From 4c633eb83ce4b01c71b5b896b933acf386f56e32 Mon Sep 17 00:00:00 2001 From: Adafede Date: Tue, 20 Aug 2024 04:23:13 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20taxonomi?= =?UTF-8?q?callyinformedannotation/tima-shinylive@0ae916c64ef23059d30d8d0b?= =?UTF-8?q?61fc7eeaffe5baa2=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app.json | 2 +- shinylive/webr/packages/metadata.rds | Bin 5387 -> 6833 bytes shinylive/webr/packages/tima/tima_2.11.0.data | Bin 2051072 -> 2051072 bytes .../packages/tima/tima_2.11.0.js.metadata | 2 +- 4 files changed, 2 insertions(+), 2 deletions(-) diff --git a/app.json b/app.json index 917a6d1..923c71d 100644 --- a/app.json +++ b/app.json @@ -1 +1 @@ -[{"name":"app.R","content":"# Check if runs in Docker environment or not\ni_am_a_whale <- file.exists(\"/.dockerenv\")\nif (i_am_a_whale) {\n options(shiny.host = \"0.0.0.0\")\n} else {\n options(shiny.host = \"127.0.0.1\")\n}\noptions(shiny.port = 3838)\noptions(shiny.maxRequestSize = 2000 * 1024^2)\n\n#' @title Label mandatory\n#'\n#' @description This function adds an asterisk to mandatory inputs\n#'\n#' @param label Label\n#'\n#' @return NULL\n#'\n#' @export\n#'\n#' @examples NULL\n.label_mandatory <- function(label) {\n shiny::tagList(label, shiny::span(\"*\", class = \"mandatory_star\"))\n}\n\napp_css <-\n \".mandatory_star { color: red; }\n .shiny-input-container { margin-top: 25px; }\n #submit_msg { margin-left: 15px; }\n #error { color: red; }\n body { background: #fcfcfc; }\n #header { margin: -20px -15px 0; padding: 15px 15px 10px; }\n \"\n\nui <- shiny::fluidPage(\n shinyjs::useShinyjs(),\n shinyjs::inlineCSS(rules = app_css),\n title = \"Taxonomically Informed Metabolite Annotation\",\n shiny::div(\n id = \"header\",\n shiny::h1(\"Taxonomically Informed Metabolite Annotation\"),\n shiny::h4(\n \"This app helps performing TIMA as described in the\",\n shiny::a(href = \"https://taxonomicallyinformedannotation.github.io/tima\", \"following documentation\")\n ),\n shiny::strong(\n shiny::span(\"Created by \"),\n shiny::a(\"Adriano Rutz\", href = \"https://adafede.github.io/\"),\n shiny::HTML(\"•\"),\n shiny::a(\"Main publication\", href = \"https://doi.org/10.3389/fpls.2019.01329\"),\n shiny::HTML(\"•\"),\n shiny::span(\"Code\"),\n shiny::a(\"on GitHub\", href = \"https://github.com/taxonomicallyinformedannotation/tima\")\n )\n ),\n shiny::fluidPage(shiny::div(\n id = \"params\",\n shiny::navlistPanel(\n widths = c(4, 5),\n \"Parameters\",\n shiny::tabPanel(\n title = \"Files\",\n shiny::h3(\"Required files\"),\n shiny::div(\n shiny::fileInput(\n inputId = \"fil_spe_raw\",\n label = .label_mandatory(\"MGF file\"),\n accept = \".mgf\"\n ),\n shiny::downloadButton(outputId = \"demo_spe\", \"Download example spectra\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"The MGF file containing your spectra.\",\n \"Should be located in `data/source`.\",\n \"Reason therefore is to find it in the future.\"\n # \"If you have a GNPS job ID, the spectra will be stored there.\"\n )\n ),\n shiny::div(\n shiny::fileInput(\n inputId = \"fil_fea_raw\",\n label = .label_mandatory(\"Features table\"),\n accept = c(\".csv\", \".tsv\", \".csv.gz\", \".tsv.gz\", \".csv.zip\", \".tsv.zip\")\n ),\n shiny::downloadButton(outputId = \"demo_fea\", \"Download example features\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"The csv or tsv file containing your features\",\n \"Should be located in `data/source`.\",\n \"Reason therefore is to find it in the future.\"\n # \"If you have a GNPS job ID, the spectra will be stored there.\"\n )\n ),\n shiny::div(\n shiny::fileInput(\n inputId = \"fil_met_raw\",\n label = \"Metadata table (mandatory if no taxon name)\",\n accept = c(\".csv\", \".tsv\", \".csv.gz\", \".tsv.gz\", \".csv.zip\", \".tsv.zip\")\n ),\n shiny::downloadButton(outputId = \"demo_met\", \"Download example metadata\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"The csv or tsv file containing the metadata of your experiment.\",\n \"Should be located in `data/source`.\",\n \"Reason therefore is to find it in the future.\",\n # \"If you have a GNPS job ID, the spectra will be stored there.\",\n \"Do not forget to change the `name` in the corresponding tab.\"\n )\n ),\n shiny::fileInput(\n inputId = \"fil_ann_raw_sir\",\n label = \"SIRIUS project space (optional)\",\n accept = c(\".zip\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"The compressed directory containing the SIRIUS project space.\",\n \"Should be located in `data/interim`.\",\n \"Reason therefore is to find it in the future.\"\n )\n ),\n shiny::selectInput(\n inputId = \"too_sir_ver\",\n label = \"SIRIUS version\",\n choices = c(5, 6),\n selected = 6\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"The SIRIUS version used.\", \"We highly recommend 6, the default.\")\n ),\n shiny::textInput(\n inputId = \"fil_pat\",\n label = .label_mandatory(\"Pattern to identify your job locally\"),\n value = \"example\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of your job.\",\n \"All intermediate steps will use this pattern.\",\n \"For example, the corresponding SIRIUS results will be named:\",\n \"`yourPattern_sirius`.\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"Annotations\",\n shiny::h3(\"Annotations-related parameters\"),\n shiny::sliderInput(\n inputId = \"ann_can_fin\",\n label = \"Number of final candidates\",\n min = 1,\n max = 500,\n value = 3,\n step = 1,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Number of final candidates to consider.\",\n \"For 12 candidates, with 10, only the first 10 will be kept.\",\n \"This can end to more than n candidates, if some are ex aequo.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"ann_ms1only\",\n label = \"Erase MS2 results and keep MS1 only\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Option to erase MS2 results.\",\n \"Usually not needed, for benchmarking mainly.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ann_thr_con\",\n label = \"Minimal consistency score (chemical) to consider a class\",\n min = 0,\n max = 1,\n value = 0.0,\n step = 0.05,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimal consistency score (chemical) to consider a class.\",\n \"Everything below will be considered as not consistent.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ann_thr_ms1_bio\",\n label = \"Minimal biological score to keep MS1 only annotation\",\n min = 0,\n max = 1,\n value = 0.0,\n step = 0.05,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimal biological score to keep an MS1 candaidate.\",\n \"Everything below will be discarded (see below).\",\n \"Check the sub-scores and weights in the `weights` panel.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ann_thr_ms1_che\",\n label = \"Minimal chemical score to keep MS1 only annotation\",\n min = 0,\n max = 1,\n value = 0.0,\n step = 0.1,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimal chemical score to keep an MS1 candaidate.\",\n \"Everything below will be discarded (see below).\",\n \"Check the sub-scores and weights in the `weights` panel.\"\n )\n ),\n shiny::selectInput(\n inputId = \"ann_thr_ms1_con\",\n label = \"Condition to be used to retain candidates\",\n choices = c(\"AND\", \"OR\"),\n selected = \"OR\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If an MS1 candidate has a bio score of 1, a chem score of 0.25\",\n \"the above thresholds set at 0.3 and 0.3 respectively\",\n \"keeps it if the condition is `OR` but discard it if `AND`.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"ann_ms2_app\",\n label = \"Perform matching without precursor? (can be very long)\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Corresponds to variable dereplication.\",\n \"Then, precurors masses are disregarded during matching.\",\n \"Allows to find (incorrect) analogs.\",\n \"Takes a lot of time.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ann_thr_ms2_sim\",\n label = \"Minimal similarity score (annotation)\",\n min = 0,\n max = 1,\n step = 0.05,\n value = 0.0,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimum similarity score between spectra.\",\n \"To keep MS2 annotation.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"edg_thr_ms2_sim\",\n label = \"Minimal similarity score (edges)\",\n min = 0.5,\n max = 1,\n step = 0.05,\n value = 0.7,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimum similarity score between spectra.\",\n \"To keep MS2 similarity edge.\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"MS\",\n shiny::h3(\"MS-related parameters\"),\n shiny::selectInput(\n inputId = \"ms_pol\",\n label = \"Polarity used\",\n choices = c(\"pos\", \"neg\"),\n selected = \"pos\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Polarity of the MS experiment.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_thr_ms2_int\",\n label = \"Intensity threshold for MS2\",\n min = 0,\n max = 1E4,\n value = 5,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"MS2 intensity threshold.\",\n \"Fragments below this threshold will be removed from spectra.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_mas_ppm_ms1\",\n label = \"Relative mass tolerance for MS1 in ppm\",\n min = 0.1,\n max = 20,\n value = 10,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Mass tolerance (in ppm) used for MS1 annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_mas_dal_ms1\",\n label = \"Absolute mass tolerance for MS1 in Dalton\",\n min = 0.005,\n max = 0.02,\n value = 0.01,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Mass tolerance (in Da) used for MS1 annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_mas_ppm_ms2\",\n label = \"Relative mass tolerance for MS2 in ppm\",\n min = 0.1,\n max = 20,\n value = 10,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Mass tolerance (in ppm) used for MS2 annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_mas_dal_ms2\",\n label = \"Absolute mass tolerance for MS2 in Dalton\",\n min = 0.005,\n max = 0.02,\n value = 0.01,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Mass tolerance (in Da) used for MS2 annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_rt_add\",\n label = \"Retention time tolerance for adducts annotation in minutes\",\n min = 0.005,\n max = 0.1,\n value = 0.02,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Retention time tolerance (minutes) used for adducts annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_rt_lib\",\n label = \"Retention time tolerance for library annotation in minutes\",\n min = 0.005,\n max = 0.2,\n value = 0.1,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Retention time tolerance (minutes) used for library annotation.\",\n \"If no experimental library is given, does not impact results.\"\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_add_pos\",\n label = \"List of adducts to be used in positive\",\n choices = list(\n \"[M+H3]3+\",\n \"[M+H2Na]3+\",\n \"[M+HNa2]3+\",\n \"[M+Na3]3+\",\n \"[M+H2]2+\",\n \"[M+HNa]2+\",\n \"[M+Mg]2+\",\n \"[M+HK]2+\",\n \"[M+Ca]2+\",\n \"[M+Na2]2+\",\n \"[M+Fe]2+\",\n \"[M+H]+\",\n \"[M+H4N]+\",\n \"[M+Na]+\",\n \"[M+K]+\",\n \"[M+Cu]+\",\n \"[2M+Mg]2+\",\n \"[2M+Ca]2+\",\n \"[2M+Fe]2+\",\n \"[2M+H]+\",\n \"[2M+H4N]+\",\n \"[2M+Na]+\",\n \"[2M+K]+\"\n ),\n selected = list(\"[M+H2]2+\", \"[M+H]+\", \"[M+H4N]+\", \"[M+Na]+\", \"[2M+H]+\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important adduct everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_add_neg\",\n label = \"List of adducts to be used in negative\",\n choices = list(\n \"[M-H3]3-\",\n \"[M-H2]2-\",\n \"[M-H]-\",\n \"[M+F]-\",\n \"[M+Na-H2]-\",\n \"[M+Cl]-\",\n \"[M+K-H2]-\",\n \"[M+Br]-\",\n \"[2M-H]-\",\n \"[3M-H]-\"\n ),\n selected = list(\"[M-H2]2-\", \"[M-H]-\", \"[2M-H]-\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important adduct everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_clu_pos\",\n label = \"List of clusters to be used in positive\",\n choices = list(\n \"H2O\", # (water)\n # \"CH4O\", # (methanol)\n \"C2H3N\", # (acetonitrile)\n # \"C2H7N\", # (ethylamine)\n # \"C2H6O\", # (ethanol)\n \"NaCl\" # (sodium chloride)\n # \"C3H8O\", # (isopropanol)\n # \"C2H6OS\" # (dmso)\n ),\n selected = list(\n \"H2O\", # (water)\n # \"CH4O\", # (methanol)\n \"C2H3N\" # (acetonitrile)\n # \"C2H7N\", # (ethylamine)\n # \"C2H6O\", # (ethanol)\n # \"NaCl\", # (sodium chloride)\n # \"C3H8O\", # (isopropanol)\n # \"C2H6OS\" # (dmso)\n )\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important cluster everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_clu_neg\",\n label = \"List of clusters to be used in negative\",\n choices = list(\n \"H2O\", # (water)\n \"CH2O2\", # (formic)\n \"NaCl\", # (sodium chloride)\n \"C2H4O2\", # (acetic)\n \"H2PO4\", # (phosphoric)\n \"C2HF3O2\" # (tfa)\n ),\n selected = list(\n \"H2O\", # (water)\n \"CH2O2\", # (formic)\n # \"NaCl\", # (sodium chloride)\n # \"C2H4O2\", # (acetic)\n \"H2PO4\" # (phosphoric)\n # \"C2HF3O2\" # (tfa)\n )\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important cluster everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_neu\",\n label = \"List of neutral losses to be used\",\n choices = list(\n \"HN\",\n \"CH3\",\n \"O\",\n \"H3N (ammonia)\",\n \"H2O (water)\",\n \"CHN\",\n \"CO\",\n \"C2H4 (2xCH2)\",\n \"C2H5 (CH2-CH3)\",\n \"CH2O\",\n \"H5ON (H2O-H3N)\",\n \"H4O2 (2xH2O)\",\n \"C2H2O\",\n \"CHON\",\n \"CO2\",\n \"CHO2\",\n \"H6O3 (3xH2O)\",\n \"C2O2 (2xCO)\",\n \"CH6O3 (combination)\",\n \"H8O4 (4xH2O)\",\n \"C2H2O3 (CH2O-CO2)\",\n \"C3H6O2 (CH3-CH2-COOH)\",\n \"C3O3 (3xCO)\",\n \"C4H9NO (combination)\",\n \"C2O4 (2xCO2)\",\n \"C3H4O3 (pyruvic)\",\n \"C4H8O2 (butyric)\",\n \"C3H7NO2 (alanine)\",\n \"C3H6O3 (sugar)\",\n \"H2O4S (sulfuric)\",\n \"H3O4P (phosphoric)\",\n \"C5H10O2 (valeric)\",\n \"C3H4O4 (malonic)\",\n \"C2H2O5 (CO2-CO2-H2O)\",\n \"C3H8O4 (combination)\",\n \"C5H9NO2 (proline)\",\n \"C4O4H6 (CO2-CO2-CH3-CH3)\",\n \"C4H8O4 (sugar)\",\n \"C2H4O6 (CO2-CO2-H2O-H2O)\",\n \"C6H6O3 (HRF)\",\n \"C5H8O4 (pentose-H2O)\",\n \"C8H8O2 (RDA-1)\",\n \"C6H8O4 (HRF)\",\n \"C9H6O2 (coumaroyl)\",\n \"C6H10O4 (methylpentose/desoxyhexose-H2O)\",\n \"C9H8O2 (cinnamoyl)\",\n \"C7H4O4 (galloyl)\",\n \"C8H8O3 (RDA-2)\",\n \"C9H6O3 (caffeoyl)\",\n \"C6H10O5 (hexose-H2O)\",\n \"C8H8O4 (RDA-3)\",\n \"C8H10O4 (RDA-2-H2O)\",\n \"C6H8O6 (glucuronic-H2O)\",\n \"C10H8O3 (feruloyl)\",\n \"C6H13NO5 (hexose-H2N)\",\n \"C6H12O6 (hexose)\",\n \"C7H12O6 (quinoyl)\",\n \"C6H15NO6 (hexose-H2N-H2O)\",\n \"C6H14O7 (hexose-H2O)\",\n \"C8H12O6 (acetylhexose-H2O)\",\n \"C11H10O4 (sinapoyl)\",\n \"C16H30O (pamitoyl)\",\n \"C9H12O8 (malonylhexose)\",\n \"C13H14O6 (benzoylhexose)\",\n \"C12H20O8 (2xmethylpentose/desoxyhexose-H2O)\",\n \"C15H16O7 (coumaroylhexose)\",\n \"C13H14O9 (galloylhexose)\",\n \"C15H16O8 (caffeoylhexose)\",\n \"C12H20O10 (2xhexose-H2O)\",\n \"C16H18O8 (feruloylhexose)\",\n \"C17H20O9 (sinapoylhexose)\",\n \"C18H30O15 (3xhexose-H2O)\"\n ),\n selected = list(\n \"O\",\n \"H3N (ammonia)\",\n \"H2O (water)\",\n \"CO\",\n \"C2H5 (CH2-CH3)\",\n \"H4O2 (2xH2O)\",\n \"CO2\",\n \"CHO2\",\n \"H6O3 (3xH2O)\",\n \"C2O2 (2xCO)\",\n \"CH6O3 (combination)\",\n \"H8O4 (4xH2O)\",\n \"C3H6O3 (sugar)\",\n \"H2O4S (sulfuric)\",\n \"H3O4P (phosphoric)\",\n \"C4H8O4 (sugar)\",\n \"C6H6O3 (HRF)\",\n \"C5H8O4 (pentose-H2O)\",\n \"C8H8O2 (RDA-1)\",\n \"C6H8O4 (HRF)\",\n \"C6H10O4 (methylpentose/desoxyhexose-H2O)\",\n \"C8H8O3 (RDA-2)\",\n \"C6H10O5 (hexose-H2O)\",\n \"C8H8O4 (RDA-3)\",\n \"C8H10O4 (RDA-2-H2O)\",\n \"C6H12O6 (hexose)\",\n \"C11H10O4 (sinapoyl)\",\n \"C16H30O (pamitoyl)\",\n \"C12H20O8 (2xmethylpentose/desoxyhexose-H2O)\",\n \"C12H20O10 (2xhexose-H2O)\"\n )\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important neutral loss everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n )\n ),\n shiny::tabPanel(\n title = \"Organisms\",\n shiny::h3(\"Organisms-related parameters\"),\n shiny::textInput(\n inputId = \"org_tax\",\n label = \"OPTIONAL. Force all features to be attributed to a taxon\n (e.g. Gentiana lutea)\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"You have to input the scientific name of your taxon.\",\n \"Will be used for the weighting of your annotations\",\n \"Example: Gentiana lutea\",\n \"All features will be attributed to this source.\",\n \"For finer attribution,\n you need to provide a metadata file in the `Files` panel.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"org_can\",\n label = \"Number of organisms to keep per feature\",\n min = 1,\n max = 5,\n value = 1,\n step = 1,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Only has impact if you provided a metadata file in the\n `Files` panel.\",\n \"In this case,\n it will take the intensity matrix of your features\",\n \"and attribute the n organisms where the highest intensity\",\n \"was observed as source to your features.\",\n \"Useful when your experiment contains\n different biological sources.\",\n \"As the organisms where the measured intensity\n is the highest are also the ones\",\n \"with higher likelihood of the\n corresponding compounds being isolated,\",\n \"the number of organisms can be kept low.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"org_fil_mod\",\n label = \"Filter library to restrict it to a\n portion of organisms only\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If you want, you can restrict your\n structure-organism pairs library\",\n \"to given taxa only.\",\n \"We do not advise doing so, but you are free to.\"\n )\n ),\n shiny::selectInput(\n inputId = \"org_fil_lev\",\n label = \"Level at which the library will be filtered\",\n choices = c(\n \"domain\",\n \"phylum\",\n \"class\",\n \"order\",\n \"family\",\n \"tribe\",\n \"genus\",\n \"species\",\n \"varietas\"\n ),\n selected = \"phylum\"\n ),\n shiny::textInput(\n inputId = \"org_fil_val\",\n label = \"Value to be applied for filtering\",\n value = \"Streptophyta\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"You can also provide multiple values.\",\n \"Example, if you want to get a bitter library:\",\n \"`Simaroubaceae|Gentianaceae`\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"Names\",\n shiny::h3(\"Variable names parameters\"),\n shiny::textInput(\n inputId = \"names_adduct\",\n label = \"Name of `adduct` variable in the input\",\n value = \"best ion\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `adduct` column in your features file.\",\n \"The default corresponds to the default in MZmine.\",\n \"If using SLAW, please input 'annotation'\"\n )\n ),\n shiny::textInput(\n inputId = \"names_features\",\n label = \"Name of `feature IDs` variable in the input\",\n value = \"row ID\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `feature id` column in your features file.\",\n \"The default corresponds to the default in MZmine.\",\n \"If using SLAW, please input 'slaw_id'\"\n )\n ),\n shiny::textInput(\n inputId = \"names_filename\",\n label = \"Name of `filename` variable in the input\",\n value = \"filename\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `file name` column in your metadata file.\",\n \"The default is filename.\",\n \"If using Sequencer, please input 'Sample Name'\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"names_extension\",\n label = \"The file(s) extension is present in the sample name\",\n value = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Uncheck the box if you removed the files extensions\",\n \"of your file in the features file\",\n \"The default corresponds to the default in MZmine.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_inchikey\",\n label = \"Name of `inchikey` variable in the input\",\n value = \"inchikey\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `inchikey` column in your file.\")\n ),\n shiny::textInput(\n inputId = \"names_precursor\",\n label = \"Name of `precursor m/z` variable in the input\",\n value = \"row m/z\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `precursor m/z` column in your features file.\",\n \"The default corresponds to the default in MZmine.\",\n \"If using SLAW, please input 'mz'\"\n )\n ),\n shiny::textInput(\n inputId = \"names_rt\",\n label = \"Name of `retention time` variable in the feature table\",\n value = \"row retention time\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `retention time` column in your features file.\",\n \"The default corresponds to the default in MZmine.\",\n \"If using SLAW, please input 'rt'\",\n \"Assumed to be in minutes.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_rt_2\",\n label = \"Name of `retention time` variable in the rt library\",\n value = \"rt\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `retention time` column in your rt library file.\")\n ),\n shiny::textInput(\n inputId = \"names_smiles\",\n label = \"Name of `SMILES` variable in the input\",\n value = \"smiles\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `SMILES` column in your file.\")\n ),\n shiny::textInput(\n inputId = \"names_source\",\n label = \"Name of `source IDs` variable in the input\",\n value = \"CLUSTERID1\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `source id` column in your edges file.\",\n \"The default corresponds to the default in GNPS.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_target\",\n label = \"Name of `target IDs` variable in the input\",\n value = \"CLUSTERID2\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `target id` column in your edges file.\",\n \"The default corresponds to the default in GNPS.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_taxon\",\n label = \"Name of `taxon name` variable in the input\",\n value = \"ATTRIBUTE_species\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `taxon name` column in your edges file.\",\n \"The default corresponds to the default in GNPS.\"\n )\n ),\n shiny::h3(\"MGF names parameters\"),\n shiny::textInput(\n inputId = \"names_mgf_ad\",\n label = \"Name of `adduct` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `adduct` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_ce\",\n label = \"Name of `collision energy` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `collision energy` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_ci\",\n label = \"Name of `compound id` variable in the MGF\",\n value = \"SPECTRUMID\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `compound id` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_em\",\n label = \"Name of `exact mass` variable in the MGF\",\n value = \"EXACTMASS\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `exact mass` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_in\",\n label = \"Name of `InChI` variable in the MGF\",\n value = \"INCHI\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `InChI` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_io\",\n label = \"Name of `InChI no stereo` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `InChI no stereo` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_ik\",\n label = \"Name of `InChIKey` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `InChIKey` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_il\",\n label = \"Name of `InChIKey no stereo` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `InChIKey no stereo` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_mf\",\n label = \"Name of `molecular formula` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `molecular formula` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_na\",\n label = \"Name of `name` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `name` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_po\",\n label = \"Name of `polarity` variable in the MGF\",\n value = \"IONMODE\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `polarity` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_rt\",\n label = \"Name of `retention time` variable in the MGF\",\n value = \"RTINSECONDS\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `retention time` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_sm\",\n label = \"Name of `SMILES` variable in the MGF\",\n value = \"SMILES\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `SMILES` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_sn\",\n label = \"Name of `SMILES no stereo` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `SMILES no stereo` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_si\",\n label = \"Name of `spectrum ID` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `spectrum ID` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_sp\",\n label = \"Name of `SPLASH` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `SPLASH` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_sy\",\n label = \"Name of `synonyms` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `synonyms` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_xl\",\n label = \"Name of `xlogP` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `xlogP` column in your MGF file.\")\n )\n ),\n shiny::tabPanel(\n title = \"Weights\",\n shiny::h3(\"Weights-related parameters\"),\n shiny::sliderInput(\n inputId = \"wei_glo_bio\",\n label = \"Weight for the biological part\",\n min = 0,\n max = 1,\n value = 0.500,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Weight attributed to the biological score.\",\n \"Depends on the organism you are studying,\n and how specialized its metabolome is.\",\n \"Also depends if you favor novelty over previous knowledge.\",\n \"We advise this value to be high.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_glo_che\",\n label = \"Weight for the chemical part\",\n min = 0,\n max = 1,\n value = 0.166,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Weight attributed to the chemical score.\",\n \"We advise this value to be low.\",\n \"Current chemical taxonomies do not perform well.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_glo_spe\",\n label = \"Weight for the spectral part\",\n min = 0,\n max = 1,\n value = 0.333,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Weight attributed to the spectral score.\",\n \"Depends on the quality of your spectra.\",\n \"We advise this value to be medium to high.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_01\",\n label = \"Score for a biological domain match\",\n min = 0,\n max = 1,\n value = 0.100,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `domain` match.\",\n \"We advise this value to be the lowest.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_02\",\n label = \"Score for a biological kingdom match\",\n min = 0,\n max = 1,\n value = 0.200,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `kingdom` match.\",\n \"We advise this value to be higher than the one of `domain`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_03\",\n label = \"Score for a biological phylum match\",\n min = 0,\n max = 1,\n value = 0.300,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `phylum` match.\",\n \"We advise this value to be higher than the one of `kingdom`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_04\",\n label = \"Score for a biological class match\",\n min = 0,\n max = 1,\n value = 0.400,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `class` match.\",\n \"We advise this value to be higher than the one of `phylum`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_05\",\n label = \"Score for a biological order match\",\n min = 0,\n max = 1,\n value = 0.500,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `order` match.\",\n \"We advise this value to be higher than the one of `class`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_06\",\n label = \"Score for a biological infraorder match\",\n min = 0,\n max = 1,\n value = 0.550,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `infraorder` match.\",\n \"We advise this value to be higher than the one of `order`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_07\",\n label = \"Score for a biological family match\",\n min = 0,\n max = 1,\n value = 0.600,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `family` match.\",\n \"We advise this value to be higher than the one of `infraorder`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_08\",\n label = \"Score for a biological subfamily match\",\n min = 0,\n max = 1,\n value = 0.650,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `subfamily` match.\",\n \"We advise this value to be higher than the one of `family`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_09\",\n label = \"Score for a biological tribe match\",\n min = 0,\n max = 1,\n value = 0.700,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `tribe` match.\",\n \"We advise this value to be higher than the one of `subfamily`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_10\",\n label = \"Score for a biological subtribe match\",\n min = 0,\n max = 1,\n value = 0.750,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `subtribe` match.\",\n \"We advise this value to be higher than the one of `tribe`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_11\",\n label = \"Score for a biological genus match\",\n min = 0,\n max = 1,\n value = 0.800,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `genus` match.\",\n \"We advise this value to be higher than the one of `subtribe`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_12\",\n label = \"Score for a biological subgenus match\",\n min = 0,\n max = 1,\n value = 0.850,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `subgenus` match.\",\n \"We advise this value to be higher than the one of `genus`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_13\",\n label = \"Score for a biological species match\",\n min = 0,\n max = 1,\n value = 0.900,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `species` match.\",\n \"We advise this value to be higher than the one of `subgenus`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_14\",\n label = \"Score for a biological subspecies match\",\n min = 0,\n max = 1,\n value = 0.950,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `subspecies` match.\",\n \"We advise this value to be higher than the one of `species`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_15\",\n label = \"Score for a biological variety match\",\n min = 0,\n max = 1,\n value = 1.000,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `variety` match.\",\n \"We advise this value to be higher than the one of `subspecies`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_11\",\n label = \"Score for a (classyfire) chemical kingdom match\",\n min = 0,\n max = 1,\n value = 0.250,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `kingdom (classyfire)` match.\",\n \"We advise this value to be the lowest.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_12\",\n label = \"Score for a (classyfire) chemical superclass match\",\n min = 0,\n max = 1,\n value = 0.500,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `superclass (classyfire)` match.\",\n \"We advise this value to be higher\n than the one of `kingdom (classyfire)`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_13\",\n label = \"Score for a (classyfire) chemical class match\",\n min = 0,\n max = 1,\n value = 0.750,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `class (classyfire)` match.\",\n \"We advise this value to be higher\n than the one of `superclass (classyfire)`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_14\",\n label = \"Score for a (classyfire) chemical parent match\",\n min = 0,\n max = 1,\n value = 1.000,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `parent (classyfire)` match.\",\n \"We advise this value to be higher\n than the one of `class (classyfire)`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_21\",\n label = \"Score for a (NPC) chemical pathway match\",\n min = 0,\n max = 1,\n value = 0.333,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `pathway (npclassifier)` match.\",\n \"We advise this value to be the lowest.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_22\",\n label = \"Score for a (NPC) chemical superclass match\",\n min = 0,\n max = 1,\n value = 0.666,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `superclass (npclassifier)` match.\",\n \"We advise this value to be higher\n than the one of `pathway (npclassifier)`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_23\",\n label = \"Score for a (NPC) chemical class match\",\n min = 0,\n max = 1,\n value = 1.000,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `class (npclassifier)` match.\",\n \"We advise this value to be higher\n than the one of `superclass (npclassifier)`.\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"Options\",\n shiny::h3(\"Options parameters\"),\n shiny::checkboxInput(\n inputId = \"compounds_names\",\n label = \"Compound names\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If ticked, compounds names will be reported.\",\n \"This will require much more memory,\n as they are the longest strings.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"high_confidence\",\n label = \"High confidence only\",\n value = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If ticked, only compounds with high confidence will be reported.\",\n \"This is an important filter. Do not hesitate removing it for more coverage.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"remove_ties\",\n label = \"Remove ties\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If checked, only one of the candidates with the exact same score will be kept randomly.\",\n \"Not adivsed, but here for convenience.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"summarise\",\n label = \"Summarise results to one row per feature\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If checked, the output will be a Cytoscape-ready table,\",\n \"with multiple candidates per line, separated by pipes (|).\",\n \"If unchecked, will output multiple lines per features\n (one per candidate).\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"force\",\n label = \"Do not use it!\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Really, do not use it.\",\n \"Corresponds to a `--force` parameter,\n allowing for crazy things that can crash.\",\n \"Really, do not use it.\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"Libraries (optional)\",\n shiny::h3(\"Additional personal libraries\"),\n shiny::h4(\"Temporal (retention time)\"),\n shiny::fileInput(\n inputId = \"lib_tmp_exp_csv\",\n label = \"Experimental (CSV)\",\n accept = c(\".csv\", \".tsv\", \".csv.gz\", \".tsv.gz\", \".csv.zip\", \".tsv.zip\"),\n multiple = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"A TSV of retention times.\",\n \"The default column names are `rt`, `smiles`, `inchikey`.\",\n \"Do not forget to adapt the names parameters if needed.\",\n \"Do not forget to fill down seconds or minutes below.\"\n )\n ),\n shiny::fileInput(\n inputId = \"lib_tmp_is_csv\",\n label = \"In silico (CSV)\",\n accept = c(\".csv\", \".tsv\", \".csv.gz\", \".tsv.gz\", \".csv.zip\", \".tsv.zip\"),\n multiple = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"A TSV of retention times.\",\n \"The default column names are `rt`, `smiles`, `inchikey`.\",\n \"Do not forget to adapt the names parameters if needed.\",\n \"Do not forget to fill down seconds or minutes below.\"\n )\n ),\n shiny::h4(\"Spectral\"),\n shiny::fileInput(\n inputId = \"lib_spe_mgf\",\n label = \"MGF\",\n accept = c(\".mgf\"),\n multiple = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"An MGF containing reference spectra.\",\n \"If it contains retention times, they will also be used.\",\n \"Do not forget to fill down seconds or minutes below.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_libraries\",\n label = \"Name of the library\",\n value = \"internal\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the library. Related content will be identified with this name.\"\n )\n ),\n shiny::selectInput(\n inputId = \"uni_rt\",\n label = \"Retention time unit\",\n choices = c(\"seconds\", \"minutes\"),\n selected = \"seconds\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Unit of the retention time.\", \"Must be `seconds` or `minutes`.\")\n ),\n ),\n shiny::tabPanel(\n title = \"GNPS (optional)\",\n shiny::h3(\"GNPS parameters\"),\n shiny::textInput(\n inputId = \"gnps_id\",\n label = \"GNPS job ID\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Your GNPS job ID.\",\n \"If you provide one, all files will be taken from there.\",\n \"You still need to provide a path for\n where we will store the files,\",\n \"in case your GNPS job gets deleted for you to find them back.\",\n \"Annotations will also be downloaded.\"\n )\n ),\n shiny::selectInput(\n inputId = \"gnps_workflow\",\n label = \"Workflow used within GNPS\",\n choices = c(\"classical\", \"fbmn\"),\n selected = \"fbmn\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"We advise `fbmn`,\n but we also support `classical` jobs.\")\n )\n )\n )\n )),\n shiny::fluidPage(\n shiny::div(\n id = \"form\",\n shiny::actionButton(\n inputId = \"save\",\n label = \"Save parameters\",\n class = \"btn-primary\"\n ),\n shinyjs::hidden(\n shiny::span(id = \"save_msg\", \"Saving parameters...\"),\n shiny::div(id = \"error\", shiny::div(\n shiny::br(),\n shiny::tags$b(\"Error: \"),\n shiny::span(id = \"error_msg\")\n ))\n ),\n shiny::actionButton(\n inputId = \"launch\",\n label = \"Launch job\",\n class = \"btn-primary\"\n ),\n ),\n shinyjs::hidden(shiny::div(\n id = \"thankyou_msg\",\n shiny::h3(\"Thanks, your parameters were saved successfully!\")\n )),\n shinyjs::hidden(shiny::div(id = \"job_msg\", shiny::h3(\"Job is running!\")))\n )\n)\n\n# save the results to a file\n.save_input <- function(input) {\n paths_data_source <- tima:::get_default_paths()$data$source$path\n paths_data_interim_annotations <-\n tima:::get_default_paths()$data$interim$annotations$path\n ## safety\n tima:::create_dir(paths_data_source)\n\n list <- tima:::load_yaml_files()\n\n yamls_params <- list$yamls_params\n yaml_files <- list$yaml_files\n yaml_names <- list$yaml_names\n\n ## This allows to keep files correctly placed in `data/source` clean\n prefil_fea_raw <- shiny::isolate(input$fil_fea_raw)\n prefil_spe_raw <- shiny::isolate(input$fil_spe_raw)\n prefil_met_raw <- shiny::isolate(input$fil_met_raw)\n prefil_sir_raw <- shiny::isolate(input$fil_ann_raw_sir)\n\n prefil_fea_raw_1 <- file.path(paths_data_source, prefil_fea_raw[[1]])\n prefil_spe_raw_1 <- file.path(paths_data_source, prefil_spe_raw[[1]])\n if (!is.null(prefil_met_raw)) {\n prefil_met_raw_1 <- file.path(paths_data_source, prefil_met_raw[[1]])\n }\n if (!is.null(prefil_sir_raw)) {\n prefil_sir_raw_1 <-\n file.path(paths_data_interim_annotations, prefil_sir_raw[[1]])\n }\n\n if (!file.exists(prefil_fea_raw_1)) {\n fs::file_copy(\n path = prefil_fea_raw[[4]],\n new_path = file.path(prefil_fea_raw_1),\n overwrite = TRUE\n )\n }\n if (!file.exists(prefil_spe_raw_1)) {\n fs::file_copy(\n path = prefil_spe_raw[[4]],\n new_path = file.path(prefil_spe_raw_1),\n overwrite = TRUE\n )\n }\n if (!is.null(prefil_met_raw)) {\n if (!file.exists(prefil_met_raw_1)) {\n fs::file_copy(\n path = prefil_met_raw[[4]],\n new_path = file.path(prefil_met_raw_1),\n overwrite = TRUE\n )\n }\n } else {\n prefil_met_raw_1 <- NULL\n }\n if (!is.null(prefil_sir_raw)) {\n if (!file.exists(prefil_sir_raw_1)) {\n ## safety\n tima:::create_dir(paths_data_interim_annotations)\n fs::file_copy(\n path = prefil_sir_raw[[4]],\n new_path = file.path(prefil_sir_raw_1),\n overwrite = TRUE\n )\n }\n } else {\n prefil_sir_raw_1 <- NULL\n }\n\n fil_fea_raw <- prefil_fea_raw_1\n fil_spe_raw <- prefil_spe_raw_1\n fil_met_raw <- prefil_met_raw_1\n fil_sir_raw <- prefil_sir_raw_1\n\n fil_pat <- shiny::isolate(input$fil_pat)\n\n gnps_job_id <- shiny::isolate(input$gnps_id)\n if (gnps_job_id == \"\") {\n gnps_job_id <- NULL\n }\n\n org_tax <- shiny::isolate(input$org_tax)\n if (org_tax == \"\") {\n org_tax <- NULL\n }\n hig_con <- shiny::isolate(input$high_confidence)\n ms_pol <- shiny::isolate(input$ms_pol)\n summarise <- shiny::isolate(input$summarise)\n\n message(x = \"Changing parameters ...\")\n message(x = \"... Small\")\n yaml_small <- yamls_params[[\"params/prepare_params\"]]\n yaml_small$files$pattern <- fil_pat\n yaml_small$files$features$raw <- fil_fea_raw\n yaml_small$files$metadata$raw <- fil_met_raw\n yaml_small$files$annotations$raw$sirius <- fil_sir_raw\n yaml_small$files$spectral$raw <- fil_spe_raw\n yaml_small$ms$polarity <- ms_pol\n yaml_small$organisms$taxon <- org_tax\n yaml_small$options$high_confidence <- hig_con\n yaml_small$options$summarise <- summarise\n tima:::create_dir(\"params\")\n yaml::write_yaml(x = yaml_small, file = tima:::get_default_paths()$params$prepare_params)\n\n message(x = \"... Advanced\")\n yaml_advanced <- yamls_params[[\"params/prepare_params_advanced\"]]\n yaml_advanced$annotations$candidates$final <-\n shiny::isolate(input$ann_can_fin)\n yaml_advanced$annotations$ms1only <-\n shiny::isolate(input$ann_ms1only)\n yaml_advanced$annotations$ms2approx <-\n shiny::isolate(input$ann_ms2_app)\n yaml_advanced$annotations$thresholds$consistency <-\n shiny::isolate(input$ann_thr_con)\n yaml_advanced$annotations$thresholds$ms1$biological <-\n shiny::isolate(input$ann_thr_ms1_bio)\n yaml_advanced$annotations$thresholds$ms1$chemical <-\n shiny::isolate(input$ann_thr_ms1_che)\n yaml_advanced$annotations$thresholds$ms1$condition <-\n shiny::isolate(input$ann_thr_ms1_con)\n yaml_advanced$annotations$thresholds$ms2$similarity$annotation <-\n shiny::isolate(input$ann_thr_ms2_sim)\n yaml_advanced$annotations$thresholds$ms2$similarity$edges <-\n shiny::isolate(input$edg_thr_ms2_sim)\n yaml_advanced$files$pattern <- fil_pat\n yaml_advanced$files$annotations$raw$spectral$gnps <-\n yaml_advanced$files$annotations$raw$spectral$gnps |>\n tima:::replace_id()\n yaml_advanced$files$annotations$raw$spectral$spectral <-\n yaml_advanced$files$annotations$raw$spectral$spectral |>\n tima:::replace_id()\n # yaml_advanced$files$annotations$raw$sirius <-\n # yaml_advanced$files$annotations$raw$sirius |>\n # tima:::replace_id()\n yaml_advanced$files$annotations$raw$sirius <- fil_sir_raw\n yaml_advanced$files$annotations$prepared$canopus <-\n yaml_advanced$files$annotations$prepared$canopus |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$formula <-\n yaml_advanced$files$annotations$prepared$formula |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$structural$gnps <-\n yaml_advanced$files$annotations$prepared$structural$gnps |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$structural$ms1 <-\n yaml_advanced$files$annotations$prepared$structural$ms1 |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$structural$sirius <-\n yaml_advanced$files$annotations$prepared$structural$sirius |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$structural$spectral <-\n yaml_advanced$files$annotations$prepared$structural$spectral |>\n tima:::replace_id()\n yaml_advanced$files$annotations$filtered <-\n yaml_advanced$files$annotations$filtered |>\n tima:::replace_id()\n # yaml_advanced$files$annotations$processed <-\n # yaml_advanced$files$annotations$processed |>\n # tima:::replace_id()\n yaml_advanced$files$features$raw <-\n fil_fea_raw\n yaml_advanced$files$features$prepared <-\n yaml_advanced$files$features$prepared |>\n tima:::replace_id()\n # TODO\n # yaml_advanced$files$libraries$sop$raw$closed <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$raw$ecmdb <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$raw$hmdb <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$raw$lotus <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$closed <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$ecmdb <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$hmdb <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$lotus <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$rt <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$spectral <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$keys <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$organisms$names <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$organisms$taxonomies$ott <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$stereo <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$metadata <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$names <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$taxonomies$cla <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$taxonomies$npc <-\n # shiny::isolate(input$todo)\n yaml_advanced$files$libraries$spectral$raw <-\n shiny::isolate(input$lib_spe_mgf)\n yaml_advanced$files$libraries$temporal$exp$csv <-\n shiny::isolate(input$lib_tmp_exp_csv)\n yaml_advanced$files$libraries$temporal$is$csv <-\n shiny::isolate(input$lib_tmp_is_csv)\n # TODO\n # other relative paths, not necessary\n # TODO\n # yaml_advanced$files$libraries$temporal$prepared <-\n # shiny::isolate(input$todo)\n yaml_advanced$files$networks$spectral$edges$raw$ms1 <-\n yaml_advanced$files$networks$spectral$edges$raw$ms1 |>\n tima:::replace_id()\n yaml_advanced$files$networks$spectral$edges$raw$spectral <-\n yaml_advanced$files$networks$spectral$edges$raw$spectral |>\n tima:::replace_id()\n yaml_advanced$files$networks$spectral$edges$prepared <-\n yaml_advanced$files$networks$spectral$edges$prepared |>\n tima:::replace_id()\n yaml_advanced$files$networks$spectral$components$raw <-\n yaml_advanced$files$networks$spectral$components$raw |>\n tima:::replace_id()\n yaml_advanced$files$networks$spectral$components$prepared <-\n yaml_advanced$files$networks$spectral$components$prepared |>\n tima:::replace_id()\n yaml_advanced$files$metadata$raw <- fil_met_raw\n yaml_advanced$files$metadata$prepared <-\n yaml_advanced$files$metadata$prepared |>\n tima:::replace_id()\n yaml_advanced$files$spectral$raw <- fil_spe_raw\n yaml_advanced$gnps$id <- gnps_job_id\n yaml_advanced$gnps$workflow <-\n shiny::isolate(input$gnps_workflow)\n yaml_advanced$ms$adducts$neg <-\n shiny::isolate(input$ms_add_neg)\n yaml_advanced$ms$adducts$pos <-\n shiny::isolate(input$ms_add_pos)\n yaml_advanced$ms$clusters$neg <-\n shiny::isolate(input$ms_clu_neg)\n yaml_advanced$ms$clusters$pos <-\n shiny::isolate(input$ms_clu_pos)\n yaml_advanced$ms$neutral_losses <-\n shiny::isolate(input$ms_neu)\n yaml_advanced$ms$polarity <- ms_pol\n yaml_advanced$ms$thresholds$ms2$intensity <-\n shiny::isolate(input$ms_thr_ms2_int)\n yaml_advanced$ms$tolerances$mass$ppm$ms1 <-\n shiny::isolate(input$ms_tol_mas_ppm_ms1)\n yaml_advanced$ms$tolerances$mass$ppm$ms2 <-\n shiny::isolate(input$ms_tol_mas_ppm_ms2)\n yaml_advanced$ms$tolerances$mass$dalton$ms1 <-\n shiny::isolate(input$ms_tol_mas_dal_ms1)\n yaml_advanced$ms$tolerances$mass$dalton$ms2 <-\n shiny::isolate(input$ms_tol_mas_dal_ms2)\n yaml_advanced$ms$tolerances$rt$adducts <-\n shiny::isolate(input$ms_tol_rt_add)\n yaml_advanced$ms$tolerances$rt$library <-\n shiny::isolate(input$ms_tol_rt_lib)\n yaml_advanced$names$adduct <-\n shiny::isolate(input$names_adduct)\n yaml_advanced$names$extension <-\n shiny::isolate(input$names_extension)\n yaml_advanced$names$features <-\n shiny::isolate(input$names_features)\n yaml_advanced$names$filename <-\n shiny::isolate(input$names_filename)\n yaml_advanced$names$inchikey <-\n shiny::isolate(input$names_inchikey)\n yaml_advanced$names$libraries <-\n shiny::isolate(input$names_libraries)\n yaml_advanced$names$mgf$adduct <-\n shiny::isolate(input$names_mgf_ad)\n yaml_advanced$names$mgf$collision_energy <-\n shiny::isolate(input$names_mgf_ce)\n yaml_advanced$names$mgf$compound_id <-\n shiny::isolate(input$names_mgf_ci)\n yaml_advanced$names$mgf$exact_mass <-\n shiny::isolate(input$names_mgf_em)\n yaml_advanced$names$mgf$inchi <-\n shiny::isolate(input$names_mgf_in)\n yaml_advanced$names$mgf$inchi_no_stereo <-\n shiny::isolate(input$names_mgf_io)\n yaml_advanced$names$mgf$inchikey <-\n shiny::isolate(input$names_mgf_ik)\n yaml_advanced$names$mgf$inchikey_no_stereo <-\n shiny::isolate(input$names_mgf_il)\n yaml_advanced$names$mgf$molecular_formula <-\n shiny::isolate(input$names_mgf_mf)\n yaml_advanced$names$mgf$name <-\n shiny::isolate(input$names_mgf_na)\n yaml_advanced$names$mgf$polarity <-\n shiny::isolate(input$names_mgf_po)\n yaml_advanced$names$mgf$retention_time <-\n shiny::isolate(input$names_mgf_rt)\n yaml_advanced$names$mgf$smiles <-\n shiny::isolate(input$names_mgf_sm)\n yaml_advanced$names$mgf$smiles_no_stereo <-\n shiny::isolate(input$names_mgf_sn)\n yaml_advanced$names$mgf$spectrum_id <-\n shiny::isolate(input$names_mgf_si)\n yaml_advanced$names$mgf$splash <-\n shiny::isolate(input$names_mgf_sp)\n yaml_advanced$names$mgf$synonyms <-\n shiny::isolate(input$names_mgf_sy)\n yaml_advanced$names$mgf$xlogp <-\n shiny::isolate(input$names_mgf_xl)\n yaml_advanced$names$precursor <-\n shiny::isolate(input$names_precursor)\n yaml_advanced$names$rt$features <-\n shiny::isolate(input$names_rt)\n yaml_advanced$names$rt$library <-\n shiny::isolate(input$names_rt_2)\n yaml_advanced$names$smiles <-\n shiny::isolate(input$names_smiles)\n yaml_advanced$names$source <-\n shiny::isolate(input$names_source)\n yaml_advanced$names$target <-\n shiny::isolate(input$names_target)\n yaml_advanced$names$taxon <-\n shiny::isolate(input$names_taxon)\n yaml_advanced$organisms$candidates <-\n shiny::isolate(input$org_can)\n yaml_advanced$organisms$filter$mode <-\n shiny::isolate(input$org_fil_mod)\n yaml_advanced$organisms$filter$level <-\n shiny::isolate(input$org_fil_lev)\n yaml_advanced$organisms$filter$value <-\n shiny::isolate(input$org_fil_val)\n yaml_advanced$organisms$taxon <-\n org_tax\n # TODO\n # yaml_advanced$tools$metadata <-\n # shiny::isolate(input$too_x)\n # yaml_advanced$tools$networks$spectral$components <-\n # shiny::isolate(input$too_x)\n # yaml_advanced$tools$networks$spectral$edges <-\n # shiny::isolate(input$too_x)\n yaml_advanced$tools$sirius$version <-\n shiny::isolate(input$too_sir_ver)\n # TODO\n # yaml_advanced$tools$taxonomies$biological <-\n # shiny::isolate(input$too_x)\n # yaml_advanced$tools$taxonomies$chemical <-\n # shiny::isolate(input$too_x)\n yaml_advanced$units$rt <-\n shiny::isolate(input$uni_rt)\n yaml_advanced$weights$global$biological <-\n shiny::isolate(input$wei_glo_bio)\n yaml_advanced$weights$global$chemical <-\n shiny::isolate(input$wei_glo_che)\n yaml_advanced$weights$global$spectral <-\n shiny::isolate(input$wei_glo_spe)\n yaml_advanced$weights$biological$domain <-\n shiny::isolate(input$wei_bio_01)\n yaml_advanced$weights$biological$kingdom <-\n shiny::isolate(input$wei_bio_02)\n yaml_advanced$weights$biological$phylum <-\n shiny::isolate(input$wei_bio_03)\n yaml_advanced$weights$biological$class <-\n shiny::isolate(input$wei_bio_04)\n yaml_advanced$weights$biological$order <-\n shiny::isolate(input$wei_bio_05)\n yaml_advanced$weights$biological$infraorder <-\n shiny::isolate(input$wei_bio_06)\n yaml_advanced$weights$biological$family <-\n shiny::isolate(input$wei_bio_07)\n yaml_advanced$weights$biological$subfamily <-\n shiny::isolate(input$wei_bio_08)\n yaml_advanced$weights$biological$tribe <-\n shiny::isolate(input$wei_bio_09)\n yaml_advanced$weights$biological$subtribe <-\n shiny::isolate(input$wei_bio_10)\n yaml_advanced$weights$biological$genus <-\n shiny::isolate(input$wei_bio_11)\n yaml_advanced$weights$biological$subgenus <-\n shiny::isolate(input$wei_bio_12)\n yaml_advanced$weights$biological$species <-\n shiny::isolate(input$wei_bio_13)\n yaml_advanced$weights$biological$subspecies <-\n shiny::isolate(input$wei_bio_14)\n yaml_advanced$weights$biological$variety <-\n shiny::isolate(input$wei_bio_15)\n yaml_advanced$weights$chemical$cla$kingdom <-\n shiny::isolate(input$wei_che_11)\n yaml_advanced$weights$chemical$npc$pathway <-\n shiny::isolate(input$wei_che_21)\n yaml_advanced$weights$chemical$cla$superclass <-\n shiny::isolate(input$wei_che_12)\n yaml_advanced$weights$chemical$npc$superclass <-\n shiny::isolate(input$wei_che_22)\n yaml_advanced$weights$chemical$cla$class <-\n shiny::isolate(input$wei_che_13)\n yaml_advanced$weights$chemical$npc$class <-\n shiny::isolate(input$wei_che_23)\n yaml_advanced$weights$chemical$cla$parent <-\n shiny::isolate(input$wei_che_14)\n yaml_advanced$options$compounds_names <-\n shiny::isolate(input$compounds_names)\n yaml_advanced$options$high_confidence <- hig_con\n yaml_advanced$options$force <-\n shiny::isolate(input$force)\n yaml_advanced$options$remove_ties <-\n shiny::isolate(input$remove_ties)\n yaml_advanced$options$summarise <- summarise\n\n if (!is.null(prefil_met_raw)) {\n yamls_params$prepare_taxa$files$metadata$raw <- fil_met_raw\n }\n if (!is.null(gnps_job_id)) {\n yamls_params$prepare_taxa$files$metadata$raw <-\n file.path(paths_data_source, paste0(gnps_job_id, \"_metadata.tsv\"))\n }\n\n yaml::write_yaml(\n x = yaml_advanced,\n file = tima:::get_default_paths()$params$prepare_params_advanced\n )\n}\n\nserver <- function(input, output, session) {\n ## Observe helpers\n shinyhelper::observe_helpers()\n\n output$demo_spe <- downloadHandler(\n filename = basename(tima:::get_default_paths()$urls$examples$spectra),\n content = function(file) {\n writeLines(readLines(tima:::get_default_paths()$urls$examples$spectra_mini), file)\n }\n )\n\n output$demo_fea <- downloadHandler(\n filename = basename(tima:::get_default_paths()$urls$examples$features),\n content = function(file) {\n writeLines(readLines(tima:::get_default_paths()$urls$examples$features), file)\n }\n )\n\n output$demo_met <- downloadHandler(\n filename = basename(tima:::get_default_paths()$urls$examples$metadata),\n content = function(file) {\n writeLines(readLines(tima:::get_default_paths()$urls$examples$metadata), file)\n }\n )\n\n ## Mandatory fields\n fields_mandatory <- c(\"fil_fea_raw\", \"fil_spe_raw\", \"fil_pat\")\n\n ## Enable the Submit button when all mandatory fields are filled out\n shiny::observe(x = {\n mandatory_filled <-\n vapply(\n X = fields_mandatory,\n FUN = function(x) {\n ## TODO improve\n suppressWarnings(any(!is.null(input[[x]]), input[[x]] != \"\"))\n },\n FUN.VALUE = logical(1)\n )\n mandatory_filled <- all(mandatory_filled)\n\n shinyjs::toggleState(id = \"save\", condition = mandatory_filled)\n shinyjs::toggleState(id = \"launch\", condition = input$save >= 1)\n })\n\n ## Special check for taxon name\n iv <- shinyvalidate::InputValidator$new()\n iv$add_rule(\"org_tax\", function(taxon) {\n if (!grepl(\n pattern = \"^[[:upper:]]\",\n x = taxon,\n perl = TRUE\n )) {\n \"Please provide your taxon name with capital letter\"\n }\n })\n iv$add_rule(\"org_tax\", function(taxon) {\n if (is.na(rotl::tnrs_match_names(names = taxon, do_approximate_matching = FALSE)$ott_id)) {\n \"Taxon not found in Open Tree of Life\"\n }\n })\n iv$enable()\n\n ## When the Save button is clicked, save the response\n shiny::observeEvent(eventExpr = input$save, handlerExpr = {\n ## User-experience stuff\n shinyjs::show(\"save_msg\")\n shinyjs::enable(\"launch\")\n shinyjs::hide(\"error\")\n\n ## Save the data (show an error message in case of error)\n tryCatch(\n expr = {\n .save_input(input = input)\n shinyjs::show(\"thankyou_msg\")\n },\n error = function(err) {\n shinyjs::html(\"error_msg\", err$message)\n shinyjs::show(\n id = \"error\",\n anim = TRUE,\n animType = \"fade\"\n )\n },\n finally = {\n shinyjs::enable(\"save\")\n shinyjs::enable(\"launch\")\n shinyjs::hide(\"save_msg\")\n shinyjs::hide(\"error\")\n }\n )\n })\n\n shiny::observeEvent(eventExpr = input$launch, handlerExpr = {\n shinyjs::show(\"job_msg\")\n shinyjs::hide(\"thankyou_msg\")\n shinyjs::hide(\"error\")\n shinyjs::hide(\"params\")\n shinyjs::hide(\"form\")\n tryCatch(expr = {\n targets::tar_watch(\n host = \"127.0.0.1\",\n port = 3839,\n display = \"graph\",\n displays = c(\"summary\", \"graph\"),\n level_separation = 300,\n degree_from = 8,\n outdated = FALSE,\n targets_only = TRUE,\n supervise = TRUE,\n verbose = TRUE,\n exclude = c(\n \"yaml_paths\",\n \"benchmark_ann_fil_ms1_neg\",\n \"benchmark_ann_fil_ms1_pos\",\n \"benchmark_ann_fil_spe_neg\",\n \"benchmark_ann_fil_spe_pos\",\n \"benchmark_ann_fil_spe_ms1_neg\",\n \"benchmark_ann_fil_spe_ms1_pos\",\n \"benchmark_ann_ms1_neg\",\n \"benchmark_ann_ms2_pos\",\n \"benchmark_ann_ms1_pre_neg\",\n \"benchmark_ann_ms1_pre_pos\",\n \"benchmark_ann_pre_ms1_ms2_b_c_neg\",\n \"benchmark_ann_pre_ms1_ms2_b_c_pos\",\n \"benchmark_ann_pre_ms1_ms2_b_neg\",\n \"benchmark_ann_pre_ms1_ms2_b_pos\",\n \"benchmark_ann_pre_ms2_b_c_neg\",\n \"benchmark_ann_pre_ms2_b_c_pos\",\n \"benchmark_ann_pre_ms2_b_neg\",\n \"benchmark_ann_pre_ms2_b_pos\",\n \"benchmark_ann_sir_pre\",\n \"benchmark_ann_sir_pre_can\",\n \"benchmark_ann_sir_pre_for\",\n \"benchmark_ann_sir_pre_str\",\n \"benchmark_ann_spe_neg\",\n \"benchmark_ann_spe_pos\",\n \"benchmark_ann_spe_pre_neg\",\n \"benchmark_ann_spe_pre_pos\",\n \"benchmark_com_neg\",\n \"benchmark_com_pos\",\n \"benchmark_com_pre_neg\",\n \"benchmark_com_pre_pos\",\n \"benchmark_converted\",\n \"benchmark_copy\",\n \"benchmark_def_ann_mas\",\n \"benchmark_def_ann_spe\",\n \"benchmark_def_cre_edg_com\",\n \"benchmark_def_cre_edg_spe\",\n \"benchmark_def_fil_ann\",\n \"benchmark_def_pre_ann_sir\",\n \"benchmark_def_pre_ann_spe\",\n \"benchmark_def_pre_fea_com\",\n \"benchmark_def_pre_fea_edg\",\n \"benchmark_def_wei_ann\",\n \"benchmark_edg_pre_neg\",\n \"benchmark_edg_pre_pos\",\n \"benchmark_edg_spe_neg\",\n \"benchmark_edg_spe_pos\",\n \"benchmark_file\",\n \"benchmark_files_neg\",\n \"benchmark_files_pos\",\n \"benchmark_path_copy\",\n \"benchmark_path_export\",\n \"benchmark_path_file\",\n \"benchmark_path_mgf_neg\",\n \"benchmark_path_mgf_pos\",\n \"benchmark_path_url\",\n \"benchmark_path_zip\",\n \"benchmark_prepared\",\n \"benchmark_pre_meta_neg\",\n \"benchmark_pre_meta_pos\",\n \"benchmark_pre_mgf_neg\",\n \"benchmark_pre_mgf_pos\",\n \"benchmark_taxed_neg\",\n \"benchmark_taxed_pos\",\n \"benchmark_wei_par\",\n \"benchmark_zip\",\n \"paths\",\n \"paths_data_interim_libraries_adducts_path\",\n \"paths_data_source_benchmark_cleaned\",\n \"paths_data_source_benchmark_copy\",\n \"paths_data_source_benchmark_mgf_neg\",\n \"paths_data_source_benchmark_mgf_pos\",\n \"paths_data_source_benchmark_set\",\n \"paths_data_source_benchmark_zip\",\n \"paths_data_source_libraries_sop_ecmdb\",\n \"paths_data_source_libraries_sop_hmdb\",\n \"paths_data_source_libraries_sop_lotus\",\n \"paths_data_source_libraries_spectra_is_lotus_pos\",\n \"paths_data_source_libraries_spectra_is_lotus_neg\",\n \"paths_data_source_spectra\",\n # \"paths_gnps_example_id\",\n \"paths_interim_a\",\n \"paths_interim_f\",\n \"paths_source\",\n \"paths_test_mode\",\n \"paths_urls_benchmarking_set\",\n \"paths_urls_ecmdb_metabolites\",\n \"paths_urls_hmdb_structures\",\n \"paths_urls_lotus_doi\",\n \"paths_urls_lotus_pattern\",\n \"paths_urls_massbank_file\",\n \"paths_urls_massbank_url\",\n \"paths_urls_massbank_version\",\n \"paths_urls_examples_spectra_mini\",\n \"paths_urls_examples_spectral_lib_pos\",\n \"paths_urls_examples_spectral_lib_neg\",\n \"par_def_ann_mas\",\n \"par_def_ann_spe\",\n \"par_def_cre_com\",\n \"par_def_cre_edg_spe\",\n \"par_def_fil_ann\",\n \"par_def_pre_ann_gnp\",\n \"par_def_pre_ann_sir\",\n \"par_def_pre_ann_spe\",\n \"par_def_pre_fea_com\",\n \"par_def_pre_fea_edg\",\n \"par_def_pre_fea_tab\",\n \"par_def_pre_lib_rt\",\n \"par_def_pre_lib_sop_clo\",\n \"par_def_pre_lib_sop_ecm\",\n \"par_def_pre_lib_sop_hmd\",\n \"par_def_pre_lib_sop_lot\",\n \"par_def_pre_lib_sop_mer\",\n \"par_def_pre_lib_spe\",\n \"par_def_pre_tax\",\n \"par_def_wei_ann\",\n \"par_fin_par\",\n \"par_fin_par2\",\n \"par_pre_par\",\n \"par_pre_par2\",\n \"par_usr_ann_mas\",\n \"par_usr_ann_spe\",\n \"par_usr_cre_com\",\n \"par_usr_cre_edg_spe\",\n \"par_usr_fil_ann\",\n \"par_usr_pre_ann_gnp\",\n \"par_usr_pre_ann_sir\",\n \"par_usr_pre_ann_spe\",\n \"par_usr_pre_fea_com\",\n \"par_usr_pre_fea_edg\",\n \"par_usr_pre_fea_tab\",\n \"par_usr_pre_lib_rt\",\n \"par_usr_pre_lib_sop_clo\",\n \"par_usr_pre_lib_sop_ecm\",\n \"par_usr_pre_lib_sop_hmd\",\n \"par_usr_pre_lib_sop_lot\",\n \"par_usr_pre_lib_sop_mer\",\n \"par_usr_pre_lib_spe\",\n \"par_usr_pre_tax\",\n \"par_usr_wei_ann\",\n \"par_ann_mas\",\n \"par_ann_spe\",\n \"par_cre_com\",\n \"par_cre_edg_spe\",\n \"par_fil_ann\",\n \"par_pre_ann_gnp\",\n \"par_pre_ann_sir\",\n \"par_pre_ann_spe\",\n \"par_pre_fea_com\",\n \"par_pre_fea_edg\",\n \"par_pre_fea_tab\",\n \"par_pre_lib_rt\",\n \"par_pre_lib_sop_clo\",\n \"par_pre_lib_sop_ecm\",\n \"par_pre_lib_sop_hmd\",\n \"par_pre_lib_sop_lot\",\n \"par_pre_lib_sop_mer\",\n \"par_pre_lib_spe\",\n \"par_pre_tax\",\n \"par_wei_ann\",\n \".Random.seed\"\n )\n )\n targets::tar_make(\n names = tidyselect::matches(\"^ann_pre$\"),\n garbage_collection = TRUE,\n reporter = \"verbose_positives\"\n )\n }, finally = {\n shiny::stopApp()\n })\n process <-\n shiny::reactiveValues(status = targets::tar_active())\n shiny::observe({\n shiny::invalidateLater(millis = 5000)\n process$status <- targets::tar_active()\n })\n\n shiny::observeEvent(eventExpr = process$status, handlerExpr = {\n message(\"Job finished\")\n shiny::stopApp()\n })\n })\n}\nurl <- \"\"\nmessage(\"Please, open:\", url, \"on your favorite browser, but not Edge.\")\nshiny::shinyApp(\n ui = ui,\n server = server,\n onStart = function() {\n if (i_am_a_whale) {\n message(\"I'm inside the matrix ;(\")\n setwd(dir = \"..\")\n } else {\n tima:::copy_backbone()\n tima:::go_to_cache()\n }\n }\n)\n","type":"text"},{"name":"CITATION","content":"citHeader(\"To cite tima in publications use:\")\n\nbibentry(\n bibtype = \"Manual\",\n title = \"tima: Taxonomically Informed Metabolite Annotation\",\n author = c(\n person(given = \"Adriano\", family = \"Rutz\", email = \"adafede@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-0443-9902\"))\n ),\n year = \"2024\",\n note = \"R package, version 2.11.0\",\n doi = \"10.5281/zenodo.5797920\"\n)\n\nbibentry(\n bibtype = \"Article\",\n title = \"Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation\",\n author = c(\n person(given = \"Adriano\", family = \"Rutz\", comment = c(ORCID = \"0000-0003-0443-9902\")),\n person(given = \"Miwa\", family = \"Dounoue-Kubo\"),\n person(given = \"Simon\", family = \"Ollivier\", comment = c(ORCID = \"0000-0002-7671-1736\")),\n person(given = \"Jonathan\", family = \"Bisson\", comment = c(ORCID = \"0000-0003-1640-9989\")),\n person(given = \"Mohsen\", family = \"Bagheri\"),\n person(given = \"Tongchai\", family = \"Saesong\"),\n person(given = \"Samad Nejad\", family = \"Ebrahimi\", comment = c(ORCID = \"0000-0003-2167-8032\")),\n person(given = \"Kornkanok\", family = \"Ingkaninan\", comment = c(ORCID = \"0000-0002-4415-8489\")),\n person(given = \"Jean-Luc\", family = \"Wolfender\", comment = c(ORCID = \"0000-0002-0125-952X\")),\n person(given = \"Pierre-Marie\", family = \"Allard\", comment = c(ORCID = \"0000-0003-3389-2191\"))\n ),\n journal = \"Frontiers in Plant Science\",\n year = \"2019\",\n volume = \"10\",\n url = \"https://doi.org/10.3389/fpls.2019.01329\",\n language = \"en\",\n keywords = \"metabolite annotation, chemotaxonomy, scoring system, natural products, computational metabolomics, taxonomic distance, specialized metabolome\",\n doi = \"10.3389/FPLS.2019.01329\",\n issn = \"1664-462X\",\n pmc = \"PMC6824209\",\n pmid = \"1708947\",\n qid = \"Q91218352\",\n textVersion = paste(\n \"Adriano Rutz; Miwa Dounoue-Kubo; et al. (25 October 2019). Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation. Frontiers in Plant Science. 10: 1329. doi:10.3389/FPLS.2019.01329. issn:1664-462X. pmc:PMC6824209. pmid:1708947. qid:Q91218352.\"\n )\n)\n","type":"text"},{"name":"DESCRIPTION","content":"Package: tima\nTitle: Taxonomically Informed Metabolite Annotation\nVersion: 2.11.0\nAuthors@R: c(\n person(\"Adriano\", \"Rutz\", , \"adafede@gmail.com\", role = c(\"aut\", \"cre\"),\n comment = c(ORCID = \"0000-0003-0443-9902\")),\n person(\"Pierre-Marie\", \"Allard\", , \"pierre-marie.allard@unifr.ch\", role = \"ctb\",\n comment = c(ORCID = \"0000-0003-3389-2191\"))\n )\nMaintainer: Adriano Rutz \nDescription: This package provides the infrastructure to perform\n Taxonomically Informed Metabolite Annotation.\nLicense: GPL (>= 3)\nURL: https://github.com/taxonomicallyinformedannotation/tima,\n https://taxonomicallyinformedannotation.github.io/tima,\n https://taxonomicallyinformedannotation.github.io/tima-shinylive\nBugReports: https://github.com/taxonomicallyinformedannotation/tima/issues\nDepends: R (>= 4.3.0)\nImports: crayon (>= 1.5.3), docopt (>= 0.7.1), dplyr (>= 1.1.4), fs (>=\n 1.6.4), httr2 (>= 1.0.1), igraph (>= 2.0.3), jsonlite (>=\n 1.8.8), MetaboCoreUtils (>= 1.10.0), MsBackendMgf (>= 1.10.0),\n MsBackendMsp (>= 1.6.0), msentropy (>= 0.1.4), pbapply (>=\n 1.7.2), rotl (>= 3.1.0), shiny (>= 1.9.0), Spectra (>= 1.12.0),\n stats, stringi (>= 1.8.4), targets (>= 1.7.1), tidyfst (>=\n 1.7.8), tidyselect (>= 1.2.1), tidytable (>= 0.11.1), utils,\n yaml (>= 2.3.10)\nSuggests: BiocManager, knitr, lifecycle, pkgload, R.utils, rlang,\n shinyhelper, shinyjs, shinytest2, shinyvalidate, shinyWidgets,\n spelling, testthat (>= 3.0.0), visNetwork\nVignetteBuilder: knitr\nbiocViews: metabolite annotation, chemotaxonomy, scoring system,\n natural products, computational metabolomics, taxonomic\n distance, specialized metabolome\nByteCompile: true\nConfig/Needs/website: rmarkdown\nConfig/testthat/edition: 3\nEncoding: UTF-8\nLanguage: en-US\nRoxygen: list(markdown = TRUE)\nRoxygenNote: 7.3.2\nX-schema.org-keywords: metabolite annotation, chemotaxonomy, scoring\n system, natural products, computational metabolomics, taxonomic\n distance, specialized metabolome\nCollate: 'round_reals.R' 'log_pipe.R' 'harmonize_adducts.R'\n 'parse_yaml_params.R' 'parse_cli_params.R'\n 'get_default_paths.R' 'get_params.R' 'dist_groups.R'\n 'decorate_masses.R' 'parse_adduct.R' 'calculate_mass_of_m.R'\n 'annotate_masses.R' 'sanitize_spectra.R' 'import_spectra.R'\n 'annotate_spectra.R' 'benchmark_taxize_spectra.R'\n 'calculate_entropy.R' 'clean_bio.R'\n 'filter_high_confidence_only.R' 'columns_model.R'\n 'clean_collapse.R' 'clean_chemo.R'\n 'complement_metadata_structures.R' 'copy_backbone.R'\n 'create_components.R' 'create_dir.R' 'create_edges.R'\n 'create_edges_spectra.R' 'decorate_bio.R' 'decorate_chemo.R'\n 'export_output.R' 'export_params.R' 'export_spectra_rds.R'\n 'extract_spectra.R' 'fake_annotations_columns.R' 'fake_ecmdb.R'\n 'fake_hmdb.R' 'fake_lotus.R' 'fake_sop_columns.R'\n 'filter_annotations.R' 'go_to_cache.R' 'get_file.R'\n 'get_example_sirius.R' 'get_example_files.R'\n 'get_gnps_tables.R' 'get_last_version_from_zenodo.R'\n 'get_massbank_spectra.R' 'get_organism_taxonomy_ott.R'\n 'globals.R' 'harmonize_names_sirius.R' 'harmonize_spectra.R'\n 'install.R' 'load_yaml_files.R' 'log_debug.R'\n 'pre_harmonize_names_sirius.R' 'select_annotations_columns.R'\n 'prepare_annotations_gnps.R' 'select_sirius_columns.R'\n 'read_from_sirius_zip.R' 'prepare_annotations_sirius.R'\n 'prepare_annotations_spectra.R' 'prepare_features_components.R'\n 'prepare_features_edges.R' 'prepare_features_tables.R'\n 'prepare_libraries_rt.R' 'select_sop_columns.R'\n 'prepare_libraries_sop_closed.R'\n 'prepare_libraries_sop_ecmdb.R' 'prepare_libraries_sop_hmdb.R'\n 'prepare_libraries_sop_lotus.R' 'split_tables_sop.R'\n 'prepare_libraries_sop_merged.R' 'prepare_libraries_spectra.R'\n 'prepare_params.R' 'prepare_taxa.R' 'replace_id.R' 'run_app.R'\n 'tima-package.R' 'tima_full.R' 'transform_score_sirius_csi.R'\n 'weight_chemo.R' 'weight_bio.R' 'weight_annotations.R'\nRepository: https://taxonomicallyinformedannotation.r-universe.dev\nRemoteUrl: https://github.com/taxonomicallyinformedannotation/tima\nRemoteRef: HEAD\nRemoteSha: e7174ea387f0beee705607011699d697eccfa7f0\nNeedsCompilation: no\nPackaged: 2024-08-16 13:19:56 UTC; root\nAuthor: Adriano Rutz [aut, cre] (),\n Pierre-Marie Allard [ctb] ()\nBuilt: R 4.4.1; ; 2024-08-16 14:15:33 UTC; unix\n","type":"text"},{"name":"INDEX","content":"annotate_masses Annotate masses\nannotate_spectra Annotate spectra\nbenchmark_taxize_spectra\n Benchmark taxize spectra\ncalculate_entropy Calculate entropy\ncalculate_mass_of_m Calculate mass of M\nclean_bio Clean bio\nclean_chemo Clean chemo\nclean_collapse Clean collapse\ncolumns_model Columns model\ncomplement_metadata_structures\n Complement metadata of structures\ncopy_backbone Copy backbone\ncreate_components Create components\ncreate_dir Create directory\ncreate_edges Create edges\ncreate_edges_spectra Create edges spectra\ndecorate_bio Decorate bio\ndecorate_chemo Decorate chemo\ndecorate_masses Decorate masses\ndist_get Distance between two elements in a distance\n matrix\ndist_groups Dist groups\nexport_output Export output\nexport_params Export parameters\nexport_spectra_rds Export spectra RDS\nextract_spectra Extract spectra from a Spectra object\nfake_annotations_columns\n Fake annotations columns\nfake_ecmdb Fake ECMDB\nfake_hmdb Fake HMDB\nfake_lotus Fake LOTUS\nfake_sop_columns Fake SOP columns\nfilter_annotations Filter annotations\nfilter_high_confidence_only\n Filter high confidence only *[Experimental]*\nget_default_paths Get default paths\nget_example_files Get example files\nget_example_sirius Get example sirius\nget_file Get file\nget_gnps_tables Get GNPS Tables\nget_last_version_from_zenodo\n Get last version from Zenodo\nget_massbank_spectra Get MassBank spectra\nget_organism_taxonomy_ott\n Get organism taxonomy (Open Tree of Life\n Taxonomy)\nget_params Get parameters\ngo_to_cache Go to cache\nharmonize_adducts Harmonize adducts\nharmonize_names_sirius\n Harmonize names sirius\nharmonize_spectra Harmonize spectra\nimport_spectra Import spectra\ninstall Install\nload_yaml_files Load yaml files\nlog_debug Log debug\nlog_pipe Log pipe\nparse_adduct Parse adduct\nparse_cli_params Parse CLI parameters\nparse_yaml_params Parse YAML parameters\npre_harmonize_names_sirius\n Pre harmonize names sirius\nprepare_annotations_gnps\n Prepare annotations GNPS\nprepare_annotations_sirius\n Prepare annotations SIRIUS\nprepare_annotations_spectra\n Prepare annotations MS2\nprepare_features_components\n Prepare features components\nprepare_features_edges\n Prepare features edges\nprepare_features_tables\n Prepare features table\nprepare_libraries_rt Prepare libraries of retention times\nprepare_libraries_sop_closed\n Prepare libraries of structure organism pairs\n CLOSED\nprepare_libraries_sop_ecmdb\n Prepare libraries of structure organism pairs\n ECMDB\nprepare_libraries_sop_hmdb\n Prepare libraries of structure organism pairs\n HMDB\nprepare_libraries_sop_lotus\n Prepare libraries of structure organism pairs\n LOTUS\nprepare_libraries_sop_merged\n Prepare merged structure organism pairs\n libraries\nprepare_libraries_spectra\n Prepare libraries of spectra\nprepare_params Prepare params\nprepare_taxa Prepare taxa\nread_from_sirius_zip Read from SIRIUS zip\nreplace_id Replace ID in file paths\nround_reals Round reals\nrun_app Run app\nsanitize_spectra Sanitize spectra\nselect_annotations_columns\n Select annotations columns\nselect_sirius_columns_canopus\n Select sirius columns (canopus)\nselect_sirius_columns_formulas\n Select sirius columns (formulas)\nselect_sirius_columns_structures\n Select sirius columns (structures)\nselect_sop_columns Select SOP columns\nsplit_tables_sop Split Structure Organism Pairs table\ntima_full Tima Full\ntransform_score_sirius_csi\n Transform score sirius CSI\nweight_annotations Weight annotations\nweight_bio Weight bio\nweight_chemo Weight 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Generated by roxygen2: do not edit by hand\n\nexport(annotate_masses)\nexport(annotate_spectra)\nexport(calculate_mass_of_m)\nexport(create_components)\nexport(create_dir)\nexport(create_edges_spectra)\nexport(export_output)\nexport(filter_annotations)\nexport(get_default_paths)\nexport(get_example_files)\nexport(get_file)\nexport(get_last_version_from_zenodo)\nexport(get_organism_taxonomy_ott)\nexport(get_params)\nexport(go_to_cache)\nexport(import_spectra)\nexport(install)\nexport(log_debug)\nexport(parse_adduct)\nexport(prepare_annotations_gnps)\nexport(prepare_annotations_sirius)\nexport(prepare_annotations_spectra)\nexport(prepare_features_components)\nexport(prepare_features_edges)\nexport(prepare_features_tables)\nexport(prepare_libraries_rt)\nexport(prepare_libraries_sop_closed)\nexport(prepare_libraries_sop_ecmdb)\nexport(prepare_libraries_sop_hmdb)\nexport(prepare_libraries_sop_lotus)\nexport(prepare_libraries_sop_merged)\nexport(prepare_libraries_spectra)\nexport(prepare_taxa)\nexport(run_app)\nexport(sanitize_spectra)\nexport(tima_full)\nexport(weight_annotations)\n","type":"text"},{"name":"NEWS.md","content":"# tima\n\n# tima 2.11.0\n\n* Added demo files download to the app\n* Better packaging\n* Improved documentation\n* Fixed all CRAN warnings\n* Reduced dependencies\n* Reduced exports\n* Removed `CompoundDb` dependency as it was causing too many issues\n* Removed `pak` install and switched to `r-universe`\n* Replaced internal functions by `Spectra` equivalents (#166)\n* Shinylive version available at \n* Simplified install and vignettes\n\n# tima 2.10.0\n\n* Added alt text to vignettes\n* Added the possibility to add internal libraries through the GUI (#159)\n* Added the possibility to filter confident annotations only (#140)\n* Added number of peaks in spectrum\n* Brought back some older dependencies to be compatible with `oldrel`\n* Changed package name, `usethis` update\n* Clearer handling of SIRIUS scores (#146, #147)\n* Exposed more parameters to the GUI (#159)\n* Facilitated install, no need to clone the directory anymore\n* Finally made it to the [r-universe](https://r-universe.dev/builds/)\n* Fixed adducts and removed nitrogen rule\n* Fixed number of matched peaks\n* Improved imports\n* Reduced warnings\n* Updated benchmarking steps\n\n# tima 2.9.6\n\n* Added light-switch thanks to `pkgdown 2.1.0`.\n* Attempt to simplify installation\n* Fixed library/adducts confusion (#123)\n* Fixed some incorrect adduct differences annotations\n* Refactored adducts / neutral losses / dimers annotation to allow for more flexibility (#141, #144)\n\n# tima 2.9.5\n\n* Do not re-package if already the latest version\n* SIRIUS 6 default and compatible (keeping SIRIUS 5 backward compatibility)\n* Updated to Massbank version `2024.06`\n\n# tima 2.9.4\n\n* Automated update\n* Added an option to remove ties (#134)\n* Added some details for SIRIUS, added manual workspace addition (#132)\n* Additional preprocessing (reduction) of noisy spectra\n* Dependencies update\n* Docker updates (#131)\n* Handle cases when same (feature_id, mslevel) pairs are present within an MGF (#133)\n* Improved documentation\n* New working directory at `$HOME/.tima`\n* Updated R and Bioconductor versions\n\n# tima 2.9.3\n\n* Allowed for SIRIUS jobs containing only summaries\n* Allowed for underscores in job pattern\n* Changed some default values (less stringent)\n* Dependencies update\n* Migrated app testing to `shinytest2`\n* Removed further some inconsistent MS1 annotations\n* Removed tests dependencies by default\n\n# tima 2.9.2\n\n* Added Nitrogen rule to filter out some annotations\n* Better handling of partial downloads (#118)\n* Dependencies update (mainly `targets 1.5.1`, will invalidate previous targets)\n* Fixed some port issues in Shiny (#122)\n* Removed completely empty columns from final output to avoid confusion (#120)\n\n# tima 2.9.1\n\n* Added [Waystation](https://caltechlibrary.github.io/waystation/) action\n* Added structures from spectral libraries to SOP library (#113)\n* Exposed all parameters (#107, #108)\n* Fixed for Zenodo API\n* HMDB structures support\n* Optimized grep/gsub by adding `perl=TRUE` or `fixed=TRUE`\n* Updated to Massbank version `2023.11`\n* Updated SIRIUS preparation (#74, #115)\n\n# tima 2.9.0\n\n* Added compounds names as parameter\n* Added MassBank spectral library (#77)\n* Allowed files outside `data/source` (#89)\n* Added RT library as annotation library (#86)\n* Better handling of download errors\n* Fixed Docker mount path\n* Improved naming (#91)\n* Internal variables refactoring\n* Multiple Shiny fixes and tests addition (#60)\n* Multiple fixes (#71, #81, #82)\n* New adducts (#79, #80)\n* Refactored adducts, clusters and neutral losses\n* Refactored biological and chemical score\n* Refactored RT matching (#76)\n* Refactored Sirius scores (#92)\n* Removed GNPS dependency by default\n\n# tima 2.8.2\n\n* Added spectral entropy\n* Added MS1 only possibility\n* Added Fluorine adduct\n* Changed from pbmclapply to pblapply\n* Documentation improvement\n* Fixed empty chemical classes\n* Fixed not classified taxa\n* Github Actions improvement\n* [renv](https://rstudio.github.io/renv/index.html) removal\n* Performance improvement by replacing the [tidyverse](https://www.tidyverse.org) by the [fastverse](https://fastverse.github.io/fastverse) (in progress)\n* Reduced warnings (CRAN and jscpd)\n\n# tima 2.8.1\n\n* Adapted tests\n* Added `retry` parameter to `get_organism_taxonomy_ott`\n* Dependencies update\n* Minor fixes\n* Moved `/params` and `paths.yaml` to `/inst` as more standard. (see )\n* Performance improvement by replacing the [tidyverse](https://www.tidyverse.org) by the [fastverse](https://fastverse.github.io/fastverse) (in progress)\n* Replaced `extdata` loading\n\n# tima 2.8.0\n\n* Added GUI prototype\n* Started using [renv](https://rstudio.github.io/renv/index.html)\n\n# tima 2.7.4\n\n* Clearer vocabulary\n* ECMDB support\n* Edges (mass and spectra-based) and components are generated if not present.\n* Fixed case when no GNPS job ID\n* Further [Targets](https://books.ropensci.org/targets/) improvements\n* Lot of fixes\n* Parameters refactoring\n* Re-introduced Classyfire support.\n* Retention time matching additionally to MS2 if RT present in library\n* Steps refactoring\n\n# tima 2.7.3\n\n* Improved calculations over redundant formulas\n* Minor fixes\n* Parameters refactoring\n* Spectral matching update (see )\n* [Targets](https://books.ropensci.org/targets/) implementation\n\n# tima 2.7.2\n\n* Benchmark update (including negative mode)\n* Improved parameters documentation\n* Minor fixes\n* Spectral comparison + intensity filtering update\n* Switched r-base Docker image to bioconductor with ARM support\n\n# tima 2.7.1\n\n* Added MONA helpers\n* Added parallelization on process_spectra\n* Added sqlite storing for spectra\n* Improved code documentation\n* Improved testing time\n* Minor fixes\n\n# tima 2.7.0\n\n* Added HMDB helpers for both taxo and ISDB\n* Added MS2 annotation capability (kudos @jorainer for the awesome *Spectra* suite)\n* Minor fixes\n\n# tima 2.6.0\n\n* Added Docker container\n* Changed data architecture\n* Minor fixes\n\n# tima 2.5.6\n\n* Dependencies removal (e.g. metabo-store)\n* Minor fixes\n* Partial functions cleanup\n\n# tima 2.5.5\n\n* Automation and parameters improvement\n* Minor fixes\n\n# tima 2.5.4\n\n* Minor fixes\n* Metadata completion improvement\n* Molecular formula and adducts formalism improvement\n\n# tima 2.5.3\n\n* Imports improvements\n* LOTUS update\n\n# tima 2.5.2\n\n* Packaging improvements\n\n# tima 2.5.1\n\n* Improved support for SIRIUS (with new summaries)\n\n# tima 2.5.0\n\n* LOTUS update\n* Minor fixes\n\n# tima 2.4.0\n\n* Added chemical names and xlogp to output (#33)\n* Added support for case when no consensus is found (#30)\n* Improved output (#34)\n* Minor fixes\n\n# tima 2.3.0\n\n* Added support for annotation without MN (#28)\n* Added support for multi tool annotations (#27)\n* Added support for classical MN GNPS jobs (#25)\n* Added support for new version of LOTUS\n* General improvements for manual inputs\n* Improved tests code coverage\n* Minor fixes\n* Updated adducts\n\n# tima 2.2.2\n\n* Additional benchmark figure ([Candidates distribution](https://taxonomicallyinformedannotation.github.io/tima/articles/IV-benchmarking.html#candidates-distribution))\n* Minor fixes\n\n# tima 2.2.1\n\n* Minor version name fixes\n\n# tima 2.2.0\n\n* Added benchmark ([here](https://taxonomicallyinformedannotation.github.io/tima/articles/IV-benchmarking.html))\n* Various fixes\n\n# tima 2.1.0\n\n* Fixes, deletion of binary dependencies.\n\n# tima 2.0.0\n\n* Initial version.\n","type":"text"},{"name":"R/tima","content":"# File share/R/nspackloader.R\n# Part of the R package, https://www.R-project.org\n#\n# Copyright (C) 1995-2012 The R Core Team\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# A copy of the GNU General Public License is available at\n# https://www.r-project.org/Licenses/\n\nlocal({\n info <- loadingNamespaceInfo()\n pkg <- info$pkgname\n ns <- .getNamespace(as.name(pkg))\n if (is.null(ns))\n stop(\"cannot find namespace environment for \", pkg, domain = NA);\n dbbase <- file.path(info$libname, pkg, \"R\", pkg)\n lazyLoad(dbbase, ns, filter = function(n) n != \".__NAMESPACE__.\")\n})\n","type":"text"},{"name":"R/tima.rdb","content":"AAAAxnicTYxLCsIwGIS/pH9EsxI8hzsXnsALqOC2jyDBmoKJB/cCtdAm0NnMg5l5WKBCRFEZ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WPbMTINW84tUAow3d32eh+9WaOuVO6oHk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\nXZviE24r/Wvmu5GIH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IWwtdcr4I4RgMjhINP90fmNocec14BuZQYeZ187ldN+KQ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BckGVYjJWkq8XmQmq5qW0GQA7J5zVeekYqpYojMcFCs8LL6pqjmU9IVJ0JQZzeZgjvZ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\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vPlHVts3tOOHYV4HHXwKXRqNZ6Hvytx\nao0ENR2MSZ/yGoSOTg6Lpdo/kODg85H/Aqn/IVz+z97597ZNhHFcICo1nbpuqvgTYTQGQzAY\n6wYDAdUYDASj29i6MbaupInbpCRxZrs/B2Pt6O+f8BfwB0KAQEhovBdeCVJfgDnHd7fLEye1\nY5/vIl+k28VLEz/P93k+z10cn30etQtxmR0GHCEGdiQ4teJ+EkU63Pjj/4Ym44778SHAefQe\nCk6JBPYo7p8HAV7AQXX70/i5O3P7xGvxBBiafqRhnGlKzAvA3kVs4ypIyCHUrsZlbxhihBjY\nkcTU6no4Yty3BCWGfnzYoaaemDIJ7DHcw8Wiazioa/iwdZfXZ4a9Fk+AoelHmw01fsy8BCxe\nx1bugpS8htqtuCwOw4wQAzuSmVpJH0D/hhtl/N/QZJRxPz7sKFN7D2WmQgL7Iu6PgwB/h4Pq\n9ifwc9RnRrwWT4AbTG8YZfxgeQWY+j027weQi7dRy8dlahhYhBjYkbDUqnk4WNy3BIWFfnzY\nAaYeFoME9mXcnwAB/hEH1e3P4Oeoz+heiyfA0PTnmg0w1ayJjuYDaOAC1J+wmb+AnBxHrRSX\nyWGgEWKgzNA823KEOZUvmijQ4QYZ/zcc8R8w8B6aovNM67dReqokwmT57wCI9K84um7/Jn6O\n+kzFa/FEGlp/zKqWiraNjj1r2ZKpZ/NzWs4oV6dsPa+5DhVzthvkGj+ssb9hA3/HJ5N2eX3G\nQK0al7F7+KtdAH7oTWJOizBUJn6IWAewWOx1CJ5gri3wOPipnpwg95q3PUQEfV2EoMgA02F2\nfCDAD3fMoy3RyT5i/sHN9eUOEfMU7uGipj+wkH/iM1q7vD5jombFJeqhhlfJjRj38C9gAUCj\nrrwhwgUZQQtzb0Zy+pv7f3B5Ppmi4HVN9Oqzb4sQGuZKj3u5W5ov8NVuNNba2WLFwia/5fM3\nh+A4ybh1OMLv8syjrRQ4HA77OMqBRUJ7BvdwTcpfOKx/4x9Yu7w+M4XaLK8QZ8aKlfyoacyQ\nKA62meTwbnbsOZ4kyc+CJH9XCgVaXdM5k63YxdFJo/YzLC9ZzgFZ3pNelsa7+QaZpZ0T4Vca\nBo/3QQJ9IELoaIPHeZ+/UYOH/37383mfR0f4HGZSelZEustUV1SC+CYInXENpj1B0D6niRjv\n4P5DIMpDLMRDcArXvNfiEQWWeM2dWnnHoUqaPpvTq7Z2/FXNNiZ0dGjXpEdwPwbG/oMN/Bcc\nIL2L2s+8jH10u+c9vDJkvzWJZGD3W5N4wdued0S4xmsqi5d80JnIkAjnwk1lG+7h3R9BE/YO\nN0QbssLtItDmkvzaNEzzn4xBm1b3y78MNPpUeo0yJX3cht8Qect0Bch0VcnkJ9MwkOmakslP\nputAps+UTH4y3QAyfa5k8pPpJpDplpLJT6YRINPtzpSpxdcv+o1jRIRrMn39CiDWpBIr4kFS\n9kITfl9NRgFwX4gQGjLVZaEvH5bPC92Wbg9ly7rfawfJcVHLmDJzvreiJH9RzDNepunwEIr0\nRw6zf3VZ+VDliK5guimCkjSUo6y3TcvRmAihVTlKCDlVjiKVI7ou7IYIStJQjnKgHOVFCK3K\nUULIqXIUqRzRRXfXRVCShnKEr1xBy9G4CKFVOUoIOVWOIpUjup5xWAQlaShHE942LUcFEUKr\ncpQQcqocRSpHdMXoFRGUpKEcFb1tWo4mRQitylFCyKlyFKkc0SW4l0VQkoZy9CUoRyURQqty\nlBByqhxFKkcDhJKLIiiRsRz1hihHbPnpxkKWQfmpiBC2oU60XP3Uo0/r5pxdKFYmGGN72yOp\nrcD1tkdK4OCS/fTHWx3a8rU/3qoQOsHbOZXsaZysQU4lMwAAVRkAaH1ieTZnGpbFaNTXhkas\nFhns+x2ghSmDFq2LQddMQa9dZCbA6HHfYfxM3C+ZRg/yGX3RCkxbdvdFKyhhplr3nGCJsegw\nxSDViYH2uaDmp2qh6GyTYnwpm8+7Kw7zU+Wye90rer0wbdwwyWX0rB6t7jFTtAtaCXml2YVs\nRTupVfTiRGHMMOsvktWJt01p56jAUwGOCljeNh2JbRGFKdSspDtnZFGMcwHH4gcO42ninslU\nchtmNDW2UlpvSGbw4moKcDUtIvt4crXkMJ4m7plMXB3E7bHB/ygDKUVqiSdSMwCpWRGJxxOp\nZYfxNHHPpESKYSClSC3zRGrO26ZIzYtIPJ5IrTiMp4l7JhNSavZXz9UKT67uetuUq69EZB9P\nrlYdxtPEPZOJKzX7o0it8kTqa4DUPRGJxxOpNYfxNHHPpESKYSClSK3xROobgNR9EYnHEynT\nYTxN3DOZkFKzv3quTJ5cLXjblKtFEdnHk6t1h/E0cc9k4krN/ihS6zyRegCQ+lZE4vFEasNh\nPE3cMymRYhhIKVIbPJFaAkgti0g8nkhtOoyniXsmE1Jq9lfP1SZPrlYAV6siso8nV1sO42ni\nnsnElZr9UaS2eCK15m1TpNZFJB5PpLYdxtPEPZMSKYaBlCK1zROpDYDUpojE44nUjsN4mrhn\nMiGlZn/1XO3w5GoLcLUtIvt4crXrMJ4m7plMXKnZH0VqlydSOwCpXRGJ93979/PTRBDFAXzQ\nSLJ3o9FoUk1qlGi8qqnxokZuavSmxpU2sQoGsRC5qdGDEA79CeVngXAk/CVAIPw8An/IkqXT\n8jopb7cwb2aSncMn015g3uv7DhMOW8pIZT1QqfLKjIwUyEBEI5WljFRWiFROx+BRRirngUqV\nV2ZSpOztrzFXOcpc5YVcFXRMH2Wu8h6oVHllJuXK3v7qkcpTRqooRKqkY/AoI1XwQKXKKzMy\nUiADEY1UgTJSo0KkxnQMHmWkih6oVHllJkXK3v4ac1WkzFVZyNW4jumjzFXJA5Uqr8ykXNnb\nXz1SJcpITQiRmtQxeJSRGvVApcorMzJSIAMRjdTRULQxNlgbjk6+TglDssEHw18T/HWCMadS\nJWdIxAnv4I/mcbv7Um7SfzhPT29/JpWM9falkumujN/I2Ce362vDQ3f87W7yLW7zR+VdqK7O\nLGPOvKzt+n2aOeORdB05kirCkTSro8iWjiTH/ZZJt/CVsPWxq+gozaQzKUSzfthmnXBgRuGw\nDjEg3z1wKEV6QNoYG6s1Y7r6vlx7Pyc0Z5c3ZI+vnLPAmLMoqzkhPr12rRs06dNr5Q+HbVZw\ns+7bZp3iELXNauFKYpsV3KwB26zwzXpimyX8DL/4A16wv8b56zhjzlKVnOL9rs8f/+JzTTbf\nVLtiF4FLiCsBriFiwA1EnLsN3EHcJfIAeIhIEHkKPEO8IPISeIV4Q+Qd8B7xkchnII3oJpIB\n+hE/ifwCfiOCzoR/iP/AUIARRBbIBSgiysA4YipAxSK3DKwgguZhHbEF7Ei0ye2f7hJUv0ec\nN9hlxFVJbiJuNdGBuKfAI8RjBZ4jOhV4jXirwAeEq8AXRI8CA4hBBf4g/koyLElekgnEtGWU\nVcSaJLsS7R0L90+QQ/F2I3kAAAGaeJxdj81OwzAQhCeuzU/EAYnnoIYAacmNCxJSxSEpEjfk\nuKZY2E4Vmz44F24FRbVFW1/s+bwzu/uaAxiB0gwjBgDsZf54OQXIxSDiTQCcA8hAcQqg4sGu\neB3sal2opux5PX56buYPs9n15Ka8a6dXk/ui4EFbwWsujRLuTX4o241rAD/YnpR2pNy6qkqA\nkh3KnLDKx8YkwpN3bdTwkXTX66V2wgDYAPiOA2cHvjPfy8EqjBY+suPItn3J78GOTBrhfczN\nYyb233tGlmZqtVtot/T/60nTJZWLEHrdfgWVCNW+uU21ppOfagFg8wfS5j9wAAFmW3icrFvd\nc9vGEQclUR+kJSuybMeWP2jXUezEki0rlW1Zkj/rjyaxY0nRh+0EgUhQvBgEOAAom03aTqeZ\ndvqRZtKXzvQlfW7Tt2bSSZpJnzvtX9BM+ty3Pqhp8yLmwFtAh+Ud+GFoZmd5uN397S3ulosl\ntJJSFKVT6epKKJ1Jhf4lX128PnZOUbo7FaWrg15IKF1KH+Xdjp219TyV7qejXkqZmqqS2M24\nT6l3FCVdQdo9VDtPDJ1+/J/C/oIp3dyYnp6YRBrJrKE5Dv0wKHJC2QL6Gqx1gESXS4qaL31m\nfGJi/DQyvFszTctVXW3N0NVHOlGzBb1oeeKcUOe67tashvFlumHP6YKIjQzuKGm2brrjeVsr\n6tv+hxQHs1axZJlUzGEQzEZPVeCJQNaz1VtlOn1VCcZAXtfcsq03RqiTbMr+Ice1y1lPT7Xs\ndc0kTlEtacRujNdQsyn8waxm5khOc6nfeWJqRmQM62SbwthZJCbdZYZadCbUNWJFQNSLNoXw\nFK/G7U8hhki4KZTdIUWLxsIllhmBJFNoLmoFsl7wtPIkp5vZqK1QL9oUQtqma9/QVZfoToT1\nsFhTlvuccrGo2cSJ8poXklmtkZcw/02pn+oue7mB0t6aIUUZBcN7vFwGtJfZUY56IJQOUkrB\nOA3jHTDuh/EA8J1wfRBs94DNYW/jwPVdlCbhWif4dx5w94D+PuD7wY43dwAwfH4Q5kZA7xCs\nZRqwMO4RwD0KuN+itNwC7ijwZ+HzMwh3BeGeAJznKRXA1kmwcQpsjCEbhLPRrF+TwF+AuTOc\nTXw/z4HMVATGBZA5z2HMwrUZkJ/j9C+BzFWQuczF4BqlIfDxBsTjJqXPkV8vgu6tCL9ug8xL\nnF+vAL9L6Rilw5TmQe4O56sX27+i+7PI+bYEvtH9kOhGvt0Deyucb/fh2gNBjN+AuddbjHEW\nrq2BfC5CvwCyeU7/TeAPYY6AniG4VyWQMVlsEimIyVyEThl0HMGaH8PcRoTPb4FMhfP5+8B/\nAHNvg94PBfg/BpkfCfB/AnPvROD/DGR+yuH/AvgvYe7noPeuAP994L8G2fe4vfQu5NPfUPoP\ny8eJKuS6Ya6IfY7SFY4DpX7FyNPrqDasWruqSkShGoB5iScJ/AB8pjz1HqN4wcYAYAo4UOp9\nSr+NG+wCANxAYB9Q+kPcYDcBYBGBfUjpz3GDLQHAKgL7hNJncYPdBwDv+ln4THnqc0p/ixus\nAAAFSOVJxlN/ZxQvGAGAP8F3ZpLx1D9oCfN03GAfAcBHUCAlGU/vYxQv2McA8Gl4g6RHKB2O\nG+wzAPgnAjtC6WLcYF8AwBcojJcYxQv2JQB8CcVYkvH0FUbxgv0LAP4L4yTj6auUVuMG+woA\nvkJhvMcoXrD/A4DHL8JnytMPGMUL9jUDSHiSpwGM8vRrlNyYwRJDADYE1UOS8fQGo3jBhgFs\nGEqGJOPpx4xiAWvSSHA5/ahgGfoCbsMlQNlvwynAE9ttuCd2NSDeQOItqOO9iU2Y7BYIdsyM\ncZL8TLJo5XQDmUlIzPRnLaNcNB3VV1I4oS1kpENipM+w1tWcvlZeRy6FW1/EcHWbmOsZ1ypl\nwpL91QjN6cx2TylDP2Ue6nrJs2OZRiXz8sIEP/+IuIVMraVSLmZSYasDUSi7MrSVRBdCspqR\ncbKWrWP1QaS+M8rcoNc0QrYUQUw7ZfeX5JFkl0xydjbaz5BfHXfm4SrvCa7mO7iz4JWhxzkO\nVCtDP4jpLARgM54ipevQlulhPPU7Sr+PCaxJIyeq9QkhqMUV4IkYa3E+ITxfRXc/Gb77J/E9\n9i125vITSLWbU92EmAo30vS0xGifS3IVv9csEuhmhxuuboUBuvFaeiUOJN7mBIUCx5GlvrDg\nNEZKyQwdQYJpiWCSOOOmJln1/iDvqLVDrjqkSAzarXQrILnFcR5uRxhuBvvdH56frTbvgU3K\njpp1CIcs44JQX8CeDDSI8E7ZZpqblfg8yDzdzraCJI093IRzwyFdksVkgNn3868gZysN4tKI\n8149FfbqNI7PUHh+Ankdmas7L9++JoCNStY3oSXrc6DaY/yHMeWpAOwWJJMFlKz/SOnjmMCe\nJFkvCpJ1XL2MgETJelf4puNkPYXlhzl5Tu8s0juHxuf5zRyVcHdHHOKow7+nweHfq0SnsaeV\nyDQ2JzqMvP6+Bvb3R9u/2MxhbyYOI4o4ySVGkeABJTJHXUZjaa47GG3nChrHntUOKfKs5iU9\nfhyVjpbgJvkcqNbo+ySmkxiALUMKWoGfrXoYT31K6S8xgT1JOloVpKO4up0BidLRYaW1dJSR\nbPcRVuWp6Jdk1XsOC6tMNbMhecgjLbp4lJN/0oy5CT+T8uNjrdndvinFskurMQnKM2g82iZK\nb444LjGzrgTnWTQ+3iZOj2bbmrkuWo7oppxAsM+1CduV050shymSGWZV3iOd7kVXzwUvdCic\nrEw3BW/hqCQnkRjl6urgDRoa8AJ5qFdU06JXdVu3QDt4TBr3OhOqZviNlBSc/gRyyFfYQaP7\nkNolLqkVqpvwOxYfxJNtBjFFz6ajqx6CIByb8GOW8FtN1lk6hJ82So5ezlncAhRB9AOHaov1\n3x/yHBhHSz3V2lKXqgjJt3M6bGcZ6d0VJSfe087xtQqYmpDFKBu2OS+z6ft0RmKoYwY9J60i\nb/tFlkUIk2GEU3hj8N8dM1ou53XQ4M5lirqr0fuqZY7PL57M6G52/ESt00YfaEsGyVc8WegU\nSjtZL4TxozoUk0j122gjTLWbr8rmum2VS3B5C5k9i8bnWoO5I/vyOI/G02263+3ohi5N6hck\nO2gga+iaSb+PDUMr+S9TBVozaDzbpm+9tcCqa3xng7c7h8YX28TpM/S8q75pEVMCdEkShl6v\nCV0iJRyAy2h8pTXHbstu+lU0vtaaXVVm9ztofL01u2syuzfQ+GYMdr3GGZILVX7c9ZQsWd9C\nfn032q/trxOvJxkcGOFx0syKauUluC+Gce5Hpcv6gqFuxnYFeh22K5Mvfk8kX7vKe/mSIt7t\niWNhd8dkYRoJ3jimvbBs2dBqxRJL5ZLIvBzGLFSbhBriXp+ORrjdJsJB7uXippdzJwYwaKvm\nLbsB2CvxgdHCswHY3TbB9vNgJXqAbK0B0nwcy/IL6Ua7Y6FNMFZNi237Mv2scA1XrPwRbFzj\nypKErK9dB+39CloUGBhCjzTsLXgZnLjN7c+m2axfbWPlZp+eMJc5swvqSdqPoM9FtCcgLxMX\nUb5/Nabv4yU0Xm7NrrQzsILGqzHZvYfG91uzu73rS2TDclXDos/ptgTrARq/FlPMX0djtTW7\nwhrIWxLILfjz/loPNPEwjuPTH+xJNWcRmVDw3zEm+1cifse+gVapRa8yJ7tTvbRl5mpEmPcU\nnAsCh1ztsWVaxYoq0NqEF6k5b5Zl6HvrLVquWytoFE6c5zxMFgUh11oQHlcFdpWWFi3ySg97\nUfcDQT48L/0dNbmhGWW+zRUFuh42+g0vV/YcRRHGF02y6JKqiIU+KBTxeMxalpiyyqPK4kWt\n0gfwxZKEmmwmmw3Z2XVmN8EYCvAqb40gEoK3xiMc3gSQJHgHDxBRSPA24B9A3qximdn5Zrb7\n2/5mt3cbHrp6e2f69/u65+vur7u/7oL11oim8n25gMLWG6nMUuc4XqIjoYu+ApNxuQfkZb5G\ntD7l114qFsuarsrHulOWboFt2odhri0Xxm9DLlBPLkDeWXB/pYzrWfBcDVDaNSJ8lscrQI09\n2luA2RXAGUKVX2nByQKvQs2jW6556DkCN1miIg3+edm2UkNhWhBoi6Xk8BNUOdJyOF0Uzn1y\nOKsoHFMOp5vCseRwkhRORg7HoHCycjgpCqdHDidN4fRWqJ9gLBcrJxUzIPfkynhvFs7zCDty\nM8kjNlPFQ9teRd4fffxz7P0xHxef2jA4LdhnPR/5yy9iYgh5f/nLFG0x+mSjsLc6CvtkYTeO\nXO4GtWS7gWAPLFmF3Tiy0A6LFJFVs6m7t3hTV9kJBT+INnUf4FUrcLn/omXO6dy8623CnppF\no1F4CetzPw+K1/BbsBBrmPdn4YRbtRMLEc9a/jke25ehNNcD2P/f7aVF7YuKWf51Jdr5+uB2\nfnE17TzMaN0+8DHxYgj5oyqNirTOJxuD5jYGW7thN45c4Qa1ZONAMA61E3bjyJVuUEs2AQQT\nqGRXuUEt2X4gOAzfKwzf7Go7NKsmOwIER8GNI+zGkRvscKNqsmNA4MT18NuOIze5QQlZNX3z\nlKBvvlmRXH4Q9c0PSvTNlyy1N+7sNVind45rZpsW1wM66IdChMkSjxE5HpaQZQHcttDnruVZ\n2e4MvZD3SPBY4YPa+71LUNZHmawSw0WraLiYhcPObPoxOdyCAtifF+6SEdE8jtJPyNFoIvHx\naId9CdhqexLxP1VhMeu0mJmyqCXy2igzqX6a0Lc59yLZniFenGuDOWtqFi9LC1XGZwmc2rip\np/EqdzitZey1P2+rmFXlS1v9KX5/q7Mx0N+an1IghDmrgfc5nndTroS8HkBNWjeD3H2EeaId\nhlfBA1S9dWSNGFwdc14BY9CT43+EfbqEJ+YkzAom0QyhyQ1K+kSf7AAMCAchhhCJ2uE6RWTV\nDAyH4MRHCGJnYFiiSC4/4IFB9JVr4la2zX1ns/dlOeQVbXfduqIN6U/EaQpaTE/aW62CTCFG\nVYRNKa1ZGXSSZzCHOOYy/iSYIH9OJ1RQvi2e6KUaAKQ5pZzPfA/ndwSUUtn3CJUZs73Q83zV\nBY1t16OsG1AnvVGuk6b8LAZyiOcFxLNJjofc03kRpTfL4VJ7OniQ4z6Y2CHTYR/kUYoW+LcE\nd9sDrDTlxiz+EI+/vhz8crrjKTgE5sUQHPNUmYnqk02DbT4Nb4fdOHKLG5SQVdMdHxfY6aou\nXvCDyE7fGtzMh1G6JUibWNyXgrWGNH5e5vMF2uoLXd8n1jMzk1qsr06nzAxps79Sfr/WjLK+\nivqF1xT0Cw7O6yj9hhzunRTumyj9liLcYZR+WwEu1jUH9x3E864czwakYxtzgPMewh0JxiV3\nsy/Ue3Tz/kynrYAME8M4lEM55mlWNNapmVqMPyrM5PGl2oak3K6o9DsQ7s4KS1/b26mbOk9S\n9NK8hLjAosqpy5iJZK9op4OtlveR+B8oqpYPEe5HctWyA+F6/+9E/w95fB/z+FtZvGpmOTOw\nxjaD1kdvc4OSEcUnOwFWqxM3wm9nIfZ2Nyghq2ZYPQnHckMQO8PqHYrk8gM1y5HQykKjMrSV\nCZG/LjtB8TLhGU+IeYbycurRwNRMgxvn1UNZzYSImG3Hn6D29qlceyPdBXbxOEXuAqP881Lb\nsWspnt1yOKRbwB45HNItYK8cDukW8JkcDukWsE8Oh3QLGJPDId0CxuVwSLeACTkc0i1gvxzO\nOhanVMzyfM7zYEN8mJLvCz5foCHeBL6vsZRh6YaVtRZrcS1hONZ4u5lK6+3s9USUYf4lT0ga\n5mFYlUfZv0L9yddyPTB5sukblP62wp79graE0b7STPVSfkaTiOiAHBF1E8JIDvF8xz8vWk/4\nnn+OHQaXBykii/MDKs+PcuUhV2cOovQhOVxyFvYTSh+Ww+VWrdh6+BnhHlFUD7+g9K9nSV+O\nVqAPbP5jfH6pAVeEN4XKPS1Xbmr2WXRjyHHE85scDznR+B3h/hGMiw2fbUT9eNOp7eW2zz+R\nHH/JyUEaYH/zOEUG2D9V6AOL868cDmmAzcjhdAXVLxWzfCd4vsJqgr02YGSTupko52C1CPkk\nj4yH+jqUHqFq5D9CwjpTt5eoDR5ll0g6e54Vdf4/BXOxemYGci5uq3ZImzwBXBDXgjkFwrMC\nnYsbrfNP2KfFd0EugF3CGlgLP8PelXC3TQRhOQ2OHcdxQq6miesmhZQbCryGUsrRcgVKSHpA\nS4E0uEnq0tiQA9o+4PVH8TPg1+gHCNv7jTxeyc7qsFbU0nvzlFkrq51vZ2dHq9XMZKvptCSc\n1yoC3XDwSathINf/HquvxNZ9tf1K3Stcqd7detihnoEWudYzIlrRVocMZ7AAmQrQjybQd4C+\nQ9RQBUzHEkxRhwJYhQSsDgroFmeWtIsDFUUyAUfjRLBZE31rtoIoL1N/DknNjCL+q6OZbeFN\n5R9boU1NeAYK+pq22PWRyxcnfSWwlgDWCbRlEgAuIFzVANNRfs0QuyaNazKIsZLFNZTXpbEA\nv8w6JY/zKMoL+L8xnAmbCXQWFs8vUufldHReCnERTQhmQpAU4hmaENBsreo3wwryabog+Eso\nt63nGM6jTLAlZ+c6iAxGugtlQFnQMCgHol0OedAoqACicccOX8pK8hyLHykPmhQ+OTKBO1fC\nKBK1OIwgItGa2KPVOI9L7YoiGGzzxpMeTfCkjoYmJrizCT7fxQRPCd42wcd1dB43wTM4AxDb\nBM/iPC3KHSZ4XvAOE1zEeS4xwTpIedCYUMoUot1yJYwifVWzASdFA66Q8pySGhJFxFsVW3tO\nawPjZGtNhNvlIESRgKx540XWW3PoLcqBuogepCw/FLl6nkWbLqKHSmh4EXakBDtShB0poe4l\nowV4AW04LXj7Ae85HWB4e8CzQ/YSdtMBsCtCTsrTmZVwItf9eQmnpdjjxIMOE1LjAZDiqNCD\nzhkJlRdijwoLkezFUJ7RIVicDCWBVfChQVxzsgD0RUlzXtIBsCfNoSjXCmqzbjEpI5cqTmpD\ndRQUXOYBo+Phq92FYG6tZxnHg8lIhy9Zx8Nx4T2bgy6D4KrFps6+HgQKYF1LwGo9zDfDf1Bd\n0+GMKrfDl2zT4Yy0UEZfWJPxy4K3J+NXdCihp8m4PVsC4ZFmAywFp4JkfQayvSr4m5YOGemG\naWmgBVXKdOdBkkzV7cblhtWDAfSa4O0B9LoO5YpoAL0h+PVkAPXnALrFB1AWCsPfPxxj7x8M\nlKehPGcFf1uL8pBTSjfOPjFUDl8dklVTAi/Af8fbPtc7t8jt8IXBXO9cpUBKTMqbkawdKe85\nCM0V902m/DoUN992Y8n6EQhBOyxztFIpg2zCCjTOb0mg3QNQjfMq/l41jOF/BIUDmjzjBc2h\nZEIzuCAVNP5vmMDG3418if8aRu54rwRhWZNMJGPq1frusuBtv+adOMjvdX03bFDOS6C8G3tQ\nWomqCJMRCZMiLEgR2JQYNiR/BvJeEPwdS4fcverU96ROvahDOE+dyjN89QqV9yVUPog9Km35\nyAiW4Q7aTvBw0Ycg6oeCL2vV8rCeTz+S+vGSDqG8aXcEby8vS6h8HHtUnIY8bEw+kTD5NPaY\nuI74sGH5TILl8/8nLF3eQOQsJmrkotEN4/AGwsu+gL4Hi+pIXn62KciwxexpoiB9qiBhuW8r\ngrdnny90KJU8wbQndHV4KnYyVwJjzWhfKl4EsAQKPXIuAogSpuNJCLlitHZJL0jltFtaLs9j\nx7RcTrun5fJxfDAhl0+Al8unsPtQLp/BVvgFyDMLeU6An8N5HuVF8CdxLoFOScvop6EAXwre\nsYyuRxHaosHKv4qcuinD2Lbcfm1m0E0Zxo4lzvaH8FcEXyH+K8HfJ35V8D8T/7XgHxC/Jvhd\n4tcFXyX+quBrxF8T/C9urTw6yWjKMH51+0+3JKPyNe55RFOGsedWozOPqHxFp2ShKcPY5wqz\npvb6w3FkQFlGvDzHiJfnGfHyAiNePs6Il08w4uVTjHj5DKOQD1+z0ZriTJhQR/LsfTzN72T7\n0cPqh36V7XqHpM3UrGF1c+5L7GF1s+XZUZ4NsFxDX3DQ1xtF+IglTEHcoR7FZH9d8LZDfSMO\nfpT29dBvJFS+jT0qrWAPvcLkpoTJrQSTZip0jsnt2GMiJ10P+wH9ewmRH2KPiJs9CfMbRLLC\nRVjhIurm6NE3iD9K6G3EHj3ne5gua4EjFpMzcrnitBaoANZhAhbqoK9CFED7LQFNXcN+T8AK\nsEy92GUWvCN4245v6gBYNtXdl6kz5Vr1YLPSTO8n//ZsbW9ns1rZ39042HxYq9Z2H/GJ6Wl+\n8vTyOcBPgn9k6ehsumGcPwdIdmr7V8R+GmxhWeGy4G0rfFfHwEyscHDFoPv107ouDQZ6oXtU\n7DMDv9OuzS3B2/HItnUof70Bj/l0mFPowKAdlfPWUYkSKihhRnJ/xlDX2xAmBSvcULQdl36P\nTOFkm5mv1g4uP9isI7pd2Wqz/5KfROgE7aEwPxzqZ41TiRBxXYeGxfGxtQtYjxOwWnPRH5bH\nVaQENMP4kxvy2XBM0VGHLzlnwzFVoZmyfjbfbl7rMaybDLp8FU9e6z3B69nOR8pu4oayR0kH\nATMIko5AXuuAGiVK6GFaFNcLIi/1vouzeJZtbjzYOyw3t3LaD921cvlwr7nbZMPed4LKLujQ\nVq/mXXWa8IN4Hdm/+JAdkUaMwuGrzSPeRs1/7V17kBxFGd8Vb/cuk1jHXS5Ek8iFHFVG6k5U\nJKgUkcNHQvmoig+MlUfteZvLae4u7N5dSDxMgiAPBYKJhARfPIwPRBCNISAk+A4+QASJSQQf\ngP9aJVBQVLHM7Pd137ffzs5re7b7bneqftX39c3OfP2br7+vu6e7J1TNUf1m/PMgy9GoDTqM\nhpt82KnrvRX8+nIkpYlMARf5KfTvPL8ZyeH5YuNvnj8TdeD5YiNwnt+KEz54fhvOmOX5s7Gc\nPH8OPnwes96ED3JIc8ySE8WHQd4qZJzqLCeObwRZThy/GGQ5cTwHspw4ngdZThzH6ety4vgY\nyHLi+DjIG4W8CeRtlJhen1j6epafIqD5zQQ0fwYBzZ9JQPPfQEDzWwlofhsBzZ9NQPPnELAj\nksfrDe/xpitCt4EaQ+S+ZRTHlGznufTkVwpftESHczapJ1+PBmLzvFoYwFkgbxfyJcwg9qMR\n7MfWRxOk1hsBagwizIJqLQqaZLH2PdsKjIQDWPD7MUVY82ycqooE0X4QhXeexhZMN2P6BabY\nvdjIddIu/NtOrfkANYrx9vnJK7JDI+PFj9EOZvOdPT09qN0XmXYHUaP7cMOGNKTWAhtvjks7\nOrHWUerSYBVA1thLdRTCpAogyJpZodMlOipeA2pbQS7rnNSETLHNJN8ucDoMD4jduR2SJ0Bu\n5yQ/gFZ9nHmrhTaWqiLZUWA7ptswvYwp8iA+YSddjH/bqXUaQI0ivP7PuWBkgzOw4PingUyu\nLzOQRRdlc7VDcPclpuohVO8QOuE0pNYigBpVnRtfzm58GG92mHHUBVBzY85RR35saCiTG9wC\ny9nyYxtGS9eyOZo9hNo8hqNMaUit022crUozh5Ivx7hU5kqQ5fDXVToKyen3XNqQtn+XywzF\nuYHi1YyUa4wnxW2vvSDB/GodJTMxmHuQtaJBVh13VoWBdETwMWKHGL4CmPqcmWhcX2E+56s6\njC2Uz0llPpsbyecJR/MZRwuQI8HNetRtHp7ThPylPb4R2MVWSqdw/ofgT6yYvhbkGwV/1+ng\nL4zr1aKgSd7EvuduQcb1LvZ1yhr54ndijfMdiIk1xY1j4Kc3FQjfzS6GdxKraOIV8w5iKDXn\nP4yB7NChoEkGIgvv3y+NpJvKSaE2P3sKkO6lljXf5x70SFWBarmYryasxhKCbU53FVwqz2Ji\nn01YaW4gey/hPYrP5RVSAU/C61QK6ZU2euNhvIuEKRHiknjt/8G9kwXU5XWkIh/BjdREirC6\nAUoqsrzZw6jQI5girB4b71R0s4AXWVwgeiXx1EfJRlwW5FtnKdJLwvmEWAGfkCM/jxdNTf7/\nFvx/Ma4k+FVX9X30/FV9cMatbmck4H9FW3sei0eufhu9ewLPs+Xb3a6VnMDMV+GkveIkOvqd\nmLyYqCTFIvwf82bV2AbKm4eSMxGtW104SyKffFC10k7oXvv+IzVFrmte/ACRfU+BKFpzBU2K\n7K1uFp/E6jDNhqNdmwUd8cwXiVT+jnhCf1COSsJiG4kIbfgmtVtlRNDeoK7WWqvscd0cxQpD\nHlPRCqUe9TS2ZdvDDSImXYHpLlZJHseK8STu9puG1DrHxntVVRIeD0q+DeBotTNYhJWl2amj\nFCZFWNXTpL8OshyevFEHwWVmEnYDsfYIbIgBStH7o+xYyMZuxs5NxrMTaoMwOSS1W0e5TKpW\nnMbSrcUFlW6fc6Lj4G4bA+wBWY5f79VBdRgXq0VBk2xBXMNv31lyRNJxRvBWQWjd29W0dsQR\nqXztalo9kTmYzvPP7XqxT1TYr2F6M6u4R7GyHsXKn4bUOhegpuKG8SxaFDTRsziFP4YFPoa/\nS0NqnQdQU3iH9W9g+k2mwAl8QifYBOf3AdQoEKYRokVBk8zDIeFbjISnsOBO+jb8206tXoAa\nEnjrZwE0IumHiEdHOu2Sj+RG5SeJb2GaPo3avYByE6TWBTZWxqXpZHPXUek7Mc4duxVk2RW4\nzYTie3cFhsZG7XfsMXJyO+Pkuw1OEvsYJ9+bgpx4eGvZ6tino1wmeetaTSD7PjOoH+ggPpxB\nlU0gmxWCI7c3gD9kHNxhAgfei/lnZMezuc2j6+0gSpSdFa1PEsm4Z0Xri4Tpg7hO33DxGncW\niDHX/MGZ5DUar2oqcyP0qKdXNbWKIj9iHvROHRWxyiii6p3HjxkXd5nAhXc0adq0PpsL2ES7\nq0DKWfNymeRs62QkMGgUvrtAnEFdG0YjCjeisI4ofDeLPD/RURENicL3MC5+agIXMUThe3SU\nyyRnW89R2G0pw89CLGUIOzqZxv814/9S+DvLZ+nCM3gNkSKsZQAlRilv9iwq5KQLJ3cftJYD\nlNws4EVcly48R74t3gL51oWK9JLwW7rgsthgCT3fPs5h8v4CXuPV0vw97LwS7RLkNyKlbjnJ\n3FctHl64ENU0nFk7uA7VTQTzznLKfkJH8Uzyzq0u9hC2CeiziG0qNAFLPGErcQKtkBY9oTIn\noL09QI6aW1yj8+XfVqrHzpeqjsV+kGUE+7mOCsfdamPn58n8qDs/HwC5bHO1mjxQuvPzvSDL\nnZ8Pgix3fr4PZLnz8/0gy52ffwGy3Pn5AZDlzs8Pgix3fj4Estz5+TDIcufnh0Bu7Pzc2Pk5\nDoSOXtO8p3+HqHffxvSXzCG9iE7oRTZz8TMANQ4pzGQdLQqa1MtxSPgVI+ElLLiTLsW/7dRa\nBVBDAo/03Zn+fphXOZzPDufH8p2ZgczgsDO/sj83sjHb3yk3x8nLqZa/ZZq/DNomnQufiZrb\nqbXaxmhcmqczuVxmeEB8hOU3Mc6h+x3IsgX3exPK7zkG0dI3ONy/NjeyKR8jLUcYLQ8bT0tq\n3eCG0WwumLeSbvWIjnKZ5K0EWVE/RvsHkOXHaP+og1Dx6Uhx48bHaOVRl93yPzHv9WcdRhnK\ne7mtNFbt0x9hrDxqPCvlK4xVc/IXxsljxnNSsq9BXLT8ldHyuPG01KICPcFY+ZvxrERrFD2h\no1wmNopaEu4jo3zUMIXEPQlysTFUcwJ5I6il/P2L2xGJ3JZgjZNGoyeAkaVJjUzi8LTQgxrX\nUZC7dRnXVnrjNKulKh5A2vvhN4wpgDGpDnl/B1mGvGM6jC9cyAuzxDBVIOWseblMDHlxT0s9\nzgzqhA7iwxmU8iWG/2AcPGUCB40lhuGXGB7X8eBM8hqN+TWNZki9l1n14OXTIMvo8E8dTiZc\ndCifUzS3Qs+52jlFtNctmhT/Arlsrk5NiKJzdf4Nspyr8x+Q5VydZ0D+HFV0bkxzZ8gRqRbN\nDViDq0BorzKd53rUo+dsPNfp+VyjDp8+C/JBXY58Bb3xVBo+tXU/IILMrzF9rpTEZBuS2Eaa\nJM5HYscBakgMM+SiRUGTOk9hXsnUPVm08LOx8E7ajoW3U+sSgJrCO6z/d/LGyYsq1NkmgnQM\nOBkhlnGKCckdBKf4YJ4HTkV0MIj7LkSchlhE4Hff0z3wFsQihsWIMwi6K+BMxBkMfnq9wwPv\nIjjbA+/2wbkuWEpwvgfej/igjQ8hlhEs98CHffCxgPg4YhnDJxCObp9CXETwaQ+scsEaD/hx\n3OeCLME6Hwx6YIhguAJEmc9D5BBOXt4D4x7Y7IMJD2wj2O6Byz1wpQ+u8cB1BNcHwLWIXdEC\nivTJXg3X5hjQFgBzFYBfszMAuhSAX/OtLujxAT//7YqwxAPvqRK9HviADy70wEcUg9/7kz5Y\n6YHVLljrgUyVGFCEkQoQZb6Ywckb9cAmD2xRhMs8cIUHrlKEHQGwcxLBGuevAWRTiXQAAAGd\neJxdj81OwzAQhCeuzU/EAYnnoIYAaanEgQsSUsUhKRI35LimsnCcKDZ9cC69tVVUW/3xxZ7P\nO7O7XymAAShNMGAAwD5nb7djgNz0ItwEwDWABBSXAF64r1te+LpdZqrMO14M3z/K2et0ej96\nyJ+q8d3oOcu417XgBZdGCfstG2NE69SwALDC7sTAM2WXk0kOUHJAmRW1cqE3CfDiRxvVf0Td\ndHqhrTAA1gD+w8zJie/KdbK3CqOFC+w8sF1fsjlZk0kjnAu5acjE8fvIyOJMlbZzbRduv540\nTVSp8L7T1Z9XkVDtysdYaxr5q+YA1luUHkC6AABKOHic7Vzdb9s2EJdsSf5MbCd20iZO3BRD\nu26o12zDMGR72NOAAd3T2mHA0GWqozQGHCez5H5gL/2TByTAMASIRllH+XShbElRbAWxgQNF\nk7yPn453FCn7t6IkSVlJUWQpq0rso7588ePTbyVJy0qSkmFfyJIiFVipmYPOwDhkvZdYLc+o\nxGiV0cNRL/ealcXnjDpkdI6NPuz2DHb5r+R+vCaj/3Zvb/drMkLt9HTTZBdVkRLSJdB/wC0D\nPRSre6zz3l+2d3fbzwjj4pvByfDUONg/cPhckFalc9JzpBad7z66vB05DUY7jBzj1xiVGa2D\n4Q+dcVA6ID4a6SNJTUZbjoVQ5qFNAx4FGFeHa4dXhdE/rk6yDaBmCehfoRJoBPpzd1zGnoqy\nYk8C1hNWdoVLLVA665bFnxn9mbSwHRDglJtwzcria5cSERaSifd16d3RSc/4hXq+DKMfomvZ\nJe7511bVI8xA/htcy2k4h0ZN0DHz/VPUE7fUOj1D7+8zN+/pp6bhzgLMUkYsz6FNKGJvL0BE\nweoefLD016PpLuqgHQ8t3TLg20siMEvqil+Bmu3nukLqq3YAX5XUtYT45kg9H43v2DuG/VFo\nChBTIPViXDHMxQb6sQh959Im7lAiYssxxWp6Z3AyCuhcqi/atyHunkOM1UQsuodGzzTIUMUy\nTAtGLgeMVLtmu6/7NcvZAbpkPxhckwoxvjrZ+HGOcWaAafSMjhXUw3hrDD5YR93+G6SGSJ1M\n/2SKwkr7sM81rgUgkD8c9jtW96SP9ME85Pcw4IIIuRQkpAyKhU59A5VAxRcuJRILPWE8Uzrl\nD3DNyuJLlxIRFpJJ2RYkhAaEAwlKJyH8mpBeHjl+jh0P3e4KccidgH5j1+l1TWtKH/VUNy1j\nSidt2O/+NZzWS/49on7yjrjDhshQCTEQldjf8zwBQjuGPfvgkwch2UHdNxuWkSMsu+VoNiTm\nCGEVc+LBColgq1Kk8L1GYs66DXzqhG9jMt9mUKRU3x0ZA8Mv5EqnHAvf4KioW8sm3cq62e4c\n6QO9YxkD1FcS4LJG9F9PCJd7hO/9aLg8pTGeQlHuim0U4aFZg+7xO5xyJQESG0TjzYSQaBK+\nW8kg8Yx83+Lytv38H2B+GRQhoia4JmDUJAnulUuJTGlP2BYktS146iq4ZfEPlxIRdp0Ety1I\ncPsJ6eVRUIKLs+xU+/p+9xC+JK60Qwdh1SQ0RjDW5x7LCBke8V8liYw0pcTzuSWJU6k2MKzh\noO83o2oLuMmA7xn0XZrxlsCoBbf6Hihp4/hh8gwctQHPELWI+zQa2q955OnvPmmdga0YG+UW\nYXNTmKgEE+0WYlKJgQnGogC25wgW+dRjgbYcbspBCgSUYvpBGW+QcFAUULSODATP12xk6MwN\n4wKVj2PFgISjq8b704FhmmwjwPypf2C8D+CTGZOQT9nVwseDg9WI4UEtyb9Nztc+2KPKAHSJ\neFR5HsBHCzne3hfHaIVgtIV0VqCuIhv4DGuBjRiXEuCw5Nbv2fPAgxtWAIEboFwdBDrP8Y+R\n0hoovezWN+aitAybalxw4aMU5hNrZhWuziwRhZ5tTPf7NgJ+KcKsw96ThxtRceverKrO44bQ\niePfR73S6ttD5couhQM6kZvKZWWjUZSbvGmHy0M5G4WBmd+4NOUhJnMbz+qVcLM6zCeWLSsh\nHTIERcFgXeQ4T9C9UcFharDpsTp2ztGT8QUK7XngUyfhfR3lKw2iDc1TKgr/GXT0XQEeS4sT\nBiGT9JwwCE6aJ5400GNkxxFoe8bf/pi2Z/3tn9J2cir8ZJo+qr//Z7Rd87fHPWD4nPfDKIc8\nUJABprPxxJipXzpCH3MFMih65ANWq3gdwRd0Wbc+ul8zN4Aur+ug3BEwrUH4yKI2Hpoch/oO\nHOUFMowvkBS3vjMXw/gS+QwhrQpyDI/t+KNMIBUId78tOc67CdPzeyyb8tNtCq1rVRwRZAgY\n3K/OQF+uQp2ooIYkrGbRT7GgqMdb5yaxBvYtA278aHXuC+frOu01F86j0zTOqxHvSWraJ5Zd\njeQ9MIoXenrUksEkFga1ZDCIbHPlejbHsrVyPVtD2zirTcsVt+5tr6zOI9jQLBRx0zKpc5I6\nwaKRBiwmbzV5r6mE2Ilp28jOmduVpp2YuxBAgnZaBI7xhY2CwZ12jMVKY7HSmEcWXnPrXuZZ\nn8dETEkWvkewuJ8GLJLLws9sZOfM7UpTsF1kYZ9j7NooGNxpx1hk4UUWnkcWhsNDL/NszmMi\npiQLNwkWW2nA4gaycHMedqUp2N7lLCx662A7xFsHWeAT9Z3UHLTxNxA0GFda/NRDyCQ9P/Wg\nbxoI3iSY9tOPb2zgcen/PvKvPXBYlkn4msXNi5aivB+68DPEENFZeOY4M/PSFJ2rAn+IugTU\nJtNtWAL6IuGN/6pp7usB9Jm5xy0eviZiUw0KTbP4m5iR4BYSvJZACMDvYRSAyjFv0FrCN6g0\npnA36X88lLXWAAABnHicXY/NTsMwEIQnqc1PxAGJ56CGAGmpuHBBQqo4JEXihhzHVBb+qWK3\nD86FW0FRbUHriz2fd2Z33woAIxCSYUQBgL4uni6nQH4xiHjnAM4BZCA4BfDAglmxOpjVppRN\n1bN6/PzSLB7n8+vJTXXXTq8m92XJgjKc1Uw4vTbWvxvXST2uAXxjd1LekbSb2awCSP6PUsuN\n9LF1HuHJh9Jy+Eja9WqpLNcAtgC+4sjZge/M92Kwcq24j+w4sl3f/OdgSyo09z7mFjET++89\nI00ztcp2yi7933pCu6QKHkKv2nWQiRDlm9tUq534lB2A7S8vokB1AAAy7Hic3Vppc9NGGJZj\n53LiYEIOcjkJ4Qi0gSQcpZQWKC2Ftinl6vnBI2SFaGJLHkmmgX7pb+IPtdNf0Bm3w/QL7kp6\nJK/X0molPJ6UzDx5ta/ea989tHrlH/KSJGWlXC4jZQcl8jf45PGdjauSNJSVpNwAYWSknDRK\n6JBlKqa6S6QLpDUCTBJorpR3Tejo75KUv8loDxPtXa2qksvXkvcX3FL159eubV1mNAaVqmxZ\n5KIYFoT0BvgX1gYgkbO1muxLb5/f2jq/CclpglkCJ/xXBOOBbUmacTSBeUeT4ATBKGgedAx0\nHLQAOgF6BLQIehR0EvQY6BToNOgM6CzocYIzTm5wbw6xOXYXwCtRvBXw1ijeKfDWKd458DZC\n+roFug16EfQS6GXQK6AfgF4F/RD0GuhHoNdBPwb9BPQG6E3QW6Cfou8XEOttKv7PwbtL8b4E\nb4fi3QfvIdXPx8i7014lWMb1FPLr8+bAK1G8EnhrFG8NvHWKtw7eBsXbAO82xbsN3l2Kdxe8\nHYq3A95DivcQ+XmCufsTwV/e4sm0MMfGmWW6RFHAWaYOHL2BVuy6zLV4SzFw5tzJgt7CNaGj\nf3jorbMxOFgEBUb/JGj22lkJDkqYolmPjv7jobfOVuBgnenZa4JWr52dgwNnwQ3imtB8hmCo\n18624OBKZ8/y5F6+0GtnV+Fgh3F2hOBkr53dhwOHnsU1ofnTHnrr7AEcPKBSSmh+3UNvnT2C\ngx+ZNJ4j2Oy1s5/hYI9xtk1wo0fOBI0E7LFf9oyq+og9AGWgrVHXGQ/+AeitQw1AG8j86p54\nvBt/4+ZQiODA9Q1Kkr5T3FVlu2GqVlkxqo2abjEWMxEWMwpjkI48D6NlrSIiVXspImXauPuG\nonSoA52hzrQiurzQ7rJcVRpV2VYrEb3Pdpo83uIEetwP1Kqrim02amVVt02j/oKjM9OlU1fl\nfYujUfI16iaJ2pYPyuR8XN7c2tf08nO5ytFc4WhaimGqyb1uW416Oq+uZkqvF53/qby6mnFe\nV8N1L1U0sy6bMY7X+MrJe6zXFTJGddlO3OO2ZkqvqUa3rZnSa6rRbWv6XiVKl90ocoIbxZIi\n6xWtQvYHga1iMMFWMR8YDtsseKEPpQjd0kytYZV3DTMi9OEEoZ+iQidxK94GTp6KTnZMx0mN\nXHAMnG4bUIyGbvvhuRtfWT2oV2VNV3nPjRUqBGe0fQuabqu6pdm8DbcUpWsZtlHnTdelCE2P\ncBQXIhRtU03j76VR0WQFClHTZCT9NCGjGjFNRhNMk9WI6BVD39Uqqq6ogsujQ9nSUrisWbrx\nXK1qVtz5IS+YtHk6ae7ylSMyNpYgY0fb4Ve1p6Zs8qZxsS0sVypkFXJk17sXXE0ja5UsFCy6\nmmwre9wlFzIivo2YpI6nmYn+xhJ1KC0kOZaFbVi6XOM+msJ01ANZsUmuLN5if+vtcS7MwEHV\neFYX21PbSpqu7Gn76ouybhCuaqoGx8LJMAsWGWMyBiL6of32jz9kctUM3ea+DKzyDLiH1NTK\nzmkktbJzqEiaN/bYl8Z5cHpLrZy228FZiqN8IUzZMJ/JumbVyoaiNEzT2eRJGgxLtXid2BI1\nRV6FVTPmybEYunBNkxx9uC+ZHL3Y184JwR1uUjFqdUMn57yobe1Igm1tPLDmvWTzIiwKRjhu\nyvp+RGxHk8Tm2tF0zda45/i8K7er6a4UrweTgj0oeE+n8C4cS9AFGIrvQ9ETfKoZZI/WFK7s\nBKIj22GM5JgnKZKaqc5eBSaGTJXMYp2Rno6QzuF8RGRnW96tWeRnDu05tOfRnkd7Ae0FtBfR\nXkR7Ce0ltEtol9BeRnsZ7RW0V9BeRdunHZnwaRHI+lOLqkAxHLIRMBx3iTfdbLhni75+xXCc\nuqluBka8qdmEw0Kfv3QEuQxOUJzCFisbXdBiJeMLWayGSAFL0AtV2hD00lHSEPRClTIEvXSU\nMFgdocIUqyRYkIrvEVuIiu9RdwFK0Eui0ekuOAl6STQ63QWmDHbEJnZY1ga/xtNExajQ5y+L\nTtjzftg5KuxhSAu+wHjCorUcZDWq9hLUbTBeUXKo0XhSMfUYT4hbe+HbCeosTVTICn3+LusM\n1YI/VEPUUPnPOIEaS/dUZOspfFMdtZMmnoSFPn8zdhKx6CdihEpEDtIhpRPvRneZxOOLl0Qi\nE0iVP5p4MBf6/HnbycuSn5c8aIEJpB+fvrv2Pl7NpWt3jq21sBrCNRZWkVtbYYXFayqspmgt\nRahfYTUUVlGodpJMCcWDZEooGgjlI6RGIuyMro0kU0raLboWwiqlqIGwJlLVPlgjcTWPBPLu\ni1AGr2ZN6reRtD5beWiiclHo829gnECX/UAnqEAH/EA7yhAer7Pk0ERho9DnX9Q4oa/4oRcp\n+E80pvyAp1lXqcHjs2UFj8uUEJoogBT6/Hsep6urflfxy+A1vz3NBNSP3/y4jqcox69+84wN\nAIMUhg8Z5iksAssUVoGTFE4DZym8B5yncOGQ4zMKd4B7FL4CvqHwLfCIwpN3FN9HzP4ieQ8j\nn9IszdCte3pFPcA6YOd/NgQjhxhLITgRgjMheD8Em/8jfBGCr0PwIATfvbsInf/j3u7fnvv/\nAaFySPAAAAGteJxdj81OwzAQhCdpwk/EAYnnoIYAaemNCxJS4ZAUiVvlOKaysJ3IdvrgXLiV\nKqqt/vhiz+ed3Z2vDMAISRJhlAJA+rl4vZ0C8c0g/B0DuAYQIcElgHfiVEdKp7p1zqvCkHL8\n9lEtXubz+8lD8VRP7ybPeU6cUJSUhLWqk1xx7ZaKO9pQR5fWmZ653nA7LgH8YXfCgDOu17NZ\nASTxAU01Vdz6XWIPL76F5MNH0K0RK6GpBLAB8OszRCe+K2vYYKVSUOvZuWe7ufH/SeyUSWqt\n75v5njh+HxnTsFMtdCP0yu7jMdkGlVHnjKh7xwNJhK0eQ61s2Q9vAGy2R8lHrAABrVJ4nMxa\nW2/UVhD23kI2uwn3hEuAACEJCVmyuUEXUggUSrlfC726jtdJ3Ox6V7bTJmqrSn0BtS8UqWp5\nQH2q+hOq/oSqj32u+guq9gEJVUjZHtuza3v22OsTDhIrDbOzZ+b75hyPx8eH3OsQBCEhJJMx\nIZESyCd15/b50eOC0JYQhGSc/BATkkKa6DZDl3Vlnnh3EqudyGYiB4lDl+1lf7d05htByD5A\n0RtI9LxaUsjXZ4LzaQwp2ieFQn4aRaTkkmQY5MsmWhLCGsh/gBYHj6SplqW693gun8+NIeB4\n0Yp/jn7tMExdNExFVypWpGcksaCYNoE/FV+AP3MyIVVHKNmqpCuamZvXpbLi5o8WiUCWbTYS\n2l6jsLouVmS65rh21MIQNancCtF2iYSYsdxNaUUklycE1e/GhqxV5SjItlsQsi3bifxBpJNE\nD1vXgEgPFOsBq1aIDABFEsZSMNYOOg0+1rXsJbLHSgG0hbfPwbd9u8DeCDHWvdUN6QfhbwPf\nLR78btCWfz9g7gC/7TC+kxGzLnspmL2AuY8R8yDofgrmAcA8xIh5GPQwBXMIMEcYMY+CHqNg\n5gAzz4g5BXqagjkJmMcYMU+APknBLADmDCPmLOgzFMzTgHnWg3kO/C+Az3lGvkugL1P4LgLf\nFUbMG6BvUjCvA+Ytyhzugs+dEL53weeeh+990B/A2HvA8SGFYw60DLoIWgE9D3oB9CJoFbA/\nAsxtgG/1Jo3Iv05vi1k9bauzHo0Hbzfg1jVI5qEjVly81vJJm6yFPVwbZNZkE6BPwHeiM986\nwpdsBxDsBg2SeUTkO95ke4DAamPX4DvRme+JPOFNdggIRqCeE47O/EjkJ95ko0AwA4WacHTm\nZyK/8CY75S6dj+xXIr/xJqsvnYyW8Xcif/ImU4DAQAXyF5G/eZMtA8EXaGb/EHnGm+xLILiP\nZkZ209kYb7IHQPAAtkwJR2fjjvAl+xoIHvqXMZsk0sGb7BEQ/OBfxqw1uy28yR4DwWO0jFsd\n4Uv2BAjWYOubcHS2h8gKb7KaQxBrByKQ7CqRrziTxTqBrBM20wlHZ+87woUsIkjj58yni5WS\ncgu/YMcguMv9buuY+4L9wqk2xAsQ+wzeiqyBpzDYRnFMlyoLYlGZW17wBAiI78htfVXVFvrM\nSl+pUlnqm6/ofVJJV6Tiap9cKVeXTaXYR95ppaJkShC/hhKIBSQQPzmKmN1Z1l/mvThxD85T\nt66bcQuFANy0qRZXTWnOPsugOaTmralR5mFdmRrYdecOTcqRF1qyPAZklAzIKCaHLLEQMha/\nOgu/rmFyuVI6a52yKAYlLi0vSrokk1X0hAuUa5PyZzxQC1iYdueKiAaKb0PXZIMfbwjhHQ7E\nL6qGqWqyCb+vIdx2ZKfXydNGDrL8q+JF7UB2Zr0shlJSqHMhn0FcR0OypBVVcgMpIimnZdlc\n1hWRrMWiuqSsilpF9Ky9l6U3zBtx9NM4jDI51TN8MV78XS09cTVlg+p/P3LsDHBMqUZOk/yr\nOlnzsGGN/bxzTueWFKUqSqUSBHTYg24/DropuhhvChXFb/TEM5TPUe9EvWW4Cdmb2XDHgnC3\nIHsrG26ehksr8IFoBe5ET0SKxgVpXwPraMebk/eabPPnOF1D49v948fQXAsRi7AAccfr/lFr\nrjtizbWRh604XkTRPZwrbgeyd3LC3YXs3ZwquRfZezjluxfZ+9hwPXstZd4UP66oWgBRH7L3\nsxEN08reeoCB30gNcA/4cY/ghNe9MRmtUbyo2xAP5GBA2hMoramgdd3mdgRlhRCJZef/nWi+\nG13fFbL1rQa47XbdymRjLy+XJF0k294y+ULJ9ikcQlEfe58jx36/Y1MTOuQfx01owrtctEQG\nWuAPhuNP0fCD+DyXKbTZUS4jc3Mc8ucd3BzJ/4U1N8fDnmgeTWEY2SOccI8ElFGXXFIkTSQb\n/pJUNRQUNYrsHFs27mZmQa8sV8W5VfgdZ3cU2WOcZp1H9jinR8IEsifZcGeCcKeQPc2GG9Sp\ns+DX6NTH/LivYqe20jzuT3MWt5zXWrSkgn+cueWdaIF/Mhx/vS3PF0+7nbrcx4jm/NVAVDSv\nXx0tSW7++iHDDENhBGcRks1Lb+evR2znG9y/VfCGn/KE8+hAp5E9y4Yb2IHOIPssG24+CPcN\nZJ9jww3qQJ24A51nKDQhZOzFOlD9zavpvXCQ9l4IFSPKi0q5opHjvvqLIW1/1+SMGA6GMYzl\nl1StCb0nwJEJedwgD+IoyJYjE/KE9W8U5An3tgt9D28ETBZVvUp2yhh8V7Cv/zo3yvnNFh39\nQnhHf+lv6m+xdC/n76G84Rc5d69LyL7MqXtdQfZVTt3rGrKv8+leXbh73Xglulf98LKxT2h5\nn5KKIS2jKplN5709AY5MyHZviYLM0lvsALu3REGe8D/Src9z/6o1iuNmi15wK7wXsB4dM/eC\n2xF7Qco6tcPHxHc4d4K3kX2XUye4h+x3OHQCa2cOfmwP+KYzmfoT5/T/7F1pbxNHGLajEEKc\npISGXOQgUUI/NR9aqiq0ggqpgiKEUBFCJTeOQwKJE+yEpActve/7PtT7vu/7vu/jt5hvbeV6\nve+ux493Znft1/YW1tJq89rzzjzzzLMzs3MF3ddbuWdGiQy/zei3zsovPWJkuGyxqoNO0II0\nlVwH1XItetM15EauONU3zCzXEbBHmeQ6BvY4r1ylNbpDuVIN6Vqu5OdGruQilesBG7mG1XIt\neu06kZ1+pkmOzWancL4shxGngtcGDrF+nmQW/EGwp5gEPw32IR7Bb6Vw0vq5zUp52jCDUU9e\nKCuWwzbCm1ELr+j15Kwb2WA9GWWWzRzY80yyOQJ2jFc20npSLhuqr6SyidvIZkEtm6LXV4vZ\n6Y9D+ltl+TrqUG6h9NKl0cnp6PgMRLEEhbnsrjDNJKpmFxdSJSMAFWO9FOzLGCSjxXM52Fe4\ni/egLN5jYF/pLl7pHMBVYB9n4uFqsK9h4uFasK9j4uF6sG9g4uFGsG9i4uFmsG9h4uFWsG9j\nivd2sO9g4vdOsO9i4vdusO9h4uFesO9jivd+sB9g4vdBsB9i4vdhsB9h4uFRsB9jiFf7E8IN\niOGE7/vh+3QDfIJWsou4HneHy2zVqsNz4zOReFgyDXce+G0EPJsMPE8Anifd4ZmS5P8sCGfZ\nv60Q0n0qO10zn8EzLTJmFcHT2RFMJy0AyQBoI0CSjAzj9wawPkfvxgTuGWD5WR6WRyDcfrAP\nycB3qt/MCeVzgPp5HtSjEG4Q7MMy1K3ScQEC+AIAfpEH8BiEGwJ7xk7cL6m1OZKU+L2s9tsv\n83tF7Tcq83tV7Tco83tN7Tcm83td7Tek4lUb7JGU14RMQC2y9zTC8wbo500e/UQgXBjsWTv9\nvKXmKSLj9221X9iG34tlPHbbzvQQgHeA0Hd5CN0D4aL5AaX5IA3YewD0fR6geyHcXH5AaXpJ\nA/YBAP2QB+g+CDcvCSed1vpILbU9SYnfx2q/vTK/T9R++0S/QG4+tskKok9WELiqQgPxKRTG\nZzyFsR3CHXGtGnGRhgbscwD6BQ/QHRAulh9Q4Tn8EoB+xQN0J4SL5wdUeA6/BqDf8ADdBeEW\nZEB7lUAzy1E0bN8C1u94sO6GcIuqZ05L93v1M7s9KfH7Qe23Q+b3o9pvp8zvJ7XfLpnfz2q/\n3Xb8/ALl9Ku7csqMeI6HY3PxOKRijvD3Cysvf4Mkf1cnaUZSo207NbcEWoVYsTQViUneTs1A\ntdPxftkyXSNMZf9k1ID7Rza8zNvw5GI0vDA9FxXSEeMILpODVfOFZ5tUCHue/6YdPsadrtpF\n/WLZ82wm9g/tAtXuXfR36l57VL9YEnMYSU8yd+N34F/aDRugu7bxe4kJl3lpdZuouiq4u3kQ\nUpNZo9OT9CVo8Bg64eqpgPAb+GbJo15gpl6/p+XBxkzA5i5WIX9mM4XzNZeAPQz2AbAnwB4A\nux/sbWDnvEEYOP+SPMhVsUiqJYtmxzJpVXGmxNenfZ+g7+qEYinFIUHpXwo5VyBIQk4QQDED\npTh4KCcDWUcJ4I/mMQIJKpu1tOe5kjZJN5pQ9C34QTpewAhf8uwZCVYezwCjy9J7dWR5PhaJ\nx1OtSPyi6ERkWRJPReayjKdWR5EVB5K1WsBTRSRVCqxXmPWQftdXbHqGSjsKnFIpAHZMZZAW\nxOaoV+xHBGlNa5C2+SSI6wTV0yKDpTh3KweteDhEgkRgiKRBcjZkDz1ZxhmR7cKu4A7K2AYT\npD7Hn8g04WZls8oLBCgrm8yRFgYnzQVw0iXkcxXZNcBVHXFTA1yFvM+V2Qt3UCObD0NNOfLl\n5RrZgqyzfbIydW56/swBaef4pGVIS8+6GnE158ZleeX5ySt/zbkNMefl+mkMkbDaqNwaSWzd\ntNO/gjr0Afp9JYmtVrfPNcRXVw7xYYGHHBR4oQUYcleAjgvEiL+BR7R55a2BR5ycTcLGpNBf\nK7nAPFa7pReTBmkQIEEkiaSU4pDZdMKnFbH3rKec6RE2lCOT/5Pe8xrg6nTvc5VX73lNOfLl\npaffTe/5lCcrSMvf3PSefdJoqZ/fey5+77lRt83e89pyiM94Svzec87nZOk9b0oK/bWSC8xj\ntduAQUY9kFGK/5aQTrhZKLXGAnqIvUIvUewVhiidFt02e4Wt5ciwq16hODpfrPeKNmBlnedZ\nyX2vaC2Akw54r+ggbjpIOSJn9cRRO3DW4XnOSqGkTmCly/OsZJ0dy/mS2uPgJXU90NXtebrM\nuXcHbWwoKWSz5NnyUhvrZM69R2BbPufuCSrtKPDMnLv/xuZem6rBpk4vqK+cD7L/NqUeYGs/\n1QXiZmDNJ8uerM0+Wc7J2uKT5ZysC3yyII5WnkbN7pNX/lp5Gj32RrEp4GycewWEWSmEqaIw\n1XRge4DCGYNW9N/jzTHxvnIIVmzle+l+hv692aBtEIA1ORDTCrqqFFc1XYUKqKl4AvJ7j0y9\nx5ZyCNtLNbGxksWIq5FHKOInrzw18oimWAIyK6BmX0D/tXceTFIcVxzfA+uEYAlia8uFjvVh\n4TICC2SDbAuwAUuCgxMcCDjSwQWOwyKeuCAUfJaNbIMkBwUHYVvOOeecsy3HD+DyJ3DZVXKV\nS6XQ2t3u6XnbG6Z7pnv6wbyperX1LvTs++3rf/frnp3hd+t8WgxNEEYaT6OunnhpSps5y7gv\nV09f5SPgecpvjZfg8wqh4PK9kiDVCUgFBGaIiK/n/i3MR+RBAIVc8p2XRh/vcuXjXeEjSLOP\nFzzkKaBTtEAn2JeClPKCyg0KpVfjp5TCPtRrFCor0VOp39G0zWSVwuRG9Ey87s29VsH1OvS4\nTPbm8gyEmXpYmKZMOntzrwe0m+/NoUAZhYD25nKX597cKgzZ57MjZ3l1JY1lyJu4L5ch1/hI\nOLhctFq8voH/XK4CrAVvjJYhqaOYLkPe4COxMSmpyZIbwYqGtZFgKW0U7UiPesSKp2hHilKR\npuVZTyATaco8rKCNgt3eFiuOgt1eFjth5oHPJCjv3yh+Dtuqf7Z3sODuPaWiUsFxib+Jgbby\noi2DI9Z7zmumsCNNkQK8DEMC+NSUNtqiTjQoLaUE4o9qq8C4ToERfOFyWPny5VNl+6ctGE8L\n6q72WTZwX24cvMlHkOpwZrj3tCSXfJOlpGyyBN+m6xRtl8S5OsW36kri27id4t2VxGdT8ecr\nrIuC7c0K61vws667y0daG1q3Kqw2omdlsqE1m4EwUw8Lk8DqzHg3AdrNN7RQoPQ826UNLQ2z\nOVOSU+2bMWSfz45scLOeLQQthLZZE9ptBC2E1q0JbRtBC6Ft1YS2naCF0Ho0od1O0EJo1Qf2\nBG0tcTvDSHrE4rPE7QwlqRnPcOLeAK2L+/Iqi80+kp9ugNb0iBUbwqsd5A3Q1vtIMGTq2hXA\nWKfAGBEAxsUK7XT+Outfudysf9uCUTlxt8MFydu4Lxd+tvoIsq6ENluQXJRLvkhmuiD5MoXn\n1YLfNoVnD36e3hYdtyusdqBnZbLoOIeBMFMPC5OI6iw63g5oN190RIEyCgEtOuYuq0VHuT27\nDUP2+ezIwZMlNKDtJmghtF2a0PYQtBBarya0fQQthFZ9nnzQ1iK3uq9zxGKwyO3YoGPG40fc\nJZ2d3JdLOrt8JDE9EarpESs2hEs68olQ3T4SDJlK7gxgbFFgTAoAU8r61n9yuVn/twWjcuJe\nh0s6e7gvy+q9PoKsK1DMlnQ6c8mXIEyXdBot5+xTWO7Hz9LkSVHyfkD7fMSFSRVMLj7JPCyD\ni082ELQQ2gFNaOsIWgitTxPaeoIWQjvIQFuddqbCcY9Y8XfamULHNeNxI279d4D7sv7r85G8\ntKXf9IgVG8L6T27p9/pIMGTquD+AsVuBcb8AcF4php8pN9VmC0blxIcc1n/93Jc1y4CPIM1q\nFrT136DCcgg/y1j136CPuDCpgsk+YOZhGewDbiBoIbR+Zlj/EbTy8MUM6z+CVmbAQFtU/xmb\n8bgRt/47zH1Z/w37SF7a/2t6xIoNYf0n9/8O+UgwZOp4KIBxkPtDgX9EgXNBALkgwE7nr/lp\n3OzAMZmne3mDmD49GPxDIuBHapcu8i8p20xbwVeoH3VYrb+Z+7LCvMNHkGYVZl21buOC8ZLG\nBePHFFbH8bMyqcblrW+O+YgLUy832Y3NPCyD3diNBC2ENsxAW3SNfHRvjFvjnOC+rHFO+kg+\n2uNqesSKDWGNI/e4jvpIMGTqdjiAMaLAeEwAeKK24MtXyob5tmBUTnza4ax5lPtyJninjyAv\nkVnzGYXVGH5WsWbNZ3zEhanXm+xhZR6WwR7WRoIWQhuBkyiaNbubNY9zX86aJ3wkH+0MND0u\nl1mz3Bk47SPBkKnbkQDGKfE6qUC5KEBczNUuuBe42YFiMuvx8gYxfWoVCHcrEJ4Ugb8gps3T\n+Wv+pWW72xaEulnrqcmJoYngaRBnwee4IMFMv6TM9EvK1Wxwxj9HnPse7ssZ/70Y8BjO+Bc7\nqI6irgAsKXcZh2wLguV9Ctu34Gcr89JVNT6lMHkreiYNnx5ctNhNIaa8wHK/gult6DHVd8uF\nKXRL9d5acwWvtyv8zuHn57zrPaAweQd6Jg27XsFC14N4gueSvlPB8y70eOq7XMFCl2vE5rzC\n5gJ+Ns6704MKk4fQM2nYnWxjeVjB8u5LE0uHBZVpNg8vibYbDVzvUfC9Fz2+ehXqsKBCUQN/\nI3bvU9g9gp+dc5V6VGHyGHomqajU4wqW91+aWFzNhT6g4Pkgejxubk2rqlCjPdMPKayewM/K\nuepcVJh8GD2TVFTnIwqWj16aWFqsNbczEGrqoWFaa9aAtZpgKW3QfltNgqxgQE8pQTKaILZH\noSe5L0ehj/lILqNRaMbw6NDJkfHhEb1us5aBSFOPDFO3MdnTzzysLGqMemmbba35uKI1n/CR\nZC61ZiUDkaYeGabuowFrDcHKttbUfK2GrgiN7kztoDNVcnWZCC7oVJXO9Enur2U+O1W70qmS\nfhDtzZPDCcRPATkniA3aKtjtrbHiKdjtnTaHNnmfwMd9JhANbX6GNo0EkTcSeZQSJLsJ4mrD\n7tPcl3XWZ3wkmcs6q5+BSFOPDFP30YB1gGDpwzpOsGiqB4vUakK4FuzPcl8K9ud8JJ9LwR5g\nINLUI8PUrTRg9REsfVgnCBYJNhTsakK4FuzPc18K9hd8JJ9LwR5kINLUI8PUrTRgHSRY+rBO\nEiwSbCjY1YQI3keHXRZRRyxWHXZZWWNpssPxRe73M5+dEOMOhwnEL3H/AEFMAPHL3B8giAkg\nfoX7fQQxAcSvcn+QICaA+DXuHySITWZ7NMOpM5vlhbx96cM+ExBTeZHFndUWCSLv1PUgJUh2\nE8TVwuDXuS8XBr/hI8nmKb+1uTA4wkCkqUeGqftowBomWPqwThEspY2MLwxWE4JYmNVf3wQy\n7a0TYay/TCB+C8g3QSRlMh3KdgZD2QM+EwjTUJbFGsP21ye/zX1ZW3zHR3K5rC12MRBp6pFh\n6jYasE4TrAxrTJAArrTmu4rWfM9HkrnUml4GIk09MkzdRwPWKMHKttaMutSa7yta8wMfSeZS\na/YyEGnqkWHqPhqw7iRY2daaagIE519oJ3aTIxanhXY4OWFqsvL1Q+7vYj47H8aVLxOIP+J+\nL0FMAPHH3N9LEJsMC0U30hgrrqIb+bM5r+gK5hVTPhOK5hV+5hWuapefcF/WLj/1kVwua5fN\nDESaemSYuo0GrDMEK8MaEySAK635maI1P/eRZC61ppuBSFOPDFP30YA1RrCyrTVjLrXmF4rW\n/NJHkrnUmq0MRJp6ZJi6jwascYKVba0Zd6k1v1K05tc+ksyl1vQwEGnqkWHqPhqwJghWtrVm\nwqXW/EbRmt/6SDKXWrODgUhTjwxT99GANUmwsq011QQIzr/YTuy2jlgMF9thaMuMO6vOzuDv\nuL+Z+ey0GHcGTSD+nvvdBDEBxD9wfytBTADxj9zvIYgJIP6J+zsIYpOJzYJ0BvdYcS5IZ9A2\nTrxizrz46szV3iSh4l+lFGN5kbRPcV8WY3/2kbxGxVj70PDY6Pg4YDQ3BiPI4ioR+18UFn/F\nwGJmhYV8lrr62yvO3jEyplmW3stAnKnHhbHSmptMkGK977nJhMaksjrLGiTGUsA+GLn+Ju7G\nUgjPMVX532eBGE0X7ZiuBl0pfjdD/K5d/F/lbi3/5edqY+Lc00BSVs59DXgVlp/kZiUp5cme\nEyrwHFDL8mv+Lm5WTqbZyLUMvK9gBH9egMyJ1/LP82ctvS9p5eMVTHx6Ff9/otF24LcBPyek\nclroL1X8c0y08ULtz6eUv6t5dznwP8ErlOU2Rb7S+PDMhqgrTg8NHDsq3m5OT52nGPj71MPD\npM7zGuSD6dpYe2uLFVPKa2M1SjgHiMAc/lpVQmsi4H0+AI7UM66c5fcx0BZd1l3D5mggTXeJ\n178rCcN4m20zas+Rv6ds52wljIaGymc+enmDmDRUA9YhgqUP6zDB0od1hGDpw1pNsPRhrSBY\n+rC6CJY+rJ0Eq74SaJstgp8NFoHKr/nz3OwEX6H+j/DEbcs0Zt/tETYzhl0NbH6EFTXtGmBR\nbS7UtEXAotq8VtNeCSyqzes07XpgUW2u0LSVwKLavFHTbgIW1eYaTVsHLKrNDZp2K7BNLSzq\nfN2a1gMsqs0dmrYbWG8L2xdhfS1sANigA+sXdiKeOEt9a1WhX+nYCg6sw4G93IEtcWDLHdgq\nB7baga13YF0tbIsD2+7A9rSw/ZZsyIEdD01v0vgifO7KiwAAAZx4nF2PzU7DMBCEJ2nMT8QB\nieeghgBpqbhwQUKqOCRF4oZs11RWHTuKTSVemwu3UkW11R9f7Pm8M7v7kQMYIMsSDAgAkPfZ\ny/UYSK96Ee4UwCWABBnOATxR37S08k27KmRddrQavr7Vs+fp9HZ0Vz7w8c3osSioVw2jFRW2\n/fnkTCy5NXJYAfjD9sS8E2lWk0kJZOkeJYY10oXWaYBnX0rL/iNq26mFMkwDWAP4DSMnR74L\n14neyrRiLrDTwLZ90/+jLYnQzLmQm4dMHL4PjCTOxJWZK7Nwu/WEtlHlzPtO8W8vI8mUq+9j\nrbZiKecA1hsG6kBTAAATEXic7VZLa9tAEF49LNuy7DipnaaPEFNKDoWIppcGXwOFnttCb0Yo\n61jULyTlRS/9EX0e2t9YcKD0EnVXO5JX65fiuHYOEXzMandmvtmZ3ZHe6wghBamqhJQMIk/m\n3dtXewcIaQpCqkwmJKSiPJGa59oubhLtInnLERQINgiqoRYbE2l8JvglWGeJddNpYzL8g9gT\nL+Huab2+/1KwyNhty/PIoDwuCHQF+AveZNBQfadjRdovzP1987ngOG9bdgs3jhyXqgCiRble\nD51xM01PmMn3Lb/VaPU6GGKQhSgzZhhEuJjMQt+yP1jHWNBXh+phMmlyaZIPCAyC+5DgJ1SX\nYJtyEOwCtwo6GujkQeqgkyV4DHbU3w7434UiqgJHGdZKU3zfA511zneV803fN+Gd2j+F8RbY\nVYDvEcFvliYpAL8Z7pDRePc4CTC+MFA7OZh5qtRg2kGKyahUQD6DMZHGV4bFkpWAoAQZVZg0\nvjEslmwNCNYghQqk8TvDYsnKQFABCTB+EPxcEFlKJ/F04azVa+M3Yg+TwLrKjSWGqIfdONQY\nvAPpI1xeunAJi9oYxWwHex7rGTK3zLNtHLrY8p3ucS1sbTXLryW1tQCsrgRSiSO9hDUuiGyQ\n9JMT3uMIdNJNGzaNAgtMcY+jTXNGNEqSvRJM2XPtsNe/oFs+wk3rpO3XLNduOT62/RMX10zT\nnMChCjvOzLnjXLhjEkOSZzMAv9qEcha8C8/HHRM+hsREh3ToYlpiqi4+awjpS3yPeqfYPXOd\nOPf68BDHDiMpQV4HMFdccvsLV2Yf3iqEoULrr8SUrGQS5GFl24gI1U/DwABjrcv4vO+Sm+z0\nut7r7hE+n+BHHmKsH4NFkfAhwbkdgG1xyV+ZkZqyfyZxNtkkBpCw61RaWsXmblmlN6NkKEIy\nlvGJH6lpmjY8gEoWl/yLMBIs37MHcOSi46cL/9rbENQO6O2iYfWzYK+x961gFZtLcW/0gAt0\n6QHesnuj81XSwVeKZ65Y9dFYJyH1Hq7TKNW7gqMHAZMP+cIfpC9MeM2noTADpSlYn4HK/Jgr\n11FelBTI3RBrU7Dx/5Du7PwDSiPUOwAAAaB4nF2PzU7DMBCEJ2nMT8QBieeghgBp6QkuSEgV\nh6RI3JDjmsrCP5Ft+uBcuBUU1RZtfbHn887s7lsJYISiyDAiAEBeF0+XUyC/GES8cwDnADIU\nOAXwQIPuaRN0v65EWzvajJ9f2sXjfH49uanvuunV5L6qaJCa0YZyJ1gQ79zq3hphgh83AH6w\nPSnzSJj1bFYDRb5DiWFa+Ng+j/DkQyoxfCRtnVxJwxSADYDvOHZ24Dvzjg9WpiTzkR1Htu2b\n/x5sSrhi3sfcMmZi/71nJGmmTpqlNCv/vx5XNqmSheBk9xVEIoX07W2qVZZ/iiWAzR97x0Ih\nAABiZ3ic7FpZb9tGEKYO6qAoH7Gdo0lqx7cTW7GN+KiTHm6AJE2BvLQp0ieDkVY2UYkSSKpO\n0JcC/V890MeiLQq06GOLAm3RPhVwgKIvZpfirLgcc2WJFmw9RMBgubsz883Mzh5c6okiSVJC\nSiZjUkKW6E9+/P69pU1JSiUkKRmnDTEpKWVpmbLMoknKlDtPaxlK401RSXoXSiD1c0p/I+k0\nlS7rFUIf/5W8X6uLGB9vba1sIgm5WNEsiz4MhRkhHQL9B9riwJG09arGuFcLKyuFZaxYN+oN\n2+3mGmPTTR3NxrTT3XNLibJL7J26ZmpVC2EmLZvUkS/D1BPNJjvFWrVeM4hhW+BSwFg3aGEd\nGYPY+zXzo9A+q06KtqlVwhSS0m64wrpJqO2k5PUFx77WsFsx6zY8/HPG8TRnnS6CoTj+c457\nVp0QN5S24UyY2j6XPGOUblFyM/ozSiqlC012SbrqDhtkuTszZkFVEnjcgZ+klIVSAZ40pSsg\nr0JbjtJ5wMHyw1CeA95BTn6U0gzYMAb9I6DrPKfrIvBfBp5LXB/TNQ7lBPBcBR3XQvTMAM9U\nG3/ngWeWw7gO5Q1oXwCMxRCMZeApcH0r0HcL7F0DrHWob4DMKiezCTK3gecO1F+H+hsg81qI\nDdvQ9yb0ufF+G8b/PqV/vGyNORD3PLf6uXEscSWQ+oVHrlzcOXa5SzrtVrgW2DVIwknglr1S\n/ZLSV70GmwaARcgo2SvVryl902uwAgDchOGWvVL9ltJ3vQZbBoBlmIuyV6rfe9RbsFXfm4Bn\nP1D6sddg6wCwDjNU9kr1J496C7YJAG/BFJe9Uv2Z0i+9BtsGgG1YS2SvVH/1qLdgdwHgHpRA\n6m+Ufu812H0AuA8rnuyV6h8e9RbsAQA8QGH806Pegj0EgIewr8leqf7lUU/AOlTSas7t79Uq\n5D18iI2BNDvESlDG/EPsiU1tEa8g9gkcKNyOF9CZCmHMWnatrpeNmo0OM1Mf1hrmRPM0O+Ee\nE+ethYlSbZ7QgjJPkGe6ZYPumEB3QqtUkBlxAWuqYVQ8hSmOOyHirmj1euU5sjj2BNx08Lns\n3uNHSEPOdanQdMKCrsM2Je9CUmBU/M4Sx+mu/Q6SlJF7KZGmra2gJn+0bL303NaeNl91whgy\nJeqRbhRtaD9EkGlUzwRNWHSCepccAU7KIhUiRMmiuhIRJftUN0o7Zm3fEgDlgornnGCdZcKC\nA/xqREPkMn2BKEHjIUovxdAKlm3qxq4FMHnBwMaKSD8/qaU2ffFH29B6BLxYq9x132WJFSKX\nLe7RF8aiTUxOnFeDLRwo0xelhkl2LDr7i+Q4Llsz6VtpiG6M8cKVCkblhijYsV0kOIhGfaj9\nKPpJqu+aWn1P0DvW7Nwpm7XqTkmzNfqkVQkwHQYh8nhJofPMpPlPWE4ozT7/J4rBsCAz4mY1\nZPEI03CufRTX8KIzIkCUrXpFxwvuqIjZoKGxEPMYqp9vPyybwvH+gHOVU7DBh0AKCcWFIH8a\nu34RGXipOwOH8BC0osHSChsZ5p1SJdWnxLT29DrHKbVx65WgmbeRWRuizLgsdTa/BoqVBr0x\nMq0dd1F7jrRcQUG72j5owiUzrZmmZuyGTShX7auoPh4RJlVt2PRqR4AygerXukMpOAK9k6g+\nFdX6hmEQS7SNTqP6THsUP/TuKaFs2SJQ06A3YRUB6GwQxM9jd40ssDWSl5gTSMTw6jIvYJRN\nd58Pdq7xoccl7xW/3Q3cZddz7xgl8ixcYk00rfMwLyZ0X7i1nPqTh0FwXF0Y8ALeavm4XI+a\nPVrRrFkWQmlFtUAHmZ1IbiDIxQ5TSXFTqXXWC+OQ9/eISZANmEnVrULYUYS3N1koGxaSUzSr\nYDSqxNSLnFQI0sPj9oqloMN4UT2SFEyuIMjZPHlWr5n+hXzAFaXZStw1FtTcDKrJAHyWwR57\nW83ZFkmo5dDyMQ61LuN5h2KQytt4OiThs4t7ShOFcKV96LdFcqsCS1MmoYdPI6hlMCwB6Ils\nym0/gLb8KV96No9IeSAVtvYYnFwSsLmy6/8kGDXXAvQMPYBnSjPOWThBgWdZFOOcUwMh3zQS\nsJUnOefY1j7LOZYFfXAZMncmjo1xH01UOP0yA2WvTDmcoaduIANMfuobBhQqPUTnsEksS68Z\nFtuAwvTEfQrVo3pWBHRQzHmns6Bdfxk0P2gLfGoPgK4Iv0i2Dxy1vVPq2EeGlUe+JQXUC7/y\n0fzqZtymHa9cZMmcQsl8Gh+ymj2BCx3+AhB38pd/B2ARm64jaLGehNGY5HagOPexNQVlGsoM\nWsBzgJFGUcr0fZT8q0sWm8EIsQnbzLIoFkrfxyJwwcrCMSrY1/F/FlgoxiENJsHNcQgFHyIV\nQpLz6kf2+1MJTTf7fe4sDOynrYsFSwmZHAlukvCjnIbgqV598UxGuasJ0LrYZ2Yqne/QkYZE\n6Xzn6vqUgfN7iEuhFAxNngsC+1ebBKX3laFvsv+4UHSa/dy/B7sJ6aoTlkz8LQZlusWn+Gj0\n053oF8nx0einvhPnIrNhsEMbeun34NEB74a6Xhrb7CPN+yR2PjrzmdQP+0ibYK2/DBbSMXKy\n2YN/kfwYOdlsijy7YvDx8wCS5gBk+MQ4jf/1HVn7/c/JuEf4KfkA3i46mAQtnwfPwtd+mgQU\nc8vxyg0WlAGvfpvVh1GQTuO/l12N4pkY2GejeOf/dq50p3EYCIOEtt1d7bLd3RJAnOG+r18g\nIU6JU0jcvA+PnAcIpfG4wzT1Fdd2IZU+RValZObLeOwZzwTIoNXNLopX28JZmnuH6D5GEf0A\nivIh41HJyctD9uN/Nt5PfSiIUw1DWv6qWXGRMI4UrJpPw7oPPUOy6k5b/QEJKjkgv57b6nfK\nF8nSZxWWI6qiaoMLRsJ3dg/IF0XEzkd8GKD6xPrYH5SwGiSdGTbsQ8GQZljjmQds9T9MDSz9\nmwDkV2jGWdgSm2yNeWD5l11HicG46NXQsmgvAoZk0Qnze5gEF00ubd4JleQlzB/Di6z16R+B\ngC/HGeAf7N4T2Zj77ckQ9BcfC/FCQuAkKsAJrG/jzMgnUQ8ncPWLcTNFuJoOnit80NgtC4oJ\nKzPBs9Iq5izCSSzgZJZwMhcCJ6jatI0RXmkKjFQJI3C2iLWFo4r5bHyW+tDSROCFbHzeMwIv\nZuMLrwKrZNEXfQgY0h6CKy8/YjKSrSrfXZeyepaVetf2YnN4as3O8YGRxjU78ZEyKzpuJC7d\nSGkgIgOZ+uoG0s9aKRRIuy5Ja5F2hbdQkZ3Z1elnpF9kZ9ZZm431Pv3QCAoN4XRGVGi4lI15\nqLTsw0jpii0OH3k3l+261BXCxWoIXHzsLKP/8q4yBUd0myI9nesVkiOCe3zm4qsGrzeKK9R9\nipzBlzaMxjPv8ApV784KZaRXvTsrU7knLrYnvoTJM1ZOnqxHNWFViO/XNUKKi++IaUUyXgQM\n7K09ABnrhAwXH2KTNlBAm2xeA8VGNm62iTsXnO7FclvL+1kfOkjXo30Tj6muwo3rE9jVJnk9\nLr65p+IFjrwKGJgXeE7VSBssSWuR9gJkbBEyXHzpUcvEvQgY2Ntqmi4mwcUnMpvsb6MHv6q7\n6Nx6SIyfOfiNMCjBPwkihBEBxiWIEWYEmJdgSYA1hHUBtg2wa4ADAY4ZTg0nwqtGIFQtiD8W\nMSrAhAZmBViwhA0BdgywZ4BDAU5aUHOCb0GWlz4AAAGZeJxdj81OwzAQhCeuzU/EAYnnoIYA\n6c+NCxJSxSEpEjfkOKaycJzKNn1wLtwKimqLtr7Y83lndvctBzACpRlGDADY6/LpegqQq0HE\nmwC4BJCB4hzAjIduzavQrTeFqkvHq/HzS718XCxuJ3flQzO9mcyKggfdCV5x6ZQI6r3VblwB\n+MHupLATZTfzeQlQskeZFZ3ysS+J8OxDGzV8JN07vdJWGABbAN9x3uzId+GdHKzCaOEjO41s\n15f8Hq3IpBHex9w8ZuLwfWBkaaZG21bblf9fT5o+qVyE4HTzFVQiVPv6PtWaXn6qFsD2D1Jj\nPxMAABkfeJzNV1tPE0EUnr30SlcuAi2XUkDFJxoxPij6ZMTEZzXxjVS6hU1KS3YL1vjiTzaB\nxBAT1jPdb9rpsFta2PSSfDmzO2fO+eacMzun37KMMYOZpsaMBKNf4uuXj7uvGUsajJk6vdCY\nyTIkk5575No10rboKU14RJgllDpawZhk7poxK6usTtHqmlO3afiXBb/ulN242N/fe6OsSBzV\nK55Hg7kwEuwGuIY1HRpmyzmtCO2X5b298gt1I3b7rOm2aPQPNpYIMwS+sQNCjpDHpra5TWyS\nB6jILeA5RXiKMQ/ITocHjyfreCrCThZjCzrc1zICyPW3CIvYqXhvYu1jrF3GWq73FnPcT4Hw\nHs8r0F2XeBWxblWyuYF9bWF+G3gCXZHQ+V5SO+//BJHUfPAylGLYlCTAi4GDr9P9O7Nv+oMS\n3nU2J23e6CFHCbWMuJ2twcFGvzMrScjE5GxII93XMz9OmnX7s3oiNawuSWMtgDiRD6bahWxA\n+4VS5hNXmEyGKOpOTdHUIjQTx659Vodyl+hZpdWy3QZ0ZZJaWdHV2qDhKxOJmtO2qzCR7YWo\n87uR6Kn1rkuhXgRtIYGcHyCWUHedLeHjs4RDmgikhW9KLM6GNDLjh9TbMo4Fg+T1psXES643\ny1eKR48qs3e7kqY8k6s6bqNyah9SJZ0o1owIayms6TfZqSsmqctymBJaRvkJCVhxRq/rLI+y\nyeMuSATS0gPE4uwhJVQIKaG4PuUDS8jsT3re789ywb9H1q8QYMnuI9VvMqLYtC1FMRWhmKWq\nLNttx2t5IZSjqA26w9fQB6xJPcE2brpkTLnoOltHjLgsY0zSSgWI11lR6t5eYUzSSgeIxdlD\nqn8jpPrj6i0GVn96UHGRjUrLjjgPQjPj2kfnrudc2DAx6Ea9Qmcc5jJTbx4fVu3v58fKp1ve\nWeaDQ/5aTffn4GPatygdbKRaxtzNAEmsZ7mtS+zAGnPb2Zm51ddo9M5HA2Dho5VD98HANBHI\n5ETZC4fm7x4xIHT1HP0Zc23Pc5oN71Ojarcj7Og9hNrJBSz6bJDPeT+QCyIo4LQing0lSOPo\ntEbK4kQITlkWCyIYuhQca8zNTYeAGRAoCUIphcg4/isOUz6FiRKcpvK5xG1z2QvOMxGctBKc\ncbQkI2VvIgSnKXvkcwef8OciKBklKOPo7W7dyn19yCgpnQj7aUqpGkm5ORP2D27bHxkZCTMR\nmAWEnuC2AGSQzDDkgYKE1SFRGgGS/XvlSsTSiAm5OyD05hUYOCjDYm1IbI4Ayf5w9fofzuNJ\nKgAAAZt4nF2PzU7DMBCEJ6nNT8QBieeghgBpKScuSEgVh6RI3JDjmMjCcSrb9MG5cCtVVFv9\n8cWezzuzux8ZgBEISTCiAEDfFy/XUyC9GkS4UwCXABIQnAN4Yr5bstJ3y1Uuq8Kycvz6Vi2e\n5/PbyV3xUE9vJo95zrzqOCuZsJJ7+SmbVrpxCeAP2xPjTqRZzWYFQNI9Sg3vpAud0wDPvpSW\nw0fUvVWtMlwDWAP4DRMnR74LZ8Vg5VpxF9hpYNu+6f/RklRo7lzIzUImDt8HRhpnqpVplGnd\nbj2h+6gy7r1V9Y+XkRDlqvtYq3vxLRsA6w1ofD/eAAAb3Hic7VjdSxtBEN/LXS6fJ35Ei22j\nUFT8qKKWlhIKfRP60pdW6Fs440YD5yXcnVXpS/9kIUIpQq5zd5M42WzMNaZJH3LwY25vZudr\nZ2c3+ZZljKlM0xSmJhk8yeOvR7vvGdNVxrQEfFCYxjJAddepOLwK0gaM0oAFwBxgJ5SK3oEa\nK4B3wuwUzK7WLA6vv1j0dFjc/l4qHe4LM5IVy3RdeJmVOcFaiN+oLYESmle7MNvSh3sHB3s9\nimv2Kb+Gl3uRUXXMM1fGaDi8ImPottuQc4wL97Ds1S3umHaFywQajYtHBTLeucPd87p1GjHD\naBcDRjAbUADkAc8w/a+C6JEGS7kRZoWxl4BikGf8pgOWcAk11JNGHTOA28gRxcclVsmSLwPW\nCUUYaxGCeQl/4Bpr/mPL2jH2IjLONgHP8R2osQF4M2pj22hgGzOiRtR4G2EkxmIq6XzOXcHy\n8y/ivlNw9g55VyK0992TXe2AKlB+YEkFjDtk6hLBxIddIkk52Zptc6ds1VxP0KUQXXfIk+ou\nlfrozni10xvPPAm7jFTgBLZ+2alfuchoCTbVPjZ1y2w0rBtBWusjrVAHpQKbQuzJfoI73ZHo\nvhDZTCD08ZbRp0Uokc34wve2EfXo+DP6offxI129tCterW4TlV190DOdM+7hrHvBUEvSVBKk\nnoPmtEIowtiMMJJ67hhbwQ66jhRhbAH2R2QsppI5X7KpN7BTM6TBpj4YkV90Uy/4A4p0rmJa\nlUvL9HiZ255Tb9w8XourwjgljLPCOCeM87Q+mVA+TCgXg2TKiGhYLiPLFBtA6e5N9esaDvcu\nHbs7jEVZmLDIS8H3JsoaYz4FQw7ldrXSHiZto02s3UVcibxwKymiy0VsmKvkdsJQJo161Gi8\n7E8iBWIABXRuFg2+JtelIIBrSQBaNNYnEsCM5DxoorMK9v+2U/mfD84r6JCKAWg4J8m6HqkH\ns/wa7sauC0eD+wmv1S2JnUR8SO3ko0i7bLQXTMMF2yLxJDF2HY8bvMcGusM+Re/TCdQzL6lc\nDWkSqU7u0kXSjzLkvq2gnumR9z8feZLr65pwIo3lhFPQbhO/GWNeuJ5tVOgo67QOfaIOtg1q\nQs9ShuxJWm9PGrrnSJK1Ok1W/GSlpsmKn6zsNFnxk5WbJit+svLTZAk65v/+4toPQ8XQtq8+\nHXFjH+8P7LH/PBKrgzxjrzKo7KJPdOV7fRr0DOXzv/w5puB/G7I2Mo4/10PDKWK4EDNYXYIM\nQRqRGzLphRFt5ewD4i3KH0hAq/YAAAGjeJxdj89LwzAcxV+71h/Fg+Df4aJVu7mbCIIwPLQT\nvI00/VqCaVqSuD/ci7cpZQm65ZK8T/Je3vctAzBBkkSYpACQvq6eLudAfDEKv8cAzgFESHAK\n4JG5bmCl64ZNTlVhWDl9fqlWD8vl9eymuKvnV7P7PGdOdpyVTBjijtbUtGTXdiDhDJ+WAL6x\nWyH2iPRmsSiAJP5HU807sr5B7OHJu1Q0XgTdG9lKzRWALYAv3zw68J1ZI0YrV5Jbz4492/0b\n/xwMmwrFrfW5mc/E/nnPmIZOtdSN1K39G0+oPqiMO2dk/ekokETa6ja8Vb34oAbA9hcGxUM3\nAADt73iczFzrc9zUFZe93rXXjhLTkBBMMArlkYTEEJOAcUiAAoFASEMS54GBjbyrtWVrpY2k\nteNQII/mRXkFhoZJCn1OO0Dp9A3TKW2/tdOZttP+JXzYmU6nM9le7f2tfHV9Je3aYujOnDl7\ndc49v3OP7j33Ie0e6pYkKSV1dLRJqbREPumR/Ts2DklSJiVJHe3kQpvUIWUJzzh23taKRFsm\npS5CNxO6ntCVuhb9Trh8mNC/uNqdpHZRNzTy9d8S/fgizZweHh7cxNVI5w3VcciXXpET0lXQ\nf2CtHRodrl5SG9qDA5s2DdzFG9bNcsX1xMzFtlvqNuoXO2vi775y97jm5sqqrZYcznaH42pl\nzudriceqq+W0wrjm5JyylndtFd4H/PLi4wgEXahjCGQpW52hl4M3y6q4fiPD2tPM964atZyt\ntdaq7pqoIabmzlj2FNPIHpFeum50TrCkJmhij6mWtJxjVey89sW0Yc4fD0p0ZzI+vNg7V7VJ\nV/livLtGFLmMj8g7lHUnbM2ZsIzCl9cp5qKjmqblqq5umaKwdvu+iqSpkjMoquToJd1Qbd2d\nnZMuE/WcVLlc+j8IQntJ3HbL0GzVzAs7XEeJ5kRc72P6wCpRUzMF1XAt88tr7Q2Mh6uZ7zcy\n3/tjW3G04lrF4pfn+XUCDwP3JaubrmY6c33PoxWEvklIJnofEr6E0HX1SqT93s0kdJOXXgjd\nBoMd0Mlgfs1C1snIulFfBl8KO8tQ7kX5GpS/gvJy2OohtNILAGz2MWWP1hC6HVi9mNeHoJci\ndANpwgHOz37YXi1owxrIbhLIboHs5iZ8WQndDka+k9A6QushvwPytYQ24tqdDO4mYG0G3wKb\ng/Ddi9e9uHYP6g8x9Yehsw06WwVtehCy7QLch8Efgc5DDO4OxOgxlB9HeSd0H4U/Twgwd0Nn\nV4uYT3OYeznMPcDcJ4jBQfBD0B2JiPModA4z2M+BPw+eAz8C3We5+Hv3dAy+ebOYBnlR4Nsk\nbExE+FSCzhTjkwVeRhyOomyj7KCOKegbvP0Z6FYY+7Pgx8FfAP8GdI/B7ouCNp0EPwV+GnVe\nbjHuZ8HPQXamibZ8C7oXGDuvgb8O2atN2BHF/C3wtyG7GDHuLkHnHZov217ixsFlyN+N8GEz\ndK4wPrwP2Xdxn7/Hlb8PvR9w13/IlX8E2+8J2vBj2PgAuh+i7kco/xR1f8LU+Rh1fg6dX6D8\nS5R/hTo/E8Tqt5D9msmZn6LvfkJi9wGT3yfptfrc9Bmhz+mk2VbDvJFl9n3rMB4aHCQfoeTV\na6/FbvQ6alF7Ox9sPSad9UhkKcpllVKyYBsBsBEzT4pyuUApWbABANyNJUCKclkjNJk02GYA\nXAQHyVOE/pI02NsA+DYH9ldCf0sa7BIALjEhJVz+O6Vkwd4FwLtYRqUol/9BKVmwywC4jLVk\ninL5n5QSAWvSyNzObYZsy7R9/FFQG2pfYb63UWocBS3aVZ9YA20vYOHsCaoQZgSKWce1ynqR\nbDu5pftNh8keXqmfCSneCYxSsDRHIXqKdkx33AHYbQux2+PVGairOowrnnYNqlcZzrraHuaq\nYY3nCtpYZTxoMBCar+yy1IJujivYuAwMDITApEJg2u/fyNn378rcZog11BFiaKleKlu2y2yh\nMlwUApu5fGMz5wUf0n7Eqh/lPpT74mKYDmucXuQ0MyGabds5xc4QxYyhmePuRDBmd7MOsrFc\n6tV84HOJ/bA6/AS7ikvWqxkOqifrqYRGkw+2BVHcgp1SmnLZoJQs2D0AuJeZ9wiXS4TMpMGG\nADAEQADLFqVkwe4DwH1YfKUpl8uUkgUbBoDHFXwnXD5KKVmwrQDYiuUkjitkm1KyYNsA8Di6\nfRpd3yX0ctJgOwGwE4cpacrlE5SSBXsSAE9inZLGOuUUpWTBdgHgaXCQfJrQ+aTB9gJgLzPA\nCZcvUEoWbB8ADnEte4XQG0mDHQbAYazA0liBvUkpWbBRABzBHJSmXH6L0KWkwfIAyAdnAPky\npWTBCgAo4JAyTbl8hVKyYBoAijihSlMuf4fQe0mDjQPA4e7Z+4Q+TBrMBYDL5caPKCULNg2A\nl1BOUy5/TOg3SYOdAMBJnJ6mKZc/IfRp0mCnAXCGaSXh8u8kSf590mBnAXAW5/BpyuXPKCUC\n1qSRO2qMX41N2jlsECRwb8P2h4T88sk7Ualxq2zPCWaVvakWsdfp36PZRcsuMdsdQ8lbJfII\nXnfqT7ck1GQRsi0gPPOQo8x4W7/6xs8gTxJdxZ3QlLKt5Su2Y9mOUtAMV93QrdQ/2jHPDUVV\n1tqaQZ5jTmvG7DrFsIiDZdsiz+4cz1dXL2lhu7LuFry7bb86pSmzFvFEM4oEdYw8uZra4F0h\nbjmaPa0puhuG1BNEGuSR/HtnerE9zkiZWkO1iH2PCHVJk6hpL8THucpysHKnCFxkrMe/X08d\nhyjMv6XN+le01XHev2VSyF7yQXH0DjbbgM4xNT+lmQVcvhqili2T++88oroqoygJWtkb3cpl\n/Ki8htGvImUJt9fDw2GuuXph1lXH6q/6CBXGdLOQs60ZB4KrHOZyrnxt0IdnQmNXHlPLZWM2\nRNxVHjMacokJlX/vDgFvRdjN5ZvMb/EFN36u8lou0ivDFLlDmX21GFSJkfHdoGE0tWNkN0SN\nS0vYp9+cer3P4/YcqPFCb4xBuJ8X0hwC6T5eKpecwZz/fgNV6p+nVC6XeKU+KK1A1lkhyj5s\nwxtRvi6s/9qlYFj310IsrApaiMzTNz6sGvmKNx+Q5F8k4a3YmnO7opmubZVnQwCubzIRdc6Z\nYav3caPlhujR8hxXfp5rd64GO6tDIpd1tKM51ZvpOEduDFY4Etdv+T4uMbpwJt9wpj/Ema5i\nxcx7bxEx9gJj+hgq/HdeJ/YSKJUdEN16/niunVk77caz6AYHyWcoJbJ28sG+jpWMxx/Fd8Ll\ns5QSAVvMAnIPdj4SuLeAPJeQXz7xC0jh8aytkfFmCrSamTyyJWf++GIV+vIY21rjnNvIzR/Y\nmQgH20dHg7YtzhkzKqs1OKMf6JAycy9kyusdMrF7EedQFU+km02Wt4qTpVkpjWm2YhUVZojO\nA1KaTJoZs2GFrb2muc7RdK68OWjHqHF4X/0CU+MtQdsl1nY706eQAS2UW056I0h0I1zSe5VS\nIn3MBzuARHcAR85ZHDm/RikRsMUkvYOCpPd6Qn75FJH03JDr9V2aoGfPcD0uscwzgmxzkMs8\nryYZkDiHvGFwa7DBkzXBMGLKwu5fRW9rZcN0O6NfxbCISCmjoYmK7OzJrMJ4lYlAXSeFTDHD\n2zjN9SGabWs4xTuCiid5yA0hhjpVZ8B71z3Yrt6oDhU2I3RjFsjpov1vFe/lME6c4J0ciGnE\nnTHyu4LyU9zdWr6QVmXpK/nhjdrEdaHB6C40Fy5vp+13G6GGNq3Zs+6EXl+shzku8unu6GHg\nI6T0An82sjlYtZOP8ZagPHA0EnZdcGRySDSCWdd6sEzjTklEqkvV/NGK7ujeTmJ3pcRoS4LY\n3BN0dFLkqKjevdExnTfIh7h+cV90vwhPLaWKS5atjFeC1FIfsFU8UhYmDG7xeoaD6+Wavbxh\ncGvQ4IVatJ2mxphn9/4mu2g3Vum5Av9GyDYuwNsXGOCugu6QVawwe3t2H+DKD7aGc74WYvch\nrvy1hOw+zJUfibY7NxV4CanohGWjjG3m8pYRAvooV97RWmNGue7XuD7FX/cz17RqUwnfc/2h\n8FhrPbc3DGpVY16jGalSYjaOVbzrIBpyS8jUmp9QbTXvanaTzVo5D6uxAariRQcG6U2uBbOi\nsceOmCeC9efNv08G5fPm110x8qeC8lNREY7yc3dIRLtJRL1f6owH4lnF8UqUZ3ti5E8vwnOp\niRbtjcHfFyPfn0BkyecNrt7FuB4zEsTlJ8sprjzLlc/UQuweCNqNnUwPMvotpJWXWfxMhP1D\njLyKN0YicE5z5bkkqZqzOavIoLIoz4T067a84HYy5eWNcqMCOc0yC3qhfpqVt7x0wf54UuKW\nsOm6Ducxq7B2zlzeqpguY47mn1xJdfMTGrsEDuNse0dbvMvPcnfhudbu9txdKOoGzbkNr6Lu\n/fMhd6V9+7ag/Xc4vKYfY+RajMORhfX20BWBypXHFhjXbLFiGLlJSzcFQHxDyOeVsOGfD9ab\nNxEVgvJ56VCLkReD8gVtBKt4P4gN3MQCA9eVt1RDc/JaCI4e057JGPlUQu01YnBKMXIz2o8F\nTaOSwE8riDMZ1e/4+vwpaYo5DzuHNzsaHOS9UpPYazU+2Hm8T3SeeeGTcPmPlJIFuwCAN7n3\nLv9E6M8JgS3mSPai4EWmpH7Y5JPoRaZysCtFPvPYuttqvMCkzGi2phTJbFnYoNBHV96DENVU\ntFLZnVXIZKqSx/AlTdFNx9XUsNOjo0H42KnB5jKS01pGEu6zqngpMSojV2JG/nSMfGYRmYG9\nHd6IbDsh+R/I36gJ5O3MApe77vt1LKbdszHtOh4jf0FafGaWwtv9Tlj7Frq0E+FIMf5V8QNo\n0SJK1o7Vf1zU+LudgHPd9asaWao1drgvBs10IVzZRjPZ0Rj2BxCMewut5zfrpZhm+f/Twzyr\n88dsQ7kD/5xUxY+9GYP8kx2ZTz9xrwuxzp6QImfFZbWQeieD9f7X3rX+xnVU8d3sbrx+LHZi\nh5Qkm3VdqlIUp+mbktLSlgCl9EWcQKMCcu3NO05xnNTm0ZpAShohkkBbQgHxEgKBgC9ULRVV\n+61Spbbq33L7rVK39+6cc+/Zc3fnzty9O3N3vVf6aTy+9twzvzlz5sx7ievw2YRlxtC18Tu9\n/3XgdyXSLpjYN1xfKoYf5Kd75GA2KQO/Ww+CwrbIXTWbAudB4AkiYEGE62tEUOMC4gfzq4Fg\ngKb/PebWo0Vvue3JhVP3L8xXl1uksy5A03RGhBRN0ygSc9biiSVbMSwbh7KsLj831ER4Ixbg\nOlaAJna319/Qt833qbpi3oJi5pmYJvbFa1UEKwKmqSIokFXpkxXUxFtraqRt75MWkLZdkbQt\nfdIC0upkZGHS2YFMZ2EhkBcfYCSZOAek7r9xH84hLlABwiITzsRW9FADFb25yIEVaFRUExvZ\nQ6J2dpeSA703mk0TW+hD2VTf7kRFNbEB36tae7FqDTMBTGzKr3+4JATZB3V/BgUagXCUCWZi\nA78nyH4U5BMifgDjG5lAJjb5h7SqYWNS6CXdlOTARihsjKZZ7w7PKpwKuKzb7m1kF0QFbG8Z\nslwBG1KGSlYBBSpD2hX4VlnzPMcyNJIVkJf2OK+CvIyz4phIQ3GQ3Vr8Fd2phWUwAhB/J/KQ\nI/FpKJNlkv8ByO+mFk2ScQ6o9fgkEWCEORO5oCGvIw8gTyxnYyS6w9lOB7S+Hl7Bg5uvEcU0\nXggp8+DmFEnb3yctIK2qSNpMn7SAtIOKpO3tkxaQdqim2ZXvk5bJHK5pduX7pGUyRxRJ29Qn\nLSCtTgamNa3o3qTsicUb5jXXXdAp21FUdPDxj2J8M1N8E8e4aXkbVgRMU810oPdJSTBx/l39\nDX0beSKDA0cmUElNHJ4XklSxP+zAkQ+ojkOZ5v3iZVDVFV/GoD+8TaE/bIQD6myWiQBDLfrD\nSfWDhxQbCs3GqGn/93qSjwLkdTu03uOBUayv/fhQ/Oztzq1399DuFCDN0YzeQNQA2dCahQGn\n/sEW4UTSc7DFB5CobDVNlrz/AN5JVvCdZvEzNfjGRyydXEa6SifWnnE67LiOmRgTChCypQ3n\nfPCX8jM+HOAea/gw8QSysHbZe4ZgI0IO1l1nSJk58Pss8GmcjywUHH54mJla+qDZJU8sczus\nbm61vYwM+c6o4nfWN0ExZt5G2/PDVfPaYKJLnT7qxbgjwH3OdjUuiTHZLOxOc+CMLieYpfHJ\nMXHycchKKR+cQyU1cWxynaKr23ROpxScUyOZoc7pNUSAbnROVZzST2s6pXlIcwzS3EIKuQDN\nIX4P9+Udhd/hrW7r4H+G+ocPqTumVg4f0nVM+WFETRzVpBxMf61TlpkJEwXpe7bo0aGDxZ2T\nMVat0W7lCdAZwTit7gMxzclY+wOF2k6KkVOhjDcIKXNSVlDLJiG8lpFi4vICrflEKwKmaVhU\nZ5p/zZPlQDtPSTBxRUbI3w6OZnPAbVEoR3/y4jobWUhTOfLOAN8jhYOYdOUadgbAT61vqjRO\nIl96RgXPk9FWurwSM1CEDHxWxM+iNuywmRGJuo5ZFTBN6urAWYiYVtSaOXQ3yBNL5hF9P005\nT9yiNR60yDueCk+sHHayYxvHuEyLePcaF9hI5xuXXTYzIjEu41YFTJtxuaHHjUvDeae9YFtK\nGfVZV2pnsJreKOL+PNhNNmpBqAloODw29Lbh4FgUtqRIchIFWtLXWC2tdaCwvPBWViAm7sar\nf/gWoRk/q4nwHGrIzRDexgQzcY+eikH3dxtaETBtBv0ORoKJCwhDNTY45NeB7Xo6ndTP2chC\nmsqx1Zz+YcggndOfyQTy4pz+50W8XoONE4n9N/xwN8/pY17aLZDdlgtkvIcKxPffb4fwLkaq\niVtQQxaPnrrtwPJ31JoNGi4b7tikC+awsIYg7btF3HfhvpgGAqQbWoP2ADnZ2gYnFchnGWbR\nKsBNGWpoBdKm3I0CV/cw7u7tQu7GE+COcjMMXNzHuPlSGrghx7iHmPGPcEdmZEtS802YKbTo\nJO1hTHw5DUw0aImCP+WvF9pjQ/o0+VOYxmi8LiM+seQ2tCRPa1j9vrWuEO43f0FdovH2FIM/\nsfIz3p6itK04cX3br4h4yLc1olS93NkYhAKhS9lyZLlahhXEV0X8kq2COEY/PKg2xBqrAAbV\nCkCH+IvtEH+/ZeJXupj4y1T2rcma4agnFgdbkzXTiZvxVnOPdCovA+9w7vFrIr5qRYGTmHt8\nQMT9uccHbWZExQOzImCaPDAHNnNgWr0w98jHe7D92EyqIPY48+Quut1Q6Lx9wfGeh0T8l1aq\nJ61Wj2pq+aM2BE6blj8C4cMkzQ2skc63AC7BHgCwJ1Z+Nui5r62gzAF+txsn3XU5i3jaKi/T\njkQ77fE3RNw3HDM2DIGOpbIiYNos1V6SVi+1xxIFuIgK8IW1rgAKZF3ukxU4RReQjDsh3MdI\nWQIiltgBWP8USIYUnWkAKwKmqdQUyFrpk6VO1rk+WSyNIlwdVoC7lorwsxuW/uXi5aQy78CC\nEZ1lVd+0IWiaSklllrjQYv6cDpsNAqHfEnF/lvgxGwSPsbfypbTBhb5IxsZM68HuCSDmakJO\nBQiZgra+2YDFARF/vmaDEHTrcakQp8C/PjgL983xP9C5Yw5zt1G9cxv1xKoWGyPcdQ1oG71e\nnlV3YMO+F36bafMqaPCPYQlWQYSlV1y8mpQ2hyqwfxe0A/OyOk3A4zaykMYmoEjIolbvdkIe\nTu99R8RfqNkgj58qW2QkolKTJxapSd7+5sA2EkrWT4Cgc6QZcMPSa5lM6f9JkeXAvpVOLbyc\nFXG/wX/CRia5RZDfJEKvONexFLM2spZGSyEh60KfLJbGWhwnjzPhPifioQl3I4qTxAD/vIj7\nA/wHbWZEUkXHrQqYpirqwHYTTKuXBviTbucPibjfzh+2oTxa7Xxx7uTs8eqpuWpC9fsIq9/H\nbDCgU7+tCJi2+n20R+t3O5p8nGnygg1F0ZmKtiJg2jT5RI9p8lp0kPG73bjwx9PB70J4klXI\nZ0HfnoXR84IIS68LJFMhdbqfVgRMm8V4kpFwHjJ+npwa7oalNwSSIYG7YbsfOom3Nk8+VV2s\nTh50JxDmd0wuVpdOLy54J2TPLkxWTzy5tDLpzi/MHlycPVGdPLJwaqk6Ow/54EdlPweyXyKW\nzjtM9E0XbyWVD+/Diwp9WtkBVksiHurTGslAEp7CaRH3PYWnbGZExVOwImDa6v0ZklYveApD\ngCx8o5tbUZ4XXHCHa8naMTfLIt695mZFxH1z8wObGVHpYlsRMG3m5vt9c8Of1JgbnKR1wz+g\n0n5PxP+I8R8yJf4VcPZiI3+lt128k5QSt7JzU/DBisTO/UjEizUbgnP/dsJ9562Lqc4fqp7C\nW6mEgIO17teeP9Vi5NkN/4za9TQrpF9DwXjhTvjZu2XyXYFkCklnWteKgGmy4XghBpImuzZt\nDAhpdm3aMyJeqtkgVHYjUZefgPAX1NRVRuwVkP0KFFpOhKX3BJIhVqcqWREwTVXJI+Fsi2rw\nEhDwEpCaE2HpfYFkyHBg8RV8OPsPDbUckGA4IWwi2CzBpwBbAFsJtklQkWBKApks1xFMAK4n\n2EEwDdhFcFMEbmuCOwh2S3CXBPdEYI8GHiD4ugRR33xEAzME+xTwWAQe18AswROAeYJDBIcl\nOBaBhRhYIjgdgWUNPE3wjAaieD8nwXmC5yLwcwkuEkRxflmC5wleaAG0Q1ck+G0Eft9B/BXg\nyfi3GPh3DPxXglcBrxCgfcf4a/Eaa799UxkDKEow0gFcFRNlCSYluEaCaxXxmQjsbIKb28Sd\nEtwtwb0dwoMSPNwh7FfAgQ5hLgJHJDjeYZzpEFY18NOEcCEhXEoIL7YA2qHfSPC7FMCT8e8x\n8J8YeFmC/6lBrRP5MRE5M5wAAAGbeJxdj81OwzAQhCepzU/EAYnnoIYAaSknLkhIFYekSNyQ\n45jKwrEr2/TBuXArKKot2vpiz+ed2d23AsAIhGQYUQCgr4unyymQXwwi3jmAcwAZCE4BPLDQ\nr1gd+tW6lE3lWD1+fmkWj/P59eSmumunV5P7smRB9ZzVrJPCOh7ke6vsuAbwg+1JcUfSrGez\nCiD5DqWG99LHznmEJx9Ky+EjaevUUhmuAWwAfMeJswPfmXdisHKtuI/sOLJt3/z3YEkqNPc+\n5hYxE/vvPSNNM7XKdMos/f96QtukCh6CU+1XkIkQ5ZvbVKut+JQdgM0fbm0/4wAAu8N4nLxa\n+VfbRhCW8cER7JCD3IeTcCSADThNuK+QcOSApJiQ25HtxdZDlniyDPXrL03/2v6YtrRpeuCu\n7LEtD0jyOvvMe/vG0sx83+w3K6HrVYcgCG7B43EJbq9A/7yb0aXQuCD43ILgaaE7XIJHaKfW\nl9USGtmm0X661UZHdzFVEH4GC8P/iyAENlF2K83elmRCf34RSn8VF1H2JicjEZThTchiNkt/\ndB1XhHAI4yugtUCER5cyYjk6Eh4dDY8g4DOioqh6TBfjMontEykWl1Qj2BTiThG9iFnL3o0z\nE2mSUXHddDqShgA7d0WNKHp4WxMzpFp9TeKFbELViFGNrKakhCjHdiQllVQzJazWwjEV2eQY\n2G2FUm57wYLz/JH83XReztlRWqfUxXjuSHq5z1aElhkN8qlakmhMfJDRoKLbYkaS80yKllMa\nnKGuSXHCNEPIaJAvRZQcWwchoy6+o2s8u0sSEskyHReVnAY590RNInqeibOSY8VZHMZpNE2H\nn0IZeJ3GkijiCsJN44wG1jg99wHvFTquGudOsG3gM8455+hoh7x6cfxgA+DrBJyTjDhnwJ4F\n32nA6WbEuQj2EvguAM5lRpzrYIPguwY4NxhxesH2wegBnH5GnEGwQ+AbAJwQI84o2Aj4RgDn\nLiPOGNhx8N0HnAkTzhTkz0LsHGzPw/YCbD+A7UXkf4j8j5B/CfmXkX8F+VeR/zHyP0H+p8j/\nDPnXkH8d+Z+b/IZ9Afu/R3EbsB0FuwmaTpu0fAmxr8G+AfsW7Duw78F+ABsD+xGsCNhbgG0c\nb3HoeYqOz6VzjqsAx6TfdNl2DXDKFob/19Iw8loKjtdpnoLdpVmF7DosvCA0zluy/t/o+J03\n2Q0guAmrxluy/gM6/uBNdgsIemDJekvW/ycdX3iT9ZqOXzPZX3R85U3WDwS3kYx/0/EPb7I7\nQDCAZvYvHf/xJhsEgiFEdkhHgTdZCAjCtUvf+BcbcPEmGwaCEUTWQoebN1kECHJgYQSMscKb\nbA8IfkJkxuw2eJN9AoJPplMXtYFoaXAhqxOksvvEflqVyQa++3ZBdvnuWwDrqt59f3OplWEG\ncP0IFxGG4wCcvmMCW6ZDpkizx5fcNm5UEY7LhHMAvmNxJyctcNt1KZkv3p5bEdNnEHrx7k8A\nr4nAV0AFua0KmJ2xwO/C9wAI0WOB6BqoBewoIIKAETT3WTD/HdpYM6m3lrSnYNMVei+Psn2m\nbBNKH0LpR9u3C4CO8nqxyK21/jsIZwDHt9XGD6L4EzyVa2dQjj6UQNkdfJUbwkqcYFSu0165\nTp7K+RmUo49XUHaAr3IhrMRJRuW67JXz81TuFINy9DkRyj7NV7kwVuIMo3Jn7ZUL8FSum0E5\n+sALZZ/jq9wwVuI8o3IX7JU7yVO5iwzK0Ud3KPsSX+VGsBKXGZW7Yq9cF0/lrjIoRx9Bouxr\nfJUbxUpcZ1QuaK/cKZ7K3WBQjj5IRdk3+SoXwUrcYlSux1650zyV663lql790mvOWJLEcylE\nZr6+79fFH1RFzRgXp3I+KCnbqpYhyaBx4UpfHQVl+ltXgx2Is8/EeQDPCGx0r3YvoYl5VbHy\nZiV5r+Zy3Mx522KeHkVT91HsHVTfANu6qGC3JaWsLikJHfajddJrldeXEJWklBR1EsvqWi6h\n5+htAMVJSzskH1NUupdoRDWhYmvVsMvFl46iLqlKNqgRTVR2aItEPain8elkEIkwZC/CPbR9\nv1BHOa3Vl5JOoW0y2SPy0JG1FEJlhtnKrOB799OSTmAnXjzDtaBj+IAdQUWMsi2YcYsTyRCK\nmzDHYcuh5RE0jbtsWk7W03Jf5Z2wU6RVx79DVd5rsOOeRF5UYB9u+H2Hho+hGsb5NDzU5IZP\noGlMskk5VU/DK59eOAVa9XsKFTndYL9bMyK93NRF2I1bPuPQ8llUxhyfloeb3PJ5NI0FNjWn\n62p5+asIp0Crlj9ARS42eojH5ZzVOf2hQ78foRqW+PR7uMn9XkbTWGGTcqauc3rlqxSnSKuG\nr6IqH3Oo8gBe39o1+SnifcanySNNbvIamsY6B/lwOd7yh0BOgVY9fo6KfNHgQe3LE1kuXrPj\nSg7gRbpdxzdQFVE+HR9tcsc30TResok5W1fHy59iOQVadXwLFfmqwY57UxohVpdqrx0a/gYV\n8ZZPwyNNbvg7NI33bFrO1dPw1uq3cE6hx7Ucx7jpXSyaxQc0i1iDK8KtkSTswuvho8N6EFEJ\ncT7r4W6T10MCTSPJpuR8Xeuh+p2iU2hpPYRt18PS1mp0ZX0zGowuPFldWw4urq9trP7f3rU3\nN3EDcY4kxgTn2TallAQToE1oHs4DUijQpm7apqVJi0159AmJmelMYZgww/Bn+Mz5AME+rWRl\nz3cnKTrp0CkzO2a5WNr9aVf7y52kazQ3tuqPqo369r2N6uZWc7u6Xq9v399qXqzOtO8mVf9v\n3wXZq75sj/d/OyEusz2s6QDQOgoA6/Xk3vOjNuHbLvivbPxHGGbomMHhYo9LAZ4pVnpB3/Hg\nWYwHpb1W+w4Quu1V6hVfAQz8AfzukOHFU+EV/uqRZ+j4Yvf5+QGxPXzoMgRPtivwwOcEXBsg\nn6VD7veN+0c77N/vGgbS89ujrTft0H31qhO4my92W29i2jnZlZ7tVIgVR9qgYFU4sAL47IPv\n98MdsQG4Z0FtLgOIsMYjvANtHMwAliYM97iFfQDGU6MqCHC6RaQfhC7m4X6UBqQSHZA0ER6w\nAOaeAzCQB9zEasOw45JYmjFDSzYMdS3NThE9kmZGwAxgHUtcmpXdS7M5Gr0DCHAT62zDjgfF\n0owZOmjDUNfS7AzRI2lmBMwAFj3FpVnFvTSbp9F7GgFuYoV52PGwWJoxQ4dtGOpamo0QPZJm\nRsAMYIVcXJqNupdmCzR6hxDgJvZWhB2Pi6UZM3TchqGupdkHRI+kmREwA1hOGZdmH7qXZos0\nescQ4CZ2FYUdT4ilGTN0woahrqUZ7G6NpJkRMANYexuXZmfdS7Majd6PEOAm9tOFHZ8TSzNm\n6DkbhrqWZp8SPZJmRsAMYKF2XJqddy/Nlmj0foIAN7GTNOx4SizNmKFTNgx1Lc0uED2SZkbA\nDGBVf1yaVd1Ls2UavZMIcBN7qMOOp8XSjBk6bcNQ19LsEtEjaWYEzAC2gMSl2WX30myFRu9F\noq9S/QoaABOnCYRX+KsS22Bo8I1A8ImerVOC/6NjeRr8/4zo7Kn/53nAo7tPJ3KF7dGhQJQR\nEJMQoLyzJXBuhuhrhzacpAaPKYxc5/MUcmoQnJpFI3jVhnN4nBLXbfDbmmQeJc/a8CyPlScB\nrBseLNTGmGBlSflRsp/23Xc8kfa5vH8i7UfJn3I6ltK2jhxvfJT8GDneuAj7iGe+lPWZKsV9\nOqG4f0F0VhrmbMwJKsUdX+H2eRYxbPjF0roJ4DyKkYV8xwjbZqvC/xaJ7hb/q6EBXLLhHB4m\nnfyPrXGq2fAsT5RGhv8VHizP//T74/mf3HjoJnTLRGdz/YqNJFco1pGvsEMcfBTIU7ZVFAXX\n8h0F9JwMFcZ2nehuMbY1NH5f2nAuS8bGlsuu2fAsTyREhrEVHizP2PT74xlb9rU6ibHdIDqb\n62/aSHL5Wh1hcvQQJh8D8nztKxQDt/IdA9w5VyqU7TbR3aJsd9AQfm3DuSwpG9t6cceGZ3li\nITKUrfBgecqm3x9P2bIv10mU7Ruis7l+3UaSK5XrCGuj5yj6MJBnbd+iMKjnOwzoUZUqlO07\nortF2TbQ+H1vw7ksKRvbxrdhw7M8sRAZylZ4sDxl0++Pp2zZ1+okyvYD0dlc/6ONJJev1ZFv\nsHOQfRDIE7ZNFAQ/vR9BoELYfia6W4TtLhq/X2w4lyVhYxvC79rwLE8cRIawFR4sT9j0++MJ\nW/a1OomwbRGdzfXbNpL8+ISNvdPAx4A8X/sVxcBv+Y6B7msjVBjbPaK7xdgaaASbNpzLkrGx\ns0UaNjzLEwmRYWyFB8szNv3+eMaWfbVOYmz3ic7m+t9tJLlKtY5wNvpWIh8F8pztAYqCh/mO\nAvbiJxXK9ojoblG2x2gA/7DhXJaUjZ1T9diGZ3liITKUrfBgecqm3x9P2bIv1kmU7U+is7n+\nLxtJrlCs8QXutYJFCAPsPrwGUTeV+xtFxz/5jg54Y6MKkfuX6G4RuSdo+J7acC5LIseObHxi\nw7M8cRMZIld4sDyR0++PJ3Jy46GbyO0Qnc31uzaSXLpUR2hc923ARQiCSPnj3nCMr+l7s3E7\nTNZpmLRQmLyG0NhHYdI5YLqhK0wEitUVqwbmqVgJgDXnwRIHa96DJQ7WggdLHKxFD5Y4WDUP\nljhYSx4scbCWPVjiYK14sKCNdp+lQwTCW3C883kB/t3+HG4S0QNCB/1n3Y6DUcE/eEoJMshJ\nWjuVBBnlJK2d8QSZ4CStnbMJcp6TtHamEmSak7R2LifIDCdp7VxNkAVO0tqpJcgqJ2ntXE+Q\nm5zc8qJNHmQoT0F21SZPNu/0aZIzmmRMk3ysSSY1ySVNMqtJFjXJNU1y24s2eZih7HRFjGy9\nA0qbPLgAAAGdeJxdj81OwzAQhCdpzE/EAYnnoIYAaanEgQsSUsUhKRI35DimWPinsk0fnAu3\ngqLagtYXez7vzO6+lABGKIoMIwIA5HnxcD4F8rNBxDsHcAogQ4FjAHc06BVtgl6tK9HWjjbj\nx6d2cT+fX06u6ptuejG5rSoapGa0ob3g1rEgXvm70HbcAPjG9qTAA2HWs1kNFPk/SgzTwsfe\neYRHb1KJ4SNp6+RSGqYAbAB8xZmzPd+Jd3ywMiWZj+wwsm3f/GdvTcIV8z7mljETu+8dI0kz\nddL00iz933pc2aRKFoKT3WcQiRTSt9epVln+IXoAm1+QzEC3AADEsHicvFpJcxtFFB5Zi3fF\nOHH2RU4cx3a8yEs2xXYWJ2FJCCELCasYSy1p8GhGjEa2BVVwAQ4spyQXqKIKThS/Co7AJYdU\nUVwiumee5ZkXzaouq+q5Z6b7fd/rfu+b1oz1qEcQhKgQi0WEaFygn/iD+zemzgtCIioIsQ56\nISLEhG7aJqpaTiMFOrqfnnVRG6S2m9pTY5R5TNtkido/yLuTehckmdDDfwXz0+wiynomMzeP\nPOI5WaxW6cFAqyCEF2D/AVoHjIjpUlncGj03PTs7nUbAQ6KiqHpWF1dlkt0gUjZXImWVDbcM\nihaJbqDa+Z187ZHTCUkaAuyriBpR9OmCJpbJdvw2x4PVnKoRA1PKiXKWrkB2TVKKebVsonU2\nWsTk6sXwuxqmd3fDgfdIC4RqrUK0rRQ4UXs5+mLf3wLEi9jFxxfngRb+ZoJcSN2cfLHiTCmV\nXLYi6qUNsR4gvzavUPllCKHyix1D5ZeBBM2vxceJ07Ahal9RY7eon5nqqO2D29NxdneAlt3q\nRoH3MJsouw9B2wV9TL17qXWDn1+cfmiT0NcHOLsC4uyGdg/0DQLOUECcA9AehL79gHMoIM4x\naFPQdxRwhgPinIR2FGwEcE4FxDkN7ST0TQDOlAVnBsbMQfzzMDZtGbMAY85Bex7aC9BmoL0I\n7SK0S4B1BrBYnpYh9hVqz8xajTQgl72WrZOt2UNLC5bcMI35dTQ898pYw217bJKxBYxSG4OF\njJptcpPaF7zJxoFgBpF9Se1r3mRpIDiLyL6h9j1vsnNAsITIfqD2mDfZMhBcQ2RPqP3Im+w6\nENxEZD9R+4U32S0guIvIfqX2G2+y+0DwLbRgyd+p/cGb7DsgeIzI/qT2F2+yJ0DwBO5sUbNN\n/m0aFzKfIM3LvRslVSb38HNCBLyfWo4jpm09J7QdatOsAJHPYctgHc+hM9FiYMfilGWktacn\nX9j6Qo2wIhas59DXEjuTccDu1qV83XiUcBiQoE9MOtHg6gtEGEXnMXsAxxt21BPofKTRApcd\nNtBE404TW15yiDtp//aG8BIOeJEJO1wPCtj4QhW59Eywfl60aK1knU5ko2hgl33gGF6FbqdV\nGHZahaMFIuo1ug4VjeTpI+OmUUjpWVpK2XXbqgiosvvoM+YV9qAp6iTvMLEeezzjaLEmGi74\n/RR6RVWqUlU3n3gElxXsbYMonq+Vy3ULsFNrJeyzE6YaDuvLpAnPQgih34LQriKeQ+FZz3dx\nwG2ltAF7/0mEM4rHv2IffwqNT/IQ0KCHLnZ79O9xLx8XtbDcpmdpdjmoZcg9ikluatnbBlEo\ntewLoBZ4PYMQ9nNWywF0fpADLntUwpV1KKBaDrurpZeHWo54qOGoR/+x0Gox9pY5ml0Oakm5\nRzHNTS3DbRCFUsvxAGqBl10I4QRntYyg85MccNnLEVxZowHVcspdLbt4qGXMQw3jHv0TodVi\n7C2c1HLaPYo0N7VMtkEUSi1TAdQCb+ARwjRntcyg8zQHXDZRXFmzAdUy566WPh5qmfdQw4JH\n/5nQajH2lnn2t321nHWPYo6bWs61QRRKLecDqAX+n4EQLnBWSwadX+SAy0SIK2sxoFqW3NUy\nwEMtyx5quOTRf7m9vYWTWq64R7HATS1X2yAKpZaVAGqBf7QihGuc1XIdnd/ggMu2ElxZrwZU\ny2vuaunnoZbXPdTwhkf/TZ9qGW69tyzkJY0mmINgbrkHcpabYN5sgyiUYG7bCZtg3bJazObJ\naq0IDlsdkU3wfMvBM14R6Uxd4hzZeoMr11OSUlC1Msmn2LtdSSmmZHqsq6keFOYdpKK33VXU\n5ErkNLGuKk69VUlet70Jt3LedZhgTNHUDTT2HorvfjCVN7G78rRMJCVnrRMLzrCT32hOVPJS\nnpZwtqprtZyhBopTktZIPauo9CrRiGpBxa1Ttg6JpjYkWsIpjWiiskZTJOopvUTQIjxAi/CO\n+yJk0PnFho9w9owZP9ioFySNjKe2f8bk5dclk3UiT75UWA9RzI+CxbxdFLm6qMA1XEjv2jEX\n8W3uPRTD+8GKZ8lhiziNxi1bx+GWQ/o/QNP4MNhSXvKT/v6x23dWxlPbP2/ycnDK+0co2GzI\nvHeWxSK9rYtwGaf+Y4/UiyiMVT6pn9rh1OfQNPLBVvOyn9Tvsynf9kMzL1enIiAo7EJY8a/K\nNQLXcAUUPSqghGKQ+FTAzA5XwCdoGmvBlvKKnwoYMMXPJfUyirccMvWJOpFl4+sAjoShKh7J\nV1EUFT7Jn93h5H+KpqEFW8yrfpI/aJN/u+mvooj1kOmPFzVCnPb9mkf211EQG3yyP7/D2d9E\n06gHW8sVP9nvNaXvN+19kHb6pfil1H+Gwv2/vXN7b6KIAviGtqGF9MbNohTCRQXE0EJbLCLQ\nhtjGaqLN1lq8olTxAmirWIVC4dVHffZ/81Ff9w+o7c7ZyezZ2ctsZ3eWZPb7zrc9mb3MuczM\nr8ns7MOYoe9Y8f138VFI4NdRFR7LCfx4yoF/gszYEPPkjSiB3+9q9nQyfdhppN2XXMHHx5QW\nq+ZsfcEsmlNz1dpMsVyvNaoNs1IrLxUb5fp8pVitmfXiVLlcX6iZx5m74rtvG//UbTy9y66V\nu+464P/hMKZjdsMjOe7ccXMf57l1u4bPfGqYX1ne+ncR/Y+c5+VODoJqwbG9KU8HtkvYUtdc\nN1zYnOdmkbrb8zV6YYaLyBz+POx3Q5kB5+2Ba3cg33Q+R75xfNIJPjnA2NdF9vlNxs7U7XJu\n2LnRrBgI9+yB5bWtrwFXV7c7sOq928trPtfZ1RTudQqkFq5rOM4qMM7Kwb4Dzu+EHyi74Nci\np87d4ERwqv2FbOrOzMEEyD7ON7oWZLpTqQJyuDP5uBOkC4TZYgWk4A1ImEQOmHOPwYj3CNli\n2efcu2NnIpykQyhJB+BaY8yTJc7nXcwvvZPQ07Hl3VDeA+UGlBUgb3YT/aySpMadG/5G34KK\n4+M838xbTJ/ezTmBfsNuwQxWx9ShkKbifO40mTzI7qbraB/RDbXtMVxbrNQbEm9aohI5LXOA\nPhYYxKZJGo/32DfuTZAC+ohOKaBfhZEpUECfCrtajQLIPbwUkIozczCL248CBg3v6KwpgG6a\nAnwoYB/RzylJ6igUcIBzHJcCDsJ+P+cEFwUcMryjraaAQAqwv/CwIG3YNEnjuVv7xkMJUsBh\nolMKeFGFkclRAEW4wyrsajUKeInoHgpIxZk5eDrFjwKOGJoCAjZNAT4UMEz0kpKkjkIBxzjH\ncSmgCPujnBNcFHDc0BQgSAH2zxwWDLxsmqSxIIZ945MJUsApolMKeFmFkclRAEW4UyrsajUK\neIXoHgpIxZk5eOrOjwJeNTQFBGyaAnwo4DTRR5QkdRQKOMs5jksBr8H+DOcEFwWcMzQFCFKA\nPbnBgoGWTZM0Vqqyb1xKkALOE51SwIgKI5OjAIpw51XY1WoUMEp0DwWk4swcPE3sRwEXDE0B\nAZumAB8KuEj0C0qSOgoFjHOO41LABOzHOCe4KOCSoSlAkALsKY0WLA3BpkkaS0jaN55MkAIu\nE51SwJsqjEyOAijCXVZhV6tRwBWieyggFWfmYJUEPwp4y9AUELBpCvChAFi/ekxJUkehgOuc\n47gUMAX7a5wTXBQwbWgKEKQA+0EGC5a8YdMkjbWd7RvfSJACKkSnFPC2CiOTowCKcBUVdrUa\nBcwQ3UMBqTgzB6u/+FHArKEpIGDTFOBDAVWiTyhJ6igUMMc5jksB78L+Hc4JLgp4z9AUIEgB\n9uOLFizltaVfcvQaSps0XsJA28k0hwY6YMRnqcAhgRMQtGGoLFveg8r3oPK9qLyAyntReR8q\n70flA6h8EPR9oO9H5QdAPwj7Q4hoDkNA6kSf3FQRGNzsoi4q5QS0PwbesU7oASe8T3SKdR9k\nwRnNVa88JXTFK8cR3cgRw1BJ1tg8GDdP9CtKIp4UmDdQBE0VxuE4BYI5u0iYyJTdhgrLsojm\nAc66qp3VxlhMA75hhG2x7OkO96VwXft3Fp9YdvTvLC6RbcQ9X8jiJHEG9xMBg/sC0enQ8KGK\nPiHO4I5L/FZNbMccYpeEkk2DiyhhPsp2wjiLVsZhwSWitxYL3kTx+1iFcThKMlmQTty+qcKy\nLOGNCAu2vbM0C8q3R7OgWDxkw90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Uqt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\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LmhXg3YXEdbyrFZZXfOwjD/oZT5sxnm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\nWdMJjVdZAxU0cKplSFl7SWgsykIK6a/vr47NpvYZfwpKYG96CpKwTPRPQSO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cSRk8L8bIyooJUEmAuTOingKNFFgfgs0Bki22/wFt+ccF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\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g136XmXxrg00aUpLk13aZZLc11a6NIy\nl1a5tNalD1za6NIWl3a6tEeD/Rp8p8FPGvyqwVHv8gQ0CDuQ14ECDpzpQGEHiiko7kBJl0q5\ndIVLZTW71oFKDlR2oKoD1f/BbQrqAOv/1QN/fcwGCu5SdHeK6OITPdPEUxnimQwxIkOMzhU3\nMVfc5AyxwCfe8Ym1PrHBJz5Nkq1JsjNNHEwTh1PEseTIiiTJSdnP+lkw67QkKZImLksT5VPE\njSniZp+okSLqJ8mdaaJZ6rPvOW3SxEOpz74mfdPEMxlivE+8nCJeTxNvpYlFSfJ29rNfQ1f6\nxEcp4lOf2JYivvQ/+8/Cd+khHEgTJ2WIMzLE2RmicK6483NZ95bwhSniqhTx7xRxXZqonCFu\nyxC3Z4i6ueLuzBXXKOfZ7wlap4kH08SjGeKpDDE4Q/w3V9xzuex7y8g0MTX12ddkeoZYkiHe\nThMrk+Td7Gf/WVvnE1t8YnuSfJ4kX+U8+7n2bZr4Iz1EjAyRL0MUTBOFM0SxDPGvFFEySS73\nibI+8W+fuM4nKidJ1exnvfeL1EwTTVOffU1aponuqc++Jr3SxODUZ1+T4WliUuqzr8mrKWJ+\nmliSId7JFbcuQ3yUITZmiE254rbnivvMJ/b7xIGcZ7+H+yE1nNhCSI6IT+T1ifw+cbpPnO0T\n5/rE+T5xoU+U8IlLfaK0T5TxiWt84lqfqOQTlX2ias6z3hsat/rEHT7RxCea5YrrlCTdkqRH\nkvRKkj5J8nj2s18Tn/KJUUkyxideShHTUsSMFPFmksz3iSUpYkWKeMcn1qaJj3PFbc0Qn6WJ\nfWnigE8c8olj/mCGfCLiE6f6xGk+caZPnOMT5/nEBT5xkU9c4hOX+8RVPnGNT1zrE5V8orJP\nVE0RdVLEXSmiSYpoliJapYh2KaJjkjyQJA8mycNJ8mj2s/bOYszHFDyuaFCaGJ4iRmcA67k1\nIQNYXadmAKvrjAwxNwNY13RxiliZIdZniI0ZYluG+DwFWK95+1OAlfX7DPFLBrCu6bFcMdFw\nrri86c967kcLZohz0p99TYuliZJponTqs59b12SIiqnPvmY3+Z+dtUaKqJMmGqaJe1JE6xTR\nOUM8mCEe8T/cD43qNsSj5x16IQleymZ/7fSKgtcUzHJhjpQVOuF4IBDIf0IwkBWInvh33q5d\nmndp2apr9Y4tWvYIBALH/gfna1ZwAAABm3icXY/NTsMwEIQnqc1PxAGJ56CGAGkpJy5ISBWH\npEjc0CY1lYXjRLapeG4u3AqKagtaX+z5vDO7+5IBGIGxBCMOAPx58XA+BdKzQYQ7BXAKIAHD\nMYA74dtelL7t17msCivK8eNTtbifzy8nV8VNPb2Y3Oa58KolUYreyp6sfPX0SeMSwDe2J8Yd\nSLOezQqApf8oN9RKFzqnAR69KS2Hj6g7q1bKkAawAfAVJk72fCfONoOVtCIX2GFg277pz96S\nvNHkXMjNQiZ23ztGHmeqlVkqs3J/6zW6iyoj762qP7yMhClXXcda3TXvcglg8wuQNT//AAGE\nEHicvFsLcNxGGZbOJ599ju0mdpw0T+WUxEkbu0nTPMYkadM0JonTkuCkhLTlkO909sV3J+dO\njg3tNHR4tEAJMDwaKHRgeBaapgWGgQHKMLTlMUwZGEKA4T0MME0oaetCaRgfK+2nu9VGkn14\nU89880vaf//v291/dyWdfCguSVKdFI3KUp0ikT/l4IHers2SVF8nSdEIuSBLUamR2PpSMVU0\nMsS7mZw1EPQ4VSW5iVoXiT9JkvYVrnaM1M5kcwY5/LdE/ypFRuFYT8+m9VwNJZXTSyVycIWf\nCGkS+A+iReARtbJ53fW+tnvduu61fOBsYWTUsouZi/JyJ4ZzMVb2P644xwcNKzmiF/V8iYsd\nLVnGCKd51kjRIM5G0tLHdaj26LH7peRT0JAxdGu06FtWV9TH6GUPf6MxbhmFUtYshDeloUwr\nNpZrFVvQ86yg5rKPimbbKcmoF6ikpVw9bgohJ31qH1w+cmaYqly8mFjKzL1KMhTi4ww7r6Eh\nb1h6Wrf0cBEzERRnBLUzx7P8BkixzJHksFgB1QlgFgf1QraU95s08ZReSGdJV7gZ7NHVRKra\nYZOmZU2/q2o9nknXVluZyw4U9WLWf2komSM+l+vzRnHQSFdLOvwix50sMvMBoWnX8D1Xb45a\nlSX1cndCu5/sBtRLV9W1lcVqYLurzT22MZfgGXvhIUVP2PUJ2p14ktRH8Bp7X8D2aG+xC5hy\nmzdGsB5bqu1ja5hHMJugA3625vm4Zh8vxHGU47LrtxBsQIxWgsUEcwiWErQRrARvlPPpgM88\n+LSDayPDxfovgP9C+M9nfBYRJFBuQ4XPYsZnGXyWw66ATwL1bd9O1F+F89U4vwp+V+N8Dequ\nRDu6AvpmLcH7OI3rOY3ratS4kdO4aQqNG6BxM/r3BNe/rq4tnK4eH5/rOZ+tPj43cj43+LRv\nJ2wvfHYw7dsFuxtx9sDntWhHn0+818Hug+/NTLzXo6wf8Q7gPIbzg+hn+/hW1N+PtvWB5xCB\nRjBAcBjl2/4PLXeg7E01aLk9RIuOcl6LOxZpbiwGQnJtED4GozcLewR2GD5DGIscE68An6Pw\nMX3mBLs2lcjlfZzeY5xei4k/hvhvRdk4k9cuz13oq80+ffU2lG+rzgV5f8BceDun456QfnsX\nfN7B9Nt9sO+Gz3sQ770ES+B/L/Tf78P/AY7/RAj/h+DzQYb/I7AfhX0A8U7C98Pg/pjP+H0S\nPg/66PoUp+uhEF2fgc+nGV2fg/08yj4LHV9g4jwMn0fg8yXsH6cIriN4FPlzmozfedR5DHW+\nijqPI+7XfNrwDa4NXw9pw7fg802mDd+BfQJl3wbXd3368vvw+R7yrRzA8zT8nmR4fgj7I+j9\nMXx+EML3DOxP4fuTgH3pZ0TORlz/OfZpe186g/JfkvL1XL/9GjHPhvTXb+HzG6Ydv4f9A9rx\nR/j8zrsvRa6r3m9ENgXMy79wY/fnKdaYv5JYBxHjb9DxLOw5xPg7dJwHdy6gbRfg/xzTthdg\nX4SdgL6XcP4v1Hme4eDH7BXYi/B92afdk1y7/1vbGMgybISW2ae2HrnuUj1yDLYBvkrVR25E\n2SyqR26mnHILzltRJ87UuQJ12pg22OftOF+FOrN9tMxD2dzqOMvz6VjLi+mYRN151Ub3zsrr\noh24Z3AtkHiWwq4XKU/5fihaDnslVCG7CTfbu2CBxDmCf4om2w2CvRzZBYIXRZPdAoL9HNlL\nBC+LJusHQT/uZxVqE69QiCU7AIJDXMsuEpRFkx0Gge4l02SCBtFkKRCk8HihUKvFKcSSpUFg\neMdMayKYJZosA4IMbvQUarVmCrFkgyA4wrWshWC2aLJhEIzgcUChVptDME802VEQHOPI5hMs\nFk02BoIxPAwq1GpLKMSSjYPgbjzNK9RqSwlWiCY7DoLjeC2jUKutpBBLdg8I3smtIKsI1ogm\nuxcEtu3GMbEaIJbsPhDcj7cZCrXaNQTrRZOdAMEJ3C4q1GobKMSSvR8ED3DzbCPBNtFkJ0Fw\nEjebCrXa9RRiyT4OAtteg2N7vLZTiCV7EASf4BLkRoIdoskeAsEj1TnmzLOdBP2iyU6B4BRe\n1CnUagcoxJKdBsGTXMtuJRgWTfYUCJ7CK0uFWi1HIZbsaRD8gmtZnuBO0WRnQHCG22LuohBL\ndhYEZ/GkrFCr3U0hluxXlMB5XGwAGbHacYIvCiZznkUV2GtBRqz2MIVYsijIYlyCfJngUdFk\n6DrH9oCMWO00hViyRpA14nFfoVZ7jEIsWRxkcfzGo1CrPU4hhGyaQSqXm8aGzJzRjwiVXJZR\n2f1aRIKVq1+LzFhqBWwA+U780DKJF0L2X72PYySb4TzlAE95GecYCXCMZUvdhdFcjnGX8POh\nxLhPMuX8K5QI04c34Wc31wLOW41zgvqwQrbTroj3NLEqEucJnhNENs0gveVLE6nyakeClQW+\n2mETaVeZG+w672Dv5sujQfm1datPHjDnHj3u33RzYyd+gnUt4AzXeUHdUiHrxe/UvXjQaqQ2\n8Q8KIWQzyQ03ZyVYWWDOhuaGEjT2W7qmGPtaLMtYH5KNzPW+csgaFJRfdUzX2i85r2Qs4Lzk\nvCCoaytkezBue/BWNUZt4nkKsWR9IOhDQseoTbxAIYRsJsm812ehE/VaOTSZYwHJ3FiyzJFs\npmBaqOAWLHujOVpU3e/NVPvDODVtGiWVuKrGeLZkIXJDQOQmu0q341ny5ms7m68Sl7cTWIKY\nkLeVOW1LHG3Ot58hwuLeKHdws6beT0WYqqYpVG1xVJHuVIfVyqdrqvNtqWEZRbU0ZI7m0uqA\noebMMXJuFlXj6KieI1XUDeCYFbjmcPvNXH7NabG9b2iX2L+gljQHJUPOHEymjYHRQa5p8jg8\n2eSbu9fU09nCoIqvQ1VLH3C+CvajbPFSHuTlu0FbESzpBEuu5cK0MmEmoMe3v3p6AggarWz6\nLa5SPwclUzT0NC5Och0RxYfPbv6wZfGC3l2yiqRHSlA3O0CdnOK4+ZuFoLLILdtxlRcWT5m5\nHfZX1s7Hfny9xtQQScMUyUKmuuQzTHOkV2cHOoxd57B3B3J+spEFrYcVstuw/tm2C8fEahEK\nsWS3g+DN3lttrY4gJohsJjuQfukOJOxHsgr8dqA2bwrpKB9wU4eN2uEuLZX9J2xtaZ/m2tLi\nRkvy838C975uFCZamotmcOcZLvUHcd4Ov6Ey4nd442bLotaAI349GDrject2w7wahmlRr1nM\n65Z1ySagdnd3B8SfX0P87SSMWiSbZLZI9vfOfYY+rOpkde6Mq56/bEEly99ovlBSV918OJ8t\nGGrGUbY6QMSVNYhYZYsge3Un2asLVrLTw9a/d/sbpuBaMM3sbPbsfFyQhZJ331sUnqTV2Wvv\ndpmSFVBcTxqSLBZwdZIjWcydL6ltZjBrhJEzUlYAy1LuXK2NxSr7xLV/n+fXn2UcT2KaXRi3\nu7DSAt/26blc0szgKj/+mhQ671tZ/X71l3O6V4TrHg1MBZKjqaHK7cEk58amfFytTLUAUSs5\nUZ21iRor+8TlVdTTCRfgedHn+gQ+efK95+riHFdzTbjqMjShlTZhhPQmv4HxNsJUqzumY9me\nMjuu9qr2rC5+3dCA/2LiHsmOlgPir+F6qWuasyZG74SzAcUddnGyaIzk9JTxP/aOtEuq4vp6\nNhickRkFFAQdmIQYOJkw7BiNSY5LQBCCHNETtO2ZeTPTML3Q3TMwiMoWs2iC2Y2KIETAhLCY\nfTkxiQSyiH5IfkOSjzmn+7Odt9yqd+u+qur3uh89wwlzTp2a6ld1761b997ab8Vt/RlM7jbx\nqBsj7RGR7ilXIJ4TkbU6J5PbN7WI40I3AFUpM12QFDRI9vYxyzBkcsk9TjUgwzQng/enar1P\nBmu9ihVm8JagcppWepqk98psaAkOOIWg4xlZ/+mpsZXjWVkOw/32HPsWlHtLReq0vfhiuxe3\nJm+WONijFXuW/pDVkzs3LAtJa3SRtWbk0AMr0C3TNJaBUDmX4tKk8HLC2RX6FlL2pVMSuVwi\nPYRpxGBXkvSqKtG0uKxSYFlN0muqxZIaLSQKqrrcRdKfCoflgEqo7ybpe6qkvi2bHMsU4iMZ\nqzlUnPo0Sd+rx5VTciqXjluWW4JFZ/+arCJsLeIzYhmvX1xMJPWzioxT8ubO+IhJBftziuxN\naZdelbnA9LK0s4Z1738N/CfLW4KjxzK8DfPvUfCweSwxMmrKUBriH0XJq2Rp3g5Af5+I/sUy\nIe9+8ftu+v0BBfnTEvYuY8qyUmSN6AUZ82RUNvb0jQOSB0UkSaVRyWV2wdVmGWRG8+dFcE8R\ncC+S9MwKzX1YVl6Hf62qyXMpjaBhCOtECKrlrXWK37XLXhQfXf5qQosx+2FeuF/c/HXOG94Z\n0WIMR3YAVqEOgK2z/7fP9X7cDdEiOwgIDsIIe4obdy9yQ7TIDgGCQwTZYjdEgqyWtbYvSdba\nojpOyoNsre0hUW61I6OF1iJOvz0msoavbASUt/ckhpJjZprvZEBJqlHrRUx0UJSllG1Q2T17\nXa5nMOdaoaA6VoJLFCoK0O97ZJRRePYKeCU7y/BuDMHj2Zuce+g2l72dITYNkwHfpGcrB3wj\nAyddNfoCglKC67rVjHTa8zb11kgtbnUUmGQM/BGS3hIOmXQdpwT3dnH60SorMXXA2hpMppXr\nUFtJ+rFweKTDTZkGPK7pa0qwJSLTkGb7/FGCZP5iBWDbKnx/QoFsSsJGRnVRqxuqGKN7sgI5\n8QrfnxK/v1augr4GLBPIupVgc0SHv6/C9/6I6IP0kTKhtzHvuCQyiHlp2LZXLCisjlHANqED\nIqFa47Vys2kt1phjbh9hb46OWwvgKWvz3Rzo2pi1+ogtOdPsygx2rU8Oml1bXK8j4wrMpohZ\nadlmipbN9gI04iwQYWCDRGeHwuksR9Y6Yg4W4tszSdlSuH3snzb0MEGcDIeYbhqx32+FfHzT\naLsIdyI2jRowGD612CEn7IgMbDtvSsUkwyCcKIH7A4TgJVLzl8uKcqkQkj3nkdG+VNLdvyoM\nm9ZWU75gj3w2blmvgJ4OKL2ziPSmkvm8hYVAyxApyoaTIlWXc5RK607x+6sqqm9BVLtKbDtQ\nSuJlWRpjNDkRje8wZV78/jX6vSB+52Q1pa1hh0jycYUCKefzLA+dlTWjYfvrsN7NYgjO7ZT7\nIxq2c2RHYb5yDLaIprhx9wNWeDBqZMcBwXHY57D/t2dka90QLbITgOAkrJmCU6TudVbYEDWy\nU4DgFOwkgreX7ofdEAmyWqaApyVTwI0R0cWDbAo4GrSf7UtmRjJDyf7ESJyfj+glwMYUmjnb\n9p4oNRpiAUFdDaKSJbi/GtRq37zO6qfZhNUy2aMjBdU0andAJtwsYQIBNW6ItnqPEcpWe31s\nXzI9oJtHPU3Se6tE1JIzSXeLoT5D0s+GwxK463lO/O4bMe8Tv9Ou6YRMcFiMRyayMcaRMskM\nhc9Uksb9IlF07KEtrzvXfxpOeLIYgm0QIjMKHNlbYA1/TMzwJitsjghZLZbxjMQyRnUHlAeZ\nZTygNwpnaf6DRFUOhVOV8zJVKcHqIE4/f5VU8MsVVPArtangBfh+RJcP5ed4v6rXsqMkLR3y\nlcABmGrYiX6v6swq1t5zcHDsHDmz+qgbIhFcjuw8aOy7ELNjpFutsD0iZLVo70WJ9kZ1z5kH\nmfa+oGntEjg6QN83kVZvJ2npYQOVNDSSBroDxRCcBtoaESM4sgtwKcKOe+F/2+nMY26IFtnb\ngOBPcOeh1Y27H7dCMiJktYgeUwkD4liEKqEVva+LopWgooShzt1spjLOkhlzKd3F/XmrRqvf\nMLQdk28l+bAhdiAvGaE6kIPYAGI43yTpb0UAF9HfWQa43yZ4vqPHoz211IB435gvwAJWJxSR\nHkxq7knt2bDezRDFuSRer++Sen2vtnq9A2ltZVqsyuyOrDZBYyyN35drh3QKN59N4dxzWLuS\nheFgR/1fNgL19VdlAx+PBS7Bpv0lsoGfckMktogjuwyG7j0yFkhbIR8RsloM8hXJWKAQEV08\nyAzyDwytweQQZ4oXP5yFft9ewisiMM9M9NszdpL5VaLir0VjIvmhdYZ8GrsU4CzClsCFrG40\n/3qF70fF73SLSnrgmMYY3jHCiTfCcUJ1fP8s5UQHm+BvzCWHkukEnIW7EIZenUZfgfn4FTI3\nL0QozBzZ+6DF/4Dh1RQ37h61wp6IkNWi0f+UaHRUvn54kGn0cb1GX6T5TxDp+2EE0lcCZ8o4\nfTIc3GMYLvp9H8nHhkRvlAHPKREP3+9jnGzyVrc6ZJKPeXPakBu0hrvICclLhKrDhPrLjLq3\nCFd+pOcKvTIxpuDKTbLfS7B0hfGdCYfPs6LmmJkbLwy7O3AGyhEkxhT9RKSArln0y9pXBues\nCEc7WOpmgyVnmNQ/YiaszquLLbZrh0vn9OrEUcywoJkD/Jkf6WGi8whWrV1dCeayMvG80amh\nfdp5JJHNm6TU2yT903DUcCxTh3KZ0Wy8Dx9TwHB/RtI/j6jWvyDpX0ZksH5F0r8OB/cVFdzf\nkPRvw8FVGcKLJB/r7rkh/J2IhxnCywxeTecLsM1jQGbnE6msNTbksJb0xgcyqQQ/EsIyzvHv\ndy3p5TkxyDk+kEvjOyxFtjITmLdJYC71surpXBbPDo+PjKYC0LmM58Qgb/WBXB5nL7ZhiLMl\nEJezjHqAK6z/B/ixEh3AFSyjvtIr44OJVHJkPEClV/KcehpXxQu5ZJ8ZgMZVLKMe4Or4kJke\nDcLF1SyjXnzWxPNZsz9pUpAy8VnjZcVAb6NAe5fEx+znpwoJCnWuH2rvEpIXa1UJHnDA5uL3\nVZrnlkR/LpOnWNjX5h50seUdgvIP4QYJ72MLJUPW1DOYpqxpS+R7grjUYIYG/f48s3B/lFs4\nDqdDK//IOKkK6c1PcFTIvqgK6SxI8DKekQhOHLIDwRF5mh68jKfMwfmN9VVVKoBCyiSrBAv0\nWPLfDads/yKS+W8mmRcJ3D+H0yhPTXcNm9LblDhTW1KuSlbe/zCCLokE8LJTB0fT/QX3hcwG\nrxwfRcvuiOvWAD4EzwoshtD9phsimf5yZGXYXrHjO+F/+/7PSTdEgqyGNQAnaoVire4P3aci\noosHugZQxQSDY5jGrpOnZV4LDJALlR42PHwfKiUpLUhJB2JQByzevBklg4wKMbYDl0WG0Unp\nWZJ2xt4yOH9RqFm7uTubyXnP5Ard4zTu4Ix1yH8VwUwF9K0MLa40faARkRU2P6/G3ypUg79w\niUzFB0Ak895zu4pHf9fz+gNVufcURLXkTGvKTbpxATuLY7Bd0Y5EsR5P4zgPMDVAHIPYIITU\nw8Gs84XuaBXBSWsRuSL+kBBXDw+nDgHNBHE9HhNyELe4TeO4JCwC+zEh9fDB6bCWIWwDT1Lw\nOJbTNAs5cJfgotfhPFGeSIKb0EuvjEBY9Z5RRoTWnUCGsGmfRxgEaekOy8JZy3fOGYO16QFz\ntwJOgxekcNpcKqQwpgIMzV9VtE3100ZDYFot/mxjDddKGq4eHmKrUgXYj/epQl0J1qhCSxkR\nWncCr6tC1arwJGu4G0jD1eP5OZ8q4OeyG8FjjQG/MVVoc9MzyxNB8I0SZ7q0ZZj0NaJRxyRq\n8XjZjROs5dsJI+vxFqBvuKb0FxyDcXgMJntFKIgprseDgj6KBV/B9CP3E1wEv8RhbOj0iaje\nZLKhMZhrUaZ1ILqYOehE3G8AmAbE7sXWScPSSqwIytJGL4Rh6XBZJsV4eS0GV39jsFldBGuH\nOVeP9zQdxDdBfDMhoB4Om31sUvscxqTVw72zw5OZIScoMyeC0MlkUYJYkllGEEsyKVg5wZYk\ny+RqBsS3EKbU4x1cn45WcjhdBAfTmNB6vKHrIzQSz9VFuGSEazMINRi8io/0+moTxgV2EfZN\nMNH1eOzXRzRyfU0/eW6vi+BbmxmPTjJhXwAGZQFQvRC0ci5yjz0PTAKe1E8D2PPcNB/W3j4Z\nOKMd1nrOuhlPZtXAk3lQz3mwEnM74VMb8OUOwqeua5BPml6aD7XumIh6TcZeenoVQoUFpxUY\nOp8IzoKJYDCVDdFlvE9yuLt4xoxWYMYcqNwM6LPng2d1Vmk2jOl2086RkbpXNgaXBBji1sqr\nYPZfVZLWWnldJJT0MbjTg8GNtC7T/QOyMOGqatgCjYZ9hGjYRydC6MJpGHrqwFdQ8Pt+XR7C\ny8NCIg8fu6blwfNO//8gCwzfrNrqqPurqv6zaqt/JLypZgqwAA39W0DeDSjHpgB3En1ZdE3p\nSxFcK9MskpdNWKU6AwpXiyTUKkid0QhSaIXqjEahrok6B5jvTC+juXXdhX0yzXcsnONlN97J\nmDIH4k8Q5uwAhmTBqjS7cbf9CvwtUTHHp+veQz30k/qRniJ4R6zmnEOPm3b8cde99gFklzdT\nz0QQOJlkl1f+Gjjn4OvghHF+DLxmxMA3Rgz8YBThBa0YLsleyLpbfOsqNrcMdLlpV1Aa4GUO\nphEGfNeU45iaFxVS2UUaIFj6doLEjRHpu9UK86KSviK8IVWNXve6aZ9e14XwMHrdOxEEXtfr\naPTam68Vfe+nVdbQJVAtvYb+r70rgW6jOMOWHcW3cWJCDpLYzT54QN+DJJBASAn3WUIgpAQK\nrZDtdSzwVVlO4pzcR4FwFAolQCAcIYQQnBYolHImpC1Q7qNQ2gKlhdfXgyf62r62iBnNP6vx\nSLs7uxppxvbqve/9O9Jq959v/vnnnglNcfofDs/kTGgZmA2WR8A1ksZUAjkmxFPh8UizJJw6\nxuq9HHRdA+34MJEG3shsr0LpzRxhloST0Qo1/DKbhK2218E6xN95WME6eI1yMj4PThohnnT4\npRG4YbmqBW4O4biaoz9X1vFxhbKfQzlO5urPSZb9NEiwH5abauDiGxw3h2nPDX9gn+yxqHkc\nI4frwIjTMLh1qKCX6tM8FbHSsfo0nDuKqUHU5WgAjGKyxApI8HrIFrT1wmaLI0j4SmpAR6kw\nIC8WrkRBnSw8CceA1uU4k5M+v87F+sMAWT27df4tXzjeIdiegb6zQU6vLv34indDfjk+bw9Q\nwWSYEPyvDKrtJfAd7QI4moSvSqnIQHU5Tn/hW6Q0U1FC8k0cmS1d+syROJTgpZ/pGBK+WomR\nUYWrQWF2xs4oZsZOKTcYWA7KH0vC19Ci5jgVkeCzaLWAxeVrWdXeLMtzrhkKfVnUav1O+zqe\nhGNKLD8E+0LSFw+laV9I93Ws7uPluFi7j684j5fjevPOUHblPjXOXOs1T2CMW4VhDl4oGpT1\nWpf1Di2wtIOhfcJFNySdWmACZF0dkJXxABdQMg4k4eto+ESOnLVAyFogOUyksTeBHHK8dDYo\nUVCn1GMjfyFEmB6cDzD2kXlwfhK2bcHym5wCF0Fd/SLo0MbXeHnVvgRyFKjnfhU/gR+lz83U\ncOZzql8M6mK5L1xjtfcjkKO6gGU3KFVQJ8tOwt5AIdjinCXjEiDgEo6MrxPIISMJe2Y4DJHT\nHejTDUJ+L3bRIXL+f/i9C7gIXw4WguX+cI2kAZATYT5nTTw1vV0czls0F/U2pSf3gJYLOS2v\nAM2uhsHiMJHGAQgHFkrL2l6sJBrJjMCxoEnY46TQQ3ink7A1YLVIBzY8rrqTPeT7LY6TM7Tn\npKI11puIdRWUlcUcK2dqz0r2QLhDmWWtdl+sIl46lVm8y6F9U+OBQHb8OwwzIspBVnAdoHMZ\noqnLOYuE11PCv62C8BCce4QV+I6YhYxl7y+6wjpZSBJ2xMDybOaZ/GBZ2AZ0B40KgE0/h694\nqRosG8n9OTUl2WM37BSBuYynKIF7KsB+vstlxnNVZC4vuV+Jgrrlfna1ZI2LxdNcz3x86Vzj\n3bqD3Fvg3AsrK6zc26Iic3jJvUoU1C33Ng+z3BuCo+ft6iE+czH9DKl6iEBmuJ1mhtNGembg\nW4yl56wimtyRIvIWStWpIFs5ytYBTeugJzlMpDGLQA5lvJqzF5qJeMxcSjqM8R6k/U0t3Z09\nfQmztWlBD+owXhQ3zabutqaTY21m0yI4uwmisISLwrWg9s1QK4caujEbYV6holDZYbYl0qfw\nglZtYrZrJUibiljoZLsyF4dME1gc0k7CVv9PTEUCZJmR0Pa8AnY1IcVEs+jR0tGuKFn1jD50\ntsd5DNv2m2hqQaUbBaJU+qyXiG/HS18YzEzKzzbPJ9+Xss/ijwfFL6zjX1jBjLJ+qVMpo9iE\n0weyIrmBuki6bLODI+cWIATLPeAaSeNwAjnkeKkjKFFQJ1+OSejkSLgVIo7lAXCNK/hHEsgh\ngfd4k07va+6MkV17E+1mE4pwAk9vWLDoZFCzh1NzPah2G9cWOQrh6EKpmRkxwip1i5mblS+6\nVURBJ3PzO6v5eyScHhEqOoHoxXezLx5is5o3UuvrIuF7aDjBkXkHEPggSIBxLMLpssgUyC4b\nlSqoU3bJtVwJE0Pnf0DtcCslbClH2AaohNwJ+8GWE2kch3C8LMK8pKgSBXVKUfTOBykZfSCX\ncaRsBCKw3A+u8fy3EwnkkMIXbA0noZ4WOr8Qxb6vI0GtbAWn392g030wlbacSOMkhPmF0q+y\nOdbVys596s8xEUH23KeVJGz1aKzSggnHGS1xkzRgCjbLZzXHyRr9OfEyy8eqq61WES+dXJWX\nZYzsyF85ELiWhK1ZPBeoIJSvuw2HZYwjcQhaZLBqpQoD0ynHoncOsMYejHI6Go5VEesPDIcc\nAJ+ElWFYXsiRsgmI2AR1Jqg7GacQyCHFS6opUVCnVEvC0hCWhM0Q8S2c6Z6KsFAWCfaVziQs\n3SlU5fNSErYqn5fpEP0CVD4vVREvnWw738rn5SRsVT6vUEFoUPm0/Qy7yufFKgxMpxxLK59J\nWBmL5ZUcKVuBOCxnwTWeB7WIQA4pXvysEgV1SjUvnbcjniw28tsgwju4sYgzEM6XFXnM+vdJ\nZGtTnAIPg1N5HiTAWIxwniwFqgAheEgS5n5ieRXIazjFBmB8D8sZcI0PfT2TQI5ifDVr8kKz\nszs93RHVt9K10SZzeXqXa2QHoOh1nKLbQbnnoJ5RSaRxFkKsUIpm9iNOwmyRQndiX0/CVp35\nBh1oqPS2Z7PIsszrVcRLJ/dEyZrsw5Bog3kakNcIhE0DB9AIJO4F1zWcse0GifADzthuVJEo\nvD35Or5GwPTGpJhYFz2WOpkeeufTqRz0hvfvXDEftrdYnesGUHzQgStUoclymhpePr6Imyyn\nWVKQJgy1Uqpr0CWs1j/exPnHH6rwHEX3jzepiOVQ8I+jkX9cHjhI+fDsIJOwRAvLmzlz3Qkm\njeVsuMbzuTsI5Jgrn/BfoxNyyJk9y2KJ9qZOMxFtjSaiTfxxmljLF0CzN7kWaSfCSllaJmFy\nNKJtJ6XtVk6RXeArX+Japl0IvbIUKXSraT0JW476NhWR5G1CXqupJcXEs+jx0sk1y1jkfjsJ\nW4vcN6ggVSDVd1OqoE6pnoTpxPRZw2SR+wts9SCo6xeueLiThK3i4S4VGcpb8ZC1K56Do7Bm\ng9ypIl46OQoBsgYCsjIeaFfggSwudlDD+BHIezgD+TUYxRvQBi4n0uhDWCHLQOwdQRLmulEb\nH5eHs5zCdYywTrMG3nUv5zTv04EOR6c5aBMKAVdgbTF+r4qo6eQKvIzcBGQBWblmO5XBbKdG\nIJNdHlkOBG4i4axDX4pCJHpxfYrIl1gF6EwkwY8v0qsFCxgfBdldbFzGyS3I6MdXnMfJLdA8\nF2zUWKtKshvMZVzDuYQz1PtJ+GUlhirgktLtJ6po0RXU0SU5kHVtQBb3jCpxl+dLxypxd+c5\noWUdcLyZhK1K3gMqjIOvx1XhSp5V782qEeNxre42Mau3NorcrCJiOlr9cD7jmLZoqWHUesgl\nbO6gPcVbSNjKHQ+qMCJvuaPKXGrG+xPtaCyKUbbWX6L7Suxaf4ktnMhJWGyP5VYuQd6ChH2L\nOyhrFYGcBPEyYKNEQZ3cjpeW5YgnC5PwEEfCOxBxLA+BaySNNQRySOC9iEGHs9MD2S0dZrTL\nbEXj2GTj0ZwD2u8S3ULUf4aJNNYi3C9LzyRMpXYaBmRbNY2Mo6eJS537AAm/mlIRkUItudtO\nwlaJ9WMVkeOtybHTrmJJvLuvJ9LcX0BWfsKx8oj2rGRPD5gjsbt3CjcPbgo8uxHehcN1EN4N\nwvUQHgPhsRBugPDuEB4H4T0gPB7CEyA8EcKTILwnl2ZTIY0e5dLsMf3TLGvMTlYr7accF49r\nz0WuE6wKPWDxBMfSz7RnyeuAhTVE9YSKqOlUU/IyKh6QxZFVz+hD92x8knzvfZvdenrTgnhs\nSawr2sFU1bRg3o0xUeZ9dkgEIxMjuEPKyyDGozpkFp3cVA6y3gjIyniW11NipL0VkJYh7U1B\n0t4JSMuQ9rYgab8JSMuQ9q4gae8HpGVIe0+QtA8C0jKk/VaQtN8HpGVI+50gaR8GpGVI+4Mg\naR8HpGVI+0iQtE8C0jKk/THFPGtOYZrNun188Uy5kdB01xnCtpNPb3Ojh97mn5Ow1dv8lIpM\nW8/96jw+EW2Jd/f2MhzVeuAo1xDq0xwHz+jAwUifH4PI/rNgifNZijHmoiecZiXOp2yJE3TU\nZusxRg4nvjgYI4cDz6WISMft9pGeeQIDofcT0DG1Z5nypwqeNbE32tnTYUasobXpMyKt3Z1R\nNARMbpiUdcPMyPmotEI32T7iwEhPe39HH71hQtYNB0VaOqKo6Lf7fRa6bjXjti+YHWmLdsY6\n+m0fcHAkEY81m7a/HxJZYnb19drGcU6kt8dsiZn0jj35O2ZMjyyNxmNmIko3iBzQIdu5ZZcC\nDy5expZZdLcCDx9fOtcIZnM/rqRYtffnSNiquT6vwpjyrL3Lml20g+Nipw5cONfiw8vazbjg\n5jl/STHxLHq8dCynh/OIu13rZ1+G+zAYxAswfbEh845m/N//Ms6oDJ7jdUpqOfxWAb+Nhv/h\n6Zb/IO8KpeDdpYxRfgnPoBJg3EsgxSitl6VAISz3gWskjfsIpLxM8CHHpRi9QvRO8FJUoi+M\nTZL0soA+J6Qg9XD4C3joaCYcYsIl4CpLM2GTC3+egmd8Ofj7Zu6+QdqVnnIM8y8qWccc4hxY\nMZLPWyFVRfee7IqCziViTro5xdxf9Djq5KTrc5qF1ybWaGcMhSbWIJdYX5LxBvVEpl2iNG+g\nvGLAfIpuc0HvlyM3r1DntA3krsEGEyoFg8FyJhgMksZmAjkG42XKsxIFdfKiXibTj3iy0DuT\nlIxfcGSMAjLKgQiAsQXhIVlk0NSqylHJLmN208m1SckvSbgipUJxvrCuQWnUE42bkUR0eZQo\nVskqNhS3nEBx+CLlIa5I/pNa06+4RIHSNC0PhURB0thGICdRBPL+K0oV1CnvC5A1NSALnoFJ\neJEjoRJIqITGehga7A8TyCHBi0krUVCnVKImy5JQBSRgORZIQNIYIJBDQhJ2D4UXlz7FkVEG\nKTgqUxzR1LOKtHJoc1RAY4eCFo013Pdhm2eVwRJeinoB7O6COg4TBDBFANNcsHce2J8By810\nBjME4KbjrDwwBzCdw6ECOEwARwjgaBccL4CTGcx3wWl5YjHgLBssZuCmyzl5IgI41wYRBs0C\nMF3QLoBOBt0OyOWbehkkBLDcBisZrAKsAay1Af29l8OFArjEBZd7xDUM1gngehfc6AG3Mljv\ngDsEcJcL7nHA/QwesMFWwEOAbQwGbPAIg0cF8LgLnnTAMwyetQHVd4cLdgngRcA2qA+4gc2D\nrzF4HfAm7IGE8TaDdxzwngs+EMCHAPadr3H4SAC5fMyfGHzqgr8C6P1/c8HnPvAvAH7+vwXw\nPwG4pEGoxAGjGIQFUOUDY3xgnAMmACb5q/Rb9WW33ovRAqhmwH7PP6vWBmMEMM4F/DMnCmCq\nAIwi4QAbzBTAQUUAr9dcAcwTwJECOEYyTtEIZ2uEFgG0SUaPA+Iu6BNAvwBWewSvx0UCuLSA\nuFYANxQItzlggwA2SsIWH9gugMcE8IQkPGcDqu9OicDPe1kAr9rgDRe864D3JYJ9J6/jxwL4\nxAWfCYLe//cCAj//PwL4vwBSchAaLYBqHxjrA3s4YGIGYp3bXwGgcUalAAABo3icXY/PS8Mw\nHMVfu9YfxYPg3+GiVbu5mwiCMDy0E7yVtM3GF9O0JNkO/tlevE0pS3BbLsn7JO/lfT8SACNE\nUYBRDADx++LlegqEV4NwewjgEkCACOcAnplte5bbtt+kosg0y8evb8XiaT6/ndxlD9X0ZvKY\npsxSy1nOtOBNudRdWxrStDblF/XjHMAPdsvHngi1mc0yIAr3aKx4K4xrEDp4tiQphguvO00r\nUlwC2AL4ds2DI9+F0fVg5ZK4cezUsd2/4e/RsHEtuTEuN3GZODwfGGPfqSLVkFqZ//Fq2XmV\ncGs1VWsrPInIFPf+rezqT9EA2P4BQ7xDagAAJ/N4nO1a7W/bRBg/J85L89ImaVavLX1JWzYG\nbVi3jqECHxASEl+GEEziG3JTd7WUulWcQoAv/KHjCwPGmxAikxCaNO9s/845X+zEab0k0mbp\np8fne+55u+ce3zn5MkcISRJZlkgyReiVuv/FxzvvEpJOEiIn6AOJyGSG0rTZarS0I8pdpK0s\nRZ5ikWLD4XLvKVUeUnSF0Rk6+khvavT2P+JeXpdmfL2/f3dPGJFqNFXTpDelICPIM+B/SEuA\nQ27rJyrjvlXf3a3fFATnTL2ln5tffaef0dZToVeGlU8h/wpFlcJ22qAoUChweMXmRwDs4K3Z\nOtHOoG0Hagv3tvxrjq2ELNiWYHwVcqt4tkcx68wLIXMUn0FnCePLGD+P8QrstO25CroI3gVg\nieN9DTxr4LHv36NYh84axT3wbgCvg14DNjk5b4DegMw3wXMdvn4Km+37+5yfol1M19ugNyFn\nh9N1Czpuo72H9h2MeQftuxi7i7nap/jHnWvJ6sXOl8x7HAWcZH7ojktYQ7NXtgYlrKesgECX\nkThJlyo/UvwUt7IKFCyDAsojir/iVrbCpRCv7G+Kf2NSFlGI9zj/zfFpU/tcLF8SRm9w95IL\nVr4ubaoHXoD0PdaC3fEEnekAxsT7Oxwn31NEDTNOUcZ4cVKIOPmBeX7g70tbGOs5d6a221rL\nwFDeMbneU+WFtqWdNdWGdqIZ7YAhRGBPHekd7RAduV68nesZR3l3EmHR0Y8EzmQYZ+0Dfxw9\nt9EuWxjB6x5ULSqo6owCzpp6FFPueMrmUSXmUeZTLlV+dhGvsmpPgVeeKFV+ofg1bmULUHAV\nFFAeU/wek7KIQopWQGlYRFIRULs0/BGTXXxpmLOETJb9mVyyQuqA3NZOxOWfClkFGZv3UG/h\ncdhyS0dUzbZRrkReQibEgBl7a1WnBebYL3JV9D4bIiB7oJqaoZ5o/n7f6h3k2ox/3IKoNxei\nV6oJjPkQxrzjodbRzbbp93E9yMawSpMQlsgmRwFniTyOKRU9ZQpmz6bbuKdU+c1FLMousx5Z\nnSCgUox1YuB6LHAT/gSngcA3zf5+yHJJnbf1phnaabjvViJkRtArOqV1vFW8Oij1yQhptoj0\nZxSwq11sFc9TtoTpW8JeHWcn5U8XsSi7TJotB5T9uHbKA9NsdnDtXRfakQvfnJC+Jb+eG2E1\nfqatH37bVg+cE3Fg4h61NPXQrzVjQUvZr6U2xJuM5ZfTe9MYat1st3TjgQnJlbBC3RDsDNqM\nBvUl7n0Yprxx2vzI/hKhmQHjZhrHaktt0B0zN5xRmjYV26cuUqs45hOgs4crYjfBvi8QGIPy\nmZ6ogUyh/EPPMCBwdEnrnLU009RPDfMT41DrhMhJ9BAop+Ba4ZNREnjYcYfaMm8F9MPequXS\nK6BlFlDCBbgoHB1e9HHcMSAJ6mYzWWOGpQSDxnG8sA1YZQbIbrvG2hnBoHEcQaKsjdWJGjhN\na4MFKxvwATSJD23MxjSChhfqhjWJ4EWYXW+hZidh4DTNruc8ZAy4LmRbtt82EZFtlbBv6CLT\naHuLtfPCRI7j60KUTFufqIHTlGldnCy6OIxL2Ht2EUQ+OOM4i/a91L1TWn8HTmhdzPMoe6vC\nJFybpnkf5WX70geL6twWX5rjODE70Z8FLQkGjON3nb5F5zt+9q1IdvTs4oDLkiwXsklhv9by\nm5UMxpfdds2ahNujvELKkzBwmlZHhGBlXgVLkJEbvrNj14VszA3f4V2kDDoTKU54iYspO2tU\nuPrA/ghCQN1PO6xITDwdhoUiajpwBo8S0tsW6S+lvq9YlOkOXwaN6HM7FHkOBYB9bZ4F5jiU\nBVQ5LAzBMoeVEKwDNQ6bQ3A9Anh5zBfm5zYwhz+2DMLu5XChBDP6EywWFCOgMgDKCFgdAVsx\ngJfHfHlLgP2s/mIRrRA8B/bs1vIAAAGZeJxdj0tPwzAQhCepzSPigMTvoIYA6ePGBQmp4pAU\niRtyHFNZOE5km/5wLtxKFdVWH77Y83lndvcjAzACIQlGFADo+/LldgqkN4MIdwrgGkACgksA\nM+bbnpW+7de5rArLyvHrW7V8XizuJw/FUz29m8zynHnVclYyK3vNhfxUzbgE8IfdiWFn0qzn\n8wIg6QGlhrfShb5pgBdfSsvhI+rOqpUyXAPYAPgN8yYnvitnxWDlWnEX2Hlgu77p/8mKVGju\nXMjNQiaO30dGGmeqlWmUWbn9ekJ3UWXce6vqHy8jIcpVj7FWd+JbNgA2W0b6PwkAADqKeJzt\nWs9PG0cUnrWNbWxMjTEQAgSMIRAaTAihEAeH/CCJmqantlKlqkUWLMSSMcg2CW0v/dNyr9Sk\nbdJWvVS9VL1UreRI/aWynd39Zj07rO21vVlzCNLH7OzMe9+bN2/fzsz6wxAhxEt8Pol4uwj9\n6/rg/bvzq4T4vYT4PPSGRHykm5b+UnGrKO/Q3hFaC1Kc0UQJeRclkPySkCkiSAeo9E4uL9PL\nP4n+ZzTJhUfp9MqyING1lc+WSvQiamUEOQb+hjYPevjKub0s6305tbiYuiQolo7ov3+Fm5HD\nklzcVC0sZPdUK/28xJRGoN0MKtVro0NoVy5vHmSL2b2SoNdXKssHwiB6D4oy7SwbEtpATGNX\nDbFqCBxky2W5WNCbTEzd2gh2Cwelk4by192KLhhSmjbLB+3ifU9u28KgHvkou3eQl23YZMj0\nqY7clneyh3nVoeWHHF2vYuUQkHARMUBxTp1SiieqHRRnNRFC4uooKBIUM1Cl+kAN/Suwxauq\npZjg+qt9VLoldbLRJ0wxptpMMYh+HnD34TqBax+n6x2Ka7iv6olRrHHtcWAJY/FC/1XYMSTw\npTm+DMc3hHFPws5J2HaeYoRiFPcn0KbaOo7rSYxXrScppnE9Bflx2DDdJNcFtM01wTULrjeb\n5FpA2yWBa7EOVwpcl5vkegvlCsel1lfrcC1z88q4rkHuOvquo34D9Zuo30L9NnStQdcGF0Mb\niPn7FH/oz6akPkL9ur+NND6CuGElkPxKhyrnURrmbZ9SL1UbZKN42CZQAsmnFN86TZYAwbRA\n9pziB6fJZkBwEemjSy+TP1L85DRZCgRLAtnPFL84TbYMgrRA9ivFb06TrYHgjkD2O8VfTpPd\nA8F9IUD+ofjPabIHIHiAHN2ll0lFhyNkNpUYt8OPH+7n5ffElZ0EabayIyil6squbVMN8Aqk\nz/EiUxteotFv0dGT2xF6SrV6JjJCT0+Nnn66+NstP+R6q6sIBb2OzfdJL+cnYtFHTLkeIQsm\nuBLQsuBTh/xrkI3hSRpHCSSfUXzjEJlNJWOKRZCxVwFBKTn4KuCDbFwR4sBrjoMJsd1XK6Iy\nGYsY4WODt4f92Y2NMW5ZMVqFNl3PHHKLQXYOay+1HMY1LZNf63CErJ3YYDFLUEoOxmzd2Oiq\nNfdr8w3mvpmSZ/Q3iMZADYukhNAxWKNjIFdKFQ7z+Tr5TbSrXpwmEDasBLTF1XOHpsggm4QD\npoQc9oLie4fI2onTaYsc5tQKs26cdjeImpC5fUapH71RPhrsRMEkNiCsBLSJeeGQAwyyJAac\nNGeu5Hc6HCFrJwqmLLKVU9FZNwrC5lm+2GCWTSzcCQrhuthNWz31qQ0an3qaI8hGONmXWFJZ\nJt10uobWQKlczBV2czWaB9XmTXqslc9uyZvZfJ5e78pH6HVspvPD9B72DJi8tPDRJwsfz7GD\nQHY7DN17cqFsIUWE7pFH8lZ5v5j7TDMGHUJcBJE6nn7DrqdxzsrLRjlZTseqoOOqUE/z6cCu\nq7ypuQW9+ZpVM9Hb1lib3eH32Ry+dz+/LYjGnB19pt7ow7OpuQuzmxfUoo4XPFOLLfqh364f\nCvJjQTT+av1wXeEG/BKnl5YbrdyOnC+xGDXitiyXypAcNEvOiwlvyNyeskp4xMKJhnM+1Y7X\n+dxozExhX28JKGbhpmfqjM2Zoqf9hWxeEB42C9+A8E3m27Pm9oSgfEWoZ4TB3GJ6RmrMUbea\nRVJC0rZQvC4ovq1Y3CcW3hmtFRxFuXyofeHgWDastNE5mFTvV9A3IuywX/U5o2qAFj69FscB\nFdQrqEdc3pFrLWLerWBXW+HOdY4F49zYEmoGdAnEbpzWasRBlAGUfsEQNzYbamxEWfCGUHZb\nTBu3PIu4vBTWbArrtq4yW3sFQ9w49j7plOqyT2yqveSrYMU5gNKH1WvcsMzYu/kVrr/ro2WE\nvi+qhgGW0lH5iH66LZVy+4XS24VtbbBWejxVWOrp0a0w6Tjh+eo6WMKbX8IbX8IrUsJLogL/\n8v5z47uFRhxtcqKjnTD0NE80VvESlg4VbEB4H7nxXUgjjtmbTMPQWCcMPc2TKWxKxGZ9MyJh\nBV/BZov3oBtf5TTieJNTHe+Eoadpqm046/prZwnOisFZQ7BlDOvhcThyDM4Yx+qQX7AHccrJ\nxhKGcwf0urZRc93JbGABLgok7rdGzFg/jB3U65GOGFuB5xlxQIgMtjPBX0tREjgZJS1FjIQt\nt43HLKJwkeC6U0/TYybhfMGG0wKvnVZ12m3+cYxBlwN/LY0lVv8JagbN+GCdBUS/Xr/B6sNC\ngLjx2x0jgoNCXo0jTV0xlFdzK35ruqJ0wmC2NjKILXKrE/k12Dg6Ws6xoRpv5zGkEv7ty85w\nRvT6nY443UaeM3bEI50w8DTlORvOWn/tLEFHyP7LoCUbQ/aT/atYBRgT3pEkf5omnK0CJHzz\nqOA7Eu8UN35IqhGPcsRPbEQImyH2U6YABx/QDYSAMIce7lsKfz9QQ1cUDujDlo6Btce5A4UB\n7HoYhoBhgG+LCRhpgPE2cZ5DI67ZNnGRQyOuVJtY4tCIa7lNpDmstYk7HDaAey0mA/bceFtE\n0AYiHPj7oq43GqDfBs4I4NtEfaMuYsZFzLuIKy4i4yDuVmHv5fc/qWWa4wAAAZp4nF2PzU7D\nMBCEJ67NT8QBieeghgBpyY0LElLFISkSN+QkprJwnMp2++BcuJUqqq3++GLP553Z3c8UwAiU\nJhgxAGAf89fbKUBuBhFuAuAaQAKKSwAF992Sl75brjNZ5ZaX47f3av4ym91PHvKneno3ec4y\n7lUneMltvzLtl5VCu3EJ4A+7E9POpFkXRQ5QckCZEZ10oTEJ8OJbaTl8RN1btVBGaAAbAL9h\n4OTEd+VsM1iFVsIFdh7Yri/5P9mRNVo4F3LTkInj95GRxZlqZVplFm6/XqP7qFLhvVX1ystI\nqHLVY6zVffMjWwCbLQyvP6AAABKqeJzdV0tv00AQ3rUdN0mbPmiSllKEyKHAgYiiClBOnJA4\nAxK3yjibYimxK9uBIC78ZKRUQqhSzW4866wHO3FClEqsNNrXvPbbnRn7Y5UQohPDoEQvEd5K\nH96/efqKEFMnxND4AiUGqfDeDHzbZz3OXeOzMqdNTrucmhOueCx6sX+IpDe4dM/pMz78ReKW\nbDH3S6fz8gWSKNl9KwgStcgJcgP0G7RpwGGEzsCS3M/bp6ftZ0ix1hXy12hV7zoXvNsWS6/r\nRDZhosGpCufa47TF6QDO2hImoRf4nUxcIeSY031xOOjLsGeCnMDuZ+wCjfh4P5ZLYXui9JJq\nMQk5LZoLphHNwi8xJg6kw0FNGJsAxsGKjBVUkixvfv3s9dk7/OYoSDeVMQU6XJGrCakK6He4\nWbFxBZumMqfKXBXUOh1FUt2phE73W2h9mkRFFoM5GIZWyGD1Jm3AjJBDGnJIT/NvR2krO1Ge\nVcv2vUnkSaupsGzbXj8AE0bOmamNfCsh38zZviWKqgKigPWZHeZwlG3PDS3Hxf5KPvWW60Ho\nD+1w6LNzNrLs8HxgBVlyVxC40sMZntbRvBFl6MN+7Ez9GPW9i0tFRBWV7Ea750rEyzmIl3tD\n1w4dz1XMqjroCASukZGbHIUl3xu63TlMVStou8MB8x07fdxDDEPyuHiadUIAfSMHrCTa65CE\nRH8G4zOI/uaqop3M6Snc+hiCvLbmzDnZUXdTeQNvTnPGGGw2oGwY4G898SWOSwq5RPKv/XzS\noPFj6hhQpvQuG136LAj4aw/eul02ytGjTSlTz1bsRUqHBKuxYJ0XoDxA9V7MK8Arz7UFQGvo\nYem3AfxiDyspCxKjFvh5BGc7AD8lRi3w+QSwO4Y1+S0ksHq4gFwV1iog90jBtQY4GvF8P7oN\nPNULNTMgTFczvKtWsjFAgXnyKtgY4MT8GZVGotKCKJHNmEMbGVRErpJBqC0V5a2/o/y2qHB2\n4Q/kTpSRkp8oWa8E11mGvwJB8C82qarXSqaiSM9jAJTCmlKz76ofKsr6EfqAwZX7HsxTtXrt\nkYVTu9L+qUTMKzUZl7jej5P/Buklw6WpJvJGwZBfsC11LumLvloqjI30Y68gJuZsWgqDvdVg\nUOzMfwCscrk9AAABlnicXY/NTsMwEIQnrs1PxAGJ56CGAGnpjQsSUsUhKRI35KSmsnCcyHb7\n4Fx6a6uotvrjiz2fd2Z3v1MAA1CaYMAAgH3N3u/HALnrRbgJgFsACSiuAeTcNx0vfNOtMlnm\nlhfDj89y9jadPo6e8pdq/DB6zTLuVSN4we3S/IiuGxYA1tifmHQhzWoyyQFKjigzopEuNCUB\nXv0qLfuPqFurFsoIDWAD4D8Mm5z5bpyte6vQSrjALgPb9yXbs/1YrYVzITcNmTh9nxhZnKlS\nZq7Mwh3Wq3UbVSq8t6paehkJVa58jrW6rf/kHMBmB8f5PfMAACWOeJztWV1P40YUHSeOHZIN\nhCV8BAgJrGGpKrLAtqKifWjVqtKKx7ZSX9NgNtaGJLKzC6gv+4f63yplparaCnecOeNMJrZJ\ng+vlgaCj8Xhm7rlz586dO+bXHCEkTVRVIekMob/MLz//ePAVIVqaEDVFXyhEJXO01By7aZsX\ntHeB1rIURYonFLvDXuyZlsYixZ40WqejL6y2SR//IuznN5mdd6enJyfSiEyz3XAc0EwoQW6B\nvyEthR5q37ps8N7H9aOj+qEkWG11nb4kd+7o+KR+SP+O5M69ru11nvfeffvHP2Abn9tvdvfK\nMW36mIMpFGi3jKGeyc5golW073hyKPaG2nurQCdNsYE+GvrMoY+O/ksUK5ClYh7zeFeErDpk\nPKWoQI8q+lTQ5tXXIHsJskTeDbSVhbZNtFVR1tCngraK0P4MdQP1XdT3UC+i/hz1fbRtwz6f\nU/zJTK240DEtuN9TyOQlYCwxeONS7p3+prpRLuaTgZxsoQSMEkU1brIqCAyJrEaxGxPZlEL8\n1/mrVrdt/iTHAAWjd4VnbAAeA+6tqg9RgPI7XN1r+IBGLaBjyrqQeiohPfNegKqb15bTd4Qh\nRNIt/6J+3m2+MW06BTTfCt1ll1UlL3omlMDQi0oxWcsnKyGelBAPMqw0lhniJVsGwfI4sbHC\nEC/ZCghWxomNVYZYyKYUUnADNsMq9ixB6W2GtZj0EjfDgit5dirEszXnxumblxFOXTWbrW5t\n59Xzy5rVcaxzs9ZvmbXLRt+2rmtf7+9gyK1EmA7bdN8cjJNlXYk8J9grSLI6LrksjdfciMno\nh/XhnyBapog6WFahHi8BbwljW0afbA3+u4n5ZlhplCkqcZNVQOCVL/FMS2OLIRay+2ycrYCN\nE9fpGrlxMiF+nO3bN983+s0W+vP3qnndszFWC3CnlLTCy0IJDFe4HNPkfLKyNxDlAp5paawz\nxEJ2nxVeh7EJSm+FN2LSK3KF9ZAV1mm46zfabbyWYwXvljFtu8tXPBvmLRdvO82+1e0I3KIQ\nxcSAjxLL7R0utI4AwUvAs1xs1vPJNmCtDfTWWWlsMsRCdh8X2gxwobgiZagLBbrOpek4jdfm\n+Em076Jb0LFDpEUtCvMpsnK4qLHNJ0yRD7hYCvNaizpSjVF+0LbeWZ3XNE0Ypgi22WjXrrp2\n+3x7J4AviDcn8Hr1fFgScXoqaeTvRqdldW5CGjX7bee7Xk8i91sbvd4PFt/JT0Ko8yxhquOT\nxdhtv9dovmGrTiQjsW8PY7w6vEGHcTXU/fyFj51vN2jwaNVH3xJ41kQkU1J/X/TeD7AnCtIF\n4/++pg5bIi5EA182c6kBZlpI+G4yoecUOW5OykwLCV9xPOIsN1o6YA5CaltI+Ebk6aZx3VRW\nr/J6RlIoibR2ONMCCMQva3v+3mJtA5S0vssV1iWFk0icfELtPROSmkSglKKXbtLDhqYWzqvO\nuXkdIi89iUB5T5hWY7KocWquYFQVRv0MhlSwigMh1CyNOIbH3kdhYVKQk5UWpyJ99lRg7Mf8\nZ1LIw8l/Ar6wHbjjB3Bg4qOgHz8UCgkbesKdS74w33r7n1RBTqi+HykGzBQL1MnYMnUM8Ccf\nHp/uFaey4XHqv8arZNPoxI82eTWFX+JeQT1xj2+ROckYSXxRmkiCpr2MUMVfcMXzkuJJ/I9l\nQnH/3iI3jO4sA2SgPG7lAv5rmIb+/BglaNMxHv+Ifek+hDnzSxFV6AtRodykZ4f9ZvL43N0x\nbJadcOxOd6jorrCYiS/CQzpUuDGmMJr2aLTp05bso7FGHvalGFrO7tj6GQF6BGBYH/PAIsBl\nLM2Acgj4YRqF7Xgx03qd3Z3K+chGIC9hQQKXUZoB6yFI4wtZgpjOl/8F016MgAAAAZ94nF2P\nP0/DMBDFX9KYPxEDEp+DGgKkpQtiQUKqGJIisSHHNdUJx4ls04GPzcJWUFRb0Hqx38/33t29\n5ABGyLIEIwYA7HnxcD4F0rNBhDsFcAogQYZjAHfctz2vfNuvC1WXllfjx6d6cT+fX06uyptm\nejG5LQruqRW84k4Y8vSpXl2vpLdiXAH4xvbEyANl1rNZCWTpP8qMaJUL3dMAj95Iq+Ej6s7S\niozQADYAvsLUyZ7vxFk5WIUm4QI7DGzbN/3ZW5RJLZwLuXnIxO57x8jiTA2ZJZmV+1tP6i6q\nXHhvqfnwKpKMXH0da3Un39USwOYX899BvwAAT3d4nO1c63Lb1hEGbyBFipKsq60rdb/YYiyn\nrm3ZiiXfMu44Haey06Y3l6IgmxOKZEEqsZ0fzY/+aR/Cj9BXaF+lb5AZeqbT6UyZA+I74OLg\nRoIIxUmCmW+WAPbs7lns7jk4ONJvkpIkRaRoNCRFYhI7Ys+fPdq+KUlyRJKiYXYhJEWlAUbl\nqppXlRPGnWZnCYapZlNJug8KyE8YXgut46z1SaGosJ//kfTDuKWUvtzdvXFTaBHLF3PVKvsx\nYmeE9H/gv5AWBke0VjjNce5r2Z2d7FWLKRUlX1Nz7Of/xD7mz2rlE038UPOqcZi5jnPFWrnE\nue7+9e2/P/7H+NcCV6JSLubUQu0N+53Urn9jlRSpVE65mP0VQ5vWrwmGqwyas98xDMLhmlFL\nmhyGtWa/9d/avRjuaQ9njmEAPDIejmbGNnhT4B0CzyCRM4x7o6Bj4NGexCxkT+DeJO6N4/cU\nbMoyXIDMaYZL4NHaLzKs4/ptyNPsm2d46mDDIvQsEBuWQVdwfRU8S4ItnxJb7GRvot06kX0Z\n17aILNHPH4D3Kni30Y8dhr8Jfv4ZeK652HEDPNeJHbdAd9HH2+C5KfTx7x59vIt2e0T2Aa7t\nC30cJ7H2McO3etiGGoiFBCkCM2jLKSA/1aG1Czc8sz7acEt0Q9ksnD8LJ8V0Kn+qI1hl8+RJ\nx1qQDxl+HbSyFSi4LCj7nOFPQSvbhoIbeG4xncpHDMWgld2Cgj2hZyUGNWhld6FgH9UhplO5\nxnAWtLJ7UHAPaRHTqfyVjkCUtSnEuJz66lW5qByKQzUfyvhQLYGGWkN116YaoAJCX6M+aTfe\n46ZswzhQLL98cawcnb0kDSRB38pBpVJ8Uyi9zFRzpUKt8DZXK5RLmfJJpvZKybTGdQntqeKQ\ng+Jw4UTgDDtwRlcLpVUX6+Kn1SfKl0pREBdxEJfIl4ul3KlSFfijDvyhfcJIGGYaZptk4bxl\n31Eu/4VSOrZxEGVLwY8PcjXqS5HypuLgEBFK6F1CgWYJPQwo7AxlC1pD0GX81nL/mY5glWWg\nYBEDRFyn8nOGzwJS1qaQ8YZNovNBSwINBTho0USfbAjRG3OI3jgL9GrupeKSP6OPCsWaomrp\n/cnhtUy5VHwDLjGXZadcvrPtkgvv8Zxkcp5wkrS765RFh0aZsbudPml24hOjFHD7ZQertCON\nJxcm/E6U9mbALPei2NukUylZFBhTDoyxQjVbEro60ABTO9UgLMxxZggFmnOczwOKTUPZKh72\nFtwQ16n8W4YXASnrJkEv2yRoUBM91wQdND/qlYZHxqSFjBkyt18V2q85jT4TemI8VZX8mVot\nq/df5dRmOZDAbJcgRsC+hfZhj4gfccrnvT2bKHaqRJFKuYrL7Qb3GmZXnALy73QE8lwNZetI\n2XW8rKV0Kv9eRyDKugnuDdQmCVQL7j8EZJdrcF9wKnh54WnrKzffSvyw3NuXXO4lJHp0Wgc3\n0D1OAc1DgXnJULaJ8Ngk6xGMyn/UEYiybkJlyyZUgqrPrqEyag6V6/S+bQhte4SQJIQAEbLb\n8Aixzts6h2CnlDplTOpscBiXzIPDhORvcEhW87mi8lTJfUHrLpU8KZxP+dQ0LgxDbjovCueX\nfOoc1nU+LtWUUlVfEbZTNy2cz/hUt6Cre1Q+UwuK+kzNlaonZfX0QK0pJ7l8zam3s8L5nE/1\naVU5Pssrh5a3cSp8Xjhf8Kls8lgtV36Zg7LP2IJ77qio2HWRhy+/TqcXAzW2aML8lMd8JAF1\nSTRI0oa2Agrk2b7H+6FTddGqmG1S/8VaTIiyAxhhaxw1JnzKZ0uL7laYKmf4oz2iz4nS5F8y\nS7fMxZbN9y+5aU8+enjw7PmvHr54/ECQsmKWMi1qWTXfN62GOF2XrO9gs2Js4Pqc3aMX+dzW\nQG5hhZBToLn+WQpooDOU7eJNZxervHGdymUdwSq7DQV3sJQc16lcYfhzQMq6mVrs2bxiBbXi\nbMBuarFmDr1lt8CfvV8+PSqUmmuaehHLFEqZfK6qZLNZ8IuJt26W7zlKb0jmYrsp+Su2Q/mm\nrS6lXbJ7dzuB2i0zY4thxT01HYqERDjcKPXE5Q49d0Xw3LZPz43qQ/LD00rtjbf33HqQdXCi\nrCq1M7Vk0x9RGkuDKe16HdfSJHV68YHNeOfhd9td1Q9hDKmjk9TwXnyssxhOFvwnsFKSED7V\n80/ba4Ze/fHVWx9gmiNaz/vCDY7C4A1iYBQGRs/DMK4w+o0kHratR5TXFZWtLbOQqT4uHSuv\nHeSEWrCVM6hbYSsjYbUlENu43LAz2ra13urfMs+SmPAAe/GJxJIl9sv6IVTPOkyjZvbi44o1\nmVt1WbxlWdOvwygxicYNg1ozjgbh73knO02CdpPJT4CmAf6hgzpH1s83+XlKcFYvtkdoBgxw\nA5KgA6BDgkG9+LxgiUPHJfQ6XNtJPKbPo0f9FI/cWSPEWSGca0Pzh3Ak3y/BeWOEj79upOHU\nYRJGPXeuGC74ilHH5SEgwtcs+fojWUusYw2d2t6Ljw0t4/4J4/4lgZqNGxWM68XytqFwRIha\nvuk1CtBDJhhowVeUj3hPEwzECZIttJ0VrF8f8BIxCDohOL0X+8cs0WxeIq4jGHkSzzhskeVT\nVb5VdpYsbM5JrdlQgmyZXYDb5hBPC5BN5/DDsGFSPzemMlP94CqXqYzT8jf341gAfqR+SsEv\nFwU/XepzP9ks2XMXDftwEXUJn1JMCy6Z6XOXtPFZgbtoKoAoErORupAPt7OCC+f63IWWTyNB\nx9S84JCFPneI8+ebTqay8+fRy36aynIZw21OFBwOX3ZznRF/6Hi67hIQiQapCz/qgAjhc2Eb\nTkv+5LSW05rO4LKmussmr8NX/6a6y7bAsvHHVHFSSB5M2JpJFCErd1G8HEu4H0dCZfTz643z\nSKwQNhkM2Wwu4MakvJe16eHrYaU6eHftMFnvUceOBZusvvo6Fmxyfi9D4+R5BGOfVfmOh8af\nnAZniE4bIXbxz5qLuE5l6puL+saVXi5o15V+kpW56j4tXDPBFi6vw1d/Z4ItbIEVvjpWrOr4\nnkkDqxd/d2qJc/MOFT+bAlb0c8umgJ50p51NAavnYZiYeOToKoG9auoPYVPAEuia8OB6sVfQ\nkiBe293q+JZDDe3FPkOLoda9bnUkRCcrUxvn0ZV+mjhwZ8WJszRbHqODEZzz8rdFHNhzxzHF\nT6jiuOBA4fDlzLh7dncyjfgFj7J10G3Bab34bwiWxLHf6ljHZspOkufKeXSnn5Knjh0v1Am9\n+KcTTcVZovhdG3NU2QNJgkEXjHhg3AUXgaSAaRfMAfMECx5YagNrwLyAdQ9s2eAKwbYHdgiu\nueC6B262gTvAjoA9D+zb4B7BFPDAZ9K9a+N9Jd4B0i64EABEfTMdIhMgRNkbXSLbAT50wc8D\nhKj3oy5B/uC7veL+HaywrK0AAAGpeJxdj01PAjEYhGeX1o+NBxN/h1RXXZCbFxMS4mEXE2+k\nWypp7AfZFn64F29oNrQR6KWdp53pvB8FgAEIyTCgAEDf56+3YyC/6UXccwDXADIQXAKYsmDW\nrA5mvS1lU3WsHk7fmvnLbHY/eqie2vHd6LksWVCGs5p5qaUIC26tCzwoZ/1COL0x1g9rAD/Y\nrxR+Ju12MqkAkh9QarmRPvbII7z4VFr2F0m7Tq2U5RrADsB37J+d+K58J3or14r7yM4j2/+b\n/56MTIXm3sfcImbi+HxkpKlTq+xS2ZX/H09ol1TBQ+hUuwkyEaJ885jeaie+5BLA7g/iJ0X+\nAABaoXic7RzLbttGkHo/LNtKLctP2YntOLZjK06R9OHkkh4K5NJLW6A3gaGoRoBEGZTUOOil\nX9VL2y9oe0h7SHto+wsFHKAoAphdamep4ZqURHlFsrAIDJa73J3XzuzOLEV9kZUkKSbF4xEp\nlpDIlfj8s4+PPpCkZEyS4lHSEJHiUoaUybau6GqN9J4ltTSBBQKbBB73etF7Uha/JfAXNzpF\nRtfqDZXc/iPRy3qkal+dnLz/ITcioTTkdpvc5J2YkC4A/gVsUegR79SbMuv9bvn+/fIxhzha\nNce/5Vqz7Y5eaXdUXW2ZI9GT2Jdqp0fAzoptgJ1zIlBd57DkTmVd1Trlmi431T7/nJIIymaP\nGhmaNhyo9ruYIzMG7Zo1BmHU5OYwjL0uI2GcMbt35LMKmZ4BWO3dvGHWTpVRMPe6uWHuwSKB\nIwKmwRbMOSCwBMa6C6iLps0ALMGclQhkoE8K+mShTwHuI8DW35SHiAEOEUcOskzgGSoBit9R\nMMdFjaEeETcGOYFFbKXnyLRchXtSFr+nIJYYI3BCYAfuSVn8gcAfook9AgKPYApjtCz+SUEI\nsRGR9K3wxfNWQ/2UXxIjMPoxuo9QYEvilVm1ACOIfA32bD54Aw+TDh2jj49QT/wk0WxV1QaH\nJuKCZlZpNbpNrV1hgyTU6YJDErUjWTLsHCQNrn/MheiG0mqeNtQmWUfNNVCuyh2ZLMF6V+l0\ndbWNRr0Bb8X1hJtGTk5cNJLp1KsvO/Kz3rbl1CHZ7HbkjgqtFxzBpAvBGb3V1aoVXZUbPM8p\nrp62o1jjNLfO1UuGCysZrp4VhHeGq+cE4Z3l6nPe8CLHURuq0nGg4mR38xzV/GCq/VDAtBOL\nkiMfsvay0qpB6wVH94aLoUQUDp0t/qipsmn2lXqVQ/eOG7odO7plN3nWAHW7osgNpdsgJl6t\ngMO7iLBgp7ltjEiqpMhatV4lFDwQKwgg1q7r9W67UmvpQ4gtiiNmRi6DiRXHJLaKiZ0SO9Tl\nIZSWRIhlLb0OxNxKzMQy53Ar47q5rOitNi+qtbGVCXdtILHKkVwbTPKmG8nEi+ck9HeQC3fK\n1dtl5bmsywrJE1y4i5drGmNu3cVz07WupnTqLQ3RwTgiZzDgLUfkwiFCjaJ4ZQ80wkqA4o8U\nhMQrFrF92IjMcgvuSVn8iYIQYiMiKRgOQdsBbMASlGbQ9rMgviwwnRzbVJIrvZh9QpMrdadN\nhVzH/CDMWuyTe0/QMIfhNguZQ8qZo2XPQoQpR3Ip8VpR4hx3w5vSDgw73rsG4Nnk8N70tiAc\ncnhZ+xHXXmb0btnx38P4ojBv0hiuXAb3LXOu/IqCkImyiN0D9zXLbbgnZfEXCkKIXcWVjx1c\n+VdBfFkgwJUfuljPQO+Na93GoJRL4qxiDimEue8rkQqRXErsvlucm20Lct8dDu/tCbvv7oTc\n9wG47APOfV9TEDJRFrGH4LIPYf3L0LL4GwUhxK7ivu85uO/vgviyICj3ldAYh7E2i5hDymCu\n+1qkMqQhJXbhO5yr7Qly4X0O74EYF7YkzpIoXOs2Vb2u2HuWDa5nTnaO16URtHPXJXZP6ipJ\nkTQ7b0nDARux8hWz/Rw8Ydbn012T6DJjgCKRVlk9xjHkxwlwL/eehQMSfHi/Bcd7W3Csxw7z\n11GcWAK32e0L0zv2OoexEbCsc8Dhu3B57qnttJF/2D9pZDpJczopAZ9Y3iTIl6T1DSMIOSc1\niSluEtNBCHfFSRStkwynk+xUJ70zaqyT3FQnPZxYJ3Ph14l1vs50EgedFJB8CVomDSSn73Ix\ngvFv+owBOI7Oq2en5Ay1Tc732k+1qnrmgifaB0c8OcqFDQdT1vwYBoQNJwMKnecMJx+Egnnb\nsL9/uWQ51rsXpoxDUMYqUkYMLMlUylP0tvAW154CQfn2DHgX3z4DtPj2WfA6vn0euL8FATeb\ngAVQ+A1a3wpkK7+keNs7KBw+vkPrO6y+QOu3Wb1A67usvkjrd1i9SOt7rA52u4/FPgS/4C/2\nc44EAtyeQoDbMwhw+wwC3D6LALfPI+BZG8f/Dy/7f9hh5PWJyTg/oowu11h6ZTRj44FnGW9c\nTUZ2jSXrjavJ6llmttQujrHvbIJvlmBpYydUeD/KwbqwTOvWfrQShmVxcCBjvasUvTevcrpY\nC4MuBu/N1lvUEYK6IwPJ6btcYQrqrsOiGYEDQt4w9pHuE2AQ6xBgLPRp9E5g36LFKAZ4vGZS\nKXiWhmdJGDczfbPuiCQ8b9bfAFJ8tByRPB1Zj3W+7/JyHa/M/OmuH/PnbZeyflrAzoJHWKCP\nDdTfd/HCtEDnnU3CayCYHAz/h0DQth5O/HckgUcF6PLd6CLwQovhWhSTbvDXWHItirG6ca1w\nmoJNMAUr0bq1uW0E4YieNrfJpWCbnC5uhkEXE0jBNoOQK0w7/DQF6xvGORxXhy0Fm/4iEhQl\nBfWLyKBSMLdfSA7KwfyYQG/blNAczBfxwrRC511s4jonYb78GjjwuABdvlvdNAkbHjNNkzDx\nSdgWrVu723YQjuhpd5tcErbD6eJ2GHQxgSRsJwi5wrTFT5OwvmGcgzOELQmbftcCipKC+q4l\nqCRMQmNYOSgB82PyvG1RQhMwX8QL0+qcd7CH65x8+fI9V+DxALp8t7hp8jU8VrpOyZflDJdt\nU4g86eG6HJlXvxLFO7Ru7cJ7QSwannbhySWK+5wuDsKgC3GJ4hMDyem7XGEKRa5zouhgGB8Z\naDG41oYxjRimEQNnD9a3g1HOOfz4G9Ie4buIcMFF+fjbppQL4O+Wsv2PgceaiMIIE5EeAXJ9\nGG1S/gM8qN7bAAABpHicXY9fS8MwFMXPutY/xQfBz+GiVbu5N0EEYfjQTvBtpFkcF9N09Kb7\n4L74NqUswW15Sc4vOSfnfqQAhojjAYYJACTv85frCRBd9cLvEYBLAAPEOAfwLFy9FoWr15tM\nl3kritHrWzl/ms1ux3f5QzW5GT9mmXBUS1EI1kYrt2BqqeOFakxXWx4VAH6wWyH3RNvNdJoD\ncbRHEytrzb5C5OHZJxndXwTdtLQiKw2ALYBvX31w5LvgVvVWaUiyZ6ee7f6Nfo+mTZSRzD43\n9Zk4PB8Yk9CpIrsku+L/8ZRpgkqlcy1VndOBxMTlfXhrGvWllwC2f/NKQ+oAAB8+eJztWd1v\n2zYQpyxZifPprnHcpUnkKGu65aNIuq0oggF76BCkL2uBdWjfBEWWF2G2bEhyEm8v+5MHtMAw\nDKh3kk4yRVOy5XhuMVTAgaR597svUuTJbxYIISKRJIGIRQJP8edXZ0dPCZFFQqQC/CAQiZSg\nlV3HcMwGcC/DaB7Ib1Wgw4Ar7ENbfQ10xUjPgXTDaprQ/YuETzxl2lenp0+PGYmi0dRdFzpl\nnhHkPdLfiFZADsmzWnrE/fjRyckjFrhQ9+X/YX5dcS3H6rralem4VtsOOQINFaAldHcd+1XK\nZQlbP3x7gSWEbAJt+b5hO49zMmKUUG4N+z7WKtCfoVVCH/p3g8wkw/091SIF4X4dyhX6I+Mr\n9bNCGitbCZWTfTRaDNvqGyBv2soOUMEBRlcM22o3pKkoGxMk/nnx+rLdNH9i17yA0odUXwgp\nWvO3NjUmGkD4HZeWP/EOJ2UOY+G7I4rTZ+ozkgIl+Q7nuEinp0mkeKbkWfWep18E+5nHILtm\n0zQ8/PU9o1BkxlLSgHI/iXqnn6al1fV0z+Rood2OuBcapu51HVOz6qi3mOK47F5bnnGZ1DrH\n4gnfIoycArN+qTuttm39Zmq23jJdLXzFpDhTCMyKHEkoesKwllt6p2PZv5yFDj2nBWmAKL5z\nTLzns+M9iJif5jiT3Azodk9rN1L0llICIxgMXDk1TXQcyoZu1606ZFzT6/VuvLwSGymeoSUf\nDCRdz4F5H78F+9voNnVHa7SdFnQ4cOWY6SzmoYG3I2M7jlnXPP1GszuGdnzS0T3tSm9yEBd/\nfPlsF6Yvr/UeA1bLAHONtmNy4KoUXO2l077QL6ym5fXGs/Ox2+2k2LniA8O06UTn8BimBnhp\npm4kEfNb+zXIpVhb8rFzGBpApRlaicFy2ehDHp/8atkpNq4+8yF7Z5Zj7r568cOLkZYOANMs\nvc9A5rc3YwVUKPAcC2EAm2b1Dhe41slre8Z6KFMqcpiduSw2WcyMaO/w8b+pW04HXjh8o7cp\nBa2269XcjmlYDcuo8VzYzVaR5sV+thLGJ8KcKiTjlFlIO00dE+y0Ke7odGbRBDyL3iLv8owv\npMFM4rVC33PKQ6LRyfg2tD2oFJbx5p6nSpCpakGhqgWC8ouoQ2RiJH38MYpvaFFsJIzNGuVf\nMWzlPuXnzP2KFEp/DAxD4kqXzRvYea5fLLrP7bp5k4JTGBAXZym0IoERBatCBcu3JbpoiRgY\nn+5g6SjggpOoRbU3CG6AMYf9aEHhXHC1nXnAo7Ui4izcp7HzBDuYhmq0LuTx1tE9mn/mbn1M\n6wh0bmBuAxIx958NPikEOiqMraOeYgbNDWgifyvD/k6D8sRsjd4Pd8e0Z8xnophENojTodzv\nodUJDjT6ACvhhsT3T3yAzX+IDcqeUckqd+gEiyvcKBjnGIwNKhgiFRQFg6KiEwo6rqKTCgZE\nxTuTgptSRVwF9agYEAUToKJlCr70VdzICt44VHwbKmirit8aFUycP76H489xvIHj+zjexPEW\njrdxrOC4hv0dHKs43gV6SCV8DxNcCsdbH/SAiROd+LIwnOjoywEYvN3ncPA+BgCvwuNlynxg\nq/HYMsp3ENnhiQwX5sCp8jizC26Q2uVJJUppYPqCx5RaIoPAA54Ap/gF1j0e64iiFsQeck1K\nK1dB4EuewFhlKAh/xV0LnAITePd5vKMKR5A74MmNrgdB8pAnma/IA5QjeoOe42nz6Ul9JjrF\nz/+bm83/ica+mUQxXb3d7WyiPK7e7jaW5yYal8SEOUBn8ZdZoHiBUrw+ZrDlDFqgqIS0NGEi\n1qd7LQ5uYUjjJelfJWK2bwAAJEl4nNVaW2vcRhSW9qL1ri8re73ekMRRojZ2ktqmKTQEt5S+\nxOCHQsApdfoiVO24Fl1Ji6RNbPelD+lj/0Mvf6Av/Qv9N4XCGkIpWBlJZ7Sj8WjvvkRwkLRz\n5ly++eaMZtj9iiAIeaFQEIV8UcBX8esXO5tPBUHKC0Ihh38QhYJQxnfJcw0XHWDtefw2g2Ub\nywdYXkZa8TO+N95g+YPpXcK9D8wWwo9vhfhKmpD9anv76cdMj6LR0j0PP8i8IIQzkP/AWg40\nCr5p6UT7k63Hj7dYw7lm2P9/5tcFz3TNjqe9Qq5nOnasEXmoY2lgCdNewTIH72HKaugR7iF8\na1EkgnAby2qYG9xnoE0CG2XotwzPoa0qln/jqMQAP9eikUnD/SV1B4ngfhP3ywUD8S0E/SBN\nnH0WOxe+gaDz8b3xM5bfpu1sHxzsA7r5+N74PZapOBvSSPLz7OtDp4X2WM6L0Psl9SzGQjg/\ncaiJ0AbEH4FaYcMpNEocxdznm5RmqBQwPUWq5ym0cS1tb6ctJS1l32we+/p30XzmKUgeaiHD\nh1/PGId55r2QDkAO0lYXgywvVsfXfZThpci8S6N5qQUcuzScJIrKAdL9jos0swmeShmASt5r\n0zcO035KrD3xUzAzk2Fm5VB3Lcc2T5Bm6xbytLh0ZYCUi8IiiaQcPWFUZUtvt037+504oV26\nI8/AHUO3m2YTD4Hm+W7HiFBAR7rha1Zcu0+hvvHSEDcZXlYyFCu6t2V3LOSaRkaOJVyxv4o9\n0nESw7NZETxiFOfSigo7c+bT7atZxGyE6T9zXcd97iKj43qO+6Ddth5y4utj7g7NQLZfeC2E\nLr/48++3v/71zy8ZuiSk29yxCkPUrBNIb2FA+tUB7fII+dD9Fvv3uzsNHIivJaYq1PpXhR4J\nw5KXVDVuNdLtY805yPC7nMVBltOykFVa6OGUe8OpN5udpNTKqYBIC93zPo8IloMT67R0Vztw\nXAs/cMzJidJOopMV+ODKkNVzEE/pVFYpXcNxERRC7cRpmrrBMT77bdSyFyoPaSupreds7UUt\nPFu3Mmz5LkIcS+UX+HeenTsZdkzP8Z02z9TcbtzEs3Y3y5rtI9sz/WOOvSV01G7ppo2au5QW\nbXWtZ9VwOrZPrLaR/oOnJd05thdxTX9G2p+H6qB01udOT6l6xpSSXIRZY6dJJvGKiAgTvgu6\n85f8BRy1pKhAf1jJ57qS8tONY48QmIftyCjbEonaniiQ7yos1QqsxGtJXrGPLmBAY1a4/pgl\nn4gEq9oUsKKxmQUsigw20nuITQGwWabyg7ykgMrz0vMiDgs/9QID4faWceFxkRfu5L1du4mO\nMuzkesK1MxdHkbJBwKpTYIWxkJU/D8DMwJdNFdobADAh1VoP3MhGCZ4JoeA92h9cOuCEK3lo\nxZsSeHgCDzAMDcKLmeF4dIPWv/S0rhOPsM+bMLaR5GHslxhw6kysg65iHyn1ZKx86+fznYaM\ngtkyPR9qQ8Yz5DUWJiSG/HRk5DqkwGS7SdWScALew7IO+dNtEtVWHNBvC/giAye34CNhEwAT\nwXe3d465SiZ45SomuAjbzC6AI8L2sQuBLnC2i12YeCRYZQhS9Ztk/fqRCUgWiDLIpCRUpkvC\nCyMtHo9bAUXelQnJyyMoISZZSBcYYspXTcxFhpjVDGKSIFcmJCRLuknJtnJxZBuFSPfolaAx\n3ZWAvcbCqXFxOAnjrBTVMbY+9FanDHxdit+TrU7tKiYUu5tJH9Cd2+skh3MEjA2m8qgQKAFF\nAVBUSEKBSaRCkioAosJSp0DFUcGuAn5UmNgKDIAKkSlQCFQAVAEgVah0CsS6Tg3ADQActhwf\nBtcC+NQh5XngySEkDvh+wNHgnSti3TWe7uBzRbbHoPNEVp85IsSBrPMCYU7/sNoDnlrqaA8r\nPeQpsYd2WO8RT49/GIe1P+Jpc4/XsPIGTZoNqFDv0TVWJd64mH3LRcrQ1Z3kWJ1sBRwL1+pk\nK9woq31yACUwRe8y/j0QOa6P+EmWgyqcJRVKyD5g7pp8ks32ZLhBegfvXsm/AAAfuXic5Vlf\nb9s2EKdtWYkdJ7abP06yZka9JN3WtYuzrSiC5WFLEcAPe1lWYG+GItGNUFsyJCVNsJc+rP06\n3SfZ9lEGtMAwDIh6ko4yxdCuYxtJ0Bo4UCfe/Y788Ugdk1/zhJAMUZQUyWQJ/LJPftm//4gQ\nNUOIkoYXKaKQHLSq6+gObYH1LGjTIBRkA+RVaBU9Q1t5DfK34D0F3i2zHbj8S6Jf3EWtk52d\nR1uCR1Zva64LDyXZIMg5yn+IlkYLxTM7GrPeflCvPxCB00bg/7/wds41HfPYbZ5QxzVtK7II\nIyyCFEGCaS+BFEAqOOVaEBHbgL7NcCSE3AZZC+aG7TT2qYiRQ78FfE5hjH+iUaV8eJ4PVyZJ\n949cixLS/TryS/vv5VfxB1EaB2tFwcnvOOhM1Fb+APlz0sFeYoCXyG4mait/RTKRYEOCxK9n\nnh/ZbXog5nwKvV9xz6lIWM6PPdRYeIDUb5haQcdb7FQlhunv73OWgZEveKY4z7fYJ0Xa2Uki\nxT05zzTOPO0w3M9Sg0PTMpq63Xax41yImRF0JTmGkp8ELvt9Ak0bpuuZlu71iZMVdHXEOKpL\n21QaRcbwlBB1enDUOEo+4DWOJB2HZp017Ra+PRfi5vqsZEoX4Ep80BbVvGOHNk0DrVhPSdcs\nwzQ0jzY1wziOp5/I3LiH91zuebqeA/0BvqV1qARBwfe8/7rM3+3AB8RtWja8ow61ZaOJbAS0\nTRkaJM2R+YyeDcTLN6y9owZYbT8WMDdkmB04MvTjtuY0W7bTgQcJZCk22o9teOAVGfBp237a\nlYBlWQePcIdD0O2AuOjTpttWyzSopVNMl3yfdFHd56anHyVzZsoXwqS+kwyouBcHOQhiiz4P\nJT6rgs8P3a5jn8Jn3GPpci7ArPaboWtK8Mt7B42dfdN6Sp3GY9mo+hHWcS37hLbhhJGlxk+2\nQdsMjnA0ylp+l84Ip0PhcmdSqBMJK+XeNNrmoaM5Z5JhqweNnxtPDgYwECedp502ra7e3Kp3\ntSQDcNL48LsUyLZ73B0b5Btd2FNykI1+IODe1I9ox7a88LQbeTQB0Fb9mWmNDTIuLyHIkLys\nDwT51jCdrub0wSFDZPZsvwPFoRDKSuaxKkNNYf6/QdvZK65Jwx6+N1HqXOjky5w3SNgiElG8\n5F1BxXeEEY+YGYEW5cbTwhdljI3yCGywGxTPSh5ZyAqsqDeelV4JyThRkJMFbn44L9Xn5nnl\n82IBlRe9gaFIvUv0tOtQN7g4uw3LoKd9cNI9keIUolEkMCa9naaExJm+DoLF3EgW/hcyJy76\nGRm7SMYqR0aGI6WKpNRwElWceA0nWUVCarijqliW1BC3inECfQ71ImZqCUkKdvQtHMMq6sEC\nfI0284K+wOl3EHMRYyyhXkF9GfUVkLvcIq7houUiveLfiMVLXKAuLh67IMGAl32JBbsAQf+K\nrL93qQGLVZlF8poCVp/IrGQ3D7C9LbONLxZgsOZH7RTbMnnOIYMOcBvAh4f4QCSwkuuBaDL4\nNgDD+FQ2XmmND8Y1KV2J0h2sPsM8CiWDOXcvKJi4/Np9Qa7lx0Zffo/MozB9AYXpI/5GOvB3\nLx74H4MM/VFjPBWH5GmSa8NiZkaTS8+xPN4c2W+kuZbHm+vIc/6Q15XVIPUBBZkiFGSsSKvi\nd7mG9UgVv9u8nhP0vKDPCHpB0GcFfU7Qi0JheAu/ajORHheGhZtQWwy+UcR/y4Fxrye/dL2L\nOzxvYLuJ7V1sP8f2C2y/xPYetl/x9VV9Mvv4un8j7a36ZM6R65ah9zh/AyXCPriK/9aFgWe5\nwEtDJp86QPKc5FAKIybE0oQTYqYnwy3SO+A2ebAAAAGheJxdj0tPwzAQhCdpzCPigMTvoIYA\naemtFySkikNSJG6V45rKwo8odvvDuXArVVRbffhiz+ed2d2vHMAAWZZgQACAfM7f7sdAeteL\ncKcAbgEkyHANYEq9bmnldbspRF12tBq+f9Tz6Wz2OHoqX5rxw+i1KKiXmtGKOqEE9wtn2wW3\naq2NG1YA/rA/MfRCmM1kUgJZekSJYVq40D8N8OpbKtF/RG07uZKGKQBbAL9h7uTMd+M63luZ\nkswFdhnYvm/6f7Yq4Yo5F3LzkInT94mRxJkaaZbSrNxhPa5sVDnzvpPN2otIMunq51irLP8R\nSwDbHSF7QpoAADVKeJztmltv2zYUx+nrmiZuvFzqxnZ6SXNpUjuxnaTNgj0OA/a8DdiboMh0\nLNSSDEpubQzo9kmG7RsOaIGiKBCXko5NWZbi4zZIXxjg4IjiOf9D/UiRCuA/7hJCUiSdTpBU\nhvC/zO+//Vw9IySbIiSd5DcSJE0WuM/aTGO0xaNzvHWHm5ta4Fb1ovxr7ktvuf0Xyv6OZ7f0\nDuWXH4j/N+6i5uvz87N6KCOjdVTb5hf5qEGQK7CPoJaEiLSjG+oounFYrx/WQsLJppv/CfLX\n3VLc3Ie64LYkHoRsuXrg3Zhdrw4hZW6b7sjB3wG/AP4u+EVuj0BzE2q47XvQXgafB/89+BWI\nW4X2GrTXoX0f2gVoP4D2BrSL0C5BuwztTbCH0B7dewzXT6C9Bf4p+G3wO+B3we+BfwZ+H/wB\n+OfgK+Cr4A9BIwvPcgSc1+DaZX/K7X9/5hJDYJEKLb7tgAfzFt9bPy85nLna0sPrFti42KJf\n3HuwDFxzX/qL2z83XawCBSqwclK+L/3r240UQ4qMby++aVsd+mt4B0hAdjVwnfBttAN89VDH\nFhRI/AmvotvxHjqzEYHJH6uBSDdoGMpMBDLfQ1+k0vn5pNK4Z8HRmwNHvfB2t6iArE07VHPg\n7tVkgYkBBbPu2Q7raU6PUcVUjTjtFRGlm1pbf0UHMZF5EWkbfCu2IW7UX55WUkxLsR3KqBXS\nWo+IbfwUEiyGC8bKrU5FjsXCT1ESoQZfklqvozKlZTGDX8SkrIkU2lc1RzH8gyUqdlnE9jvW\nZTf0SAXR7ah9xexqSq3eVZ2Qyv5EmGVaxsCNdQ80vaVT5ie136gDhH7D7nVD+pWZ+m4SZaMj\ndFaJY82DFyyxN7PEcZR6aVKdhyhamxqWyd8RRAVPctDS+b1g2jXjdyvU6q90cz51L+WyaRkI\n9YgJOJihjsbvFYjAvzOjQCT8YoT2SVNnXZUhFlBQ3s3iOxZPpKYT86rkLHapmrptXLc9FcZB\n4zqWIyY1HF6cDq/V+Syp3vxG7gcRGY3JucUUOe62B51eXMZGRMZJcAYwCacWa1I2x5hetFS+\nGQ7mKPHSYfoFnSPh7JKaPXsOtD/YXarpNC6lPJ1Sr71WmU4dNS4nx496fiaYGlWalg6dVwEf\nPK1TMadzllG+mkOLxDtaw2r8vF9277+D2Nwtf+d5PcHeic+H/FTq6NPhnT9275+AHHwwL8FH\nM4G+jO+zw0D8rT/fqGD6bzEwsMjsPO13GeXHimXav5hN2o/RSQqL1FnyRzGhgYC1ImHhYa1K\nWHhYaxIWHtZ9CUscUOtDHLQHEpqAVkBC25DQ8K9lUcLCwypJWHhYmxKW2LvKyL3rkYQmoD1E\nQnsioQloj5HQnkpoAtoWEtqOhCagbSOh7UloAtouEtq+hCagPUNCey6hCWgHSGgVCQ3/UVuV\nsPCwDiUsPKwjCQsPqyZh4WHVJSw8rIaEhYd1LGHhYZ1IWHhYpxIWHtYLCQsP66WEhYd1JmGB\nRvBXNyQE4zZ+0O0VTgUKX0w/TKRlpd2Y1QJ2BNb4wsU4mr+UtG9idWG4TeAzHRMT8QAAAZ94\nnF2PzU7DMBCEJ67NT8QBieeghgBp6QVxQUKqOCRF4lY5qaksHCfKmj44F24FRbUFrS/2fN6Z\n3X1LAYzAeYKRAADxuni6nALsYhDhZgDOASTgOAXwIH3TycI33SbTZd7LYvz8Ui4e5/PryU1+\nV02vJvdZJr1plCwkddb4pVeV1bSkthsXAL6xOzHySLvNbJYDnP2jwqlGU+jOAjx5N1YPH1G3\nvVkbpyyALYCvMHVy4Dujvh6syhpFgR0HtuvLfg4WFbVVRCE3DZnYf+8ZRZypMm5l3Jr+1qtt\nG1WqvO9N9el1JNxQeRtrbVt/6BWA7S/yjEG+AAFg3nicrFrdb9NWFHeatKUFSlcKhYJbl5aP\nQhtIC6UtLWXAGOVzUGB8Z67jJh6JHWwHWm1v06RpH9KE9h9s2svQNO1p2scT8P/wUGmaJhGu\n45Pk+sQ3scONdHR8zr3n9zv3+t5zbbe3OwVBiAqxWESItgrk13rzxrnxaUFoiwpCrIU4IkJM\n6CC6zTIVU10hvTcTawORTUS2EXlV6uVeEy3GiPyDottJ9IqWVcnlv4L7qzSp+pPZ2ekJFNGq\nZGXLIhfdfkkIb0D+A7QW6BGztZxc7j0RTyTiRzCwLS+XEvkfIJy8XxAh44p0wrj6YEx7HEgi\ng04gkX0lKtfn9GmDPh2gO6FPO5HdRETAE138UttGItuJdFE4Za4eisuJ2Qr+XojtrsPdB322\nUdw7wbcDOPt9OAcQ5yD4JYjd7RMzjGJGwL8XYoZC5nkAfPshz1Eq/hD0iYM+DNxHIGYMYhJ1\nOI9B30mK8zjoaWibApyZkDjzoE9C2xzgLIScgzPgOw3xZ33m/Tya90XwX4DYcyFzvwz6CrRd\nAu6rFM416HMD+lyHNmct3YS12QX33MntNviuUr474OunfHfBN0r57oEvQfnug2+B8j0A3wzl\newjr5xaMIekzBgV0CrQKegV0GnQGtAZjlgGrF3Cd+6ETee0Wl0gR9moPqo97KQ1Sqo8xN66l\n2LAgxor1amCFzNFR0ItwTbTY6gpfsi1AMAAVJupqsZ3Idt5kg0AwCEsv6mqxzxW+ZENAMIZG\n5sgIb7JxIEiABhGdRTLKm2wCCGYQ2UEiCd5kJ4DgPJrGSSKneJNdAIJrcEZEXS2eJrLIm2wJ\nCHQosFFXixeJrPAmywPBN4gsQ+QL3mTfAcH36J59SeQr3mTPgOAZKldfu8KX7Acg+AVN47dE\nfuRN9hwInsOTXtTV4k+u8CX7FQj+gEeCqKvFn4n8xpvsTyB4AbpcQX4n8hdvspdA8BJ6R10t\n/u0KF7KAIBX3xqcZI6su4fegCES/oq4jrpTfg9451YrQAJHP4G3EaViHxjafjh1ZI51MqcuF\nNARUEFahJ008tZTParat6WnJzqiSYuiKbKs6kZSU1ZZN2VyTNN02JCsnZ7OqKeU1VVEtQHqD\ncoowcmqZG6d60i2dpRe05CN1zUJYLRTWenUj12LPzjKwO2wttVZ+AfTr0GapWVWxqcHQhDFk\nt3oTGCp6UfcUWSzkXdhWTQZLG7Lbm2RpL+hp0yjkGTQbkN3RJE2HnEolFaOgs2atE9kbmyTa\nUBpNcnmNwbMJ2Zub5UlpFln+zFXQhewt4XiGiwzcbmS/Fw53hIXbg+ytnHB7kb2NA65zSfdb\nh5c5v20eGUId+xgdWzUrrsuMu91j2WZBsQummiT3PKORukPlhTVNt8NLN4rz3ultP8hab93V\nDKwc+UhGl9J6/P0N+HcF5N9smGlZ16xcUpdzKkXGInc+yiCwMWSPM8nImaaaqq6oyZShUeDN\nkggNkl2Hzx2+K+hz1FFsMKUD9ac07pcQHT/ISKRlfp4xXxEdNXQ5zoXXAv1rNH6JNX50BieC\nTuiQF1CEeRgox3seZC6RBwfbQAh7GCnFdNN46s1KorOip4JmGa6sq5RU2U/j5ZUt5WXNZD2i\nDHtTGWQt3T73waSCbpFLQolHNiJ4y+JeIVRZPMAqt/uQvT8cLvPYOYDs0XC4zOPhILIPccId\nQ/Y4J9w4sg9zwPU7zo5422tqTMLbjmvMIZoHaxpnogHPZH2esaA8RxvwHKvPU9leu2sP4qRu\nVPdYkFymGuRyPGAu/fhIDpVJgKPrGLKngs72tHcEdStv9xnDJGUqb+ip0tscrlQzXqxJlNPR\nok8OmEMq6NrjglqtuZb0VLMzkjtZ8XjcB8Phng0xjqisp1D4iTqpO+1zaDPPh9vMvkW4PCX4\nNgrU1DQxVUbBlmqWFj2Wk94cmOfTzprzKafackq2ZQS4QAHyOJlOIft9TifTaWSfCYfLrPRn\nkf0BJ9xzyP6QE+55ZC9ywr2A7IsccAWfE++St72mKl/2tjd94l1pwHO1Pk/gE++jBjzX6vNU\nNuyu6imTI18WlUJWNpMrhpkjFwFTud4glaWAqfRWU1FXZcVO5tz/vgiSw40GOdwMmMOWag6r\n5Isl/d2KRR/gpJ1G9gyyZ4Pe9VveYTDL8PaaMuy8VuNvmR970SrRXUpWlXXyPS2blfOWinbo\nbWTfCbdj97MqwV1k3wuHyzwZ7iP7QThc5snwENnJcLjMivgJsmVOuMvIVjjg+lXalLe9Ziuq\n3vamK+1KA550fZ7AlTbTgEerz1PdVdXSwu8b13yxiXi6X+WbB9ntFozoU0ZdiChoaN3vNDyH\n6lHAgibWFDRbXjV0I0fez/IKAs2ilZ4Lt9KrX41yBZv8yYlKnUbVvahzRdRuIDsfLgtmnXyM\nbDMcLrNOWsi2w+Ey62QB2U/esnelP20cUdwQbG4wJZQG4ihFVT6kCYVAS9tIvdIr7Zcq/dwi\n1yxgBWzLQAM9SXoBve+7JekF9L7v62/yx0p1dr1vljdvZ/YcjBG2NFrNzs57b37zZua3O4cV\n9DuGnDMkPq9Arqg/W+DTbe3/Ief27/mt+mFezlVUzyMu6Y+62PmYx36qe7Mh623MaF6jA4O5\nJJ6JEpn/uIv6xRDqj83MeWFihpqzLmacC2HGUMqRE1NzWOWj+C0kfoLEbxU5i8yZ9Ps3FKFY\nT/DFupEWK+6tgv1kgUrxk0UCIK6fJ0mrfipo351M5bMz9HM7S432o+HuaaLyGWeVlpAmYyLf\nmqsXPRE9M6l/UxGUFD/Uos8MpiaTef2Nh5uOx/bW9Y9nmLlLvHnWMw3jc/p8cTqLJ2vw16z/\niOz/BWtHa9GaktMwvceuEBIpMyhZU2Ipm4LJ/yn4+NxoXhNjZlCizKOQnqJgYc00rIGIwNVY\nWKMpsssKxgdo7GwxcvXj/015LTeVTBlkDFKI/x1w6iJaM9nZE8bmh/R4WhtDAgSCOIdpQ1i1\nmdeSwyjDKuJyxX3JMo9dEHJpdlhY6EqwirkZ99y4t3mWl2Ojkc+R+PP+9Epp5Ask/qI/uVIa\n+RKJv+xPrpRGvkLir/qTK6WRr5H46wrkRgQ08g0+3UZM3uTTKTERfuVnVyznLRc9bzvrsRpF\nLz9+6w1hNDWpTWcz+qjnwRTB/NK9MlXddlUDg6fTGR+PC8iI0+NDfLvGj/cIHh8eS+dzSTw4\ny4qL7lvc7B0ecUdu5oK6LJtHBOP+UYz7RzIeGk0DtXdJu3zPX7u8jdTG7aw23idyP3CWeweJ\n30nksvsnyf27mL4Pefl3Y3lh6NkSULIlQs8WzaBktLWULQMlWyb07KwZlCgLQ89WBPTsnCK7\nrKCAnt0j8R71ZGwJCNgKIWOLKpGJuFxxe17lkZKSsS6TjLFVU+KZi/PB4JeSlgsk/pEi0vIx\niX+yReTiU5dB/zPnQd/TIkY/ix3XeH2OKyuiSX14myAC1nkBdFnIfZJ2xC89gbUOlitJjN3w\n6JkdlmeWxrLs7CwR9Hkwr5S+GnxB4l8q8vavSPxrRd7+DYl/q8DbDTnfkfj3CuSKWtEPLq3o\nRwWtyJDzk4uen531bPIxa9G09b6qO6VXek6XR4/K1PTY1QwMjmWnk1KG3ivIcUynoxN6Lh9K\nhnKTC1Nzshz7BDmG2YkNXjNcnc2Plb66ebXpmvHkdHpqwYeKkdl8+gHNR4ZrJ7TM3IwPaK+b\nyWmptCbLst+eZXDgwWQ+rc0mResrsCv+Qprer/6anpSF/0bk/u4sNzQL/4OXr4yFrwHzXiMs\nfNUMSmiWpWwdmPc6YeHnzaBEWRgWviFg4RcU2WWFHcXC14B5bxAWvqoSmYjLFbfnP3mkLIm1\nedLNxooSCX95ZEuxEluixP1vif66qfQMo1Ps3h5rG9TBIklq5kmYsRqHPtKkf3nglksP0yfq\njSemNRAwIkzWXz7M5OM2E4xk+KZhPnJK+oi5RMFYpYpRjQjQ/YfHJ0vQPUji95M4XRs8QuLH\nSfwUid8kq/V/JfUWy2v6xx3CA3KiUtbAa0AB7rWiZlKOk0NKKTg1+B7jGugqaqCrKWyaX9bz\nSWxF4rYWx21Z2VRlAaqrCyDvkJw41UdOnNqPdqcloEc7ZNlvbrItQF6MTbTysbE2QzNM2gNg\ngrFohLLHCBb1FY8F2rKtGowGAkZjxYPBbSxncHSFaC8JKGoCio9hagFYmghMzRUPE94Wr9pp\nWggarZWPBtq8z9DoVugzCXTKH0OrFdBpI2i1Vzxam+PSVo1HcYJJR+VjYhuPVGNyCcGks4pJ\n6TAJjEnXDsSkDjDZi8oXNa+xIipn2cvFFNYtbhoGQZg7rs3n8pr+up3NzJzMjGnzEjm1m0Eo\np8W0gpPBwGoGsHrAlr0A4OWwtb0Wdnkye+sBwEvN+GEGaPd2AFoDq/uZ4mY7ILaAfoFAZzr2\neAueK4TJ7/BQBlIO0S9Q2Tr8lS10mcM64WXECfdtkxMeqTqh8LcrnLCHOGHvNjnh0aoTCn87\nygkduMuVRfR+U3YHq0Tu4gDWkSpY3sE6WgXLO1j9VbCIjG41/bTsF6hc3Wr679D9OrOnPRxG\ngTBoD4eBykZjjV1Nu73R+Bm7qmD5GLt2PVhMRtfWdMeBytO1Nd1wtfvdZWVkHUFnxNs7ehSd\nixyHd/Uh6BiuhwLXwDt/AT6P43f4A9vRmWBymTDjpZNg2wTHzRbgFFtmZKeHyo9CiEGoh9AA\nIawzdKpt7L4bwG78XhBDI6Ph0IehEGyELMCxw8yXyu7UTGGMjJBhAY/JK9sziDWwmqwANhWg\nJ8EgleN/j0opXKp1VjOr54YIP8+XAFPwHF4MitBnxoeK21EUD5TNwrxvOwysRMrWADIcfoFs\na3DvET3bSn3U40nfBTh6G9dzOf7ey2YvXpFiGHUFctZyrUw5ZMYvtnf9z1EbV1zHjR3juM61\nGZoSVxAy01Bo3WIMBGPIOIYkBdKW4ATSNsEc5wNfsM/GZwf6nSRNy7e0NKXfm5YkNIWU0pTm\nK/k+yR+UGee3zORyd/sk7z3rpJW00q58b2ferPck7+777Edvv72V7N31lToAo9gz5asIk1X6\nYxK5F8ZqhMnXCJOarjwm3YRJ7V35PCbfTCAmIt4631Chl04DhbB71GtY2p7f9qgAlLx1GoZA\nuqmacwYl4VpEwl5FJCRvHefQFCRch0i4XhEJ1xMJHUNTkHADIuHtiki4gUjoGBJFQpFt99tU\nEEzHAbTItjuB5Q3WegJLHKwNBBbKg1zGGmOUgncYzMI3MavxRkScbsi3p74M8yuGYa6SRZwM\nBtDhO3ZB9qf6WHre/lQsSgk8rjb6fSoqqOPjmsT9KZEvIs6COwjfyGuhYftQI682DLNHViPj\nysKnFYM8Tf0sPe9pikWPqBa2N7O0vbC9RYVyuJEW+z3wLmJjNqvQTCcb42ew2fRgNeM8dwF2\nQnXfmuUJ3Q/4fAtNCHoNwxyQRehZ+Pas9eQtDWG8G3k/mLA6xhv3Tih7gKVt436nChB8Gffo\nvSAGESZb9cck8t3tbQiTuwiT2reNeUzuIUxquvKYbCdMat+U5jHZmUBMRDxDdqjQS6cRYdit\nqHtZ2t6K+rYKQMkzpGFIxBA9LAm/g0j4XUUkJM8Q59AUJNyFSHifIhJuJBI6hqYg4W5EwiFF\nJOwjEjqGpiDh/YiEDygi4SYioWNIFAlF3JMGVBBMx1mciHsSgeUN1kYCSxysPgJLHKxNBBbK\nY6mcTs0rBNJvqZxOT1qnmILv0cwahnEHS2+20nsRoXZA/vfBAbg0i81BwzC3yyKUxXjrxSph\n9gD5dWjrxSoPsrS9Dv09FUpm0FV3R44Iv+PwfYTGD/RHw8GtZYkErlj7xTxKHYDKQwilh7VH\nKfr94X0Ik2H9MYl8j2s/wiRLmBgHECa5BGIisu93QIVeOo2+wq7xjLC0vcaTVwEo7fs1DIF0\nU7XGE5SEBxEJDykiIe37OYemIOEoImFBEQm3EAkdQ6JIKLLa/ZAKguk4dhFZ7SawvMHaQmCh\nPOgd2PPr0QzvhxYxvw82+0Pix/w2PVhWHjfKtSiB9FD1gm1MmAzXJtYx1Efgdz6vTvvY03Ax\nO56HO5Xsc/ilgiilgsCZMow7yizut56zPRCPI3CGIP8iQJ1msbnTMMyDssDJ4KvjM9PZaau9\nxjgGdBj1y5XWwWQT2LGcW7Y04J42yKfI0rX9rthVjGq9dYKl7fXWSRXK4fZTtr91BKExpT8a\nCr9TXkJoTWuPVvT7XDMIk0f1xyTyPZ2jCJNjCcREZE/nqAq9dBprhl3J/CFL2yuZP1IBKL1W\nsGEIpJuqlcwVgiRsQfdcx93TCve0wXt2FqN7r+fubYd7O9Anmm4AMv8Ykftnisg9aFXgpxAf\nZ79vtX7/OcQ/gfhxdn2bdf0xruIrBIjVAtLqIm0gi0HaXcQaw4cl5go5xIyN2M38MLt0v/bL\nCUsqHigdu18XsAYJLHGwthJY4mBtI7BQHvSOUG+MmnxPx+67JuihocFNhmsDa1viCfi9KmnI\n66a5bYnp7LHh4mRueE3PZHba5fLa0syky+Xe3FgWCizqwEQvBsWxu2Hl1QF5+QiB6twhyPwg\nT1eYkw/L0crwcpeTD79gaXul70kVZMqgq+4rfdnc1ESpFMFewi8RFr/SAYv2Khb2Iji+2nJ0\nND+VF+u6tpc5PWPXS8euayGPZVKGcXfZgRirOOxbgBAn4DjejXNlLKv+7yecMUpDPn63Gq6D\na21wrRX+r7oQ9xErK1WGshdxpDwMC3dWDGLmmEghpV3YGFiBatwDf/cYhjnCREphgpncXObq\nlYJbxwFIA+KUYZh5SfWypdpoZWi9avpjyLSVS6e4tAGmctFc+laU3lWGPD6t/30Zuq+udp8r\nTkxvHcuWSoWDhfwIl4EV8zY6hWxZHC3pr79qn8pPjmVzVYcUqLMhZq+Xlbn7Y9dRJ3ud8WKI\n37lIq7skYS5SZyg7ORvRyeKaoZRmI5QPF7gQO/1ShrGjzOVFrr512AxYduowIsok5HcaEWXU\nMMwnZBGlWvApSf5ip1l6nr9YLIpE5ctxhqXtHvMpFcr56jGj9Bf7NULjN/qj4eAv1hWCI438\nxUwYlJuQN4+etSN+FqH3W+3Ri95/7GmEye/0xyRy/7FzCJPfJxATEf+xcyr00mlkHtZ/7A8s\nbbvY/FEFoPRt5IYhCTOh0CT8EyLhnxWRcIhI6BgSRUKXbsP+qNhZFQTTsdtwAWuIwBIH634C\nSxysBwgscbD2EFjiYO0lsFAeXXJGAKIhkJ5dckYKouIbw4W8S+1n1HSGHioaVme4NmgFYvwF\nfq9KC+TVVe8zlxvLDudG8+MTxenCCNxy0/xb1vQcLhRdLjd2yqtdrjnlscs3O1xeN1KYmsxO\nQa1P60BnLxqS256/RzQut72/srS9wPo3FWTKoKuK3PaeQVj8XQcsInDbe0aFXjr2fwt5QOTH\nbe8fGrrtnYD11xPIbe84EymktAs7CVbgJHKEeoyJlMIEM3F02ztlzHfbe1xSvWxZEG57cbSk\nv/5KvtteLDrqZK8zXgzxO6FpdZckTGjqDGUnZyMst73jMm2E8uECF2KnH7ntuWKz27JTJxFR\nnoL8zqLPSj1ZkROyiFIt+NkIPVCeQx3O8yqU9NXhRO+pdAFh8k/9MQnklXNBhV46dbxhHSJe\nYGnbIeJfKgCtVKCbN9/kEGGHJAx0RJ7Y7jJnrWMnmE5PbDMSpILzwxYBzkN8ERHhaci3Gm+H\nvyuxeYqJHCLgXqclW9knOMRRuA11xNaZAr6TtTYjLrF0b1mFJgLPnA35JRUV1PGZa/PedwhU\ntzbvZ1m4rhlc9kyxcGQmf8vE1KFssVAax6/wOQcN+G802TtTkedlNeYsFCB7Qf8yS9vj1P+o\nUM7XONXpjMbKEKN30TMaJpRlwgKCCa1gQv1MmEVU4y8gtJcAulcQ2v/VHu3oZ0ovIUz+pz8m\nkZ/puIow+X8CMRGZPV5VoZdO/WLY2ePLLG3PHl9RASjNHhuGREwOwpLwVUTC1xSRcJhI6BgS\nRUKRJYwrKgimY7fhAtYwgSUO1n4CSxysLIElDtYBAkscrByBJQ7WCIElDlaewBIH6yCBJQ7W\nIQJLHKxRAkscrAKBhfJYKWc+HFUIhIulU1pP8d1GC/nAQFwnjl5naXv5/w0VBiCDrio6cXQN\nYfGmDlhEcOLomgq9dDTyC9mA+Dlx9JaGJ44uwu7ARXTi6DwTKaS0C7sEVuASOsPxLBMphQlm\n4nji6EVj/omj5yTVy5YFceIojpb011/JP3EUi4462euMF0OsQkR3sVrdJZCCMe9i1RnKTs5G\nWCeOzsu0EcqHC1yInX504sgVm32WnXqRpSes9NuIOJch/8vQ/adZbF5gIoc4fnyClFRQJ8Na\nKfOIBca7CIwrAMDrAFyaxeYLFXlJFhiVCkyVWVyCeBriGYgfhfgoxMcgnrQq/g7E7yEFrkGl\nP6intnm1Im/KUkCAbreUVVZQJ7oJgLWPwBIHax2BJQ7W7QSWOFj9BJY4WLsJLHGwBggshznV\nh6Dwh/B/aRabbzGRo3wV9ffnCk61C85jFsHM0k2ud5EbOMkEkCUe8iUH6eLkywFkRQDxqudK\nB1nNydcFZS0nvR6ywUU2ceKVz2YXGeDEC4NBB7mLk7sDiFfd73WRXZzsdpE9Gst+TrISZRjk\ncDDjaNuXtIC0SZLPB5AvhhQzgNwaQG4LKd0BZJ0k6Zckd4aUewLITkky5CJ7EyIHJMojcyI2\naPoMrvwWQQAAAZh4nF2PzW7CMBCEJ8buT9RDpT5HcZu2IeXWS6VKqIeESr1VTjDIquOg2PDg\nXLgBCtjix5f1fPbM7v7GAHqgNEKPAQD7GX8+ZgB56ISvBMA9gAgUtwAy7uo5z109XyaySFue\n97++i/HHaPQ8eEnfyuxp8J4k3Kla8Hxf/qYLrfs5gDUOJ2RdSbMcDlOAkhPKjKil9W2JhzdT\npWX3EHTTqpkyQgPYAFj5caML351tq84qtBLWs2vPDn3J9mJDVmlhrc+NfSbO72dGFmYqlZko\nM7PH9SrdBBUL51pVLpwMhCpbvIa/uqn+5QTAZgfabD69AAAiD3ic5VhLb9tGECb1sh7WW/Kj\nsRNaZYpebDTNxRDag+EmbVoXKOoU6KkCLa0kohIZkFQaoZf85AIyUORiZSkOpeVoKVEKIQsx\ngQ+z5M7OfLM7++KfaUEQokIsJgrRuECf+B+vX56eC0IiKgixCP0gCjEhRWXCNJoGaVPtLH1L\nUuxS5CiqEy2nTKV8SXGFWu/Q1m21R2jxf8F5plVEe1uvnz9HLeLNnmKatFDgkRDuAB/AWgQ0\nYpbaV1ztb8+ePTv7BjRtmvsUNv0roL8P1L+aWBCEPdsCwK5LUNQobGvHFGnQ3eHoZEEnB+95\n0M1wdEugW0a6BUa3At+qzLd90P8C5CPQOaA4Aps2nlA8hrojpj2OVeJwk8HGU9CtQbuvKf5z\nBkkcA/cUSodDRgIm6XDptIuMl45/bLxoyKfObBkFWYUylfIPDsJ1lgMHtryGMpXyCwfhOsuD\nA1s+hzKV8ksH4TorgANbnkOZSvlHB+E6K4KDojdK+ScH4TorgQNbfg9lKuVXDsJ1VgYHZW9Z\n/tlBuM4q4MCWL6BsZ+IvDkJxFtDI9HPmn67eI9d4hxChdZUpiw7cHeKTqU7BGhD/hUXOrriF\nygRHMfLdKaPJ1sRNSzEsZEb0MZO8HppndOch8P2OkWz7iE/7VE/vNFrkZtBBbNiwM6+7qimZ\nTUN9g3lFGbu3sKZzw63XfcJNNA1lqGt+ndExCNHg490Cjk9bOjEl8pYYQ6urah1pqA8mr1LL\nIErfkvT2mZRmDAmcXop72e+NFzhMXQysrm6YdQkZSaAu2fEa3UdGD9D74RicLYo2cvF7AC0x\nzVG6hSkSNM7cpa5ZhnozsHjBplCw6dWCnXqJ3fQGbA77sYn+9utFADXfyDM++Znp6A1LbzSV\nZpf4NN1FoWYXhzpbU+h07hDL9KlO0upGX/kbu53OAE3pExN85hCHfEAOaUttDU3SI03Lj2Vf\nsWjsJmLB69zUX4qmNd4YRGaUBU6PFbzsyn7sokRrMVVMkyo7GXguiitkcul6soJJbVVTzS5p\nSaqGrJVQ95ZDmLq3sGPysi7R1g3a70i76qMtoh3jBLmv8DrLT4rQuSPYXLIbPuPaTisuAceI\nM3ojCCq74XPwpGbBrleFDsmjG1wNdr0a7B/uLecIviXgG8Q4MT2CGETIqRHYuPeYZ5sxrplu\nxLgi8MbrBpd/7xCOLIHPw42qQN7R9cg0VV0zX2kt8g7iwz6j64Hrc9fpSY+/ESRBdsP3qrlR\n8ZxOwk7dBErdnW2IeI3UdU5RDyEvceST89Fottxv9F4+x2b+iBl2xqZQxqa3IWz/jHVPw/g7\nnH4fQsbS0XrkjlYGjdYm/ud40iWLCGziH8/c4DP3CFzF3iFGMGXCnkI51Cf5begT771mrsdm\ndxpc5bnDPJD59HiMhmwTfw9txyduzhRAFhGRTfxZnEsB/nVwnWlzvGDalNC0KW9D6AHOSm4/\nJFE/HANHNtYExFZx3mvj+4gREwYyU+enHMLwe/jkXgi7F+Cp4/czgiIzy5lnrRUmuXxVC7yK\nYJsLnq3h+jmv6q6/K58Y4wx2AiCzAgoroMrBwRIcLQGO9YRBbTnWGterAOOaDIDdFVBcAXsc\nHH4inizAl8sRLJc/AiOgEg8AAAGpeJxdjz9PwzAUxK9pwp+IAYnPQQMB0tKNBalSxZAUiS1y\nXLd6wnEqP7cfnIWtoKi2oPVi38++872PFMAQcTzAMAGA5H3xejsBopte+D0CcA1ggBiXAGaZ\nazdZ6drNLldVYbNyNHurFi/z+f34oXhqJnfj5zzPHLUiKzNnheFVZ9uaZWdVzWRpy7VkGpUA\nvnFYIfxMmd10WgBx9I8mRrSKfY/Iw4sVadVfBN1ZWpMRGsAewJfvPzjxXbGVvVVoEuzZuWeH\nf6Ofk5ETqQWzz019Jo7PR8YkdGrILMms+W88qbugUuGcpWbrVCAxcfUY3upOfqolgP0v63FG\nBAAACe54nL1UW2vUQBSeXHezF5tstz74IkJopdqtLX0oRdAnodg3FRTRZYkpBnazJWm14Is/\nudAFEaHxTOabbTImddG2A9+e2Tnfuc7JvG0xxgxmmhozLEbLevP6xcYuY7bBmKnTgcZM5pC0\n0yRIwkNid+lfk9Ah3Cf0cpbYk/TfEYaKdYOsD6NxSNsfTKy5Koy/7O3t7igWVjAepSlt3Kok\n2AXwE950MMzjaDKS7O3B1tbgieLYCdJomAbThCfzS1FqL+nnDt8832dY5bTSYJSXIUg+k4sn\ns0JoE3iHVtGhe+hOn3siPCCs5QmLM971xzxZwl3eDvB42U5B9qFbRwx5PkCcPuJuIrdtwhJ8\nuzj3kAvP80wUpmW0X86H4PJmuf1OQQL+ewFup2d/vUozu+r25sFcETyXa9iT9D8IXG8wDwE8\ndM4Q0v8ocC3BFnQyP25//Twdh6/Uz0uDda+w1wTk5/Xfqc5RdKB9w3hyxQxKu4KoP90oMIua\n5sHB8CiJJqHiSavxpD1SiHoNsTVKB/HJJEyioBzazsC8KJ83iueqfoYJqIpkJ+HxSRIrbLOu\ngE2lE/nz8OyMySV1lcYPlShWmdjLFnQ+g11VECM8PVKojTJ1JVP0zbp81xWiU0dU5sOruaQ/\nCqrgNKsuclFJc7fM7c/hs3vL7w9PIJ9QDRN5LvYcnvzP0Ai1Gd1bfr/yZBxIvERNmWQD0oa0\nIE1Io5Dw6neRhH4FrMK424gn4ShoAG1AnneALiCbuAS4gAf0arrg0ieShGkaTeN0P/4UnqIf\naj3GP6J1c6ispyNu9bKW3yWm3o0AAAGheJxdj81OwzAQhCdpzE/EAYnnoIYAaemtFySkikNS\nJG7ISU1Y4dhVbMp7c+FWqqi2+uOLPZ93ZnffUgADJEmEAQMA9jp/uh4D8VUv/B0DuAQQIcE5\ngCl37ZIXrl2uMlnmHS+Gzy/lfDqb3Y7u8odqfDN6zDLuqBW84D+Smk/3LrQ2Tjgy2g4LAH/Y\nnhB6IvVqMsmBJN6jTItWWt8/9vDsg5TsP4I2HTWkhQKwBvDr546OfBe2q3urUCSsZ6eebfvG\n/0ersloJa31u6jNx+D4wsjBTRXpBurG79WplgkqFcx1V304GkpAt70OtMvWXXABYbwAmYkKe\nAAFJwXiczJr9cxNFGMeftmmapFQBBVqgMCICIjAURREd38ZhVGQcERVfw+Zuk5zN7Z67e9D6\nOuqML8yoo/ibf4A/+uc5/mLspU9ym83THgHSXGdudnP73X0+3+d29y6XXq0AwBQUChMwNQ0A\nMP32lQsnzwEUpwAKkwAwAQUoA0BRK0/xOsDUHACUAOBvANgDMHGro+rUk/LonwDHvnZ6z2jl\n1YMWB4B/YP2v18TF9fPnz511ekx7LaY1AGynIOA/PP7F0SZRUTBByLrqM6eWlk6ddlFaQU0x\ntZoIrNMThzujdE7OtIer9wapNLipRkyxUDtRC9rwyHGz8wYPGk1TZUJIw0wghUZXfYlI8kY1\nlNeNBGTjlJYRcboYctXgPtFSWOarOFAf+KxUjaphK1VpzJ1l53bqpfZ62HJ7mBxV2ml91qpv\ns+pzbSp1UjWYCHRIpa5i2IoUMtwgset5cNNU0UZVteGKy7vPxhgzk/iIPRMr0nyx53BgmvRj\nbJm53ZSJUj1oJaA+ATrjMSGjWI/ratDAkeIRU/bCnG9T8HWpwrjF8gC/YNX3UrAVT4aRFFyY\nO0z2vWVPky24uSHVsqbadMQ9o1grbdtHeO6/U3G/wXNhcb9VX7TqB7IsFAxbYVtJmiY85Ib5\nzLAMwqKMTRSbrWQkV2o5UtLjWpN7y3aPCT/wmeG6Wg9EZxaNFJdErKQUROt0F8yFvx8DWStg\npOzpprauIu83jZas2Ytxsb0JudfkYeCNnvyolfVj5KS2SFzYHThyLZAt2cgFbqWPxQXeoz2p\nuMVb9WXIArGV2Cco7GKPw0WeH0BeDkTDl+HYmWdSkOw8R83VVjx+5mKPw0XePYDc/dI2FuJC\n+7YYpfK5Ghdjug93MVzgfQPAgagrlg/qSh9L9hSuszBorY4du9jjcJH3DiDruJYT6rKNkj2v\njQpqfOzQ010MF3iBynQ+mEsWSXaeG1zEY9vj0jx3MW4rz/lgLlkk2ffs5EE04OOnnklBsvdq\nHdfywl3pY8nO9/XkXaIZ/7Y3k4JssFt3H6+Tx43hH+3urYXjlIUpr2V9oT3dJqwsElZ0HHE1\n5CPU6Nxc3GBSWYwbTKo+U7n0Qz4qLhD0yVs5YfKGnz7Y9PiyloqIvGrETPMGW82Dm3SpiMij\nNoGUNWvlJM7ytXIuW/VL1EybJyzkkp5cJw+EgQjCZIFIoQNtuPBGfteg33SZpuK6KVu+Jlpn\n+/EG3hp1XYR6KbkNbmXqbTfvkqsi1Evp6RPUVdhh8yczKRcO3icmVR/2rj5sKfwgGTlv6L2R\nyjai66WUeJAi66vi3WOm22KolzDgwHzu/NwSC19XBQuznj/v9p2tjJyma9TFvr8ZNJrJVa4H\nPhceHwUTs/JUoyBmFQ/ldV41wWiSYgN4FEBZx2HIVKBH7t+nwie3UsOVGEVw+/cSTgWfrks1\n+ute79aTYxcA/AUAcwCTrwPAtvR/U2Ax+bELAA4maABwBCkKjqaEmjJqioRmG2rmUFMhNNtR\nswM19xGaB1GzCzU7Cc08ahZQs9vS7AWAQ6hLNAdQs4/QPORoDlqaQ3g8guURPB4GgP04/jHs\n8yh+Po6fH8M+J/DzSex7FFlPESxnHJbTQ7KcdViezGB5AlmeIliecVie3oTlOdQ8a7G8gOWL\n2PY8xnqJiHXBifXykL5fdXy/luH7FWS5SLC84bBcGpLlssPyVgbLm8hyhWC56rC8swnLB6h5\nz2L5CM99iDE+JmLUrBhJHw/P+9j32iYxG6jhVswAy0+wXMayhdomsoQEy6eOX2lpFJ7T2D8e\nkmsVy8+w/BzLL9aOwxj3S+y3gjG+GjLGt3juG+z/3ZBr5gcsf8S2753rluyLN3E/fHzt+Lnz\nr4oAv6xtiz9tEOc3HOtXK84tLP/Att+tOJNrY93E/fl/Zs41NooqiuO7nXZxW2gpqFCo2ym2\nziB2fZaYhhibGh9RS6wSMdFstu22Xbp2y84uohgwET/oByWakKhBjcFWBFFifMSoUaNRRGhE\nQSVQSksgaGJqFDUxst6Z89/dOzcz+4jXwId/zs7eM+d37mPecyfD3Gxe4oD5LJjPMb8RF+YW\nxH2eY74I+xLKXhCYoxyTHxNbhTHxcoltOgr7KspGwN3GxdkOn52wb8C+iXV2OOT1lpDXrjx5\nObXHO7Dvouxt5PWeA+sDgfU+5/Mh4nwM30/g8xHifcr5fgbfL2C/hO/n8N3t4LsXdh9898B3\njPP9Bj7fIc8DWD6I5e+R2w9Y/hGx9jvEOCzEOCLEGBdiHOJiHMU6k8I+bYrbJ5vrTDj0/QmU\nHefG/RjON35mmqYzMm8a28IS7r1fM8cQZyFtN8lcryxd8EXfcvFkz/ZubxY2jg1wHDtMhaz2\nFUku7CgApr0Jv5nV9pDkwiYAmMBhVyGrfU2SCzsGgGk78JtZbS9JLmwSgEmcEClktX0kubAp\nAE7AQtoY037ZsJMAnMRhVCGrfUuSCzsFwCmctytktQMkubCfAPhFaMaDTIdkw6YBmMYRQiGr\nHSbJhf0KwGmhZkeYjsmG/QmAaS/Hb2a1KZJc2F8E8M7ABapCVjvO9JtkmNcPmB9HGoWsdpok\nF1YJWA0u0xWy2h9Mf8uG1QJWK9TsH5Jc2BzA5gk1Yz66IhtWB1gdziYUsnoFSS5sAWAqTuUU\nsrqPaaZsGGrjvQIgSK9mqpcNuwqwq3Fhq5DVA0wNsmGtgLUKsEaSXNhSwJYKfbaIJBd2LWA3\n24e+3sR0jWzYLYB14aaZQlZvZbpONuwuwEJCza5nulU2LAxYRBj6tzPdIRvWB1hUgHUxrZAN\nGwRsEDfzFLL63SS5sBhgqz22g6e+kuk+2TADMAOX7QpZPUSSC0sBZtplgDGrh0lyYWsAM28s\ntQHGrN7NNCAbth4w06qAMauvIsmFbQBsA25fK2T1QZJc2COAPZrbxqztLMYUlw3bCNhG3JhV\nyOrDJLmwxwB7XKjZaqaUbNgTgD0pwNYwPSQb9hRgpr0SMGb1dSS5sE2AbcIDJ4Ws/jBJLuxp\nwDJ3ShWy+nqSFFiRQbJ/Vz0wEI9F7kSE7DWQFys/k/ttWW9uEvp/TjUrPoB3HR4dmgW/o9Dn\n4Og3kvHhaB97gik8Q29qzz3WVM1J3rqxWO2N6xFmWIEaWRs1kgjudQmuhGMxIY8yF1dfaihG\nAX2ct+LmHQsPD1vvGfApe1eSl/VuAl+g3LiiU4hQZVYpaFXCQNGZPJavQrk9qc0irfGeeCqh\nYh6/1XSs3SJGrtWCCFRhD7QlzYHMonSBRHwFEmmyEsnNc82Xy4z8uSwslMt5BXJpsHKx5qLm\nS8OfP41AoTQqC6QRsNIw55Pmy6IqfxYNhbKYWSALtSPz2o56f8pIqt0RVVvepanxhKq1d96g\nZfKY5TL8y5ujQ832nO5NCzlUu6zr7bGvaNuxlC3vylOqsNwcau7UAjUudH8s3h/qjXSn+oUm\n8a6FJw+svS0e7o0O9Vt9ZajBYNAFN9uOewWNMZJ2CFrNwqi2yd9OEWtdKlC2rEVooUzJ7FzM\nUDLcbX1NhI84x2Pft811I7S1uRD8yWjvg2Jo3qGiLxEJ9+LPM0ILl+MLJ5mtmS+rHAoHjWSC\nNbWB7M63Z7c1nWdcePKNqM52/CvmU9kTj3WYb5VGDIf1/D0D4US4J2nN5PJwwcWOuqCErveb\nXZ+ZD+8U7EJ7sNG0S0NXWUEcu3keF4ILtU0I9ZqwvJ3fp7D/d2A5AL/X04g////qmJ2OLVZs\nN9SV0A31Zjdkv+TRkvnaiTocjibcemZBkT0TyMYNZeKGrLiOnbVQ2CbrS+s0brcW6UuGVsWt\n+b6Z9PnAFwnLgdJAu9IucRuEZVXOqPOJo67x7I86vt8WCdW+WE6154rVbjr71fY4VL9ZqP4l\ncqpfI1ZfO3eq73FoBt1T/D6nyjrqZz+u4xRusae4XcwshHHco1zKBZHQJfPFLlly7nQJX+3L\nSu2K3KeCnMK1FNsV/zJ3Zb9xE2E8m2RzkYQWJAgViFSwpTwQ2nIf4S4tgrQlKTfUddazicmu\nd/Fmm2y5L3GfpVDacpdnJMQDhcIDDxQkhISQ4AH+APrcSOGFLmN7bH/z7Xxee7OCrGTN2jPf\n9/tmvsPjmfFYsFGqYqS1qliFVXHh8lTFugSqONlRhbx+WcVyfUx1rAhZKTWyAcWti5JpJmyL\nSdMyNLs4BwWGjC+WGe8XjA6INvAHCA76qrxkaSYSmMSly8ckAHHQLJclMI2OTcGQkE9+OWrl\nK5pUXzpbrLhvBfoyQqZXovOrmgTpmbKLlZI2WSVwrkbno83iGHzgwLSyVH2uQefXNonT5e0a\np0Dhvy9qSFXXyfkBl9RqVPB6omDaLI9YOiHLmmAPJy3sbvM2mDZnWFWziuFWg22AWmWVkGtf\njukeJ4MosTLEDbfJBAWP1NQQ38LrKhGg4Z9qWnxoRs/HiIs3yI1HxsVBUTN1l+FGZCE3JbOQ\nL2HlKJtwFqdS0rXbs1ROYbe4omo4R9aNCSLK0Di7QLdt3ZpyhnQat+7NMVs3XSgaDI8zbyLs\neoCPOlQKVlnziaJqtzmmBKfg+562HnG6BXBaqm4XxFsV8PzWJqNKt6cQRsDchs7HmoTpKrM8\nU4ZIVfDaglC3RqOGAcQZHwuQlHLoVlUr5sRVrO5tMs4hLNftcn43kuM7Sq4VgfsL4yPwx5vk\nfxbY2C+r57O8QzzLjAZgE02CrQJgYjO+BkjbW1CtcN9XBRiVQiHuQDZ1Z5OW3GmwclaB0aZs\nIs17k75sFsy8bpuzsDOi4IBD9BFKinNVN+AyB+HtA2+/7YCmd2SGsZLmTck5vz430zvaIpru\nrqaj4AbE6W7AqRVR8B50fm8yvocpvveh8/uT8f1exVcV6B5AODuicY6i8x8gDuSryXzqAtnO\nZB75I4Wjt8KzTdusuA7eIIxMJgP7CQqNU8g3K/MNu8rURM+ZKudjtl20Nam/BEEMGeTXWnLe\nNrx9qjCYjPEV1npOzv+6cYQPwperomzZBMiNUog8JSPjCFLnF9OgfAK/I0fJTXT+YDK+YQjN\nVfL5qGH+GXSeTwb0O1WBAjq3WsDXea8MlftF5ejO+3XE9c+pwFBE8paSyUs90nwmyn3j5+MH\nLK3Mp/ez1PToUFDK7bdUChqfrLWLpSpR/rS68iWmz0T1PGAbPCTLHj5UmTlU0qZKjo7Kku3A\nhqm6hfv5+CW5drRK94ww9Q93le5Yi9YlBWCT3lKXlCGGx9NeunYLP7a1CCwmkz01xeIsf6ly\nm0hTLVyqHBz8txeHubKs94ZhEa+OqiTzqsOEV9Xh7CLsMbUGFZyLvqP8TIWH+Wi63yBd3BTy\nr8r8/8AV3C3n/4nwd9YIN/N/J2K6Wc6bOfVT/3DXp4+3yMICsCnhZtNioD3tpWsn+LG9RWBL\ncTNT4WatWqQfHCo3ezihmz2C3OzRZG4Wu/f/mJxP9gmHVH1CZxMlgNMohbiPy7iRY3bn6Ia7\nACvciHM4WK4Bvo2ggnkiutnDu2zdY+OsPs8MxO1JwG2pXUKVMp5CSn86GQ41aXi2KBfMED0j\n811uM0TPyuKFcHZBFgaPLsfqSi6IfTTiWt/5JWY7M7yOBfqfPeImmK8Om5ZznRnDzhMKzybA\nnotpg918YKmSdxcDQvLnmzM6ZQRw+L1AtO5J2TzTLbhNXkDyIkEyaDBecycmqKheIqj6xZZV\nKpqXCZpeT7hgD8KA4BUKJBCtnuZVgqZPCOZRtIEScVKos9cQ4uvRujtKmUR6bpoPohEgbxDV\ncN/98QuqCnTwAWh1EelxHVwnH9NTcDFs0hRW5k0ZMNIjBzbOl4r2rOOQ9GrcPTJD0uucr+Hh\nybC3iIbrcvxdxzPSe4nSPRPV8ojgjiXEVRrMVDOFjKFlNmfGMlsnAIWqbm/HrBufFrY1Rf3e\nobzMfR/B2VYWEeyTCaRhrwWxB1BU/v4G+QcIgVY6Hws0WE7ncdHdmlc1LgcJOom+gLNcr9aI\nVlQcl3EEPEhZREnn49zrZI4LUfdT9w1YkMmgWDiFIrwXU+n97teLtOBjUJDH+zKPvxGPRXS+\nWiUb5PcB0SwDzPXQ4DuPLlEQZt2rjPcH/HXeH8pseoQYvSrnHxTfgQt5Q8ECS+aWz4dLiraY\n7FhsnqVf149kIU8IjjW/U/VxgkqcLiNqurFLt7JMXjWfahOki0tmFdbikwYag2bjB99jAt/v\nT/5TI5gfkpn/hczpGEX3KeVdNuPPGZbMRUL305R4weW4uDYAnv3+i/2R3DcABsTRL5aK+198\n7RBrr/3dNDvFU/h5AaAn6HHxnx/7av9HJTjwu34rtoNKDSq2CO0QC7//Ze9Kn+O4iviuJa11\nWJJtbEkOSCsrUZhwiBTYJoRgAlQKm8Q5HIcEcjiytbYVS15HlhMZTOIQ7vusQDB3UQWfoOAj\nd8EXjgJMAQX8AdzHhwXCVTWZ2e6e7e3ZnX0zO/vekzRb9eq5d+WZX/9ed79j3vTrZsrRRvAr\nmWJ9eD2cbT9pRDFSpBsV2cYA9kA94jKg2gHSDbsv1IBhafi/N5dWTi+Vzpzxd67sPzVXWmly\nnQ210vA6mwBF3TXyuEdRgbRPZqTVSLvITXsIr5Xgkwj7UBi7alHWke41KHTrblLS0GswmV5x\n2u0JMuJuYcQ6ct0FXtbbJMbyWFpAoNz7jAGOCAsFlwHVDtCmsBAo3zocJMLW29o94rjCp6jh\nCqLhdGRiTOQKG0EOuYJWwBGu8EyXAdUOMHOFxK7waWq4XtFwOvKEJnKFPpBDrqAVcIQrTLgM\nqHaAmSskdoXPUMP1i4bTkcU2kSsMgBxyBa2AI1yh6DKg2gFmrpDYFT5LDbdJNJyOHMuBZfU3\ncYVxtDruEhsR8CDIn3dNAo9wiXtcBlQ7QJtcQpK1meGhADeE3/tlA17Lz5sD/8IcOdaw2YoF\nVTYZ4NgBpl99QSYRxv7WgSZJwPmcC/UXyD2GRYPqyLcemBr92jAnUh6fGFTwjzlKHYnaQyjD\nSZby+Oy9gvvkOEQd6d1DEOuyKckfg0xKFczcFGe+udWEejaF0Tw+sFIJp9sY+xROc1jDJidr\nKDUZS/O4hyxkxXyHVx63geVzuS+SMW7BertgUMcpB2G0PAEXR6PjGIQqDaMxJ8yjJoDa5Msq\nPjym5MNWUGnYh79MdjWC9Q5BylN43ac6eERHyC1bJWSrYIzgQHUc7xGOHzzPWgUTuJGFbhFT\nsym02in2/H8DO/WrgPVGMW3rx2uPgxyMWiasYCBq1FJHD9GyIwEtRaRlHFUt4tLhFNJTxPUT\nTtsQ0lQUtE1aT1sw2IvzfK1oQi2bOoY4g7ydjPXmHYQVlBruINQHeXTDHYqz3zY/icjYESaj\nEyX2YEZ3TJwCOYiJl5uw9I7FxGe4TE3taq3WmHgFY715TLSC0iwmNv6siZhIuLakw1kiTrak\nw4n1/cA0yEE/cKUJ7+5YPzDsMjW1q7Va+4FnM9ab9wNWUJr1A40/a6IfyOdyXyEHvgxrp97q\ntByzGmphkbqb49FxEmuViOeoRcAxl/29dqA2RUCVyPfcnErks4JKw5Hvq2RXV2H9PEGKjlOC\nGztmLZE7x6PjIOEqETNqjnmZy/5eO9DV5pgvUHJMK6g07JhfI7t6PtZXC1J0HHIdcswGaf0r\nmG6fY9NxJnZ4vMZz91cwVxEZ5YiYj7V625Ee3xRxVDyFc7EizsX4HGwQ77cLZPk2pB424rwN\nucsEQJtClXwbsr/BbL2Lzdob7b3cDfKXjLR2yPaVZtoEc5Vum7voKvYyexgJzXsZK7zAcC8T\nf+I7om47cT+JCBhRt7W4JQ6R1axVFTzRxq9fLKwLQ0r+6vrGcoZyOedZaVmXbEk8aoV8ZjgX\nf1Gy0Wv/14AcLDa+xAZdo0MgHQzTqS0d1wpKXmo9JfwYG2Jlexus0Kr1OBoKZ2kTsnKdYOll\n9rPEDuHplO3sFay83HpWakcGKQw7g9C414ReNg07iawBJIvyyWxDAil/zAYMIISXhpzXg/x1\nIvQVJgjN4+kRdOMBhQ6YfRKRPhAe5UQV5Qah66/HJ5QRHvtNl8Vr7QZmo8dGkPWtjCx1sr6R\nkSWusT2d0CM/ifTZnk4o6mRoCrzt2syA1l/fRfcdbk/nRLoOt6erso5yrN3k1MgKnufIfeCF\ncKv8i/ARaBfUzoRXimn5gMTHZ0gVTPYRZ1bwKhNK2OTIcQZkGVmtyfpORpY6Wd/NyKrNa79N\nZLwS6xsEKbuRlN2ClJ1Q0iFFBtio02090N8j0K8WYPcg2D3iecYUlHTA5vF8vAoeq8sBXIMA\n9uXqHts7V3hlV1oAKngGbqcW514DcrA4d6MJJaVJRC7OsZN3O0XKTYKUA9aTUjsnOM7Y5CYT\netkUFNN+cnSzMJxbTBAsbaP+dOeQ5QQnOxMZE7nw8m0XhqFuBL5TfF9Ab5Lf9yI58vt+XCaW\nKcVpv/+tIFdP+dFOIA/6t4H8A5IPgvxDkm8H+UckHwL5xxz4hHimTNOmblb49wVW+Pe9rPDv\n+1kRn0QeNaE4/exAUfbc9TBVXo9LIGlH5DtADiLya00ElFhdOR2UrtCR/8RlWmrXyqaOPAsH\nazscqCwe7V/vThDnAVVGVmuyfpqRVRsR/8yF+vtEyj5Byn4k5SC+HtyFR2Pv9sretEjxb3xX\nB2f+rwM5GC683oSSsYYL4b1Knd7ldrfg6B77OUq0OnK3Cb1s8vq0x+L3CsO5zwTB0jbiro6M\n5TqzOsJXQcjTDoMcWgXRQhRfBbkf5GAVZBbkn5N8BOS6VY+xDq16sE8ijxhTHD63UbJpS4Mo\nUmAhN4+vJ+fw3z1oR0dB/oURg6cbFkTobZfoQvPG7QiJcyD/MiMx25OmbFhJ92yXQA72bB8z\nYXQegF9xa8/2bAefRLrpXuXx2u8SGdCdwoAOwbWqR1Dzufb1XrkxLQPyb3yig9PKeZCDke8D\nJpSMNWXqO3Z2YUFmxE2blpOCloWMFp+GRUHLqdVJS8QcO1hEWjShmo1z7AiyLmVkreO+UcFA\nglwqJzMDyQykgYEEWbDmMwNZvwYy2sYwhfIE0XOB8VzzPEFlkIPhy2kTRhdr+NLoLfgIdwrO\nIimb0Mwmd1Ig69cZWbVZbnWvggJpv8lIU7ew32ZkiWuMptO7Nfsk0ms0nV6v7d7QN5Tj0D73\nucJgZuFa+VL9tZ0DXrktLYOhnbpkKD6gJawfxPqsAHYELDc/h0v+PVA7N3vl1rSAbZa/BjsL\nfEhxn54vm1DBJmcksrbmWq+s898K7LcezD5FuvQhuQ+BHKy6P2yCbL4BdQXkulX4rQpRqCei\nsE+ixtuaXsSJFV3wxbX7ZXQ5BtfKz9df2znolTvSahQeXXII6A1Yn8P6TQLYcbSWE5hUtAdq\n53avHEoL2ObQr7Tnwod0PmZ0OW9CBRuji8oD4UdA/p1rkjQbHwj75LwR60cFOSfRW/16D3qr\nVzt3QkmHHOkUl8/OVY+oPjJfXigfnz86uzAZnCm5WFqenZtdlgnDFxDng2gMXVA7d3nl3rRw\n+gQ9FtNFHzMB1EYX1X1WzptBDtZfHjfRENKy+9I6K6foMjW1q2WTfcU5K+ctjPXmKYOtoLQV\nFaqUJhm+ZWflJCtxCP49OfAFkP9A8luF9Z2Ba1frvXgvr3YOQ0nH+uL0aEYA2hRx4jzZz8hq\nTdaljCw2wHybIOEskuDX1yEJXu3MQkmHBBnprzpdWvJP5vFH4cuzK+VT5UV/GL5wbnL+lP99\naW7yzNHykvezQPsQoj2Pyfu6oHaOeOVEWmh9mt7BbKtXDC3H8cZ8WEhDgHeC/CfXBPAkgN8F\n8p9XDeB3g/yXVQP4PSD/ddUAfi/IfzMKuCAAc6C05PI+kP9uBKhcImnwSRTg01hyCchojq0t\njL2tB+MZ1gzrmsSa9vua7wc5WEj6gIlgJgdo0e9r9jx8orRUajD6dph+3ajPB03oI0fL7RqQ\n6ug9yrjX8uuHHh9/JAN+O9YfEg3/CDa8X0/iPbzaeQBKOg0vTXXwhpXT5aVlf6IxMzMDSP9J\nSD8iED6KCP16BhF6tXMSSjoI4wxwPgryv1wTQG0e4MhmHpo+N704PXd4et/0gelbMF3cP6id\nPwzyv0l+QtB5Ael8vN76nQWvlNOis1m/Qf0Ef7Wdv86+H2/ai38/gEp8DOT/kFKfMKGUf+Mn\nAch/CcjHQZ5yof6fqxAD+evq8hX1du1NVwykBt7UZKpFA4RxNkDI4d/0IncXQf7/0+3dzW8M\nYRwH8Gc18dqQkGwPJAiJuTRsOCDEAeFQwgEhkjXRERc1FksQL0kracl200RJVWmrDsKBcEDC\nQTggIdy4kBD8BU41MuaZ3Z9HO8/3mXk6M022ySdP5jL7e77P88w+nU46ThKDCdxLqyyp3iQK\nVP02Hst7aeIV6O/zj0CCVi1B4Rz18k2/+BOq1npw/xWmLxnGfvkD283bPmGAW/kAu20jH+BG\nxgzbo2eAgRlYKTSRAtM0A4Gw5tfCqk7xYT+Ma97xb//4uhBOGw+nXbiWHWLMOKorHH/0Rnvv\nsv9Wbvo96/9DhRve8WQnicL/27zaBcs2C1beNgvmAf5g4xRa2jh917LjKF6REplH6VpkGeYo\nTRE3tn4htg4eW0mIrciYcUJXbFGW38Aoyy+WwsVsG/7NNm82F82WvVYzCXk8r0OV9ZfIRErT\n+lOaG5mMn9ugkFsnz81tczy3HGPGSY+e3ICB/ZlogWkaWCCs4VpY/BxuCDeFEMo8hDIPss5r\njVMePSGoTOlECkzTKNHOd/HOu22Wdz7LmHHao6fzbupD1Q+e0KTwC/VEwFTAdMBMQBYwGzAX\nsEBiUQSLiSUAWS3LIlhOrACsklgTYC2xDiDr98YImohNAFktWyPYRmwH7JTYPYI8sScES2K/\nghbiIOCwoEjI6joe0hlC9hnnRtBKyNbM+QDtRAd3kT/v5SoRnRJdAS5xJUE3d4X/kcjVQ1yV\n6AvQz/UIBgCyTG8FuE3cUXAPIMvjYYBHxGPAU4nnxAuJV8RriXfEew0+avA5hK8B3nI/wm0S\nK/usOsAkwDTADMAsQANgDmAeYGFMcoClMVkJWK3JesCGmGwGbInJDsCuiMwQ9o0RG3AkwLEY\nnI2oTZMLEmUNxHNelujVQDznIGBIk7sK7gMeaPIE8EzBSwVvFHzQ4JMGX0L4FuB7FXYT7Q/e\nl1c8AAABmXicXY9LT8MwEIQnqc0j4oDE76CGAOnjxgUJqeKQFIkbclITVjhOFZvyv7lwK1VU\nW334Ys/nndndtwTAAIxFGHAA4K/zp+sxEF/1wt8xgEsAERjOAUyEa5Yid81ylaoi60Q+fH4p\n5o+z2e3oLnsoxzejSZoKR40UufhRVH+695LaYQ7gD9sTwk6UWU2nGcDiPcqNbJT1fWMPzz5I\nq/4j6LajmozUANYAfv280ZHvwnZVb5WapPXs1LNt3/j/aEVeaWmtz018Jg7fB0YeZirJLMjU\ndrdepdugEulcR+W3U4EwssV9qNVt9aUWANYbZH8/IgAB6kp4nOybe5AcRR3HZ+9uL3eXHElM\ncnmHy2MX1OS8u+QuyZmImpiAEnwAITziZm539m7I7uw6O5fkBHyURcQnpeWzLAwPI0r5LMtS\nEKggQQRCaWH8S/wLSykFtcooZQFZe7a/M9vzu57Z3bkW//Gqfvltz/x+v093T/evZ6YnB3o0\nTWvXOjoSWntSY3/Jq6/as2mbpnW2a1pHGzuQ0Dq0bqY7K3bWNvLMupeVuphcXHPVEo9w7Unq\nfZqW/gvxnsO882bBYD9f0viff8qwjoyNbRslHslsQa9U2I8Fskpo5yH/RrQ2WHQ4ZlH3rIcH\nhoYGBkngPt2ySo7umCUr4+jjBYP9e8zIufaCVfuE4dTCBisQ6hysO2uSaZOI88q6bVjOQN7W\ni0a9BQHHNRXHnso6U7aRKdkTumVWipmybtoVDuMR51QlFWvo6XK6qjxCdzWEP/+oYU5MOplK\n2cg6tl6IAM40bYqwEG7jZqlQmjCzkQyZcVOUpZVsifVE3S+TKxV104pghbs0RVw2w/2waU2w\nEBHICJ+YrSxPThemii210nNpitg3w92bpWHAUI+YvJKdM+yWePCI2aN5vWgWplvqUc8lZgsd\n2xw3WmohPGLyJgxrqrUrCI+Y88JNGKZRaWle+D4xmUd02zSc6ZaYvk8YsyZLmJxg0stCPe9m\neXdI1OJq2jp3PYJ2F9c0uKuYrHZXPugunHNXiz4m3fCTxemF7VwhznzoBTh3AeIsjIizBLaL\nhDhLoZdBL4deAb0S2rP3ZA30hdD90Guh10Gvh94AnYJOQ18EfTH066HfAP1G2PZBNgrtGwBn\nCDZvwjk3xjDsRyT222G/VTg3At4O2OxE296C8iUovxXlt6H8dpR3obwbscfAf4eEfxls9kr4\nl8NmH+FfQfjvJvz3EP47wX+vhH81bK6U8A/A5lrCv47wryf8Gwh/P/gHJXwdNhkJPwcbg/Dz\nhD9B+JOEPw6+KeEXYXNYwi/D5gOEbxN+hfAdwrfAn5Lwp2FzVMK/GTa3EP6HCP/DhP8Rwv8g\n+B+V8G+Fzcck/Ntg8wnC/yThf4rwP034x8H/jIT/OdjcLuF/ATZfJPwvEf6XCf8rhP958L8q\n4d8Bm69J+HfC5i7Cv5vw7yH8bxD+18E/KeF/Gzb3Svjfgc13Cf97hP99wv8B4d8H/g8l/B/D\n5kcRa4Vs7fop9P1gPQCbn4D1MyHeQ7A9Bf0I9M+hH4U+Df0Y9C+gH4f+JfQT0E9CPwV9Bvpp\n1OVh1GEx6uO26xkmf+dreqKK9W+N8FDr9v0hQUNSGS6uX1u14VNsRzXqwdWHjaJz96Cc5Drl\nAsuqYZcCcBPuPJJcp2wmJ1XDbgHgDgK7l8njqmEnADiBOZDkOvUEF7WwOwF4gLTsSSZ/UA17\nEIAHcZeV5Dr1Ry5qYQ8B8Chp2Z+YvKgadhqA08gfSa5Tf+WiFvYYAE+Rlv2NyT9Vw84AcIZc\ns39xUQt7GoDfkJa9xORV1bCzAJxFnk1ynTrPRS3stwD8jrSMeaeTqmHPAvBssGXpTi5qYb8H\n4Llgy9Ku9KqGvQDAC8HRmO7johb2IgD/IC1bymS1atgrALxCrlmai1rYqxyQaCctu4jJJsWw\nxDzA5pGWjXJRC+sFbBFp2VYmO1XDVgG2Kpj103u4qIWh6xLrScv2MtmnGrYRsI14/5HkOr2f\ni1rYJsA2k5Zdw+SgatgIYCO4K05ync5wUQsbBexbpGWHmDyjGnYfYA8DBEmfZfK8atgpwE6R\nof9nLkpgTQbxD889OlkqGFfSjb0EnL2NPQ06Ud/Ym3VVfREDJG7CU6Z74hxOdkoM23ZsEizF\nM+25/CAJkhCCnMM5adCxsZCg3Y6Zm/Z24GQGncUptilo4Oh5AmwPAXaxV82Zslk2iH0HKSeD\n/m+uBmuxoxpSq66cWXFMK+vg+PlgnM4wv7VZ3cqZOdagTH1Xke1zuls3mcGhw7V9tJYcrXKW\nOZZ1JxZxuDJVjkWM41gjbmb/xiJGOG6IJG7JmTbbIBaulBgjsFOh53Jsm7B/HduI3uXupZl5\n08it6++R+J7DFRdH05yYo6lTz9ql2l6fSPHOJgeypUIFiC6C7I5G+kF63HlWMQpGNmygzCnq\nTnbSoLWQ9dNiWX+H1L5jIG95le8Jm635KTaV2Ma/wBRjJI7B4WVJ5egrpzYh3e7CazVPQ1JF\nLkrSrQ/bjb0jV2/Gb6ZTFhclsCaDjFZnrjm1PZJu/O7mx1MlRfXyhf1tE8dgJ9GtTIse2ygX\n9KyRsXScIYNyXzVikPYGJrEQQBIoMGAWCH21gOvagFHWV1oDLaaYucG+2x7WV2xxHiKu80iq\n6I15GboLRt7J3FiqrU1eHcXAF5Dy/NZAl1RD4i4g5YUxG9Dp5z1KiVqq07Ikx5b8SfOwMZ2x\nSuyoYRulEO/lFb1YLghfDA0O1T+UkTmsmOEwLHzn0hxic/0rFZnDshkOW/yPTJqzH/E/Emmu\nQqP1jzyaA2z1v9Fozn6b/41Fc326XfhGQuaxknoMDeJrBV1cGD0tTrfXkdG6qLXRujNsFiwm\n5SWKZlcfKS9tLe7+sLjLSHl53FmbNwuOYUso7tJQJb2/Ini+fuuwlhiuDDFMmpUBK+wGc1WD\nDKAJPmR8XIN+Mrwa+zHr+UWaJET+Ct+C3dWWrFJxmpkG0olnuVoWVcwkYtiVkrDDwaQTWVsh\n3zSq7eZAZvIrIItaT0pi0OWSoFvE7BUZs564GsUcETNcZOuF5Nao9aOBNBhZ03oGbFTTrWKq\njIxZz5KNYm4T02nkmBIzaaMxtT2YdD3TNZK4M/Otfw1mBh4abDI3ryI5aXVrOWlvNRj30iri\nrCFxL4yOe1lYrksenWSZRFJ50Wgey1DZSd3Ws8G0yGzf5VWoP1iBy0VgG26YtRgPUkcR29OQ\n1F1clNwX+7BjuId0dQq/mU7dzUUJbDYPUtO4mdag3QepexTVyxeVD1JJS8+YeRwkwyvyGUoT\nfCS+geGxWOiZxVzXhoeyntGa1OLEXxvssajHp2Hiuo7M7fWtdb70vov9XUXsDpDytaR8HSlf\nT8o3kPJBUn4/KWdI+RAp66ScJ+VJUr6RlAukbJFymZRtUnZI+QgpHxP71dPiddsQcoPX7b4S\nzhnjUxPBgIERN5jVC9mpgu64rwHrXyz31z5h7tedfr1Q6C8YR4xCpX9gYCD0zWCqyXG3iH8b\nXTbsTC2q+5U0CZWOTul+qLacN8NfJmd6/HW2EmIxhz/2hJ1O1moZFp03weL/9UaLsbicxAp9\nkryl+xUXJdnDh30Tb3bux8uVbq5Tv2bynCLYbBYX7+MnDTqh8OMnX8IWF8lQvU1mN9vn2hhx\nxv8f5zWJE7Zu3RoyXrItHq8PXN194C6Sdz3HRTzVCvCfJc39eBQvBjenqJti1VOLX++Gl6tt\n7c4YNRWOB9JS2xW7BStt9vX/b/X78f9Rv98ep35hvd1wK0Rr3AA/WOJmiUFEghFuMtgLC2vC\nDDm90D3NbtAc9qo+YxsThrfp51XIj8M2u9kTsPUaXmgR387qqRatOgc1nss7Vc7llkeXFt6k\nehXH/tPelQbHcVznBQmAuAgsCICUKK5AkwZEkyIJ3pd46L4syZYoUZRELlfAklwSWEC74CVF\nMq2Dhw9J1n1RFCWRclk5bceHLNuKZMWx5cSpRPkV55dTiStlJ64CkvywzfXMzuvZnrfdM292\ne3eGRE/Vq97evl5/772enj7Xq6k0rtJxLznLOKpP7TR6/Xi8rdboVI+Wx+ETuSCE4VDpQ2zT\naKl1KLQO82mVOSarTKuZycbfRvjHi/9JaTYIn08dcT5nCS7Hgd0jPwU9cNYjB+r5SGWPXMbI\nOGza5CB8JOci9xbza9SaEZB+lM5z5if9KG0w5jVG4s6Jx3HY8MmlfzTn9D8M+dktKc+f12zH\nJE6/+XRe86jHRIlcziiYxLUAfJKOoiR9SyCaTDzzfYhnqikemFmRymeBL/k414qNw6bdUuXj\nOW8kE5DnvLVQQm5nOvgQ0VKIJhPRQr8WZM1SSSW0yJeEjMxQ+sWqLcg540+0IGciogXxSaji\nWQbRZOLp8yGeZlM8+fk+qXSW+JKOcznhOGxmL1U6HjOZMuF4LMkQykZ+UogP0SznMIwIsFzm\nVzT5aVOpaJb7Eo2RF0q/QrVoHCtZiKJxpCGKhktBFc0KiCYTzUq/jZo1+SyVzSpfsjEyQ+lX\nq27UnKuGiI2aMxGxUeOTUMWzEqLJxLPGr+Xkp/Gl0lnrSzpGXij9OtWW41iiRbQcRxqi5XAp\nqKJZBdFkornMr2jyqyGkolnvSzRGXij9BtWicayGI4rGkYYoGi4FVTSrIZpMNBt9iCbfmYYl\nJVLhbPIlHCM3lP7yMoTjuWCG3plGqYidaUcaqojWQDSZiK7wIaJWU0RscY5URlf6kpGRHUp/\nVRky8l58JBOSxxpRupQKR2fRpbSkD6LJpHS1DynNzX+VJpMj+dluYyCreMpbJrpriKLryJ/q\nCAc6HkimBBPc13JZcVmWvGD2OuS/3l++0oWtNyD/jYr4/TTy36Qo35ud+WzIofBbkP8z/sq9\nXVbuZ5H/VkX434b8m/3la6tkUyqdTmbcNjTcjvx3+CtJuuR6C/LfWWINpiQymUR6l2xT5Fbk\nv6tUoDL5I0JdgLob+e/xV5JUdbch/3ZFKhRH/h2K+E0g/73+8pWaUj/yDyjKF2+L3ako313I\nv1tRvink36Mo373IP6go3yHkTyvKdxj5RxTlex/yZxTlm0X+UQX5ch075j/Bx+P+f1Xy/0nJ\n/69J/j8l+f91yf9vSP5/U/L/acn/Z/D/rGWO4n4h4LvPiZMdv3ZkKHHQmbfbl8ASetSl9KjL\n6FGX06OuoEddSY+6ih51NT3qGnLUJX0oKp7Y5LWhLp1YlGGbcJogIot8lnNZhqaq7JeoSs1C\nFPEAsuGD7jYs3ePQ0D+cHjUm4SgbzNuLABHUhs+EJVws2rRpf6gN9/fvy2SS6f6kMWQ+nLVm\n7sdh3Tpfx/v9tVN28Y39iazxmbM7mUb8sYwfkKH+EBeRi/AsKult5J8qaTl2iv4fh4NPufzf\nzZVWbquk3N2ych9UU26bpNw9snIfUlNuVFLuoKzcz6kpt11SblpW7mE15U6TlDsiK/fzasrt\nkJSbkZX7sJpyOyXljsrKfURNuV2ScvfLyn1UTbnTJeUexP/brfeigeTOxL5Btos+Cq+Yxpzx\ncGkEeW6W1eUxjmfTf8RZB3KrWzuQzPYLymD1duFtC8oztLtx7DfMInO8Lp4YHIxYj+h9z2VQ\nLwP/qBPs7+RQ+DEknOPuwsGv/e9KFMzxmp9WPAbLpRJx/QUPrr+IuP5SBbhuQ+O/ApYFWayl\nZL0gv8iB24Jk3dKSHPAemBWMa1udqGwqk9qXjfdnU/HRoRHA5ctOXOzUM0eNgaXszuHMUFFq\nifHNlJcnAUTK6Ug2uW9gOJ5Kp0ZT9ifN40iiT/hrJL4n0/8naQ1poYfWK45AWpAecQfMmIQ2\npgJG2U5ykQaVUOL7orbPJd9CVRd7RZjnFWGBR4Qmc/PHvqFkJtXvZPpDiZm859GQWx9ImyKO\nRxR3HE7GJ8ieCvSHbrxx6X7iJ904nK6vkM/3PTB8T4S9ycfTLnxE0DuoSCYR53NW4PJlPeMs\n6zs5oh7Y7xTLqFjDCfNN43DXAZfz3+F3x3PO8Jdw+PMu4dz/P8bpXnCG/21OhBK34pmS54su\ndTGX/SB/s1tTwFycXvD/2zINeckDu5dLwE4lZhXEqlmC1Qd+8jExekXSVk7KoAOd8JFVa5Ef\nbzEXDpdy/leR/yTyv4b8p5D/deR/A/nfRP7TyH9GhOA4XHEgQqQ+kzQGdtBJWm+J8K6BHsIY\nxJ3Kre+vxhUR+RA+1HHeKQ4snHU6Bi1nFxyo0ITuv4rB2afsLpFem0drn8JY4VzZ/Lxv1evN\nGI/5vLirHlz+Ai/TbQS3CdxmLu8Yd3kXAyEKINQiJagLAgxfSsAfLctQrIWadnI1rLPc+hxX\n06rXjBVYe7jAGJAwdTR5cCSTzGZTw+ns9emB/B4/UT6TCiTMp8XiwpEHAaxNGiw6WJdrsOhg\nXaHBooN1pQaLDtZVGiw6WFdrsOhgXaPBQnnEivMQkuKnpPoyXidXl8iY4n6t2+H6rE5NhyPU\npyTMmojyLcXQuiL+v3S60ZdON3zp8F8yLWCk9Zbf/pKZEoTR+vqSKVxrwDBq84ERO3+Px6IR\n6t6AsGgMAxbOKxZwKHe9Ag6SXafAatRWXqtUkqW0lde6kK2nBk5Txa+rT4HE2etqDL79O4Cg\njPzplb/jLHEy5NMe8X9h+BzQlt6CteXHGfQNE8WZhOeGiXHItJ7z13B+wiThrTnI46zz//Lu\nl+AbqBrUQFVDkv4aa+dlG2NQLqEXeVOOi1/1OoapFxn10hBWSDuxQa93p5Iq2K6m20gFxdFQ\nRit9s0rV+wBYbbin6upXA+eAs7y6KvMxU1K9utRoXalaaGKzlm+nxqBd4xWmGrd4F7fJ/OU6\nY9DKltJdZp8Uou4yzMPbb6O2UNTc7yRITwlolDqVFAPUYoBWDPiLQdeSR7kTUI0ilNtDj3Lh\nYiQ/E03RIOoVpvc8AawtGiw6WHdqsOhgbdVg0cG6S4NFB+tuDRYdrHs0WHSwtmmw6GBt12DR\nwYprsOhg7dBg0cFKaLBQHj2VGU8q9ykJD1YXBWNQKsm3TM7nqUFmpH6n8vhxH36cpgkMeprl\nt8dpOoIw8CgKJa4LVz0y2Imw6Ao9FqKRwXVlaEg3GhnsRiOD3RHnyGA3GhnsBv7YyGA3aFgM\nNKsbUI4Bv6Z/OvhngP8C8F8I/pngvwj8s5D0ukFa05H0ZoReesUjjqqt+wKEyYXhx8S+6JrQ\nLWnKcfWser3C1C1hYDUDWDOBl04A8BNwg8YkaCAYv2x7DMQfYIBeFASgNXAxOCu4mfA+556S\nQG/29/723SdpV9NPLKluVZ7dplhsMse11lVXsDBZLJsXIYC2S4NWAG0nEbSUBq0A2m4iaHs1\naAXQ9hBBG9KgFUAbJII2rEErgJYmgnafBq0A2ggRtKwGrQBahgjaPg1aAbRRImgHNGgF0PYT\nQTukQSuAlj9xkOW1Ts2n47nylIQ3w2jyxCCyLjF8JsKUSFfE/6Cpn72Gsyy/PYgaC6KhiqLQ\nxsrtNXSbIrkYYdEdBizc9xrWHdidzCRpL6MHclw9q16vML2MJkID4me75ewQbrc8AHwxF6jn\nlEVKlNIu7CC0IqbbA78Nt+d1i5QURsxEuN3yEIAfAdfcbvmGIr5sqsB2y8M5yOOs83/X7ZYR\nLg1z3XZYVkN4/l5RdelEPLUT2I3QWmfp5sqqVC9MrXNUoA8sX+qM0/m0n/IAKAxrBDotN98S\nKmsEAu8PcE/VNU7vp/TuK02kud4aOFl1DPoppjsXGcmDYBgnkJG8ZdCPVRkJbgr5JVFj0LEi\nvFvsyswJohJhercQwLpTg0UHa6sGiw7WXRosOlh3a7DoYN2jwaKDtU2DRQdruwaLDlZcg0UH\na4cGiw5WQoNFB2unBosO1m4NFh2sPRosOliDGiw6WGkNFh2sEQ0WHayMBosO1qgGiw7Wfg0W\nHayDGizIowbuqRuDrZuG/xHm/yQC5yQAYrpXwG/D7fmJRWrAiaLQPv5e3qLreBOjsxODg7MH\nk/uTg9nZ5jW+gtPKHzNL/J31+wj3+yj3+xj3+3gOLbe5AjSqEzTKz3Ibtu6PLbeZDmvc2iCP\nqR5Lb05DfqfRSec/t0gJ6HZhZ4C578JcW6Pl9vyjQb9UVBgxE+HSm3cA1Ai45tKbf1fEl01e\nS2+MZw0fHgHt4ePXRJxLdSZFfC3VWZ+LWM9ZlM9k5K/1l++9snzrkL9eUb5TkL9BUb6NyN+k\nKN9m5G9RlO9U5G/1l+9GUb4RaNl4vWtzhvfj8KhHeLsz/HHExxGeD+zy+UzzKKfDI7zTnY+j\nbnyI+OnyKG+6M3wAh8/wSH+BR/iFCuoTcanfTI/yL/IIn+UMfwKHx3zwz/3vaG0n3XwVFytC\nqNXFHlx3e0httkf6T3iEzynDGiKE+s31KP+THuE9HlLr9cG/TGqe921EXCp4iZOBJzGD87jw\ncVgP7dIsfgX5n0IVeDoH+cz3AG6BR/il5TWDzzA+FnqUs8gjfLGCZoPPr8+jvCUe4Uud4c/i\n8GU++FWmcBFBRZc7GXkOM7rCo6IrPcJXuVf0uIeC2PmsdubzPOTzAlOgNR58rPUIX+cML2oh\nLqumwIwMXmQVW+/B+AaP8I3O8KKb7ze5V+yYrGL4hvuIvC4vszxauS8UtzTYdbslvRpfYkXf\n2o2l3ZKu+iyxyQib2vBjU3SWmGpM6hAm9RoTNgJgY9KgMcmXwmPSpDHJ15PHpOUcxKSjDEz4\nIUiGDVtJPxVh0xp6bEQnkboM9T+W42pa9ZqFaaifgdWCVCkGADIVWgfg8ac3NgCIMGr9OAO1\nPQhQCVI/EiiDYZL6GMRhebUU5+WgOiDuKYlnVk4ltgSp0ORpSJM7g1AUgiYfDZTBsGlyx3mm\nyayMDo+6MCI+JdWzw389lWCAuzjsROEZYBj8KSHMmqeA2+By4jDr4nRZfvvE4elBGBLf1Fzo\n0/IvDILhsFn+BeDOcLGaOglNAWoAkljUOWU1rPx2NS1HSXVvV1N33y1F1OO9HwW5s1FgvsWI\nQBr2cTgTGWcsCGPz0xoEwmCYWgP8QWjNK47Bx+8YOlM+Suwl1AOxtoJ7SqphtHzb0G1BgL0G\nOH/M7jV0B2F2fMM0x+eX75wgGA5TOzEGUjbd2VyeutcwMVsKlb2Gucg4e4MwNj+tQSAMhqk1\nwL2GolnrMVhQM4ZWPusOxPndLPRE/M2sTHG5vYwtuJoBVhWDMnqhjCjMsCyDofR18N86GKle\nCwDWgDWPQSeFn6H5VBCWjK1nSnY0k0rvSgmC2s2g+EByNNk/Gs8kd+UBH4PFXipGbudbfrvt\nvTQIQPy0vYEwGKa2dwyW3LG8zoeR2xpYbqhCoxcijV4chML4GYMIhMGwafSi80+j8wtXeU1c\n5lMxlk10xSB1M5eCuwTcPq5w6nXP9T6oRUKtQG1AjPl2oHK19Xy57nkidoubBO+zyei9FoGw\nKaDHyy1/frV11VsBFS/iFZbfbv5WBVkRl/b2eKAMhqm9HYPF+ufTi5gpwDSJCXZHCkPcddw2\nqDno61S03m+15X8+UBONephoFCpGGYlbY/ltk70syIpRukiBMBgmkyV1kdaBu5YrdCKMxNXA\n5h+/prKIYCrrLb9tKpuC0ESCqRwLlMEwmUqrYEfSGOx+Mt0NE8868jvIWgW7rmpgRxbja9rh\niKqnJFymET8aCOT7g6WJXveS6tZEr5sf2dqGH+HK6lTwQdzkpJLq3Fm+npeq93YDeAps9h3O\nfs3jiT5SeQ7KmGC68DVoPFjBdVDwT1UWbGjAo0wD2EzcJYiRd6Fw070Yfhtuz39YpIaRKBaJ\nebDQwPBQIpWezV6meBrm+8DMBwih/zToN6oYI7w87dOcAmEwTC9PLMVZ/Yn0QGogMZqMD2d2\nJdKp7FC8b0nckqvF45GcIN2F2cTQyKA80VFRohn586nihQOrHEmOiZJ0FCXpW2JFz3/uG+4J\nJtt54M5HMv4RyPVH8BUIX4c9/22RGhljvqea1rHXmA00amibB57z+hC4+Qhp3/8Y9L+qOPNj\nHoEwGGbziInMY2kcBCtIMLPILtxiX1Ck3fK4AktYaqH0KhPfAnAXIjH+DET3M/R++D+L1IhR\n+H4Y2X1ocF/BAPAU2d8DM/+M9Ov/DfqDKsb8GEAgDIbZAITvh2VxS66k94JL5OL3gTSqQPuX\nWRCdZLJbBG4fkuHHILePudFBw+05a5EaGWJ+m03t7zcHcGzlX4r4+hfg5V+RbhkF9dap4suP\n8gfCYJiV/yKR8i+P58UqbM2x7svjTi/SZ1lMgebDlNJrTHBsyhRPNP8ChPYLp+b31lukRoBC\nzR/ODCQztuavQHz9G/DyS6di9Zo0VRVffjQ/EAbPOc1fEc+LlaT58rjFmi+LKdB8mIs8xQS3\nHNyVSIC/BqH92tnj6Z1ukRoBYn7zPZ6diaHU4CFb9Vcjxn4DzIwhzZphUEwVY35UPxAGw6z6\nwh7PyrglV0H84h6PS+TiHo80qkD7V1oQvc5ktwrcNUiGvwe5/R61+70WqZGhsN0fzaTuTdrK\nvw7x9QeLl5rJSLcuMWihKr78KH8gDIZZ+YXt/qp4Xqykdl8et7jdl8UUaP4qC583mODWgovm\njWtaQIAtSPNXWqRGgELN35VM7yv09TcgvqYCXx1IsVYZtF4VX340PxAGzznNXx3Pi5Wk+fK4\nxZoviynQfFib8iYT3HpwNyIBzgIBznKOcvZeY5EaAWJ+86Oc2ZFkfypZ0P3LEWdghzVzkWpd\na9BNqjjzo/uBMBhm3ReOcq6Jg2AFCYpHOd1iF49yyuMKLAAWM51m4tsE7hVIjJeCGE13NojR\ncHvvsEiNGDG/raYF7E9kUsnRRMEErkKsLQTWliEN22LQNlWs+TGBQBgMswlcLDCBJX1xJlnR\nCwPbgHv0YiOwItO6/kv6LJzOMAFeCe7VSJArQJArYFSjznJ74xapESTmd25+siuZHMlfR5PM\njhbfSdOE+FwJfH4VKdwOg/5JFZ8mQNdylqH6DM3rLL+9C/H6ICqJheF+hmY2OZh0nBJZzhma\nbmtqb0DY3Bh+bOyznCuNzacRNjedg9jItkIwjERbIW62/BtyQdS3Uo3ALUiYnwmicr6E2TiY\n3Dka3zOcX3ihQtdjLrr+WQTPraGHp7iNVK0ytyFMNocek6ZUOp3MYJ2Zp0Bn2GkBMcAnBroT\ng7xjUJbptoLbBm4U3HY4naIb9ofwmHcBxrcjzO8IPeaiM51Va+IWhMqdoUdlSiKTSaR3JSsI\nylYEyl2hB6Upk9q1W2mT7tZ9uRvhc0/o8SnuvqjWmW0Ik+3hx6RqnwJxhM2O8GNTcX1JIEzu\nDT0mwl6jalj6ESwDGhYThiSCZaeGxYRhF4Jlt4bFhCGFYNmjYTFh2ItgGdSwmDAMIVjSGhYT\nhmEEy4iGxYThPgRLRsNiwpBFsIyem7BQpg6zQVQtTFOHBLBOaLBQHu3EDcseT0n8s7KrtDGZ\noiD2lq37tIJoBREoiL2raVgriFYQgYLYm3+GtIJoBREoiL1HZq9WEK0gAgWxt5GktIJoBREo\niL3bYpdWEK0gAgWxNyUktYJoBREoiL1mv18riFYQgYLY69kTWkEmroJ0R5yj8THIKwbKEwMF\nwCPxpktZQzgHymFrBFvRKH47KOE+y/+nuSCUkWAtf5bjGK06g2GyFgJYf67BooP1FxosOlh/\nqcGig/VXGiw6WF/XYNHB+oYGiw7WNzVYdLD+WoNFB+tbGizIQ3IA/7dzXP7dhyNBPiVh0l2M\nSTWJjL+B9ds81h1EvolPSdgFdad5KQvd5oCh9sJn9nyoeA230G2/5bcXuh0MwuijKLTJXOhm\nb2fBoQ39w+nRRCqdBZYPCOK0F524wNWLOiQjumShXP2p9pCM3zp7POdEnZmt+L30WuV21jnA\nZzfYLD881QmKewjZ3v1hsD33Rab9iWwyfmB3UrTIdB5XxVqo0gNBVAm/+8vVX2pfhNLhEoH0\nJxokb5Ae1CB5g/SQBskbpM9pkLxBOqxB8gbp8xokb5Ae1iB5g/SIBskbpEcnOkiF7rjRFc8Z\nj/X7+zkuf+rV+lV+SsLqXL9a35DNO7xs9ACW68jz5hwY+tYwGLqXgVZy5JnlMRFHbdoi/kdt\n+NGVRlCixyy/PbpyJAiliqJQ19GV2oFktp9mKm/nuFpWvVZhNJW28kylJL7byjMR3RyU977Y\nwozg9oluBASw7tRg0cHaqsGig3WXBosO1t0aLDpY92iw6GBt02DRwdquwaKDFddg0cHaocGi\ng5XQYEEeRpk/zHF5zVPzqVfJpySMWL0mh5dUKng9U/DbJrqCszwm4hiG6kWbRy2/PbR5PAjl\niqJQf4s2jwniTCu+s6sExTkfVm1WSnG+gBTnS+ee4nxREKcNXd02QbWGlaunGV3fyWuZ9t8S\nhPbrd3KwRoLbjgXmXYf9icH+fYOJUfO+wwNJ8+6V5ID7nYdNhyPUpyRYmogiKeWDrAG9Vdjx\nDPwsaj3YyJct/9/kgrAVgjG/n+MYrTqDYTTmBm/NLIm3Bm+N9PO1+15O8LaYyG+sLh99Pa/d\nKrwlt4CBPG757b7fE0EYDG591e42eTKIKmHbLVcPy1kgSAHpKxokb5Ce0iB5g/T0RAeJ5dGl\n9vXFnpLq06X2Neb7dWbg94F+tTuNqJ7rv9agoZk6MKZnLL+jW1R1o6pHxlAu8PVy4fs2son0\nkcoUh13Ia8W3FGgyuGYrfCkoxGJQMha+CJSlE/qFi6Bf6CefWkjXBP5m8LdA/Ajk1QYK/Cx/\nYsbG39qAV12ZDaZacpbbzDq7L4L7ArjPW+H2MtrnwH2Za8FM/0tcBeYh42CCrQWqA2LDhyx8\nClADUCMQNR/2fxNQM1ALULnKTZ1qDJD8vH1+CrJ/i8nwGsv/EfO/gpTya6CUP+CwN6j3Y4N+\npUop/cwVBsJgmIYy/AziarC8wdqswaKD9bAGiw7WCQ0WHaxXNVh0sE5qsOhgvabBooN1SoNF\nB+t1DRYdrDc0WHSw3tRg0cE6rcGig3VGg4XyMCv/HlTedLuh8obb+18Wqam8ifqJQsE1vyKO\nLdW7UJNg7EtGrS40jaMOD5p+DtJCjha70DKglRytcqG1FaJrOLrWhW6oEN3K0W0udEeFaDtH\ncRe6t0K0h6O9LpSuEO3n6IAL3V8hepijR1zoSIXocY6ecKGnKkQvcfSyC71aITrD0Vsu9LUK\n0dc5+oYLeb27vk2gdzn6QQjoe0A/L61TYr/XJyuiFkXUeZ5TnwstF9BqF1pXBbrOhW6sAm12\noS1VoB0u1F8FGnSh4SrQQRd6oAr0qAsdrQI96UJPV4FecaGTVaCvutDbVaBvutC3FNMPQ0D/\nUCDaIMEfAZlu82YAAAGbeJxdj81OwzAQhCepzU/EAYnnoIYAaSknLkhIFYekSNyQ45rUwnaq\n2JT35sKtoKi2+uOLPZ93ZnffMgADEJJgQAGAvs6eLsdAetGLcKcAzgEkIDgF8MC8WbLSm+Uq\nl1XRsXL4/FLNHqfT69FNcVePr0b3ec68MpyV7FuqZuHfxUKadlgC+MXmxLgjaVeTSQGQdIdS\ny410oXMa4MmH0rL/iLrtVKMs1wDWAH7CxMmB78x1ordyrbgL7DiwTd/072BJKjR3LuRmIRP7\n7z0jjTPVys6Vbdx2PaHbqDLufafqLy8jIcpVt7FWt+JTzgGs/wGFWD/2AADntXic7FtdcNxW\nFdbaXsd27MRu4iROk3SdrI2d1Bv/JQ4moaUhKYE4CdCSpEC38q7WVrzeXVZy4pCm0wINDIWm\nMDAl5e+Bvxd4Bjq8ADNM+XkD3oA34A0y053pFGa8XEmfdq+O79VKWzm8ZGeuj6/uOef77j26\nV9LR1aUuRVFalba2mNIaV9gv/uQTp8eOKUp7q6K0tbADMaVN6WSy3ShnylqOafewWgcrg7ap\novwWEiX5gqIMjRLrTcw6p+c19u9bivOrNWmFq7Ozx2aIRTyTVw2D/dMrIqGsobwNby3QaDP1\nZdXVnkxNTKTGieMBtVAommlTnc9r6Wuanp7Xi+lMXlMLlgmn2LqgmbZnLwc/e28PWMf0MnHa\nXVLLWsFM5crqslbvh8dwK/O6sGimjZKWMctq3nGxqSogs17V8tRRdUw6qxKEPpgx6vnigp7x\nxRApB0JxyWUWteUGGOtVAyHsNjLFslazYmFQ00t6YSFbXPYB87UKhLtX4MFYKWll96SVQTcy\nDIS+S+CkEbCPTSDMAYG9cyr7gPoZNRXfQimTLqnm4jX1eoj4eqyaiq/loan4UsOm4ms5CRtf\nzkaGaZd+Vk6z0sNcrVrrEys7bb+Kst9aTyGti8MwcPdYHbVWbsgOyE7ILsjNkN2QPZBbILdC\n9kL2QT4AuQ1yO2Q/5A7InZC7IAcgd0M+CLkH3Ns52zbYP4T+7YdOAm2jrByA/rBA/yCOj3Bt\nw/A9Bp0U9A+jPo76BOqTqE+hPo36EdSPoj4D34fA55iAz3HozAr4PAKdRwmf9xE+jxE+Jwmf\n9xM+J8DnlIDPGeg8LuBzFjpzhM85wuc84XOB8Pkw4fNB8PmIgM/HoPOEgM9l6DxF+Hyc8PkE\n4fNJwudpwuci+KQFfDLQUQV8ctBZIHwWCR+d8LlC+CwRPlnwyQv4lKBTEPAxoGMSPiuEz1XC\n5xrhs0r4fAp8rgv43ITODQGf56HzAuHzGcLns4TP5wifFwm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Created by use_targets().\n# Follow the comments below to fill in this target script.\n# Then follow the manual to check and run the pipeline:\n# https://books.ropensci.org/targets/walkthrough.html#inspect-the-pipeline # nolint\n\n# Load packages required to define the pipeline:\nlibrary(targets)\n\n# Set target options:\ntar_option_set(\n packages = c(\"tima\"),\n imports = c(\"tima\"),\n memory = \"transient\",\n garbage_collection = TRUE,\n resources = tar_resources(\n qs = tar_resources_qs(preset = \"fast\")\n )\n)\n\n# tar_make_clustermq() configuration (okay to leave alone):\n\n# tar_make_future() configuration (okay to leave alone):\n# Install packages {{future}}, {{future.callr}}, and {{future.batchtools}}\n# to allow use_targets() to configure tar_make_future() options.\n\n# Run the R scripts in the R/ folder with your custom functions:\ntar_source(files = list.files(\n path = system.file(\"R\", package = \"tima\"),\n pattern = \".R$\",\n full.names = TRUE\n))\n\n# Replace the target list below with your own:\nlist(\n ## Architecture\n list( ## Paths\n list(\n tar_target(\n name = yaml_paths,\n format = \"file\",\n command = {\n yaml_paths <- system.file(\"paths.yaml\", package = \"tima\")\n }\n ),\n tar_target(\n name = paths,\n command = {\n paths <- get_default_paths(yaml = yaml_paths)\n }\n ),\n # tar_target(\n # name = paths_gnps_example_id,\n # command = {\n # paths_gnps_example_id <- paths$gnps$example\n # }\n # ),\n tar_target(\n name = paths_source,\n command = {\n paths_source <- paths$data$source$path\n }\n ),\n tar_target(\n name = paths_interim_a,\n command = {\n paths_interim_a <- paths$data$interim$annotations$path\n }\n ),\n tar_target(\n name = paths_interim_f,\n command = {\n paths_interim_f <- paths$data$interim$features$path\n }\n ),\n tar_target(\n name = paths_test_mode,\n command = {\n paths_test_mode <- paths$tests$mode\n }\n ),\n tar_target(\n name = paths_urls_massbank_file,\n command = {\n paths_urls_massbank_file <- paths$urls$massbank$file\n }\n ),\n tar_target(\n name = paths_urls_massbank_url,\n command = {\n paths_urls_massbank_url <- paths$urls$massbank$url\n }\n ),\n tar_target(\n name = paths_urls_massbank_version,\n command = {\n paths_urls_massbank_version <- paths$urls$massbank$version\n }\n ),\n tar_target(\n name = paths_urls_examples_spectra_mini,\n command = {\n paths_urls_examples_spectra_mini <- paths$urls$examples$spectra_mini\n }\n ),\n tar_target(\n name = paths_data_source_spectra,\n command = {\n paths_data_source_spectra <- paths$data$source$spectra\n }\n ),\n tar_target(\n name = paths_urls_ecmdb_metabolites,\n command = {\n paths_urls_ecmdb_metabolites <- paths$urls$ecmdb$metabolites\n }\n ),\n tar_target(\n name = paths_data_source_libraries_sop_ecmdb,\n command = {\n paths_data_source_libraries_sop_ecmdb <-\n paths$data$source$libraries$sop$ecmdb\n }\n ),\n tar_target(\n name = paths_urls_hmdb_structures,\n command = {\n paths_urls_hmdb_structures <- paths$urls$hmdb$structures\n }\n ),\n tar_target(\n name = paths_data_source_libraries_sop_hmdb,\n command = {\n paths_data_source_libraries_sop_hmdb <-\n paths$data$source$libraries$sop$hmdb\n }\n ),\n tar_target(\n name = paths_urls_lotus_doi,\n command = {\n paths_urls_lotus_doi <- paths$urls$lotus$doi\n }\n ),\n tar_target(\n name = paths_urls_lotus_pattern,\n command = {\n paths_urls_lotus_pattern <- paths$urls$lotus$pattern\n }\n ),\n tar_target(\n name = paths_data_source_libraries_sop_lotus,\n command = {\n paths_data_source_libraries_sop_lotus <-\n paths$data$source$libraries$sop$lotus\n }\n ),\n tar_target(\n name = paths_urls_examples_spectral_lib_pos,\n command = {\n paths_urls_examples_spectral_lib_pos <-\n paths$urls$examples$spectral_lib$pos\n }\n ),\n tar_target(\n name = paths_data_source_libraries_spectra_is_lotus_pos,\n command = {\n paths_data_source_libraries_spectra_is_lotus_pos <-\n paths$data$source$libraries$spectra$is$lotus$pos\n }\n ),\n tar_target(\n name = paths_urls_examples_spectral_lib_neg,\n command = {\n paths_urls_examples_spectral_lib_neg <-\n paths$urls$examples$spectral_lib$neg\n }\n ),\n tar_target(\n name = paths_data_source_libraries_spectra_is_lotus_neg,\n command = {\n paths_data_source_libraries_spectra_is_lotus_neg <-\n paths$data$source$libraries$spectra$is$lotus$neg\n }\n ),\n tar_target(\n name = paths_data_interim_libraries_adducts_path,\n command = {\n paths_data_interim_libraries_adducts_path <-\n paths$data$interim$libraries$adducts$path\n }\n ),\n tar_target(\n name = paths_urls_benchmarking_set,\n command = {\n paths_urls_benchmarking_set <-\n paths$urls$benchmarking_set\n }\n ),\n tar_target(\n name = paths_data_source_benchmark_zip,\n command = {\n paths_data_source_benchmark_zip <-\n paths$data$source$benchmark$zip\n }\n ),\n tar_target(\n name = paths_data_source_benchmark_cleaned,\n command = {\n paths_data_source_benchmark_cleaned <-\n paths$data$source$benchmark$cleaned\n }\n ),\n tar_target(\n name = paths_data_source_benchmark_mgf_neg,\n command = {\n paths_data_source_benchmark_mgf_neg <-\n paths$data$source$benchmark$mgf$neg\n }\n ),\n tar_target(\n name = paths_data_source_benchmark_mgf_pos,\n command = {\n paths_data_source_benchmark_mgf_pos <-\n paths$data$source$benchmark$mgf$pos\n }\n )\n )\n ),\n ## Params\n list(\n ## Default\n list(\n tar_target(\n name = par_def_ann_mas,\n format = \"file\",\n command = {\n par_def_ann_mas <- paths$params$default$annotate$masses\n }\n ),\n tar_target(\n name = par_def_ann_spe,\n format = \"file\",\n command = {\n par_def_ann_spe <- paths$params$default$annotate$spectra\n }\n ),\n tar_target(\n name = par_def_cre_com,\n format = \"file\",\n command = {\n par_def_cre_com <- paths$params$default$create$components\n }\n ),\n tar_target(\n name = par_def_cre_edg_spe,\n format = \"file\",\n command = {\n par_def_cre_edg_spe <- paths$params$default$create$edges$spectra\n }\n ),\n tar_target(\n name = par_def_fil_ann,\n format = \"file\",\n command = {\n par_def_fil_ann <-\n paths$params$default$filter$annotations\n }\n ),\n tar_target(\n name = par_def_pre_ann_gnp,\n format = \"file\",\n command = {\n par_def_pre_ann_gnp <- paths$params$default$prepare$annotations$gnps\n }\n ),\n tar_target(\n name = par_def_pre_ann_sir,\n format = \"file\",\n command = {\n par_def_pre_ann_sir <-\n paths$params$default$prepare$annotations$sirius\n }\n ),\n tar_target(\n name = par_def_pre_ann_spe,\n format = \"file\",\n command = {\n par_def_pre_ann_spe <-\n paths$params$default$prepare$annotations$spectra\n }\n ),\n tar_target(\n name = par_def_pre_fea_com,\n format = \"file\",\n command = {\n par_def_pre_fea_com <-\n paths$params$default$prepare$features$components\n }\n ),\n tar_target(\n name = par_def_pre_fea_edg,\n format = \"file\",\n command = {\n par_def_pre_fea_edg <- paths$params$default$prepare$features$edges\n }\n ),\n tar_target(\n name = par_def_pre_fea_tab,\n format = \"file\",\n command = {\n par_def_pre_fea_tab <- paths$params$default$prepare$features$tables\n }\n ),\n tar_target(\n name = par_def_pre_lib_rt,\n format = \"file\",\n command = {\n par_def_pre_lib_rt <-\n paths$params$default$prepare$libraries$rt\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_clo,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_clo <-\n paths$params$default$prepare$libraries$sop$closed\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_ecm,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_ecm <-\n paths$params$default$prepare$libraries$sop$ecmdb\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_hmd,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_hmd <-\n paths$params$default$prepare$libraries$sop$hmdb\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_lot,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_lot <-\n paths$params$default$prepare$libraries$sop$lotus\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_mer,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_mer <-\n paths$params$default$prepare$libraries$sop$merged\n }\n ),\n tar_target(\n name = par_def_pre_lib_spe,\n format = \"file\",\n command = {\n par_def_pre_lib_spe <-\n paths$params$default$prepare$libraries$spectra\n }\n ),\n tar_target(\n name = par_def_pre_tax,\n format = \"file\",\n command = {\n par_def_pre_tax <- paths$params$default$prepare$taxa\n }\n ),\n tar_target(\n name = par_def_wei_ann,\n format = \"file\",\n command = {\n par_def_wei_ann <- paths$params$default$weight$annotations\n }\n )\n ),\n list(\n ## Prepare params\n list(\n tar_target(\n name = par_pre_par,\n format = \"file\",\n command = {\n par_pre_par <- paths$params$prepare_params\n }\n ),\n tar_target(\n name = par_pre_par2,\n format = \"file\",\n command = {\n par_pre_par2 <- paths$params$prepare_params_advanced\n }\n ),\n tar_target(\n name = par_fin_par,\n command = {\n par_fin_par <- tima:::parse_yaml_params(\n def = par_pre_par,\n usr = par_pre_par\n )\n }\n ),\n tar_target(\n name = par_fin_par2,\n command = {\n par_fin_par2 <- tima:::parse_yaml_params(\n def = par_pre_par2,\n usr = par_pre_par2\n )\n }\n )\n ),\n ## User\n list(\n tar_target(\n name = par_usr_ann_mas,\n format = \"file\",\n command = {\n par_usr_ann_mas <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"annotate_masses\"\n )\n }\n ),\n tar_target(\n name = par_usr_ann_spe,\n format = \"file\",\n command = {\n par_usr_ann_spe <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"annotate_spectra\"\n )\n }\n ),\n tar_target(\n name = par_usr_cre_com,\n format = \"file\",\n command = {\n par_usr_cre_com <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"create_components\"\n )\n }\n ),\n tar_target(\n name = par_usr_fil_ann,\n format = \"file\",\n command = {\n par_usr_fil_ann <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"filter_annotations\"\n )\n }\n ),\n tar_target(\n name = par_usr_cre_edg_spe,\n format = \"file\",\n command = {\n par_usr_cre_edg_spe <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"create_edges_spectra\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_ann_gnp,\n format = \"file\",\n command = {\n par_usr_pre_ann_gnp <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_annotations_gnps\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_ann_sir,\n format = \"file\",\n command = {\n par_usr_pre_ann_sir <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_annotations_sirius\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_ann_spe,\n format = \"file\",\n command = {\n par_usr_pre_ann_spe <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_annotations_spectra\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_fea_com,\n format = \"file\",\n command = {\n par_usr_pre_fea_com <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_features_components\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_fea_edg,\n format = \"file\",\n command = {\n par_usr_pre_fea_edg <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_features_edges\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_fea_tab,\n format = \"file\",\n command = {\n par_usr_pre_fea_tab <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_features_tables\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_rt,\n format = \"file\",\n command = {\n par_usr_pre_lib_rt <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_rt\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_clo,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_clo <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_closed\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_ecm,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_ecm <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_ecmdb\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_hmd,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_hmd <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_hmdb\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_lot,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_lot <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_lotus\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_mer,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_mer <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_merged\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_spe,\n format = \"file\",\n command = {\n par_usr_pre_lib_spe <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_spectra\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_tax,\n format = \"file\",\n command = {\n par_usr_pre_tax <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_taxa\"\n )\n }\n ),\n tar_target(\n name = par_usr_wei_ann,\n format = \"file\",\n command = {\n par_usr_wei_ann <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"weight_annotations\"\n )\n }\n )\n )\n ),\n ## Final\n list(\n tar_target(\n name = par_ann_mas,\n command = {\n par_ann_mas <-\n tima:::parse_yaml_params(\n def = par_def_ann_mas,\n usr = par_usr_ann_mas[1]\n )\n }\n ),\n tar_target(\n name = par_ann_spe,\n command = {\n par_ann_spe <-\n tima:::parse_yaml_params(\n def = par_def_ann_spe,\n usr = par_usr_ann_spe[1]\n )\n }\n ),\n tar_target(\n name = par_cre_com,\n command = {\n par_cre_com <-\n tima:::parse_yaml_params(\n def = par_def_cre_com,\n usr = par_usr_cre_com[1]\n )\n }\n ),\n tar_target(\n name = par_cre_edg_spe,\n command = {\n par_cre_edg_spe <-\n tima:::parse_yaml_params(\n def = par_def_cre_edg_spe,\n usr = par_usr_cre_edg_spe[1]\n )\n }\n ),\n tar_target(\n name = par_fil_ann,\n command = {\n par_fil_ann <-\n tima:::parse_yaml_params(\n def = par_def_fil_ann,\n usr = par_usr_fil_ann[1]\n )\n }\n ),\n tar_target(\n name = par_pre_ann_gnp,\n command = {\n par_pre_ann_gnp <-\n tima:::parse_yaml_params(\n def = par_def_pre_ann_gnp,\n usr = par_usr_pre_ann_gnp[1]\n )\n }\n ),\n tar_target(\n name = par_pre_ann_sir,\n command = {\n par_pre_ann_sir <-\n tima:::parse_yaml_params(\n def = par_def_pre_ann_sir,\n usr = par_usr_pre_ann_sir[1]\n )\n }\n ),\n tar_target(\n name = par_pre_ann_spe,\n command = {\n par_pre_ann_spe <-\n tima:::parse_yaml_params(\n def = par_def_pre_ann_spe,\n usr = par_usr_pre_ann_spe[1]\n )\n }\n ),\n tar_target(\n name = par_pre_fea_com,\n command = {\n par_pre_fea_com <-\n tima:::parse_yaml_params(\n def = par_def_pre_fea_com,\n usr = par_usr_pre_fea_com[1]\n )\n }\n ),\n tar_target(\n name = par_pre_fea_edg,\n command = {\n par_pre_fea_edg <-\n tima:::parse_yaml_params(\n def = par_def_pre_fea_edg,\n usr = par_usr_pre_fea_edg[1]\n )\n }\n ),\n tar_target(\n name = par_pre_fea_tab,\n command = {\n par_pre_fea_tab <-\n tima:::parse_yaml_params(\n def = par_def_pre_fea_tab,\n usr = par_usr_pre_fea_tab[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_rt,\n command = {\n par_pre_lib_rt <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_rt,\n usr = par_usr_pre_lib_rt[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_clo,\n command = {\n par_pre_lib_sop_clo <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_clo,\n usr = par_usr_pre_lib_sop_clo[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_ecm,\n command = {\n par_pre_lib_sop_ecm <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_ecm,\n usr = par_usr_pre_lib_sop_ecm[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_hmd,\n command = {\n par_pre_lib_sop_hmd <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_hmd,\n usr = par_usr_pre_lib_sop_hmd[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_lot,\n command = {\n par_pre_lib_sop_lot <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_lot,\n usr = par_usr_pre_lib_sop_lot[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_mer,\n command = {\n par_pre_lib_sop_mer <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_mer,\n usr = par_usr_pre_lib_sop_mer[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_spe,\n command = {\n par_pre_lib_spe <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_spe,\n usr = par_usr_pre_lib_spe[1]\n )\n }\n ),\n tar_target(\n name = par_pre_tax,\n command = {\n par_pre_tax <-\n tima:::parse_yaml_params(\n def = par_def_pre_tax,\n usr = par_usr_pre_tax[1]\n )\n }\n ),\n tar_target(\n name = par_wei_ann,\n command = {\n par_wei_ann <-\n tima:::parse_yaml_params(\n def = par_def_wei_ann,\n usr = par_usr_wei_ann[1]\n )\n }\n )\n )\n ),\n # ## GNPS\n # list(\n # tar_target(\n # name = gnps_tables,\n # format = \"file\",\n # command = {\n # gnps_tables <- tima:::get_gnps_tables(\n # gnps_job_id = par_fin_par$gnps$id,\n # gnps_job_example = paths_gnps_example_id,\n # filename = par_fin_par$files$pattern,\n # workflow = par_fin_par$gnps$workflow,\n # path_features = par_pre_fea_tab$files$features$raw,\n # path_metadata = par_pre_tax$files$metadata$raw,\n # path_spectra = par_ann_spe$files$spectral$raw,\n # path_source = paths_source,\n # path_interim_a = paths_interim_a,\n # path_interim_f = paths_interim_f\n # )\n # }\n # ),\n # list(\n # tar_target(\n # name = gnps_features,\n # format = \"file\",\n # command = {\n # gnps_features <- gnps_tables[[1]]\n # }\n # ),\n # tar_target(\n # name = gnps_metadata,\n # format = \"file\",\n # command = {\n # gnps_metadata <- gnps_tables[[2]]\n # }\n # ),\n # tar_target(\n # name = gnps_spectra,\n # format = \"file\",\n # command = {\n # gnps_spectra <- gnps_tables[[3]]\n # }\n # ),\n # # TODO think to change this also if uncommenting\n # tar_target(\n # name = gnps_annotations,\n # format = \"file\",\n # command = {\n # gnps_annotations <- gnps_tables[[4]]\n # }\n # ),\n # tar_target(\n # name = gnps_components,\n # format = \"file\",\n # command = {\n # gnps_components <- gnps_tables[[5]]\n # }\n # ),\n # tar_target(\n # name = gnps_edges,\n # format = \"file\",\n # command = {\n # gnps_edges <- gnps_tables[[6]]\n # }\n # )\n # )\n # ),\n ## Inputs\n list(\n tar_target(\n name = input_features,\n format = \"file\",\n command = {\n input_features <- par_pre_tax$files$features$raw\n # input_features <-\n # ifelse(\n # test = !is.null(gnps_features),\n # yes = ifelse(test = file.exists(gnps_features),\n # yes = gnps_features,\n # no = par_pre_tax$files$features$raw\n # ),\n # no = par_pre_tax$files$features$raw\n # )\n }\n ),\n tar_target(\n name = input_spectra,\n format = \"file\",\n command = {\n input_spectra <-\n ifelse(\n test = paths_test_mode == FALSE,\n yes = par_ann_spe$files$spectral$raw,\n # yes = ifelse(\n # test = !is.null(gnps_spectra),\n # yes =\n # ifelse(\n # test = file.exists(gnps_spectra),\n # yes = gnps_spectra,\n # no = par_ann_spe$files$spectral$raw\n # ),\n # no = par_ann_spe$files$spectral$raw\n # ),\n no = {\n get_file(\n url = paths_urls_examples_spectra_mini,\n export = paths_data_source_spectra\n )\n }\n )\n }\n )\n ),\n ## libraries\n list(\n ## Spectra\n list( ## In silico\n list( ## Raw\n list(\n ## TODO ADD ISDB HMDB,\n tar_target(\n name = lib_spe_is_lot_pos,\n format = \"file\",\n command = {\n lib_spe_is_lot_pos <-\n # if (paths_test_mode == FALSE) {\n get_file(\n url = paths_urls_examples_spectral_lib_pos,\n export = paths_data_source_libraries_spectra_is_lotus_pos |>\n gsub(\n pattern = \"isdb_pos.mgf\",\n replacement = \"lotus_pos.rds\",\n fixed = TRUE\n )\n )\n # get_last_version_from_zenodo(\n # doi = paths_urls_lotus_isdb_doi,\n # pattern = paths_urls_lotus_isdb_pattern_pos,\n # path = paths_data_source_libraries_spectra_is_lotus_pos\n # )\n # } else {\n # get_file(\n # url = paths_urls_examples_spectral_lib_pos,\n # export = paths_data_source_libraries_spectra_is_lotus_pos\n # )\n # }\n }\n ## To always check if a newest version is available\n ,\n cue = tar_cue(mode = \"always\")\n # cue = tar_cue(mode = \"thorough\")\n ),\n tar_target(\n name = lib_spe_is_lot_neg,\n format = \"file\",\n command = {\n lib_spe_is_lot_neg <-\n # if (paths_test_mode == FALSE) {\n get_file(\n url = paths_urls_examples_spectral_lib_neg,\n export = paths_data_source_libraries_spectra_is_lotus_neg |>\n gsub(\n pattern = \"isdb_neg.mgf\",\n replacement = \"lotus_neg.rds\",\n fixed = TRUE\n )\n )\n # get_last_version_from_zenodo(\n # doi = paths_urls_lotus_isdb_doi,\n # pattern = paths_urls_lotus_isdb_pattern_neg,\n # path = paths_data_source_libraries_spectra_is_lotus_neg\n # )\n # } else {\n # get_file(\n # url = paths_urls_examples_spectral_lib_neg,\n # export = paths_data_source_libraries_spectra_is_lotus_neg\n # )\n # }\n }\n ## To always check if a newest version is available\n ,\n cue = tar_cue(mode = \"always\")\n # cue = tar_cue(mode = \"thorough\")\n )\n )\n ),\n ## Prepared\n list(\n ## TODO ADD IS HMDB PREPARED,\n tar_target(\n name = lib_spe_is_lot_pre_pos,\n format = \"file\",\n command = {\n lib_spe_is_lot_pre_pos <-\n lib_spe_is_lot_pos\n }\n ),\n tar_target(\n name = lib_spe_is_lot_pre_neg,\n format = \"file\",\n command = {\n lib_spe_is_lot_pre_neg <-\n lib_spe_is_lot_neg\n }\n )\n ),\n ## Experimental\n list(\n ## RAW\n list(\n ### Internal\n ## This does not work as it forces the file to exist.\n ## So targets will not check if the input file changed automatically.\n # tar_target(\n # name = lib_spe_exp_int_raw,\n # . format = \"file\",\n # command = {\n # lib_spe_exp_int_raw <-\n # par_pre_lib_spe$files$libraries$spectral$exp$raw\n # }\n # ),\n ### MassBank\n tar_target(\n name = lib_spe_exp_mb_raw,\n format = \"file\",\n command = {\n lib_spe_exp_mb_raw <- tima:::get_massbank_spectra(\n mb_file = paths_urls_massbank_file,\n mb_url = paths_urls_massbank_url,\n mb_version = paths_urls_massbank_version\n )\n }\n )\n ),\n ## Prepared\n list(\n tar_target(\n name = lib_spe_exp_int_pre,\n format = \"file\",\n command = {\n lib_spe_exp_int_pre <-\n prepare_libraries_spectra(\n input = par_pre_lib_spe$files$libraries$spectral$raw,\n nam_lib = par_pre_lib_spe$names$libraries,\n col_ad = par_pre_lib_spe$names$mgf$adduct,\n col_ce = par_pre_lib_spe$names$mgf$collision_energy,\n col_ci = par_pre_lib_spe$names$mgf$compound_id,\n col_em = par_pre_lib_spe$names$mgf$exact_mass,\n col_in = par_pre_lib_spe$names$mgf$inchi,\n col_io = par_pre_lib_spe$names$mgf$inchi_no_stereo,\n col_ik = par_pre_lib_spe$names$mgf$inchikey,\n col_il = par_pre_lib_spe$names$mgf$inchikey_no_stereo,\n col_mf = par_pre_lib_spe$names$mgf$molecular_formula,\n col_na = par_pre_lib_spe$names$mgf$name,\n col_po = par_pre_lib_spe$names$mgf$polarity,\n col_sm = par_pre_lib_spe$names$mgf$smiles,\n col_sn = par_pre_lib_spe$names$mgf$smiles_no_stereo,\n col_si = par_pre_lib_spe$names$mgf$spectrum_id,\n col_sp = par_pre_lib_spe$names$mgf$splash,\n col_sy = par_pre_lib_spe$names$mgf$synonyms,\n col_xl = par_pre_lib_spe$names$mgf$xlogp\n )\n }\n ),\n tar_target(\n name = lib_spe_exp_int_pre_pos,\n format = \"file\",\n command = {\n lib_spe_exp_int_pre_pos <- lib_spe_exp_int_pre[[1]]\n }\n ),\n tar_target(\n name = lib_spe_exp_int_pre_neg,\n format = \"file\",\n command = {\n lib_spe_exp_int_pre_neg <- lib_spe_exp_int_pre[[2]]\n }\n ),\n tar_target(\n name = lib_spe_exp_int_pre_sop,\n format = \"file\",\n command = {\n lib_spe_exp_int_pre_sop <- lib_spe_exp_int_pre[[3]]\n }\n ),\n tar_target(\n name = lib_spe_exp_mb_pre,\n format = \"file\",\n command = {\n lib_spe_exp_mb_pre <-\n prepare_libraries_spectra(\n input = lib_spe_exp_mb_raw,\n nam_lib = paste(\"MassBank\",\n paths_urls_massbank_version,\n sep = \" - \"\n ),\n col_ad = \"Precursor_type\",\n col_ce = \"Collision_energy\",\n col_ci = NULL,\n col_em = \"ExactMass\",\n col_in = \"InChI\",\n col_io = NULL,\n col_ik = \"InChIKey\",\n col_il = NULL,\n col_mf = \"Formula\",\n col_na = \"Name\",\n col_po = \"Ion_mode\",\n col_sm = \"smiles\",\n col_sn = NULL,\n col_si = \"accession\",\n col_sp = \"Splash\",\n col_sy = \"Synon\",\n col_xl = NULL\n )\n }\n ),\n tar_target(\n name = lib_spe_exp_mb_pre_pos,\n format = \"file\",\n command = {\n lib_spe_exp_mb_pre_pos <- lib_spe_exp_mb_pre[[1]]\n }\n ),\n tar_target(\n name = lib_spe_exp_mb_pre_neg,\n format = \"file\",\n command = {\n lib_spe_exp_mb_pre_neg <- lib_spe_exp_mb_pre[[2]]\n }\n ),\n tar_target(\n name = lib_spe_exp_mb_pre_sop,\n format = \"file\",\n command = {\n lib_spe_exp_mb_pre_sop <- lib_spe_exp_mb_pre[[3]]\n }\n )\n )\n )\n ),\n ## Retention times\n list(tar_target(\n name = lib_rt,\n format = \"file\",\n command = {\n lib_rt <- prepare_libraries_rt(\n mgf_exp = par_pre_lib_rt$files$libraries$temporal$exp$mgf,\n mgf_is = par_pre_lib_rt$files$libraries$temporal$is$mgf,\n temp_exp = par_pre_lib_rt$files$libraries$temporal$exp$csv,\n temp_is = par_pre_lib_rt$files$libraries$temporal$is$csv,\n output_rt = par_pre_lib_rt$files$libraries$temporal$prepared,\n output_sop = par_pre_lib_rt$files$libraries$sop$prepared$rt,\n col_ik = par_pre_lib_rt$names$mgf$inchikey,\n col_rt = par_pre_lib_rt$names$mgf$retention_time,\n col_sm = par_pre_lib_rt$names$mgf$smiles,\n name_inchikey = par_pre_lib_rt$names$inchikey,\n name_rt = par_pre_lib_rt$names$rt$library,\n name_smiles = par_pre_lib_rt$names$smiles,\n unit_rt = par_pre_lib_rt$units$rt\n )\n }\n )),\n tar_target(\n name = lib_rt_rts,\n format = \"file\",\n command = {\n lib_rt_rts <- lib_rt[[1]]\n }\n ),\n tar_target(\n name = lib_rt_sop,\n format = \"file\",\n command = {\n lib_rt_sop <- lib_rt[[2]]\n }\n ),\n ## Structure organism pairs\n list(\n ## Raw\n list(\n ## This does not work as it forces the file to exist.\n ## So targets will not check if the input file changed automatically.\n # tar_target(\n # name = lib_sop_clo,\n # format = \"file\",\n # command = {\n # lib_sop_clo <- paths$data$source$libraries$sop$closed\n # }\n # ),\n tar_target(\n name = lib_sop_ecm,\n format = \"file\",\n command = {\n ## Because ECMDB certificate is expired\n lib_sop_ecm <- tryCatch(\n expr = {\n get_file(\n url = paths_urls_ecmdb_metabolites,\n export = paths_data_source_libraries_sop_ecmdb\n )\n },\n error = function(e) {\n return(tima:::fake_ecmdb(export = paths_data_source_libraries_sop_ecmdb))\n }, finally = {\n return(paths_data_source_libraries_sop_ecmdb)\n }\n )\n }\n ),\n tar_target(\n name = lib_sop_hmd,\n format = \"file\",\n command = {\n lib_sop_hmd <- tryCatch(\n expr = {\n get_file(\n url = paths_urls_hmdb_structures,\n export = paths_data_source_libraries_sop_hmdb\n )\n },\n warning = function(w) {\n ## See #118\n log_debug(\"HMDB download failed partially, returning empty file instead\")\n unlink(paths_data_source_libraries_sop_hmdb)\n return(tima:::fake_hmdb(export = paths_data_source_libraries_sop_hmdb))\n },\n error = function(e) {\n return(tima:::fake_hmdb(export = paths_data_source_libraries_sop_hmdb))\n }, finally = {\n return(paths_data_source_libraries_sop_hmdb)\n }\n )\n }\n ),\n ## TODO ADD GET HMDB\n tar_target(\n name = lib_sop_lot,\n format = \"file\",\n command = {\n lib_sop_lot <- tryCatch(\n expr = {\n get_last_version_from_zenodo(\n doi = paths_urls_lotus_doi,\n pattern = paths_urls_lotus_pattern,\n path = paths_data_source_libraries_sop_lotus\n )\n },\n error = function(e) {\n return(tima:::fake_lotus(export = paths_data_source_libraries_sop_lotus))\n }, finally = {\n return(paths_data_source_libraries_sop_lotus)\n }\n )\n },\n ## To always check if a newest version is available\n cue = tar_cue(mode = \"always\")\n # cue = tar_cue(mode = \"thorough\")\n )\n ),\n ## Prepared\n list(\n tar_target(\n name = lib_sop_clo_pre,\n format = \"file\",\n command = {\n lib_sop_clo_pre <-\n prepare_libraries_sop_closed(\n input = par_pre_lib_sop_clo$files$libraries$sop$raw$closed,\n output = par_pre_lib_sop_clo$files$libraries$sop$prepared$closed\n )\n }\n ),\n tar_target(\n name = lib_sop_ecm_pre,\n format = \"file\",\n command = {\n lib_sop_ecm_pre <-\n prepare_libraries_sop_ecmdb(\n input = lib_sop_ecm,\n output = par_pre_lib_sop_ecm$files$libraries$sop$prepared$ecmdb\n )\n }\n ),\n tar_target(\n name = lib_sop_hmd_pre,\n format = \"file\",\n command = {\n lib_sop_hmd_pre <-\n prepare_libraries_sop_hmdb(\n input = lib_sop_hmd,\n output = par_pre_lib_sop_hmd$files$libraries$sop$prepared$hmdb\n )\n }\n ),\n tar_target(\n name = lib_sop_lot_pre,\n format = \"file\",\n command = {\n lib_sop_lot_pre <-\n prepare_libraries_sop_lotus(\n input = if (paths_test_mode == FALSE) {\n lib_sop_lot\n } else {\n paths_data_source_libraries_sop_lotus\n },\n output = par_pre_lib_sop_lot$files$libraries$sop$prepared$lotus\n )\n }\n )\n ),\n ## Merged\n list(\n tar_target(\n name = lib_sop_mer,\n format = \"file\",\n command = {\n lib_sop_mer <- prepare_libraries_sop_merged(\n files = c(\n lib_sop_clo_pre,\n lib_sop_ecm_pre,\n lib_sop_hmd_pre,\n lib_sop_lot_pre,\n lib_rt_sop,\n lib_spe_exp_int_pre_sop,\n lib_spe_exp_mb_pre_sop\n ),\n filter = par_pre_lib_sop_mer$organisms$filter$mode,\n level = par_pre_lib_sop_mer$organisms$filter$level,\n value = par_pre_lib_sop_mer$organisms$filter$value,\n output_key = par_pre_lib_sop_mer$files$libraries$sop$merged$keys,\n output_org_tax_ott = par_pre_lib_sop_mer$files$libraries$sop$merged$organisms$taxonomies$ott,\n output_str_stereo = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$stereo,\n output_str_met = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$metadata,\n output_str_nam = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$names,\n output_str_tax_cla = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$taxonomies$cla,\n output_str_tax_npc = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$taxonomies$npc\n )\n }\n ),\n tar_target(\n name = lib_mer_key,\n format = \"file\", command = {\n lib_mer_key <- lib_sop_mer[[1]]\n }\n ),\n tar_target(\n name = lib_mer_org_tax_ott,\n format = \"file\", command = {\n lib_mer_org_tax_ott <- lib_sop_mer[[2]]\n }\n ),\n tar_target(\n name = lib_mer_str_stereo,\n format = \"file\", command = {\n lib_mer_str_stereo <- lib_sop_mer[[3]]\n }\n ),\n tar_target(\n name = lib_mer_str_met,\n format = \"file\", command = {\n lib_mer_str_met <- lib_sop_mer[[4]]\n }\n ),\n tar_target(\n name = lib_mer_str_nam,\n format = \"file\", command = {\n lib_mer_str_nam <- lib_sop_mer[[5]]\n }\n ),\n tar_target(\n name = lib_mer_str_tax_cla,\n format = \"file\", command = {\n lib_mer_str_tax_cla <- lib_sop_mer[[6]]\n }\n ),\n tar_target(\n name = lib_mer_str_tax_npc,\n format = \"file\", command = {\n lib_mer_str_tax_npc <- lib_sop_mer[[7]]\n }\n )\n )\n )\n ),\n ## Annotations\n list(\n ## MS1\n list(\n tar_target(\n name = ann_ms1_pre,\n format = \"file\",\n command = {\n ann_ms1_pre <-\n annotate_masses(\n features = fea_pre,\n library = lib_mer_key,\n output_annotations = par_ann_mas$files$annotations$prepared$structural$ms1,\n output_edges = par_ann_mas$files$networks$spectral$edges$raw,\n name_source = par_ann_mas$names$source,\n name_target = par_ann_mas$names$target,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc,\n adducts_list = par_ann_mas$ms$adducts,\n clusters_list = par_ann_mas$ms$clusters,\n neutral_losses_list = par_ann_mas$ms$neutral_losses,\n ms_mode = par_ann_mas$ms$polarity,\n tolerance_ppm = par_ann_mas$ms$tolerances$mass$ppm$ms1,\n tolerance_rt = par_ann_mas$ms$tolerances$rt$adducts\n )\n }\n ),\n tar_target(\n name = ann_ms1_pre_ann,\n format = \"file\",\n command = {\n ann_ms1_pre_ann <-\n ann_ms1_pre[[1]]\n }\n ),\n tar_target(\n name = ann_ms1_pre_edg,\n format = \"file\",\n command = {\n ann_ms1_pre_edg <- ann_ms1_pre[[2]]\n }\n )\n ),\n ## Spectral\n list(\n # ## GNPS\n list(\n tar_target(\n name = ann_spe_exp_gnp_pre,\n format = \"file\",\n command = {\n ann_spe_exp_gnp_pre <-\n prepare_annotations_gnps(\n # input = gnps_annotations,\n input = par_pre_ann_gnp$files$annotations$raw$spectral$gnps,\n output = par_pre_ann_gnp$files$annotations$prepared$structural$gnps,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n )\n ),\n ## Classic\n list(\n ## TODO improve polarity handling, suboptimal\n tar_target(\n name = ann_spe_pos,\n format = \"file\",\n command = {\n ann_spe_pos <- annotate_spectra(\n input = input_spectra,\n library = c(\n lib_spe_is_lot_pre_pos,\n ## TODO add is hmdb\n lib_spe_exp_int_pre_pos,\n lib_spe_exp_mb_pre_pos\n ),\n polarity = \"pos\",\n output = gsub(\n pattern = \".tsv.gz\",\n replacement = \"_pos.tsv.gz\",\n x = par_ann_spe$files$annotations$raw$spectral$spectral,\n fixed = TRUE\n ),\n threshold = par_ann_spe$annotations$thresholds$ms2$similarity$annotation,\n ppm = par_ann_spe$ms$tolerances$mass$ppm$ms2,\n dalton = par_ann_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = par_ann_spe$ms$thresholds$ms2$intensity,\n approx = par_ann_spe$annotations$ms2approx\n )\n }\n ),\n tar_target(\n name = ann_spe_neg,\n format = \"file\",\n command = {\n ann_spe_neg <- annotate_spectra(\n input = input_spectra,\n library = c(\n lib_spe_is_lot_pre_neg,\n ## TODO add is hmdb\n lib_spe_exp_int_pre_neg,\n lib_spe_exp_mb_pre_neg\n ),\n polarity = \"neg\",\n output = gsub(\n pattern = \".tsv.gz\",\n replacement = \"_neg.tsv.gz\",\n x = par_ann_spe$files$annotations$raw$spectral$spectral,\n fixed = TRUE\n ),\n threshold = par_ann_spe$annotations$thresholds$ms2$similarity$annotation,\n ppm = par_ann_spe$ms$tolerances$mass$ppm$ms2,\n dalton = par_ann_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = par_ann_spe$ms$thresholds$ms2$intensity,\n approx = par_ann_spe$annotations$ms2approx\n )\n }\n ),\n tar_target(\n name = ann_spe_pre,\n format = \"file\",\n command = {\n ann_spe_pre <- prepare_annotations_spectra(\n input = c(\n ann_spe_neg,\n ann_spe_pos\n ),\n output = par_pre_ann_spe$files$annotations$prepared$structural$spectral,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n )\n )\n ),\n # SIRIUS\n tar_target(\n name = ann_sir_pre,\n format = \"file\",\n command = {\n ann_sir_pre <-\n prepare_annotations_sirius(\n input_directory = par_pre_ann_sir$files$annotations$raw$sirius,\n output_ann = par_pre_ann_sir$files$annotations$prepared$structural$sirius,\n output_can = par_pre_ann_sir$files$annotations$prepared$canopus,\n output_for = par_pre_ann_sir$files$annotations$prepared$formula,\n sirius_version = par_pre_ann_sir$tools$sirius$version,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n ),\n tar_target(\n name = ann_sir_pre_can,\n format = \"file\", command = {\n ann_sir_pre_can <- ann_sir_pre[[1]]\n }\n ),\n tar_target(\n name = ann_sir_pre_for,\n format = \"file\", command = {\n ann_sir_pre_for <- ann_sir_pre[[2]]\n }\n ),\n tar_target(\n name = ann_sir_pre_str,\n format = \"file\", command = {\n ann_sir_pre_str <- ann_sir_pre[[3]]\n }\n ),\n list()\n ),\n ## Features\n list(\n tar_target(\n name = fea_edg_spe,\n format = \"file\",\n command = {\n fea_edg_spe <- create_edges_spectra(\n input = input_spectra,\n output = par_cre_edg_spe$files$networks$spectral$edges$raw,\n name_source = par_cre_edg_spe$names$source,\n name_target = par_cre_edg_spe$names$target,\n threshold = par_cre_edg_spe$annotations$thresholds$ms2$similarity$edges,\n ppm = par_cre_edg_spe$ms$tolerances$mass$ppm$ms2,\n dalton = par_cre_edg_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = par_cre_edg_spe$ms$thresholds$ms2$intensity\n )\n }\n ),\n tar_target(\n name = fea_com,\n format = \"file\",\n command = {\n fea_com <- create_components(\n input = fea_edg_pre,\n output = par_cre_com$files$networks$spectral$components$raw\n )\n }\n ),\n ## Interim\n list(\n tar_target(\n name = int_com,\n format = \"file\",\n command = {\n int_com <- fea_com\n # int_com <-\n # if (file.exists(fea_com)) {\n # fea_com\n # } else {\n # gnps_components\n # }\n }\n ),\n tar_target(\n name = edg_spe,\n format = \"file\",\n command = {\n edg_spe <- fea_edg_spe\n # edg_spe <-\n # ifelse(test = file.exists(fea_edg_spe),\n # yes = fea_edg_spe,\n # no = gnps_edges\n # )\n }\n )\n ),\n tar_target(\n name = fea_edg_pre,\n format = \"file\",\n command = {\n fea_edg_pre <- prepare_features_edges(\n input = c(\"ms1\" = ann_ms1_pre_edg, \"spectral\" = edg_spe),\n output = par_pre_fea_edg$files$networks$spectral$edges$prepared,\n name_source = par_pre_fea_edg$names$source,\n name_target = par_pre_fea_edg$names$target\n )\n }\n ),\n tar_target(\n name = fea_com_pre,\n format = \"file\",\n command = {\n fea_com_pre <- prepare_features_components(\n input = int_com,\n output = par_pre_fea_com$files$networks$spectral$components$prepared\n )\n }\n ),\n tar_target(\n name = fea_pre,\n format = \"file\",\n command = {\n fea_pre <- prepare_features_tables(\n features = input_features,\n output = par_pre_fea_tab$files$features$prepared,\n name_features = par_pre_fea_tab$names$features,\n name_rt = par_pre_fea_tab$names$rt$features,\n name_mz = par_pre_fea_tab$names$precursor\n )\n }\n )\n ),\n tar_target(\n name = tax_pre,\n format = \"file\",\n command = {\n tax_pre <- prepare_taxa(\n input = input_features,\n name_features = par_pre_tax$names$features,\n name_filename = par_pre_tax$names$filename,\n extension = par_pre_tax$names$extension,\n colname = par_pre_tax$names$taxon,\n metadata = par_pre_tax$files$metadata$raw,\n top_k = par_pre_tax$organisms$candidates,\n org_tax_ott = lib_mer_org_tax_ott,\n output = par_pre_tax$files$metadata$prepared,\n taxon = par_pre_tax$organisms$taxon\n )\n }\n ),\n tar_target(\n name = ann_fil,\n format = \"file\",\n command = {\n ann_fil <- filter_annotations(\n annotations = c(\n ann_spe_exp_gnp_pre,\n ann_spe_pre,\n ann_sir_pre_str,\n ann_ms1_pre_ann\n ),\n features = fea_pre,\n rts = lib_rt_rts,\n output = par_fil_ann$files$annotations$filtered,\n tolerance_rt = par_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = ann_pre,\n format = \"file\",\n command = {\n ann_pre <- weight_annotations(\n library = lib_mer_key,\n org_tax_ott = lib_mer_org_tax_ott,\n str_stereo = lib_mer_str_stereo,\n annotations = ann_fil,\n canopus = ann_sir_pre_can,\n formula = ann_sir_pre_for,\n components = fea_com_pre,\n edges = fea_edg_pre,\n taxa = tax_pre,\n output = par_wei_ann$files$annotations$processed,\n candidates_final = par_wei_ann$annotations$candidates$final,\n weight_spectral = par_wei_ann$weights$global$spectral,\n weight_chemical = par_wei_ann$weights$global$chemical,\n weight_biological = par_wei_ann$weights$global$biological,\n score_biological_domain = par_wei_ann$weights$biological$domain,\n score_biological_kingdom = par_wei_ann$weights$biological$kingdom,\n score_biological_phylum = par_wei_ann$weights$biological$phylum,\n score_biological_class = par_wei_ann$weights$biological$class,\n score_biological_order = par_wei_ann$weights$biological$order,\n score_biological_infraorder = par_wei_ann$weights$biological$infraorder,\n score_biological_family = par_wei_ann$weights$biological$family,\n score_biological_subfamily = par_wei_ann$weights$biological$subfamily,\n score_biological_tribe = par_wei_ann$weights$biological$tribe,\n score_biological_subtribe = par_wei_ann$weights$biological$subtribe,\n score_biological_genus = par_wei_ann$weights$biological$genus,\n score_biological_subgenus = par_wei_ann$weights$biological$subgenus,\n score_biological_species = par_wei_ann$weights$biological$species,\n score_biological_subspecies = par_wei_ann$weights$biological$subspecies,\n score_biological_variety = par_wei_ann$weights$biological$variety,\n score_chemical_cla_kingdom = par_wei_ann$weights$chemical$cla$kingdom,\n score_chemical_cla_superclass =\n par_wei_ann$weights$chemical$cla$superclass,\n score_chemical_cla_class = par_wei_ann$weights$chemical$cla$class,\n score_chemical_cla_parent = par_wei_ann$weights$chemical$cla$parent,\n score_chemical_npc_pathway = par_wei_ann$weights$chemical$npc$pathway,\n score_chemical_npc_superclass =\n par_wei_ann$weights$chemical$npc$superclass,\n score_chemical_npc_class = par_wei_ann$weights$chemical$npc$class,\n minimal_consistency = par_wei_ann$annotations$thresholds$consistency,\n minimal_ms1_bio = par_wei_ann$annotations$thresholds$ms1$biological,\n minimal_ms1_chemo = par_wei_ann$annotations$thresholds$ms1$chemical,\n minimal_ms1_condition =\n par_wei_ann$annotations$thresholds$ms1$condition,\n ms1_only = par_wei_ann$annotations$ms1only,\n compounds_names = par_wei_ann$options$compounds_names,\n high_confidence = par_wei_ann$options$high_confidence,\n remove_ties = par_wei_ann$options$remove_ties,\n summarise = par_wei_ann$options$summarise,\n pattern = par_wei_ann$files$pattern,\n force = par_wei_ann$options$force\n )\n }\n ),\n list(\n ## Benchmark\n tar_target(\n name = benchmark_path_url,\n command = {\n benchmark_path_url <- paths_urls_benchmarking_set\n }\n ),\n tar_target(\n name = benchmark_path_zip,\n command = {\n benchmark_path_zip <- paths_data_source_benchmark_zip\n }\n ),\n tar_target(\n name = benchmark_path_file,\n command = {\n benchmark_path_file <- paths_data_source_benchmark_cleaned\n }\n ),\n tar_target(\n name = benchmark_path_mgf_neg,\n command = {\n benchmark_path_mgf_neg <- paths_data_source_benchmark_mgf_neg\n }\n ),\n tar_target(\n name = benchmark_path_mgf_pos,\n command = {\n benchmark_path_mgf_pos <- paths_data_source_benchmark_mgf_pos\n }\n ),\n tar_target(\n name = benchmark_zip,\n format = \"file\",\n command = {\n benchmark_zip <- get_file(\n url = benchmark_path_url,\n export = benchmark_path_zip\n )\n return(benchmark_path_zip)\n }\n ),\n tar_target(\n name = benchmark_file,\n format = \"file\",\n command = {\n utils::unzip(zipfile = benchmark_zip)\n dir.create(dirname(benchmark_path_file), recursive = TRUE)\n file.copy(from = \"cleaned_libraries_matchms/results_library_cleaning/cleaned_spectra.mgf\", to = benchmark_path_file)\n unlink(\"cleaned_libraries_matchms\", recursive = TRUE)\n return(benchmark_path_file)\n }\n ),\n tar_target(\n name = benchmark_converted,\n format = \"file\",\n command = {\n sp <- benchmark_file |>\n import_spectra()\n sp |>\n Spectra::filterEmptySpectra() |>\n tima:::extract_spectra() |>\n data.frame() |>\n saveRDS(file = \"data/interim/benchmark/benchmark_spectra.rds\")\n return(\"data/interim/benchmark/benchmark_spectra.rds\")\n }\n ),\n tar_target(\n name = benchmark_prepared,\n format = \"file\",\n command = {\n sp <- benchmark_converted |>\n import_spectra()\n\n sp@backend@spectraData$precursorMz <-\n sp@backend@spectraData$PRECURSOR_MZ |>\n as.numeric()\n\n log_debug(\"Imported\")\n sp_clean <- sp\n\n log_debug(\"Cleaned\")\n df_meta <- tidytable::tidytable(\n adduct = sp_clean$ADDUCT,\n inchikey = sp_clean$INCHIKEY,\n instrument = sp_clean$INSTRUMENT_TYPE,\n fragments = lapply(sp_clean@backend@peaksData, length) |>\n as.character() |>\n as.numeric() / 2,\n precursorMz = sp_clean$precursorMz,\n smiles = sp_clean$SMILES,\n ccmslib = sp_clean$SPECTRUM_ID,\n charge = sp_clean$precursorCharge,\n name = sp_clean$COMPOUND_NAME\n ) |>\n tidytable::mutate(\n tidytable::across(\n .cols = tidyselect::everything(),\n .fns = function(x) {\n tidytable::na_if(x, \"\")\n }\n )\n )\n\n log_debug(\"Framed\")\n df_clean <- df_meta |>\n tidytable::filter(!is.na(inchikey)) |>\n tidytable::filter(fragments >= 5) |>\n tidytable::filter(fragments <= 250) |>\n tidytable::filter(!grepl(\n pattern = \"QQQ\",\n x = instrument,\n fixed = TRUE\n )) |>\n ## fragments are nominal mass\n tidytable::filter(!grepl(\n pattern = \"ReSpect\",\n x = name,\n fixed = TRUE\n )) |>\n ## remove spectral matches\n tidytable::filter(!grepl(\n pattern = \"Spectral Match to\",\n x = name,\n fixed = TRUE\n )) |>\n ## remove putatives\n tidytable::filter(!grepl(\n pattern = \"putative\",\n x = name,\n fixed = TRUE\n )) |>\n tidytable::select(-name) |>\n tidytable::mutate(mass = precursorMz) |>\n tidytable::separate(\n col = mass,\n sep = \"\\\\.\",\n into = c(\"a\", \"b\")\n ) |>\n tidytable::filter(!is.na(b)) |>\n tidytable::filter(stringi::stri_length(as.numeric(b)) > 1) |>\n tidytable::select(-a, -b) |>\n tidytable::mutate(inchikey_no_stereo = gsub(\n pattern = \"-.*\",\n replacement = \"\",\n x = inchikey,\n perl = TRUE\n )) |>\n tidytable::distinct(inchikey_no_stereo, adduct, .keep_all = TRUE) |>\n tidytable::mutate(mz = precursorMz) |>\n ## Weird way to have some kind of retention time\n tidytable::mutate(\n rt = tidytable::cur_group_id(),\n .by = \"inchikey_no_stereo\"\n )\n\n df_clean_neg <- df_clean |>\n tidytable::filter(grepl(\n pattern = \"]-\",\n x = adduct,\n fixed = TRUE\n ))\n\n df_clean_pos <- df_clean |>\n tidytable::filter(grepl(\n pattern = \"]+\",\n x = adduct,\n fixed = TRUE\n ))\n\n sp_pos <-\n sp_clean[sp_clean$SPECTRUM_ID %in% df_clean_pos$ccmslib]\n sp_neg <-\n sp_clean[sp_clean$SPECTRUM_ID %in% df_clean_neg$ccmslib]\n\n extract_benchmark_spectra <- function(x, mode) {\n df <- x |>\n tima:::extract_spectra() |>\n tidytable::mutate(acquisitionNum = tidytable::row_number()) |>\n tidytable::mutate(spectrum_id = acquisitionNum) |>\n tidytable::mutate(short_ik = gsub(\n pattern = \"-.*\",\n replacement = \"\",\n INCHIKEY,\n perl = TRUE\n )) |>\n tidytable::mutate(\n rtime = tidytable::cur_group_id(),\n .by = \"short_ik\"\n ) |>\n tidytable::mutate(precursorCharge = ifelse(\n test = mode == \"pos\",\n yes = as.integer(1),\n no = as.integer(-1)\n )) |>\n tidytable::select(\n acquisitionNum,\n precursorCharge,\n precursorMz,\n MS_LEVEL,\n rtime,\n name = COMPOUND_NAME,\n smiles = SMILES,\n inchi = INCHI,\n inchikey = INCHIKEY,\n adduct = ADDUCT,\n instrument = INSTRUMENT_TYPE,\n ccmslib = SPECTRUM_ID,\n spectrum_id = acquisitionNum,\n mz,\n intensity\n ) |>\n data.frame()\n return(df)\n }\n\n spectra_harmonized_pos <- sp_pos |>\n extract_benchmark_spectra(mode = \"pos\")\n\n spectra_harmonized_neg <- sp_neg |>\n extract_benchmark_spectra(mode = \"neg\")\n\n select_benchmark_columns <- function(x) {\n df <- x |>\n tidytable::select(\n adduct,\n inchikey,\n instrument,\n smiles,\n ccmslib,\n charge = precursorCharge,\n mz = precursorMz,\n rt = rtime,\n feature_id = spectrum_id\n ) |>\n tidytable::mutate(inchikey_no_stereo = gsub(\n pattern = \"-.*\",\n replacement = \"\",\n x = inchikey,\n perl = TRUE\n )) |>\n data.frame()\n return(df)\n }\n\n df_clean_pos <- spectra_harmonized_pos |>\n select_benchmark_columns()\n\n df_clean_neg <- spectra_harmonized_neg |>\n select_benchmark_columns()\n\n log_debug(\"Exporting\")\n spectra_harmonized_pos |>\n Spectra::Spectra() |>\n Spectra::export(\n backend = MsBackendMgf::MsBackendMgf(),\n file = benchmark_path_mgf_pos\n )\n spectra_harmonized_neg |>\n Spectra::Spectra() |>\n Spectra::export(\n backend = MsBackendMgf::MsBackendMgf(),\n file = benchmark_path_mgf_neg\n )\n df_clean_pos |>\n export_output(\"data/interim/benchmark/benchmark_meta_pos.tsv\")\n df_clean_neg |>\n export_output(\"data/interim/benchmark/benchmark_meta_neg.tsv\")\n\n return(\n c(\n \"spectra_pos\" = benchmark_path_mgf_pos,\n \"spectra_neg\" = benchmark_path_mgf_neg,\n \"meta_pos\" = \"data/interim/benchmark/benchmark_meta_pos.tsv\",\n \"meta_neg\" = \"data/interim/benchmark/benchmark_meta_neg.tsv\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_pre_mgf_pos,\n format = \"file\",\n command = {\n benchmark_pre_mgf_pos <- benchmark_prepared[[1]]\n }\n ),\n tar_target(\n name = benchmark_pre_mgf_neg,\n format = \"file\",\n command = {\n benchmark_pre_mgf_neg <- benchmark_prepared[[2]]\n }\n ),\n tar_target(\n name = benchmark_pre_meta_pos,\n format = \"file\",\n command = {\n benchmark_pre_meta_pos <- benchmark_prepared[[3]]\n }\n ),\n tar_target(\n name = benchmark_pre_meta_neg,\n format = \"file\",\n command = {\n benchmark_pre_meta_neg <- benchmark_prepared[[4]]\n }\n ),\n tar_target(\n name = benchmark_taxed_pos,\n format = \"file\",\n command = {\n benchmark_taxed_pos <- benchmark_pre_meta_pos |>\n tima:::benchmark_taxize_spectra(\n keys = lib_mer_key,\n org_tax_ott = lib_mer_org_tax_ott,\n output = \"data/interim/benchmark/benchmark_taxed_pos.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_taxed_neg,\n format = \"file\",\n command = {\n benchmark_taxed_neg <- benchmark_pre_meta_neg |>\n tima:::benchmark_taxize_spectra(\n keys = lib_mer_key,\n org_tax_ott = lib_mer_org_tax_ott,\n output = \"data/interim/benchmark/benchmark_taxed_neg.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_def_ann_mas,\n command = {\n benchmark_def_ann_mas <- tima:::parse_yaml_params(\n def = par_def_ann_mas,\n usr = par_def_ann_mas\n )\n }\n ),\n tar_target(\n name = benchmark_ann_ms1_pre_pos,\n format = \"file\",\n command = {\n benchmark_ann_ms1_pre_pos <-\n annotate_masses(\n features = benchmark_pre_meta_pos,\n library = lib_mer_key,\n output_annotations = \"data/interim/benchmark/benchmark_ann_ms1_pos.tsv.gz\",\n output_edges = \"data/interim/benchmark/benchmark_edges_ms1_pos.tsv.gz\",\n name_source = benchmark_def_ann_mas$names$source,\n name_target = benchmark_def_ann_mas$names$target,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc,\n adducts_list = par_ann_mas$ms$adducts,\n clusters_list = par_ann_mas$ms$clusters,\n neutral_losses_list = par_ann_mas$ms$neutral_losses,\n ms_mode = \"pos\",\n tolerance_ppm = benchmark_def_ann_mas$ms$tolerances$mass$ppm$ms1,\n tolerance_rt = benchmark_def_ann_mas$ms$tolerances$rt$adducts\n )\n }\n ),\n tar_target(\n name = benchmark_ann_ms1_pre_neg,\n format = \"file\",\n command = {\n benchmark_ann_ms1_pre_neg <-\n annotate_masses(\n features = benchmark_pre_meta_neg,\n library = lib_mer_key,\n output_annotations = \"data/interim/benchmark/benchmark_ann_ms1_neg.tsv.gz\",\n output_edges = \"data/interim/benchmark/benchmark_edges_ms1_neg.tsv.gz\",\n name_source = benchmark_def_ann_mas$names$source,\n name_target = benchmark_def_ann_mas$names$target,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc,\n adducts_list = par_ann_mas$ms$adducts,\n clusters_list = par_ann_mas$ms$clusters,\n neutral_losses_list = par_ann_mas$ms$neutral_losses,\n ms_mode = \"neg\",\n tolerance_ppm = benchmark_def_ann_mas$ms$tolerances$mass$ppm$ms1,\n tolerance_rt = benchmark_def_ann_mas$ms$tolerances$rt$adducts\n )\n }\n ),\n tar_target(\n name = benchmark_def_cre_edg_spe,\n command = {\n benchmark_def_cre_edg_spe <- tima:::parse_yaml_params(\n def = par_def_cre_edg_spe,\n usr = par_def_cre_edg_spe\n )\n }\n ),\n tar_target(\n name = benchmark_edg_spe_pos,\n format = \"file\",\n command = {\n benchmark_edg_spe_pos <- create_edges_spectra(\n input = benchmark_pre_mgf_pos,\n output = \"data/interim/benchmark/benchmark_edges_spe_pos.tsv.gz\",\n name_source = benchmark_def_cre_edg_spe$names$source,\n name_target = benchmark_def_cre_edg_spe$names$target,\n threshold = benchmark_def_cre_edg_spe$annotations$thresholds$ms2$similarity$edges,\n ppm = benchmark_def_cre_edg_spe$ms$tolerances$mass$ppm$ms2,\n dalton = benchmark_def_cre_edg_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = 0\n )\n }\n ),\n tar_target(\n name = benchmark_edg_spe_neg,\n format = \"file\",\n command = {\n benchmark_edg_spe_neg <- create_edges_spectra(\n input = benchmark_pre_mgf_neg,\n output = \"data/interim/benchmark/benchmark_edges_spe_neg.tsv.gz\",\n name_source = benchmark_def_cre_edg_spe$names$source,\n name_target = benchmark_def_cre_edg_spe$names$target,\n threshold = benchmark_def_cre_edg_spe$annotations$thresholds$ms2$similarity$edges,\n ppm = benchmark_def_cre_edg_spe$ms$tolerances$mass$ppm$ms2,\n dalton = benchmark_def_cre_edg_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = 0\n )\n }\n ),\n tar_target(\n name = benchmark_def_pre_fea_edg,\n command = {\n benchmark_def_pre_fea_edg <- tima:::parse_yaml_params(\n def = par_def_pre_fea_edg,\n usr = par_def_pre_fea_edg\n )\n }\n ),\n tar_target(\n name = benchmark_edg_pre_pos,\n format = \"file\",\n command = {\n benchmark_edg_pre_pos <- prepare_features_edges(\n input = list(\"spectral\" = benchmark_edg_spe_pos, \"ms1\" = benchmark_ann_ms1_pre_pos[[2]]),\n output = \"data/interim/benchmark/benchmark_edges_pos.tsv.gz\",\n name_source = benchmark_def_pre_fea_edg$names$source,\n name_target = benchmark_def_pre_fea_edg$names$target\n )\n }\n ),\n tar_target(\n name = benchmark_edg_pre_neg,\n format = \"file\",\n command = {\n benchmark_edg_pre_neg <- prepare_features_edges(\n input = list(\"spectral\" = benchmark_edg_spe_neg, \"ms1\" = benchmark_ann_ms1_pre_neg[[2]]),\n output = \"data/interim/benchmark/benchmark_edges_neg.tsv.gz\",\n name_source = benchmark_def_pre_fea_edg$names$source,\n name_target = benchmark_def_pre_fea_edg$names$target\n )\n }\n ),\n tar_target(\n name = benchmark_def_cre_edg_com,\n command = {\n benchmark_def_cre_edg_com <- tima:::parse_yaml_params(\n def = par_def_cre_com,\n usr = par_def_cre_com\n )\n }\n ),\n tar_target(\n name = benchmark_com_pos,\n format = \"file\",\n command = {\n benchmark_com_pos <- create_components(\n input = benchmark_edg_pre_pos,\n output = \"data/interim/benchmark/benchmark_components_pos.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_com_neg,\n format = \"file\",\n command = {\n benchmark_com_neg <- create_components(\n input = benchmark_edg_pre_neg,\n output = \"data/interim/benchmark/benchmark_components_neg.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_def_pre_fea_com,\n command = {\n benchmark_def_pre_fea_com <- tima:::parse_yaml_params(\n def = par_def_pre_fea_com,\n usr = par_def_pre_fea_com\n )\n }\n ),\n tar_target(\n name = benchmark_com_pre_pos,\n format = \"file\",\n command = {\n benchmark_com_pre_pos <- prepare_features_components(\n input = benchmark_com_pos,\n output = \"data/interim/benchmark/benchmark_com_pre_pos.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_com_pre_neg,\n format = \"file\",\n command = {\n benchmark_com_pre_neg <- prepare_features_components(\n input = benchmark_com_neg,\n output = \"data/interim/benchmark/benchmark_com_pre_neg.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_def_ann_spe,\n command = {\n benchmark_def_ann_spe <- tima:::parse_yaml_params(\n def = par_def_ann_spe,\n usr = par_def_ann_spe\n )\n }\n ),\n tar_target(\n name = benchmark_ann_spe_pos,\n format = \"file\",\n command = {\n benchmark_ann_spe_pos <- annotate_spectra(\n input = benchmark_pre_mgf_pos,\n library = c(\n lib_spe_is_lot_pre_pos,\n lib_spe_exp_mb_pre_pos\n ),\n polarity = \"pos\",\n output = \"data/interim/benchmark/benchmark_ann_spe_pos.tsv.gz\",\n threshold =\n benchmark_def_ann_spe$annotations$thresholds$ms2$similarity$annotation,\n ppm = benchmark_def_ann_spe$ms$tolerances$mass$ppm$ms2,\n dalton = benchmark_def_ann_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = 0,\n approx = benchmark_def_ann_spe$annotations$ms2approx\n )\n }\n ),\n tar_target(\n name = benchmark_ann_spe_neg,\n format = \"file\",\n command = {\n benchmark_ann_spe_neg <- annotate_spectra(\n input = benchmark_pre_mgf_neg,\n library = c(\n lib_spe_is_lot_pre_neg,\n lib_spe_exp_mb_pre_neg\n ),\n polarity = \"neg\",\n output = \"data/interim/benchmark/benchmark_ann_spe_neg.tsv.gz\",\n threshold =\n benchmark_def_ann_spe$annotations$thresholds$ms2$similarity$annotation,\n ppm = benchmark_def_ann_spe$ms$tolerances$mass$ppm$ms2,\n dalton = benchmark_def_ann_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = 0,\n approx = benchmark_def_ann_spe$annotations$ms2approx\n )\n }\n ),\n tar_target(\n name = benchmark_def_pre_ann_spe,\n command = {\n benchmark_def_pre_ann_spe <- tima:::parse_yaml_params(\n def = par_def_pre_ann_spe,\n usr = par_def_pre_ann_spe\n )\n }\n ),\n tar_target(\n name = benchmark_ann_spe_pre_pos,\n format = \"file\",\n command = {\n benchmark_ann_spe_pre_pos <- prepare_annotations_spectra(\n input = c(benchmark_ann_spe_pos),\n output = \"data/interim/benchmark/benchmark_ann_spe_pre_pos.tsv.gz\",\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n ),\n tar_target(\n name = benchmark_ann_spe_pre_neg,\n format = \"file\",\n command = {\n benchmark_ann_spe_pre_neg <- prepare_annotations_spectra(\n input = c(benchmark_ann_spe_neg),\n output = \"data/interim/benchmark/benchmark_ann_spe_pre_neg.tsv.gz\",\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n ),\n tar_target(\n name = benchmark_def_pre_ann_sir,\n command = {\n benchmark_def_pre_ann_sir <- tima:::parse_yaml_params(\n def = par_def_pre_ann_sir,\n usr = par_def_pre_ann_sir\n )\n }\n ),\n tar_target(\n name = benchmark_ann_sir_pre,\n format = \"file\",\n command = {\n benchmark_ann_sir_pre <-\n prepare_annotations_sirius(\n input_directory = \"doesNotExist4Now\",\n output_ann = \"data/interim/benchmark/benchmark_ann_sir_pre.tsv.gz\",\n output_can = \"data/interim/benchmark/benchmark_ann_sir_pre_can.tsv.gz\",\n output_for = \"data/interim/benchmark/benchmark_ann_sir_pre_for.tsv.gz\",\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n ),\n tar_target(\n name = benchmark_ann_sir_pre_can,\n format = \"file\", command = {\n benchmark_ann_sir_pre_can <- benchmark_ann_sir_pre[[1]]\n }\n ),\n tar_target(\n name = benchmark_ann_sir_pre_for,\n format = \"file\", command = {\n benchmark_ann_sir_pre_for <- benchmark_ann_sir_pre[[2]]\n }\n ),\n tar_target(\n name = benchmark_ann_sir_pre_str,\n format = \"file\", command = {\n benchmark_ann_sir_pre_str <- benchmark_ann_sir_pre[[3]]\n }\n ),\n tar_target(\n name = benchmark_def_fil_ann,\n command = {\n benchmark_def_fil_ann <- tima:::parse_yaml_params(\n def = par_def_fil_ann,\n usr = par_def_fil_ann\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_spe_neg,\n format = \"file\",\n command = {\n benchmark_ann_fil_spe_neg <- filter_annotations(\n annotations = c(\n benchmark_ann_spe_pre_neg,\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_neg,\n rts = list(),\n output = \"data/interim/benchmark/benchmark_ann_spe_fil_neg.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_spe_ms1_neg,\n format = \"file\",\n command = {\n benchmark_ann_fil_spe_ms1_neg <- filter_annotations(\n annotations = c(\n benchmark_ann_spe_pre_neg,\n benchmark_ann_ms1_pre_neg[[1]],\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_neg,\n rts = list(),\n output =\n \"data/interim/benchmark/benchmark_ann_spe_ms1_fil_neg.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_ms1_neg,\n format = \"file\",\n command = {\n benchmark_ann_fil_ms1_neg <- filter_annotations(\n annotations = c(\n benchmark_ann_ms1_pre_neg[[1]],\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_neg,\n rts = list(),\n output = \"data/interim/benchmark/benchmark_ann_ms1_fil_neg.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_spe_pos,\n format = \"file\",\n command = {\n benchmark_ann_fil_spe_pos <- filter_annotations(\n annotations = c(\n benchmark_ann_spe_pre_pos,\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_pos,\n rts = list(),\n output = \"data/interim/benchmark/benchmark_ann_spe_fil_pos.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_spe_ms1_pos,\n format = \"file\",\n command = {\n benchmark_ann_fil_spe_ms1_pos <- filter_annotations(\n annotations = c(\n benchmark_ann_spe_pre_pos,\n benchmark_ann_ms1_pre_pos[[1]],\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_pos,\n rts = list(),\n output =\n \"data/interim/benchmark/benchmark_ann_spe_ms1_fil_pos.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_ms1_pos,\n format = \"file\",\n command = {\n benchmark_ann_fil_ms1_pos <- filter_annotations(\n annotations = c(\n benchmark_ann_ms1_pre_pos[[1]],\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_pos,\n rts = list(),\n output = \"data/interim/benchmark/benchmark_ann_ms1_fil_pos.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_def_wei_ann,\n command = {\n benchmark_def_wei_ann <- tima:::parse_yaml_params(\n def = par_def_wei_ann,\n usr = par_def_wei_ann\n )\n }\n ),\n tar_target(\n name = benchmark_wei_par,\n command = {\n benchmark_wei_par <- list(\n canopus = benchmark_ann_sir_pre_can,\n formula = benchmark_ann_sir_pre_for,\n library = lib_mer_key,\n org_tax_ott = lib_mer_org_tax_ott,\n str_stereo = lib_mer_str_stereo,\n candidates_final = 500,\n score_biological_domain =\n benchmark_def_wei_ann$weights$biological$domain,\n score_biological_kingdom =\n benchmark_def_wei_ann$weights$biological$kingdom,\n score_biological_phylum =\n benchmark_def_wei_ann$weights$biological$phylum,\n score_biological_class =\n benchmark_def_wei_ann$weights$biological$class,\n score_biological_order =\n benchmark_def_wei_ann$weights$biological$order,\n score_biological_infraorder =\n benchmark_def_wei_ann$weights$biological$infraorder,\n score_biological_family =\n benchmark_def_wei_ann$weights$biological$family,\n score_biological_subfamily =\n benchmark_def_wei_ann$weights$biological$subfamily,\n score_biological_tribe =\n benchmark_def_wei_ann$weights$biological$tribe,\n score_biological_subtribe =\n benchmark_def_wei_ann$weights$biological$subtribe,\n score_biological_genus =\n benchmark_def_wei_ann$weights$biological$genus,\n score_biological_subgenus =\n benchmark_def_wei_ann$weights$biological$subgenus,\n score_biological_species =\n benchmark_def_wei_ann$weights$biological$species,\n score_biological_subspecies =\n benchmark_def_wei_ann$weights$biological$subspecies,\n score_biological_variety =\n benchmark_def_wei_ann$weights$biological$variety,\n score_chemical_cla_kingdom =\n benchmark_def_wei_ann$weights$chemical$cla$kingdom,\n score_chemical_cla_superclass =\n benchmark_def_wei_ann$weights$chemical$cla$superclass,\n score_chemical_cla_class =\n benchmark_def_wei_ann$weights$chemical$cla$class,\n score_chemical_cla_parent =\n benchmark_def_wei_ann$weights$chemical$cla$parent,\n score_chemical_npc_pathway =\n benchmark_def_wei_ann$weights$chemical$npc$pathway,\n score_chemical_npc_superclass =\n benchmark_def_wei_ann$weights$chemical$npc$superclass,\n score_chemical_npc_class =\n benchmark_def_wei_ann$weights$chemical$npc$class,\n minimal_consistency =\n benchmark_def_wei_ann$annotations$thresholds$consistency,\n minimal_ms1_bio =\n benchmark_def_wei_ann$annotations$thresholds$ms1$biological,\n minimal_ms1_chemo =\n benchmark_def_wei_ann$annotations$thresholds$ms1$chemical,\n minimal_ms1_condition =\n benchmark_def_wei_ann$annotations$thresholds$ms1$condition,\n compounds_names = benchmark_def_wei_ann$options$compounds_names,\n high_confidence = FALSE,\n remove_ties = benchmark_def_wei_ann$options$remove_ties,\n summarise = benchmark_def_wei_ann$options$summarise,\n pattern = benchmark_def_wei_ann$files$pattern,\n force = benchmark_def_wei_ann$options$force\n )\n }\n ),\n tar_target(\n name = benchmark_files_pos,\n command = {\n benchmark_files_pos <- list(\n components = benchmark_com_pre_pos,\n edges = benchmark_edg_pre_pos,\n taxa = benchmark_taxed_pos\n )\n }\n ),\n tar_target(\n name = benchmark_files_neg,\n command = {\n benchmark_files_pos <- list(\n components = benchmark_com_pre_neg,\n edges = benchmark_edg_pre_neg,\n taxa = benchmark_taxed_neg\n )\n }\n ),\n # tar_target(\n # name = benchmark_ann_pre_ms1_pos,\n # . format = \"file\",\n # command = {\n # benchmark_ann_pre_ms1_pos <-\n # do.call(\n # what = weight_annotations,\n # args = c(benchmark_wei_par,\n # benchmark_files_pos,\n # annotations = benchmark_ann_fil_ms1_pos,\n # weight_spectral = benchmark_def_wei_ann$weights$global$spectral,\n # weight_chemical = benchmark_def_wei_ann$weights$global$chemical,\n # weight_biological =\n # benchmark_def_wei_ann$weights$global$biological,\n # ms1_only = TRUE,\n # output = \"benchmark_lotus_ms1_pos.tsv.gz\"\n # )\n # )\n # }\n # ),\n tar_target(\n name = benchmark_ann_pre_ms2_b_pos,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms2_b_pos <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_pos,\n annotations = benchmark_ann_fil_spe_pos,\n weight_spectral = 0.333,\n weight_chemical = 0,\n weight_biological = 0.666,\n ms1_only = FALSE,\n output = \"benchmark_lotus_ms2_bio_pos.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms1_ms2_b_pos,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms1_ms2_b_pos <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_pos,\n annotations = benchmark_ann_fil_spe_ms1_pos,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0,\n weight_biological = 0.666,\n output = \"benchmark_lotus_ms1_ms2_bio_pos.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms2_b_c_pos,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms2_b_c_pos <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_pos,\n annotations = benchmark_ann_fil_spe_pos,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0.166,\n weight_biological = 0.500,\n output = \"benchmark_lotus_ms2_bio_chemo_pos.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms1_ms2_b_c_pos,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms1_ms2_b_c_pos <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_pos,\n annotations = benchmark_ann_fil_spe_ms1_pos,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0.166,\n weight_biological = 0.500,\n output = \"benchmark_lotus_ms1_ms2_bio_chemo_pos.tsv.gz\"\n )\n )\n }\n ),\n # tar_target(\n # name = benchmark_ann_pre_ms1_neg,\n # . format = \"file\",\n # command = {\n # benchmark_ann_pre_ms1_neg <-\n # do.call(\n # what = weight_annotations,\n # args = c(benchmark_wei_par,\n # benchmark_files_neg,\n # annotations = benchmark_ann_fil_ms1_neg,\n # ms1_only = TRUE,\n # weight_spectral = benchmark_def_wei_ann$weights$global$spectral,\n # weight_chemical = benchmark_def_wei_ann$weights$global$chemical,\n # weight_biological =\n # benchmark_def_wei_ann$weights$global$biological,\n # output = \"benchmark_lotus_ms1_neg.tsv.gz\"\n # )\n # )\n # }\n # ),\n tar_target(\n name = benchmark_ann_pre_ms2_b_neg,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms2_b_neg <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_neg,\n annotations = benchmark_ann_fil_spe_neg,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0,\n weight_biological = 0.666,\n output = \"benchmark_lotus_ms2_bio_neg.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms1_ms2_b_neg,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms1_ms2_b_neg <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_neg,\n annotations = benchmark_ann_fil_spe_ms1_neg,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0,\n weight_biological = 0.666,\n output = \"benchmark_lotus_ms1_ms2_bio_neg.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms2_b_c_neg,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms2_b_c_neg <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_neg,\n annotations = benchmark_ann_fil_spe_neg,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0.166,\n weight_biological = 0.500,\n output = \"benchmark_lotus_ms2_bio_chemo_neg.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms1_ms2_b_c_neg,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms1_ms2_b_c_neg <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_neg,\n annotations = benchmark_ann_fil_spe_ms1_neg,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0.166,\n weight_biological = 0.500,\n output = \"benchmark_lotus_ms1_ms2_bio_chemo_neg.tsv.gz\"\n )\n )\n }\n )\n )\n)\n","type":"text"},{"name":"doc/index.html","content":"\n\nR: Vignettes and other documentation<\/title>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0, user-scalable=yes\" />\n<link rel=\"stylesheet\" type=\"text/css\" href=\"/doc/html/R.css\" />\n<\/head><body><div class=\"container\">\n<h1> Vignettes and other documentation\n<img class=\"toplogo\" src=\"/doc/html/Rlogo.svg\" alt=\"[R logo]\" />\n<\/h1>\n<hr/>\n<div style=\"text-align: center;\">\n<a href=\"/doc/html/index.html\"><img class=\"arrow\" src=\"/doc/html/up.jpg\" alt=\"[Top]\" /><\/a>\n<\/div>\n<h2>Vignettes from package 'tima'<\/h2>\n<table style=\"width: 100%;\">\n<col style=\"width: 22%;\" />\n<col style=\"width: 2%;\" />\n<col style=\"width: 50%;\" />\n<col style=\"width: 8%;\" />\n<col style=\"width: 8%;\" />\n<col style=\"width: 8%;\" />\n<tr><td style=\"text-align: right; vertical-align: top;\"><a href=\"../../../library/tima/doc/tima.html\">tima::tima<\/a><\/td>\n<td><\/td><td style=\"vertical-align: top;\">General comments about the infrastructure<\/td>\n<td style=\"vertical-align: top;\"><a href=\"../../../library/tima/doc/tima.html\">HTML<\/a><\/td>\n<td style=\"vertical-align: top;\"><a href=\"../../../library/tima/doc/tima.Rmd\">source<\/a><\/td>\n<td style=\"vertical-align: top; white-space: nowrap\"><a href=\"../../../library/tima/doc/tima.R\">R code<\/a><\/td><\/tr>\n<\/table>\n<\/div><\/body><\/html>\n","type":"text"},{"name":"doc/tima.R","content":"## ----setup, include = FALSE---------------------------------------------------\nknitr::opts_chunk$set(\n collapse = TRUE,\n comment = \"#>\"\n)\nknitr::opts_knit$set(root.dir = system.file(package = \"tima\"))\n\n## ----tima, results=\"hide\", message=FALSE, warning=FALSE, include = FALSE------\nlibrary(\"tima\")\n\n## ----chunk-1, echo=FALSE, message=FALSE, warning=FALSE, out.width=\"100%\"------\nlibrary(targets)\nSys.setenv(TAR_WARN = \"false\")\ntargets::tar_visnetwork(\n names = starts_with(\"ann\"),\n exclude = c(\n tidyselect::contains(\"benchmark\"),\n tidyselect::contains(\"par_\"),\n tidyselect::contains(\"gnps\"),\n tidyselect::contains(\"input\"),\n tidyselect::contains(\"paths\"),\n tidyselect::contains(\"_is\"),\n tidyselect::contains(\"_exp\"),\n tidyselect::contains(\"library_sop_lotus\"),\n tidyselect::contains(\"library_sop_closed\")\n ),\n targets_only = TRUE,\n degree_from = 8,\n level_separation = 200\n)\n\n## ----eval=FALSE, include=TRUE-------------------------------------------------\n# install.packages(\n# \"tima\",\n# repos = c(\n# \"https://taxonomicallyinformedannotation.r-universe.dev\",\n# \"https://bioc.r-universe.dev\",\n# \"https://cloud.r-project.org\"\n# )\n# )\n# tima::install()\n# tima::get_example_files()\n\n## ----eval=FALSE, include=TRUE-------------------------------------------------\n# tima::run_app()\n\n## ----eval=FALSE, include=TRUE-------------------------------------------------\n# tima::tima_full()\n\n","type":"text"},{"name":"doc/tima.Rmd","content":"---\ntitle: \"General comments about the infrastructure\"\nauthor: \"Adriano Rutz\"\ncreative_commons: CC BY-SA\ndate: \"`r Sys.Date()`\"\noutput: rmarkdown::html_vignette\nopengraph:\n image:\n src: https://github.com/taxonomicallyinformedannotation/tima/blob/main/man/figures/logo.svg\n alt: \"Taxonomically Informed Metabolite Annotation\"\n twitter:\n creator: \"@adafede\"\nvignette: >\n %\\VignetteIndexEntry{General comments about the infrastructure}\n %\\VignetteEngine{knitr::rmarkdown}\n %\\VignetteEncoding{UTF-8}\n---\n\n```{r setup, include = FALSE}\nknitr::opts_chunk$set(\n collapse = TRUE,\n comment = \"#>\"\n)\nknitr::opts_knit$set(root.dir = system.file(package = \"tima\"))\n```\n\n```{r tima, results=\"hide\", message=FALSE, warning=FALSE, include = FALSE}\nlibrary(\"tima\")\n```\n\nThis vignette describes the philosophy behind the infrastructure of **TIMA**.\n\n## Philosophy\n\nOur main goals were **flexibility** and **reproducibility**.\n\n### Flexibility\n\nTo ensure flexibility, we tried to split the process in as much tiny parts as needed.\nSo you can decide whether to skip an optional part, add your own processing, etc.\nWe tried to cover most use cases, but of course they are not exhaustive.\nIf you feel like something useful to other users is missing, please fill an [issue](https://github.com/taxonomicallyinformedannotation/tima/issues).\n\n### Reproducibility\n\nAfter some time using TIMA, you will probably wonder:\n\"*What was the parameters I used to generate this file?*\" ...\nOr a collaborator might ask you to share your data and parameters.\nWriting them down each time might be time consuming and not really in line with modern computational approaches.\nTherefore, we chose to implement all parameters of all steps (almost...) as YAML files.\nThey are human-readable and can be used in batches.\nIf you do not like YAML, parameters of each steps can also be given as command line arguments.\nThey will then be saved as YAML you will be able to share.\n\nTo ensure optimal reproducibility and avoiding re-computing endlessly steps that did not change, we decided to build a [{targets}](https://books.ropensci.org/targets/) pipeline.\nEach step of the whole pipeline will be described next.\n\n\n```{r chunk-1, echo=FALSE, message=FALSE, warning=FALSE, out.width=\"100%\"}\nlibrary(targets)\nSys.setenv(TAR_WARN = \"false\")\ntargets::tar_visnetwork(\n names = starts_with(\"ann\"),\n exclude = c(\n tidyselect::contains(\"benchmark\"),\n tidyselect::contains(\"par_\"),\n tidyselect::contains(\"gnps\"),\n tidyselect::contains(\"input\"),\n tidyselect::contains(\"paths\"),\n tidyselect::contains(\"_is\"),\n tidyselect::contains(\"_exp\"),\n tidyselect::contains(\"library_sop_lotus\"),\n tidyselect::contains(\"library_sop_closed\")\n ),\n targets_only = TRUE,\n degree_from = 8,\n level_separation = 200\n)\n```\n\n## Use\n\nEach step is detailed in the related [vignettes](https://taxonomicallyinformedannotation.github.io/tima/articles).\n\nAll coming steps admit you already installed `tima`:\n\n```{r eval=FALSE, include=TRUE}\ninstall.packages(\n \"tima\",\n repos = c(\n \"https://taxonomicallyinformedannotation.r-universe.dev\",\n \"https://bioc.r-universe.dev\",\n \"https://cloud.r-project.org\"\n )\n)\ntima::install()\ntima::get_example_files()\n```\n\nWe now recommend you to read the [first vignette](https://taxonomicallyinformedannotation.github.io/tima/articles/I-gathering.html).\n\n### tl;dr\n\nIf you do not feel like going through all the steps, then just do 🚀:\n\n```{r eval=FALSE, include=TRUE}\ntima::run_app()\n```\n\nIf you do not even need a GUI ☠️:\n\n```{r eval=FALSE, include=TRUE}\ntima::tima_full()\n```\n","type":"text"},{"name":"doc/tima.html","content":"<!doctype html>\n<html>\n <head>\n <meta charset=\"utf-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1, shrink-to-fit=no\">\n <meta name=\"generator\" content=\"r-universe/pandoc\">\n <meta name=\"author\" content=\"Adriano Rutz\">\n \n <title>General comments about the infrastructure<\/title>\n <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.6.0/css/bootstrap.min.css\" data-external=\"1\">\n <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css\" data-external=\"1\">\n <link rel=\"stylesheet\" href=\"https://r-universe.dev/static/article-styles.css\" data-external=\"1\">\n <\/head>\n <body>\n <div class=\"container\" id=\"topcontainer\">\n <script>if (window.self != window.top) try {document.getElementById('topcontainer').className = \"container-fluid article-in-iframe\"; } catch(e) {}<\/script>\n <h1 class=\"title\">General comments about the\ninfrastructure<\/h1>\n <div class=\"row\">\n <div class=\"col-xl-10\"><p>This vignette describes the\nphilosophy behind the infrastructure of <strong>TIMA<\/strong>.<\/p>\n<div id=\"philosophy\" class=\"section level2\">\n<h2>Philosophy<\/h2>\n<p>Our main goals were <strong>flexibility<\/strong> and\n<strong>reproducibility<\/strong>.<\/p>\n<div id=\"flexibility\" class=\"section level3\">\n<h3>Flexibility<\/h3>\n<p>To ensure flexibility, we tried to split the process in as much tiny\nparts as needed. So you can decide whether to skip an optional part, add\nyour own processing, etc. We tried to cover most use cases, but of\ncourse they are not exhaustive. If you feel like something useful to\nother users is missing, please fill an <a href=\"https://github.com/taxonomicallyinformedannotation/tima/issues\">issue<\/a>.<\/p>\n<\/div>\n<div id=\"reproducibility\" class=\"section level3\">\n<h3>Reproducibility<\/h3>\n<p>After some time using TIMA, you will probably wonder: “<em>What was\nthe parameters I used to generate this file?<\/em>” … Or a collaborator\nmight ask you to share your data and parameters. Writing them down each\ntime might be time consuming and not really in line with modern\ncomputational approaches. Therefore, we chose to implement all\nparameters of all steps (almost…) as YAML files. They are human-readable\nand can be used in batches. If you do not like YAML, parameters of each\nsteps can also be given as command line arguments. They will then be\nsaved as YAML you will be able to share.<\/p>\n<p>To ensure optimal reproducibility and avoiding re-computing endlessly\nsteps that did not change, we decided to build a <a href=\"https://books.ropensci.org/targets/\">{targets}<\/a> pipeline. Each\nstep of the whole pipeline will be described next.<\/p>\n<div class=\"visNetwork html-widget html-fill-item\" id=\"htmlwidget-31e2d35c17dd7e20a104\" style=\"width:100%;height:480px;\"><\/div>\n<script type=\"application/json\" 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class=\"section level2\">\n<h2>Use<\/h2>\n<p>Each step is detailed in the related <a href=\"https://taxonomicallyinformedannotation.github.io/tima/articles\">vignettes<\/a>.<\/p>\n<p>All coming steps admit you already installed <code>tima<\/code>:<\/p>\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb1-1\"><a href=\"#cb1-1\" tabindex=\"-1\"><\/a><span class=\"fu\">install.packages<\/span>(<\/span>\n<span id=\"cb1-2\"><a href=\"#cb1-2\" tabindex=\"-1\"><\/a> <span class=\"st\">"tima"<\/span>,<\/span>\n<span id=\"cb1-3\"><a href=\"#cb1-3\" tabindex=\"-1\"><\/a> <span class=\"at\">repos =<\/span> <span class=\"fu\">c<\/span>(<\/span>\n<span id=\"cb1-4\"><a href=\"#cb1-4\" tabindex=\"-1\"><\/a> <span class=\"st\">"https://taxonomicallyinformedannotation.r-universe.dev"<\/span>,<\/span>\n<span id=\"cb1-5\"><a href=\"#cb1-5\" tabindex=\"-1\"><\/a> <span class=\"st\">"https://bioc.r-universe.dev"<\/span>,<\/span>\n<span id=\"cb1-6\"><a href=\"#cb1-6\" tabindex=\"-1\"><\/a> <span class=\"st\">"https://cloud.r-project.org"<\/span><\/span>\n<span id=\"cb1-7\"><a href=\"#cb1-7\" tabindex=\"-1\"><\/a> )<\/span>\n<span id=\"cb1-8\"><a href=\"#cb1-8\" tabindex=\"-1\"><\/a>)<\/span>\n<span id=\"cb1-9\"><a href=\"#cb1-9\" tabindex=\"-1\"><\/a>tima<span class=\"sc\">::<\/span><span class=\"fu\">install<\/span>()<\/span>\n<span id=\"cb1-10\"><a href=\"#cb1-10\" tabindex=\"-1\"><\/a>tima<span class=\"sc\">::<\/span><span class=\"fu\">get_example_files<\/span>()<\/span><\/code><\/pre><\/div>\n<p>We now recommend you to read the <a href=\"https://taxonomicallyinformedannotation.github.io/tima/articles/I-gathering.html\">first\nvignette<\/a>.<\/p>\n<div id=\"tldr\" class=\"section level3\">\n<h3>tl;dr<\/h3>\n<p>If you do not feel like going through all the steps, then just do\n🚀:<\/p>\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb2-1\"><a href=\"#cb2-1\" tabindex=\"-1\"><\/a>tima<span class=\"sc\">::<\/span><span class=\"fu\">run_app<\/span>()<\/span><\/code><\/pre><\/div>\n<p>If you do not even need a GUI ☠️:<\/p>\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb3-1\"><a href=\"#cb3-1\" tabindex=\"-1\"><\/a>tima<span class=\"sc\">::<\/span><span class=\"fu\">tima_full<\/span>()<\/span><\/code><\/pre><\/div>\n<\/div>\n<\/div><\/div>\n <div class=\"d-none d-xl-block col-xl-2 bd-toc\">\n <ul class=\"section-nav\">\n <li class=\"toc-entry\"><ul>\n<li><a href=\"#philosophy\" id=\"toc-philosophy\">Philosophy<\/a><\/li>\n<li><a href=\"#use\" id=\"toc-use\">Use<\/a><\/li>\n<\/ul><\/li>\n <\/ul>\n <\/div>\n <\/div>\n <\/div>\n <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.0/jquery.slim.js\" data-external=\"1\"><\/script>\n <script src=\"https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.6.0/js/bootstrap.min.js\" data-external=\"1\"><\/script>\n <script src=\"https://r-universe.dev/static/article.js\" data-external=\"1\"><\/script>\n\n <!-- Mathjax -->\n <!-- <script src=\"https://polyfill.io/v3/polyfill.min.js?features=es6\" data-external=\"1\"><\/script> -->\n <script id=\"MathJax-script\" async src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.1.2/es5/tex-mml-chtml.min.js\" data-external=\"1\"><\/script>\n 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552.492188 575.761719 552.402344 C 575.816406 552.375 575.839844 552.316406 575.816406 552.261719 C 575.804688 552.214844 575.761719 552.191406 575.71875 552.191406 C 575.710938 552.191406 575.699219 552.191406 575.691406 552.199219 C 575.570312 552.230469 575.464844 552.246094 575.367188 552.246094 C 575.308594 552.246094 575.261719 552.242188 575.21875 552.234375 C 575.152344 552.222656 575.082031 552.199219 575.015625 552.160156 C 574.953125 552.125 574.890625 552.082031 574.832031 552.039062 L 574.820312 552.03125 C 574.800781 552.019531 574.78125 552.007812 574.761719 551.996094 C 574.714844 551.957031 574.679688 551.902344 574.6875 551.839844 C 574.695312 551.773438 574.699219 551.707031 574.699219 551.644531 C 574.707031 551.589844 574.75 551.539062 574.808594 551.539062 C 574.808594 551.539062 574.847656 551.539062 574.847656 551.539062 C 574.960938 551.539062 575.074219 551.519531 575.179688 551.484375 C 575.292969 551.445312 575.382812 551.390625 575.453125 551.351562 L 575.464844 551.347656 C 575.535156 551.308594 575.601562 551.273438 575.660156 551.242188 C 575.730469 551.210938 575.804688 551.191406 575.871094 551.191406 C 575.890625 551.191406 575.910156 551.191406 575.929688 551.191406 C 576.046875 551.191406 576.1875 551.222656 576.339844 551.28125 C 576.351562 551.285156 576.363281 551.285156 576.375 551.285156 C 576.414062 551.285156 576.449219 551.261719 576.46875 551.222656 C 576.492188 551.167969 576.476562 551.101562 576.425781 551.070312 C 576.222656 550.953125 576.050781 550.890625 575.871094 550.875 C 575.847656 550.875 575.820312 550.867188 575.796875 550.867188 C 575.71875 550.867188 575.636719 550.878906 575.558594 550.898438 C 575.464844 550.921875 575.378906 550.953125 575.304688 550.984375 L 575.28125 550.988281 C 575.207031 551.019531 575.132812 551.046875 575.058594 551.058594 C 575.015625 551.0625 574.980469 551.070312 574.941406 551.070312 C 574.917969 551.070312 574.890625 551.070312 574.867188 551.0625 L 574.859375 551.0625 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574.503906 548.363281 C 574.484375 548.367188 574.472656 548.375 574.453125 548.382812 C 574.429688 548.386719 574.402344 548.394531 574.378906 548.386719 C 574.328125 548.382812 574.289062 548.339844 574.269531 548.289062 C 574.246094 548.226562 574.21875 548.171875 574.195312 548.109375 C 574.152344 548.003906 574.109375 547.90625 574.066406 547.808594 C 574.054688 547.78125 574.058594 547.753906 574.070312 547.734375 C 574.082031 547.710938 574.109375 547.699219 574.140625 547.691406 C 574.195312 547.6875 574.242188 547.679688 574.285156 547.671875 C 574.390625 547.65625 574.496094 547.617188 574.582031 547.570312 C 574.773438 547.464844 574.910156 547.316406 575.019531 547.183594 C 575.039062 547.160156 575.046875 547.136719 575.046875 547.113281 C 575.046875 547.089844 575.027344 547.0625 575.007812 547.042969 C 574.992188 547.03125 574.964844 547.019531 574.949219 547.019531 C 574.921875 547.019531 574.898438 547.027344 574.878906 547.042969 C 574.726562 547.171875 574.59375 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545.921875 574.789062 545.917969 C 574.644531 545.886719 574.523438 545.867188 574.390625 545.867188 C 574.332031 545.867188 574.269531 545.871094 574.207031 545.878906 C 574.101562 545.898438 574.003906 545.925781 573.90625 545.976562 C 573.8125 546.027344 573.734375 546.082031 573.660156 546.136719 C 573.652344 546.144531 573.648438 546.152344 573.628906 546.15625 C 573.554688 546.199219 573.460938 546.167969 573.429688 546.082031 C 573.382812 545.925781 573.324219 545.769531 573.300781 545.664062 C 573.289062 545.625 573.296875 545.589844 573.320312 545.558594 C 573.335938 545.527344 573.375 545.511719 573.410156 545.503906 C 573.46875 545.496094 573.503906 545.492188 573.53125 545.492188 C 573.632812 545.484375 573.738281 545.460938 573.839844 545.421875 C 574.035156 545.347656 574.1875 545.230469 574.324219 545.121094 C 574.351562 545.101562 574.363281 545.078125 574.363281 545.046875 C 574.363281 545.023438 574.355469 544.996094 574.332031 544.980469 C 574.3125 544.960938 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574.167969 544.300781 574.25 544.292969 574.324219 544.277344 C 574.527344 544.234375 574.699219 544.140625 574.847656 544.058594 C 574.875 544.046875 574.890625 544.023438 574.898438 544 C 574.902344 543.972656 574.898438 543.949219 574.878906 543.929688 C 574.863281 543.90625 574.832031 543.894531 574.792969 543.894531 C 574.78125 543.894531 574.769531 543.894531 574.75 543.898438 C 574.613281 543.949219 574.441406 544.003906 574.28125 544.003906 L 574.273438 544.003906 C 574.226562 544.003906 574.171875 544.003906 574.121094 543.992188 C 574.039062 543.972656 573.960938 543.941406 573.84375 543.886719 L 573.824219 543.875 C 573.75 543.839844 573.671875 543.800781 573.578125 543.769531 C 573.46875 543.734375 573.355469 543.714844 573.246094 543.707031 C 573.238281 543.707031 573.226562 543.707031 573.222656 543.707031 C 573.207031 543.707031 573.195312 543.707031 573.183594 543.707031 L 573.175781 543.707031 C 573.117188 543.707031 573.054688 543.679688 573.015625 543.625 C 572.992188 543.59375 572.984375 543.546875 572.984375 543.503906 C 572.992188 543.386719 573 543.273438 573.011719 543.160156 C 573.023438 543.066406 573.097656 543 573.195312 543 C 573.234375 543 573.269531 542.992188 573.308594 542.992188 C 573.40625 542.980469 573.492188 542.957031 573.570312 542.9375 L 573.597656 542.933594 C 573.683594 542.90625 573.757812 542.894531 573.832031 542.890625 C 573.84375 542.890625 573.863281 542.890625 573.875 542.890625 C 573.929688 542.890625 573.984375 542.894531 574.039062 542.90625 C 574.1875 542.945312 574.335938 543.03125 574.480469 543.117188 C 574.496094 543.128906 574.523438 543.136719 574.539062 543.136719 C 574.566406 543.136719 574.589844 543.128906 574.601562 543.117188 C 574.621094 543.097656 574.632812 543.078125 574.632812 543.054688 C 574.632812 543.03125 574.621094 543 574.601562 542.980469 C 574.460938 542.859375 574.324219 542.742188 574.140625 542.652344 C 574.046875 542.609375 573.941406 542.574219 573.839844 542.5625 C 573.777344 542.554688 573.707031 542.550781 573.628906 542.550781 C 573.597656 542.550781 573.570312 542.550781 573.546875 542.550781 C 573.511719 542.550781 573.480469 542.550781 573.441406 542.550781 C 573.386719 542.550781 573.34375 542.550781 573.308594 542.542969 C 573.300781 542.542969 573.300781 542.542969 573.292969 542.542969 C 573.238281 542.53125 573.195312 542.488281 573.175781 542.433594 C 573.164062 542.394531 573.164062 542.359375 573.175781 542.320312 C 573.238281 542.117188 573.3125 541.902344 573.40625 541.691406 C 573.449219 541.585938 573.546875 541.519531 573.660156 541.507812 C 573.703125 541.5 573.746094 541.5 573.78125 541.5 C 573.800781 541.5 573.820312 541.5 573.84375 541.5 C 573.917969 541.507812 573.984375 541.519531 574.054688 541.542969 C 574.117188 541.570312 574.183594 541.605469 574.28125 541.667969 L 574.324219 541.699219 C 574.394531 541.742188 574.472656 541.796875 574.550781 541.839844 C 574.664062 541.898438 574.773438 541.933594 574.886719 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511.15625 1167.125 511.929688 1167.054688 512.28125 C 1166.992188 512.628906 1166.675781 512.828125 1166.710938 513.015625 C 1166.75 513.199219 1167.328125 513.953125 1167.558594 513.859375 C 1167.785156 513.769531 1168.136719 513.394531 1168.273438 513.632812 C 1168.421875 513.875 1168.886719 514.203125 1168.777344 514.296875 C 1168.675781 514.382812 1168.683594 514.609375 1168.484375 514.808594 C 1168.289062 515 1168.464844 515.574219 1168.433594 515.742188 C 1168.398438 515.921875 1168.644531 516.285156 1168.644531 516.285156 C 1168.878906 516.324219 1169.113281 516.367188 1169.351562 516.410156 C 1169.351562 516.410156 1169.53125 515.828125 1169.492188 515.644531 C 1169.460938 515.457031 1169.886719 515.453125 1170.03125 515.558594 C 1170.171875 515.65625 1170.777344 516.515625 1170.777344 516.515625 C 1170.496094 516.777344 1170.214844 517.035156 1169.925781 517.300781 C 1170.03125 517.546875 1170.136719 517.792969 1170.242188 518.046875 C 1170.242188 518.046875 1171.203125 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1159.699219 508.484375 1159.789062 507.816406 1159.789062 507.816406 C 1159.550781 507.875 1159.316406 507.933594 1159.074219 507.996094 C 1159.074219 507.996094 1158.816406 507.230469 1157.863281 507.074219 Z M 1170.160156 521.507812 C 1169.574219 520.71875 1172.027344 519.539062 1172.691406 519.496094 C 1173.351562 519.449219 1173.503906 520.179688 1173.992188 520.628906 C 1174.488281 521.078125 1173.699219 521.4375 1173.75 521.886719 C 1173.804688 522.339844 1174.140625 521.894531 1174.546875 521.777344 C 1174.953125 521.65625 1175.320312 522.664062 1175.734375 523.117188 C 1176.144531 523.570312 1175.855469 524.621094 1176.117188 525.042969 C 1176.386719 525.457031 1175.488281 524.992188 1176.070312 525.652344 C 1176.664062 526.3125 1176.769531 525.664062 1176.359375 524.707031 C 1175.9375 523.746094 1177.007812 524.703125 1177.429688 525.074219 C 1177.847656 525.453125 1178.121094 526.433594 1178.425781 526.75 C 1178.742188 527.066406 1177.859375 527.054688 1178.269531 527.542969 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dybXCSz0XR6ODjqn6TDg8FpFIXIBQkFkWHKJL0XpFoukDzzF7ZQJaNY\nkV6cAXjbOnn0/vp2A3psotGcKIvmGrN9dZlM5hSN3FpJbODc+z/nT13nyyS0TII3ltQ2hNmG\nGH3Lc6Y7Z3QXuWAKiSiN4T5lhunDfJFT42uriuqUWvE/j9cZgbkPm+tG6k7KpSZVtkNM0ryw\nKiCIFU8fkFnH5yo5tmfzgi7Zivwb8N9kQQAAHPp4nM2ZbW/TSBDHx4nThyQNKVfuuCs0e6pO\nuko9cld0wLWvrlAQqAKUBsSLSNVibxMLP7FeVwlfnHcUbT1ubWMLOdmURlr9J/FDfp7Z2R3v\nvqsDQBV0XYNqDQCg9qb/9K9HANWfAGAZoCJVA6i0z1WXv8HtfeYaI4fyD0TQsfWJkcBnhuAU\nQK8kzlvomceCDgEgebXeP3jXz54ZcIOzE4DqivwmQQBgCbWDdidz1WLAjRPLZgDwGaLPxQ2Z\ne7q7ez9zQc2waRBkeC7+G75ETV6inWXPGghLyL+SRytnGVINlaBNovOqZ99Dq519jyY/Au/j\nCBxHEThOReAbev3tQW8/h70qvYi6gfaGOvZcltrApQ4rgNFQO2h31MHM1ZE6dlH92+6qgr2g\nS1LbokEBjYY6ry5ZyXhSqxe57DLfU5C3AKCVUGzKIJNHt/ojKyAnoWsIy3MJNc1ADl2UCI+I\nESMXvaDsQ/yM3pW6hfaWuofIZWkOTBYY3PLlsxQ4VnL8gopNFVO56Nd6j189OcihXMAsiRWb\nMsrk0b2iLP/Tcv1QbJMPbBJsE48P5fFjT4ht4oXCD8VW6edaRI9L3UN7T91zFYwEYUCHrMDL\nGnp3Xn2hOetIsBzd5EKxKYWUZUDeBFA77wElgesYX6kNtBvqgHNB289dS1jUJieMipCzYArm\nFupdtO+qY85PkoElmFMAo6FuoK2w7pitO9ZxDo+1o3Yunw2ugd6KdR4JXZQruhwmS/I2MT2a\n6bRRxZvLuXT06jWZknUFdQ3tNXWs5VKkicFtJmb0W+pgZuuFTSx5YsV2PeBW0FuxXmWKNBI1\nREnsFmaH1FW0V9Vh52dKn44913MmU7Cuoa6jva6OtVymtDDGUu+gfUcdzGydsZWebuN2PeBu\noLdivcpMWYgK7JLEbUyMdnpqUUWcS7r6mjOfcmZOW3q10cVtLCZuqC0oymVLdFKk81gbmKlD\nynN+Tyg2VXC5LPUB5cPQYa7IW0pZRhetos4pRWZz220srmKtqy2yUnCb+8ygYcDkusmEUM5I\nICzbJh9DSzBiWlxMyuL/in6Vuon2pjr8XJalgckEteygwJ2S4bfrHPOb+FZ/8wrWT0h/xIhP\nxSheLxN0zMzpV81wlD3XOYxBuSwLg1Nqh6zAkxoWMT883IXrSuv40hdrQ+2aQ3pR5OWbw8PS\nhInaK7kpooowF2Z5wMbU8W0WFLhMw5prDoHVM3tduBWVvOZFVzh+tycc/3SHHT3g3d695y+P\n+v8fHv7z8P6Df98/+vvhfzs7XWE5tOtQt1u0TnmvZxZQVeIjl0nnMEETxVcbnzJ51fYf5Blz\nGadCpvGEcG88GTJ3Z5eYHnE9QZhpCXlgRN3s/Wty+ybeRKvgj4umZ/QnPovDMozu7vFLV6VC\nE3/ixR9QF5oU7JKPtVvifVDuL335CviTHxYAAAJxeJyNkd9KwzAUxk//ua0XIvgWgotW3ebl\nkAmDodJO2JWSplGCSVqaTPGpfBlvfA0fQCUzwa32YrlJvi/Jd345WcQAEEAYehBEAADR7fzy\ncATg7xvxN8MeAHgQQm/9UJsZ+uumxIKqxvXuRJKyYPIRAL4aexdIiwqlWlTPCc0GNUr706ts\nPp7Njocng7N8dDQ8TxKkmcBIYIkI5mTJsab3VOq6rF77aQEQ7JrI8cfb+x2YsclEOFaOybdm\n5+Y6my6ItnJnJXUL4MH2gL91vW+bENmEYCKJ6wR1nbD6gXFqWmZ1TzNBlcaisob/UlimT/s3\nXoOvo2piUhrF/z3eZMT2PmyuW6m7OZOGVLkOUUl46VSMta5ZvtTUOSFT2ak7y0vyRFfkP14H\nYWgAACbueJzNmv1P20YYxx2TBJJSSsh4ySj0RtsNVkZotrUdaBIdY1Ml1kkhnfpDJHTYl2DJ\nL5F94eUv329ld87jcAm22iPnLJGO70McJx8/93ztu7M/FjVNm9Ky2Yw2ldPYK/eh8ccPb9h7\nZfbPjKbpXDNM50PNagWmpUNsG10bU4KIS32vc61pWV34QL5unlLcZpG4W7Zx9LEx/MnAN3zS\nYj/4kP/HCXo/HOoKxCtDe02zvVqWTVj4r9Z79b+QuBd7ez8N7ZAzbBwEQzz939Y+9RrfJXMz\n/KkmtSj/Kb5VvxkizYBWIK70Pjd18zm03M3naOJTb0SpPx1I/R3s7D9H9d9ioKd4+kCXIV5W\nBx3Lkmu62CEJMBnQFYhX1MGkk8EsFGX2boGqgE4oQmxbOEigyYCmVYT6UAozxaSU3Tp8APIb\n1uYEhaYMUtx60Di3AtTquga1PBf1ezuIzlUosBzLxr5Fr9EZoZeEuIheeijoEIP6WPbYNiDp\nXA8gPlB3bLEss02TBIZvdfghJuSbczwFhaaKKSyKwpcWRa5++PfvRzGUeTBPpNCUUYpby3dc\nvynNOw2Z5FqGuJwSbwEhyzXJ1bY05QyQzfT6KIwLKVEWEaLYbxMqjymgaUWIiylhsmS2fNwO\n5CkFstSTWULICWqn1LOJj12DyNM+AEKuJYhL6dF2Os4otLNAODsG2gVWqOc+Cc4920S/ot2d\nl9K4DwGR6wLECynh6lvSeHOAxFWHWFeHFwuTb3YD3I4b2OWB4BFoGheghVFHJfNQfJFCUwqp\n314rBmBz4dldErgEF58SeHy6p6qAY0FX33FQ5LWYgQhqWX5AYaTUde6BPwe6CfGmOvx4vzQt\nSpwEmAzoFsRb6mBGq0wO9b2g0CYDTjj5pebtJNvke8MNSeKyMGibhVih0WNJHw/4JiCG55r3\nNU4ZzqRlKIVHastBzjjCyFd7AfELdTCj1SaH2hYU2mTAfQXZinScxsmFA2BJ4EXwyuIYrzel\nY4tdYZhtOLDD5mr3gZ4DXYd4XR20nFkWoZu5PoH4iTqY0eqRQyFBoU0G3BJkK9JxmuXhwDxM\nElxYVBUvPKrA44H/OqmhUYCXQDcg3lAHLGeYZejqZWH16qk6mNFqkkM9ExTaZMAJ6+fjN8zA\nUoAkeAVMUhmnYRgwGgV4CfR/N4yw5j95hqmASSqTaJivIVuRjtMwhf5qlCT0KhhkFaYB0z1V\nBR0Lu9boL51dYLvLVgE8vzerMTyf3OMAFkB3IN5RdwBy5lmFbudahbiqDma0+uRQu4JCUwUX\ny1Jssvl1Vxh231kw4yl6nKJjpkZN2xrc+1gbw12l/bfI5tMVNrmn2HIttx36on+vyYy75xh6\nKJA9rHXIN9d9iPdTOqxt5u/3H46PkdW69Tlid1XPiO1dhm/1z1+yxyHMgsLZeUbtDD2WJd8M\nc55QLBmY+qRV0fqXVnTi2v4zmH9H+kDtPHxwoYB3vDThc8gdV+GxGFWEsTCFJrnCTscmQULK\nOMS36XRsduhpJ3gYSdznsEqdTrXO/lzUyMkrv1rfeff+pPH2+Pjl6x9f/Xz2Zvf1L7ValVoO\nrjrYrd65Qb1TNxNw9GjLrdscQrEw5piHwxtw9XP0J3GJH56Xzq6R711dt4lb20Omh1yPImJa\nlG84x+7w9+f4AzzR81M6vDltekbjukOi/mj3vt3zb3M00CfR6zvoD01dnwzAznR80sE+MYW7\ndvwJo0//AZYMLzcAAAJzeJyNkU1OwzAQhSd/tM0CIXELJBoI0JZlF0GqVAFKitQVkeO4yMJ2\notiFY3EZNlyDAwBysUUbsqg39nu233weL0MA8MD3HfACAIDgYXFzOgFwj7X4m+EIABzwYbB9\nqMv03W1TIE5k63o/EbgqqXgCgK/WXhIpXkep4vVLTLJRE6XD2W22mM7n5+OL0VUxORtfx3Gk\nKEcRRyLCiOE1Q4rkHEmZV6ucD9MSwDvUodOPt/dH0GOXCjMkLZVrzN79XTZbYmXkwUaqDsST\n/RF/6zrfJiEwCV4isO0Fsb0wekUZ0U0zeqAoJ1IhXhvDfS0N06f5HafF15MN1imt4v8erzNC\ncx92153U/YIKTSpth4jArLIqREo1tFgrYh2fyuzSnmUVfiYb8h+s2mINAAAZwniczZnvT9s4\nGMeftCkdpeXYcccd7I3F7gQcjJZuLT+kInFc7zhpMKkr06R1qkzilkj5USUuAv7xvRuT2yeQ\nZolQWhetkvV9kGPn8zz249jmYw4A0qCqCqQzAACZ8+a/r/YA0osA8AwgJVQBSC0MVIVZAFg8\npqbWNylnxKKeR5wOOQVQU4FHZhp6m9MuAAQbqs36x2b4Sc/VXNYBSBfEX4Jh+OqBrqC9EmqV\n9VytY5gMAL7A8HffIbOvDg4qoQYZzaSeF+K5fzd8HRbRRLkLP9XiBhevErWpuxCpgvoC7RfD\n59J3j6Fl7h6jiQ6+5ge/LYLfdjptKwZc/VBv/B2BnRYBRF1Ge1kediRLpmVTi8XAKKgraK/I\ng5lWDFWcmOr3k1QGdsxEpKZBvRgaBXVaEzEVCqKSiwvZQ5aPQIpO5gOKRRpksHateWl4pNO3\nNW44Nrkfb4/wy8CqldSFXzC2QtfQXpPnQiRLvqUzT3ONnvAkJqyC41dULLKYko19pnH87p96\nBOUM5oivWKRRBmvrEem9TnW9r/G2x13D7m4R63aLMJNp3HXswVOkRkrbpVKp8mavsru/v5HY\nwyzGXmgd7bo8D2NWhL5Huywm3grGeVqzYnbSFWEwvQOKRSpk6iFFRmALIxMiIXgOxzmHnWeH\nKgs8EnjxaABMuEMuGOlR12P6GNhLqOtor8vDjs6XlsGZFQOjoG6gvSEPZrKZKaD+CiiWHwNu\nDqPl6zRyOy5tUtZtQto85kceV6Os3JVfIuVcYBmak7sUJUuNPA6q0ALaBXkwk82+PG7d8k+w\njUsMF4jWk6dGYWQ/kRB8HjNj/gm/KIU6ApMxgZdQV9FelQecLGHmcaiFvkT7pTyYyeakgPoj\noFhkwUWy5FrU7fYtZvOo4+Ishuin6WbJZGFbwO/FwhOcGLJHgxRIivgcY/d8dPsvCzGSZaZ1\nRc0+iwmYIPh5iqOqTnwQXMKPq69YpFEGa2tRB0HrltTITvn19ptKdXeLjBwDSI2sfjrdPPm8\nuZr8APgbxlxoDe3alDw7HNOzMzqWa7+jO0IP0T6ckmvnj7kW5Vn5dOfVcfVkp/yuStYv2bXj\nsY3NM3psbp58Lo/j8TJ6KfQc7XN5HkfCzLbYNbV6JvNi8kYJ3JFKzm41dHuPl+vBNvUit3rF\nBrd6V2X2vuoWG9v/n71vHr19u7P7ulq52Cvt7pfLRW5YtGhRuxgxjtsNPQYo5dc8LLoW4zSw\n5VpAB4Ottv4k/zGbuZQznVzcENe5vukyu3xAdIfYDidMN7iouKR2uP+MuJD2/yOQ8j8EuqM1\nb3rMH5HusHfHfYjSyKj4P//SFeSNygjss57LetQVJ/77va24Mf/6DexC5lQAAAJpeJyNkU1O\nwzAUhCc/pW0WCIlbINFAgLaIVRdFqlQBSorUFchxDLKInSgxcCwuw4ZrcABALrZoQxb1xp6x\nPe/z8zIA4MH3HXgdAOjcLi4Px4C7r8XfjD0ADnz01w+1mb67bkoiWN243ptKWmRcPgL4auxd\nhEqUYaxE+RKxZFiF8WB2lSwm8/nx6GR4lo6PRudRFCouSCiIDGnOiLxPeTGIM8Db1VGTj7f3\nO+ixyUJzUlsW15jdm+tktqTKyJ2VVC1gB9uD/dZ1vk1CxyR4U0ltB5jtgNEPPGe6VUb3FRes\nVkSUxnBfM8P0af7EafB164rqlEbxf4/XGYG5j811K3Uv5VKT1rZDTNK8sCogSlU8fVbMOj6v\nk1N7Ni/oE1uR/wCiQl3eAAAfXHiczZn9T9tGGMcvr7yklDDa7o11B2MS1RgZTKPA+jLWUVqR\ntVKaTv0hErrYT8xp9jk6X6D5y/tbqS55DE6wRQ3ntpFO3yc5O/48z91zfnx+M00IKZBiMUcK\nJUIIKb1uPv11i5DCPCFkipC81hwh+epAi/o3MvXEBSZom/uEFPORjnLDPlTMIYREDy829940\nx48MpCWhQ0hhRn/TVyaETKJW0a6OnTURSKvDXSCEvCPDz9kfgjje2dkcO6FkuSwIxnjOrk3e\nD5s+JXc6flRLcaUvpXvzp2OkOdQ5tOeGxxVOL0MrnV5GEx9yS4f88CzkF3CL/+01/o6BLeiw\noc6iPWsONpal1BLMgwSYHGoV7ao5GLORK+IkLF6ckCZgEyYdczkLEmhyqFlNusJY6HLTSSE7\nz+gRyG8JITcjis0YZLR3sXnEA9rpCUtxX9DBKAdUHQGVEPRcFaSF/w6jqnUR7cWM4Jf9tmJc\ngE1ZR4HUS6nrO9xiLj0B7hwpLpy0/N8js9ZltJfN8cey3GjZEFiSd/UYJEwIzbGAis0U02DW\nlj921pYaT17+sxdDWcbsDhWbMcpob+VsOVpJzTmBEdRaQbuSEWedUiaErw4Va7tweAJcM9OH\n1AG1shTTtbRKQRxzSR/SLpMg1FpHMg9W7t1bTe3oJDqntY52PSNHH1AKtgPB0JvQwchPJh2b\nQmcGF0b7QUaOHVDqccE95h5avgh4oEBY/dDBmK5kR1P7OY2+aT1A+yAjP/Pp8SLJQ/Jo583h\nxcKUW72AOXFVURkJbqBmsUhOXffWPoOAoWIzCqmHIq6Em49ZblLi38RlM6xOJoZqCp8g/gj2\nneZgRbF8oe/1XDi07/fk9BXIF1DX0V43Rz5CHKlD3D5WIrpG0eFn57f4j2afHQ7mQCna1Bx7\nfNK3uAIvIZAhTNb1Xer00lBLEcX2ZcANjznXLBaopNyvRO7EKbHnMM/nRm1T2LG4v1zIef1E\nMvCBtkGdAAjaAaZ6EoIruHML9RHaj8y5ky6ZIo+g5DHaj83BXG++aqi/Iorty4D7CqMV6qdM\npvmYqi8l/jwm0nyGN9Jx7NV/h9g0WscGli+BdnxJGR3s8q3Rp/WXu80r+LOAuo/2vjl/0mUV\nOj/QZ2g/MwdzvYmroZ5HFJspuFiW6RaTTs8DoeL2xGYwRLdQs0ila++J3cbn2NufYHNha5eq\nuPvPZWVdWp/uYLC1bqG9lZFP2ydHIIH6wu1TRh1+DIKKntcGSf0O5YIrrlcGJmxuMwUBZRLo\n/9BVaZ36Gh3Ruo32tjmnYlnKrWPm9iBh2miCbzKc2/nrzm2Kj/ihYjMGGe2tDOetfvZLixmp\n9rPYN4tlmWwFAMwN/ISw5bDI/+xjm7hJ8gNGK9SK2aiN0JVevK7XUxPexdjdHX0pZ4owFmaq\nBW+Z13UhSAiZhvgxm4Etjr1rxVeh0XP+rCmvW2sor3u8Aa82Za2x9vzFq+Zuvb5+//fNP9pb\nv93f3tioKe6xmsdE7Ww3eq1hJ2Dkw57zPPNAsUipWUW3omet/kz3QYBk+k7T7lPpv+07IDZ2\nqO1T4SsKNle644iJ8f8v6deH4VvbPP44YftWs9+FcByc4b/78jw2I2MRfn7CcSDmxmIEdrIr\nQW+X2pGCRL/ffP8BOW+oigAAAmt4nI2RTU7DMBSEJz+lbRYIiVsg0UCAtixYdFGkShWgpEhd\ngRzHgEXsRImBY3EZNlyDAwBysUUbssAbe8b2vM/PywCAB9934HUAoHO9ON8fA+6uFr8zdgA4\n8NFfP9Rm+u66KYlgdeN6byppkXF5D+CzsXcWKlGGsRLlc8SSYRXGg9lFspjM54ejo+FJOj4Y\nnUZRqLggoSAypDkj8pY+MFEM4gzwtnXY5P317QZ6bNLQnNSWxjVm9+oymS2pMnJrJVUL2t7/\n0X7qOl8moWMSvKmktgfM9sDoO54z3Syj+4oLVisiSmO4L5lh+jC/4jT4unVFdUqj+J/H64zA\n3MfmupW6l3KpSWvbISZpXlgVEKUqnj4pZh2f18mxPZsX9JGtyL8BNuBesgAAQjR4nM2c/VPT\nSBzGt6WA4iGC4il6uFR7wgEtLycoh3MH4tsdIFbuxh8600nbbZuxSTpJivKX32963eZJ2YZU\nXbrp1Zn1+Zq08Hk2+90k++L7MULIEEkkYmRomBBChv8+frH8mJCh64SQy4TEucYIiV9raYIf\nI1ee1Zhm0mKVGRYhibhwaiRbyrtahRAifiFx/Pz9cfCTjl20WZmQoXH+L/67CSGXoFOIpwLf\nGnXsYlmvMULIv8T70/6BzDzZ2toMfGG4WNMcJ8DT/t3ks1f4V2Jfgp/KubrLfxU/G/8SII1B\nryO+7n1u6Mu30Ia/fIsmvNKLvNLzQqWfA0788zy7G4I7xCsOOol4Uh1uKMtwztQM1gUmBp1C\nPKUORnXdJdAQE+cbpQrcLg1Pq+ma04UmBo2q4Q0FKi821q3KzrK6A3KdEHJVUBRlkOLZueOq\n7tBywyy6umXS1nV2qFtl1GZOo+Y6svC/ola5ziGeiwg+aRVcTTdZiWpll9mtDlUvajX6kemV\nqqubFVn6RyDmmkScVEcfyvJDrsScoq3X+RXo0hw4xwYURRVTq81OfG+bHc4+e7P3PIRyBLnt\nK4oySvHsuNAdzUuTjqIOuY4jHo+I9IhSzTQtN+9qhRrLf2S6R02f0gpz55OhJ5NLlJknuk2f\n0rpmM9NNl23NYPMLC0vSZi/BINcjxEcRmX1BadEy6pbJTNfxTPk+g8dVWrwMWy0ExC8isrhL\naZlpbsNmAYOdR1XaG4MlrruIdyOy95ZSx7UbRW4lb9kVzdQdI1/XdDtg9+ufOrMv7fYKHHJ9\ni/htRG6nKaW9X58fQMl1GvF0RMQ8wzSzpJc0lzn5sm5qtXaGBY6rbIJCLxl5hu1Rauimbmi1\nvOGs5gt6u68MHFZp8CpMcd1DvBeRwVedBjtuB+dOqDQ5AWNcXyF+FZHJo4BJyyzprQfMMKP+\nSZVmvQ95GvV9b4/Sql6pciNlvcTMYrubDBxWaXASpib70GS3KbWZYZ2wvKszxzcnHFJpTHin\nJduItyMytkmp0zAMzdads1ubf6C7KWlPwqsm2US8GZGnuDzeDSBxjSOOq8MLhRnJNRytEjbA\nMQIC/2YaxfvOXK/v6Ddxx/cVRSlk/KzldD63hL41SBr4EW9AXO8gvqPOAIGBDvCbx61nyKJl\n8td23azQU6thj12AfBa6jnhdHXkH8aw/pFA7xaACH2zg1a+dva1/N/kttOZboI55qoo8POlz\nusuMLtXow9xDfE8dTG/pxaGooCiDAXcbteVrFB1Ut9y/FnyTlmSfQarPoO8a9VQVeyjzrSOb\n8TtoSRgeoN4khDT7begK4hV17HLpM4MLz3UV8ao6mN5aKIdaExRlMODuoLZ87Wf6XO0cp5Ek\nv4uEuYtnpVFPVZGHEt9sJ4+PfpHUuYsOn+sS4iV15HKpcxcXnesy4mV1ML21Tg6VFhRlMOB+\nQm352s/Umf36mJ+kk1mkD9cHiB+ocxL6+Dl97vHTrbKxC4A/hP6G+Dd14B3AM+0qp8vUr3Ta\nqnRJauEBT7xzqaKWy/9ZAUa4GaiC6S3F7iH3fUUZDDiK2vK1n/l/LTjCLMk+hyyf6+OTZ/Kw\nYRSYTa0y9QbLzzxQ16IfGKtfwMVt6BbiLXUu5BJJmF9v9UWxCPsi6bbKobYFRRkMOGFuv++J\nNBGYyJBEv4/cuY8hslG1802hyI8OPGRa0K2aVWmtsXCKls38LKIH71ZpQXN6GR25j/d9rvuI\n99UZk0ut+2gUXA8QH6iD6a31cqhDQVEGA+4BasvXfqbW5LkJNEn4FBIq1TlCqgo+FHrdT672\n8iXlqZXCY18KqTXzf6ZWCo0iNYiplUI6pQYxtX5Gbfnaz9SaDp2ylTTwECn1MMJJhyD40rP2\n5HMzowqMNpqplKYHDcfl/0q+ySab71Y0uXO4l0xfwI//Qvga8Wt1fuTy6iFaBNc/Ef+pDqa3\npsuh/hIUZTDg5lFbvvYzryYCawQk0ReQRQt9fBrMZFndst3WogcqLHoQXq4ss3aaprtv3uw/\n3zm8gKVb0BeIFa42kkuqBTQHri8Rv1QH01u7XcBKHl9fRbiiRxruF9SWr/1MqivC2hRJ7EUk\n0WI0ez1CcW9kW7iU4140axYxJ7GITvaGp6q45bJmEdd7sbNzVQXTW8NcRMb4ijIYcEuoLV/7\nmTWX20ufJKGXkSdcJxBPqIMOhU3vWhbfBZAWVnBhywqdX6W29ZHWmyN/mDRbuICjKejviH9X\n50gum4Q5MvIH4j/UwfTWYDnUjqAoquBCWcZyml1pGHwdQQjRTVRRGhpFCvW8xyuDPRmZPmyX\n2dihbsjMF/362iZZRyuo6hVhq9JGRI6efKwym7WeMqlGK/oJM6nZHupvOnT1zsF+rfnpD6zu\nypoSVnaQJ4ifqDMVyjKSO9FqDdal0cSwnCOqlh3vtWVvYa+KryjKIMWz41679Yb6ZEGFGZMo\n9oGFslzKOYxpNcfqUnGcYZsMwNXtulZ4E5tzfEVRRtkBcfj3/r404WPUHVdho7kqwlCYyzn2\nSTPqNeZ0qbKY0IEovrCJwP8fgO394neeZlyjnsk2/zpZY+827Ew2/frw3fHO/v7q5vrGo8Lj\nlc0na2sZtznAlzE0MyPsr0xnS11A4v6Zs0wzmKsJD5nXYEz81lKKvmQmszV+tymcNh/UPp1W\nmLm2RUsWbd57KGsO1vET1WbvHfj5w3xLvP9/EcRxcLRkFY9P68y/EhXvp1v2We10XA3/z1Nc\nCaLuanTAXqpj4ZbQvfA9+5//A9Im5tMAAAJueJyNkctOAyEYhc9cai8LY+JbmNjRqm3d2UVN\nmjRqOjXpSkMZNERgJgPqY/kybnwNH0ANFWI7zqJs4Bzg/B8/iw6ACHEcIGoAQON2fnk4BMJ9\nK/5m7AEIEKO9fqjOjMN1UxHJdOV6a6xonnH1COCrsneRGFkkMyOLlx5L+2Uy606u0vloOj0e\nnPTPlsOjwXmvlxguSSKJSqhgRN3TXAhSaNadZUC0a/NGH2/vd7BjE4gKoj1Q6MzmzXU6WVDj\n5M5Kmhq6g+3pfusG3y6h4RKisaK+Dcy3wekHLpjtl9NtwyXThsjCGeFr5pg+3ccEFb6mLqlN\nqRT/93ib0XH3sbmupW4tubKk2neIKSpyrzrEmJIvnw3zTsx1eurPipw+sRX5D6yuX/wAABSd\neJzNmP1L20AYx5+0qZ1ddTqt+20c+IJvs1vH1AqDOedkUBzUOvyhIGdzrYG8kVzF/uX7zY5r\nn7RJTJDY67bC8X307pLPPS+X5K4KAJAFVVUgmwMAyF02vr87BMguAcALgIxQBSCzMFAVZgFg\n/sRg1CIt2zCo4zEANRPonalr15x2ACA4R22cXjWiIz235bI2QHZO/CVuP7zrQEtolyKz8p7b\nausGA4DfMPyNLsisu6Ojw8iEXMugnhfhGd0bHoZNTFH60VFNrnMDV5jpR0gV1BW0V4bjsv2n\n0HL9p2ji/d4Sfr8O+/0Rs/rrtP41hjgrfIe6jPayPOJYllzToiZLgFFQS2iX5MFMwX0qpqP6\nODVlECekHzV06iXQKKjTSr9MxH9KIcll49oOQS4KpwcUmzTIYG+1cat7pN21Wly3x3uTRyjp\nuHbXYRrRKKdtl5qMUEsj3NVNj+g87aJeo7eFVtGuyltULEuxqTGv5eqOWFuCowXHEio2WUzp\nsiFXP/n57TSGcgarxlds0iiDvRvhWt/EHLjW2rsiNzzymZwfb6VeQh6dK3QD7Q15S0jYBLoe\n7bAEhyroyGmFfWbSTWCQvwHFJhUyM879EGxhHPaU1AUMcgHH54cqizqWdvEsulE9A3oJdRXt\nVXnQ8aXS1DkzE2AU1DW01+TBTJaUAmo9oNj+D7iX6C1fp1HWSRWjis0xJW8R66MYrhtZvLGc\nb09so2tam94W4TahjmP0Rs98wu1nrGAOdRvtbXkrSFc4RQy50B20d+TBTJabAmo3oNhkwcWy\nFJrU7XRNZvG4N+FZdNEc6pSqZTK3zeNTev4vvPqQwdc50wZvuX5NBB4qadlfoVOFErSJPPZY\nlpnmHTW6LMGTymj0Pw534nviMm7fvmKTRhmCOL+s1VITBj66g2cZsghjYWab7J6ajsG8BJcp\nga9ZyYFVI0dUeIIUnPOlzE2nXOemc1dhF/tuub734/yicVyrfTj4uP/p5vD9QbVSKXPdpGWT\nWuXwJ8ZeXUtgyfg941IzGaeBJ/ECri04a3ednDGLuZQzjdz0iGvf9zrMqhwRzSaWzQnTdC46\nbqkVvX5OnLr4J14Z/Gdes1uNnsP8YHSGV7fdsYNCAfF/bzAYIC8gIdgXjssc6jItsImLY6GH\nP/1oQYQAAAJteJyNkU1OwzAUhCc/pW0WCIlbINFAgLasUBdFqlQBSorUFShxDLKInSh24Vhc\nhg3X4ACAXGzRhizwxp6xPe/z8zIA4MH3HXgdAOjcLi4Px4C7r8XvjD0ADnz0Nw+1mb67aYqU\nU9m43psKUuZMPAL4bOxdhIpXYax49RzRZFiH8WB2lSwm8/nx6GR4lo2PRudRFCrG05CnIiRl\nseJC3vMyp8UgzgFvV8dN3l/f7qDHNg8pUml5XGN2b66T2ZIoI3fWUrXAHfwf7qeu82USOibB\nmwpiu0BtF4x+YAXV7TK6rxinUqW8Mob7khumD/MvToOvK2uiUxrF/zxeZwTmPrbXrdS9jAlN\nKm2HqCBFaVWQKlWzbKWodXwmk1N7tijJE12TfwMelF+3AAANVHiczZZda9swFIblxG6arNm6\nJL0M+GawwVZvGetHbsbWZWMQOnDd0YtAUW0lMfgL2wnNL99dMyk+TmzXogTUrQblPbF8rIf3\n6BhdNRBCVSTLEqoqiF7KpfH93Qm916Z/aghVmEpU91cqozrV5pnvzFwvUl3fIg5CciUzuaNb\n1zGe0CibIhuDK6P4ZBSaIRnTxZrsH1udjl3QDsSdQlaNZo1th9DwD0qu9QuJN+/3TwsJiung\nKCrwrNdGd8lgKdKy+NQotmO2FJutLAukEugBxAfJc9XlQ2jK8iGactvNxPbrjO33kOXfA/1r\nCXA1qedK2xC3xQGXsigjD7uEAyOBdiDuiIMR754Mm1G+vzFFAHM2H3ZsHHFoJNDH2nyVgn1S\ng2fZprNzkHt0PM8oDGGQ2dmuMbUjdTzzzNj2veSzFKlQ8G3Bm+Ao0y7EXXHgpSx7I4tEZmgH\njJ9jpgQmSpshimm7iiv62a9vgxLKHeiMVGEIo8zO7ufa+fWbrWFrYGNtnbBSUbCclp5FeEI4\n1klg2X8vMK8z6gBY/wf1bRlTknZw0tDb0jbARqYtiFviaDkFnmNnRjjeMYJnT6HA3KZ4AYSp\nwhBGmYM4vxwOtybMtGr2ECaKsBSmPiK32A0cEnEsYxAvH6ewcuFsDUffbM5nLXYDTac/8x65\nOAo1/fDn+YXxZTj8cPzx6NPNyfvj015Pi20Xay72tNyX81C3OCiVdGbTaS6J8WavrZ5Ghay3\nr9QfxCMhjoml3izU0L9dTIjX66uWr3p+rBLLjtnEFHvF9yvsuJie1Ctws2b5prEISFqLSfJ2\nP9z4k6tHeqUtj8TVIwe7G4QkwCGxMkcGdp69+wssI3S4AAACfnicjZFNTsMwEIUnP6VtFgiJ\nWyDRQIC2LLsoUqUKqqRIXVE5jkEWsRPFEzgWl2HDNTgAIBdbtCGLemO/Z/vN5/EqAAAPfN8B\nrwMA0Llf3pyOAdxjLf5mOAIAB3zobx9qM31325REMNW43ptKWmRcPgHAV2NvEaIowxhF+RKx\nZFiF8WB2mywn8/n56GJ4lY7PRtdRFCIXJBREhrQQZc4Ek7gWDElGkKwVVjXFumJqEGcA3qHO\nn3y8vT+AHruANCfKArrG7C7uktmKopEHG4kttCf70/7Wdb5NQsckeFNJbVuYbYvRjzxnun9G\n95ELppCI0hjua2aYPs1HOQ2+rqqoTmkU//d4nRGY+7C7bqXupVxqUmU7xCTNC6sCgljxtEZm\nHZ+r5NKezQv6zDbkPyvdZu4AACe1eJzNWu9P2zgYdtKW8mPbsQHHcb1xhsEEElDoGD8Kk8YY\nO52EuKnrTvtQCZnELZEap0pcDv7y+zZObt60bi5hS+t2RbKelyZOn+f1+ySunS+TCKEUSqc1\nlMoghFDmc/nD5gFCqRmE0DhCukANIX26hWk0gRBaOnXsRp3alHFsU05Mwgl2qtjjbtPgTZd6\nCKV1qcNYybzkpIYQki+TLp99KYfP9FzDpVWEUo/Ff4KRT6SFaxCvhXplPdeoWnWKEPoX+X/t\nQ5TdFIs726EeGaNOPC9EqP3l6KvfRBftPnxWhVtcfJc4qt+HqGqA6xCv++el7r/JLXP/LTqR\ng7FotAfjMhiMy/BI/E9E+u+z0rsICSlBC3AF4hWFEiLJZCqM2DSGjQa4CvGqQjZDSGgaSjYN\nEsYVS4gpUVK3iBdDRwN8CfFLhXRaGdVDGdUm45LWuQN0scQIoScSQlPHUj66Wb62PFxtMoNb\nDsOd0W/fz0i9fZtLqmQJcixwE+JNhUoiyTyqmNQzXKshBMWkVxBZBoSmjFSrCCa+twgypdO/\n3p9F0BwDuwQITR1N+Sh+2PVridlnIbFZKeF4UOzHMDarG4k5jgOvIMuaj4PhWMTCUJcepy51\n8Btco3xtufPJ8gam7MZy8RvcIC5lfKvqEpuura8n1zUBWlpfC3FxULp2fV025bIom3KViiZB\nhcBdiHcHrIgRW1bEiK1S0RSomBqGomNfESe3l0adyKrgI5XKHoEagccQHw9K2VFHGWsYYWWs\nYcQrSyzsMYgReATx0aCE6cn5PQFOAnWIdYX8ItmMVZoeqUXNXoM76k+AA3nMPut3rjUNlRog\nNLUsxWhEzbV1s5qQ7lN4pD6Fx1ZW8aQgkubee/ELt+Ub/I/Fr6Nmhpg7+IpK0whq9iBtCvAt\nxG8VSos2U8Xi1I5howGeQHyikE1/VStYvZMQ2oiwewb5CnAgzo/z1GRnTpWQ9gz4aQbOz/qo\njHYk3aUPVl3YhnFiMYvVpFUk3LOKoL3qoDIVyWwEkls48GlO4kIVrF5LCG1E2M1CvgIcqo2y\nMItPyHkOfDMH86Ssj8o4R3J98YCHgudTDzqmAQsQFxTqSOaiORj9OfC0ptjT/dXpHHg7wN2R\n8vjPkK8Ah+8iRuyEnOfBOfPDdBF+wEVigdrrQcQ0YB7ivEIRySw0D0MvcBvibYVs+itSwWpH\nQmgjwu4XyFeAQ7XQlLRMkZD3AlhnofuHkzLe3/cwOhVbbXdVy6WYk1uHOfZdDzpmAfcg3lOo\nI5mTFqACBO5DvK+QTX+1KlgdSAhtRNj9CvkK8Mc4iTWMhLxz4J7cMJ20GnbSxceWl6yqRd1e\nvZQD/+SgNmYV10cyL+WgBgQeQnyokE1/1ZqD/YMAi4r3ESLJTFaIW2uKFaSoDVz/TIR+G7CB\n+kvcc1idez6Mrbud8OqcvAanYhN3EfK8CBMTTfHkJJLMWOWG1Js0JrmCwu8jUQKxq/EvYAkz\nQGjqaHaxuPh8fp6Y4gpkT6D08o4yipFsJir0logC9WKSpklvu6ge23TotSx4a0ru8zHP7Ua+\nxO3GTYF+2nPzpa0/Lz6VT87Pd/Zf7b2+OtjePywU8tyySd4mLP/wxvlWyYzhpgdHOv4T/aVn\n+DSIlXttrOI/KKMuEd6+usOuc3tXo6xQxKaDmcMxNS0uDlwTFr5+Bn7FtS6oBxk0HaN816DB\n6NT8qztuJ2FdIxT8BW/PIIUj1MV2vOFSsWNnSjtv4kWor/8BLeafOgAAAm14nI2RS07DMBiE\nJ4/SxwIhcQskGgjQlhXqokiVKkBJkboCOY5BVmMnSgyIU3EZNlyDAwBysUUbsqg39ozt+T//\nXvQAePB9B14LAFq388vDEeDua/E3Yw+AAx/d9UNNpu+um5IIVtWudyaS5imXjwC+ansXgRJF\nEClRPIcsHpRB1J9exfPxbHY8PBmcJaOj4XkYBooLEggiA5oXr/cJocskl6wfpYC3q+PGH2/v\nd9Bjk4dmpLI8rjHbN9fxdEGVkTsrqRrgDraH+63rfJuElknwJpLaLjDbBaMfeMZ0u4zuKi5Y\npYgojOG+pIbp0/yLU+NrVyXVKbXi/x6vM3rmPjbXjdSdhEtNWtkOMUmz3KoeUarkyZNi1vF5\nFZ/as1lOl2xF/gPmZV+VAAASsXiczZjvT9pAGMefliKDyXSKulfmJFniEieTZepIzLKJW5YQ\ntyAuviAhR3tAI/2Rthr5y/dOlpOn0NY2pnIkI7l8H7he+7nnx3G9qwIAZEBRJMhkAQCyl63v\n748BMusAkAOQuUoA8uqDKpAHgOKpZY9Il6rXXctkAIoc6Fxqah2P9gEgOERpnV21ole6juqw\nHkCmyL/xpwPAC9QS2qXIqJzrqD19yADgL0w+0y5m3tZqBweREVl1SF03AjR9ONxPGh8ijaNX\ntT3dG+IU5XEEVULdQHtjcl1m/CRbdvwUTrzjVcsedcKOf8Ss/Dlrfoshzkwi+qDraK8LJI6F\nybZNarAEGgm1hHZJII14/ymYkMrj5BRCnJCAdKhTNwFHQl1YAsoRB0qFJKfN6jtE+Sq+iaMM\n9m63BrpLejem6umWSVTL1pk7XafSoq+gU7luo70tED0WZrmtMVd1dJvPIMGf0nTMtAmDShf1\nbPP0V/0sBnMJ68NXbOIwg731UFXvqlQdsI6mO+SE9NxazabeoDOwDLZb3vd0g5bf7RGbqte0\nz8gJKU9+Sj3DHDqfax3tusAZJqwGNy7tswSHS+joheXF0ryrwUOGBxSbWEoZ/9+ii39+mhgp\nqQsY5gIuBrmJCqOOpV055bRE0x2mepYzegbzGipBmwhkji+Wtu4xI4FGQt1Be0cgzXxZyanK\nAcX2n9C9RH/5upDKTqqZHC6UKZmXsUq4FtEuCmSOZ/39bNZV1E20NwWypquUZYww1y20twTS\nzJeLnOpNQLEJo4uFKbSp078xmOnFbYPz6KQi6qLKY84N0WusYF+xicMMUZxfNhqpEdfQe2vh\nt1thiLE0+Ta7o4Y9ZG6C06RZtQuPrRI5t8BjheCYLxXPsCtNz7Bvq+zi0Kk093+eX7S+NhoH\nRx8PP3WPPxx9rlYrfBNZMahZCW1F95taAors98xKzmAeDazGqzi34Ki9t+QHM5lDPaaR7og4\n1t2oz8xqjWgWMS2PME33eMeAmtH7Z/l7uH8IIvsO0yy1NbKZH4z+5O6WM/NPKCD+x39hB4EB\nCdG+sB1mU4dpgcrmJwX3/wAu9QSkAAACcXicjZHfSsMwFMZP/8xtvRDBtxBctOo2L4dMGAyV\ndsKulDSNEmzS0hz1sXwZb3wNH0AlM8Gt9mK5Sb4vyXd+OVlGABBAGHoQdAAAOreLy8MxgL9v\nxN8MewDgQQj99UNtZuivm4pKrhvXe1PFylyoRwD4auxdEJQVSVBWLzFPhzVJBrOrdDGZz49H\nJ8OzbHw0Oo9jgkJSIqkirOYU+T0rZVUqrlAPkhwg2DWRk4+39zswY5OJFVQ7Jt+a3ZvrdLZk\naOXOSmIL4MH2gL91vW+b0LEJwVQx1wnuOmH1gyi4aZnVfRSSa6Sysob/mlumT/s3XoOvq2tm\nUhrF/z3eZET2PmyuW6l7mVCGVLsOccWK0qmIItYie0bunFDo9NSdLUr2xFfkP1YgYWMAACIc\neJzNmv1P20YYxy+vvJSXMtiglLEjhC6IgFvQ2i4aP2yMTZWqVUrTqUiRosO+JBbxnXU+8/KX\n77cynfOYOImj1nDuhnT6PsQ2+TzPPc+9mY+zCKEcyuczKFdACKHCh8Yf+68Ryi0jhKYRyirN\nIJR9HGgezSCElk4EJZJikzsuZ5RJD6F8NnJDsW61JOkghKKP5RunHxujd3rCFLSNUG5e/aYI\n+l8c6BrYayNPTXnCbNs9ihD6B/V/7i5RdlmrvTgceaJg9ojnjQDdfTn61G/qkczt6F1NaUv1\nXepq9nYENQP6BOwn/ftyt59lK9x+Dic++GYQ/NZo8Me483+f1n+Loc4pEtBVsFc1UsfCFJqM\nOHQCTQZ0Dew1jTTpxDAPiZkfT1It1BMSkfRs4k3AyYCmlojZkSBmZicFbVDnQ5QrCKGFiELT\nRxm9utfo2h5u+8yUNmfYHB2xcFtwB1OrQ72kfnwLEVa6B/aeRj9iYeaaFvVMYbvKnQnBVSDf\ngULTBhWkQOFLU6BQP3n3+2kMZhGKJVRo+jCjV5fHyrySGHgKYql0GezltIDPMLaZ60t8jDtU\ntlwiiONVPEldfIxLY96UdstqFvTKjMorLi68sudSUwrSKweJXXYFdYmgVjWx29PgqtIzsM9S\ndJv7Uovfg5vKglwldnsGXJ35Gm5ndxPzzQKT0izYWY18sTTFpu+RTtzkXQSER4MBR/+gU3zo\nvDMX3/RSZgfjwxBtISjohMTzMODMQ2yn+qqNOJZ0800w9KjKqni72ORMEpvZrNOfJe/hwgLo\nHtipT5KFpi2pM4EmA1oFu6qR5mEJqnQ/otD+J3QLEK9QUynySeVT7E8MCZEXoWYWodqnNFd8\nLOrcu/4cpgro4B7AYVsfqDbgZNWyCL2s9CnYTzXSPCwfFdVGRKFpo4uFmW0S0fGdwVZxbIbJ\n3D2RXok8LHBLsJ5b+hpr7+1Gl2KXyC6WHMsuhR2YFdmCJeX/BiKrdBvsbY38sTDF5iXp+XRC\nODORXUEqnZ578I5rFdYQoT7SvJYIMBe+ODcnHKiswaI2VGj6KKNXV6XtkFqtZnL3pnVOzItz\nzmhlNylz5KQliG5G80HaEPNCh7ckb5nE7N4DdR3w1ofn83RQjY4tu/45/mUfl7pSul7NMAS5\nOuh/7HtUqLUlZfLA5I5RSupLZEZCBthGWr6cCOrywBNJrjnjjm2SXu/GZm0uHGoRxrgk6kjG\nUDm1T6+J4/bofrA9NRxis+T+bYBPSk/APknLP2wRSVo2k1TYTuCn+sCAD5LDfw/ASjHYOC34\nDcsWitklnqTPK/38qmLVZYkrZBNgN4d7IB3wrWFwyxbVoY5ITP8DECvdAnsrLfpZdUYTbBOT\nYkYyInqUkQ7mEca+6OHjoSiX2pRIX1DPgFJtBZvbA+ldlnarSf2JhBodgX2Ulj9nGNNrlwsN\np2TDp4NJvS6Bp6X/9pBsEl5kaZjKGdkQ3sr4EXNi4DJAliPH+CtpAW/5rGezi0owzpfUYGn6\nwrMvKT7GjfqH08T0O0C8k9LQEwsz3bQ4k8KPexMSvkV8Bqp9XR69mnw9/gzSMtSs5vSMpZlp\nwnAXt3sN164/phSw/Mi/BsCb++gzJ4Z0XKMuHffykL5/KYz6wZu/3jd+ffv2xaujlz+dv37+\n6ufDw2CNZTiEGWNVd1C3JuBkwyuDTHaoJJFzp8fgX/Sp6g7+kzIqgi3r+Q0W/PqmQ9lhDVsc\nMy4xtWypLnQJG/37BfWqO/xfg2wYNIubjRuXhh3S6f91LgYxGuqU8KcCHYI0dsoQ7XQ4DUSO\nSdXL+E//Av5AHSQAAAJqeJyNkd9KwzAYxU//zG29EMG3EFy16jbBm11MGAyVdsKulDSNEmzS\n0kZ9LF/GG1/DB1DJTHCrvVhuknOSnO+XL8sAgAffd+B1AKBzu7g8HAPuvhZ/M/YAOPDRXz/U\nZvruuimJYHXjem8qaZFx+Qjgq7F3ESpRhrES5UvEkmEVxoPZVbKYzOfHo5PhWTo+Gp1HUai4\nIKEgMqQVI4rdZ7waxBng7eqsycfb+x302IShOaktjGvM7s11MltSZeTOSqoWsoPtyX7rOt8m\noWMSvKmktgXMtsDoB54z3Suj+4oLVisiSmO4r5lh+jSf4jT4unVFdUqj+L/H64zA3MfmupW6\nl3KpSWvbISZpXlgVEKUqnj4rZh2f18mpPZsX9ImtyH8AgdBeVQAAEyd4nL2Y32/TSBDHx47T\nH7lyBMJdeaq2PZ3ESVxzSXuQq44HfhSExC+FgHiIVG3tSbIi/qH1pmr+ct4oWnvSOMYWhG6I\ntPpOsrvxZ2dmx2t/qAFABRzHgkoVAKD6rvf07w5ApQEAGwC2VgvArifqwCYA1B9L5AqZJyS6\nKpRTAMfO9K91vRPFh3pk5lend/yhlx8ZS1fiAKByTX/TAOl1E90mezs3az2W7kCMEQA+Qfq5\n7MLg7OiodZCbUXXHPI5zQJcXh89p01Osi/yovhJKX0v32hc5VIv0Ntm303GVi2+yVS++hVPo\n+5qb+P7EE7IE2Hl/3H1UgFvRCKR1susGcQthqv2A+1hCY5HeIPuGQRrDznMoFR3C3TCMW5J6\nfCx4XIJjkd4k+6ZBnMR7lZz3rFqZ0+Y7e4HyOgD8mlFq5iizvZ3eSMRsMAlcJcKApZGOGZ9X\nKcYVUyNkcYSuGAj0WMTVaNlFpWNS7ZDdWdWidsSACcW8EGMWhIrxsUTuTRmei1jtL4ue2Wew\nQ/aOQfRCmK2+h7ErRaTDUpIkViaNqRmDSlLZ/t5UrnYfv35yXIC5Rpt+ptTMYWZ7G/M6dQfP\no1Cqv5YGXidfam2Q3TAIXFKxJjEfYon/LPLbysJ85YqVJGxGqZmltOcRWaBdS0O9JHKNYlwj\n3HXDyIWof7zhasRUmNTTeYFVITtFKr3eD6zjOmmL7JbBdRTvnr5Q6JfQWKRtstsGaa6Wpprq\nIKPUjNEVwtT6XA4nPgaq6ESySU76ZZX7276q47aoAm39jCp++BJjXQ2ZCDzhciWCYXr+UFxN\nYhYOMjsn2TMiDJZd0DVytdZDsg8NLqgQZq1/xscTLPGvRSe9lWVB5co38wYl6kypmcPM9ra+\nupmzB2xPHz2bKmxepsBJODjRD7V7y9/qb5Gnb1HptAyXzoXl7E6CsQg+3klWsHeXSXQnMhZn\nyB6wXvfd8fL4vxGy1l2ydw3iF9Js9vGc+9EY45L80BS/ryiNndzbEnqZkZ3zf1P5UbOr/Ois\njW/vyWZ3//mrt72HL1607h/c+/e088/9/9rtphI+b/o8aM6TbL/rlXDYs555WfFR8cyxpE4L\ny866+yd7hgFKfU9np1Mmw/PpEIP2EfPC5KEEPaF0x4gH+f+v6qf/2XsXe+YtL3R70whnkRim\n/x7KuXMWojH7bFMkwGA0Fmg3IokRl/rocllY9fuJz18AE/w4BwAAAmx4nI2RTU7DMBCFJz+l\nbRYIiVsgUUOAtmyQuihSpQpQUqSuQI4zVBaxE8UGjsVl2HANDgDIxRZtyAJv7Pdsv/k8XkYA\nEEAYehB0AAA6t4vLwzGAv2/E7wx7AOBBCP3NQ21m6G+akgpUjeu9qWRlzuUKAD4bexdEi4ok\nWlTPMabDmiSD2VW6mMznx6OT4Vk2PhqdxzHRXFAiqCSsRqrxHvMVqkGSAwS7Jm3y/vp2B2Zs\n47CCKofjW7N7c53OlkxbubOWuoXt4P9sP3W9L5vQsQnBVDLXBHRNsPqBF2i6ZXVfc4FKU1FZ\nw3/JLdOH/RavwddVNTMpjeJ/Hm8yInsfttet1L2MS0OqXIdQsqJ0KqJa1zx70uickKv01J0t\nSvaIa/JvCKhfIAAAKHl4nM2a228aRxTGDzdj8CW+YEwodaZO0ouUhoa2ScpDpTZ1qkiuW2FS\n5QHJGu8OeKW9aXaJ7L+8b3EzcNYMeDfJsrMES6PvWMPCjzPnG2Zm900ZAHKQz2cgVwAAKLzu\nvvz+OUBuFwCKAFmhGYDs1kjzUAKA9RecUZ8Rpg+YB5DPSn0rHf3MpwMAkK/Id4/edGdf6XGN\nsz5AbkP8Jz4cAFZRdzHenbmq6HGtb5gMAP6D8d9NF7PftttPfpq5oqCZ1PNmgG4+HN6Nm7gk\ncz37qp5v+OKzRG/2egY1g1rBuDJ+Xe76o2yF64/hhOddG+X9TMr7LeT8v0ed30OAc+PxHOkO\nxjsKgUNhCj2bWiyCJoO6i/GuQhrl6ctjOeZvl6YS4Ijyo6ZBvQicDGpq5ZebyV+mHJW0ibun\nKAkAbEqKTR2l3NvuXhge6Q9tzTccm1DXNQ3mEc+wDJNyw78iGjW1oUlH3b5DKDENzydOP+7X\n+goTLrSNcTutr1XzXKb5nApiTZp240IfIqjQGsY1hdChMOs9nXkaN1yR84gCESD3UbEpgxqV\ncflTy7jQefH3H0chmCto+ECxqcOUezflWerb2KxFTGMR/ZZJ03MlQgxbZ5ePYmOuIprQEsal\nFDH7nA68+JgS2kIwXc60OTDLiFZeBGaZEFtMSnNwriHb2jRzOpzbhFhe68x3TMaprbH4uOuI\nKHQb4+0UcV3XSoK7gYgbi8BdJ8S/4My7cEw9Nqk0MclJToc0+11svjvIJDSLcVYhXyjNSm/o\n0UHYankFEbZQU/mZ3Eu62tuWnLI+aWops5MNwxRtYfQ7FJNY2geNpqTiWJURh5IWX9m6oUVv\nQD7AuolawVjlij/cID3DZ1YETQZ1D+M9hTTJKlFQVSXFtiR00n43PTdH+mS0EIpJXEFvVBbp\nk9JLTgcWs/15aDdRqxirHP14TpF257CP8b5CmmS1WMHdYKA1xbvCZHTSzPIZnDJai8ckrqI7\nqot0SvkfzrQh9xw+D+4m6j7GKosznlWqOMTVlI4okhWjoLorKbYloZOmlsVbZWW8H4yJXEN/\n1HCtWFS8XgxF3T4ZWueME6dP8GBtDuo7qPcwvqeQOp5lJJvcHKYRhTTJirKGh6SBYlsSuruY\nr0AXapmNqaOJmOR1dEsd37yo+GZEOPFfpy2ShLiKeojxoULieK6p42jXpaPn+wppktWloHog\nKbYlofsC8xXoYl0zdUIWk7yBTmks1DWua5EkxFXUz++aBo52Yyld00CnNJbSNV9ivgJdqGtK\nNwe1MakP0CUHuM4pjlUZdThtNwHtDmod47pC2nhuOcBRPpieM5XRJKvHA3RKoNiU0YXClHuU\nD4bSkc6t4+MMLqrTtMinJS7y1sAD3NkHuqZ4hz99BnHy+vg4NuJDzJ5Q6TklZYihNKUeu6SW\nazIvImmC4uuUxjY/8wQaPiAmX/Nr07fcZse33LctdvqUNzuPX52cdn87Pn7y7MenP58//+HZ\nL61W0zcs2rSo3ZTvwj/u6BEk2aBn4jiL+VSakLfwq8lXPXpI/mQ24+8/QCfnV4Q7l1cDZrfa\nRHeI7fiE6YYvOi6oPfv+BfFEVfA0WzbIl+5o3SuXBWMxGL+7wyfpmRqP4O8bHAtQOB5TtKvv\nD9Fcypku3Z4Uz3y9+x/EiCi8AAACdHicjZFNTsMwEIUnf7TNAiFxCyQaCNCWZRetVKkClBSp\nKyrHGSqL2IliA8fiMmy4BgcA5GKLNmRRb+z3bL/5PF6GAOCB7zvgBQAAwf1iejoCcI+1+Jvh\nCAAc8KG3fajN9N1tUxCOsnG9OxG0zJlYA8BXY28aKV5FieLVS4zpoI6S/uwmXYzn8/PhxeAq\nG50Nr+M4UoyTiBMR0RqJwhXma5QrWSFVNeknOYB3qFPHH2/vD6DHLhYtiLRYrjE7d7fpbEmV\nkQcbqVoYT/Zn/K3rfJuEwCR4E0FtM9A2w+hHVqDumtE9xThKRXhlDPc1N0yf5nucBl9H1lSn\nNIr/e7zOCM192F23UnczJjSptB1CQYvSqpAoVbPsWaF1fCbTS3u2KOkTbsh/AH4kYnkAADjx\neJzNW+1T2kgYDwhaX2p7Pc966Hlbjp7YUqjUt3OmH1q1pzM9O1J64wdmmDUskGleuGSj9S+/\nb/V2kycaaLh2YUNhJv4eTAK/37PPk91ndzmbURRlQkmlEspEWmGv9Ifqm2e77H8/sjd3FCXJ\nMcHwvocpZZrhwr5NMCWINFrEQU6HqNTGipJKhq6ZrDTqFLeYFb4zVT08q/Ze6diqTZrsO+/y\nd5yE/90eLoO93HPXFLurqemEmf8q/uvmFDEv9vY2tnruSKs6dpweQjdfrnz2D35L4rr3qhrV\nKP8ufjZ53UM1AbgC9op/3cT1V7mlr79GJ9r/quf/uuf/epf/v6Ce+vuw8jqC+AQnA5gBOyOR\neCSZdM3EBunDJgG4DPayRDaxuTEF4Zn6MlSlEO8TjljXsNOHTgIwtnCc6PFjYqaf026zvYvl\nGjvmQwiHPJbhs8vVtuagpmuqVLNMpIYeXaK88+DRfFxRGj775Bw7pIEY5aaNWwYxKfYEQJwi\nRzM0HdsavRKVsQ7UOT4B+4lEGZFk5moN4qi21uEi+sREIkQIDmmkvMid+9bITVf23x0cRtCc\nhBwPEA55NMNnF6MeUHlhzlPgTo6LYC/GxfkIIc3suBS9RC1C6x1sY8PJO5R02H+yUYKy6zne\nkTs5eK/nbHxZEFZ5B5RxPAL7KC6VZwhZLh1MpknopWV/DOn1rhtM9TQo9UiBfRaXata2vP+u\nO5Zrq0RIOr+PKfZuFFc5A8pmRtG2gUqKbaZvAJX+jeIqZ0HZ7ChUIoRo2yZO29IbURqF2c8B\nY44IbBQX+2PEXv1aAZum5XeWrC0CjU7OcMq5207TTzrxRroL0jgeg30cl8wThDodQygEDabZ\n0omNTZWFosEqnhz7CC5eXOs86ON4AvZJXFqrCDWwTtkIZ0i5/qcMpvgeqORYBbsal+JThP5x\nqdVsCivuDmrNpMR0/mcg2Feuf5GPp2CfxiU3uS7M7wfgxDEJdlIiv0g2kzXXwa2oKnUSKDwA\njGWMujRsdQUD3RuEQy7LJHxBb02d9gZ/gowXYHC6AJ3flI/SGEcy3Tl1iX2F+JgMqRarrjRT\nM1tBeVVE+65ts6JLv0LYRGtFo9Vc8y4eQNs84AHYBxK1RSdUTaPE6MMmAXgI9qFENsNFLmf1\nJoRwjAm7n8BfAcaS/f3yatIvNwQpLyq3Vd8c2DIfBZFU5975lRFPluIAhO8BZsAewaxkn2wJ\nFcvxz/cIx+MizPEFuCJ7rm8odg/BXwGONFtmQ2WqIO8lSJMlGHZM+SiNdyTf7Anji6wmK8kI\nguq6yUZ/Lhvnsc5Jdw1zABkLgFtgb0mUIZZISxAAHLfB3pbIZrhQ5ax2QgjHmLD7GfwV4HdI\nJH9CQ5B3aFHpOyUSTOAMl0gZSJ7MWCRSBgIgM5aJlIHkyYxlIoV68NEn0vRNzS7IegVSZwVG\nQ1M+SmMdyXblL1YMGVgPLTIhaiGbdCybDiDgAeAzsJ9JFCCWQKFFUaUIdlEim+FClLMqhRCO\nMWH3C/grwJEm0ASbxRTkuwrpsgrrI1M+SuMbyfNRhehs7vmCeDO3N3OTPH3OCXLZku4AKuYA\nC2AXJKoQy59VaPdVyOaE5GweLkJXIasDLI5Vdv8K/gpwpPkz6U+LC1JGkDZolBMIuVfnDhuq\nUYIO/AUBCVmEoPvkWAa7LFGIWBaF1seUF2C/kMhmuDjlrDZDCMeYsHsE/gpwtFnkL9gIUs5C\n5mRHmUWbx8GyEHLNBrHRZVtT24gtGN1sKHLQpabrPKNsYlgXpFEcQNg9wNdgv5YoTCyrshAN\nHPfB3pfIZri4zcICQIAHo1gImKmxGtj12jqCEoSN8lvMqTSc43KwqSc3ii1Xa1U2cdDBtM27\nGj6J4K/CNoJ1IX2wnYOPwbuPQzvd1iRqiCQzWbvAukv6uJRT+D3Ohv/mbaJ9l3+fwnpggLOS\n1wU9mtPfHJ99tgAXYGNSgHDIYxk++5Cyunxvb0+1Olf1c6x+PLdMkl8X5Rwauodn6uPhPN+y\n6tSqq1htD0A1VKWHN6TEQ3WG78PgC1d5UZoloFbq3q4WD81dhFxbh20jDdLErs63j9C2k1/P\nsTNOjnzCRie0qbJusBmdgqio5yCE4y7Yu3GJOkKIfOITTENuHRUVuQHCNkax7S4pHP9loFSO\naQ9M9+MlcsexMOdQTRP/4+WRa+qa+TGfbWCKswU2llVd2+EzMC9RtfLhUJj9JjDe7K42pLGP\nJHOn1rBMartRe+MLQGILUHp3PVw3vQWRGWBScoRGspmuBY+4CEZPwUnbMTks1fNLN/ghWvie\nNyVqdEoV9ueiTN5v26VK8fjkffXV27cbOy+2t853n+/8US6XeHdeMrBZikq8YqXRh1EyOHMb\nzAahOFS93geJ4bsKj9GfxCS2N6A9v0K29emqRczyHmpYyLQoG9tqlJ9oY7P389Pe3mb4wGTg\nt4alVq86JGiTlv/pln3rpq52CV470CaKxHbpYnunw9YqsM2niW527PLfln3+Dzr72SwAAAJs\neJyNkd9KwzAYxU//zG29EMG3EFy16jZvhF1MGAyVdsKulDSNEmzS0kZ9LF/GG1/DB1DJTHCr\nvVhuknOSnO+XL8sAgAffd+B1AKBzu7g8HAPuvhZ/M/YAOPDRXz/UZvruuimJYHXjem8qaZFx\n+Qjgq7F3ESpRhrES5UvEkmEVxoPZVbKYzOfHo5PhWTo+Gp1HUai4IKEgMswYLSqi2H3Ki0Gc\nAd6uTpt8vL3fQY9NHJqT2uK4xuzeXCezJVVG7qykamE72J7tt67zbRI6JsGbSmqbwGwTjH7g\nOdPdMrqvuGC1IqI0hvuaGaZP8y1Og69bV1SnNIr/e7zOCMx9bK5bqXspl5q0th1ikuaFVQFR\nquLps2LW8XmdnNqzeUGf2Ir8BxDdXyUAADl2eJzNW+1P20YcvrxBS6FvtKWMhlzIskB5SUu1\ntqvEpK0v0zRGpzSgfoiEnOSSWIvtyHZo+cv3rYyznwMntdU6ORsjmefg4vh5fvd7fC8+f5wj\nhGRINpsimRwhhOQO6++2XxKSWSSEXCMkzTFFSPq2g1lynRAy/4a1DFOxGW2qBiHZtKduptY+\ntpUuIcR7Rrb+9mN9/JOW2TJZh5DMAv+LX9y9poOLKC+OnTVrma2O2meEkP+I+3NRxfSTV6+e\nPh87I9fqK5Y1Ruji4uSLe/BTUmfjn2rYqs2vxWvTZ2NUU8B7KN9zP5c5+ya33Nm36PjHvY24\nH1/E/SvK2aO3td99CGc4CeBdlO9KJOxLJtfQFY0FsEkBF1FelMhGeviySMfs16kphXBA+il9\nVbEC6KSAkaVfeix+qbmgoF26e4TlNiHkpgdxyGPprd2r91SLdoZ6y1YNnRpDezC0LarqHcPU\nFOd/StMY2vye1Te6akvp009M7fZsVe+GVbaDmHPcQ3lPojJfMvONNrNapjrgYgLCzYlUgTik\nkXKSYuF7kyJXe/3+zVsfmjOwj0Ac8mh6a296Pb8emusswjiL7E1FmcEHlCq6btjHttLss+NP\nTOWk6R7tMnt9bbyq1WOasbZFmX6imnSPDhST6fZOx1Q0tr6xsRVa6zXo43iA8kFUWo8otVqG\n6TQL3Hj8r6p324YmBAfVy9R8HTodSigfRaW57qN50DvtD4Mlu9UyFc9BJcc6yvWoFNd8FDuj\nsUDBTq1MvTegkWMN5Vqceg2zzcxAvU6tTL3z0Dgfh16/jO4omto/DRTsVstUvACVC1eV0bap\nNlmgYKdWpl5PPxR9C/vp7TJ9GOxgp1am3lvQeCsOvX79kjVgLZUFK0a9TM3uh1yMvF869NF8\nopgqs4NtjPpgzaEl34FMjocoH0YlOR2e311w4phGOS2Rny+bmcbQUrp+M+gZUBCz50gG+6vT\nzgDvoScSiEMuy/RlGEbYLvqMpkPyv4+xP8dllJcl8vflfafOGVPboGLyMgHrR8AtlLcksvY3\nTkO1mRbAJgUUU9RtiWymy9D7mMULxJEQdg8QL4GRuDzIPw+DZmAhRSzBOBxXUV6VKMKX/MJf\nmE06IiZgXARuoywzZcMZaAkNvzS68CSNzXQpuoQVJ4E4EsLuIeIlMFYDLQXM50Nq8HQ8JI9y\nXqIGX+7z/7grE5PYhxOlwE2UNyUSDmefZTT7MnrDlOTecLoEXcYtRuC25FvNdOx+QLwExmqf\nB/6rQyElrMAxKxgIzbooTYIv9RuvnVWuSczDeRaAGyhvSOQbzjwraHSOj1F+LJHNdOm5ghuM\nwE3JN5rp2D1CvAResXmcpcaQEjzdTYzmee8smU5injwMk0+EefJo9HwizZOHYfKJNM8q4iUw\nVvMsBSxbh9RQgGMKsQ7c3rkL8JPYp4DBWiERA7cCmr2QyIFbAYO1QiIHbhTxEhirfR74PwQJ\nKaEIxxRj7XvqzsOcScxTRDIUE9H3FNHoxUT2PUXcYIqJ7HvWEC+BV2we54laSAklGKYUq3n+\ncJ4MTmKeEgxTSoR5Smj0UiLNU4JhSok0z4+Il8BYzfMw6OFsSBFlWKYc65L1BzxonsRAZdxN\ny4lYsi6j4cuJXLIuY5m6nMgl658QL4FXbCA86Q8pogLTVGI10BF2LUxioApMU0mEgSpo+Eoi\nDVSBaSoRGciXzFxDMbtDjem23454sQt+PUrXZKYN3Ab2127Esfd5+29m8d0jVNXb51bm+9yp\n3WNUH2rN8/U1o+NuN3b2xlthlXiGRdE/1V9zN+qzNlVsypRWz7uJv89OWD8s/U1Q3hwd7kuj\n70tmpnGi9IcsIC9SnmWOSLI3/b3ZG7gH6gk20grEIY/mCIuDw/390BSfInocPS9pSaPoy+Z6\ng31WtEGfWQFB4yx2I2rb7Njrd3g7znvOr1VbG1Rr579OdtmH52a1tvPnwYf6b/v7T188e/5z\n8+WTF7/s7lZtVVOqmqJXvS9N7NTaAUzSoubSbRqzFc8I4zakec/aKtPzaSLjX9+mzVNqGp9P\nz2e4u69o26DntyPK2qrNK3qKPv79Of46mXiVLy3i1TZa9dMBE23Rdb/dMC/DM9Ie4ucZ2oJI\nbI8RttcGJuO7ItueTcr8hbcv/wP7MN/NAAACbnicjZFNTsMwFIQnP6VtFgiJWyDRQIC27Oii\nSJUqQEmRugI5jgGL2IkSA8fiMmy4BgcA5GKLNmSBN/aM7Xmfn5cBAA++78DrAEDnenG+Pwbc\nXS1+Z+wAcOCjv36ozfTddVMSwerG9d5U0iLj8h7AZ2PvLFSiDGMlyueIJcMqjAezi2Qxmc8P\nR0fDk3R8MDqNolBxQUJBZJgxWlREsVv6wEQxiDPA29Z5k/fXtxvosQlEc1JbINeY3avLZLak\nysitlVQtdHv/p/up63yZhI5J8KaS2jYw2waj73jOdL+M7isuWK2IKI3hvmSG6cN8jNPg69YV\n1SmN4n8erzMCcx+b61bqXsqlJq1th5ikeWFVQJSqePqkmHV8XifH9mxe0Ee2Iv8GqABf+QAA\nMuh4nNVb/U/bRhi+fEFLv1coUEa5wNrSDUhLW6CwSv3cNIkVRANCVaTocC6JtdiObAfKX77f\nyrjkMbkEG+r4nKVI1+cttsnzvPc+9t35sj9CCEmRdDpBUhlCCMns5v9YXCMkdZcQcoWQpMAE\nIcnbTUyTq4SQmx+4ZtnM5VSrcsMiJJ2Ujg7tlIouqxBC5GvS+Y/7+e4zHVuzeZmQ1A3xP/Hx\nrU9t4hjisa6rhh1bK+s1Tgj5l7R+zg5x83B9/dlq1xUZrcYcp4vQ2YeTb60mLkmcdJ9VcHVX\nfJY4mjzpopoA3kN8r3Ve6uRSbpmTy+j4Z76EzBelzJ8jnd77uPPOh3JK0ACOIh5VSNmXTKZg\nMoMHsEkAxxCPKWQTQwLTKMn0+fJUQjmgBFlNZ04AnQQwthJMdmUwMRKUtLbDO1g+EWmXEE0d\nS/noRr6qO7TcMDVXt0xqNdx6w3WobpYt22DN37EDq+E271y6xmr0iOuVqqublbC6fkXGBW4g\n3lCoy5fM9UKJO5qt14WUgGQLIr8B0ZSRapbEje8ticzO+60PH31oDsE8HqKpoykfvd3p+fnQ\nbIeRyOGzC5oYD9ttSplpWm7RZQc1Xjzieos2fU0r3J2f9T04u0C5eajb9DWtM5ub7lLZZgaf\nf/JkIbTaK1AocBvxdlxqv1DqaJbd6hrhx6JWY8V/dLNSsgxPcvAZKnVfhdYmLcRf4tK95avb\nadS53RysXCC9fVJbfWixIxAocAvxVlxixyil0fvnGmhei2u8IB/d8+2fy7qmq1eia74OnQL3\nEO/FpXnfV3NLwwWiWyeoVH0DSgXuI96PS/X5O5BZ14p15laP2HGAbOkMlbpvQuvN/+cOJFRd\negfqPCnCHegWBN76ce5A0rM//jvQrm//XNY1l9yBQku+A5kCdxHvxiU5GZ7fT+AkMIk4qZCf\nL5uhQsNhFb857RAo3AXGMvyeiDojG8UTxUM0tSyT7TR0WtF3ABtSwRhG4wKnEE8pVODL/E5e\ncKauRb0JRQ+sHwCXEC8pZO1vnYLuciOATQKYQ5xTyCZajQpWTyVEGxB20mpLfD4PctD94PlQ\nSBnjsI5AipgqlOFLf+K9eEYdl3WbU2+i11TUA/k54DridYXkw7lpHFUwHtNqULR6Fax+lxBt\nQNhNIF8e9tVN0xdOsUMqmYSJJlGPwy1UpsRXwZRkKGn83ounBO9HwDeI3yjkH85Tk6gFgW8R\nv1XIJlrVClbvJEQbEHb3kS8P++qpiaC1kZAipKFdc7A03EJlInzJ35Ps1LuTBOUscAXxikLq\n4Zw0hQoQuIp4VSGbaLUqWK1JiDYg7H5Gvjzsq5MmAxfcQqqYhn0EziCeUajCl/24ZCWsI/bi\nJcF5FriGWGV9hPPSNGpA4CvErxSyiVat0xgNe7iueFQcjd0D5MvDvnrpfvAqbkgZkoH6N2+6\n82n7PfXWpXux0QwGpQKfI36ukHU4G82g+wW+QPxCIZtohSpYvZQQbUDYUeTLw77aaPrCNwIh\nlWThnmw/J0yjwkkRZ0pZzI6yeAo8UvwUCGemLIogizt+YqDu+lmsiXi4MVBrI7PIl4d9NdNE\n0DuckCIk7/RvpnRL+Kh3C81Jvs8hVrkAHc5Cc+j6OSw2JwZqwVmweiYhmjJ2vmRGCsyuNIzT\nYbvf7sNRJOmXOH2Tipq4h9jJ9LAfO80W/+aOeDNIdbN0amexq5C6VU7NhnHAbWqVW1u7mvsQ\nnbBKHiHHAhcRL8alBNsieYkyl3KmVdsbJmv8kNfCkn8Mwo87Ry/KyPuSGSocslqDB1SFoDAf\nZ+0mv7d2A99uL2C7k4do6mh2sPi0u7kZmqJUifKWeGUUfdlcLfCvzKjXuBOQtATesMbRt+mu\nLzvguwjyNW9yrlHP7Zz+c7jMP6/YuZ2lvz59zr/d3Hy2+nzl5cHa09VXy8s5VzdYzmBmrnOD\n6tJOKYBL0jvS9pvBXSaNMW5DnHzVwkP6Jze5+IASPTimtvX1uMLN5XVasujp7Yjyku6KA1Vm\ndv/9jNi87311IullrGRp+eM693qj0vrrlt1OUEePeD/eW2aisEc62F6p21ysM5WkTWTi6wXf\n/gOeOTGPAAACb3icjZFLTsMwGIQnj9LHAiFxCyQaKNCWZRZFqlQBSorUFchxDLKInSg2cCwu\nw4ZrcABALrZoQxZ4Y8/Ynv/z79UAQIAw9BB0AKBzs7w4nAL+vhG/M/YAeAjR3zzUZob+pimJ\nYKpxvTeTtMy5fADw2diLIy2qKNGieh6xdFxHyXB+mS7jxeJ4cjI+y6ZHk/PRKNJckEgQGeWM\nljXR7E4QpZgaJjkQ7JrA+P317RZmbBPRgihH5Fuze32VzldUW7mzlroF7+D/eD91vS+b0LEJ\nwUxS1wfm+mD1PS+YaZjVfc0FU5qIyhr+S26ZPuzPeA2+rqqpSWkU//N4kzGw97G9bqXuZVwa\nUuU6xCQtSqcGROuaZ0+aOSfkKj11Z4uSPrI1+TeZ42B6AAATHHicxZjva9pAGMefxNjfv1yr\nhcLgKAxaaHU6VruCL7q1GwPtwNrRF4Kc5rQBc5HklPYv37s6Tp/UmCZ0rucmHN/HXC73eZ48\nz+WS2xUASIBhaJBIAgAkb2pfj08BEtsAsASgS9UA9K2RGrAMAJsXrOW4VDBiU89jHoChB7oX\nqmZD0A4ABAcZtcvbWvhMz225rA2QWJf/5PzjaUeaQTsTGrXoua221WUA8AvGv6cuxgdnZ/nT\n0Ihkq0s9LwT0NDk8jpscog3DZ9WFJeRcslcfhlA11F20d8fnJYYvsiWHL+FEh97E0DeCoX9G\nbfy8rH6OYE5IDtQ02mmFzJEwyTqnNouh0VAzaGcU0swjggYmpfE8QZUwxyQh7VrUi8HRUOeW\nhHoohNpKXNAmNT5FmQKAjYBiU0cZ7C3W7iyPtPu8JSyHE6cven3hEYu3Hdemo2O06fQFqVzn\nCeXcEaODs/r0BqMttYh2UaFPkTBrdZN5LdfqSeKYQEuQbVRsyqBG6WD8aTokq19+XFxGYC5g\n4fiKTR1msDcVKviDmXEXMZKLgQin5oVbIYGkbAja7LKG7eVJiXSYONiP6ts/IowPLJeUSI+6\njIts26U2Ozg8nNnVJXRPagXtyrxc1WfnW0am0XC0dYV8Mctv36MdFpPIEmEFdS71lnjt8rsq\nV46AYlNLqU+WnSnanaiMndGBNSw+qXto7yl0IBI8VZO0RDjEX0D+gvot6jHaxwqpoyunbglm\nx9BoqFm0swppXpeikioXUGzK6CJhVurU7fRtxkXU1moVg7SOOpfafvXWagMX641/8SgtVZgn\nV0JicdNqUWHxDhF3jPC+3WQucdqBR5dHnKagFmcmaT7Ivdasnm1izKWW0C4p9CwSZqE+oN0+\niwm09nT2/06H2AfkDmaur9jUYU5RXN2UyzMjpjF66emXaGWIkTTLdXZP7V6XeTFB0wIvoarv\nrRH6PIJfL4JjznPC7uWqwu4NCuz6xM1Vs9+vrmvn5XK++OHkY/P0ffFToZATlk1zNuW50KY2\nWzVjYHS/Z1JwNhM08NTeQu+Co47ekW+MMznDqH5d5/6hw3jhjJgO4Y4gzLSE7LijPHz9pHzd\n97+26H7ITKdVe+gx/3Z0xld33EmEpm6J//NfakHhLZmiXeq5TO6azcBSLz9IPP4Gv5w05gAA\nAmh4nI2RTU7DMBCFJz+lbRYIiVsgUUOAtrDrokiVKkBJkboCOY6pLGInigc4FpdhwzU4ACAX\nW7QhC7yx37P95vN4GQFAAGHoQdABAOjcLi4PxwD+vhG/M+wBgAch9DcPtZmhv2kqKrluXO9N\nFStzoVYA8NnYuyAoK5KgrJ5jng5rkgxmV+liMp8fj06GZ9n4aHQexwSFpERSRXKh8X7FcZDk\nAMGuSZq8v77dgRnbKKyg2qH41uzeXKezJUMrd9YSW7gO/s/1U9f7sgkdmxBMFXMN4K4BVj+I\ngptOWd1HIblGKitr+C+5ZfqwX+I1+Lq6ZialUfzP401GZO/D9rqVupcJZUi16xBXrCidiihi\nLbIn5M4JhU5P3dmiZI98Tf4NDQ5dlAAAGpR4nM2Za2/TPBTHT9p0dHu2rjzcxgaaNYmbBC0r\n4rK+AzYQ0gRSKYgXlZCXnHYRuVSJO7pPzjuG3B3TJCQaYe7USt7/bI7TX87Fs50vSwBQBtM0\noFwBAKh86r559AKgfAUAFgFKUg2AUn2ipvwbtHadSHDfQnaA4juiz8T3gKGLHvoiYo7POLPV\nJR4XoTMGMEuxOyx07K+CDwAgfl+zu/elm74yCq0Q+wDlFfmbRASAKukO2TupUZei0Oo7LgLA\nDzj9/O5C/6jd3k6PqFguj6IU0O8vh5+nTQ4xTtJX9YQj5HfJ3tJJCtUgbZPdPr2ufHImW+Xk\nLJzM6FSl678OUOTgmp/3Oq8yYMsSgLRGdk0jbCZMpedzD3NoDNJVslc10mh1nUlpaBJsVTNs\nTtpx1+FRDo5BWie7rhFn4rtyynfGUp7TplWdoLxCeaa0pjnfEnSN7qETsf7It4QT+MzirjVy\nucCIiUOcTlY0nxV9lKvkZKkNshuzepStMyfbovjXCFnqFtlbGvEzYZZ7NkZW6AxlQHLSQ4Jc\nJ6WmDWqSxKW/TeJK5/WH3b0MzAUqd6XU9GHGe9fU/HTffsgce7w9+dl6UJj6EjlU6hrZaxqp\ncyasUcQHmONEg5w3s1gvnnfCmmRtTKnppSxNUz9JaxekXaLwKqWmjTaTcnU3a9lXiFm1talq\nY84ukp4j0MuhMUhvkn1TI835slFSrceU2pzQ/Uf+UjqTis6rFVNOjQWBl6lElpOlow04E3T9\nnW/jmAX9ySqk74SRUP/Z/wF/hfQu2Xc14hernGWKuNR7ZN/TSHO+3JRU92NKbU7oVshfSi+8\ncloFgWN7xYurnI1E5URoBb79j6VTI2fXKFlXNCdrsdKpUchrlJjGXCWnpHoQU2ra6DJhlno8\nHIwm250MpEVykjokmFW9nM9xN6g4biRXOfoo472br0O0HcEGgdzfBmwUodw4sCG3vvEBFmWP\nbRBgk+xNjeyZMNWejYI7bpTjTCO2KJvPkNdpQ1O/iJ3h1p9HsoeYOJYtin+ZHHv5Ig8FFnpH\n3B1hjjclwv9zEfPcjfU6rXmVUtOHmaB4/2l/vzDiBnlvI3lArQ0xk2axh2PuDV2McpwmKW7N\nKLZm6tUDvRmIj2k3hTdsdoQ3PGrhx2dhs9N49/5j9+X+/vbzJ8+eHrx4/Hyn1WoKx+NNj/tN\ndRDT6Ng5FCXVM600DwWPLcDq9FjxUQ/vsLfoY8gF2uzgmIXB+HiAfqvN7ID5gWCTif3gmB1y\nP33/ijxKV68wSspXdmB1j4eo4jA4vXsQTl2TiIX63KY4gMZYJGirwxCHPEQ7ttaVh/0/fwH5\nU+NJAAACa3icjZFLTsMwGIQnj9LHAiFxCyQaCNCWBYsuilSpApQUqStQ4pjKInai2IVjcRk2\nXIMDAHKxRRuyqDf2jO35P/9e9AB48H0HXgsAWvfz6+MR4B5q8TfjAIADH93NQ02m726aIuFU\n1q53JoIUGRNLAF+1vatA8TKIFC9fQhoPqiDqT2/i+Xg2Ox2eDS7S0cnwMgwDxXgS8EQEGZPq\ncVkVq1L2owzw9nXY+OPt/QF6bNOQPJGWxjVm++42ni6IMnJvLVUD2tHuaL91nW+T0DIJ3kQQ\n2wNqe2D0E8upbpbRXcU4lSrhpTHc18wwfZpfcWp8bVkRnVIr/u/xOqNn7mN73UjdSZnQpNJ2\niAqSF1b1EqUqlq4UtY7PZHxuz+YFeaZr8h+oJl73AAAZW3iczZhbb9s2FMePb7k1zRqnu7Ub\nQAQYsiIXd87mdgn2kK3ZMCDoANcd+mCgoCVa5mBRAkk7zSffWzPQOrJpVUKhWQpqgPifhLr8\nzk0i9WYLAGpQr1eg1gAAaLzu/X78HKC2BwAbAFWjFYDqg5nWYRMA7r3gShNPBpNQAdSr1tRa\n132rqQcA9gn13uWbXvJIJR3JhgC1++Yvc+/oljNtot1MnLWupDPkYwYA/0L0m08xMT07az9N\nnNFwxlSpBND85vA+GuaUym3yqL7m2tzLzFZvE6gV1D2096LjarcfZWvcfgwnPewuV/qtHfYP\niOt/X3Z/TeGtGQbUXbR3C+RNhWn0BfVZBk0FtYl2s0CaoqNXx2Ksf1iYhfBmFB8dc6oycCqo\npRVfNRG+ylZW0Ba9vUT5JQDsWIqjOEp79qA34ooMJ8LRPBDEY1oRk28qHKZIyGT0tMrrw1cY\nXaMHaB8U6EMqzHbfZcqRPDSuZATWgHyNiqMwqHzpb3R/++vFZQrmGjZKrDiKw7Rnm1Zzf+8e\nEe9Jbt51DOV6SQ+ljCafKOqxjPBVMGylZXlt1Saf1aulOIqlrC6ec8u0bk7aLUxvrDgKo02l\n3L2YP4lIMPiHOfp/UMfj0UILo05vkz7XzM+gqaA+RvtxgTSr1aOh+sZSHJ8I3T2MV6yl9HRm\nt3g5abexQ7bvslueXERvai48MmWODiQZBpLoEVuxi7axKoy20W4X6E2+LtrG7Bs9Rfu0QJrV\n6tRQ/WgpjsLoUmG2+lR6E58Jnbbm3cQg3UctpXU2Vg3cDr6wd+5izXN8QVyqKRlK6jPiBEJT\nLkzfzBuFi2EgfWrWkXk9+QxjbPQY7eOyPOkMmL5mTJCQcqlIMCTBQDE5nZErwkVa/x/ldSk6\nJtIO2p2yXDq/5npEnGA88YWaP8LMfliRQBIuXO4Yczj7v+1ubr920Rej52ifl+XXgcGdPaHJ\nmA7YOPKNUWdkO5HbB2vBXcpGa8mHQyrc5YqK6y+Zi5O8fljbcThE+7AsP057IxZlAQstKiuq\nmSLXI6ZHLPHi5CqvPw/Rh4d38Z7aNz3DBaFYYIGcJyba2eVOx+eIbHQf7f0C8VNh1vpTOp6k\n7el2EOEL1FJeYqvv3B/hEjVWHMVhLlG8fH11lRvR2nnY32ILQ0yl2eyzd9QPx0xlBK2CO44y\ncltPfGXHj+D2Ob+0tB+2utoPp232qiNb3ZM/X77qXVxd/fDstPPT4PnTZz+32y3NfdryqWhZ\n30xOum4GSDWeWTSbzzS1NhsP0DP7rKPvyB9MMEk1c8nghsjg3Y3HRPuMuAERgSbM5dpMjKhI\nXr8xe0viBatxuNzA6d2ELE6FF109kIvoLKUj/n2LqYAC07FEuxFKFlLJXGtxar5rv/8PgTf0\n1QAAAm14nI2RTU7DMBCFJz+lbRYIiVsg0UCAtqxQF0WqVAFKitQVyHEMsoidKJ4At+IybLgG\nBwDkYos2ZFFv7Pdsv/k8XgYA4IHvO+B1AAA6t4vLwzGAu6/F3wx7AOCAD/31Q22m766bkgim\nGtd7U0mLjMtHAPhq7F2EKMowRlE+RywZVmE8mF0li8l8fjw6GZ6l46PReRSFyAUJBZEhey2L\nCu+LGssaB3EG4O3quMnH2/sd6LHJQ3OiLI9rzO7NdTJbUjRyZyWxBe5ge7jfus63SeiYBG8q\nqe0Cs10w+oHnTLfL6D5ywRQSURrDfckM06f5F6fB11UV1SmN4v8erzMCcx82163UvZRLTaps\nh5ikeWFVQBArntbIrONzlZzas3lBn9iK/AeJIl/4AAAWdHiczZj7b9MwEMcvbTrY6MZjG4Nf\nJmsDMbSxQhGvSgjxKAwJgVQK4odKyEvc1qJ5yHGr7i/nN4acXNo0JIIwB1HJ+l7luPn4fHe9\n5MsKAFTBNA2o1gAAap+6r+88BqiuA8B5gIpSA6ByKVQTlgFgtT31PSGJN5b+WAKYlcTkUsf+\nKukAAJJLzG77Szd9ZSAswfoA1VX1Td09ummoG2hvpFadC4TV5yMGAN8h+symmDtptZr3Uitq\n1ogGQQpodnP4EQ21xDhNX9WTXKp7qdnKaQrVQN1EezO6rnr6W7ba6e9wsh3PQsd/TTr+F2bz\nc7vzIoO4qihQ19Fe10icCVPrudRhOTQG6gbaGxpp9PvPxIA0fw1OLcQ5AUhHnAY5OAZqaQFY\nTTnQWMlz2jy/FygvA8BaQnHoo0zO7neHPCD9sWtJ7rnEEoxKFhA5ZFiqiM0Fs6QnToru4wp6\nWOk+2vtl7eOI9wmXxPZY4N6ShE15IAl1bRKFb7Qjm0pK+oI6jEiPUCLp8R2bjbjDJbOJqpCH\nRTeJKRPqEdpHGjeZCVPv2SywBPfVmeVEkJEoEji0QYVxXvnTOK91Xn541c7AXMKKECsOfZjJ\nWbJQx/amB+F5k6cY5rcL059DxyolaBON9Dm1bRzQActxpoFOLO3Ml85a28LoTSgOvZSVeV4u\n0k4L0q7g8caKQxttJuX1xSp1zDBymf0X9PHYmas2+ux06XHJnBwaA3UX7V2NNGeLS0V1I6E4\n/hO6C+ivWEvJ7bysMaMnh0LAdUyW+mISaQPOBN3yqRyqlEk0L3/Jvoq6i7bOQC2WNnU87joG\npfFfBaaiuplQHNroMmFWelQMxg5zZVaPv4xOWkUtK1fO5rg1/Kde+xetz3Z3yEiYHF4/TI74\nDyVMj6LoF9GpSrfR3taIngmz1JvQ0ZjleNKYXV3SeVfP3OtuYvmOFYc+zORsK9Xrkqfho89h\n2FTs3Z61vjvRBY3wyUcGk53iXfBVdLnSFtqtsva1O3ZH3P22h9w7B0QwaywCPlG76XY+tYtv\nYAuht0rqSzJplntsSh1/xIKcUFEU10qKaDP16hHfDCbXPGtIx290pONPmuzjQ9HoHL59/7H7\n/N27e4/uP3xw/PjuoyfNZkNyhzYc6jYW4u2wY+egVOKZeZFxmKSJBuQS7i256uAmecNcJqiq\nWMcnRHjTkwFzmy1ie8T1JGE2l2piSN3079fUq7T4PWYldpjtWd0Tn8WHMYh+3RNz/ywcSPy5\njocBGg9kgfa8L5hPher0Z/9f6mXfj5+CSpBwAAACbXicjZFLTsMwGIQnj9LHAiFxCyQaKNCW\nFeqiSJUqQEmRugI5jkEWsRPF5nErLsOGa3AAQC62aEMWeGPP2J7/8+9lD0CAMPQQtACgdb04\n3x8D/q4RvzN2AHgI0V0/1GSG/ropiWCqdr0zlbTIuLwH8FnbO4u0KKNYi/JpwJJhFcX92UWy\nmMznh6Oj4Uk6PhidDgaR5oJEgsiIvZRFpW9LUhGh+nEGBNsmbvL++nYDMzZ5aE6U4/Gt2b66\nTGZLqq3cWkndALf3f7ifut6XTWjZhGAqqesCc12w+o7nzLTL6q7mgilNRGkN/zmzTB/2X7wa\nX1tV1KTUiv95vMno2fvYXDdSd1IuDalyHWKS5oVTPaJ1xdNHzZwTcpUcu7N5QR/YivwbP4tf\nywAAGdJ4nM2ZbW/bNhDHZVm2Y69O2rjd0u0NV3hAgmTx5qQPM5Kt6ZYNA7wOcN2hAwwEjEXb\nBGRJkOg8fPK+a0pKp4gWJLSqqaIGmP/ZpKTfHXkkxbxpaJpW1gyjpJUrGv9UXg///PEZ/63F\nv9Q0TRda4no3UEOrc713euU6HkMu9vCcMOL5mmboUoPqwDxjeMot+TJjePpmmGzpe2OPTPgD\nm+KbIOBlDXQL7K3EVTV+1YRahJtvtfBzW0Xsi16v201cURlb2PcTQLcP196FRVxSukm2GjHK\nxLNErX6TQC2BPgT7YdiufPNBtsrNh3BSg98kQfDPguD7GczGf6eDFynE5bBXA22B3VJInApT\nGdl8mGTQlEDvg31fIY36+BkwIA0gXlNMnDEAsUWxn4FTAn0A9gOFOEEAy4kAlhpZQYvze4lS\nBGpdUijqKOXa9nBGfTRZ2GNGHRtdepQRH7EZkWarvA5IodXaYLeLcmCXOQij/0/+6SMxxSFq\nB/S+S8Z0QomJTOqRMXO86/28fnwN7EJ3wd5V6EcqzJ2RSfyxR13RHxmjQ4B8AwpFGVQwhqsf\nO4Yrg9///eM0BbMK2R4pFHWYcu3G0hy1nRu2BnEUugH2RlGwPSSlFjpGU8K2H8W/PNpD/O7U\n4zX8R2Kz/Ymo2t7Z2cvt1xr4IrQHdq8ov45QnGqhW2cmmeCFJfqFzfztnbaJGW5Tm7tJ5+2w\ns7iwWX7P6uBN8GCwj4ryrIaQz4ibG7IBYI3lIVYMpL6Tm+8rYBKqg60r5EulqY4WPp6mbW+q\ngHAHtJCZrb7q6twEwkihqKXUYb+Z3Iw1kq8QH429DmNwHdrXQlWGnYr7vUV9hpyJPOHx5fqc\noHDCJuYneNECPQD7QKEX6fkz4nuieQZNCfQQ7EOFNKsNVEH1WFIoXwidtMwWl+xZaVS/Xahy\nUkuZE5DXFG8UUml342X1ckY8Euxv4w3vJbUskVI+vsidT6LNJugR2CqX0Xz5FDYK9RjsY4U0\nq41Y0eZXSaF8IXT3IF6RftZ8MsT2KCfwJqTPprRBaigETgU9FKCImnxbLV4OPViNqD22FiZ/\nV4SXxzi54pOYXI41QZ+D/VyhY/lyahPGgtATsE8U0qw2agXVC0mhKKNLhWmMsDddzPkYSDsj\nakKQWqBFJdKK79dbsG+PFIo6zCWKl6/7/dyI0hGvfPSrDDGVpj4iV3juWsTPCJqg+LagvjUS\nh/pw5i5f81uHzd3OgP+56JJXT7zOYP/vl6+GJ/3+z08Pnjw+f/bT01+63Q6jc9yZY7uzdLKx\nPzAzUPSoJk45vt/G0rx9F3yTr9r7Af1FbOJhvhNH59fIc66up8Tu9pDpINthiJiUiYoZtpP3\nr4ipMfoPgR4FzHTGw2uXRJ0xDe/ueHF8ljok+nwHnaEp7JAl2jXXI+I4xZQyXByjv3sPbdnt\nGQAAAnJ4nI2RzU4DIRSF7/zZdhbGxLcwseiobV02sSZNGjUzNenKhmHQEAdmAvjzVr6MG1/D\nB1BDhdiOsygbOAc49+OyiAEggDD0IIgAAKLb+eXhCMDfN+Jvhj0A8CCE3vqhNjP0102BOVWN\n692JIFXBxAMAfDX2LpDmNUo1r58Tmg0kSvvTq2w+ns2OhyeDs3x0NDxPEqQZx4hjgehrXUm9\nVDUlWuKlLFQ/LQCCXZM5/nh7vwMzNqFIiZWD8q3ZubnOpguirdxZSd1CeLA94W9d79smRDYh\nmAjiWkFdK6y+ZyU1PbO6pxmnSmNeW8N/KSzTp/0cr8HXUZKYlEbxf483GbG9D5vrVupuzoQh\nVa5DVJCycirGWkuWP2nqnJCp7NSdLSvySFfkP07jYeYAABLeeJzNmO1P2kAcx1toRRluzu2F\n+KoxI9kyJ4qZOtybbeKyhLik1sUXJOagJ3ShD7meT3/53snu6K+l1DamciQ2Ob4/uB793O/h\neu15WZKkoqQoslRUJXaoZ8bxpwP22xv2pSRJBa4y05WxKtIS09XWrecSqvke7lGCNP3oVJKU\nQuyMBd28oKjPrPg4xWidG8kzfdIj+JJdcZl/4wisLYJWwa4mRpXYqEtriJn5TwqOqAs7181m\nYzcxQu0Nke8ngKKLS/dB40PkUfKsDrUovxbvLYwSqDLoOtjrwXnF0aNs6ugxnHTv47H3L8D7\nF8T0M8CVPy39ewp2MYjtWNfAXhOInQqjdhxk4wwaGbQKdlUgzZycqEBqKg/TVAh2RiqioYX8\nDBwZdG6pWEh4US5nOW1S6VOUL9ObOMp4b80YWL52eeX0qOU6WhBxP1q1LIcvXHln8Ap8y7UG\ndk3gDFJhKh0T+z1ieXwiGW6VozFREwaVL/iq/uP3USsFcwHKJFRo4jDjvRsPK/w9v2Vshgnw\nITd/CVzLdQPsDYH8GSV/5aM+znCnDG6cW9QXZi35cf7GFJpYSn6nT1vmlWCTkAu4DBEuT9vC\ngFNB3x0zUO1mgAmOFqgbazjUuhgWLmxuPWEiy6DbYG8LnEh68XQsiu0MGhl0B+wdgTSzZSmn\nasQU2jOhewH+CnUulZ5VQyXIx5zMFSidCmReKVBhzKmsH43BpH7c7l9mJIoKcZu6BJtPmM8K\naBPspsD55KumCmQB10OwDwXSzJavnOprTKEJo0uFKXcQ6V/Z2KFp++MlcNIy6LxKaMYt0muo\n8lChicOcojg5a7dzI66C91anH4CFIabSLHXwLbK9IfYznCZPVgThsVUSrzbgzUN8zFGd2l5d\nZx/XDXy6R+r61q+TU+Nbu72zv7v3uXuwvf+l0ahTy0Z1Gzn1h5vTLd3M4CmEPZO6szFFsWV7\nBSYYH7VZ035iBxPEdg5a904j7u1dHzuNpma6muNSDZsW5R0D5CT/X+WP6uHLkkLoNdPtGXce\nDiPSD/7dJRMnTUUlPN5CRCSBUZmiXfQI9tB4RY/Km79MuP8Px8Qa3wAAAm94nI2RTU7DMBCF\nJ3+0zQIhcQskGgjQlmUWRapUAUqK1BXIcQyyiJ0oHn5uxWXYcA0OAMjFFm3Iot7Y79l+83m8\nDAHAA993wAsAAIKbxcXhBMDd1+Jvhj0AcMCHwfqhLtN3101JBFOt6/2ppFXB5QMAfLX2kghF\nHaUo6ueYZaMmSoezy2yRzOfH45PRWT45Gp/HcYRckEgQGbFXbAjFO1Uzig0ZpgWAt6sDk4+3\n91vQY5OIlkRZIteYveurbLakaOTOSmIH3sH2eL91nW+TEJgEbyqp7QOzfTD6npdMN8zoAXLB\nFBJRG8N9KQzTp/kZp8XXUw3VKa3i/x6vM0JzHzbXndT9nEtNqmyHmKRlZVVIEBuePyGzjs9V\ndmrPlhV9ZCvyH8XJYJQAABNaeJzFmFtP2zAUx0/StEAHg124PEZCZZtE6Vaugyc2umlSBVJb\nJh4iIZO4JVNzUeIi+OR7o5OTE0ijGOhwt0rW/0SOk1/OOT52fVYGgAJomgKFIgBA8bTzrboH\nUHgDANMAKlcFQF2IVIMZAKg0rllATKaHPjVZQPRu4Dk60dt46V38oiYD0NTUoFLLOmekBwDp\nR2mdxlkne2cYmAHtAhTm+BWnimEiXUd7PTNqKgzMrt2nAPAb4t9dF3Wv9vfrW5kRRbNPwjAD\ndPdyuI0bH6IMs3cZzGb8XbxXHWZQFdQq2tX4vsLwUbbi8DGc3IDM0zgg5xgQAbX2s9H6ksNc\n4Byoi2gvSmTOhSkaLnGogEZBXUJ7SSLNJDyoYVJqyDwtmVmQhKRvk1CAo6Auo70sESdyoZZx\noVIWOe1+jo9QzgPAy5Rik0eZ7l3rXNqh3h24JrM9V8dwh9kSNuYnxPfEuob2msxPyMvVqfaD\nOVpsfT05aghgq6ibaG9KhBVMc9OzqIDmHeoW2lsSaUZdFi9J5b8I7zbqAdoHEhlzYWYNi4Zm\nYPs8UQXThufZK1Rs0qCinFOfOrmF2VbCMpgoNnmY6d6VTPl+H8f7w9jUU+hQritor0ikzqUp\nGYOQ9KjAiQo6b2KxLjy3kEdZm1Jscin5zi2vGpbSu80nI5cxxmXEnZKMnIu6fBKh6l5Xj7ae\n+sOF/CH2edQK2hWJ7PkzxrAZdQQ0CupElsBnpWYZV5ZEsUmjy4UpGyToDRzqsrzN2Qw66cUk\n57T6XMfNYtWZ/Rfle/WIML4FIw7VTc9lxHZtt3e3N7MII+Pyz6Fnua6ivSqRPxemZFyR/oAK\n3KngHvf/B124/L3GvEwUmzzMEYrj02ZzbEQsrJGm/mxLQ8ylmTHoNXH8Pg0FTuMUbycUWy1z\njIKnHOkxhzXm+LUWc/yrOm3vBLXWxo/jduew2fy0u7mzfbH3cfdzvV5jtkNqDnFrme3SRssS\nwKhJz/2EcygjqTV5Ab8uPWq9on+nLg0Io5Z+caMH3vVNj7r1fd3ydNdjOrVsxjsuiZt9fpEf\nCySnMmriMsszOzc+TcLRi5/uBfceGglJ8lvEcIDEkIzQTvsB9UlArVTJ4QcXt38AjtMllQAA\nAnh4nI2RzU4CMRSF7/wgMAtj4luYSHVUwCULTDBEzQwmrCSdTjEN03YyLfpYvowbX8MHUFNs\nI4yzoJv2nLbnfr2dRwAQQBh6ELQAAFqPs5vTIYB/bMTfDEcA4EEI3e1DTWbob5sCc6pq1ztj\nQWTOxDMAfNX2bpHmJUo0L19imvYrlPQmd+lsNJ2eDy76V9nwbHAdx0gzjhHHAi3xii6wEFJj\nzaRQCyKLNReql+QAwaFJHn28vT+BGbtopMDKofnWbD/cp5M50VYebKRu4DzZn/O3rvdtE1o2\nIRgL4hpCXUOsXrKCms5Z3dWMU6UxL63hv+aW6dN+kVfja6uKmJRa8X+PNxmRvQ+760bqTsaE\nIVWuQ1SQQjoVYa0rlq01dU7IVHrpzhaSrOiG/AcROmRVAAALxXiczZbfa9swEMdlx07brNkG\nexkEGr0MMtjqLaM/lrf9SMdG6MB1Rx8CQbWV1Cz+geWU5i/fWzOpOTe2a1ECCtSgfC85nfXx\n3cnRRQMhVEOGoaGaifhlnjsn74/5b6/4lzpCulCN68s7NdAO19cn5C/FJAyjlKR+FDLsRtNZ\nEDKEDD03r257o5RMuJWPNpz+hVOeyRI3oWO+bnO5LhI026BtsNulqC0eNfanlJv/0PK6d9Hw\nutfrHpQiTHdKGCsB3S+ObpdDhGiL8qxh6qdiLeHVFyVUDRSDjZfzaotH2czFYzjVNRjzGoxy\nNRgVavAA3/jTt79WwNcEEOge2HsK4SthzGFIAiqh0UDbYLcV0mw0lQa0qfGwZZXAS9qSTH3C\nJDga6MbaUi/lUmvIkrba9QVK4XueUxjqKPPejnPlMzyeha6oMxZ1Z1XvsXUfogHpFdoBu6Pw\nISphdoceZW7ixwJdklkB8gwUhjKo9epv2t9+f+9XYNZhp2QKQx1m3tuSbfXO27XBtyCnQltg\ntxSCS7b7jJEJleRRg/w94XLvQkdmCkMdZoHi9HwwWBuxCdlrFv/blSFW0uwM6Q0J4illkqRp\n8HLcRG2N0qkNDlX5mF9WGsSWzT+uu/TsMLHs/Z+nZ86XweDj0afDg8vjD0efu10r9QNiBSS0\nZBtt3/YkVHrmWb1uA5qSVefdzUalqHdv8A8a0oSk1MOXc5xEN/MJDbs97EWYr46p56fCcUXC\n8v1NcQrJToN6ljsvcp15TLO6TJZ3j5JVqgq1ya4XUBeksDYF2u04oTFJqJdrUXFOuv0PXL9i\n9QAAAmp4nI2RS07DMBiEJ4/SxwIhcQskGgjQFolNF0WqVAFKitQVyHFcZDV2osTAsbgMG67B\nAQC52KINWdQbe8b2/J9/L3oAPPi+A68FAK37+fXxCHAPtfibcQDAgY/u5qEm03c3TUkEq2rX\nOxNJ85TLJwBftb2rQIkiiJQoXkIWD8og6k9v4vl4Njsdng0uktHJ8DIMA8UFCQSRwZKs2COj\nIk36UQp4+zpr/PH2/gA9tmFoRioL4xqzfXcbTxdUGbm3lqqB7Gh3st+6zrdJaJkEbyKpbQGz\nLTB6yTOme2V0V3HBKkVEYQz3NTVMn+ZTnBpfuyqpTqkV//d4ndEz97G9bqTuJFxq0sp2iEma\n5Vb1iFIlT54Vs47Pq/jcns1yumJr8h9LtV40AAAPaHictZfPb9owFMedkJTC2hXKZUdf1nbS\nVjam0a7aZW3pNIl1E6VTD0iVmxiIRn4oMQz+8t3K7OalCVmiimEiPb6P2E4+fs927JsyQqiA\nNE1BBR3xS7/uXrw55vdq/E8RIVWowrXyoBoqcS1fkF8Ut86+nZ8ipKmJko2OecvIgHvJ+lq3\nddNN1wx8w6d9/qZt8U+8mtsmaBX8aqpVkbfqWyPK3T8ovB6LqDM5OWk0Uy10Y0SCIAX0+HJ0\nH5pooszTtXrMYuJdolSdp1AV0F3wd8N6hfmTbPr8KZzsqPd51G+pYZt3OcDaz1bnNAO3EOby\nQSvgVyTiZsLoPYfYNIdGAa2CX5VIIzl4GgxF7d9hKQU3Z+iRkUWCHBwFdG1DT01FTynnBS2e\n2QuUW9yeJxRMHmWytNkdWgHujx2DWa6DRaaDcHXCloMNElDMhhSb7m9n5BKTV+AriLlsl7Yh\n2EKb4DcldikTZqtn0sDwLU/0LCfOCsRXiU0a1HKjQe+cfT9vZWBuwLyJFEweZrK0Fk/1Azr1\nXJ+9Whq4CLEUWgO/JhE4Z9KPAzKgOfFTIG5rS3Nh1UlfggEZKZhcSjXOyALtRpjqJZHLkOMy\n4BYlI2eivvhB2BAzFwdkEi5NYkvDb/wH/A7oHvh7EuGzp0zPYtTOoVFA98Hfl0iz2tgUVAcJ\nBZNGlwlT7hF/MLapw7K+5CUI0rN1TurV1+4dQIwUTB7mAsXldbu9NGJYKdTE1lwaYiZNqUen\nxPZGNMgJmpLY38rOrZY6dMGZKNnmU53ZXr3DfyYNetX0653Dr5dX3c/t9ruj980Pd8dvjz42\nGnVm2aRuE6cefzUPO2YOhxqVxPPNpowkluYKdCzZ6vVL/IU61CeMmvhuhn13OhtQp3HCt2TY\ncRmmpsVEwZA46efr4hARHd/UKFqma3RnHo0yMQif7vpxcBayEV3RfhlJzMYC7abnU4/41Ezs\nF8Ux5/4vmN2sngAAAml4nI2RS07DMBiEJ4/SxwIhcQskGgjQFrHqokiVKkBJkboCOY4LVmMn\nSgwci8uw4RocAJCLLdqQBd7YM7bn//x70QPgwfcdeC0AaN3OLw9HgLuvxe+MPQAOfHQ3DzWZ\nvrtpSiJYVbvemUiap1w+APis7V0EShRBpETxHLJ4UAZRf3oVz8ez2fHwZHCWjI6G52EYKC5I\nIIgMlmTF7h9FmvSjFPB2ddT4/fXtDnpss9CMVJbFNWb75jqeLqgycmctVQPYwf/Bfuo6Xyah\nZRK8iaS2A8x2wOglz5huldFdxQWrFBGFMdyX1DB9mD9xanztqqQ6pVb8z+N1Rs/cx/a6kbqT\ncKlJK9shJmmWW9UjSpU8eVLMOj6v4lN7Nsvpiq3JvwGQVV3TAAAPYnictZfPb9owFMcdSKCw\ndlB22dGXtZ20lY2J0rHTutJtEusmSqcekCpDDEQjP5QYBn/5bmU2eYGQJaoYJtLj+4jt5JP3\n/PMujxBKI1VVUFpD/NJu21evz/m9Z/xPFqGUUIVrcaEqynHNXZFfFH/5dnmBkJoKFWRa+j0j\nA+6Fq6vtxl07WtNzey7t8xcdiH/izdz2QIvgFyOtsrxV3xhR7v5B/rUsotakXq/UIi203oh4\nXgRo+XL04JtoosyjtTrMYOJdojQ1j6AqoIfgH/r10vNH2bT5YzjxQe/zoN8PTb2bwKv+bLQu\nYmjTfiYXWgC/IJE2FkbrWMSkCTQKaBH8okQaubFToSOq/3ZKKbQJHY+MDOIl4CigO+t4qUjw\nlHxS0Fbjeo1yn9vTkILJowyXVttDw8P9sdVjhm1hkWhvMTVhw8I94lHMhhTr9m9rZBOdl/Pp\nQ9/0iw4g1kKr4FclflEszH5Hp17PNRzxYQlhViC8ysqkQW3WGbTWp++XjRjMDAybQMHkYYZL\nS8uBfkKnju2ylxvzZiGUQkvglyTyJgz5sUcGNCF8CoRtZ1lObzvkc9AfAwWTSynW9bjZPeOn\nekPkPOQ4D7hZycixqM9/EDbEzMYemfgzk9jO8Bv/AV8APQL/SCJ8/JDpGIyaCTQK6DH4xxJp\ntuubguokpGDS6GJh8h3iDsYmtVjcOp6DID3Z5aDefuouAGKgYPIw1yiub5vNjRH9Sr6GtuXS\nEGNpch06JaYzol5C0JTQ/kx2btXIgQvOQ+E2H8rMdMot/jOp0Jszt9w6/Xp90/7YbL6tvTur\nds/f1N5XKmVmmKRsEqu8XDRPW3oCRiooWQ03kzISmpmL8F3hVq9e4M/Uoi5hVMfdGXbt6WxA\nrUqdb8iwZTNMdYOJgiGxos/XxAEiOLmlgmDpdq89c2iQiIH/dNtdxWYtGcEVLORIYjLWaPcc\nlzrEpXpotyiOOA9/ASO0qp4AAAJqeJyNkctKAzEYhc9cai8LEXwLwY5WbSu46aJCoah0KnSl\nZDKphE4ywyTVx/Jl3PgaPoBKaoLtOAuzSc5Jcv4vfxYdAAHC0EPQAIDG/fz6eAj4h0b8zjgA\n4CFEe/tQnRn626YkgqnK9dZY0jzl8gnAZ2XvKtKiiGZaFM89FvfLaNad3MTz0XR6OjjrXyTD\nk8FlrxdpLkgkiIyWZMUes1yvVXeWAsG+yRq9v749wIxdGJoR5WB8azbvbuPJgmor9zZS15Ad\n/Z/sp673ZRMaNiEYS+pawFwLrF7yjJleWd3WXDCliSis4b+klunDfopX4WuqkpqUSvE/jzcZ\nHXsfu+ta6lbCpSFVrkNM0ix3qkO0Lnmy1sw5IVfxuTub5XTFNuTfrZRecAAAD2h4nLWXz2/a\nMBTHnZCUwtoVymVHX9Z20lY2ptGu2mU/2mkSaicIUw9IlUsMRCM/lBgGf/luZXbz0oQsUUUx\nkR7fR2wnH79nO/Z1GSFUQJqmoIKO+KV3jYs3p/xejf8pIqQKVbhW7lVDJa7lC/Kb4taV0e0g\npKmJkq22ecPIkHvJ+ppxfm2kawZ+36cD/qZd8U+8mts2aBX8aqpVkbcaWGPK3b8ovB6KqDM9\nO2ucplro/TEJghTQw8vRXWiiibJI1+oxi4l3iVJ1kUJVQPfB3w/rFRaPsumLx3Cyoz7gUb8Z\nu2wS5ABrv87bXzJwC2Eu77UCfkUibiaM3nOITXNoFNAq+FWJNJKDp8FQ1P4fllJwc4YeGVsk\nyMFRQDc29NRU9JRyXtDimb1EucPteULB5FEmS5vGyArwYOL0meU6WGQ6CFcnbDm4TwKK2Yhi\n0/3jjF1i8gp8BTFX7dIuBFtoE/ymxC5lwuz0TBr0fcsTPcuJswLxVWKTBrXaaNDbX6++nWdg\nbsG8iRRMHmaytBZP9SM681yfvVoZuAixFFoDvyYROGfSTwIypDnxUyBuG0tzYd1JX4IBGSmY\nXEo1zsgS7VaY6hWRy5DjMuAWJSNnor74SdgIMxcHZBouTWJLw288AX4P9AD8A4nw2VOmZzFq\n59AooIfgH0qkWW9sCqqjhIJJo8uEKfeIP5zY1GFZX/ISBOnZJif1+mv3HiBGCiYPc4nisttq\nrYwYVgo1sTWXhphJU+rRGbG9MQ1ygqYk9reyc6ulDl1wJkq2+VRntldv859pg3aafr19/OOy\nY3xutd6dvG9+uD19e/Kx0agzyyZ1mzj1+Kt53DZzONSoJJ5vNmUksTRXoGPJVq9f4u/UoT5h\n1MS3c+y7s/mQOo0zviXDjsswNS0mCkbEST9fF4eI6PimRtEy3b4x92iUiWH4dNePg7OUjeiK\n9stIYjaWaLc9n3rEp2ZivyiOOXf/AGrBrjIAAAJweJyNkctOAjEYhc9ckMvCmPgWJjKKCrjE\nBBMSoobBhJWkdIppmHYm06KP5cu48TV8ADXFNsI4C7ppz2l7/q9/Zy0AAcLQQ1ADgNrj9Pa0\nD/jHRvzNOALgIURz+1CVGfrbpiSCqdL1xlDSLOHyGcBXae8m0iKPJlrkLx0Wd4to0h7dxdPB\neHzeu+heLfpnvetOJ9JckEgQGS3Jis1Vls9plq6FVO1JAgSHJnHw8fb+BDN2kWhKlEPyrVl/\nuI9HM6qtPNhIXcF3sj/fb13v2ybUbEIwlNQ1grlGWL3kKTMds7qpuWBKE5Fbw39NLNOn/Rqv\nxFdXBTUppeL/Hm8yWvY+dteV1I0Fl4ZUuQ4xSdPMqRbRuuCLtWbOCbmKL93ZNKMrtiH/AXup\nYPEAAAuVeJzNlttr2zAUxmXH7iVrtkKg6aNeBhts9ZaxXvK2bukYhHY47uhDIKi2kprFFyyn\nNH/53ppJzXFjqzYloEANyqf4SNYv3zlydFVHCNWQYWioZiJ+mZfO2cdjfq/Jv2wgpAvVuO4+\nqIG2ue6ekb8U9y9+YzeaTIOQIWToufiG7Q1TMhYjc3cNp3vlyCNZ4iZ0xNdrLNZDgmILtAX9\nljRrk88a+RPKu//Q4noM0fC202mfSDNMd0IYk4AeF0f3iyamaHN51CD1U7GWiOpzCVUD3Yf+\n/mJcbf4smzl/Dqfc+xH3fsiieFjw/gm28adrn5ZA1wQI6B709xRCl8KYg5AEtIJGA21Bv6WQ\nZi0WGlCWxtMSVQJdUYZk4hNWgaOBrq0MdclDrV5l2nKXFyhF7HVOoamjzEexc+MzPJqGbupH\nIRb5ZpgnPHtfrQpfB1uFYuhjhfClMDsDjzI38WPxGyocFSCvQKEpg1ot76b9/eJHtwRzA3ZI\nptDUYeajTXlrv3u/MvAmeCm0Cf2mQuCK7T1lZEwr/NPAtxec5h2oxEyhqcMsUJxf9norIzbA\nvUbxv1sZYinN9oDekSCeUFZhmgYvw3Xk1pBOZXBoys85tdIgtmz+cdum/cPEsg9+nfedb73e\n56Mvh1+vjz8dnbTbVuoHxApIaMkb7MD2Kmj0LLJ8vQY0JcuKexiNpFkf3uKfNKQJSamHr2c4\nie5mYxq2O9iLcBilmHp+KgI3JJSfb4pTRnbK0zPPvMh1ZjHN8jFePD1KlhYVcpJdbyAfSGFO\nCrRbcUJjklAvV5riHHT/H/6cTf0AAAJyeJyNkd9KwzAUxk//zG29EMG3EFy06jYvB04YDJV2\nwq6UNM0k2KSlOepj+TLe+Bo+gEpmglvthblJvi/Jd345WUYAEEAYehB0AAA6t4vLwzGAv2/E\n7wx7AOBBCP3NQ21m6G+aikquG9d7U8XKXKgHAPhs7F0QlBVJUFbPMU+HNUkGs6t0MZnPj0cn\nw7NsfDQ6j2OCQlIiqSIrUSCv76lSJVIUpdKDJAcIdk3m5P317Q7M2IZiBdUOyrdm9+Y6nS0Z\nWrmzlthCePB/wp+63pdN6NiEYKqYawV3rbB6JQpuemZ1H4XkGqmsrOG/5Jbpw36O1+Dr6pqZ\nlEbxP483GZG9D9vrVupeJpQh1a5DXLGidCqiiLXInpA7JxQ6PXVni5I98jX5N0A8Yd4AADzn\neJzVm/lP20gUx50QztITaEqP7TSFNtCQUNhypKUXpauVKliFdFWpqdDgDMGqY0f2mIXV/t/7\nW+lM/BwmwaFM4okgknkvcUw+3znejN+Mv4xomtanJRIxra9fY6/+z8WPcyvsszH2ZkjT4tzG\nmL1RtwltmNlbHw2TEgdhy7IppoZtuZqWiAvfGCiUdyiuME+8LlHc+FJs/abr6A7ZY794lb/j\nCP4v1+0k+JMtVw2yq/YMkzD3f81/NU4R6yCfX5xvuaJfN7HrtgA1flz74R/8kthx67dK1KD8\nt/jZ+HELagzsXfDv+t/rO/4lW//xr3DCS3+vXvo7p0r/FHji743C+xDsPo4C9g74dyLEDoXp\nL1m4StrQxMBOgj8ZIY2iQkxA00ycbqaRYLdpitg0sNsGJwZWWVOMt5RibKRdoZ309CbK39hx\nTbBwREcpnn1W3DdctOdZOq9f5Ne4iwzLoAY2xeiVldXxEEqY22fgP4tQRyjMaKlMXN0xapy5\nTeFyEAQWjsig6k1g+LxNoL+wvvVhIwRzADpLYOGIDlM8O366n6eliQehMLkdB39cFXEKiS0T\nraEKoTs17OCqm3YpqaE1af4hYOY2BX5KFf87xF6p08WempniI7Y7JXw2VXMIk0bKUy51PJ16\nDjYz0vKGQVL918F/p0reJkJ7BDNSElo3ZyoPLmzIltc6Avq43QR/U5XWbYQcKi/TNHYd7BjM\no6Ras1mddiH4Cojkdhv8bVWCtxCyPVrzqLRmsVH7X+tE7CgI5HYL/C1VYgsIUdskDrZ0suPI\nSK6ymg2udKccCjV+JK33KmjktgB+QZXe+Iw03zVg4jYOfjxCvlCagZLn4krYTHkAEK6DVTLA\nX+92jncDGnBg4YiWktdG2Lz+yjkm9G24b8IIf7PZj4w7lHfyL4iMTYO+f2srTT8ONgN+JkL6\n8I5TMliIb0MTAzsH/lyENN21UE6VFSwcF4TuFpRXYJX08nb9ZyiYo0hCj0GHGYNoORjxjV0o\n7O1G52lMyTroOWPQErh9Av6TCNHleg7orNun4D+NkKa7tsmp0oKF44LQCfdmve85fWyqLMk7\nAR1lAm5eBn0bGW8o58NGp3EIJVY9NUKNKkEwietAwyjYWfBnI9Qg13smoNYnFOVlumufEzA2\nBzYT8RjdHd1tKK/A9rT3DPj3XZLISeg0SWh4gxFPNcOnmFv+LWIHQ00SJu9JyBlf70XOuG1n\nSUIlJ1WljLtqjpzqnmDhuCB0d6C8AtvTzjIq3rdLgk9CN5mEGeagbyMDDwVOFQNglnKARHzL\n8NOBjAmwS+AvRShDricJy1baMvjLEdJ011Y51Ypg4YiMLhRmpISdildlNRy2WuV/8yTsqOo+\n3RXcPciZ3+vFSsV0cZ+gGqb7vItQ5gdpRDEzIKvgPpQtt9PgT0eoIBRmoHSATY+0KVCO8EBl\ntfedt9rbpv0eQb45sHBEi/no3K2zzQJ0CpYBAgtHdJTi2SSL0Tifz+t27WhnF+vfd22LpGdk\nmR9DpT9unn6oYb5WsXeovaNjfb8D1CnAm2rOA6tBzVUMuu/toldzKLVPac3N53IO/ifrf+y5\nxNFtiw+XWd2u5lKyWoSur+XAz6nSss7uL+26EooPbcuuGjo2zSPD2rOdKimfRLMcb1Nz5BBX\nayaZq6+j5KrYsOT1PQFN3K6Dv65K3/2y4XB5NcwWSebTfhVlEFct3ciEDI8Yq9WAEzHFzAR0\nvsIVsmz79evCt2/ov9eyJZAG1dwS8ImqEhhCqOJ6u2lZyBkAm2leTFcDOcEX0NlEgFWExesk\nW/k3lZElngXK2eakiBri25yYNQkT64TP+Di1PLKQrxFzEWqQb3LkPeOQTa/WULHweUOWNgOE\nmebVDjW0CYSkQ4uw7CJuDlMDuNVIwEvHlVObIjoIIlnQl+3FunmnQUQYeC9JEJkHyvnLE0Se\nA+bzSxFEFoBw4aIGkUWAWuxFECnyzUbS8eOM3UYdRJLfQSS3RfCLqgR3GkleANiLSxNJloBy\n6fJEEiGPdwkiyQoQrlzUSLIKUKu9iCRveAjh4SHtOSYrUbhVYzduGTGbNpNB5JCFDN5CxI9l\nteVBD7dvwH+jStvqGdqCuZQorPGZrKqXoORlc+2pUZU7QxUbFkRB/K2sllfA/6oXCRCZPeft\ngNcAcq0XW84ftG45F95Jx83XQPtayPM+UEWebNqNHbjSzEKnVZ+WHA12VbO/sqBvAe5t82Zh\nNaBx6W4m7L5Xsl+2Ce+RZ5mG9T2dKmOKUzwBp3uOaxwQGEKl6d8D8XtI/cd8Gxl9KMxQqcwy\nu44X9kBP8KxGkNKEIzKgLpdJ1qGWAxuPuLZDaYZLkDAOW1YMau2DogJLtDzjCo+gitd8yNFq\nLVdgfw4WyPaSkytk/9zcLr779On58uLSi92V+eXVhYV66pslu63c6bEiWyi34YkHZ06acpVQ\nLOwWuAECxasy0+gPYhEHUza73D1Cjn14VCHWQh6VbcR+FZGyQfmJfWy1/v9+/sxm8NRsPCi1\nsq0Xj2okqJGK/99t56SQmmoleG1AjWgR1koT7VBw4yc8WsCfKv3xE2m/YpUAAAJ7eJyNkUtO\nwzAURV9+tM0AIbELJBoI0JZhB0WqVD5KitQRUeI4rYXtRLEBsSo2w4RtsABALrZoQwb1xL7X\n9n3HzwsfABxwXQscDwDAu59fHY8A7EMl/mY4AAALXOhtHmozXXvT5CnDonG9O+GozAlfAsBX\nY+86kKwKIsmq5xDHgzqI+tObeD6ezU6HZ4OLbHQyvAzDQBKWBizlQUGoxHWyIstVgkpekBxz\nhJOS09d+lAM4+yp8/PH2/gBqbNMhmgpDZ2uzc3cbTxdIarm3lrIF9Wh31N+61rdO8HSCM+HI\n9ASbnmhdEIpV87TuScKwkCmrtGG/5JrpU/+S1eDriBqplEbxf49XGb6+D9vrVupuRrgiFaZD\nmCNaGuWnUtYke5LYOC4R8bk5S0v0iNfkPzvKZWcAAB7AeJzNmetu2zYUxyXHzj1Nul7XbYDa\nYOgGNFabIo1jIB+6xkkLuB3qukOBeTAYiZIJyJJB0UH8Hm2BfdwL9PJ0+9aMlI9s2RMTKKbT\nGmH+x7rQv3PIw4v0ZlHTtBktn9e1mYLGP4XX9f2NEj92hX+Z17Sc0Bz/W+Oqa3ltgeuP+8Rj\nmBot4rYMK/AdYmPfwkbgez1eYT6XuHa2ZjcZcrmVrCFfr7ypj18ZUotih//2ivgmYPoMka6D\nvT521xy/yyEe5ua/Wv8zOIX9o3L54YOxOwqWh8JwDGjw49qXfokcn+FFqD4WgHyLtb1+tfqJ\n3LXcScIZURZAl8Fe7l83c3ImfeHkLOABKLRYKvhui7FOWDZNjzjY6lkeLtINjxwWA+qaiDLC\nj4RmyBsMh0Xh5Do+7mBK2thnSOrxH5XabxKPr4G+BPvlRXt8a+DpRtKVYnjknsOdBmgX7O40\n3Ele9VPQYSTww7KBPLZ7989Kwoe/7p7Dg2PQt2C/VehBKsxcwyFul2IJTx30Hdjvps1TaLSi\ni1JpVkHfg/1eIc1Idx1v50ujLXuOseUf0A9gf1CILmnYkNHAdyU8f4N+BPvj9Hl4pnuhrKPp\noJ/A/jRtntkGI8xLwykkcD6D/VkhTmoP+8GJJu2mmLSbw0m7KSbtjOPIjGACvQ32bYX8krz1\nURtLaHTQO2DfUUgz7WjmYWWTB/55xfySzok8gkIJjg66Dva6QpzUFaW+KAuaZMS7KsbMhEJR\nR5k8u1NvkdBwur4l5mKj3/RhtPz1eoMFMDOQ7wcMRfN1tBYuZvXqGsRb6A7YOwq9SoVZbtg4\ntCiJFhqSUAuQ66BQlEFl6xCF2pPf9yopmHOQOrFCUYeZPFs9Jf1/sZ17RmgFFDcPSdBsE9/Y\nNe4XS1vxUeKTwdHtrV8zezoPjSC0CnZVoaeSAaMb8l2BJPA6bGim1j8WJh0wFmHbFeuy4u3X\nqZuQnO1kxF2CwC6N9mpluKmYC3uIIYcOptlMtHG5MlRltOn50SAMtyU0Omg8eF1VSDNZR1yC\n8T1WKN8I3TLEK9apJLMsTVZGRs2M5CuQJStQ+ZziPphKvPmcD+Zt5Bmc2QtcYnEzcqJoPOlS\nKhYFNnZQ12MGC6JJoHgOt66DHoB9oNCtbKm1Al1C6FOwnyqkmazzCn2WUCjfCN0liFesXyO1\nYOmRkXwV0mn1IlPLjFOLCyNn5NV25rxahVwSugf2nkKfsuXVKvQHoRWwKwppJuu5gmo/oVCU\n0aXCLDYQdbvieVjaLnURgrQGOq1kmixwl2H1dvki9iSlx4Ydr9/EppQhnjm+G+1E//eqJrFV\nzerUdxBuoSWwSwqdSoWZbRwhr4slMdaHY8hX7gnSTdsNWPLdGF0uq8McoXjxulrNjHgToncz\n8YiyoBAxlWahgY9Ru8NfO0mCJii+n1Lb5sfeVMKLxOQ9z03W7pg1/u9oE796RM1a8dmLV/XH\n1eqD7YePtg5L97d3NjdNxqcqs41885QHA8WaLQHLxWeGydfGDCXm9DXwNHnXvZ+NA+xjihi2\njcOeQYPjnov9zbJhBwZPbwPbhIkTLeSP118QD1Pjl6C5OHx2YNV7HRw3jduvPaDDaI00T/y5\nBU2jKWyeEdr5DsUdRLGdWIKKx71f/gMklprIAAACcXicjZHfTsIwFMbP/iCwC2PiW5jIdCrg\nJTGYkBA1GyZcSbquYOPaLetBH8uX8cbX8AHUFNsIcxf2pv2+tt/59XQeAIAHvu+A1wIAaN3P\nro+HAO6hFr8zHACAAz50tw81mb67bUoimKpd74wlLTIuVwDwWdu7ClGUYYyifI5Y0q/CuDe5\nSWaj6fR0cNa/SIcng8soCpELEgoiwxXDRcaWZJ3joiT4qHpxBuDt68jR++vbA+ixy0RzoiyT\na8z23W0ymVM0cm8jsQHw6P+AP3WdL5PQMgneWFLbCWY7YfSS50y3zOgucsEUElEaw33JDNOH\n+RunxtdWFdUpteJ/Hq8zAnMfdteN1J2US02qbIeYpHlhVUAQK56ukVnH5yo5t2fzgj6xDfk3\nMfZhTQAAEnl4nM2Y7U/aQBjAWyi+oDidbqL7crIs0cTRDTNxJvvgnJglbkuQLS4hMWd7wGVt\nadrDyF++b7I7eKqltDGdx7Imx/PA3dHfPW937UVeUZSsommqks0p/Mp9b9ReH/DfVvmXOUXJ\nCKlyuTyUmjLP5copYcgkLdyzGHIx6/iKomVCA2bq5iXDba6Fp2mNk4tGdKTvGR5p8RsWxDdB\nMLrxUBZBL0ZmzfJZLWoRrv5WRtddF3GuDw/3KpEZOcPCvh8Buru5cjtqYoo6iI5qMsrEvURv\nZhBBVUFugL4xGpcdPMiWGzyEE2/8NmGXYPzLkPEnuLUfJ/WPMdRZQQJyHfR1idSxMLmmg22S\nQKOCLIJelEgzHRtqEJjaZJBKoU4IRGxR7CfgqCCnFoiZiBHVfJLR7vN8jFKMWQpJaPIow71b\njQ71UavnGIx2HcQd7o+XrLT0K2BXIbdA35JIHwuz2DSJb3jUFYtIMKkAeQoSmjSodI7P1Y+/\nfTqJwZyBFAkkNHmY4d7aRHJv97FtoQ/I7/uM2GWxc2yXhj1l0VPa5RFh/MJtwseUGLVxaWcn\n9RpnwfxC1kCvSVxjQkno+Zw7weQqmHpqkZF9bEkYxnhIQpNLKc4BcbuAJpyfEjgPHs6P69KA\nlbiEe9noEPTz6MsZEqGLjK7DMHWo00aMd/xVNRPsBZA66LrEdYzxb2wHlZeX5HDi7aSkXoBA\nXpjSPhef8U3Kq0aCDQOaTdA3JdI8LrUE1YuQhCaNLhYm38Reu2cTh8UdU+bBSIsgp1KTHn1M\nKUDVLPyLzap6hCzqs9icRr5LDNqixETUGf58VwPSrmkJrC1kFfSqxDXFwsw0r7HVIwkmFghP\n/otASNzSVyF9AglNHma4d23y2JL+ELIGxlwb16URx9LMN8kNtl2L+Ak2FBTPpuRqLfLeAV4L\nhOcc68x29Tr/uK6Q831Pr5c/fz1vHJ2dva3u7b+7OnhTfV+p6OLgp9vY0SccUa6bCTiZoOc+\nA23CcOjgsQzrC8/afYVOiUM8zHhqX/WR173pt4lTOURmFzldhohJmejoYCf6/znxHB28yMgE\nRjO7RqPvksAh7dG/d717G405Jbieg0MUiU4Zo51zPeJij5ihpBdP+rd/APKCIj0AAAJxeJyN\nkctKAzEYhc9cai8LEXwLwY5WbeuySIVCUelU6MqSycQSnGSGSby8lS/jxtfwAVRSE2zHWZhN\nck6Sk+//s+gACBCGHoIGADRu55eHQ8DfN+J3xh4ADyHam4fqzNDfNCURTFWut8aS5imXKwCf\nlb2LSIsimmlRPPVY3C+jWXdyFc9H0+nx4KR/lgyPBue9XqS5IJEgMloxvWQvRBQZW97zjKnu\nLAWCXRM5en99u4MZ20w0I8ox+dZs3lzHkwXVVu6spa4BPPg/4M+73pdNaNiEYCyp6wRznbDa\nlGBaZnVbc8GUJqKwhv+cWqYP+zdeha+pSmpSKo//Kd5kdOx9bK9rqVsJl4ZUuQ4xSbPcqQ7R\nuuTJo2bOCbmKT93ZLKcPbE3+DSioYUcAABfUeJzNmG1v0zoUx5M07R7uxjZ2792AN14R0yaN\nFVoep43nDiFNIHUF8aLSZBK3s2idKnEG++S8Y9jtyeqmiSDUQURy/6dxnPzOsU9i++O8YRgF\nw7ZNo1A0xFF83zy8/UicWxV/Zg3DkmoKXR6obcwJXXlNOCJfca/fJahNuyQwDNtSLig13BOO\nO8JSm9nN+sdm/MrAd3zSFg9clP8kwfDBA10Hez3Waka0ks8V5jdjeFxWEXa2t1erxVoUnS4O\nghjQ5cON78Mim5gX8atanHL5LFlrXcRQTdBrYF8bXle4+Clb8eJnOMnB7xB+AsE/UYI/wW1/\nqDdeJFAXJAnoGthrGqkTYYothnskhcYEXQd7XSNNPjG0YWDak4NUC3XKQMRdioMUHBM0t4Fo\nxYJozqcFbZTnY5T/inJFUSj6KNXazeYpDVA7ZA6nHkOu94V1PewG4++trC78B8GVugn2pkYX\nEmEWWi4JHJ/2pScpcZUg/4NC0QY16P3ir/Z+sfHy3at6AmYJ8iRSKPow1drViQzfygw8A7GU\nugr2al7A++hyZB4gZ6vcJpiHPgnKO6jcIxy7mGNpB9Sn4eBs0CcO93F5eyezZ7PgjdR9sPfz\n8uwqQpSdONg5la41G+/rmXnngHFwO7Cv5sVrbWfmmwcmqRbYlka+RJpSKwxwJ+mbWgKEf0Yv\nA/0vhNK0n4OF5KKX0hrl7hjtDCRbRuZFeB0sjtvamBNZbzZF5gDvVrCNzr0QfcGMI+5dftd+\nw49l0CrYVY1+JKdPi3LSS6ExQWtg1zTSTDdOpd5TFMpfQncF4hVpLrmelkWz0Ys9I/QSpM4S\ngM9ongkmwu4ednFH5gxlLnUwJ4i2UcA9nzJx+hRmhKIW/a5LK6BPwH6i0aVsGbUEI0HqU7Cf\naqSZbsxKqmeKQtFGlwgz38J+J+wRxpNWUAsQpGXQvNJousCtwJxt5U9MoZfq6kppNyurMk9T\nh6M21kSYUusMd0OSEjpTmcjn0sGFqRdJazBtixSKPky19mBykZS8BtneUafwh8+PjuvZJ8nK\n7o5xAPZBXq5thKxL2eetMqybfOKEfkDPoiVIdnxlX8XYAHtDI34izVwLOiRIGSuS4npOQ9qO\nbenCjqva5mWF9/qVhvg5q5LjB36lsfvm7XHz+dHR3Ye1B/c/Pbrz8HG1WuG0hys9zCoTA263\n4abgWFHN6E0j18HKjGQZ/FNb7dxCrwkjvvi8u+jTOfK9r+cdwqp7YqaMmCe2rV3KZcUpZvH7\nF+UWZbRHbEVBcz2ned4nUYd0hnf3/FGMxjolOm5AhxgaO2WMdrbvkz72iatMoOUm6vcfgty1\nXQAAAnJ4nI2Ry04CMRiFz1yQy8KY+BYmMooKuCQRExKihsGElaR0KmmcdibTor6VL+PG1/AB\n1BTbCOMs6KY9p+35v/6dtQAECEMPQQ0AavfT6+M+4B8a8TfjAICHEM3NQ1Vm6G+akgimStcb\nQ0mzhMslgK/S3lWkRR5NtMifOyzuFtGkPbqJp4Px+LR31r1Y9E96l51OpLkgkSAyWjI9Z69E\n5CmbK17wlWpPEiDYN5mDj7f3B5ixDUVTohyUb8363W08mlFt5d5a6grCo90Jf+t63zahZhOC\noaSuFcy1wupHnjLTM6ubmgumNBG5NfyXxDJ92s/xSnx1VVCTUir+7/Emo2XvY3tdSd1YcGlI\nlesQkzTNnGoRrQu+WGnmnJCr+NydTTP6xNbkPy93YdQAABUeeJzNmFtP2zAUx9M0aYFxG5cN\neJgsrZNAgpaVDRjSHrbBEFLFpl4mHipVpnHbSE1axS6DT743Ors9adM0EcvqTItk/qc4bn7n\n+Bw79c2CoihJRdMSSlJX+KVXyl8PTvn/1vmHtKKoQhNcVweqKfNc1y4JQ+QeW902QdR0zB5V\nFE313JEqGjWGm9zyjtPKFzdl/53UqTukwZ+4JD4JBN7mQLfB3vaNSvNRDbNNuPlLGV6jLmLf\nnZ0dvfON0OttTKkPaPRw5XHYxJBE339XlZlMPEv0qn0fagJ0B+yd4X3J/pNsev8pnODoNwmr\nQfRr3uhPgWs/LoqfA7CTw7kd6BbYWxKxA2H0qo0tEkKTAN0Ge1siTUxB1CA1tek0lYIdkoq4\nbWIagpMAjS0VVV8UEwthQRtX+gTlc96WPQpNHqW3N1tumRQ1enadmR0b8Rmno1WrdFW8qpQQ\ntu0Ow6KbRnVlDYIsNAt2VqIrgTCLVYPQumN2BXJIfAXIOig0aVCDLND/NAv04pdv5xcBmCmo\nF1ehycP09m5Ml/puZOI0BFPoBtgbcRHnEeo5bfRRpGzNIA3ca7NaF7MW3d3L8B6aAXdoZujP\nfmR/5sAHoXmw83H5c4l43XU7DgtxycAMZ0ybEce0Mp6SzEzOWmQn58GxAQPYl3E5qe5F5lsA\nJqEq2KpEvkCaVLVHcTNoI04BwrPxwiF/8UjNuoUsBje5lOp4DZ2gTfLqi8i7BKvGEiRieqjS\neAM5X1eKBfSzRRyCWIuMdj2+HeJ6nVBq3rrvs5H8WATNgh37fqdXTUasEJoEaA7snESa2XJU\n6KFHof0ndMsQL1djqfOwCkoNd4KIyCtQOCuQeGnJRR+I+uo7Zi23ijqI4ruJavoLF9y2P1Zp\nLkSrnxWYd6EHYB9IpJktQ1dgjXE1+y/WmoUqdpo9i9gs6PfVIgRpFTSuopnxzXoD9m9XocnD\nnKC4rhQKkRE3IXqbkwco0hADaear7itzSNAExYuY5lbzHY3ByZV3zHmOWd1ckf+5y5PSsZMr\nZq+uS+VPhcLbk6Pj97enhycf8vkcMy2cs7Cdm/5Nky0aITyq2zOuO4sw7FmoV8FB76j9N+iS\n2MTBjBjo9gE5nfuHJrHzZ8joIP6KjohhMtHRwrb/+3Vx1OMetqlu1IxOvfzQJe6MNIff3nHG\nQZqYFfd6CTOiSJyVCdq5rkO62CGG571CHEY9/gbiL2paAAACaHicjZFNTsMwFIQnP6VtFgiJ\nWyDRQIC2sOuiSJUqQEmRugIljqksYieKDRyLy7DhGhwAkIst2pAF3tgztsffe14GADz4vgOv\nAwCd28Xl4Rhw97X4nbEHwIGP/uahNtN3N02Rciob13tTQcqciRWAz8beRah4FcaKV88RTYZ1\nGA9mV8liMp8fj06GZ9n4aHQeRaFiPA15KsIVVfcPrKCDOAe8XZ00eX99u4Me2yikSKVFcY3Z\nvblOZkuijNxZS9XCdfB/rp93nS+T0DEJ3lQQ2wBqG2C0rkB3yui+YpxKlfLKGO5Lbpg+zJc4\nDb6urIlOaTz+p3idEZj72F63UvcyJjSptB2ighSlVUGqVM2yJ0Wt4zOZnNqzRUke6Zr8G+xN\nXYAAAB56eJzNme9P20YYxy+/oKQphHV0lJVxREwLqokZqMDQENoPWk1Cm5SGqS8iocM+HGu2\nzzqfA/zle1eqcx4nTuqoMjmjRjp9n8R2/Hmee57z3flDFSFUQuVyAZUqCCFUuei83TlCqPQc\nIfQEoaLUAkLFeqRltCB/f0cFvrYdilC5mPh9rm1eCmIhhJJnlztnHzqTZwbc4PQaodIz+U3e\neHC/SJfAXpq4aj7gxuC26H80+AwPUa9/fLz/ZuKKiuGQIJgAGt4cfRw0eUnhfvKsrrBF7GLx\nfgK1AFoHuz44r3T/RbbK/Zdw0mNuUXE5ivlnuOV/z9q/p8CWJADoItiLCmFTYSpdj7h0Ck0B\ndAnsJYU0SkNXhjQsf56SSmCnpB1xbBJMwSmA5pZ2xYnYFarTgjaq6jHK55BnsS4qzrcxutVO\nzw7wdegZwmYetmBYCrJCfwvhlLoK9qpC6FSYWtekgcFtX7JPiaQEWQGFpgwqW39X2n/88+dZ\nCuYcVEas0NRhJo9uxaXcDLmjYXrrMy407NiuLfAJ3j/Y3d3O7ME8BFfqFthbCj2YUudhQCw6\nJaAFCGRu/b4wa51HGZxQaGopi6MyGKMthdzJyFuFDq4C8/xAlfGmcuKL9jlm11j0aDQwYcHw\nFcUmu/EcRkxqPsCJGuhrsF8rdCK9brq2oO4UmgKoBramkGa2BJVUOwmF9pXQPYV4xZpLkU8r\nn7nBuJkRuQZVUxu3lSGnojbfyrLxiejhmx7ldFRKQY+FjinLKSD9zJVUgzmd1AOwDxQ6k62S\napABUg/BPlRIM1uuSqqjhEL7SuieQbxifdRKqkQzj4zEiWVYVP/zA1VGnEq61rFdykIBc6Wm\n7eGAGswzg+0H4Mftp5Eqw89WOovQ5VKbYDcV0syWnJJqO6HQlNGlwlS7hFuhSz2RtoxcgCDF\ny++86mW2wNVhAlx/jFXFSqcHzxjBhg+YrMTLEMvlxJJtRSFxKsxct0+ckE4JoET4Js9ufjrz\n6vEFjIGxPlU8Fo7hrVu2wL/u4EZPCD841nXLFr3wqmUwV29kRv8Ooip1Hez1vNC3GbcidEFu\nmcdc2yCOc2d714y71CSexwSR+wjZ3Ujsd0TDU0HxEDXeA5z6bOCH7ZIdektc36E70aZNdvSX\ngPvyMXpg+YoTz+hF8C6xHxDpNUBcGx8t8sHdiCbJEhaCfOlSQUwiSEsE/ezw3wOw1A2wN/KC\nrw63eTJzvgK2V+ML1Hw4TzEOuYNPsE8CQZuWLTTMuKVhmeYabnBy09DwIHG06Kmi4YD6+AQ3\n9Ma2ltm5RJKjU7BP83KuhWF/TeLKxNEDFnKD6moS6gfgl9oCu5WXL8Xsu4KJJEdFsIt58W2G\nnmN7/zWjODdk/hghD+w+xSe40744y46PAVnqJtibCvFTaRa6kBtpE88XI4pcZiTliVeR8KYw\nec2xLlxfbwvX7+/R9wdcb7f++vt957fz858P9w/eXB3tHv6yt6fLZ5PuEk+Ph6FW25xCUYyP\nDH8py7JILBvr4FbyKu1H/I56lBNBTXx1hzm7vbOot3eMTYY9JjA1bSEP9Ig3+f8V+WotfqVZ\njGNlMqNz59O4H6zBvzM+Cs1YX8SfBvQDUtgXY7RPfE59wuX+zHCFLl/+ffwEFUBmsgAAAm94\nnI2RTU7DMBCFJz+lbRYIiVsg0UCAtiyzKFKlClBSpK6oHMdEFrETxQMci8uw4RocAJCLLdqQ\nBd7Y79l+83m8CgDAA993wOsBAPTullfHUwD3UIvfGQ4AwAEfhtuHukzf3TYlEUy1rg9mklY5\nlwUAfLb24hBFHSYo6ueIpeMmTEbz63QZLxank7PxRTY9mVxGUYhckFAQGRYM14Ws1RpJVjI1\nSnIAb18Hxu+vb/egxy4RLYmyRK4x+7c36XxF0ci9jcQOvKP/4/3Udb5MQs8keDNJbR+Y7YPR\nD7xkumFGD5ELppCI2hjuS26YPszPOC2+vmqoTmkV//N4nRGY+7C77qQeZFxqUmU7xCQtK6sC\ngtjw7AmZdXyu0nN7tqzoI9uQfwOYtGB5AAA3mXiczZv9U9pIGMcDgljfvfpynpZuKVq0VirO\nac+Za6tt7XTG8TqW3vQHZpgIC+YOEiYJffnL77d6u/CNLjS0XbJBnInfR5PA53l2v8tmEz6M\na5o2osViEW0krrGf+Pv88aMn7H+32R9jmhblGmE629KYdovpzGvqktenb9+RvH5eo46mxaLC\n7tGzctHVqywST4rlX33Idx/p2CWbVtjbTfG/+Pu337ali4gXu85KsLMqRo2y8D+t/XO1i5of\nDw5297rOiJdquuN0AV29ufa1vfFTIpfdRxVcw+XvxfdGL7tQI9AlxEvt40Yuf8gWv/wRjn/p\nq9QtVs2Gwyp8XfpvqGN/vzo78mEe4RzQBcQLCpl9YeIFU6/THjQR6CLiRYU0YVQwhk4Z+7aD\nKmHu0Qn1mqE7PXAi0NA6YbSrhJHxXkW79ngH5SO2TQuKTR2luPcgf2E4pNI0S65hmYQ1t9Me\nrNrtTSq2VScly7ap07DMsmFWyT/WOXnzcls2rW0UnOsB4gOFafnCTBbK1CnZRoNn16PWHCQL\nxaYMqtUjpn62R8TPXvz18pUP5ii84yk2dZji3rkuz2ekcROoJNc5xHNh4c4Q0oJl3bJolLek\nYccAyHUG8UxYsPsCLP2s1xs1Sv7kriuWaUVv1txiQ3cvnMxGmh+WxiHySd1CIq33RLwfVlKT\nhPAZBv/ckicdBx3XScSTYZEuEPLJsv+t1KxPrOypynndTMkjTwBzAvOCiOJ5QacbCeFdolih\nuttko7A8rlDV8N3o4dapq5d1V5fHnQLi1CBwZ4HrNGjJtfugnQbh9NUJLQ2Hds+jtZp2qdfA\nwauebh+R5v+Tz0kYBbU9xHth5XSMnAzTpbZRL+rfSwsHpXXTtFydf6Y7feYotJV2jPg4rByP\nunKs/EyOnuFbCUrnJzhHO0J8FFZ+0Q1pvl/AxDWKOKqQz5dmtNB09KrfxdUoEG5DQ5kHJoNe\nGcxjFPd0UvVn5W20ht+l4IQwy5LkFi6eWx0yoXhI9+dtXca0L1b64J2HJhEnFfL6W6VguLTe\ngyYCvYv4rkKaYH2SUxFBsQ0JnbBQEp6vezlmtnuqLwm/BKssIYGE4tUTf+gr2/QPvQzNIM4o\nhJbzjrDKo20g3lBIE6x3cqpNQbENCd2vqJenA/XOmHdFKQm9DJ8sY06eULx45u+ZUwZKrApx\nLyhpL7ZLQ89BCWKVY6icZ5bR1lzvIb6nkCZYr+RUKUGxDQndb6iXp4P1jLe2IQm9Ap+shOmZ\n7mXx3IsL3dZL7FqHOK7N15UNtrxcYhd1LOQ+cr80Wp7yktoa7yOtOejR9aWQsrQ60lmlBoO2\nsaZELBa1bqOxjGopSfBVdJzVzlgZuNxwsCLQ3EF8RyFNMMOtYoruaVLxVD0YnVCvwQ8HUx3r\nhpLkSYwDSbx4oq3KyP0/R9/q7gVxLRIAehG6hnhNIbSccZJocK7riNcV0gTrmpzqgaDYhoRO\nuM69KeN4K9iS5ARmITdinADQi9CbNw5Bg5OhNA6BWchQGkeYsA/eOJPivRRJ8BS8ksKicKKt\nysB9gWc83/TPvAC9j/i+QmY526TQ3FzTiNMKaYJ1zBSGFk/XFA8xwejuo16eDtQ2E8JNPUnu\nNJySHuTKevLKNa5l09bFGu5I8rUPp48U5qE7iHcUpiBnIsE4Wg5xTiFNsG7KqXYFxTYkdGuo\nl6cDNdF0511kSfR1eGcdHTDRVmXovsib3/oI/ES4wd3PguI6FpW5Pkf8XGE6cp4S5nDaIeJD\nhTTBeu06loc8PQpzmUia7gHq5ekNeqoiiZ6BjzKD9FSmt6e8RYW+PqUywi2tp4ifKkxGzlEZ\n9ASuzxA/U0gTrM9mMOp4+nwQo894QberzTo1Xb8nv+dRJO8eYFg2Cla4TTyGujmIx3xX8swb\nZeuTWbP0Mi2LT33Lcj9ERR8Ky8UrCrl9YUYLH/Vak/YoI0fYGorG7vkI1GM8QuopNnWYHRSn\n709OpBF3UD2uwjd4lCH60twq4MkDp0fRIsLcXHXbxrq+m4WvTonnHGbdeiN7xn59zNF3e3b2\nbPvN6bv84cnJzv7u3u/nTx7v/5HLZV2jrmfrupntepx++6zcAybq7bk2HF/PEz6kZ5GdeNbW\nGnlNTWrrLjPx+RdiW5+/VKmZO2DmJmwWSWjZcPmOC93sfv04v8PufdUr6pWsbJXy7Oab1xzV\n9qtb9nWFOprE+9lFc2gKm6SDdqxh04Zu07LwlDD/NtTX/wGROia4AAACfHicjZHfSsMwFMZP\n/7mtFyL4FoKrVt3m5S4mDMaUdsKuLGmajWCTlCZO8KV8GW98DR9AJTPBrfbC3CTfl+Q7v5ws\nQwDwwPcd8AIAgOB+cXM6AnCPtfid4QgAHPCht3uozfTdXZMjRmTjenfCsSgoXwPAZ2NvHilW\nRYli1SYm6aCOkv50ni7Gs9n58GJwlY/OhtdxHCnKUMQQj9ZEZSWSKtuQWlLBs1UtWPZCuChE\nPykAvEOdPn5/fXsAPfbxcImkxXON2bm7TadLrIw82ErVwnryf9afus6XSQhMgjfh2DaF2KYY\nvaIl0d0zuqcoI1IhVhnDfS4M04f5JqfB15E11imN4n8erzNCcx/2163U3ZxyTSpthwjHpbAq\nRErVNH9SxDo+lemlPVsK/Ei25N9z02YOAAAfMHiczZn9b9pGGMfPYPLCkizd2i1Z83IN6ZRJ\nGYR0CSTSftjarKpUtRVlUzUhoQMfcJrxIftgJf94f2uqM4+TM7HTOpyrIJ2+D/iFzz33PI/v\nzu/yCKEsMk0DZXMIIZT7u/7Xr1WEst8jhBYRykg1EMqs+mrK39DGcyqwTTyBR9T1GHdwx+V9\n/C91uMURMjPKuXM1qylIFyGk3sGsn72rT5/puW2XdhDKLstvEgYhtABaALswddW857Y7zKYI\noQ9o8rk8RJ3R6emTytQVubZNPG8K6PLP0cdJk5cYF9NnNQQT8r/k0czFFKoBugv27uS87MVn\n2XIXn8OJHocuFU05Dk0Yh6Ych+a5Mg7XumD+c1b7M6IDWQkFugP2jsYORMLkGg7p0xgaA7QA\ndkEjTeruNCFczeuhq6UDMeFJbEa8GBwDNLXwzE7508jHOe0q+0OU8iYrikLTR6ke3a33mIc7\nQ6ctZAXrUuFh0aOhupa0A/fBtffTcrN6dJt3MMGy9k3KL4ECjF3a5q6VlP0B8ErdBntbI3sk\nzFLDol7bZQM5BjERIUF+AIWmDcqP28yXxm2u9vT1s7MIzDnI8ECh6cNUj5ZvKlN7Fmf7eECE\noK7jG71fEndnHjwttQx2WWN3YorX0CNdGuNdA7yaWhAszlq8/HBWFJpeysxVToRosxZnCXnz\nMMB5YJ6fqDbeSM61Z69fYN7xC22oWt0Cfgm0AHbqj9dcgwnaj6ExQFMv+4kDU1I9VhTaHaH7\nBvwVaCrJHZc281AoEzIvQbpIXQZ7WSNzJOvemwkrFhwzizqCdcZ+HvkzAMGxxf93bE6sW3Rm\nFbQCdkVjZ5Kl0hKEgNQq2FWNNLMFq6Q6URTaHaFbBn8F+lVTyZRTjYTAK5A7K+HHkTbg6EfQ\nGyJ6Ml88MqJqAt0Cfhl0F2ydRT9Z3qzAeK9AgTfuVJGXVD8rCk0bXSRMvkHc7rBPHRG1Il4E\nJ32bZrLMvCL+ETIj0LzmDAnvfzx1qcUE7nLqyQQZEFcwYttjLHhS8DVwqdQNsDfSAi/0hBh4\np6USc1q82GWiN2wVGfe/ukPBbK+UlH8dmNdT2oCKhFloWFQQZnsxkSAhfkozXjOzxusqLNNW\nv8ZC+EG9R/0VrgzWoJAnJb4HvrwX3gLRRhwJM9cYEXtIYxwoEb5Lc5jnZ97weAgz9kCh6cNU\nj27duOGRmF0pR2gL7K202HcwtjjDv+Od8kHx6LBaLk2wi0eVk9/KB8c7+4n5N4F5E3bqDc07\n9SH+TRxsJsk+dFx+Tp1i2xsVu+e3QFfcrXYjHfR1H713nTsxtrIdqj4Y0sHOJN+vw8CEYX5u\nTDQdvrWhYzPnv70ppyanfgSkj8LTBW3UkTSLDfqe9Ac2jXrGPgSKIKt0F19z6sUovLdUr3lV\nEv1BqSb6g9EhfXvslmrFF6/e1v94+bJceXJ81KoeVE4OD0uC9UmpT5zSTaWxWLNiyDLBkctf\nzD4VRFnirUJX1av2H+Pn1KEuEdTCrTF2+ftxlzqHp9ji2OEC+7PH1hj3iDN9/5x80Re8dM0E\n/rN4uz4e0GBsupO7c/fKXaHxCT7BhAxpHJ8Q7cLApQPiUktZTctXkR8/AeHnjHUAAAJ0eJyN\nkU1OwzAQhSd/tM0CIXELJBoI0JZlF61UqQKUFKkrKscxldXYieIBjsVl2HANDgDIxRZtyAJv\n7Pdsv/k8XoYA4IHvO+AFAADB/WJ6OgJwj7X4neEIABzwobd7qM303V1TEsFU43p3ImmZc7kG\ngM/G3jRCUUUJiuo5ZumgjpL+7CZdjOfz8+HF4CobnQ2v4zhCLkgkiIzWDFeCKJURuVmpilGs\nST/JAbxDnTp+f317AD32sWhBlMVyjdm5u01nS4pGHmwltjCe/J/xp67zZRICk+BNJLXNYLYZ\nRj/ygumuGd1DLphCIipjuC+5Yfow3+M0+DqqpjqlUfzP43VGaO7D/rqVuptxqUmV7RCTtCit\nCglizbMnZNbxuUov7dmipBu2Jf8Gn69ijQAAHvx4nM2ZbU/bSBDHbeMkkAsUjkJ7PN0ezUmt\n1KvvAm0B6V5c72h1UtpKaaj6IlK0iZfEavwge03hk/dd6W48ThxjF0zWEZGW/wSv49/M7KzX\n609lSZLmJFWVpbmCxD6Fk+brPw7Y/1bZl3lJUrjKTJeHqkoLTO+/IRS9xZ73ClufkeeQLnWx\nJKlKpE+xobcp7jEreqbaPP7UjPf03K5LTtk1F/k3DhFce6ibYG/Gziqxs06NAWHmVyn4jA4R\n6+zoaO8gdkahO2DIMaDRxaVvQeOnyJfxXi1qUH4tflS5jKHKoFtgbwX95i6vZStcXoeTHP8e\noW2TOdNh8W9PxP8KuvrxuPEqAXyOw4BugL0hEDwRptCysElSaGTQTbA3BdLkFkYVhqd6dagK\nAU8ZjnhgYC8FRwbNbTgqsTjK5bSgjat9gpKPt6WIQhNHGT1abfYND536VpcatoVYzr0rc1dW\nByJDVKqCXRXoQCJMpaUTr+saDvcjJapyJO/QhEENc1+6ae4LjX/f/3ecgFmEKgkVmjjM6NH1\npBJ/nJm5BOHkug72el7M+wjZPnV82tYNF/2NdnVMsebZvtsl2sDouNg1iKeBMxo5d3afZvZo\nHrzgug/2fo4emZ02v1szd3hGdHKK/QFtO5j2vcdPqr478Kphmqq8Y3aPFsCLhVl4VBt6xLBv\n5BD7lt2fMvjAtQZ2LS9/Dof+nBHX43PkTXyCvpnd+glc4XoI9mFebilPMvNVgImrArYikC+R\nptjyPdxLWhgVAWERNJdJvTLtDX0JAhYqNLGUPBtJy7jyeLbMiH0PpvR70L8UqDDsRNzf3g9x\n0Zc+cQmiNvKozY0+QdcsQX/gxSroHth7Ar1Irp+WQYmZQiOD5j4NZx6onOp5RKHdEbqgz1hz\nKfa0MirBLToj8wrUzQpMUqVAhTEnsi6OFvC3JF4G3QJb5DNStnpZgTxz3QZ7WyDNdCOSU+1E\nFNodofsZ4hXqTOulGCwAMyLDM9RQK2CLvFMmolZG5XLSqN8COLzDbIA9g32ilGoB74aayz7R\nVONxFWaUULcEzyzT0d2HeIU602opjx8vMmKvQZWsQf+ZLNKWRxVze+hV0F2wdwVCZ6uaNcg2\n10dgPxJIM924XINNtFCrs9hMK7ew2/NNYtGkbdQlCFK415NXqUwXuAewf/NgFjtp2032gMK3\nAvgjy7T7pw8hpg9zWvQkwhRbZ3jgk5RAcoRf7kS6U7cmtmHbJFRo4jAnKN6d1OuZEXcgejuT\nr8aEISbSLLTIOTadAfFSgsYpfs0pt2rspSe8k4ye81qjpqM12J+zGvnwwtUaz/5/96H5T73+\n18u9F887B3++PKzVNGqYWDOxpSXtWT9r6ClESnhkXHUmoThyP14GF6NnPf0dvSEWcTElOupc\nINc+v+gRq3aEdBtZNkVENyg/0MdW/PcL/CVe+CJVCeOm293mhUPCnPSCX7fdcZgm8hJ+EORE\nEpiXCdp5xyUOdoke2UXjrxm/fQc0IXfDAAACeXicjZFLTsMwFEVfPqWfAUJiF0g0UKAtww6K\nVFEBaorUEZXjmMgitqP48VsVm2HCNlgAIBdbtCGDemLfa/u+4+dFBwACCEMPggYAQON2fnE4\nBPD3jfibYQ8APAihvX6ozgz9dVMSwXTlemssqUq5zADgq7J3GaEoohmK4qnH4n4ZzbqTq3g+\nmk6PByf9s2R4NDjv9SLkgkSCyChjuFRlRiTXYonkRUklXpcKsTtLAYJdEz36eHu/AzM22WhO\ntGPzrdm8uY4nC4pW7qwk1oAebA/6W9f7tgkNmxCMJXUdYa4jVt/znJnWWd1GLphGIgpr+M+p\nZfq0f+RV+Jq6pCalUvzf401Gx96HzXUtdSvh0pBq1yEmaa6c6hDEkiePyJwTch2furO5og9s\nRf4DIZZk5QAAHXV4nM2Z70/bRhjHnZ/8KpDRbmvHRo+MoSBBDKH8itiLdWXdNDSk1J36IhI6\n4nNiLfZZ50tG/vK9K7uLH4NJbbUm56qRzPcJZzufe+6e556z381rmlbQisWcVihp4lN6a/y2\ncyz+91h8mdW0vNSc0MpYi9qc0MZrwhFlXezavoM4vqYudUaoduERFxmMEEQtdG5bBBnQtqVp\nxXzkDuWWeclxV1jR+xaNs3fG5Jk+6zBiCaJF+U0iBmRjPQH7ZOKqGXGVZfeJMP/Tgs9tE3GH\nzeb+5BWlTh/7/gTQ7Y9r74NDXpK7mTyrzW0uf0u25m8mUHOgTbCbwXmFm4+ylW4+hhM7Os+6\nhF+Go3MZjs4l5TyBv/j3WetlDH1BEoE+B/u5QvpYmFLbxQ5JoMmBIrCRQppsfVmEiVoE+lnF\n9AkTE/dt7Cfg5EDXwV5XiDN2ZmHCmbn5JKfdxf09ym/FsRRRONRRRlvXjZ7tI2vgdrhNXcQI\nZzYZEv82v6Wlfwp+fZqVj6OtGxajIhX3CPowCT+0A88AWuoG2BsKOxAL86htEr/DbE+OQsKc\nkCDfgcKhDGo8c8ufOnNLrV8vXp3FYJYhxkOFQx1mtHU1MUvVUoPPgE+lroK9mhV4GSHT2k7N\nOAtcoYNzgWbDeIrQgPXRz6ja49zzm7qOPbtORZRxEWTU6osQqwvn68N9PXS9jq/ogFfT92wO\nejP+YbBPs+rZMhpnuZHom9F6e5Yadh4ApS6DvZwVbH4rNd8CMEnNg51XyBdLU24PfNyNK2DC\nmfoINJPMNTftmrsIhKHCoZZSjkZcuZU3rZS4S5CqliAdzCjOs7GYO68wxxYTdSrqUJdj27Xd\nLhrRAbvdFdX8LSQLWaEP6NEC6BHYRwp7FB9DbZsTJ4EmB3oM9rFCmukm6xJsAUM9udsKfgF0\nkZyYXcAnhVJBLFopeSsQPxVYgGYCVcYby7klF1dRqcrSVRQsSCyuqGZRhjjxuQwrb8A86pPU\ncVSB5CX1BdgvFPYmXRwFJwV6APaBQprpZqo85zCicHwhdF+Bv0L9rHFUGpdIKYlXIHpWIH/P\nBKqMOJb0x5eU9gl266g1rur+tXkPdYlLmN1BxBPfCH9AP5ZAd8HeVdiPdDG0AmMvdQ/sPYU0\n081SSdWIKBzK6GJh5tuYdQeO2IvEPeVZBCc9Bs0qcKZz3BMo3Z58jq3ypiHWGQ+LyOA0WHOu\nZP1GTHRhGA9+UvI1OFfqJtibCrsQC1NuD3F/QBI8KhG+yXLci5867on7s1XIjqEuKM6S9/Dq\npoVOd5ApKvf6uHSvVcNavSo3979ThyJfVB/E9avp95rfg5ul1sGuZ9WXteTnPaaVnv0H4JW6\nBvZaVuzrA7dvu//UqnIkqtviCURnwHx7SOApRHr8CHImz1ljaeba5Bo7Xp/4CRM7By9Nsoi/\n4sSrNHjTFb3mT507nt4Sf4YN8uaQ6a36H3+9MX45P9872j88uDrePTppNHRuO1h3sKsnzqh6\ny0zAyoctd+nRIRxH6qkK9DN61fZP6LWsSTAXOfdqhBi9HokipdFEJkUu5YiYNpcNPexO3r8k\nd9ThO7p86DyTdoyRR8KB6QZ3p+zOV/cGJ/yE75A0hYNzj3bWY8TDjJiRlVi+vHr/Pw93bJsA\nAAJqeJyNkUtOwzAYhCeP0scCIXELJBoo0BaJTRdFqlQBSorUFchxTGURO1Fs4Fhchg3X4ACA\nXGzRhizwxp6xPf/n38segABh6CFoAUDrdnF5OAb8fSN+Z+wB8BCiu3moyQz9TVMSwVTtemcq\naZFxuQLwWdu7iLQoo1iL8nnAkmEVxf3ZVbKYzOfHo5PhWTo+Gp0PBpHmgkSCyGjF9H1JKiJU\nP86AYNdkTd5f3+5gxjYMzYlyML412zfXyWxJtZU7a6kbyA7+T/ZT1/uyCS2bEEwldS1grgVW\nP/CcmV5Z3dVcMKWJKK3hv2SW6cN+ilfja6uKmpRa8T+PNxk9ex/b60bqTsqlIVWuQ0zSvHCq\nR7SuePqkmXNCrpJTdzYv6CNbk38DnZxeZgAAFVp4nL2Y62/TMBDAL226R9nYxhgDpEkWEhpI\n2wLjXfGFx0BICKRSEB8qVW5yzQLNQ4470b+cbwy5va5ulAhKHSpZd6nj5Hfnu7Pjr3UAqIJt\nW1CtAQDUPrfeHD4FqG4DwApARUkLoLI5kjasAsDltyhZwgUPUaJIAeyK1rvU9DqS+wCgj7Fb\nJ19b2TtT4QrsAVTX1ZV6/fitI7lD+k5m1HIq3F7QRwD4CePfRRdGZ43Gw3uZETW3z9M0A3Tx\ncvg1bmqIdZ69qy0Dqd6leivnGVSL5DXSr43vq57/ka12/iecXM/XfZSdkefTAmD7y0nzZQ5u\nVSGQ3CR90yBuLkytHfEQC2gsklukbxmkMew8m0LRJtwVw7gFocf7AU8LcCySV0i/YhBn5L1q\nxntWvchp08yeodxQlUKT1MxR6r1O6zRIWW8QuTKII+ajTJk8Ra1KsV4sRn99i7tH89oyvmcs\nHdKdsmzZfxWH3SDiI0viHnP7AeORx4Y87GsGzWuDlmewT/q+QRtyYdbaHqauCBJlTEGQWFoY\nUzMGNQrlyt+Gcq356uPrkxzMJUr6iaRmDlPv3ZrWqTupxOTu3LjL5MnlksprQb0apNzHAu9Z\n5LXSJnnhejUKV01SM0uptiV5a5OtJnpO4DrNcH1WNwacC8o+8BBVVVKlVEGzLgaRzxIUvViE\n6P2DEeskD0g/MGhEfuK0A4lhAY1F8pD0Q4M0i0WoojrSJDVjdLkw9TYX/iDESOZtRVbJSZfK\nTO3Koo5bo+Kz9j/K90ZrZt8xL+s6eXFdWx03DLLmwiy1z3h/gAWus2j3VtoEVxdeoLcpBieS\nmllM+6/jsOA74iol71Wtapus2DN0uzIIeaPRcONk2Oly93s3jvDO3XmZd2jSldwlfbcs5st+\n3JFxx+Xu6T+gap/hF/Fa2hfHnrZLu5UITLhAur41N7nmWNgjfc8geS7MStuLIykGUUGkKojr\nZaX9Yul+nbZSE0nNGF4uzWobf/Aw6WPeQrhNTrpRksPszBEbnYDpY547MkycpgyTs2P89Fg4\nzaN3Hz61Xrx/f//Jg8ePuk/vPXl2fOyosuCEPHKmAXzU9Ao4KpOeaQiHKLm2m90kw/RRB7fZ\nW4xQcIke6w6ZiH8MfYyOG8yLWRRLhl4gVccpj7LPr6kjo8lhXWXiLS92W8MEJzPhj58ei6lz\nZmZj8rtJMwEGZ2OGdoUy39NWbnWo9es36SdZhAAAAmt4nI2RS07DMBiEJ4/SxwIhcQskGgjQ\nlgWLLopUqQKUFKkrKscxxSJ2osTAsbgMG67BAQC52KINWeCNPWN7/s+/Fz0AHnzfgdcCgNbt\n/PJwBLj7WvzO2APgwEd381CT6bubpiSCVbXrnYmkecrlCsBnbe8iUKIIIiWK55DFgzKI+tOr\neD6ezY6HJ4OzZHQ0PA/DQHFBAkFksMqXKl9SQh9YP0oBb1eHjd9f3+6gxzYNzUhlaVxjtm+u\n4+mCKiN31lI1oB38H+2nrvNlElomwZtIanvAbA+MvucZ080yuqu4YJUiojCG+5Iapg/zK06N\nr12VVKfUiv95vM7omfvYXjdSdxIuNWllO8QkzXKrekSpkidPilnH51V8as9mOX1ka/JvMoBe\nrwAAESh4nMWXz0/bMBTH7TQBWgqDArvAIUKatEkbHd0GDInDNhiaVDGplIlDJGQSN43UJJXj\nVvQv340uaV5aNyRCXd0tkvt9qeP4k/f8/OO2hBAqIFXFqKCh8NJumt/fnYT/bYU3KwgpkeJQ\nN0aqomKoq5e+zn3dJGabIqQqQtVSw7rjxA4tsYHavLhtpp8MmMloK+xqLbqL+o67HGkF7Eqq\n1XLYquV0aGj+RvE1rqJe//T042GqhWZ2SBCkgMado8e4RE3wMP2UwR3egU9UhilUDLoF9lb8\nXGH4LJs2fA4n2+22f8f9O8HtT4jVXxeNrxm8hYgBdBPsTYm8mTCa4RGX5tBg0ArYFYk0sr2n\nwmBUnw5MKbw5g490HBLk4GDQhQ0+JeU+XMpz2iS3pyhfhGVdUCjyKMXavWbbCfRWzzO543u6\n7dNgPEPNCh4/E+se2HsSwTNhyoZFA5M53Yg/x5sYkhdPijSo2WKuNb79PL/IwFyC7EgUijxM\nsXZXyOjXlsP0M33/gDsu2X8zM/gy+DTSXbB3JYLnpHgvIDbN8SMG/y0s3IV5U3w0cAWFIpdS\nmcxyU7SFMOIz8pYgwCVgXo5VGm8mZ/HcYdTkPhv8BW0ZdBvsbYm02QliOJy6OTQYdAfsHYk0\n843EiOqloFCk0WXClAzC7J5LPZ61ShfBSauLTOG5V+kyTDLlfzFhr1/Osy6vgRPXYDOBJW8o\nMmGWjD7p9GiO57CA8n/jm7uwVWAIJgpFHuYUxdVNvT4zorCHFQ9W0hAzaYoGfSBut0ODHKdh\nmHYXEVs1dWSGE63Y5qzK3W61Ef70a/T6iFUbBz+urptf6vXD4w9Hn+5P3h9/rtWq0Xan6hKv\nKuyFDhpWDoiS1EySzaWcCCvtBnyZ2OrtK/2SepQRTi39fqAz/2FgU692qlu+7vlcp5bDo4o2\n8dLv16IjYHL6VhJ3Wb7ZHHRpEgo7frvPJt6ZCkdyJesOkhiOKdqVLqNdwqglTDfRIfXxD8jt\nz9gAAAJxeJyNkctKAzEYhc9cai8LEXwLwY5WbeuySIVCUelU6ErJZKIGJ5lhkir4Ur6MG1/D\nB1BJTbAdZ2E2yTlJzv/lz6IDIEAYeggaANC4np/vDwF/14jfGTsAPIRorx+qM0N/3ZREMFW5\n3hpLmqdc3gP4rOydRVoU0UyL4qnH4n4ZzbqTi3g+mk4PB0f9k2R4MDjt9SLNBYkEkdEDKUUu\n+Qu7JWm6pFp1ZykQbJvI0fvr2w3M2GSiGVGOybdm8+oyniyotnJrJXUN4N7/AX/qel82oWET\ngrGkrhPMdcLqO54x0zKr25oLpjQRhTX859Qyfdi/8Sp8TVVSk1Ip/ufxJqNj72NzXUvdSrg0\npMp1iEma5U51iNYlT5aaOSfkKj52Z7OcPrIV+TdOrGFeAAAUpXiczZjvT9pAGMefliID8dfQ\nia+8mExd4mRjmzqTvXDqfiTEJYiLL0jM2R7QhLakPZzuH987WU6eQiltTMexjeTyfeDu2k+/\nd0/vuMscAKRA0xRIpQEA0he1Ty8PAFIFAHgCoApVANTFB9UgCwBLX6hrObb5kxFqGF2dewCa\nGmgwUzWuOG0CQLCbVju9rIVbeq7usgZAak58EwT9Gz9oEeNiqFfGc/WG2WYA8Av6n0EVs28O\nD9+WQz3Sept6XghocHO47xfRRemFW9W5ycW9RK3aC6EqqGsYr/XbpXqPsqV7j+FEm9/yzb8a\nMX+MW/t+Wv0YQZ0SJKirGK9KpI6ESddtarEYGgW1iHFRIs10PNRwYmrjk1QKdcxEpG2TejE4\nCurUJqIaMlHJxZk2zPMRyiUAmA8oFnmUwdr1Wsv0SKNr69x0bEJt2+GUM49Y1POYl5T9Kboq\ndB3jdYnskTD5usE83TU74hFiDBUgBVQs0qCSDXu6evzt5DQCcwYTxFcs8jCDteWx1N42Gjv+\nOnWlO23xIiIfyEb/p40XiZ8ng1YLLWNclvg8Mcnf9WiTxdiroK1TmwUzkyb/w3wOKBa5lOow\nGUZoVaOREDeH45tDYzOS52wkZvaEctpwBwtlItpZ1ALGBYm00flRNzmzYmgU1GWMlyXSTDYR\nBdVKQLH8J3Sz6JevU0nmuDRZCL0lE7LnMU/y6GpGsrPRzEd9ZvLnzEXUTYw3JTIny5w8jrjQ\nLYy3JNJMNjcF1XZAsUiji4TJ1anb7FrM5lEb3yyaNIc6rXSZzLh5XETm/8YGiBwRTq/bjPww\neYsMdkOGvwVKCr+AtgolGBOJ8JEwM/Ub2u6yGC+VQet/PeKxe8RlfIf7ikUe5gjF2UWlkhhx\nBd1bGT3JkIYYSZOts1tqddrMizFNUDyb0thqoTMqPEIK9jkucatTqnKrc1Nm53tuqbr79ey8\ndlSpvN5/s/fu+uDV/vtyucRNi5YsapfG/m3sVo0YHNWvGaacxTgNrMmL+HzBXjvPyWdmM5dy\nZpDrO+I6t3dNZpcPieEQ2+GEGSYXFS1qh6+fFguif+il+qYZjl676zB/QJr9qzvu0KORQfE/\nqzggIHFQRmifdFzWoS4zAu9zcSp0/xvspUAkAAACdnicjZFNTsMwEIUnf7TNAiFxCyRqCNCW\nZRdFVKoAJUXqispxDFjEThS7IHEpLsOGa3AAQA62aEMW9cZ+z/abz+NFCAAe+L4DXgAAENzO\nLw5HAO6+Fn8z7AGAAz701g+1mb67bgrMqWxc704EKTImHgDgq7F3iRQvUax4+RzRZFChuD+9\nSubj2ex4eDI4S0dHw/MoQopxjDgW6BFXvBDslS7rSkvJKraS/TgD8HZ17vjj7f0O9NgEIzmW\nFsw1ZufmOpkuiDJyp5aqhfJge8rfus63SQhMgjcRxLaD2nYYfc9yql9jdE8xTqXCvDSG+5IZ\npk/zQU6DryMrolMaxf89XmeE5j5srlupuykTmlTaDlFB8sKqECtVsXSlqHV8JpNTezYvyBOt\nyX8AhpNjjQAAE1d4nM2Y70/TQBjH261DmIAoigkBciExaGRMhwIue6OIYoKajGl4sYQc7W27\nuP5Ie8XNf9x3zLvu6daVNthwMzY5vs96vfbz/Ljr0bOioih5RdNUJV9Q+FH41vhQOuDnlvmP\nWUXJCVW5LgWqKXNcV46xa9oW/UWQhU3iIY+61PcURctFrpqpG+cMt7kVHas1js4a8Ss9V3dJ\niz91QfwSGMOnB7oO9nps1B0+qkW7hJu/leEx6iLWZbX6ajc2oqB3sefFgEYPV66GTQxRB/Gr\nmowy8SzRmxvEUFXQDbA3htflBzeyFQY34SRnoBNm4DzIwHk0A9fgte9H9XcJ6HmBA7oG9ppE\n9ESYQlMQp9CooOtgr0ukmWIgNShR7Xq5SkFPKUncpdhLwVFBp1aS+Vgk1WJa0MYzfoJSXLMY\nUWjyKKO9pUaHeqjlWzqjtoVGWfcQ64SrmN1Cp0H2s3pyH2IstAR2aVqeINtnjs84uI1M/IMI\nB0yk26aDGb3okqzwDwBYKAIbSYRPhJlvGsTTXeqIbKTUhgBZBoUmDSqo4NzfVnChfvj1/VEC\n5gzM9VChycOM9q4mL1VPe88yc9+BkApdBXtVInfKeuV7uE1SwqhC+KaW7VuvV0HdRhSaXMrc\nuOgnaXsZaYuQ3lChSaNNpNw85DWKdUZc5DGXWm2+LlkMU0uYGI1f/JncCNvzsUpzI3neNCkj\nZgqNCroN9rZEmtsVaBFeP6GWJL+GEmGKTey2fZNYLGknMgdBuvtfz+x5WHvm/8UyXrk2R35S\n1hltQ5BpG7RFiYGwrtuuwS/I6s4CBFpoBezKtNzZ4tsQwe76XfGPoEP0IT21gtPhjiurD4vA\nLXQL7C2JPiTCzDQvcdcnKRUiEO5Ns45vvx95CFMtVGjyMKO9tdF+xAg2JKhWQoyauFqtpuxU\nNj/3gxOb2XcsjyDoQmtg1yR6lkgz1yQ9bDq8sFNiLShWplQSWuxbCnzqiI45LjPTKdf5n8sK\nOd1zy/WdT19OG29PTl7u7+69vjh4sf+mUimLpJRNbJWT07JTN1KYcmHPeLqahOHIVmUJnIyO\n2n6CPhKLuJjxZeCij1y7128Tq1JFho0smyFiUCY6OtiK378QgMENc2HkDFtv9B0SZqU9vLvt\njgM1kZnweAxZUSRmZoJ21nGJg11iRBZd8fXi6g/qYk9zAAACcXicjZHLSgMxGIXPXGovCxF8\nC8GOVm3rskiFQlHpVOhKyWSiBieZYRIVfClfxo2v4QOopCbYjrNoNsk5Sc7/5c+iAyBAGHoI\nGgDQuJ6f7w8Bf9eIvxk7ADyEaK8eqjNDf9WURDBVud4aS5qnXN4D+KrsnUVaFNFMi+K5x+J+\nGc26k4t4PppODwdH/ZNkeDA47fUizQWJBJHRAylFLvkru1UFo7ok3VkKBNsmcvTx9n4DM9aZ\naEaUY/Kt2by6jCcLqq3cWkpdA7i3OeBvXe/bJjRsQjCW1HWCuU5YfcczZlpmdVtzwZQmorCG\n/5Japk/7N16Fr6lKalIqxf893mR07H2sr2upWwmXhlS5DjFJs9ypDtG65MmTZs4JuYqP3dks\np49sSf4DX4JhaAAAX8J4nMya71MaRxzGFwQx/owxiZo0cdVaoIioTdJOom+a2mma2s4Y28kL\nZ5zzWOAqd8fcHan2H++7Whee0z08ahaWFWZuni/DMXyevX3Y2y98HCeEjJBUKkFG0oQQkv79\n8Mfid4SMzBFCxghJck0Qkrzf0hS5RwiZ/cnwbNex/mbUbzAz8AxCUknhhNGD8nFgVAkh4ttS\nh3sfDzvP9D3TYxVCRqb4M07Q/uCWLqBe6HhXxvfMilVnhJB/SPtx9RJzPr1+/eJFxzvSZt3w\n/Q6gqw8n/7YP/pbERedZR4EV8M/iryYvOlAT0EXUi+3zRi5uZUtf3IYTP/i1cPCPI4N/gzv1\nx97B9zHUI5wEOo96XiF1LEz6yDFs1oUmAV1AvaCQZjBjmMLETN2cpEqou0xEo24ZfhecBHRg\nEzHZMYiJ8W6Ddp3zCOXPhJBpQXGooxRfzR/WLJ9Wmo4ZWK5Dry64H35l0RozyszzZV28x/hy\nzaPOK3QRCzN5VGa+6VkNbqbL0HKQX6A4lEG1JsD8506A9MHb337Yi8EcRVRCxaEOU3x17kbI\nc9LAGYwl1znUc4MCnqDh3FyXBh0DHNcJ1BODAt2k1GaBUaa7tMqC3Erryco6Zc4ny6O7tGF4\nzAk2Kp5hs1w+L+/mHhy0Pgz15qDcjFFqu2UmTzkOsvHo+A+GcptS060fXw96+5nKURfmDdlG\nvT1gPyYT/ZhMpZ9JeJjU6ceK+LFU+pmChymNfpgt+mG2Sj/T8DCt0Y/liH4sR6WfGXiY0enH\njfhxVfppn9RWbX5OI35OVfqZhYdZnX7qET91lX4ewMMDjX7siujHrqj0I9xgafPjGKIfx1Dp\n5yE8PNTopxH5Pmgo/T54BA+PNPrxI+uPr3T9eQwPj3X6cSJ+lK4/8/Awr9OPFfGj9H5HaAfp\n89OI+Gmo9CN0ZvT5OY/4OVfp5wk8PNHhZ6vt5yyynp79z3oqbecpLHDdQr01KDtJeb4vwMQ1\niTqpkC+WZvSo6RvVuMbtKBCeQXEow2m1nHb67Tk+x/4vVBxqKZPXNy8R2swtzeUuzEtoOC1h\nr5dpqzLmWNalD2iLuid/MjOggUtP2HXbtNyDh/vQDdQbCj3ER+fICpjdhSYBLaEuKaTpb44u\nocsV6uYgu13SdBTjFepAct4tQelWa1GSeBmZWUYvK6O4FxpLSvc5qREYPDlWmTmBVTmnQY3R\nunXiGd55DyamoUXURYUm5AK0jAu/jDgnFMe5vym6jFiHWhqqeK9gvELVGqAUb21LAq8iM6vo\nbWfaqgw4FrS0/4FafKkxWr/XcewNut/0A2q6TmBYDs02XD9LXY9mHVbN9uBpCvoG9RuFnuTi\ntIppwHUH9Y5Cmv4mLKfaFRTHkNB9ifEKVWucRtu/ukgiryFEa7gBzSi+CY1FXfzVsBl1K601\nyCiXm2ZALYfa1UoP9DPQLOqsQnq56KzhknPNoc4ppOlvcq7hTwGh5hX/OaA/uq8wXqHqj44p\nuxZlEZeszuisitG5xK5bPl+TmMO86nlvIRKC02ouzChuLsiFKIuLn9XRuZGeppzqG0FxDAmd\n8KVzVyGyJJHzCE5eZ4ieR0NkN9ymU77cGfWWnzwyw3Ud9bpCC3L5Ef731dqcJRRvzvqboXls\n0kLdGKrN2tcYr1D15ye8pJ+NXEBmCjrz80zMDzszLm/fbMP3e4tPAZEpRGtlDuTiU8BlLyDM\nCcVh7m+CFhDqUItDFW5hvO4oPpYjiVxEZIo647Mgxued87b2rrfkFJGWIu6bZ9qqDF4uOcJi\nI97EKaPpb24WcVsUam6o9mZC3/KukuNKIpeQlpLO5GRvJucvK6i5zYD6AfOY21uQSggP15eo\nXyr0Ihck4Vcg8gr1K4U0/U1VTvWtoDiGhE74R/ZdBelUEnkL4dnSGaSnN4L0nvXYPhBaBuJW\n6L9mzqY3bSAIw1WkVP04pLfcKh9ataqq0ECJcSzXdV03iUTSCOiNixO7BMlfMiaCP19V27wr\ndhs4rL0sGwmNJQf0MrMPHs+OR5p+MXiY7gg2nZemptnyPEbudszkuzJz3WbqmHLLruBJBCV3\nAExHJTwf1sIj40LUAT/E9nDck/h1xFjqYA0Qa+HYkqim2Wolqk4Zi5cm6j7DX9SqZykVXXxd\n8NNVydIblqU0T+LbeRKWxu+8TOdJWI8iJoX791t2ILmELEZRF9Hv8kRJU9NsnXaxRqnFSxN1\nTPq7I4oy0Q45E+SYKik6ZCnKyHEtcEzAYvJbq9K0i4FjIuAmvz0oTU2zpWmizEHtO63KHT34\ni1r14BSiBQULsFgqweHug4o8CctpVfM+yAIwlhb3QUy+puF9kIV7H0vL+6BT+Ita9fDMRLeB\nbABjq4SHa+MZXl70g2E9dGzgYmvRxmMj5LaWbTw2fl5sLdt4mI7BXaEjugXkABdHJTrv16Aj\no4DggB4HSfOB5NqxGEkOVoDDZ3PS1DRbqw7yI2p7kostzdR9gb+o3QFJor08LuhxVZLE9fI8\nPNQ0T2v38rhgxtWil8dF3F0te3lcbFi6WvbyfIW/qN0BP4WgZA/MeCr54ZO46743PK+Hjgdc\nPC2SOA8h97RM4jwkbp6WSdw3+IvaHaAj+kiaD1x8lehwxYPZMsuzZVqzCc4HML4WxQMfQfe1\nLB74KBj4WhYPvsNf1KqHZyG6iRoAmEAlPFwT3CLJJ0U9cgLQEmjRBBcg4oGWTXABLobBlprg\n1op5MQ7LyTyNs2rdeNjXcNKPLePSzHFnGKN4pmI46OHojp1uQAdvimo+hzfP+fE70jSvFfN0\nfB8m83iDC4mECy0CvXGSySUezKf2peQH9Dl5+1e/+n1hiVfwHrHMmG9pEteqeT6OF2FaJPFs\ng9OIip9bii35r9XZQYT56ux7/FaVFq1BlRb37Xh4UrYGRxdXw5HX7x+bnZPuTe+TabXbrWqa\nhq00zFqPBvAeDaINcvbomRVyZPQDcyl+he/HvuvjW+OMPJUXVnFk3CyNMl8sJ3HWPjWi3Mjy\nyoijaUVO3IXZ/5+/T7Zw6UT4Peq0KL8dLYuYBmTy8Ol5ufIRFxT6d42APJEYFE7ts6KMyYCj\niJkDSkam//kLyUGjLQAAAm54nI2RTU7DMBCFJz+lbRYIiVsgUUOAtuzookiVKkBJkboCOY5B\nFrETxQMci8uw4RocAJCLLdqQRb2x37P95vN4GQFAAGHoQdABAOjcLi4PxwD+vhF/M+wBgAch\n9NcPtZmhv24qKrluXO9NFStzoR4B4Kuxd0FQViRBWb3EPB3WJBnMrtLFZD4/Hp0Mz7Lx0eg8\njgkKSYmkighZlTXe64ozrOkgyQGCXZM3+Xh7vwMzNoFYQbUD8q3ZvblOZ0uGVu6sJLbQHWxP\n91vX+7YJHZsQTBVzbeCuDVY/iIKbflndRyG5Riora/ivuWX6tB/jNfi6umYmpVH83+NNRmTv\nw+a6lbqXCWVItesQV6wonYooYi2yZ+TOCYVOT93ZomRPfEX+AwhJYDMAACxMeJzNmu9TE0cY\nxy8hIQFBCRg1WnVbYwXFREJVpHYsKnScsdYJseOLzDBLsgnXuR/p3cZC//G+k+7mvoeXeBm4\nZC+SmeX7wOW4z/Pc8+zt7e7HWU3TprRUKqFNpTXxSX+o7TzcEH9bEr9kNS0pNSF0oacpbUbo\nxTdmx3Y4cTuswR2qaalk4Oh0tbnHaVtYwXNSte2PtcFvuk7DYS1xtXn5m7y8d9We5mHnB87K\niLNausGE+Z/mfU4OMevT5uZPjwfOSDcM6roDQCcX1z57TZ6SOB78Vp3rXF5LHk0eD6AmoFdg\nX/G+N3V8Klv6+DSc8Mjrvcjv9UX+K+jUn9vVlyHIUxIDehn2ZYXIoTDpukVNNoQmAc3Dziuk\niSGAKaRk6uv0VII8JAWpoVN3CE4CGlsKJgcimJgdFrQvFd5HSWTYA4qmjjJ49HntQHdJq2s1\nuG5bxLvbrt9TkZZjm0SI6D7Icslst4jtkJL7t6FzthLVse8RcqnPYT9X6FgozFy9ydyGo3ek\nf0OiLUF+gKIpg+rlxMxZcyJdffXH6+0QzGlUj69o6jCDRxf6i345Mm0GgcycnNDTeGizpJef\nq5EpsyDL9tvxUF4kpNHldqtFfiGPorPOgG8GHUIizk4hR0iTGlz0B4K19GgtOu4sEKXmYOdi\nxO3YBnV0fiSA321Fx70AxAuTwJ0TuB1TkK6NkAdzoJvrt+MhXSTEpZbO9X+ZwK1VP2xH5p0H\no9RF2Itx8SZXIvMFaklLwk4q5Aulma53XdoOG+NNA+ESNJan0eK4I5QFJN9CIBGVJuES7kbY\niDTlvctEAs7hWZRDl5TxVBlwKOjDHTlq6lB+QOwW4QcMo6qu6Y2nuE32GUZcrDmCR/PQZ7Cf\nKfQovIrqYthnDqFJQDdhbyqkGS9dJdXPAUU7J3SBXjG+kh9WTNPemCQi8hJKaAm1n1Fc/6Go\n17f2XdvockZM3dJNahDd4sxyxVN/BPxL0GXYywrxo9UOfO3pCuwVhTTjZaekuh9QtHNCdxnx\n8nWyteONkSMi51Ev+UnWzsJrbzjPbYM51GqwEaAvQW/BvqUQOlrFBCa/tNuwbyukGS8n85hU\n8BXtnNAFpr0mXzFZ/zUtIvRV1MlVDNQzil9+w2Hfjw6bg16DfU0hbLRauYp7LLUAu6CQZrxs\nlFTXA4p2TuiuIV6+TrRWpsQcQUTeAkqjgHmijKfKeEM559+//33k50oBD8ECojz3TWslUB/n\nsFYKqI/CuayV64iXrxOtlaw/SxUR+gaK5MYknytrOwZti3eXpt6gXLfa5J8DJiYHHDkf4PtR\nIq9Zi3YN3pt0G8GrHHQL9pZCr6IV1Q0kg9SXsF8qpBkvbSXVq4CiKaMLhZmtU6fdNZnFwxYp\nvW9q2ncxV9J4gbuJNZObk1iPKu9iOdLe/0sYpGFbnIpJAFE8clrNn0LzVy2j+nILUZZahl1W\n6EsozHT9EzW6bEhoE4FXm1gSYPasCTB05vwO1kt8RVOH2Vcybcb35Kxp9LXIIkJY7F+Wiodz\ng5CuY4jFEsnb9PrwPTkF7C6vFMURt8gOqdkxmFtEsu7J2azo60B34YnUDdgbcXm1Tgg77O1g\nCnesSTktunbXabDiKTU41KEf4YTUddjrcTk0wkLRPTDdi2mhqI/vxcACfG+94Myxj+7cMhyS\n+gL2i7icG3t3QWCW9ORpGdvugoq3u+Ds0Y9ey/fhg9QK7Epc/iz1renubL3djb6o+wCQD/qn\nr+MBHqFWV8G0GlOthtLM1P2uPYToDigeQlU/01MDG0qx3zN4zq9lbnbKVfHjU4XtPnHK1dKb\nd7u1rbdv156uP3m8v/Ho6bNKpczF0krZpFa5v0pL1eYQlqR/5OQvKZNxGnhlW4BzwbNW75Lf\nmMUcKsdt+0fEsQ+P2syqbJKmTSybE9bUuTxwQK3B/5+WGyT97alJP2JNu1E76jD/brS9/247\nXwLUd0f8Twl3Q1N4R/posx2HdagjV3hPdkPILZyf/wdZleOpAAACZ3icjZFLTsMwGIQnj9LH\nAiFxCyQaCNDHsosiVaoAJUXqCslxDLKInSg2cCwuw4ZrcABALrZoQxZ4Y8/YHn//7/UAQIAw\n9BB0AKBzu7o8ngD+oRG/Mw4AeAjR3z7UZob+timJYKpxvTeXtMy5fADw2dibRlpUUaJF9Ryz\ndFRHyXBxla5my+Xp+Gx0kU1OxtM4jjQXJBJERlwqTYpimORAsG+CZu+vb3cwY5eEFkQ5Et+a\n3ZvrdLGm2sq9jdQtWEf/x/p51/uyCR2bEMwldfUzV7/V97xgplFW9zUXTGkiKmv4L7ll+rA/\n4jX4uqqmJqXx+J/iTcbA3sfuupW6l3FpSJXrEJO0KJ0aEK1rnj1p5pyQq/TcnS1K+sg25N9Z\nDl03AAAdP3iczZnxb9pGFMdtx4SQAmGMJFvSTadIk0rXwpZtaRtpP3QZVJVQWxFS9Qek6mJf\niFt8du0jSv7y/tb0LjyHw7HVOpyrWjq+D87Gn3vvnv3se7OqadqSZpq6tlTQ+FY4GnQfPua/\n/ci/FDXNEKpzrV2pqZXE789pyPB4rGmmIf283LffMjzilryzOei8GcT3DAMrICf8NBXxTZyX\ntxXQKtjV2FFFftSJMybc/KhNt+suQs/29//eix1RsMY4DGNA1yfXPk2bOES/jO81ZA4T5xK9\nxmUMVQddA3ttut/S5RfZCpdfwkl2uSO7/Aat+brT/y+BdWkaxSutgF1RyJoIUxhS7JIUGh20\nCnZVIY1Kz5kwCc2bE1IJa8qkw2MHhyk4Omhuk86IuU5fTXPaLKfnKH8CZ0VaVey0Obrm4NQJ\n0cmEWszxKAomNESh5xIUkA8TJyA2gshnHcXP4F+hTbCbCkeRCFMe2iS0AscXg0lxrQDZAoWm\nDOpqAqx87QQo9A9e/t9JwFyGVIkUmjpMubcEAb6XGbMIHhRaAruUF2YDIR9b7/GIoH/RDnNc\nvPMgM/EKUAptgN3Ii7iLeBL5Xsh5rXs7p4z54X67zfC5Rz3XsbjLLxx64gUusTGlHsNi0raC\nhxPqnJEgJC2bnN1ijFIktC7Y3bzGeID4dj22Y8ezbgxg1m2NvYnN+/3Ae0cs1vKC0U4z+wBX\nYVBCD8A+yGuAmwjZxCfUJtRyiIjloH/UyQ59B0CFboK9mRf0GkKMhIzDdp/2DjuZYcsAWM7r\nPin3Gs3MfBVgEmqAbSjkS6RZHk5CfvVJuVzrUjWWy12lvGhZsSYFtDxrailFNBLrR7h2Z2Su\nwd2lllPlncz66tasUduYqTLW5BQZOoy4KTQ6aO7Xm8xzsQa1WKTQvhO6H8BfkeaSz2mZUriq\nGTIS1yE36nCHKU5VGXEyaf+WpFVQyVZGmi1L6hDd+myIQpXRLDYP61ClRtrIs1rNTCf569tn\nSVkuyjKCNyBBGvOJoww8EXi9O8YjxKt9tCD5OmgT7NyfqVOTR3qA0u6DfV8hzWLTU1D9Lim0\n74RuHfwV6TdNHlM8HGQE3oBE2YAHr+JUlQEnglavk0YQh7dAroBugb2lEDlbtmxAlIVug72t\nkGax+Sio7koKTRldIszqEAejiUsoS3pZGz14RtVrXimy4Lu6bSi5Ir2juPSaL7leHPV6mRHv\ngveESusuyhATaUpDco5df0zCFKcJil9yiq0ZW1GDBS/5mCdt5vrtPv842yWHe0G733r+4nDw\ntNf789Ffe/8cP/7j0ZPd3bZ40dh2MW3DW9JW306BMKKeWbK5hGHpWlyDUclHPfgNPSOUBJjx\nV+3HFyjwzi9GhO7uI9tD/KUgIrbDRMcppvH/L4gVomhhzohcZXvW4MInURhG03/3gpln5kIR\nbb9CGDSFoZijXfH5kw3mKwrSCxyxhvXpMwyuH2cAAAJveJyNkd9OwyAUxk//ua0XxsS3MHFo\n1W1e9mImSxY17Ux25UIpGiLQpqDGp/JlvPE1fAA1TIhb7YXcwPcBH79zWMYAEEAYehBEAADR\nzeLicALg7xvxO8MeAHgQwmDzUJcZ+pumxIKq1vX+VJKqZPIeAD5beynSokaZFvVTQvNRg7Lh\n7DJfpPP58fhkdFZMjsbnSYI0ExgJLBGvcLl6wYKv7hinapiVAMGuCUzfX99uwYxtIsKxckS+\nNXvXV/lsSbSVO2upO/AO/o/38673ZRMimxBMJXF9oK4PVpsSTMOsHmgmqNJY1Nbwn0vL9GF/\nxmvx9VRDTErr8T/Fm4zY3oftdSd1v2DSkCrXISoJr5yKsdYNKx41dU7IVH7qzvKKPNA1+TeD\nLmBsAAANfHiczZbvb9JAGMev0I6Bm1PM2BtC+sZEE10VdUzeoaIxITPpOrMXJORGD9akv9IW\nMv5y3w3v6FMopZeFeKhNbt+H3R334fvc0zzXFYRQEcmyhIoKoo9yZXx9fU7/94x+KCFUYCpR\nfbJQGZWpHvU8bKoz7NjqyLJJiJBcSE3v6eYgwmMapTfJRvfayK4Mg2FARvS4Q/aJnU/HPmgN\n4lpmV4nuYufS8BeKn+UUcaft9vtWZocytHEYZoCWh6P7eLAt0jy7qh9ZETuLzRbmGVQJ9ATi\nk3hdcf4gmzJ/CCffeptaP2DWD1LWb1DLP7v6pxzmYpzVhR5DfCyQORdG6bvYIRwaCbQGcU0g\nzS4clOFSypsXVAgz5xJi28IhB0cC3dklLGQslCo801Y1vkZ5QMfjlMIQR5mebRi3VqiOJu4w\nsjxXtdffVduiH4KpTBsQNwSi58Ic9E0SDgPLZ7+A46cEPkqrIQxqu6wr+ucfX7o5mHtQH4nC\nEIeZnq1m6vrFy615S2Al0yrEVYG8nNqehHhMOPZJYNu/zzKvQMpAWP4bSa53VNsKI9UbLeqa\n/EllV8BSpnWI6wLBOdmeYntCOD4yhEf/Rba5NXIEiInCEIe5RnFx1ettjRgvijXVpAlDzKUp\n98kddvxlQ7FhGqN4uqPcypn2G7rj9J6OFjm+ptM/0ya5PAs0/fT7xaXR6fXett6dfbg5f9P6\n2GxqkeVgzcGulnmbnuomB6aQzKwKziERXl24xWqU2fXqufqNuCTAEa3hm5kaeHezMXGbbdX0\nVNeLVGJaEZu4xW72+xXWTibdfCGxzPSGxswnSTrG8bd7wcqhtZQkT/KeRwJTska77wfExwEx\nU80Ea3jvfwPWu383AAACaXicjZFNTsMwFIQnP6VtFgiJWyDRQIC2iFUXRapUAUqK1BUocUxk\nETtR4sKxuAwbrsEBALnYog1Z1Bt7xva8z89LD4AD17XgdACgc7+4Ph4D9qESfzMOAFhw0d88\n1Ga69qYpYk7rxvXeVJAiZSID8NXYu/IlL/1Q8vIloNGw8sPB7CZaTObz09HZ8CIZn4wug8CX\njMc+j4WfF9ljSpNVNghTwNlXUZOPt/cHqLHNQvK4Niy2Nrt3t9FsSaSWe2spW8COdgf7rWt9\n64SOTnCmgpgOUNMBrZ9YTlWrtO5LxmktY15qw35NNdOn/hOrwdetK6JSGsX/PV5lePo+ttet\n1L2ECUVamw5RQfLCKC+WsmLJSlLjuKyOzs3ZvCDPdE3+A7YXXeoAABFSeJzFl99v2jAQxx0I\nUBhd6X70qdKsSkhb28HGtrbrnvajmyZ1m0TZ1IdIlSHXNFJ+KQlV+5fvrSwmFzBpoophtgjz\nPXCMP7k7+/BpjRBSJKqqkGKJRFfpZ+/z84Pou4fRhxVCClyVSBtjVUk10uqxa1Ad+kODELUg\ndJS7+lnIom+JeLvaOzrtpe8M/IEP59FEq/wTnzmecKwNtBupUZVo1LlpQWT+JvE16QLn8vDw\n9UFqRGlgsSBIAU0mJzdx40OUUfouLTRDPhfvLYxSqArqOtrr8X3F0Z1spdFdONlOt1zjTHD6\nLV7111H3QwZtkROgrqG9JpE2E6akOcyGHBoFtYF2QyKNXN+pmIjq7aSUQpuTeMwyWZCDo6Au\nLfEKKecptTynTdf1DCVPsvuCYpNHKfZunpi2ZwG9AMsDn567frwzGaZjzAsu5CPZRHtTIngm\nTF3TIRj4phearpPjTUWINzZpUPPFvNT9+OPTUQZmGVdHotjkYYq9a5P1/LTVaj2bG7aCfqwI\n/l36lljWhgEzIMd3CvpsaSEuLrqsx8kqKDa5lLx2Z+3gxSjOc/LWMMA1ZK7EKo03k/OJ6wCN\n9iDb9YFeMmsIAQ1d2gcapawB+l88Qx11G+1tic+QvWw0MwQ7h0ZB3UF7RyLNYvnJqXYFxSaN\nLhOmpjHfGNrghFn1uopOurfMhb1wva7j1lP/F1v3xjcI+Da4WKVeRWdy3UB7QyJyJkxZGy/o\nHA8q+E/n/8c5t+w9wFRMFJs8TLG3Oa3RW70LM6DRi8URp3acA1vzV27cc8faRLsp8REyaaoa\nXDH+LzPIcSqneLSk2KupAzaef8Ux79qh7bW70dtlB072/Ha39fX7Se/98fHL/Vd7b/oHL/bf\ndjrt0LRZ22ZOexKZVlfPwSgkPdOlaEPIhCrdwOcSR+026RdwwGch6LR/TX336toAp3NIdZc6\nbkhBN0PeccGc9O+X+IExOakXEmfp7qB37UESCCP+ddef+mYmGMn1GANBJAZjhnbF88FjPi/o\nk82IH2lv/gBHqOBKAAACaHicjZFNTsMwFIQnP6VtFgiJWyDRQIC2sOuiSJUqQEmRugIljqks\nYieKDRyLy7DhGhwAkIst2pAF3tgztud9fl4GADz4vgOvAwCd28Xl4Rhw97X4nbEHwIGP/uah\nNtN3N02Rciob13tTQcqciRWAz8beRah4FcaKV88RTYZ1GA9mV8liMp8fj06GZ9n4aHQeRaFi\nPA15KsKiXN1XrKKDOAe8XZ00eX99u4Me2yikSKVFcY3ZvblOZkuijNxZS9XCdfB/rp+6zpdJ\n6JgEbyqIbQC1DTD6gRVUd8rovmKcSpXyyhjuS26YPsyXOA2+rqyJTmkU//N4nRGY+9het1L3\nMiY0qbQdooIUpVVBqlTNsidFreMzmZzas0VJHuma/BsGnF2QAAAUbXiczZjfb9JQFMdPocCG\nG7DNzQdnvC8myiYo6n7w5o9pTHAmjJk9kCyFnnVN+ittmewv922YC6dQunYLclFJTr4X2ks/\n93vPaW/vWR4A0iDLEqQzAACZ09bnlwcA6Q0AWAJIcZUAUqWhyrDMf2/YGnN0BwHkVOj3bFM9\n9xUNAMJny62js1b0TM/tungBkF7l3/iFR9cbapHaxUivnOd2L3QDAeAXjD7jQ2hd1etvDyM9\nMl1D8bwI0PjicDMK3kUaRM9q+7pv0BBTgwiqRFqidml0XnpwL1tmcB9OvOeGrZ1PPL+FK/84\nan6IgU1zANICtQsCYWNhMm1LMTGBRiItUrsokEaodTKloXw7JYXAJqSdYuiKl4AjkS4s7VIR\n76R8kmmTqp6iXKM8C7QgON+m6MonuukYyC7RcNBlF7bLVOz0NE23NNZB/yeiNbxRebMOY50M\n5lqmdlngMGJhVtoqel1Xd3zdthK85SAbpBTCoGbLgEzz4/dPRzGYWaqVQCnEYYaPrgXF/by/\nyyqVyouZcXPkZC7k8JpA3IQy73mKhgnuSeTawiY5O2+ZD9M1pBRiKVOTXJ+m7c9Im6fpDZRC\nGG0s5fqVYvRweFPyL5Hd9QC6AzuIxxMVhh1fJ23dRzOBRiLdpva2QJr5EpJTPQkpxX9C94D8\nCnQhRZ1ULulKpTIj7woVSVDiuZEK443lfGpbyGyXmbaLbFg8HvNt1kFm2JqG6h+MIYjyRIWN\nYbbqWaFZ57pD7R2BNPPlJ6faDSmFMLpYmHxbcbWeiZYft9xdJpNWSRdVMvMZV6AndeFvrHW2\nvqHHVw3TC91ZkUPvXbBF7S2ByLEw2fawoBMclMZn/+t5TlwlPqS7d6AU4jCnKI5PG42ZETfJ\nvc3pPQphiLE0y23sK/wlzEswTQqlmei5lSO7T7Q5FO5Tr/qmU236pnNVw5M9t9qsfD0+ab1v\nNF7vv9l71zl4tX9Yq1V93VSqpmJVg9eJSlNNoEgFRyaVZqKvhB7CJRpWuNfuM/YFLXQVH1XW\nuWau3b/W0KrVmWozy/YZqrrPD1wqVvT/M3w3JdjFSgVeqXa3de1gMA/a6N9td2LN1FwEn0c0\nDyBwLqZolxwXHcXlz+vxzZvv99z8Bv3aJycAAAJseJyNkUtOwzAYhCeP0scCIXELJBoo0JYN\nUhdFqlQBSorUFchxDLKInSh24Vhchg3X4ACAXGzRhizwxp6xPf/n38segABh6CFoAUDrdnF5\nOAb8fSN+Z+wB8BCiu3moyQz9TVMSwVTtemcqaZFx+Qjgs7Z3EWlRRrEW5fOAJcMqivuzq2Qx\nmc+PRyfDs3R8NDofDCLNBYkEkVFJKsXuSZatqO7HGRDsmrTJ++vbHczYxqE5UQ7Ht2b75jqZ\nLam2cmctdQPbwf/Zfup6XzahZROCqaSuCcw1weoHnjPTLau7mgumNBGlNfyXzDJ92G/xanxt\nVVGTUiv+5/Emo2fvY3vdSN1JuTSkynWISZoXTvWI1hVPV5o5J+QqOXVn84I+sTX5NymlXzQA\nABePeJzNmP1P00AYx9uu3QBBXqb4BsmJmmwgbMyISqJEEV8SUTOnkqwLOdbbaLK2S9sh+I/7\nG9hrn7FbaYNl18Qlx/cp12s/99zz3F1vb0IQhIwgy6KQUQTvp3yvvVt97v0v712MCYJEVfR0\nxldZGPd08iu2HYKwpvWariDIElOXrWr7Lm57FttCru3s1cJ3OnbTJi3vXVP0ir48eKevebDz\noVY5r1VL7xDP/CMEv/MqYh5tbj4th1oozQ52nBDQ+cuF06DQJuJZ+C7V1V36LlornYVQRdAb\nYN8I7sucXcqmnF2GE+33LvX7Puv3C8jyj53qmwjgDIUAnQN7jiNwJIyimtggMTQiaB7sPEca\n7u6TIRzli6HJBTgm/HBHx04MjgiaWvhJIf+JE3FOG2T3ECUlu84oFH6UbO1i7VB3UKtnNl3d\nMpE/1g7MUU5S8pvgU6qLYC9yJI+EmVQ14jRtvUs7EONOCjIPCoUblD/oyr8OulLd/vJ2JwIz\nC+nRVyj8MNna62xOFxKz5sCNOYhOMc0InUUQjvuOa+tm+3Fi3DFApDoL9mxauB8RskmbHKOX\naElV6wVV1ZaLu4Wt+3W8+rtRDK4L9ZXVRl1Vf3lXjbXl4tbasqo2mLri1lLibo5D16j+BPtn\nWt2Uion5JoCJqgS2xJEvkiar9hzcjlpMs4BwbTAn8J8XsqMuBpPRhS+lNNhPDNFODSVeQvIp\nmCCm4OE5zmt/JHH+tU+MXAsdkGA1067APQ9aALvAkTs6aVTdJUYMjQhaBLvIkWa06KS6zCiU\n/4SOWZrSy/C43FH8VSAh8TTkyTTMS7lAuRFHks5X/fWq56UKalm2nzXJ030aHE31AdgPOKIn\nS5tpGG6qD8F+yJFmtMCkVI8YhcKNLhJmQsV2u2cQ0436NJoEJ82AppUrozlultnFpb5JXvDP\nazREOsT3GmrZlgF70aTgc+BSqgtgL3AEj4TJqke40yMxfhSZQ4RURjsz8jfRLZgG+3qN83Q4\nhHdr6Jtoqb678qGxspR8m3sb/HkbwMVA04Euh6Aru+ur2xsf1itfNlDhkBxbDimufMbbHa8z\nlav05g70gGoZ7DLH3kTSjKvkGBvdDnFigoJS3E0pduXQuSwcm7JtXpVco1uqen+OKuTbhl2q\nrn38/K32+tOn9WdPNp4ePC8/e1GplFzdwCUDmyV2kNaqWgyJ1K8ZzCYGcTGzz5iBrrGtHj9C\n74lJbOx6U9XBCbKt45M2MSubSLOQabmIaLpLKw6xGX6+Qs8Z+2e8Ut9fmtWsnXRJfyzawdMt\ne+CeofHo/+7BWAgcx2OIdqxrE8+TdEN/vi2mJ6GnfwE66ZZtAAACcHicjZHdSsMwHMVPP+Y+\nLkTwLQRXnbrNywkTBkOlnbArR5pGCTZpaaI+li/jja/hA6hkJrjVXpib5Jwk5//LP8segABh\n6CFoAUDrdnF5OAb8fSN+Z+wB8BCiu3moyQz9TVMSwVTtemcqaZFx+QDgs7Z3EWlRRrEW5fOA\nJcMqivuzq2Qxmc+PRyfDs3R8NDofDCLNBYkEkVFJKsVWNOerklREqH6cAcGuSZy8v77dwYxt\nJJoT5ZB8a7ZvrpPZkmord9ZSN/Ad/J/vp673ZRNaNiGYSuoawVwjrL7nOTMds7qruWBKE1Fa\nw3/JLNOH/RqvxtdWFTUpteJ/Hm8yevY+tteN1J2US0OqXIeYpHnhVI9oXfH0STPnhFwlp+5s\nXtBHtib/Bl01YN4AABSpeJzNmNtP2zAUxk/StEBXKOMuccvLpE6idHTiMt4GYxNSxVApEw+V\nkEncEqm5yHER/OV7o5PbkzbNErGqLqOS9Z3KdvPzZx/H9U0WAFKgaQqk0gAA6eva9+IRQGoR\nAKYBVKEKgDrfVQ1mAGDpkjCf6qeVc90jjNiUU+YDaGqoTaZq3nLSBIBwT612dlOLtvSZwWgD\nIDUrvgmI3rO7uo7xeqTXlM+MhtWiAPAbep9+FXUejo/39yI90kaL+H4EqP9weO4V0UXpRFvV\nucXFs0St2omgKqgbGG/02qU6L7KlOy/hxPo/7wn/b42Wddv130/A1n6dVU9ioFMCBHUF4xWJ\n0LEw6bpDbJpAo6CuYrwqkWYiFmq4LDWEnpYMnbAMScsifgKOgrqG8ZpEnK6HasRDJZtk2iDL\nhyjfA8BcSLHIowzXFmv3lq832o7BLdfRu/Pt64Zr28Qx9Zbl0NDmNepIFtBjoUWMixJHEguT\nq5vUN5jliQEl2CtAFlGxSIMabRGkq6c/v53FYGYwXQLFIg8zXFuI5nmBsGbbpg73d0Lz/3Hk\nUUyhwUILGBckjiJhA2j7pEkTTFXQzInNfWbcDaC7ikOKRS6lOkiBIdqZ/syPSJ3FaRaaxzgv\nkTqWdlacrsYhXkDdwnhLInF8qtQtTu0EGgV1G+NtiTTjrUlBpYcUyxuhe4d+BTqRvE7KmGz0\ndP/P2DlMkxy2n+qpNOx43MtxcBdRNzCWeYofLV9yOM9CNzHelEgz3orM4Z4S6NZr7C3ZemQr\n/Ou1IkyaRZ1Ukoxn3By+nude49yzPcgFcea1nKbeYK6t8/vu//ZR2fPoan5CW3gsTKb+QFpt\nmmCl0m/9vyc88Yy4hBt3oFjkYQ5RXFxXKiMjLqN7y8N3GdIQY2lm6vSR2F6L+gmmKXgxMYm5\n1SK3VHiJFO5zUuK2V6py23so06sDVqrunl9c1b5WKnuHnw/2744+HX4pl0vcsknJJk4p+h9j\nt2om0KhBzSDjbMpJ6D08j8ML99r5oP+gDmWEU1O/e9KZ+/jUpE75WDdd3XG5Tk2Li4p74kR/\nPy1uXYJbLzXwzHSN2pNHg/lo9n7dZQOLhuYk+ATXMyBxToZopz1GPcKoGdrNxb3Q8x/Q8EHc\nAAACcXicjZHLSgMxGIXPXGovCxF8C8GOVm3rskiFQlHpVOjKkslECU4ywyQqPpUv48bX8AFU\nUhNsx1k0m+ScJOf/8mfRARAgDD0EDQBo3M4vD4eAv2/E34w9AB5CtNcP1Zmhv25KIpiqXG+N\nJc1TLh8AfFX2LiItimimRfHcY3G/jGbdyVU8H02nx4OT/lkyPBqc93qR5oJEgsioIKViy1ci\nsmVBSiJUd5YCwa6JHH28vd/BjE0mmhHlmHxrNm+u48mCait3VlLXAB5sD/hb1/u2CQ2bEIwl\ndZ1grhNW3/OMmZZZ3dZcMKWJKKzhv6SW6dP+jVfha6qSmpRK8X+PNxkdex+b61rqVsKlIVWu\nQ0zSLHeqQ7QuefKkmXNCruJTdzbL6SNbkf8ASEdhWgAAFuF4nMWY8U/aQBTH21IQGIiOOXTR\n5GJCoomTyYI6E35w6pYlzC2Ii0tIzEkPaAKFtIeRv3y/ye7Kq5TaxlUO1+T8Prxe+3nv7rWv\nd5WUJCkiqaosRaISO6KXtS/vD9n/suxHXJIUrjLTJVtVKcF05Sc2LYJ+H3+voD42cZdQYlqS\npCquk2JV7ZriFrPcQ9Xa2VXNe6ZlNkzSZDdN81+cYnxzW9fBXveMWmCjmnqHMPOPND4euohx\ne3RUKnpGRBsdbFkeoIebS/fjxofII+9ZdapTfi/eq4w8qDLoBtgb4/MioyfZoqOncHwnYLnP\nJ+B6iLuda3sCrABu9ddZ9bMPdYSTgObAzgmk9oWJ1g22VAJoZNBVsFcF0swnhiosTBWo44Kp\nAxYi7ujYCsCRQdfAXhOIYwdR8QRRTgYFbZLnU5RvWFt0KTRxlO7efK2tW6g5MBpU7xnInnDL\n+9gK68EKxJZrHuy8QA98YVJ1jVgNU+9zRwLCykHegkITBmVPfvRfJz9aPflxeuaDGYM0cRSa\nOEx3b/ZRgm+FBl6AWHLNgp2dF3AJIY00URm1CN3aZCYedChDp+3NHcQurJusj3lCDLrb5Mt3\na3t7J7RLcXCDawns0rxc2kNoYJmOSwOLmE/4E9qdBLhg3w3svXm5o4TnSwITVwVsRSCfL02s\nPrBwy+9VGwOEV5OHhPgHRWzWt0TKv4mlVCY5PUUb0fipoXjT8IhIw0JcGKswXl/O1On4CYF4\nRj0DOAWaA/sF6j+dkm4AjQw6l/pvpsWYhoLK0TXRhdVMdIsQL0fnktCBqcIe7iF5M5AemZdM\nlcQle/M8J08ykBsZqLFSYxVGGy5PMjC/mekyVBjNbCsxA/Wno9CE0fnCJOvYbA26rITw+yZK\nQZCWQOeVHLMFbhmqseWXqIrLx6ijWxQ1egbFuqEbLUTbxPU9hKw+aehNnWhIN+w++4OJb7WE\n/lZ6DTHnWga7LNAzX5hY/RZ3BiQg0LKrkv+/yyGwZMxBfeYoNHGYUxTnl5VKaETXW9q9AyYM\n0ZcmUSd3uNtn6zAgaLJr60P03KqevU3YenSPOSnQbr9QZX9ui+Ri3yxUd7+dX9SOK5W9g4/7\npZvDDwefisUC1bu40MVG4dH36W5VC8BRnJ5JyrFsxa7X8xL45x61k0dfiUFMTFky3wyR2bsb\ntohRPEJaDxk9ioimU97Rxob3+lG+V+dslipO0LReozbsE2dCWuOr98xJjKYmxTnewYRIAidl\nijbeNwn/ktRc1STfTbz/C9uGiCoAAAJ6eJyNkU1OwzAQhSc/pW0WCIlbINFAgLYsuyhSpQhQ\nUqSuiBzHgEXsRLYLEpfiMmy4BgcA5GCLNmSBN/Z7tt98Hq8CAPDA9x3wegAAvZvlxeEUwN3X\n4neGPQBwwIfh5qEu03c3TY4Yka3rgznHVUH5PQB8tvbiULE6TBSrnyKSjkWYjBaX6XIWx8eT\nk/FZPj2anEdRqChDIUM8rAXJHpBgFacvJGuqZZIKupajpADwdnX27P317Rb02IbDJZIWzjVm\n//oqXaywMnKnkaqD9OD/pD91nS+T0DMJ3pxj2xJiW2L0HS2Jfo3RQ0UZkQqx2hjuc2GYPswn\nOS2+vhRYp7SK/3m8zgjMfdhed1IPcso1qbQdIhyXlVUBUkrQfK2IdXwq01N7tqzwI2nIvwHL\nKWU3AAATaXiczZhbb9MwFMeTNt2NbQzGZeJhciuhwdhW6LQLFS8wCkIaF3UF7aFS5SVua9Fc\n5Lij5YvztmKnJ2uaJYxqLiKS9z+t7eR3Lna9nC5ompbVDEPXsjlNXLmvtXfbh+K7VfFhTtMy\nUnWhK4Ea2rzQR18YQW3MbNehPwlysE185FNGu76mGZnIyJmq1eC4JazofKNWOa3FR/rMZKQp\nnrwkP0mUIUGgebDzsVmzYlaTdogwf2nD67KLOOfl8t5ubEbO7GDfjwFdPly7GDY5RR/ER9U5\n5fJZsjcziKHqoAWwC8Nx2cG1bLnBdTjJWfAYaVxmoRFkoRHNwhUHjG+V6psE/KxEAkVgI4X4\niTC5uiROodFB82DnFdJMOZgGlKpxtWyV4KeUJu5Q7Kfg6KBTK81sLJr6QlrQRit/jFKOWY4o\nNHWU0d7NWpv6qNl1TE5dB3nRzcxHJ0HSh5vapG7cgQBL3QR7c1purHEX2fg7QbxNbGS6toc5\nPeuQSaHvAqjUNbDXFEInwizWLeKbjHoyBSkFIUFWQaEpgwrKNvO3ZZurHn1+W0nAnIEFHio0\ndZjR3vX0PepJ7+nE7LMQVqnrYK8rZE/ZqLo+bpGUUOoQwqll/MYbVVC7EYWmljIzKvxx2t6E\ntAuQ3lChKaNNpCwciRrFJicM+ZxRpyX2Jodj6kgTo9Gv/kRuhO3ZSJW5kbxu6pQTO4VGB90C\ne0shzc0KVFJtRxSaMrpEmIU6Zq2uTRyedASZhyDd+q9X9iLsPYv/YisvXVkjPyhvy1/yYH0g\n27VokxILYdN0mSUGTOrOEgRaagns0rTc2RBHEcnOuh35n6BHzCE9dYKvw6PWpD4sA7fUDbA3\nFPqQCDNTP8edLkmpEIlwe5p1fPMzyT1YaqFCU4cZ7a2IM4mHGbGC4wh6tY04tXG5XP7DWaXw\nsR9UedHvNpu0V5j86HIfoi+1AnZFoYuJNPN10sO2Jyo8JeiS4sGUasOIvVWBlx7ROcdFbnvF\nqvhzXiIn+6xY3fnw6aT2+vj4xcHu/t7Z4fODl6VSUeanaGOnmJ6hnaqVwpUJe0Zr1yYcR84t\nK+BodNbWY/SeOIRhLvaEsz5ibq/fIk6pjCwXOS5HxKJcdrSxE79/LgCDG2bC6FmuWet7JMxM\na3h3l42CNZad8HoImdEUZmeMdi5cDpEdWL7HuPgNgudW7QAAAnh4nI2RzU4DIRSF7/zZdhbG\nxLcwseiobV12UZOaRs1MTbqyYRhsiANDBtTH8mXc+Bo+gBoqxHacRdnAOcC5H5dFDAABhKEH\nQQQAEN3Pr45HAP6hEX8zHACAByH0Ng+1maG/aQrMqWpc704EqQomVgDw1di7RppLlGouXxKa\nDWqU9qc32Xw8m50OzwYX+ehkeJkkSDOOEccCyZpKXNMlFqLSWLNKqOVKSNVPC4Bg3ySPP97e\nH8CMbTRSYuXQfGt27m6z6YJoK/fWUrdwHu3O+VvX+7YJkU0IJoK4hlDXEKsfWUlN56zuacap\n0phLa/ivhWX6tF/kNfg6qiYmpVH83+NNRmzvw/a6lbqbM2FIlesQFaSsnIqx1jXLnzV1TshU\ndu7OlhV5omvyHyupZGQAADVveJzNm+9T2kgYxwOCWG2tvdrTlloWhKv2rFT81TLTF3c92+mN\ntQ5Fpy+YYSKsmCkkmSR4+pffu3q75Ju6cklrYENlZv0+mkQ+zz77hOyzy+dJRVHGlEQipowl\nFfZKHlTfPn/J/naf/TKhKHGuMaYzPU0ot5jO71vUVC1KVF03HNXRDN0m7/b2PylKIi6cN15p\n1h21xSzx6kR153O1/0zbalj0mL3vHf4bB3Hfv6cZ2Jm+q1LsqmOtTZn5r+K+vh2i+mm5vLnR\nd0Wy0VZtuw/o25srX93GL4ld9J9VczSHvxc/Gr/oQ41BCWzinjd28UO25MWPcPxjYLoxqAsx\nqLd00w7ATxzuVP70gR/jQNAnsJ9IhPeFSdZ0tUMDaGLQDOyMRJpIuzKBYZr4/5CVAh8wLNW2\nptoBODFoZMMy3teXscmgTrvM+iuUz1ibFhRNHqV4dK16otnkuKs3eJAJ4u7eu4hx5KiaTpvi\nXS2sN7+jn7muwV6T6I0vzO1ak9oNSzM5ckAXc5AVKJo0qN5AuHfdgZCsvPn4144P5jhSxlM0\neZji0UdBOb8UmjuFLuX6CPajqLgfE6LpZtchr0mLOnXmgtqxl2yHmuR1aPIJ0HJ9DPtxVOQ7\nhL1yQd2eW87zT3I7LxzIW+o/edukDcdS23l+1kpoF2/BrR4B7J2oXFwgxOg6sqIzCVyuC7AX\nokLfDR8dnNzM247VbTjdwYM0Be+47sLejcrTLCEMuM6CYlFDSqBuA5lrFnY2Kvy96wWqrR1Z\nqqUxyzbMfIdaLSFQ/K8998OH6g7847oHey8qX5+4oepQOQk1Dd5pPOrGJD/qXmHflxMn5rva\nVB01fKTuwkOu+7D3o/IWkWJP9VIi5Z7kauSR+iAnUnxGM8C97x7c4/oB9oeoXM25YXLUszqb\nCEsJ1S9g5pqDnYuK/1BOqJj/hm50+AmsG8LH7D785HoI+zAqn4WY6WZDSsxmwTw7ipgdSI8Z\n64bQLj+Am1wPYB9E5XJ8OTTfr2DiGocdl8jnSzNe69pqy68OMw6EOWgkU8YH150yBs225/HQ\n5SmaXMr4ZcJfoU325mEhiR9invgQT7spV6UR+5JOvu/NGN1KaWjcaegc7DmJuP45UtMc2gmg\niUHnYc9LpBluMD70bzeETihMRJfQQaky7s6KQyKnkR9pZHZKcnb7ok59dCfwAyRLGg/baQT/\nruQBEC5Z0ghyehRVqdDDMe3fbgidUAsbfbJMXlYnQmIvIEkWcH7KVWnYvrjZtyxTSMPQefFc\n01vk8pmNDOzFfeg67HWJXoRLI6HwpmzA3pBIM9xA5VSbgqLdEDph5j76NEqhchSSOYO8yaDG\nlXJVGrMv6+J3csgrAA3gxwy0BLsk0Y9wWSQs5fZyOiY5p4cbpxnktqcbNyrHhaXbn5RFrLIV\nkjmLzMmOMovId7KoV5wbwIkZaBF2UaIT4VJIWFhQXsB+IZFmuEGaxXq3p2uS172HoxNqXKNP\noSmh4hqSexGps4j6cMpVadzX+zB6w7dMnR9rFiUowp0P4McsdAv2lkQ/wmXSIkYA123Y2xJp\nhhurnOqloGg3hC6P/vL052QSKwCH5C4gewqjzKRCfybt7fdySTvWqDVoLhWQPwWMjVnJ4yNc\nLhUwBri+gv1KIs1wo5VTlQVFk0bnCzNZU61Wt0N1x29Tnle3/C3iBBqu455iU9DTUWzFelY9\nocRUnRPiGMThNjaWuNvyhtiNt4QOXhI2wT2T6IYvzHjtVG13aUCvcoTlKGM/dt3YBy5FPcfa\nhKdTktcoepjJaw/RgM22q9gZ5SmaPErx6JyjddRyudwwzPP6kdr4cmTodGk5LHMRQS8KK2sy\nl1OuME+3jLpj1Btq42QAVGGKIe6biQY1HbgZMzS3sOlWLIFHw53t6m1N/7KU4wWa3AqxaKNr\n2dopZav01crBTmj6EohLEe0k84WZqDXZI4rV9ds6vAoIr2gj/ZY13K1qHU+LnsZHsWp+q0bP\n1I7Zpn6f78/RSRsRdVii7/s1+PqLeM3fRadjFivsx2mJftqyipXV93ufqn/s7q5tr29tHr18\nsf2qVCryW1qxo+rFoORbrTQDqOLekcsBzcuUwuP7DNwUr1opkHdUp5bqsI/2o3NiGWfnLaqX\nyqRpEPbWhDY1hx84UfX+/59EAaf3D+Ne3zWNRvXcpF5cWu5/N6zLrroSG++1ibgoEmNzhXbC\ne4IRNkzyb7R8/Q+NMHOgAAACenicjZHfTsMgFMZP/7mtF8bEtzBxaNVtXu5iJksaNe1MduVC\nKRpioQ0wfSxfxhtfwwdQw4S41V7IDXwf8J0fh2UMAAGEoQdBBAAQ3S2ujicA/qERvzMcAIAH\nIQy2D3WZob9tCsypal3vzwSpSyYeAeCztZcizRuUad48JzQfSZQN59f5Ypqmp+Oz0UUxORlf\nJgnSjGPEsUCNpA2WdIWFqDXWrBZqpZhkazXMSoBg32RP31/f7sGMXThSYeXgfGv2bm/y+ZJo\nK/c2UneQHv2f9Keu92UTIpsQzARxLaGuJVY/sIqa3lk90IxTpTFvrOG/lJbpw36S1+LrKUlM\nSqv4n8ebjNjeh911J3W/YMKQKtchKkhVOxVjrSUr1po6J2QqP3dnq5o80Q35N++3ZU0AAEQM\neJzNXP1T00oUbUsLioIgKuoTXqgRgYcUUb6q4AfCG2ccYUp94kxnOiENbcY26SQpyl/+fpO3\n25zYbV+KbLqJ7cxyLiRtzt17z256N8vRcCwWG4glk/HYQCpGXqlP+b0nG+RvE+SXK7FYgmKc\n4FgTk7GrBO8fWFpdsTRJMQzTURzdNGzp8H3u/afDWCyZYM4czJWKjlImFvv+ZH73KN95pm2p\nlnZCrjxCf6NUXAZNnIE90/GuIfKuE72qEfPfmPv6eUgzTrPZ1dWOd6TUqmLbHYR+Xjz2w230\nLfHzzrMKju7Qa9GjifMOqnFgGnbaPW/g/JfcUue/ouMfhbobhSIThaKtW3rD7uJA8p/d3Fsf\n+gOUElCCLQmk70smVTCUmtaFTRw4A3tGIJuQOzOJVE3+P22F0O+SmkpVV+wudOLA0FIz0dGb\n8eFundZSfhvLVdJGGUQTx5I9up2v6LZ00jBUGmYJkScjWDPcEjEbVceWHFOqKV81yaloNUk1\na3WSFcdVjde1NXQ6xW3Y2wJd8yVzvVDSbNXS69TDLv1NiawD0YSRambFnctmRSq3s/9u14fm\nIPTjIZo4muzRB92HgDlu5kPoVIoPYD8Ii/msJOlGveEUS7qlqY5pnUlbUllzisQdpWbP2Y5W\nl7a4fbgC3hRnYc+G5cNribzS3UOQnpfpRG/LzCHZUr7J7uFFbu+uwqPmxWG/Dsu7GUkyGw4N\nEaEvJDjDoDwc1izJHj0IEhycXpJtx2qoTsNSqoGDdQ0eUjyAfRCWt61gqYqYYF0H5etRBGuv\np2ARl816kBCNwC+Ke7D3wvKxFaIT0xISolFQHu3/EBGXa42qwh+iG/DrRhQh+iJJrjPFU82y\n6Y2WT5h+0QmOaVZtDBoyPobfbfckF7/A/hKW2yQzyXhXJO5ZmikkM8dBeTyKzLz0SF/Vjy3F\n0ollm3W5plllZqSnf212AH+wbsLDm1GM9NNusGqaIyRSE+BLcRr2dFjcc6IiRbxXSooTYDy5\nBR8p5mDnwvIXsSJFAyGxug2+t6OI1b6oWNGSSYC5+Q4cpLgPez8sZ9NuoBzle5HU2oQEaxKc\nJ8MqYrBHj0QFi/SAaZg1eoIaZLq+C08pHsE+CstrJmpGXRUStXvgfC+KqH0OIWqkI7idvg9H\nKX6G/TkspxPz3Pz+ACeKCdgJgfx82QwWGrZS9iv2DoKCVyNBE0Yngbm4t/LkFL67eYgmlmWi\ndQfRxvZGR22Hk/t0zK1CTWOyG3JRGHdfzo/f/axEqabhKLqhG2VaSe0osgbw5S5wG3botdRU\nQXe0Whc2ceAr2K8EsuktZ6dR1/LwdZj1LW52f6K/PAxF990UNdyqxXHSZlbHmucPuSiMti/d\n5f0mXelbhXylossStnKqecsWJWYRNqCqJFyX4lvYbwU6xacqCdlAcQf2jkA2veUtZfWOQbQ+\nYceUB36fqkgFkZN2GkpKR6mqJxerCnXQgJJK46JpTA0TgqcGPkkxN9/hL3VwJy1l9YZBtD5h\n9xD95eFvkRSp+HLSliEjuY8khbp1QEnJuKjcF5KSkQpyX0pKhozkvpTUI/SXh5FKarR9UYKT\n+iykNIuq55CLwqj7U8Z3puCUJ4EybFkgZT7dMM8RsHkgjE1vmTnr3/qE3WP0l4eR6ma4tarF\nSXsOOpnD+ZFMRTN7pNjH1h5aFT4psBcTwGewnwn0gk9Gcwg/xeewnwtk01uizuEJMw/R+oTd\nPPrLw0hlNIT1Rk7OC9ANxRHYIwI5+3J9eIGGvEXDAH6MAVdgrwj0g09FC4j+AjQdF6zp3vJ0\nAdr28Hlfafwv9JeH0auIrIVycl6EchajVJF0gYqay7kBnBgDZmBnBDrBJ6FFhJ7iMuxlgWx6\nS1LK6imDaH3C7gn6y8NIJXSNWaPn5L0E6VAchz0ukPflJqMduo/n7ESnlQZ3yfYsgB+3gGuw\n1wT6waekJWTAEvNY/rpANr3lKmW1wSBan7DLoL88/D1KMuoqJ+9lqGc5SiU96lTSx4OmlvQT\nXbOCamkZ+llGbtwSnB98WmJmotgm7E2BbHrLVsoqyyCaMHa+ZIYLilVu1DTD8dslNoVOegoM\nS0C9ddwKNqOsRLEdaDFfIbVrxanQQrZTYerY7k5XdvMrryPMlw32DkqYI75kBgunSrWhdenX\nOFNJCCX6A5eNftdHlzawAcRDNLE0U5dO0i77PzexKcdDNHEs2aOTjl5TstmsatbPiseK+vXY\nNLS5eV7OWQQ92/6sZTicR8tm0THJMrFaCUD1Bei9aN+zEQ7VqQu2BHIzfwm2L5nxdios5jMN\no6obX+fStEiTXiRrbmqDrB6Q5bgtKZ/7tMvNfguMt0LaheBL5kqhRG5TrIbfFlZvXvf21Aof\ntHobrLZxx+hhIornLK8WtO9KrV7V/Ob4DXTSq5A6LNn27xxyJfxfBvY9HzJOrZ7JkR+nK9rh\nmpXJLb3/eJh/8+HD0/Vna6vHG8vrmysrGTqoZWqKkekuv6VcqQuvhHekldK0WMncxI/B0bbZ\n/pH0t2ZoluKQCf74TLLM72dlzVjJSiVTIheXtJLu0AMVxej8/BTKOM0PTHi9VzLV/Fld8yJT\ndj+dLuR7ndUWHe/lLSbHBEanje0V7z6G2bNH/9XCj/8AwL51zwAAAnt4nI2RTU7DMBCFJz+l\nbRYIiVsg0UCAtiy7KFKl8qOkSF1ROY5BFrET2QMci8uw4RocAJCLLdqQBd7Y79l+83m8jAAg\ngDD0IOgAAHRuFxeHYwB/34jfGfYAwIMQ+puH2szQ3zQlEUw3rvemklYFlw8A8NnYu4xR1HGK\non5OWDZUcTqYXWWLyXx+PDoZnuXjo9F5ksTIBYkFkXGtWE0UWxEpKyTIK6lXumYUFRmkBUCw\na8In769vd2DGNh0tiXZ0vjW7N9fZbEnRyp21xBbUg/+j/tT1vmxCxyYEU0ldT5jridX3vGSm\neVb3kQumkYjaGv5LYZk+7C95Db6uVtSkNIr/ebzJiOx92F63UvdyLg2pdh1ikpaVUxFBVDx/\nQuackOvs1J0tK/rI1uTfnXJloQAARVt4nM2c/VPTSBjH01JeRA/x7dBDZClwwl1ptApiT/QU\nwcPxBQveoNOZTpoubcbmZZIU4eb+7/tNbjd5Qre9FNl2twcz8XlsGvr57rNPmn3yhL1RRVEG\nlFQqoQwMKuRn8OPu5tIqee0a+c+IoiSpTRA7HtiUcoHYiW0XO5qLkWZZtq/5hm156O1OTlFS\nSeZtQ4VKydeqxGMPTu1u7O22v9NzdRfvk4/9gf6PcoQfH9g74N9pO2qYHLVv1DFx/1HCn5Nd\n2DrI55dX2o4Y1Oua57UBnXy48i3c6CGJ4/Z3FX3Dp59F9yaP21ATYKfBnw7fN3D8XbbB4+/h\nxIZg0glDUGJCUPIcrPuu1kFB6s+NwosY/gHKBHYG/BmB/LEwg0VLM3EHmgTYNPhpgTSyRzMF\nkzUF/COC+TtMTq1uaF4HnATYWfBnBeIEwznQNpyJ0U6D1sz9FspfyDbGWNjEUbJ7M7s1w0P7\nDUuncUYQeg/5NYwg5nVkar5ewx6vkF9hiKnNgJ+RJsQu+5ph4QrVcGDYDa9+hHybsH/BVI2J\ndNt0yGwu1zGvEAZeipBYmEvFCvZ013BoYDpMEwqyBBY2YVDBZL5y1sk8WFh//3IjBnMI0j6y\nsInDZPdOnXLqWuBGH4ZRpXYK/ClZ6LcRMiyn4aM1VMV+iajQTG/B87GD1rjJR4CW2tvg35ZF\n/gaRn/QpI59enKNXJt4cs2/O1b7ORSeYEyfDrfQCqAtAwH8jS+kUQnbDFxWkUcAd7cf02ukq\nSPD+ypznuw3db/QWq4sgktod8HdkCZ5BiECXSGxcbAuJ1yVAvgSXhgnBl4Yt+B/OHK+6UXY1\n1yCeZztzJnarTLzoq8EI8EfrB5BI7QfwP8iSeyeMlonFpNYY8I7BginRXDCJZ98RFioiX6to\nvsYfrMsg8nI/UguCRdYsQoIVvim00oO1LSxYdMnm8UfqCiikdhv8bVlq02GkfO2wRBb7QqJ1\nFZivylqSsns/CYsWGQLbsk36BjIS/GG7BlKp/QT+J1mymbBZji4kbNeB+Xo/wrYnI2xkJLhV\n3wCl1O6BvydLdXKRm+9HYKI2CX5SIF8szVCx4WnVuHLTECBMgJWypLxx1iVlp9X4TbgGiyxs\nYimTzZxvoR0MFmmcxLdgEXkLLn6HQyuMOJZ0dCtYToY1YW7cMbAT4E8IxI3PkaLhY7MDTQLs\nTfBvCqTpbTLeit/OCd1PMF6RlZLQnVJlKFwrcyJPQn5MQmYPC87uWNSL78NlfRfJMglX3ZMQ\n/MuCJwBfskxCkCdbgy+MprfpOBm/nRM6plDW/2QZbRYrOLGnlGaFchz8cYHYsbgzmyRTSEXd\nojV3w6qi5jUb6lrFNbAPwH8gUAVfGjHlOOUh+A8F0vQ2USnVMmNhOyd0zBK+/2k0DFUkTuZp\nyJtpqHcNh1YYcyzr7Ck5FFWCutAxDjYHfk6gDr4sYu7/BzmdEJzTvc3TacjtyD48VzmOYLwi\n2/8sIvUtTmamX6J/WYROyaKgRNeFiHGwKviqQBF8KcTcZ1DugX9PIE1vk5RS3WcsbOeEjilz\n9T+FLjJ1V07uWUidWagSD4dWGPfZvozWaXPY0b7hYgRFuKMudFwHuwL+ikAdfJnE9Psoj8B/\nJJCmt7lKqVYZC9s5oZuD8Yrs/5NJpADMyT0P2TPfz0yab8+kd9tBLhn7Bna7zaV5yJ95mBvX\nBc8PvlyahzlA7WPwHwuk6W22Uqo8Y2ETRhcLM1rU3GrDxJYf13gY1S1/lpxAvQ3cXegYutuP\nVq3sbg0jR/NrtEnPpz70mjTbDpkbR7xSFmCQqc2CnxUoJRZmqHig1Ru4w8hShEWZ8T9z42nH\n21FZuD8R2YuC71MEmDfPPE07NBWr0DMVWdjEUbJ7J3zD1PL5vG47R6Wypn8p2xZeWORlZq7d\nT+6uibyl0sI8VrVLvl3SNb3WBep9wLvf2kcjB1WtGn6tUUZPllC65vuOl1dVV/uaDV9ueNil\nX6TkrJrVbVNN82rJAX8OpktC8GKuRcs6OYHZgZLorrWu1etHhrVvuyauNM9mKp1TS/hQM506\nXgpuhKsmuVjg18dUVZR18Ndl6UO0IFUySDhcwwx00hdUeIEfnimssuUOOfC3K4ZLmR2NlKfv\nLYTzK4NoyLgzZBlgl/vRXYvDrmCC/p8mEEFNt+jvp7wjsAKqqcXgY1kjMIJQ1WuUF3ghmVUe\n2xAtB/IWbXkhlzMkFywamazvHWSrf6UzvNSrQLraeutbDvUdSk3mUF3TMb12peQlx/YoPT86\nsxSQ38h3haLvG4fkgnEN7RY+bvDS5oEw39qUJ4c2hRD3aeY3gKKWeXJJDuADenahJ4yFhlsn\nIwrnSXLWzITPJSxmED50bJfOkfAFXj1PQMOTftwOmGk93wc62O8vbvo1IF7rR/d3V8+xdCJ/\nCrRP+/GcwZXmYyyB5T6JPAPCZ/1Iy9dtDwmwsyV90puoerajhr2JavPughoeFZwqF7ll/g7S\nqH0N/mtZMrfY7noujdF9yC5VPgdl1G6BvyVL5Sbbls6lMrhP1KXEFyCL2k3wN2VJ/Nzez80l\ns9lMq+on1f0uZTOrDeUz+J9lyS6090N3K5scHerllfsSJFJbAL8gS26SG28DkDYk9RG3frU2\nrLphfVkIln9puobSG65nHGC4EOOmZ9JGyldrLMxIsUIqC24j7tHaqFTwCixswoB6LNO9gihH\nNik42rE0F4pQsIirb0cl1j8kDViq7S9pwB+6YI95q/qmoxbIPwc5vLPiqoXs1rud3edv3tx/\n9GBlubx679HjXC4ovZBii6WecoWVLVQ6gCWjPc05Tb8hmTtY46CUPSozj15hC7uaTxYr5SPk\n2odHVWzl8qhiI/LpCFcMn+6oaVb77x+EHobgFyaj4avY+u6Rg6PQVMPfbrvN0WoJT/QTfQUr\nAsPTQjsSFfGZ5wfp36749i/qa+FzAAACe3icjZFNTsMwEIUnf7TNAiFxCyQaCNCWZRdFqlR+\nlBSpKyrHmSKL2IliF47FZdhwDQ4AyMUWbcgCb+z3bL/5PF6EAOCB7zvgBQAAwf386ngE4B5q\n8TvDAQA44ENv+1Cb6bvbpiAcZeN6dyJomTPxCACfjb3rSPEqShSvnmNMB3WU9Kc36Xw8m50O\nzwYX2ehkeBnHkWKcRJyIqKqxIjUuV0jUuka5pCWvSoFCyX6SA3j7Onz8/vr2AHrs0tGCSEvn\nGrNzd5tOF1QZubeRqgX16P+oP3WdL5MQmARvIqjtCdqeGL1iBermGd1TjKNUhFfGcF9yw/Rh\nfslp8HVkTXVKo/ifx+uM0NyH3XUrdTdjQpNK2yEUtCitColSNcvWCq3jM5me27NFSZ9wQ/4N\npLtlpgAAIwV4nM2aW1PbRhTH5SvEEC690XJpF8eAPQMWhUlIPc1DS2knk7R0HKeTB894Fnmx\nNeg2qxWBT9630F3pyBZGIhFeZeqZ5X+wJPt39pxd7R75XUVRlIJSLOaUQknhr9Lbzu97z/l7\nX/J/ZhUlLzTHdcnXovKI69rflDiYEnROMPMocZFmm45tEYu5ilLMR04tt/s9hgfcin5AsXPy\nrjN5pks1Ss75Vz8W/wmWAMHXKtjViatm+FXnukG4+a8SvEaHiHXZaj09mriipBnYdSeARl+u\nfAiauCR3M3lWl+lMfJc4mr+ZQM2BPgH7SXBe4eajbKWbj+HEh8EJwtALw9CbDMMdD4r/nLR/\njeEvCCbQTbA3JfLHwpS6FjZJAk0OtAp2VSJN1r1ZhGQt3k1cKfwJyYkNHbsJODnQzJKzMNGd\nuUpSp43H/i3Kb3hbiCg0eZTRo9udoe6ic8/SmG5bCELvIjYkkbksrQsr0LlCt8HezsqF/bpm\neC4j1EW6hUzbIJpnYIoswt7b9KKBzm3KPaTcJYo8l6R15ltwQOg+2PsSnYmFme/2iatR3RFh\nSUgSAfIdKDRpUH4qz3xqKpfax6e/ncRglmHQhwpNHmb06MY9E1c9NfoM9KrQDbA3skJfRzx3\nHY+hF2hAWI97gU23zpPaQS9Sk88CrdB1sNezIn+F+Kt6T89XGzWxLnFrMB7dmusQjVFs1MYn\n1Sh+v5va0UfgnM8B9qusHN1AyPaYrBhVALfyObLrVE6M4AP6qZ2dAweFnoJ9mpWz+UZqvnlg\nEpoHOy+RL5am3PVcPIhb9ZUB4TFoJnN7edplygJ0WKjQ5FKKaMStUUv+bJmSeBFm80VIxZlA\npRHHklZe+vN6sDVLjbsAugL2ikTc+DHS1RkxE2hyoJEVkTSa6ZJxEZZCoUL7n9AF54w1kwGd\nNFTKwV0rJfIyjI9lGNkzkkd3LOrcaXCDfcBgWYbgL99OBGm86QbLMgRZ6CrYqxJppktHQbUW\nUWjS6GJhKl1MB545LiLcuZmITvoi4xEyXcdBKXCkme5mmh2+BXcwGyJm+9vxcPk1KjbuTLFD\n/wo6WWgT7KZEV2Jhyt1LbHgkoWcFwtdZxr8w9XZ2FVYOoc5JXkH4mPOfnKYJVbc12FeECk0e\nZfToCtNN3Gq1NNu57p1h7eKMJ2S9kZY5sk+NVo+yYV4Y2D1m9zSsDR+AGtmujeatzOpz6kBn\nQ+8M/byHqkPGHLelqnyv3Aze5tUsqtkW4zNAk88FajWtL98Dv1AVbDUrX475BGb7njB8ZVu2\nqWvYMK51ixfoTNLHlmUzLOpdqsipPXKFTccge/7OVDWxbqX37wfwSegx2MdZ+bfe16lwz8G8\nKLBfD0K0i4TXqZMMASz6HDUcHNSeOPqdwoaEsgFP17Teb4LHQjHYOCvvD4XTwo+6Rw3uMYSP\nB3M3KMo1dhG5cmwqKj/BG2n9iTy7UQ7BPszKnweVQZPII49Jsq9TLY2roL6mha0BYG10vq/Z\nwOZTZ8EWIG1lVGa6hbfpWYZuXdSrfcxwVcxCmkdd/ZLwzu20356kpo88x4mOT2n0sTCz3T6/\nvVEv7hHIGkDsgEpfM0aPpl8r7kCUQ81LjnYszaMu3DXjNlnhTrSeUYcVJ37vAD9HiF7zp8pM\nR23zP5cH5M0zqrabL/960/nl9esfjw6fPT17vn/008GBf//nd3xLvWcqa7b7CWD58Mg4p03C\ncKROsgSeRq/a3UJ/EItQzPgW6+waUfvqekCsgxbq24ivTBDp60wcGGJr8vNL4vl9+FOKfNh9\nfVvrXDskDM0g+HSbjnvrVnjCVwNCo0gMzy3a2XAnGakxi18YfPgP5ihPIwAAAnZ4nI2R307C\nMBTGz/4gsAtj4luYyHQq4CUXGEmImg0TriRddyCNa7esRR/Ll/HG1/AB1BTbCHMX9Kb9vrbf\n+fV0HgCAB77vgNcCAGg9zm5OhwDusRZ/MxwBgAM+dLcPNZm+u20KwlHWrnfGghYZEysA+Krt\n3YaKl2GsePkSYdKvwrg3uUtmo+n0fHDRv0qHZ4PrKAoV4yTkRIRlhSWpcLFEotYVygVmK5S9\nOAPwDnXu6OPt/Qn02AWjOZEWzDVm++E+mcypMvJgI1UD5cn+lL91nW+T0DIJ3lhQ2w607TB6\nyXLUfTO6qxhHqQgvjeG+Zobp03yQU+Nry4rqlFrxf4/XGYG5D7vrRupOyoQmlbZDKGheWBUQ\npSqWrhVax2cyubRn84I+44b8B0HCY2MAACvDeJzNmv1zEkcYx5cD8qoxTS1Gm5gVsSEKwdAx\nsUz9oU2TNo6jHcSOzuAwy7HAjfc2e3t5+cv7mzHLPZdcyDG6YS8mM+f3kWXh893dh9uXez+D\nEEqjTCaF0lmEEMq+a+yWnyOU/hEhNIWQJjSFkDY/0AyaRgjl/mXUJYziLiXcZ9TDtNOjHkIZ\nLfKuiXqnxUkPIRStm2nsvG8Mv9NjOqNdhNI3xf8ERvDtA12GeHmo1qTH9K5hUoTQ/yj4Oy2i\n9n6t9uz5UI2sbhLPGwI6/XL0ObhEldTx8Lua3ODiu0SpdjyEmgK9D/H94H3p46+yZY+/hhPf\nA27QA62wB1qRHrgAn/lvp/5nDHpa4IAuQbykED0WJtu0iUVH0KRAlyFeVkiTYENmYIhmLg5X\nJegjhiQxDeKNwEmBJjYktaGWTM2MarSzjD9H+TNCaC6icKmjjJaWG33Dw13f1rnh2Bh6HX64\ncNdhuOsz3qcM+x6VdbIEbSy0DHFZoZNYmBvNDvV0ZrjC0IjmTUVSCS5lUFp4h/imQZCtb7/5\naycGcwLSJVS41GFGSxfjs70oTT0JDSp0EeLFpKg/YGzYrs/xC9yjvOUSRiyv6HHq4hc4H28p\nv1YQ90avYFN+4LBPXsFzqc4ZMQuD8gIjByVp21NgVegHiD8kZXsZY8fnI3xLo08D7nRSd5do\n6S7G+NI9A9U68t0zA76E7kK8m5THlxiLG3nLc3ymU8mxKWp6haCqvM9Z8Cb0JcQvk/K5Bz45\nYT3KL+UzqCpt8wZYE7oH8V5SNrU1ab6bwCRUg1hTyBdLM9H0PdKLmz9OAMIcaCI3vBvjznpu\nQYeGCpdaStEbcbPd7OA+Ikk8D/e5eci4yUCVEceS5vYGdzzxS4mNLl61iO0Tc/US6HOgDyF+\nqBA9Pl+aBqfWCJoUaAHigkKa8QameM+jiMJ1Teh+gPYKNZHkHpU2E8E8RBJ5AXJlAbJ8UnGm\nx6LOvgmmTMHGiDTvLdC7EN9VyCuXLAvQyULvQXxPIc14w3EBFlihwnVN6GBgnOqVJstsZFYo\nyX0bsuQ2JPpkoMq4Y3nzr4lFsdPFvE8xTGZP9zV1x/Qt+xI2wuvZmSqzIZdIt2EACN2EeFMh\nzXhDVVBtRRSua0L3E7RXqN8hkYK1gyR3DpIn990SCVZL4yVSDgZG7lokUg4GQO5aJlIOkieX\nUCLFwsw0Cev5FrV53Db0LWikOwlnz3gNtwj7WYtXsQG50uhT7BLex9wZ5Em42RNsRMuy34VW\nFboC8YpC9liYieY+MX06oilTkflaIh2eHnvL+T4sZEOdVbygHWDOffO4HHGmtAIbeaHCpY4y\nWnqHGxap1Wq64x612kT/1HZsWlyTZcbQ6TiS9XeSYp7rOS3utHSi9y+B+gDwHkR2jBI7far0\nDN732/j3Ms73OXe9WqXCyMF68LLvUaY7Nqc2X9cdq5KX9ZIHfqEViCtJedlm1HUGTjg5dGzH\nMnRimkeG3XWYRTvEth1OxJlURYypMj0klmvS8mDPu2IRw5b39xA8Cd2GeDspf0sdgwl7LvE4\nfVoMuqiEhWvpQRbZ5YkeEiYD/nGwudfaEPBKz4kKlrch6/wRuBX6EeKPSTlvB86r6p2HL8na\n/wUsC21D3E7K/pbwLLwUfWbiF+HI7RishGFMrJUwPXQdJs4vwpdkPa2Cj1WYXaYUzzAlPVUv\neqpKeyqCj+JVeJI9fR4FvQaga1dx+Lx5dvhsGh4v5i1vI382iko4H+ZIfox+eAw2Hl/F2kqT\nxnsCSE8SOuk6h/fAt03D/lTMdwgneXHn0X3mGfvijLVRf7cjTV8C4tL5aY8y+liYqWbnZErD\n/LhHU8KlSvisjPJ1QrRUfn1Qhl4OVVPc27E0002YKcWtpMOHuNYTarDM0HOb8FhltM4/FW65\nlfrJP/tV+naTVerre6/fNv549Wpj69fNZ+3nT7d+q1YHc76TWZ5dif+tW693RjBpYcnZcLYo\nJ5E9snkwGa1VeoT/pjZlhJ8sodtHmDmHRz1qV2u44+CTiejJstrgoqBP7OHPzw4O5+EDtbDl\nOo7eOHJp2Cu94NMddtZQ53om/Aun3Uhhz5yjnQp3CiIH7+JJyc9fAIviboUAAAJ3eJyNkU1O\nwzAQhSc/pW0WCIlbINFAgLYsuyhSpApQUqSuqBxniixiJ4pdOBaXYcM1OAAgF1u0IYt6Y79n\n+83n8SIAAA983wGvAwDQeZjfnI4B3GMt/mY4AgAHfOhvH2ozfXfbFISjbFzvTQUtcyaeAOCr\nsReHildhonj1EmE6rMNkEN+m88lsdj66GF5l47PRdRSFinESciLCqsaK1LhcIVHrGuVSkaxA\nOUhyAO9QB08+3t4fQY9dMloQaclcY3bv79J4QZWRBxupWjBP9sf8ret8m4SOSfCmgtp+oO2H\n0StWoG6c0X3FOEpFeGUM9zU3TJ/mh5wGX1fWVKc0iv97vM4IzH3YXbdS9zImNKm0HUJBi9Kq\ngChVs2yt0Do+k+mlPVuU9Bk35D8h5WPXAAAvZ3ic1Zr9U9NIHMbT0vKmKMohVzk0YtWiQD3O\n1/pyox46jAxorTf+0JlOSJaSsUk6eeGEf/x+k9ttnoW0k4ws2SDXmeX5kpf28+zuN9lk98u4\noihDSqGQU4aKCv0UPzfeLj2h26boP6OKkmeaozrZ04IyRvXKB5d0NZeo20TzA5d4qq9tdYii\nFPKRo4brRsvX2jSKnltorH5pDB7pubpLtumvTrD/GEb46z2dQzw3cNYIPWvbZD+r/KuEn8Nd\nxN6t1R4+HTijqHc0zxsAOvxx5XtY2Cm5g8Gjmr7pc4v5gwHUHPQa4mvhcUMHP2QrHvwIJ7YF\nZrphC7R4C7R6LeAl0Bf+Xq2/jmEfYjzQOcRzEtljYYpNW7NIAk0O+n+ryQI6aQHso5LZEzql\n1jE1LwEnB72O+LpEnF5V5geqMjeeVGlHOd9HqdJyIaIo8iije9XGjump24Gt+6Zjq2h27/Aa\nJgp/A9V6AwZyoUqDj4U53zSIp7tml3lIqFEGMg9FkQbVa/ex47Z7sf5m86/VGMxhZAhXFHmY\n0b2lhAyvCGOPoEaZlhCXssLeUI9ury/UNvFb1IRmeRXPJ126ZT7B1vxCmd0VvTLfXna1fxaF\nvY7CH9MNxBtZef2oqk7gdwM/pVMcaIjbHYPFHg3ij1nZfa+q7BbY0gwj0IU9s1O9cniuuNFx\nmGP6HvH7rIxuwuhJO3JolW8XN3sOBpluIt7Myuw6zLonbFHXT+H0PNwxXUe8npXTNTi19k/m\nlO7XA9dzXGGbE7DGdA3xWlY28wvCfBfAxDSPOC+RL5ZmuBl4WjtuRD0MhIvQTIYDl9MOAyfR\nb7miyKVkrRE3/h/lnVQQ+hLGAZfQ2iOSx66xsEsfNH9H9R3V36EP3/Tep+qO7WumbdrtcBu/\n0Bqar53AES8vjlSao/hMapo+sRJoctCXiF9KpEnXZRnVnxFFOSN0l1FfXDNJ+6SEGg5HcoLI\nU0ihKeT/iORrwDHTicbkW9eht3C2ySJumxi9XKJ7TuDoIvQZ4mcSHYmlE+z39Dni5xJp0nXY\nKVxyuL6QfOlJR/cL6ovrqabTuciTgiD3NPJouv92JY07lnd5g/KqznYvgfCAozudwLJV0059\nj5pGMzB9hfiVREtiSTWNzsD0NeLXEmnSdVtG9SaiKGeE7grqi+upJtVE31OpIPkMUmmm/7Yl\njTyW+H40rQ4zSF5izaApmK4iXpVoSiyxZtAhmL5F/FYiTbquy6jeRRTljND9ivrieqqJNYI3\nIILMJeXopeoE4gmJzLGsj6Ip5RKf2L2JAd+kW+UlVgnHlZBYkz81sSJvrc9gYpWQTKUzmVhX\nUV9cf0JiWfuCzLNIptnTTKx70cSyqvsSs2kWxzGtIa5J9COWTbPoBbN4xMtJfsRL118Z1fOI\nokiji4UZb2puO7DotTRuWjo8UlF+yziF0lVcZMFJ9rOTdxo0JbqR9xB8DovnSbi2RtRDZP2E\ncgfxHYkeYmGGm7taJyAJVZqLrEXIpOGHUs9L38RcEVcUuZhjx+6fCWtNypi+44oijzK6d4aO\nh7RaraY73b3WlqZ/3XJsUlkQZb6FRr/VP7rPhvlC22n5TkvX9J0ToN4G3u3++ZxsUMfZ9Bl7\n9VgRxYxkdXRSNxvMB6oauB3M9hlkWws6bNbP3/EqC2W6xyuTb5rV7RxntjbJUAUmmD5A/CAr\nQ3QKHi96U6+tEPW5AG8LpzEDnxfu/neBdDejKcw+vKtJa3OEse8B9V7/SD0b7BuB3THtr5V5\nNoSdX1TD+Wxzl9C+06h/XhWmXwTxYv/CMmn0sTCjTYPOHrpB3EKyMiCWoNJv2Olu1EvonFzz\nkjtpLM1Yk1/kYohuopKWM6qwwsDSaqx8jp6zVvWtbrVO/+yukE+P3Gp9eW3jU+PV+vrvj/94\n9HDryf3HT1dWquyGXrU0u5qQe8t1IwEqz/cc9WeLsGe4w0fXSbiMnrV4S31HbOJqPh3Ubu2p\nrvNtr03slZpqOKrt+CoxTJ/t2NHswe8v9haM4AvzvOoMR2/sdQlvlnb47Y57VFN9TcM/VTSL\nIrFp+mhH+dg9slKFLWb+/h+6lMRDAAACdHicjZHLTgIxGIXPXJDLwpj4FiYyigq4ZAEJCVHD\nYMJK0inVNE47k7bqY/kybnwNH0BNsY0wzsJu2nPanv/r32UHQIQ4DhA1AKBxu5gcD4Hw0Irf\nGQcAAsRobx+qM+Nw25REMF253hpLWqy5fADwWdmbJEaUydyI8rnH0r5K5t3pVboYzWang7P+\nRTY8GVz2eonhgiSCyKRUrCSKrXKeKaI40ytluvM1EO3b1NH769sd7NjFojnRHit0ZvPmOp0u\nqXFybyNNDePR/xl/6gZfLqHhEqKxpL4ZzDfD6XueM9s1p9uGC6YNEaUzwpe1Y/pw3xNU+Jpa\nUZtSKf7n8Taj4+5jd11L3cq4tKTad4hJmhdedYgximdPhnkn5jo992fzgj6yDfk3ul5inQAA\nSnV4nM1c+1fTSBhNy6PIrsquCrvIsmO3YoHaQllf9f1ARFH2ICpqz+kJ7VBybJOeJOXIX76/\n6c40NyXtpquTTLLlnOF+NA/unfnuZDKZdG9SUZQRZXQ0oYyMKexn7M3u06s32Wfn2B8TipLk\nmGA41cFR5RTDzF8mbakmJQ1t31RNjVrEOCAmtalua4ZObK1JLUUZTXqOGd+pVWy1ziLvmUZ3\n1/d2+/e0zKpJDxiH0/wvTsrh0sFlxMt9R6XYUQdag7Lwb8X56W6i+lGpdH2l74ixakO1rD5C\n3X+ufHEKPyTxtX+vsq3Z/H/xrcmvfVQTwBzinLPfyNdvchv7+i06vu1xvuW0R6XbHhXTHkB9\n9O36ziMf4iOcDHAW8axE4r5kxsq62qQD2CSAFxFflMgmsmocRXqOgviEZOID0lFtaKo1gE4C\nOId4TiKdTj2O9NVjYnJQpZ24vYflNVbOeBBFHkvv1tXdQ80iB2292umr0OZWf/d10reJqrmO\neua4ing1KjUXbIPsU9K2aI0cGCZpqDY1RRnfAEuOFxBfkMjYl8yP5Rq1qqbW4nU+ICk4kZtA\nFGmkOqk79b2pO7bzePvJug/NcZjcRRR5NL1bp/16qKww5xSqk+M04umoOO8R0qwfVOjnFrlL\n6tSuMAFq08paNuWfpP0kpRcz/FpuZbofZmzabBmm2siwE2XYCXPCqieglOMe4r2oVL91VLOe\nRopozQqm+RR0dighfhuV5veEcMJym7pqHYnLnoRUju8Rv49K9jvIltjWgUT/AKEc3yF+F5Xo\nj4QYbbvVtpkYObKxf01c+I8Qy/Ej4o9RCf/QFW4Z4dKcHd8VnTFtcd2noZXjB8QfotK9SUjV\naFS0T0Ka+fC+03VlNL16qH2ix+Iyz0Aax03Em1HJ3HZkCib1iczuKLLCR5HiYs9CIMdtxNtR\nid1wxFrNgGKtJk9pcZHOTg5uIN6ISuQWIZxxxU3AAFqD5+5PUMdxC/FWVEqfQWmg5DVtdE0B\nVP4MZRyfIX4WtUon+QIoDZq156DsXBwq7xPS1jXhqys/hrelsLjzEMTxPuL7UYlLLgrz89yW\nKknESYn8fNmMl9uWWvebnxoHBffuKZKb0yth51VmMChyEUUuy+SJNXrYpnDXJ8j5F9yX/oKB\nTcpBaZx9uS6/3HjKLoW6rWq6ptcJo01Ndv1mHzT857WF9EwBS4hLEvX426issXH1ADYJ4G3E\ntyWyCZevnNUdD6IMCbtfUV8uRuL5QW4ad2YTBCl7JvI75k9J7gB8qRb7zKTpxNIaWtXgc601\nrWqzqcpwnprFAJnjA8QPJMoS89QscoHjQ8QPJbIJl7Wc1SMPogwJO8+znfg9NeHOVgmSnoOT\n5nBTmpL8uMKXbO4pG7VGdI2awx3KHPrcnyT3u2J+8jynUu4iviuRTbiM5azueRBlSNj9hvpy\nMVY/pTANKsh5Hhaaj3PEt9ZvJ/lXqXnsN49ed0pyzyvmqnlkA8fHiB9LZBMubzmrJx5EGRJ2\nv6O+XIzVVae68+yCrAm8RDBCSjkojbUv29ntDts+6xBnZY4w/Z+By4iXJdIXsw9Bs5Oo1vWE\nSlDO6qoHUYaE3SXUl4ux2mfy5GmNIO00PJPG/ikHpdH2pTsN/7Qs2q4ZhD9jCuCdNM7OMYs4\nK5G6mHfSaHOOi4gXJbIJl52c1ZIHUYaE3R+oLxdj9c6488RPkHIGXsnEOelw8RWb2+erUO1D\nSjb1x4ebL9ijHjaoY/MmAfifBS4ilpmuYubJoNEzSM3EUKVnBhdnF5clX6TDsbuM+nIxfvMI\nD9oWYJiFOM1zyWuevpFbIAstwDYcC4gLElWIWWgBTc9xBfGKRDbhknQBy0RdXI1yuagwuyuo\nLxfjt5DVFKSchW2ycVroV6+FXr/c3Fp/Hcw6Wdgli0o/66A09mLWyaLJs72xNDbhkjOLK7SL\ni5Kv1OHYeYa68VvndM+aF0HmS3DNEk6eclAac1/Gc4NGcAHuf5bwaH4J/em05D5VzESeYZtS\nRFyUyCZcmnJWax5EGRJ2y6gvF2M1UQrLqQQ552CZXJwT2un/HsMFMFEO++Vgoqn/1USeSbch\nNFEOxskNpYmuor5cjNVEP3hW6wnyzsM8eTyTTDkojbf/DLb/SC6AgfJYT5dH1Z+XPCsrZqA8\nGj7fG0tjEy5F87hTdLEg+Y4xHLsC6svFWA2UwkJQQc4rMMxKnFeh0hvG1f8qlCcv25bNX1ZM\nW5Q9eK1ZaWKYJN3U9LZNrXQAeVPATcQy3woQM5dnWkF5jvi5RDbh0pezeuFBFGnsfMlMllWz\n3ubrU/xeTJ5BJbkvyEblqHAVV8TrgsU4Xu5c2WWeaan2IbGNjn/cl4P6h3NYpy8qZg3VvBbR\nNJgvmfHykdpo0wF1yyn8GWUGfPer6QMXnt/CG3UuosilOfbdiTrgawdKeNnRRRR5LL1bZ1gW\nqqVSqWq0jiv7avXTvqHT7KIoZ8/64253MBMV5zN1o2IblapaPQxA9Q7o3el9HS0aqjO+L3kL\nc/YsnYu+ei+19Yamf8qma6qtpnOsx6q2TUs7ouQu2d15sy7M/h4Y3+tdPyCNvS+ZiXLN0G2z\n7fd1BCWQcF+Okd5dheum7mMA6SKKNHq+bE6V6We12ere2P2r40xggXYUFcb3Otm6U8OX/3iP\neVqwm63CDvt1VKSvr5uFnfzmq9e7D7e2Vm+sXb+2f3Plxq1iscC7s0JT1Qt+xsvv1AYwSrpb\nTpK5SW3VM5qfgkTvUbnLZIPq1FT5AsP9Y2Ian4/rVC+WSM0gumETWmMjaLbhUNX7zz/WeYEN\nJ0y69VYzqrvHLeq2Sd05u2GeVFNPu7g/7lJ1RWK79LCdcMcvnleV+ff5fPkHbSQEfQAAAnx4\nnI2R3U7DIBzF//1yWy+MiW9h4qpVt3m5i5ksWaZpZ7IrG0rREAslgPpYvow3voYPoIYJcau9\nsDflHODw47COASCAMPQgiAAAotvV1fEEwD804vcPBwDgQQiD7UVdZuhvmxwxolrb+zOOm4ry\nBwD4bM0tE81EkmkmnlOSj2SSDefLfDVdLE7HZ6OLcnIyvkzTRFOGEoZ4IiQRSJKipqVEkhJV\nqEYUuG4UqYZZBRDsm/Tp++vbHZhvFw/XSDk835q9m+t8vsbayr2N1B2sR/9n/TnX+7IJkU0I\nZhy7Uogrxep7WhPTntUDTRlRGjFhDf+lskwf9pm8Fl9PSWxSWof/ubzJiO1+2B13UvdLyg2p\ncg0Rbgq3KkZaS1o+aeKckKr83K2tG/xINuTfOGFl6gAAHOx4nM2ZW2/TSBTHHefaFALZW6HX\nachKrVTI0t0WNsBDl2a3SBVdpQHxECma2NPUIr7IHhfyyXmjzMTHiWPZBSdjhKXJ/yRjO79z\nZs54ZvyuLElSVsrlMlI2L7Ej/6bz78On7Lef2ZeSJMlcM0zvjjUnLTH963+bWNgmaKj1bWxr\nxEHmBXKo7SrUZT+b9gAbmqMjC2u2g16enp23jiUpJwfuUWirPYoHzAreOddpveuEz3RsxSYX\njOk2/8YhPbaxPgP7WeiqIrvqQhsSZn6SvGNSRYyrZvPwceiKvDLEjhMCmvy59Nkr/JLMdfis\nLtUo/y9eK1+HUDOgz8F+7p2Xvf4qW/76aziR7bNuee3Tm7RPzzGtnjI0HaLGuJB722r/E+FA\nlkOB1sCuCXQgEibfNbBOYmgyoA/AfiCQJvVw5qC75sCBkmAHYronHmrYicHJgNbBrgvEGcdT\nDsUzU44L2jT7Zyh/Y6USUCjiKIO1B/MMbkk9WoFYcz0A+0CgR5Ewt7oqcRRbs6hmGjFh5iD3\nQKEIgxp3huK3doZ8++XZcSsCswBp4ysUcZjB2s2bcn8nMXsRwsp1E+zNtNjXEdIMy6XoBRoQ\n2mNuYN3ZcSix0IvE5CWg5boO9npa5MeIHbWbQl/brfOnu1OfVNZZZd3GH+reCXuJPVwCr8YA\nYB+n5eEGQqZLRTVOGXC5boC9kRb6ybyNA5eo0EKJvVwGz7iegH2SlpfybmK+W8DEVQZbFsgX\nSVPoug4eRM2VCoBwGzSV4byw6LO9AgHzFYpYSt4aUTO7/Hh8TEh8B8bvO9AVi54KI44kLb8a\nj+TeiiYxbgV0BewVgbjROdLVKNFjaDKg/jzjnkCaxTojp7ofUCg/CJ13zlRTSei4VCl4j6uE\nyFXIjypkdlFwdkeiLp95T9Y5kqUKjV+d7QjCeJMlSxUamesq2KsCaRbrjpxqLaBQhNFFwpS7\n2B64OjFo1HK2AkH6KeUMWSxwsLU20VQXMEedS8KWqvQSURNRbsMsbLqkfRha0nqzttGcS9tf\nIO5cj8A+EuhdJEyhe4WHLokJNkf4Nc0ukV14UXsfJhO+LgueVIwx89/cc2N2r1ZhjeErFHGU\nwdoVqum42WwqpjXq9bHyvm8aZGc3KfMaNPra7LZLOsyVgdmjZk/ByuUcqIF19WQoS22fa+vG\nnY3E7IFlp7QF9lZa7NuuMdSM9zs1FVNc20M2UVzb0a4IW0132m9aiekDezHSNtjbAukjYUpd\n1TTYIBy1F7c6DWE6w9Ziw9UWzBR9lb/HUnepSz5i3WIbCzEDKA8SSilgudDLK3i3FLzmdYPq\nVqPNPq72yfmh3Wg/evX6vHN0evr4yZ+HB/2nfzz5e3+/wYe1ho6Nxk0J+KitxpDJfs20U+uE\n4sD0/S64Grxq73f0HzGIjSl78vdHyDY/jgbE2G8i1USGSRFRNcorLrERvn+ev4zxX4zJfvxU\nU+mMLOK3zcC7u2lPwzXTPv7hZ5cksH1maEv+BCew9cFfF33+Ak4xa/gAAAJ7eJyNkUtOwzAU\nRV9+tM0AIbELJBoI0JZhB0WqVD5KitQRkeMYZBE7lm1gWWyGCdtgAYBcbNGGDPDEvtf2fcfP\nqxgAAghDD4IIACC6XV4cTgD8fSN+Z9gDAA9CGGwe6jJDf9PkiBHVut6fcdxUlD8AwGdr7zLR\nTCSZZuI5JflIJtlwfpUvp4vF8fhkdFZOjsbnaZpoylDCEE+EJAJJUtS0lEhSogrViIJgVpXD\nrAIIdk349P317Q7M2KbDNVKOzrdm7+Y6n6+wtnJnLXUH6sH/UX/qel82IbIJwYxj1xPiemL1\nPa2JaZ7VA00ZURoxYQ3/pbJMH/aXvBZfT0lsUlrF/zzeZMT2PmyvO6n7JeWGVLkOEY7rxqkY\naS1p+aSJc0Kq8lN3tm7wI1mTfwNCoWVqAAAcPniczZn9b9NGGMcd56VpSkvGXtrStbuGTGql\nQrbQFRY0qQPSCQnGFMKEqkjRxb6mFvGL7HMhfzm/Ue7sx4lj2QUn54Kly/dJznY+99w95+fO\nbyqSJOWlQiEn5YsSO4qvuyd3H7LfvmdfypIkc80xrXpakJaZ3v/PJha2CRppAxvbGnGQeYYc\narsKddnPpj3EhuboyMKa7aD2kxdPH0tSQQ7dotRR+xQPmRW+caHbftONnunYik3OGNIq/8YZ\nfTRPW2C3IlctsavOtBFh5gfJPyZVxLhotY6akSuKygg7TgRo8ufSR7/wS3KX0bN6VKP8v3it\nfBlBzYE+AvuRf17+8rNsxcvP4cR2z5bld09/0j19x7T6RNHVQUILCv+3O49j+POcCXQX7F2B\n/LEwxZ6BdZJAkwOtgV0TSJO1NwswWAvAXxbMnzA48UjDTgJODvQO2HcE4njulCPuzFWSnDaN\n/RnKH1lZCykUcZTh2sM5Zra0DfoJXM31EOxDgQ2KhbnRU4mj2JpFNdNI8DIHWQeFIgzKGwul\nLx0Lxc6Tl0/bMZgliJpAoYjDDNduXxH5e6nRl8CrXLfB3s4K/RQhzbBciv5CQ0L7rBVYd/Yc\nSiz2S+2KdtX26/yx6dQndXVWV7fxu7pXf5C63WVoK9dTsE+zavc2QqZLExqeGn0ZcJevo8tO\nEDvm6Bq4QvX7J3UbK9Auridgn2TVRnk/Nd8KMHGVwZYF8sXSlHqug4dxKUgJEG6Aft15MukR\nswqEgUIRS8l7Iy5hKnozT0riNZgZ16Crl3wVRhxLWnnmzZH+OiE1blDWpyoMNz5GeholegJN\nDnQD7A2BNIsNRk61GVIo3wjdTfBXoJkEdFKolPyHVUrkKsRHFSJ7SXB0x6KuvPSfq3MESxXc\nW4XOvyl4AKQLFv8kX2+DfVsgzWLDkZ+zFVIowuhiYSo9bA9dnRg0bpm4Ck76LuMIWcxxtyDF\nvHUdK4Pj7jlhS0B6jqiJKLchCZsuFe9Glop+0jaeb8kIAenpMdjHAhsXC1PqXeCRSxJ8zRF+\nyHJE5BdeLG5ALhHoiuCcwsMsfvHATdgU2oTkO1Ao4ijDtetU03Gr1VJMa9wfYOXtwDTI3n5a\n5tC8OdkxEJn+zDCvDc0+NfsKVs7nQN0CvK3ZPCkb1J2rdgxSo/8MuFx3wN7JCn3XNUaa8Xav\npmKKawfIJoprO9oFYSvpbud1OzV9aMHsbR3nrmPruNxTTYNNwXFbXJtTF2Yzay02W+1Anhio\nfB0L3eUeeY91i+0qJMyf3Em/ZOSwQuSFELyvCV/zokF1q9FhHxdN8urIbnTuPfv3Vffv589/\nf3D/6I/Bw98e/NlsNvis1tCx0bgi/u511AQwOaiZjmmdUBzK3avQ0vBVB7+if4hBbEzZY38w\nRrb5fjwkRrOFVBMZJkVE1SivOMdG9P5F/oIjeNckB+5TTaU7tkjQNUP/7qY99dZM9wQHgq6R\nBHbPDG05yG5CySB/BfPxE0hNV+MAAAJ6eJyNkd9OwyAUxk//zG29MCa+hYmrVt3m5S5msqRR\n085kVzaUohILJYD6WL6MN76GD6CGCXGrvZAb+D7gOz8OqwgAAghDD4IeAEDvZnlxOAXw9434\nnWEPADwIYbh5qMsM/U2TI0ZU6/pgznFTUX4PAJ+tvTTWTMSZZuI5IflYxtlocZkvZ2l6PDkZ\nn5XTo8l5ksSaMhQzxGMhiUCSFDUtJZKUqEI1onhgVTnKKoBg12TP3l/fbsGMbThcI+XgfGv2\nr6/yxQprK3fWUneQHvyf9Keu92UTejYhmHPsWkJcS6y+ozUxvbN6qCkjSiMmrOG/VJbpw36S\n1+LrK4lNSqv4n8ebjMjeh+11J/WgpNyQKtchwnHdOBUhrSUtnzRxTkhVfurO1g1+JGvyb3/6\nZQkAABw4eJzNmWtv2zYUhmX5EsdpUq/bmnvHGB6QAGm9uVjaGBiCdc62Atk6uO7gDwIMWmJs\nodYFEpXVv3zfmpHSkS0LUlvVVFsB9HtsSvJzDskjkhrUJEkqSqVSQSqWJXaUX/V/e/iU/fY1\n+1KVJJlrgWnd15K0zrT9t0Ns7BA01UcOdnTiIusaudTxVOqxny1njE3dNZCNdcdFf/zZfSZJ\nJTlyh0pPG1I8Zlb0vqX+5aAfP9N1VIdcM6JN/o0jBmS+noN9HrtqjV11rU8JM/+TgmNeRcyb\nTufsceyKsjrFrhsDmv+59DYo/JLCbfwsheqU/xevlW9jqAXQDtid4Lzi7XvZyrfvw0lsnT07\naJ3hvHWGrmUPJ4Y2SnGg9M9l71kCfpEjgSKwkUD8RJiyYmKDpNAUQI/APhJIk3MwS9BVS4Bf\nFYyf0jXxVMduCk4BtAF2QyCOH005Fs1CLS1oi5G/RPktK1sRhSKOMlp73p/oLrr2TJXqlomg\n5V1EJ8TPYYsM93A5w2X16j7E+z5ksMIigwnxKhHmjqIRV3V0mzuXEmoOsg0KRRiU3yEqH9oh\nyr1fX3QvEzArMHRChSIOM1p7kD76jzOTr0FQuR6AfZAX+QAh3bQ9in5GY0KHzAlsuMcuJTb7\npZHuVuOkyZ+bbnNe1WRVTQf/2+TVp5m9roKnXAdgD/Ly+hAhy6MpbmdGXwdc/85gH+aF3kXs\nyN4wcIHmt05mD2vgFdcu2N28PJRPMvNtABNXGWxZIF8iTUXxXDxOmoFUAOEO6OdNkWlPl00g\nDBWKWEreGknzpbKfdTISb0FW3IKmXgtUGHEiae25nx+DVUJm3LBsL1QYbvIYUXRKjBSaAugO\n2DsCaVbrjJxqN6JQvhC6uxCvUHMZ0GlDpRI8qjIi12F81GFkrwke3YmoGy+Cp+pHDJY6hLcO\njX9XcAfINliCkwLdA3tPIM1q3ZGfsx9RKMLoEmFqCnbGnkFMmrRM3IQgfZXzCFktcPdggnnv\nUywKLvpsPWhjOkHU8teG4RwsdX0I+2QzfxWZ1TcYj75egH0h0LdEmIpyg6ceSQk1R/gmzw5R\nXHmZuANTiVA3BE8pfMzyB/fblD2hXZh7hwpFHGW0dpvqBu50Oqplz4YjrL4eWSY5PsnKHEmb\n870CkbOfJeatsTWk1lDF6uQjUPcBb395mpQP6uE79goyk0f2B/Jfeh555lQ3Xx83NExx4xQ5\nRPUcV78hbBXd7726zEwfIc5lpzYRpqpolskScNLe1i5APAAVnrRWS1YPYJYYqvwplrnrCnmD\nDZttKaSkTx6k73IKWCn2Mgje1USvuWpRw2712MdNm7w8c1q9R8//etn/5erqxyePz34aPf3h\nyXm73eJJrWVgs5U+/B71tBQuOaxZdGmDUByZuNfB0ehVp9+j34lJHEzZM380Q471ZjYmZruD\nNAuZFkVE0ymvmGAzfv8yf7kRvmaSw+hpltqf2SRsmXFwd8tZBGupdcIDQctIAltnibYaTm0i\nM0H++uXt/zZSVkIAAAJ7eJyNkUtOwzAURV8+pW0GCIldINFAgLYMOyhSpfJRUqSOiBzHIIvY\nsWwXlsVmmLANFgDIxRZtyKCe2Pfavu/4eRkBQABh6EHQAQDo3C+ujscA/qERfzMcAIAHIfQ3\nD7WZob9pcsSIalzvTTmuS8qfAOCrsXcdaybiVDPxkpBsKON0MLvJFpP5/HR0NrwoxiejyySJ\nNWUoZojHQhKBJMkrWkgkKVG5qkVe1XqlBmkJEOyb8MnH2/sDmLFNhyukHJ1vze7dbTZbYm3l\n3lrqFtSj3VF/63rfNqFjE4Ipx64nxPXE6kdaEdM8q/uaMqI0YsIa/mtpmT7tL3kNvq6S2KQ0\niv97vMmI7H3YXrdS9wrKDalyHSIcV7VTEdJa0mKliXNCqrJzd7aq8TNZk/8ApIBlpgAAHER4\nnM2Z/U/bRhjHbeeFEArNuq1AGexIMwkk2mx0o12qSewFpkpsTCFMFYoUXewjsRq/yD6z5i/f\nb6V39uPEsWxakzOtpcv3Sc52Ps9zd4/vzq+rkiQVpGJRlgoliR2l887xkxfsty/Zl4okKVxl\npjVfi9Ii02f/OMTGDkEjve9gRycusi6RSx1PpR772XIG2NRdA9lYd1x0cto5P5OkohK5Rbmt\n9SgeMCt642Ln6HUnfqbrqA65ZEjL/BtnDNB8bYHdil21wK661EeEmf9LwTGpIuZVq3XwY+yK\nkjrCrhsDmvy59C4o/BL5On5Wl+qU/xevVa5jqDLoS7BfBucVrj/IVrr+EE5i82zYQfP0Js3T\ncy27N7Ko56Z4UPz3qP1bAn+BM4Fug70tkD8RptQ1sUFSaGTQOth1gTR5R7MInbUI/BXB/Cmd\nE4907KbgyKCPwX4sEMcPpxILp1xNC9p07M9Qfs3KSkShiKOM1rY6Q91Fl56pUt0yETS9i+iQ\nBGlsmuWezGa5rG49hIA/hCQmT5OYELcSYe51NeKqjm5z71JizUFWQaEIg/J7RPlje0Sp/fvp\nH0cJmGUYO6FCEYcZrd28YfzvZEZfgKhy3QR7My/0C4R00/Yo+gUNCO0xL7Dh7riU2OyX+g1+\n1Xcb/OHpNiZ1DVbXcPB/Db9+L7PfFfCV6wXYF3n5vYmQ5dEUxzOjLwLu4l002TFixy2aBq7Q\ngvbJ7GMV/OJ6DPZxXj4qu5n5loCJqwK2IpAvkabc9Vw8SJqIlAHhHuinzZNpj5hlIAwVilhK\n3hpJ06aSn3kyEq9AZlyBpl4IVBhxImn1lZ8jg9VCZtywrE5VGG7yGOnqlBgpNDLoGthrAmnm\n64ycaj2iUD4TuvsQr1BzGdBpQ6UcPKwyItdgfNRgZC8IHt2JqEunwXP1FoOlBuGtQePfF9wB\nsg2W4KRAH4H9SCDNfN2Rn7MRUSjC6BJhql3sDDyDmDRpsbgMQfoi5xEyX+AewBTzwV2sDA47\nbFVoYzpE1PJXiOEkLHWRCBtm42AtmdU5GJC+HoJ9KNC5RJhy9wqPPJISa47wVZ49ojD3YnEN\n5hKhLgmeU/iYpY/uuClbQ+sw+Q4VijjKaO0q1Q3carVUyx73+lh907dMsrOblTmSNyc7BiKn\nPzPMKwOrR62eitXhLVA3AG9jdp6UD+rWTTsGmdG/AVyuW2Bv5YW+7Zkj3XyzU9cwxfU95BDV\nc1z9irCVdKd9fpSZPrJg9jeQ5bvYQK50NctkKThpi2t9GsJ8stZ82WoL5omhKnex0F3skrfY\nsNmuQkr+5EH6NqeAFWOvheCtTfSav5rUsJtt9nG1T84OnGb76au/zzq/npz88PzZwU/9F98/\n/3l/v8mzWtPAZvOG8fe0raWAKWHNtE8bhOLI3L0Gnkav2vsO/UlM4mDKHvv9MXKst+MBMfdb\nSLOQaVFENJ3yiiE24/cv8dcc4RsnJQyfZqmdsU3CphkEd7ecabRmmic8EDSNJLB5Zmgr4ewm\nMhnkL2LevQeFkV2CAAACfHicjZFNTsMwEIUnP6U/C4TELZBooEBbll0UqVJVUFKkrogcZ6gs\nYieyDRyLy7DhGhwAkIst2pBFvbHfs/3m83jVA4AAwtCDoAUA0Lpf3pyOAfxjI/5mOAIAD0Lo\nbh9qMkN/2xSEo6pd70wFLXMm1gDwVdtbRJpXUax59TLAZCijuD9bJMvJfH4+uhheZeOz0fVg\nEGnGScSJiCqJFZGYFiyTRDJUqSqrlKNcY96Pc4Dg0KRPPt7eH8CMXTxaEOXwfGu2726T2Ypq\nKw82UjewnuzP+lvX+7YJLZsQTAV1TUHXFKsfWYGme1Z3NeOoNOGVNfzX3DJ92m/yanxtJalJ\nqRX/93iT0bP3YXfdSN3JmDCkynUIBS1Kp3pEa8myZ43OCZlKLt3ZoqRPuCH/AS6oZeQAAFQP\neJzNXPtz08YWVpwnoQ3l3ZbXxjjEvvEDyCVAeNwmweEVAhj3TmEy49nIa0cTvUZap3Gn/3d/\nK/VKR/HayClr74pkRnyHyFa+b885u9o9Wv02q2nauDYxMaaNT2qapk3+Wt0sPNC08fOapp3S\ntBTDMU1L/RDgBPudduedR1zsEWQRr0nqyKdeS6ctjyDHa2Lb8C3kYsPzkWnsetgziK9pEynu\nAlOVeo3ipqZp/GUnquXfqv2f9D3dIw1NG/+e/Y8x1DRtBvAB2A/6vjXte3rDMImmaX9p4c/R\nKWIfrK6u3Ov7xqRuYt/vI3T0x7W/w4N9Zexz/6d2qEHZ32JnU5/7qI4BPgT7Yfi58c//ym3y\n87/RiXXOVTd0Tu2o8Wu+49ZCVw2QMPH/cmU9RsA4IwWYBjstUUAsmckdG1tkAJsxwJtg35TI\nRnlzTkC4ToCAGckCBoQnNg3sD6AzBpgBOyORTtCe433tOTY7qNG62d/DkuX4HIdwyGPJny1U\n9wwfNVq2Tg3HRuB7H9E90u3MkIXrBDkNUSVcF6AVwC6oUrKCTRP5rd1Cl7bu2BQbtmE3uz12\nobfHFpW0CjIYroC9IlFSLJnvdurE1z3DZT4aEDGMyCNAOKSRCuL64tfG9WRl4+2zcgzNKegB\nIoRDHk3+7PXjurGsMPdpaFaG18G+ror7J4TYYO6jJ6hJaM3FHrb8rE+Ji56g9HHC0rlM8M3M\n0cmM77gZ+Eo9L6x7BrQy/AT2J1W6q4FuSryhhEeJ7WfCi2Qsp07EFZ8ClQEhsKsKFZvkgJhy\nBAeXElc8CypnE1J8gM0WkaM4uJS44tOg8nQSiucRclrUbdHaPmnHyRam/x1QZjgP9rwq+mWE\n0FCdTngys0/avriHvgdZDMtgl1VJzB15yPGaNYoPaw6lUjw1B9QZ5sDOqZLxcURPdbOL4kPH\ndix23qFU3HtnQCrDj2B/VCU7e+Q9n3o1nxKPOFKcF34oxCzYWVUqKiM67+gG18+ETSDutbOg\nkWEF7IoqvQs9XrOInHw7B7QZLoC9oEpCVZ7LLEJxHVMs7rTzoPJ8EkNZr9NsbElx2gWgfSEJ\np72X5zS2ajPE0HYRJDJ8D/Z7VXJzPR5jQ5tuYileuwTULyUxtH2S5zVubNPNIRLuMmi9nMSM\n6Ev32a4uxX0/AvUfk3DfRyXus11dWPZPIPWnJO5MUjlhfj8DJ4YpsFMS+cWymdpp+bgZtwI9\nBRSuAMIhjU6wsjQ/6orpVZgLRQiHXJap7mDbw3YyiFtBxtdgKekaTEKnQ5TGOJbp9S3Dp8hp\ncAu71EG7UVVrCAlzgEtgL0mUEJ83OwYl1gA2Y4B5sPMS2YwWoNdgATzCgsqFcGF23HqmuiQf\nlD5T4UqOIOUbkDM3INunJWd8LNUH645jEmwXUbXyaxk5Htpc2/pQRkYDtZ0W+h3blCUULGF2\nKyjtIcSdAdwEe1OiOLHMugERwfA52M8lshktdhmrFxzCcULYIWivCBPNrMlgOViQ8Twk07zK\ngSnVxxStG47pNA0dm8jD9j4MS2EikXpxdggRc4AFsJV1tzdfG3az7lh55O61zZaVRw1sGWY7\nj5rEbvl5VCwWBfmnIVTSiur+Yh3APMdGSdV8pBRLw6JBhAsqFw+E2XHe+wYdQFAdEWScgaTP\nJNkB3NrGFnvAIBg0gxkeG10pPsTQFewTd/Ay9HFS5gCXwV6WKKVHwg1SbBbR4nNiUwPbWCeY\n/LnmOno7tBcFuXPrYPwILI27WBeQ4dhwY5o0NqMl2QJ0UhHOqyyHCbO7Be0VYaJdwGy38ihI\nexFyfxE+Px2iNNqxdC+8DegGTz+gRqcLYMXDIYifB7wF9i2JxMUyZxE8vthrS2MzWmwuQmUr\nwqzKCpcwO67mlnzmnPuyIixIPwdZk4NV1ukQpdGPpZ3tz6Cjom44rjpWG2XfVqu5IcRcAVwH\ne12iGLGs4hastQ2wNySyGS1uGatnHMJxQtj9B9orwkSz6uwXlXpB9kuQSEtQgZkOURr7WNbz\n/UnVrUegoVX8DLgK9qpEFWLZtARRsMQ9QvpIIpvR4pWxeswhHCeEHbfWnHw2zfU+QSFIvQDp\nU4DS+3SI0qjHUr55TCpFD0EMoeMy4EOwZW46EUsm7jl3/mFxaWxGC9cCpHiEj05UqhehvSL8\nZslkY0uQegkSqJRkMqFjkil4OGUIEZcBV8BWvrlhYCaVIAIY3gf7vkQ2o8VqCfZdRAjHCWF3\nG9orwkQz6dyXTxwJ0r8DGXQnyanT7WOyqTt32mDbJtsNwyO5IURdAXwF9iuJosSy6w5EBcPX\nYL+WyGa0+GWstjiE44SwuwvtFeE3zS7b1QXpL0NGLSeZXbmvyq7tdxu5IdRcAdwAW+ZagFha\nLUM4LMO8f+xEzf2XYTdFhGXJuypiyczuYK/ZsjqFkbg9uVehkf6rMpdG3pP7GLZHRQiHPJb8\n2eJLinRss7pXJzuoZ+iU1FkhzHeJbjQM/agytocPiKiUJ9DIT3pvuNVIyVmOR9DuUXnfbHdm\neZjtyG20TIRt26GYbWoVLvE/BeZPFT2+GktmZqfemaIapj8gQhiJ/6mM49SocXwPtnLeS2IL\n7lq1U811Md1joUqZDdtQB+7Gjh6YQm/KleflZ6LquA3Z2hrYaxLVxZKZ2uHK7F809hg3eVES\nEl/dtQ185PgXqPRHeFpyxT+geearI3fAqyTWoNddS6IHvkwNC6+uruqO267tYn1/17FJNifK\neR2cvt67bUAN57mmU6NOTcf63hBUuSoKv2lRDdVS06B7rV30uIDSe5S6/mqp5OHfi+GvWz7x\n2JsbOjcMRd2xSmlRLdx9F7+IoEbLRqdPcwIl0c6EYJgz7M49rkXq3SGuxGKqQA6x5ZqkEDw0\nXrKwYYvr4/bDqq9+Xa0bHpPn4k6l43Y2dFEeMdXCQbYJZDe5+76rqojj8BUKHepy36GQMR3a\n8tGfT0XVcw/PahhsrEr9DEJNv7WbFSX5Aoi96H33gxqSF9m2oM4NQqeEZjOvFJt/pPOijF8C\ny5e9W+vUML7EGHcCwcQ6YTMaxlqc8iug+ap3G50aymcZ5YZx2LntehI8uC7Kllsd4rcIq2E7\ngZBwt7IFpBhyr3hSQ3CZ9Sasf8i2PPbaDugXO71kPnxpSy6PyKHreCw2wl+I6nkDGt70rieo\n0ZM/9j050WtohhOyDeS3k9iUM9+yTcPez6ZZCTHNRii95fnGAYGwF2b/Fhi/VfSWjVgynSlm\n59bHa8W95ima0rwDlD6f4M+KzyPewaJlhHBIoxfL5tQO3FHFzcl/gUZ6r6jBJlL82Uod3sDI\nf2e7RC23VOn8c3CXfFjxSpXiy+0P1bWtrTv3l1fu7T64ff/h3bvBvWHnbtAuHZeNxUp9ALNU\ndKYb1KyYzq0k/wBSe1J/AT0nNvEwW17abSPPOWw3iX13FdUd1LltRaRuUHZiD9v915+E+mJw\nwVTUfnVHr7ZdEvmmGV7d8brN1eOf6Cd62YQm0T89bGeimzfuzTLspYp//wMAub9ZAAACeXic\njZHNTgMhFIXv/NS2szAmvoWJRUdt67KLmjQ2amZq0pUNw6AhDgwB1MfyZdz4Gj6AGirEdpyF\nbOAc4NyPyzIBgAjiOICoAwDQuV1cHI4Bwn0rfmfYA4AAYuhvHmoz43DTFJhT3bjemwpSl0w8\nAMBnY+8SGS5RZrh8Tmk+VCgbzK7yxWQ+Px6dDM+K8dHoPE2RYRwjjgWSikqs6KpihcKKUb3S\nkhKj8CArAaJdGz15f327Azu22UiFtWcLndm9uc5nS2Kc3FlL0wJ68H/Qn7rBl0vouIRoKojv\nCPUdcfqeVdS2zum+YZxqg7l0RvhSOqYP90dBg6+rFbEpjeJ/Hm8zEncfttet1L2CCUuqfYeo\nIFXtVYKNUax4MtQ7MdP5qT9b1eSRrsm/AcdjZK4AAGZVeJzMWltT2zgYdUICLdtCt3dKLyqk\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+uZER9h9B5Fr7yVISbSzHTYIbXdAfNT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6YuXp7wpS2IoVp783Ql6U2Xf6u4L5VsXK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\nO3kEsEf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html>\n<html>\n<head><title>R: Taxonomically Informed Metabolite Annotation<\/title>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0, user-scalable=yes\" />\n<link rel=\"stylesheet\" type=\"text/css\" href=\"R.css\" />\n<\/head><body><div class=\"container\">\n<h1> Taxonomically Informed Metabolite Annotation\n<img class=\"toplogo\" src=\"../../../doc/html/Rlogo.svg\" alt=\"[R logo]\" />\n<\/h1>\n<hr/>\n<div style=\"text-align: center;\">\n<a href=\"../../../doc/html/packages.html\"><img class=\"arrow\" src=\"../../../doc/html/left.jpg\" alt=\"[Up]\" /><\/a>\n<a href=\"../../../doc/html/index.html\"><img class=\"arrow\" src=\"../../../doc/html/up.jpg\" alt=\"[Top]\" /><\/a>\n<\/div><h2>Documentation for package ‘tima’ version 2.11.0<\/h2>\n\n<ul><li><a href=\"../DESCRIPTION\">DESCRIPTION file<\/a>.<\/li>\n<li><a href=\"../doc/index.html\">User guides, package vignettes and other documentation.<\/a><\/li>\n<li><a href=\"../NEWS\">Package NEWS<\/a>.<\/li>\n<\/ul>\n\n<h2>Help Pages<\/h2>\n\n\n<table style=\"width: 100%;\">\n<tr><td style=\"width: 25%;\"><a href=\"annotate_masses.html\">annotate_masses<\/a><\/td>\n<td>Annotate masses<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"annotate_spectra.html\">annotate_spectra<\/a><\/td>\n<td>Annotate spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"benchmark_taxize_spectra.html\">benchmark_taxize_spectra<\/a><\/td>\n<td>Benchmark taxize spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"calculate_entropy.html\">calculate_entropy<\/a><\/td>\n<td>Calculate entropy<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"calculate_mass_of_m.html\">calculate_mass_of_m<\/a><\/td>\n<td>Calculate mass of M<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"clean_bio.html\">clean_bio<\/a><\/td>\n<td>Clean bio<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"clean_chemo.html\">clean_chemo<\/a><\/td>\n<td>Clean chemo<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"clean_collapse.html\">clean_collapse<\/a><\/td>\n<td>Clean collapse<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"columns_model.html\">columns_model<\/a><\/td>\n<td>Columns model<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"complement_metadata_structures.html\">complement_metadata_structures<\/a><\/td>\n<td>Complement metadata of structures<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"copy_backbone.html\">copy_backbone<\/a><\/td>\n<td>Copy backbone<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"create_components.html\">create_components<\/a><\/td>\n<td>Create components<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"create_dir.html\">create_dir<\/a><\/td>\n<td>Create directory<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"create_edges.html\">create_edges<\/a><\/td>\n<td>Create edges<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"create_edges_spectra.html\">create_edges_spectra<\/a><\/td>\n<td>Create edges spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"decorate_bio.html\">decorate_bio<\/a><\/td>\n<td>Decorate bio<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"decorate_chemo.html\">decorate_chemo<\/a><\/td>\n<td>Decorate chemo<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"decorate_masses.html\">decorate_masses<\/a><\/td>\n<td>Decorate masses<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"dist_get.html\">dist_get<\/a><\/td>\n<td>Distance between two elements in a distance matrix<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"dist_groups.html\">dist_groups<\/a><\/td>\n<td>Dist groups<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"export_output.html\">export_output<\/a><\/td>\n<td>Export output<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"export_params.html\">export_params<\/a><\/td>\n<td>Export parameters<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"export_spectra_rds.html\">export_spectra_rds<\/a><\/td>\n<td>Export spectra RDS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"extract_spectra.html\">extract_spectra<\/a><\/td>\n<td>Extract spectra from a Spectra object<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_annotations_columns.html\">fake_annotations_columns<\/a><\/td>\n<td>Fake annotations columns<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_ecmdb.html\">fake_ecmdb<\/a><\/td>\n<td>Fake ECMDB<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_hmdb.html\">fake_hmdb<\/a><\/td>\n<td>Fake HMDB<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_lotus.html\">fake_lotus<\/a><\/td>\n<td>Fake LOTUS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_sop_columns.html\">fake_sop_columns<\/a><\/td>\n<td>Fake SOP columns<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"filter_annotations.html\">filter_annotations<\/a><\/td>\n<td>Filter annotations<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"filter_high_confidence_only.html\">filter_high_confidence_only<\/a><\/td>\n<td>Filter high confidence only *[Experimental]*<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_default_paths.html\">get_default_paths<\/a><\/td>\n<td>Get default paths<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_example_files.html\">get_example_files<\/a><\/td>\n<td>Get example files<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_example_sirius.html\">get_example_sirius<\/a><\/td>\n<td>Get example sirius<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_file.html\">get_file<\/a><\/td>\n<td>Get file<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_gnps_tables.html\">get_gnps_tables<\/a><\/td>\n<td>Get GNPS Tables<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_last_version_from_zenodo.html\">get_last_version_from_zenodo<\/a><\/td>\n<td>Get last version from Zenodo<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_massbank_spectra.html\">get_massbank_spectra<\/a><\/td>\n<td>Get MassBank spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_organism_taxonomy_ott.html\">get_organism_taxonomy_ott<\/a><\/td>\n<td>Get organism taxonomy (Open Tree of Life Taxonomy)<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_params.html\">get_params<\/a><\/td>\n<td>Get parameters<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"go_to_cache.html\">go_to_cache<\/a><\/td>\n<td>Go to cache<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"harmonize_adducts.html\">harmonize_adducts<\/a><\/td>\n<td>Harmonize adducts<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"harmonize_names_sirius.html\">harmonize_names_sirius<\/a><\/td>\n<td>Harmonize names sirius<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"harmonize_spectra.html\">harmonize_spectra<\/a><\/td>\n<td>Harmonize spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"import_spectra.html\">import_spectra<\/a><\/td>\n<td>Import spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"install.html\">install<\/a><\/td>\n<td>Install<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"load_yaml_files.html\">load_yaml_files<\/a><\/td>\n<td>Load yaml files<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"log_debug.html\">log_debug<\/a><\/td>\n<td>Log debug<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"log_pipe.html\">log_pipe<\/a><\/td>\n<td>Log pipe<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"parse_adduct.html\">parse_adduct<\/a><\/td>\n<td>Parse adduct<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"parse_cli_params.html\">parse_cli_params<\/a><\/td>\n<td>Parse CLI parameters<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"parse_yaml_params.html\">parse_yaml_params<\/a><\/td>\n<td>Parse YAML parameters<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_annotations_gnps.html\">prepare_annotations_gnps<\/a><\/td>\n<td>Prepare annotations GNPS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_annotations_sirius.html\">prepare_annotations_sirius<\/a><\/td>\n<td>Prepare annotations SIRIUS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_annotations_spectra.html\">prepare_annotations_spectra<\/a><\/td>\n<td>Prepare annotations MS2<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_features_components.html\">prepare_features_components<\/a><\/td>\n<td>Prepare features components<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_features_edges.html\">prepare_features_edges<\/a><\/td>\n<td>Prepare features edges<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_features_tables.html\">prepare_features_tables<\/a><\/td>\n<td>Prepare features table<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_rt.html\">prepare_libraries_rt<\/a><\/td>\n<td>Prepare libraries of retention times<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_closed.html\">prepare_libraries_sop_closed<\/a><\/td>\n<td>Prepare libraries of structure organism pairs CLOSED<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_ecmdb.html\">prepare_libraries_sop_ecmdb<\/a><\/td>\n<td>Prepare libraries of structure organism pairs ECMDB<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_hmdb.html\">prepare_libraries_sop_hmdb<\/a><\/td>\n<td>Prepare libraries of structure organism pairs HMDB<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_lotus.html\">prepare_libraries_sop_lotus<\/a><\/td>\n<td>Prepare libraries of structure organism pairs LOTUS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_merged.html\">prepare_libraries_sop_merged<\/a><\/td>\n<td>Prepare merged structure organism pairs libraries<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_spectra.html\">prepare_libraries_spectra<\/a><\/td>\n<td>Prepare libraries of spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_params.html\">prepare_params<\/a><\/td>\n<td>Prepare params<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_taxa.html\">prepare_taxa<\/a><\/td>\n<td>Prepare taxa<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"pre_harmonize_names_sirius.html\">pre_harmonize_names_sirius<\/a><\/td>\n<td>Pre harmonize names sirius<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"read_from_sirius_zip.html\">read_from_sirius_zip<\/a><\/td>\n<td>Read from SIRIUS zip<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"replace_id.html\">replace_id<\/a><\/td>\n<td>Replace ID in file paths<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"round_reals.html\">round_reals<\/a><\/td>\n<td>Round reals<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"run_app.html\">run_app<\/a><\/td>\n<td>Run app<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"sanitize_spectra.html\">sanitize_spectra<\/a><\/td>\n<td>Sanitize spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_annotations_columns.html\">select_annotations_columns<\/a><\/td>\n<td>Select annotations columns<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_sirius_columns_canopus.html\">select_sirius_columns_canopus<\/a><\/td>\n<td>Select sirius columns (canopus)<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_sirius_columns_formulas.html\">select_sirius_columns_formulas<\/a><\/td>\n<td>Select sirius columns (formulas)<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_sirius_columns_structures.html\">select_sirius_columns_structures<\/a><\/td>\n<td>Select sirius columns (structures)<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_sop_columns.html\">select_sop_columns<\/a><\/td>\n<td>Select SOP columns<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"split_tables_sop.html\">split_tables_sop<\/a><\/td>\n<td>Split Structure Organism Pairs table<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"tima_full.html\">tima_full<\/a><\/td>\n<td>Tima Full<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"transform_score_sirius_csi.html\">transform_score_sirius_csi<\/a><\/td>\n<td>Transform score sirius CSI<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"weight_annotations.html\">weight_annotations<\/a><\/td>\n<td>Weight annotations<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"weight_bio.html\">weight_bio<\/a><\/td>\n<td>Weight bio<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"weight_chemo.html\">weight_chemo<\/a><\/td>\n<td>Weight chemo<\/td><\/tr>\n<\/table>\n<\/div><\/body><\/html>\n","type":"text"},{"name":"html/R.css","content":"@media screen {\n .container {\n\tpadding-right: 10px;\n\tpadding-left: 10px;\n\tmargin-right: auto;\n\tmargin-left: auto;\n\tmax-width: 900px;\n }\n}\n\n.rimage img { /* from knitr - for examples and demos */\n width: 96%;\n margin-left: 2%;\n} \t\n\n.katex { font-size: 1.1em; }\n\ncode {\n color: inherit;\n background: inherit;\n}\n\nbody {\n line-height: 1.4;\n background: white;\n color: black;\n}\n\na:link {\n background: white;\n color: blue;\n}\n\na:visited {\n background: white;\n color: rgb(50%, 0%, 50%);\n}\n\nh1 {\n background: white;\n color: rgb(55%, 55%, 55%);\n font-family: monospace;\n font-size: 1.4em; /* x-large; */\n text-align: center;\n}\n\nh2 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n font-size: 1.2em; /* large; */\n text-align: center;\n}\n\nh3 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n font-size: 1.2em; /* large; */\n}\n\nh4 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n font-style: italic;\n font-size: 1.2em; /* large; */\n}\n\nh5 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n}\n\nh6 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n font-style: italic;\n}\n\nimg.toplogo {\n width: 4em;\n vertical-align: middle;\n}\n\nimg.arrow {\n width: 30px;\n height: 30px;\n border: 0;\n}\n\nspan.acronym {\n font-size: small;\n}\n\nspan.env {\n font-family: monospace;\n}\n\nspan.file {\n font-family: monospace;\n}\n\nspan.option{\n font-family: monospace;\n}\n\nspan.pkg {\n font-weight: bold;\n}\n\nspan.samp{\n font-family: monospace;\n}\n\ndiv.vignettes a:hover {\n background: rgb(85%, 85%, 85%);\n}\n\ntr {\n vertical-align: top;\n}\n\nspan.rlang {\n font-family: Courier New, Courier;\n color: #666666;\n}\n\n","type":"text"},{"name":"params/default/annotate_masses.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' List of prepared annotations.\n prepared:\n #' List of prepared structural annotations.\n structural:\n #' Prepared MS1 structural annotations. STRING\n ms1: data/interim/annotations/example_ms1Prepared.tsv.gz\n\n #' Features files.\n features:\n #' Prepared features file. STRING\n prepared: data/interim/features/example_features.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Merged structure organism pairs library keys. STRING\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Raw edges file.\n raw:\n #' Raw ms1 edges file. STRING\n ms1: data/interim/features/example_edgesMasses.tsv\n\n#' MS related parameters.\nms:\n #' List of adducts to be used.\n adducts:\n #' List of adducts to be used in negative. STRING\n neg:\n - \"[M-H3]3-\"\n - \"[M-H2]2-\"\n - \"[M-H]-\"\n - \"[M+F]-\"\n - \"[M+Na-H2]-\"\n - \"[M+Cl]-\"\n - \"[M+K-H2]-\"\n - \"[M+Br]-\"\n - \"[2M-H]-\"\n - \"[3M-H]-\"\n\n #' List of adducts to be used in positive. STRING\n pos:\n - \"[M+H3]3+\"\n - \"[M+H2Na]3+\"\n - \"[M+HNa2]3+\"\n - \"[M+Na3]3+\"\n - \"[M+H2]2+\"\n - \"[M+HNa]2+\"\n - \"[M+Mg]2+\"\n - \"[M+HK]2+\"\n - \"[M+Ca]2+\"\n - \"[M+Na2]2+\"\n - \"[M+Fe]2+\"\n - \"[M+H]+\"\n - \"[M+H4N]+\"\n - \"[M+Na]+\"\n - \"[M+K]+\"\n - \"[M+Cu]+\"\n - \"[2M+Mg]2+\"\n - \"[2M+Ca]2+\"\n - \"[2M+Fe]2+\"\n - \"[2M+H]+\"\n - \"[2M+H4N]+\"\n - \"[2M+Na]+\"\n - \"[2M+K]+\"\n\n #' List of clusters to be used.\n clusters:\n #' List of clusters to be used in negative. STRING\n neg:\n - \"H2O\" # (water)\n - \"CH2O2\" # (formic)\n - \"NaCl\" # (sodium chloride)\n - \"C2H4O2\" # (acetic)\n - \"H2PO4\" # (phosphoric)\n - \"C2HF3O2\" # (tfa)\n\n #' List of clusters to be used in positive. STRING\n pos:\n - \"H2O\" # (water)\n # - CH4O # (methanol)\n - \"C2H3N\" # (acetonitrile)\n # - C2H7N # (ethylamine)\n # - C2H6O # (ethanol)\n - \"NaCl\" # (sodium chloride)\n # - C3H8O # (isopropanol)\n # - C2H6OS # (dmso)\n\n #' List of neutral losses to be used. STRING\n neutral_losses:\n - \"HN\"\n - \"CH3\"\n - \"O\"\n - \"H3N (ammonia)\"\n - \"H2O (water)\"\n - \"CHN\"\n - \"CO\"\n - \"C2H4 (2xCH2)\"\n - \"C2H5 (CH2-CH3)\"\n - \"CH2O\"\n - \"H5ON (H2O-H3N)\"\n - \"H4O2 (2xH2O)\"\n - \"C2H2O\"\n - \"CHON\"\n - \"CO2\"\n - \"CHO2\"\n - \"H6O3 (3xH2O)\"\n - \"C2O2 (2xCO)\"\n - \"CH6O3 (combination)\"\n - \"H8O4 (4xH2O)\"\n - \"C2H2O3 (CH2O-CO2)\"\n - \"C3H6O2 (CH3-CH2-COOH)\"\n - \"C3O3 (3xCO)\"\n - \"C4H9NO (combination)\"\n - \"C2O4 (2xCO2)\"\n - \"C3H4O3 (pyruvic)\"\n - \"C4H8O2 (butyric)\"\n - \"C3H7NO2 (alanine)\"\n - \"C3H6O3 (sugar)\"\n - \"H2O4S (sulfuric)\"\n - \"H3O4P (phosphoric)\"\n - \"C5H10O2 (valeric)\"\n - \"C3H4O4 (malonic)\"\n - \"C2H2O5 (CO2-CO2-H2O)\"\n - \"C3H8O4 (combination)\"\n - \"C5H9NO2 (proline)\"\n - \"C4O4H6 (CO2-CO2-CH3-CH3)\"\n - \"C4H8O4 (sugar)\"\n - \"C2H4O6 (CO2-CO2-H2O-H2O)\"\n - \"C6H6O3 (HRF)\"\n - \"C5H8O4 (pentose-H2O)\"\n - \"C8H8O2 (RDA-1)\"\n - \"C6H8O4 (HRF)\"\n - \"C9H6O2 (coumaroyl)\"\n - \"C6H10O4 (methylpentose/desoxyhexose-H2O)\"\n - \"C9H8O2 (cinnamoyl)\"\n - \"C7H4O4 (galloyl)\"\n - \"C8H8O3 (RDA-2)\"\n - \"C9H6O3 (caffeoyl)\"\n - \"C6H10O5 (hexose-H2O)\"\n - \"C8H8O4 (RDA-3)\"\n - \"C8H10O4 (RDA-2-H2O)\"\n - \"C6H8O6 (glucuronic-H2O)\"\n - \"C10H8O3 (feruloyl)\"\n - \"C6H13NO5 (hexose-H2N)\"\n - \"C6H12O6 (hexose)\"\n - \"C7H12O6 (quinoyl)\"\n - \"C6H15NO6 (hexose-H2N-H2O)\"\n - \"C6H14O7 (hexose-H2O)\"\n - \"C8H12O6 (acetylhexose-H2O)\"\n - \"C11H10O4 (sinapoyl)\"\n - \"C16H30O (pamitoyl)\"\n - \"C9H12O8 (malonylhexose)\"\n - \"C13H14O6 (benzoylhexose)\"\n - \"C12H20O8 (2xmethylpentose/desoxyhexose-H2O)\"\n - \"C15H16O7 (coumaroylhexose)\"\n - \"C13H14O9 (galloylhexose)\"\n - \"C15H16O8 (caffeoylhexose)\"\n - \"C12H20O10 (2xhexose-H2O)\"\n - \"C16H18O8 (feruloylhexose)\"\n - \"C17H20O9 (sinapoylhexose)\"\n - \"C18H30O15 (3xhexose-H2O)\"\n\n #' Polarity used. Must be \"pos\" or \"neg\". STRING\n polarity: pos\n\n #' Tolerances.\n tolerances:\n #' For mass.\n mass:\n #' Relative in ppm.\n ppm:\n #' Relative mass tolerance for MS1 in ppm. FLOAT\n ms1: 10\n\n #' Absolute in Dalton.\n dalton:\n #' Absolute mass tolerance for MS1 in Dalton. FLOAT\n ms1: 0.01\n\n #' For retention time.\n rt:\n #' Retention time tolerance for adducts in minutes. FLOAT\n adducts: 0.02\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"source IDs\" variable in the input. STRING\n source: CLUSTERID1\n\n #' Name of \"target IDs\" variable in the input. STRING\n target: CLUSTERID2\n\n#' Options.\noptions:\n #' Do not use it. BOOLEAN\n force: no\n","type":"text"},{"name":"params/default/annotate_spectra.yaml","content":"---\n#' Annotations related parameters.\nannotations:\n #' Perform approximative matching without precursor matching? BOOLEAN\n ms2approx: no\n\n #' Thresholds related parameters.\n thresholds:\n #' MS2 annotation related parameters.\n ms2:\n #' For similarity.\n similarity:\n #' For annotation. FLOAT\n annotation: 0.0\n\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' List of spectral matching results.\n spectral:\n #' Spectral spectral matching results. STRING\n spectral: data/interim/annotations/example_spectralMatches.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Spectral libraries. STRING\n spectral:\n #' List of negative spectral libraries. STRING\n neg:\n - data/interim/libraries/spectra/exp/massbank_neg.rds\n - data/interim/libraries/spectra/is/lotus_neg.rds\n\n #' List of positive spectral libraries. STRING\n pos:\n - data/interim/libraries/spectra/exp/massbank_pos.rds\n - data/interim/libraries/spectra/is/lotus_pos.rds\n\n #' Spectral files. STRING\n spectral:\n #' Raw spectral file. STRING\n raw: data/source/example_spectra.mgf\n\n#' MS related parameters.\nms:\n #' Thresholds related parameters.\n thresholds:\n #' MS2 related parameters.\n ms2:\n #' Intensity threshold for MS2. INTEGER\n intensity: 5\n\n #' Polarity used. Must be \"pos\" or \"neg\". STRING\n polarity: pos\n\n #' Tolerances.\n tolerances:\n #' For mass.\n mass:\n #' Relative in ppm.\n ppm:\n #' Relative mass tolerance for MS2 in ppm. FLOAT\n ms2: 10\n\n #' Absolute in Dalton.\n dalton:\n #' Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms2: 0.01\n","type":"text"},{"name":"params/default/create_components.yaml","content":"---\n#' Files.\nfiles:\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_edges.tsv\n\n #' Components files.\n components:\n #' Raw components file. STRING\n raw: data/interim/features/example_components.tsv\n","type":"text"},{"name":"params/default/create_edges_spectra.yaml","content":"---\n#' Annotations related parameters.\nannotations:\n #' Thresholds related parameters.\n thresholds:\n #' MS2 annotation related parameters.\n ms2:\n #' For similarity.\n similarity:\n #' For edges. FLOAT\n edges: 0.7\n\n#' Files.\nfiles:\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Raw edges file.\n raw:\n #' Raw spectral edges file. STRING\n spectral: data/interim/features/example_edgesSpectra.tsv\n\n #' Spectral files. STRING\n spectral:\n #' Raw spectral file. STRING\n raw: data/source/example_spectra.mgf\n\n#' MS related parameters.\nms:\n #' Thresholds related parameters.\n thresholds:\n #' MS2 related parameters.\n ms2:\n #' Intensity threshold for MS2. INTEGER\n intensity: 5\n\n #' Tolerances.\n tolerances:\n #' For mass.\n mass:\n #' Relative in ppm.\n ppm:\n #' Relative mass tolerance for MS2 in ppm. FLOAT\n ms2: 10\n\n #' Absolute in Dalton.\n dalton:\n #' Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms2: 0.01\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"source IDs\" variable in the input. STRING\n source: CLUSTERID1\n\n #' Name of \"target IDs\" variable in the input. STRING\n target: CLUSTERID2\n","type":"text"},{"name":"params/default/filter_annotations.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' List of filtered annotations. STRING\n filtered:\n - data/interim/annotations/example_annotationsFiltered.tsv.gz\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared structural annotations.\n structural:\n #' Prepared GNPS structural annotations. STRING\n gnps: data/interim/annotations/example_gnpsPrepared.tsv.gz\n #' Prepared MS1 structural annotations. STRING\n ms1: data/interim/annotations/example_ms1Prepared.tsv.gz\n #' Prepared SIRIUS structural annotations. STRING\n sirius: data/interim/annotations/example_siriusPrepared.tsv.gz\n #' Prepared SPECTRAL structural annotations. STRING\n spectral: data/interim/annotations/example_spectralMatchesPrepared.tsv.gz\n\n #' Features files.\n features:\n #' Prepared features file. STRING\n prepared: data/interim/features/example_features.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Temporal libraries. STRING\n temporal:\n #' List of prepared temporal libraries. STRING\n prepared:\n - data/interim/libraries/rt/prepared.tsv.gz\n\n#' MS related parameters.\nms:\n #' Tolerances.\n tolerances:\n #' For retention time.\n rt:\n #' Retention time tolerance for library in minutes. FLOAT\n library: 0.1\n","type":"text"},{"name":"params/default/prepare_annotations_gnps.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' List of spectral matching annotations.\n spectral:\n #' GNPS spectral matching annotations. STRING\n gnps: data/interim/annotations/example_gnps.tsv\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared structural annotations.\n structural:\n #' Prepared GNPS structural annotations. STRING\n gnps: data/interim/annotations/example_gnpsPrepared.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n","type":"text"},{"name":"params/default/prepare_annotations_sirius.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' Directory containing the sirius annotations. STRING\n sirius: data/interim/annotations/example_sirius\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared canopus annotations. STRING\n canopus: data/interim/annotations/example_canopusPrepared.tsv.gz\n\n #' List of prepared formula annotations. STRING\n formula: data/interim/annotations/example_formulaPrepared.tsv.gz\n\n #' List of prepared structural annotations.\n structural:\n #' Prepared SIRIUS structural annotations. STRING\n sirius: data/interim/annotations/example_siriusPrepared.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n\n#' Tools used for each step.\ntools:\n #' Sirius.\n sirius:\n #' Sirius version.\n #' Currently only 5 or 6 supported. INTEGER\n version: 6\n #' Taxonomies related parameters.\n taxonomies:\n #' Chemical taxonomy used for matching.\n #' Currently only \"npc\" or supported. STRING\n chemical: npc\n","type":"text"},{"name":"params/default/prepare_annotations_spectra.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' List of spectral matching annotations.\n spectral:\n #' Spectral spectral matching annotations. STRING\n spectral: data/interim/annotations/example_spectralMatches.tsv.gz\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared structural annotations.\n structural:\n #' Prepared spectral structural annotations. STRING\n spectral: data/interim/annotations/example_spectralMatchesPrepared.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n","type":"text"},{"name":"params/default/prepare_features_components.yaml","content":"---\n#' Files.\nfiles:\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Components files.\n components:\n #' Raw components file. STRING\n raw: data/interim/features/example_components.tsv\n\n #' Prepared components file. STRING\n prepared: data/interim/features/example_componentsPrepared.tsv\n","type":"text"},{"name":"params/default/prepare_features_edges.yaml","content":"---\n#' Files.\nfiles:\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Raw edges file.\n raw:\n #' Raw ms1 edges file. STRING\n ms1: data/interim/features/example_edgesMasses.tsv\n\n #' Raw spectral edges file. STRING\n spectral: data/interim/features/example_edgesSpectra.tsv\n\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_edges.tsv\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"source IDs\" variable in the input. STRING\n source: CLUSTERID1\n\n #' Name of \"target IDs\" variable in the input. STRING\n target: CLUSTERID2\n","type":"text"},{"name":"params/default/prepare_features_tables.yaml","content":"---\n#' Files.\nfiles:\n #' Features files.\n features:\n #' Raw features file. STRING\n raw: data/source/example_features.csv\n\n #' Prepared features file. STRING\n prepared: data/interim/features/example_features.tsv.gz\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"adduct\" variable in the feature table. STRING\n adduct: best ion\n \n #' Name of \"feature IDs\" variable in the input. STRING\n features: row ID\n\n #' Name of \"precursor m/z\" variable in the input. STRING\n precursor: row m/z\n\n #' Name of \"retention time\" variable.\n rt: \n #' Name of \"retention time\" variable in the feature table. STRING\n features: row retention time\n","type":"text"},{"name":"params/default/prepare_libraries_rt.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' RT prepared structure organism pairs libraries. STRING\n rt: data/interim/libraries/sop/rt_prepared.tsv.gz\n\n #' Temporal libraries. STRING\n temporal:\n #' List of experimental temporal libraries.\n exp:\n #' List of experimental temporal libraries (CSV). STRING\n csv: [ ]\n\n #' List of experimental temporal libraries (MGF).\n mgf:\n #' List of negative experimental temporal libraries (MGF). STRING\n neg: [ ]\n\n #' List of positive experimental temporal libraries (MGF). STRING\n pos: [ ]\n\n #' List of in silico temporal libraries.\n is:\n #' List of in silico temporal libraries (CSV). STRING\n csv: [ ]\n\n #' List of in silico temporal libraries (MGF).\n mgf:\n #' List of negative in silico temporal libraries (MGF). STRING\n neg: [ ]\n\n #' List of positive in silico temporal libraries (MGF). STRING\n pos: [ ]\n\n #' List of prepared temporal libraries. STRING\n prepared:\n - data/interim/libraries/rt/prepared.tsv.gz\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"InChIKey\" variable in the input. STRING\n inchikey: inchikey\n\n #' Name of fields present in the MGF.\n mgf:\n #' Name of \"InChIKey\" in MGF. STRING\n inchikey: ~\n\n #' Name of \"retention time\" in MGF. STRING\n retention_time: RTINSECONDS\n\n #' Name of \"SMILES\" in MGF. STRING\n smiles: SMILES\n\n #' Name of \"retention time\" variable.\n rt: \n #' Name of \"retention time\" variable in the library. STRING\n library: rt\n\n #' Name of \"SMILES\" variable in the input. STRING\n smiles: smiles\n\n#' Units of the different variables in the input files.\nunits:\n #' Unit of the \"retention time\" variable in the input files.\n #' Must be \"seconds\" or \"minutes\". STRING\n rt: seconds\n","type":"text"},{"name":"params/default/prepare_libraries_sop_closed.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' Closed structure organism pairs library. STRING\n closed: ../lotus-processor/data/processed/230106_closed_metadata.csv.gz\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' Closed prepared structure organism pairs library. STRING\n closed: data/interim/libraries/sop/closed_prepared.tsv.gz\n","type":"text"},{"name":"params/default/prepare_libraries_sop_ecmdb.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' ECMDB structure organism pairs library. STRING\n ecmdb: data/source/libraries/sop/ecmdb.json.zip\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' ECMDB prepared structure organism pairs library. STRING\n ecmdb: data/interim/libraries/sop/ecmdb_prepared.tsv.gz\n","type":"text"},{"name":"params/default/prepare_libraries_sop_hmdb.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' HMDB structure organism pairs library. STRING\n hmdb: data/source/libraries/sop/hmdb/structures.zip\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' HMDB prepared structure organism pairs library. STRING\n hmdb: data/interim/libraries/sop/hmdb_prepared.tsv.gz\n","type":"text"},{"name":"params/default/prepare_libraries_sop_lotus.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' Lotus raw structure organism pairs library. STRING\n lotus: data/source/libraries/sop/lotus.csv.gz\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' Lotus prepared structure organism pairs library. STRING\n lotus: data/interim/libraries/sop/lotus_prepared.tsv.gz\n","type":"text"},{"name":"params/default/prepare_libraries_sop_merged.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' Closed prepared structure organism pairs library. STRING\n closed: data/interim/libraries/sop/closed_prepared.tsv.gz\n #' ECMDB prepared structure organism pairs library. STRING\n ecmdb: data/interim/libraries/sop/ecmdb_prepared.tsv.gz\n #' HMDB prepared structure organism pairs library. STRING\n hmdb: data/interim/libraries/sop/hmdb_prepared.tsv.gz\n #' Lotus prepared structure organism pairs library. STRING\n lotus: data/interim/libraries/sop/lotus_prepared.tsv.gz\n #' RT prepared structure organism pairs library. STRING\n rt: data/interim/libraries/sop/rt_prepared.tsv.gz\n #' Spectral prepared structure organism pairs library. STRING\n spectral: data/interim/libraries/sop/spectral_prepared.tsv.gz\n\n #' Merged structure organism pairs library.\n merged:\n #' Merged structure organism pairs library keys. STRING\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n\n #' Organisms libraries.\n organisms:\n #' Organisms names. STRING\n names: data/interim/libraries/sop/merged/organisms/names.tsv.gz\n\n #' Organisms taxonomies.\n taxonomies:\n #' OTT organisms taxonomy. STRING\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n\n#' Organisms related parameters.\norganisms:\n #' Organisms filter related parameters.\n filter:\n #' Filter library to restrict it to a portion of organisms only. BOOLEAN\n mode: no\n\n #' Level at which the library will be filtered.\n #' Must be one of \"domain\", \"phylum\", \"class\", \"order\",\n #' \"family\", \"tribe\", \"genus\", \"species\", \"varietas\" . STRING\n level: phylum\n\n #' Value to be applied for filtering.\n #' \"OR\" can be used as \"|\".\n # Example: \"Simaroubaceae|Gentianaceae\". STRING\n value: Streptophyta\n","type":"text"},{"name":"params/default/prepare_libraries_spectra.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Spectral libraries.\n spectral:\n #' List of raw spectral libraries. STRING\n raw:\n - data/source/libraries/spectra/exp/spectral_lib_mini_with_rt.mgf\n\n#' Names of the different variables in the input files.\nnames:\n #' Names of the prepared libraries. STRING\n libraries: \n - internal\n\n #' Name of fields present in the MGF.\n mgf:\n #' Name of \"adduct\" in MGF. STRING\n adduct: ~\n\n #' Name of \"collision energy\" in MGF. STRING\n collision_energy: ~\n\n #' Name of \"compound ID\" in MGF. STRING\n compound_id: FILENAME\n\n #' Name of \"exact mass\" in MGF. STRING\n exact_mass: EXACTMASS\n\n #' Name of \"InChI\" in MGF. STRING\n inchi: INCHI\n\n #' Name of \"InChI without stereo\" in MGF. STRING\n inchi_no_stereo: ~\n\n #' Name of \"InChIKey\" in MGF. STRING\n inchikey: ~\n\n #' Name of \"InChIKey without stereo\" in MGF. STRING\n inchikey_no_stereo: ~\n\n #' Name of \"molecular formula\" in MGF. STRING\n molecular_formula: ~\n\n #' Name of \"name\" in MGF. STRING\n name: NAME\n\n #' Name of \"polarity\" in MGF. STRING\n polarity: IONMODE\n\n #' Name of \"SMILES\" in MGF. STRING\n smiles: SMILES\n\n #' Name of \"SMILES without stereo\" in MGF. STRING\n smiles_no_stereo: ~\n\n #' Name of \"spectrum ID\" in MGF. STRING\n spectrum_id: SPECTRUMID\n\n #' Name of \"SPLASH\" in MGF. STRING\n splash: ~\n\n #' Name of \"synonyms\" in MGF. STRING\n synonyms: ~\n\n #' Name of \"xlogP\" in MGF. STRING\n xlogp: ~\n","type":"text"},{"name":"params/default/prepare_taxa.yaml","content":"---\n#' Files.\nfiles:\n #' Features files.\n features:\n #' Raw features file. STRING\n raw: data/source/example_features.csv\n\n #' Metadata files.\n metadata:\n #' The path to the file containing your metadata.\n #' Must be formatted accordingly.\n #' If your experiment contains a single taxon, you can provide it below instead. STRING\n raw: data/source/example_metadata.tsv\n\n #' Taxonomically informed features file. STRING\n prepared: data/interim/taxa/example_taxed.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Organisms libraries.\n organisms:\n #' Organisms taxonomies.\n taxonomies:\n #' OTT organisms taxonomy. STRING\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n\n#' Names of the different variables in the input files.\nnames:\n #' The file(s) extension is present in the sample name. BOOLEAN\n extension: yes\n\n #' Name of \"feature IDs\" variable in the input. STRING\n features: row ID\n\n #' Name of \"filename\" variable in the input. STRING\n filename: filename\n\n #' Name of the \"taxon name\" variable in the input. STRING\n taxon: ATTRIBUTE_species\n\n#' Organisms related parameters.\norganisms:\n #' Number of organisms to keep per feature. INTEGER\n candidates: 1\n\n #' OPTIONAL. If your experiment contains a single taxon, its scientific name.\n #' Else, you will need to provide a metadata file above.\n #' If you provide both, this one will have priority.\n #' Example: \"Homo sapiens\". STRING\n taxon: ~\n","type":"text"},{"name":"params/default/weight_annotations.yaml","content":"---\n#' Annotations related parameters.\nannotations:\n #' Number of candidates to keep per feature.\n candidates:\n #' Number of final candidates. INTEGER\n final: 3\n\n #' Erase MS2 results and keep MS1 only. BOOLEAN\n ms1only: no\n\n #' Thresholds related parameters.\n thresholds:\n #' Minimal consistency score (chemical) to consider a class. FLOAT\n consistency: 0.0\n\n #' MS1 annotation related parameters.\n ms1:\n #' Minimal biological score to keep MS1 only annotation. FLOAT\n biological: 0.0\n\n #' Minimal chemical score to keep MS1 only annotation. FLOAT\n chemical: 0.0\n\n #' Condition to be used to retain candidates.\n #' Must be \"OR\" or \"AND\".\n #' Example: Minimum 0.3 biological AND 0.3 chemical. STRING\n condition: OR\n\n#' Files.\nfiles:\n #' Pattern to identify the files. STRING\n pattern: example\n\n #' Annotations files.\n annotations:\n #' List of filtered annotations. STRING\n filtered:\n - data/interim/annotations/example_annotationsFiltered.tsv.gz\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared canopus annotations. STRING\n canopus: data/interim/annotations/example_canopusPrepared.tsv.gz\n\n #' List of prepared formula annotations. STRING\n formula: data/interim/annotations/example_formulaPrepared.tsv.gz\n\n #' Final results file. STRING\n processed: annotations.tsv\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Merged structure organism pairs library keys. STRING\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n\n #' Organisms libraries.\n organisms:\n #' Organisms taxonomies.\n taxonomies:\n #' OTT organisms taxonomy. STRING\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Components files.\n components:\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_componentsPrepared.tsv\n\n #' Edges files.\n edges:\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_edges.tsv\n\n #' Metadata files.\n metadata:\n #' Taxonomically informed features file. STRING\n prepared: data/interim/taxa/example_taxed.tsv.gz\n\n#' Weights to apply to each part of the final score.\nweights:\n #' Global weights.\n global:\n #' Weight for the biological part. FLOAT\n biological: 0.500\n\n #' Weight for the chemical part. FLOAT\n chemical: 0.166\n\n #' Weight for the spectral part. FLOAT\n spectral: 0.333\n\n #' Weights for the biological part.\n biological:\n #' Score for a biological domain match. FLOAT\n domain: 0.10\n\n #' Score for a biological kingdom match. FLOAT\n kingdom: 0.20\n\n #' Score for a biological phylum match. FLOAT\n phylum: 0.30\n\n #' Score for a biological class match. FLOAT\n class: 0.40\n\n #' Score for a biological order match. FLOAT\n order: 0.50\n\n #' Score for a biological infraorder match. FLOAT\n infraorder: 0.55\n\n #' Score for a biological family match. FLOAT\n family: 0.60\n\n #' Score for a biological subfamily match. FLOAT\n subfamily: 0.65\n\n #' Score for a biological tribe match. FLOAT\n tribe: 0.70\n\n #' Score for a biological subtribe match. FLOAT\n subtribe: 0.75\n\n #' Score for a biological genus match. FLOAT\n genus: 0.80\n\n #' Score for a biological subgenus match. FLOAT\n subgenus: 0.85\n\n #' Score for a biological species match. FLOAT\n species: 0.90\n\n #' Score for a biological subspecies match. FLOAT\n subspecies: 0.95\n\n #' Score for a biological variety match. FLOAT\n variety: 1.00\n\n #' Weights for the chemical part.\n chemical:\n #' Score for a classyfire match.\n cla:\n #' Score for a chemical kingdom match. FLOAT\n kingdom: 0.25\n\n #' Score for a chemical superclass match. FLOAT\n superclass: 0.50\n\n #' Score for a chemical class match. FLOAT\n class: 0.75\n\n #' Score for a chemical parent match. FLOAT\n parent: 1.00\n\n #' Score for a NPClassifier match.\n npc:\n #' Score for a chemical kingdom match. FLOAT\n pathway: 0.333\n\n #' Score for a chemical superclass match. FLOAT\n superclass: 0.666\n\n #' Score for a chemical class match. FLOAT\n class: 1.000\n\n#' Options.\noptions:\n #' Report compounds names. Can be very large. BOOLEAN\n compounds_names: no\n\n #' Do not use it. BOOLEAN\n force: no\n\n #' Filter high confidence candidates only. BOOLEAN\n high_confidence: yes\n\n #' Remove ties. BOOLEAN\n remove_ties: no\n\n #' Summarise results to one row per feature. BOOLEAN\n summarise: no\n","type":"text"},{"name":"params/prepare_params.yaml","content":"---\nfiles:\n #' The pattern identifying your whole job. You can put whatever you want. STRING\n pattern: example\n features:\n #' The path to the file containing your features' intensities.\n #' Can be generated by MZMine3 or SLAW. STRING\n raw: data/source/example_features.csv\n metadata:\n #' The path to the file containing your metadata.\n #' Must be formatted accordingly.\n #' If your experiment contains a single taxon, you can provide it below instead. STRING\n raw: data/source/example_metadata.tsv\n spectral:\n #' The path to the file containing your features' spectra.\n #' Can contain MS1 and/or MS2 spectra. STRING\n raw: data/source/example_spectra.mgf\n #' Annotations files.\n annotations:\n raw:\n #' Directory containing the sirius annotations. STRING\n sirius: data/interim/annotations/example_sirius.zip\nms:\n #' The polarity used. Must be either \"pos\" or \"neg\". STRING\n polarity: pos\norganisms:\n #' OPTIONAL. If your experiment contains a single taxon, its scientific name.\n #' Else, you will need to provide a metadata file above.\n #' If you provide both, this one will have priority.\n #' Example: \"Homo sapiens\". STRING\n taxon: ~\noptions:\n #' Filter high confidence candidates only. BOOLEAN\n high_confidence: yes\n #' Do you want to summarize all candidates per feature to a single row? BOOLEAN\n summarise: no\n","type":"text"},{"name":"params/prepare_params_advanced.yaml","content":"---\n#' Annotations related parameters.\nannotations:\n #' Number of candidates to keep per feature.\n candidates:\n #' Number of final candidates. INTEGER\n final: 3\n\n #' Erase MS2 results and keep MS1 only. BOOLEAN\n ms1only: no\n\n #' Perform approximative matching without precursor matching? BOOLEAN\n ms2approx: no\n\n #' Thresholds related parameters.\n thresholds:\n #' Minimal consistency score (chemical) to consider a class. FLOAT\n consistency: 0.0\n\n #' MS1 annotation related parameters.\n ms1:\n #' Minimal biological score to keep MS1 only annotation. FLOAT\n biological: 0.0\n\n #' Minimal chemical score to keep MS1 only annotation. FLOAT\n chemical: 0.0\n\n #' Condition to be used to retain candidates.\n #' Must be \"OR\" or \"AND\".\n #' Example: Minimum 0.3 biological AND 0.3 chemical. STRING\n condition: OR\n\n #' MS2 annotation related parameters.\n ms2:\n #' For similarity.\n similarity:\n #' For annotation. FLOAT\n annotation: 0.0\n\n #' For edges. FLOAT\n edges: 0.7\n\n#' Files.\nfiles:\n #' Pattern to identify the files. STRING\n pattern: example\n\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' List of spectral matching annotations.\n spectral:\n #' GNPS spectral matching annotations. STRING\n gnps: data/interim/annotations/example_gnps.tsv\n\n #' Spectral spectral matching annotations. STRING\n spectral: data/interim/annotations/example_spectralMatches.tsv.gz\n\n #' Directory containing the sirius annotations. STRING\n sirius: data/interim/annotations/example_sirius\n\n #' List of filtered annotations. STRING\n filtered: data/interim/annotations/example_annotationsFiltered.tsv.gz\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared canopus annotations. STRING\n canopus: data/interim/annotations/example_canopusPrepared.tsv.gz\n\n #' List of prepared formula annotations. STRING\n formula: data/interim/annotations/example_formulaPrepared.tsv.gz\n\n #' List of prepared structural annotations.\n structural:\n #' Prepared GNPS structural annotations. STRING\n gnps: data/interim/annotations/example_gnpsPrepared.tsv.gz\n\n #' Prepared MS1 structural annotations. STRING\n ms1: data/interim/annotations/example_ms1Prepared.tsv.gz\n\n #' Prepared SIRIUS structural annotations. STRING\n sirius: data/interim/annotations/example_siriusPrepared.tsv.gz\n\n #' Prepared SPECTRAL structural annotations. STRING\n spectral: data/interim/annotations/example_spectralMatchesPrepared.tsv.gz\n\n #' Final results file. STRING\n processed: example_results.tsv\n\n #' Features files.\n features:\n #' Raw features file. STRING\n raw: data/source/example_features.csv\n\n #' Prepared features file. STRING\n prepared: data/interim/features/example_features.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' Closed structure organism pairs library. STRING\n closed: ../lotus-processor/data/processed/230106_closed_metadata.csv.gz\n\n #' ECMDB structure organism pairs library. STRING\n ecmdb: data/source/libraries/sop/ecmdb.json.zip\n\n #' HMDB structure organism pairs library. STRING\n hmdb: data/source/libraries/sop/hmdb/structures.zip\n\n #' Lotus raw structure organism pairs library. STRING\n lotus: data/source/libraries/sop/lotus.csv.gz\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' Closed prepared structure organism pairs library. STRING\n closed: data/interim/libraries/sop/closed_prepared.tsv.gz\n\n #' ECMDB prepared structure organism pairs library. STRING\n ecmdb: data/interim/libraries/sop/ecmdb_prepared.tsv.gz\n\n #' HMDB prepared structure organism pairs library. STRING\n hmdb: data/interim/libraries/sop/hmdb_prepared.tsv.gz\n\n #' Lotus prepared structure organism pairs library. STRING\n lotus: data/interim/libraries/sop/lotus_prepared.tsv.gz\n\n #' RT prepared structure organism pairs library. STRING\n rt: data/interim/libraries/sop/rt_prepared.tsv.gz\n\n #' Spectral prepared structure organism pairs library. STRING\n spectral: data/interim/libraries/sop/spectral_prepared.tsv.gz\n\n #' Merged structure organism pairs library.\n merged:\n #' Merged structure organism pairs library keys. STRING\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n\n #' Organisms libraries.\n organisms:\n #' Organisms names. STRING\n names: data/interim/libraries/sop/merged/organisms/names.tsv.gz\n\n #' Organisms taxonomies.\n taxonomies:\n #' OTT organisms taxonomy. STRING\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n\n #' Spectral libraries. STRING\n spectral:\n #' List of negative spectral libraries. STRING\n neg:\n - data/interim/libraries/spectra/exp/internal_neg.rds\n \n #' List of positive spectral libraries. STRING\n pos:\n - data/interim/libraries/spectra/exp/internal_pos.rds\n\n #' List of raw spectral libraries. STRING\n raw: \n - data/source/libraries/spectra/exp/spectral_lib_mini_with_rt.mgf\n\n #' Temporal libraries. STRING\n temporal:\n #' List of experimental temporal libraries.\n exp:\n #' List of experimental temporal libraries (CSV). STRING\n csv: ~\n\n #' List of experimental temporal libraries (MGF).\n mgf:\n #' List of negative experimental temporal libraries (MGF). STRING\n neg: [ ]\n\n #' List of positive experimental temporal libraries (MGF). STRING\n pos: [ ]\n\n #' List of in silico temporal libraries. STRING\n is:\n #' List of in silico temporal libraries (CSV). STRING\n csv: ~\n\n #' List of in silico temporal libraries (MGF).\n mgf:\n #' List of negative in silico temporal libraries (MGF). STRING\n neg: [ ]\n\n #' List of positive in silico temporal libraries (MGF). STRING\n pos: [ ]\n\n #' List of prepared temporal libraries. STRING\n prepared:\n - data/interim/libraries/rt/prepared.tsv.gz\n\n #' Metadata files.\n metadata:\n #' The path to the file containing your metadata.\n #' Must be formatted accordingly.\n #' If your experiment contains a single taxon, you can provide it below instead. STRING\n raw: data/source/example_metadata.tsv\n\n #' Taxonomically informed features file. STRING\n prepared: data/interim/taxa/example_taxed.tsv.gz\n\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Raw edges file.\n raw:\n #' Raw ms1 edges file. STRING\n ms1: data/interim/features/example_edgesMasses.tsv\n\n #' Raw spectral edges file. STRING\n spectral: data/interim/features/example_edgesSpectra.tsv\n\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_edges.tsv\n\n #' Components files.\n components:\n #' Raw components file. STRING\n raw: data/interim/features/example_components.tsv\n\n #' Prepared components file. STRING\n prepared: data/interim/features/example_componentsPrepared.tsv\n\n #' Spectral files.\n spectral:\n #' Raw spectral file. STRING\n raw: data/source/example_spectra.mgf\n\n#' GNPS related parameters.\ngnps:\n #' GNPS job ID. STRING\n id: 41c6068e905d482db5698ad81d145d7c\n\n #' Workflow used within GNPS.\n #' Currently \"fbmn\" or \"classical\" supported. STRING\n workflow: fbmn\n\n#' MS related parameters.\nms:\n #' List of adducts to be used.\n adducts:\n #' List of adducts to be used in negative. STRING\n neg:\n - \"[M-H3]3-\"\n - \"[M-H2]2-\"\n - \"[M-H]-\"\n - \"[M+F]-\"\n - \"[M+Na-H2]-\"\n - \"[M+Cl]-\"\n - \"[M+K-H2]-\"\n - \"[M+Br]-\"\n - \"[2M-H]-\"\n - \"[3M-H]-\"\n\n #' List of adducts to be used in positive. STRING\n pos:\n - \"[M+H3]3+\"\n - \"[M+H2Na]3+\"\n - \"[M+HNa2]3+\"\n - \"[M+Na3]3+\"\n - \"[M+H2]2+\"\n - \"[M+HNa]2+\"\n - \"[M+Mg]2+\"\n - \"[M+HK]2+\"\n - \"[M+Ca]2+\"\n - \"[M+Na2]2+\"\n - \"[M+Fe]2+\"\n - \"[M+H]+\"\n - \"[M+H4N]+\"\n - \"[M+Na]+\"\n - \"[M+K]+\"\n - \"[M+Cu]+\"\n - \"[2M+Mg]2+\"\n - \"[2M+Ca]2+\"\n - \"[2M+Fe]2+\"\n - \"[2M+H]+\"\n - \"[2M+H4N]+\"\n - \"[2M+Na]+\"\n - \"[2M+K]+\"\n\n #' List of clusters to be used.\n clusters:\n #' List of clusters to be used in negative. STRING\n neg:\n - \"H2O\" # (water)\n - \"CH2O2\" # (formic)\n - \"NaCl\" # (sodium chloride)\n - \"C2H4O2\" # (acetic)\n - \"H2PO4\" # (phosphoric)\n - \"C2HF3O2\" # (tfa)\n\n #' List of clusters to be used in positive. STRING\n pos:\n - \"H2O\" # (water)\n # - CH4O # (methanol)\n - \"C2H3N\" # (acetonitrile)\n # - C2H7N # (ethylamine)\n # - C2H6O # (ethanol)\n - \"NaCl\" # (sodium chloride)\n # - C3H8O # (isopropanol)\n # - C2H6OS # (dmso)\n\n #' List of neutral losses to be used. STRING\n neutral_losses:\n - \"HN\"\n - \"CH3\"\n - \"O\"\n - \"H3N (ammonia)\"\n - \"H2O (water)\"\n - \"CHN\"\n - \"CO\"\n - \"C2H4 (2xCH2)\"\n - \"C2H5 (CH2-CH3)\"\n - \"CH2O\"\n - \"H5ON (H2O-H3N)\"\n - \"H4O2 (2xH2O)\"\n - \"C2H2O\"\n - \"CHON\"\n - \"CO2\"\n - \"CHO2\"\n - \"H6O3 (3xH2O)\"\n - \"C2O2 (2xCO)\"\n - \"CH6O3 (combination)\"\n - \"H8O4 (4xH2O)\"\n - \"C2H2O3 (CH2O-CO2)\"\n - \"C3H6O2 (CH3-CH2-COOH)\"\n - \"C3O3 (3xCO)\"\n - \"C4H9NO (combination)\"\n - \"C2O4 (2xCO2)\"\n - \"C3H4O3 (pyruvic)\"\n - \"C4H8O2 (butyric)\"\n - \"C3H7NO2 (alanine)\"\n - \"C3H6O3 (sugar)\"\n - \"H2O4S (sulfuric)\"\n - \"H3O4P (phosphoric)\"\n - \"C5H10O2 (valeric)\"\n - \"C3H4O4 (malonic)\"\n - \"C2H2O5 (CO2-CO2-H2O)\"\n - \"C3H8O4 (combination)\"\n - \"C5H9NO2 (proline)\"\n - \"C4O4H6 (CO2-CO2-CH3-CH3)\"\n - \"C4H8O4 (sugar)\"\n - \"C2H4O6 (CO2-CO2-H2O-H2O)\"\n - \"C6H6O3 (HRF)\"\n - \"C5H8O4 (pentose-H2O)\"\n - \"C8H8O2 (RDA-1)\"\n - \"C6H8O4 (HRF)\"\n - \"C9H6O2 (coumaroyl)\"\n - \"C6H10O4 (methylpentose/desoxyhexose-H2O)\"\n - \"C9H8O2 (cinnamoyl)\"\n - \"C7H4O4 (galloyl)\"\n - \"C8H8O3 (RDA-2)\"\n - \"C9H6O3 (caffeoyl)\"\n - \"C6H10O5 (hexose-H2O)\"\n - \"C8H8O4 (RDA-3)\"\n - \"C8H10O4 (RDA-2-H2O)\"\n - \"C6H8O6 (glucuronic-H2O)\"\n - \"C10H8O3 (feruloyl)\"\n - \"C6H13NO5 (hexose-H2N)\"\n - \"C6H12O6 (hexose)\"\n - \"C7H12O6 (quinoyl)\"\n - \"C6H15NO6 (hexose-H2N-H2O)\"\n - \"C6H14O7 (hexose-H2O)\"\n - \"C8H12O6 (acetylhexose-H2O)\"\n - \"C11H10O4 (sinapoyl)\"\n - \"C16H30O (pamitoyl)\"\n - \"C9H12O8 (malonylhexose)\"\n - \"C13H14O6 (benzoylhexose)\"\n - \"C12H20O8 (2xmethylpentose/desoxyhexose-H2O)\"\n - \"C15H16O7 (coumaroylhexose)\"\n - \"C13H14O9 (galloylhexose)\"\n - \"C15H16O8 (caffeoylhexose)\"\n - \"C12H20O10 (2xhexose-H2O)\"\n - \"C16H18O8 (feruloylhexose)\"\n - \"C17H20O9 (sinapoylhexose)\"\n - \"C18H30O15 (3xhexose-H2O)\"\n\n #' Polarity used. Must be \"pos\" or \"neg\". STRING\n polarity: pos\n\n #' Thresholds related parameters.\n thresholds:\n #' MS2 related parameters.\n ms2:\n #' Intensity threshold for MS2. INTEGER\n intensity: 5\n\n #' Tolerances.\n tolerances:\n #' For mass.\n mass:\n #' Relative in ppm.\n ppm:\n #' Relative mass tolerance for MS1 in ppm. FLOAT\n ms1: 10\n\n #' Relative mass tolerance for MS2 in ppm. FLOAT\n ms2: 10\n\n #' Absolute in Dalton.\n dalton:\n #' Absolute mass tolerance for MS1 in Dalton. FLOAT\n ms1: 0.01\n\n #' Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms2: 0.01\n\n #' For retention time.\n rt:\n #' Retention time tolerance for adducts in minutes. FLOAT\n adducts: 0.02\n\n #' Retention time tolerance for library in minutes. FLOAT\n library: 0.1\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"adduct\" variable in the feature table. STRING\n adduct: best ion\n\n #' The file(s) extension is present in the sample name. BOOLEAN\n extension: yes\n\n #' Name of \"feature IDs\" variable in the input. STRING\n features: row ID\n\n #' Name of \"filename\" variable in the input. STRING\n filename: filename\n\n #' Names of the prepared libraries. STRING\n libraries:\n - internal\n\n #' Name of \"InChIKey\" variable in the input. STRING\n inchikey: inchikey\n\n #' Name of fields present in the MGF.\n mgf:\n #' Name of \"adduct\" in MGF. STRING\n adduct: ~\n\n #' Name of \"collision energy\" in MGF. STRING\n collision_energy: ~\n\n #' Name of \"compound ID\" in MGF. STRING\n compound_id: FILENAME\n\n #' Name of \"exact mass\" in MGF. STRING\n exact_mass: EXACTMASS\n\n #' Name of \"InChI\" in MGF. STRING\n inchi: INCHI\n\n #' Name of \"InChI without stereo\" in MGF. STRING\n inchi_no_stereo: ~\n\n #' Name of \"InChIKey\" in MGF. STRING\n inchikey: ~\n\n #' Name of \"InChIKey without stereo\" in MGF. STRING\n inchikey_no_stereo: ~\n\n #' Name of \"molecular formula\" in MGF. STRING\n molecular_formula: ~\n\n #' Name of \"name\" in MGF. STRING\n name: NAME\n\n #' Name of \"polarity\" in MGF. STRING\n polarity: IONMODE\n\n #' Name of \"retention time\" in MGF. STRING\n retention_time: RTINSECONDS\n\n #' Name of \"SMILES\" in MGF. STRING\n smiles: SMILES\n\n #' Name of \"SMILES without stereo\" in MGF. STRING\n smiles_no_stereo: ~\n\n #' Name of \"spectrum ID\" in MGF. STRING\n spectrum_id: SPECTRUMID\n\n #' Name of \"SPLASH\" in MGF. STRING\n splash: ~\n\n #' Name of \"synonyms\" in MGF. STRING\n synonyms: ~\n\n #' Name of \"xlogP\" in MGF. STRING\n xlogp: ~\n\n #' Name of \"precursor m/z\" variable in the input. STRING\n precursor: row m/z\n\n #' Name of \"retention time\" variable.\n rt:\n #' Name of \"retention time\" variable in the feature table. STRING\n features: row retention time\n\n #' Name of \"retention time\" variable in the library. STRING\n library: rt\n\n #' Name of \"SMILES\" variable in the input. STRING\n smiles: smiles\n\n #' Name of \"source IDs\" variable in the input. STRING\n source: CLUSTERID1\n\n #' Name of \"target IDs\" variable in the input. STRING\n target: CLUSTERID2\n\n #' Name of the \"taxon name\" variable in the input. STRING\n taxon: ATTRIBUTE_species\n\n#' Organisms related parameters.\norganisms:\n #' Number of organisms to keep per feature. INTEGER\n candidates: 1\n\n #' Organisms filter related parameters.\n filter:\n #' Filter library to restrict it to a portion of organisms only. BOOLEAN\n mode: no\n\n #' Level at which the library will be filtered.\n #' Must be one of \"domain\", \"phylum\", \"class\", \"order\",\n #' \"family\", \"tribe\", \"genus\", \"species\", \"varietas\" . STRING\n level: phylum\n\n #' Value to be applied for filtering.\n #' \"OR\" can be used as \"|\".\n # Example: \"Simaroubaceae|Gentianaceae\". STRING\n value: Streptophyta\n\n #' OPTIONAL. If your experiment contains a single taxon, its scientific name.\n #' Else, you will need to provide a metadata file above.\n #' If you provide both, this one will have priority.\n #' Example: \"Homo sapiens\". STRING\n taxon: ~\n\n#' Tools used for each step.\ntools:\n #' Tool used to store metadata.\n #' Currently \"gnps\" or \"manual\" supported. STRING\n metadata: gnps\n\n #' Networks related parameters.\n networks:\n #' Spectral networks related parameters.\n spectral:\n #' Tool used to generate components.\n #' Currently \"gnps\" or \"manual\" supported. STRING\n components: gnps\n\n #' Tool used to generate edges.\n #' Currently \"gnps\" or \"manual\" supported. STRING\n edges: gnps\n\n #' Sirius.\n sirius:\n #' Sirius version.\n #' Currently only 5 or 6 supported. INTEGER\n version: 6\n\n #' Taxonomies related parameters.\n taxonomies:\n #' Biological taxonomy used for matching.\n #' Currently only \"otl\" supported. STRING\n biological: otl\n\n #' Chemical taxonomy used for matching.\n #' Currently only \"npc\" or supported. STRING\n chemical: npc\n\n#' Units of the different variables in the input files.\nunits:\n #' Unit of the \"retention time\" variable in the input files. STRING\n rt: seconds\n\n#' Weights to apply to each part of the final score.\nweights:\n #' Global weights.\n global:\n #' Weight for the biological part. FLOAT\n biological: 0.500\n\n #' Weight for the chemical part. FLOAT\n chemical: 0.166\n\n #' Weight for the spectral part. FLOAT\n spectral: 0.333\n\n #' Weights for the biological part.\n biological:\n #' Score for a biological domain match. FLOAT\n domain: 0.10\n\n #' Score for a biological kingdom match. FLOAT\n kingdom: 0.20\n\n #' Score for a biological phylum match. FLOAT\n phylum: 0.30\n\n #' Score for a biological class match. FLOAT\n class: 0.40\n\n #' Score for a biological order match. FLOAT\n order: 0.50\n\n #' Score for a biological infraorder match. FLOAT\n infraorder: 0.55\n\n #' Score for a biological family match. FLOAT\n family: 0.60\n\n #' Score for a biological subfamily match. FLOAT\n subfamily: 0.65\n\n #' Score for a biological tribe match. FLOAT\n tribe: 0.70\n\n #' Score for a biological subtribe match. FLOAT\n subtribe: 0.75\n\n #' Score for a biological genus match. FLOAT\n genus: 0.80\n\n #' Score for a biological subgenus match. FLOAT\n subgenus: 0.85\n\n #' Score for a biological species match. FLOAT\n species: 0.90\n\n #' Score for a biological subspecies match. FLOAT\n subspecies: 0.95\n\n #' Score for a biological variety match. FLOAT\n variety: 1.00\n\n #' Weights for the chemical part.\n chemical:\n #' Score for a classyfire match.\n cla:\n #' Score for a chemical kingdom match. FLOAT\n kingdom: 0.25\n\n #' Score for a chemical superclass match. FLOAT\n superclass: 0.50\n\n #' Score for a chemical class match. FLOAT\n class: 0.75\n\n #' Score for a chemical parent match. FLOAT\n parent: 1.00\n\n #' Score for a NPClassifier match.\n npc:\n #' Score for a chemical kingdom match. FLOAT\n pathway: 0.333\n\n #' Score for a chemical superclass match. FLOAT\n superclass: 0.666\n\n #' Score for a chemical class match. FLOAT\n class: 1.000\n\n#' Options.\noptions:\n #' Report compounds names. Can be very large. BOOLEAN\n compounds_names: no\n\n #' Do not use it. BOOLEAN\n force: no\n\n #' Filter high confidence candidates only. BOOLEAN\n high_confidence: yes\n\n #' Remove ties. BOOLEAN\n remove_ties: no\n\n #' Summarise results to one row per feature. BOOLEAN\n summarise: no\n","type":"text"},{"name":"paths.yaml","content":"---\nbase_dir: ./\ndata:\n path: data\n interim:\n path: data/interim\n annotations:\n path: data/interim/annotations\n example_sirius:\n v5: data/interim/annotations/example_sirius_5.zip\n v6: data/interim/annotations/example_sirius_6.zip\n features:\n path: data/interim/features\n libraries:\n path: data/interim/libraries\n adducts:\n path: data/interim/libraries/adducts\n neg: data/interim/libraries/adducts/neg.tsv.gz\n pos: data/interim/libraries/adducts/pos.tsv.gz\n sop:\n path: data/interim/libraries/sop\n closed: data/interim/libraries/sop/closed_prepared.tsv.gz\n hmdb: data/interim/libraries/sop/hmdb_prepared.tsv.gz\n hmdb_minimal: data/interim/libraries/sop/hmdb_minimal.tsv.gz\n lotus: data/interim/libraries/sop/lotus_prepared.tsv.gz\n merged:\n path: data/interim/libraries/sop/merged\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n organisms:\n path: data/interim/libraries/sop/merged/organisms\n names: data/interim/libraries/sop/merged/organisms/names.tsv.gz\n taxonomies:\n path: data/interim/libraries/sop/merged/organisms/taxonomies\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n structures:\n path: data/interim/libraries/sop/merged/structures\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n taxonomies:\n path: data/interim/libraries/sop/merged/structures/taxonomies\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n spectra:\n path: data/interim/libraries/spectra\n exp:\n path: data/interim/libraries/spectra/exp\n mona:\n pos: data/interim/libraries/spectra/exp/mona_pos.rds\n neg: data/interim/libraries/spectra/exp/mona_neg.rds\n is:\n path: data/interim/libraries/spectra/is\n hmdb:\n pos: data/interim/libraries/spectra/is/hmdb_pos.rds\n neg: data/interim/libraries/spectra/is/hmdb_neg.rds\n lotus:\n pos: data/interim/libraries/spectra/is/lotus_pos.rds\n neg: data/interim/libraries/spectra/is/lotus_neg.rds\n params:\n path: data/interim/params\n taxa:\n path: data/interim/taxa\n processed:\n path: data/processed\n source:\n path: data/source\n features: data/source/example_features.csv\n metadata: data/source/example_metadata.tsv\n spectra: data/source/example_spectra.mgf\n benchmark:\n path: data/source/benchmark\n cleaned: data/interim/benchmark/cleaned_libraries_matchms/results_library_cleaning/cleaned_spectra.mgf\n mgf:\n neg: data/interim/benchmark/benchmark_neg.mgf\n pos: data/interim/benchmark/benchmark_pos.mgf\n zip: data/source/benchmark/cleaned_libraries_matchms.zip\n libraries:\n path: data/source/libraries\n rt:\n path: data/source/libraries/rt\n example_mini: data/source/libraries/rt/example_lib_mini_rt.tsv\n sop:\n path: data/source/libraries/sop\n closed: ../lotus-processor/data/processed/230224_closed_metadata.csv.gz\n ecmdb: data/source/libraries/sop/ecmdb.json.zip\n hmdb: data/source/libraries/sop/hmdb/structures.zip\n lotus: data/source/libraries/sop/lotus.csv.gz\n spectra:\n path: data/source/libraries/spectra\n exp:\n path: data/source/libraries/spectra/exp\n mona_lcmsms: data/source/libraries/spectra/exp/MoNA-export-LC-MS-MS_Spectra.sdf\n with_rt: data/source/libraries/spectra/exp/spectral_lib_mini_with_rt.mgf\n is:\n hmdb: data/source/libraries/spectra/is/hmdb_predicted_msms_spectra.zip\n lotus:\n pos: data/source/libraries/spectra/is/isdb_pos.mgf\n neg: data/source/libraries/spectra/is/isdb_neg.mgf\ndoc:\n path: doc\nimg:\n path: img\ninst:\n path: inst\n scripts:\n path: scripts\n docopt:\n path: scripts/docopt\ngnps:\n example: 41c6068e905d482db5698ad81d145d7c\n example2: 958d60d32e0c43bc9a06798b744f41c7\nparams:\n path: params\n default:\n path: params/default\n annotate:\n masses: params/default/annotate_masses.yaml\n spectra: params/default/annotate_spectra.yaml\n create:\n components: params/default/create_components.yaml\n edges:\n spectra: params/default/create_edges_spectra.yaml\n filter:\n annotations: params/default/filter_annotations.yaml\n prepare:\n features:\n components: params/default/prepare_features_components.yaml\n edges: params/default/prepare_features_edges.yaml\n tables: params/default/prepare_features_tables.yaml\n libraries:\n rt: params/default/prepare_libraries_rt.yaml\n sop:\n closed: params/default/prepare_libraries_sop_closed.yaml\n ecmdb: params/default/prepare_libraries_sop_ecmdb.yaml\n hmdb: params/default/prepare_libraries_sop_hmdb.yaml\n lotus: params/default/prepare_libraries_sop_lotus.yaml\n merged: params/default/prepare_libraries_sop_merged.yaml\n spectra: params/default/prepare_libraries_spectra.yaml\n annotations:\n gnps: params/default/prepare_annotations_gnps.yaml\n sirius: params/default/prepare_annotations_sirius.yaml\n spectra: params/default/prepare_annotations_spectra.yaml\n taxa: params/default/prepare_taxa.yaml\n weight:\n annotations: params/default/weight_annotations.yaml\n prepare_params: params/prepare_params.yaml\n prepare_params_advanced: params/prepare_params_advanced.yaml\n user:\n path: params/user\n annotate:\n masses: params/user/annotate_masses.yaml\n spectra: params/user/annotate_spectra.yaml\n create:\n components: params/user/create_components.yaml\n edges:\n spectra: params/user/create_edges_spectra.yaml\n filter:\n annotations: params/user/filter_annotations.yaml\n prepare:\n features:\n components: params/user/prepare_features_components.yaml\n edges: params/user/prepare_features_edges.yaml\n tables: params/user/prepare_features_tables.yaml\n libraries:\n rt: params/user/prepare_libraries_rt.yaml\n sop:\n closed: params/user/prepare_libraries_sop_closed.yaml\n ecmdb: params/user/prepare_libraries_sop_ecmdb.yaml\n hmdb: params/user/prepare_libraries_sop_hmdb.yaml\n lotus: params/user/prepare_libraries_sop_lotus.yaml\n merged: params/user/prepare_libraries_sop_merged.yaml\n spectra: params/user/prepare_libraries_spectra.yaml\n annotations:\n gnps: params/user/prepare_annotations_gnps.yaml\n sirius: params/user/prepare_annotations_sirius.yaml\n spectra: params/user/prepare_annotations_spectra.yaml\n taxa: params/user/prepare_taxa.yaml\n weight:\n annotations: params/user/weight_annotations.yaml\ntests:\n mode: no\n path: tests\nurls:\n benchmarking_set: https://zenodo.org/records/10160791/files/cleaned_libraries_matchms.zip?download=1\n examples:\n features: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_features.csv\n lib_mini:\n rt: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_lib_mini_rt.tsv\n metadata: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_metadata.tsv\n sirius:\n v5: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_sirius_5.zip\n v6: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_sirius_6.zip\n spectra: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_spectra.mgf\n spectra_mini: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_spectra_mini.mgf\n spectra_ms1: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_spectra_ms1.mgf\n spectral_lib:\n neg: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/lotus_neg.rds\n pos: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/lotus_pos.rds\n spectral_lib_mini:\n neg: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/spectral_lib_mini_neg.mgf\n pos: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/spectral_lib_mini_pos.mgf\n with_rt: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/spectral_lib_mini_with_rt.mgf\n ecmdb:\n metabolites: https://ecmdb.ca/download/ecmdb.json.zip\n hmdb:\n metabolites: https://hmdb.ca/system/downloads/current/hmdb_metabolites.zip\n spectra:\n predicted: https://hmdb.ca/system/downloads/current/spectral_data/spectra_xml/hmdb_predicted_msms_spectra.zip\n structures: https://hmdb.ca/system/downloads/current/structures.zip\n lotus:\n doi: 10.5281/zenodo.5794106\n pattern: frozen_metadata.csv.gz\n metadata_doi: 10.5281/zenodo.6378223\n metadata_pattern:\n organisms: organism_metadata.tsv.gz\n references: reference_metadata.tsv.gz\n structures: structure_metadata.tsv.gz\n lotus_isdb:\n doi: 10.5281/zenodo.5607185\n pattern:\n pos: isdb_pos.mgf\n neg: isdb_neg.mgf\n massbank:\n file: MassBank_NIST.msp\n url: https://github.com/MassBank/MassBank-data/releases/download\n version: 2024.06\n","type":"text"},{"name":"scripts/annotate_masses.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"annotates based on exact mass (MS1)\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: \", crayon::blue(\"CH\"), crayon::cyan(\"MS\"), \"\\n\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_ms1_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/annotate_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\n \"performs spectral similarity calculation\",\n \"to annotate against a spectral library. \\n\"\n )\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_spe_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/change_tests.R","content":"paths <- yaml::read_yaml(file = system.file(\"paths.yaml\", package = \"tima\"))\n\npaths$tests$mode <- \"yes\"\npaths$urls$examples$spectra <- paths$urls$examples$spectra_mini\n\nyaml::write_yaml(\n x = paths,\n file = system.file(\"paths.yaml\", package = \"tima\")\n)\n","type":"text"},{"name":"scripts/create_components.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"creates components from edges. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_com\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/create_edges_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"performs spectral similarity calculation to create edges. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: \", crayon::blue(\"CH\"), crayon::cyan(\"MS\"), \"\\n\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_edg_spe\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/docopt/annotate_masses.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/annotate_masses.R\n\nUsage:\n annotate_masses.R [--fil-ann-pre-str=<fil-ann-pre-str>] [--fil-lib-sop-mer=<fil-lib-sop-mer>] [--fil-lib-add-neg=<fil-lib-add-neg>] [--fil-lib-add-pos=<fil-lib-add-pos>] [--fil-lib-add-pro=<fil-lib-add-pro>] [--ms-add-neg=<ms-add-neg>] [--ms-add-pos=<ms-add-pos>] [--ms-clu-neg=<ms-clu-neg>] [--ms-clu-pos=<ms-clu-pos>] [--ms-neu=<ms-neu>] [--ms-pol=<ms-pol>] [--ms-tol-mas-ppm-ms1=<ms-tol-mas-ppm-ms1>] [--ms-tol-mas-dal-ms1=<ms-tol-mas-dal-ms1>] [--ms-tol-rt-add=<ms-tol-rt-add>] [--force=<force>]\n\nArguments:\n fil-ann-pre-str List of prepared structural annotations. STRING\n fil-fea-pre Prepared features file. STRING\n fil-lib-add-neg Negative adducts. STRING\n fil-lib-add-pos Positive adducts. STRING\n fil-lib-add-pro Prepared adducts library. STRING\n fil-lib-sop-mer Merged structure organism pairs library. STRING\n\n ms-add-neg List of adducts to be used in negative. STRING\n ms-add-pos List of adducts to be used in positive. STRING\n ms-clu-neg List of clusters to be used in negative. STRING\n ms-clu-pos List of clusters to be used in positive. STRING\n ms-neu List of neutral losses to be used. STRING\n ms-pol Polarity used. Must be \"pos\" or \"neg\". STRING\n ms-tol-mas-ppm-ms1 Relative mass tolerance for MS1 in ppm. FLOAT\n ms-tol-mas-dal-ms1 Absolute mass tolerance for MS1 in Dalton. FLOAT\n ms-tol-rt-add Retention time tolerance for adducts in minutes. FLOAT\n\n force Do not use it. BOOLEAN\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/annotate_spectra.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/annotate_spectra.R --fil-spe-raw data/source/examples/spectra.mgf --fil-lib-spe-pos data/interim/libraries/spectra/is/lotus_pos.rds --fil-ann-raw-spe-spe data/interim/annotations/example_spectralMatches.tsv.gz --ms-tol-mas-dal-ms2 0.01 --ms-tol-mas-ppm-ms2 10 --ann-thr-ms2-sim-ann 0.0 --ms-thr-ms2-int 5 --ann-ms2-app false\n\nUsage:\n annotate_spectra.R [--ann-ms2-app=<ann-ms2-app>] [--ann-thr-ms2-sim-ann=<ann-thr-ms2-sim-ann>] [--fil-ann-raw-spe=<fil-ann-raw-spe>] [--fil-lib-spe-neg=<fil-lib-spe-neg>] [--fil-lib-spe-pos=<fil-lib-spe-pos>] [--fil-spe-raw=<fil-spe-raw>] [--ms-thr-ms2-int=<ms-thr-ms2-int>] [--ms-pol=<ms-pol>] [--ms-tol-mas-ppm-ms2=<ms-tol-mas-ppm-ms2>] [--ms-tol-mas-dal-ms2=<ms-tol-mas-dal-ms2>] [--ms-tol-rt-min=<ms-tol-rt-min>]\n\nArguments:\n ann-ms2-app Perform approximative matching without precursor matching? BOOLEAN\n ann-thr-ms2-sim-ann Similarity threhsold for annotation. FLOAT\n\n fil-ann-raw-spe-spe List of spectral matching results. STRING\n fil-lib-spe-neg List of negative spectral libraries. STRING\n fil-lib-spe-pos List of positive spectral libraries. STRING\n fil-spe-raw Raw spectral file. STRING\n\n ms-pol Polarity used. Must be \"pos\" or \"neg\". STRING\n ms-thr-ms2-int Intensity threshold for MS2. INTEGER\n ms-tol-mas-ppm-ms2 Relative mass tolerance for MS2 in ppm. FLOAT\n ms-tol-mas-dal-ms2 Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms-tol-rt-min Retention time tolerance in minutes. FLOAT\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/create_components.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/create_components.R --fil-net-spe-edg-pre data/interim/features/example_edges.tsv.gz --fil-net-spe-com-raw data/interim/features/example_components.tsv.gz\n\nUsage:\n create_components.R [--fil-net-spe-edg-pre=<fil-net-spe-edg-pre>] [--fil-net-spe-com-raw=<fil-net-spe-com-raw>]\n\nArguments:\n fil-net-spe-edg-pre Prepared edges file. STRING\n fil-net-spe-com-raw Raw components file. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/create_edges_spectra.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/create_edges_spectra.R --fil-spe-raw data/source/examples/spectra.mgf --fil-net-spe-edg-raw data/interim/features/example_edgesSpectra.tsv.gz --ms-tol-mas-dal-ms2 0.01 --ms-tol-mas-ppm-ms2 10 --ann-thr-ms2-sim-edg 0.7 --ms-thr-ms2-int 5\n\nUsage:\n create_edges_spectra.R [--ann-thr-ms2-sim-edg=<ann-thr-ms2-sim-edg>] [--fil-net-spe-edg-raw=<fil-net-spe-edg-raw>] [--fil-spe-raw=<fil-spe-raw>] [--ms-thr-ms2-int=<ms-thr-ms2-int>] [--ms-tol-mas-ppm-ms2=<ms-tol-mas-ppm-ms2>] [--ms-tol-mas-dal-ms2=<ms-tol-mas-dal-ms2>] [--ms-tol-rt-min=<ms-tol-rt-min>]\n\nArguments:\n ann-thr-ms2-sim-edg Similarity threhsold for edges. FLOAT\n\n fil-net-spe-edg-raw Raw edges file. STRING\n fil-spe-raw Raw spectral file. STRING\n\n ms-thr-ms2-int Intensity threshold for MS2. INTEGER\n ms-tol-mas-ppm-ms2 Relative mass tolerance for MS2 in ppm. FLOAT\n ms-tol-mas-dal-ms2 Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms-tol-rt-min Retention time tolerance in minutes. FLOAT\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/filter_annotations.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/filter_annotations.R\n\nUsage:\n filter_annotations.R [--fil-ann-fil=<fil-ann-fil>] [--fil-ann-pre-str=<fil-ann-pre-str>] [--fil-fea-pre=<fil-fea-prer>] [--fil-lib-tem-pre=<fil-lib-tem-pre>] [--ms-tol-rt-lib=<ms-tol-rt-lib>]\n\nArguments:\n fil-ann-fil Filtered annotation file. STRING\n fil-ann-pre-str List of prepared structural annotations. STRING\n fil-fea-pre Prepared features file. STRING\n fil-lib-tem-pre List of prepared temporal libraries. STRING\n\n ms-tol-rt-lib Retention time tolerance for library in minutes. FLOAT\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_annotations_gnps.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_annotations_gnps.R --fil-ann-raw-spe-gnp data/interim/annotations/example_gnps.tsv --fil-ann-pre-str data/interim/annotations/example_gnpsPrepared.tsv.gz\n\nUsage:\n prepare_annotations_gnps.R [--=fil-ann-pre-str<fil-ann-pre-str>] [--gnps-id=<gnps-id>] [--gnps-workflow=<gnps-workflow>]\n\nArguments:\n fil-ann-raw-spe-gnp List of GNPS spectral matching results. STRING\n fil-ann-pre-str List of prepared structural annotations. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_annotations_sirius.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_annotations_sirius.R --fil-ann-raw-sir data/interim/annotations/example_sirius/ --fil-ann-pre-can data/interim/annotations/example_canopusPrepared.tsv.gz --fil-ann-pre-for data/interim/annotations/example_formulaPrepared.tsv.gz --fil-ann-pre-str data/interim/annotations/example_siriusPrepared.tsv.gz\n\nUsage:\n prepare_annotations_sirius.R [--fil-ann-raw-sir=<fil-ann-raw-sir>] [--fil-ann-pre-can=<fil-ann-pre-can>] [--fil-ann-pre-for=<fil-ann-pre-for>] [--fil-ann-pre-str=<fil-ann-pre-str>]\n\nArguments:\n fil-ann-raw-sir Directory containing the sirius results. STRING\n fil-ann-pre-can List of prepared canopus annotations. STRING\n fil-ann-pre-for List of prepared formula annotations. STRING\n fil-ann-pre-str List of prepared structural annotations. STRING\n \nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_annotations_spectra.txt","content":"You can use this script with the following example:\nRscript inst/scripts/prepare_annotations_spectra.R --fil-ann-raw-spe-spe data/interim/annotations/example_isdb.tsv.gz --fil-ann-pre-str data/interim/annotations/example_isdbPrepared.tsv.gz\n\nUsage:\n prepare_annotations_spectra.R [--fil-ann-raw-spe=<fil-ann-raw-spe>] [--fil-ann-pre-str=<fil-ann-pre-str>]\n\nArguments:\n fil-ann-raw-spe-spe List of spectral matching results. STRING\n fil-ann-pre-str List of prepared structural annotations. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_features_components.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_features_components.R\nUsage:\n prepare_features_components.R [--fil-net-spe-com-raw=<fil-net-spe-com-raw>] [--fil-net-spe-com-pre=<fil-net-spe-com-pre>]\n\nArguments:\n fil-net-spe-com-raw Raw components file. STRING\n fil-net-spe-com-pre Prepared components file. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_features_edges.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_features_edges.R --fil-net-spe-edg-pre data/interim/features/example_edges.tsv.gz\n\nUsage:\n prepare_features_edges.R [--fil-net-spe-edg-raw=<fil-net-spe-edg-raw>] [--fil-net-spe-edg-pre=<fil-net-spe-edg-pre>] [--names-source=<names-source>] [--names-target=<names-target>]\n\nArguments:\n fil-net-spe-edg-raw Raw edges file. STRING\n fil-net-spe-edg-pre Prepared edges file. STRING\n\n names-source Name of \"source IDs\" variable in the input. STRING\n names-target Name of \"target IDs\" variable in the input. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_features_tables.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_features_tables.R --fil-fea-raw data/source/example_features.csv --fil-fea-pre data/interim/features/example_features.tsv.gz --names-adduct 'best ion' --names-features 'row ID' --names-precursor 'row m/z' --names-rt 'row retention time'\n\nUsage:\n prepare_features_tables.R [--fil-fea-raw=<fil-fea-raw>] [--fil-fea-pre=<fil-fea-pre>] [--names-features=<names-features>] [--names-precursor=<names-precursor>] [--names-rt-fea=<names-rt-fea>]\n\nArguments:\n fil-fea-raw Raw features file. STRING\n fil-fea-pre Prepared features file. STRING\n\n names-adduct Name of \"adduct\" variable in the input. STRING\n names-features Name of \"feature IDs\" variable in the input. STRING\n names-precursor Name of \"precursor m/z\" variable in the input. STRING\n names-rt-fea Name of \"retention time\" variable (fea) in the input. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_rt.txt","content":"You can use this script with the following example:\nRscript inst/scripts/prepare_libraries_rt.R --fil-lib-tem-exp-csv data/source/libraries/rt/internal.tsv --fil-lib-tem-is-csv data/source/libraries/rt/predicted.tsv --fil-lib-tem-pre data/interim/libraries/rt/prepared.tsv.gz\n\nUsage:\n prepare_libraries_rt.R [--fil-lib-tem-exp-csv=<fil-lib-tem-exp-csv>] [--fil-lib-tem-exp-mgf-neg=<fil-lib-tem-exp-mgf-neg>] [--fil-lib-tem-exp-mgf-pos=<fil-lib-tem-exp-mgf-pos>] [--fil-lib-tem-is-csv=<fil-lib-tem-is-csv>] [--fil-lib-tem-is-mgf-neg=<fil-lib-tem-is-mgf-neg>] [--fil-lib-tem-is-mgf-pos=<fil-lib-tem-is-mgf-pos>] [--fil-lib-tem-pre=<fil-lib-tem-pre>] [--names-inchikey=<names-inchikey>] [--names-mgf-ik=<names-mgf-ik>] [--names-mgf-rt=<names-mgf-rt>] [--names-mgf-sm=<names-mgf-sm>] [--names-rt-lib=<names-rt-lib>] [--names-smiles=<names-smiles>] [--units-rt=<units-rt>]\n\nArguments:\n fil-lib-tem-exp-csv List of experimental temporal libraries (CSV). STRING\n fil-lib-tem-exp-mgf-neg List of negative experimental temporal libraries (MGF). STRING\n fil-lib-tem-exp-mgf-pos List of positive experimental temporal libraries (MGF). STRING\n fil-lib-tem-is-csv List of in silico temporal libraries (CSV). STRING\n fil-lib-tem-is-mgf-neg List of negative in silico temporal libraries. (MGF) STRING\n fil-lib-tem-is-mgf-pos List of positive in silico temporal libraries (MGF). STRING\n fil-lib-tem-pre List of prepared temporal libraries. STRING\n\n names-inchikey Name of \"InChIKey\" variable in the input. STRING\n names-mgf-ik Name of \"InChIKey\" in MGF. STRING\n names-mgf-rt Name of \"retention time\" in MGF. STRING\n names-mgf-sm Name of \"SMILES\" in MGF. STRING\n names-rt-lib Name of \"retention time\" variable (lib) in the input. STRING\n names-smiles Name of \"SMILES\" variable in the input. STRING\n\n units-rt Unit of the \"retention time\" variable in the input. Must be \"seconds\" or \"minutes\". STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_closed.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_closed.R --fil-lib-sop-raw-clo ../lotus-processor/data/processed/220226_closed_metadata.csv.gz --fil-lib-sop-pre data/interim/libraries/sop/closed_prepared.tsv.gz\n\nUsage:\n prepare_libraries_sop_closed.R [--fil-lib-sop-raw-clo=<fil-lib-sop-raw-clo>] [--fil-lib-sop-pre=<fil-lib-sop-pre>]\n\nArguments:\n fil-lib-sop-raw-clo Closed raw structure organism pairs library. STRING\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_ecmdb.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_ecmdb.R --fil-lib-sop-raw-ecm data/source/libraries/sop/ecmdb.json.zip --fil-lib-sop-pre data/interim/libraries/sop/ecmdb_prepared.tsv.gz\n\nUsage:\n prepare_libraries_sop_ecmdb.R [--fil-lib-sop-raw-ecm=<fil-lib-sop-raw-ecm>] [--fil-lib-sop-pre=<fil-lib-sop-pre>]\n\nArguments:\n fil-lib-sop-raw-ecm ECMDB raw structure organism pairs library. STRING\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_hmdb.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_hmdb.R --fil-lib-sop-raw-hmd data/source/libraries/sop/hmdb/structures.zip --fil-lib-sop-pre data/interim/libraries/sop/hmdb_prepared.tsv.gz\n\nUsage:\n prepare_libraries_sop_hmdb.R [--fil-lib-sop-raw-hmd=<fil-lib-sop-raw-hmd>] [--fil-lib-sop-pre=<fil-lib-sop-pre>]\n\nArguments:\n fil-lib-sop-raw-hmd HMDB raw structure organism pairs library. STRING\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_lotus.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_lotus.R --fil-lib-sop-raw-ecm data/source/libraries/sop/lotus.csv.gz --fil-lib-sop-pre data/interim/libraries/sop/lotus_prepared.tsv.gz\n\nUsage:\n prepare_libraries_sop_ecmdb.R [--fil-lib-sop-raw-ecm=<fil-lib-sop-raw-ecm>] [--fil-lib-sop-pre=<fil-lib-sop-pre>]\n\nArguments:\n fil-lib-sop-raw-lot LOTUS raw structure organism pairs library. STRING\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_merged.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_merged.R --fil-lib-sop-pre data/interim/libraries/sop/lotus_prepared.tsv.gz --fil-lib-sop-mer bitter_db.tsv.gz --org-fil-mod TRUE --org-fil-lev family --org-fil-val 'Simaroubaceae|Gentianaceae'\n\nUsage:\n prepare_libraries_sop_merged.R [--fil-lib-sop-pre=<fil-lib-sop-pre>] [--fil-lib-sop-mer=<fil-lib-sop-mer>] [--org-fil-mod=<org-fil-mod>] [--org-fil-lev=<org-fil-lev>] [--org-fil-val=<org-fil-val>]\n\nArguments:\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n fil-lib-sop-mer Merged structure organism pairs library. STRING\n \n org-fil-mod Filter library to restrict it to a portion of organisms only. BOOLEAN\n org-fil-lev Level at which the library will be filtered. Must be one of \"domain\", \"phylum\", \"class\", \"order\", \"family\", \"tribe\", \"genus\", \"species\", \"varietas\" . STRING\n org-fil-val Value to be applied for filtering. \"OR\" can be used as \"|\". Example: \"Simaroubaceae|Gentianaceae\". STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_spectra.txt","content":"You can use this script with the following example:\nRscript inst/scripts/prepare_libraries_spectra.R --fil-lib-spe-raw data/source/libraries/spectra/exp/spectral_lib_mini_with_rt.mgf --names-lib internal --ms-pol pos\n\nUsage:\n prepare_libraries_spectra.R [--fil-lib-spe-raw=<fil-lib-spe-raw>] [--names-lib=<names-lib>] [--ms-pol=<ms-pol>] [--names-mgf-ce=<names-mgf-ce>] [--names-mgf-ci=<names-mgf-ci>] [--names-mgf-em=<names-mgf-em>] [--names-mgf-in=<names-mgf-in>] [--names-mgf-io=<names-mgf-io>] [--names-mgf-ik=<names-mgf-ik>] [--names-mgf-il=<names-mgf-il>] [--names-mgf-mf=<names-mgf-mf>] [--names-mgf-na=<names-mgf-na>] [--names-mgf-po=<names-mgf-po>] [--names-mgf-sm=<names-mgf-sm>] [--names-mgf-sn=<names-mgf-sn>] [--names-mgf-si=<names-mgf-si>] [--names-mgf-sp=<names-mgf-sp>] [--names-mgf-sy=<names-mgf-sy>] [--names-mgf-xl=<names-mgf-xl>] [--names-precursor=<names-precursor>] [--names-rt-lib=<names-rt-lib>] [--names-source=<names-source>] [--names-target=<names-target>] [--names-taxon=<names-taxon>]\n\nArguments:\n fil-lib-spe-raw List of experimental raw spectral libraries. STRING\n\n ms-pol Polarity used. Must be \"pos\" or \"neg\". STRING\n\n names-lib Metadata to identify the library. STRING\n names-mgf-ad Name of \"adduct\" in MGF. STRING\n names-mgf-ce Name of \"collision energy\" in MGF. STRING\n names-mgf-ci Name of \"compound ID\" in MGF. STRING\n names-mgf-em Name of \"exact mass\" in MGF. STRING\n names-mgf-in Name of \"InChI\" in MGF. STRING\n names-mgf-io Name of \"InChI without stereo\" in MGF. STRING\n names-mgf-ik Name of \"InChIKey\" in MGF. STRING\n names-mgf-il Name of \"InChIKey without stereo\" in MGF. STRING\n names-mgf-mf Name of \"molecular formula\" in MGF. STRING\n names-mgf-na Name of \"name\" in MGF. STRING\n names-mgf-po Name of \"polarity\" in MGF. STRING\n names-mgf-sm Name of \"SMILES\" in MGF. STRING\n names-mgf-sn Name of \"SMILES without stereo\" in MGF. STRING\n names-mgf-si Name of \"spectrum ID\" in MGF. STRING\n names-mgf-sp Name of \"SPLASH\" in MGF. STRING\n names-mgf-sy Name of \"synonyms\" in MGF. STRING\n names-mgf-xl Name of \"xlogp\" in MGF. STRING\n names-precursor Name of \"precursor m/z\" variable in the input. STRING\n names-rt-lib Name of \"retention time\" variable (lib) in the input. STRING\n names-source Name of \"source IDs\" variable in the input. STRING\n names-target Name of \"target IDs\" variable in the input. STRING\n names-taxon Name of the \"taxon name\" variable in the input. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.\n","type":"text"},{"name":"scripts/docopt/prepare_params.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_params.R\n\nUsage:\n prepare_params.R [--fil-pat=<fil-pat>] [--gnps-id=<gnps-id>] [--gnps-workflow=<gnps-workflow>] [--ms-pol=<ms-pol>] [--org-tax=<org-tax>]\n\nArguments:\n fil-pat Pattern to identify the files. STRING\n gnps-id GNPS job ID. STRING\n gnps-workflow Workflow used within GNPS. Currently \"fbmn\" or \"classical\" supported. STRING\n ms-pol Polarity used. Must be \"pos\" or \"neg\". STRING\n org-tax OPTIONAL. Force all features to be attributed to given taxon. Example: \"Homo sapiens\". STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_taxa.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_taxa.R --fil-met-pre data/interim/taxa/example_taxed.tsv.gz --org-can 1 --names-extension TRUE --names-features \"row ID\" --names-filename filename --names-taxon ATTRIBUTE_species\n\nUsage:\n prepare_taxa.R [--fil-fea-raw=<fil-fea-raw>] [--fil-met-raw=<fil-met-raw>] [--fil-met-pre=<fil-met-pre>] [--gnps-id=<gnps-id>] [--names-extension=<names-extension>] [--names-features=<names-features>] [--names-filename=<names-filename>] [--names-taxon=<names-taxon>] [--org-can=<org-can>] [--org-tax=<org-tax>] [--too-met=<too-met>]\n\nArguments:\n fil-fea-raw Raw features file. STRING\n fil-met-raw OPTIONAL. If tool is \"manual\". Biological origin metadata file. STRING\n fil-met-pre Taxonomically informed features file. STRING\n\n names-extension The file(s) extension is present in the sample name. BOOLEAN\n names-features Name of \"feature IDs\" variable in the input. STRING\n names-filename Name of \"filename\" variable in the input. STRING\n names-taxon Name of the \"taxon name\" variable in the input. STRING\n\n org-can Number of organisms to keep per feature. INTEGER\n org-tax OPTIONAL. Force all features to be attributed to given taxon. Example: \"Homo sapiens\". STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/weight_annotations.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/weight_annotations.R\n\nUsage:\n weight_annotations.R [--ann-can-fin=<ann-can-fin>] [--ann-ms1only=<ann-ms1only>] [--ann-thr-ms1-bio=<ann-thr-ms1-bio>] [--ann-thr-ms1-che=<ann-thr-ms1-che>] [--ann-thr-ms1-con=<ann-thr-ms1-con>] [--ann-thr-con=<ann-thr-con>] [--fil-ann-fil=<fil-ann-fil>] [--fil-ann-pre-can=<fil-ann-pre-can>] [--fil-ann-pre-for=<fil-ann-pre-for>] [--fil-ann-pro=<fil-ann-pro>] [--fil-lib-sop-mer=<fil-lib-sop-mer>] [--fil-net-spe-edg-pre=<fil-net-spe-edg-pre>] [--fil-met-pre=<fil-met-pre>] [--wei-glo-bio=<wei-glo-bio>] [--wei-glo-che=<wei-glo-che>] [--wei-glo-spe=<wei-glo-spe>] [--wei-bio-01=<wei-bio-01>] [--wei-bio-02=<wei-bio-02>] [--wei-bio-03=<wei-bio-03>] [--wei-bio-04=<wei-bio-04>] [--wei-bio-05=<wei-bio-05>] [--wei-bio-06=<wei-bio-06>] [--wei-bio-07=<wei-bio-07>] [--wei-bio-08=<wei-bio-08>] [--wei-bio-09=<wei-bio-09>] [--wei-bio-10=<wei-bio-10>] [--wei-bio-11=<wei-bio-11>] [--wei-bio-12=<wei-bio-12>] [--wei-bio-13=<wei-bio-13>] [--wei-bio-14=<wei-bio-14>] [--wei-bio-15=<wei-bio-15>] [--wei-che-11=<wei-che-11>] [--wei-che-12=<wei-che-12>] [--wei-che-13=<wei-che-13>] [--wei-che-14=<wei-che-14>] [--wei-che-21=<wei-che-21>] [--wei-che-22=<wei-che-22>] [--wei-che-23=<wei-che-23>] [--compounds-names=<compounds-names>] [--high-confidence=<high-confidence>] [--remove-ties=<remove-ties>] [--summarise=<summarise>] [--force=<force>]\n\nArguments:\n ann-can-fin Number of final candidates. INTEGER\n ann-ms1only Erase MS2 results and keep MS1 only. BOOLEAN\n ann-thr-ms1-bio Minimal biological score to keep MS1 only annotation. FLOAT\n ann-thr-ms1-che Minimal chemical score to keep MS1 only annotation. FLOAT\n ann-thr-ms1-con Condition to be used to retain candidates. Must be \"OR\" or \"AND\". Example: Minimum 0.5 biological AND 0.5 chemical. STRING\n ann-thr-con Minimal consistency score (chemical) to consider a class. FLOAT\n\n fil-ann-fil Filtered annotation file. STRING\n fil-ann-pre-can List of prepared canopus annotations. STRING\n fil-ann-pre-for List of prepared formula annotations. STRING\n fil-ann-pro Final results file. STRING\n fil-lib-sop-mer Merged structure organism pairs library. STRING\n fil-net-spe-edg-pre Prepared edges file. STRING\n fil-met-pre Taxonomically informed features file. STRING\n\n wei-glo-bio Weight for the biological part. FLOAT\n wei-glo-che Weight for the chemical part. FLOAT\n wei-glo-spe Weight for the spectral part. FLOAT\n\n wei-bio-01 Score for a biological domain match. FLOAT\n wei-bio-02 Score for a biological kingdom match. FLOAT\n wei-bio-03 Score for a biological phylum match. FLOAT\n wei-bio-04 Score for a biological class match. FLOAT\n wei-bio-05 Score for a biological order match. FLOAT\n wei-bio-06 Score for a biological infraorder match. FLOAT\n wei-bio-07 Score for a biological family match. FLOAT\n wei-bio-08 Score for a biological subfamily match. FLOAT\n wei-bio-09 Score for a biological tribe match. FLOAT\n wei-bio-10 Score for a biological subtribe match. FLOAT\n wei-bio-11 Score for a biological genus match. FLOAT\n wei-bio-12 Score for a biological subgenus match. FLOAT\n wei-bio-13 Score for a biological species match. FLOAT\n wei-bio-14 Score for a biological subspecies match. FLOAT\n wei-bio-15 Score for a biological variety match. FLOAT\n\n wei-che-11 Score for a chemical classyfire kingdom match. FLOAT\n wei-che-12 Score for a chemical classyfire superclass match. FLOAT\n wei-che-13 Score for a chemical classyfire class match. FLOAT\n wei-che-14 Score for a chemical classyfire parent match. FLOAT\n wei-che-21 Score for a chemical NPC pathway match. FLOAT\n wei-che-22 Score for a chemical NPC superclass match. FLOAT\n wei-che-23 Score for a chemical NPC class match. FLOAT\n\n compounds-names Report compounds names. Can be very large. BOOLEAN\n force Do not use it. BOOLEAN\n high-confidence Report high confidence candidates only. BOOLEAN\n remove-ties Remove ties. BOOLEAN\n summarise Summarize results (1 row per feature). BOOLEAN\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt.R","content":"start <- Sys.time()\n\nrequire(\n package = \"tima\",\n quietly = TRUE\n)\n\nstep <- \"params\"\npaths <- get_default_paths()\nparams <- get_params(step = step)\n\nlog_debug(\n \"This script\",\n crayon::green(\"is a test script\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/filter_annotations.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script performs\",\n crayon::green(\"filters annotations\"),\n \"based on\",\n crayon::blue(\"retention time matching\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_fil\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_benchmark.R","content":"start <- Sys.time()\n\nrequire(\n package = \"tima\",\n quietly = TRUE\n)\n\npaths <- get_default_paths()\n\nlog_debug(\n \"This script\",\n crayon::green(\n \"downloads benchmarking set\",\n \"from matchms work on GNPS dataset. \\n\"\n )\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_file(\n url = paths$urls$benchmarking_set,\n export = paths$data$source$benchmark$set\n)\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_ecmdb.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\npaths <- get_default_paths()\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads E. coli metabolome database structures. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nlog_debug(\"Downloading E. coli structures (should not take long)\")\nget_file(\n url = paths$urls$ecmdb$metabolites,\n export = paths$data$source$libraries$sop$ecmdb\n)\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_example_features.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads an example of minimal feature table \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_file(\n url = get_default_paths()$urls$examples$features,\n export = get_default_paths()$data$source$features\n)\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_example_metadata.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads an example of metadata table. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_file(\n url = get_default_paths()$urls$examples$metadata,\n export = get_default_paths()$data$source$metadata\n)\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_example_sirius.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads an example of SIRIUS output. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_example_sirius()\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_example_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads an example of prepared MGF file. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_file(\n url = get_default_paths()$urls$examples$spectra,\n export = get_default_paths()$data$source$spectra\n)\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_gnps_tables.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"gets gnps tables\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"gnps_tables\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_hmdb.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\npaths <- get_default_paths()\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads HMDB structures. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\n## TODO check md5 if possible\n## (see https://twitter.com/Adafede/status/1592543895094788096)\nlog_debug(\"Downloading HMDB (might be long)\")\nget_file(\n url = paths$urls$hmdb$structures,\n export = paths$data$source$libraries$sop$hmdb\n)\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_isdb_hmdb.R","content":"start <- Sys.time()\n\nrequire(\n package = \"tima\",\n quietly = TRUE\n)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads HMDB structures in silico fragmented. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\n## TODO check md5 if possible\n## (see https://twitter.com/Adafede/status/1592543895094788096)\nlog_debug(\"Downloading HMDB (might be long)\")\nget_file(\n url = get_default_paths()$urls$hmdb$spectra$predicted,\n export = get_default_paths()$data$source$libraries$spectra$is$hmdb\n)\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_isdb_lotus.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads the In Silico DataBase (ISDB) \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_spe_is_lot\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_lotus.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads LOTUS referenced structure-organism pairs. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_lot$\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_massbank_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads MassBank spectra \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_spe_exp_mb_raw\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_annotations_gnps.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"formats GNPS results\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_spe_exp_gnp_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_annotations_sirius.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"formats SIRIUS results\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_sir_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_annotations_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"formats spectral annotations results\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\n## Not ann_spe because of `ann_spe_int` (see #69)\ntargets::tar_make(names = c(tidyselect::matches(\"^ann_spe_is\")))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_features_components.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares features metadata (mz, rt and component id)\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_com_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_features_edges.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares edges\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_edg_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_features_tables.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares features\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_rt.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\n \"prepares a library of retention times\",\n \"from MGF or tabular data. \\n\"\n )\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_rt\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_closed.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares closed referenced structure-organism pairs \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_clo_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_ecmdb.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares ecmdb structure-organism pairs \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_ecm_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_hmdb.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares hmdb structure-organism pairs \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_hmd_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_lotus.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares LOTUS referenced structure-organism pairs \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_lot_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_merged.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares a library from prepared sub-libraries. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_mer\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"Prepares the internal library spectra \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_spe_exp_int_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_spectra_is_lotus.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"Prepares the predicted spectra from LOTUS \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_spe_is_lot_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_params.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares parameters\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"))\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"par_\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_taxa.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"informs taxonomically features\")\n)\nlog_debug(\n \"Authors: \",\n crayon::green(\"AR\"),\n \",\",\n crayon::blue(\"PMA\"),\n \"\\n\"\n)\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"tax_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/tima_full.R","content":"library(\"tima\")\n\ntima_full()\n","type":"text"},{"name":"scripts/weight_annotations.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script performs\",\n crayon::green(\"taxonomically informed scoring\"),\n \"and followed by\",\n crayon::blue(\"chemical consistency informed scoring\")\n)\nlog_debug(\n \"Authors: \",\n crayon::green(\"AR\"),\n \",\",\n crayon::blue(\"PMA\"),\n \"\\n\"\n)\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"ann_pre$\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"}] +[{"name":"app.R","content":"# Check if runs in Docker environment or not\ni_am_a_whale <- file.exists(\"/.dockerenv\")\nif (i_am_a_whale) {\n options(shiny.host = \"0.0.0.0\")\n} else {\n options(shiny.host = \"127.0.0.1\")\n}\noptions(shiny.port = 3838)\noptions(shiny.maxRequestSize = 2000 * 1024^2)\n\n#' @title Label mandatory\n#'\n#' @description This function adds an asterisk to mandatory inputs\n#'\n#' @param label Label\n#'\n#' @return NULL\n#'\n#' @export\n#'\n#' @examples NULL\n.label_mandatory <- function(label) {\n shiny::tagList(label, shiny::span(\"*\", class = \"mandatory_star\"))\n}\n\napp_css <-\n \".mandatory_star { color: red; }\n .shiny-input-container { margin-top: 25px; }\n #submit_msg { margin-left: 15px; }\n #error { color: red; }\n body { background: #fcfcfc; }\n #header { margin: -20px -15px 0; padding: 15px 15px 10px; }\n \"\n\nui <- shiny::fluidPage(\n shinyjs::useShinyjs(),\n shinyjs::inlineCSS(rules = app_css),\n title = \"Taxonomically Informed Metabolite Annotation\",\n shiny::div(\n id = \"header\",\n shiny::h1(\"Taxonomically Informed Metabolite Annotation\"),\n shiny::h4(\n \"This app helps performing TIMA as described in the\",\n shiny::a(href = \"https://taxonomicallyinformedannotation.github.io/tima\", \"following documentation\")\n ),\n shiny::strong(\n shiny::span(\"Created by \"),\n shiny::a(\"Adriano Rutz\", href = \"https://adafede.github.io/\"),\n shiny::HTML(\"•\"),\n shiny::a(\"Main publication\", href = \"https://doi.org/10.3389/fpls.2019.01329\"),\n shiny::HTML(\"•\"),\n shiny::span(\"Code\"),\n shiny::a(\"on GitHub\", href = \"https://github.com/taxonomicallyinformedannotation/tima\")\n )\n ),\n shiny::fluidPage(shiny::div(\n id = \"params\",\n shiny::navlistPanel(\n widths = c(4, 5),\n \"Parameters\",\n shiny::tabPanel(\n title = \"Files\",\n shiny::h3(\"Required files\"),\n shiny::div(\n shiny::fileInput(\n inputId = \"fil_spe_raw\",\n label = .label_mandatory(\"MGF file\"),\n accept = \".mgf\"\n ),\n shiny::downloadButton(outputId = \"demo_spe\", \"Download example spectra\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"The MGF file containing your spectra.\",\n \"Should be located in `data/source`.\",\n \"Reason therefore is to find it in the future.\"\n # \"If you have a GNPS job ID, the spectra will be stored there.\"\n )\n ),\n shiny::div(\n shiny::fileInput(\n inputId = \"fil_fea_raw\",\n label = .label_mandatory(\"Features table\"),\n accept = c(\".csv\", \".tsv\", \".csv.gz\", \".tsv.gz\", \".csv.zip\", \".tsv.zip\")\n ),\n shiny::downloadButton(outputId = \"demo_fea\", \"Download example features\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"The csv or tsv file containing your features\",\n \"Should be located in `data/source`.\",\n \"Reason therefore is to find it in the future.\"\n # \"If you have a GNPS job ID, the spectra will be stored there.\"\n )\n ),\n shiny::div(\n shiny::fileInput(\n inputId = \"fil_met_raw\",\n label = \"Metadata table (mandatory if no taxon name)\",\n accept = c(\".csv\", \".tsv\", \".csv.gz\", \".tsv.gz\", \".csv.zip\", \".tsv.zip\")\n ),\n shiny::downloadButton(outputId = \"demo_met\", \"Download example metadata\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"The csv or tsv file containing the metadata of your experiment.\",\n \"Should be located in `data/source`.\",\n \"Reason therefore is to find it in the future.\",\n # \"If you have a GNPS job ID, the spectra will be stored there.\",\n \"Do not forget to change the `name` in the corresponding tab.\"\n )\n ),\n shiny::fileInput(\n inputId = \"fil_ann_raw_sir\",\n label = \"SIRIUS project space (optional)\",\n accept = c(\".zip\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"The compressed directory containing the SIRIUS project space.\",\n \"Should be located in `data/interim`.\",\n \"Reason therefore is to find it in the future.\"\n )\n ),\n shiny::selectInput(\n inputId = \"too_sir_ver\",\n label = \"SIRIUS version\",\n choices = c(5, 6),\n selected = 6\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"The SIRIUS version used.\", \"We highly recommend 6, the default.\")\n ),\n shiny::textInput(\n inputId = \"fil_pat\",\n label = .label_mandatory(\"Pattern to identify your job locally\"),\n value = \"example\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of your job.\",\n \"All intermediate steps will use this pattern.\",\n \"For example, the corresponding SIRIUS results will be named:\",\n \"`yourPattern_sirius`.\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"Annotations\",\n shiny::h3(\"Annotations-related parameters\"),\n shiny::sliderInput(\n inputId = \"ann_can_fin\",\n label = \"Number of final candidates\",\n min = 1,\n max = 500,\n value = 3,\n step = 1,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Number of final candidates to consider.\",\n \"For 12 candidates, with 10, only the first 10 will be kept.\",\n \"This can end to more than n candidates, if some are ex aequo.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"ann_ms1only\",\n label = \"Erase MS2 results and keep MS1 only\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Option to erase MS2 results.\",\n \"Usually not needed, for benchmarking mainly.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ann_thr_con\",\n label = \"Minimal consistency score (chemical) to consider a class\",\n min = 0,\n max = 1,\n value = 0.0,\n step = 0.05,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimal consistency score (chemical) to consider a class.\",\n \"Everything below will be considered as not consistent.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ann_thr_ms1_bio\",\n label = \"Minimal biological score to keep MS1 only annotation\",\n min = 0,\n max = 1,\n value = 0.0,\n step = 0.05,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimal biological score to keep an MS1 candaidate.\",\n \"Everything below will be discarded (see below).\",\n \"Check the sub-scores and weights in the `weights` panel.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ann_thr_ms1_che\",\n label = \"Minimal chemical score to keep MS1 only annotation\",\n min = 0,\n max = 1,\n value = 0.0,\n step = 0.1,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimal chemical score to keep an MS1 candaidate.\",\n \"Everything below will be discarded (see below).\",\n \"Check the sub-scores and weights in the `weights` panel.\"\n )\n ),\n shiny::selectInput(\n inputId = \"ann_thr_ms1_con\",\n label = \"Condition to be used to retain candidates\",\n choices = c(\"AND\", \"OR\"),\n selected = \"OR\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If an MS1 candidate has a bio score of 1, a chem score of 0.25\",\n \"the above thresholds set at 0.3 and 0.3 respectively\",\n \"keeps it if the condition is `OR` but discard it if `AND`.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"ann_ms2_app\",\n label = \"Perform matching without precursor? (can be very long)\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Corresponds to variable dereplication.\",\n \"Then, precurors masses are disregarded during matching.\",\n \"Allows to find (incorrect) analogs.\",\n \"Takes a lot of time.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ann_thr_ms2_sim\",\n label = \"Minimal similarity score (annotation)\",\n min = 0,\n max = 1,\n step = 0.05,\n value = 0.0,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimum similarity score between spectra.\",\n \"To keep MS2 annotation.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"edg_thr_ms2_sim\",\n label = \"Minimal similarity score (edges)\",\n min = 0.5,\n max = 1,\n step = 0.05,\n value = 0.7,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Minimum similarity score between spectra.\",\n \"To keep MS2 similarity edge.\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"MS\",\n shiny::h3(\"MS-related parameters\"),\n shiny::selectInput(\n inputId = \"ms_pol\",\n label = \"Polarity used\",\n choices = c(\"pos\", \"neg\"),\n selected = \"pos\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Polarity of the MS experiment.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_thr_ms2_int\",\n label = \"Intensity threshold for MS2\",\n min = 0,\n max = 1E4,\n value = 5,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"MS2 intensity threshold.\",\n \"Fragments below this threshold will be removed from spectra.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_mas_ppm_ms1\",\n label = \"Relative mass tolerance for MS1 in ppm\",\n min = 0.1,\n max = 20,\n value = 10,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Mass tolerance (in ppm) used for MS1 annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_mas_dal_ms1\",\n label = \"Absolute mass tolerance for MS1 in Dalton\",\n min = 0.005,\n max = 0.02,\n value = 0.01,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Mass tolerance (in Da) used for MS1 annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_mas_ppm_ms2\",\n label = \"Relative mass tolerance for MS2 in ppm\",\n min = 0.1,\n max = 20,\n value = 10,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Mass tolerance (in ppm) used for MS2 annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_mas_dal_ms2\",\n label = \"Absolute mass tolerance for MS2 in Dalton\",\n min = 0.005,\n max = 0.02,\n value = 0.01,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Mass tolerance (in Da) used for MS2 annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_rt_add\",\n label = \"Retention time tolerance for adducts annotation in minutes\",\n min = 0.005,\n max = 0.1,\n value = 0.02,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Retention time tolerance (minutes) used for adducts annotation.\")\n ),\n shiny::sliderInput(\n inputId = \"ms_tol_rt_lib\",\n label = \"Retention time tolerance for library annotation in minutes\",\n min = 0.005,\n max = 0.2,\n value = 0.1,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Retention time tolerance (minutes) used for library annotation.\",\n \"If no experimental library is given, does not impact results.\"\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_add_pos\",\n label = \"List of adducts to be used in positive\",\n choices = list(\n \"[M+H3]3+\",\n \"[M+H2Na]3+\",\n \"[M+HNa2]3+\",\n \"[M+Na3]3+\",\n \"[M+H2]2+\",\n \"[M+HNa]2+\",\n \"[M+Mg]2+\",\n \"[M+HK]2+\",\n \"[M+Ca]2+\",\n \"[M+Na2]2+\",\n \"[M+Fe]2+\",\n \"[M+H]+\",\n \"[M+H4N]+\",\n \"[M+Na]+\",\n \"[M+K]+\",\n \"[M+Cu]+\",\n \"[2M+Mg]2+\",\n \"[2M+Ca]2+\",\n \"[2M+Fe]2+\",\n \"[2M+H]+\",\n \"[2M+H4N]+\",\n \"[2M+Na]+\",\n \"[2M+K]+\"\n ),\n selected = list(\"[M+H2]2+\", \"[M+H]+\", \"[M+H4N]+\", \"[M+Na]+\", \"[2M+H]+\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important adduct everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_add_neg\",\n label = \"List of adducts to be used in negative\",\n choices = list(\n \"[M-H3]3-\",\n \"[M-H2]2-\",\n \"[M-H]-\",\n \"[M+F]-\",\n \"[M+Na-H2]-\",\n \"[M+Cl]-\",\n \"[M+K-H2]-\",\n \"[M+Br]-\",\n \"[2M-H]-\",\n \"[3M-H]-\"\n ),\n selected = list(\"[M-H2]2-\", \"[M-H]-\", \"[2M-H]-\")\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important adduct everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_clu_pos\",\n label = \"List of clusters to be used in positive\",\n choices = list(\n \"H2O\", # (water)\n # \"CH4O\", # (methanol)\n \"C2H3N\", # (acetonitrile)\n # \"C2H7N\", # (ethylamine)\n # \"C2H6O\", # (ethanol)\n \"NaCl\" # (sodium chloride)\n # \"C3H8O\", # (isopropanol)\n # \"C2H6OS\" # (dmso)\n ),\n selected = list(\n \"H2O\", # (water)\n # \"CH4O\", # (methanol)\n \"C2H3N\" # (acetonitrile)\n # \"C2H7N\", # (ethylamine)\n # \"C2H6O\", # (ethanol)\n # \"NaCl\", # (sodium chloride)\n # \"C3H8O\", # (isopropanol)\n # \"C2H6OS\" # (dmso)\n )\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important cluster everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_clu_neg\",\n label = \"List of clusters to be used in negative\",\n choices = list(\n \"H2O\", # (water)\n \"CH2O2\", # (formic)\n \"NaCl\", # (sodium chloride)\n \"C2H4O2\", # (acetic)\n \"H2PO4\", # (phosphoric)\n \"C2HF3O2\" # (tfa)\n ),\n selected = list(\n \"H2O\", # (water)\n \"CH2O2\", # (formic)\n # \"NaCl\", # (sodium chloride)\n # \"C2H4O2\", # (acetic)\n \"H2PO4\" # (phosphoric)\n # \"C2HF3O2\" # (tfa)\n )\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important cluster everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n ),\n shiny::checkboxGroupInput(\n inputId = \"ms_neu\",\n label = \"List of neutral losses to be used\",\n choices = list(\n \"HN\",\n \"CH3\",\n \"O\",\n \"H3N (ammonia)\",\n \"H2O (water)\",\n \"CHN\",\n \"CO\",\n \"C2H4 (2xCH2)\",\n \"C2H5 (CH2-CH3)\",\n \"CH2O\",\n \"H5ON (H2O-H3N)\",\n \"H4O2 (2xH2O)\",\n \"C2H2O\",\n \"CHON\",\n \"CO2\",\n \"CHO2\",\n \"H6O3 (3xH2O)\",\n \"C2O2 (2xCO)\",\n \"CH6O3 (combination)\",\n \"H8O4 (4xH2O)\",\n \"C2H2O3 (CH2O-CO2)\",\n \"C3H6O2 (CH3-CH2-COOH)\",\n \"C3O3 (3xCO)\",\n \"C4H9NO (combination)\",\n \"C2O4 (2xCO2)\",\n \"C3H4O3 (pyruvic)\",\n \"C4H8O2 (butyric)\",\n \"C3H7NO2 (alanine)\",\n \"C3H6O3 (sugar)\",\n \"H2O4S (sulfuric)\",\n \"H3O4P (phosphoric)\",\n \"C5H10O2 (valeric)\",\n \"C3H4O4 (malonic)\",\n \"C2H2O5 (CO2-CO2-H2O)\",\n \"C3H8O4 (combination)\",\n \"C5H9NO2 (proline)\",\n \"C4O4H6 (CO2-CO2-CH3-CH3)\",\n \"C4H8O4 (sugar)\",\n \"C2H4O6 (CO2-CO2-H2O-H2O)\",\n \"C6H6O3 (HRF)\",\n \"C5H8O4 (pentose-H2O)\",\n \"C8H8O2 (RDA-1)\",\n \"C6H8O4 (HRF)\",\n \"C9H6O2 (coumaroyl)\",\n \"C6H10O4 (methylpentose/desoxyhexose-H2O)\",\n \"C9H8O2 (cinnamoyl)\",\n \"C7H4O4 (galloyl)\",\n \"C8H8O3 (RDA-2)\",\n \"C9H6O3 (caffeoyl)\",\n \"C6H10O5 (hexose-H2O)\",\n \"C8H8O4 (RDA-3)\",\n \"C8H10O4 (RDA-2-H2O)\",\n \"C6H8O6 (glucuronic-H2O)\",\n \"C10H8O3 (feruloyl)\",\n \"C6H13NO5 (hexose-H2N)\",\n \"C6H12O6 (hexose)\",\n \"C7H12O6 (quinoyl)\",\n \"C6H15NO6 (hexose-H2N-H2O)\",\n \"C6H14O7 (hexose-H2O)\",\n \"C8H12O6 (acetylhexose-H2O)\",\n \"C11H10O4 (sinapoyl)\",\n \"C16H30O (pamitoyl)\",\n \"C9H12O8 (malonylhexose)\",\n \"C13H14O6 (benzoylhexose)\",\n \"C12H20O8 (2xmethylpentose/desoxyhexose-H2O)\",\n \"C15H16O7 (coumaroylhexose)\",\n \"C13H14O9 (galloylhexose)\",\n \"C15H16O8 (caffeoylhexose)\",\n \"C12H20O10 (2xhexose-H2O)\",\n \"C16H18O8 (feruloylhexose)\",\n \"C17H20O9 (sinapoylhexose)\",\n \"C18H30O15 (3xhexose-H2O)\"\n ),\n selected = list(\n \"O\",\n \"H3N (ammonia)\",\n \"H2O (water)\",\n \"CO\",\n \"C2H5 (CH2-CH3)\",\n \"H4O2 (2xH2O)\",\n \"CO2\",\n \"CHO2\",\n \"H6O3 (3xH2O)\",\n \"C2O2 (2xCO)\",\n \"CH6O3 (combination)\",\n \"H8O4 (4xH2O)\",\n \"C3H6O3 (sugar)\",\n \"H2O4S (sulfuric)\",\n \"H3O4P (phosphoric)\",\n \"C4H8O4 (sugar)\",\n \"C6H6O3 (HRF)\",\n \"C5H8O4 (pentose-H2O)\",\n \"C8H8O2 (RDA-1)\",\n \"C6H8O4 (HRF)\",\n \"C6H10O4 (methylpentose/desoxyhexose-H2O)\",\n \"C8H8O3 (RDA-2)\",\n \"C6H10O5 (hexose-H2O)\",\n \"C8H8O4 (RDA-3)\",\n \"C8H10O4 (RDA-2-H2O)\",\n \"C6H12O6 (hexose)\",\n \"C11H10O4 (sinapoyl)\",\n \"C16H30O (pamitoyl)\",\n \"C12H20O8 (2xmethylpentose/desoxyhexose-H2O)\",\n \"C12H20O10 (2xhexose-H2O)\"\n )\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Choose wisely.\",\n \"If a very important neutral loss everyone should have is missing,\",\n \"please open an issue at:\",\n as.character(\n shiny::tags$a(\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\",\n href =\n \"https://github.com/taxonomicallyinformedannotation/tima/issues\"\n )\n )\n )\n )\n ),\n shiny::tabPanel(\n title = \"Organisms\",\n shiny::h3(\"Organisms-related parameters\"),\n shiny::textInput(\n inputId = \"org_tax\",\n label = \"OPTIONAL. Force all features to be attributed to a taxon\n (e.g. Gentiana lutea)\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"You have to input the scientific name of your taxon.\",\n \"Will be used for the weighting of your annotations\",\n \"Example: Gentiana lutea\",\n \"All features will be attributed to this source.\",\n \"For finer attribution,\n you need to provide a metadata file in the `Files` panel.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"org_can\",\n label = \"Number of organisms to keep per feature\",\n min = 1,\n max = 5,\n value = 1,\n step = 1,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Only has impact if you provided a metadata file in the\n `Files` panel.\",\n \"In this case,\n it will take the intensity matrix of your features\",\n \"and attribute the n organisms where the highest intensity\",\n \"was observed as source to your features.\",\n \"Useful when your experiment contains\n different biological sources.\",\n \"As the organisms where the measured intensity\n is the highest are also the ones\",\n \"with higher likelihood of the\n corresponding compounds being isolated,\",\n \"the number of organisms can be kept low.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"org_fil_mod\",\n label = \"Filter library to restrict it to a\n portion of organisms only\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If you want, you can restrict your\n structure-organism pairs library\",\n \"to given taxa only.\",\n \"We do not advise doing so, but you are free to.\"\n )\n ),\n shiny::selectInput(\n inputId = \"org_fil_lev\",\n label = \"Level at which the library will be filtered\",\n choices = c(\n \"domain\",\n \"phylum\",\n \"class\",\n \"order\",\n \"family\",\n \"tribe\",\n \"genus\",\n \"species\",\n \"varietas\"\n ),\n selected = \"phylum\"\n ),\n shiny::textInput(\n inputId = \"org_fil_val\",\n label = \"Value to be applied for filtering\",\n value = \"Streptophyta\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"You can also provide multiple values.\",\n \"Example, if you want to get a bitter library:\",\n \"`Simaroubaceae|Gentianaceae`\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"Names\",\n shiny::h3(\"Variable names parameters\"),\n shiny::textInput(\n inputId = \"names_adduct\",\n label = \"Name of `adduct` variable in the input\",\n value = \"best ion\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `adduct` column in your features file.\",\n \"The default corresponds to the default in MZmine.\",\n \"If using SLAW, please input 'annotation'\"\n )\n ),\n shiny::textInput(\n inputId = \"names_features\",\n label = \"Name of `feature IDs` variable in the input\",\n value = \"row ID\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `feature id` column in your features file.\",\n \"The default corresponds to the default in MZmine.\",\n \"If using SLAW, please input 'slaw_id'\"\n )\n ),\n shiny::textInput(\n inputId = \"names_filename\",\n label = \"Name of `filename` variable in the input\",\n value = \"filename\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `file name` column in your metadata file.\",\n \"The default is filename.\",\n \"If using Sequencer, please input 'Sample Name'\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"names_extension\",\n label = \"The file(s) extension is present in the sample name\",\n value = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Uncheck the box if you removed the files extensions\",\n \"of your file in the features file\",\n \"The default corresponds to the default in MZmine.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_inchikey\",\n label = \"Name of `inchikey` variable in the input\",\n value = \"inchikey\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `inchikey` column in your file.\")\n ),\n shiny::textInput(\n inputId = \"names_precursor\",\n label = \"Name of `precursor m/z` variable in the input\",\n value = \"row m/z\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `precursor m/z` column in your features file.\",\n \"The default corresponds to the default in MZmine.\",\n \"If using SLAW, please input 'mz'\"\n )\n ),\n shiny::textInput(\n inputId = \"names_rt\",\n label = \"Name of `retention time` variable in the feature table\",\n value = \"row retention time\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `retention time` column in your features file.\",\n \"The default corresponds to the default in MZmine.\",\n \"If using SLAW, please input 'rt'\",\n \"Assumed to be in minutes.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_rt_2\",\n label = \"Name of `retention time` variable in the rt library\",\n value = \"rt\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `retention time` column in your rt library file.\")\n ),\n shiny::textInput(\n inputId = \"names_smiles\",\n label = \"Name of `SMILES` variable in the input\",\n value = \"smiles\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `SMILES` column in your file.\")\n ),\n shiny::textInput(\n inputId = \"names_source\",\n label = \"Name of `source IDs` variable in the input\",\n value = \"CLUSTERID1\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `source id` column in your edges file.\",\n \"The default corresponds to the default in GNPS.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_target\",\n label = \"Name of `target IDs` variable in the input\",\n value = \"CLUSTERID2\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `target id` column in your edges file.\",\n \"The default corresponds to the default in GNPS.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_taxon\",\n label = \"Name of `taxon name` variable in the input\",\n value = \"ATTRIBUTE_species\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the `taxon name` column in your edges file.\",\n \"The default corresponds to the default in GNPS.\"\n )\n ),\n shiny::h3(\"MGF names parameters\"),\n shiny::textInput(\n inputId = \"names_mgf_ad\",\n label = \"Name of `adduct` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `adduct` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_ce\",\n label = \"Name of `collision energy` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `collision energy` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_ci\",\n label = \"Name of `compound id` variable in the MGF\",\n value = \"SPECTRUMID\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `compound id` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_em\",\n label = \"Name of `exact mass` variable in the MGF\",\n value = \"EXACTMASS\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `exact mass` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_in\",\n label = \"Name of `InChI` variable in the MGF\",\n value = \"INCHI\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `InChI` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_io\",\n label = \"Name of `InChI no stereo` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `InChI no stereo` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_ik\",\n label = \"Name of `InChIKey` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `InChIKey` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_il\",\n label = \"Name of `InChIKey no stereo` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `InChIKey no stereo` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_mf\",\n label = \"Name of `molecular formula` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `molecular formula` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_na\",\n label = \"Name of `name` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `name` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_po\",\n label = \"Name of `polarity` variable in the MGF\",\n value = \"IONMODE\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `polarity` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_rt\",\n label = \"Name of `retention time` variable in the MGF\",\n value = \"RTINSECONDS\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `retention time` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_sm\",\n label = \"Name of `SMILES` variable in the MGF\",\n value = \"SMILES\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `SMILES` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_sn\",\n label = \"Name of `SMILES no stereo` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `SMILES no stereo` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_si\",\n label = \"Name of `spectrum ID` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `spectrum ID` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_sp\",\n label = \"Name of `SPLASH` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `SPLASH` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_sy\",\n label = \"Name of `synonyms` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `synonyms` column in your MGF file.\")\n ),\n shiny::textInput(\n inputId = \"names_mgf_xl\",\n label = \"Name of `xlogP` variable in the MGF\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Name of the `xlogP` column in your MGF file.\")\n )\n ),\n shiny::tabPanel(\n title = \"Weights\",\n shiny::h3(\"Weights-related parameters\"),\n shiny::sliderInput(\n inputId = \"wei_glo_bio\",\n label = \"Weight for the biological part\",\n min = 0,\n max = 1,\n value = 0.500,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Weight attributed to the biological score.\",\n \"Depends on the organism you are studying,\n and how specialized its metabolome is.\",\n \"Also depends if you favor novelty over previous knowledge.\",\n \"We advise this value to be high.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_glo_che\",\n label = \"Weight for the chemical part\",\n min = 0,\n max = 1,\n value = 0.166,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Weight attributed to the chemical score.\",\n \"We advise this value to be low.\",\n \"Current chemical taxonomies do not perform well.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_glo_spe\",\n label = \"Weight for the spectral part\",\n min = 0,\n max = 1,\n value = 0.333,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Weight attributed to the spectral score.\",\n \"Depends on the quality of your spectra.\",\n \"We advise this value to be medium to high.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_01\",\n label = \"Score for a biological domain match\",\n min = 0,\n max = 1,\n value = 0.100,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `domain` match.\",\n \"We advise this value to be the lowest.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_02\",\n label = \"Score for a biological kingdom match\",\n min = 0,\n max = 1,\n value = 0.200,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `kingdom` match.\",\n \"We advise this value to be higher than the one of `domain`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_03\",\n label = \"Score for a biological phylum match\",\n min = 0,\n max = 1,\n value = 0.300,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `phylum` match.\",\n \"We advise this value to be higher than the one of `kingdom`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_04\",\n label = \"Score for a biological class match\",\n min = 0,\n max = 1,\n value = 0.400,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `class` match.\",\n \"We advise this value to be higher than the one of `phylum`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_05\",\n label = \"Score for a biological order match\",\n min = 0,\n max = 1,\n value = 0.500,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `order` match.\",\n \"We advise this value to be higher than the one of `class`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_06\",\n label = \"Score for a biological infraorder match\",\n min = 0,\n max = 1,\n value = 0.550,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `infraorder` match.\",\n \"We advise this value to be higher than the one of `order`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_07\",\n label = \"Score for a biological family match\",\n min = 0,\n max = 1,\n value = 0.600,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `family` match.\",\n \"We advise this value to be higher than the one of `infraorder`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_08\",\n label = \"Score for a biological subfamily match\",\n min = 0,\n max = 1,\n value = 0.650,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `subfamily` match.\",\n \"We advise this value to be higher than the one of `family`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_09\",\n label = \"Score for a biological tribe match\",\n min = 0,\n max = 1,\n value = 0.700,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `tribe` match.\",\n \"We advise this value to be higher than the one of `subfamily`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_10\",\n label = \"Score for a biological subtribe match\",\n min = 0,\n max = 1,\n value = 0.750,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `subtribe` match.\",\n \"We advise this value to be higher than the one of `tribe`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_11\",\n label = \"Score for a biological genus match\",\n min = 0,\n max = 1,\n value = 0.800,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `genus` match.\",\n \"We advise this value to be higher than the one of `subtribe`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_12\",\n label = \"Score for a biological subgenus match\",\n min = 0,\n max = 1,\n value = 0.850,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `subgenus` match.\",\n \"We advise this value to be higher than the one of `genus`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_13\",\n label = \"Score for a biological species match\",\n min = 0,\n max = 1,\n value = 0.900,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `species` match.\",\n \"We advise this value to be higher than the one of `subgenus`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_14\",\n label = \"Score for a biological subspecies match\",\n min = 0,\n max = 1,\n value = 0.950,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `subspecies` match.\",\n \"We advise this value to be higher than the one of `species`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_bio_15\",\n label = \"Score for a biological variety match\",\n min = 0,\n max = 1,\n value = 1.000,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `variety` match.\",\n \"We advise this value to be higher than the one of `subspecies`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_11\",\n label = \"Score for a (classyfire) chemical kingdom match\",\n min = 0,\n max = 1,\n value = 0.250,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `kingdom (classyfire)` match.\",\n \"We advise this value to be the lowest.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_12\",\n label = \"Score for a (classyfire) chemical superclass match\",\n min = 0,\n max = 1,\n value = 0.500,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `superclass (classyfire)` match.\",\n \"We advise this value to be higher\n than the one of `kingdom (classyfire)`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_13\",\n label = \"Score for a (classyfire) chemical class match\",\n min = 0,\n max = 1,\n value = 0.750,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `class (classyfire)` match.\",\n \"We advise this value to be higher\n than the one of `superclass (classyfire)`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_14\",\n label = \"Score for a (classyfire) chemical parent match\",\n min = 0,\n max = 1,\n value = 1.000,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `parent (classyfire)` match.\",\n \"We advise this value to be higher\n than the one of `class (classyfire)`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_21\",\n label = \"Score for a (NPC) chemical pathway match\",\n min = 0,\n max = 1,\n value = 0.333,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `pathway (npclassifier)` match.\",\n \"We advise this value to be the lowest.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_22\",\n label = \"Score for a (NPC) chemical superclass match\",\n min = 0,\n max = 1,\n value = 0.666,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `superclass (npclassifier)` match.\",\n \"We advise this value to be higher\n than the one of `pathway (npclassifier)`.\"\n )\n ),\n shiny::sliderInput(\n inputId = \"wei_che_23\",\n label = \"Score for a (NPC) chemical class match\",\n min = 0,\n max = 1,\n value = 1.000,\n step = 0.001,\n ticks = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Score for a `class (npclassifier)` match.\",\n \"We advise this value to be higher\n than the one of `superclass (npclassifier)`.\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"Options\",\n shiny::h3(\"Options parameters\"),\n shiny::checkboxInput(\n inputId = \"compounds_names\",\n label = \"Compound names\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If ticked, compounds names will be reported.\",\n \"This will require much more memory,\n as they are the longest strings.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"high_confidence\",\n label = \"High confidence only\",\n value = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If ticked, only compounds with high confidence will be reported.\",\n \"This is an important filter. Do not hesitate removing it for more coverage.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"remove_ties\",\n label = \"Remove ties\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If checked, only one of the candidates with the exact same score will be kept randomly.\",\n \"Not adivsed, but here for convenience.\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"summarise\",\n label = \"Summarise results to one row per feature\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"If checked, the output will be a Cytoscape-ready table,\",\n \"with multiple candidates per line, separated by pipes (|).\",\n \"If unchecked, will output multiple lines per features\n (one per candidate).\"\n )\n ),\n shiny::checkboxInput(\n inputId = \"force\",\n label = \"Do not use it!\",\n value = FALSE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Really, do not use it.\",\n \"Corresponds to a `--force` parameter,\n allowing for crazy things that can crash.\",\n \"Really, do not use it.\"\n )\n )\n ),\n shiny::tabPanel(\n title = \"Libraries (optional)\",\n shiny::h3(\"Additional personal libraries\"),\n shiny::h4(\"Temporal (retention time)\"),\n shiny::fileInput(\n inputId = \"lib_tmp_exp_csv\",\n label = \"Experimental (CSV)\",\n accept = c(\".csv\", \".tsv\", \".csv.gz\", \".tsv.gz\", \".csv.zip\", \".tsv.zip\"),\n multiple = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"A TSV of retention times.\",\n \"The default column names are `rt`, `smiles`, `inchikey`.\",\n \"Do not forget to adapt the names parameters if needed.\",\n \"Do not forget to fill down seconds or minutes below.\"\n )\n ),\n shiny::fileInput(\n inputId = \"lib_tmp_is_csv\",\n label = \"In silico (CSV)\",\n accept = c(\".csv\", \".tsv\", \".csv.gz\", \".tsv.gz\", \".csv.zip\", \".tsv.zip\"),\n multiple = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"A TSV of retention times.\",\n \"The default column names are `rt`, `smiles`, `inchikey`.\",\n \"Do not forget to adapt the names parameters if needed.\",\n \"Do not forget to fill down seconds or minutes below.\"\n )\n ),\n shiny::h4(\"Spectral\"),\n shiny::fileInput(\n inputId = \"lib_spe_mgf\",\n label = \"MGF\",\n accept = c(\".mgf\"),\n multiple = TRUE\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"An MGF containing reference spectra.\",\n \"If it contains retention times, they will also be used.\",\n \"Do not forget to fill down seconds or minutes below.\"\n )\n ),\n shiny::textInput(\n inputId = \"names_libraries\",\n label = \"Name of the library\",\n value = \"internal\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Name of the library. Related content will be identified with this name.\"\n )\n ),\n shiny::selectInput(\n inputId = \"uni_rt\",\n label = \"Retention time unit\",\n choices = c(\"seconds\", \"minutes\"),\n selected = \"seconds\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"Unit of the retention time.\", \"Must be `seconds` or `minutes`.\")\n ),\n ),\n shiny::tabPanel(\n title = \"GNPS (optional)\",\n shiny::h3(\"GNPS parameters\"),\n shiny::textInput(\n inputId = \"gnps_id\",\n label = \"GNPS job ID\",\n value = NULL\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\n \"Your GNPS job ID.\",\n \"If you provide one, all files will be taken from there.\",\n \"You still need to provide a path for\n where we will store the files,\",\n \"in case your GNPS job gets deleted for you to find them back.\",\n \"Annotations will also be downloaded.\"\n )\n ),\n shiny::selectInput(\n inputId = \"gnps_workflow\",\n label = \"Workflow used within GNPS\",\n choices = c(\"classical\", \"fbmn\"),\n selected = \"fbmn\"\n ) |>\n shinyhelper::helper(\n type = \"inline\",\n content = c(\"We advise `fbmn`,\n but we also support `classical` jobs.\")\n )\n )\n )\n )),\n shiny::fluidPage(\n shiny::div(\n id = \"form\",\n shiny::actionButton(\n inputId = \"save\",\n label = \"Save parameters\",\n class = \"btn-primary\"\n ),\n shinyjs::hidden(\n shiny::span(id = \"save_msg\", \"Saving parameters...\"),\n shiny::div(id = \"error\", shiny::div(\n shiny::br(),\n shiny::tags$b(\"Error: \"),\n shiny::span(id = \"error_msg\")\n ))\n ),\n shiny::actionButton(\n inputId = \"launch\",\n label = \"Launch job\",\n class = \"btn-primary\"\n ),\n ),\n shinyjs::hidden(shiny::div(\n id = \"thankyou_msg\",\n shiny::h3(\"Thanks, your parameters were saved successfully!\")\n )),\n shinyjs::hidden(\n shiny::div(\n id = \"targets\",\n shiny::mainPanel(\n targets::tar_watch_ui(\n id = \"targets-shiny\",\n seconds = 10,\n targets_only = TRUE,\n degree_from = 8,\n display = \"graph\"\n )\n )\n )\n ),\n shinyjs::hidden(shiny::downloadButton(outputId = \"results\", \"Download results\")),\n shinyjs::hidden(shiny::actionButton(inputId = \"close\", label = \"Close\")),\n shinyjs::hidden(shiny::div(id = \"job_msg\", shiny::h3(\"Job is running!\"))),\n shinyjs::hidden(shiny::div(id = \"job_end\", shiny::h3(\"Job finished!\")))\n )\n)\n\n# save the results to a file\n.save_input <- function(input) {\n paths_data_source <- tima:::get_default_paths()$data$source$path\n paths_data_interim_annotations <-\n tima:::get_default_paths()$data$interim$annotations$path\n ## safety\n tima:::create_dir(paths_data_source)\n\n list <- tima:::load_yaml_files()\n\n yamls_params <- list$yamls_params\n yaml_files <- list$yaml_files\n yaml_names <- list$yaml_names\n\n ## This allows to keep files correctly placed in `data/source` clean\n prefil_fea_raw <- shiny::isolate(input$fil_fea_raw)\n prefil_spe_raw <- shiny::isolate(input$fil_spe_raw)\n prefil_met_raw <- shiny::isolate(input$fil_met_raw)\n prefil_sir_raw <- shiny::isolate(input$fil_ann_raw_sir)\n\n prefil_fea_raw_1 <- file.path(paths_data_source, prefil_fea_raw[[1]])\n prefil_spe_raw_1 <- file.path(paths_data_source, prefil_spe_raw[[1]])\n if (!is.null(prefil_met_raw)) {\n prefil_met_raw_1 <- file.path(paths_data_source, prefil_met_raw[[1]])\n }\n if (!is.null(prefil_sir_raw)) {\n prefil_sir_raw_1 <-\n file.path(paths_data_interim_annotations, prefil_sir_raw[[1]])\n }\n\n if (!file.exists(prefil_fea_raw_1)) {\n fs::file_copy(\n path = prefil_fea_raw[[4]],\n new_path = file.path(prefil_fea_raw_1),\n overwrite = TRUE\n )\n }\n if (!file.exists(prefil_spe_raw_1)) {\n fs::file_copy(\n path = prefil_spe_raw[[4]],\n new_path = file.path(prefil_spe_raw_1),\n overwrite = TRUE\n )\n }\n if (!is.null(prefil_met_raw)) {\n if (!file.exists(prefil_met_raw_1)) {\n fs::file_copy(\n path = prefil_met_raw[[4]],\n new_path = file.path(prefil_met_raw_1),\n overwrite = TRUE\n )\n }\n } else {\n prefil_met_raw_1 <- NULL\n }\n if (!is.null(prefil_sir_raw)) {\n if (!file.exists(prefil_sir_raw_1)) {\n ## safety\n tima:::create_dir(paths_data_interim_annotations)\n fs::file_copy(\n path = prefil_sir_raw[[4]],\n new_path = file.path(prefil_sir_raw_1),\n overwrite = TRUE\n )\n }\n } else {\n prefil_sir_raw_1 <- NULL\n }\n\n fil_fea_raw <- prefil_fea_raw_1\n fil_spe_raw <- prefil_spe_raw_1\n fil_met_raw <- prefil_met_raw_1\n fil_sir_raw <- prefil_sir_raw_1\n\n fil_pat <- shiny::isolate(input$fil_pat)\n\n gnps_job_id <- shiny::isolate(input$gnps_id)\n if (gnps_job_id == \"\") {\n gnps_job_id <- NULL\n }\n\n org_tax <- shiny::isolate(input$org_tax)\n if (org_tax == \"\") {\n org_tax <- NULL\n }\n hig_con <- shiny::isolate(input$high_confidence)\n ms_pol <- shiny::isolate(input$ms_pol)\n summarise <- shiny::isolate(input$summarise)\n\n message(x = \"Changing parameters ...\")\n message(x = \"... Small\")\n yaml_small <- yamls_params[[\"params/prepare_params\"]]\n yaml_small$files$pattern <- fil_pat\n yaml_small$files$features$raw <- fil_fea_raw\n yaml_small$files$metadata$raw <- fil_met_raw\n yaml_small$files$annotations$raw$sirius <- fil_sir_raw\n yaml_small$files$spectral$raw <- fil_spe_raw\n yaml_small$ms$polarity <- ms_pol\n yaml_small$organisms$taxon <- org_tax\n yaml_small$options$high_confidence <- hig_con\n yaml_small$options$summarise <- summarise\n tima:::create_dir(\"params\")\n yaml::write_yaml(x = yaml_small, file = tima:::get_default_paths()$params$prepare_params)\n\n message(x = \"... Advanced\")\n yaml_advanced <- yamls_params[[\"params/prepare_params_advanced\"]]\n yaml_advanced$annotations$candidates$final <-\n shiny::isolate(input$ann_can_fin)\n yaml_advanced$annotations$ms1only <-\n shiny::isolate(input$ann_ms1only)\n yaml_advanced$annotations$ms2approx <-\n shiny::isolate(input$ann_ms2_app)\n yaml_advanced$annotations$thresholds$consistency <-\n shiny::isolate(input$ann_thr_con)\n yaml_advanced$annotations$thresholds$ms1$biological <-\n shiny::isolate(input$ann_thr_ms1_bio)\n yaml_advanced$annotations$thresholds$ms1$chemical <-\n shiny::isolate(input$ann_thr_ms1_che)\n yaml_advanced$annotations$thresholds$ms1$condition <-\n shiny::isolate(input$ann_thr_ms1_con)\n yaml_advanced$annotations$thresholds$ms2$similarity$annotation <-\n shiny::isolate(input$ann_thr_ms2_sim)\n yaml_advanced$annotations$thresholds$ms2$similarity$edges <-\n shiny::isolate(input$edg_thr_ms2_sim)\n yaml_advanced$files$pattern <- fil_pat\n yaml_advanced$files$annotations$raw$spectral$gnps <-\n yaml_advanced$files$annotations$raw$spectral$gnps |>\n tima:::replace_id()\n yaml_advanced$files$annotations$raw$spectral$spectral <-\n yaml_advanced$files$annotations$raw$spectral$spectral |>\n tima:::replace_id()\n # yaml_advanced$files$annotations$raw$sirius <-\n # yaml_advanced$files$annotations$raw$sirius |>\n # tima:::replace_id()\n yaml_advanced$files$annotations$raw$sirius <- fil_sir_raw\n yaml_advanced$files$annotations$prepared$canopus <-\n yaml_advanced$files$annotations$prepared$canopus |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$formula <-\n yaml_advanced$files$annotations$prepared$formula |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$structural$gnps <-\n yaml_advanced$files$annotations$prepared$structural$gnps |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$structural$ms1 <-\n yaml_advanced$files$annotations$prepared$structural$ms1 |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$structural$sirius <-\n yaml_advanced$files$annotations$prepared$structural$sirius |>\n tima:::replace_id()\n yaml_advanced$files$annotations$prepared$structural$spectral <-\n yaml_advanced$files$annotations$prepared$structural$spectral |>\n tima:::replace_id()\n yaml_advanced$files$annotations$filtered <-\n yaml_advanced$files$annotations$filtered |>\n tima:::replace_id()\n # yaml_advanced$files$annotations$processed <-\n # yaml_advanced$files$annotations$processed |>\n # tima:::replace_id()\n yaml_advanced$files$features$raw <-\n fil_fea_raw\n yaml_advanced$files$features$prepared <-\n yaml_advanced$files$features$prepared |>\n tima:::replace_id()\n # TODO\n # yaml_advanced$files$libraries$sop$raw$closed <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$raw$ecmdb <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$raw$hmdb <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$raw$lotus <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$closed <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$ecmdb <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$hmdb <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$lotus <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$rt <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$prepared$spectral <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$keys <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$organisms$names <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$organisms$taxonomies$ott <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$stereo <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$metadata <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$names <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$taxonomies$cla <-\n # shiny::isolate(input$todo)\n # yaml_advanced$files$libraries$sop$merged$structures$taxonomies$npc <-\n # shiny::isolate(input$todo)\n yaml_advanced$files$libraries$spectral$raw <-\n shiny::isolate(input$lib_spe_mgf)\n yaml_advanced$files$libraries$temporal$exp$csv <-\n shiny::isolate(input$lib_tmp_exp_csv)\n yaml_advanced$files$libraries$temporal$is$csv <-\n shiny::isolate(input$lib_tmp_is_csv)\n # TODO\n # other relative paths, not necessary\n # TODO\n # yaml_advanced$files$libraries$temporal$prepared <-\n # shiny::isolate(input$todo)\n yaml_advanced$files$networks$spectral$edges$raw$ms1 <-\n yaml_advanced$files$networks$spectral$edges$raw$ms1 |>\n tima:::replace_id()\n yaml_advanced$files$networks$spectral$edges$raw$spectral <-\n yaml_advanced$files$networks$spectral$edges$raw$spectral |>\n tima:::replace_id()\n yaml_advanced$files$networks$spectral$edges$prepared <-\n yaml_advanced$files$networks$spectral$edges$prepared |>\n tima:::replace_id()\n yaml_advanced$files$networks$spectral$components$raw <-\n yaml_advanced$files$networks$spectral$components$raw |>\n tima:::replace_id()\n yaml_advanced$files$networks$spectral$components$prepared <-\n yaml_advanced$files$networks$spectral$components$prepared |>\n tima:::replace_id()\n yaml_advanced$files$metadata$raw <- fil_met_raw\n yaml_advanced$files$metadata$prepared <-\n yaml_advanced$files$metadata$prepared |>\n tima:::replace_id()\n yaml_advanced$files$spectral$raw <- fil_spe_raw\n yaml_advanced$gnps$id <- gnps_job_id\n yaml_advanced$gnps$workflow <-\n shiny::isolate(input$gnps_workflow)\n yaml_advanced$ms$adducts$neg <-\n shiny::isolate(input$ms_add_neg)\n yaml_advanced$ms$adducts$pos <-\n shiny::isolate(input$ms_add_pos)\n yaml_advanced$ms$clusters$neg <-\n shiny::isolate(input$ms_clu_neg)\n yaml_advanced$ms$clusters$pos <-\n shiny::isolate(input$ms_clu_pos)\n yaml_advanced$ms$neutral_losses <-\n shiny::isolate(input$ms_neu)\n yaml_advanced$ms$polarity <- ms_pol\n yaml_advanced$ms$thresholds$ms2$intensity <-\n shiny::isolate(input$ms_thr_ms2_int)\n yaml_advanced$ms$tolerances$mass$ppm$ms1 <-\n shiny::isolate(input$ms_tol_mas_ppm_ms1)\n yaml_advanced$ms$tolerances$mass$ppm$ms2 <-\n shiny::isolate(input$ms_tol_mas_ppm_ms2)\n yaml_advanced$ms$tolerances$mass$dalton$ms1 <-\n shiny::isolate(input$ms_tol_mas_dal_ms1)\n yaml_advanced$ms$tolerances$mass$dalton$ms2 <-\n shiny::isolate(input$ms_tol_mas_dal_ms2)\n yaml_advanced$ms$tolerances$rt$adducts <-\n shiny::isolate(input$ms_tol_rt_add)\n yaml_advanced$ms$tolerances$rt$library <-\n shiny::isolate(input$ms_tol_rt_lib)\n yaml_advanced$names$adduct <-\n shiny::isolate(input$names_adduct)\n yaml_advanced$names$extension <-\n shiny::isolate(input$names_extension)\n yaml_advanced$names$features <-\n shiny::isolate(input$names_features)\n yaml_advanced$names$filename <-\n shiny::isolate(input$names_filename)\n yaml_advanced$names$inchikey <-\n shiny::isolate(input$names_inchikey)\n yaml_advanced$names$libraries <-\n shiny::isolate(input$names_libraries)\n yaml_advanced$names$mgf$adduct <-\n shiny::isolate(input$names_mgf_ad)\n yaml_advanced$names$mgf$collision_energy <-\n shiny::isolate(input$names_mgf_ce)\n yaml_advanced$names$mgf$compound_id <-\n shiny::isolate(input$names_mgf_ci)\n yaml_advanced$names$mgf$exact_mass <-\n shiny::isolate(input$names_mgf_em)\n yaml_advanced$names$mgf$inchi <-\n shiny::isolate(input$names_mgf_in)\n yaml_advanced$names$mgf$inchi_no_stereo <-\n shiny::isolate(input$names_mgf_io)\n yaml_advanced$names$mgf$inchikey <-\n shiny::isolate(input$names_mgf_ik)\n yaml_advanced$names$mgf$inchikey_no_stereo <-\n shiny::isolate(input$names_mgf_il)\n yaml_advanced$names$mgf$molecular_formula <-\n shiny::isolate(input$names_mgf_mf)\n yaml_advanced$names$mgf$name <-\n shiny::isolate(input$names_mgf_na)\n yaml_advanced$names$mgf$polarity <-\n shiny::isolate(input$names_mgf_po)\n yaml_advanced$names$mgf$retention_time <-\n shiny::isolate(input$names_mgf_rt)\n yaml_advanced$names$mgf$smiles <-\n shiny::isolate(input$names_mgf_sm)\n yaml_advanced$names$mgf$smiles_no_stereo <-\n shiny::isolate(input$names_mgf_sn)\n yaml_advanced$names$mgf$spectrum_id <-\n shiny::isolate(input$names_mgf_si)\n yaml_advanced$names$mgf$splash <-\n shiny::isolate(input$names_mgf_sp)\n yaml_advanced$names$mgf$synonyms <-\n shiny::isolate(input$names_mgf_sy)\n yaml_advanced$names$mgf$xlogp <-\n shiny::isolate(input$names_mgf_xl)\n yaml_advanced$names$precursor <-\n shiny::isolate(input$names_precursor)\n yaml_advanced$names$rt$features <-\n shiny::isolate(input$names_rt)\n yaml_advanced$names$rt$library <-\n shiny::isolate(input$names_rt_2)\n yaml_advanced$names$smiles <-\n shiny::isolate(input$names_smiles)\n yaml_advanced$names$source <-\n shiny::isolate(input$names_source)\n yaml_advanced$names$target <-\n shiny::isolate(input$names_target)\n yaml_advanced$names$taxon <-\n shiny::isolate(input$names_taxon)\n yaml_advanced$organisms$candidates <-\n shiny::isolate(input$org_can)\n yaml_advanced$organisms$filter$mode <-\n shiny::isolate(input$org_fil_mod)\n yaml_advanced$organisms$filter$level <-\n shiny::isolate(input$org_fil_lev)\n yaml_advanced$organisms$filter$value <-\n shiny::isolate(input$org_fil_val)\n yaml_advanced$organisms$taxon <-\n org_tax\n # TODO\n # yaml_advanced$tools$metadata <-\n # shiny::isolate(input$too_x)\n # yaml_advanced$tools$networks$spectral$components <-\n # shiny::isolate(input$too_x)\n # yaml_advanced$tools$networks$spectral$edges <-\n # shiny::isolate(input$too_x)\n yaml_advanced$tools$sirius$version <-\n shiny::isolate(input$too_sir_ver)\n # TODO\n # yaml_advanced$tools$taxonomies$biological <-\n # shiny::isolate(input$too_x)\n # yaml_advanced$tools$taxonomies$chemical <-\n # shiny::isolate(input$too_x)\n yaml_advanced$units$rt <-\n shiny::isolate(input$uni_rt)\n yaml_advanced$weights$global$biological <-\n shiny::isolate(input$wei_glo_bio)\n yaml_advanced$weights$global$chemical <-\n shiny::isolate(input$wei_glo_che)\n yaml_advanced$weights$global$spectral <-\n shiny::isolate(input$wei_glo_spe)\n yaml_advanced$weights$biological$domain <-\n shiny::isolate(input$wei_bio_01)\n yaml_advanced$weights$biological$kingdom <-\n shiny::isolate(input$wei_bio_02)\n yaml_advanced$weights$biological$phylum <-\n shiny::isolate(input$wei_bio_03)\n yaml_advanced$weights$biological$class <-\n shiny::isolate(input$wei_bio_04)\n yaml_advanced$weights$biological$order <-\n shiny::isolate(input$wei_bio_05)\n yaml_advanced$weights$biological$infraorder <-\n shiny::isolate(input$wei_bio_06)\n yaml_advanced$weights$biological$family <-\n shiny::isolate(input$wei_bio_07)\n yaml_advanced$weights$biological$subfamily <-\n shiny::isolate(input$wei_bio_08)\n yaml_advanced$weights$biological$tribe <-\n shiny::isolate(input$wei_bio_09)\n yaml_advanced$weights$biological$subtribe <-\n shiny::isolate(input$wei_bio_10)\n yaml_advanced$weights$biological$genus <-\n shiny::isolate(input$wei_bio_11)\n yaml_advanced$weights$biological$subgenus <-\n shiny::isolate(input$wei_bio_12)\n yaml_advanced$weights$biological$species <-\n shiny::isolate(input$wei_bio_13)\n yaml_advanced$weights$biological$subspecies <-\n shiny::isolate(input$wei_bio_14)\n yaml_advanced$weights$biological$variety <-\n shiny::isolate(input$wei_bio_15)\n yaml_advanced$weights$chemical$cla$kingdom <-\n shiny::isolate(input$wei_che_11)\n yaml_advanced$weights$chemical$npc$pathway <-\n shiny::isolate(input$wei_che_21)\n yaml_advanced$weights$chemical$cla$superclass <-\n shiny::isolate(input$wei_che_12)\n yaml_advanced$weights$chemical$npc$superclass <-\n shiny::isolate(input$wei_che_22)\n yaml_advanced$weights$chemical$cla$class <-\n shiny::isolate(input$wei_che_13)\n yaml_advanced$weights$chemical$npc$class <-\n shiny::isolate(input$wei_che_23)\n yaml_advanced$weights$chemical$cla$parent <-\n shiny::isolate(input$wei_che_14)\n yaml_advanced$options$compounds_names <-\n shiny::isolate(input$compounds_names)\n yaml_advanced$options$high_confidence <- hig_con\n yaml_advanced$options$force <-\n shiny::isolate(input$force)\n yaml_advanced$options$remove_ties <-\n shiny::isolate(input$remove_ties)\n yaml_advanced$options$summarise <- summarise\n\n if (!is.null(prefil_met_raw)) {\n yamls_params$prepare_taxa$files$metadata$raw <- fil_met_raw\n }\n if (!is.null(gnps_job_id)) {\n yamls_params$prepare_taxa$files$metadata$raw <-\n file.path(paths_data_source, paste0(gnps_job_id, \"_metadata.tsv\"))\n }\n\n yaml::write_yaml(\n x = yaml_advanced,\n file = tima:::get_default_paths()$params$prepare_params_advanced\n )\n}\n\nserver <- function(input, output, session) {\n ## Observe helpers\n shinyhelper::observe_helpers()\n\n output$demo_spe <- downloadHandler(\n filename = basename(tima:::get_default_paths()$urls$examples$spectra),\n content = function(file) {\n writeLines(readLines(tima:::get_default_paths()$urls$examples$spectra_mini), file)\n }\n )\n\n output$demo_fea <- downloadHandler(\n filename = basename(tima:::get_default_paths()$urls$examples$features),\n content = function(file) {\n writeLines(readLines(tima:::get_default_paths()$urls$examples$features), file)\n }\n )\n\n output$demo_met <- downloadHandler(\n filename = basename(tima:::get_default_paths()$urls$examples$metadata),\n content = function(file) {\n writeLines(readLines(tima:::get_default_paths()$urls$examples$metadata), file)\n }\n )\n\n ## Mandatory fields\n fields_mandatory <- c(\"fil_fea_raw\", \"fil_spe_raw\", \"fil_pat\")\n\n ## Enable the Submit button when all mandatory fields are filled out\n shiny::observe(x = {\n mandatory_filled <-\n vapply(\n X = fields_mandatory,\n FUN = function(x) {\n ## TODO improve\n suppressWarnings(any(!is.null(input[[x]]), input[[x]] != \"\"))\n },\n FUN.VALUE = logical(1)\n )\n mandatory_filled <- all(mandatory_filled)\n\n shinyjs::toggleState(id = \"save\", condition = mandatory_filled)\n shinyjs::toggleState(id = \"launch\", condition = input$save >= 1)\n })\n\n ## Special check for taxon name\n iv <- shinyvalidate::InputValidator$new()\n iv$add_rule(\"org_tax\", function(taxon) {\n if (!grepl(\n pattern = \"^[[:upper:]]\",\n x = taxon,\n perl = TRUE\n )) {\n \"Please provide your taxon name with capital letter\"\n }\n })\n iv$add_rule(\"org_tax\", function(taxon) {\n if (is.na(rotl::tnrs_match_names(names = taxon, do_approximate_matching = FALSE)$ott_id)) {\n \"Taxon not found in Open Tree of Life\"\n }\n })\n iv$enable()\n\n ## When the Save button is clicked, save the response\n shiny::observeEvent(eventExpr = input$save, handlerExpr = {\n ## User-experience stuff\n shinyjs::show(\"save_msg\")\n shinyjs::enable(\"launch\")\n shinyjs::hide(\"error\")\n shinyjs::hide(\"job_msg\")\n shinyjs::hide(\"job_end\")\n shinyjs::hide(\"results\")\n shinyjs::hide(\"close\")\n\n ## Save the data (show an error message in case of error)\n tryCatch(\n expr = {\n .save_input(input = input)\n shinyjs::show(\"thankyou_msg\")\n },\n error = function(err) {\n shinyjs::html(\"error_msg\", err$message)\n shinyjs::show(\n id = \"error\",\n anim = TRUE,\n animType = \"fade\"\n )\n },\n finally = {\n shinyjs::enable(\"save\")\n shinyjs::enable(\"launch\")\n shinyjs::hide(\"save_msg\")\n shinyjs::hide(\"error\")\n shinyjs::hide(\"job_msg\")\n shinyjs::hide(\"job_end\")\n shinyjs::hide(\"results\")\n shinyjs::hide(\"close\")\n }\n )\n })\n\n shiny::observeEvent(eventExpr = input$launch, handlerExpr = {\n shinyjs::show(\"job_msg\")\n shinyjs::show(\"targets\")\n shinyjs::hide(\"thankyou_msg\")\n shinyjs::hide(\"error\")\n shinyjs::hide(\"params\")\n shinyjs::hide(\"form\")\n shinyjs::hide(\"job_end\")\n shinyjs::hide(\"results\")\n shinyjs::hide(\"close\")\n targets::tar_watch_server(\n id = \"targets-shiny\",\n exclude = c(\n \"yaml_paths\",\n \"benchmark_ann_fil_ms1_neg\",\n \"benchmark_ann_fil_ms1_pos\",\n \"benchmark_ann_fil_spe_neg\",\n \"benchmark_ann_fil_spe_pos\",\n \"benchmark_ann_fil_spe_ms1_neg\",\n \"benchmark_ann_fil_spe_ms1_pos\",\n \"benchmark_ann_ms1_neg\",\n \"benchmark_ann_ms2_pos\",\n \"benchmark_ann_ms1_pre_neg\",\n \"benchmark_ann_ms1_pre_pos\",\n \"benchmark_ann_pre_ms1_ms2_b_c_neg\",\n \"benchmark_ann_pre_ms1_ms2_b_c_pos\",\n \"benchmark_ann_pre_ms1_ms2_b_neg\",\n \"benchmark_ann_pre_ms1_ms2_b_pos\",\n \"benchmark_ann_pre_ms2_b_c_neg\",\n \"benchmark_ann_pre_ms2_b_c_pos\",\n \"benchmark_ann_pre_ms2_b_neg\",\n \"benchmark_ann_pre_ms2_b_pos\",\n \"benchmark_ann_sir_pre\",\n \"benchmark_ann_sir_pre_can\",\n \"benchmark_ann_sir_pre_for\",\n \"benchmark_ann_sir_pre_str\",\n \"benchmark_ann_spe_neg\",\n \"benchmark_ann_spe_pos\",\n \"benchmark_ann_spe_pre_neg\",\n \"benchmark_ann_spe_pre_pos\",\n \"benchmark_com_neg\",\n \"benchmark_com_pos\",\n \"benchmark_com_pre_neg\",\n \"benchmark_com_pre_pos\",\n \"benchmark_converted\",\n \"benchmark_copy\",\n \"benchmark_def_ann_mas\",\n \"benchmark_def_ann_spe\",\n \"benchmark_def_cre_edg_com\",\n \"benchmark_def_cre_edg_spe\",\n \"benchmark_def_fil_ann\",\n \"benchmark_def_pre_ann_sir\",\n \"benchmark_def_pre_ann_spe\",\n \"benchmark_def_pre_fea_com\",\n \"benchmark_def_pre_fea_edg\",\n \"benchmark_def_wei_ann\",\n \"benchmark_edg_pre_neg\",\n \"benchmark_edg_pre_pos\",\n \"benchmark_edg_spe_neg\",\n \"benchmark_edg_spe_pos\",\n \"benchmark_file\",\n \"benchmark_files_neg\",\n \"benchmark_files_pos\",\n \"benchmark_path_copy\",\n \"benchmark_path_export\",\n \"benchmark_path_file\",\n \"benchmark_path_mgf_neg\",\n \"benchmark_path_mgf_pos\",\n \"benchmark_path_url\",\n \"benchmark_path_zip\",\n \"benchmark_prepared\",\n \"benchmark_pre_meta_neg\",\n \"benchmark_pre_meta_pos\",\n \"benchmark_pre_mgf_neg\",\n \"benchmark_pre_mgf_pos\",\n \"benchmark_taxed_neg\",\n \"benchmark_taxed_pos\",\n \"benchmark_wei_par\",\n \"benchmark_zip\",\n \"paths\",\n \"paths_data_interim_libraries_adducts_path\",\n \"paths_data_source_benchmark_cleaned\",\n \"paths_data_source_benchmark_copy\",\n \"paths_data_source_benchmark_mgf_neg\",\n \"paths_data_source_benchmark_mgf_pos\",\n \"paths_data_source_benchmark_set\",\n \"paths_data_source_benchmark_zip\",\n \"paths_data_source_libraries_sop_ecmdb\",\n \"paths_data_source_libraries_sop_hmdb\",\n \"paths_data_source_libraries_sop_lotus\",\n \"paths_data_source_libraries_spectra_is_lotus_pos\",\n \"paths_data_source_libraries_spectra_is_lotus_neg\",\n \"paths_data_source_spectra\",\n # \"paths_gnps_example_id\",\n \"paths_interim_a\",\n \"paths_interim_f\",\n \"paths_source\",\n \"paths_test_mode\",\n \"paths_urls_benchmarking_set\",\n \"paths_urls_ecmdb_metabolites\",\n \"paths_urls_hmdb_structures\",\n \"paths_urls_lotus_doi\",\n \"paths_urls_lotus_pattern\",\n \"paths_urls_massbank_file\",\n \"paths_urls_massbank_url\",\n \"paths_urls_massbank_version\",\n \"paths_urls_examples_spectra_mini\",\n \"paths_urls_examples_spectral_lib_pos\",\n \"paths_urls_examples_spectral_lib_neg\",\n \"par_def_ann_mas\",\n \"par_def_ann_spe\",\n \"par_def_cre_com\",\n \"par_def_cre_edg_spe\",\n \"par_def_fil_ann\",\n \"par_def_pre_ann_gnp\",\n \"par_def_pre_ann_sir\",\n \"par_def_pre_ann_spe\",\n \"par_def_pre_fea_com\",\n \"par_def_pre_fea_edg\",\n \"par_def_pre_fea_tab\",\n \"par_def_pre_lib_rt\",\n \"par_def_pre_lib_sop_clo\",\n \"par_def_pre_lib_sop_ecm\",\n \"par_def_pre_lib_sop_hmd\",\n \"par_def_pre_lib_sop_lot\",\n \"par_def_pre_lib_sop_mer\",\n \"par_def_pre_lib_spe\",\n \"par_def_pre_tax\",\n \"par_def_wei_ann\",\n \"par_fin_par\",\n \"par_fin_par2\",\n \"par_pre_par\",\n \"par_pre_par2\",\n \"par_usr_ann_mas\",\n \"par_usr_ann_spe\",\n \"par_usr_cre_com\",\n \"par_usr_cre_edg_spe\",\n \"par_usr_fil_ann\",\n \"par_usr_pre_ann_gnp\",\n \"par_usr_pre_ann_sir\",\n \"par_usr_pre_ann_spe\",\n \"par_usr_pre_fea_com\",\n \"par_usr_pre_fea_edg\",\n \"par_usr_pre_fea_tab\",\n \"par_usr_pre_lib_rt\",\n \"par_usr_pre_lib_sop_clo\",\n \"par_usr_pre_lib_sop_ecm\",\n \"par_usr_pre_lib_sop_hmd\",\n \"par_usr_pre_lib_sop_lot\",\n \"par_usr_pre_lib_sop_mer\",\n \"par_usr_pre_lib_spe\",\n \"par_usr_pre_tax\",\n \"par_usr_wei_ann\",\n \"par_ann_mas\",\n \"par_ann_spe\",\n \"par_cre_com\",\n \"par_cre_edg_spe\",\n \"par_fil_ann\",\n \"par_pre_ann_gnp\",\n \"par_pre_ann_sir\",\n \"par_pre_ann_spe\",\n \"par_pre_fea_com\",\n \"par_pre_fea_edg\",\n \"par_pre_fea_tab\",\n \"par_pre_lib_rt\",\n \"par_pre_lib_sop_clo\",\n \"par_pre_lib_sop_ecm\",\n \"par_pre_lib_sop_hmd\",\n \"par_pre_lib_sop_lot\",\n \"par_pre_lib_sop_mer\",\n \"par_pre_lib_spe\",\n \"par_pre_tax\",\n \"par_wei_ann\",\n \".Random.seed\"\n )\n )\n targets::tar_make(\n names = tidyselect::matches(\"^ann_pre$\"),\n garbage_collection = TRUE,\n reporter = \"verbose_positives\"\n )\n results <- tail(\n list.files(\n path = \"data/processed\",\n full.names = TRUE,\n recursive = TRUE\n ),\n n = 1\n )\n process <- reactiveValues(status = targets::tar_active())\n observe({\n shiny::invalidateLater(millis = 5000)\n process$status <- targets::tar_active()\n shinyjs::hide(\"job_msg\")\n shinyjs::show(\"job_end\")\n shinyjs::show(\"results\")\n output$results <- downloadHandler(\n filename = basename(results),\n content = function(file) {\n writeLines(readLines(results), file)\n }\n )\n shinyjs::show(\"close\")\n shiny::observeEvent(eventExpr = input$close, handlerExpr = {\n shiny::stopApp()\n })\n })\n })\n}\nurl <- \"<http://127.0.0.1:3838>\"\nmessage(\"Please, open:\", url, \" on your favorite browser, but not Edge.\")\nshiny::shinyApp(\n ui = ui,\n server = server,\n onStart = function() {\n if (i_am_a_whale) {\n message(\"I'm inside the matrix ;(\")\n setwd(dir = \"..\")\n } else {\n tima:::copy_backbone()\n tima:::go_to_cache()\n }\n }\n)\n","type":"text"},{"name":"CITATION","content":"citHeader(\"To cite tima in publications use:\")\n\nbibentry(\n bibtype = \"Manual\",\n title = \"tima: Taxonomically Informed Metabolite Annotation\",\n author = c(\n person(given = \"Adriano\", family = \"Rutz\", email = \"adafede@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-0443-9902\"))\n ),\n year = \"2024\",\n note = \"R package, version 2.11.0\",\n doi = \"10.5281/zenodo.5797920\"\n)\n\nbibentry(\n bibtype = \"Article\",\n title = \"Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation\",\n author = c(\n person(given = \"Adriano\", family = \"Rutz\", comment = c(ORCID = \"0000-0003-0443-9902\")),\n person(given = \"Miwa\", family = \"Dounoue-Kubo\"),\n person(given = \"Simon\", family = \"Ollivier\", comment = c(ORCID = \"0000-0002-7671-1736\")),\n person(given = \"Jonathan\", family = \"Bisson\", comment = c(ORCID = \"0000-0003-1640-9989\")),\n person(given = \"Mohsen\", family = \"Bagheri\"),\n person(given = \"Tongchai\", family = \"Saesong\"),\n person(given = \"Samad Nejad\", family = \"Ebrahimi\", comment = c(ORCID = \"0000-0003-2167-8032\")),\n person(given = \"Kornkanok\", family = \"Ingkaninan\", comment = c(ORCID = \"0000-0002-4415-8489\")),\n person(given = \"Jean-Luc\", family = \"Wolfender\", comment = c(ORCID = \"0000-0002-0125-952X\")),\n person(given = \"Pierre-Marie\", family = \"Allard\", comment = c(ORCID = \"0000-0003-3389-2191\"))\n ),\n journal = \"Frontiers in Plant Science\",\n year = \"2019\",\n volume = \"10\",\n url = \"https://doi.org/10.3389/fpls.2019.01329\",\n language = \"en\",\n keywords = \"metabolite annotation, chemotaxonomy, scoring system, natural products, computational metabolomics, taxonomic distance, specialized metabolome\",\n doi = \"10.3389/FPLS.2019.01329\",\n issn = \"1664-462X\",\n pmc = \"PMC6824209\",\n pmid = \"1708947\",\n qid = \"Q91218352\",\n textVersion = paste(\n \"Adriano Rutz; Miwa Dounoue-Kubo; et al. (25 October 2019). Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation. Frontiers in Plant Science. 10: 1329. doi:10.3389/FPLS.2019.01329. issn:1664-462X. pmc:PMC6824209. pmid:1708947. qid:Q91218352.\"\n )\n)\n","type":"text"},{"name":"DESCRIPTION","content":"Package: tima\nTitle: Taxonomically Informed Metabolite Annotation\nVersion: 2.11.0\nAuthors@R: c(\n person(\"Adriano\", \"Rutz\", , \"adafede@gmail.com\", role = c(\"aut\", \"cre\"),\n comment = c(ORCID = \"0000-0003-0443-9902\")),\n person(\"Pierre-Marie\", \"Allard\", , \"pierre-marie.allard@unifr.ch\", role = \"ctb\",\n comment = c(ORCID = \"0000-0003-3389-2191\"))\n )\nMaintainer: Adriano Rutz <adafede@gmail.com>\nDescription: This package provides the infrastructure to perform\n Taxonomically Informed Metabolite Annotation.\nLicense: GPL (>= 3)\nURL: https://github.com/taxonomicallyinformedannotation/tima,\n https://taxonomicallyinformedannotation.github.io/tima,\n https://taxonomicallyinformedannotation.github.io/tima-shinylive\nBugReports: https://github.com/taxonomicallyinformedannotation/tima/issues\nDepends: R (>= 4.3.0)\nImports: crayon (>= 1.5.3), docopt (>= 0.7.1), dplyr (>= 1.1.4), DT, fs\n (>= 1.6.4), gt, httr2 (>= 1.0.1), igraph (>= 2.0.3), jsonlite\n (>= 1.8.8), MetaboCoreUtils (>= 1.10.0), MsBackendMgf (>=\n 1.10.0), MsBackendMsp (>= 1.6.0), msentropy (>= 0.1.4), pbapply\n (>= 1.7.2), rotl (>= 3.1.0), shiny (>= 1.9.0), shinybusy,\n shinyWidgets, Spectra (>= 1.12.0), stats, stringi (>= 1.8.4),\n targets (>= 1.7.1), tidyfst (>= 1.7.8), tidyselect (>= 1.2.1),\n tidytable (>= 0.11.1), utils, visNetwork, yaml (>= 2.3.10)\nSuggests: BiocManager, knitr, lifecycle, pkgload, R.utils, rlang,\n shinyhelper, shinyjs, shinytest2, shinyvalidate, spelling,\n testthat (>= 3.0.0)\nVignetteBuilder: knitr\nbiocViews: metabolite annotation, chemotaxonomy, scoring system,\n natural products, computational metabolomics, taxonomic\n distance, specialized metabolome\nByteCompile: true\nConfig/Needs/website: rmarkdown\nConfig/testthat/edition: 3\nEncoding: UTF-8\nLanguage: en-US\nRoxygen: list(markdown = TRUE)\nRoxygenNote: 7.3.2\nX-schema.org-keywords: metabolite annotation, chemotaxonomy, scoring\n system, natural products, computational metabolomics, taxonomic\n distance, specialized metabolome\nCollate: 'round_reals.R' 'log_pipe.R' 'harmonize_adducts.R'\n 'parse_yaml_params.R' 'parse_cli_params.R'\n 'get_default_paths.R' 'get_params.R' 'dist_groups.R'\n 'decorate_masses.R' 'parse_adduct.R' 'calculate_mass_of_m.R'\n 'annotate_masses.R' 'sanitize_spectra.R' 'import_spectra.R'\n 'annotate_spectra.R' 'benchmark_taxize_spectra.R'\n 'calculate_entropy.R' 'clean_bio.R'\n 'filter_high_confidence_only.R' 'columns_model.R'\n 'clean_collapse.R' 'clean_chemo.R'\n 'complement_metadata_structures.R' 'copy_backbone.R'\n 'create_components.R' 'create_dir.R' 'create_edges.R'\n 'create_edges_spectra.R' 'decorate_bio.R' 'decorate_chemo.R'\n 'export_output.R' 'export_params.R' 'export_spectra_rds.R'\n 'extract_spectra.R' 'fake_annotations_columns.R' 'fake_ecmdb.R'\n 'fake_hmdb.R' 'fake_lotus.R' 'fake_sop_columns.R'\n 'filter_annotations.R' 'go_to_cache.R' 'get_file.R'\n 'get_example_sirius.R' 'get_example_files.R'\n 'get_gnps_tables.R' 'get_last_version_from_zenodo.R'\n 'get_massbank_spectra.R' 'get_organism_taxonomy_ott.R'\n 'globals.R' 'harmonize_names_sirius.R' 'harmonize_spectra.R'\n 'install.R' 'load_yaml_files.R' 'log_debug.R'\n 'pre_harmonize_names_sirius.R' 'select_annotations_columns.R'\n 'prepare_annotations_gnps.R' 'select_sirius_columns.R'\n 'read_from_sirius_zip.R' 'prepare_annotations_sirius.R'\n 'prepare_annotations_spectra.R' 'prepare_features_components.R'\n 'prepare_features_edges.R' 'prepare_features_tables.R'\n 'prepare_libraries_rt.R' 'select_sop_columns.R'\n 'prepare_libraries_sop_closed.R'\n 'prepare_libraries_sop_ecmdb.R' 'prepare_libraries_sop_hmdb.R'\n 'prepare_libraries_sop_lotus.R' 'split_tables_sop.R'\n 'prepare_libraries_sop_merged.R' 'prepare_libraries_spectra.R'\n 'prepare_params.R' 'prepare_taxa.R' 'replace_id.R' 'run_app.R'\n 'tima-package.R' 'tima_full.R' 'transform_score_sirius_csi.R'\n 'weight_chemo.R' 'weight_bio.R' 'weight_annotations.R'\nRepository: https://taxonomicallyinformedannotation.r-universe.dev\nRemoteUrl: https://github.com/taxonomicallyinformedannotation/tima\nRemoteRef: HEAD\nRemoteSha: d193099cf9e8c0c932aa85abfa300d0f3c956443\nNeedsCompilation: no\nPackaged: 2024-08-20 03:20:49 UTC; root\nAuthor: Adriano Rutz [aut, cre] (<https://orcid.org/0000-0003-0443-9902>),\n Pierre-Marie Allard [ctb] (<https://orcid.org/0000-0003-3389-2191>)\nBuilt: R 4.4.1; ; 2024-08-20 04:21:48 UTC; unix\n","type":"text"},{"name":"INDEX","content":"annotate_masses Annotate masses\nannotate_spectra Annotate spectra\nbenchmark_taxize_spectra\n Benchmark taxize spectra\ncalculate_entropy Calculate entropy\ncalculate_mass_of_m Calculate mass of M\nclean_bio Clean bio\nclean_chemo Clean chemo\nclean_collapse Clean collapse\ncolumns_model Columns model\ncomplement_metadata_structures\n Complement metadata of structures\ncopy_backbone Copy backbone\ncreate_components Create components\ncreate_dir Create directory\ncreate_edges Create edges\ncreate_edges_spectra Create edges spectra\ndecorate_bio Decorate bio\ndecorate_chemo Decorate chemo\ndecorate_masses Decorate masses\ndist_get Distance between two elements in a distance\n matrix\ndist_groups Dist groups\nexport_output Export output\nexport_params Export parameters\nexport_spectra_rds Export spectra RDS\nextract_spectra Extract spectra from a Spectra object\nfake_annotations_columns\n Fake annotations columns\nfake_ecmdb Fake ECMDB\nfake_hmdb Fake HMDB\nfake_lotus Fake LOTUS\nfake_sop_columns Fake SOP columns\nfilter_annotations Filter annotations\nfilter_high_confidence_only\n Filter high confidence only *[Experimental]*\nget_default_paths Get default paths\nget_example_files Get example files\nget_example_sirius Get example sirius\nget_file Get file\nget_gnps_tables Get GNPS Tables\nget_last_version_from_zenodo\n Get last version from Zenodo\nget_massbank_spectra Get MassBank spectra\nget_organism_taxonomy_ott\n Get organism taxonomy (Open Tree of Life\n Taxonomy)\nget_params Get parameters\ngo_to_cache Go to cache\nharmonize_adducts Harmonize adducts\nharmonize_names_sirius\n Harmonize names sirius\nharmonize_spectra Harmonize spectra\nimport_spectra Import spectra\ninstall Install\nload_yaml_files Load yaml files\nlog_debug Log debug\nlog_pipe Log pipe\nparse_adduct Parse adduct\nparse_cli_params Parse CLI parameters\nparse_yaml_params Parse YAML parameters\npre_harmonize_names_sirius\n Pre harmonize names sirius\nprepare_annotations_gnps\n Prepare annotations GNPS\nprepare_annotations_sirius\n Prepare annotations SIRIUS\nprepare_annotations_spectra\n Prepare annotations MS2\nprepare_features_components\n Prepare features components\nprepare_features_edges\n Prepare features edges\nprepare_features_tables\n Prepare features table\nprepare_libraries_rt Prepare libraries of retention times\nprepare_libraries_sop_closed\n Prepare libraries of structure organism pairs\n CLOSED\nprepare_libraries_sop_ecmdb\n Prepare libraries of structure organism pairs\n ECMDB\nprepare_libraries_sop_hmdb\n Prepare libraries of structure organism pairs\n HMDB\nprepare_libraries_sop_lotus\n Prepare libraries of structure organism pairs\n LOTUS\nprepare_libraries_sop_merged\n Prepare merged structure organism pairs\n libraries\nprepare_libraries_spectra\n Prepare libraries of spectra\nprepare_params Prepare params\nprepare_taxa Prepare taxa\nread_from_sirius_zip Read from SIRIUS zip\nreplace_id Replace ID in file paths\nround_reals Round reals\nrun_app Run app\nsanitize_spectra Sanitize spectra\nselect_annotations_columns\n Select annotations columns\nselect_sirius_columns_canopus\n Select sirius columns (canopus)\nselect_sirius_columns_formulas\n Select sirius columns (formulas)\nselect_sirius_columns_structures\n 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Generated by roxygen2: do not edit by hand\n\nexport(annotate_masses)\nexport(annotate_spectra)\nexport(calculate_mass_of_m)\nexport(create_components)\nexport(create_dir)\nexport(create_edges_spectra)\nexport(export_output)\nexport(filter_annotations)\nexport(get_default_paths)\nexport(get_example_files)\nexport(get_file)\nexport(get_last_version_from_zenodo)\nexport(get_organism_taxonomy_ott)\nexport(get_params)\nexport(go_to_cache)\nexport(import_spectra)\nexport(install)\nexport(log_debug)\nexport(parse_adduct)\nexport(prepare_annotations_gnps)\nexport(prepare_annotations_sirius)\nexport(prepare_annotations_spectra)\nexport(prepare_features_components)\nexport(prepare_features_edges)\nexport(prepare_features_tables)\nexport(prepare_libraries_rt)\nexport(prepare_libraries_sop_closed)\nexport(prepare_libraries_sop_ecmdb)\nexport(prepare_libraries_sop_hmdb)\nexport(prepare_libraries_sop_lotus)\nexport(prepare_libraries_sop_merged)\nexport(prepare_libraries_spectra)\nexport(prepare_taxa)\nexport(run_app)\nexport(sanitize_spectra)\nexport(tima_full)\nexport(weight_annotations)\n","type":"text"},{"name":"NEWS.md","content":"# tima\n\n# tima 2.11.0\n\n* Added demo files download to the app\n* Better packaging\n* Improved documentation\n* Fixed all CRAN warnings\n* Reduced dependencies\n* Reduced exports\n* Removed `CompoundDb` dependency as it was causing too many issues\n* Removed `pak` install and switched to `r-universe`\n* Replaced internal functions by `Spectra` equivalents (#166)\n* Shinylive version available at <https://taxonomicallyinformedannotation.github.io/tima-shinylive>\n* Simplified install and vignettes\n\n# tima 2.10.0\n\n* Added alt text to vignettes\n* Added the possibility to add internal libraries through the GUI (#159)\n* Added the possibility to filter confident annotations only (#140)\n* Added number of peaks in spectrum\n* Brought back some older dependencies to be compatible with `oldrel`\n* Changed package name, `usethis` update\n* Clearer handling of SIRIUS scores (#146, #147)\n* Exposed more parameters to the GUI (#159)\n* Facilitated install, no need to clone the directory anymore\n* Finally made it to the [r-universe](https://r-universe.dev/builds/)\n* Fixed adducts and removed nitrogen rule\n* Fixed number of matched peaks\n* Improved imports\n* Reduced warnings\n* Updated benchmarking steps\n\n# tima 2.9.6\n\n* Added light-switch thanks to `pkgdown 2.1.0`.\n* Attempt to simplify installation\n* Fixed library/adducts confusion (#123)\n* Fixed some incorrect adduct differences annotations\n* Refactored adducts / neutral losses / dimers annotation to allow for more flexibility (#141, #144)\n\n# tima 2.9.5\n\n* Do not re-package if already the latest version\n* SIRIUS 6 default and compatible (keeping SIRIUS 5 backward compatibility)\n* Updated to Massbank version `2024.06`\n\n# tima 2.9.4\n\n* Automated update\n* Added an option to remove ties (#134)\n* Added some details for SIRIUS, added manual workspace addition (#132)\n* Additional preprocessing (reduction) of noisy spectra\n* Dependencies update\n* Docker updates (#131)\n* Handle cases when same (feature_id, mslevel) pairs are present within an MGF (#133)\n* Improved documentation\n* New working directory at `$HOME/.tima`\n* Updated R and Bioconductor versions\n\n# tima 2.9.3\n\n* Allowed for SIRIUS jobs containing only summaries\n* Allowed for underscores in job pattern\n* Changed some default values (less stringent)\n* Dependencies update\n* Migrated app testing to `shinytest2`\n* Removed further some inconsistent MS1 annotations\n* Removed tests dependencies by default\n\n# tima 2.9.2\n\n* Added Nitrogen rule to filter out some annotations\n* Better handling of partial downloads (#118)\n* Dependencies update (mainly `targets 1.5.1`, will invalidate previous targets)\n* Fixed some port issues in Shiny (#122)\n* Removed completely empty columns from final output to avoid confusion (#120)\n\n# tima 2.9.1\n\n* Added [Waystation](https://caltechlibrary.github.io/waystation/) action\n* Added structures from spectral libraries to SOP library (#113)\n* Exposed all parameters (#107, #108)\n* Fixed for Zenodo API\n* HMDB structures support\n* Optimized grep/gsub by adding `perl=TRUE` or `fixed=TRUE`\n* Updated to Massbank version `2023.11`\n* Updated SIRIUS preparation (#74, #115)\n\n# tima 2.9.0\n\n* Added compounds names as parameter\n* Added MassBank spectral library (#77)\n* Allowed files outside `data/source` (#89)\n* Added RT library as annotation library (#86)\n* Better handling of download errors\n* Fixed Docker mount path\n* Improved naming (#91)\n* Internal variables refactoring\n* Multiple Shiny fixes and tests addition (#60)\n* Multiple fixes (#71, #81, #82)\n* New adducts (#79, #80)\n* Refactored adducts, clusters and neutral losses\n* Refactored biological and chemical score\n* Refactored RT matching (#76)\n* Refactored Sirius scores (#92)\n* Removed GNPS dependency by default\n\n# tima 2.8.2\n\n* Added spectral entropy\n* Added MS1 only possibility\n* Added Fluorine adduct\n* Changed from pbmclapply to pblapply\n* Documentation improvement\n* Fixed empty chemical classes\n* Fixed not classified taxa\n* Github Actions improvement\n* [renv](https://rstudio.github.io/renv/index.html) removal\n* Performance improvement by replacing the [tidyverse](https://www.tidyverse.org) by the [fastverse](https://fastverse.github.io/fastverse) (in progress)\n* Reduced warnings (CRAN and jscpd)\n\n# tima 2.8.1\n\n* Adapted tests\n* Added `retry` parameter to `get_organism_taxonomy_ott`\n* Dependencies update\n* Minor fixes\n* Moved `/params` and `paths.yaml` to `/inst` as more standard. (see <https://r-pkgs.org/misc.html#other-directories>)\n* Performance improvement by replacing the [tidyverse](https://www.tidyverse.org) by the [fastverse](https://fastverse.github.io/fastverse) (in progress)\n* Replaced `extdata` loading\n\n# tima 2.8.0\n\n* Added GUI prototype\n* Started using [renv](https://rstudio.github.io/renv/index.html)\n\n# tima 2.7.4\n\n* Clearer vocabulary\n* ECMDB support\n* Edges (mass and spectra-based) and components are generated if not present.\n* Fixed case when no GNPS job ID\n* Further [Targets](https://books.ropensci.org/targets/) improvements\n* Lot of fixes\n* Parameters refactoring\n* Re-introduced Classyfire support.\n* Retention time matching additionally to MS2 if RT present in library\n* Steps refactoring\n\n# tima 2.7.3\n\n* Improved calculations over redundant formulas\n* Minor fixes\n* Parameters refactoring\n* Spectral matching update (see <https://github.com/rformassspectrometry/MetaboAnnotation/issues/93>)\n* [Targets](https://books.ropensci.org/targets/) implementation\n\n# tima 2.7.2\n\n* Benchmark update (including negative mode)\n* Improved parameters documentation\n* Minor fixes\n* Spectral comparison + intensity filtering update\n* Switched r-base Docker image to bioconductor with ARM support\n\n# tima 2.7.1\n\n* Added MONA helpers\n* Added parallelization on process_spectra\n* Added sqlite storing for spectra\n* Improved code documentation\n* Improved testing time\n* Minor fixes\n\n# tima 2.7.0\n\n* Added HMDB helpers for both taxo and ISDB\n* Added MS2 annotation capability (kudos @jorainer for the awesome *Spectra* suite)\n* Minor fixes\n\n# tima 2.6.0\n\n* Added Docker container\n* Changed data architecture\n* Minor fixes\n\n# tima 2.5.6\n\n* Dependencies removal (e.g. metabo-store)\n* Minor fixes\n* Partial functions cleanup\n\n# tima 2.5.5\n\n* Automation and parameters improvement\n* Minor fixes\n\n# tima 2.5.4\n\n* Minor fixes\n* Metadata completion improvement\n* Molecular formula and adducts formalism improvement\n\n# tima 2.5.3\n\n* Imports improvements\n* LOTUS update\n\n# tima 2.5.2\n\n* Packaging improvements\n\n# tima 2.5.1\n\n* Improved support for SIRIUS (with new summaries)\n\n# tima 2.5.0\n\n* LOTUS update\n* Minor fixes\n\n# tima 2.4.0\n\n* Added chemical names and xlogp to output (#33)\n* Added support for case when no consensus is found (#30)\n* Improved output (#34)\n* Minor fixes\n\n# tima 2.3.0\n\n* Added support for annotation without MN (#28)\n* Added support for multi tool annotations (#27)\n* Added support for classical MN GNPS jobs (#25)\n* Added support for new version of LOTUS\n* General improvements for manual inputs\n* Improved tests code coverage\n* Minor fixes\n* Updated adducts\n\n# tima 2.2.2\n\n* Additional benchmark figure ([Candidates distribution](https://taxonomicallyinformedannotation.github.io/tima/articles/IV-benchmarking.html#candidates-distribution))\n* Minor fixes\n\n# tima 2.2.1\n\n* Minor version name fixes\n\n# tima 2.2.0\n\n* Added benchmark ([here](https://taxonomicallyinformedannotation.github.io/tima/articles/IV-benchmarking.html))\n* Various fixes\n\n# tima 2.1.0\n\n* Fixes, deletion of binary dependencies.\n\n# tima 2.0.0\n\n* Initial version.\n","type":"text"},{"name":"R/tima","content":"# File share/R/nspackloader.R\n# Part of the R package, https://www.R-project.org\n#\n# Copyright (C) 1995-2012 The R Core Team\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# A copy of the GNU General Public License is available at\n# https://www.r-project.org/Licenses/\n\nlocal({\n info <- loadingNamespaceInfo()\n pkg <- info$pkgname\n ns <- .getNamespace(as.name(pkg))\n if (is.null(ns))\n stop(\"cannot find namespace environment for \", pkg, domain = NA);\n dbbase <- file.path(info$libname, pkg, \"R\", pkg)\n lazyLoad(dbbase, ns, filter = function(n) n != \".__NAMESPACE__.\")\n})\n","type":"text"},{"name":"R/tima.rdb","content":"AAAAxnicTYxLCsIwGIS/pH9EsxI8hzsXnsALqOC2jyDBmoKJB/cCtdAm0NnMg5l5WKBCRFEZ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\nNX5UTVf+c5KIRQF1Q5siHHXp9Zt0oz/EnaC62WhXU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zk7ERc1xf9dz/3b6f1uKHxTpTd6B9OLsvc3o6jrQPYO91LE1antsvA9LjYg8P5F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U84pXA4w2dH+fhe5Xa+qUO6kHkl/VoWV4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\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k2SgDJcHBqlQSsOl6Z2o986LiuNw62m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\nDukW8JkcDukWsE8Oh3QLGJPDId0CxuVwSLeACTkc0i1gvxzOOha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Bk8zDIeFbjISnsOBO+jb8206tXoAaEnjrZwE0IumHiEdHOu2Sj+RG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VGDgow2JtSGyOAMn+cPX6H87jSSoAAAGbeJxdj81OwzAQhCeuzU/EAYnnoAYpVKic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B4mv7+R942n8OBHo3H0huEQWj3VNEHNlBHo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owBgKCggpiitQUJCiEiigSIGhiE2OIicEcA5y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\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Tdi4pLCVKEU0v0D/qpvJFvMdvjmAi0Y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\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zUb5NJ/XXrOpRdcGqfZK5q9rtmbms3T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4Fl1Ji6RNbPelD+lj/0Mvf6Av/Qv9N4XC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6msaPKrIbii2SQlGiMvlH6zatE4VsMJisaR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\n2QXZDdkDuRmyF7IPcgvkVsh+yAHIByC3QW6HHITcAbkTchf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g\nbioconductor\ncanopus\ncli\ncolname\ncsi\ncsv\ndimers\ndr\netabolite\nextdata\nfastverse\nfbmn\ngithub\ngsub\ngui\nhttps\ninbo\ninborutils\nio\njscpd\nmetabo\nmgf\nmslevel\nmz\nnformed\nnnotation\nott\nparallelization\nparams\npblapply\npbmclapply\npos\npre\nrenv\nreproducibility\nsilico\nsirius\nsqlite\nsummarise\ntaxize\ntaxo\ntidyverse\ntl\nusedist\nworkspaces\nxlogp\nyaml\n️\n","type":"text"},{"name":"_targets.R","content":"# Created by use_targets().\n# Follow the comments below to fill in this target script.\n# Then follow the manual to check and run the pipeline:\n# https://books.ropensci.org/targets/walkthrough.html#inspect-the-pipeline # nolint\n\n# Load packages required to define the pipeline:\nlibrary(targets)\n\n# Set target options:\ntar_option_set(\n packages = c(\"tima\"),\n imports = c(\"tima\"),\n memory = \"transient\",\n garbage_collection = TRUE,\n resources = tar_resources(\n qs = tar_resources_qs(preset = \"fast\")\n )\n)\n\n# tar_make_clustermq() configuration (okay to leave alone):\n\n# tar_make_future() configuration (okay to leave alone):\n# Install packages {{future}}, {{future.callr}}, and {{future.batchtools}}\n# to allow use_targets() to configure tar_make_future() options.\n\n# Run the R scripts in the R/ folder with your custom functions:\ntar_source(files = list.files(\n path = system.file(\"R\", package = \"tima\"),\n pattern = \".R$\",\n full.names = TRUE\n))\n\n# Replace the target list below with your own:\nlist(\n ## Architecture\n list( ## Paths\n list(\n tar_target(\n name = yaml_paths,\n format = \"file\",\n command = {\n yaml_paths <- system.file(\"paths.yaml\", package = \"tima\")\n }\n ),\n tar_target(\n name = paths,\n command = {\n paths <- get_default_paths(yaml = yaml_paths)\n }\n ),\n # tar_target(\n # name = paths_gnps_example_id,\n # command = {\n # paths_gnps_example_id <- paths$gnps$example\n # }\n # ),\n tar_target(\n name = paths_source,\n command = {\n paths_source <- paths$data$source$path\n }\n ),\n tar_target(\n name = paths_interim_a,\n command = {\n paths_interim_a <- paths$data$interim$annotations$path\n }\n ),\n tar_target(\n name = paths_interim_f,\n command = {\n paths_interim_f <- paths$data$interim$features$path\n }\n ),\n tar_target(\n name = paths_test_mode,\n command = {\n paths_test_mode <- paths$tests$mode\n }\n ),\n tar_target(\n name = paths_urls_massbank_file,\n command = {\n paths_urls_massbank_file <- paths$urls$massbank$file\n }\n ),\n tar_target(\n name = paths_urls_massbank_url,\n command = {\n paths_urls_massbank_url <- paths$urls$massbank$url\n }\n ),\n tar_target(\n name = paths_urls_massbank_version,\n command = {\n paths_urls_massbank_version <- paths$urls$massbank$version\n }\n ),\n tar_target(\n name = paths_urls_examples_spectra_mini,\n command = {\n paths_urls_examples_spectra_mini <- paths$urls$examples$spectra_mini\n }\n ),\n tar_target(\n name = paths_data_source_spectra,\n command = {\n paths_data_source_spectra <- paths$data$source$spectra\n }\n ),\n tar_target(\n name = paths_urls_ecmdb_metabolites,\n command = {\n paths_urls_ecmdb_metabolites <- paths$urls$ecmdb$metabolites\n }\n ),\n tar_target(\n name = paths_data_source_libraries_sop_ecmdb,\n command = {\n paths_data_source_libraries_sop_ecmdb <-\n paths$data$source$libraries$sop$ecmdb\n }\n ),\n tar_target(\n name = paths_urls_hmdb_structures,\n command = {\n paths_urls_hmdb_structures <- paths$urls$hmdb$structures\n }\n ),\n tar_target(\n name = paths_data_source_libraries_sop_hmdb,\n command = {\n paths_data_source_libraries_sop_hmdb <-\n paths$data$source$libraries$sop$hmdb\n }\n ),\n tar_target(\n name = paths_urls_lotus_doi,\n command = {\n paths_urls_lotus_doi <- paths$urls$lotus$doi\n }\n ),\n tar_target(\n name = paths_urls_lotus_pattern,\n command = {\n paths_urls_lotus_pattern <- paths$urls$lotus$pattern\n }\n ),\n tar_target(\n name = paths_data_source_libraries_sop_lotus,\n command = {\n paths_data_source_libraries_sop_lotus <-\n paths$data$source$libraries$sop$lotus\n }\n ),\n tar_target(\n name = paths_urls_examples_spectral_lib_pos,\n command = {\n paths_urls_examples_spectral_lib_pos <-\n paths$urls$examples$spectral_lib$pos\n }\n ),\n tar_target(\n name = paths_data_source_libraries_spectra_is_lotus_pos,\n command = {\n paths_data_source_libraries_spectra_is_lotus_pos <-\n paths$data$source$libraries$spectra$is$lotus$pos\n }\n ),\n tar_target(\n name = paths_urls_examples_spectral_lib_neg,\n command = {\n paths_urls_examples_spectral_lib_neg <-\n paths$urls$examples$spectral_lib$neg\n }\n ),\n tar_target(\n name = paths_data_source_libraries_spectra_is_lotus_neg,\n command = {\n paths_data_source_libraries_spectra_is_lotus_neg <-\n paths$data$source$libraries$spectra$is$lotus$neg\n }\n ),\n tar_target(\n name = paths_data_interim_libraries_adducts_path,\n command = {\n paths_data_interim_libraries_adducts_path <-\n paths$data$interim$libraries$adducts$path\n }\n ),\n tar_target(\n name = paths_urls_benchmarking_set,\n command = {\n paths_urls_benchmarking_set <-\n paths$urls$benchmarking_set\n }\n ),\n tar_target(\n name = paths_data_source_benchmark_zip,\n command = {\n paths_data_source_benchmark_zip <-\n paths$data$source$benchmark$zip\n }\n ),\n tar_target(\n name = paths_data_source_benchmark_cleaned,\n command = {\n paths_data_source_benchmark_cleaned <-\n paths$data$source$benchmark$cleaned\n }\n ),\n tar_target(\n name = paths_data_source_benchmark_mgf_neg,\n command = {\n paths_data_source_benchmark_mgf_neg <-\n paths$data$source$benchmark$mgf$neg\n }\n ),\n tar_target(\n name = paths_data_source_benchmark_mgf_pos,\n command = {\n paths_data_source_benchmark_mgf_pos <-\n paths$data$source$benchmark$mgf$pos\n }\n )\n )\n ),\n ## Params\n list(\n ## Default\n list(\n tar_target(\n name = par_def_ann_mas,\n format = \"file\",\n command = {\n par_def_ann_mas <- paths$params$default$annotate$masses\n }\n ),\n tar_target(\n name = par_def_ann_spe,\n format = \"file\",\n command = {\n par_def_ann_spe <- paths$params$default$annotate$spectra\n }\n ),\n tar_target(\n name = par_def_cre_com,\n format = \"file\",\n command = {\n par_def_cre_com <- paths$params$default$create$components\n }\n ),\n tar_target(\n name = par_def_cre_edg_spe,\n format = \"file\",\n command = {\n par_def_cre_edg_spe <- paths$params$default$create$edges$spectra\n }\n ),\n tar_target(\n name = par_def_fil_ann,\n format = \"file\",\n command = {\n par_def_fil_ann <-\n paths$params$default$filter$annotations\n }\n ),\n tar_target(\n name = par_def_pre_ann_gnp,\n format = \"file\",\n command = {\n par_def_pre_ann_gnp <- paths$params$default$prepare$annotations$gnps\n }\n ),\n tar_target(\n name = par_def_pre_ann_sir,\n format = \"file\",\n command = {\n par_def_pre_ann_sir <-\n paths$params$default$prepare$annotations$sirius\n }\n ),\n tar_target(\n name = par_def_pre_ann_spe,\n format = \"file\",\n command = {\n par_def_pre_ann_spe <-\n paths$params$default$prepare$annotations$spectra\n }\n ),\n tar_target(\n name = par_def_pre_fea_com,\n format = \"file\",\n command = {\n par_def_pre_fea_com <-\n paths$params$default$prepare$features$components\n }\n ),\n tar_target(\n name = par_def_pre_fea_edg,\n format = \"file\",\n command = {\n par_def_pre_fea_edg <- paths$params$default$prepare$features$edges\n }\n ),\n tar_target(\n name = par_def_pre_fea_tab,\n format = \"file\",\n command = {\n par_def_pre_fea_tab <- paths$params$default$prepare$features$tables\n }\n ),\n tar_target(\n name = par_def_pre_lib_rt,\n format = \"file\",\n command = {\n par_def_pre_lib_rt <-\n paths$params$default$prepare$libraries$rt\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_clo,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_clo <-\n paths$params$default$prepare$libraries$sop$closed\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_ecm,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_ecm <-\n paths$params$default$prepare$libraries$sop$ecmdb\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_hmd,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_hmd <-\n paths$params$default$prepare$libraries$sop$hmdb\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_lot,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_lot <-\n paths$params$default$prepare$libraries$sop$lotus\n }\n ),\n tar_target(\n name = par_def_pre_lib_sop_mer,\n format = \"file\",\n command = {\n par_def_pre_lib_sop_mer <-\n paths$params$default$prepare$libraries$sop$merged\n }\n ),\n tar_target(\n name = par_def_pre_lib_spe,\n format = \"file\",\n command = {\n par_def_pre_lib_spe <-\n paths$params$default$prepare$libraries$spectra\n }\n ),\n tar_target(\n name = par_def_pre_tax,\n format = \"file\",\n command = {\n par_def_pre_tax <- paths$params$default$prepare$taxa\n }\n ),\n tar_target(\n name = par_def_wei_ann,\n format = \"file\",\n command = {\n par_def_wei_ann <- paths$params$default$weight$annotations\n }\n )\n ),\n list(\n ## Prepare params\n list(\n tar_target(\n name = par_pre_par,\n format = \"file\",\n command = {\n par_pre_par <- paths$params$prepare_params\n }\n ),\n tar_target(\n name = par_pre_par2,\n format = \"file\",\n command = {\n par_pre_par2 <- paths$params$prepare_params_advanced\n }\n ),\n tar_target(\n name = par_fin_par,\n command = {\n par_fin_par <- tima:::parse_yaml_params(\n def = par_pre_par,\n usr = par_pre_par\n )\n }\n ),\n tar_target(\n name = par_fin_par2,\n command = {\n par_fin_par2 <- tima:::parse_yaml_params(\n def = par_pre_par2,\n usr = par_pre_par2\n )\n }\n )\n ),\n ## User\n list(\n tar_target(\n name = par_usr_ann_mas,\n format = \"file\",\n command = {\n par_usr_ann_mas <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"annotate_masses\"\n )\n }\n ),\n tar_target(\n name = par_usr_ann_spe,\n format = \"file\",\n command = {\n par_usr_ann_spe <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"annotate_spectra\"\n )\n }\n ),\n tar_target(\n name = par_usr_cre_com,\n format = \"file\",\n command = {\n par_usr_cre_com <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"create_components\"\n )\n }\n ),\n tar_target(\n name = par_usr_fil_ann,\n format = \"file\",\n command = {\n par_usr_fil_ann <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"filter_annotations\"\n )\n }\n ),\n tar_target(\n name = par_usr_cre_edg_spe,\n format = \"file\",\n command = {\n par_usr_cre_edg_spe <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"create_edges_spectra\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_ann_gnp,\n format = \"file\",\n command = {\n par_usr_pre_ann_gnp <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_annotations_gnps\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_ann_sir,\n format = \"file\",\n command = {\n par_usr_pre_ann_sir <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_annotations_sirius\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_ann_spe,\n format = \"file\",\n command = {\n par_usr_pre_ann_spe <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_annotations_spectra\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_fea_com,\n format = \"file\",\n command = {\n par_usr_pre_fea_com <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_features_components\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_fea_edg,\n format = \"file\",\n command = {\n par_usr_pre_fea_edg <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_features_edges\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_fea_tab,\n format = \"file\",\n command = {\n par_usr_pre_fea_tab <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_features_tables\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_rt,\n format = \"file\",\n command = {\n par_usr_pre_lib_rt <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_rt\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_clo,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_clo <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_closed\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_ecm,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_ecm <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_ecmdb\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_hmd,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_hmd <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_hmdb\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_lot,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_lot <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_lotus\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_sop_mer,\n format = \"file\",\n command = {\n par_usr_pre_lib_sop_mer <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_sop_merged\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_lib_spe,\n format = \"file\",\n command = {\n par_usr_pre_lib_spe <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_libraries_spectra\"\n )\n }\n ),\n tar_target(\n name = par_usr_pre_tax,\n format = \"file\",\n command = {\n par_usr_pre_tax <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"prepare_taxa\"\n )\n }\n ),\n tar_target(\n name = par_usr_wei_ann,\n format = \"file\",\n command = {\n par_usr_wei_ann <-\n tima:::prepare_params(\n params_small = par_fin_par,\n params_advanced = par_fin_par2,\n step = \"weight_annotations\"\n )\n }\n )\n )\n ),\n ## Final\n list(\n tar_target(\n name = par_ann_mas,\n command = {\n par_ann_mas <-\n tima:::parse_yaml_params(\n def = par_def_ann_mas,\n usr = par_usr_ann_mas[1]\n )\n }\n ),\n tar_target(\n name = par_ann_spe,\n command = {\n par_ann_spe <-\n tima:::parse_yaml_params(\n def = par_def_ann_spe,\n usr = par_usr_ann_spe[1]\n )\n }\n ),\n tar_target(\n name = par_cre_com,\n command = {\n par_cre_com <-\n tima:::parse_yaml_params(\n def = par_def_cre_com,\n usr = par_usr_cre_com[1]\n )\n }\n ),\n tar_target(\n name = par_cre_edg_spe,\n command = {\n par_cre_edg_spe <-\n tima:::parse_yaml_params(\n def = par_def_cre_edg_spe,\n usr = par_usr_cre_edg_spe[1]\n )\n }\n ),\n tar_target(\n name = par_fil_ann,\n command = {\n par_fil_ann <-\n tima:::parse_yaml_params(\n def = par_def_fil_ann,\n usr = par_usr_fil_ann[1]\n )\n }\n ),\n tar_target(\n name = par_pre_ann_gnp,\n command = {\n par_pre_ann_gnp <-\n tima:::parse_yaml_params(\n def = par_def_pre_ann_gnp,\n usr = par_usr_pre_ann_gnp[1]\n )\n }\n ),\n tar_target(\n name = par_pre_ann_sir,\n command = {\n par_pre_ann_sir <-\n tima:::parse_yaml_params(\n def = par_def_pre_ann_sir,\n usr = par_usr_pre_ann_sir[1]\n )\n }\n ),\n tar_target(\n name = par_pre_ann_spe,\n command = {\n par_pre_ann_spe <-\n tima:::parse_yaml_params(\n def = par_def_pre_ann_spe,\n usr = par_usr_pre_ann_spe[1]\n )\n }\n ),\n tar_target(\n name = par_pre_fea_com,\n command = {\n par_pre_fea_com <-\n tima:::parse_yaml_params(\n def = par_def_pre_fea_com,\n usr = par_usr_pre_fea_com[1]\n )\n }\n ),\n tar_target(\n name = par_pre_fea_edg,\n command = {\n par_pre_fea_edg <-\n tima:::parse_yaml_params(\n def = par_def_pre_fea_edg,\n usr = par_usr_pre_fea_edg[1]\n )\n }\n ),\n tar_target(\n name = par_pre_fea_tab,\n command = {\n par_pre_fea_tab <-\n tima:::parse_yaml_params(\n def = par_def_pre_fea_tab,\n usr = par_usr_pre_fea_tab[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_rt,\n command = {\n par_pre_lib_rt <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_rt,\n usr = par_usr_pre_lib_rt[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_clo,\n command = {\n par_pre_lib_sop_clo <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_clo,\n usr = par_usr_pre_lib_sop_clo[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_ecm,\n command = {\n par_pre_lib_sop_ecm <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_ecm,\n usr = par_usr_pre_lib_sop_ecm[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_hmd,\n command = {\n par_pre_lib_sop_hmd <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_hmd,\n usr = par_usr_pre_lib_sop_hmd[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_lot,\n command = {\n par_pre_lib_sop_lot <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_lot,\n usr = par_usr_pre_lib_sop_lot[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_sop_mer,\n command = {\n par_pre_lib_sop_mer <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_sop_mer,\n usr = par_usr_pre_lib_sop_mer[1]\n )\n }\n ),\n tar_target(\n name = par_pre_lib_spe,\n command = {\n par_pre_lib_spe <-\n tima:::parse_yaml_params(\n def = par_def_pre_lib_spe,\n usr = par_usr_pre_lib_spe[1]\n )\n }\n ),\n tar_target(\n name = par_pre_tax,\n command = {\n par_pre_tax <-\n tima:::parse_yaml_params(\n def = par_def_pre_tax,\n usr = par_usr_pre_tax[1]\n )\n }\n ),\n tar_target(\n name = par_wei_ann,\n command = {\n par_wei_ann <-\n tima:::parse_yaml_params(\n def = par_def_wei_ann,\n usr = par_usr_wei_ann[1]\n )\n }\n )\n )\n ),\n # ## GNPS\n # list(\n # tar_target(\n # name = gnps_tables,\n # format = \"file\",\n # command = {\n # gnps_tables <- tima:::get_gnps_tables(\n # gnps_job_id = par_fin_par$gnps$id,\n # gnps_job_example = paths_gnps_example_id,\n # filename = par_fin_par$files$pattern,\n # workflow = par_fin_par$gnps$workflow,\n # path_features = par_pre_fea_tab$files$features$raw,\n # path_metadata = par_pre_tax$files$metadata$raw,\n # path_spectra = par_ann_spe$files$spectral$raw,\n # path_source = paths_source,\n # path_interim_a = paths_interim_a,\n # path_interim_f = paths_interim_f\n # )\n # }\n # ),\n # list(\n # tar_target(\n # name = gnps_features,\n # format = \"file\",\n # command = {\n # gnps_features <- gnps_tables[[1]]\n # }\n # ),\n # tar_target(\n # name = gnps_metadata,\n # format = \"file\",\n # command = {\n # gnps_metadata <- gnps_tables[[2]]\n # }\n # ),\n # tar_target(\n # name = gnps_spectra,\n # format = \"file\",\n # command = {\n # gnps_spectra <- gnps_tables[[3]]\n # }\n # ),\n # # TODO think to change this also if uncommenting\n # tar_target(\n # name = gnps_annotations,\n # format = \"file\",\n # command = {\n # gnps_annotations <- gnps_tables[[4]]\n # }\n # ),\n # tar_target(\n # name = gnps_components,\n # format = \"file\",\n # command = {\n # gnps_components <- gnps_tables[[5]]\n # }\n # ),\n # tar_target(\n # name = gnps_edges,\n # format = \"file\",\n # command = {\n # gnps_edges <- gnps_tables[[6]]\n # }\n # )\n # )\n # ),\n ## Inputs\n list(\n tar_target(\n name = input_features,\n format = \"file\",\n command = {\n input_features <- par_pre_tax$files$features$raw\n # input_features <-\n # ifelse(\n # test = !is.null(gnps_features),\n # yes = ifelse(test = file.exists(gnps_features),\n # yes = gnps_features,\n # no = par_pre_tax$files$features$raw\n # ),\n # no = par_pre_tax$files$features$raw\n # )\n }\n ),\n tar_target(\n name = input_spectra,\n format = \"file\",\n command = {\n input_spectra <-\n ifelse(\n test = paths_test_mode == FALSE,\n yes = par_ann_spe$files$spectral$raw,\n # yes = ifelse(\n # test = !is.null(gnps_spectra),\n # yes =\n # ifelse(\n # test = file.exists(gnps_spectra),\n # yes = gnps_spectra,\n # no = par_ann_spe$files$spectral$raw\n # ),\n # no = par_ann_spe$files$spectral$raw\n # ),\n no = {\n get_file(\n url = paths_urls_examples_spectra_mini,\n export = paths_data_source_spectra\n )\n }\n )\n }\n )\n ),\n ## libraries\n list(\n ## Spectra\n list( ## In silico\n list( ## Raw\n list(\n ## TODO ADD ISDB HMDB,\n tar_target(\n name = lib_spe_is_lot_pos,\n format = \"file\",\n command = {\n lib_spe_is_lot_pos <-\n # if (paths_test_mode == FALSE) {\n get_file(\n url = paths_urls_examples_spectral_lib_pos,\n export = paths_data_source_libraries_spectra_is_lotus_pos |>\n gsub(\n pattern = \"isdb_pos.mgf\",\n replacement = \"lotus_pos.rds\",\n fixed = TRUE\n )\n )\n # get_last_version_from_zenodo(\n # doi = paths_urls_lotus_isdb_doi,\n # pattern = paths_urls_lotus_isdb_pattern_pos,\n # path = paths_data_source_libraries_spectra_is_lotus_pos\n # )\n # } else {\n # get_file(\n # url = paths_urls_examples_spectral_lib_pos,\n # export = paths_data_source_libraries_spectra_is_lotus_pos\n # )\n # }\n }\n ## To always check if a newest version is available\n ,\n cue = tar_cue(mode = \"always\")\n # cue = tar_cue(mode = \"thorough\")\n ),\n tar_target(\n name = lib_spe_is_lot_neg,\n format = \"file\",\n command = {\n lib_spe_is_lot_neg <-\n # if (paths_test_mode == FALSE) {\n get_file(\n url = paths_urls_examples_spectral_lib_neg,\n export = paths_data_source_libraries_spectra_is_lotus_neg |>\n gsub(\n pattern = \"isdb_neg.mgf\",\n replacement = \"lotus_neg.rds\",\n fixed = TRUE\n )\n )\n # get_last_version_from_zenodo(\n # doi = paths_urls_lotus_isdb_doi,\n # pattern = paths_urls_lotus_isdb_pattern_neg,\n # path = paths_data_source_libraries_spectra_is_lotus_neg\n # )\n # } else {\n # get_file(\n # url = paths_urls_examples_spectral_lib_neg,\n # export = paths_data_source_libraries_spectra_is_lotus_neg\n # )\n # }\n }\n ## To always check if a newest version is available\n ,\n cue = tar_cue(mode = \"always\")\n # cue = tar_cue(mode = \"thorough\")\n )\n )\n ),\n ## Prepared\n list(\n ## TODO ADD IS HMDB PREPARED,\n tar_target(\n name = lib_spe_is_lot_pre_pos,\n format = \"file\",\n command = {\n lib_spe_is_lot_pre_pos <-\n lib_spe_is_lot_pos\n }\n ),\n tar_target(\n name = lib_spe_is_lot_pre_neg,\n format = \"file\",\n command = {\n lib_spe_is_lot_pre_neg <-\n lib_spe_is_lot_neg\n }\n )\n ),\n ## Experimental\n list(\n ## RAW\n list(\n ### Internal\n ## This does not work as it forces the file to exist.\n ## So targets will not check if the input file changed automatically.\n # tar_target(\n # name = lib_spe_exp_int_raw,\n # . format = \"file\",\n # command = {\n # lib_spe_exp_int_raw <-\n # par_pre_lib_spe$files$libraries$spectral$exp$raw\n # }\n # ),\n ### MassBank\n tar_target(\n name = lib_spe_exp_mb_raw,\n format = \"file\",\n command = {\n lib_spe_exp_mb_raw <- tima:::get_massbank_spectra(\n mb_file = paths_urls_massbank_file,\n mb_url = paths_urls_massbank_url,\n mb_version = paths_urls_massbank_version\n )\n }\n )\n ),\n ## Prepared\n list(\n tar_target(\n name = lib_spe_exp_int_pre,\n format = \"file\",\n command = {\n lib_spe_exp_int_pre <-\n prepare_libraries_spectra(\n input = par_pre_lib_spe$files$libraries$spectral$raw,\n nam_lib = par_pre_lib_spe$names$libraries,\n col_ad = par_pre_lib_spe$names$mgf$adduct,\n col_ce = par_pre_lib_spe$names$mgf$collision_energy,\n col_ci = par_pre_lib_spe$names$mgf$compound_id,\n col_em = par_pre_lib_spe$names$mgf$exact_mass,\n col_in = par_pre_lib_spe$names$mgf$inchi,\n col_io = par_pre_lib_spe$names$mgf$inchi_no_stereo,\n col_ik = par_pre_lib_spe$names$mgf$inchikey,\n col_il = par_pre_lib_spe$names$mgf$inchikey_no_stereo,\n col_mf = par_pre_lib_spe$names$mgf$molecular_formula,\n col_na = par_pre_lib_spe$names$mgf$name,\n col_po = par_pre_lib_spe$names$mgf$polarity,\n col_sm = par_pre_lib_spe$names$mgf$smiles,\n col_sn = par_pre_lib_spe$names$mgf$smiles_no_stereo,\n col_si = par_pre_lib_spe$names$mgf$spectrum_id,\n col_sp = par_pre_lib_spe$names$mgf$splash,\n col_sy = par_pre_lib_spe$names$mgf$synonyms,\n col_xl = par_pre_lib_spe$names$mgf$xlogp\n )\n }\n ),\n tar_target(\n name = lib_spe_exp_int_pre_pos,\n format = \"file\",\n command = {\n lib_spe_exp_int_pre_pos <- lib_spe_exp_int_pre[[1]]\n }\n ),\n tar_target(\n name = lib_spe_exp_int_pre_neg,\n format = \"file\",\n command = {\n lib_spe_exp_int_pre_neg <- lib_spe_exp_int_pre[[2]]\n }\n ),\n tar_target(\n name = lib_spe_exp_int_pre_sop,\n format = \"file\",\n command = {\n lib_spe_exp_int_pre_sop <- lib_spe_exp_int_pre[[3]]\n }\n ),\n tar_target(\n name = lib_spe_exp_mb_pre,\n format = \"file\",\n command = {\n lib_spe_exp_mb_pre <-\n prepare_libraries_spectra(\n input = lib_spe_exp_mb_raw,\n nam_lib = paste(\"MassBank\",\n paths_urls_massbank_version,\n sep = \" - \"\n ),\n col_ad = \"Precursor_type\",\n col_ce = \"Collision_energy\",\n col_ci = NULL,\n col_em = \"ExactMass\",\n col_in = \"InChI\",\n col_io = NULL,\n col_ik = \"InChIKey\",\n col_il = NULL,\n col_mf = \"Formula\",\n col_na = \"Name\",\n col_po = \"Ion_mode\",\n col_sm = \"smiles\",\n col_sn = NULL,\n col_si = \"accession\",\n col_sp = \"Splash\",\n col_sy = \"Synon\",\n col_xl = NULL\n )\n }\n ),\n tar_target(\n name = lib_spe_exp_mb_pre_pos,\n format = \"file\",\n command = {\n lib_spe_exp_mb_pre_pos <- lib_spe_exp_mb_pre[[1]]\n }\n ),\n tar_target(\n name = lib_spe_exp_mb_pre_neg,\n format = \"file\",\n command = {\n lib_spe_exp_mb_pre_neg <- lib_spe_exp_mb_pre[[2]]\n }\n ),\n tar_target(\n name = lib_spe_exp_mb_pre_sop,\n format = \"file\",\n command = {\n lib_spe_exp_mb_pre_sop <- lib_spe_exp_mb_pre[[3]]\n }\n )\n )\n )\n ),\n ## Retention times\n list(tar_target(\n name = lib_rt,\n format = \"file\",\n command = {\n lib_rt <- prepare_libraries_rt(\n mgf_exp = par_pre_lib_rt$files$libraries$temporal$exp$mgf,\n mgf_is = par_pre_lib_rt$files$libraries$temporal$is$mgf,\n temp_exp = par_pre_lib_rt$files$libraries$temporal$exp$csv,\n temp_is = par_pre_lib_rt$files$libraries$temporal$is$csv,\n output_rt = par_pre_lib_rt$files$libraries$temporal$prepared,\n output_sop = par_pre_lib_rt$files$libraries$sop$prepared$rt,\n col_ik = par_pre_lib_rt$names$mgf$inchikey,\n col_rt = par_pre_lib_rt$names$mgf$retention_time,\n col_sm = par_pre_lib_rt$names$mgf$smiles,\n name_inchikey = par_pre_lib_rt$names$inchikey,\n name_rt = par_pre_lib_rt$names$rt$library,\n name_smiles = par_pre_lib_rt$names$smiles,\n unit_rt = par_pre_lib_rt$units$rt\n )\n }\n )),\n tar_target(\n name = lib_rt_rts,\n format = \"file\",\n command = {\n lib_rt_rts <- lib_rt[[1]]\n }\n ),\n tar_target(\n name = lib_rt_sop,\n format = \"file\",\n command = {\n lib_rt_sop <- lib_rt[[2]]\n }\n ),\n ## Structure organism pairs\n list(\n ## Raw\n list(\n ## This does not work as it forces the file to exist.\n ## So targets will not check if the input file changed automatically.\n # tar_target(\n # name = lib_sop_clo,\n # format = \"file\",\n # command = {\n # lib_sop_clo <- paths$data$source$libraries$sop$closed\n # }\n # ),\n tar_target(\n name = lib_sop_ecm,\n format = \"file\",\n command = {\n ## Because ECMDB certificate is expired\n lib_sop_ecm <- tryCatch(\n expr = {\n get_file(\n url = paths_urls_ecmdb_metabolites,\n export = paths_data_source_libraries_sop_ecmdb\n )\n },\n error = function(e) {\n return(tima:::fake_ecmdb(export = paths_data_source_libraries_sop_ecmdb))\n }, finally = {\n return(paths_data_source_libraries_sop_ecmdb)\n }\n )\n }\n ),\n tar_target(\n name = lib_sop_hmd,\n format = \"file\",\n command = {\n lib_sop_hmd <- tryCatch(\n expr = {\n get_file(\n url = paths_urls_hmdb_structures,\n export = paths_data_source_libraries_sop_hmdb\n )\n },\n warning = function(w) {\n ## See #118\n log_debug(\"HMDB download failed partially, returning empty file instead\")\n unlink(paths_data_source_libraries_sop_hmdb)\n return(tima:::fake_hmdb(export = paths_data_source_libraries_sop_hmdb))\n },\n error = function(e) {\n return(tima:::fake_hmdb(export = paths_data_source_libraries_sop_hmdb))\n }, finally = {\n return(paths_data_source_libraries_sop_hmdb)\n }\n )\n }\n ),\n ## TODO ADD GET HMDB\n tar_target(\n name = lib_sop_lot,\n format = \"file\",\n command = {\n lib_sop_lot <- tryCatch(\n expr = {\n get_last_version_from_zenodo(\n doi = paths_urls_lotus_doi,\n pattern = paths_urls_lotus_pattern,\n path = paths_data_source_libraries_sop_lotus\n )\n },\n error = function(e) {\n return(tima:::fake_lotus(export = paths_data_source_libraries_sop_lotus))\n }, finally = {\n return(paths_data_source_libraries_sop_lotus)\n }\n )\n },\n ## To always check if a newest version is available\n cue = tar_cue(mode = \"always\")\n # cue = tar_cue(mode = \"thorough\")\n )\n ),\n ## Prepared\n list(\n tar_target(\n name = lib_sop_clo_pre,\n format = \"file\",\n command = {\n lib_sop_clo_pre <-\n prepare_libraries_sop_closed(\n input = par_pre_lib_sop_clo$files$libraries$sop$raw$closed,\n output = par_pre_lib_sop_clo$files$libraries$sop$prepared$closed\n )\n }\n ),\n tar_target(\n name = lib_sop_ecm_pre,\n format = \"file\",\n command = {\n lib_sop_ecm_pre <-\n prepare_libraries_sop_ecmdb(\n input = lib_sop_ecm,\n output = par_pre_lib_sop_ecm$files$libraries$sop$prepared$ecmdb\n )\n }\n ),\n tar_target(\n name = lib_sop_hmd_pre,\n format = \"file\",\n command = {\n lib_sop_hmd_pre <-\n prepare_libraries_sop_hmdb(\n input = lib_sop_hmd,\n output = par_pre_lib_sop_hmd$files$libraries$sop$prepared$hmdb\n )\n }\n ),\n tar_target(\n name = lib_sop_lot_pre,\n format = \"file\",\n command = {\n lib_sop_lot_pre <-\n prepare_libraries_sop_lotus(\n input = if (paths_test_mode == FALSE) {\n lib_sop_lot\n } else {\n paths_data_source_libraries_sop_lotus\n },\n output = par_pre_lib_sop_lot$files$libraries$sop$prepared$lotus\n )\n }\n )\n ),\n ## Merged\n list(\n tar_target(\n name = lib_sop_mer,\n format = \"file\",\n command = {\n lib_sop_mer <- prepare_libraries_sop_merged(\n files = c(\n lib_sop_clo_pre,\n lib_sop_ecm_pre,\n lib_sop_hmd_pre,\n lib_sop_lot_pre,\n lib_rt_sop,\n lib_spe_exp_int_pre_sop,\n lib_spe_exp_mb_pre_sop\n ),\n filter = par_pre_lib_sop_mer$organisms$filter$mode,\n level = par_pre_lib_sop_mer$organisms$filter$level,\n value = par_pre_lib_sop_mer$organisms$filter$value,\n output_key = par_pre_lib_sop_mer$files$libraries$sop$merged$keys,\n output_org_tax_ott = par_pre_lib_sop_mer$files$libraries$sop$merged$organisms$taxonomies$ott,\n output_str_stereo = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$stereo,\n output_str_met = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$metadata,\n output_str_nam = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$names,\n output_str_tax_cla = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$taxonomies$cla,\n output_str_tax_npc = par_pre_lib_sop_mer$files$libraries$sop$merged$structures$taxonomies$npc\n )\n }\n ),\n tar_target(\n name = lib_mer_key,\n format = \"file\", command = {\n lib_mer_key <- lib_sop_mer[[1]]\n }\n ),\n tar_target(\n name = lib_mer_org_tax_ott,\n format = \"file\", command = {\n lib_mer_org_tax_ott <- lib_sop_mer[[2]]\n }\n ),\n tar_target(\n name = lib_mer_str_stereo,\n format = \"file\", command = {\n lib_mer_str_stereo <- lib_sop_mer[[3]]\n }\n ),\n tar_target(\n name = lib_mer_str_met,\n format = \"file\", command = {\n lib_mer_str_met <- lib_sop_mer[[4]]\n }\n ),\n tar_target(\n name = lib_mer_str_nam,\n format = \"file\", command = {\n lib_mer_str_nam <- lib_sop_mer[[5]]\n }\n ),\n tar_target(\n name = lib_mer_str_tax_cla,\n format = \"file\", command = {\n lib_mer_str_tax_cla <- lib_sop_mer[[6]]\n }\n ),\n tar_target(\n name = lib_mer_str_tax_npc,\n format = \"file\", command = {\n lib_mer_str_tax_npc <- lib_sop_mer[[7]]\n }\n )\n )\n )\n ),\n ## Annotations\n list(\n ## MS1\n list(\n tar_target(\n name = ann_ms1_pre,\n format = \"file\",\n command = {\n ann_ms1_pre <-\n annotate_masses(\n features = fea_pre,\n library = lib_mer_key,\n output_annotations = par_ann_mas$files$annotations$prepared$structural$ms1,\n output_edges = par_ann_mas$files$networks$spectral$edges$raw,\n name_source = par_ann_mas$names$source,\n name_target = par_ann_mas$names$target,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc,\n adducts_list = par_ann_mas$ms$adducts,\n clusters_list = par_ann_mas$ms$clusters,\n neutral_losses_list = par_ann_mas$ms$neutral_losses,\n ms_mode = par_ann_mas$ms$polarity,\n tolerance_ppm = par_ann_mas$ms$tolerances$mass$ppm$ms1,\n tolerance_rt = par_ann_mas$ms$tolerances$rt$adducts\n )\n }\n ),\n tar_target(\n name = ann_ms1_pre_ann,\n format = \"file\",\n command = {\n ann_ms1_pre_ann <-\n ann_ms1_pre[[1]]\n }\n ),\n tar_target(\n name = ann_ms1_pre_edg,\n format = \"file\",\n command = {\n ann_ms1_pre_edg <- ann_ms1_pre[[2]]\n }\n )\n ),\n ## Spectral\n list(\n # ## GNPS\n list(\n tar_target(\n name = ann_spe_exp_gnp_pre,\n format = \"file\",\n command = {\n ann_spe_exp_gnp_pre <-\n prepare_annotations_gnps(\n # input = gnps_annotations,\n input = par_pre_ann_gnp$files$annotations$raw$spectral$gnps,\n output = par_pre_ann_gnp$files$annotations$prepared$structural$gnps,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n )\n ),\n ## Classic\n list(\n ## TODO improve polarity handling, suboptimal\n tar_target(\n name = ann_spe_pos,\n format = \"file\",\n command = {\n ann_spe_pos <- annotate_spectra(\n input = input_spectra,\n library = c(\n lib_spe_is_lot_pre_pos,\n ## TODO add is hmdb\n lib_spe_exp_int_pre_pos,\n lib_spe_exp_mb_pre_pos\n ),\n polarity = \"pos\",\n output = gsub(\n pattern = \".tsv.gz\",\n replacement = \"_pos.tsv.gz\",\n x = par_ann_spe$files$annotations$raw$spectral$spectral,\n fixed = TRUE\n ),\n threshold = par_ann_spe$annotations$thresholds$ms2$similarity$annotation,\n ppm = par_ann_spe$ms$tolerances$mass$ppm$ms2,\n dalton = par_ann_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = par_ann_spe$ms$thresholds$ms2$intensity,\n approx = par_ann_spe$annotations$ms2approx\n )\n }\n ),\n tar_target(\n name = ann_spe_neg,\n format = \"file\",\n command = {\n ann_spe_neg <- annotate_spectra(\n input = input_spectra,\n library = c(\n lib_spe_is_lot_pre_neg,\n ## TODO add is hmdb\n lib_spe_exp_int_pre_neg,\n lib_spe_exp_mb_pre_neg\n ),\n polarity = \"neg\",\n output = gsub(\n pattern = \".tsv.gz\",\n replacement = \"_neg.tsv.gz\",\n x = par_ann_spe$files$annotations$raw$spectral$spectral,\n fixed = TRUE\n ),\n threshold = par_ann_spe$annotations$thresholds$ms2$similarity$annotation,\n ppm = par_ann_spe$ms$tolerances$mass$ppm$ms2,\n dalton = par_ann_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = par_ann_spe$ms$thresholds$ms2$intensity,\n approx = par_ann_spe$annotations$ms2approx\n )\n }\n ),\n tar_target(\n name = ann_spe_pre,\n format = \"file\",\n command = {\n ann_spe_pre <- prepare_annotations_spectra(\n input = c(\n ann_spe_neg,\n ann_spe_pos\n ),\n output = par_pre_ann_spe$files$annotations$prepared$structural$spectral,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n )\n )\n ),\n # SIRIUS\n tar_target(\n name = ann_sir_pre,\n format = \"file\",\n command = {\n ann_sir_pre <-\n prepare_annotations_sirius(\n input_directory = par_pre_ann_sir$files$annotations$raw$sirius,\n output_ann = par_pre_ann_sir$files$annotations$prepared$structural$sirius,\n output_can = par_pre_ann_sir$files$annotations$prepared$canopus,\n output_for = par_pre_ann_sir$files$annotations$prepared$formula,\n sirius_version = par_pre_ann_sir$tools$sirius$version,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n ),\n tar_target(\n name = ann_sir_pre_can,\n format = \"file\", command = {\n ann_sir_pre_can <- ann_sir_pre[[1]]\n }\n ),\n tar_target(\n name = ann_sir_pre_for,\n format = \"file\", command = {\n ann_sir_pre_for <- ann_sir_pre[[2]]\n }\n ),\n tar_target(\n name = ann_sir_pre_str,\n format = \"file\", command = {\n ann_sir_pre_str <- ann_sir_pre[[3]]\n }\n ),\n list()\n ),\n ## Features\n list(\n tar_target(\n name = fea_edg_spe,\n format = \"file\",\n command = {\n fea_edg_spe <- create_edges_spectra(\n input = input_spectra,\n output = par_cre_edg_spe$files$networks$spectral$edges$raw,\n name_source = par_cre_edg_spe$names$source,\n name_target = par_cre_edg_spe$names$target,\n threshold = par_cre_edg_spe$annotations$thresholds$ms2$similarity$edges,\n ppm = par_cre_edg_spe$ms$tolerances$mass$ppm$ms2,\n dalton = par_cre_edg_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = par_cre_edg_spe$ms$thresholds$ms2$intensity\n )\n }\n ),\n tar_target(\n name = fea_com,\n format = \"file\",\n command = {\n fea_com <- create_components(\n input = fea_edg_pre,\n output = par_cre_com$files$networks$spectral$components$raw\n )\n }\n ),\n ## Interim\n list(\n tar_target(\n name = int_com,\n format = \"file\",\n command = {\n int_com <- fea_com\n # int_com <-\n # if (file.exists(fea_com)) {\n # fea_com\n # } else {\n # gnps_components\n # }\n }\n ),\n tar_target(\n name = edg_spe,\n format = \"file\",\n command = {\n edg_spe <- fea_edg_spe\n # edg_spe <-\n # ifelse(test = file.exists(fea_edg_spe),\n # yes = fea_edg_spe,\n # no = gnps_edges\n # )\n }\n )\n ),\n tar_target(\n name = fea_edg_pre,\n format = \"file\",\n command = {\n fea_edg_pre <- prepare_features_edges(\n input = c(\"ms1\" = ann_ms1_pre_edg, \"spectral\" = edg_spe),\n output = par_pre_fea_edg$files$networks$spectral$edges$prepared,\n name_source = par_pre_fea_edg$names$source,\n name_target = par_pre_fea_edg$names$target\n )\n }\n ),\n tar_target(\n name = fea_com_pre,\n format = \"file\",\n command = {\n fea_com_pre <- prepare_features_components(\n input = int_com,\n output = par_pre_fea_com$files$networks$spectral$components$prepared\n )\n }\n ),\n tar_target(\n name = fea_pre,\n format = \"file\",\n command = {\n fea_pre <- prepare_features_tables(\n features = input_features,\n output = par_pre_fea_tab$files$features$prepared,\n name_features = par_pre_fea_tab$names$features,\n name_rt = par_pre_fea_tab$names$rt$features,\n name_mz = par_pre_fea_tab$names$precursor\n )\n }\n )\n ),\n tar_target(\n name = tax_pre,\n format = \"file\",\n command = {\n tax_pre <- prepare_taxa(\n input = input_features,\n name_features = par_pre_tax$names$features,\n name_filename = par_pre_tax$names$filename,\n extension = par_pre_tax$names$extension,\n colname = par_pre_tax$names$taxon,\n metadata = par_pre_tax$files$metadata$raw,\n top_k = par_pre_tax$organisms$candidates,\n org_tax_ott = lib_mer_org_tax_ott,\n output = par_pre_tax$files$metadata$prepared,\n taxon = par_pre_tax$organisms$taxon\n )\n }\n ),\n tar_target(\n name = ann_fil,\n format = \"file\",\n command = {\n ann_fil <- filter_annotations(\n annotations = c(\n ann_spe_exp_gnp_pre,\n ann_spe_pre,\n ann_sir_pre_str,\n ann_ms1_pre_ann\n ),\n features = fea_pre,\n rts = lib_rt_rts,\n output = par_fil_ann$files$annotations$filtered,\n tolerance_rt = par_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = ann_pre,\n format = \"file\",\n command = {\n ann_pre <- weight_annotations(\n library = lib_mer_key,\n org_tax_ott = lib_mer_org_tax_ott,\n str_stereo = lib_mer_str_stereo,\n annotations = ann_fil,\n canopus = ann_sir_pre_can,\n formula = ann_sir_pre_for,\n components = fea_com_pre,\n edges = fea_edg_pre,\n taxa = tax_pre,\n output = par_wei_ann$files$annotations$processed,\n candidates_final = par_wei_ann$annotations$candidates$final,\n weight_spectral = par_wei_ann$weights$global$spectral,\n weight_chemical = par_wei_ann$weights$global$chemical,\n weight_biological = par_wei_ann$weights$global$biological,\n score_biological_domain = par_wei_ann$weights$biological$domain,\n score_biological_kingdom = par_wei_ann$weights$biological$kingdom,\n score_biological_phylum = par_wei_ann$weights$biological$phylum,\n score_biological_class = par_wei_ann$weights$biological$class,\n score_biological_order = par_wei_ann$weights$biological$order,\n score_biological_infraorder = par_wei_ann$weights$biological$infraorder,\n score_biological_family = par_wei_ann$weights$biological$family,\n score_biological_subfamily = par_wei_ann$weights$biological$subfamily,\n score_biological_tribe = par_wei_ann$weights$biological$tribe,\n score_biological_subtribe = par_wei_ann$weights$biological$subtribe,\n score_biological_genus = par_wei_ann$weights$biological$genus,\n score_biological_subgenus = par_wei_ann$weights$biological$subgenus,\n score_biological_species = par_wei_ann$weights$biological$species,\n score_biological_subspecies = par_wei_ann$weights$biological$subspecies,\n score_biological_variety = par_wei_ann$weights$biological$variety,\n score_chemical_cla_kingdom = par_wei_ann$weights$chemical$cla$kingdom,\n score_chemical_cla_superclass =\n par_wei_ann$weights$chemical$cla$superclass,\n score_chemical_cla_class = par_wei_ann$weights$chemical$cla$class,\n score_chemical_cla_parent = par_wei_ann$weights$chemical$cla$parent,\n score_chemical_npc_pathway = par_wei_ann$weights$chemical$npc$pathway,\n score_chemical_npc_superclass =\n par_wei_ann$weights$chemical$npc$superclass,\n score_chemical_npc_class = par_wei_ann$weights$chemical$npc$class,\n minimal_consistency = par_wei_ann$annotations$thresholds$consistency,\n minimal_ms1_bio = par_wei_ann$annotations$thresholds$ms1$biological,\n minimal_ms1_chemo = par_wei_ann$annotations$thresholds$ms1$chemical,\n minimal_ms1_condition =\n par_wei_ann$annotations$thresholds$ms1$condition,\n ms1_only = par_wei_ann$annotations$ms1only,\n compounds_names = par_wei_ann$options$compounds_names,\n high_confidence = par_wei_ann$options$high_confidence,\n remove_ties = par_wei_ann$options$remove_ties,\n summarise = par_wei_ann$options$summarise,\n pattern = par_wei_ann$files$pattern,\n force = par_wei_ann$options$force\n )\n }\n ),\n list(\n ## Benchmark\n tar_target(\n name = benchmark_path_url,\n command = {\n benchmark_path_url <- paths_urls_benchmarking_set\n }\n ),\n tar_target(\n name = benchmark_path_zip,\n command = {\n benchmark_path_zip <- paths_data_source_benchmark_zip\n }\n ),\n tar_target(\n name = benchmark_path_file,\n command = {\n benchmark_path_file <- paths_data_source_benchmark_cleaned\n }\n ),\n tar_target(\n name = benchmark_path_mgf_neg,\n command = {\n benchmark_path_mgf_neg <- paths_data_source_benchmark_mgf_neg\n }\n ),\n tar_target(\n name = benchmark_path_mgf_pos,\n command = {\n benchmark_path_mgf_pos <- paths_data_source_benchmark_mgf_pos\n }\n ),\n tar_target(\n name = benchmark_zip,\n format = \"file\",\n command = {\n benchmark_zip <- get_file(\n url = benchmark_path_url,\n export = benchmark_path_zip\n )\n return(benchmark_path_zip)\n }\n ),\n tar_target(\n name = benchmark_file,\n format = \"file\",\n command = {\n utils::unzip(zipfile = benchmark_zip)\n dir.create(dirname(benchmark_path_file), recursive = TRUE)\n file.copy(from = \"cleaned_libraries_matchms/results_library_cleaning/cleaned_spectra.mgf\", to = benchmark_path_file)\n unlink(\"cleaned_libraries_matchms\", recursive = TRUE)\n return(benchmark_path_file)\n }\n ),\n tar_target(\n name = benchmark_converted,\n format = \"file\",\n command = {\n sp <- benchmark_file |>\n import_spectra()\n sp |>\n Spectra::filterEmptySpectra() |>\n tima:::extract_spectra() |>\n data.frame() |>\n saveRDS(file = \"data/interim/benchmark/benchmark_spectra.rds\")\n return(\"data/interim/benchmark/benchmark_spectra.rds\")\n }\n ),\n tar_target(\n name = benchmark_prepared,\n format = \"file\",\n command = {\n sp <- benchmark_converted |>\n import_spectra()\n\n sp@backend@spectraData$precursorMz <-\n sp@backend@spectraData$PRECURSOR_MZ |>\n as.numeric()\n\n log_debug(\"Imported\")\n sp_clean <- sp\n\n log_debug(\"Cleaned\")\n df_meta <- tidytable::tidytable(\n adduct = sp_clean$ADDUCT,\n inchikey = sp_clean$INCHIKEY,\n instrument = sp_clean$INSTRUMENT_TYPE,\n fragments = lapply(sp_clean@backend@peaksData, length) |>\n as.character() |>\n as.numeric() / 2,\n precursorMz = sp_clean$precursorMz,\n smiles = sp_clean$SMILES,\n ccmslib = sp_clean$SPECTRUM_ID,\n charge = sp_clean$precursorCharge,\n name = sp_clean$COMPOUND_NAME\n ) |>\n tidytable::mutate(\n tidytable::across(\n .cols = tidyselect::everything(),\n .fns = function(x) {\n tidytable::na_if(x, \"\")\n }\n )\n )\n\n log_debug(\"Framed\")\n df_clean <- df_meta |>\n tidytable::filter(!is.na(inchikey)) |>\n tidytable::filter(fragments >= 5) |>\n tidytable::filter(fragments <= 250) |>\n tidytable::filter(!grepl(\n pattern = \"QQQ\",\n x = instrument,\n fixed = TRUE\n )) |>\n ## fragments are nominal mass\n tidytable::filter(!grepl(\n pattern = \"ReSpect\",\n x = name,\n fixed = TRUE\n )) |>\n ## remove spectral matches\n tidytable::filter(!grepl(\n pattern = \"Spectral Match to\",\n x = name,\n fixed = TRUE\n )) |>\n ## remove putatives\n tidytable::filter(!grepl(\n pattern = \"putative\",\n x = name,\n fixed = TRUE\n )) |>\n tidytable::select(-name) |>\n tidytable::mutate(mass = precursorMz) |>\n tidytable::separate(\n col = mass,\n sep = \"\\\\.\",\n into = c(\"a\", \"b\")\n ) |>\n tidytable::filter(!is.na(b)) |>\n tidytable::filter(stringi::stri_length(as.numeric(b)) > 1) |>\n tidytable::select(-a, -b) |>\n tidytable::mutate(inchikey_no_stereo = gsub(\n pattern = \"-.*\",\n replacement = \"\",\n x = inchikey,\n perl = TRUE\n )) |>\n tidytable::distinct(inchikey_no_stereo, adduct, .keep_all = TRUE) |>\n tidytable::mutate(mz = precursorMz) |>\n ## Weird way to have some kind of retention time\n tidytable::mutate(\n rt = tidytable::cur_group_id(),\n .by = \"inchikey_no_stereo\"\n )\n\n df_clean_neg <- df_clean |>\n tidytable::filter(grepl(\n pattern = \"]-\",\n x = adduct,\n fixed = TRUE\n ))\n\n df_clean_pos <- df_clean |>\n tidytable::filter(grepl(\n pattern = \"]+\",\n x = adduct,\n fixed = TRUE\n ))\n\n sp_pos <-\n sp_clean[sp_clean$SPECTRUM_ID %in% df_clean_pos$ccmslib]\n sp_neg <-\n sp_clean[sp_clean$SPECTRUM_ID %in% df_clean_neg$ccmslib]\n\n extract_benchmark_spectra <- function(x, mode) {\n df <- x |>\n tima:::extract_spectra() |>\n tidytable::mutate(acquisitionNum = tidytable::row_number()) |>\n tidytable::mutate(spectrum_id = acquisitionNum) |>\n tidytable::mutate(short_ik = gsub(\n pattern = \"-.*\",\n replacement = \"\",\n INCHIKEY,\n perl = TRUE\n )) |>\n tidytable::mutate(\n rtime = tidytable::cur_group_id(),\n .by = \"short_ik\"\n ) |>\n tidytable::mutate(precursorCharge = ifelse(\n test = mode == \"pos\",\n yes = as.integer(1),\n no = as.integer(-1)\n )) |>\n tidytable::select(\n acquisitionNum,\n precursorCharge,\n precursorMz,\n MS_LEVEL,\n rtime,\n name = COMPOUND_NAME,\n smiles = SMILES,\n inchi = INCHI,\n inchikey = INCHIKEY,\n adduct = ADDUCT,\n instrument = INSTRUMENT_TYPE,\n ccmslib = SPECTRUM_ID,\n spectrum_id = acquisitionNum,\n mz,\n intensity\n ) |>\n data.frame()\n return(df)\n }\n\n spectra_harmonized_pos <- sp_pos |>\n extract_benchmark_spectra(mode = \"pos\")\n\n spectra_harmonized_neg <- sp_neg |>\n extract_benchmark_spectra(mode = \"neg\")\n\n select_benchmark_columns <- function(x) {\n df <- x |>\n tidytable::select(\n adduct,\n inchikey,\n instrument,\n smiles,\n ccmslib,\n charge = precursorCharge,\n mz = precursorMz,\n rt = rtime,\n feature_id = spectrum_id\n ) |>\n tidytable::mutate(inchikey_no_stereo = gsub(\n pattern = \"-.*\",\n replacement = \"\",\n x = inchikey,\n perl = TRUE\n )) |>\n data.frame()\n return(df)\n }\n\n df_clean_pos <- spectra_harmonized_pos |>\n select_benchmark_columns()\n\n df_clean_neg <- spectra_harmonized_neg |>\n select_benchmark_columns()\n\n log_debug(\"Exporting\")\n spectra_harmonized_pos |>\n Spectra::Spectra() |>\n Spectra::export(\n backend = MsBackendMgf::MsBackendMgf(),\n file = benchmark_path_mgf_pos\n )\n spectra_harmonized_neg |>\n Spectra::Spectra() |>\n Spectra::export(\n backend = MsBackendMgf::MsBackendMgf(),\n file = benchmark_path_mgf_neg\n )\n df_clean_pos |>\n export_output(\"data/interim/benchmark/benchmark_meta_pos.tsv\")\n df_clean_neg |>\n export_output(\"data/interim/benchmark/benchmark_meta_neg.tsv\")\n\n return(\n c(\n \"spectra_pos\" = benchmark_path_mgf_pos,\n \"spectra_neg\" = benchmark_path_mgf_neg,\n \"meta_pos\" = \"data/interim/benchmark/benchmark_meta_pos.tsv\",\n \"meta_neg\" = \"data/interim/benchmark/benchmark_meta_neg.tsv\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_pre_mgf_pos,\n format = \"file\",\n command = {\n benchmark_pre_mgf_pos <- benchmark_prepared[[1]]\n }\n ),\n tar_target(\n name = benchmark_pre_mgf_neg,\n format = \"file\",\n command = {\n benchmark_pre_mgf_neg <- benchmark_prepared[[2]]\n }\n ),\n tar_target(\n name = benchmark_pre_meta_pos,\n format = \"file\",\n command = {\n benchmark_pre_meta_pos <- benchmark_prepared[[3]]\n }\n ),\n tar_target(\n name = benchmark_pre_meta_neg,\n format = \"file\",\n command = {\n benchmark_pre_meta_neg <- benchmark_prepared[[4]]\n }\n ),\n tar_target(\n name = benchmark_taxed_pos,\n format = \"file\",\n command = {\n benchmark_taxed_pos <- benchmark_pre_meta_pos |>\n tima:::benchmark_taxize_spectra(\n keys = lib_mer_key,\n org_tax_ott = lib_mer_org_tax_ott,\n output = \"data/interim/benchmark/benchmark_taxed_pos.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_taxed_neg,\n format = \"file\",\n command = {\n benchmark_taxed_neg <- benchmark_pre_meta_neg |>\n tima:::benchmark_taxize_spectra(\n keys = lib_mer_key,\n org_tax_ott = lib_mer_org_tax_ott,\n output = \"data/interim/benchmark/benchmark_taxed_neg.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_def_ann_mas,\n command = {\n benchmark_def_ann_mas <- tima:::parse_yaml_params(\n def = par_def_ann_mas,\n usr = par_def_ann_mas\n )\n }\n ),\n tar_target(\n name = benchmark_ann_ms1_pre_pos,\n format = \"file\",\n command = {\n benchmark_ann_ms1_pre_pos <-\n annotate_masses(\n features = benchmark_pre_meta_pos,\n library = lib_mer_key,\n output_annotations = \"data/interim/benchmark/benchmark_ann_ms1_pos.tsv.gz\",\n output_edges = \"data/interim/benchmark/benchmark_edges_ms1_pos.tsv.gz\",\n name_source = benchmark_def_ann_mas$names$source,\n name_target = benchmark_def_ann_mas$names$target,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc,\n adducts_list = par_ann_mas$ms$adducts,\n clusters_list = par_ann_mas$ms$clusters,\n neutral_losses_list = par_ann_mas$ms$neutral_losses,\n ms_mode = \"pos\",\n tolerance_ppm = benchmark_def_ann_mas$ms$tolerances$mass$ppm$ms1,\n tolerance_rt = benchmark_def_ann_mas$ms$tolerances$rt$adducts\n )\n }\n ),\n tar_target(\n name = benchmark_ann_ms1_pre_neg,\n format = \"file\",\n command = {\n benchmark_ann_ms1_pre_neg <-\n annotate_masses(\n features = benchmark_pre_meta_neg,\n library = lib_mer_key,\n output_annotations = \"data/interim/benchmark/benchmark_ann_ms1_neg.tsv.gz\",\n output_edges = \"data/interim/benchmark/benchmark_edges_ms1_neg.tsv.gz\",\n name_source = benchmark_def_ann_mas$names$source,\n name_target = benchmark_def_ann_mas$names$target,\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc,\n adducts_list = par_ann_mas$ms$adducts,\n clusters_list = par_ann_mas$ms$clusters,\n neutral_losses_list = par_ann_mas$ms$neutral_losses,\n ms_mode = \"neg\",\n tolerance_ppm = benchmark_def_ann_mas$ms$tolerances$mass$ppm$ms1,\n tolerance_rt = benchmark_def_ann_mas$ms$tolerances$rt$adducts\n )\n }\n ),\n tar_target(\n name = benchmark_def_cre_edg_spe,\n command = {\n benchmark_def_cre_edg_spe <- tima:::parse_yaml_params(\n def = par_def_cre_edg_spe,\n usr = par_def_cre_edg_spe\n )\n }\n ),\n tar_target(\n name = benchmark_edg_spe_pos,\n format = \"file\",\n command = {\n benchmark_edg_spe_pos <- create_edges_spectra(\n input = benchmark_pre_mgf_pos,\n output = \"data/interim/benchmark/benchmark_edges_spe_pos.tsv.gz\",\n name_source = benchmark_def_cre_edg_spe$names$source,\n name_target = benchmark_def_cre_edg_spe$names$target,\n threshold = benchmark_def_cre_edg_spe$annotations$thresholds$ms2$similarity$edges,\n ppm = benchmark_def_cre_edg_spe$ms$tolerances$mass$ppm$ms2,\n dalton = benchmark_def_cre_edg_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = 0\n )\n }\n ),\n tar_target(\n name = benchmark_edg_spe_neg,\n format = \"file\",\n command = {\n benchmark_edg_spe_neg <- create_edges_spectra(\n input = benchmark_pre_mgf_neg,\n output = \"data/interim/benchmark/benchmark_edges_spe_neg.tsv.gz\",\n name_source = benchmark_def_cre_edg_spe$names$source,\n name_target = benchmark_def_cre_edg_spe$names$target,\n threshold = benchmark_def_cre_edg_spe$annotations$thresholds$ms2$similarity$edges,\n ppm = benchmark_def_cre_edg_spe$ms$tolerances$mass$ppm$ms2,\n dalton = benchmark_def_cre_edg_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = 0\n )\n }\n ),\n tar_target(\n name = benchmark_def_pre_fea_edg,\n command = {\n benchmark_def_pre_fea_edg <- tima:::parse_yaml_params(\n def = par_def_pre_fea_edg,\n usr = par_def_pre_fea_edg\n )\n }\n ),\n tar_target(\n name = benchmark_edg_pre_pos,\n format = \"file\",\n command = {\n benchmark_edg_pre_pos <- prepare_features_edges(\n input = list(\"spectral\" = benchmark_edg_spe_pos, \"ms1\" = benchmark_ann_ms1_pre_pos[[2]]),\n output = \"data/interim/benchmark/benchmark_edges_pos.tsv.gz\",\n name_source = benchmark_def_pre_fea_edg$names$source,\n name_target = benchmark_def_pre_fea_edg$names$target\n )\n }\n ),\n tar_target(\n name = benchmark_edg_pre_neg,\n format = \"file\",\n command = {\n benchmark_edg_pre_neg <- prepare_features_edges(\n input = list(\"spectral\" = benchmark_edg_spe_neg, \"ms1\" = benchmark_ann_ms1_pre_neg[[2]]),\n output = \"data/interim/benchmark/benchmark_edges_neg.tsv.gz\",\n name_source = benchmark_def_pre_fea_edg$names$source,\n name_target = benchmark_def_pre_fea_edg$names$target\n )\n }\n ),\n tar_target(\n name = benchmark_def_cre_edg_com,\n command = {\n benchmark_def_cre_edg_com <- tima:::parse_yaml_params(\n def = par_def_cre_com,\n usr = par_def_cre_com\n )\n }\n ),\n tar_target(\n name = benchmark_com_pos,\n format = \"file\",\n command = {\n benchmark_com_pos <- create_components(\n input = benchmark_edg_pre_pos,\n output = \"data/interim/benchmark/benchmark_components_pos.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_com_neg,\n format = \"file\",\n command = {\n benchmark_com_neg <- create_components(\n input = benchmark_edg_pre_neg,\n output = \"data/interim/benchmark/benchmark_components_neg.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_def_pre_fea_com,\n command = {\n benchmark_def_pre_fea_com <- tima:::parse_yaml_params(\n def = par_def_pre_fea_com,\n usr = par_def_pre_fea_com\n )\n }\n ),\n tar_target(\n name = benchmark_com_pre_pos,\n format = \"file\",\n command = {\n benchmark_com_pre_pos <- prepare_features_components(\n input = benchmark_com_pos,\n output = \"data/interim/benchmark/benchmark_com_pre_pos.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_com_pre_neg,\n format = \"file\",\n command = {\n benchmark_com_pre_neg <- prepare_features_components(\n input = benchmark_com_neg,\n output = \"data/interim/benchmark/benchmark_com_pre_neg.tsv.gz\"\n )\n }\n ),\n tar_target(\n name = benchmark_def_ann_spe,\n command = {\n benchmark_def_ann_spe <- tima:::parse_yaml_params(\n def = par_def_ann_spe,\n usr = par_def_ann_spe\n )\n }\n ),\n tar_target(\n name = benchmark_ann_spe_pos,\n format = \"file\",\n command = {\n benchmark_ann_spe_pos <- annotate_spectra(\n input = benchmark_pre_mgf_pos,\n library = c(\n lib_spe_is_lot_pre_pos,\n lib_spe_exp_mb_pre_pos\n ),\n polarity = \"pos\",\n output = \"data/interim/benchmark/benchmark_ann_spe_pos.tsv.gz\",\n threshold =\n benchmark_def_ann_spe$annotations$thresholds$ms2$similarity$annotation,\n ppm = benchmark_def_ann_spe$ms$tolerances$mass$ppm$ms2,\n dalton = benchmark_def_ann_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = 0,\n approx = benchmark_def_ann_spe$annotations$ms2approx\n )\n }\n ),\n tar_target(\n name = benchmark_ann_spe_neg,\n format = \"file\",\n command = {\n benchmark_ann_spe_neg <- annotate_spectra(\n input = benchmark_pre_mgf_neg,\n library = c(\n lib_spe_is_lot_pre_neg,\n lib_spe_exp_mb_pre_neg\n ),\n polarity = \"neg\",\n output = \"data/interim/benchmark/benchmark_ann_spe_neg.tsv.gz\",\n threshold =\n benchmark_def_ann_spe$annotations$thresholds$ms2$similarity$annotation,\n ppm = benchmark_def_ann_spe$ms$tolerances$mass$ppm$ms2,\n dalton = benchmark_def_ann_spe$ms$tolerances$mass$dalton$ms2,\n qutoff = 0,\n approx = benchmark_def_ann_spe$annotations$ms2approx\n )\n }\n ),\n tar_target(\n name = benchmark_def_pre_ann_spe,\n command = {\n benchmark_def_pre_ann_spe <- tima:::parse_yaml_params(\n def = par_def_pre_ann_spe,\n usr = par_def_pre_ann_spe\n )\n }\n ),\n tar_target(\n name = benchmark_ann_spe_pre_pos,\n format = \"file\",\n command = {\n benchmark_ann_spe_pre_pos <- prepare_annotations_spectra(\n input = c(benchmark_ann_spe_pos),\n output = \"data/interim/benchmark/benchmark_ann_spe_pre_pos.tsv.gz\",\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n ),\n tar_target(\n name = benchmark_ann_spe_pre_neg,\n format = \"file\",\n command = {\n benchmark_ann_spe_pre_neg <- prepare_annotations_spectra(\n input = c(benchmark_ann_spe_neg),\n output = \"data/interim/benchmark/benchmark_ann_spe_pre_neg.tsv.gz\",\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n ),\n tar_target(\n name = benchmark_def_pre_ann_sir,\n command = {\n benchmark_def_pre_ann_sir <- tima:::parse_yaml_params(\n def = par_def_pre_ann_sir,\n usr = par_def_pre_ann_sir\n )\n }\n ),\n tar_target(\n name = benchmark_ann_sir_pre,\n format = \"file\",\n command = {\n benchmark_ann_sir_pre <-\n prepare_annotations_sirius(\n input_directory = \"doesNotExist4Now\",\n output_ann = \"data/interim/benchmark/benchmark_ann_sir_pre.tsv.gz\",\n output_can = \"data/interim/benchmark/benchmark_ann_sir_pre_can.tsv.gz\",\n output_for = \"data/interim/benchmark/benchmark_ann_sir_pre_for.tsv.gz\",\n str_stereo = lib_mer_str_stereo,\n str_met = lib_mer_str_met,\n str_nam = lib_mer_str_nam,\n str_tax_cla = lib_mer_str_tax_cla,\n str_tax_npc = lib_mer_str_tax_npc\n )\n }\n ),\n tar_target(\n name = benchmark_ann_sir_pre_can,\n format = \"file\", command = {\n benchmark_ann_sir_pre_can <- benchmark_ann_sir_pre[[1]]\n }\n ),\n tar_target(\n name = benchmark_ann_sir_pre_for,\n format = \"file\", command = {\n benchmark_ann_sir_pre_for <- benchmark_ann_sir_pre[[2]]\n }\n ),\n tar_target(\n name = benchmark_ann_sir_pre_str,\n format = \"file\", command = {\n benchmark_ann_sir_pre_str <- benchmark_ann_sir_pre[[3]]\n }\n ),\n tar_target(\n name = benchmark_def_fil_ann,\n command = {\n benchmark_def_fil_ann <- tima:::parse_yaml_params(\n def = par_def_fil_ann,\n usr = par_def_fil_ann\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_spe_neg,\n format = \"file\",\n command = {\n benchmark_ann_fil_spe_neg <- filter_annotations(\n annotations = c(\n benchmark_ann_spe_pre_neg,\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_neg,\n rts = list(),\n output = \"data/interim/benchmark/benchmark_ann_spe_fil_neg.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_spe_ms1_neg,\n format = \"file\",\n command = {\n benchmark_ann_fil_spe_ms1_neg <- filter_annotations(\n annotations = c(\n benchmark_ann_spe_pre_neg,\n benchmark_ann_ms1_pre_neg[[1]],\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_neg,\n rts = list(),\n output =\n \"data/interim/benchmark/benchmark_ann_spe_ms1_fil_neg.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_ms1_neg,\n format = \"file\",\n command = {\n benchmark_ann_fil_ms1_neg <- filter_annotations(\n annotations = c(\n benchmark_ann_ms1_pre_neg[[1]],\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_neg,\n rts = list(),\n output = \"data/interim/benchmark/benchmark_ann_ms1_fil_neg.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_spe_pos,\n format = \"file\",\n command = {\n benchmark_ann_fil_spe_pos <- filter_annotations(\n annotations = c(\n benchmark_ann_spe_pre_pos,\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_pos,\n rts = list(),\n output = \"data/interim/benchmark/benchmark_ann_spe_fil_pos.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_spe_ms1_pos,\n format = \"file\",\n command = {\n benchmark_ann_fil_spe_ms1_pos <- filter_annotations(\n annotations = c(\n benchmark_ann_spe_pre_pos,\n benchmark_ann_ms1_pre_pos[[1]],\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_pos,\n rts = list(),\n output =\n \"data/interim/benchmark/benchmark_ann_spe_ms1_fil_pos.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_ann_fil_ms1_pos,\n format = \"file\",\n command = {\n benchmark_ann_fil_ms1_pos <- filter_annotations(\n annotations = c(\n benchmark_ann_ms1_pre_pos[[1]],\n benchmark_ann_sir_pre_str\n ),\n features = benchmark_pre_meta_pos,\n rts = list(),\n output = \"data/interim/benchmark/benchmark_ann_ms1_fil_pos.tsv.gz\",\n tolerance_rt = benchmark_def_fil_ann$ms$tolerances$rt$library\n )\n }\n ),\n tar_target(\n name = benchmark_def_wei_ann,\n command = {\n benchmark_def_wei_ann <- tima:::parse_yaml_params(\n def = par_def_wei_ann,\n usr = par_def_wei_ann\n )\n }\n ),\n tar_target(\n name = benchmark_wei_par,\n command = {\n benchmark_wei_par <- list(\n canopus = benchmark_ann_sir_pre_can,\n formula = benchmark_ann_sir_pre_for,\n library = lib_mer_key,\n org_tax_ott = lib_mer_org_tax_ott,\n str_stereo = lib_mer_str_stereo,\n candidates_final = 500,\n score_biological_domain =\n benchmark_def_wei_ann$weights$biological$domain,\n score_biological_kingdom =\n benchmark_def_wei_ann$weights$biological$kingdom,\n score_biological_phylum =\n benchmark_def_wei_ann$weights$biological$phylum,\n score_biological_class =\n benchmark_def_wei_ann$weights$biological$class,\n score_biological_order =\n benchmark_def_wei_ann$weights$biological$order,\n score_biological_infraorder =\n benchmark_def_wei_ann$weights$biological$infraorder,\n score_biological_family =\n benchmark_def_wei_ann$weights$biological$family,\n score_biological_subfamily =\n benchmark_def_wei_ann$weights$biological$subfamily,\n score_biological_tribe =\n benchmark_def_wei_ann$weights$biological$tribe,\n score_biological_subtribe =\n benchmark_def_wei_ann$weights$biological$subtribe,\n score_biological_genus =\n benchmark_def_wei_ann$weights$biological$genus,\n score_biological_subgenus =\n benchmark_def_wei_ann$weights$biological$subgenus,\n score_biological_species =\n benchmark_def_wei_ann$weights$biological$species,\n score_biological_subspecies =\n benchmark_def_wei_ann$weights$biological$subspecies,\n score_biological_variety =\n benchmark_def_wei_ann$weights$biological$variety,\n score_chemical_cla_kingdom =\n benchmark_def_wei_ann$weights$chemical$cla$kingdom,\n score_chemical_cla_superclass =\n benchmark_def_wei_ann$weights$chemical$cla$superclass,\n score_chemical_cla_class =\n benchmark_def_wei_ann$weights$chemical$cla$class,\n score_chemical_cla_parent =\n benchmark_def_wei_ann$weights$chemical$cla$parent,\n score_chemical_npc_pathway =\n benchmark_def_wei_ann$weights$chemical$npc$pathway,\n score_chemical_npc_superclass =\n benchmark_def_wei_ann$weights$chemical$npc$superclass,\n score_chemical_npc_class =\n benchmark_def_wei_ann$weights$chemical$npc$class,\n minimal_consistency =\n benchmark_def_wei_ann$annotations$thresholds$consistency,\n minimal_ms1_bio =\n benchmark_def_wei_ann$annotations$thresholds$ms1$biological,\n minimal_ms1_chemo =\n benchmark_def_wei_ann$annotations$thresholds$ms1$chemical,\n minimal_ms1_condition =\n benchmark_def_wei_ann$annotations$thresholds$ms1$condition,\n compounds_names = benchmark_def_wei_ann$options$compounds_names,\n high_confidence = FALSE,\n remove_ties = benchmark_def_wei_ann$options$remove_ties,\n summarise = benchmark_def_wei_ann$options$summarise,\n pattern = benchmark_def_wei_ann$files$pattern,\n force = benchmark_def_wei_ann$options$force\n )\n }\n ),\n tar_target(\n name = benchmark_files_pos,\n command = {\n benchmark_files_pos <- list(\n components = benchmark_com_pre_pos,\n edges = benchmark_edg_pre_pos,\n taxa = benchmark_taxed_pos\n )\n }\n ),\n tar_target(\n name = benchmark_files_neg,\n command = {\n benchmark_files_pos <- list(\n components = benchmark_com_pre_neg,\n edges = benchmark_edg_pre_neg,\n taxa = benchmark_taxed_neg\n )\n }\n ),\n # tar_target(\n # name = benchmark_ann_pre_ms1_pos,\n # . format = \"file\",\n # command = {\n # benchmark_ann_pre_ms1_pos <-\n # do.call(\n # what = weight_annotations,\n # args = c(benchmark_wei_par,\n # benchmark_files_pos,\n # annotations = benchmark_ann_fil_ms1_pos,\n # weight_spectral = benchmark_def_wei_ann$weights$global$spectral,\n # weight_chemical = benchmark_def_wei_ann$weights$global$chemical,\n # weight_biological =\n # benchmark_def_wei_ann$weights$global$biological,\n # ms1_only = TRUE,\n # output = \"benchmark_lotus_ms1_pos.tsv.gz\"\n # )\n # )\n # }\n # ),\n tar_target(\n name = benchmark_ann_pre_ms2_b_pos,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms2_b_pos <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_pos,\n annotations = benchmark_ann_fil_spe_pos,\n weight_spectral = 0.333,\n weight_chemical = 0,\n weight_biological = 0.666,\n ms1_only = FALSE,\n output = \"benchmark_lotus_ms2_bio_pos.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms1_ms2_b_pos,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms1_ms2_b_pos <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_pos,\n annotations = benchmark_ann_fil_spe_ms1_pos,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0,\n weight_biological = 0.666,\n output = \"benchmark_lotus_ms1_ms2_bio_pos.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms2_b_c_pos,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms2_b_c_pos <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_pos,\n annotations = benchmark_ann_fil_spe_pos,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0.166,\n weight_biological = 0.500,\n output = \"benchmark_lotus_ms2_bio_chemo_pos.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms1_ms2_b_c_pos,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms1_ms2_b_c_pos <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_pos,\n annotations = benchmark_ann_fil_spe_ms1_pos,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0.166,\n weight_biological = 0.500,\n output = \"benchmark_lotus_ms1_ms2_bio_chemo_pos.tsv.gz\"\n )\n )\n }\n ),\n # tar_target(\n # name = benchmark_ann_pre_ms1_neg,\n # . format = \"file\",\n # command = {\n # benchmark_ann_pre_ms1_neg <-\n # do.call(\n # what = weight_annotations,\n # args = c(benchmark_wei_par,\n # benchmark_files_neg,\n # annotations = benchmark_ann_fil_ms1_neg,\n # ms1_only = TRUE,\n # weight_spectral = benchmark_def_wei_ann$weights$global$spectral,\n # weight_chemical = benchmark_def_wei_ann$weights$global$chemical,\n # weight_biological =\n # benchmark_def_wei_ann$weights$global$biological,\n # output = \"benchmark_lotus_ms1_neg.tsv.gz\"\n # )\n # )\n # }\n # ),\n tar_target(\n name = benchmark_ann_pre_ms2_b_neg,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms2_b_neg <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_neg,\n annotations = benchmark_ann_fil_spe_neg,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0,\n weight_biological = 0.666,\n output = \"benchmark_lotus_ms2_bio_neg.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms1_ms2_b_neg,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms1_ms2_b_neg <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_neg,\n annotations = benchmark_ann_fil_spe_ms1_neg,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0,\n weight_biological = 0.666,\n output = \"benchmark_lotus_ms1_ms2_bio_neg.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms2_b_c_neg,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms2_b_c_neg <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_neg,\n annotations = benchmark_ann_fil_spe_neg,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0.166,\n weight_biological = 0.500,\n output = \"benchmark_lotus_ms2_bio_chemo_neg.tsv.gz\"\n )\n )\n }\n ),\n tar_target(\n name = benchmark_ann_pre_ms1_ms2_b_c_neg,\n format = \"file\",\n command = {\n benchmark_ann_pre_ms1_ms2_b_c_neg <-\n do.call(\n what = weight_annotations,\n args = c(\n benchmark_wei_par,\n benchmark_files_neg,\n annotations = benchmark_ann_fil_spe_ms1_neg,\n ms1_only = FALSE,\n weight_spectral = 0.333,\n weight_chemical = 0.166,\n weight_biological = 0.500,\n output = \"benchmark_lotus_ms1_ms2_bio_chemo_neg.tsv.gz\"\n )\n )\n }\n )\n )\n)\n","type":"text"},{"name":"doc/index.html","content":"<!DOCTYPE html>\n<html>\n<head><title>R: Vignettes and other documentation<\/title>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0, user-scalable=yes\" />\n<link rel=\"stylesheet\" type=\"text/css\" href=\"/doc/html/R.css\" />\n<\/head><body><div class=\"container\">\n<h1> Vignettes and other documentation\n<img class=\"toplogo\" src=\"/doc/html/Rlogo.svg\" alt=\"[R logo]\" />\n<\/h1>\n<hr/>\n<div style=\"text-align: center;\">\n<a href=\"/doc/html/index.html\"><img class=\"arrow\" src=\"/doc/html/up.jpg\" alt=\"[Top]\" /><\/a>\n<\/div>\n<h2>Vignettes from package 'tima'<\/h2>\n<table style=\"width: 100%;\">\n<col style=\"width: 22%;\" />\n<col style=\"width: 2%;\" />\n<col style=\"width: 50%;\" />\n<col style=\"width: 8%;\" />\n<col style=\"width: 8%;\" />\n<col style=\"width: 8%;\" />\n<tr><td style=\"text-align: right; vertical-align: top;\"><a href=\"../../../library/tima/doc/tima.html\">tima::tima<\/a><\/td>\n<td><\/td><td style=\"vertical-align: top;\">General comments about the infrastructure<\/td>\n<td style=\"vertical-align: top;\"><a href=\"../../../library/tima/doc/tima.html\">HTML<\/a><\/td>\n<td style=\"vertical-align: top;\"><a href=\"../../../library/tima/doc/tima.Rmd\">source<\/a><\/td>\n<td style=\"vertical-align: top; white-space: nowrap\"><a href=\"../../../library/tima/doc/tima.R\">R code<\/a><\/td><\/tr>\n<\/table>\n<\/div><\/body><\/html>\n","type":"text"},{"name":"doc/tima.R","content":"## ----setup, include = FALSE---------------------------------------------------\nknitr::opts_chunk$set(\n collapse = TRUE,\n comment = \"#>\"\n)\nknitr::opts_knit$set(root.dir = system.file(package = \"tima\"))\n\n## ----tima, results=\"hide\", message=FALSE, warning=FALSE, include = FALSE------\nlibrary(\"tima\")\n\n## ----chunk-1, echo=FALSE, message=FALSE, warning=FALSE, out.width=\"100%\"------\nlibrary(targets)\nSys.setenv(TAR_WARN = \"false\")\ntargets::tar_visnetwork(\n names = starts_with(\"ann\"),\n exclude = c(\n tidyselect::contains(\"benchmark\"),\n tidyselect::contains(\"par_\"),\n tidyselect::contains(\"gnps\"),\n tidyselect::contains(\"input\"),\n tidyselect::contains(\"paths\"),\n tidyselect::contains(\"_is\"),\n tidyselect::contains(\"_exp\"),\n tidyselect::contains(\"library_sop_lotus\"),\n tidyselect::contains(\"library_sop_closed\")\n ),\n targets_only = TRUE,\n degree_from = 8\n)\n\n## ----eval=FALSE, include=TRUE-------------------------------------------------\n# install.packages(\n# \"tima\",\n# repos = c(\n# \"https://taxonomicallyinformedannotation.r-universe.dev\",\n# \"https://bioc.r-universe.dev\",\n# \"https://cloud.r-project.org\"\n# )\n# )\n# tima::install()\n# tima::get_example_files()\n\n## ----eval=FALSE, include=TRUE-------------------------------------------------\n# tima::run_app()\n\n## ----eval=FALSE, include=TRUE-------------------------------------------------\n# tima::tima_full()\n\n","type":"text"},{"name":"doc/tima.Rmd","content":"---\ntitle: \"General comments about the infrastructure\"\nauthor: \"Adriano Rutz\"\ncreative_commons: CC BY-SA\ndate: \"`r Sys.Date()`\"\noutput: rmarkdown::html_vignette\nopengraph:\n image:\n src: https://github.com/taxonomicallyinformedannotation/tima/blob/main/man/figures/logo.svg\n alt: \"Taxonomically Informed Metabolite Annotation\"\n twitter:\n creator: \"@adafede\"\nvignette: >\n %\\VignetteIndexEntry{General comments about the infrastructure}\n %\\VignetteEngine{knitr::rmarkdown}\n %\\VignetteEncoding{UTF-8}\n---\n\n```{r setup, include = FALSE}\nknitr::opts_chunk$set(\n collapse = TRUE,\n comment = \"#>\"\n)\nknitr::opts_knit$set(root.dir = system.file(package = \"tima\"))\n```\n\n```{r tima, results=\"hide\", message=FALSE, warning=FALSE, include = FALSE}\nlibrary(\"tima\")\n```\n\nThis vignette describes the philosophy behind the infrastructure of **TIMA**.\n\n## Philosophy\n\nOur main goals were **flexibility** and **reproducibility**.\n\n### Flexibility\n\nTo ensure flexibility, we tried to split the process in as much tiny parts as needed.\nSo you can decide whether to skip an optional part, add your own processing, etc.\nWe tried to cover most use cases, but of course they are not exhaustive.\nIf you feel like something useful to other users is missing, please fill an [issue](https://github.com/taxonomicallyinformedannotation/tima/issues).\n\n### Reproducibility\n\nAfter some time using TIMA, you will probably wonder:\n\"*What was the parameters I used to generate this file?*\" ...\nOr a collaborator might ask you to share your data and parameters.\nWriting them down each time might be time consuming and not really in line with modern computational approaches.\nTherefore, we chose to implement all parameters of all steps (almost...) as YAML files.\nThey are human-readable and can be used in batches.\nIf you do not like YAML, parameters of each steps can also be given as command line arguments.\nThey will then be saved as YAML you will be able to share.\n\nTo ensure optimal reproducibility and avoiding re-computing endlessly steps that did not change, we decided to build a [{targets}](https://books.ropensci.org/targets/) pipeline.\nEach step of the whole pipeline will be described next.\n\n\n```{r chunk-1, echo=FALSE, message=FALSE, warning=FALSE, out.width=\"100%\"}\nlibrary(targets)\nSys.setenv(TAR_WARN = \"false\")\ntargets::tar_visnetwork(\n names = starts_with(\"ann\"),\n exclude = c(\n tidyselect::contains(\"benchmark\"),\n tidyselect::contains(\"par_\"),\n tidyselect::contains(\"gnps\"),\n tidyselect::contains(\"input\"),\n tidyselect::contains(\"paths\"),\n tidyselect::contains(\"_is\"),\n tidyselect::contains(\"_exp\"),\n tidyselect::contains(\"library_sop_lotus\"),\n tidyselect::contains(\"library_sop_closed\")\n ),\n targets_only = TRUE,\n degree_from = 8\n)\n```\n\n## Use\n\nEach step is detailed in the related [vignettes](https://taxonomicallyinformedannotation.github.io/tima/articles).\n\nAll coming steps admit you already installed `tima`:\n\n```{r eval=FALSE, include=TRUE}\ninstall.packages(\n \"tima\",\n repos = c(\n \"https://taxonomicallyinformedannotation.r-universe.dev\",\n \"https://bioc.r-universe.dev\",\n \"https://cloud.r-project.org\"\n )\n)\ntima::install()\ntima::get_example_files()\n```\n\nWe now recommend you to read the [first vignette](https://taxonomicallyinformedannotation.github.io/tima/articles/I-gathering.html).\n\n### tl;dr\n\nIf you do not feel like going through all the steps, then just do 🚀:\n\n```{r eval=FALSE, include=TRUE}\ntima::run_app()\n```\n\nIf you do not even need a GUI ☠️:\n\n```{r eval=FALSE, include=TRUE}\ntima::tima_full()\n```\n","type":"text"},{"name":"doc/tima.html","content":"<!doctype html>\n<html>\n <head>\n <meta charset=\"utf-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1, shrink-to-fit=no\">\n <meta name=\"generator\" content=\"r-universe/pandoc\">\n <meta name=\"author\" content=\"Adriano Rutz\">\n \n <title>General comments about the infrastructure<\/title>\n <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.6.0/css/bootstrap.min.css\" data-external=\"1\">\n <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css\" data-external=\"1\">\n <link rel=\"stylesheet\" href=\"https://r-universe.dev/static/article-styles.css\" data-external=\"1\">\n <\/head>\n <body>\n <div class=\"container\" id=\"topcontainer\">\n <script>if (window.self != window.top) try {document.getElementById('topcontainer').className = \"container-fluid article-in-iframe\"; } catch(e) {}<\/script>\n <h1 class=\"title\">General comments about the\ninfrastructure<\/h1>\n <div class=\"row\">\n <div class=\"col-xl-10\"><p>This vignette describes the\nphilosophy behind the infrastructure of <strong>TIMA<\/strong>.<\/p>\n<div id=\"philosophy\" class=\"section level2\">\n<h2>Philosophy<\/h2>\n<p>Our main goals were <strong>flexibility<\/strong> and\n<strong>reproducibility<\/strong>.<\/p>\n<div id=\"flexibility\" class=\"section level3\">\n<h3>Flexibility<\/h3>\n<p>To ensure flexibility, we tried to split the process in as much tiny\nparts as needed. So you can decide whether to skip an optional part, add\nyour own processing, etc. We tried to cover most use cases, but of\ncourse they are not exhaustive. If you feel like something useful to\nother users is missing, please fill an <a href=\"https://github.com/taxonomicallyinformedannotation/tima/issues\">issue<\/a>.<\/p>\n<\/div>\n<div id=\"reproducibility\" class=\"section level3\">\n<h3>Reproducibility<\/h3>\n<p>After some time using TIMA, you will probably wonder: “<em>What was\nthe parameters I used to generate this file?<\/em>” … Or a collaborator\nmight ask you to share your data and parameters. Writing them down each\ntime might be time consuming and not really in line with modern\ncomputational approaches. Therefore, we chose to implement all\nparameters of all steps (almost…) as YAML files. They are human-readable\nand can be used in batches. If you do not like YAML, parameters of each\nsteps can also be given as command line arguments. They will then be\nsaved as YAML you will be able to share.<\/p>\n<p>To ensure optimal reproducibility and avoiding re-computing endlessly\nsteps that did not change, we decided to build a <a href=\"https://books.ropensci.org/targets/\">{targets}<\/a> pipeline. 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class=\"section level2\">\n<h2>Use<\/h2>\n<p>Each step is detailed in the related <a href=\"https://taxonomicallyinformedannotation.github.io/tima/articles\">vignettes<\/a>.<\/p>\n<p>All coming steps admit you already installed <code>tima<\/code>:<\/p>\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb1-1\"><a href=\"#cb1-1\" tabindex=\"-1\"><\/a><span class=\"fu\">install.packages<\/span>(<\/span>\n<span id=\"cb1-2\"><a href=\"#cb1-2\" tabindex=\"-1\"><\/a> <span class=\"st\">"tima"<\/span>,<\/span>\n<span id=\"cb1-3\"><a href=\"#cb1-3\" tabindex=\"-1\"><\/a> <span class=\"at\">repos =<\/span> <span class=\"fu\">c<\/span>(<\/span>\n<span id=\"cb1-4\"><a href=\"#cb1-4\" tabindex=\"-1\"><\/a> <span class=\"st\">"https://taxonomicallyinformedannotation.r-universe.dev"<\/span>,<\/span>\n<span id=\"cb1-5\"><a href=\"#cb1-5\" tabindex=\"-1\"><\/a> <span class=\"st\">"https://bioc.r-universe.dev"<\/span>,<\/span>\n<span id=\"cb1-6\"><a href=\"#cb1-6\" tabindex=\"-1\"><\/a> <span class=\"st\">"https://cloud.r-project.org"<\/span><\/span>\n<span id=\"cb1-7\"><a href=\"#cb1-7\" tabindex=\"-1\"><\/a> )<\/span>\n<span id=\"cb1-8\"><a href=\"#cb1-8\" tabindex=\"-1\"><\/a>)<\/span>\n<span id=\"cb1-9\"><a href=\"#cb1-9\" tabindex=\"-1\"><\/a>tima<span class=\"sc\">::<\/span><span class=\"fu\">install<\/span>()<\/span>\n<span id=\"cb1-10\"><a href=\"#cb1-10\" tabindex=\"-1\"><\/a>tima<span class=\"sc\">::<\/span><span class=\"fu\">get_example_files<\/span>()<\/span><\/code><\/pre><\/div>\n<p>We now recommend you to read the <a href=\"https://taxonomicallyinformedannotation.github.io/tima/articles/I-gathering.html\">first\nvignette<\/a>.<\/p>\n<div id=\"tldr\" class=\"section level3\">\n<h3>tl;dr<\/h3>\n<p>If you do not feel like going through all the steps, then just do\n🚀:<\/p>\n<div class=\"sourceCode\" id=\"cb2\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb2-1\"><a href=\"#cb2-1\" tabindex=\"-1\"><\/a>tima<span class=\"sc\">::<\/span><span class=\"fu\">run_app<\/span>()<\/span><\/code><\/pre><\/div>\n<p>If you do not even need a GUI ☠️:<\/p>\n<div class=\"sourceCode\" id=\"cb3\"><pre class=\"sourceCode r\"><code class=\"sourceCode r\"><span id=\"cb3-1\"><a href=\"#cb3-1\" tabindex=\"-1\"><\/a>tima<span class=\"sc\">::<\/span><span class=\"fu\">tima_full<\/span>()<\/span><\/code><\/pre><\/div>\n<\/div>\n<\/div><\/div>\n <div class=\"d-none d-xl-block col-xl-2 bd-toc\">\n <ul class=\"section-nav\">\n <li class=\"toc-entry\"><ul>\n<li><a href=\"#philosophy\" id=\"toc-philosophy\">Philosophy<\/a><\/li>\n<li><a href=\"#use\" id=\"toc-use\">Use<\/a><\/li>\n<\/ul><\/li>\n <\/ul>\n <\/div>\n <\/div>\n <\/div>\n <script src=\"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.0/jquery.slim.js\" data-external=\"1\"><\/script>\n <script src=\"https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.6.0/js/bootstrap.min.js\" data-external=\"1\"><\/script>\n <script src=\"https://r-universe.dev/static/article.js\" data-external=\"1\"><\/script>\n\n <!-- Mathjax -->\n <!-- <script src=\"https://polyfill.io/v3/polyfill.min.js?features=es6\" data-external=\"1\"><\/script> -->\n <script id=\"MathJax-script\" async src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/3.1.2/es5/tex-mml-chtml.min.js\" data-external=\"1\"><\/script>\n <\/body>\n<\/html>\n","type":"text"},{"name":"help/AnIndex","content":"tima-package\ttima-package\nannotate_masses\tannotate_masses\nannotate_spectra\tannotate_spectra\nbenchmark_taxize_spectra\tbenchmark_taxize_spectra\ncalculate_entropy\tcalculate_entropy\ncalculate_mass_of_m\tcalculate_mass_of_m\nclean_bio\tclean_bio\nclean_chemo\tclean_chemo\nclean_collapse\tclean_collapse\ncolumns_model\tcolumns_model\ncomplement_metadata_structures\tcomplement_metadata_structures\ncopy_backbone\tcopy_backbone\ncreate_components\tcreate_components\ncreate_dir\tcreate_dir\ncreate_edges\tcreate_edges\ncreate_edges_spectra\tcreate_edges_spectra\ndecorate_bio\tdecorate_bio\ndecorate_chemo\tdecorate_chemo\ndecorate_masses\tdecorate_masses\ndist_get\tdist_get\ndist_groups\tdist_groups\nexport_output\texport_output\nexport_params\texport_params\nexport_spectra_rds\texport_spectra_rds\nextract_spectra\textract_spectra\nfake_annotations_columns\tfake_annotations_columns\nfake_ecmdb\tfake_ecmdb\nfake_hmdb\tfake_hmdb\nfake_lotus\tfake_lotus\nfake_sop_columns\tfake_sop_columns\nfilter_annotations\tfilter_annotations\nfilter_high_confidence_only\tfilter_high_confidence_only\nget_default_paths\tget_default_paths\nget_example_files\tget_example_files\nget_example_sirius\tget_example_sirius\nget_file\tget_file\nget_gnps_tables\tget_gnps_tables\nget_last_version_from_zenodo\tget_last_version_from_zenodo\nget_massbank_spectra\tget_massbank_spectra\nget_organism_taxonomy_ott\tget_organism_taxonomy_ott\nget_params\tget_params\ngo_to_cache\tgo_to_cache\nharmonize_adducts\tharmonize_adducts\nharmonize_names_sirius\tharmonize_names_sirius\nharmonize_spectra\tharmonize_spectra\nimport_spectra\timport_spectra\ninstall\tinstall\nload_yaml_files\tload_yaml_files\nlog_debug\tlog_debug\nlog_pipe\tlog_pipe\nparse_adduct\tparse_adduct\nparse_cli_params\tparse_cli_params\nparse_yaml_params\tparse_yaml_params\nprepare_annotations_gnps\tprepare_annotations_gnps\nprepare_annotations_sirius\tprepare_annotations_sirius\nprepare_annotations_spectra\tprepare_annotations_spectra\nprepare_features_components\tprepare_features_components\nprepare_features_edges\tprepare_features_edges\nprepare_features_tables\tprepare_features_tables\nprepare_libraries_rt\tprepare_libraries_rt\nprepare_libraries_sop_closed\tprepare_libraries_sop_closed\nprepare_libraries_sop_ecmdb\tprepare_libraries_sop_ecmdb\nprepare_libraries_sop_hmdb\tprepare_libraries_sop_hmdb\nprepare_libraries_sop_lotus\tprepare_libraries_sop_lotus\nprepare_libraries_sop_merged\tprepare_libraries_sop_merged\nprepare_libraries_spectra\tprepare_libraries_spectra\nprepare_params\tprepare_params\nprepare_taxa\tprepare_taxa\npre_harmonize_names_sirius\tpre_harmonize_names_sirius\nread_from_sirius_zip\tread_from_sirius_zip\nreplace_id\treplace_id\nround_reals\tround_reals\nrun_app\trun_app\nsanitize_spectra\tsanitize_spectra\nselect_annotations_columns\tselect_annotations_columns\nselect_sirius_columns_canopus\tselect_sirius_columns_canopus\nselect_sirius_columns_formulas\tselect_sirius_columns_formulas\nselect_sirius_columns_structures\tselect_sirius_columns_structures\nselect_sop_columns\tselect_sop_columns\nsplit_tables_sop\tsplit_tables_sop\ntima\ttima-package\ntima_full\ttima_full\ntransform_score_sirius_csi\ttransform_score_sirius_csi\nweight_annotations\tweight_annotations\nweight_bio\tweight_bio\nweight_chemo\tweight_chemo\n","type":"text"},{"name":"help/aliases.rds","content":"H4sIAAAAAAAAA+1Wy3LaMBR1GjIJhCaEMmlC0kx/IPt+Qb+g7XSnubZko0GvkeQW8vGdXmHJ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\nxwfK8YFyfKAcHyj/7wNlEC7/f/Iwyf78BZGokP5AEQAA","type":"binary"},{"name":"help/figures/lifecycle-archived.svg","content":"<svg 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552.492188 575.761719 552.402344 C 575.816406 552.375 575.839844 552.316406 575.816406 552.261719 C 575.804688 552.214844 575.761719 552.191406 575.71875 552.191406 C 575.710938 552.191406 575.699219 552.191406 575.691406 552.199219 C 575.570312 552.230469 575.464844 552.246094 575.367188 552.246094 C 575.308594 552.246094 575.261719 552.242188 575.21875 552.234375 C 575.152344 552.222656 575.082031 552.199219 575.015625 552.160156 C 574.953125 552.125 574.890625 552.082031 574.832031 552.039062 L 574.820312 552.03125 C 574.800781 552.019531 574.78125 552.007812 574.761719 551.996094 C 574.714844 551.957031 574.679688 551.902344 574.6875 551.839844 C 574.695312 551.773438 574.699219 551.707031 574.699219 551.644531 C 574.707031 551.589844 574.75 551.539062 574.808594 551.539062 C 574.808594 551.539062 574.847656 551.539062 574.847656 551.539062 C 574.960938 551.539062 575.074219 551.519531 575.179688 551.484375 C 575.292969 551.445312 575.382812 551.390625 575.453125 551.351562 L 575.464844 551.347656 C 575.535156 551.308594 575.601562 551.273438 575.660156 551.242188 C 575.730469 551.210938 575.804688 551.191406 575.871094 551.191406 C 575.890625 551.191406 575.910156 551.191406 575.929688 551.191406 C 576.046875 551.191406 576.1875 551.222656 576.339844 551.28125 C 576.351562 551.285156 576.363281 551.285156 576.375 551.285156 C 576.414062 551.285156 576.449219 551.261719 576.46875 551.222656 C 576.492188 551.167969 576.476562 551.101562 576.425781 551.070312 C 576.222656 550.953125 576.050781 550.890625 575.871094 550.875 C 575.847656 550.875 575.820312 550.867188 575.796875 550.867188 C 575.71875 550.867188 575.636719 550.878906 575.558594 550.898438 C 575.464844 550.921875 575.378906 550.953125 575.304688 550.984375 L 575.28125 550.988281 C 575.207031 551.019531 575.132812 551.046875 575.058594 551.058594 C 575.015625 551.0625 574.980469 551.070312 574.941406 551.070312 C 574.917969 551.070312 574.890625 551.070312 574.867188 551.0625 L 574.859375 551.0625 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574.503906 548.363281 C 574.484375 548.367188 574.472656 548.375 574.453125 548.382812 C 574.429688 548.386719 574.402344 548.394531 574.378906 548.386719 C 574.328125 548.382812 574.289062 548.339844 574.269531 548.289062 C 574.246094 548.226562 574.21875 548.171875 574.195312 548.109375 C 574.152344 548.003906 574.109375 547.90625 574.066406 547.808594 C 574.054688 547.78125 574.058594 547.753906 574.070312 547.734375 C 574.082031 547.710938 574.109375 547.699219 574.140625 547.691406 C 574.195312 547.6875 574.242188 547.679688 574.285156 547.671875 C 574.390625 547.65625 574.496094 547.617188 574.582031 547.570312 C 574.773438 547.464844 574.910156 547.316406 575.019531 547.183594 C 575.039062 547.160156 575.046875 547.136719 575.046875 547.113281 C 575.046875 547.089844 575.027344 547.0625 575.007812 547.042969 C 574.992188 547.03125 574.964844 547.019531 574.949219 547.019531 C 574.921875 547.019531 574.898438 547.027344 574.878906 547.042969 C 574.726562 547.171875 574.59375 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545.921875 574.789062 545.917969 C 574.644531 545.886719 574.523438 545.867188 574.390625 545.867188 C 574.332031 545.867188 574.269531 545.871094 574.207031 545.878906 C 574.101562 545.898438 574.003906 545.925781 573.90625 545.976562 C 573.8125 546.027344 573.734375 546.082031 573.660156 546.136719 C 573.652344 546.144531 573.648438 546.152344 573.628906 546.15625 C 573.554688 546.199219 573.460938 546.167969 573.429688 546.082031 C 573.382812 545.925781 573.324219 545.769531 573.300781 545.664062 C 573.289062 545.625 573.296875 545.589844 573.320312 545.558594 C 573.335938 545.527344 573.375 545.511719 573.410156 545.503906 C 573.46875 545.496094 573.503906 545.492188 573.53125 545.492188 C 573.632812 545.484375 573.738281 545.460938 573.839844 545.421875 C 574.035156 545.347656 574.1875 545.230469 574.324219 545.121094 C 574.351562 545.101562 574.363281 545.078125 574.363281 545.046875 C 574.363281 545.023438 574.355469 544.996094 574.332031 544.980469 C 574.3125 544.960938 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574.167969 544.300781 574.25 544.292969 574.324219 544.277344 C 574.527344 544.234375 574.699219 544.140625 574.847656 544.058594 C 574.875 544.046875 574.890625 544.023438 574.898438 544 C 574.902344 543.972656 574.898438 543.949219 574.878906 543.929688 C 574.863281 543.90625 574.832031 543.894531 574.792969 543.894531 C 574.78125 543.894531 574.769531 543.894531 574.75 543.898438 C 574.613281 543.949219 574.441406 544.003906 574.28125 544.003906 L 574.273438 544.003906 C 574.226562 544.003906 574.171875 544.003906 574.121094 543.992188 C 574.039062 543.972656 573.960938 543.941406 573.84375 543.886719 L 573.824219 543.875 C 573.75 543.839844 573.671875 543.800781 573.578125 543.769531 C 573.46875 543.734375 573.355469 543.714844 573.246094 543.707031 C 573.238281 543.707031 573.226562 543.707031 573.222656 543.707031 C 573.207031 543.707031 573.195312 543.707031 573.183594 543.707031 L 573.175781 543.707031 C 573.117188 543.707031 573.054688 543.679688 573.015625 543.625 C 572.992188 543.59375 572.984375 543.546875 572.984375 543.503906 C 572.992188 543.386719 573 543.273438 573.011719 543.160156 C 573.023438 543.066406 573.097656 543 573.195312 543 C 573.234375 543 573.269531 542.992188 573.308594 542.992188 C 573.40625 542.980469 573.492188 542.957031 573.570312 542.9375 L 573.597656 542.933594 C 573.683594 542.90625 573.757812 542.894531 573.832031 542.890625 C 573.84375 542.890625 573.863281 542.890625 573.875 542.890625 C 573.929688 542.890625 573.984375 542.894531 574.039062 542.90625 C 574.1875 542.945312 574.335938 543.03125 574.480469 543.117188 C 574.496094 543.128906 574.523438 543.136719 574.539062 543.136719 C 574.566406 543.136719 574.589844 543.128906 574.601562 543.117188 C 574.621094 543.097656 574.632812 543.078125 574.632812 543.054688 C 574.632812 543.03125 574.621094 543 574.601562 542.980469 C 574.460938 542.859375 574.324219 542.742188 574.140625 542.652344 C 574.046875 542.609375 573.941406 542.574219 573.839844 542.5625 C 573.777344 542.554688 573.707031 542.550781 573.628906 542.550781 C 573.597656 542.550781 573.570312 542.550781 573.546875 542.550781 C 573.511719 542.550781 573.480469 542.550781 573.441406 542.550781 C 573.386719 542.550781 573.34375 542.550781 573.308594 542.542969 C 573.300781 542.542969 573.300781 542.542969 573.292969 542.542969 C 573.238281 542.53125 573.195312 542.488281 573.175781 542.433594 C 573.164062 542.394531 573.164062 542.359375 573.175781 542.320312 C 573.238281 542.117188 573.3125 541.902344 573.40625 541.691406 C 573.449219 541.585938 573.546875 541.519531 573.660156 541.507812 C 573.703125 541.5 573.746094 541.5 573.78125 541.5 C 573.800781 541.5 573.820312 541.5 573.84375 541.5 C 573.917969 541.507812 573.984375 541.519531 574.054688 541.542969 C 574.117188 541.570312 574.183594 541.605469 574.28125 541.667969 L 574.324219 541.699219 C 574.394531 541.742188 574.472656 541.796875 574.550781 541.839844 C 574.664062 541.898438 574.773438 541.933594 574.886719 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511.15625 1167.125 511.929688 1167.054688 512.28125 C 1166.992188 512.628906 1166.675781 512.828125 1166.710938 513.015625 C 1166.75 513.199219 1167.328125 513.953125 1167.558594 513.859375 C 1167.785156 513.769531 1168.136719 513.394531 1168.273438 513.632812 C 1168.421875 513.875 1168.886719 514.203125 1168.777344 514.296875 C 1168.675781 514.382812 1168.683594 514.609375 1168.484375 514.808594 C 1168.289062 515 1168.464844 515.574219 1168.433594 515.742188 C 1168.398438 515.921875 1168.644531 516.285156 1168.644531 516.285156 C 1168.878906 516.324219 1169.113281 516.367188 1169.351562 516.410156 C 1169.351562 516.410156 1169.53125 515.828125 1169.492188 515.644531 C 1169.460938 515.457031 1169.886719 515.453125 1170.03125 515.558594 C 1170.171875 515.65625 1170.777344 516.515625 1170.777344 516.515625 C 1170.496094 516.777344 1170.214844 517.035156 1169.925781 517.300781 C 1170.03125 517.546875 1170.136719 517.792969 1170.242188 518.046875 C 1170.242188 518.046875 1171.203125 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1159.699219 508.484375 1159.789062 507.816406 1159.789062 507.816406 C 1159.550781 507.875 1159.316406 507.933594 1159.074219 507.996094 C 1159.074219 507.996094 1158.816406 507.230469 1157.863281 507.074219 Z M 1170.160156 521.507812 C 1169.574219 520.71875 1172.027344 519.539062 1172.691406 519.496094 C 1173.351562 519.449219 1173.503906 520.179688 1173.992188 520.628906 C 1174.488281 521.078125 1173.699219 521.4375 1173.75 521.886719 C 1173.804688 522.339844 1174.140625 521.894531 1174.546875 521.777344 C 1174.953125 521.65625 1175.320312 522.664062 1175.734375 523.117188 C 1176.144531 523.570312 1175.855469 524.621094 1176.117188 525.042969 C 1176.386719 525.457031 1175.488281 524.992188 1176.070312 525.652344 C 1176.664062 526.3125 1176.769531 525.664062 1176.359375 524.707031 C 1175.9375 523.746094 1177.007812 524.703125 1177.429688 525.074219 C 1177.847656 525.453125 1178.121094 526.433594 1178.425781 526.75 C 1178.742188 527.066406 1177.859375 527.054688 1178.269531 527.542969 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IioQEEfOWlXS0Ve2CsuZYNUtWSclQDevk6efLy576DHNhqt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R8whOXmeInkdDZDfcplO+3Bn1lp88MsN1HfW6Qgty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\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TzMKYrD40ZjZsRNcm9zeo9CGmIizXIb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\nXVzC3/aE22a0NGfH90TnLEdc92lo5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DMBTGT//Mbb0QwbcQXBWqeLuLCZUypZ24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\nm7MX6ez7A2efY+cinG/YcYydxUzNoghf8lRa1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LXctQu4EFk3Ura5UmGPIiEJew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\ndd8DMEVULYrJAPO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html>\n<html>\n<head><title>R: Taxonomically Informed Metabolite Annotation<\/title>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0, user-scalable=yes\" />\n<link rel=\"stylesheet\" type=\"text/css\" href=\"R.css\" />\n<\/head><body><div class=\"container\">\n<h1> Taxonomically Informed Metabolite Annotation\n<img class=\"toplogo\" src=\"../../../doc/html/Rlogo.svg\" alt=\"[R logo]\" />\n<\/h1>\n<hr/>\n<div style=\"text-align: center;\">\n<a href=\"../../../doc/html/packages.html\"><img class=\"arrow\" src=\"../../../doc/html/left.jpg\" alt=\"[Up]\" /><\/a>\n<a href=\"../../../doc/html/index.html\"><img class=\"arrow\" src=\"../../../doc/html/up.jpg\" alt=\"[Top]\" /><\/a>\n<\/div><h2>Documentation for package ‘tima’ version 2.11.0<\/h2>\n\n<ul><li><a href=\"../DESCRIPTION\">DESCRIPTION file<\/a>.<\/li>\n<li><a href=\"../doc/index.html\">User guides, package vignettes and other documentation.<\/a><\/li>\n<li><a href=\"../NEWS\">Package NEWS<\/a>.<\/li>\n<\/ul>\n\n<h2>Help Pages<\/h2>\n\n\n<table style=\"width: 100%;\">\n<tr><td style=\"width: 25%;\"><a href=\"annotate_masses.html\">annotate_masses<\/a><\/td>\n<td>Annotate masses<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"annotate_spectra.html\">annotate_spectra<\/a><\/td>\n<td>Annotate spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"benchmark_taxize_spectra.html\">benchmark_taxize_spectra<\/a><\/td>\n<td>Benchmark taxize spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"calculate_entropy.html\">calculate_entropy<\/a><\/td>\n<td>Calculate entropy<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"calculate_mass_of_m.html\">calculate_mass_of_m<\/a><\/td>\n<td>Calculate mass of M<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"clean_bio.html\">clean_bio<\/a><\/td>\n<td>Clean bio<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"clean_chemo.html\">clean_chemo<\/a><\/td>\n<td>Clean chemo<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"clean_collapse.html\">clean_collapse<\/a><\/td>\n<td>Clean collapse<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"columns_model.html\">columns_model<\/a><\/td>\n<td>Columns model<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"complement_metadata_structures.html\">complement_metadata_structures<\/a><\/td>\n<td>Complement metadata of structures<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"copy_backbone.html\">copy_backbone<\/a><\/td>\n<td>Copy backbone<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"create_components.html\">create_components<\/a><\/td>\n<td>Create components<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"create_dir.html\">create_dir<\/a><\/td>\n<td>Create directory<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"create_edges.html\">create_edges<\/a><\/td>\n<td>Create edges<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"create_edges_spectra.html\">create_edges_spectra<\/a><\/td>\n<td>Create edges spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"decorate_bio.html\">decorate_bio<\/a><\/td>\n<td>Decorate bio<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"decorate_chemo.html\">decorate_chemo<\/a><\/td>\n<td>Decorate chemo<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"decorate_masses.html\">decorate_masses<\/a><\/td>\n<td>Decorate masses<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"dist_get.html\">dist_get<\/a><\/td>\n<td>Distance between two elements in a distance matrix<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"dist_groups.html\">dist_groups<\/a><\/td>\n<td>Dist groups<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"export_output.html\">export_output<\/a><\/td>\n<td>Export output<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"export_params.html\">export_params<\/a><\/td>\n<td>Export parameters<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"export_spectra_rds.html\">export_spectra_rds<\/a><\/td>\n<td>Export spectra RDS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"extract_spectra.html\">extract_spectra<\/a><\/td>\n<td>Extract spectra from a Spectra object<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_annotations_columns.html\">fake_annotations_columns<\/a><\/td>\n<td>Fake annotations columns<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_ecmdb.html\">fake_ecmdb<\/a><\/td>\n<td>Fake ECMDB<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_hmdb.html\">fake_hmdb<\/a><\/td>\n<td>Fake HMDB<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_lotus.html\">fake_lotus<\/a><\/td>\n<td>Fake LOTUS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"fake_sop_columns.html\">fake_sop_columns<\/a><\/td>\n<td>Fake SOP columns<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"filter_annotations.html\">filter_annotations<\/a><\/td>\n<td>Filter annotations<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"filter_high_confidence_only.html\">filter_high_confidence_only<\/a><\/td>\n<td>Filter high confidence only *[Experimental]*<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_default_paths.html\">get_default_paths<\/a><\/td>\n<td>Get default paths<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_example_files.html\">get_example_files<\/a><\/td>\n<td>Get example files<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_example_sirius.html\">get_example_sirius<\/a><\/td>\n<td>Get example sirius<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_file.html\">get_file<\/a><\/td>\n<td>Get file<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_gnps_tables.html\">get_gnps_tables<\/a><\/td>\n<td>Get GNPS Tables<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_last_version_from_zenodo.html\">get_last_version_from_zenodo<\/a><\/td>\n<td>Get last version from Zenodo<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_massbank_spectra.html\">get_massbank_spectra<\/a><\/td>\n<td>Get MassBank spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_organism_taxonomy_ott.html\">get_organism_taxonomy_ott<\/a><\/td>\n<td>Get organism taxonomy (Open Tree of Life Taxonomy)<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"get_params.html\">get_params<\/a><\/td>\n<td>Get parameters<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"go_to_cache.html\">go_to_cache<\/a><\/td>\n<td>Go to cache<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"harmonize_adducts.html\">harmonize_adducts<\/a><\/td>\n<td>Harmonize adducts<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"harmonize_names_sirius.html\">harmonize_names_sirius<\/a><\/td>\n<td>Harmonize names sirius<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"harmonize_spectra.html\">harmonize_spectra<\/a><\/td>\n<td>Harmonize spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"import_spectra.html\">import_spectra<\/a><\/td>\n<td>Import spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"install.html\">install<\/a><\/td>\n<td>Install<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"load_yaml_files.html\">load_yaml_files<\/a><\/td>\n<td>Load yaml files<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"log_debug.html\">log_debug<\/a><\/td>\n<td>Log debug<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"log_pipe.html\">log_pipe<\/a><\/td>\n<td>Log pipe<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"parse_adduct.html\">parse_adduct<\/a><\/td>\n<td>Parse adduct<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"parse_cli_params.html\">parse_cli_params<\/a><\/td>\n<td>Parse CLI parameters<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"parse_yaml_params.html\">parse_yaml_params<\/a><\/td>\n<td>Parse YAML parameters<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_annotations_gnps.html\">prepare_annotations_gnps<\/a><\/td>\n<td>Prepare annotations GNPS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_annotations_sirius.html\">prepare_annotations_sirius<\/a><\/td>\n<td>Prepare annotations SIRIUS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_annotations_spectra.html\">prepare_annotations_spectra<\/a><\/td>\n<td>Prepare annotations MS2<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_features_components.html\">prepare_features_components<\/a><\/td>\n<td>Prepare features components<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_features_edges.html\">prepare_features_edges<\/a><\/td>\n<td>Prepare features edges<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_features_tables.html\">prepare_features_tables<\/a><\/td>\n<td>Prepare features table<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_rt.html\">prepare_libraries_rt<\/a><\/td>\n<td>Prepare libraries of retention times<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_closed.html\">prepare_libraries_sop_closed<\/a><\/td>\n<td>Prepare libraries of structure organism pairs CLOSED<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_ecmdb.html\">prepare_libraries_sop_ecmdb<\/a><\/td>\n<td>Prepare libraries of structure organism pairs ECMDB<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_hmdb.html\">prepare_libraries_sop_hmdb<\/a><\/td>\n<td>Prepare libraries of structure organism pairs HMDB<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_lotus.html\">prepare_libraries_sop_lotus<\/a><\/td>\n<td>Prepare libraries of structure organism pairs LOTUS<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_sop_merged.html\">prepare_libraries_sop_merged<\/a><\/td>\n<td>Prepare merged structure organism pairs libraries<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_libraries_spectra.html\">prepare_libraries_spectra<\/a><\/td>\n<td>Prepare libraries of spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_params.html\">prepare_params<\/a><\/td>\n<td>Prepare params<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"prepare_taxa.html\">prepare_taxa<\/a><\/td>\n<td>Prepare taxa<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"pre_harmonize_names_sirius.html\">pre_harmonize_names_sirius<\/a><\/td>\n<td>Pre harmonize names sirius<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"read_from_sirius_zip.html\">read_from_sirius_zip<\/a><\/td>\n<td>Read from SIRIUS zip<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"replace_id.html\">replace_id<\/a><\/td>\n<td>Replace ID in file paths<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"round_reals.html\">round_reals<\/a><\/td>\n<td>Round reals<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"run_app.html\">run_app<\/a><\/td>\n<td>Run app<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"sanitize_spectra.html\">sanitize_spectra<\/a><\/td>\n<td>Sanitize spectra<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_annotations_columns.html\">select_annotations_columns<\/a><\/td>\n<td>Select annotations columns<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_sirius_columns_canopus.html\">select_sirius_columns_canopus<\/a><\/td>\n<td>Select sirius columns (canopus)<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_sirius_columns_formulas.html\">select_sirius_columns_formulas<\/a><\/td>\n<td>Select sirius columns (formulas)<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_sirius_columns_structures.html\">select_sirius_columns_structures<\/a><\/td>\n<td>Select sirius columns (structures)<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"select_sop_columns.html\">select_sop_columns<\/a><\/td>\n<td>Select SOP columns<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"split_tables_sop.html\">split_tables_sop<\/a><\/td>\n<td>Split Structure Organism Pairs table<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"tima_full.html\">tima_full<\/a><\/td>\n<td>Tima Full<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"transform_score_sirius_csi.html\">transform_score_sirius_csi<\/a><\/td>\n<td>Transform score sirius CSI<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"weight_annotations.html\">weight_annotations<\/a><\/td>\n<td>Weight annotations<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"weight_bio.html\">weight_bio<\/a><\/td>\n<td>Weight bio<\/td><\/tr>\n<tr><td style=\"width: 25%;\"><a href=\"weight_chemo.html\">weight_chemo<\/a><\/td>\n<td>Weight chemo<\/td><\/tr>\n<\/table>\n<\/div><\/body><\/html>\n","type":"text"},{"name":"html/R.css","content":"@media screen {\n .container {\n\tpadding-right: 10px;\n\tpadding-left: 10px;\n\tmargin-right: auto;\n\tmargin-left: auto;\n\tmax-width: 900px;\n }\n}\n\n.rimage img { /* from knitr - for examples and demos */\n width: 96%;\n margin-left: 2%;\n} \t\n\n.katex { font-size: 1.1em; }\n\ncode {\n color: inherit;\n background: inherit;\n}\n\nbody {\n line-height: 1.4;\n background: white;\n color: black;\n}\n\na:link {\n background: white;\n color: blue;\n}\n\na:visited {\n background: white;\n color: rgb(50%, 0%, 50%);\n}\n\nh1 {\n background: white;\n color: rgb(55%, 55%, 55%);\n font-family: monospace;\n font-size: 1.4em; /* x-large; */\n text-align: center;\n}\n\nh2 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n font-size: 1.2em; /* large; */\n text-align: center;\n}\n\nh3 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n font-size: 1.2em; /* large; */\n}\n\nh4 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n font-style: italic;\n font-size: 1.2em; /* large; */\n}\n\nh5 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n}\n\nh6 {\n background: white;\n color: rgb(40%, 40%, 40%);\n font-family: monospace;\n font-style: italic;\n}\n\nimg.toplogo {\n width: 4em;\n vertical-align: middle;\n}\n\nimg.arrow {\n width: 30px;\n height: 30px;\n border: 0;\n}\n\nspan.acronym {\n font-size: small;\n}\n\nspan.env {\n font-family: monospace;\n}\n\nspan.file {\n font-family: monospace;\n}\n\nspan.option{\n font-family: monospace;\n}\n\nspan.pkg {\n font-weight: bold;\n}\n\nspan.samp{\n font-family: monospace;\n}\n\ndiv.vignettes a:hover {\n background: rgb(85%, 85%, 85%);\n}\n\ntr {\n vertical-align: top;\n}\n\nspan.rlang {\n font-family: Courier New, Courier;\n color: #666666;\n}\n\n","type":"text"},{"name":"params/default/annotate_masses.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' List of prepared annotations.\n prepared:\n #' List of prepared structural annotations.\n structural:\n #' Prepared MS1 structural annotations. STRING\n ms1: data/interim/annotations/example_ms1Prepared.tsv.gz\n\n #' Features files.\n features:\n #' Prepared features file. STRING\n prepared: data/interim/features/example_features.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Merged structure organism pairs library keys. STRING\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Raw edges file.\n raw:\n #' Raw ms1 edges file. STRING\n ms1: data/interim/features/example_edgesMasses.tsv\n\n#' MS related parameters.\nms:\n #' List of adducts to be used.\n adducts:\n #' List of adducts to be used in negative. STRING\n neg:\n - \"[M-H3]3-\"\n - \"[M-H2]2-\"\n - \"[M-H]-\"\n - \"[M+F]-\"\n - \"[M+Na-H2]-\"\n - \"[M+Cl]-\"\n - \"[M+K-H2]-\"\n - \"[M+Br]-\"\n - \"[2M-H]-\"\n - \"[3M-H]-\"\n\n #' List of adducts to be used in positive. STRING\n pos:\n - \"[M+H3]3+\"\n - \"[M+H2Na]3+\"\n - \"[M+HNa2]3+\"\n - \"[M+Na3]3+\"\n - \"[M+H2]2+\"\n - \"[M+HNa]2+\"\n - \"[M+Mg]2+\"\n - \"[M+HK]2+\"\n - \"[M+Ca]2+\"\n - \"[M+Na2]2+\"\n - \"[M+Fe]2+\"\n - \"[M+H]+\"\n - \"[M+H4N]+\"\n - \"[M+Na]+\"\n - \"[M+K]+\"\n - \"[M+Cu]+\"\n - \"[2M+Mg]2+\"\n - \"[2M+Ca]2+\"\n - \"[2M+Fe]2+\"\n - \"[2M+H]+\"\n - \"[2M+H4N]+\"\n - \"[2M+Na]+\"\n - \"[2M+K]+\"\n\n #' List of clusters to be used.\n clusters:\n #' List of clusters to be used in negative. STRING\n neg:\n - \"H2O\" # (water)\n - \"CH2O2\" # (formic)\n - \"NaCl\" # (sodium chloride)\n - \"C2H4O2\" # (acetic)\n - \"H2PO4\" # (phosphoric)\n - \"C2HF3O2\" # (tfa)\n\n #' List of clusters to be used in positive. STRING\n pos:\n - \"H2O\" # (water)\n # - CH4O # (methanol)\n - \"C2H3N\" # (acetonitrile)\n # - C2H7N # (ethylamine)\n # - C2H6O # (ethanol)\n - \"NaCl\" # (sodium chloride)\n # - C3H8O # (isopropanol)\n # - C2H6OS # (dmso)\n\n #' List of neutral losses to be used. STRING\n neutral_losses:\n - \"HN\"\n - \"CH3\"\n - \"O\"\n - \"H3N (ammonia)\"\n - \"H2O (water)\"\n - \"CHN\"\n - \"CO\"\n - \"C2H4 (2xCH2)\"\n - \"C2H5 (CH2-CH3)\"\n - \"CH2O\"\n - \"H5ON (H2O-H3N)\"\n - \"H4O2 (2xH2O)\"\n - \"C2H2O\"\n - \"CHON\"\n - \"CO2\"\n - \"CHO2\"\n - \"H6O3 (3xH2O)\"\n - \"C2O2 (2xCO)\"\n - \"CH6O3 (combination)\"\n - \"H8O4 (4xH2O)\"\n - \"C2H2O3 (CH2O-CO2)\"\n - \"C3H6O2 (CH3-CH2-COOH)\"\n - \"C3O3 (3xCO)\"\n - \"C4H9NO (combination)\"\n - \"C2O4 (2xCO2)\"\n - \"C3H4O3 (pyruvic)\"\n - \"C4H8O2 (butyric)\"\n - \"C3H7NO2 (alanine)\"\n - \"C3H6O3 (sugar)\"\n - \"H2O4S (sulfuric)\"\n - \"H3O4P (phosphoric)\"\n - \"C5H10O2 (valeric)\"\n - \"C3H4O4 (malonic)\"\n - \"C2H2O5 (CO2-CO2-H2O)\"\n - \"C3H8O4 (combination)\"\n - \"C5H9NO2 (proline)\"\n - \"C4O4H6 (CO2-CO2-CH3-CH3)\"\n - \"C4H8O4 (sugar)\"\n - \"C2H4O6 (CO2-CO2-H2O-H2O)\"\n - \"C6H6O3 (HRF)\"\n - \"C5H8O4 (pentose-H2O)\"\n - \"C8H8O2 (RDA-1)\"\n - \"C6H8O4 (HRF)\"\n - \"C9H6O2 (coumaroyl)\"\n - \"C6H10O4 (methylpentose/desoxyhexose-H2O)\"\n - \"C9H8O2 (cinnamoyl)\"\n - \"C7H4O4 (galloyl)\"\n - \"C8H8O3 (RDA-2)\"\n - \"C9H6O3 (caffeoyl)\"\n - \"C6H10O5 (hexose-H2O)\"\n - \"C8H8O4 (RDA-3)\"\n - \"C8H10O4 (RDA-2-H2O)\"\n - \"C6H8O6 (glucuronic-H2O)\"\n - \"C10H8O3 (feruloyl)\"\n - \"C6H13NO5 (hexose-H2N)\"\n - \"C6H12O6 (hexose)\"\n - \"C7H12O6 (quinoyl)\"\n - \"C6H15NO6 (hexose-H2N-H2O)\"\n - \"C6H14O7 (hexose-H2O)\"\n - \"C8H12O6 (acetylhexose-H2O)\"\n - \"C11H10O4 (sinapoyl)\"\n - \"C16H30O (pamitoyl)\"\n - \"C9H12O8 (malonylhexose)\"\n - \"C13H14O6 (benzoylhexose)\"\n - \"C12H20O8 (2xmethylpentose/desoxyhexose-H2O)\"\n - \"C15H16O7 (coumaroylhexose)\"\n - \"C13H14O9 (galloylhexose)\"\n - \"C15H16O8 (caffeoylhexose)\"\n - \"C12H20O10 (2xhexose-H2O)\"\n - \"C16H18O8 (feruloylhexose)\"\n - \"C17H20O9 (sinapoylhexose)\"\n - \"C18H30O15 (3xhexose-H2O)\"\n\n #' Polarity used. Must be \"pos\" or \"neg\". STRING\n polarity: pos\n\n #' Tolerances.\n tolerances:\n #' For mass.\n mass:\n #' Relative in ppm.\n ppm:\n #' Relative mass tolerance for MS1 in ppm. FLOAT\n ms1: 10\n\n #' Absolute in Dalton.\n dalton:\n #' Absolute mass tolerance for MS1 in Dalton. FLOAT\n ms1: 0.01\n\n #' For retention time.\n rt:\n #' Retention time tolerance for adducts in minutes. FLOAT\n adducts: 0.02\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"source IDs\" variable in the input. STRING\n source: CLUSTERID1\n\n #' Name of \"target IDs\" variable in the input. STRING\n target: CLUSTERID2\n\n#' Options.\noptions:\n #' Do not use it. BOOLEAN\n force: no\n","type":"text"},{"name":"params/default/annotate_spectra.yaml","content":"---\n#' Annotations related parameters.\nannotations:\n #' Perform approximative matching without precursor matching? BOOLEAN\n ms2approx: no\n\n #' Thresholds related parameters.\n thresholds:\n #' MS2 annotation related parameters.\n ms2:\n #' For similarity.\n similarity:\n #' For annotation. FLOAT\n annotation: 0.0\n\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' List of spectral matching results.\n spectral:\n #' Spectral spectral matching results. STRING\n spectral: data/interim/annotations/example_spectralMatches.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Spectral libraries. STRING\n spectral:\n #' List of negative spectral libraries. STRING\n neg:\n - data/interim/libraries/spectra/exp/massbank_neg.rds\n - data/interim/libraries/spectra/is/lotus_neg.rds\n\n #' List of positive spectral libraries. STRING\n pos:\n - data/interim/libraries/spectra/exp/massbank_pos.rds\n - data/interim/libraries/spectra/is/lotus_pos.rds\n\n #' Spectral files. STRING\n spectral:\n #' Raw spectral file. STRING\n raw: data/source/example_spectra.mgf\n\n#' MS related parameters.\nms:\n #' Thresholds related parameters.\n thresholds:\n #' MS2 related parameters.\n ms2:\n #' Intensity threshold for MS2. INTEGER\n intensity: 5\n\n #' Polarity used. Must be \"pos\" or \"neg\". STRING\n polarity: pos\n\n #' Tolerances.\n tolerances:\n #' For mass.\n mass:\n #' Relative in ppm.\n ppm:\n #' Relative mass tolerance for MS2 in ppm. FLOAT\n ms2: 10\n\n #' Absolute in Dalton.\n dalton:\n #' Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms2: 0.01\n","type":"text"},{"name":"params/default/create_components.yaml","content":"---\n#' Files.\nfiles:\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_edges.tsv\n\n #' Components files.\n components:\n #' Raw components file. STRING\n raw: data/interim/features/example_components.tsv\n","type":"text"},{"name":"params/default/create_edges_spectra.yaml","content":"---\n#' Annotations related parameters.\nannotations:\n #' Thresholds related parameters.\n thresholds:\n #' MS2 annotation related parameters.\n ms2:\n #' For similarity.\n similarity:\n #' For edges. FLOAT\n edges: 0.7\n\n#' Files.\nfiles:\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Raw edges file.\n raw:\n #' Raw spectral edges file. STRING\n spectral: data/interim/features/example_edgesSpectra.tsv\n\n #' Spectral files. STRING\n spectral:\n #' Raw spectral file. STRING\n raw: data/source/example_spectra.mgf\n\n#' MS related parameters.\nms:\n #' Thresholds related parameters.\n thresholds:\n #' MS2 related parameters.\n ms2:\n #' Intensity threshold for MS2. INTEGER\n intensity: 5\n\n #' Tolerances.\n tolerances:\n #' For mass.\n mass:\n #' Relative in ppm.\n ppm:\n #' Relative mass tolerance for MS2 in ppm. FLOAT\n ms2: 10\n\n #' Absolute in Dalton.\n dalton:\n #' Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms2: 0.01\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"source IDs\" variable in the input. STRING\n source: CLUSTERID1\n\n #' Name of \"target IDs\" variable in the input. STRING\n target: CLUSTERID2\n","type":"text"},{"name":"params/default/filter_annotations.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' List of filtered annotations. STRING\n filtered:\n - data/interim/annotations/example_annotationsFiltered.tsv.gz\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared structural annotations.\n structural:\n #' Prepared GNPS structural annotations. STRING\n gnps: data/interim/annotations/example_gnpsPrepared.tsv.gz\n #' Prepared MS1 structural annotations. STRING\n ms1: data/interim/annotations/example_ms1Prepared.tsv.gz\n #' Prepared SIRIUS structural annotations. STRING\n sirius: data/interim/annotations/example_siriusPrepared.tsv.gz\n #' Prepared SPECTRAL structural annotations. STRING\n spectral: data/interim/annotations/example_spectralMatchesPrepared.tsv.gz\n\n #' Features files.\n features:\n #' Prepared features file. STRING\n prepared: data/interim/features/example_features.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Temporal libraries. STRING\n temporal:\n #' List of prepared temporal libraries. STRING\n prepared:\n - data/interim/libraries/rt/prepared.tsv.gz\n\n#' MS related parameters.\nms:\n #' Tolerances.\n tolerances:\n #' For retention time.\n rt:\n #' Retention time tolerance for library in minutes. FLOAT\n library: 0.1\n","type":"text"},{"name":"params/default/prepare_annotations_gnps.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' List of spectral matching annotations.\n spectral:\n #' GNPS spectral matching annotations. STRING\n gnps: data/interim/annotations/example_gnps.tsv\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared structural annotations.\n structural:\n #' Prepared GNPS structural annotations. STRING\n gnps: data/interim/annotations/example_gnpsPrepared.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n","type":"text"},{"name":"params/default/prepare_annotations_sirius.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' Directory containing the sirius annotations. STRING\n sirius: data/interim/annotations/example_sirius\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared canopus annotations. STRING\n canopus: data/interim/annotations/example_canopusPrepared.tsv.gz\n\n #' List of prepared formula annotations. STRING\n formula: data/interim/annotations/example_formulaPrepared.tsv.gz\n\n #' List of prepared structural annotations.\n structural:\n #' Prepared SIRIUS structural annotations. STRING\n sirius: data/interim/annotations/example_siriusPrepared.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n\n#' Tools used for each step.\ntools:\n #' Sirius.\n sirius:\n #' Sirius version.\n #' Currently only 5 or 6 supported. INTEGER\n version: 6\n #' Taxonomies related parameters.\n taxonomies:\n #' Chemical taxonomy used for matching.\n #' Currently only \"npc\" or supported. STRING\n chemical: npc\n","type":"text"},{"name":"params/default/prepare_annotations_spectra.yaml","content":"---\n#' Files.\nfiles:\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' List of spectral matching annotations.\n spectral:\n #' Spectral spectral matching annotations. STRING\n spectral: data/interim/annotations/example_spectralMatches.tsv.gz\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared structural annotations.\n structural:\n #' Prepared spectral structural annotations. STRING\n spectral: data/interim/annotations/example_spectralMatchesPrepared.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n","type":"text"},{"name":"params/default/prepare_features_components.yaml","content":"---\n#' Files.\nfiles:\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Components files.\n components:\n #' Raw components file. STRING\n raw: data/interim/features/example_components.tsv\n\n #' Prepared components file. STRING\n prepared: data/interim/features/example_componentsPrepared.tsv\n","type":"text"},{"name":"params/default/prepare_features_edges.yaml","content":"---\n#' Files.\nfiles:\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Raw edges file.\n raw:\n #' Raw ms1 edges file. STRING\n ms1: data/interim/features/example_edgesMasses.tsv\n\n #' Raw spectral edges file. STRING\n spectral: data/interim/features/example_edgesSpectra.tsv\n\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_edges.tsv\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"source IDs\" variable in the input. STRING\n source: CLUSTERID1\n\n #' Name of \"target IDs\" variable in the input. STRING\n target: CLUSTERID2\n","type":"text"},{"name":"params/default/prepare_features_tables.yaml","content":"---\n#' Files.\nfiles:\n #' Features files.\n features:\n #' Raw features file. STRING\n raw: data/source/example_features.csv\n\n #' Prepared features file. STRING\n prepared: data/interim/features/example_features.tsv.gz\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"adduct\" variable in the feature table. STRING\n adduct: best ion\n \n #' Name of \"feature IDs\" variable in the input. STRING\n features: row ID\n\n #' Name of \"precursor m/z\" variable in the input. STRING\n precursor: row m/z\n\n #' Name of \"retention time\" variable.\n rt: \n #' Name of \"retention time\" variable in the feature table. STRING\n features: row retention time\n","type":"text"},{"name":"params/default/prepare_libraries_rt.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' RT prepared structure organism pairs libraries. STRING\n rt: data/interim/libraries/sop/rt_prepared.tsv.gz\n\n #' Temporal libraries. STRING\n temporal:\n #' List of experimental temporal libraries.\n exp:\n #' List of experimental temporal libraries (CSV). STRING\n csv: [ ]\n\n #' List of experimental temporal libraries (MGF).\n mgf:\n #' List of negative experimental temporal libraries (MGF). STRING\n neg: [ ]\n\n #' List of positive experimental temporal libraries (MGF). STRING\n pos: [ ]\n\n #' List of in silico temporal libraries.\n is:\n #' List of in silico temporal libraries (CSV). STRING\n csv: [ ]\n\n #' List of in silico temporal libraries (MGF).\n mgf:\n #' List of negative in silico temporal libraries (MGF). STRING\n neg: [ ]\n\n #' List of positive in silico temporal libraries (MGF). STRING\n pos: [ ]\n\n #' List of prepared temporal libraries. STRING\n prepared:\n - data/interim/libraries/rt/prepared.tsv.gz\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"InChIKey\" variable in the input. STRING\n inchikey: inchikey\n\n #' Name of fields present in the MGF.\n mgf:\n #' Name of \"InChIKey\" in MGF. STRING\n inchikey: ~\n\n #' Name of \"retention time\" in MGF. STRING\n retention_time: RTINSECONDS\n\n #' Name of \"SMILES\" in MGF. STRING\n smiles: SMILES\n\n #' Name of \"retention time\" variable.\n rt: \n #' Name of \"retention time\" variable in the library. STRING\n library: rt\n\n #' Name of \"SMILES\" variable in the input. STRING\n smiles: smiles\n\n#' Units of the different variables in the input files.\nunits:\n #' Unit of the \"retention time\" variable in the input files.\n #' Must be \"seconds\" or \"minutes\". STRING\n rt: seconds\n","type":"text"},{"name":"params/default/prepare_libraries_sop_closed.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' Closed structure organism pairs library. STRING\n closed: ../lotus-processor/data/processed/230106_closed_metadata.csv.gz\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' Closed prepared structure organism pairs library. STRING\n closed: data/interim/libraries/sop/closed_prepared.tsv.gz\n","type":"text"},{"name":"params/default/prepare_libraries_sop_ecmdb.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' ECMDB structure organism pairs library. STRING\n ecmdb: data/source/libraries/sop/ecmdb.json.zip\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' ECMDB prepared structure organism pairs library. STRING\n ecmdb: data/interim/libraries/sop/ecmdb_prepared.tsv.gz\n","type":"text"},{"name":"params/default/prepare_libraries_sop_hmdb.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' HMDB structure organism pairs library. STRING\n hmdb: data/source/libraries/sop/hmdb/structures.zip\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' HMDB prepared structure organism pairs library. STRING\n hmdb: data/interim/libraries/sop/hmdb_prepared.tsv.gz\n","type":"text"},{"name":"params/default/prepare_libraries_sop_lotus.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' Lotus raw structure organism pairs library. STRING\n lotus: data/source/libraries/sop/lotus.csv.gz\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' Lotus prepared structure organism pairs library. STRING\n lotus: data/interim/libraries/sop/lotus_prepared.tsv.gz\n","type":"text"},{"name":"params/default/prepare_libraries_sop_merged.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' Closed prepared structure organism pairs library. STRING\n closed: data/interim/libraries/sop/closed_prepared.tsv.gz\n #' ECMDB prepared structure organism pairs library. STRING\n ecmdb: data/interim/libraries/sop/ecmdb_prepared.tsv.gz\n #' HMDB prepared structure organism pairs library. STRING\n hmdb: data/interim/libraries/sop/hmdb_prepared.tsv.gz\n #' Lotus prepared structure organism pairs library. STRING\n lotus: data/interim/libraries/sop/lotus_prepared.tsv.gz\n #' RT prepared structure organism pairs library. STRING\n rt: data/interim/libraries/sop/rt_prepared.tsv.gz\n #' Spectral prepared structure organism pairs library. STRING\n spectral: data/interim/libraries/sop/spectral_prepared.tsv.gz\n\n #' Merged structure organism pairs library.\n merged:\n #' Merged structure organism pairs library keys. STRING\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n\n #' Organisms libraries.\n organisms:\n #' Organisms names. STRING\n names: data/interim/libraries/sop/merged/organisms/names.tsv.gz\n\n #' Organisms taxonomies.\n taxonomies:\n #' OTT organisms taxonomy. STRING\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n\n#' Organisms related parameters.\norganisms:\n #' Organisms filter related parameters.\n filter:\n #' Filter library to restrict it to a portion of organisms only. BOOLEAN\n mode: no\n\n #' Level at which the library will be filtered.\n #' Must be one of \"domain\", \"phylum\", \"class\", \"order\",\n #' \"family\", \"tribe\", \"genus\", \"species\", \"varietas\" . STRING\n level: phylum\n\n #' Value to be applied for filtering.\n #' \"OR\" can be used as \"|\".\n # Example: \"Simaroubaceae|Gentianaceae\". STRING\n value: Streptophyta\n","type":"text"},{"name":"params/default/prepare_libraries_spectra.yaml","content":"---\n#' Files.\nfiles:\n #' Libraries files.\n libraries:\n #' Spectral libraries.\n spectral:\n #' List of raw spectral libraries. STRING\n raw:\n - data/source/libraries/spectra/exp/spectral_lib_mini_with_rt.mgf\n\n#' Names of the different variables in the input files.\nnames:\n #' Names of the prepared libraries. STRING\n libraries: \n - internal\n\n #' Name of fields present in the MGF.\n mgf:\n #' Name of \"adduct\" in MGF. STRING\n adduct: ~\n\n #' Name of \"collision energy\" in MGF. STRING\n collision_energy: ~\n\n #' Name of \"compound ID\" in MGF. STRING\n compound_id: FILENAME\n\n #' Name of \"exact mass\" in MGF. STRING\n exact_mass: EXACTMASS\n\n #' Name of \"InChI\" in MGF. STRING\n inchi: INCHI\n\n #' Name of \"InChI without stereo\" in MGF. STRING\n inchi_no_stereo: ~\n\n #' Name of \"InChIKey\" in MGF. STRING\n inchikey: ~\n\n #' Name of \"InChIKey without stereo\" in MGF. STRING\n inchikey_no_stereo: ~\n\n #' Name of \"molecular formula\" in MGF. STRING\n molecular_formula: ~\n\n #' Name of \"name\" in MGF. STRING\n name: NAME\n\n #' Name of \"polarity\" in MGF. STRING\n polarity: IONMODE\n\n #' Name of \"SMILES\" in MGF. STRING\n smiles: SMILES\n\n #' Name of \"SMILES without stereo\" in MGF. STRING\n smiles_no_stereo: ~\n\n #' Name of \"spectrum ID\" in MGF. STRING\n spectrum_id: SPECTRUMID\n\n #' Name of \"SPLASH\" in MGF. STRING\n splash: ~\n\n #' Name of \"synonyms\" in MGF. STRING\n synonyms: ~\n\n #' Name of \"xlogP\" in MGF. STRING\n xlogp: ~\n","type":"text"},{"name":"params/default/prepare_taxa.yaml","content":"---\n#' Files.\nfiles:\n #' Features files.\n features:\n #' Raw features file. STRING\n raw: data/source/example_features.csv\n\n #' Metadata files.\n metadata:\n #' The path to the file containing your metadata.\n #' Must be formatted accordingly.\n #' If your experiment contains a single taxon, you can provide it below instead. STRING\n raw: data/source/example_metadata.tsv\n\n #' Taxonomically informed features file. STRING\n prepared: data/interim/taxa/example_taxed.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Organisms libraries.\n organisms:\n #' Organisms taxonomies.\n taxonomies:\n #' OTT organisms taxonomy. STRING\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n\n#' Names of the different variables in the input files.\nnames:\n #' The file(s) extension is present in the sample name. BOOLEAN\n extension: yes\n\n #' Name of \"feature IDs\" variable in the input. STRING\n features: row ID\n\n #' Name of \"filename\" variable in the input. STRING\n filename: filename\n\n #' Name of the \"taxon name\" variable in the input. STRING\n taxon: ATTRIBUTE_species\n\n#' Organisms related parameters.\norganisms:\n #' Number of organisms to keep per feature. INTEGER\n candidates: 1\n\n #' OPTIONAL. If your experiment contains a single taxon, its scientific name.\n #' Else, you will need to provide a metadata file above.\n #' If you provide both, this one will have priority.\n #' Example: \"Homo sapiens\". STRING\n taxon: ~\n","type":"text"},{"name":"params/default/weight_annotations.yaml","content":"---\n#' Annotations related parameters.\nannotations:\n #' Number of candidates to keep per feature.\n candidates:\n #' Number of final candidates. INTEGER\n final: 3\n\n #' Erase MS2 results and keep MS1 only. BOOLEAN\n ms1only: no\n\n #' Thresholds related parameters.\n thresholds:\n #' Minimal consistency score (chemical) to consider a class. FLOAT\n consistency: 0.0\n\n #' MS1 annotation related parameters.\n ms1:\n #' Minimal biological score to keep MS1 only annotation. FLOAT\n biological: 0.0\n\n #' Minimal chemical score to keep MS1 only annotation. FLOAT\n chemical: 0.0\n\n #' Condition to be used to retain candidates.\n #' Must be \"OR\" or \"AND\".\n #' Example: Minimum 0.3 biological AND 0.3 chemical. STRING\n condition: OR\n\n#' Files.\nfiles:\n #' Pattern to identify the files. STRING\n pattern: example\n\n #' Annotations files.\n annotations:\n #' List of filtered annotations. STRING\n filtered:\n - data/interim/annotations/example_annotationsFiltered.tsv.gz\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared canopus annotations. STRING\n canopus: data/interim/annotations/example_canopusPrepared.tsv.gz\n\n #' List of prepared formula annotations. STRING\n formula: data/interim/annotations/example_formulaPrepared.tsv.gz\n\n #' Final results file. STRING\n processed: annotations.tsv\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Merged structure organism pairs library.\n merged:\n #' Merged structure organism pairs library keys. STRING\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n\n #' Organisms libraries.\n organisms:\n #' Organisms taxonomies.\n taxonomies:\n #' OTT organisms taxonomy. STRING\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Components files.\n components:\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_componentsPrepared.tsv\n\n #' Edges files.\n edges:\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_edges.tsv\n\n #' Metadata files.\n metadata:\n #' Taxonomically informed features file. STRING\n prepared: data/interim/taxa/example_taxed.tsv.gz\n\n#' Weights to apply to each part of the final score.\nweights:\n #' Global weights.\n global:\n #' Weight for the biological part. FLOAT\n biological: 0.500\n\n #' Weight for the chemical part. FLOAT\n chemical: 0.166\n\n #' Weight for the spectral part. FLOAT\n spectral: 0.333\n\n #' Weights for the biological part.\n biological:\n #' Score for a biological domain match. FLOAT\n domain: 0.10\n\n #' Score for a biological kingdom match. FLOAT\n kingdom: 0.20\n\n #' Score for a biological phylum match. FLOAT\n phylum: 0.30\n\n #' Score for a biological class match. FLOAT\n class: 0.40\n\n #' Score for a biological order match. FLOAT\n order: 0.50\n\n #' Score for a biological infraorder match. FLOAT\n infraorder: 0.55\n\n #' Score for a biological family match. FLOAT\n family: 0.60\n\n #' Score for a biological subfamily match. FLOAT\n subfamily: 0.65\n\n #' Score for a biological tribe match. FLOAT\n tribe: 0.70\n\n #' Score for a biological subtribe match. FLOAT\n subtribe: 0.75\n\n #' Score for a biological genus match. FLOAT\n genus: 0.80\n\n #' Score for a biological subgenus match. FLOAT\n subgenus: 0.85\n\n #' Score for a biological species match. FLOAT\n species: 0.90\n\n #' Score for a biological subspecies match. FLOAT\n subspecies: 0.95\n\n #' Score for a biological variety match. FLOAT\n variety: 1.00\n\n #' Weights for the chemical part.\n chemical:\n #' Score for a classyfire match.\n cla:\n #' Score for a chemical kingdom match. FLOAT\n kingdom: 0.25\n\n #' Score for a chemical superclass match. FLOAT\n superclass: 0.50\n\n #' Score for a chemical class match. FLOAT\n class: 0.75\n\n #' Score for a chemical parent match. FLOAT\n parent: 1.00\n\n #' Score for a NPClassifier match.\n npc:\n #' Score for a chemical kingdom match. FLOAT\n pathway: 0.333\n\n #' Score for a chemical superclass match. FLOAT\n superclass: 0.666\n\n #' Score for a chemical class match. FLOAT\n class: 1.000\n\n#' Options.\noptions:\n #' Report compounds names. Can be very large. BOOLEAN\n compounds_names: no\n\n #' Do not use it. BOOLEAN\n force: no\n\n #' Filter high confidence candidates only. BOOLEAN\n high_confidence: yes\n\n #' Remove ties. BOOLEAN\n remove_ties: no\n\n #' Summarise results to one row per feature. BOOLEAN\n summarise: no\n","type":"text"},{"name":"params/prepare_params.yaml","content":"---\nfiles:\n #' The pattern identifying your whole job. You can put whatever you want. STRING\n pattern: example\n features:\n #' The path to the file containing your features' intensities.\n #' Can be generated by MZMine3 or SLAW. STRING\n raw: data/source/example_features.csv\n metadata:\n #' The path to the file containing your metadata.\n #' Must be formatted accordingly.\n #' If your experiment contains a single taxon, you can provide it below instead. STRING\n raw: data/source/example_metadata.tsv\n spectral:\n #' The path to the file containing your features' spectra.\n #' Can contain MS1 and/or MS2 spectra. STRING\n raw: data/source/example_spectra.mgf\n #' Annotations files.\n annotations:\n raw:\n #' Directory containing the sirius annotations. STRING\n sirius: data/interim/annotations/example_sirius.zip\nms:\n #' The polarity used. Must be either \"pos\" or \"neg\". STRING\n polarity: pos\norganisms:\n #' OPTIONAL. If your experiment contains a single taxon, its scientific name.\n #' Else, you will need to provide a metadata file above.\n #' If you provide both, this one will have priority.\n #' Example: \"Homo sapiens\". STRING\n taxon: ~\noptions:\n #' Filter high confidence candidates only. BOOLEAN\n high_confidence: yes\n #' Do you want to summarize all candidates per feature to a single row? BOOLEAN\n summarise: no\n","type":"text"},{"name":"params/prepare_params_advanced.yaml","content":"---\n#' Annotations related parameters.\nannotations:\n #' Number of candidates to keep per feature.\n candidates:\n #' Number of final candidates. INTEGER\n final: 3\n\n #' Erase MS2 results and keep MS1 only. BOOLEAN\n ms1only: no\n\n #' Perform approximative matching without precursor matching? BOOLEAN\n ms2approx: no\n\n #' Thresholds related parameters.\n thresholds:\n #' Minimal consistency score (chemical) to consider a class. FLOAT\n consistency: 0.0\n\n #' MS1 annotation related parameters.\n ms1:\n #' Minimal biological score to keep MS1 only annotation. FLOAT\n biological: 0.0\n\n #' Minimal chemical score to keep MS1 only annotation. FLOAT\n chemical: 0.0\n\n #' Condition to be used to retain candidates.\n #' Must be \"OR\" or \"AND\".\n #' Example: Minimum 0.3 biological AND 0.3 chemical. STRING\n condition: OR\n\n #' MS2 annotation related parameters.\n ms2:\n #' For similarity.\n similarity:\n #' For annotation. FLOAT\n annotation: 0.0\n\n #' For edges. FLOAT\n edges: 0.7\n\n#' Files.\nfiles:\n #' Pattern to identify the files. STRING\n pattern: example\n\n #' Annotations files.\n annotations:\n #' Raw annotations files.\n raw:\n #' List of spectral matching annotations.\n spectral:\n #' GNPS spectral matching annotations. STRING\n gnps: data/interim/annotations/example_gnps.tsv\n\n #' Spectral spectral matching annotations. STRING\n spectral: data/interim/annotations/example_spectralMatches.tsv.gz\n\n #' Directory containing the sirius annotations. STRING\n sirius: data/interim/annotations/example_sirius\n\n #' List of filtered annotations. STRING\n filtered: data/interim/annotations/example_annotationsFiltered.tsv.gz\n\n #' List of prepared annotations.\n prepared:\n #' List of prepared canopus annotations. STRING\n canopus: data/interim/annotations/example_canopusPrepared.tsv.gz\n\n #' List of prepared formula annotations. STRING\n formula: data/interim/annotations/example_formulaPrepared.tsv.gz\n\n #' List of prepared structural annotations.\n structural:\n #' Prepared GNPS structural annotations. STRING\n gnps: data/interim/annotations/example_gnpsPrepared.tsv.gz\n\n #' Prepared MS1 structural annotations. STRING\n ms1: data/interim/annotations/example_ms1Prepared.tsv.gz\n\n #' Prepared SIRIUS structural annotations. STRING\n sirius: data/interim/annotations/example_siriusPrepared.tsv.gz\n\n #' Prepared SPECTRAL structural annotations. STRING\n spectral: data/interim/annotations/example_spectralMatchesPrepared.tsv.gz\n\n #' Final results file. STRING\n processed: example_results.tsv\n\n #' Features files.\n features:\n #' Raw features file. STRING\n raw: data/source/example_features.csv\n\n #' Prepared features file. STRING\n prepared: data/interim/features/example_features.tsv.gz\n\n #' Libraries files.\n libraries:\n #' Structure organism pairs libraries.\n sop:\n #' Raw structure organism pairs libraries.\n raw:\n #' Closed structure organism pairs library. STRING\n closed: ../lotus-processor/data/processed/230106_closed_metadata.csv.gz\n\n #' ECMDB structure organism pairs library. STRING\n ecmdb: data/source/libraries/sop/ecmdb.json.zip\n\n #' HMDB structure organism pairs library. STRING\n hmdb: data/source/libraries/sop/hmdb/structures.zip\n\n #' Lotus raw structure organism pairs library. STRING\n lotus: data/source/libraries/sop/lotus.csv.gz\n\n #' List of prepared structure organism pairs libraries.\n prepared:\n #' Closed prepared structure organism pairs library. STRING\n closed: data/interim/libraries/sop/closed_prepared.tsv.gz\n\n #' ECMDB prepared structure organism pairs library. STRING\n ecmdb: data/interim/libraries/sop/ecmdb_prepared.tsv.gz\n\n #' HMDB prepared structure organism pairs library. STRING\n hmdb: data/interim/libraries/sop/hmdb_prepared.tsv.gz\n\n #' Lotus prepared structure organism pairs library. STRING\n lotus: data/interim/libraries/sop/lotus_prepared.tsv.gz\n\n #' RT prepared structure organism pairs library. STRING\n rt: data/interim/libraries/sop/rt_prepared.tsv.gz\n\n #' Spectral prepared structure organism pairs library. STRING\n spectral: data/interim/libraries/sop/spectral_prepared.tsv.gz\n\n #' Merged structure organism pairs library.\n merged:\n #' Merged structure organism pairs library keys. STRING\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n\n #' Organisms libraries.\n organisms:\n #' Organisms names. STRING\n names: data/interim/libraries/sop/merged/organisms/names.tsv.gz\n\n #' Organisms taxonomies.\n taxonomies:\n #' OTT organisms taxonomy. STRING\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n\n #' Structures libraries.\n structures:\n #' Structures stereo. STRING\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n\n #' Structures metadata. STRING\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n\n #' Structures names. STRING\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n\n #' Structures taxonomies.\n taxonomies:\n #' Classyfire structures taxonomy. STRING\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n\n #' NPClassifier structures taxonomy. STRING\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n\n #' Spectral libraries. STRING\n spectral:\n #' List of negative spectral libraries. STRING\n neg:\n - data/interim/libraries/spectra/exp/internal_neg.rds\n \n #' List of positive spectral libraries. STRING\n pos:\n - data/interim/libraries/spectra/exp/internal_pos.rds\n\n #' List of raw spectral libraries. STRING\n raw: \n - data/source/libraries/spectra/exp/spectral_lib_mini_with_rt.mgf\n\n #' Temporal libraries. STRING\n temporal:\n #' List of experimental temporal libraries.\n exp:\n #' List of experimental temporal libraries (CSV). STRING\n csv: ~\n\n #' List of experimental temporal libraries (MGF).\n mgf:\n #' List of negative experimental temporal libraries (MGF). STRING\n neg: [ ]\n\n #' List of positive experimental temporal libraries (MGF). STRING\n pos: [ ]\n\n #' List of in silico temporal libraries. STRING\n is:\n #' List of in silico temporal libraries (CSV). STRING\n csv: ~\n\n #' List of in silico temporal libraries (MGF).\n mgf:\n #' List of negative in silico temporal libraries (MGF). STRING\n neg: [ ]\n\n #' List of positive in silico temporal libraries (MGF). STRING\n pos: [ ]\n\n #' List of prepared temporal libraries. STRING\n prepared:\n - data/interim/libraries/rt/prepared.tsv.gz\n\n #' Metadata files.\n metadata:\n #' The path to the file containing your metadata.\n #' Must be formatted accordingly.\n #' If your experiment contains a single taxon, you can provide it below instead. STRING\n raw: data/source/example_metadata.tsv\n\n #' Taxonomically informed features file. STRING\n prepared: data/interim/taxa/example_taxed.tsv.gz\n\n #' Networks files.\n networks:\n #' Spectral networks files.\n spectral:\n #' Edges files.\n edges:\n #' Raw edges file.\n raw:\n #' Raw ms1 edges file. STRING\n ms1: data/interim/features/example_edgesMasses.tsv\n\n #' Raw spectral edges file. STRING\n spectral: data/interim/features/example_edgesSpectra.tsv\n\n #' Prepared edges file. STRING\n prepared: data/interim/features/example_edges.tsv\n\n #' Components files.\n components:\n #' Raw components file. STRING\n raw: data/interim/features/example_components.tsv\n\n #' Prepared components file. STRING\n prepared: data/interim/features/example_componentsPrepared.tsv\n\n #' Spectral files.\n spectral:\n #' Raw spectral file. STRING\n raw: data/source/example_spectra.mgf\n\n#' GNPS related parameters.\ngnps:\n #' GNPS job ID. STRING\n id: 41c6068e905d482db5698ad81d145d7c\n\n #' Workflow used within GNPS.\n #' Currently \"fbmn\" or \"classical\" supported. STRING\n workflow: fbmn\n\n#' MS related parameters.\nms:\n #' List of adducts to be used.\n adducts:\n #' List of adducts to be used in negative. STRING\n neg:\n - \"[M-H3]3-\"\n - \"[M-H2]2-\"\n - \"[M-H]-\"\n - \"[M+F]-\"\n - \"[M+Na-H2]-\"\n - \"[M+Cl]-\"\n - \"[M+K-H2]-\"\n - \"[M+Br]-\"\n - \"[2M-H]-\"\n - \"[3M-H]-\"\n\n #' List of adducts to be used in positive. STRING\n pos:\n - \"[M+H3]3+\"\n - \"[M+H2Na]3+\"\n - \"[M+HNa2]3+\"\n - \"[M+Na3]3+\"\n - \"[M+H2]2+\"\n - \"[M+HNa]2+\"\n - \"[M+Mg]2+\"\n - \"[M+HK]2+\"\n - \"[M+Ca]2+\"\n - \"[M+Na2]2+\"\n - \"[M+Fe]2+\"\n - \"[M+H]+\"\n - \"[M+H4N]+\"\n - \"[M+Na]+\"\n - \"[M+K]+\"\n - \"[M+Cu]+\"\n - \"[2M+Mg]2+\"\n - \"[2M+Ca]2+\"\n - \"[2M+Fe]2+\"\n - \"[2M+H]+\"\n - \"[2M+H4N]+\"\n - \"[2M+Na]+\"\n - \"[2M+K]+\"\n\n #' List of clusters to be used.\n clusters:\n #' List of clusters to be used in negative. STRING\n neg:\n - \"H2O\" # (water)\n - \"CH2O2\" # (formic)\n - \"NaCl\" # (sodium chloride)\n - \"C2H4O2\" # (acetic)\n - \"H2PO4\" # (phosphoric)\n - \"C2HF3O2\" # (tfa)\n\n #' List of clusters to be used in positive. STRING\n pos:\n - \"H2O\" # (water)\n # - CH4O # (methanol)\n - \"C2H3N\" # (acetonitrile)\n # - C2H7N # (ethylamine)\n # - C2H6O # (ethanol)\n - \"NaCl\" # (sodium chloride)\n # - C3H8O # (isopropanol)\n # - C2H6OS # (dmso)\n\n #' List of neutral losses to be used. STRING\n neutral_losses:\n - \"HN\"\n - \"CH3\"\n - \"O\"\n - \"H3N (ammonia)\"\n - \"H2O (water)\"\n - \"CHN\"\n - \"CO\"\n - \"C2H4 (2xCH2)\"\n - \"C2H5 (CH2-CH3)\"\n - \"CH2O\"\n - \"H5ON (H2O-H3N)\"\n - \"H4O2 (2xH2O)\"\n - \"C2H2O\"\n - \"CHON\"\n - \"CO2\"\n - \"CHO2\"\n - \"H6O3 (3xH2O)\"\n - \"C2O2 (2xCO)\"\n - \"CH6O3 (combination)\"\n - \"H8O4 (4xH2O)\"\n - \"C2H2O3 (CH2O-CO2)\"\n - \"C3H6O2 (CH3-CH2-COOH)\"\n - \"C3O3 (3xCO)\"\n - \"C4H9NO (combination)\"\n - \"C2O4 (2xCO2)\"\n - \"C3H4O3 (pyruvic)\"\n - \"C4H8O2 (butyric)\"\n - \"C3H7NO2 (alanine)\"\n - \"C3H6O3 (sugar)\"\n - \"H2O4S (sulfuric)\"\n - \"H3O4P (phosphoric)\"\n - \"C5H10O2 (valeric)\"\n - \"C3H4O4 (malonic)\"\n - \"C2H2O5 (CO2-CO2-H2O)\"\n - \"C3H8O4 (combination)\"\n - \"C5H9NO2 (proline)\"\n - \"C4O4H6 (CO2-CO2-CH3-CH3)\"\n - \"C4H8O4 (sugar)\"\n - \"C2H4O6 (CO2-CO2-H2O-H2O)\"\n - \"C6H6O3 (HRF)\"\n - \"C5H8O4 (pentose-H2O)\"\n - \"C8H8O2 (RDA-1)\"\n - \"C6H8O4 (HRF)\"\n - \"C9H6O2 (coumaroyl)\"\n - \"C6H10O4 (methylpentose/desoxyhexose-H2O)\"\n - \"C9H8O2 (cinnamoyl)\"\n - \"C7H4O4 (galloyl)\"\n - \"C8H8O3 (RDA-2)\"\n - \"C9H6O3 (caffeoyl)\"\n - \"C6H10O5 (hexose-H2O)\"\n - \"C8H8O4 (RDA-3)\"\n - \"C8H10O4 (RDA-2-H2O)\"\n - \"C6H8O6 (glucuronic-H2O)\"\n - \"C10H8O3 (feruloyl)\"\n - \"C6H13NO5 (hexose-H2N)\"\n - \"C6H12O6 (hexose)\"\n - \"C7H12O6 (quinoyl)\"\n - \"C6H15NO6 (hexose-H2N-H2O)\"\n - \"C6H14O7 (hexose-H2O)\"\n - \"C8H12O6 (acetylhexose-H2O)\"\n - \"C11H10O4 (sinapoyl)\"\n - \"C16H30O (pamitoyl)\"\n - \"C9H12O8 (malonylhexose)\"\n - \"C13H14O6 (benzoylhexose)\"\n - \"C12H20O8 (2xmethylpentose/desoxyhexose-H2O)\"\n - \"C15H16O7 (coumaroylhexose)\"\n - \"C13H14O9 (galloylhexose)\"\n - \"C15H16O8 (caffeoylhexose)\"\n - \"C12H20O10 (2xhexose-H2O)\"\n - \"C16H18O8 (feruloylhexose)\"\n - \"C17H20O9 (sinapoylhexose)\"\n - \"C18H30O15 (3xhexose-H2O)\"\n\n #' Polarity used. Must be \"pos\" or \"neg\". STRING\n polarity: pos\n\n #' Thresholds related parameters.\n thresholds:\n #' MS2 related parameters.\n ms2:\n #' Intensity threshold for MS2. INTEGER\n intensity: 5\n\n #' Tolerances.\n tolerances:\n #' For mass.\n mass:\n #' Relative in ppm.\n ppm:\n #' Relative mass tolerance for MS1 in ppm. FLOAT\n ms1: 10\n\n #' Relative mass tolerance for MS2 in ppm. FLOAT\n ms2: 10\n\n #' Absolute in Dalton.\n dalton:\n #' Absolute mass tolerance for MS1 in Dalton. FLOAT\n ms1: 0.01\n\n #' Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms2: 0.01\n\n #' For retention time.\n rt:\n #' Retention time tolerance for adducts in minutes. FLOAT\n adducts: 0.02\n\n #' Retention time tolerance for library in minutes. FLOAT\n library: 0.1\n\n#' Names of the different variables in the input files.\nnames:\n #' Name of \"adduct\" variable in the feature table. STRING\n adduct: best ion\n\n #' The file(s) extension is present in the sample name. BOOLEAN\n extension: yes\n\n #' Name of \"feature IDs\" variable in the input. STRING\n features: row ID\n\n #' Name of \"filename\" variable in the input. STRING\n filename: filename\n\n #' Names of the prepared libraries. STRING\n libraries:\n - internal\n\n #' Name of \"InChIKey\" variable in the input. STRING\n inchikey: inchikey\n\n #' Name of fields present in the MGF.\n mgf:\n #' Name of \"adduct\" in MGF. STRING\n adduct: ~\n\n #' Name of \"collision energy\" in MGF. STRING\n collision_energy: ~\n\n #' Name of \"compound ID\" in MGF. STRING\n compound_id: FILENAME\n\n #' Name of \"exact mass\" in MGF. STRING\n exact_mass: EXACTMASS\n\n #' Name of \"InChI\" in MGF. STRING\n inchi: INCHI\n\n #' Name of \"InChI without stereo\" in MGF. STRING\n inchi_no_stereo: ~\n\n #' Name of \"InChIKey\" in MGF. STRING\n inchikey: ~\n\n #' Name of \"InChIKey without stereo\" in MGF. STRING\n inchikey_no_stereo: ~\n\n #' Name of \"molecular formula\" in MGF. STRING\n molecular_formula: ~\n\n #' Name of \"name\" in MGF. STRING\n name: NAME\n\n #' Name of \"polarity\" in MGF. STRING\n polarity: IONMODE\n\n #' Name of \"retention time\" in MGF. STRING\n retention_time: RTINSECONDS\n\n #' Name of \"SMILES\" in MGF. STRING\n smiles: SMILES\n\n #' Name of \"SMILES without stereo\" in MGF. STRING\n smiles_no_stereo: ~\n\n #' Name of \"spectrum ID\" in MGF. STRING\n spectrum_id: SPECTRUMID\n\n #' Name of \"SPLASH\" in MGF. STRING\n splash: ~\n\n #' Name of \"synonyms\" in MGF. STRING\n synonyms: ~\n\n #' Name of \"xlogP\" in MGF. STRING\n xlogp: ~\n\n #' Name of \"precursor m/z\" variable in the input. STRING\n precursor: row m/z\n\n #' Name of \"retention time\" variable.\n rt:\n #' Name of \"retention time\" variable in the feature table. STRING\n features: row retention time\n\n #' Name of \"retention time\" variable in the library. STRING\n library: rt\n\n #' Name of \"SMILES\" variable in the input. STRING\n smiles: smiles\n\n #' Name of \"source IDs\" variable in the input. STRING\n source: CLUSTERID1\n\n #' Name of \"target IDs\" variable in the input. STRING\n target: CLUSTERID2\n\n #' Name of the \"taxon name\" variable in the input. STRING\n taxon: ATTRIBUTE_species\n\n#' Organisms related parameters.\norganisms:\n #' Number of organisms to keep per feature. INTEGER\n candidates: 1\n\n #' Organisms filter related parameters.\n filter:\n #' Filter library to restrict it to a portion of organisms only. BOOLEAN\n mode: no\n\n #' Level at which the library will be filtered.\n #' Must be one of \"domain\", \"phylum\", \"class\", \"order\",\n #' \"family\", \"tribe\", \"genus\", \"species\", \"varietas\" . STRING\n level: phylum\n\n #' Value to be applied for filtering.\n #' \"OR\" can be used as \"|\".\n # Example: \"Simaroubaceae|Gentianaceae\". STRING\n value: Streptophyta\n\n #' OPTIONAL. If your experiment contains a single taxon, its scientific name.\n #' Else, you will need to provide a metadata file above.\n #' If you provide both, this one will have priority.\n #' Example: \"Homo sapiens\". STRING\n taxon: ~\n\n#' Tools used for each step.\ntools:\n #' Tool used to store metadata.\n #' Currently \"gnps\" or \"manual\" supported. STRING\n metadata: gnps\n\n #' Networks related parameters.\n networks:\n #' Spectral networks related parameters.\n spectral:\n #' Tool used to generate components.\n #' Currently \"gnps\" or \"manual\" supported. STRING\n components: gnps\n\n #' Tool used to generate edges.\n #' Currently \"gnps\" or \"manual\" supported. STRING\n edges: gnps\n\n #' Sirius.\n sirius:\n #' Sirius version.\n #' Currently only 5 or 6 supported. INTEGER\n version: 6\n\n #' Taxonomies related parameters.\n taxonomies:\n #' Biological taxonomy used for matching.\n #' Currently only \"otl\" supported. STRING\n biological: otl\n\n #' Chemical taxonomy used for matching.\n #' Currently only \"npc\" or supported. STRING\n chemical: npc\n\n#' Units of the different variables in the input files.\nunits:\n #' Unit of the \"retention time\" variable in the input files. STRING\n rt: seconds\n\n#' Weights to apply to each part of the final score.\nweights:\n #' Global weights.\n global:\n #' Weight for the biological part. FLOAT\n biological: 0.500\n\n #' Weight for the chemical part. FLOAT\n chemical: 0.166\n\n #' Weight for the spectral part. FLOAT\n spectral: 0.333\n\n #' Weights for the biological part.\n biological:\n #' Score for a biological domain match. FLOAT\n domain: 0.10\n\n #' Score for a biological kingdom match. FLOAT\n kingdom: 0.20\n\n #' Score for a biological phylum match. FLOAT\n phylum: 0.30\n\n #' Score for a biological class match. FLOAT\n class: 0.40\n\n #' Score for a biological order match. FLOAT\n order: 0.50\n\n #' Score for a biological infraorder match. FLOAT\n infraorder: 0.55\n\n #' Score for a biological family match. FLOAT\n family: 0.60\n\n #' Score for a biological subfamily match. FLOAT\n subfamily: 0.65\n\n #' Score for a biological tribe match. FLOAT\n tribe: 0.70\n\n #' Score for a biological subtribe match. FLOAT\n subtribe: 0.75\n\n #' Score for a biological genus match. FLOAT\n genus: 0.80\n\n #' Score for a biological subgenus match. FLOAT\n subgenus: 0.85\n\n #' Score for a biological species match. FLOAT\n species: 0.90\n\n #' Score for a biological subspecies match. FLOAT\n subspecies: 0.95\n\n #' Score for a biological variety match. FLOAT\n variety: 1.00\n\n #' Weights for the chemical part.\n chemical:\n #' Score for a classyfire match.\n cla:\n #' Score for a chemical kingdom match. FLOAT\n kingdom: 0.25\n\n #' Score for a chemical superclass match. FLOAT\n superclass: 0.50\n\n #' Score for a chemical class match. FLOAT\n class: 0.75\n\n #' Score for a chemical parent match. FLOAT\n parent: 1.00\n\n #' Score for a NPClassifier match.\n npc:\n #' Score for a chemical kingdom match. FLOAT\n pathway: 0.333\n\n #' Score for a chemical superclass match. FLOAT\n superclass: 0.666\n\n #' Score for a chemical class match. FLOAT\n class: 1.000\n\n#' Options.\noptions:\n #' Report compounds names. Can be very large. BOOLEAN\n compounds_names: no\n\n #' Do not use it. BOOLEAN\n force: no\n\n #' Filter high confidence candidates only. BOOLEAN\n high_confidence: yes\n\n #' Remove ties. BOOLEAN\n remove_ties: no\n\n #' Summarise results to one row per feature. BOOLEAN\n summarise: no\n","type":"text"},{"name":"paths.yaml","content":"---\nbase_dir: ./\ndata:\n path: data\n interim:\n path: data/interim\n annotations:\n path: data/interim/annotations\n example_sirius:\n v5: data/interim/annotations/example_sirius_5.zip\n v6: data/interim/annotations/example_sirius_6.zip\n features:\n path: data/interim/features\n libraries:\n path: data/interim/libraries\n adducts:\n path: data/interim/libraries/adducts\n neg: data/interim/libraries/adducts/neg.tsv.gz\n pos: data/interim/libraries/adducts/pos.tsv.gz\n sop:\n path: data/interim/libraries/sop\n closed: data/interim/libraries/sop/closed_prepared.tsv.gz\n hmdb: data/interim/libraries/sop/hmdb_prepared.tsv.gz\n hmdb_minimal: data/interim/libraries/sop/hmdb_minimal.tsv.gz\n lotus: data/interim/libraries/sop/lotus_prepared.tsv.gz\n merged:\n path: data/interim/libraries/sop/merged\n keys: data/interim/libraries/sop/merged/keys.tsv.gz\n organisms:\n path: data/interim/libraries/sop/merged/organisms\n names: data/interim/libraries/sop/merged/organisms/names.tsv.gz\n taxonomies:\n path: data/interim/libraries/sop/merged/organisms/taxonomies\n ott: data/interim/libraries/sop/merged/organisms/taxonomies/ott.tsv.gz\n structures:\n path: data/interim/libraries/sop/merged/structures\n stereo: data/interim/libraries/sop/merged/structures/stereo.tsv.gz\n metadata: data/interim/libraries/sop/merged/structures/metadata.tsv.gz\n names: data/interim/libraries/sop/merged/structures/names.tsv.gz\n taxonomies:\n path: data/interim/libraries/sop/merged/structures/taxonomies\n cla: data/interim/libraries/sop/merged/structures/taxonomies/classyfire.tsv.gz\n npc: data/interim/libraries/sop/merged/structures/taxonomies/npc.tsv.gz\n spectra:\n path: data/interim/libraries/spectra\n exp:\n path: data/interim/libraries/spectra/exp\n mona:\n pos: data/interim/libraries/spectra/exp/mona_pos.rds\n neg: data/interim/libraries/spectra/exp/mona_neg.rds\n is:\n path: data/interim/libraries/spectra/is\n hmdb:\n pos: data/interim/libraries/spectra/is/hmdb_pos.rds\n neg: data/interim/libraries/spectra/is/hmdb_neg.rds\n lotus:\n pos: data/interim/libraries/spectra/is/lotus_pos.rds\n neg: data/interim/libraries/spectra/is/lotus_neg.rds\n params:\n path: data/interim/params\n taxa:\n path: data/interim/taxa\n processed:\n path: data/processed\n source:\n path: data/source\n features: data/source/example_features.csv\n metadata: data/source/example_metadata.tsv\n spectra: data/source/example_spectra.mgf\n benchmark:\n path: data/source/benchmark\n cleaned: data/interim/benchmark/cleaned_libraries_matchms/results_library_cleaning/cleaned_spectra.mgf\n mgf:\n neg: data/interim/benchmark/benchmark_neg.mgf\n pos: data/interim/benchmark/benchmark_pos.mgf\n zip: data/source/benchmark/cleaned_libraries_matchms.zip\n libraries:\n path: data/source/libraries\n rt:\n path: data/source/libraries/rt\n example_mini: data/source/libraries/rt/example_lib_mini_rt.tsv\n sop:\n path: data/source/libraries/sop\n closed: ../lotus-processor/data/processed/230224_closed_metadata.csv.gz\n ecmdb: data/source/libraries/sop/ecmdb.json.zip\n hmdb: data/source/libraries/sop/hmdb/structures.zip\n lotus: data/source/libraries/sop/lotus.csv.gz\n spectra:\n path: data/source/libraries/spectra\n exp:\n path: data/source/libraries/spectra/exp\n mona_lcmsms: data/source/libraries/spectra/exp/MoNA-export-LC-MS-MS_Spectra.sdf\n with_rt: data/source/libraries/spectra/exp/spectral_lib_mini_with_rt.mgf\n is:\n hmdb: data/source/libraries/spectra/is/hmdb_predicted_msms_spectra.zip\n lotus:\n pos: data/source/libraries/spectra/is/isdb_pos.mgf\n neg: data/source/libraries/spectra/is/isdb_neg.mgf\ndoc:\n path: doc\nimg:\n path: img\ninst:\n path: inst\n scripts:\n path: scripts\n docopt:\n path: scripts/docopt\ngnps:\n example: 41c6068e905d482db5698ad81d145d7c\n example2: 958d60d32e0c43bc9a06798b744f41c7\nparams:\n path: params\n default:\n path: params/default\n annotate:\n masses: params/default/annotate_masses.yaml\n spectra: params/default/annotate_spectra.yaml\n create:\n components: params/default/create_components.yaml\n edges:\n spectra: params/default/create_edges_spectra.yaml\n filter:\n annotations: params/default/filter_annotations.yaml\n prepare:\n features:\n components: params/default/prepare_features_components.yaml\n edges: params/default/prepare_features_edges.yaml\n tables: params/default/prepare_features_tables.yaml\n libraries:\n rt: params/default/prepare_libraries_rt.yaml\n sop:\n closed: params/default/prepare_libraries_sop_closed.yaml\n ecmdb: params/default/prepare_libraries_sop_ecmdb.yaml\n hmdb: params/default/prepare_libraries_sop_hmdb.yaml\n lotus: params/default/prepare_libraries_sop_lotus.yaml\n merged: params/default/prepare_libraries_sop_merged.yaml\n spectra: params/default/prepare_libraries_spectra.yaml\n annotations:\n gnps: params/default/prepare_annotations_gnps.yaml\n sirius: params/default/prepare_annotations_sirius.yaml\n spectra: params/default/prepare_annotations_spectra.yaml\n taxa: params/default/prepare_taxa.yaml\n weight:\n annotations: params/default/weight_annotations.yaml\n prepare_params: params/prepare_params.yaml\n prepare_params_advanced: params/prepare_params_advanced.yaml\n user:\n path: params/user\n annotate:\n masses: params/user/annotate_masses.yaml\n spectra: params/user/annotate_spectra.yaml\n create:\n components: params/user/create_components.yaml\n edges:\n spectra: params/user/create_edges_spectra.yaml\n filter:\n annotations: params/user/filter_annotations.yaml\n prepare:\n features:\n components: params/user/prepare_features_components.yaml\n edges: params/user/prepare_features_edges.yaml\n tables: params/user/prepare_features_tables.yaml\n libraries:\n rt: params/user/prepare_libraries_rt.yaml\n sop:\n closed: params/user/prepare_libraries_sop_closed.yaml\n ecmdb: params/user/prepare_libraries_sop_ecmdb.yaml\n hmdb: params/user/prepare_libraries_sop_hmdb.yaml\n lotus: params/user/prepare_libraries_sop_lotus.yaml\n merged: params/user/prepare_libraries_sop_merged.yaml\n spectra: params/user/prepare_libraries_spectra.yaml\n annotations:\n gnps: params/user/prepare_annotations_gnps.yaml\n sirius: params/user/prepare_annotations_sirius.yaml\n spectra: params/user/prepare_annotations_spectra.yaml\n taxa: params/user/prepare_taxa.yaml\n weight:\n annotations: params/user/weight_annotations.yaml\ntests:\n mode: no\n path: tests\nurls:\n benchmarking_set: https://zenodo.org/records/10160791/files/cleaned_libraries_matchms.zip?download=1\n examples:\n features: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_features.csv\n lib_mini:\n rt: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_lib_mini_rt.tsv\n metadata: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_metadata.tsv\n sirius:\n v5: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_sirius_5.zip\n v6: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_sirius_6.zip\n spectra: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_spectra.mgf\n spectra_mini: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_spectra_mini.mgf\n spectra_ms1: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/example_spectra_ms1.mgf\n spectral_lib:\n neg: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/lotus_neg.rds\n pos: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/lotus_pos.rds\n spectral_lib_mini:\n neg: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/spectral_lib_mini_neg.mgf\n pos: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/spectral_lib_mini_pos.mgf\n with_rt: https://github.com/taxonomicallyinformedannotation/tima-example-files/raw/main/spectral_lib_mini_with_rt.mgf\n ecmdb:\n metabolites: https://ecmdb.ca/download/ecmdb.json.zip\n hmdb:\n metabolites: https://hmdb.ca/system/downloads/current/hmdb_metabolites.zip\n spectra:\n predicted: https://hmdb.ca/system/downloads/current/spectral_data/spectra_xml/hmdb_predicted_msms_spectra.zip\n structures: https://hmdb.ca/system/downloads/current/structures.zip\n lotus:\n doi: 10.5281/zenodo.5794106\n pattern: frozen_metadata.csv.gz\n metadata_doi: 10.5281/zenodo.6378223\n metadata_pattern:\n organisms: organism_metadata.tsv.gz\n references: reference_metadata.tsv.gz\n structures: structure_metadata.tsv.gz\n lotus_isdb:\n doi: 10.5281/zenodo.5607185\n pattern:\n pos: isdb_pos.mgf\n neg: isdb_neg.mgf\n massbank:\n file: MassBank_NIST.msp\n url: https://github.com/MassBank/MassBank-data/releases/download\n version: 2024.06\n","type":"text"},{"name":"scripts/annotate_masses.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"annotates based on exact mass (MS1)\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: \", crayon::blue(\"CH\"), crayon::cyan(\"MS\"), \"\\n\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_ms1_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/annotate_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\n \"performs spectral similarity calculation\",\n \"to annotate against a spectral library. \\n\"\n )\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_spe_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/change_tests.R","content":"paths <- yaml::read_yaml(file = system.file(\"paths.yaml\", package = \"tima\"))\n\npaths$tests$mode <- \"yes\"\npaths$urls$examples$spectra <- paths$urls$examples$spectra_mini\n\nyaml::write_yaml(\n x = paths,\n file = system.file(\"paths.yaml\", package = \"tima\")\n)\n","type":"text"},{"name":"scripts/create_components.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"creates components from edges. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_com\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/create_edges_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"performs spectral similarity calculation to create edges. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: \", crayon::blue(\"CH\"), crayon::cyan(\"MS\"), \"\\n\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_edg_spe\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/docopt/annotate_masses.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/annotate_masses.R\n\nUsage:\n annotate_masses.R [--fil-ann-pre-str=<fil-ann-pre-str>] [--fil-lib-sop-mer=<fil-lib-sop-mer>] [--fil-lib-add-neg=<fil-lib-add-neg>] [--fil-lib-add-pos=<fil-lib-add-pos>] [--fil-lib-add-pro=<fil-lib-add-pro>] [--ms-add-neg=<ms-add-neg>] [--ms-add-pos=<ms-add-pos>] [--ms-clu-neg=<ms-clu-neg>] [--ms-clu-pos=<ms-clu-pos>] [--ms-neu=<ms-neu>] [--ms-pol=<ms-pol>] [--ms-tol-mas-ppm-ms1=<ms-tol-mas-ppm-ms1>] [--ms-tol-mas-dal-ms1=<ms-tol-mas-dal-ms1>] [--ms-tol-rt-add=<ms-tol-rt-add>] [--force=<force>]\n\nArguments:\n fil-ann-pre-str List of prepared structural annotations. STRING\n fil-fea-pre Prepared features file. STRING\n fil-lib-add-neg Negative adducts. STRING\n fil-lib-add-pos Positive adducts. STRING\n fil-lib-add-pro Prepared adducts library. STRING\n fil-lib-sop-mer Merged structure organism pairs library. STRING\n\n ms-add-neg List of adducts to be used in negative. STRING\n ms-add-pos List of adducts to be used in positive. STRING\n ms-clu-neg List of clusters to be used in negative. STRING\n ms-clu-pos List of clusters to be used in positive. STRING\n ms-neu List of neutral losses to be used. STRING\n ms-pol Polarity used. Must be \"pos\" or \"neg\". STRING\n ms-tol-mas-ppm-ms1 Relative mass tolerance for MS1 in ppm. FLOAT\n ms-tol-mas-dal-ms1 Absolute mass tolerance for MS1 in Dalton. FLOAT\n ms-tol-rt-add Retention time tolerance for adducts in minutes. FLOAT\n\n force Do not use it. BOOLEAN\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/annotate_spectra.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/annotate_spectra.R --fil-spe-raw data/source/examples/spectra.mgf --fil-lib-spe-pos data/interim/libraries/spectra/is/lotus_pos.rds --fil-ann-raw-spe-spe data/interim/annotations/example_spectralMatches.tsv.gz --ms-tol-mas-dal-ms2 0.01 --ms-tol-mas-ppm-ms2 10 --ann-thr-ms2-sim-ann 0.0 --ms-thr-ms2-int 5 --ann-ms2-app false\n\nUsage:\n annotate_spectra.R [--ann-ms2-app=<ann-ms2-app>] [--ann-thr-ms2-sim-ann=<ann-thr-ms2-sim-ann>] [--fil-ann-raw-spe=<fil-ann-raw-spe>] [--fil-lib-spe-neg=<fil-lib-spe-neg>] [--fil-lib-spe-pos=<fil-lib-spe-pos>] [--fil-spe-raw=<fil-spe-raw>] [--ms-thr-ms2-int=<ms-thr-ms2-int>] [--ms-pol=<ms-pol>] [--ms-tol-mas-ppm-ms2=<ms-tol-mas-ppm-ms2>] [--ms-tol-mas-dal-ms2=<ms-tol-mas-dal-ms2>] [--ms-tol-rt-min=<ms-tol-rt-min>]\n\nArguments:\n ann-ms2-app Perform approximative matching without precursor matching? BOOLEAN\n ann-thr-ms2-sim-ann Similarity threhsold for annotation. FLOAT\n\n fil-ann-raw-spe-spe List of spectral matching results. STRING\n fil-lib-spe-neg List of negative spectral libraries. STRING\n fil-lib-spe-pos List of positive spectral libraries. STRING\n fil-spe-raw Raw spectral file. STRING\n\n ms-pol Polarity used. Must be \"pos\" or \"neg\". STRING\n ms-thr-ms2-int Intensity threshold for MS2. INTEGER\n ms-tol-mas-ppm-ms2 Relative mass tolerance for MS2 in ppm. FLOAT\n ms-tol-mas-dal-ms2 Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms-tol-rt-min Retention time tolerance in minutes. FLOAT\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/create_components.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/create_components.R --fil-net-spe-edg-pre data/interim/features/example_edges.tsv.gz --fil-net-spe-com-raw data/interim/features/example_components.tsv.gz\n\nUsage:\n create_components.R [--fil-net-spe-edg-pre=<fil-net-spe-edg-pre>] [--fil-net-spe-com-raw=<fil-net-spe-com-raw>]\n\nArguments:\n fil-net-spe-edg-pre Prepared edges file. STRING\n fil-net-spe-com-raw Raw components file. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/create_edges_spectra.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/create_edges_spectra.R --fil-spe-raw data/source/examples/spectra.mgf --fil-net-spe-edg-raw data/interim/features/example_edgesSpectra.tsv.gz --ms-tol-mas-dal-ms2 0.01 --ms-tol-mas-ppm-ms2 10 --ann-thr-ms2-sim-edg 0.7 --ms-thr-ms2-int 5\n\nUsage:\n create_edges_spectra.R [--ann-thr-ms2-sim-edg=<ann-thr-ms2-sim-edg>] [--fil-net-spe-edg-raw=<fil-net-spe-edg-raw>] [--fil-spe-raw=<fil-spe-raw>] [--ms-thr-ms2-int=<ms-thr-ms2-int>] [--ms-tol-mas-ppm-ms2=<ms-tol-mas-ppm-ms2>] [--ms-tol-mas-dal-ms2=<ms-tol-mas-dal-ms2>] [--ms-tol-rt-min=<ms-tol-rt-min>]\n\nArguments:\n ann-thr-ms2-sim-edg Similarity threhsold for edges. FLOAT\n\n fil-net-spe-edg-raw Raw edges file. STRING\n fil-spe-raw Raw spectral file. STRING\n\n ms-thr-ms2-int Intensity threshold for MS2. INTEGER\n ms-tol-mas-ppm-ms2 Relative mass tolerance for MS2 in ppm. FLOAT\n ms-tol-mas-dal-ms2 Absolute mass tolerance for MS2 in Dalton. FLOAT\n ms-tol-rt-min Retention time tolerance in minutes. FLOAT\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/filter_annotations.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/filter_annotations.R\n\nUsage:\n filter_annotations.R [--fil-ann-fil=<fil-ann-fil>] [--fil-ann-pre-str=<fil-ann-pre-str>] [--fil-fea-pre=<fil-fea-prer>] [--fil-lib-tem-pre=<fil-lib-tem-pre>] [--ms-tol-rt-lib=<ms-tol-rt-lib>]\n\nArguments:\n fil-ann-fil Filtered annotation file. STRING\n fil-ann-pre-str List of prepared structural annotations. STRING\n fil-fea-pre Prepared features file. STRING\n fil-lib-tem-pre List of prepared temporal libraries. STRING\n\n ms-tol-rt-lib Retention time tolerance for library in minutes. FLOAT\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_annotations_gnps.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_annotations_gnps.R --fil-ann-raw-spe-gnp data/interim/annotations/example_gnps.tsv --fil-ann-pre-str data/interim/annotations/example_gnpsPrepared.tsv.gz\n\nUsage:\n prepare_annotations_gnps.R [--=fil-ann-pre-str<fil-ann-pre-str>] [--gnps-id=<gnps-id>] [--gnps-workflow=<gnps-workflow>]\n\nArguments:\n fil-ann-raw-spe-gnp List of GNPS spectral matching results. STRING\n fil-ann-pre-str List of prepared structural annotations. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_annotations_sirius.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_annotations_sirius.R --fil-ann-raw-sir data/interim/annotations/example_sirius/ --fil-ann-pre-can data/interim/annotations/example_canopusPrepared.tsv.gz --fil-ann-pre-for data/interim/annotations/example_formulaPrepared.tsv.gz --fil-ann-pre-str data/interim/annotations/example_siriusPrepared.tsv.gz\n\nUsage:\n prepare_annotations_sirius.R [--fil-ann-raw-sir=<fil-ann-raw-sir>] [--fil-ann-pre-can=<fil-ann-pre-can>] [--fil-ann-pre-for=<fil-ann-pre-for>] [--fil-ann-pre-str=<fil-ann-pre-str>]\n\nArguments:\n fil-ann-raw-sir Directory containing the sirius results. STRING\n fil-ann-pre-can List of prepared canopus annotations. STRING\n fil-ann-pre-for List of prepared formula annotations. STRING\n fil-ann-pre-str List of prepared structural annotations. STRING\n \nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_annotations_spectra.txt","content":"You can use this script with the following example:\nRscript inst/scripts/prepare_annotations_spectra.R --fil-ann-raw-spe-spe data/interim/annotations/example_isdb.tsv.gz --fil-ann-pre-str data/interim/annotations/example_isdbPrepared.tsv.gz\n\nUsage:\n prepare_annotations_spectra.R [--fil-ann-raw-spe=<fil-ann-raw-spe>] [--fil-ann-pre-str=<fil-ann-pre-str>]\n\nArguments:\n fil-ann-raw-spe-spe List of spectral matching results. STRING\n fil-ann-pre-str List of prepared structural annotations. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_features_components.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_features_components.R\nUsage:\n prepare_features_components.R [--fil-net-spe-com-raw=<fil-net-spe-com-raw>] [--fil-net-spe-com-pre=<fil-net-spe-com-pre>]\n\nArguments:\n fil-net-spe-com-raw Raw components file. STRING\n fil-net-spe-com-pre Prepared components file. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_features_edges.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_features_edges.R --fil-net-spe-edg-pre data/interim/features/example_edges.tsv.gz\n\nUsage:\n prepare_features_edges.R [--fil-net-spe-edg-raw=<fil-net-spe-edg-raw>] [--fil-net-spe-edg-pre=<fil-net-spe-edg-pre>] [--names-source=<names-source>] [--names-target=<names-target>]\n\nArguments:\n fil-net-spe-edg-raw Raw edges file. STRING\n fil-net-spe-edg-pre Prepared edges file. STRING\n\n names-source Name of \"source IDs\" variable in the input. STRING\n names-target Name of \"target IDs\" variable in the input. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_features_tables.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_features_tables.R --fil-fea-raw data/source/example_features.csv --fil-fea-pre data/interim/features/example_features.tsv.gz --names-adduct 'best ion' --names-features 'row ID' --names-precursor 'row m/z' --names-rt 'row retention time'\n\nUsage:\n prepare_features_tables.R [--fil-fea-raw=<fil-fea-raw>] [--fil-fea-pre=<fil-fea-pre>] [--names-features=<names-features>] [--names-precursor=<names-precursor>] [--names-rt-fea=<names-rt-fea>]\n\nArguments:\n fil-fea-raw Raw features file. STRING\n fil-fea-pre Prepared features file. STRING\n\n names-adduct Name of \"adduct\" variable in the input. STRING\n names-features Name of \"feature IDs\" variable in the input. STRING\n names-precursor Name of \"precursor m/z\" variable in the input. STRING\n names-rt-fea Name of \"retention time\" variable (fea) in the input. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_rt.txt","content":"You can use this script with the following example:\nRscript inst/scripts/prepare_libraries_rt.R --fil-lib-tem-exp-csv data/source/libraries/rt/internal.tsv --fil-lib-tem-is-csv data/source/libraries/rt/predicted.tsv --fil-lib-tem-pre data/interim/libraries/rt/prepared.tsv.gz\n\nUsage:\n prepare_libraries_rt.R [--fil-lib-tem-exp-csv=<fil-lib-tem-exp-csv>] [--fil-lib-tem-exp-mgf-neg=<fil-lib-tem-exp-mgf-neg>] [--fil-lib-tem-exp-mgf-pos=<fil-lib-tem-exp-mgf-pos>] [--fil-lib-tem-is-csv=<fil-lib-tem-is-csv>] [--fil-lib-tem-is-mgf-neg=<fil-lib-tem-is-mgf-neg>] [--fil-lib-tem-is-mgf-pos=<fil-lib-tem-is-mgf-pos>] [--fil-lib-tem-pre=<fil-lib-tem-pre>] [--names-inchikey=<names-inchikey>] [--names-mgf-ik=<names-mgf-ik>] [--names-mgf-rt=<names-mgf-rt>] [--names-mgf-sm=<names-mgf-sm>] [--names-rt-lib=<names-rt-lib>] [--names-smiles=<names-smiles>] [--units-rt=<units-rt>]\n\nArguments:\n fil-lib-tem-exp-csv List of experimental temporal libraries (CSV). STRING\n fil-lib-tem-exp-mgf-neg List of negative experimental temporal libraries (MGF). STRING\n fil-lib-tem-exp-mgf-pos List of positive experimental temporal libraries (MGF). STRING\n fil-lib-tem-is-csv List of in silico temporal libraries (CSV). STRING\n fil-lib-tem-is-mgf-neg List of negative in silico temporal libraries. (MGF) STRING\n fil-lib-tem-is-mgf-pos List of positive in silico temporal libraries (MGF). STRING\n fil-lib-tem-pre List of prepared temporal libraries. STRING\n\n names-inchikey Name of \"InChIKey\" variable in the input. STRING\n names-mgf-ik Name of \"InChIKey\" in MGF. STRING\n names-mgf-rt Name of \"retention time\" in MGF. STRING\n names-mgf-sm Name of \"SMILES\" in MGF. STRING\n names-rt-lib Name of \"retention time\" variable (lib) in the input. STRING\n names-smiles Name of \"SMILES\" variable in the input. STRING\n\n units-rt Unit of the \"retention time\" variable in the input. Must be \"seconds\" or \"minutes\". STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_closed.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_closed.R --fil-lib-sop-raw-clo ../lotus-processor/data/processed/220226_closed_metadata.csv.gz --fil-lib-sop-pre data/interim/libraries/sop/closed_prepared.tsv.gz\n\nUsage:\n prepare_libraries_sop_closed.R [--fil-lib-sop-raw-clo=<fil-lib-sop-raw-clo>] [--fil-lib-sop-pre=<fil-lib-sop-pre>]\n\nArguments:\n fil-lib-sop-raw-clo Closed raw structure organism pairs library. STRING\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_ecmdb.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_ecmdb.R --fil-lib-sop-raw-ecm data/source/libraries/sop/ecmdb.json.zip --fil-lib-sop-pre data/interim/libraries/sop/ecmdb_prepared.tsv.gz\n\nUsage:\n prepare_libraries_sop_ecmdb.R [--fil-lib-sop-raw-ecm=<fil-lib-sop-raw-ecm>] [--fil-lib-sop-pre=<fil-lib-sop-pre>]\n\nArguments:\n fil-lib-sop-raw-ecm ECMDB raw structure organism pairs library. STRING\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_hmdb.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_hmdb.R --fil-lib-sop-raw-hmd data/source/libraries/sop/hmdb/structures.zip --fil-lib-sop-pre data/interim/libraries/sop/hmdb_prepared.tsv.gz\n\nUsage:\n prepare_libraries_sop_hmdb.R [--fil-lib-sop-raw-hmd=<fil-lib-sop-raw-hmd>] [--fil-lib-sop-pre=<fil-lib-sop-pre>]\n\nArguments:\n fil-lib-sop-raw-hmd HMDB raw structure organism pairs library. STRING\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_lotus.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_lotus.R --fil-lib-sop-raw-ecm data/source/libraries/sop/lotus.csv.gz --fil-lib-sop-pre data/interim/libraries/sop/lotus_prepared.tsv.gz\n\nUsage:\n prepare_libraries_sop_ecmdb.R [--fil-lib-sop-raw-ecm=<fil-lib-sop-raw-ecm>] [--fil-lib-sop-pre=<fil-lib-sop-pre>]\n\nArguments:\n fil-lib-sop-raw-lot LOTUS raw structure organism pairs library. STRING\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_sop_merged.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_libraries_sop_merged.R --fil-lib-sop-pre data/interim/libraries/sop/lotus_prepared.tsv.gz --fil-lib-sop-mer bitter_db.tsv.gz --org-fil-mod TRUE --org-fil-lev family --org-fil-val 'Simaroubaceae|Gentianaceae'\n\nUsage:\n prepare_libraries_sop_merged.R [--fil-lib-sop-pre=<fil-lib-sop-pre>] [--fil-lib-sop-mer=<fil-lib-sop-mer>] [--org-fil-mod=<org-fil-mod>] [--org-fil-lev=<org-fil-lev>] [--org-fil-val=<org-fil-val>]\n\nArguments:\n fil-lib-sop-pre List of prepared structure organism pairs libraries. STRING\n fil-lib-sop-mer Merged structure organism pairs library. STRING\n \n org-fil-mod Filter library to restrict it to a portion of organisms only. BOOLEAN\n org-fil-lev Level at which the library will be filtered. Must be one of \"domain\", \"phylum\", \"class\", \"order\", \"family\", \"tribe\", \"genus\", \"species\", \"varietas\" . STRING\n org-fil-val Value to be applied for filtering. \"OR\" can be used as \"|\". Example: \"Simaroubaceae|Gentianaceae\". STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_libraries_spectra.txt","content":"You can use this script with the following example:\nRscript inst/scripts/prepare_libraries_spectra.R --fil-lib-spe-raw data/source/libraries/spectra/exp/spectral_lib_mini_with_rt.mgf --names-lib internal --ms-pol pos\n\nUsage:\n prepare_libraries_spectra.R [--fil-lib-spe-raw=<fil-lib-spe-raw>] [--names-lib=<names-lib>] [--ms-pol=<ms-pol>] [--names-mgf-ce=<names-mgf-ce>] [--names-mgf-ci=<names-mgf-ci>] [--names-mgf-em=<names-mgf-em>] [--names-mgf-in=<names-mgf-in>] [--names-mgf-io=<names-mgf-io>] [--names-mgf-ik=<names-mgf-ik>] [--names-mgf-il=<names-mgf-il>] [--names-mgf-mf=<names-mgf-mf>] [--names-mgf-na=<names-mgf-na>] [--names-mgf-po=<names-mgf-po>] [--names-mgf-sm=<names-mgf-sm>] [--names-mgf-sn=<names-mgf-sn>] [--names-mgf-si=<names-mgf-si>] [--names-mgf-sp=<names-mgf-sp>] [--names-mgf-sy=<names-mgf-sy>] [--names-mgf-xl=<names-mgf-xl>] [--names-precursor=<names-precursor>] [--names-rt-lib=<names-rt-lib>] [--names-source=<names-source>] [--names-target=<names-target>] [--names-taxon=<names-taxon>]\n\nArguments:\n fil-lib-spe-raw List of experimental raw spectral libraries. STRING\n\n ms-pol Polarity used. Must be \"pos\" or \"neg\". STRING\n\n names-lib Metadata to identify the library. STRING\n names-mgf-ad Name of \"adduct\" in MGF. STRING\n names-mgf-ce Name of \"collision energy\" in MGF. STRING\n names-mgf-ci Name of \"compound ID\" in MGF. STRING\n names-mgf-em Name of \"exact mass\" in MGF. STRING\n names-mgf-in Name of \"InChI\" in MGF. STRING\n names-mgf-io Name of \"InChI without stereo\" in MGF. STRING\n names-mgf-ik Name of \"InChIKey\" in MGF. STRING\n names-mgf-il Name of \"InChIKey without stereo\" in MGF. STRING\n names-mgf-mf Name of \"molecular formula\" in MGF. STRING\n names-mgf-na Name of \"name\" in MGF. STRING\n names-mgf-po Name of \"polarity\" in MGF. STRING\n names-mgf-sm Name of \"SMILES\" in MGF. STRING\n names-mgf-sn Name of \"SMILES without stereo\" in MGF. STRING\n names-mgf-si Name of \"spectrum ID\" in MGF. STRING\n names-mgf-sp Name of \"SPLASH\" in MGF. STRING\n names-mgf-sy Name of \"synonyms\" in MGF. STRING\n names-mgf-xl Name of \"xlogp\" in MGF. STRING\n names-precursor Name of \"precursor m/z\" variable in the input. STRING\n names-rt-lib Name of \"retention time\" variable (lib) in the input. STRING\n names-source Name of \"source IDs\" variable in the input. STRING\n names-target Name of \"target IDs\" variable in the input. STRING\n names-taxon Name of the \"taxon name\" variable in the input. STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.\n","type":"text"},{"name":"scripts/docopt/prepare_params.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_params.R\n\nUsage:\n prepare_params.R [--fil-pat=<fil-pat>] [--gnps-id=<gnps-id>] [--gnps-workflow=<gnps-workflow>] [--ms-pol=<ms-pol>] [--org-tax=<org-tax>]\n\nArguments:\n fil-pat Pattern to identify the files. STRING\n gnps-id GNPS job ID. STRING\n gnps-workflow Workflow used within GNPS. Currently \"fbmn\" or \"classical\" supported. STRING\n ms-pol Polarity used. Must be \"pos\" or \"neg\". STRING\n org-tax OPTIONAL. Force all features to be attributed to given taxon. Example: \"Homo sapiens\". STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/prepare_taxa.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/prepare_taxa.R --fil-met-pre data/interim/taxa/example_taxed.tsv.gz --org-can 1 --names-extension TRUE --names-features \"row ID\" --names-filename filename --names-taxon ATTRIBUTE_species\n\nUsage:\n prepare_taxa.R [--fil-fea-raw=<fil-fea-raw>] [--fil-met-raw=<fil-met-raw>] [--fil-met-pre=<fil-met-pre>] [--gnps-id=<gnps-id>] [--names-extension=<names-extension>] [--names-features=<names-features>] [--names-filename=<names-filename>] [--names-taxon=<names-taxon>] [--org-can=<org-can>] [--org-tax=<org-tax>] [--too-met=<too-met>]\n\nArguments:\n fil-fea-raw Raw features file. STRING\n fil-met-raw OPTIONAL. If tool is \"manual\". Biological origin metadata file. STRING\n fil-met-pre Taxonomically informed features file. STRING\n\n names-extension The file(s) extension is present in the sample name. BOOLEAN\n names-features Name of \"feature IDs\" variable in the input. STRING\n names-filename Name of \"filename\" variable in the input. STRING\n names-taxon Name of the \"taxon name\" variable in the input. STRING\n\n org-can Number of organisms to keep per feature. INTEGER\n org-tax OPTIONAL. Force all features to be attributed to given taxon. Example: \"Homo sapiens\". STRING\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt/weight_annotations.txt","content":"You can use this script with the following example:\n Rscript inst/scripts/weight_annotations.R\n\nUsage:\n weight_annotations.R [--ann-can-fin=<ann-can-fin>] [--ann-ms1only=<ann-ms1only>] [--ann-thr-ms1-bio=<ann-thr-ms1-bio>] [--ann-thr-ms1-che=<ann-thr-ms1-che>] [--ann-thr-ms1-con=<ann-thr-ms1-con>] [--ann-thr-con=<ann-thr-con>] [--fil-ann-fil=<fil-ann-fil>] [--fil-ann-pre-can=<fil-ann-pre-can>] [--fil-ann-pre-for=<fil-ann-pre-for>] [--fil-ann-pro=<fil-ann-pro>] [--fil-lib-sop-mer=<fil-lib-sop-mer>] [--fil-net-spe-edg-pre=<fil-net-spe-edg-pre>] [--fil-met-pre=<fil-met-pre>] [--wei-glo-bio=<wei-glo-bio>] [--wei-glo-che=<wei-glo-che>] [--wei-glo-spe=<wei-glo-spe>] [--wei-bio-01=<wei-bio-01>] [--wei-bio-02=<wei-bio-02>] [--wei-bio-03=<wei-bio-03>] [--wei-bio-04=<wei-bio-04>] [--wei-bio-05=<wei-bio-05>] [--wei-bio-06=<wei-bio-06>] [--wei-bio-07=<wei-bio-07>] [--wei-bio-08=<wei-bio-08>] [--wei-bio-09=<wei-bio-09>] [--wei-bio-10=<wei-bio-10>] [--wei-bio-11=<wei-bio-11>] [--wei-bio-12=<wei-bio-12>] [--wei-bio-13=<wei-bio-13>] [--wei-bio-14=<wei-bio-14>] [--wei-bio-15=<wei-bio-15>] [--wei-che-11=<wei-che-11>] [--wei-che-12=<wei-che-12>] [--wei-che-13=<wei-che-13>] [--wei-che-14=<wei-che-14>] [--wei-che-21=<wei-che-21>] [--wei-che-22=<wei-che-22>] [--wei-che-23=<wei-che-23>] [--compounds-names=<compounds-names>] [--high-confidence=<high-confidence>] [--remove-ties=<remove-ties>] [--summarise=<summarise>] [--force=<force>]\n\nArguments:\n ann-can-fin Number of final candidates. INTEGER\n ann-ms1only Erase MS2 results and keep MS1 only. BOOLEAN\n ann-thr-ms1-bio Minimal biological score to keep MS1 only annotation. FLOAT\n ann-thr-ms1-che Minimal chemical score to keep MS1 only annotation. FLOAT\n ann-thr-ms1-con Condition to be used to retain candidates. Must be \"OR\" or \"AND\". Example: Minimum 0.5 biological AND 0.5 chemical. STRING\n ann-thr-con Minimal consistency score (chemical) to consider a class. FLOAT\n\n fil-ann-fil Filtered annotation file. STRING\n fil-ann-pre-can List of prepared canopus annotations. STRING\n fil-ann-pre-for List of prepared formula annotations. STRING\n fil-ann-pro Final results file. STRING\n fil-lib-sop-mer Merged structure organism pairs library. STRING\n fil-net-spe-edg-pre Prepared edges file. STRING\n fil-met-pre Taxonomically informed features file. STRING\n\n wei-glo-bio Weight for the biological part. FLOAT\n wei-glo-che Weight for the chemical part. FLOAT\n wei-glo-spe Weight for the spectral part. FLOAT\n\n wei-bio-01 Score for a biological domain match. FLOAT\n wei-bio-02 Score for a biological kingdom match. FLOAT\n wei-bio-03 Score for a biological phylum match. FLOAT\n wei-bio-04 Score for a biological class match. FLOAT\n wei-bio-05 Score for a biological order match. FLOAT\n wei-bio-06 Score for a biological infraorder match. FLOAT\n wei-bio-07 Score for a biological family match. FLOAT\n wei-bio-08 Score for a biological subfamily match. FLOAT\n wei-bio-09 Score for a biological tribe match. FLOAT\n wei-bio-10 Score for a biological subtribe match. FLOAT\n wei-bio-11 Score for a biological genus match. FLOAT\n wei-bio-12 Score for a biological subgenus match. FLOAT\n wei-bio-13 Score for a biological species match. FLOAT\n wei-bio-14 Score for a biological subspecies match. FLOAT\n wei-bio-15 Score for a biological variety match. FLOAT\n\n wei-che-11 Score for a chemical classyfire kingdom match. FLOAT\n wei-che-12 Score for a chemical classyfire superclass match. FLOAT\n wei-che-13 Score for a chemical classyfire class match. FLOAT\n wei-che-14 Score for a chemical classyfire parent match. FLOAT\n wei-che-21 Score for a chemical NPC pathway match. FLOAT\n wei-che-22 Score for a chemical NPC superclass match. FLOAT\n wei-che-23 Score for a chemical NPC class match. FLOAT\n\n compounds-names Report compounds names. Can be very large. BOOLEAN\n force Do not use it. BOOLEAN\n high-confidence Report high confidence candidates only. BOOLEAN\n remove-ties Remove ties. BOOLEAN\n summarise Summarize results (1 row per feature). BOOLEAN\n\nOptions:\n -h --help Shows this screen.\n -v --version Shows version.","type":"text"},{"name":"scripts/docopt.R","content":"start <- Sys.time()\n\nrequire(\n package = \"tima\",\n quietly = TRUE\n)\n\nstep <- \"params\"\npaths <- get_default_paths()\nparams <- get_params(step = step)\n\nlog_debug(\n \"This script\",\n crayon::green(\"is a test script\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/filter_annotations.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script performs\",\n crayon::green(\"filters annotations\"),\n \"based on\",\n crayon::blue(\"retention time matching\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_fil\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_benchmark.R","content":"start <- Sys.time()\n\nrequire(\n package = \"tima\",\n quietly = TRUE\n)\n\npaths <- get_default_paths()\n\nlog_debug(\n \"This script\",\n crayon::green(\n \"downloads benchmarking set\",\n \"from matchms work on GNPS dataset. \\n\"\n )\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_file(\n url = paths$urls$benchmarking_set,\n export = paths$data$source$benchmark$set\n)\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_ecmdb.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\npaths <- get_default_paths()\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads E. coli metabolome database structures. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nlog_debug(\"Downloading E. coli structures (should not take long)\")\nget_file(\n url = paths$urls$ecmdb$metabolites,\n export = paths$data$source$libraries$sop$ecmdb\n)\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_example_features.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads an example of minimal feature table \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_file(\n url = get_default_paths()$urls$examples$features,\n export = get_default_paths()$data$source$features\n)\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_example_metadata.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads an example of metadata table. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_file(\n url = get_default_paths()$urls$examples$metadata,\n export = get_default_paths()$data$source$metadata\n)\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_example_sirius.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads an example of SIRIUS output. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_example_sirius()\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_example_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads an example of prepared MGF file. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\nget_file(\n url = get_default_paths()$urls$examples$spectra,\n export = get_default_paths()$data$source$spectra\n)\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_gnps_tables.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"gets gnps tables\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"gnps_tables\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_hmdb.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\npaths <- get_default_paths()\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads HMDB structures. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\n## TODO check md5 if possible\n## (see https://twitter.com/Adafede/status/1592543895094788096)\nlog_debug(\"Downloading HMDB (might be long)\")\nget_file(\n url = paths$urls$hmdb$structures,\n export = paths$data$source$libraries$sop$hmdb\n)\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_isdb_hmdb.R","content":"start <- Sys.time()\n\nrequire(\n package = \"tima\",\n quietly = TRUE\n)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads HMDB structures in silico fragmented. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\n## TODO check md5 if possible\n## (see https://twitter.com/Adafede/status/1592543895094788096)\nlog_debug(\"Downloading HMDB (might be long)\")\nget_file(\n url = get_default_paths()$urls$hmdb$spectra$predicted,\n export = get_default_paths()$data$source$libraries$spectra$is$hmdb\n)\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_isdb_lotus.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads the In Silico DataBase (ISDB) \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_spe_is_lot\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_lotus.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads LOTUS referenced structure-organism pairs. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_lot$\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/get_massbank_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"downloads MassBank spectra \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_spe_exp_mb_raw\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_annotations_gnps.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"formats GNPS results\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_spe_exp_gnp_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_annotations_sirius.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"formats SIRIUS results\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"^ann_sir_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_annotations_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"formats spectral annotations results\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\n## Not ann_spe because of `ann_spe_int` (see #69)\ntargets::tar_make(names = c(tidyselect::matches(\"^ann_spe_is\")))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_features_components.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares features metadata (mz, rt and component id)\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_com_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_features_edges.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares edges\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_edg_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_features_tables.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares features\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"fea_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_rt.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\n \"prepares a library of retention times\",\n \"from MGF or tabular data. \\n\"\n )\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_rt\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_closed.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares closed referenced structure-organism pairs \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_clo_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_ecmdb.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares ecmdb structure-organism pairs \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_ecm_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_hmdb.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares hmdb structure-organism pairs \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_hmd_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_lotus.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares LOTUS referenced structure-organism pairs \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_lot_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_sop_merged.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares a library from prepared sub-libraries. \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_sop_mer\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_spectra.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"Prepares the internal library spectra \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_spe_exp_int_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_libraries_spectra_is_lotus.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"Prepares the predicted spectra from LOTUS \\n\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"), \"\\n\")\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"lib_spe_is_lot_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_params.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"prepares parameters\")\n)\nlog_debug(\"Authors: \", crayon::green(\"AR\"))\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"par_\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/prepare_taxa.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script\",\n crayon::green(\"informs taxonomically features\")\n)\nlog_debug(\n \"Authors: \",\n crayon::green(\"AR\"),\n \",\",\n crayon::blue(\"PMA\"),\n \"\\n\"\n)\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"tax_pre\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"},{"name":"scripts/tima_full.R","content":"library(\"tima\")\n\ntima_full()\n","type":"text"},{"name":"scripts/weight_annotations.R","content":"start <- Sys.time()\n\nlibrary(tima)\n\nlog_debug(\n \"This script performs\",\n crayon::green(\"taxonomically informed scoring\"),\n \"and followed by\",\n crayon::blue(\"chemical consistency informed scoring\")\n)\nlog_debug(\n \"Authors: \",\n crayon::green(\"AR\"),\n \",\",\n crayon::blue(\"PMA\"),\n \"\\n\"\n)\nlog_debug(\"Contributors: ...\")\n\ntargets::tar_make(names = tidyselect::matches(\"ann_pre$\"))\n\nend <- Sys.time()\n\nlog_debug(\"Script finished in\", crayon::green(format(end - start)))\n","type":"text"}] diff --git a/shinylive/webr/packages/metadata.rds b/shinylive/webr/packages/metadata.rds index 41331edf9a5d90319f533b726ea3c832e5808440..cf9e3634ed70d5d08c22fb07ca3937f35d8f43a5 100644 GIT binary patch literal 6833 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