From c171666849806f0c9a8290310e4365d3390dd38d Mon Sep 17 00:00:00 2001 From: Pepe Marquez Date: Fri, 11 Oct 2024 23:53:30 +0200 Subject: [PATCH] Cleaning some of the folders --- .../getting_started/auto-xrd-analysis.ipynb | 60 +- ...rain_xrd_cnn.ipynb => train-xrd-cnn.ipynb} | 0 .../getting_started/train_analyse.ipynb | 1693 ----------------- 3 files changed, 31 insertions(+), 1722 deletions(-) rename src/nomad_auto_xrd/example_uploads/getting_started/{train_xrd_cnn.ipynb => train-xrd-cnn.ipynb} (100%) delete mode 100644 src/nomad_auto_xrd/example_uploads/getting_started/train_analyse.ipynb diff --git a/src/nomad_auto_xrd/example_uploads/getting_started/auto-xrd-analysis.ipynb b/src/nomad_auto_xrd/example_uploads/getting_started/auto-xrd-analysis.ipynb index 7b95fa8..c1e199b 100644 --- a/src/nomad_auto_xrd/example_uploads/getting_started/auto-xrd-analysis.ipynb +++ b/src/nomad_auto_xrd/example_uploads/getting_started/auto-xrd-analysis.ipynb @@ -1,28 +1,13 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 8, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'AnalysisSettings' from 'nomad_auto_xrd.auto_xrd_analysis' (/home/pepe_marquez/NOMAD/nomad/plugins/nomad-auto-xrd/src/nomad_auto_xrd/auto_xrd_analysis.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnomad_auto_xrd\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauto_xrd_analysis\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 2\u001b[0m AnalysisSettings,\n\u001b[1;32m 3\u001b[0m run_analysis_existing_spectra,\n\u001b[1;32m 4\u001b[0m run_analysis_with_patterns_archive,\n\u001b[1;32m 5\u001b[0m )\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'AnalysisSettings' from 'nomad_auto_xrd.auto_xrd_analysis' (/home/pepe_marquez/NOMAD/nomad/plugins/nomad-auto-xrd/src/nomad_auto_xrd/auto_xrd_analysis.py)" - ] - } - ], "source": [ - "from nomad_auto_xrd.auto_xrd_analysis import (\n", - " AnalysisSettings,\n", - " run_analysis_existing_spectra,\n", - " run_analysis_with_patterns_archive,\n", - ")\n" + "# Running Auto XRD Analysis\n", + "\n", + "We can now run inference in our train models. We will data coming from a NOMAD archive in \n", + "the first example and the mdoels that we have in the archive." ] }, { @@ -32,15 +17,28 @@ "outputs": [], "source": [ "# from nomad_auto_xrd.auto_xrd_analysis import run_analysis\n", - "\n", - "settings = AnalysisSettings(\n", - " # structure_references_directory='References',\n", - " # xrd_model='Models/XRD_Model.h5',\n", - " max_phases=5,\n", - " min_confidence=30,\n", - " include_pdf=True,\n", + "from nomad_auto_xrd.auto_xrd_analysis import (\n", + " AnalysisSettings,\n", + " run_analysis_existing_spectra,\n", + " run_analysis_with_patterns_archive,\n", ")\n", - "run_analysis_with_patterns_archive(settings, 'model_metadata.archive.json', 'xrd_archive.json', 'custom_results.json')" + "\n", + "settings = AnalysisSettings()\n", + "run_analysis_with_patterns_archive(\n", + " settings,\n", + " 'model_metadata.archive.json',\n", + " 'xrd_archive.json',\n", + " 'custom_results.json'\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optionally, we can run it as it is classically done in AutoXRD, specifying the model path,\n", + "the path to the cif files and the path to the folder where the patterns are located in \n", + "`.xy` format. We can also override the default parameters of the model." ] }, { @@ -49,6 +47,7 @@ "metadata": {}, "outputs": [], "source": [ + "\n", "settings = AnalysisSettings(\n", " structure_references_directory='References',\n", " patterns_folder_directory='Spectra',\n", @@ -57,7 +56,10 @@ " min_confidence=30,\n", " include_pdf=True,\n", ")\n", - "run_analysis_existing_spectra(settings, 'model_metadata.archive.json', 'custom_results.json')" + "run_analysis_existing_spectra(\n", + " settings,\n", + " 'model_metadata.archive.json',\n", + " 'custom_results.json')" ] } ], diff --git a/src/nomad_auto_xrd/example_uploads/getting_started/train_xrd_cnn.ipynb b/src/nomad_auto_xrd/example_uploads/getting_started/train-xrd-cnn.ipynb similarity index 100% rename from src/nomad_auto_xrd/example_uploads/getting_started/train_xrd_cnn.ipynb rename to src/nomad_auto_xrd/example_uploads/getting_started/train-xrd-cnn.ipynb diff --git a/src/nomad_auto_xrd/example_uploads/getting_started/train_analyse.ipynb b/src/nomad_auto_xrd/example_uploads/getting_started/train_analyse.ipynb deleted file mode 100644 index acc4d6a..0000000 --- a/src/nomad_auto_xrd/example_uploads/getting_started/train_analyse.ipynb +++ /dev/null @@ -1,1693 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9f03d1fc-643a-470e-b0bf-1f0ebbf7d490", - "metadata": { - "tags": [] - }, - "source": [ - "# Auto XRD analyisis\n", - "\n", - "In this notebook we exemplify how to train an [XRD-AutoAnalyzer](https://github.com/njszym/XRD-AutoAnalyzer) (CNN) model on a chemical space. \n", - "\n", - "Then, we will save the model(s) trainned as an entry in NOMAD, so we can serach for them and reuse them easily. \n", - "\n", - "Once we have done this, we will analyse some of the diffraction patterns that we have already uploaded in NOMAD, to match the phases to the diffraction patterns. " - ] - }, - { - "cell_type": "markdown", - "id": "c96aaa56-0d37-4d79-ae2a-96b8d55902d6", - "metadata": {}, - "source": [ - "## Training the model\n", - "\n", - "The first thing that we need to train the model is a set of structure files for the chemical space that we want to epxlore. Then we will also need to set some parameters for our model, based on the data that we want to evaluate." - ] - }, - { - "cell_type": "markdown", - "id": "18bf7e20", - "metadata": {}, - "source": [ - "First, let's make sure that none of the CIF files are problematic. We will use the `pymatgen` lybrary ot help us with this. " - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "bdfa5db7-aa50-45da-9a4b-a6a098de647f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1224/1222366715.py:17: FutureWarning: get_structures is deprecated\n", - "get_structures is deprecated and will be removed in 2024. Use parse_structures instead.The only difference is that primitive defaults to False in the new parse_structures method.So parse_structures(primitive=True) is equivalent to the old behavior of get_structures().\n", - " structures = parser.get_structures() # Attempt to parse the CIF file\n", - "/home/pepe_marquez/NOMAD/nomad/.pyenv/lib/python3.11/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 2 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/home/pepe_marquez/NOMAD/nomad/.pyenv/lib/python3.11/site-packages/pymatgen/io/cif.py:1138: UserWarning: Incorrect stoichiometry:\n", - " CIF={'Cu': 7.0, 'P': 1.0, 'S': 6.0}\n", - " PMG={'Cu': 27.972000000000012, 'P': 4.0, 'S': 23.855999999999995}\n", - " ratios={'P': 4.0, 'Cu': 3.9960000000000018, 'S': 3.975999999999999}\n", - " warnings.warn(cif_failure_reason)\n", - "/home/pepe_marquez/NOMAD/nomad/.pyenv/lib/python3.11/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 6 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "All files parsed successfully! No files were removed.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/pepe_marquez/NOMAD/nomad/.pyenv/lib/python3.11/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 1 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "from pymatgen.io.cif import CifParser\n", - "\n", - "\n", - "def remove_problematic_cif_files(directory):\n", - " problematic_files = []\n", - "\n", - " # Ensure the directory exists\n", - " if not os.path.isdir(directory):\n", - " print(f\"The directory '{directory}' does not exist.\")\n", - " return\n", - "\n", - " for filename in os.listdir(directory):\n", - " if filename.lower().endswith(('.cif', '.mcif')): # Case-insensitive check\n", - " filepath = os.path.join(directory, filename)\n", - " try:\n", - " parser = CifParser(filepath)\n", - " structures = ( # noqa: F841\n", - " parser.get_structures()\n", - " ) # Attempt to parse the CIF file\n", - " except Exception as e:\n", - " print(f'Problem with file: {filename}, Error: {e}')\n", - " problematic_files.append(filepath)\n", - "\n", - " if problematic_files:\n", - " for file_path in problematic_files:\n", - " try:\n", - " os.remove(file_path)\n", - " print(f'Removed problematic file: {os.path.basename(file_path)}')\n", - " except Exception as e:\n", - " print(f'Failed to remove {os.path.basename(file_path)}. Error: {e}')\n", - " print('\\nAll problematic files have been removed.')\n", - " else:\n", - " print('All files parsed successfully! No files were removed.')\n", - "\n", - "\n", - "# Directory containing CIF files\n", - "cif_directory = 'All_CIFs'\n", - "\n", - "# Call the function to remove problematic files\n", - "remove_problematic_cif_files(cif_directory)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6253d770-f5a3-4219-b51c-f36769974f42", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'cnn' from 'autoXRD' (/home/pepe_marquez/NOMAD/nomad/.pyenv/lib/python3.11/site-packages/autoXRD/__init__.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[9], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mautoXRD\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cnn, solid_solns, spectrum_generation, tabulate_cifs \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'cnn' from 'autoXRD' (/home/pepe_marquez/NOMAD/nomad/.pyenv/lib/python3.11/site-packages/autoXRD/__init__.py)" - ] - } - ], - "source": [ - "import shutil\n", - "import sys\n", - "\n", - "import numpy as np\n", - "from autoXRD import cnn, solid_solns, spectrum_generation, tabulate_cifs # type: ignore" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "7d1f9cb0", - "metadata": {}, - "outputs": [], - "source": [ - "# Define default values for the parameters\n", - "max_texture = 0.5\n", - "min_domain_size = 0.3\n", - "max_domain_size = 30.0\n", - "max_strain = 0.03\n", - "num_spectra = 100\n", - "min_angle = 20.00\n", - "max_angle = 80.00\n", - "max_shift = 0.5\n", - "separate = True\n", - "impur_amt = 70\n", - "skip_filter = True\n", - "include_elems = True\n", - "inc_pdf = True\n", - "num_epochs = 50\n", - "test_fraction = 0.2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7538abf0", - "metadata": {}, - "outputs": [], - "source": [ - "def run_xrd_model( # noqa: PLR0913\n", - " max_texture=max_texture,\n", - " min_domain_size=min_domain_size,\n", - " max_domain_size=max_domain_size,\n", - " max_strain=max_strain,\n", - " num_spectra=num_spectra,\n", - " min_angle=min_angle,\n", - " max_angle=max_angle,\n", - " max_shift=max_shift,\n", - " separate=separate,\n", - " impur_amt=impur_amt,\n", - " skip_filter=skip_filter,\n", - " include_elems=include_elems,\n", - " inc_pdf=inc_pdf,\n", - " num_epochs=num_epochs,\n", - " test_fraction=test_fraction,\n", - "):\n", - " if not skip_filter:\n", - " assert 'All_CIFs' in os.listdir(\n", - " '.'\n", - " ), 'No All_CIFs directory was provided. Please create or use --skip_filter'\n", - " assert 'References' not in os.listdir(\n", - " '.'\n", - " ), 'References directory already exists. Please remove or use --skip_filter'\n", - "\n", - " # Clean up the Filtered_CIFs directory if it exists\n", - " filtered_cif_directory = 'Filtered_CIFs'\n", - " if os.path.exists(filtered_cif_directory):\n", - " shutil.rmtree(filtered_cif_directory)\n", - "\n", - " tabulate_cifs.main('All_CIFs', 'References', include_elems)\n", - " else:\n", - " assert 'References' in os.listdir(\n", - " '.'\n", - " ), '--skip_filter was specified, but no References directory was provided'\n", - "\n", - " # Manually remove '.ipynb_checkpoints' from 'Filtered_CIFs' before running the rest\n", - " filtered_cif_directory = 'Filtered_CIFs'\n", - " if os.path.exists(filtered_cif_directory):\n", - " for item in os.listdir(filtered_cif_directory):\n", - " item_path = os.path.join(filtered_cif_directory, item)\n", - " if os.path.isdir(item_path):\n", - " shutil.rmtree(\n", - " item_path\n", - " ) # Remove any directories like .ipynb_checkpoints\n", - "\n", - " # Optionally, generate hypothetical solid solutions\n", - " solid_solns.main('References')\n", - "\n", - " # Remove the Models directory if it exists\n", - " models_directory = 'Models'\n", - " if os.path.exists(models_directory):\n", - " shutil.rmtree(models_directory)\n", - "\n", - " # Simulate and save augmented XRD spectra\n", - " xrd_obj = spectrum_generation.SpectraGenerator(\n", - " 'References',\n", - " num_spectra,\n", - " max_texture,\n", - " min_domain_size,\n", - " max_domain_size,\n", - " max_strain,\n", - " min_angle,\n", - " max_angle,\n", - " separate,\n", - " )\n", - " xrd_specs = xrd_obj.augmented_spectra\n", - " np.save('XRD', xrd_specs)\n", - "\n", - " # Train, test, and save the CNN\n", - " cnn.main(\n", - " xrd_specs,\n", - " num_epochs=50,\n", - " testing_fraction=0.2,\n", - " is_pdf=False,\n", - " )\n", - "\n", - " # If specified, train another model on PDFs\n", - " if inc_pdf:\n", - " pdf_obj = spectrum_generation.SpectraGenerator(\n", - " 'References',\n", - " num_spectra,\n", - " max_texture,\n", - " min_domain_size,\n", - " max_domain_size,\n", - " max_strain,\n", - " max_shift,\n", - " impur_amt,\n", - " min_angle,\n", - " max_angle,\n", - " separate,\n", - " is_pdf=True,\n", - " )\n", - " pdf_specs = pdf_obj.augmented_spectra\n", - "\n", - " # Save PDFs if flag is specified\n", - " if '--save' in sys.argv:\n", - " np.save('PDF', np.array(pdf_specs))\n", - "\n", - " # Move trained XRD model to new directory\n", - " os.mkdir('Models')\n", - " os.rename('Model.h5', 'Models/XRD_Model.h5')\n", - "\n", - " # Train, test, and save the CNN\n", - " test_fraction = 0.2\n", - " cnn.main(pdf_specs, num_epochs, test_fraction, is_pdf=True)\n", - " os.rename('Model.h5', 'Models/PDF_Model.h5')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "786e83de", - "metadata": {}, - "outputs": [], - "source": [ - "def get_cif_files_from_folder(folder_name):\n", - " \"\"\"Returns a list of CIF files with their full paths in the specified folder\"\"\"\n", - " cif_files_names = []\n", - " for file in os.listdir(folder_name):\n", - " if file.endswith('.cif'):\n", - " full_path = os.path.join(folder_name, file)\n", - " cif_files_names.append(full_path)\n", - " return cif_files_names\n", - "\n", - "\n", - "# now one function to get thhe file objects\n", - "def get_cif_files_from_folder(folder_name): # noqa: F811\n", - " \"\"\"Returns a list of CIF files names in the specified folder\"\"\"\n", - " cif_files = []\n", - " for file in os.listdir(folder_name):\n", - " if file.endswith('.cif'):\n", - " cif_files.append(file)\n", - " return cif_files" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e4946b75", - "metadata": {}, - "outputs": [], - "source": [ - "def get_cif_files_from_folder(folder_name): # noqa: F811\n", - " \"\"\"Returns a list of CIF files with their full paths in the specified folder\"\"\"\n", - " cif_files_names = []\n", - " for file in os.listdir(folder_name):\n", - " if file.endswith('.cif'):\n", - " full_path = os.path.join(folder_name, file)\n", - " cif_files_names.append(full_path)\n", - " return cif_files_names" - ] - }, - { - "cell_type": "markdown", - "id": "364d0b47", - "metadata": {}, - "source": [ - "## Save the Model Entry in NOMAD\n", - "\n", - "Now that we have finished creating the model, Let's create a NOMAD entry to be able to find it and reuse it easily. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2f677e95", - "metadata": {}, - "outputs": [], - "source": [ - "# from nomad_material_processing.utils import create_archive\n", - "import logging\n", - "\n", - "from nomad.datamodel.datamodel import EntryArchive\n", - "\n", - "from nomad_auto_xrd.schema_packages.auto_xrd import AutoXRDModel\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "\n", - "def create_auto_xrd_model(cif_folder, xrd_model_path, pdf_model_path, output_file):\n", - " archive = EntryArchive(\n", - " data=AutoXRDModel(\n", - " cif_files=get_cif_files_from_folder(cif_folder),\n", - " xrd_model_file=xrd_model_path,\n", - " pdf_model_file=pdf_model_path,\n", - " max_texture=max_texture,\n", - " min_domain_size=min_domain_size,\n", - " max_domain_size=max_domain_size,\n", - " max_strain=max_strain,\n", - " num_patterns=num_spectra,\n", - " min_angle=min_angle,\n", - " max_angle=max_angle,\n", - " max_shift=max_shift,\n", - " separate=separate,\n", - " impur_amt=impur_amt,\n", - " skip_filter=skip_filter,\n", - " include_elems=include_elems,\n", - " inc_pdf=inc_pdf,\n", - " num_epochs=num_epochs,\n", - " test_fraction=test_fraction,\n", - " )\n", - " )\n", - "\n", - " with open(output_file, 'w') as f:\n", - " f.write(archive.m_to_json(indent=4))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "7bf25be5", - "metadata": {}, - "outputs": [], - "source": [ - "create_auto_xrd_model(\n", - " 'All_CIFs',\n", - " 'Models/XRD_Model.h5',\n", - " 'Models/PDF_Model.h5',\n", - " 'auto_xrd_model.archive.json',\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "00c50a80", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "aec98a8e-021b-4f10-9c6b-1f2bc255e1e6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 8 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 12 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 8 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 12 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 8 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 12 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 8 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 12 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 8 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n", - "/opt/conda/lib/python3.10/site-packages/pymatgen/io/cif.py:1225: UserWarning: Issues encountered while parsing CIF: 12 fractional coordinates rounded to ideal values to avoid issues with finite precision.\n", - " warnings.warn(\"Issues encountered while parsing CIF: \" + \"\\n\".join(self.warnings))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 117ms/step - categorical_accuracy: 0.5383 - loss: 2.0828 - val_categorical_accuracy: 0.4854 - val_loss: 11.9627\n", - "Epoch 2/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 113ms/step - categorical_accuracy: 0.7705 - loss: 0.9952 - val_categorical_accuracy: 0.6500 - val_loss: 3.4942\n", - "Epoch 3/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 115ms/step - categorical_accuracy: 0.8367 - loss: 0.6783 - val_categorical_accuracy: 0.7917 - val_loss: 1.4896\n", - "Epoch 4/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 120ms/step - categorical_accuracy: 0.8432 - loss: 0.6083 - val_categorical_accuracy: 0.6812 - val_loss: 3.0404\n", - "Epoch 5/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.8480 - loss: 0.5144 - val_categorical_accuracy: 0.6792 - val_loss: 3.7634\n", - "Epoch 6/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 116ms/step - categorical_accuracy: 0.8654 - loss: 0.4614 - val_categorical_accuracy: 0.8687 - val_loss: 0.5662\n", - "Epoch 7/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 119ms/step - categorical_accuracy: 0.8945 - loss: 0.3211 - val_categorical_accuracy: 0.9042 - val_loss: 0.3010\n", - "Epoch 8/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 120ms/step - categorical_accuracy: 0.9084 - loss: 0.2866 - val_categorical_accuracy: 0.9125 - val_loss: 0.2681\n", - "Epoch 9/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 113ms/step - categorical_accuracy: 0.8884 - loss: 0.3142 - val_categorical_accuracy: 0.9167 - val_loss: 0.2865\n", - "Epoch 10/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 119ms/step - categorical_accuracy: 0.9063 - loss: 0.3103 - val_categorical_accuracy: 0.9167 - val_loss: 0.2537\n", - "Epoch 11/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 119ms/step - categorical_accuracy: 0.9300 - loss: 0.2089 - val_categorical_accuracy: 0.9312 - val_loss: 0.1974\n", - "Epoch 12/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 114ms/step - categorical_accuracy: 0.9322 - loss: 0.2193 - val_categorical_accuracy: 0.9271 - val_loss: 0.2721\n", - "Epoch 13/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 116ms/step - categorical_accuracy: 0.9292 - loss: 0.1894 - val_categorical_accuracy: 0.9500 - val_loss: 0.1849\n", - "Epoch 14/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 114ms/step - categorical_accuracy: 0.9453 - loss: 0.1612 - val_categorical_accuracy: 0.9604 - val_loss: 0.1138\n", - "Epoch 15/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 114ms/step - categorical_accuracy: 0.9418 - loss: 0.1709 - val_categorical_accuracy: 0.9604 - val_loss: 0.1449\n", - "Epoch 16/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 115ms/step - categorical_accuracy: 0.9606 - loss: 0.1309 - val_categorical_accuracy: 0.9625 - val_loss: 0.1109\n", - "Epoch 17/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.9637 - loss: 0.1273 - val_categorical_accuracy: 0.9354 - val_loss: 0.2427\n", - "Epoch 18/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 117ms/step - categorical_accuracy: 0.9559 - loss: 0.1362 - val_categorical_accuracy: 0.9375 - val_loss: 0.2284\n", - "Epoch 19/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 113ms/step - categorical_accuracy: 0.9497 - loss: 0.1526 - val_categorical_accuracy: 0.9458 - val_loss: 0.1781\n", - "Epoch 20/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 114ms/step - categorical_accuracy: 0.9553 - loss: 0.1400 - val_categorical_accuracy: 0.9792 - val_loss: 0.0746\n", - "Epoch 21/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 117ms/step - categorical_accuracy: 0.9474 - loss: 0.1870 - val_categorical_accuracy: 0.6708 - val_loss: 5.7360\n", - "Epoch 22/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 114ms/step - categorical_accuracy: 0.8678 - loss: 0.4185 - val_categorical_accuracy: 0.7937 - val_loss: 0.8728\n", - "Epoch 23/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.8943 - loss: 0.3407 - val_categorical_accuracy: 0.9083 - val_loss: 0.3017\n", - "Epoch 24/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 117ms/step - categorical_accuracy: 0.8972 - loss: 0.2626 - val_categorical_accuracy: 0.9021 - val_loss: 2.5786\n", - "Epoch 25/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 116ms/step - categorical_accuracy: 0.9363 - loss: 0.1968 - val_categorical_accuracy: 0.9229 - val_loss: 39.8349\n", - "Epoch 26/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.9329 - loss: 0.1816 - val_categorical_accuracy: 0.9521 - val_loss: 0.1619\n", - "Epoch 27/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 117ms/step - categorical_accuracy: 0.9442 - loss: 0.1444 - val_categorical_accuracy: 0.9604 - val_loss: 65.6584\n", - "Epoch 28/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 119ms/step - categorical_accuracy: 0.9441 - loss: 0.1439 - val_categorical_accuracy: 0.9375 - val_loss: 0.2351\n", - "Epoch 29/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 113ms/step - categorical_accuracy: 0.9524 - loss: 0.1471 - val_categorical_accuracy: 0.9312 - val_loss: 25.7426\n", - "Epoch 30/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.9629 - loss: 0.1217 - val_categorical_accuracy: 0.9646 - val_loss: 0.1021\n", - "Epoch 31/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 116ms/step - categorical_accuracy: 0.9573 - loss: 0.1206 - val_categorical_accuracy: 0.9583 - val_loss: 0.1178\n", - "Epoch 32/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 119ms/step - categorical_accuracy: 0.9714 - loss: 0.0909 - val_categorical_accuracy: 0.7875 - val_loss: 32.5773\n", - "Epoch 33/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 117ms/step - categorical_accuracy: 0.8905 - loss: 0.3527 - val_categorical_accuracy: 0.8417 - val_loss: 10.6044\n", - "Epoch 34/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 115ms/step - categorical_accuracy: 0.9072 - loss: 0.3465 - val_categorical_accuracy: 0.9417 - val_loss: 0.3013\n", - "Epoch 35/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 119ms/step - categorical_accuracy: 0.9228 - loss: 0.2489 - val_categorical_accuracy: 0.9667 - val_loss: 0.0876\n", - "Epoch 36/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 113ms/step - categorical_accuracy: 0.9531 - loss: 0.1477 - val_categorical_accuracy: 0.9417 - val_loss: 1.7419\n", - "Epoch 37/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 116ms/step - categorical_accuracy: 0.9594 - loss: 0.1363 - val_categorical_accuracy: 0.9667 - val_loss: 0.0993\n", - "Epoch 38/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.9418 - loss: 0.1753 - val_categorical_accuracy: 0.9625 - val_loss: 0.0962\n", - "Epoch 39/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 114ms/step - categorical_accuracy: 0.9650 - loss: 0.1001 - val_categorical_accuracy: 0.9688 - val_loss: 0.0970\n", - "Epoch 40/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.9567 - loss: 0.1130 - val_categorical_accuracy: 0.9688 - val_loss: 0.1209\n", - "Epoch 41/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.9648 - loss: 0.1038 - val_categorical_accuracy: 0.9833 - val_loss: 0.0479\n", - "Epoch 42/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 113ms/step - categorical_accuracy: 0.9671 - loss: 0.1008 - val_categorical_accuracy: 0.9896 - val_loss: 0.0597\n", - "Epoch 43/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 117ms/step - categorical_accuracy: 0.9698 - loss: 0.0885 - val_categorical_accuracy: 0.9729 - val_loss: 0.0858\n", - "Epoch 44/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.9641 - loss: 0.1015 - val_categorical_accuracy: 0.9792 - val_loss: 0.0509\n", - "Epoch 45/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 119ms/step - categorical_accuracy: 0.9663 - loss: 0.1132 - val_categorical_accuracy: 0.9771 - val_loss: 0.0699\n", - "Epoch 46/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 113ms/step - categorical_accuracy: 0.9709 - loss: 0.0797 - val_categorical_accuracy: 0.9875 - val_loss: 0.0384\n", - "Epoch 47/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 117ms/step - categorical_accuracy: 0.9778 - loss: 0.0590 - val_categorical_accuracy: 0.9750 - val_loss: 0.0693\n", - "Epoch 48/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 117ms/step - categorical_accuracy: 0.9863 - loss: 0.0386 - val_categorical_accuracy: 0.9812 - val_loss: 0.0733\n", - "Epoch 49/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 113ms/step - categorical_accuracy: 0.9840 - loss: 0.0513 - val_categorical_accuracy: 0.9833 - val_loss: 0.0513\n", - "Epoch 50/50\n", - "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 118ms/step - categorical_accuracy: 0.9788 - loss: 0.0775 - val_categorical_accuracy: 0.9729 - val_loss: 0.0770\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m19/19\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - categorical_accuracy: 0.9717 - loss: 0.1309\n", - "Test Accuracy: 97.66666889190674%\n" - ] - } - ], - "source": [ - "run_xrd_model()" - ] - }, - { - "cell_type": "markdown", - "id": "c02c0672-d19a-4a63-ab5d-4253aef6fdb0", - "metadata": {}, - "source": [ - "## Let's try to run some inference" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "eaaf9f16-fc7f-41de-a300-596ba48e4a3e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import json\n", - "import time\n", - "\n", - "from autoXRD import spectrum_analysis, visualizer\n", - "\n", - "\n", - "def convert_to_serializable(obj):\n", - " \"\"\"Convert non-serializable objects like numpy arrays to serializable formats.\"\"\"\n", - " if isinstance(obj, np.ndarray):\n", - " return obj.tolist()\n", - " elif isinstance(obj, dict):\n", - " return {key: convert_to_serializable(value) for key, value in obj.items()}\n", - " elif isinstance(obj, list):\n", - " return [convert_to_serializable(item) for item in obj]\n", - " return obj\n", - "\n", - "\n", - "def run_analysis( # noqa: PLR0913\n", - " references_folder='References',\n", - " spectra_folder='Spectra',\n", - " max_phases=3,\n", - " cutoff_intensity=1,\n", - " min_conf=40,\n", - " wavelength='CuKa',\n", - " unknown_threshold=25.0,\n", - " show_reduced=False,\n", - " inc_pdf=False,\n", - " parallel=False,\n", - " raw=True,\n", - " show_indiv=False,\n", - " min_angle=25.00,\n", - " max_angle=80.00,\n", - "):\n", - " start = time.time()\n", - "\n", - " # Check for spectra\n", - " if not os.path.exists(spectra_folder) or len(os.listdir(spectra_folder)) == 0:\n", - " print(f'Please provide at least one pattern in the {spectra_folder} directory.')\n", - " return\n", - "\n", - " results = {'XRD': {}, 'PDF': {}}\n", - "\n", - " # XRD/PDF ensemble requires all predictions\n", - " if inc_pdf:\n", - " final_conf = min_conf\n", - " min_conf = 10.0\n", - "\n", - " model_path = 'Models/XRD_Model.h5' if inc_pdf else 'Model.h5'\n", - "\n", - " # Ensure temp directory exists\n", - " if not os.path.exists('temp'):\n", - " os.mkdir('temp')\n", - "\n", - " # Get predictions from XRD analysis\n", - " (\n", - " results['XRD']['filenames'],\n", - " results['XRD']['phases'],\n", - " results['XRD']['confs'],\n", - " results['XRD']['backup_phases'],\n", - " results['XRD']['scale_factors'],\n", - " results['XRD']['reduced_spectra'],\n", - " ) = spectrum_analysis.main(\n", - " spectra_folder,\n", - " references_folder,\n", - " max_phases,\n", - " cutoff_intensity,\n", - " min_conf,\n", - " wavelength,\n", - " min_angle,\n", - " max_angle,\n", - " parallel,\n", - " model_path,\n", - " is_pdf=False,\n", - " )\n", - "\n", - " if inc_pdf:\n", - " # Get predictions from PDF analysis\n", - " model_path = 'Models/PDF_Model.h5'\n", - " (\n", - " results['PDF']['filenames'],\n", - " results['PDF']['phases'],\n", - " results['PDF']['confs'],\n", - " results['PDF']['backup_phases'],\n", - " results['PDF']['scale_factors'],\n", - " results['PDF']['reduced_spectra'],\n", - " ) = spectrum_analysis.main(\n", - " spectra_folder,\n", - " references_folder,\n", - " max_phases,\n", - " cutoff_intensity,\n", - " min_conf,\n", - " wavelength,\n", - " min_angle,\n", - " max_angle,\n", - " parallel,\n", - " model_path,\n", - " is_pdf=True,\n", - " )\n", - "\n", - " # Merge results\n", - " results['Merged'] = spectrum_analysis.merge_results(\n", - " results, final_conf, max_phases\n", - " )\n", - " else:\n", - " results['Merged'] = results['XRD']\n", - "\n", - " # Process results\n", - " for idx, (\n", - " spectrum_fname,\n", - " phase_set,\n", - " confidence,\n", - " backup_set,\n", - " heights,\n", - " final_spectrum,\n", - " ) in enumerate(\n", - " zip(\n", - " results['Merged']['filenames'],\n", - " results['Merged']['phases'],\n", - " results['Merged']['confs'],\n", - " results['Merged']['backup_phases'],\n", - " results['Merged']['scale_factors'],\n", - " results['Merged']['reduced_spectra'],\n", - " )\n", - " ):\n", - " # Display phase ID info\n", - " print(f'Filename: {spectrum_fname}')\n", - " print(f'Predicted phases: {phase_set}')\n", - " print(f'Confidence: {confidence}')\n", - "\n", - " # Check for unknown peaks\n", - " if len(phase_set) > 0 and 'None' not in phase_set:\n", - " remaining_I = max(final_spectrum)\n", - " if remaining_I > unknown_threshold:\n", - " print(\n", - " f'WARNING: some peaks (I ~ {int(remaining_I)}%) were not identified.' # noqa: E501\n", - " )\n", - " else:\n", - " print('WARNING: no phases were identified')\n", - " continue\n", - "\n", - " # Show backup predictions\n", - " if show_indiv:\n", - " print(f\"XRD predicted phases: {results['XRD']['phases'][idx]}\")\n", - " print(f\"XRD confidence: {results['XRD']['confs'][idx]}\")\n", - " if inc_pdf:\n", - " print(f\"PDF predicted phases: {results['PDF']['phases'][idx]}\")\n", - " print(f\"PDF confidence: {results['PDF']['confs'][idx]}\")\n", - "\n", - " # Plot the results\n", - " phasenames = [f'{phase}.cif' for phase in phase_set]\n", - " visualizer.main(\n", - " spectra_folder,\n", - " spectrum_fname,\n", - " phasenames,\n", - " heights,\n", - " final_spectrum,\n", - " min_angle,\n", - " max_angle,\n", - " wavelength,\n", - " save=False,\n", - " show_reduced=show_reduced,\n", - " inc_pdf=inc_pdf,\n", - " plot_both=False,\n", - " raw=raw,\n", - " )\n", - "\n", - " end = time.time()\n", - " print(f'Total time: {round(end - start, 1)} sec')\n", - "\n", - " # Convert results to a JSON serializable format\n", - " serializable_results = convert_to_serializable(results)\n", - "\n", - " # Save the results dictionary as a JSON file\n", - " results_file = 'results.json'\n", - " with open(results_file, 'w') as f:\n", - " json.dump(serializable_results, f, indent=4)\n", - " print(f'Results saved to {results_file}')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "19d7c813-7e99-4266-b71c-a0f0f8bce3ef", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: mittma_0015_FR_0-Copy1.0_1point.xy\n", - "Predicted phases: ['CuPS3_136']\n", - "Confidence: [76.0]\n", - "WARNING: some peaks (I ~ 84%) were not identified.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: mittma_0019_R_slowscans.xy\n", - "Predicted phases: ['CuPS3_136']\n", - "Confidence: [55.0]\n", - "WARNING: some peaks (I ~ 91%) were not identified.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 22.7 sec\n", - "Results saved to results.json\n" - ] - } - ], - "source": [ - "run_analysis()" - ] - }, - { - "cell_type": "markdown", - "id": "e5a205bb-f57f-44d3-894c-cbd801e21747", - "metadata": {}, - "source": [ - "## Analysis with PDF" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f47f7200-71f6-4e6c-a127-46d472be3c20", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "04e1586e-fdb6-4ed2-b618-1a87feb911b2", - "metadata": {}, - "outputs": [], - "source": [ - "def convert_to_serializable(obj): # noqa: F811\n", - " \"\"\"Convert non-serializable objects like numpy arrays to serializable formats.\"\"\"\n", - " if isinstance(obj, np.ndarray):\n", - " return obj.tolist()\n", - " elif isinstance(obj, dict):\n", - " return {key: convert_to_serializable(value) for key, value in obj.items()}\n", - " elif isinstance(obj, list):\n", - " return [convert_to_serializable(item) for item in obj]\n", - " return obj\n", - "\n", - "\n", - "def run_analysis( # noqa: PLR0913\n", - " references_folder='References',\n", - " spectra_folder='Spectra',\n", - " max_phases=3,\n", - " cutoff_intensity=1,\n", - " min_conf=40,\n", - " wavelength='CuKa',\n", - " unknown_threshold=25.0,\n", - " show_reduced=False,\n", - " inc_pdf=True,\n", - " parallel=False,\n", - " raw=True,\n", - " show_indiv=False,\n", - " min_angle=25.00,\n", - " max_angle=80.00,\n", - "):\n", - " start = time.time()\n", - "\n", - " # Check for spectra\n", - " if not os.path.exists(spectra_folder) or len(os.listdir(spectra_folder)) == 0:\n", - " print(f'Please provide at least one pattern in the {spectra_folder} directory.')\n", - " return\n", - "\n", - " results = {'XRD': {}, 'PDF': {}}\n", - "\n", - " # XRD/PDF ensemble requires all predictions\n", - " if inc_pdf:\n", - " final_conf = min_conf\n", - " min_conf = 10.0\n", - "\n", - " model_path = 'Models/XRD_Model.h5' if inc_pdf else 'Model.h5'\n", - "\n", - " # Ensure temp directory exists\n", - " if not os.path.exists('temp'):\n", - " os.mkdir('temp')\n", - "\n", - " # Get predictions from XRD analysis\n", - " (\n", - " results['XRD']['filenames'],\n", - " results['XRD']['phases'],\n", - " results['XRD']['confs'],\n", - " results['XRD']['backup_phases'],\n", - " results['XRD']['scale_factors'],\n", - " results['XRD']['reduced_spectra'],\n", - " ) = spectrum_analysis.main(\n", - " spectra_folder,\n", - " references_folder,\n", - " max_phases,\n", - " cutoff_intensity,\n", - " min_conf,\n", - " wavelength,\n", - " min_angle,\n", - " max_angle,\n", - " parallel,\n", - " model_path,\n", - " is_pdf=False,\n", - " )\n", - "\n", - " if inc_pdf:\n", - " # Get predictions from PDF analysis\n", - " model_path = 'Models/PDF_Model.h5'\n", - " (\n", - " results['PDF']['filenames'],\n", - " results['PDF']['phases'],\n", - " results['PDF']['confs'],\n", - " results['PDF']['backup_phases'],\n", - " results['PDF']['scale_factors'],\n", - " results['PDF']['reduced_spectra'],\n", - " ) = spectrum_analysis.main(\n", - " spectra_folder,\n", - " references_folder,\n", - " max_phases,\n", - " cutoff_intensity,\n", - " min_conf,\n", - " wavelength,\n", - " min_angle,\n", - " max_angle,\n", - " parallel,\n", - " model_path,\n", - " is_pdf=True,\n", - " )\n", - "\n", - " # Merge results\n", - " results['Merged'] = spectrum_analysis.merge_results(\n", - " results, final_conf, max_phases\n", - " )\n", - " else:\n", - " results['Merged'] = results['XRD']\n", - "\n", - " # Process results\n", - " for idx, (\n", - " spectrum_fname,\n", - " phase_set,\n", - " confidence,\n", - " backup_set,\n", - " heights,\n", - " final_spectrum,\n", - " ) in enumerate(\n", - " zip(\n", - " results['Merged']['filenames'],\n", - " results['Merged']['phases'],\n", - " results['Merged']['confs'],\n", - " results['Merged']['backup_phases'],\n", - " results['Merged']['scale_factors'],\n", - " results['Merged']['reduced_spectra'],\n", - " )\n", - " ):\n", - " # Display phase ID info\n", - " print(f'Filename: {spectrum_fname}')\n", - " print(f'Predicted phases: {phase_set}')\n", - " print(f'Confidence: {confidence}')\n", - "\n", - " # Check for unknown peaks\n", - " if len(phase_set) > 0 and 'None' not in phase_set:\n", - " remaining_I = max(final_spectrum)\n", - " if remaining_I > unknown_threshold:\n", - " print(\n", - " f'WARNING: some peaks (I ~ {int(remaining_I)}%) were not identified.' # noqa: E501\n", - " )\n", - " else:\n", - " print('WARNING: no phases were identified')\n", - " continue\n", - "\n", - " # Show backup predictions\n", - " if show_indiv:\n", - " print(f\"XRD predicted phases: {results['XRD']['phases'][idx]}\")\n", - " print(f\"XRD confidence: {results['XRD']['confs'][idx]}\")\n", - " if inc_pdf:\n", - " print(f\"PDF predicted phases: {results['PDF']['phases'][idx]}\")\n", - " print(f\"PDF confidence: {results['PDF']['confs'][idx]}\")\n", - "\n", - " # Plot the results\n", - " phasenames = [f'{phase}.cif' for phase in phase_set]\n", - " visualizer.main(\n", - " spectra_folder,\n", - " spectrum_fname,\n", - " phasenames,\n", - " heights,\n", - " final_spectrum,\n", - " min_angle,\n", - " max_angle,\n", - " wavelength,\n", - " save=False,\n", - " show_reduced=show_reduced,\n", - " inc_pdf=inc_pdf,\n", - " plot_both=False,\n", - " raw=raw,\n", - " )\n", - "\n", - " end = time.time()\n", - " print(f'Total time: {round(end - start, 1)} sec')\n", - "\n", - " # Convert results to a JSON serializable format\n", - " serializable_results = convert_to_serializable(results)\n", - "\n", - " # Save the results dictionary as a JSON file\n", - " results_file = 'results.json'\n", - " with open(results_file, 'w') as f:\n", - " json.dump(serializable_results, f, indent=4)\n", - " print(f'Results saved to {results_file}')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "ade5779c-c44e-4234-8ec8-dac9fee637c2", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: Background_Subtracted_Data_with_Padding.xy\n", - "Predicted phases: ['Cu3PS4_31']\n", - "Confidence: [49.0]\n", - "WARNING: some peaks (I ~ 83%) were not identified.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: mittma_0015_FR_0-Copy1.0_1point.xy\n", - "Predicted phases: ['Cu3PS4_31', 'CuPS3_136']\n", - "Confidence: [50.0, 49.5]\n", - "WARNING: some peaks (I ~ 84%) were not identified.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Filename: mittma_0019_R_slowscans.xy\n", - "Predicted phases: ['CuPS3_136']\n", - "Confidence: [59.0]\n", - "WARNING: some peaks (I ~ 81%) were not identified.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['international']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['equivalent_atoms']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['wyckoffs']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n", - "/opt/conda/lib/python3.10/site-packages/spglib/spglib.py:115: DeprecationWarning: dict interface (SpglibDataset['hall_number']) is deprecated.Use attribute interface ({self.__class__.__name__}.{key}) instead\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total time: 142.3 sec\n" - ] - }, - { - "ename": "TypeError", - "evalue": "Object of type ndarray is not JSON serializable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mrun_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[24], line 115\u001b[0m, in \u001b[0;36mrun_analysis\u001b[0;34m(references_folder, spectra_folder, max_phases, cutoff_intensity, min_conf, wavelength, unknown_threshold, show_reduced, inc_pdf, parallel, raw, show_indiv, min_angle, max_angle)\u001b[0m\n\u001b[1;32m 113\u001b[0m results_file \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresults.json\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(results_file, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m--> 115\u001b[0m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mserializable_results\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResults saved to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresults_file\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/json/__init__.py:179\u001b[0m, in \u001b[0;36mdump\u001b[0;34m(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)\u001b[0m\n\u001b[1;32m 173\u001b[0m iterable \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(skipkeys\u001b[38;5;241m=\u001b[39mskipkeys, ensure_ascii\u001b[38;5;241m=\u001b[39mensure_ascii,\n\u001b[1;32m 174\u001b[0m check_circular\u001b[38;5;241m=\u001b[39mcheck_circular, allow_nan\u001b[38;5;241m=\u001b[39mallow_nan, indent\u001b[38;5;241m=\u001b[39mindent,\n\u001b[1;32m 175\u001b[0m separators\u001b[38;5;241m=\u001b[39mseparators,\n\u001b[1;32m 176\u001b[0m default\u001b[38;5;241m=\u001b[39mdefault, sort_keys\u001b[38;5;241m=\u001b[39msort_keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\u001b[38;5;241m.\u001b[39miterencode(obj)\n\u001b[1;32m 177\u001b[0m \u001b[38;5;66;03m# could accelerate with writelines in some versions of Python, at\u001b[39;00m\n\u001b[1;32m 178\u001b[0m \u001b[38;5;66;03m# a debuggability cost\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m 180\u001b[0m fp\u001b[38;5;241m.\u001b[39mwrite(chunk)\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/json/encoder.py:431\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m _iterencode_list(o, _current_indent_level)\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(o, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m--> 431\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m _iterencode_dict(o, _current_indent_level)\n\u001b[1;32m 432\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 433\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m markers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/json/encoder.py:405\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode_dict\u001b[0;34m(dct, _current_indent_level)\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 404\u001b[0m chunks \u001b[38;5;241m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[0;32m--> 405\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m chunks\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m newline_indent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 407\u001b[0m _current_indent_level \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/json/encoder.py:405\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode_dict\u001b[0;34m(dct, _current_indent_level)\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 404\u001b[0m chunks \u001b[38;5;241m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[0;32m--> 405\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m chunks\n\u001b[1;32m 406\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m newline_indent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 407\u001b[0m _current_indent_level \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/json/encoder.py:325\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode_list\u001b[0;34m(lst, _current_indent_level)\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 324\u001b[0m chunks \u001b[38;5;241m=\u001b[39m _iterencode(value, _current_indent_level)\n\u001b[0;32m--> 325\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m chunks\n\u001b[1;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m newline_indent \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 327\u001b[0m _current_indent_level \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/json/encoder.py:438\u001b[0m, in \u001b[0;36m_make_iterencode.._iterencode\u001b[0;34m(o, _current_indent_level)\u001b[0m\n\u001b[1;32m 436\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCircular reference detected\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 437\u001b[0m markers[markerid] \u001b[38;5;241m=\u001b[39m o\n\u001b[0;32m--> 438\u001b[0m o \u001b[38;5;241m=\u001b[39m \u001b[43m_default\u001b[49m\u001b[43m(\u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m _iterencode(o, _current_indent_level)\n\u001b[1;32m 440\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m markers \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m/opt/conda/lib/python3.10/json/encoder.py:179\u001b[0m, in \u001b[0;36mJSONEncoder.default\u001b[0;34m(self, o)\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdefault\u001b[39m(\u001b[38;5;28mself\u001b[39m, o):\n\u001b[1;32m 161\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Implement this method in a subclass such that it returns\u001b[39;00m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;124;03m a serializable object for ``o``, or calls the base implementation\u001b[39;00m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;124;03m (to raise a ``TypeError``).\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 177\u001b[0m \n\u001b[1;32m 178\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mObject of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mo\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not JSON serializable\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mTypeError\u001b[0m: Object of type ndarray is not JSON serializable" - ] - } - ], - "source": [ - "run_analysis()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5323f5c-273b-47f9-b64a-469bf2371a91", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}