diff --git a/data_viz.ipynb b/data_viz.ipynb new file mode 100644 index 0000000..d370591 --- /dev/null +++ b/data_viz.ipynb @@ -0,0 +1,50247 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import matplotlib.pyplot as plt \n", + "from matplotlib import cm\n", + "import seaborn as sns\n", + "from sklearn.decomposition import PCA\n", + "plt.tight_layout(h_pad = 3, w_pad=3)\n", + "degree_sign= u'\\N{DEGREE SIGN}'\n", + "\n", + "plt.rcParams['axes.autolimit_mode'] = 'round_numbers'\n", + "plt.rcParams['axes.xmargin'] = 0\n", + "plt.rcParams['axes.ymargin'] = 0\n", + "\n", + "plt.rcParams.update({'font.size': 12})\n", + "\n", + "from pandas.plotting import parallel_coordinates\n", + "\n", + "import plotly.express as px" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib\n", + "#matplotlib.rcParams.update(matplotlib.rcParamsDefault)\n", + "%pylab --no-import-all inline\n", + "\n", + "import plotly\n", + "import plotly.graph_objs as go\n", + "\n", + "# mode offline\n", + "plotly.offline.init_notebook_mode()\n", + "\n", + "import scipy as sp\n", + "from scipy.interpolate import SmoothBivariateSpline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df_spectra = pd.read_csv(\"data/df_spectra.csv\", index_col=\"name\", dtype=object)\n", + "df_props_id_spectra = pd.read_csv(\"data/df_props_id_spectra.csv\", index_col=\"name\", dtype=object)\n", + "df_props_id = pd.read_csv(\"data/df_props_id.csv\", index_col=\"name\", dtype=object)\n", + "df_props_id = df_props_id.apply(pd.to_numeric, errors='ignore')\n", + "df_blend_composition = pd.read_csv(\"data/blend_composition.csv\", index_col=\"name\", dtype=object)\n", + "df_neat_props = pd.read_csv(\"data/neat_properties.csv\", index_col=\"name\", dtype=object)\n", + "\n", + "wavenumbers = np.asarray(df_spectra.columns.values.astype(float))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ternary composition plots" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def ternary(components, marker_color):\n", + " \n", + " df_props_id = pd.read_csv(\"data/df_props_id.csv\", index_col=\"name\", dtype=object)\n", + " df_props_id = df_props_id.apply(pd.to_numeric, errors='ignore')\n", + "\n", + " a, b, c = components[0], components[1], components[2]\n", + " \n", + " df = df_props_id[(df_props_id[a]+\n", + " df_props_id[b]+\n", + " df_props_id[c]) == 100]\n", + "\n", + " trace = go.Scatterternary(\n", + " a=df[a],\n", + " b=df[b],\n", + " c=df[c],\n", + " mode='markers',\n", + " marker={\n", + " 'color': df[marker_color],\n", + " 'colorscale': \"plasma\", # \"Viridis\",\n", + " 'size': 20,\n", + " 'opacity': 0.9,\n", + " 'line': { 'width': 2 },\n", + " 'colorbar': dict(title=marker_color)}\n", + " )\n", + "\n", + " data=[trace]\n", + " layout=go.Layout(\n", + " height=800, \n", + " width=800, \n", + " title=f'{a}, {b}, {c}', \n", + " ternary=dict(\n", + " sum=1,\n", + " aaxis=dict(title=a),\n", + " baxis=dict(title=b),\n", + " caxis=dict(title=c)\n", + " ),\n", + " # showlegend= True\n", + " )\n", + " fig=go.Figure(data=data, layout=layout)\n", + " plotly.offline.iplot(fig)\n", + " \n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# plotly express not reliable\n", + "#df = df_props_id[(df_props_id['xylenes']+\n", + "# df_props_id['n-heptane']+\n", + "# df_props_id['isooctane']) == 100]\n", + "\n", + "#fig = px.scatter_ternary(df, \n", + "# a=\"xylenes\", b=\"n-heptane\", \n", + "# c=\"isooctane\", size=\"cn\", \n", + "# color='H/C')\n", + "#fig.show()" + ] 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_ = ternary(['xylenes', 'n-heptane', 'toluene'], \"H/C\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parallel coordinates plot of properties\n", + "\n", + "https://plotly.github.io/plotly.py-docs/generated/plotly.graph_objects.parcoords.html#plotly.graph_objects.parcoords.Line\n", + "\n", + "https://plotly.com/python/plotly-express/" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "def paracoord(properties, color_var, colorscale=\"plasma\", filter_component=None):\n", + " \n", + " df = pd.read_csv(\"data/df_props_id.csv\", index_col=\"name\", dtype=object)\n", + " df = df.apply(pd.to_numeric, errors='ignore')\n", + " \n", + " if filter_component:\n", + " df = df[df[filter_component] > 0.0] \n", + " \n", + " df = df.dropna(subset = properties)\n", + " df = df.dropna(subset = [color_var])\n", + " \n", + " # if filter_component:\n", + " # df = df[df[filter_component] > 0.0] \n", + " \n", + " labels={\"fp_c\": \"Flash pt\",\n", + " \"mp_c\": \"Freezing pt\", \n", + " \"cn\": \"CN\", \n", + " \"H/C\": \"H/C\",\n", + " \"MW\": \"MW\",\n", + " \"id_n_components\": \"Number of components\",\n", + " \"id_n_C\": \"Carbon number\"}\n", + " try:\n", + " labels[color_var] == 1\n", + " except:\n", + " labels[color_var] = str(color_var)\n", + " \n", + " for prop in properties:\n", + " try:\n", + " labels[prop] == 1\n", + " except:\n", + " labels[prop] = str(prop)\n", + " \n", + " dimensions = list([dict(label = labels[prop], \n", + " values = df[prop],\n", + " range = [min(df[prop].values), max(df[prop].values)]) for prop in properties])\n", + "\n", + " data = go.Parcoords(\n", + "\n", + " line = dict(color = df[color_var], \n", + " colorscale = colorscale,\n", + " colorbar= dict(title=labels[color_var]),\n", + " showscale=True,\n", + " ),\n", + " dimensions = dimensions,\n", + " # color_continuous_scale=px.colors.diverging.Tealrose, \n", + " #color_continuous_midpoint=2\n", + " )\n", + "\n", + " layout = go.Layout(title=f'Properties', \n", + " height=600, \n", + " width=900,\n", + " plot_bgcolor = 'white',\n", + " paper_bgcolor = 'white',\n", + " )\n", + "\n", + " fig = go.Figure(data=data, layout=layout)\n", + " plotly.offline.iplot(fig)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Properties of F-24 blends" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "linkText": "Export to plot.ly", + "plotlyServerURL": "https://plot.ly", + "showLink": false + }, + "data": [ + { + "dimensions": [ + { + "label": "MW", + "range": [ + 157.15179252526994, + 178.5932430649076 + ], + "values": [ + 167.6, + 171.0830632866119, + 174.74287626867118, + 178.5932430649076, + 163.92110320443246, + 160.44374033404958, + 157.15179252526994, 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