{ "cells": [ { "cell_type": "markdown", "id": "173884b6-1ec0-4dab-97a6-c6ed615b6eae", "metadata": {}, "source": [ "Loading experimental data with GlassPy\n", "======================================\n", "\n", "**Author:** Daniel R. Cassar\n", "\n" ] }, { "cell_type": "markdown", "id": "b33cff5e-2108-414e-b8b4-89fcbb42c89f", "metadata": {}, "source": [ "## Introduction\n", "\n" ] }, { "cell_type": "markdown", "id": "060cb17f-4311-4c70-a72d-0d1cb3e2d178", "metadata": {}, "source": [ "GlassPy can load experimental data through its `glasspy.data` subpackage. Currently, SciGlass is the only available data source.\n", "\n" ] }, { "cell_type": "markdown", "id": "4d92149d-cf89-4025-bf9d-1547aebf8ad1", "metadata": {}, "source": [ "## Basic Usage\n", "\n" ] }, { "cell_type": "markdown", "id": "c0414f45-c464-4a27-90ed-d1c84d5ebc70", "metadata": {}, "source": [ "The minimal example below loads SciGlass data into a `pandas` DataFrame using the default configuration, which includes most of the available data and metadata.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "5bdc7e94-efad-4db2-8994-3d2a9cef2de9", "metadata": {}, "outputs": [], "source": [ "from glasspy.data import SciGlass\n", "\n", "source = SciGlass()\n", "df = source.data" ] }, { "cell_type": "markdown", "id": "caa40d5f-3a56-48d4-9e18-96685626990e", "metadata": {}, "source": [ "The first run may take a while, as GlassPy performs several computations to prepare the data. Subsequent runs will be significantly faster, since the data is cached locally on your machine.\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "952af934-af20-4ab8-a0fb-b8d09f9c2726", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
elements...propertymetadata
HLiBeBCNOFNaMg...SurfaceTensionAboveTgSurfaceTension1173KSurfaceTension1473KSurfaceTension1573KSurfaceTension1673KChemicalAnalysisAuthorYearNumberElementsNumberCompounds
ID
204000200000.00.00.00.0000000.00.00.6666670.00.0000000.000000...NaNNaNNaNNaNNaNFalseVolarovich M.P.193621
205000200010.00.00.00.0000000.00.00.5792130.00.1968150.000000...NaNNaNNaNNaNNaNFalseHoj J.W.199254
205000200020.00.00.00.0000000.00.00.5808690.00.1934490.000000...NaNNaNNaNNaNNaNFalseHoj J.W.199254
205000200030.00.00.00.0000000.00.00.5819860.00.1871670.000000...NaNNaNNaNNaNNaNFalseHoj J.W.199254
205000200040.00.00.00.0000000.00.00.5836720.00.1830800.000000...NaNNaNNaNNaNNaNFalseHoj J.W.199254
..................................................................
44933006116940.00.00.00.0000000.00.00.6254850.00.0000000.049125...NaNNaNNaNNaNNaNFalseMurata T.201976
44933006116950.00.00.00.0019480.00.00.6375400.00.0000000.009932...NaNNaNNaNNaNNaNFalseMurata T.2019109
44933006116960.00.00.00.0000000.00.00.6359210.00.0000000.000000...NaNNaNNaNNaNNaNFalseMurata T.201987
44933006116970.00.00.00.0145440.00.00.6222260.00.0358900.000000...NaNNaNNaNNaNNaNFalseMurata T.201998
44933006116980.00.00.00.0415320.00.00.6344620.00.0000000.000487...NaNNaNNaNNaNNaNFalseMurata T.201976
\n", "

283102 rows × 793 columns

\n", "
" ], "text/plain": [ " elements \\\n", " H Li Be B C N O F Na \n", "ID \n", "20400020000 0.0 0.0 0.0 0.000000 0.0 0.0 0.666667 0.0 0.000000 \n", "20500020001 0.0 0.0 0.0 0.000000 0.0 0.0 0.579213 0.0 0.196815 \n", "20500020002 0.0 0.0 0.0 0.000000 0.0 0.0 0.580869 0.0 0.193449 \n", "20500020003 0.0 0.0 0.0 0.000000 0.0 0.0 0.581986 0.0 0.187167 \n", "20500020004 0.0 0.0 0.0 0.000000 0.0 0.0 0.583672 0.0 0.183080 \n", "... ... ... ... ... ... ... ... ... ... \n", "4493300611694 0.0 0.0 0.0 0.000000 0.0 0.0 0.625485 0.0 0.000000 \n", "4493300611695 0.0 0.0 0.0 0.001948 0.0 0.0 0.637540 0.0 0.000000 \n", "4493300611696 0.0 0.0 0.0 0.000000 0.0 0.0 0.635921 0.0 0.000000 \n", "4493300611697 0.0 0.0 0.0 0.014544 0.0 0.0 0.622226 0.0 0.035890 \n", "4493300611698 0.0 0.0 0.0 0.041532 0.0 0.0 0.634462 0.0 0.000000 \n", "\n", " ... property \\\n", " Mg ... SurfaceTensionAboveTg SurfaceTension1173K \n", "ID ... \n", "20400020000 0.000000 ... NaN NaN \n", "20500020001 0.000000 ... NaN NaN \n", "20500020002 0.000000 ... NaN NaN \n", "20500020003 0.000000 ... NaN NaN \n", "20500020004 0.000000 ... NaN NaN \n", "... ... ... ... ... \n", "4493300611694 0.049125 ... NaN NaN \n", "4493300611695 0.009932 ... NaN NaN \n", "4493300611696 0.000000 ... NaN NaN \n", "4493300611697 0.000000 ... NaN NaN \n", "4493300611698 0.000487 ... NaN NaN \n", "\n", " \\\n", " SurfaceTension1473K SurfaceTension1573K SurfaceTension1673K \n", "ID \n", "20400020000 NaN NaN NaN \n", "20500020001 NaN NaN NaN \n", "20500020002 NaN NaN NaN \n", "20500020003 NaN NaN NaN \n", "20500020004 NaN NaN NaN \n", "... ... ... ... \n", "4493300611694 NaN NaN NaN \n", "4493300611695 NaN NaN NaN \n", "4493300611696 NaN NaN NaN \n", "4493300611697 NaN NaN NaN \n", "4493300611698 NaN NaN NaN \n", "\n", " metadata \\\n", " ChemicalAnalysis Author Year NumberElements \n", "ID \n", "20400020000 False Volarovich M.P. 1936 2 \n", "20500020001 False Hoj J.W. 1992 5 \n", "20500020002 False Hoj J.W. 1992 5 \n", "20500020003 False Hoj J.W. 1992 5 \n", "20500020004 False Hoj J.W. 1992 5 \n", "... ... ... ... ... \n", "4493300611694 False Murata T. 2019 7 \n", "4493300611695 False Murata T. 2019 10 \n", "4493300611696 False Murata T. 2019 8 \n", "4493300611697 False Murata T. 2019 9 \n", "4493300611698 False Murata T. 2019 7 \n", "\n", " \n", " NumberCompounds \n", "ID \n", "20400020000 1 \n", "20500020001 4 \n", "20500020002 4 \n", "20500020003 4 \n", "20500020004 4 \n", "... ... \n", "4493300611694 6 \n", "4493300611695 9 \n", "4493300611696 7 \n", "4493300611697 8 \n", "4493300611698 6 \n", "\n", "[283102 rows x 793 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "bd243c7a-9e79-43bc-97f1-06b46933d66d", "metadata": {}, "source": [ "To avoid naming conflicts and simplify navigation, the DataFrame is organized into two levels. The first level groups information by composition, property, or metadata.\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "f1a4bc65-6234-490a-a5d4-893d28fc1d21", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['elements', 'compounds', 'property', 'metadata'], dtype='str')\n" ] } ], "source": [ "print(df.columns.levels[0])" ] }, { "cell_type": "markdown", "id": "d7e789ae-f564-45d1-a289-7b82833802ce", "metadata": {}, "source": [ "To explore the chemical composition data, simply filter the DataFrame by the `compounds` or `elements` level.\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "6aae12c3-712a-4084-bf12-b1366faa4d89", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
HLiBeBCNOFNaMg...WRePtAuHgTlPbBiThU
ID
204000200000.00.00.00.0000000.00.00.6666670.00.0000000.000000...0.00.00.00.00.00.00.00.00.00.0
205000200010.00.00.00.0000000.00.00.5792130.00.1968150.000000...0.00.00.00.00.00.00.00.00.00.0
205000200020.00.00.00.0000000.00.00.5808690.00.1934490.000000...0.00.00.00.00.00.00.00.00.00.0
205000200030.00.00.00.0000000.00.00.5819860.00.1871670.000000...0.00.00.00.00.00.00.00.00.00.0
205000200040.00.00.00.0000000.00.00.5836720.00.1830800.000000...0.00.00.00.00.00.00.00.00.00.0
..................................................................
44933006116940.00.00.00.0000000.00.00.6254850.00.0000000.049125...0.00.00.00.00.00.00.00.00.00.0
44933006116950.00.00.00.0019480.00.00.6375400.00.0000000.009932...0.00.00.00.00.00.00.00.00.00.0
44933006116960.00.00.00.0000000.00.00.6359210.00.0000000.000000...0.00.00.00.00.00.00.00.00.00.0
44933006116970.00.00.00.0145440.00.00.6222260.00.0358900.000000...0.00.00.00.00.00.00.00.00.00.0
44933006116980.00.00.00.0415320.00.00.6344620.00.0000000.000487...0.00.00.00.00.00.00.00.00.00.0
\n", "

283102 rows × 76 columns

\n", "
" ], "text/plain": [ " H Li Be B C N O F Na \\\n", "ID \n", "20400020000 0.0 0.0 0.0 0.000000 0.0 0.0 0.666667 0.0 0.000000 \n", "20500020001 0.0 0.0 0.0 0.000000 0.0 0.0 0.579213 0.0 0.196815 \n", "20500020002 0.0 0.0 0.0 0.000000 0.0 0.0 0.580869 0.0 0.193449 \n", "20500020003 0.0 0.0 0.0 0.000000 0.0 0.0 0.581986 0.0 0.187167 \n", "20500020004 0.0 0.0 0.0 0.000000 0.0 0.0 0.583672 0.0 0.183080 \n", "... ... ... ... ... ... ... ... ... ... \n", "4493300611694 0.0 0.0 0.0 0.000000 0.0 0.0 0.625485 0.0 0.000000 \n", "4493300611695 0.0 0.0 0.0 0.001948 0.0 0.0 0.637540 0.0 0.000000 \n", "4493300611696 0.0 0.0 0.0 0.000000 0.0 0.0 0.635921 0.0 0.000000 \n", "4493300611697 0.0 0.0 0.0 0.014544 0.0 0.0 0.622226 0.0 0.035890 \n", "4493300611698 0.0 0.0 0.0 0.041532 0.0 0.0 0.634462 0.0 0.000000 \n", "\n", " Mg ... W Re Pt Au Hg Tl Pb Bi Th U \n", "ID ... \n", "20400020000 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "20500020001 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "20500020002 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "20500020003 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "20500020004 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "... ... ... ... ... ... ... ... ... ... ... ... ... \n", "4493300611694 0.049125 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "4493300611695 0.009932 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "4493300611696 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "4493300611697 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "4493300611698 0.000487 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", "[283102 rows x 76 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "els = df[\"elements\"]\n", "\n", "els" ] }, { "cell_type": "markdown", "id": "631b059f-7388-4576-b5cf-ecf944ddebff", "metadata": {}, "source": [ "The example below shows how to retrieve $T_g$ data from the `property` level.\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "d47d334e-a732-423f-bd41-7003b06944bc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ID\n", "20400020000 NaN\n", "20500020001 1017.15\n", "20500020002 1096.15\n", "20500020003 1013.15\n", "20500020004 1013.15\n", " ... \n", "4493300611694 NaN\n", "4493300611695 NaN\n", "4493300611696 NaN\n", "4493300611697 NaN\n", "4493300611698 NaN\n", "Name: Tg, Length: 283102, dtype: float64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Tg = df[\"property\"][\"Tg\"]\n", "\n", "Tg" ] }, { "cell_type": "markdown", "id": "d021e99c-7288-4c43-8003-cd5a12b1dca2", "metadata": {}, "source": [ "As you can see, not all entries have a value for $T_g$.\n", "\n", "To check for all available properties in GlassPy, run:\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "719392a7-be3c-47d5-9d78-ddfa0d67b9e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['T0', 'T1', 'T2', 'T3', 'T4', 'T5', 'T6', 'T7', 'T8', 'T9', 'T10', 'T11', 'T12', 'Viscosity773K', 'Viscosity873K', 'Viscosity973K', 'Viscosity1073K', 'Viscosity1173K', 'Viscosity1273K', 'Viscosity1373K', 'Viscosity1473K', 'Viscosity1573K', 'Viscosity1673K', 'Viscosity1773K', 'Viscosity1873K', 'Viscosity2073K', 'Viscosity2273K', 'Viscosity2473K', 'Tg', 'Tmelt', 'Tliquidus', 'TLittletons', 'TAnnealing', 'Tstrain', 'Tsoft', 'TdilatometricSoftening', 'AbbeNum', 'RefractiveIndex', 'RefractiveIndexLow', 'RefractiveIndexHigh', 'MeanDispersion', 'Permittivity', 'TangentOfLossAngle', 'TresistivityIs1MOhm.m', 'Resistivity293K', 'Resistivity373K', 'Resistivity423K', 'Resistivity573K', 'Resistivity1073K', 'Resistivity1273K', 'Resistivity1473K', 'Resistivity1673K', 'YoungModulus', 'ShearModulus', 'Microhardness', 'PoissonRatio', 'Density293K', 'Density1073K', 'Density1273K', 'Density1473K', 'Density1673K', 'ThermalConductivity', 'ThermalShockRes', 'CTEbelowTg', 'CTE328K', 'CTE373K', 'CTE433K', 'CTE483K', 'CTE623K', 'Cp293K', 'Cp473K', 'Cp673K', 'Cp1073K', 'Cp1273K', 'Cp1473K', 'Cp1673K', 'NucleationTemperature', 'NucleationRate', 'TMaxGrowthVelocity', 'MaxGrowthVelocity', 'CrystallizationPeak', 'CrystallizationOnset', 'SurfaceTensionAboveTg', 'SurfaceTension1173K', 'SurfaceTension1473K', 'SurfaceTension1573K', 'SurfaceTension1673K']\n" ] } ], "source": [ "print(SciGlass.available_properties())" ] }, { "cell_type": "markdown", "id": "251549e1-c600-4733-8c24-143880dbee41", "metadata": {}, "source": [ "If you are unfamiliar with `pandas` DataFrames, refer to the `pandas` [documentation](https://pandas.pydata.org/docs/).\n", "\n" ] }, { "cell_type": "markdown", "id": "55650755-c658-47f5-be6e-0132c3b61838", "metadata": {}, "source": [ "## Controlling the Initial Data Load\n", "\n" ] }, { "cell_type": "markdown", "id": "6f60256f-d59e-4d07-b050-33083feda5e0", "metadata": {}, "source": [ "Loading the complete SciGlass dataset can be time-consuming, so it is advisable to load only the data you need. You can control what is loaded by passing configuration dictionaries to the `SciGlass` class.\n", "\n", "For example, suppose you want to exclude glasses containing silver or gold, retrieve only glass transition temperature data, and omit compound information. You can do so as follows:\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "260cac67-e104-4f21-86c3-e550d1423c7d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
elementspropertymetadata
HLiBeBCNOFNaMg...TlPbBiThUTgChemicalAnalysisAuthorYearNumberElements
ID
205000200010.00.0000000.00.0000000.00.057.9212490.019.6815300.0...0.0000000.00.0000000.00.01017.15FalseHoj J.W.19925
205000200020.00.0000000.00.0000000.00.058.0869410.019.3449400.0...0.0000000.00.0000000.00.01096.15FalseHoj J.W.19925
205000200030.00.0000000.00.0000000.00.058.1986010.018.7166900.0...0.0000000.00.0000000.00.01013.15FalseHoj J.W.19925
205000200040.00.0000000.00.0000000.00.058.3672410.018.3080010.0...0.0000000.00.0000000.00.01013.15FalseHoj J.W.19925
205000200050.00.0000000.00.0000000.00.058.2827680.018.2645610.0...0.0000000.00.0000000.00.0978.15FalseHoj J.W.19925
..................................................................
44932006114150.07.2506380.02.3688010.00.059.3892210.00.0000000.0...8.9648280.05.5364470.00.0543.15FalseJung Woo Man20199
44932006114160.07.4459310.02.3588260.00.059.5958710.00.0000000.0...6.6501830.05.8089630.00.0545.15FalseJung Woo Man20199
44932006114170.06.5930680.010.2884800.00.059.6000900.00.0000000.0...10.7825700.00.0000000.00.0532.15FalseJung Woo Man20199
44932006114180.05.9190640.01.9360390.00.064.0140760.00.0000000.0...7.3225530.00.0000000.00.0506.15FalseJung Woo Man20199
44932006114190.06.3717980.02.0199260.00.063.7617610.00.0000000.0...7.8826360.00.0000000.00.0522.15FalseJung Woo Man20199
\n", "

91738 rows × 78 columns

\n", "
" ], "text/plain": [ " elements \\\n", " H Li Be B C N O F \n", "ID \n", "20500020001 0.0 0.000000 0.0 0.000000 0.0 0.0 57.921249 0.0 \n", "20500020002 0.0 0.000000 0.0 0.000000 0.0 0.0 58.086941 0.0 \n", "20500020003 0.0 0.000000 0.0 0.000000 0.0 0.0 58.198601 0.0 \n", "20500020004 0.0 0.000000 0.0 0.000000 0.0 0.0 58.367241 0.0 \n", "20500020005 0.0 0.000000 0.0 0.000000 0.0 0.0 58.282768 0.0 \n", "... ... ... ... ... ... ... ... ... \n", "4493200611415 0.0 7.250638 0.0 2.368801 0.0 0.0 59.389221 0.0 \n", "4493200611416 0.0 7.445931 0.0 2.358826 0.0 0.0 59.595871 0.0 \n", "4493200611417 0.0 6.593068 0.0 10.288480 0.0 0.0 59.600090 0.0 \n", "4493200611418 0.0 5.919064 0.0 1.936039 0.0 0.0 64.014076 0.0 \n", "4493200611419 0.0 6.371798 0.0 2.019926 0.0 0.0 63.761761 0.0 \n", "\n", " ... \\\n", " Na Mg ... Tl Pb Bi Th U \n", "ID ... \n", "20500020001 19.681530 0.0 ... 0.000000 0.0 0.000000 0.0 0.0 \n", "20500020002 19.344940 0.0 ... 0.000000 0.0 0.000000 0.0 0.0 \n", "20500020003 18.716690 0.0 ... 0.000000 0.0 0.000000 0.0 0.0 \n", "20500020004 18.308001 0.0 ... 0.000000 0.0 0.000000 0.0 0.0 \n", "20500020005 18.264561 0.0 ... 0.000000 0.0 0.000000 0.0 0.0 \n", "... ... ... ... ... ... ... ... ... \n", "4493200611415 0.000000 0.0 ... 8.964828 0.0 5.536447 0.0 0.0 \n", "4493200611416 0.000000 0.0 ... 6.650183 0.0 5.808963 0.0 0.0 \n", "4493200611417 0.000000 0.0 ... 10.782570 0.0 0.000000 0.0 0.0 \n", "4493200611418 0.000000 0.0 ... 7.322553 0.0 0.000000 0.0 0.0 \n", "4493200611419 0.000000 0.0 ... 7.882636 0.0 0.000000 0.0 0.0 \n", "\n", " property metadata \n", " Tg ChemicalAnalysis Author Year NumberElements \n", "ID \n", "20500020001 1017.15 False Hoj J.W. 1992 5 \n", "20500020002 1096.15 False Hoj J.W. 1992 5 \n", "20500020003 1013.15 False Hoj J.W. 1992 5 \n", "20500020004 1013.15 False Hoj J.W. 1992 5 \n", "20500020005 978.15 False Hoj J.W. 1992 5 \n", "... ... ... ... ... ... \n", "4493200611415 543.15 False Jung Woo Man 2019 9 \n", "4493200611416 545.15 False Jung Woo Man 2019 9 \n", "4493200611417 532.15 False Jung Woo Man 2019 9 \n", "4493200611418 506.15 False Jung Woo Man 2019 9 \n", "4493200611419 522.15 False Jung Woo Man 2019 9 \n", "\n", "[91738 rows x 78 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_properties_except_Tg = SciGlass.available_properties()\n", "all_properties_except_Tg.remove(\"Tg\")\n", "\n", "config_el = {\n", " \"drop\": [\"Ag\", \"Au\"],\n", "}\n", "\n", "config_prop = {\n", " \"keep\": [\"Tg\"],\n", " \"drop\": all_properties_except_Tg,\n", "}\n", "\n", "config_comp = {}\n", "\n", "source = SciGlass(\n", " elements_cfg=config_el,\n", " properties_cfg=config_prop,\n", " compounds_cfg=config_comp,\n", ")\n", "\n", "df = source.data\n", "df" ] }, { "cell_type": "markdown", "id": "2603dfb2-9724-4826-ad09-02fca06eac0b", "metadata": {}, "source": [ "See the documentation for the `SciGlass` class for more information on how to control your initial data collection.\n", "\n" ] } ], "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": "3.14.3" }, "org": null }, "nbformat": 4, "nbformat_minor": 5 }