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Merge plot notebooks for both Olympus/Sciaps

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Jeff Moe 2022-05-27 16:51:00 -06:00
parent 032f8f5a49
commit 0e92e91d0e
7 changed files with 46 additions and 148 deletions
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0
examples/golden_buffalo.csv → examples/sciaps-x555.csv
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0
examples/golden_buffalo.mca → examples/sciaps-x555.mca
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0
notebooks/examples/golden_buffalo.csv → notebooks/examples/sciaps-x555.csv
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notebooks/examples/golden_buffalo.mca → notebooks/examples/sciaps-x555.mca
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129
notebooks/witzit-plot-x555.ipynb
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@ -1,129 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# witzit-plot-x555 --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers.\n",
"#\n",
"# Copyright (C) 2019-2022, Jeff Moe\n",
"#\n",
"# This program is free software: you can redistribute it and/or modify\n",
"# it under the terms of the GNU General Public License as published by\n",
"# the Free Software Foundation, either version 3 of the License, or\n",
"# (at your option) any later version.\n",
"#\n",
"# This program is distributed in the hope that it will be useful,\n",
"# but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
"# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
"# GNU General Public License for more details.\n",
"#\n",
"# You should have received a copy of the GNU General Public License\n",
"# along with this program. If not, see <http://www.gnu.org/licenses/>.\n",
"#\n",
"# Plotting Application.\n",
"# https://spacecruft.org/spacecruft/witzit\n",
"#\n",
"# Based on wut?\n",
"# https://spacecruft.org/spacecruft/satnogs-wut"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import locale"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"locale.setlocale(locale.LC_ALL, \"en_US.utf8\")\n",
"plt.rcParams['axes.formatter.use_locale'] = True"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"template = pd.read_csv('template/sciaps-x555.csv')\n",
"mca = pd.read_csv('examples/golden_buffalo.mca', skiprows=21)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df=template.join(mca)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.set_theme()\n",
"plt.figure(facecolor='xkcd:off white', figsize=(15, 8))\n",
"g=sns.lineplot(x=\"energy (eV)\", y=\"2048\", data=df, linestyle='-', linewidth=1, color='xkcd:pale orange')\n",
"g.set_title('XRF Spectrum'.format('seaborn'), color='xkcd:cornflower blue', fontsize='large', fontweight='bold')\n",
"g.set_xlabel('energy (eV)', color='xkcd:goldenrod', fontsize='large', fontweight='bold')\n",
"g.set_ylabel('Counts', color='xkcd:cerulean', fontsize='large', fontweight='bold')\n",
"g.patch.set_alpha(1.0)\n",
"g.set_facecolor('xkcd:powder blue')\n",
"plt.xlim(0,50000)\n",
"plt.ylim(0,100000)\n",
"plt.tight_layout()\n",
"plt.savefig(\"xrf-spectrum.png\", dpi=72, transparent=False, facecolor='xkcd:off white', edgecolor=g.get_facecolor())\n",
"plt.show()\n",
"plt.close()"
]
}
],
"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": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

63
notebooks/witzit-plot-vanta.ipynb → notebooks/witzit-plot.ipynb
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@ -8,7 +8,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"# witzit-plot-vanta --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers.\n", "# witzit-plot --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers.\n",
"#\n", "#\n",
"# Copyright (C) 2019-2022, Jeff Moe\n", "# Copyright (C) 2019-2022, Jeff Moe\n",
"#\n", "#\n",
@ -41,7 +41,24 @@
"import pandas as pd\n", "import pandas as pd\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import seaborn as sns\n", "import seaborn as sns\n",
"import locale" "import locale\n",
"import sys\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Olympus Vanta-M XRF Example\n",
"#datafile=('examples/olympus-vanta.csv')\n",
"# SciAps X-555 XRF Example\n",
"#datafile=('examples/sciaps-x555.mca')\n",
"# Fail examples\n",
"#datafile=('examples/foo.csv')\n",
"#datafile=('examples/sciaps-x555.csv')"
] ]
}, },
{ {
@ -60,21 +77,31 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df = pd.read_csv('examples/olympus-vanta.csv', header = 0,\n", "with open(datafile) as f:\n",
" skiprows=39,\n", " firstline = f.readline().rstrip()\n",
" names=('eV', 'Counts'),\n",
" usecols = [0, 1])\n",
"\n", "\n",
"df['eV'] = df['eV'] * 1000" "if firstline == 'File Version = 2':\n",
] " print('SciAps X-555 XRF MCA')\n",
}, " template = pd.read_csv('template/sciaps-x555-ev.csv', header=0,\n",
{ " skiprows=0,\n",
"cell_type": "code", " usecols = [0])\n",
"execution_count": null, " mca = pd.read_csv(datafile, skiprows=21, header=0,\n",
"metadata": {}, " usecols = [0])\n",
"outputs": [], " df=template.join(mca)\n",
"source": [ " df.rename(columns = {'2048':'Counts'}, inplace = True)\n",
"print(df)" "\n",
"elif re.match(re.compile('Date,*'), firstline):\n",
" print('Olympus Vanta-M XRF')\n",
" df = pd.read_csv(datafile, header=0,\n",
" skiprows=39,\n",
" names=('energy (eV)', 'Counts'),\n",
" usecols = [0, 1])\n",
" df['energy (eV)'] = df['energy (eV)'] * 1000\n",
"\n",
"elif re.match(re.compile('\"Date\",*'), firstline):\n",
" print('Possibly SciAps X-555 XRF CSV, not processed. Use .mca file instead of .csv.')\n",
"else:\n",
" print('Unknown file type.')\n"
] ]
}, },
{ {
@ -85,7 +112,7 @@
"source": [ "source": [
"sns.set_theme()\n", "sns.set_theme()\n",
"plt.figure(facecolor='xkcd:off white', figsize=(15, 8))\n", "plt.figure(facecolor='xkcd:off white', figsize=(15, 8))\n",
"g=sns.lineplot(x=\"eV\", y=\"Counts\", data=df, linestyle='-', linewidth=1, color='xkcd:pale orange', legend=False)\n", "g=sns.lineplot(x=\"energy (eV)\", y=\"Counts\", data=df, linestyle='-', linewidth=1, color='xkcd:pale orange', legend=False)\n",
"g.set_title('XRF Spectrum'.format('seaborn'), color='xkcd:cornflower blue', fontsize='large', fontweight='bold')\n", "g.set_title('XRF Spectrum'.format('seaborn'), color='xkcd:cornflower blue', fontsize='large', fontweight='bold')\n",
"g.set_xlabel('energy (eV)', color='xkcd:goldenrod', fontsize='large', fontweight='bold')\n", "g.set_xlabel('energy (eV)', color='xkcd:goldenrod', fontsize='large', fontweight='bold')\n",
"g.set_ylabel('Counts', color='xkcd:cerulean', fontsize='large', fontweight='bold')\n", "g.set_ylabel('Counts', color='xkcd:cerulean', fontsize='large', fontweight='bold')\n",
@ -94,7 +121,7 @@
"plt.xlim(0,50000)\n", "plt.xlim(0,50000)\n",
"plt.ylim(0,100000)\n", "plt.ylim(0,100000)\n",
"plt.tight_layout()\n", "plt.tight_layout()\n",
"plt.savefig(\"xrf-spectrum-vanta.png\", dpi=72, transparent=False, facecolor='xkcd:off white', edgecolor=g.get_facecolor())\n", "plt.savefig(\"tmp/xrf-spectrum.png\", dpi=72, transparent=False, facecolor='xkcd:off white', edgecolor=g.get_facecolor())\n",
"plt.show()\n", "plt.show()\n",
"plt.close()" "plt.close()"
] ]

2
witzit-load
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@ -24,7 +24,7 @@
# witzit-load [filename] # witzit-load [filename]
# Examples: # Examples:
# witzit-load ./examples/olympus-vanta.csv # witzit-load ./examples/olympus-vanta.csv
# witzit-load ./examples/golden_buffalo.mca # witzit-load ./examples/sciaps-x555.mca
import pandas as pd import pandas as pd
import sys import sys