Merge plot notebooks for both Olympus/Sciaps
Jeff Moe
parent
032f8f5a49
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0e92e91d0e
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@ -1,129 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# witzit-plot-x555 --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers.\n",
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"#\n",
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"# Copyright (C) 2019-2022, Jeff Moe\n",
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"#\n",
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"# This program is free software: you can redistribute it and/or modify\n",
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"# it under the terms of the GNU General Public License as published by\n",
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"# the Free Software Foundation, either version 3 of the License, or\n",
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"# (at your option) any later version.\n",
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"#\n",
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"# This program is distributed in the hope that it will be useful,\n",
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"# but WITHOUT ANY WARRANTY; without even the implied warranty of\n",
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"# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n",
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"# GNU General Public License for more details.\n",
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"#\n",
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"# You should have received a copy of the GNU General Public License\n",
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"# along with this program. If not, see <http://www.gnu.org/licenses/>.\n",
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"#\n",
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"# Plotting Application.\n",
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"# https://spacecruft.org/spacecruft/witzit\n",
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"#\n",
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"# Based on wut?\n",
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"# https://spacecruft.org/spacecruft/satnogs-wut"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import locale"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"locale.setlocale(locale.LC_ALL, \"en_US.utf8\")\n",
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"plt.rcParams['axes.formatter.use_locale'] = True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"template = pd.read_csv('template/sciaps-x555.csv')\n",
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"mca = pd.read_csv('examples/golden_buffalo.mca', skiprows=21)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df=template.join(mca)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.set_theme()\n",
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"plt.figure(facecolor='xkcd:off white', figsize=(15, 8))\n",
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"g=sns.lineplot(x=\"energy (eV)\", y=\"2048\", data=df, linestyle='-', linewidth=1, color='xkcd:pale orange')\n",
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"g.set_title('XRF Spectrum'.format('seaborn'), color='xkcd:cornflower blue', fontsize='large', fontweight='bold')\n",
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"g.set_xlabel('energy (eV)', color='xkcd:goldenrod', fontsize='large', fontweight='bold')\n",
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"g.set_ylabel('Counts', color='xkcd:cerulean', fontsize='large', fontweight='bold')\n",
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"g.patch.set_alpha(1.0)\n",
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"g.set_facecolor('xkcd:powder blue')\n",
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"plt.xlim(0,50000)\n",
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"plt.ylim(0,100000)\n",
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"plt.tight_layout()\n",
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"plt.savefig(\"xrf-spectrum.png\", dpi=72, transparent=False, facecolor='xkcd:off white', edgecolor=g.get_facecolor())\n",
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"plt.show()\n",
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"plt.close()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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@ -8,7 +8,7 @@
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},
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"outputs": [],
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"source": [
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"# witzit-plot-vanta --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers.\n",
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"# witzit-plot --- What In The Zap Is That? AI categorization of spectra from LIBS/XRF analyzers.\n",
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"#\n",
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"# Copyright (C) 2019-2022, Jeff Moe\n",
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"#\n",
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@ -41,7 +41,24 @@
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import locale"
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"import locale\n",
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"import sys\n",
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"import re"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Olympus Vanta-M XRF Example\n",
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"#datafile=('examples/olympus-vanta.csv')\n",
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"# SciAps X-555 XRF Example\n",
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"#datafile=('examples/sciaps-x555.mca')\n",
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"# Fail examples\n",
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"#datafile=('examples/foo.csv')\n",
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"#datafile=('examples/sciaps-x555.csv')"
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]
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},
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{
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@ -60,21 +77,31 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv('examples/olympus-vanta.csv', header = 0,\n",
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" skiprows=39,\n",
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" names=('eV', 'Counts'),\n",
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" usecols = [0, 1])\n",
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"with open(datafile) as f:\n",
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" firstline = f.readline().rstrip()\n",
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"\n",
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"df['eV'] = df['eV'] * 1000"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(df)"
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"if firstline == 'File Version = 2':\n",
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" print('SciAps X-555 XRF MCA')\n",
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" template = pd.read_csv('template/sciaps-x555-ev.csv', header=0,\n",
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" skiprows=0,\n",
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" usecols = [0])\n",
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" mca = pd.read_csv(datafile, skiprows=21, header=0,\n",
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" usecols = [0])\n",
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" df=template.join(mca)\n",
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" df.rename(columns = {'2048':'Counts'}, inplace = True)\n",
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"\n",
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"elif re.match(re.compile('Date,*'), firstline):\n",
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" print('Olympus Vanta-M XRF')\n",
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" df = pd.read_csv(datafile, header=0,\n",
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" skiprows=39,\n",
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" names=('energy (eV)', 'Counts'),\n",
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" usecols = [0, 1])\n",
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" df['energy (eV)'] = df['energy (eV)'] * 1000\n",
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"\n",
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"elif re.match(re.compile('\"Date\",*'), firstline):\n",
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" print('Possibly SciAps X-555 XRF CSV, not processed. Use .mca file instead of .csv.')\n",
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"else:\n",
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" print('Unknown file type.')\n"
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]
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},
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{
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@ -85,7 +112,7 @@
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"source": [
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"sns.set_theme()\n",
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"plt.figure(facecolor='xkcd:off white', figsize=(15, 8))\n",
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"g=sns.lineplot(x=\"eV\", y=\"Counts\", data=df, linestyle='-', linewidth=1, color='xkcd:pale orange', legend=False)\n",
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"g=sns.lineplot(x=\"energy (eV)\", y=\"Counts\", data=df, linestyle='-', linewidth=1, color='xkcd:pale orange', legend=False)\n",
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"g.set_title('XRF Spectrum'.format('seaborn'), color='xkcd:cornflower blue', fontsize='large', fontweight='bold')\n",
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"g.set_xlabel('energy (eV)', color='xkcd:goldenrod', fontsize='large', fontweight='bold')\n",
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"g.set_ylabel('Counts', color='xkcd:cerulean', fontsize='large', fontweight='bold')\n",
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@ -94,7 +121,7 @@
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"plt.xlim(0,50000)\n",
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"plt.ylim(0,100000)\n",
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"plt.tight_layout()\n",
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"plt.savefig(\"xrf-spectrum-vanta.png\", dpi=72, transparent=False, facecolor='xkcd:off white', edgecolor=g.get_facecolor())\n",
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"plt.savefig(\"tmp/xrf-spectrum.png\", dpi=72, transparent=False, facecolor='xkcd:off white', edgecolor=g.get_facecolor())\n",
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"plt.show()\n",
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"plt.close()"
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]
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@ -24,7 +24,7 @@
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# witzit-load [filename]
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# Examples:
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# witzit-load ./examples/olympus-vanta.csv
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# witzit-load ./examples/golden_buffalo.mca
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# witzit-load ./examples/sciaps-x555.mca
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import pandas as pd
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import sys
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