2023-10-05 18:34:18 +03:00
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{
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"cells": [
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{
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"cell_type": "code",
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2023-10-07 19:36:09 +03:00
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"execution_count": 18,
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2023-10-05 18:34:18 +03:00
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"id": "c14446e5-2129-4747-85de-4784341bfc13",
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"metadata": {},
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"outputs": [],
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"source": [
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"# %load /home/glebi/git/experiment-automation/processing_tools.py\n",
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"import numpy as np\n",
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"from scipy.optimize import curve_fit\n",
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"import pandas as pd\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib\n",
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"import scienceplots\n",
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"\n",
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"plt.style.use(['science', 'russian-font'])\n",
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"\n",
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"matplotlib.rcParams.update({\n",
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" 'figure.figsize': [10, 4],\n",
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" 'savefig.facecolor': 'white',\n",
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" 'figure.dpi': 150.0,\n",
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" 'font.size': 12.0,\n",
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"})\n"
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]
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},
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{
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"cell_type": "code",
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2023-10-07 19:36:09 +03:00
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"execution_count": 19,
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2023-10-05 18:34:18 +03:00
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"id": "b6e60ac4-5589-4b8f-ab38-42169e7fd345",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.colors as mcolors\n",
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"from scipy.signal import find_peaks"
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]
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},
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{
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"cell_type": "code",
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2023-10-07 19:36:09 +03:00
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"execution_count": 20,
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2023-10-05 18:34:18 +03:00
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"id": "3de58523-c5e9-427a-8f44-0c2e825fe184",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"data_4.csv\", sep=\"\\t\")\n",
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"wl = df[\"ангстремы\"]\n",
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"cols = df.columns[1:]\n",
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"colors = [\"red\", \"green\", \"green\", \"blue\"]"
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]
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},
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{
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"cell_type": "code",
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2023-10-07 19:36:09 +03:00
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"execution_count": 21,
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2023-10-05 18:34:18 +03:00
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"id": "0f5054ea-587f-44f7-9f1c-0a13140d0c5f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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2023-10-07 19:36:09 +03:00
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"image/png": "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2023-10-05 18:34:18 +03:00
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"text/plain": [
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"<Figure size 1500x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"for i, col in enumerate(cols):\n",
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2023-10-07 19:36:09 +03:00
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" plt.plot(wl, df[col], color = colors[i], label=fr\"${col}$\")\n",
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"\n",
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"plt.ylabel(\"Интенсивность по каждому каналу, у.е.\")\n",
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"plt.xlabel(r\"Длина волны, $\\textup{~\\AA}$\")\n",
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2023-10-05 18:34:18 +03:00
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"plt.legend() \n",
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"\n",
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"plt.savefig(\"rggb.png\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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2023-10-07 19:36:09 +03:00
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"execution_count": 22,
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2023-10-05 18:34:18 +03:00
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"id": "4d9b0b6e-fc51-4a28-956b-cae2a494dd81",
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"metadata": {},
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"outputs": [
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{
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"data": {
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2023-10-07 19:36:09 +03:00
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"image/png": "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
|
2023-10-05 18:34:18 +03:00
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"text/plain": [
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"<Figure size 1500x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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|
|
|
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"sum_int = df[cols].sum(axis=1)\n",
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"\n",
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"from_idx = 800\n",
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"to_idx = -100\n",
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"\n",
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"y = np.array(sum_int[from_idx:to_idx])\n",
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"x = np.array(wl[from_idx:to_idx])\n",
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"\n",
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|
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"peaks, prop = find_peaks(-y, prominence=5, distance=10)\n",
|
2023-10-07 19:36:09 +03:00
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"plt.plot(x[peaks], y[peaks], \"x\", color=\"C3\", markersize=3, label=\"Полосы поглощения\")\n",
|
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"plt.plot(x, y, lw=.5, label=r\"$R + G_1 + G_2 + B$\")\n",
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"\n",
|
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|
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"plt.ylabel(\"Суммарная интенсивность, у.е.\")\n",
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"plt.xlabel(r\"Длина волны, $\\textup{~\\AA}$\")\n",
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"plt.legend()\n",
|
2023-10-05 18:34:18 +03:00
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"\n",
|
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|
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"plt.savefig(\"sum.png\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
|
2023-10-07 19:36:09 +03:00
|
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"execution_count": 23,
|
2023-10-05 18:34:18 +03:00
|
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|
"id": "e604a87a-9543-431b-bec7-c23737c32be5",
|
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|
|
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"metadata": {
|
|
|
|
|
|
"jupyter": {
|
|
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|
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"source_hidden": true
|
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},
|
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"tags": []
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},
|
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"outputs": [],
|
|
|
|
|
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"source": [
|
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|
|
|
"def wavelength_to_rgb(wavelength, gamma=0.8):\n",
|
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|
|
|
|
" ''' taken from http://www.noah.org/wiki/Wavelength_to_RGB_in_Python\n",
|
|
|
|
|
|
" This converts a given wavelength of light to an \n",
|
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" approximate RGB color value. The wavelength must be given\n",
|
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" in nanometers in the range from 380 nm through 750 nm\n",
|
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" (789 THz through 400 THz).\n",
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"\n",
|
|
|
|
|
|
" Based on code by Dan Bruton\n",
|
|
|
|
|
|
" http://www.physics.sfasu.edu/astro/color/spectra.html\n",
|
|
|
|
|
|
" Additionally alpha value set to 0.5 outside range\n",
|
|
|
|
|
|
" '''\n",
|
|
|
|
|
|
" wavelength = float(wavelength)\n",
|
|
|
|
|
|
" if wavelength >= 380 and wavelength <= 750:\n",
|
|
|
|
|
|
" A = 1.\n",
|
|
|
|
|
|
" else:\n",
|
|
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|
|
|
" A=0.5\n",
|
|
|
|
|
|
" if wavelength < 380:\n",
|
|
|
|
|
|
" wavelength = 380.\n",
|
|
|
|
|
|
" if wavelength >750:\n",
|
|
|
|
|
|
" wavelength = 750.\n",
|
|
|
|
|
|
" if wavelength >= 380 and wavelength <= 440:\n",
|
|
|
|
|
|
" attenuation = 0.3 + 0.7 * (wavelength - 380) / (440 - 380)\n",
|
|
|
|
|
|
" R = ((-(wavelength - 440) / (440 - 380)) * attenuation) ** gamma\n",
|
|
|
|
|
|
" G = 0.0\n",
|
|
|
|
|
|
" B = (1.0 * attenuation) ** gamma\n",
|
|
|
|
|
|
" elif wavelength >= 440 and wavelength <= 490:\n",
|
|
|
|
|
|
" R = 0.0\n",
|
|
|
|
|
|
" G = ((wavelength - 440) / (490 - 440)) ** gamma\n",
|
|
|
|
|
|
" B = 1.0\n",
|
|
|
|
|
|
" elif wavelength >= 490 and wavelength <= 510:\n",
|
|
|
|
|
|
" R = 0.0\n",
|
|
|
|
|
|
" G = 1.0\n",
|
|
|
|
|
|
" B = (-(wavelength - 510) / (510 - 490)) ** gamma\n",
|
|
|
|
|
|
" elif wavelength >= 510 and wavelength <= 580:\n",
|
|
|
|
|
|
" R = ((wavelength - 510) / (580 - 510)) ** gamma\n",
|
|
|
|
|
|
" G = 1.0\n",
|
|
|
|
|
|
" B = 0.0\n",
|
|
|
|
|
|
" elif wavelength >= 580 and wavelength <= 645:\n",
|
|
|
|
|
|
" R = 1.0\n",
|
|
|
|
|
|
" G = (-(wavelength - 645) / (645 - 580)) ** gamma\n",
|
|
|
|
|
|
" B = 0.0\n",
|
|
|
|
|
|
" elif wavelength >= 645 and wavelength <= 750:\n",
|
|
|
|
|
|
" attenuation = 0.3 + 0.7 * (750 - wavelength) / (750 - 645)\n",
|
|
|
|
|
|
" R = (1.0 * attenuation) ** gamma\n",
|
|
|
|
|
|
" G = 0.0\n",
|
|
|
|
|
|
" B = 0.0\n",
|
|
|
|
|
|
" else:\n",
|
|
|
|
|
|
" R = 0.0\n",
|
|
|
|
|
|
" G = 0.0\n",
|
|
|
|
|
|
" B = 0.0\n",
|
|
|
|
|
|
" return (R,G,B,A)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2023-10-07 19:36:09 +03:00
|
|
|
|
"execution_count": 24,
|
2023-10-05 18:34:18 +03:00
|
|
|
|
"id": "267ef041-f675-4e84-b431-0a4f2d5e6430",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
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"image/png": "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"text/plain": [
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"<Figure size 1500x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"prominences = prop[\"prominences\"]\n",
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"\n",
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"for i, peak in enumerate(peaks):\n",
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" plt.axvline(x[peak], color=wavelength_to_rgb(x[peak] / 10), lw=prominences[i]/25)\n",
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"\n",
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"# plt.axis(\"x\")\n",
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"plt.xlabel(r\"Длина волны, $\\textup{~\\AA}$\")\n",
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"\n",
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"plt.savefig(\"lines.png\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
|
2023-10-07 19:36:09 +03:00
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"execution_count": 25,
|
2023-10-05 18:34:18 +03:00
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"id": "8cad90df-f11e-45c3-9e0a-b3f570b7e9ef",
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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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"np.savetxt(\"peaks.csv\", np.vstack((x[peaks], prominences)).T, header=\"wavelength[A],prominence[c.u.]\", comments=\"\", fmt=\"%.4f\", delimiter=\",\")"
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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": "3.10.12"
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}
|
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|
