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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2023-10-21 01:37:03 +03:00
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"execution_count": 1,
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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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2023-10-21 01:37:03 +03:00
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"execution_count": 2,
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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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2023-10-21 01:37:03 +03:00
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"execution_count": 3,
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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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2023-10-21 01:37:03 +03:00
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"execution_count": 4,
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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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2023-10-21 01:37:03 +03:00
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"execution_count": 5,
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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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"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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|
|
|
"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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|
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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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|
|
"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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|
|
"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",
|
2023-10-21 01:37:03 +03:00
|
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|
"execution_count": 6,
|
2023-10-05 18:34:18 +03:00
|
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|
|
"id": "e604a87a-9543-431b-bec7-c23737c32be5",
|
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|
|
|
|
"metadata": {
|
|
|
|
|
|
"jupyter": {
|
|
|
|
|
|
"source_hidden": true
|
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},
|
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"tags": []
|
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},
|
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|
|
|
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"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"def wavelength_to_rgb(wavelength, gamma=0.8):\n",
|
|
|
|
|
|
" ''' 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",
|
|
|
|
|
|
" 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",
|
2023-10-21 01:37:03 +03:00
|
|
|
|
"execution_count": 35,
|
|
|
|
|
|
"id": "2783bf6b-3812-4071-87cd-380855c44c1a",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"matplotlib.rcParams.update({\n",
|
|
|
|
|
|
" 'figure.figsize': [10, 2],\n",
|
|
|
|
|
|
"})"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 36,
|
2023-10-05 18:34:18 +03:00
|
|
|
|
"id": "267ef041-f675-4e84-b431-0a4f2d5e6430",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
2023-10-21 01:37:03 +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": [
|
2023-10-21 01:37:03 +03:00
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"<Figure size 1500x300 with 1 Axes>"
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2023-10-05 18:34:18 +03:00
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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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"prominences = prop[\"prominences\"]\n",
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"\n",
|
2023-10-21 01:37:03 +03:00
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"for lx in np.linspace(x.min(), x.max(), 1000):\n",
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" plt.axvline(lx, color=wavelength_to_rgb(lx / 10), lw=1)\n",
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"\n",
|
2023-10-05 18:34:18 +03:00
|
|
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"for i, peak in enumerate(peaks):\n",
|
2023-10-21 01:37:03 +03:00
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" plt.axvline(x[peak], color='k', lw=prominences[i]/25)\n",
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2023-10-05 18:34:18 +03:00
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"\n",
|
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|
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"# plt.axis(\"x\")\n",
|
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"plt.xlabel(r\"Длина волны, $\\textup{~\\AA}$\")\n",
|
2023-10-21 01:37:03 +03:00
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|
|
"plt.gca().axes.get_yaxis().set_visible(False)\n",
|
2023-10-05 18:34:18 +03:00
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"\n",
|
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|
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"plt.savefig(\"lines.png\")\n",
|
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"plt.show()"
|
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]
|
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},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
2023-10-21 01:37:03 +03:00
|
|
|
|
"execution_count": 37,
|
2023-10-05 18:34:18 +03:00
|
|
|
|
"id": "8cad90df-f11e-45c3-9e0a-b3f570b7e9ef",
|
|
|
|
|
|
"metadata": {
|
|
|
|
|
|
"tags": []
|
|
|
|
|
|
},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"np.savetxt(\"peaks.csv\", np.vstack((x[peaks], prominences)).T, header=\"wavelength[A],prominence[c.u.]\", comments=\"\", fmt=\"%.4f\", delimiter=\",\")"
|
|
|
|
|
|
]
|
2023-10-21 01:37:03 +03:00
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
|
"id": "69c8119a-2493-4c92-ab19-8d745a683534",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"### Сопоставление линиям Фраунгофера"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 38,
|
|
|
|
|
|
"id": "47ca35ac-b45c-480d-874b-3d15335a94f1",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"df_fh = pd.read_csv(\"fraunhofer_lines.csv\", sep=\"\\t\")\n",
|
|
|
|
|
|
"fh_arr = np.array(df_fh)"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 41,
|
|
|
|
|
|
"id": "57a540bd-40b3-4916-bd58-c871ae3e3dee",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 1500x300 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"for lx in np.linspace(x.min(), x.max(), 1000):\n",
|
|
|
|
|
|
" plt.axvline(lx, color=wavelength_to_rgb(lx / 10), lw=1)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"offset = 0\n",
|
|
|
|
|
|
"for (i, fh) in enumerate(fh_arr):\n",
|
|
|
|
|
|
" l = fh[2]*10\n",
|
|
|
|
|
|
" name = fh[1]\n",
|
|
|
|
|
|
" plt.axvline(l, color='k', lw=1)\n",
|
|
|
|
|
|
" \n",
|
|
|
|
|
|
" if (i > 0 and abs(fh[2] - fh_arr[i - 1][2]) < 2):\n",
|
|
|
|
|
|
" offset += 0.2\n",
|
|
|
|
|
|
" else:\n",
|
|
|
|
|
|
" offset = 0\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
" plt.text(l, 0.9-offset, fr\"${name}$\",\n",
|
|
|
|
|
|
" size=12, rotation=40,\n",
|
|
|
|
|
|
" horizontalalignment=\"center\", verticalalignment=\"baseline\",\n",
|
|
|
|
|
|
" rotation_mode=\"anchor\", backgroundcolor=\"g\", color=\"w\")\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"plt.xlabel(r\"Длина волны, $\\textup{~\\AA}$\")\n",
|
|
|
|
|
|
"plt.gca().axes.get_yaxis().set_visible(False)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"plt.savefig(\"fraunhofer_lines.png\")\n",
|
|
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 51,
|
|
|
|
|
|
"id": "03ab9c98-670d-4cde-95c5-f42fb0ad41a6",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
|
"text": [
|
|
|
|
|
|
"name\tdelta\twavelength \n",
|
|
|
|
|
|
"O_2\t0.34\t6276.61\n",
|
|
|
|
|
|
"Na\t0.45\t5895.92\n",
|
|
|
|
|
|
"Na\t0.44\t5889.95\n",
|
|
|
|
|
|
"He\t13.89\t5875.62\n",
|
|
|
|
|
|
"Hg\t2.58\t5460.73\n",
|
|
|
|
|
|
"Fe\t4.59\t5270.39\n",
|
|
|
|
|
|
"Mg\t4.47\t5183.62\n",
|
|
|
|
|
|
"Mg\t6.45\t5172.70\n",
|
|
|
|
|
|
"Fe\t6.62\t5168.91\n",
|
|
|
|
|
|
"Mg\t5.04\t5167.33\n",
|
|
|
|
|
|
"Fe\t0.40\t4957.61\n",
|
|
|
|
|
|
"H\\beta\t2.22\t4861.34\n"
|
|
|
|
|
|
]
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"closer_lines = []\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"print(\"name\\tdelta\\twavelength \")\n",
|
|
|
|
|
|
"for fh in fh_arr:\n",
|
|
|
|
|
|
" l = fh[2]*10\n",
|
|
|
|
|
|
" name = fh[1]\n",
|
|
|
|
|
|
" min_idx = 0\n",
|
|
|
|
|
|
" delta = 2000\n",
|
|
|
|
|
|
" for (i, peak) in enumerate(peaks):\n",
|
|
|
|
|
|
" if (abs(x[peak] - l) < delta):\n",
|
|
|
|
|
|
" min_idx = i\n",
|
|
|
|
|
|
" delta = abs(x[peak] - l)\n",
|
|
|
|
|
|
" if (delta < 20):\n",
|
|
|
|
|
|
" closer_lines.append([name, l, min_idx])\n",
|
|
|
|
|
|
" print(f\"{name}\\t{delta:.2f}\\t{l:.2f}\")"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 52,
|
|
|
|
|
|
"id": "10d8af20-d6af-4207-8b39-49f22f75b7e8",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"[['O_2', 6276.61, 80],\n",
|
|
|
|
|
|
" ['Na', 5895.92, 73],\n",
|
|
|
|
|
|
" ['Na', 5889.95, 72],\n",
|
|
|
|
|
|
" ['He', 5875.6179999999995, 72],\n",
|
|
|
|
|
|
" ['Hg', 5460.73, 54],\n",
|
|
|
|
|
|
" ['Fe', 5270.389999999999, 38],\n",
|
|
|
|
|
|
" ['Mg', 5183.62, 29],\n",
|
|
|
|
|
|
" ['Mg', 5172.7, 29],\n",
|
|
|
|
|
|
" ['Fe', 5168.91, 28],\n",
|
|
|
|
|
|
" ['Mg', 5167.33, 28],\n",
|
|
|
|
|
|
" ['Fe', 4957.610000000001, 10],\n",
|
|
|
|
|
|
" ['H\\\\beta', 4861.34, 1]]"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"execution_count": 52,
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"closer_lines"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 53,
|
|
|
|
|
|
"id": "d0be8e86-95fa-44b5-81f5-d2ee8f52ac58",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"matplotlib.rcParams.update({\n",
|
|
|
|
|
|
" 'figure.figsize': [10, 4],\n",
|
|
|
|
|
|
"})"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": 54,
|
|
|
|
|
|
"id": "f5e56747-7559-4676-913a-407fbd4a0692",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [
|
|
|
|
|
|
{
|
|
|
|
|
|
"data": {
|
|
|
|
|
|
"image/png": "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
|
|
|
|
|
|
"text/plain": [
|
|
|
|
|
|
"<Figure size 1500x600 with 1 Axes>"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"source": [
|
|
|
|
|
|
"plt.plot(x, y, lw=.5)\n",
|
|
|
|
|
|
"for i, closer_line in enumerate(closer_lines):\n",
|
|
|
|
|
|
" plt.axvline(closer_line[1], color=f\"C{i}\", label=f\"${closer_line[0]}$\", lw=.5)\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"plt.ylabel(\"Суммарная интенсивность, у.е.\")\n",
|
|
|
|
|
|
"plt.xlabel(r\"Длина волны, $\\textup{~\\AA}$\")\n",
|
|
|
|
|
|
"plt.legend()\n",
|
|
|
|
|
|
"\n",
|
|
|
|
|
|
"plt.savefig(\"sum_and_fraunhofer.png\")\n",
|
|
|
|
|
|
"plt.show()"
|
|
|
|
|
|
]
|
|
|
|
|
|
},
|
|
|
|
|
|
{
|
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
|
"id": "2102c412-4119-432a-8886-b6b9b95fe98f",
|
|
|
|
|
|
"metadata": {},
|
|
|
|
|
|
"outputs": [],
|
|
|
|
|
|
"source": []
|
2023-10-05 18:34:18 +03:00
|
|
|
|
}
|
|
|
|
|
|
],
|
|
|
|
|
|
"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": "3.10.12"
|
|
|
|
|
|
}
|
|
|
|
|
|
},
|
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
|
"nbformat_minor": 5
|
|
|
|
|
|
}
|
