mirror of
https://github.com/galera951/experiment-automation.git
synced 2024-11-15 02:15:58 +03:00
414 lines
203 KiB
Plaintext
414 lines
203 KiB
Plaintext
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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": 2,
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"id": "cc0657aa-fe90-463e-95d4-ff62649064f8",
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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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"# %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': [6, 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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"\n",
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"import os"
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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": 3,
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"id": "461ecc72-8495-4e8a-9f5f-5f4c2d42ee3a",
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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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"exposure_time = {\n",
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" \"17.4\" : 15557,\n",
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" \"35\" : 4393,\n",
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" \"20\" : 3258,\n",
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" \"45\" : 5386,\n",
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" \"25\" : 2408,\n",
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" \"40\" : 2191,\n",
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"}\n",
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"\n",
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"colors = {\n",
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" \"17.4\" : \"#f21821\",\n",
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" \"20\" : \"#f8631f\",\n",
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" \"25\" : \"#fa931a\",\n",
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" \"35\" : \"#ffc309\",\n",
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" \"40\" : \"#fff600\",\n",
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" \"45\" : \"#cdde25\",\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b5e53a7-79b4-4e5c-acd7-3d7a43af8b7e",
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"metadata": {},
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"source": [
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" Red\n",
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"#f21821\n",
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"Orange Red\n",
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"#f8631f\n",
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"Orange\n",
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"#fa931a\n",
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"Orange Yellow\n",
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"#ffc309\n",
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"Yellow\n",
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"#fff600\n",
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"Yellow Green\n",
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"#cdde25\n",
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"Green\n",
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"#8bc83b\n",
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"Blue Green\n",
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"#04b99e\n",
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"Blue\n",
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"#01aef3\n",
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"Blue Purple\n",
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"#5954a8\n",
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"Purple\n",
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"#8f59a7\n",
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"Purple Red\n",
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"#bf168d "
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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": 4,
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"id": "cbcdff6c-9df7-40b9-a106-55ee10de5109",
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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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"matplotlib.rcParams.update({\n",
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" 'figure.figsize': [8, 4],\n",
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"})"
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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": 5,
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"id": "1d8dddf8-ea76-40e2-b937-ee283716aca8",
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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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"def get_df_from_filename(filename, time_lims = [1.5, 15]):\n",
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" with open(filename, \"r\") as file:\n",
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" raw_lines = file.read()\n",
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" raw_samples = raw_lines.split(\"#\")\n",
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"\n",
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" text_info = [\" \".join(i.split()) for i in raw_samples[1:6]]\n",
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" resolution = float((text_info[4].split()[-1]).replace(\",\", \".\"))\n",
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"\n",
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" hist = np.array(raw_samples[6].split()[1:], dtype=int)\n",
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" n = len(hist)\n",
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" time = resolution * np.arange(1, n + 1)\n",
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" \n",
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" df = pd.DataFrame(data={\"time\" : time, \"counts\" : hist})\n",
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" df = df[lambda x : (x[\"time\"] < time_lims[1]) * (x[\"time\"] > time_lims[0])]\n",
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" \n",
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" return 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": 13,
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"id": "18751df3-1eb2-480d-92e0-36622f294f46",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1200x600 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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"filenames = [i[2] for i in os.walk(\".\") if i[0] == \"./data\"][0]\n",
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"filenames.sort(key = lambda x : float(x.split(\"_\")[0]))\n",
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"\n",
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"counts_data = {}\n",
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"\n",
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"for name in filenames:\n",
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" filename = \"data/\" + name\n",
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" angle = name.split(\"_\")[0]\n",
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" df = get_df_from_filename(filename)\n",
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" \n",
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" time = df[\"time\"]\n",
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" counts = df[\"counts\"] * 3600 / exposure_time[angle]\n",
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" \n",
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" plt.step(time, counts, where=\"mid\", lw=.4, label=f\"Angle {angle}\")\n",
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" # plt.plot(df[\"time\"], df[\"counts\"], lw=.3, label=f\"Angle {angle}\")\n",
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" \n",
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" counts_data[angle] = counts\n",
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"\n",
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"# plt.xscale(\"log\")\n",
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" \n",
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"plt.xlabel(\"Transit time, ns\")\n",
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"plt.ylabel(\"Counts\")\n",
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"plt.legend()\n",
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"\n",
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"plt.savefig(\"all.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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"execution_count": 7,
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"id": "326b4750-443b-4c84-8763-a2c5c9637853",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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|
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1200x600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(counts_data.values())\n",
|
||
|
|
"plt.axis('off')\n",
|
||
|
|
"\n",
|
||
|
|
"plt.savefig(\"heatmap.png\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"id": "df0f9844-01b2-4173-9e9b-a663033cd7b1",
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1200x600 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for angle, counts in counts_data.items():\n",
|
||
|
|
" t = np.array(time)[:-1:2]\n",
|
||
|
|
" c = np.array(counts)\n",
|
||
|
|
" c2 = c[1::2] + c[:-1:2]\n",
|
||
|
|
" \n",
|
||
|
|
" plt.step(t, c2, where=\"mid\", lw=.8, label=f\"Angle {angle}\", color=colors[angle])\n",
|
||
|
|
"\n",
|
||
|
|
"# plt.xscale('log')\n",
|
||
|
|
" \n",
|
||
|
|
"plt.xlabel(\"Transit time, ns\")\n",
|
||
|
|
"plt.ylabel(\"Counts\")\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.savefig(\"all_c2.png\")\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"id": "4445bbab-bc55-4d46-8bc3-22d3cb0a61d7",
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"23 18.046788\n",
|
||
|
|
"24 13.367991\n",
|
||
|
|
"25 12.031192\n",
|
||
|
|
"26 16.709989\n",
|
||
|
|
"27 14.036391\n",
|
||
|
|
" ... \n",
|
||
|
|
"229 1.336799\n",
|
||
|
|
"230 0.000000\n",
|
||
|
|
"231 0.668400\n",
|
||
|
|
"232 1.336799\n",
|
||
|
|
"233 0.000000\n",
|
||
|
|
"Name: counts, Length: 211, dtype: float64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"counts_data[\"45\"]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "53a08ed6-d7b0-4a67-9a87-59877568f1aa",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import BayesPowerlaw as bp\n",
|
||
|
|
"\n",
|
||
|
|
"data_dir = os.path.dirname(os.path.abspath(__file__)) + '/examples/data'\n",
|
||
|
|
"data = np.loadtxt(data_dir + '/tweet_count.txt')\n",
|
||
|
|
"\n",
|
||
|
|
"fit=bp.bayes(data)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(6, 4))\n",
|
||
|
|
"fit.plot_fit(np.mean(fit.gamma_posterior[0]), fit_color='black', scatter_size=100,\n",
|
||
|
|
" data_color='gray', edge_color='black', line_width=2)\n",
|
||
|
|
"plt.ylim(10**-5, 10**0)\n",
|
||
|
|
"plt.xlabel('likes')\n",
|
||
|
|
"plt.ylabel('frequency')\n",
|
||
|
|
"plt.title('Likes per Tweet')\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(6, 4))\n",
|
||
|
|
"fit.plot_posterior(fit.gamma_posterior[0], range=[1.6, 1.9], color='blue')\n",
|
||
|
|
"plt.xlabel('exponent')\n",
|
||
|
|
"plt.ylabel('posterior')\n",
|
||
|
|
"plt.title('Posterior for Likes per Tweet')\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 1,
|
||
|
|
"id": "4f6b60b1-1b01-4a81-890b-22a4f6bc83b4",
|
||
|
|
"metadata": {
|
||
|
|
"tags": []
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Running /home/glebi/miniconda3/lib/python3.10/site-packages/BayesPowerlaw/src/../examples/scripts/tweets.py:\n",
|
||
|
|
"-------------------------------------------------------------\n",
|
||
|
|
"import BayesPowerlaw as bp\n",
|
||
|
|
"import numpy as np\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"import os\n",
|
||
|
|
"\n",
|
||
|
|
"data_dir = os.path.dirname(os.path.abspath(__file__)) + '/examples/data'\n",
|
||
|
|
"data = np.loadtxt(data_dir + '/tweet_count.txt')\n",
|
||
|
|
"\n",
|
||
|
|
"fit=bp.bayes(data)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(6, 4))\n",
|
||
|
|
"fit.plot_fit(np.mean(fit.gamma_posterior[0]), fit_color='black', scatter_size=100,\n",
|
||
|
|
" data_color='gray', edge_color='black', line_width=2)\n",
|
||
|
|
"plt.ylim(10**-5, 10**0)\n",
|
||
|
|
"plt.xlabel('likes', fontsize=16)\n",
|
||
|
|
"plt.ylabel('frequency', fontsize=16)\n",
|
||
|
|
"plt.title('Likes per Tweet', fontsize=18)\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"plt.figure(figsize=(6, 4))\n",
|
||
|
|
"fit.plot_posterior(fit.gamma_posterior[0], range=[1.6, 1.9], color='blue')\n",
|
||
|
|
"plt.xlabel('exponent', fontsize=16)\n",
|
||
|
|
"plt.ylabel('posterior', fontsize=16)\n",
|
||
|
|
"plt.title('Posterior for Likes per Tweet', fontsize=18)\n",
|
||
|
|
"plt.tight_layout()\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"-------------------------------------------------------------\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 600x400 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import BayesPowerlaw as bp\n",
|
||
|
|
"bp.demo()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "c1875341-f433-4ebb-aeb6-3700887d865f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
}
|