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added moench04 support
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Erik Frojdh committed
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@@ -0,0 +1,205 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "7c600ac0",
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"metadata": {},
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"source": [
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"# Using RawFile"
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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": 1,
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"id": "38912c9c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"import numpy as np \n",
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"import matplotlib.pyplot as plt\n",
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"import boost_histogram as bh\n",
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"import time\n",
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"\n",
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"#Python cluster reader\n",
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"from creader import RawFile\n",
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"path = Path('/mnt/sls_det_storage/moench_data/cluster_reader_test/')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f06378a1",
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"metadata": {},
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"source": [
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"## Moench03 data\n",
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"\n",
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"Fetches info from the master file\n",
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"\n",
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"### Frames only"
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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": 2,
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"id": "218e2897",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 1000x500 with 3 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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"with RawFile(path/'Moench03_master_0.json') as f:\n",
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" frames = f.read(100)\n",
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" \n",
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"fig, ax = plt.subplots(1,3, figsize = (10,5))\n",
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"im0 = ax[0].imshow(frames[0])\n",
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"im1 = ax[1].imshow(frames[9])\n",
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"im1 = ax[2].imshow(frames[0]-frames[9])\n",
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"fig.tight_layout()"
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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": "3f92db90",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fc4ccea5d50>]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"ax.plot(frames.sum(axis =1).sum(axis = 1))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d25663bd",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Frames and header"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "befafb31",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Frame Number, Timestamp \n",
|
||||
"201 1107824650\n",
|
||||
"202 1108024651\n",
|
||||
"203 1108224652\n",
|
||||
"204 1108424653\n",
|
||||
"205 1108624654\n",
|
||||
"206 1108824655\n",
|
||||
"207 1109024656\n",
|
||||
"208 1109224657\n",
|
||||
"209 1109424658\n",
|
||||
"210 1109624659\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"with RawFile(path/'Moench03_master_0.json', header = True) as f:\n",
|
||||
" frames, header = f.read(10)\n",
|
||||
" \n",
|
||||
" \n",
|
||||
"print(f'{\"Frame Number,\":<15} {\"Timestamp\":<10}')\n",
|
||||
"for h in header:\n",
|
||||
" print(f\"{h['Frame Number']:<15} {h['Timestamp']:<10}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "991c2729",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Moench 04 data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "15fcde89",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x1000 with 4 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#if the file has analog and digital data it's automatically read\n",
|
||||
"\n",
|
||||
"with RawFile(path/'Moench04_digital_master_0.raw', header = True) as f:\n",
|
||||
" analog, digital, header = f.read(20)\n",
|
||||
" \n",
|
||||
"fig, ax = plt.subplots(2,2, figsize = (10,10))\n",
|
||||
"im0 = ax[0][0].imshow(analog[0], origin = 'lower')\n",
|
||||
"im1 = ax[0][1].imshow(digital[0], origin = 'lower')\n",
|
||||
"im2 = ax[1][0].imshow(analog[9], origin = 'lower')\n",
|
||||
"im3 = ax[1][1].imshow(digital[9], origin = 'lower')\n",
|
||||
"\n",
|
||||
"fig.tight_layout()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.11.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in new issue
Block a user