Delete jupyternotebook/lecturefichier/test4.ipynb
This commit is contained in:
yalmansour1998
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Users\\yazan\\anaconda3\\Lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
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" \"class\": algorithms.Blowfish,\n"
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]
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}
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],
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"source": [
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"# Import data\n",
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"import time\n",
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"import numpy as np\n",
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"from skimage import io\n",
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"import plotly.graph_objects as go\n",
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"from scipy.ndimage import uniform_filter1d\n",
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"from scipy.signal import savgol_filter\n",
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"from scipy import *\n",
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"from scipy.signal import *\n",
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"from util import *"
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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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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"['Shear_x001-x101_y010_Rot00.csv', 'Shear_x001-x101_y011_Rot00.csv', 'Shear_x001-x101_y012_Rot00.csv', 'Shear_x001-x101_y013_Rot00.csv', 'Shear_x001-x101_y014_Rot00.csv', 'Shear_x001-x101_y015_Rot00.csv', 'Shear_x001-x101_y016_Rot00.csv', 'Shear_x001-x101_y017_Rot00.csv', 'Shear_x001-x101_y018_Rot00.csv', 'Shear_x001-x101_y019_Rot00.csv', 'Shear_x001-x101_y020_Rot00.csv', 'Shear_x001-x101_y021_Rot00.csv', 'Shear_x001-x101_y022_Rot00.csv', 'Shear_x001-x101_y023_Rot00.csv', 'Shear_x001-x101_y024_Rot00.csv', 'Shear_x001-x101_y025_Rot00.csv', 'Shear_x001-x101_y026_Rot00.csv', 'Shear_x001-x101_y027_Rot00.csv', 'Shear_x001-x101_y028_Rot00.csv', 'Shear_x001-x101_y029_Rot00.csv', 'Shear_x001-x101_y030_Rot00.csv', 'Shear_x001-x101_y031_Rot00.csv', 'Shear_x001-x101_y032_Rot00.csv', 'Shear_x001-x101_y033_Rot00.csv', 'Shear_x001-x101_y034_Rot00.csv', 'Shear_x001-x101_y035_Rot00.csv', 'Shear_x001-x101_y036_Rot00.csv', 'Shear_x001-x101_y037_Rot00.csv', 'Shear_x001-x101_y038_Rot00.csv', 'Shear_x001-x101_y039_Rot00.csv', 'Shear_x001-x101_y040_Rot00.csv', 'Shear_x001-x101_y041_Rot00.csv', 'Shear_x001-x101_y042_Rot00.csv', 'Shear_x001-x101_y043_Rot00.csv', 'Shear_x001-x101_y044_Rot00.csv', 'Shear_x001-x101_y045_Rot00.csv', 'Shear_x001-x101_y046_Rot00.csv', 'Shear_x001-x101_y047_Rot00.csv', 'Shear_x001-x101_y048_Rot00.csv', 'Shear_x001-x101_y049_Rot00.csv', 'Shear_x001-x101_y050_Rot00.csv', 'Shear_x001-x101_y051_Rot00.csv', 'Shear_x001-x101_y052_Rot00.csv', 'Shear_x001-x101_y053_Rot00.csv', 'Shear_x001-x101_y054_Rot00.csv', 'Shear_x001-x101_y055_Rot00.csv', 'Shear_x001-x101_y056_Rot00.csv', 'Shear_x001-x101_y057_Rot00.csv', 'Shear_x001-x101_y058_Rot00.csv', 'Shear_x001-x101_y059_Rot00.csv', 'Shear_x001-x101_y060_Rot00.csv', 'Shear_x001-x101_y061_Rot00.csv']\n"
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]
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}
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],
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"source": [
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"\n",
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"\"\"\"\n",
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"vol = io.imread(\"https://s3.amazonaws.com/assets.datacamp.com/blog_assets/attention-mri.tif\")\n",
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"volume = vol.T\n",
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"r, c = volume[0].shape\n",
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"\"\"\"\n",
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"\n",
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"# Import data\n",
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"dossier = \"dataverse_files\\Dataset\\Shear_Wave_Rot00_CSV_Data\"\n",
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"fichiers_selectionnes= fichiers_selectionnes = ['Shear_x001-x101_y{:03d}_Rot00.csv'.format(i) for i in range(10, 62)]\n",
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"print(fichiers_selectionnes)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def butter_lowpass(cutoff, fs, order=5):\n",
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" nyq = 0.5 * fs\n",
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" normal_cutoff = cutoff / nyq\n",
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" b, a = butter(order, normal_cutoff, btype='low', analog=False)\n",
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" return b, a\n",
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"\n",
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"def butter_lowpass_filter(data, cutoff, fs, order=5):\n",
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" b, a = butter_lowpass(cutoff, fs, order=order)\n",
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" y = lfilter(b, a, data)\n",
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" return y"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"pre_volume= np.array(lire_fichier_csv(dossier,fichiers_selectionnes))\n",
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"\n",
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"pre_traitement = abs(hilbert(pre_volume))\n",
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"fs = 600.0\n",
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"cutoff = 3.667\n",
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"filtered_m_hat = butter_lowpass_filter(pre_traitement, cutoff, fs)\n",
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"traitement = savgol_filter(pre_traitement, window_length=50, polyorder=5)\n",
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"\n",
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"volume = traitement[:, ::10, :]\n",
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"#volume = np.load('Dataset/3D_Dataset_All_Wave_PY_Data/3D_Dataset_shear_Wave_Rot00.npy')\n",
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"r, c = volume[0].shape\n",
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"# Define the number of frames based on the depth of the 3D volume\n",
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"nb_frames = volume.shape[0]\n",
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"\n",
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"# Define the z-scale based on the number of frames\n",
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"z_scale = 6.7 / nb_frames\n",
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"\n",
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"#custom_colorscale = [(0, 'blue'), (0.5, 'cyan'), (1, 'red')]\n",
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"\n",
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"fig = go.Figure(frames=[go.Frame(data=go.Surface(\n",
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" z=(z_scale * nb_frames - k * z_scale) * np.ones((r, c)),\n",
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" surfacecolor=np.flipud(volume[nb_frames - 1 - k]),\n",
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" cmin=0, cmax=20000,\n",
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" colorscale='Jet'\n",
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" ),\n",
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" name=str(k) # you need to name the frame for the animation to behave properly\n",
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" )\n",
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" for k in range(nb_frames)])"
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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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"# Add data to be displayed before animation starts\n",
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"fig.add_trace(go.Surface(\n",
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" z=z_scale * nb_frames * np.ones((r, c)),\n",
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" surfacecolor=np.flipud(volume[nb_frames - 1]),\n",
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" colorscale='Jet',\n",
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" cmin=-20000, cmax=20000,\n",
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" colorbar=dict(thickness=20, ticklen=4)\n",
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" ))\n",
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"\n",
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"def frame_args(duration):\n",
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" return {\n",
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" \"frame\": {\"duration\": duration},\n",
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" \"mode\": \"immediate\",\n",
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" \"fromcurrent\": True,\n",
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" \"transition\": {\"duration\": duration, \"easing\": \"linear\"},\n",
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" }\n",
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"\n",
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"sliders = [\n",
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" {\n",
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" \"pad\": {\"b\": 10, \"t\": 60},\n",
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" \"len\": 0.9,\n",
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" \"x\": 0.1,\n",
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" \"y\": 0,\n",
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" \"steps\": [\n",
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" {\n",
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" \"args\": [[f.name], frame_args(0)],\n",
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" \"label\": str(k),\n",
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" \"method\": \"animate\",\n",
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" }\n",
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" for k, f in enumerate(fig.frames)\n",
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" ],\n",
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" }\n",
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" ]\n",
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"\n",
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"# Layout\n",
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"fig.update_layout(\n",
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" title='Slices in volumetric data',\n",
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" width=600,\n",
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" height=600,\n",
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" scene=dict(\n",
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" zaxis=dict(range=[-0.1, z_scale * nb_frames], autorange=False),\n",
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" aspectratio=dict(x=1, y=1, z=1),\n",
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" ),\n",
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" updatemenus = [\n",
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" {\n",
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" \"buttons\": [\n",
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" {\n",
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" \"args\": [None, frame_args(50)],\n",
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" \"label\": \"▶\", # play symbol\n",
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" \"method\": \"animate\",\n",
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" },\n",
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" {\n",
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" \"args\": [[None], frame_args(0)],\n",
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" \"label\": \"◼\", # pause symbol\n",
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" \"method\": \"animate\",\n",
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" },\n",
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" ],\n",
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" \"direction\": \"left\",\n",
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" \"pad\": {\"r\": 10, \"t\": 70},\n",
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" \"type\": \"buttons\",\n",
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" \"x\": 0.1,\n",
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" \"y\": 0,\n",
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" }\n",
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" ],\n",
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" sliders=sliders\n",
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")\n",
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"\n",
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"fig.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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig = go.Figure()\n",
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"\n",
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"for k in range(nb_frames):\n",
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" fig.add_trace(go.Surface(\n",
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" z=np.ones((r, c)) * (z_scale * nb_frames - k * z_scale),\n",
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" surfacecolor=np.flipud(volume[k]),\n",
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" colorscale='Jet',\n",
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" cmin=0, cmax=20000\n",
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" ))\n",
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"\n",
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"fig.update_layout(\n",
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" title='Volumetric Data Visualization',\n",
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" scene=dict(\n",
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" xaxis=dict(title='X'),\n",
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" yaxis=dict(title='Y'),\n",
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" zaxis=dict(title='Z'),\n",
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" )\n",
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")\n",
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"fig.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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"seuil = 10000\n",
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"indices = np.argwhere(volume > seuil)\n",
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"x, y, z = indices[:, 1], indices[:, 2], indices[:, 0] * z_scale \n",
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"\n",
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"fig = go.Figure(data=[go.Scatter3d(\n",
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" x=x,\n",
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" y=y,\n",
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" z=z,\n",
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" mode='markers',\n",
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" marker=dict(\n",
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" size=2,\n",
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" color=volume[indices[:, 0], indices[:, 1], indices[:, 2]], \n",
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" colorscale='Jet',\n",
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" opacity=0.8,\n",
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" colorbar=dict(title='Intensité')\n",
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" )\n",
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")])\n",
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"fig.update_layout(\n",
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" title='Volumetric Data Points',\n",
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" scene=dict(\n",
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" xaxis=dict(title='X'),\n",
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" yaxis=dict(title='Y'),\n",
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" zaxis=dict(title='Z'),\n",
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" )\n",
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")\n",
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"fig.show()\n"
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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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"\n",
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"\n",
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"pre_volume_transposed = np.transpose(pre_volume)\n",
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"\n",
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"# Apply the Hilbert transform to the transposed array\n",
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"pre_traitement = abs(hilbert(pre_volume_transposed))\n",
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"\n",
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"traitement = savgol_filter(pre_traitement, window_length=50, polyorder=5)\n",
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"\n",
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"volume = traitement[:, ::10, :]\n",
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"r, c = volume[0].shape\n",
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"\n",
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"# Définition du nombre de frames basé sur la profondeur du volume 3D\n",
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"nb_frames = volume.shape[0]\n",
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"\n",
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"# Définition de l'échelle Z basée sur le nombre de frames\n",
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"z_scale = 6.7 / nb_frames\n",
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"\n",
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"# Création de la figure avec les frames\n",
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"fig = go.Figure(frames=[go.Frame(data=go.Surface(\n",
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" z=(z_scale * nb_frames - k * z_scale) * np.ones((r, c)),\n",
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" surfacecolor=np.flipud(volume[nb_frames - 1 - k]),\n",
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" cmin=0, cmax=20000,\n",
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" colorscale='Jet'\n",
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" ),\n",
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" name=str(k) # vous devez nommer la frame pour que l'animation se comporte correctement\n",
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" )\n",
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" for k in range(nb_frames)])\n",
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"\n",
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"# Ajout des données à afficher avant le début de l'animation\n",
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"fig.add_trace(go.Surface(\n",
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" z=z_scale * nb_frames * np.ones((r, c)),\n",
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" surfacecolor=np.flipud(volume[nb_frames - 1]),\n",
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" colorscale='Jet',\n",
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" cmin=-20000, cmax=20000,\n",
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" colorbar=dict(thickness=20, ticklen=4)\n",
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" ))\n",
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"\n",
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"def frame_args(duration):\n",
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" return {\n",
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" \"frame\": {\"duration\": duration},\n",
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" \"mode\": \"immediate\",\n",
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" \"fromcurrent\": True,\n",
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" \"transition\": {\"duration\": duration, \"easing\": \"linear\"},\n",
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" }\n",
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"\n",
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"sliders = [\n",
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" {\n",
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" \"pad\": {\"b\": 10, \"t\": 60},\n",
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" \"len\": 0.9,\n",
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" \"x\": 0.1,\n",
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" \"y\": 0,\n",
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" \"steps\": [\n",
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" {\n",
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" \"args\": [[f.name], frame_args(0)],\n",
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" \"label\": str(k),\n",
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" \"method\": \"animate\",\n",
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" }\n",
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" for k, f in enumerate(fig.frames)\n",
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" ],\n",
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" }\n",
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" ]\n",
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"\n",
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"# Layout\n",
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"fig.update_layout(\n",
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" title='Slices in volumetric data',\n",
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" width=600,\n",
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" height=600,\n",
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" scene=dict(\n",
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" zaxis=dict(range=[-0.1, z_scale * nb_frames], autorange=False),\n",
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" aspectratio=dict(x=1, y=1, z=1),\n",
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" xaxis=dict(title=\"X Axis\"),\n",
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" yaxis=dict(title=\"Y Axis\"),\n",
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" ),\n",
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" updatemenus = [\n",
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" {\n",
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" \"buttons\": [\n",
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" {\n",
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" \"args\": [None, frame_args(50)],\n",
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" \"label\": \"▶\", # play symbol\n",
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" \"method\": \"animate\",\n",
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" },\n",
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" {\n",
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" \"args\": [[None], frame_args(0)],\n",
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" \"label\": \"◼\", # pause symbol\n",
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" \"method\": \"animate\",\n",
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" },\n",
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" ],\n",
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" \"direction\": \"left\",\n",
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" \"pad\": {\"r\": 10, \"t\": 70},\n",
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" \"type\": \"buttons\",\n",
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" \"x\": 0.1,\n",
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" \"y\": 0,\n",
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" }\n",
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" ],\n",
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" sliders=sliders\n",
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")\n",
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"\n",
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"fig.show()\n"
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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": "base",
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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": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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Reference in New Issue
Block a user