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jeanpat/Download_from_github_2164-dataset.ipynb

Created January 31, 2020 12:15
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Download_from_github_2164-dataset.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2164 Full resolution pairs of grayscaled+groundtruth label images\n",
"The pairs of images were generated from a [previous dataset](https://github.com/jeanpat/DeepFISH/blob/master/dataset/overlapping_chromosomes_examples.h5?raw=true) which suffered from several defects both in the grayscale and in the ground truth components:\n",
"\n",
" * The grayscaled images could suffer from two problems:\n",
" * Some images had black dots: those images were removed (with the corresponding groundtruth)\n",
" * The images dtype is now *np.uint8*\n",
" * The overlapping domain is now more realistic compared with real overlapping chromosomes\n",
" * The labels of the groundtruth don't have no more spurious pixels\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import ndimage as nd\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2020-01-30 16:48:29-- https://github.com/jeanpat/DeepFISH/blob/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true\n",
"Résolution de github.com (github.com)… 140.82.118.4\n",
"Connexion à github.com (github.com)|140.82.118.4|:443… connecté.\n",
"requête HTTP transmise, en attente de la réponse… 302 Found\n",
"Emplacement : https://github.com/jeanpat/DeepFISH/raw/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz [suivant]\n",
"--2020-01-30 16:48:29-- https://github.com/jeanpat/DeepFISH/raw/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz\n",
"Réutilisation de la connexion existante à github.com:443.\n",
"requête HTTP transmise, en attente de la réponse… 302 Found\n",
"Emplacement : https://raw.githubusercontent.com/jeanpat/DeepFISH/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz [suivant]\n",
"--2020-01-30 16:48:30-- https://raw.githubusercontent.com/jeanpat/DeepFISH/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz\n",
"Résolution de raw.githubusercontent.com (raw.githubusercontent.com)… 151.101.120.133\n",
"Connexion à raw.githubusercontent.com (raw.githubusercontent.com)|151.101.120.133|:443… connecté.\n",
"requête HTTP transmise, en attente de la réponse… 200 OK\n",
"Taille : 9166198 (8,7M) [application/octet-stream]\n",
"Enregistre : «Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true»\n",
"\n",
"Cleaned_FullRes_216 100%[===================>] 8,74M 1,22MB/s ds 6,9s \n",
"\n",
"2020-01-30 16:48:38 (1,26 MB/s) - «Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true» enregistré [9166198/9166198]\n",
"\n"
]
}
],
"source": [
"!wget https://github.com/jeanpat/DeepFISH/blob/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true\n",
"!mv Cleaned_FullRes_2164_overlapping_pairs.npz?raw=true Clean2164.npz"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2164, 190, 189, 2)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# There's a trick to load and uncompress a numpy .npz array\n",
"# https://stackoverflow.com/questions/18231135/load-compressed-data-npz-from-file-using-numpy-load/44693995\n",
"#\n",
"dataset = np.load('Clean2164.npz')\n",
"data = dataset.f.arr_0\n",
"data.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Have a look to the downloaded images"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fd7164d2e10>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"N=203\n",
"plt.figure(figsize=(10,8))\n",
"plt.subplot(121)\n",
"plt.imshow(data[N,:,:,0], cmap=plt.cm.gray)\n",
"plt.subplot(122)\n",
"plt.imshow(data[N,:,:,1], cmap=plt.cm.flag_r)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The same dataset saved as a hdf5 file can be downloaded"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2020-01-30 22:49:04-- https://github.com/jeanpat/DeepFISH/blob/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true\n",
"Résolution de github.com (github.com)… 140.82.118.4\n",
"Connexion à github.com (github.com)|140.82.118.4|:443… connecté.\n",
"requête HTTP transmise, en attente de la réponse… 302 Found\n",
"Emplacement : https://github.com/jeanpat/DeepFISH/raw/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5 [suivant]\n",
"--2020-01-30 22:49:05-- https://github.com/jeanpat/DeepFISH/raw/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5\n",
"Réutilisation de la connexion existante à github.com:443.\n",
"requête HTTP transmise, en attente de la réponse… 302 Found\n",
"Emplacement : https://raw.githubusercontent.com/jeanpat/DeepFISH/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5 [suivant]\n",
"--2020-01-30 22:49:05-- https://raw.githubusercontent.com/jeanpat/DeepFISH/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5\n",
"Résolution de raw.githubusercontent.com (raw.githubusercontent.com)… 151.101.120.133\n",
"Connexion à raw.githubusercontent.com (raw.githubusercontent.com)|151.101.120.133|:443… connecté.\n",
"requête HTTP transmise, en attente de la réponse… 200 OK\n",
"Taille : 4487537 (4,3M) [application/octet-stream]\n",
"Enregistre : «Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true.1»\n",
"\n",
"Cleaned_FullRes_216 100%[===================>] 4,28M 1,55MB/s ds 2,8s \n",
"\n",
"2020-01-30 22:49:08 (1,55 MB/s) - «Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true.1» enregistré [4487537/4487537]\n",
"\n"
]
}
],
"source": [
"!wget https://github.com/jeanpat/DeepFISH/blob/master/dataset/Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true\n",
"!mv Cleaned_FullRes_2164_overlapping_pairs.h5?raw=true Clean2164.h5"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dataset is a numpy array of shape: (2164, 190, 189, 2)\n"
]
}
],
"source": [
"import h5py\n",
"filename = './Clean2164.h5'\n",
"h5f = h5py.File(filename,'r')\n",
"pairs = h5f['chroms_data'][:]\n",
"h5f.close()\n",
"print('dataset is a numpy array of shape:', pairs.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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