saving ground truth
This commit is contained in:
Erik Fröjdh committed
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commit
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3 files changed
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@@ -24,12 +24,12 @@
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"import matplotlib.pyplot as plt\n",
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"from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)\n",
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"import time \n",
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"\n",
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"import h5py\n",
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"import numpy as np\n",
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"import aare\n",
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"import boost_histogram as bh\n",
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"\n",
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"from simple_eta import gaussian_2d, plot_gaussian\n",
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"from simple_eta import plot_gaussian\n",
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"from simple_eta import generate, Generator\n",
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"\n",
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"#General configuration\n",
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@@ -60,7 +60,13 @@
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"cell_type": "code",
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"execution_count": 2,
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"id": "8ae6ce69-313f-41d4-8019-64e93a5610a0",
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"metadata": {},
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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@@ -71,7 +77,7 @@
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},
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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 4 Axes>"
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]
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@@ -110,11 +116,17 @@
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"cell_type": "code",
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"execution_count": 3,
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"id": "6390e1ef-2216-47ed-a8ce-106b9a7766ed",
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"metadata": {},
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"metadata": {
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"editable": true,
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"slideshow": {
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"slide_type": ""
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},
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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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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x500 with 2 Axes>"
|
||||
]
|
||||
@@ -169,15 +181,20 @@
|
||||
"\n",
|
||||
"generate_hits = False\n",
|
||||
"n_hits = 1000\n",
|
||||
"n_frames = 1000\n",
|
||||
"n_frames = 10000\n",
|
||||
"\n",
|
||||
"if generate_hits:\n",
|
||||
" t0 = time.perf_counter()\n",
|
||||
" frame_number = np.int32(1)\n",
|
||||
" number_of_clusters = np.uint32(n_hits)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" with open(f'.data/test_{grid_size}x{grid_size}.clust', 'wb') as f:\n",
|
||||
"\n",
|
||||
" #Set up an hdf5 file for truth\n",
|
||||
" hf = h5py.File(f'.data/test_{grid_size}x{grid_size}.h5', 'w')\n",
|
||||
" hf.create_dataset(\"x\", (n_frames, n_hits), dtype = np.float32)\n",
|
||||
" hf.create_dataset(\"y\", (n_frames, n_hits), dtype = np.float32)\n",
|
||||
" \n",
|
||||
" for i in range(n_frames):\n",
|
||||
" print(f'{i}',end = '\\r')\n",
|
||||
" #Generate a new set of n_hits hits\n",
|
||||
@@ -192,12 +209,18 @@
|
||||
" frame_number.tofile(f)\n",
|
||||
" number_of_clusters.tofile(f)\n",
|
||||
" clusters.tofile(f)\n",
|
||||
"\n",
|
||||
" #Write ground truth to hdf5\n",
|
||||
" hf['x'][frame_number-1] = mx[:,0,0].cpu()\n",
|
||||
" hf['y'][frame_number-1] = my[:,0,0].cpu()\n",
|
||||
" \n",
|
||||
" frame_number += 1\n",
|
||||
" print()\n",
|
||||
" hf.close()\n",
|
||||
" t = time.perf_counter()-t0\n",
|
||||
" print(f'Duration: {t:.3f}s, FPS: {n_frames/t:.2f}, hits/s: {n_frames*n_hits/t:.2}s')\n",
|
||||
" \n",
|
||||
"# \n"
|
||||
"# "
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -208,7 +231,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 2 Axes>"
|
||||
]
|
||||
@@ -239,6 +262,8 @@
|
||||
" eta2 = aare.calculate_eta2(clusters)\n",
|
||||
" # energy = clusters.sum_2x2()\n",
|
||||
" eta_hist.fill(eta2['x'], eta2['y'], eta2['sum'])\n",
|
||||
" # if eta_hist.sum()>1e5:\n",
|
||||
" # break\n",
|
||||
" # eta_hist.fill(eta2[:,0], eta2[:,1], energy['sum'])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
@@ -250,20 +275,35 @@
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"mesh = ax.pcolormesh(x,y,data)\n",
|
||||
"fig.colorbar(mesh)\n",
|
||||
"ax.set_title(f'ETA[{energy_index}]')\n",
|
||||
"ax.grid()\n",
|
||||
"# mesh.set_clim(0,25)"
|
||||
"ax.set_title(f'ETA[{energy_index}] - {data.sum()/1e6}M hits')\n",
|
||||
"ax.grid()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 6,
|
||||
"id": "cada1860-6b7b-416a-a39b-295586121531",
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"editable": true,
|
||||
"slideshow": {
|
||||
"slide_type": ""
|
||||
},
|
||||
"tags": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"(15.0, 60.0)"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 1000x1000 with 1 Axes>"
|
||||
]
|
||||
@@ -318,19 +358,113 @@
|
||||
"ax.set_yticks(ticks)\n",
|
||||
"ax.grid(visible=True, which='minor', color='silver', linestyle='--')\n",
|
||||
"ax.grid(visible=True, which='major', color='black', linestyle='-')\n",
|
||||
"# ax.set_xlim(15,60)\n",
|
||||
"# ax.set_ylim(15,60)"
|
||||
"ax.set_xlim(15,60)\n",
|
||||
"ax.set_ylim(15,60)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "033d20a4-818c-4585-ae8b-43b8e23d3d64",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Interpolate one pixel from the ETA dataset\n",
|
||||
"\n",
|
||||
"- Uniformity\n",
|
||||
"- Correct assignment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "b0398535-b646-4b3c-9029-0a8b8aa764f1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Lets take the same eta dataset and interpolate\n",
|
||||
"n_subpixels = 25\n",
|
||||
"n_bins = grid_size*n_subpixels\n",
|
||||
"image_hist = bh.Histogram(\n",
|
||||
" bh.axis.Regular(n_bins, 0, grid_size), \n",
|
||||
" bh.axis.Regular(n_bins, 0, grid_size),\n",
|
||||
" # bh.axis.Regular(e_bins, e_min, e_max),\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"with aare.ClusterFile(f'.data/test_{grid_size}x{grid_size}.clust', cluster_size = (grid_size,grid_size)) as f:\n",
|
||||
" for clusters in f:\n",
|
||||
" # eta_hist.fill(eta2[:,0], eta2[:,1], energy['sum'])\n",
|
||||
" hits = p.interpolate(clusters)\n",
|
||||
" image_hist.fill(hits['x'], hits['y'])\n",
|
||||
"\n",
|
||||
"image = image_hist.view()\n",
|
||||
"x = image_hist.axes.edges.T[0].flat\n",
|
||||
"y = image_hist.axes.edges.T[1].flat\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"mesh = ax.pcolormesh(x,y,image)\n",
|
||||
"fig.colorbar(mesh)\n",
|
||||
"ax.set_title(f'Image - {image.sum()/1e6}M hits')\n",
|
||||
"ax.grid()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "4559f6f0-cc46-41cd-b345-8ff81ef4fbd5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"err_hist = bh.Histogram(\n",
|
||||
" bh.axis.Regular(100, 0, 3) #um\n",
|
||||
")\n",
|
||||
"with aare.ClusterFile(f'.data/test_{grid_size}x{grid_size}.clust', cluster_size = (grid_size,grid_size)) as f:\n",
|
||||
" with h5py.File(f'.data/test_{grid_size}x{grid_size}.h5') as hf:\n",
|
||||
" \n",
|
||||
" for i in range(100):\n",
|
||||
" clusters = f.read_frame()\n",
|
||||
" x,y = hf['x'][i], hf['y'][i]\n",
|
||||
" # eta_hist.fill(eta2[:,0], eta2[:,1], energy['sum'])\n",
|
||||
" hits = p.interpolate(clusters)\n",
|
||||
" # image_hist.fill(hits['x'], hits['y'])\n",
|
||||
" error = np.sqrt(np.abs(hits['x']*pixel_size-x)**2+np.abs(hits['y']*pixel_size-y)**2)\n",
|
||||
" err_hist.fill(error)\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"ax.stairs(err_hist.values(), err_hist.axes[0].edges);\n",
|
||||
"ax.set_xlabel(r'Distance from truth [$\\mu$m]')\n",
|
||||
"ax.set_ylabel('Intensity [a.u.]')\n",
|
||||
"ax.grid()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b0398535-b646-4b3c-9029-0a8b8aa764f1",
|
||||
"id": "5867d1ce-5c41-42ad-853e-ee4aaf151621",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tmp"
|
||||
]
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
@@ -1,2 +1,2 @@
|
||||
from .generate import gaussian_2d, Generator
|
||||
from .generate import Generator
|
||||
from .plotting import plot_gaussian
|
||||
+17
-95
@@ -2,19 +2,11 @@ import torch
|
||||
import math
|
||||
import numpy as np
|
||||
|
||||
def gaussian_2d(mx, my, sigma = 1, res=100, pixel_size = 25, grid_size = 2):
|
||||
"""
|
||||
Generate a 2D gaussian as position mx, my, with sigma=sigma.
|
||||
The gaussian is placed on a grid_size x grid_size pixel matrix with resolution
|
||||
res in one dimesion.
|
||||
"""
|
||||
x = torch.linspace(0, pixel_size*grid_size, res)
|
||||
x,y = torch.meshgrid(x,x, indexing="ij")
|
||||
return 1 / (2*math.pi*sigma**2) * \
|
||||
torch.exp(-((x - my)**2 / (2*sigma**2) + (y - mx)**2 / (2*sigma**2)))
|
||||
|
||||
|
||||
def sum_pixels(t, grid):
|
||||
"""
|
||||
Given a charge density as a torch array sum it to pixels
|
||||
"""
|
||||
if t.ndim == 2:
|
||||
resolution = t.shape[0]
|
||||
pixels = np.zeros((grid,grid))
|
||||
@@ -45,14 +37,25 @@ class Generator:
|
||||
self.device = device
|
||||
|
||||
def hit(self, mx, my):
|
||||
x = torch.linspace(0, self.pixel_size*self.grid_size, self.resolution)
|
||||
x = torch.linspace(0, self.pixel_size*self.grid_size,
|
||||
self.resolution, device = self.device)
|
||||
x,y = torch.meshgrid(x,x, indexing="ij")
|
||||
t = 1 / (2*math.pi*self.sigma**2) * \
|
||||
torch.exp(-((x - my)**2 / (2*self.sigma**2) + (y - mx)**2 / (2*self.sigma**2)))
|
||||
|
||||
p = sum_pixels(t, self.grid_size)
|
||||
if self.device != 'cpu':
|
||||
t = t.cpu()
|
||||
return t, p
|
||||
|
||||
|
||||
def dx(self):
|
||||
"""
|
||||
Return the step for normalizing of the gaussian
|
||||
"""
|
||||
x = torch.linspace(0, self.pixel_size*self.grid_size,
|
||||
self.resolution, device = self.device)
|
||||
return x[1]-x[0]
|
||||
|
||||
def uniform_hits(self, n_hits):
|
||||
x = torch.linspace(0, self.pixel_size*self.grid_size, self.resolution, device = self.device)
|
||||
x,y = torch.meshgrid(x,x, indexing="ij")
|
||||
@@ -126,87 +129,6 @@ class Generator:
|
||||
pixels[:,i,j] = ts[:,i*step:(i+1)*step, j*step:(j+1)*step].sum(axis = 1).sum(axis = 1)
|
||||
|
||||
return mx, my, pixels
|
||||
def generate_triangle_hits(sigma, pixel_size, grid_size, resolution, N=100, device = 'cpu'):
|
||||
"""
|
||||
Generate N gaussians. For even grid size use the four inner corners of the central pixel
|
||||
For odd corners the whole central pixel
|
||||
"""
|
||||
|
||||
x = torch.linspace(0, pixel_size*grid_size, resolution, device = device)
|
||||
x,y = torch.meshgrid(x,x, indexing="ij")
|
||||
xs = x.unsqueeze(0).repeat(N,1,1)
|
||||
ys = y.unsqueeze(0).repeat(N,1,1)
|
||||
|
||||
|
||||
|
||||
|
||||
if grid_size % 2 == 0:
|
||||
#For even grids spread hits over the inner quadrants around the center
|
||||
low = (grid_size-1)//2*pixel_size+pixel_size/2
|
||||
high =pixel_size+low
|
||||
else:
|
||||
#For odd grids spread hits over the central pixel
|
||||
low = pixel_size*(grid_size//2)
|
||||
high = low+pixel_size
|
||||
|
||||
mx = torch.rand(N,1,1, device = device)
|
||||
my = torch.rand(N,1,1, device = device)
|
||||
mask = (mx + my > 1)
|
||||
mx[mask] = 1 - mx[mask]
|
||||
my[mask] = 1 - my[mask]
|
||||
mx = mx * (high-low) + low
|
||||
my = my * (high-low) + low
|
||||
|
||||
# mx = torch.rand(N,1,1, device = device) * (high-low)+low
|
||||
# my = torch.rand(N,1,1, device = device) * (high-low) +low
|
||||
|
||||
ts = 1 / (2*math.pi*sigma**2) * \
|
||||
torch.exp(-((xs - my)**2 / (2*sigma**2) + (ys - mx)**2 / (2*sigma**2)))
|
||||
|
||||
|
||||
#Sum signal in pixels for all N depositions
|
||||
step = resolution//grid_size
|
||||
pixels = torch.zeros((N,grid_size,grid_size))
|
||||
for i in range(grid_size):
|
||||
for j in range(grid_size):
|
||||
pixels[:,i,j] = ts[:,i*step:(i+1)*step, j*step:(j+1)*step].sum(axis = 1).sum(axis = 1)
|
||||
|
||||
return mx, my, pixels
|
||||
|
||||
def generate_uniform_hits(sigma, pixel_size, grid_size, resolution, N=100, device = 'cpu'):
|
||||
"""
|
||||
Generate N gaussians. For even grid size use the four inner corners of the central pixel
|
||||
For odd corners the whole central pixel
|
||||
"""
|
||||
|
||||
x = torch.linspace(0, pixel_size*grid_size, resolution, device = device)
|
||||
x,y = torch.meshgrid(x,x, indexing="ij")
|
||||
xs = x.unsqueeze(0).repeat(N,1,1)
|
||||
ys = y.unsqueeze(0).repeat(N,1,1)
|
||||
|
||||
|
||||
|
||||
if grid_size % 2 == 0:
|
||||
#For even grids spread hits over the inner quadrants around the center
|
||||
low = (grid_size-1)//2*pixel_size+pixel_size/2
|
||||
high =pixel_size+low
|
||||
else:
|
||||
#For odd grids spread hits over the central pixel
|
||||
low = pixel_size*(grid_size//2)
|
||||
high = low+pixel_size
|
||||
|
||||
mx = torch.rand(N,1,1, device = device) * (high-low)+low
|
||||
my = torch.rand(N,1,1, device = device) * (high-low) +low
|
||||
|
||||
ts = 1 / (2*math.pi*sigma**2) * \
|
||||
torch.exp(-((xs - my)**2 / (2*sigma**2) + (ys - mx)**2 / (2*sigma**2)))
|
||||
|
||||
|
||||
#Sum signal in pixels for all N depositions
|
||||
step = resolution//grid_size
|
||||
pixels = torch.zeros((N,grid_size,grid_size))
|
||||
for i in range(grid_size):
|
||||
for j in range(grid_size):
|
||||
pixels[:,i,j] = ts[:,i*step:(i+1)*step, j*step:(j+1)*step].sum(axis = 1).sum(axis = 1)
|
||||
|
||||
return mx, my, pixels
|
||||
|
||||
Reference in new issue
Block a user