Interpolate missing data
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parent
34aac07fdd
commit
f003715d34
@ -6,11 +6,16 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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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 laspy\n",
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"\n",
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"triskele_path = Path('../triskele/python/')\n",
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"sys.path.append(str(triskele_path.resolve()))\n",
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"import triskele\n",
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"\n",
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"figsize = (16, 9)"
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]
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},
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@ -64,12 +69,42 @@
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" print('file: {}, point count: {}'.format(file, infile.header.get_count()))\n",
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" xdata.append(infile.x)\n",
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" ydata.append(infile.y)\n",
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" idata.append(infile.intensity)\n",
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" #idata.append(infile.intensity)\n",
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" idata.append(infile.z)\n",
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" \n",
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" \n",
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"data = np.array((np.concatenate(xdata), np.concatenate(ydata), np.concatenate(idata))).T\n",
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"data.shape"
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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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"plt.hist(data[:,2], 1000)\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"extremum = .001\n",
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"tresholds = np.percentile(data[:,2], [extremum, 1 - extremum])\n",
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"display(tresholds)\n",
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"\n",
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"filtered_data = data.copy()\n",
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"filtered_data[:,2][data[:,2] < tresholds[0]] = tresholds[0]\n",
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"filtered_data[:,2][data[:,2] > tresholds[1]] = tresholds[1]\n",
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"\n",
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"plt.hist(filtered_data[:,2], 1000)\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": "markdown",
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"metadata": {},
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@ -83,11 +118,21 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"resolution = 2\n",
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"resolution = 0.5\n",
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"ground_size = np.array((data[:,0].max() - data[:,0].min(), data[:,1].max() - data[:,1].min()))\n",
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"display(ground_size)\n",
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"\n",
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"bins = ground_size / resolution"
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"bins = np.rint(ground_size / resolution).astype(int)\n",
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"bins"
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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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"data[:,0].max(), data[:,0].min(), data[:,1].max(), data[:,1].min()"
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]
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},
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{
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@ -178,15 +223,276 @@
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"plt.imsave('../Res/hist_int.png', idisp, origin='lower')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Create rasters DFC comptabible\n",
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"\n",
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"[Official description](http://www.grss-ieee.org/community/technical-committees/data-fusion/data-fusion-contest/) announce .5 m resolution on the DSMs.\n",
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"\n",
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"## Compare with existing DSMs\n",
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"\n",
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"### Shape"
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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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"plt.scatter(infile.x, infile.y)\n",
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"dfc_dsm = triskele.read('../Data/phase1_rasters/Intensity_C1/UH17_GI1F051_TR.tif')\n",
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"dfc_dsm.shape"
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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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"idisp.shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Offset\n",
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"\n",
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"#### Filter DFC raster"
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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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"nodata_filter = dfc_dsm > 1e4\n",
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"dfc_dsm[nodata_filter] = dfc_dsm[nodata_filter == False].max()"
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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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"plt.hist(dfc_dsm.reshape(-1), 1000, label='DFC')\n",
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"plt.hist(idisp[f], 1000, label='our', alpha=.8)\n",
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"\n",
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"plt.legend()\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": "markdown",
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"metadata": {},
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"source": [
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"#### Visual test"
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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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"offset_disp = np.moveaxis(np.array((np.flip(idisp, 0), dfc_dsm, np.zeros_like(idisp))), 0, 2)\n",
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"display(offset_disp.shape)\n",
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"\n",
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"plt.figure(figsize=figsize)\n",
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"plt.imshow(offset_disp / np.nanmax(offset_disp), origin='upper', extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])\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": "markdown",
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"metadata": {},
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"source": [
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"Seems good."
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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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"plt.imsave('../Res/offset.png',offset_disp / np.nanmax(offset_disp), origin='upper')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Better raster with interpolation of missing data"
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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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"from scipy.interpolate import griddata"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Test Griddata"
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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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"gridx, gridy = np.mgrid[0:10, 0:10] + 10\n",
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"gridx, gridy"
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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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"sampled_data = np.array([[0, 0, 5], [9, 9, 5], [0, 9, 10], [9, 0, 10]])\n",
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"sampled_data[:, :2] += 10\n",
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"display(sampled_data)\n",
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"\n",
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"inter_data = griddata(sampled_data[:,:2], sampled_data[:,2], (gridx, gridy), method='cubic') # linear, nearest, cubic\n",
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"\n",
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"plt.imshow(inter_data, origin='lower')\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": "markdown",
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"metadata": {},
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"source": [
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"### Automatic grid computation"
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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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"test_resolution = .01\n",
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"\n",
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"sampled_data = np.array([[0, 0, 5], [9, 9, 5], [0, 9, 10], [20, 0, 10]])\n",
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"\n",
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"def rasterize(coords, values, resolution, method='cubic'):\n",
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" xmin, xmax = coords[:,0].min(), coords[:,0].max()\n",
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" ymin, ymax = coords[:,1].min(), coords[:,1].max()\n",
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"\n",
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" gridx, gridy = np.mgrid[xmin:xmax:resolution, ymin:ymax:resolution]\n",
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" \n",
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" return griddata(coords, values, (gridx, gridy), method=method) # linear, nearest, cubic\n",
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"\n",
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"plt.imshow(rasterize(sampled_data[:,:2], sampled_data[:,2], test_resolution, 'nearest').T, origin='lower')\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.hist(data[:,2], 1000)\n",
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"plt.show()\n",
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"\n",
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"should_have_keep_idata = data[:,2].copy()\n",
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"should_have_keep_idata[data[:,2] > 1e4] = 1e4\n",
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"\n",
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"plt.hist(should_have_keep_idata, 700)\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"treshold = -1\n",
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"raster = rasterize(data[:treshold,:2], should_have_keep_idata[:treshold], 0.5, 'nearest')\n",
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"\n",
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"plt.figure(figsize=figsize)\n",
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"plt.imshow(raster.T, origin='lower')\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.imsave('../Res/my_raster.png', raster.T, origin='lower')"
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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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"idisp_complete = griddata(data[:,:2], data[:,2], test)"
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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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"X, Y = np.meshgrid(*([np.linspace(-1,1,200)] * 2))"
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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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"test = np.mgrid[0:bins[1], 0:bins[0]]\n",
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"test"
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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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"data[:,:2]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Save TIFF"
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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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"triskele.write('../Res/my_intensity.tiff', np.flip(idisp, 0))"
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]
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}
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],
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"metadata": {
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