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e75aa11cc1
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demo.ipynb
225
demo.ipynb
@ -25,7 +25,6 @@
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}
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],
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"source": [
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"import numpy as np\n",
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"import rasterio as rio\n",
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"import sap\n",
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"import matplotlib as mpl\n",
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@ -33,6 +32,7 @@
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"import inspect\n",
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"import ipywidgets as ipw\n",
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"import higra as hg\n",
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"import numpy as np\n",
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"\n",
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"plt.style.use('dark_background')\n",
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"plt.set_cmap('plasma')"
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@ -75,8 +75,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"execution_count": 3,
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"metadata": {
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"jupyter": {
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"source_hidden": true
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"def ui8_clip(x, vmin=None, vmax=None):\n",
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@ -138,13 +143,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"execution_count": 4,
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"metadata": {
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"jupyter": {
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"source_hidden": true
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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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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6a5848b7f64b487fae0ecb76d2ff5f72",
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"model_id": "81086011013945a497e4ffb7163998f9",
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"version_major": 2,
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"version_minor": 0
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},
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@ -158,10 +168,10 @@
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{
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"data": {
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"text/plain": [
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"<function __main__.spectrum_widget(tree=<class 'sap.trees.MaxTree'>, x='area', y='compactness', x_log=True, y_log=False, hillshade_overlay=True, highlight=True)>"
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"<function __main__.spectrum_widget(tree=<class 'sap.trees.MaxTree'>, x='area', y='compactness', x_log=True, y_log=False, hillshade_overlay=True, highlight=False)>"
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]
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},
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"execution_count": 13,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -185,7 +195,7 @@
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" 'dtm_x2': 0,\n",
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" 'dtm_y1': 0,\n",
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" 'dtm_y2': 0,\n",
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" 'overlay': False,\n",
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" 'overlay': True,\n",
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" 'pixel_mask': None,\n",
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"})\n",
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"\n",
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@ -207,6 +217,7 @@
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" pmask = np.zeros_like(dtm, dtype=np.bool)\n",
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" pmask[x1:x2,y1:y2] = True\n",
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" model['pixel_mask'] = pmask\n",
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" model['highlight'] = True\n",
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" show_spectrum()\n",
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" \n",
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"def show_spectrum():\n",
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@ -243,7 +254,6 @@
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" if not model['overlay']:\n",
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" model['ax_dtm'].imshow(dtm, **kwargs)\n",
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" else:\n",
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" #model['ax_dtm'].imshow(dtm_hs, cmap=plt.cm.Greys)\n",
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" model['ax_dtm'].imshow(dtm, **kwargs)\n",
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" model['ax_dtm'].imshow(dtm_hs, cmap=plt.cm.Greys, alpha=alpha)\n",
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"\n",
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@ -256,13 +266,10 @@
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"\n",
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" img = model['tree_cache'].reconstruct((X < x1) | (X > x2) | (Y < y1) | (Y > y2), filtering='subtractive')\n",
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" \n",
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" model['ax_dtm'].clear()\n",
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" if not model['overlay']:\n",
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" model['ax_dtm'].imshow(img)\n",
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" else:\n",
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" #over = img != model['tree_cache']._alt[-1]\n",
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" #model['ax_dtm'].imshow(dtm_hs, cmap=plt.cm.Greys)\n",
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" #model['ax_dtm'].imshow(over, cmap=cm_hud, alpha=.4)\n",
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" model['ax_dtm'].clear()\n",
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" model['ax_dtm'].imshow(dtm, **kwargs)\n",
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" model['ax_dtm'].imshow(dtm_hs, cmap=plt.cm.Greys, alpha=alpha)\n",
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" model['ax_dtm'].contour(cX, cY, img, [model['tree_cache']._alt[-1]], colors='lime')\n",
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@ -270,7 +277,7 @@
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" plt.gcf().canvas.draw()\n",
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"\n",
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"def spectrum_widget(tree=sap.MaxTree, x='area', y='compactness', x_log=True, y_log=False,\n",
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" hillshade_overlay=True, highlight=True):\n",
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" hillshade_overlay=True, highlight=False):\n",
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" # Update only toggle of hillshade_overlay\n",
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" if hillshade_overlay != model['overlay']:\n",
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" model['overlay'] = hillshade_overlay\n",
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@ -305,7 +312,7 @@
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"\n",
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"\n",
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"plt.close()\n",
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"fig = plt.figure('Spectrum', figsize=(15, 4), constrained_layout=True)\n",
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"fig = plt.figure('Spectrum', figsize=(18, 5), constrained_layout=True)\n",
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"grid = mpl.gridspec.GridSpec(1, 3, fig)\n",
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"\n",
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"model['ax_spectrum'] = fig.add_subplot(grid[:2])\n",
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@ -319,198 +326,16 @@
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" pass\n",
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"\n",
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"\n",
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"plt.connect('key_press_event', selector)\n",
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"#plt.connect('key_press_event', selector)\n",
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"\n",
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"\n",
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"#filter_dtm()\n",
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"filter_dtm()\n",
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"\n",
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"ipw.interact(spectrum_widget, \n",
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" tree=inspect.getmembers(sap.trees, lambda t: inspect.isclass(t) and issubclass(t, sap.Tree) and t != sap.Tree),\n",
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" x=sap.available_attributes().keys(),\n",
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" y=sap.available_attributes().keys())"
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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": 70,
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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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"(2001, 2001)"
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]
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},
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"execution_count": 70,
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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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"source": [
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"model['tree_cache'].reconstruct().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": 73,
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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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"2001"
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]
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},
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"execution_count": 73,
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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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"source": [
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"len(X)"
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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": 66,
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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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"16.93000030517578"
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]
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},
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"execution_count": 66,
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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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"source": [
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"model['tree_cache']._alt[-1]"
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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": 62,
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"metadata": {},
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"outputs": [
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{
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"ename": "NameError",
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"evalue": "name 'img' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-62-77a7949ba908>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mNameError\u001b[0m: name 'img' is not defined"
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]
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}
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],
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"source": [
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"img"
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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": 82,
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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.contour.QuadContourSet at 0x7fe61585e430>"
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]
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},
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"execution_count": 82,
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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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"source": [
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"plt.close()\n",
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"plt.figure()\n",
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"ax = plt.gca()\n",
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"\n",
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"plt.imshow(dtm, **kwargs)\n",
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"plt.colorbar()\n",
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"ax.imshow(dtm_hs, cmap=plt.cm.Greys, alpha=alpha)\n",
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"ax.contour(X, Y, dtm, [30], colors='lime')"
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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": 21,
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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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"(2001, 2001)"
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]
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},
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"execution_count": 21,
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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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"source": [
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"dtm_hs.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": 22,
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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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"(2001, 2001)"
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]
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},
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"execution_count": 22,
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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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"source": [
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"dtm.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": 37,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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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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"0.12586920037702484"
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]
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},
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"execution_count": 42,
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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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"source": [
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"alpha.mean()"
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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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}
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],
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"metadata": {
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41
utils.py
41
utils.py
@ -0,0 +1,41 @@
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import numpy as np
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def ui8_clip(x, vmin=None, vmax=None):
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vmin = vmin if vmin else np.nanmin(x)
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vmax = vmax if vmax else np.nanmax(x)
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ui8 = ((np.clip(x, vmin, vmax) - vmin) / (vmax - vmin) * 255).astype(np.uint8)
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return np.ma.array(ui8, mask=np.isnan(x))
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def hillshades(x):
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hs = np.zeros_like(x)
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hs[:-1,:-1] = x[:-1,:-1] - x[1:,1:]
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return hs
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from sap.spectra import get_bins
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def spectrum2d(tree, x_attribute, y_attribute, x_count=100, y_count=100,
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x_log=False, y_log=False, weighted=True, normalized=True,
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node_mask=None):
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x = tree.get_attribute(x_attribute)
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y = tree.get_attribute(y_attribute)
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bins = (get_bins(x, x_count, 'geo' if x_log else 'lin'),
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get_bins(y, y_count, 'geo' if y_log else 'lin'))
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weights = tree.get_attribute('area') if weighted else None
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weights = weights / tree._image.size if normalized and weighted else weights
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s, xedges, yedges = np.histogram2d(x[node_mask].ravel(), y[node_mask].ravel(),
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bins=bins, density=None, weights=weights[node_mask].ravel())
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return s, xedges, yedges, x_log, y_log
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def pixel_to_node(tree, mask):
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"""Compute the node mask from the pixel mask."""
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node_mask = hg.accumulate_and_min_sequential(tree._tree,
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np.ones(tree._tree.num_vertices(), dtype=np.uint8),
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mask.ravel(),
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hg.Accumulators.min).astype(np.bool)
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return node_mask
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