Add contour highlight
This commit is contained in:
parent
3810964728
commit
e75aa11cc1
214
demo.ipynb
214
demo.ipynb
@ -75,7 +75,7 @@
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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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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -121,6 +121,10 @@
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"\n",
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"dtm_hs = ui8_clip(hillshades(dtm), -1, 1)\n",
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"cm_hud = mpl.colors.LinearSegmentedColormap.from_list('GreenHUD', [(0.,0.,0.,0.), (0.,1.,0.,1.)], 256)\n",
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"alpha = ((dtm_hs.astype(float) - 127) / 127) ** 2 * .4\n",
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"cX, cY = np.meshgrid(np.arange(dtm.shape[1]), np.arange(dtm.shape[0]))\n",
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"kwargs = {}\n",
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"kwargs['vmin'], kwargs['vmax'] = np.quantile(dtm, (.01, .99))\n",
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"\n",
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"model = {}"
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]
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@ -134,13 +138,13 @@
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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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"execution_count": 13,
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"metadata": {},
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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": "87b90d0f6d584b83af83965b32f15118",
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"model_id": "6a5848b7f64b487fae0ecb76d2ff5f72",
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"version_major": 2,
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"version_minor": 0
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},
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@ -154,10 +158,10 @@
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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=False, 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=True)>"
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]
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},
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"execution_count": 4,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -218,7 +222,6 @@
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" ps_mask = spectrum2d(model['tree_cache'], x, y, 200, 100, x_log, y_log, node_mask=node_mask)\n",
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" ps[0][:] = ps_mask[0] / ps[0]\n",
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"\n",
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" \n",
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" # Display spectrum\n",
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" plt.sca(model['ax_spectrum'])\n",
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" plt.cla()\n",
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@ -238,9 +241,12 @@
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" # No filter\n",
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" if x1 == x2 == y1 == y2 == 0:\n",
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" if not model['overlay']:\n",
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" model['ax_dtm'].imshow(dtm)\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_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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" return\n",
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" \n",
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" # Filter\n",
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@ -253,14 +259,18 @@
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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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" \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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"\n",
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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=False, highlight=True):\n",
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" hillshade_overlay=True, highlight=True):\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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@ -312,8 +322,7 @@
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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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"#model['ax_dtm'].imshow(dtm_hs, cmap=plt.cm.Greys)\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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@ -321,6 +330,181 @@
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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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