Add newer app
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app.ipynb
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app.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Interactive pixel to Spectrum x DTM Analysis in Attribute Space\n",
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"\n"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/florent/.local/share/micromamba/lib/python3.12/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
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" setattr(self, word, getattr(machar, word).flat[0])\n",
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"/home/florent/.local/share/micromamba/lib/python3.12/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
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" return self._float_to_str(self.smallest_subnormal)\n",
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"/home/florent/.local/share/micromamba/lib/python3.12/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
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" setattr(self, word, getattr(machar, word).flat[0])\n",
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"/home/florent/.local/share/micromamba/lib/python3.12/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
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" return self._float_to_str(self.smallest_subnormal)\n"
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]
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}
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],
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"source": [
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"from matplotlib import pyplot as plt\n",
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"import higra as hg\n",
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"from pathlib import Path\n",
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"import ipywidgets as ipw\n",
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"\n",
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"from spectra_app import *\n",
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"\n",
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"plt.style.use('dark_background')\n",
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"plt.set_cmap('plasma')\n",
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"plt.close()"
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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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"## Load dataset"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import rasterio as rio\n",
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"image = np.random.random((100, 100))\n",
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"#image = rio.open('data/dsm_vox_50cm_tile_-12_0.tif').read()[0]\n",
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"#image = rio.open('data/dsm_vox_50cm_tile_-7_1.tif').read()[0]\n",
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"\n",
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"load_image(image)"
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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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"tags": []
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},
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"source": [
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"## Run"
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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": 3,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2024-02-14 17:46:43,794 - spectra_app - INFO - Initialize spectra app\n",
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"2024-02-14 17:46:43,854 - spectra_app - INFO - init spectrum axis...\n",
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"2024-02-14 17:46:43,855 - spectra_app - INFO - compute spectrum...\n",
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"2024-02-14 17:46:43,859 - spectra_app - INFO - compute spectrum done\n",
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"2024-02-14 17:46:44,053 - spectra_app - INFO - init spectrum axis done\n",
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"2024-02-14 17:46:44,054 - spectra_app - INFO - init lidar view\n"
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]
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},
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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": "898e72645d384b3da97065dc823ff386",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"VBox(children=(HBox(children=(Dropdown(description='Attribute X', options=('area', 'child_number', 'children_p…"
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]
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},
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"execution_count": 3,
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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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"# \"widget\" or \"qt\", qt provide a snappier GUI\n",
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"#%matplotlib widget\n",
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"%matplotlib qt\n",
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"\n",
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"plt.style.use('dark_background')\n",
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"\n",
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"init()\n",
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"\n",
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"plt.close()\n",
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"fig = plt.figure('Interactive 2D Shape Size Spectrum Analysis', figsize=(17, 5), constrained_layout=False)\n",
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"grid = mpl.gridspec.GridSpec(1, 3, fig, \n",
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" top=0.915,\n",
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" bottom=0.11,\n",
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" left=0.045,\n",
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" right=1.0,\n",
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" hspace=0.17,\n",
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" wspace=0.1)\n",
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"\n",
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"model['ax_spectrum'] = fig.add_subplot(grid[:2])\n",
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"model['ax_dtm'] = fig.add_subplot(grid[-1])\n",
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"model['fig'] = fig\n",
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"\n",
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"def draw_e(e):\n",
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" refresh_lidar_axis()\n",
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" refresh_spectrum_axis()\n",
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" \n",
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"def on_xlim_change(*args):\n",
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" ax = args[0]\n",
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" print('x change triggered')\n",
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" if ax.get_xlim() == model['ax_dtm_xlim']:\n",
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" return\n",
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" model['ax_dtm_xlim'] = ax.get_xlim()\n",
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" #reset_lidar_axis()\n",
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" \n",
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"def on_ylim_change(*args):\n",
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" ax = args[0]\n",
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" print(f'y change triggered: {ax.get_ylim()}')\n",
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" if ax.get_ylim() == model['ax_dtm_ylim']:\n",
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" return\n",
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" model['ax_dtm_ylim'] = ax.get_ylim()\n",
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" \n",
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" reset_lidar_axis()\n",
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" return\n",
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"\n",
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"def redraw_lidar_contours(event):\n",
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" ax = model['ax_dtm']\n",
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" path_patch = model['dtm_contours']\n",
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" ax.draw_artist(path_patch)\n",
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" fig.canvas.blit(path_patch.clipbox)\n",
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" #fig.canvas.flush_events()\n",
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"\n",
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"#fig.canvas.mpl_connect(\"resize_event\", resize_callback)\n",
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"#fig.canvas.mpl_connect('draw_event', draw_e)\n",
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"\n",
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"init_spectrum_axis()\n",
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"init_lidar_axis()\n",
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"\n",
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"plt.show()\n",
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"\n",
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"#refresh_spectrum_axis()\n",
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"#refresh_lidar_axis()\n",
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"\n",
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"init_selector()\n",
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"ax = model['ax_dtm']\n",
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"ax.callbacks.connect('xlim_changed', on_xlim_change)\n",
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"ax.callbacks.connect('ylim_changed', on_ylim_change)\n",
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"fig.canvas.mpl_connect('draw_event', redraw_lidar_contours)\n",
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"\n",
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"\n",
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"#refresh_spectrum_axis()\n",
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"\n",
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"\n",
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"#model['tree_type'] = ipw.Dropdown(description='Hierarchy', value=sap.trees.MaxTree, options=inspect.getmembers(sap.trees, lambda t: inspect.isclass(t) and issubclass(t, sap.Tree) and t != sap.Tree))\n",
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"load_button = ipw.Button(description='Analyse', icon='rocket')\n",
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"#model['tree_type'].observe(tree_change, names='value')\n",
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"load_button.on_click(load_change)\n",
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"\n",
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"ipw.VBox([\n",
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" #ipw.HBox([model['tile_name'], model['lidar_feature'], model['tree_type'] , load_button]),\n",
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" ipw.HBox([model['x'], model['x_log']]),\n",
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" ipw.HBox([model['y'], model['y_log']]),\n",
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" #load_button,\n",
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" model['highlight'],\n",
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" model['overlay'],\n",
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" load_button\n",
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"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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spectra_app.py
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spectra_app.py
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import logging
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import matplotlib as mpl
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from matplotlib import path, patches, pyplot as plt
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import numpy as np
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from skimage import measure
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import ipywidgets as ipw
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import higra as hg
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import sap
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from sap.spectra import get_bins
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log = logging.getLogger(__name__)
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log.setLevel(logging.DEBUG)
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ch = logging.StreamHandler()
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ch.setLevel(logging.DEBUG)
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formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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ch.setFormatter(formatter)
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log.addHandler(ch)
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def compute_contours(image, threshold):
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contours = measure.find_contours(image > threshold, 0, positive_orientation='high', fully_connected='high')
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return contours
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def convert_contours_to_verts(contours):
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if len(contours) == 0:
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return np.zeros((1, 2)), None
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codes = []
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for c in contours:
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code = np.repeat(path.Path.LINETO, len(c))
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code[0] = path.Path.MOVETO
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codes += [code]
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verts = np.concatenate(contours)[:, ::-1]
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codes = np.concatenate(codes)
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return verts, codes
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def get_contours_verts(image, threshold):
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return convert_contours_to_verts(compute_contours(image, threshold))
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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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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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def load_image(image):
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model['im'] = image
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model['im_hs'] = ui8_clip(hillshades(model['im']), -1, 1)
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model['im_hs_alpha'] = ((model['im_hs'].astype(float) - 127) / 127) ** 2 * .4
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kwargs = {}
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kwargs['vmin'], kwargs['vmax'] = np.quantile(model['im'], (.01, .99))
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model['im_kwargs'] = kwargs
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model['im_mesh'] = np.meshgrid(np.arange(model['im'].shape[1]), np.arange(model['im'].shape[0]))
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model['tree_cache'] = sap.MaxTree(model['im'])
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def line_select_callback(eclick, erelease):
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log.info('spectrum mouse release callback triggered')
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model['x1'], model['y1'] = eclick.xdata, eclick.ydata
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model['x2'], model['y2'] = erelease.xdata, erelease.ydata
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refresh_lidar_axis()
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def dtm_select_callback(eclick, erelease):
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log.info('lidar mouse release callback triggered')
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model['dtm_y1'], model['dtm_x1'] = np.int(eclick.xdata), np.int(eclick.ydata)
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model['dtm_y2'], model['dtm_x2'] = np.int(erelease.xdata), np.int(erelease.ydata)
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filter_nodes()
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def filter_nodes():
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log.info('filter selected nodes')
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x1, x2 = model['dtm_x1'], model['dtm_x2']
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y1, y2 = model['dtm_y1'], model['dtm_y2']
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pmask = np.zeros_like(model['im'], dtype=np.bool)
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pmask[x1:x2,y1:y2] = True
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model['pixel_mask'] = pmask
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compute_spectrum_highlight()
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if model['highlight'].value:
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refresh_spectrum_axis()
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else:
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# This trigger spectrum refresh
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model['highlight'].value = True
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def init_spectrum_axis():
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log.info('init spectrum axis...')
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compute_spectrum()
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fig = model['fig']
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ax = model['ax_spectrum']
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x, y = model['x'].value, model['y'].value
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spectrum, xedges, yedges, x_log, y_log = model['spectrum']
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#x_log, y_log = model['x_log'].value, model['y_log'].value
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#void = np.ma.zeros((100, 200))
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#void.mask = True
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#reset_ax(ax)
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ax.clear()
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pc = ax.pcolor(xedges, yedges, spectrum.T, norm=mpl.colors.LogNorm(), animated=False)
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if x_log:
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ax.set_xscale('log')
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if y_log:
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ax.set_yscale('log')
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ax.set_xlabel(x)
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ax.set_ylabel(y)
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ax.set_title('Pattern Spectrum')
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ax.grid(True)
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ax.grid(which='minor', color='#BBBBBB', linestyle=':')
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model['spectrum_pcolor'] = pc
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fig.canvas.draw()
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log.info('init spectrum axis done')
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def _compute_spectrum(node_mask=None):
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tree = model['tree_cache']
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x, y = model['x'].value, model['y'].value
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x_log, y_log = model['x_log'].value, model['y_log'].value
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ps = sap.spectrum2d(tree, x, y, 200, 100, x_log, y_log, node_mask=node_mask)
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ps = (np.ma.array(ps[0], mask=ps[0]==0),) + ps[1:]
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return ps
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def compute_spectrum():
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log.info('compute spectrum...')
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model['spectrum'] = _compute_spectrum()
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log.info('compute spectrum done')
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def compute_spectrum_highlight():
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log.info('compute spectrum highlight...')
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tree = model['tree_cache']
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mask = model['pixel_mask']
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spectrum = model['spectrum']
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node_mask = pixel_to_node(tree, mask)# if mask is not None else None
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#model['spectrum_highlight'] = _compute_spectrum(node_mask)
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spectrum_mask = _compute_spectrum(node_mask)
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spectrum_highlight = np.zeros_like(spectrum[0])
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#spectrum_highlight = np.tile(np.nan, spectrum[0].shape)
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spectrum_highlight = np.divide(spectrum_mask[0],
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spectrum[0],
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where=spectrum[0]!=0,
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out=spectrum_highlight)
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spectrum_highlight.mask = spectrum[0].mask
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model['spectrum_highlight'] = spectrum_highlight
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log.info('compute spectrum highlight done')
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def refresh_spectrum_axis():
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log.info('refresh spectrum axis...')
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fig = model['fig']
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pc = model['spectrum_pcolor']
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highlight = model['highlight'].value
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spectrum = model['spectrum_highlight'] if highlight else model['spectrum'][0]
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pc.set_array(spectrum.T.compressed())
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pc.set_norm(mpl.colors.Normalize(0, 1) if highlight else mpl.colors.LogNorm())
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# TODO: if highlight remove norm log and set to [0, 1]
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#norm=mpl.colors.LogNorm() if log_scale else None)
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fig.draw_artist(pc)
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fig.canvas.blit(pc.clipbox)
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#fig.canvas.flush_events()
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model['widget_spectrum'].update_background(None)
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log.info('refresh spectrum axis done')
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def show_spectrum():
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assert False, 'LEGACY FUNCTION NEVER CALLED DAMNIT'
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x, y = model['x'].value, model['y'].value
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x_log, y_log = model['x_log'].value, model['y_log'].value
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highlight = model['highlight'].value
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# Compute spectrum
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log.info('compute spectrum...')
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ps = sap.spectrum2d(model['tree_cache'], x, y, 200, 100, x_log, y_log)
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log.info('compute done')
|
||||
|
||||
if highlight:
|
||||
node_mask = pixel_to_node(model['tree_cache'], model['pixel_mask']) if model['pixel_mask'] is not None else None
|
||||
log.info('compute masked spectrum...')
|
||||
ps_mask = sap.spectrum2d(model['tree_cache'], x, y, 200, 100, x_log, y_log, node_mask=node_mask)
|
||||
ps[0][:] = np.divide(ps_mask[0], ps[0], where=ps[0]!=0, out=np.tile(np.nan, ps_mask[0].shape))
|
||||
log.info('compute done')
|
||||
|
||||
# Display spectrum
|
||||
plt.sca(model['ax_spectrum'])
|
||||
plt.cla()
|
||||
log.info('draw spectrum...')
|
||||
sap.show_spectrum(*ps, log_scale=not highlight)
|
||||
plt.xlabel(x)
|
||||
plt.ylabel(y)
|
||||
plt.grid(which='minor', color='#BBBBBB', linestyle=':')
|
||||
plt.grid(True)
|
||||
|
||||
log.info('draw complete')
|
||||
#plt.gcf().canvas.draw_idle()
|
||||
plt.gcf().canvas.blit(model['ax_spectrum'].bbox)
|
||||
log.info('matplotlib redraw complete')
|
||||
|
||||
# Refactor selector?
|
||||
#init_selector()
|
||||
|
||||
|
||||
def get_attribute_extends():
|
||||
x1, x2 = model['x1'], model['x2']
|
||||
y1, y2 = model['y1'], model['y2']
|
||||
|
||||
return x1, x2, y1, y2
|
||||
|
||||
|
||||
def filter_tree():
|
||||
log.info('filtering the tree...')
|
||||
|
||||
x1, x2, y1, y2 = get_attribute_extends()
|
||||
|
||||
t = model['tree_cache']
|
||||
X = t.get_attribute(model['x'].value)
|
||||
Y = t.get_attribute(model['y'].value)
|
||||
|
||||
im_f = model['tree_cache'].reconstruct((X < x1) | (X > x2) |
|
||||
(Y < y1) | (Y > y2),
|
||||
filtering='subtractive')
|
||||
model['im_f'] = im_f
|
||||
|
||||
log.info('tree filtering complete')
|
||||
|
||||
def refresh_lidar_axis():
|
||||
log.info('refresh lidar view...')
|
||||
|
||||
x1, x2, y1, y2 = get_attribute_extends()
|
||||
|
||||
# No filter
|
||||
if x1 == x2 == y1 == y2 == 0:
|
||||
log.info('no filtering applied')
|
||||
return
|
||||
|
||||
# Filter
|
||||
filter_tree()
|
||||
draw_lidar_contours()
|
||||
|
||||
#model['ax_dtm'].contour(*model['im_mesh'], img, [model['tree_cache']._alt[-1]], colors='lime')
|
||||
|
||||
log.info('refresh complete')
|
||||
#plt.gcf().canvas.draw_idle()
|
||||
#log.info('matplotlib redraw complete')
|
||||
|
||||
|
||||
def reset_ax(ax):
|
||||
ax.clear()
|
||||
ax.collections.clear()
|
||||
ax.patches.clear()
|
||||
|
||||
|
||||
def draw_lidar_contours():
|
||||
log.info('draw lidar contours...')
|
||||
path_patch = model['dtm_contours']
|
||||
contour_path = path_patch.get_path()
|
||||
|
||||
im_f = model['im_f']
|
||||
tree = model['tree_cache']
|
||||
|
||||
root_val = tree._alt[-1]
|
||||
|
||||
contour_path.vertices, contour_path.codes = get_contours_verts(im_f, root_val)
|
||||
|
||||
fig = model['fig']
|
||||
fig.canvas.restore_region(model['dtm_bg'])
|
||||
fig.draw_artist(path_patch)
|
||||
fig.canvas.blit(path_patch.clipbox)
|
||||
model['widget_dtm'].update_background(None)
|
||||
#fig.canvas.flush_events()
|
||||
|
||||
|
||||
def init_dtm_contours():
|
||||
contour_path = path.Path([(0,0)])
|
||||
path_patch = patches.PathPatch(contour_path,
|
||||
color='lime',
|
||||
linewidth=2,
|
||||
fill=False,
|
||||
animated=True)
|
||||
model['dtm_contours'] = path_patch
|
||||
model['ax_dtm'].add_artist(path_patch)
|
||||
|
||||
|
||||
def init_lidar_axis():
|
||||
#reset_ax(model['ax_dtm']
|
||||
_init_lidar_axis()
|
||||
init_dtm_contours()
|
||||
|
||||
|
||||
def reset_lidar_axis():
|
||||
log.info('reset lidar axis')
|
||||
|
||||
path_patch = model['dtm_contours']
|
||||
fig = model['fig']
|
||||
ax = model['ax_dtm']
|
||||
|
||||
path_patch.set_visible(False)
|
||||
fig.canvas.draw()
|
||||
model['dtm_bg'] = fig.canvas.copy_from_bbox(ax.bbox)
|
||||
|
||||
path_patch.set_visible(True)
|
||||
|
||||
|
||||
def _init_lidar_axis():
|
||||
log.info('init lidar view')
|
||||
ax = model['ax_dtm']
|
||||
fig = model['fig']
|
||||
# xlim = model['ax_dtm_xlim']
|
||||
# ylim = model['ax_dtm_ylim']
|
||||
|
||||
#reset_ax(ax)
|
||||
|
||||
ax.imshow(model['im'], **model['im_kwargs'])
|
||||
ax.imshow(model['im_hs'], cmap=plt.cm.Greys, alpha=model['im_hs_alpha'])
|
||||
ax.set_title('LiDAR')
|
||||
# ax.set_xlim(xlim)
|
||||
# ax.set_ylim(ylim)
|
||||
|
||||
fig.canvas.draw()
|
||||
model['dtm_bg'] = fig.canvas.copy_from_bbox(ax.bbox)
|
||||
|
||||
|
||||
def load_tile():
|
||||
model['im'] = rio.open(get_tile(model['lidar_feature'].value, model['tile_name'].value)).read()[0]
|
||||
model['im_hs'] = ui8_clip(hillshades(model['im']), -1, 1)
|
||||
model['im_hs_alpha'] = ((model['im_hs'].astype(float) - 127) / 127) ** 2 * .4
|
||||
kwargs = {}
|
||||
kwargs['vmin'], kwargs['vmax'] = np.quantile(model['im'], (.01, .99))
|
||||
model['im_kwargs'] = kwargs
|
||||
model['im_mesh'] = np.meshgrid(np.arange(model['im'].shape[1]), np.arange(model['im'].shape[0]))
|
||||
|
||||
|
||||
def load_tree():
|
||||
model['tree_cache'] = model['tree_type'].value(model['im'])
|
||||
|
||||
|
||||
def reset_selections():
|
||||
model.update({
|
||||
'x1': 0,
|
||||
'x2': 0,
|
||||
'y1': 0,
|
||||
'y2': 0,
|
||||
'dtm_x1': 0,
|
||||
'dtm_x2': 0,
|
||||
'dtm_y1': 0,
|
||||
'dtm_y2': 0,
|
||||
})
|
||||
model['overlay'].value = True
|
||||
model['highlight'].value = False
|
||||
|
||||
|
||||
def init_selector():
|
||||
"""Call after creating plot axes"""
|
||||
# Selectors
|
||||
# Setup selector spectrum
|
||||
model['widget_spectrum'] = mpl.widgets.RectangleSelector(model['ax_spectrum'], line_select_callback,
|
||||
useblit=True,
|
||||
button=[1, 3], # don't use middle button
|
||||
minspanx=5, minspany=5,
|
||||
spancoords='pixels',
|
||||
props = dict(facecolor=(0,1,0,.2), edgecolor=(0,1,0), fill=True, ls='--', lw=2),
|
||||
interactive=True)
|
||||
|
||||
# Setup selector DTM
|
||||
model['widget_dtm'] = mpl.widgets.RectangleSelector(model['ax_dtm'], dtm_select_callback,
|
||||
useblit=True,
|
||||
button=[1, 3], # don't use middle button
|
||||
minspanx=5, minspany=5,
|
||||
spancoords='pixels',
|
||||
props = dict(facecolor=(1,1,0,.2), edgecolor=(1,1,0), fill=True, ls='--', lw=2),
|
||||
interactive=True)
|
||||
|
||||
def load_change(change):
|
||||
#print('Loading tile...')
|
||||
#load_tile()
|
||||
#print('Loading tree...')
|
||||
#load_tree()
|
||||
#print('Loading complete')
|
||||
log.info('change triggered redraw')
|
||||
reset_selections()
|
||||
#show_spectrum()
|
||||
init_spectrum_axis()
|
||||
init_lidar_axis()
|
||||
#refresh_lidar_axis()
|
||||
#plt.show()
|
||||
|
||||
def resize_callback(resize_event):
|
||||
log.info('resize callback triggered')
|
||||
|
||||
reset_lidar_axis()
|
||||
# Try to update only when resizing finished
|
||||
#lw = model['last_width'] if 'last_width' in model else 0
|
||||
#lh = model['last_height'] if 'last_height' in model else 0
|
||||
|
||||
#if resize_event.width == lw and resize_event.height == lh:
|
||||
# init_lidar_axis()
|
||||
#
|
||||
#model['last_width'] = resize_event.width
|
||||
#model['last_height'] = resize_event.height
|
||||
|
||||
|
||||
def spectrum_change(change):
|
||||
if change['name'] != 'value':
|
||||
return
|
||||
|
||||
#show_spectrum()
|
||||
log.info('spetrum change triggered')
|
||||
#compute_spectrum()
|
||||
reset_selections()
|
||||
init_spectrum_axis()
|
||||
#compute_spectrum_highlight()
|
||||
#refresh_spectrum_axis()
|
||||
|
||||
|
||||
def spectrum_change_hl(change):
|
||||
log.info('spetrum highlight toggle change triggered')
|
||||
refresh_spectrum_axis()
|
||||
|
||||
|
||||
def init_model():
|
||||
model.update({
|
||||
'ax_spectrum': None,
|
||||
'ax_dtm': None,
|
||||
'x': ipw.Dropdown(description='Attribute X', options=sap.available_attributes().keys(), value='area'),
|
||||
'y': ipw.Dropdown(description='Attribute Y', options=sap.available_attributes().keys(), value='compactness'),
|
||||
'x_log': ipw.Checkbox(description='X log', value=True),
|
||||
'y_log': ipw.Checkbox(description='Y log', value=False),
|
||||
'highlight': ipw.Checkbox(description='Spectrum higlight'),
|
||||
'overlay': ipw.Checkbox(description='Filtering overlay', value=True),
|
||||
'x1': 0,
|
||||
'x2': 0,
|
||||
'y1': 0,
|
||||
'y2': 0,
|
||||
'dtm_x1': 0,
|
||||
'dtm_x2': 0,
|
||||
'dtm_y1': 0,
|
||||
'dtm_y2': 0,
|
||||
'pixel_mask': None,
|
||||
'ax_dtm_xlim': None,
|
||||
'ax_dtm_ylim': None
|
||||
})
|
||||
|
||||
def init_observer():
|
||||
model['x_log'].observe(spectrum_change)
|
||||
model['y_log'].observe(spectrum_change)
|
||||
model['highlight'].observe(spectrum_change_hl)
|
||||
model['x'].observe(spectrum_change)
|
||||
model['y'].observe(spectrum_change)
|
||||
|
||||
|
||||
model = {
|
||||
}
|
||||
|
||||
|
||||
def init():
|
||||
log.info('Initialize spectra app')
|
||||
init_model()
|
||||
init_observer()
|
||||
Loading…
Reference in New Issue
Block a user