adding snow compare example

docs/_quarto.yml

@@ -58,6 +58,7 @@ website:
             contents:
               - examples/dfs2/bathy.qmd
               - examples/dfs2/gfs.qmd
+              - examples/dfs2/snowcompare.qmd
           - section: Dfsu
             href: examples/dfsu/index.qmd
             contents:

docs/examples/dfs2/snowcompare.qmd

@@ -0,0 +1,133 @@
+---
+title: Dfs2 - Snow Compare
+jupyter: python3
+---
+
+
+
+## Using This Notebook
+- Provides a simple differencing tool to compare two Dfs2 files, in this case MIKE SHE and MODIS snow cover
+- Due to the ipywidgets controls which dynamically query Dfs2 files, this notebook is best downloaded from the right hand menu using the Jupyter link, and executed locally
+- Modify the inputs section cell to provide file paths and then execute all cells
+- Use the horizontal slider controls in the Analysis section to move back and forth in time for each Dfs2. A simple grid calculation will be performed showing which cells have lower, similar, or higher values, allowing you to identify areas of concern for further investigation
+- If the notebook is running as expected, the Analysis section will appear as follows:
+
+![Example of Analysis section](../../images/dfs2_snowcompare_preview.png)
+
+## Environment Setup
+- The following packages were used at the time of development, and you may be able to use more recent versions:
+    - ipykernel 6.29.0
+    - mikeio 1.6.3
+    - matplotlib 3.8.2
+    - ipywidgets 8.1.5
+
+```{python}
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import mikeio
+import ipywidgets as widgets
+```
+
+## Inputs Section
+
+```{python}
+mikeSheSnowDfs2Path = "../../data/MikeSheExtract.dfs2"
+modisSnowDfs2Path = "../../data/ModisExtract.dfs2"
+
+mikeSheSnowDfs2 = mikeio.read(mikeSheSnowDfs2Path)
+modisSnowDfs2 = mikeio.read(modisSnowDfs2Path)
+```
+
+## Functions Section
+
+```{python}
+def sliders_changed(msheSlider, modisSlider):
+    modisItemName = 'Snow Cover'
+    msheItemName = 'Fraction of cell area covered by Snow'
+
+    timeIndex = modisSnowDfs2[modisItemName].time.get_loc(modisSlider)
+    modisSingleTimestep = modisSnowDfs2[modisItemName].isel(
+        time=timeIndex) / 100.0
+
+    timeIndex = mikeSheSnowDfs2[msheItemName].time.get_loc(msheSlider)
+    mikeSheSingleTimestep = mikeSheSnowDfs2[msheItemName].isel(time=timeIndex)
+
+    # use nans in MSHE to set nans in MODIS to mask out catchment
+    modisSingleTimestep.values = np.where(np.isnan(
+        mikeSheSingleTimestep.values), mikeSheSingleTimestep.values, modisSingleTimestep.values)
+    modisSingleDataArray = modisSingleTimestep.to_xarray()
+    modisSingleDataArray.attrs['units'] = 'Fraction'
+
+    # 3 column layout
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(21, 5))
+    modisSingleDataArray.plot.pcolormesh(
+        ax=ax1, vmin=0.0, vmax=1.0, cmap="jet")
+    ax1.set_title('MODIS')
+
+    mikeSheSingleDataArray = mikeSheSingleTimestep.to_xarray()
+    mikeSheSingleDataArray.attrs['units'] = 'Fraction'
+    mikeSheSingleDataArray.plot.pcolormesh(
+        ax=ax2, vmin=0.0, vmax=1.0, cmap="jet")
+    ax2.set_title('MIKE SHE')
+
+    diff = modisSingleTimestep.copy()
+    diff.values = modisSingleTimestep.values - mikeSheSingleTimestep.values
+
+    categories = ['MSHE Higher', 'Similar', 'Similar', 'MODIS Higher']
+    colors = ['yellow', 'green', 'red']
+    boundaries = [-1.0, -0.1, 0.1, 1.0]
+
+    cmap = mpl.colors.ListedColormap(colors)
+    norm = mpl.colors.BoundaryNorm(boundaries, cmap.N)
+
+    diffPlot = diff.to_xarray().plot.pcolormesh(ax=ax3, cmap=cmap, norm=norm)
+    cbar = diffPlot.colorbar
+    cbar.set_ticklabels(categories)
+    cbar.set_label(None)
+
+    ax3.set_title('Difference (MODIS - MIKE SHE)')
+    plt.tight_layout()
+
+
+style = {'description_width': 'initial'}
+
+msheSlider = widgets.SelectionSlider(
+    options=mikeSheSnowDfs2.time,
+    value=mikeSheSnowDfs2.time[0],
+    description='MSHE Timestep',
+    disabled=False,
+    continuous_update=False,
+    orientation='horizontal',
+    readout=True,
+    style=style,
+    layout=widgets.Layout(width='800px', margin='0 30px 0 0 ')
+)
+
+# constrain the modis timesteps to the mshe, as modis covers far more
+modis_time_slicer = modisSnowDfs2.time.slice_indexer(
+    start=mikeSheSnowDfs2.time[0], end=mikeSheSnowDfs2.time[-1])
+filtered_modis_times = modisSnowDfs2.time[modis_time_slicer]
+
+modisSlider = widgets.SelectionSlider(
+    options=filtered_modis_times,
+    value=filtered_modis_times[0],
+    description='MODIS Timestep',
+    disabled=False,
+    continuous_update=False,
+    orientation='horizontal',
+    readout=True,
+    style=style,
+    layout=widgets.Layout(width='800px', margin='0 30px 0 0 ')
+)
+```
+
+## Analysis Section
+
+- This section uses ipywidgets to make dynamic queries to the Dfs2 files, so it can only be run locally after you have downloaded this notebook
+
+```{python}
+ui = widgets.HBox([modisSlider, msheSlider])
+out = widgets.interactive_output(sliders_changed, {'msheSlider' : msheSlider, 'modisSlider' : modisSlider})
+display(ui, out)
+```