From 7b24593d0a7ac173bd5ef926dd874a9bd3fc5e06 Mon Sep 17 00:00:00 2001 From: dyk dhi Date: Thu, 21 Nov 2024 15:49:53 +0000 Subject: [PATCH] adding snow compare example --- docs/_quarto.yml | 1 + docs/examples/dfs2/snowcompare.qmd | 133 ++++++++++++++++++++++++++++ images/dfs2_snowcompare_preview.png | Bin 0 -> 194950 bytes tests/testdata/MikeSheExtract.dfs2 | Bin 0 -> 3712291 bytes tests/testdata/ModisExtract.dfs2 | Bin 0 -> 1855863 bytes 5 files changed, 134 insertions(+) create mode 100644 docs/examples/dfs2/snowcompare.qmd create mode 100644 images/dfs2_snowcompare_preview.png create mode 100644 tests/testdata/MikeSheExtract.dfs2 create mode 100644 tests/testdata/ModisExtract.dfs2 diff --git a/docs/_quarto.yml b/docs/_quarto.yml index 469daf756..499252e3c 100644 --- a/docs/_quarto.yml +++ b/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: diff --git a/docs/examples/dfs2/snowcompare.qmd b/docs/examples/dfs2/snowcompare.qmd new file mode 100644 index 000000000..3057073a1 --- /dev/null +++ b/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, 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