Merge pull request #51 from TUW-GEO/dev

add remote cmap

Author: MartinSchobben

Diff for: .isort.cfg

@@ -0,0 +1,2 @@
+[settings]
+known_third_party = eo_datascience,nbformat,pytest,setuptools,yaml

Diff for: .pre-commit-config.yaml

@@ -1,13 +1,72 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0  # the release, git tag, or commit you want to use
+    rev: v4.6.0 # the release, git tag, or commit you want to use
     hooks:
       - id: trailing-whitespace
       - id: check-added-large-files
       - id: check-yaml
       - id: detect-private-key
+      - id: end-of-file-fixer
+      - id: check-docstring-first
+      - id: check-json
+      - id: double-quote-string-fixer
+
+  - repo: https://github.com/psf/black
+    rev: 23.12.1
+    hooks:
+      - id: black
+
+  - repo: https://github.com/keewis/blackdoc
+    rev: v0.3.9
+    hooks:
+      - id: blackdoc
+
+  - repo: https://github.com/PyCQA/flake8
+    rev: 7.0.0
+    hooks:
+      - id: flake8
+
+  - repo: https://github.com/asottile/seed-isort-config
+    rev: v2.2.0
+    hooks:
+      - id: seed-isort-config
+
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.13.2
+    hooks:
+      - id: isort
+
+  - repo: https://github.com/pre-commit/mirrors-prettier
+    rev: v3.1.0
+    hooks:
+      - id: prettier
+        additional_dependencies: [[email protected]]
+
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.7.1
+    hooks:
+      - id: nbqa-black
+        additional_dependencies: [black]
+      - id: nbqa-pyupgrade
+        additional_dependencies: [pyupgrade]
+        exclude: foundations/quickstart.ipynb
+      - id: nbqa-isort
+        additional_dependencies: [isort]
+
+  - repo: https://github.com/s-weigand/flake8-nb
+    rev: "v0.5.3"
+    hooks:
+      - id: flake8-nb
+
+  # - repo: local
+  #   hooks:
+  #     - id: convert-ipynb
+  #       name: Convert Jupyter notebooks
+  #       entry: make convert
+  #       language: system
+  #       pass_filenames: false
 
   - repo: https://github.com/kynan/nbstripout
     rev: 0.7.1
     hooks:
-      - id: nbstripout
+      - id: nbstripout

Diff for: Makefile

@@ -6,6 +6,7 @@ YML = $(wildcard notebooks/**/*.yml)
 REQ := $(basename $(notdir $(YML)))
 NB != find chapters -name "*.quarto_ipynb" -o  -name "*.ipynb" -not -path \
 	"*/.jupyter_cache/*"
+QN != git diff --cached --name-only "***.ipynb"
 
 CONDA_ENV != conda info --base
 CONDA_ACTIVATE := source $(CONDA_ENV)/etc/profile.d/conda.sh ; \
@@ -53,6 +54,11 @@ post-render:
 	cp ./Makefile ./notebooks/
 	cp -r ./chapters/images ./notebooks
 
+convert:
+	$(foreach f, $(QN), \
+		quarto convert $(f); \
+		mv $(subst .ipynb,.qmd, $(f)) $(subst notebooks,chapters,$(subst .ipynb,.qmd,$(f))); )
+
 preview: $(CONDA_ENV)/envs/eo-datascience $(CONDA_ENV_DIR) $(KERNEL_DIR)
 	$(CONDA_ACTIVATE) eo-datascience
 	- mkdir -p _preview/notebooks
@@ -79,4 +85,4 @@ teardown:
 		jupyter kernelspec uninstall -y $(f); \
 		conda deactivate; \
 		conda remove -n $(f) --all -y ; \
-		conda deactivate; )
+		conda deactivate; )

Diff for: chapters/courses/microwave-remote-sensing/02_in_class_exercise.qmd

@@ -23,7 +23,7 @@ import pystac_client
 import odc.stac
 import matplotlib.pyplot as plt
 import xarray as xr
-import rioxarray  # noqa
+import rioxarray  # noqa: F401
 ```
 
 ## Exploring the Data
@@ -288,4 +288,3 @@ data_2_save.rio.to_raster(
 loaded_data = xr.open_dataset("sig0_mean_mosaic_july.tif", engine="rasterio")
 loaded_data
 ```
-

Diff for: chapters/courses/microwave-remote-sensing/03_in_class_exercise.qmd

@@ -14,13 +14,9 @@ In this notebook, we will introduce some of the steps involved in the processing
 import numpy as np
 import xarray as xr
 import intake
-
-import rioxarray #noqa
-import matplotlib.pyplot as plt # noqa
-import hvplot.xarray  # noqa
-
-from pathlib import Path
-from functools import partial
+import rioxarray # noqa: F401
+import matplotlib.pyplot as plt # noqa: F401
+import hvplot.xarray  # noqa: F401
 ```
 
 ## Loading Backscatter Data
@@ -156,4 +152,3 @@ rtc_dc.hvplot.image(x="x", y="y", robust=True, data_aspect=1, cmap="Greys_r",
 *Figure 7: $\sigma^0_E$, $\gamma^0_E$, and $\gamma^0_T$ can be selected on the right-hand side of the graphic.*
 
 When comparing $\gamma^0_E$ and $\gamma^0_T$ in the plot we can clearly see the impact of the radiometric correction in the mountainous areas. This correction is necessary, because for slopes facing towards the sensor, a larger ground area contributes to the backscatter value of a slant range resolution cell, than for slopes lying in the opposite direction. This results in significant brightness changes, where foreshortening areas appear brighter and lengthening areas darker. $\gamma^0_T$ adjusts the backscatter to represent what it would be if the terrain was flat, thus reducing these effects. This significantly reduces the impact of the terrain on the backscatter values, allowing for more accurate comparisons across different terrain types and locations. The correction is done by using a DEM to determine the local illuminated area at each radar position. The above illustrated approach is also referred to as terrain flattening because in the resulting image, mountains appear flat. As $\gamma^0_T$ is corrected for geometric and radiometric distortions, it is also referred to as Normalized Radar Backscatter (NRB) and is the current standard for Analysis-Ready-Backscatter (ARD).
-

Diff for: chapters/courses/microwave-remote-sensing/04_in_class_exercise.qmd

@@ -40,15 +40,13 @@ In this notebook we will study two different SAR products. SAR data from the Adv
 import numpy as np
 import pandas as pd
 import xarray as xr
-import rioxarray  # noqa
-import hvplot.xarray  # noqa
+import rioxarray  # noqa: F401
+import hvplot.xarray  # noqa: F401
 import folium
-
 from huggingface_hub import snapshot_download
 from shapely.geometry import mapping, box
 from shapely import affinity
 from rasterio.enums import Resampling
-
 from pathlib import Path
 from functools import partial
 ```
@@ -299,4 +297,3 @@ fused_da.\
 ```
 
 *Figure 3: Stacked array with ALOS-2 L-band and Sentinel-1 C-band $\gamma^0_T (dB)$.*
-

Diff for: chapters/courses/microwave-remote-sensing/05_in_class_exercise.qmd

@@ -20,11 +20,9 @@ In this notebook, we aim to demonstrate how C-band (4–8 GHz, wavelengths of ap
 
 ```{python}
 import numpy as np
-import xarray as xr
 import intake
-
 import matplotlib.pyplot as plt
-import hvplot.xarray  # noqa
+import hvplot.xarray  # noqa: F401
 import holoviews as hv
 ```
 
@@ -154,4 +152,3 @@ LAI_image + time_series
 Can you see some patterns when analyzing the different wavelengths and polarizations?
 
 Remember again that we deal with a logarithmic scale. A measurement of 10 dB is 10 times brighter than the intensity measured at 0 dB, and 100 times brighter at 20 dB. The most notable difference is that the offset between cross- and co-polarised signals becomes larger at low LAI and lower at higher LAI. This might indicate the effect of volume scattering in forested areas where co- and cross-polarization render backscattering values more equal. You will study the differences among cross- and co-polarized backscattering in more detail in the homework exercise.
-

Diff for: chapters/courses/microwave-remote-sensing/06_in_class_exercise.qmd

@@ -14,14 +14,9 @@ In this notebook, we will investigate the varying backscatter values associated
 ```{python}
 import numpy as np
 import xarray as xr
-import rioxarray  # noqa
-
+import rioxarray  # noqa: F401
 import intake
 import json
-
-from functools import partial
-from pathlib import Path
-
 import matplotlib.pyplot as plt
 import matplotlib.patches as mpatches
 from matplotlib.colors import ListedColormap, BoundaryNorm
@@ -62,7 +57,7 @@ For the different land cover types we use the official color encoding which can
 
 ```{python}
 # Load encoding
-with open("../../images/cmaps/06_color_mapping.json", "r") as f:
+with cat.corine_cmap.read()[0] as f:
     color_mapping_data = json.load(f)
 
 # Get mapping
@@ -220,4 +215,3 @@ hist_opts = hv.opts.Histogram(width=350, height=555)
 
 dmap.opts(image_opts, hist_opts)
 ```
-

Diff for: chapters/courses/microwave-remote-sensing/07_in_class_exercise.qmd

@@ -18,10 +18,8 @@ Speckle is defined as a kind of noise that affects all radar images. Given the m
 
 ```{python}
 import numpy as np
-import xarray as xr
 import intake
 import json
-from pathlib import Path
 import matplotlib.pyplot as plt
 import holoviews as hv
 from holoviews.streams import RangeXY
@@ -139,7 +137,7 @@ We also create the same dashboard for backscatter of different landcover types o
 
 ```{python}
 # Load encoding
-with open("../../images/cmaps/06_color_mapping.json", "r") as f:
+with cat.corine_cmap.read()[0] as f:
     color_mapping_data = json.load(f)
 
 # Get mapping
@@ -354,4 +352,3 @@ plot_variability(fused_ds, filter_fun_temporal=temporal_filter)
 *Figure 4: Lake Neusiedl $\sigma^0_E$ with a temporal filter applied.*
 
 Let´s observe the histograms of the two plots. Especially in the region around the lake, it is clear that the distribution is now less dispersed and more centered around a central value.
-

Diff for: chapters/courses/microwave-remote-sensing/08_in_class_exercise.qmd

@@ -19,16 +19,13 @@ This notebook will outline the process of working with interferograms and the st
 
 
 ```{python}
-import xarray as xr
-import numpy as np
 import matplotlib.pyplot as plt
 import hvplot.xarray  # noqa: F401
 import seaborn as sns
 import holoviews as hv
 import folium
 import base64
 import intake
-from pathlib import Path
 from io import BytesIO
 
 hv.extension("bokeh")
@@ -285,4 +282,3 @@ folium.LayerControl().add_to(m)
 
 m
 ```
-

Diff for: chapters/courses/microwave-remote-sensing/09_in_class_exercise.qmd

@@ -27,7 +27,6 @@ import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.patches as patches
 import xarray as xr
-from pathlib import Path
 import cmcrameri as cmc  # noqa: F401
 import snaphu
 import seaborn as sns
@@ -427,4 +426,3 @@ In the following animation, we can capture the 3D displacement caused by the Rid
 
 ![](../../images/ridgecrest.gif)
 *Credits: NASA*
-

Diff for: chapters/images/cmaps/06_color_mapping.json

@@ -24,7 +24,7 @@ import numpy as np
 import xarray as xr
 import numpy as np
 import datetime
-import rioxarray
+import rioxarray  # noqa: F401
 import pandas as pd
 
 import hvplot.pandas
@@ -93,8 +93,9 @@ def post_process_eodc_cube(dc, items, bands):
 
 
 def post_process_eodc_cube_(dc, items, band):
-    scale = items[0].assets[band].extra_fields.get("raster:bands")[0]["scale"]
-    nodata = items[0].assets[band].extra_fields.get("raster:bands")[0]["nodata"]
+    fields = items[0].assets[band].extra_fields
+    scale = fields.get("raster:bands")[0]["scale"]
+    nodata = fields.get("raster:bands")[0]["nodata"]
     return dc.where(dc != nodata) / scale
 
 
@@ -312,7 +313,9 @@ likelihood_pdi = hvplot.bind(
 ).interactive()
 
 view_likelihoods = (
-    likelihood_pdi.hvplot("sigma", "water_likelihood", ylabel="likelihoods").dmap()
+    likelihood_pdi.hvplot(
+        "sigma", "water_likelihood",
+        ylabel="likelihoods").dmap()
     * likelihood_pdi.hvplot("sigma", "land_likelihood").dmap()
     * likelihood_pdi.hvplot("observed", "land_likelihood").dmap()
 ).opts(frame_height=200, frame_width=300)
@@ -349,7 +352,9 @@ def calc_posteriors(sigma, x=None, y=None):
     land_likelihood = calc_land_likelihood(sigma=sigma, x=x, y=y)
     water_likelihood = calc_water_likelihood(sigma=sigma, x=x, y=y)
     evidence = (water_likelihood * 0.5) + (land_likelihood * 0.5)
-    return (water_likelihood * 0.5) / evidence, (land_likelihood * 0.5) / evidence
+    water_posterior = (water_likelihood * 0.5) / evidence
+    land_posterior = (land_likelihood * 0.5) / evidence
+    return water_posterior, land_posterior
 ```
 
 We can plot the posterior probabilities of flooding and non-flooding again and compare these to pixel's measured $\sigma^0$. For reference we model the flood and non-flood posteriors (`model_posteriors`) over a range of $\sigma^0$ values. **Hover** on a pixel to calculate the posterior probability.
@@ -446,8 +451,8 @@ colorbar_opts = {
     "ticker": FixedTicker(ticks=[0, 1]),
 }
 flood_view = flood_dc.decision.hvplot.image(
-    x="x", y="y", rasterize=True, geo=True, cmap=["rgba(0, 0, 1, 0.1)", "darkred"]
+    x="x", y="y", rasterize=True, geo=True,
+    cmap=["rgba(0, 0, 1, 0.1)", "darkred"]
 ).opts(frame_height=400, colorbar_opts={**colorbar_opts})
 mrs_view.get(0) * flood_view
 ```
-