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fix: expose all the reduction parameters in the plotting methods #380

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Nov 26, 2024
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fix: expose all the reduction parameters in the plotting methods #380

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566fa72
fix: add back all the reduction parameters in the plotting functions
12rambau Nov 23, 2024
80df342
style: cleaning
12rambau Nov 23, 2024
e289b4d
fix: add all parameters to the ic plotting methods
12rambau Nov 23, 2024
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126 changes: 105 additions & 21 deletions geetools/ee_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1591,10 +1591,13 @@ def byBands(
self,
regions: ee.featurecollection,
reducer: str | ee.Reducer = "mean",
scale: int = 10000,
bands: list = [],
regionId: str = "system:index",
labels: list = [],
scale: int = 10000,
crs: str | None = None,
crsTransform: list | None = None,
tileScale: float = 1,
) -> ee.Dictionary:
"""Compute a reducer for each band of the image in each region.

Expand All @@ -1611,10 +1614,13 @@ def byBands(
Parameters:
regions: The regions to compute the reducer in.
reducer: The name of the reducer or a reducer object to use. Default is "mean".
scale: The scale to use for the computation. Default is 10000m.
regionId: The property used to label region. Defaults to "system:index".
labels: The labels to use for the output dictionary. Default to the band names.
bands: The bands to compute the reducer on. Default to all bands.
scale: The scale to use for the computation. Default is 10000m.
crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default.

Returns:
A dictionary with all the bands as keys and their values in each region as a list.
Expand Down Expand Up @@ -1656,7 +1662,14 @@ def byBands(

# retrieve the reduce bands for each feature
image = self._obj.select(eeBands).rename(eeLabels)
fc = image.reduceRegions(collection=regions, reducer=red, scale=scale)
fc = image.reduceRegions(
collection=regions,
reducer=red,
scale=scale,
crs=crs,
crsTransform=crsTransform,
tileScale=tileScale,
)

# extract the data as a list of dictionaries (one for each label) aggregating
# the values for each feature
Expand All @@ -1668,10 +1681,13 @@ def byRegions(
self,
regions: ee.featurecollection,
reducer: str | ee.Reducer = "mean",
scale: int = 10000,
bands: list = [],
regionId: str = "system:index",
labels: list = [],
scale: int = 10000,
crs: str | None = None,
crsTransform: list | None = None,
tileScale: float = 1,
) -> ee.Dictionary:
"""Compute a reducer in each region of the image for eah band.

Expand All @@ -1688,10 +1704,14 @@ def byRegions(
Parameters:
regions: The regions to compute the reducer in.
reducer: The name of the reducer or a reducer object to use. Default is "mean".
scale: The scale to use for the computation. Default is 10000m.
regionId: The property used to label region. Defaults to "system:index".
labels: The labels to use for the output dictionary. Default to the band names.
bands: The bands to compute the reducer on. Default to all bands.
scale: The scale to use for the computation. Default is 10000m.
crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default.


Returns:
A dictionary with all the bands as keys and their values in each region as a list.
Expand Down Expand Up @@ -1733,7 +1753,14 @@ def byRegions(

# retrieve the reduce bands for each feature
image = self._obj.select(bands).rename(labels)
fc = image.reduceRegions(regions, red, scale)
fc = image.reduceRegions(
collection=regions,
reducer=red,
scale=scale,
crs=crs,
crsTransform=crsTransform,
tileScale=tileScale,
)

# extract the data as a list of dictionaries (one for each label) aggregating
# we are force to turn the fc into a list because GEE don't accept to map a featureCollection
Expand All @@ -1748,12 +1775,15 @@ def plot_by_regions(
type: str,
regions: ee.FeatureCollection,
reducer: str | ee.Reducer = "mean",
scale: int = 10000,
bands: list = [],
regionId: str = "system:index",
labels: list = [],
colors: list = [],
ax: Axes | None = None,
scale: int = 10000,
crs: str | None = None,
crsTransform: list | None = None,
tileScale: float = 1,
) -> Axes:
"""Plot the reduced values for each region.

Expand All @@ -1768,12 +1798,16 @@ def plot_by_regions(
type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue!
regions: The regions to compute the reducer in.
rreducer: The name of the reducer or a reducer object to use. Default is "mean".
scale: The scale to use for the computation. Default is 10000m.
bands: The bands to compute the reducer on. Default to all bands.
regionId: The property used to label region. Defaults to "system:index".
labels: The labels to use for the output dictionary. Default to the band names.
colors: The colors to use for the plot. Default to the default matplotlib colors.
ax: The matplotlib axis to plot the data on. If None, a new figure is created.
scale: The scale to use for the computation. Default is 10000m.
crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default.


Returns:
The matplotlib axis with the plot.
Expand All @@ -1798,7 +1832,17 @@ def plot_by_regions(
normClim.plot_by_regions(ecoregions, ee.Reducer.mean(), scale=10000)
"""
# get the data from the server
data = self.byBands(regions, reducer, scale, bands, regionId, labels).getInfo()
data = self.byBands(
regions=regions,
reducer=reducer,
bands=bands,
regionId=regionId,
labels=labels,
scale=scale,
crs=crs,
crsTransform=crsTransform,
tileScale=tileScale,
).getInfo()

# get all the id values, they must be string so we are forced to cast them manually
# the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322
Expand All @@ -1823,12 +1867,15 @@ def plot_by_bands(
type: str,
regions: ee.FeatureCollection,
reducer: str | ee.Reducer = "mean",
scale: int = 10000,
bands: list = [],
regionId: str = "system:index",
labels: list = [],
colors: list = [],
ax: Axes | None = None,
scale: int = 10000,
crs: str | None = None,
crsTransform: list | None = None,
tileScale: float = 1,
) -> Axes:
"""Plot the reduced values for each bands.

Expand All @@ -1844,12 +1891,15 @@ def plot_by_bands(
type: The type of plot to use. Defaults to "bar". can be any type of plot from the python lib `matplotlib.pyplot`. If the one you need is missing open an issue!
regions: The regions to compute the reducer in.
reducer: The name of the reducer or a reducer object to use. Default is "mean".
scale: The scale to use for the computation. Default is 10000m.
bands: The bands to compute the reducer on. Default to all bands.
regionId: The property used to label region. Defaults to "system:index".
labels: The labels to use for the output dictionary. Default to the band names.
colors: The colors to use for the plot. Default to the default matplotlib colors.
ax: The matplotlib axis to plot the data on. If None, a new figure is created.
scale: The scale to use for the computation. Default is 10000m.
crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default.

Returns:
The matplotlib axis with the plot.
Expand All @@ -1874,7 +1924,17 @@ def plot_by_bands(
normClim.plot_by_bands(ecoregions, ee.Reducer.mean(), scale=10000)
"""
# get the data from the server
data = self.byRegions(regions, reducer, scale, bands, regionId, labels).getInfo()
data = self.byRegions(
regions=regions,
reducer=reducer,
bands=bands,
regionId=regionId,
labels=labels,
scale=scale,
crs=crs,
crsTransform=crsTransform,
tileScale=tileScale,
).getInfo()

# get all the id values, they must be string so we are forced to cast them manually
# the default casting is broken from Python side: https://issuetracker.google.com/issues/329106322
Expand All @@ -1901,23 +1961,33 @@ def plot_hist(
bands: list = [],
labels: list = [],
colors: list = [],
scale: int = 10000,
precision: int = 2,
ax: Axes | None = None,
scale: int = 10000,
crs: str | None = None,
crsTransform: list | None = None,
bestEffort: bool = False,
maxPixels: int = 10**7,
tileScale: float = 1,
**kwargs,
) -> Axes:
"""Plot the histogram of the image bands.

Parameters:
bins: The number of bins to use for the histogram. Default is 30.
region: The region to compute the histogram in. Default is the image geometry.
bands: The bands to plot the histogram for. Default to all bands.
labels: The labels to use for the output dictionary. Default to the band names.
colors: The colors to use for the plot. Default to the default matplotlib colors.
prescision: The number of decimal to keep for the histogram bins values. Default is 2.
ax: The matplotlib axis to plot the data on. If None, a new figure is created.
scale: The scale to use for the computation. Default is 10,000m.
prescision: The number of decimal to keep for the histogram bins values. Default is 2.
crs: The projection to work in. If unspecified, the projection of the image's first band is used. If specified in addition to scale, rescaled to the specified scale.
crsTransform: The list of CRS transform values. This is a row-major ordering of the 3x2 transform matrix. This option is mutually exclusive with 'scale', and replaces any transform already set on the projection.
bestEffort: If the polygon would contain too many pixels at the given scale, compute and use a larger scale which would allow the operation to succeed.
maxPixels: The maximum number of pixels to reduce. default to 10**7.
tileScale: A scaling factor between 0.1 and 16 used to adjust aggregation tile size; setting a larger tileScale (e.g., 2 or 4) uses smaller tiles and may enable computations that run out of memory with the default.
kwargs: Keyword arguments passed to the matplotlib fill_between() function.
region: The region to compute the histogram in. Default is the image geometry.

Returns:
The matplotlib axis with the plot.
Expand Down Expand Up @@ -1950,16 +2020,30 @@ def plot_hist(
# extract the data from the server
image = self._obj.select(eeBands).rename(eeLabels).clip(region)

# compute the min and ma values of the bands so w can scale the bins of the histogram
min = image.reduceRegion(ee.Reducer.min(), region, scale).values().reduce(ee.Reducer.min())
max = image.reduceRegion(ee.Reducer.max(), region, scale).values().reduce(ee.Reducer.max())
# set the common parameters of the 3 reducers
params = {
"geometry": region,
"scale": scale,
"crs": crs,
"crsTransform": crsTransform,
"bestEffort": bestEffort,
"maxPixels": maxPixels,
"tileScale": tileScale,
}

# compute the min and max values of the bands so w can scale the bins of the histogram
min = image.reduceRegion(**{"reducer": ee.Reducer.min(), **params})
min = min.values().reduce(ee.Reducer.min())

max = image.reduceRegion(**{"reducer": ee.Reducer.max(), **params})
max = max.values().reduce(ee.Reducer.max())

# compute the histogram. The result is a dictionary with each band as key and the histogram
# as values. The histograp is a list of [start of bin, value] pairs
reduce = image.reduceRegion(ee.Reducer.fixedHistogram(min, max, bins), region, scale)
raw_data = reduce.getInfo()
reducer = ee.Reducer.fixedHistogram(min, max, bins)
raw_data = image.reduceRegion(**{"reducer": reducer, **params}).getInfo()

# massage raw data to reqhape them as usable source for a Axes plot
# massage raw data to reshape them as usable source for an Axes plot
# first extract the x coordinates of the plot as a list of bins borders
# every value is duplicated but the first one to create a scale like display.
# the values are treated the same way we simply drop the last duplication to get the same size.
Expand Down
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