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♻️ Turn BBox into a dataclass with bounds and subset methods
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Refactor the BBox namedtuple into a Python 3.7+ dataclass, complete with convenience functions to get the bounding box coordinates as a tuple (either in a PyGMT or Shapely compatible format), as well as geographically subsetting an xarray.Dataset! This BBox class also allows us to set a name, and has a automatic 'scale' property, handy for making PyGMT plots later. Also moved the optional geographic subsetting section nearer to the top of the notebook as some people might want to do that before all the compute heavy stuff.
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@weiji14
weiji14 committed May 26, 2020
1 parent 9768fba commit 4c91dfe
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349 changes: 200 additions & 149 deletions atl11_play.ipynb
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220 changes: 130 additions & 90 deletions atl11_play.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@
# Adapted from https://github.com/suzanne64/ATL11/blob/master/intro_to_ATL11.ipynb

# %%
import collections
import dataclasses
import os
import glob

Expand Down Expand Up @@ -136,6 +136,109 @@

# %%

# %% [markdown]
# ## Subset to geographic region of interest (optional)
#
# Take a geographical subset and save to a NetCDF/Zarr format for distribution.

# %%
# Bounding Box in EPSG:3031 as minx, maxx, miny, maxy
@dataclasses.dataclass(frozen=True)
class BBox:
name: str # name of region
xmin: float # left coordinate
xmax: float # right coordinate
ymin: float # bottom coordinate
ymax: float # top coordinate

@property
def scale(self) -> int:
"""
Automatically set a map scale (1:scale)
based on x-coordinate range divided by 0.2
"""
return int((self.xmax - self.xmin) / 0.2)

def bounds(self, style="lrbt") -> tuple:
"""
Convenience function to get the bounding box coordinates
of the region in two different styles, lrbt or lbrt.
Defaults to 'lrbt', i.e. left, right, bottom, top.
"""
if style == "lrbt": # left, right, bottom, top (for PyGMT)
return (self.xmin, self.xmax, self.ymin, self.ymax)
elif style == "lbrt": # left, bottom, right, top (for Shapely, etc)
return (self.xmin, self.ymin, self.xmax, self.ymax)
else:
raise ValueError(f"Unknown style type {style}")

def subset(self, ds: xr.Dataset, x_dim: str = "x", y_dim: str = "y") -> xr.Dataset:
"""
Convenience function to find datapoints in an xarray.Dataset
that fit within the bounding boxes of this region
"""
return xr.ufuncs.logical_and(
xr.ufuncs.logical_and(ds[x_dim] > self.xmin, ds[x_dim] < self.xmax),
xr.ufuncs.logical_and(ds[y_dim] > self.ymin, ds[y_dim] < self.ymax),
)


# %%
# Dictionary of Antarctic bounding box locations with EPSG:3031 coordinates
regions = {
"kamb": BBox(
name="Kamb Ice Stream",
xmin=-739741.7702261859,
xmax=-411054.19240523444,
ymin=-699564.516934089,
ymax=-365489.6822096751,
),
"antarctica": BBox("Antarctica", -2700000, 2800000, -2200000, 2300000),
"siple_coast": BBox("Siple Coast", -1000000, 250000, -1000000, -100000),
"kamb2": BBox("Kamb Ice Stream", -500000, -400000, -600000, -500000),
"whillans": BBox("Whillans Ice Stream", -350000, -100000, -700000, -450000),
}

# %%
# Do the actual computation to find data points within region of interest
region = regions["kamb"] # Select Kamb Ice Stream region
ds_subset = ds.where(cond=region.subset(ds=ds), drop=True)
ds_subset = ds_subset.unify_chunks()
ds_subset = ds_subset.compute()

# %%
# Save to NetCDF/Zarr formats for distribution
ds_subset.to_netcdf(
path="atl11_subset.nc", engine="h5netcdf",
)
ds_subset.to_zarr(
store="atl11_subset.zarr", mode="w", consolidated=True,
)

# %%
# Look at Cycle Number 6 only for plotting
points_subset = hv.Points(
data=ds_subset.sel(cycle_number=6)[[*essential_columns]],
label="Cycle_6",
kdims=["x", "y"],
vdims=["utc_time", "h_corr", "cycle_number"],
datatype=["xarray"],
)
df_subset = points_subset.dframe()

# %%
# Plot our subset of points on an interactive map
df_subset.hvplot.points(
title=f"Elevation (metres) at Cycle {cycle_number}",
x="x",
y="y",
c="h_corr",
cmap="Blues",
rasterize=True,
hover=True,
)


# %% [markdown]
# # Pivot into a pandas/dask dataframe
#
Expand Down Expand Up @@ -202,63 +305,6 @@
hover=True,
)

# %% [markdown]
# ## Subset to geographic region of interest (optional)
#
# Take a geographical subset and save to a NetCDF/Zarr format for distribution.

# %%
# Kamb Ice Stream bounding box in EPSG:3031 coordinates
xmin, xmax, ymin, ymax = (
-739741.7702261859,
-411054.19240523444,
-699564.516934089,
-365489.6822096751,
)
cond = xr.ufuncs.logical_and(
xr.ufuncs.logical_and(ds.x > xmin, ds.x < xmax),
xr.ufuncs.logical_and(ds.y > ymin, ds.y < ymax),
)

# %%
# Do the actual computation to find data points within region of interest
ds_subset = ds.where(cond=cond, drop=True)
ds_subset = ds_subset.unify_chunks()
ds_subset = ds_subset.compute()

# %%
# Save to NetCDF/Zarr formats for distribution
ds_subset.to_netcdf(
path="atl11_subset.nc", engine="h5netcdf",
)
ds_subset.to_zarr(
store="atl11_subset.zarr", mode="w", consolidated=True,
)

# %%
# Look at Cycle Number 6 only for plotting
points_subset = hv.Points(
data=ds_subset.sel(cycle_number=6)[[*essential_columns]],
label="Cycle_6",
kdims=["x", "y"],
vdims=["utc_time", "h_corr", "cycle_number"],
datatype=["xarray"],
)
df_subset = points_subset.dframe()

# %%
# Plot our subset of points on an interactive map
df_subset.hvplot.points(
title=f"Elevation (metres) at Cycle {cycle_number}",
x="x",
y="y",
c="h_corr",
cmap="Blues",
rasterize=True,
hover=True,
)


# %%

# %% [markdown]
Expand Down Expand Up @@ -317,27 +363,9 @@ def calculate_delta_height(
# from [Smith et al., 2009](https://doi.org/10.3189/002214309789470879).

# %%
# Bounding Box in EPSG:3031 as minx, maxx, miny, maxy
BBox = collections.namedtuple(
typename="BBox", field_names=["xmin", "xmax", "ymin", "ymax"]
)
region = BBox(-2700000, 2800000, -2200000, 2300000) # Antarctica
region = BBox(-1000000, 250000, -1000000, -100000) # Siple Coast
region = BBox(-500000, -400000, -600000, -500000) # Kamb Ice Stream
region = BBox(-350000, -100000, -700000, -450000) # Whillans Ice Stream

# %%
# Datashade our height values (vector points) onto a grid (raster image)
canvas = datashader.Canvas(
plot_width=450,
plot_height=450,
x_range=(region.xmin, region.xmax),
y_range=(region.ymin, region.ymax),
)
agg_grid = canvas.points(
source=dhdf, x="x", y="y", agg=datashader.mean(column="delta_height")
)
agg_grid
# Select region here, see dictionary of regions at top
placename: str = "whillans"
region: BBox = regions[placename]

# %%
# Find subglacial lakes (Smith et al., 2009) within region of interest
Expand All @@ -346,9 +374,7 @@ def calculate_delta_height(
)
subglacial_lakes_gdf = subglacial_lakes_gdf.loc[
subglacial_lakes_gdf.within(
shapely.geometry.Polygon.from_bounds(
xmin=region.xmin, xmax=region.xmax, ymin=region.ymin, ymax=region.ymax
)
shapely.geometry.Polygon.from_bounds(*region.bounds(style="lbrt"))
)
]
subglacial_lakes_geom = [g for g in subglacial_lakes_gdf.geometry]
Expand All @@ -358,13 +384,26 @@ def calculate_delta_height(
]


# %%
# Datashade our height values (vector points) onto a grid (raster image)
canvas = datashader.Canvas(
plot_width=450,
plot_height=450,
x_range=(region.xmin, region.xmax),
y_range=(region.ymin, region.ymax),
)
agg_grid = canvas.points(
source=dhdf, x="x", y="y", agg=datashader.mean(column="delta_height")
)
agg_grid

# %%
# Plot our map!
scale = "1:1500000"
scale: int = region.scale
fig = pygmt.Figure()
fig.coast(
region=region,
projection=f"s0/-90/-71/{scale}",
region=region.bounds(),
projection=f"s0/-90/-71/1:{scale}",
area_thresh="+ag",
resolution="i",
shorelines="0.5p",
Expand All @@ -381,16 +420,17 @@ def calculate_delta_height(
pygmt.makecpt(cmap="roma", series=[-2, 2])
fig.grdimage(
grid=agg_grid,
region=region,
projection=f"x{scale}",
frame=["afg", 'WSne+t"ICESat-2 Ice Surface Change from Cycle 5 to 6"'],
region=region.bounds(),
projection=f"x1:{scale}",
frame=["afg", f'WSne+t"ICESat-2 Ice Surface Change over {region.name}"',],
Q=True,
)
for subglacial_lake in subglacial_lakes:
fig.plot(data=subglacial_lake, L=True, pen="thick")
fig.plot(data=subglacial_lake, L=True, pen="thin")
fig.colorbar(
position="JCR", frame=["af", 'x+l"Elevation Change"', "y+lm"],
position="JCR+e", frame=["af", 'x+l"Elevation Change from Cycle 5 to 6"', "y+lm"],
)
fig.savefig(f"figures/plot_atl11_{placename}.png")
fig.show(width=600)

# %%
Expand Down

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