# Recipe 6: Zonal Statistics - Polar Grid#

[1]:
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings

warnings.filterwarnings("ignore")
try:
get_ipython().run_line_magic("matplotlib inline")
except:
plt.ion()
import numpy as np
import xarray as xr

## Setup Examples#

[2]:
def testplot(
ds,
obj,
col="mean",
levels=[0, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 40, 50, 100],
title="",
):
"""Quick test plot layout for this example file"""
colors = plt.cm.viridis(np.linspace(0, 1, len(levels)))
mycmap, mynorm = from_levels_and_colors(levels, colors, extend="max")

radolevels = [0, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 40, 50, 100]

fig = plt.figure(figsize=(10, 16))

# Average rainfall sum
obj.zdata.trg.geo.plot(
column=col,
ax=ax,
cmap=mycmap,
norm=mynorm,
edgecolor="white",
lw=0.5,
legend=True,
legend_kwds=dict(shrink=0.5),
)
ax.autoscale()
plt.xlabel("UTM Zone 32 Easting")
plt.ylabel("UTM Zone 32 Northing")
plt.title(title)
plt.draw()

pm = ds.plot(
x="xc",
y="yc",
ax=ax1,
cbar_kwargs=dict(shrink=0.5),
)
obj.zdata.trg.geo.plot(ax=ax1, facecolor="None", edgecolor="white")
plt.xlabel("UTM Zone 32 Easting")
plt.ylabel("UTM Zone 32 Northing")
plt.draw()
plt.tight_layout()
[3]:
from matplotlib.collections import PatchCollection
from matplotlib.colors import from_levels_and_colors
import matplotlib.patches as patches
import datetime as dt
from osgeo import osr
[4]:
# check for GEOS enabled GDAL
if not wrl.util.has_geos():
print("NO GEOS support within GDAL, aborting...")
exit(0)
[5]:
# create radolan projection osr object

# create UTM Zone 32 projection osr object
proj_utm = osr.SpatialReference()
proj_utm.ImportFromEPSG(32632)

# Source projection of the shape data (in GK2)
proj_gk2 = osr.SpatialReference()
proj_gk2.ImportFromEPSG(31466)
[5]:
0
[6]:
def create_center_coords(ds, crs=None):
# create polar grid centroids in GK2
center = wrl.georef.spherical_to_centroids(
ds.range.values,
ds.azimuth.values,
0.5,
(ds.longitude.values, ds.latitude.values, ds.altitude.values),
crs=crs,
)
ds = ds.assign_coords(
{
"xc": (["azimuth", "range"], center[..., 0]),
"yc": (["azimuth", "range"], center[..., 1]),
"zc": (["azimuth", "range"], center[..., 2]),
}
)
return ds
[7]:
ds = xr.open_dataset(filename)
ds = ds.rename_dims({"phony_dim_0": "azimuth", "phony_dim_1": "range"})
ds = ds.assign_coords(
{
"latitude": ds.data.Latitude,
"longitude": ds.data.Longitude,
"altitude": 99.5,
"azimuth": ds.data.az,
"range": ds.data.r,
"sweep_mode": "azimuth_surveillance",
"elevation": 0.5,
}
)

ds = ds.wrl.georef.georeference(crs=proj_utm)
ds = ds.pipe(create_center_coords, crs=proj_utm)
display(ds)
<xarray.Dataset>
Dimensions:     (azimuth: 360, range: 1000)
Coordinates: (12/17)
latitude    float64 50.73
longitude   float64 7.072
altitude    float64 99.5
* azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
* range       (range) float64 100.0 200.0 300.0 ... 9.98e+04 9.99e+04 1e+05
sweep_mode  <U20 'azimuth_surveillance'
...          ...
rays        (azimuth, range) float64 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
bins        (azimuth, range) float64 100.0 200.0 300.0 ... 9.99e+04 1e+05
crs_wkt     int64 0
xc          (azimuth, range) float64 3.639e+05 3.639e+05 ... 3.657e+05
yc          (azimuth, range) float64 5.622e+06 5.622e+06 ... 5.722e+06
zc          (azimuth, range) float64 100.0 100.9 ... 1.558e+03 1.56e+03
Data variables:
data        (azimuth, range) float64 ...
[8]:
def write_prj(filename, proj):
with open(filename, "w") as f:
f.write(proj.ExportToWkt())
[9]:
flist = ["shapefiles/agger/agger_merge.shx", "shapefiles/agger/agger_merge.dbf"]
# reshape
write_prj(shpfile[:-3] + "prj", proj_gk2)
trg = wrl.io.VectorSource(shpfile, trg_crs=proj_utm, name="trg")

bbox = trg.extent

# create catchment bounding box
buffer = 5000.0
bbox = dict(
left=bbox[0] - buffer,
right=bbox[1] + buffer,
bottom=bbox[2] - buffer,
top=bbox[3] + buffer,
)
[10]:
ds_clip = ds.where(
(
((ds.yc > bbox["bottom"]) & (ds.yc < bbox["top"]))
& ((ds.xc > bbox["left"]) & (ds.xc < bbox["right"]))
),
drop=True,
)
display(ds_clip)
<xarray.Dataset>
Dimensions:     (azimuth: 86, range: 695)
Coordinates: (12/17)
latitude    float64 50.73
longitude   float64 7.072
altitude    float64 99.5
* azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 4.5 ... 82.5 83.5 84.5 85.5
* range       (range) float64 2.8e+03 2.9e+03 3e+03 ... 7.21e+04 7.22e+04
sweep_mode  <U20 'azimuth_surveillance'
...          ...
rays        (azimuth, range) float64 0.5 0.5 0.5 0.5 ... 85.5 85.5 85.5 85.5
bins        (azimuth, range) float64 2.8e+03 2.9e+03 ... 7.21e+04 7.22e+04
crs_wkt     int64 0
xc          (azimuth, range) float64 3.64e+05 3.64e+05 ... 4.359e+05
yc          (azimuth, range) float64 5.624e+06 5.624e+06 ... 5.625e+06
zc          (azimuth, range) float64 124.1 124.9 ... 1.034e+03 1.036e+03
Data variables:
data        (azimuth, range) float64 nan nan nan nan nan ... nan nan nan nan
[11]:
[11]:
(59770, 2)

## Zonal Stats Polar Grid - Points#

[12]:
trg = wrl.io.VectorSource(shpfile, trg_crs=proj_utm, name="trg", src_crs=proj_gk2)
[13]:
###########################################################################
# Approach #1: Assign grid points to each polygon and compute the average.
#
# - Uses matplotlib.path.Path
# - Each point is weighted equally (assumption: polygon >> grid cell)
# - this is quick, but theoretically dirty
# - for polar grids a range-area dependency has to be taken into account
###########################################################################

t1 = dt.datetime.now()

# Create instance of type ZonalDataPoint from source grid and
# catchment array
zd = wrl.zonalstats.ZonalDataPoint(src, trg=trg, crs=proj_utm, buf=500.0)
# dump to file
zd.dump_vector("test_zonal_points")
# Create instance of type ZonalStatsPoint from zonal data object
obj1 = wrl.zonalstats.ZonalStatsPoint(zd)

isecs1 = obj1.zdata.isecs
t2 = dt.datetime.now()

t3 = dt.datetime.now()

# Create instance of type ZonalStatsPoint from zonal data file
obj1 = wrl.zonalstats.ZonalStatsPoint("test_zonal_points")
# Compute stats for target polygons
avg1 = obj1.mean(ds_clip.data.values.ravel())
var1 = obj1.var(ds_clip.data.values.ravel())

t4 = dt.datetime.now()

print("Approach #1 computation time:")
print("\tCreate object from scratch: %f seconds" % (t2 - t1).total_seconds())
print("\tCreate object from dumped file: %f seconds" % (t4 - t3).total_seconds())
print("\tCompute stats using object: %f seconds" % (t3 - t2).total_seconds())
Approach #1 computation time:
Create object from scratch: 4.169448 seconds
Create object from dumped file: 1.690017 seconds
Compute stats using object: 0.000045 seconds
[14]:
trg1 = wrl.io.VectorSource(shpfile, trg_crs=proj_utm, name="trg", src_crs=proj_gk2)

# Just a test for plotting results with zero buffer
zd = wrl.zonalstats.ZonalDataPoint(src1, trg=trg1, buf=0)
# Create instance of type ZonalStatsPoint from zonal data object
obj2 = wrl.zonalstats.ZonalStatsPoint(zd)
obj2.zdata.trg.set_attribute("mean", avg1)
obj2.zdata.trg.set_attribute("var", var1)

isecs2 = obj2.zdata.isecs
[15]:
# Illustrate results for an example catchment i
i = 0  # try e.g. 5, 2
fig = plt.figure(figsize=(10, 8))

# Target polygon patches
trg_patch = obj2.zdata.trg.get_data_by_idx([i], mode="geo")
trg_patch.plot(ax=ax, facecolor="None", edgecolor="black", linewidth=2)
trg_patch = obj1.zdata.trg.get_data_by_idx([i], mode="geo")
trg_patch.plot(ax=ax, facecolor="None", edgecolor="grey", linewidth=2)

# pips
sources = obj1.zdata.src.geo
sources.plot(ax=ax, label="all points", c="grey", markersize=200)
isecs1 = obj2.zdata.dst.get_data_by_att(attr="trg_index", value=[i], mode="geo")
isecs1.plot(ax=ax, label="buffer=0 m", c="green", markersize=200)
isecs2 = obj1.zdata.dst.get_data_by_att(attr="trg_index", value=[i], mode="geo")
isecs2.plot(ax=ax, label="buffer=500 m", c="red", markersize=50)

cat = trg.get_data_by_idx([i])[0]
bbox = wrl.zonalstats.get_bbox(cat[..., 0], cat[..., 1])
plt.xlim(bbox["left"] - 2000, bbox["right"] + 2000)
plt.ylim(bbox["bottom"] - 2000, bbox["top"] + 2000)
plt.legend()
plt.title("Catchment #%d: Points considered for stats" % i)
[15]:
Text(0.5, 1.0, 'Catchment #0: Points considered for stats')
[16]:
# Plot average rainfall and original data
testplot(
ds_clip.data, obj2, col="mean", title="Catchment rainfall mean (ZonalStatsPoint)"
)
[17]:
testplot(
ds_clip.data,
obj2,
col="var",
levels=np.arange(0, 20, 1.0),
title="Catchment rainfall variance (ZonalStatsPoint)",
)

## Zonal Stats Polar Grid - Polygons#

[18]:
ds.range.values,
ds.azimuth.values,
0.5,
(ds.longitude.values, ds.latitude.values, ds.altitude.values),
crs=proj_utm,
)
radar_utm.shape = (360, 1000, 5, 3)
ds = ds.assign_coords(
{
"xp": (["azimuth", "range", "verts"], radar_utm[..., 0]),
"yp": (["azimuth", "range", "verts"], radar_utm[..., 1]),
"zp": (["azimuth", "range", "verts"], radar_utm[..., 2]),
}
)
display(ds)
trg = wrl.io.VectorSource(shpfile, trg_crs=proj_utm, name="trg", src_crs=proj_gk2)
bbox = trg.extent

# create catchment bounding box
buffer = 5000.0
bbox = dict(
left=bbox[0] - buffer,
right=bbox[1] + buffer,
bottom=bbox[2] - buffer,
top=bbox[3] + buffer,
)
ds_clip = ds.where(
(
((ds.yc > bbox["bottom"]) & (ds.yc < bbox["top"]))
& ((ds.xc > bbox["left"]) & (ds.xc < bbox["right"]))
),
drop=True,
)
display(ds_clip)
<xarray.Dataset>
Dimensions:     (azimuth: 360, range: 1000, verts: 5)
Coordinates: (12/20)
latitude    float64 50.73
longitude   float64 7.072
altitude    float64 99.5
* azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
* range       (range) float64 100.0 200.0 300.0 ... 9.98e+04 9.99e+04 1e+05
sweep_mode  <U20 'azimuth_surveillance'
...          ...
xc          (azimuth, range) float64 3.639e+05 3.639e+05 ... 3.657e+05
yc          (azimuth, range) float64 5.622e+06 5.622e+06 ... 5.722e+06
zc          (azimuth, range) float64 100.0 100.9 ... 1.558e+03 1.56e+03
xp          (azimuth, range, verts) float64 3.639e+05 ... 3.648e+05
yp          (azimuth, range, verts) float64 5.622e+06 ... 5.721e+06
zp          (azimuth, range, verts) float64 99.56 100.4 ... 1.559e+03
Dimensions without coordinates: verts
Data variables:
data        (azimuth, range) float64 ...
<xarray.Dataset>
Dimensions:     (azimuth: 86, range: 695, verts: 5)
Coordinates: (12/20)
latitude    float64 50.73
longitude   float64 7.072
altitude    float64 99.5
* azimuth     (azimuth) float64 0.5 1.5 2.5 3.5 4.5 ... 82.5 83.5 84.5 85.5
* range       (range) float64 2.8e+03 2.9e+03 3e+03 ... 7.21e+04 7.22e+04
sweep_mode  <U20 'azimuth_surveillance'
...          ...
xc          (azimuth, range) float64 3.64e+05 3.64e+05 ... 4.359e+05
yc          (azimuth, range) float64 5.624e+06 5.624e+06 ... 5.625e+06
zc          (azimuth, range) float64 124.1 124.9 ... 1.034e+03 1.036e+03
xp          (azimuth, range, verts) float64 3.64e+05 3.64e+05 ... 4.358e+05
yp          (azimuth, range, verts) float64 5.624e+06 ... 5.626e+06
zp          (azimuth, range, verts) float64 123.6 124.5 ... 1.035e+03
Dimensions without coordinates: verts
Data variables:
data        (azimuth, range) float64 nan nan nan nan nan ... nan nan nan nan
[19]:
radar_utm = np.stack([ds_clip.xp, ds_clip.yp], axis=-1).reshape(-1, 5, 2)
trg = wrl.io.VectorSource(shpfile, trg_crs=proj_utm, name="trg", src_crs=proj_gk2)
(59770, 5, 2)
[20]:
###########################################################################
# Approach #2: Compute weighted mean based on fraction of source polygons
# in target polygons
#
# - This is more accurate (no assumptions), but probably slower...
###########################################################################

t1 = dt.datetime.now()

# Create instance of type ZonalDataPoly from source grid and
# catchment array
zd = wrl.zonalstats.ZonalDataPoly(src, trg=trg, crs=proj_utm)
# dump to file
zd.dump_vector("test_zonal_poly")
# Create instance of type ZonalStatsPoint from zonal data object
obj3 = wrl.zonalstats.ZonalStatsPoly(zd)

obj3.zdata.dump_vector("test_zonal_poly")
t2 = dt.datetime.now()

t3 = dt.datetime.now()

# Create instance of type ZonalStatsPoly from zonal data file
obj4 = wrl.zonalstats.ZonalStatsPoly("test_zonal_poly")

avg3 = obj4.mean(ds_clip.data.values.ravel())
var3 = obj4.var(ds_clip.data.values.ravel())

t4 = dt.datetime.now()

print("Approach #2 computation time:")
print("\tCreate object from scratch: %f seconds" % (t2 - t1).total_seconds())
print("\tCreate object from dumped file: %f seconds" % (t4 - t3).total_seconds())
print("\tCompute stats using object: %f seconds" % (t3 - t2).total_seconds())

obj4.zdata.trg.dump_raster(
"test_zonal_hdr.nc", driver="netCDF", attr="mean", pixel_size=100.0
)

obj4.zdata.trg.dump_vector("test_zonal_shp")
obj4.zdata.trg.dump_vector("test_zonal_json.geojson", driver="GeoJSON")

Approach #2 computation time:
Create object from scratch: 2.789290 seconds
Create object from dumped file: 1.298372 seconds
Compute stats using object: 0.000043 seconds
[21]:
# Plot average rainfall and original data
testplot(
ds_clip.data,
obj4,
col="mean",
title="Catchment rainfall mean (PolarZonalStatsPoly)",
)
[22]:
testplot(
ds_clip.data,
obj4,
col="var",
levels=np.arange(0, 20, 1.0),
title="Catchment rainfall variance (PolarZonalStatsPoly)",
)
[23]:
# Illustrate results for an example catchment i
i = 0  # try e.g. 5, 2
fig = plt.figure(figsize=(10, 8))

# Grid cell patches
src_index = obj3.zdata.get_source_index(i)
trg_patch = obj3.zdata.src.get_data_by_idx(src_index, mode="geo")
trg_patch.plot(ax=ax, facecolor="None", edgecolor="black")

# Target polygon patches
trg_patch = obj3.zdata.trg.get_data_by_idx([i], mode="geo")
trg_patch.plot(ax=ax, facecolor="None", edgecolor="red", linewidth=2)

# intersections
isecs1 = obj3.zdata.dst.get_data_by_att(attr="trg_index", value=[i], mode="geo")
isecs1.plot(column="src_index", ax=ax, cmap=plt.cm.plasma, alpha=0.5)

cat = trg.get_data_by_idx([i])[0]
bbox = wrl.zonalstats.get_bbox(cat[..., 0], cat[..., 1])
plt.xlim(bbox["left"] - 2000, bbox["right"] + 2000)
plt.ylim(bbox["bottom"] - 2000, bbox["top"] + 2000)
plt.legend()
plt.title("Catchment #%d: Polygons considered for stats" % i)
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
[23]:
Text(0.5, 1.0, 'Catchment #0: Polygons considered for stats')
[24]:
# Compare estimates
maxlim = np.max(np.concatenate((avg1, avg3)))
fig = plt.figure(figsize=(10, 8))