Zonal Statistics Example

[1]:
import wradlib as wrl
import matplotlib.pyplot as pl
import matplotlib as mpl
import warnings

warnings.filterwarnings("ignore")
try:
    get_ipython().magic("matplotlib inline")
except:
    pl.ion()
import numpy as np
import xarray as xr
/home/runner/micromamba-root/envs/wradlib-notebooks/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

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 = pl.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]
    radocolors = pl.cm.viridis(np.linspace(0, 1, len(radolevels)))
    radocmap, radonorm = from_levels_and_colors(radolevels, radocolors, extend="max")

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

    # Average rainfall sum
    ax = fig.add_subplot(211, aspect="equal")
    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()
    pl.xlabel("UTM Zone 32 Easting")
    pl.ylabel("UTM Zone 32 Northing")
    pl.title(title)
    pl.draw()

    # Original radar data
    ax1 = fig.add_subplot(212, aspect="equal")
    pm = ds.plot(
        x="x", y="y", cmap=radocmap, norm=radonorm, ax=ax1, cbar_kwargs=dict(shrink=0.5)
    )
    obj.zdata.trg.geo.plot(ax=ax1, facecolor="None", edgecolor="white")
    pl.xlabel("UTM Zone 32 Easting")
    pl.ylabel("UTM Zone 32 Northing")
    pl.title("Original radar rain sums")
    pl.draw()
    pl.tight_layout()

Zonal Stats Rectangular Grid

[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]:
# Read and prepare the actual data (RADOLAN)
f = wrl.util.get_wradlib_data_file(
    "radolan/misc/raa01-sf_10000-1406100050-dwd---bin.gz"
)
ds = wrl.io.open_radolan_dataset(f)
[6]:
gridres = ds.x.diff("x")[0].values
gridres
[6]:
array(1000.)
[7]:
# create radolan projection osr object
if ds.attrs["formatversion"] >= 5:
    proj_stereo = wrl.georef.create_osr("dwd-radolan-wgs84")
else:
    proj_stereo = wrl.georef.create_osr("dwd-radolan-sphere")

# 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)
[7]:
0
[8]:
print(proj_gk2)
PROJCS["DHDN / 3-degree Gauss-Kruger zone 2",
    GEOGCS["DHDN",
        DATUM["Deutsches_Hauptdreiecksnetz",
            SPHEROID["Bessel 1841",6377397.155,299.1528128,
                AUTHORITY["EPSG","7004"]],
            AUTHORITY["EPSG","6314"]],
        PRIMEM["Greenwich",0,
            AUTHORITY["EPSG","8901"]],
        UNIT["degree",0.0174532925199433,
            AUTHORITY["EPSG","9122"]],
        AUTHORITY["EPSG","4314"]],
    PROJECTION["Transverse_Mercator"],
    PARAMETER["latitude_of_origin",0],
    PARAMETER["central_meridian",6],
    PARAMETER["scale_factor",1],
    PARAMETER["false_easting",2500000],
    PARAMETER["false_northing",0],
    UNIT["metre",1,
        AUTHORITY["EPSG","9001"]],
    AXIS["Northing",NORTH],
    AXIS["Easting",EAST],
    AUTHORITY["EPSG","31466"]]
[9]:
shpfile = wrl.util.get_wradlib_data_file("shapefiles/agger/agger_merge.shp")
trg = wrl.io.VectorSource(shpfile, srs=proj_utm, name="trg", projection_source=proj_gk2)
print(f"Found {len(trg)} sub-catchments in shapefile.")
Found 13 sub-catchments in shapefile.
[10]:
print(trg.crs)
PROJCS["WGS 84 / UTM zone 32N",
    GEOGCS["WGS 84",
        DATUM["WGS_1984",
            SPHEROID["WGS 84",6378137,298.257223563,
                AUTHORITY["EPSG","7030"]],
            AUTHORITY["EPSG","6326"]],
        PRIMEM["Greenwich",0,
            AUTHORITY["EPSG","8901"]],
        UNIT["degree",0.0174532925199433,
            AUTHORITY["EPSG","9122"]],
        AUTHORITY["EPSG","4326"]],
    PROJECTION["Transverse_Mercator"],
    PARAMETER["latitude_of_origin",0],
    PARAMETER["central_meridian",9],
    PARAMETER["scale_factor",0.9996],
    PARAMETER["false_easting",500000],
    PARAMETER["false_northing",0],
    UNIT["metre",1,
        AUTHORITY["EPSG","9001"]],
    AXIS["Easting",EAST],
    AXIS["Northing",NORTH],
    AUTHORITY["EPSG","32632"]]
[11]:
bbox = trg.extent
buffer = 5000.0
bbox = dict(
    left=bbox[0] - buffer,
    right=bbox[1] + buffer,
    bottom=bbox[2] - buffer,
    top=bbox[3] + buffer,
)
print(bbox)
{'left': 365059.5928799211, 'right': 419830.11388741195, 'bottom': 5624046.706676126, 'top': 5668055.540990271}
[12]:
# Get RADOLAN grid coordinates
x_rad, y_rad = np.meshgrid(ds.x, ds.y)
grid_xy_radolan = np.stack([x_rad, y_rad], axis=-1)

# Reproject the RADOLAN coordinates
xy = wrl.georef.reproject(
    grid_xy_radolan, projection_source=proj_stereo, projection_target=proj_utm
)

# assign as coordinates
ds = ds.assign_coords(
    {
        "xc": (
            ["y", "x"],
            xy[..., 0],
            dict(long_name="UTM Zone 32 Easting", units="m"),
        ),
        "yc": (
            ["y", "x"],
            xy[..., 1],
            dict(long_name="UTM Zone 32 Northing", units="m"),
        ),
    }
)
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:  (y: 47, x: 58, time: 1)
Coordinates:
  * time     (time) datetime64[ns] 2014-06-10T00:50:00
  * y        (y) float64 -4.233e+06 -4.232e+06 ... -4.188e+06 -4.187e+06
  * x        (x) float64 -2.15e+05 -2.14e+05 -2.13e+05 ... -1.59e+05 -1.58e+05
    xc       (y, x) float64 3.655e+05 3.664e+05 ... 4.179e+05 4.189e+05
    yc       (y, x) float64 5.624e+06 5.624e+06 ... 5.669e+06 5.669e+06
Data variables:
    SF       (y, x) float32 nan nan nan nan nan nan ... nan nan nan nan nan nan
Attributes:
    radarid:         10000
    formatversion:   3
    radolanversion:  2.13.1
    radarlocations:  ['asw', 'boo', 'emd', 'han', 'umd', 'pro', 'ess', 'drs',...
    radardays:       ['asw 10', 'boo 24', 'drs 24', 'emd 24', 'ess 24', 'fbg ...
[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
###########################################################################

t1 = dt.datetime.now()

# Get RADOLAN center grid points for each grid cell
# (MUST BE DONE IN NATIVE RADOLAN COORDINATES)
grid_x, grid_y = np.meshgrid(ds_clip.x, ds_clip.y)
grdpoints = np.dstack([grid_x, grid_y]).reshape(-1, 2)

src = wrl.io.VectorSource(
    grdpoints, srs=proj_utm, name="src", projection_source=proj_stereo
)
trg = wrl.io.VectorSource(shpfile, srs=proj_utm, name="trg", projection_source=proj_gk2)

# Create instance of type ZonalDataPoint from source grid and
# catchment array
zd = wrl.zonalstats.ZonalDataPoint(src, trg, srs=proj_utm, buf=500.0)
# dump to file (for later use - see below)
zd.dump_vector("test_zonal_points_cart")
# Create instance of type ZonalStatsPoint from zonal data object
obj1 = wrl.zonalstats.ZonalStatsPoint(zd)

isecs1 = obj1.zdata.isecs  # for plotting (see below)

t2 = dt.datetime.now()

t3 = dt.datetime.now()

# Create instance of type ZonalStatsPoint from zonal data file
# (much faster)
obj1 = wrl.zonalstats.ZonalStatsPoint("test_zonal_points_cart")

# Compute stats for target polygons
avg1 = obj1.mean(ds_clip.SF.values.ravel())
var1 = obj1.var(ds_clip.SF.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())

# PLOTTING Approach #1

src = wrl.io.VectorSource(
    grdpoints, srs=proj_utm, name="src", projection_source=proj_stereo
)
trg = wrl.io.VectorSource(shpfile, srs=proj_utm, name="trg", projection_source=proj_gk2)
# Just a test for plotting results with zero buffer
zd2 = wrl.zonalstats.ZonalDataPoint(src, trg, buf=0)
# Create instance of type ZonalStatsPoint from zonal data object
obj2 = wrl.zonalstats.ZonalStatsPoint(zd2)
# copy attributes to target layer
obj2.zdata.trg.set_attribute("mean", avg1)
obj2.zdata.trg.set_attribute("var", var1)
isecs2 = obj2.zdata.isecs
Approach #1 computation time:
        Create object from scratch: 2.159083 seconds
        Create object from dumped file: 0.089241 seconds
        Compute stats using object: 0.000034 seconds
[14]:
# Illustrate results for an example catchment i
i = 6  # try e.g. 5, 2
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, aspect="equal")

# 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])
pl.xlim(bbox["left"] - 2000, bbox["right"] + 2000)
pl.ylim(bbox["bottom"] - 2000, bbox["top"] + 2000)
pl.legend()
pl.title("Catchment #%d: Points considered for stats" % i)
[14]:
Text(0.5, 1.0, 'Catchment #6: Points considered for stats')
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_17_1.png
[15]:
# Plot average rainfall and original data
testplot(
    ds_clip.SF, obj2, col="mean", title="Catchment rainfall mean (ZonalStatsPoint)"
)
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_18_0.png
[16]:
testplot(
    ds_clip.SF,
    obj2,
    col="var",
    levels=np.arange(0, np.max(var1), 1.0),
    title="Catchment rainfall variance (ZonalStatsPoint)",
)
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_19_0.png
[17]:
###########################################################################
# Approach #2: Compute weighted mean based on fraction of source polygons
# in target polygons
#
# - This is more accurate (no assumptions), but probably slower...
###########################################################################

# Create vertices for each grid cell
# (MUST BE DONE IN NATIVE RADOLAN COORDINATES)
grid_x, grid_y = np.meshgrid(ds_clip.x, ds_clip.y)
grdverts = wrl.zonalstats.grid_centers_to_vertices(grid_x, grid_y, gridres, gridres)
# And reproject to Cartesian reference system (here: UTM Zone 32)
src = wrl.io.VectorSource(
    grdverts, srs=proj_utm, name="src", projection_source=proj_stereo
)
trg = wrl.io.VectorSource(shpfile, srs=proj_utm, name="trg", projection_source=proj_gk2)

t1 = dt.datetime.now()

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

t2 = dt.datetime.now()

t3 = dt.datetime.now()

# Create instance of type ZonalStatsPoly from zonal data file
obj3 = wrl.zonalstats.ZonalStatsPoly("test_zonal_poly_cart")
# Compute stats for target polygons
avg3 = obj3.mean(ds_clip.SF.values.ravel())
var3 = obj3.var(ds_clip.SF.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())
Approach #2 computation time:
        Create object from scratch: 0.267671 seconds
        Create object from dumped file: 0.096162 seconds
        Compute stats using object: 0.000036 seconds
[18]:
# PLOTTING Approach #2

# Plot average rainfall and original data
testplot(ds.SF, obj3, col="mean", title="Catchment rainfall mean (ZonalStatsPoly)")
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_21_0.png
[19]:
testplot(
    ds.SF,
    obj3,
    col="var",
    levels=np.arange(0, np.max(var3), 1.0),
    title="Catchment rainfall variance (ZonalStatsPoly)",
)
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_22_0.png
[20]:
ds_clip
[20]:
<xarray.Dataset>
Dimensions:  (y: 47, x: 58, time: 1)
Coordinates:
  * time     (time) datetime64[ns] 2014-06-10T00:50:00
  * y        (y) float64 -4.233e+06 -4.232e+06 ... -4.188e+06 -4.187e+06
  * x        (x) float64 -2.15e+05 -2.14e+05 -2.13e+05 ... -1.59e+05 -1.58e+05
    xc       (y, x) float64 3.655e+05 3.664e+05 ... 4.179e+05 4.189e+05
    yc       (y, x) float64 5.624e+06 5.624e+06 ... 5.669e+06 5.669e+06
Data variables:
    SF       (y, x) float32 nan nan nan nan nan nan ... nan nan nan nan nan nan
Attributes:
    radarid:         10000
    formatversion:   3
    radolanversion:  2.13.1
    radarlocations:  ['asw', 'boo', 'emd', 'han', 'umd', 'pro', 'ess', 'drs',...
    radardays:       ['asw 10', 'boo 24', 'drs 24', 'emd 24', 'ess 24', 'fbg ...
[21]:
# Illustrate results for an example catchment i
i = 6  # try e.g. 5, 2
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, aspect="equal")

# 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=pl.cm.plasma, alpha=0.5)

# scatter center points
ds_clip.plot.scatter(x="xc", y="yc", s=10)

cat = trg.get_data_by_idx([i])[0]
bbox = wrl.zonalstats.get_bbox(cat[..., 0], cat[..., 1])
pl.xlim(bbox["left"] - 2000, bbox["right"] + 2000)
pl.ylim(bbox["bottom"] - 2000, bbox["top"] + 2000)
pl.legend()
pl.title("Catchment #%d: Polygons considered for stats" % i)
# pl.gca().set_xlim(402000, 404000)
# pl.gca().set_ylim(5642000, 5644000)
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.
[21]:
Text(0.5, 1.0, 'Catchment #6: Polygons considered for stats')
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_24_2.png
[22]:
# Compare estimates
maxlim = np.max(np.concatenate((avg1, avg3)))
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, aspect="equal")
pl.scatter(avg1, avg3, edgecolor="None", alpha=0.5)
pl.xlabel("Average of points in or close to polygon (mm)")
pl.ylabel("Area-weighted average (mm)")
pl.xlim(0, maxlim)
pl.ylim(0, maxlim)
pl.plot([-1, maxlim + 1], [-1, maxlim + 1], color="black")
pl.show()
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_25_0.png

Zonal Stats Polar Grid

[23]:
def create_center_coords(ds, proj=None):
    # create polar grid centroids in GK2
    center = wrl.georef.spherical_to_centroids(
        ds.data.r,
        ds.azimuth.values,
        ds.elevation.values,
        (ds.longitude.values, ds.latitude.values, ds.altitude.values),
        proj=proj,
    )
    ds = ds.assign_coords(
        {
            "xc": (["azimuth", "range"], center[..., 0]),
            "yc": (["azimuth", "range"], center[..., 1]),
            "zc": (["azimuth", "range"], center[..., 2]),
        }
    )
    return ds
[24]:
filename = wrl.util.get_wradlib_data_file("hdf5/rainsum_boxpol_20140609.h5")
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,
        # bin centers
        "range": ds.data.r - np.median(np.diff(ds.data.r)) / 2.0,
        "sweep_mode": "azimuth_surveillance",
        "elevation": 0.5,
    }
)
[25]:
ds = ds.pipe(wrl.georef.georeference_dataset, proj=proj_utm)
ds = ds.pipe(create_center_coords, proj=proj_utm)
display(ds)
<xarray.Dataset>
Dimensions:     (azimuth: 360, range: 1000)
Coordinates: (12/16)
    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 50.0 150.0 250.0 ... 9.985e+04 9.995e+04
    sweep_mode  <U20 'azimuth_surveillance'
    ...          ...
    gr          (azimuth, range) float64 49.99 150.0 ... 9.981e+04 9.991e+04
    rays        (azimuth, range) float64 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5
    bins        (azimuth, range) float64 50.0 150.0 ... 9.985e+04 9.995e+04
    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 ...
[26]:
# reshape
shpfile = wrl.util.get_wradlib_data_file("shapefiles/agger/agger_merge.shp")
trg = wrl.io.VectorSource(shpfile, srs=proj_utm, name="trg", projection_source=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,
)
[27]:
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: (12/16)
    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 2.75e+03 2.85e+03 ... 7.205e+04 7.215e+04
    sweep_mode  <U20 'azimuth_surveillance'
    ...          ...
    gr          (azimuth, range) float64 2.749e+03 2.849e+03 ... 7.212e+04
    rays        (azimuth, range) float64 0.5 0.5 0.5 0.5 ... 85.5 85.5 85.5 85.5
    bins        (azimuth, range) float64 2.75e+03 2.85e+03 ... 7.215e+04
    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
[28]:
radar_utmc = np.dstack([ds_clip.xc, ds_clip.yc]).reshape(-1, 2)
radar_utmc.shape
[28]:
(59770, 2)
[29]:
trg = wrl.io.VectorSource(shpfile, srs=proj_utm, name="trg", projection_source=proj_gk2)
src = wrl.io.VectorSource(radar_utmc, srs=proj_utm, name="src")
[30]:
###########################################################################
# 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, srs=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: 4.247128 seconds
        Create object from dumped file: 1.294230 seconds
        Compute stats using object: 0.000053 seconds
[31]:
# PLOTTING Approach #2
src1 = wrl.io.VectorSource(radar_utmc, srs=proj_utm, name="src")
trg1 = wrl.io.VectorSource(
    shpfile, srs=proj_utm, name="trg", projection_source=proj_gk2
)

# Just a test for plotting results with zero buffer
zd = wrl.zonalstats.ZonalDataPoint(src1, 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
[32]:
# Illustrate results for an example catchment i
i = 0  # try e.g. 5, 2
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, aspect="equal")

# 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])
pl.xlim(bbox["left"] - 2000, bbox["right"] + 2000)
pl.ylim(bbox["bottom"] - 2000, bbox["top"] + 2000)
pl.legend()
pl.title("Catchment #%d: Points considered for stats" % i)
[32]:
Text(0.5, 1.0, 'Catchment #0: Points considered for stats')
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_36_1.png
[33]:
# Plot average rainfall and original data
testplot(
    ds_clip.data, obj2, col="mean", title="Catchment rainfall mean (ZonalStatsPoint)"
)
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_37_0.png
[34]:
testplot(
    ds_clip.data,
    obj2,
    col="var",
    levels=np.arange(0, 20, 1.0),
    title="Catchment rainfall variance (ZonalStatsPoint)",
)
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_38_0.png
[35]:
radar_utm = wrl.georef.spherical_to_polyvert(
    ds.range.values + np.median(np.diff(ds.range.values)) / 2.0,
    ds.azimuth.values,
    0.5,
    (ds.longitude.values, ds.latitude.values, ds.altitude.values),
    proj=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]),
    }
)

trg = wrl.io.VectorSource(shpfile, srs=proj_utm, name="trg", projection_source=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: 86, range: 695, verts: 5)
Coordinates: (12/19)
    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 2.75e+03 2.85e+03 ... 7.205e+04 7.215e+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
[36]:
radar_utm = np.stack([ds_clip.xp, ds_clip.yp], axis=-1).reshape(-1, 5, 2)
print(radar_utm.shape)
src = wrl.io.VectorSource(radar_utm, srs=proj_utm, name="src")
trg = wrl.io.VectorSource(shpfile, srs=proj_utm, name="trg", projection_source=proj_gk2)
(59770, 5, 2)
[37]:
###########################################################################
# 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, srs=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", "netCDF", "mean", pixel_size=100.0)

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

# Target polygon patches
trg_patches = [patches.Polygon(item, True) for item in obj3.zdata.trg.data]
Approach #2 computation time:
        Create object from scratch: 3.003707 seconds
        Create object from dumped file: 1.211138 seconds
        Compute stats using object: 0.000039 seconds
[38]:
ds_clip.data.plot(x="x", y="y")
[38]:
<matplotlib.collections.QuadMesh at 0x7fc17095d2a0>
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_42_1.png
[39]:
# Plot average rainfall and original data
testplot(
    ds_clip.data,
    obj4,
    col="mean",
    title="Catchment rainfall mean (PolarZonalStatsPoly)",
)
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_43_0.png
[40]:
testplot(
    ds_clip.data,
    obj4,
    col="var",
    levels=np.arange(0, 20, 1.0),
    title="Catchment rainfall variance (PolarZonalStatsPoly)",
)
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_44_0.png
[41]:
# Illustrate results for an example catchment i
i = 0  # try e.g. 5, 2
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, aspect="equal")

# 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=pl.cm.plasma, alpha=0.5)

# scatter center points
ds_clip.plot.scatter(x="xc", y="yc", s=10)

cat = trg.get_data_by_idx([i])[0]
bbox = wrl.zonalstats.get_bbox(cat[..., 0], cat[..., 1])
pl.xlim(bbox["left"] - 2000, bbox["right"] + 2000)
pl.ylim(bbox["bottom"] - 2000, bbox["top"] + 2000)
pl.legend()
pl.title("Catchment #%d: Polygons considered for stats" % i)
pl.gca().set_xlim(402000, 404000)
pl.gca().set_ylim(5654000, 5656000)
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.
[41]:
(5654000.0, 5656000.0)
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_45_2.png
[42]:
# Compare estimates
maxlim = np.max(np.concatenate((avg1, avg3)))
fig = pl.figure(figsize=(10, 8))
ax = fig.add_subplot(111, aspect="equal")
pl.scatter(avg1, avg3, edgecolor="None", alpha=0.5)
pl.xlabel("Average of points in or close to polygon (mm)")
pl.ylabel("Area-weighted average (mm)")
pl.xlim(0, maxlim)
pl.ylim(0, maxlim)
pl.plot([-1, maxlim + 1], [-1, maxlim + 1], color="black")
pl.show()
../../_images/notebooks_zonalstats_wradlib_zonalstats_example_46_0.png