In [1]:
import wradlib
import numpy as np
import os
import datetime as dt


C:\Anaconda3\envs\wradlib1_0\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters

In [2]:
%matplotlib


Using matplotlib backend: Qt5Agg

In [3]:
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.collections import PatchCollection
import datetime
import warnings
warnings.simplefilter('once', DeprecationWarning)

In [4]:
from scipy import ndimage as ndi
from skimage import feature
from skimage.feature import match_template

In [5]:
import h5py
import pandas as pd

In [30]:
import matplotlib
from matplotlib.patches import Circle, Wedge, Polygon, Rectangle
from matplotlib.collections import PatchCollection

In [7]:
from skimage import measure
from skimage import filters
from scipy import ndimage
from skimage.measure import label, regionprops
import math
from matplotlib.patches import Ellipse

In [8]:
for_year = "2016"
rootdir = r"e:\data\radolan\ry"
tmpdir = r"e:\data\radolan\tmp"
h5file = 'ry_%s.hdf5' % for_year
hourlyfile = 'hdf/ry_hourly_%s.hdf5' % for_year
hourlyobjfile = 'hdf/ry_hourly_objects_%s.pickle' % for_year
tstart = "%s-01-01" % for_year 
tend = "%s-12-31" % for_year
dffile = "exc_%s.csv" % for_year
nx = 900
ny = 900
thresh = 20. # mm/h
minarea = 10
maxarea = 1500

Extract hourly features (initial detection)


In [9]:
days = wradlib.util.from_to(dt.datetime.strptime(tstart, "%Y-%m-%d"),
                            dt.datetime.strptime(tend, "%Y-%m-%d"), tdelta=3600*24)
dtimes = wradlib.util.from_to(days[0].strftime("%Y-%m-%d 00:00:00"),
                              (days[-1]+dt.timedelta(days=1)).strftime("%Y-%m-%d 00:00:00"), tdelta=60*60)
hrs = np.arange(24).astype("i4")

In [10]:
dummy = regionprops(np.ones((4,4)).astype("i4"), intensity_image=np.ones((4,4)) )
keys = list(dummy[0])
props = [ dummy[0].__getitem__(key) for key in keys ]
keys.insert(0, "dtime")
props.insert(0, "1900-01-01 00:00:00")

In [91]:
df = pd.DataFrame( dict([(key, [props[i]]) for i,key in enumerate(keys)] ) )

In [92]:
with h5py.File(hourlyfile, 'r') as f:
    for day in days:
        print(day.strftime("%Y/%m/%d"), end="")
        try:
            dset = f[day.strftime("%Y/%m/%d")][:]
        except KeyError:
            print(" does not exist.")
            continue
        found = 0
        for i, hr in enumerate(hrs):
            hset = dset[i]
            label_im = measure.label(hset > thresh, background=0)
            nb_labels = len(np.unique(label_im))
            regions = regionprops(label_im, intensity_image=hset)
            for region in regions:
                if (region.area < minarea) or (region.area > maxarea):
                    continue
                found += 1
                thetime = day.strftime("%Y-%m-%d") + " %02d:00:00" % hr
                theprops = [region.__getitem__(prop) for prop in region]
                theprops.insert(0, thetime)
                df = df.append(dict([(key, theprops[i]) for i,key in enumerate(keys)] ), ignore_index=True)
        print(" Found %d regions." % found)


2016/01/01 Found 0 regions.
2016/01/02 Found 0 regions.
2016/01/03 Found 0 regions.
2016/01/04 Found 0 regions.
2016/01/05 Found 0 regions.
2016/01/06 Found 0 regions.
2016/01/07 Found 0 regions.
2016/01/08 Found 0 regions.
2016/01/09 Found 0 regions.
2016/01/10 Found 3 regions.
2016/01/11 Found 0 regions.
2016/01/12 Found 0 regions.
2016/01/13 Found 0 regions.
2016/01/14 Found 0 regions.
2016/01/15 Found 0 regions.
2016/01/16 Found 0 regions.
2016/01/17 Found 0 regions.
2016/01/18 Found 0 regions.
2016/01/19 Found 0 regions.
2016/01/20 Found 0 regions.
2016/01/21 Found 0 regions.
2016/01/22 Found 0 regions.
2016/01/23 Found 0 regions.
2016/01/24 Found 0 regions.
2016/01/25 Found 0 regions.
2016/01/26 Found 0 regions.
2016/01/27 Found 0 regions.
2016/01/28 Found 0 regions.
2016/01/29 Found 0 regions.
2016/01/30 Found 0 regions.
2016/01/31 Found 0 regions.
2016/02/01 Found 0 regions.
2016/02/02 Found 2 regions.
2016/02/03 Found 0 regions.
2016/02/04 Found 0 regions.
2016/02/05 Found 0 regions.
2016/02/06 Found 0 regions.
2016/02/07 Found 0 regions.
2016/02/08 Found 0 regions.
2016/02/09 Found 5 regions.
2016/02/10 Found 0 regions.
2016/02/11 Found 0 regions.
2016/02/12 Found 0 regions.
2016/02/13 Found 0 regions.
2016/02/14 Found 0 regions.
2016/02/15 Found 0 regions.
2016/02/16 Found 0 regions.
2016/02/17 Found 0 regions.
2016/02/18 Found 0 regions.
2016/02/19 Found 0 regions.
2016/02/20 Found 0 regions.
2016/02/21 Found 0 regions.
2016/02/22 Found 0 regions.
2016/02/23 Found 0 regions.
2016/02/24 does not exist.
2016/02/25 does not exist.
2016/02/26 does not exist.
2016/02/27 does not exist.
2016/02/28 does not exist.
2016/02/29 does not exist.
2016/03/01 does not exist.
2016/03/02 does not exist.
2016/03/03 Found 0 regions.
2016/03/04 Found 0 regions.
2016/03/05 Found 0 regions.
2016/03/06 Found 0 regions.
2016/03/07 Found 0 regions.
2016/03/08 Found 0 regions.
2016/03/09 Found 0 regions.
2016/03/10 Found 0 regions.
2016/03/11 Found 0 regions.
2016/03/12 Found 0 regions.
2016/03/13 Found 0 regions.
2016/03/14 Found 0 regions.
2016/03/15 Found 0 regions.
2016/03/16 does not exist.
2016/03/17 does not exist.
2016/03/18 Found 0 regions.
2016/03/19 Found 0 regions.
2016/03/20 Found 0 regions.
2016/03/21 Found 0 regions.
2016/03/22 Found 0 regions.
2016/03/23 Found 0 regions.
2016/03/24 Found 0 regions.
2016/03/25 Found 0 regions.
2016/03/26 Found 0 regions.
2016/03/27 Found 0 regions.
2016/03/28 Found 0 regions.
2016/03/29 Found 0 regions.
2016/03/30 Found 0 regions.
2016/03/31 Found 3 regions.
2016/04/01 Found 0 regions.
2016/04/02 Found 0 regions.
2016/04/03 Found 0 regions.
2016/04/04 Found 1 regions.
2016/04/05 Found 0 regions.
2016/04/06 Found 0 regions.
2016/04/07 Found 0 regions.
2016/04/08 Found 0 regions.
2016/04/09 Found 0 regions.
2016/04/10 Found 0 regions.
2016/04/11 Found 0 regions.
2016/04/12 Found 6 regions.
2016/04/13 Found 10 regions.
2016/04/14 Found 2 regions.
2016/04/15 Found 0 regions.
2016/04/16 does not exist.
2016/04/17 does not exist.
2016/04/18 does not exist.
2016/04/19 does not exist.
2016/04/20 does not exist.
2016/04/21 does not exist.
2016/04/22 does not exist.
2016/04/23 does not exist.
2016/04/24 does not exist.
2016/04/25 does not exist.
2016/04/26 Found 0 regions.
2016/04/27 Found 0 regions.
2016/04/28 Found 0 regions.
2016/04/29 Found 0 regions.
2016/04/30 Found 0 regions.
2016/05/01 Found 0 regions.
2016/05/02 Found 0 regions.
2016/05/03 Found 2 regions.
2016/05/04 Found 0 regions.
2016/05/05 Found 0 regions.
2016/05/06 Found 0 regions.
2016/05/07 Found 0 regions.
2016/05/08 Found 1 regions.
2016/05/09 Found 0 regions.
2016/05/10 Found 2 regions.
2016/05/11 Found 4 regions.
2016/05/12 Found 3 regions.
2016/05/13 Found 45 regions.
2016/05/14 Found 2 regions.
2016/05/15 Found 0 regions.
2016/05/16 Found 0 regions.
2016/05/17 Found 0 regions.
2016/05/18 Found 0 regions.
2016/05/19 Found 0 regions.
2016/05/20 Found 0 regions.
2016/05/21 Found 0 regions.
2016/05/22 Found 4 regions.
2016/05/23 Found 65 regions.
2016/05/24 Found 17 regions.
2016/05/25 Found 0 regions.
2016/05/26 Found 1 regions.
2016/05/27 Found 80 regions.
2016/05/28 Found 163 regions.
2016/05/29 Found 122 regions.
2016/05/30 Found 58 regions.
2016/05/31 does not exist.
2016/06/01 does not exist.
2016/06/02 Found 8 regions.
2016/06/03 Found 62 regions.
2016/06/04 Found 111 regions.
2016/06/05 Found 206 regions.
2016/06/06 Found 136 regions.
2016/06/07 Found 217 regions.
2016/06/08 Found 123 regions.
2016/06/09 Found 3 regions.
2016/06/10 Found 2 regions.
2016/06/11 Found 12 regions.
2016/06/12 Found 24 regions.
2016/06/13 Found 82 regions.
2016/06/14 Found 23 regions.
2016/06/15 Found 40 regions.
2016/06/16 Found 35 regions.
2016/06/17 Found 15 regions.
2016/06/18 Found 23 regions.
2016/06/19 Found 1 regions.
2016/06/20 Found 0 regions.
2016/06/21 Found 0 regions.
2016/06/22 Found 0 regions.
2016/06/23 Found 55 regions.
2016/06/24 Found 192 regions.
2016/06/25 Found 204 regions.
2016/06/26 Found 1 regions.
2016/06/27 Found 0 regions.
2016/06/28 Found 0 regions.
2016/06/29 Found 56 regions.
2016/06/30 Found 13 regions.
2016/07/01 Found 0 regions.
2016/07/02 Found 6 regions.
2016/07/03 Found 0 regions.
2016/07/04 Found 21 regions.
2016/07/05 Found 334 regions.
2016/07/06 does not exist.
2016/07/07 does not exist.
2016/07/08 Found 0 regions.
2016/07/09 Found 0 regions.
2016/07/10 Found 9 regions.
2016/07/11 Found 41 regions.
2016/07/12 Found 8 regions.
2016/07/13 Found 41 regions.
2016/07/14 Found 0 regions.
2016/07/15 Found 0 regions.
2016/07/16 Found 0 regions.
2016/07/17 Found 0 regions.
2016/07/18 Found 2 regions.
2016/07/19 Found 0 regions.
2016/07/20 Found 14 regions.
2016/07/21 Found 127 regions.
2016/07/22 Found 269 regions.
2016/07/23 Found 115 regions.
2016/07/24 Found 60 regions.
2016/07/25 Found 99 regions.
2016/07/26 Found 218 regions.
2016/07/27 Found 153 regions.
2016/07/28 Found 12 regions.
2016/07/29 Found 0 regions.
2016/07/30 Found 6 regions.
2016/07/31 Found 24 regions.
2016/08/01 Found 0 regions.
2016/08/02 Found 0 regions.
2016/08/03 Found 0 regions.
2016/08/04 Found 0 regions.
2016/08/05 Found 3 regions.
2016/08/06 Found 0 regions.
2016/08/07 Found 0 regions.
2016/08/08 Found 1 regions.
2016/08/09 Found 1 regions.
2016/08/10 Found 0 regions.
2016/08/11 does not exist.
2016/08/12 does not exist.
2016/08/13 does not exist.
2016/08/14 does not exist.
2016/08/15 does not exist.
2016/08/16 does not exist.
2016/08/17 does not exist.
2016/08/18 does not exist.
2016/08/19 does not exist.
2016/08/20 does not exist.
2016/08/21 does not exist.
2016/08/22 does not exist.
2016/08/23 does not exist.
2016/08/24 does not exist.
2016/08/25 does not exist.
2016/08/26 does not exist.
2016/08/27 does not exist.
2016/08/28 does not exist.
2016/08/29 does not exist.
2016/08/30 does not exist.
2016/08/31 does not exist.
2016/09/01 Found 0 regions.
2016/09/02 Found 0 regions.
2016/09/03 Found 0 regions.
2016/09/04 Found 3 regions.
2016/09/05 Found 0 regions.
2016/09/06 Found 0 regions.
2016/09/07 Found 0 regions.
2016/09/08 Found 0 regions.
2016/09/09 Found 0 regions.
2016/09/10 Found 3 regions.
2016/09/11 Found 1 regions.
2016/09/12 Found 13 regions.
2016/09/13 Found 0 regions.
2016/09/14 Found 2 regions.
2016/09/15 Found 0 regions.
2016/09/16 Found 1 regions.
2016/09/17 Found 0 regions.
2016/09/18 Found 0 regions.
2016/09/19 Found 0 regions.
2016/09/20 Found 0 regions.
2016/09/21 Found 0 regions.
2016/09/22 Found 0 regions.
2016/09/23 Found 0 regions.
2016/09/24 Found 0 regions.
2016/09/25 Found 0 regions.
2016/09/26 Found 5 regions.
2016/09/27 Found 5 regions.
2016/09/28 Found 0 regions.
2016/09/29 Found 0 regions.
2016/09/30 Found 1 regions.
2016/10/01 Found 3 regions.
2016/10/02 Found 0 regions.
2016/10/03 Found 0 regions.
2016/10/04 Found 0 regions.
2016/10/05 Found 0 regions.
2016/10/06 Found 0 regions.
2016/10/07 Found 0 regions.
2016/10/08 Found 0 regions.
2016/10/09 Found 0 regions.
2016/10/10 Found 0 regions.
2016/10/11 Found 0 regions.
2016/10/12 Found 0 regions.
2016/10/13 Found 0 regions.
2016/10/14 Found 0 regions.
2016/10/15 does not exist.
2016/10/16 does not exist.
2016/10/17 does not exist.
2016/10/18 does not exist.
2016/10/19 does not exist.
2016/10/20 does not exist.
2016/10/21 does not exist.
2016/10/22 does not exist.
2016/10/23 does not exist.
2016/10/24 does not exist.
2016/10/25 does not exist.
2016/10/26 does not exist.
2016/10/27 does not exist.
2016/10/28 does not exist.
2016/10/29 does not exist.
2016/10/30 does not exist.
2016/10/31 does not exist.
2016/11/01 does not exist.
2016/11/02 does not exist.
2016/11/03 does not exist.
2016/11/04 does not exist.
2016/11/05 does not exist.
2016/11/06 does not exist.
2016/11/07 does not exist.
2016/11/08 does not exist.
2016/11/09 does not exist.
2016/11/10 does not exist.
2016/11/11 does not exist.
2016/11/12 does not exist.
2016/11/13 does not exist.
2016/11/14 does not exist.
2016/11/15 does not exist.
2016/11/16 does not exist.
2016/11/17 does not exist.
2016/11/18 does not exist.
2016/11/19 does not exist.
2016/11/20 does not exist.
2016/11/21 Found 0 regions.
2016/11/22 Found 0 regions.
2016/11/23 Found 0 regions.
2016/11/24 Found 0 regions.
2016/11/25 Found 0 regions.
2016/11/26 Found 0 regions.
2016/11/27 Found 0 regions.
2016/11/28 Found 0 regions.
2016/11/29 Found 0 regions.
2016/11/30 Found 0 regions.
2016/12/01 Found 0 regions.
2016/12/02 Found 0 regions.
2016/12/03 Found 0 regions.
2016/12/04 Found 0 regions.
2016/12/05 Found 0 regions.
2016/12/06 Found 0 regions.
2016/12/07 Found 0 regions.
2016/12/08 Found 0 regions.
2016/12/09 Found 0 regions.
2016/12/10 Found 0 regions.
2016/12/11 Found 0 regions.
2016/12/12 Found 0 regions.
2016/12/13 Found 0 regions.
2016/12/14 Found 0 regions.
2016/12/15 Found 0 regions.
2016/12/16 Found 0 regions.
2016/12/17 Found 0 regions.
2016/12/18 Found 0 regions.
2016/12/19 Found 0 regions.
2016/12/20 Found 0 regions.
2016/12/21 Found 0 regions.
2016/12/22 Found 0 regions.
2016/12/23 Found 0 regions.
2016/12/24 Found 0 regions.
2016/12/25 Found 0 regions.
2016/12/26 Found 0 regions.
2016/12/27 Found 0 regions.
2016/12/28 Found 0 regions.
2016/12/29 Found 0 regions.
2016/12/30 Found 0 regions.
2016/12/31 Found 0 regions.

In [11]:
#df.to_pickle(hourlyobjfile)
df = pd.read_pickle(hourlyobjfile)

In [32]:
df = df.set_index(pd.DatetimeIndex(df['dtime']))

In [13]:
df.keys()


Out[13]:
Index(['area', 'bbox', 'bbox_area', 'centroid', 'convex_area', 'convex_image',
       'coords', 'dtime', 'eccentricity', 'equivalent_diameter',
       'euler_number', 'extent', 'filled_area', 'filled_image', 'image',
       'label', 'major_axis_length', 'max_intensity', 'mean_intensity',
       'min_intensity', 'minor_axis_length', 'moments', 'moments_central',
       'moments_hu', 'moments_normalized', 'orientation', 'perimeter',
       'solidity', 'weighted_centroid', 'weighted_moments',
       'weighted_moments_central', 'weighted_moments_hu',
       'weighted_moments_normalized'],
      dtype='object')

Analyse spatial extent of cells


In [118]:
expandby = np.arange(50)
toleft = np.zeros((len(df), len(expandby))) * np.nan
toright = toleft.copy()
tobottom = toleft.copy()
totop = toleft.copy()

In [119]:
with h5py.File(hourlyfile, 'r') as f:
    for i in range(len(df)):
        dtime = dt.datetime.strptime(df.dtime.iloc[i], "%Y-%m-%d %H:%M:%S")
        print(dtime)
        try:
            hset = f[dtime.strftime("%Y/%m/%d")][dtime.hour]
        except KeyError:
            continue
        left, bottom, right, top = df.bbox.iloc[i][0], df.bbox.iloc[i][1], df.bbox.iloc[i][2], df.bbox.iloc[i][3]
        for j, step in enumerate(expandby):
            try:
                toleft[i,j] = np.nanmean(hset[(left-step):right, bottom:top])
            except IndexError:
                continue
            try:
                toright[i,j] = np.nanmean(hset[left:(right+step), bottom:top])
            except IndexError:
                continue
            try:
                tobottom[i,j] = np.nanmean(hset[left:right, (bottom-step):top])
            except IndexError:
                continue
            try:
                totop[i,j] = np.nanmean(hset[left:right, bottom:(top+step)])
            except IndexError:
                continue


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C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:12: RuntimeWarning: Mean of empty slice
  if sys.path[0] == '':
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C:\Anaconda3\envs\wradlib1_0\lib\site-packages\numpy\core\_methods.py:32: RuntimeWarning: overflow encountered in reduce
  return umr_sum(a, axis, dtype, out, keepdims)
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In [134]:
leftnorm = toleft / toleft[:,0].reshape((-1,1))
leftnorm[leftnorm>1] = np.nan

rightnorm = toright / toright[:,0].reshape((-1,1))
rightnorm[rightnorm>1] = np.nan

bottomnorm = tobottom / tobottom[:,0].reshape((-1,1))
bottomnorm[bottomnorm>1] = np.nan

topnorm = totop / totop[:,0].reshape((-1,1))
topnorm[topnorm>1] = np.nan


C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in true_divide
  """Entry point for launching an IPython kernel.
C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in greater
  
C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in true_divide
  after removing the cwd from sys.path.
C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in greater
  """
C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:7: RuntimeWarning: invalid value encountered in true_divide
  import sys
C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in greater
  
C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in true_divide
  # Remove the CWD from sys.path while we load stuff.
C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:11: RuntimeWarning: invalid value encountered in greater
  # This is added back by InteractiveShellApp.init_path()

In [149]:
print("left")
for i, item in enumerate(leftnorm):
    plt.plot(expandby, np.ma.masked_invalid(item), "b-", alpha=0.005)
print("right")
for i, item in enumerate(rightnorm):
    plt.plot(expandby, np.ma.masked_invalid(item), "r-", alpha=0.005)
print("bottom")
for i, item in enumerate(bottomnorm):
    plt.plot(expandby, np.ma.masked_invalid(item), "g-", alpha=0.005)
print("top")
for i, item in enumerate(topnorm):
    plt.plot(expandby, np.ma.masked_invalid(item), "k-", alpha=0.005)


left
right
bottom
top

Analyse impact of threshold on mean intensity


In [72]:
# Thresholds of hourly rainfall depths for detection of contiguous regions
threshs = np.arange(19,0,-1)

In [73]:
def get_regions(im, thresh):
    """Extract regions from im which exceed thresh.
    """
    label_im = measure.label(im > thresh, background=0)
    nb_labels = len(np.unique(label_im))
    regions = regionprops(label_im, intensity_image=im)
    return(regions)

In [74]:
#means = np.load("hdf/means_2016.numpy.npy")
#areas = np.load("hdf/areas_2016.numpy.npy")

In [75]:
means = np.zeros( (len(df), len(threshs)+1) )
areas = np.zeros( (len(df), len(threshs)+1) )
_dtime = None
with h5py.File(hourlyfile, 'r') as f:
    for i in range(len(df)):
        dtime = dt.datetime.strptime(df.dtime.iloc[i], "%Y-%m-%d %H:%M:%S")
        bottom, left, top, right = df.bbox.iloc[i][0], df.bbox.iloc[i][1], df.bbox.iloc[i][2], df.bbox.iloc[i][3]
        means[i, 0] = df.mean_intensity.iloc[i]
        areas[i, 0] = df.area.iloc[i]
        if dtime != _dtime:
            print("")
            print(dtime, end="")
            # Only process new hourly set for a new datetime
            try:
                hset = f[dtime.strftime("%Y/%m/%d")][dtime.hour]
            except KeyError:
                continue
            threshregions = [get_regions(hset, thresh) for thresh in threshs]
        else:
            print(".", end="")
        for trix, tr in enumerate(threshregions):
            for r in tr:
                # Looking for region that contains core region
                if (left >= r.bbox[1]) and \
                   (right <= r.bbox[3]) and \
                   (bottom >= r.bbox[0]) and \
                   (top <= r.bbox[2]):
                        # Found
                        means[i,trix+1] = r.mean_intensity
                        areas[i,trix+1] = r.area
        _dtime = dtime


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In [76]:
np.save("hdf/means_2016", means)
np.save("hdf/areas_2016", areas)

In [121]:
meansnorm = means / means[:,0].reshape((-1,1))
#meansnorm[meansnorm>1] = np.nan

areasnorm = areas / areas[:,0].reshape((-1,1))
#areasnorm[areasnorm>1] = np.nan

vols = areas * means
volsnorm = (vols - vols[:,0].reshape((-1,1)) ) / vols[:,0].reshape((-1,1))

In [111]:
for i, item in enumerate(areasnorm):
    plt.plot(np.arange(20,0,-1), item, "b-", alpha=0.005)

In [152]:
matplotlib.rcParams.update({'font.size': 7})
plt.figure(figsize=(14,10)) 
for i, item in enumerate(range(1300,1400)):
    ax1 = plt.subplot(10,10,i+1)
    plt.plot(np.arange(20,0,-1), means[item], "b-")
    plt.ylim(0,30)
    plt.grid()
    plt.title(df.dtime.iloc[item] + ", " + str(df.label.iloc[item]), fontsize=7)
    ax2 = ax1.twinx()
    plt.semilogy(np.arange(20,0,-1), areas[item], "r-")
    plt.ylim(10,10000)
plt.tight_layout()

In [174]:
from scipy.signal import argrelextrema

In [176]:
np.gradient(areas[item])


Out[176]:
array([  23. ,   19.5,  212. ,  223. ,   39.5,   38. ,   42. ,   52. ,
         49. ,   51. ,   55.5,   70.5,   87. ,   87.5,   90.5,  116. ,
        159. ,  269. ,  684.5, 1008. ])

In [177]:
argrelextrema(np.gradient(areas[item]), np.greater)[0]


Out[177]:
array([3, 7], dtype=int64)

In [219]:
blacklist_hours = ["2016-06-29 02:00:00"
             "2016-06-29 13:00:00",
             "2016-06-29 14:00:00",
             "2016-07-05 05:00:00",
             "2016-07-05 16:00:00",
             "2016-07-05 17:00:00"]
blacklist_days = ["2016-06-16", "2016-06-29", "2016-07-04", "2016-07-05"]

In [221]:
for day in blacklist_days:
    df.mean_intensity.loc[day] = -9999
bigx = np.argsort(df.mean_intensity)[::-1]
plt.figure()
plt.plot( np.array(df.mean_intensity)[bigx])
plt.ylim(0,60)


C:\Anaconda3\envs\wradlib1_0\lib\site-packages\pandas\core\indexing.py:194: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self._setitem_with_indexer(indexer, value)
Out[221]:
(0, 60)

In [225]:
matplotlib.rcParams.update({'font.size': 7})
plt.figure(figsize=(14,10))
# Look at the 100 most intensive objects
for i, item in enumerate(bigx[0:100]):
    ax1 = plt.subplot(10,10,i+1)
    plt.plot(np.arange(20,0,-1), np.gradient(areas[item]), "b-")
    # for local maxima
    extr = argrelextrema(np.gradient(areas[item]), np.greater)[0]
    plt.plot(np.arange(20,0,-1)[extr], np.gradient(areas[item])[extr], "bo")
    plt.grid()
    plt.ylim(0,20)
    plt.title(df.dtime.iloc[item] + ", " + str(df.label.iloc[item]), fontsize=7)
    ax2 = ax1.twinx()
    plt.plot(np.arange(20,0,-1), areasnorm[item], "g-")
    plt.grid()
    #plt.plot(np.arange(20,0,-1), areas[item], "r-")
    plt.ylim(1,10)
plt.tight_layout()

In [148]:
plt.figure(figsize=(6,6))
ax1 = plt.subplot(2,1,1)
ax2 = plt.subplot(2,1,2)
for item in range(len(means[1:])):
    ax1.plot(np.arange(20,0,-1), meansnorm[item], "b-", alpha=0.005)
    ax1.grid()
    ax2.semilogy(np.arange(20,0,-1), areasnorm[item], "r-", alpha=0.005)


C:\Anaconda3\envs\wradlib1_0\lib\site-packages\matplotlib\axes\_base.py:3443: UserWarning: Attempting to set identical bottom==top results
in singular transformations; automatically expanding.
bottom=1.0, top=1.0
  'bottom=%s, top=%s') % (bottom, top))

In [124]:
plt.figure(figsize=(6,6))
for i, item in enumerate(volsnorm):
    plt.plot(np.arange(20,0,-1), item, "b-", alpha=0.005)

Analyze impact of temporal duration


In [228]:
time_window = np.arange(1, 7)
tdeltas = [dt.timedelta(seconds=i*3600.) for i in time_window]

In [234]:
tmeans = np.zeros( (len(df), len(time_window)+1) )
tareas = np.zeros( (len(df), len(time_window)+1) )
_dtime = None
with h5py.File(hourlyfile, 'r') as f:
    for i in range(len(df)):
        dtime = dt.datetime.strptime(df.dtime.iloc[i], "%Y-%m-%d %H:%M:%S")
        bottom, left, top, right = df.bbox.iloc[i][0], df.bbox.iloc[i][1], df.bbox.iloc[i][2], df.bbox.iloc[i][3]
        tmeans[i, 0] = df.mean_intensity.iloc[i]
        tareas[i, 0] = df.area.iloc[i]
        if dtime != _dtime:
            print("")
            print(dtime, end="")
            # Only process new hourly set for a new datetime
            thewindow = [dtime + item for item in tdeltas]
            daystrings = [item.strftime("%Y/%m/%d") for item in thewindow]
            hours = [item.hour for item in thewindow]
            hsets = np.zeros((len(time_window), 900, 900)) * np.nan
            for i in range(len(time_window)):      
                try:
                    hsets[i] = f[dtime.strftime("%Y/%m/%d")][dtime.hour]
                except KeyError:
                    continue
            hsets = np.cumsum(hsets, axis=0)
            threshregions = [get_regions(hset, 20.) for hset in hsets]
        else:
            print(".", end="")
        for trix, tr in enumerate(threshregions):
            for r in tr:
                # Looking for region that contains core region
                if (left >= r.bbox[1]) and \
                   (right <= r.bbox[3]) and \
                   (bottom >= r.bbox[0]) and \
                   (top <= r.bbox[2]):
                        # Found
                        tmeans[i,trix+1] = r.mean_intensity
                        tareas[i,trix+1] = r.area
        _dtime = dtime


1900-01-01 00:00:00
C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in greater
  after removing the cwd from sys.path.
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In [236]:
np.save("hdf/tmeans_2016", tmeans)
np.save("hdf/tareas_2016", tareas)

In [237]:
tmeansnorm = tmeans / tmeans[:,0].reshape((-1,1))
#meansnorm[meansnorm>1] = np.nan

tareasnorm = tareas / tareas[:,0].reshape((-1,1))

In [239]:
matplotlib.rcParams.update({'font.size': 7})
plt.figure(figsize=(14,10)) 
for i, item in enumerate(range(1300,1400)):
    ax1 = plt.subplot(10,10,i+1)
    plt.plot(np.arange(0,7), tmeans[item], "b-")
    plt.grid()
    plt.title(df.dtime.iloc[item] + ", " + str(df.label.iloc[item]), fontsize=7)
    ax2 = ax1.twinx()
    plt.plot(np.arange(0,7), tareas[item], "r-")
plt.tight_layout()

View specific situations


In [60]:
radolan_grid_xy = wradlib.georef.get_radolan_grid(900,900)
x = radolan_grid_xy[:,:,0]
y = radolan_grid_xy[:,:,1]

In [242]:
dtime = "2016-06-07 21:00:00"
dtime_ = dt.datetime.strptime(dtime, "%Y-%m-%d %H:%M:%S")
with h5py.File(hourlyfile, 'r') as f:
    hset = f[dtime_.strftime("%Y/%m/%d")][dtime_.hour]
sub = df.loc[dtime]

cmap=plt.cm.nipy_spectral
norm = matplotlib.colors.BoundaryNorm(np.arange(0,21), cmap.N)

plt.figure(figsize=(8,8))
pm = plt.pcolormesh(np.ma.masked_array(hset, ~np.isfinite(hset)), cmap=cmap, norm=norm)
plt.xlabel("RADOLAN easting (km)")
plt.ylabel("RADOLAN northing (km)")
plt.colorbar(pm)

ax = plt.gca()
patches = []
for i in range(0,len(sub)):
    polygon = Rectangle(
        (sub.iloc[i]["bbox"][1], sub.iloc[i]["bbox"][0]),   # (x,y)
        sub.iloc[i]["bbox"][3]-sub.iloc[i]["bbox"][1],          # height
        sub.iloc[i]["bbox"][2]-sub.iloc[i]["bbox"][0]          # width
    )
    patches.append(polygon)
p = PatchCollection(patches, facecolor="None", edgecolor="white", linewidth=2)
ax.add_collection(p)

for i in range(0,len(sub)):
    plt.text(sub.iloc[i].centroid[1], sub.iloc[i].centroid[0], str(sub.iloc[i].label), color="red", fontsize=18)

In [132]:
sub.centroid


Out[132]:
dtime
2016-06-07 21:00:00    (102.73913043478261, 480.17391304347825)
2016-06-07 21:00:00                 (129.0, 488.09090909090907)
2016-06-07 21:00:00                  (220.5, 483.6666666666667)
2016-06-07 21:00:00      (230.0612244897959, 471.9183673469388)
2016-06-07 21:00:00                        (275.6875, 431.1875)
2016-06-07 21:00:00                             (329.76, 423.0)
2016-06-07 21:00:00                            (362.825, 462.1)
2016-06-07 21:00:00      (640.5454545454545, 518.9090909090909)
Name: centroid, dtype: object

In [38]:
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, aspect="equal")

patches = []
for i in range(1,len(df)):
    polygon = Rectangle(
        df.iloc[i]["bbox"][0:2],   # (x,y)
        df.iloc[i]["bbox"][2]-df.iloc[i]["bbox"][0],          # width
        df.iloc[i]["bbox"][3]-df.iloc[i]["bbox"][1],          # height
    )
    patches.append(polygon)

colors = 100*np.random.rand(len(patches))
p = PatchCollection(patches, alpha=0.4)
p.set_array(np.array(colors))
ax.add_collection(p)

plt.xlim(0,900)
plt.ylim(0,900)

#plt.draw()


Out[38]:
(0, 900)

Junkyard


In [ ]:
proj = wradlib.georef.create_osr("dwd-radolan")
watersheds_shp = r"E:\src\git\heisterm_bitbucket\tsms_data\tsms-data-misc\shapefiles\watersheds_kocher.shp"
dataset, inLayer = wradlib.io.open_vector(watersheds_shp)
cats, ids = wradlib.georef.get_vector_coordinates(inLayer, dest_srs=proj,
                                                 key="value")
ids = np.array(ids)
left, right, bottom, top = inLayer.GetExtent()

In [96]:
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, aspect="equal")

patches = []
for i in range(1,len(df)):
    polygon = Rectangle(
        df.iloc[i]["bbox"][0:2],   # (x,y)
        df.iloc[i]["bbox"][2]-df.iloc[i]["bbox"][0],          # width
        df.iloc[i]["bbox"][3]-df.iloc[i]["bbox"][1],          # height
    )
    patches.append(polygon)

colors = 100*np.random.rand(len(patches))
p = PatchCollection(patches, alpha=0.4)
p.set_array(np.array(colors))
ax.add_collection(p)

#for i in range(1,len(df)):
#    plt.plot(df.ix[i]["centroid"][0], df.ix[i]["centroid"][1], "bo")

plt.xlim(0,900)
plt.ylim(0,900)

#plt.draw()

#wradlib.vis.add_lines(ax, cats, color='red', lw=0.5, zorder=4, alpha=0.3)
#plt.xlim(-40,20)
#plt.ylim(-4440,-4390)


C:\Anaconda3\envs\wradlib1_0\lib\site-packages\ipykernel_launcher.py:8: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
  
Out[96]:
(0, 900)

In [84]:
toobigx = np.where(df["area"]>1500)[0]
print(len(toobigx))


5

In [85]:
for i in toobigx:
    plt.figure()
    plt.pcolormesh(df.iloc[i]["image"])

In [77]:
i


Out[77]:
array([2177, 2183, 2195, 2202, 2353], dtype=int64)

In [155]:
plt.hist(df["area"], bins=100, range=(0,200), log=True)


Out[155]:
(array([  0.,   0.,   0.,   0.,   0., 573., 400., 378., 277., 229., 210.,
        156., 138., 113.,  95.,  77.,  78.,  78.,  60.,  68.,  45.,  56.,
         43.,  34.,  34.,  37.,  38.,  31.,  26.,  32.,  21.,  25.,  21.,
         23.,  14.,  11.,  17.,  26.,  12.,  16.,  16.,  13.,  14.,  13.,
         12.,   5.,  13.,  12.,   7.,   8.,   8.,  10.,   7.,   5.,   9.,
          6.,   8.,   3.,   6.,  10.,   7.,   5.,   3.,   6.,   2.,   5.,
          6.,   3.,   5.,   4.,   2.,   3.,   3.,   1.,   2.,   1.,   4.,
          5.,   3.,   3.,   2.,   1.,   1.,   1.,   2.,   5.,   2.,   4.,
          4.,   0.,   2.,   0.,   0.,   0.,   0.,   2.,   1.,   0.,   0.,
          3.]),
 array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,
         22.,  24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,
         44.,  46.,  48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,
         66.,  68.,  70.,  72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,
         88.,  90.,  92.,  94.,  96.,  98., 100., 102., 104., 106., 108.,
        110., 112., 114., 116., 118., 120., 122., 124., 126., 128., 130.,
        132., 134., 136., 138., 140., 142., 144., 146., 148., 150., 152.,
        154., 156., 158., 160., 162., 164., 166., 168., 170., 172., 174.,
        176., 178., 180., 182., 184., 186., 188., 190., 192., 194., 196.,
        198., 200.]),
 <a list of 100 Patch objects>)

In [ ]:
plt.figure(figsize=(8,8))
#plt.imshow(im.mean(axis=0), cmap=plt.cm.gray, origin="lower")
plt.imshow(frames[start:end].sum(axis=0), cmap=plt.cm.gray, origin="lower", vmin=0, vmax=30)
plt.xlabel("RADOLAN easting (km)")
plt.ylabel("RADOLAN northing (km)")
plt.title("Rainfall accumulation and cell tracks\nMay 29, 2016, 15:00-18:00 UTC")
ax = plt.gca()
for label in labels[1:]:
    #for i in range(len(im)):
    tmp = (label_im == label).astype("int")
    #tmp = label_im[i]
    regions = regionprops(tmp, intensity_image=im)
    centx, centy = [], []
    for region in regions:

        y0, x0 = region.centroid
        centx.append(x0)
        centy.append(y0)
        orientation = region.orientation

        angle=-np.rad2deg( orientation)
        e = Ellipse([x0,y0], region.major_axis_length, region.minor_axis_length, 
                angle=angle, facecolor="none", edgecolor="blue", linewidth=1.3, alpha=0.5)
        ax.add_artist(e)
        #plt.plot(x0, y0, "o", markerfacecolor=plt.cm.rainbow(i/len(im)), markeredgecolor="none", alpha=0.5)
        plt.contour(tmp, [0.5], linewidths=1., colors="red", alpha=0.5)
        

#pm=plt.scatter([], [], c=[], cmap=plt.cm.rainbow, vmin=0, vmax=len(im)*5)
#cb=plt.colorbar(pm, label="Minutes from 2016-05-29 16:00", shrink=0.75)

In [ ]:
fig = plt.figure(figsize=(12,12))
ax = fig.add_subplot(111)
for i, label in enumerate(labels):
    tmp = (label_im == label)
    areal_avg = np.array([np.mean(frames[i][tmp]) for i in range(len(frames))])
    ax.plot(np.cumsum(areal_avg))

In [ ]:
from matplotlib import animation

In [ ]:
# Animate features

# Prepare canvas
fig = plt.figure(figsize=(5,5))
ax = plt.subplot(111,aspect="equal")
im1 = ax.imshow(frames[0], origin="lower", cmap="gray", interpolation="none", vmin=10,  vmax=20)
plt.xlabel("Easting (km)")
plt.ylabel("Northing (km)")
plt.grid(color="white")
plt.xlim(150,450)
plt.ylim(550,900)
#ax1.plot(x[0,goodtrack], y[0,goodtrack], linestyle="None", marker="o", mfc="None", mec="limegreen")
#ax1.plot(x[0,~goodtrack], y[0,~goodtrack], linestyle="None", marker="o", mfc="None", mec="red")
ax.grid(color="white")
tstamp1 = ax.text(160, 560, dtimes[0].isoformat(), color="white", fontsize=12)

def animate(j):
    im1.set_array(frames[0+j])
    tstamp1.set_text(dtimes[0+j].isoformat())
    for label in labels[1:]:
        #break
        tmp = (label_im[j] == label).astype("int")
        #tmp = label_im[i]
        regions = regionprops(tmp, intensity_image=im[j])
        centx, centy = [], []
        for region in regions:

            y0, x0 = region.centroid
            centx.append(x0)
            centy.append(y0)
            orientation = region.orientation

            angle=-np.rad2deg( orientation)
            e = Ellipse([x0,y0], region.major_axis_length, region.minor_axis_length, 
                    angle=angle, facecolor="none", edgecolor=plt.cm.rainbow(j/len(im)), linewidth=1.3, alpha=0.3)
            ax.add_artist(e)
            #ax.plot(x0, y0, "o", markerfacecolor=plt.cm.rainbow(j/len(im)), markeredgecolor="none", alpha=0.5)
            tstamp1.set_text(dtimes[0+j].isoformat())

    return im1

# ATTENTION: THIS IS SLOW - Rendering each frame of the animation might take more time than the interval between the frames
#    This can cause the temporal sequence to be confused in the matplotlib interactive mode.
#    The animation thus looks better if saved as movie, or you have to increase the interval argument
#    Animation not shown in notebook if you use %pylab inline
maxi = len(frames)-1
ani = animation.FuncAnimation(fig, animate, frames=np.arange(0, maxi), interval=400, blit=False)
ani.save("features.gif", writer="imagemagick", dpi=150)

In [ ]:
len(region)

In [ ]:
#fig, ax = plt.subplots()
plt.imshow(im, cmap=plt.cm.gray, origin="lower")
plt.contour(label_im, [0.5], linewidths=1.2, colors='y')
plt.xlabel("RADOLAN easting (km)")
plt.ylabel("RADOLAN northing (km)")
plt.title("Snaphot at 2016-05-29 16:00 UTC")

ax = plt.gca()

for i, props in enumerate(regions):
    y0, x0 = props.centroid
    orientation = props.orientation
    x1 = x0 + math.cos(orientation) * 0.5 * props.major_axis_length
    y1 = y0 - math.sin(orientation) * 0.5 * props.major_axis_length
    x2 = x0 - math.sin(orientation) * 0.5 * props.minor_axis_length
    y2 = y0 - math.cos(orientation) * 0.5 * props.minor_axis_length

    #plt.plot((x0, x1), (y0, y1), '--r', linewidth=2)
    #plt.plot((x0, x2), (y0, y2), '--r', linewidth=2)
    #plt.plot(x0, y0, '.r', markersize=15)
    angle=-np.rad2deg( props.orientation)
    e = Ellipse([x0,y0], props.major_axis_length, props.minor_axis_length, 
                angle=angle, facecolor="none", edgecolor="red", linewidth=2)
    ax.add_artist(e)

    minr, minc, maxr, maxc = props.bbox
    bx = (minc, maxc, maxc, minc, minc)
    by = (minr, minr, maxr, maxr, minr)
    #plt.plot(bx, by, '-b', linewidth=2.5)
    try: 
        label = "ID=%s\navg=%d mm/h\nmax=%d mm/h" % (props.label, props.mean_intensity, props.max_intensity)
    except:
        label = "ID=%s, avg=%s mm/h, max=%s mm/h" % (props.label, "nan", "nan")
    plt.text((minc+maxc)/2, maxr+2, label, color="red", fontsize=10, horizontalalignment='center')  
    

#plt.axis((0, 900, 900, 0))
plt.xlim(200,900)
plt.ylim(0,470)

In [ ]:
minr, minc, maxr, maxc = props.bbox
plt.imshow(im[minr:maxr, minc:maxc])

In [ ]:
im2 = frames[1]

fig = plt.figure(figsize=(8, 8))
ax2 = plt.subplot(1, 1, 1)
    
for i, props in enumerate(regions):

    minr, minc, maxr, maxc = props.bbox
    roi = im[minr:maxr, minc:maxc]
    result = match_template(im2, roi)
    ij = np.unravel_index(np.argmax(result), result.shape)
    x, y = ij[::-1]
    print(ij)

    #ax1.imshow(roi, cmap=plt.cm.gray)
    #ax1.set_axis_off()
    #ax1.set_title('Feature #1 at t+0')

    ax2.imshow(im2, cmap=plt.cm.gray, origin="lower")
    ax2.set_axis_off()
    ax2.set_title('Feature #1 at t+2')
    # highlight matched region
    hcoin, wcoin = roi.shape
    rect = plt.Rectangle((x, y), wcoin, hcoin, edgecolor='r', facecolor='none')
    ax2.add_patch(rect)
    plt.plot(x,y,".r")
    plt.plot(ij[0],ij[1],".b")
    # highlight matched region
    bx = (minc, maxc, maxc, minc, minc)
    by = (minr, minr, maxr, maxr, minr)
    plt.plot(bx, by, '-b', linewidth=1.)

In [ ]:
ij

In [ ]:
ndimage.find_objects(label_im==15)

In [ ]:
image = frames[2]
coin = roi

result = match_template(image, coin)
ij = np.unravel_index(np.argmax(result), result.shape)
x, y = ij[::-1]

fig = plt.figure(figsize=(8, 3))
ax1 = plt.subplot(1, 2, 1)
ax2 = plt.subplot(1, 2, 2, adjustable='box-forced')

ax1.imshow(coin, cmap=plt.cm.gray)
ax1.set_axis_off()
ax1.set_title('Feature #1 at t+0')

ax2.imshow(image, cmap=plt.cm.gray)
ax2.set_axis_off()
ax2.set_title('Feature #1 at t+2')
# highlight matched region
hcoin, wcoin = coin.shape
rect = plt.Rectangle((x, y), wcoin, hcoin, edgecolor='r', facecolor='none')
ax2.add_patch(rect)

In [ ]: