Note
Click here to download the full example code
Optical flow¶
This tutorial offers a short overview of the optical flow routines available in pysteps and it will cover how to compute and plot the motion field from a sequence of radar images.
from datetime import datetime
from pprint import pprint
import matplotlib.pyplot as plt
import numpy as np
from pysteps import io, motion, rcparams
from pysteps.utils import conversion, transformation
from pysteps.visualization import plot_precip_field, quiver
Read the radar input images¶
First, we will import the sequence of radar composites. You need the pysteps-data archive downloaded and the pystepsrc file configured with the data_source paths pointing to data folders.
# Selected case
date = datetime.strptime("201505151630", "%Y%m%d%H%M")
data_source = rcparams.data_sources["mch"]
Load the data from the archive¶
root_path = data_source["root_path"]
path_fmt = data_source["path_fmt"]
fn_pattern = data_source["fn_pattern"]
fn_ext = data_source["fn_ext"]
importer_name = data_source["importer"]
importer_kwargs = data_source["importer_kwargs"]
timestep = data_source["timestep"]
# Find the input files from the archive
fns = io.archive.find_by_date(
date, root_path, path_fmt, fn_pattern, fn_ext, timestep=5, num_prev_files=9
)
# Read the radar composites
importer = io.get_method(importer_name, "importer")
R, quality, metadata = io.read_timeseries(fns, importer, **importer_kwargs)
del quality # Not used
Out:
/home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.0.1/lib/python3.7/site-packages/pysteps-1.0.0-py3.7-linux-x86_64.egg/pysteps/io/importers.py:504: RuntimeWarning: invalid value encountered in greater
if np.any(R > np.nanmin(R)):
/home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.0.1/lib/python3.7/site-packages/pysteps-1.0.0-py3.7-linux-x86_64.egg/pysteps/io/importers.py:505: RuntimeWarning: invalid value encountered in greater
metadata["threshold"] = np.nanmin(R[R > np.nanmin(R)])
Preprocess the data¶
# Convert to mm/h
R, metadata = conversion.to_rainrate(R, metadata)
# Store the reference frame
R_ = R[-1, :, :].copy()
# Log-transform the data [dBR]
R, metadata = transformation.dB_transform(R, metadata, threshold=0.1, zerovalue=-15.0)
# Nicely print the metadata
pprint(metadata)
Out:
/home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.0.1/lib/python3.7/site-packages/pysteps-1.0.0-py3.7-linux-x86_64.egg/pysteps/utils/transformation.py:232: RuntimeWarning: invalid value encountered in less
zeros = R < threshold
{'accutime': 5,
'institution': 'MeteoSwiss',
'product': 'AQC',
'projection': '+proj=somerc +lon_0=7.43958333333333 +lat_0=46.9524055555556 '
'+k_0=1 +x_0=600000 +y_0=200000 +ellps=bessel '
'+towgs84=674.374,15.056,405.346,0,0,0,0 +units=m +no_defs',
'threshold': -10.0,
'timestamps': array([datetime.datetime(2015, 5, 15, 15, 45),
datetime.datetime(2015, 5, 15, 15, 50),
datetime.datetime(2015, 5, 15, 15, 55),
datetime.datetime(2015, 5, 15, 16, 0),
datetime.datetime(2015, 5, 15, 16, 5),
datetime.datetime(2015, 5, 15, 16, 10),
datetime.datetime(2015, 5, 15, 16, 15),
datetime.datetime(2015, 5, 15, 16, 20),
datetime.datetime(2015, 5, 15, 16, 25),
datetime.datetime(2015, 5, 15, 16, 30)], dtype=object),
'transform': 'dB',
'unit': 'mm/h',
'x1': 255000.0,
'x2': 965000.0,
'xpixelsize': 1000.0,
'y1': -160000.0,
'y2': 480000.0,
'yorigin': 'upper',
'ypixelsize': 1000.0,
'zerovalue': -15.0}
Lucas-Kanade (LK)¶
The Lucas-Kanade optical flow method implemented in pysteps is a local tracking approach that relies on the OpenCV package. Local features are tracked in a sequence of two or more radar images. The scheme includes a final interpolation step in order to produce a smooth field of motion vectors.
oflow_method = motion.get_method("LK")
V1 = oflow_method(R[-3:, :, :])
# Plot the motion field on top of the reference frame
plot_precip_field(R_, geodata=metadata, title="LK")
quiver(V1, geodata=metadata, step=25)
plt.show()
Out:
Computing the motion field with the Lucas-Kanade method.
--- 13 outliers removed ---
--- LK found 800 sparse vectors ---
--- 156 sparse vectors left after declustering ---
--- 3.37 seconds ---
/home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.0.1/lib/python3.7/site-packages/pysteps-1.0.0-py3.7-linux-x86_64.egg/pysteps/visualization/precipfields.py:210: RuntimeWarning: invalid value encountered in less
R[R < 0.1] = np.nan
Variational echo tracking (VET)¶
This module implements the VET algorithm presented by Laroche and Zawadzki (1995) and used in the McGill Algorithm for Prediction by Lagrangian Extrapolation (MAPLE) described in Germann and Zawadzki (2002). The approach essentially consists of a global optimization routine that seeks at minimizing a cost function between the displaced and the reference image.
oflow_method = motion.get_method("VET")
V2 = oflow_method(R[-3:, :, :])
# Plot the motion field
plot_precip_field(R_, geodata=metadata, title="VET")
quiver(V2, geodata=metadata, step=25)
plt.show()
Out:
Running VET algorithm
original image shape: (3, 640, 710)
padded image shape: (3, 640, 710)
padded template_image image shape: (3, 640, 710)
Number of sectors: 2,2
Sector Shape: (320, 355)
Minimizing
residuals 3630622.364497493
smoothness_penalty 0.0
original image shape: (3, 640, 710)
padded image shape: (3, 640, 712)
padded template_image image shape: (3, 640, 712)
Number of sectors: 4,4
Sector Shape: (160, 178)
Minimizing
residuals 2592747.959869525
smoothness_penalty 0.5045368802200889
original image shape: (3, 640, 710)
padded image shape: (3, 640, 720)
padded template_image image shape: (3, 640, 720)
Number of sectors: 16,16
Sector Shape: (40, 45)
Minimizing
residuals 2479618.252823427
smoothness_penalty 101.18168573997124
original image shape: (3, 640, 710)
padded image shape: (3, 640, 736)
padded template_image image shape: (3, 640, 736)
Number of sectors: 32,32
Sector Shape: (20, 23)
Minimizing
residuals 2508994.6200837744
smoothness_penalty 286.0529705370135
/home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.0.1/lib/python3.7/site-packages/pysteps-1.0.0-py3.7-linux-x86_64.egg/pysteps/visualization/precipfields.py:210: RuntimeWarning: invalid value encountered in less
R[R < 0.1] = np.nan
Dynamic and adaptive radar tracking of storms (DARTS)¶
DARTS uses a spectral approach to optical flow that is based on the discrete Fourier transform (DFT) of a temporal sequence of radar fields. The level of truncation of the DFT coefficients controls the degree of smoothness of the estimated motion field, allowing for an efficient motion estimation. DARTS requires a longer sequence of radar fields for estimating the motion, here we are going to use all the available 10 fields.
oflow_method = motion.get_method("DARTS")
R[~np.isfinite(R)] = metadata["zerovalue"]
V3 = oflow_method(R) # needs longer training sequence
# Plot the motion field
plot_precip_field(R_, geodata=metadata, title="DARTS")
quiver(V3, geodata=metadata, step=25)
plt.show()
# sphinx_gallery_thumbnail_number = 1
Out:
Computing the motion field with the DARTS method.
--- 3.394226312637329 seconds ---
Total running time of the script: ( 0 minutes 58.299 seconds)