Update tutorials

Author: dnerini

examples/plot_noise_generators.py

@@ -33,9 +33,7 @@
 
 # Import the example radar composite
 root_path = rcparams.data_sources["mch"]["root_path"]
-filename = os.path.join(
-    root_path, "20160711", "AQC161932100V_00005.801.gif"
-)
+filename = os.path.join(root_path, "20160711", "AQC161932100V_00005.801.gif")
 R, _, metadata = io.import_mch_gif(filename, product="AQC", unit="mm", accutime=5.0)
 
 # Convert to mm/h
@@ -158,16 +156,16 @@
 
 ###############################################################################
 # The above figure highlights the main limitation of the parametric approach
-# (top row), that is, the assumption of an isotropic power law scaling 
-# relationship, meaning that anisotropic structures such as rainfall bands 
-# cannot be represented. 
+# (top row), that is, the assumption of an isotropic power law scaling
+# relationship, meaning that anisotropic structures such as rainfall bands
+# cannot be represented.
 #
-# Instead, the nonparametric approach (bottom row) allows generating 
-# perturbation fields with anisotropic  structures, but it also requires a 
-# larger sample size and is sensitive to the quality of the input data, e.g. 
+# Instead, the nonparametric approach (bottom row) allows generating
+# perturbation fields with anisotropic  structures, but it also requires a
+# larger sample size and is sensitive to the quality of the input data, e.g.
 # the presence of residual clutter in the radar image.
 #
-# In addition, both techniques assume spatial stationarity of the covariance 
+# In addition, both techniques assume spatial stationarity of the covariance
 # structure of the field.
 
-# sphinx_gallery_thumbnail_number = 3
+# sphinx_gallery_thumbnail_number = 3

examples/plot_optical_flow.py

@@ -3,14 +3,13 @@
 Optical flow
 ============
 
-This tutorial offers a short overview to the optical flow routines available in 
+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 matplotlib import pyplot
 import numpy as np
 from pprint import pprint
 from pysteps import io, motion, rcparams
@@ -21,20 +20,25 @@
 # Read the radar input images
 # ---------------------------
 #
-# First thing, the sequence of radar composites is imported, converted and 
+# First thing, the sequence of radar composites is imported, converted and
 # transformed into units of dBR.
 
 date = datetime.strptime("201505151630", "%Y%m%d%H%M")
 data_source = "mch"
+
+# Load data source config
 root_path = rcparams.data_sources[data_source]["root_path"]
 path_fmt = rcparams.data_sources[data_source]["path_fmt"]
 fn_pattern = rcparams.data_sources[data_source]["fn_pattern"]
 fn_ext = rcparams.data_sources[data_source]["fn_ext"]
 importer_name = rcparams.data_sources[data_source]["importer"]
 importer_kwargs = rcparams.data_sources[data_source]["importer_kwargs"]
+timestep = rcparams.data_sources[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)
+fns = io.archive.find_by_date(
+    date, root_path, path_fmt, fn_pattern, fn_ext, timestep, num_prev_files=9
+)
 
 # Read the radar composites
 importer = io.get_method(importer_name, "importer")
@@ -44,7 +48,7 @@
 R, metadata = conversion.to_rainrate(R, metadata)
 
 # Store the last frame for polotting it later later
-R_ = R[-1, : , :].copy()
+R_ = R[-1, :, :].copy()
 
 # Log-transform the data
 R, metadata = transformation.dB_transform(R, metadata, threshold=0.1, zerovalue=-15.0)
@@ -57,53 +61,53 @@
 # -----------------
 #
 # The Lucas-Kanade optical flow method implemented in pysteps is a local
-# tracking apporach that relies on the OpenCV package.
+# 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 
+# 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
-plot_precip_field(R_, geodata=metadata, title="Lucas-Kanade")
+plot_precip_field(R_, geodata=metadata, title="LK")
 quiver(V1, geodata=metadata, step=25)
 
 ###############################################################################
 # 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 
+# 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. 
+# 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="Variational echo tracking")
+plot_precip_field(R_, geodata=metadata, title="VET")
 quiver(V2, geodata=metadata, step=25)
 
 ###############################################################################
 # 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 
+# 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
+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)
 
-# sphinx_gallery_thumbnail_number = 1
+# sphinx_gallery_thumbnail_number = 1

examples/plot_steps_nowcast.py

@@ -20,23 +20,28 @@
 from pysteps.visualization import plot_precip_field
 
 # Set nowcast parameters
-date = datetime.strptime("201609281600", "%Y%m%d%H%M")
 n_ens_members = 12
+n_leadtimes = 12
 seed = 24
 
 ###############################################################################
 # Read precipitation field
 # ------------------------
 #
-# First thing, the sequence of Finnish radar composites is imported, converted and 
+# First thing, the sequence of Finnish radar composites is imported, converted and
 # transformed into units of dBR.
 
+date = datetime.strptime("201701311200", "%Y%m%d%H%M")
+data_source = "mch"
+
 # Load data source config
-root_path = rcparams.data_sources["fmi"]["root_path"]
-path_fmt = rcparams.data_sources["fmi"]["path_fmt"]
-fn_pattern = rcparams.data_sources["fmi"]["fn_pattern"]
-fn_ext = rcparams.data_sources["fmi"]["fn_ext"]
-timestep = rcparams.data_sources["fmi"]["timestep"]
+root_path = rcparams.data_sources[data_source]["root_path"]
+path_fmt = rcparams.data_sources[data_source]["path_fmt"]
+fn_pattern = rcparams.data_sources[data_source]["fn_pattern"]
+fn_ext = rcparams.data_sources[data_source]["fn_ext"]
+importer_name = rcparams.data_sources[data_source]["importer"]
+importer_kwargs = rcparams.data_sources[data_source]["importer_kwargs"]
+timestep = rcparams.data_sources[data_source]["timestep"]
 
 # Find the radar files in the archive
 inputfns = find_by_date(
@@ -73,7 +78,7 @@
 R_f = nowcast_method(
     R[-3:, :, :],
     V,
-    12,
+    n_leadtimes,
     n_cascade_levels=8,
     R_thr=-10.0,
     decomp_method="fft",
@@ -86,15 +91,7 @@
 
 # Plot the S-PROG forecast
 figure()
-bm = plot_precip_field(
-    R_f[-1, :, :],
-    map="basemap",
-    geodata=metadata,
-    drawlonlatlines=False,
-    basemap_resolution="h",
-    basemap_scale_args=[30.0, 58.5, 30.2, 58.5, 120],
-    title="S-PROG",
-)
+bm = plot_precip_field(R_f[-1, :, :], geodata=metadata, title="S-PROG")
 
 ###############################################################################
 # As we can see from the figure above, the forecast produced by S-PROG is a
@@ -140,35 +137,20 @@
 # Plot the ensemble mean
 R_f_mean = np.mean(R_f[:, -1, :, :], axis=0)
 figure()
-bm = plot_precip_field(
-    R_f_mean,
-    map="basemap",
-    geodata=metadata,
-    drawlonlatlines=False,
-    basemap_resolution="h",
-    basemap_scale_args=[30.0, 58.5, 30.2, 58.5, 120],
-    title="Ensemble mean",
-)
+bm = plot_precip_field(R_f_mean, geodata=metadata, title="Ensemble mean")
 
 ###############################################################################
 # The mean of the ensemble displays similar properties as the S-PROG
 # forecast seen above, although the degree of smoothing strongly depends on
-# the ensemble size. In this sense, the S-PROG forecast can be seen as 
+# the ensemble size. In this sense, the S-PROG forecast can be seen as
 # the mean forecast from an ensemble of infinite size.
 
 # Plot the first two realizations
 fig = figure()
 for i in range(2):
     ax = fig.add_subplot(121 + i)
     ax.set_title("Member %02d" % i)
-    bm = plot_precip_field(
-        R_f[i, -1, :, :],
-        map="basemap",
-        geodata=metadata,
-        drawlonlatlines=False,
-        basemap_resolution="h",
-        basemap_scale_args=[30.0, 58.5, 30.2, 58.5, 120],
-    )
+    bm = plot_precip_field(R_f[i, -1, :, :], geodata=metadata)
 tight_layout()
 
 ###############################################################################
@@ -183,11 +165,8 @@
 figure()
 bm = plot_precip_field(
     P,
-    map="basemap",
     geodata=metadata,
     drawlonlatlines=False,
-    basemap_resolution="h",
-    basemap_scale_args=[30.0, 58.5, 30.2, 58.5, 120],
     type="prob",
     units="mm/h",
     probthr=0.5,