Docstring revision for arguments that have a discrete set of allowed values.

pulkkins · pulkkins · commit 1cff4d05ddcb · 2018-08-03T22:33:56.000+02:00
diff --git a/doc/source/nowcasting.rst b/doc/source/nowcasting.rst
@@ -9,10 +9,10 @@ pysteps\.nowcasts\.interface
 .. automodule:: pysteps.nowcasts.interface
     :members:
 
-pysteps\.nowcasts\.simple\_advection
-------------------------------------
+pysteps\.nowcasts\.extrapolation
+--------------------------------
 
-.. automodule:: pysteps.nowcasts.simple_advection
+.. automodule:: pysteps.nowcasts.extrapolation
     :members:
 
 pysteps\.nowcasts\.steps
diff --git a/pysteps/cascade/interface.py b/pysteps/cascade/interface.py
@@ -26,6 +26,7 @@ def get_method(name):
     |  fft              | decomposition based on Fast Fourier Transform (FFT)    |
     |                   | and a bandpass filter                                  |
     +-------------------+--------------------------------------------------------+
+    
     """
     if name.lower() == "fft":
         return decomposition.decomposition_fft
diff --git a/pysteps/io/exporters.py b/pysteps/io/exporters.py
@@ -26,7 +26,7 @@
   metadata : dict
       Metadata dictionary containing the projection,x1,x2,y1,y2 and unit 
       attributes described in the documentation of pysteps.io.importers.
-  incremental : str
+  incremental : {'timestep', 'member'}
       Allow incremental writing of datasets into the netCDF file. The 
       available options are: 'timestep'=write a forecast or a forecast 
       ensemble for a given time step or 'member'=write a forecast sequence 
diff --git a/pysteps/io/importers.py b/pysteps/io/importers.py
@@ -445,7 +445,7 @@ def import_odim_hdf5(filename, **kwargs):
     
     Other Parameters
     ----------------
-    qty : str
+    qty : {'RATE', 'ACRR', 'DBZH'}
         The quantity to read from the file. The currently supported identitiers
         are: 'RATE'=instantaneous rain rate (mm/h), 'ACRR'=hourly rainfall
         accumulation (mm) and 'DBZH'=max-reflectivity (dBZ). The default value
diff --git a/pysteps/io/nowcast_importers.py b/pysteps/io/nowcast_importers.py
@@ -55,6 +55,7 @@
 |    zerovalue      | it is the value assigned to the no rain pixels with the  |
 |                   | same unit, transformation and accutime of the data.      |
 +-------------------+----------------------------------------------------------+
+
 """
 
 import numpy as np
@@ -92,7 +93,6 @@ def import_netcdf_pysteps(filename, **kwargs):
     metadata["leadtimes"] = leadtimes
     timestamps = netCDF4.num2date(time_var[:], time_var.units)
     metadata["timestamps"] = timestamps
-
     
     projdef = ""
     if "polar_stereographic" in var_names:
diff --git a/pysteps/noise/interface.py b/pysteps/noise/interface.py
@@ -32,6 +32,7 @@ def get_method(name):
     |                   | time-dependent velocity perturbations are sampled     |
     |                   | from the exponential distribution                     |
     +-------------------+-------------------------------------------------------+
+    
     """
     if name.lower() == "parametric":
         return fftgenerators.initialize_param_2d_fft_filter, \
diff --git a/pysteps/noise/utils.py b/pysteps/noise/utils.py
@@ -56,8 +56,8 @@ def compute_noise_stddev_adjs(R, R_thr_1, R_thr_2, F, decomp_method, num_iter,
         If set to True, compute the statistics conditionally by excluding areas 
         of no precipitation.
     num_workers : int
-      The number of workers to use for parallel computation. Set to None to use 
-      all available CPUs. Applicable if dask is enabled.
+        The number of workers to use for parallel computation. Set to None to 
+        use all available CPUs. Applicable if dask is enabled.
     
     Returns
     -------
diff --git a/pysteps/nowcasts/extrapolation.py b/pysteps/nowcasts/extrapolation.py
@@ -20,9 +20,9 @@ def forecast(R, V, num_timesteps, extrap_method="semilagrangian", extrap_kwargs=
     
     Other Parameters
     ----------------
-    extrap_method : str
+    extrap_method : {'semilagrangian'}
       Name of the extrapolation method to use. See the documentation of the 
-      advection module for the available choices.
+      advection module.
     extrap_kwargs : dict
       Optional dictionary that is supplied as keyword arguments to the 
       extrapolation method.
diff --git a/pysteps/nowcasts/steps.py b/pysteps/nowcasts/steps.py
@@ -51,24 +51,24 @@ def forecast(R, V, n_timesteps, n_ens_members, n_cascade_levels,
     R_thr : float
       Specifies the threshold value for minimum observable precipitation 
       intensity. Must be set if use_probmatching is True or conditional is True.
-    extrap_method : str
+    extrap_method : {'semilagrangian'}
       Name of the extrapolation method to use. See the documentation of 
-      pysteps.advection for the available choices.
-    decomp_method : str
+      pysteps.advection.
+    decomp_method : {'fft'}
       Name of the cascade decomposition method to use. See the documentation 
       of pysteps.cascade.decomposition.
-    bandpass_filter_method : str
+    bandpass_filter_method : {'gaussian', 'uniform'}
       Name of the bandpass filter method to use with the cascade decomposition. 
       See the documentation of pysteps.cascade.bandpass_filters.
-    noise_method : str
+    noise_method : {'parametric','nonparametric','ssft','nested'}
       Name of the noise generator to use for perturbating the precipitation 
       field. See the documentation of pysteps.noise.interface.
     noise_stddev_adj : bool
       Optional adjustment for the standard deviations of the noise fields added 
       to each cascade level. See pysteps.noise.utils.compute_noise_stddev_adjs.
     ar_order : int
-      The order of the autoregressive model to use.
-    vel_pert_method : str
+      The order of the autoregressive model to use. Must be >= 1.
+    vel_pert_method : {'bps'}
       Name of the noise generator to use for perturbing the velocity field. See 
       the documentation of pysteps.noise.interface.
     conditional : bool
@@ -78,7 +78,7 @@ def forecast(R, V, n_timesteps, n_ens_members, n_cascade_levels,
     use_precip_mask : bool
       If True, set pixels outside precipitation areas to the minimum value of 
       the observed field.
-    mask_method : str
+    mask_method : {'obs', 'sprog', 'incremental'}
       The precipitation/no precipitation method to use with mask: 'obs' = apply R_thr
       to the most recently observed precipitation intensity field, 'sprog' = use the
       smoothed forecast field from S-PROG, where the AR(p) model has been applied,
diff --git a/pysteps/optflow/darts.py b/pysteps/optflow/darts.py
@@ -47,7 +47,7 @@ def DARTS(Z, **kwargs):
       y-axis.
     print_info : bool
       If True, print information messages.
-    lsq_method : int
+    lsq_method : {1, 2}
       The method to use for solving the linear equations in the least squares 
       sense: 1=numpy.linalg.lstsq, 2=explicit computation of the Moore-Penrose 
       pseudoinverse and SVD.
diff --git a/pysteps/utils/conversion.py b/pysteps/utils/conversion.py
@@ -3,7 +3,11 @@
 
 import numpy as np
 import warnings
-warnings.filterwarnings("ignore", category=RuntimeWarning) # To desactivate warnings for comparison operators with NaNs
+
+# TODO: This should not be done. Instead fix the code so that it doesn't 
+# produce the warnings.
+# to deactivate warnings for comparison operators with NaNs
+warnings.filterwarnings("ignore", category=RuntimeWarning)
 
 from . import transformation
 
@@ -78,8 +82,8 @@ def to_rainrate(R, metadata, a=None, b=None):
 def to_raindepth(R, metadata, a=None, b=None):
     """Convert to rain depth [mm].
     
-    Parameters 
-    ---------- 
+    Parameters
+    ----------
     R : array-like 
         Array of any shape to be (back-)transformed.
     metadata : dict
diff --git a/pysteps/utils/dimension.py b/pysteps/utils/dimension.py
@@ -2,9 +2,10 @@
 
 import numpy as np
 
+# TODO: If time_window_min can be set to None, it should be a keyword argument.
 def aggregate_fields_time(R, metadata, time_window_min, method="mean"):
     """Aggregate fields in time.
-
+    
     Parameters
     ----------
     R : array-like
@@ -19,7 +20,7 @@ def aggregate_fields_time(R, metadata, time_window_min, method="mean"):
     method : string
         Optional argument that specifies the operation to use to aggregate the values within the time
         window.
-        
+    
     Returns
     -------
     outputarray : array-like
@@ -227,5 +228,3 @@ def square_domain(R, metadata, method="pad", inverse=False):
             R = R[:, :, idx_buffer:(idx_buffer + shape[0]), :]
             
         return R.squeeze(),metadata
-    
-
diff --git a/pysteps/utils/transformation.py b/pysteps/utils/transformation.py
@@ -2,7 +2,6 @@
 """
 
 import numpy as np
-import scipy
 import scipy.stats as scipy_stats
 import warnings
 warnings.filterwarnings("ignore", category=RuntimeWarning) # To deactivate warnings for comparison operators with NaNs
@@ -89,7 +88,8 @@ def dB_transform(R, metadata=None, threshold=None, zerovalue=None, inverse=False
             
         return R, metadata
     
-def boxcox_transform(R, metadata=None, Lambda=None, threshold=None, zerovalue=None, inverse=False):
+def boxcox_transform(R, metadata=None, Lambda=None, threshold=None, 
+                     zerovalue=None, inverse=False):
     """The one-parameter Box–Cox transformation.
     
     Parameters 
@@ -185,10 +185,10 @@ def boxcox_transform(R, metadata=None, Lambda=None, threshold=None, zerovalue=No
         metadata["threshold"] = threshold
     
     return R, metadata
-    
+
 def boxcox_transform_test_lambdas(R, Lambdas=None, threshold=0.1):
-    """Test and plot various lambdas for the Box-Cox transformation.
-    """
+    """Test and plot various lambdas for the Box-Cox transformation."""
+    
     import matplotlib.pyplot as plt
     
     R = R[R>threshold].flatten()
