Revert "Improve docstrings"

This reverts commit 290c3a3.
pySTEPS · Mar 25, 2019 · ed0e8b3 · ed0e8b3
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diff --git a/pysteps/cascade/__init__.py b/pysteps/cascade/__init__.py
@@ -1,36 +1,4 @@
-"""
-.. _pysteps.cascade:
-
-Scale-based decomposition of precipitation fields (:mod:`pysteps.cascade`)
-**************************************************************************
-
-Methods for constructing bandpass filters and decomposing 2d precipitation 
-fields into different spatial scales.
-
-pysteps.cascade.interface
--------------------------
-
-.. automodule:: pysteps.cascade.interface
-    :members:
-
-pysteps.cascade.bandpass_filters
---------------------------------
-
-.. currentmodule:: pysteps.cascade.bandpass_filters
-
-.. autosummary::
-    filter_uniform
-    filter_gaussian
-
-.. automodule:: pysteps.cascade.bandpass_filters
-    :members:
-
-pysteps.cascade.decomposition
------------------------------
-
-.. automodule:: pysteps.cascade.decomposition
-    :members:
-    
-"""
+"""Methods for constructing bandpass filters and decomposing 2d precipitation 
+fields into different spatial scales."""
 
 from .interface import get_method
diff --git a/pysteps/extrapolation/__init__.py b/pysteps/extrapolation/__init__.py
@@ -1,27 +1,6 @@
-"""
-.. _pysteps.extrapolation:
-
-Advection-based extrapolation (:mod:`pysteps.extrapolation`)
-************************************************************
-
-Methods for advection-based extrapolation of precipitation fields. Currently 
-the module contains an implementation of the semi-Lagrangian method described 
-in :cite:`GZ2002` and the eulerian persistence.
-
-pysteps\.extrapolation\.interface
----------------------------------
-
-.. automodule:: pysteps.extrapolation.interface
-    :members:
-
-pysteps\.extrapolation\.semilagrangian
---------------------------------------
-
-.. automodule:: pysteps.extrapolation.semilagrangian
-    :members:
-    
-"""
+"""Methods for advection-based extrapolation of precipitation fields.
+Currently the module contains an implementation of the
+semi-Lagrangian method described in :cite:`GZ2002` and the
+eulerian persistence."""
 
 from pysteps.extrapolation.interface import get_method
-
-
diff --git a/pysteps/io/__init__.py b/pysteps/io/__init__.py
@@ -1,67 +1,5 @@
-"""
-.. _pysteps.io:
-
-Input/output routines (:mod:`pysteps.io`)
-*****************************************
-
-Methods for browsing data archives, reading 2d precipitation fields and writing 
-forecasts into files.
-
-pysteps\.io\.interface
-----------------------
-
-.. automodule:: pysteps.io.interface
-    :members:
-
-pysteps\.io\.archive
---------------------
-
-.. automodule:: pysteps.io.archive
-    :members:
-
-pysteps\.io\.importers
-----------------------
-
-.. currentmodule:: pysteps.io.importers
-
-.. autosummary::
-    import_bom_rf3
-    import_fmi_pgm
-    import_knmi_hdf5
-    import_mch_gif
-    import_mch_hdf5
-    import_mch_metranet
-    import_odim_hdf5
-
-.. automodule:: pysteps.io.importers
-    :members:
-
-pysteps\.io\.readers
---------------------
-
-.. automodule:: pysteps.io.readers
-    :members:
-
-pysteps\.io\.exporters
-----------------------
-
-.. currentmodule:: pysteps.io.exporters
-
-.. autosummary::
-    initialize_forecast_exporter_kineros
-    initialize_forecast_exporter_netcdf
-    export_forecast_dataset
-    close_forecast_file
-
-.. automodule:: pysteps.io.exporters
-    :members:
-
-pysteps\.io\.nowcast\_importers
--------------------------------
-
-.. automodule:: pysteps.io.nowcast_importers
-    :members:
-"""
+"""Methods for browsing data archives, reading 2d precipitation fields and writing 
+forecasts into files."""
 
 from .interface import get_method
 from .archive import *

diff --git a/pysteps/io/importers.py b/pysteps/io/importers.py
@@ -95,8 +95,6 @@
 except ImportError:
     h5py_imported = False
 try:
-    import sys
-    sys.path.append('/store/mch/msrad/python/library/radar/io/') # path to metranet library
     import metranet
     metranet_imported = True
 except ImportError:

diff --git a/pysteps/io/nowcast_importers.py b/pysteps/io/nowcast_importers.py
@@ -194,141 +194,3 @@ def _convert_grid_mapping_to_proj4(grid_mapping):
         return proj_str
     else:
         return None
-
-def import_netcdf_cosmo1(filename, timebounds=None):
-    """Read a COSMO-1 forecast from a NetCDF file conforming to the
-    CF 1.7 specification."""   
-    if not netcdf4_imported:
-        raise Exception("netCDF4 not imported")
-
-    ds = netCDF4.Dataset(filename, 'r')
-
-    var_names = list(ds.variables.keys())
-
-    metadata = {}
-
-    time_var = ds.variables['time']
-    if timebounds is None:
-        timebounds = np.ones(len(time_var[:]), dtype=bool)
-    leadtimes = np.array(time_var[timebounds])/60. # minutes leadtime
-    timestamps = np.array([netCDF4.num2date(time,time_var.units) for time in time_var[timebounds]])
-    metadata["leadtimes"] = leadtimes
-    metadata["timestamps"] = timestamps
-    f = np.vectorize(lambda x: x.replace(microsecond=0))
-    metadata["timestamps"] = f(metadata["timestamps"])
-
-    no_data = ds.variables[var_names[-1]]._FillValue 
-    R = ds.variables[var_names[-1]][timebounds, :, :]
-    R = R[...].squeeze().astype(float)
-    R=R[:,::-1,:] # flip upside-down
-    R[R==no_data] = np.nan
-
-    # TODO: Read the metadata to the dictionary.
-    time_step = (timestamps[1] - timestamps[0]).total_seconds()/60
-    metadata["accutime"] = time_step
-    metadata["unit"] = "mm"
-    metadata["transform"] = None
-    metadata["zerovalue"] = np.nanmin(R)
-    if np.any(np.isfinite(R)):
-        metadata["threshold"] = np.nanmin(R[R > np.nanmin(R)])
-    else:
-        metadata["threshold"] = np.nan
-
-    # LV03 Swiss projection definition in Proj4
-    projdef = ""
-    projdef += "+proj=somerc "
-    projdef += " +lon_0=7.43958333333333"
-    projdef += " +lat_0=46.9524055555556"
-    projdef += " +k_0=1"
-    projdef += " +x_0=600000"
-    projdef += " +y_0=200000"
-    projdef += " +ellps=bessel"
-    projdef += " +towgs84=674.374,15.056,405.346,0,0,0,0"
-    projdef += " +units=m"
-    projdef += " +no_defs"
-    metadata["projection"] = projdef
-
-    metadata["x1"] = 255000.
-    metadata["y1"] = -160000.
-    metadata["x2"] = 965000.
-    metadata["y2"] = 480000.
-
-    metadata["xpixelsize"] = 1000.
-    metadata["ypixelsize"] = 1000.
-
-    metadata["yorigin"] = "upper"
-
-    ds.close()
-
-    return R,metadata
-
-def import_netcdf_cosmoe(filename):
-    """Read a COSMO-E ensemble forecast from a NetCDF file conforming to the
-    CF 1.7 specification."""   
-    if not netcdf4_imported:
-        raise Exception("netCDF4 not imported")
-
-    ds = netCDF4.Dataset(filename, 'r')
-
-    var_names = list(ds.variables.keys())
-
-    R = ds.variables[var_names[-1]] 
-    no_data = R._FillValue 
-    R = R[...].squeeze().astype(float)
-    R=R[:,:,::-1,:] # flip upside-down
-    R[R==no_data] = np.nan
-
-    metadata = {}
-
-    time_var = ds.variables['time']
-    leadtimes = np.array(time_var[:])/60. # minutes leadtime
-    metadata["leadtimes"] = np.array(leadtimes)
-    timestamps = [netCDF4.num2date(time,time_var.units) for time in time_var[:]]
-    metadata["timestamps"] = np.array(timestamps)
-
-    # remove milliseconds
-    f = np.vectorize(lambda x: x.replace(microsecond=0))
-    metadata["timestamps"] = f(metadata["timestamps"])
-
-    # swpax axes: [time, eps, y, x] -> [eps, time, y, x]
-    R = np.swapaxes(R, 0, 1)
-
-    # TODO: Read the metadata to the dictionary.
-    time_step = (timestamps[1] - timestamps[0]).total_seconds()/60
-    metadata["accutime"] = time_step
-    metadata["unit"] = "mm"
-    metadata["transform"] = None
-    metadata["zerovalue"] = np.nanmin(R)
-    if np.any(np.isfinite(R)):
-        metadata["threshold"] = np.nanmin(R[R > np.nanmin(R)])
-    else:
-        metadata["threshold"] = np.nan
-    metadata["eps"] = R.shape[0]
-
-    # LV03 Swiss projection definition in Proj4
-    projdef = ""
-    projdef += "+proj=somerc "
-    projdef += " +lon_0=7.43958333333333"
-    projdef += " +lat_0=46.9524055555556"
-    projdef += " +k_0=1"
-    projdef += " +x_0=600000"
-    projdef += " +y_0=200000"
-    projdef += " +ellps=bessel"
-    projdef += " +towgs84=674.374,15.056,405.346,0,0,0,0"
-    projdef += " +units=m"
-    projdef += " +no_defs"
-    metadata["projection"] = projdef
-
-    metadata["x1"] = 255000.
-    metadata["y1"] = -160000.
-    metadata["x2"] = 965000.
-    metadata["y2"] = 480000.
-
-    metadata["xpixelsize"] = 1000.
-    metadata["ypixelsize"] = 1000.
-
-    metadata["yorigin"] = "upper"
-
-    ds.close()
-
-    return R,metadata
diff --git a/pysteps/verification/detcatscores.py b/pysteps/verification/detcatscores.py
@@ -81,7 +81,7 @@ def det_cat_fct(pred, obs, thr, scores="", axis=None):
 
     See also
     --------
-    pysteps.verification.detcontscores.det_cont_fct
+    pysteps.verification.detcontscores
 
 
     """

diff --git a/pysteps/verification/detcontscores.py b/pysteps/verification/detcontscores.py
@@ -63,9 +63,11 @@ def det_cont_fct(pred, obs, scores="", axis=None, conditioning=None):
         |  scatter*  | half the distance between the 16% and 84% percentiles  |
         |            | of the weighted cumulative error distribution,         |
         |            | where error = dB(pred/obs),                            |
-        |            | as in Germann et al. (2006)                            |
+        |            | as in `Germann et al. (2006)`_                         |
         +------------+--------------------------------------------------------+
 
+    .. _`Germann et al. (2006)`:https://doi.org/10.1256/qj.05.190
+
     axis : {int, tuple of int, None}, optional
         Axis or axes along which a score is integrated. The default, axis=None,
         will integrate all of the elements of the input arrays.\n
@@ -88,19 +90,14 @@ def det_cont_fct(pred, obs, scores="", axis=None, conditioning=None):
 
     Note
     ----
-    Score names denoted by * can only be computed offline.\n
-    Multiplicative scores can be computed by passing log-tranformed values.
+    Score names denoted by * can only be computed offline.
 
-    References
-    ----------
+    Multiplicative scores can be computed by passing log-tranformed values.
 
-    Germann, U. , Galli, G. , Boscacci, M. and Bolliger, M. (2006), Radar
-    precipitation measurement in a mountainous region. Q.J.R. Meteorol. Soc.,
-    132: 1669-1692. doi:10.1256/qj.05.190
 
     See also
     --------
-    pysteps.verification.detcatscores.det_cat_fct
+    pysteps.verification.detcatscores
 
     """
 

diff --git a/pysteps/verification/interface.py b/pysteps/verification/interface.py
@@ -70,7 +70,7 @@ def get_method(name, type="deterministic"):
         |  scatter*  | half the distance between the 16% and 84% percentiles  |
         |            | of the weighted cumulative error distribution,         |
         |            | where error = dB(pred/obs),                            |
-        |            | as in Germann et al. (2006)                            |
+        |            | as in `Germann et al. (2006)`_                         |
         +------------+--------------------------------------------------------+
         |  binary_mse| binary MSE                                             |
         +------------+--------------------------------------------------------+
@@ -105,15 +105,11 @@ def get_method(name, type="deterministic"):
         |  ROC       | ROC curve                                              |
         +------------+--------------------------------------------------------+
 
-    type : {'deterministic', 'ensemble', 'probabilistic'}, optional
-        Type of the verification method.
+    type : str
+        Type of the method. The available options are 'deterministic', 'ensemble'
+        and 'probabilistic'.
 
-    References
-    ----------
-    
-    Germann, U. , Galli, G. , Boscacci, M. and Bolliger, M. (2006), Radar 
-    precipitation measurement in a mountainous region. Q.J.R. Meteorol. Soc., 
-    132: 1669-1692. doi:10.1256/qj.05.190
+    .. _`Germann et al. (2006)`:https://doi.org/10.1256/qj.05.190
 
     """