removed some issues

Author: mengaldo

pyspod/__init__.py

@@ -1,6 +1,4 @@
-"""
-PySPOD init
-"""
+"""PySPOD init"""
 __all__ = ['spod_base', 'spod_low_storage', 'spod_low_ram', 'spod_streaming']
 
 from .spod_base        import SPOD_base

pyspod/plotting_support/__init__.py

@@ -0,0 +1 @@
+"""subpackaging maps."""

pyspod/postprocessing.py

@@ -1,6 +1,4 @@
-"""
-Derived module from spodbase.py for classic spod.
-"""
+"""Derived module from spodbase.py for classic spod."""
 
 # import standard python packages
 import os
@@ -9,11 +7,8 @@
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 from matplotlib import animation
-import matplotlib.gridspec as gridspec
-from mpl_toolkits.axes_grid1 import make_axes_locatable
 mpl.rc('figure', max_open_warning = 0)
 from os.path import splitext
-import warnings
 
 # Current, parent and file paths
 CWD = os.getcwd()
@@ -55,7 +50,7 @@ def find_nearest_coords(coords, x, data_space_dim):
 	coords = np.asarray(coords)
 	if isinstance(x, list):
 		grid = np.array(np.meshgrid(*x))
-	elif isinstance(x,np.ndarray) == data:
+	elif isinstance(x,np.ndarray):
 		if x.shape == data_space_dim:
 			grid = x
 	else:
@@ -108,7 +103,7 @@ def get_mode_from_file(filename):
 		file at a given frequency.
 	:rtype: numpy.ndarray
 	"""
-	basename, ext = splitext(filename)
+	_, ext = splitext(filename)
 	if ext.lower() == '.npy':
 		m = np.load(filename)
 	elif ext.lower() == '.mat':
@@ -670,7 +665,6 @@ def plot_2D_mode_slice_vs_time(modes, freq_required, freq, vars_idx=[0],
 			# axis management
 			ax.set_xlim(np.nanmin(t )*1.05,np.nanmax(t )*1.05)
 			ax.set_ylim(np.nanmin(x2)*1.05,np.nanmax(x2)*1.05)
-			xlim = ax.get_xlim()
 			ax_divider = make_axes_locatable(ax)
 			cax = ax_divider.append_axes("bottom", size="5%", pad=0.65)
 			plt.colorbar(ax_obj, cax=cax, orientation="horizontal")
@@ -1243,7 +1237,8 @@ def plot_data_tracers(X, coords_list, x=None, time_limits=[0,10],
 
 def generate_2D_data_video(X, time_limits=[0,10], vars_idx=None, sampling=1,
 	x1=None, x2=None, coastlines='', figsize=(12,8), path='CWD', filename='data_video.mp4'):
-	"""Make movie of 2D data.
+	"""
+        Make movie of 2D data.
 
 	:param numpy.ndarray X: 2D data to be plotted. \
 		First dimension must be time. Last dimension must be variable.
@@ -1283,10 +1278,6 @@ def generate_2D_data_video(X, time_limits=[0,10], vars_idx=None, sampling=1,
 	# filename
 	basename, ext = splitext(filename)
 
-	# figure dimensions
-	axes_ratio = (np.nanmax(x2) - np.nanmin(x2)) \
-			   / (np.nanmax(x1) - np.nanmin(x1))
-
 	# check dimension axes and data
 	if x1.shape[0] != X[0,:,:].shape[0] or \
 	   x2.shape[0] != X[0,:,:].shape[1]:
@@ -1304,8 +1295,8 @@ def generate_2D_data_video(X, time_limits=[0,10], vars_idx=None, sampling=1,
 		cst = True
 
 	# Generate movie
-	vmin = np.nanmin(X)
-	vmax = np.nanmax(X)
+	#vmin = np.nanmin(X)
+	#vmax = np.nanmax(X)
 	vmean = np.nanmean(X)
 	for i in vars_idx:
 		fig = plt.figure()

pyspod/spod_base.py

@@ -395,7 +395,7 @@ def parse_parameters(self, isrealx, params):
 			else:
 				raise ValueError(mean_type, 'not recognized.')
 		elif isinstance(mean_type,np.ndarray):
-			x_mean = kwargs.get('mean')
+			x_mean = mean_type
 			mean_name = 'user-specified'
 		else:
 			raise ValueError(type(mean_type), 'data type not recognized. ',
@@ -528,6 +528,7 @@ def _nextpow2(a):
 			of 2 that satisfy 2^p >= abs(a)
 		'''
 		p = 0
+                v = 0
 		while v < np.abs(a):
 			v = 2 ** p
 			p += 1

pyspod/spod_low_ram.py

@@ -11,7 +11,6 @@
 from numpy import linalg as la
 import scipy.special as sc
 from scipy.fft import fft
-import warnings
 import shutil
 
 # Import PySPOD base class for SPOD_low_ram

pyspod/spod_low_storage.py

@@ -1,6 +1,4 @@
-"""
-Derived module from spod_base.py for low storage SPOD.
-"""
+"""Derived module from spod_base.py for low storage SPOD."""
 
 # import standard python packages
 import os

pyspod/spod_streaming.py

@@ -29,7 +29,8 @@ class SPOD_streaming(SPOD_base):
 	the `SPOD_base` class.
 	"""
 	def __init__(self, X, params, data_handler, variables):
-		super().__init__(X, params, data_handler, variables)
+                """Constructor of SPOD_streaming."""
+	        super().__init__(X, params, data_handler, variables)
 
 	def fit(self):
 		"""

pyspod/weights.py

@@ -1,6 +1,4 @@
-"""
-Derived module from spodbase.py for classic spod.
-"""
+"""Module implementing weights for standard cases.."""
 
 # import standard python packages
 import numpy as np
@@ -9,7 +7,8 @@
 
 def geo_weights_trapz_2D(lat, lon, R, n_vars):
 	'''
-	2D integration weights for geospatial data via trapezoidal rule
+	2D integration weights for geospatial
+        data via trapezoidal rule
 	'''
 	n_lat = len(lat)
 	n_lon = len(lon)
@@ -40,7 +39,8 @@ def geo_weights_trapz_2D(lat, lon, R, n_vars):
 
 def geo_weights_trapz_3D(lat, lon, R, z, n_vars):
 	'''
-	3D integration weights for geospatial data via trapezoidal rule
+	3D integration weights for geospatial
+        data via trapezoidal rule
 	'''
 	n_lat = len(lat)
 	n_lon = len(lon)
@@ -101,6 +101,7 @@ def geo_weights_trapz_3D(lat, lon, R, z, n_vars):
 
 
 def apply_normalization(X, weights, method='variance'):
+        '''Normalization of weights if required.'''
 
 	# variable-wise normalization by variance via weight matrix
 	if method.lower() == 'variance':

tests/test_basic.py

@@ -152,7 +152,7 @@ def test_basic_spod_streaming():
 		modes_idx=[0,1],
 		vars_idx=[0],
 		filename='tmp.png')
-	tol = 1e-10
+	# tol = 1e-10
 	# assert((np.abs(modes_at_freq[5,10,0,0]) < 0.010067915390717594 +tol) & \
 	# 	   (np.abs(modes_at_freq[5,10,0,0]) > 0.010067915390717594 -tol))
 	# assert((np.abs(modes_at_freq[0,0,0,0])  < 0.012179481869151793 +tol) & \

tests/test_basic_file.py

@@ -55,7 +55,7 @@ def read_data_netCDF(data, t_0, t_end, variables):
 	if t_0 == t_end: ti = [t_0]
 	else           : ti = np.arange(t_0,t_end)
 	X = np.empty([len(ti), x2.shape[0], x1.shape[0], len(variables)])
-	for i,var in enumerate(variables):
+	for _,var in enumerate(variables):
 		X = np.array(ds[var].isel(time=ti))
 	return X
 x_nc = read_data_netCDF('data.nc', t_0=0, t_end=t.shape[0], variables=variables)
@@ -205,7 +205,7 @@ def test_basic_file_spod_streaming():
 		modes_idx=[0,1],
 		vars_idx=[0],
 		filename='tmp.png')
-	tol = 1e-10
+	# tol = 1e-10
 	# assert((np.abs(modes_at_freq[5,10,0,0]) < 0.010067915390717594 +tol) & \
 	# 	   (np.abs(modes_at_freq[5,10,0,0]) > 0.010067915390717594 -tol))
 	# assert((np.abs(modes_at_freq[0,0,0,0])  < 0.012179481869151793 +tol) & \