Mcpca doc

pyleoclim/core/ensemblegeoseries.py

@@ -281,11 +281,9 @@ def make_labels(self):
         Returns
         -------
         time_header : str
-
             Label for the time axis
 
         value_header : str
-
             Label for the value axis
 
         '''
@@ -318,7 +316,7 @@ def dashboard(self, figsize=[11, 8], gs=None, plt_kwargs=None, histplt_kwargs=No
                   hue='archiveType', marker='archiveType', size=None, scatter_kwargs=None,
                   gridspec_kwargs=None,
                   savefig_settings=None):
-        '''
+        '''Dashboard that plots the trace, histogram, map, and power spectrum of the ensemble.
 
         Parameters
         ----------
@@ -393,7 +391,7 @@ def dashboard(self, figsize=[11, 8], gs=None, plt_kwargs=None, histplt_kwargs=No
 
         See also
         --------
-
+        
         pyleoclim.core.series.Series.plot : plot a timeseries
 
         pyleoclim.core.ensembleseries.EnsembleSeries.plot_envelope: Envelope plots for an ensemble

pyleoclim/core/ensmultivardecomp.py

@@ -131,27 +131,17 @@ def modeplot(self,index=0,flip=False,plot_envelope_kwargs = None, psd_envelope_k
 
         .. jupyter-execute::
 
-            n = 3 # number of ensembles
-            nn = 30 # number of noise realizations
-            nt = 500
-            ens_list = []
-
-            t,v = pyleo.utils.gen_ts(model='colored_noise',nt=nt,alpha=1.0)
-            signal = pyleo.Series(t,v)
-
-            for _ in range(n): 
-                series_list = []
-                lat = np.random.randint(-90,90)
-                lon = np.random.randint(-180,180)
-                for idx in range(nn):  # noise
-                    noise = np.random.randn(nt,nn)*100
-                    ts = pyleo.GeoSeries(time=signal.time, value=signal.value+noise[:,idx], lat=lat, lon=lon, verbose=False)
-                    series_list.append(ts)
-
-                ts_ens = pyleo.EnsembleSeries(series_list)
-                ens_list.append(ts_ens)
-
-            mul_ens = pyleo.MulEnsGeoSeries([ts_ens])
+            n = 100 # number of series
+
+            soi = pyleo.utils.load_dataset('SOI')
+            soi_time_axes = [pyleo.utils.random_time_axis(n=len(soi.time)) for _ in range(n)]
+            soi_ens = pyleo.EnsembleGeoSeries([pyleo.GeoSeries(time=time, value=soi.value,lat=-5,lon=-85,auto_time_params=True) for time in soi_time_axes])
+
+            nino3 = pyleo.utils.load_dataset('NINO3')
+            nino3_time_axes = [pyleo.utils.random_time_axis(n=len(nino3.time)) for _ in range(n)]
+            nino3_ens = pyleo.EnsembleGeoSeries([pyleo.GeoSeries(time=time, value=nino3.value,lat=-5,lon=-85,auto_time_params=True) for time in nino3_time_axes])
+
+            mul_ens = pyleo.MulEnsGeoSeries([nino3_ens,soi_ens])
             mcpca = mul_ens.mcpca(nsim=10,seed=42)
             mcpca.modeplot()'''
 
@@ -328,27 +318,17 @@ def screeplot(self,clr_eig='C0', linewidth=.3, title='Screeplot', violin_kwargs
 
         .. jupyter-execute::
 
-            n = 3 # number of ensembles
-            nn = 30 # number of noise realizations
-            nt = 500
-            ens_list = []
-
-            t,v = pyleo.utils.gen_ts(model='colored_noise',nt=nt,alpha=1.0)
-            signal = pyleo.Series(t,v)
-
-            for _ in range(n): 
-                series_list = []
-                lat = np.random.randint(-90,90)
-                lon = np.random.randint(-180,180)
-                for idx in range(nn):  # noise
-                    noise = np.random.randn(nt,nn)*100
-                    ts = pyleo.GeoSeries(time=signal.time, value=signal.value+noise[:,idx], lat=lat, lon=lon, verbose=False)
-                    series_list.append(ts)
-
-                ts_ens = pyleo.EnsembleSeries(series_list)
-                ens_list.append(ts_ens)
-
-            mul_ens = pyleo.MulEnsGeoSeries([ts_ens])
+            n = 100 # number of series
+
+            soi = pyleo.utils.load_dataset('SOI')
+            soi_time_axes = [pyleo.utils.random_time_axis(n=len(soi.time)) for _ in range(n)]
+            soi_ens = pyleo.EnsembleGeoSeries([pyleo.GeoSeries(time=time, value=soi.value,lat=0,lon=0,auto_time_params=True) for time in soi_time_axes])
+
+            nino3 = pyleo.utils.load_dataset('NINO3')
+            nino3_time_axes = [pyleo.utils.random_time_axis(n=len(nino3.time)) for _ in range(n)]
+            nino3_ens = pyleo.EnsembleGeoSeries([pyleo.GeoSeries(time=time, value=nino3.value,lat=0,lon=0,auto_time_params=True) for time in nino3_time_axes])
+
+            mul_ens = pyleo.MulEnsGeoSeries([nino3_ens,soi_ens])
             mcpca = mul_ens.mcpca(nsim=10,seed=42)
             mcpca.screeplot()'''
 

pyleoclim/core/multiplegeoseries.py

@@ -82,15 +82,14 @@ def __init__(self, series_list, time_unit=None, label=None):
 
         super().__init__(series_list, time_unit, label)
 
-    # ============ MAP goes here ================
 
     def map(self, marker='archiveType', hue='archiveType', size=None, cmap=None,
             edgecolor='k', projection='auto',
             proj_default=True, crit_dist=5000, colorbar=True,color_scale_type=None,
             background=True, borders=False, coastline=True,rivers=False, lakes=False, land=True,ocean=True,
             figsize=None, fig=None, scatter_kwargs=None, gridspec_kwargs=None, legend=True, gridspec_slot=None,
             lgd_kwargs=None, savefig_settings=None, **kwargs):
-        '''
+        '''Mapping of the collection of GeoSeries objects.
 
 
         Parameters

pyleoclim/core/multipleseries.py

@@ -1932,7 +1932,6 @@ def stripes(self, cmap = 'RdBu_r', sat=1.0, ref_period=None,
         figsize : list
             a list of two integers indicating the figure size (in inches)
 
-
         sat : float > 0
             Controls the saturation of the colormap normalization by scaling the vmin, vmax in https://matplotlib.org/stable/tutorials/colors/colormapnorms.html
             default = 1.0
@@ -1941,15 +1940,13 @@ def stripes(self, cmap = 'RdBu_r', sat=1.0, ref_period=None,
             flag indicating whether or not the x-axis should be shown (default = False) 
 
         savefig_settings : dictionary
-
             the dictionary of arguments for plt.savefig(); some notes below:
 
             - 'path' must be specified; it can be any existing or non-existing path,
               with or without a suffix; if the suffix is not given in 'path', it will follow 'format'
             - 'format' can be one of {"pdf", 'eps', 'png', ps'} The default is None.
 
         time_unit : str
-
             the target time unit, possible inputs:
             {
                 'year', 'years', 'yr', 'yrs',

pyleoclim/core/multivardecomp.py

@@ -186,12 +186,10 @@ def modeplot(self, index=0, figsize=[8, 8], fig=None, savefig_settings=None,gs=N
                  hue='EOF', marker=None, size=None, scatter_kwargs=None,
                  flip = False, map_kwargs=None, gridspec_kwargs=None):
 
-        ''' Dashboard visualizing the properties of a given mode, including:
-            1. The temporal coefficient (PC or similar)
-            2. its spectrum
-            3. The loadings (EOF or similar), possibly geolocated. If the object
-                does not have geolocation information, a spaghetti plot of the standardized
-                series is displayed.
+        '''Dashboard visualizing the properties of a given mode.
+
+        Includes: The temporal coefficient (PC or similar), its spectrum, and the loadings (EOF or similar), possibly geolocated. 
+        If the object does not have geolocation information, a spaghetti plot of the standardized series is displayed.
 
         Parameters
         ----------

pyleoclim/core/series.py

@@ -457,7 +457,7 @@ def to_csv(self, metadata_header=True, path = None):
         -------
         None
 
-        See Also
+        See also
         --------
 
         pyleoclim.Series.from_csv
@@ -513,7 +513,7 @@ def from_csv(cls, path):
         Series
             pyleoclim Series object containing data and metadata.
 
-        See Also
+        See also
         --------
 
         pyleoclim.Series.to_csv