Archive graphics (#593)

* candidate colors/shapes * map_neighbors no longer assumes the first neighbor in the list is itself map_neighbors now checks for itself and removes duplicate rows in the neighbors dataframe, keeping the final row as the target * tweaking colors * implemented map_neighbors() title with CI test * map title + CI tests --------- Co-authored-by: Jordan Landers <[email protected]>
LinkedEarth · Jun 27, 2024 · c355874 · c355874
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commit c355874
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Showing 4 changed files with 177 additions and 36 deletions.
diff --git a/pyleoclim/core/geoseries.py b/pyleoclim/core/geoseries.py
@@ -296,7 +296,7 @@ def from_Series(lat, lon, elevation=None,sensorType=None,observationType=None,
 
     def map(self, projection='Orthographic', proj_default=True,
             background=True, borders=False, coastline=True, rivers=False, lakes=False, ocean=True,
-            land=True, fig=None, gridspec_slot=None,
+            land=True, fig=None, gridspec_slot=None, title = None,
             figsize=None, marker='archiveType', hue='archiveType', size=None, edgecolor='w',
             markersize=None, scatter_kwargs=None, cmap=None, colorbar=False, gridspec_kwargs=None,
             legend=True, lgd_kwargs=None, savefig_settings=None):
@@ -388,17 +388,36 @@ def map(self, projection='Orthographic', proj_default=True,
 
         .. jupyter-execute::
 
-            import pyleoclim as pyleo
             ts = pyleo.utils.datasets.load_dataset('EDC-dD')
             fig, ax = ts.map()
+            
+        By default, the figure has no title. For a title built from the available labels:
+            
+        .. jupyter-execute::
+            
+           fig, ax = ts.map(title=True) 
+           
+        For a custom title, and custom projection:
+            
+        .. jupyter-execute::
+            
+           fig, ax = ts.map(title='Insert title here', projection='RotatedPole',
+                            proj_default={'pole_longitude':0.0, 'pole_latitude':-90.0, 'central_rotated_longitude':45.0}) 
 
 
         '''
         if markersize != None:
             scatter_kwargs['markersize'] = markersize
+
+        if type(title)==bool:
+            if title == False:
+                title = None
+            else:
+                if self.label is not None:
+                    title = f"{self.label} location"
 
         fig, ax_d = mapping.scatter_map(self, hue=hue, size=size, marker=marker, projection=projection,
-                    proj_default=proj_default,
+                    proj_default=proj_default, title = title,
                     background=background, borders=borders, rivers=rivers, lakes=lakes,
                     ocean=ocean, land=land, coastline=coastline,
                     figsize=figsize, scatter_kwargs=scatter_kwargs, gridspec_kwargs=gridspec_kwargs,
@@ -409,7 +428,7 @@ def map(self, projection='Orthographic', proj_default=True,
 
     def map_neighbors(self, mgs, radius=3000, projection='Orthographic', proj_default=True,
             background=True, borders=False, rivers=False, lakes=False, ocean=True,
-            land=True, fig=None, gridspec_slot=None,
+            land=True, fig=None, gridspec_slot=None, title = None,
             figsize=None, marker='archiveType', hue='archiveType', size=None, edgecolor=None,#'w',
             markersize=None, scatter_kwargs=None, cmap=None, colorbar=False, gridspec_kwargs=None,
             legend=True, lgd_kwargs=None, savefig_settings=None):
@@ -470,6 +489,10 @@ def map_neighbors(self, mgs, radius=3000, projection='Orthographic', proj_defaul
             - "path" must be specified; it can be any existed or non-existed 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.
+            
+        title : bool or str
+            the title for the figure. If True or None, made automatically from the objects' labels. 
+            Set to False for an empty title. 
 
         Returns
         -------
@@ -500,10 +523,23 @@ def map_neighbors(self, mgs, radius=3000, projection='Orthographic', proj_defaul
                                                 archiveType = row['archiveType'], verbose = False, 
                                                 label=row['dataSetName']+'_'+row['paleoData_variableName'])) 
 
-            mgs = pyleo.MultipleGeoSeries(series_list=ts_list,time_unit='years AD') 
+            mgs = pyleo.MultipleGeoSeries(series_list=ts_list,time_unit='years AD',label='Euro2k') 
             gs = ts_list[6] # extract one record as the target one
             gs.map_neighbors(mgs, radius=4000)
             
+        By default, the figure has no title. For a title built from the available labels:
+            
+        .. jupyter-execute::
+            
+           gs.map_neighbors(mgs, radius=4000, title=True) 
+           
+        For a custom title:
+            
+        .. jupyter-execute::
+            
+           gs.map_neighbors(mgs, radius=4000, title='Insert title here') 
+              
+            
         '''
         from ..core.multiplegeoseries import MultipleGeoSeries
         if markersize != None:
@@ -512,42 +548,69 @@ def map_neighbors(self, mgs, radius=3000, projection='Orthographic', proj_defaul
         # find neighbors
         lats = [ts.lat for ts in mgs.series_list]
         lons = [ts.lon for ts in mgs.series_list]
+        # compute distance from self to all points in supplied catalog
         dist = mapping.compute_dist(self.lat, self.lon, lats, lons)
+        # identify indices of neighbors within specified radius
         neigh_idx = mapping.within_distance(dist, radius)
 
-        neighbors =[mgs.series_list[i] for i in neigh_idx if i !=0]
+        # create a new MultipleGeoSeries object with only the neighbors
+        neighbors =[mgs.series_list[i] for i in neigh_idx]
+        dists = [dist[i] for i in neigh_idx]
         neighbors = MultipleGeoSeries(neighbors)
 
+        # create a dataframe for neighbors and add distance to target
         df = mapping.make_df(neighbors, hue=hue, marker=marker, size=size)
+        df['distance'] = dists
+
+        # create a dataframe for the target
         df_self = mapping.make_df(self, hue=hue, marker=marker, size=size)
+        df_self['distance'] = 0
 
         neighborhood = pd.concat([df, df_self], axis=0)
 
-        # additional columns are added manually
-        neighbor_coloring = ['w' for ik in range(len(neighborhood))]
+        # remove duplicates
+        # keep = "last" is specified to make sure that if the target was included
+        # in the potential neighbor catalog, it is kept as the last record
+        neighborhood = neighborhood.drop_duplicates(keep='last')
 
+        # add a column to specify the status of the record as either neighbor or target
         neighbor_status = ['neighbor' for ik in range(len(neighborhood))]
         neighbor_status[-1] = 'target'
         neighborhood['neighbor_status'] = neighbor_status
 
+        # neighbors are assigned white as edgecolor
+        neighbor_coloring = ['w' for ik in range(len(neighborhood))]
+
+        # if edgecolor is not specified, use black, otherwise, use the specified color
         if edgecolor is None:
             edgecolor = 'k'
             if isinstance(scatter_kwargs, dict):
                 edgecolor = scatter_kwargs.pop('edgecolor', 'k')
 
         neighbor_coloring[-1] = edgecolor
-        neighborhood['original'] =neighbor_coloring
+        neighborhood['edgecolor'] =neighbor_coloring
 
-        # plot neighbors
+        if type(title)==bool:
+            if title == False:
+                title = None
+            else:
+                if mgs.label is not None and self.label is not None:
+                    title = f"{mgs.label} neighbors for {self.label} within {radius} km"
+
 
+        # plot neighbors
+        # in future version, if edgecolor is specified as a dictionary with keys "neighbor" and "target",
+        # and values that are colors, that mapping will be used to color the edges of the points
         fig, ax_d = mapping.scatter_map(neighborhood, fig=fig, gs_slot=gridspec_slot, hue=hue, size=size,
                                         marker=marker, projection=projection,
                                            proj_default=proj_default,
                                            background=background, borders=borders, rivers=rivers, lakes=lakes,
-                                           ocean=ocean, land=land,
+                                           ocean=ocean, land=land, title = title,
                                            figsize=figsize, scatter_kwargs=scatter_kwargs, lgd_kwargs=lgd_kwargs,
                                            gridspec_kwargs=gridspec_kwargs, colorbar=colorbar,
-                                           legend=legend, cmap=cmap, edgecolor=neighborhood['original'].values)
+                                           legend=legend, cmap=cmap, edgecolor=neighborhood['edgecolor'].values)
+
+
         return fig, ax_d
 
     def dashboard(self, figsize=[11, 8], gs=None, plt_kwargs=None, histplt_kwargs=None, spectral_kwargs=None,

diff --git a/pyleoclim/tests/test_core_GeoSeries.py b/pyleoclim/tests/test_core_GeoSeries.py
@@ -86,7 +86,7 @@ def test_init_dropna(self, evenly_spaced_series):
             print(ts2.value)
             assert ~np.isnan(ts2.value[0])
 
-@pytest.mark.xfail   # will fail until pandas is fixed
+#@pytest.mark.xfail   # will fail until pandas is fixed
 class TestUIGeoSeriesResample():
     ''' test GeoSeries.Resample()
     '''
@@ -111,8 +111,42 @@ def test_map_neighbors_t1(self, pinkgeoseries):
         mgs = multiple_pinkgeoseries()
         fig, ax = ts.map_neighbors(mgs, radius=5000)
         pyleo.closefig(fig)
+
+    @pytest.mark.parametrize('title',[None, False, True, 'Untitled'])    
+    def test_map_neighbors_t2(self, title):
+        PLOT_DEFAULT = pyleo.utils.lipdutils.PLOT_DEFAULT
+        ntypes = len(PLOT_DEFAULT)
+
+        lat = np.random.uniform(20,70,ntypes)
+        lon = np.random.uniform(-20,60,ntypes)
+
+        dummy = [1, 2, 3]
 
-class TestUiGeoSeriesMap():
+        ts = pyleo.GeoSeries(time = dummy, value=dummy, lat=lat.mean(), lon=lon.mean(),
+                             auto_time_params=True, verbose=False, archiveType='Wood',
+                             label='Random Tree')
+        series_list = []
+        for i, key in enumerate(PLOT_DEFAULT.keys()):
+            ser = ts.copy()
+            ser.lat=lat[i]
+            ser.lon=lon[i]
+            ser.archiveType=key
+            ser.label=key
+            series_list.append(ser)
+
+        mgs = pyleo.MultipleGeoSeries(series_list,time_unit='Years CE', label = 'multi-archive maelstrom')
+
+        fig, ax = ts.map_neighbors(mgs,radius=5000, title = title)
+
+        if title is None or title == False:
+            assert ax['map'].get_title() == ''
+        elif title == True:
+            assert ax['map'].get_title() == 'multi-archive maelstrom neighbors for Random Tree within 5000 km'
+        else:
+            ax['map'].get_title() == 'Untitled'
+        pyleo.closefig(fig)
+
+class TestUIGeoSeriesMap():
     ''' test GeoSeries.map()
     '''
 
@@ -121,6 +155,19 @@ def test_map_t0(self, pinkgeoseries):
         fig, ax = ts.map()
         pyleo.closefig(fig)
 
+    @pytest.mark.parametrize('title',[None, False, True, 'Untitled'])    
+    def test_map_t1(self, pinkgeoseries, title):
+        ts = pinkgeoseries
+        fig, ax = ts.map(title=title)
+        if title is None or title == False:
+            assert ax['map'].get_title() == ''
+        elif title == True:
+            assert ax['map'].get_title() == 'pink noise geoseries location'
+        else:
+            ax['map'].get_title() == 'Untitled'
+        pyleo.closefig(fig)
+        pyleo.closefig(fig)
+
 
 def test_segment():
     '''
@@ -133,7 +180,7 @@ def test_segment():
     assert np.array_equal(mgs.series_list[0].value,gs.value[:4000]) 
     assert np.array_equal(mgs.series_list[1].value,gs.value[5000:]) 
 
-class TestUiGeoSeriesDashboard():
+class TestUIGeoSeriesDashboard():
     ''' test GeoSeries.Dashboard
     '''
 

diff --git a/pyleoclim/utils/lipdutils.py b/pyleoclim/utils/lipdutils.py
@@ -21,27 +21,53 @@ def __setitem__(self, key, value):
     def __getitem__(self, key):
         return super().__getitem__(key.lower().replace(" ", ""))
 
-PLOT_DEFAULT = {'GroundIce': ['#86CDFA', 'h'],
-                     'Borehole': ['#00008b', 'h'],
-                     'Coral': ['#FF8B00', 'o'],
-                     'Documents': ['#f8d568', 'p'],
-                     'GlacierIce': ['#86CDFA', 'd'],
-                     'Hybrid': ['#808000', '*'],
-                     'LakeSediment': ['#8A4513', '^'],
-                     'MarineSediment': ['#8A4513', 's'],
-                     'Sclerosponge': ['r', 'o'],
+# Old one:
+# PLOT_DEFAULT = {'GroundIce': ['#86CDFA', 'h'],
+#                      'Borehole': ['#00008b', 'h'],
+#                      'Coral': ['#FF8B00', 'o'],
+#                      'Documents': ['#f8d568', 'p'],
+#                      'GlacierIce': ['#86CDFA', 'd'],
+#                      'Hybrid': ['#808000', '*'],
+#                      'LakeSediment': ['#8A4513', '^'],
+#                      'MarineSediment': ['#8A4513', 's'],
+#                      'Sclerosponge': ['r', 'o'],
+#                      'Speleothem': ['#FF1492', 'd'],
+#                      'Wood': ['#32CC32', '^'],
+#                      'MolluskShell': ['#FFD600', 'h'],
+#                      'Peat': ['#2F4F4F', '*'],
+#                      'Midden': ['#824E2B', 'o'],
+#                      'FluvialSediment': ['#4169E0','o'],
+#                      'TerrestrialSediment': ['#8A4513','o'],
+#                      'Shoreline': ['#add8e6','o'],
+#                      'Instrumental' : ['#8f21d8', '*'],
+#                      'Model' : ['#b4a7d6', "d"],
+#                      'Other': ['k', 'o']
+#                     }
+
+PLOT_DEFAULT = {'GlacierIce': ['deepskyblue', '*'],
+                'GroundIce': ['slategray', '*'],
+                     'Borehole': ['#FFD600', 's'],
+                     'Coral': ['#FF8B00', 'v'],
+                     'Sclerosponge': ['r', 'v'],
+                     'Documents': ['#f8d568', 'p'],     
+                     'Hybrid': ['#808000', 'H'],
+                     'LakeSediment': ['#1170aa', 'o'],
+                     'MarineSediment': ['#8A4513', 'o'],
+                     'FluvialSediment': ['#5fa2ce','o'],
+                     'TerrestrialSediment': ['#57606c','o'],
                      'Speleothem': ['#FF1492', 'd'],
-                     'Wood': ['#32CC32', '^'],
-                     'MolluskShell': ['#FFD600', 'h'],
-                     'Peat': ['#2F4F4F', '*'],
-                     'Midden': ['#824E2B', 'o'],
-                     'FluvialSediment': ['#4169E0','d'],
-                     'TerrestrialSediment': ['#8A4513','o'],
-                     'Shoreline': ['#add8e6','o'],
-                     'Instrumental' : ['#8f21d8', '*'],
-                     'Model' : ['#b4a7d6', "d"],
+                     'Wood': ['#8CD17D', '^'], 
+                     'MolluskShell': ['#f8d568', 'h'],
+                     'Peat': ['#8A9A5B', 'X'],
+                     'Midden': ['#824E2B', 'X'], 
+                     'Shoreline': ['#40826D','o'],
+                     'Instrumental' : ['#B07AA1', 'D'],
+                     'Model' : ['#E15759', "D"],
                      'Other': ['k', 'o']
                     }
+# as per lipd convention, communicated by David Edge, 06.25.2024
+# var colorPal = {"Borehole":"#FFD600","MolluskShell":"#7b03fc","GlacierIce":"#86CDFA","GroundIce":"#ff6db6","Coral":"#FF8B00","FluvialSediment":"#4169E0","LakeSediment":"#8f8fa1","MarineSediment":"#8A4513","Speleothem":"#FF1492","Midden":"#824E2B","Peat":"#8A9A5B","Sclerosponge":"#D2042D","Shoreline":"#40826D","Wood":"#32CC32","TerrestrialSediment":"#d2b48c"}
+# var shapePal ={"Borehole":"square","MolluskShell":"triangle","GlacierIce":"snowflake","GroundIce":"snowflake","Coral":"triangle-down","FluvialSediment":"circle","LakeSediment":"circle","MarineSediment":"circle","Speleothem":"square","Midden":"diamond","Peat":"triangle-down","Sclerosponge":"triangle","Shoreline":"diamond","Wood":"triangle","TerrestrialSediment":"circle"}
 
 
 """

diff --git a/pyleoclim/utils/mapping.py b/pyleoclim/utils/mapping.py
@@ -563,7 +563,7 @@ def make_df(geo_ms, hue=None, marker=None, size=None, cols=None, d=None):
 
 
 def scatter_map(geos, hue='archiveType', size=None, marker='archiveType', edgecolor='k',
-                proj_default=True, projection='auto', crit_dist=5000,
+                proj_default=True, projection='auto', crit_dist=5000, title = None,
                 background=True, borders=False, coastline=True, rivers=False, lakes=False, ocean=True, land=True,
                 figsize=None, scatter_kwargs=None, gridspec_kwargs=None, extent='global', edgecolor_var=None,
                 lgd_kwargs=None, legend=True, colorbar=True, cmap=None, color_scale_type=None,
@@ -681,7 +681,9 @@ def scatter_map(geos, hue='archiveType', size=None, marker='archiveType', edgeco
         - 'missing_val_hue', 'missing_val_marker', 'missing_val_label' can all be used to change the way missing values are represented ('k', '?',  are default hue and marker values will be associated with the label: 'missing').
         - 'hue_mapping' and 'marker_mapping' can be used to submit dictionaries mapping hue values to colors and marker values to markers. Does not replace passing a string value for hue or marker.
         - 'scalar_mappable' can be used to pass a matplotlib scalar mappable. See pyleoclim.utils.plotting.make_scalar_mappable for documentation on using the Pyleoclim utility, or the `Matplotlib tutorial on customizing colorbars <https://matplotlib.org/stable/users/explain/colors/colorbar_only.html>`_.
-
+    
+    title : str
+        the title for the figure
 
     Returns
     -------
@@ -1323,9 +1325,12 @@ def replace_last(source_string, replace_what, replace_with):
     x = 'lon'
     y = 'lat'
     _, ax_d = plot_scatter(df=df, x=x, y=y, hue_var=hue, size_var=size, marker_var=marker, ax_d=ax_d, proj=None,
-                           edgecolor=edgecolor, colorbar=colorbar, color_scale_type=color_scale_type,
+                           edgecolor=edgecolor, colorbar=colorbar, color_scale_type=color_scale_type, title = title,
                            cmap=cmap, scatter_kwargs=scatter_kwargs, legend=legend, lgd_kwargs=lgd_kwargs,
                            **kwargs)  # , **kwargs)
+    if title is not None:
+        ax_d['map'].set_title(title)
+
     return fig, ax_d