DuraMAT Live Demo

pvdeg/fatigue.py

@@ -27,10 +27,10 @@ def _avg_daily_temp_change(time_range, temp_cell):
         Average of Daily Maximum Temperature for 1-year (Celsius)
 
     """
-    
+
     if time_range.dtype == 'object':
         time_range = pd.to_datetime(time_range)
-    
+
     # Setup frame for vector processing
     timeAndTemp_df = pd.DataFrame(columns=['Cell Temperature'])
     timeAndTemp_df['Cell Temperature'] = temp_cell
@@ -111,7 +111,7 @@ def solder_fatigue(weather_df, meta,
     This function uses the default values for 60-min input intervals from Table 4 of the above
     paper. For other use cases, please refer to the paper for recommended values of C1 and
     the reversal temperature.
-    
+
     Parameters
     ------------
     weather_df : pd.dataframe
@@ -143,9 +143,9 @@ def solder_fatigue(weather_df, meta,
 
     """
 
-    # TODO this, and many other functions with temp_cell or temp_module would benefit from an 
+    # TODO this, and many other functions with temp_cell or temp_module would benefit from an
     # optional parameter "conf = 'open_rack_glass_glass' or equivalent"
-    
+
     # TODO Make this function have more utility.
     # People want to run all the scenarios from the bosco paper.
     # Currently have everything hard coded for hourly calculation
@@ -158,10 +158,10 @@ def solder_fatigue(weather_df, meta,
 
     if time_range is None:
         time_range = weather_df.index
-    
+
     if temp_cell is None:
         temp_cell = temperature.cell(weather_df, meta)
-    
+
     temp_amplitude, temp_max_avg = _avg_daily_temp_change(time_range, temp_cell)
 
     temp_max_avg = convert_temperature(temp_max_avg, 'Celsius', 'Kelvin')

pvdeg/geospatial.py

@@ -10,6 +10,10 @@
 import pandas as pd
 from dask.distributed import Client, LocalCluster
 
+import matplotlib.pyplot as plt
+import cartopy.crs as ccrs
+import cartopy.io.shapereader as shpreader
+
 
 def start_dask(hpc=None):
     """
@@ -97,7 +101,6 @@ def calc_gid(ds_gid, meta_gid, func, **kwargs):
     """
 
     df_weather = ds_gid.to_dataframe()
-
     df_res = func(weather_df=df_weather, meta=meta_gid, **kwargs)
     ds_res = xr.Dataset.from_dataframe(df_res)
 
@@ -170,14 +173,15 @@ def analysis(weather_ds, meta_df, func, template=None, **func_kwargs):
 
     stacked = weather_ds.map_blocks(calc_block, kwargs=kwargs, template=template).compute()
 
-    lats = stacked.latitude.values.flatten()
-    lons = stacked.longitude.values.flatten()
-    #stacked = stacked.drop(['gid'])
-    stacked = stacked.drop_vars(['latitude', 'longitude'])
-    stacked.coords['gid'] = pd.MultiIndex.from_arrays([lats, lons], names=['latitude', 'longitude'])
-
-    res = stacked.unstack('gid', sparse=True)
+    # lats = stacked.latitude.values.flatten()
+    # lons = stacked.longitude.values.flatten()
+    stacked = stacked.drop(['gid'])
+    # stacked = stacked.drop_vars(['latitude', 'longitude'])
+    stacked.coords['gid'] = pd.MultiIndex.from_arrays([
+        meta_df['latitude'], meta_df['longitude']],
+        names=['latitude', 'longitude'])
 
+    res = stacked.unstack('gid') #, sparse=True
     return res
 
 
@@ -210,7 +214,7 @@ def output_template(ds_gids, shapes, attrs=dict(), add_dims=dict()):
     output_template = xr.Dataset(
         data_vars = {var: (dim, da.empty([dims_size[d] for d in dim])
                            ) for var, dim in shapes.items()},
-        coords = {'gid': ds_gids['gid']},
+        coords = {dim: ds_gids[dim] for dim in dims},
         attrs = attrs
     ).chunk({dim: ds_gids.chunks[dim] for dim in dims})
 
@@ -227,16 +231,16 @@ def template_parameters(func):
         Dictionary of variable names and their associated dimensions.
     attrs : dict
         Dictionary of attributes for each variable (e.g. units).
+    add_dims : dict
+        Dictionary of dimensions to add to the output template.
     """
 
     if func == standards.standoff:
 
         shapes = {'x':        ('gid',),
-                'T98_inf':  ('gid',),
-                'T98_0':    ('gid',),
-                'latitude': ('gid',),
-                'longitude':('gid',),
-                }
+                  'T98_inf':  ('gid',),
+                  'T98_0':    ('gid',),
+                  }
 
         attrs = {'x' :      {'units': 'cm'},
                 'T98_0' :   {'units': 'Celsius'},
@@ -263,4 +267,45 @@ def template_parameters(func):
                   'attrs': attrs,
                   'add_dims': add_dims}
 
-    return parameters
+    return parameters
+
+
+def plot_USA(xr_res,
+             cmap='viridis',
+             vmin=None,
+             vmax=None,
+             title=None,
+             cb_title=None,
+             fp=None):
+
+    fig = plt.figure()
+    ax = fig.add_axes([0, 0, 1, 1],
+                      projection=ccrs.LambertConformal(),
+                      frameon=False)
+    ax.patch.set_visible(False)
+    ax.set_extent([-120, -74, 22, 50], ccrs.Geodetic())
+
+    shapename = 'admin_1_states_provinces_lakes'
+    states_shp = shpreader.natural_earth(
+        resolution='110m', category='cultural', name=shapename)
+    ax.add_geometries(
+        shpreader.Reader(states_shp).geometries(),
+        ccrs.PlateCarree(), facecolor='w', edgecolor='gray')
+
+    cm = xr_res.plot(transform=ccrs.PlateCarree(),
+                zorder=10,
+                add_colorbar=False,
+                cmap=cmap,
+                vmin=vmin,
+                vmax=vmax,
+                subplot_kws={"projection": ccrs.LambertConformal(
+                central_longitude=-95, central_latitude=45)})
+
+    cb = plt.colorbar(cm, shrink=0.5)
+    cb.set_label(cb_title)
+    ax.set_title(title)
+
+    if fp is not None:
+        plt.savefig(fp, dpi=600)
+
+    return fig, ax

pvdeg/standards.py

@@ -152,9 +152,7 @@ def standoff(
 
     res = {'x': x,
            'T98_0': T98_0,
-           'T98_inf': T98_inf,
-           'latitude': meta['latitude'],
-           'longitude': meta['longitude']}
+           'T98_inf': T98_inf}
 
     df_res = pd.DataFrame.from_dict(res, orient='index').T
 

pvdeg/weather.py

@@ -264,6 +264,10 @@ def ini_h5_geospatial(fps):
 
     ds = xr.merge(dss)
     ds = xr.decode_cf(ds)
+
+    # Rechunk time axis
+    ds = ds.chunk(chunks={'time': -1, 'gid': ds.chunks['gid']})
+
     weather_ds = ds
 
     return weather_ds, meta_df
@@ -308,9 +312,6 @@ def get_NSRDB_fnames(satellite, names, NREL_HPC = False, **_):
         hpc_fp = '/nrel/nsrdb/'
         hsds = True
 
-    if type(names) in [int, float]:
-        nsrdb_fp = os.path.join(hpc_fp, sat_map[satellite], '*_{}.h5'.format(int(names)))
-
     if type(names) in [int, float]:
         nsrdb_fp = os.path.join(hpc_fp, sat_map[satellite], '*_{}.h5'.format(int(names)))
         nsrdb_fnames = glob.glob(nsrdb_fp)