Kempe edge seals

pvdeg/data/H2Opermeation.json

@@ -22,11 +22,11 @@
 		"source": "unpublished measurements",
 		"Fickian": true,
 		"Ead": 61.4781422330562,
-		"Do": 25790.6020262449,
+		"Do": 257.906020262449,
 		"Eas": 5.88752263485353,
-		"So": 0.00982242435416737,
-		"Eap": 67.3656648679097,
-		"Po": 5559396276.60964
+		"So": 0.0982242435416737,
+		"Eap": 66.9611315410624,
+		"Po": 189338932521.637
     },
     "W003": {
 		"name": "Coveme",

pvdeg/geospatial.py

@@ -86,7 +86,7 @@ def start_dask(hpc=None):
 
     client = Client(cluster)
     print("Dashboard:", client.dashboard_link)
-    client.wait_for_workers(n_workers=1)
+    # client.wait_for_workers(n_workers=1)
 
     return client
 
@@ -999,7 +999,10 @@ def elevation_stochastic_downselect(
 
 
 def interpolate_analysis(
-    result: xr.Dataset, data_var: str, method="nearest", resolution=100j,
+    result: xr.Dataset,
+    data_var: str,
+    method="nearest",
+    resolution=100j,
 ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
     Interpolate sparse spatial result data against DataArray coordinates.
@@ -1037,12 +1040,12 @@ def interpolate_analysis(
 
 # api could be updated to match that of plot_USA
 def plot_sparse_analysis(
-    result: xr.Dataset, 
-    data_var: str, 
-    method="nearest", 
-    resolution:complex=100j,
-    figsize:tuple=(10,8),
-    show_plot:bool=False,
+    result: xr.Dataset,
+    data_var: str,
+    method="nearest",
+    resolution: complex = 100j,
+    figsize: tuple = (10, 8),
+    show_plot: bool = False,
 ) -> None:
     """
     Plot the output of a sparse geospatial analysis using interpolation.
@@ -1054,7 +1057,7 @@ def plot_sparse_analysis(
     data_var: str
         name of datavariable to plot from result
     method: str
-        interpolation method. 
+        interpolation method.
         Options: `'nearest', 'linear', 'cubic'`
         See [`scipy.interpolate.griddata`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html)
     resolution: complex
@@ -1072,7 +1075,9 @@ def plot_sparse_analysis(
     )
 
     fig = plt.figure(figsize=figsize)
-    ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False) # these should be the same ccrs
+    ax = fig.add_axes(
+        [0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False
+    )  # these should be the same ccrs
     ax.patch.set_visible(False)
 
     extent = [lon.min(), lon.max(), lat.min(), lat.max()]
@@ -1082,7 +1087,7 @@ def plot_sparse_analysis(
         extent=extent,
         origin="lower",
         cmap="viridis",
-        transform=ccrs.PlateCarree(), # why are ccrs different
+        transform=ccrs.PlateCarree(),  # why are ccrs different
     )
 
     shapename = "admin_1_states_provinces_lakes"
@@ -1103,22 +1108,22 @@ def plot_sparse_analysis(
     plt.title(f"Interpolated Sparse Analysis, {data_var}")
     plt.xlabel("Longitude")
     plt.ylabel("Latitude")
-    
+
     if show_plot:
         plt.show()
 
     return fig, ax
 
+
 def plot_sparse_analysis_land(
-    result: xr.Dataset, 
-    data_var: str, 
-    method="nearest", 
-    resolution:complex=100j,
-    figsize:tuple=(10,8),
-    show_plot:bool=False,
+    result: xr.Dataset,
+    data_var: str,
+    method="nearest",
+    resolution: complex = 100j,
+    figsize: tuple = (10, 8),
+    show_plot: bool = False,
     proj=ccrs.PlateCarree(),
 ):
-
     import matplotlib.path as mpath
     from cartopy.mpl.patch import geos_to_path
 
@@ -1133,7 +1138,7 @@ def plot_sparse_analysis_land(
     extent = [lon.min(), lon.max(), lat.min(), lat.max()]
     ax.set_extent(extent, crs=proj)
 
-    mesh = ax.pcolormesh(lon, lat, grid_values, transform=proj, cmap='viridis')
+    mesh = ax.pcolormesh(lon, lat, grid_values, transform=proj, cmap="viridis")
 
     land_path = geos_to_path(list(cfeature.LAND.geometries()))
     land_path = mpath.Path.make_compound_path(*land_path)
@@ -1150,15 +1155,15 @@ def plot_sparse_analysis_land(
         proj,
         facecolor="none",
         edgecolor="black",
-        linestyle=':'
+        linestyle=":",
     )
 
     cbar = plt.colorbar(mesh, ax=ax, orientation="vertical", fraction=0.02, pad=0.04)
     cbar.set_label("Value")
 
     utilities._add_cartopy_features(
-        ax=ax, 
-        features = [
+        ax=ax,
+        features=[
             cfeature.BORDERS,
             cfeature.COASTLINE,
             cfeature.LAND,
@@ -1169,4 +1174,4 @@ def plot_sparse_analysis_land(
     if show_plot:
         plt.show()
 
-    return fig, ax
+    return fig, ax

pvdeg/spectral.py

@@ -65,12 +65,73 @@ def solar_position(weather_df: pd.DataFrame, meta: dict) -> pd.DataFrame:
     ],
 )
 def poa_irradiance(
+    weather_df: pd.DataFrame,
+    meta: dict,
+    module_mount="fixed",
+    sol_position=None,
+    **kwargs_irradiance,
+) -> pd.DataFrame:
+    """
+    Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the
+    National Solar Radiation Database (NSRDB) for a given location (gid).
+
+    Parameters
+    ----------
+    weather_df : pd.DataFrame
+        The file path to the NSRDB file.
+    meta : dict
+        The geographical location ID in the NSRDB file.
+    module_mount: string
+        Module mounting configuration. Can either be `fixed` for fixed tilt systems or
+        `1_axis` for single-axis tracker systems.
+    sol_position : pd.DataFrame, optional
+        pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated.
+    kwargs_irradiance : dict
+        Contains kwarg arguments for the poa model based on mounting configuration. See
+        `poa_irradiance_fixed` or `poa_irradiance_tracker` for details.
+
+    Returns
+    -------
+    poa : pandas.DataFrame
+         Contains keys/columns 'poa_global', 'poa_direct', 'poa_diffuse',
+         'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2]
+    """
+
+    if sol_position is None:
+        sol_position = solar_position(weather_df, meta)
+
+    if module_mount == "fixed":
+        poa = poa_irradiance_fixed(weather_df, meta, sol_position, **kwargs_irradiance)
+    elif module_mount == "1_axis":
+        poa = poa_irradiance_tracker(
+            weather_df, meta, sol_position, **kwargs_irradiance
+        )
+    else:
+        raise NotImplementedError(
+            f"The input module_mount '{module_mount}' is not implemented"
+        )
+
+    return poa
+
+
+@geospatial_quick_shape(
+    1,
+    [
+        "poa_global",
+        "poa_direct",
+        "poa_diffuse",
+        "poa_sky_diffuse",
+        "poa_ground_diffuse",
+    ],
+)
+def poa_irradiance_fixed(
     weather_df: pd.DataFrame,
     meta: dict,
     sol_position=None,
     tilt=None,
     azimuth=None,
     sky_model="isotropic",
+    **kwargs_irradiance,
 ) -> pd.DataFrame:
     """
     Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the
@@ -104,7 +165,7 @@ def poa_irradiance(
         try:
             tilt = float(meta["tilt"])
         except:
-            tilt = float(meta["latitude"])
+            tilt = float(abs(meta["latitude"]))
             print(
                 f"The array tilt angle was not provided, therefore the latitude tilt of {tilt:.1f} was used."
             )
@@ -135,3 +196,92 @@ def poa_irradiance(
     )
 
     return poa
+
+
+@geospatial_quick_shape(
+    1,
+    [
+        "poa_global",
+        "poa_direct",
+        "poa_diffuse",
+        "poa_sky_diffuse",
+        "poa_ground_diffuse",
+    ],
+)
+def poa_irradiance_tracker(
+    weather_df: pd.DataFrame,
+    meta: dict,
+    sol_position=None,
+    axis_tilt=0,
+    axis_azimuth=None,
+    max_angle=90,
+    backtrack=True,
+    gcr=0.2857142857142857,
+    cross_axis_tilt=0,
+    sky_model="isotropic",
+    **kwargs_irradiance,
+) -> pd.DataFrame:
+    """
+    Calculate plane-of-array (POA) irradiance using pvlib based on weather data from the
+    National Solar Radiation Database (NSRDB) for a given location (gid).
+
+    Parameters
+    ----------
+    weather_df : pd.DataFrame
+        The file path to the NSRDB file.
+    meta : dict
+        The geographical location ID in the NSRDB file.
+    sol_position : pd.DataFrame, optional
+        pvlib.solarposition.get_solarposition Dataframe. If none is given, it will be calculated.
+    tilt : float, optional
+        The tilt angle of the PV panels in degrees, if None, the latitude of the
+        location is used.
+    azimuth : float, optional
+        The azimuth angle of the PV panels in degrees. Equatorial facing by default.
+    sky_model : str, optional
+        The pvlib sky model to use, 'isotropic' by default.
+        Options: 'isotropic', 'klucher', 'haydavies', 'reindl', 'king', 'perez'.
+
+    Returns
+    -------
+    tracker_poa : pandas.DataFrame
+         Contains keys/columns 'poa_global', 'poa_direct', 'poa_diffuse',
+         'poa_sky_diffuse', 'poa_ground_diffuse'. [W/m2]
+    """
+
+    if axis_azimuth is None:  # Sets the default orientation to north-south.
+        try:
+            axis_azimuth = float(meta["axis_azimuth"])
+        except:
+            if float(meta["latitude"]) < 0:
+                axis_azimuth = 0
+            else:
+                axis_azimuth = 180
+                print(f"The array axis_azimuth was not provided, therefore an azimuth of {axis_azimuth:.1f} was used.")
+
+    if sol_position is None:
+        sol_position = solar_position(weather_df, meta)
+
+    tracker_data = pvlib.tracking.singleaxis(
+        sol_position["apparent_zenith"],
+        sol_position["azimuth"],
+        axis_tilt=axis_tilt,
+        axis_azimuth=axis_azimuth,
+        max_angle=max_angle,
+        backtrack=backtrack,
+        gcr=gcr,
+        cross_axis_tilt=cross_axis_tilt,
+    )
+
+    tracker_poa = pvlib.irradiance.get_total_irradiance(
+        surface_tilt=tracker_data["surface_tilt"],
+        surface_azimuth=tracker_data["surface_azimuth"],
+        dni=weather_df["dni"],
+        ghi=weather_df["ghi"],
+        dhi=weather_df["dhi"],
+        solar_zenith=sol_position["apparent_zenith"],
+        solar_azimuth=sol_position["azimuth"],
+        model=sky_model,
+    )
+
+    return tracker_poa