Merge branch 'development' into Kempe_Gap_Calc

docs/source/whatsnew/releases/v0.3.2.rst

@@ -0,0 +1,19 @@
+v0.3.2 (2024-XX-XX)
+=======================
+
+Enhancements
+------------
+
+Documentation 
+-------------
+* Renamted pvdeg_tutorials folder to tutorials_and_tools, and added more tutorials 
+to be run easily in google collab.
+* Jupyter Journals now have information regarding the version of the programs used near 
+the beginning, to simplify debugging and user support.
+
+Contributors
+~~~~~~~~~~~~
+* Martin Springer (:ghuser:`martin-springer`)
+* Mike Kempe (:ghuser:`MDKempe`)
+* Silvana Ovaitt (:ghuser:`shirubana`)
+* Tobin Ford (:ghuser:`tobin-ford`)

pvdeg/geospatial.py

@@ -130,10 +130,10 @@ def calc_block(weather_ds_block, future_meta_df, func, func_kwargs):
         Dataset with results for a block of gids.
     """
 
-    res = weather_ds_block.groupby("gid").map(
+    res = weather_ds_block.groupby("gid", squeeze=False).map(
         lambda ds_gid: calc_gid(
-            ds_gid=ds_gid,
-            meta_gid=future_meta_df.loc[ds_gid["gid"].values].to_dict(),
+            ds_gid=ds_gid.squeeze(),
+            meta_gid=future_meta_df.loc[ds_gid["gid"].values[0]].to_dict(),
             func=func,
             **func_kwargs,
         )
@@ -179,9 +179,14 @@ def analysis(weather_ds, meta_df, func, template=None, **func_kwargs):
     # lons = stacked.longitude.values.flatten()
     stacked = stacked.drop(["gid"])
     # stacked = stacked.drop_vars(['latitude', 'longitude'])
-    stacked.coords["gid"] = pd.MultiIndex.from_arrays(
+    # stacked.coords["gid"] = pd.MultiIndex.from_arrays(
+    #     [meta_df["latitude"], meta_df["longitude"]], names=["latitude", "longitude"]
+    # )
+    mindex_obj = pd.MultiIndex.from_arrays(
         [meta_df["latitude"], meta_df["longitude"]], names=["latitude", "longitude"]
     )
+    mindex_coords = xr.Coordinates.from_pandas_multiindex(mindex_obj, "gid")
+    stacked = stacked.assign_coords(mindex_coords)
 
     stacked = stacked.drop_duplicates("gid")
     res = stacked.unstack("gid")  # , sparse=True

pvdeg/humidity.py

@@ -542,7 +542,7 @@ def back_encap(
     Csat = _csat(temp_module=temp_module, So=So, Eas=Eas)
     Ceq = _ceq(Csat=Csat, rh_SurfaceOutside=rh_surface)
 
-    start = Ceq[0]
+    start = Ceq.iloc[0]
 
     # Need to convert these series to numpy arrays for numba function
     temp_module_numba = temp_module.to_numpy()

pvdeg/spectral.py

@@ -73,12 +73,24 @@ def poa_irradiance(
 
     # TODO: change for handling HSAT tracking passed or requested
     if tilt is None:
-        tilt = float(meta["latitude"])
+        try:
+            tilt = float(meta["tilt"])
+        except:
+            tilt = float(meta["latitude"])
+            print(
+                f"The array tilt angle was not provided, therefore the latitude tilt of {tilt:.1f} was used."
+            )
     if azimuth is None:  # Sets the default orientation to equator facing.
-        if float(meta["latitude"]) < 0:
-            azimuth = 0
-        else:
-            azimuth = 180
+        try:
+            azimuth = float(meta["azimuth"])
+        except:
+            if float(meta["latitude"]) < 0:
+                azimuth = 0
+            else:
+                azimuth = 180
+                print(
+                    f"The array azimuth was not provided, therefore an azimuth of {azimuth:.1f} was used."
+                )
 
     if sol_position is None:
         sol_position = solar_position(weather_df, meta)

pvdeg/standards.py

@@ -47,7 +47,7 @@ def eff_gap_parameters(
     temp_model : str, optional
         Options: 'sapm'.  'pvsyst' and 'faiman' and others from PVlib.
         Performs the calculation for the cell temperature.
-    conf_0 : str, optional        
+    conf_0 : str, optional
         Model for the high temperature module on the exponential decay curve.
         Default: 'insulated_back_glass_polymer'
     conf_inf : str, optional
@@ -96,24 +96,16 @@ def eff_gap_parameters(
     elif weather_df is None:
         weather_df, meta = weather.get(**weather_kwarg)
 
-    if tilt == None:
-        try:
-            tilt = meta["tilt"]
-        except:
-            tilt = meta["latitude"]
-            print("The array tilt angle was not provided, therfore the latitude tilt of" & "%.1f" % tilt & "was used.")
-
-    if azimuth == None:  # Sets the default orientation to equator facing.
-        try:
-            azimuth = meta["azimuth"]
-        except:
-            if float(meta["latitude"]) < 0:
-                azimuth = 0
-            else:
-                azimuth = 180
-            print("The array azimuth was not provided, therfore an azimuth of" & "%.1f" % azimuth & "was used.")
-    if "wind_height" not in meta.keys(): #if the anenometer hight is not give, this will make it just use the unadjusted wind speed as given.
-        wind_factor = 1
+    # if tilt == None:
+    #     tilt = meta["latitude"]
+
+    # if azimuth == None:  # Sets the default orientation to equator facing. MSP: Defaults are already set in temperature.py
+    #     if float(meta["latitude"]) < 0:
+    #         azimuth = 0
+    #     else:
+    #         azimuth = 180
+    # if "wind_height" not in meta.keys():
+    #     wind_factor = 1
 
     solar_position = spectral.solar_position(weather_df, meta)
     poa = spectral.poa_irradiance(
@@ -168,10 +160,10 @@ def eff_gap(T_0, T_inf, T_measured, T_ambient, poa, x_0=6.5, poa_min=400, t_amb_
         Thermal decay constant [cm], [Kempe, PVSC Proceedings 2023].
         According to edition 2 of IEC TS 63126 a value of 6.5 cm is recommended.
     poa_min : float
-        The minimum value for which the data will be used, [W/m²]. 
+        The minimum value for which the data will be used, [W/m²].
         400 W/m² is recommended.
     t_amb_min : float
-        The minimum ambient temperature for which the calculation will be made, [°C]. 
+        The minimum ambient temperature for which the calculation will be made, [°C].
         A value of 0 °C is recommended to avoid data where snow might be present.
 
     Returns
@@ -212,7 +204,7 @@ def standoff(
     weather_df=None,
     meta=None,
     weather_kwarg=None,
-    tilt=0,
+    tilt=None,
     azimuth=None,
     sky_model="isotropic",
     temp_model="sapm",
@@ -283,13 +275,13 @@ def standoff(
     to IEC TS 63126, PVSC Proceedings 2023
     """
 
-    if azimuth == None:  # Sets the default orientation to equator facing.
-        if float(meta["latitude"]) < 0:
-            azimuth = 0
-        else:
-            azimuth = 180
-    if "wind_height" not in meta.keys():
-        wind_factor = 1
+    # if azimuth == None:  # Sets the default orientation to equator facing.
+    #     if float(meta["latitude"]) < 0:
+    #         azimuth = 0
+    #     else:
+    #         azimuth = 180
+    # if "wind_height" not in meta.keys():
+    #     wind_factor = 1
     parameters = ["temp_air", "wind_speed", "dhi", "ghi", "dni"]
 
     if isinstance(weather_df, dd.DataFrame):
@@ -331,9 +323,8 @@ def standoff(
     try:
         x = -x_0 * np.log(1 - (T98_0 - T98) / (T98_0 - T98_inf))
     except RuntimeWarning as e:
-        x = (
-            np.nan
-        )  # results if the specified T₉₈ is cooler than an open_rack temperature
+        x = np.nan
+        # results if the specified T₉₈ is cooler than an open_rack temperature
     if x < 0:
         x = 0
 
@@ -508,16 +499,16 @@ def T98_estimate(
 
     """
 
-    if tilt == None:
-        tilt = meta["latitude"]
+    # if tilt == None:
+    #     tilt = meta["latitude"]
 
-    if azimuth == None:  # Sets the default orientation to equator facing.
-        if float(meta["latitude"]) < 0:
-            azimuth = 0
-        else:
-            azimuth = 180
-    if "wind_height" not in meta.keys():
-        wind_factor = 1
+    # if azimuth == None:  # Sets the default orientation to equator facing.
+    #     if float(meta["latitude"]) < 0:
+    #         azimuth = 0
+    #     else:
+    #         azimuth = 180
+    # if "wind_height" not in meta.keys():
+    #     wind_factor = 1
     parameters = ["temp_air", "wind_speed", "dhi", "ghi", "dni"]
 
     if isinstance(weather_df, dd.DataFrame):

pvdeg/weather.py

@@ -165,7 +165,7 @@ def read(file_in, file_type, map_variables=True, **kwargs):
     if weather_df.index.tzinfo is None:
         tz = "Etc/GMT%+d" % -meta["tz"]
         weather_df = weather_df.tz_localize(tz)
-         
+
     return weather_df, meta
 
 
@@ -196,9 +196,11 @@ def csv_read(filename):
     metadata_values = file1.readline().split(",")
     metadata_values[-1] = metadata_values[-1].strip()  # strip trailing newline
     meta = dict(zip(metadata_fields, metadata_values))
-    for key in meta: #converts everything to a float that is possible to convert to a float
-        try: 
-            meta[key]=float(meta[key])
+    for (
+        key
+    ) in meta:  # converts everything to a float that is possible to convert to a float
+        try:
+            meta[key] = float(meta[key])
         except:
             pass
     # get the column headers
@@ -462,8 +464,8 @@ def ini_h5_geospatial(fps):
     ds = xr.merge(dss)
     ds = xr.decode_cf(ds)
 
-    # Rechunk time axis - TODO: fix this
-    # ds = ds.chunk(chunks={"time": -1, "gid": ds.chunks["gid"]})
+    # Rechunk time axis
+    ds = ds.chunk(chunks={"time": -1, "gid": ds.chunks["gid"]})
 
     weather_ds = ds