Renormalized files in repository

compressions/4/az-tilt-submission/requirements.txt

@@ -1,6 +1,6 @@
-numpy==1.24.4
-pandas==1.5.3
-pvanalytics==0.2.0
-pvlib==0.10.3
-rdtools==2.1.8
+numpy==1.24.4
+pandas==1.5.3
+pvanalytics==0.2.0
+pvlib==0.10.3
+rdtools==2.1.8
 ruptures==1.1.9

compressions/4/az-tilt-submission/submission_wrapper.py

@@ -1,160 +1,160 @@
-"""
-PVWatts-based model submission.
-"""
-
-import pvlib
-import pandas as pd
-from pvanalytics import system
-from pvanalytics.quality import gaps
-from pvanalytics.quality import data_shifts as ds
-from pvanalytics.quality.outliers import hampel, zscore
-import rdtools
-
-
-def estimate_az_tilt(time_series, latitude, longitude):
-    """
-    Estimate the azimuth and tilt of a system based on its time series data,
-    using the PVAnalytics PVWatts-based method.
-
-    Parameters
-    ----------
-    time_series : Pandas series
-        Time series of an AC power stream associated with the system.
-    latitude: Float.
-        Latitude coordinate of the site.
-
-    longitude: Float.
-        Longitude coordinate of the site.
-
-    Returns
-    -------
-    azimuth: Float.
-        Predicted azimuth value.
-    tilt: Float.
-        Predicted tilt value.
-
-    """
-    psm3s = []
-    years = list(time_series.index.year.drop_duplicates())
-    years = [x for x in years if x < 2021]
-    for year in years:
-        psm3 = None
-        try:
-            # connect
-            psm3, _ = pvlib.iotools.get_psm3(
-                latitude,
-                longitude,
-                "4z5fRAXbGB3qldVVd3c6WH5CuhtY5mhgC2DyD952",
-                "[email protected]",
-                year,
-                attributes=[
-                    "air_temperature",
-                    "ghi",
-                    "clearsky_ghi",
-                    "clearsky_dni",
-                    "clearsky_dhi",
-                ],
-                map_variables=True,
-                interval=30,
-                leap_day=True,
-            )
-        except Exception as e:
-            print(e)
-        psm3s.append(psm3)
-    psm3 = pd.concat(psm3s)
-    psm3 = psm3.reindex(
-        pd.date_range(psm3.index[0], psm3.index[-1], freq="15T")
-    ).interpolate()
-    psm3 = psm3.reindex(time_series.index)
-    # psm3[(psm3.index.year == year) | (psm3.index.year == year+1)]
-    psm3_sub = psm3
-    is_clear = psm3_sub.ghi_clear == psm3_sub.ghi
-    is_daytime = psm3_sub.ghi > 0
-    # Run the associated data-cleaning routines on the time series:
-    # 1) Data shift detection + removal
-    # 2) Removal of frozen/stuck data
-    # 3) Removal of data periods with low data 'completeness'
-    # 4) Removal of negative data
-    # 5) Removal of outliers via Hampel + outlier filter
-    # 6) Removal of clipped values via clipping filter
-    # 7) Filter to clearsky data only, as determined by PSM3
-    # try:
-    # Detect any data shifts and remove them
-    time_series_daily = time_series.resample("D").sum()
-    start_date, end_date = ds.get_longest_shift_segment_dates(
-        time_series_daily
-    )
-    time_series = time_series[start_date:end_date]
-    # Trim based on frozen data values
-    stale_data_mask = gaps.stale_values_diff(time_series)
-    time_series = time_series.asfreq("15T")
-    time_series = time_series[~stale_data_mask]
-    time_series = time_series.asfreq("15T")
-    # Remove negative data
-    time_series = time_series[(time_series >= 0) | (time_series.isna())]
-    time_series = time_series.asfreq("15T")
-    # Remove any outliers via Hampel and z-score filters
-    hampel_outlier_mask = hampel(time_series, window=5)
-    zscore_outlier_mask = zscore(time_series, zmax=2, nan_policy="omit")
-    time_series = time_series[(~hampel_outlier_mask) & (~zscore_outlier_mask)]
-    time_series = time_series.asfreq("15T")
-    # Apply clipping filter from Rdtools.
-    clip_mask = rdtools.logic_clip_filter(time_series)
-    time_series = time_series[clip_mask]
-    # Reindex the time series after all of the filtering so it
-    # has the same index as PSM3
-    time_series = time_series.reindex(is_clear.index)
-    # Trim based on clearsky values
-    time_series_clearsky = time_series[(is_clear) & (is_daytime)]
-    # Remove any incomplete days (less than 25% data)
-    time_series_clearsky = time_series_clearsky.asfreq("15T")
-    completeness = gaps.complete(
-        time_series_clearsky, minimum_completeness=0.25
-    )
-    time_series_clearsky = time_series_clearsky[completeness]
-    # Get the peak value for each day, and if it's less than 25% of the
-    # monthly average peak value, then throw it out
-    daily_max = time_series_clearsky.groupby(
-        time_series_clearsky.index.date
-    ).transform("max")
-    average_max_day_month = daily_max.groupby(
-        time_series_clearsky.index.month
-    ).transform("median")
-    time_series_clearsky = time_series_clearsky[
-        daily_max > 0.25 * average_max_day_month
-    ]
-    time_series_clearsky = time_series_clearsky.dropna()
-    # Get the clearsky data
-    psm3_clearsky = psm3.loc[time_series_clearsky.index]
-    solpos_clearsky = pvlib.solarposition.get_solarposition(
-        time_series_clearsky.index, latitude, longitude
-    )
-    # Run PVWatts estimator
-    best_tilt, best_azimuth, r2 = system.infer_orientation_fit_pvwatts(
-        time_series_clearsky,
-        psm3_clearsky.ghi_clear,
-        psm3_clearsky.dhi_clear,
-        psm3_clearsky.dni_clear,
-        solpos_clearsky.zenith,
-        solpos_clearsky.azimuth,
-        temperature=psm3_clearsky.temp_air,
-        azimuth_min=85,
-        azimuth_max=275,
-    )
-    return best_azimuth, best_tilt
-
-
-if __name__ == "__main__":
-    # Test the function
-    print("Reading data")
-    file_name = "./compressions/4/1332_inv_total_ac_power__2654.csv"
-
-    time_series = pd.read_csv(file_name, index_col=0, parse_dates=True)
-    print(time_series.head())
-
-    time_series = time_series.asfreq("15min").squeeze()
-
-    print(time_series.head())
-    print("Estimating azimuth and tilt...")
-    results = estimate_az_tilt(time_series, 40.0150, -105.2705)
-    print(results)
+"""
+PVWatts-based model submission.
+"""
+
+import pvlib
+import pandas as pd
+from pvanalytics import system
+from pvanalytics.quality import gaps
+from pvanalytics.quality import data_shifts as ds
+from pvanalytics.quality.outliers import hampel, zscore
+import rdtools
+
+
+def estimate_az_tilt(time_series, latitude, longitude):
+    """
+    Estimate the azimuth and tilt of a system based on its time series data,
+    using the PVAnalytics PVWatts-based method.
+
+    Parameters
+    ----------
+    time_series : Pandas series
+        Time series of an AC power stream associated with the system.
+    latitude: Float.
+        Latitude coordinate of the site.
+
+    longitude: Float.
+        Longitude coordinate of the site.
+
+    Returns
+    -------
+    azimuth: Float.
+        Predicted azimuth value.
+    tilt: Float.
+        Predicted tilt value.
+
+    """
+    psm3s = []
+    years = list(time_series.index.year.drop_duplicates())
+    years = [x for x in years if x < 2021]
+    for year in years:
+        psm3 = None
+        try:
+            # connect
+            psm3, _ = pvlib.iotools.get_psm3(
+                latitude,
+                longitude,
+                "4z5fRAXbGB3qldVVd3c6WH5CuhtY5mhgC2DyD952",
+                "[email protected]",
+                year,
+                attributes=[
+                    "air_temperature",
+                    "ghi",
+                    "clearsky_ghi",
+                    "clearsky_dni",
+                    "clearsky_dhi",
+                ],
+                map_variables=True,
+                interval=30,
+                leap_day=True,
+            )
+        except Exception as e:
+            print(e)
+        psm3s.append(psm3)
+    psm3 = pd.concat(psm3s)
+    psm3 = psm3.reindex(
+        pd.date_range(psm3.index[0], psm3.index[-1], freq="15T")
+    ).interpolate()
+    psm3 = psm3.reindex(time_series.index)
+    # psm3[(psm3.index.year == year) | (psm3.index.year == year+1)]
+    psm3_sub = psm3
+    is_clear = psm3_sub.ghi_clear == psm3_sub.ghi
+    is_daytime = psm3_sub.ghi > 0
+    # Run the associated data-cleaning routines on the time series:
+    # 1) Data shift detection + removal
+    # 2) Removal of frozen/stuck data
+    # 3) Removal of data periods with low data 'completeness'
+    # 4) Removal of negative data
+    # 5) Removal of outliers via Hampel + outlier filter
+    # 6) Removal of clipped values via clipping filter
+    # 7) Filter to clearsky data only, as determined by PSM3
+    # try:
+    # Detect any data shifts and remove them
+    time_series_daily = time_series.resample("D").sum()
+    start_date, end_date = ds.get_longest_shift_segment_dates(
+        time_series_daily
+    )
+    time_series = time_series[start_date:end_date]
+    # Trim based on frozen data values
+    stale_data_mask = gaps.stale_values_diff(time_series)
+    time_series = time_series.asfreq("15T")
+    time_series = time_series[~stale_data_mask]
+    time_series = time_series.asfreq("15T")
+    # Remove negative data
+    time_series = time_series[(time_series >= 0) | (time_series.isna())]
+    time_series = time_series.asfreq("15T")
+    # Remove any outliers via Hampel and z-score filters
+    hampel_outlier_mask = hampel(time_series, window=5)
+    zscore_outlier_mask = zscore(time_series, zmax=2, nan_policy="omit")
+    time_series = time_series[(~hampel_outlier_mask) & (~zscore_outlier_mask)]
+    time_series = time_series.asfreq("15T")
+    # Apply clipping filter from Rdtools.
+    clip_mask = rdtools.logic_clip_filter(time_series)
+    time_series = time_series[clip_mask]
+    # Reindex the time series after all of the filtering so it
+    # has the same index as PSM3
+    time_series = time_series.reindex(is_clear.index)
+    # Trim based on clearsky values
+    time_series_clearsky = time_series[(is_clear) & (is_daytime)]
+    # Remove any incomplete days (less than 25% data)
+    time_series_clearsky = time_series_clearsky.asfreq("15T")
+    completeness = gaps.complete(
+        time_series_clearsky, minimum_completeness=0.25
+    )
+    time_series_clearsky = time_series_clearsky[completeness]
+    # Get the peak value for each day, and if it's less than 25% of the
+    # monthly average peak value, then throw it out
+    daily_max = time_series_clearsky.groupby(
+        time_series_clearsky.index.date
+    ).transform("max")
+    average_max_day_month = daily_max.groupby(
+        time_series_clearsky.index.month
+    ).transform("median")
+    time_series_clearsky = time_series_clearsky[
+        daily_max > 0.25 * average_max_day_month
+    ]
+    time_series_clearsky = time_series_clearsky.dropna()
+    # Get the clearsky data
+    psm3_clearsky = psm3.loc[time_series_clearsky.index]
+    solpos_clearsky = pvlib.solarposition.get_solarposition(
+        time_series_clearsky.index, latitude, longitude
+    )
+    # Run PVWatts estimator
+    best_tilt, best_azimuth, r2 = system.infer_orientation_fit_pvwatts(
+        time_series_clearsky,
+        psm3_clearsky.ghi_clear,
+        psm3_clearsky.dhi_clear,
+        psm3_clearsky.dni_clear,
+        solpos_clearsky.zenith,
+        solpos_clearsky.azimuth,
+        temperature=psm3_clearsky.temp_air,
+        azimuth_min=85,
+        azimuth_max=275,
+    )
+    return best_azimuth, best_tilt
+
+
+if __name__ == "__main__":
+    # Test the function
+    print("Reading data")
+    file_name = "./compressions/4/1332_inv_total_ac_power__2654.csv"
+
+    time_series = pd.read_csv(file_name, index_col=0, parse_dates=True)
+    print(time_series.head())
+
+    time_series = time_series.asfreq("15min").squeeze()
+
+    print(time_series.head())
+    print("Estimating azimuth and tilt...")
+    results = estimate_az_tilt(time_series, 40.0150, -105.2705)
+    print(results)