Merge pull request #84 from slacgismo/hotfix/pvlib

Hotfix/pvlib

.github/workflows/build.yml

@@ -60,7 +60,7 @@ jobs:
               conda config --set anaconda_upload no
               echo yes | anaconda login --username ${{ secrets.ANACONDA_CLOUD_USERNAME }} --password ${{ secrets.ANACONDA_CLOUD_PASSWORD }}
               git fetch --prune --unshallow --tags
-              VERSION_FROM_GIT_TAG=$(git tag --list "v*[0-9]" --sort=version:refname | tail -1 | cut -c 2-) conda build . -c conda-forge -c slacgismo --numpy 1.22.0
+              VERSION_FROM_GIT_TAG=$(git tag --list "v*[0-9]" --sort=version:refname | tail -1 | cut -c 2-) conda build . -c anaconda -c pvlib -c slacgismo -c conda-forge --numpy 1.22.0
               echo '::set-output name=gitversion::$(git tag --list "v*[0-9]" --sort=version:refname | tail -1 | cut -c 2-)'
 
           - name: Upload the Anaconda Package

.github/workflows/test.yml

@@ -21,8 +21,9 @@ jobs:
 
           - name: Install Dependencies
             run: |
-              sudo pip install -r requirements.txt
-              sudo pip install awscli coverage
+              sudo $pythonLocation/bin/python3 -m pip install -r requirements.txt
+              sudo $pythonLocation/bin/python3 -m pip install awscli coverage
+              $pythonLocation/bin/python3 -m pip list
 
           - name: Setup Mosek License File
             run: |
@@ -33,7 +34,7 @@ jobs:
 
 # Current unit test is not consistent. Occasionally fails despite usually passing. Needs to be fixed.
           - name: Run Unit Tests
-            run: sudo coverage run -m unittest
+            run: sudo $pythonLocation/bin/python3 -m coverage run -m unittest
 
     build-pypi:
         runs-on: ubuntu-latest
@@ -89,5 +90,6 @@ jobs:
               conda install anaconda-client
               conda config --set anaconda_upload no
               git fetch --prune --unshallow --tags
-              VERSION_FROM_GIT_TAG=$(git tag --list "v*[0-9]" --sort=version:refname | tail -1 | cut -c 2-)test conda build . -c conda-forge -c slacgismo --numpy 1.22.0
-              echo '::set-output name=gitversion::$(git tag --list "v*[0-9]" --sort=version:refname | tail -1 | cut -c 2-)'
+              VERSION_FROM_GIT_TAG=$(git tag --list "v*[0-9]" --sort=version:refname | tail -1 | cut -c 2-)test conda build . -c anaconda -c pvlib -c slacgismo -c conda-forge --numpy 1.22.0
+              echo '::set-output name=gitversion::$(git tag --list "v*[0-9]" --sort=version:refname | tail -1 | cut -c 2-)'
+

conda_recipe/meta.yaml

@@ -29,7 +29,7 @@ requirements:
     - seaborn
     - requests
     - scikit-learn
-    - pvlib-python
+    - pvlib
     - cvxpy
     - statistical-clear-sky
     - pv-system-profiler

setup.py

@@ -26,12 +26,13 @@
     ["git", "tag", "--list", "v*[0-9]", "--sort=version:refname"],
     stdout=subprocess.PIPE,
 )
+tags = git_tags.stdout.read()
+git_tags.stdout.close()
+tags = tags.decode("utf-8").split("\n")
+tags.sort()
 
-gt_decoded = git_tags.communicate()[0].decode()
-tag_list = gt_decoded.split("\n")
-tag_list.sort()
-
-VERSION_FROM_GIT_TAG = tag_list[-1][1:]
+# PEP 440 won't accept the v in front, so here we remove it, strip the new line and decode the byte stream
+VERSION_FROM_GIT_TAG = tags[-1][1:]
 
 with open((here / "requirements.txt"), encoding="utf-8") as f:
     install_requires = f.read().splitlines()

solardatatools/algorithms/shade.py

@@ -315,7 +315,7 @@ def transform_data(self, power=8):
         agg_by_azimuth = pd.DataFrame(
             data=normalized.T,
             index=np.arange(normalized.shape[1]),
-            columns=np.linspace(0, 1, 2 ** power),
+            columns=np.linspace(0, 1, 2**power),
         )
         agg_by_azimuth["delta"] = my_round(
             delta_cooper(self.dh.day_index.dayofyear.values), 1
@@ -399,7 +399,7 @@ def batch_process(data, mask, power=8, scale=None):
     """
     if scale is None:
         scale = 1
-    N = 2 ** power
+    N = 2**power
     output = np.zeros((N, data.shape[1]))
     xs_new = np.linspace(0, 1, N)
     for col_ix in range(data.shape[1]):

solardatatools/circular_statistics.py

@@ -68,7 +68,7 @@ def rayleightest(data, axis=None, weights=None):
         tmp = (
             1.0
             + (2.0 * z - z * z) / (4.0 * n)
-            - (24.0 * z - 132.0 * z ** 2.0 + 76.0 * z ** 3.0 - 9.0 * z ** 4.0)
+            - (24.0 * z - 132.0 * z**2.0 + 76.0 * z**3.0 - 9.0 * z**4.0)
             / (288.0 * n * n)
         )
 

solardatatools/data_quality.py

@@ -9,20 +9,22 @@
 from scipy.stats import mode
 from solardatatools.signal_decompositions import tl1_l2d2p365
 
+
 def make_quality_flags(
     density_scores,
     linearity_scores,
     density_lower_threshold=0.6,
     density_upper_threshold=1.05,
-    linearity_threshold=0.1
+    linearity_threshold=0.1,
 ):
     density_flags = np.logical_and(
         density_scores > density_lower_threshold,
-        density_scores < density_upper_threshold
+        density_scores < density_upper_threshold,
     )
     linearity_flags = linearity_scores < linearity_threshold
     return density_flags, linearity_flags
 
+
 def make_density_scores(
     data_matrix,
     threshold=0.2,
@@ -50,35 +52,37 @@ def make_density_scores(
         out = tuple(out)
     return out
 
+
 def make_linearity_scores(data_matrix, capacity, density_baseline):
     temp_mat = np.copy(data_matrix)
     temp_mat[temp_mat < 0.005 * capacity] = np.nan
     difference_mat = np.round(temp_mat[1:] - temp_mat[:-1], 4)
     modes, counts = mode(difference_mat, axis=0, nan_policy="omit")
     n = data_matrix.shape[0] - 1
-    linearity_scores = counts.data.squeeze() / (
-            n * density_baseline
-    )
+    linearity_scores = counts.data.squeeze() / (n * density_baseline)
     # Label detected infill points with a boolean mask
     infill = np.zeros_like(data_matrix, dtype=bool)
     slct = linearity_scores >= 0.1
     reference_diffs = np.tile(modes[0][slct], (data_matrix.shape[0], 1))
     found_infill = np.logical_or(
         np.isclose(
-            np.r_[np.zeros(data_matrix.shape[1]).reshape((1, -1)),
-                  difference_mat][:, slct],
+            np.r_[np.zeros(data_matrix.shape[1]).reshape((1, -1)), difference_mat][
+                :, slct
+            ],
             reference_diffs,
         ),
         np.isclose(
-            np.r_[difference_mat,
-                  np.zeros(data_matrix.shape[1]).reshape((1, -1))][:, slct],
+            np.r_[difference_mat, np.zeros(data_matrix.shape[1]).reshape((1, -1))][
+                :, slct
+            ],
             reference_diffs,
         ),
     )
     infill[:, slct] = found_infill
     infill_mask = infill
     return linearity_scores, infill_mask
 
+
 def daily_missing_data_simple(data_matrix, threshold=0.2, return_density_signal=False):
     """
     This function takes a PV power data matrix and returns a boolean array,

solardatatools/dataio.py

@@ -53,9 +53,8 @@ def get_pvdaq_data(sysid=2, api_key="DEMO_KEY", year=2011, delim=",", standardiz
     # concatenate the list of yearly data frames
     df = pd.concat(df_list, axis=0, sort=True)
     if standardize:
-        print('\n')
-        df, _ = standardize_time_axis(df, datetimekey='Date-Time',
-                                      timeindex=False)
+        print("\n")
+        df, _ = standardize_time_axis(df, datetimekey="Date-Time", timeindex=False)
     return df
 
 

solardatatools/signal_decompositions.py

@@ -233,7 +233,7 @@ def tl1_l1d1_l2d2p365(
         )
         + c1 * cvx.norm1(cvx.multiply(tv_weights, cvx.diff(s_hat, k=1)))
         + c2 * cvx.norm(cvx.diff(s_seas, k=2))
-        + c3 * beta ** 2
+        + c3 * beta**2
     )
     constraints = [
         signal[use_ixs] == s_hat[use_ixs] + s_seas[:n][use_ixs] + s_error[use_ixs],