diff --git a/.gitignore b/.gitignore
index d8972ad040d4ec91e6ded9970b6c503f21822beb..81acaf9123c7f5b07a2448b785f98ae9feb4d8da 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,7 @@
# Folders not needed for repo (old)
# e.g.: /src
+/.idea/
target/
__manuscripts/
__workbench/
diff --git a/.idea/csv-editor.xml b/.idea/csv-editor.xml
index 67be0495c4daa237825d625d2dab085b01c8824a..4273d501e522189e65f7dba3f6273902375076d9 100644
--- a/.idea/csv-editor.xml
+++ b/.idea/csv-editor.xml
@@ -87,6 +87,13 @@
</Attribute>
</value>
</entry>
+ <entry key="\notebooks\Workbench\CH-OE2_SW_IN_2021.csv">
+ <value>
+ <Attribute>
+ <option name="separator" value=":" />
+ </Attribute>
+ </value>
+ </entry>
</map>
</option>
</component>
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9333f33f83a79f09d650000b6022c26017a27151..a5810a31793ed3d7e45f08cac09cbbf67540a15e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,20 @@
+TODO TimeSeriesSplit like in https://www.youtube.com/watch?v=z3ZnOW-S550
+
+## v0.XX.X | X Oct 2023
+
+- Removed large data files for migration to GitHub
+
+### New features
+
+- Added first code for XGBoost gap-filling, not production-ready yet
+
+### Environment
+
+- Added new package [xgboost](https://xgboost.readthedocs.io/en/stable/#)
+
## v0.63.0 | 5 Oct 2023
### New features
diff --git a/diive/pkgs/gapfilling/randomforest_ts.py b/diive/pkgs/gapfilling/randomforest_ts.py
index fa557fe203ddcf8524ccc32e4d01ff4ee3424504..a2f41d5dccf7257cbea92dabbb83a13dfc068f91 100644
--- a/diive/pkgs/gapfilling/randomforest_ts.py
+++ b/diive/pkgs/gapfilling/randomforest_ts.py
@@ -220,6 +220,8 @@ class RandomForestTS:
was trained on the combined observed + .PREDICTIONS_FULLMODEL data, using
only the timestamp info as features.
"""
+
+ # Args
self.model_df = input_df.copy()
self.target_col = target_col
self.kwargs = kwargs
@@ -1234,11 +1236,11 @@ def example_rfts():
# add_continuous_record_number=False,
add_continuous_record_number=True,
sanitize_timestamp=True,
+ perm_n_repeats=9,
n_estimators=9,
random_state=42,
min_samples_split=2,
min_samples_leaf=1,
- perm_n_repeats=9,
n_jobs=-1
)
rfts.reduce_features()
@@ -1250,8 +1252,8 @@ def example_rfts():
rfts.fillgaps(showplot_scores=True, showplot_importance=True)
rfts.report_gapfilling()
- # observed = df[TARGET_COL]
- # gapfilled = rfts.get_gapfilled_target()
+ observed = df[TARGET_COL]
+ gapfilled = rfts.get_gapfilled_target()
# rfts.feature_importances
# rfts.scores
# rfts.gapfilling_df
@@ -1265,21 +1267,21 @@ def example_rfts():
# # shap.summary_plot(shap_values[0], xtest)
# shap.dependence_plot("Feature 12", shap_values, xtest, interaction_index="Feature 11")
- # # Plot
- # from diive.core.plotting.heatmap_datetime import HeatmapDateTime
- # HeatmapDateTime(series=observed).show()
- # HeatmapDateTime(series=gapfilled).show()
+ # Plot
+ from diive.core.plotting.heatmap_datetime import HeatmapDateTime
+ HeatmapDateTime(series=observed).show()
+ HeatmapDateTime(series=gapfilled).show()
# mds = df_orig['NEE_CUT_REF_f'].copy()
# mds = mds[mds.index.year >= 2016]
- # import matplotlib.pyplot as plt
+ import matplotlib.pyplot as plt
# # rfts.gapfilling_df_['.PREDICTIONS_FALLBACK'].cumsum().plot()
# # rfts.gapfilling_df_['.PREDICTIONS_FULLMODEL'].cumsum().plot()
# # rfts.gapfilling_df_['.PREDICTIONS'].cumsum().plot()
- # rfts.get_gapfilled_target().cumsum().plot()
+ rfts.get_gapfilled_target().cumsum().plot()
# mds.cumsum().plot()
# plt.legend()
- # plt.show()
+ plt.show()
# d = rfts.gapfilling_df['NEE_CUT_REF_orig'] - rfts.gapfilling_df['.PREDICTIONS']
# d.plot()
@@ -1329,7 +1331,7 @@ def example_optimize():
if __name__ == '__main__':
- example_quickfill()
+ # example_quickfill()
# example_longterm_rfts()
- # example_rfts()
+ example_rfts()
# example_optimize()
diff --git a/diive/pkgs/gapfilling/xgboost_ts.py b/diive/pkgs/gapfilling/xgboost_ts.py
new file mode 100644
index 0000000000000000000000000000000000000000..18ab7d346f81971d41bd009dc8b03e5f6197ec75
--- /dev/null
+++ b/diive/pkgs/gapfilling/xgboost_ts.py
@@ -0,0 +1,993 @@
+# TODO generalization bias
+# TODO SHAP values
+# https://pypi.org/project/shap/
+# https://mljar.com/blog/feature-importance-in-random-forest/
+
+"""
+=========================================
+RANDOM FOREST GAP-FILLING FOR TIME SERIES
+randomforest_ts
+=========================================
+
+This module is part of the diive library:
+https://gitlab.ethz.ch/diive/diive
+
+ - Example notebook available in:
+ notebooks/GapFilling/RandomForestGapFilling.ipynb
+
+Kudos, optimization of hyper-parameters, grid search
+- https://scikit-learn.org/stable/modules/grid_search.html
+- https://www.kaggle.com/code/carloscliment/random-forest-regressor-and-gridsearch
+
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import xgboost as xgb
+from pandas import DataFrame
+from sklearn.ensemble import RandomForestRegressor # Import the model we are using
+from sklearn.inspection import permutation_importance
+from sklearn.model_selection import train_test_split
+
+import diive.core.dfun.frames as fr
+from diive.core.ml.common import prediction_scores_regr, plot_prediction_residuals_error_regr
+from diive.core.times.times import TimestampSanitizer
+from diive.core.times.times import include_timestamp_as_cols
+
+pd.set_option('display.max_rows', 50)
+pd.set_option('display.max_columns', 12)
+pd.set_option('display.width', 1000)
+
+
+class XGBoostTS:
+
+ def __init__(
+ self,
+ input_df: DataFrame,
+ target_col: str or tuple,
+ verbose: int = 0,
+ perm_n_repeats: int = 10,
+ test_size: float = 0.25,
+ features_lag: list = None,
+ features_lagmax: int = None,
+ include_timestamp_as_features: bool = False,
+ add_continuous_record_number: bool = False,
+ sanitize_timestamp: bool = False,
+ **kwargs
+ ):
+ """
+ Gap-fill timeseries with predictions from random forest model
+
+ Args:
+ input_df:
+ Contains timeseries of 1 target column and 1+ feature columns.
+
+ target_col:
+ Column name of variable in *input_df* that will be gap-filled.
+
+ perm_n_repeats:
+ Number of repeats for calculating permutation feature importance.
+
+ test_size:
+ Proportion of the dataset to include in the test split,
+ between 0.0 and 1.0.
+
+ features_lag:
+ List of integers (number of records), includes lagged variants of predictors.
+ If features_lag=None, no lagged variants are added.
+ Example:
+ - features_lag=[-2, +2] includes variants that are lagged by -2, -1, +1 and
+ +2 records in the dataset, for each feature already present in the data.
+ For a variable named *TA*, this created the following output:
+ TA = [ 5, 6, 7, 8 ]
+ TA-2 = [NaN, NaN, 5, 6 ]
+ TA-1 = [NaN, 5, 6, 7 ] --> each TA record is paired with the preceding record TA-1
+ TA+1 = [ 6, 7, 8, NaN] --> each TA record is paired with the next record TA+1
+ TA+2 = [ 7, 8, NaN, NaN]
+
+ include_timestamp_as_features:
+ Include timestamp info as integer data: year, season, month, week, doy, hour
+
+ add_continuous_record_number:
+ Add continuous record number as new column
+
+ sanitize_timestamp:
+ Validate and prepare timestamps for further processing
+
+ Attributes:
+ gapfilled_df
+ - .PREDICTIONS_FULLMODEL uses the output from the full RF model where
+ all features where available.
+ - .PREDICTIONS_FALLBACK uses the output from the fallback RF model, which
+ was trained on the combined observed + .PREDICTIONS_FULLMODEL data, using
+ only the timestamp info as features.
+ """
+
+ # Args
+ self.model_df = input_df.copy()
+ self.target_col = target_col
+ self.kwargs = kwargs
+ self.perm_n_repeats = perm_n_repeats
+ self.test_size = test_size
+ self.features_lag = features_lag
+ self.features_lagmax = features_lagmax
+ self.verbose = verbose
+
+ self._check_n_cols()
+
+ if self.features_lag:
+ self.model_df = self._lag_features()
+
+ if include_timestamp_as_features:
+ self.model_df = include_timestamp_as_cols(df=self.model_df, txt="")
+
+ if add_continuous_record_number:
+ self.model_df = fr.add_continuous_record_number(df=self.model_df)
+
+ if sanitize_timestamp:
+ verbose = True if verbose > 0 else False
+ tss = TimestampSanitizer(data=self.model_df, output_middle_timestamp=True, verbose=verbose)
+ self.model_df = tss.get()
+
+ self.random_col = None
+
+ # Attributes
+ self._gapfilling_df = None # Will contain gapfilled target and auxiliary variables
+ self._model = None
+ self._feature_importances = dict()
+ self._feature_importances_traintest = dict()
+ self._feature_importances_reduction = dict()
+ self._scores = dict()
+ self._scores_test = dict()
+ self._accepted_features = []
+ self._rejected_features = []
+
+ def get_gapfilled_target(self):
+ """Gap-filled target time series"""
+ return self.gapfilling_df_[self.target_gapfilled_col]
+
+ def get_flag(self):
+ """Gap-filling flag, where 0=observed, 1=gap-filled, 2=gap-filled with fallback"""
+ return self.gapfilling_df_[self.target_gapfilled_flag_col]
+
+ @property
+ def model_(self) -> RandomForestRegressor:
+ """Return model, trained on test data"""
+ if not self._model:
+ raise Exception(f'Not available: model.')
+ return self._model
+
+ @property
+ def feature_importances_(self) -> DataFrame:
+ """Return feature importance for model used in gap-filling"""
+ if not isinstance(self._feature_importances, DataFrame):
+ raise Exception(f'Not available: feature importances for gap-filling.')
+ return self._feature_importances
+
+ @property
+ def feature_importances_traintest_(self) -> DataFrame:
+ """Return feature importance from model training on training data,
+ with importances calculated using test data (holdout set)"""
+ if not isinstance(self._feature_importances_traintest, DataFrame):
+ raise Exception(f'Not available: feature importances from training & testing.')
+ return self._feature_importances_traintest
+
+ @property
+ def feature_importances_reduction_(self) -> DataFrame:
+ """Return feature importance from feature reduction, model training on training data,
+ with importances calculated using test data (holdout set)"""
+ if not isinstance(self._feature_importances_reduction, DataFrame):
+ raise Exception(f'Not available: feature importances from feature reduction.')
+ return self._feature_importances_reduction
+
+ @property
+ def scores_(self) -> dict:
+ """Return model scores for model used in gap-filling"""
+ if not self._scores:
+ raise Exception(f'Not available: model scores for gap-filling.')
+ return self._scores
+
+ @property
+ def scores_test_(self) -> dict:
+ """Return model scores for model trained on training data,
+ with scores calculated using test data (holdout set)"""
+ if not self._scores_test:
+ raise Exception(f'Not available: model scores for gap-filling.')
+ return self._scores_test
+
+ @property
+ def gapfilling_df_(self) -> DataFrame:
+ """Return gapfilled data and auxiliary variables"""
+ if not isinstance(self._gapfilling_df, DataFrame):
+ raise Exception(f'Gapfilled data not available.')
+ return self._gapfilling_df
+
+ @property
+ def traintest_details_(self) -> dict:
+ """Return details from train/test splits"""
+ if not self._traintest_details:
+ raise Exception(f'Not available: details about training & testing.')
+ return self._traintest_details
+
+ @property
+ def accepted_features_(self) -> list:
+ """Return list of accepted features from feature reduction"""
+ if not self._accepted_features:
+ raise Exception(f'Not available: accepted features from feature reduction.')
+ return self._accepted_features
+
+ @property
+ def rejected_features_(self) -> list:
+ """Return list of rejected features from feature reduction"""
+ if not self._rejected_features:
+ raise Exception(f'Not available: accepted features from feature reduction.')
+ return self._rejected_features
+
+ def reduce_features(self):
+ """Reduce number of features using permutation importance
+
+ A random variable is added to features and the permutation importances
+ are calculated. The permutation importance of the random variable is the
+ benchmark to determine whether a feature is relevant. All features where
+ permutation importance is smaller or equal to the importance of the random
+ variable are rejected.
+ """
+
+ df = self.model_df.copy()
+
+ # Info
+ print(f"Feature reduction ...")
+
+ # Add random variable as feature
+ df, self.random_col = self._add_random_variable(df=df)
+
+ # Data as arrays, y = targets, X = features
+ y, X, X_names, timestamp = fr.convert_to_arrays(
+ df=df, target_col=self.target_col, complete_rows=True)
+
+ # Train and test set
+ X_train, X_test, y_train, y_test = train_test_split(
+ X, y, test_size=self.test_size, random_state=self.kwargs['random_state'])
+
+ # TODO -------------------------------------------------------------------------->
+ # TODO -------------------------------------------------------------------------->
+ # TODO -------------------------------------------------------------------------->
+ from sklearn.model_selection import TimeSeriesSplit
+ splitter = TimeSeriesSplit(n_splits=5)
+
+ from sklearn.model_selection import GridSearchCV
+ params = {
+ 'max_depth': [2, 4, 8],
+ 'n_estimators': [5, 10, 20],
+ 'learning_rate': [0.1, 0.05, 0.01],
+ 'gamma': [0],
+ 'reg_lambda': [0, 1, 5],
+ 'early_stopping_rounds': [10],
+ 'colsample_bytree': [0.3, 0.5, 1],
+ 'scale_pos_weight': [0.1, 1, 3]
+ }
+ grid = GridSearchCV(estimator=xgb.XGBRegressor(subsample=0.1),
+ param_grid=params,
+ scoring='neg_mean_squared_error',
+ cv=TimeSeriesSplit(n_splits=10),
+ n_jobs=-1, verbose=1)
+ grid.fit(X_train, y_train,
+ eval_set=[(X_train, y_train), (X_test, y_test)],
+ verbose=100)
+ grid.best_params_
+
+ # # Plot
+ # fig, axs = plt.subplots(10, 1, figsize=(15, 30), sharex=True)
+ # fold = 0
+ # for train_idx, val_idx in splitter.split(df):
+ # train = df.iloc[train_idx]
+ # test = df.iloc[val_idx]
+ # train[self.target_col].plot(ax=axs[fold], label='Training Set', title=f'Data Train/Test Split Fold {fold}')
+ # test[self.target_col].plot(ax=axs[fold], label='Test Set')
+ # axs[fold].axvline(test.index.min(), color='black', ls='--')
+ # fold += 1
+ # plt.show()
+
+ fold = 0
+ preds = []
+ scores = []
+ df = df.dropna()
+ for train_idx, val_idx in splitter.split(df):
+ train = df.iloc[train_idx]
+ test = df.iloc[val_idx]
+
+ features_list = df.columns.tolist()
+ features_list.remove(self.target_col)
+
+ X_train = train[features_list]
+ y_train = train[self.target_col]
+
+ X_test = test[features_list]
+ y_test = test[self.target_col]
+
+ reg = xgb.XGBRegressor(base_score=0.5, booster='gbtree',
+ n_estimators=1000,
+ early_stopping_rounds=50,
+ objective='reg:squarederror',
+ max_depth=3,
+ learning_rate=0.02)
+ reg.fit(X_train, y_train,
+ early_stopping_rounds=10,
+ eval_set=[(X_train, y_train), (X_test, y_test)],
+ verbose=100)
+
+ y_pred = reg.predict(X_test)
+ preds.append(y_pred)
+ from sklearn.metrics import mean_squared_error
+ score = np.sqrt(mean_squared_error(y_test, y_pred))
+ scores.append(score)
+ fold += 1
+
+ print(f'Score across folds {np.mean(scores):0.4f}')
+ print(f'Fold scores:{scores}')
+
+ # TODO <--------------------------------------------------------------------------
+ # TODO <--------------------------------------------------------------------------
+ # TODO <--------------------------------------------------------------------------
+
+ # Instantiate model with params
+ model = xgb.XGBRegressor(**self.kwargs)
+ # model = RandomForestRegressor(**self.kwargs)
+
+ # Train the model
+ model.fit(X=X_train, y=y_train)
+
+ # # Predict targets in test data
+ # pred_y_test = model.predict(X=X_test)
+
+ # Calculate permutation importance and store in dataframe
+ self._feature_importances_reduction = self._permutation_importance(
+ model=model, X=X_test, y=y_test, X_names=X_names, showplot_importance=False)
+
+ # Threshold for feature acceptance
+ fi_threshold = self.feature_importances_reduction_['PERM_IMPORTANCE'][self.random_col]
+
+ # Get accepted and rejected features
+ fidf_accepted = self.feature_importances_reduction_.loc[
+ self.feature_importances_reduction_['PERM_IMPORTANCE'] > fi_threshold].copy()
+ self._accepted_features = fidf_accepted.index.tolist()
+ fidf_rejected = self.feature_importances_reduction_.loc[
+ self.feature_importances_reduction_['PERM_IMPORTANCE'] <= fi_threshold].copy()
+ self._rejected_features = fidf_rejected.index.tolist()
+
+ # Assemble dataframe for next model
+ usecols = [self.target_col]
+ usecols = usecols + self._accepted_features
+ self.model_df = df[usecols].copy()
+
+ # # This could be a way to combine permutation importance with RFECV,
+ # # but at the time of this writing an import failed (Oct 2023)
+ # # Train model with random variable included, to detect unimportant features
+ # df = df.dropna()
+ # targets = df[self.target_col].copy()
+ # df = df.drop(self.target_col, axis=1, inplace=False)
+ # features = df.copy()
+ # estimator = RandomForestRegressor(**self.kwargs)
+ # splitter = TimeSeriesSplit(n_splits=10)
+ # from eli5.sklearn import PermutationImportance
+ # rfecv = RFECV(estimator=PermutationImportance(estimator, scoring='r2', n_iter=10, random_state=42, cv=splitter),
+ # step=1,
+ # min_features_to_select=3,
+ # cv=splitter,
+ # scoring='r2',
+ # verbose=self.verbose,
+ # n_jobs=-1)
+ # rfecv.fit(features, targets)
+ # # Feature importances
+ # features.drop(features.columns[np.where(rfecv.support_ == False)[0]], axis=1, inplace=True)
+ # rfecv_fi_df = pd.DataFrame()
+ # rfecv_fi_df['FEATURE'] = list(features.columns)
+ # rfecv_fi_df['IMPORTANCE'] = rfecv.estimator_.feature_importances_
+ # rfecv_fi_df = rfecv_fi_df.set_index('FEATURE')
+ # rfecv_fi_df = rfecv_fi_df.sort_values(by='IMPORTANCE', ascending=False)
+ # # rfecv.cv_results_
+ # # rfecv.n_features_
+ # # rfecv.n_features_in_
+ # # rfecv.ranking_
+ # # rfecv.support_
+
+ def trainmodel(self,
+ showplot_scores: bool = True,
+ showplot_importance: bool = True):
+ """
+ Train random forest model for gap-filling
+
+ No gap-filling is done here, only the model is trained.
+
+ Args:
+ showplot_predictions: shows plot of predicted vs observed
+ showplot_importance: shows plot of permutation importances
+ verbose: if > 0 prints more text output
+
+ """
+
+ df = self.model_df.copy()
+
+ # Info
+ idtxt = f"TRAIN & TEST "
+ print(f"Building random forest model based on data between "
+ f"{df.index[0]} and {df.index[-1]} ...")
+
+ # Data as arrays
+ # y = targets, X = features
+ y, X, X_names, timestamp = fr.convert_to_arrays(
+ df=df, target_col=self.target_col, complete_rows=True)
+
+ # Train and test set
+ X_train, X_test, y_train, y_test = train_test_split(
+ X, y, test_size=self.test_size, random_state=self.kwargs['random_state'])
+
+ # Instantiate model with params
+ self._model = xgb.XGBRegressor(**self.kwargs)
+ # self._model = RandomForestRegressor(**self.kwargs)
+
+ # Train the model
+ self._model.fit(X=X_train, y=y_train,
+ eval_set=[(X_train, y_train), (X_test, y_test)],
+ # early_stopping_rounds=50,
+ verbose=1)
+
+ # Predict targets in test data
+ pred_y_test = self._model.predict(X=X_test)
+
+ # Calculate permutation importance and store in dataframe
+ self._feature_importances_traintest = self._permutation_importance(
+ model=self._model, X=X_test, y=y_test, X_names=X_names, showplot_importance=showplot_importance)
+
+ # Stats
+ self._scores_test = prediction_scores_regr(
+ predictions=pred_y_test, targets=y_test, showplot=showplot_scores,
+ infotxt=f"{idtxt} trained on training set, tested on test set")
+
+ if showplot_scores:
+ plot_prediction_residuals_error_regr(
+ model=self._model, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test,
+ infotxt=f"{idtxt} trained on training set, tested on test set")
+
+ # Collect results
+ self._traintest_details = dict(
+ X=X,
+ y=y,
+ timestamp=timestamp,
+ predictions=pred_y_test,
+ X_names=X_names,
+ y_name=self.target_col,
+ X_train=X_train,
+ y_train=y_train,
+ X_test=X_test,
+ y_test=y_test,
+ model=self._model,
+ )
+
+ def fillgaps(self,
+ showplot_scores: bool = True,
+ showplot_importance: bool = True):
+ """
+ Gap-fill data with previously built model
+
+ No new model is built here, instead the last model built in
+ the preceding step .trainmodel() is used.
+
+ y = target
+ X = features
+
+ """
+ self._fillgaps_fullmodel(showplot_scores, showplot_importance)
+ self._fillgaps_fallback()
+ self._fillgaps_combinepredictions()
+
+ def report_feature_reduction(self):
+ """Results from feature reduction"""
+
+ idtxt = "FEATURE REDUCTION"
+
+ fi = self.feature_importances_reduction_
+
+ # TODO hier weiter
+
+ _X_names = [x for x in fi.index if x != self.random_col] # Original features without random variable
+ print(
+ f"\n"
+ f"{'=' * len(idtxt)}\n"
+ f"{idtxt}\n"
+ f"{'=' * len(idtxt)}\n"
+ f"\n"
+ f"- the random variable {self.random_col} was added to the original features, "
+ f"used as benchmark for detecting relevant feature importances\n"
+ f"- target variable: {self.target_col}\n"
+ f"- features before reduction: {fi.index.to_list()}\n"
+ f"- permutation importance was calculated from {self.perm_n_repeats} permutations\n"
+ f"- These results are from feature reduction. Note that feature importances for "
+ f"the final model are calculated during gap-filling.\n"
+ f"\n"
+ f"\n"
+ f"PERMUTATION IMPORTANCE (FULL RESULTS):\n"
+ f"\n"
+ f"{fi}"
+ f"\n"
+ f"\n"
+ f"--> {len(fi.index)} input features, "
+ f"including {self.random_col}: {fi.index.tolist()}\n"
+ f"--> {len(self.accepted_features_)} accepted features, "
+ f"larger than {self.random_col}: {self.accepted_features_}\n"
+ f"--> {len(self.rejected_features_)} rejected features, "
+ f"smaller than or equal to {self.random_col}: {self.rejected_features_}\n"
+ )
+
+ def report_traintest(self):
+ """Results from model training on test data"""
+
+ idtxt = "MODEL TRAINING & TESTING RESULTS"
+
+ results = self.traintest_details_
+ fi = self.feature_importances_traintest_
+
+ test_size_perc = self.test_size * 100
+ training_size_perc = 100 - test_size_perc
+ n_vals_observed = len(results['y'])
+ n_vals_train = len(results['y_train'])
+ n_vals_test = len(results['y_test'])
+ timestamp = results['timestamp']
+ used_features = results['X_names']
+ model = results['model']
+
+ print(
+ f"\n"
+ f"{'=' * len(idtxt)}\n"
+ f"{idtxt}\n"
+ f"{'=' * len(idtxt)}\n"
+ f"\n"
+ f"- the model was trained and tested based on data between "
+ f"{timestamp[0]} and {timestamp[-1]}.\n"
+ f"- in total, {n_vals_observed} observed target values were available for training and testing\n"
+ f"- the dataset was split into training and test datasets\n"
+ f" > the training dataset comprised {n_vals_train} target values ({training_size_perc:.1f}%)\n"
+ f" > the test dataset comprised {n_vals_test} target values ({test_size_perc:.1f}%)\n"
+ f"\n"
+ f"## FEATURE IMPORTANCES\n"
+ f"- feature importances were calculated for test data ({n_vals_test} target values).\n"
+ f"- permutation importances were calculated from {self.perm_n_repeats} repeats."
+ f"\n"
+ f"{fi}"
+ f"\n"
+ f"\n"
+ f"## MODEL\n"
+ f"The model was trained on the training set.\n"
+ f"- estimator: {model}\n"
+ f"- parameters: {model.get_params()}\n"
+ f"- names of features used in model: {used_features}\n"
+ f"- number of features used in model: {len(used_features)}\n"
+ f"\n"
+ f"## MODEL SCORES\n"
+ f"- the model was trained on training data ({n_vals_train} values).\n"
+ f"- the model was tested on test data ({n_vals_test} values).\n"
+ f"- all scores were calculated for test split.\n"
+ f" > MAE: {self.scores_test_['mae']} (mean absolute error)\n"
+ f" > MedAE: {self.scores_test_['medae']} (median absolute error)\n"
+ f" > MSE: {self.scores_test_['mse']} (mean squared error)\n"
+ f" > RMSE: {self.scores_test_['rmse']} (root mean squared error)\n"
+ f" > MAXE: {self.scores_test_['maxe']} (max error)\n"
+ f" > MAPE: {self.scores_test_['mape']:.3f} (mean absolute percentage error)\n"
+ f" > R2: {self.scores_test_['r2']}\n"
+ )
+
+ def report_gapfilling(self):
+ """Results from gap-filling"""
+ # Setup
+ idtxt = "GAP-FILLING RESULTS"
+
+ df = self.gapfilling_df_
+ model = self.model_
+ scores = self.scores_
+ fi = self.feature_importances_
+
+ feature_names = fi.index.to_list()
+ n_features = len(feature_names)
+
+ locs_observed = df[self.target_gapfilled_flag_col] == 0
+ locs_hq = df[self.target_gapfilled_flag_col] == 1
+ locs_observed_missing_fromflag = df[self.target_gapfilled_flag_col] > 0
+ locs_fallback = df[self.target_gapfilled_flag_col] == 2
+
+ n_observed = locs_observed.sum()
+ n_hq = locs_hq.sum()
+ n_observed_missing_fromflag = locs_observed_missing_fromflag.sum()
+ n_available = len(df[self.target_gapfilled_col].dropna())
+ n_potential = len(df.index)
+ n_fallback = locs_fallback.sum()
+ test_size_perc = self.test_size * 100
+
+ print(
+ f"\n"
+ f"{'=' * len(idtxt)}\n"
+ f"{idtxt}\n"
+ f"{'=' * len(idtxt)}\n"
+ f"\n"
+ f"Model scores and feature importances were calculated from high-quality "
+ f"predicted targets ({n_hq} values, {self.target_gapfilled_col} where flag=1) "
+ f"in comparison to observed targets ({n_observed} values, {self.target_col}).\n"
+ f"\n"
+ f"## TARGET\n"
+ f"- first timestamp: {df.index[0]}\n"
+ f"- last timestamp: {df.index[-1]}\n"
+ f"- potential number of values: {n_potential} values)\n"
+ f"- target column (observed): {self.target_col}\n"
+ f"- missing records (observed): {df[self.target_col].isnull().sum()} "
+ f"(cross-check from flag: {n_observed_missing_fromflag})\n"
+ f"- target column (gap-filled): {self.target_gapfilled_col} ({n_available} values)\n"
+ f"- missing records (gap-filled): {df[self.target_gapfilled_col].isnull().sum()}\n"
+ f"- gap-filling flag: {self.target_gapfilled_flag_col}\n"
+ f" > flag 0 ... observed targets ({n_observed} values)\n"
+ f" > flag 1 ... targets gap-filled with high-quality, all features available ({n_hq} values)\n"
+ f" > flag 2 ... targets gap-filled with fallback ({n_fallback} values)\n"
+ f"\n"
+ f"## FEATURE IMPORTANCES\n"
+ f"- names of features used in model: {feature_names}\n"
+ f"- number of features used in model: {n_features}\n"
+ f"- permutation importances were calculated from {self.perm_n_repeats} repeats.\n"
+ f"\n"
+ f"{fi}"
+ f"\n"
+ f"\n"
+ f"## MODEL\n"
+ f"The model was trained on a training set with test size {test_size_perc:.2f}.\n"
+ f"- estimator: {model}\n"
+ f"- parameters: {model.get_params()}\n"
+ f"\n"
+ f"## MODEL SCORES\n"
+ f"- MAE: {scores['mae']} (mean absolute error)\n"
+ f"- MedAE: {scores['medae']} (median absolute error)\n"
+ f"- MSE: {scores['mse']} (mean squared error)\n"
+ f"- RMSE: {scores['rmse']} (root mean squared error)\n"
+ f"- MAXE: {scores['maxe']} (max error)\n"
+ f"- MAPE: {scores['mape']:.3f} (mean absolute percentage error)\n"
+ f"- R2: {scores['r2']}\n"
+ )
+
+ def _permutation_importance(self, model, X, y, X_names, showplot_importance) -> DataFrame:
+ """Calculate permutation importance"""
+ # https://scikit-learn.org/stable/modules/permutation_importance.html#permutation-feature-importance
+ fi = permutation_importance(estimator=model,
+ X=X, y=y,
+ n_repeats=self.perm_n_repeats,
+ random_state=42,
+ scoring='r2',
+ n_jobs=-1)
+
+ # Store permutation importance
+ fidf = pd.DataFrame({'PERM_IMPORTANCE': fi.importances_mean,
+ 'PERM_SD': fi.importances_std},
+ index=X_names)
+
+ fidf = fidf.sort_values(by='PERM_IMPORTANCE', ascending=False)
+
+ if showplot_importance:
+ fig, axs = plt.subplots(ncols=1, figsize=(9, 16))
+ _fidf = fidf.copy().sort_values(by='PERM_IMPORTANCE', ascending=True)
+ _fidf['PERM_IMPORTANCE'].plot.barh(color='#008bfb', yerr=_fidf['PERM_SD'], ax=axs)
+ axs.set_xlabel("Feature importance")
+ axs.set_ylabel("Feature")
+ axs.set_title(f"Permutation importance ({self.perm_n_repeats} permutations)")
+ axs.legend(loc='lower right')
+ fig.tight_layout()
+ fig.show()
+
+ return fidf
+
+ def _add_random_variable(self, df: DataFrame) -> tuple[DataFrame, str]:
+ # Add random variable as benchmark for relevant feature importances
+ random_col = '.RANDOM' # Random variable as benchmark for relevant importances
+ df[random_col] = np.random.RandomState(self.kwargs['random_state']).randn(df.shape[0], 1)
+ # df[random_col] = np.random.rand(df.shape[0], 1)
+ return df, random_col
+
+ def _lag_features(self):
+ """Add lagged variants of variables as new features"""
+ return fr.lagged_variants(df=self.model_df,
+ stepsize=1,
+ lag=self.features_lag,
+ exclude_cols=[self.target_col])
+
+ def _check_n_cols(self):
+ """Check number of columns"""
+ if len(self.model_df.columns) == 1:
+ raise Exception(f"(!) Stopping execution because dataset comprises "
+ f"only one single column : {self.model_df.columns}")
+
+ def _fillgaps_fullmodel(self, showplot_scores, showplot_importance):
+ """Apply model to fill missing targets for records where all features are available
+ (high-quality gap-filling)"""
+
+ # Original input data, contains target and features
+ # This dataframe has the full timestamp
+ df = self.model_df.copy()
+
+ # Test how the model performs with all y data
+ # Since the model was previously trained on test data,
+ # here it is checked how well the model performs when
+ # predicting all available y data.
+ # This is needed to calculate feature importance and scores.
+ y, X, X_names, timestamp = fr.convert_to_arrays(
+ df=df, target_col=self.target_col, complete_rows=True)
+
+ # Predict all targets (no test split)
+ pred_y = self.model_.predict(X=X)
+
+ # Calculate permutation importance and store in dataframe
+ self._feature_importances = self._permutation_importance(
+ model=self._model, X=X, y=y, X_names=X_names, showplot_importance=showplot_importance)
+
+ # Model scores, using all targets
+ self._scores = prediction_scores_regr(predictions=pred_y,
+ targets=y,
+ infotxt="trained on training set, "
+ "tested on full set",
+ showplot=showplot_scores)
+
+ # In the next step, all available features are used to
+ # predict the target for records where all features are available.
+ # Feature data for records where all features are available:
+ features_df = df.drop(self.target_col, axis=1) # Remove target data
+ features_df = features_df.dropna() # Keep rows where all features available
+ X = features_df.to_numpy() # Features are needed as numpy array
+ feature_names = features_df.columns.tolist()
+
+ # Predict targets for all records where all features are available
+ pred_y = self.model_.predict(X=X)
+
+ # Collect gapfilling results in df
+ # Define column names for gapfilled_df
+ self._define_cols()
+
+ # Collect predictions in dataframe
+ self._gapfilling_df = pd.DataFrame(data={self.pred_fullmodel_col: pred_y}, index=features_df.index)
+
+ # Add target to dataframe
+ self._gapfilling_df[self.target_col] = df[self.target_col].copy()
+
+ # Gap locations
+ # Make column that contains predicted values
+ # for rows where target is missing
+ _gap_locs = self._gapfilling_df[self.target_col].isnull() # Locations where target is missing
+ self._gapfilling_df[self.pred_gaps_col] = self._gapfilling_df.loc[
+ _gap_locs, self.pred_fullmodel_col]
+
+ # Flag
+ # Make flag column that indicates where predictions for
+ # missing targets are available, where 0=observed, 1=gapfilled
+ # todo Note that missing predicted gaps = 0. change?
+ _gapfilled_locs = self._gapfilling_df[self.pred_gaps_col].isnull() # Non-gapfilled locations
+ _gapfilled_locs = ~_gapfilled_locs # Inverse for gapfilled locations
+ self._gapfilling_df[self.target_gapfilled_flag_col] = _gapfilled_locs
+ self._gapfilling_df[self.target_gapfilled_flag_col] = self._gapfilling_df[
+ self.target_gapfilled_flag_col].astype(
+ int)
+
+ # Gap-filled time series
+ # Fill missing records in target with predicions
+ self._gapfilling_df[self.target_gapfilled_col] = \
+ self._gapfilling_df[self.target_col].fillna(self._gapfilling_df[self.pred_fullmodel_col])
+
+ # Restore original full timestamp
+ self._gapfilling_df = self._gapfilling_df.reindex(df.index)
+
+ # SHAP values
+ # https://pypi.org/project/shap/
+ # https://mljar.com/blog/feature-importance-in-random-forest/
+
+ def _fillgaps_fallback(self):
+
+ # Fallback gapfilling
+ # Fill still existing gaps in full timestamp data
+ # Build fallback model exclusively from timestamp features.
+ # Here, the model is trained on the already gapfilled time series,
+ # using info from the timestamp, e.g. DOY
+ _still_missing_locs = self._gapfilling_df[self.target_gapfilled_col].isnull()
+ _num_still_missing = _still_missing_locs.sum() # Count number of still-missing values
+ if _num_still_missing > 0:
+
+ fallback_predictions, \
+ fallback_timestamp = \
+ self._predict_fallback(series=self._gapfilling_df[self.target_gapfilled_col])
+
+ fallback_series = pd.Series(data=fallback_predictions, index=fallback_timestamp)
+ self._gapfilling_df[self.pred_fallback_col] = fallback_series
+ self._gapfilling_df[self.target_gapfilled_col] = \
+ self._gapfilling_df[self.target_gapfilled_col].fillna(fallback_series)
+
+ self._gapfilling_df.loc[_still_missing_locs, self.target_gapfilled_flag_col] = 2 # Adjust flag, 2=fallback
+ else:
+ self._gapfilling_df[self.pred_fallback_col] = None
+
+ # Cumulative
+ self._gapfilling_df[self.target_gapfilled_cumu_col] = \
+ self._gapfilling_df[self.target_gapfilled_col].cumsum()
+
+ def _fillgaps_combinepredictions(self):
+ """Combine predictions of full model with fallback predictions"""
+ # First add predictions from full model
+ self._gapfilling_df[self.pred_col] = self._gapfilling_df[self.pred_fullmodel_col].copy()
+ # Then fill remaining gaps with predictions from fallback model
+ self._gapfilling_df[self.pred_col] = (
+ self._gapfilling_df[self.pred_col].fillna(self._gapfilling_df[self.pred_fallback_col]))
+
+ def _predict_fallback(self, series: pd.Series):
+ """Fill data gaps using timestamp features only, fallback for still existing gaps"""
+ gf_fallback_df = pd.DataFrame(series)
+ gf_fallback_df = include_timestamp_as_cols(df=gf_fallback_df, txt="(ONLY FALLBACK)")
+
+ # Build model for target predictions *from timestamp*
+ y_fallback, X_fallback, _, _ = \
+ fr.convert_to_arrays(df=gf_fallback_df,
+ target_col=self.target_gapfilled_col,
+ complete_rows=True)
+
+ # Instantiate new model with same params as before
+ self.kwargs['early_stopping_rounds'] = 0
+ model_fallback = xgb.XGBRegressor(**self.kwargs)
+ # model_fallback = RandomForestRegressor(**self.kwargs)
+
+ # Train the model on all available records ...
+ model_fallback.fit(X=X_fallback, y=y_fallback)
+
+ # ... and use it to predict all records for full timestamp
+ full_timestamp_df = gf_fallback_df.drop(self.target_gapfilled_col, axis=1) # Remove target data
+ X_fallback_full = full_timestamp_df.to_numpy() # Features are needed as numpy array
+ pred_y_fallback = model_fallback.predict(X=X_fallback_full) # Predict targets in test data
+ full_timestamp = full_timestamp_df.index
+
+ return pred_y_fallback, full_timestamp
+
+ def _results(self, gapfilled_df, most_important_df, model_r2, still_missing_locs):
+ """Summarize gap-filling results"""
+
+ _vals_max = len(gapfilled_df.index)
+ _vals_before = len(gapfilled_df[self.target_col].dropna())
+ _vals_after = len(gapfilled_df[self.target_gapfilled_col].dropna())
+ _vals_fallback_filled = still_missing_locs.sum()
+ _perc_fallback_filled = (_vals_fallback_filled / _vals_max) * 100
+
+ print(f"Gap-filling results for {self.target_col}\n"
+ f"max possible: {_vals_max} values\n"
+ f"before gap-filling: {_vals_before} values\n"
+ f"after gap-filling: {_vals_after} values\n"
+ f"gap-filled with fallback: {_vals_fallback_filled} values / {_perc_fallback_filled:.1f}%\n"
+ f"used features:\n{most_important_df}\n"
+ f"predictions vs targets, R2 = {model_r2:.3f}")
+
+ def _define_cols(self):
+ self.pred_col = ".PREDICTIONS"
+ self.pred_fullmodel_col = ".PREDICTIONS_FULLMODEL"
+ self.pred_fallback_col = ".PREDICTIONS_FALLBACK"
+ self.pred_gaps_col = ".GAP_PREDICTIONS"
+ self.target_gapfilled_col = f"{self.target_col}_gfRF"
+ self.target_gapfilled_flag_col = f"FLAG_{self.target_gapfilled_col}_ISFILLED" # "[0=measured]"
+ self.target_gapfilled_cumu_col = ".GAPFILLED_CUMULATIVE"
+
+
+def example_rfts():
+ # Setup, user settings
+ # TARGET_COL = 'LE_orig'
+ TARGET_COL = 'NEE_CUT_REF_orig'
+ subsetcols = [TARGET_COL, 'Tair_f', 'VPD_f', 'Rg_f']
+
+ # from datetime import datetime
+ # dt_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+ # print(f"This page was last modified on: {dt_string}")
+ # import importlib.metadata
+ # version_diive = importlib.metadata.version("diive")
+ # print(f"diive version: v{version_diive}")
+
+ # # Show docstring for QuickFillRFTS
+ # print(RandomForestTS.__name__)
+ # print(RandomForestTS.__doc__)
+
+ # Example data
+ from diive.configs.exampledata import load_exampledata_parquet
+ df_orig = load_exampledata_parquet()
+
+ # # Create a large gap
+ # remove = df.index.year != 2014
+ # # df = df.drop(df.index[100:2200])
+ # df = df[remove].copy()
+
+ # Subset
+ keep = df_orig.index.year <= 2014
+ df = df_orig[keep].copy()
+ # df = df_orig.copy()
+
+ # Subset with target and features
+ # Only High-quality (QCF=0) measured NEE used for model training in this example
+ lowquality = df["QCF_NEE"] > 0
+ df.loc[lowquality, TARGET_COL] = np.nan
+ df = df[subsetcols].copy()
+
+ # Time series stats
+ # from diive.core.dfun.stats import sstats
+ # statsdf = sstats(df[TARGET_COL])
+ # print(statsdf)
+
+ # from diive.core.plotting.timeseries import TimeSeries # For simple (interactive) time series plotting
+ # TimeSeries(series=df[TARGET_COL]).plot()
+
+ # XGBoost
+ # https://xgboost.readthedocs.io/en/stable/parameter.html#parameters-for-tree-booster
+ xts = XGBoostTS(
+ input_df=df,
+ target_col=TARGET_COL,
+ verbose=1,
+ # features_lag=None,
+ features_lag=[-1, -1],
+ # include_timestamp_as_features=False,
+ include_timestamp_as_features=True,
+ # add_continuous_record_number=False,
+ add_continuous_record_number=True,
+ sanitize_timestamp=True,
+ perm_n_repeats=9,
+ n_estimators=99,
+ random_state=42,
+ early_stopping_rounds=5,
+ max_depth=10,
+ learning_rate=0.05,
+ colsample_bytree=1,
+ n_jobs=-1
+ )
+ xts.reduce_features()
+ xts.report_feature_reduction()
+
+ xts.trainmodel(showplot_scores=False, showplot_importance=False)
+ xts.report_traintest()
+
+ xts.fillgaps(showplot_scores=False, showplot_importance=False)
+ xts.report_gapfilling()
+
+ observed = df[TARGET_COL]
+ gapfilled = xts.get_gapfilled_target()
+ # rfts.feature_importances
+ # rfts.scores
+ # rfts.gapfilling_df
+
+ # # https://www.datacamp.com/tutorial/introduction-to-shap-values-machine-learning-interpretability
+ # import shap
+ # explainer = shap.TreeExplainer(rfts.model_)
+ # xtest = rfts.traintest_details_['X_test']
+ # shap_values = explainer.shap_values(xtest)
+ # shap.summary_plot(shap_values, xtest)
+ # # shap.summary_plot(shap_values[0], xtest)
+ # shap.dependence_plot("Feature 12", shap_values, xtest, interaction_index="Feature 11")
+
+ # # Plot
+ # from diive.core.plotting.heatmap_datetime import HeatmapDateTime
+ # HeatmapDateTime(series=observed).show()
+ # HeatmapDateTime(series=gapfilled).show()
+
+ # mds = df_orig['NEE_CUT_REF_f'].copy()
+ # mds = mds[mds.index.year >= 2016]
+ import matplotlib.pyplot as plt
+ # # rfts.gapfilling_df_['.PREDICTIONS_FALLBACK'].cumsum().plot()
+ # # rfts.gapfilling_df_['.PREDICTIONS_FULLMODEL'].cumsum().plot()
+ # # rfts.gapfilling_df_['.PREDICTIONS'].cumsum().plot()
+ xts.get_gapfilled_target().cumsum().plot()
+ # mds.cumsum().plot()
+ # plt.legend()
+ plt.show()
+
+ # d = rfts.gapfilling_df['NEE_CUT_REF_orig'] - rfts.gapfilling_df['.PREDICTIONS']
+ # d.plot()
+ # plt.show()
+ # d = abs(d)
+ # d.mean() # MAE
+
+ print("Finished.")
+
+
+if __name__ == '__main__':
+ # example_quickfill()
+ # example_longterm_rfts()
+ example_rfts()
+ # example_optimize()
diff --git a/notebooks/Workbench/CH-OE2_SW_IN_2021.csv b/notebooks/Workbench/CH-OE2_SW_IN_2021.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7cef1ae17c3b1f73dee169e189803ec1752157cf
--- /dev/null
+++ b/notebooks/Workbench/CH-OE2_SW_IN_2021.csv
@@ -0,0 +1,17519 @@
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diff --git a/notebooks/Workbench/DownloadMeteoScreenedVariables.ipynb b/notebooks/Workbench/DownloadMeteoScreenedVariables.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1349438e2a0f7fbe6fc659cd70f970d17c2fcfdf
--- /dev/null
+++ b/notebooks/Workbench/DownloadMeteoScreenedVariables.ipynb
@@ -0,0 +1,1014 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2cb69589-01ea-4fa7-b60d-00c7d87ecc42",
+ "metadata": {},
+ "source": [
+ "<span style='background:#DCEDC8; font-size:30px; display:block; border-left:40px solid #C5E1A5; padding-left:40px;'>\n",
+ "<b>\n",
+ " Download multiple meteoscreening variables\n",
+ "</b>\n",
+ "</span>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b302c76-a358-42e4-86f1-605239b7ba3a",
+ "metadata": {},
+ "source": [
+ "**notebook version**: `1.1` (26 Apr 2023) \n",
+ "**new in this version**: added check if downloaded data is indeed in 30MIN time resolution\n",
+ "\n",
+ "- This notebook can be used to download data from the database `InfluxDB`\n",
+ "- Specify what you want to download in the `User settings`\n",
+ "- Data are stored to a `.csv` file in this folder"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "169d1235-b429-46f9-bc7a-ab0a1c56fa50",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# **User settings** (please adjust)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f07d3540-dcd2-43f1-bfaa-b8a952ff4d07",
+ "metadata": {},
+ "source": [
+ "## Site"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "3a121390-4cd1-415c-9327-fe70a0bf957f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "SITE='ch-oe2'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "56b92b29-059d-4baf-8666-448992dd13b5",
+ "metadata": {},
+ "source": [
+ "## Variables to download\n",
+ "- Specify variables as shown in the database"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "b084d444-2cfd-41b1-a12d-c874312d0e8f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Variable names\n",
+ "# Variables are called FIELDS in the database; InfluxDB stores variable names as '_field'\n",
+ "FIELDS = ['SW_IN_M1_1_1'] \n",
+ "\n",
+ "# Measurement names\n",
+ "# used to group similar variable together, e.g., 'TA' contains all air temperature variables\n",
+ "MEASUREMENTS = ['SW']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2280f041-9ab0-463e-ad61-ec224699eda6",
+ "metadata": {},
+ "source": [
+ "## Time range to download\n",
+ "- `START`: Download data starting with this date (the start date itself IS included)\n",
+ "- `STOP`: Download data before this date (the stop date itself IS NOT included)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "64ac9ce0-e206-41c8-a9c4-b9b38db3e676",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "START = '2021-01-01 00:00:01' \n",
+ "STOP = '2022-01-01 00:00:01'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da135ba6-a468-4352-8543-9fb5f3d7620d",
+ "metadata": {},
+ "source": [
+ "## Output file\n",
+ "- Name of the output file that will contain the downloaded data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "398d0fc1-04de-4908-b528-b6fe668f2889",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "OUTFILE = f\"CH-OE2_SW_IN_2021.csv\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "01673648-2743-4026-9699-ef921ca8cac7",
+ "metadata": {},
+ "source": [
+ "# **Auto-settings**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ad1202e4-be6b-49db-824e-4adb164ab637",
+ "metadata": {},
+ "source": [
+ "## Data settings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "d0aaaf11-fd0c-47fe-a869-79ca6e4507b1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DIRCONF = r'P:\\Flux\\RDS_calculations\\_scripts\\_configs\\configs' # Folder with configuration files: needed e.g. for connection to database\n",
+ "DATA_VERSION = 'meteoscreening'\n",
+ "TIMEZONE_OFFSET_TO_UTC_HOURS = 1 # Timezone, e.g. \"1\" is translated to timezone \"UTC+01:00\" (CET, winter time)\n",
+ "REQUIRED_TIME_RESOLUTION = '30T' # 30MIN time resolution"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "acd7176f-9419-40c0-8028-6b94929c1937",
+ "metadata": {},
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "335cc79e-7c01-4dfe-8f72-b2406b8ea136",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\holukas\\AppData\\Local\\Temp\\ipykernel_14192\\2644345916.py:2: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n",
+ " import pkg_resources\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<style>\n",
+ " .bk-notebook-logo {\n",
+ " display: block;\n",
+ " width: 20px;\n",
+ " height: 20px;\n",
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+ " }\n",
+ " </style>\n",
+ " <div>\n",
+ " <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
+ " <span id=\"ffc7fcab-8e23-4b50-9b21-8c7d2830f82d\">Loading BokehJS ...</span>\n",
+ " </div>\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "(function(root) {\n",
+ " function now() {\n",
+ " return new Date();\n",
+ " }\n",
+ "\n",
+ " const force = true;\n",
+ "\n",
+ " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+ " root._bokeh_onload_callbacks = [];\n",
+ " root._bokeh_is_loading = undefined;\n",
+ " }\n",
+ "\n",
+ "const JS_MIME_TYPE = 'application/javascript';\n",
+ " const HTML_MIME_TYPE = 'text/html';\n",
+ " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+ " const CLASS_NAME = 'output_bokeh rendered_html';\n",
+ "\n",
+ " /**\n",
+ " * Render data to the DOM node\n",
+ " */\n",
+ " function render(props, node) {\n",
+ " const script = document.createElement(\"script\");\n",
+ " node.appendChild(script);\n",
+ " }\n",
+ "\n",
+ " /**\n",
+ " * Handle when an output is cleared or removed\n",
+ " */\n",
+ " function handleClearOutput(event, handle) {\n",
+ " const cell = handle.cell;\n",
+ "\n",
+ " const id = cell.output_area._bokeh_element_id;\n",
+ " const server_id = cell.output_area._bokeh_server_id;\n",
+ " // Clean up Bokeh references\n",
+ " if (id != null && id in Bokeh.index) {\n",
+ " Bokeh.index[id].model.document.clear();\n",
+ " delete Bokeh.index[id];\n",
+ " }\n",
+ "\n",
+ " if (server_id !== undefined) {\n",
+ " // Clean up Bokeh references\n",
+ " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+ " cell.notebook.kernel.execute(cmd_clean, {\n",
+ " iopub: {\n",
+ " output: function(msg) {\n",
+ " const id = msg.content.text.trim();\n",
+ " if (id in Bokeh.index) {\n",
+ " Bokeh.index[id].model.document.clear();\n",
+ " delete Bokeh.index[id];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " });\n",
+ " // Destroy server and session\n",
+ " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+ " cell.notebook.kernel.execute(cmd_destroy);\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " /**\n",
+ " * Handle when a new output is added\n",
+ " */\n",
+ " function handleAddOutput(event, handle) {\n",
+ " const output_area = handle.output_area;\n",
+ " const output = handle.output;\n",
+ "\n",
+ " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+ " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+ " return\n",
+ " }\n",
+ "\n",
+ " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+ "\n",
+ " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+ " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+ " // store reference to embed id on output_area\n",
+ " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+ " }\n",
+ " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+ " const bk_div = document.createElement(\"div\");\n",
+ " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+ " const script_attrs = bk_div.children[0].attributes;\n",
+ " for (let i = 0; i < script_attrs.length; i++) {\n",
+ " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+ " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+ " }\n",
+ " // store reference to server id on output_area\n",
+ " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function register_renderer(events, OutputArea) {\n",
+ "\n",
+ " function append_mime(data, metadata, element) {\n",
+ " // create a DOM node to render to\n",
+ " const toinsert = this.create_output_subarea(\n",
+ " metadata,\n",
+ " CLASS_NAME,\n",
+ " EXEC_MIME_TYPE\n",
+ " );\n",
+ " this.keyboard_manager.register_events(toinsert);\n",
+ " // Render to node\n",
+ " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+ " render(props, toinsert[toinsert.length - 1]);\n",
+ " element.append(toinsert);\n",
+ " return toinsert\n",
+ " }\n",
+ "\n",
+ " /* Handle when an output is cleared or removed */\n",
+ " events.on('clear_output.CodeCell', handleClearOutput);\n",
+ " events.on('delete.Cell', handleClearOutput);\n",
+ "\n",
+ " /* Handle when a new output is added */\n",
+ " events.on('output_added.OutputArea', handleAddOutput);\n",
+ "\n",
+ " /**\n",
+ " * Register the mime type and append_mime function with output_area\n",
+ " */\n",
+ " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+ " /* Is output safe? */\n",
+ " safe: true,\n",
+ " /* Index of renderer in `output_area.display_order` */\n",
+ " index: 0\n",
+ " });\n",
+ " }\n",
+ "\n",
+ " // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+ " if (root.Jupyter !== undefined) {\n",
+ " const events = require('base/js/events');\n",
+ " const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+ "\n",
+ " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+ " register_renderer(events, OutputArea);\n",
+ " }\n",
+ " }\n",
+ " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+ " root._bokeh_timeout = Date.now() + 5000;\n",
+ " root._bokeh_failed_load = false;\n",
+ " }\n",
+ "\n",
+ " const NB_LOAD_WARNING = {'data': {'text/html':\n",
+ " \"<div style='background-color: #fdd'>\\n\"+\n",
+ " \"<p>\\n\"+\n",
+ " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+ " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+ " \"</p>\\n\"+\n",
+ " \"<ul>\\n\"+\n",
+ " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
+ " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
+ " \"</ul>\\n\"+\n",
+ " \"<code>\\n\"+\n",
+ " \"from bokeh.resources import INLINE\\n\"+\n",
+ " \"output_notebook(resources=INLINE)\\n\"+\n",
+ " \"</code>\\n\"+\n",
+ " \"</div>\"}};\n",
+ "\n",
+ " function display_loaded() {\n",
+ " const el = document.getElementById(\"ffc7fcab-8e23-4b50-9b21-8c7d2830f82d\");\n",
+ " if (el != null) {\n",
+ " el.textContent = \"BokehJS is loading...\";\n",
+ " }\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " if (el != null) {\n",
+ " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+ " }\n",
+ " } else if (Date.now() < root._bokeh_timeout) {\n",
+ " setTimeout(display_loaded, 100)\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function run_callbacks() {\n",
+ " try {\n",
+ " root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+ " if (callback != null)\n",
+ " callback();\n",
+ " });\n",
+ " } finally {\n",
+ " delete root._bokeh_onload_callbacks\n",
+ " }\n",
+ " console.debug(\"Bokeh: all callbacks have finished\");\n",
+ " }\n",
+ "\n",
+ " function load_libs(css_urls, js_urls, callback) {\n",
+ " if (css_urls == null) css_urls = [];\n",
+ " if (js_urls == null) js_urls = [];\n",
+ "\n",
+ " root._bokeh_onload_callbacks.push(callback);\n",
+ " if (root._bokeh_is_loading > 0) {\n",
+ " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+ " return null;\n",
+ " }\n",
+ " if (js_urls == null || js_urls.length === 0) {\n",
+ " run_callbacks();\n",
+ " return null;\n",
+ " }\n",
+ " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+ " root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+ "\n",
+ " function on_load() {\n",
+ " root._bokeh_is_loading--;\n",
+ " if (root._bokeh_is_loading === 0) {\n",
+ " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+ " run_callbacks()\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function on_error(url) {\n",
+ " console.error(\"failed to load \" + url);\n",
+ " }\n",
+ "\n",
+ " for (let i = 0; i < css_urls.length; i++) {\n",
+ " const url = css_urls[i];\n",
+ " const element = document.createElement(\"link\");\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error.bind(null, url);\n",
+ " element.rel = \"stylesheet\";\n",
+ " element.type = \"text/css\";\n",
+ " element.href = url;\n",
+ " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " for (let i = 0; i < js_urls.length; i++) {\n",
+ " const url = js_urls[i];\n",
+ " const element = document.createElement('script');\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error.bind(null, url);\n",
+ " element.async = false;\n",
+ " element.src = url;\n",
+ " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+ " document.head.appendChild(element);\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " function inject_raw_css(css) {\n",
+ " const element = document.createElement(\"style\");\n",
+ " element.appendChild(document.createTextNode(css));\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n",
+ " const css_urls = [];\n",
+ "\n",
+ " const inline_js = [ function(Bokeh) {\n",
+ " Bokeh.set_log_level(\"info\");\n",
+ " },\n",
+ "function(Bokeh) {\n",
+ " }\n",
+ " ];\n",
+ "\n",
+ " function run_inline_js() {\n",
+ " if (root.Bokeh !== undefined || force === true) {\n",
+ " for (let i = 0; i < inline_js.length; i++) {\n",
+ " inline_js[i].call(root, root.Bokeh);\n",
+ " }\n",
+ "if (force === true) {\n",
+ " display_loaded();\n",
+ " }} else if (Date.now() < root._bokeh_timeout) {\n",
+ " setTimeout(run_inline_js, 100);\n",
+ " } else if (!root._bokeh_failed_load) {\n",
+ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+ " root._bokeh_failed_load = true;\n",
+ " } else if (force !== true) {\n",
+ " const cell = $(document.getElementById(\"ffc7fcab-8e23-4b50-9b21-8c7d2830f82d\")).parents('.cell').data().cell;\n",
+ " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " if (root._bokeh_is_loading === 0) {\n",
+ " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+ " run_inline_js();\n",
+ " } else {\n",
+ " load_libs(css_urls, js_urls, function() {\n",
+ " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+ " run_inline_js();\n",
+ " });\n",
+ " }\n",
+ "}(window));"
+ ],
+ "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"ffc7fcab-8e23-4b50-9b21-8c7d2830f82d\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.2.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"ffc7fcab-8e23-4b50-9b21-8c7d2830f82d\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "import pkg_resources\n",
+ "import matplotlib\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.gridspec as gridspec\n",
+ "%matplotlib inline\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "sns.set_theme('notebook')\n",
+ "\n",
+ "from dbc_influxdb import dbcInflux\n",
+ "from diive.core.plotting.heatmap_datetime import HeatmapDateTime\n",
+ "from diive.core.times.times import DetectFrequency"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "7668024e-c650-40c1-bba8-8fc08824525d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "This page was last modified on: 2023-10-05 13:55:08\n",
+ "diive version: v0.63.0\n",
+ "dbc-influxdb version: v0.10.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "dt_string = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
+ "print(f\"This page was last modified on: {dt_string}\")\n",
+ "version_diive = pkg_resources.get_distribution(\"diive\").version\n",
+ "print(f\"diive version: v{version_diive}\")\n",
+ "version_dbc_influxdb = pkg_resources.get_distribution(\"dbc_influxdb\").version\n",
+ "print(f\"dbc-influxdb version: v{version_dbc_influxdb}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fffb17f5-5d53-4ffa-b896-00fcd17a998e",
+ "metadata": {},
+ "source": [
+ "## Connect to database"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "f38a21dd-4e66-4830-82f4-e31439786fd2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Reading configuration files was successful.\n",
+ "Connection to database works.\n"
+ ]
+ }
+ ],
+ "source": [
+ "dbc = dbcInflux(dirconf=DIRCONF)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3a5b0943-54e8-4825-82f4-067190287f7f",
+ "metadata": {},
+ "source": [
+ "# **Download**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "7ae84d33-62ec-4b4f-8898-008015370784",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading from bucket ch-oe2_processing:\n",
+ " variables ['SW_IN_M1_1_1'] from measurements ['SW']\n",
+ " between 2021-01-01 00:00:01 and 2022-01-01 00:00:01\n",
+ " in data version meteoscreening\n",
+ " with timezone offset to UTC of 1\n",
+ "Downloaded data for 1 variables:\n",
+ " SW_IN_M1_1_1 (17518 records) first date: 2021-01-01 00:30:00 last date: 2022-01-01 00:00:00\n",
+ "========================================\n",
+ "Fields in measurement SW of bucket ch-oe2_processing:\n",
+ "#1 ch-oe2_processing SW NIGHT\n",
+ "#2 ch-oe2_processing SW SW_IN_ERA\n",
+ "#3 ch-oe2_processing SW SW_IN_F\n",
+ "#4 ch-oe2_processing SW SW_IN_F_MDS\n",
+ "#5 ch-oe2_processing SW SW_IN_F_MDS_QC\n",
+ "#6 ch-oe2_processing SW SW_IN_F_QC\n",
+ "#7 ch-oe2_processing SW SW_IN_M1_1_1\n",
+ "#8 ch-oe2_processing SW SW_IN_M1_1_2\n",
+ "#9 ch-oe2_processing SW SW_IN_POT\n",
+ "Found 9 fields in measurement SW of bucket ch-oe2_processing.\n",
+ "========================================\n",
+ "CPU times: total: 1.41 s\n",
+ "Wall time: 1.57 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "data_simple, data_detailed, assigned_measurements = dbc.download(bucket=f'{SITE}_processing',\n",
+ " measurements=MEASUREMENTS,\n",
+ " fields=FIELDS,\n",
+ " start=START,\n",
+ " stop=STOP,\n",
+ " timezone_offset_to_utc_hours=TIMEZONE_OFFSET_TO_UTC_HOURS,\n",
+ " data_version=DATA_VERSION)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "45e05d39-2c46-47fa-b597-1dd27e5f6cbf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>SW_IN_M1_1_1</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>TIMESTAMP_END</th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2021-01-01 00:30:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2021-01-01 01:00:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2021-01-01 01:30:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2021-01-01 02:00:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2021-01-01 02:30:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2021-12-31 22:00:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2021-12-31 22:30:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2021-12-31 23:00:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2021-12-31 23:30:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2022-01-01 00:00:00</th>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>17518 rows × 1 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " SW_IN_M1_1_1\n",
+ "TIMESTAMP_END \n",
+ "2021-01-01 00:30:00 0.0\n",
+ "2021-01-01 01:00:00 0.0\n",
+ "2021-01-01 01:30:00 0.0\n",
+ "2021-01-01 02:00:00 0.0\n",
+ "2021-01-01 02:30:00 0.0\n",
+ "... ...\n",
+ "2021-12-31 22:00:00 0.0\n",
+ "2021-12-31 22:30:00 0.0\n",
+ "2021-12-31 23:00:00 0.0\n",
+ "2021-12-31 23:30:00 0.0\n",
+ "2022-01-01 00:00:00 0.0\n",
+ "\n",
+ "[17518 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_simple"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ac8f44ed-8c9c-4bf2-93f7-f8b956fda3c9",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Check time resolution of downloaded data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a7a1949c-3e18-47bc-95d1-9d2aea414b61",
+ "metadata": {},
+ "source": [
+ "<span style='background:#FFF8E1; font-size:14px;'>\n",
+ " new in notebook v1.1\n",
+ "</span>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "0872c076-7601-46dc-b512-034780835ce1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Detecting time resolution from timestamp TIMESTAMP_END ... OK (detected 30T time resolution 30T with MEDIUM confidence)\n",
+ "OK\n",
+ " Detected 30T time resolution with MEDIUM confidence.\n",
+ " Records at start and end of file have consistent timestamp:\n",
+ " from full data = None / -failed- (not used)\n",
+ " from timedelta = 30T / >90% occurrence (not used)\n",
+ " from progressive = 30T / data 1000+1000 (OK)\n",
+ "\n",
+ ">>> TEST PASSED - The downloaded dataset has a time resolution of 30T. <<<\n"
+ ]
+ }
+ ],
+ "source": [
+ "_checkfreq = DetectFrequency(index=data_simple.index, verbose=True).get()\n",
+ "if _checkfreq == '30T':\n",
+ " print(f\">>> TEST PASSED - The downloaded dataset has a time resolution of {_checkfreq}. <<<\")\n",
+ "else:\n",
+ " print(f\"{'#' * 20}(!)TEST FAILED - The downloaded dataset does not have the expected time resolution of {_checkfreq}.{'#' * 20}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5cef7fb4-94ff-45b8-a615-1b4f7d9f0c0b",
+ "metadata": {},
+ "source": [
+ "## Time series plot"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "fc3e3fc1-7ef0-4435-9630-18dbd789b338",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 2000x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data_simple.plot(subplots=True, figsize=(20, 9), title=\"Downloaded data\", alpha=.9);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "d200aa9d-d1a0-4e30-9772-ee80bdea5181",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 2000x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "data_simple.plot(subplots=False, figsize=(20, 9), title=\"Downloaded data\", alpha=.9);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "36330e39-6fe2-4aa2-b953-36c43c1c29cc",
+ "metadata": {},
+ "source": [
+ "## Heatmap"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "7d2f9201-8ff0-4d0e-b811-604c0000a6bc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 400x800 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "for col in data_simple.columns: \n",
+ " series = data_simple[col].asfreq('30T')\n",
+ " HeatmapDateTime(series, figsize=(4,8)).show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "286acff9-690e-4ffc-bf1f-9366c513185e",
+ "metadata": {},
+ "source": [
+ "## Stats"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "d67dff50-05ed-4c45-90ef-f90e3c3588cd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>SW_IN_M1_1_1</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>17518.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>146.055129</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>238.516252</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>2.983408</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>196.370146</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>1048.238438</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " SW_IN_M1_1_1\n",
+ "count 17518.000000\n",
+ "mean 146.055129\n",
+ "std 238.516252\n",
+ "min 0.000000\n",
+ "25% 0.000000\n",
+ "50% 2.983408\n",
+ "75% 196.370146\n",
+ "max 1048.238438"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_simple.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9a2cd1cb-1a58-4347-8c2e-cdfef3ec9192",
+ "metadata": {},
+ "source": [
+ "## Save to file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "021bf1f2-5cc6-4125-8975-75fbed4cfefc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_simple.to_csv(OUTFILE)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "e2d9655c-e15f-406c-858a-2788ef401aac",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Finished. 2023-10-05 13:55:58\n"
+ ]
+ }
+ ],
+ "source": [
+ "dt_string = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
+ "print(f\"Finished. {dt_string}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c1c10496-92ec-49ba-a658-dbb3dff99d12",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.7"
+ },
+ "toc-autonumbering": false,
+ "toc-showcode": false,
+ "toc-showmarkdowntxt": false
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/poetry.lock b/poetry.lock
index 1c5f00bb74c5d422ff9dd3f025e86fd0cf560f08..9e530d79f7d43fc9a448adc3cbe1d0caa38f999c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -3520,6 +3520,33 @@ files = [
{file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"},
]
+[[package]]
+name = "xgboost"
+version = "2.0.0"
+description = "XGBoost Python Package"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "xgboost-2.0.0-py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.macosx_12_0_x86_64.whl", hash = "sha256:602134f13df6ac0747191febe29b020699c3ef7eae7c35bb55004b3288c21bb4"},
+ {file = "xgboost-2.0.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:0a1041718a0d7026aa51c9abc5270f22620b9ac930c0785c2178216fe073c512"},
+ {file = "xgboost-2.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:b73b954096775b7012dec7834420e051f98c51529263b5724386858bb54300af"},
+ {file = "xgboost-2.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:5bf02fa47d727eb5e8e71e39f3c21a5d46490e43b3d985e24f7a0126ef33eabc"},
+ {file = "xgboost-2.0.0-py3-none-win_amd64.whl", hash = "sha256:fe83a3a47dcb56880f3957e0cdb7780d5f886007d9cd10c51a9c0a30d9e970bc"},
+ {file = "xgboost-2.0.0.tar.gz", hash = "sha256:a89a4504c486043dbfdad41e5f426e2a0b4e5494a5f3ca99cf7ad85a665c79e7"},
+]
+
+[package.dependencies]
+numpy = "*"
+scipy = "*"
+
+[package.extras]
+dask = ["dask", "distributed", "pandas"]
+datatable = ["datatable"]
+pandas = ["pandas"]
+plotting = ["graphviz", "matplotlib"]
+pyspark = ["cloudpickle", "pyspark", "scikit-learn"]
+scikit-learn = ["scikit-learn"]
+
[[package]]
name = "xyzservices"
version = "2023.7.0"
@@ -3567,4 +3594,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.11"
-content-hash = "1978d3ab82b93edb94fe31dfc9dcf5443ca9b4f4ae67658c8e37e7cb1c2edbd4"
+content-hash = "7d6d311797f7a0d76ab5a8ff1c85c1b733dfc686b11d893422903cf3ddeae9fc"
diff --git a/pyproject.toml b/pyproject.toml
index d305c1bbadc105e74208391d58a2422adcf34972..bb99cb0292ff000216c9f87467a6917123258b79 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "diive"
-version = "0.63.0"
+version = "0.63.1"
description = "Time series processing"
authors = ["holukas <holukas@ethz.ch>"]
readme = "README.md"
@@ -25,6 +25,7 @@ pymannkendall = "^1.4.3"
yellowbrick = "^1.5"
shap = "^0.42.1"
eli5 = "^0.13.0"
+xgboost = "^2.0.0"
[tool.poetry.dev-dependencies]
dbc-influxdb = { path = "../../22 - POET/dbc-influxdb", develop = true }