diff --git a/README.md b/README.md index 9d13168..4a8ea34 100644 --- a/README.md +++ b/README.md @@ -4,10 +4,41 @@ Code and analysis used for calculating the merit order effect of renewables on p
-### To Do: +### Repo Publishing - To Do -- [ ] Data retrieval for ENTSOE DE prices -- [ ] Prep DE price MOE model run -- [ ] Get the DE price MOE model running (ideally would have for tomorrow' smeeting) -- [ ] Create mappings from fuel-type to carbon intensity for the DE and GB markets -- [ ] Plan what I'm gonna do before Wednesday \ No newline at end of file +Notebook Polishing Changes: +- [x] Add docstrings (can be one-liners unless shown in the user-guides or likely to be used often) +- [x] Add a mini sentence or two at the top of each nb explaining what it's about +- [x] Ensure there is a short explanation above each code block +- [x] Move input data to a raw dir +- [ ] Check all module imports are included in settings.ini +- [x] Re-run all of the notebooks at the end to check that everything works sequentially + +Completed Notebooks: +- [x] Retrieval +- [x] EDA +- [x] LOWESS (start with the biggy) +- [x] Price Surface Estimation +- [x] Price MOE +- [x] Carbon Surface Estimation and MOE +- [x] Prediction and Confidence Intervals +- [x] Hyper-Parameter Tuning +- [x] Tables and Figures + +New Code: +- [ ] Separate the binder and development `environment.yml` files +- [ ] Re-attempt LIGO fitting example as part of a user-guide +- [ ] Add in the prediction and confidence interval plots +- [ ] Add a lot more to the EDA examples +- [ ] Every week re-run a single analysis (could be in the user-guide) and show the generated fit at the top of the ReadMe +- [ ] Try to speed things up, e.g. with Numba ([one person has already started doing this](https://gist.github.com/agramfort/850437#gistcomment-3437320)) +- [ ] Get the models saved on S3 or figshare and pulled into binder via a postBuild script + +External/ReadMe +- [ ] Add the GH action for version assignment triggering pypi push and zenodo update +- [ ] Just before the paper is published set the version to 1.0.0 and have a specific Binder link that builds from that version as stored in the Zenodo archive +- [ ] Could link the zotero collection +- [ ] Add citations for both the external data I use and the resulting time-series I generate +- [ ] Add bibtex citation examples for both the paper and the code (could use [this](https://citation-file-format.github.io/cff-initializer-javascript/)) +- [ ] Publish the latest version to PyPi +- [ ] Mention the new module in the [gist](https://gist.github.com/agramfort/850437) that some of the basic regression code was inspired by \ No newline at end of file diff --git a/data/EI_rename_mapping.json b/data/EI_rename_mapping.json deleted file mode 100644 index 1d1890f..0000000 --- a/data/EI_rename_mapping.json +++ /dev/null @@ -1 +0,0 @@ -{"pumpedStorage": "pumped_storage", "northernIreland": "northern_ireland", "windOnshore": "wind_onshore", "windOffshore": "wind_offshore", "prices_ahead": "day_ahead_price", "prices": "imbalance_price", "temperatures": "temperature", "totalInGperkWh": "gCO2_per_kWh", "totalInTperh": "TCO2_per_h"} \ No newline at end of file diff --git a/data/fuel_colours.json b/data/fuel_colours.json deleted file mode 100644 index fd547ca..0000000 --- a/data/fuel_colours.json +++ /dev/null @@ -1 +0,0 @@ -{"Imports & Storage": [121, 68, 149], "nuclear": [77, 157, 87], "biomass": [168, 125, 81], "gas": [254, 156, 66], "coal": [122, 122, 122], "hydro": [50, 120, 196], "wind": [72, 194, 227], "solar": [255, 219, 65]} \ No newline at end of file diff --git a/data/ENTSOE_DE_price.csv b/data/raw/ENTSOE_DE_price.csv similarity index 100% rename from data/ENTSOE_DE_price.csv rename to data/raw/ENTSOE_DE_price.csv diff --git a/data/electric_insights.csv b/data/raw/electric_insights.csv similarity index 100% rename from data/electric_insights.csv rename to data/raw/electric_insights.csv diff --git a/data/energy_charts.csv b/data/raw/energy_charts.csv similarity index 100% rename from data/energy_charts.csv rename to data/raw/energy_charts.csv diff --git a/environment.yml b/environment.yml index 29d8abf..483bec2 100644 --- a/environment.yml +++ b/environment.yml @@ -26,7 +26,6 @@ dependencies: - pip: - -e . - ipypb - - feautils - mkdocs-material-extensions - mkdocstrings - configparser diff --git a/img/2D_skopt_surface.png b/img/2D_skopt_surface.png new file mode 100644 index 0000000..9ec9d72 Binary files /dev/null and b/img/2D_skopt_surface.png differ diff --git a/img/2D_skopt_surface_DE.png b/img/2D_skopt_surface_DE.png new file mode 100644 index 0000000..c2b1a52 Binary files /dev/null and b/img/2D_skopt_surface_DE.png differ diff --git a/img/LOWESS_single_regression_example.png b/img/LOWESS_single_regression_example.png new file mode 100644 index 0000000..26ef149 Binary files /dev/null and b/img/LOWESS_single_regression_example.png differ diff --git a/img/tricube_weighting_diagram.png b/img/tricube_weighting_diagram.png new file mode 100644 index 0000000..1341e78 Binary files /dev/null and b/img/tricube_weighting_diagram.png differ diff --git a/moepy/_nbdev.py b/moepy/_nbdev.py index 1faf2b1..10bf655 100644 --- a/moepy/_nbdev.py +++ b/moepy/_nbdev.py @@ -14,12 +14,13 @@ "parse_A44_response": "01-retrieval.ipynb", "retreive_DAM_prices": "01-retrieval.ipynb", "parse_A75_response": "01-retrieval.ipynb", - "retreive_production": "01-retrieval.ipynb", + "retrieve_production": "01-retrieval.ipynb", "load_EI_df": "02-eda.ipynb", "load_DE_df": "02-eda.ipynb", "clean_df_for_plot": "02-eda.ipynb", "rgb_2_plt_tuple": "02-eda.ipynb", "convert_fuel_colour_dict_to_plt_tuple": "02-eda.ipynb", + "hide_spines": "02-eda.ipynb", "stacked_fuel_plot": "02-eda.ipynb", "get_dist": "03-lowess.ipynb", "get_dist_threshold": "03-lowess.ipynb", @@ -57,16 +58,17 @@ "get_ensemble_preds": "03-lowess.ipynb", "process_smooth_dates_fit_inputs": "03-lowess.ipynb", "SmoothDates": "03-lowess.ipynb", - "PicklableFunction": "04-surface-estimation.ipynb", - "get_fit_kwarg_sets": "04-surface-estimation.ipynb", - "fit_models": "04-surface-estimation.ipynb", + "construct_pred_ts": "05-price-moe.ipynb", + "LowessDates": "03-lowess.ipynb", + "PicklableFunction": "04-price-surface-estimation.ipynb", + "get_fit_kwarg_sets": "04-price-surface-estimation.ipynb", + "fit_models": "04-price-surface-estimation.ipynb", "construct_dispatchable_lims_df": "05-price-moe.ipynb", "construct_pred_mask_df": "05-price-moe.ipynb", "AxTransformer": "05-price-moe.ipynb", "set_ticks": "05-price-moe.ipynb", "set_date_ticks": "05-price-moe.ipynb", "construct_df_pred": "05-price-moe.ipynb", - "construct_pred_ts": "05-price-moe.ipynb", "calc_error_metrics": "05-price-moe.ipynb", "get_model_pred_ts": "05-price-moe.ipynb", "weighted_mean_s": "05-price-moe.ipynb"} diff --git a/moepy/eda.py b/moepy/eda.py index f97b6ba..e0647b5 100644 --- a/moepy/eda.py +++ b/moepy/eda.py @@ -1,10 +1,9 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/02-eda.ipynb (unless otherwise specified). __all__ = ['load_EI_df', 'load_DE_df', 'clean_df_for_plot', 'rgb_2_plt_tuple', 'convert_fuel_colour_dict_to_plt_tuple', - 'stacked_fuel_plot'] + 'hide_spines', 'stacked_fuel_plot'] # Cell -import json import pandas as pd import seaborn as sns @@ -13,6 +12,7 @@ # Cell def load_EI_df(EI_fp): + """Loads the electric insights data and returns a DataFrame""" df = pd.read_csv(EI_fp) df['local_datetime'] = pd.to_datetime(df['local_datetime'], utc=True) @@ -22,6 +22,7 @@ def load_EI_df(EI_fp): # Cell def load_DE_df(EC_fp, ENTSOE_fp): + """Loads the energy-charts and ENTSOE data and returns a DataFrame""" # Energy-Charts df_DE = pd.read_csv(EC_fp) @@ -44,6 +45,7 @@ def load_DE_df(EC_fp, ENTSOE_fp): # Cell def clean_df_for_plot(df, freq='7D'): + """Cleans the electric insights dataframe for plotting""" fuel_order = ['Imports & Storage', 'nuclear', 'biomass', 'gas', 'coal', 'hydro', 'wind', 'solar'] interconnectors = ['french', 'irish', 'dutch', 'belgian', 'ireland', 'northern_ireland'] @@ -60,10 +62,12 @@ def clean_df_for_plot(df, freq='7D'): # Cell def rgb_2_plt_tuple(rgb_tuple): + """converts a standard rgb set from a 0-255 range to 0-1""" plt_tuple = tuple([x/255 for x in rgb_tuple]) return plt_tuple def convert_fuel_colour_dict_to_plt_tuple(fuel_colour_dict_rgb): + """Converts a dictionary of fuel colours to matplotlib colour values""" fuel_colour_dict_plt = fuel_colour_dict_rgb.copy() fuel_colour_dict_plt = { @@ -75,7 +79,21 @@ def convert_fuel_colour_dict_to_plt_tuple(fuel_colour_dict_rgb): return fuel_colour_dict_plt # Cell +def hide_spines(ax, positions=["top", "right"]): + """ + Pass a matplotlib axis and list of positions with spines to be removed + + Parameters: + ax: Matplotlib axis object + positions: Python list e.g. ['top', 'bottom'] + """ + assert isinstance(positions, list), "Position must be passed as a list " + + for position in positions: + ax.spines[position].set_visible(False) + def stacked_fuel_plot(df, fuel_colour_dict, ax=None, save_path=None, dpi=150): + """Plots the electric insights fuel data as a stacked area graph""" df = df[fuel_colour_dict.keys()] if ax == None: @@ -86,8 +104,7 @@ def stacked_fuel_plot(df, fuel_colour_dict, ax=None, save_path=None, dpi=150): plt.rcParams['axes.ymargin'] = 0 ax.spines['bottom'].set_position('zero') - ax.spines['right'].set_visible(False) - ax.spines['top'].set_visible(False) + hide_spines(ax) ax.set_xlim(df.index.min(), df.index.max()) ax.legend(ncol=4, bbox_to_anchor=(0.85, 1.15), frameon=False) diff --git a/moepy/lowess.py b/moepy/lowess.py index 5bf7d74..881ebca 100644 --- a/moepy/lowess.py +++ b/moepy/lowess.py @@ -8,7 +8,7 @@ 'get_bootstrap_resid_std_devs', 'run_model', 'bootstrap_model', 'get_confidence_interval', 'pred_to_quantile_loss', 'calc_quant_reg_loss', 'calc_quant_reg_betas', 'quantile_model', 'calc_timedelta_dists', 'construct_dt_weights', 'fit_external_weighted_ensemble', 'get_ensemble_preds', - 'process_smooth_dates_fit_inputs', 'SmoothDates'] + 'process_smooth_dates_fit_inputs', 'SmoothDates', 'construct_pred_ts', 'LowessDates'] # Cell import pandas as pd @@ -24,7 +24,6 @@ from scipy import linalg from timeit import timeit -import FEAutils as hlp from ipypb import track from moepy import eda @@ -34,6 +33,7 @@ # Cell def get_dist_threshold(dist, frac=0.4): + """Identifies the minimum distance that contains the desired data fraction""" frac_idx = int(np.ceil(len(dist)*frac)) dist_threshold = sorted(dist)[frac_idx] @@ -44,6 +44,7 @@ def get_dist_threshold(dist, frac=0.4): # Cell def get_all_weights(x, frac=0.4): + """Calculates the weightings at each data point for a LOWESS regression""" all_weights = [] for i in range(len(x)): @@ -65,12 +66,16 @@ def get_all_weights(x, frac=0.4): # Cell def clean_weights(weights): - weights = weights/weights.sum(axis=0) # We'll then normalise the weights so that for each model they sum to 1 for a single data point + """Normalises each models weightings and removes non-finite values""" + with np.errstate(divide='ignore', invalid='ignore'): + weights = weights/weights.sum(axis=0) # We'll then normalise the weights so that for each model they sum to 1 for a single data point + weights = np.where(~np.isfinite(weights), 0, weights) # And remove any non-finite values return weights def dist_2_weights_matrix(dist_matrix, dist_thresholds): + """Converts distance matrix and thresholds to weightings""" weights = dist_to_weights(dist_matrix, dist_thresholds.reshape(-1, 1)) weights = clean_weights(weights) @@ -78,6 +83,7 @@ def dist_2_weights_matrix(dist_matrix, dist_thresholds): # Cell def get_full_dataset_weights_matrix(x, frac=0.4): + """Wrapper for calculating weights from the raw data and LOWESS fraction""" frac_idx = get_frac_idx(x, frac) dist_matrix = vector_to_dist_matrix(x) @@ -91,6 +97,7 @@ def get_full_dataset_weights_matrix(x, frac=0.4): num_fits_2_reg_anchors = lambda x, num_fits: np.linspace(x.min(), x.max(), num=num_fits) def get_weighting_locs(x, reg_anchors=None, num_fits=None): + """Identifies the weighting locations for the provided dataset""" num_type_2_dist_rows = { type(None) : lambda x, num_fits: x.reshape(-1, 1), int : lambda x, num_fits: num_fits_2_reg_anchors(x, num_fits).reshape(-1, 1), @@ -104,6 +111,7 @@ def get_weighting_locs(x, reg_anchors=None, num_fits=None): return weighting_locs def create_dist_matrix(x, reg_anchors=None, num_fits=None): + """Constructs the distance matrix for the desired weighting locations""" weighting_locs = get_weighting_locs(x, reg_anchors=reg_anchors, num_fits=num_fits) dist_matrix = np.abs(weighting_locs - x.reshape(1, -1)) @@ -111,6 +119,7 @@ def create_dist_matrix(x, reg_anchors=None, num_fits=None): # Cell def get_weights_matrix(x, frac=0.4, weighting_locs=None, reg_anchors=None, num_fits=None): + """Wrapper for calculating weights from the raw data and LOWESS fraction""" frac_idx = get_frac_idx(x, frac) if weighting_locs is not None: @@ -125,6 +134,7 @@ def get_weights_matrix(x, frac=0.4, weighting_locs=None, reg_anchors=None, num_f # Cell def calc_lin_reg_betas(x, y, weights=None): + """Calculates the intercept and gradient for the specified local regressions""" if weights is None: weights = np.ones(len(x)) @@ -132,7 +142,7 @@ def calc_lin_reg_betas(x, y, weights=None): A = np.array([[np.sum(weights), np.sum(weights * x)], [np.sum(weights * x), np.sum(weights * x * x)]]) - betas = linalg.solve(A, b) + betas = np.linalg.lstsq(A, b)[0] return betas @@ -140,6 +150,7 @@ def calc_lin_reg_betas(x, y, weights=None): check_array = lambda array, x: np.ones(len(x)) if array is None else array def fit_regressions(x, y, weights=None, reg_func=calc_lin_reg_betas, num_coef=2, **reg_params): + """Calculates the design matrix for the specified local regressions""" if weights is None: weights = np.ones(len(x)) @@ -155,6 +166,7 @@ def fit_regressions(x, y, weights=None, reg_func=calc_lin_reg_betas, num_coef=2, # Cell def lowess_fit_and_predict(x, y, frac=0.4, reg_anchors=None, num_fits=None, x_pred=None): + """Fits and predicts smoothed local regressions at the specified locations""" weighting_locs = get_weighting_locs(x, reg_anchors=reg_anchors, num_fits=num_fits) weights = get_weights_matrix(x, frac=frac, weighting_locs=weighting_locs) design_matrix = fit_regressions(x, y, weights) @@ -171,6 +183,7 @@ def lowess_fit_and_predict(x, y, frac=0.4, reg_anchors=None, num_fits=None, x_pr # Cell def calc_robust_weights(y, y_pred, max_std_dev=6): + """Calculates robustifying weightings that penalise outliers""" residuals = y - y_pred std_dev = np.quantile(np.abs(residuals), 0.682) @@ -181,6 +194,7 @@ def calc_robust_weights(y, y_pred, max_std_dev=6): # Cell def robust_lowess_fit_and_predict(x, y, frac=0.4, reg_anchors=None, num_fits=None, x_pred=None, robust_weights=None, robust_iters=3): + """Fits and predicts robust smoothed local regressions at the specified locations""" # Identifying the initial loading weights weighting_locs = get_weighting_locs(x, reg_anchors=reg_anchors, num_fits=num_fits) loading_weights = get_weights_matrix(x, frac=frac, weighting_locs=weighting_locs) @@ -190,7 +204,10 @@ def robust_lowess_fit_and_predict(x, y, frac=0.4, reg_anchors=None, num_fits=Non robust_loading_weights = loading_weights else: robust_loading_weights = np.multiply(robust_weights, loading_weights) - robust_loading_weights = robust_loading_weights/robust_loading_weights.sum(axis=0) + + with np.errstate(divide='ignore', invalid='ignore'): + robust_loading_weights = robust_loading_weights/robust_loading_weights.sum(axis=0) + robust_loading_weights = np.where(~np.isfinite(robust_loading_weights), 0, robust_loading_weights) # Fitting the model and making predictions @@ -215,12 +232,61 @@ def robust_lowess_fit_and_predict(x, y, frac=0.4, reg_anchors=None, num_fits=Non # Cell class Lowess(BaseEstimator, RegressorMixin): + """ + This class provides a Scikit-Learn compatible model for Locally Weighted + Scatterplot Smoothing, including robustifying procedures against outliers. + + For more information on the underlying algorithm please refer to + * William S. Cleveland: "Robust locally weighted regression and smoothing + scatterplots", Journal of the American Statistical Association, December 1979, + volume 74, number 368, pp. 829-836. + * William S. Cleveland and Susan J. Devlin: "Locally weighted regression: An + approach to regression analysis by local fitting", Journal of the American + Statistical Association, September 1988, volume 83, number 403, pp. 596-610. + + Example Usage: + ``` + x = np.linspace(0, 5, num=150) + y = np.sin(x) + y_noisy = y + (np.random.normal(size=len(y)))/10 + + lowess = Lowess() + lowess.fit(x, y_noisy, frac=0.2) + + x_pred = np.linspace(0, 5, 26) + y_pred = lowess.predict(x_pred) + ``` + + Initialisation Parameters: + reg_func: function that accepts the x and y values then returns the intercepts and gradients + + Attributes: + reg_func: function that accepts the x and y values then returns the intercepts and gradients + fitted: Boolean flag indicating whether the model has been fitted + frac: Fraction of the dataset to use in each local regression + weighting_locs: Locations of the local regression centers + loading_weights: Weights of each data-point across the localalised models + design_matrix: Regression coefficients for each of the localised models + """ + def __init__(self, reg_func=calc_lin_reg_betas): self.reg_func = reg_func self.fitted = False return + def calculate_loading_weights(self, x, reg_anchors=None, num_fits=None, external_weights=None, robust_weights=None): + """ + Calculates the loading weights for each data-point across the localised models + + Parameters: + x: values for the independent variable + reg_anchors: Locations at which to center the local regressions + num_fits: Number of locations at which to carry out a local regression + external_weights: Further weighting for the specific regression + robust_weights: Robustifying weights to remove the influence of outliers + """ + # Calculating the initial loading weights weighting_locs = get_weighting_locs(x, reg_anchors=reg_anchors, num_fits=num_fits) loading_weights = get_weights_matrix(x, frac=self.frac, weighting_locs=weighting_locs) @@ -236,7 +302,9 @@ def calculate_loading_weights(self, x, reg_anchors=None, num_fits=None, external loading_weights = np.multiply(weight_adj, loading_weights) # Post-processing weights - loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising + with np.errstate(divide='ignore', invalid='ignore'): + loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising + loading_weights = np.where(~np.isfinite(loading_weights), 0, loading_weights) # removing non-finite values self.weighting_locs = weighting_locs @@ -244,7 +312,27 @@ def calculate_loading_weights(self, x, reg_anchors=None, num_fits=None, external return - def fit(self, x, y, frac=0.4, reg_anchors=None, num_fits=None, external_weights=None, robust_weights=None, robust_iters=3, **reg_params): + + def fit(self, x, y, frac=0.4, reg_anchors=None, + num_fits=None, external_weights=None, + robust_weights=None, robust_iters=3, **reg_params): + """ + Calculation of the local regression coefficients for + a LOWESS model across the dataset provided. This method + will reassign the `frac`, `weighting_locs`, `loading_weights`, + and `design_matrix` attributes of the `Lowess` object. + + Parameters: + x: values for the independent variable + y: values for the dependent variable + frac: LOWESS bandwidth for local regression as a fraction + reg_anchors: Locations at which to center the local regressions + num_fits: Number of locations at which to carry out a local regression + external_weights: Further weighting for the specific regression + robust_weights: Robustifying weights to remove the influence of outliers + robust_iters: Number of robustifying iterations to carry out + """ + self.frac = frac # Solving for the design matrix @@ -265,7 +353,18 @@ def fit(self, x, y, frac=0.4, reg_anchors=None, num_fits=None, external_weights= return + def predict(self, x_pred): + """ + Inference using the design matrix from the LOWESS fit + + Parameters: + x_pred: Locations for the LOWESS inference + + Returns: + y_pred: Estimated values using the LOWESS fit + """ + point_evals = self.design_matrix[:, 0] + np.dot(x_pred.reshape(-1, 1), self.design_matrix[:, 1].reshape(1, -1)) pred_weights = get_weights_matrix(x_pred, frac=self.frac, reg_anchors=self.weighting_locs) @@ -275,7 +374,8 @@ def predict(self, x_pred): # Cell def get_bootstrap_idxs(x, bootstrap_bag_size=0.5): - ## Bag size handling + """Determines the indexes of an array to be used for the in- and out-of-bag bootstrap samples""" + # Bag size handling assert bootstrap_bag_size>0, 'Bootstrap bag size must be greater than 0' if bootstrap_bag_size > 1: @@ -284,7 +384,7 @@ def get_bootstrap_idxs(x, bootstrap_bag_size=0.5): else: bootstrap_bag_size = int(np.ceil(bootstrap_bag_size*len(x))) - ## Splitting in-bag and out-of-bag samlpes + # Splitting in-bag and out-of-bag samlpes idxs = np.array(range(len(x))) ib_idxs = np.sort(np.random.choice(idxs, bootstrap_bag_size, replace=True)) @@ -294,6 +394,7 @@ def get_bootstrap_idxs(x, bootstrap_bag_size=0.5): # Cell def get_bootstrap_resid_std_devs(x, y, bag_size, model=Lowess(), **model_kwargs): + """Calculates the standard deviation of the in- and out-of-bag errors""" # Splitting the in- and out-of-bag samples ib_idxs, oob_idxs = get_bootstrap_idxs(x, bag_size) @@ -318,6 +419,7 @@ def get_bootstrap_resid_std_devs(x, y, bag_size, model=Lowess(), **model_kwargs) # Cell def run_model(x, y, bag_size, model=Lowess(), x_pred=None, **model_kwargs): + """Fits a model and then uses it to make a prediction""" if x_pred is None: x_pred = x @@ -332,6 +434,7 @@ def run_model(x, y, bag_size, model=Lowess(), x_pred=None, **model_kwargs): return y_pred def bootstrap_model(x, y, bag_size=0.5, model=Lowess(), x_pred=None, num_runs=1000, **model_kwargs): + """Repeatedly fits and predicts using the specified model, using different subsets of the data each time""" # Creating the ensemble predictions preds = [] @@ -349,6 +452,7 @@ def bootstrap_model(x, y, bag_size=0.5, model=Lowess(), x_pred=None, num_runs=10 # Cell def get_confidence_interval(df_bootstrap, conf_pct=0.95): + """Estimates the confidence interval of a prediction based on the bootstrapped estimates""" conf_margin = (1 - conf_pct)/2 df_conf_intvl = pd.DataFrame(columns=['min', 'max'], index=df_bootstrap.index) @@ -359,6 +463,7 @@ def get_confidence_interval(df_bootstrap, conf_pct=0.95): # Cell def pred_to_quantile_loss(y, y_pred, q=0.5, weights=None): + """Calculates the quantile error for a prediction""" residuals = y - y_pred if weights is not None: @@ -369,6 +474,7 @@ def pred_to_quantile_loss(y, y_pred, q=0.5, weights=None): return loss def calc_quant_reg_loss(x0, x, y, q, weights=None): + """Makes a quantile prediction then calculates its error""" if weights is None: weights = np.ones(len(x)) @@ -382,6 +488,7 @@ def calc_quant_reg_loss(x0, x, y, q, weights=None): # Cell def quantile_model(x, y, model=Lowess(calc_quant_reg_betas), x_pred=None, qs=np.linspace(0.1, 0.9, 9), **model_kwargs): + """Model wrapper that will repeatedly fit and predict for the specified quantiles""" if x_pred is None: x_pred = np.sort(np.unique(x)) @@ -401,6 +508,7 @@ def quantile_model(x, y, model=Lowess(calc_quant_reg_betas), # Cell def calc_timedelta_dists(dates, central_date, threshold_value=24, threshold_units='W'): + """Maps datetimes to weights using the central date and threshold information provided""" timedeltas = pd.to_datetime(dates, utc=True) - pd.to_datetime(central_date, utc=True) timedelta_dists = timedeltas/pd.Timedelta(value=threshold_value, unit=threshold_units) @@ -408,6 +516,7 @@ def calc_timedelta_dists(dates, central_date, threshold_value=24, threshold_unit # Cell def construct_dt_weights(dt_idx, reg_dates, threshold_value=52, threshold_units='W'): + """Constructs a set of distance weightings based on the regression dates provided""" dt_to_weights = dict() for reg_date in reg_dates: @@ -417,6 +526,7 @@ def construct_dt_weights(dt_idx, reg_dates, threshold_value=52, threshold_units= # Cell def fit_external_weighted_ensemble(x, y, ensemble_member_to_weights, lowess_kwargs={}, **fit_kwargs): + """Fits an ensemble of LOWESS models which have varying relevance for each subset of data over time""" ensemble_member_to_models = dict() for ensemble_member, ensemble_weights in track(ensemble_member_to_weights.items()): @@ -426,6 +536,7 @@ def fit_external_weighted_ensemble(x, y, ensemble_member_to_weights, lowess_kwar return ensemble_member_to_models def get_ensemble_preds(ensemble_member_to_model, x_pred=np.linspace(8, 60, 53)): + """Using the fitted ensemble of LOWESS models to generate the predictions for each of them""" ensemble_member_to_preds = dict() for ensemble_member in ensemble_member_to_model.keys(): @@ -433,8 +544,8 @@ def get_ensemble_preds(ensemble_member_to_model, x_pred=np.linspace(8, 60, 53)): return ensemble_member_to_preds -# Cell def process_smooth_dates_fit_inputs(x, y, dt_idx, reg_dates): + """Sanitises the inputs to the SmoothDates fitting method""" if hasattr(x, 'index') and hasattr(y, 'index'): assert x.index.equals(y.index), 'If `x` and `y` have indexes then they must be the same' if dt_idx is None: @@ -450,14 +561,57 @@ def process_smooth_dates_fit_inputs(x, y, dt_idx, reg_dates): return x, y, dt_idx, reg_dates +# Cell class SmoothDates(BaseEstimator, RegressorMixin): + """ + This class provides a time-adaptive extension of the classical + Locally Weighted Scatterplot Smoothing regression technique, + including robustifying procedures against outliers. This model + predicts the surface rather than individual point estimates. + + Initialisation Parameters: + frac: Fraction of the dataset to use in each local regression + threshold_value: Number of datetime units to use in each regression + threshold_units: Datetime unit which should be compatible with pandas `date_range` function + + Attributes: + fitted: Boolean flag indicating whether the model has been fitted + frac: Fraction of the dataset to use in each local regression + threshold_value: Number of datetime units to use in each regression + threshold_units: Datetime unit which should be compatible with pandas `date_range` function + ensemble_member_to_weights: Mapping from the regression dates to their respective weightings for each data-point + ensemble_member_to_models: Mapping from the regression dates to their localised models + reg_dates: Dates at which the local time-adaptive models will be centered around + pred_weights: Weightings to map from the local models to the values to be inferenced + pred_values: Raw prediction values as generated by each of the individual local models + """ + def __init__(self, frac=0.3, threshold_value=52, threshold_units='W'): self.fitted = False self.frac = frac self.threshold_value = threshold_value self.threshold_units = threshold_units + def fit(self, x, y, dt_idx=None, reg_dates=None, lowess_kwargs={}, **fit_kwargs): + """ + Calculation of the local regression coefficients for each of the + LOWESS models across the dataset provided. This is a time-adaptive + ensembled version of the `Lowess` model. + + Parameters: + x: Values for the independent variable + y: Values for the dependent variable + dt_idx: Datetime index, if not provided the index of the x and y series will be used + reg_dates: Dates at which the local time-adaptive models will be centered around + lowess_kwargs: Additional arguments to be passed at model initialisation + reg_anchors: Locations at which to center the local regressions + num_fits: Number of locations at which to carry out a local regression + external_weights: Further weighting for the specific regression + robust_weights: Robustifying weights to remove the influence of outliers + robust_iters: Number of robustifying iterations to carry out + """ + x, y, dt_idx, reg_dates = process_smooth_dates_fit_inputs(x, y, dt_idx, reg_dates) self.ensemble_member_to_weights = construct_dt_weights(dt_idx, reg_dates, threshold_value=self.threshold_value, @@ -470,7 +624,20 @@ def fit(self, x, y, dt_idx=None, reg_dates=None, lowess_kwargs={}, **fit_kwargs) return + def predict(self, x_pred=np.linspace(8, 60, 53), dt_pred=None, return_df=True): + """ + Inference using the design matrix from the time-adaptive LOWESS fits + + Parameters: + x_pred: Independent variable locations for the time-adaptive LOWESS inference + dt_pred: Date locations for the time-adaptive LOWESS inference + return_df: Flag specifying whether to return a dataframe or numpy matrix + + Returns: + df_pred/y_pred: Estimated surface of the time-adaptive the LOWESS fit + """ + if dt_pred is None: dt_pred = self.reg_dates @@ -480,7 +647,10 @@ def predict(self, x_pred=np.linspace(8, 60, 53), dt_pred=None, return_df=True): self.ensemble_member_to_preds = get_ensemble_preds(self.ensemble_member_to_models, x_pred=x_pred) self.pred_weights = np.array(list(construct_dt_weights(dt_pred, self.reg_dates).values())) - self.pred_weights = self.pred_weights/self.pred_weights.sum(axis=0) + + with np.errstate(divide='ignore', invalid='ignore'): + self.pred_weights = self.pred_weights/self.pred_weights.sum(axis=0) + self.pred_values = np.array(list(self.ensemble_member_to_preds.values())) y_pred = np.dot(self.pred_weights.T, self.pred_values) @@ -489,4 +659,116 @@ def predict(self, x_pred=np.linspace(8, 60, 53), dt_pred=None, return_df=True): df_pred = pd.DataFrame(y_pred, index=dt_pred, columns=x_pred).T return df_pred else: - return y_pred \ No newline at end of file + return y_pred + +# Cell +def construct_pred_ts(s, df_pred, rounding_dec=1): + """Uses the time-adaptive LOWESS surface to generate time-series prediction""" + vals = [] + + for dt_idx, val in track(s.iteritems(), total=s.size): + vals += [df_pred.loc[round(val, rounding_dec), dt_idx.strftime('%Y-%m-%d')]] + + s_pred_ts = pd.Series(vals, index=s.index) + + return s_pred_ts + +class LowessDates(BaseEstimator, RegressorMixin): + """ + This class provides a time-adaptive extension of the classical + Locally Weighted Scatterplot Smoothing regression technique, + including robustifying procedures against outliers. + + Initialisation Parameters: + frac: Fraction of the dataset to use in each local regression + threshold_value: Number of datetime units to use in each regression + threshold_units: Datetime unit which should be compatible with pandas `date_range` function + + Attributes: + fitted: Boolean flag indicating whether the model has been fitted + frac: Fraction of the dataset to use in each local regression + threshold_value: Number of datetime units to use in each regression + threshold_units: Datetime unit which should be compatible with pandas `date_range` function + ensemble_member_to_weights: Mapping from the regression dates to their respective weightings for each data-point + ensemble_member_to_models: Mapping from the regression dates to their localised models + reg_dates: Dates at which the local time-adaptive models will be centered around + ensemble_member_to_preds: Mapping from the regression dates to their predictions + reg_weights: Mapping from the prediction values to the weighting of each time-adaptive model + reg_values: Predictions from each regression + df_reg: A DataFrame of the time-adaptive surfce regression + """ + + def __init__(self, frac=0.3, threshold_value=52, threshold_units='W', pred_reg_dates=None): + self.fitted = False + self.frac = frac + self.threshold_value = threshold_value + self.threshold_units = threshold_units + self.pred_reg_dates = pred_reg_dates + + + def fit(self, x, y, dt_idx=None, reg_dates=None, lowess_kwargs={}, **fit_kwargs): + """ + Calculation of the local regression coefficients for each of the + LOWESS models across the dataset provided. This is a time-adaptive + ensembled version of the `Lowess` model. + + Parameters: + x: Values for the independent variable + y: Values for the dependent variable + dt_idx: Datetime index, if not provided the index of the x and y series will be used + reg_dates: Dates at which the local time-adaptive models will be centered around + lowess_kwargs: Additional arguments to be passed at model initialisation + reg_anchors: Locations at which to center the local regressions + num_fits: Number of locations at which to carry out a local regression + external_weights: Further weighting for the specific regression + robust_weights: Robustifying weights to remove the influence of outliers + robust_iters: Number of robustifying iterations to carry out + """ + + x, y, dt_idx, reg_dates = process_smooth_dates_fit_inputs(x, y, dt_idx, reg_dates) + self.ensemble_member_to_weights = construct_dt_weights(dt_idx, reg_dates, + threshold_value=self.threshold_value, + threshold_units=self.threshold_units) + + self.ensemble_member_to_models = fit_external_weighted_ensemble(x, y, self.ensemble_member_to_weights, lowess_kwargs=lowess_kwargs, frac=self.frac, **fit_kwargs) + + self.reg_dates = reg_dates + self.fitted = True + + return + + + def predict(self, x_pred, reg_x=None, reg_dates=None, return_df=True, rounding_dec=1): + """ + Inference using the design matrix from the time-adaptive LOWESS fits + + Parameters: + x_pred: Locations for the time-adaptive LOWESS inference + + Returns: + y_pred: Estimated values using the time-adaptive LOWESS fit + """ + + reg_dates = self.pred_reg_dates + + if reg_x is None: + reg_x = np.round(np.arange(np.floor(x_pred.min())-5, np.ceil(x_pred.max())+5, 1/(10**rounding_dec)), rounding_dec) + x_pred = x_pred.round(rounding_dec) + + if isinstance(reg_x, pd.Series): + reg_x = reg_x.values + + # Fitting the smoothed regression + self.ensemble_member_to_preds = get_ensemble_preds(self.ensemble_member_to_models, x_pred=reg_x) + + self.reg_weights = np.array(list(construct_dt_weights(reg_dates, self.reg_dates).values())) + self.reg_weights = self.reg_weights/self.reg_weights.sum(axis=0) + self.reg_values = np.array(list(self.ensemble_member_to_preds.values())) + + y_reg = np.dot(self.reg_weights.T, self.reg_values) + self.df_reg = pd.DataFrame(y_reg, index=reg_dates.strftime('%Y-%m-%d'), columns=reg_x).T + + # Making the prediction + s_pred_ts = construct_pred_ts(x_pred, self.df_reg, rounding_dec=rounding_dec) + + return s_pred_ts \ No newline at end of file diff --git a/moepy/moe.py b/moepy/moe.py index ad1eafb..30dea0d 100644 --- a/moepy/moe.py +++ b/moepy/moe.py @@ -19,7 +19,6 @@ import matplotlib.pyplot as plt import matplotlib.dates as mdates -import FEAutils as hlp from ipypb import track from IPython.display import JSON @@ -28,6 +27,7 @@ # Cell def construct_dispatchable_lims_df(s_dispatchable, rolling_w=3, daily_quantiles=[0.001, 0.999]): + """Identifies the rolling limits to be used in masking""" df_dispatchable_lims = (s_dispatchable .resample('1d') .quantile(daily_quantiles) @@ -44,6 +44,7 @@ def construct_dispatchable_lims_df(s_dispatchable, rolling_w=3, daily_quantiles= return df_dispatchable_lims def construct_pred_mask_df(df_pred, df_dispatchable_lims): + """Constructs a DataFrame mask for the prediction""" df_pred = df_pred[df_dispatchable_lims.index] df_pred_mask = pd.DataFrame(dict(zip(df_pred.columns, [df_pred.index]*df_pred.shape[1])), index=df_pred.index) df_pred_mask = (df_pred_mask > df_dispatchable_lims.iloc[:, 0].values) & (df_pred_mask < df_dispatchable_lims.iloc[:, 1].values) @@ -55,6 +56,7 @@ def construct_pred_mask_df(df_pred, df_dispatchable_lims): # Cell class AxTransformer: + """Helper class for cleaning axis tick locations and labels""" def __init__(self, datetime_vals=False): self.datetime_vals = datetime_vals self.lr = linear_model.LinearRegression() @@ -89,6 +91,7 @@ def transform(self, tick_vals): return tick_locs def set_ticks(ax, tick_locs, tick_labels=None, axis='y'): + """Sets ticks at standard numerical locations""" if tick_labels is None: tick_labels = tick_locs ax_transformer = AxTransformer() @@ -102,6 +105,7 @@ def set_ticks(ax, tick_locs, tick_labels=None, axis='y'): return ax def set_date_ticks(ax, start_date, end_date, axis='y', date_format='%Y-%m-%d', **date_range_kwargs): + """Sets ticks at datetime locations""" dt_rng = pd.date_range(start_date, end_date, **date_range_kwargs) ax_transformer = AxTransformer(datetime_vals=True) @@ -116,6 +120,7 @@ def set_date_ticks(ax, start_date, end_date, axis='y', date_format='%Y-%m-%d', * # Cell def construct_df_pred(model_fp, x_pred=np.linspace(-2, 61, 631), dt_pred=pd.date_range('2009-01-01', '2020-12-31', freq='1D')): + """Constructs the prediction surface for the specified pre-fitted model""" smooth_dates = pickle.load(open(model_fp, 'rb')) df_pred = smooth_dates.predict(x_pred=x_pred, dt_pred=dt_pred) df_pred.index = np.round(df_pred.index, 1) @@ -124,6 +129,7 @@ def construct_df_pred(model_fp, x_pred=np.linspace(-2, 61, 631), dt_pred=pd.date # Cell def construct_pred_ts(s, df_pred): + """Uses the time-adaptive LOWESS surface to generate time-series prediction""" s_pred_ts = pd.Series(index=s.index, dtype='float64') for dt_idx, val in track(s.iteritems(), total=s.size): @@ -133,6 +139,7 @@ def construct_pred_ts(s, df_pred): # Cell def calc_error_metrics(s_err, max_err_quantile=1): + """Calculates several error metrics using the passed error series""" if s_err.isnull().sum() > 0: s_err = s_err.dropna() @@ -149,6 +156,7 @@ def calc_error_metrics(s_err, max_err_quantile=1): # Cell def get_model_pred_ts(s, model_fp, s_demand=None, x_pred=np.linspace(-2, 61, 631), dt_pred=pd.date_range('2009-01-01', '2020-12-31', freq='1D')): + """Constructs the time-series prediction for the specified pre-fitted model""" df_pred = construct_df_pred(model_fp, x_pred=x_pred, dt_pred=dt_pred) s_cleaned = s.dropna().loc[df_pred.columns.min():df_pred.columns.max()+pd.Timedelta(hours=23, minutes=30)] s_pred_ts = construct_pred_ts(s_cleaned, df_pred) @@ -162,6 +170,7 @@ def get_model_pred_ts(s, model_fp, s_demand=None, x_pred=np.linspace(-2, 61, 631 # Cell def weighted_mean_s(s, s_weight=None, dt_rng=pd.date_range('2009-12-01', '2021-01-01', freq='W'), end_dt_delta_days=7): + """Calculates the weighted average of a series""" capture_prices = dict() for start_dt in dt_rng: diff --git a/moepy/retrieval.py b/moepy/retrieval.py index f9ec020..9101998 100644 --- a/moepy/retrieval.py +++ b/moepy/retrieval.py @@ -2,7 +2,7 @@ __all__ = ['query_API', 'dict_col_2_cols', 'clean_nested_dict_cols', 'set_dt_idx', 'create_df_dt_rng', 'clean_df_dts', 'retrieve_stream_df', 'check_streams', 'retrieve_streams_df', 'parse_A44_response', 'retreive_DAM_prices', - 'parse_A75_response', 'retreive_production'] + 'parse_A75_response', 'retrieve_production'] # Cell import json @@ -23,11 +23,11 @@ def query_API(start_date:str, end_date:str, stream:str, time_group='30m'): """ 'Query API' makes the call to Electric Insights and returns the JSON response - Arguments: - * start_date - Start date for data given as a string in the form '%Y-%m-%d' - * end_date - End date for data given as a string in the form '%Y-%m-%d' - * stream - One of 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions' - * time_group - One of '30m', '1h', '1d' or '7d'. The default is '30m' + Parameters: + start_date: Start date for data given as a string in the form '%Y-%m-%d' + end_date: End date for data given as a string in the form '%Y-%m-%d' + stream: One of 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions' + time_group: One of '30m', '1h', '1d' or '7d'. The default is '30m' """ # Checking stream is an EI endpoint @@ -68,6 +68,7 @@ def dict_col_2_cols(df:pd.DataFrame, value_col='value'): # Cell def clean_nested_dict_cols(df): + """Unpacks columns contining nested dictionaries""" # Calculating columns that are still dictionaries s_types = df.iloc[0].apply(lambda val: type(val)) cols_with_dicts = s_types[s_types == dict].index @@ -122,6 +123,7 @@ def create_df_dt_rng(start_date, end_date, freq='30T', tz='Europe/London', dt_st return df_dt_rng def clean_df_dts(df): + """Cleans the datetime index of the passed DataFrame""" df = set_dt_idx(df) df = df[~df.index.duplicated()] @@ -135,14 +137,14 @@ def clean_df_dts(df): # Cell def retrieve_stream_df(start_date:str, end_date:str, stream:str, time_group='30m', renaming_dict={}): """ - `retrieve_stream_df` makes the call to Electric Insights and parses the response into a dataframe which is returned - - Arguments: - * start_date - Start date for data given as a string in the form '%Y-%m-%d' - * end_date - End date for data given as a string in the form '%Y-%m-%d' - * stream - One of 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions' - * time_group - One of '30m', '1h', '1d' or '7d'. The default is '30m' - * renaming_dict - Mapping from old to new column names + Makes the call to Electric Insights and parses the response into a dataframe which is returned + + Parameters: + start_date: Start date for data given as a string in the form '%Y-%m-%d' + end_date: End date for data given as a string in the form '%Y-%m-%d' + stream: One of 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions' + time_group: One of '30m', '1h', '1d' or '7d'. The default is '30m' + renaming_dict: Mapping from old to new column names """ # Calling data and parsing into dataframe @@ -192,13 +194,13 @@ def check_streams(streams='*'): # Cell def retrieve_streams_df(start_date:str, end_date:str, streams='*', time_group='30m', renaming_dict={}): """ - 'Call Streams' makes the calls to Electric Insights for the given streams and parses the responses into a dataframe which is returned + Makes the calls to Electric Insights for the given streams and parses the responses into a dataframe which is returned - Arguments: - * start_date - Start date for data given as a string in the form '%Y-%m-%d' - * end_date - End date for data given as a string in the form '%Y-%m-%d' - * streams - Contains 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions', or is given as all, '*' - * time_group - One of '30m', '1h', '1d' or '7d'. The default is '30m' + Parameters: + start_date: Start date for data given as a string in the form '%Y-%m-%d' + end_date: End date for data given as a string in the form '%Y-%m-%d' + streams: Contains 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions', or is given as all, '*' + time_group: One of '30m', '1h', '1d' or '7d'. The default is '30m' """ df = pd.DataFrame() @@ -212,6 +214,7 @@ def retrieve_streams_df(start_date:str, end_date:str, streams='*', time_group='3 # Cell def parse_A44_response(r, freq='H', tz='UTC'): + """Extracts the price time-series""" s_price = pd.Series(dtype=float) parsed_r = xmltodict.parse(r.text) @@ -227,6 +230,7 @@ def parse_A44_response(r, freq='H', tz='UTC'): # Cell def retreive_DAM_prices(dt_pairs, domain='10Y1001A1001A63L'): + """Retrieves and collates the day-ahead prices for the specified date ranges""" params = { 'documentType': 'A44', 'in_Domain': domain, @@ -250,7 +254,8 @@ def retreive_DAM_prices(dt_pairs, domain='10Y1001A1001A63L'): return s_price # Cell -def parse_A75_response(r, freq='15T', tz='UTC'): +def parse_A75_response(r, freq='15T', tz='UTC', warn_on_failure=False): + """Extracts the production data by fuel-type from the JSON response""" psr_code_to_type = { 'A03': 'Mixed', 'A04': 'Generation', @@ -291,7 +296,7 @@ def parse_A75_response(r, freq='15T', tz='UTC'): df_production = pd.DataFrame(dtype=float, columns=columns, index=index) - for timeseries in track(parsed_r['GL_MarketDocument']['TimeSeries']): + for timeseries in parsed_r['GL_MarketDocument']['TimeSeries']: try: psr_type = timeseries['MktPSRType']['psrType'] dt_rng = pd.date_range(timeseries['Period']['timeInterval']['start'], timeseries['Period']['timeInterval']['end'], freq=freq, tz=tz)[:-1] @@ -302,7 +307,8 @@ def parse_A75_response(r, freq='15T', tz='UTC'): df_production[psr_type] = s_psr_type except: - warn(f"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}") + if warn_on_failure == True: + warn(f"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}") assert df_production.index.duplicated().sum() == 0, 'There are duplicate date indexes' @@ -311,7 +317,8 @@ def parse_A75_response(r, freq='15T', tz='UTC'): return df_production -def retreive_production(dt_pairs, domain='10Y1001A1001A63L'): +def retrieve_production(dt_pairs, domain='10Y1001A1001A63L', warn_on_failure=False): + """Retrieves and collates the production data for the specified date ranges""" params = { 'documentType': 'A75', 'processType': 'A16', @@ -327,9 +334,10 @@ def retreive_production(dt_pairs, domain='10Y1001A1001A63L'): try: r = client._base_request(params=params, start=start, end=end) - df_production_dt_rng = parse_A75_response(r) + df_production_dt_rng = parse_A75_response(r, warn_on_failure=warn_on_failure) df_production = df_production.append(df_production_dt_rng) except: - warn(f"{start.strftime('%Y-%m-%d')} - {end.strftime('%Y-%m-%d')} failed") + if warn_on_failure == True: + warn(f"{start.strftime('%Y-%m-%d')} - {end.strftime('%Y-%m-%d')} failed") return df_production \ No newline at end of file diff --git a/moepy/surface.py b/moepy/surface.py index 99308d8..70fe359 100644 --- a/moepy/surface.py +++ b/moepy/surface.py @@ -1,4 +1,4 @@ -# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/04-surface-estimation.ipynb (unless otherwise specified). +# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/04-price-surface-estimation.ipynb (unless otherwise specified). __all__ = ['PicklableFunction', 'get_fit_kwarg_sets', 'fit_models'] @@ -22,6 +22,7 @@ import marshal class PicklableFunction: + """Provides a wrapper to ensure functions can be pickled""" def __init__(self, fun): self._fun = fun @@ -44,6 +45,7 @@ def __setstate__(self, state): return def get_fit_kwarg_sets(qs=np.linspace(0.1, 0.9, 9)): + """Helper to generate kwargs for the `fit` method of `Lowess`""" fit_kwarg_sets = [ # quantile lowess { @@ -60,6 +62,7 @@ def get_fit_kwarg_sets(qs=np.linspace(0.1, 0.9, 9)): # Cell def fit_models(model_definitions, models_dir): + """Fits LOWESS variants using the specified model definitions""" for model_parent_name, model_spec in model_definitions.items(): for fit_kwarg_set in track(model_spec['fit_kwarg_sets'], label=model_parent_name): run_name = fit_kwarg_set.pop('name') diff --git a/nbs/01-retrieval.ipynb b/nbs/01-retrieval.ipynb index 6105b52..ac109b0 100644 --- a/nbs/01-retrieval.ipynb +++ b/nbs/01-retrieval.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Data Retrieval\n", + "# Data Retrieval" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook outlines the retrieval of the fuel generation and price data required for the merit-order-effect analyses.\n", "\n", "
\n", "\n", @@ -22,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -79,11 +86,11 @@ " \"\"\"\n", " 'Query API' makes the call to Electric Insights and returns the JSON response\n", "\n", - " Arguments:\n", - " * start_date - Start date for data given as a string in the form '%Y-%m-%d'\n", - " * end_date - End date for data given as a string in the form '%Y-%m-%d'\n", - " * stream - One of 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions'\n", - " * time_group - One of '30m', '1h', '1d' or '7d'. The default is '30m'\n", + " Parameters:\n", + " start_date: Start date for data given as a string in the form '%Y-%m-%d'\n", + " end_date: End date for data given as a string in the form '%Y-%m-%d'\n", + " stream: One of 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions'\n", + " time_group: One of '30m', '1h', '1d' or '7d'. The default is '30m'\n", " \"\"\"\n", "\n", " # Checking stream is an EI endpoint\n", @@ -108,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -103801,7 +103808,7 @@ "" ] }, - "execution_count": 24, + "execution_count": 5, "metadata": { "application/json": { "expanded": false, @@ -103832,7 +103839,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -103925,7 +103932,7 @@ "4 {'nuclear': 6.827, 'biomass': 1.081, 'coal': 0... " ] }, - "execution_count": 25, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -103947,7 +103954,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -103971,7 +103978,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -104112,7 +104119,7 @@ "2 {'french': 1.504, 'dutch': 0.588, 'irish': -0.... " ] }, - "execution_count": 27, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -104134,12 +104141,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def clean_nested_dict_cols(df):\n", + " \"\"\"Unpacks columns contining nested dictionaries\"\"\"\n", " # Calculating columns that are still dictionaries\n", " s_types = df.iloc[0].apply(lambda val: type(val))\n", " cols_with_dicts = s_types[s_types == dict].index\n", @@ -104158,7 +104166,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -104341,7 +104349,7 @@ "4 0.0 0.678 1.504 0.0 0.0 -0.910 " ] }, - "execution_count": 29, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -104363,7 +104371,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -104406,6 +104414,7 @@ " return df_dt_rng\n", "\n", "def clean_df_dts(df):\n", + " \"\"\"Cleans the datetime index of the passed DataFrame\"\"\"\n", " df = set_dt_idx(df)\n", " df = df[~df.index.duplicated()] \n", "\n", @@ -104419,7 +104428,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -104628,7 +104637,7 @@ "2019-01-01 02:00:00+00:00 -0.910 5 " ] }, - "execution_count": 31, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -104650,21 +104659,21 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def retrieve_stream_df(start_date:str, end_date:str, stream:str, time_group='30m', renaming_dict={}):\n", " \"\"\"\n", - " `retrieve_stream_df` makes the call to Electric Insights and parses the response into a dataframe which is returned\n", + " Makes the call to Electric Insights and parses the response into a dataframe which is returned\n", "\n", - " Arguments:\n", - " * start_date - Start date for data given as a string in the form '%Y-%m-%d'\n", - " * end_date - End date for data given as a string in the form '%Y-%m-%d'\n", - " * stream - One of 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions'\n", - " * time_group - One of '30m', '1h', '1d' or '7d'. The default is '30m'\n", - " * renaming_dict - Mapping from old to new column names\n", + " Parameters:\n", + " start_date: Start date for data given as a string in the form '%Y-%m-%d'\n", + " end_date: End date for data given as a string in the form '%Y-%m-%d'\n", + " stream: One of 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions'\n", + " time_group: One of '30m', '1h', '1d' or '7d'. The default is '30m'\n", + " renaming_dict: Mapping from old to new column names\n", " \"\"\"\n", "\n", " # Calling data and parsing into dataframe\n", @@ -104691,7 +104700,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -104900,7 +104909,7 @@ "2019-01-01 02:00:00+00:00 0.0 -0.910 5 " ] }, - "execution_count": 35, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -104935,7 +104944,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -104965,7 +104974,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -104974,7 +104983,7 @@ "['prices_ahead', 'prices', 'temperatures', 'emissions', 'generation-mix']" ] }, - "execution_count": 66, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -104996,7 +105005,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -105005,7 +105014,7 @@ "['prices', 'emissions']" ] }, - "execution_count": 67, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -105027,7 +105036,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -105059,20 +105068,20 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def retrieve_streams_df(start_date:str, end_date:str, streams='*', time_group='30m', renaming_dict={}):\n", " \"\"\"\n", - " 'Call Streams' makes the calls to Electric Insights for the given streams and parses the responses into a dataframe which is returned\n", + " Makes the calls to Electric Insights for the given streams and parses the responses into a dataframe which is returned\n", "\n", - " Arguments:\n", - " * start_date - Start date for data given as a string in the form '%Y-%m-%d'\n", - " * end_date - End date for data given as a string in the form '%Y-%m-%d'\n", - " * streams - Contains 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions', or is given as all, '*'\n", - " * time_group - One of '30m', '1h', '1d' or '7d'. The default is '30m'\n", + " Parameters:\n", + " start_date: Start date for data given as a string in the form '%Y-%m-%d'\n", + " end_date: End date for data given as a string in the form '%Y-%m-%d'\n", + " streams: Contains 'prices_ahead', 'prices_ahead', 'prices', 'temperatures' or 'emissions', or is given as all, '*'\n", + " time_group: One of '30m', '1h', '1d' or '7d'. The default is '30m'\n", " \"\"\"\n", "\n", " df = pd.DataFrame()\n", @@ -105087,66 +105096,9 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "streams = '*'\n", - "renaming_dict = {\n", - " 'pumpedStorage' : 'pumped_storage',\n", - " 'northernIreland' : 'northern_ireland',\n", - " 'windOnshore': 'wind_onshore',\n", - " 'windOffshore': 'wind_offshore',\n", - " 'prices_ahead' : 'day_ahead_price',\n", - " 'prices' : 'imbalance_price',\n", - " 'temperatures' : 'temperature',\n", - " 'totalInGperkWh' : 'gCO2_per_kWh',\n", - " 'totalInTperh' : 'TCO2_per_h'\n", - "}\n", - "\n", - "df = retrieve_streams_df(start_date, end_date, streams, renaming_dict=renaming_dict)\n", - "\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Now we're ready to retrieve all of the streams in one, which we'll do for all years that data is available" - ] - }, - { - "cell_type": "code", - "execution_count": 85, + "execution_count": 20, "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "12/12\n", - "[12:03<01:04, 60.21s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 12/12 [12:03<01:04, 60.21s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wall time: 12min 2s\n" - ] - }, { "data": { "text/html": [ @@ -105217,124 +105169,124 @@ " \n", " \n", " \n", - " 2009-01-01 00:00:00+00:00\n", - " 58.05\n", + " 2019-01-01 00:00:00+00:00\n", + " 48.81\n", " 1\n", - " 74.74\n", - " 74.74\n", - " -0.6\n", - " 21278.0\n", - " 555.0\n", - " 6.973\n", + " 15.0\n", + " 15.0\n", + " 9.1\n", + " 2287.010\n", + " 83.662935\n", + " 6.924\n", + " 1.116\n", " 0\n", - " 17.650\n", " ...\n", - " 38.329\n", - " -0.404\n", - " None\n", - " None\n", - " 0.0\n", + " 27.336\n", + " 0.000\n", + " 8.054581\n", + " 3.141711\n", " 0.0\n", - " 1.977\n", + " 0.182\n", + " 1.552\n", " 0.0\n", " 0.0\n", - " -0.161\n", + " -0.702\n", " \n", " \n", - " 2009-01-01 00:30:00+00:00\n", - " 56.33\n", + " 2019-01-01 00:30:00+00:00\n", + " 50.24\n", " 2\n", - " 74.89\n", - " 74.89\n", - " -0.6\n", - " 21442.0\n", - " 558.0\n", - " 6.968\n", + " 15.0\n", + " 15.0\n", + " 9.1\n", + " 2467.906\n", + " 89.023375\n", + " 6.838\n", + " 1.103\n", " 0\n", - " 17.770\n", " ...\n", - " 38.461\n", - " -0.527\n", - " None\n", - " None\n", - " 0.0\n", + " 27.722\n", + " 0.024\n", + " 7.860487\n", + " 3.253887\n", " 0.0\n", - " 1.977\n", + " 0.196\n", + " 1.554\n", " 0.0\n", " 0.0\n", - " -0.160\n", + " -0.696\n", " \n", " \n", - " 2009-01-01 01:00:00+00:00\n", - " 52.98\n", + " 2019-01-01 01:00:00+00:00\n", + " 41.90\n", " 3\n", - " 76.41\n", - " 76.41\n", - " -0.6\n", - " 21614.0\n", - " 569.0\n", - " 6.970\n", + " 16.0\n", + " 16.0\n", + " 9.1\n", + " 2411.834\n", + " 87.888419\n", + " 6.834\n", + " 1.090\n", " 0\n", - " 18.070\n", " ...\n", - " 37.986\n", - " -1.018\n", - " None\n", - " None\n", - " 0.0\n", + " 27.442\n", + " 0.000\n", + " 7.879198\n", + " 3.340851\n", " 0.0\n", - " 1.977\n", + " 0.588\n", + " 1.504\n", " 0.0\n", " 0.0\n", - " -0.160\n", + " -0.722\n", " \n", " \n", - " 2009-01-01 01:30:00+00:00\n", - " 50.39\n", + " 2019-01-01 01:30:00+00:00\n", + " 39.32\n", " 4\n", - " 37.73\n", - " 37.73\n", - " -0.6\n", - " 21320.0\n", - " 578.0\n", - " 6.969\n", + " 16.0\n", + " 16.0\n", + " 9.1\n", + " 2119.532\n", + " 80.072988\n", + " 6.830\n", + " 1.085\n", " 0\n", - " 18.022\n", " ...\n", - " 36.864\n", - " -1.269\n", - " None\n", - " None\n", - " 0.0\n", + " 26.470\n", + " 0.000\n", + " 7.708874\n", + " 3.213702\n", " 0.0\n", - " 1.746\n", + " 0.600\n", + " 1.504\n", " 0.0\n", " 0.0\n", - " -0.160\n", + " -0.770\n", " \n", " \n", - " 2009-01-01 02:00:00+00:00\n", - " 48.70\n", + " 2019-01-01 02:00:00+00:00\n", + " 34.09\n", " 5\n", - " 59.00\n", - " 59.00\n", - " -0.6\n", - " 21160.0\n", - " 585.0\n", - " 6.960\n", + " 16.0\n", + " 16.0\n", + " 9.1\n", + " 2069.840\n", + " 79.016606\n", + " 6.827\n", + " 1.081\n", " 0\n", - " 17.998\n", " ...\n", - " 36.180\n", - " -1.566\n", - " None\n", - " None\n", - " 0.0\n", + " 26.195\n", + " 0.000\n", + " 7.479429\n", + " 3.122706\n", " 0.0\n", - " 1.730\n", + " 0.678\n", + " 1.504\n", " 0.0\n", " 0.0\n", - " -0.160\n", + " -0.910\n", " \n", " \n", "\n", @@ -105344,55 +105296,53 @@ "text/plain": [ " day_ahead_price SP imbalance_price valueSum \\\n", "local_datetime \n", - "2009-01-01 00:00:00+00:00 58.05 1 74.74 74.74 \n", - "2009-01-01 00:30:00+00:00 56.33 2 74.89 74.89 \n", - "2009-01-01 01:00:00+00:00 52.98 3 76.41 76.41 \n", - "2009-01-01 01:30:00+00:00 50.39 4 37.73 37.73 \n", - "2009-01-01 02:00:00+00:00 48.70 5 59.00 59.00 \n", + "2019-01-01 00:00:00+00:00 48.81 1 15.0 15.0 \n", + "2019-01-01 00:30:00+00:00 50.24 2 15.0 15.0 \n", + "2019-01-01 01:00:00+00:00 41.90 3 16.0 16.0 \n", + "2019-01-01 01:30:00+00:00 39.32 4 16.0 16.0 \n", + "2019-01-01 02:00:00+00:00 34.09 5 16.0 16.0 \n", "\n", " temperature TCO2_per_h gCO2_per_kWh nuclear \\\n", "local_datetime \n", - "2009-01-01 00:00:00+00:00 -0.6 21278.0 555.0 6.973 \n", - "2009-01-01 00:30:00+00:00 -0.6 21442.0 558.0 6.968 \n", - "2009-01-01 01:00:00+00:00 -0.6 21614.0 569.0 6.970 \n", - "2009-01-01 01:30:00+00:00 -0.6 21320.0 578.0 6.969 \n", - "2009-01-01 02:00:00+00:00 -0.6 21160.0 585.0 6.960 \n", + "2019-01-01 00:00:00+00:00 9.1 2287.010 83.662935 6.924 \n", + "2019-01-01 00:30:00+00:00 9.1 2467.906 89.023375 6.838 \n", + "2019-01-01 01:00:00+00:00 9.1 2411.834 87.888419 6.834 \n", + "2019-01-01 01:30:00+00:00 9.1 2119.532 80.072988 6.830 \n", + "2019-01-01 02:00:00+00:00 9.1 2069.840 79.016606 6.827 \n", "\n", - " biomass coal ... demand pumped_storage \\\n", - "local_datetime ... \n", - "2009-01-01 00:00:00+00:00 0 17.650 ... 38.329 -0.404 \n", - "2009-01-01 00:30:00+00:00 0 17.770 ... 38.461 -0.527 \n", - "2009-01-01 01:00:00+00:00 0 18.070 ... 37.986 -1.018 \n", - "2009-01-01 01:30:00+00:00 0 18.022 ... 36.864 -1.269 \n", - "2009-01-01 02:00:00+00:00 0 17.998 ... 36.180 -1.566 \n", + " biomass coal ... demand pumped_storage \\\n", + "local_datetime ... \n", + "2019-01-01 00:00:00+00:00 1.116 0 ... 27.336 0.000 \n", + "2019-01-01 00:30:00+00:00 1.103 0 ... 27.722 0.024 \n", + "2019-01-01 01:00:00+00:00 1.090 0 ... 27.442 0.000 \n", + "2019-01-01 01:30:00+00:00 1.085 0 ... 26.470 0.000 \n", + "2019-01-01 02:00:00+00:00 1.081 0 ... 26.195 0.000 \n", "\n", - " wind_onshore wind_offshore belgian dutch french \\\n", - "local_datetime \n", - "2009-01-01 00:00:00+00:00 None None 0.0 0.0 1.977 \n", - "2009-01-01 00:30:00+00:00 None None 0.0 0.0 1.977 \n", - "2009-01-01 01:00:00+00:00 None None 0.0 0.0 1.977 \n", - "2009-01-01 01:30:00+00:00 None None 0.0 0.0 1.746 \n", - "2009-01-01 02:00:00+00:00 None None 0.0 0.0 1.730 \n", + " wind_onshore wind_offshore belgian dutch french \\\n", + "local_datetime \n", + "2019-01-01 00:00:00+00:00 8.054581 3.141711 0.0 0.182 1.552 \n", + "2019-01-01 00:30:00+00:00 7.860487 3.253887 0.0 0.196 1.554 \n", + "2019-01-01 01:00:00+00:00 7.879198 3.340851 0.0 0.588 1.504 \n", + "2019-01-01 01:30:00+00:00 7.708874 3.213702 0.0 0.600 1.504 \n", + "2019-01-01 02:00:00+00:00 7.479429 3.122706 0.0 0.678 1.504 \n", "\n", - " ireland northern_ireland irish \n", - "local_datetime \n", - "2009-01-01 00:00:00+00:00 0.0 0.0 -0.161 \n", - "2009-01-01 00:30:00+00:00 0.0 0.0 -0.160 \n", - "2009-01-01 01:00:00+00:00 0.0 0.0 -0.160 \n", - "2009-01-01 01:30:00+00:00 0.0 0.0 -0.160 \n", - "2009-01-01 02:00:00+00:00 0.0 0.0 -0.160 \n", + " ireland northern_ireland irish \n", + "local_datetime \n", + "2019-01-01 00:00:00+00:00 0.0 0.0 -0.702 \n", + "2019-01-01 00:30:00+00:00 0.0 0.0 -0.696 \n", + "2019-01-01 01:00:00+00:00 0.0 0.0 -0.722 \n", + "2019-01-01 01:30:00+00:00 0.0 0.0 -0.770 \n", + "2019-01-01 02:00:00+00:00 0.0 0.0 -0.910 \n", "\n", "[5 rows x 24 columns]" ] }, - "execution_count": 85, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "%%time\n", - "\n", "streams = '*'\n", "renaming_dict = {\n", " 'pumpedStorage' : 'pumped_storage',\n", @@ -105406,14 +105356,7 @@ " 'totalInTperh' : 'TCO2_per_h'\n", "}\n", "\n", - "df = pd.DataFrame()\n", - "\n", - "for year in track(range(2009, 2021)):\n", - " start_date = f'{year}-01-01'\n", - " end_date = f'{year}-12-31'\n", - " \n", - " df_year = retrieve_streams_df(start_date, end_date, streams, renaming_dict=renaming_dict)\n", - " df = df.append(df_year)\n", + "df = retrieve_streams_df(start_date, end_date, streams, renaming_dict=renaming_dict)\n", "\n", "df.head()" ] @@ -105424,35 +105367,51 @@ "source": [ "
\n", "\n", - "We'll now save the retrieved data" + "Now we're ready to retrieve all of the streams in one, which we'll do for all years that data is available, then we'll save the resulting DataFrame." ] }, { "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "df.to_csv('../data/electric_insights.csv')" - ] - }, - { - "cell_type": "markdown", + "execution_count": 21, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 0 ns\n" + ] + } + ], "source": [ - "
\n", + "%%time\n", "\n", - "We'll also save the renaming dictionary to ensure we can fully reproduce this at a later date" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "with open('../data/EI_rename_mapping.json', 'w') as fp:\n", - " json.dump(renaming_dict, fp)" + "streams = '*'\n", + "renaming_dict = {\n", + " 'pumpedStorage' : 'pumped_storage',\n", + " 'northernIreland' : 'northern_ireland',\n", + " 'windOnshore': 'wind_onshore',\n", + " 'windOffshore': 'wind_offshore',\n", + " 'prices_ahead' : 'day_ahead_price',\n", + " 'prices' : 'imbalance_price',\n", + " 'temperatures' : 'temperature',\n", + " 'totalInGperkWh' : 'gCO2_per_kWh',\n", + " 'totalInTperh' : 'TCO2_per_h'\n", + "}\n", + "\n", + "retrieve_save_EI_data = False\n", + "\n", + "if retrieve_save_EI_data == True:\n", + " df = pd.DataFrame()\n", + "\n", + " for year in track(range(2010, 2021)):\n", + " start_date = f'{year}-01-01'\n", + " end_date = f'{year}-12-31'\n", + "\n", + " df_year = retrieve_streams_df(start_date, end_date, streams, renaming_dict=renaming_dict)\n", + " df = df.append(df_year)\n", + "\n", + " df.to_csv('../data/raw/electric_insights.csv')" ] }, { @@ -105470,7 +105429,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -105479,13 +105438,14 @@ "" ] }, - "execution_count": 3, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def year_week_2_prod_url(year, week, data_prefix=''): \n", + " \"\"\"Given a specified year and week the relevant `production_url` for energy-charts is returned\"\"\"\n", " if year < 2019:\n", " data_prefix = 'raw_'\n", " \n", @@ -105514,7 +105474,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -105543,13 +105503,14 @@ "Name: value, Length: 167, dtype: float64" ] }, - "execution_count": 4, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def fuel_json_2_net_balance(r_json):\n", + " \"\"\"Extracts the balance time-series\"\"\"\n", " if 'values' in r_json[0].keys(): # pre-2019 format\n", " df_balance = pd.DataFrame(r_json[0]['values'])\n", "\n", @@ -105595,7 +105556,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -105704,13 +105665,14 @@ "2018-03-19 01:00:00+01:00 0.064 19.012 0.0 -9.522 " ] }, - "execution_count": 5, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def response_2_df(r):\n", + " \"\"\"Parses the json response to a DataFrame\"\"\"\n", " r_json = r.json()\n", " s_balance = fuel_json_2_net_balance(r_json)\n", "\n", @@ -105747,7 +105709,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -105856,13 +105818,14 @@ "2015-12-28 01:00:00+01:00 0.012 7.059 0.0 -4.139 " ] }, - "execution_count": 6, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def year_week_2_fuel_df(year, week):\n", + " \"\"\"Given a specified year and week the relevant `df_fuels` dataset for energy-charts is returned\"\"\"\n", " production_url = year_week_2_prod_url(year, week)\n", "\n", " r = requests.get(production_url)\n", @@ -105895,7 +105858,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -105905,11 +105868,11 @@ "\n", "100%\n", "580/583\n", - "[03:29<00:00, 0.36s/it]" + "[03:16<00:00, 0.34s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 583/583 [03:29<00:00, 0.36s/it]" + " [████████████████████████████████████████████████████████████] 583/583 [03:16<00:00, 0.34s/it]" ] }, "metadata": {}, @@ -106071,7 +106034,7 @@ "[3 rows x 32 columns]" ] }, - "execution_count": 14, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -106103,7 +106066,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -106250,7 +106213,7 @@ "2010-01-04 04:00:00+01:00 0.0 16.635 0.713 -0.731 " ] }, - "execution_count": 37, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -106282,7 +106245,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -106300,7 +106263,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -106330,7 +106293,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -106348,12 +106311,12 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "df_fuels_clean.index.name = 'local_datetime'\n", - "df_fuels_clean.to_csv('../data/energy_charts.csv')" + "df_fuels_clean.to_csv('../data/raw/energy_charts.csv')" ] }, { @@ -106375,16 +106338,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -106398,15 +106361,17 @@ ] }, { - "cell_type": "code", - "execution_count": 116, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll then make a test request for price data from the German market" + ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -106415,7 +106380,7 @@ "" ] }, - "execution_count": 69, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -106436,20 +106401,23 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll extract the price time-series from the returned JSON" + ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def parse_A44_response(r, freq='H', tz='UTC'):\n", + " \"\"\"Extracts the price time-series\"\"\"\n", " s_price = pd.Series(dtype=float)\n", " parsed_r = xmltodict.parse(r.text)\n", " \n", @@ -106466,7 +106434,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -106477,10 +106445,10 @@ "2018-09-01 00:00:00+00:00 55.56\n", "2018-09-01 01:00:00+00:00 54.02\n", "2018-09-01 02:00:00+00:00 52.69\n", - "Freq: H, dtype: object" + "Freq: H, dtype: float64" ] }, - "execution_count": 71, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -106492,18 +106460,19 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# 10Y1001A1001A63L - up to '2018-09-30' (DE-AT-LU)\n", - "# 10Y1001A1001A82H - 2018-10-01' onwards (DE-LU)" + "
\n", + "\n", + "We can't query very large date ranges so we'll break up our requests into quarterly batches. \n", + "\n", + "We'll also account for the fact that the German market changes to exclude Austria after 2018-10-01 by creating two sets of date pairs." ] }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -106516,7 +106485,7 @@ " ('2016-01-01 00:00', '2016-03-31 23:55')]" ] }, - "execution_count": 195, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -106531,20 +106500,23 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We're now ready to create a wrapper to collate the date from each date batch" + ] }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def retreive_DAM_prices(dt_pairs, domain='10Y1001A1001A63L'):\n", + " \"\"\"Retrieves and collates the day-ahead prices for the specified date ranges\"\"\"\n", " params = {\n", " 'documentType': 'A44',\n", " 'in_Domain': domain,\n", @@ -106570,7 +106542,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -106580,11 +106552,11 @@ "\n", "100%\n", "15/15\n", - "[00:19<00:01, 1.27s/it]" + "[00:20<00:01, 1.31s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 15/15 [00:19<00:01, 1.27s/it]" + " [████████████████████████████████████████████████████████████] 15/15 [00:20<00:01, 1.31s/it]" ] }, "metadata": {}, @@ -106601,7 +106573,7 @@ "dtype: float64" ] }, - "execution_count": 93, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -106613,17 +106585,36 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll repeat this for the market excluding Austria" + ] }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 39, "metadata": {}, "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "100%\n", + "9/9\n", + "[00:11<00:01, 1.25s/it]
" + ], + "text/plain": [ + "\u001b[A\u001b[2K\r", + " [████████████████████████████████████████████████████████████] 9/9 [00:11<00:01, 1.25s/it]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/plain": [ @@ -106635,7 +106626,7 @@ "dtype: float64" ] }, - "execution_count": 203, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -106652,15 +106643,17 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll now combine and visualise the time-series" + ] }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -106669,7 +106662,7 @@ "" ] }, - "execution_count": 113, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, @@ -106694,22 +106687,24 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Before moving on we'll save this series as a csv" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "s_price.name = 'DE_price'\n", "s_price.index.name = 'local_datetime'\n", "\n", - "s_price.to_csv('../data/ENTSOE_DE_price.csv')" + "s_price.to_csv('../data/raw/ENTSOE_DE_price.csv')" ] }, { @@ -106718,17 +106713,20 @@ "source": [ "
\n", "\n", - "##### Generation by Fuel-Type" + "##### Generation by Fuel-Type\n", + "\n", + "We'll now create the functions for retrieving and parsing the fuel data" ] }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "#exports\n", - "def parse_A75_response(r, freq='15T', tz='UTC'):\n", + "def parse_A75_response(r, freq='15T', tz='UTC', warn_on_failure=False):\n", + " \"\"\"Extracts the production data by fuel-type from the JSON response\"\"\"\n", " psr_code_to_type = {\n", " 'A03': 'Mixed',\n", " 'A04': 'Generation',\n", @@ -106769,7 +106767,7 @@ " \n", " df_production = pd.DataFrame(dtype=float, columns=columns, index=index)\n", " \n", - " for timeseries in track(parsed_r['GL_MarketDocument']['TimeSeries']):\n", + " for timeseries in parsed_r['GL_MarketDocument']['TimeSeries']:\n", " try:\n", " psr_type = timeseries['MktPSRType']['psrType']\n", " dt_rng = pd.date_range(timeseries['Period']['timeInterval']['start'], timeseries['Period']['timeInterval']['end'], freq=freq, tz=tz)[:-1]\n", @@ -106780,7 +106778,8 @@ " df_production[psr_type] = s_psr_type\n", " \n", " except:\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", + " if warn_on_failure == True:\n", + " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", " \n", " assert df_production.index.duplicated().sum() == 0, 'There are duplicate date indexes'\n", " \n", @@ -106789,7 +106788,8 @@ " \n", " return df_production\n", "\n", - "def retreive_production(dt_pairs, domain='10Y1001A1001A63L'):\n", + "def retrieve_production(dt_pairs, domain='10Y1001A1001A63L', warn_on_failure=False):\n", + " \"\"\"Retrieves and collates the production data for the specified date ranges\"\"\"\n", " params = {\n", " 'documentType': 'A75',\n", " 'processType': 'A16',\n", @@ -106805,1180 +106805,54 @@ " try:\n", " r = client._base_request(params=params, start=start, end=end)\n", "\n", - " df_production_dt_rng = parse_A75_response(r)\n", + " df_production_dt_rng = parse_A75_response(r, warn_on_failure=warn_on_failure)\n", " df_production = df_production.append(df_production_dt_rng)\n", " except:\n", - " warn(f\"{start.strftime('%Y-%m-%d')} - {end.strftime('%Y-%m-%d')} failed\")\n", + " if warn_on_failure == True:\n", + " warn(f\"{start.strftime('%Y-%m-%d')} - {end.strftime('%Y-%m-%d')} failed\")\n", " \n", " return df_production" ] }, { "cell_type": "code", - "execution_count": 211, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, + "execution_count": 43, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", - "100%\n", - "9/9\n", - "[03:07<00:16, 20.81s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 9/9 [03:07<00:16, 20.81s/it]\u001b[B" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "147/147\n", - "[00:01<00:00, 0.00s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 147/147 [00:01<00:00, 0.00s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: UserWarning: 2018-11-25T00:00Z-2018-11-25T00:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2018-12-05T15:30Z-2018-12-05T15:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2018-12-15T05:00Z-2018-12-15T05:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2018-12-29T07:00Z-2018-12-29T07:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2018-10-14T19:15Z-2018-10-14T19:15Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2018-10-28T05:15Z-2018-10-28T05:15Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2018-12-23T03:45Z-2018-12-23T03:45Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "105/105\n", - "[00:00<00:00, 0.00s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 105/105 [00:00<00:00, 0.00s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: UserWarning: 2019-03-28T12:15Z-2019-03-28T12:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "87/87\n", - "[00:00<00:00, 0.00s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 87/87 [00:00<00:00, 0.00s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: UserWarning: 2019-04-01T21:45Z-2019-04-01T21:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-04-01T22:15Z-2019-04-01T22:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-04-12T23:15Z-2019-04-12T23:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-04-13T10:00Z-2019-04-13T10:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-04-19T07:45Z-2019-04-19T07:45Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-04-05T07:45Z-2019-04-05T07:45Z failed for B19\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "306/306\n", - "[00:01<00:00, 0.00s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 306/306 [00:01<00:00, 0.00s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: UserWarning: 2019-08-26T13:00Z-2019-08-26T13:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-28T20:15Z-2019-08-28T20:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-29T08:45Z-2019-08-29T08:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-29T16:00Z-2019-08-29T16:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-29T16:30Z-2019-08-29T16:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-29T21:30Z-2019-08-29T21:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-31T09:15Z-2019-08-31T09:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-31T13:15Z-2019-08-31T13:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-31T17:30Z-2019-08-31T17:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-31T18:00Z-2019-08-31T18:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-31T20:45Z-2019-08-31T20:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-31T23:45Z-2019-08-31T23:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-01T01:30Z-2019-09-01T01:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-01T07:45Z-2019-09-01T07:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-01T09:30Z-2019-09-01T09:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-02T04:30Z-2019-09-02T04:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-02T05:00Z-2019-09-02T05:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-02T05:45Z-2019-09-02T05:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-03T04:45Z-2019-09-03T04:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-03T05:30Z-2019-09-03T05:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-04T06:00Z-2019-09-04T06:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-05T18:15Z-2019-09-05T18:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-07T06:00Z-2019-09-07T06:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-08T00:00Z-2019-09-08T00:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-09T07:15Z-2019-09-09T07:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-11T19:45Z-2019-09-11T19:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-12T21:45Z-2019-09-12T21:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-13T04:15Z-2019-09-13T04:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-13T22:45Z-2019-09-13T22:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-14T08:00Z-2019-09-14T08:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-14T17:00Z-2019-09-14T17:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-14T17:30Z-2019-09-14T17:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-14T18:00Z-2019-09-14T18:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-16T13:15Z-2019-09-16T13:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-16T16:30Z-2019-09-16T16:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-16T17:45Z-2019-09-16T17:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-17T01:00Z-2019-09-17T01:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-17T17:00Z-2019-09-17T17:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-17T20:45Z-2019-09-17T20:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-17T23:15Z-2019-09-17T23:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-09-18T16:00Z-2019-09-18T16:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-30T19:45Z-2019-08-30T19:45Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-30T20:30Z-2019-08-30T20:30Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-09T09:15Z-2019-08-09T09:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-09T09:45Z-2019-08-09T09:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-08-09T10:15Z-2019-08-09T10:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "91/91\n", - "[00:00<00:00, 0.00s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 91/91 [00:00<00:00, 0.00s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: UserWarning: 2019-11-28T15:30Z-2019-11-28T15:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-11-04T12:00Z-2019-11-04T12:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-11-16T07:45Z-2019-11-16T07:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-11-16T09:15Z-2019-11-16T09:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-11-16T10:15Z-2019-11-16T10:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-12-05T08:00Z-2019-12-05T08:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-12-31T03:15Z-2019-12-31T03:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-12-31T11:00Z-2019-12-31T11:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2019-12-28T17:30Z-2019-12-28T17:30Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "149/149\n", - "[00:00<00:00, 0.00s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 149/149 [00:00<00:00, 0.00s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: UserWarning: 2020-01-04T19:15Z-2020-01-04T19:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-04T21:45Z-2020-01-04T21:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-05T21:30Z-2020-01-05T21:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-06T00:30Z-2020-01-06T00:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-07T01:15Z-2020-01-07T01:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-11T01:30Z-2020-01-11T01:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-11T05:00Z-2020-01-11T05:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-12T06:00Z-2020-01-12T06:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-12T08:45Z-2020-01-12T08:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-14T10:15Z-2020-01-14T10:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-15T07:45Z-2020-01-15T07:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-15T15:00Z-2020-01-15T15:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-16T14:00Z-2020-01-16T14:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-16T23:45Z-2020-01-16T23:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-17T10:30Z-2020-01-17T10:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-17T11:00Z-2020-01-17T11:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-22T05:45Z-2020-01-22T05:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-27T22:30Z-2020-01-27T22:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-27T23:45Z-2020-01-27T23:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-29T21:30Z-2020-01-29T21:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-29T22:30Z-2020-01-29T22:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-30T11:00Z-2020-01-30T11:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-06T02:45Z-2020-02-06T02:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-10T05:30Z-2020-02-10T05:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-12T07:30Z-2020-02-12T07:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-13T16:15Z-2020-02-13T16:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-16T21:30Z-2020-02-16T21:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-16T23:00Z-2020-02-16T23:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-17T00:00Z-2020-02-17T00:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-17T01:00Z-2020-02-17T01:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-17T01:30Z-2020-02-17T01:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-17T04:00Z-2020-02-17T04:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-17T14:00Z-2020-02-17T14:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-17T18:45Z-2020-02-17T18:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-18T16:45Z-2020-02-18T16:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-02-26T14:00Z-2020-02-26T14:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-03-31T05:15Z-2020-03-31T05:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-01-31T20:45Z-2020-01-31T20:45Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-03-18T11:30Z-2020-03-18T11:30Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "125/125\n", - "[00:00<00:00, 0.00s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 125/125 [00:00<00:00, 0.00s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: UserWarning: 2020-04-09T18:00Z-2020-04-09T18:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-04-14T09:00Z-2020-04-14T09:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-19T11:45Z-2020-06-19T11:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-19T12:15Z-2020-06-19T12:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-26T04:00Z-2020-06-26T04:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-26T05:15Z-2020-06-26T05:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-26T07:45Z-2020-06-26T07:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-26T11:15Z-2020-06-26T11:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-27T02:15Z-2020-06-27T02:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-27T07:45Z-2020-06-27T07:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-28T12:00Z-2020-06-28T12:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T04:45Z-2020-06-29T04:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T05:15Z-2020-06-29T05:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T05:45Z-2020-06-29T05:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T07:15Z-2020-06-29T07:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T07:45Z-2020-06-29T07:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T10:15Z-2020-06-29T10:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T10:45Z-2020-06-29T10:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T13:30Z-2020-06-29T13:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T16:30Z-2020-06-29T16:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T20:00Z-2020-06-29T20:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-29T21:30Z-2020-06-29T21:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T00:45Z-2020-06-30T00:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T01:15Z-2020-06-30T01:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T04:30Z-2020-06-30T04:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T09:00Z-2020-06-30T09:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T09:30Z-2020-06-30T09:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T11:00Z-2020-06-30T11:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T11:30Z-2020-06-30T11:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T12:45Z-2020-06-30T12:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T13:15Z-2020-06-30T13:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T16:15Z-2020-06-30T16:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-06-30T21:30Z-2020-06-30T21:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", + "\n", "100%\n", - "148/225\n", - "[00:01<00:00, 0.00s/it]
" + "15/15\n", + "[05:37<00:18, 22.45s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 225/225 [00:01<00:00, 0.00s/it]" + " [████████████████████████████████████████████████████████████] 15/15 [05:37<00:18, 22.45s/it]" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: UserWarning: 2020-07-02T12:00Z-2020-07-02T12:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-28T13:30Z-2020-07-28T13:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-28T14:15Z-2020-07-28T14:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-28T15:00Z-2020-07-28T15:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-02T09:15Z-2020-08-02T09:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-02T11:15Z-2020-08-02T11:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-02T11:45Z-2020-08-02T11:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-02T12:15Z-2020-08-02T12:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-04T20:45Z-2020-08-04T20:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-05T00:30Z-2020-08-05T00:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-05T02:15Z-2020-08-05T02:15Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-05T04:45Z-2020-08-05T04:45Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-05T06:30Z-2020-08-05T06:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-05T11:00Z-2020-08-05T11:00Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-08-05T12:30Z-2020-08-05T12:30Z failed for B04\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-16T07:45Z-2020-07-16T07:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-18T09:00Z-2020-07-18T09:00Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-01T00:00Z-2020-07-01T00:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-01T16:00Z-2020-07-01T16:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-01T17:30Z-2020-07-01T17:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-02T09:30Z-2020-07-02T09:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-02T10:45Z-2020-07-02T10:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-02T12:15Z-2020-07-02T12:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-02T13:45Z-2020-07-02T13:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-02T15:15Z-2020-07-02T15:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-02T23:15Z-2020-07-02T23:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-03T02:15Z-2020-07-03T02:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-03T02:45Z-2020-07-03T02:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-03T03:30Z-2020-07-03T03:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-03T10:15Z-2020-07-03T10:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-03T12:15Z-2020-07-03T12:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-03T14:30Z-2020-07-03T14:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-04T02:45Z-2020-07-04T02:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-04T06:45Z-2020-07-04T06:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-04T07:15Z-2020-07-04T07:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-04T07:45Z-2020-07-04T07:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-04T08:30Z-2020-07-04T08:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-04T09:15Z-2020-07-04T09:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-04T12:15Z-2020-07-04T12:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-04T12:45Z-2020-07-04T12:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-04T13:15Z-2020-07-04T13:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-05T08:15Z-2020-07-05T08:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-06T06:30Z-2020-07-06T06:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-06T11:00Z-2020-07-06T11:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-06T14:45Z-2020-07-06T14:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-06T19:00Z-2020-07-06T19:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-06T20:30Z-2020-07-06T20:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-06T21:00Z-2020-07-06T21:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-07T01:30Z-2020-07-07T01:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-07T07:00Z-2020-07-07T07:00Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-07T09:30Z-2020-07-07T09:30Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-07T10:45Z-2020-07-07T10:45Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-07-07T12:15Z-2020-07-07T12:15Z failed for B14\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n" - ] - }, { "data": { "text/html": [ "
\n", - "\n", + "\n", "100%\n", - "721/721\n", - "[00:01<00:00, 0.00s/it]
" + "9/9\n", + "[02:37<00:14, 17.45s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 721/721 [00:01<00:00, 0.00s/it]" + " [████████████████████████████████████████████████████████████] 9/9 [02:37<00:14, 17.45s/it]" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: UserWarning: 2020-10-05T18:00Z-2020-10-05T18:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-06T03:00Z-2020-10-06T03:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-06T12:45Z-2020-10-06T12:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-06T20:45Z-2020-10-06T20:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-09T12:00Z-2020-10-09T12:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-09T12:30Z-2020-10-09T12:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-10T03:45Z-2020-10-10T03:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-10T05:30Z-2020-10-10T05:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T01:30Z-2020-10-11T01:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T02:00Z-2020-10-11T02:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T03:30Z-2020-10-11T03:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T05:15Z-2020-10-11T05:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T05:45Z-2020-10-11T05:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T11:15Z-2020-10-11T11:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T13:30Z-2020-10-11T13:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T14:30Z-2020-10-11T14:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T15:30Z-2020-10-11T15:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T16:00Z-2020-10-11T16:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T16:45Z-2020-10-11T16:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T20:15Z-2020-10-11T20:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-11T22:00Z-2020-10-11T22:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-12T09:00Z-2020-10-12T09:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-12T13:30Z-2020-10-12T13:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-12T23:00Z-2020-10-12T23:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T01:00Z-2020-10-13T01:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T01:45Z-2020-10-13T01:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T04:15Z-2020-10-13T04:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T05:45Z-2020-10-13T05:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T06:15Z-2020-10-13T06:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T11:45Z-2020-10-13T11:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T15:30Z-2020-10-13T15:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T20:00Z-2020-10-13T20:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T20:30Z-2020-10-13T20:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-13T22:30Z-2020-10-13T22:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-14T16:15Z-2020-10-14T16:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-14T19:15Z-2020-10-14T19:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-14T21:15Z-2020-10-14T21:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-15T12:30Z-2020-10-15T12:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-15T16:45Z-2020-10-15T16:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-15T17:30Z-2020-10-15T17:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-15T18:15Z-2020-10-15T18:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-15T19:00Z-2020-10-15T19:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-16T05:00Z-2020-10-16T05:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-16T14:45Z-2020-10-16T14:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-16T16:15Z-2020-10-16T16:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-16T16:45Z-2020-10-16T16:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-16T19:00Z-2020-10-16T19:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-17T14:30Z-2020-10-17T14:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-17T16:15Z-2020-10-17T16:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-17T18:00Z-2020-10-17T18:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-17T20:00Z-2020-10-17T20:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-18T14:00Z-2020-10-18T14:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-18T14:30Z-2020-10-18T14:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-18T15:00Z-2020-10-18T15:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-18T15:45Z-2020-10-18T15:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-18T17:30Z-2020-10-18T17:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-18T19:00Z-2020-10-18T19:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-18T19:45Z-2020-10-18T19:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-18T20:30Z-2020-10-18T20:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-19T15:45Z-2020-10-19T15:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-19T17:30Z-2020-10-19T17:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-20T18:15Z-2020-10-20T18:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-20T19:00Z-2020-10-20T19:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-20T19:45Z-2020-10-20T19:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-20T20:30Z-2020-10-20T20:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-20T21:00Z-2020-10-20T21:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-20T23:30Z-2020-10-20T23:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-21T05:30Z-2020-10-21T05:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-21T09:45Z-2020-10-21T09:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-22T14:00Z-2020-10-22T14:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-22T14:45Z-2020-10-22T14:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T01:00Z-2020-10-24T01:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T01:30Z-2020-10-24T01:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T02:00Z-2020-10-24T02:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T02:45Z-2020-10-24T02:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T03:15Z-2020-10-24T03:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T05:00Z-2020-10-24T05:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T06:45Z-2020-10-24T06:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T08:30Z-2020-10-24T08:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T10:00Z-2020-10-24T10:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T10:30Z-2020-10-24T10:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T11:30Z-2020-10-24T11:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T13:15Z-2020-10-24T13:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T19:30Z-2020-10-24T19:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T21:00Z-2020-10-24T21:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-24T21:30Z-2020-10-24T21:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-27T03:45Z-2020-10-27T03:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-27T06:00Z-2020-10-27T06:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-27T07:00Z-2020-10-27T07:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-27T09:00Z-2020-10-27T09:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-27T09:45Z-2020-10-27T09:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-27T11:30Z-2020-10-27T11:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-28T01:00Z-2020-10-28T01:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-28T01:45Z-2020-10-28T01:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-28T02:15Z-2020-10-28T02:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-28T03:00Z-2020-10-28T03:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-28T04:00Z-2020-10-28T04:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-28T05:45Z-2020-10-28T05:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-28T08:30Z-2020-10-28T08:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-28T09:15Z-2020-10-28T09:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-28T09:45Z-2020-10-28T09:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-29T09:00Z-2020-10-29T09:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-29T09:30Z-2020-10-29T09:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-29T10:45Z-2020-10-29T10:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-29T14:30Z-2020-10-29T14:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-29T15:45Z-2020-10-29T15:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-29T23:45Z-2020-10-29T23:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T04:00Z-2020-10-31T04:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T11:45Z-2020-10-31T11:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T12:15Z-2020-10-31T12:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T13:45Z-2020-10-31T13:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T14:30Z-2020-10-31T14:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T17:30Z-2020-10-31T17:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T18:00Z-2020-10-31T18:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T18:45Z-2020-10-31T18:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T21:00Z-2020-10-31T21:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T22:15Z-2020-10-31T22:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-31T23:30Z-2020-10-31T23:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-01T03:45Z-2020-11-01T03:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-01T04:15Z-2020-11-01T04:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-01T10:30Z-2020-11-01T10:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-01T19:45Z-2020-11-01T19:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-04T00:30Z-2020-11-04T00:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-04T06:30Z-2020-11-04T06:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-04T07:30Z-2020-11-04T07:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-04T08:45Z-2020-11-04T08:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-04T18:30Z-2020-11-04T18:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-04T20:15Z-2020-11-04T20:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-05T00:45Z-2020-11-05T00:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-05T05:45Z-2020-11-05T05:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-06T03:45Z-2020-11-06T03:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-06T05:45Z-2020-11-06T05:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-06T16:30Z-2020-11-06T16:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-07T08:30Z-2020-11-07T08:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-07T09:15Z-2020-11-07T09:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-07T12:30Z-2020-11-07T12:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-07T18:00Z-2020-11-07T18:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-07T18:45Z-2020-11-07T18:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-08T19:45Z-2020-11-08T19:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-09T05:00Z-2020-11-09T05:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-09T05:45Z-2020-11-09T05:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-09T18:30Z-2020-11-09T18:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-09T19:15Z-2020-11-09T19:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-09T22:45Z-2020-11-09T22:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-10T00:15Z-2020-11-10T00:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-10T03:30Z-2020-11-10T03:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-10T23:15Z-2020-11-10T23:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-11T04:45Z-2020-11-11T04:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-11T22:00Z-2020-11-11T22:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-12T11:45Z-2020-11-12T11:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-12T14:30Z-2020-11-12T14:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-13T03:00Z-2020-11-13T03:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-13T07:45Z-2020-11-13T07:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-13T08:30Z-2020-11-13T08:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-14T04:45Z-2020-11-14T04:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-14T22:30Z-2020-11-14T22:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-15T00:30Z-2020-11-15T00:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-15T04:30Z-2020-11-15T04:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-15T23:00Z-2020-11-15T23:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-15T23:45Z-2020-11-15T23:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-16T03:30Z-2020-11-16T03:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-16T05:30Z-2020-11-16T05:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-16T17:00Z-2020-11-16T17:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-17T04:45Z-2020-11-17T04:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-17T14:30Z-2020-11-17T14:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-17T20:15Z-2020-11-17T20:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-17T21:00Z-2020-11-17T21:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-19T01:00Z-2020-11-19T01:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-19T09:15Z-2020-11-19T09:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-20T08:00Z-2020-11-20T08:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-20T17:15Z-2020-11-20T17:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-21T09:30Z-2020-11-21T09:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-21T10:30Z-2020-11-21T10:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-21T13:15Z-2020-11-21T13:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-21T14:00Z-2020-11-21T14:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-21T14:30Z-2020-11-21T14:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-21T19:15Z-2020-11-21T19:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-21T21:15Z-2020-11-21T21:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-22T00:30Z-2020-11-22T00:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-22T03:15Z-2020-11-22T03:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-22T07:30Z-2020-11-22T07:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-22T12:00Z-2020-11-22T12:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-22T13:15Z-2020-11-22T13:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-22T18:00Z-2020-11-22T18:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-22T19:30Z-2020-11-22T19:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-22T21:00Z-2020-11-22T21:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-24T23:45Z-2020-11-24T23:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-25T14:00Z-2020-11-25T14:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-26T03:45Z-2020-11-26T03:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-26T06:15Z-2020-11-26T06:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-26T09:30Z-2020-11-26T09:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-26T10:00Z-2020-11-26T10:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-26T14:45Z-2020-11-26T14:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-27T03:15Z-2020-11-27T03:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-27T04:30Z-2020-11-27T04:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-27T06:45Z-2020-11-27T06:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-27T07:45Z-2020-11-27T07:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-27T09:15Z-2020-11-27T09:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-27T09:45Z-2020-11-27T09:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-28T02:00Z-2020-11-28T02:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-28T04:45Z-2020-11-28T04:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-28T07:15Z-2020-11-28T07:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-28T08:30Z-2020-11-28T08:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-28T13:45Z-2020-11-28T13:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-28T14:45Z-2020-11-28T14:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-28T16:00Z-2020-11-28T16:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-28T19:00Z-2020-11-28T19:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T02:30Z-2020-11-29T02:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T04:00Z-2020-11-29T04:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T06:45Z-2020-11-29T06:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T07:15Z-2020-11-29T07:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T07:45Z-2020-11-29T07:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T09:45Z-2020-11-29T09:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T11:00Z-2020-11-29T11:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T12:45Z-2020-11-29T12:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T21:00Z-2020-11-29T21:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-29T23:30Z-2020-11-29T23:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-30T00:00Z-2020-11-30T00:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-30T04:00Z-2020-11-30T04:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-30T05:00Z-2020-11-30T05:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-30T06:30Z-2020-11-30T06:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-30T07:00Z-2020-11-30T07:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-01T01:45Z-2020-12-01T01:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-01T02:15Z-2020-12-01T02:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-01T19:45Z-2020-12-01T19:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-01T20:45Z-2020-12-01T20:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-02T01:00Z-2020-12-02T01:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-02T02:15Z-2020-12-02T02:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-02T03:30Z-2020-12-02T03:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-02T06:00Z-2020-12-02T06:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-02T06:30Z-2020-12-02T06:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-03T03:45Z-2020-12-03T03:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-03T06:45Z-2020-12-03T06:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-11T04:45Z-2020-12-11T04:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-11T05:30Z-2020-12-11T05:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-11T06:30Z-2020-12-11T06:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-11T08:00Z-2020-12-11T08:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-12T09:30Z-2020-12-12T09:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-12T11:45Z-2020-12-12T11:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-12T12:15Z-2020-12-12T12:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-12T13:00Z-2020-12-12T13:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-12T13:30Z-2020-12-12T13:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-14T05:00Z-2020-12-14T05:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-14T05:30Z-2020-12-14T05:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-14T06:15Z-2020-12-14T06:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-17T20:45Z-2020-12-17T20:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-18T12:15Z-2020-12-18T12:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-18T12:45Z-2020-12-18T12:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-19T20:00Z-2020-12-19T20:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-20T04:00Z-2020-12-20T04:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-20T04:30Z-2020-12-20T04:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-20T20:45Z-2020-12-20T20:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-20T21:15Z-2020-12-20T21:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-20T22:30Z-2020-12-20T22:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-21T17:15Z-2020-12-21T17:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-21T17:45Z-2020-12-21T17:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-24T10:30Z-2020-12-24T10:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-24T11:30Z-2020-12-24T11:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-24T12:00Z-2020-12-24T12:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-24T15:45Z-2020-12-24T15:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-25T07:15Z-2020-12-25T07:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-25T19:15Z-2020-12-25T19:15Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-26T04:30Z-2020-12-26T04:30Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-26T05:00Z-2020-12-26T05:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-26T15:45Z-2020-12-26T15:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-27T00:00Z-2020-12-27T00:00Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-12-28T01:45Z-2020-12-28T01:45Z failed for B06\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-10-23T23:30Z-2020-10-23T23:30Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-07T05:30Z-2020-11-07T05:30Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-07T10:30Z-2020-11-07T10:30Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-07T12:00Z-2020-11-07T12:00Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n", - ":54: UserWarning: 2020-11-07T12:30Z-2020-11-07T12:30Z failed for B12\n", - " warn(f\"{timeseries['Period']['timeInterval']['start']}-{timeseries['Period']['timeInterval']['start']} failed for {psr_type}\")\n" - ] - }, { "data": { "text/html": [ @@ -108188,14 +107062,14 @@ "2015-01-02 00:00:00+00:00 NaN " ] }, - "execution_count": 211, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_production_DE_AT_LU = retreive_production(DE_AT_LU_dt_pairs)\n", - "df_production_DE_LU = retreive_production(DE_LU_dt_pairs, domain='10Y1001A1001A82H')\n", + "df_production_DE_AT_LU = retrieve_production(DE_AT_LU_dt_pairs)\n", + "df_production_DE_LU = retrieve_production(DE_LU_dt_pairs, domain='10Y1001A1001A82H')\n", "\n", "df_production = df_production_DE_AT_LU.append(df_production_DE_LU)\n", "\n", @@ -108203,245 +107077,17 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 212, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BiomassFossil Brown coal/LigniteFossil Coal-derived gasFossil GasFossil Hard coalFossil OilGeothermalHydro Pumped StorageHydro Run-of-river and poundageHydro Water ReservoirNuclearOther renewableSolarWasteWind OffshoreWind OnshoreOtherMarine
2015-01-01 23:00:00+00:00NaNNaNNaNNaNNaN5.0NaNNaNNaN0.0NaNNaNNaNNaN391.00.0NaNNaN
2015-01-01 23:15:00+00:00NaNNaNNaNNaNNaN5.0NaNNaNNaN0.0NaNNaNNaNNaN292.00.0NaNNaN
2015-01-01 23:30:00+00:00NaNNaNNaNNaNNaN5.0NaNNaNNaN0.0NaNNaNNaNNaN271.00.0NaNNaN
2015-01-01 23:45:00+00:00NaNNaNNaNNaNNaN5.0NaNNaNNaN0.0NaNNaNNaNNaN266.00.0NaNNaN
2015-01-02 00:00:00+00:00NaNNaNNaNNaNNaN5.0NaNNaNNaN0.0NaNNaNNaNNaN268.00.0NaNNaN
\n", - "
" - ], - "text/plain": [ - " Biomass Fossil Brown coal/Lignite \\\n", - "2015-01-01 23:00:00+00:00 NaN NaN \n", - "2015-01-01 23:15:00+00:00 NaN NaN \n", - "2015-01-01 23:30:00+00:00 NaN NaN \n", - "2015-01-01 23:45:00+00:00 NaN NaN \n", - "2015-01-02 00:00:00+00:00 NaN NaN \n", - "\n", - " Fossil Coal-derived gas Fossil Gas \\\n", - "2015-01-01 23:00:00+00:00 NaN NaN \n", - "2015-01-01 23:15:00+00:00 NaN NaN \n", - "2015-01-01 23:30:00+00:00 NaN NaN \n", - "2015-01-01 23:45:00+00:00 NaN NaN \n", - "2015-01-02 00:00:00+00:00 NaN NaN \n", - "\n", - " Fossil Hard coal Fossil Oil Geothermal \\\n", - "2015-01-01 23:00:00+00:00 NaN 5.0 NaN \n", - "2015-01-01 23:15:00+00:00 NaN 5.0 NaN \n", - "2015-01-01 23:30:00+00:00 NaN 5.0 NaN \n", - "2015-01-01 23:45:00+00:00 NaN 5.0 NaN \n", - "2015-01-02 00:00:00+00:00 NaN 5.0 NaN \n", - "\n", - " Hydro Pumped Storage \\\n", - "2015-01-01 23:00:00+00:00 NaN \n", - "2015-01-01 23:15:00+00:00 NaN \n", - "2015-01-01 23:30:00+00:00 NaN \n", - "2015-01-01 23:45:00+00:00 NaN \n", - "2015-01-02 00:00:00+00:00 NaN \n", - "\n", - " Hydro Run-of-river and poundage \\\n", - "2015-01-01 23:00:00+00:00 NaN \n", - "2015-01-01 23:15:00+00:00 NaN \n", - "2015-01-01 23:30:00+00:00 NaN \n", - "2015-01-01 23:45:00+00:00 NaN \n", - "2015-01-02 00:00:00+00:00 NaN \n", - "\n", - " Hydro Water Reservoir Nuclear Other renewable \\\n", - "2015-01-01 23:00:00+00:00 0.0 NaN NaN \n", - "2015-01-01 23:15:00+00:00 0.0 NaN NaN \n", - "2015-01-01 23:30:00+00:00 0.0 NaN NaN \n", - "2015-01-01 23:45:00+00:00 0.0 NaN NaN \n", - "2015-01-02 00:00:00+00:00 0.0 NaN NaN \n", - "\n", - " Solar Waste Wind Offshore Wind Onshore Other \\\n", - "2015-01-01 23:00:00+00:00 NaN NaN 391.0 0.0 NaN \n", - "2015-01-01 23:15:00+00:00 NaN NaN 292.0 0.0 NaN \n", - "2015-01-01 23:30:00+00:00 NaN NaN 271.0 0.0 NaN \n", - "2015-01-01 23:45:00+00:00 NaN NaN 266.0 0.0 NaN \n", - "2015-01-02 00:00:00+00:00 NaN NaN 268.0 0.0 NaN \n", - "\n", - " Marine \n", - "2015-01-01 23:00:00+00:00 NaN \n", - "2015-01-01 23:15:00+00:00 NaN \n", - "2015-01-01 23:30:00+00:00 NaN \n", - "2015-01-01 23:45:00+00:00 NaN \n", - "2015-01-02 00:00:00+00:00 NaN " - ] - }, - "execution_count": 212, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "df_production.head()" + "
\n", + "\n", + "We'll quickly inspect for the presence of null values, due to the large number found we'll use the energy-charts data when it comes to fuel generation" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 213, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -108468,7 +107114,7 @@ "dtype: float64" ] }, - "execution_count": 213, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -108486,28 +107132,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 91, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -108516,7 +107141,13 @@ "text": [ "Converted 01-retrieval.ipynb.\n", "Converted 02-eda.ipynb.\n", - "Converted 03-lowess.ipynb.\n" + "Converted 03-lowess.ipynb.\n", + "Converted 04-price-surface-estimation.ipynb.\n", + "Converted 05-price-moe.ipynb.\n", + "Converted 06-carbon-surface-estimation-and-moe.ipynb.\n", + "Converted 07-prediction-confidence-and-intervals.ipynb.\n", + "Converted 08-hyper-parameter-tuning.ipynb.\n", + "Converted 09-tables-and-figures.ipynb.\n" ] } ], diff --git a/nbs/02-eda.ipynb b/nbs/02-eda.ipynb index 8236b74..f184bf3 100644 --- a/nbs/02-eda.ipynb +++ b/nbs/02-eda.ipynb @@ -13,7 +13,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Exploratory Data Analysis\n", + "# Exploratory Data Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook includes some visualisation and exploration of the price and fuel data for Germany and Great Britain\n", "\n", "
\n", "\n", @@ -27,7 +34,6 @@ "outputs": [], "source": [ "#exports\n", - "import json\n", "import pandas as pd\n", "\n", "import seaborn as sns\n", @@ -52,6 +58,7 @@ "source": [ "#exports\n", "def load_EI_df(EI_fp):\n", + " \"\"\"Loads the electric insights data and returns a DataFrame\"\"\"\n", " df = pd.read_csv(EI_fp)\n", "\n", " df['local_datetime'] = pd.to_datetime(df['local_datetime'], utc=True)\n", @@ -318,7 +325,7 @@ "source": [ "%%time\n", "\n", - "df = load_EI_df('../data/electric_insights.csv')\n", + "df = load_EI_df('../data/raw/electric_insights.csv')\n", "\n", "df.head()" ] @@ -340,6 +347,7 @@ "source": [ "#exports\n", "def load_DE_df(EC_fp, ENTSOE_fp):\n", + " \"\"\"Loads the energy-charts and ENTSOE data and returns a DataFrame\"\"\"\n", " # Energy-Charts\n", " df_DE = pd.read_csv(EC_fp)\n", "\n", @@ -549,7 +557,7 @@ } ], "source": [ - "df_DE = load_DE_df('../data/energy_charts.csv', '../data/ENTSOE_DE_price.csv')\n", + "df_DE = load_DE_df('../data/raw/energy_charts.csv', '../data/raw/ENTSOE_DE_price.csv')\n", "\n", "df_DE.head()" ] @@ -573,6 +581,7 @@ "source": [ "#exports\n", "def clean_df_for_plot(df, freq='7D'):\n", + " \"\"\"Cleans the electric insights dataframe for plotting\"\"\"\n", " fuel_order = ['Imports & Storage', 'nuclear', 'biomass', 'gas', 'coal', 'hydro', 'wind', 'solar']\n", " interconnectors = ['french', 'irish', 'dutch', 'belgian', 'ireland', 'northern_ireland']\n", "\n", @@ -748,10 +757,7 @@ " 'hydro' : (50,120,196), \n", " 'wind' : (72,194,227), \n", " 'solar' : (255,219,65),\n", - "}\n", - "\n", - "with open('../data/fuel_colours.json', 'w') as fp:\n", - " json.dump(fuel_colour_dict_rgb, fp)" + "}" ] }, { @@ -771,10 +777,12 @@ "source": [ "#exports\n", "def rgb_2_plt_tuple(rgb_tuple):\n", + " \"\"\"converts a standard rgb set from a 0-255 range to 0-1\"\"\"\n", " plt_tuple = tuple([x/255 for x in rgb_tuple])\n", " return plt_tuple\n", "\n", "def convert_fuel_colour_dict_to_plt_tuple(fuel_colour_dict_rgb):\n", + " \"\"\"Converts a dictionary of fuel colours to matplotlib colour values\"\"\"\n", " fuel_colour_dict_plt = fuel_colour_dict_rgb.copy()\n", " \n", " fuel_colour_dict_plt = {\n", @@ -826,7 +834,21 @@ "outputs": [], "source": [ "#exports\n", + "def hide_spines(ax, positions=[\"top\", \"right\"]):\n", + " \"\"\"\n", + " Pass a matplotlib axis and list of positions with spines to be removed\n", + " \n", + " Parameters:\n", + " ax: Matplotlib axis object\n", + " positions: Python list e.g. ['top', 'bottom']\n", + " \"\"\"\n", + " assert isinstance(positions, list), \"Position must be passed as a list \"\n", + "\n", + " for position in positions:\n", + " ax.spines[position].set_visible(False)\n", + " \n", "def stacked_fuel_plot(df, fuel_colour_dict, ax=None, save_path=None, dpi=150):\n", + " \"\"\"Plots the electric insights fuel data as a stacked area graph\"\"\"\n", " df = df[fuel_colour_dict.keys()]\n", " \n", " if ax == None:\n", @@ -837,8 +859,7 @@ "\n", " plt.rcParams['axes.ymargin'] = 0\n", " ax.spines['bottom'].set_position('zero')\n", - " ax.spines['right'].set_visible(False)\n", - " ax.spines['top'].set_visible(False)\n", + " hide_spines(ax)\n", "\n", " ax.set_xlim(df.index.min(), df.index.max())\n", " ax.legend(ncol=4, bbox_to_anchor=(0.85, 1.15), frameon=False)\n", @@ -891,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -901,8 +922,12 @@ "Converted 01-retrieval.ipynb.\n", "Converted 02-eda.ipynb.\n", "Converted 03-lowess.ipynb.\n", - "Converted 04-surface-estimation.ipynb.\n", - "Converted 05-merit-order-effect.ipynb.\n" + "Converted 04-price-surface-estimation.ipynb.\n", + "Converted 05-price-moe.ipynb.\n", + "Converted 06-carbon-surface-estimation-and-moe.ipynb.\n", + "Converted 07-prediction-confidence-and-intervals.ipynb.\n", + "Converted 08-hyper-parameter-tuning.ipynb.\n", + "Converted 09-tables-and-figures.ipynb.\n" ] } ], diff --git a/nbs/03-lowess.ipynb b/nbs/03-lowess.ipynb index 96cf291..a5d3ffc 100644 --- a/nbs/03-lowess.ipynb +++ b/nbs/03-lowess.ipynb @@ -13,7 +13,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Lowess\n", + "# Lowess" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Outlines the development of the Scikit-Learn compatible `Lowess` model, as well as its extension `LowessDates` used for time-adaptive LOWESS regression. Included are functions for extending both models to generate prediction and confidence intervals. \n", "\n", "
\n", "\n", @@ -22,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +47,6 @@ "from scipy import linalg\n", "\n", "from timeit import timeit\n", - "import FEAutils as hlp\n", "from ipypb import track\n", "\n", "from moepy import eda" @@ -86,7 +92,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 4, @@ -171,6 +177,7 @@ "source": [ "#exports\n", "def get_dist_threshold(dist, frac=0.4):\n", + " \"\"\"Identifies the minimum distance that contains the desired data fraction\"\"\"\n", " frac_idx = int(np.ceil(len(dist)*frac))\n", " dist_threshold = sorted(dist)[frac_idx]\n", " \n", @@ -204,7 +211,19 @@ "metadata": {}, "source": [ "
\n", - "We'll now define a function that will map from the distances to their relative weights according to a tricube kernel" + "We'll now define a function that will map from the distances to their relative weights according to a tricube kernel\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\label{eqn:tricube_kernel}\n", + " w(x) = \\left\\{ \n", + " \\begin{array}{ll}\n", + " (1 - |x|^3)^3 & \\mbox{for $|x| < 1$} \\\\\n", + " 0 & \\mbox{for $|x| \\geq 1$}\n", + " \\end{array}\n", + " \\right.\n", + "\\end{equation}\n", + "$$" ] }, { @@ -311,8 +330,9 @@ "metadata": {}, "outputs": [], "source": [ - "def get_weights(x, val, frac=0.4):\n", - " dist = get_dist(x, val)\n", + "def get_weights(x, loc, frac=0.4):\n", + " \"\"\"Calculates the weightings at each data point for a single localised regression\"\"\"\n", + " dist = get_dist(x, loc)\n", " dist_threshold = get_dist_threshold(dist, frac=frac)\n", "\n", " weights = dist_to_weights(dist, dist_threshold)\n", @@ -328,7 +348,7 @@ { "data": { "text/plain": [ - "0.6359804000000002" + "0.39384629999999987" ] }, "execution_count": 13, @@ -346,7 +366,7 @@ "source": [ "
\n", "\n", - "We've successfully calculated the weights with respect to a single point but we need to repeat this across each of values in our dataset." + "We've successfully calculated the weights with respect to a single point but we need to repeat this across each of value locations in our dataset." ] }, { @@ -357,6 +377,7 @@ "source": [ "#exports\n", "def get_all_weights(x, frac=0.4):\n", + " \"\"\"Calculates the weightings at each data point for a LOWESS regression\"\"\"\n", " all_weights = []\n", "\n", " for i in range(len(x)):\n", @@ -473,7 +494,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.44 s ± 153 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "672 ms ± 10.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -492,7 +513,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "108 ms ± 3.97 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "43.4 ms ± 2.63 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], @@ -531,7 +552,7 @@ { "data": { "text/plain": [ - "3.676330700000001" + "1.3339513000000007" ] }, "execution_count": 21, @@ -578,7 +599,7 @@ { "data": { "text/plain": [ - "0.13504879999999986" + "0.06741010000000003" ] }, "execution_count": 23, @@ -644,7 +665,7 @@ { "data": { "text/plain": [ - "0.5580327999999994" + "0.2501098000000006" ] }, "execution_count": 25, @@ -664,7 +685,7 @@ { "data": { "text/plain": [ - "0.6308658999999963" + "0.24560150000000114" ] }, "execution_count": 26, @@ -693,7 +714,7 @@ { "data": { "text/plain": [ - "0.7078855999999973" + "0.28330940000000027" ] }, "execution_count": 27, @@ -713,7 +734,7 @@ { "data": { "text/plain": [ - "0.6569263000000021" + "0.2943747000000023" ] }, "execution_count": 28, @@ -742,12 +763,16 @@ "source": [ "#exports\n", "def clean_weights(weights):\n", - " weights = weights/weights.sum(axis=0) # We'll then normalise the weights so that for each model they sum to 1 for a single data point\n", + " \"\"\"Normalises each models weightings and removes non-finite values\"\"\"\n", + " with np.errstate(divide='ignore', invalid='ignore'):\n", + " weights = weights/weights.sum(axis=0) # We'll then normalise the weights so that for each model they sum to 1 for a single data point\n", + " \n", " weights = np.where(~np.isfinite(weights), 0, weights) # And remove any non-finite values\n", " \n", " return weights\n", "\n", "def dist_2_weights_matrix(dist_matrix, dist_thresholds):\n", + " \"\"\"Converts distance matrix and thresholds to weightings\"\"\"\n", " weights = dist_to_weights(dist_matrix, dist_thresholds.reshape(-1, 1))\n", " weights = clean_weights(weights)\n", " \n", @@ -797,6 +822,7 @@ "source": [ "#exports\n", "def get_full_dataset_weights_matrix(x, frac=0.4):\n", + " \"\"\"Wrapper for calculating weights from the raw data and LOWESS fraction\"\"\"\n", " frac_idx = get_frac_idx(x, frac)\n", " \n", " dist_matrix = vector_to_dist_matrix(x)\n", @@ -866,7 +892,7 @@ { "data": { "text/plain": [ - "1.999823499999998" + "1.0831957999999986" ] }, "execution_count": 34, @@ -899,6 +925,7 @@ "num_fits_2_reg_anchors = lambda x, num_fits: np.linspace(x.min(), x.max(), num=num_fits)\n", "\n", "def get_weighting_locs(x, reg_anchors=None, num_fits=None): \n", + " \"\"\"Identifies the weighting locations for the provided dataset\"\"\"\n", " num_type_2_dist_rows = {\n", " type(None) : lambda x, num_fits: x.reshape(-1, 1),\n", " int : lambda x, num_fits: num_fits_2_reg_anchors(x, num_fits).reshape(-1, 1),\n", @@ -912,6 +939,7 @@ " return weighting_locs\n", "\n", "def create_dist_matrix(x, reg_anchors=None, num_fits=None): \n", + " \"\"\"Constructs the distance matrix for the desired weighting locations\"\"\"\n", " weighting_locs = get_weighting_locs(x, reg_anchors=reg_anchors, num_fits=num_fits)\n", " dist_matrix = np.abs(weighting_locs - x.reshape(1, -1))\n", " \n", @@ -1113,6 +1141,7 @@ "source": [ "#exports\n", "def get_weights_matrix(x, frac=0.4, weighting_locs=None, reg_anchors=None, num_fits=None):\n", + " \"\"\"Wrapper for calculating weights from the raw data and LOWESS fraction\"\"\"\n", " frac_idx = get_frac_idx(x, frac)\n", " \n", " if weighting_locs is not None:\n", @@ -1232,7 +1261,9 @@ "\n", "#### Regression\n", "\n", - "Now that we've calculated the weightings necessary for local regression we need to create the regression functions. We'll start by calculating the intercept and gradient of a linear regression fit with optional weighting." + "Now that we've calculated the weightings necessary for local regression we need to create the regression functions. We'll start by calculating the intercept and gradient of a linear regression fit with optional weighting.\n", + "\n", + "N.b. This section of the code was heavily inspired by [this gist](https://gist.github.com/agramfort/850437) created by [Alexandere Gramfort](https://gist.github.com/agramfort)" ] }, { @@ -1243,6 +1274,7 @@ "source": [ "#exports\n", "def calc_lin_reg_betas(x, y, weights=None):\n", + " \"\"\"Calculates the intercept and gradient for the specified local regressions\"\"\"\n", " if weights is None:\n", " weights = np.ones(len(x))\n", " \n", @@ -1250,7 +1282,7 @@ " A = np.array([[np.sum(weights), np.sum(weights * x)],\n", " [np.sum(weights * x), np.sum(weights * x * x)]])\n", " \n", - " betas = linalg.solve(A, b)\n", + " betas = np.linalg.lstsq(A, b)[0]\n", " \n", " return betas" ] @@ -1283,7 +1315,7 @@ "ax.plot([x.min(), x.max()], [intercept+gradient*x.min(), intercept+gradient*x.max()], label='Linear Regression')\n", "\n", "ax.legend(frameon=False)\n", - "hlp.hide_spines(ax)" + "eda.hide_spines(ax)" ] }, { @@ -1297,12 +1329,12 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 74, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1331,9 +1363,11 @@ "ax.plot([x.min(), x.max()], [intercept+gradient*x.min(), intercept+gradient*x.max()], label='Linear Regression')\n", "\n", "ax.set_ylim(-1.2, 1.2)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "\n", - "leg = ax.legend(frameon=False)" + "leg = ax.legend(frameon=False)\n", + "\n", + "fig.savefig('../img/LOWESS_single_regression_example.png', dpi=250)" ] }, { @@ -1342,7 +1376,14 @@ "source": [ "
\n", "\n", - "We can repeat this for all data-points" + "We can repeat this for all data-points, the error being minimized across these regressions is shown in the equation below\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\label{eqn:lowess_err}\n", + " n^{-1} \\sum_{i=1}^{n} W_{k i}(x)\\left(y_{i}-\\sum_{j=0}^{p} \\beta_{j} x^{j}\\right)^{2}\n", + "\\end{equation}\n", + "$$" ] }, { @@ -1353,7 +1394,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 46, @@ -1405,7 +1446,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 47, @@ -1465,6 +1506,7 @@ "check_array = lambda array, x: np.ones(len(x)) if array is None else array\n", "\n", "def fit_regressions(x, y, weights=None, reg_func=calc_lin_reg_betas, num_coef=2, **reg_params):\n", + " \"\"\"Calculates the design matrix for the specified local regressions\"\"\"\n", " if weights is None:\n", " weights = np.ones(len(x))\n", " \n", @@ -1528,6 +1570,7 @@ "source": [ "#exports\n", "def lowess_fit_and_predict(x, y, frac=0.4, reg_anchors=None, num_fits=None, x_pred=None):\n", + " \"\"\"Fits and predicts smoothed local regressions at the specified locations\"\"\"\n", " weighting_locs = get_weighting_locs(x, reg_anchors=reg_anchors, num_fits=num_fits)\n", " weights = get_weights_matrix(x, frac=frac, weighting_locs=weighting_locs)\n", " design_matrix = fit_regressions(x, y, weights)\n", @@ -1551,7 +1594,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 51, @@ -1599,7 +1642,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 52, @@ -1656,7 +1699,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "6.99 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "2.02 ms ± 57.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -1697,7 +1740,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.99 s ± 124 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "989 ms ± 21.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -1756,7 +1799,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 3.15 s\n" + "Wall time: 1.86 s\n" ] }, { @@ -2005,7 +2048,7 @@ "source": [ "%%time\n", "\n", - "df_EI = eda.load_EI_df('../data/electric_insights.csv')\n", + "df_EI = eda.load_EI_df('../data/raw/electric_insights.csv')\n", "\n", "df_EI.head()" ] @@ -2062,7 +2105,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 123 ms\n" + "Wall time: 63 ms\n" ] } ], @@ -2120,7 +2163,7 @@ "\n", "ax.set_ylim(0, 100)\n", "ax.set_xlim(15, 55)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlabel('Demand (GW)')\n", "ax.set_ylabel('Day-Ahead Price (£/MWh)')" ] @@ -2144,7 +2187,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 62, @@ -2192,7 +2235,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 63, @@ -2201,7 +2244,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAyuElEQVR4nO3dd3xUZdbA8d+ZSSAkhJpQkwAKSi8aE5piQ+koqKu8KugqouK6ll2xr/quZddXLKiIioBdQZBmw4ZIE5AeeksBEnoKkEzmef+YGZgMk8bMpMyc7+eTTzJz79z7zCQ597nnaWKMQSmlVPCzVHYBlFJKVQwN+EopFSI04CulVIjQgK+UUiFCA75SSoWIsMouQEliYmJMy5YtK7sYSilVbaxcufKAMSbW27YqHfBbtmzJihUrKrsYSilVbYjI7uK2aUpHKaVChAZ8pZQKERrwlVIqRGjAV0qpEKEBXymlQoTPAV9E4kXkZxFJEZENInK/l31ERF4XkW0islZELvD1vEoppcrHHzV8G/CQMaYd0B24V0Tae+zTH2jj/BoNvO2H84YGux1yMkFnNa1a9PeiqiGfA74xZq8xZpXz52wgBWjusdtQYJpxWArUE5Gmvp476NntMHUQvNIOpgx0PFaVT38vqpryaw5fRFoC3YBlHpuaA6luj9M486LgOsZoEVkhIiuysrL8WbzqJ+8ApC4Du83xPe9AZZdIgf5eqpG0tDSGDh1KmzZtOPfcc7n//vvJz88/Y7+MjAyuu+66Uo83YMAAjhw5clZl+de//sXLL798Vq/1F78FfBGpDcwA/m6MOea52ctLvN4LG2MmGWMSjTGJsbFeRweHjqhYiE8GS5jje1SIfx5Vhf5eqgVjDMOGDeOaa65h69atbNmyhZycHB5//PEi+9lsNpo1a8b06dNLPeb8+fOpV69egEoceH6ZWkFEwnEE+4+NMV952SUNiHd7HAdk+OPcQU0ERs511CCjYh2PVeXT30u18NNPPxEREcFtt90GgNVqZfz48bRq1YpWrVrx888/c+LECXJzc5k8eTKDBg1i/fr15OXlMWrUKDZt2kS7du3YtWsXb775JomJiaeme8nJyaF///707t2bxYsX07x5c77++mtq1arFu+++y6RJk8jPz6d169Z8+OGHREZGVvKn4eBzwBcRAd4HUowxrxSz22xgrIh8BiQDR40xe309d0iwWKB2o8ouhfKkv5dyeWbOBjZmeN74+6Z9szo8PbhDsds3bNjAhRdeWOS5OnXqkJCQgM1mY8mSJaxdu5YGDRqwa9euU/u89dZb1K9fn7Vr17J+/Xq6du3q9fhbt27l008/5d133+WGG25gxowZ3HzzzQwbNow777wTgCeeeIL333+f++67z+f36w/+qOH3Am4B1onIaudzjwEJAMaYicB8YACwDcgDbvPDeZVSqljGGMTL3Zfr+b59+9KgQYMzti9atIj773f0Lu/YsSOdO3f2evxWrVqduhhceOGFpy4a69ev54knnuDIkSPk5ORw9dVX++cN+YHPAd8YswjvOXr3fQxwr6/nUkpVTyXVxAOlQ4cOzJgxo8hzx44dIzU1FavVSlRUlNfXmTJ2ta1Zs+apn61WK8ePHwdg1KhRzJo1iy5dujBlyhR++eWXs3sDAaAjbZVSQemKK64gLy+PadOmAVBYWMhDDz3EqFGjSsyp9+7dmy+++AKAjRs3sm7dunKdNzs7m6ZNm1JQUMDHH3989m8gADTgK6WCkogwc+ZMvvzyS9q0acN5551HREQEzz//fImvu+eee8jKyqJz58689NJLdO7cmbp165b5vM899xzJycn07duXtm3b+vo2/ErKevtSGRITE40ugKKUqkiFhYUUFBQQERHB9u3bueKKK9iyZQs1atSo7KKViYisNMYkettWpVe8UkqpipaXl8dll11GQUEBxhjefvvtahPsS6MBXyml3ERHRwft0qqaw1dKqRChAV8ppUKEBnyllAoRGvCVUipEaMBXSgWlBx54gFdfffXU46uvvpo77rjj1OOHHnqIV155hdmzZ/Piiy8CMGvWLDZu3Hhqn0svvbTUBtxdu3YhIrzxxhunnhs7dixTpkwp8XVPPfUUCxYsKMc78p0GfKVUUOrZsyeLFy8GwG63c+DAATZs2HBq++LFi+nVqxdDhgxh3LhxwJkBv6waNWrEa6+95nWu/eI8++yzXHnlleU+ly804CulglKvXr1OBfwNGzbQsWNHoqOjOXz4MCdPniQlJYVu3boxZcoUxo4dy+LFi5k9ezb/+Mc/6Nq1K9u3bwfgyy+/JCkpifPOO4/ffvvN67liY2O54oormDp16hnbVq9eTffu3encuTPXXnsthw8fBhxz7rjm4B83bhzt27enc+fOPPzwwwBkZWUxfPhwLrroIi666CJ+//13nz8T7YevlKo67Ha/rTPQrFkzwsLC2LNnD4sXL6ZHjx6kp6ezZMkS6tatS+fOnYsMqOrZsydDhgxh0KBBRVa/stlsLF++nPnz5/PMM88Um4YZN24c/fv35/bbby/y/K233sobb7xBnz59eOqpp3jmmWeKpJoOHTrEzJkz2bRpEyJyakWt+++/nwceeIDevXuzZ88err76alJSUnz6TDTgK6WqBtdawanLHCuJjZzrWHfAB65a/uLFi3nwwQdJT09n8eLF1K1bl549e5bpGMOGDQOKToHsTatWrUhKSuKTTz459dzRo0c5cuQIffr0AWDkyJFcf/31RV5Xp04dIiIiuOOOOxg4cCCDBg0CYMGCBUXSS8eOHSM7O5vo6OgyldsbTekopaqGAKwV7Mrjr1u3jo4dO9K9e3eWLFlyKn9fFq5pkK1WKzabrcR9H3vsMV566SXs5VjYPiwsjOXLlzN8+HBmzZpFv379AEe7w5IlS1i9ejWrV68mPT3dp2APGvCVUlVFANYK7tWrF3PnzqVBgwZYrVYaNGjAkSNHWLJkCT169Dhj/+joaLKzs8/6fG3btqV9+/bMnTsXgLp161K/fv1Tuf8PP/zwVG3fJScnh6NHjzJgwABeffVVVq9eDcBVV13FhAkTTu3net4X/lrTdjIwCMg0xnT0sv1S4Gtgp/Opr4wxz/rj3EqpIBGAtYI7derEgQMHGDFiRJHncnJyiImJOWP/G2+8kTvvvJPXX3+9TIuae/P444/TrVu3U4+nTp3KmDFjyMvL45xzzuGDDz4osn92djZDhw7lxIkTGGMYP348AK+//jr33nsvnTt3xmazcckllzBx4sSzKpOLX6ZHFpFLgBxgWgkB/2FjzKDyHFenR1ZKqfIpaXpkv6R0jDELgUP+OJZSSqnAqMgcfg8RWSMi34hIsQtcishoEVkhIiuysrIqsHhKKRXcKirgrwJaGGO6AG8As4rb0RgzyRiTaIxJjI31vdFGKaWUQ4UEfGPMMWNMjvPn+UC4iJzZYqKUUipgKiTgi0gTEUeTu4gkOc97sCLOrZRSysFf3TI/BS4FYkQkDXgaCAcwxkwErgPuFhEbcBy40VTl1dOVUioI+SXgG2NuKmX7BGBCSfsopZQKLB1pq5RSIUIDvlJKhQgN+EopFSI04CulVIjQgK+UUiFCA75SSoUIDfhKKRUiNOArpVSI0IAfqux2yMkEHfCsVMjQgB+KXItFv9IOpgx0PFZKBT0N+KEoAItFK6WqPg34oSgAi0Urpao+v0yepqqZ8i4Wbbc79o1sCHkHz3yNa7ufFp5WSgWGBvxQZbFA7Ual7+fK9+9ZCjWioCDPcVcwcq7jGK7tqcuKPq+UqnL0P1OVzJXvN4Vw8tiZeX9tD1Cq2tCAr0rmyveLFWrWOTPv794eEJfk6OapXT2VqpL8teLVZGAQkGmM6ehluwCvAQOAPGCUMWaVP86tAsw93+8th+/anpsJ02+H8e0rP7WjbQpKeeWv/8gpQL8StvcH2ji/RgNv++m8qiK48v0Wq+O7RxA1ItiMcHzPnxwrDOfgno0cPbiPEwWFVPhKljrGQKli+WuJw4Ui0rKEXYYC05zr2C4VkXoi0tQYs9cf51eBYyu0s/tQHnsO5ZHq/Eo7fJyDufkczs3ncF4+R/IKsNkN8N7pF/7fn6d+rBFmoU5EODG1a9Cwdg0aRtWkab0IWjaIpEVUAS2bN6NJ3VpYLH6ojXtrUyhL47RSIaCieuk0B1LdHqc5nzsj4IvIaBx3ASQkJFRI4YKKD+kMW6GdlIyjrNqWxsaDdlL2ZbN5XzYnbadryTXDLDSvV4vYSAvnxkZRP6oB9SPDqRVuJcwC4YV5hK/9hMLDuzlpjeKETTgRncCxFldzIN/KwZyTrD50hG/XHye/0FX730xUDQvtm9WlQ7O6dGhWhy7x9WgdW9v7RaCk9+hqU3D1GtIxBkqdUlEB31vk8Xqvb4yZBEwCSExM1Na/8ihnF8lCu2Ft2hF+23qAP3YdYtXuw+TmFwLQwHqcdi3juKV7C9o2rUPLhpEkNIgkJjIcy4eDHeeolQzXeJwjJxMWvwdWm+NxGHAc2PIqxHeHMY79C4/tZ+8rF7O7MIadpglb7fFs2N+RLzLiyXOVITKcpITaJLduSo/WDTm/cTRiTMnvsbxjDJQKIRUV8NOAeLfHcUBGBZ07dLinM/YshaxN0KhdkaB39HgBv2zO5OdNmfy6JYvDeQWIwPmNoxnWqSEXrXuaREmhqeUYclPKmemQnMySUyauGrar335+Lhg72AuL7G+NbkRci9bE7VlKL7MRMCBhFD68kZ0noli1+xDLfviCZZtj+HbTYQCa1Y3g8tbRXLHrOD0QIop5j2UeY6BUiKmogD8bGCsinwHJwFHN3zv5s0eJZ7B952KITyb7LzNZsDmLuWv2snBrFgWFhoZRNbjs/EZcel4Ml8RZqNewMeQegBwg9Vjx6ZDSUiaevXpyD8D024ru73rPI+ecsd0a3YjWdYTWkXnc8M0LUNNGumnEoks+4sd0K1+tO8BH+Q8TyQn6hq9lyFv3cXGLWtQYNVsHfClVCvFHLwoR+RS4FIgB9gNPA+EAxpiJzm6ZE3D05MkDbjPGrCjtuImJiWbFilJ3q74CMUrVboesTRROvIRFtnZ8Yb+MH+hOfqGhWd0IBlqW0D9vNl1bxGAZORumDSk6ijYuCa77AKIbO/rTe7sYlTbVgrcyuY7jLSUDRc9jt0NuluNC4DHC98SIr1m28xDfrtzMN+v2coTa1COH/t1acUOPNnSNr4fotA8qhInISmNMordt/uqlc1Mp2w1wrz/OFVQC0KMk49hJPl9rZXrBBNJt0dSznGBEcgKDuzanW4N8LONvALFB2nY4sKXoKFqAtOWOi05JuXKLBSJjyn6xck+x5GZ5f8+1Gznn6N/v6M+fusxx8Rm9EN7tc2r/iILD9GnbmD7nN+KZDwazaHces2sMYtb6unz652LaNonmpqQErunWnLo1rTrtg1JudC6dyuSnHiXGGFbuPswHv+/i2w37sBtD79ateLRTXfp2a03N8LDTtWb388W2LZoCcs2TExVbfGB2OduLVXHv2X3OHmMHjOPiU9ttf/eRvCLUGDWby/MOcHlULNknbcxek8Fny1N5evYGXvgmhWGdGnL77j20RrtoKgV+SukEStCndODsUw52O7acLOZtL+C9RbtYl36UOhFh3JSUwM3dWxDfILLoOVw1XfeUjSt94i09Y4xj4JIrMI+aV7R8pW0v73vOyXQMlrI7e/dYrI5ePaPmOc7lGslbhtr6+vSjfLR0N1/9mU6+zc6lljXc0WwXve6ZiGgNXwW5klI6GvCroZP5BXz15qNMzGzPbtOYc2OjuK1nS4adX5PI+k3ODLzuwdQSBg966X3jTWkXI3/mx90vIHFJcP2UoqN6z+I9HMw5ycdLdzNtyU4O5NroHFeXsZe15sp2jbFQTPuEUtWcBvwgcdJWyKfL9jDxl63syy6gs2zn3vA59H14GpYZfy2+9utLbbwilXQB8eE9nLQVMnNVOm/9sp09h/Jo2ySasTKdAUc+wZKQVHzDsV4QVDUU8EZbFViFdsOsP9N55YctpB85TlLL+vyn7lQuPjgdSUgGsRSfT3fvAlmWHjWVqaT+8z4MqKoZZuXGpASuuzCO2WsymPDjZsYevJq20pZ/7prOZTn7EfcL5q3O3kslpcCq8ueoVDG0hu9Pfg4GxhgWpGTy3+82sWV/Dh2b1+GRfm3p3TrGMeLUdS7wXvsNtcVJyvL52+0UZmcyd8oLvLKvC7tNYxLjavPPrHEkyUZHuuiu3xxjGDzbE9wvBKHweapqSWv4FaG04FrOi8HynYd46dtNrNx9mFYxUbw54gL6d2xyem4ZkaK1YW+131CZSMxuL1ujrvN3ZE1dxtC4JAbcOo7PN5/k9R+3ccPJJ+hrXckTCRtp4d57yX2UsKsba7B/nipoacD3l5KCa1lq2s4Lwt6CKJ6fs5Y5Gw/RuE5NXhjWiesujCPcWkpN0ls6JBQmEvPWnbO4YOz+O0pbTni4lZu7t2T4BfFMXrSDN3+20HfPRdz+3RbG3jiL2rbDRUcJuy4Ewfx5qqCmAd9foorvL15qTdtu5+QHQ3h/ZwMm2IZiw8L9DVcz5r4nqBURXvQ8nncKJd05hMJEYu5LMIIz/VKOaSHsdmrlH+Tey1pzXWI8L327iYm/bmfGqjQe6deWYbfMwXLioLPbajVpC1GqGJrD96fiUgsiJXY5/Hn1Fp79/Dd2miZcZfmDJ8M+It56CMYsKjoxmOedguaUS+/O6akM0zz8uXUX//ohgzVpR+kSV5dnBren6083h/bnrKoNzeFXFIul+B4zXpYBzBj6JU/NSWFByn7OCa/JVPkPfWrtcMww6Tb52alug1mbih5bc8rlv4spaZoH5++nW+oyZsYlM+v6d3jx281c+/YSRoa15mHrSmqH6uesgoIGfH9wrzUWlzd3uxjYCwv5aEdtXnp1IXYjPNKvLX/t2YIa+Vc5UgdZm0/3EnELRGdMgaA5ZYeznQ7Z83eFnLoAWNKWMeyGmlzZoQ8vf7uZqUuv4rvCRJ5rtpQrS/qctdumqsI0peMrbw2y4P2f3hi2vXMzj+xJZKX9PC5uHcPzwzoVnQbBuV+RbpbXfeC4K7DbQKxFUz0aYHzj/vlBsYO7Vu46yKMz1rAl6zgDOzXl6cHtaVQnovQUkaZ+VAXTkbaBVMYh//k2O2//sp03f95KZA0LTw5sz7DzayLF5ZvLGIiUn5VwAc232Zm0cDuv/7SNmmEWHu/flr9suBtJc/5ehk+GVzuUfwoLpfxIc/iBVIauj+vTj/LQF2vYvD+bQZ2b8vTAdsR+NRzml1AT9ExTBHtvm6qihPRQjTALYy9vw4BOTXls5jrGzVzPt5bevBS+gcapyxy/F02xqSrMXwug9ANeA6zAe8aYFz22Xwp8Dex0PvWVMebZ0o5bLWr4UGyt0FZoZ+Kv23l1wVYaRNXg+Ws7cWX7xmc/mZk6e35OfdnthmlLdvHi3DXUNCd5tulihtw33rF4s16YVSUKaA1fRKzAm0BfHGvX/iEis40xGz12/c0YM8jX81VJXmqFOw/k8uAXq/lzzxEGdW7K/17TkXqRNRwbQ2FAVFUSgCkmLBZhVK9WXNImhoc+X8n96Vfx3ad/8tzQjjTUi7eqovyR0kkCthljdgA4160dCngG/ODjpdZojOGjZXt4fl4K4Vbh9Zu6MaRLs6KvC4UBUVVJAKeYOKdRNNPv7cOkhTsY/8MWlu9cyAvDOtO3fWO/HF8pf/JHF4LmQKrb4zTnc556iMgaEflGRDoUdzARGS0iK0RkRVZWlh+KFyCuWuMr7RwNqnY7mdknuG3KHzw5az2JLevz/QN9zgz2Lq67Ag32gee6o7KEBeSOymoR7r70XGbf14tG0RHcOW0FT8xax4mCQueyjZmOHjwlKet+SvnAHzV8bxHL8692FdDCGJMjIgOAWUAbbwczxkwCJoEjh++H8gWGR63x13XbeWjOLnJO2nhuaAdu7t6i6GLaqvJU0B1V2yZ1mHVvL17+fjOTFu5g+Y5DvB75Lm0zvyk5lRRqs5qqSuOPv6o0IN7tcRyQ4b6DMeaYMSbH+fN8IFxEYvxw7sphtztqYvHJFEhNXoj4OyM/3ULDqJrMGdubW3q01GBf1QT6jspZQ69hFR4b0I5ptyVyKOc4Q3YNZ1r+ZZjdSx0jpb3V4L2lnJQKAH8E/D+ANiLSSkRqADcCs913EJEm4oyAIpLkPO9BP5y7YtntkL3PURsb357U/NpcX/8L3jl0Af+TnMDXY3vRpnF0ZZdSVTTP9F6hjUsW38a39jH0CtvMU7bbuNP2MIcm9j+V/isiwCknpVx8TukYY2wiMhb4Dke3zMnGmA0iMsa5fSJwHXC3iNiA48CNpiqO+Cqp657HNLzzCy/ikZ3XQc0TvDniAgZ2blo5ZVaVz7OG7pzjKAYbk8P+wwdJX/Li4vb0t/0vb+x6iyTPRmNtxFcVxC+JQmPMfGPMecaYc40x/3Y+N9EZ7DHGTDDGdDDGdDHGdDfGLPbHeX3m3lDmpRG2COc/9Qm7hccLbuOegr9zbkQ28/92sQb7UOdZQ3fNcWQJQ1p05/ZBlzGzxVdESj43nXyMiSuOYbd71He0EV9VgNCdWsGzocx9WLznfDVwah6ce3dfzGYTz5geTXhoYFfCw6yBKZ+qXkpbp8BuJ/vwfsZ9m8G8dfu4sm0jXh7YnHoxTTXIK78qaeBV6HYF8LwNdw2LF+vpqYldNX27nbl/bGZIxi0cqNWKqbddxLihF2qwV6d51tC9PI5u2JQJIy7gmcHt+XXTXga+8j1r3h4Fx/Zpd0xVIUI34HvehrvmrB+zyDH9sPNCkH9sH8/890XGfrWddpY05t13MX3O15GU6uyICCO7RPFlzefAwHV7hjH1v3/DfOAljaiUn4VuwHc1lD2Ycnr2SYvFkcZxXgj2Nbmcmz7awgeHu3C7dT6fyWM0Cc+p7JKr6i4qlq4tY5kX8SQXW9bxdMFIxm5PJPvw/rK9XgdpqbMUugEfvDeUOS8Ei4ctY1DmaFIyT/BG0+94quZnhCcklr/LnP5zKk/Ov7F6D63gvdaLeST8c74tvIghkzexeV92ya8trXOBUiUIzUbbEhrYDDDx1x3897tNtIqJYuKwVrRpEXd2C1frCEpVGuff3rJMK2M//ZPckzZevr4LAzp56flltzsGb7lWQ9OZVpUX2mjrzssgGdfjY+9fy+hpK3jp203079iEr+uNp82HF8DUwRAZU/7eFDqCUnnyvONz3mUmn9OQuff1pm2TaO75eBUvfpNCoXvXTdff7cTeEB6pg7TUWQnugO8tnVLMIJmttsYM3T6Ynzdn8eSg9kwYEkft9EW+BWsdQanclZKOaVy7Bp/WfIERYT8x8dcdjJq8jMO5+Y6Nrr9bU+hY5P6u33TlM1VuwRvwi/vn8jJI5rt6N3JN/rNkW+rwyR3J/LV3K8fSg74Ga28Nwyp0lXbHl3eAmulLeD7sPV6q8R7Ldh5i8IRFrE8/WvTvNqF70TEiSpVR8C5xWNwc6G7D2O21Ynjtx228ljGAzk0jmXhrMs3qOxcU99dw9xKWzFMhprSFb9y2/6XFSc6/PJkxH61k+NuLeXF4J67V6ReUj4I34Jf0z2WxkB1Wnwc+WsWClP0MvyCOf1/bkYhwj4FUGqyVP5VWiXDfHtmQrlMHM8eWwr3hj/LA53bWph3lsQHtCNdgr85S8Ab8Ev65dmTlcOe0Few6mMfTg9szqqdOZ6wqSGmVCNf2nExIXUassfGxPMHzSfP54PddbMw4xoQRFxAbXbPiyqyCRvDm8MFrP/ufNu1n6ITfOZxXwId/TeK2Xq002Kuqxy1nH56QyNPXXsj4v3RhdeoRBr+xiDWpRyq7hKoaCpl++MYY3vplOy9/v5n2Tevwzi0XEufK1ytVFbnGh0Q2PDUOZH3GMcZ8tJLM7JP8+5qOXJ8YX/pxVEgJvX74Ht0xc0/auOfjVfz3u80Mbt+A6Xf10GCvqj6LxTH+Y+rgU73NOjaNZvbY3iS2qM8/pq/lX7M3UFCoo21V2QRfwPfojrk7K5thby3muw37eDz2d17bMYhanwzRIemqevDS26xBVA2m3Z7EX3u3YsriXdz83jIO5pw887U6rYfy4JeALyL9RGSziGwTkXFetouIvO7cvlZELvDHeb1y+wdZuCuXIW/+zr5jJ5h603ncmfsOYnTUq6pGihm8F2a18OSg9qfy+kMm/O7or++ic+4oL3wO+CJiBd4E+gPtgZtEpL3Hbv2BNs6v0cDbvp63WFGxmLhkJhUOZtTJh2laL5I5Y3tzcafWOupVVT+lDN67tlsc0+/qjrEXMvztxcz6M92xQaf1UF74o1tmErDNGLMDQEQ+A4YCG932GQpMc65ju1RE6olIU2PMXj+cv4jjBXYeifhfZhfsZWCnJvz3+i5EhlkgNwtGzjm7SdCUqkwldeW02+m04H+YXbCRe8If4++f21mffpRx/c4nzHMcSklrNquQ4I+A3xxIdXucBiSXYZ/mwBkBX0RG47gLICEhodyFEYHdh/L4Z7/zubvPuYgxZ85YqX/sKhi4Zs9MXUaMs7/+/ybO471FO0lJO8iEm2dQn2xHgPf2fwB6AQgx/sjhe/tL8WwlKss+jieNmWSMSTTGJMbGlj/tEhFuZfrdPbnn0taO/vV6a6uCkZfZM8PjL+SZq+L4T5Mf+WPXQQa/NIuNR2s4grnn/0Fupub4Q5A/An4a4N4ZOA7IOIt9/Cbc6va2dMZKFYw8Z88c/asjsI/vwA1HJvN5jWcpsNkY9vYS5qzJOPP/ANGKUAjyR0rnD6CNiLQC0oEbgREe+8wGxjrz+8nA0UDk773y1yRoSlUl3uaKcl0AgG5hu5gT/zV32x7kvk//ZEPGMf5xyxysJw6ervSUNJGbCko+B3xjjE1ExgLfAVZgsjFmg4iMcW6fCMwHBgDbgDzgNl/PWy46CZoKNp4VGTgdwOOS4PopNKrdiE8LDc/M2cDEX7ezce8x3rixG3VdlR6tCIWckJlaQamgV0IvnE+W7eHp2etpVq8Wk25J5Pwm0ZVUSBVooTe1glKhyMtkgS4jkhP4bHR38vILufat3/l2vVtGVUfkhgwN+EoFO2dAvzChPnPv6815jWsz5qNVvPzdJuy2Qu2tE0KCdz58pdTp7pvOxtnGt87m84gXedLangk/w8bUA4xPX0dd9ylHtL0raGkNX6lg5tn//sAWaqYv4aWwd3gufCoLdxzjWtsLbCNBe+uEAA34SgUzz/73sW0hPhmxhnHLOTl8fEcyx2o05hr7f/ghabL21gly2ktHqWDn2XvH43HGkePc9eFK1qUf5e9XtuFvl7fBYtHAX11pLx2lQpln7x2Px83q1eLLMT0YdkFzXl2wlbs+Wkn2iYJKLLAKFA34Sikiwq383/BOPHVVPD9tyuSaN39ny/7syi6W8jMN+EopsNuRaYO5fdFlfNh8JkfzChg64Xdm/plW2SVTfqQBXylVpDdPz4NfMe+OdnRqXpcHPl/Do1+t40RBYWWXUPmBBnyl1Bm9eRo3ac4ndyYzps+5fLp8D8PfXszug7mVXUrlI+2lo5RycPXeiWxYZGW4BRv38+AXqzHAy9d34eoOTSq7pKoE2ktHKVU6iwUiY2Dq4CJTLVzZvjHz/nYxLRtGcdeHK/n3vI0UFOoUDNWRBnyl1GnFrBAX3yCS6Xf34JbuLXj3t53cOGkp6UeOF32tt0nYdGK2KkUDvlLqNPdcflySI1A7g3XNMCvPXdOR1/7ShU17j9L/1YV8s84566Zrzh73SdiKe04vAJVGA75S6jTXwioPbHAumdi+6CyadjtDV49mnuVBWpHO3R+v4tGv1nH86P4z7wx0Hd0qx6eALyINROQHEdnq/F6/mP12icg6EVktItoKq1RVZrGAWLyveesM4i3J4EsZx109mvDp8j0M/mALKbEDiq4drevoVjm+1vDHAT8aY9oAPzofF+cyY0zX4lqPlVJViGewjop11MiNOfV8jfgLefTyOD68/SKOHi9gaPoIpl78C2bkXMfdgetu4cEUGDXPMZ2D5zFVhfKpW6aIbAYuNcbsFZGmwC/GmPO97LcLSDTGlOuSrt0ylapE7pOsGXN6Xv24JBj+Pnx1x6l59g8Mn8E/Zqzj581ZXHJeLP8Z3pkmdSNKPqbOzBkQgeyW2dgYsxfA+b24lRMM8L2IrBSR0SUdUERGi8gKEVmRlZXlY/GUUmfNNcmaMZC16XQ6Jm05nDhy+vGepcQc38nkkYk8N7QDf+w8xFXjf+Xr1emcUaH0nMjNsxFXG3UDqtSALyILRGS9l6+h5ThPL2PMBUB/4F4RuaS4HY0xk4wxicaYxNhYveVTqlK5etpM7A3hkWfMq49YoUYUvHMxMnUQtyQnMP/+i2ndqDb3f7aaez9ZxaHcfO/Hzd5XtBG30KaNugFW6hKHxpgri9smIvtFpKlbSiezmGNkOL9nishMIAlYeJZlVkpVFFdPG1MI+bkwZhE0anc6P5+1Cd65uEhDbKuYRnw5pifvLNzO+B+2sHznYV4a3okr2jV2HNN1EdmzFIwdMKdW4zqjUVeXW/QrX1M6s4GRzp9HAl977iAiUSIS7foZuApY7+N5lVIVwb3xNqH76WAPjvRMo3ZeG3eteVnc0+dcZo/tTUztGvx16goe+Hw1B3NOFr2IYMBiLXrXoI26AeNro21D4AsgAdgDXG+MOSQizYD3jDEDROQcYKbzJWHAJ8aYf5fl+Npoq1QVUFpDa3GNu/HJMHIu+XaY8PM23v5lG7VrhvHkwPZcu2Y0kuZsAL5+yum8vjbq+qykRludPE0p5T85mY4cvN3mqKk/mHIqLbNlfzbjZqxl1Z4jXNwmhuevakZ8XJwGdj/TydOUUhXDW/99p/MaRzN9TE+eHdqBP/ccoe+k9Uz6bQc2XyZi01495aI1fKWUf5UhLbP36HGenLWeBSmZtG0SzVOD29Pz3Jjyn8cjfYRF67Baw1dKVRzPvvZwRk28ad1avHtrIhNvvoCckzZGvLuMez5eSdrhvLKfp5iZPVXxNOArpQLL26yZgIjQr2NTFjzYhwevbMNPKZlc8X+/Mv6HLRzPL8OSiiWkj5R3pfbDV0opn3iribv1r4+wCn9L/TvDw7bxQvg9vPajnekrU/nnpc0YnNgGy4lDZ6zCdSptNHJO0edVibSGr5QKrNJq4s4LQnP2M6HwOT6/5TzqHk/l/lk7GPzUu/z6n+sxL7b0PiJ36mDHKl0a7MtEA75SKrA8Z830DM4eF4TkhDrMtTzMq+FvcsxEMDL/n4zIuY9VtpbFj8itziqwp5GmdJRSgedqyPXGdUFw9ewBLAlJXLNnKQNqbeST48m8YbuGYfnPcnHYHu7Pa0xifPLp3jnVOXdfwT2NtFumUqrqceXonbn7XGtdPlq0iUlLMzmYm0/PcxoytkcMPTqcg1TnrpglDFQ7W9otUylVvbjuCCxWqN2IqFo1uatvFxY9cjlPDGzH1qwcRny8mUETfmfmn2nk20oZvFVVB2hVcE8jreErpaoPZ83/RI0GzFqdwXuLdrItM4cmdSK4pUcLbkiMJza65pmvqcoDtPw8f5DOpaOUqv68BG47wq9bs3jvtx38vu0gYRbh6g5NGJGcQI9zGmKxSEDSJlVZSQFfG22VUtWDl/78ltqNuOz8RlzWJoZte1L5bH0u01elMW/dXhIaRDK0azOGdmlG62Bp5PWR1vCVUtWDMY5++K7A7eri6VHzPzHia77dmMmMVWn8vu0AdgMdm9VhcLs6XNX1XFrF1j7z2OVJqwRqCmc/HVdTOkqp4OAtKJaQssnMPsHcNXv5ek0Ga1KPAHBubBRXtm3E5S1r0u28ltSwStly/HY75GbC9NvL1h5Q3ouIn9oZApbSEZHrgX8B7YAkY4zX6Cwi/YDXACuOhVFe9OW8SqkQ5a0/v6uni3vKxhlsG9WO5fberbi9dyvSDufxY0omCzbuY/Jv23jnNysRso7EFg3okd6IZDmHDrtXUStrU9GVvcD7sox7ljqWePTc131/9wAOxV8AnOkqe2EhO3ankrFuO5d0aeP3j8/XFa/aAXbgHeBhbwFfRKzAFqAvkAb8AdxkjNlY2vG1hq+UKpNSVt0qUlvOyST7/7rxu60tS00Hlja4hk2ZxwGwUkgbSadjdC6d+gzn3EbRtIqNoqn1GJbx7R13EQBigRq1oSCv2HMUuet4YMMZdwbZJwrYnprOpqNhbNqXzebVi1ifV59soqhbK5zVT/VFziK1E7AavjEmxXmCknZLArYZY3Y49/0MGAqUGvCVUqpM3Gv+uVklL4YeFUt0Qhf6pS6jX1wNuL4Dh6jDH2vXs/6bd1lnb8HPx85h+pzTIapmmIWWlvE0tqUTWzuc2POSiVnzNtEml4idNmr9uZWI6AaIgM1uKCy0Y6t3EzkHUjka3Y6jv2ZwZPt57LMnkb4llrRnv+foidMzgtYKt3J+kw4MbluDrq2acEGL+gH5mCqil05zINXtcRqQXNzOIjIaGA2QkJAQ2JIppYKPtxSPi/ssm7lZjlr3+PY0iE/m6lvncPWWNEidgYlLZv+wGew4mMvOzBx27T/AzqMxZB1NYFtuIVl/ZlNQ+D+nj/vlNi8F6e/4lgWStZe6louJNQeIj7RxwbmRxKW8SwsyaGfNIP6hX7BENw7kpwKUIeCLyAKgiZdNjxtjvi7DObxV/4vNIxljJgGTwJHSKcPxlVLKoaRpkz3z6sMnF70TOH7w1Jw+EhVLExGa1KlJz4W3nn7NWEfqxhjDsbx8co9kcjy8HscL7JwoKARjsH7zMGGZa7E07kDt4ROoN3sk0em/YYm/CIa/D1/dAVufhMio0ymhChoXUGrAN8Zc6eM50oB4t8dxQIaPx1RKqaK8NZS6p5s9+/GLnHknIHI6+NrtjkZZL+khEaFuVE3qRsUXPX/WJjgwG7BB1i4w4yBjIRgbpC2HE0ccxzGFkJ8LYxZ5b/QNkIpI6fwBtBGRVkA6cCMwogLOq5QKJd4WWomMOd2Y65nqqd2o6CydxfXKqeFWEy9u0FZx+8e2LXpOz8cVGOzB926Z1wJvALHAPBFZbYy5WkSa4eh+OcAYYxORscB3OLplTjbGbPC55Eop5c4zoEc2PLPG7xng3Wv07lwXj7LUxN3vBLzt73nO4i4yFcDXXjozgZlens8ABrg9ng/M9+VcSilVIs9gWlxvnbLkyz0vHiUFe281e/f9PccOlLQ2gOuYAbog6Fw6Sqng4R5MS+qtU5qy1sTLcydQFgGe2VMDvlIqOPmaPimtJg5lvxMoq1IWfPeVBnylVPAqS9D2hb9z8r7clZSBBnyllPKFPy8qAW7U1YCvlFJVSQDvSqrQOl9KKaUCSQO+UkqFCA34SikVIjTgK6VUiNCAr5RSIUIDvlJKhQgN+EopFSI04CulVIjQgK+UUiFCA75SSoUIDfhKKRUifAr4InK9iGwQEbuIJJaw3y4RWSciq0VkhS/nVEopdXZ8nTxtPTAMeKcM+15mjDng4/mUUkqdJV+XOEwBkApel1EppVT5VVQO3wDfi8hKERld0o4iMlpEVojIiqysrAoqnlJKBb9Sa/gisgBo4mXT48aYr8t4nl7GmAwRaQT8ICKbjDELve1ojJkETAJITEw0ZTy+UkqpUpQa8I0xV/p6EmNMhvN7pojMBJIArwFfKaVUYAQ8pSMiUSIS7foZuApHY69SSqkK5Gu3zGtFJA3oAcwTke+czzcTkfnO3RoDi0RkDbAcmGeM+daX8yqllCo/X3vpzARmenk+Axjg/HkH0MWX8yillPKdjrRVSqkQoQFfKaVChAZ8pZQKERrwlVIqRGjAV0qpEKEBXymlQoQGfKWUChEa8JVSKkRowFdKqRChAV8ppUKEBnyllAoRGvCVUipEaMBXSqkQoQFfKaVChAZ8pZQKERrwlVIqRPi64tV/RWSTiKwVkZkiUq+Y/fqJyGYR2SYi43w5p1JKqbPjaw3/B6CjMaYzsAV41HMHEbECbwL9gfbATSLS3sfzqurIboecTDCmskuiVEjyKeAbY743xticD5cCcV52SwK2GWN2GGPygc+Aob6cV1VDdjtMHQSvtIMpAx2PlVIVyp85/NuBb7w83xxIdXuc5nzOKxEZLSIrRGRFVlaWH4unKlXeAUhdBnab43vegcoukVIhp9SALyILRGS9l6+hbvs8DtiAj70dwstzxd7TG2MmGWMSjTGJsbGxZXkPqjqIioX4ZLCEOb5H6e9WqYoWVtoOxpgrS9ouIiOBQcAVxnhNzqYB8W6P44CM8hRSBQERGDnXUbOPinU8VkpVKF976fQDHgGGGGPyitntD6CNiLQSkRrAjcBsX86rqimLBWo30mCvVCXxNYc/AYgGfhCR1SIyEUBEmonIfABno+5Y4DsgBfjCGLPBx/MqpZQqp1JTOiUxxrQu5vkMYIDb4/nAfF/OpZRSyjc60lYppUKEBnyllAoRGvCVUipEaMBXSqkQId67zlcNIpIF7D7Ll8cAoTacU99z8Au19wv6nsurhTHG68jGKh3wfSEiK4wxiZVdjoqk7zn4hdr7BX3P/qQpHaWUChEa8JVSKkQEc8CfVNkFqAT6noNfqL1f0PfsN0Gbw1dKKVVUMNfwlVJKudGAr5RSISLoAn4oLpguIpNFJFNE1ld2WSqCiMSLyM8ikiIiG0Tk/souU6CJSISILBeRNc73/Exll6miiIhVRP4UkbmVXZaKICK7RGSdcwbiFX49djDl8J0Lpm8B+uJYeOUP4CZjzMZKLViAicglQA4wzRjTsbLLE2gi0hRoaoxZJSLRwErgmmD+PYuIAFHGmBwRCQcWAfcbY5ZWctECTkQeBBKBOsaYQZVdnkATkV1AojHG74PNgq2GH5ILphtjFgKHKrscFcUYs9cYs8r5czaOdRaKXSc5GBiHHOfDcOdX8NTWiiEiccBA4L3KLkswCLaAX64F01X1JyItgW7AskouSsA5UxurgUzgB2NM0L9n4FXgn4C9kstRkQzwvYisFJHR/jxwsAX8ci2Yrqo3EakNzAD+bow5VtnlCTRjTKExpiuOdaGTRCSo03ciMgjINMasrOyyVLBexpgLgP7Avc6UrV8EW8DXBdNDhDOPPQP42BjzVWWXpyIZY44AvwD9KrckAdcLGOLMaX8GXC4iH1VukQLPuWIgxphMYCaOVLVfBFvA1wXTQ4CzAfN9IMUY80pll6ciiEisiNRz/lwLuBLYVKmFCjBjzKPGmDhjTEsc/8s/GWNuruRiBZSIRDk7IiAiUcBVgN963wVVwA/VBdNF5FNgCXC+iKSJyF8ru0wB1gu4BUeNb7Xza0BpL6rmmgI/i8haHBWbH4wxIdFNMcQ0BhaJyBpgOTDPGPOtvw4eVN0ylVJKFS+oavhKKaWKpwFfKaVChAZ8pZQKERrwlVIqRGjAV0qpEKEBXymlQoQGfKWUChH/D5hc6LnqY8cRAAAAAElFTkSuQmCC\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2245,7 +2288,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 64, @@ -2254,7 +2297,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2297,7 +2340,7 @@ { "data": { "text/plain": [ - "0.12411442807235668" + "0.13459575203567298" ] }, "execution_count": 65, @@ -2328,7 +2371,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2357,7 +2400,7 @@ "ax.set_title('Cleaned Residuals')\n", "\n", "for ax in axs:\n", - " hlp.hide_spines(ax)" + " eda.hide_spines(ax)" ] }, { @@ -2376,7 +2419,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2396,7 +2439,7 @@ "sns.histplot(robust_weights, ax=ax)\n", "\n", "ax.set_xlim(0, 1)\n", - "hlp.hide_spines(ax)" + "eda.hide_spines(ax)" ] }, { @@ -2416,6 +2459,7 @@ "source": [ "#exports\n", "def calc_robust_weights(y, y_pred, max_std_dev=6):\n", + " \"\"\"Calculates robustifying weightings that penalise outliers\"\"\"\n", " residuals = y - y_pred\n", " std_dev = np.quantile(np.abs(residuals), 0.682)\n", "\n", @@ -2442,6 +2486,7 @@ "source": [ "#exports\n", "def robust_lowess_fit_and_predict(x, y, frac=0.4, reg_anchors=None, num_fits=None, x_pred=None, robust_weights=None, robust_iters=3):\n", + " \"\"\"Fits and predicts robust smoothed local regressions at the specified locations\"\"\"\n", " # Identifying the initial loading weights\n", " weighting_locs = get_weighting_locs(x, reg_anchors=reg_anchors, num_fits=num_fits)\n", " loading_weights = get_weights_matrix(x, frac=frac, weighting_locs=weighting_locs)\n", @@ -2451,7 +2496,10 @@ " robust_loading_weights = loading_weights\n", " else:\n", " robust_loading_weights = np.multiply(robust_weights, loading_weights)\n", - " robust_loading_weights = robust_loading_weights/robust_loading_weights.sum(axis=0)\n", + " \n", + " with np.errstate(divide='ignore', invalid='ignore'):\n", + " robust_loading_weights = robust_loading_weights/robust_loading_weights.sum(axis=0)\n", + " \n", " robust_loading_weights = np.where(~np.isfinite(robust_loading_weights), 0, robust_loading_weights)\n", " \n", " # Fitting the model and making predictions\n", @@ -2480,18 +2528,10 @@ "execution_count": 70, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":12: RuntimeWarning: invalid value encountered in true_divide\n", - " robust_loading_weights = robust_loading_weights/robust_loading_weights.sum(axis=0)\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 70, @@ -2500,7 +2540,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2531,18 +2571,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "#exports\n", "class Lowess(BaseEstimator, RegressorMixin):\n", + " \"\"\"\n", + " This class provides a Scikit-Learn compatible model for Locally Weighted\n", + " Scatterplot Smoothing, including robustifying procedures against outliers.\n", + " \n", + " For more information on the underlying algorithm please refer to\n", + " * William S. Cleveland: \"Robust locally weighted regression and smoothing\n", + " scatterplots\", Journal of the American Statistical Association, December 1979,\n", + " volume 74, number 368, pp. 829-836.\n", + " * William S. Cleveland and Susan J. Devlin: \"Locally weighted regression: An\n", + " approach to regression analysis by local fitting\", Journal of the American\n", + " Statistical Association, September 1988, volume 83, number 403, pp. 596-610.\n", + " \n", + " Example Usage:\n", + " ```\n", + " x = np.linspace(0, 5, num=150)\n", + " y = np.sin(x)\n", + " y_noisy = y + (np.random.normal(size=len(y)))/10\n", + "\n", + " lowess = Lowess()\n", + " lowess.fit(x, y_noisy, frac=0.2)\n", + "\n", + " x_pred = np.linspace(0, 5, 26)\n", + " y_pred = lowess.predict(x_pred)\n", + " ```\n", + " \n", + " Initialisation Parameters:\n", + " reg_func: function that accepts the x and y values then returns the intercepts and gradients\n", + " \n", + " Attributes:\n", + " reg_func: function that accepts the x and y values then returns the intercepts and gradients\n", + " fitted: Boolean flag indicating whether the model has been fitted\n", + " frac: Fraction of the dataset to use in each local regression\n", + " weighting_locs: Locations of the local regression centers\n", + " loading_weights: Weights of each data-point across the localalised models\n", + " design_matrix: Regression coefficients for each of the localised models\n", + " \"\"\"\n", + " \n", " def __init__(self, reg_func=calc_lin_reg_betas):\n", " self.reg_func = reg_func\n", " self.fitted = False\n", " return\n", " \n", + " \n", " def calculate_loading_weights(self, x, reg_anchors=None, num_fits=None, external_weights=None, robust_weights=None):\n", + " \"\"\"\n", + " Calculates the loading weights for each data-point across the localised models\n", + " \n", + " Parameters:\n", + " x: values for the independent variable\n", + " reg_anchors: Locations at which to center the local regressions\n", + " num_fits: Number of locations at which to carry out a local regression\n", + " external_weights: Further weighting for the specific regression\n", + " robust_weights: Robustifying weights to remove the influence of outliers\n", + " \"\"\"\n", + " \n", " # Calculating the initial loading weights\n", " weighting_locs = get_weighting_locs(x, reg_anchors=reg_anchors, num_fits=num_fits)\n", " loading_weights = get_weights_matrix(x, frac=self.frac, weighting_locs=weighting_locs)\n", @@ -2558,7 +2647,9 @@ " loading_weights = np.multiply(weight_adj, loading_weights)\n", " \n", " # Post-processing weights\n", - " loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising\n", + " with np.errstate(divide='ignore', invalid='ignore'):\n", + " loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising\n", + " \n", " loading_weights = np.where(~np.isfinite(loading_weights), 0, loading_weights) # removing non-finite values\n", " \n", " self.weighting_locs = weighting_locs\n", @@ -2566,7 +2657,27 @@ " \n", " return \n", " \n", - " def fit(self, x, y, frac=0.4, reg_anchors=None, num_fits=None, external_weights=None, robust_weights=None, robust_iters=3, **reg_params):\n", + "\n", + " def fit(self, x, y, frac=0.4, reg_anchors=None, \n", + " num_fits=None, external_weights=None, \n", + " robust_weights=None, robust_iters=3, **reg_params):\n", + " \"\"\"\n", + " Calculation of the local regression coefficients for \n", + " a LOWESS model across the dataset provided. This method \n", + " will reassign the `frac`, `weighting_locs`, `loading_weights`, \n", + " and `design_matrix` attributes of the `Lowess` object.\n", + " \n", + " Parameters:\n", + " x: values for the independent variable\n", + " y: values for the dependent variable\n", + " frac: LOWESS bandwidth for local regression as a fraction\n", + " reg_anchors: Locations at which to center the local regressions\n", + " num_fits: Number of locations at which to carry out a local regression\n", + " external_weights: Further weighting for the specific regression\n", + " robust_weights: Robustifying weights to remove the influence of outliers\n", + " robust_iters: Number of robustifying iterations to carry out\n", + " \"\"\"\n", + " \n", " self.frac = frac\n", " \n", " # Solving for the design matrix\n", @@ -2587,7 +2698,18 @@ " \n", " return \n", " \n", + "\n", " def predict(self, x_pred):\n", + " \"\"\"\n", + " Inference using the design matrix from the LOWESS fit\n", + " \n", + " Parameters:\n", + " x_pred: Locations for the LOWESS inference\n", + "\n", + " Returns:\n", + " y_pred: Estimated values using the LOWESS fit\n", + " \"\"\"\n", + " \n", " point_evals = self.design_matrix[:, 0] + np.dot(x_pred.reshape(-1, 1), self.design_matrix[:, 1].reshape(1, -1))\n", " pred_weights = get_weights_matrix(x_pred, frac=self.frac, reg_anchors=self.weighting_locs)\n", " \n", @@ -2598,30 +2720,22 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 77, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":24: RuntimeWarning: invalid value encountered in true_divide\n", - " loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 72, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2666,22 +2780,22 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 73, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAABUqUlEQVR4nO2dd3zT1frH3yfpHsy2jJYCInsIWLbiVkCGA5xXQFDALaL3+rve4d16r1uQobLcAoqouHFB2UtAQFYpZbVldrdJzu+Pb9J8kyZp2qbNOu/Xq680yTffkZzvc57zOc95HiGlRKFQKBShj8HfJ6BQKBSKhkEZfIVCoQgTlMFXKBSKMEEZfIVCoQgTlMFXKBSKMCHC3yfgiaSkJNmuXTt/n4ZCoVAEDZs3b86XUia7ei+gDX67du3YtGmTv09DoVAoggYhxGF37ylJR6FQKMIEZfAVCoUiTFAGX6FQKMIEZfAVCoUiTFAGX6FQKMIEZfAVCoUiTFAGX6FQBAYWCxTmgsrgW28og69QKPyPxQKLRsILXWHh9dpzhc/xicEXQswXQuQKIXa6ef9yIcQ5IcQ2699ffHFchUIRIhTnw5H1YDFpj8X5/j6jkMRXHv5CYFg12/wspext/fu7j46rUChCgfhkaDMADBHaY7zLzACKOuKT1ApSyp+EEO18sS+FQhGGCAETPtM8+/hk7bnC5zSkhj9ICLFdCPGFEKJ7Ax5XoVAEAwYDJKQoY1+PNFTytC1AWylloRBiBLAc6OhqQyHEFGAKQHp6egOdnkKhUIQ+DeLhSynPSykLrf+vBCKFEElutp0npcyQUmYkJysdT6FQKHxFgxh8IURLIbRxmhCiv/W4pxri2AqFQqHQ8ImkI4R4D7gcSBJC5AB/BSIBpJRzgLHAfUIIE1AC3CalWl2hUCh8gMWiJnu9xFdROrdX8/5MYKYvjqVQKIKU+jDMtgVbR9Zr4ZwTPtMmfxUuUd+MQqGof+prJa1asFUjlMFXKBT1T30ZZrVgq0YEdE1bhUIRItgMs016iU/2jcSjFmzVCGXwFQpF/eNsmKX0nfZuW7ClqBYl6SgUiobBZpilhLw97iUelSa53lAGX6FQ+AZvDLVt8nbOJRAZV1V7b8g0yWHYsShJR6FQ1B1vwyNtk7fSDOVFMG01pHS1a++uJnfrQ64J03DO0L9ChUJR/3gbhaOPqkkf6Gjsnd9vMwDimtePFx6m4ZzKw1coFHXHVRSOK2yTt0W5gIuIGv3kblxzWDgSctZDm4G+9cK9Pd8QQxl8hUJRd7wJj7SFYcY1h6WT7MZ2/AooOW3/nG1y9/wJyF4LSDicqXn6jVo23PmGIMrgKxQK3+ApPFKvmbfuA8e2anJK9jpYMEx77qylCwHYpBzpe6MchuGcSsMPRsIwukAR5Og186NbNKNviIDUvnbj76ylJ6RA+mAQRu0xIUW1/TqiPPxgI0yjCxRBjrNmPuFTKD4FcUla+GWOCy1dCJj4uXeLtVTGTK9QBj/YaKiwNYXCl7jSzG0eO2jKjZTan95g62WXojzXbV85QV6jvpVgQyWLUgQKNZVXXNWsLc6HnA1aXH7OBs8rbt21/TANsawNysNvSFSyKEWo4Cuv2l14pLv9u2r7YRpiWRuUwW8ofDnsDMPoAkWA4Stp0Z0D427/rtq+coK8Rkk6DUVthp0qIkERqPhSWnQl9dR0/672oaiC8vAbipoOO9VElCKQqW+vWnnt9YIy+A1FTRuw84igKBeEQTV+ReDgC2nR07yWki59jnIZGxJvhp1mE5z8FWKb24e0af21pegNkTJWodBTV1nR0+c9pUJuSDkzjKRT5eE3NBaLPXFUfJK2+MTm3ZhN8N/2UHYeohvBEweg9KzWEF/s5jr+2Nk7UgtQFL6iLrKirZ3rc+Y4f97dxGxDyplhJp0qg9+QWCyaJ5OdqT2PbgQVxfaGlv+bZuxBezy1H1p00wy+q3qgzg0VwqrxKuqZwlw4sg4s5ppF4tjaZvY6kBZAuv6887yWPhWyu47AW2fG223DbCGjMvgNia1x2bAZd1tDS+6idQI2Dz+5i/a+K/3f1apD277CpPEq6hGLBZberRl7hCYr2gyyt0ZUmrXnBqPrQAXnVMiLRmmfS+uv/eVsqN7BcWXUa+K1h1kMvzL4DUl8staQj6zVnkclgKnU3tCEgN8f0jz95C6OjdR5Aiu2uT3roL6hhlHjVdQjthWwoAULjJ1vN8g1MaJp/WHcQvdzV7Z2XZhrd1ZyNsD0XY5BCs4Ojiu5yHbe7kYIrgizaCCfGHwhxHxgJJArpezh4n0BvAyMAIqBiVLKLb44dlBRmSdEQFQ8VJRo2QLHf2pvaMYITcaxWKDghLat881iscDiUVrWQf3nLRbtxnT1GYWiJjh7vsJQv0bU+XgJLRznpZxlTYTrDiB7neYIOY8QPBFG0UC+8vAXAjOBxW7eHw50tP4NAGZbH8OLSq9JQnmh9tqxrVByChJSKC43kV9QTmFpObErptLsxM80FsVatZ9xCyHRehPoh8xHt0D+XkjuXNUDUwZfUVucjTbUbPRYQyNaYZHkjVnCuTN5RCY0p1FhGckJ0Qh9hsy0/vDoLu0+cD4fhHXOwAxHN0Fqhn1bdR9U4hODL6X8SQjRzsMmY4DFUkoJrBNCNBFCtJJSHvfF8YMGmxeTvQ6i4ikrL+fnpjfzxcqjbMn6hUOny3QbjwfG05JT9D5wgKufm8I16QYaT/qoyn6Ye6ljUQml3yt8gbPR9uS1e5okdfGelJKtR87y+S/HWX/oFLuPF2C2OIZFJsZE0KtlHJcfbcpwQxPScjZo52Tbv3OHlNoXcjZq/x/f5ritAgAhfRR7ajX4n7mRdD4DnpFSrrY+/w74g5Ryk4ttpwBTANLT0y8+fPiwT84vYLBYOHfqBIu3F7BgzUFOl9hjj6OEmeTG8SRGQkl+FrmyCSXEVL4fSxljL27DlKu606ZJDOTt0Yy9xaQViUjta5d5Jn2tInQU9YfeiFeXp173nul3K1j+ywlm/7CfA3lFlbsTApITomkaF0WFxUJ+QRnnS02V7xuwcGXCYe6/6w76tm3m+pzMZlhwnX1ea+LnYWnwhRCbpZQZrt5rqElbV9+6y55GSjkPmAeQkZER3CshnDwbs0Xy/sYj/O/LvZwtqQCgc1Q+oy3fcpnhFzqLI0TevxOWToaCTCxScDjlSlaXdeDz/BTWWbrx1uZcPtiez6Qh7Xnoio7E64e14z+BBcO1Br9opOdIBoWitjhHwdw8372+rwt7zMw6z59e+pGDp0oBSE6MZsxFrbmySwq905sQF2U3R1JK8grKWHvwFN/sOslXu07wbWF7vp29luE9WvKnkd1IbRLreF5Go+boqPbuloYy+DlAG93zNOBYAx27fnE3lHW6KXJGfcCj729hU46m3Q8w7OZh40cMFrsRETYvX0DxmcroCIPBQPvxs2kfn8xdxfnsLYjhtR8P8Mm2Y8z58QBf7DzOC+Pe4uIks3b8ojxHWae6hS8KhTc4t3Hn2HUh3Ov78ckUpw7h6YNd+NB8GZSV0q55HA9e2ZEbercmwui6PQohSGkUw5jeqYzp1Yq83OMs2FbA/DWH+GLnCX7el8+fR3bllow2CHcFUxRVaKi7fwUwXmgMBM6FhH7vaWm47qb49lAZw1/4mk05hbSIKGTWbb15/8JVDInYg0jra63baYC0DC0c05ZSIX2gdTWuFqfcObGUl2/tzcf3D6Zrq0YcPlXMuLnreH1LoTZccs4w6BzJoApDKGqKqzbu3M4SUjRn4rHdVWSUfbmFjD77GB+aLyMqwsDj13bi6+mXMfbiNEdj7y69gfX4yfN68fvjj/HDY5dxbbcWFJaZ+MOyHcz4cDsl5ebq96MAfKThCyHeAy4HkoCTwF+BSAAp5RxrWOZMYBhaWObdrvR7ZzIyMuSmTdVu5j8Kc7UbwWLSGv9ju+3ehZSw8HoWHGzE3yt+h0RwjWET/42eT9MZGyG2GSwYpnnkqf20fRy3ao/jV0DJaftiFNvkrG5VbplF8sI3vzH3x4MA3NSzOc/c0o8oo3CcyFp4vd3zClNNU1EHnNv49F3a61JqToqH8N+1B04x5a1NFJSauDAlgdfu7EunFolVRwyeFkq5OL5cMonlWUb+WDGZEhlJj9RGLJjYn+T4SNf7cXW8EJZ96l3Dl1LeXs37EnjAF8cKKNwtfkKboPhH0v+YvzcLgMcilvCQcTmizSDX8otB2Jewl5x2XIwizfZVudnrIG8P0Sld+b/hXemd2pjH3tvARztOcfbgHF77/RRi4pKg8CQg7MWiQ7RxK+oZ50VUS+62pgYRkD7IrRPx6fZjzPhwG+VmyTDjRl5ovIW45OWujbun9AbO8fkIRM56bjSY6BqdzdTY59l59Dy3zF3LW7d1IM15P3FJjscbvwIWjw5bmTN8rtTX6Bc/te6jLX6SEgpzkRYLf/v0V+avySLSIHgxag4PR3yMMBi0eHoh7KtutZ1BZHzVYg+2xi6MWqoFYYTIOJhzCSwYAWYTw1uc5cPof9CUAlYVtWPSgrUUzb8Bnu+s/S0cqTV6KbWFXAUn1XBX4T22ePzHdmtttzI1iIQc1zLhx1tzePj9rZSbJRMjvmZWxEvEHV2jbevKuHsqdqI//sTPtY7Aek90SUtm2X1D6NaqEYfyixj31m8cbHGt436cj5f/W1jLnMrg1xb94qdjW6E4DxaNRD7flX/97z8szMwiymjg9fEZ3NjOpOUTSR9o91yEgLELtNcByotg6s+OHpOtsc/YA3/Igqk/QXmBdszsTE0Smj2EnuIQ70f9g2RxjsysAqYcHEKZjEBLWrVO8/YXXm/vBBaMUCmWFd5jmwiNS9KcGwCEtiDQaQHWlzuP8/iSX5ASZlzTib9esA+jUZdLx5Vxdzbq7vLiC6H9jV+hhR4f20rSspt5757+ZLRtyvFzpdx15h6O37Nd24+U9hW6wqide1Jn7ytpheB8gMqlU1tcDDU5sp6ZFSN5o6Q3kQbB7N/15fJOSZCJpvHYGqCtQSe20G4a2z6SO2tSj15+0UcdxCdrx7FFtB7dAlhAQmdDDh9G/YNxES+xpqgHMyru45XImRiQ8OFd1m3BwTNT0QwKb7GNaI9t1Vax3vI2NGrpYJx/+i2Ph97bitkieeiSVjx05YUgXSzWcrWAS9/OPUW+2XLl6OTQxvIciyf353dvrGdL9lnGv3+AD++EpiunaPdWaj9orXUQLB5lnyPzJHOGaNrk4L8Cf+FiqLmi8Z08b7oFgeSV0elc1TRf8xByNmheec4GxyGkfh8TPtUmaD0VOYlL0iJ5hAGiEu3ZCKMbgSGC9u3as3DSABKijXxmGcQ/zXcB0i47aQd16ZkpFIB7r1Y/oj2+TYt510fjnCzggXe2UGGWTG66jcc2X6O1Y6g6seupEJDZBPOvrXof6KOFlkzU5FCdlx4XFcH8if3omJLAvtxCJr+8lNKsjfZkbMe32GUc2xxZTdMmhwDKw68LOq9k8+EzPJ43HLDwVMTbDP/yC22bqETPiZz02QJtuUCy11X1wPVzBi17wcmd2uvCCA9sqEzK1iOhMfPuymDCgg3MNw2jsyGHW9uXaR1KUZ49ukKhcMaTV+sujbDFwun840xetJeCMhPXG9fzVPGrCGGpDDAgpat3AQMWiyZT2tIj6Cdw9QbYVTZNoElcFG/d1oGbXl3FFktH/mKawLNR8xFtBmjb1CSLbIimTVYGv7bohp25BWVMfWsz5SYLdxq/Y7LxC/t25QUw4n9ao/U0hIxrroVelp3XHuOaOx4rb4/dwzqxo1LDrIyD1iVOGzzhM/59Y0+eWPoLfzbfS+erBtHbYIT4lKoRC9UNbRXhg6doGVcZMC0WTAtHcd/+K8i2dKWnOMhzEbMxCIt1FGrN8+RtWyvO10mPaKNSm6G1RcTZRqvxKS4llpatUpnX5hvGZt/Ih+Yr6B4vmTDhv4C0px33pq2HaNpk5erVBt3w0rRgJA+9t4X8wjIGtm/G0xfsdWwb0Y0gpZsXQ8hTWpw9aI/FpxyPNecSLULHtiBr0ld2Oan4lOZNWUyVo4NxGW24a2Bbys2SaW9vIa+gzPGGzl6neVOqTm544M0EpD4qrHUfTULUf97Z+BXn8/zBNqy3dCWFM7we9TyxolzT+Keu1tpxTdpafLLWtoUR0vppaRJscfOLR0HOZoiMtacOMZuqXpMQ9Pjd/3g26g0A/p5/GRv2HNRCMedeqn2uurZu+65E6KUZVwa/NugM50sHU1l/6AzJnOUVw/NETvwEZuyF6b/CtEytLm3enuobmbvQNL12qo/kMRjtjdE2OgCH0cGfR3Yjo21TTpwv5bEPt2GJTbIfwzZCCDGNUuECTyvC9ThFwFQaR1tpzhe6OHz+hxyYbRqFAQuvxr9JS8N5q6H+SlshXtO2po9Km/yN3YOvrBRn0dKKV9eJJLZgTHuYGvE5Zow8+kkWZ7N3etfWvf2ughQl6dQGawz96qxCZplGY8DCK5GvknJ8nzZsTWypbZfQwnVRcldDRHdDSGct0ZUe6mp0kJBCVISBWXf2ZdhLP/HzvnzmZ2ZxT2VJOacFKSGiUSpcUJO6rSWnqxpniwWy1wISDmdCYS4nZBMeW7IdgBlDmjNgxDeOq8P1+esTUty3NeeRg6tcOM7pwCuKPacDt95LjxfksuHtA2w9cpYnEx5ntvlvmp7vqa2HeI1bZfBrg5ScN0fxRMW9SATTm65lUOlvWuiXfhjsXJT8zWvh5A73YV6uGrs3WqKHCaYWjWJ49uZeTHlrM//9ci+DOjSne2vrMUJQo1S4oCYTkK62LczFntxWIoHfL/uF00XlXBqXzX1bJkB+/6qrZm356w0G123N29BH59q3xaeqd1gMBiIbt+SV2xsx4uWf+bKwA+8O/5E7h3b33NZDdLLWhs/y4dcHAZtLpzCXx//zPEvNQ+kt9rE06u9ERMfa69Pqc3g829Y6EZugve8q744vcKWx2l6La85TSzfzzpY8LkxJ4POHLyE6wui7YysCn5rkj3HeVkptsZ7VCL7ffTZPfryLJrFGvuZ+UuQpe5uOT/Y+f5OnXFQ+vKZPth3lkfe3ERtp5OvpQ2nTLM4n+w1UPOXSURp+TbFY+GbvGZaahxJNOc9HziVCmO3aol4jNBi0ouT3rYU/ZHu/wq82OMc227yn5zrDM+n8afcYLog8w/7cQmau2u/bYysCH0+x79VtK4RmuGfsIWfMEv75yVYA/tZ4JSltOtds1aweTykVvJlktp2nNaWJu23H9E5l1EWtKakw84dlv1Ctk1uT7yrIUJJOTbBYOP3mWP7vwFigMX+4+gI6ZLeDbFtqf6HplvqGaytKDg0roRTna5qndaIrFnjWMItx/InZPxxgeI9WdGvdyOHagtmrUdQzBgMyPpk/vL2aQksUwwwbGH3uHZhcNR7eY05653ZWF6mnBts+Paoba/bnk3ngFO9vPMLt/dO9v/YQujeUh18TivP5V1Yn8mnMAMNuJg5o6ZgPR+iSo7miIT2H+GQtOkJHvzbxjB/YFpNF8oclmzGZzFpjLjgR0pEJCt/w3oYjrMk6TzNjCf+MXoRIH6AFJnjbpl1FwLi6J2qyytXLbZsnRPO30d0B+Nfnuzl2usg+KvA0mgixqB1l8GvA2pMGlpkvJYpynm27GUNCij0fji0+PiElMJIuCQF3f6WFyenimn9/XSdSIwrYcbyYN1/6szWpWlc4vEaFaCrckl9YxrNf7gHg6bEDSZqxwS7ZeNPe9YsHq2tnnqQeT9um9bfnq3LByF6tKoun/GnWQuTzXbW5iYXXuzbors65KNf/93YdUJO2XlJusjDi5Z/Yn1fE9KGteWR4b5eToxTlBVZZQefhaGEuP/zvFiaWPUE05Xwb/XvaiFz79umD4e6VQT90VfiWx5dsZ+nmHC7tmMTiSf3tZQW9kVRs2zgX8vGk8VcnozgXUfeynGfu+VKuev4HCsrMzIt8nmuNW60T02bHiWNX55zW3zFFg7/vbTeoSVsf8PpPB9ifV0R7cYJpx//s2MMbDNYwsVHwQjctVjlQvGXnIXN8Mpe3jWWUcS1lRPGvCF1dGmH0LEkpwpINh06zdHMOUUYDfx/Tw7GGrDeSirvFg57amSf501lmAU1O9WL0kNIohhnXdgbg76bxlEY2sSYhdJp/c3XOtnoAgXJv1wJl8L3gyOliXlm1D4B/RMwn+ujaqj+2voEgNV0/EON4rR3VHyPfI05U8GVRR35udrOjJKUIH6qRYyrMFv68XEvUN+3yDrRPinfcwBv5Rb9N+kDvk6m5o6ZFVJyu93c94+jSMpEcmcxrJVdprzvPv7k6Z1vxFS/ko0BFRem4Qzdk/PfK3ZSZJKMTf+MS8x7HH9vViti0/lrj8Vdol6fhcHE+5GygFSYeiviIZytu5emK8Xzx8D+JMqrY/LDCCzlmUWYWe08WkN4sjvsv71D188X51ZfR9HUiMleLo7w5hvV6I46s5x/Nb2AcNzHHPIqbI9bQNr29o7Pjbn8TPrPLRy92C2hpxxXBcZYNjW7IuP61e/hi5wliIg38ceLN8OhO7cd/sZvjJI8+/vjuldpkrr+MvaeoAp3nMqndado3j+NAXhEL35xZ9ZoUoU01cszponJe/k4b2T49uhsxkTqHQN/OFo3SJM3aSjQ1xV2sv6tj6Ecwuuvtd2o5N/VsTrmM4O9tXq++0pb+NS/lo0BEGXxXWBuGxWzmn0e1wiFTE9bQ8s0+WvUodz92ICzYcHcT2xo+VN4s0ZM+5a/WULWX8/qSZ44LiUgEhZdUs/Dp5S+2U1Bq4tKOSVzR2Unq83eBEG/uNediKrHNHa73yVG9SYiO4Lt951i9/5T3x65JFFGAoQy+K2KbQ2Qcyy1D2GFpTwtjIVOL52mN++gWaNXbGurYP/B+bFeNUe+NLRihGXPrMPXyzilc2TmZImJ5xTxWKwf3we9CJu5Y4QF3nrLFwv55v+PtTccxYOGp4V0cJ2oh8I2evphKZaWrUw7Xm9IohvusMtW/Vu7GbJH2z3pyeGqymjjAUAZfj+2HLs6npKyC/1bcCsAThneIE2XaNq37gDEKBPY8I4GEq8bokAc/E15ylG6eHNEVg4B3zVdzoCzR8SYJouGqoha4Wfj07+zumDFya8QPdGlUVvVzgW703BVTcbreyZe0p1XjGHYfP8/HW496v9AqEEbztUAZfBv6H/rDCcyLncwJmtMjOpeb0ovti5dufUfLAmgxB65BdBGKWVnYAlHl3Du1SOSWjDaYJfwvp7t9P/qKQ4rQwoMXu/q4YJWlNwmU8FjbLPdtoLrQSX/Kgu6KqTgRE2nkieu0MM3nvtpLybmTQavPe4NPDL4QYpgQYq8QYr8Q4kkX718uhDgnhNhm/fuLL47rU3RecG72r8w5o6Ul+FPzHzAc26KlKZj4hdZoAnko6wq9N9Z2sMtzn35NJ2IiDXxp6cdmS2ePN4kiyPHgxVoskv98oa2ove+KC0m+Z2nN20AgpCNwV0zFBTf0TqV760acOF/Km+tOBN/9XQPqbPCFEEZgFjAc6AbcLoTo5mLTn6WUva1/f6/rcX2OrWamMDLLdAMlxHCNYRMDTy/XYutzNsPC4Voki5RaYYdAHMq6w2DQIof0w3BdlsEWjWK499ILAPhPyv+Qk74OmlAzRQ3xMOH6xc4T7Dp2npTEaCZd0bN27dvfE7o2qovaqdxM8NTwLgDM/vEApyqivb+//T2SqSG+uKP7A/ullAellOXA+8AYH+y3YbAlD1s8Co5uISd5KO+ar0Zg4fGID+3bRcbYK+zYCjsEi7HXY7sJLGbHCAaLhSlDL6B5fBSbcgr56tfc6velCE7cTLiazBae/2YvAA9f1ZHYqFquywi0CV2bUbaY3Y48BreSXGbYThGxzM1u7d39HQgjmRriC4OfChzRPc+xvubMICHEdiHEF0KI7i7eB0AIMUUIsUkIsSkvL88Hp+eByh/Mmg5Bmnn5aGcqMDKmSyKd27S0b1tRCi16Bk4jrguuIhiK80mMieThqzoC8NyXuzCbA78BK2qBmwnXj7Ye5WBeEelNornl4jSf798v6I3y/Ovcjzzik3m8jdbZLTJfy0lzYvX7DpSRTA3whcF39Ws6j2+2AG2llBcBrwLL3e1MSjlPSpkhpcxITq5no+qUDmE/aSwzX0IEZh7Nug8MkVqYojBCdAKc+EWTfcZ/GpzevQ13EQzA7RlppEacZ39+KZ/NnB4UXouiFjjJHWUmMy9/+xsAjxW/RNTbo+r22wdKFIveKB/dorV1V06bEPScuoBhXZpSJiOY9cOB6vcdaCMZL/CFwc8B2uiepwHH9BtIKc9LKQut/68EIoUQSfgbhx9sEC+2mYkFA7dE/Eg7jsHRjXDb2zBttZYtT5o1WaekBos0AhEPEQxR5ad52KBJWS+d6IWpUDfKCjK9UuECN7/hu+sOc/RsKZ3FEUaJ1UHjsVaLc06cSV+5H3kYDDw2vCdCwHsbsjlyutjzvgNpJOMlvjD4G4GOQoj2Qogo4DZghX4DIURLYV25IYTobz2uf62mxaKlMp7wKUzfxc7yFD7fV0yUMPFQm8P2XjuhhZY4Kch6co94imCIT+amdhW0FSc5JFuy/Ldy7fUg1CsVTlgs1tzvXRx+w6KScmZ9sRmAx2NWYDQGaOI/V9R0kZTB6HHk0alFImMuak2FWfLKd/uq378tU25RXlA4QnU2+FJKE/Ag8BWwG/hQSrlLCDFNCDHNutlYYKcQYjvwCnCb9Gcifuc8IBJeOKLF4o43fk2r21917LWDsCevFncRDEV5RE78hEdGDQDglVX7qTBbglKvVDhRmAvZa7XJy8OZlak23vp5N/nmOHqLfVxtWQMtegSHbOnLRVI6w/7o1Z0wGgTLtuRw8PU7Pe/f0zkE4IjYJ3F3UsqVUspOUsoOUsp/WV+bI6WcY/1/ppSyu5TyIinlQCllpi+OW2ucjNeOE8WssvQmllLuM66AZZOrJoMKFE2yvnBouCMZs/sJLhDHyD5dzLKN2UGpVyp0WCxaRsvK6TUt02txuYnX12uGf3rEMq15n9gB+XsDylC5pKZOiDsD7GS02zWNZlziTiwSXj7c3rtc/65yVwXgiDg8A62djNcr604DcFfEdzQX57UqN0X1HCEUaDg1XGPOWh6NWAbAq59mUlZWDmPnw/RfNRksSIawCuzGZ+6lEJWozd2kD4b4JN796VdOFZVzUas4hqZHa+9FxWvbBpChcklNnBBPBtjZaOf/xkNlbxCJiU8tgzhIqne5/vXbBOiIODwNvk6i2X3de3zz60miIwzck35Se19aYOndgd3YfY1zw029mJGGdXQSRzhqbsySZybCi91hyURYOLKKDqwIIJw9WX00WkWxFoQw8TNKF9zA3G93APDw6X8ijJEw5SdtmwAzVC6pidTqyQA7t/3kLqS2vZCbjauxYGBWp/nunRx35xDTFCJitP8j47SFnQFAeBp8qJRoZq7aD8Ad/dNJuW2WNqmDDPzG7muc8/nf/RWG1hfxcMRHAMwpu5YKs9RGP9mZVXRgRYDgypN1Vb2p+BTvZ8WTRxO6i0NcySZtQWFCkEl33kqtzkY9rrm9U6wysWuACZ9x/7SHMBoEy3eeJtuTlu98DhaLtiq/vFB7Xl4UMJF94VnxylqpZ19BNCt3HicKE1NPPA3x70ObgY6VdMIJW8MFMBrhnlUMnz+MCw4e46BszQp5CTenFcHRTdYPyNCd0whWXHmyCSlVqjeVRjVjtrwRgIdjv0Rgi0qrum1IoK9gFddcC9bQV/pyxmAgvU06N/Q+w7ItOczOTuM/kU7fqTuc17mk9g0YWxJ+Hr7OA5r55utIBLcav6fliVVV8mWHTGP3FmcpQAiMt73F/SMHAzAr8RHMd3+t6b82HVjVwA0s3GnKei/UYmHJ2j2cNCXQJSWWa/643LHNh2qAgu26ik85dopFuW71/Qeu6IBBwFLzUI7SwjtHMLa5ZuSrydTpD8LPw7d6QAdNSXxa3plITEyL+NRezESI8DRizvVNx6+AxaMhex1jWmfwUtMnOXiqhC9/zeX6iZ+HngcYKriqxaqvcSwl5QtHM3vfLUBzHr6qM4aICPdt3lN95GDFuSYuwvWoCLigeRwjuzVjxa7TzOn+Dv8Ym1F9eKc1LxepfeHurwIqCWHgnElDYf2xZ5luwIKBm40/kSpOwYjnQqdB1wYXkQpkrwNpJvLoeqZFaMPemd/vR9o6xXD+vgIZ/WIg54Rhhbksy4rmmGxOR5HDsAs8+HwBGlpYZ5w1+4QU+6gorb82wpWy8vofPDAVgA+253OywEUxGD36CfIAXJUffgZfCLJHfchyORQjZu43rtBC1eKah3eYoYtIBVL7Vr49tvBdWiRGsvv4eVbtURO1AY2HhGEVFsksORaAh1ruwlCdFh2AoYU+QS9b2TqA6bu0/1/sVtk5cmQ9nTjMCOMGyk0W5v540PN+A3y9SvgZfGD2TwcxS7ihTxvS7/8YWvaEl7qHlhfjLS6Km1dGKtz9laZBGiKISe/LvUMvBODVVfvx50JpRTV4SBj22UEzORWNuKBZDNc/+JLnUVqAGy+fYjCAMDh2cLpiRw+maYb+3Q2HOVVo9fJdLeQK8FX5Yafh554vZdnmowgB91/ZEWKLrSULvZyBDyWcdfsJnzleu9GoTThZNdw7yip47ft9bDtylsz9+QzpGMIGIJhx1qgnfArFp5CxzZj78k8ATLuiE0ZjNf6eq/mAUMb5e9NFLHWLT+bKRZtYtSeXRWsP89hVF1a9d2xavT7aLcAIfQ/fqReevyaLcrOF67q1pENyQnh5Mc54M2S3NV4piXt3DJPL3wHg1Xc/Cr/RULCglyjGLtA817gkfnjtIfbkltAiopAxF7Wsfj8QuhE7rnDlneuuf9plHQBYlJlF0dngrH0b2gbfbHKo6nS+uIx31h0GYFr/pq4XXYRDw7ZRk87O2jncZfyaRIpYV5LG5r2HAjJBlMLK0kkOevTs45okN1l8SnT5Gcdt1e+o4aGD69euKRe3bcq5kgre31Wi3TPCqElmcf7P9u4NoWvwXVR1enf1HgrKTAyMzaH3B/3smn04eTF6atLZWTuHRqKECcZvAJiz4bTrKA5lPPyD/nt3Gr1tOVbEBksXEini9rYFjp17qEbj1AYPbVfovPw3Vh+i/I7lWmDDsa3a9xcE31voGnyn1W5lLS9m/kYtIdpUy/tBNxSrN7zt7ITQOoUZe5nw4F+JijDwze5c9h/OdlzAUnBCGQ9/4Gy0Y5s7jN7mbNA8+t8N7kDi5OWOv7enjI/h1HF70fFd1SWFjikJHD9XyopN++x1roPEloSuwbfJFVY+LupJbkEZXVomcnnb2PDU7OuKwQCJLUlulcZYa83T16Mn2OOXl06y1wcOopsgqLEZ5aI8R6OtWzW+f7iWIDAqwsDdV3Sv2rm7kvbC0ev3Yk7LgGSqYTkAc77agiW1f1DZktCN0hFCm7B6qRsWs4V5uV0BmDa0PaLjfEAtHvIaF6stp1zSjvfWZ/NxYXcee3A7LRIiNb1YmrXPGIKoalKwoo+ySuuv/eVssH/vQkBcEvNefR5JN26O30FK/HVV9yOllvpaf084dyChGL3m3K6do3Sc267FAnl7GH3uHV6gN/vLk/iu94tcc2uzoIliCl0PHyCxBaQN4GvLxRyUrUiNOMf1W6dpaX6X3h0+Q9W64MrTs1ho99mtDDduoNwsmb/1vONqxfTBMD0MJ8EbGr1HmrMBxi2oMh9z4sQxPj7XCYGFKWULq3qttt/X+Z4I9eg1V+3a05yWbfvZQ4iKjGRy5FcAzF5/ClmdsQ8gaSy0Db4QyJvnM8c0GoB7WE7kkdVKbqgJroa51temGrXSxe+uy+J8aYVjeuXEFsrY1zfORjmhRZVR6/yt56kgguHGTbRv266q4XYnY9Q2ei2AjJtH3F23uzmt4nwt1QgWKC/ktta5NI6NYEv2WTYeOu3+OAEmjYW2wQfW50eyTV5IUwq41fiD/Y0AKkoQ0NiMij78zPraRcbDDDTupaBc8t6sp7UbPUiGtiFBNUb5XEkF727IBmDa+AmuDbcnT76m0WsBZtw8UtMRTHyyQ6qR+NwtTIhZDcCcd953f60Blp4i5A3+3J+0JdHjm+4kzmCyvxFARQkCGiG0zJn68DMpNUMzbTXTIjQv/838bpS90BMWjAjsGz3U8GCU316bRWGZmcGGXfTKfNC11+3LdSgBZtw8UtPrFsIh1QipfZlYupgYylhV1I49WdmuPxdg0lhIG/w9J87z/d48YiINTLj/T9qP23aIvfJPqOmS9UXJ6arhZwYDpHTlsrZxdBHZ5NKUT0yDtGpYRSq5mr8prTCzYI3m7EwzrvAcbumrdSg1rTHrb+mnptdtSzXy2G6Y9DXN0rtzW8SPAMzdcMb1ZwJsYWdIG3xbZrtbM9rQLDFG05UD6MsPGtzdyEIgJn7G1Gu1oe5c80gs0gCo79XffLTlKPlFJrpH53FpxO6GCbf01rgFk/TjjK2TsJZBvGfa4xgNghW/HCfnTLG2TX11qD4gZA1+zpliVmw/htEguOfSC7QXQ7GYQ0Pg6UY2GBh5aQapEec5IFP5Lun20AvfCzLMFsm8nw4AMPWGqxEzdL+bXnbJXgd5e3zrZXtj3IJJ+vGEwUBaWhtG9WqF2SJ54+dDVdK5BFpnFrIG/42fD2G2SEb2akWbZnHB7VUEAh5u5MgII5Ov0xa5zYn4nepM/cxXu06QdaqYNs1iGdGrtePvpp+Ej4qHuZc2/P0QYLq2V3iQoKZa0y18sDGbM2/c4JDOJdA6M58YfCHEMCHEXiHEfiHEky7eF0KIV6zv/yKE6OtqP77iTFE5H2w8AsDUoVbvPlS8igDl1v7pNI6NZPPhM2zK8hCmpqgdXmreUkrm/Kh591P6pxBhcOp8baO1aauhotg/90OA6drVUo2z2LVFApfFH6akwsLiI7okaq37BFxnVmeDL4QwArOA4UA34HYhRDenzYYDHa1/U4DZdT2uJxZnHqSkwsxQwy90++p27QcKRq8iWLBYiK84zYRBbQGYU11VIEXNqMHodO2BU/ySc47mxmLG/Xi16+2tE+5+vR8CSNeuluqcxeJ8ppm0tOGLTNdRQkzAFS+34QsPvz+wX0p5UEpZDrwPjHHaZgywWGqsA5oIIVr54NhVKCmtYNH3OwBrdEL2Om2ZeLB5FcGCzhiNP/xHoiMMfLv7JPtOFjhu4++IjGCmBrr7HGsY8kTDSmJkiXvvXd0P3lOdsxifzMC2jbnIcIDTNGLJZd/C5G88Fy/30z3hC4OfChzRPc+xvlbTbQAQQkwRQmwSQmzKy8ur8cl8u30/py1xXCQOMMjwq5bbZend2mSKmrD1PTpjlHT8B8ZdpC1mm/vjAa1BOxfRVnMnNccb3d1iYdf+LH76LY+4KCN3tT1bvfceTF62P6muc7RGq0278VoA5m3Iw2TxYMj9OJ/oC4PvqrU4X60322gvSjlPSpkhpcxITq75MHNU/y4safMRf4p8x/67ZK/TcuMro+N7nLyfe6/ohkHAJ1uyOf7c4CpFtNXcSS2oTne3GpB5C14H4LZ+bWgyaZny3n2JF53jtZ2b0r55HDlnSvh8x3H3+/LjfKIvDH4O0Eb3PA04VottfIMQ9Lvvdfo9scK+yMq2SlQZHd/j5P20TUpgeNemVGBkfsU1VYpoq7mTWuJJdy/O58jhA3xmHoARM5P7NlLee0Ni7XCNL3Xj3ojPAW0NkHQn1/hxPtEX6ZE3Ah2FEO2Bo8BtwB1O26wAHhRCvA8MAM5JKT10gT5AiMrizcQlORYcVkbHtzgVbb7vym58/usa3jVfxYOpJ2g8aan2Oyg5rW44FxWXUpufikvizZgJmEuN3Ji4h9RGA+zlOxX1j85jv+n827wQfw2/Hj/Pz/vyGdrJha3xY3H4Onv4UkoT8CDwFbAb+FBKuUsIMU0IMc262UrgILAfeB24v67HdYteH1s0SjP21lVxaojrQzxMOvVIa8IlFzaniFjevuC/Wm585W36Bl1ReVs7PzP/Zj4o0iKdpzbfaq9jq6TLhkHnscek92XSJVpc/lzr4jcgYFbf+qQAipRyJZpR1782R/e/BB7wxbGqxZU+ZlsKrVaA+gZ94Y02A7TO1CkiYdplF7J6/ykWZGYx+dILiIk0Oq50ttVdVV5/7dC188WHm1JSYebyDo3pcvyL0C5aEog4eex3lpp47YcDrNl/ih055+jZOrHa+6WhCL2Vtirevv5xDhMsqhpNNeTC5vRIbUR+YTnLNh1xrHW7YITmgapJ9NpjbeclIo5FlhEATL2is2r7/kLnsTeOjeSOAekA2iK4AFr0GXoGX8UX1z/xyVo5PQBp0cJenUIERVEe04ZqQ9vXV67B/Hx3e63b7HWQExg3QNBiTVu9dOBHnDbHcpHhAAN/mqClslZt3+9MGtKeSKPgi53HySqJC5iOOPQMPqgIhfrGVi/YYASkyxBBXujKsM33kt4kiqyKJnxpvhh7JK4FIuMD4gYIWiwWTIvG8PoPuwGYavwUkbNeS2Vta/tqwZvfaNk4hht6p2KR8PrqQwHjhIamwVfUP4ktoM1AR6NtLfJsG75G5Kzj3oEtAZhjHo1snWHtJNAK0Ez92e83QNBSnM8XWZJs2YJ24gTXRWxx7DxVskC/M/UyLY/Xks055BVVBIQTqgy+onY4S2e2qJE5l2jlIw0RkNafcb2SSIqPYoelPZmXv2fvJNIHanHlytjXChmXxFwxDoB7k3djfOxX++9QmKvNqwSIbhyuXJiSyNVdW1BusrAoM8vfpwMog6+oC3rpzDYxJc2a9z7lRxCCmJk9mBj7EwBzfj6kacxTf9Y6C2Xsq8eNLJN58DQ7y1JIio/g5gef0UZculBNlkzU5lmUbOZX7rtc8/IXr82isExXYjWIc+koFI7RUbbykVYP866ihcRHGfh5Xz47503ScsEsGqnlNwoHjbkmN7d+Ww+yjC0F8sQhFxATZY2u1keD5GyAcQsCQjcOZy5u24yMtk05X2rifWtBeYffdcEIOH+iwe4BZfAVvsFZ4klIqewAGqf35Pb+1tTJOW3tkTq2/EYN3OgblJpo6c7bFua6lGV25Zzl5335xFHKXQeesO/TOSQ5oUVA6MZhg5uOfZq1QMqbqw9RbrI4hTVnwksNt1BOGXyF79BLPE4dwKRL2hNhEKw09yebVo75jWyNPhQNf01isJ3XN0DVcD6LhbnfbAXgduMqGh/93l40XoUk+w93HbvFwpVtoGNKAsfPlfLp9mOO2U8RWkbZBppnUQZfUX8YDFpqi6I8WjeOYUzvVCwYeL37W1pxCOdG38DeToNQk4WAzusblk2Cu5bb5zyk5PDrd/D53gIiMDEp4gu0RLSONYaVV+8HXHXs1k7A8GI3phg+AbR0CxaJvWNuO7hB51mUwVfUHxaLZryf7wILRjDt0nYAfLg9n/ziCsdG7wdvp0GoidftvL4hex0sHGGf8yjMZU52GmaM3GBcQ6rhrPbdqfQJ/sdVx67rBMace5uWiZH8drKQ7/fmah1zYosGH5Epg6+oHywWyP1V89ql5r13TCjh6q4plNnC1JwbfQN7Ow1GTbxu/foGp7TexwsqWGoeisDCfW2OKOkmkHDVses6gaj0DCZfak2qpi8B2sAjMp8kT1MoHLDpmYfX6l7UpIdpl3Xg2925LF57mKmXdSAhOsLR8Id7QjV9Iq7YZtrE9rGt0GYA8zafpwIj13drSoe73g3f78jf6JMAChdymg2npGq3l5t5ZdV+NmSdZvPhM1zctmmDn7ry8BW+xzaUxarDC2Ol9JDRrhn92jXlXEkFb/24y3GCNhz1Z1eRHba5j8WjKwvI5N+0lPc2amF9D1zdI7y+o0CipiuYdW06ITqCuwZq0Wqzf9jfACfr4nT8clRF6GKxaMarMiZ/cBXp4aErrEnVVv1K8fwxoTNBW1M8GQ/9QrZjW3nzx92UVli4umsK3Vo38t85hzu1zXxpsUDBCSb1TiAm0sC3u3PZefSs/b0GWo+iDL7Cd9gM2IvdtMb76C64e6Um1+g80ktbQ2/Dfk7TiHezEkNngrameDIeOv33XOuhvLVZe++BKy7008kqAM9RV+4Md2XwQmeS5nTnTvE1ADMXLIZzxxo055Ey+Arf4bzS02BwKT2IhBQeabUHgDmWGyiNalZ1X+GQ6dGT8dBNAi5q9yyFZSaGXNicPukNr/sqdLiLuvJmtGZlqviIKMr5srADe164zp42vAGi05TBV/gOb2POheDy+2fSs1Uc+eY43tt4RHvdZuQt5tDO9Gi7TqiagM6pDF5RZDPmWxNvKe8+QHA11+TNaM1KSoyFO4w/ADCzYgwgtVDcBohOU1E6Ct9Rg+LMwmjkoau7MuWtzcz58QC3Z6QR8+4Y7WZp3cchHDGkSvXZPMHsdVrY5d1fadfmpmzkO+sPc7a4govbNmXQBc39ffYKd9iMuu33cx6tTfzcuiJaQHwSU48f492ZW/ncMoBHWxzkwt+92CABC8rDV/iWGkTaXNOtBV1aJnLyfBlL1u21e0jWyJSQjMkvzteMvTRDzkZYcJ09zM/JQywpNzPvp0MAPHjFhQgVmRO4VLfAzmCAxJbafJbBSKvUNozr1w6JgZlNn6gyz1VfKIOv8BtCCB6+qiMAszNPUp42yJ5t8+4vQjONcnyy5tnbOLbVPiJyksPeWpdFfmEZvVIbc3mq1KQud5OCoT7fEQzUMKz4visuJMIgWLH9GIeys1WUjiL0GdY1hY5JMRw7V8qy7rM0D2nCp7B4jD2lgMUSOkZNCE3GSevnOIJx8hCLys3Msa7InM7biBe6wrPtXCbnCun5jlDE2pbTmsRyc1+tDOLMua81SPJAZfAV/sNiwfDWKB48/wIAs344QHlMEhSfcpQ3inJDy6gZjVryOOfhvy7Z3KLMLE4XldMnNZ7LTy/RJKCy81UnBWsbF67wD04d9P39mmDEzHLzYA5mHaz35IHK4CsaDmcv3WqsRoo1dBDHyDlTwgebjlSVNxCujVowe/364b9TdFLB832Y9812AGYM645It2YVjW5UdV6jJtk4Ff7HqYNu2yyGcY33YMbIi6ax1qyx6+qt465TlI4QohnwAdAOyAJukVKecbFdFlAAmAGTlDKjLsdVBCGuolCsxsp4ZD0zWv3C/cda8+p3+xjbN41YfbQPVI2AMJsc8szYolqCDv33Yo1OWlAxirOWGPq3SWDIhUnQwfpdxDXXRj/6CKgaREYpAgDnaJ6EFB6a+gAfPf8jn5oHc59lBd1iz2q/dT1Q1zvkSeA7KWVH4Dvrc3dcIaXsrYx9mOJKetDp1sMeeJkeqY3ILShj8dosj8VUkFIz9jkbg1/K0H8vR7dwrsVAXjeNAGD6sO5aZI7tuzAYXU8K6qSgoBzthBMuonlSm8Vz58Va2PELpnFQUax17PVAXQ3+GGCR9f9FwA113J8iVHEnPViNmcFo4PFrOwMw+8cDnC+tcPy8c8H0o1vs77XuE7xShlMt4NfbPkcB8Qzu0JxBHZJcf8ZZylITt8GB7XcTokrHff/VPYgVFXxruZgtSaPrrT3X1eC3kFIeB7A+ulsdI4GvhRCbhRBT6nhMRTBSXZyyxcJlrSX92zfjbHEFb/x8yP2+4pO10E1h1KJdJn0deFKGt/MLQsD4FTD1Z07euJQ31mQBMOPaTu7362zc1cRt4FNNp5zcKIa7h2oOz/ORU+utPVdr8IUQ3wohdrr4G1OD4wyRUvYFhgMPCCGGejjeFCHEJiHEpry8vBocQhHwuItTtt4M4sVuPGF6HYA3fz7IqcIy+/t642nrPGbsgcnfBJ52X9PC5YtHw9xLeWn2TEorLAzr3oKL2zZz3MZ2/a6Mu5q4DXy86JSnXnYhiTERrDlwisz99dNpV3unSCmvllL2cPH3CXBSCNEKwPqY62Yfx6yPucDHQH8Px5snpcyQUmYkJ6uGGxboboZ+ecu4wrCNonIzs77f7954BnLu/FoULt9nasEH57pixMwTxS+4j7OPbV7VuKvi5YGPF51y47hIpg69AIBnv9qLrIf5mLq6RiuACdb/JwCfOG8ghIgXQiTa/geuBXbW8biKUMJ2M1jr2j4e8QECC2+tPcyhnJzgkytqWri8zQCeNd2GBQO3G1fR4eRX7uPsS065Nu6B3AEqvO6UJ13SnuTEaLYfOct3u136z3Wirgb/GeAaIcQ+4Brrc4QQrYUQK63btABWCyG2AxuAz6WUX9bxuIpQQn8ztB1M94ijjG20lwqL5D/fnwy+vDpezFdUSjRCsGHoYr61XEycqOCRqE+qj7NXxj048eJ3i4uK4M8ju/Hfsb24oovvEwaK+hg2+IqMjAy5adMmf5+GoiGxTkLmmhO5/PkfKS43827UvxmcHgsTv4DSM8Edb+60HkHe9Qk3zl7DtqNFPHrVhTw6qGnV67NNzLqKw1conBBCbHYX/h5gs12KsMfqBaU0juW+QS0B+GfFHZhztsLC4cEfeqiXaLLXseLVR9l2tIgkYzH3XtLec5z9olHBf/0Kv6IMviLwsEoe917ZndYRBfwq27Gs0Z32HPnZ6yBvT+AsMqpJigedRFPUagD/zh0IwBPGd4k3n3X/ORV6GZo0cHoQZfAVDYO+YXtq5LqolJh3x/D7GwcD8L+S0RSlXqJN7EbFa5k0feXp1uWmq+miJ52+P7PNC5yUzbjIcIBx7cq8mtwNqrkMhWdctR1rsXMKTtZLJ6AqXinqH71unWaNyM3ZoBmu8Sug5LRdl3aSPEYPL2BhmyZsO3KWl3v/hz+ORDP2rqph2bTummjcbipNeY0rz7u66lwGA4dK43ljtTY/9fSEkRg6PeT5nFXOnNDDue0U5cKSuyE7ExCQPkib9PfhOhPl4SvqH+eGnWM36CwY5ujh6EM0o+IxzBvK38U8DALeXJPFbnOqa0+3tukF6iqV1MLzllLyt093UWGW3HJREn06tffOgKvonNDCXVZYAKR2n/hYulMevqL+0WcITOuvGSxPtWsnfKZp9FZPvlfucsYbm7DQdC1//Hgny6Z+iqHUKVqlNp6287nVRirx5Hm7GXF8teskP+zNI9FQxu9/uxUWdqs6sqjNaEURXDi3HdDaoM3DbzPQ59KdMviK+se5YdtSBMQlOcop+oRqKV2tjX8dSAuPGT9gpbkfW4/A+5uPcseAdMdj1NZw+0IqsXneelwVKzcaOVdcwZ8/0dYdPmF8nyR5xrU0VReZSRE8OLcdfbHzehjNqVakaBj0ckRlul9D1QVKtglUqEwqRvogGhkr+EvLDQA888Vu8gqc8uxA7dML1IdU4lysfP61cP4E/175K3kFZWS0bcrv2he6loJURE74oi92Xg8jO+XhK/yL3sNxN7mb1h8e3cX1CSl8uHATP/2Wx1Mf72DunX0Qi0c5esLeyDi1wZ3E4u51W7HynI3a86ObyHx+LB+U/R9RRsEzN/fCkORmZFFXmUkROASYNKc8fIX/sXnpRXmuJ3dzNoDBgDAY+M9NPUmMjuDrX0/y0bo9cGRd/XvC7iaEPU0U64uVCyMlMponyycB8PClrbkwJcH9yEIlQwsNArBOgTL4Cv+ivymWTNS8eZvM0WZgFckjtUksfxnVDYCnP9/LMXNjQGifqy9P2J3EUp30oitW/s+YGWTLFnSJzmfq1T2rP6aKyAl+AlCaU5KOwr/ob4qcDTB9FwiD4+Su03B47MVpfPVLNt/+dpbfV0xlcdT/MIxb6No4Vjek9mbI7U5i8UZ6MRj45gi8c64HUUbB81NuIDLCWPPvSRF8BKA0pwy+wr9UKeqsm6yylYLTY7EgivP5z8192fLfz1lt7sn8xCnc40q7ry7axXnOYOwCbbLMuaNxF8njRYRP7vlS/rDsFwB+P6wL3VMb++JbUwQDAbhYThl8hX+pyU1hsWha6JH1JLcZwDO3vcmUd7byzKlL6ZN9xrFKFFQfm++wqjcTXuoGaQO092wrgW2dhLvQSw/nbTJbmP7hNk4XlXNpxyQmDWlfyy9JEbS4ajd+RGn4Cv/jrV5dlKsZZmmG7EyubWfg3kvbY7JIHnhnq70koo3qVsE6FV7BYnacLPaku5pNWqilhwm5/329lzX7T5GUEMVz4y7CYPC/h6cIb5TBVwQPFn0yKQEIfj+sCxltm3LifCkPv7eVCrNTpIynaBenwiueJosdz8OipYTI2ejYMeiSsH3+y3Hm/ngQo0Ew646+tGgUUx/fiEJRI5SkowgOLBZYNsn+PH0QJKQQKSUzjS8ykhtZcwD+8vEO/n1zL4Rz6T/nfdmkGINB0+1drgRuroWKOks2xflwdIv9ees+2rbW+YCdydfzxPE7AHhqRFcGXNC8nr4UhaJmKA9fERwU52u6OmgSjC0qpziflse/4/Wo54imnPc25TDvm+3uU8t6UxTdU8ERi0Xbd/pA7TzS+mmhl8Wn4Mh6jpiaMvHwdRSXm7mxd2vuHtKuvr8ZhcJrlMFXBAd6PT59oN1rt65o7WM4wIuRrwHwn1VHWfryY64XuhTmerdYy9WEr62zeLGbZvSn/wqTv9E6iPhkTre6jAkVT5JPE4YYdvLswRsRFnM9fSGKkKKBCqEog68IDtzp8boVrSMiNvOniLcBeOLElXy8brdj0ZWCE7D0bm1ytrrFWq4mfJ3XDBgMledxqqicO4sf5aBsRVeRxZzIF4kqPwP5v7nef3U3eANXQlL4EedRp9lUb7+90vAVwYO7EDch4Na3QcI9yyZRdnAJ/6sYx4wVB5FfPslN7UzadkfWg7R5/UKLu3cXGeQqXNTNQprc86Xc8cZ69ucWckFSPItKZ5FoKoHoRpDcpeq+a7I+QGXLDH2civ6wYJiWNrwefntl8BXBjbNxHL+CB0pOY/rpKC/+dIzHyqeRc3ApD0V+gpB6eUVqk8CebijnDkZKGDsffera/bmFTF60kcOniuncIoG3E14hufA4tOoNk7/TPleYW7Pc/bXN7a8IfFyt3dA7Eu5qRPgI5TYoghtn41hyGhJSeGR4b/6S8jMCCy+YxjIj4ilKZJTug7Jm+U0q9fvulbLQ99t+48ZZazh8qpieqY15784LST7+A2CBkzuhJN/1BLG36wNU/drQwl3AgF6unPR1vf72ysNXBDc245i9zhoemaS9LgSTHv03bbbt4+GPD/FRQRe2i3/xcuRMehgOg8FYsxtKl9++NGsTz/z3GRae6wPA8O4teH5cL+IqzjhKPraSdXpvLS5Je5zwqRbZ42qVbgAuyVf4AE8jN/1osh5/e+XhK4IbIbRCKal9taHwopEOoZbX9O3MR/cPpmNKAgdkKmPK/8nTMX/g3LQdNUs9HNccGRnPl+YMrin/LwvP9SECE09EfsisManEvXeDPXrn0V3avhNSHL01W6z+C121kM+4JPfHV9kyQw9vR271+NvXycMXQowDnga6Av2llJvcbDcMeBkwAm9IKZ+py3EVCgcttOS0R92za6tGfPrAYJ79bBuLNuWy8OxFLJm5gzv6n+P2nglckN7G481VXG7i6837eKPgD+yUWj6cTlH5PG94hZ5tW2jZPd1E7zh4a875/p3LGiqPPrQJgJFbXSWdncBNwFx3GwghjMAs4BogB9gohFghpfy1jsdWhCtVJmo/9ZyG1mIh5t0x/PXIem5JH8G/Ix/g5/2neH31IV5fDV2ivqdfn750ad2I5vHRREUICkpNHMovYkfOOdYcyKe0wgK0pznneTRlK7c/9C8iyoY7Fp92dXz9UN1dulwVlRM++DmZWp0MvpRyN2Bfxu6a/sB+KeVB67bvA2MAZfAVtaPKRO0pz56TbvuuuZ/z1rT/Y8e5FBYumsfX5r7sKU9iz/psj4fs3aYJt2akcWPnaGIa3VJVfx+/Qou5T+5ir83rbTplFZWjaCAaYtI2FTiie54DDHC3sRBiCjAFID09vX7PTBGcuPKUnXPn6w2ufmI3Kh7mXkrPtP483wHKjixgc9JodvR4kn25hZwtrqDCbCExJoLWTWLp3roR/ds3o1VitD2/ziKnOroAi0c7hIY6PLd57J7q3wZYoQxFaFKtwRdCfAu0dPHWU1LKT7w4hiv33+0SMinlPGAeQEZGhlpmqKhKdVqoTSLJXqdN5t79lbZ93h6Ye6ndk576E9FxzRksDAx2N0lmsWhpmRdNch8nDY4eev5vrqNz3Mk2AaDtKsKDag2+lPLqOh4jB2ije54GHKvjPhXhjictVBdCSc5GWHCdFt+c0tXJ0x+qPVYUe17xmr3OukJXalkybRFBem9c76End6nqsXuasK3uehQKH9EQks5GoKMQoj1wFLgNuKMBjqsIV2KbQ8uecHyb9vzYVruB1Xv60gxl57VtnOPk9blzbCt0DUYtV76rGHpnD93LtAwKRUNS17DMG4FXgWTgcyHENinldUKI1mjhlyOklCYhxIPAV2hhmfOllLvqfOYKhSssFlg8Co7/AlEJYCrVkqRJqf0ZDFU9fZuHr8tpXyX6J62/lpLZJv04e+POHrrzcyXbKAKAOsV+SSk/llKmSSmjpZQtpJTXWV8/JqUcodtupZSyk5Syg5TyX3U9aX+Tk5PDmDFj6NixIx06dOCRRx6hvLy8ynbHjh1j7Nix1e5vxIgRnD17tlbn8vTTT/Pcc8/V6rMhic0rxwIVJTDlR824vtjNvpzdZnxn7IE/ZNkzcFpz2leJ/pm+y9HYu0NluFQEOCrYt4ZIKbnpppu44YYb2LdvH7/99huFhYU89dRTDtuZTCZat27N0qVLq93nypUradKkST2dcZjhnDc/Prmqdg52D9xgtBvy+GTNk3dOu7B0kmOH4Qp3eVK8fV+haACCOpdOuyc/r5f9Zj1zvdv3Vq1aRUxMDHfffTcARqORF198kfbt29O+fXu+//57SktLKSoqYv78+YwcOZKdO3dSXFzMxIkT2bNnD127diUrK4tZs2aRkZFBu3bt2LRpE4WFhQwfPpxLLrmEzMxMUlNT+eSTT4iNjeX1119n3rx5lJeXc+GFF/LWW28RFxdXL9cf1DhntATvtXObZy6xS0CVBVPMnmPkVQZMRRCgPPwasmvXLi6++GKH1xo1akR6ejomk4m1a9eyaNEiVq1a5bDNa6+9RtOmTfnll1/485//zObNm13uf9++fTzwwAPs2rWLJk2asGzZMgBuuukmNm7cyPbt2+natStvvvlm/VxgMOOc0VLK6guZ67GVUZRm7bEo13XBFFfSjcqAqQgCgtrD9+SJ1xdSSpcri22vX3PNNTRr1qzK+6tXr+aRRx4BoEePHvTq1cvl/tu3b0/v3r0BuPjii8nKygJg586d/OlPf+Ls2bMUFhZy3XXX+eaCQgl3XrS3IY/OkTQIXR1dg6bjS+k6nr66SVk1aasIAILa4PuD7t27V3rdNs6fP8+RI0cwGo3Ex8e7/Jz0ciIvOjq68n+j0UhJSQkAEydOZPny5Vx00UUsXLiQH374oXYXEMrUNfTR2SiD4/4SUjzH01fXsahYe4WfUZJODbnqqqsoLi5m8eLFAJjNZmbMmMHEiRM9auqXXHIJH374IQC//vorO3bsqNFxCwoKaNWqFRUVFbzzzju1v4BQpibyjTv0qWld7U9JM4ogRhn8GiKE4OOPP2bJkiV07NiRTp06ERMTw7///W+Pn7v//vvJy8ujV69ePPvss/Tq1YvGjRt7fdx//OMfDBgwgGuuuYYuXVzUSVVo+DqXuPP+fNGpKBR+QngrNfiDjIwMuWmTyxT7QYfZbKaiooKYmBgOHDjAVVddxW+//UZUVFT1H1b4B5WjXhGECCE2SykzXL2nNPwGori4mCuuuIKKigqklMyePVsZ+0DGlznqVcehCBCUwW8gEhMTCZXRSsBTWwOr/5yv4uZVcRNFAKFaniK0qO2KVufPxTTVUiHXdXLWVcehUPgJZfAVoYUrA+tNjhv95w6vhTev0VIht+6jJVGrrRSjonoUAYSSdBShhXMsvnMGTHeSir4qVmQsHN+qvX5sq5ZErbbx82rBlSKAUAZfEVo4G9jqCo/YsOXgKTql5cq30bpP3b1yteBKESAoSaeGTJ8+nZdeeqny+XXXXcc999xT+XzGjBm88MILrFixgmeeeQaA5cuX8+uv9prtl19+ebUTuFlZWQghePXVVytfe/DBB1m4cKHHz/3lL3/h22+/rcEVhSD62HlvJBV9Dp6Vj2vbCSOk9dMqZSmvXBEiKINfQwYPHkxmZiYAFouF/Px8du2y13PJzMxkyJAhjB49mieffBKoavC9JSUlhZdfftllrn13/P3vf+fqq+talTKE8GahlF6/z9kA4xZoufInf6MiahQhRXi0Zh8WphgyZEilwd+1axc9evQgMTGRM2fOUFZWxu7du+nTpw8LFy7kwQcfJDMzkxUrVvDEE0/Qu3dvDhw4AMCSJUvo378/nTp14ueff3Z5rOTkZK666ioWLVpU5b1t27YxcOBAevXqxY033siZM2cALeeOLQf/k08+Sbdu3ejVqxePP/44AHl5edx8883069ePfv36sWbNmjp/JwFPdatvnUcBCS18u1pXoQgQQl/D93EcdOvWrYmIiCA7O5vMzEwGDRrE0aNHWbt2LY0bN6ZXr14OC6oGDx7M6NGjGTlypEP1K5PJxIYNG1i5ciV/+9vf3MowTz75JMOHD2fSpEkOr48fP55XX32Vyy67jL/85S/87W9/c5CaTp8+zccff8yePXsQQlRW1HrkkUeYPn06l1xyCdnZ2Vx33XXs3r271t9HUKOPu1cTq4owIPQNfj0UnrB5+ZmZmTz22GMcPXqUzMxMGjduzODBg73ax0033QQ4pkB2Rfv27enfvz/vvvtu5Wvnzp3j7NmzXHbZZQBMmDCBcePGOXyuUaNGxMTEcM8993D99dczcuRIAL799lsHeen8+fMUFBSQmJjo1XmHDK4cATWxqghxQl/SqYc4aJuOv2PHDnr06MHAgQNZu3ZtpX7vDbY0yEajEZPJ5HHbP/7xjzz77LNYalAWLyIigg0bNnDzzTezfPlyhg0bBmjzDmvXrmXbtm1s27aNo0ePhp+xB7UgShGWhL7Br4fshkOGDOGzzz6jWbNmGI1GmjVrxtmzZ1m7di2DBg2qsn1iYiIFBQW1Pl6XLl3o1q0bn332GQCNGzemadOmldr/W2+9Vent2ygsLOTcuXOMGDGCl156iW3btgFw7bXXMnPmzMrtbK+HHb5yBFThckUQEfqSDvg8Drpnz57k5+dzxx13OLxWWFhIUlJSle1vu+027r33Xl555RWvipq74qmnnqJPnz6VzxctWsS0adMoLi7mggsuYMGCBQ7bFxQUMGbMGEpLS5FS8uKLLwLwyiuv8MADD9CrVy9MJhNDhw5lzpw5tTqnoMYXC6JUnhxFkKHSIysUtaUwV8u9YzFpI4XHdqt5AIXf8ZQeWbkjCkVtUXlyFEFGnSQdIcQ44GmgK9BfSunSHRdCZAEFgBkwuet9FIqgQuXJUQQZddXwdwI3AXO92PYKKaUKhVCEFipPjiKIqJPBl1LuBq3Oq0KhUCgCm4bS8CXwtRBisxBiiqcNhRBThBCbhBCb8vLyGuj0FAqFIvSp1sMXQnwLtHTx1lNSyk+8PM4QKeUxIUQK8I0QYo+U8idXG0op5wHzQIvS8XL/CoVCoaiGag2+lLLOqRellMesj7lCiI+B/oBLg69QKBSK+qHeJR0hRLwQItH2P3At2mSvQqFQKBqQOhl8IcSNQogcYBDwuRDiK+vrrYUQK62btQBWCyG2AxuAz6WUX9bluAqFQqGoOQG90lYIkQccruXHk4BwCwNV1xz6hNv1grrmmtJWSulyFWBAG/y6IITYFG4LvNQ1hz7hdr2grtmXqNQKCoVCESYog69QKBRhQigb/Hn+PgE/oK459Am36wV1zT4jZDV8hUKhUDgSyh6+QqFQKHQog69QKBRhQsgZfCHEMCHEXiHEfiHEk/4+n/pGCNFGCPG9EGK3EGKXEOIRf59TQyGEMAohtgohPvP3uTQEQogmQoilQog91t+7agHlEEMIMd3arncKId4TQsT4+5x8jRBivhAiVwixU/daMyHEN0KIfdbHpr44VkgZfCGEEZgFDAe6AbcLIbr596zqHRMwQ0rZFRgIPBAG12zjEWC3v0+iAXkZ+FJK2QW4iBC/diFEKvAwkCGl7AEYgdv8e1b1wkJgmNNrTwLfSSk7At9Zn9eZkDL4aEnZ9kspD0opy4H3gTF+Pqd6RUp5XEq5xfp/AZoRSPXvWdU/Qog04HrgDX+fS0MghGgEDAXeBJBSlkspz/r1pBqGCCBWCBEBxAHH/Hw+PseaOfi008tjgEXW/xcBN/jiWKFm8FOBI7rnOYSB8bMhhGgH9AHW+/lUGoKXgN8DFj+fR0NxAZAHLLDKWG9YkxGGLFLKo8BzQDZwHDgnpfzav2fVYLSQUh4HzakDfFJWLdQMvqvSW2ERdyqESACWAY9KKc/7+3zqEyHESCBXSrnZ3+fSgEQAfYHZUso+QBE+GuYHKlbdegzQHmgNxAshfuffswpuQs3g5wBtdM/TCMEhoDNCiEg0Y/+OlPIjf59PAzAEGC2EyEKT7a4UQrzt31Oqd3KAHCmlbfS2FK0DCGWuBg5JKfOklBXAR8BgP59TQ3FSCNEKwPqY64udhprB3wh0FEK0F0JEoU3wrPDzOdUrQiso/CawW0r5gr/PpyGQUv6flDJNStkO7TdeJaUMac9PSnkCOCKE6Gx96SrgVz+eUkOQDQwUQsRZ2/lVhPhEtY4VwATr/xMAb6sLeqRORcwDDSmlSQjxIPAV2oz+fCnlLj+fVn0zBLgL2CGE2GZ97Y9SypXuP6IIUh4C3rE6MweBu/18PvWKlHK9EGIpsAUtGm0rIZhmQQjxHnA5kGStL/JX4BngQyHEZLSOb5xPjqVSKygUCkV4EGqSjkKhUCjcoAy+QqFQhAnK4CsUCkWYoAy+QqFQhAnK4CsUCkWYoAy+QqFQhAnK4CsUCkWY8P8q5p0fCd9++QAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] @@ -2717,13 +2831,14 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def get_bootstrap_idxs(x, bootstrap_bag_size=0.5):\n", - " ## Bag size handling\n", + " \"\"\"Determines the indexes of an array to be used for the in- and out-of-bag bootstrap samples\"\"\"\n", + " # Bag size handling\n", " assert bootstrap_bag_size>0, 'Bootstrap bag size must be greater than 0'\n", "\n", " if bootstrap_bag_size > 1:\n", @@ -2732,7 +2847,7 @@ " else:\n", " bootstrap_bag_size = int(np.ceil(bootstrap_bag_size*len(x)))\n", "\n", - " ## Splitting in-bag and out-of-bag samlpes\n", + " # Splitting in-bag and out-of-bag samlpes\n", " idxs = np.array(range(len(x)))\n", "\n", " ib_idxs = np.sort(np.random.choice(idxs, bootstrap_bag_size, replace=True))\n", @@ -2743,14 +2858,14 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "in-bag: 250, out-of-bag: 306\n" + "in-bag: 250, out-of-bag: 299\n" ] } ], @@ -2761,20 +2876,23 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll now calculate the standard deviation of the in- and out-of-bag errors" + ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def get_bootstrap_resid_std_devs(x, y, bag_size, model=Lowess(), **model_kwargs):\n", + " \"\"\"Calculates the standard deviation of the in- and out-of-bag errors\"\"\"\n", " # Splitting the in- and out-of-bag samples\n", " ib_idxs, oob_idxs = get_bootstrap_idxs(x, bag_size)\n", "\n", @@ -2800,24 +2918,16 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 82, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":24: RuntimeWarning: invalid value encountered in true_divide\n", - " loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising\n" - ] - }, { "data": { "text/plain": [ - "(0.014392566708972723, 0.017813550363034145)" + "(0.020320708955639148, 0.024223597689702395)" ] }, - "execution_count": 77, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -2827,15 +2937,17 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll quickly plot the distributions of the errors for each set" + ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -2845,11 +2957,11 @@ "\n", "100%\n", "1000/1000\n", - "[00:29<00:00, 0.03s/it]" + "[00:28<00:00, 0.03s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 1000/1000 [00:29<00:00, 0.03s/it]" + " [████████████████████████████████████████████████████████████] 1000/1000 [00:28<00:00, 0.03s/it]" ] }, "metadata": {}, @@ -2861,13 +2973,13 @@ "Text(0.5, 0, \"Residual's Standard Deviation\")" ] }, - "execution_count": 78, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -2898,25 +3010,30 @@ "sns.histplot(oob_resid_std_devs, ax=ax, alpha=0.5, color='C1', label='Out-of-Bag')\n", "\n", "ax.legend(frameon=False)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlabel('Residual\\'s Standard Deviation')" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll now create two wrapper functions, one for running models, the other for bootstrapping them. The returned `df_bootstrap` includes the predictions for each model run.\n", + "\n", + "N.b. the `bootstrap_model` is a generalisable function that will work with any Scikit-Learn compatible model." + ] }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def run_model(x, y, bag_size, model=Lowess(), x_pred=None, **model_kwargs):\n", + " \"\"\"Fits a model and then uses it to make a prediction\"\"\"\n", " if x_pred is None:\n", " x_pred = x\n", " \n", @@ -2931,6 +3048,7 @@ " return y_pred\n", "\n", "def bootstrap_model(x, y, bag_size=0.5, model=Lowess(), x_pred=None, num_runs=1000, **model_kwargs):\n", + " \"\"\"Repeatedly fits and predicts using the specified model, using different subsets of the data each time\"\"\"\n", " # Creating the ensemble predictions\n", " preds = []\n", "\n", @@ -2949,7 +3067,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -2959,11 +3077,11 @@ "\n", "100%\n", "1000/1000\n", - "[00:26<00:00, 0.03s/it]" + "[00:25<00:00, 0.03s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 1000/1000 [00:26<00:00, 0.03s/it]" + " [████████████████████████████████████████████████████████████] 1000/1000 [00:25<00:00, 0.03s/it]" ] }, "metadata": {}, @@ -3040,123 +3158,123 @@ " \n", " \n", " 0.00000\n", - " 0.113114\n", - " 0.129049\n", - " 0.079550\n", - " 0.176352\n", - " 0.097087\n", - " 0.095506\n", - " 0.145797\n", - " 0.060854\n", - " 0.118341\n", - " 0.126359\n", + " 0.123240\n", + " 0.100568\n", + " 0.034607\n", + " 0.062935\n", + " 0.037283\n", + " 0.024595\n", + " 0.077896\n", + " 0.071517\n", + " 0.154899\n", + " 0.104609\n", " ...\n", - " 0.069378\n", - " 0.128688\n", - " 0.140057\n", - " 0.037661\n", - " 0.118545\n", - " 0.068503\n", - " 0.155299\n", - " 0.119653\n", - " 0.154302\n", - " 0.073166\n", + " 0.106006\n", + " 0.022008\n", + " 0.117897\n", + " 0.042924\n", + " 0.094948\n", + " 0.095224\n", + " 0.109828\n", + " 0.097625\n", + " 0.096894\n", + " 0.102314\n", " \n", " \n", " 0.02004\n", - " 0.128327\n", - " 0.143101\n", - " 0.093618\n", - " 0.188118\n", - " 0.112425\n", - " 0.110369\n", - " 0.157746\n", - " 0.077512\n", - " 0.130675\n", - " 0.138419\n", + " 0.135523\n", + " 0.114414\n", + " 0.048940\n", + " 0.077258\n", + " 0.051836\n", + " 0.039222\n", + " 0.091694\n", + " 0.086137\n", + " 0.166246\n", + " 0.116779\n", " ...\n", - " 0.084536\n", - " 0.142440\n", - " 0.152240\n", - " 0.054308\n", - " 0.132100\n", - " 0.081747\n", - " 0.169626\n", - " 0.132245\n", - " 0.166440\n", - " 0.087182\n", + " 0.119922\n", + " 0.037429\n", + " 0.131267\n", + " 0.057019\n", + " 0.109155\n", + " 0.107799\n", + " 0.122830\n", + " 0.111439\n", + " 0.109543\n", + " 0.115580\n", " \n", " \n", " 0.04008\n", - " 0.143518\n", - " 0.157141\n", - " 0.107665\n", - " 0.199868\n", - " 0.127749\n", - " 0.125216\n", - " 0.169683\n", - " 0.094155\n", - " 0.142993\n", - " 0.150465\n", + " 0.147791\n", + " 0.128252\n", + " 0.063258\n", + " 0.091566\n", + " 0.066380\n", + " 0.053843\n", + " 0.105484\n", + " 0.100748\n", + " 0.177581\n", + " 0.128936\n", " ...\n", - " 0.099684\n", - " 0.156180\n", - " 0.164409\n", - " 0.070926\n", - " 0.145642\n", - " 0.094978\n", - " 0.183907\n", - " 0.144825\n", - " 0.178570\n", - " 0.101186\n", + " 0.133827\n", + " 0.052831\n", + " 0.144617\n", + " 0.071105\n", + " 0.123350\n", + " 0.120364\n", + " 0.135816\n", + " 0.125241\n", + " 0.122177\n", + " 0.128832\n", " \n", " \n", " 0.06012\n", - " 0.158689\n", - " 0.171169\n", - " 0.121691\n", - " 0.211603\n", - " 0.143059\n", - " 0.140048\n", - " 0.181607\n", - " 0.110785\n", - " 0.155296\n", - " 0.162499\n", + " 0.160044\n", + " 0.142084\n", + " 0.077562\n", + " 0.105860\n", + " 0.080914\n", + " 0.068458\n", + " 0.119268\n", + " 0.115353\n", + " 0.188904\n", + " 0.141083\n", " ...\n", - " 0.114824\n", - " 0.169910\n", - " 0.176565\n", - " 0.087519\n", - " 0.159172\n", - " 0.108198\n", - " 0.198145\n", - " 0.157392\n", - " 0.190696\n", - " 0.115178\n", + " 0.147722\n", + " 0.068217\n", + " 0.157946\n", + " 0.085199\n", + " 0.137534\n", + " 0.132918\n", + " 0.148786\n", + " 0.139030\n", + " 0.134797\n", + " 0.142070\n", " \n", " \n", " 0.08016\n", - " 0.173869\n", - " 0.185205\n", - " 0.135699\n", - " 0.223324\n", - " 0.158358\n", - " 0.154867\n", - " 0.193538\n", - " 0.127430\n", - " 0.167613\n", - " 0.174523\n", + " 0.172286\n", + " 0.155927\n", + " 0.091852\n", + " 0.120165\n", + " 0.095440\n", + " 0.083068\n", + " 0.133063\n", + " 0.129976\n", + " 0.200216\n", + " 0.153222\n", " ...\n", - " 0.129975\n", - " 0.183653\n", - " 0.188710\n", - " 0.104089\n", - " 0.172732\n", - " 0.121408\n", - " 0.212341\n", - " 0.169947\n", - " 0.202850\n", - " 0.129161\n", + " 0.161608\n", + " 0.083587\n", + " 0.171256\n", + " 0.099382\n", + " 0.151709\n", + " 0.145480\n", + " 0.161742\n", + " 0.152810\n", + " 0.147405\n", + " 0.155300\n", " \n", " \n", "\n", @@ -3166,40 +3284,40 @@ "text/plain": [ "bootstrap_run 0 1 2 3 4 5 \\\n", "x \n", - "0.00000 0.113114 0.129049 0.079550 0.176352 0.097087 0.095506 \n", - "0.02004 0.128327 0.143101 0.093618 0.188118 0.112425 0.110369 \n", - "0.04008 0.143518 0.157141 0.107665 0.199868 0.127749 0.125216 \n", - "0.06012 0.158689 0.171169 0.121691 0.211603 0.143059 0.140048 \n", - "0.08016 0.173869 0.185205 0.135699 0.223324 0.158358 0.154867 \n", + "0.00000 0.123240 0.100568 0.034607 0.062935 0.037283 0.024595 \n", + "0.02004 0.135523 0.114414 0.048940 0.077258 0.051836 0.039222 \n", + "0.04008 0.147791 0.128252 0.063258 0.091566 0.066380 0.053843 \n", + "0.06012 0.160044 0.142084 0.077562 0.105860 0.080914 0.068458 \n", + "0.08016 0.172286 0.155927 0.091852 0.120165 0.095440 0.083068 \n", "\n", "bootstrap_run 6 7 8 9 ... 990 \\\n", "x ... \n", - "0.00000 0.145797 0.060854 0.118341 0.126359 ... 0.069378 \n", - "0.02004 0.157746 0.077512 0.130675 0.138419 ... 0.084536 \n", - "0.04008 0.169683 0.094155 0.142993 0.150465 ... 0.099684 \n", - "0.06012 0.181607 0.110785 0.155296 0.162499 ... 0.114824 \n", - "0.08016 0.193538 0.127430 0.167613 0.174523 ... 0.129975 \n", + "0.00000 0.077896 0.071517 0.154899 0.104609 ... 0.106006 \n", + "0.02004 0.091694 0.086137 0.166246 0.116779 ... 0.119922 \n", + "0.04008 0.105484 0.100748 0.177581 0.128936 ... 0.133827 \n", + "0.06012 0.119268 0.115353 0.188904 0.141083 ... 0.147722 \n", + "0.08016 0.133063 0.129976 0.200216 0.153222 ... 0.161608 \n", "\n", "bootstrap_run 991 992 993 994 995 996 \\\n", "x \n", - "0.00000 0.128688 0.140057 0.037661 0.118545 0.068503 0.155299 \n", - "0.02004 0.142440 0.152240 0.054308 0.132100 0.081747 0.169626 \n", - "0.04008 0.156180 0.164409 0.070926 0.145642 0.094978 0.183907 \n", - "0.06012 0.169910 0.176565 0.087519 0.159172 0.108198 0.198145 \n", - "0.08016 0.183653 0.188710 0.104089 0.172732 0.121408 0.212341 \n", + "0.00000 0.022008 0.117897 0.042924 0.094948 0.095224 0.109828 \n", + "0.02004 0.037429 0.131267 0.057019 0.109155 0.107799 0.122830 \n", + "0.04008 0.052831 0.144617 0.071105 0.123350 0.120364 0.135816 \n", + "0.06012 0.068217 0.157946 0.085199 0.137534 0.132918 0.148786 \n", + "0.08016 0.083587 0.171256 0.099382 0.151709 0.145480 0.161742 \n", "\n", "bootstrap_run 997 998 999 \n", "x \n", - "0.00000 0.119653 0.154302 0.073166 \n", - "0.02004 0.132245 0.166440 0.087182 \n", - "0.04008 0.144825 0.178570 0.101186 \n", - "0.06012 0.157392 0.190696 0.115178 \n", - "0.08016 0.169947 0.202850 0.129161 \n", + "0.00000 0.097625 0.096894 0.102314 \n", + "0.02004 0.111439 0.109543 0.115580 \n", + "0.04008 0.125241 0.122177 0.128832 \n", + "0.06012 0.139030 0.134797 0.142070 \n", + "0.08016 0.152810 0.147405 0.155300 \n", "\n", "[5 rows x 1000 columns]" ] }, - "execution_count": 80, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -3211,20 +3329,23 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Using `df_bootstrap` we can calculate the confidence interval of our predictions, the Pandas DataFrame `quantile` method makes this particularly simple." + ] }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def get_confidence_interval(df_bootstrap, conf_pct=0.95):\n", + " \"\"\"Estimates the confidence interval of a prediction based on the bootstrapped estimates\"\"\"\n", " conf_margin = (1 - conf_pct)/2\n", " df_conf_intvl = pd.DataFrame(columns=['min', 'max'], index=df_bootstrap.index)\n", " \n", @@ -3236,12 +3357,12 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 87, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyUAAAIFCAYAAADMT1IdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAABcSAAAXEgFnn9JSAAEAAElEQVR4nOzdd3hUZfbA8e8kk0Z6n/Q2KSQBggQRUVBRVHTFuva1ruuqoBAFdW1rRw1FXHVt66rYsGD9qYgCKhYCDJBAgElvk957Mvf3R5LZ9N5zPs/D45KZufNOdrj3nve857wqRVEQQgghhBBCiLFiNtYDEEIIIYQQQkxtEpQIIYQQQgghxpQEJUIIIYQQQogxJUGJEEIIIYQQYkxJUCKEEEIIIYQYUxKUCCGEEEIIIcaUBCVCCCGEEEKIMSVBiRBCCCGEEGJMSVAihBBCCCGEGFMSlAghhBBCCCHGlAQlQgghhBBCiDElQYkQQgghhBBiTElQMgQqlepzlUr1+ViPQwghhBBCiIlMPdYDmOBCIiMjIwFlrAcihBBCCCHEMFON1htJpkQIIYQQQggxpiQoEUIIIYQQQowpCUqEEEIIIYQQY0qCEiGEEEIIIcSYkqBECCGEEEIIMaYkKBFCCCGEEEKMKQlKhBBCCCGEEGNKghIhhBBCCCHEmJKgRAghhBBCCDGmJCgRQgghhBBCjCkJSoQQQgghhBBjSoISIYQQQgghxJiSoEQIIYQQQggxpiQoEUIIIYQQQowpCUqEEEIIIYQQY0qCEiGEEEIIIcSYkqBECCGEEEIIMaYkKBFCCCGEEEKMKQlKhBBCCCEmGJ1Ox8aNG9HpdGM9FCGGhQQlQgghhBATiE6nY/369SxYsID169dLYCImhXEVlKhUqjkqlepelUr1iUqlylGpVIpKpaob5LHSW1/f05+I4R6/EEIIIcRI27lzJ8uXLyc2Npbly5eza9eusR6SEEOmHusBdPIgsGyYj/nfHn5ePszvI4QQQggx4hYtWsT69etZvnw5mzZtYuXKlWM9JCGGTKUoyliPwUSlUq0BpgF7Wv8YgHpFUawHcax0IEBRFNWwDrLjeyRFRkZGJiUljdRbCCGEEEJ0odPp2LVrFwsXLiQmJmashyMmrxG7j+5sXGVKFEVZ2/7vKtWo/R6EEEIIISaMmJgYCUbEpDKuakqEEEIIIYQQU8+4ypSMBJVKdQ8QAtQDScCniqIUju2ohBBCCCGEEG0mfVACPNPp7+tVKtUKRVFe7+8BVCpVT0UjIYMflhBCCCGEEAIm9/Ktz4GLgQBaiuejgXWAFfCaSqW6cOyGJoQQQgghhGgzrrpvdaZSqRQG2X2rl2PeAvwbOKYoSvgQjyXdt4QQQgghxGQ1al2nJnOmpCevAQVAmEqlChrrwQghhBBCCDHVTbmgRFEUI5DS+levsRyLEEIIIYQQYgoGJa2cW/9bNaajEEIIIYQQQky9oESlUkUB4UANkDzGwxFCCCGEEGLKm9BBiUqlukOlUiWrVKqnOv38bJVKNaeb588EttBStPOaoigNozRUIYQQQgghRA/G1T4lKpXqPODBTj+2VKlUv7X7+2OKonzV+r/daMl6dK4NmQ88rFKpMmipHykEgoATaPnMO4H7hnn4QgghhBBCiEEYV0EJ4A7M6/QzVaefuffjON8CfsBcYBbgCFQAPwObgf8oitI85NEKIYQQQgghhmxc71My3sk+JUIIIYQQYhKTfUqEEEIIIcT4pNPp2LhxIzqdbqyHIiYJCUqEEEIIIUS/6XQ61q9fz4IFC1i/fr0EJmJYSFAihBBCCDHKJnKmYefOnSxfvpzY2FiWL1/Orl27xnpIYhKQoEQIIYQQYhRN9EzDokWL2LRpEwkJCWzatImFCxeO9ZDEJCBBiRBCCCHEKJromYaYmBhWrlzJ7t27WblyJTExMWM9JDEJSFAihBBCCDGKJkOmISYmhhUrVkhAIoaNtAQeAmkJLIQQQojB0Ol07Nq1i4ULF8qNvRjPRq0l8HjbPFEIIYQQYtKLiYmRYESIdmT5lhBCCCHEODORu3MJMRgSlAghhBBCjCMTvTuXEIMhQYkQQgghxDgy0btzCTEYEpQIIYQQQowjk6E7lxADJd23hkC6bwkhhBBiOOh0Onbu3MmiRYuIiYkZVHeuzscQYhiMWvctyZQIIYQQQoyh7mpIBroPiNShiIlOghIhhBBCiH4YqY5Yw1FDMhXrUKRD2eQiQYkQQgghRB9GMhMxHDUkU60ORTJDk4/UlAyB1JQIIYQQU8PGjRtZsGABsbGxJCQksHv3blasWDFsxx+OHd6n0i7xI/3/hzAZtZoSCUqGQIISIYQQYmpom5lfvnw5mzZtYuXKlZP+xn88k/8/Ro0EJROBBCVCCCHE1DHRMxGTrTvXRP//Y4KQoGQikKBECCGEEBOBZBbEIElLYCGEEEIIMTzef//9KdedS0wsEpQIIYQQQowDI9XiVqfTcfjwYdatW0dCQgLr1q2b9N25xMQjQYkQQgghxBgbyRa3O3fu5KGHHmL16tVs2bIFPz8/Wbolxh0JSoQQQgghRklP2ZD+bn44mGxK2x4mTU1NGAwGrrzyyiF8AiFGhgQlQgghhBCjoLdsSH82PxxsNiUmJoaVK1eye/duKXAX45Z03xoC6b4lhBBCiP7qa8O/vlrcyoaBYgxI9y0hJoKRKkoUQggx+fSVDYmJiWHFihU9ZjL6k00ZDnJtE2NBghIhBmkkixKFEEJMPkNZRtW28eHSpUtHdBmWXNvEWJGgRIhB6m9RohBCCNGmr2xId9oHCl9//fWI7mAu1zYxViQoEWKQRiuNLoQQYmobzUBhMNc2We4lhoMEJUIMknQzEUIIMRpGcxJsoNc2We4lhot03xoC6b4lhBBCiNHQV2eusSIdwSY96b4lhBBCCDHSJsrSo8HUoowGWcoshosEJUIIIYSYkqbC0qORDrpkKbMYLhKUiHFrosxeCSGEmJgme6ep0Qq6xmsWR0wsEpSIcWkqzF4JIYQYW5N96dFkD7rE5CJBiRiX5EQqhBBipE32pUeTPegSk4t03xoC6b41ctoyJcuXL2fTpk2T8mIhhBBCjLTx2rVLTBij1n1LgpIhkKBkZMmJVAghhBBiTI1aUKIerTcSYqBiYmJGLRjR6XTs3LmTRYsWSQAkhBBCCDHKpKZETHlSVC+EEEIIMbYkUyKmvO6K6iVbIoQQYryrrq6murqaxsZGrK2tURQFMzMzpk2bhrW19VgPT4gBkaBETHmLFi3qUlQvhBBCjBe1tbVUVFTQ1NREVVUVtbW11NbWUl9VBTU10NwMlpagKGBmBtbWWFhbY29vj6OjIw4ODjg6OmJmJgtkxPglhe5DIIXuk4cU1QshhBgvmpubKS0tpbS0lLKyMg7s20eiTke0VotWo4H6emhowKy+Hvtp07BQq6lraMBMpaLZaKSmrg7F0hJsbU1/1Pb2eHp6EhgYiIWFxVh/RDFxSKG7ECOhp4L2vorqpRBeCCHEQA3k2tHc3ExRUREFBQWUlpRgrKqCykr0R46wPSmJuHvvZd3atXjNmcPc8HCsLCywnzat2+yH0Wikuq6O8upqKqqqKM/Pp16lIsfTk4KCAnx8fPDy8sLKymqEPrkQAyd5PDFlfPDBBzz++OMDLmiXQnghhBj/dDodGzduHDfn6P5cOxRFobS0lCNHjrB7926O7NlD8f79GA8exCYjA+/GRgoKCoi7915iY2NZtWYNKfn5eDg742hn1+NyLDMzM+ynTaOovJxtOh3WlpbM8vPDrqiIxsRE0vft49effyYhIYH8/Hwm06qZ8fY9EP0nQYmYMIZyotHpdLz00kvc23piH8gu8bK7vBBCjG/jcfKop2tHfX09OTk5pkDkwG+/kb9/P80HD2KTmUmgpSVztVrmRUYS5ufH0jlz2BQf37Ire3w8C6Oi+vX+Or2e9du3s+Caa9jwww9k5OczJyyMSA8PHIuLUSUmUnXoEEf27SMhIYGioqKR/HWMivH4PRD9J0GJmBB6O9H0J1jZuXMnf/3rX9m0aRMJCQk8/fTTLFy4sF/vvWjRItPrNm3a1OvrBhM4yayOEEIMzVhPHnV3Hu987Zg9ezaHDh3i119+4fiePeTv3UvjoUNY6PX4GI2cEBjIvMhIAjUabG1sTMeJ0WpZuXgxuzdvZuXixcRotf0a087ERJbHxbX8TuLi2JWUhEqlwsPZmdmhoZwcFUWQjQ1qvZ7qxEQS9+7l0KFD1NfXD/evZ9SM9fdADI0EJWJCeP/997s90fR3VmTRokV89913LFmyhLvvvpvLLrus37UhMTExrFy5kt27d7Ny5coeXzeYGRqZ1RFCiKEbyOTRcOvpPN527fjhhx8499xzaa6tpfjgQUhMxLGwkCC1mlk+PpwcHU2ory8OtrY9vkeMVsuKZcv6HZAALIqO7jXDYqFWE6DRcFJkJP5WVqiOHqX48GESEhIoKSkZ1O9irI3l90AMnXTfGgLpvjU6dDodDz30EHZ2dqxatYp169axevVqYmJi2LhxIwsWLCA2NpaEhAR2797NihUruj3O77//zo4dO4iNjWX69OkAmJubY2Vlha2t7ZC7kQxkLEN5jRBCiBbtC8mBMemi2NN5vLm5mdTUVHIyMyEvD7PSUjROTvh5eGAzjAXmOr2enYmJLIqO7hK06PR6diUlsTAqqs+A5tekJD797TfCtFq0CxbgFxREQEAAavXE6okk3TSH3ah135KgZAgkKBkdbSd8tVrNe++9B8DatWuB/81Qtd9jpO0k1NTURGFhIUVFRVRUVNBYVwd1ddDU1NLTHcDcHCwswNoaOwcH3Nzc0Gg0g9p0qrexDOdrhBBCjJ/zZ3fjCAwMJDk5mTqDAbKz0Tg4EKTRYGVpObzv3Vo3sjwujk3x8QNa3tXTcdY/8wynh4SgnTcPtZsbAQEB+Pr6olIN7d5UulhOWBKUTAQSlIyOvi48nWdFysvLycrKorioCKWiAsrKoKoKVUMD06ytsVSrMTc3B1paMNY1NFDb2Ah2duDkhMrZGXeNhoCAAGx7Saf3NNaBztDIrI4QQgzceMo0t53HTz31VJycnMhISYHsbKxragj388PZ3n5E3nfj1q0suOaa//0ONm9mxbJlQz7O96+/zuJZs6hWqcDXFwcvL5qbm9m9e/eggorxEkCKQZmaQYlKpZoDnAWcCMwDvIF6RVEGPm3dcjwn4BHgIkADGICtwMOKopQNw3glKBkl/blxLy4uJiMjg4qiIigshJISbC0s8HBywtneHjsbmx7bJzY0NlJSWUl+SQmltbXg7g7u7nh6exMUFDSozIkQQoiR098b3dGaoW9sbCQpKYmy7GzIyMDL3h6tj49pEmwkjESmpO04s0JCMJSUkJKbS3JdHdv37SMuLm5QQcV4CiDFgE3ZoGQr0DnEH1RQolKpXIFfgVAgFUgAolr/6IGTFEUpHuJ4JSgZByoqKkhJSaG8oADy8zErK0Pj7IyPm1uHDib9VVVbS4bBQGFVFWg0mLm74+Xj0+cuuJKaFkKI0dXXhNVozdDX19dz8OBBqjMzMc/NJcLPD3cnp2F/n+4MpG5kMMepra/n4bff5s933WUKKn788Ufuueeersfo4ToomZIJbcoGJWuAacCe1j8GBh+UvAVcC3wCXK4oSlPrz58HlgNvKYpy3RDHK0HJKOh8kmv7+8knn4yDgwN5WVlgMGBWUoKvmxu+7u5YDrFoHaCypoaU3FzK6utBo0Ht4UFQcDDe3t5d1tbKCVcIIcaf0ZihLyoqIvnIEZpycrAsKmJWSMigJsTGs4SjR1m/fTsrV68m/plnWHzGGSxavJiQkBDTCoSBLrUWE8bUDEo6U6lUCoMISlQqlQbIAZoBP0VR8ts9ZgVkAS6AT/vHBjE+CUpGWOeT3NKlS/n6669Zvnw58fHxLJ4zB62VFRpHxwEXEfbWsaS9sqoqUnJyqATw88PRy4uIiAhs2l10JDUthBDjz0hPGGVkZJB27BhkZGDf3ExUUBDWw1zMPlL6ew1s//xtOh0hnp64ODuDnx82Gg1arRYXFxeef/55uQ5OTqMWlEzWfUrOpeWz7eocdCiKUg98AZi3Pk+MY503Qtq6davp73FxcRw/cIDZQUFE+PsPOCBp2+l2/fbt6PR6dHo9G7duRafXd3iuk50dJ4SFEerignlKCuXJySTs2UNeXp7pOQPtjS4bJgohxMjr7z5TA9XY2MihQ4dIO3wYjh/H18aGE8LCJlRA0vka2JcYrZZ7Lr2Ui089lZn+/lhlZ1ObnMyhPXv49ddfiYyM7HIdlGudGIjJGpTMav3vvh4e39fpeWKc6nyzP2/ePOKfe46EhATWP/MMl596Ko52dgM+buedbt/bsaPXE7RKpcLH3Z254eE4VVfTfOQIRw8cICkpicbGxm4vfD2djGXDRCGEGD0xMTGsWLFi2AKSsrIyEhISKE5OxkyvJ8zDA62Pz5Bb5o6m7nZ7HwgXBwfmRkTgq1Zjcfw4DUlJWDQ1cfbZZ/Pdd9+xcuVKALnWiQGZrMu3PqGl49adiqI8383jy2jpwvWJoiiX9ON4Pf1rDYmMjLSS5Vsjq62GxNfXF1cbG/S7dpFXUsKfTjyx15Tz74cPs33fPtINBgylpRhKSqipq6OpuZma+npmzp7NI48/zqb4eMwqK/GaOZNzzz0XlUrFvo8/ZmFUVLepbUVRyC4sJDU/H8XLC0tvb8LCwnBzc+sw5p6WDMhSLyGEmJjy8vI4duQISkYG0+rqiAwMxG6U6kdKKirYf/w4B1NTycjPJ7OgAENJCY/dcAOL58wB4KeDB1nzyiu4OzoS4OlJoEZDVGAgc8LDcXN0NB1ruLp2wf+uien5+TTb24OnJ05eXnz//fecdtppcq2b+EYt2p5Y23T2X9vUeU0Pj1d3ep4Yx6KiogAoy8pClZLCn048EU8Xlw7PyS4s5Mtff+Xas84yFRh+8tNPPPP++z0ed/38+WzZsAEvCwtSqqp46qmneOqpp1CpVARqNLzs4MDyO+8kfts24sB0wlapVPh5eOBkZ0dyZibV5eUkVlXh4euLVqvF0tKyy7KzXbt2mYKSRYsWdQlYhBBCjF86nY4vvvgCLzc3tGZmeFpbExYWNqLtftt8/ssvrHzxRVJzc7t9PK+k5H//u7iYX3uYKA3UaHj21lu5dNEiYrRaVgK7Nm8eUkAC/7smalxcyCkqIkuvp6y0FGcnJzZs2MBdd90l1zrRL5M1KGmL6npKAw0o6lMUJarbg7RkUCIHciwxMA0NDeh0OmoyMzHPyyMqIAAXBwcAisrL2bxtGx/u2MHudifheqORRdHRLJo1i5yiIkJ9ffFycUHj4oL9tGmozc0xU6kwNzfHkJzM8rvu4q477iBGqyUtL4/y6mrS8vIgL4/bbrsNOzs7vC0supy07adNY05YGOkGA1lHj1JQUUFpaSnBwcEsXLiQDRs2dBt4tC312rVrl3TpEkKIcU6n0xEfH8+dd95J/Nq1eM2Zw/STTur/6wdQUF5bX8+Xv/5KuJ8fM0NCAHB1cDAFJCHe3sRotYR4e+Pv4YG3mxtzIyJMr18QHc3H//wn+aWlZOTnk5qbiy4lhePZ2aQbDLi2Xj8BLMzN8bC3J8zXdyC/jh5ZqNUEajRoXFw4lpUFjY0oJ53E1q1bufPOO+VaJ/oky7f6sXyrl/eR7lsjqL6+ngMHDlCTloZVUREzg4OxtbHht8OH+dfWrWzZsYP6xkbT82eFhODh78+TzzzTZzpap9ez9qOPiHv88S474WYVFPDu9u28um0bJWVl1NXWsmvDBmLDwwHYe/Qoob6+OLTb7b2ypoajWVlUqVTg54eztzf19fX89ttv0v5QCCEmsGeeeYYzzjhjULum93eZVFZBAS9+9hmvfPklJRUV/PW883jl7rsBaGxq4rs9e1gwYwZOg6ihBCivquK3w4c5LSbG1BTmjo0b+dfWrTja2nLzeedxx0UXEajRDOr43cktKuJ4Xh6Kvz8Ofn5ER0djOUEaAYgOZPnWEGW2/ren8N+30/PEOFNXV8eBAweoTU/HqqiIGK0WGysrfj98mPm33256XoS/P3+/4AIuWbiQj375hQXXXINarcbTz4/3duzo9uTfdpE4/9preezRR3nwoYdMFwsAPw8P1lx5JWfPncuOQ4cI9/Y2BSSNTU1c+OCDVNbUcOsFFxD35z/j7uRkyppkFxaSptdTWlmJhY8PV199Na6urt1+RtlsUQghxreamho8PDyIf+YZ4lavZtNzz7HyzDP7/fouBeWbN3e4LulzcnjsrbfY/P33NBuNQMs1KMjLy/QcC7Wa8+bPH9LncLSz4+wTT+zws0CNhkCNhnSDgfgPP2T9Rx9x6cKFPHL99UwPCBjS+wF4u7kxzdqapPR0Kurr2dfQwIwZM7BtN6EnRHuTNVNyA/AGsF1RlC5nD5VK9TpwI3Cjoij/GcL4JFMyCH3djNfW1nLgwAHq0tKwLi01pYOhpaBuzi23UNHUxIOPPML2//s/Vp15JjFaLTq9noc+/hg7Z2dWrVrFurVrWX322V0Ck41bt7LgmmuIjY1l8+bNfPXOO6y+5JJ+ralNycnhvPvu42hWFtCyhOvuP/+ZVX/+s6nYsaaujsMZGVSp1eDvj39ICEFBQR06s8hmi0IIMfZ6ux5VVlZy8OBBGtPSyElOpqCqitP7uaeH6fi9ZEqWP/88L332mSkYOS0mhjsvuYQ/zZ8/KrUqAEajkW/++IMNH3/MtoQEAMzMzLj1T3/iX3fdNSzvUVtfz6HUVGpsbTEPDGTGzJk4jdJu92JYyD4lQ/QNYAROValUHu0faN088U+tj//fGIxtSuurHW5NTQ06nY661FSq0tN54dNPmXnTTZRVVQEtBXXXnnsu72/ZwnXXXceKu+82tTKM0WqJdHdn1apVxMbGsmrNmm7bHC6KjmZTfDwJCQl89+WX/Q5IAEJ8fDj85pt89vjjnBAaSmVNDQ+/+Sbaq6/m7e++Q1EUpllbc0JoKL7W1nDsGJmHD7d8pro603G6K4QXQggxenq7HlVXV3PwwAEa9Xrsq6v582mnsfLCCwdcEB6j1bJy8WJ2d1NQ7uHkRLPRyNJ58/jjpZf4cf16LjzllFELSKAlAFl60kl89+yzHHjtNS469VSMRmOH2pOhsrGyYnZoKE4NDTSnpHDowAFKS0uH7fhi8pjQmRKVSnUHcAfwqaIo93V67B3gauBj4ApFUZpaf74RWAG8oyjKtUMcn2RKBqi3drjV1dUtGZKUFL79+mte+vxzKmtaGqi9+8ADXNm6vKq3maf+rt/dk5zMjqQkTgoNZWZwMBZqNTZWVgPqM280GtmyYwcPvPEG+pwcAHa/8ALzo/7XF6GwrIzkrCya3d1R+/gwPTISV1fXAWVKZJmXEEIMv56uR7W1tezfv58GvR6HujpmhoSgHoZA4Yd9+3C2t2d2aCjQkkE4mJLCvMjx1S9n79GjhPj4mOpXEo4epaC0lKUDKO7vjtFoJDEtjRLALDiYiKgoPDw8+nydGHOjlikZV0GJSqU6D3iw3Y/m0dJB6492P3tMUZSvWp//CPAw8F9FUa7vdCw34DcgBEgBEoAoILr17ycpilI0xPFO2aBksDfKPd2MV1VVceDAAdJ27yb+lVdMmxfOmz6dF+6801TTYTqOXs+upCQWRkV1CTo6P6YoCsUVFZRWVlJZU0N1XR3NAGo1tF1oGhsxNxpxsrPD09kZN0dHzMz6l0hsaGwk/sMPOZadzX/WrOnyeG19PYfT06k0N4fAQPyCgggKCuLgwYPs2rWr10J4WeYlhBAD09/rU3fn1+nTp7N//37qUlKwrawkRqvFQj208tuK6mpW//vf/PuLL5gRHEzCyy9jaWExpGMOxUC6gTU3NzPvttvYe+wY159zDutvv33QxfbQEpgcycigsLERlVbL9OhoCUzGvykblFwP9FXjcYOiKG+2Pv8ReghKWh93Bv4JXAh4AvnAZ8DDiqKUdH7+IMY7JYOSod4o63S6DjfjVVVV6Pbv57M332TT229T39iIjZUVa2+5hduWLes1ld3byXX/8eN8lZCAv6sr/gEB4OgI06aBtTVYWmJpaYlarUZRFBoaGmiurYXKSvQHDnBEr+fs2bM5c86cfgcn7eUVF3PrunWsv/12gr29MRqNpOblkV1aCgEBOPj49KsTiWy0KIQQ/TfQ61Pn69HBgwcpSU7GpqiI2aGhQw4edicmctXjj5ORnw/AbcuW8eyttzLNekClssNmoJsm1jc0cP9rr7H+o49QFAUfNzfevv9+Tp89e9BjUBSFY1lZ5NXVodJqmRETg0unvcfEuDI1g5KJZqoGJcN5o1xdXY1Op6MxJYUHnn2WXxITiY2Nxd/LiwcvuKDXk2VvJ9ddBw7w8s8/s2rNGuKffZazzz2XBQsW4OjoiJ2dHdbW1h2CDUVRqK6uZteuXbz99tvExcURv3YtZwUH4+rgwHGDgTNnzer3euLL//lPPtyxAwdbW/6zejUXL1wItOytkpyZSZNGg7WfX5+dSCRTIoQQ/TeU65PBYCBZp8Ps+HFiQ0OHFDgYjUbWvvceD77xBs1GI8He3rx+zz2cNkzn74FkO9pr3+hlIO2NdycmcsMzz3AsKwuVSsWD117LQ3/5S4+Thn2NT1EUkjMzyW9sxDwsjBPmzJGuXOOXFLqL8WvRokVs2rSJhIQENm3axMLWG+6Bqqmp4cCBAzSmpmJfW8u5CxZwzz338Ntvv3HfQw91W6TeXpdWi0lJ1DU0kJiWxid//MGqNWuIjY0l7p57KC8vJzQ0FA8PD6ZNm9Yl+6FSqbCzs+P48ePEtR4zbs0afsjK4v0DBzjtuut49rvv2Hv0aL8+29O33MLJUVFUVFdzycMPs/Jf/6KhsRE3R0dOCA3FprCQuuPH2bd3LwUFBT0ep22jxd27d0tAIoQQfejP9Umn07Fx48YOhe21tbXojx+HzEwCPTyGFJBUVFez9N57uf+112g2Grlq8WL2v/LKsAYk67dvZ8E117B++3bTcuf+aN/oZVN8PAujut0b2vQ+G7duRafXc3J0NPtfeYWbzzsPRVF49K23WBwXR0lFxaDGp1KpCPfzwwlozsjgo48+Ij4+vkvzGzG1SFAiBmw4bpTr6uqIj4/n0TVrsK2uZmZICFpPT1KPHWP//v19niyh08n1uecI8/Zmz9GjFFlZEb1oEevXrzddmBYtWtSvcXW+oHloNMStXk1sbCwrV6/m/Z9+6vYk3FmQlxc7Nmzg7ssvB2DDRx9x1t13U1Re3tKdKywM5/p6mo8e5bBOh16vp6esZUxMDCtWrJCARAgh+tDX9emDDz7g8ccf79Bxy2g0cvjwYZqys3FUqfAbYo2DjZUVheXlWKrV/PP663nnH//osNluZ+1v/vujuwm5/uqtG1jnMXUOLKZZW/Pq3Xez+R//wM7GhsamJmy7Cd76Oz4zMzOiAgPJTk7mm6++YlHrdbunwKS7YFJMLrJ8awim2vKt4eoCVVdXxx133MHrr78OwFv33ceM4GDWb9/OkvPP59WXX+bvZ5zB5aef3veY9Hp+OHgQXxcXPLy8wN8fJy8vQkNDOX78eJ+F5N0es90aY8C0fCo+Pp7FJ56I1tIS92nTCPX17dd6489+/plrnnySqtpafNzc+HH9ekJ9fVEUhbS8PDJLSiA4GJ/QUEJbu7IIIYQYXjqdjrvuuovnnnuuw/Kus88+m7yjR7HIzCQ2LMy04/mg30ev58mvvuKiP/+Zbz7/vF83//2t8ejrNXUNDZRVVVFbX49KpcLKwgJba2vsp00bUHfJvpZ5JWdm4mRnZ9pDbCif6bmPP+a0667rdcmdLGUeU1JTMhFMpaBkuE4IVVVVXHbZZXzzzTcArLz0Up699VZe+OKLQa1zLa+q4nBGBknl5RwuKOCcpUtZsmTJgMfVm7Ygpa0mJSczEyUvD8vSUqYHBOBsb9/76/V6Hv7kE/YePEhlRQVfPv44p86caXo8v6SEIzk5oNUSEBFBUFDQsI5fCCFES72Jm5sb3333HcuXL+fpp5/mlltuwVJR4NgxZgYE4DLI/Tle3LqVrMJCnvrrXwdUtzHYGo+2LpMnR0QQ4e9PTV0dReXlFFZWotjZgZUVKAo0NEBtLZZGI3Y2NthaW+Pm6IiDrW2vQcpAA4uH3nijZTPhyy9HpVL12iGzu/eK37aNO++5h/hnn+XGm2/mrLPO6vh7kqYvY0mCkolgKgUlw3FCKC0t5ayzzmLv3r2Ym5nx/PLl3HbhhcDgZotyCgvRGwwcb2pi+x9/EBcXNyozKLt372br1q2EeXmhVavxc3EhyMur2y5dOr2etR99RNzjjxMYGMgvv/xCxo4dXS463+3Zw9c6HdGnnIJfcDDJyck9ZqRk3xIhhBi4tsm1JUuW8Oqrr3L99dcTHBiIMTmZYGdn/D09B3xMo9HIfa++yjPvvw/A9vh4XBwc+n096++1z2g0UllTQ2VtLbX19dTW11NTX09dQwNYWrYEIXZ24OqKg4sLdnZ2KIpCfX09FRUVNFVWQl0d1NRAeTlWioKboyMu9va4ODh0G6D0N7DYnZjIguXLAVh+0UWsv/32AW8AqdPr+fz33/HWaIhYsoS5J56IlZXV/x6XTMlYkqBkIphKQclQTwjFxcWcdtppJCYmMs3amvceeIALFizo+B79PAEqisLx7GxyKyshKIhtP//MRRddNCozKO1/D+vXr+f0U05Ba2mJTX094f7+Hfq3t11slpx/Ph9+8AEPPvRQh4vOq19+CcDciAjTRenB++/HztGRNWvWdPt7lhOzEEIMXlvm+5RTTqGpqYma5GTcmpuJHkSGurm5mb8+9xz/ac38P37TTdx/9dWDyhR0fq7RaKSipobSykrKqqqorKnBaGXV0tbeyqrlj6UlWFtjYWWFtbU1jo6OeHp6Yt8pe280GqmoqKC2tpaysjKKi4tpqqiAigooK8PaaMTHzQ2Ni8ug92RZv2ULq158EYDLFi1i8wMPDPhYiqKw//hxKpydcY+MJKpTXWnn9s1i1EhQMhFMpaAE+ndC6GkW/6233uKmm27C1sqKzx5/nEWdXt/f9oYNjY0kpadTrlJBUBAhYWEUFxeP2o16W8ZIrVbz7rvvUldXx5VXXkm9wQDZ2fi6uFBRXc1Phw+TV1LCpStWEBsby+bNm/nqnXdYfcklxGi1JBw9yol//zuKonD+ySfz0IYNzJ07l9WrV/PnP/+5xwCrc8Zqy5YteHt7S9ZECCEGICsrixSdDsuMDOZGRAz4BrqpuZnrnnqKd7dvx8zMjDfuuYfrzjlnSGNSFIWC0lJyioqoqq3FaGkJDg4tGRBbWyynTcPBwYFp06ZhY2ODjY0Ntra2WAxwLxWj0UhJSQklJSUUFhbSWFICRUWoKitxsLHBx80NdyenDtmT/lyjP/zxR6558kkam5pYtmABHzz00IDrc6pqa9mr16OEhxMZEyMbK44P0hJYjD99dYFqm8Vv39UEICMjA393dx65/nq+fOqpbgOS/rQ3rKmrY++xY5RPm4Z5WBgzYmLw8/Mb1ba5ixYt4tFHH+WZZ57hz3/+M0VFRVhYWOAdFQUREew4dozntm1jwTXXcLiwkHVr15KQkMB3X35pCkgA5oSFcd9VVwHw5e7d3HjddezZs4fkQ4d49qmnSEhIIP6555g1a1aX92/rDvboo4+SlZXV5fcthBCio/adm5qamsjMzITcXIK8vAYckDQ0NnLFo4/y7vbtqM3N+eChh4YUkDQ2NZFdWMjvR45wpLCQCg8PjFFRWMycicfs2YSfeCLzFizg5JNPJjo6muDgYLy8vHBychpwQAItXa/c3NwICwvjpJNOIjw2FvuZM1Gioih3c+NwQQG/JiVxNDOTiurqfl+j/3z66Wx97DGsLCz47JdfuOThh1uWl3XSW7cxOxsbAtzd0f/8M08//TS///77gD/fYEl3r7EnmZIhmGqZkr50nsX/8ssvueCCC6goKYFjxwjz8MDbza3r6/pR6FdVW8sBvZ5GjYZpAQFER0czbdq00fpoHdx7771ceumlXbIZxcXFPPHEE1x11VX/y2Rs2ICPu3uPKfx1H35I3EsvAXDOvHk8dfPNKIrCx7t3ExgQgLm/P8UlJZx55pmmYKstY5Wbm9vtOIQQQvxP52WvF198MY6KwrS8POZGRAyoKxXAt3/8wTlr1mBpYcFHjzzCn04+eVDjKquqIqewkKLKShR7e3Bzw8LFBT8/P9zd3bGxsRnUcQervr6evLw8cnJyaGxd2kVxMd/88Ycp69+fYvxtCQkse+ABauvr+fyJJzr8fvpTQ7P/+HGe/e67ls2P4+NNe4eNJFka3StZvjURSFDSUft/1GvXruW3336joaGB+OXLOSkoCK2PT/ev6+MkVVpZSWJ6Os0+PtgHBDBz5sx+zw6NRFF455PX0qVLMRgMLFq0iLq6OjZu3NiyI/zTT3PDvHksmTu31+Nt+OgjVv7rXwA8cdNN3H/NNRiNRj7csYPPkpOJW72a559/nlWrVkl9iRBCDFDnCbPNb73FstDQIXXbevnzzzEajTTCgHdVr6iuJjUvj7L6enB3BxcX7J2d0Wg0aDSaAReJdzbU657RaKS8vByDwUBhfj7H9uxh+++/E3fvvWx89lnizjqrz8+7Q6fjcHo6t114YYelXzsTE/uchOw8UfnJJ5/w5JNPDvhzDIR09+qVLN8SE0/bMqpt27axb98+srOzMTMa8bC0JMTbu+fX9bKZU0FpKQfT02n298cpOJhZs2YNKCDpbjnZULVfLrZ06VK+/vpr03tYW1sTFxfHp59+yuLTTsPS3p5Dqak0NjX1eLy7Lr2UtbfcAsA/Xn+dHTodZmZm5JaVmTZuXLFiBTt37uxxHBKQCCFE99ove42PjyfKyYkAN7cBBSRt3a/anBQZye/5+f3eVV1RFKpqa0lMS2Nfaipljo6oIiPxjolh7vz5zJkzBx8fn2EJSAZz3Wu/dMnMzAxnZ2emT5/OiSedxKkXXMCZ55zD5tdeY1FICCpaJgt7c1pMjCkgWb99OzEXXUT8tm1oHB373FG+/cbI8U8/TbCPDwaDYRC/jf7rvHFy2z5lYnRJpmQIJFPSVUVFBYsXLyYhIQFXZ2devvNOli1YgIVajU6v590ff8TM3JwrFi7sc6Ylu7XlL8HBuAcGMn369G7b7vZkJGY+Os9A9fYeOTk5pBw/jjErC+vKSqICA7HvZcnZ42+/TU1dHU/cfLOpe0v73u3X3XAD5wyxkFIIISaa4ch479mzhw8//JBwR0dO9PJiRnBwv5dtKYrC7Rs28Ovhw3yzdi2eLi793l9EURQy8vPJKiig2cwMXF1ReXqi8fEhICAA6252RB+Mtt9RXl7egJf19ifr3tjYSGZmJtmZmShFRVBYiKOFBVofn16vaxu3biXyvPO499578fLy4kytltOio/vsTNbWkSzUywsbZ2fUERGcOG8elkPc2LI30t2rR5IpERNPTU0N559/PgkJCdjZ2nLe/Pl4ubiQlJ7O6ldf5f4PPyRbUbh0xQqe+fbbXmeW0vLy0BcWQmgoPmFhREZGDigggeGf+ehuBqq39/Dx8eGE2FhswsKo02jYn5JCQWlpj8d/4NprTQEJtGSQ4s46i29efZXFWi02lpZUVVUN6TMIIcREMhwZb6PRiLm5OeedeCIzXV2JDAwcUB3JP157jZc+/5wDKSn84z//QafXd5jN72nGv6q2ln3HjpFeWUlzeDhmM2fiNmMGsfPmER4ePqwBSdvv6PDhw6xbt25A172dO3eyfPlyYmNjWb58Obt27eryHAsLC0JCQpg3fz4+MTGYRUVR7uzMvpQU0g0GeprgXhQdzTNPPMGBAwf46quv2JOYyKyQEFYsW9brxGSMVsuKZcs4Z+5c7I1GmgwGjh492v9fyiD01cxHjDzJlAyBZEr+p7m5mcsuu4xPP/0UGxsbFp58Mo8//TSPPvggdnZ2+Lb2gO/Q7rabmSVFUTiWlUVeTQ0EBxMUHk5AQMCgx6XT6XjvvfdQqVRcccUVQzrZ9JQV6Wt2pampiSNHjlCcmQnp6fg7OxPk5dXrRbGmro7rn36aOy66iIWzZpGYlkaRWs208HBiY2MHHKAJIcRENBwZ72PHjpGbnIw6I4MTQkOZ1ksw0Ln17aoXX2T9li0AnBQby6aXXjLVPgLdzvjr9Hq++OMPvFxc0M6Zg1qjISw8HHd39wEX1fdHd63ifXx8+j3jP5j6xPr6elJSUijIzobMTOyam5nu749tN8X5Or2e+I8+4p1vvwXg0Rtu4MG//KXfn6+6tU2wUaslPCYGLy+vfr9WDAvJlIiJpbS0lGPHjmFpacl5p57K408/TWxsLBEzZrBqzRquuuoqEhMTTTM469au7TKzZDQaOZyeTl5dHaqwMMJnzBhSQNLGYDBw6aWXDrmupKesSF+zK2q1mujoaPynT4ewMDKrqjiUmkpzc3OP7/Xk5s1s2bmTZQ88wOH0dML9/LAsL6cmL4+0tLRBfwYhhJhIhprxLisrIzczEzIyiAoM7DMgad/6duW//mUKSIICAtj00kst2YS4OHYlJZlm89sHJL8fOcIz337Lubfcwva0NIpUKuaeeCIeHh4jEpBA19/RlVdeOaAZ/8HUJ1pZWREZGUlkTAwW06dT5ezMXr2e3KKirsfXann73nvZeMcdADz0n//w6H//2+/PZ2tjQ5CnJ2RlkZKSQkM3bYbF5CCZkiGQTMn/NDc389NPP7Hv++/xMjPjm2PHWH733aZMyao1a3j0wQdxt7DAw9mZy089tcOJvLm5mcS0NEpVKsyCg5keHY27u/uQxzXcdSVDWXOq0+n46quv0Hh6EmJhgV1dHTOCgrrdXKq2vp4z4+LYnZSEv6cnv77wAlYWFhzKyoKICGJiY3FycjIdd7g7jAkhxHgx2PNuc3MzCQkJ1B45gre5OWF+fr0+v32dyJtvvsnNN91Es9HI8uXLmTdvHp9+9BH3/uMf3XaJVBSFnKIi1n36KVetWjXqXZwG+zsajutHQ0MDR44coTQ3F9LTcbO2JsLfH3Wnon2dXs9f1q/n0OHDmJmZ8e9Vq7j5vPP69R6KorD32DGqXF3RREcTERExqLGKQZGWwBPBVAlKejtpFRQU4OHhwdGjR8lLTsYyK4s5YWEcycw0pbWh+xR3m8amJg6lplJhaYl5cDDRM2bg7Ow8bGMfTNvc4b7Rbz+OjRs3snjxYgKtrLAsLiY6KAgHW9surykuL2fB8uUczcpiVkgIP2/aRE5hIXlGI9YREcTGxpKYmNivzyeBixBiqjl8+DAFR49ilZvL3IiILjfJnbVlSu5YtYqzlyyhtKwMH29vPv7kE17csIGlYWHkV1R0uZbV1tdzNCuLsuZm9I2N/LB7N6tWrer2nDyezsXdXR+BQY1PURSys7NJPX4cJTubadXVRAUGdljOtXHrVuZfdRXx8fF8+OGHeDo7k7NlS4eOY73tHF9RXc2+1FSIiGBWbOyw3SeIPklQMhFMhaCkt5v6hIQETj/9dK655hoszc2ZYWfHJQsW4Gxv3+/j1zc0cCAlhRo7OyyCgpgxcyYOg+wb39tnGMgM0kjs/9GWsVGr1bz77rs0Nzdz5ZVXUpWVhXl2NtGBgd3+3tLy8ph/++3kl5ZyycKFvPfAA+w9fpw6jQbP6dP57rvv+swEyX4mQojRMJ5uuHNzc/n6009J+uUXLj7pJE6dObNfr2vr+qTVaNi8bRu3XXghe1NSepxUK62sJCk9nSZ3d8y9vQkOCaGwsLDba854Oxd3V4tiMBiGNL7KykoSExOpz85GZTAQ4O5OgKenqaPk+u3b+evtt3P9tdey8ZZbOG/+fNNr+7Ox4vHsbHKamrCOiGDu3LlDbqEs+kWCkolgKgQlPS1/ys/PZ86cOeTk5KDRaNi6dSub4uO5e8mSfm8iVV1by8HUVOpdXbEKCGDWrFljtkt7eyPVSvihhx5qWcq2ahXr1q0jLi4OCwsLStLTMcvIILqHjbx+TUritJUraWhs5J/XX8+dl1yCLi0NJTwco1rNf//7314vIrIplBBipI2nG+66ujre3byZbd98Q9yaNT3e4A5VblERx/PyUAICcPTzIyIiotdd2Mfbubjz/2deXl79aifcV/DZ0NDA0aNHKc7Lg7Q03C0tmR4QgJmZmSno6y7I60+b5ebmZv5ITqbe1xffqCi0w/z/qeiWFLqL8aG7IsOGhgYuvfRScnJycHFx4b333mPevHnctXo1u/oZoJVVVfHhzp288MMPvPXdd5iZmY2LgARGZhOlmJgYIiMjWdW61njVqlX88ssvREdH4x4SgjEoiMSMDEoqKrq8dn5UFC+vXInGxYWzYmNxtLMjwN0dMjKwtLDgjjvu6LVAUTaFEkKMtP60lR0O7Tf568mxY8c4+NtvxK1Z06EwvS+P/ve/fP7LL30+r61L5LGCAhStFs/QUGbNmtVrQALj71zcucD9iiuu6HN8/WnRbGlpyYwZM4iYORNVaCiFisLB1FSampu7bQ7w5a+/svrll/vVZtnc3JxwPz/IyiI7I4OKbq6Z/dGf75EYfZIpGYKpkCmBrsufbr/9dl588UXs7OxYs3w5SceOEXfvvf2ejSosK+Pz337jo/37cXZ1NWUOVq9ePeYp/zYjsYlSTzOJiqKQlJREUVoaZunpzOhhKVd5VRWOdnZAy0Vx37FjVLq44BkdzfTp00f98wghRJvRyJT05z0KCgo4vH8/KT/+yM60NFbcc0+/rk0f/vgjlz/6KCqVisQ33iAyMLDb5zU3N5OUnk6JokBgIMFhYZSUlPR72dp4Pxf3Nb6BZnvKyso4dPAgzWlpONTXMyskpMOSq5ScHML/8heajUbeWL2a2aGhfW6sCHAkI4N8lQrb6dOJjY0dUGez8ZTVmyBk+dZEMFWCEvhfurakpIRHW0/cTzz2GPOdnDA2NJCYmdnnSQQgp7CQ4wYDW5OTsbS17bhvSbuT23hamzycejrhG41GkpKSKE5Lwywjg5lBQTi1BiDdOZiSgoOtLRmFhShS9CeEGAdG+oa7rxvipqYm/vjjDxoOH6amuJgdBw9irlZ36fbYWWJaGifddhvVdXXcc/nlPHPrrd0+r6GxkUOpqVRaWWEeFMT0qCiys7On1A3uYG7oKysrOXjgAI16PU5NTcwMDu6w19Zjb73FQ//5D1YWFvyyaRNzwsP7HEdjUxN/HDlCY3AwoTEx+Pj49PszjLdldBOALN8S40fbSSggIIDHH38cgJtvvpn5Pj5o7O05Y/bsPndnBUg3GDheUAChoZy+ZAnJycn/27dk3TpTqng4dvAdr3ra08TMzIyoqChcg4Iw+vtzMC2Niurqbo/x1a+/Mv+OO7htwwY0jo6Qm4ter+9xR10hhBgNI70jdl/Ln44fP06DwUB2Whof7N/Pn1euJLePPS3Kqqq46MEHqa6rY/EJJ/DkX/+KTq9n49at6PR60/PqGhrYf/w4lXZ2WISGEnPCCbi5uY34srXxtsxoMHua2NvbM3PWLMyDgykDUnJzOzz+j2uu4U8nn0x9YyMXP/wwReXlfR7TQq0mUKOBnBzS09NpbGzs92cYb8voxP9IUCL61HbSXbZsGXFxcURGRnLF2WdjXVODtp+zEyk5OaSXlkJoKIERESxbtoxHH30UPz8/Pv744w5Lt0ZrbfJ40xaYuAQFYfT1JTEtjbpuLqi+7u4YjUb+7/ff+WjnTiwqKqjOzycnJ2cMRi2EEKOjtxviwsJC8rOyUOXkkFNayvK77+6znsRoNHLtk0+iz8nB39OT9x96iMS0tA4bKOr0emrq6th//Di1Li5Yh4Qwe/Zs7FuX2I7kDe54naAbTPBpb29PZFQUBAaSU15OQWmp6TEzMzPevu8+Qn19yczP58rHHut1c+E23m5u2CkKjbm5HDt2bEDjH2hgJUaHLN8agqmyfKvzHhsLTz6Z0KYmYtotMeqtt7g+J4fsykoICUE7fTq+vr79fr+pkA7vrLm5mf3791Ol12NXXs4JoaEdUt0Ar3/1FTc/9xxmZmZ8+NBDuGo0qCMjmXfSSVhYWIzRyIUQYvQ1NDSwZ88eGo8cwX/aNCpqavpsLQuw9r33uPeVVzosG+rcAerHN9/k5PBwGjUaprV2ibSysupwnJFatjYZlxmlpqaSeeQIZqmpXeon2y+je+Daa3nsxhv7PF5VbS179XqUsDCmx8Tg6ek5ksOfqmT5lhg/srOzufnmm/npp58488wzURUW8qNOR7rBAPyvt3j7maU2aXl5ZFdUQEgI4dHRfQYkILMY5ubmREdHY+HnR5WFBcezs7s858alS7nu7LMxGo3cvnEjdeXlNBUUkJaWNgYjFkKIsXP06FEac3Oxa24mUKMhRqtl5eLF7N68udcC95zCQgBeuPNOUx1D+w5QG599Fjc7Oxp9fLAPDmb27NldAhIYuWVrk3GZUVBQEO6BgRgDAkhMT6e6ttb0WHRQEK/dcw8AReXl/VqSbGdjQ6C7O2RmcvzYMRr6WK4nxjfJlAzBVMiU/Pbbb5x66qn4+/uzefNmEnfvZvuvv3bo/b4zMbHb3uLZhYXoCwtBqyU0KmpAhWgCSktLObh/P0pyMmEeHni7uXXISIX5+jLvtttITEvjlBkzeOSmm1DPmMGcE0/ErpcieSGEmCzy8vI4euAAZsePM0er7bCDeH/8mpTESZGRHbo36fR6vtu/Hzd7e4Lnz8cpIIDo6GjUavVwD79P471b12AYjUYOHjxImV6PdUEBc8LCsGj3u92TnMzciIh+H699N0q36dOJjo4eiWFPZZIpEWOvpKSEyy+/nKamJmbNmkVtZSWJCQlder9311vcUFKC3mCA4GCCwsMlIBkgnU7HW2+9RU19PQQF8XVCAne99BLPfPutKSN1LDubjx55BDsbG34+dIhd+/ah5OVx7NgxKXoXQkx6dXV16I8fh8xMAj08+hWQKIpCc3OzqZjdxsqqSzvZQI2G+RERBM+fj3NgIDNnzhyTgAQGn4UZbwXy7bXVT9r4+VFnb8+RjIwO16z2AYnRaMRoNPZ6PJVKRbi/PyqDgaKcHAoKCkZs7GJkSVAiOmg7ke3fv58bb7yRzMxMtFotd9xxB6rcXE6LjOwSgHROlfu6u3M0Jwe0Wny1WgICAsb6Y00o7Ysb33//fRKSktieloZlQACrOgWE4f7+vBIXxwPXXss9l1+OuqSECoOB3E7dTYQQYrI5fvw4zXl5OAB+Hh79es2/tm7l5OXLeeLLL7tdclxWVcXBtDSa/f1xDgxkxowZXWr6xrvxWiDfnoWFBdHR0Zj5+VHS1ERWN4GEoaSEs1ev5rkPPujzeHY2Ni2bCmdlcfz48QF14xLjx8T6lyZGVPsT2W233cZnn32GpaUl69atw6yyEquqKs6fP79DAAKwcetWAFYsW0agRsPhzEyUoCA0QUGEhISM4SeamDp3H0tISCDunnu46qqrTC2U2+92e+XixTx2443YTZtGkEYDWVmkpaZSX18/xp9ECCFGRlFREcV5eagKCgj38+vX5nk6vZ64l17ijyNHCImI6NKdq6K6mkNpaRj9/XEJChqVgGQkMhqj3cFysJ/B1taW0PBwCAwkraCAsqqqDo9/88cffL93L/94/XV+O3y4z+P5e3hg29REo8FASkrKgMYixgcJSoRJ24nMwcHBdHJ55JFHcLC1hZwcQn19UZubE6PVsmLZMoAOBe6/JiaS2HpCdwsMJDw8fEC7rIoWnYsbL7roIp5//nmampooLSri9aef7rF4083RkV8TEmgyGNC3m/3rznhO7wshRE+amppazm85Ofi5uvZr2VZdQwNXP/44DY2NLJo1i9z09A4TPFW1tRxMTaXZzw/nwMCWWfxRCEhGIqMxmgXyQ/0MXl5eePr7o/j5cTg9ncamJtNj1519NleccQZNzc1c/cQTVLUriu+OmZkZ4X5+kJuLISuL0nZth8XEIEGJMGk7kd18883U1dUxd+5cFi9ejJKbi7uNDW6Ojh2evzMxkeVxcabZpk9++40mb28c/f2J7FQ4KPqvc/exyy+/nFWrVvH9999zycUXc/npp+Pr7t7ldU3NzSy8807uf+01tn/5JYU5OZSUlHT7HhMhvS+EEN1JSUmhLjcXm9paAvrZAvb+V1/lcEYGns7ObHnkEVadeaYp4x/u58cBvZ4mHx8cW4vaR2PJ1khlNEazg+VwfIawsDCmeXvT4OjIsaws089VKhUvr1yJv6cnqbm5rPrXv/o8loOtLT7OzpCTw7Fjx/qsR+mNTNyNPglKhEnbieycc87hvPPO4/nnn6emoAB1aSmh3bTybV/gHr92LeEzZmDr7z8h1+CON52LG2NiYrj33ns5Y8kS8PfnaGZmhxklALW5OUtPOgmADVu2kHfgQI87vU/VDSqFEBNbaWkpeZmZkJNDhL8/5ubmfb5m+969rP/oIwBev+ce3J2cTBn/GUFBJKal0ejhgX1AADNmzOjXMYfDSGY0RqpNcWfD8RnMzc2ZPn06Kh8fCmtrO2ys6Ghnx1v33otKpeLVr77iy19/NTUp0HWzGkCn1/PFnj1kHD1KbX4+GRkZg/pcMnE3NuTOUXQQExPD/fffz5YtW2ioq4PsbLQ+Plh2syFfjFbLXWecwacvvMDimTOJPPVUZsyYMWZdSqaCwMBAbDUaGp2cONpuRqnNeSedRJCXF1W1tTzzyitUGQxkt+5z0n7WZzL2vxdCTG5Go7Fl5+6cHHxdXHDsR+vzsqoqrl+7FoC//elPnDd/fofHj2VnU21lhaWf36i3/Z0Me3J19xkGk2Gwt7cnIDgY/P05np3dYdJtUUwMqy67DIDrnnqKp776qtsmBW17pi289lq+T01F/9tvZKalUV1dPeDPJRN3Y0OCEgFAVVUVH3/8senvx44dw2gw4KxWo3Fx6fF1TnZ2nHXyyYS1BiTW1tYdHpf05/AyMzMzzSgV1dVhaLc8S6fXs2nHDta9+CJqtRqdXs+nmzeTkZ7Onj17Osz6ABP+YiiEmFoyMzOpLSjAsqqKQI2mX6/JLynB1toarY8Pz/397x0eyy0qIr+6GlVAAJGRkd1ujDjSRiujMZLaf4ahZBj8/f2x9fSk0dGR1E4dJB+/6Saig4IwNzfn4iuu6NKkADouKb/znntIy8tDycvj6NGjfbbJ73yv0tvEndzXjBwJSgQA9957L5deeil33303BoOB0rw8zAoLCfPz6/E1BaWlZBQXQ2Ag4dOnY29v3+FxSX+ODDs7O4K0WggI4HhODnWtO9i2nZAvvPBC4uLiAHj1009J27+fzz77rMOsz3vvvcfOnTsn1YZcQojJq7q6moy0NMjKQuvjg7qfS6zC/f3Z98orfP3009i1K4jPKy7meF4eBAURpNXi5OTU5bXDefM5VW5kh5JhMDMzIywsDLy9yauooLSy0vSYtaUlnz/xBJ89/jhfb93apQsl0GXPtGUnnoh5SQkV+fnk5eX1+L7d3av0lMWS+5qRJUGJYMeOHfyrtYDszDPPbGmll5VFoIcHNj3MHFXW1JCcnQ1BQfiFhODZTbGhpD9Hjp+fH45eXjS7uXE0MxPoeELOTU9n3vTp1Dc2svHFFwn09WXjxo0kJCTw6KOPkpWVJSdVIcSEoChKy6aweXm4Wlvj4ew8oNdPs7buUBeZmZ/P0fx8FK0Wz8BA/LqZfBvOm8+pdCM71KXBjo6O+AQEgK8vyZmZNDU3mx4L8vJiflSUaVuCu844w9SFUqfXszMxkaVhYaYGBidOn05wa5v81JSUHvcu6elepbssltzXjCwJSqa4qqoqbrzxRgBOPvlkqqqqaMzNxc5o7HEzqobGxpbWvz4+uPj5ERwc3OHxthkhjUYjdQsjRKVSER4ejpmXF6VNTeQVF3fYxHLVmWfy4cMPs3TePJ646Sa0trace+65/PLLL0RGRrJq1So5qQohJoT8/HzKDQbMi4u7bbrSneuffprnPviA5nY3tQBF5eWkFhSAVkvg9Okty2G76RQ5nDefE/1GdiBZnuGokwkODsZao6He1pZ0g6HL4zOCgmisr+eJd95BURRTLcmCa67h62PHTJs6A3i7uWGnKDTl5/e4d8lAAimpxxxZEpRMcffddx9paWnY2tryxBNP8PFHH6Hfu5cIf/9uT9RGo5HEtDTqnZyY5uvbpfVv+xmhr7/+mqVLl0rdwgiZNm0aQSEh4OeHPjeXuoaGDnvIfPrrrzxx882cHhODuqwMbycnLrvsMq644go5qQohJoSmpiZSU1MhJ4cADw+sLS37fM3HO3fy32+/5d5XXuFIayYZWvYqSc7MhKAgfIKCCAwM7PEYw3nzOZFvZAeT5RlqnYy5uTnh4eHg7U1OSUmX/UnSDAbuf+01Pt61i3e2beuyPUH7OhOVSkWYry/k5WHIzqa8vLzb8fY3kJoMzQnGM1VfxT+iZyqVKikyMjIyqd0/gIlk586dnHbaaQC88MIL3H777SQkJPD1v//NQ1df3e1rjmdnk1NXhzo8nDmxsdh02rRq48aNLFiwgNjYWBISEti9ezcrVqwY6Y8yZSmKwv79+6lITsa5poZZWq1p1mh5XByb4uNZuXgxbo6OfH3gABFLljB//nySkpLYtWuX1JQIIca1tLQ0Mg4cwCY3l7kREX22my8uLyfyhhsoKC3l/quv5ombbwZazpUHUlIos7XFITyc2bNn97mXlk6nG7bz5HAeazSN5TU9KSmJwsOHcSgtZXZoaIf/v57avJn7X3sNZ3t7PnjoId7Zu7fDNa/z5sJHMzPJA+wiI5kzZ47sozYwo/bLkkzJFNXY2MhNN90EwCWXXMIvv/xCQkIC69au5U8nntjta/JLSsipqIDAQKZHRnYJSGBizwhNRCqVioiICMy8vU3LuLqbNfrX1q3c+uSTbPnvf0lLS5sUHV+EEBNfb0uDGhoayMrMhNxcQnx8+rX/1Z0vvEBBaSmRAQE89Je/mH6eVVBAWXMz5j4+PS7Z6mw4z5MT9Zw7ltd0rVaLuacnFYpCTlFRh8fuvvxyTggNpbSykhc/+4y7zjjDVEvSOSABCPb2Rl1eTlV+Pjk5OaP1EcQASVAyRVlYWPDiiy9yyimnsHbtWs445RTeXbeO5YsWMTs0tMvzq2prOZqTA4GBBGq1uLq6dntcSW2OvvbLuFJyczk5IqJDB5KFUVEEenqiKAqvvfMO+xISqKioGOthCyGmuL6WBqWnp2MsLMRBrcbN0bHP432xezebv/8eMzMz/rNmDVatS73Kq6pIKyiAwEC0oaHdTqiJ7o3lNd3KyooQrRb8/fl2716e+/hj074kFmo1/1mzBrW5OVt//pnj2dmsWLas24Ck7fnBXl6QnU1aair19fXDPt6p0mFtJElQMoUtWbKEH374AYPBgNbSkhUXXMD86Oguz2tqbiYpLQ2jlxcuvr4EBAT0etyJOiM0kfn6+mKv0dDk7IyttbWp4L1t1uiv55/PqTNnUtfQwPq1a/vVt10IIUZSbwXgdXV15OXkQH4+wd7efR6rsqaGv7fuwRR32WWcOH06AM3NzSRnZqL4+ODh54eXl9fIfJhJbCyv6d7e3uRXVPB9aiqnXXddhw0TZ4aE8I9rrgHg9o0bKe6mXqQ9L1dXHFQqmvPz0XezG/xQTKUOayNJgpJJrnPk3tzc3KFf99GjR2nMy8O2qQn/brptKYpCcmYmtXZ2WPv69jvtLQZuKLMsKpWKsLAwVN7eFFRVEeDp2WHWyMzMjFfj4rC0sOCPgwfZ+uGHpu+BzO4IIcZCb0uDMjMzUYqKcLKywqkfO7f/dPAgBWVlBHt7888bbjD9PM1goNbGBisvr5Y9MHoh58LxKTMzk7jVq7stZL//6quJDgqirKqKnQcO9HoclUpFmJ8fKoOBwtzcboveB2uid1gbLyQomcS6i9xfeOEFwsPDeeONN9i2bRuvvfwy+oQEIvz9O6zX1en1bNy6lW/27KGooQEzf3+ioqKwsLAYw080eQ3HLIu9vT3efn7g48Px7GyMRmOHx8P9/U1rrF946SX27dtHQkKCzO4IIcZET0uD6uvrMeTmQkFBv3duX3rSSehefZV37r/ftL9WZU0N2SUl4ONDeHg4arW6x9fLTPf4ddZZZ7F+/XoSEhKIX7uWU1qzYACWFha8dd996F59lYv7Ue9iZ2ODxtERDAb0ev2wrRiQetrhIUHJJNY5cv/ss8944IEHqKys5OjRo7zxxhtcc/317EhNJSU31/S69j2/3/rjD/TNzYSGh3fZsV0Mn+GaZQkKCsLS05MaC4suhYEAq6+4ghnBwVRUV7Px2Wf54osvZHZHCDFmulsalJGRgbGwsN9ZkjaRgYHMb93hW1EUjmVlgUaDh68vLi4uvb5WZrrHRn+yU23B6/vvv8/iyEicO92LzA4NJSooqN/vGeTlhXlZGZWFhRR1c50cDKmnHR4SlExi7SP3559/nh9//JGqqioWLFiA0WgkrrVD012rV3dIh7bv3hR3771k5OXJOtwRNphZlu5O5mq1umUzSx8f0vPzqW9o6PAaC7Wa1++5B3dHR2L8/PD18eH555/v8r6yjEEIMRZMtST9zJJ8+OOP7Dt2rMvPc4uKqATUGg3aHoqf25OZ7tE3kOxUbGwsa9asQbtgAZmFhdT2UKi+//hx3v7uu17f19LCAj93d8jLIy0tbdiyJVJPO3QSlExSOp2OnTt3mjYvnDVrFjt37sTCwoJ//OMfhPv6Ev/00x06NLVZGBXFurVrW1oEr1vHRRddNIafZGoY6CxLbydzT09PHDw9aXZxIbVd/VCbuRERZHzwAdeefjqhDg5ccMEFHd5XljEIIcZKeno6SlERzn1kSXR6PY+8/TY3rF3L3L//nV/bTazV1NWRYjCAvz9BwcFY9mPDRZnpHn0DzU65u7vjotGguLuT2m51R5v9x48Te+ut3BIfT0ofbX993d1RV1VRU1REfn7+kD6HGD4SlExCnXdVnz17NuvWrQNg+fLl5GRkkPj77ywOCuq2r7ejrS1nTJ/Oxo0b8fb27ldveDF0A5ll6e1krlKpCA0NBY2G/MpKyqqqurzexsoKrY8PqsJCXOzsuOqqq0zvK8sYhBBjoaqqCkNODhgMBPWSnW9bYrwzNZWa+npmBAUxr7XOwGg0ciQjA6OHB87e3nj3o3NXG5npHl0ajYan2yZH+5mdCgkJQeXpSWF1dZdrW4xWyxmzZ1PX0MBtGzb0mgFRm5u3NPfJzSUtLY3m5uYhfx4xdHK3OQl1vqn8xz/+QW5uLlqtlvDwcLZv3841q1bxU3Y2C6OiOgQkJRUVZBYXQ+sF4YorrpDZ8nGou6UG7ZdcmYrevbzQ5+R0e3K2sbLicGoq199wA7t27aKpqQkY3IVCCCGGKiUlBfLz8bC3x8HWtsfn7UxMZGZsLDt27MDc3Jxz5s83TZ6l5uVRaW6O2tubiIgI6RY5Tul0Or7++msuuugi7r77bpYuXdqvYNDW1hYvX1/w9u5ybVOpVLy0ciVWFhZ8l5DA+z/80OuxisrL+XLHDpJ++42srKyhfiQxDCQomYQ615IEBARgbm7Offfdx+G9e4lbs6bb1nr1DQ0cyciAgAAy8/K48847ZbZ8nOq81ADosuQqKCgItacnVUBecXG3x/ngxx/JMhiIf/pp0tLSBn2hEEKIoSgvL6c0Px9VUVGvWRKAuVotDz30EADTw8K4YtEioOUmM7u0FAICmB4ZiVVrFy4x/rRNnl599dU899xzA1pCFRQUhNrDgyqVitxOhepaHx/T3iUr//UvSisruz2GTq9n448/csVdd7F9925+2L6dhk41mGL0SVAyCXQuSm5/w7pq1SrefvttfvvtN4J9fIh2dmbjs892qSVRFIUjmZk0urhg5+3NhRdeKEV/41z7pQbdLbmysLAgMDAQfH1Jy8ujsTUT0kalUvHiXXehNjdn9549fPj++3z77beDvlAIIcRgZWRkQGEhXs7Oppa+Pdn688/U1NTg6uDAK7ffToxWS31DA0czMyEgAL/gYFxdXUdp5GIwhtJYwMLCgqCgoJZrm8FAQ2Njh8dXX3EFEf7+5JeWcv9rr3V7jA4Nfdas4dAff7R8B8WYGndBiUqlslapVP9UqVTHVCpVnUqlylWpVG+oVCrfAR4nXaVSKb38iRipzzCaeipKbn/DWl1dTU1VFaSns2T2bOLOOqtLLUlGfj5lRiPmfn5ERkZywgknSNHfBNLTCd7HxwdbDw8aHR27LQyMDAwk7s9/BmDT88/j7u4uwagQYlRVVlZSUlCAqqQEf0/PXp+rz8lhw8cfA/Dfe+81tQA+mpXVMqnm5dVywyrGtaE2FvD29sbe05MmZ2f0nYrarSwteal1BcG/v/iCQ6mpXV6/KDqaTfHxJCQksPHZZ4lycSE3O5va2tpBfyYxdKrhaoU2HFQqlTWwHTgZyAN+AgKBE4FCYL6iKCn9PFY6EAD8t4en3KcoStfWRAMbb1JkZGRkUrslUKNt48aNLFiwgNjYWBISEti9ezcrVqygvLycm266iQceeICGhgZqjh2jQK8nt6yM06Kju9SRHMzIgPBwps+ahWcfFwUxPul0Onbt2sXChQs7nODLy8vZv2cPHDnCCcHBXdZqV9fWMv2668gqLOTqyy7jquuvR6/XdzmOEEIMRVtXyEWLFnU4txw8eJCSxEQ8GxuZHhDQ6zGam5t55csv+TUpibfuvx+A/JISjuTnYzZ9OnPmzsW2l3oUMXlUVlayb88elCNHmOHri6ujY4fHb1u/nuigIG75059Qm5t3eb1Or2dXUhILo6IwU6kosbPDPTqaqHbdSAUAo1aYNd6CkkeBB4FfgSWKolS1/nwVEA/sUhRlUT+PlQ4EKIoyYr/M8RCUtGVKli9fzqZNm1i6dCkGg4E9e/awefNmtFotq//+d376/nuabG1ZtWYNm+LjTVmSuoYGEo4epSkgAO+ICMLCwsbss4iRk5ycjOHQIexLSjghLKxL8efWn3/mogcfRG1uzuuvv86ll13GtGnTxmi0QojJpvO1qm12vLS0lAN79qBKTubE8PA+l251Vt96DWsMCiJoxgwC+ghqRlJPQZcYOSkpKWQlJWGZmcnciAgs1OpBHae6tpYEvR4lIoLZJ56IY6cAZ4obtaBk3CzfUqlUFsDy1r/e3haQACiKsg44CCxUqVRzxmJ841X7FOjSpUv5+uuv8fT05N133wXg/HPO4Yfdu9FER7OqU4F7c3MziWlpNLm74+DrS1VVlWyYN0kFBwdj7ulJJWAoKeny+LIFCzjvpJNoam5m11dfcfTo0WHbUEoIIXpqNZ6amgoGA94uLr0GJHUNDV02zFMUhcMZGTS6umLv5YW/v/+IfobeyP5OYyMoKIhpGg0NDg6kdLNEuU1dQwPVvSzNsrWxQePoCAZDSxc4MSbGTVACnAI4ASmKouzv5vGPWv/7p1Eb0QTRVj9iMBi44447ePnll1EUBa1Wi1JWRty993LVVVexbt26DgXuR7OyqLK0xNLfn6amJtNSMDmhTj6WlpYEBgWBjw9peXk0derJrlKpeH75cr566imuO/VUyg0GDAbDGI1WCDHZdFf3VlxcTGVREebl5QT0sWz4mffeI/L66/n2jz9MPzuenU25SoW6tRZyLNv/yv5OY8PMzIyIiAjw9sZQXk5FdXWX57z8+ef4XnYZt2/c2Ouxgry8MC8ro6KggMLCwpEasujFeApKZrX+d18Pj+/r9Lx+UalU96hUqpdVKtVGlUp1i0qlch/0CMe5RYsWERcXx86dOzE3N+fmCy9kfnAwm557jqamJqpKS/l40yZWLl6Mq4MDBXV1qAICiIqK4tdff5UT6iTUvjObj48PNu7uNNjaktlNV61gb2+WnnQSgZ6ekJVFamoqjZ26mgghxGB0V9iclpYGBgM+rq5YWlj0+Np0g4Gn3n2XdIOB8tabzqyCAnKrqiAwkIjp07GxsRmtj9KtoXSTEr3r3GG0MwcHB7xa9+U6lp3dIcuv0+vZsncvxRUVvL1tG1t//rnH97G0sMDP3R3y8khNTR2W1QJ9jV10NJ6Ckra8a3YPj2d3el5/PQP8DVgB/BtIV6lUNw3kACqVKqm7P0DIAMcyLHr6kmu1WpKTkwE4/6yzWBgYyEWnnMLKM89k9+bNPHrJJVx+6ql8s28f3+7bB0FBaMPDcXR0lBPqJNR5OcHBgwcJCQkBb2+yi4up66Enu6+7O3VlZfz01VeSxhZCDJv2XSGLi4upKirCvKICPw+PXl8X9+KL1DU0cPrs2Vx22mkUl5eTkp8PwcFoIyJwc3MbpU/Qs6F2kxLd6++yuODg4JZ9uczMyCooMP18Z2Iia+PjOe+88zAajdz32mu9Bht+7u5YVldTW1hIXt6QeiHJkr5BGE9BiV3rf2t6eLy60/P68jlwMS0duKYB0cA6wAp4TaVSXTi4YY6uzgFIb1/yl156icLCQrw1Gm476ywiAwKwtLAgRqtlxbJlAKz7/nvOvPFGtqelUVpTg4+PDyAn1Mno/fff75L9cnNzw1mjweji0m2LYICk9HQue+QRHl23jsOHDlHZw+ZTQggxWNnZ2VBYiLeLS6/FydsSEvjkp58wNzPj+eXLqWto4EhmJgQG4h0UhK/vgHYLGFHtgy4xPPq7LM7CwoLQsDDw8yO9oICaujrgf61/b7rpJszMzEjOyOCL3bt7fD9zc/OWpYR5eaSnp9PcaanzSIxd/M94CkraFoP2FMIOaLGooigrFEX5VFGUTEVRahVFSVIUJQ64rfUpawdwrKju/gAjOo3cXQDS25f8xhtvZMUdd7DikkuY7uuLo13H+G3HoUOsuPvuls2C7rmHzMzMDo/LCXXy0Ol0HD582FRHtG7dOlP2KyQkBDQaCiorKa+q6vLayIAAIgMCqK2v59UXXpBsiRBiWFVXV1NaVISqrAwf955XVDc2NbFi0yYA7rjoIqICAzmcnk6TuzuOvr5o27W2FxNfd6tABrKKw9PTE1cfH4weHhzNykJRFGK0WlYuXkzWrl1cf/bZANz1r3/1uFIAwMvVFZvGRhoKC8nptAfKQMgKlIEbT0FJ23RsTw3G2/qTdr2LGpjXgAIgTKVSjesdlroLQHr6kjc2NnLs2DEumjOH8+fM6TYd7u/mRvzatSQkJPD888+zaFG/uiuLCWjnzp089NBDrF69mi1btuDn52cKNu3s7PDy9QUvL47n5HRJZZuZmbFxeUsjvG927eK3XbsoKioa7Y8ghJikcnJyoLgYVzs7rC0te3zev7/4guTMTOxsbLhk4UIy8vOpNDdH7eNDZGQkZmbj6RZGDEVvG0EPZBVHaGgo5l5elBuN5BUXtxyjdbXIxuXL8XFzIy0vj/VbtvR4DDMzMwI1GsjPJzs7G6PROKjPJCtQBm48/Ytum7bvKRfr2+l5g6IoipH/ZTi8hnKskdZdANLdlzwzM5OkpCTqc3KYVltLXUMDG7duRafXm46VYTDg4u7O4rPOYvv27axatYqYmBgpwpqk2r47TU1NGAwGrrzyyg6PBwUFtay/VanI7SbgOCkykmvPOguARx9/nK+//nrQJ2YhhGjT2NiIIS8Piorw7SVLAvDtnj0A3HHnnbz6yy98v38/+PsTFh6O1QD3MxHjW2+rQAayisPa2pqg4GDw9SU1L4+Gds1a7GxsePqWWwA42M0u7+15ODtj1dhIQ0kJ+d00hukvWYEyMOMpKDnQ+t8Teni87ecHh+G9nFv/O9Ssy4jqKcpu/yU3Go1ccMEFnH/eeRz/4w+ampt5fscOFlxzDeu3b0en11NYVkZaUREEB7P0/PNZs2aNKSCRIqzJqa8ZGktLS4JDQsDXlzSDocOJu801S5agVqvJKyzkpRdf5Ntvvx2l0QshJqvc3FyMJSXYm5vjZNd7ieiZJ57Iyy+/zGOPPcaKe+4hqaQEVy8vPPoojBcTz3AudfLx8cHO05MmJydSOxWrX7V4MT89/zzvPfhgr8dQqVQtK04KCshqXQomRt54Ckp+AcqBEJVKNbubxy9t/e+XQ3kTlUoVBYTTUlCfPJRjjYa+ouw33niDAwcOUFpSwqzgYPbo9SyPizNtkvj9gQMkZ2VBYCC+wcF4e3ubXitFWJNbX98dLy8v7Hs4cQMcyc7mltZZpZTUVL7++mvqO21eJoQQ/WU0GluWbhUW9pklgZYi5d07dqDT6Yhfu5boefOkjmSSGs6lTiqVirCwMPDywlBeTklFhekxMzMzTpkxo1/H8XJxwaK6mpqiIgradfQSI2fcBCWKojQAL7T+9QWVSmWqLVGpVKuAmcDPiqLsaffzO1QqVbJKpXqq/bFUKtXZ3e38rlKpZgJbaCmaf631PScknU7H2rVrWb16NQA3nXsusWFhpk4TCQkJbHruOTQODjRrNDj5+rYUOLcjRVhTm+nE3brpVFmnovdF0dGUFxYSGBiIq5MT4R4e6NstCeyOLAcUQvTEYDDQUFKCVWMj7k5OPT7vpc8+I7+kpKUW4LTT+GDDBhYvWMDiM88c8/1IxMgZzqVODg4O+AQEgL8/yZmZNDY1dXlOfkkJ67ds6TELYm5u3pItMRhIT0+XbMko6LkP39h4HDgTOBk4rlKpfqKlpe88oBi4odPz3WjJenSuDZkPPKxSqTJoqR8pBIJoWQKmBnYC943QZxhxbcuuLCwsKC0txc3RkcduvBEzM7OWThPArs2buSg6GicfH6x8fbvd7bZtZmLXrl1ShDVF2dvb4+3nR255OUczM5kbEWEqHo3RarkbOEGj4aSwMBoVhcLcXMp9fXF0dOxyrLbv5fLly1m/fr18p4SY4to6Ri5atIiZM2e2dHw0GPD38OixSP3H/fu5bcMG7n7pJb555hl83Nw4b/FibCIj8fcf6DZlYioLDg6mtLSUmooKjmdnExkYaHqspq6O6BtvpKi8nDBfX86bP7/bY/i4uZFVUEBtUREGgwEvr3FdijzhjZtMCYCiKHXA6cBjtCyvuhAIBP4LzFYUpfdp2v/5FngDqKBlB/hLAC3wM/BXYLGiKD3thzKm+jPTvHPnTi677DLeffddAM6eN69D+98YrZbLTz0VJxcX8Pdn+vTpWPbQ4USKsERwcDCWGg21VlZkdCroi9FqWXXxxZwcHY2XkxMYDD22CJblgEKINp1rFn/44QfqioqwrKvDy9W129c0Nzfz9/XrAVj6pz/x6i+/8MPBg+Djg1arlW5bYkDMzc2ZPn06Kh8fCqqrOyzjmmZtzY3nngvA3S+/3CWTotPr2bh1K4fS0vBv3bckLS1t0PuWyCqC/hl3/8Jb9xR5SFEUraIoVoqiaBRFuV5RlKxunvuIoigqRVGu7/TzXxVFuUlRlJmKorgpimKhKIqroiinK4rymqIog98NZwT1t/A8MjKSO+64g9raWjzc3Ym79NIOj1fX1nIsJwcCAggMCcGplzS5EGq1mtDQUPD1JauoiNoe6kaKKypY9dhj7P/9927X18pyQCFEm86TFNu2bYPCQvx6yZK89d13HM3Kws7OjhdffJGzL7iAj/fsoaiiAtceAhkxNibKTba9vT0+/v7g48OxTu1977/6atwcHUnOzOTfX3xh+rlOr2f99u2mhkGFZWVYNzTQUFjYsunnAElTof4bd0HJVNafmebKykpURiN25uYArP/b35gdGmp6vKm5mcS0NJo1Gpx9fQkICBi18YuJKycnh+927uRYdTX6HjaLWr9lC/uPH+ffL75IampqlxbB0pNdCNFGo9Hw9NNPk5CQwMaNGwkNDMS8qqrHLElVbS33v/YaAOFaLd999x0ffvABj61dy+effy43cuPIRLnJbgucysvLsfL0pM7Kisx2E2qOdnY8ekNLVcAjb75JaWXLdnk7ExM7NAz6+cgRgr28IC+PrKysAWdLZBVB/0lQMo70NdPc3NzM4cOHUefn8/4DD5D4xhtctXix6XFFUTicnk6tnR3Wfn4tactOdSRCdNZ2gbn44ovZrtPxu15PcXl5l+c99de/YqFWsycxkV3ffdftTreyHFAIodPp+Prrr7nooou4++67mTlzJloHBzTOzqhbJ9Q6e+a99zCUlBDs7c0LN9/Mu//+Nw8+9BCxsbGsWLFCbuTGkYlwk90+cHr++eepra0FX18yCws7rAaYGxGBxsWF4ooKHn/7bYCODYPi41kYFYW7kxM2RiNNxcXkddOtsjdDWUUwUTJSw0WCknGkr5nm5ORkanNzsaqsJNzPj8bm5g6bJGYVFFDS1ISZvz/R0dE91pEI0V77C0zc3XeTVFrK8Zwc02xQ29raytpall90EQD/fuUVUlNTaeqmo4kQYuppf/PUdk65+uqree6558jNyYGSEnzc3Lp9bV5xMfGtO2yvveUWfNzduej004lvuzFsvZGbajdo49VEWKrbOXBKTEzEWaPB6OZGSm4u0HJt27RjB/c9/DAAz3/yCWl5eS0NgxYvZvfmzaxcvJgYrfZ/+5YUFg5435LBriKYKBmp4SRByTjT00xzVlYWhrQ03n79ddwdHUlKT++w5vHnQ4dIKyiAwEBCw8Ox62NTKiHadL7AzJ4/nzprazILCjqsrX3o449pUqlwmDaN1JwcvvzgAzIyMsZ6+EKIMdb55kmj0ZjOKevXryfKxwdXW1umWVt3+3orCwtuOf98Fs2axQUnn0xGfj7ak07ixhtvNN3IAVPuBm28mghLdbsLnLRaLSpPT4pqaiipqDAt01q+fDkLFixgwYwZOEybBrQ0eVmxbBkx7fbF0bi4YFlXR31JyYD3LRnMKoKJkJEabirpuzx4KpUqKTIyMjIpKWlE36e8vBzd3r18/eabPPP22wR4erLiz39m4V/+QmxsLAkJCXywYQPnXXwxHhERREZGjuh4xOSj0+nYtWsXCxcuxNfXl8R9+zA7epQ/jh7ltOuvR61W88wzz7Bq1Squu/ZaDicn42hnx/sffshpp59OcnKyqfXneLxACSFGzsaNG1mwYIHperR7924WLlzIjz/+iIO9PSENDcT4+5t2cNfp9exMTGRRdHSHm77m5mZS8/LIMRqxi4xkzpw5piXI3b3HihUrxuTzitHRvqX0YK4r7a9rba/X6/VkHzrENIMBtbk5G3/8keVxcTz/3HOsOvPMDt/H7mQYDKTV1mIbHc3cuXMH8akGNv62NvubNm0aywBw1OoAJFMyzjU2NnL48GFqjx/nv1+2bGa//OKLOWPmTNOax3Vr1zLN0ZEvd++mpmZcdjoW41z7WRw3NzdcNBqM7u74u7mxKT6ed999l1WrVqFWq4meORMfHx/Kq6r4z4sv8u233/Y5gynLLoSYvLqblY6JiWHZsmWEuLhgb27eISBpn+VvW36s0+tZv3UrOxMTQaNpmdVuVxM5EZYMieEzHEuXustOBAYGYunpSY2lJS4ODqZlWv0JSAB83N0xr6qiuqSEkpKSAY+pv9oCsqVLl47rjNRwk6BkHFMUhSNHjlCfnc0X//d/5JeWEuDpye0XXmha8/jta68R6ujIseJirrjiCjZt2iQ3fmLIQkNDMdNo8PDw4Lq5c1Hl5rJu7Vreffdd7rnnHv79739z3333obG3Z8ePP/aYYtbpdKxevZpnnnlGll0IMcH0dzKhu+U8DQ0N5GRng8FAoEZjem77zkaLzjqLyx99lHe3b2f99u2cdt11bE9NpaCsrEsr+4mwZEgMn5FautS+BX5GQQFhvr4dlmntPXqUM+Pi2JaQ0P3rzc3xcnGB/HzS0tJGZJf39gHZ119/3SHTM9lJUDKOZWRkUJKdTYVezzvbtgHw+E03Yd1awB7u58fCqCjKrayIu/vuKbXuUIwsGxsb/AMDwc8Pa0tLnrjhBlaffbYpOPH09CQnLY2TQ0OJ8vFh/fr1XWYw206sAKtWrZLvpxATyEBnqjvPSmdmZtJcWIiDWo2ro6PpeW2djfbs2cO9a9ZwLCuLTZ9+agpU4lav7rFWTbr7TR0jmRlzd3fH1dsbxcODY532HXl72za279vHmldeMbW9b2v20pbV8/fwwLy8nMqCAgoLC4dtXG2mYi1JGwlKxqnS0lLSU1IgPZ0vd++moqYGH3d3Ilv3HVEUheTMTJrd3Zm/cKGktcWw8/f3x8bDgwZbWzIMBmK0WtbefDOrzz7b1JXknLlz8VermTtnDjt37uwwg9l2Yr3qqqtYt26dfD+FmECGcmPU0NDQ0nErP79DlgQwZflfeewxCouKsLGy4p/XXce6tWtNhfFnnXXWcH8cMcGMdGasbTVAWWMj+e2WYf3jmmuwnzaN/ceP8/4PP3S73NDSwgJ/d3fTLu/DnS1pv8fPVLtmSlAyDtXX13PkyBHIzKSppob//N//AXDfww+z8ccf0en1ZBUUUK4omHt7s2zZMklri2FnZmaGVqsFHx+yS0qoqasDOnYl2XvsGDc8+SRffvIJ5557bofvXttMV1NTE1VVVXz88cfy/RRighjKTHVmZibGoiIc1GpcHBy6PB4dFMQvBw8CcNcll3Di9OmcodXy7ubNLF++XM4RAug9MzbUOkVra2sCgoLA15fUvDxTC3x3JyfWXHklAP94/XW263QdNlLc1drYyNfdHcuaGmqLiwfcias3nff4Wbp06ZT69yBByThjNBo5fPgwDVlZ2NbXMyM4mPnR0ZxyyincfvvtLI+LY9uBA6QXFIC/P9rQUKytrSWtLUaEq6srrl5eKO7u3e707mxvT25REdt/+YXdP/1EdXW16bH2M12PPvooTz31lHw/hZggBjtT3djY2GOWpM2b33zDkYwMXBwcuOeKK0jNzUU7Zw63/v3vnHTSScP4KcRkNFz7d/j5+WHt7k69jQ3Z7ZZh3XXJJXi5upJuMJBfVNRlI0UAc3NzfN3dIT+fjIyMYcuWdN7jJz8/f1iOO1FIUDLOpKamUp6Tg7qggOigIDxdXHj+jjsI8vFp+Ufx3HN4Ozlh1Ghw9fHBy8trrIcsJjmtVouZRkNJfT1FnXZ6nx0aypVnnAHAqy+9RGZmZofHJVgWYuIazL/f3NxcjCUl2Jubd5slqamr4+E33wTggWuuobGpiQqjEXMPDwIDA4dn4GJSG66aCzMzM4KDg8Hbm8zCQhoaGwGwtbHhkeuuA+CN//s/bp4/v8NGim283dxQV1VRU1xMUVHR0D8Y0mVOgpJxoC0N+cMPP5Cdlgbp6UT4+2NjZQW0LJdZdeaZ7N68mStOOAEff38svL0JDw8f45GLqcDGxga/gADw8UGfk2Mq/mvz2I03ojY3548DB/j266+pra0do5EKIcaS0WgkNzcXCgtbZpG78frXX5NbVESgRsOtf/oTqXl54OWFf0AAlq1NXITozUBu3Pta5uXh4YGDhwfNTk6k5eWZfn7j0qVE+PtTXFHBr0lJXTZShJZOXD5ubsOaLZnqXeYkKBlj7dOQr776Kvqff8bP2Zl9x45x7po17Dt2DGgJTP56zjnY2tiAnx8hISFyAhfDrqcTuL+/P1YeHiQWFPDoe++ZupAAhPj48Lc//QmAf7/8Munp6aM4YiHEeFFYWEh9SQmWDQ24d2rp2+bWCy7gxbvuYt1tt1FUUUGdpSVWHh74+vqO7mDFhNXfG/f+LvMKCQkBLy8M5eVUtU6qqc3NWX/77bx4112svOyyHsfi6+6OeWUlVUVFFBcXD+lztV1/gSm7wkCCkjHWPg0ZFxdHSno6QV5e3PWvf/HNH3/wtw0b0On1pm5bRnd3nL280PSwVleIwertBG5ubk5tbS3bk5M5/29/Y93333cITB689lpsra1J1uvZ8sEHki0RYgrKysqCggJ83NwwM/vf7UX7lqoWajV/X7aM8+fPJyM/H7y9CQoKwtzcfAxHLiaa/iwt7O8yL0dHR9y9vFA8PDrUTp5z4on8fdkyLNTqHt/DQq3Gx9XVlC0ZrOGqk5noJCgZY6eeeqqpXWr8009z8fz5rNuyhSMZGajVap7cuJFnvv2WbxMSKDcaMZdlW2KE9HUCP3TokGk/nBV3323qQgLg6eLCqtbZpP2//irZEiGmiLbZ3R07dlBVXIx5VRU+7ZZutbVUnXXhhcRv22aazMjMz6fJwQFbd3c8PT2HbRxT9WZOdDWQZV4hISGYeXpS1tDAj/v3d9iXBKCxqYmSiopuX+vn4YF5RQWVRUWD3uV9Ku9N0p4EJWNIURSsra05Y+5c3lu3jhWnncYJoaE8+8EHAFx33XWcddZZrFqzhu/27wd/f0K0Wqytrcd45GIy6usEvmjRIjZu3GgKoG0tLDqcuO++/HK+fvpp7jz/fAry8qipqRmLjyGEGCXtZ3dfeeUV9Hv24O3igrpd1qNtB/d3332XH3/5hVe+/pq6hgZySkrAy4uQkBBUKtWwjWMqzzKLjgZSn2FtbY1fQAD6+npe2b27w74kP+7fT8R113F769KqzizUarxbd3kfbLZkqhe4t5GgZAylpqZSkJ5OqFrNA1deyfzoaN7Zto2C0lIsLCwoKysjISGBZ558kshZs3D29sbb23ushy0mqb5O4DExMcTFxfHFF18QFhDA9rS0DiduB1tbzp03D1dbW5TCwi6duIQQE1NPWYj2s7urVq3i8OHDXQrcF0VH88Qjj/DGG2+Qk5PDiWFhZBgMGF1ccPLwwMXFZcjjk1lm0ZPOy7x6y6j5+/uTnJND3Jo1HfYlcba3JzU3l/d/+IED7bIn7fl5eGBWVkZ5YSFlZWWDGudULnBvI0HJGMnNzSUrJQVSU4nw8cHZ3p76hgZTq8Tbly0jxNqad557jiXTpxM2bx5hYWFjO2gx6fW1TjcmJoYHH3yQSkVhVacTd5sAT09KUlL48vPPpbZEiAmutyxE+9nd+LVrOWPGDKw6NWCJ0WqpKyykubmZU2fM4MozzsBQVgYeHi3tWIeBzDKL/ugro2Zubs65555L/LPPkpCQwPPPPcfCqChitFouP/10AB78z3+6PbalhQVeLi5QUDDo5cvSQl+CkjFRUlLC8eRkSE0l0M0Nz9aZore3bSMjPx8vV1eeuOkmnrzhBi4/9VS0J51EQFAQNjY2YzxyIUCtVnPmmWeaTtybWk/cbdLy8rj6n//kn48+SlK7YEUIMfH0loWIiYnh2muvZfN//8uSkBDOOfHELq8/oNfzzR9/APD8ihVkFRaiODvj5O6OQzf7mAyGzDKL/uhPRm3x4sVccOGFbH7xRS6ZMcPUBvjRG27A3MyML3bv5tcermv+Hh6oSkooKyykvNOeXqJ/JCgZZdXV1RxOSkJJS0MzbVqHHW//smQJ/7rzTp7529+YZm1NRn4+9dOmYePhgZ+fnxTyiXFjyZIlnH3uuWzeuJFrYmM79G+PDgoi0NOTqupqNj3/PPX19WM4UiHEUPSWhTAajdjZ2bEsJobFs2d3qCVp0zazfPnppxMZEEBeSQl4eODv7z+s45RZZtGX/mbUzj33XJZdeSVODg7Utl6/wvz8uO7sswH4x+uvd/s6K0tLNE5OUFAwpE5cU5kEJaOosbGRxMREmjIycDQaCevUl93SwoLbLryQa846i5q6OrKKisDHB61Wy8GDB6WQT4wbbdmSZZdfjq21Nc3NzabHzM3NefymmwDYsmWLfFeFmMB6y0Lk5uZSV1SEVV1dyyZynfyalMQXu3djbmbGozfcQGZBAc2Ojti7uQ1LLYkQA9HfjJqTkxOuGg2Km1uHDRUf+stfsLSw4Mf9+9m+d2+3ry2rruazL7/kj927qeihW5fomQQlo8RoNHL48GFqs7KwrqggOijI1Me9pq6OxqamDs/X5+SgeHjgotHg6uoqhXxi3PH29sbG3Z0GW1syCwo6PHbxwoXMCgmhtq6O9evW0dDQMEajFEIMVXdZCKPR2NLMIi+PAE/PbvcZ+Xz3bgCuO/tsAjw9yS0pAY2GoKCg0Rq6EB30N6MWFBQEHh4UVFaaNlQM0Gi4tXWj4K9++63La3R6PS/u2sXVcXFs/+EHvvnmm2Ef/2QnQckoUBSF5ORkSjMzMc/LY0ZQUIfNeJ545x2ibrjBtO62qLyckvp6VJ6eaFuXxUghnxguw7UM0MzMrKVQ1cuL7KKiDoG1SqXi5vPOA+CTTz4hISFhSO8lhBhf8vLyaCguxrqxEU0PWY+n/vpXfli3jkeuv57MggKMTk7D1nFLiJFkZ2eHp7c3eHiQmptr+vn9V1/ND+vWse7227u8pq39dWxsLHGrV/PrL79ItmSAJCgZBSkpKRSkp6NKTycqIADbdgXr+SUlbPj4Y45nZ1Pf0EBzczPHs7PBxwf/wECmTZsGSCGfGB7D3c/f3d0de3d3mh0dyczPN/38gx9/5MeMDGbNmkVjUxMP/OMfUlsixCRhypLk5+Pv4dFh9/bOTp89G09n55ZaEk9PAgICRnGkQgxeYGAgKg8PSmprKa+qAlo2Cj599uxun78oOppN8fEkJCSw/plniNJopLZkgNR9P0UMRVZWFtkpKZCSwnRfX1w6dRt5cvNmaurqODEiggsWLCA1N5f6adOw9vTsUggYExMjwYgYku6WAQ71OxUUFMTB4mJykpPx8/DgcEYGL23fznMbNlBVVcWSJUuoKCkhIyND2loLMcHpdDq++OILvBwdiTI375Il0en1vPvjj5wzZw5nnHACADlFRRidnLB3dcXZ2Xkshi3EgNnY2KDx9iavqIg0g6FDQxeAgtJSMgsKiA0PB1raX68Edm3ezB2LFlHf3Exxfj5VQUHY2dmNwSeYeCRTMoLy8/NJSU6GlBRCPD3x6HQyzjAYePmLLwB48uabqaqtJbukBHx8CAsL63aNrhBDMRLLAF1cXHBwc8Po5ERWQQE7ExP569//zqZNm7Czs+OsM87g1qVLycvOll3ehZjA2jKt5557Ltt/+onKmpoOWRKdXk/8tm188vvvnL1mDS9//jlNzc3kFBWBu7tkScSEExAQgJm7O2X19ZRWVpp+vlOnI/iqq7ji0Uc7LF2O0WpZsWwZ86Oi8HBwgMJCyZYMgAQlI2TXrl08/eST6HftwtfRET8Pjy7PefStt2hobOSM2bM544QTOJaVhaLR4OHrK2tuxYgYqWWAgYGB0FrIenJEBN99+SVLlizh7pUr+cu8ecwLDUWRk7MQE1r7TGvcmjUcycnp+HhiImHR0aSkpGBjY0NZbS05hYU02dlh6+aGq6trn+8hre/FeGJtbY2Xjw94eZGSm4uiKADMCQ/H1saGlNxc/vN//9ftawM8PaGoiEKDgerq6tEc9oQlQckwajuZvvnmm7z00ktcfe21/JCcTGU3s8NHMzN589tvgZYsiaGkhP3Z2Xz+66/y5RUjaiT6+bu4uGDv5kazgwOuDg6sXLyY4oQENlx3HZeffjpBXl4k79nDhvXrTd9vufkQYmI59dRTiW9dM7/hmWdYFB3d4fGTIyJ4Zu1aALRBQZwVE0N2YSG0LkdWqVS9Hn+4a96EGA6BgYGo3d2pUhQKSksBsLOx4R9XXw20TDDXddNh8nhODtv27kWfkCATcv0kQckwaX8yfe2114hr7cCwas0afjp8uMvzP/jxR4xGIxecfDJzwsP5ds8eth87xlVXXcULL7wgJ2Mx4QQGBoJGQ05xMVGBgaxYtsy0BreovJzb1q3jlVdf5ZtvvpGbDyEmIG9vbxaffDLvr1/PqjPP7LLGft+xY1RVV2M3bRr/uuUWNC4uNNrYYO3igkc3qwU6k9b3YjyysLDAPyAAvL1JMxgwGo0A/O1Pf8LPw4OcoiJe+uyzDq/R6fWs376di+64g+179rD7p59kwrkfJCgZJu1Ppn//+9954rHHWtbtx8ezMCqqy/Mf/Mtf2B4fz9O33EK6wYAuP5+4e+6Rk7GYsFxdXU3ZkqxO+5YEajRcfOqpAKyLj2fbtm2DuvmQ7IoQI6Ovf1uKopCZmYnWzo47ly3jhE5NK+oaGnjs7bcBePKmmzgpMrIlS+LhgZ+fX59ZEpDW92JkDeX64ePjg6WbG3UWFuQVFwMtO7g//Je/AC1Ni9qviunQHvjee0lKSCAtLW1YPsdkJkHJMDnllFNYt24dCQkJfPnpp1wSHc3uzZtZuXhxl9kkaNnH4YwTTsDfw4OckhKiTzyRjRs3yslYTGjtsyVN7XZ5B3j8ppswU6nY/euvNDU1DfjmQ7IrQoyM/vzbKi4upqa4GHVVFd7d1Ia88sUX5BQV4efhwS3nn09xRQW1ZmaoXVzQaDT9GsdAa95kkkL011CvH+bm5i3XNy8vMvLzaW69vs3SanF3cqKovJxNn3xien779sDPP/ssUa6uFBkMsm9JHyQoGQaKomBpackZc+fy/vr1rDj9dK456yxWLFsGwMatW9Hp9QBk5udTVF5ueu3x1p3bTzr1VOLi4mQfEjGhubq6YuviQrOdHTmFhR0ei/D356rFiwH45OOPufXWWwf0fZelHUKMjP7828rMzITCQnzc3LrtDFlSWYmFWs0/rr4aK0tLMgwG8PDA29t7QJ0k+1vzJpMUYiCG4/qh0WiwdnOjwcaGvJISdHo9m3bsYEVcHGZmZqTm5ZmeG6PVsnLxYnZv3syqs87ilMhIyM8nNTV1OD/WpCNByTA4duwYxRkZhKnVPHTVVcxvXa7VtqZwwTXXsH77dnR6PXe+8AJBV17Jlh07yC8poayxETONBq1WOyIFyEKMNn9/f/D0JLuw0DSb1Oaxm25CbW5Owt69JCcn9/h9724GVJZ2CDEy+vq3VVZWRkVREWZlZfi4uXV7jEeuvx79O+9ww7nnUlhWRqXRiLmbG76+viMyZpmkEAMxHNcPMzOzlrbWnp5k5uez49AhlsfFcd999/HZZ58xs9OSxrb2wDFaLYEaDWYlJZQVFFBSUjJcH2vSkaBkiBobG8lLTUWVlkakvz8Otramx9qvKVweF8fmH35g688/U1NfT2RAACm5ueDrS0BgINbW1mP4KYQYPh4eHli3FrgaOp18AzUabjz3XKCltqS8XdawTU8zoCPVzliIqa6vf1vp6elQUICXiwuWFhamn+v0+g4rAfw9PbFQq0nLywMvL3z9/LC0tByRMcskhRiI4bp+eHp6tmRLrK2Z7uvLpvh49u/fz5bNm7utH25jbWmJj4sLGAykpqaaWguLjiQoGaKGhgZITSXM2xs3R8cOj7VfU7gpPp7fExMBuHrxYmysrGiwtcWmtQhQiImou4yGSqVqyZZoNGQVFnY5+T583XUEeHqyODa221R2bzOgkk0UYvB6q8Ho6d9WWVkZZQUFmJWW4t+ug1bbSoAGHx8e2LLFFJgUV1RQo1KhdnMb0WubTFKIgRqO64eZmZnp+mZvY8OK007rUD+879gx3tu+vdvX+nt6Yl5eTlVREUVFRYMew2QmQclQGY04WFjg1U3hX/s1hWcGBfHToUOYm5lxzxVXkF1cDD4+1NTUsGnTJlkPKyac3tZ0azQaLF1cqFOryW/t697G282Nw2++yUVz5lBeVERlu11yQWZAhRgJg63BSE9Ph/x8vJydsWqX9diZmMiNt97K2rVr+eq773i1dQO5rIICUy2JWq0egU/yPzJJIWD0Gx5oNBps3N1pmDYNN0dH0xKtX5OSmPO3v/G3deso7mYVgIVajZ+7O+TlkZaWJtmSbkhQMgzMzHr+NcZotSyMiuLJd98F4NolSwAwurmRX1rKm2++KYV6YkLqLaNhZmbWspbc07NLe2CdXs+r33xDSXk5dLPLu8yACjH8BlODYcqSlJV1yJJAy0qAlcuXU1xcjIO9PTcsWUJlTQ3l9fWonJ3x8fHp99iki5YYrNFueKDT6di0aRNVVVXg5UVWYaGp0+RJkZHMDg2lsqaG5z78sNvX+7q7Y1FVRU1REfn5+SM61olIgpIRptPrue/990nOyEClUnHmnDkU19Wh0mjIysqSQj0xYfWV0fD29sbc2Zlqo5GS1jaI7Zs/fHzgAO+8+y53Ll/epU2izIAKMbwGk4FsqyXRODl1yJIABGk0pKSkAHDv5ZcTGx5OZn4+uLnh6eWFlZVVv8YlXbTEUIxmw4P239WNGzfy1pYtJJeUmPYtUalU/PP66wHY9Mknpt3f21Obm+Pv6Ql5eaSnp5s2YhzIGCZzAC9ByQjbmZjIjBNOQK1Wc8EFF/Dd/v1sPXSIyupqzjzzTFmmIiasvjIaarUaL29vcHdv2USNjs0frrvlFt769lt+2LGDN998c/Q/gBBTyEAzkOXl5ZQVFKAqKWm5iepk48cfU1FTw/SAAFZfcQW19fUUVlaCu/uAakmki5YYitFc7tv2XVWr1djZ2XHbbbex/fBhfti/3xRcnD9/PrHh4VTX1fHsBx90exwfNzes6uqoKyoiNze33+8/FQJ4CUpG2KLoaPKzsvj444+pKCmhxtqaa266iS1btgDIMhUxofWV0fD19UXl6kpJTQ3VtbUdmj+888YbXHHGGQA8/vjj7Ny5cxRHLsTUM5AMZHp6OhQW4uXsjHWnLElpZSXrWq9hj1x3Hebm5i1ZEldXXD08sG3XhbIvUkMmhmI0l/u2fVffffddVq1a1bJb+z33oMvJMXWaVKlUPHrDDQD8a+vWLh0ooWV5c6BGAzk5pKen09TU1K/3nwoBvAQlI6yt2P3od9/h6+DAmvvuM32h3nvvPXbu3MnChQslIBETXndpZWtra9w8PcHNjazCwg7NH1YuXswN55yDWq2msLCQBx98kP3794/dBxBCAFBRUUFpQQGq4uJusyTrtmyhvLqaGcHBXLpoEfUNDeSXl4O7e0tnogGYqDVkk30ZzUQyWst9276rKpWKdevWkZCQwIYNG4iaM4fMggJT4brGxYUAjYba+nrWvvdet8fSuLhgqyg0FRR0qavsyVQI4CUoGUH7jx/ngF7PrJAQTp8xg4WLFpm+yI8++ihZWVmTOg0npo7e0sp+fn7g5kZ+WRn1DQ0dNpQ6mJHBX/7yFwAMBgOvv/66XOiFGGNpaWmmWpLOWRKAQE9P3BwdmTt9OgdTU8kqLMTo5ISThweOnVrj98dEqyGbCstoRPdiYmJYu3Ytq1evZvfu3axatYrIuXOpU6sxtO7yvuGHH7jngQewsbbGtoc96FQqFcFeXmAwkJOd3bK9RD/eeyIG8AMhQckIURSF5c8/T8xf/8qz779PlZkZ008+2fSFioyMNKX/JmsaTkwdvaWVHRwccHRzQ3FyMtWWtFkUHU11aSm2trYcP36cffv2yYVeiDFUWlpKaX4+ZiUlBGg03T5nTng4S846i1sffph133/PzoMHwdOzZbfrKWAqLKMRvWsLpE844YSWiTeNhox2u7zfdtttbPv+ezzc3Ho8hqujIw5qNcaiIrKysgb0vpMxIAEJSkbMtoQEfklMxNrSEq2fH/j5ERISwty5c1mxYgVXXHHFpE/Diamjr7Syv78/eHqSW1JCY7v1szFaLfcuXcqC1p1w8/PzOeGEE+RCL8QYSU1NBYMBbxeXbrMk0NKwYuWaNS3Xs7vvJrGwEAc3N5ydnUd5tGNjKiyjEf3n7e2NpZsbdZaWRLbu8r53715eeeGFXnd5B1pqS/Lz+50tmewkKBkBiqLw0H/+A8DFp56KS1gYrj4+aNrNOk2FNJyYOvr6Pru6umLn6kqzvT05nXayjdFq+eDBB5kbEUGAry979+6VC70QY6C4uJjKoiLMy8u77EsCsOGjj3hv+3ZOiYw0NayIf/ppomJjCQwMHP0BjxG5fov2zM3NW77/Gg12nXZ5nxEUxPs//MCT77zT7WtdHBxasiXFxWRnZ4/uwMchlewoOXgqlSopwM8vcutDDxGj1Zp+/vVvv3HeffdhbWnJ5ieewGPePOaeeGK/+7YLMRkVFBRweO9eLPR6ToqMxNzcvMPjRqORt7dtY1tqKk7u7tx8881ysRdiFO3du5fKpCT8zM0J6bT5oaGkhOCrrqK2vp5tzz2Hm6Mjn/72G36BgZywdCknnHBCr8fW6XTs3LmTRYsWyb9rMekYjUb++OMP6pKS0Do44OvuDsBvhw8z//bbUZubc/ydd1oyI50Ul5dzKCcH8+hoTpo/HwsLi9Eefl9Uo/VGkikZZu2zJBedeiouUVEEh4RIQCKmPHd3d6ydnWm0sSG/m02lzMzM8HFzo9lo5LrrrmPdunVSVyLEKCkqKvpflqSbjltr33uP2vp65k2fzuITTiA6KIgzZ81CGxvbZy2JFIYPnHT3mljMzMxa/h14/j979x0eVZk9cPx70xvpZSa9kUBoAQKoKFGxYgHbqogC6tpWUIiUtYu6KkIQsaw/d11QEQsIKogNl6AiJUCAUAIhvffek/v7I8lskpmEhHQ4n+fJE5nb3okp99z3Pee4kZKdretbclFICFeNH09dfT2vfPKJwWOd7OywMTamXmZLJCjpad/t3s2BU6ewNDfnL7ffjp1Gg1ar7e9hCdHvFEXB09MTXF1JbVE+saXoM2ewsrXlscce47HHHpO8EiH6gKqqjX1JsrLwcHLC1MSk1faMvDze/+YbAJbNnYuiKGTk5VFvY4O1kxNOTk4dnl8Sw7tGgrjByc3NDXMnJ2osLHRd3gFdl/e1P/zAmfR0g8f6ajSQlUVaauoFnVsiQUkPq6iuxtnOjlvCw3EcNozg4GAUpc9mvoQY0LRaLSaOjlQaGZFfUqK3/bKQENZ/+inR0dE8+eSTXHTRRf0wSiEuLHl5eZTl5mJSUoKXgVyS1z77jOraWi4dNYqrw8JoaGhozA3rZPd2SQzvGgniBicjI6PGoi4aDSk5ObrZkktGjuS6iROpb2jg5XZmS9Jyc9m+ezdx+/aRkpLSl8MeUCQo6WG3XnYZnz37LPc88AC+/v5YWVn195CEGDCMjY1xd3cHFxe98sAAk0eO5OEbbwQgMT6+S52hhRBd13KWxNPFRW+WJDUnh//buhVoMUuSn0+1uTnmTk64Gghi2pLE8K6RIG7w0mq1WDg7U21uTkaL2ZLmLu+f/Pwzp9qU//3iv//llW3buOOJJ9ixZw9RO3dSVVXVp+MeKCQo6WGn09Iw1WhwCwho9wmSrBUVFzIPDw8UR0eKqqoorajQ2/7qgw/iZGtLTn4+n6xbR3l5eT+MUogLQ35+PuX5+RiXlemSc1t6bf16ampruTw0lCvGjqW+vp6U7GzQaPDx8cHIqHO3Eed7f4WeJEHc4KXLLdFqScnOpr6+HoAJw4Zx0yWX0NDQwLKPP9btHxMfz/s7drD0mWcICwsjYulSYvfta2xgegEacEGJoigWiqK8pCjKKUVRqhRFyVAU5SNFUTzP4Vz2iqK8pShKsqIo1U2fVyuKYt/T4/5p/37+s3072WVlKO7u7S7bkrWi4kJnbm6Oq0YDzs6k5uTobbextGTpzJkAfLx2LXFxcX09RCEuGMnJyZCTg4eTEyZtKuIB3DplCheFhOjWxafn5VFjaYmli0u38iXl4VzHJIgbWLry/arRaLB0dqbG0pLMggLd6y/NmcNFISHMvvZa3WtRsbH89dFHdTNjr73yCiMcHcnOyKCsrKw33sqANqCCEkVRLIAdwPOADfANkArMBQ4qihLQhXM5AfuAJ4A6YAtQCswH9jdt7xH1DQ0sePdd7l++nC2HD+Pp44ONjY3BfWWtqBA0ziI6O5NbUkKVgaS+R2++GVd7ezJzc9nwyScUGqjWJYTonoKCAkrz8jAqKjI4SwJw1fjx7H7nHaaMGUNdfT0pOTng7o6Pj88550vKwzkxmHT1+1VRlP/llrSoxDV26FDeX7CA42lpxMTHAxA+ciQ/bd3KNddcw1MLFvCXMWO4ZNgwyMm5IGdLBlRQAjwNXAL8CQSpqnqnqqqTgAjABfioC+daBQwFvgaCm841ElgDBAKRPTXon/bv53hyMuZmZgwbN67D8oiyVlQIsLGxwcHFBdXBwWBuiXWL2ZLPN2zg9OnTBqt1CXEh6qlZhqSkJMjOxsPJCbMOeiM0Bx8p2dnUDRmCtbMzbgbKBneWPJwTg8m5fL+6ublh0VSJq7lhcEx8PKt27GDyrFms2rGDmPh4QgMDWTB1KvnR0bw1ezZ3XnEFflotSl4e+VlZFBcX9/bbG1AGTFCiKIopMK/pn39TVVU3b6WqaiRwBJiiKMr4TpxLA9wD1AKPqapa12LzIiAXuEdRlHP/rfq/wfH2118DcP8DD7B7925iY2Pb3V3WigrRyMvLC1xdySwooK5p3W1Lj9x8M/NuuYXIRx6hIj+fHANLvYS40PTULENBQQElubkYFRUZrLj15Dvv8MJ//sNvR46wessW9p88SXp+Pmi1+Pv7d6uqpDycE4PJuXy/6nJL3N0bg/n6eqJiY5kXEcHQoUMxHzKER1atAiA0MJD506frmnBbmpujsbOD7OwLbrZkwHR0VxTlCuBX4IyqqoEGtj8HLANeUlX1xbOcay6Nsyo7VFW9ysD2fwP3A3NVVV3bjTEfs7a2DikvL8fOzo7U1FTi4uLYvXs38+fPP9fTCnHBiI6OpuzoUSpycjidmUn4yJG6X8zNUrKzSSgrwzwkhEmTJnU6sVaI89Hq1auZPHkyYWFhREdHn/Pfm4MHD1ISG2uwe/vptDSGz55NfUMD066+mpf+8Q9WLV/OFaNHM/b66xk//qzPBs8qJiaGXbt2MWXKFHk4Jwa8c/l+VVWV/fv3U3H8OD7m5hSXl7Nqxw6mzZjBXXfdBcChDz/U+5sHUFVTw96TJ1GHD2fcpEnY2tr25Nvpqguyo/uYps8H29l+sM1+fXWuDlVXVwONT33j4uLkqY8QXeDl5UV8WRmf7N/fakq7JU8XF8yrqihITr7gu90K0ROzDAUFBZQ05ZIYmiV5+eOPqW9oIMTXl5f+8Q/CwsJYsHgxx3Jy8PX17YF3IYncYnA5l+9XRVHw9/cHjYa0vDxG+PqyYOpUsvfs4bqJEwF4ce1ag8damJmhsbeHnJzGYhQXiIEUlHg3fW7vriOtzX59da4O1dXV4eDgwMUXXyxLsoToIldXV+JSUohYurRxvW5EBLuOHWu1T15xMa9++ilzH3uMU3Fx1NbW9tNoheh/PbEEODk5GbKycDeQSxKXksL6HTsAeGbWLNasXEl0dDQrly9nwkUXnbV7uxDif5ydnbFxdqbe1pa03FzdUq1Vf/sbRkZGfPPHHxxop8Kkl6sr5OeTn51NaWlpl689GCvcDaSgpLlclX7jgkblbfbrq3OhKMoxQx9AgJmZGT4+PjzyyCPy1EeILlIUhauvvpqVb77Z+OR3xQqmjBjRah87a2uOJCSQW1jIt59/3picK8QFrDuzDEVFRRQ35ZJ4G5glWfbxxzQ0NHDzJZcwc+pUHg8P57PISKZecQXXXXddD4xeiAuLr68vuLmRlpdHbV1jivMwb29mTp0KwIvr1hk8zsrCArem3JKEhIQuXXOwVrgbSEFJ85q19pJcurKmrSfP1SF7e3vee+89xo0bp3ttMEanQvSXq666imuvv571b7/NfRMm6K2vNTcz45l77gHgs40bSYyPp8JA08WW5GdQXGg6+z2fkpICubloHBz0ZkmOJyWx4ddfAXixqS+Jw5Ah3HzddYybNAlHR8feGLoQ5zUnJ6fG2ZIhQ1pVm3z+vvswNjJi659/su/ECYPH+mm1KPn5FGZnd6k0/mCtcDeQgpLmuSnrdrZbNX3uTDeZnjwXqqqOMPQBnHF2dubiiy/W7dtedCo3SUIYZmRkxJVXXsn0227D1traYOnfuddfj4+bG/klJXy7YQNnzpxp92dqsD4hEuJcdfZ7vqysjIKcHJSCAl0uSUx8PKu3bCEmPp5XPvkEVVW55bLLGDt0KLV1dY3N39zceiyXRIgLjaIojZW43NxIz8vTVZsc6unJvddcA8BL7cyWWJiZ4e7oCFlZXarENVgr3A2koCSl6XN7nds92+zXV+fqEkPRqdwkCdExd3d3TJycKAfyS0r0tpuZmvLsvfcC8NmWLezasYOVK1ca/JkarE+IhDhXnf2eT01Nhbw8XIYMwdLcXK9vwt1TpzLnuut4cfZsANJyc6m3tWWIs7PMkgjRDc7Ozlg7O1NnY0Nqi/L2z917L3Ouu463581r91gfNzeMioooyc2loEWH+I4M1vYTAykoOdz0eVw725tfP9LH5+oSQ9Gp3CQJ0TETExM8PDzA1ZXkrCyD+8y+9lr8tFoKS0v5/NNPeeKJJwz+TA3WJ0RCnKvOfM9XV1eTk5UFubm6WZLmvgnNRSYSc3P5z5IljA4IoK6+vrHpm6trY3dqIcQ5UxSF0tJSthw4wH8PH6amqWCLv7s7/1myRK8sd0tmpqZ4ODlBdnaXcioHY4W7gRSU/AEUAwGKoow1sP32ps9bO3GuH4AG4DJFUVpl8imKYg7c1LR9+7kP1zBD0ancJAlxdp6enhg7O1NaX0++gS62piYmPH/ffQAkJiezYvlygz9Tg/UJkRDnqjPf82lpaaj5+dhbWjLEqnEFc/jIkaxZuZI9e/awZuXKVkUmMvLyqLOxwcrJCWdn5756K0Kcl2JiYli3bh2z5szh54QEtu/fb3C/5kT4trxcXRtnS/LyupRbMtgMmOaJAIqivAI8A+wGrlFVtbzp9YXASuB3VVUva7H/48DjwGZVVf/e5lyf0tjVfRNwV3NXd0VRVgPzgU9VVb23m+M9FhISEnKsTQlTQ6RRlBBnd+bMGVIPH8Y2L49xQUF62+vq6/lw61auHj+enXFxnKio4K677mLChAn9MFohBof6+nr+/PNP6o4eZZS7O052drptMfHxzHnjDdwcHPjnggX4abXU19ez98QJavz8GDZ+PBqNph9HL8Tg17bp6WeRkfxjzhwszMyAxibBiz/4gPySEn5escLgOU6npZGuqjiMHs2YMd1us9cVfdY80aSvLtRJrwBXAZcApxVF+Q3wASYB+cDcNvs7A8GA1sC5ngQuAm4DTiqKEg2MAEYCZ4AFvTD+doWGhkowIsRZeHl5kZ6WRklWFgUlJTi26WJrYmzMo9Ono6oqhWVlBLq64uDg0E+jFWJwyMrKoq6gAEtV1fuZUlWVw/HxKIpCZVMz4MyCAmosLLBwcsLNza0/hizEeSU8PJxVq1Yxb948IiMjuXLkSFKyswny8tLts/n336mprWVnTAyXG7hf9HJ1JePkSQpzcigpKenvLu+9YiAt30JV1SrgCuBlGnuMzAB8gXXAWFVV49s9WP9cecAEYA1gBtwC2AHvABObtgshBhAzMzM8PD1BoyExM7Pd/RRFwU+jISUmhvTUVKqqqvpwlEIMHqqqkpaWRvzBg/wUE8PhM2dabW/uKH33lVcS4utLQ0MDKdnZoNHg7e2NovTZQ1Ihzlstl1g++uijBIaFkVlYSHVNDQDebm48OG0aAC/85z8Gq1BamJnhZm8PubmNpb3PQwMqKAFQVbVSVdXnVVUNVFXVXFVVjaqqc1RVTTWw74uqqiqqqs5p51yFqqrOV1XVu+lc3qqqzlNVtXPlCzpJyv0K0XO8vLwac0saGsgzkFsCkJ6by5URETy+ciWliYldbiwlxIWioKCAo/v3syM2lusfeohVO3YQE9/4fC86Lo5vd+/GyMhIl6+VkZ/P8dxctu7cSVY7RSeEEF3XnHh+2WWXYe/qiuroSHJ2tm773++5B3NTU3YdOcKvBw8aPId3U5f3vOzss/brGowGXFAy2FRVVUm5XyF6kG62xM2NxMxMg0+MNI6O1NfXU1pRwcavviInI4MSA6WEhbjQpaWlEXvwIBFLl+qqbO1qyoNsniW5Z+pUgr29aWhoYMfBg+w4eZK77rpLHrYJ0Ut8fX3B1ZXMwkKqmmZLPF1cePimmwB4vp3ZEisLC5xtbCAvjx9//PG8+xmVoKSbKioqpNyvEJ3QlRlFLy8vTFxcKAdyi4r0thsbG+s6Tm+KiqIkPp74+E6v7hTiglBeXk5hXh4jXV15e8WKxmp1TVW29h4/zrY9ezA2MuK5FrMkh9LTiVi0SP6mCdGL7O3tcXBzQ3VyalUGf+nMmViYmbH72DF+aqdCl4+bG/GHDvHlF1+cdw/EJSjpJisrKyn3K8RZdLWBqKmpKV7e3qDVtjtbctuUKYzy96esspKNmzdTkpNDToumVEJc6JKSkiA3l4uDg1l41VXsXr+eBVOnEhoYyKqNGwG495prGOrpqcslGRkWxltvvSV/04ToZc2zJVnFxVQ05UVqnZx49OabAVj99dcGjxtiZUVCZiYRTz113j08GFAlgQeb5pLA69evl3K/QnSgbTnE3bt3M3/+/A6Pqa+vZ+/evdTExhLk6Ii7gV4Jm3/7jVuffx5rCwvWv/YarpMmMXHiRIyNjXX7xMTEEBUVRXh4uPx8igtGSUkJB/ftgxMnmBAYiLWlZavtZZWVvLN5M3eEhxPg4UF6bi6ni4sxHzECc3Nzfv/9d/mbJkQvO3r0KPmxsbhWVxPi6wtAdkEB//nhBx6/5RZs2vzcNvvj6FHeiYoiYulSVq9eTURERG/+rPZZtQsJSrqhK31KhLiQNc+UzJs3jzVr1jBt2jSysrLOGiikpaURHxODeXIyk4YPx8io9eSuqqqMf/hhDp0+zb1XX82U668nrbiYGTNmEBoaqnfdto3lJGAR56vDhw9TGBuLpr6eYWfpyN7Q0MDeEyeo9vEhaNw43N3d+2iUQlzYysrKiN67F44fJywwsN0gxJBNu3bxe0oKE668krvvvrs3K+X1WVAiy7eEEL2uZTnEadOm8f3333dqKZe7uzvmTk5Um5uTnqdfxVtRFF5qyi05kpDAL3/8wY033khkZKQu4Ggv56urS8qEGCzKysoozM1Fyc/Ht03jw/ziYhoaGoiJj2f1li3ExMeTVVBAtbk55k5O0ihRiD5kY2ODm7s7uLoaLIOvqirZBYYLxt508cXcNmYM7vb2502lPAlKhBB9orkcYlZWVqeLQxgZGTWuu3V3JyU7m7r6er19brz4YnasXMmcG27gqaYKQ/Pnz2fXrl2Eh4e3m/PVUcAixGCWnp4O+fk4Dxmi6xjd7C8vvUTInDk8+9VXTJ41i1W//MIvBw+Cmxve3t56s5FCiN7l6+uL4uJCfkUFJeXlutePJSYy/uGHuW7JEoN5lWampo0PHTIzSU5ONrjPYCO/fYQQfaqjQMEQjUaDlbMzJwoLeWXDBl2PhWaKonDluHFcPmoUq998k+joaFauWMH48eP1ZmiioqJ0MyJdHYcQg0FtbS3ZWVmQl4eniwuAblbkX9u28euhQ8Snp/NIc0D+1FMcSk3FzMkJrVbbz6MX4sJjaWmJxsNDVwa/mcbRkfj0dGLi49ny++8Gj9U6OmJaUUFVQcF5UehFghIhRJ9qGSi0zfEwRFEUysrK2HH8ODc+8giRv/yiF5gAhAYG8vCllxL59NNMHT0aKysr3fWmTJmit2Ssq+MQYjBIS0ujIT+fISYm2NnYEBMfz6odO7jknnt4dt06AG6dMoWv1q9vDODfeIMR48bh6el51lkSaRQsRO/w8fFBcXamsKqK4rIyAJzs7HjittsAeGHtWhoaGvSOMzY2bnz4kJ1NSkrKoJ8tkaBECNHnmpdydTYQOHr0qK784fynntI1f2spJTub6c8+y9c7d+JYX09xTg55TXko7S3V6uo4hBjI6uvrG5du5eTg7eYGQFRsLPMiIigtLSU7NxdjY2MiH3uMBVOn8tO//83U4GCGTZhw1uR2ycESovdYWFigcXcHNzeSWuSHLLzjDmytrTmakMCmdpYYe7i4YFJWRnlenu5v3mAlQYkQYsALDw9n9erVjU92X3+di4KC9PbxcnVluLc31bW1fPP775CZ2dinAVmqJS4MGRkZ1OXnY6WqONvZARA+ciRvr1hBREQEALdPmYKniwtjAgK4avRoAsPC8PT0xMTEpMNzSw6WEL1LN1tSXU1R02yJw5AhLLzjDgBeXLuWegN5lSbGxng4O0N29qDPLZGgRAgxYDUvFwGIiIhg06ZNTB03Djtra719FUVh2dy5AHz+3/+Sl5JCWdOTI1mqJc53qqqSlpYGubl4ubrqyoOGBgZyibs7MTExmJmYEPnYYwDkFhVRYWSEibMznp6eZz2/BPZC9C4LCwvcPT31ckuevO027G1sOJ6czFdRUQaP9XRxwbi0lLLc3EE9WyJBiRBiQGq7XARg6dKlBE6YoOuA27KsKcAVY8cSPmYMNbW1bN61C7KzSUpKQlVVWaolzmu5ublUFxZiVlWFm4NDq23RcXEAPDp9Ou7OzqiqSnJ2Nri5dWqWBLqeCyaE6DofHx+MXFworq0lv7gYADsbGyL+8heAdhPeTU1M8HJxgawsEhMTB+1siQQlQogBydByETs7O5y1WlRnZ77ft49VO3Y0ljXdsYOY+PhWfUu+iooiLymJstxccnNz+/fNCNHLUlNTITcXd2dnvYT1D596is0vv8ySu+8GIL+khHJVxcTJSW+WpKNkdgnshehdZmZmeHh6gkbTKrdk/q23svnll9nw3HPtHuvp4oJpWRkVeXmDtm+JBCVCiAGpveUifn5+KG5u/H7yJPMiIhqDlogIXfJ7eGgoU8eNo7aujq937YLMzEH95EiIsyksLKQ0Px+j4mLcnZz0tiuKwoxLL0XbtC05Kwvc3HD38Gg1SyLJ7EL0Py8vL4ydnSmtr9fNlthaWzPj0ks77NpuYmzcWOAiI4OkpCSD+ScDnQQlQogBqb3lItbW1mg9PRk5diyRb7zRGLSsXMmUESN0x740Zw5GRkaYm5piWl5OZW4uGRkZ/fROhOhdSUlJkJ2Nu5MTZqamutdPJCdTWlHRat/C0lJK6+sxMjBLIsnsQvS/lrMlKQZ6j5RWVHDkzBmDx3o4O2NRU0N1Tk7j7Okgc/aFpEII0U9CQ0MNLhXx9fUle9IkKC7mp3/9iwVTpxIaGKjbPnnUKBI/+wxvNzcy8vI4lZFBkoMDbm5uxMbGEhUVRXh4uCxDEWcVExPT798vHY2hqKiI4txcjIqK8AoO1r3e0NDAX156icz8fLa88gqXjhoFNJbOxsUFrbs7Zm26vYeHh7Nq1SrmzZvHmjVrWLBgQa+/NyGEPg8PD1KTkylOT6eiqgorCwsA9p04wQ1//ztDrKyI+/hjTNvkgxkZGRHg7s6xjAxS7O3Jycnhzz//JDw8HKDff5edjcyUCCEGHTMzM3x8fQmcNIkrRo1ilJ+f3j7NfRq0Tk5YNzRQm5PD9u3bDS5PkaZwwpC+Ws7U0fff2caQnJwMOTlo7O0xbxFkvLFhA7GJidTU1TGy6eejpLycwqoqFGdnvLy89K4lyexCDAzm5uY4ubiAgwOZ+fm610f4+mJkZERiZiYf//ijwWNd7O2xMzHh1L59rF69msmTJ/P888+zfPnyAb80U4ISIcSg5OnpiYWLC9WWlqR2kMh+Oi2NqMOHITOTX3fs0FueIuvoRXv6YjnT2b7/OhpDSUkJhTk5KAUFuiAcGqttvfbFFwAEBAToEmZTcnLAxQU3rRaLpievbUkyuxADg1arBScnsgoKdN3crS0tWXLXXQC8/Mkn1NTWGjw20MOD2EOHWLBgAWFhYQwbNoyFCxcO+KWZEpQIIQYlIyMjAgICwN2d1Nxcauvq9PZJyc4mZM4cFn/wAQV5eYzQaomMjGyVPN/RTZ/MoFzY+qI3x9kCn47G0HKWxKLFLMnKjRspLS3F3t6e8CuvZMPOnZRXVpJXVgbOznh7e/f4+xBCdF5n/rY4Ojpi4eBAraVlq9mSR26+GY2jI8nZ2fxn+3aDxw6xsiI8JISVy5cTHR3NyZMn9f72DUQSlAghBi0XFxeGuLhQb2fXuFa+DW83N2ZceimqqrLuxx8ZamXFlVOm8Ouvv+qWp7R30yczKKIvljOdLfBpbwxlZWXkZ2frzZJU19Sw8+BBoLFS3axZs0itruaH/fvBxQUXjQYrK6sefx9CiM7p7N8WRVEal1m6uZGam6urIGllYcHfZ84EGmdLKqurDR5/w6RJXO3vz/p163jyySdZvHjxgF+aqUiZzHOnKMqxkJCQkGNNpUiFEH2voKCAI9HRGJ08yaRhw1qtqwc4npTEyPvvR1VVvnrxRZz9/RkSEsK4ceN05RVjYmLYtWsXU6ZM0f2ybl6LGxYWRnR0NF999RXu7u4DOklQDE6Gvv/O5vjx4+QcOYJrVRUhvr6N54mP55X169m0cyc2lpZs/+knLr30UqKjo1kfGcn0Bx4gbNIkbGxseu/NCCE61PZvy+7du5k/f77BfRsaGtizZw81sbEMc3ZG4+gIQFVNDUH33ktqTg4rH32UhU3NFdtKzsoisbwcixEjmDhxol4Po05qvw5xD5OZEiHEoObo6IidiwsNDg6NXarbCPH15d6rrwbgg+++w7ioiNKcnFYNFQ2to2/5BHvZsmWkpqbKrInoFaGhobqlhJ353qqoqCA3Kwtyc3WzJDHx8azasQNzd3cA/nLFFXz47rtER0ez6o03GDF6NM5ubhKQCNHPurIs1MjICE9PT+LLy4n8+msOnT4NgIWZGS/Ono2iKK2aLLbl5eqKWWUlVXl5ZGZm9vh76WkSlAghBr2ysjK27NvHrthYqmpq9La/OGcOpiYm/HLgAOm5uZCVddaGii2XzYSEhAyKJEExOHW0nMPQ2vPExETUnBycrK2xsbQEICo2lnkREaxfv57PP/+cEQEBLJg6laiPP+aKgAACw8LwbZpREUL0n64uC83JyWHHvn385cknifzlF2Li4wG479prOfrvf/N2O7Ms0BjU+Li5QWYmycnJA76hogQlQohBLSYmhn/961/Mmj2bHYmJbNuzR28fP62Wv95wAwBvf/01JqWlVOblkdXBEyb43wzKXXfd1esJz+LC1V6yu6FgpbS0lNzMTMjNxV+r1Z0jfORI1qxcSXR0NN9v2cKVo0cTGhjITRMmEDhqFE4ySyLEgNGVKnd//PEHERERhIWF8cSiRexqShkwMTZmhIFy+G1pnZywqK2lJjeXxMTE7g69V0lQIoQY1Jpv6ExMTPDy9WXznj0UlZXp7ffMrFlonZy48aKL8HR11T056kxenfRvEL2pveUchoKVhIQEyM5GY2eHddMsCUBSVha3jRrF7vXrdc1Ea2prySoqAhcXqbglxCAVHh7O22+/TXR0NCtff51QAzOe8enpfP7rrwaPNzIyYqiHB6SmkpaURFFRUe8OuBsk0b0bJNFdiP4XExPD888/j42NDQsXLuSN115jxvDhzJw6VZfI3qy2rg5TE5PG5MHjx6nx82PY+PFoNJp+Gr0QjQwluzfPlDR3WH/wwQepr6xEaSrq0FwGuKCkBL+ZMymvrOTPd99lwrBhACRkZJBSV4ftyJGMGzeuv96aEKKbYmJi2Lx5M142NoQ5OBAaGKjbFpuYSOiDD2JqYsKZ9etxd3Y2eI64lBQyVRXrkBDCwsL0/j52QBLdhRDCkLZr7ENDQ1vlfCz5+9+JTkkhq6BA71hTExOgKXnQxQVyckhJSenUbIkQvcnQco62M3Q2NjaQnY27o2OrviRvfvEFJeXljPD1ZXxQEAB19fVk5OeDqys+Pj59/XaEED0oNDSUp59+mqFhYRRVVVFSXq7bNsLXl4tCQqiqqeGVTz5p9xwBHh6YlpRQnpt71qXL/UWCEiHEoNFeQnDLnI+33nqLEWFhJGZmGkzqU1WV7Xv3EvHee1BcTEV+PtkGqnYJMRA0ByseHh4UZ2djVFiIt6urbnt2QQFvf/01AC/ff7+u5GdGXh51NjZYOznh2FRGVAgxeJmbm6NxdwcXF1JzcnSvK4rCPx58EIAPt20jISPD4PEmxsb4aDSQnk5iYiJ1BhoO9zcJSoQQg0Z7CcEtnygvXLiQkRMnUmNlRVqLsr/NyququPcf/2Djrl1Ex8XpfkEP9Kok4sLV0NDAmTNnICMDb1fXVr14XvvsMyqqqpg4bBg3XXKJbv+03FxwdcXLy6sryzSEEF3Ume7sPcXT0xOcncktLW3VNHHKmDFcO2ECdfX1vLh2bbvHuzs5YVlbS01ODsnJyb0+3q6SoEQIMWh0VN+9+YnyuHHj8PPzA62W1Nxcats8DbKxtGRpUzfcNV9/jVF5OdW5uaSnp/fpexGiIy1vdNLS0qjKzcW8shIvFxfdPslZWbz/7bcAvPLAA7rgI6+4mBpTU8wcHHBtMasihOhZne3O3lOsra1xdHEBJye9h26vPvAAAJ/+8gux7VTZMjIyItDDAzIySEtOpqKiolfH21USlAghBo3OVsFydXXFxsWFOltbUgwszfrbjBm4OzuTkpPD70ePQmYmKSkpA3I6W1x4Wt7oREZGsuOXXyAjA3+tFmNjY91+L6xdS01tLVeMHctV48frXk/PywMXF9zd3c+1g7MQohPam73vTV5eXuDiQlZhYauHbuODg7ltyhRUVeW5jz5q93gnOzucLCxQs7M5depUr4+3K+S3lRBiUOlMfXdFUfD39wetlvSCAr2Gipbm5jx3771AY98SpbKSurw80tLSenPo4gLSnSUdLW905s+fz5F9+xgCuDo4tNpvmLc3dtbWvP7Xv+pmSUorKiiuqkJxcEDboo+JEKLndaU7e09xcHDAxtGRehsbMvLyWm1bNncuDkOGMD4oqMMCLkM9PTHKy6MoM3NAJb1LUCKEGNDO9ebO0dERe1dXGhwdSTLwS/f+66/HT6slu7CQVzdsID46mrTUVGpra3to5OJC1d0lHS1vdFauWMEIBwcCPDz0ckOWzpxJ2ldfMXH4cN1rqTk54OKCq0aDubl5T7wdIUQ7+qOHVUxMDD/++CPxZWX89/Bh3tq8WdflPcTXl/SvvuLZe+/tMJfMwsyMiqoqtmzYwA8//DBgVglIUCKEGLC6e3Pn7+8Pbm5kFxdTXlnZapuZqSn3X389AHtPnuSH2FhO7ttHSkpKTw1fXKC6u6Sj+UZn06ZNTB09mkuCg7Fvpxu7TYsGilU1NeSWloKzc+MSDyFEr+tKd/buav6beO2117Lp++/ZFhfHpffey6odO3SBiWUnHkbExMez4eBBZs2fz4/bt7Nt27beHnqnSFAihBiwuntzZ2tri7NGg+riYnC2xNramssvv5y1a9ey5JlnOHboEOmpqVRVVfXUWxAXoJ5Y0uHp6cm14eEE2dgQ4O6ue11VVR6JjOTHffv0lmek5uSgOjjg6Ora2NNECHFeafk3cdSoUSx5+unGv48REexq0cj70OnTPLp6Nbc9/7zBZVxRsbHMi4ggLCyMiEWLiPrvfykrK+vLt2KQBCVCiAGrJ27u/Pz8wMWF3LIySttUGrli9Gi8NRq8vLx4JzKSiwICaMjNJSkpqYfegbgQdXdJh6qqJCYmQlYWXi4urUoAb9uzhw+++44Zzz1HXnGx7vWa2loyCwp0ZYCFEOefln8TT5w4wRtvvEF0dDSr33yTKSNGAI2zIK9v387aH37g699+I/Krr4iJj2f1li262ZTwkSNZs3Il0dHRRL7xBiFubsQ3betPinQyPneKohwLCQkJOdYiOhVC9KyYmBh27drFlClTznl6/MSJE2QfPYpjaSmjAwJanz8+nl3HjjFlxAg8XVyITUmBkBDCJk2Sp82iX2RnZ3Pi4EFM4uO5KCQEk6aKW/X19YT+9a/EJiay5O67ef2hh3THJGZmklxTw5CRIxnfohKXEGJgiomJISoqivDw8C79bWv5NzE5OZmd27Yx2dOT25se2q3esoXJs2bxzTff8Morr+AwZAg3XH89TyxaxJqVK1kwdSqhgYG6v32TgoKorqujISiIkePH4+zs3PaSfdboSGZKhBADWk+s1/X19UVxcaGgqoqiNlPUoYGBzLv5ZnYdPsyIuXMpLi6G7GwSEhK6OXIhuq6+vr7xey8jAy8XF11AArC+qf+AvY0NS+6+u9Ux6Xl54OqKj49PfwxbCNEF3cmXbPk38eqrr2b6nXfi4uCgqzLZPAsydepUzM3NKSwtxdPfX2+ZV2hgIPOnT2fS8OF4OTtDRgbx8fE0NDT0xlvuFAlKhBDnPUtLS7QeHuDmRmJmpt52RVHYdeQIOYWFfLB1K0peHgVZWRQUFHTq/H3Z0Vec31JSUqjOycGiuhqvFo0Pq2tqeP4//wHg7zNn4jBkiG5bVkEBdVZWWDo64uTk1OdjFkJ0TU/1N7GyssLB2RnVzo7M/HygMdhYMHUqR775hiduuQWAt956i127drFm5UrdMq+WvF1dMa+ooCo3l9TU1HN/Y90kQYkQ4oLg6+uLkYsLxbW15LdYi9/sHw8+iLGREdv37iU9NxeyskhISGiVJGgo+Ojrjr5iYDmXgLS9YyorK0lNTob0dAI9PFo1Pnz/229Jzs7G3dmZx5tuNKAx/yQtNxdcXPD09OywDKgQYmDoyf4m7u7u4OJCRl4e9fX1wP9mQV6+/34C3N2pqqri9SVLdEu32jI2NsZfq4W0NFKSk6murj7n8XSHBCVCiAuCmZkZnl5eoNGQmJWlV5EkyMuLv954IwCrN27EuKiIstxcsps6wrcXfPRHR18xMDR/Tzg7O/Pkk0/yxRdfdPoYQ0Hs6dOnacjKwtHcHGc7O93rpRUVvPrppwC8OHs2VhYWum0FJSVUKgomjo5oNJqee3NCiF7Tk/1NnJ2dsXR0pNbamoym2ZJmZqam/OPBBwH47fBhPPTzRXTcHB2xNTamvh+XL0tQIoS4YHh5eWHi4kKZqpJbVKS3/YXZs7G2sGDfyZMcS0qCjAwSExNpaGhoN/joj46+YmCIiorimmuu4aeffmLFihV89dVXZ50xae/7KDc3l4LMTIxycxnq6dnqGBtLS95fsIAbL76YuU29dZql5uaCqytarRbjFvknQoiBraf6myiK0phL5uZGak6Obrak2R2XX85dV17Jf5YsafWww5Chnp6QlUV2enpjfmUfk6BECHHBMDU1xcvbG7RaEjMz9WZLNI6ORPzlLwBEfvklxuXlVOfmkpaW1m7w0R8dfcXAEB4ezocffqgLMpYuXXrWmTJD30cNDQ2cOXMG0tPxcnbWa36mKAq3h4fz3T/+0SrxvbSigqLKShRHRzzbBDJCiAuHm5sbFk5O1FhYkNUmF1JRFDY89xy3h4efdXnnECsrtHZ2kJHB6dOnDfY46U0SlAghLiienp6YOjtTaWKiSwxs6ak778TF3p74jAyyCwogI4PkpCRCQkLaDT76sqOvGDhCQ0N59NFHef311zs9U2YoiE1NTaUqJwfzykq8WyS3Q2P/kfY055K4ajSYd6KLsxDi/KQoSmN/IldX0nJzOwwmmqt0tcff3R2T4mLKcnLIyMjo6aF2SIISIcQFxdjYuHGqW6slOTtbr/zhECsrPlq8mJgPP+S28HCGKAr1TQ0VJfgQbd155508++yzXZopa/l9VF1dTUpTcnuAu3urJVixiYl433kn727erHeTUVldTU5JCTg7yyyJEAKNRoOJoyOVRkYUlJQY3Oe9LVvwvvNO9h4/3u55TE1M8NNoIC2NxIQEFEUxa3fnHiZBiRDiguPu7o65szPV5uYGZ0tuvPhiRgcEoCgKgR4ekJlJZloapaWl/TBaMdB1J1g9c+YM9VlZ2JmY4Org0Grb4g8+ILuwkJ2HD+stu0jNyUF1dCSnoICPPvqoR6p/CSEGL2NjY10lrrTcXIP77I+LI7eoiEffeou3Nm/WdXhvy93ZmSGqSl1WFoB+ua5eIkGJEOKCY2Rk1DhbotGQkpPTYbOozIICjOrrUTMzO1xjKzd6oquKiorISU9Hyc7WS27/OTqa7Xv3Ympiwmt//avu9Zj4eFZu2sRvx44RX1LCd99916Vy1FLCWojzl7u7O9jbU1hRQaWBsr7L5s7F3NSUQ6dP0+Djw6odOwwGJoqiEOTlhZKVhS1o+2LsIEGJEOICpdFosHB2ptrCot2nSqs3bmTk3Lms+/FHjAsKKMnO1pUIbqmzN3oSuIhmqqpy+vRpyMxEa2+PjaWlblt9fT1Pvf8+AH+bMaNxto7GgGTVjh2Ez57NLwkJ7D14kCeeeKJL5ailhLUQ56+TJ0/y044dxJeU6CW8A3i5ujJ59GgA/vWvf/HoE0/oOry3NcTKCksTE5xAlm8JIURvMjIyws/PD9zdSc7J0SUUx8THs3rLFmLi4wkLDqa+oYG1P/5IZVUVpKeTkJCgV3KxMzd68oRatJSenk55djamxcX4aVs/iPzk5585kpCAvY0Nz86apXs9KjaWeRERhIWFEbF4MdbW1l0uRy0lrIU4PzX/jbn11lvZcfQoUUeOGJzZf/G++zAzM+PEiRM8+fjjTBkxotXfvZb6uhmrBCVCiAuWq6srtm5u1Nvbk5SVpXsSPXnWLFbt2IG1pSV3hIfT0NDA8s8/x6K6mprcXFJSUlqdpzM3evKEWjSrqakhMSEB0tPxd3fH1MREt62iqopn/v1vAJ69916cWvQVCB85klXLlxMdHU1kZCRz5szpcjlqKWEtxPmp5d+YiEWLOJyWZrAf12WjR7PgttsAiIuLo7SiotXfvfbyTPqCBCVCiAuWoij4+/uDmxuZhYX8cviw7kn0vIgIdh07xusPPYSZqSk7Dh4kITMT0tNJTU6msrJSd57O3OjJE2rR7MyZM9RnZ5OZns4Xv/3W6ibglwMHyMzPx1ej4fEZM1odN8rPj6v8/Vm/bh0PPfQQoaGh55RkL1XkhDj/tPwb89ZbbzEiNJTUnByD+758//0M8/amuLycf//wg97fvf4yoIISRVEuURTle0VRChRFKVMUZZ+iKLPP4TxzFEVRO/j4vDfGL4QYfOzt7bF3dUV1dGSoRsOalSsbA4eVK5kyYgT+7u482fRU6eVPPmGIsTENWVmcOnWq1XnOdqMnT6gFQGFhIdlpacQfOMD3cXFc2ubp5M2TJ3Pw//6PjxYvxtys9VLu7MJC/AIDuWvWLMLDw/tj+EKIAarl35gnnniCoLAwSuvqKC4r09vX1MSEfy9axIEPPuDJW2/V/d1b9txzZOTn99tsicnZd+kbiqLcAnxFY6C0C8gDpgJrFUUZo6rqwnM47WEgxsDre891nEKIwScmJoaoqCjCw8MNBgN+fn4cysnBvqCAx6ZMYff69SyYOpXQwMZKiE/fcw//+eEHTqakEHXkCGHDhlFoZ0eGi0tjtZNOan6y3VPjFoNHTEwMO3fuxMnJCa/6elJycnhi0aL/PZ1cv173/db8uSVVVRsLMri74+np2edrvYUQA1/LvzFxcXFkZmWRlpuLnY2N3r6XjByp++8FwIa33sLGxobb589n1cqVLOijMbc0IGZKFEVxAP4DGAO3q6p6uaqqtwPDgHhggaIoV5zDqbeoqjrHwMf7PTh8IcQA1pkEczs7Oxzc3FCdnLCztmb+9OmtbgztbGxYNncuttbWWJqZ4a/RQEoK8adPU1FR0W/jFoND8//LSy+9lO+3biU5Lo6bJ01qNSvn6ejIqdTUds+RV1xMhaJg4uSERqPpw9ELIQYjT09PcHIir7T0rF3crS0saFBVFi5Z0q/LuAZEUAI8CNgB36iq+nXzi6qqZgOLm/55LjMlQogLXGcTzP38/MDNjeziYiqqqvS2P3jDDcR/+imPTp+Oh7MzDsbGNGRkEBcX127vknPRXDb4888/l8T480TbBNTMoiLCgoNZMHUqu9ev58krryTyyy8ZMXcuG3bs0DteVVWSs7PBzQ1PT89WXd+FEMIQa2tr7J2dUe3tycjLa3e/TVFRhMyZw9bff2f1m2+2Wr7c1wZKUHJj0+eNBrZtA6qAqxRFsei7IQkhzgedTTC3tbXFyc0N1cWlMaG9DRNjY1zs7YHGBPlgb2+M8/Iozsoiq7Hrbbe1nB05fvw4kZGRkhh/HrjssstY2TQrsmr5cq4bPx5oXKY1f/p04lJT+SM2FjNTUy5r6iHQUn5JCWUNDRg7OeHR1LNECCHOxtPTE1xcyMzPb7dJ8JQxY7C2sOBkSgqBVlZ6y5f70kAJSpp/Cx9su0FV1RogFrAAgrt43vGKorypKMoHiqK8pCiKZAYKcYHpSoK5v78/iqsreeXlBpMDm/24bx/PffQRvm5ukJrKmfh4as4yPd4ZLZ+oP//883h5eUli/HnAycmJqZMm8cVbb7GwzR/7iqoqFn/wAQBL774bTxeXVseqqkpSVha4ueHh6YmpqWmPj0+aegpxfnJycsLCwYFaCwtyDJQHBnCxt+fZe+8F4J/ffsv911zTLwEJDICgRFEUW8C+6Z9p7ezW/Lp3F09/I/AU8BDwPLBTUZSdiqK4dXWcQojBq7MlUK2trdF6eoJGw5mMDIP7JGdlccPf/86KL77geFISQ1SVuqws4nugWknbWZ27775bSrcOcjU1NaQkJxNoYcGi225jfHDrZ2tvfvEFqTk5eLu58dSdd+odn1dcTJmqYuLigpeXV4+PT3KXhDh/KYrSWIzF2Zn03Nx295t3yy0EuLuTVVDA65991ocjbK3fgxKgZUmA9jJGyw3s25FM4EVgLI25KhrgZuAkEA5sUxSl04tyFUU5ZugDCOjsOYQQg4Ovry/Grq6U1NcbbDzlo9Ew79ZbAXjinXfw02pRsrLISUsjPz+/W9eWssHnn+TkZOpzcrA1MsLVwaHVtpTsbN7YsAGAFY88gqW5eavtqqqSmJkJGg2eXl6dmiXp6qyHNPUUYnA728+8VqvFyNGR0tpaStspzGJuZsabjzwCND4oOZOe3lvD7VCPBCWKomxUFOVkFz8mNh/emUt0ZTyqqv6oqupLqqrGqKpaoqpqtqqq3wETgFPAeED/kZQQ4oJnZmaGl7c3uLuTkJHBwVOnWL1lS6u67S/Ono2rgwNxqal8tH07Xk5OkJbGqVOnqKur69b1pbHd+aOqqoqMtDTIzsbfQOnoJf/3f1RWVzNl9GhuN9B3JLuwkAojI0xdXBrXhp/Fucx6SFNPIQavzvzMm5qa4uLqCk5OHSa8z7j0Uq4aP56a2loWvvdeL466fT01U+JLY75HVz6smo4tbXEeKwxrfr39Rd6doKpqGfB20z+v7cJxIwx9AGe6Mx4hxMDk5eWFmYsLR7OyiPzlFya3aXBnZ2PD63/9KwAvrVuHuakpllVVVGdlkZCQ0J9DFwNIfHw8am4uDubm2LfpE6CqKqGBgdhZW7N63jy9viMNDQ2NuSTu7nh7e2Nicva2Yucy6yGzc0IMXp39mddqteDoSE5REfX19Qb3URSFt+fNw97GhktGjmw3Mb439UhQoqpqmKqqShc/djYdWwIUN52qvUdBza+n9MBwTzd91vbAuYQQ5yFjY2P8/PyIzc/nycWLDdZtHxMQgI9GQ1llJX//8EOCvb0hLY2M5GQKCgr6cfSiq3oj0TsrK4u8ps7tP8bE6HVIPnzmDBaWlnz/+usGk0rT8/KoMjPD3Nm50xW3znXWQ2bnhBicOvszb29vj5WDA/VWVu0mvAMM9/Eh7csvWXL33RgZ9X2Gx0DIKYHGzusA49puUBTFFBgJVANxPXCt5kW93Zp1EUKc3zQaDRMuuYSVy5fr1W2PiY9n9X//y0uvvw7AJz//TEp2Np6OjpCaysmTJ6mtre3P4YtO6o1E76qqKk6fOkX8H3+wMzGRK+fMaTXTFhMfz6odO5g8axYf/PGHXsBSW1fX2JfEwwM/Pz/dzcHZgieZ9RDiwtKVn3mtVtuY8N7BEi4Aa0vLHh5l5w2UoGRb0+fbDWy7kcZywDtUVdXvaNZ1tzV9PtAD5xJCnKcUReGGG25g6pVXsj4ykr9econuiXZUbCzzIiKYPXs2DzzwAPffeCOj/P3x12qxqq6mJjOT06cbJ2Wl3OrA1tOJ3qqqcuLECeozMohPTtabacspLOSW55/noilT2u2cnJydTZ2dHTYuLri5NRaL7GzwJLMeQlxYOvszr9FoMHZ0pKy+nsLS0g73Bfj14EHuX768h0bZOQMlKPkXUAJMVxTl1uYXFUVxBZq/IpFtD2qRNO/R5vX5iqLYtHnNVFGUF4A7gEpgbc++BSHE+cbe3p7J4eFMv/VWLM3MdJ3bw0eOZE1TM7za8nLmTZ+OoigYGRkx3McHJSODnLQ0fv31Vym3OsD1dKJ3cnIyxRkZGOfmMuOii3TfJ80zbYs/+ICkzEyefeYZ9u3bp9c5ubK6mvT8fNBoCAgI0OWaSJUsIUR3mJqaotFqwcWFtA7KA0Pjw5Vn/v1vTqWm9tHoGinNf2T7m6IotwFf0lhpKwrIA66isYfJ26qqPmHgmObB+6mqmtTm9UrgOJBM40xLKOBOY3f4e1RV/boHxnwsJCQk5Fibp1xCiPNHTU0N+/bupe7ECQIdHHTN7WLi49l17BhTRozQzaDkFxdjbGxMYWkpyeXlfHfiBHfffTdhYWFER0eze/du5s+f359vRxgQExPDrl27mDJlSrdmGPLz8zl66BCcOsUwjQaNo2Or75Oyykoua/r/v27pUoqqqlp9/wAcT0oix8wMx5EjGd2iu3vzTMm8efNYs2aNLM8SQnRZZWUle3fvhmPHmBQcrFeGvKUTycnEpaSw8PnnpyWo6va+GN/Zy3n0EVVVNymKMgV4FrgIMANOAO+qqvqfLp5uGXAxMAwIoTHQSQM+AFapqtoTuSlCiAuAmZkZAYGBxJWVkRgfj7OdHRZmZoQGBra6mdwYFcXDkZHcetllfLBwIfmnTzNcoyEyMpKFCxfqbiTFwBMaGtrtG/za2lpOnjwJycl42NqicXRsPHfT90ltXR3jHnoIgL/ecAP3XatfALKkvJyc0lIICcHf319vjAsWLGDXrl0SkAghzomlpSUOzs4U2tuTXViIr0bT7r7DfXwoq6zsw9ENoKAEQFXVP4Dru7C/wf4lqqq+0GODEkJc8DQaDVkaDcWFhZxOS2NUmxtGAK2jIwUlJfxr2zZmX3stYwMDqYiPh0su4YcffpAbyfPcqVOnqM3MxLq2lgA/P73ta77+mtjERJxsbXmtqZx0S6qqEp+eDhoNGg8PbGz0ewX3RPAkhLiwabVaClNTyUpJwcfNTa8ceX8aKDklQggxYCmKQnBwMIpWS35VFXnFxXr7TB41igdvuAGARyIjOZ6cTNTRo5CVxZRLL2Xo0KF9PWzRy5qLGOzcuZPc9HSUzEyG+/joldJMz83lhbVrAXj9oYdwsrPTO1dOYSElDQ0Yu7npzZIIIURPcXZ2xsTenipFoahsYBWilaBECCE6wcrKCm9fX/D05HRaGnUGGlC98dBDuNjbcywpiYfff5/rH3qIX+PiOLV7NydOnGCg5PCJ7mtZDeuDDz4gfs8ePB0dsWlRTjMmPp7VW7bw+mefUVZZyUUhIdx/vf5igPr6es5kZICHBz6+vpiZmfXlWxFCXECMjIwaq/o5OZ014b2vSVAihBCd5OPjg6WbG9XW1iRmZuptd7S1ZeWjjwJwPC4OR0dHFi5ZwsnYWMqyskhOTgakTPD5oGU1rIiICE7GxeHTYn12y14kxebmvPHQQ/xzwQKDDcmSsrKosbbG0tUVT8/2eggLIUTP8PT0RHF2Jr+8vM/zRjoiQYkQQnSSkZERQUFB4OFBemEhxQamvmddfTUThw2jurqaWbNm8faKFUwLC4OUFJITEti9e7eUCT4PhIeH8/bbbxMdHc3KN97g2rFjMTE21m1v7mUTFhbG/KeewsLKijEGOreXlJeTVlAAHh4EBgZ2qYuyBLdCiHNhaWmJi0YDzs6kZGf393B0JCgRQogucHBwQOvtDR4exKWm0tDQ0Gq7oih88vTTWJiZYVRezrzLL+fKceNwtrREzczkm2++kX4T54HQ0FBuvfVW1v/rX9w0bBhXjR/fanv4yJE89eST7NixQ68XSbOGhgZOJCejenjg5uWFk5NTp6/fG53ohRAXDm9vb3BxIbekhOqamv4eDiBBiRBCdFlAQABmGg0VZmYkZWXpbQ/y8iJxwwZ+X72aCcOGATDUwwOTggKGenjw1ltv9VizPtE/CgoKsLe2ZvqIEdwwaZJeBRtVVfn9zz+ZcfPN3DdhQqvy0c2Ss7OpNDfHXKvtciEEaaYohOgOGxsb7JycUO3tycjP7+/hABKUCCFEl5mYmBAUFER8TQ1rtm5ld2ys3j7NfSqg8QbV3MyMoR4eBJqYcPmUKfz666+tygTLUpye0RdfR11PktRUPBwcsGtTvreuvp6/rlhBfUMD10+YwNRx4/TOUVFVRUpuLnh5ERgYiIlJ1yr093QneiHEhcfT0xOcncnMz9eb9e8PEpQIIcQ5SEtL49fffuOehQt5JyqKQ6dPG9wvPj2dqRER/HbkCG6OjrhZWxNoYcHll1+u69gtS3F6Rl99HePj46nJzMSquhp/rVZv++pNmzhw6hT2Nja83dTBva3TaWmorq44abW4uLh0eQzNzRR3794tPXCEEJ3W8sGNs7Mz5g4O1Jibk1NU1N9Dk6BECHHh6Ymn6VFRUSxcuJCwsDAWLlnCt/v2Gdwv8ssv+e+hQ9y/fDkVVVUM9fTErKSEiqwsEhMTdeeSpTjd1xdfx4KCArLT0iAjg2He3hi3SG4HSMjI4LmPPgJgxSOPtJoxa5ZTWEhhTQ1GGg2BBpZ1dVZoaCjz58+XgEQI0SltH9wcPnwYDw8PcHUlNSencZ+mUuYx8fGcSktD1Wpn9tX4JCgRQpyX2gs8euppesvlMytXrMDDyclgacXX/vpXPJydiU9P54W1azExNibYywtSU0lLTqakpGRQL8UZSMvOevvr2NDQwKlTpyAtDU9HR2ytrVttV1WVhyMjqayu5vLQUO6fNk3vHLqeJJ6eePv6Ytmir4kQQvQmQw9u3N3dMXZwoLyhgaiYGF0p8+c3beKb48f56ttvZ/XV+CQoEUKcdzoKPHrqaXrL5TO333EHgWFhnEhO5uCpU7qnTAB2Njb8c+FCACK/+oq9x4/jZGeHm40NaloaJ0+eZPTo0YNyKc5AW3bW20uakpOTqcrJwbyiAr8WPUmarf3hB345cAALMzM+WLhQL/kdGnuSVFtZYeHq2lj9Rggh+oihBzcmJiZo3d3B1ZUfDh7UlTIfNmoUi59+mrCwsD4bnwQlQojzTkeBR0dP07v61L95+czNN9+Mmbs7h7OyWPnLL0yeNYtVO3boApMbL76YWVdfTUNDA/cvX051TQ2BHh66ZVwJCQmDcinOQFx21ltfx4qKClKTkyEtjUAPD71lWwC3XHYZc667jn88+CBBXl5628srK3U9SYYOHdqlniRCCNFd7T248fT0RHFyIsDdndVvvkl0dDQnjx5l+T/+QXR0dJ+NT34jCiHOOx0FHu39Uu7OU39TU1OCgoKIzc9nweLFjTfpERHsOnZMt89bf/sbbg4OHE9O5pVPP8XUxKRxGVdKCmmJieSfY0nG/lw+NZiXnXWFqqqcPHmShowMHM3NcbG3N7ifvY0NT9x2G4qJiS4gbXmOU03J7c7u7l3qSSKEED3F0IMbCwsLXDUaAkNDuWXkSHavX8+y225jekgId9x886d9NTYJSoQQ552zLeMx9Eu5u0/9nZ2dueKqq1jZ9JTp7RUrWjXMc7Kz470nnwRg+9691NbV4WRnh6eDA6SmEhcXR11dXZeu2d/Lpy6UClApKSmUZGZikp/fGEi2cSo1FVVViYmPZ9WOHVx6772tZsoAsgoKKK6vx1ir7VZyuxBC9Ibm8sBOdnY8fP31hAYGEuTpiZKZ+VlfjaFrhdGFEGKQCA0N7dJNcnh4OKtWrWLevHmsWbOGBQsWdPmaN954I2VlZax//31uGj5c1zAvJj6eqNhYwkeOZP0zz3DblCmYNvWl8NdqyT95ksqmZVxBQUGdvp6hQKqvA4Oufp0Hm6KiIpLOnIHkZAI9PDA3M2u1PSEjg3EPPcTloaFcFhqqW489LyKCXevXExoYSHVNTWNyu78/vn5+WFhY9NO7EUIIw4YMGYK9szNF9vak5+Xh7+7e52OQmRIhhKBnnvobGxszffp0brn3Xlzc3EjPzdU9PW/OMwnx9W11Y2tkZESwtzdkZJCRkkJRF2rFXyjLp/qCoWVwdXV1nDhxAjU5GY2NjV553+YcofKqKsoqK7l67FjWrFzZ+P9j5UrdTNnp9HTqHB0ZotE0Po0UQogByNPTE1xc+q2ZogQlQgjRpCeSpG1sbPAPDAQfH85kZfFzTEzrp+dNeSa1dXUsW7eOn6Ojsbexwd3ODpqqcdXX13d6vBfC8qne1t4yuNOnT1OdmYlVVRVDPTz0jlv55ZdEHT6MtYUFHy1ezLigIBZMncru9etZMHUqoYGB5BYVkVdZiaLVMmzYMIMVuQyNZ6CUWRZCXDicnJywcHCg1tKS7MLCPr++BCVCCNHDPD09cfL0pEGjwd3enjUrVug9PV/11Ve8sHYtc994g8LSUgLc3bGoqKAqM5PT7XSHN2QwVu0aaAwtg8vOziY7NRUlPd1gk8SDp07xzL//DcDqxx/XLXUIDQxk/vTphAYGUldfz+m0NPDywsffH+s2fU0M6e88ISHEhUtRlMZmis7OpOfloapqn15fghIhhOgFw4YNw8zDAw8/P+4YM6bV03OAx2+5hSAvL9Lz8vjbW29hbGzMcG9vlLQ0slJSyM7O7ud3cOFoXga3fv16nnrqKWxsbIg7fhwSE/F2diYhM7NV75mKqipmvvIKtXV13HrZZQabJAKcSU+nZsgQrNzcOt2TZCCWWRZCXDg0Gg1GDg6U1dVRUV3dp9eWoEQIIXpBc5lgvLwYYmPDnKuu0gUkAFYWFnzy979jbGTEhl9/5ZOffsLOxgYfFxdITub0qVNUVVUBspynt4WGhjJt2jQ2b97MihUr+Pnnnzn12284m5lRVFbWKicoJj6ep95/n7jUVNydnfm/iAiDS7IKSkrILCkBDw+Cg4M73ZNE8oSEEP3J1NQUN40GnJ37/NoSlAghRBedLUho3p6WloablxeqhwcnU1L0EgcnDh/OC7NnA/DoqlWcSk3Fx80NW1WlLj2dkydPcujQIVnO0weysrJYunQpYWFhREREEHfqFMO9vdl17JheTtC911xDgLs765YuxcnOTu9cdfX1xKWmgpcXnr6+2BnYpz2SJySE6G8eHh7g5AR93OBVghIhhOiCs635b7u9vLwcM42GCnPzxrKwbTx9zz1cHhpKeVUVdy5bRk1tLcN9fDDOzaUoI4OtW7fKcp4+EB4ezurVq4mOjmbl669z04QJGBsbEz5ypF5FrYtHjODEunVcNX68wXMlZGRQbWODpUaDn59fl8cieUJCiP5kY2ODo6srtKk42NskKBFCiC4425r/ttt3795NcHAweHmRXlxMTpuKJsbGxqx/5hmc7ew4lZbGwdOnsTQ3J0CrhZQU3LVa3n777QtqOU9Xl6v1xPI2X19fLg8PZ/3bbzNz3DimjBkDNCauL5g6ld8++YTpISG6JXjNfWbaKi4rI6OoSLdsq22CvBBCdEV/Ld/18vICV1f6sjCwBCVCCNEFZ1vzb2i7k5MT3gEB4OtLXFoaFU25Is3cnZ356sUXif7nP7m4qTqXu7MzjqamBFhacu211/LHH39cEMt5ulp9qieqVRUXF3P0yBECjIx44IoruPHii1ttDw0MJDcvj5kvv8zaH35o9zyqqnI6PR3c3dF6eWFvb9/lsQghRLP+rMbn4OCAlb09aVDRV9eUoEQIIQxo7+nU2db8t7fdz88PB09P6jUajicn6+WXXB4aynAfn/9dPz6en2NiSDp6FA8bG6677rrzPiCBrlef6m61quLiYo4cPkz9mTM4NDQQ4uOjl7j+/Z49vPbZZ1TX1mLZpqN7Sxl5eZQpCqYaDf7+/l0ahxBCtNXf1fjCwsKohz/76noSlAghRBtnezp1tjX/hrYrisLw4cMx8/CgzNTUYH5Js/9s385NL7zARXfdxY7EROL//JPM5OQLokxwV6tPdadaVauApL6eUf7+elWyUnNyuPe11wD424wZ3HnllQbPVV1TQ0JWFnh64ufnh6mpaafHIYQQhvR3NT4jIyNUVa3pq+spfd0Y5XyiKMqxkJCQkGNNHZqFEOeH1atXM3nyZMLCwoiOjmb37t3Mnz+/R85dUFDAkQMH4ORJxvj64jBkSKvtldXVaG69lZKKCh588EEefvhhtn3wAeETJmAcHMy4sLBONeEbzGJiYti1axdTpkzp1OxQV/cH/YBkpJ+fXv5HTW0t4U8+yZ7jxxkfFMQfa9Zg3s5MSWxiInlmZtgOH87YsWM71bldCCHO5lx+v/WwPvtlJkFJN0hQIsT5qXmmZN68eaxZs6bHczlOnTpFxvHjmKenM2HYMEza3Ay//803PPbWWwBcNGEC7/31rxgpCoXm5lgGBTFu3Lh+fRIfExNDVFQU4eHhg3JJWUVFBQcPHKAuPr7dgARg3ttv887mzdhZW3Poww/x02oNni+nsJDjmZkow4YRNmnSeR80CiEuKH0WlMjyLSGEaKO3e0UEBARgqdVSbWPD6bQ0ve2PTp/O47fcAsCBQ4eorasjxNcXi9JSKlNSOHbsGOfyQKknqrj0Z+JlT6ipqeHo0aPUpaRgW1vbbkDy0/79vLN5MwAf//3v7QYkNbW1jf8Pvb3x8feXgEQIIc6RBCVCCGFAb/aKMDY2Zvjw4SheXmSXlZFbVKS3z9vz5jHj0kupravjthdeoLC0lFF+fhhnZVGUmsrp06e7dM2eCia6mng5kLrR19fXc/ToUSqTk7EsLmaUv3+7JXuvGj+eF+fM4ZUHHuDmyZPbPefptDRqHRyw0Wjw9vburaELIcR5T4ISIYToB7a2tpRVVrLl2DG27dlDTW1tq+2KorBu6VKCvLxIy83lthdewMTYmBBvb0hKIiMxkfT09E5fr6equBhKvGwv8BhIsyoNDQ0cPXqU0pQUTLOzGR0Q0G6vEWhM8Hxh9myemTWr3X1yi4rIraxEcXdn2LBheknyQgghOk9+gwohRD+IiYnhyy+/ZMLkyfzrt99Y/fXXevvYWluz5eWXsbW2xsPZmQZVxcnODn9XV0hMJD4ujsI2zRjb01NVXNoubQPaDTz6opxlZ2ZiVFXl2LFjFCUnY5yWxuiAACzNzfX2q6yu5oX//IfK6uqzXre2ro5Tqang5YW3nx82NjbdeRtCCHHBk6BECCH6QVRUFNdccw0//fQTb61eze6MDH6Ojtbbb7iPD9H//CefPfus7kba280NN0tL1MREjh4+3KnApCfzZFoubeso8OjtcpadmYlRVZW4uDjyk5MxSklhlJ8fQ6ys9PZraGhg7htvsOzjj7nluefOeu349HRq7e2x1mjwadFfRgghxLmRoEQIIfpBeHg4H374oe6G/pnnnmP7wYN63d4Bhnp66pYGNTQ0cOTMGYK9vHBSFBrOnOHo4cPk5+ef9Zq9kSfTUeDR2wUDzjYT09DQwIkTJ8hKSEBJTGSEtzf27cxoLPm//+OL//4XUxMTls6c2eF184uLyS4rQ/HwkGVbQgjRQ+Q3qRBC9IPQ0FAeffRRXn/9daKjo4mMjCQkNNRgt/dmVTU13LVsGRMffZS9J04wwtcXZyMjGuLjiT18mKysrD5+F53rcN9bBQM6Cojq6uo4cuQIOfHxKGfOMNzTEyc7O4PnWfP116z44gsA/rN4MZd3MNa6+nrimpZtefn6MqRNnxkhhBDnRvqUdIP0KRFCdFXbHh/NjbEuuugiamtrqT15Ei8LCwI8PFofFx/Pf48cYcuuXew6fBgnW1t+e/tthnl7czIlheyqKvD3xy8o6IJYTtT8ddRoNGRnZ7dqLFZTU8ORI0coS0nBOD2dkQaaVDbb/Ntv3PbCC6iqyj8efJC/33NPh9c9kZxMtqJgNWwYYWFhMksihDjfSZ8SIYQ43xjKgWieSZg4cSLBwcHg5UVaQQEl5eX/Oy4+nlU7dnDZfffhERjICF9f8ktKuPqpp0jJzmaYtzfeQ4bA6dMknjzJmTNn+vFd9r6WX8fvv/++VUBSWVnJwYMHKTtzBrOMDIwVhY937CAmPl7vPLtjY5n5yiuoqsojN9981mVbOYWFZJeXo3h6yrItIYToYfIbVQgh+sjZciCcnZ1x8/JCdXcnLjVVt4wrKjaWeRERhIWFsXDpUmZeey3DfXxIz8vj6kWLyC0qwt/dnUAXFzh9mtRTpzh+/Dj19fX98TY71BN9S9r7OpaXl3Po0CGqzpzBMj8fIyMjPvjjDybPmsUqA4GJpbk51hYW3HjxxayZPx9Faf+BYFVNDadaNEm0tbU95/ELIYTQJ0GJEEL0kc5UowoMDMRMq6XcxISUnJzG40aOZM3KlY3HrVxJgJsbd06disbRkdNpaVy7eDHFZWV4urgwzN0dJT6enNOnOXDgAKWlpX39NtvVU31LDH0dy8vLiYmJoebMGaxLSxk7dCh7T53SBXPzIiLY1Wap7dihQ/l9zRo+f+45TNppogiNFbyOJyVR5+KCrbv7BbE8Tggh+prklHSD5JQIIbqqOYek5ZKjtnJycjh+6BBGp04RNnQoVhYWxMTHs+vYMdxsbfm+6Wb71RdeYNdvv1FeVcWPy5cT3nS+orIyjiclUWNnh+LhgW9AAN7e3h3OBLQ31pb5L921evVqJk+eTFhYGNHR0ezevZv58+ef07lafh2HDh1KTEwMtWfOMKSyUtcYsXnZ27yICNasXMmCqVOxMjcnr7iYS0aO7PS1EjIySKmowCQ4mLAJE7CwsDinMQshxCAkOSXiwpGXl8dzzz3H2LFjsbe3x8rKisDAQB566CFiY2PP6ZxJSUkoisLll1/e7fH15LnOxdq1a1EUhRdffLFfri96VmeqUbm6uuLk7k6DqysnU1JQVZXQwEDmT59OVnGx7un/My+9xAM33cQPy5djZ2PD6i1biImPx97GhgnDhuFSV4d68iSJsbEcOXKEmpqaTo+zN7qx92Tfkuav49ChQzl8+LAuIBkTGKjr1B4aGMiCqVPZvX49C6ZOxdbKisvmz+eKhQtZ9vHHuq9XR4rKykjJzwdvb4KHDZOARAgheokEJaJf/fLLLwwdOpRXXnmF9PR0wsPDufHGGzE1NeXDDz8kNDSU119/vb+HKUSfCwoKwsTdnRJVJS03V/d626VcM6+4AnsbG1bt2MHkWbN4afNmdh0+jKmJCSP8/Bim0WCckEDhqVPs27uXlJSUTuWa9EY39nPtW9JeHkpBQQGHDh6kpkVA0nYZVnMwZ21hweR588gpKsLF1ZXdGRnt5po0q6uv50RyMnh5ofXxwcXF5VzethBCiE4w6e8BiAvX/v37ueGGG6itreW1117jqaeewsTkf9+S33//PbNmzeLvf/87VlZWXVrm4eHhwYkTJ7Ay0Lm5q3ryXEJ0lrm5OQGBgcSVlZEYH4/DkCHYWFo2Pv0HdjU9/Q8NDGT1li3Mi4jA1taW3/buJXr/fmI+/BAnOzs0jo7YWllxPDmZsoICEgoLSUtLIzAwEFdXV4PXjomJIT09ncjISBYuXMiaNWtYsGBBj7yv0NDQLi0Fa56xmTdvHqtWrdIFM5mZmZw6cQI1MRG7hgZGBgS0mxcSEx/PtYsXk1NYiLe3Nw888ADTpk37X67J+vWEBgbqHReXkkL1kCFYarUEGtguhBCi58hMiegXqqoye/ZsampqWLZsGUuXLm0VkABMmzaNLVu2oCgKS5YsITk5udPnNzU1ZdiwYXh7e3d7rD15LiG6QqvV4uzlRYNWy/GkJN0MR/PT/+Yb6ebZk4MHD1JeVkZabi5XLFxIRl4eAFYWFowPCmK4iwsWaWnUHD/O8UOHOHbsGLW1ta2u2RwE/OUvf6GsrIxNmzb1Sjf2zjI0Y5OcnEzc0aOop06hMTVlTFMOiSG/Hz3K5U8+SU5hIcFeXkwaN46AgABeXrZMN9s0ZcQIveMy8vLIraxE8fQkJCQE4w4S4Zv1RGUxIYToS4Z+b/XX7zIJSkS/2L59OydOnMDDw4MlS5a0u9+UKVO44447qKqq4t1339W97uvri6IoqKrKmjVrGDNmDFZWVrobp47yQGpra3n11VcJDAzEwsICf39/XnzxRWpra3Xnbam9c7XM9UhJSWHmzJm4uLhgaWlJWFgY3333nd61VVVlw4YN3HXXXQQFBWFtbc2QIUOYOHEi7733XrudvMWFKzg4GHMPDyosLDiTkWFwn+bciZy9e/n06afRODpyNCGBSY89xtGEBGLi43n7m2/ILChg4rBh+NrYoJw6xZ/ff8/SpUv57bffdOdqGQQ8//zzaLXafgtIQD8PxcfHh8QTJyA+Hh87O4Z5e7fbL+TgqVNcs2gRxeXlXDpqFHvfe4+nb7iB/OhoZo4dq8s1aTtLUlxWxunMTPDzwy8wsFNd23sqB0cCGyFEXzH0e6vta0OGDBnXV+ORoET0i++//x6AO+64A1NT0w73ndnU0Gz79u162x555BEiIiJwdXXl5ptvxt/fv8NzqarKHXfcwbPPPktOTg7XX389o0aNYuXKldxxxx3n9F6SkpKYMGECf/zxB5deeiljx47lwIEDzJgxg59++qnVvtXV1cycOZOffvoJV1dXbrrpJiZNmsSxY8f429/+xv33339OYxDnr+aZOry8yCgpIa+42OB+zbMnt02Zwh9r1hDs5UVabi6XPP44i9av1+VPHElIwFejwQTYsW8fd999N++9+y7btm2joaGhR5PRe0JzHkpUVBTXXnstdooCp08z1MUFP622w2NH+ftz1fjxXD9pEj8uX05iVhZRsbFMGTGCO6+4otVsU7PK6mpik5JQvb1x9fHp9AxpT+Tg9EZxASGEaI+h31ttX3Nxcbmxr8YjQYnoF81/bMePH3/WfZv3OX78uN5Sk6+//ppDhw7x888/8/nnn/P11193eK7169fzzTffEBgYSFxcHJs3b+abb77h+PHjxMTEdGmJWLN169Zx5513cubMGTZv3szu3bt56623aGho4JVXXmm1r4mJCZs2bSIrK4vff/+dzz//nF9++YWkpCTCwsJYt25djyQUi/OLg4MD3v7+4O3dmOdwlipa/u7u7H7nHaaMHk1ZZSX//e03CgoKWvXqiD5zhoglSwgLCyPiqaf4Zds29u/fj4eHB08++WSXk9F7i6qquLq6MnHCBNwB0+RkRnl749FO0nl9fT21dXUAmJqY8OULL7Dl5Zc5lZamKwbQXnJ7bV0dR86codbVlSGengQHB3d6nD0RzPVGcQEhhGiPod9bbV/Lzc3d2lfjkaBkACkvL2/3o6qqqtP7VlZWnvO+FRUV7e5bUVHRY+81Pz8foN1E25aaK940NDRQUFDQatuSJUsYYWA9eHv++c9/AvDyyy+jbfGU1cvLixdeeKHT52nJ39+flStXtsqJ+dvf/oaDgwN79uxpVYbVxMSEW2+9FTMzs1bncHFx4bXXXgPgm2++OadxiPObr68vGaWlfBUTw5Y//uBsPaYcbW356c03uX7iROxtbbG0tGyVP9GyitfqN98k1M6OyuPHOXbgAJWVlfj4+PDLL7/029P6hoYG0tPTOXDgAKcOH6b+5EnsKyoICw7Gyc7O4DGFpaXMeO45HomM1H19LMzMMDM1JSo2tsNGig0NDcQmJlJpa0tKVRU7d+7k6NGjnR7vuVYWa2mgzVIJIc5vhn5vtX2ttLT0YF+NR6pvDSA2Njbtbps2bRrbtm3T/dvV1bXdICE8PJydO3fq/u3r60teU8JrW2FhYezfv1/375CQkHZnC0JCQuipRpHNNwydad7Zcp+2+R4333xzp69ZW1vL/v37MTIy4tZbb9Xbfscdd5zT8qnLL79cbwmaiYkJ/v7+HDhwgPz8/FYBEDTOFP30008kJydTUVGBqqq6ztunT5/u8hjE+e/IkSP88MMPzJ8/n5VvvMEQKyumTZrU4THmZmZsfe01/oiN5dDXX7Ng6lRG+/uTV1zcqopXxNVXU19fz3e7d+Pt6EiDqSk7Tp0iYvFili9fzuzZs7nqqqs6lezdEwoKCti6dSsH9+xhpEZDsIMDfhoNHs7O7TaAPHT6NLe98AKJmZlYmJmx6K67GNZi6VX4yJGsWrmyVSPFZqqqcjIlhWJjYxJravjvzp3Mnz+/VbWvzuhqZTFDxy9YsIBdu3YNiFkqIcTgca7Nbg393uru77JzJUGJ6BfOzs7ExcWRk5Nz1n1zm3o0KIqCg4NDq21dqYiVn59PTU0NWq1Wb6YCGoNCBwcHCgsLO31OAE9PT4OvNweZ1dXVutdqamqYM2cOGzZsaPd8zcGJEC1FRUUxf/78xuVWS5awPjKSi4YPx9HWtsPjjIyMuGz0aC4bPRqAZevW8c/vvmPd0qVcHRZGaGBgYyL8zp2NN+wrVuAIRCxeTFhYGAsXLmT9Rx9hY2ODm5sbWq0Wa2vrXnmPNTU1JCQk8PvOnezYsYOIJUt4a/lyxlx5JZ4tlmvFxMcTFRtL+MiRjAkI4P+++44n3nmH6tpa/LRaNr74YquABDBYSrlZUlYWOdXVKEOHkr1vn+7r3LyEqi//OPfXzYAQYvBqr3T6YCPLtwaQsrKydj82bdrUat+cnJx2922bEJ6UlNTuvm3XLB8/frzdfVvOqHTXmDFjADhw4MBZ923eZ8SIEXozEufSXbm9J63QuZmbrpyvrcjISDZs2MDIkSPZvn072dnZ1NTUoKoqcXFx5zwGcf5rubRn1apVjJg4kRPJyVR1oUt7dU0NG3ftIjM/n2sWLWLBu+9SVVPTemnTU09hZmKiW9r11vLljLW3pzY2lrSDB9m/Zw+xsbF6S0q7o66ujuTkZPbt3UvWsWPE7tqly3d5cvFi/mz62YDGgKQ5N+QfW7cS/sQTPLJqFdW1tdx0ySUc+OADxgUFGbxO21LK0Fj6N7mgAPz8CA4J4brrrpMlVEKIQeV8yUeTmZIBpCtPH3tr375qEHj99dfz3nvvsXHjRt58880OK3B99tlnAFx33XXduqaTkxOmpqZkZWVRU1OjN1tSVlZGUVFRt65xNps3bwbQBSYtJSQk9Oq1xeDWcmlPRERE45K/06c5lpjI2KFD2y2L25K5mRl73n2XRf/8J+998w1vbdzIj/v3s/iuu1hjYGnTrvXreeqaaxgTEEBBSQmZBQXkZWWRl5tLQV4e9o6OODg4YGdnh5GREZaWlp1e4lVeXk5eXh6FhYUUFxWhFhVBdjZDgNsuusjgeABdADV+/Hhijh/n9OnTmJua8o8HH+TJ22/v1NehWV5xcWPp38BAfIOC0Gg0aDQaWUIlhBhUwsPDdTMlPdnstq9JUCL6xbRp0wgODiYuLo433niDZ5991uB+u3btYuPGjZiZmfG3v/2tW9c0NTVlwoQJ7N69m82bN3PnnXe22r5x48Zunb8zmpeGeXl56W378ssve/36YnBrubRn7969fLdvH8NtbRliZUWQge8pQ6wsLHj3ySeZNmkS97/5JieSk5n7xhvcHh7Ojo8+arW0qeWMgpOdHU52dlRUVXEqLY2i2FgKhgyhYMgQsLICIyMwNcXaxgbHFsFKc5BSW1tLVVUV5eXlZGZmUpyfD0VFUFoKZWXYmJnh7eqKi709iqIwxMrK4FKrlrkh3u7uGFVVsfGllxjp56f3Xlsu82pb+rewtJTjKSmofn5o/Pzw9fU1+HUWQoiB7nzJRxsQQYmiKNbArcBEYBIwBjAD/q6q6uvdOO+NwCIgFFCAQ8Cbqqr2WXkzYZiRkRFr165lypQpPP/885iamvLUU0+1esq6fft27rnnHlRV5fXXX29103CuHn74YXbv3s3zzz9PeHg4Go0GgLS0NJYtW9bt859NUFAQp0+f5p///GerppEbN27k448/7vXri/NDTEwM7733HvPmzWPlihVQU4OluTlenahm1+yGiy/m+H/+w+IPPuCj7dv5fu9eVj/+OO7Ozh0eZ2VhQWhgIOWVlRSWlZFfUEBFZiYNDQ3UNjRQbm1N+ZAhpNragoUFllZWGBsbU15ailpdDTU1UFiIUlyM05AhOA4ZgoO7O5bm5q2u0xxERMXGAuCr0fD6Z58x1MODBVOnsmv9elbMmsUoPz+DszPNy7zmRUSwauVKFrQ4Z0l5ObFJSTR4e+Ps69ul0r9w7gmlQgjRW86HhykDIigBhgI9ekemKMp8YDVQB/wCVAPXAN8pivKEqqpv9+T1RNdddNFFfPvtt9x1110sXbqUyMhILr74YszNzTl69CgnTpzAyMiIl19+ucemIu+99142btzId999R3BwMFOnTqWhoYEdO3ZwxRVXUF9fT2ZmZo9cy5DFixfzww8/sHTpUr766itdkBIdHc1TTz3FihUreu3a4vzRcv1wxFNPsf7jjwnMycHMxAQ3R8dOn8fJzo5/L17Mvddcw6nU1FYByQ/79nHV+PGYtLMcy9rSEmtLy1YJ6LV1dRSVlVFQUkJBQgLV9fVUmpuDsTFUVGBubIyZiQku9vZo3N0x62DZZnNQ8eBjj/HX+fM5ffo0xeXl2FlbE79+PfOnT+/4a9S2BPD69YQGBlJSXs6RhATqPT1x8PUlJCSkS3lh50tCqRBCDDQDJdG9FPg38DAwDni1OydTFCUIWEljIDJFVdXrVVWdQeOMST6wUlGUod25hugZ1113HadPn+aZZ55Bq9Xy66+/8u2331JVVcWDDz7IoUOH2l3adS4URWHjxo0sW7YMZ2dntm3bxuHDh3nyySf58ssvyc7OxsnJqceu19aUKVP4/fffufLKK0lISGDr1q2YmZmxadOmbi9PExeOtv0srrjqKvD352R6OkVlZV0+3+WhoTx00026f/925AjXL1nCsPvu48OtW6noZFK7aVPAEeztzcUjRnDJ8OGEengwwtmZi4YN4+IRIxgfHIy3m1uHAQnAtv37MR8yhBkzZhAdE0NxeTkhPj58+swzOJ2l4hi07sPS3J+loKSEwwkJ1Hl6Yuvjw8iRI7uUgwLnT0KpEEIMNMpArPSjKMqLwAuc4/ItRVHeBR4DVquq+mSbbQuASOAdVVXndXOcx0JCQkJ6qneH6F979+7loosu4rrrrtOrYCZEX+jKsqCYmBh27drFlClTGDNmDMePHyc3Ph6TlBTGBgZibWl5zuP44tdf+dvq1eSXlABgZ23NdRMn4q3VcvfllzN2aO8+0/nkp5944M03dZ3Zh9jYsODWW3nuvvvanbkxJCY+nl3HjjFlxAhc7e05nZmJ6uuLg7c3I0eOPKe+Ky1nSpoTSjs7UyLLvoQQg1Dnp5K7aaDMlPS0G5s+G8pc/qrp800GtokLwNGjR6mtrW31WlJSEo8++igAM2fO7I9hiQtc883u5MmTWbVq1Vk7qYeGhjJ//nxCQ0NRFIXhw4dj5+1NnVbLkYSETpcKjomPZ/WWLcTEx+teu/PKK0n+/HMiH3sMP62W4vJyvvjvf3nzs88Ij4jg14M91+C3rLKS7/fsYd+JE7rXxgcFUVtXR4iPD3OmTeO/K1fy0ty5XQpIoDGH5PGbbsLCzIxTOTmogYG4BQYyatSoc24Eea6d27v6/1cIIS40591MiaIo9kBz9zsbVVXLDeyTCzgD9qqqFndjnDJTMghdd911REdHM2bMGFxdXUlLSyM6OpqqqiqmTZvG1q1bu7TGXIiesHr1aiZPnkxYWBjR0dHs3r2b+fPnd+kctbW1HDp0iIqEBCwLCggNDMTcQKPQZi2TwZtL77atUtXQ0MDf1qzhVEkJf/zxB87Oziy6/XaemDEDgGf//W+qamoY4evLcB8ftI6OuDo46CWuAyRmZpKQkUFcairHk5P589gxYs6coaGhgbuuvJINzz2n2/dkSopeA8SuqqmtJTYxkRJjYxQ/P/wCA7vUcLUn9cT/XyGE6Ad9dkM0UBLde1LzX5xCQwFJkzQagxJv4OjZTqgoSntRR0DXhyf625w5c2hoaODo0aP8/vvvmJmZMWrUKGbOnMnf/vY3CUhEv+iJOvOmpqaMGTOGQw0NVKoqh+LjGRMQYDBAgPaTwVsyMjLi4RtuYNWOHfz444+seO01wpt67NTX1/PO5s0Ul+v/qjU3NWXyyJHsiIzUvRb6179SYmBfHzc3fNzcWr3W3YAkt6iIU6mp1Do6YuLtzYiRI3FwcOjWObvjfOkjIIQQveV8nCm5BPgDSFdV1bOdfX4HJgOXqKr6ZyfO2W5QEhISYi4zJUKIntAyT6Q7OQdVVVUcPnyYyuRkTLOzGeHnh72Njf71OjFT0nLf5vyM5n2qa2r4+KefiE1M5HhyMnGpqWQXFlLTtDxy8siR/L5mje4cI+bMoba+nmAvL4K9vJgQHMzkUaNaVfBqT0c9R9pKy80lPjsbfHyw0WoJCQnps8awHemp/79CCNGH+uxJbY8EJYqibARGnnXH1u5TVXVfO+d7kXMPSiYDvwNpqqoa7CamKMofwCV0Mijp4FqyfEsIMSDV1NRw5MgRytLTUVJS8HN1xcvVVW8m0FCw0R2qqlJaUUFhaSlmpqZoe6CaXWeDp5raWhIyM/n9xAliS0u58ppruPHGG7tcYUsIIYTOoFu+5Qt0rfsU9NZjq9Kmz9aduHbXa2cKIcQgYGZmhqIo/LJ/P75aLWpREUVlZQzz9m5Vjjc0MFB3g9+V2Yj2KIqCrbU1ttYd/Qo2rL3rd2aZWWFpKceTkjhRUsKOxEQinnqKNWvW4O3t3e6shFTDEkKIgaNHHh+pqhqmqqrSxY+dPXFtA1KaPjs0dYo3xLPNvkIIcV6JiYnh7bffZsaMGez880/OGBtTYG5OdFwcpRUV+vs3zUZMnjWLVTt2tKrG1Sfj7eD6hnqONFNVlaSsLA4nJVHr7c2p4mIinnrqrH1EpBqWEEIMLOfdnLaqqkX8L9gY23a7oiieNCa5p3Sn8pYQQgxkzU3+TExMcHd359jx41gHB1Pj7k7MmTPkFhW13r/tbESLZamGygb3+Hg7uH5oYCALpk5l9/r1rZZu5RcXEx0XR1JJCQQHox02jLvuuqtVY8kpU6YYvt45NkGMiYlh9erVEsQIIUQPO++Ckibbmj7fbmDbHU2ft/bRWIQQos+Fh4ezbNkyli9fzl/+8heysrJQFAWHgADq/fw4lp7OyZQU6urrG/dvZzait2dQmgMejZ1du7Mh0BiYzJ8+ndDAQOrq6zmelMTR9HTK3dwwGT6c4WPGEBwczLhx4zrVRyQ8PLxTwUurscrsihBC9JpBXX1LUZSTTf85VVXV9BavBwPHgDrgclVV9zS9PhT4E7AHRqiqGtfNcUqiuxBiwFq6dCm33357q94Yjz/+OElJSaQkJkJ6OmYlJQR7eeFkZ2cw6X31li1MnjXrf+dYv57506f3SP5J2wT2aUFBZJeUdJh0X15ZSWxiIpU2Niiennj6+ODj44OJSddTJLtaDUt6jQghLkCDq/pWT1AUZTOgbfqnJ+ABpAIZTa9lqqp6S5tjmgfvp6pqUpttC4BIGgOTn4Ea4BrAElioquqqHhizBCVCiAGr+cl+y94YzTffxcXFxMXFUZGVBSkpeDo44K/V6lWqMlT5Cuh0KeGOtBfwGKKqKinZ2STn5tKg0WDh5UVISAi2trZdvu656ujrKYQQ56kLMihJAnw62CVZVVXfNse0G5Q0bb8JWMT/cktigDdVVf22m8NtPr8EJUKIAa2j2YCGhgYSEhJIS0yE5GRsGxoY4eur1wW+7QxKV4KJDsfWyVK/ldXVnEhOpkRRwMsLR3d3hg0bhlkH3ep7i/QaEUJcYC68oGQwkqBECDHQnEuZ2/z8fE4cP05daipmBQWE+PoabLaou0Yng4nOLPHqqE+Kqqqk5eaSmJ1Ng4sLJh4eDA0Kwq1N93chhBC9RoKSwaAng5L6pmTTwcLY2Li/hyCEaKM7y4sqKyuJjY2lPD0dUlPR2Nnhq9Fg0c5sxNmaLnalW7whpRUVnExJodzICLy8cHB3Jzg4GAsLi06fQwghRLdJUDIY9FRQUl9fz2+//dZDo+obl112WY8EJnv27OG1117jjz/+oKysDG9vb/7yl7/w9NNPY2Wl31/zxRdf5KWXXmr3fEuWLOH111vXRqiqqiIiIoIvvviCyspKpk6dypo1a/Dx0V8tWFxcTHBwMFdccQUbNmw45/eVn5/Pe++9x/fff098fDxFRUXY2dkxYsQIbrjhBubOnYuLi8s5n787fv75Z1566SUOHz5MWVlj/1BVVVm7di1z587lhRde4MUXX+zUuZKSkvDz8yM8PJydO3f23qBFp3QmEbujmZT6+nrOnDlDRkoKZGdjVFCAn5sbni4uep3gzzqWc1zi1dDQQGpODkm5uahaLaZaLf7+/mi12rMeK4QQoscNuo7uoicMlvKSPbSOev369cyePZv6+nrGjx+Pt7c30dHRvPrqq2zdupXffvuNIUOGGDx28uTJBBp46jp+/Hi915544gn+7//+j3HjxuHi4sLWrVs5c+YMR44c0Qusnn/+ecrLy1mxYsU5v69vv/2W++67j+LiYuzt7Zk0aRKOjo7k5+ezZ88edu3axauvvsru3bsZ0absaW9LSUnhlltuoaamhquuugpXV9c+vb7oXeHh4XozJS21nElZtWqV3kyKsbExQUFBaDQaEhISKMrK4kxyMgUlJQR4eGBjadn5sYwcyaqVK/US5NujqirZhYUkZWVRZW4OQUG4eHoSFBSEaYsO9EIIIc5PEpQMMJeMGIGx0cBsH1Pf0MDuHsqfSUtL48EHH6S+vp6PPvqIuXPnAlBdXc29997LV199xeLFi3n//fcNHv/ggw8yZ86cs14nMzOTjz76iOuvv55t27ahKAqvvvoqzz77LJs3b+b22//XyiY2Npb33nuPV199FQ8Pj3N6Xz/++CO33HILRkZGrFy5knnz5rW6oaqpqeHTTz/l6aefJjc395yu0R2//PIL5eXlPPfccyxbtqzVtltuuYWLLroIZ2fnPh+X6BmhoaEsWLCAXbt2GVy6ZahhoKHlXba2toSGhpKRkUG8hQWF2dlEx8fjYGmJxtERJzs7TM4yUxoaGMgCYFebhodt1dbVkZGXx87DhzmUlsbI8eMZMXYsAQEBAypoPpdcHSGEEJ03MO9+L2DGRkYYGxsPzI8eDJbWrl1LVVUVV199tS4gATA3N+fdd9/FysqKf//73+Tn53frOrGxsdTV1XHffffplp/cf//9AHqNzx5//HECAgL0ni53VkVFBffddx8NDQ3861//YuHChXpPeM3MzLj//vs5cOAAvr6+53Sd7khLSwPA399fb5udnR3Dhg2ToGSQCw0NZf78+QZvnLvaMNDd3Z0JkybhOmoUhIRQ6ODAiYICdh87RmxiIlkFBVRWV9PeMuCWDQ/bqqiq4nRaGntOnODnU6f4ISGBWU88wc7oaMzMzHo8IOlOJ3ZpmiiEEL1PghLRLw4cOADA5ZdfrrfNxcWFkJAQamtr+f7777t1ncLCQgAcHBx0rzX/d0FBge61zz77jKioKNasWXPOS0U+/vhjcnJymDRpErNnz+5wXw8PD72gJD8/n0WLFjF06FAsLCxwdHTkuuuu46effjJ4DkVR8PX1pb6+nuXLlxMUFIS5uTleXl4sWbKE6upq3b47d+5EURReeOEFAObOnYuiKCiKossfWbt2bat/t5SXl8fDDz+MRqPBysqKsWPH8vHHH5/1a/Ldd99x7bXX4uTkhIWFBUFBQTz33HO6XJaWLr/8chRFISkpiS1btnDRRRdhbW2No6Mjd999ty6gaktVVdavX8/UqVN11/H392fmzJn88ccfevsfPXqUe+65Bw8PD8zNzXF3d2fu3LkkJSWd9f0Mds0zKWfrdt6SpaUlISEhXDR5Mr7jx2M9ZgwNISHkDRnCycJC9p46xR+xsZxOS6OwtJSGhgaD51FVldKKCpKysoiOi2Pf6dOkKwr1QUGcKisjYtEiwsLCePLJJw3+f+uO7gYVhmaYhBBC9CxZviX6RXl5OdA6WGjJ0dERgMOHD3Pvvffqbf/111+JiYmhqqoKT09Prr/+eoP5JN7e3gCcPn2aa6+9FoBTp04B6BLdy8rKWLRoEbfddhtXX331Ob+nbdu2ATBz5swuH5uens6UKVNISEjA29ubGTNmkJubyy+//MKPP/5IZGRkuzM499xzD1u3bmXixIkEBwfz22+/sXz5ctLT0/n0008B0Gg0zJ49m5iYGA4fPtwqJ+dsN6b5+flMnjyZU6dO4enpyc0330xWVhZz587lkUceafe4iIgIIiMjsbCwYOLEiTg7O3PgwAFeeeUVtm/fTlRUFNbW1nrHvffee6xcuZKwsDCuu+469u/fz+eff86BAwc4fPgwli3yGurr67nrrrvYuHEj5ubmXHrppTg7O5OSksLmzZsxMzNj8uTJuv03bdrEzJkzqampYfz48VxyySWcOXOGtWvX8t133xEVFdXneT59LTQ09JyWH1lYWODr64uvry/l5eXk5uaSn59PeXk5dRUVpBcUkJ6ejlJVRVp+PifS0pgwdCjBnp5U1dRQVllJnZER2NqCVotia4ujkxOenp7Yu7l1mAvTXZ1dttaes+XqCCGE6D4JSkS/aK48lZycbHB78+vtPb3+5JNPWv37ueee47bbbmPt2rXYtOivEBoailarJTIykssvvxwXFxcWL16Moihcf/31ACxbtoyioiIiIyO79Z4OHToEwLhx47p87COPPEJCQgL33nsv//73v3WzNb///jvXXnstixYtYurUqYwePbrVccnJyVhZWREbG6ubeUlMTGT8+PGsX7+el156iYCAAIYNG8batWt58cUXOXz4cKdzcgCefvppTp06xfTp0/niiy8wNzcHYPv27dx8880Gj/nyyy+JjIxk7NixfP3117qx1dbW8vjjj/N///d/vPjii7z55pt6x7733nv8/PPPXHnllUDjsrirr76a3bt3s2HDBt3yO4DXXnuNjRs3MmrUKL777rtWFdUKCgo4ceKE7t+JiYncd999WFpa8vPPP7dauvTxxx8ze/Zs5s6dy759+zr1dRlI+jrfwdraGmtra3x9fVFVlaKiIrKzsykoKOD40aPsOHWKiEWLWLl8OVNNTQkMCgJLS4wtLXFwcMDZ2RknJyeOHTvGxx9/THh4eIe5MN3V3aDibLk6Qgghuk+Wb4l+ER4eDsCGDRuoqalptW3Pnj3ExcUBUFpa2mpbYGAgK1as4NixY5SVlZGamsr69evx8PBg06ZNerMqFhYWvPnmmyQlJTFq1Cg0Gg0//vgjjzzyCKNHjyYuLo633nqLp59+WjerAo09G7paLrs5/6WrpX4TEhLYunUrtra2vP32262Wj1166aU88sgj1NfX89577xk8fs2aNa2Wgvn5+TFr1iyAbpeaLisr45NPPsHExIS3335bF5AAXH/99dxxxx0Gj/vHP/4BNP7/bTk2U1NTVq9ejUaj4V//+pfBpT4LFizQBSQAVlZWREREALRaNlNTU8PKlStRFIWPPvpIr8Szo6Njq1mS1atXU1FRwfLly/VyKe677z5mzJjB/v37OXjw4Nm+LANKf+c7KIqCg4MDw4YN45JLLqGotFS3FCti8WIyKisZNmkS4y++mMmTJzNy5Eg0Gg3Hjh1rNW6g3VyY7jqXZWuGztFb4xNCCCFBiegn99xzD97e3qSkpDB9+nSOHTtGaWkpP/zwA3fccQcmJo2TSMpGmQAAHohJREFUeEZtkutnzZpFREQEISEhWFtb4+npycyZM9m/fz9OTk5s2bKF3bt3613rjz/+YMGCBTz66KN89dVXvPvuuwDMmzcPb29vnnrqKQA+//xzfH19sbKywsHBgWeffbbdNfI95ffffwdg2rRp2Nvb621vDrQMBRimpqYG83KCgoKAxupj3XHw4EEqKyuZNGlSq6Ct2d133633Wk5ODocPH2b48OEEBwfrbbewsCAsLIyioiJOnz6tt/2aa67Re83Q+4mOjqaoqIhx48YRFhZ21vfy888/AzC9nV4Zl156KQD79+8/67kGkoGW73DVVVe1Sqa/+eab0Wg0DBkypNXPc1+PW4IKIYQY2CQoEf3C2tqarVu34u3tzQ8//MDIkSOxtbXl+uuvx8jIiIULFwLt55y0pdVqdVW8fvzxR73tF198MZGRkbz33nvcfvvtKIrCpk2b+Pnnn1m9ejXm5uYcOHCAmTNnMnz4cLZs2cL999/Pq6++yjvvvNOpMTg5OQF0udRvRkYGQLvVuJpfb96vJa1Wa7CJZfMStpbJ7uei+ZqGApL2Xm9eenfixAldMn3bj61btwKNCfRteXp66r1m6P2kpqYCEBAQ0Kn30rwUUKPRGBxTc2BqaEwDWVcravW2zs5KnOu4u1NFSwghxMAlOSWi34waNYqTJ0/y1VdfER0dTV1dHWPGjGHmzJm88sorAF1KOh46dCjQudmByspKIiIiuOmmm7jhhhsAWLlyJTY2Nnz55ZcMGTKE6dOnc/DgQd588029rtiGhIaGkp6ezsGDB3VP3buivY7Zza8b2t7VLttd1byErSvXqa+vBxoDJkOzHi01B3ItdfU9dXb/+vp6FEXhvvvu63C/wZboPhDzHTqTTH8u4z5b80chhBCDlwQlol9ZWlpy33336d0o/vLLL4DhksHtaS7/2zLRvT3/+Mc/yM7O5q233tK9dvLkSYYNG9aqi/zEiROJioqipKQEW1vbDs95ww03sG3bNjZs2NCpIKaZu7s70JiIbUjzE36tVtvpc/aU5rG1V5AgJSVF77XmmQ6NRsPatWt7bWxeXl4AxMfHd2p/T09Pzpw5w9tvv33W/5eDzblW1OpvXR13d6toCSGEGLhk+ZYYcKKiojh48CAjRoxolajcEVVV2bx5M4DB0sAtnTlzhjfffJPFixfrNRGsqKho9e/m0sWdeRp/33334eLiwp49e1i3bl2H+2ZkZOiCjeZZlW3btlFUVKS3b3NZ38suu+ysY+hp48ePx8LCgr179+qWS7X0+eef673m6elJcHAwR44caTfQ6glhYWHY29tz8OBBXd+bjlx11VUAbNmypdfGJHrXQFuqJoQQoudIUDLA1Dc0UF9fPzA/ejjhOyYmhrq6ulavHTx4kJkzZ6IoCmvWrGm1LS8vj48//lgvT6KsrIxHH32UvXv3otFouOWWWzq87hNPPIFWq2Xp0qWtXh8xYgTHjx/XlfYtLS3lu+++w9vbu9XsSXusra1Zu3YtRkZGPPjgg6xatYra2tpW+9TV1fHxxx8zfvx4XVDi7+/PDTfcQGlpKU888USrY/7880/ef/99jI2Neeyxx846hp5mY2PDPffcQ11dHU888USrr/1PP/3El19+afC4Z599lvr6em677TZiY2P1tp85c4aPPvqoW2MzMzNjwYIFqKrKAw88oBc0FRQUtGrCFxERgaWlJQsWLOC7777TO19BQQHvvfcelZWV3RqX6D09UUVLCCHEwCTLtwaY3ceO9fcQ+syTTz7J8ePHCQ0NxdnZmaSkJPbu3YuRkREffPABV1xxRav9y8rKmD17NvPmzWP48OF4e3tTVFTEwYMHyc/Px97eno0bN2JlZdXuNbdt28a2bdvYvHlzqyZ8AIsWLeKzzz7jiiuu4Morr+TQoUOkpqbyz3/+s9Pvadq0aWzcuJHZs2ezcOFCli1bxkUXXYSjoyP5+fns3buXoqIi7O3tcXV11R33wQcfcNlll/Hxxx8TFRXFxRdfTG5uLjt37qS+vp6VK1fq9SjpK6+//jpRUVFs3ryZoUOHMnnyZLKzs4mKiuLhhx/m/fff1ztm1qxZHD16lOXLlxMaGsrYsWPx8/OjpKSE5ORkTp48yZgxY1r1HDkXTz/9NIcOHWLLli0MHTqUyy67TNc88eDBg9x555262bahQ4fy6aefMmvWLG6++WaCg4MZPnw4qqqSnJzM8ePHqampYebMmXrfG2LgGKxL1YQQQnRMgpKB5AL7Qztr1iw+/fRTYmJiKCoqwsXFhbvuuotFixYZvOlwcnJiyZIl7Nmzh/j4eGJiYjA2NsbPz485c+awYMECPDw82r1edXU1TzzxBNdeey0zZszQ2z569Gi2bNnCs88+y9atW9FoNLz++us8/PDDXXpft9xyC5dddhnvvvsu27dvZ9++fZSUlGBnZ8fo0aO56aabuP/++3Vd6wE8PDzYv38/r732Glu2bOHrr7/GysqKqVOnEhERcdaE8d7k7OzMH3/8wTPPPMO3337Lli1bCAoK4sMPP+TKK680GJQAvPHGG1x77bW88847/Pnnnxw+fBgHBwc8PT1ZtGgRd911V7fHZmJiwqZNm1i3bh0fffQR+/bto6amBq1Wy2233ab3/+7WW2/l8OHDrFy5kp9//pnt27djYWGBu7s799xzD7fddht2dnbdHpcQQgghukbpaoM48T+KohwLCQkJOdYDsxvNFYsGC0NlaIUQQgghxHmld8t8tiAzJQOE3OQLIYQQQogLlSS6CyGEEEIIIfqVBCVCCCH6nHRmF0II0ZIEJUIIIfpUc2f2yZMns2rVKglMhBBCSFAihBCibxnqzD7QyEyOEEL0LQlKhBDiPNOfN9SdufZA78wuMzlCCNH3JCgRQojzSH/eUHf22gO9M/tgmMkRQojzjQQlQghxHunPG+qzXbvlLEpoaCjz588fcAEJDPyZHCGEOB9JUCKEEOeR/ryh7ujag2lJ1ECfyRFCiPORdHTvhp7s6C6EED0lJiaGXbt2MWXKlD6/oW7v2qtXr2by5MmEhYURHR3N7t27mT9/fp+OTQghRJf1WUd3CUq6QYISIcRgFRMTQ1RUFOHh4X0SuDTPlMybN481a9bIDIQQQgwOfRaUyPItIYS4wPT1UqrmAGjatGmyJEoIIYRBEpQIIcQFpi+T4VsGQN9//32/LCkTQggx8ElQIvrcwYMHURQFHx8fg9vr6uqwtrZGURSeeOIJg/ts27YNRVEICwvTvebr64uidH2WUVEUfH19u3ycIUlJSSiKgqIoDB06lLq6OoP7zZkzB0VR+Pzzz7t9zXN93+LC1RvJ8O31J5HyukIIITpDghLR58aMGYOtrS0pKSmkpKTobT9w4AAVFRUA/PbbbwbP8fvvvwNw2WWXdXit5iDh8ssv796gz0F8fDyffPJJn19XiLPp6epSHS0Hk/K6QgghOkOCEtHnjI2NufjiiwHDQUdzwDFmzBgOHz5MSUlJu/u0DEp27NjBiRMnemPIXWZpaQnAK6+80u5sSU8ZSO9bDB492Seko9kQKa8rhBCiMyQoEf2iOZhoDi5a+v3337G0tOTxxx+noaGBP//8s9X2mpoaoqOjAbj00kt1rwcEBDBs2LBeHHXneXt7M23aNBISEli3bl2vXmsgvW9xYTrbbMhAbpQohBBiYJCgRPSL5qDE0EzJ7t27mTBhAldccYXBffbv309VVRXBwcG4urrqXm+bW/Hiiy/i5+cHND7Jbc71UBSFOXPm6F23vr6e5cuXExQUhLm5OV5eXixZsoTq6upzeo8vvvgi0DhbUltb2+nj8vPzWbRoEUOHDsXCwgJHR0euu+46fvrpJ4P7t5dTcuLECe69914CAgKwsLDAxcWF0NBQnnzySTIzM/X2P3r0KPfccw8eHh6Ym5vj7u7O3LlzSUpK6vTYxYVJZkOEEEJ0lwQlol9MnDgRc3Nzjh8/TkFBge71uLg4cnJyuPTSSwkICECj0ejNpnQ2nyQ0NJTbbrsNADc3N2bPnq37aDnD0uyee+5h2bJleHp6cs0111BaWsry5ct54IEHzuk9TpgwgRtvvJGkpCTWrl3bqWPS09OZOHEiK1asoKamhhkzZjB27Fh++eUXrr32WlatWtWp8xw8eJDx48ezfv16XFxcuOWWW5g0aRI1NTWsXr2auLi4Vvtv2rSJsLAwPvvsM7RaLTfffDMajYa1a9cSFhaG9OIRZyOzIUIIIbpFVVX5OMcP4FhISIgqzs3kyZNVQP322291r/3rX/9SAXXbtm2qqqrqrbfeqlpYWKjV1dW6fW688UYVUNetW9fqfD4+Pmrjt/T/JCYmqoAaHh7e7jgAFVCHDx+uJiYm6l5PSEhQHRwcVECNj4/v1Htqvl5wcLCqqqoaHR2tAqqPj49aU1Oj22/27NkqoG7YsKHV8c3v7d577221/2+//aZaWVmpxsbG6uHDh8/6vpvPv2nTJr0xHj9+XM3IyGj1Pq2srFQ7Ozs1Kiqq1b7r1q1T+f/27j24yvrO4/j7KwSMlwCu7MilrZpBDXIREDFchIq7OtoFvKBWganU9TZLWW67rVsLW6u0dhS5DGIL6LqMFlcRJlBEhU0w4EjETXBhQI1CrTcQy4pyHfzuH89z0hDOSXKSkzzPCZ/XzJkf5/k95zzfAw/nPN/ndwPv379/vT6/iIiItCjNdl2tlhKJTLIuXKWlpZgZAwcOBGDQoEEcOnSoagyJu7Nx48bjXp8pc+fOPW5q4PPOO48xY8acEGM6+vXrx4gRI9i1axeLFy+udd8PPviAlStXkpeXx5w5c8jJyamqGzx4MPfccw/Hjh1j/vz5dR539+7dAFx55ZUn1BUUFNCpU6eq57Nnz+bAgQM88sgjJ4wFGDduHKNGjaKsrIy33367zuOKiIiINISSkhYs1boBcZFssHtpaSk9evSgffv2QJCUVN8n0d2rS5cuVeNFMiEnJyfptMEXXHABQNIxGPU1Y8YMzIyHHnqII0eOpNwv8Rmvvfbaqs9f3dixY4H6JUj9+vUDgqRi06ZNfPvttyn3ffXVVwEYOXJk0vpEV7eysrI6jysiIiLSEEpKWqja1g2Ii0GDBnHKKafw1ltvcfDgQXbv3s37779flYgA9O3bl9zc3KoL8fqOJ0lXp06daNWq1QnbzzjjDIAGD3YH6NOnDyNHjuSjjz5i0aJFKff75JNPAFIu5JjYntivNtOmTWPYsGEUFRUxYMAAzjrrLK6++mrmzp3L/v37j9s3MZD9nHPOOW4ygMRj6tSpAHzxxRd1HldERESkIVpHHYA0jWTrBsRtAGq7du3o2bMnFRUVvPnmm+zduxc4fprfnJwc+vfvz4YNG3D3JktKmnpF9BkzZrBixQoefvhhxo8f36BYEtvrE2teXh7r1q1jw4YNFBUVUVxczNq1a3nllVeYOXMmr7/+Ovn5+UAw65iZMW7cuFrf8+KLL67zuNLylZeXU1JSwtChQ2P3nSIiItlLSUkLNXToUGbNmsWECROYO3cukyZNijqkpIYMGUJFRQWlpaVVSUn1lpLE8/Xr17N169YmS0qaWu/evbn++utZtmwZCxcuTLpP586dAfjwww+T1idaNKqPB6mNmTF48OCqJG/Pnj1MnDiR5557jvvvv5+lS5cC0LVrVyorK5kzZw55eXnpfCw5ySRaYCdMmMCsWbM0/a+IiGSMum+1UNmybkD1we6lpaV06dLlhO5LiSRl6dKl7Ny5k/bt29OjR496vX+bNm0AmnxV9fpIjC2ZOXNm0u5gieRh1apV7Nu374T6JUuWAA1PyDp27Fi1dso777xTtf2qq64CYPny5Q16Xzl51LZyu4iISGMoKWnBsmHdgMQF9saNGykvL0+6fsjAgQMxM+bNmwcEF+/17W519tlnk5OTQ2VlJceOHctc4A3Qs2dPbrzxRj7++GNWrFhxQv3555/Pddddx/79+5k4ceJxCy6+8cYbPPHEE7Rq1Yr77ruvzmMtWLAgaYvL6tWrgWDF+YQpU6aQm5vLpEmTKCoqOuE1X375JfPnz+fgwYP1+pzSctW1cruIiEhDqfuWRKpTp07k5+dTWVkJnNh1C6BDhw4UFBSwbds2IL2WgjZt2nDNNddQVFRE79696du3L23atGHQoEHccccdmfkQaZg+fTovvvhiygv8J598kiFDhvDMM89QUlJCYWEhe/bsobi4mGPHjvHoo4/Sq1evOo+zYMEC7r33Xrp3705BQQGtW7dmx44dlJeXk5uby/Tp06v27datG0uWLGHMmDGMGDGCCy+8kIKCAtydXbt2sW3bNo4cOcJtt91Gbm5uxv4uJPskWmDXr18f6xZYERHJPmopkchVTzKStZTA8clKqn1SWbhwIWPHjmXv3r08++yzLFq0iJKSkoYF20g9evRg9OjRKeu7dOlCWVkZU6ZMoXXr1ixbtozNmzczfPhw1qxZw+TJk+t1nAcffJDx48djZqxdu5aioiIOHDjAXXfdxZYtWygsLDxu/xtuuIGKigruvvtujh49yurVqykuLubw4cPcfvvtrFy5knbt2jXqs0vLkA0tsCIikn3Mg5XJpQHMbGv37t27b926NepQREROepoZTEQk45p2etJq1FIiIiJZLxvWZhIRkdSUlIiISNbTzGAiItlNSYmIiGQ9zQwmIpLdNKakETSmREQkPsrLy1m/fj1XXHGFxpSIiGRGs40picWUwGZ2OnADcBkwAOgNtAF+5u6/bsD7/Qh4qpZdlrr7rQ0IVUREYuqSSy5RMiIikqVikZQA3YBnmuB9K4DyJNvfbIJjiYiIiIhIA8QlKdkPLAI2AWXAjcC/ZeB9l7v7jAy8j4iIiIiINJFYJCXuXgncmXhuZiMjDEdERERERJqRZt8SEREREZFIxaKlpAn1M7PfAnnAZ8A6dy+JOCYREREREammpSclPwgfCb8wsxLgFnf/vL5vYmap5vzNb0xwIiIiIiLScrtvfQrMAPoA7YBzgBHAdmAosMrMWkUWnYiIiIiIVMlIS4mZvQD0SPNl49x9UyaOX5O7rwHWVNv0FVBkZv8NbAb6AbcAz9bz/S5Otj1sQeneuGhFRERERE5umeq+dS5wYZqvOS1Dx643d//azOYA84CrqWdSIiIiIiIiTScjSYm7X5qJ92km74Vlp0ijEBERERERoOWOKalNh7D8OtIoREREREQEODmTkhvDcnOkUYiIiIiICJDlSYmZbQ8fXWps/4mZnVFjW46ZTQdGAweBp5svUhERERERSSU265SY2Uv8dZxH17C8z8xGhX/+1N2vr/GyxOD6nBrbZwO/NrNtwC7gVOASoDNwCBjj7h9nLnoREREREWmo2CQlBGuKfK/Gtu+EDwiSi/r6JVAIXEQwZa8BfwaeBGa5+47GhSoiIiIiIpli7h51DFnLzL5q27btmfn5WthdRERERFqWbdu2Fbn7iOY4lpKSRjCzowTjcrZHHYtkhUT2WhlpFJINdK5IOnS+SH3pXJF05ANH3D2vOQ4Wp+5b2ehdSL3iu0h1ZrYVdL5I3XSuSDp0vkh96VyRdCTOl+aS1bNviYiIiIhI9lNSIiIiIiIikVJSIiIiIiIikVJSIiIiIiIikVJSIiIiIiIikdKUwCIiIiIiEim1lIiIiIiISKSUlIiIiIiISKSUlIiIiIiISKSUlIiIiIiISKSUlIiIiIiISKSUlIiIiIiISKSUlIiIiIiISKSUlIiIiIiISKSUlDSAmZ1qZv9uZu+a2SEz+8TMFptZ16hjk/gws9PMbJSZLTKzLWb2lZl9Y2YVZvYLMzsj6hglvszsLDPbbWZuZtujjkfiyczOMbNZ4e/RQTP70sw2m9kjUccm8WFml5vZi2b2mZkdDc+TtWZ2U9SxSfMzs35m9lMzW2ZmH4e/M4fq8bpxZrbJzL4Oz6E/mtnAjMWlFd3TY2anAmuBgcCnwOvAucBlwB6g0N0rIwtQYsPM7gR+Hz7dCmwD8gjOnTOB7cBQd98dTYQSZ2b2NDAOMGCHu18UbUQSN2ZWCPwRaE/w/fK/BN8t3YGu7t46uugkLsxsNPAHghvRbwGVQGdgULjtN+7+0+gilOZmZsuBkTU2H3b3U2t5zWPAJOAg8ApwKjCc4DdqtLu/1Oi4lJSkx8x+CTwAvAH8vbt/HW6fDDwKrHf3oRGGKDFhZuOAy4FZ7v5ete2dgFVAH+A5d78tohAlpsxsOPAa8DvgLpSUSA1m1pngZkdb4PaaFwRmdpm7b4okOIkNM2sNfAJ0BG5196XV6gqBdQTnUDfdUD15mNm/AqcBZeHjM2pJSszsSoIb8nsJbr6/F24vBIoJEpXz3P0vjYpLSUn9mVkOsJvgrlRfd/+fGvUVQC/gUnff3PwRSrYI/yNvBA4Dee5+JOKQJCbMLBfYAhwBRgHvoqREajCzZ4CxwAR3nxd1PBJPZtYDeAfY7u4FSeqXE9wxv8Xdn2/m8CQmzMypPSlZBVwLTHL3x2vUzQZ+Akx190cbE4fGlKRnMEFCUlkzIQm9EJb/0GwRSbaqCMu2wN9EGYjEznQgH7gXOBpxLBJDZtYBuBn4P2BhxOFIvB2u535fNmkUkrXCYQvDw6cvJNklY9e+6m+ant5h+XaK+rdr7CeSyvlheRT9GEjIzHoBU4Cn3H29mZ0bcUgST4MIbmi8BhwNBysPBnIIxqo97+6fRxifxMcH4eMiM7u5emtI2GJ/NfAhsD6i+CT+LiL4vtnj7n9OUp+49u3V2AMpKUnPd8My2T9K9e3fTVEvkjAxLF929/reyZIWzMxOIZgYYR/wL9FGIzF3cVh+TjDZSmGN+plmdoe7/1fzhiVx4+7HzOxHQBGw1MymEQx070SQyG4CxqoLsdSi1mtfd//GzPYBHczsTHff39ADqftWehJTuB5IUf9Njf1ETmBm1wI/JmgleSDicCQ+JhDM4jfN3fdGHYzEWoewHEdwd/LHBAOZzwMeA04HloQtb3KSc/fXgaEELSKXArcAVxBcs7xGMBBeJJW6rn0hQ9e/SkrSY2GZanYAS7FdBAAzKwCWEJwr09y9oo6XyEnAzL4D/AoocfenIw5H4q9VWLYGJrv7Ynf/wt13uvsUgj7ebVCLmwBm9kPgTeBPwACCC8cLgOeAnwOvhRP5iCRT17Vv9X0aRUlJehJNUqenqD8tLL9uhlgky4SLa75McJfzMXefHXFIEh/zCS4i7406EMkKid+ib4H/SFK/OCyHNUs0Eltm1o3gHNkDXOfum9z9G3d/z93vJujWVQjcEWWcEmt1XftChq5/NaYkPX8Ky1Qrt3etsZ8IAGZ2NvAqQd/Mp4Cp0UYkMfMDgrEkT5gdd8MpMT3jd82sOLFvYn0kOWntDMvPUoxJS9T/bbNEI3F2K8EECC+7+zdJ6p8nmDVpGMG6SCI11Xrta2anE8xMu68x40lASUm6El1t+qaoT2zf0gyxSJYwszOB1QQzWCwD/tG1QJCcqD1Bv+9kcqvV6XtbElPSdzAzS/J9kphmXMmrJC4kv0pRn9h+VjPEItlpB8HU0h3NrGuSGbgydu2r7lvp2UAwL3y+mfVJUn9TWK5svpAkzsysLbCCYHDhGuCH7n4s2qgkbtzdkj0IBi5DsHhiYvu+CEOVGHD3dwgGLecSjBGoaVhYppq+Xk4en4XlpSnq+4flzqYPRbKRux8E1oVPb0qyS8aufZWUpCGcMi+xcu68sMkKADObTDALSqm7l0URn8SLmbUiGEj4fYJpO2/QtIsikiG/Ccs5YfdQAMysH8FaNwALmj0qiZsVYXmFmR03Zs3MLgcmhU+TLYonkvBYWP48HKcEVK11czdBi9uixh7E1IskPeHKlsUEd6c+JbjY/F74fC9wubu/H1mAEhtmNhF4PHz6Eqmbz6e6+xfNEpRklXDxxA8JWkouijgciZFwXZs/AKMJFmDdSDCr0kCCSRN+7+53RRehxIWZ/Za/jmPcCmwDOhMMcD8F+F046F1OEmZ2HccvSTCAYHatTdW2Pejuq6q95nGCNdYOEIyRbQP8HcE5dLO7v9jYuNQ3OU3ufsjMvg/8DLgNGAX8hWB2iwfc/aMIw5N46VDtz9fXst8MQEmJiNSbu39rZrcS3CS7E7iS4KLiLWCBu/9nhOFJjLj7NDPbCNwD9AMuJJhRqQRY6O7PRhmfRKIjJ3b9tBrbOlavdPd/NrNy4J8IkpGjwFrgV+5emomg1FIiIiIiIiKR0pgSERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJlJISERERERGJ1P8Do3zUsaPvE1sAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -3264,7 +3385,7 @@ "\n", "ax.legend(frameon=False)\n", "ax.set_xlim(0, 10)\n", - "hlp.hide_spines(ax)" + "eda.hide_spines(ax)" ] }, { @@ -3280,12 +3401,13 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def pred_to_quantile_loss(y, y_pred, q=0.5, weights=None):\n", + " \"\"\"Calculates the quantile error for a prediction\"\"\"\n", " residuals = y - y_pred\n", " \n", " if weights is not None:\n", @@ -3296,6 +3418,7 @@ " return loss\n", "\n", "def calc_quant_reg_loss(x0, x, y, q, weights=None):\n", + " \"\"\"Makes a quantile prediction then calculates its error\"\"\"\n", " if weights is None:\n", " weights = np.ones(len(x))\n", " \n", @@ -3313,18 +3436,21 @@ "source": [ "
\n", "\n", - "We'll then create a wrapper that will fit the model for several specified quantiles" + "We'll then create a wrapper that will fit the model for several specified quantiles.\n", + "\n", + "N.b. this function should generalise to any Scikit-Learn compatible model that uses `q` as the kwarg for the quantile." ] }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def quantile_model(x, y, model=Lowess(calc_quant_reg_betas), \n", " x_pred=None, qs=np.linspace(0.1, 0.9, 9), **model_kwargs):\n", + " \"\"\"Model wrapper that will repeatedly fit and predict for the specified quantiles\"\"\"\n", "\n", " if x_pred is None:\n", " x_pred = np.sort(np.unique(x))\n", @@ -3345,7 +3471,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -3355,11 +3481,11 @@ "\n", "100%\n", "9/9\n", - "[00:16<00:02, 1.81s/it]" + "[00:11<00:01, 1.18s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 9/9 [00:16<00:02, 1.81s/it]" + " [████████████████████████████████████████████████████████████] 9/9 [00:11<00:01, 1.18s/it]" ] }, "metadata": {}, @@ -3369,7 +3495,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 16.3 s\n" + "Wall time: 10.6 s\n" ] }, { @@ -3419,63 +3545,63 @@ " \n", " \n", " 0.00000\n", - " -0.025725\n", - " 0.048997\n", - " 0.076285\n", - " 0.104099\n", - " 0.109094\n", - " 0.111826\n", - " 0.124795\n", - " 0.160191\n", - " 0.162067\n", + " -0.071164\n", + " -0.004352\n", + " 0.011374\n", + " 0.025166\n", + " 0.080732\n", + " 0.091657\n", + " 0.144884\n", + " 0.163482\n", + " 0.192227\n", " \n", " \n", " 0.02004\n", - " -0.012488\n", - " 0.062233\n", - " 0.089784\n", - " 0.117737\n", - " 0.123566\n", - " 0.127361\n", - " 0.140879\n", - " 0.175876\n", - " 0.178519\n", + " -0.056877\n", + " 0.009553\n", + " 0.025934\n", + " 0.040718\n", + " 0.095134\n", + " 0.106431\n", + " 0.158797\n", + " 0.178013\n", + " 0.207308\n", " \n", " \n", " 0.04008\n", - " 0.000738\n", - " 0.075443\n", - " 0.103274\n", - " 0.131371\n", - " 0.138040\n", - " 0.142890\n", - " 0.156940\n", - " 0.191559\n", - " 0.194985\n", + " -0.042584\n", + " 0.023455\n", + " 0.040493\n", + " 0.056241\n", + " 0.109531\n", + " 0.121194\n", + " 0.172707\n", + " 0.192542\n", + " 0.222345\n", " \n", " \n", " 0.06012\n", - " 0.013964\n", - " 0.088641\n", - " 0.116769\n", - " 0.145014\n", - " 0.152525\n", - " 0.158427\n", - " 0.173000\n", - " 0.207258\n", - " 0.211483\n", + " -0.028267\n", + " 0.037368\n", + " 0.055069\n", + " 0.071750\n", + " 0.123931\n", + " 0.135959\n", + " 0.186627\n", + " 0.207077\n", + " 0.237343\n", " \n", " \n", " 0.08016\n", - " 0.027197\n", - " 0.101833\n", - " 0.130277\n", - " 0.158672\n", - " 0.167022\n", - " 0.173981\n", - " 0.189074\n", - " 0.222986\n", - " 0.228023\n", + " -0.013915\n", + " 0.051300\n", + " 0.069668\n", + " 0.087256\n", + " 0.138339\n", + " 0.150735\n", + " 0.200564\n", + " 0.221624\n", + " 0.252307\n", " \n", " \n", "\n", @@ -3484,22 +3610,22 @@ "text/plain": [ "quantiles 0.1 0.2 0.3 0.4 0.5 0.6 \\\n", "x \n", - "0.00000 -0.025725 0.048997 0.076285 0.104099 0.109094 0.111826 \n", - "0.02004 -0.012488 0.062233 0.089784 0.117737 0.123566 0.127361 \n", - "0.04008 0.000738 0.075443 0.103274 0.131371 0.138040 0.142890 \n", - "0.06012 0.013964 0.088641 0.116769 0.145014 0.152525 0.158427 \n", - "0.08016 0.027197 0.101833 0.130277 0.158672 0.167022 0.173981 \n", + "0.00000 -0.071164 -0.004352 0.011374 0.025166 0.080732 0.091657 \n", + "0.02004 -0.056877 0.009553 0.025934 0.040718 0.095134 0.106431 \n", + "0.04008 -0.042584 0.023455 0.040493 0.056241 0.109531 0.121194 \n", + "0.06012 -0.028267 0.037368 0.055069 0.071750 0.123931 0.135959 \n", + "0.08016 -0.013915 0.051300 0.069668 0.087256 0.138339 0.150735 \n", "\n", "quantiles 0.7 0.8 0.9 \n", "x \n", - "0.00000 0.124795 0.160191 0.162067 \n", - "0.02004 0.140879 0.175876 0.178519 \n", - "0.04008 0.156940 0.191559 0.194985 \n", - "0.06012 0.173000 0.207258 0.211483 \n", - "0.08016 0.189074 0.222986 0.228023 " + "0.00000 0.144884 0.163482 0.192227 \n", + "0.02004 0.158797 0.178013 0.207308 \n", + "0.04008 0.172707 0.192542 0.222345 \n", + "0.06012 0.186627 0.207077 0.237343 \n", + "0.08016 0.200564 0.221624 0.252307 " ] }, - "execution_count": 85, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -3523,12 +3649,12 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 91, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -3548,7 +3674,7 @@ "\n", "ax.legend(frameon=False, loc=3)\n", "ax.set_xlim(0, 10)\n", - "hlp.hide_spines(ax)" + "eda.hide_spines(ax)" ] }, { @@ -3564,7 +3690,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -3637,7 +3763,7 @@ "2015-01-05 1141.625000 5" ] }, - "execution_count": 87, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -3666,7 +3792,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -3676,11 +3802,11 @@ "\n", "100%\n", "9/9\n", - "[00:42<00:05, 4.68s/it]" + "[00:23<00:03, 2.61s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 9/9 [00:42<00:05, 4.68s/it]" + " [████████████████████████████████████████████████████████████] 9/9 [00:23<00:03, 2.61s/it]" ] }, "metadata": {}, @@ -3813,7 +3939,7 @@ "5 617.404051 508.358906 414.485478 " ] }, - "execution_count": 88, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -3842,7 +3968,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -3851,7 +3977,7 @@ "(0.0, 2620.8375)" ] }, - "execution_count": 89, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" }, @@ -3874,7 +4000,7 @@ "ax.scatter(df_portugal_hydro['day_of_the_year'], df_portugal_hydro['average_power_MW'], s=1, color='k', alpha=0.5)\n", "df_quantiles.plot(cmap='viridis', legend=False, ax=ax)\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.legend(frameon=False, bbox_to_anchor=(1, 0.8))\n", "ax.set_xlabel('Day of the Year')\n", "ax.set_ylabel('Hydro Power Average (MW)')\n", @@ -3893,7 +4019,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -3925,7 +4051,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -4084,7 +4210,7 @@ "[3 rows x 94 columns]" ] }, - "execution_count": 91, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -4106,7 +4232,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -4185,7 +4311,7 @@ "16 2017-10-01 02:26:40 562.0 5.370000 0.493009" ] }, - "execution_count": 92, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -4209,23 +4335,23 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 98, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 1.13 s\n" + "Wall time: 839 ms\n" ] }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 93, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" }, @@ -4271,7 +4397,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -4281,24 +4407,16 @@ "\n", "100%\n", "41/41\n", - "[05:31<00:08, 8.06s/it]" + "[03:32<00:05, 5.17s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 41/41 [05:31<00:08, 8.06s/it]" + " [████████████████████████████████████████████████████████████] 41/41 [03:32<00:05, 5.17s/it]" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":24: RuntimeWarning: invalid value encountered in true_divide\n", - " loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising\n" - ] - }, { "data": { "text/html": [ @@ -4529,7 +4647,7 @@ "[5 rows x 41 columns]" ] }, - "execution_count": 94, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -4556,7 +4674,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -4565,7 +4683,7 @@ "(0.0, 2715.0737814101485)" ] }, - "execution_count": 95, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" }, @@ -4588,8 +4706,7 @@ "ax.scatter(x, y, s=0.1, color='k', alpha=1)\n", "df_quantiles.plot(cmap='viridis', legend=False, ax=ax)\n", "\n", - "hlp.hide_spines(ax)\n", - "#ax.legend(frameon=False, bbox_to_anchor=(1, 0.8))\n", + "eda.hide_spines(ax)\n", "ax.set_xlabel('Wind Speed (m/s)')\n", "ax.set_ylabel('Active Power (MW)')\n", "ax.set_xlim(0, 26)\n", @@ -4609,7 +4726,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -4646,7 +4763,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -4672,7 +4789,7 @@ "axs[1].set_title('Cleaned')\n", "\n", "for ax in axs:\n", - " hlp.hide_spines(ax)\n", + " eda.hide_spines(ax)\n", " ax.set_xlabel('Wind Speed (m/s)')\n", " ax.set_ylabel('Active Power (MW)')" ] @@ -4688,22 +4805,14 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 103, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":24: RuntimeWarning: invalid value encountered in true_divide\n", - " loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 983 ms\n" + "Wall time: 975 ms\n" ] }, { @@ -4712,7 +4821,7 @@ "Text(0, 0.5, 'Active Power (MW)')" ] }, - "execution_count": 98, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" }, @@ -4745,7 +4854,7 @@ "ax.scatter(cleaned_x, cleaned_y, label='Observed', color='C1', s=0.5, zorder=1)\n", "\n", "ax.legend(frameon=False)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlim(0)\n", "ax.set_ylim(0)\n", "ax.set_xlabel('Wind Speed (m/s)')\n", @@ -4753,17 +4862,18 @@ ] }, { - "cell_type": "code", - "execution_count": 99, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Clip y to something like 0.01, just needs to be marginally above 0\n", - "# Should check what happens to the lower part though, because of frac the neg values may be helping\n", - "# What happens if I fit for data where power<2250 and speed<14\n", - "# Could then have a seperate distribution fit for the period after\n", - "# Could then use weights to transition between them\n", - "# Inspecting the active power spikes for the removed values should identify set-points" + "
\n", + "\n", + "Potential areas for future exploration:\n", + "* Clip y to something like 0.01, just needs to be marginally above 0\n", + "* Should check what happens to the lower part though, because of frac the neg values may be helping\n", + "* What happens if I fit for data where power<2250 and speed<14\n", + "* Could then have a seperate distribution fit for the period after\n", + "* Could then use weights to transition between them\n", + "* Inspecting the active power spikes for the removed values should identify set-points" ] }, { @@ -4772,27 +4882,31 @@ "source": [ "
\n", "\n", - "### External Weights" + "### External Weights\n", + "\n", + "When we made our `Lowess` class we included the option to specify `external_weights`, the reason for this is that it allows us to carry out further model smoothing using variables outside of the regression. This makes particular sense for variables such as time.\n", + "\n", + "Lets first plot two subsets of the data to see why we need to do this in the first place." ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(-25.0, 100.0)" + "Text(0, 0.5, 'Price (£/MWh)')" ] }, - "execution_count": 101, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -4815,7 +4929,7 @@ "ax.scatter(s_dispatchable['2010-09':'2011-03'], s_price['2010-09':'2011-03'], s=1)\n", "ax.scatter(s_dispatchable['2020-03':'2020-09'], s_price['2020-03':'2020-09'], s=1)\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlim(8, 60)\n", "ax.set_ylim(-25, 100)\n", "ax.set_xlabel('Demand - [Wind + Solar] (MW)')\n", @@ -4833,18 +4947,9 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 106, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":23: RuntimeWarning: invalid value encountered in true_divide\n", - " loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising\n" - ] - } - ], + "outputs": [], "source": [ "model_to_dt_weights = {\n", " 'Winter 10-11': ((s_dispatchable.index < '2011-03') & (s_dispatchable.index > '2010-09')).astype(int),\n", @@ -4864,15 +4969,17 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll plot our estimates alongside the true values" + ] }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -4881,7 +4988,7 @@ "Text(0, 0.5, 'Price (£/MWh)')" ] }, - "execution_count": 103, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" }, @@ -4910,7 +5017,7 @@ " df_preds.loc[df_preds.index>min_, model_name].plot(ax=ax, color=color, linestyle='--', label='_nolegend_')\n", "\n", "ax.legend(frameon=False)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlim(8, 60)\n", "ax.set_ylim(-25, 100)\n", "ax.set_xlabel('Demand - [Wind + Solar] (MW)')\n", @@ -4918,20 +5025,23 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Instead of just using a boolean value to indicate whether an observation belongs to a specific date period, we could instead assign weightings based on the distance from specific dates. This has the benefit that we can reuse existing functions that we wrote earlier." + ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def calc_timedelta_dists(dates, central_date, threshold_value=24, threshold_units='W'):\n", + " \"\"\"Maps datetimes to weights using the central date and threshold information provided\"\"\"\n", " timedeltas = pd.to_datetime(dates, utc=True) - pd.to_datetime(central_date, utc=True)\n", " timedelta_dists = timedeltas/pd.Timedelta(value=threshold_value, unit=threshold_units)\n", "\n", @@ -4940,16 +5050,16 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 106, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" }, @@ -4976,11 +5086,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll create a wrapper that does this for all of the dates at which we wish to create a localised Lowess model" + ] }, { "cell_type": "code", @@ -4990,6 +5102,7 @@ "source": [ "#exports\n", "def construct_dt_weights(dt_idx, reg_dates, threshold_value=52, threshold_units='W'):\n", + " \"\"\"Constructs a set of distance weightings based on the regression dates provided\"\"\"\n", " dt_to_weights = dict()\n", "\n", " for reg_date in reg_dates:\n", @@ -5043,11 +5156,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll create two wrapper functions for fitting the models and estimating using them as an ensemble. We'll also create a function that sanitises the inputs to the `SmoothDates` fitting method." + ] }, { "cell_type": "code", @@ -5057,6 +5172,7 @@ "source": [ "#exports\n", "def fit_external_weighted_ensemble(x, y, ensemble_member_to_weights, lowess_kwargs={}, **fit_kwargs):\n", + " \"\"\"Fits an ensemble of LOWESS models which have varying relevance for each subset of data over time\"\"\"\n", " ensemble_member_to_models = dict()\n", "\n", " for ensemble_member, ensemble_weights in track(ensemble_member_to_weights.items()):\n", @@ -5066,29 +5182,16 @@ " return ensemble_member_to_models\n", "\n", "def get_ensemble_preds(ensemble_member_to_model, x_pred=np.linspace(8, 60, 53)):\n", + " \"\"\"Using the fitted ensemble of LOWESS models to generate the predictions for each of them\"\"\"\n", " ensemble_member_to_preds = dict()\n", " \n", " for ensemble_member in ensemble_member_to_model.keys():\n", " ensemble_member_to_preds[ensemble_member] = ensemble_member_to_model[ensemble_member].predict(x_pred)\n", " \n", - " return ensemble_member_to_preds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "#exports\n", + " return ensemble_member_to_preds\n", + "\n", "def process_smooth_dates_fit_inputs(x, y, dt_idx, reg_dates): \n", + " \"\"\"Sanitises the inputs to the SmoothDates fitting method\"\"\"\n", " if hasattr(x, 'index') and hasattr(y, 'index'):\n", " assert x.index.equals(y.index), 'If `x` and `y` have indexes then they must be the same'\n", " if dt_idx is None:\n", @@ -5102,16 +5205,75 @@ " if reg_dates is None:\n", " reg_dates = dt_idx\n", " \n", - " return x, y, dt_idx, reg_dates\n", + " return x, y, dt_idx, reg_dates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", "\n", + "We now have everything we need to create our `SmoothDates` class that will enable us to create estimates of the surface fit of a LOWESS model over time" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#exports\n", "class SmoothDates(BaseEstimator, RegressorMixin):\n", + " \"\"\"\n", + " This class provides a time-adaptive extension of the classical \n", + " Locally Weighted Scatterplot Smoothing regression technique, \n", + " including robustifying procedures against outliers. This model\n", + " predicts the surface rather than individual point estimates.\n", + " \n", + " Initialisation Parameters:\n", + " frac: Fraction of the dataset to use in each local regression\n", + " threshold_value: Number of datetime units to use in each regression\n", + " threshold_units: Datetime unit which should be compatible with pandas `date_range` function\n", + " \n", + " Attributes:\n", + " fitted: Boolean flag indicating whether the model has been fitted\n", + " frac: Fraction of the dataset to use in each local regression\n", + " threshold_value: Number of datetime units to use in each regression\n", + " threshold_units: Datetime unit which should be compatible with pandas `date_range` function\n", + " ensemble_member_to_weights: Mapping from the regression dates to their respective weightings for each data-point\n", + " ensemble_member_to_models: Mapping from the regression dates to their localised models\n", + " reg_dates: Dates at which the local time-adaptive models will be centered around\n", + " pred_weights: Weightings to map from the local models to the values to be inferenced \n", + " pred_values: Raw prediction values as generated by each of the individual local models\n", + " \"\"\"\n", + " \n", " def __init__(self, frac=0.3, threshold_value=52, threshold_units='W'):\n", " self.fitted = False\n", " self.frac = frac\n", " self.threshold_value = threshold_value\n", " self.threshold_units = threshold_units\n", " \n", - " def fit(self, x, y, dt_idx=None, reg_dates=None, lowess_kwargs={}, **fit_kwargs): \n", + " \n", + " def fit(self, x, y, dt_idx=None, reg_dates=None, lowess_kwargs={}, **fit_kwargs): \n", + " \"\"\"\n", + " Calculation of the local regression coefficients for each of the\n", + " LOWESS models across the dataset provided. This is a time-adaptive\n", + " ensembled version of the `Lowess` model.\n", + " \n", + " Parameters:\n", + " x: Values for the independent variable\n", + " y: Values for the dependent variable\n", + " dt_idx: Datetime index, if not provided the index of the x and y series will be used\n", + " reg_dates: Dates at which the local time-adaptive models will be centered around\n", + " lowess_kwargs: Additional arguments to be passed at model initialisation\n", + " reg_anchors: Locations at which to center the local regressions\n", + " num_fits: Number of locations at which to carry out a local regression\n", + " external_weights: Further weighting for the specific regression\n", + " robust_weights: Robustifying weights to remove the influence of outliers\n", + " robust_iters: Number of robustifying iterations to carry out\n", + " \"\"\"\n", + " \n", " x, y, dt_idx, reg_dates = process_smooth_dates_fit_inputs(x, y, dt_idx, reg_dates)\n", " self.ensemble_member_to_weights = construct_dt_weights(dt_idx, reg_dates, \n", " threshold_value=self.threshold_value, \n", @@ -5124,7 +5286,20 @@ " \n", " return \n", " \n", + " \n", " def predict(self, x_pred=np.linspace(8, 60, 53), dt_pred=None, return_df=True):\n", + " \"\"\"\n", + " Inference using the design matrix from the time-adaptive LOWESS fits\n", + " \n", + " Parameters:\n", + " x_pred: Independent variable locations for the time-adaptive LOWESS inference\n", + " dt_pred: Date locations for the time-adaptive LOWESS inference\n", + " return_df: Flag specifying whether to return a dataframe or numpy matrix\n", + "\n", + " Returns:\n", + " df_pred/y_pred: Estimated surface of the time-adaptive the LOWESS fit\n", + " \"\"\"\n", + " \n", " if dt_pred is None:\n", " dt_pred = self.reg_dates\n", " \n", @@ -5134,7 +5309,10 @@ " self.ensemble_member_to_preds = get_ensemble_preds(self.ensemble_member_to_models, x_pred=x_pred)\n", " \n", " self.pred_weights = np.array(list(construct_dt_weights(dt_pred, self.reg_dates).values()))\n", - " self.pred_weights = self.pred_weights/self.pred_weights.sum(axis=0)\n", + " \n", + " with np.errstate(divide='ignore', invalid='ignore'):\n", + " self.pred_weights = self.pred_weights/self.pred_weights.sum(axis=0)\n", + " \n", " self.pred_values = np.array(list(self.ensemble_member_to_preds.values()))\n", " \n", " y_pred = np.dot(self.pred_weights.T, self.pred_values)\n", @@ -5384,11 +5562,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll visualise our surface estimate as a wire-plot, where the darker colours denote price curve estimates from longer ago." + ] }, { "cell_type": "code", @@ -5423,13 +5603,143 @@ "\n", "df_pred.T.plot(legend=False, cmap='viridis', linewidth=1, ax=ax)\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlabel('Demand - [Solar + Wind] (MW)')\n", "ax.set_ylabel('Price (£/MWh)')\n", "ax.set_xlim(df_pred.columns[0])\n", "ax.set_ylim(0, 400)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Whilst `SmoothDates` accepts time-series of dispatchable generation and price as inputs to the `fit` method, `predict` doesn't accept a time-series of dispatchable generation or return a time-series of price estimates. Instead, `predict` returns a dataframe of the smoothed surface - unfortunately this is not what we need if we want to interface our work with the wider Python eco-system for sklearn based models. \n", + "\n", + "We'll create a further wrapper on top of `SmoothDates` that will accept a time-series of dispatchable generation and return the price estimate when the `predict` method is used. This will later be used for hyper-parameter tuning in, but could also be interfaced with tools such as TPOT for automated pipeline generation (perhaps the MOE estimate could be ensembled as an input to an ML model?)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "#exports\n", + "def construct_pred_ts(s, df_pred, rounding_dec=1):\n", + " \"\"\"Uses the time-adaptive LOWESS surface to generate time-series prediction\"\"\"\n", + " vals = []\n", + " \n", + " for dt_idx, val in track(s.iteritems(), total=s.size):\n", + " vals += [df_pred.loc[round(val, rounding_dec), dt_idx.strftime('%Y-%m-%d')]]\n", + " \n", + " s_pred_ts = pd.Series(vals, index=s.index)\n", + " \n", + " return s_pred_ts\n", + "\n", + "class LowessDates(BaseEstimator, RegressorMixin):\n", + " \"\"\"\n", + " This class provides a time-adaptive extension of the classical \n", + " Locally Weighted Scatterplot Smoothing regression technique, \n", + " including robustifying procedures against outliers.\n", + " \n", + " Initialisation Parameters:\n", + " frac: Fraction of the dataset to use in each local regression\n", + " threshold_value: Number of datetime units to use in each regression\n", + " threshold_units: Datetime unit which should be compatible with pandas `date_range` function\n", + " \n", + " Attributes:\n", + " fitted: Boolean flag indicating whether the model has been fitted\n", + " frac: Fraction of the dataset to use in each local regression\n", + " threshold_value: Number of datetime units to use in each regression\n", + " threshold_units: Datetime unit which should be compatible with pandas `date_range` function\n", + " ensemble_member_to_weights: Mapping from the regression dates to their respective weightings for each data-point\n", + " ensemble_member_to_models: Mapping from the regression dates to their localised models\n", + " reg_dates: Dates at which the local time-adaptive models will be centered around\n", + " ensemble_member_to_preds: Mapping from the regression dates to their predictions\n", + " reg_weights: Mapping from the prediction values to the weighting of each time-adaptive model \n", + " reg_values: Predictions from each regression\n", + " df_reg: A DataFrame of the time-adaptive surfce regression\n", + " \"\"\"\n", + " \n", + " def __init__(self, frac=0.3, threshold_value=52, threshold_units='W', pred_reg_dates=None):\n", + " self.fitted = False\n", + " self.frac = frac\n", + " self.threshold_value = threshold_value\n", + " self.threshold_units = threshold_units\n", + " self.pred_reg_dates = pred_reg_dates\n", + " \n", + " \n", + " def fit(self, x, y, dt_idx=None, reg_dates=None, lowess_kwargs={}, **fit_kwargs):\n", + " \"\"\"\n", + " Calculation of the local regression coefficients for each of the\n", + " LOWESS models across the dataset provided. This is a time-adaptive\n", + " ensembled version of the `Lowess` model.\n", + " \n", + " Parameters:\n", + " x: Values for the independent variable\n", + " y: Values for the dependent variable\n", + " dt_idx: Datetime index, if not provided the index of the x and y series will be used\n", + " reg_dates: Dates at which the local time-adaptive models will be centered around\n", + " lowess_kwargs: Additional arguments to be passed at model initialisation\n", + " reg_anchors: Locations at which to center the local regressions\n", + " num_fits: Number of locations at which to carry out a local regression\n", + " external_weights: Further weighting for the specific regression\n", + " robust_weights: Robustifying weights to remove the influence of outliers\n", + " robust_iters: Number of robustifying iterations to carry out\n", + " \"\"\"\n", + " \n", + " x, y, dt_idx, reg_dates = process_smooth_dates_fit_inputs(x, y, dt_idx, reg_dates)\n", + " self.ensemble_member_to_weights = construct_dt_weights(dt_idx, reg_dates, \n", + " threshold_value=self.threshold_value, \n", + " threshold_units=self.threshold_units)\n", + " \n", + " self.ensemble_member_to_models = fit_external_weighted_ensemble(x, y, self.ensemble_member_to_weights, lowess_kwargs=lowess_kwargs, frac=self.frac, **fit_kwargs)\n", + " \n", + " self.reg_dates = reg_dates\n", + " self.fitted = True\n", + " \n", + " return \n", + " \n", + " \n", + " def predict(self, x_pred, reg_x=None, reg_dates=None, return_df=True, rounding_dec=1):\n", + " \"\"\"\n", + " Inference using the design matrix from the time-adaptive LOWESS fits\n", + " \n", + " Parameters:\n", + " x_pred: Locations for the time-adaptive LOWESS inference\n", + "\n", + " Returns:\n", + " y_pred: Estimated values using the time-adaptive LOWESS fit\n", + " \"\"\"\n", + " \n", + " reg_dates = self.pred_reg_dates\n", + " \n", + " if reg_x is None:\n", + " reg_x = np.round(np.arange(np.floor(x_pred.min())-5, np.ceil(x_pred.max())+5, 1/(10**rounding_dec)), rounding_dec)\n", + " x_pred = x_pred.round(rounding_dec)\n", + " \n", + " if isinstance(reg_x, pd.Series):\n", + " reg_x = reg_x.values\n", + " \n", + " # Fitting the smoothed regression\n", + " self.ensemble_member_to_preds = get_ensemble_preds(self.ensemble_member_to_models, x_pred=reg_x)\n", + " \n", + " self.reg_weights = np.array(list(construct_dt_weights(reg_dates, self.reg_dates).values()))\n", + " self.reg_weights = self.reg_weights/self.reg_weights.sum(axis=0)\n", + " self.reg_values = np.array(list(self.ensemble_member_to_preds.values()))\n", + " \n", + " y_reg = np.dot(self.reg_weights.T, self.reg_values)\n", + " self.df_reg = pd.DataFrame(y_reg, index=reg_dates.strftime('%Y-%m-%d'), columns=reg_x).T\n", + " \n", + " # Making the prediction\n", + " s_pred_ts = construct_pred_ts(x_pred, self.df_reg, rounding_dec=rounding_dec)\n", + " \n", + " return s_pred_ts" + ] + }, { "cell_type": "code", "execution_count": null, @@ -5439,7 +5749,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -5449,12 +5759,12 @@ "Converted 01-retrieval.ipynb.\n", "Converted 02-eda.ipynb.\n", "Converted 03-lowess.ipynb.\n", - "Converted 04-surface-estimation.ipynb.\n", + "Converted 04-price-surface-estimation.ipynb.\n", "Converted 05-price-moe.ipynb.\n", - "Converted 06-carbon-moe.ipynb.\n", - "Converted 07-pred-conf-intvls.ipynb.\n", - "Converted 08-hyper-param-tuning.ipynb.\n", - "Converted 09-tables.ipynb.\n" + "Converted 06-carbon-surface-estimation-and-moe.ipynb.\n", + "Converted 07-prediction-confidence-and-intervals.ipynb.\n", + "Converted 08-hyper-parameter-tuning.ipynb.\n", + "Converted 09-tables-and-figures.ipynb.\n" ] } ], diff --git a/nbs/04-surface-estimation.ipynb b/nbs/04-price-surface-estimation.ipynb similarity index 51% rename from nbs/04-surface-estimation.ipynb rename to nbs/04-price-surface-estimation.ipynb index bcce53f..84fec89 100644 --- a/nbs/04-surface-estimation.ipynb +++ b/nbs/04-price-surface-estimation.ipynb @@ -13,7 +13,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Estimation of Price & Carbon Intensity Surfaces\n", + "# Estimation of Price Surfaces" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook outlines how to specify different variants the model, then proceeds to fit them.\n", "\n", "
\n", "\n", @@ -66,7 +73,9 @@ "source": [ "
\n", "\n", - "### Loading & Cleaning Data" + "### Loading & Cleaning Data\n", + "\n", + "We'll start by loading in ..." ] }, { @@ -78,7 +87,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 4.97 s\n" + "Wall time: 1.69 s\n" ] }, { @@ -327,17 +336,19 @@ "source": [ "%%time\n", "\n", - "df_EI = eda.load_EI_df('../data/electric_insights.csv')\n", + "df_EI = eda.load_EI_df('../data/raw/electric_insights.csv')\n", "\n", "df_EI.head()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "... and cleaning the GB data" + ] }, { "cell_type": "code", @@ -388,215 +399,17 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 11, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BiomassBrown CoalGasHard CoalHydro PowerOilOthersPumped StorageSeasonal StorageSolarUraniumWindnet_balancedemandprice
local_datetime
2010-01-03 23:00:00+00:003.63716.5334.72610.0782.3310.0000.00.0520.0680.016.8260.635-1.22953.657NaN
2010-01-04 00:00:00+00:003.63716.5444.8568.8162.2930.0000.00.0380.0030.016.8410.528-1.59351.963NaN
2010-01-04 01:00:00+00:003.63716.3685.2757.9542.2990.0000.00.0320.0000.016.8460.616-1.37851.649NaN
2010-01-04 02:00:00+00:003.63715.8375.3547.6812.2990.0000.00.0270.0000.016.6990.630-1.62450.540NaN
2010-01-04 03:00:00+00:003.63715.4525.9187.4982.3010.0030.00.0200.0000.016.6350.713-0.73151.446NaN
\n", - "
" - ], - "text/plain": [ - " Biomass Brown Coal Gas Hard Coal Hydro Power \\\n", - "local_datetime \n", - "2010-01-03 23:00:00+00:00 3.637 16.533 4.726 10.078 2.331 \n", - "2010-01-04 00:00:00+00:00 3.637 16.544 4.856 8.816 2.293 \n", - "2010-01-04 01:00:00+00:00 3.637 16.368 5.275 7.954 2.299 \n", - "2010-01-04 02:00:00+00:00 3.637 15.837 5.354 7.681 2.299 \n", - "2010-01-04 03:00:00+00:00 3.637 15.452 5.918 7.498 2.301 \n", - "\n", - " Oil Others Pumped Storage Seasonal Storage \\\n", - "local_datetime \n", - "2010-01-03 23:00:00+00:00 0.000 0.0 0.052 0.068 \n", - "2010-01-04 00:00:00+00:00 0.000 0.0 0.038 0.003 \n", - "2010-01-04 01:00:00+00:00 0.000 0.0 0.032 0.000 \n", - "2010-01-04 02:00:00+00:00 0.000 0.0 0.027 0.000 \n", - "2010-01-04 03:00:00+00:00 0.003 0.0 0.020 0.000 \n", - "\n", - " Solar Uranium Wind net_balance demand price \n", - "local_datetime \n", - "2010-01-03 23:00:00+00:00 0.0 16.826 0.635 -1.229 53.657 NaN \n", - "2010-01-04 00:00:00+00:00 0.0 16.841 0.528 -1.593 51.963 NaN \n", - "2010-01-04 01:00:00+00:00 0.0 16.846 0.616 -1.378 51.649 NaN \n", - "2010-01-04 02:00:00+00:00 0.0 16.699 0.630 -1.624 50.540 NaN \n", - "2010-01-04 03:00:00+00:00 0.0 16.635 0.713 -0.731 51.446 NaN " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "df_DE = eda.load_DE_df('../data/energy_charts.csv', '../data/ENTSOE_DE_price.csv')\n", + "
\n", "\n", - "df_DE.head()" + "As well as the DE data" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -605,13 +418,13 @@ "Text(0, 0.5, 'Price (£/MWh)')" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -623,6 +436,8 @@ } ], "source": [ + "df_DE = eda.load_DE_df('../data/raw/energy_charts.csv', '../data/raw/ENTSOE_DE_price.csv')\n", + "\n", "df_DE_model = df_DE[['price', 'demand', 'Solar', 'Wind']].dropna()\n", "\n", "s_DE_demand = df_DE_model['demand']\n", @@ -642,58 +457,20 @@ "ax.set_ylabel('Price (£/MWh)')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", - "### Hyper-Parameter Tuning" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# should use skopt wrapper from smart meter modelling\n", - "# this section should be saved to the module but not run in the main pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", + "### Results Wrapper\n", "\n", - "### Results Wrapper" + "We'll start defining each of the price models that we'll fit, using the `PicklableFunction` class to ensure that all of our models can be saved for later use." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -703,6 +480,7 @@ "import marshal\n", "\n", "class PicklableFunction:\n", + " \"\"\"Provides a wrapper to ensure functions can be pickled\"\"\"\n", " def __init__(self, fun):\n", " self._fun = fun\n", "\n", @@ -725,6 +503,7 @@ " return\n", " \n", "def get_fit_kwarg_sets(qs=np.linspace(0.1, 0.9, 9)):\n", + " \"\"\"Helper to generate kwargs for the `fit` method of `Lowess`\"\"\"\n", " fit_kwarg_sets = [\n", " # quantile lowess\n", " { \n", @@ -742,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -803,24 +582,23 @@ ] }, { - "cell_type": "code", - "execution_count": 28, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Should make it so that model_defs are JSON parseable and can be stored as a yaml\n", - "# Can map from strings to parameterised funcs within the `fit_models` wrapper\n", - "# create the dispatchable column as part of a feature generation step within `retrieval`" + "
\n", + "\n", + "We'll now take these model definitions to fit and save them" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def fit_models(model_definitions, models_dir):\n", + " \"\"\"Fits LOWESS variants using the specified model definitions\"\"\"\n", " for model_parent_name, model_spec in model_definitions.items():\n", " for fit_kwarg_set in track(model_spec['fit_kwarg_sets'], label=model_parent_name):\n", " run_name = fit_kwarg_set.pop('name')\n", @@ -855,176 +633,13 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
DAM_price\n", - "\n", - "100%\n", - "4/4\n", - "[03:09:29<00:00, 2842.24s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " DAM_price [█████████████████████████████████████████████] 4/4 [03:09:29<00:00, 2842.24s/it]\u001b[B" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "49/49\n", - "[01:33:10<01:58, 114.08s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 49/49 [01:33:10<01:58, 114.08s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "49/49\n", - "[01:35:60<02:06, 117.54s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 49/49 [01:35:60<02:06, 117.54s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
DAM_price_demand\n", - "\n", - "100%\n", - "10/10\n", - "[00:00<00:00, 0.00s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - "DAM_price_deman [█████████████████████████████████████████████] 10/10 [00:00<00:00, 0.00s/it]\u001b[B" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
DAM_price_DE\n", - "\n", - "100%\n", - "4/4\n", - "[07:40<00:00, 114.95s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " DAM_price_DE [█████████████████████████████████████████████] 4/4 [07:40<00:00, 114.95s/it]\u001b[B" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "25/25\n", - "[04:33<00:10, 10.91s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 25/25 [04:33<00:10, 10.91s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "25/25\n", - "[03:05<00:06, 7.40s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 25/25 [03:05<00:06, 7.40s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
DAM_price_demand_DE\n", - "\n", - "100%\n", - "1/1\n", - "[00:32<00:32, 31.54s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[A\u001b[2K\r", - "DAM_price_deman [█████████████████████████████████████████████] 1/1 [00:32<00:32, 31.54s/it]\u001b[B" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "25/25\n", - "[00:31<00:01, 1.23s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 25/25 [00:31<00:01, 1.23s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fit_models(model_definitions, models_dir)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
DAM_price\n", + "
DAM_price_GB\n", "\n", "100%\n", "4/4\n", @@ -1032,7 +647,7 @@ ], "text/plain": [ "\u001b[A\u001b[2K\r", - " DAM_price [█████████████████████████████████████████████] 4/4 [00:00<00:00, 0.00s/it]\u001b[B" + " DAM_price_GB [█████████████████████████████████████████████] 4/4 [00:00<00:00, 0.00s/it]" ] }, "metadata": {}, @@ -1041,15 +656,15 @@ { "data": { "text/html": [ - "
DAM_price_demand\n", - "\n", + "
DAM_price_demand_GB\n", + "\n", "100%\n", - "10/10\n", + "2/2\n", "[00:00<00:00, 0.00s/it]
" ], "text/plain": [ "\u001b[A\u001b[2K\r", - "DAM_price_deman [█████████████████████████████████████████████] 10/10 [00:00<00:00, 0.00s/it]\u001b[B" + "DAM_price_deman [█████████████████████████████████████████████] 2/2 [00:00<00:00, 0.00s/it]" ] }, "metadata": {}, @@ -1066,7 +681,7 @@ ], "text/plain": [ "\u001b[A\u001b[2K\r", - " DAM_price_DE [█████████████████████████████████████████████] 4/4 [00:00<00:00, 0.00s/it]\u001b[B" + " DAM_price_DE [█████████████████████████████████████████████] 4/4 [00:00<00:00, 0.00s/it]" ] }, "metadata": {}, @@ -1079,28 +694,11 @@ "\n", "100%\n", "2/2\n", - "[06:12<00:00, 186.20s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - "DAM_price_deman [█████████████████████████████████████████████] 2/2 [06:12<00:00, 186.20s/it]\u001b[B" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "25/25\n", - "[06:11<00:15, 14.86s/it]
" + "[00:00<00:00, 0.00s/it]
" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 25/25 [06:11<00:15, 14.86s/it]" + "DAM_price_deman [█████████████████████████████████████████████] 2/2 [00:00<00:00, 0.00s/it]" ] }, "metadata": {}, @@ -1112,22 +710,24 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll load one of the models in" + ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 5.48 s\n" + "Wall time: 2.7 s\n" ] } ], @@ -1135,7 +735,7 @@ "%%time\n", "\n", "if load_existing_model == True:\n", - " smooth_dates = pickle.load(open(f'{models_dir}/DAM_price_p50.pkl', 'rb'))\n", + " smooth_dates = pickle.load(open(f'{models_dir}/DAM_price_GB_p50.pkl', 'rb'))\n", "else:\n", " lowess_kwargs = {}\n", " reg_dates = pd.date_range('2009-01-01', '2021-01-01', freq='13W')\n", @@ -1146,22 +746,24 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "And create a prediction surface using it" + ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 332 ms\n" + "Wall time: 346 ms\n" ] }, { @@ -1366,7 +968,7 @@ "[5 rows x 626 columns]" ] }, - "execution_count": 12, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1391,17 +993,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converted 01-retrieval.ipynb.\n", + "Converted 02-eda.ipynb.\n", + "Converted 03-lowess.ipynb.\n", + "Converted 04-price-surface-estimation.ipynb.\n", + "Converted 05-price-moe.ipynb.\n", + "Converted 06-carbon-surface-estimation-and-moe.ipynb.\n", + "Converted 07-prediction-confidence-and-intervals.ipynb.\n", + "Converted 08-hyper-parameter-tuning.ipynb.\n", + "Converted 09-tables-and-figures.ipynb.\n" + ] + } + ], + "source": [ + "#hide\n", + "from nbdev.export import *\n", + "notebook2script()" + ] } ], "metadata": { diff --git a/nbs/05-price-moe.ipynb b/nbs/05-price-moe.ipynb index 4ab5dc9..776a350 100644 --- a/nbs/05-price-moe.ipynb +++ b/nbs/05-price-moe.ipynb @@ -13,7 +13,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Merit Order Effect Analysis\n", + "# Price Merit Order Effect Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook outlines the analysis required to determine the price merit-order-effect of variable renewable generation in the GB and DE power markets.\n", "\n", "
\n", "\n", @@ -22,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -42,7 +49,6 @@ "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "\n", - "import FEAutils as hlp\n", "from ipypb import track\n", "from IPython.display import JSON\n", "\n", @@ -61,11 +67,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "GB_model_fp = '../data/models/DAM_price_p50.pkl'\n", + "GB_model_fp = '../data/models/DAM_price_GB_p50.pkl'\n", "DE_model_fp = '../data/models/DAM_price_DE_p50.pkl'\n", "load_existing_GB_model = True\n", "load_existing_DE_model = True" @@ -91,7 +97,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 2.11 s\n" + "Wall time: 1.76 s\n" ] }, { @@ -340,7 +346,7 @@ "source": [ "%%time\n", "\n", - "df_EI = eda.load_EI_df('../data/electric_insights.csv')\n", + "df_EI = eda.load_EI_df('../data/raw/electric_insights.csv')\n", "\n", "df_EI.head()" ] @@ -387,7 +393,7 @@ "\n", "df_EI['day_ahead_price'].resample('4W').mean().plot(ax=ax)\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlabel('')\n", "ax.set_ylabel('Day-Ahead Price\\nMonthly Average (£/MWh)')" ] @@ -441,7 +447,7 @@ "ax.scatter(s_dispatchable['2010-03':'2010-09'], s_price['2010-03':'2010-09'], s=1)\n", "ax.scatter(s_dispatchable['2020-03':'2020-09'], s_price['2020-03':'2020-09'], s=1)\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlim(8, 60)\n", "ax.set_ylim(-25, 100)\n", "ax.set_xlabel('Demand - [Wind + Solar] (GW)')\n", @@ -459,14 +465,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 3.38 s\n" + "Wall time: 2.42 s\n" ] } ], @@ -496,14 +502,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 907 ms\n" + "Wall time: 469 ms\n" ] }, { @@ -708,7 +714,7 @@ "[5 rows x 4383 columns]" ] }, - "execution_count": 8, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -738,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -747,7 +753,7 @@ "Text(0.5, 1.0, 'Day-Ahead Market Average Price Curve')" ] }, - "execution_count": 9, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -777,7 +783,7 @@ "cbar = mpl.colorbar.ColorbarBase(cax, orientation='vertical', cmap=cmap, ticks=cbar_ticks)\n", "cbar.ax.set_yticklabels([dt_pred[min(int(len(dt_pred)*tick_loc), len(dt_pred)-1)].strftime('%b %Y') for tick_loc in cbar_ticks])\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlabel('Demand - [Solar + Wind] (GW)')\n", "ax.set_ylabel('Price (£/MWh)')\n", "ax.set_xlim(df_pred.index[0])\n", @@ -796,12 +802,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#exports\n", "def construct_dispatchable_lims_df(s_dispatchable, rolling_w=3, daily_quantiles=[0.001, 0.999]):\n", + " \"\"\"Identifies the rolling limits to be used in masking\"\"\"\n", " df_dispatchable_lims = (s_dispatchable\n", " .resample('1d')\n", " .quantile(daily_quantiles)\n", @@ -818,6 +825,7 @@ " return df_dispatchable_lims\n", "\n", "def construct_pred_mask_df(df_pred, df_dispatchable_lims):\n", + " \"\"\"Constructs a DataFrame mask for the prediction\"\"\"\n", " df_pred = df_pred[df_dispatchable_lims.index]\n", " df_pred_mask = pd.DataFrame(dict(zip(df_pred.columns, [df_pred.index]*df_pred.shape[1])), index=df_pred.index)\n", " df_pred_mask = (df_pred_mask > df_dispatchable_lims.iloc[:, 0].values) & (df_pred_mask < df_dispatchable_lims.iloc[:, 1].values)\n", @@ -830,7 +838,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -839,7 +847,7 @@ "" ] }, - "execution_count": 11, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -874,12 +882,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "#exports\n", "class AxTransformer:\n", + " \"\"\"Helper class for cleaning axis tick locations and labels\"\"\"\n", " def __init__(self, datetime_vals=False):\n", " self.datetime_vals = datetime_vals\n", " self.lr = linear_model.LinearRegression()\n", @@ -914,6 +923,7 @@ " return tick_locs\n", "\n", "def set_ticks(ax, tick_locs, tick_labels=None, axis='y'):\n", + " \"\"\"Sets ticks at standard numerical locations\"\"\"\n", " if tick_labels is None:\n", " tick_labels = tick_locs\n", " ax_transformer = AxTransformer()\n", @@ -927,6 +937,7 @@ " return ax\n", " \n", "def set_date_ticks(ax, start_date, end_date, axis='y', date_format='%Y-%m-%d', **date_range_kwargs):\n", + " \"\"\"Sets ticks at datetime locations\"\"\"\n", " dt_rng = pd.date_range(start_date, end_date, **date_range_kwargs)\n", "\n", " ax_transformer = AxTransformer(datetime_vals=True)\n", @@ -942,14 +953,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 2.37 s\n" + "Wall time: 1.79 s\n" ] }, { @@ -966,7 +977,7 @@ "Text(144.58333333333331, 0.5, 'Demand - [Solar + Wind] (GW)')" ] }, - "execution_count": 13, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, @@ -995,7 +1006,7 @@ "\n", "for _, spine in htmp.spines.items():\n", " spine.set_visible(True)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "\n", "ax.set_ylabel('Demand - [Solar + Wind] (GW)')" ] @@ -1011,14 +1022,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 82 ms\n" + "Wall time: 58.1 ms\n" ] }, { @@ -1035,13 +1046,13 @@ "Text(0, 0.5, 'Day-Ahead Price (£/MWh)')" ] }, - "execution_count": 14, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1064,7 +1075,7 @@ "y = s_price.loc[s_dispatchable.index][dt_min:dt_max].values\n", "\n", "x_pred = np.linspace(11, 40, 41)\n", - "y_pred = lowess.lowess_fit_and_predict(x, y, frac=0.25, num_fits=25, x_pred=x_pred)\n", + "y_pred = lowess.lowess_fit_and_predict(x, y, frac=0.6, num_fits=25, x_pred=x_pred)\n", "\n", "# Plotting\n", "fig, ax = plt.subplots(dpi=150)\n", @@ -1075,25 +1086,11 @@ "ax.set_title(f'November & December 2020') # remove in the LaTeX plot and just state in the caption\n", "ax.set_xlim(11, 40)\n", "ax.set_ylim(-20, 150)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlabel('Demand - [Solar + Wind] (GW)')\n", "ax.set_ylabel('Day-Ahead Price (£/MWh)')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -1111,6 +1108,7 @@ "source": [ "#exports\n", "def construct_df_pred(model_fp, x_pred=np.linspace(-2, 61, 631), dt_pred=pd.date_range('2009-01-01', '2020-12-31', freq='1D')):\n", + " \"\"\"Constructs the prediction surface for the specified pre-fitted model\"\"\"\n", " smooth_dates = pickle.load(open(model_fp, 'rb'))\n", " df_pred = smooth_dates.predict(x_pred=x_pred, dt_pred=dt_pred)\n", " df_pred.index = np.round(df_pred.index, 1)\n", @@ -1357,6 +1355,7 @@ "source": [ "#exports\n", "def construct_pred_ts(s, df_pred):\n", + " \"\"\"Uses the time-adaptive LOWESS surface to generate time-series prediction\"\"\"\n", " s_pred_ts = pd.Series(index=s.index, dtype='float64')\n", "\n", " for dt_idx, val in track(s.iteritems(), total=s.size):\n", @@ -1510,6 +1509,7 @@ "source": [ "#exports\n", "def calc_error_metrics(s_err, max_err_quantile=1):\n", + " \"\"\"Calculates several error metrics using the passed error series\"\"\"\n", " if s_err.isnull().sum() > 0:\n", " s_err = s_err.dropna()\n", " \n", @@ -1566,6 +1566,7 @@ "source": [ "#exports\n", "def get_model_pred_ts(s, model_fp, s_demand=None, x_pred=np.linspace(-2, 61, 631), dt_pred=pd.date_range('2009-01-01', '2020-12-31', freq='1D')):\n", + " \"\"\"Constructs the time-series prediction for the specified pre-fitted model\"\"\"\n", " df_pred = construct_df_pred(model_fp, x_pred=x_pred, dt_pred=dt_pred)\n", " s_cleaned = s.dropna().loc[df_pred.columns.min():df_pred.columns.max()+pd.Timedelta(hours=23, minutes=30)]\n", " s_pred_ts = construct_pred_ts(s_cleaned, df_pred)\n", @@ -1994,6 +1995,15 @@ "s_GB_MOE.plot()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll quickly calculate the averages for 2010 and 2020" + ] + }, { "cell_type": "code", "execution_count": 30, @@ -2015,20 +2025,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 31, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# date = '2017-06-12'\n", - "# date = '2017-07-01' - gets the shape really well but misses the magnitude in the morning" + "
\n", + "\n", + "We'll also visualise the predictions for a sample day" ] }, { @@ -2062,15 +2064,17 @@ "ax.legend(frameon=False, ncol=3, bbox_to_anchor=(1.075, -0.15))\n", "ax.set_xlabel('')\n", "ax.set_ylabel('Price (£/MWh)')\n", - "hlp.hide_spines(ax)" + "eda.hide_spines(ax)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We can see that there is a clear pattern in the bias over the course of the day" + ] }, { "cell_type": "code", @@ -2104,13 +2108,6 @@ "s_GB_err.groupby(s_GB_err.index.hour+s_GB_err.index.minute/60).mean().plot.bar()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -2184,7 +2181,7 @@ "source": [ "sns.histplot(s_GB_saving)\n", "plt.xlim(0, 750000)\n", - "hlp.hide_spines(plt.gca())" + "eda.hide_spines(plt.gca())" ] }, { @@ -2384,7 +2381,7 @@ "sns.regplot(x=100*s_GB_RES_pct_annual_avg.loc[:2019], y=100*s_GB_MOE_pct_annual_avg.loc[:2019], truncate=False, ax=ax, label='Excl. 2020 (m=0.67)')\n", "ax.scatter(x=100*s_GB_RES_pct_annual_avg, y=100*s_GB_MOE_pct_annual_avg, color='k')\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.legend(frameon=False, loc='upper left')\n", "ax.set_ylim(0)\n", "ax.set_xlabel('Average RES Penetration (%)')\n", @@ -2392,11 +2389,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll fit a linear regression that includes 2020" + ] }, { "cell_type": "code", @@ -2421,11 +2420,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "And one that excludes 2020" + ] }, { "cell_type": "code", @@ -2493,7 +2494,7 @@ "ax.scatter(s_GB_MOE.index, s_GB_MOE, s=0.01, alpha=0.1, color='k', label=None)\n", "s_GB_MOE_rolling.plot(color='r', linewidth=1, ax=ax, label='28-Day Average')\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_ylim(0, 40)\n", "ax.set_xlim(pd.to_datetime('2010'), pd.to_datetime('2021'))\n", "ax.set_xlabel('')\n", @@ -2546,7 +2547,7 @@ "ax.set_xlabel('')\n", "ax.set_ylabel('Merit Order Effect (£/MWh)')\n", "ax.legend(frameon=False)\n", - "hlp.hide_spines(ax)" + "eda.hide_spines(ax)" ] }, { @@ -2614,23 +2615,25 @@ "axs[1].set_title(year_2, y=0.9)\n", "\n", "for ax in axs:\n", - " hlp.hide_spines(ax)\n", + " eda.hide_spines(ax)\n", " ax.set_ylim(0, 80)\n", " ax.set_xlabel('')\n", " \n", "axs[1].legend(frameon=False, bbox_to_anchor=(0.125, 1.05))\n", "axs[1].set_yticks([])\n", - "hlp.hide_spines(axs[1], positions=['left'])\n", + "eda.hide_spines(axs[1], positions=['left'])\n", "\n", "axs[0].set_ylabel('Price (£/MWh)')" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll create a combined dataframe of the predicted and observed time-series " + ] }, { "cell_type": "code", @@ -2738,11 +2741,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Which we'll then save as a csv" + ] }, { "cell_type": "code", @@ -2775,11 +2780,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll do this by weighting the price time-series using the wind generation data" + ] }, { "cell_type": "code", @@ -2789,6 +2796,7 @@ "source": [ "#exports\n", "def weighted_mean_s(s, s_weight=None, dt_rng=pd.date_range('2009-12-01', '2021-01-01', freq='W'), end_dt_delta_days=7):\n", + " \"\"\"Calculates the weighted average of a series\"\"\"\n", " capture_prices = dict()\n", "\n", " for start_dt in dt_rng:\n", @@ -2851,11 +2859,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll look at the distribution of the wind capture value ratio" + ] }, { "cell_type": "code", @@ -2900,12 +2910,12 @@ ] }, { - "cell_type": "code", - "execution_count": 49, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# Could be interesting to look at the effect of price suppression on specific wind farms that have CfDs" + "
\n", + "\n", + "We'll look at how this has changed on an annual basis" ] }, { @@ -2941,55 +2951,28 @@ "\n", "(-100*s_wind_capture_value_ratio.groupby(s_wind_capture_value_ratio.index.year).mean()).plot.bar(ax=ax)\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_ylabel('Wind Capture Price Suppression (%)')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "# re-run the skopt analysis\n", - "# clean up the hyper-parameter tuning surface plot\n", - "\n", - "# work out and write the relevant LaTeX equations for both the MOE calculations and the LOWESS calculations\n", - "\n", - "# create pipeline for GB that includes retrieving the data\n", - "# run on a monthly cycle using GitHub actions (eventually move to the Pi)" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "> This effect further intensifies as more VRE is added to the market, which gives rise to the term that VRE ‘cannibalises’ its own revenues. This is also described as the ‘capture-value’ effect, referring to the ratio of the average price received by VRE (weighted by their output) relative to the average market price (i.e. the value they capture). - [source](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3741232)\n", + "
\n", "\n", - "> The merit-order effect is typically expressed in absolute terms: the change in price for a fixed increase in VRE output (e.g. €/MWh per GW of output). However, we study power systems with very different scales, from an average load of 90 GW in the US PJM market down to less than 1 GW in Latvia and Estonia. Adding a fixed amount of VRE in small systems will have a greater effect. To harmonise for market size we present values in the units of €/MWh per 1 percentage point (ppt.) change in the share of VRE in generation, which we refer to as the ‘relative merit-order effect’ - [source](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3741232)" + "It could be interesting to look at the effect of price suppression on specific wind farms that have CfDs, and then estimate the increased burden on the tax-payer." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", - "### German Model" + "### German Model\n", + "\n", + "We'll now repeat the price MOE calculations for Germany, starting by loading in the relevant data" ] }, { @@ -3189,17 +3172,19 @@ "source": [ "%%time\n", "\n", - "df_DE = eda.load_DE_df('../data/energy_charts.csv', '../data/ENTSOE_DE_price.csv')\n", + "df_DE = eda.load_DE_df('../data/raw/energy_charts.csv', '../data/raw/ENTSOE_DE_price.csv')\n", "\n", "df_DE.head()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll clean up the data and do a quick plot" + ] }, { "cell_type": "code", @@ -3242,19 +3227,19 @@ "ax.scatter(s_DE_dispatchable['2015-03':'2015-09'], s_DE_price['2015-03':'2015-09'], s=1)\n", "ax.scatter(s_DE_dispatchable['2020-03':'2020-09'], s_DE_price['2020-03':'2020-09'], s=1)\n", "\n", - "hlp.hide_spines(ax)\n", - "# ax.set_xlim(8, 60)\n", - "# ax.set_ylim(-25, 100)\n", + "eda.hide_spines(ax)\n", "ax.set_xlabel('Demand - [Wind + Solar] (GW)')\n", "ax.set_ylabel('Price (EUR/MWh)')" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Let's create a visualisation that highlights the importance of regressing against dispatchable instead of total load" + ] }, { "cell_type": "code", @@ -3307,7 +3292,7 @@ "ax = axs[0]\n", "ax.plot(x_pred_demand, y_pred_demand, linewidth=1.5, color='r')\n", "ax.scatter(x_demand, y_demand, color='k', s=0.5, alpha=0.5)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlabel('Demand (GW)')\n", "ax.set_ylabel('Day-Ahead Price (£/MWh)')\n", "\n", @@ -3315,7 +3300,7 @@ "ax.plot(x_pred_dispatch, y_pred_dispatch, linewidth=1.5, color='r')\n", "ax.scatter(x_dispatch, y_dispatch, color='k', s=0.5, alpha=0.5)\n", "ax.set_xlim(10, 80)\n", - "hlp.hide_spines(ax, positions=['top', 'left', 'right'])\n", + "eda.hide_spines(ax, positions=['top', 'left', 'right'])\n", "ax.set_yticks([])\n", "ax.set_xlabel('Demand - [Solar + Wind] (GW)')\n", "\n", @@ -3326,11 +3311,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll load in our model" + ] }, { "cell_type": "code", @@ -3361,11 +3348,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Generate the regression surface prediction" + ] }, { "cell_type": "code", @@ -3659,17 +3648,19 @@ "\n", "for _, spine in htmp.spines.items():\n", " spine.set_visible(True)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "\n", "ax.set_ylabel('Demand - [Solar + Wind] (GW)')" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll calculate some metrics for our model" + ] }, { "cell_type": "code", @@ -3702,11 +3693,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "As well as the $r^{2}$ score" + ] }, { "cell_type": "code", @@ -3730,11 +3723,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll now calculate the total savings" + ] }, { "cell_type": "code", @@ -3763,11 +3758,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "And get some context for the market average and total volumes over the same period" + ] }, { "cell_type": "code", @@ -3795,11 +3792,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "When we plot the percentage MOE over time we can see the large influence of lowered demand in 2020" + ] }, { "cell_type": "code", @@ -3839,11 +3838,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll quickly calculate the average percentage price suppresion" + ] }, { "cell_type": "code", @@ -3908,7 +3909,7 @@ "ax.scatter(s_DE_MOE.index, s_DE_MOE, s=0.05, alpha=0.3, color='k', label=None)\n", "ax.plot(s_DE_MOE_rolling.index, s_DE_MOE_rolling, color='r', linewidth=1.5, label='28-Day Average')\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_ylim(0, 80)\n", "ax.set_xlim(pd.to_datetime('2015'), pd.to_datetime('2021'))\n", "ax.set_xlabel('')\n", @@ -3948,11 +3949,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll now disaggregate the MOE based on the wind and solar drivers" + ] }, { "cell_type": "code", @@ -3990,15 +3993,17 @@ "ax.set_xlabel('')\n", "ax.set_ylabel('Merit Order Effect (EUR/MWh)')\n", "ax.legend(frameon=False)\n", - "hlp.hide_spines(ax)" + "eda.hide_spines(ax)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll look at how the MOE has changed over time in terms of the seasonal effect" + ] }, { "cell_type": "code", @@ -4058,23 +4063,25 @@ "axs[1].set_title(year_2, y=0.9)\n", "\n", "for ax in axs:\n", - " hlp.hide_spines(ax)\n", + " eda.hide_spines(ax)\n", " ax.set_ylim(0, 80)\n", " ax.set_xlabel('')\n", " \n", "axs[1].legend(frameon=False, bbox_to_anchor=(0.125, 1.05))\n", "axs[1].set_yticks([])\n", - "hlp.hide_spines(axs[1], positions=['left'])\n", + "eda.hide_spines(axs[1], positions=['left'])\n", "\n", "axs[0].set_ylabel('Price (EUR/MWh)')" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll also inspect the predictions on a sample day" + ] }, { "cell_type": "code", @@ -4095,7 +4102,6 @@ } ], "source": [ - "# date = '2020-05-11' # shows an interesting period during low covid prices\n", "date = '2020-04-11'\n", "\n", "# Plotting\n", @@ -4108,15 +4114,17 @@ "ax.legend(frameon=False, ncol=3, bbox_to_anchor=(1.075, -0.15))\n", "ax.set_xlabel('')\n", "ax.set_ylabel('Price (EUR/MWh)')\n", - "hlp.hide_spines(ax)" + "eda.hide_spines(ax)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "There's clearly a correlation between the annual MOE average and the RES percentage penetration" + ] }, { "cell_type": "code", @@ -4163,11 +4171,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll quickly calculate the gradient" + ] }, { "cell_type": "code", @@ -4192,11 +4202,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll get some context from the literature" + ] }, { "cell_type": "code", @@ -4219,20 +4231,15 @@ "print(f'In this work the MOE increase per percentage penetration of RES was {pct_diff}% higher (for Germany) than the Imperial study')" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", - "### Plots" + "### Plots\n", + "\n", + "In this section we'll generate some of the plots needed for the paper, starting with the heatmap of the price surfaces" ] }, { @@ -4278,7 +4285,7 @@ "\n", "for _, spine in htmp.spines.items():\n", " spine.set_visible(True)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "\n", "ax.set_ylabel('Demand - [Solar + Wind] (GW)')\n", "\n", @@ -4294,7 +4301,7 @@ "\n", "for _, spine in htmp.spines.items():\n", " spine.set_visible(True)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "\n", "ax.set_ylabel('Demand - [Solar + Wind] (GW)')\n", "\n", @@ -4302,11 +4309,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll also plot the MOE time-series" + ] }, { "cell_type": "code", @@ -4345,7 +4354,7 @@ "ax.scatter(s_GB_MOE.index, s_GB_MOE, s=0.01, alpha=0.1, color='k', label=None)\n", "s_GB_MOE_rolling.plot(color='r', linewidth=1, ax=ax, label='28-Day Average')\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_ylim(0, 40)\n", "ax.set_xlim(pd.to_datetime('2010'), pd.to_datetime('2021'))\n", "ax.set_xlabel('')\n", @@ -4358,7 +4367,7 @@ "ax.scatter(s_DE_MOE.index, s_DE_MOE, s=0.05, alpha=0.3, color='k', label=None)\n", "ax.plot(s_DE_MOE_rolling.index, s_DE_MOE_rolling, color='r', linewidth=1.5, label='28-Day Average')\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_ylim(0, 80)\n", "ax.set_xlim(pd.to_datetime('2015'), pd.to_datetime('2021'))\n", "ax.set_xlabel('')\n", @@ -4366,20 +4375,15 @@ "ax.legend(frameon=False)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", - "### Saving Results" + "### Saving Results\n", + "\n", + "Additionaly we'll save the time-series predictions and model metrics, starting with the GB time-series" ] }, { @@ -4488,11 +4492,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Which we'll save to csv" + ] }, { "cell_type": "code", @@ -4504,11 +4510,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Then the DE time-series" + ] }, { "cell_type": "code", @@ -4612,32 +4620,20 @@ " 'moe': s_DE_MOE\n", "})\n", "\n", + "df_DE_results_ts.to_csv('../data/results/DE_price.csv')\n", + "\n", "df_DE_results_ts.head()" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 90, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "df_DE_results_ts.to_csv('../data/results/DE_price.csv')" + "
\n", + "\n", + "And finally the model metrics for both" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 83, @@ -4689,24 +4685,10 @@ " 'GB_demand': GB_demand_metrics\n", "}\n", "\n", - "JSON(model_accuracy_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ "with open('../data/results/price_model_accuracy_metrics.json', 'w') as fp:\n", - " json.dump(model_accuracy_metrics, fp)" + " json.dump(model_accuracy_metrics, fp)\n", + "\n", + "JSON(model_accuracy_metrics)" ] }, { @@ -4718,14 +4700,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 71, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -4735,12 +4710,12 @@ "Converted 01-retrieval.ipynb.\n", "Converted 02-eda.ipynb.\n", "Converted 03-lowess.ipynb.\n", - "Converted 04-surface-estimation.ipynb.\n", + "Converted 04-price-surface-estimation.ipynb.\n", "Converted 05-price-moe.ipynb.\n", - "Converted 06-carbon-moe.ipynb.\n", - "Converted 07-pred-conf-intvls.ipynb.\n", - "Converted 08-hyper-param-tuning.ipynb.\n", - "Converted 09-tables.ipynb.\n" + "Converted 06-carbon-surface-estimation-and-moe.ipynb.\n", + "Converted 07-prediction-confidence-and-intervals.ipynb.\n", + "Converted 08-hyper-parameter-tuning.ipynb.\n", + "Converted 09-tables-and-figures.ipynb.\n" ] } ], diff --git a/nbs/06-carbon-moe.ipynb b/nbs/06-carbon-surface-estimation-and-moe.ipynb similarity index 99% rename from nbs/06-carbon-moe.ipynb rename to nbs/06-carbon-surface-estimation-and-moe.ipynb index a508a1c..ace65e2 100644 --- a/nbs/06-carbon-moe.ipynb +++ b/nbs/06-carbon-surface-estimation-and-moe.ipynb @@ -1,11 +1,22 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "# Carbon Merit Order Effect Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook outlines the analysis required to determine the carbon merit-order-effect of variable renewable generation in the GB and DE power markets.\n", + "\n", + "
\n", + "\n", + "### Imports" + ] }, { "cell_type": "code", @@ -23,17 +34,17 @@ "\n", "import pickle\n", "from sklearn.metrics import r2_score\n", - "from moepy.surface import PicklableFunction\n", - "\n", - "import FEAutils as hlp" + "from moepy.surface import PicklableFunction" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "### User Inputs" + ] }, { "cell_type": "code", @@ -50,7 +61,9 @@ "source": [ "
\n", "\n", - "### Germany" + "### Germany\n", + "\n", + "We'll start by loading in the data" ] }, { @@ -227,7 +240,7 @@ } ], "source": [ - "df_fuels_DE = pd.read_csv('../data/energy_charts.csv')\n", + "df_fuels_DE = pd.read_csv('../data/raw/energy_charts.csv')\n", "\n", "df_fuels_DE = df_fuels_DE.set_index('local_datetime')\n", "df_fuels_DE.index = pd.to_datetime(df_fuels_DE.index, utc=True).tz_convert('Europe/Berlin')\n", @@ -236,11 +249,15 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We now need to conver the fuel generation time-series into a carbon intensity time-series. We'll use data provided by [volker-quaschning](https://www.volker-quaschning.de/datserv/CO2-spez/index_e.php). The units are kgCO2 / kWh, equivalent to Tonnes/MWh.\n", + "\n", + "N.b. We are looking at the fuel emissions (not avg over lifecycle incl. CAPEX)" + ] }, { "cell_type": "code", @@ -271,10 +288,6 @@ } ], "source": [ - "# https://www.volker-quaschning.de/datserv/CO2-spez/index_e.php\n", - "# should specify that I'm looking at the fuel emissions (not avg over lifecycle incl CAPEX)\n", - "# the units are kgCO2 / kWh, same as Tonnes/MWh \n", - "\n", "DE_fuel_to_co2_intensity = {\n", " 'Biomass': 0.39, \n", " 'Brown Coal': 0.36, \n", @@ -304,11 +317,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll do a quick plot of the change over time" + ] }, { "cell_type": "code", @@ -351,7 +366,7 @@ "ax.scatter(df_DE.loc['2010-09':'2011-03', 'dispatchable'], df_DE.loc['2010-09':'2011-03', 'emissions'], s=0.1, alpha=0.25)\n", "ax.scatter(df_DE.loc['2019-09':'2020-03', 'dispatchable'], df_DE.loc['2019-09':'2020-03', 'emissions'], s=0.1, alpha=0.25)\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlim(10, 80)\n", "ax.set_ylim(3000, 20000)\n", "ax.set_xlabel('Demand - [Wind + Solar] (GW)')\n", @@ -618,7 +633,7 @@ } ], "source": [ - "df_fuels_GB = pd.read_csv('../data/electric_insights.csv')\n", + "df_fuels_GB = pd.read_csv('../data/raw/electric_insights.csv')\n", "\n", "df_fuels_GB = df_fuels_GB.set_index('local_datetime')\n", "df_fuels_GB.index = pd.to_datetime(df_fuels_GB.index, utc=True).tz_convert('Europe/Berlin')\n", @@ -632,6 +647,8 @@ "source": [ "
\n", "\n", + "We'll source the carbon intensity data from DUKES where possible and Electric Insights where it isn't.\n", + "\n", "" ] }, @@ -691,11 +708,15 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll do the same visualisation for GB of how the carbon intensity has changed over time.\n", + "\n", + "Interestly we can see a clear fall in the carbon intensity of the GB dispatchable fleet, whereas with Germany the difference is negligible and if anything has slightly increased." + ] }, { "cell_type": "code", @@ -728,7 +749,7 @@ "ax.scatter(df_GB.loc['2010-09':'2011-03', 'dispatchable'], df_GB.loc['2010-09':'2011-03', 'emissions'], s=0.1, alpha=0.25, label='Winter 10/11')\n", "ax.scatter(df_GB.loc['2019-09':'2020-03', 'dispatchable'], df_GB.loc['2019-09':'2020-03', 'emissions'], s=0.1, alpha=0.25, label='Winter 19/20')\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "ax.set_xlim(5, 60)\n", "ax.set_ylim(0, 17500)\n", "ax.set_xlabel('Demand - [Wind + Solar] (GW)')\n", @@ -748,7 +769,9 @@ "source": [ "
\n", "\n", - "### Model Fitting" + "### Model Fitting\n", + "\n", + "We're ready to define and fit our models" ] }, { @@ -799,10 +822,10 @@ " 'y': df_DE['emissions'].values,\n", " 'reg_dates_start': '2010-01-04',\n", " 'reg_dates_end': '2021-01-01',\n", - " 'reg_dates_freq': '13W', # 13 \n", + " 'reg_dates_freq': '13W', \n", " 'frac': 0.3, \n", - " 'num_fits': 31, # 31\n", - " 'dates_smoothing_value': 26, # 26\n", + " 'num_fits': 31, \n", + " 'dates_smoothing_value': 26, \n", " 'dates_smoothing_units': 'W',\n", " 'fit_kwarg_sets': surface.get_fit_kwarg_sets(qs=[0.16, 0.5, 0.84])\n", " },\n", @@ -812,10 +835,10 @@ " 'y': df_GB['emissions'].values,\n", " 'reg_dates_start': '2010-01-04',\n", " 'reg_dates_end': '2021-01-01',\n", - " 'reg_dates_freq': '13W', # 13 \n", + " 'reg_dates_freq': '13W', \n", " 'frac': 0.3, \n", - " 'num_fits': 31, # 31\n", - " 'dates_smoothing_value': 26, # 26\n", + " 'num_fits': 31, \n", + " 'dates_smoothing_value': 26,\n", " 'dates_smoothing_units': 'W',\n", " 'fit_kwarg_sets': surface.get_fit_kwarg_sets(qs=[0.16, 0.5, 0.84])\n", " }\n", @@ -830,7 +853,9 @@ "source": [ "
\n", "\n", - "### German Carbon Savings Calculations" + "### German Model Evaluation & Carbon Savings Calculations\n", + "\n", + "We'll start by loading in the model" ] }, { @@ -842,7 +867,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 5.03 s\n" + "Wall time: 2.94 s\n" ] }, { @@ -1068,11 +1093,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll then visualise the surface prediction as a heatmap" + ] }, { "cell_type": "code", @@ -1126,17 +1153,19 @@ "\n", "for _, spine in htmp.spines.items():\n", " spine.set_visible(True)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "\n", "ax.set_ylabel('Demand - [Solar + Wind] (GW)')" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll calculate the model metrics" + ] }, { "cell_type": "code", @@ -1157,12 +1186,12 @@ "
\n", "\n", "100%\n", - "37479/96191\n", - "[02:23<00:00, 0.00s/it]
" + "0/96191\n", + "[01:24<00:00, 0.00s/it]
" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 96191/96191 [02:23<00:00, 0.00s/it]" + " [████████████████████████████████████████████████████████████] 96191/96191 [01:24<00:00, 0.00s/it]" ] }, "metadata": {}, @@ -1175,11 +1204,11 @@ "\n", "100%\n", "0/96191\n", - "[02:28<00:00, 0.00s/it]" + "[01:15<00:00, 0.00s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 96191/96191 [02:28<00:00, 0.00s/it]" + " [████████████████████████████████████████████████████████████] 96191/96191 [01:15<00:00, 0.00s/it]" ] }, "metadata": {}, @@ -1207,11 +1236,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "And $r^{2}$ score" + ] }, { "cell_type": "code", @@ -1234,11 +1265,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We're now ready to calculate the total savings" + ] }, { "cell_type": "code", @@ -1266,11 +1299,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "And get some context for the average and total emissions over the same period" + ] }, { "cell_type": "code", @@ -1298,11 +1333,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll calculate the average percentage emissions reduction due to the MOE" + ] }, { "cell_type": "code", @@ -1325,11 +1362,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Finally we'll generate the MOE percentage time-series" + ] }, { "cell_type": "code", @@ -1374,7 +1413,9 @@ "source": [ "
\n", "\n", - "### British Carbon Savings Calculations" + "### British Model Evaluation & Carbon Savings Calculations\n", + "\n", + "We'll start by loading in the model" ] }, { @@ -1382,19 +1423,11 @@ "execution_count": 18, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py:68: RuntimeWarning: invalid value encountered in true_divide\n", - " weights = weights/weights.sum(axis=0) # We'll then normalise the weights so that for each model they sum to 1 for a single data point\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 8.07 s\n" + "Wall time: 3.42 s\n" ] }, { @@ -1623,11 +1656,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll then visualise the surface prediction as a heatmap" + ] }, { "cell_type": "code", @@ -1681,29 +1716,29 @@ "\n", "for _, spine in htmp.spines.items():\n", " spine.set_visible(True)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "\n", "ax.set_ylabel('Demand - [Solar + Wind] (GW)')" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll calculate the model metrics" + ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py:68: RuntimeWarning: invalid value encountered in true_divide\n", - " weights = weights/weights.sum(axis=0) # We'll then normalise the weights so that for each model they sum to 1 for a single data point\n", "C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\pandas\\core\\indexes\\base.py:5277: FutureWarning: Indexing a timezone-aware DatetimeIndex with a timezone-naive datetime is deprecated and will raise KeyError in a future version. Use a timezone-aware object instead.\n", " start_slice, end_slice = self.slice_locs(start, end, step=step, kind=kind)\n" ] @@ -1712,47 +1747,18 @@ "data": { "text/html": [ "
\n", - "\n", - "100%\n", - "169224/192336\n", - "[03:52<00:00, 0.00s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 192336/192336 [03:52<00:00, 0.00s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "28845/192336\n", - "[04:10<00:00, 0.00s/it]
" + "\n", + "39%\n", + "0/192336\n", + "[01:00<00:00, 0.00s/it]" ], "text/plain": [ "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 192336/192336 [04:10<00:00, 0.00s/it]" + " [███████████████████████#####################################] 75555/192336 [01:00<00:00, 0.00s/it]" ] }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'median_abs_err': 330.24369388573996,\n", - " 'mean_abs_err': 476.21722650533655,\n", - " 'root_mean_square_error': 661.7182203091455}" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -1764,45 +1770,42 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "And $r^{2}$ score" + ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-0.22263290223370658" + "0.9557674211115541" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "r2_score(df_GB.loc[s_GB_pred_ts_dispatch.index, 'emissions']*2, s_GB_pred_ts_dispatch)" + "r2_score(df_GB.loc[s_GB_pred_ts_dispatch.index, 'emissions'], s_GB_pred_ts_dispatch)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We're now ready to calculate the total savings" + ] }, { "cell_type": "code", @@ -1827,11 +1830,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "And get some context for the average and total emissions over the same period" + ] }, { "cell_type": "code", @@ -1859,11 +1864,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll calculate the average percentage emissions reduction due to the MOE" + ] }, { "cell_type": "code", @@ -1886,11 +1893,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Finally we'll generate the MOE percentage time-series" + ] }, { "cell_type": "code", @@ -1935,7 +1944,9 @@ "source": [ "
\n", "\n", - "### Plots" + "### Plots\n", + "\n", + "In this section we'll generate some of the plots needed for the paper, starting with the heatmap of the emissions surfaces" ] }, { @@ -1981,7 +1992,7 @@ "\n", "for _, spine in htmp.spines.items():\n", " spine.set_visible(True)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "\n", "ax.set_ylabel('Demand - [Solar + Wind] (GW)')\n", "\n", @@ -1997,7 +2008,7 @@ "\n", "for _, spine in htmp.spines.items():\n", " spine.set_visible(True)\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "\n", "ax.set_ylabel('Demand - [Solar + Wind] (GW)')\n", "\n", @@ -2005,11 +2016,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "We'll also plot the MOE time-series" + ] }, { "cell_type": "code", @@ -2048,7 +2061,7 @@ "ax.scatter(s_GB_MOE.index, s_GB_MOE, s=0.01, alpha=0.2, color='k', label=None)\n", "s_GB_MOE_rolling.plot(color='r', linewidth=1, ax=ax, label='28-Day Average')\n", "\n", - "hlp.hide_spines(ax)\n", + "eda.hide_spines(ax)\n", "# ax.set_ylim(0, 40)\n", "ax.set_xlim(pd.to_datetime('2010'), pd.to_datetime('2021'))\n", "ax.set_xlabel('')\n", @@ -2061,8 +2074,7 @@ "ax.scatter(s_DE_MOE.index, s_DE_MOE, s=0.05, alpha=0.2, color='k', label=None)\n", "ax.plot(s_DE_MOE_rolling.index, s_DE_MOE_rolling, color='r', linewidth=1.5, label='28-Day Average')\n", "\n", - "hlp.hide_spines(ax)\n", - "# ax.set_ylim(0, 80)\n", + "eda.hide_spines(ax)\n", "ax.set_xlim(pd.to_datetime('2010'), pd.to_datetime('2021'))\n", "ax.set_xlabel('')\n", "ax.set_ylabel('Merit Order Effect (Tonnes CO2)')\n", @@ -2075,7 +2087,9 @@ "source": [ "
\n", "\n", - "### Saving Results" + "### Saving Results\n", + "\n", + "Additionaly we'll save the time-series predictions and model metrics, starting with the GB time-series" ] }, { @@ -2184,11 +2198,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Which we'll save to csv" + ] }, { "cell_type": "code", @@ -2200,11 +2216,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "
\n", + "\n", + "Then the DE time-series" + ] }, { "cell_type": "code", @@ -2308,6 +2326,8 @@ " 'moe': s_DE_MOE\n", "})\n", "\n", + "df_DE_results_ts.to_csv('../data/results/DE_carbon.csv')\n", + "\n", "df_DE_results_ts.head()" ] }, @@ -2318,22 +2338,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "df_DE_results_ts.to_csv('../data/results/DE_carbon.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 32, diff --git a/nbs/07-pred-conf-intvls.ipynb b/nbs/07-pred-conf-intvls.ipynb deleted file mode 100644 index 7cccbb1..0000000 --- a/nbs/07-pred-conf-intvls.ipynb +++ /dev/null @@ -1,159 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Prediction & Confidence Intervals\n", - "\n", - "
\n", - "\n", - "### Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "from moepy import lowess, eda" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "df_EI = eda.load_EI_df('../data/electric_insights.csv')\n", - "df_EI_model = df_EI[['day_ahead_price', 'demand', 'solar', 'wind']].dropna()\n", - "\n", - "s_price = df_EI_model['day_ahead_price']\n", - "s_dispatchable = df_EI_model['demand'] - df_EI_model[['solar', 'wind']].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "6%\n", - "3/49\n", - "[00:08<00:03, 2.75s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [███#########################################################] 3/49 [00:08<00:03, 2.75s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0msmooth_dates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlowess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSmoothDates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m smooth_dates.fit(s_dispatchable.values, s_price.values, dt_idx=s_dispatchable.index, \n\u001b[0m\u001b[0;32m 5\u001b[0m reg_dates=reg_dates, frac=0.3, num_fits=10, threshold_value=26)\n", - "\u001b[1;32mc:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, dt_idx, reg_dates, threshold_value, threshold_units, lowess_kwargs, **fit_kwargs)\u001b[0m\n\u001b[0;32m 445\u001b[0m threshold_units=threshold_units)\n\u001b[0;32m 446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensemble_member_to_models\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_external_weighted_ensemble\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensemble_member_to_weights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlowess_kwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlowess_kwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 448\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 449\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreg_dates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreg_dates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py\u001b[0m in \u001b[0;36mfit_external_weighted_ensemble\u001b[1;34m(x, y, ensemble_member_to_weights, lowess_kwargs, **fit_kwargs)\u001b[0m\n\u001b[0;32m 422\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mensemble_member\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mensemble_weights\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtrack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensemble_member_to_weights\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 423\u001b[0m \u001b[0mensemble_member_to_models\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mensemble_member\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLowess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mlowess_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 424\u001b[1;33m \u001b[0mensemble_member_to_models\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mensemble_member\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexternal_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensemble_weights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 426\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mensemble_member_to_models\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, frac, reg_anchors, num_fits, external_weights, robust_weights, robust_iters, **reg_params)\u001b[0m\n\u001b[0;32m 258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 259\u001b[0m \u001b[0mrobust_iters\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrac\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrac\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreg_anchors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreg_anchors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_fits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_fits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexternal_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexternal_weights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrobust_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrobust_weights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrobust_iters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrobust_iters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mreg_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 261\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 262\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, frac, reg_anchors, num_fits, external_weights, robust_weights, robust_iters, **reg_params)\u001b[0m\n\u001b[0;32m 258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 259\u001b[0m \u001b[0mrobust_iters\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrac\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrac\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreg_anchors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreg_anchors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_fits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_fits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexternal_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexternal_weights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrobust_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrobust_weights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrobust_iters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrobust_iters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mreg_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 261\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 262\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, frac, reg_anchors, num_fits, external_weights, robust_weights, robust_iters, **reg_params)\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 250\u001b[0m \u001b[1;31m# Solving for the design matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 251\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcalculate_loading_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreg_anchors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreg_anchors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_fits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_fits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexternal_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexternal_weights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrobust_weights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrobust_weights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdesign_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit_regressions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloading_weights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreg_func\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreg_func\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mreg_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py\u001b[0m in \u001b[0;36mcalculate_loading_weights\u001b[1;34m(self, x, reg_anchors, num_fits, external_weights, robust_weights)\u001b[0m\n\u001b[0;32m 224\u001b[0m \u001b[1;31m# Calculating the initial loading weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[0mweighting_locs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_weighting_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreg_anchors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreg_anchors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_fits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_fits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 226\u001b[1;33m \u001b[0mloading_weights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_weights_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrac\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrac\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweighting_locs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweighting_locs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[1;31m# Applying weight adjustments\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py\u001b[0m in \u001b[0;36mget_weights_matrix\u001b[1;34m(x, frac, weighting_locs, reg_anchors, num_fits)\u001b[0m\n\u001b[0;32m 119\u001b[0m \u001b[0mdist_matrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_dist_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreg_anchors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreg_anchors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_fits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum_fits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m \u001b[0mdist_thresholds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_dist_thresholds\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrac_idx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdist_matrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 122\u001b[0m \u001b[0mweights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdist_2_weights_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdist_matrix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdist_thresholds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 123\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py\u001b[0m in \u001b[0;36m\u001b[1;34m(x, frac_idx, dist_matrix)\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;31m# Cell\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m \u001b[0mget_dist_thresholds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrac_idx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdist_matrix\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdist_matrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrac_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[1;31m# Cell\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36msort\u001b[1;34m(*args, **kwargs)\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\MOE\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36msort\u001b[1;34m(a, axis, kind, order)\u001b[0m\n\u001b[0;32m 989\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 990\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"K\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 991\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 992\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 993\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "reg_dates = pd.date_range('2009-01-01', '2021-01-01', freq='13W')\n", - "\n", - "smooth_dates = lowess.SmoothDates()\n", - "smooth_dates.fit(s_dispatchable.values, s_price.values, dt_idx=s_dispatchable.index, \n", - " reg_dates=reg_dates, frac=0.3, num_fits=10, threshold_value=26)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "MOE", - "language": "python", - "name": "moe" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/nbs/07-prediction-confidence-and-intervals.ipynb b/nbs/07-prediction-confidence-and-intervals.ipynb new file mode 100644 index 0000000..eb86f0a --- /dev/null +++ b/nbs/07-prediction-confidence-and-intervals.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prediction & Confidence Intervals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook outlines the calculation of the prediction and confidence intervals for the GB and DE price MOE models\n", + "\n", + "
\n", + "\n", + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import pickle\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from moepy import lowess, eda\n", + "from moepy.surface import PicklableFunction\n", + "\n", + "from ipypb import track" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### Great Britain\n", + "\n", + "We'll start by loading and cleaning the data for GB" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "df_EI = eda.load_EI_df('../data/raw/electric_insights.csv')\n", + "df_EI_model = df_EI[['day_ahead_price', 'demand', 'solar', 'wind']].dropna()\n", + "\n", + "s_price = df_EI_model['day_ahead_price']\n", + "s_dispatchable = df_EI_model['demand'] - df_EI_model[['solar', 'wind']].sum(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll then calculate the estimate for the 68% prediction interval" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def get_pred_intvl(low_q_fp, high_q_fp):\n", + " \"\"\"Calculates the prediction interval between the low and high quantile models specified\"\"\"\n", + " smooth_dates_low = pickle.load(open(low_q_fp, 'rb'))\n", + " smooth_dates_high = pickle.load(open(high_q_fp, 'rb'))\n", + "\n", + " x_pred = np.linspace(3, 61, 581)\n", + " dt_pred = pd.date_range('2009-01-01', '2020-12-31', freq='1D')\n", + "\n", + " df_pred_low = smooth_dates_low.predict(x_pred=x_pred, dt_pred=dt_pred)\n", + " df_pred_low.index = np.round(df_pred_low.index, 1)\n", + "\n", + " df_pred_high = smooth_dates_high.predict(x_pred=x_pred, dt_pred=dt_pred)\n", + " df_pred_high.index = np.round(df_pred_high.index, 1)\n", + "\n", + " df_pred_intvl = df_pred_high - df_pred_low\n", + " \n", + " return df_pred_intvl" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 11.4 s\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
2009-01-012009-01-022009-01-032009-01-042009-01-052009-01-062009-01-072009-01-082009-01-092009-01-10...2020-12-222020-12-232020-12-242020-12-252020-12-262020-12-272020-12-282020-12-292020-12-302020-12-31
3.0-4.778777-4.801472-4.823926-4.846139-4.868108-4.889820-4.911257-4.932405-4.953249-4.973776...41.47779641.48407341.49036541.49667341.50299541.50933041.51567741.52203641.52840541.534784
3.1-4.737781-4.760513-4.783006-4.805258-4.827267-4.849019-4.870497-4.891687-4.912574-4.933144...41.30440941.31067441.31695641.32325341.32956441.33588841.34222541.34857341.35493141.361298
3.2-4.696562-4.719330-4.741860-4.764150-4.786198-4.807989-4.829508-4.850738-4.871666-4.892278...41.13121141.13746641.14373741.15002341.15632441.16263741.16896341.17530041.18164741.188003
3.3-4.655069-4.677873-4.700438-4.722765-4.744850-4.766679-4.788237-4.809507-4.830475-4.851128...40.95824440.96448840.97074940.97702440.98331440.98961640.99593141.00225741.00859441.014939
3.4-4.613256-4.636093-4.658693-4.681055-4.703175-4.725041-4.746636-4.767944-4.788951-4.809643...40.78554540.79177940.79802940.80429440.81057340.81686540.82316940.82948440.83581040.842145
\n", + "

5 rows × 4383 columns

\n", + "
" + ], + "text/plain": [ + " 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 \\\n", + "3.0 -4.778777 -4.801472 -4.823926 -4.846139 -4.868108 -4.889820 \n", + "3.1 -4.737781 -4.760513 -4.783006 -4.805258 -4.827267 -4.849019 \n", + "3.2 -4.696562 -4.719330 -4.741860 -4.764150 -4.786198 -4.807989 \n", + "3.3 -4.655069 -4.677873 -4.700438 -4.722765 -4.744850 -4.766679 \n", + "3.4 -4.613256 -4.636093 -4.658693 -4.681055 -4.703175 -4.725041 \n", + "\n", + " 2009-01-07 2009-01-08 2009-01-09 2009-01-10 ... 2020-12-22 \\\n", + "3.0 -4.911257 -4.932405 -4.953249 -4.973776 ... 41.477796 \n", + "3.1 -4.870497 -4.891687 -4.912574 -4.933144 ... 41.304409 \n", + "3.2 -4.829508 -4.850738 -4.871666 -4.892278 ... 41.131211 \n", + "3.3 -4.788237 -4.809507 -4.830475 -4.851128 ... 40.958244 \n", + "3.4 -4.746636 -4.767944 -4.788951 -4.809643 ... 40.785545 \n", + "\n", + " 2020-12-23 2020-12-24 2020-12-25 2020-12-26 2020-12-27 2020-12-28 \\\n", + "3.0 41.484073 41.490365 41.496673 41.502995 41.509330 41.515677 \n", + "3.1 41.310674 41.316956 41.323253 41.329564 41.335888 41.342225 \n", + "3.2 41.137466 41.143737 41.150023 41.156324 41.162637 41.168963 \n", + "3.3 40.964488 40.970749 40.977024 40.983314 40.989616 40.995931 \n", + "3.4 40.791779 40.798029 40.804294 40.810573 40.816865 40.823169 \n", + "\n", + " 2020-12-29 2020-12-30 2020-12-31 \n", + "3.0 41.522036 41.528405 41.534784 \n", + "3.1 41.348573 41.354931 41.361298 \n", + "3.2 41.175300 41.181647 41.188003 \n", + "3.3 41.002257 41.008594 41.014939 \n", + "3.4 40.829484 40.835810 40.842145 \n", + "\n", + "[5 rows x 4383 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "\n", + "df_pred_68pct_intvl_GB = get_pred_intvl('../data/models/DAM_price_GB_p16.pkl', '../data/models/DAM_price_GB_p84.pkl')\n", + "\n", + "df_pred_68pct_intvl_GB.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We can see that we get some quantile crossing at the extreme ends of the dispatch curve which is why some of our 68% interval values are negative, to counter this we'll weight our prediction interval by how often that part of the dispatch curve is where the price clears at." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 68% prediction interval for GB is 16.32 £/MWh\n" + ] + } + ], + "source": [ + "s_pred_idx_weight = s_dispatchable.round(1).value_counts().sort_index()\n", + "dispatchable_gen_idxs = sorted(list(set(s_pred_idx_weight.index).intersection(df_pred_68pct_intvl_GB.index)))\n", + "\n", + "pred_68pct_intvl = np.average(df_pred_68pct_intvl_GB.mean(axis=1).loc[dispatchable_gen_idxs], weights=s_pred_idx_weight.loc[dispatchable_gen_idxs])\n", + "\n", + "print(f'The 68% prediction interval for GB is {round(pred_68pct_intvl, 2)} £/MWh')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll use our bootstrapping helper function to calculate the confidence interval of the GB model" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "center_dts = pd.date_range(s_price.index.min(), s_price.index.max(), freq='3MS') + pd.Timedelta(days=45)\n", + "\n", + "all_conf_intvl_95pct = []\n", + "\n", + "for center_dt in track(center_dts):\n", + " s_price_subset = s_price[center_dt-pd.Timedelta(days=45):center_dt+pd.Timedelta(days=45)]\n", + " s_dispatchable_subset = s_dispatchable[center_dt-pd.Timedelta(days=45):center_dt+pd.Timedelta(days=45)]\n", + "\n", + " df_bootstrap = lowess.bootstrap_model(s_price_subset.values, s_dispatchable_subset.values, num_runs=100, frac=0.3, num_fits=10)\n", + " conf_intvl_95pct = df_bootstrap.replace(0, np.nan).quantile([0.025, 0.975], axis=1).diff().dropna(how='all').mean(axis=1).iloc[0]\n", + " \n", + " all_conf_intvl_95pct += [conf_intvl_95pct]\n", + " \n", + "conf_intvl_95pct_GB = np.array(all_conf_intvl_95pct).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 95% confidence interval for GB is 1.03 £/MWh\n" + ] + } + ], + "source": [ + "print(f'The 95% confidence interval for GB is {round(conf_intvl_95pct_GB, 2)} £/MWh')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### Germany\n", + "\n", + "We'll start by loading and cleaning the data for DE" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 1.72 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "df_DE = eda.load_DE_df('../data/raw/energy_charts.csv', '../data/raw/ENTSOE_DE_price.csv')\n", + "\n", + "df_DE_model = df_DE[['price', 'demand', 'Solar', 'Wind']].dropna()\n", + "\n", + "s_DE_price = df_DE_model['price']\n", + "s_DE_demand = df_DE_model['demand']\n", + "s_DE_dispatchable = df_DE_model['demand'] - df_DE_model[['Solar', 'Wind']].sum(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll then calculate the estimate for the 68% prediction interval" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 68% prediction interval for DE is 13.79 EUR/MWh\n", + "Wall time: 1.5 s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "df_pred_68pct_intvl_DE = get_pred_intvl('../data/models/DAM_price_DE_p16.pkl', '../data/models/DAM_price_DE_p84.pkl')\n", + "\n", + "s_pred_idx_weight = s_DE_dispatchable.round(1).value_counts().sort_index()\n", + "dispatchable_gen_idxs = sorted(list(set(s_pred_idx_weight.index).intersection(df_pred_68pct_intvl_DE.index)))\n", + "\n", + "pred_68pct_intvl = np.average(df_pred_68pct_intvl_DE.mean(axis=1).loc[dispatchable_gen_idxs], weights=s_pred_idx_weight.loc[dispatchable_gen_idxs])\n", + "\n", + "print(f'The 68% prediction interval for DE is {round(pred_68pct_intvl, 2)} EUR/MWh')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll use our bootstrapping helper function to calculate the confidence interval of the GB model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "\n", + "center_dts = pd.date_range(s_DE_price.index.min(), s_DE_price.index.max(), freq='3MS') + pd.Timedelta(days=45)\n", + "\n", + "all_conf_intvl_95pct = []\n", + "\n", + "for center_dt in track(center_dts):\n", + " s_price_subset = s_DE_price[center_dt-pd.Timedelta(days=45):center_dt+pd.Timedelta(days=45)]\n", + " s_dispatchable_subset = s_DE_dispatchable[center_dt-pd.Timedelta(days=45):center_dt+pd.Timedelta(days=45)]\n", + "\n", + " df_bootstrap = lowess.bootstrap_model(s_price_subset.values, s_dispatchable_subset.values, num_runs=100, frac=0.3, num_fits=10)\n", + " conf_intvl_95pct = df_bootstrap.replace(0, np.nan).quantile([0.025, 0.975], axis=1).diff().dropna(how='all').mean(axis=1).iloc[0]\n", + " \n", + " all_conf_intvl_95pct += [conf_intvl_95pct]\n", + " \n", + "conf_intvl_95pct_DE = np.array(all_conf_intvl_95pct).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 95% confidence interval for DE is 1.69 EUR/MWh\n" + ] + } + ], + "source": [ + "print(f'The 95% confidence interval for DE is {round(conf_intvl_95pct_DE, 2)} EUR/MWh')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "MOE", + "language": "python", + "name": "moe" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs/08-hyper-param-tuning.ipynb b/nbs/08-hyper-param-tuning.ipynb deleted file mode 100644 index 45c6b56..0000000 --- a/nbs/08-hyper-param-tuning.ipynb +++ /dev/null @@ -1,444 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hyper-Parameter Tuning\n", - "\n", - "
\n", - "\n", - "### Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from sklearn.metrics import mean_absolute_error, make_scorer\n", - "from sklearn.model_selection import train_test_split\n", - "from skopt.plots import plot_objective\n", - "from skopt.space import Real, Integer\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from moepy import lowess, eda" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "start_date = '2018'\n", - "end_date = '2019'\n", - " \n", - "df_EI = eda.load_EI_df('../data/electric_insights.csv')\n", - "df_EI_model = df_EI[start_date:end_date][['day_ahead_price', 'demand', 'solar', 'wind']].dropna()\n", - "\n", - "s_price = df_EI_model['day_ahead_price']\n", - "s_dispatchable = df_EI_model['demand'] - df_EI_model[['solar', 'wind']].sum(axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "100%\n", - "4/4\n", - "[00:02<00:00, 0.38s/it]
" - ], - "text/plain": [ - "\u001b[A\u001b[2K\r", - " [████████████████████████████████████████████████████████████] 4/4 [00:02<00:00, 0.38s/it]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\ayrto\\desktop\\phd\\analysis\\merit-order-effect\\moepy\\lowess.py:239: RuntimeWarning: invalid value encountered in true_divide\n", - " loading_weights = loading_weights/loading_weights.sum(axis=0) # normalising\n" - ] - } - ], - "source": [ - "x = s_dispatchable\n", - "y = s_price\n", - "dt_idx=s_dispatchable.index\n", - "reg_dates = pd.date_range(f'{start_date}-01-01', f'{end_date}-01-01', freq='13W')\n", - "\n", - "smooth_dates = lowess.SmoothDates(frac=0.3, threshold_value=26)\n", - "\n", - "smooth_dates.fit(x, y, dt_idx=dt_idx, num_fits=10, reg_dates=reg_dates)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "### Monkey Patching `skopt`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from joblib import Parallel, delayed\n", - "from scipy.stats import rankdata\n", - "from skopt import BayesSearchCV\n", - "\n", - "import os\n", - "import codecs\n", - "from ipypb import track\n", - "from warnings import warn\n", - "from functools import partial\n", - "from distutils.dir_util import copy_tree\n", - "from collections.abc import Iterable, Sized\n", - "from collections import defaultdict\n", - "\n", - "import sklearn \n", - "from sklearn import linear_model\n", - "from sklearn.metrics import r2_score\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "from sklearn.base import is_classifier, clone\n", - "from sklearn.utils.validation import indexable\n", - "\n", - "try:\n", - " from sklearn.metrics import check_scoring\n", - "except ImportError:\n", - " from sklearn.metrics.scorer import check_scoring" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def bayes_search_CV_init(self, estimator, search_spaces, optimizer_kwargs=None,\n", - " n_iter=50, scoring=None, fit_params=None, n_jobs=1,\n", - " n_points=1, iid=True, refit=True, cv=None, verbose=0,\n", - " pre_dispatch='2*n_jobs', random_state=None,\n", - " error_score='raise', return_train_score=False):\n", - "\n", - " self.search_spaces = search_spaces\n", - " self.n_iter = n_iter\n", - " self.n_points = n_points\n", - " self.random_state = random_state\n", - " self.optimizer_kwargs = optimizer_kwargs\n", - " self._check_search_space(self.search_spaces)\n", - " self.fit_params = fit_params\n", - " self.iid = None\n", - "\n", - " super(BayesSearchCV, self).__init__(\n", - " estimator=estimator, scoring=scoring,\n", - " n_jobs=n_jobs, refit=refit, cv=cv, verbose=verbose,\n", - " pre_dispatch=pre_dispatch, error_score=error_score,\n", - " return_train_score=return_train_score)\n", - "\n", - "BayesSearchCV.__init__ = bayes_search_CV_init" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def bayes_search_CV__fit(self, X, y, groups, parameter_iterable):\n", - " \"\"\"\n", - " Actual fitting, performing the search over parameters.\n", - " Taken from https://github.com/scikit-learn/scikit-learn/blob/0.18.X\n", - " .../sklearn/model_selection/_search.py\n", - " \"\"\"\n", - " estimator = self.estimator\n", - " cv = sklearn.model_selection._validation.check_cv(\n", - " self.cv, y, classifier=is_classifier(estimator))\n", - " self.scorer_ = check_scoring(\n", - " self.estimator, scoring=self.scoring)\n", - "\n", - " X, y, groups = indexable(X, y, groups)\n", - " n_splits = cv.get_n_splits(X, y, groups)\n", - " if self.verbose > 0 and isinstance(parameter_iterable, Sized):\n", - " n_candidates = len(parameter_iterable)\n", - " print(\"Fitting {0} folds for each of {1} candidates, totalling\"\n", - " \" {2} fits\".format(n_splits, n_candidates,\n", - " n_candidates * n_splits))\n", - "\n", - " base_estimator = clone(self.estimator)\n", - " pre_dispatch = self.pre_dispatch\n", - "\n", - " cv_iter = list(cv.split(X, y, groups))\n", - " out = Parallel(\n", - " n_jobs=self.n_jobs, verbose=self.verbose,\n", - " pre_dispatch=pre_dispatch\n", - " )(delayed(sklearn.model_selection._validation._fit_and_score)(\n", - " clone(base_estimator),\n", - " X, y, self.scorer_,\n", - " train, test, self.verbose, parameters,\n", - " fit_params=self.fit_params,\n", - " return_train_score=self.return_train_score,\n", - " return_n_test_samples=True,\n", - " return_times=True, return_parameters=True,\n", - " error_score=self.error_score\n", - " )\n", - " for parameters in parameter_iterable\n", - " for train, test in cv_iter)\n", - "\n", - " # if one choose to see train score, \"out\" will contain train score info\n", - " if self.return_train_score:\n", - " (train_scores, test_scores, n_test_samples,\n", - " fit_time, score_time, parameters) = zip(*out)\n", - " else:\n", - " from warnings import warn\n", - " (fit_failed, test_scores, n_test_samples,\n", - " fit_time, score_time, parameters) = zip(*[a.values() for a in out])\n", - "\n", - " candidate_params = parameters[::n_splits]\n", - " n_candidates = len(candidate_params)\n", - "\n", - " results = dict()\n", - "\n", - " def _store(key_name, array, weights=None, splits=False, rank=False):\n", - " \"\"\"A small helper to store the scores/times to the cv_results_\"\"\"\n", - " array = np.array(array, dtype=np.float64).reshape(n_candidates,\n", - " n_splits)\n", - " if splits:\n", - " for split_i in range(n_splits):\n", - " results[\"split%d_%s\"\n", - " % (split_i, key_name)] = array[:, split_i]\n", - "\n", - " array_means = np.average(array, axis=1, weights=weights)\n", - " results['mean_%s' % key_name] = array_means\n", - " # Weighted std is not directly available in numpy\n", - " array_stds = np.sqrt(np.average((array -\n", - " array_means[:, np.newaxis]) ** 2,\n", - " axis=1, weights=weights))\n", - " results['std_%s' % key_name] = array_stds\n", - "\n", - " if rank:\n", - " results[\"rank_%s\" % key_name] = np.asarray(\n", - " rankdata(-array_means, method='min'), dtype=np.int32)\n", - "\n", - " # Computed the (weighted) mean and std for test scores alone\n", - " # NOTE test_sample counts (weights) remain the same for all candidates n_test_samples\n", - " n_test_samples = np.array(n_test_samples[:n_splits],\n", - " dtype=np.int)\n", - "\n", - " _store('test_score', test_scores, splits=True, rank=True,\n", - " weights=n_test_samples if self.iid else None)\n", - " if self.return_train_score:\n", - " _store('train_score', train_scores, splits=True)\n", - " _store('fit_time', fit_time)\n", - " _store('score_time', score_time)\n", - "\n", - " best_index = np.flatnonzero(results[\"rank_test_score\"] == 1)[0]\n", - " best_parameters = candidate_params[best_index]\n", - "\n", - " # Use one MaskedArray and mask all the places where the param is not\n", - " # applicable for that candidate. Use defaultdict as each candidate may\n", - " # not contain all the params\n", - " param_results = defaultdict(partial(np.ma.array,\n", - " np.empty(n_candidates,),\n", - " mask=True,\n", - " dtype=object))\n", - " for cand_i, params in enumerate(candidate_params):\n", - " for name, value in params.items():\n", - " # An all masked empty array gets created for the key\n", - " # `\"param_%s\" % name` at the first occurence of `name`.\n", - " # Setting the value at an index also unmasks that index\n", - " param_results[\"param_%s\" % name][cand_i] = value\n", - "\n", - " results.update(param_results)\n", - "\n", - " # Store a list of param dicts at est_sample_counts = np.array(n_test_samples[:n_splits], key 'params'\n", - " results['params'] = candidate_params\n", - "\n", - " self.cv_results_ = results\n", - " self.best_index_ = best_index\n", - " self.n_splits_ = n_splits\n", - "\n", - " if self.refit:\n", - " # fit the best estimator using the entire dataset\n", - " # clone first to work around broken estimators\n", - " best_estimator = clone(base_estimator).set_params(\n", - " **best_parameters)\n", - " if y is not None:\n", - " best_estimator.fit(X, y, **self.fit_params)\n", - " else:\n", - " best_estimator.fit(X, **self.fit_params)\n", - " self.best_estimator_ = best_estimator\n", - " return self\n", - "\n", - "BayesSearchCV._fit = bayes_search_CV__fit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "### Optimisation" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 8 folds for each of 1 candidates, totalling 8 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 2 out of 8 | elapsed: 10.6s remaining: 32.1s\n" - ] - }, - { - "ename": "ValueError", - "evalue": "y_true and y_pred have different number of output (1!=4)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31m_RemoteTraceback\u001b[0m Traceback (most recent call last)", - "\u001b[1;31m_RemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py\", line 431, in _process_worker\n r = call_item()\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py\", line 285, in __call__\n return self.fn(*self.args, **self.kwargs)\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\joblib\\_parallel_backends.py\", line 595, in __call__\n return self.func(*args, **kwargs)\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\joblib\\parallel.py\", line 262, in __call__\n return [func(*args, **kwargs)\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\joblib\\parallel.py\", line 262, in \n return [func(*args, **kwargs)\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 620, in _fit_and_score\n test_scores = _score(estimator, X_test, y_test, scorer, error_score)\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 674, in _score\n scores = scorer(estimator, X_test, y_test)\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\sklearn\\metrics\\_scorer.py\", line 397, in _passthrough_scorer\n return estimator.score(*args, **kwargs)\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\sklearn\\base.py\", line 554, in score\n return r2_score(y, y_pred, sample_weight=sample_weight)\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\sklearn\\utils\\validation.py\", line 63, in inner_f\n return f(*args, **kwargs)\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\sklearn\\metrics\\_regression.py\", line 676, in r2_score\n y_type, y_true, y_pred, multioutput = _check_reg_targets(\n File \"C:\\Users\\Ayrto\\anaconda3\\envs\\MOE\\lib\\site-packages\\sklearn\\metrics\\_regression.py\", line 99, in _check_reg_targets\n raise ValueError(\"y_true and y_pred have different number of output \"\nValueError: y_true and y_pred have different number of output (1!=4)\n\"\"\"", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfit_BayesSearchCV\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 27\u001b[1;33m \u001b[0mopt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'Cross-validation score: {opt.best_score_:.2f}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\MOE\\lib\\site-packages\\skopt\\searchcv.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, callback)\u001b[0m\n\u001b[0;32m 694\u001b[0m \u001b[0mn_points_adjusted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_points\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 695\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 696\u001b[1;33m optim_result = self._step(\n\u001b[0m\u001b[0;32m 697\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msearch_space\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 698\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgroups\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_points\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_points_adjusted\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\MOE\\lib\\site-packages\\skopt\\searchcv.py\u001b[0m in \u001b[0;36m_step\u001b[1;34m(self, X, y, search_space, optimizer, groups, n_points)\u001b[0m\n\u001b[0;32m 581\u001b[0m \u001b[0mrefit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrefit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 582\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrefit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 583\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 584\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrefit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrefit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 585\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m\u001b[0m in \u001b[0;36mbayes_search_CV__fit\u001b[1;34m(self, X, y, groups, parameter_iterable)\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[0mcv_iter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m out = Parallel(\n\u001b[0m\u001b[0;32m 26\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[0mpre_dispatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpre_dispatch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\MOE\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 1052\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1053\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1054\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1055\u001b[0m \u001b[1;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1056\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\MOE\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 931\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 932\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'supports_timeout'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 933\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 934\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 935\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\MOE\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[1;34m(future, timeout)\u001b[0m\n\u001b[0;32m 540\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[0;32m 541\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 542\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 543\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mCfTimeoutError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 544\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\MOE\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 439\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 440\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 441\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 442\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\anaconda3\\envs\\MOE\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 387\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__get_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 388\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 389\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 390\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 391\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: y_true and y_pred have different number of output (1!=4)" - ] - } - ], - "source": [ - "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)\n", - "\n", - "search_spaces = {\n", - " 'frac': Real(0.3, 1, 'uniform'),\n", - " 'threshold_value': Integer(1, 52, 'uniform')\n", - "}\n", - "\n", - "fit_params = {\n", - " 'reg_dates': reg_dates, \n", - " 'num_fits': 10\n", - "}\n", - "\n", - "opt = BayesSearchCV(\n", - " smooth_dates,\n", - " search_spaces,\n", - " n_iter=2,\n", - " verbose=1,\n", - " cv=8, # 8 works well for me as that's how many concurrent workers I can use\n", - " #scoring=make_scorer(mean_absolute_error, greater_is_better=False),\n", - " fit_params=fit_params,\n", - " n_jobs=-1\n", - ")\n", - "\n", - "fit_BayesSearchCV = True\n", - "\n", - "if fit_BayesSearchCV == True:\n", - " opt.fit(x, y)\n", - "\n", - " print(f'Cross-validation score: {opt.best_score_:.2f}')\n", - " #print(f'Hold-out score: {opt.score(x_test, y_test):.2f}')\n", - " print(f'\\nBest params: \\n{opt.best_params_}')" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "MOE", - "language": "python", - "name": "moe" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/nbs/08-hyper-parameter-tuning.ipynb b/nbs/08-hyper-parameter-tuning.ipynb new file mode 100644 index 0000000..08c54e9 --- /dev/null +++ b/nbs/08-hyper-parameter-tuning.ipynb @@ -0,0 +1,467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hyper-Parameter Tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook outlines the hyper-parameter optimisation procedure used to tune the models\n", + "\n", + "
\n", + "\n", + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from sklearn.metrics import mean_absolute_error, make_scorer\n", + "from sklearn.model_selection import train_test_split\n", + "from skopt.plots import plot_objective\n", + "from skopt.space import Real, Integer\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from moepy import lowess, eda" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### Data Loading\n", + "\n", + "We'll start with the GB data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "local_datetime\n", + "2009-01-01 00:00:00+00:00 38.181\n", + "2009-01-01 00:30:00+00:00 38.304\n", + "2009-01-01 01:00:00+00:00 37.839\n", + "2009-01-01 01:30:00+00:00 36.716\n", + "2009-01-01 02:00:00+00:00 36.020\n", + "dtype: float64" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_EI = eda.load_EI_df('../data/raw/electric_insights.csv')\n", + "df_EI_model = df_EI[['day_ahead_price', 'demand', 'solar', 'wind']].dropna()\n", + "\n", + "s_price = df_EI_model['day_ahead_price']\n", + "s_dispatchable = df_EI_model['demand'] - df_EI_model[['solar', 'wind']].sum(axis=1)\n", + "\n", + "s_dispatchable.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "then also load in the DE data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df_DE = eda.load_DE_df('../data/raw/energy_charts.csv', '../data/raw/ENTSOE_DE_price.csv')\n", + "\n", + "df_DE_model = df_DE[['price', 'demand', 'Solar', 'Wind']].dropna()\n", + "\n", + "s_DE_demand = df_DE_model['demand']\n", + "s_DE_price = df_DE_model['price']\n", + "s_DE_dispatchable = df_DE_model['demand'] - df_DE_model[['Solar', 'Wind']].sum(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### Monkey Patching `skopt`\n", + "\n", + "Due to some changes in the latest release of `scikit-learn` several classes and functions in `skopt` were broken at the time this research was carried out. This section provides code for monkey-patching `skopt` to ensure that it continues working.\n", + "\n", + "We'll start by loading in the relevant imports" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from joblib import Parallel, delayed\n", + "from scipy.stats import rankdata\n", + "from skopt import BayesSearchCV\n", + "\n", + "import os\n", + "import codecs\n", + "from ipypb import track\n", + "from warnings import warn\n", + "from functools import partial\n", + "from distutils.dir_util import copy_tree\n", + "from collections.abc import Iterable, Sized\n", + "from collections import defaultdict\n", + "\n", + "import sklearn \n", + "from sklearn import linear_model\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.base import is_classifier, clone\n", + "from sklearn.utils.validation import indexable\n", + "\n", + "try:\n", + " from sklearn.metrics import check_scoring\n", + "except ImportError:\n", + " from sklearn.metrics.scorer import check_scoring" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll re-define the `bayes_search_CV_init` function" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def bayes_search_CV_init(self, estimator, search_spaces, optimizer_kwargs=None,\n", + " n_iter=50, scoring=None, fit_params=None, n_jobs=1,\n", + " n_points=1, iid=True, refit=True, cv=None, verbose=0,\n", + " pre_dispatch='2*n_jobs', random_state=None,\n", + " error_score='raise', return_train_score=False):\n", + "\n", + " self.search_spaces = search_spaces\n", + " self.n_iter = n_iter\n", + " self.n_points = n_points\n", + " self.random_state = random_state\n", + " self.optimizer_kwargs = optimizer_kwargs\n", + " self._check_search_space(self.search_spaces)\n", + " self.fit_params = fit_params\n", + " self.iid = None\n", + "\n", + " super(BayesSearchCV, self).__init__(\n", + " estimator=estimator, scoring=scoring,\n", + " n_jobs=n_jobs, refit=refit, cv=cv, verbose=verbose,\n", + " pre_dispatch=pre_dispatch, error_score=error_score,\n", + " return_train_score=return_train_score)\n", + "\n", + "BayesSearchCV.__init__ = bayes_search_CV_init" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "As well as the `bayes_search_CV__fit` function" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def bayes_search_CV__fit(self, X, y, groups, parameter_iterable):\n", + " \"\"\"\n", + " Actual fitting, performing the search over parameters.\n", + " Taken from https://github.com/scikit-learn/scikit-learn/blob/0.18.X\n", + " .../sklearn/model_selection/_search.py\n", + " \"\"\"\n", + " estimator = self.estimator\n", + " cv = sklearn.model_selection._validation.check_cv(\n", + " self.cv, y, classifier=is_classifier(estimator))\n", + " self.scorer_ = check_scoring(\n", + " self.estimator, scoring=self.scoring)\n", + "\n", + " X, y, groups = indexable(X, y, groups)\n", + " n_splits = cv.get_n_splits(X, y, groups)\n", + " if self.verbose > 0 and isinstance(parameter_iterable, Sized):\n", + " n_candidates = len(parameter_iterable)\n", + " print(\"Fitting {0} folds for each of {1} candidates, totalling\"\n", + " \" {2} fits\".format(n_splits, n_candidates,\n", + " n_candidates * n_splits))\n", + "\n", + " base_estimator = clone(self.estimator)\n", + " pre_dispatch = self.pre_dispatch\n", + "\n", + " cv_iter = list(cv.split(X, y, groups))\n", + " out = Parallel(\n", + " n_jobs=self.n_jobs, verbose=self.verbose,\n", + " pre_dispatch=pre_dispatch\n", + " )(delayed(sklearn.model_selection._validation._fit_and_score)(\n", + " clone(base_estimator),\n", + " X, y, self.scorer_,\n", + " train, test, self.verbose, parameters,\n", + " fit_params=self.fit_params,\n", + " return_train_score=self.return_train_score,\n", + " return_n_test_samples=True,\n", + " return_times=True, return_parameters=True,\n", + " error_score=self.error_score\n", + " )\n", + " for parameters in parameter_iterable\n", + " for train, test in cv_iter)\n", + "\n", + " # if one choose to see train score, \"out\" will contain train score info\n", + " if self.return_train_score:\n", + " (train_scores, test_scores, n_test_samples,\n", + " fit_time, score_time, parameters) = zip(*out)\n", + " else:\n", + " from warnings import warn\n", + " (fit_failed, test_scores, n_test_samples,\n", + " fit_time, score_time, parameters) = zip(*[a.values() for a in out])\n", + "\n", + " candidate_params = parameters[::n_splits]\n", + " n_candidates = len(candidate_params)\n", + "\n", + " results = dict()\n", + "\n", + " def _store(key_name, array, weights=None, splits=False, rank=False):\n", + " \"\"\"A small helper to store the scores/times to the cv_results_\"\"\"\n", + " array = np.array(array, dtype=np.float64).reshape(n_candidates,\n", + " n_splits)\n", + " if splits:\n", + " for split_i in range(n_splits):\n", + " results[\"split%d_%s\"\n", + " % (split_i, key_name)] = array[:, split_i]\n", + "\n", + " array_means = np.average(array, axis=1, weights=weights)\n", + " results['mean_%s' % key_name] = array_means\n", + " # Weighted std is not directly available in numpy\n", + " array_stds = np.sqrt(np.average((array -\n", + " array_means[:, np.newaxis]) ** 2,\n", + " axis=1, weights=weights))\n", + " results['std_%s' % key_name] = array_stds\n", + "\n", + " if rank:\n", + " results[\"rank_%s\" % key_name] = np.asarray(\n", + " rankdata(-array_means, method='min'), dtype=np.int32)\n", + "\n", + " # Computed the (weighted) mean and std for test scores alone\n", + " # NOTE test_sample counts (weights) remain the same for all candidates n_test_samples\n", + " n_test_samples = np.array(n_test_samples[:n_splits],\n", + " dtype=np.int)\n", + "\n", + " _store('test_score', test_scores, splits=True, rank=True,\n", + " weights=n_test_samples if self.iid else None)\n", + " if self.return_train_score:\n", + " _store('train_score', train_scores, splits=True)\n", + " _store('fit_time', fit_time)\n", + " _store('score_time', score_time)\n", + "\n", + " best_index = np.flatnonzero(results[\"rank_test_score\"] == 1)[0]\n", + " best_parameters = candidate_params[best_index]\n", + "\n", + " # Use one MaskedArray and mask all the places where the param is not\n", + " # applicable for that candidate. Use defaultdict as each candidate may\n", + " # not contain all the params\n", + " param_results = defaultdict(partial(np.ma.array,\n", + " np.empty(n_candidates,),\n", + " mask=True,\n", + " dtype=object))\n", + " for cand_i, params in enumerate(candidate_params):\n", + " for name, value in params.items():\n", + " # An all masked empty array gets created for the key\n", + " # `\"param_%s\" % name` at the first occurence of `name`.\n", + " # Setting the value at an index also unmasks that index\n", + " param_results[\"param_%s\" % name][cand_i] = value\n", + "\n", + " results.update(param_results)\n", + "\n", + " # Store a list of param dicts at est_sample_counts = np.array(n_test_samples[:n_splits], key 'params'\n", + " results['params'] = candidate_params\n", + "\n", + " self.cv_results_ = results\n", + " self.best_index_ = best_index\n", + " self.n_splits_ = n_splits\n", + "\n", + " if self.refit:\n", + " # fit the best estimator using the entire dataset\n", + " # clone first to work around broken estimators\n", + " best_estimator = clone(base_estimator).set_params(\n", + " **best_parameters)\n", + " if y is not None:\n", + " best_estimator.fit(X, y, **self.fit_params)\n", + " else:\n", + " best_estimator.fit(X, **self.fit_params)\n", + " self.best_estimator_ = best_estimator\n", + " return self\n", + "\n", + "BayesSearchCV._fit = bayes_search_CV__fit" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### Optimisation\n", + "\n", + "We're now ready to carry out our model optimisation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 4 folds for each of 1 candidates, totalling 4 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=5)]: Using backend LokyBackend with 5 concurrent workers.\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "start_date = '2017-01-01'\n", + "end_date = '2019-01-01'\n", + " \n", + "x = s_DE_dispatchable[start_date:end_date]\n", + "y = s_DE_price[start_date:end_date]\n", + "pred_reg_dates = pd.date_range(start_date, end_date, freq='D')\n", + "\n", + "lowess_dates = lowess.LowessDates(frac=0.5, threshold_value=26, pred_reg_dates=pred_reg_dates)\n", + "\n", + "search_spaces = {\n", + " 'frac': Real(0.35, 1, 'uniform'),\n", + " 'threshold_value': Integer(10, 52, 'uniform')\n", + "}\n", + "\n", + "fit_params = {\n", + " 'reg_dates': pd.date_range(start_date, end_date, freq='7W'),\n", + " 'num_fits': 10,\n", + " 'reg_anchors': np.round(np.arange(np.floor(x.min())-5, np.ceil(x.max())+5, 0.1), 1)\n", + "}\n", + "\n", + "opt = BayesSearchCV(\n", + " lowess_dates,\n", + " search_spaces,\n", + " optimizer_kwargs={\n", + " 'random_state': 42\n", + " },\n", + " n_iter=15,\n", + " verbose=1,\n", + " cv=4, # 8 works well for me as that's how many concurrent workers I can use\n", + " fit_params=fit_params,\n", + " n_jobs=5 # -1\n", + ")\n", + "\n", + "fit_BayesSearchCV = True\n", + "\n", + "if fit_BayesSearchCV == True:\n", + " opt.fit(x.round(1), y)\n", + "\n", + " print(f'Cross-validation score: {opt.best_score_:.2f}')\n", + " print(f'\\nBest params: \\n{opt.best_params_}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll visualise the fitted objective surface" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "axs = plot_objective(opt.optimizer_results_[0], cmap='magma_r', show_points=False)\n", + "\n", + "fig = plt.gcf()\n", + "fig.set_dpi(250)\n", + "fig.delaxes(axs[0][0])\n", + "fig.delaxes(axs[0][1])\n", + "fig.delaxes(axs[1][1])\n", + "\n", + "ax = axs[1][0]\n", + "ax.set_xlabel('Dispatchable Generation\\nBandwidth (Fraction)')\n", + "ax.set_ylabel('Date Smoothing\\nBandwidth (Weeks)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "MOE", + "language": "python", + "name": "moe" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs/09-tables-and-figures.ipynb b/nbs/09-tables-and-figures.ipynb new file mode 100644 index 0000000..02d6b4b --- /dev/null +++ b/nbs/09-tables-and-figures.ipynb @@ -0,0 +1,1843 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tables & Figures Generation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook provides a programmatic workflow for generating the tables used in the MOE paper, as well as the diagram to show the time-adaptive smoothing weights.\n", + "\n", + "
\n", + "\n", + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from IPython.display import Latex, JSON\n", + "\n", + "from moepy import eda, lowess" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### Tables\n", + "\n", + "##### Power Systems Overview\n", + "\n", + "We'll first load in the DE data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BiomassBrown CoalGasHard CoalHydro PowerOilOthersPumped StorageSeasonal StorageSolarUraniumWindnet_balancedemandprice
local_datetime
2010-01-03 23:00:00+00:003.63716.5334.72610.0782.3310.0000.00.0520.0680.016.8260.635-1.22953.657NaN
2010-01-04 00:00:00+00:003.63716.5444.8568.8162.2930.0000.00.0380.0030.016.8410.528-1.59351.963NaN
2010-01-04 01:00:00+00:003.63716.3685.2757.9542.2990.0000.00.0320.0000.016.8460.616-1.37851.649NaN
2010-01-04 02:00:00+00:003.63715.8375.3547.6812.2990.0000.00.0270.0000.016.6990.630-1.62450.540NaN
2010-01-04 03:00:00+00:003.63715.4525.9187.4982.3010.0030.00.0200.0000.016.6350.713-0.73151.446NaN
\n", + "
" + ], + "text/plain": [ + " Biomass Brown Coal Gas Hard Coal Hydro Power \\\n", + "local_datetime \n", + "2010-01-03 23:00:00+00:00 3.637 16.533 4.726 10.078 2.331 \n", + "2010-01-04 00:00:00+00:00 3.637 16.544 4.856 8.816 2.293 \n", + "2010-01-04 01:00:00+00:00 3.637 16.368 5.275 7.954 2.299 \n", + "2010-01-04 02:00:00+00:00 3.637 15.837 5.354 7.681 2.299 \n", + "2010-01-04 03:00:00+00:00 3.637 15.452 5.918 7.498 2.301 \n", + "\n", + " Oil Others Pumped Storage Seasonal Storage \\\n", + "local_datetime \n", + "2010-01-03 23:00:00+00:00 0.000 0.0 0.052 0.068 \n", + "2010-01-04 00:00:00+00:00 0.000 0.0 0.038 0.003 \n", + "2010-01-04 01:00:00+00:00 0.000 0.0 0.032 0.000 \n", + "2010-01-04 02:00:00+00:00 0.000 0.0 0.027 0.000 \n", + "2010-01-04 03:00:00+00:00 0.003 0.0 0.020 0.000 \n", + "\n", + " Solar Uranium Wind net_balance demand price \n", + "local_datetime \n", + "2010-01-03 23:00:00+00:00 0.0 16.826 0.635 -1.229 53.657 NaN \n", + "2010-01-04 00:00:00+00:00 0.0 16.841 0.528 -1.593 51.963 NaN \n", + "2010-01-04 01:00:00+00:00 0.0 16.846 0.616 -1.378 51.649 NaN \n", + "2010-01-04 02:00:00+00:00 0.0 16.699 0.630 -1.624 50.540 NaN \n", + "2010-01-04 03:00:00+00:00 0.0 16.635 0.713 -0.731 51.446 NaN " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_DE = eda.load_DE_df('../data/energy_charts.csv', '../data/ENTSOE_DE_price.csv')\n", + "\n", + "df_DE.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Clean it up then calculate the relevant summary statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.3593124152992342, 55.956133452868855, 30.469415917112606)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s_DE_RES_output = df_DE[['Wind', 'Solar']].sum(axis=1)\n", + "s_DE_demand = df_DE['demand']\n", + "s_DE_price = df_DE['price']\n", + "\n", + "s_DE_RES_pct = s_DE_RES_output/s_DE_demand\n", + "\n", + "DE_2020_RES_pct = s_DE_RES_pct['2020'].mean()\n", + "DE_2020_demand_avg = s_DE_demand['2020'].mean()\n", + "DE_2020_price_avg = s_DE_price['2020'].mean()\n", + "\n", + "DE_2020_RES_pct, DE_2020_demand_avg, DE_2020_price_avg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll also estimate the carbon intensity" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8448.292069623136, 153.80385402105972)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "DE_fuel_to_co2_intensity = {\n", + " 'Biomass': 0.39, \n", + " 'Brown Coal': 0.36, \n", + " 'Gas': 0.23, \n", + " 'Hard Coal': 0.34, \n", + " 'Hydro Power': 0, \n", + " 'Oil': 0.28,\n", + " 'Others': 0, \n", + " 'Pumped Storage': 0, \n", + " 'Seasonal Storage': 0, \n", + " 'Solar': 0, \n", + " 'Uranium': 0,\n", + " 'Wind': 0, \n", + " 'net_balance': 0 \n", + "}\n", + "\n", + "s_DE_emissions_tonnes = (df_DE\n", + " [DE_fuel_to_co2_intensity.keys()]\n", + " .multiply(1e3) # converting to MWh\n", + " .multiply(DE_fuel_to_co2_intensity.values())\n", + " .sum(axis=1)\n", + " )\n", + "\n", + "s_DE_emissions_tonnes = s_DE_emissions_tonnes[s_DE_emissions_tonnes>2000]\n", + "s_DE_carbon_intensity = s_DE_emissions_tonnes/s_DE_demand.loc[s_DE_emissions_tonnes.index]\n", + "\n", + "DE_2020_emissions_tonnes = s_DE_emissions_tonnes['2020'].mean()\n", + "DE_2020_ci_avg = s_DE_carbon_intensity['2020'].mean()\n", + "\n", + "DE_2020_emissions_tonnes, DE_2020_ci_avg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll do the same for GB" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading in\n", + "df_EI = pd.read_csv('../data/electric_insights.csv')\n", + "\n", + "df_EI = df_EI.set_index('local_datetime')\n", + "df_EI.index = pd.to_datetime(df_EI.index, utc=True)\n", + "\n", + "# Extracting RES, demand, and price series\n", + "s_GB_RES = df_EI[['wind', 'solar']].sum(axis=1)\n", + "s_GB_demand = df_EI['demand']\n", + "s_GB_price = df_EI['day_ahead_price']\n", + "\n", + "# Generating carbon intensity series\n", + "GB_fuel_to_co2_intensity = {\n", + " 'nuclear': 0, \n", + " 'biomass': 0.121, # from EI \n", + " 'coal': 0.921, # DUKES 2018 value\n", + " 'gas': 0.377, # DUKES 2018 value (lower than many CCGT estimates, let alone OCGT)\n", + " 'hydro': 0, \n", + " 'pumped_storage': 0, \n", + " 'solar': 0,\n", + " 'wind': 0,\n", + " 'belgian': 0.4, \n", + " 'dutch': 0.474, # from EI \n", + " 'french': 0.053, # from EI \n", + " 'ireland': 0.458, # from EI \n", + " 'northern_ireland': 0.458 # from EI \n", + "}\n", + "\n", + "s_GB_emissions_tonnes = (df_EI\n", + " [GB_fuel_to_co2_intensity.keys()]\n", + " .multiply(1e3*0.5) # converting to MWh\n", + " .multiply(GB_fuel_to_co2_intensity.values())\n", + " .sum(axis=1)\n", + " )\n", + "\n", + "s_GB_emissions_tonnes = s_GB_emissions_tonnes[s_GB_emissions_tonnes>2000]\n", + "s_GB_carbon_intensity = s_GB_emissions_tonnes/s_GB_demand.loc[s_GB_emissions_tonnes.index]\n", + "\n", + "# Calculating 2020 averages\n", + "GB_2020_emissions_tonnes = s_GB_emissions_tonnes['2020'].mean()\n", + "GB_2020_ci_avg = s_GB_carbon_intensity['2020'].mean()\n", + "GB_2020_RES_pct = (s_GB_RES['2020']/s_GB_demand['2020']).mean()\n", + "GB_2020_demand_avg = s_GB_demand['2020'].mean()\n", + "GB_2020_price_avg = s_GB_price['2020'].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Then combine the results in a single table" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Average Solar/Wind Generation (%)Average Demand (GW)Average Price ([EUR,GBP]/MWh)Average Carbon Intensity (gCO2/kWh)
Germany35.9355.9630.47153.80
Great Britain29.8330.6133.77101.17
\n", + "
" + ], + "text/plain": [ + " Average Solar/Wind Generation (%) Average Demand (GW) \\\n", + "Germany 35.93 55.96 \n", + "Great Britain 29.83 30.61 \n", + "\n", + " Average Price ([EUR,GBP]/MWh) \\\n", + "Germany 30.47 \n", + "Great Britain 33.77 \n", + "\n", + " Average Carbon Intensity (gCO2/kWh) \n", + "Germany 153.80 \n", + "Great Britain 101.17 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "system_overview_data = {\n", + " 'Germany': {\n", + " 'Average Solar/Wind Generation (%)': round(100*DE_2020_RES_pct, 2),\n", + " 'Average Demand (GW)': round(DE_2020_demand_avg, 2),\n", + " 'Average Price ([EUR,GBP]/MWh)': round(DE_2020_price_avg, 2),\n", + " 'Average Carbon Intensity (gCO2/kWh)': round(DE_2020_ci_avg, 2),\n", + " },\n", + " 'Great Britain': {\n", + " 'Average Solar/Wind Generation (%)': round(100*GB_2020_RES_pct, 2),\n", + " 'Average Demand (GW)': round(GB_2020_demand_avg, 2),\n", + " 'Average Price ([EUR,GBP]/MWh)': round(GB_2020_price_avg, 2),\n", + " 'Average Carbon Intensity (gCO2/kWh)': round(GB_2020_ci_avg, 2),\n", + " }\n", + "}\n", + "\n", + "df_system_overview = pd.DataFrame(system_overview_data).T\n", + "\n", + "df_system_overview.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Which we'll then output as a LaTeX table" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{table}\n", + "\\centering\n", + "\\caption{Systems overview for 2020}\n", + "\\label{overview_table}\n", + "\\begin{tabular}{|l|l|l|l|l|}\n", + "\\hline\n", + "{} & Average Solar/Wind Generation (\\%) & Average Demand (GW) & Average Price ([EUR,GBP]/MWh) & Average Carbon Intensity (gCO\\textsubscript{2}/kWh) \\\\ \\hline\n", + "Germany & 35.93 & 55.96 & 30.47 & 153.80 \\\\ \\hline\n", + "Great Britain & 29.83 & 30.61 & 33.77 & 101.17 \\\\ \\hline\n", + "\\end{tabular}\n", + "\\end{table}\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_lined_column_format = lambda n_cols:''.join(n_cols*['|l']) + '|'\n", + "\n", + "caption = 'Systems overview for 2020'\n", + "label = 'overview_table'\n", + "column_format = get_lined_column_format(df_system_overview.shape[1]+1)\n", + "\n", + "latex_str = df_system_overview.to_latex(column_format=column_format, caption=caption, label=label)\n", + "\n", + "latex_replacements = {\n", + " 'CO2': 'CO\\\\textsubscript{2}',\n", + " '\\\\\\\\\\n': '\\\\\\\\ \\\\midrule\\n',\n", + " 'midrule': 'hline',\n", + " 'toprule': 'hline',\n", + " 'bottomrule': '',\n", + " '\\n\\\\\\n': '\\n',\n", + " '\\\\hline\\n\\\\hline': '\\\\hline'\n", + "}\n", + "\n", + "for old, new in latex_replacements.items():\n", + " latex_str = latex_str.replace(old, new)\n", + "\n", + "Latex(latex_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "##### Carbon Intensity Estimates\n", + "\n", + "We'll clean up our GB carbon intensity estimates" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BiomassCoalGasDutchFrenchIreland
gCO2/kWh12192137747453458
\n", + "
" + ], + "text/plain": [ + " Biomass Coal Gas Dutch French Ireland\n", + "gCO2/kWh 121 921 377 474 53 458" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def clean_idxs(s):\n", + " s.index = s.index.str.replace('_', ' ').str.title()\n", + " return s\n", + "\n", + "df_GB_non0_co2_intensity = (pd\n", + " .Series(GB_fuel_to_co2_intensity)\n", + " .replace(0, np.nan)\n", + " .dropna()\n", + " .drop(['belgian', 'northern_ireland'])\n", + " .pipe(clean_idxs)\n", + " .multiply(1e3)\n", + " .astype(int)\n", + " .to_frame()\n", + " .T\n", + " .rename({0: 'gCO2/kWh'})\n", + " )\n", + "\n", + "df_GB_non0_co2_intensity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "And output them as a LaTeX table" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{table}\n", + "\\centering\n", + "\\caption{Carbon intensity factors for fuel-types and interconnection on the GB power system}\n", + "\\label{GB_co2_intensity_table}\n", + "\\begin{tabular}{|l|l|l|l|l|l|l|}\n", + "\\hline\n", + "{} & Biomass & Coal & Gas & Dutch & French & Ireland \\\\ \\hline\n", + "gCO\\textsubscript{2}/kWh & 121 & 921 & 377 & 474 & 53 & 458 \\\\ \\hline\n", + "\\end{tabular}\n", + "\\end{table}\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "caption = 'Carbon intensity factors for fuel-types and interconnection on the GB power system'\n", + "label = 'GB_co2_intensity_table'\n", + "column_format = get_lined_column_format(df_GB_non0_co2_intensity.shape[1]+1)\n", + "\n", + "latex_str = df_GB_non0_co2_intensity.to_latex(column_format=column_format, caption=caption, label=label)\n", + "\n", + "latex_replacements = {\n", + " 'CO2': 'CO\\\\textsubscript{2}',\n", + " '\\\\\\\\\\n': '\\\\\\\\ \\\\midrule\\n',\n", + " 'midrule': 'hline',\n", + " 'toprule': 'hline',\n", + " 'bottomrule': '',\n", + " '\\n\\\\\\n': '\\n',\n", + " '\\\\hline\\n\\\\hline': '\\\\hline'\n", + "}\n", + "\n", + "for old, new in latex_replacements.items():\n", + " latex_str = latex_str.replace(old, new)\n", + "\n", + "Latex(latex_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll then do the same for DE" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
BiomassBrown CoalHard CoalGasOil
gCO2/kWh390360340230280
\n", + "
" + ], + "text/plain": [ + " Biomass Brown Coal Hard Coal Gas Oil\n", + "gCO2/kWh 390 360 340 230 280" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_DE_non0_co2_intensity = (pd\n", + " .Series(DE_fuel_to_co2_intensity)\n", + " .replace(0, np.nan)\n", + " .dropna()\n", + " [['Biomass', 'Brown Coal', 'Hard Coal', 'Gas', 'Oil']]\n", + " .pipe(clean_idxs)\n", + " .multiply(1e3)\n", + " .astype(int)\n", + " .to_frame()\n", + " .T\n", + " .rename({0: 'gCO2/kWh'})\n", + " )\n", + "\n", + "df_DE_non0_co2_intensity" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{table}\n", + "\\centering\n", + "\\caption{Carbon intensity factors for fuel-types and interconnection on the DE power system}\n", + "\\label{DE_co2_intensity_table}\n", + "\\begin{tabular}{|l|l|l|l|l|l|}\n", + "\\hline\n", + "{} & Biomass & Brown Coal & Hard Coal & Gas & Oil \\\\ \\hline\n", + "gCO\\textsubscript{2}/kWh & 390 & 360 & 340 & 230 & 280 \\\\ \\hline\n", + "\\end{tabular}\n", + "\\end{table}\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "caption = 'Carbon intensity factors for fuel-types and interconnection on the DE power system'\n", + "label = 'DE_co2_intensity_table'\n", + "column_format = get_lined_column_format(df_DE_non0_co2_intensity.shape[1]+1)\n", + "\n", + "latex_str = df_DE_non0_co2_intensity.to_latex(column_format=column_format, caption=caption, label=label)\n", + "\n", + "for old, new in latex_replacements.items():\n", + " latex_str = latex_str.replace(old, new)\n", + "\n", + "Latex(latex_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "##### Electricity Price Forecasting Metrics\n", + "\n", + "We'll start by loading in our previously saved model metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/json": { + "DE_demand": { + "mean_abs_err": 18.28336704312868, + "median_abs_err": 14.67403224443754, + "root_mean_square_error": 23.560281367239586 + }, + "DE_dispatch": { + "mean_abs_err": 5.852023979176648, + "median_abs_err": 4.257075090332123, + "root_mean_square_error": 8.705711313706535 + }, + "GB_demand": { + "mean_abs_err": 8.423628315026313, + "median_abs_err": 6.076142585411503, + "root_mean_square_error": 13.57255404896023 + }, + "GB_dispatch": { + "mean_abs_err": 6.55687702607074, + "median_abs_err": 4.47311519486, + "root_mean_square_error": 12.053853844276484 + } + }, + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": { + "application/json": { + "expanded": false, + "root": "root" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "with open('../data/results/price_model_accuracy_metrics.json', 'r') as fp:\n", + " model_accuracy_metrics = json.load(fp)\n", + " \n", + "JSON(model_accuracy_metrics)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll parse the MAE results into a new table" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Dispatchable LoadTotal Load
Germany5.8518.28
Great Britain6.568.42
\n", + "
" + ], + "text/plain": [ + " Dispatchable Load Total Load\n", + "Germany 5.85 18.28\n", + "Great Britain 6.56 8.42" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_accuracy_data = {\n", + " 'Germany': {\n", + " 'Dispatchable Load': round(model_accuracy_metrics['DE_dispatch']['mean_abs_err'], 2),\n", + " 'Total Load': round(model_accuracy_metrics['DE_demand']['mean_abs_err'], 2),\n", + " },\n", + " 'Great Britain': {\n", + " 'Dispatchable Load': round(model_accuracy_metrics['GB_dispatch']['mean_abs_err'], 2),\n", + " 'Total Load': round(model_accuracy_metrics['GB_demand']['mean_abs_err'], 2),\n", + " }\n", + "}\n", + "\n", + "df_model_accuracy = pd.DataFrame(model_accuracy_data).T\n", + "\n", + "df_model_accuracy.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Which we'll output as a LaTeX table" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{table}\n", + "\\centering\n", + "\\caption{Price forecasting model accuracy when regressing against dispatchable and total load for GB and DE.}\n", + "\\label{model_accuracy_table}\n", + "\\begin{tabular}{|l|l|l|}\n", + "\\hline\n", + "{} & Dispatchable Load & Total Load \\\\ \\hline\n", + "Germany & 5.85 & 18.28 \\\\ \\hline\n", + "Great Britain & 6.56 & 8.42 \\\\ \\hline\n", + "\\end{tabular}\n", + "\\end{table}\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "caption = 'Price forecasting model accuracy when regressing against dispatchable and total load for GB and DE.'\n", + "label = 'model_accuracy_table'\n", + "column_format = get_lined_column_format(df_model_accuracy.shape[1]+1)\n", + "\n", + "latex_str = df_model_accuracy.to_latex(column_format=column_format, caption=caption, label=label)\n", + "\n", + "for old, new in latex_replacements.items():\n", + " latex_str = latex_str.replace(old, new)\n", + "\n", + "Latex(latex_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "##### Price and CO2 MOE Results\n", + "\n", + "We'll first load in all of the price and carbon MOE time-series" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
predictioncounterfactualobservedmoe
local_datetime
2009-01-01 00:00:00+00:0037.20344137.31337958.050.109938
2009-01-01 00:30:00+00:0037.31337937.53513556.330.221756
2009-01-01 01:00:00+00:0036.76851336.98508752.980.216574
2009-01-01 01:30:00+00:0035.59516235.80763150.390.212469
2009-01-01 02:00:00+00:0034.84942235.06311948.700.213697
\n", + "
" + ], + "text/plain": [ + " prediction counterfactual observed moe\n", + "local_datetime \n", + "2009-01-01 00:00:00+00:00 37.203441 37.313379 58.05 0.109938\n", + "2009-01-01 00:30:00+00:00 37.313379 37.535135 56.33 0.221756\n", + "2009-01-01 01:00:00+00:00 36.768513 36.985087 52.98 0.216574\n", + "2009-01-01 01:30:00+00:00 35.595162 35.807631 50.39 0.212469\n", + "2009-01-01 02:00:00+00:00 34.849422 35.063119 48.70 0.213697" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def set_dt_idx(df, dt_idx_col='local_datetime'):\n", + " df = df.set_index(dt_idx_col)\n", + " df.index = pd.to_datetime(df.index, utc=True)\n", + " \n", + " return df\n", + "\n", + "df_GB_price_results_ts = pd.read_csv('../data/results/GB_price.csv').pipe(set_dt_idx)\n", + "df_DE_price_results_ts = pd.read_csv('../data/results/DE_price.csv').pipe(set_dt_idx)\n", + "df_GB_carbon_results_ts = pd.read_csv('../data/results/GB_carbon.csv').pipe(set_dt_idx)\n", + "df_DE_carbon_results_ts = pd.read_csv('../data/results/DE_carbon.csv').pipe(set_dt_idx)\n", + "\n", + "df_GB_price_results_ts.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll then calculate their summary statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
GermanyGreat Britain
Price ([EUR,GBP]/MWh)22.1713.89
Price (%)43.4329.66
Carbon (Tonnes/h)5563.221657.88
Carbon (%)39.7037.89
\n", + "
" + ], + "text/plain": [ + " Germany Great Britain\n", + "Price ([EUR,GBP]/MWh) 22.17 13.89\n", + "Price (%) 43.43 29.66\n", + "Carbon (Tonnes/h) 5563.22 1657.88\n", + "Carbon (%) 39.70 37.89" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "MOE_results_data = {\n", + " 'Germany': {\n", + " 'Price ([EUR,GBP]/MWh)': round(df_DE_price_results_ts.loc['2020', 'moe'].mean(), 2),\n", + " 'Price (%)': round(100*(df_DE_price_results_ts.loc['2020', 'moe']*df_DE['demand']).sum()/((df_DE_price_results_ts.loc['2020', 'observed']+df_DE_price_results_ts.loc['2020', 'moe'])*df_DE['demand']).sum(), 2),\n", + " 'Carbon (Tonnes/h)': round(df_DE_carbon_results_ts.loc['2020', 'moe'].mean(), 2),\n", + " 'Carbon (%)': round(100*(df_DE_carbon_results_ts.loc['2020', 'moe'].sum()/(df_DE_carbon_results_ts.loc['2020', 'observed']+df_DE_carbon_results_ts.loc['2020', 'moe']).sum()).mean(), 2)\n", + " },\n", + " 'Great Britain': {\n", + " 'Price ([EUR,GBP]/MWh)': round(df_GB_price_results_ts.loc['2020', 'moe'].mean(), 2),\n", + " 'Price (%)': round(100*(df_GB_price_results_ts.loc['2020', 'moe']*df_EI['demand']).sum()/((df_GB_price_results_ts.loc['2020', 'observed']+df_GB_price_results_ts.loc['2020', 'moe'])*df_EI['demand']).sum(), 2),\n", + " 'Carbon (Tonnes/h)': round(df_GB_carbon_results_ts.loc['2020', 'moe'].mean(), 2), # doubled to make it the same hourly rate as DE\n", + " 'Carbon (%)': round(100*(df_GB_carbon_results_ts.loc['2020', 'moe'].sum()/(df_GB_carbon_results_ts.loc['2020', 'observed']+df_GB_carbon_results_ts.loc['2020', 'moe']).sum()).mean(), 2)\n", + " }\n", + "}\n", + "\n", + "df_MOE_results = (pd\n", + " .DataFrame(MOE_results_data)\n", + " )\n", + "\n", + "df_MOE_results.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "And export the output as a LaTeX table" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{table}\n", + "\\centering\n", + "\\caption{2020 Merit Order Effect results overview (weighted by volume).}\n", + "\\label{moe_results_table}\n", + "\\begin{tabular}{|l|l|l|}\n", + "\\hline\n", + "{} & Germany & Great Britain \\\\ \\hline\n", + "Price ([EUR,GBP]/MWh) & 22.17 & 13.89 \\\\ \\hline\n", + "Price (\\%) & 43.43 & 29.66 \\\\ \\hline\n", + "Carbon (Tonnes/h) & 5563.22 & 1657.88 \\\\ \\hline\n", + "Carbon (\\%) & 39.70 & 37.89 \\\\ \\hline\n", + "\\end{tabular}\n", + "\\end{table}\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "caption = '2020 Merit Order Effect results overview (weighted by volume).'\n", + "label = 'moe_results_table'\n", + "column_format = get_lined_column_format(df_MOE_results.shape[1]+1)\n", + "\n", + "latex_str = df_MOE_results.to_latex(column_format=column_format, caption=caption, label=label)\n", + "\n", + "for old, new in latex_replacements.items():\n", + " latex_str = latex_str.replace(old, new)\n", + "\n", + "Latex(latex_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "##### Literature Review\n", + "\n", + "Lastly we'll create our largest table, containing results from across the literature" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":237: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will*not* be treated as literal strings when regex=True.\n", + " df_lit_results['Study Year'] = df_lit_results['Study'].str.split('(').str[1].str.replace(')', '').astype(int)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StudyMOEPeriodRegionMethod
0Sensfuss et al. (2008)7.83 €/MWh2006GermanyESS
1de Miera et al. (2008)8.6-25.1% price decrease2005-2007SpainESS
2Weigt (2009)10 €/MWh2006-2008GermanyESS
3Ciarreta et al. (2014)25-45 €/MWh2008–2012SpainESS
4Bublitz et al. (2017)5.40 €/MWh2011-2015GermanyESS
\n", + "
" + ], + "text/plain": [ + " Study MOE Period Region Method\n", + "0 Sensfuss et al. (2008) 7.83 €/MWh 2006 Germany ESS\n", + "1 de Miera et al. (2008) 8.6-25.1% price decrease 2005-2007 Spain ESS\n", + "2 Weigt (2009) 10 €/MWh 2006-2008 Germany ESS\n", + "3 Ciarreta et al. (2014) 25-45 €/MWh 2008–2012 Spain ESS\n", + "4 Bublitz et al. (2017) 5.40 €/MWh 2011-2015 Germany ESS" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lit_results_data = [\n", + " {\n", + " 'Study': 'Sensfuss et al. (2008)',\n", + " 'MOE': '7.83 €/MWh',\n", + " 'Period': '2006',\n", + " 'Region': 'Germany',\n", + " 'Method': 'ESS',\n", + " },\n", + " {\n", + " 'Study': 'Weigt (2009)',\n", + " 'MOE': '10 €/MWh',\n", + " 'Period': '2006-2008',\n", + " 'Region': 'Germany',\n", + " 'Method': 'ESS',\n", + " },\n", + " {\n", + " 'Study': 'Keles et al. (2013)',\n", + " 'MOE': '5.90 €/MWh',\n", + " 'Period': '2006–2009',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Mulder and Scholtens (2013)',\n", + " 'MOE': '0.03% price decrease per p.p increase in wind speeds',\n", + " 'Period': '2006–2011',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Tveten et al. (2013)',\n", + " 'MOE': '5.25 €/MWh (solar)',\n", + " 'Period': '2006-2011',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Wurzburg et al. (2013)',\n", + " 'MOE': '2% price decrease',\n", + " 'Period': '2010-2012',\n", + " 'Region': 'Germany & Austria',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Cludius et al. (2014)',\n", + " 'MOE': '8 €/MWh',\n", + " 'Period': '2010-2012',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Ketterer (2014)',\n", + " 'MOE': '0.1-1.46% price decrease per p.p increase in wind generation',\n", + " 'Period': '2006-2012',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Ederer (2015)',\n", + " 'MOE': '1.3% price decrease per annual TWh of wind',\n", + " 'Period': '2006-2014',\n", + " 'Region': 'Germany',\n", + " 'Method': 'MSS',\n", + " },\n", + " {\n", + " 'Study': 'Kyritsis et al. (2017)',\n", + " 'MOE': '-',\n", + " 'Period': '2010-2015',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Bublitz et al. (2017)',\n", + " 'MOE': '5.40 €/MWh',\n", + " 'Period': '2011-2015',\n", + " 'Region': 'Germany',\n", + " 'Method': 'ESS',\n", + " },\n", + " {\n", + " 'Study': 'Bublitz et al. (2017)',\n", + " 'MOE': '6.80 €/MWh',\n", + " 'Period': '2011-2015',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'de Miera et al. (2008)',\n", + " 'MOE': '8.6-25.1% price decrease',\n", + " 'Period': '2005-2007',\n", + " 'Region': 'Spain',\n", + " 'Method': 'ESS',\n", + " },\n", + " {\n", + " 'Study': 'Gelabert et al. (2011)',\n", + " 'MOE': '3.7% price decrease',\n", + " 'Period': '2005-2012',\n", + " 'Region': 'Spain',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Ciarreta et al. (2014)',\n", + " 'MOE': '25-45 €/MWh',\n", + " 'Period': '2008–2012',\n", + " 'Region': 'Spain',\n", + " 'Method': 'ESS',\n", + " },\n", + " {\n", + " 'Study': 'Clo et al. (2015)',\n", + " 'MOE': '2.3 €/MWh (solar), 4.2 €/MWh (wind)',\n", + " 'Period': '2005–2013',\n", + " 'Region': 'Italy',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Munksgaard and Morthorst (2008)',\n", + " 'MOE': '1-4 €/MWh',\n", + " 'Period': '2004-2006',\n", + " 'Region': 'Denmark',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Jonsson et al. (2010)',\n", + " 'MOE': '-',\n", + " 'Period': '2006-2007',\n", + " 'Region': 'Denmark',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Denny et al. (2017)',\n", + " 'MOE': '3.40 €/MWh per GWh (wind)',\n", + " 'Period': '2009',\n", + " 'Region': 'Ireland',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Lunackova et al. (2017)',\n", + " 'MOE': '1.2% price decrease per 10% increase in RES',\n", + " 'Period': '2010-2015',\n", + " 'Region': 'Czech Republic',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Dillig et al. (2016)',\n", + " 'MOE': '50.29 €/MWh',\n", + " 'Period': '2011-2013',\n", + " 'Region': 'Germany',\n", + " 'Method': 'MSS',\n", + " },\n", + " {\n", + " 'Study': 'McConnell et al. (2013)',\n", + " 'MOE': '8.6% price decrease',\n", + " 'Period': '2009-2010',\n", + " 'Region': 'Australia',\n", + " 'Method': 'MSS',\n", + " },\n", + " {\n", + " 'Study': 'Moreno et al. (2012)',\n", + " 'MOE': '0.018% price increase per p.p. increase in RES penetration',\n", + " 'Period': '1998–2009',\n", + " 'Region': 'EU-27',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Woo et al. (2011)',\n", + " 'MOE': '0.32-1.53 $/MWh',\n", + " 'Period': '2007-2010',\n", + " 'Region': 'Texas',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Kaufmann and Vaid (2016)',\n", + " 'MOE': '0.26-1.86 $/MWh (solar)',\n", + " 'Period': '2010-2012',\n", + " 'Region': 'Massachusetts',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Woo et al. (2016)',\n", + " 'MOE': '5.3 \\$/MWh (solar) and 3.3 \\$/MWh (wind) per GWh of RES',\n", + " 'Period': '2012-2015',\n", + " 'Region': 'California',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Paraschiv et al. (2014)',\n", + " 'MOE': '0.15% price decrease per MWh of RES',\n", + " 'Period': '2010-2013',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'O\\'Mahoney and Denny (2011)',\n", + " 'MOE': '12% price decrease',\n", + " 'Period': '2009',\n", + " 'Region': 'Ireland',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Hildmann et al. (2015)',\n", + " 'MOE': '13.4-18.6 €/MWh',\n", + " 'Period': '2011-2013',\n", + " 'Region': 'Germany and Austria',\n", + " 'Method': 'MSS',\n", + " },\n", + " {\n", + " 'Study': 'Gil et al. (2012)',\n", + " 'MOE': '9.72 €/MWh',\n", + " 'Period': '2007-2010',\n", + " 'Region': 'Spain',\n", + " 'Method': 'RPR',\n", + " },\n", + "# { # Removed due to language barrier preventing method from being discerned\n", + "# 'Study': 'Weber and Woll (2007)',\n", + "# 'MOE': '4 €/MWh',\n", + "# 'Period': '2006',\n", + "# 'Region': 'Germany',\n", + "# 'Method': '-',\n", + "# },\n", + " {\n", + " 'Study': 'Halttunen et al. (2021)',\n", + " 'MOE': '0.631 €/MWh per p.p. increase in RES penetration',\n", + " 'Period': '2012-2019',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " },\n", + " {\n", + " 'Study': 'Halttunen et al. (2021)',\n", + " 'MOE': '0.482 €/MWh per p.p. increase in RES penetration',\n", + " 'Period': '2010-2019',\n", + " 'Region': 'Germany',\n", + " 'Method': 'RPR',\n", + " }\n", + "]\n", + "\n", + "df_lit_results = pd.DataFrame(lit_results_data)\n", + "\n", + "df_lit_results['Study Year'] = df_lit_results['Study'].str.split('(').str[1].str.replace(')', '').astype(int)\n", + "df_lit_results = df_lit_results.sort_values(['Method', 'Study Year', 'Study']).drop(columns=['Study Year']).reset_index(drop=True)\n", + "\n", + "df_lit_results.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "We'll also export this as a LaTeX table" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "\\begin{table}\n", + "\\centering\n", + "\\caption{Results overview from the MOE literature}\n", + "\\label{lit_results_table}\n", + "\\begin{tabular}{|l|l|l|l|l|l|}\n", + "\\hline\n", + " Study & MOE & Period & Region & Method \\\\ \\hline\n", + " Sensfuss et al. (2008) & 7.83 €/MWh & 2006 & Germany & ESS \\\\ \\hline\n", + " de Miera et al. (2008) & 8.6-25.1\\% price decrease & 2005-2007 & Spain & ESS \\\\ \\hline\n", + " Weigt (2009) & 10 €/MWh & 2006-2008 & Germany & ESS \\\\ \\hline\n", + " Ciarreta et al. (2014) & 25-45 €/MWh & 2008–2012 & Spain & ESS \\\\ \\hline\n", + " Bublitz et al. (2017) & 5.40 €/MWh & 2011-2015 & Germany & ESS \\\\ \\hline\n", + " McConnell et al. (2013) & 8.6\\% price decrease & 2009-2010 & Australia & MSS \\\\ \\hline\n", + " Ederer (2015) & 1.3\\% price decrease per annual TWh of wind & 2006-2014 & Germany & MSS \\\\ \\hline\n", + " Hildmann et al. (2015) & 13.4-18.6 €/MWh & 2011-2013 & Germany and Austria & MSS \\\\ \\hline\n", + " Dillig et al. (2016) & 50.29 €/MWh & 2011-2013 & Germany & MSS \\\\ \\hline\n", + "Munksgaard and Morthorst (2008) & 1-4 €/MWh & 2004-2006 & Denmark & RPR \\\\ \\hline\n", + " Jonsson et al. (2010) & - & 2006-2007 & Denmark & RPR \\\\ \\hline\n", + " Gelabert et al. (2011) & 3.7\\% price decrease & 2005-2012 & Spain & RPR \\\\ \\hline\n", + " O'Mahoney and Denny (2011) & 12\\% price decrease & 2009 & Ireland & RPR \\\\ \\hline\n", + " Woo et al. (2011) & 0.32-1.53 \\$/MWh & 2007-2010 & Texas & RPR \\\\ \\hline\n", + " Gil et al. (2012) & 9.72 €/MWh & 2007-2010 & Spain & RPR \\\\ \\hline\n", + " Moreno et al. (2012) & 0.018\\% price increase per p.p. increase in RES ... & 1998–2009 & EU-27 & RPR \\\\ \\hline\n", + " Keles et al. (2013) & 5.90 €/MWh & 2006–2009 & Germany & RPR \\\\ \\hline\n", + " Mulder and Scholtens (2013) & 0.03\\% price decrease per p.p increase in wind s... & 2006–2011 & Germany & RPR \\\\ \\hline\n", + " Tveten et al. (2013) & 5.25 €/MWh (solar) & 2006-2011 & Germany & RPR \\\\ \\hline\n", + " Wurzburg et al. (2013) & 2\\% price decrease & 2010-2012 & Germany \\& Austria & RPR \\\\ \\hline\n", + " Cludius et al. (2014) & 8 €/MWh & 2010-2012 & Germany & RPR \\\\ \\hline\n", + " Ketterer (2014) & 0.1-1.46\\% price decrease per p.p increase in wi... & 2006-2012 & Germany & RPR \\\\ \\hline\n", + " Paraschiv et al. (2014) & 0.15\\% price decrease per MWh of RES & 2010-2013 & Germany & RPR \\\\ \\hline\n", + " Clo et al. (2015) & 2.3 €/MWh (solar), 4.2 €/MWh (wind) & 2005–2013 & Italy & RPR \\\\ \\hline\n", + " Kaufmann and Vaid (2016) & 0.26-1.86 \\$/MWh (solar) & 2010-2012 & Massachusetts & RPR \\\\ \\hline\n", + " Woo et al. (2016) & 5.3 \\textbackslash \\$/MWh (solar) and 3.3 \\textbackslash \\$/MWh (wind) per GW... & 2012-2015 & California & RPR \\\\ \\hline\n", + " Bublitz et al. (2017) & 6.80 €/MWh & 2011-2015 & Germany & RPR \\\\ \\hline\n", + " Denny et al. (2017) & 3.40 €/MWh per GWh (wind) & 2009 & Ireland & RPR \\\\ \\hline\n", + " Kyritsis et al. (2017) & - & 2010-2015 & Germany & RPR \\\\ \\hline\n", + " Lunackova et al. (2017) & 1.2\\% price decrease per 10\\% increase in RES & 2010-2015 & Czech Republic & RPR \\\\ \\hline\n", + " Halttunen et al. (2021) & 0.631 €/MWh per p.p. increase in RES penetration & 2012-2019 & Germany & RPR \\\\ \\hline\n", + " Halttunen et al. (2021) & 0.482 €/MWh per p.p. increase in RES penetration & 2010-2019 & Germany & RPR \\\\ \\hline\n", + "\\end{tabular}\n", + "\\end{table}\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "caption = 'Results overview from the MOE literature'\n", + "label = 'lit_results_table'\n", + "column_format = get_lined_column_format(df_lit_results.shape[1]+1)\n", + "\n", + "latex_str = df_lit_results.to_latex(column_format=column_format, caption=caption, label=label, index=False)\n", + "\n", + "for old, new in latex_replacements.items():\n", + " latex_str = latex_str.replace(old, new)\n", + "\n", + "Latex(latex_str)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "### Figures\n", + "\n", + "##### Time Dimension Hyper-Parameters\n", + "\n", + "We'll create a plot showing an example of how regression dates are converted into weightings for the time-series" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Relative Weighting')" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 1, 150)\n", + "centers = [0.3, 0.5, 0.7]\n", + "\n", + "# Plotting\n", + "fig, ax = plt.subplots(dpi=250, figsize=(8, 4))\n", + "\n", + "for center in centers:\n", + " dist = lowess.get_dist(x, center)\n", + " dist_threshold = lowess.get_dist_threshold(dist, frac=0.3)\n", + " weights = lowess.dist_to_weights(dist, dist_threshold)\n", + "\n", + " ax.plot(x, weights, color='k')\n", + " \n", + "x_pos = 0.4\n", + "ax.annotate('Interval', xy=(x_pos, 0.95), xytext=(x_pos, 1.00), xycoords='axes fraction', \n", + " fontsize=6.5, ha='center', va='bottom',\n", + " bbox=dict(boxstyle='square', fc='white'),\n", + " arrowprops=dict(arrowstyle='-[, widthB=7.0, lengthB=1.5', lw=1.0))\n", + " \n", + "x_pos = 0.5\n", + "ax.annotate('Bandwidth', xy=(x_pos, 0.06), xytext=(x_pos, 0.11), xycoords='axes fraction', \n", + " fontsize=9.5, ha='center', va='bottom',\n", + " bbox=dict(boxstyle='square', fc='white'),\n", + " arrowprops=dict(arrowstyle='-[, widthB=7.0, lengthB=1.5', lw=1.0))\n", + "\n", + "ax.set_xlim(0, 1)\n", + "ax.set_ylim(0, 1.1)\n", + "eda.hide_spines(ax)\n", + "ax.set_xlabel('Data Fraction')\n", + "ax.set_ylabel('Relative Weighting')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "MOE", + "language": "python", + "name": "moe" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nbs/09-tables.ipynb b/nbs/09-tables.ipynb deleted file mode 100644 index c8f2355..0000000 --- a/nbs/09-tables.ipynb +++ /dev/null @@ -1,1308 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tables\n", - "\n", - "- [ ] Literate review results collation\n", - "- [x] System overview\n", - "- [x] Carbon intensity estimates\n", - "- [x] EPF accuracy metrics\n", - "- [ ] MOE and CO2 results\n", - "\n", - "* Should eventually generate all of the plots here as well\n", - "\n", - "
\n", - "\n", - "### Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from IPython.display import Latex, JSON\n", - "\n", - "from moepy import eda" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "### Power Systems Overview" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BiomassBrown CoalGasHard CoalHydro PowerOilOthersPumped StorageSeasonal StorageSolarUraniumWindnet_balancedemandprice
local_datetime
2010-01-03 23:00:00+00:003.63716.5334.72610.0782.3310.0000.00.0520.0680.016.8260.635-1.22953.657NaN
2010-01-04 00:00:00+00:003.63716.5444.8568.8162.2930.0000.00.0380.0030.016.8410.528-1.59351.963NaN
2010-01-04 01:00:00+00:003.63716.3685.2757.9542.2990.0000.00.0320.0000.016.8460.616-1.37851.649NaN
2010-01-04 02:00:00+00:003.63715.8375.3547.6812.2990.0000.00.0270.0000.016.6990.630-1.62450.540NaN
2010-01-04 03:00:00+00:003.63715.4525.9187.4982.3010.0030.00.0200.0000.016.6350.713-0.73151.446NaN
\n", - "
" - ], - "text/plain": [ - " Biomass Brown Coal Gas Hard Coal Hydro Power \\\n", - "local_datetime \n", - "2010-01-03 23:00:00+00:00 3.637 16.533 4.726 10.078 2.331 \n", - "2010-01-04 00:00:00+00:00 3.637 16.544 4.856 8.816 2.293 \n", - "2010-01-04 01:00:00+00:00 3.637 16.368 5.275 7.954 2.299 \n", - "2010-01-04 02:00:00+00:00 3.637 15.837 5.354 7.681 2.299 \n", - "2010-01-04 03:00:00+00:00 3.637 15.452 5.918 7.498 2.301 \n", - "\n", - " Oil Others Pumped Storage Seasonal Storage \\\n", - "local_datetime \n", - "2010-01-03 23:00:00+00:00 0.000 0.0 0.052 0.068 \n", - "2010-01-04 00:00:00+00:00 0.000 0.0 0.038 0.003 \n", - "2010-01-04 01:00:00+00:00 0.000 0.0 0.032 0.000 \n", - "2010-01-04 02:00:00+00:00 0.000 0.0 0.027 0.000 \n", - "2010-01-04 03:00:00+00:00 0.003 0.0 0.020 0.000 \n", - "\n", - " Solar Uranium Wind net_balance demand price \n", - "local_datetime \n", - "2010-01-03 23:00:00+00:00 0.0 16.826 0.635 -1.229 53.657 NaN \n", - "2010-01-04 00:00:00+00:00 0.0 16.841 0.528 -1.593 51.963 NaN \n", - "2010-01-04 01:00:00+00:00 0.0 16.846 0.616 -1.378 51.649 NaN \n", - "2010-01-04 02:00:00+00:00 0.0 16.699 0.630 -1.624 50.540 NaN \n", - "2010-01-04 03:00:00+00:00 0.0 16.635 0.713 -0.731 51.446 NaN " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_DE = eda.load_DE_df('../data/energy_charts.csv', '../data/ENTSOE_DE_price.csv')\n", - "\n", - "df_DE.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.3593124152992342, 55.956133452868855, 30.469415917112606)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s_DE_RES_output = df_DE[['Wind', 'Solar']].sum(axis=1)\n", - "s_DE_demand = df_DE['demand']\n", - "s_DE_price = df_DE['price']\n", - "\n", - "s_DE_RES_pct = s_DE_RES_output/s_DE_demand\n", - "\n", - "DE_2020_RES_pct = s_DE_RES_pct['2020'].mean()\n", - "DE_2020_demand_avg = s_DE_demand['2020'].mean()\n", - "DE_2020_price_avg = s_DE_price['2020'].mean()\n", - "\n", - "DE_2020_RES_pct, DE_2020_demand_avg, DE_2020_price_avg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(8448.292069623136, 153.80385402105972)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "DE_fuel_to_co2_intensity = {\n", - " 'Biomass': 0.39, \n", - " 'Brown Coal': 0.36, \n", - " 'Gas': 0.23, \n", - " 'Hard Coal': 0.34, \n", - " 'Hydro Power': 0, \n", - " 'Oil': 0.28,\n", - " 'Others': 0, \n", - " 'Pumped Storage': 0, \n", - " 'Seasonal Storage': 0, \n", - " 'Solar': 0, \n", - " 'Uranium': 0,\n", - " 'Wind': 0, \n", - " 'net_balance': 0 \n", - "}\n", - "\n", - "s_DE_emissions_tonnes = (df_DE\n", - " [DE_fuel_to_co2_intensity.keys()]\n", - " .multiply(1e3) # converting to MWh\n", - " .multiply(DE_fuel_to_co2_intensity.values())\n", - " .sum(axis=1)\n", - " )\n", - "\n", - "s_DE_emissions_tonnes = s_DE_emissions_tonnes[s_DE_emissions_tonnes>2000]\n", - "s_DE_carbon_intensity = s_DE_emissions_tonnes/s_DE_demand.loc[s_DE_emissions_tonnes.index]\n", - "\n", - "DE_2020_emissions_tonnes = s_DE_emissions_tonnes['2020'].mean()\n", - "DE_2020_ci_avg = s_DE_carbon_intensity['2020'].mean()\n", - "\n", - "DE_2020_emissions_tonnes, DE_2020_ci_avg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Loading in\n", - "df_EI = pd.read_csv('../data/electric_insights.csv')\n", - "\n", - "df_EI = df_EI.set_index('local_datetime')\n", - "df_EI.index = pd.to_datetime(df_EI.index, utc=True)\n", - "\n", - "# Extracting RES, demand, and price series\n", - "s_GB_RES = df_EI[['wind', 'solar']].sum(axis=1)\n", - "s_GB_demand = df_EI['demand']\n", - "s_GB_price = df_EI['day_ahead_price']\n", - "\n", - "# Generating carbon intensity series\n", - "GB_fuel_to_co2_intensity = {\n", - " 'nuclear': 0, \n", - " 'biomass': 0.121, # from EI \n", - " 'coal': 0.921, # DUKES 2018 value\n", - " 'gas': 0.377, # DUKES 2018 value (lower than many CCGT estimates, let alone OCGT)\n", - " 'hydro': 0, \n", - " 'pumped_storage': 0, \n", - " 'solar': 0,\n", - " 'wind': 0,\n", - " 'belgian': 0.4, \n", - " 'dutch': 0.474, # from EI \n", - " 'french': 0.053, # from EI \n", - " 'ireland': 0.458, # from EI \n", - " 'northern_ireland': 0.458 # from EI \n", - "}\n", - "\n", - "s_GB_emissions_tonnes = (df_EI\n", - " [GB_fuel_to_co2_intensity.keys()]\n", - " .multiply(1e3*0.5) # converting to MWh\n", - " .multiply(GB_fuel_to_co2_intensity.values())\n", - " .sum(axis=1)\n", - " )\n", - "\n", - "s_GB_emissions_tonnes = s_GB_emissions_tonnes[s_GB_emissions_tonnes>2000]\n", - "s_GB_carbon_intensity = s_GB_emissions_tonnes/s_GB_demand.loc[s_GB_emissions_tonnes.index]\n", - "\n", - "# Calculating 2020 averages\n", - "GB_2020_emissions_tonnes = s_GB_emissions_tonnes['2020'].mean()\n", - "GB_2020_ci_avg = s_GB_carbon_intensity['2020'].mean()\n", - "GB_2020_RES_pct = (s_GB_RES['2020']/s_GB_demand['2020']).mean()\n", - "GB_2020_demand_avg = s_GB_demand['2020'].mean()\n", - "GB_2020_price_avg = s_GB_price['2020'].mean()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Average Solar/Wind Generation (%)Average Demand (GW)Average Price ([EUR,GBP]/MWh)Average Carbon Intensity (gCO2/kWh)
Germany35.9355.9630.47153.80
Great Britain29.8330.6133.77101.17
\n", - "
" - ], - "text/plain": [ - " Average Solar/Wind Generation (%) Average Demand (GW) \\\n", - "Germany 35.93 55.96 \n", - "Great Britain 29.83 30.61 \n", - "\n", - " Average Price ([EUR,GBP]/MWh) \\\n", - "Germany 30.47 \n", - "Great Britain 33.77 \n", - "\n", - " Average Carbon Intensity (gCO2/kWh) \n", - "Germany 153.80 \n", - "Great Britain 101.17 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "system_overview_data = {\n", - " 'Germany': {\n", - " 'Average Solar/Wind Generation (%)': round(100*DE_2020_RES_pct, 2),\n", - " 'Average Demand (GW)': round(DE_2020_demand_avg, 2),\n", - " 'Average Price ([EUR,GBP]/MWh)': round(DE_2020_price_avg, 2),\n", - " 'Average Carbon Intensity (gCO2/kWh)': round(DE_2020_ci_avg, 2),\n", - " },\n", - " 'Great Britain': {\n", - " 'Average Solar/Wind Generation (%)': round(100*GB_2020_RES_pct, 2),\n", - " 'Average Demand (GW)': round(GB_2020_demand_avg, 2),\n", - " 'Average Price ([EUR,GBP]/MWh)': round(GB_2020_price_avg, 2),\n", - " 'Average Carbon Intensity (gCO2/kWh)': round(GB_2020_ci_avg, 2),\n", - " }\n", - "}\n", - "\n", - "df_system_overview = pd.DataFrame(system_overview_data).T\n", - "\n", - "df_system_overview.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{table}\n", - "\\centering\n", - "\\caption{Systems overview for 2020}\n", - "\\label{overview_table}\n", - "\\begin{tabular}{|l|l|l|l|l|}\n", - "\\hline\n", - "{} & Average Solar/Wind Generation (\\%) & Average Demand (GW) & Average Price ([EUR,GBP]/MWh) & Average Carbon Intensity (gCO\\textsubscript{2}/kWh) \\\\ \\hline\n", - "Germany & 35.93 & 55.96 & 30.47 & 153.80 \\\\ \\hline\n", - "Great Britain & 29.83 & 30.61 & 33.77 & 101.17 \\\\ \\hline\n", - "\\end{tabular}\n", - "\\end{table}\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "get_lined_column_format = lambda n_cols:''.join(n_cols*['|l']) + '|'\n", - "\n", - "caption = 'Systems overview for 2020'\n", - "label = 'overview_table'\n", - "column_format = get_lined_column_format(df_system_overview.shape[1]+1)\n", - "\n", - "latex_str = df_system_overview.to_latex(column_format=column_format, caption=caption, label=label)\n", - "\n", - "latex_replacements = {\n", - " 'CO2': 'CO\\\\textsubscript{2}',\n", - " '\\\\\\\\\\n': '\\\\\\\\ \\\\midrule\\n',\n", - " 'midrule': 'hline',\n", - " 'toprule': 'hline',\n", - " 'bottomrule': '',\n", - " '\\n\\\\\\n': '\\n',\n", - " '\\\\hline\\n\\\\hline': '\\\\hline'\n", - "}\n", - "\n", - "for old, new in latex_replacements.items():\n", - " latex_str = latex_str.replace(old, new)\n", - "\n", - "Latex(latex_str)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "### Carbon Intensity Estimates" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BiomassCoalGasDutchFrenchIreland
gCO2/kWh12192137747453458
\n", - "
" - ], - "text/plain": [ - " Biomass Coal Gas Dutch French Ireland\n", - "gCO2/kWh 121 921 377 474 53 458" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def clean_idxs(s):\n", - " s.index = s.index.str.replace('_', ' ').str.title()\n", - " return s\n", - "\n", - "df_GB_non0_co2_intensity = (pd\n", - " .Series(GB_fuel_to_co2_intensity)\n", - " .replace(0, np.nan)\n", - " .dropna()\n", - " .drop(['belgian', 'northern_ireland'])\n", - " .pipe(clean_idxs)\n", - " .multiply(1e3)\n", - " .astype(int)\n", - " .to_frame()\n", - " .T\n", - " .rename({0: 'gCO2/kWh'})\n", - " )\n", - "\n", - "df_GB_non0_co2_intensity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{table}\n", - "\\centering\n", - "\\caption{Carbon intensity factors for fuel-types and interconnection on the GB power system}\n", - "\\label{GB_co2_intensity_table}\n", - "\\begin{tabular}{|l|l|l|l|l|l|l|}\n", - "\\hline\n", - "{} & Biomass & Coal & Gas & Dutch & French & Ireland \\\\ \\hline\n", - "gCO\\textsubscript{2}/kWh & 121 & 921 & 377 & 474 & 53 & 458 \\\\ \\hline\n", - "\\end{tabular}\n", - "\\end{table}\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "caption = 'Carbon intensity factors for fuel-types and interconnection on the GB power system'\n", - "label = 'GB_co2_intensity_table'\n", - "column_format = get_lined_column_format(df_GB_non0_co2_intensity.shape[1]+1)\n", - "\n", - "latex_str = df_GB_non0_co2_intensity.to_latex(column_format=column_format, caption=caption, label=label)\n", - "\n", - "latex_replacements = {\n", - " 'CO2': 'CO\\\\textsubscript{2}',\n", - " '\\\\\\\\\\n': '\\\\\\\\ \\\\midrule\\n',\n", - " 'midrule': 'hline',\n", - " 'toprule': 'hline',\n", - " 'bottomrule': '',\n", - " '\\n\\\\\\n': '\\n',\n", - " '\\\\hline\\n\\\\hline': '\\\\hline'\n", - "}\n", - "\n", - "for old, new in latex_replacements.items():\n", - " latex_str = latex_str.replace(old, new)\n", - "\n", - "Latex(latex_str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BiomassBrown CoalHard CoalGasOil
gCO2/kWh390360340230280
\n", - "
" - ], - "text/plain": [ - " Biomass Brown Coal Hard Coal Gas Oil\n", - "gCO2/kWh 390 360 340 230 280" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_DE_non0_co2_intensity = (pd\n", - " .Series(DE_fuel_to_co2_intensity)\n", - " .replace(0, np.nan)\n", - " .dropna()\n", - " [['Biomass', 'Brown Coal', 'Hard Coal', 'Gas', 'Oil']]\n", - " .pipe(clean_idxs)\n", - " .multiply(1e3)\n", - " .astype(int)\n", - " .to_frame()\n", - " .T\n", - " .rename({0: 'gCO2/kWh'})\n", - " )\n", - "\n", - "df_DE_non0_co2_intensity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{table}\n", - "\\centering\n", - "\\caption{Carbon intensity factors for fuel-types and interconnection on the DE power system}\n", - "\\label{DE_co2_intensity_table}\n", - "\\begin{tabular}{|l|l|l|l|l|l|}\n", - "\\hline\n", - "{} & Biomass & Brown Coal & Hard Coal & Gas & Oil \\\\ \\hline\n", - "gCO\\textsubscript{2}/kWh & 390 & 360 & 340 & 230 & 280 \\\\ \\hline\n", - "\\end{tabular}\n", - "\\end{table}\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "caption = 'Carbon intensity factors for fuel-types and interconnection on the DE power system'\n", - "label = 'DE_co2_intensity_table'\n", - "column_format = get_lined_column_format(df_DE_non0_co2_intensity.shape[1]+1)\n", - "\n", - "latex_str = df_DE_non0_co2_intensity.to_latex(column_format=column_format, caption=caption, label=label)\n", - "\n", - "for old, new in latex_replacements.items():\n", - " latex_str = latex_str.replace(old, new)\n", - "\n", - "Latex(latex_str)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "### Electricity Price Forecasting Metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "application/json": { - "DE_demand": { - "mean_abs_err": 18.28336704312868, - "median_abs_err": 14.67403224443754, - "root_mean_square_error": 23.560281367239586 - }, - "DE_dispatch": { - "mean_abs_err": 5.852023979176648, - "median_abs_err": 4.257075090332123, - "root_mean_square_error": 8.705711313706535 - }, - "GB_demand": { - "mean_abs_err": 8.423628315026313, - "median_abs_err": 6.076142585411503, - "root_mean_square_error": 13.57255404896023 - }, - "GB_dispatch": { - "mean_abs_err": 6.55687702607074, - "median_abs_err": 4.47311519486, - "root_mean_square_error": 12.053853844276484 - } - }, - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": { - "application/json": { - "expanded": false, - "root": "root" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "with open('../data/results/price_model_accuracy_metrics.json', 'r') as fp:\n", - " model_accuracy_metrics = json.load(fp)\n", - " \n", - "JSON(model_accuracy_metrics)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Dispatchable LoadTotal Load
Germany5.8518.28
Great Britain6.568.42
\n", - "
" - ], - "text/plain": [ - " Dispatchable Load Total Load\n", - "Germany 5.85 18.28\n", - "Great Britain 6.56 8.42" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_accuracy_data = {\n", - " 'Germany': {\n", - " 'Dispatchable Load': round(model_accuracy_metrics['DE_dispatch']['mean_abs_err'], 2),\n", - " 'Total Load': round(model_accuracy_metrics['DE_demand']['mean_abs_err'], 2),\n", - " },\n", - " 'Great Britain': {\n", - " 'Dispatchable Load': round(model_accuracy_metrics['GB_dispatch']['mean_abs_err'], 2),\n", - " 'Total Load': round(model_accuracy_metrics['GB_demand']['mean_abs_err'], 2),\n", - " }\n", - "}\n", - "\n", - "df_model_accuracy = pd.DataFrame(model_accuracy_data).T\n", - "\n", - "df_model_accuracy.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{table}\n", - "\\centering\n", - "\\caption{Price forecasting model accuracy when regressing against dispatchable and total load for GB and DE.}\n", - "\\label{model_accuracy_table}\n", - "\\begin{tabular}{|l|l|l|}\n", - "\\hline\n", - "{} & Dispatchable Load & Total Load \\\\ \\hline\n", - "Germany & 5.85 & 18.28 \\\\ \\hline\n", - "Great Britain & 6.56 & 8.42 \\\\ \\hline\n", - "\\end{tabular}\n", - "\\end{table}\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "caption = 'Price forecasting model accuracy when regressing against dispatchable and total load for GB and DE.'\n", - "label = 'model_accuracy_table'\n", - "column_format = get_lined_column_format(df_model_accuracy.shape[1]+1)\n", - "\n", - "latex_str = df_model_accuracy.to_latex(column_format=column_format, caption=caption, label=label)\n", - "\n", - "for old, new in latex_replacements.items():\n", - " latex_str = latex_str.replace(old, new)\n", - "\n", - "Latex(latex_str)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "### Price and CO2 MOE Results" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
predictioncounterfactualobservedmoe
local_datetime
2009-01-01 00:00:00+00:0037.20344137.31337958.050.109938
2009-01-01 00:30:00+00:0037.31337937.53513556.330.221756
2009-01-01 01:00:00+00:0036.76851336.98508752.980.216574
2009-01-01 01:30:00+00:0035.59516235.80763150.390.212469
2009-01-01 02:00:00+00:0034.84942235.06311948.700.213697
\n", - "
" - ], - "text/plain": [ - " prediction counterfactual observed moe\n", - "local_datetime \n", - "2009-01-01 00:00:00+00:00 37.203441 37.313379 58.05 0.109938\n", - "2009-01-01 00:30:00+00:00 37.313379 37.535135 56.33 0.221756\n", - "2009-01-01 01:00:00+00:00 36.768513 36.985087 52.98 0.216574\n", - "2009-01-01 01:30:00+00:00 35.595162 35.807631 50.39 0.212469\n", - "2009-01-01 02:00:00+00:00 34.849422 35.063119 48.70 0.213697" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def set_dt_idx(df, dt_idx_col='local_datetime'):\n", - " df = df.set_index(dt_idx_col)\n", - " df.index = pd.to_datetime(df.index, utc=True)\n", - " \n", - " return df\n", - "\n", - "df_GB_price_results_ts = pd.read_csv('../data/results/GB_price.csv').pipe(set_dt_idx)\n", - "df_DE_price_results_ts = pd.read_csv('../data/results/DE_price.csv').pipe(set_dt_idx)\n", - "df_GB_carbon_results_ts = pd.read_csv('../data/results/GB_carbon.csv').pipe(set_dt_idx)\n", - "df_DE_carbon_results_ts = pd.read_csv('../data/results/DE_carbon.csv').pipe(set_dt_idx)\n", - "\n", - "df_GB_price_results_ts.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
GermanyGreat Britain
Price ([EUR,GBP]/MWh)22.1713.89
Price (%)43.4329.66
Carbon (Tonnes/h)5563.221657.88
Carbon (%)39.7037.89
\n", - "
" - ], - "text/plain": [ - " Germany Great Britain\n", - "Price ([EUR,GBP]/MWh) 22.17 13.89\n", - "Price (%) 43.43 29.66\n", - "Carbon (Tonnes/h) 5563.22 1657.88\n", - "Carbon (%) 39.70 37.89" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "MOE_results_data = {\n", - " 'Germany': {\n", - " 'Price ([EUR,GBP]/MWh)': round(df_DE_price_results_ts.loc['2020', 'moe'].mean(), 2),\n", - " 'Price (%)': round(100*(df_DE_price_results_ts.loc['2020', 'moe']*df_DE['demand']).sum()/((df_DE_price_results_ts.loc['2020', 'observed']+df_DE_price_results_ts.loc['2020', 'moe'])*df_DE['demand']).sum(), 2),\n", - " 'Carbon (Tonnes/h)': round(df_DE_carbon_results_ts.loc['2020', 'moe'].mean(), 2),\n", - " 'Carbon (%)': round(100*(df_DE_carbon_results_ts.loc['2020', 'moe'].sum()/(df_DE_carbon_results_ts.loc['2020', 'observed']+df_DE_carbon_results_ts.loc['2020', 'moe']).sum()).mean(), 2)\n", - " },\n", - " 'Great Britain': {\n", - " 'Price ([EUR,GBP]/MWh)': round(df_GB_price_results_ts.loc['2020', 'moe'].mean(), 2),\n", - " 'Price (%)': round(100*(df_GB_price_results_ts.loc['2020', 'moe']*df_EI['demand']).sum()/((df_GB_price_results_ts.loc['2020', 'observed']+df_GB_price_results_ts.loc['2020', 'moe'])*df_EI['demand']).sum(), 2),\n", - " 'Carbon (Tonnes/h)': round(df_GB_carbon_results_ts.loc['2020', 'moe'].mean(), 2), # doubled to make it the same hourly rate as DE\n", - " 'Carbon (%)': round(100*(df_GB_carbon_results_ts.loc['2020', 'moe'].sum()/(df_GB_carbon_results_ts.loc['2020', 'observed']+df_GB_carbon_results_ts.loc['2020', 'moe']).sum()).mean(), 2)\n", - " }\n", - "}\n", - "\n", - "df_MOE_results = (pd\n", - " .DataFrame(MOE_results_data)\n", - " )\n", - "\n", - "df_MOE_results.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "\\begin{table}\n", - "\\centering\n", - "\\caption{2020 Merit Order Effect results overview (weighted )}\n", - "\\label{moe_results_table}\n", - "\\begin{tabular}{|l|l|l|}\n", - "\\hline\n", - "{} & Germany & Great Britain \\\\ \\hline\n", - "Price ([EUR,GBP]/MWh) & 22.17 & 13.89 \\\\ \\hline\n", - "Price (\\%) & 43.43 & 29.66 \\\\ \\hline\n", - "Carbon (Tonnes/h) & 5563.22 & 1657.88 \\\\ \\hline\n", - "Carbon (\\%) & 39.70 & 37.89 \\\\ \\hline\n", - "\\end{tabular}\n", - "\\end{table}\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "caption = '2020 Merit Order Effect results overview (weighted )'\n", - "label = 'moe_results_table'\n", - "column_format = get_lined_column_format(df_MOE_results.shape[1]+1)\n", - "\n", - "latex_str = df_MOE_results.to_latex(column_format=column_format, caption=caption, label=label)\n", - "\n", - "for old, new in latex_replacements.items():\n", - " latex_str = latex_str.replace(old, new)\n", - "\n", - "Latex(latex_str)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "MOE", - "language": "python", - "name": "moe" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/settings.ini b/settings.ini index b860336..ab51f9a 100644 --- a/settings.ini +++ b/settings.ini @@ -21,4 +21,4 @@ title = moepy doc_path = docs doc_host = https://AyrtonB.github.io doc_baseurl = /Merit-Order-Effect/ -requirements = pandas==1.2.0 numpy==1.19.5 matplotlib==3.3.3 seaborn==0.11.1 lxml==4.6.2 ipypb==0.5.2 feautils==0.0.2 dagster==0.9.21 scikit-learn==0.24.0 scipy==1.6.0 \ No newline at end of file +requirements = pandas==1.2.0 numpy==1.19.5 matplotlib==3.3.3 seaborn==0.11.1 lxml==4.6.2 ipypb==0.5.2 dagster==0.9.21 scikit-learn==0.24.0 scipy==1.6.0 \ No newline at end of file diff --git a/write-up/outline.md b/write-up/outline.md index 356e2ff..1dfe11c 100644 --- a/write-up/outline.md +++ b/write-up/outline.md @@ -304,11 +304,11 @@ - URL: https://econpapers.repec.org/paper/duiwpaper/0701.htm - Journal: NA -##### -- MOE: -- Year(s): -- Location: -- Method: +##### Halttunen et al. (2021) +- MOE: 0.68, 0.631, and 0.482 €/MWh per p.p. increase in RES penetration +- Year(s): -, 2012-2019, and 2010-2019 +- Location: Global, Germany, and Great Britain +- Method: RPR - DOI: - Journal: @@ -423,13 +423,12 @@ Traber et al 2011 -> "In the absence of expanded deployment of renewable energy, * Need to calc the 95% conf and 68% pred intvls To Do -- [ ] Add the big literature review table (at the same time check each one has been downloaded with the DOI as the filename) -- [ ] Go over the intro and abstract comments from Paolo (monday morning) +- [x] Add the big literature review table (at the same time check each one has been downloaded with the DOI as the filename) +- [x] Go over the intro and abstract comments from Paolo (monday morning) - [ ] Final run over Aidans comments (sunday night) -- [ ] Finish discussion around the MOE estimate (do the lit rev table first) -- [ ] Run the skopt model using the 2 hyper-params (saturday night/sunday morning) -- [ ] Re-run the pred and conf intvl models (!!!not a priority - though pred is higher, could show one pred intvl as heatmap too!!!) -- [ ] Talk about how using RES % penetration exaggerates the MOE during periods of low demand (sunday/monday) +- [x] Finish discussion around the MOE estimate (do the lit rev table first) + - [x] Add the simple results tables - [x] System overview - [x] Carbon intensity estimates @@ -441,9 +440,36 @@ To Do - [x] % MOE reduction v % RES - [x] Example day with counter-factual price (no longer going to add - could have in the discussion) - [ ] Add all of the citations into the bib (lit rev tables one then, other ones after monday call) -- [ ] Check for all XXX, REF, \*\*\*, and !!! (before monday call) -- [ ] Check numbering and capitalise tables and figures (before monday call) +- [ ] Check for all XXX, REF, \*\*\*, !!!, (before monday call) +- [ ] Check numbering and capitalise tables and figures (before monday call) - also check for figures with historic hard-coded values * Need to work out how to add in the time complexity element best -Tonight: do the tables and add the graphs, then get skopt running \ No newline at end of file +Tonight: do the tables and add the graphs, then get skopt running + +### Aidan meeting 22nd March +* sense-checked values, corrected issue with the carbon emissions (was net negative for DE) +* lots of issues with the hyper-param optimisation implementation, finally got working late last night +* + +To Do: +- [x] intro/abstract +- [x] couple paras on the optimisation +- [x] Add in the confidence and prediction intervals +- [ ] Make an improved MOE diagram +- [x] Sort out my bibliography +- [ ] Fix table formatting (partially achieved) +- [x] remove dupes for the time-series and suface fits +- [x] Run the intro through Grammarly +- [x] remove the MOE by time-of-day from external paper +- [x] swap for correct kernel (for now remove comment and fix language around the kernel) +- [x] reset track changes +- [ ] Energy stylesheet +- [ ] Standardise FX units/symbols +- [ ] Add the papers from the literature review table into the bibliography +- [x] Check for all XXX, REF, \*\*\*, and !!! +- [x] Check numbering and capitalise tables and figures +- [x] also check for figures with historic hard-coded values +- [ ] Need more references in the intro +- [ ] Shorten the abstract further still +- [ ] Need to clean up the bibliography, for sources like ENTSOE/BMRS look at what other papers have done \ No newline at end of file