From f6504baa24bb88fa0319535d3dceb56f6d43abe7 Mon Sep 17 00:00:00 2001 From: Paul Date: Wed, 12 Jul 2023 11:15:50 -0600 Subject: [PATCH 01/17] add polars energy ratio --- flasc/energy_ratio/energy_ratio_polars.py | 758 ++++++++++++++++++++++ 1 file changed, 758 insertions(+) create mode 100644 flasc/energy_ratio/energy_ratio_polars.py diff --git a/flasc/energy_ratio/energy_ratio_polars.py b/flasc/energy_ratio/energy_ratio_polars.py new file mode 100644 index 00000000..c3d84ee6 --- /dev/null +++ b/flasc/energy_ratio/energy_ratio_polars.py @@ -0,0 +1,758 @@ +# This is a work in progress as we try to synthesize ideas from the +# table based methods and energy ratios back into one thing, +# some ideas we're incorporating: + +# Conversion from polars to pandas +# Constructing tables (but now including tables of ratios) +# Keeping track of frequencies is matching sized tables + +import numpy as np +import polars as pl +import warnings + +# def get_mid_bins(bin_edges): +# """_summary_ + +# Args: +# bin_edges (NDArray): a set of bin edges +# """ + +# print(bin_edges[:-1] + np.diff(bin_edges)/2.0) + +def convert_to_polars(df_): + """_summary_ + + Args: + df_ (Pandas DataFrame): a pandas dataframe + + Returns: + Polars DataFrame: a polars dataframe + """ + return pl.from_pandas(df_) + +def cut(col_name, edges): + """ + Bins the values in the specified column according to the given edges. + + Parameters: + col_name (str): The name of the column to bin. + edges (array-like): The edges of the bins. Values will be placed into the bin + whose left edge is the largest edge less than or equal to + the value, and whose right edge is the smallest edge + greater than the value. + + Returns: + expression: An expression object that can be used to bin the column. + """ + c = pl.col(col_name) + labels = edges[:-1] + np.diff(edges)/2.0 + expr = pl.when(c < edges[0]).then(None) + for edge, label in zip(edges[1:], labels): + expr = expr.when(c < edge).then(label) + expr = expr.otherwise(None) + + return expr + + +def bin_column(df_, col_name, bin_col_name, edges): + """ + Bins the values in the specified column of a Polars DataFrame according to the given edges. + + Parameters: + df_ (pl.DataFrame): The Polars DataFrame containing the column to bin. + col_name (str): The name of the column to bin. + bin_col_name (str): The name to give the new column containing the bin labels. + edges (array-like): The edges of the bins. Values will be placed into the bin + whose left edge is the largest edge less than or equal to + the value, and whose right edge is the smallest edge + greater than the value. + + Returns: + pl.DataFrame: A new Polars DataFrame with an additional column containing the bin labels. + """ + return df_.with_columns( + cut( + col_name=col_name, + edges = edges + ).alias(bin_col_name) + ) + +def add_ws_bin(df_, ws_cols, ws_step=1.0, ws_min=-0.5, ws_max=50.0): + """ + Add the ws_bin column to a dataframe, given which columns to average over + and the step sizes to use + + Parameters: + df_ (pl.DataFrame): The Polars DataFrame containing the column to bin. + ws_cols (str): The name of the columns to average across. + ws_step (float): Step size for binning + ws_min (float): Minimum wind speed + ws_max (float): Maximum wind speed + + Returns: + pl.DataFrame: A new Polars DataFrame with an additional ws_bin column + """ + + edges = np.arange(ws_min, ws_max+ws_step,ws_step) + + df_with_mean_ws = ( + # df_.select(pl.exclude('ws_bin')) # In case ws_bin already exists + df_.with_columns( + # df_.select(ws_cols).mean(axis=1).alias('ws_bin') + ws_bin = pl.concat_list(ws_cols).list.mean() # Initially ws_bin is just the mean + ) + .filter( + pl.all(pl.col(ws_cols).is_not_null()) # Select for all bin cols present + ) + + .filter( + (pl.col('ws_bin') > ws_min) & # Filter the mean wind speed + (pl.col('ws_bin') < ws_max) & + (pl.col('ws_bin').is_not_null()) + ) + ) + + return bin_column(df_with_mean_ws, 'ws_bin', 'ws_bin', edges) + +def add_wd_bin(df_, wd_cols, wd_step=2.0, wd_min=0.0, wd_max=360.0): + """ + Add the wd_bin column to a dataframe, given which columns to average over + and the step sizes to use + + Parameters: + df_ (pl.DataFrame): The Polars DataFrame containing the column to bin. + wd_cols (str): The name of the columns to average across. + wd_step (float): Step size for binning + wd_min (float): Minimum wind direction + wd_max (float): Maximum wind direction + + Returns: + pl.DataFrame: A new Polars DataFrame with an additional ws_bin column + """ + + edges = np.arange(wd_min, wd_max + wd_step, wd_step) + + # Gather up intermediate column names and final column names + wd_cols_cos = [c + '_cos' for c in wd_cols] + wd_cols_sin = [c + '_sin' for c in wd_cols] + cols_to_return = df_.columns + if 'wd_bin' not in cols_to_return: + cols_to_return = cols_to_return + ['wd_bin'] + + + df_with_mean_wd = ( + # df_.select(pl.exclude('wd_bin')) # In case wd_bin already exists + df_.filter( + pl.all(pl.col(wd_cols).is_not_null()) # Select for all bin cols present + ) + # Add the cosine columns + .with_columns( + [ + pl.col(wd_cols).mul(np.pi/180).cos().suffix('_cos'), + pl.col(wd_cols).mul(np.pi/180).sin().suffix('_sin'), + ] + ) + ) + df_with_mean_wd = ( + df_with_mean_wd + .with_columns( + [ + # df_with_mean_wd.select(wd_cols_cos).mean(axis=1).alias('cos_mean'), + # df_with_mean_wd.select(wd_cols_sin).mean(axis=1).alias('sin_mean'), + pl.concat_list(wd_cols_cos).list.mean().alias('cos_mean'), + pl.concat_list(wd_cols_sin).list.mean().alias('sin_mean'), + ] + ) + .with_columns( + wd_bin = np.mod(pl.reduce(np.arctan2, [pl.col('sin_mean'), pl.col('cos_mean')]) + .mul(180/np.pi), 360.0) + ) + .filter( + (pl.col('wd_bin') > wd_min) & # Filter the mean wind speed + (pl.col('wd_bin') < wd_max) & + (pl.col('wd_bin').is_not_null()) + ) + .select(cols_to_return) # Select for just the columns we want to return + ) + + return bin_column(df_with_mean_wd, 'wd_bin', 'wd_bin', edges) + + +def add_test_power(df_, test_cols): + + return df_.with_columns( + test_pow = pl.concat_list(test_cols).list.mean() + #df_.select(test_cols).mean(axis=1).alias('test_pow') + ) + + +def add_ref_power(df_, ref_cols): + + return df_.with_columns( + ref_pow = pl.concat_list(ref_cols).list.mean() + # df_.select(ref_cols).mean(axis=1).alias('ref_pow') + ) + +def generate_block_list(N, num_blocks=10): + """Generate an np.array of length N where each element is an integer between 0 and num_blocks-1 + with each value repeating N/num_blocks times. + + Args: + N (int): Length of the array to generate + num_blocks (int): Number of blocks to generate + + """ + + # Test than N and num_blocks are integers greater than 0 + if not isinstance(N, int) or not isinstance(num_blocks, int): + raise ValueError('N and num_blocks must be integers') + if N <= 0 or num_blocks <= 0: + raise ValueError('N and num_blocks must be greater than 0') + + # Num blocks must be less than or equal to N + if num_blocks > N: + raise ValueError('num_blocks must be less than or equal to N') + + + block_list = np.zeros(N) + for i in range(num_blocks): + block_list[i*N//num_blocks:(i+1)*N//num_blocks] = i + return block_list.astype(int) + +def get_energy_table( + df_list_in, + df_names=None, + num_blocks=10,): + """ + Given a list of PANDAS dataframes, return a single + POLARS dataframe with a column + indicating which dataframe the row came from as well as a block + list to use in bootstrapping. + + Parameters: + df_list_in (list): A list of PANDAS dataframes to combine. + df_names (list): A list of names to give to the dataframes. If None, + the dataframes will be named df_0, df_1, etc. + n_blocks (int): The number of blocks to add to the block column for later bootstrapping. + + Returns: + pl.DataFrame: A new Polars DataFrame with an additional column containing the df_names + """ + + # Convert to polars + df_list = [pl.from_pandas(df) for df in df_list_in] + + if df_names is None: + df_names = ['df_'+str(i) for i in range(len(df_list))] + + # Add a name column to each dataframe + for i in range(len(df_list)): + df_list[i] = df_list[i].with_columns([ + pl.lit(df_names[i]).alias('df_name') + ]) + + # Add a block column to each dataframe + for i in range(len(df_list)): + df_list[i] = df_list[i].with_columns([ + pl.Series(generate_block_list(df_list[i].shape[0], num_blocks=num_blocks)).alias('block') + ]) + + return pl.concat(df_list) + +def resample_energy_table(df_e_, i): + """Use the block column of an energy table to resample the data. + + Args: + df_e_ (pl.DataFrame): An energy table with a block column + + Returns: + pl.DataFrame: A new energy table with (approximately) + the same number of rows as the original + """ + + if i == 0: #code to return as is + return df_e_ + + else: + + num_blocks = df_e_['block'].max() + 1 + + # Generate a random np.array, num_blocks long, where each element is + # an integer between 0 and num_blocks-1 + block_list = np.random.randint(0, num_blocks, num_blocks) + + return pl.DataFrame( + { + 'block':block_list + } + ).join(df_e_, how='inner', on='block') + + + +def compute_energy_ratio(df_, + df_names, + ref_turbines=None, + test_turbines= None, + ws_turbines=None, + wd_turbines=None, + use_predefined_ref = False, + use_predefined_ws = False, + use_predefined_wd = False, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + bin_cols_in = ['wd_bin','ws_bin'], + ): + + """ + Compute the energy ratio between two sets of turbines. + + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_turbines (list[int]): A list of turbine numbers to use as the reference. + test_turbines (list[int]): A list of turbine numbers to use as the test. + ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds + wd_turbines (list[int]): A list of turbine numbers to use for the wind directions + use_predefined_ref (bool): If True, use the ref_pow column of df_ as the reference power. + use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. + use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + + Returns: + pl.DataFrame: A dataframe containing the energy ratio for each wind direction bin + """ + + # If use_predefined_ref is True, df_ must have a column named 'ref_pow' + if use_predefined_ref: + if 'ref_pow' not in df_.columns: + raise ValueError('df_ must have a column named ref_pow when use_predefined_ref is True') + # If ref_turbines supplied, warn user that it will be ignored + if ref_turbines is not None: + warnings.warn('ref_turbines will be ignored when use_predefined_ref is True') + else: + # ref_turbine must be supplied + if ref_turbines is None: + raise ValueError('ref_turbines must be supplied when use_predefined_ref is False') + + # If use_predefined_ws is True, df_ must have a column named 'ws' + if use_predefined_ws: + if 'ws' not in df_.columns: + raise ValueError('df_ must have a column named ws when use_predefined_ws is True') + # If ws_turbines supplied, warn user that it will be ignored + if ws_turbines is not None: + warnings.warn('ws_turbines will be ignored when use_predefined_ws is True') + else: + # ws_turbine must be supplied + if ws_turbines is None: + raise ValueError('ws_turbines must be supplied when use_predefined_ws is False') + + # If use_predefined_wd is True, df_ must have a column named 'wd' + if use_predefined_wd: + if 'wd' not in df_.columns: + raise ValueError('df_ must have a column named wd when use_predefined_wd is True') + # If wd_turbines supplied, warn user that it will be ignored + if wd_turbines is not None: + warnings.warn('wd_turbines will be ignored when use_predefined_wd is True') + else: + # wd_turbine must be supplied + if wd_turbines is None: + raise ValueError('wd_turbines must be supplied when use_predefined_wd is False') + + + # Identify the number of dataframes + num_df = len(df_names) + + # Set up the column names for the reference and test power + if not use_predefined_ref: + ref_cols = [f'pow_{i:03d}' for i in ref_turbines] + else: + ref_cols = ['ref_pow'] + + if not use_predefined_ws: + ws_cols = [f'ws_{i:03d}' for i in ws_turbines] + else: + ws_cols = ['ws'] + + if not use_predefined_wd: + wd_cols = [f'wd_{i:03d}' for i in wd_turbines] + else: + wd_cols = ['wd'] + + # Convert the numbered arrays to appropriate column names + test_cols = [f'pow_{i:03d}' for i in test_turbines] + + + # Filter df_ that all the columns are not null + df_ = df_.filter(pl.all(pl.col(ref_cols + test_cols + ws_cols + wd_cols).is_not_null())) + + # Assign the wd/ws bins + df_ = add_ws_bin(df_, ws_cols, ws_step, ws_min, ws_max) + df_ = add_wd_bin(df_, wd_cols, wd_step, wd_min, wd_max) + + # Assign the reference and test power columns + df_ = add_ref_power(df_, ref_cols) + df_ = add_test_power(df_, test_cols) + + bin_cols_without_df_name = [c for c in bin_cols_in if c != 'df_name'] + bin_cols_with_df_name = bin_cols_without_df_name + ['df_name'] + + df_ = (df_ + .filter(pl.all(pl.col(bin_cols_with_df_name).is_not_null())) # Select for all bin cols present + .groupby(bin_cols_with_df_name, maintain_order=True) + .agg([pl.mean("ref_pow"), pl.mean("test_pow"),pl.count()]) + .with_columns( + [ + pl.col('count').min().over(bin_cols_without_df_name).alias('count_min')#, # Find the min across df_name + ] + ) + .with_columns( + [ + pl.col('ref_pow').mul(pl.col('count_min')).alias('ref_energy'), # Compute the reference energy + pl.col('test_pow').mul(pl.col('count_min')).alias('test_energy'), # Compute the test energy + ] + ) + .groupby(['wd_bin','df_name'], maintain_order=True) + .agg([pl.sum("ref_energy"), pl.sum("test_energy"),pl.sum("count")]) + .with_columns( + energy_ratio = pl.col('test_energy') / pl.col('ref_energy') + ) + .pivot(values=['energy_ratio','count'], columns='df_name', index='wd_bin',aggregate_function='first') + .rename({f'energy_ratio_df_name_{n}' : n for n in df_names}) + .rename({f'count_df_name_{n}' : f'count_{n}' for n in df_names}) + .sort('wd_bin') + ) + + # This probably doesn't belong in here but for now + if num_df == 2: + df_ = df_.with_columns( + uplift = 100 * (pl.col(df_names[1]) - pl.col(df_names[0])) / pl.col(df_names[0]) + ) + + # Enforce a column order + df_ = df_.select(['wd_bin'] + df_names + ['uplift'] + [f'count_{n}' for n in df_names]) + + else: + # Enforce a column order + df_ = df_.select(['wd_bin'] + df_names + [f'count_{n}' for n in df_names]) + + + return(df_) + +def compute_energy_ratio_bootstrap(df_, + df_names, + ref_turbines=None, + test_turbines= None, + ws_turbines=None, + wd_turbines=None, + use_predefined_ref = False, + use_predefined_ws = False, + use_predefined_wd = False, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + bin_cols_in = ['wd_bin','ws_bin'], + N = 20, + ): + + """ + Compute the energy ratio between two sets of turbines with bootstrapping + + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_turbines (list[int]): A list of turbine numbers to use as the reference. + test_turbines (list[int]): A list of turbine numbers to use as the test. + ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds + wd_turbines (list[int]): A list of turbine numbers to use for the wind directions + use_predefined_ref (bool): If True, use the ref_pow column of df_ as the reference power. + use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. + use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + N (int): The number of bootstrap samples to use. + + Returns: + pl.DataFrame: A dataframe containing the energy ratio between the two sets of turbines. + + """ + + df_concat = pl.concat([compute_energy_ratio(resample_energy_table(df_, i), + df_names, + ref_turbines, + test_turbines, + ws_turbines, + wd_turbines, + use_predefined_ref, + use_predefined_ws, + use_predefined_wd, + ws_step, + ws_min, + ws_max, + wd_step, + wd_min, + wd_max, + bin_cols_in, + ) for i in range(N)]) + + if 'uplift' in df_concat.columns: + df_names_with_uplift = df_names + ['uplift'] + else: + df_names_with_uplift = df_names + + + return(df_concat + .groupby(['wd_bin'], maintain_order=True) + .agg([pl.first(n) for n in df_names_with_uplift] + + [pl.quantile(n, 0.95).alias(n + "_ub") for n in df_names_with_uplift] + + [pl.quantile(n, 0.05).alias(n + "_lb") for n in df_names_with_uplift] + + [pl.first(f'count_{n}') for n in df_names] + ) + .sort('wd_bin') + ) + +# Use method of Eric Simley's slide 2 +def compute_uplift_in_region(df_, + df_names, + ref_turbines=None, + test_turbines= None, + ws_turbines=None, + wd_turbines=None, + use_predefined_ref = False, + use_predefined_ws = False, + use_predefined_wd = False, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + bin_cols_in = ['wd_bin','ws_bin'], + N = 20, + ): + + """ + Compute the energy uplift between two dataframes using method of Eric Simley's slide 2 + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_turbines (list[int]): A list of turbine numbers to use as the reference. + test_turbines (list[int]): A list of turbine numbers to use as the test. + ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds + wd_turbines (list[int]): A list of turbine numbers to use for the wind directions + use_predefined_ref (bool): If True, use the ref_pow column of df_ as the reference power. + use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. + use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + + Returns: + pl.DataFrame: A dataframe containing the energy uplift + """ + + # If use_predefined_ref is True, df_ must have a column named 'ref_pow' + if use_predefined_ref: + if 'ref_pow' not in df_.columns: + raise ValueError('df_ must have a column named ref_pow when use_predefined_ref is True') + # If ref_turbines supplied, warn user that it will be ignored + if ref_turbines is not None: + warnings.warn('ref_turbines will be ignored when use_predefined_ref is True') + else: + # ref_turbine must be supplied + if ref_turbines is None: + raise ValueError('ref_turbines must be supplied when use_predefined_ref is False') + + # If use_predefined_ws is True, df_ must have a column named 'ws' + if use_predefined_ws: + if 'ws' not in df_.columns: + raise ValueError('df_ must have a column named ws when use_predefined_ws is True') + # If ws_turbines supplied, warn user that it will be ignored + if ws_turbines is not None: + warnings.warn('ws_turbines will be ignored when use_predefined_ws is True') + else: + # ws_turbine must be supplied + if ws_turbines is None: + raise ValueError('ws_turbines must be supplied when use_predefined_ws is False') + + # If use_predefined_wd is True, df_ must have a column named 'wd' + if use_predefined_wd: + if 'wd' not in df_.columns: + raise ValueError('df_ must have a column named wd when use_predefined_wd is True') + # If wd_turbines supplied, warn user that it will be ignored + if wd_turbines is not None: + warnings.warn('wd_turbines will be ignored when use_predefined_wd is True') + else: + # wd_turbine must be supplied + if wd_turbines is None: + raise ValueError('wd_turbines must be supplied when use_predefined_wd is False') + + + num_df = len(df_names) + + # Confirm num_df == 2 + if num_df != 2: + raise ValueError('Number of dataframes must be 2') + + # Set up the column names for the reference and test power + if not use_predefined_ref: + ref_cols = [f'pow_{i:03d}' for i in ref_turbines] + else: + ref_cols = ['ref_pow'] + + if not use_predefined_ws: + ws_cols = [f'ws_{i:03d}' for i in ws_turbines] + else: + ws_cols = ['ws'] + + if not use_predefined_wd: + wd_cols = [f'wd_{i:03d}' for i in wd_turbines] + else: + wd_cols = ['wd'] + + # Convert the numbered arrays to appropriate column names + test_cols = [f'pow_{i:03d}' for i in test_turbines] + + + # Filter df_ that all the columns are not null + df_ = df_.filter(pl.all(pl.col(ref_cols + test_cols + ws_cols + wd_cols).is_not_null())) + + # Assign the wd/ws bins + df_ = add_ws_bin(df_, ws_cols, ws_step, ws_min, ws_max) + df_ = add_wd_bin(df_, wd_cols, wd_step, wd_min, wd_max) + + # Assign the reference and test power columns + df_ = add_ref_power(df_, ref_cols) + df_ = add_test_power(df_, test_cols) + + bin_cols_without_df_name = [c for c in bin_cols_in if c != 'df_name'] + bin_cols_with_df_name = bin_cols_without_df_name + ['df_name'] + + df_ = (df_.with_columns( + power_ratio = pl.col('test_pow') / pl.col('ref_pow')) + .filter(pl.all(pl.col(bin_cols_with_df_name).is_not_null())) # Select for all bin cols present + .groupby(bin_cols_with_df_name, maintain_order=True) + .agg([pl.mean("ref_pow"), pl.mean("power_ratio"),pl.count()]) + .with_columns( + [ + pl.col('count').min().over(bin_cols_without_df_name).alias('count_min'), # Find the min across df_name + pl.col('ref_pow').mul(pl.col('power_ratio')).alias('test_pow'), # Compute the test power + ] + ) + + .pivot(values=['power_ratio','test_pow','ref_pow','count_min'], columns='df_name', index=['wd_bin','ws_bin'],aggregate_function='first') + .drop_nulls() + .with_columns( + f_norm = pl.col(f'count_min_df_name_{df_names[0]}') / pl.col(f'count_min_df_name_{df_names[0]}').sum() + ) + .with_columns( + delta_power_ratio = pl.col(f'power_ratio_df_name_{df_names[1]}') - pl.col(f'power_ratio_df_name_{df_names[0]}'), + ref_pow_both_cases = pl.concat_list([f'ref_pow_df_name_{n}' for n in df_names]).list.mean() + ) + .with_columns( + delta_energy = pl.col('delta_power_ratio') * pl.col('f_norm') * pl.col('ref_pow_both_cases'), # pl.col(f'ref_pow_df_name_{df_names[0]}'), + base_test_energy = pl.col(f'test_pow_df_name_{df_names[0]}') * pl.col('f_norm') + ) + + ) + + return pl.DataFrame({'delta_energy':8760 * df_['delta_energy'].sum(), + 'base_test_energy':8760 * df_['base_test_energy'].sum(), + 'uplift':100 * df_['delta_energy'].sum() / df_['base_test_energy'].sum()}) + + +def compute_uplift_in_region_bootstrap(df_, + df_names, + ref_turbines=None, + test_turbines= None, + ws_turbines=None, + wd_turbines=None, + use_predefined_ref = False, + use_predefined_ws = False, + use_predefined_wd = False, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + bin_cols_in = ['wd_bin','ws_bin'], + N = 20, + ): + + """ + Compute the uplift in a region using bootstrap resampling + + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_turbines (list[int]): A list of turbine numbers to use as the reference. + test_turbines (list[int]): A list of turbine numbers to use as the test. + ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds + wd_turbines (list[int]): A list of turbine numbers to use for the wind directions + use_predefined_ref (bool): If True, use the ref_pow column of df_ as the reference power. + use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. + use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + N (int): The number of bootstrap samples to use. + + Returns: + pl.DataFrame: A dataframe containing the energy uplift + """ + + df_concat = pl.concat([compute_uplift_in_region(resample_energy_table(df_, i), + df_names, + ref_turbines, + test_turbines, + ws_turbines, + wd_turbines, + use_predefined_ref, + use_predefined_ws, + use_predefined_wd, + ws_step, + ws_min, + ws_max, + wd_step, + wd_min, + wd_max, + bin_cols_in, + ) for i in range(N)]) + + return pl.DataFrame({ + 'delta_energy_exp':df_concat['delta_energy'][0], + 'delta_energy_ub':df_concat['delta_energy'].quantile(0.95), + 'delta_energy_lb':df_concat['delta_energy'].quantile(0.05), + 'base_test_energy_exp':df_concat['base_test_energy'][0], + 'base_test_energy_ub':df_concat['base_test_energy'].quantile(0.95), + 'base_test_energy_lb':df_concat['base_test_energy'].quantile(0.05), + 'uplift_exp':df_concat['uplift'][0], + 'uplift_ub':df_concat['uplift'].quantile(0.95), + 'uplift_lb':df_concat['uplift'].quantile(0.05), + }) \ No newline at end of file From e8ed456a10268f1b41b53f889598056fb9f462ca Mon Sep 17 00:00:00 2001 From: Paul Date: Wed, 12 Jul 2023 11:15:56 -0600 Subject: [PATCH 02/17] update init --- flasc/energy_ratio/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/flasc/energy_ratio/__init__.py b/flasc/energy_ratio/__init__.py index d30c1e6f..30ebde4a 100644 --- a/flasc/energy_ratio/__init__.py +++ b/flasc/energy_ratio/__init__.py @@ -13,5 +13,6 @@ energy_ratio_gain, energy_ratio_suite, energy_ratio_visualization, - energy_ratio_wd_bias_estimation + energy_ratio_wd_bias_estimation, + energy_ratio_polars ) \ No newline at end of file From 6c934629e7e4a4d165308f6e9df1aeefb09b11f3 Mon Sep 17 00:00:00 2001 From: Paul Date: Wed, 12 Jul 2023 11:16:04 -0600 Subject: [PATCH 03/17] Start converting timing test --- flasc/timing_tests/energy_ratio_timing.py | 59 ++++++++++++----------- 1 file changed, 30 insertions(+), 29 deletions(-) diff --git a/flasc/timing_tests/energy_ratio_timing.py b/flasc/timing_tests/energy_ratio_timing.py index 00d7a01c..de9d558f 100644 --- a/flasc/timing_tests/energy_ratio_timing.py +++ b/flasc/timing_tests/energy_ratio_timing.py @@ -25,6 +25,7 @@ import pandas as pd from flasc.energy_ratio import energy_ratio_suite +from flasc.energy_ratio import energy_ratio_polars as erp N_ITERATIONS = 5 @@ -33,7 +34,7 @@ def load_data_and_prep_data(): # Load dataframe with artificial SCADA data root_dir = os.path.dirname(os.path.abspath(__file__)) ftr_path = os.path.join( - root_dir, '..','examples_artificial_data', 'raw_data_processing', 'postprocessed', 'df_scada_data_600s_filtered_and_northing_calibrated.ftr' + root_dir, '..','..','examples_artificial_data', 'raw_data_processing', 'postprocessed', 'df_scada_data_600s_filtered_and_northing_calibrated.ftr' ) if not os.path.exists(ftr_path): @@ -62,39 +63,39 @@ def time_energy_ratio_with_bootstrapping(): # Load the data df = load_data_and_prep_data() - # Load an energy ratio suite from FLASC - s = energy_ratio_suite.energy_ratio_suite(verbose=False) + # Build the polars energy table object + # Speciy num_blocks = num_rows to implement normal boostrapping + df_energy = erp.get_energy_table([df],['baseline'],num_blocks=df.shape[0]) - # Add dataframe to energy suite - s.add_df(df, 'data') + print(df_energy) - # For forward consistency, define the bins by the edges - ws_edges = np.arange(5,25,1.) - wd_edges = np.arange(0,360,2.) + # # For forward consistency, define the bins by the edges + # ws_edges = np.arange(5,25,1.) + # wd_edges = np.arange(0,360,2.) - # Create bins - ws_bins = [(ws_edges[i], ws_edges[i+1]) for i in range(len(ws_edges)-1)] - wd_bins = [(wd_edges[i], wd_edges[i+1]) for i in range(len(wd_edges)-1)] + # # Create bins + # ws_bins = [(ws_edges[i], ws_edges[i+1]) for i in range(len(ws_edges)-1)] + # wd_bins = [(wd_edges[i], wd_edges[i+1]) for i in range(len(wd_edges)-1)] - # Run this calculation N_ITERATIONS times and take the average time + # # Run this calculation N_ITERATIONS times and take the average time - time_results = np.zeros(N_ITERATIONS) - for i in range(N_ITERATIONS): - start_time = time.time() - er = s.get_energy_ratios( - test_turbines=1, - ws_bins=ws_bins, - wd_bins=wd_bins, - N=N, - percentiles=[5.0, 95.0], - verbose=False - ) - - end_time = time.time() - time_results[i] = end_time - start_time - - # Return the average time - return np.mean(time_results) + # time_results = np.zeros(N_ITERATIONS) + # for i in range(N_ITERATIONS): + # start_time = time.time() + # er = s.get_energy_ratios( + # test_turbines=1, + # ws_bins=ws_bins, + # wd_bins=wd_bins, + # N=N, + # percentiles=[5.0, 95.0], + # verbose=False + # ) + + # end_time = time.time() + # time_results[i] = end_time - start_time + + # # Return the average time + # return np.mean(time_results) From 1a7da12755e78e8d20ceb37ae4bfa842660b1725 Mon Sep 17 00:00:00 2001 From: Paul Date: Wed, 12 Jul 2023 11:31:40 -0600 Subject: [PATCH 04/17] switch to pow_ref and include test on test_turbine --- flasc/energy_ratio/energy_ratio_polars.py | 65 ++++++++++++++--------- 1 file changed, 40 insertions(+), 25 deletions(-) diff --git a/flasc/energy_ratio/energy_ratio_polars.py b/flasc/energy_ratio/energy_ratio_polars.py index c3d84ee6..a9d22c84 100644 --- a/flasc/energy_ratio/energy_ratio_polars.py +++ b/flasc/energy_ratio/energy_ratio_polars.py @@ -186,11 +186,11 @@ def add_test_power(df_, test_cols): ) -def add_ref_power(df_, ref_cols): +def add_power_ref(df_, ref_cols): return df_.with_columns( - ref_pow = pl.concat_list(ref_cols).list.mean() - # df_.select(ref_cols).mean(axis=1).alias('ref_pow') + pow_ref = pl.concat_list(ref_cols).list.mean() + # df_.select(ref_cols).mean(axis=1).alias('pow_ref') ) def generate_block_list(N, num_blocks=10): @@ -317,7 +317,7 @@ def compute_energy_ratio(df_, test_turbines (list[int]): A list of turbine numbers to use as the test. ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds wd_turbines (list[int]): A list of turbine numbers to use for the wind directions - use_predefined_ref (bool): If True, use the ref_pow column of df_ as the reference power. + use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. ws_step (float): The width of the wind speed bins. @@ -332,10 +332,10 @@ def compute_energy_ratio(df_, pl.DataFrame: A dataframe containing the energy ratio for each wind direction bin """ - # If use_predefined_ref is True, df_ must have a column named 'ref_pow' + # If use_predefined_ref is True, df_ must have a column named 'pow_ref' if use_predefined_ref: - if 'ref_pow' not in df_.columns: - raise ValueError('df_ must have a column named ref_pow when use_predefined_ref is True') + if 'pow_ref' not in df_.columns: + raise ValueError('df_ must have a column named pow_ref when use_predefined_ref is True') # If ref_turbines supplied, warn user that it will be ignored if ref_turbines is not None: warnings.warn('ref_turbines will be ignored when use_predefined_ref is True') @@ -369,6 +369,14 @@ def compute_energy_ratio(df_, raise ValueError('wd_turbines must be supplied when use_predefined_wd is False') + # Confirm that test_turbines is a list of ints or a numpy array of ints + if not isinstance(test_turbines, list) and not isinstance(test_turbines, np.ndarray): + raise ValueError('test_turbines must be a list or numpy array of ints') + + # Confirm that test_turbines is not empty + if len(test_turbines) == 0: + raise ValueError('test_turbines cannot be empty') + # Identify the number of dataframes num_df = len(df_names) @@ -376,7 +384,7 @@ def compute_energy_ratio(df_, if not use_predefined_ref: ref_cols = [f'pow_{i:03d}' for i in ref_turbines] else: - ref_cols = ['ref_pow'] + ref_cols = ['pow_ref'] if not use_predefined_ws: ws_cols = [f'ws_{i:03d}' for i in ws_turbines] @@ -400,7 +408,7 @@ def compute_energy_ratio(df_, df_ = add_wd_bin(df_, wd_cols, wd_step, wd_min, wd_max) # Assign the reference and test power columns - df_ = add_ref_power(df_, ref_cols) + df_ = add_power_ref(df_, ref_cols) df_ = add_test_power(df_, test_cols) bin_cols_without_df_name = [c for c in bin_cols_in if c != 'df_name'] @@ -409,7 +417,7 @@ def compute_energy_ratio(df_, df_ = (df_ .filter(pl.all(pl.col(bin_cols_with_df_name).is_not_null())) # Select for all bin cols present .groupby(bin_cols_with_df_name, maintain_order=True) - .agg([pl.mean("ref_pow"), pl.mean("test_pow"),pl.count()]) + .agg([pl.mean("pow_ref"), pl.mean("test_pow"),pl.count()]) .with_columns( [ pl.col('count').min().over(bin_cols_without_df_name).alias('count_min')#, # Find the min across df_name @@ -417,7 +425,7 @@ def compute_energy_ratio(df_, ) .with_columns( [ - pl.col('ref_pow').mul(pl.col('count_min')).alias('ref_energy'), # Compute the reference energy + pl.col('pow_ref').mul(pl.col('count_min')).alias('ref_energy'), # Compute the reference energy pl.col('test_pow').mul(pl.col('count_min')).alias('test_energy'), # Compute the test energy ] ) @@ -477,7 +485,7 @@ def compute_energy_ratio_bootstrap(df_, test_turbines (list[int]): A list of turbine numbers to use as the test. ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds wd_turbines (list[int]): A list of turbine numbers to use for the wind directions - use_predefined_ref (bool): If True, use the ref_pow column of df_ as the reference power. + use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. ws_step (float): The width of the wind speed bins. @@ -557,7 +565,7 @@ def compute_uplift_in_region(df_, test_turbines (list[int]): A list of turbine numbers to use as the test. ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds wd_turbines (list[int]): A list of turbine numbers to use for the wind directions - use_predefined_ref (bool): If True, use the ref_pow column of df_ as the reference power. + use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. ws_step (float): The width of the wind speed bins. @@ -572,10 +580,10 @@ def compute_uplift_in_region(df_, pl.DataFrame: A dataframe containing the energy uplift """ - # If use_predefined_ref is True, df_ must have a column named 'ref_pow' + # If use_predefined_ref is True, df_ must have a column named 'pow_ref' if use_predefined_ref: - if 'ref_pow' not in df_.columns: - raise ValueError('df_ must have a column named ref_pow when use_predefined_ref is True') + if 'pow_ref' not in df_.columns: + raise ValueError('df_ must have a column named pow_ref when use_predefined_ref is True') # If ref_turbines supplied, warn user that it will be ignored if ref_turbines is not None: warnings.warn('ref_turbines will be ignored when use_predefined_ref is True') @@ -608,6 +616,13 @@ def compute_uplift_in_region(df_, if wd_turbines is None: raise ValueError('wd_turbines must be supplied when use_predefined_wd is False') + # Confirm that test_turbines is a list of ints or a numpy array of ints + if not isinstance(test_turbines, list) and not isinstance(test_turbines, np.ndarray): + raise ValueError('test_turbines must be a list or numpy array of ints') + + # Confirm that test_turbines is not empty + if len(test_turbines) == 0: + raise ValueError('test_turbines cannot be empty') num_df = len(df_names) @@ -619,7 +634,7 @@ def compute_uplift_in_region(df_, if not use_predefined_ref: ref_cols = [f'pow_{i:03d}' for i in ref_turbines] else: - ref_cols = ['ref_pow'] + ref_cols = ['pow_ref'] if not use_predefined_ws: ws_cols = [f'ws_{i:03d}' for i in ws_turbines] @@ -643,35 +658,35 @@ def compute_uplift_in_region(df_, df_ = add_wd_bin(df_, wd_cols, wd_step, wd_min, wd_max) # Assign the reference and test power columns - df_ = add_ref_power(df_, ref_cols) + df_ = add_power_ref(df_, ref_cols) df_ = add_test_power(df_, test_cols) bin_cols_without_df_name = [c for c in bin_cols_in if c != 'df_name'] bin_cols_with_df_name = bin_cols_without_df_name + ['df_name'] df_ = (df_.with_columns( - power_ratio = pl.col('test_pow') / pl.col('ref_pow')) + power_ratio = pl.col('test_pow') / pl.col('pow_ref')) .filter(pl.all(pl.col(bin_cols_with_df_name).is_not_null())) # Select for all bin cols present .groupby(bin_cols_with_df_name, maintain_order=True) - .agg([pl.mean("ref_pow"), pl.mean("power_ratio"),pl.count()]) + .agg([pl.mean("pow_ref"), pl.mean("power_ratio"),pl.count()]) .with_columns( [ pl.col('count').min().over(bin_cols_without_df_name).alias('count_min'), # Find the min across df_name - pl.col('ref_pow').mul(pl.col('power_ratio')).alias('test_pow'), # Compute the test power + pl.col('pow_ref').mul(pl.col('power_ratio')).alias('test_pow'), # Compute the test power ] ) - .pivot(values=['power_ratio','test_pow','ref_pow','count_min'], columns='df_name', index=['wd_bin','ws_bin'],aggregate_function='first') + .pivot(values=['power_ratio','test_pow','pow_ref','count_min'], columns='df_name', index=['wd_bin','ws_bin'],aggregate_function='first') .drop_nulls() .with_columns( f_norm = pl.col(f'count_min_df_name_{df_names[0]}') / pl.col(f'count_min_df_name_{df_names[0]}').sum() ) .with_columns( delta_power_ratio = pl.col(f'power_ratio_df_name_{df_names[1]}') - pl.col(f'power_ratio_df_name_{df_names[0]}'), - ref_pow_both_cases = pl.concat_list([f'ref_pow_df_name_{n}' for n in df_names]).list.mean() + pow_ref_both_cases = pl.concat_list([f'pow_ref_df_name_{n}' for n in df_names]).list.mean() ) .with_columns( - delta_energy = pl.col('delta_power_ratio') * pl.col('f_norm') * pl.col('ref_pow_both_cases'), # pl.col(f'ref_pow_df_name_{df_names[0]}'), + delta_energy = pl.col('delta_power_ratio') * pl.col('f_norm') * pl.col('pow_ref_both_cases'), # pl.col(f'pow_ref_df_name_{df_names[0]}'), base_test_energy = pl.col(f'test_pow_df_name_{df_names[0]}') * pl.col('f_norm') ) @@ -711,7 +726,7 @@ def compute_uplift_in_region_bootstrap(df_, test_turbines (list[int]): A list of turbine numbers to use as the test. ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds wd_turbines (list[int]): A list of turbine numbers to use for the wind directions - use_predefined_ref (bool): If True, use the ref_pow column of df_ as the reference power. + use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. ws_step (float): The width of the wind speed bins. From 19fdd3cdfcd8cb0e60788ecc8cd6136d23e93d7e Mon Sep 17 00:00:00 2001 From: Paul Date: Wed, 12 Jul 2023 11:31:49 -0600 Subject: [PATCH 05/17] Update timing test to polars --- flasc/timing_tests/energy_ratio_timing.py | 64 +++++++++++++---------- 1 file changed, 35 insertions(+), 29 deletions(-) diff --git a/flasc/timing_tests/energy_ratio_timing.py b/flasc/timing_tests/energy_ratio_timing.py index de9d558f..94f89173 100644 --- a/flasc/timing_tests/energy_ratio_timing.py +++ b/flasc/timing_tests/energy_ratio_timing.py @@ -67,37 +67,43 @@ def time_energy_ratio_with_bootstrapping(): # Speciy num_blocks = num_rows to implement normal boostrapping df_energy = erp.get_energy_table([df],['baseline'],num_blocks=df.shape[0]) - print(df_energy) - - # # For forward consistency, define the bins by the edges - # ws_edges = np.arange(5,25,1.) - # wd_edges = np.arange(0,360,2.) - - # # Create bins - # ws_bins = [(ws_edges[i], ws_edges[i+1]) for i in range(len(ws_edges)-1)] - # wd_bins = [(wd_edges[i], wd_edges[i+1]) for i in range(len(wd_edges)-1)] + # For forward consistency, define the bins by the edges + ws_edges = np.arange(5,25,1.) + wd_edges = np.arange(0,360,2.) + + # Get what new polars needs from this + ws_max = np.max(ws_edges) + ws_min = np.min(ws_edges) + ws_step = ws_edges[1] - ws_edges[0] + wd_max = np.max(wd_edges) + wd_min = np.min(wd_edges) + wd_step = wd_edges[1] - wd_edges[0] # # Run this calculation N_ITERATIONS times and take the average time - - # time_results = np.zeros(N_ITERATIONS) - # for i in range(N_ITERATIONS): - # start_time = time.time() - # er = s.get_energy_ratios( - # test_turbines=1, - # ws_bins=ws_bins, - # wd_bins=wd_bins, - # N=N, - # percentiles=[5.0, 95.0], - # verbose=False - # ) - - # end_time = time.time() - # time_results[i] = end_time - start_time - - # # Return the average time - # return np.mean(time_results) - - + time_results = np.zeros(N_ITERATIONS) + for i in range(N_ITERATIONS): + start_time = time.time() + + df_erb = erp.compute_energy_ratio_bootstrap( + df_energy, + ['baseline'], + test_turbines=[1], + use_predefined_ref=True, + use_predefined_wd=True, + use_predefined_ws=True, + ws_max=ws_max, + ws_min=ws_min, + ws_step=ws_step, + wd_max=wd_max, + wd_min=wd_min, + wd_step=wd_step, + N=N) + + end_time = time.time() + time_results[i] = end_time - start_time + + # Return the average time + return np.mean(time_results) From 05a2d24dede70772252bb18db7f974c23b7078c9 Mon Sep 17 00:00:00 2001 From: Paul Date: Wed, 12 Jul 2023 12:02:14 -0600 Subject: [PATCH 06/17] Add polars requirement --- requirements.txt | 1 + setup.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d3b67ba6..192b5d05 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,3 +13,4 @@ sqlalchemy streamlit tkcalendar seaborn +polars \ No newline at end of file diff --git a/setup.py b/setup.py index bdb6ecf2..587f7ce5 100644 --- a/setup.py +++ b/setup.py @@ -26,7 +26,8 @@ 'sqlalchemy', 'streamlit', 'tkcalendar', - 'seaborn' + 'seaborn', + 'polars' ] ROOT = Path(__file__).parent From 0f5f02ed9d4d1dc3b10d4c2cfb26fa72515d3a51 Mon Sep 17 00:00:00 2001 From: Paul Date: Wed, 12 Jul 2023 13:28:18 -0600 Subject: [PATCH 07/17] Add a first unit test --- tests/energy_ratio_test.py | 40 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) diff --git a/tests/energy_ratio_test.py b/tests/energy_ratio_test.py index 02301b1c..d29c1e82 100644 --- a/tests/energy_ratio_test.py +++ b/tests/energy_ratio_test.py @@ -9,6 +9,7 @@ from flasc.dataframe_operations import dataframe_manipulations as dfm from flasc import floris_tools as ftools from flasc.examples.models import load_floris_artificial as load_floris +from flasc.energy_ratio import energy_ratio_polars as erp def load_data(): @@ -151,3 +152,42 @@ def test_energy_ratio_regression(self): self.assertEqual(out.loc[3, "bin_count"], 34) self.assertEqual(out.loc[4, "bin_count"], 38) self.assertEqual(out.loc[5, "bin_count"], 6) + + def test_energy_ratio_regression_polars(self): + # Load data and FLORIS model + fi, _ = load_floris() + df = load_data() + df = dfm.set_wd_by_all_turbines(df) + df_upstream = ftools.get_upstream_turbs_floris(fi) + df = dfm.set_ws_by_upstream_turbines(df, df_upstream) + df = dfm.set_pow_ref_by_turbines(df, turbine_numbers=[0, 6]) + + wd_step=2.0 + ws_step=1.0 + # wd_bin_width=3.0, + + df_energy = erp.get_energy_table([df],['baseline']) + + df_erb = erp.compute_energy_ratio_bootstrap( + df_energy, + ['baseline'], + test_turbines=[1], + use_predefined_ref=True, + use_predefined_wd=True, + use_predefined_ws=True, + ws_max=30.0, + ws_min=ws_step/2.0, + ws_step=ws_step, + wd_max=360.0, + wd_min=wd_step/2.0, + wd_step=wd_step, + ) + + + self.assertAlmostEqual(df_erb['baseline'].item(1), 0.8087321721301793, places=4) + self.assertAlmostEqual(df_erb['baseline'].item(2), 0.903263, places=4) + self.assertAlmostEqual(df_erb['baseline'].item(3), 0.930883, places=4) + + self.assertEqual(df_erb['count_baseline'].item(1), 25) + self.assertEqual(df_erb['count_baseline'].item(2), 27) + self.assertEqual(df_erb['count_baseline'].item(3), 22) \ No newline at end of file From 3d08b11001a689d4695327bfde0ef9e4cc468234 Mon Sep 17 00:00:00 2001 From: Paul Date: Wed, 12 Jul 2023 13:57:11 -0600 Subject: [PATCH 08/17] Delete table analysis --- .../energy_ratio_visualization.py | 399 ------------------ 1 file changed, 399 deletions(-) diff --git a/flasc/energy_ratio/energy_ratio_visualization.py b/flasc/energy_ratio/energy_ratio_visualization.py index 9bb4c747..2887430e 100644 --- a/flasc/energy_ratio/energy_ratio_visualization.py +++ b/flasc/energy_ratio/energy_ratio_visualization.py @@ -265,402 +265,3 @@ def plot( plt.tight_layout() return axarr - -# def table_analysis( -# df_list, -# fout_xlsx, -# hide_bin_count_columns=False, -# hide_ws_ti_columns=False, -# hide_pow_columns=False, -# hide_unbalanced_cols=True, -# fi=None -# ): -# # Save some useful info -# header_row = 2 -# first_data_row = header_row + 1 -# first_data_col = 1 - -# # Extract variables -# ws_step = df_list[0]["er_ws_step"] -# wd_bin_width = df_list[0]["er_wd_bin_width"] - -# # Extract relevant details -# name_list = [df["name"] for df in df_list] -# df_per_wd_bin_list = [ -# d["er_results_info_dict"]["df_per_wd_bin"]for d in df_list -# ] -# df_per_ws_bin_list = [ -# d["er_results_info_dict"]["df_per_ws_bin"] for d in df_list -# ] -# test_turbines = np.array(df_list[0]["er_test_turbines"], dtype=int) - -# # Append column names with data label for df_per_wd_bin -# for ii, df in enumerate(df_per_wd_bin_list): -# name = name_list[ii] -# df.columns = ["{:s}_{:s}".format(c, name) for c in df.columns] - -# # Append column names with data label for df_per_ws_bin -# for ii, df in enumerate(df_per_ws_bin_list): -# name = name_list[ii] -# df = df.reset_index(drop=False).set_index(["wd_bin", "ws_bin"]) -# df.columns = ["{:s}_{:s}".format(c, name) for c in df.columns] -# df_per_ws_bin_list[ii] = df - -# # Concatenate information from different dataframes -# df_per_wd_bin = pd.concat(df_per_wd_bin_list, axis=1) -# df_per_ws_bin = pd.concat(df_per_ws_bin_list, axis=1) - -# # Merge df_per_wd_bin information into df_per_ws_bin -# df_per_ws_bin = df_per_ws_bin.reset_index(drop=False).set_index("wd_bin") -# df_per_wd_bin["ws_bin"] = 999999.9 # very high number -# df_merged = pd.concat([df_per_ws_bin, df_per_wd_bin]) -# df_merged = df_merged.sort_values(by=["wd_bin", "ws_bin"]) -# df_merged = df_merged.reset_index(drop=False) - -# wd_intervals = [pd.Interval(a, b, "left") for a, b in zip( -# df_merged["wd_bin"] - wd_bin_width / 2.0, -# df_merged["wd_bin"] + wd_bin_width / 2.0 -# ) -# ] -# ws_intervals = [pd.Interval(a, b, "left") for a, b in zip( -# df_merged["ws_bin"] - ws_step / 2.0, -# df_merged["ws_bin"] + ws_step / 2.0 -# ) -# ] - -# df_table = pd.DataFrame( -# { -# "wd_bin": wd_intervals, -# "ws_bin": ws_intervals, -# } -# ) - -# # Overwrite placeholder large numbers with new interval -# total_ids = [i.right >= 999999.9 for i in ws_intervals] -# df_table.loc[total_ids, "ws_bin"] = "TOTALS" -# df_merged.loc[total_ids, "ws_bin"] = "TOTALS" - -# # Add bin counts for the dataframes -# cols = ["bin_count_{:s}".format(n) for n in name_list] -# for c in cols: -# df_table[c] = df_merged[c].fillna(0).astype(int) - -# # Add balanced bin count per ws and in total -# df_table["bin_count_balanced"] = df_table[cols].min(axis=1).astype(int) -# df_table.loc[total_ids, "bin_count_balanced"] = int(0) -# Ntot = df_table.groupby(["wd_bin"])["bin_count_balanced"].sum() -# df_table.loc[total_ids, "bin_count_balanced"] = np.array(Ntot, dtype=int) - -# df_merged["bin_count_balanced_tot"] = df_table.loc[total_ids, "bin_count_balanced"] -# df_merged["bin_count_balanced_tot"] = df_merged["bin_count_balanced_tot"].bfill().astype(int) - -# # add ws_mean and ti_mean for all dataframes -# for col in ["ws_mean", "ti_mean"]: -# for n in name_list: -# c = "{:s}_{:s}".format(col, n) -# if c in df_merged.columns: -# df_table[c] = df_merged[c] - -# # Add reference power and energy -# bin_totals = np.array(df_merged["bin_count_balanced_tot"]) -# for n in name_list: -# pow_mean = df_merged["pow_ref_mean_{:s}".format(n)] -# energy_unbal = df_merged["energy_ref_unbalanced_{:s}".format(n)] -# energy_bal_norm = df_merged["energy_ref_balanced_norm_{:s}".format(n)] -# energy_bal = bin_totals * energy_bal_norm - -# df_table["ref_pow_{:s}".format(n)] = pow_mean -# df_table["ref_energy_unbalanced_{:s}".format(n)] = energy_unbal -# df_table["ref_energy_balanced_{:s}".format(n)] = energy_bal - -# # Fill empty entries with 0.0 for energy -# df_table["ref_energy_unbalanced_{:s}".format(n)] = ( -# df_table["ref_energy_unbalanced_{:s}".format(n)].fillna(0.0) -# ) -# df_table["ref_energy_balanced_{:s}".format(n)] = ( -# df_table["ref_energy_balanced_{:s}".format(n)].fillna(0.0) -# ) - -# # Add empty column/spacer -# df_table["___"] = None - -# # Add test power and energy -# bin_totals = np.array(df_merged["bin_count_balanced_tot"]) -# for n in name_list: -# pow_mean = df_merged["pow_test_mean_{:s}".format(n)] -# energy_unbal = df_merged["energy_test_unbalanced_{:s}".format(n)] -# energy_bal_norm = df_merged["energy_test_balanced_norm_{:s}".format(n)] -# energy_bal = bin_totals * energy_bal_norm -# energy_ratio = df_merged["energy_ratio_unbalanced_{:s}".format(n)] -# energy_ratio_bal = df_merged["energy_ratio_balanced_{:s}".format(n)] - -# df_table["test_pow_{:s}".format(n)] = pow_mean -# df_table["test_energy_unbalanced_{:s}".format(n)] = energy_unbal -# df_table["test_energy_balanced_{:s}".format(n)] = energy_bal -# df_table["energy_ratio_unbalanced_{:s}".format(n)] = energy_ratio -# df_table["energy_ratio_balanced_{:s}".format(n)] = energy_ratio_bal - -# # Fill empty entries with 0.0 for energy -# df_table["test_energy_unbalanced_{:s}".format(n)] = ( -# df_table["test_energy_unbalanced_{:s}".format(n)].fillna(0.0) -# ) -# df_table["test_energy_balanced_{:s}".format(n)] = ( -# df_table["test_energy_balanced_{:s}".format(n)].fillna(0.0) -# ) - -# # Define change in unbalanced and balanced energy ratios -# bl = df_table["energy_ratio_unbalanced_{:s}".format(name_list[0])] -# bl_bal = df_table["energy_ratio_balanced_{:s}".format(name_list[0])] - -# for n in name_list[1::]: -# df_table["change_energy_ratio_unbalanced_{:s}".format(n)] = ( -# (df_table["energy_ratio_unbalanced_{:s}".format(n)] - bl) / bl -# ) -# df_table["change_energy_ratio_balanced_{:s}".format(n)] = ( -# (df_table["energy_ratio_balanced_{:s}".format(n)] - bl_bal) -# / bl_bal -# ) - -# # Add empty column/spacer -# df_table["___0"] = None - -# # Add empty rows in df_table after each wd_bin -# df_empty = pd.DataFrame([None]) -# df_array = [] -# splits = np.where(total_ids)[0] -# splits = np.hstack([0, splits + 1]) # Add zero - -# for ii in range(len(splits) - 1): -# lb = splits[ii] -# ub = splits[ii+1] -# df_array.append(df_table[lb:ub]) -# df_array.append(df_empty) - -# df_table_spaced = pd.concat(df_array, axis=0, ignore_index=True) -# df_table_spaced = df_table_spaced[df_table.columns] -# df_table = df_table_spaced - -# # Write out the dataframe with xslxwriter -# writer = pd.ExcelWriter(fout_xlsx, engine="xlsxwriter") -# df_table.to_excel( -# writer, -# index=False, -# sheet_name="results", -# startcol=first_data_col, -# startrow=header_row, -# ) -# workbook = writer.book -# worksheet = writer.sheets["results"] - -# # FORMATTING - -# # Format large numbers to 2 decimal -# fmt_rate = workbook.add_format({"num_format": "0.00", "bold": False}) -# cols = df_table.columns -# change_list = [ -# i -# for i in range(len(cols)) -# if ("_mean_" in cols[i]) or ("_pow_" in cols[i]) or ( -# ("_energy_" in cols[i]) and not ("energy_ratio_" in cols[i]) -# ) -# ] -# for c in change_list: -# worksheet.set_column( -# c + first_data_col, c + first_data_col, 10, fmt_rate -# ) - -# # Format energy ratios to 3 decimal -# fmt_rate = workbook.add_format({"num_format": "0.000", "bold": False}) -# cols = df_table.columns -# change_list = [i for i in range(len(cols)) if "energy_ratio" in cols[i]] -# for c in change_list: -# worksheet.set_column( -# c + first_data_col, c + first_data_col, 10, fmt_rate -# ) - -# # Format change and TI into a percentage -# fmt_rate = workbook.add_format({"num_format": "%0.0", "bold": False}) -# cols = df_table.columns -# change_list = [ -# i -# for i in range(len(cols)) -# if ("change" in cols[i]) or ("ti_" in cols[i]) -# ] -# for c in change_list: -# worksheet.set_column( -# c + first_data_col, c + first_data_col, 10, fmt_rate -# ) - -# # # Make "totals" rows bold -# # bold_format = workbook.add_format({'bold': True}) -# # total_ids = np.where(df_table["ws_bin"] == "TOTALS")[0] -# # for ri in total_ids: -# # worksheet.set_row(first_data_row + ri, 15, bold_format) - -# # Make the seperator columns very narrow and black -# fmt_black = workbook.add_format({"fg_color": "#000000"}) -# change_list = [i for i in range(len(cols)) if "___" in cols[i]] -# for c in change_list: -# worksheet.set_column( -# c + first_data_col, c + first_data_col, 1, fmt_black -# ) - -# # Add data bars to the bins counts -# change_list = [i for i in range(len(cols)) if "bin" in cols[i]] -# for c in change_list: -# worksheet.conditional_format( -# first_data_row, -# c + first_data_col, -# df_table.shape[0] + first_data_row, -# c + first_data_col, -# {"type": "data_bar", "max_value": 100}, -# ) - -# # Add color to the change columns -# change_list = [i for i in range(len(cols)) if "change" in cols[i]] - -# for c in change_list: -# worksheet.conditional_format( -# first_data_row, -# c + first_data_col, -# df_table.shape[0] + first_data_row, -# c + first_data_col, -# { -# "type": "3_color_scale", -# "min_value": -1.0, -# "min_type": "num", -# "max_value": 1.0, -# "mid_value": 0.0, -# "mid_type": "num", -# "min_color": "#FF0000", -# "mid_color": "#FFFFFF", -# "max_color": "#00FF00", -# "max_type": "num", -# }, -# ) - -# # Add color to energy ratios -# change_list = [ -# i -# for i in range(len(cols)) -# if ("er_" in cols[i]) and not ("change" in cols[i]) -# ] -# for c in change_list: -# worksheet.conditional_format( -# first_data_row, -# c + first_data_col, -# df_table.shape[0] + first_data_row, -# c + first_data_col, -# { -# "type": "3_color_scale", -# "min_value": 0.25, -# "min_type": "num", -# "max_value": 2.0, -# "mid_value": 1.0, -# "mid_type": "num", -# "min_color": "#0000FF", -# "mid_color": "#FFFFFF", -# "max_color": "#00FF00", -# "max_type": "num", -# }, -# ) - -# # Header -# # Adding formats for header row. -# fmt_header = workbook.add_format( -# { -# "bold": True, -# "text_wrap": True, -# "valign": "top", -# "fg_color": "#5DADE2", -# "font_color": "#FFFFFF", -# "border": 1, -# } -# ) -# for col, value in enumerate(df_table.columns.values): -# worksheet.write(header_row, col + first_data_col, value, fmt_header) - -# # If a floris model is provided, use it to make layout images -# if fi is not None: -# # Make that first colum wide -# worksheet.set_column("A:A", 30) - -# # Create image folder -# img_folder = os.path.join(os.path.dirname(fout_xlsx), "xlsx_images") -# if not os.path.exists(img_folder): -# os.makedirs(img_folder, exist_ok=True) - -# # For each bin were checking, make image of wake scenarios -# sort_df = df_table[["wd_bin", "ws_bin"]].copy() -# sort_df = sort_df.sort_values(["wd_bin", "ws_bin"]).dropna() -# for wdb in sort_df.wd_bin.unique(): -# row_top_ws_bin = sort_df.index[wdb == sort_df["wd_bin"]][0] -# wd_arrow = wdb.mid # Put arrow in middle of bin -# fig, ax = plt.subplots(figsize=(2, 2)) -# fi.reinitialize_flow_field( -# wind_direction=wd_arrow, wind_speed=8.0 -# ) -# fi.calculate_wake() -# hor_plane = fi.get_hor_plane() -# hor_plane = wfct.cut_plane.change_resolution( -# hor_plane, -# resolution=(200, 200), -# ) -# wfct.visualization.visualize_cut_plane(hor_plane, ax=ax) -# im_name = os.path.join(img_folder, "wd_%03d.png" % wd_arrow) -# fig.savefig(im_name, bbox_inches="tight") - -# # Insert the figure -# worksheet.insert_image(first_data_row + row_top_ws_bin, 0, im_name) - -# # Make the first row bigger -# worksheet.set_row(0, 120) - -# # Get a list of blank columns indicating turbine starts -# blank_cols = [i for i in range(len(cols)) if "___" in cols[i]] - -# # Plot the layout on top -# fig, ax = plt.subplots(figsize=(3, 2)) -# fi.vis_layout(ax=ax) -# xt = np.array(fi.layout_x) -# yt = np.array(fi.layout_y) -# ax.plot(xt[test_turbines], yt[test_turbines], "mo", ms=25) -# im_name = os.path.join(img_folder, "layout.png") -# fig.savefig(im_name, bbox_inches="tight") -# worksheet.insert_image(0, blank_cols[0] + 1, im_name) - -# # Hide columns if necessary -# if hide_bin_count_columns: -# cols = [i for i, c in enumerate(df_table.columns) if "bin_count" in c] -# for ii in cols: -# worksheet.set_column(ii + 1, ii + 1, None, None, {'hidden': 1}) - -# if hide_ws_ti_columns: -# cols = [ -# i for i, c in enumerate(df_table.columns) if ( -# ("ws_mean_" in c) or ("ws_std_" in c) or -# ("ti_mean_" in c) or ("ti_std_" in c) -# ) -# ] -# for ii in cols: -# worksheet.set_column(ii + 1, ii + 1, None, None, {'hidden': 1}) - -# if hide_pow_columns: -# cols = [ -# i for i, c in enumerate(df_table.columns) if ( -# ("ref_pow_" in c) or ("test_pow_" in c) -# ) -# ] -# for ii in cols: -# worksheet.set_column(ii + 1, ii + 1, None, None, {'hidden': 1}) - -# if hide_unbalanced_cols: -# cols = [i for i, c in enumerate(df_table.columns) if "unbalance" in c] -# for ii in cols: -# worksheet.set_column(ii + 1, ii + 1, None, None, {'hidden': 1}) - -# # Freeze the panes -# worksheet.freeze_panes(first_data_row, first_data_col) - -# writer.save() -# print("File successfully written to {:s}.".format(fout_xlsx)) From dd61b8410b79d8165012c352439e8f40b5fb0904 Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 13 Jul 2023 08:53:26 -0600 Subject: [PATCH 09/17] Update test to new framework --- tests/energy_ratio_test.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/tests/energy_ratio_test.py b/tests/energy_ratio_test.py index d29c1e82..f70336dc 100644 --- a/tests/energy_ratio_test.py +++ b/tests/energy_ratio_test.py @@ -168,21 +168,23 @@ def test_energy_ratio_regression_polars(self): df_energy = erp.get_energy_table([df],['baseline']) - df_erb = erp.compute_energy_ratio_bootstrap( + ero = erp.compute_energy_ratio( df_energy, ['baseline'], test_turbines=[1], use_predefined_ref=True, use_predefined_wd=True, use_predefined_ws=True, - ws_max=30.0, - ws_min=ws_step/2.0, - ws_step=ws_step, wd_max=360.0, wd_min=wd_step/2.0, wd_step=wd_step, + ws_max=30.0, + ws_min=ws_step/2.0, + ws_step=ws_step, ) + # Get the underlying polars data frame + df_erb = ero.df_result self.assertAlmostEqual(df_erb['baseline'].item(1), 0.8087321721301793, places=4) self.assertAlmostEqual(df_erb['baseline'].item(2), 0.903263, places=4) From c62f7439cceba348ef9a38fb66ce5f50a2f52bb5 Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 13 Jul 2023 08:53:34 -0600 Subject: [PATCH 10/17] Add temporary testing notebook --- flasc/energy_ratio/demo_capabilities.ipynb | 796 +++++++++++++++++++++ 1 file changed, 796 insertions(+) create mode 100644 flasc/energy_ratio/demo_capabilities.ipynb diff --git a/flasc/energy_ratio/demo_capabilities.ipynb b/flasc/energy_ratio/demo_capabilities.ipynb new file mode 100644 index 00000000..e4b8fef6 --- /dev/null +++ b/flasc/energy_ratio/demo_capabilities.ipynb @@ -0,0 +1,796 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demo polars capabilities and interactively develop it" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "import pandas as pd\n", + "import polars as pl\n", + "import seaborn as sns\n", + "\n", + "from floris import tools as wfct\n", + "from floris.utilities import wrap_360\n", + "\n", + "\n", + "from flasc.energy_ratio import energy_ratio_suite\n", + "from flasc.energy_ratio import energy_ratio_polars as erp\n", + "\n", + "from flasc.visualization import plot_layout_with_waking_directions, plot_binned_mean_and_ci\n", + "\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use FLORIS to generate a wake steering data set" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/pfleming/Projects/FLORIS/flasc/flasc/energy_ratio/../examples/floris_input_artificial/gch.yaml\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "file_path = Path.cwd()\n", + "fi_path = file_path / \"../examples/floris_input_artificial/gch.yaml\"\n", + "print(fi_path)\n", + "fi = wfct.floris_interface.FlorisInterface(fi_path)\n", + "fi.reinitialize(layout_x = [0, 0, 5*126], layout_y = [5*126, 0, 0])\n", + "\n", + "# # Show the wind farm\n", + "plot_layout_with_waking_directions(fi)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num Points 16000\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a time history of points where the wind speed and wind direction step different combinations\n", + "ws_points = np.arange(5.0,10.0,0.25)\n", + "wd_points = np.arange(250.0, 290.0, 0.25,)\n", + "num_points_per_combination = 5 # How many \"seconds\" per combination\n", + "\n", + "# I know this is dumb but will come back, can't quite work out the numpy version\n", + "ws_array = []\n", + "wd_array = []\n", + "for ws in ws_points:\n", + " for wd in wd_points:\n", + " for i in range(num_points_per_combination):\n", + " ws_array.append(ws)\n", + " wd_array.append(wd)\n", + "t = np.arange(len(ws_array))\n", + "\n", + "print(f'Num Points {len(t)}')\n", + "\n", + "fig, axarr = plt.subplots(2,1,sharex=True)\n", + "axarr[0].plot(t, ws_array,label='Wind Speed')\n", + "axarr[0].set_ylabel('m/s')\n", + "axarr[0].legend()\n", + "axarr[0].grid(True)\n", + "axarr[1].plot(t, wd_array,label='Wind Direction')\n", + "axarr[1].set_ylabel('deg')\n", + "axarr[1].legend()\n", + "axarr[1].grid(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the power of the second turbine for two cases\n", + "# Baseline: The front turbine is aligned to the wind\n", + "# WakeSteering: The front turbine is yawed 25 deg\n", + "fi.reinitialize(wind_speeds=ws_array, wind_directions=wd_array, time_series=True)\n", + "fi.calculate_wake()\n", + "power_baseline_ref = fi.get_turbine_powers().squeeze()[:,0].flatten() / 1000.\n", + "power_baseline_control = fi.get_turbine_powers().squeeze()[:,1].flatten() / 1000.\n", + "power_baseline_downstream = fi.get_turbine_powers().squeeze()[:,2].flatten() / 1000.\n", + "\n", + "yaw_angles = np.zeros([len(t),1,3]) * 25\n", + "yaw_angles[:,:,1] = 25 # Set control turbine yaw angles to 25 deg\n", + "fi.calculate_wake(yaw_angles=yaw_angles)\n", + "power_wakesteering_ref = fi.get_turbine_powers().squeeze()[:,0].flatten() / 1000.\n", + "power_wakesteering_control = fi.get_turbine_powers().squeeze()[:,1].flatten() /1000.\n", + "power_wakesteering_downstream = fi.get_turbine_powers().squeeze()[:,2].flatten() /1000." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas version (for time)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Build up the data frames needed for energy ratio suite\n", + "df_baseline_pd = pd.DataFrame({\n", + " 'wd':wd_array,\n", + " 'ws':ws_array,\n", + " 'pow_ref':power_baseline_ref,\n", + " 'pow_000':power_baseline_ref, \n", + " 'pow_001':power_baseline_control,\n", + " 'pow_002':power_baseline_downstream\n", + "})\n", + "\n", + "df_wakesteering_pd = pd.DataFrame({\n", + " 'wd':wd_array,\n", + " 'ws':ws_array,\n", + " 'pow_ref':power_wakesteering_ref,\n", + " 'pow_000':power_wakesteering_ref, \n", + " 'pow_001':power_wakesteering_control,\n", + " 'pow_002':power_wakesteering_downstream\n", + "})\n", + "\n", + "# Create alternative versions of each of the above dataframes where the wd/ws are perturbed by noise\n", + "df_baseline_noisy_pd = pd.DataFrame({\n", + " 'wd':wd_array + np.random.randn(len(wd_array))*5,\n", + " 'ws':ws_array + np.random.randn(len(ws_array)),\n", + " 'pow_ref':power_baseline_ref,\n", + " 'pow_000':power_baseline_ref, \n", + " 'pow_001':power_baseline_control,\n", + " 'pow_002':power_baseline_downstream\n", + "})\n", + "\n", + "df_wakesteering_noisy_pd = pd.DataFrame({\n", + " 'wd':wd_array + np.random.randn(len(wd_array))*5,\n", + " 'ws':ws_array + np.random.randn(len(ws_array)),\n", + " 'pow_ref':power_wakesteering_ref,\n", + " 'pow_000':power_wakesteering_ref, \n", + " 'pow_001':power_wakesteering_control,\n", + " 'pow_002':power_wakesteering_downstream\n", + "})\n", + "\n", + "color_palette = sns.color_palette(\"Paired\",4)[::-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the energy ratio suite object and add each dataframe\n", + "# separately. \n", + "fsc = energy_ratio_suite.energy_ratio_suite()\n", + "# fsc.add_df(df_baseline_pd, 'Baseline', color_palette[0])\n", + "# fsc.add_df(df_wakesteering_pd, 'WakeSteering', color_palette[1])\n", + "fsc.add_df(df_baseline_noisy_pd, 'Baseline (Noisy)', color_palette[2])\n", + "fsc.add_df(df_wakesteering_noisy_pd, 'WakeSteering (Noisy)', color_palette[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataframes differ in wd and ws. Rebalancing.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n", + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n", + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n", + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "done\n", + "Dataframes differ in wd and ws. Rebalancing.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n" + ] + }, + { + "data": { + "text/plain": [ + "[{'name': 'WakeSteering (Noisy)/Baseline (Noisy)',\n", + " 'color': (0.12156862745098039, 0.47058823529411764, 0.7058823529411765),\n", + " 'er_results': baseline baseline_lb baseline_ub wd_bin bin_count\n", + " 0 0.998822 0.998791 0.998884 235.0 2\n", + " 1 0.995258 0.993638 0.998203 237.0 2\n", + " 2 0.997264 0.990900 1.000186 239.0 16\n", + " 3 0.998522 0.993120 1.010756 241.0 28\n", + " 4 0.985829 0.970631 0.994559 243.0 65\n", + " 5 0.983269 0.965738 0.991220 245.0 130\n", + " 6 0.972870 0.959708 0.982437 247.0 217\n", + " 7 0.973321 0.963169 0.980622 249.0 344\n", + " 8 0.967568 0.953978 0.977599 251.0 451\n", + " 9 0.961280 0.949570 0.982164 253.0 601\n", + " 10 0.940554 0.933701 0.960864 255.0 652\n", + " 11 0.931804 0.903085 0.944257 257.0 730\n", + " 12 0.892420 0.859754 0.935450 259.0 782\n", + " 13 0.899908 0.874993 0.937526 261.0 771\n", + " 14 0.942003 0.899937 0.995433 263.0 774\n", + " 15 1.017115 0.986823 1.051867 265.0 766\n", + " 16 1.157586 1.115922 1.228063 267.0 790\n", + " 17 1.289355 1.250793 1.331258 269.0 803\n", + " 18 1.441737 1.365641 1.533941 271.0 777\n", + " 19 1.440991 1.390693 1.491528 273.0 764\n", + " 20 1.407508 1.367686 1.460167 275.0 795\n", + " 21 1.252298 1.204170 1.293490 277.0 788\n", + " 22 1.221448 1.192820 1.260181 279.0 819\n", + " 23 1.132397 1.113457 1.145533 281.0 716\n", + " 24 1.103128 1.071284 1.125142 283.0 706\n", + " 25 1.065693 1.062284 1.075546 285.0 664\n", + " 26 1.036448 1.031518 1.044857 287.0 539\n", + " 27 1.025215 1.021451 1.028925 289.0 445\n", + " 28 1.013079 1.008682 1.018727 291.0 324\n", + " 29 1.009353 1.004584 1.018281 293.0 203\n", + " 30 1.003626 1.002363 1.005703 295.0 114\n", + " 31 1.004066 1.001818 1.009156 297.0 60\n", + " 32 1.000598 1.000060 1.000921 299.0 28\n", + " 33 1.001022 1.000453 1.001294 301.0 9\n", + " 34 NaN NaN NaN 303.0 1,\n", + " 'df_freq': ws_bin wd_bin ws_bin_edges wd_bin_edges freq\n", + " 0 7.5 235.0 [7.0, 8.0) [234.0, 236.0) 1\n", + " 1 9.5 235.0 [9.0, 10.0) [234.0, 236.0) 0\n", + " 2 4.5 237.0 [4.0, 5.0) [236.0, 238.0) 0\n", + " 3 5.5 237.0 [5.0, 6.0) [236.0, 238.0) 1\n", + " 4 7.5 237.0 [7.0, 8.0) [236.0, 238.0) 1\n", + " .. ... ... ... ... ...\n", + " 335 2.5 297.0 [2.0, 3.0) [296.0, 298.0) 0\n", + " 336 4.5 299.0 [4.0, 5.0) [298.0, 300.0) 0\n", + " 337 6.5 301.0 [6.0, 7.0) [300.0, 302.0) 0\n", + " 338 8.5 301.0 [8.0, 9.0) [300.0, 302.0) 0\n", + " 339 6.5 303.0 [6.0, 7.0) [302.0, 304.0) 0\n", + " \n", + " [340 rows x 5 columns],\n", + " 'er_test_turbines': [2],\n", + " 'er_wd_step': 2.0,\n", + " 'er_ws_step': 1.0,\n", + " 'er_wd_bin_width': 2.0,\n", + " 'er_bootstrap_N': 50}]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "# Print out the energy ratio\n", + "# fsc.print_dfs()\n", + "\n", + "\n", + "# Calculate and plot the energy ratio for the downstream turbine [2]\n", + "# With respect to reference turbine [0]\n", + "# datasets with uncertainty quantification using 50 bootstrap samples\n", + "fsc.get_energy_ratios(\n", + " test_turbines=[2],\n", + " wd_step=2.0,\n", + " ws_step=1.0,\n", + " N=50,\n", + " percentiles=[5., 95.],\n", + " verbose=False,\n", + " num_blocks=10\n", + ")\n", + "print('done')\n", + "# fsc.plot_energy_ratios(superimpose=True)\n", + "\n", + "fsc.get_energy_ratios_gain(\n", + " test_turbines=[2],\n", + " wd_step=2.0,\n", + " ws_step=1.0,\n", + " N=50,\n", + " percentiles=[5., 95.],\n", + " verbose=False,\n", + " num_blocks=10\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Polars implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# # Build the dataframes\n", + "# # Build up the data frames needed for energy ratio suite\n", + "# df_baseline = pd.DataFrame({\n", + "# 'wd_000':wd_array,\n", + "# 'ws_000':ws_array,\n", + "# # 'pow_ref':power_baseline_ref,\n", + "# 'pow_000':power_baseline_ref, \n", + "# 'pow_001':power_baseline_control,\n", + "# 'pow_002':power_baseline_downstream\n", + "# })\n", + "\n", + "# df_wakesteering = pd.DataFrame({\n", + "# 'wd_000':wd_array,\n", + "# 'ws_000':ws_array,\n", + "# # 'pow_ref':power_wakesteering_ref,\n", + "# 'pow_000':power_wakesteering_ref, \n", + "# 'pow_001':power_wakesteering_control,\n", + "# 'pow_002':power_wakesteering_downstream\n", + "# })\n", + "\n", + "# # Create alternative versions of each of the above dataframes where the wd/ws are perturbed by noise\n", + "# df_baseline_noisy = pd.DataFrame({\n", + "# 'wd_000':df_baseline_noisy_pd.wd.values,# wd_array + np.random.randn(len(wd_array))*5,\n", + "# 'ws_000':df_baseline_noisy_pd.ws.values,# ws_array + np.random.randn(len(ws_array)),\n", + "# # 'pow_ref':power_baseline_ref,\n", + "# 'pow_000':power_baseline_ref, \n", + "# 'pow_001':power_baseline_control,\n", + "# 'pow_002':power_baseline_downstream\n", + "# })\n", + "\n", + "# df_wakesteering_noisy = pl.DataFrame({\n", + "# 'wd_000':df_wakesteering_noisy_pd.wd.values,# wd_array + np.random.randn(len(wd_array))*5,\n", + "# 'ws_000':df_wakesteering_noisy_pd.ws.values,#ws_array + np.random.randn(len(ws_array)),\n", + "# # 'pow_ref':power_wakesteering_ref,\n", + "# 'pow_000':power_wakesteering_ref, \n", + "# 'pow_001':power_wakesteering_control,\n", + "# 'pow_002':power_wakesteering_downstream\n", + "# })" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (32_000, 8)\n", + "┌────────────┬───────────┬─────────────┬────────────┬────────────┬────────────┬────────────┬───────┐\n", + "│ wd ┆ ws ┆ pow_ref ┆ pow_000 ┆ pow_001 ┆ pow_002 ┆ df_name ┆ block │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ str ┆ i64 │\n", + "╞════════════╪═══════════╪═════════════╪════════════╪════════════╪════════════╪════════════╪═══════╡\n", + "│ 254.006236 ┆ 2.325801 ┆ 383.695142 ┆ 383.695142 ┆ 383.695142 ┆ 383.588139 ┆ baseline ┆ 0 │\n", + "│ 246.247545 ┆ 4.099678 ┆ 383.695142 ┆ 383.695142 ┆ 383.695142 ┆ 383.588139 ┆ baseline ┆ 0 │\n", + "│ 245.567504 ┆ 3.358879 ┆ 383.695142 ┆ 383.695142 ┆ 383.695142 ┆ 383.588139 ┆ baseline ┆ 0 │\n", + "│ 245.999543 ┆ 4.740228 ┆ 383.695142 ┆ 383.695142 ┆ 383.695142 ┆ 383.588139 ┆ baseline ┆ 0 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 287.187211 ┆ 10.000708 ┆ 3063.446736 ┆ 3063.44673 ┆ 2547.65638 ┆ 3063.44318 ┆ wakesteeri ┆ 9 │\n", + "│ ┆ ┆ ┆ 6 ┆ 5 ┆ 1 ┆ ng ┆ │\n", + "│ 296.536423 ┆ 9.455998 ┆ 3063.446736 ┆ 3063.44673 ┆ 2547.65638 ┆ 3063.44318 ┆ wakesteeri ┆ 9 │\n", + "│ ┆ ┆ ┆ 6 ┆ 5 ┆ 1 ┆ ng ┆ │\n", + "│ 283.906273 ┆ 11.0921 ┆ 3063.446736 ┆ 3063.44673 ┆ 2547.65638 ┆ 3063.44318 ┆ wakesteeri ┆ 9 │\n", + "│ ┆ ┆ ┆ 6 ┆ 5 ┆ 1 ┆ ng ┆ │\n", + "│ 289.11435 ┆ 10.923032 ┆ 3063.446736 ┆ 3063.44673 ┆ 2547.65638 ┆ 3063.44318 ┆ wakesteeri ┆ 9 │\n", + "│ ┆ ┆ ┆ 6 ┆ 5 ┆ 1 ┆ ng ┆ │\n", + "└────────────┴───────────┴─────────────┴────────────┴────────────┴────────────┴────────────┴───────┘" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "df_energy = erp.get_energy_table([df_baseline_noisy_pd, df_wakesteering_noisy_pd], ['baseline', 'wakesteering'])\n", + "df_energy" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['wd', 'ws', 'pow_ref', 'pow_000', 'pow_001', 'pow_002', 'df_name', 'block']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_energy.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# erp.compute_energy_ratio(df_energy, [0], [2], df_names=['Baseline', 'WakeSteering'])\n", + "\n", + "ero = erp.compute_energy_ratio(df_energy,\n", + " ['baseline', 'wakesteering'],\n", + " test_turbines=[2],\n", + " use_predefined_ref=True,\n", + " use_predefined_wd=True,\n", + " use_predefined_ws=True,\n", + " N=50)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compare the energy ratio plots" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the mean values" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_erb = ero.df_result\n", + "\n", + "axarr = fsc.plot_energy_ratios(superimpose=True)\n", + "\n", + "ax = axarr[0]\n", + "ax.plot(df_erb['wd_bin'], df_erb['baseline'], color='k',label='POLARS result (baseline)',ls='--')\n", + "ax.plot(df_erb['wd_bin'], df_erb['wakesteering'], color='k',label='POLARS result (wake steering)',ls='-')\n", + "\n", + "ax.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare uncertainty bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig ,ax = plt.subplots()\n", + "\n", + "# Plot pandas results with uncertainty\n", + "df_pandas_base = fsc.df_list[0]['er_results']\n", + "ax.plot(df_pandas_base['wd_bin'], df_pandas_base['baseline_lb'],'-', color='k', label='PANDAS lower bound')\n", + "ax.plot(df_pandas_base['wd_bin'], df_pandas_base['baseline_ub'],'--', color='k', label='PANDAS upper bound')\n", + "\n", + "ax.plot(df_erb['wd_bin'], df_erb['baseline_lb'],':', color='r', label='POLARS lower bound')\n", + "ax.plot(df_erb['wd_bin'], df_erb['baseline_ub'],':', color='orange', label='POLARS upper bound')\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare the gain values" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig ,ax = plt.subplots()\n", + "\n", + "# NOTE THAT PANDAS VERSION IS RATIO WHILE POLARS IS PERCENT CHANGE\n", + "\n", + "# Plot pandas results with uncertainty\n", + "df_pandas_base = fsc.df_list_gains[0]['er_results']\n", + "ax.plot(df_pandas_base['wd_bin'], df_pandas_base['baseline'],'-', color='k', label='PANDAS')\n", + "ax.fill_between(df_pandas_base['wd_bin'], df_pandas_base['baseline_lb'], df_pandas_base['baseline_ub'], color='k', alpha=0.2)\n", + "\n", + "ax.plot(df_erb['wd_bin'], df_erb['uplift'] * .01 + 1.0,'--', color='r', label='POLARS')\n", + "\n", + "ax.plot(df_erb['wd_bin'], df_erb['uplift_lb'] * .01 + 1.0,':', color='r', label='POLARS lower bound')\n", + "ax.plot(df_erb['wd_bin'], df_erb['uplift_ub'] * .01 + 1.0,':', color='orange', label='POLARS upper bound')\n", + "\n", + "ax.grid(True)\n", + "ax.axhline(1, color='k')\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test out simley approach to calculating energy ratio uplift in regions" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (1, 3)
delta_energybase_test_energyuplift
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732234.6340549.6320e67.602095
" + ], + "text/plain": [ + "shape: (1, 3)\n", + "┌───────────────┬──────────────────┬──────────┐\n", + "│ delta_energy ┆ base_test_energy ┆ uplift │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ f64 ┆ f64 ┆ f64 │\n", + "╞═══════════════╪══════════════════╪══════════╡\n", + "│ 732234.634054 ┆ 9.6320e6 ┆ 7.602095 │\n", + "└───────────────┴──────────────────┴──────────┘" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "ero = erp.compute_uplift_in_region(df_energy,\n", + " ['baseline', 'wakesteering'],\n", + " test_turbines=[2],\n", + " use_predefined_ref=True,\n", + " use_predefined_wd=True,\n", + " use_predefined_ws=True)\n", + "\n", + "ero.df_result" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (1, 9)
delta_energy_expdelta_energy_ubdelta_energy_lbbase_test_energy_expbase_test_energy_ubbase_test_energy_lbuplift_expuplift_ubuplift_lb
f64f64f64f64f64f64f64f64f64
732234.634054931394.533294581022.7371359.6320e61.2662e77.5244e67.6020958.1043937.34586
" + ], + "text/plain": [ + "shape: (1, 9)\n", + "┌────────────┬────────────┬────────────┬────────────┬───┬────────────┬──────────┬─────────┬─────────┐\n", + "│ delta_ener ┆ delta_ener ┆ delta_ener ┆ base_test_ ┆ … ┆ base_test_ ┆ uplift_e ┆ uplift_ ┆ uplift_ │\n", + "│ gy_exp ┆ gy_ub ┆ gy_lb ┆ energy_exp ┆ ┆ energy_lb ┆ xp ┆ ub ┆ lb │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ f64 ┆ f64 ┆ f64 ┆ f64 ┆ ┆ f64 ┆ f64 ┆ f64 ┆ f64 │\n", + "╞════════════╪════════════╪════════════╪════════════╪═══╪════════════╪══════════╪═════════╪═════════╡\n", + "│ 732234.634 ┆ 931394.533 ┆ 581022.737 ┆ 9.6320e6 ┆ … ┆ 7.5244e6 ┆ 7.602095 ┆ 8.10439 ┆ 7.34586 │\n", + "│ 054 ┆ 294 ┆ 135 ┆ ┆ ┆ ┆ ┆ 3 ┆ │\n", + "└────────────┴────────────┴────────────┴────────────┴───┴────────────┴──────────┴─────────┴─────────┘" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "ero = erp.compute_uplift_in_region(df_energy,\n", + " ['baseline', 'wakesteering'],\n", + " test_turbines=[2],\n", + " use_predefined_ref=True,\n", + " use_predefined_wd=True,\n", + " use_predefined_ws=True,\n", + " N=20)\n", + "\n", + "ero.df_result" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env_scada", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From f577151d0f1ec80dd5ff007d0d597c1696515b3b Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 13 Jul 2023 08:53:51 -0600 Subject: [PATCH 11/17] Convert output to class and hide hidden functions --- flasc/energy_ratio/energy_ratio_polars.py | 612 +++++++++++++++------- 1 file changed, 409 insertions(+), 203 deletions(-) diff --git a/flasc/energy_ratio/energy_ratio_polars.py b/flasc/energy_ratio/energy_ratio_polars.py index a9d22c84..52e93e30 100644 --- a/flasc/energy_ratio/energy_ratio_polars.py +++ b/flasc/energy_ratio/energy_ratio_polars.py @@ -19,16 +19,55 @@ # print(bin_edges[:-1] + np.diff(bin_edges)/2.0) -def convert_to_polars(df_): - """_summary_ - Args: - df_ (Pandas DataFrame): a pandas dataframe - - Returns: - Polars DataFrame: a polars dataframe +class EnergyRatioResult: + """ This class is used to store the results of the energy ratio calculations + and provide convenient methods for plotting and saving the results. """ - return pl.from_pandas(df_) + def __init__(self, + df_result, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N + ): + + self.df_result = df_result + self.df_names = df_names + self.num_df = len(df_names) + self.ref_cols = ref_cols + self.test_cols = test_cols + self.wd_cols = wd_cols + self.ws_cols = ws_cols + self.wd_step = wd_step + self.wd_min = wd_min + self.wd_max = wd_max + self.ws_step = ws_step + self.ws_min = ws_min + self.ws_max = ws_max + self.bin_cols_in = bin_cols_in + self.N = N + + +# def convert_to_polars(df_): +# """_summary_ + +# Args: +# df_ (Pandas DataFrame): a pandas dataframe + +# Returns: +# Polars DataFrame: a polars dataframe +# """ +# return pl.from_pandas(df_) def cut(col_name, edges): """ @@ -178,11 +217,11 @@ def add_wd_bin(df_, wd_cols, wd_step=2.0, wd_min=0.0, wd_max=360.0): return bin_column(df_with_mean_wd, 'wd_bin', 'wd_bin', edges) -def add_test_power(df_, test_cols): +def add_power_test(df_, test_cols): return df_.with_columns( - test_pow = pl.concat_list(test_cols).list.mean() - #df_.select(test_cols).mean(axis=1).alias('test_pow') + pow_test = pl.concat_list(test_cols).list.mean() + #df_.select(test_cols).mean(axis=1).alias('pow_test') ) @@ -288,22 +327,19 @@ def resample_energy_table(df_e_, i): ).join(df_e_, how='inner', on='block') - -def compute_energy_ratio(df_, +# Internal version, returns a polars dataframe +def _compute_energy_ratio_single(df_, df_names, - ref_turbines=None, - test_turbines= None, - ws_turbines=None, - wd_turbines=None, - use_predefined_ref = False, - use_predefined_ws = False, - use_predefined_wd = False, - ws_step = 1.0, - ws_min = 0.0, - ws_max = 50.0, + ref_cols, + test_cols, + wd_cols, + ws_cols, wd_step = 2.0, wd_min = 0.0, wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, bin_cols_in = ['wd_bin','ws_bin'], ): @@ -313,93 +349,25 @@ def compute_energy_ratio(df_, Args: df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. df_names (list): A list of names to give to the dataframes. - ref_turbines (list[int]): A list of turbine numbers to use as the reference. - test_turbines (list[int]): A list of turbine numbers to use as the test. - ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds - wd_turbines (list[int]): A list of turbine numbers to use for the wind directions - use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. - use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. - use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. - ws_step (float): The width of the wind speed bins. - ws_min (float): The minimum wind speed to use. - ws_max (float): The maximum wind speed to use. + ref_cols (list[str]): A list of columns to use as the reference turbines + test_cols (list[str]): A list of columns to use as the test turbines + wd_cols (list[str]): A list of columns to derive the wind directions from + ws_cols (list[str]): A list of columns to derive the wind speeds from wd_step (float): The width of the wind direction bins. wd_min (float): The minimum wind direction to use. wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. Returns: pl.DataFrame: A dataframe containing the energy ratio for each wind direction bin """ - # If use_predefined_ref is True, df_ must have a column named 'pow_ref' - if use_predefined_ref: - if 'pow_ref' not in df_.columns: - raise ValueError('df_ must have a column named pow_ref when use_predefined_ref is True') - # If ref_turbines supplied, warn user that it will be ignored - if ref_turbines is not None: - warnings.warn('ref_turbines will be ignored when use_predefined_ref is True') - else: - # ref_turbine must be supplied - if ref_turbines is None: - raise ValueError('ref_turbines must be supplied when use_predefined_ref is False') - - # If use_predefined_ws is True, df_ must have a column named 'ws' - if use_predefined_ws: - if 'ws' not in df_.columns: - raise ValueError('df_ must have a column named ws when use_predefined_ws is True') - # If ws_turbines supplied, warn user that it will be ignored - if ws_turbines is not None: - warnings.warn('ws_turbines will be ignored when use_predefined_ws is True') - else: - # ws_turbine must be supplied - if ws_turbines is None: - raise ValueError('ws_turbines must be supplied when use_predefined_ws is False') - - # If use_predefined_wd is True, df_ must have a column named 'wd' - if use_predefined_wd: - if 'wd' not in df_.columns: - raise ValueError('df_ must have a column named wd when use_predefined_wd is True') - # If wd_turbines supplied, warn user that it will be ignored - if wd_turbines is not None: - warnings.warn('wd_turbines will be ignored when use_predefined_wd is True') - else: - # wd_turbine must be supplied - if wd_turbines is None: - raise ValueError('wd_turbines must be supplied when use_predefined_wd is False') - - - # Confirm that test_turbines is a list of ints or a numpy array of ints - if not isinstance(test_turbines, list) and not isinstance(test_turbines, np.ndarray): - raise ValueError('test_turbines must be a list or numpy array of ints') - - # Confirm that test_turbines is not empty - if len(test_turbines) == 0: - raise ValueError('test_turbines cannot be empty') - # Identify the number of dataframes num_df = len(df_names) - # Set up the column names for the reference and test power - if not use_predefined_ref: - ref_cols = [f'pow_{i:03d}' for i in ref_turbines] - else: - ref_cols = ['pow_ref'] - - if not use_predefined_ws: - ws_cols = [f'ws_{i:03d}' for i in ws_turbines] - else: - ws_cols = ['ws'] - - if not use_predefined_wd: - wd_cols = [f'wd_{i:03d}' for i in wd_turbines] - else: - wd_cols = ['wd'] - - # Convert the numbered arrays to appropriate column names - test_cols = [f'pow_{i:03d}' for i in test_turbines] - - # Filter df_ that all the columns are not null df_ = df_.filter(pl.all(pl.col(ref_cols + test_cols + ws_cols + wd_cols).is_not_null())) @@ -409,7 +377,7 @@ def compute_energy_ratio(df_, # Assign the reference and test power columns df_ = add_power_ref(df_, ref_cols) - df_ = add_test_power(df_, test_cols) + df_ = add_power_test(df_, test_cols) bin_cols_without_df_name = [c for c in bin_cols_in if c != 'df_name'] bin_cols_with_df_name = bin_cols_without_df_name + ['df_name'] @@ -417,7 +385,7 @@ def compute_energy_ratio(df_, df_ = (df_ .filter(pl.all(pl.col(bin_cols_with_df_name).is_not_null())) # Select for all bin cols present .groupby(bin_cols_with_df_name, maintain_order=True) - .agg([pl.mean("pow_ref"), pl.mean("test_pow"),pl.count()]) + .agg([pl.mean("pow_ref"), pl.mean("pow_test"),pl.count()]) .with_columns( [ pl.col('count').min().over(bin_cols_without_df_name).alias('count_min')#, # Find the min across df_name @@ -426,7 +394,7 @@ def compute_energy_ratio(df_, .with_columns( [ pl.col('pow_ref').mul(pl.col('count_min')).alias('ref_energy'), # Compute the reference energy - pl.col('test_pow').mul(pl.col('count_min')).alias('test_energy'), # Compute the test energy + pl.col('pow_test').mul(pl.col('count_min')).alias('test_energy'), # Compute the test energy ] ) .groupby(['wd_bin','df_name'], maintain_order=True) @@ -440,7 +408,7 @@ def compute_energy_ratio(df_, .sort('wd_bin') ) - # This probably doesn't belong in here but for now + # In the case of two turbines, compute an uplift column if num_df == 2: df_ = df_.with_columns( uplift = 100 * (pl.col(df_names[1]) - pl.col(df_names[0])) / pl.col(df_names[0]) @@ -455,24 +423,22 @@ def compute_energy_ratio(df_, return(df_) - -def compute_energy_ratio_bootstrap(df_, + +# Bootstrap function wraps the _compute_energy_ratio function +def _compute_energy_ratio_bootstrap(df_, df_names, - ref_turbines=None, - test_turbines= None, - ws_turbines=None, - wd_turbines=None, - use_predefined_ref = False, - use_predefined_ws = False, - use_predefined_wd = False, - ws_step = 1.0, - ws_min = 0.0, - ws_max = 50.0, + ref_cols, + test_cols, + wd_cols, + ws_cols, wd_step = 2.0, wd_min = 0.0, wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, bin_cols_in = ['wd_bin','ws_bin'], - N = 20, + N = 1, ): """ @@ -481,19 +447,16 @@ def compute_energy_ratio_bootstrap(df_, Args: df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. df_names (list): A list of names to give to the dataframes. - ref_turbines (list[int]): A list of turbine numbers to use as the reference. - test_turbines (list[int]): A list of turbine numbers to use as the test. - ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds - wd_turbines (list[int]): A list of turbine numbers to use for the wind directions - use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. - use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. - use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. - ws_step (float): The width of the wind speed bins. - ws_min (float): The minimum wind speed to use. - ws_max (float): The maximum wind speed to use. + ref_cols (list[str]): A list of columns to use as the reference turbines + test_cols (list[str]): A list of columns to use as the test turbines + wd_cols (list[str]): A list of columns to derive the wind directions from + ws_cols (list[str]): A list of columns to derive the wind speeds from wd_step (float): The width of the wind direction bins. wd_min (float): The minimum wind direction to use. wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. N (int): The number of bootstrap samples to use. @@ -501,22 +464,20 @@ def compute_energy_ratio_bootstrap(df_, pl.DataFrame: A dataframe containing the energy ratio between the two sets of turbines. """ - - df_concat = pl.concat([compute_energy_ratio(resample_energy_table(df_, i), + + # Otherwise run the function N times and concatenate the results to compute statistics + df_concat = pl.concat([_compute_energy_ratio_single(resample_energy_table(df_, i), df_names, - ref_turbines, - test_turbines, - ws_turbines, - wd_turbines, - use_predefined_ref, - use_predefined_ws, - use_predefined_wd, - ws_step, - ws_min, - ws_max, + ref_cols, + test_cols, + wd_cols, + ws_cols, wd_step, wd_min, wd_max, + ws_step, + ws_min, + ws_max, bin_cols_in, ) for i in range(N)]) @@ -525,39 +486,39 @@ def compute_energy_ratio_bootstrap(df_, else: df_names_with_uplift = df_names + return (df_concat + .groupby(['wd_bin'], maintain_order=True) + .agg([pl.first(n) for n in df_names_with_uplift] + + [pl.quantile(n, 0.95).alias(n + "_ub") for n in df_names_with_uplift] + + [pl.quantile(n, 0.05).alias(n + "_lb") for n in df_names_with_uplift] + + [pl.first(f'count_{n}') for n in df_names] + ) + .sort('wd_bin') + ) + - return(df_concat - .groupby(['wd_bin'], maintain_order=True) - .agg([pl.first(n) for n in df_names_with_uplift] + - [pl.quantile(n, 0.95).alias(n + "_ub") for n in df_names_with_uplift] + - [pl.quantile(n, 0.05).alias(n + "_lb") for n in df_names_with_uplift] + - [pl.first(f'count_{n}') for n in df_names] - ) - .sort('wd_bin') - ) - -# Use method of Eric Simley's slide 2 -def compute_uplift_in_region(df_, +def compute_energy_ratio(df_, df_names, ref_turbines=None, test_turbines= None, - ws_turbines=None, wd_turbines=None, + ws_turbines=None, use_predefined_ref = False, - use_predefined_ws = False, use_predefined_wd = False, - ws_step = 1.0, - ws_min = 0.0, - ws_max = 50.0, + use_predefined_ws = False, wd_step = 2.0, wd_min = 0.0, wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, bin_cols_in = ['wd_bin','ws_bin'], - N = 20, + N = 1, ): """ - Compute the energy uplift between two dataframes using method of Eric Simley's slide 2 + Compute the energy ratio between two sets of turbines with bootstrapping + Args: df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. df_names (list): A list of names to give to the dataframes. @@ -568,18 +529,21 @@ def compute_uplift_in_region(df_, use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. - ws_step (float): The width of the wind speed bins. - ws_min (float): The minimum wind speed to use. - ws_max (float): The maximum wind speed to use. wd_step (float): The width of the wind direction bins. wd_min (float): The minimum wind direction to use. wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + N (int): The number of bootstrap samples to use. Returns: - pl.DataFrame: A dataframe containing the energy uplift + pl.DataFrame: A dataframe containing the energy ratio between the two sets of turbines. + """ + # Check that the inputs are valid # If use_predefined_ref is True, df_ must have a column named 'pow_ref' if use_predefined_ref: if 'pow_ref' not in df_.columns: @@ -616,6 +580,7 @@ def compute_uplift_in_region(df_, if wd_turbines is None: raise ValueError('wd_turbines must be supplied when use_predefined_wd is False') + # Confirm that test_turbines is a list of ints or a numpy array of ints if not isinstance(test_turbines, list) and not isinstance(test_turbines, np.ndarray): raise ValueError('test_turbines must be a list or numpy array of ints') @@ -623,14 +588,8 @@ def compute_uplift_in_region(df_, # Confirm that test_turbines is not empty if len(test_turbines) == 0: raise ValueError('test_turbines cannot be empty') - - num_df = len(df_names) - - # Confirm num_df == 2 - if num_df != 2: - raise ValueError('Number of dataframes must be 2') - - # Set up the column names for the reference and test power + + # Set up the column names for the reference and test power if not use_predefined_ref: ref_cols = [f'pow_{i:03d}' for i in ref_turbines] else: @@ -649,6 +608,97 @@ def compute_uplift_in_region(df_, # Convert the numbered arrays to appropriate column names test_cols = [f'pow_{i:03d}' for i in test_turbines] + # If N=1, don't use bootstrapping + if N == 1: + # Compute the energy ratio + df_res = _compute_energy_ratio_single(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in) + else: + df_res = _compute_energy_ratio_bootstrap(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N) + + # Return the results as an EnergyRatioResult object + return EnergyRatioResult(df_res, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N) + + + + + +# Use method of Eric Simley's slide 2 +def _compute_uplift_in_region_single(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + bin_cols_in = ['wd_bin','ws_bin'] + ): + + """ + Compute the energy uplift between two dataframes using method of Eric Simley's slide 2 + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_cols (list[str]): A list of columns to use as the reference turbines + test_cols (list[str]): A list of columns to use as the test turbines + wd_cols (list[str]): A list of columns to derive the wind directions from + ws_cols (list[str]): A list of columns to derive the wind speeds from + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + + Returns: + pl.DataFrame: A dataframe containing the energy uplift + """ + + + # Filter df_ that all the columns are not null df_ = df_.filter(pl.all(pl.col(ref_cols + test_cols + ws_cols + wd_cols).is_not_null())) @@ -659,24 +709,24 @@ def compute_uplift_in_region(df_, # Assign the reference and test power columns df_ = add_power_ref(df_, ref_cols) - df_ = add_test_power(df_, test_cols) + df_ = add_power_test(df_, test_cols) bin_cols_without_df_name = [c for c in bin_cols_in if c != 'df_name'] bin_cols_with_df_name = bin_cols_without_df_name + ['df_name'] df_ = (df_.with_columns( - power_ratio = pl.col('test_pow') / pl.col('pow_ref')) + power_ratio = pl.col('pow_test') / pl.col('pow_ref')) .filter(pl.all(pl.col(bin_cols_with_df_name).is_not_null())) # Select for all bin cols present .groupby(bin_cols_with_df_name, maintain_order=True) .agg([pl.mean("pow_ref"), pl.mean("power_ratio"),pl.count()]) .with_columns( [ pl.col('count').min().over(bin_cols_without_df_name).alias('count_min'), # Find the min across df_name - pl.col('pow_ref').mul(pl.col('power_ratio')).alias('test_pow'), # Compute the test power + pl.col('pow_ref').mul(pl.col('power_ratio')).alias('pow_test'), # Compute the test power ] ) - .pivot(values=['power_ratio','test_pow','pow_ref','count_min'], columns='df_name', index=['wd_bin','ws_bin'],aggregate_function='first') + .pivot(values=['power_ratio','pow_test','pow_ref','count_min'], columns='df_name', index=['wd_bin','ws_bin'],aggregate_function='first') .drop_nulls() .with_columns( f_norm = pl.col(f'count_min_df_name_{df_names[0]}') / pl.col(f'count_min_df_name_{df_names[0]}').sum() @@ -687,7 +737,7 @@ def compute_uplift_in_region(df_, ) .with_columns( delta_energy = pl.col('delta_power_ratio') * pl.col('f_norm') * pl.col('pow_ref_both_cases'), # pl.col(f'pow_ref_df_name_{df_names[0]}'), - base_test_energy = pl.col(f'test_pow_df_name_{df_names[0]}') * pl.col('f_norm') + base_test_energy = pl.col(f'pow_test_df_name_{df_names[0]}') * pl.col('f_norm') ) ) @@ -697,21 +747,18 @@ def compute_uplift_in_region(df_, 'uplift':100 * df_['delta_energy'].sum() / df_['base_test_energy'].sum()}) -def compute_uplift_in_region_bootstrap(df_, +def _compute_uplift_in_region_bootstrap(df_, df_names, - ref_turbines=None, - test_turbines= None, - ws_turbines=None, - wd_turbines=None, - use_predefined_ref = False, - use_predefined_ws = False, - use_predefined_wd = False, - ws_step = 1.0, - ws_min = 0.0, - ws_max = 50.0, + ref_cols, + test_cols, + wd_cols, + ws_cols, wd_step = 2.0, wd_min = 0.0, wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, bin_cols_in = ['wd_bin','ws_bin'], N = 20, ): @@ -722,13 +769,10 @@ def compute_uplift_in_region_bootstrap(df_, Args: df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. df_names (list): A list of names to give to the dataframes. - ref_turbines (list[int]): A list of turbine numbers to use as the reference. - test_turbines (list[int]): A list of turbine numbers to use as the test. - ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds - wd_turbines (list[int]): A list of turbine numbers to use for the wind directions - use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. - use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. - use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. + ref_cols (list[str]): A list of columns to use as the reference turbines + test_cols (list[str]): A list of columns to use as the test turbines + wd_cols (list[str]): A list of columns to derive the wind directions from + ws_cols (list[str]): A list of columns to derive the wind speeds from ws_step (float): The width of the wind speed bins. ws_min (float): The minimum wind speed to use. ws_max (float): The maximum wind speed to use. @@ -742,21 +786,18 @@ def compute_uplift_in_region_bootstrap(df_, pl.DataFrame: A dataframe containing the energy uplift """ - df_concat = pl.concat([compute_uplift_in_region(resample_energy_table(df_, i), + df_concat = pl.concat([_compute_uplift_in_region_single(resample_energy_table(df_, i), df_names, - ref_turbines, - test_turbines, - ws_turbines, - wd_turbines, - use_predefined_ref, - use_predefined_ws, - use_predefined_wd, - ws_step, - ws_min, - ws_max, + ref_cols, + test_cols, + wd_cols, + ws_cols, wd_step, wd_min, wd_max, + ws_step, + ws_min, + ws_max, bin_cols_in, ) for i in range(N)]) @@ -770,4 +811,169 @@ def compute_uplift_in_region_bootstrap(df_, 'uplift_exp':df_concat['uplift'][0], 'uplift_ub':df_concat['uplift'].quantile(0.95), 'uplift_lb':df_concat['uplift'].quantile(0.05), - }) \ No newline at end of file + }) + + +def compute_uplift_in_region(df_, + df_names, + ref_turbines=None, + test_turbines= None, + wd_turbines=None, + ws_turbines=None, + use_predefined_ref = False, + use_predefined_wd = False, + use_predefined_ws = False, + wd_step = 2.0, + wd_min = 0.0, + wd_max = 360.0, + ws_step = 1.0, + ws_min = 0.0, + ws_max = 50.0, + bin_cols_in = ['wd_bin','ws_bin'], + N = 1, + ): + + """ + Compute the energy ratio between two sets of turbines with bootstrapping + + Args: + df_ (pl.DataFrame): A dataframe containing the data to use in the calculation. + df_names (list): A list of names to give to the dataframes. + ref_turbines (list[int]): A list of turbine numbers to use as the reference. + test_turbines (list[int]): A list of turbine numbers to use as the test. + ws_turbines (list[int]): A list of turbine numbers to use for the wind speeds + wd_turbines (list[int]): A list of turbine numbers to use for the wind directions + use_predefined_ref (bool): If True, use the pow_ref column of df_ as the reference power. + use_predefined_ws (bool): If True, use the ws column of df_ as the wind speed. + use_predefined_wd (bool): If True, use the wd column of df_ as the wind direction. + wd_step (float): The width of the wind direction bins. + wd_min (float): The minimum wind direction to use. + wd_max (float): The maximum wind direction to use. + ws_step (float): The width of the wind speed bins. + ws_min (float): The minimum wind speed to use. + ws_max (float): The maximum wind speed to use. + bin_cols_in (list[str]): A list of column names to use for the wind speed and wind direction bins. + N (int): The number of bootstrap samples to use. + + Returns: + pl.DataFrame: A dataframe containing the energy ratio between the two sets of turbines. + + """ + + # Check if inputs are valid + # If use_predefined_ref is True, df_ must have a column named 'pow_ref' + if use_predefined_ref: + if 'pow_ref' not in df_.columns: + raise ValueError('df_ must have a column named pow_ref when use_predefined_ref is True') + # If ref_turbines supplied, warn user that it will be ignored + if ref_turbines is not None: + warnings.warn('ref_turbines will be ignored when use_predefined_ref is True') + else: + # ref_turbine must be supplied + if ref_turbines is None: + raise ValueError('ref_turbines must be supplied when use_predefined_ref is False') + + # If use_predefined_ws is True, df_ must have a column named 'ws' + if use_predefined_ws: + if 'ws' not in df_.columns: + raise ValueError('df_ must have a column named ws when use_predefined_ws is True') + # If ws_turbines supplied, warn user that it will be ignored + if ws_turbines is not None: + warnings.warn('ws_turbines will be ignored when use_predefined_ws is True') + else: + # ws_turbine must be supplied + if ws_turbines is None: + raise ValueError('ws_turbines must be supplied when use_predefined_ws is False') + + # If use_predefined_wd is True, df_ must have a column named 'wd' + if use_predefined_wd: + if 'wd' not in df_.columns: + raise ValueError('df_ must have a column named wd when use_predefined_wd is True') + # If wd_turbines supplied, warn user that it will be ignored + if wd_turbines is not None: + warnings.warn('wd_turbines will be ignored when use_predefined_wd is True') + else: + # wd_turbine must be supplied + if wd_turbines is None: + raise ValueError('wd_turbines must be supplied when use_predefined_wd is False') + + # Confirm that test_turbines is a list of ints or a numpy array of ints + if not isinstance(test_turbines, list) and not isinstance(test_turbines, np.ndarray): + raise ValueError('test_turbines must be a list or numpy array of ints') + + # Confirm that test_turbines is not empty + if len(test_turbines) == 0: + raise ValueError('test_turbines cannot be empty') + + num_df = len(df_names) + + # Confirm num_df == 2 + if num_df != 2: + raise ValueError('Number of dataframes must be 2') + + # Set up the column names for the reference and test power + if not use_predefined_ref: + ref_cols = [f'pow_{i:03d}' for i in ref_turbines] + else: + ref_cols = ['pow_ref'] + + if not use_predefined_ws: + ws_cols = [f'ws_{i:03d}' for i in ws_turbines] + else: + ws_cols = ['ws'] + + if not use_predefined_wd: + wd_cols = [f'wd_{i:03d}' for i in wd_turbines] + else: + wd_cols = ['wd'] + + # Convert the numbered arrays to appropriate column names + test_cols = [f'pow_{i:03d}' for i in test_turbines] + + # If N=1, don't use bootstrapping + if N == 1: + # Compute the energy ratio + df_res = _compute_uplift_in_region_single(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in) + else: + df_res = _compute_uplift_in_region_bootstrap(df_, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N) + + # Return the results as an EnergyRatioResult object + return EnergyRatioResult(df_res, + df_names, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N) \ No newline at end of file From 02cf2084d7cf8d5c047c332d8d23c125b58b5f2f Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 13 Jul 2023 08:59:56 -0600 Subject: [PATCH 12/17] fix timing test --- flasc/timing_tests/energy_ratio_timing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/flasc/timing_tests/energy_ratio_timing.py b/flasc/timing_tests/energy_ratio_timing.py index 94f89173..83a14a6f 100644 --- a/flasc/timing_tests/energy_ratio_timing.py +++ b/flasc/timing_tests/energy_ratio_timing.py @@ -84,7 +84,7 @@ def time_energy_ratio_with_bootstrapping(): for i in range(N_ITERATIONS): start_time = time.time() - df_erb = erp.compute_energy_ratio_bootstrap( + df_erb = erp.compute_energy_ratio( df_energy, ['baseline'], test_turbines=[1], From e6aea0259077c39758d12f5c323896a6bd0633e9 Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 13 Jul 2023 10:01:56 -0600 Subject: [PATCH 13/17] Split energy ratio result to separate file --- flasc/energy_ratio/energy_ratio_polars.py | 45 ++--------------------- flasc/energy_ratio/energy_ratio_result.py | 38 +++++++++++++++++++ 2 files changed, 41 insertions(+), 42 deletions(-) create mode 100644 flasc/energy_ratio/energy_ratio_result.py diff --git a/flasc/energy_ratio/energy_ratio_polars.py b/flasc/energy_ratio/energy_ratio_polars.py index 52e93e30..07ed4247 100644 --- a/flasc/energy_ratio/energy_ratio_polars.py +++ b/flasc/energy_ratio/energy_ratio_polars.py @@ -9,6 +9,7 @@ import numpy as np import polars as pl import warnings +from energy_ratio_result import EnergyRatioResult # def get_mid_bins(bin_edges): # """_summary_ @@ -19,45 +20,6 @@ # print(bin_edges[:-1] + np.diff(bin_edges)/2.0) - -class EnergyRatioResult: - """ This class is used to store the results of the energy ratio calculations - and provide convenient methods for plotting and saving the results. - """ - def __init__(self, - df_result, - df_names, - ref_cols, - test_cols, - wd_cols, - ws_cols, - wd_step, - wd_min, - wd_max, - ws_step, - ws_min, - ws_max, - bin_cols_in, - N - ): - - self.df_result = df_result - self.df_names = df_names - self.num_df = len(df_names) - self.ref_cols = ref_cols - self.test_cols = test_cols - self.wd_cols = wd_cols - self.ws_cols = ws_cols - self.wd_step = wd_step - self.wd_min = wd_min - self.wd_max = wd_max - self.ws_step = ws_step - self.ws_min = ws_min - self.ws_max = ws_max - self.bin_cols_in = bin_cols_in - self.N = N - - # def convert_to_polars(df_): # """_summary_ @@ -643,6 +605,7 @@ def compute_energy_ratio(df_, # Return the results as an EnergyRatioResult object return EnergyRatioResult(df_res, df_names, + energy_table, ref_cols, test_cols, wd_cols, @@ -697,9 +660,6 @@ def _compute_uplift_in_region_single(df_, pl.DataFrame: A dataframe containing the energy uplift """ - - - # Filter df_ that all the columns are not null df_ = df_.filter(pl.all(pl.col(ref_cols + test_cols + ws_cols + wd_cols).is_not_null())) @@ -965,6 +925,7 @@ def compute_uplift_in_region(df_, # Return the results as an EnergyRatioResult object return EnergyRatioResult(df_res, df_names, + energy_table, ref_cols, test_cols, wd_cols, diff --git a/flasc/energy_ratio/energy_ratio_result.py b/flasc/energy_ratio/energy_ratio_result.py new file mode 100644 index 00000000..9d4e96ee --- /dev/null +++ b/flasc/energy_ratio/energy_ratio_result.py @@ -0,0 +1,38 @@ +class EnergyRatioResult: + """ This class is used to store the results of the energy ratio calculations + and provide convenient methods for plotting and saving the results. + """ + def __init__(self, + df_result, + df_names, + energy_table, + ref_cols, + test_cols, + wd_cols, + ws_cols, + wd_step, + wd_min, + wd_max, + ws_step, + ws_min, + ws_max, + bin_cols_in, + N + ): + + self.df_result = df_result + self.df_names = df_names + self.energy_table = energy_table + self.num_df = len(df_names) + self.ref_cols = ref_cols + self.test_cols = test_cols + self.wd_cols = wd_cols + self.ws_cols = ws_cols + self.wd_step = wd_step + self.wd_min = wd_min + self.wd_max = wd_max + self.ws_step = ws_step + self.ws_min = ws_min + self.ws_max = ws_max + self.bin_cols_in = bin_cols_in + self.N = N \ No newline at end of file From 683567cc6e45769c7e57f883436c765287b76130 Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 13 Jul 2023 10:02:06 -0600 Subject: [PATCH 14/17] cleaning up --- flasc/timing_tests/energy_ratio_timing.py | 1 - 1 file changed, 1 deletion(-) diff --git a/flasc/timing_tests/energy_ratio_timing.py b/flasc/timing_tests/energy_ratio_timing.py index 83a14a6f..22e2ffd8 100644 --- a/flasc/timing_tests/energy_ratio_timing.py +++ b/flasc/timing_tests/energy_ratio_timing.py @@ -29,7 +29,6 @@ N_ITERATIONS = 5 - def load_data_and_prep_data(): # Load dataframe with artificial SCADA data root_dir = os.path.dirname(os.path.abspath(__file__)) From 8baceeb549e42b7a9a8e8b1c5df030bfc06b7dc0 Mon Sep 17 00:00:00 2001 From: Paul Date: Thu, 13 Jul 2023 16:41:29 -0600 Subject: [PATCH 15/17] incremental update --- flasc/energy_ratio/demo_capabilities.ipynb | 513 +++++++++++++++++---- flasc/energy_ratio/energy_ratio_polars.py | 253 +++++++++- flasc/energy_ratio/energy_ratio_result.py | 38 -- 3 files changed, 683 insertions(+), 121 deletions(-) delete mode 100644 flasc/energy_ratio/energy_ratio_result.py diff --git a/flasc/energy_ratio/demo_capabilities.ipynb b/flasc/energy_ratio/demo_capabilities.ipynb index e4b8fef6..365f5732 100644 --- a/flasc/energy_ratio/demo_capabilities.ipynb +++ b/flasc/energy_ratio/demo_capabilities.ipynb @@ -34,6 +34,15 @@ "\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "N = 2" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -44,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -60,7 +69,7 @@ "" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, @@ -91,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -143,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -174,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -236,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -257,6 +266,10 @@ "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", " r, k = function_base._ureduce(a,\n", "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n", + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n", + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", " r, k = function_base._ureduce(a,\n" ] }, @@ -272,6 +285,8 @@ "name": "stderr", "output_type": "stream", "text": [ + "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", + " r, k = function_base._ureduce(a,\n", "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", " r, k = function_base._ureduce(a,\n" ] @@ -282,63 +297,63 @@ "[{'name': 'WakeSteering (Noisy)/Baseline (Noisy)',\n", " 'color': (0.12156862745098039, 0.47058823529411764, 0.7058823529411765),\n", " 'er_results': baseline baseline_lb baseline_ub wd_bin bin_count\n", - " 0 0.998822 0.998791 0.998884 235.0 2\n", - " 1 0.995258 0.993638 0.998203 237.0 2\n", - " 2 0.997264 0.990900 1.000186 239.0 16\n", - " 3 0.998522 0.993120 1.010756 241.0 28\n", - " 4 0.985829 0.970631 0.994559 243.0 65\n", - " 5 0.983269 0.965738 0.991220 245.0 130\n", - " 6 0.972870 0.959708 0.982437 247.0 217\n", - " 7 0.973321 0.963169 0.980622 249.0 344\n", - " 8 0.967568 0.953978 0.977599 251.0 451\n", - " 9 0.961280 0.949570 0.982164 253.0 601\n", - " 10 0.940554 0.933701 0.960864 255.0 652\n", - " 11 0.931804 0.903085 0.944257 257.0 730\n", - " 12 0.892420 0.859754 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" + ], + "text/plain": [ + " wd_bin ws_bin df_name count\n", + "0 257.0 5.5 baseline 104\n", + "1 257.0 3.5 baseline 17\n", + "2 267.0 4.5 baseline 50\n", + "3 271.0 6.5 baseline 156\n", + "4 279.0 3.5 baseline 19\n", + ".. ... ... ... ...\n", + "639 249.0 11.5 wakesteering 6\n", + "640 243.0 10.5 wakesteering 4\n", + "641 253.0 12.5 wakesteering 1\n", + "642 279.0 11.5 wakesteering 9\n", + "643 239.0 11.5 wakesteering 1\n", + "\n", + "[644 rows x 4 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_freq_plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "could not convert string to float: 'baseline'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/Users/pfleming/Projects/FLORIS/flasc/flasc/energy_ratio/demo_capabilities.ipynb Cell 21\u001b[0m in \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m ero\u001b[39m.\u001b[39;49m_plot()\n", + "File \u001b[0;32m~/Projects/FLORIS/flasc/flasc/energy_ratio/energy_ratio_polars.py:1173\u001b[0m, in \u001b[0;36mEnergyRatioResult._plot\u001b[0;34m(self, df_names_subset, labels, color_dict, axarr, polar_plot, show_wind_speed_distrubution)\u001b[0m\n\u001b[1;32m 1170\u001b[0m \u001b[39m# Get the data for this df_name\u001b[39;00m\n\u001b[1;32m 1171\u001b[0m df_sub \u001b[39m=\u001b[39m df_freq[df_freq[\u001b[39m\"\u001b[39m\u001b[39mdf_name\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m==\u001b[39m df_name]\n\u001b[0;32m-> 1173\u001b[0m sns\u001b[39m.\u001b[39;49mheatmap(data\u001b[39m=\u001b[39;49mdf_sub, x\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mwd_bin\u001b[39;49m\u001b[39m\"\u001b[39;49m, y\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mws_bin\u001b[39;49m\u001b[39m\"\u001b[39;49m, value\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcount\u001b[39;49m\u001b[39m\"\u001b[39;49m, ax\u001b[39m=\u001b[39;49max, cmap\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mviridis\u001b[39;49m\u001b[39m\"\u001b[39;49m, cbar\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, annot\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, fmt\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mg\u001b[39;49m\u001b[39m'\u001b[39;49m)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/floris/lib/python3.10/site-packages/seaborn/_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args..inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m 37\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mPass the following variable\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m as \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39mkeyword arg\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 38\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[39mFutureWarning\u001b[39;00m\n\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 45\u001b[0m kwargs\u001b[39m.\u001b[39mupdate({k: arg \u001b[39mfor\u001b[39;00m k, arg \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(sig\u001b[39m.\u001b[39mparameters, args)})\n\u001b[0;32m---> 46\u001b[0m \u001b[39mreturn\u001b[39;00m f(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "File \u001b[0;32m~/opt/anaconda3/envs/floris/lib/python3.10/site-packages/seaborn/matrix.py:540\u001b[0m, in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[39m\"\"\"Plot rectangular data as a color-encoded matrix.\u001b[39;00m\n\u001b[1;32m 363\u001b[0m \n\u001b[1;32m 364\u001b[0m \u001b[39mThis is an Axes-level function and will draw the heatmap into the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[39m ... ax = sns.heatmap(corr, mask=mask, vmax=.3, square=True)\u001b[39;00m\n\u001b[1;32m 538\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 539\u001b[0m \u001b[39m# Initialize the plotter object\u001b[39;00m\n\u001b[0;32m--> 540\u001b[0m plotter \u001b[39m=\u001b[39m _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,\n\u001b[1;32m 541\u001b[0m annot_kws, cbar, cbar_kws, xticklabels,\n\u001b[1;32m 542\u001b[0m yticklabels, mask)\n\u001b[1;32m 544\u001b[0m \u001b[39m# Add the pcolormesh kwargs here\u001b[39;00m\n\u001b[1;32m 545\u001b[0m kwargs[\u001b[39m\"\u001b[39m\u001b[39mlinewidths\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m linewidths\n", + "File \u001b[0;32m~/opt/anaconda3/envs/floris/lib/python3.10/site-packages/seaborn/matrix.py:159\u001b[0m, in \u001b[0;36m_HeatMapper.__init__\u001b[0;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mylabel \u001b[39m=\u001b[39m ylabel \u001b[39mif\u001b[39;00m ylabel \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 158\u001b[0m \u001b[39m# Determine good default values for the colormapping\u001b[39;00m\n\u001b[0;32m--> 159\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_determine_cmap_params(plot_data, vmin, vmax,\n\u001b[1;32m 160\u001b[0m cmap, center, robust)\n\u001b[1;32m 162\u001b[0m \u001b[39m# Sort out the annotations\u001b[39;00m\n\u001b[1;32m 163\u001b[0m \u001b[39mif\u001b[39;00m annot \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mor\u001b[39;00m annot \u001b[39mis\u001b[39;00m \u001b[39mFalse\u001b[39;00m:\n", + "File \u001b[0;32m~/opt/anaconda3/envs/floris/lib/python3.10/site-packages/seaborn/matrix.py:193\u001b[0m, in \u001b[0;36m_HeatMapper._determine_cmap_params\u001b[0;34m(self, plot_data, vmin, vmax, cmap, center, robust)\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[39m\"\"\"Use some heuristics to set good defaults for colorbar and range.\"\"\"\u001b[39;00m\n\u001b[1;32m 192\u001b[0m \u001b[39m# plot_data is a np.ma.array instance\u001b[39;00m\n\u001b[0;32m--> 193\u001b[0m calc_data \u001b[39m=\u001b[39m plot_data\u001b[39m.\u001b[39;49mastype(\u001b[39mfloat\u001b[39;49m)\u001b[39m.\u001b[39mfilled(np\u001b[39m.\u001b[39mnan)\n\u001b[1;32m 194\u001b[0m \u001b[39mif\u001b[39;00m vmin \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 195\u001b[0m \u001b[39mif\u001b[39;00m robust:\n", + "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'baseline'" + ] + }, + { + "data": { + "image/png": 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", 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""" + + # Temporary copy of energy table + df_ = self.energy_table.clone() + + # Filter df_ that all the columns are not null + df_ = df_.filter(pl.all(pl.col(self.ref_cols + self.test_cols + self.ws_cols + self.wd_cols).is_not_null())) + + # Assign the wd/ws bins + df_ = add_ws_bin(df_, self.ws_cols, self.ws_step, self.ws_min, self.ws_max) + df_ = add_wd_bin(df_, self.wd_cols, self.wd_step, self.wd_min, self.wd_max) + + # Get the bin count by wd, ws and df_name + df_ = df_.groupby(['wd_bin','ws_bin','df_name']).count() + + return df_ + + def _plot(self, + df_names_subset = None, + labels = None, + color_dict = None, + axarr = None, + polar_plot=False, + show_wind_speed_distrubution=True, + ): + + # Only allow showing the wind speed distribution if polar_plot is False + if polar_plot and show_wind_speed_distrubution: + raise ValueError('show_wind_speed_distrubution cannot be True if polar_plot is True') + + # If df_names_subset is None, plot all the dataframes + if df_names_subset is None: + df_names_subset = self.df_names + + # If df_names_subset is not a list, convert it to a list + if not isinstance(df_names_subset, list): + df_names_subset = [df_names_subset] + + # Total number of energy ratios to plot + N = len(df_names_subset) + + # If labels is None, use the dataframe names + if labels is None: + labels = df_names_subset + + # If labels is not a list, convert it to a list + if not isinstance(labels, list): + labels = [labels] + + # Confirm that the length of labels is the same as the length of df_names_subset + if len(labels) != N: + raise ValueError('Length of labels must be the same as the length of df_names_subset') + + # Generate the default colors using the seaborn color palette + default_colors = sns.color_palette('colorblind', N) + + # If color_dict is None, use the default colors + if color_dict is None: + color_dict = {labels[i]: default_colors[i] for i in range(N)} + + # If color_dict is not a dictionary, raise an error + if not isinstance(color_dict, dict): + raise ValueError('color_dict must be a dictionary') + + # Make sure the keys of color_dict are in df_names_subset + if not all([label in df_names_subset for label in color_dict.keys()]): + raise ValueError('color_dict keys must be in df_names_subset') + + if axarr is None: + if polar_plot: + _, axarr = plt.subplots(nrows=1, ncols=2, figsize=(10, 5), subplot_kw={'projection': 'polar'}) + else: + if show_wind_speed_distrubution: + num_rows = 2 + N # Add rows to show wind speed distribution + else: + num_rows = 2 + _, axarr = plt.subplots(nrows=num_rows, ncols=1, sharex=True, figsize=(10, 5)) + + # Come up with hatch patterns + hatch_patterns = [ + '//', '++', 'xx', 'oo', '\\\\', '--', 'OO', '..', '**', '||', + '/o', '\\|', '|*', '-\\', '+o', 'x*', 'o-', 'O|', 'O.', '*-', + '/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*', + ] * 10 # Repeat 10 times to ensure always sufficiently large size + + # Set the bar width using self.wd_step + bar_width = (0.7 / N) * self.wd_step + if polar_plot: + bar_width = bar_width * np.pi / 180.0 + + # For plotting, get a pandas dataframe + df = self.df_result.to_pandas() + + # Get x-axis values + x = np.array(df["wd_bin"], dtype=float) + + # Add NaNs to avoid connecting plots over gaps + dwd = np.min(x[1::] - x[0:-1]) + jumps = np.where(np.diff(x) > dwd * 1.50)[0] + if len(jumps) > 0: + df = pd.concat( + [ + df, + pd.DataFrame( + { + "wd_bin": x[jumps] + dwd / 2.0, + "N_bin": [0] * len(jumps), + } + ) + ], + axis=0, + ignore_index=False, + ) + df = df.iloc[np.argsort(df["wd_bin"])].reset_index(drop=True) + x = np.array(df["wd_bin"], dtype=float) + + # Plot horizontal black line at 1. + xlims = np.linspace(np.min(x) - 4.0, np.max(x) + 4.0, 1000) + + # Save the x_bins + # x_bins = copy.deepcopy(x) + + if polar_plot: + x = (90.0 - x) * np.pi / 180.0 # Convert to radians + xlims = (90.0 - xlims) * np.pi / 180.0 # Convert to radians + + # Plot the horizontal line at 1 + axarr[0].plot(xlims, np.ones_like(xlims), color="black") + + # Plot the energy ratios + for df_name, label in zip(df_names_subset, labels): + + axarr[0].plot(x, df[df_name], "-o", markersize=3.0, label=label, color=color_dict[label]) + + # If data includes upper and lower bounds plot them + if df_name + "_ub" in df.columns: + + axarr[0].fill_between( + x, + df[df_name + "_lb"], + df[df_name + "_ub"], + alpha=0.25, + color=color_dict[label], + ) + + # Format the energy ratio plot + axarr[0].legend() + axarr[0].grid(visible=True, which="major", axis="both", color="gray") + axarr[0].grid(visible=True, which="minor", axis="both", color="lightgray") + axarr[0].minorticks_on() + # axarr[0].set_grid(True) + + # Plot the bin counts + df_freq = self._compute_df_freq().to_pandas() + df_freq_sum_all_ws = df_freq.groupby(["wd_bin","df_name"]).sum().reset_index() + + + for i, (df_name, label) in enumerate(zip(df_names_subset, labels)): + df_sub = df_freq_sum_all_ws[df_freq_sum_all_ws["df_name"] == df_name] + + x = np.array(df_sub["wd_bin"], dtype=float) + if polar_plot: + x = (90.0 - x) * np.pi / 180.0 # Convert to radians + axarr[1].bar(x - (i - N / 2) * bar_width, df_sub["count"], width=bar_width, label = label, color=color_dict[label]) + + axarr[1].legend() + + # Get the bins + wd_bins = np.array(df_freq["wd_bin"].unique(), dtype=float) + ws_bins = np.array(df_freq["ws_bin"].unique(), dtype=float) + num_wd_bins = len(wd_bins) + num_ws_bins = len(ws_bins) + + if show_wind_speed_distrubution: + # Plot the wind speed distribution in df_freq as a heat map with wd on the x-axis and ws on the y-axis + + for i, (df_name, label) in enumerate(zip(df_names_subset, labels)): + ax = axarr[2 + i] + + # Get the data for this df_name + df_sub = df_freq[df_freq["df_name"] == df_name] + + # Scatter plot the data with wd on the x-axis and ws on the y-axis and count as the color + im = ax.scatter(df_sub["wd_bin"], df_sub["ws_bin"], c=df_sub["count"], cmap="viridis", s=10, marker="s") + ax.set_ylabel(label) + # ax.colorbar(im, ax=ax) + + # df_pivot = df_sub.pivot(index="ws_bin", columns="wd_bin", values="count") + + # sns.heatmap(data=df_pivot, ax=ax, cmap="viridis", cbar=True, annot=True, fmt='g') + + + diff --git a/flasc/energy_ratio/energy_ratio_result.py b/flasc/energy_ratio/energy_ratio_result.py deleted file mode 100644 index 9d4e96ee..00000000 --- a/flasc/energy_ratio/energy_ratio_result.py +++ /dev/null @@ -1,38 +0,0 @@ -class EnergyRatioResult: - """ This class is used to store the results of the energy ratio calculations - and provide convenient methods for plotting and saving the results. - """ - def __init__(self, - df_result, - df_names, - energy_table, - ref_cols, - test_cols, - wd_cols, - ws_cols, - wd_step, - wd_min, - wd_max, - ws_step, - ws_min, - ws_max, - bin_cols_in, - N - ): - - self.df_result = df_result - self.df_names = df_names - self.energy_table = energy_table - self.num_df = len(df_names) - self.ref_cols = ref_cols - self.test_cols = test_cols - self.wd_cols = wd_cols - self.ws_cols = ws_cols - self.wd_step = wd_step - self.wd_min = wd_min - self.wd_max = wd_max - self.ws_step = ws_step - self.ws_min = ws_min - self.ws_max = ws_max - self.bin_cols_in = bin_cols_in - self.N = N \ No newline at end of file From 3415f75a28f9be5063760dd90631953998d26ea8 Mon Sep 17 00:00:00 2001 From: Paul Date: Fri, 14 Jul 2023 14:03:11 -0600 Subject: [PATCH 16/17] Update plotting --- flasc/energy_ratio/demo_capabilities.ipynb | 369 ++++++++------------- flasc/energy_ratio/energy_ratio_polars.py | 51 ++- 2 files changed, 162 insertions(+), 258 deletions(-) diff --git a/flasc/energy_ratio/demo_capabilities.ipynb b/flasc/energy_ratio/demo_capabilities.ipynb index 365f5732..1b74d29f 100644 --- a/flasc/energy_ratio/demo_capabilities.ipynb +++ b/flasc/energy_ratio/demo_capabilities.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -230,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -266,10 +266,6 @@ "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", " r, k = function_base._ureduce(a,\n", "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", - " r, k = function_base._ureduce(a,\n", - "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", - " r, k = function_base._ureduce(a,\n", - "/Users/pfleming/opt/anaconda3/envs/floris/lib/python3.10/site-packages/numpy/lib/nanfunctions.py:1559: RuntimeWarning: All-NaN slice encountered\n", " r, k = function_base._ureduce(a,\n" ] }, @@ -281,71 +277,60 @@ "Dataframes differ in wd and ws. 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303.00.9998530.9999930.0139590.9998530.9999930.0139590.9998530.9999910.0137734
305.00.999773nullnull0.999773nullnull0.999773nullnull2null
307.00.998718nullnull0.998718nullnull0.99841nullnull2null
" ], "text/plain": [ - "shape: (37, 12)\n", + "shape: (38, 12)\n", "┌────────┬──────────┬────────────┬───────────┬───┬────────────┬───────────┬────────────┬────────────┐\n", "│ wd_bin ┆ baseline ┆ wakesteeri ┆ uplift ┆ … ┆ wakesteeri ┆ uplift_lb ┆ count_base ┆ count_wake │\n", "│ --- ┆ --- ┆ ng ┆ --- ┆ ┆ ng_lb ┆ --- ┆ line ┆ steering │\n", "│ f64 ┆ f64 ┆ --- ┆ f64 ┆ ┆ --- ┆ f64 ┆ --- ┆ --- │\n", "│ ┆ ┆ f64 ┆ ┆ ┆ f64 ┆ ┆ u32 ┆ u32 │\n", "╞════════╪══════════╪════════════╪═══════════╪═══╪════════════╪═══════════╪════════════╪════════════╡\n", - "│ 231.0 ┆ 0.999716 ┆ null ┆ null ┆ … ┆ null ┆ null ┆ 1 ┆ null │\n", - "│ 235.0 ┆ 0.998841 ┆ 0.992024 ┆ -0.682536 ┆ … ┆ 0.992024 ┆ -0.682536 ┆ 2 ┆ 1 │\n", - "│ 237.0 ┆ 0.999907 ┆ 0.993721 ┆ -0.618655 ┆ … ┆ 0.993721 ┆ -0.618655 ┆ 3 ┆ 5 │\n", - "│ 239.0 ┆ 0.999172 ┆ 0.996788 ┆ -0.238604 ┆ … ┆ 0.996788 ┆ -0.238604 ┆ 18 ┆ 13 │\n", + "│ 233.0 ┆ 0.999801 ┆ null ┆ null ┆ … ┆ null ┆ null ┆ 2 ┆ null │\n", + "│ 235.0 ┆ 0.999893 ┆ null ┆ null ┆ … ┆ null ┆ null ┆ 1 ┆ null │\n", + "│ 237.0 ┆ 0.998107 ┆ 0.980529 ┆ -1.761142 ┆ … ┆ 0.980529 ┆ -1.761142 ┆ 6 ┆ 4 │\n", + "│ 239.0 ┆ 0.999278 ┆ 0.995383 ┆ -0.389829 ┆ … ┆ 0.994991 ┆ -0.408202 ┆ 11 ┆ 14 │\n", "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", - "│ 299.0 ┆ 0.997014 ┆ 0.999964 ┆ 0.295947 ┆ … ┆ 0.999964 ┆ 0.137382 ┆ 28 ┆ 22 │\n", - "│ 301.0 ┆ 0.998877 ┆ 0.999881 ┆ 0.10051 ┆ … ┆ 0.999879 ┆ 0.10051 ┆ 12 ┆ 11 │\n", - "│ 303.0 ┆ 0.998914 ┆ 0.999997 ┆ 0.108439 ┆ … ┆ 0.999997 ┆ 0.108439 ┆ 2 ┆ 3 │\n", - "│ 305.0 ┆ 0.9994 ┆ null ┆ null ┆ … ┆ null ┆ null ┆ 1 ┆ null │\n", + "│ 301.0 ┆ 0.998964 ┆ 0.999959 ┆ 0.099525 ┆ … ┆ 0.999959 ┆ 0.088127 ┆ 5 ┆ 15 │\n", + "│ 303.0 ┆ 0.999853 ┆ 0.999993 ┆ 0.013959 ┆ … ┆ 0.999991 ┆ 0.01377 ┆ 3 ┆ 4 │\n", + "│ 305.0 ┆ 0.999773 ┆ null ┆ null ┆ … ┆ null ┆ null ┆ 2 ┆ null │\n", + "│ 307.0 ┆ 0.998718 ┆ null ┆ null ┆ … ┆ null ┆ null ┆ 2 ┆ null │\n", "└────────┴──────────┴────────────┴───────────┴───┴────────────┴───────────┴────────────┴────────────┘" ] }, @@ -585,16 +570,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "df_freq_plot = ero._compute_df_freq().to_pandas()" + "_, df_freq_plot = ero._compute_df_freq()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -627,38 +612,38 @@ " \n", " \n", " 0\n", - " 257.0\n", + " 255.0\n", " 5.5\n", " baseline\n", - " 104\n", + " 111\n", " \n", " \n", " 1\n", - " 257.0\n", - " 3.5\n", + " 243.0\n", + " 5.5\n", " baseline\n", - " 17\n", + " 11\n", " \n", " \n", " 2\n", - " 267.0\n", + " 243.0\n", " 4.5\n", " baseline\n", - " 50\n", + " 4\n", " \n", " \n", " 3\n", - " 271.0\n", + " 247.0\n", " 6.5\n", " baseline\n", - " 156\n", + " 40\n", " \n", " \n", " 4\n", - " 279.0\n", - " 3.5\n", + " 251.0\n", + " 6.5\n", " baseline\n", - " 19\n", + " 86\n", " \n", " \n", " ...\n", @@ -668,60 +653,60 @@ " ...\n", " \n", " \n", - " 639\n", - " 249.0\n", - " 11.5\n", + " 625\n", + " 299.0\n", + " 9.5\n", " wakesteering\n", - " 6\n", + " 1\n", " \n", " \n", - " 640\n", - " 243.0\n", - " 10.5\n", + " 626\n", + " 279.0\n", + " 11.5\n", " wakesteering\n", - " 4\n", + " 12\n", " \n", " \n", - " 641\n", - " 253.0\n", - " 12.5\n", + " 627\n", + " 243.0\n", + " 10.5\n", " wakesteering\n", - " 1\n", + " 5\n", " \n", " \n", - " 642\n", - " 279.0\n", + " 628\n", + " 249.0\n", " 11.5\n", " wakesteering\n", - " 9\n", + " 5\n", " \n", " \n", - " 643\n", - " 239.0\n", + " 629\n", + " 243.0\n", " 11.5\n", " wakesteering\n", " 1\n", " \n", " \n", "\n", - "

644 rows × 4 columns

\n", + "

630 rows × 4 columns

\n", "" ], "text/plain": [ " wd_bin ws_bin df_name count\n", - "0 257.0 5.5 baseline 104\n", - "1 257.0 3.5 baseline 17\n", - "2 267.0 4.5 baseline 50\n", - "3 271.0 6.5 baseline 156\n", - "4 279.0 3.5 baseline 19\n", + "0 255.0 5.5 baseline 111\n", + "1 243.0 5.5 baseline 11\n", + "2 243.0 4.5 baseline 4\n", + "3 247.0 6.5 baseline 40\n", + "4 251.0 6.5 baseline 86\n", ".. ... ... ... ...\n", - "639 249.0 11.5 wakesteering 6\n", - "640 243.0 10.5 wakesteering 4\n", - "641 253.0 12.5 wakesteering 1\n", - "642 279.0 11.5 wakesteering 9\n", - "643 239.0 11.5 wakesteering 1\n", + "625 299.0 9.5 wakesteering 1\n", + "626 279.0 11.5 wakesteering 12\n", + "627 243.0 10.5 wakesteering 5\n", + "628 249.0 11.5 wakesteering 5\n", + "629 243.0 11.5 wakesteering 1\n", "\n", - "[644 rows x 4 columns]" + "[630 rows x 4 columns]" ] }, "execution_count": 15, @@ -735,30 +720,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [ { - "ename": "ValueError", - "evalue": "could not convert string to float: 'baseline'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/Users/pfleming/Projects/FLORIS/flasc/flasc/energy_ratio/demo_capabilities.ipynb Cell 21\u001b[0m in \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m ero\u001b[39m.\u001b[39;49m_plot()\n", - "File \u001b[0;32m~/Projects/FLORIS/flasc/flasc/energy_ratio/energy_ratio_polars.py:1173\u001b[0m, in \u001b[0;36mEnergyRatioResult._plot\u001b[0;34m(self, df_names_subset, labels, color_dict, axarr, polar_plot, show_wind_speed_distrubution)\u001b[0m\n\u001b[1;32m 1170\u001b[0m \u001b[39m# Get the data for this df_name\u001b[39;00m\n\u001b[1;32m 1171\u001b[0m df_sub \u001b[39m=\u001b[39m df_freq[df_freq[\u001b[39m\"\u001b[39m\u001b[39mdf_name\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m==\u001b[39m df_name]\n\u001b[0;32m-> 1173\u001b[0m sns\u001b[39m.\u001b[39;49mheatmap(data\u001b[39m=\u001b[39;49mdf_sub, x\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mwd_bin\u001b[39;49m\u001b[39m\"\u001b[39;49m, y\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mws_bin\u001b[39;49m\u001b[39m\"\u001b[39;49m, value\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcount\u001b[39;49m\u001b[39m\"\u001b[39;49m, ax\u001b[39m=\u001b[39;49max, cmap\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mviridis\u001b[39;49m\u001b[39m\"\u001b[39;49m, cbar\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, annot\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, fmt\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mg\u001b[39;49m\u001b[39m'\u001b[39;49m)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/floris/lib/python3.10/site-packages/seaborn/_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args..inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 36\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m 37\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mPass the following variable\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m as \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39mkeyword arg\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 38\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[39mFutureWarning\u001b[39;00m\n\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 45\u001b[0m kwargs\u001b[39m.\u001b[39mupdate({k: arg \u001b[39mfor\u001b[39;00m k, arg \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(sig\u001b[39m.\u001b[39mparameters, args)})\n\u001b[0;32m---> 46\u001b[0m \u001b[39mreturn\u001b[39;00m f(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", - "File \u001b[0;32m~/opt/anaconda3/envs/floris/lib/python3.10/site-packages/seaborn/matrix.py:540\u001b[0m, in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[39m\"\"\"Plot rectangular data as a color-encoded matrix.\u001b[39;00m\n\u001b[1;32m 363\u001b[0m \n\u001b[1;32m 364\u001b[0m \u001b[39mThis is an Axes-level function and will draw the heatmap into the\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[39m ... ax = sns.heatmap(corr, mask=mask, vmax=.3, square=True)\u001b[39;00m\n\u001b[1;32m 538\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 539\u001b[0m \u001b[39m# Initialize the plotter object\u001b[39;00m\n\u001b[0;32m--> 540\u001b[0m plotter \u001b[39m=\u001b[39m _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,\n\u001b[1;32m 541\u001b[0m annot_kws, cbar, cbar_kws, xticklabels,\n\u001b[1;32m 542\u001b[0m yticklabels, mask)\n\u001b[1;32m 544\u001b[0m \u001b[39m# Add the pcolormesh kwargs here\u001b[39;00m\n\u001b[1;32m 545\u001b[0m kwargs[\u001b[39m\"\u001b[39m\u001b[39mlinewidths\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m linewidths\n", - "File \u001b[0;32m~/opt/anaconda3/envs/floris/lib/python3.10/site-packages/seaborn/matrix.py:159\u001b[0m, in \u001b[0;36m_HeatMapper.__init__\u001b[0;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mylabel \u001b[39m=\u001b[39m ylabel \u001b[39mif\u001b[39;00m ylabel \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 158\u001b[0m \u001b[39m# Determine good default values for the colormapping\u001b[39;00m\n\u001b[0;32m--> 159\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_determine_cmap_params(plot_data, vmin, vmax,\n\u001b[1;32m 160\u001b[0m cmap, center, robust)\n\u001b[1;32m 162\u001b[0m \u001b[39m# Sort out the annotations\u001b[39;00m\n\u001b[1;32m 163\u001b[0m \u001b[39mif\u001b[39;00m annot \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mor\u001b[39;00m annot \u001b[39mis\u001b[39;00m \u001b[39mFalse\u001b[39;00m:\n", - "File \u001b[0;32m~/opt/anaconda3/envs/floris/lib/python3.10/site-packages/seaborn/matrix.py:193\u001b[0m, in \u001b[0;36m_HeatMapper._determine_cmap_params\u001b[0;34m(self, plot_data, vmin, vmax, cmap, center, robust)\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[39m\"\"\"Use some heuristics to set good defaults for colorbar and range.\"\"\"\u001b[39;00m\n\u001b[1;32m 192\u001b[0m \u001b[39m# plot_data is a np.ma.array instance\u001b[39;00m\n\u001b[0;32m--> 193\u001b[0m calc_data \u001b[39m=\u001b[39m plot_data\u001b[39m.\u001b[39;49mastype(\u001b[39mfloat\u001b[39;49m)\u001b[39m.\u001b[39mfilled(np\u001b[39m.\u001b[39mnan)\n\u001b[1;32m 194\u001b[0m \u001b[39mif\u001b[39;00m vmin \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 195\u001b[0m \u001b[39mif\u001b[39;00m robust:\n", - "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'baseline'" + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pfleming/Projects/FLORIS/flasc/flasc/energy_ratio/energy_ratio_polars.py:1160: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " ax.scatter(df_unbinned[\"wd_bin\"], df_unbinned[\"ws_bin\"], c=color_dict[label],alpha=0.25, s=1)\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -766,19 +743,19 @@ } ], "source": [ - "ero._plot()" + "ero.plot()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "shape: (627, 4)\n", - "┌────────┬────────┬──────────────┬───────┐\n", - "│ wd_bin ┆ ws_bin ┆ df_name ┆ count │\n", - "│ --- ┆ --- ┆ --- ┆ --- │\n", - "│ f64 ┆ f64 ┆ str ┆ u32 │\n", - "╞════════╪════════╪══════════════╪═══════╡\n", - "│ 245.0 ┆ 3.5 ┆ baseline ┆ 7 │\n", - "│ 245.0 ┆ 5.5 ┆ baseline ┆ 22 │\n", - "│ 255.0 ┆ 4.5 ┆ baseline ┆ 50 │\n", - "│ 249.0 ┆ 7.5 ┆ baseline ┆ 74 │\n", - "│ … ┆ … ┆ … ┆ … │\n", - "│ 249.0 ┆ 11.5 ┆ wakesteering ┆ 5 │\n", - "│ 243.0 ┆ 11.5 ┆ wakesteering ┆ 1 │\n", - "│ 239.0 ┆ 10.5 ┆ wakesteering ┆ 1 │\n", - "│ 291.0 ┆ 12.5 ┆ wakesteering ┆ 2 │\n", - "└────────┴────────┴──────────────┴───────┘" + "
" ] }, - "execution_count": 19, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "df_f.groupby(['wd_bin','ws_bin','df_name']).count()" + "ero.plot(polar_plot=True, show_wind_speed_distrubution=False)" ] }, { diff --git a/flasc/energy_ratio/energy_ratio_polars.py b/flasc/energy_ratio/energy_ratio_polars.py index f4787c32..fc81e02a 100644 --- a/flasc/energy_ratio/energy_ratio_polars.py +++ b/flasc/energy_ratio/energy_ratio_polars.py @@ -1001,11 +1001,11 @@ def _compute_df_freq(self): df_ = add_wd_bin(df_, self.wd_cols, self.wd_step, self.wd_min, self.wd_max) # Get the bin count by wd, ws and df_name - df_ = df_.groupby(['wd_bin','ws_bin','df_name']).count() + df_group = df_.groupby(['wd_bin','ws_bin','df_name']).count() - return df_ + return df_.to_pandas(), df_group.to_pandas() - def _plot(self, + def plot(self, df_names_subset = None, labels = None, color_dict = None, @@ -1061,18 +1061,11 @@ def _plot(self, _, axarr = plt.subplots(nrows=1, ncols=2, figsize=(10, 5), subplot_kw={'projection': 'polar'}) else: if show_wind_speed_distrubution: - num_rows = 2 + N # Add rows to show wind speed distribution + num_rows = 3 # Add rows to show wind speed distribution else: num_rows = 2 _, axarr = plt.subplots(nrows=num_rows, ncols=1, sharex=True, figsize=(10, 5)) - # Come up with hatch patterns - hatch_patterns = [ - '//', '++', 'xx', 'oo', '\\\\', '--', 'OO', '..', '**', '||', - '/o', '\\|', '|*', '-\\', '+o', 'x*', 'o-', 'O|', 'O.', '*-', - '/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*', - ] * 10 # Repeat 10 times to ensure always sufficiently large size - # Set the bar width using self.wd_step bar_width = (0.7 / N) * self.wd_step if polar_plot: @@ -1107,9 +1100,6 @@ def _plot(self, # Plot horizontal black line at 1. xlims = np.linspace(np.min(x) - 4.0, np.max(x) + 4.0, 1000) - # Save the x_bins - # x_bins = copy.deepcopy(x) - if polar_plot: x = (90.0 - x) * np.pi / 180.0 # Convert to radians xlims = (90.0 - xlims) * np.pi / 180.0 # Convert to radians @@ -1141,7 +1131,7 @@ def _plot(self, # axarr[0].set_grid(True) # Plot the bin counts - df_freq = self._compute_df_freq().to_pandas() + df_unbinned, df_freq = self._compute_df_freq() df_freq_sum_all_ws = df_freq.groupby(["wd_bin","df_name"]).sum().reset_index() @@ -1164,20 +1154,23 @@ def _plot(self, if show_wind_speed_distrubution: # Plot the wind speed distribution in df_freq as a heat map with wd on the x-axis and ws on the y-axis - for i, (df_name, label) in enumerate(zip(df_names_subset, labels)): - ax = axarr[2 + i] - - # Get the data for this df_name + ax = axarr[2] + for df_name, label in zip(df_names_subset, labels): df_sub = df_freq[df_freq["df_name"] == df_name] - - # Scatter plot the data with wd on the x-axis and ws on the y-axis and count as the color - im = ax.scatter(df_sub["wd_bin"], df_sub["ws_bin"], c=df_sub["count"], cmap="viridis", s=10, marker="s") - ax.set_ylabel(label) - # ax.colorbar(im, ax=ax) - - # df_pivot = df_sub.pivot(index="ws_bin", columns="wd_bin", values="count") - - # sns.heatmap(data=df_pivot, ax=ax, cmap="viridis", cbar=True, annot=True, fmt='g') - + ax.scatter(df_unbinned["wd_bin"], df_unbinned["ws_bin"], c=color_dict[label],alpha=0.25, s=1) + def plot_uplift(self, + axarr = None, + polar_plot=False, + show_wind_speed_distrubution=True, + ): + self.plot( + df_names_subset = 'uplift', + labels = ['uplift'], + color_dict = {'uplift':'k'}, + axarr = axarr, + polar_plot=polar_plot, + show_wind_speed_distrubution=show_wind_speed_distrubution, + ) + From a7e48503128e2a56ab513f8bdae6f02c71d4edda Mon Sep 17 00:00:00 2001 From: Paul Date: Sat, 22 Jul 2023 14:01:46 -0600 Subject: [PATCH 17/17] re-work sqldatabase code to polars --- .../sqldatabase_management.py | 562 ++++++++---------- 1 file changed, 256 insertions(+), 306 deletions(-) diff --git a/flasc/raw_data_handling/sqldatabase_management.py b/flasc/raw_data_handling/sqldatabase_management.py index b017f96f..bd468260 100644 --- a/flasc/raw_data_handling/sqldatabase_management.py +++ b/flasc/raw_data_handling/sqldatabase_management.py @@ -11,9 +11,10 @@ # the License. -import os import numpy as np import pandas as pd +import polars as pl +from pathlib import Path from time import perf_counter as timerpc import datetime @@ -27,6 +28,7 @@ import sqlalchemy as sqlalch + class sql_database_manager: # Private methods @@ -44,45 +46,88 @@ def _create_sql_engine(self, password): name = self.db_name usn = self.username address = "%s:%d" % (self.host, self.port) + self.url = "%s://%s:%s@%s/%s" % (dr, usn, password, address, name) self.engine = sqlalch.create_engine( - url="%s://%s:%s@%s/%s" % (dr, usn, password, address, name) + url= self.url ) + self.inspector = sqlalch.inspect(self.engine) self.print_properties() def _get_table_names(self): - return self.engine.table_names() + return self.inspector.get_table_names() + + def _does_table_exist(self, table_name): + return table_name in self._get_table_names() def _get_column_names(self, table_name): - df = pd.read_sql_query( - "SELECT * FROM " + table_name + " WHERE false;", self.engine + columns = self.inspector.get_columns(table_name) + return [c['name'] for c in columns] + + def _create_table_from_df(self, table_name, df): + + print(f'Creating Table: {table_name} with {df.shape[1]} columns') + + # Convert to pandas for upload + df_pandas = df.to_pandas() + df_pandas = df_pandas.iloc[:10] + + df_pandas.to_sql( + table_name, + self.engine, + index=False, + method="multi" + ) + + # Make time unique and an index to speed queries + query = 'CREATE UNIQUE INDEX idx_time_%s ON %s (time);' % (table_name, table_name) + print('Setting time to unique index') + with self.engine.connect() as con: + rs = con.execute(sqlalch.text(query)) + print(f'...RESULT: {rs}') + con.commit() # commit the transaction + + def _remove_duplicated_time(self, table_name, df): + + start_time = df.select(pl.min("time"))[0, 0] + end_time = df.select(pl.max("time"))[0, 0] + original_size = df.shape[0] + + print(f'Checking for time entries already in {table_name} between {start_time} and {end_time}') + time_in_db = self.get_data(table_name, + ['time'], + start_time=start_time, + end_time=end_time, + end_inclusive=True ) - return list(df.columns) + + df = df.join(time_in_db, on='time',how="anti") + new_size = df.shape[0] + if new_size < original_size: + print(f'...Dataframe size reduced from {original_size} to {new_size} by time values already in {table_name}') + return df + def _get_first_time_entry(self, table_name): - tn = table_name - column_names = self._get_column_names(tn) - if 'time' in column_names: - df_time = pd.read_sql_query( - sql="SELECT time FROM %s ORDER BY time asc LIMIT 1" % tn, - con=self.engine - ) - if df_time.shape[0] > 0: - return df_time["time"][0] - return None + # Get the table corresponding to the table name + table = sqlalch.Table(table_name, sqlalch.MetaData(), autoload_with=self.engine) + + stmt = sqlalch.select(table.c.time).order_by(table.c.time.asc()).limit(1) + with self.engine.begin() as conn: + result = conn.execute(stmt) + for row in result: + return row[0] def _get_last_time_entry(self, table_name): - tn = table_name - column_names = self._get_column_names(tn) - if 'time' in column_names: - df_time = pd.read_sql_query( - sql="SELECT time FROM %s ORDER BY time desc LIMIT 1" % tn, - con=self.engine - ) - if df_time.shape[0] > 0: - return df_time["time"][0] + # Get the table corresponding to the table name + table = sqlalch.Table(table_name, sqlalch.MetaData(), autoload_with=self.engine) - return None + stmt = sqlalch.select(table.c.time).order_by(table.c.time.desc()).limit(1) + print(stmt) + with self.engine.begin() as conn: + result = conn.execute(stmt) + for row in result: + return row[0] # General info functions from data def print_properties(self): @@ -103,7 +148,11 @@ def print_properties(self): def launch_gui(self, turbine_names=None, sort_columns=False): root = tk.Tk() - sql_db_explorer_gui(master=root, dbc=self, turbine_names=turbine_names, sort_columns=sort_columns) + sql_db_explorer_gui(master=root, + dbc=self, + turbine_names=turbine_names, + sort_columns=sort_columns + ) root.mainloop() def get_column_names(self, table_name): @@ -113,8 +162,8 @@ def batch_get_data(self, table_name, columns=None, start_time=None, end_time=None, fn_out=None, no_rows_per_file=10000): if fn_out is None: fn_out = table_name + ".ftr" - if not (fn_out[-4::] == ".ftr"): - fn_out = fn_out + ".ftr" + if not (fn_out.suffix == ".ftr"): + fn_out = fn_out.with_suffix(".ftr") # Ensure 'time' in database column_names = self._get_column_names(table_name=table_name) @@ -122,13 +171,16 @@ def batch_get_data(self, table_name, columns=None, start_time=None, raise KeyError("Cannot find 'time' column in database table.") # Get time column from database + print("Getting time column from database...") time_in_db = self.get_data(table_name=table_name, columns=['time'], start_time=start_time, end_time=end_time) - time_in_db = list(time_in_db['time']) + time_in_db = list(time_in_db.select("time").to_numpy().flatten()) + print("...finished, N.o. entries: %d." % len(time_in_db)) splits = np.arange(0, len(time_in_db) - 1, no_rows_per_file, dtype=int) splits = np.append(splits, len(time_in_db) - 1) splits = np.unique(splits) + print(f"Splitting {len(time_in_db)} entries data into {len(splits)} subsets of {no_rows_per_file}.") for ii in range(len(splits) - 1): print("Downloading subset %d out of %d." % (ii, len(splits) - 1)) @@ -138,12 +190,17 @@ def batch_get_data(self, table_name, columns=None, start_time=None, start_time=time_in_db[splits[ii]], end_time=time_in_db[splits[ii+1]] ) - fn_out_ii = fn_out + '.%d' % ii - print("Saving file to %s." % fn_out_ii) - df.to_feather(fn_out_ii) + fn_out_ii = fn_out.with_suffix(".ftr.%03d" % ii) + print("Saving file to %s" % fn_out_ii) + df.write_ipc(fn_out_ii) def get_data( - self, table_name, columns=None, start_time=None, end_time=None + self, + table_name, + columns=None, + start_time=None, + end_time=None, + end_inclusive=False, ): # Get the data from tables if columns is None: @@ -156,22 +213,33 @@ def get_data( query_string += " WHERE time >= '" + str(start_time) + "'" if (start_time is not None) and (end_time is not None): - query_string += " AND time < '" + str(end_time) + "'" + if end_inclusive: + query_string += " AND time <= '" + str(end_time) + "'" + else: + query_string += " AND time < '" + str(end_time) + "'" elif (start_time is None) and (end_time is not None): - query_string += " WHERE time < '" + str(end_time) + "'" + if end_inclusive: + query_string += " WHERE time <= '" + str(end_time) + "'" + else: + query_string += " WHERE time < '" + str(end_time) + "'" - query_string += " ORDER BY time;" - df = pd.read_sql_query(query_string, self.engine) + query_string += " ORDER BY time" + + df = pl.read_database(query_string,self.url) # Drop a column called index if "index" in df.columns: - df = df.drop(["index"], axis=1) + df = df.drop("index") - # Make sure time column is in datetime format - df["time"] = pd.to_datetime(df.time) + # Confirm that the time column is in datetime format + if "time" in df.columns: + if not (df.schema["time"] == pl.Datetime): + df = df.with_columns(pl.col("time").cast(pl.Datetime)) return df + + #TODO: This is a fresh redo check it works def send_data( self, table_name, @@ -181,81 +249,135 @@ def send_data( df_chunk_size=2000, sql_chunk_size=50 ): - table_name = table_name.lower() - table_names = [t.lower() for t in self._get_table_names()] - - if (if_exists == "append"): - print("Warning: risk of adding duplicate rows using 'append'.") - print("You are suggested to use 'append_new' instead.") - - if (if_exists == "append_new") and (table_name in table_names): - if len(unique_cols) > 1: - raise NotImplementedError("Not yet implemented.") - - col = unique_cols[0] - idx_in_db = self.get_data(table_name=table_name, columns=[col])[ - col - ] - - # Check if values in SQL database are unique - if not idx_in_db.is_unique: - raise IndexError( - "Column '%s' is not unique in the SQL database." % col - ) + + # Make a local copy + df_ = df.clone() + + # Check if table exists + if not self._does_table_exist(table_name): + + print(f'{table_name} does not yet exist') + + # Create the table + self._create_table_from_df(table_name, df_) + + # Check for times already in database + df_ = self._remove_duplicated_time(table_name, df_) + + # Check if df_ is now + if df_.shape[0] == 0: + print('Dataframe is empty') + return + + # Write to database + print(f'Inserting {df_.shape[0]} rows into {table_name} in chunks of {df_chunk_size}') + time_start_total = timerpc() + + # Parition into chunks + df_list = (df_.with_row_count('id') + .with_columns(pl.col('id').apply(lambda i: int(i/df_chunk_size))) + .partition_by('id') + ) - idx_in_df = set(df[col]) - idx_in_db = set(idx_in_db) - idx_to_add = np.sort(list(idx_in_df - idx_in_db)) - print( - "{:d} entries already exist in SQL database.".format( - len(idx_in_df) - len(idx_to_add) - ) + num_par = len(df_list) + for df_par_idx, df_par in enumerate(df_list): + print(f'...inserting chunk {df_par_idx} of {num_par}') + + df_par.drop('id').write_database( + table_name, + self.url, + if_exists='append' ) - - print("Adding {:d} new entries...".format(len(idx_to_add))) - df_subset = df.set_index('time').loc[idx_to_add].reset_index( - drop=False) - - else: - df_subset = df - - if (if_exists == "append_new"): - if_exists = "append" - - # Upload data - N = df_subset.shape[0] - if N < 1: - print("Skipping data upload. Dataframe is empty.") - else: - print("Attempting to insert %d rows into table '%s'." - % (df_subset.shape[0], table_name)) - df_chunks_id = np.arange(0, df_subset.shape[0], df_chunk_size) - df_chunks_id = np.append(df_chunks_id, df_subset.shape[0]) - df_chunks_id = np.unique(df_chunks_id) - - time_start_total = timerpc() - for i in range(len(df_chunks_id)-1): - Nl = df_chunks_id[i] - Nu = df_chunks_id[i+1] - print("Inserting rows %d to %d." % (Nl, Nu)) - time_start_i = timerpc() - df_sub = df_subset[Nl:Nu] - df_sub.to_sql( - table_name, - self.engine, - if_exists=if_exists, - index=False, - method="multi", - chunksize=sql_chunk_size, - ) - time_i = timerpc() - time_start_i - total_time = timerpc() - time_start_total - est_time_left = (total_time / Nu) * (N - Nu) - eta = datetime.datetime.now() + td(seconds=est_time_left) - eta = eta.strftime("%a, %d %b %Y %H:%M:%S") - print("Data insertion took %.1f s. ETA: %s." % (time_i, eta)) - - + total_time = timerpc() - time_start_total + print(f'...Finished in {total_time}') + + + # #TODO: UPDATE TO POLARS + # #TODO: Paul note (may 31 2023), POLARS API not up to PANDAS so using PANDAS here + # def send_data( + # self, + # table_name, + # df, + # if_exists="append_new", + # unique_cols=["time"], + # df_chunk_size=2000, + # sql_chunk_size=50 + # ): + # table_name = table_name.lower() + # table_names = [t.lower() for t in self._get_table_names()] + + # if (if_exists == "append"): + # print("Warning: risk of adding duplicate rows using 'append'.") + # print("You are suggested to use 'append_new' instead.") + + # if (if_exists == "append_new") and (table_name in table_names): + # if len(unique_cols) > 1: + # raise NotImplementedError("Not yet implemented.") + + # col = unique_cols[0] + # idx_in_db = self.get_data(table_name=table_name, columns=[col])[ + # col + # ] + + # # Check if values in SQL database are unique + # if not idx_in_db.is_unique: + # raise IndexError( + # "Column '%s' is not unique in the SQL database." % col + # ) + + # idx_in_df = set(df[col]) + # idx_in_db = set(idx_in_db) + # idx_to_add = np.sort(list(idx_in_df - idx_in_db)) + # print( + # "{:d} entries already exist in SQL database.".format( + # len(idx_in_df) - len(idx_to_add) + # ) + # ) + + # print("Adding {:d} new entries...".format(len(idx_to_add))) + # df_subset = df.set_index('time').loc[idx_to_add].reset_index( + # drop=False) + + # else: + # df_subset = df + + # if (if_exists == "append_new"): + # if_exists = "append" + + # # Upload data + # N = df_subset.shape[0] + # if N < 1: + # print("Skipping data upload. Dataframe is empty.") + # else: + # print("Attempting to insert %d rows into table '%s'." + # % (df_subset.shape[0], table_name)) + # df_chunks_id = np.arange(0, df_subset.shape[0], df_chunk_size) + # df_chunks_id = np.append(df_chunks_id, df_subset.shape[0]) + # df_chunks_id = np.unique(df_chunks_id) + + # time_start_total = timerpc() + # for i in range(len(df_chunks_id)-1): + # Nl = df_chunks_id[i] + # Nu = df_chunks_id[i+1] + # print("Inserting rows %d to %d." % (Nl, Nu)) + # time_start_i = timerpc() + # df_sub = df_subset[Nl:Nu] + # df_sub.to_sql( + # table_name, + # self.engine, + # if_exists=if_exists, + # index=False, + # method="multi", + # chunksize=sql_chunk_size, + # ) + # time_i = timerpc() - time_start_i + # total_time = timerpc() - time_start_total + # est_time_left = (total_time / Nu) * (N - Nu) + # eta = datetime.datetime.now() + td(seconds=est_time_left) + # eta = eta.strftime("%a, %d %b %Y %H:%M:%S") + # print("Data insertion took %.1f s. ETA: %s." % (time_i, eta)) + +#TODO: UPDATE TO POLARS class sql_db_explorer_gui: def __init__(self, master, dbc, turbine_names = None, sort_columns=False): @@ -264,7 +386,7 @@ def __init__(self, master, dbc, turbine_names = None, sort_columns=False): self.master = master # Get basic database properties - self.df = pd.DataFrame() + self.df = pl.DataFrame() table_names = dbc._get_table_names() min_table_dates = [ dbc._get_first_time_entry(table_name=t) for t in table_names @@ -453,7 +575,7 @@ def load_data(self): start_time=start_time, end_time=end_time, ) - df = df.set_index("time", drop=True) + # df = df.set_index("time", drop=True) if df.shape[0] <= 0: print( @@ -463,29 +585,40 @@ def load_data(self): else: print("...Imported data successfully.") - old_col_names = list(df.columns) + old_col_names = [c for c in list(df.columns) if not c=='time'] new_col_names = [ chr(97 + tables_selected[ii]).upper() + "_%s" % c - for c in df.columns + for c in old_col_names ] col_mapping = dict(zip(old_col_names, new_col_names)) - df = df.rename(columns=col_mapping) + df = df.rename(col_mapping) # If specific turbine names are supplied apply them here if self.turbine_names is not None: columns = df.columns for t in range(len(self.turbine_names)): columns = [c.replace('%03d' % t,self.turbine_names[t]) for c in columns] - df.columns = columns + # df.columns = columns + df = df.rename(dict(zip(df.columns,columns))) df_array.append(df) # Merge dataframes - self.df = pd.concat(df_array, axis=1).reset_index(drop=False) + # self.df = pl.concat(df_array, axis=1)# .reset_index(drop=False) + df_merge = df_array[0] + + if len(df_array) > 1: + for df_ in df_array[1:]: + df_merge = df_merge.join(df_, on='time',how='outer') + + #Save it now + self.df = df_merge # If sorting the columns do it now if self.sort_columns: - self.df = self.df[sorted(self.df.columns)] + # self.df = self.df[sorted(self.df.columns)] + self.df = self.df.select(sorted(self.df.columns)) + self.update_channel_cols() self.create_figures() @@ -533,7 +666,7 @@ def update_plot(self, channel_no): ax = self.axes[channel_no] ax.clear() for c in self.channel_selection[channel_no]: - ax.plot(self.df.time, np.array(self.df[c].values), label=c) + ax.plot(self.df['time'], np.array(self.df[c]), label=c) ax.legend() ax.grid(True) @@ -572,186 +705,3 @@ def ci_select(self, channel_no, evt=None): channels = [self.df.columns[idx] for idx in indices] self.channel_selection[channel_no] = channels self.update_plot(channel_no=channel_no) - - -# def get_timestamp_of_last_downloaded_datafile(filelist): -# time_latest = None -# for fi in filelist: -# df = pd.read_feather(fi) -# time_array = df['time'] -# if not all(np.isnan(time_array)): -# tmp_time_max = np.max(df['time']) -# if time_latest is None: -# time_latest = tmp_time_max -# else: -# if tmp_time_max > time_latest: -# time_latest = tmp_time_max - -# return time_latest - - -# # # # # # # # # # # # # # # # # # # # # # # # # # -# # # # RAW DATA READING FUNCTIONS -# # # # # # # # # # # # # # # # # # # # # # # # # # -# def find_files_to_add_to_sqldb(sqldb_engine, files_paths, filenames_table): -# """This function is used to figure out which files are already -# uploaded to the SQL database, and which files still need to be -# uploaded. - -# Args: -# sqldb_engine ([SQL engine]): SQL Engine from sqlalchemy.create_engine -# used to access the SQL database of interest. This is used to call -# which files have previously been uploaded. -# files_paths ([list, str]): List of strings or a single string containing -# the path to the raw data files. One example is: -# files_paths = ['/home/user/data/windfarm1/year1/*.csv', -# '/home/user/data/windfarm1/year2/*.csv', -# '/home/user/data/windfarm1/year3/*.csv',] -# filenames_table ([str]): SQL table name containing the filenames of -# the previously uploaded data files. - -# Returns: -# files ([list]): List of files that are not yet in the SQL database -# """ -# # Convert files_paths to a list -# if isinstance(files_paths, str): -# files_paths = [files_paths] - -# # Figure out which files exists on local system -# files = [] -# for fpath in files_paths: -# fl = glob.glob(fpath) -# if len(fl) <= 0: -# print('No files found in directory %s.' % fpath) -# else: -# files.extend(fl) - -# # Figure out which files have already been uploaded to sql db -# query_string = "select * from " + filenames_table + ";" -# df = pd.read_sql_query(query_string, sqldb_engine) - -# # # Check for the files not in the database -# files = [f for f in files -# if os.path.basename(f) not in df['filename'].values] - -# # Sort the file list according to ascending name -# files = sorted(files, reverse=False) - -# return files - - -# # # # # # # # # # # # # # # # # # # # # # # # # # -# # # # DATA UPLOAD FUNCTIONS -# # # # # # # # # # # # # # # # # # # # # # # # # # - - -# def omit_last_rows_by_buffer(df, omit_buffer): -# if not 'time' in df.columns: -# df = df.reset_index(drop=False) - -# num_rows = df.shape[0] -# df = df[df['time'] < max(df['time']) - omit_buffer] -# print('Omitting last %d rows (%s s) as a buffer for future files.' -# % (num_rows-df.shape[0], omit_buffer)) -# return df - - -# def remove_duplicates_with_sqldb(df, sql_engine, table_name): -# min_time = df.time.min() -# max_time = df.time.max() - -# time_query = ( -# "select time from "+ table_name + -# " where time BETWEEN '%s' and '%s';" % (min_time, max_time) -# ) - -# df_time = pd.read_sql_query(time_query, sql_engine) -# df_time["time"] = pd.to_datetime(df_time.time) -# print("......Before duplicate removal there are %d rows" % df.shape[0]) -# df = df[~df.time.isin(df_time.time)] -# print("......After duplicate removal there are %d rows" % df.shape[0]) - -# # Check for self duplicates in to-be-uploaded dataset -# print("......Before SELF duplicate removal there are %d rows" % df.shape[0]) -# if "turbid" in df.columns: -# df = df.drop_duplicates(subset=["time", "turbid"], keep="first") -# else: -# df = df.drop_duplicates(subset=["time"], keep="first") -# print("......After SELF duplicate removal there are %d rows" % df.shape[0]) - -# # Drop null times in to-be-uploaded dataset -# print( -# "......Before null time/turbid duplicate removal there are %d rows" -# % df.shape[0] -# ) -# df = df.dropna(subset=["time"]) -# print("......After null time duplicate removal there are %d rows" % df.shape[0]) - -# return df - - -# def batch_download_data_from_sql(dbc, destination_path, table_name): -# print("Batch downloading data from table %s." % table_name) - -# # Check if output directory exists, if not, create -# if not os.path.exists(destination_path): -# os.makedirs(destination_path) - -# # Check current start and end time of database -# db_end_time = get_last_time_entry_sqldb(dbc.engine, table_name) -# db_end_time = db_end_time + datetime.timedelta(minutes=10) - -# # Check for past files and continue download or start a fresh download -# files_result = fsio.browse_downloaded_datafiles(destination_path, -# table_name=table_name) -# print('A total of %d existing files found.' % len(files_result)) - -# # Next timestamp is going to be next first of the month -# latest_timestamp = get_timestamp_of_last_downloaded_datafile(files_result) -# if latest_timestamp is None: -# db_start_time = get_first_time_entry_sqldb(dbc.engine, table_name) -# db_start_time = db_start_time - datetime.timedelta(minutes=10) -# current_timestamp = db_start_time -# elif latest_timestamp.month == 12: -# current_timestamp = pd.to_datetime('%s-01-01' % -# str(latest_timestamp.year+1)) -# else: -# current_timestamp = pd.to_datetime("%s-%s-01" % ( -# str(latest_timestamp.year), str(latest_timestamp.month+1))) - -# print('Continuing import from timestep: ', current_timestamp) -# while current_timestamp <= db_end_time: -# print('Importing data for ' + -# str(current_timestamp.strftime("%B")) + -# ', ' + str(current_timestamp.year) + '.') -# if current_timestamp.month == 12: -# next_timestamp = current_timestamp.replace( -# year=current_timestamp.year+1, month=1, -# day=1, hour=0, minute=0, second=0) -# else: -# next_timestamp = current_timestamp.replace( -# month=current_timestamp.month+1, -# day=1, hour=0, minute=0, second=0) - -# df = dbc.get_table_data_from_db_wide( -# table_name=table_name, -# start_time=current_timestamp, -# end_time=next_timestamp -# ) - -# # Drop NaN rows -# df = dfm.df_drop_nan_rows(df) - -# # Save dataset as a .ftr file -# fout = os.path.join(destination_path, "%s_%s.ftr" % -# (current_timestamp.strftime("%Y-%m"), table_name)) - -# df = df.reset_index(drop=('time' in df.columns)) -# df.to_feather(fout) - -# print('Data for ' + table_name + -# ' saved to .ftr files for ' + -# str(current_timestamp.strftime("%B")) + -# ', ' + str(current_timestamp.year) + '.') - -# current_timestamp = next_timestamp