Updating to v1.1

Updating to v1.1

docs/source/conf.py

@@ -22,7 +22,7 @@
 author = 'Bart Doekemeijer, Paul Fleming, Eric Simley'
 
 # The full version, including alpha/beta/rc tags
-release = '0.1'
+release = '1.1'
 
 
 # -- General configuration ---------------------------------------------------

examples/_legacy/sensitivity_analysis/simple_sobol_example.py

@@ -5,7 +5,7 @@
 
 import floris.tools as wfct
 from floris.logging_manager import configure_console_log
-from pandas.core.base import DataError
+from pandas.errors import DataError
 
 from flasc import floris_sensitivity_analysis as fsasa
 

examples/demo_dataset/demo_floris_input.yaml

@@ -38,7 +38,7 @@ logging:
     level: INFO
 name: floris_input_file_example
 solver:
-  turbine_grid_points: 5
+  turbine_grid_points: 3
   type: turbine_grid
 wake:
   enable_secondary_steering: true

examples/wake_steering_design/00_analyze_single_ws_vs_range.py

@@ -15,8 +15,9 @@
 from matplotlib import pyplot as plt
 
 from flasc.wake_steering.lookup_table_tools import get_yaw_angles_interpolant
-from flasc.wake_steering.yaw_optimizer_visualization import plot_uplifts_by_atmospheric_conditions
-from flasc.visualization import plot_floris_layout
+from flasc.wake_steering.yaw_optimizer_visualization import \
+    plot_uplifts_by_atmospheric_conditions, plot_offsets_wswd_heatmap, plot_offsets_wd
+from flasc.visualization import plot_floris_layout, plot_layout_with_waking_directions
 
 from _local_helper_functions import load_floris, optimize_yaw_angles, evaluate_optimal_yaw_angles
 
@@ -25,6 +26,7 @@
     # Load FLORIS model and plot layout (and additional information)
     fi = load_floris()
     plot_floris_layout(fi)
+    plot_layout_with_waking_directions(fi, limit_dist_D=5, limit_num=3)
 
     # Compare optimizing over all wind speeds vs. optimizing over a single wind speed
     AEP_baseline_array = []
@@ -47,6 +49,18 @@
         AEP_opt_array.append(AEP_opt)
         df_out_array.append(df_out.copy())
 
+    # Vizualize optimal wake steering schedule for all wind speeds
+    # for a single turbine (index 2)
+    ax, cbar = plot_offsets_wswd_heatmap(df_offsets=df_opt, turb_id=2)
+    ax.set_title("T02 offset schedule")
+
+    ax = plot_offsets_wd(df_offsets=df_opt, turb_id=2, ws_plot=[5, 12], 
+                         alpha=0.5)
+    ax = plot_offsets_wd(df_offsets=df_opt, turb_id=2, ws_plot=7.0, 
+                         color="C0", label="7.0 m/s", ax=ax)
+    ax.set_title("T02 offset schedule")
+    ax.legend()
+
     # Calculate AEP uplifts
     uplift_one_ws = (
         100.0 * (AEP_opt_array[0] - AEP_baseline_array[0]) /

flasc/dataframe_operations/dataframe_filtering.py

@@ -92,7 +92,7 @@ def plot_highlight_data_by_conds(df, conds, ti):
         conds = [conds]
 
     # Convert time arrays to a string with 'year+week'
-    tfull = [int('%04d%02d' % (i.year, i.week)) for i in df.time]
+    tfull = [int('%04d%02d' % (i.isocalendar().year, i.isocalendar().week)) for i in df.time]
     time_array = np.unique(tfull)
 
     # Get number of non-NaN entries before filtering
@@ -113,7 +113,7 @@ def plot_highlight_data_by_conds(df, conds, ti):
         # Convert time array of occurrences to year+week no.
         conds_new = conds[ii] & (~conds_combined)
         conds_combined = (conds_combined | np.array(conds[ii], dtype=bool))
-        subset_time_array = [int('%04d%02d' % (i.year, i.week)) for i in df.loc[conds_new, 'time']]
+        subset_time_array = [int('%04d%02d' % (i.isocalendar().year, i.isocalendar().week)) for i in df.loc[conds_new, 'time']]
 
         for iii in range(len(time_array)):
             # Count occurrences for condition

flasc/energy_ratio/energy_ratio.py

@@ -15,7 +15,7 @@
 import pandas as pd
 
 from floris.utilities import wrap_360
-from pandas.core.base import DataError
+from pandas.errors import DataError
 
 from ..dataframe_operations import dataframe_manipulations as dfm
 from ..energy_ratio import energy_ratio_visualization as ervis
@@ -479,16 +479,20 @@ def get_energy_ratio_fast(
         energy_ratios = df_summed.reset_index(drop=False)
         return energy_ratios
 
-    def plot_energy_ratio(self):
+    def plot_energy_ratio(self, hide_uq_labels=True):
         """This function plots the energy ratio against the wind direction,
         potentially with uncertainty bounds if N > 1 was specified by
         the user. One must first run get_energy_ratio() before attempting
         to plot the energy ratios.
 
+        Args:
+            hide_uq_labels (bool, optional): If true, do not specifically label
+                the confidence intervals in the plot
+
         Returns:
             ax [plt.Axes]: Axis handle for the figure.
         """
-        return ervis.plot(self.energy_ratio_out)
+        return ervis.plot(self.energy_ratio_out, hide_uq_labels=hide_uq_labels)
 
 
 # Support functions not included in energy_ratio class

flasc/energy_ratio/energy_ratio_suite.py

@@ -13,7 +13,7 @@
 
 import numpy as np
 import pandas as pd
-from pandas.core.base import DataError
+from pandas.errors import DataError
 from scipy.interpolate import NearestNDInterpolator
 
 from ..energy_ratio import energy_ratio as er
@@ -625,7 +625,7 @@ def get_energy_ratios_fast(
 
         return self.df_list
 
-    def plot_energy_ratios(self, superimpose=True):
+    def plot_energy_ratios(self, superimpose=True, hide_uq_labels=True):
         """This function plots the energy ratios of each dataset against
         the wind direction, potentially with uncertainty bounds if N > 1
         was specified by the user. One must first run get_energy_ratios()
@@ -636,19 +636,21 @@ def plot_energy_ratios(self, superimpose=True):
             of all datasets into the same figure. If False, will plot the
             energy ratio of each dataset into a separate figure. Defaults
             to True.
+            hide_uq_labels (bool, optional): If true, do not specifically label
+            the confidence intervals in the plot
 
         Returns:
             ax [plt.Axes]: Axis handle for the figure.
         """
         if superimpose:
             results_array = [df["er_results"] for df in self.df_list]
             labels_array = [df["name"] for df in self.df_list]
-            fig, ax = vis.plot(results_array, labels_array)
+            fig, ax = vis.plot(results_array, labels_array, hide_uq_labels=hide_uq_labels)
 
         else:
             ax = []
             for df in self.df_list:
-                fig, axi = vis.plot(df["er_results"], df["name"])
+                fig, axi = vis.plot(df["er_results"], df["name"], hide_uq_labels=hide_uq_labels)
                 ax.append(axi)
 
         return ax

flasc/energy_ratio/energy_ratio_visualization.py

@@ -20,7 +20,7 @@
 from floris import tools as wfct
 
 
-def plot(energy_ratios, labels=None):
+def plot(energy_ratios, labels=None, hide_uq_labels=True):
     """This function plots energy ratios against the reference wind
     direction. The plot may or may not include uncertainty bounds,
     depending on the information contained in the provided energy ratio
@@ -43,6 +43,8 @@ def plot(energy_ratios, labels=None):
                         with UQ.
         labels ([iteratible], optional): Label for each of the energy ratio
             dataframes. Defaults to None.
+        hide_uq_labels (bool, optional): If true, do not specifically label
+            the confidence intervals in the plot
 
     Returns:
         fig ([plt.Figure]): Figure in which energy ratios are plotted.
@@ -60,6 +62,9 @@ def plot(energy_ratios, labels=None):
     else:
         uq_labels = ["%s confidence bounds" % lb for lb in labels]
 
+    if hide_uq_labels:
+        uq_labels = ['_nolegend_' for l in uq_labels]
+
     N = len(energy_ratios)
     fig, ax = plt.subplots(nrows=2, sharex=True, figsize=(10, 5))
 

flasc/floris_tools.py

@@ -15,7 +15,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from pandas.core.base import DataError
+from pandas.errors import DataError
 from scipy import interpolate
 from time import perf_counter as timerpc
 

flasc/model_estimation/floris_sensitivity_analysis.py

@@ -18,7 +18,7 @@
 from SALib.sample import saltelli
 from SALib.analyze import sobol
 
-from pandas.core.base import DataError
+from pandas.errors import DataError
 
 from .. import floris_tools as ftools
 

flasc/optimization.py

@@ -14,7 +14,7 @@
 import copy
 from datetime import timedelta as td
 import numpy as np
-from pandas.core.base import DataError
+from pandas.errors import DataError
 import scipy.optimize as opt
 import scipy.stats as spst
 

flasc/raw_data_handling/sqldatabase_management.py

@@ -100,9 +100,10 @@ def print_properties(self):
             print("    N.o. columns: %d." % len(cols))
         print("")
 
-    def launch_gui(self):
+    def launch_gui(self, turbine_names=None, sort_columns=False):
         root = tk.Tk()
-        sql_db_explorer_gui(master=root, dbc=self)
+
+        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):
@@ -256,7 +257,7 @@ def send_data(
 
 
 class sql_db_explorer_gui:
-    def __init__(self, master, dbc):
+    def __init__(self, master, dbc, turbine_names = None, sort_columns=False):
 
         # Create the options container
         frame_1 = tk.Frame(master)
@@ -408,6 +409,12 @@ def mapper_func(evt):
         # Set up the database connection
         self.dbc = dbc
 
+        # Save the turbine names
+        self.turbine_names = turbine_names
+
+        # Save the sort columns
+        self.sort_columns = sort_columns
+
     def channel_add(self):
         if self.N_channels < self.N_channels_max:
             ci = self.N_channels  # New channel
@@ -463,11 +470,23 @@ def load_data(self):
                 ]
                 col_mapping = dict(zip(old_col_names, new_col_names))
                 df = df.rename(columns=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_array.append(df)
 
         # Merge dataframes
         self.df = pd.concat(df_array, axis=1).reset_index(drop=False)
 
+        # If sorting the columns do it now
+        if self.sort_columns:
+            self.df = self.df[sorted(self.df.columns)]
+
         self.update_channel_cols()
         self.create_figures()
         # # Clear all axes