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Update docstrings in floris_tools
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@paulf81
paulf81 committed Jul 24, 2024
1 parent ab0f8d9 commit 4d5c73f
Showing 1 changed file with 49 additions and 40 deletions.
89 changes: 49 additions & 40 deletions flasc/utilities/floris_tools.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
"""Utility functions that use FlorisModels."""

import copy
from time import perf_counter as timerpc

Expand Down Expand Up @@ -31,7 +33,9 @@ def interpolate_floris_from_df_approx(
mirror_nans=True,
verbose=True,
):
"""This function generates the FLORIS predictions for a set of historical
"""Interpolate FLORIS predictions from a precalculated table of solutions.
This function generates the FLORIS predictions for a set of historical
data, 'df', quickly by linearly interpolating from a precalculated set of
FLORIS solutions, 'df_approx'. We use linear interpolation to eliminate
dependency of the computation time on the size of the dataframe/number of
Expand Down Expand Up @@ -120,7 +124,6 @@ def interpolate_floris_from_df_approx(
52103 2018-12-31 23:30:00 15.6 11.0 0.08 4235128.7 3825108.4 ... 2725108.3
52104 2018-12-31 23:40:00 15.3 11.1 0.08 3860281.3 3987634.7 ... 2957021.7
"""

# Format dataframe and get number of turbines
# df = df.reset_index(drop=('time' in df.columns))
nturbs = fsut.get_num_turbines(df_approx)
Expand Down Expand Up @@ -327,7 +330,9 @@ def calc_floris_approx_table(
ti_array=np.arange(0.03, 0.1801, 0.03),
save_turbine_inflow_conditions_to_df=False,
):
"""This function calculates a large number of floris solutions for a rectangular grid
"""Calculate the FLORIS approximate table from a FlorisModel object.
This function calculates a large number of floris solutions for a rectangular grid
of wind directions ('wd_array'), wind speeds ('ws_array'), and optionally turbulence
intensities ('ti_array'). The variables that are saved are each turbine's power
production, and optionally also each turbine's inflow wind direction, wind speed and
Expand All @@ -346,7 +351,7 @@ def calc_floris_approx_table(
dataframe size but can provide useful information. Defaults to False.
Returns:
df_approx (pd.DataFrame): A Pandas DataFrame containing the floris simulation results for all wind
pd.DataFrame: A Pandas DataFrame containing the floris simulation results for all wind
direction, wind speed and turbulence intensity combinations. The outputs are the power production
for each turbine, 'pow_000' until 'pow_{nturbs-1}', and optionally als each turbine's inflow wind
direction, wind speed and turbulence intensity when save_turbine_inflow_conditions_to_df==True.
Expand All @@ -357,8 +362,7 @@ def calc_floris_approx_table(
ti_array=np.arange(0.03, 0.1801, 0.03)
save_turbine_inflow_conditions_to_df=True
Yields:
Yields:
df_approx=
wd ws ti pow_000 ws_000 wd_000 ti_000 pow_001 ... pow_006 ws_006 wd_006 ti_006
0 0.0 1.0 0.03 0.0 1.0 0.0 0.03 0.0 ... 0.0 1.0 0.0 0.03
Expand All @@ -374,7 +378,6 @@ def calc_floris_approx_table(
32399 357.0 25.0 0.18 0.0 24.880829 357.0 0.18 0.0 ... 0.0 24.881165 357.0 0.18
32400 360.0 25.0 0.18 0.0 24.880829 360.0 0.18 0.0 ... 0.0 24.881165 360.0 0.18
"""

# if ti_array is None, use the current value in the FLORIS object
if ti_array is None:
ti = fm.core.flow_field.turbulence_intensity
Expand Down Expand Up @@ -430,7 +433,9 @@ def calc_floris_approx_table(


def add_gaussian_blending_to_floris_approx_table(df_fi_approx, wd_std=3.0, pdf_cutoff=0.995):
"""This function applies a Gaussian blending across the wind direction for the predicted
"""Add Gaussian blending to the precalculated FLORIS solutions.
This function applies a Gaussian blending across the wind direction for the predicted
turbine power productions from FLORIS. This is a post-processing step and achieves the
same result as evaluating FLORIS directly with the UncertainFlorisModel module. However,
having this as a postprocess step allows for rapid generation of the FLORIS solutions for
Expand All @@ -446,7 +451,7 @@ def add_gaussian_blending_to_floris_approx_table(df_fi_approx, wd_std=3.0, pdf_c
deviations to the left and to the right of the evaluation.
Returns:
df_fi_approx_gauss (pd.DataFrame): Pandas DataFrame with Gaussian-blurred precalculated
pd.DataFrame: Pandas DataFrame with Gaussian-blurred precalculated
FLORIS solutions. The DataFrame typically has the columns "wd", "ws", "ti", and
"pow_000" until "pow_{nturbs-1}", with nturbs being the number of turbines.
Expand Down Expand Up @@ -497,27 +502,31 @@ def add_gaussian_blending_to_floris_approx_table(df_fi_approx, wd_std=3.0, pdf_c
return df_fi_approx_gauss


# TODO Is this function in the right module?
# TODO Should include itself have a default?
def get_turbs_in_radius(
x_turbs, y_turbs, turb_no, max_radius, include_itself, sort_by_distance=False
):
"""Determine which turbines are within a certain radius of other
"""Find turbines within a certain radius of a turbine.
Determine which turbines are within a certain radius of other
wind turbines.
Args:
x_turbs ([list, array]): Long. locations of turbines
y_turbs ([list, array]): Lat. locations of turbines
turb_no ([int]): Turbine number for which the distance is
turb_no (int): Turbine number for which the distance is
calculated w.r.t. the other turbines.
max_radius ([float]): Maximum distance between turbines to be
max_radius (float): Maximum distance between turbines to be
considered within the radius of turbine [turb_no].
include_itself ([type]): Include itself in the list of turbines
include_itself (bool): Include itself in the list of turbines
within the radius.
sort_by_distance (bool, optional): Sort the output list of turbines
according to distance to the turbine, from closest to furthest (but
still within radius). Defaults to False.
Returns:
turbs_within_radius ([list]): List of turbines that are within the
list: List of turbines that are within the
prespecified distance from turbine [turb_no].
"""
dr_turb = np.sqrt((x_turbs - x_turbs[turb_no]) ** 2.0 + (y_turbs - y_turbs[turb_no]) ** 2.0)
Expand All @@ -538,33 +547,31 @@ def get_turbs_in_radius(
def get_all_impacting_turbines_geometrical(
fm, turbine_weights, wd_array=np.arange(0.0, 360.0, 3.0), wake_slope=0.30
):
"""Determine which turbines affect the turbines of interest
"""Get all impacting turbines using a simple geometric model.
Determine which turbines affect the turbines of interest
(i.e., those with a turbine_weights > 0.00001). This function
uses very simplified geometric functions to very quickly
derive which turbines are supposedly waking at least one
turbine in the farm of interest.
Args:
fm ([floris object]): FLORIS object of the farm of interest.
turbine_weights [list]: List of with turbine weights with length
equal to the number of wind turbines, and typically filled with
0s (neighbouring farms) and 1s (farm of interest).
wd_step (float, optional): Wind direction discretization step.
Defaults to 3.0.
fm (FlorisModel): FLORIS object of the farm of interest.
turbine_weights (list): List of with turbine weights with length
equal to the number of wind turbines, and typically filled with
0s (neighboring farms) and 1s (farm of interest).
wd_array (array, optional): Array of wind directions to evaluate in [deg].
wake_slope (float, optional): linear slope of the wake (dy/dx)
plot_lines (bool, optional): Enable plotting wakes/turbines.
Defaults to False.
Returns:
df_impacting_simple ([pd.DataFrame]): A Pandas Dataframe in which each row
pd.Dataframe: A Pandas Dataframe in which each row
contains a wind direction and a list of turbine numbers. The turbine
numbers are those turbines that should be modelled to accurately
capture the wake losses for the wind farm of interest. Turbine numbers
that are not in the 'impacting_turbines' can safely be removed from
the simulation without affecting any of the turbines that have a nonzero
turbine weight.
"""

# Get farm layout
x = fm.layout_x
y = fm.layout_y
Expand Down Expand Up @@ -639,14 +646,16 @@ def is_in_wake(xt, yt):


def get_upstream_turbs_floris(fm, wd_step=0.1, wake_slope=0.10, plot_lines=False):
"""Determine which turbines are operating in freestream (unwaked)
"""Use FLORIS to determine which turbines are operating in freestream flow.
Determine which turbines are operating in freestream (unwaked)
flow, for the entire wind rose. This function will return a data-
frame where each row will present a wind direction range and a set
of wind turbine numbers for which those turbines are operating
upstream. This is useful in determining the freestream conditions.
Args:
fi ([floris object]): FLORIS object of the farm of interest.
fm ([FlorisModel): FLORIS object of the farm of interest.
wd_step (float, optional): Wind direction discretization step.
It will test what the upstream turbines are every [wd_step]
degrees. A lower number means more accurate results, but
Expand All @@ -657,7 +666,7 @@ def get_upstream_turbs_floris(fm, wd_step=0.1, wake_slope=0.10, plot_lines=False
Defaults to False.
Returns:
df_upstream ([pd.DataFrame]): A Pandas Dataframe in which each row
df_upstream (pd.Dataframe): A Pandas Dataframe in which each row
contains a wind direction range and a list of turbine numbers. For
that particular wind direction range, the turbines numbered are
all upstream according to the FLORIS predictions. Depending on
Expand All @@ -668,7 +677,6 @@ def get_upstream_turbs_floris(fm, wd_step=0.1, wake_slope=0.10, plot_lines=False
turbines are waked/unwaked and has served useful for determining
what turbines to use as reference.
"""

# Get farm layout
x = fm.layout_x
y = fm.layout_y
Expand Down Expand Up @@ -787,14 +795,15 @@ def get_dependent_turbines_by_wd(
ws_test=9.0,
return_influence_magnitudes=False,
):
"""
"""Get dependent turbines for a test turbine for each wind direction.
Computes all turbines that depend on the operation of a specified
turbine (test_turbine) for each wind direction in wd_array, using
the FLORIS model specified by fm_in to detect dependencies.
Args:
fi ([floris object]): FLORIS object of the farm of interest.
test_turbine ([int]): Turbine for which dependencies are found.
fm_in (FlorisModels): FLORIS object of the farm of interest.
test_turbine (int): Turbine for which dependencies are found.
wd_array ([np.array]): Wind directions at which to determine
dependencies. Defaults to [0, 2, ... , 358].
change_threshold (float): Fractional change in power needed
Expand Down Expand Up @@ -880,13 +889,14 @@ def get_all_dependent_turbines(
limit_number=None,
ws_test=9.0,
):
"""
"""Get all dependent turbines for each turbine in the farm.
Wrapper for get_dependent_turbines_by_wd() that loops over all
turbines in the farm and packages their dependencies as a pandas
dataframe.
Args:
fi ([floris object]): FLORIS object of the farm of interest.
fm_in (FlorisModel): FLORIS object of the farm of interest.
wd_array ([np.array]): Wind directions at which to determine
dependencies. Defaults to [0, 2, ... , 358].
change_threshold (float): Fractional change in power needed
Expand All @@ -899,15 +909,14 @@ def get_all_dependent_turbines(
determine dependencies. Defaults to 9. m/s.
Returns:
df_out ([pd.DataFrame]): A Pandas Dataframe in which each row
df_out (pd.Dataframe): A Pandas Dataframe in which each row
contains a wind direction, each column is a turbine, and
each entry is the turbines that depend on the column turbine
at the row wind direction. Dependencies can be extracted
as: For wind direction wd, the turbines that depend on
turbine T are df_out.loc[wd, T]. Dependencies are ordered,
with strongest dependencies appearing first.
"""

results = []
for t_i in range(len(fm_in.layout_x)):
results.append(
Expand All @@ -934,14 +943,15 @@ def get_all_impacting_turbines(
limit_number=None,
ws_test=9.0,
):
"""
"""Get all impacting turbines for each turbine in the farm.
Calculate which turbines impact a specified turbine based on the
FLORIS model. Essentially a wrapper for
get_dependent_turbines_by_wd() that loops over all turbines and
extracts their impact magnitudes, then sorts.
Args:
fi ([floris object]): FLORIS object of the farm of interest.
fm_in (FlorisModel): FLORIS object of the farm of interest.
wd_array ([np.array]): Wind directions at which to determine
dependencies. Defaults to [0, 2, ... , 358].
change_threshold (float): Fractional change in power needed
Expand All @@ -954,15 +964,14 @@ def get_all_impacting_turbines(
determine dependencies. Defaults to 9. m/s.
Returns:
df_out ([pd.DataFrame]): A Pandas Dataframe in which each row
df_out (pd.Dataframe): A Pandas Dataframe in which each row
contains a wind direction, each column is a turbine, and
each entry is the turbines that the column turbine depends
on at the row wind direction. Dependencies can be extracted
as: For wind direction wd, the turbines that impact turbine
T are df_out.loc[wd, T]. Impacting turbines are simply
ordered by magnitude of impact.
"""

dependency_magnitudes = np.zeros((len(wd_array), len(fm_in.layout_x), len(fm_in.layout_x)))

for t_i in range(len(fm_in.layout_x)):
Expand Down

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