import numpy as np
import pandas as pd
from pathlib import Path
from scipy.interpolate import interp1d
from foxes.core import States
from foxes.utils import PandasFileHelper
from foxes.data import STATES
import foxes.variables as FV
import foxes.constants as FC
from foxes.utils import wd2uv, uv2wd
[docs]
class MultiHeightStates(States):
"""
States with multiple heights data per entry.
The input data is taken from a csv file or
pandas data frame with columns. The format
of the data columns is as in the following
example for wind speed at heights 50, 60, 100 m:
WS-50, WS-60, WS-100, ...
Attributes
----------
data_source: str or pandas.DataFrame
Either path to a file or data
ovars: list of str
The output variables
heights: list of float
The heights at which to search data
var2col: dict, optional
Mapping from variable names to data column names
fixed_vars: dict, optional
Fixed uniform variable values, instead of
reading from data
pd_read_pars: dict, optional
pandas file reading parameters
states_sel: slice or range or list of int
States subset selection
states_loc: list
State index selection via pandas loc function
RDICT: dict
Default pandas file reading parameters
:group: input.states
"""
RDICT = {"index_col": 0}
[docs]
def __init__(
self,
data_source,
output_vars,
heights,
var2col={},
fixed_vars={},
pd_read_pars={},
states_sel=None,
states_loc=None,
ipars={},
):
"""
Constructor.
Parameters
----------
data_source: str or pandas.DataFrame
Either path to a file or data
output_vars: list of str
The output variables
heights: list of float
The heights at which to search data
var2col: dict, optional
Mapping from variable names to data column names
fixed_vars: dict, optional
Fixed uniform variable values, instead of
reading from data
pd_read_pars: dict, optional
pandas file reading parameters
states_sel: slice or range or list of int, optional
States subset selection
states_loc: list, optional
State index selection via pandas loc function
ipars: dict, optional
Parameters for scipy.interpolate.interp1d
"""
super().__init__()
self.data_source = data_source
self.ovars = output_vars
self.heights = np.array(heights, dtype=FC.DTYPE)
self.rpars = pd_read_pars
self.var2col = var2col
self.fixed_vars = fixed_vars
self.ipars = ipars
self.states_sel = states_sel
self.states_loc = states_loc
self._cmap = None
self._solo = None
self._weights = None
self._N = None
[docs]
def reset(self, algo=None, states_sel=None, states_loc=None, verbosity=0):
"""
Reset the states, optionally select states
Parameters
----------
states_sel: slice or range or list of int, optional
States subset selection
states_loc: list, optional
State index selection via pandas loc function
verbosity: int
The verbosity level, 0 = silent
"""
if self.initialized:
if algo is None:
raise KeyError(f"{self.name}: Missing algo for reset")
elif algo.states is not self:
raise ValueError(f"{self.states}: algo.states differs from self")
self.finalize(algo, verbosity)
self.states_sel = states_sel
self.states_loc = states_loc
def _find_cols(self, v, cols):
"""
Helper function for searching height columns
"""
c0 = self.var2col.get(v, v)
if v in self.fixed_vars:
return []
elif c0 in cols:
return [c0]
else:
cls = []
for h in self.heights:
hh = int(h) if int(h) == h else h
c = f"{c0}-{hh}"
oc = self.var2col.get(c, c)
if oc in cols:
cls.append(oc)
else:
raise KeyError(
f"Missing: '{v}' in fixed_vars, or '{c0}' or '{oc}' in columns. Maybe make use of var2col?"
)
return cls
[docs]
def load_data(self, algo, verbosity=0):
"""
Load and/or create all model data that is subject to chunking.
Such data should not be stored under self, for memory reasons. The
data returned here will automatically be chunked and then provided
as part of the mdata object during calculations.
Parameters
----------
algo: foxes.core.Algorithm
The calculation algorithm
verbosity: int
The verbosity level, 0 = silent
Returns
-------
idata: dict
The dict has exactly two entries: `data_vars`,
a dict with entries `name_str -> (dim_tuple, data_ndarray)`;
and `coords`, a dict with entries `dim_name_str -> dim_array`
"""
if not isinstance(self.data_source, pd.DataFrame):
if not Path(self.data_source).is_file():
if verbosity:
print(
f"States '{self.name}': Reading static data '{self.data_source}' from context '{STATES}'"
)
self.data_source = algo.dbook.get_file_path(
STATES, self.data_source, check_raw=False
)
if verbosity:
print(f"Path: {self.data_source}")
elif verbosity:
print(f"States '{self.name}': Reading file {self.data_source}")
rpars = dict(self.RDICT, **self.rpars)
data = PandasFileHelper().read_file(self.data_source, **rpars)
isorg = False
else:
isorg = True
data = self.data_source
if self.states_sel is not None:
data = data.iloc[self.states_sel]
elif self.states_loc is not None:
data = data.loc[self.states_loc]
self._N = len(data.index)
self._inds = data.index.to_numpy()
col_w = self.var2col.get(FV.WEIGHT, FV.WEIGHT)
self._weights = np.zeros((self._N, algo.n_turbines), dtype=FC.DTYPE)
if col_w in data:
self._weights[:] = data[col_w].to_numpy()[:, None]
elif FV.WEIGHT in self.var2col:
raise KeyError(
f"Weight variable '{col_w}' defined in var2col, but not found in states table columns {data.columns}"
)
else:
self._weights[:] = 1.0 / self._N
if isorg:
data = data.copy()
data[col_w] = self._weights[:, 0]
cols = []
self._cmap = {}
self._solo = {}
for v in self.ovars:
vcols = self._find_cols(v, data.columns)
if len(vcols) == 1:
self._solo[v] = data[vcols[0]].to_numpy()
elif len(vcols) > 1:
self._cmap[v] = (len(cols), len(cols) + len(vcols))
cols += vcols
data = data[cols]
self.H = self.var(FV.H)
self.VARS = self.var("vars")
self.DATA = self.var("data")
idata = super().load_data(algo, verbosity)
idata["coords"][self.H] = self.heights
idata["coords"][self.VARS] = list(self._cmap.keys())
n_hts = len(self.heights)
n_vrs = int(len(data.columns) / n_hts)
dims = (FC.STATE, self.VARS, self.H)
idata["data_vars"][self.DATA] = (
dims,
data.to_numpy().reshape(self._N, n_vrs, n_hts),
)
for v, d in self._solo.items():
idata["data_vars"][self.var(v)] = ((FC.STATE,), d)
return idata
[docs]
def size(self):
"""
The total number of states.
Returns
-------
int:
The total number of states
"""
return self._N
[docs]
def index(self):
"""
The index list
Returns
-------
indices: array_like
The index labels of states, or None for default integers
"""
return self._inds
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def output_point_vars(self, algo):
"""
The variables which are being modified by the model.
Parameters
----------
algo: foxes.core.Algorithm
The calculation algorithm
Returns
-------
output_vars: list of str
The output variable names
"""
return self.ovars
[docs]
def weights(self, algo):
"""
The statistical weights of all states.
Parameters
----------
algo: foxes.core.Algorithm
The calculation algorithm
Returns
-------
weights: numpy.ndarray
The weights, shape: (n_states, n_turbines)
"""
return self._weights
[docs]
def calculate(self, algo, mdata, fdata, tdata):
""" "
The main model calculation.
This function is executed on a single chunk of data,
all computations should be based on numpy arrays.
Parameters
----------
algo: foxes.core.Algorithm
The calculation algorithm
mdata: foxes.core.MData
The model data
fdata: foxes.core.FData
The farm data
tdata: foxes.core.TData
The target point data
Returns
-------
results: dict
The resulting data, keys: output variable str.
Values: numpy.ndarray with shape
(n_states, n_targets, n_tpoints)
"""
n_states = tdata.n_states
n_targets = tdata.n_targets
n_tpoints = tdata.n_tpoints
h = mdata[self.H]
z = tdata[FC.TARGETS][..., 2].reshape(n_states, n_targets * n_tpoints)
n_h = len(h)
vrs = list(mdata[self.VARS])
n_vars = len(vrs)
coeffs = np.zeros((n_h, n_h), dtype=FC.DTYPE)
np.fill_diagonal(coeffs, 1.0)
ipars = dict(assume_sorted=True, bounds_error=True)
ipars.update(self.ipars)
intp = interp1d(h, coeffs, axis=0, **ipars)
ires = intp(z)
del coeffs, intp
has_wd = FV.WD in vrs
if has_wd:
i_wd = vrs.index(FV.WD)
if FV.WS in vrs:
i_ws = vrs.index(FV.WS)
uvh = wd2uv(
mdata[self.DATA][:, i_wd], mdata[self.DATA][:, i_ws], axis=-1
)
elif FV.WS in self.fixed_vars:
uvh = wd2uv(mdata[self.DATA][:, i_wd], self.fixed_vars[FV.WS], axis=-1)
elif self.var(FV.WS) in mdata:
uvh = wd2uv(
mdata[self.DATA][:, i_wd], mdata[self.var(FV.WS)][:, None], axis=-1
)
else:
raise KeyError(
f"States '{self.name}': Found variable '{FV.WD}', but missing variable '{FV.WS}'"
)
uv = np.einsum("shd,sph->spd", uvh, ires).reshape(
n_states, n_targets, n_tpoints, 2
)
del uvh
ires = np.einsum("svh,sph->vsp", mdata[self.DATA], ires).reshape(
n_vars, n_states, n_targets, n_tpoints
)
results = {}
for v in self.ovars:
if has_wd and v == FV.WD:
results[v] = uv2wd(uv, axis=-1)
elif has_wd and v == FV.WS:
results[v] = np.linalg.norm(uv, axis=-1)
elif v in self.fixed_vars:
results[v] = np.zeros((n_states, n_targets, n_tpoints), dtype=FC.DTYPE)
results[v][:] = self.fixed_vars[v]
elif v in self._solo.keys():
results[v] = np.zeros((n_states, n_targets, n_tpoints), dtype=FC.DTYPE)
results[v][:] = mdata[self.var(v)][:, None, None]
else:
results[v] = ires[vrs.index(v)]
return results
[docs]
def finalize(self, algo, verbosity=0):
"""
Finalizes the model.
Parameters
----------
algo: foxes.core.Algorithm
The calculation algorithm
verbosity: int
The verbosity level
"""
super().finalize(algo, verbosity)
self._cmap = None
self._solo = None
self._weights = None
self._N = None
[docs]
class MultiHeightTimeseries(MultiHeightStates):
"""
Multi-height timeseries states data.
:group: input.states
"""
RDICT = {"index_col": 0, "parse_dates": [0]}