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model.py
1539 lines (1264 loc) · 53.2 KB
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model.py
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"""The Model class is the main object for creating model in Pastas.
Examples
--------
>>> oseries = pd.Series([1,2,1], index=pd.to_datetime(range(3), unit="D"))
>>> ml = Model(oseries)
"""
from collections import OrderedDict
from copy import copy
from inspect import isclass
from os import getlogin
import numpy as np
import pandas as pd
from .decorators import get_stressmodel
from .io.base import dump, load_model
from .noisemodels import NoiseModel
from .plots import Plotting
from .solver import LeastSquares
from .modelstats import Statistics
from .stressmodels import Constant
from .timeseries import TimeSeries
from .utils import get_dt, get_time_offset, get_sample, \
frequency_is_supported, validate_name
from .version import __version__
from logging import getLogger
class Model:
"""Initiates a time series model.
Parameters
----------
oseries: pandas.Series or pastas.TimeSeries
pandas Series object containing the dependent time series. The
observation can be non-equidistant.
constant: bool, optional
Add a constant to the model (Default=True).
noisemodel: bool, optional
Add the default noisemodel to the model. A custom noisemodel can be
added later in the modelling process as well.
name: str, optional
String with the name of the model, used in plotting and saving.
metadata: dict, optional
Dictionary containing metadata of the oseries, passed on the to
oseries when creating a pastas TimeSeries object. hence,
ml.oseries.metadata will give you the metadata.
Returns
-------
ml: pastas.Model
Pastas Model instance, the base object in Pastas.
Examples
--------
>>> oseries = pd.Series([1,2,1], index=pd.to_datetime(range(3), unit="D"))
>>> ml = Model(oseries)
"""
def __init__(self, oseries, constant=True, noisemodel=True, name=None,
metadata=None):
self.logger = getLogger(__name__)
# Construct the different model components
self.oseries = TimeSeries(oseries, settings="oseries",
metadata=metadata)
if name is None:
name = self.oseries.name
if name is None:
name = 'Observations'
self.name = validate_name(name)
self.parameters = pd.DataFrame(
columns=["initial", "name", "optimal", "pmin", "pmax", "vary",
"stderr"])
# Define the model components
self.stressmodels = OrderedDict()
self.constant = None
self.transform = None
self.noisemodel = None
# Default solve/simulation settings
self.settings = {
"tmin": None,
"tmax": None,
"freq": "D",
"warmup": 3650,
"time_offset": pd.Timedelta(0),
"noise": noisemodel,
"solver": None,
"fit_constant": True,
}
if constant:
constant = Constant(initial=self.oseries.series.mean(),
name="constant")
self.add_constant(constant)
if noisemodel:
self.add_noisemodel(NoiseModel())
# File Information
self.file_info = self.get_file_info()
# initialize some attributes for solving and simulation
self.sim_index = None
self.oseries_calib = None
self.interpolate_simulation = None
self.normalize_residuals = False
self.fit = None
# Load other modules
self.stats = Statistics(self)
self.plots = Plotting(self)
self.plot = self.plots.plot # because we are lazy
def __repr__(self):
"""Prints a simple string representation of the model.
"""
template = ('{cls}(oseries={os}, name={name}, constant={const}, '
'noisemodel={noise})')
return template.format(cls=self.__class__.__name__,
os=self.oseries.name,
name=self.name,
const=not self.constant is None,
noise=not self.noisemodel is None)
def add_stressmodel(self, stressmodel, *args, replace=False):
"""Adds a stressmodel to the main model.
Parameters
----------
stressmodel: pastas.stressmodel.stressmodelBase
instance of a pastas.stressmodel object. Multiple stress models
can be provided (e.g., ml.add_stressmodel(sm1, sm2) in one call.
replace: bool, optional
replace the stressmodel if a stressmodel with the same name
already exists. Not recommended but useful at times. Default is
False.
Notes
-----
To obtain a list of the stressmodel names, type:
>>> ml.stressmodels.keys()
Examples
--------
>>> sm = ps.StressModel(stress, rfunc=ps.Gamma, name="stress")
>>> ml.add_stressmodel(sm)
"""
# Method can take multiple stressmodels at once through args
if args:
for arg in args:
self.add_stressmodel(arg)
if (stressmodel.name in self.stressmodels.keys()) and not replace:
self.logger.error("The name for the stressmodel you are trying "
"to add already exists for this model. Select "
"another name.")
else:
self.stressmodels[stressmodel.name] = stressmodel
self.parameters = self.get_init_parameters(initial=False)
if self.settings["freq"] is None:
self._set_freq()
stressmodel.update_stress(freq=self.settings["freq"])
# Check if stress overlaps with oseries, if not give a warning
if (stressmodel.tmin > self.oseries.series.index.max()) or \
(stressmodel.tmax < self.oseries.series.index.min()):
self.logger.warning("The stress of the stressmodel has no "
"overlap with ml.oseries.")
def add_constant(self, constant):
"""Adds a Constant to the time series Model.
Parameters
----------
constant: pastas.Constant
Pastas constant instance, possibly more things in the future.
Examples
--------
>>> d = ps.Constant()
>>> ml.add_constant(d)
"""
self.constant = constant
self.parameters = self.get_init_parameters(initial=False)
def add_transform(self, transform):
"""Adds a Transform to the time series Model.
Parameters
----------
transform: pastas.transform
instance of a pastas.transform object.
Examples
--------
>>> tt = ps.ThresholdTransform()
>>> ml.add_transform(tt)
"""
if isclass(transform):
# keep this line for backwards compatibilty for now
transform = transform()
transform.set_model(self)
self.transform = transform
self.parameters = self.get_init_parameters(initial=False)
def add_noisemodel(self, noisemodel):
"""Adds a noisemodel to the time series Model.
Parameters
----------
noisemodel: pastas.noisemodels.NoiseModelBase
Instance of NoiseModelBase
Examples
--------
>>> n = ps.NoiseModel()
>>> ml.add_noisemodel(n)
"""
self.noisemodel = noisemodel
self.noisemodel.set_init_parameters(oseries=self.oseries.series)
self.parameters = self.get_init_parameters(initial=False)
@get_stressmodel
def del_stressmodel(self, name):
""" Safely delete a stressmodel from the stressmodels dict.
Parameters
----------
name: str
string with the name of the stressmodel object.
Notes
-----
To obtain a list of the stressmodel names type:
>>> ml.stressmodels.keys()
"""
self.stressmodels.pop(name, None)
self.parameters = self.get_init_parameters(initial=False)
def del_constant(self):
""" Safely delete the constant from the Model.
"""
if self.constant is None:
self.logger.warning("No constant is present in this model.")
else:
self.constant = None
self.parameters = self.get_init_parameters(initial=False)
def del_transform(self):
"""Safely delete the transform from the Model.
"""
if self.transform is None:
self.logger.warning("No transform is present in this model.")
else:
self.transform = None
self.parameters = self.get_init_parameters(initial=False)
def del_noisemodel(self):
"""Safely delete the noisemodel from the Model.
"""
if self.noisemodel is None:
self.logger.warning("No noisemodel is present in this model.")
else:
self.noisemodel = None
self.parameters = self.get_init_parameters(initial=False)
def simulate(self, parameters=None, tmin=None, tmax=None, freq=None,
warmup=None, return_warmup=False):
"""Method to simulate the time series model.
Parameters
----------
parameters: array-like, optional
Array with the parameters used in the time series model. See
Model.get_parameters() for more info if parameters is None.
tmin: str, optional
tmax: str, optional
freq: str, optional
Frequency at which the time series are simulated.
warmup: int, optional
Length of the warmup period in days
return_warmup: bool, optional
Return the simulation including the the warmup period or not,
default is False.
Returns
-------
sim: pandas.Series
pandas.Series containing the simulated time series
Notes
-----
This method can be used without any parameters. When the model is
solved, the optimal parameters values are used and if not,
the initial parameter values are used. This allows the user to
get an idea of how the simulation looks with only the initial
parameters and no calibration.
"""
# Default options when tmin, tmax, freq and warmup are not provided.
if tmin is None and self.settings['tmin']:
tmin = self.settings['tmin']
else:
tmin = self.get_tmin(tmin, freq, use_oseries=False,
use_stresses=True)
if tmax is None and self.settings['tmax']:
tmax = self.settings['tmax']
else:
tmax = self.get_tmax(tmax, freq, use_oseries=False,
use_stresses=True)
if freq is None:
freq = self.settings["freq"]
if warmup is None:
warmup = self.settings["warmup"]
elif isinstance(warmup, str):
warmup = get_dt(warmup)
# Get the simulation index and the time step
sim_index = self.get_sim_index(tmin, tmax, freq, warmup)
dt = get_dt(freq)
# Get parameters if none are provided
if parameters is None:
parameters = self.get_parameters()
sim = pd.Series(data=np.zeros(sim_index.size, dtype=float),
index=sim_index, fastpath=True)
istart = 0 # Track parameters index to pass to stressmodel object
for sm in self.stressmodels.values():
contrib = sm.simulate(parameters[istart: istart + sm.nparam],
sim_index.min(), sim_index.max(), freq, dt)
sim = sim + contrib
istart += sm.nparam
if self.constant:
sim = sim + self.constant.simulate(parameters[istart])
istart += 1
if self.transform:
sim = self.transform.simulate(sim, parameters[
istart:istart + self.transform.nparam])
# Respect provided tmin/tmax at this point, since warmup matters for
# simulation but should not be returned, unless return_warmup=True.
if not return_warmup:
sim = sim.loc[tmin:tmax]
if sim.hasnans:
sim = sim.dropna()
self.logger.warning('Nan-values were removed from the simulation.')
sim.name = 'Simulation'
return sim
def residuals(self, parameters=None, tmin=None, tmax=None, freq=None,
warmup=None):
"""Method to calculate the residual series.
Parameters
----------
parameters: list, optional
Array of the parameters used in the time series model. See
Model.get_parameters() for more info if parameters is None.
tmin: str, optional
tmax: str, optional
freq: str, optional
frequency at which the time series are simulated.
warmup: int, optional
length of the warmup period in days
Returns
-------
res: pandas.Series
pandas.Series with the residuals series.
"""
# Default options when tmin, tmax, freq and warmup are not provided.
if tmin is None:
tmin = self.settings['tmin']
if tmax is None:
tmax = self.settings['tmax']
if freq is None:
freq = self.settings["freq"]
if warmup is None:
warmup = self.settings["warmup"]
elif isinstance(warmup, str):
warmup = get_dt(warmup)
# simulate model
sim = self.simulate(parameters, tmin, tmax, freq, warmup,
return_warmup=False)
# Get the oseries calibration series
oseries_calib = self.observations(tmin, tmax, freq)
# Get simulation at the correct indices
if self.interpolate_simulation is None:
if oseries_calib.index.difference(sim.index).size is not 0:
self.interpolate_simulation = True
self.logger.info('There are observations between the '
'simulation timesteps. Linear interpolation '
'between simulated values is used.')
if self.interpolate_simulation:
# interpolate simulation to measurement-times
# TODO RC: Somehow switch to pandas methods with maximum gap (gap_limit?)
sim_interpolated = np.interp(oseries_calib.index.asi8,
sim.index.asi8, sim)
else:
# all of the observation indexes are in the simulation
sim_interpolated = sim.loc[oseries_calib.index]
# Calculate the actual residuals here
res = oseries_calib.subtract(sim_interpolated)
if res.hasnans:
res = res.dropna()
self.logger.warning('Nan-values were removed from the residuals.')
if self.normalize_residuals:
res = res - res.values.mean()
res.name = "Residuals"
return res
def noise(self, parameters=None, tmin=None, tmax=None, freq=None,
warmup=None):
"""Method to simulate the noise when a noisemodel is present.
Parameters
----------
parameters: list, optional
Array of the parameters used in the time series model. See
Model.get_parameters() for more info if parameters is None.
tmin: str, optional
tmax: str, optional
freq: str, optional
frequency at which the time series are simulated.
warmup: int, optional
length of the warmup period in days
Returns
-------
noise : pandas.Series
Pandas series of the noise.
Notes
-----
The noise are the time series that result when applying a noise
model.
"""
if self.noisemodel is None:
self.logger.error("Noise cannot be calculated if there is "
"no noisemodel.")
return None
if freq is None:
freq = self.settings["freq"]
# Get parameters if none are provided
if parameters is None:
parameters = self.get_parameters()
# Calculate the residuals
res = self.residuals(parameters, tmin, tmax, freq, warmup)
# Calculate the noise
noise = self.noisemodel.simulate(res,
parameters[-self.noisemodel.nparam:])
return noise
def observations(self, tmin=None, tmax=None, freq=None):
"""Method that returns the observations series used for calibration.
Parameters
----------
tmin: str or pandas.TimeStamp, optional
tmax: str or pandas.TimeStamp, optional
freq: str, optional
Returns
-------
oseries_calib: pandas.Series
pandas series of the oseries used for calibration of the model
Notes
-----
This method makes sure the simulation is compared to the nearest
observation. It finds the index closest to sim_index, and then returns
a selection of the oseries. in the residuals method, the simulation is
interpolated to the observation-timestamps.
"""
if tmin is None and self.settings['tmin']:
tmin = self.settings['tmin']
else:
tmin = self.get_tmin(tmin, freq, use_oseries=False,
use_stresses=True)
if tmax is None and self.settings['tmax']:
tmax = self.settings['tmax']
else:
tmax = self.get_tmax(tmax, freq, use_oseries=False,
use_stresses=True)
if freq is None:
freq = self.settings["freq"]
update_observations = False
for key, setting in zip([tmin, tmax, freq], ["tmin", "tmax", "freq"]):
if key != self.settings[setting]:
update_observations = True
if self.oseries_calib is None or update_observations:
oseries_calib = self.oseries.series.loc[tmin:tmax]
# sample measurements, so that frequency is not higher than model
# keep the original timestamps, as they will be used during
# interpolation of the simulation
sim_index = self.get_sim_index(tmin, tmax, freq,
self.settings["warmup"])
if not oseries_calib.empty:
index = get_sample(oseries_calib.index, sim_index)
oseries_calib = oseries_calib.loc[index]
if not update_observations:
# tmin, tmax and freq are equal to the settings
# so we can set self.oseries_calib to improve speed of next run
self.oseries_calib = oseries_calib
else:
oseries_calib = self.oseries_calib
return oseries_calib
def initialize(self, tmin=None, tmax=None, freq=None, warmup=None,
noise=None, weights=None, initial=True, fit_constant=None):
"""Method to initialize the model.
This method is called by the solve-method, but can also be triggered
manually. See the solve-method for a description of the arguments.
"""
if noise is None and self.noisemodel:
noise = True
elif noise is True and self.noisemodel is None:
self.logger.warning("""Warning, solving with noisemodel while no
noisemodel is defined. No noisemodel is used.""")
noise = False
self.settings["noise"] = noise
self.settings["weights"] = weights
# Set the frequency & warmup
if freq:
self.settings["freq"] = frequency_is_supported(freq)
if warmup is not None:
if isinstance(warmup, str):
warmup = get_dt(warmup)
self.settings["warmup"] = warmup
# Set the time offset from the frequency (this does not work as expected yet)
# self._set_time_offset()
# Set tmin and tmax
# Only overwrite settings dic if tmin is not None or if
# settins['tmin'] is None. Same for tmax
if tmin is not None:
self.settings["tmin"] = self.get_tmin(tmin)
elif self.settings["tmin"] is None:
self.settings["tmin"] = self.get_tmin(tmin)
if tmax is not None:
self.settings["tmax"] = self.get_tmax(tmax)
elif self.settings["tmax"] is None:
self.settings["tmax"] = self.get_tmax(tmax)
# set fit_constant
if fit_constant is not None:
self.settings["fit_constant"] = fit_constant
# make sure calibration data is renewed
self.sim_index = None
self.oseries_calib = None
self.interpolate_simulation = None
# Initialize parameters
self.parameters = self.get_init_parameters(noise, initial)
# Prepare model if not fitting the constant as a parameter
if not self.settings["fit_constant"]:
self.parameters.loc["constant_d", "vary"] = 0
self.parameters.loc["constant_d", "initial"] = 0.0
self.normalize_residuals = True
def solve(self, tmin=None, tmax=None, freq=None, warmup=None, noise=True,
solver=None, report=True, initial=True, weights=None,
fit_constant=True, **kwargs):
"""Method to solve the time series model.
Parameters
----------
tmin: str, optional
String with a start date for the simulation period (E.g. '1980').
If none is provided, the tmin from the oseries is used.
tmax: str, optional
String with an end date for the simulation period (E.g. '2010').
If none is provided, the tmax from the oseries is used.
freq: str, optional
String with the frequency the stressmodels are simulated. Must
be one of the following: (D, h, m, s, ms, us, ns) or a multiple of
that e.g. "7D".
warmup: float/int, optinal
Warmup period (in Days) for which the simulation is calculated,
but not used for the calibration period.
noise: bool, optional
Argument that determines if a noisemodel is used (only if
present). The default is noise=True.
solver: pastas.solver.BaseSolver class, optional
Class used to solve the model. Options are: ps.LeastSquares
(default) or ps.LmfitSolve. A class is needed, not an instance
of the class!
report: bool, optional
Print a report to the screen after optimization finished. This
can also be manually triggered after optimization by calling
print(ml.fit_report()) on the Pastas model instance.
initial: bool, optional
Reset initial parameters from the individual stressmodels.
Default is True. If False, the optimal values from an earlier
optimization are used.
weights: pandas.Series, optional
Pandas Series with values by which the residuals are multiplied,
index-based.
fit_constant: bool, optional
Argument that determines if the constant is fitted as a parameter.
If it is set to False, the constant is set equal to the mean of
the residuals.
**kwargs: dict, optional
All keyword arguments will be passed onto minimization method
from the solver. It depends on the solver used which arguments
can be used.
Notes
-----
- The solver object including some results are stored as ml.fit. From
here one can access the covariance (ml.fit.pcov) and correlation
matrix (ml.fit.pcor).
- Each solver return a number of results after optimization. These
solver specific results are stored in ml.fit.result and can be
accessed from there.
"""
# Initialize the model
self.initialize(tmin, tmax, freq, warmup, noise, weights, initial,
fit_constant)
# Store the solve instance
if solver is None:
if self.fit is None:
self.fit = LeastSquares(model=self)
elif not issubclass(solver, self.fit.__class__):
self.fit = solver(model=self)
self.settings["solver"] = self.fit._name
# Solve model
success, optimal, stderr = self.fit.solve(noise=noise, weights=weights,
**kwargs)
if not success:
self.logger.warning("Model parameters could not be estimated "
"well.")
if not self.settings['fit_constant']:
# Determine the residuals and set the constant to their mean
self.normalize_residuals = False
res = self.residuals(optimal).mean()
optimal[self.parameters.name == self.constant.name] = res
self.parameters.optimal = optimal
self.parameters.stderr = stderr
if report:
print(self.fit_report())
def set_initial(self, name, value, move_bounds=False):
"""Method to set the initial value of any parameter.
Parameters
----------
name: str
name of the parameter to update.
value: float
parameters value to use as initial estimate.
move_bounds: bool, optional
Reset pmin/pmax based on new initial value.
"""
if move_bounds:
factor = value / self.parameters.loc[name, 'initial']
min_new = self.parameters.loc[name, 'pmin'] * factor
self.set_parameter(name, min_new, 'pmin')
max_new = self.parameters.loc[name, 'pmax'] * factor
self.set_parameter(name, max_new, 'pmax')
self.set_parameter(name, value, "initial")
def set_vary(self, name, value):
"""Method to set if the parameter is allowed to vary.
Parameters
----------
name: str
name of the parameter to update.
value: bool
boolean to vary a parameter (True) or not (False).
"""
self.set_parameter(name, bool(value), "vary")
def set_pmin(self, name, value):
"""Method to set the minimum value of a parameter.
Parameters
----------
name: str
name of the parameter to update.
value: float
minimum value for the parameter.
"""
self.set_parameter(name, value, "pmin")
def set_pmax(self, name, value):
"""Method to set the maximum values of a parameter.
Parameters
----------
name: str
name of the parameter to update.
value: float
maximum value for the parameter.
"""
self.set_parameter(name, value, "pmax")
def set_parameter(self, name, value, kind):
"""Internal method to set the parameter value for some kind.
"""
if name not in self.parameters.index:
msg = "parameters with name {} is not present in the " \
"model".format(name)
self.logger.error(msg)
raise Exception(msg)
cat = self.parameters.loc[name, "name"]
# Because either of the following is not necessarily present
noisemodel = self.noisemodel.name if self.noisemodel else "NotPresent"
constant = self.constant.name if self.constant else "NotPresent"
if cat in self.stressmodels.keys():
self.stressmodels[cat].__getattribute__("set_" + kind)(name, value)
self.parameters.loc[name, kind] = value
elif cat == noisemodel:
self.noisemodel.__getattribute__("set_" + kind)(name, value)
self.parameters.loc[name, kind] = value
elif cat == constant:
self.constant.__getattribute__("set_" + kind)(name, value)
self.parameters.loc[name, kind] = value
def _set_freq(self):
"""Internal method to set the frequency in the settings. This is
method is not yet applied and is for future development.
"""
freqs = set()
if self.oseries.freq:
# when the oseries has a constant frequency, us this
freqs.add(self.oseries.freq)
else:
# otherwise determine frequency from the stressmodels
for stressmodel in self.stressmodels.values():
if stressmodel.stress:
for stress in stressmodel.stress:
if stress.settings['freq']:
# first check the frequency, and use this
freqs.add(stress.settings['freq'])
elif stress.freq_original:
# if this is not available, and the original frequency is, take the original frequency
freqs.add(stress.freq_original)
if len(freqs) == 1:
# if there is only one frequency, use this frequency
self.settings["freq"] = next(iter(freqs))
elif len(freqs) > 1:
# if there are more frequencies, take the highest frequency (lowest dt)
freqs = list(freqs)
dt = np.array([get_dt(f) for f in freqs])
self.settings["freq"] = freqs[np.argmin(dt)]
else:
self.logger.info("Frequency of model cannot be determined. "
"Frequency is set to daily")
self.settings["freq"] = "D"
def _set_time_offset(self):
"""Internal method to set the time offset for the model class.
Notes
-----
Method to check if the StressModel timestamps match (e.g. similar hours)
"""
time_offsets = set()
for stressmodel in self.stressmodels.values():
for st in stressmodel.stress:
if st.freq_original:
# calculate the offset from the default frequency
time_offset = get_time_offset(
st.series_original.index.min(),
self.settings["freq"])
time_offsets.add(time_offset)
if len(time_offsets) > 1:
msg = (
"The time-differences with the default frequency is not the "
"same for all stresses.")
self.logger.error(msg)
raise (Exception(msg))
if len(time_offsets) == 1:
self.settings["time_offset"] = next(iter(time_offsets))
else:
self.settings["time_offset"] = pd.Timedelta(0)
def get_stressmodel_names(self):
"""Returns list of stressmodel names"""
return list(self.stressmodels.keys())
def get_sim_index(self, tmin, tmax, freq, warmup):
"""Internal method to get the simulation index, including the warmup
period.
Parameters
----------
tmin: pandas.TimeStamp
tmax: pandas.TimeStamp
freq: str
warmup: int
Returns
-------
sim_index: pandas.DatetimeIndex
Pandas DatetimeIndex instance with the datetimes values for
which the model is simulated.
"""
# Check if any of the settings are updated
update_sim_index = False
for key, setting in zip([tmin, tmax, freq, warmup],
["tmin", "tmax", "freq", "warmup"]):
if key != self.settings[setting]:
update_sim_index = True
if self.sim_index is None or update_sim_index:
tmin = (tmin - pd.DateOffset(days=warmup)).floor(freq) + \
self.settings["time_offset"]
sim_index = pd.date_range(tmin, tmax, freq=freq)
if not update_sim_index:
# tmin, tmax, freq and warmup are equal to the settings
# so we can set self.sim_index to improve speed of next run
self.sim_index = sim_index
else:
sim_index = self.sim_index
return sim_index
def get_tmin(self, tmin=None, freq=None, use_oseries=True,
use_stresses=False):
"""Method that checks and returns valid values for tmin.
Parameters
----------
tmin: str, optional
string with a year or date that can be turned into a pandas
Timestamp (e.g. pd.Timestamp(tmin)).
freq: str, optional
string with the frequency.
use_oseries: bool, optional
Obtain the tmin and tmax from the oseries. Default is True.
use_stresses: bool, optional
Obtain the tmin and tmax from the stresses. The minimum/maximum
time from all stresses is taken.
Returns
-------
tmin: pandas.Timestamp
returns pandas timestamps for tmin.
Notes
-----
The parameters tmin and tmax are leading, unless use_oseries is
True, then these are checked against the oseries index. The tmin and
tmax are checked and returned according to the following rules:
A. If no value for tmin is provided:
1. If use_oseries is True, tmin is based on the oseries.
2. If use_stresses is True, tmin is based on the stressmodels.
B. If a values for tmin is provided:
1. A pandas timestamp is made from the string
2. if use_oseries is True, tmin is checked against oseries.
C. In all cases an offset for the tmin is added.
A detailed description of dealing with tmin and timesteps in general
can be found in the developers section of the docs.
"""
# Get tmin from the oseries
if use_oseries:
ts_tmin = self.oseries.series.index.min()
# Get tmin from the stressmodels
elif use_stresses:
ts_tmin = pd.Timestamp.max
for stressmodel in self.stressmodels.values():
if stressmodel.tmin < ts_tmin:
ts_tmin = stressmodel.tmin
# Get tmin and tmax from user provided values
else:
ts_tmin = pd.Timestamp(tmin)
# Set tmin properly
if tmin is not None and use_oseries:
tmin = max(pd.Timestamp(tmin), ts_tmin)
elif tmin is not None:
tmin = pd.Timestamp(tmin)
else:
tmin = ts_tmin
# adjust tmin and tmax so that the time-offset is equal to the stressmodels.
if freq is None:
freq = self.settings["freq"]
tmin = tmin.floor(freq) + self.settings["time_offset"]
# assert tmax > tmin, \
# self.logger.error('Error: Specified tmax not larger than '
# 'specified tmin')
# if use_oseries:
# assert self.oseries.series.loc[tmin: tmax].size > 0, \
# self.logger.error(
# 'Error: no observations between tmin and tmax')
return tmin
def get_tmax(self, tmax=None, freq=None, use_oseries=True,
use_stresses=False):
"""Method that checks and returns valid values for tmin and tmax.
Parameters
----------
tmax: str, optional
string with a year or date that can be turned into a pandas
Timestamp (e.g. pd.Timestamp(tmax)).
freq: str, optional
string with the frequency.
use_oseries: bool, optional
Obtain the tmin and tmax from the oseries. Default is True.
use_stresses: bool, optional
Obtain the tmin and tmax from the stresses. The minimum/maximum
time from all stresses is taken.
Returns
-------
tmax: pandas.Timestamp
returns pandas timestamps for tmax.
Notes
-----
The parameters tmin and tmax are leading, unless use_oseries is
True, then these are checked against the oseries index. The tmin and
tmax are checked and returned according to the following rules:
A. If no value for tmax is provided: