# -*- coding: utf-8 -*-
"""This module implements tools to determine time-related indices."""
# import...
# ...from standard library
import copy
# ...from site-packages
import numpy
# ...from HydPy
import hydpy
from hydpy import config
from hydpy.core import exceptiontools
from hydpy.core import objecttools
from hydpy.core import propertytools
from hydpy.core import timetools
from hydpy.core.typingtools import *
def _get_timegrids(func):
timegrids = exceptiontools.getattr_(hydpy.pub, "timegrids", None)
if timegrids is None:
name = func.__name__[1:]
raise exceptiontools.AttributeNotReady(
f"An Indexer object has been asked for an `{name}` array. "
f"Such an array has neither been determined yet nor can it "
f"be determined automatically at the moment. Either define "
f"an `{name}` array manually and pass it to the Indexer "
f"object, or make a proper Timegrids object available within "
f"the pub module."
)
return timegrids
[docs]
class IndexerProperty(propertytools.BaseProperty):
"""A property for handling time-related indices.
Some models (e.g. |lland_dd|) require time related index values.
|IndexerProperty| provides some caching functionalities to avoid
recalculating the same indices for different model instances over
and over again. We illustrate this by taking property
|Indexer.monthofyear| as an example.
Generally, |Indexer| needs to know the relevant initialisation
period before being able to calculate any time-related index values.
If you forget to define one first, you get the following error
message:
>>> from hydpy import print_vector, pub
>>> pub.indexer.monthofyear
Traceback (most recent call last):
...
hydpy.core.exceptiontools.AttributeNotReady: An Indexer object has \
been asked for an `monthofyear` array. Such an array has neither been \
determined yet nor can it be determined automatically at the moment. \
Either define an `monthofyear` array manually and pass it to the Indexer \
object, or make a proper Timegrids object available within the pub module.
For efficiency, repeated querying of |Indexer.monthofyear| returns
the same |numpy| |numpy.array| object:
>>> pub.timegrids = "27.02.2004", "3.03.2004", "1d"
>>> monthofyear = pub.indexer.monthofyear
>>> print_vector(monthofyear)
1, 1, 1, 2, 2
>>> print_vector(pub.indexer.monthofyear)
1, 1, 1, 2, 2
>>> pub.indexer.monthofyear is monthofyear
True
When the |Timegrids| object handled by module |pub| changes,
|IndexerProperty| calculates and returns a new index array:
>>> pub.timegrids.init.firstdate += "1d"
>>> print_vector(pub.indexer.monthofyear)
1, 1, 2, 2
>>> pub.indexer.monthofyear is monthofyear
False
When in doubt, you can manually delete the cached |numpy| |numpy.ndarray|
and receive a freshly calculated index array afterwards:
>>> monthofyear = pub.indexer.monthofyear
>>> pub.indexer.monthofyear is monthofyear
True
>>> del pub.indexer.monthofyear
>>> print_vector(pub.indexer.monthofyear)
1, 1, 2, 2
>>> pub.indexer.monthofyear is monthofyear
False
You are allowed to define alternative values manually, which
seems advisable only for testing purposes:
>>> pub.indexer.monthofyear = 0, 1, 2, 3
>>> print_vector(pub.indexer.monthofyear)
0, 1, 2, 3
>>> pub.timegrids.init.firstdate -= "1d"
>>> print_vector(pub.indexer.monthofyear)
1, 1, 1, 2, 2
When assigning inadequate data, you get errors like the following:
>>> pub.indexer.monthofyear = 1
Traceback (most recent call last):
...
TypeError: While trying to assign a new `monthofyear` index array to an Indexer \
object, the following error occurred: 'int' object is not subscriptable
>>> pub.indexer.monthofyear = [[0, 1, 2, 3], [4, 5, 6, 7]]
Traceback (most recent call last):
...
ValueError: The `monthofyear` index array of an Indexer object \
must be 1-dimensional. However, the given value has interpreted as \
a 2-dimensional object.
>>> pub.indexer.monthofyear = 0, 1, 2, 3
Traceback (most recent call last):
...
ValueError: The `monthofyear` index array of an Indexer object must have \
a number of entries fitting to the initialization time period precisely. \
However, the given value has been interpreted to be of length `4` and the \
length of the Timegrid object representing the actual initialisation \
period is `5`.
"""
def __init__(self, fget):
super().__init__()
self.fget = fget
self.fset = self._fset
self.fdel = self._fdel
self.__doc__ = fget.__doc__
self.values = None
self.timegrids = None
[docs]
def call_fget(self, obj) -> NDArrayFloat:
timegrids = exceptiontools.getattr_(hydpy.pub, "timegrids", None)
if (self.values is None) or (self.timegrids != timegrids):
self.values = self._calcidxs(self.fget(obj))
self.timegrids = copy.deepcopy(timegrids)
return self.values
[docs]
def call_fset(self, obj, value):
self._fset(value)
def _fset(self, values):
self.values = self._convertandtest(values, self.name)
self.timegrids = copy.deepcopy(exceptiontools.getattr_(hydpy.pub, "timegrids"))
[docs]
def call_fdel(self, obj):
self.fdel()
def _fdel(self):
self.values = None
self.timegrids = None
@staticmethod
def _convertandtest(values, name):
try:
type_ = type(values[0])
array = numpy.array(values, dtype=config.TYPES_PY2NP.get(type_, type_))
except BaseException:
objecttools.augment_excmessage(
f"While trying to assign a new `{name}` "
f"index array to an Indexer object"
)
if array.ndim != 1:
raise ValueError(
f"The `{name}` index array of an Indexer object must be "
f"1-dimensional. However, the given value has interpreted "
f"as a {array.ndim}-dimensional object."
)
timegrids = exceptiontools.getattr_(hydpy.pub, "timegrids")
if timegrids is not None:
if len(array) != len(timegrids.init):
raise ValueError(
f"The `{name}` index array of an Indexer object must have "
f"a number of entries fitting to the initialization time "
f"period precisely. However, the given value has been "
f"interpreted to be of length `{len(array)}` and the "
f"length of the Timegrid object representing the actual "
f"initialisation period is `{len(timegrids.init)}`."
)
return array
@staticmethod
def _calcidxs(func):
timegrids = _get_timegrids(func)
type_ = type(func(timegrids.init[0]))
idxs = numpy.empty(
len(timegrids.init), dtype=config.TYPES_PY2NP.get(type_, type_)
)
with hydpy.pub.options.timestampleft(True):
for jdx, date in enumerate(hydpy.pub.timegrids.init):
idxs[jdx] = func(date)
return idxs
[docs]
class Indexer:
"""Handles different |IndexerProperty| objects defining time-related
indices.
One can specify the index arrays manually, but they are usually
determined automatically based on the |Timegrids| object made
available through module |pub|.
"""
def __init__(self):
self._monthofyear = None
self._monthofyear_timegrids = hash(None)
self._dayofyear = None
self._dayofyear_hash = hash(None)
self._timeofyear = None
self._timeofyear_hash = hash(None)
[docs]
@IndexerProperty
def monthofyear(self):
"""Index values, representing the month of the year.
The following example shows the month indices of the last days of
February and the first days of March for a leap year:
>>> from hydpy import print_vector, pub
>>> pub.timegrids = "27.02.2004", "3.03.2004", "1d"
>>> monthofyear = pub.indexer.monthofyear
>>> print_vector(monthofyear)
1, 1, 1, 2, 2
"""
def _monthofyear(date):
return date.month - 1
return _monthofyear
[docs]
@IndexerProperty
def dayofyear(self):
"""Index values, representing the month of the year.
For reasons of consistency between leap years and non-leap years,
assuming a daily time step, index 59 is always associated with the
29th of February. Hence, it is missing in non-leap years:
>>> from hydpy import print_vector, pub
>>> from hydpy.core.indextools import Indexer
>>> pub.timegrids = "27.02.2004", "3.03.2004", "1d"
>>> print_vector(Indexer().dayofyear)
57, 58, 59, 60, 61
>>> pub.timegrids = "27.02.2005", "3.03.2005", "1d"
>>> print_vector(Indexer().dayofyear)
57, 58, 60, 61
"""
def _dayofyear(date):
return date.dayofyear - 1 + ((date.month > 2) and (not date.leapyear))
return _dayofyear
[docs]
@IndexerProperty
def timeofyear(self):
"""Index values, representing the time of the year.
Let us reconsider one of the examples of the documentation on
property |Indexer.dayofyear|:
>>> from hydpy import print_vector, pub, Timegrids, Timegrid
>>> from hydpy.core.indextools import Indexer
>>> pub.timegrids = "27.02.2005", "3.03.2005", "1d"
Due to the simulation step size of one day, the index arrays
calculated by properties |Indexer.dayofyear| and |Indexer.timeofyear|
are identical:
>>> print_vector(Indexer().dayofyear)
57, 58, 60, 61
>>> print_vector(Indexer().timeofyear)
57, 58, 60, 61
In the next example, we halve the step size:
>>> pub.timegrids = "27.02.2005", "3.03.2005", "12h"
Now two subsequent simulation steps associated are with the same day:
>>> print_vector(Indexer().dayofyear)
57, 57, 58, 58, 60, 60, 61, 61
However, the `timeofyear` array gives the index of the
respective simulation steps of the actual year:
>>> print_vector(Indexer().timeofyear)
114, 115, 116, 117, 120, 121, 122, 123
Note the gap in the returned index array due to 2005 being not a
leap year.
"""
def _timeofyear(date):
date = copy.deepcopy(date)
date.year = 2000
return refgrid[date]
refgrid = timetools.Timegrid(
timetools.Date("2000.01.01"),
timetools.Date("2001.01.01"),
_get_timegrids(_timeofyear).stepsize,
)
return _timeofyear
[docs]
@IndexerProperty
def standardclocktime(self):
"""Standard clock time at the midpoints of the initialisation time
steps in hours.
Note that the standard clock time is not usable as an index. Hence,
we might later move property |Indexer.standardclocktime| somewhere
else or give class |Indexer| a more general purpose (and name) later.
The following examples demonstrate the calculation of the standard
clock time for simulation step sizes of one day, one hour, one minute,
and one second, respectively:
>>> from hydpy import pub, print_vector
>>> pub.timegrids = "27.02.2004", "3.03.2004", "1d"
>>> print_vector(pub.indexer.standardclocktime)
12.0, 12.0, 12.0, 12.0, 12.0
>>> pub.timegrids = "27.02.2004 21:00", "28.02.2004 03:00", "1h"
>>> print_vector(pub.indexer.standardclocktime)
21.5, 22.5, 23.5, 0.5, 1.5, 2.5
>>> pub.timegrids = "27.02.2004 23:57:0", "28.02.2004 00:03:00", "1m"
>>> print_vector(pub.indexer.standardclocktime)
23.958333, 23.975, 23.991667, 0.008333, 0.025, 0.041667
>>> pub.timegrids = "27.02.2004 23:59:57", "28.02.2004 00:00:03", "1s"
>>> print_vector(pub.indexer.standardclocktime)
23.999306, 23.999583, 23.999861, 0.000139, 0.000417, 0.000694
"""
def _standardclocktime(date):
t0 = date.hour + (date.minute + date.second / 60.0) / 60.0
return t0 + hydpy.pub.timegrids.stepsize.hours / 2.0
return _standardclocktime
