def __init__(self, data=None, lon=None, lat=None, timezone=None):
self._data = None
self._lon = lon
self._lat = lat
self._timezone = timezone
if data is not None:
self.data = data
self.wind = Bunch(a=None, b=None, t_shift=None)
self.precip = Bunch(months=None, stats=None)
self.hum = Bunch(a0=None,
a1=None,
kr=None,
month_hour_precip_mean=None)
self.temp = Bunch(max_delta=None, mean_course=None)
angstroem_df = pd.DataFrame(index=np.arange(12) + 1,
columns=['a', 'b'])
angstroem_df.a = 0.25
angstroem_df.b = 0.75
bristcamp_df = pd.DataFrame(index=np.arange(12) + 1,
columns=['a', 'c'])
bristcamp_df.a = 0.75
bristcamp_df.c = 2.4
self.glob = Bunch(angstroem=angstroem_df,
bristcamp=bristcamp_df,
mean_course=None)
def __init__(self, data=None, lon=None, lat=None, timezone=None):
self._data = None
self._lon = lon
self._lat = lat
self._timezone = timezone
if data is not None:
self.data = data
self.wind = Bunch(a=None, b=None, t_shift=None)
self.precip = Bunch(months=None, stats=None)
self.hum = Bunch(a0=None, a1=None, kr=None, month_hour_precip_mean=None)
self.temp = Bunch(max_delta=None, mean_course=None)
angstroem_df = pd.DataFrame(index=np.arange(12) + 1, columns=['a', 'b'])
angstroem_df.a = 0.25
angstroem_df.b = 0.75
bristcamp_df = pd.DataFrame(index=np.arange(12) + 1, columns=['a', 'c'])
bristcamp_df.a = 0.75
bristcamp_df.c = 2.4
self.glob = Bunch(angstroem=angstroem_df, bristcamp=bristcamp_df, mean_course=None)
def __init__(self, data=None, lon=None, lat=None, timezone=None):
self._data = None
self._lon = lon
self._lat = lat
self._timezone = timezone
if data is not None:
self.data = data
self.wind = Bunch(a=None, b=None, t_shift=None)
self.precip = Bunch(months=None, stats=None)
self.hum = Bunch(a0=None, a1=None, kr=None)
self.temp = Bunch(max_delta=None)
self.glob = Bunch(angstroem_a=0.25,
angstroem_b=0.75,
bristcamp_a=0.75,
bristcamp_c=2.4)
class StationStatistics(object):
"""
Class representing objects that include statistical information about
diurnal features of selected variables. A StationStatistics object is
generally associated to a Station object for which this infomration
is valid.
"""
def __init__(self, data=None, lon=None, lat=None, timezone=None):
self._data = None
self._lon = lon
self._lat = lat
self._timezone = timezone
if data is not None:
self.data = data
self.wind = Bunch(a=None, b=None, t_shift=None)
self.precip = Bunch(months=None, stats=None)
self.hum = Bunch(a0=None,
a1=None,
kr=None,
month_hour_precip_mean=None)
self.temp = Bunch(max_delta=None, mean_course=None)
angstroem_df = pd.DataFrame(index=np.arange(12) + 1,
columns=['a', 'b'])
angstroem_df.a = 0.25
angstroem_df.b = 0.75
bristcamp_df = pd.DataFrame(index=np.arange(12) + 1,
columns=['a', 'c'])
bristcamp_df.a = 0.75
bristcamp_df.c = 2.4
self.glob = Bunch(angstroem=angstroem_df,
bristcamp=bristcamp_df,
mean_course=None)
@property
def data(self):
return self._data
@data.setter
def data(self, df):
assert isinstance(df, pd.DataFrame)
assert df.index.is_all_dates
assert df.index.resolution == 'hour'
self._data = df.copy()
def calc_precipitation_stats(self,
months=None,
avg_stats=True,
percentile=50):
"""
Calculates precipitation statistics for the cascade model while aggregating hourly observations
Parameters
----------
months : Months for each seasons to be used for statistics (array of numpy array, default=1-12, e.g., [np.arange(12) + 1])
avg_stats : average statistics for all levels True/False (default=True)
percentile : percentil for splitting the dataset in small and high intensities (default=50)
"""
if months is None:
months = [np.arange(12) + 1]
self.precip.months = months
self.precip.stats = melodist.build_casc(self.data,
months=months,
avg_stats=avg_stats,
percentile=percentile)
def calc_wind_stats(self):
"""
Calculates statistics in order to derive diurnal patterns of wind speed
"""
a, b, t_shift = melodist.fit_cosine_function(self.data.wind)
self.wind.update(a=a, b=b, t_shift=t_shift)
def calc_humidity_stats(self):
"""
Calculates statistics in order to derive diurnal patterns of relative humidity.
"""
a1, a0 = melodist.calculate_dewpoint_regression(self.data,
return_stats=False)
self.hum.update(a0=a0, a1=a1)
self.hum.kr = 12
self.hum.month_hour_precip_mean = melodist.calculate_month_hour_precip_mean(
self.data)
def calc_temperature_stats(self):
"""
Calculates statistics in order to derive diurnal patterns of temperature
"""
self.temp.max_delta = melodist.get_shift_by_data(
self.data.temp, self._lon, self._lat, self._timezone)
self.temp.mean_course = melodist.util.calculate_mean_daily_course_by_month(
self.data.temp, normalize=True)
def calc_radiation_stats(self,
data_daily=None,
day_length=None,
how='all'):
"""
Calculates statistics in order to derive solar radiation from sunshine duration or
minimum/maximum temperature.
Parameters
----------
data_daily : DataFrame, optional
Daily data from the associated ``Station`` object.
day_length : Series, optional
Day lengths as calculated by ``calc_sun_times``.
"""
assert how in ('all', 'seasonal', 'monthly')
self.glob.mean_course = melodist.util.calculate_mean_daily_course_by_month(
self.data.glob)
if data_daily is not None:
pot_rad = melodist.potential_radiation(
melodist.util.hourly_index(data_daily.index), self._lon,
self._lat, self._timezone)
pot_rad_daily = pot_rad.resample('D').mean()
obs_rad_daily = self.data.glob.resample('D').mean()
if how == 'all':
month_ranges = [np.arange(12) + 1]
elif how == 'seasonal':
month_ranges = [[3, 4, 5], [6, 7, 8], [9, 10, 11], [12, 1, 2]]
elif how == 'monthly':
month_ranges = zip(np.arange(12) + 1)
def myisin(s, v):
return pd.Series(s).isin(v).values
def extract_months(s, months):
return s[myisin(s.index.month, months)]
if 'ssd' in data_daily and day_length is not None:
for months in month_ranges:
a, b = melodist.fit_angstroem_params(
extract_months(data_daily.ssd, months),
extract_months(day_length, months),
extract_months(pot_rad_daily, months),
extract_months(obs_rad_daily, months),
)
for month in months:
self.glob.angstroem.loc[month] = a, b
if 'tmin' in data_daily and 'tmax' in data_daily:
df = pd.DataFrame(data=dict(
tmin=data_daily.tmin,
tmax=data_daily.tmax,
pot_rad=pot_rad_daily,
obs_rad=obs_rad_daily,
)).dropna(how='any')
for months in month_ranges:
a, c = melodist.fit_bristow_campbell_params(
extract_months(df.tmin, months),
extract_months(df.tmax, months),
extract_months(df.pot_rad, months),
extract_months(df.obs_rad, months),
)
for month in months:
self.glob.bristcamp.loc[month] = a, c
def to_json(self, filename=None):
"""
Exports statistical data to a JSON formatted file
Parameters
----------
filename: output file that holds statistics data
"""
def json_encoder(obj):
if isinstance(obj, pd.DataFrame) or isinstance(obj, pd.Series):
if isinstance(obj.index, pd.core.index.MultiIndex):
obj = obj.reset_index() # convert MultiIndex to columns
return json.loads(obj.to_json(date_format='iso'))
elif isinstance(obj, melodist.cascade.CascadeStatistics):
return obj.__dict__
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
raise TypeError('%s not supported' % type(obj))
d = dict(temp=self.temp,
wind=self.wind,
precip=self.precip,
hum=self.hum,
glob=self.glob)
j = json.dumps(d, default=json_encoder, indent=4)
if filename is None:
return j
else:
with open(filename, 'w') as f:
f.write(j)
@classmethod
def from_json(cls, filename):
"""
Imports statistical data from a JSON formatted file
Parameters
----------
filename: input file that holds statistics data
"""
def json_decoder(d):
if 'p01' in d and 'pxx' in d: # we assume this is a CascadeStatistics object
return melodist.cascade.CascadeStatistics.from_dict(d)
return d
with open(filename) as f:
d = json.load(f, object_hook=json_decoder)
stats = cls()
stats.temp.update(d['temp'])
stats.hum.update(d['hum'])
stats.precip.update(d['precip'])
stats.wind.update(d['wind'])
stats.glob.update(d['glob'])
if stats.temp.max_delta is not None:
stats.temp.max_delta = pd.read_json(json.dumps(
stats.temp.max_delta),
typ='series').sort_index()
if stats.temp.mean_course is not None:
mc = pd.read_json(json.dumps(stats.temp.mean_course),
typ='frame').sort_index()[np.arange(1, 12 + 1)]
stats.temp.mean_course = mc.sort_index()[np.arange(1, 12 + 1)]
if stats.hum.month_hour_precip_mean is not None:
mhpm = pd.read_json(json.dumps(stats.hum.month_hour_precip_mean),
typ='frame').sort_index()
mhpm = mhpm.set_index(['level_0', 'level_1',
'level_2']) # convert to MultiIndex
mhpm = mhpm.squeeze() # convert to Series
mhpm = mhpm.rename_axis([None, None, None]) # remove index labels
stats.hum.month_hour_precip_mean = mhpm
for var in ('angstroem', 'bristcamp', 'mean_course'):
if stats.glob[var] is not None:
stats.glob[var] = pd.read_json(json.dumps(
stats.glob[var])).sort_index()
if stats.glob.mean_course is not None:
stats.glob.mean_course = stats.glob.mean_course[np.arange(
1, 12 + 1)]
return stats
class StationStatistics(object):
"""
Class representing objects that include statistical information about
diurnal features of selected variables. A StationStatistics object is
generally associated to a Station object for which this infomration
is valid.
"""
def __init__(self, data=None, lon=None, lat=None, timezone=None):
self._data = None
self._lon = lon
self._lat = lat
self._timezone = timezone
if data is not None:
self.data = data
self.wind = Bunch(a=None, b=None, t_shift=None)
self.precip = Bunch(months=None, stats=None)
self.hum = Bunch(a0=None, a1=None, kr=None)
self.temp = Bunch(max_delta=None)
self.glob = Bunch(angstroem_a=0.25,
angstroem_b=0.75,
bristcamp_a=0.75,
bristcamp_c=2.4)
@property
def data(self):
return self._data
@data.setter
def data(self, df):
assert isinstance(df, pd.DataFrame)
assert df.index.is_all_dates
assert df.index.resolution == 'hour'
self._data = df.copy()
def calc_precipitation_stats(self,
months=None,
avg_stats=True,
percentile=50):
"""
Calculates precipitation statistics for the cascade model while aggregating hourly observations
Parameters
----------
months : Months for each seasons to be used for statistics (array of numpy array, default=1-12, e.g., [np.arange(12) + 1])
avg_stats : average statistics for all levels True/False (default=True)
percentile : percentil for splitting the dataset in small and high intensities (default=50)
"""
if months is None:
months = [np.arange(12) + 1]
self.precip.months = months
self.precip.stats = melodist.build_casc(self.data,
months=months,
avg_stats=avg_stats,
percentile=percentile)
def calc_wind_stats(self):
"""
Calculates statistics in order to derive diurnal patterns of wind speed
"""
a, b, t_shift = melodist.fit_cosine_function(self.data.wind)
self.wind.update(a=a, b=b, t_shift=t_shift)
def calc_humidity_stats(self):
"""
Calculates statistics in order to derive diurnal patterns of relative humidity (requires temperature data as well)
"""
a1, a0 = melodist.calculate_dewpoint_regression(self.data,
return_stats=False)
self.hum.update(a0=a0, a1=a1)
self.hum.kr = 12
def calc_temperature_stats(self):
"""
Calculates statistics in order to derive diurnal patterns of temperature
"""
self.temp.max_delta = melodist.get_shift_by_data(
self.data.temp, self._lon, self._lat, self._timezone)
def calc_radiation_stats(self, data_daily, day_length=None):
"""
Calculates statistics in order to derive solar radiation from sunshine duration or
minimum/maximum temperature.
Parameters
----------
data_daily : DataFrame
Daily data from the associated ``Station`` object.
day_length : Series, optional
Day lengths as calculated by ``calc_sun_times``.
"""
if 'ssd' in data_daily and day_length is not None:
df = pd.DataFrame(
data=dict(ssd=data_daily.ssd, day_length=day_length)).dropna(
how='any')
pot_rad = melodist.potential_radiation(
melodist.util.hourly_index(df.index), self._lon, self._lat,
self._timezone)
pot_rad_daily = pot_rad.resample('D', how='mean')
obs_rad_daily = self.data.glob.resample('D', how='mean')
a, b = melodist.fit_angstroem_params(data_daily.ssd, day_length,
pot_rad_daily, obs_rad_daily)
self.glob.angstroem_a = a
self.glob.angstroem_b = b
if 'tmin' in data_daily and 'tmax' in data_daily:
pot_rad = melodist.potential_radiation(
melodist.util.hourly_index(df.index), self._lon, self._lat,
self._timezone)
pot_rad_daily = pot_rad.resample('D', how='mean')
obs_rad_daily = self.data.glob.resample('D', how='mean')
df = pd.DataFrame(data=dict(
tmin=data_daily.tmin,
tmax=data_daily.tmax,
pot_rad=pot_rad_daily,
obs_rad=obs_rad_daily,
)).dropna(how='any')
a, c = melodist.fit_bristow_campbell_params(
df.tmin, df.tmax, df.pot_rad, df.obs_rad)
self.glob.bristcamp_a = a
self.glob.bristcamp_c = c
def to_json(self, filename=None):
"""
Exports statistical data to a JSON formatted file
Parameters
----------
filename: output file that holds statistics data
"""
def json_encoder(obj):
if isinstance(obj, pd.DataFrame) or isinstance(obj, pd.Series):
return json.loads(obj.to_json(date_format='iso'))
elif isinstance(obj, melodist.cascade.CascadeStatistics):
return obj.__dict__
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
raise TypeError('%s not supported' % type(obj))
d = dict(
temp=self.temp,
wind=self.wind,
precip=self.precip,
hum=self.hum,
)
j = json.dumps(d, default=json_encoder, indent=4)
if filename is None:
return j
else:
with open(filename, 'w') as f:
f.write(j)
@classmethod
def from_json(cls, filename):
"""
Imports statistical data from a JSON formatted file
Parameters
----------
filename: input file that holds statistics data
"""
def json_decoder(d):
if 'p01' in d and 'pxx' in d: # we assume this is a CascadeStatistics object
return melodist.cascade.CascadeStatistics.from_dict(d)
return d
with open(filename) as f:
d = json.load(f, object_hook=json_decoder)
stats = cls()
stats.temp.update(d['temp'])
stats.hum.update(d['hum'])
stats.precip.update(d['precip'])
stats.wind.update(d['wind'])
if stats.temp.max_delta is not None:
stats.temp.max_delta = pd.read_json(json.dumps(
stats.temp.max_delta),
typ='series')
return stats
class StationStatistics(object):
"""
Class representing objects that include statistical information about
diurnal features of selected variables. A StationStatistics object is
generally associated to a Station object for which this infomration
is valid.
"""
def __init__(self, data=None, lon=None, lat=None, timezone=None):
self._data = None
self._lon = lon
self._lat = lat
self._timezone = timezone
if data is not None:
self.data = data
self.wind = Bunch(a=None, b=None, t_shift=None)
self.precip = Bunch(months=None, stats=None)
self.hum = Bunch(a0=None, a1=None, kr=None, month_hour_precip_mean=None)
self.temp = Bunch(max_delta=None, mean_course=None)
angstroem_df = pd.DataFrame(index=np.arange(12) + 1, columns=['a', 'b'])
angstroem_df.a = 0.25
angstroem_df.b = 0.75
bristcamp_df = pd.DataFrame(index=np.arange(12) + 1, columns=['a', 'c'])
bristcamp_df.a = 0.75
bristcamp_df.c = 2.4
self.glob = Bunch(angstroem=angstroem_df, bristcamp=bristcamp_df, mean_course=None)
@property
def data(self):
return self._data
@data.setter
def data(self, df):
assert isinstance(df, pd.DataFrame)
assert df.index.is_all_dates
assert df.index.resolution == 'hour'
self._data = df.copy()
def calc_precipitation_stats(self, months=None, avg_stats=True, percentile=50):
"""
Calculates precipitation statistics for the cascade model while aggregating hourly observations
Parameters
----------
months : Months for each seasons to be used for statistics (array of numpy array, default=1-12, e.g., [np.arange(12) + 1])
avg_stats : average statistics for all levels True/False (default=True)
percentile : percentil for splitting the dataset in small and high intensities (default=50)
"""
if months is None:
months = [np.arange(12) + 1]
self.precip.months = months
self.precip.stats = melodist.build_casc(self.data, months=months, avg_stats=avg_stats, percentile=percentile)
def calc_wind_stats(self):
"""
Calculates statistics in order to derive diurnal patterns of wind speed
"""
a, b, t_shift = melodist.fit_cosine_function(self.data.wind)
self.wind.update(a=a, b=b, t_shift=t_shift)
def calc_humidity_stats(self):
"""
Calculates statistics in order to derive diurnal patterns of relative humidity.
"""
a1, a0 = melodist.calculate_dewpoint_regression(self.data, return_stats=False)
self.hum.update(a0=a0, a1=a1)
self.hum.kr = 12
self.hum.month_hour_precip_mean = melodist.calculate_month_hour_precip_mean(self.data)
def calc_temperature_stats(self):
"""
Calculates statistics in order to derive diurnal patterns of temperature
"""
self.temp.max_delta = melodist.get_shift_by_data(self.data.temp, self._lon, self._lat, self._timezone)
self.temp.mean_course = melodist.util.calculate_mean_daily_course_by_month(self.data.temp, normalize=True)
def calc_radiation_stats(self, data_daily=None, day_length=None, how='all'):
"""
Calculates statistics in order to derive solar radiation from sunshine duration or
minimum/maximum temperature.
Parameters
----------
data_daily : DataFrame, optional
Daily data from the associated ``Station`` object.
day_length : Series, optional
Day lengths as calculated by ``calc_sun_times``.
"""
assert how in ('all', 'seasonal', 'monthly')
self.glob.mean_course = melodist.util.calculate_mean_daily_course_by_month(self.data.glob)
if data_daily is not None:
pot_rad = melodist.potential_radiation(
melodist.util.hourly_index(data_daily.index),
self._lon, self._lat, self._timezone)
pot_rad_daily = pot_rad.resample('D').mean()
obs_rad_daily = self.data.glob.resample('D').mean()
if how == 'all':
month_ranges = [np.arange(12) + 1]
elif how == 'seasonal':
month_ranges = [[3, 4, 5], [6, 7, 8], [9, 10, 11], [12, 1, 2]]
elif how == 'monthly':
month_ranges = zip(np.arange(12) + 1)
def myisin(s, v):
return pd.Series(s).isin(v).values
def extract_months(s, months):
return s[myisin(s.index.month, months)]
if 'ssd' in data_daily and day_length is not None:
for months in month_ranges:
a, b = melodist.fit_angstroem_params(
extract_months(data_daily.ssd, months),
extract_months(day_length, months),
extract_months(pot_rad_daily, months),
extract_months(obs_rad_daily, months),
)
for month in months:
self.glob.angstroem.loc[month] = a, b
if 'tmin' in data_daily and 'tmax' in data_daily:
df = pd.DataFrame(
data=dict(
tmin=data_daily.tmin,
tmax=data_daily.tmax,
pot_rad=pot_rad_daily,
obs_rad=obs_rad_daily,
)
).dropna(how='any')
for months in month_ranges:
a, c = melodist.fit_bristow_campbell_params(
extract_months(df.tmin, months),
extract_months(df.tmax, months),
extract_months(df.pot_rad, months),
extract_months(df.obs_rad, months),
)
for month in months:
self.glob.bristcamp.loc[month] = a, c
def to_json(self, filename=None):
"""
Exports statistical data to a JSON formatted file
Parameters
----------
filename: output file that holds statistics data
"""
def json_encoder(obj):
if isinstance(obj, pd.DataFrame) or isinstance(obj, pd.Series):
if isinstance(obj.index, pd.core.index.MultiIndex):
obj = obj.reset_index() # convert MultiIndex to columns
return json.loads(obj.to_json(date_format='iso'))
elif isinstance(obj, melodist.cascade.CascadeStatistics):
return obj.__dict__
elif isinstance(obj, np.ndarray):
return obj.tolist()
else:
raise TypeError('%s not supported' % type(obj))
d = dict(
temp=self.temp,
wind=self.wind,
precip=self.precip,
hum=self.hum,
glob=self.glob
)
j = json.dumps(d, default=json_encoder, indent=4)
if filename is None:
return j
else:
with open(filename, 'w') as f:
f.write(j)
@classmethod
def from_json(cls, filename):
"""
Imports statistical data from a JSON formatted file
Parameters
----------
filename: input file that holds statistics data
"""
def json_decoder(d):
if 'p01' in d and 'pxx' in d: # we assume this is a CascadeStatistics object
return melodist.cascade.CascadeStatistics.from_dict(d)
return d
with open(filename) as f:
d = json.load(f, object_hook=json_decoder)
stats = cls()
stats.temp.update(d['temp'])
stats.hum.update(d['hum'])
stats.precip.update(d['precip'])
stats.wind.update(d['wind'])
stats.glob.update(d['glob'])
if stats.temp.max_delta is not None:
stats.temp.max_delta = pd.read_json(json.dumps(stats.temp.max_delta), typ='series').sort_index()
if stats.temp.mean_course is not None:
mc = pd.read_json(json.dumps(stats.temp.mean_course), typ='frame').sort_index()[np.arange(1, 12 + 1)]
stats.temp.mean_course = mc.sort_index()[np.arange(1, 12 + 1)]
if stats.hum.month_hour_precip_mean is not None:
mhpm = pd.read_json(json.dumps(stats.hum.month_hour_precip_mean), typ='frame').sort_index()
mhpm = mhpm.set_index(['level_0', 'level_1', 'level_2']) # convert to MultiIndex
mhpm = mhpm.squeeze() # convert to Series
mhpm = mhpm.rename_axis([None, None, None]) # remove index labels
stats.hum.month_hour_precip_mean = mhpm
for var in ('angstroem', 'bristcamp', 'mean_course'):
if stats.glob[var] is not None:
stats.glob[var] = pd.read_json(json.dumps(stats.glob[var])).sort_index()
if stats.glob.mean_course is not None:
stats.glob.mean_course = stats.glob.mean_course[np.arange(1, 12 + 1)]
return stats