def stdtoswmm5(start_date=None, end_date=None, input_ts="-"):
"""Take the toolbox standard format and return SWMM5 format."""
import csv
sys.tracebacklimit = 1000
tsd = tsutils.read_iso_ts(input_ts)[start_date:end_date]
try:
# Header
print(";Datetime,", ", ".join(str(i) for i in tsd.columns))
# Data
cols = tsd.columns.tolist()
tsd["date_tmp_tstoolbox"] = tsd.index.format(
formatter=lambda x: x.strftime("%m/%d/%Y")
)
tsd["time_tmp_tstoolbox"] = tsd.index.format(
formatter=lambda x: x.strftime("%H:%M:%S")
)
tsd.to_csv(
sys.stdout,
float_format="%g",
header=False,
index=False,
cols=["date_tmp_tstoolbox", "time_tmp_tstoolbox"] + cols,
sep=" ",
quoting=csv.QUOTE_NONE,
)
except IOError:
return
def csvtowdm(wdmpath,
dsn,
start_date=None,
end_date=None,
columns=None,
force_freq=None,
groupby=None,
round_index=None,
clean=False,
target_units=None,
source_units=None,
input_ts='-'):
"""Write data from a CSV file to a DSN.
File can have comma separated
'year', 'month', 'day', 'hour', 'minute', 'second', 'value'
OR
'date/time string', 'value'
Parameters
----------
wdmpath
Path and WDM
filename.
dsn
The Data Set Number in the WDM
file.
{input_ts}
{start_date}
{end_date}
{columns}
{force_freq}
{groupby}
{round_index}
{clean}
{target_units}
{source_units}
"""
tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts),
start_date=start_date,
end_date=end_date,
pick=columns,
force_freq=force_freq,
groupby=groupby,
round_index=round_index,
clean=clean,
target_units=target_units,
source_units=source_units)
if len(tsd.columns) > 1:
raise ValueError("""
*
* The input data set must contain only 1 time series.
* You gave {0}.
*
""".format(len(tsd.columns)))
_writetodsn(wdmpath, dsn, tsd)
def daily_to_daytime_hourly_trapezoid(
latitude,
statistic='mean',
input_ts='-',
start_date=None,
end_date=None,
float_format='%g',
print_input=''):
'''
Daily to hourly disaggregation based on a trapezoidal shape.
'''
from tstoolbox import tstoolbox
tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts),
start_date=start_date,
end_date=end_date,
pick=None)
lrad = latitude*np.pi/180.0
ad = 0.40928*np.cos(0.0172141*(172 - tsd.index.dayofyear))
ss = np.sin(lrad)*np.sin(ad)
cs = np.cos(lrad)*np.cos(ad)
x2 = -ss/cs
delt = 7.6394*(np.pi/2.0 - np.arctan(x2/np.square(1 - x2**2)))
sunr = 12.0 - delt/2.0
#develop hourly distribution given sunrise,
#sunset and length of day (DELT)
dtr2 = delt / 2.0
dtr4 = delt / 4.0
crad = 2.0/3.0/dtr2/60 # using minutes...
tr2 = sunr + dtr4
tr3 = tr2 + dtr2
tr4 = tr3 + dtr4
sdate = datetime.datetime(tsd.index[0].year, tsd.index[0].month,
tsd.index[0].day)
edate = datetime.datetime(tsd.index[-1].year, tsd.index[-1].month,
tsd.index[-1].day) + datetime.timedelta(days=1) - datetime.timedelta(hours=1)
datevalue = pandas.DatetimeIndex(start=sdate, end=edate,
freq='MIN')
fdata = pandas.Series([np.nan]*(len(datevalue)), index=datevalue)
fdata[0] = 0.0
fdata[-1] = 0.0
for index in range(len(sunr)):
cdate = tsd.index[index]
fdata[datetime.datetime(cdate.year, cdate.month, cdate.day, int(sunr[index]), int((sunr[index] - int(sunr[index]))*60))] = 0.0
fdata[datetime.datetime(cdate.year, cdate.month, cdate.day, int(tr4[index]), int((tr4[index] - int(tr4[index]))*60))] = 0.0
fdata[datetime.datetime(cdate.year, cdate.month, cdate.day, int(tr2[index]), int((tr2[index] - int(tr2[index]))*60))] = crad[index]
fdata[datetime.datetime(cdate.year, cdate.month, cdate.day, int(tr3[index]), int((tr3[index] - int(tr3[index]))*60))] = crad[index]
fdata = fdata.interpolate('linear')
fdata = fdata.fillna(0.0)
fdata = tstoolbox.aggregate(statistic=statistic,
agg_interval='H',
input_ts=fdata
)
return tsutils.print_input(print_input, tsd, fdata, None,
float_format=float_format)
def csvtowdm(wdmpath,
dsn,
input=None,
start_date=None,
end_date=None,
columns=None,
input_ts='-'):
"""Write data from a CSV file to a DSN.
File can have comma separated
'year', 'month', 'day', 'hour', 'minute', 'second', 'value'
OR
'date/time string', 'value'
Parameters
----------
wdmpath
Path and WDM
filename.
dsn
The Data Set Number in the WDM
file.
input
DEPRECATED - use input_ts instead.
{input_ts}
{start_date}
{end_date}
{columns}
"""
if input is not None:
raise ValueError("""
*
* The '--input' option has been deprecated. Please use '--input_ts'
* instead.
*
""")
tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts),
start_date=start_date,
end_date=end_date,
pick=columns)
if len(tsd.columns) > 1:
raise ValueError("""
*
* The input data set must contain only 1 time series.
* You gave {0}.
*
""".format(len(tsd.columns)))
_writetodsn(wdmpath, dsn, tsd)
def csvtowdm(wdmpath,
dsn,
input=None,
start_date=None,
end_date=None,
columns=None,
input_ts='-'):
"""Write data from a CSV file to a DSN.
File can have comma separated
'year', 'month', 'day', 'hour', 'minute', 'second', 'value'
OR
'date/time string', 'value'
:param wdmpath: Path and WDM filename.
:param dsn: The Data Set Number in the WDM file.
:param input: DEPRECATED - use input_ts instead.
:param input_ts: Filename with data in 'ISOdate,value' format or '-' for
stdin.
:param start_date: The start_date of the series in ISOdatetime format, or
'None' for beginning.
:param end_date: The end_date of the series in ISOdatetime format, or
'None' for end.
:param columns: Columns to pick out of input. Can use column names or
column numbers. If using numbers, column number 1 is the first column.
To pick multiple columns; separate by commas with no spaces. As used in
'pick' command.
"""
if input is not None:
raise ValueError("""
*
* The '--input' option has been deprecated. Please use '--input_ts'
* instead.
*
""")
tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts),
start_date=start_date,
end_date=end_date,
pick=columns)
if len(tsd.columns) > 1:
raise ValueError("""
*
* The input data set must contain only 1 time series.
* You gave {0}.
*
""".format(len(tsd.columns)))
_writetodsn(wdmpath, dsn, tsd)
def stdtoswmm5(start_date=None, end_date=None, input_ts='-'):
"""Take the toolbox standard format and return SWMM5 format.
Toolbox standard::
Datetime, Column_Name
2000-01-01 00:00:00 , 45.6
2000-01-01 01:00:00 , 45.2
...
SWMM5 format::
; comment line
01/01/2000 00:00, 45.6
01/01/2000 01:00, 45.2
...
Parameters
----------
{input_ts}
{start_date}
{end_date}
"""
import csv
sys.tracebacklimit = 1000
tsd = tsutils.read_iso_ts(input_ts)[start_date:end_date]
try:
# Header
print(';Datetime,', ', '.join(str(i) for i in tsd.columns))
# Data
cols = tsd.columns.tolist()
tsd['date_tmp_tstoolbox'] = tsd.index.format(
formatter=lambda x: x.strftime('%m/%d/%Y'))
tsd['time_tmp_tstoolbox'] = tsd.index.format(
formatter=lambda x: x.strftime('%H:%M:%S'))
tsd.to_csv(sys.stdout,
float_format='%g',
header=False,
index=False,
cols=['date_tmp_tstoolbox', 'time_tmp_tstoolbox'] + cols,
sep=' ',
quoting=csv.QUOTE_NONE)
except IOError:
return
def stdtoswmm5(start_date=None, end_date=None, input_ts='-'):
''' Take the toolbox standard format and return SWMM5 format.
Toolbox standard:
Datetime, Column_Name
2000-01-01 00:00:00 , 45.6
2000-01-01 01:00:00 , 45.2
...
SWMM5 format:
; comment line
01/01/2000 00:00, 45.6
01/01/2000 01:00, 45.2
...
:param input_ts: Filename with data in 'ISOdate,value' format or '-' for
stdin. Default is stdin.
:param start_date: The start_date of the series in ISOdatetime format, or
'None' for beginning.
:param end_date: The end_date of the series in ISOdatetime format, or
'None' for end.
'''
import csv
sys.tracebacklimit = 1000
tsd = tsutils.read_iso_ts(input_ts)[start_date:end_date]
try:
# Header
print(';Datetime,', ', '.join(str(i) for i in tsd.columns))
# Data
cols = tsd.columns.tolist()
tsd['date_tmp_tstoolbox'] = tsd.index.format(
formatter=lambda x: x.strftime('%m/%d/%Y'))
tsd['time_tmp_tstoolbox'] = tsd.index.format(
formatter=lambda x: x.strftime('%H:%M:%S'))
tsd.to_csv(sys.stdout,
float_format='%g',
header=False,
index=False,
cols=['date_tmp_tstoolbox', 'time_tmp_tstoolbox'] + cols,
sep=' ',
quoting=csv.QUOTE_NONE)
except IOError:
return
def csvtowdm(wdmpath, dsn, input=None, start_date=None,
end_date=None, columns=None, input_ts='-'):
''' Writes data from a CSV file to a DSN.
File can have comma separated
'year', 'month', 'day', 'hour', 'minute', 'second', 'value'
OR
'date/time string', 'value'
:param wdmpath: Path and WDM filename.
:param dsn: The Data Set Number in the WDM file.
:param input: DEPRECATED - use input_ts instead.
:param input_ts: Filename with data in 'ISOdate,value' format or '-' for
stdin.
:param start_date: The start_date of the series in ISOdatetime format, or
'None' for beginning.
:param end_date: The end_date of the series in ISOdatetime format, or
'None' for end.
:param columns: Columns to pick out of input. Can use column names or
column numbers. If using numbers, column number 1 is the first column.
To pick multiple columns; separate by commas with no spaces. As used in
'pick' command.
'''
if input is not None:
raise ValueError('''
*
* The '--input' option has been deprecated. Please use '--input_ts'
* instead.
*
''')
tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts),
start_date=start_date,
end_date=end_date,
pick=columns)
if len(tsd.columns) > 1:
raise ValueError('''
*
* The input data set must contain only 1 time series.
* You gave {0}.
*
'''.format(len(tsd.columns)))
_writetodsn(wdmpath, dsn, tsd)
def stdtoswmm5(start_date=None, end_date=None, input_ts='-'):
"""Take the toolbox standard format and return SWMM5 format.
Toolbox standard::
Datetime, Column_Name
2000-01-01 00:00:00 , 45.6
2000-01-01 01:00:00 , 45.2
...
SWMM5 format::
; comment line
01/01/2000 00:00, 45.6
01/01/2000 01:00, 45.2
...
Parameters
----------
{input_ts}
{start_date}
{end_date}
"""
import csv
sys.tracebacklimit = 1000
tsd = tsutils.read_iso_ts(input_ts)[start_date:end_date]
try:
# Header
print(';Datetime,', ', '.join(str(i) for i in tsd.columns))
# Data
cols = tsd.columns.tolist()
tsd['date_tmp_tstoolbox'] = tsd.index.format(formatter=lambda x:
x.strftime('%m/%d/%Y'))
tsd['time_tmp_tstoolbox'] = tsd.index.format(formatter=lambda x:
x.strftime('%H:%M:%S'))
tsd.to_csv(sys.stdout, float_format='%g', header=False, index=False,
cols=['date_tmp_tstoolbox', 'time_tmp_tstoolbox'] + cols,
sep=' ', quoting=csv.QUOTE_NONE)
except IOError:
return
def et0_pm(
input_ts="-",
columns=None,
start_date=None,
end_date=None,
dropna="no",
clean=False,
round_index=None,
skiprows=None,
index_type="datetime",
names=None,
source_units=None,
target_units=None,
print_input=False,
tablefmt="csv",
avp=None,
avp_from_tdew=None,
avp_from_twet_tdry=None,
avp_from_rhmin_rh_max=None,
avp_from_rhmax=None,
avp_from_rhmean=None,
avp_from_tmin=None,
lat=None,
):
"""Penman-Monteith evaporation."""
tsd = tsutils.common_kwds(
tsutils.read_iso_ts(
input_ts, skiprows=skiprows, names=names, index_type=index_type
),
start_date=start_date,
end_date=end_date,
pick=columns,
round_index=round_index,
dropna=dropna,
source_units=source_units,
target_units=target_units,
clean=clean,
)
return tsd
def stdtoswmm5(start_date=None, end_date=None, input_ts='-'):
""" Take the toolbox standard format and return SWMM5 format.
Toolbox standard:
Datetime, Column_Name
2000-01-01 00:00:00 , 45.6
2000-01-01 01:00:00 , 45.2
...
SWMM5 format:
; comment line
01/01/2000 00:00, 45.6
01/01/2000 01:00, 45.2
...
:param input_ts: Filename with data in 'ISOdate,value' format or '-' for
stdin. Default is stdin.
:param start_date: The start_date of the series in ISOdatetime format, or
'None' for beginning.
:param end_date: The end_date of the series in ISOdatetime format, or
'None' for end.
"""
import csv
sys.tracebacklimit = 1000
tsd = tsutils.read_iso_ts(input_ts)[start_date:end_date]
try:
# Header
print(';Datetime,', ', '.join(str(i) for i in tsd.columns))
# Data
cols = tsd.columns.tolist()
tsd['date_tmp_tstoolbox'] = tsd.index.format(formatter=lambda x:
x.strftime('%m/%d/%Y'))
tsd['time_tmp_tstoolbox'] = tsd.index.format(formatter=lambda x:
x.strftime('%H:%M:%S'))
tsd.to_csv(sys.stdout, float_format='%g', header=False, index=False,
cols=['date_tmp_tstoolbox', 'time_tmp_tstoolbox'] + cols,
sep=' ', quoting=csv.QUOTE_NONE)
except IOError:
return
def csvtowdm(
wdmpath,
dsn,
start_date=None,
end_date=None,
columns=None,
force_freq=None,
groupby=None,
round_index=None,
clean=False,
target_units=None,
source_units=None,
input_ts="-",
):
"""Write data from a CSV file to a DSN.
File can have comma separated
'year', 'month', 'day', 'hour', 'minute', 'second', 'value'
OR
'date/time string', 'value'
Parameters
----------
wdmpath
Path and WDM
filename.
dsn
The Data Set Number in the WDM
file.
{input_ts}
{start_date}
{end_date}
{columns}
{force_freq}
{groupby}
{round_index}
{clean}
{target_units}
{source_units}
"""
tsd = tsutils.common_kwds(
tsutils.read_iso_ts(input_ts),
start_date=start_date,
end_date=end_date,
pick=columns,
force_freq=force_freq,
groupby=groupby,
round_index=round_index,
clean=clean,
target_units=target_units,
source_units=source_units,
)
if len(tsd.columns) > 1:
raise ValueError(
tsutils.error_wrapper("""
The input data set must contain only 1 time series.
You gave {0}.
""".format(len(tsd.columns))))
_writetodsn(wdmpath, dsn, tsd)
def wind_speed(
method: Literal["equal", "cosine", "random"],
source_units,
input_ts="-",
columns=None,
start_date=None,
end_date=None,
dropna="no",
clean=False,
round_index=None,
skiprows=None,
index_type="datetime",
names=None,
target_units=None,
print_input=False,
a=None,
b=None,
t_shift=None,
):
"""Disaggregate daily to hourly data.
disaggregate_wind(wind_daily,
method='equal',
a=None,
b=None,
t_shift=None):
Args:
wind_daily: daily values
method: keyword specifying the disaggregation method to be used
a: parameter a for the cosine function
b: parameter b for the cosine function
t_shift: parameter t_shift for the cosine function
"""
target_units = single_target_units(source_units, target_units, "m/s")
target_units = target_units[0] * len(source_units)
pd.options.display.width = 60
if method == "cosine" and (a is None or b is None or t_shift is None):
raise ValueError(
tsutils.error_wrapper("""
For the "cosine" method, requires the `a`, `b`, and `t_shift`
keywords. You gave:
a = {a}
b = {b}
t_shift = {t_shift}
""".format(**locals())))
tsd = tsutils.common_kwds(
tsutils.read_iso_ts(input_ts,
skiprows=skiprows,
names=names,
index_type=index_type),
start_date=start_date,
end_date=end_date,
pick=columns,
round_index=round_index,
dropna=dropna,
source_units=source_units,
target_units=target_units,
clean=clean,
)
return tsutils.return_input(
print_input,
tsd,
pd.DataFrame(
disaggregate_wind(tsd, method=method, a=a, b=b, t_shift=t_shift)),
)
def radiation(
method: Literal["pot_rad", "pot_rad_via_ssd", "pot_rad_via_bc",
"mean_course"],
source_units,
input_ts="-",
columns=None,
start_date=None,
end_date=None,
dropna="no",
clean=False,
round_index=None,
skiprows=None,
index_type="datetime",
names=None,
target_units=None,
print_input=False,
pot_rad=None,
angstr_a=0.25,
angstr_b=0.5,
bristcamp_a=0.75,
bristcamp_c=2.4,
hourly_rad=None,
lat=None,
lon=None,
glob_swr_col=None,
):
"""Disaggregate daily to hourly data.
disaggregate_radiation(data_daily,
sun_times=None,
pot_rad=None,
method='pot_rad',
angstr_a=0.25,
angstr_b=0.5,
bristcamp_a=0.75,
bristcamp_c=2.4,
mean_course=None):
Args:
daily_data: daily values
sun_times: daily dataframe including results of the util.sun_times function
pot_rad: hourly dataframe including potential radiation
method: keyword specifying the disaggregation method to be used
angstr_a: parameter a of the Angstrom model (intercept)
angstr_b: parameter b of the Angstrom model (slope)
bristcamp_a: ?
bristcamp_c: ?
mean_course: monthly values of the mean hourly radiation course
if method == 'mean_course':
HOURLY radiation in "mean_course"
if method in ('pot_rad', 'mean_course')
.glob from glob_swr_col
if method == 'pot_rad_via_ssd'
daily sun_times
if method == 'pot_rad_via_bc'
bristcamp_a
bristcamp_c
"""
target_units = single_target_units(source_units, target_units, "W/m2")
target_units = target_units[0] * len(source_units)
pd.options.display.width = 60
if method == "mean_course" and hourly_rad is None:
raise ValueError(
tsutils.error_wrapper("""
If method is "mean_course" need to supply CSV filename of hourly
radiation by the `hourly_rad` keyword."""))
if method in ["pot_rad", "mean_course"] and glob_swr_col is None:
raise ValueError(
tsutils.error_wrapper("""
If method is "pot_rad" or "mean_course" need to supply the daily global
short wave radiation as column name or index with keyword
`glob_swr_col`"""))
if method == "pot_rad_via_bc" and (bristcamp_a is None
or bristcamp_c is None):
raise ValueError(
tsutils.error_wrapper("""
If method is "pot_rad_via_bc" need to supply the keywords `bristcamp_a`
and `bristcamp_c`."""))
tsd = tsutils.common_kwds(
tsutils.read_iso_ts(input_ts,
skiprows=skiprows,
names=names,
index_type=index_type),
start_date=start_date,
end_date=end_date,
pick=columns,
round_index=round_index,
dropna=dropna,
source_units=source_units,
target_units=target_units,
clean=clean,
)
if method in ["pot_rad", "mean_course"]:
try:
glob_swr_col = glob_swr_col - 1
except ValueError:
pass
tsd["glob"] = tsd[glob_swr_col]
sun_times = None
if method == "pot_rad_via_ssd":
sun_times = get_sun_times(tsd.index, float(lon), float(lat),
round(lon / 15.0))
return tsutils.return_input(
print_input,
tsd,
pd.DataFrame(
disaggregate_radiation(
tsd,
sun_times=sun_times,
pot_rad=pot_rad,
method=method,
angstr_a=angstr_a,
angstr_b=angstr_b,
bristcamp_a=bristcamp_a,
bristcamp_c=bristcamp_c,
mean_course=hourly_rad,
)),
)
def temperature(
method: Literal["sine_min_max", "sine_mean", "sine", "mean_course_min_max",
"mean_course_mean"],
source_units,
min_max_time: Literal["fix", "sun_loc", "sun_loc_shift"] = "fix",
mod_nighttime: bool = False,
input_ts="-",
start_date=None,
end_date=None,
dropna="no",
clean=False,
round_index=None,
skiprows=None,
index_type="datetime",
names=None,
print_input=False,
target_units=None,
max_delta: bool = False,
temp_min_col: Optional[Union[tsutils.IntGreaterEqualToOne, str]] = None,
temp_max_col: Optional[Union[tsutils.IntGreaterEqualToOne, str]] = None,
temp_mean_col: Optional[Union[tsutils.IntGreaterEqualToOne, str]] = None,
lat: Optional[FloatLatitude] = None,
lon: Optional[FloatLongitude] = None,
hourly: Optional[str] = None,
):
"""Disaggregate daily to hourly data.
disaggregate_temperature(data_daily,
method='sine_min_max',
min_max_time='fix',
mod_nighttime=False,
max_delta=None,
mean_course=None,
sun_times=None):
data_daily : daily data
method : method to disaggregate
min_max_time: "fix" - min/max temperature at fixed times 7h/14h,
"sun_loc" - min/max calculated by sunrise/sunnoon + 2h,
"sun_loc_shift" - min/max calculated by sunrise/sunnoon +
monthly mean shift,
mod_nighttime: ?
max_delta: maximum monthly temperature shift as returned by
get_shift_by_data()
mean_course: ?
sun_times: times of sunrise/noon as returned by get_sun_times()
if method in ('sine_min_max', 'sine_mean', 'sine')
.tmin from temp_min_col
.tmax from temp_max_col
.temp from temp_mean_col
if method in ('mean_course_min', 'mean_course_mean')
.tmin from temp_min_col
.tmax from temp_max_col
require hourly temperature perhaps from nearby station in 'mean_course'
if method == 'mean_course_mean'
.temp from temp_mean_col
sun_times = get_sun_times(dates, lon, lat, round(lon/15.0))
max_delta = get_shift_by_data(temp_hourly, lon, lat, round(lon/15.0))
"""
target_units = single_target_units(source_units, target_units, "degC")
pd.options.display.width = 60
if (method in ["mean_course_min", "mean_course_mean"]
or max_delta is True) and hourly is None:
raise ValueError(
tsutils.error_wrapper("""
The methods "mean_course_min", "mean_course_mean", or if `max_delta` is
True, require a HOURLY temperature values in the CSV file specified by the
keyword `hourly`."""))
if method in ["mean_course_min", "mean_course_mean"] or max_delta is True:
hourly = tstoolbox.read(hourly)
if max_delta is True:
max_delta = get_shift_by_data(hourly, lon, lat, round(lon / 15.0))
else:
max_delta = None
if temp_min_col is None or temp_max_col is None:
raise ValueError(
tsutils.error_wrapper("""
For "temperature" disaggregation you need to supply the daily minimum
column (name or number, data column numbering starts at 1) and the daily
maximum column (name or number).
Instead `temp_min_col` is {temp_min_col} and `temp_max_col` is
{temp_max_col}""".format(**locals())))
columns = []
try:
temp_min_col = int(temp_min_col)
except TypeError:
pass
columns.append(temp_min_col)
try:
temp_max_col = int(temp_max_col)
except TypeError:
pass
columns.append(temp_max_col)
if temp_mean_col is not None:
try:
temp_mean_col = int(temp_mean_col)
except TypeError:
pass
columns.append(temp_mean_col)
tsd = tsutils.common_kwds(
tsutils.read_iso_ts(input_ts,
skiprows=skiprows,
names=names,
index_type=index_type),
start_date=start_date,
end_date=end_date,
pick=columns,
round_index=round_index,
dropna=dropna,
source_units=source_units,
target_units=target_units,
clean=clean,
)
if len(tsd.columns) == 3:
tsd.columns = ["tmin", "tmax", "temp"]
else:
tsd.columns = ["tmin", "tmax"]
if any((tsd.tmax <= tsd.tmin).dropna()):
raise ValueError(
tsutils.error_wrapper("""
On the following dates:
{0},
minimum temperature values in column "{1}" are greater than or equal to
the maximum temperature values in column "{2}".""".format(
tsd[tsd.tmax <= tsd.tmin].index, temp_min_col, temp_max_col)))
if temp_mean_col is None:
warnings.warn(
tsutils.error_wrapper("""
Since `temp_mean_col` is None, the average daily temperature will be
estimated by the average of `temp_min_col` and `temp_max_col`""".format(
**locals())))
tsd["temp"] = (tsd.tmin + tsd.tmax) / 2.0
if any((tsd.tmin >= tsd.temp).dropna()) or any(
(tsd.tmax <= tsd.temp).dropna()):
raise ValueError(
tsutils.error_wrapper("""
On the following dates:
{0},
the daily average is either below or equal to the minimum temperature in column {1}
or higher or equal to the maximum temperature in column {2}.""".format(
tsd[tsd.tmin >= tsd.temp | tsd.tmax <= tsd.temp],
temp_min_col,
temp_max_col,
)))
if min_max_time == "fix":
# Not dependent on sun, just average values.
sun_times = pd.DataFrame(
index=[1], columns=["sunrise", "sunnoon", "sunset", "daylength"])
sun_times.sunrise = 7
sun_times.sunnoon = 12
sun_times.sunset = 19
sun_times.daylength = 12
else:
if lat is None or lon is None:
raise ValueError(
tsutils.error_wrapper("""
The `min_max_time` options other than "fix" require calculation of
sunrise, sun noon, sunset, and day length. The calculation requires the
latitude with keyword "lat" and longitude with keyword "lon".
You gave:
lat={lat}
lon={lon}
""".format(**locals())))
sun_times = get_sun_times(tsd.index, float(lon), float(lat),
round(lon / 15.0))
ntsd = pd.DataFrame(
disaggregate_temperature(
tsd,
method=method,
min_max_time=min_max_time,
mod_nighttime=mod_nighttime,
max_delta=max_delta,
mean_course=hourly,
sun_times=sun_times,
))
ntsd.columns = ["temperature:{0}:disagg".format(target_units[0])]
return tsutils.return_input(print_input, tsd, ntsd)
def precipitation(
method: Literal["equal", "cascade", "masterstation"],
source_units,
input_ts="-",
start_date=None,
end_date=None,
dropna="no",
clean=False,
round_index=None,
skiprows=None,
index_type="datetime",
names=None,
target_units=None,
print_input=False,
columns=None,
masterstation_hour_col: Optional[Union[tsutils.IntGreaterEqualToOne,
str]] = None,
):
"""Disaggregate daily to hourly data."""
target_units = single_target_units(source_units, target_units, "mm")
pd.options.display.width = 60
tsd = tsutils.common_kwds(
tsutils.read_iso_ts(input_ts,
skiprows=skiprows,
names=names,
index_type=index_type),
start_date=start_date,
end_date=end_date,
pick=columns,
round_index=round_index,
dropna=dropna,
source_units=source_units,
target_units=target_units,
clean=clean,
)
if method == "masterstation":
try:
# If masterstations_hour_col is a column name:
masterstation_hour_col = tsd.columns.get_loc(
masterstation_hour_col)
except KeyError:
# If masterstations_hour_col is a column number:
masterstation_hour_col = int(masterstation_hour_col) - 1
masterstation_hour_col = tsd.columns[masterstation_hour_col]
# Should only be one hourly column in the input.
mhour = tsd[masterstation_hour_col].to_frame()
dsum = mhour.groupby(pd.Grouper(freq="D")).sum().asfreq("H",
method="ffill")
master = mhour.join(dsum, rsuffix="sum")
mask = master.iloc[:, 0] > 0.0
master = (master.loc[mask, master.columns[0]] /
master.loc[mask, master.columns[1]]).to_frame()
print(master)
ntsd = tsd.loc[:, tsd.columns != masterstation_hour_col].asfreq(
"H", method="ffill")
print(ntsd)
ntsd = ntsd.join(master)
print(ntsd)
ntsd = ntsd.loc[:, tsd.columns != masterstation_hour_col].multiply(
ntsd.iloc[:, -1:], axis="index")
print(ntsd)
sys.exit()
# All the remaining columns are daily.
ntsd = (tsd.loc[:, tsd.columns != masterstation_hour_col].asfreq(
"H", method="ffill").mul(master, axis="rows"))
return tsutils.return_input(print_input, tsd, ntsd)
def evaporation(
method: Literal["trap", "fixed"],
source_units,
input_ts="-",
columns=None,
start_date=None,
end_date=None,
dropna="no",
clean=False,
round_index=None,
skiprows=None,
index_type="datetime",
names=None,
target_units=None,
print_input=False,
lat: Optional[FloatLatitude] = None,
):
"""Disaggregate daily to hourly data."""
target_units = single_target_units(source_units, target_units)
pd.options.display.width = 60
if method == "trap" and lat is None:
raise ValueError(
tsutils.error_wrapper("""
The "trap" method requires latitude with the `lat` keyword. You gave
"{lat}". """.format(**locals())))
tsd = tsutils.common_kwds(
tsutils.read_iso_ts(input_ts,
skiprows=skiprows,
names=names,
index_type=index_type),
start_date=start_date,
end_date=end_date,
pick=columns,
round_index=round_index,
dropna=dropna,
source_units=source_units,
target_units=target_units,
clean=clean,
)
ntsd = tsd.append(
pd.DataFrame(columns=tsd.columns,
index=[tsd.index[-1] + datetime.timedelta(days=1)]))
ndata = ntsd.resample("H").ffill()
fdata = pd.DataFrame(columns=ndata.columns, index=ndata.index, dtype="f")
if method == "trap":
lrad = lat * np.pi / 180.0
ad = 0.40928 * np.cos(0.0172141 * (172 - tsd.index.dayofyear))
ss = np.sin(lrad) * np.sin(ad)
cs = np.cos(lrad) * np.cos(ad)
x2 = -ss / cs
delt = 7.6394 * (np.pi / 2.0 - np.arctan(x2 / np.square(1 - x2**2)))
sunr = 12.0 - delt / 2.0
# develop hourly distribution given sunrise,
# sunset and length of day (DELT)
dtr2 = delt / 2.0
dtr4 = delt / 4.0
tr2 = sunr + dtr4
tr3 = tr2 + dtr2
tr4 = tr3 + dtr4
for index, toss in enumerate(sunr):
cdate = ntsd.index[index]
fdata.ix[datetime.datetime(cdate.year, cdate.month, cdate.
day, int(sunr[index])), :, ] = 0.0
fdata.ix[datetime.datetime(cdate.year, cdate.month, cdate.day,
int(tr4[index]) + 1), :, ] = 0.0
fdata.ix[datetime.datetime(cdate.year, cdate.month, cdate.
day, int(round(tr2[index]))), :, ] = 1.0
fdata.ix[datetime.datetime(cdate.year, cdate.month, cdate.
day, int(round(tr3[index]))), :, ] = 1.0
fdata.ix[0, :] = 0.0
fdata.ix[-1, :] = 0.0
fdata = fdata.interpolate("linear")
fdata = fdata.fillna(0.0)
fdata = fdata / fdata.groupby(
pd.Grouper(freq="D")).sum().resample("H").ffill()
fdata = fdata * ndata
fdata = fdata.ix[:-1, :]
elif method == "fixed":
# DATA EVAPDIST / 0.000,0.000,0.000,0.000,0.000,0.000,0.019,0.041,
# $ 0.067,0.088,0.102,0.110,0.110,0.110,0.105,0.095,
# $ 0.081,0.055,0.017,0.000,0.000,0.000,0.000,0.000
fdata = fdata.fillna(0.0)
fdata[fdata.index.hour == 7] = 0.019
fdata[fdata.index.hour == 8] = 0.041
fdata[fdata.index.hour == 9] = 0.067
fdata[fdata.index.hour == 10] = 0.088
fdata[fdata.index.hour == 11] = 0.102
fdata[fdata.index.hour == 12] = 0.110
fdata[fdata.index.hour == 13] = 0.110
fdata[fdata.index.hour == 14] = 0.110
fdata[fdata.index.hour == 15] = 0.105
fdata[fdata.index.hour == 16] = 0.095
fdata[fdata.index.hour == 17] = 0.081
fdata[fdata.index.hour == 18] = 0.055
fdata[fdata.index.hour == 19] = 0.017
fdata = fdata * ndata
fdata = fdata.ix[:-1, :]
return tsutils.print_input(print_input, tsd, fdata, None)
def PET_hargreaves(latitude,
tmax=None,
tmin=None,
input_ts='-',
start_date=None,
end_date=None,
float_format='%g',
print_input=''):
'''
Calculate potential evaporation as expected from a shallow body of water
using the Hargreaves equation which needs the daily maximum and minimum
temperatures. Input is degrees C. and returns mm/day.
:param -i, --input_ts <str>: Filename with data in 'ISOdate,value' format or '-'
for stdin.
:param -s, --start_date <str>: The start_date of the series in ISOdatetime format,
or 'None' for beginning.
:param -e, --end_date <str>: The end_date of the series in ISOdatetime format, or
'None' for end.
:param latitude <float>: Latitude of the station.
:param tmax: Specify comma separated column names or column numbers that
represent the daily maximum temperature in degrees C. The
default is None, and if None there has to be an even number of
columns and tmax is the first half of the columns, and tmin is
the last half of the columns.
:param tmin: Specify comma separated column names or column numbers that
represent the daily minimum temperature in degrees C. The
default is None, and if None there has to be an even number of
columns and tmax is the first half of the columns, and tmin is
the last half of the columns.
'''
tsd = tsutils.common_kwds(tsutils.read_iso_ts(input_ts),
start_date=start_date,
end_date=end_date,
pick=None)
if tmax is None and tmin is None:
tmaxcol = tsd.columns[:len(tsd.columns)/2]
tmincol = tsd.columns[len(tsd.columns)/2:]
if len(tsd.columns) % 2 == 1:
raise ValueError('''
*
* Without explicitly defining tmax and tmin columns in the input dataset, the
* first half of the columns are tmax and the second half are tmin, which
* means that you need an even number of columns. Instead there are {0}.
*
'''.format(len(tsd.columns)))
elif tmax is None or tmin is None:
raise ValueError('''
*
* Either tmax and tmin must be None OR tmax and tmin must have the same
* number of comma separated columns names/numbers.
*
''')
else:
tmaxcol = tmax.split(',')
tmincol = tmin.split(',')
tmaxdf = tsd[tmaxcol]
tmindf = tsd[tmincol]
if pd.np.any(tmindf.values > tmaxdf.values):
raise ValueError('''
*
* There is at least one day where the minimum temperature is greater than the
* maximum temperature.
*
''')
# 'Roll-out' the distribution from day to day.
jday = pd.np.arange(1, 367)
# calculate right ascension
dec = 0.4102*pd.np.sin((2.0*pd.np.pi*(jday - 80.0))/365.0)
lrad = latitude*pd.np.pi/180.0
s = pd.np.arccos(-pd.np.tan(dec)*pd.np.tan(lrad))
ra = 118.0/pd.np.pi*(s*pd.np.sin(lrad)*pd.np.sin(dec) +
pd.np.cos(lrad)*pd.np.cos(dec)*pd.np.sin(s))
# ra just covers 1 year - need to map onto all years...
newra = tmindf.copy()
for day in jday:
newra[newra.index.dayofyear == day] = ra[day - 1]
tsavg = (tmaxdf.values + tmindf.values)/2.0
tsdiff = tmaxdf.values - tmindf.values
# Copy tsavg in order to get all of the time components correct.
pe = tmaxdf.copy()
pe.values[:] = 0.408*0.0023*newra*(tsavg + 17.8)*pd.np.sqrt(abs(tsdiff))
pe.columns = ['PET_hargreaves_{0}'.format(i) for i in range(len(pe.columns))]
return tsutils.print_input(print_input, tsd, pe, None,
float_format=float_format)
def humidity(
method: Literal["equal", "minimal", "dewpoint_regression",
"linear_dewpoint_variation", "min_max",
"month_hour_precip_mean", ],
source_units,
input_ts="-",
columns=None,
start_date=None,
end_date=None,
dropna="no",
clean=False,
round_index=None,
skiprows=None,
index_type="datetime",
names=None,
target_units=None,
print_input=False,
hum_min_col=None,
hum_max_col=None,
hum_mean_col=None,
temp_min_col=None,
temp_max_col=None,
a0=None,
a1=None,
kr=None,
hourly_temp=None,
preserve_daily_mean=None,
):
"""Disaggregate daily humidity to hourly humidity data.
disaggregate_humidity(data_daily, method='equal', temp=None,
a0=None, a1=None, kr=None,
month_hour_precip_mean=None, preserve_daily_mean=False):
Args:
daily_data: daily values
method: keyword specifying the disaggregation method to be used
temp: hourly temperature time series (necessary for some methods)
kr: parameter for linear_dewpoint_variation method (6 or 12)
month_hour_precip_mean: [month, hour, precip(y/n)] categorical mean values
preserve_daily_mean: if True, correct the daily mean values of the disaggregated
data with the observed daily means.
if method=equal
.hum from hum_mean_col
if method in ["minimal", "dewpoint_regression", "linear_dewpoint_variation"]
.tmin from temp_min_col
if method=min_max need
.hum_min from hum_min_col
.hum_max from hum_max_col
.tmin from temp_min_col
.tmax from temp_max_col
if method in ["dewpoint_regression", "linear_dewpoint_variation"]
a0
a1
if method in ["linear_dewpoint_variation"]
kr
if preserve_daily_mean is True
.hum from hum_mean_col
if method in ["minimal",
"dewpoint_regression",
"linear_dewpoint_variation",
"min_max"]
need HOURLY temperature in 'temp'
"""
target_units = single_target_units(source_units, target_units, "")
if method == "equal" and hum_mean_col is None:
raise ValueError(
tsutils.error_wrapper("""
If `method` is "equal" then the mean daily humidity is a required column
identified with the keyword `hum_mean_col`"""))
if (method
in ["minimal", "dewpoint_regression", "linear_dewpoint_variation"]
and temp_min_col is None):
raise ValueError(
tsutils.error_wrapper("""
If `method` is "minimal", "dewpoint_regression", or
"linear_dewpoint_variation" then the minimum daily temperature is a required
column identified with the keyword `temp_min_col`."""))
if method == "min_max" and (hum_min_col is None or hum_max_col is None or
temp_min_col is None or temp_max_col is None):
raise ValueError(
tsutils.error_wrapper("""
If `method` is "min_max" then:
Minimum daily humidity is a required column identified with the keyword
`hum_min_col`. You gave {hum_min_col}.
Maximum daily humidity is a required column identified with the keyword
`hum_max_col`. You gave {hum_max_col}.
Minimum daily temperature is a required column identified with the
keyword `temp_min_col`. You gave {temp_min_col}.
Maximum daily temperature is a required column identified with the
keyword `temp_max_col`. You gave {temp_max_col}.
""".format(**locals())))
if method in ["dewpoint_regression", "linear_dewpoint_variation"
] and (a0 is None or a1 is None):
raise ValueError(
tsutils.error_wrapper("""
If `method` is "dewpoint_regression" or "linear_dewpoint_variation" then
a0 and a1 must be given."""))
if method == "linear_dewpoint_variation" and kr is None:
raise ValueError(
tsutils.error_wrapper("""
If `method` is "linear_dewpoint_variation" then kr must be given"""))
if (method in [
"minimal",
"dewpoint_regression",
"linear_dewpoint_variation",
"min_max",
"month_hour_precip_mean",
] and hourly_temp is None):
raise ValueError(
tsutils.error_wrapper("""
If `method` is "minimal", "dewpoint_regression",
"linear_dewpoint_variation", or "min_max" then hourly temperature is
required identified by the filename in keyword `hourly_temp`."""))
pd.options.display.width = 60
tsd = tsutils.common_kwds(
tsutils.read_iso_ts(input_ts,
skiprows=skiprows,
names=names,
index_type=index_type),
start_date=start_date,
end_date=end_date,
pick=columns,
round_index=round_index,
dropna=dropna,
source_units=source_units,
target_units=target_units,
clean=clean,
)
if method == "equal":
tsd["hum"] = tsd["hum_mean_col"]
if method in [
"minimal", "dewpoint_regression", "linear_dewpoint_variation"
]:
tsd["tmin"] = tsd["temp_min_col"]
if method == "min_max":
tsd["hum_min"] = tsd["hum_min_col"]
tsd["hum_max"] = tsd["hum_max_col"]
tsd["tmin"] = tsd["temp_min_col"]
tsd["tmax"] = tsd["temp_max_col"]
if preserve_daily_mean is not None:
tsd["hum"] = tsd[preserve_daily_mean]
preserve_daily_mean = True
if method in [
"minimal",
"dewpoint_regression",
"linear_dewpoint_variation",
"min_max",
"month_hour_precip_mean",
]:
hourly_temp = tstoolbox.read(hourly_temp)
if method == "month_hour_precip_mean":
month_hour_precip_mean = calculate_month_hour_precip_mean(hourly_temp)
ntsd = pd.DataFrame(
disaggregate_humidity(
tsd,
method=method,
temp=hourly_temp,
a0=a0,
a1=a1,
kr=kr,
preserve_daily_mean=preserve_daily_mean,
month_hour_precip_mean=month_hour_precip_mean,
))
ntsd.columns = ["humidity:{0}:disagg"]
return tsutils.return_input(print_input, tsd, ntsd)
