Skip to content

Commit

Permalink
Merge github.com:timcera/mettoolbox
Browse files Browse the repository at this point in the history
  • Loading branch information
@timcera
timcera committed Sep 9, 2022
2 parents 7f66fc0 + 1df4704 commit 48fe515
Show file tree
Hide file tree
Showing 4 changed files with 2,778 additions and 257 deletions.
288 changes: 271 additions & 17 deletions src/mettoolbox/disaggregate.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@
calculate_month_hour_precip_mean,
disaggregate_humidity,
)
from . import tdew as tdew_melo
from .melodist.melodist.radiation import disaggregate_radiation
from .melodist.melodist.temperature import disaggregate_temperature, get_shift_by_data
from .melodist.melodist.util.util import (
Expand Down Expand Up @@ -428,19 +429,18 @@ def humidity(
pass
columns.append(precip_col)

if preserve_daily_mean is not None:
if method in [
"minimal",
"dewpoint_regression",
"linear_dewpoint_variation",
"min_max",
"month_hour_precip_mean",
]:
try:
hum_mean_col = int(preserve_daily_mean)
except TypeError:
pass
columns.append(hum_mean_col)
if preserve_daily_mean is not None and method in [
"minimal",
"dewpoint_regression",
"linear_dewpoint_variation",
"min_max",
"month_hour_precip_mean",
]:
try:
hum_mean_col = int(preserve_daily_mean)
except TypeError:
pass
columns.append(hum_mean_col)

tsd = tsutils.common_kwds(
tsutils.read_iso_ts(
Expand All @@ -462,19 +462,19 @@ def humidity(
if preserve_daily_mean is not None:
if method in ["minimal", "dewpoint_regression", "linear_dewpoint_variation"]:
tsd.columns = ["tmin", "hum"]
if method == "month_hour_precip_mean":
tsd.columns = ["precip", "hum"]
if method == "min_max":
tsd.columns = ["tmin", "tmax", "hum_min", "hum_max", "hum"]
elif method == "month_hour_precip_mean":
tsd.columns = ["precip", "hum"]
preserve_daily_mean = True
else:
if method in ["minimal", "dewpoint_regression", "linear_dewpoint_variation"]:
tsd.columns = ["tmin"]
if method == "month_hour_precip_mean":
tsd.columns = ["precip"]
if method == "min_max":
tsd.columns = ["tmin", "tmax", "hum_min", "hum_max"]

elif method == "month_hour_precip_mean":
tsd.columns = ["precip"]
if method in [
"minimal",
"dewpoint_regression",
Expand Down Expand Up @@ -508,6 +508,260 @@ def humidity(
return tsutils.return_input(print_input, tsd, ntsd)


@typic.al
def tdew(
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: Optional[Union[tsutils.IntGreaterEqualToOne, str, pd.Series]] = None,
hum_max_col: Optional[Union[tsutils.IntGreaterEqualToOne, str, pd.Series]] = None,
hum_mean_col: Optional[Union[tsutils.IntGreaterEqualToOne, str, pd.Series]] = None,
temp_min_col: Optional[Union[tsutils.IntGreaterEqualToOne, str, pd.Series]] = None,
temp_max_col: Optional[Union[tsutils.IntGreaterEqualToOne, str, pd.Series]] = None,
precip_col: Optional[Union[tsutils.IntGreaterEqualToOne, str, pd.Series]] = None,
a0=None,
a1=None,
kr=None,
hourly_temp=None,
hourly_precip_hum=None,
preserve_daily_mean=None,
):
"""Disaggregate daily humidity to hourly humidity data."""
#target_units = single_target_units(source_units, target_units, "")
target_units = single_target_units(source_units, target_units, "degK")

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 == "month_hour_precip_mean" and precip_col is None:
raise ValueError(
tsutils.error_wrapper(
"""
If `method` is "month_hour_precip_mean" then the daily precip is a required column
identified with the keyword `precip_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(
f"""
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}.
"""
)
)

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",
]
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

columns = []
if method == "equal":
try:
hum_mean_col = int(hum_mean_col)
except TypeError:
pass
columns.append(hum_mean_col)

if method == "min_max":
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)
try:
hum_min_col = int(hum_min_col)
except TypeError:
pass
columns.append(hum_min_col)
try:
hum_max_col = int(hum_max_col)
except TypeError:
pass
columns.append(hum_max_col)

if method in ["minimal", "dewpoint_regression", "linear_dewpoint_variation"]:
try:
temp_min_col = int(temp_min_col)
except TypeError:
pass
columns.append(temp_min_col)

if method == "month_hour_precip_mean":
try:
precip_col = int(precip_col)
except TypeError:
pass
columns.append(precip_col)

if preserve_daily_mean is not None and method in [
"minimal",
"dewpoint_regression",
"linear_dewpoint_variation",
"min_max",
"month_hour_precip_mean",
]:
try:
hum_mean_col = int(preserve_daily_mean)
except TypeError:
pass
columns.append(hum_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 method == "equal":
tsd.columns = ["hum"]

if preserve_daily_mean is not None:
if method in ["minimal", "dewpoint_regression", "linear_dewpoint_variation"]:
tsd.columns = ["tmin", "hum"]
if method == "min_max":
tsd.columns = ["tmin", "tmax", "hum_min", "hum_max", "hum"]
elif method == "month_hour_precip_mean":
tsd.columns = ["precip", "hum"]
preserve_daily_mean = True
else:
if method in ["minimal", "dewpoint_regression", "linear_dewpoint_variation"]:
tsd.columns = ["tmin"]
if method == "min_max":
tsd.columns = ["tmin", "tmax", "hum_min", "hum_max"]

elif method == "month_hour_precip_mean":
tsd.columns = ["precip"]
hourly_temp = tstoolbox.read(hourly_temp)
hourly_temp = hourly_temp.astype(float).squeeze()

if method == "month_hour_precip_mean":
hourly_precip_hum = tstoolbox.read(hourly_precip_hum)
month_hour_precip_mean = calculate_month_hour_precip_mean(hourly_precip_hum)
else:
month_hour_precip_mean = "None"

ntsd = pd.DataFrame(
tdew_melo.disaggregate_tdew(
tsd.astype(float),
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 = ["tdew:degK:disagg"]
ntsd_units = tsutils._normalize_units(ntsd,source_units='degK',target_units=target_units[0])
return tsutils.return_input(print_input, tsd, ntsd)


@typic.al
def wind_speed(
method: Literal["equal", "cosine", "random"],
Expand Down

0 comments on commit 48fe515

Please sign in to comment.