def read_beliefs_from_csv(sensor, source, cp, event_resolution: timedelta, tz_hour_difference: float = 0) -> list:
"""
Returns a timely_beliefs DataFrame read from a csv file
@param sensor : beliefsensor
@param source : BeliefSource
@param cp : float, cummulative probability
@param event_resolution : timedelta object, event resolution
@param tz_hour_difference : float,time difference
"""
sensor_descriptions = (("Temperature", "°C"),)
cols = [0, 1] # Columns with datetime index and observed values
horizons = list(range(0, 169, 1))
cols.extend([h + 2 for h in horizons])
n_horizons = 169
n_events = None
beliefs = pd.read_csv("%s-%s-%s.csv" % (sensor.name.replace(' ', '_').lower(), source.name.replace(' ', '_').lower(), cp),
index_col=0, parse_dates=[0], date_parser=lambda col: pd.to_datetime(col, utc=True) - timedelta(hours=tz_hour_difference),
nrows=n_events, usecols=cols)
beliefs = beliefs.resample(event_resolution).mean()
assert beliefs.index.tzinfo == pytz.utc
# Construct the BeliefsDataFrame by looping over the belief horizons
blfs = load_time_series(beliefs.iloc[:, 0].head(n_events), sensor=sensor, source=source,
belief_horizon=timedelta(hours=0), cumulative_probability=0.5) # load the observations (keep cp=0.5)
for h in beliefs.iloc[:, 1 :n_horizons + 1] :
try:
blfs += load_time_series(beliefs[h].head(n_events), sensor=sensor, source=source,
belief_horizon=(isodate.parse_duration(
"PT%s" % h)) + event_resolution, cumulative_probability=cp) # load the forecasts
except isodate.isoerror.ISO8601Error: # In case of old headers that don't yet follow the ISO 8601 standard
blfs += load_time_series(beliefs[h].head(n_events), sensor=sensor, source=source,
belief_horizon=(isodate.parse_duration(
"%s" % h)) + event_resolution, cumulative_probability=cp) # load the forecasts
return blfs
def read_beliefs_from_csv(sensor,
csv_in,
source,
event_resolution: timedelta = None,
tz_hour_difference: float = 0,
n_events: int = None) -> list:
beliefs = pd.read_csv(
csv_in,
index_col=0,
parse_dates=[0],
date_parser=lambda col: pd.to_datetime(col, utc=True) - timedelta(
hours=tz_hour_difference),
nrows=n_events,
usecols=["datetime", sensor.name.replace(' ', '_').lower()])
if event_resolution is not None:
beliefs = beliefs.resample(event_resolution).mean()
assert beliefs.index.tzinfo == pytz.utc
# Construct the BeliefsDataFrame by looping over the belief horizons
blfs = load_time_series(
beliefs[sensor.name.replace(' ', '_').lower()],
sensor=sensor,
source=source,
belief_horizon=timedelta(hours=0),
cumulative_probability=0.5) # load the observations (keep cp=0.5)
return blfs
def read_beliefs_from_csv(sensor,
source,
cp,
event_resolution: timedelta,
tz_hour_difference: float = 0) -> list:
beliefs = pd.read_csv("../%s-%s-%s.csv" % (sensor.name.replace(
' ', '_').lower(), source.name.replace(' ', '_').lower(), cp),
index_col=0,
parse_dates=[0],
date_parser=lambda col: pd.to_datetime(col, utc=True)
- timedelta(hours=tz_hour_difference),
nrows=n_events,
usecols=cols)
beliefs = beliefs.resample(event_resolution).mean()
assert beliefs.index.tzinfo == pytz.utc
# Construct the BeliefsDataFrame by looping over the belief horizons
blfs = load_time_series(
beliefs.iloc[:, 0].head(n_events),
sensor=sensor,
source=source,
belief_horizon=timedelta(hours=0),
cumulative_probability=0.5) # load the observations (keep cp=0.5)
for h in beliefs.iloc[:, 1:n_horizons + 1]:
try:
blfs += load_time_series(
beliefs[h].head(n_events),
sensor=sensor,
source=source,
belief_horizon=(isodate.parse_duration("PT%s" % h)) +
event_resolution,
cumulative_probability=cp) # load the forecasts
except isodate.isoerror.ISO8601Error: # In case of old headers that don't yet follow the ISO 8601 standard
blfs += load_time_series(
beliefs[h].head(n_events),
sensor=sensor,
source=source,
belief_horizon=(isodate.parse_duration("%s" % h)) +
event_resolution,
cumulative_probability=cp) # load the forecasts
return blfs
def append_from_time_series(
self,
event_value_series: pd.Series,
source: BeliefSource,
belief_horizon: timedelta,
cumulative_probability: float = 0.5,
) -> "BeliefsDataFrame":
"""Append beliefs from time series entries into this BeliefsDataFrame. Sensor is assumed to be the same.
Returns a new BeliefsDataFrame object."""
beliefs = belief_utils.load_time_series(
event_value_series,
self.sensor,
source,
belief_horizon,
cumulative_probability,
)
return self.append(
BeliefsDataFrame(sensor=self.sensor, beliefs=beliefs))