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Le Roux Erwan authoredd5e81395
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import numpy as np
class SafranSnowfall(object):
""""
Hypothesis:
-How to count how much snowfall in one hour ?
I take the average between the rhythm of snowfall per second between the start and the end
and multiply that by 60 x 60 which corresponds to the number of seconds in one hour
-How do how I define the limit of a day ?
From the start, i.e. in August at 4am something like that,then if I add a 24H duration, I arrive to the next day
-How do you aggregate several days ?
We aggregate all the N consecutive days into a value x_i, then we take the max
(but here the problem might be that the x_i are not idnependent, they are highly dependent one from another)
"""
def __init__(self, dataset):
self.dataset = dataset
# Corresponding starting times
# start_datetime = datetime(year=year, month=8, day=1, hour=6)
# start_times = np.array(dataset.variables['time'])[:-1]
# Compute the daily snowfall in kg/m2
snowfall_rates = np.array(dataset.variables['Snowf'])
mean_snowfall_rates = 0.5 * (snowfall_rates[:-1] + snowfall_rates[1:])
hourly_snowfall = 60 * 60 * mean_snowfall_rates
# Transform the snowfall amount into a dataframe
nb_days = len(hourly_snowfall) // 24
self.daily_snowfall = [sum(hourly_snowfall[24 * i:24 * (i+1)]) for i in range(nb_days)]
def annual_snowfall(self, nb_days_of_snowfall=1):
# Aggregate the daily snowfall by the number of consecutive days
shifted_list = [self.daily_snowfall[i:] for i in range(nb_days_of_snowfall)]
# First element of shifted_list is of length n, Second element of length n-1, Third element n-2....
# The zip is done with respect to the shortest list
snowfall_in_consecutive_days = [sum(e) for e in zip(*shifted_list)]
# Return the maximum of the aggregated list
# The returned array is of size n-nb_days+1 x nb_massif
return np.array(snowfall_in_consecutive_days)