71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
import numpy
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from app.models import Temperature, Dataset
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def getTemps(datasets, community_id, minyear, maxyear):
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temps = Temperature.query.join(Dataset). \
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filter(Dataset.id == Temperature.dataset_id,
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Dataset.id == datasets,
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Temperature.community_id == community_id,
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Temperature.year >= minyear,
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Temperature.year <= maxyear)
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length = int(maxyear) - int(minyear)
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temps_arr = numpy.zeros((length+1, 12))
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i = 0
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for t in temps.all():
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temps_arr[i,:] = [t.january, t.february, t.march,
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t.april, t.may, t.june,
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t.july, t.august, t.september,
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t.october, t.november, t.december]
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i += 1
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return temps_arr
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def avg_air_temp(temps):
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return numpy.average(temps)
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def ann_air_indices(temps):
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ATI, AFI = 0.0, 0.0
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indices = numpy.zeros((temps.shape[0], 2), dtype='int')
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months = [0.0 for m in range(12)]
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days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
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i = 0
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for year in temps:
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j = 0
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for month in months:
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months[j] = days[j] * year[j]
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j += 1
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for ind in months:
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if ind >= 0.0:
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ATI = ATI + ind
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else:
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AFI = AFI + ind
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indices[i, 0], indices[i, 1] = int(ATI), int(AFI)
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ATI, AFI = 0.0, 0.0
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i += 1
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return indices
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def avg_air_indices(indices):
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temp = numpy.average(indices, axis=0)
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return (int(temp[0]), int(temp[1]))
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def des_air_indices(indices):
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if indices.shape[0] > 2:
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ati = numpy.sort(indices[:,0])
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afi = numpy.sort(indices[:,1])
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dti = (ati[-1] + ati[-2] + ati[-3]) / 3.0
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dfi = (afi[0] + afi[1] + afi[2]) / 3.0
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return (int(dti), int(dfi))
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else:
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return (None, None)
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def c_to_f(temp):
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return (temp * 9. / 5.) + 32.
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