73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
from .models import DB
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def getTemps(session):
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modelname, scenario = session['datasets'].split(',')
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data = DB.getTemps(session['minyear'],
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session['maxyear'],
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session['community_data']['id'],
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modelname,
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scenario)
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return data
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def avg_air_temp(temps):
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year_counter, total = 0, 0
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for temp in temps:
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total += sum(temp[1])
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year_counter += 1
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return total / (year_counter * 12)
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def ann_air_indices(temps):
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ATI, AFI = 0.0, 0.0
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indices = [[0 for x in range(2)] for y in range(len(temps))]
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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[1][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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year_counter, total_freezing, total_thawing = 0, 0, 0
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for index in indices:
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total_thawing += index[0]
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total_freezing += index[1]
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year_counter += 1
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return (int(total_thawing / year_counter), int(total_freezing / year_counter))
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def des_air_indices(indices):
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if len(indices) > 2:
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ati = sorted(indices, key=lambda arr: arr[0])
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afi = sorted(indices, key=lambda arr: arr[1])
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dti = (ati[-1][0] + ati[-2][0] + ati[-3][0]) / 3.0
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dfi = (afi[0][1] + afi[1][1] + afi[2][1]) / 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 communitiesSelect():
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return [(c.id, c.name) for c in DB.getCommunities()]
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def datasetsSelect():
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return [("{0.modelname},{0.scenario}".format(d),
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"{x} ({d.resolution}) - {d.modelname} {d.scenario}".format(d=d,
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x=d.datatype.title()))
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for d in DB.getDatasets()]
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