| @@ -1,5 +1,6 @@ |
| import os, datetime, netCDF4, pandas, csv, bz2, sys, json, bisect, psycopg2, math |
| import psycopg2.extras |
| +from pymongo import MongoClient |
| |
| from collections import namedtuple |
| import numpy as np |
| @@ -246,7 +247,7 @@ |
| ['nwcsc_historical_{model}.nc', datetime.date(1950, 1, 1), datetime.date(2005, 12, 31)], |
| ] |
| |
| - if forecast_option == 'rcp45': |
| + if not forecast_option or forecast_option == 'rcp45': |
| ncfiles.append(['nwcsc_rcp45_{model}.nc', datetime.date(2006, 1, 1), datetime.date(2099, 12, 31)]) |
| elif forecast_option == 'rcp85': |
| ncfiles.append(['nwcsc_rcp85_{model}.nc', datetime.date(2006, 1, 1), datetime.date(2099, 12, 31)]) |
| @@ -257,7 +258,7 @@ |
| ['nwcsc_historical.nc', datetime.date(1950, 1, 1), datetime.date(2005, 12, 31)], |
| ] |
| |
| - if forecast_option == 'rcp45': |
| + if not forecast_option or forecast_option == 'rcp45': |
| ncfiles.append(['nwcsc_rcp45.nc', datetime.date(2006, 1, 1), datetime.date(2099, 12, 31)]) |
| elif forecast_option == 'rcp85': |
| ncfiles.append(['nwcsc_rcp85.nc', datetime.date(2006, 1, 1), datetime.date(2099, 12, 31)]) |
| @@ -351,7 +352,8 @@ |
| data_dict[cell_name][var] = [] |
| data = f.variables[var] |
| if testNew: |
| - darray = np.ascontiguousarray(data[jday_start:jday_end, gridx, gridy]) |
| + # not sure why the coordinate order is flipped -- it seems like it should be gridx, gridy |
| + darray = np.ascontiguousarray(data[jday_start:jday_end, gridy, gridx]) |
| else: |
| darray = np.ascontiguousarray(data[model, jday_start:jday_end, gridx, gridy]) |
| if hasattr(data, 'scale_factor'): |
| @@ -375,7 +377,7 @@ |
| ]) |
| return { 'output': climate_data, 'model': models[model], } |
| |
| - def WEgetDataPRISM(self, input_zone_file, units, start_date, end_date): |
| + def WEgetDataPRISM_CSIP(self, input_zone_file, units, start_date, end_date): |
| tiles, names, cell_extents, cell_defs = self.getInputZoneInfo(input_zone_file, 'prism') |
| |
| ident = lambda x: x |
| @@ -412,6 +414,96 @@ |
| climate_data += cell_data |
| return climate_data, cell_defs, cell_extents |
| |
| + def WEgetDataPRISM(self, input_zone_file, units, start_date, end_date): |
| + prec = 2 |
| + ident = lambda x: round(x, prec) if x != None else None |
| + to_metric_fn = { |
| + 'tmp': ident, |
| + 'apcp': lambda x: round(x, prec)/10 if x != None else None, # mm -> cm |
| + } |
| + to_english_fn = { |
| + 'tmp': lambda x: round(x * 1.8 + 32, prec) if x != None else None, # C -> F |
| + 'apcp': lambda x: round(x / 25.4, prec) if x != None else None, # mm -> in |
| + } |
| + if units == "metric": |
| + header = ["cell", "date", "tmpmin (C)", "tmpmax (C)", "apcp (cm)"] |
| + conv_dict = to_metric_fn |
| + else: |
| + header = ["cell", "date", "tmpmin (F)", "tmpmax (F)", "apcp (in)"] |
| + conv_dict = to_english_fn |
| + |
| + mc = MongoClient('eds0.engr.colostate.edu') |
| + db = mc.climate |
| + |
| + ret = [header,] |
| + |
| + # generate tile info |
| + tiles = [] |
| + if input_zone_file: |
| + geojson_obj = json.loads(input_zone_file) |
| + for feat in geojson_obj['features']: |
| + pt = feat['geometry']['coordinates'] |
| + tiles.append(pt) |
| + |
| + for tile in tiles: |
| + x, y = tile |
| + |
| + # query the locations for x, y data |
| + query = [ |
| + { |
| + '$match': { |
| + 'boundary': { |
| + '$geoIntersects': { |
| + '$geometry': { |
| + 'type': "Point", |
| + 'coordinates': [x, y] |
| + } |
| + } |
| + } |
| + } |
| + }, |
| + { |
| + '$project': {'x': 1, 'y': 1} |
| + }, |
| + { |
| + '$limit': 10 |
| + } |
| + ] |
| + |
| + results = db.prism_locs.aggregate(query) |
| + |
| + cells = [] |
| + for x, y in [(d['x'], d['y']) for d in results]: |
| + cells.append([x, y]) |
| + assert len(cells) == 1 |
| + |
| + # get data for multiple cells and multiple years |
| + for cell in cells: |
| + data_results = db.prism_data.find({'_id': {'$in': ['{0}_{1}_{2}'.format(cell[0], cell[1], y) for y in range(start_date.year, end_date.year+1)]}}) |
| + |
| + # sort data |
| + data_results = sorted(data_results, key=lambda d: d['year']) |
| + |
| + # cap start and end dates |
| + if start_date.year < data_results[0]['year']: |
| + start_date = datetime.datetime(data_results[0]['year'], 1, 1) |
| + |
| + # build precip |
| + ppt = [v for d in data_results for v in d['data']['ppt']] |
| + |
| + # build tmin |
| + tmin = [v for d in data_results for v in d['data']['tmin']] |
| + |
| + # build tmax |
| + tmax = [v for d in data_results for v in d['data']['tmax']] |
| + |
| + dt = start_date |
| + for i in range(len(tmax)): |
| + # convert precip from mm to cm |
| + ret.append(['{0} {1}'.format(cell[0], cell[1]), dt.strftime('%Y-%m-%d'), conv_dict['tmp'](tmin[i]), conv_dict['tmp'](tmax[i]), conv_dict['apcp'](ppt[i])]) |
| + dt += datetime.timedelta(days=1) |
| + return ret |
| + |
| def WEgetDataGHCND(self, input_zone_file_or_station_list, units, start_date, end_date, generate_average=False, IDW_center=False): |
| names = [] |
| stations = [] |