include main code in raw version outcommented

albatross/processes/wps_drought.py

@@ -17,14 +17,14 @@ def __init__(self):
                          abstract='??? netcdf file',
                          # keywords=['name', 'firstname'],
                          data_type='string'),
-            LiteralInput('gph', 'Monthly global geopotential height file',
-                         abstract='??? netcdf file',
-                         # keywords=['name', 'firstname'],
-                         data_type='string'),
-            LiteralInput('slp', 'Monthly global sea level pressure file',
-                         abstract='??? netcdf file',
-                         # keywords=['name', 'firstname'],
-                         data_type='string'),
+ #           LiteralInput('gph', 'Monthly global geopotential height file',
+ #                        abstract='??? netcdf file',
+ #                        # keywords=['name', 'firstname'],
+ #                        data_type='string'),
+ #           LiteralInput('slp', 'Monthly global sea level pressure file',
+ #                        abstract='??? netcdf file',
+ #                        # keywords=['name', 'firstname'],
+ #                        data_type='string'),
             LiteralInput('indicator', 'Indicator ???',
                          abstract='examples: climate indicator of tele-connection patterns, ???',
                          # keywords=['name', 'firstname'],
@@ -73,7 +73,159 @@ def __init__(self):
 
     @staticmethod
     def _handler(request, response):
-        LOGGER.info("processing")
-        response.outputs['negative_list'].data = 'PR ' + request.inputs['pr'][0].data
-        response.outputs['positive_list'].data = 'sst ' + request.inputs['sst'][0].data
+
+        ############################
+        LOGGER.info("get input parameter")
+
+        pr = request.inputs['pr'][0].data
+        sst= request.inputs['sst'][0].data
+
+        ############################
+        ### original code https://github.com/mxgiuliani00/CSI/blob/master/NIPA/run_nipa.py 
+        LOGGER.info("start processing")
+
+
+# import pandas as pd
+# from matplotlib import cm, pyplot as plt
+# import numpy as np
+# from climdiv_data import *
+# from simpleNIPA import *
+# from atmos_ocean_data import *
+# from utils import sstMap
+# import matplotlib as mpl
+# from utils import *
+# import pydap.client
+# import mpl_toolkits
+# import csv
+# import math
+
+
+# #### USER INPUT ####
+
+# # Select the input-output files:
+# index_file = './DATA/nao.txt'
+# clim_file = './DATA/APGD_prcpComo.txt'
+# filename = 'testComoNAO'
+
+# # Settings:
+# M = 2               # number of climate signal's phases
+# n_obs = 3           # number of observations (months)
+# lag = 3             # lag-time (months) --> 3 = seasonal
+# months = [1,2,3]    # months to consider (J,F,M)
+# startyr = 1971      # beginning of the time period to analyze
+# n_yrs = 38          # number of years to analyze
+
+# # Select the type of experiment:
+# # crv_flag:
+# #   True  = runs NIPA with crossvalidation
+# #   False = runs NIPA without crossvalidation and save the first SST-Principal Component for multi-variate model
+# #
+# crv_flag = True
+# map_flag = True
+
+# ####################
+
+# # Don't mess with the next few lines
+# years = np.arange(startyr, startyr+n_yrs)
+
+# kwgroups = create_kwgroups(debug = True, climdata_months = months,
+#                         climdata_startyr = startyr, n_yrs = n_yrs,
+#                         n_mon_sst = n_obs, n_mon_index = n_obs, sst_lag = lag,
+#                         n_phases = M, phases_even = True,
+#                         index_lag = lag,
+#                         index_fp = index_file,
+#                         climdata_fp = clim_file)
+
+# climdata, sst, index, phaseind = get_data(kwgroups)
+# fp = './maps/%s' % (filename)
+# fig, axes = plt.subplots(M, 1, figsize = (6, 12))
+# timeseries = {'years' : [], 'data' : [], 'hindcast': []}
+# pc1 = {'pc1':[]}
+
+# print('NIPA running...')
+
+# if M == 1:
+#     phase = 'allyears'
+#     model = NIPAphase(climdata, sst, index, phaseind[phase])
+#     model.phase = phase
+#     model.years = years[phaseind[phase]]
+#     model.bootcorr(corrconf = 0.95)
+#     model.gridCheck()
+#     model.crossvalpcr(xval = crv_flag)
+#     timeseries['years'] = model.years
+#     timeseries['data'] = model.clim_data
+#     timeseries['hindcast'] = model.hindcast
+#     print( timeseries['years'])
+#     print( timeseries['data'])
+
+#     if map_flag:
+#         fig, axes, m = sstMap(model, fig = fig, ax = axes)
+#         axes.set_title('%s, %.2f' % (phase, model.correlation))
+#         fig.savefig(fp)
+#         plt.close(fig)
+
+
+# else:
+#     for phase, ax in zip(phaseind, axes):
+#         model = NIPAphase(climdata, sst, index, phaseind[phase])
+#         model.phase = phase
+#         model.years = years[phaseind[phase]]
+#         model.bootcorr(corrconf = 0.95)
+#         model.gridCheck()
+#         model.crossvalpcr(xval = crv_flag)
+#         timeseries['years'].append(model.years)
+#         timeseries['data'].append(model.clim_data)
+#         timeseries['hindcast'].append(model.hindcast)
+#         if not crv_flag:
+#             if hasattr(model,'pc1'):
+#                 pc1['pc1'].append(model.pc1)
+
+#         if map_flag:
+#             fig, ax, m = sstMap(model, fig = fig, ax = ax)
+#             ax.set_title('%s, %.2f' % (phase, model.correlation))
+#             fig.savefig(fp)
+#             plt.close(fig)
+
+
+# # save timeseries (exceptions handled only for 2 phase analysis)
+# if np.size(timeseries['hindcast'][0]) == 1:
+#     if math.isnan(timeseries['hindcast'][0]):
+#         # no result for the first phase -> consider only the second set of results
+#         timeseries['years'] = timeseries['years'][1]
+#         timeseries['data'] = timeseries['data'][1]
+#         timeseries['hindcast'] = timeseries['hindcast'][1]
+
+# elif np.size(timeseries['hindcast'][1]) == 1:
+#     if math.isnan(timeseries['hindcast'][1]):
+#         # no result for the second phase -> consider only the first set of results
+#         timeseries['years'] = timeseries['years'][0]
+#         timeseries['data'] = timeseries['data'][0]
+#         timeseries['hindcast'] = timeseries['hindcast'][0]
+
+# else:
+#     timeseries['years'] = np.concatenate(timeseries['years'])
+#     timeseries['data'] = np.concatenate(timeseries['data'])
+#     timeseries['hindcast'] = np.concatenate(timeseries['hindcast'])
+
+# df = pd.DataFrame(timeseries)
+# ts_file = './output/%s_timeseries.csv' % (filename)
+# df.to_csv(ts_file)
+
+# if not crv_flag:
+#     # save PC
+#     pc1['pc1'] = np.concatenate(pc1['pc1'])
+#     pc_file = './output/%s_pc1SST.csv' % (filename)
+#     df1 = pd.DataFrame(pc1)
+#     df1.to_csv(pc_file)
+
+# print( 'NIPA run completed')
+
+
+        ############################
+        LOGGER.  info("start processing")
+
+        response.outputs['graphic'].data = 'graphic' + request.inputs['pr'][0].data
+
         return response
+
+