HG_REPOSITORIES/LPP/SciQLOP_Repos/SciQLop Commit - r1433:aaf8b0146447

Few fixes with merging empty csv files from Web Services...

jeandet -

r1433:aaf8b0146447

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r1433:aaf8b0146447

core +1 -1

	@@ -1,1 +1,1
	1	Subproject commit 9a080a34054c21a67236f761e8843005c07ff0ba		1	Subproject commit 5f1aaa704ac36252027b9da0064bdf1de063df0d

plugins/python_providers/resources/amda.py +4 -3

             import sys
             sys.path.append("/home/jeandet/Documents/prog/build-SciQLop-Desktop-Debug/core")
             import traceback
             import os
             from datetime import datetime, timedelta, timezone
             import PythonProviders
             import pysciqlopcore
             import numpy as np
             import pandas as pds
             import requests
             import copy
             from spwc.amda import AMDA
             amda = AMDA()
             def get_sample(metadata,start,stop):
                 ts_type = pysciqlopcore.ScalarTimeSerie
+                default_ctor_args = 1
                 try:
                     param_id = None
                     for key,value in metadata:
                         if key == 'xml:id':
                             param_id = value
                         elif key == 'type':
                             if value == 'vector':
                                 ts_type = pysciqlopcore.VectorTimeSerie
                             elif value == 'multicomponent':
                                 ts_type = pysciqlopcore.MultiComponentTimeSerie
+                                default_ctor_args = (0,2)
                     tstart=datetime.datetime.fromtimestamp(start, tz=timezone.utc)
                     tend=datetime.datetime.fromtimestamp(stop, tz=timezone.utc)
-                    df = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id)
+                    df = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST")
                     #t = np.array([d.timestamp()-7200 for d in df.index])
                     t = np.array([d.timestamp() for d in df.index])
                     values = df.values
                     return ts_type(t,values.transpose())
-                    return ts_type(1)
                 except Exception as e:
                     print(traceback.format_exc())
                     print("Error in amda.py ",str(e))
-                    return ts_type(1)
+                    return ts_type(default_ctor_args)
             if len(amda.component) is 0:
                 amda.update_inventory()
             parameters = copy.deepcopy(amda.parameter)
             for name,component in amda.component.items():
                 if 'components' in parameters[component['parameter']]:
                     parameters[component['parameter']]['components'].append(component)
                 else:
                     parameters[component['parameter']]['components']=[component]
             products = []
             for key,parameter in parameters.items():
                 path = f"/AMDA/{parameter['mission']}/{parameter['instrument']}/{parameter['dataset']}/{parameter['name']}"
                 components = [component['name'] for component in parameter.get('components',[])]
                 metadata = [ (key,item) for key,item in parameter.items() if key is not 'components' ]
                 n_components = parameter.get('size',0)
                 if n_components is '3':
                     metadata.append(("type","vector"))
                 elif n_components !=0:
                     metadata.append(("type","multicomponent"))
                 else:
                     metadata.append(("type","scalar"))
                 products.append( (path, components, metadata))
             PythonProviders.register_product(products, get_sample)

plugins/python_providers/resources/test.py +2 -1

             import sys
             sys.path.append("/home/jeandet/Documents/prog/build-SciQLop-Desktop-Debug/core")
             import PythonProviders
             import pysciqlopcore
             import numpy as np
+            import math
             someglobal = 1
             def make_scalar(x):
                 y = np.cos(x/10.)
                 return pysciqlopcore.ScalarTimeSerie(x,y)
             def make_vector(x):
                 v=np.ones((3,len(x)))
                 for i in range(3):
                     v[:][i] = np.cos(x/10. + float(i))
                 return pysciqlopcore.VectorTimeSerie(x,v)
             def make_multicomponent(x):
                 v=np.ones((4,len(x)))
                 for i in range(4):
                     v[:][i] = float(i+1) * np.cos(x/10. + float(i))
                 return pysciqlopcore.MultiComponentTimeSerie(x,v)
             def get_data(metadata,start,stop):
-                x = np.arange(start, stop)
+                x = np.arange(math.ceil(start), math.floor(stop))
                 for key,value in metadata:
                     if key == 'xml:id':
                         param_id = value
                     elif key == 'type':
                         if value == 'vector':
                             return make_vector(x)
                         elif value == 'multicomponent':
                             return make_multicomponent(x)
                 return make_scalar(x)
             PythonProviders.register_product([("/tests/scalar",[],[("type","scalar")]), ("/tests/vector",[],[("type","vector")]), ("/tests/multicomponent",[],[("type","multicomponent"),('size','4')])],get_data)

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