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1 | Subproject commit 9a080a34054c21a67236f761e8843005c07ff0ba | |
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1 | Subproject commit 5f1aaa704ac36252027b9da0064bdf1de063df0d | |
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1 | 1 | import sys |
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2 | 2 | sys.path.append("/home/jeandet/Documents/prog/build-SciQLop-Desktop-Debug/core") |
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3 | 3 | import traceback |
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4 | 4 | import os |
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5 | 5 | from datetime import datetime, timedelta, timezone |
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6 | 6 | import PythonProviders |
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7 | 7 | import pysciqlopcore |
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8 | 8 | import numpy as np |
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9 | 9 | import pandas as pds |
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10 | 10 | import requests |
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11 | 11 | import copy |
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12 | 12 | from spwc.amda import AMDA |
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13 | 13 | |
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14 | 14 | amda = AMDA() |
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15 | 15 | |
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16 | 16 | def get_sample(metadata,start,stop): |
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17 | 17 | ts_type = pysciqlopcore.ScalarTimeSerie |
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18 | default_ctor_args = 1 | |
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18 | 19 | try: |
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19 | 20 | param_id = None |
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20 | 21 | for key,value in metadata: |
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21 | 22 | if key == 'xml:id': |
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22 | 23 | param_id = value |
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23 | 24 | elif key == 'type': |
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24 | 25 | if value == 'vector': |
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25 | 26 | ts_type = pysciqlopcore.VectorTimeSerie |
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26 | 27 | elif value == 'multicomponent': |
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27 | 28 | ts_type = pysciqlopcore.MultiComponentTimeSerie |
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29 | default_ctor_args = (0,2) | |
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28 | 30 | tstart=datetime.datetime.fromtimestamp(start, tz=timezone.utc) |
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29 | 31 | tend=datetime.datetime.fromtimestamp(stop, tz=timezone.utc) |
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30 | df = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id) | |
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32 | df = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST") | |
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31 | 33 | #t = np.array([d.timestamp()-7200 for d in df.index]) |
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32 | 34 | t = np.array([d.timestamp() for d in df.index]) |
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33 | 35 | values = df.values |
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34 | 36 | return ts_type(t,values.transpose()) |
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35 | return ts_type(1) | |
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36 | 37 | except Exception as e: |
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37 | 38 | print(traceback.format_exc()) |
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38 | 39 | print("Error in amda.py ",str(e)) |
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39 |
return ts_type( |
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40 | return ts_type(default_ctor_args) | |
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40 | 41 | |
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41 | 42 | |
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42 | 43 | if len(amda.component) is 0: |
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43 | 44 | amda.update_inventory() |
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44 | 45 | parameters = copy.deepcopy(amda.parameter) |
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45 | 46 | for name,component in amda.component.items(): |
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46 | 47 | if 'components' in parameters[component['parameter']]: |
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47 | 48 | parameters[component['parameter']]['components'].append(component) |
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48 | 49 | else: |
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49 | 50 | parameters[component['parameter']]['components']=[component] |
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50 | 51 | |
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51 | 52 | products = [] |
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52 | 53 | for key,parameter in parameters.items(): |
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53 | 54 | path = f"/AMDA/{parameter['mission']}/{parameter['instrument']}/{parameter['dataset']}/{parameter['name']}" |
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54 | 55 | components = [component['name'] for component in parameter.get('components',[])] |
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55 | 56 | metadata = [ (key,item) for key,item in parameter.items() if key is not 'components' ] |
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56 | 57 | n_components = parameter.get('size',0) |
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57 | 58 | if n_components is '3': |
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58 | 59 | metadata.append(("type","vector")) |
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59 | 60 | elif n_components !=0: |
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60 | 61 | metadata.append(("type","multicomponent")) |
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61 | 62 | else: |
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62 | 63 | metadata.append(("type","scalar")) |
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63 | 64 | products.append( (path, components, metadata)) |
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64 | 65 | |
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65 | 66 | PythonProviders.register_product(products, get_sample) |
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66 | 67 | |
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67 | 68 | |
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1 | 1 | import sys |
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2 | 2 | sys.path.append("/home/jeandet/Documents/prog/build-SciQLop-Desktop-Debug/core") |
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3 | 3 | import PythonProviders |
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4 | 4 | import pysciqlopcore |
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5 | 5 | import numpy as np |
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6 | import math | |
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6 | 7 | |
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7 | 8 | someglobal = 1 |
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8 | 9 | |
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9 | 10 | def make_scalar(x): |
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10 | 11 | y = np.cos(x/10.) |
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11 | 12 | return pysciqlopcore.ScalarTimeSerie(x,y) |
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12 | 13 | |
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13 | 14 | def make_vector(x): |
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14 | 15 | v=np.ones((3,len(x))) |
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15 | 16 | for i in range(3): |
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16 | 17 | v[:][i] = np.cos(x/10. + float(i)) |
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17 | 18 | return pysciqlopcore.VectorTimeSerie(x,v) |
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18 | 19 | |
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19 | 20 | |
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20 | 21 | def make_multicomponent(x): |
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21 | 22 | v=np.ones((4,len(x))) |
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22 | 23 | for i in range(4): |
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23 | 24 | v[:][i] = float(i+1) * np.cos(x/10. + float(i)) |
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24 | 25 | return pysciqlopcore.MultiComponentTimeSerie(x,v) |
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25 | 26 | |
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26 | 27 | |
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27 | 28 | def get_data(metadata,start,stop): |
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28 | x = np.arange(start, stop) | |
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29 | x = np.arange(math.ceil(start), math.floor(stop)) | |
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29 | 30 | for key,value in metadata: |
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30 | 31 | if key == 'xml:id': |
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31 | 32 | param_id = value |
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32 | 33 | elif key == 'type': |
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33 | 34 | if value == 'vector': |
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34 | 35 | return make_vector(x) |
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35 | 36 | elif value == 'multicomponent': |
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36 | 37 | return make_multicomponent(x) |
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37 | 38 | return make_scalar(x) |
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38 | 39 | |
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39 | 40 | |
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40 | 41 | |
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41 | 42 | |
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42 | 43 | PythonProviders.register_product([("/tests/scalar",[],[("type","scalar")]), ("/tests/vector",[],[("type","vector")]), ("/tests/multicomponent",[],[("type","multicomponent"),('size','4')])],get_data) |
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43 | 44 | |
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44 | 45 | |
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