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@@ -1,38 +1,38
1 1 import traceback
2 2 from SciQLopBindings import PyDataProvider, Product, VectorTimeSerie, ScalarTimeSerie, DataSeriesType
3 3 import numpy as np
4 from spwc.cache import _cache
5 from spwc.common.datetime_range import DateTimeRange
4 from speasy.cache import _cache
5 from speasy.common.datetime_range import DateTimeRange
6 6 from datetime import datetime, timedelta, timezone
7 from spwc.common.variable import SpwcVariable
8 from spwc.amda import AMDA
7 from speasy.common.variable import SpeasyVariable
8 from speasy.amda import AMDA
9 9
10 10 amda = AMDA()
11 11
12 12
13 13 def vp_make_scalar(var=None):
14 14 if var is None:
15 15 return (((np.array([]), np.array([])), np.array([])), DataSeriesType.SCALAR)
16 16 else:
17 17 return (((var.time, np.array([])), var.data), DataSeriesType.SCALAR)
18 18
19 19 class DemoVP(PyDataProvider):
20 20 def __init__(self):
21 21 super().__init__()
22 22 self.register_products([Product("/VP/thb_fgm_gse_mod",[],{"type":"scalar"})])
23 23
24 24 def get_data(self,metadata,start,stop):
25 25 try:
26 26 tstart = datetime.fromtimestamp(start, tz=timezone.utc)
27 27 tend = datetime.fromtimestamp(stop, tz=timezone.utc)
28 28 thb_bs = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id='thb_bs', method="REST")
29 29 thb_bs.data = np.sqrt((thb_bs.data*thb_bs.data).sum(axis=1))
30 30 return vp_make_scalar(thb_bs)
31 31 except Exception as e:
32 32 print(traceback.format_exc())
33 33 print(f"Error in {__file__} ",str(e))
34 34 return (((np.array([]), np.array([])), np.array([])), ts_type)
35 35
36 36
37 37 t=DemoVP()
38 38
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@@ -1,128 +1,128
1 1 import traceback
2 2 import os
3 3 from datetime import datetime, timedelta, timezone
4 4 from SciQLopBindings import PyDataProvider, Product, VectorTimeSerie, ScalarTimeSerie, DataSeriesType
5 5 import numpy as np
6 6 import requests
7 7 import copy
8 from spwc.amda import AMDA
8 from speasy.amda import AMDA
9 9
10 10 amda = AMDA()
11 11
12 12
13 13 def amda_make_scalar(var=None):
14 14 if var is None:
15 15 return (((np.array([]), np.array([])), np.array([])), DataSeriesType.SCALAR)
16 16 else:
17 17 return (((var.time, np.array([])), var.data), DataSeriesType.SCALAR)
18 18
19 19
20 20 def amda_make_vector(var=None):
21 21 if var is None:
22 22 return (((np.array([]), np.array([])), np.array([])), DataSeriesType.VECTOR)
23 23 else:
24 24 return (((var.time, np.array([])), var.data), DataSeriesType.VECTOR)
25 25
26 26
27 27 def amda_make_multi_comp(var=None):
28 28 if var is None:
29 29 return (((np.array([]), np.array([])), np.array([])), DataSeriesType.MULTICOMPONENT)
30 30 else:
31 31 return (((var.time, np.array([])), var.data), DataSeriesType.MULTICOMPONENT)
32 32
33 33
34 34 def amda_make_spectro(var=None):
35 35 if var is None:
36 36 return (((np.array([]), np.array([])), np.array([])), DataSeriesType.SPECTROGRAM)
37 37 else:
38 38 min_sampling = float(var.meta.get("DATASET_MIN_SAMPLING", "nan"))
39 39 max_sampling = float(var.meta.get("DATASET_MAX_SAMPLING", "nan"))
40 40 if var.y is None and len(var.data):
41 41 var.y = np.logspace(1, 3, var.data.shape[1])[::-1]
42 42 return (((var.time, var.y), var.data), DataSeriesType.SPECTROGRAM)
43 43 #return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data,min_sampling,max_sampling,True)
44 44
45 45
46 46 def amda_get_sample(metadata, start, stop):
47 47 ts_type = amda_make_scalar
48 48 try:
49 49 param_id = None
50 50 for key, value in metadata:
51 51 if key == 'xml:id':
52 52 param_id = value
53 53 elif key == 'type':
54 54 if value == 'vector':
55 55 ts_type = amda_make_vector
56 56 elif value == 'multicomponent':
57 57 ts_type = amda_make_multi_comp
58 58 elif value == 'spectrogram':
59 59 ts_type = amda_make_spectro
60 60 tstart = datetime.fromtimestamp(start, tz=timezone.utc)
61 61 tend = datetime.fromtimestamp(stop, tz=timezone.utc)
62 62 var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST")
63 63 return ts_type(var)
64 64 except Exception as e:
65 65 print(traceback.format_exc())
66 66 print("Error in amda.py ", str(e))
67 67 return ts_type()
68 68
69 69
70 70 class AmdaProvider(PyDataProvider):
71 71 def __init__(self):
72 72 super(AmdaProvider, self).__init__()
73 73 if len(amda.component) is 0:
74 74 amda.update_inventory()
75 75 parameters = copy.deepcopy(amda.parameter)
76 76 for name, component in amda.component.items():
77 77 if 'components' in parameters[component['parameter']]:
78 78 parameters[component['parameter']]['components'].append(component)
79 79 else:
80 80 parameters[component['parameter']]['components']=[component]
81 81
82 82 products = []
83 83 for key, parameter in parameters.items():
84 84 mission_name = amda.mission[parameter['mission']]['name']
85 85 observatory_name = parameter.get('observatory','')
86 86 if observatory_name != '':
87 87 observatory_name = amda.observatory[observatory_name]['name']
88 88 instrument_name = amda.instrument[parameter['instrument']]['name']
89 89 dataset_name = amda.dataset[parameter['dataset']]['name']
90 90 path = f"/AMDA/{mission_name}/{observatory_name}/{instrument_name}/{dataset_name}/{parameter['name']}"
91 91 components = [component['name'] for component in parameter.get('components',[])]
92 92 metadata = {key: item for key, item in parameter.items() if key is not 'components'}
93 93 n_components = parameter.get('size', 0)
94 94 if n_components == '3':
95 95 metadata["type"] = "vector"
96 96 elif parameter.get('display_type', '')=="spectrogram":
97 97 metadata["type"] = "spectrogram"
98 98 elif n_components != 0:
99 99 metadata["type"] = "multicomponent"
100 100 else:
101 101 metadata["type"] = "scalar"
102 102 products.append(Product(path, components, metadata))
103 103 self.register_products(products)
104 104 for _,mission in amda.mission.items():
105 105 if ('target' in mission) and (mission['xml:id'] != 'Ephemerides') and (mission['target'] != 'Earth'):
106 106 self.set_icon(f'/AMDA/{mission["name"]}','satellite')
107 107
108 108 def get_data(self, metadata, start, stop):
109 109 ts_type = amda_make_scalar
110 110 try:
111 111 param_id = metadata['xml:id']
112 112 ts_type_str = metadata['type']
113 113 if ts_type_str == 'vector':
114 114 ts_type = amda_make_vector
115 115 elif ts_type_str == 'multicomponent':
116 116 ts_type = amda_make_multi_comp
117 117 elif ts_type_str == 'spectrogram':
118 118 ts_type = amda_make_spectro
119 119 tstart = datetime.fromtimestamp(start, tz=timezone.utc)
120 120 tend = datetime.fromtimestamp(stop, tz=timezone.utc)
121 121 var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST")
122 122 return ts_type(var)
123 123 except Exception as e:
124 124 print(traceback.format_exc())
125 125 print("Error in amda.py ", str(e))
126 126 return ts_type()
127 127
128 128 _amda = AmdaProvider()
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@@ -1,93 +1,93
1 1 import traceback
2 2 from SciQLopBindings import PyDataProvider, Product, VectorTimeSerie, ScalarTimeSerie, DataSeriesType
3 3 import numpy as np
4 4 import math
5 from spwc.cache import _cache
6 from spwc.common.datetime_range import DateTimeRange
5 from speasy.cache import _cache
6 from speasy.common.datetime_range import DateTimeRange
7 7 from functools import partial
8 8 from datetime import datetime, timedelta, timezone
9 from spwc.common.variable import SpwcVariable
9 from speasy.common.variable import SpeasyVariable
10 10
11 11
12 12 def make_scalar(x):
13 13 y = np.cos(x/10.)
14 return SpwcVariable(time=x, data=y)
14 return SpeasyVariable(time=x, data=y)
15 15
16 16 def make_vector(x):
17 17 v=np.ones((len(x),3))
18 18 for i in range(3):
19 19 v.transpose()[:][i] = np.cos(x/10. + float(i)) + (100. * np.cos(x/10000. + float(i)))
20 return SpwcVariable(time=x, data=v)
20 return SpeasyVariable(time=x, data=v)
21 21
22 22
23 23 def make_multicomponent(x):
24 24 v=np.ones((len(x),4))
25 25 for i in range(4):
26 26 v.transpose()[:][i] = float(i+1) * np.cos(x/10. + float(i))
27 return SpwcVariable(time=x, data=v)
27 return SpeasyVariable(time=x, data=v)
28 28
29 29 def make_spectrogram(x):
30 30 v=np.ones((len(x),32))
31 31 for i in range(32):
32 32 v.transpose()[:][i] = 100.*(2.+ float(i+1) * np.cos(x/1024. + float(i)))
33 return SpwcVariable(time=x, data=v)
33 return SpeasyVariable(time=x, data=v)
34 34
35 35
36 36 def _get_data(p_type, start, stop):
37 37 if type(start) is datetime:
38 38 start = start.timestamp()
39 39 stop = stop.timestamp()
40 40 x = np.arange(math.ceil(start), math.floor(stop))*1.
41 41 if p_type == 'scalar':
42 42 return make_scalar(x)
43 43 if p_type == 'vector':
44 44 return make_vector(x)
45 45 if p_type == 'multicomponent':
46 46 return make_multicomponent(x)
47 47 if p_type == 'spectrogram':
48 48 return make_spectrogram(np.arange(math.ceil(start), math.floor(stop),15.))
49 49 return None
50 50
51 51 class MyProvider(PyDataProvider):
52 52 def __init__(self):
53 53 super(MyProvider,self).__init__()
54 54 self.register_products([Product("/tests/without_cache/scalar",[],{"type":"scalar"}),
55 55 Product("/tests/without_cache/vector",[],{"type":"vector"}),
56 56 Product("/tests/without_cache/multicomponent",[],{"type":"multicomponent",'size':'4'}),
57 57 Product("/tests/without_cache/spectrogram",[],{"type":"spectrogram",'size':'32'}),
58 58 Product("/tests/with_cache/scalar",[],{"type":"scalar", "cache":"true"}),
59 59 Product("/tests/with_cache/vector",[],{"type":"vector", "cache":"true"}),
60 60 Product("/tests/with_cache/multicomponent",[],{"type":"multicomponent",'size':'4', "cache":"true"})
61 61 ])
62 62
63 63 def get_data(self,metadata,start,stop):
64 64 ts_type = DataSeriesType.SCALAR
65 65 default_ctor_args = 1
66 66 use_cache = False
67 67 p_type = 'scalar'
68 68 try:
69 69 for key,value in metadata.items():
70 70 if key == 'type':
71 71 p_type = value
72 72 if value == 'vector':
73 73 ts_type = DataSeriesType.VECTOR
74 74 elif value == 'multicomponent':
75 75 ts_type = DataSeriesType.MULTICOMPONENT
76 76 elif value == 'spectrogram':
77 77 ts_type = DataSeriesType.SPECTROGRAM
78 78 if key == 'cache' and value == 'true':
79 79 use_cache = True
80 80 if use_cache:
81 81 cache_product = f"tests/{p_type}"
82 82 var = _cache.get_data(cache_product, DateTimeRange(datetime.fromtimestamp(start, tz=timezone.utc), datetime.fromtimestamp(stop, tz=timezone.utc)), partial(_get_data, p_type), fragment_hours=24)
83 83 else:
84 84 var = _get_data(p_type, start, stop)
85 85 return (((var.time, np.array([])),var.data), ts_type)
86 86 except Exception as e:
87 87 print(traceback.format_exc())
88 88 print("Error in test.py ",str(e))
89 89 return (((np.array([]), np.array([])), np.array([])), ts_type)
90 90
91 91
92 92 t=MyProvider()
93 93
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@@ -1,31 +1,31
1 1 #!/bin/bash
2 2 # guess using centos 7 as build host
3 3 yum install -y gtk3 openssl-devel.x86_64 ncurses-devel.x86_64 sqlite-devel.x86_64 tkinter.x86_64 readline-devel.x86_64 xz-devel.x86_64 gdbm-devel.x86_64 bzip2-devel.x86_64 tk-devel.x86_64 libffi-devel.x86_64 make
4 4 HERE="$(dirname "$(readlink -f "${0}")")"
5 5 SCIQLOP_SCR=$HERE/../../
6 6 SCIQLOP_BUILD=$SCIQLOP_SCR/build/
7 7 SCIQLOP_APPDIR=$SCIQLOP_BUILD/AppDir
8 8 mkdir $SCIQLOP_BUILD
9 9 cd $SCIQLOP_BUILD
10 10 # need to build python to easily install/relocate in AppImage
11 11 wget https://www.python.org/ftp/python/3.7.3/Python-3.7.3.tgz
12 12 tar -xf Python-3.7.3.tgz
13 13 cd Python-3.7.3
14 14 # Optimisation is damn slow maybe enabled later
15 15 ./configure --enable-shared --prefix=/usr
16 16 make -j
17 17 DESTDIR=$SCIQLOP_BUILD/AppDir make install
18 18 cd ..
19 19 cp $HERE/AppRun $SCIQLOP_APPDIR/
20 20 chmod +x $SCIQLOP_APPDIR/AppRun
21 21 # Tweak to find custom python from build dir
22 22 sed "s|/usr|$SCIQLOP_APPDIR/usr|" -i $SCIQLOP_APPDIR/usr/lib/pkgconfig/python3.pc
23 LD_PRELOAD=$SCIQLOP_APPDIR/usr/lib/libpython3.7m.so.1.0 PATH=$SCIQLOP_APPDIR/usr/bin/:/usr/bin/ LD_LIBRARY_PATH=AppDir/usr/lib/:AppDir/usr/lib/python3.7/ $SCIQLOP_APPDIR/usr/bin/python3 $SCIQLOP_APPDIR/usr/bin/pip3 install git+https://github.com/jeandet/spwc
23 LD_PRELOAD=$SCIQLOP_APPDIR/usr/lib/libpython3.7m.so.1.0 PATH=$SCIQLOP_APPDIR/usr/bin/:/usr/bin/ LD_LIBRARY_PATH=AppDir/usr/lib/:AppDir/usr/lib/python3.7/ $SCIQLOP_APPDIR/usr/bin/python3 $SCIQLOP_APPDIR/usr/bin/pip3 install speasy
24 24 LD_LIBRARY_PATH=$SCIQLOP_APPDIR/usr/lib/ PKG_CONFIG_PATH=$SCIQLOP_APPDIR/usr/lib/pkgconfig/:$PKG_CONFIG_PATH PATH=$SCIQLOP_APPDIR/usr/bin/:$PATH meson --prefix=/usr ..
25 25 ninja
26 26 DESTDIR=$SCIQLOP_APPDIR ninja install
27 27 cp -rf $SCIQLOP_APPDIR/usr/lib64/* $SCIQLOP_APPDIR/usr/lib/
28 28 rm -rf $SCIQLOP_APPDIR/usr/lib64/
29 29 wget https://github.com/probonopd/linuxdeployqt/releases/download/continuous/linuxdeployqt-continuous-x86_64.AppImage
30 30 chmod +x linuxdeployqt-continuous-x86_64.AppImage && ./linuxdeployqt-continuous-x86_64.AppImage --appimage-extract
31 31 LD_LIBRARY_PATH=$SCIQLOP_APPDIR/usr/lib:$SCIQLOP_APPDIR/usr/lib/SciQLop/:$SCIQLOP_APPDIR/usr/lib/python3.7/site-packages/numpy/.libs/ ./squashfs-root/AppRun $SCIQLOP_APPDIR/usr/share/applications/*.desktop -appimage -extra-plugins=iconengines,platformthemes/libqgtk3.so
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@@ -1,15 +1,15
1 1 #!/bin/bash
2 2 # On OS X only 0.49.1 works :(
3 3 pip3 install --upgrade --user meson==0.49.1
4 4 HERE=$( cd "$(dirname "$0")" ; pwd -P )
5 5 mkdir build
6 6 ~/Library/Python/3.7/bin/meson -Dcpp_args='-DQT_STATICPLUGIN' -Ddefault_library=static --prefix=/tmp/SciQLOP.app --bindir=Contents/MacOS build
7 7 cd build
8 8 ninja
9 9 ninja install
10 10 ~/Library/Python/3.7/bin/virtualenv --always-copy /tmp/SciQLOP.app
11 11 ~/Library/Python/3.7/bin/virtualenv --relocatable /tmp/SciQLOP.app
12 12 source /tmp/SciQLOP.app/bin/activate
13 /tmp/SciQLOP.app/bin/pip install git+https://github.com/jeandet/spwc
13 /tmp/SciQLOP.app/bin/pip install speasy
14 14 cp $HERE/SciQLOP_wrapper /tmp/SciQLOP.app/Contents/MacOS/
15 15 chmod +x /tmp/SciQLOP.app/Contents/MacOS/SciQLOP_wrapper
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