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124 lines
4.4 KiB
Python
124 lines
4.4 KiB
Python
import os
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import gzip
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import numpy as np
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import pyerrors as pe
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import pyerrors.input.json as jsonio
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def test_jsonio():
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o = pe.pseudo_Obs(1.0, .2, 'one')
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o2 = pe.pseudo_Obs(0.5, .1, 'two|r1')
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o3 = pe.pseudo_Obs(0.5, .1, 'two|r2')
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o4 = pe.merge_obs([o2, o3])
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otag = 'This has been merged!'
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o4.tag = otag
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do = o - .2 * o4
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co1 = pe.cov_Obs(1., .123, 'cov1')
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co3 = pe.cov_Obs(4., .1 ** 2, 'cov3')
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do *= co1 / co3
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do.tag = {'A': 2}
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o5 = pe.pseudo_Obs(0.8, .1, 'two|r2')
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co2 = pe.cov_Obs([1, 2], [[.12, .004], [.004, .02]], 'cov2')
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o5 /= co2[0]
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o3 /= co2[1]
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o5.tag = 2 * otag
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testl = [o3, o5]
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arr = np.array([o3, o5])
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mat = np.array([[pe.pseudo_Obs(1.0, .1, 'mat'), pe.pseudo_Obs(0.3, .1, 'mat')], [pe.pseudo_Obs(0.2, .1, 'mat'), pe.pseudo_Obs(2.0, .4, 'mat')]])
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mat[0][1].tag = ['This', 'is', 2, None]
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mat[1][0].tag = '{testt}'
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mat[1][1].tag = '[tag]'
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tt1 = pe.Obs([np.random.rand(100)], ['t|r1'], idl=[range(2, 202, 2)])
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tt2 = pe.Obs([np.random.rand(100)], ['t|r2'], idl=[range(2, 202, 2)])
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tt3 = pe.Obs([np.random.rand(102)], ['qe'])
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tt = tt1 + tt2 + tt3
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tt.tag = 'Test Obs: Ä'
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ol = [o4, do, testl, mat, arr, np.array([o]), np.array([tt, tt]), [tt, tt], co1, co2, np.array(co2), co1 / co2[0]]
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fname = 'test_rw'
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jsonio.dump_to_json(ol, fname, indent=1, description='[I am a tricky description]')
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rl = jsonio.load_json(fname)
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os.remove(fname + '.json.gz')
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for o, r in zip(ol, rl):
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assert np.all(o == r)
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for i in range(len(ol)):
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if isinstance(ol[i], pe.Obs):
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o = ol[i] - rl[i]
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assert(o.is_zero())
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assert(ol[i].tag == rl[i].tag)
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or1 = np.ravel(ol[i])
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or2 = np.ravel(rl[i])
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for j in range(len(or1)):
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o = or1[j] - or2[j]
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assert(o.is_zero())
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description = {'I': {'Am': {'a': 'nested dictionary!'}}}
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jsonio.dump_to_json(ol, fname, indent=0, gz=False, description=description)
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rl = jsonio.load_json(fname, gz=False, full_output=True)
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os.remove(fname + '.json')
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for o, r in zip(ol, rl['obsdata']):
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assert np.all(o == r)
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assert(description == rl['description'])
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def test_json_string_reconstruction():
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my_obs = pe.Obs([np.random.rand(100)], ['name'])
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json_string = pe.input.json.create_json_string(my_obs)
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reconstructed_obs1 = pe.input.json.import_json_string(json_string)
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assert my_obs == reconstructed_obs1
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compressed_string = gzip.compress(json_string.encode('utf-8'))
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reconstructed_string = gzip.decompress(compressed_string).decode('utf-8')
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reconstructed_obs2 = pe.input.json.import_json_string(reconstructed_string)
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assert reconstructed_string == json_string
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assert my_obs == reconstructed_obs2
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def test_json_corr_io():
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my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
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rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list)
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for obs_list in [my_list, rw_list]:
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for tag in [None, "test"]:
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obs_list[3].tag = tag
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for fp in [0, 2]:
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for bp in [0, 7]:
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for corr_tag in [None, 'my_Corr_tag']:
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my_corr = pe.Corr(obs_list, padding_front=fp, padding_back=bp)
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my_corr.tag = corr_tag
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pe.input.json.dump_to_json(my_corr, 'corr')
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recover = pe.input.json.load_json('corr')
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assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]])
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assert my_corr.tag == recover.tag
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assert my_corr.reweighted == recover.reweighted
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def test_json_corr_2d_io():
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obs_list = [np.array([[pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test'), pe.pseudo_Obs(0.0, 0.1 * i, 'test')], [pe.pseudo_Obs(0.0, 0.1 * i, 'test'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test')]]) for i in range(8)]
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for tag in [None, "test"]:
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obs_list[3][0, 1].tag = tag
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for padding in [0, 1]:
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my_corr = pe.Corr(obs_list, padding_front=padding, padding_back=padding)
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my_corr.tag = tag
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pe.input.json.dump_to_json(my_corr, 'corr')
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recover = pe.input.json.load_json('corr')
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assert np.all([np.all([o.is_zero() for o in q]) for q in [x.ravel() for x in (my_corr - recover) if x is not None]])
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assert my_corr.tag == recover.tag
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