diff --git a/pyerrors/input/json.py b/pyerrors/input/json.py index 6b854874..cdf203f2 100644 --- a/pyerrors/input/json.py +++ b/pyerrors/input/json.py @@ -171,6 +171,9 @@ def create_json_string(ol, description='', indent=1): names.append(key) idl.append(value) my_obs = Obs(samples, names, idl) + my_obs._covobs = obs._covobs + for name in obs._covobs: + my_obs.names.append(name) my_obs.reweighted = obs.reweighted my_obs.is_merged = obs.is_merged return my_obs diff --git a/tests/json_io_test.py b/tests/json_io_test.py new file mode 100644 index 00000000..04ccfd9f --- /dev/null +++ b/tests/json_io_test.py @@ -0,0 +1,253 @@ +import os +import gzip +import numpy as np +import pyerrors as pe +import pyerrors.input.json as jsonio +import pytest + + +def test_jsonio(): + o = pe.pseudo_Obs(1.0, .2, 'one') + o2 = pe.pseudo_Obs(0.5, .1, 'two|r1') + o3 = pe.pseudo_Obs(0.5, .1, 'two|r2') + o4 = pe.merge_obs([o2, o3]) + otag = 'This has been merged!' + o4.tag = otag + do = o - .2 * o4 + co1 = pe.cov_Obs(1., .123, 'cov1') + co3 = pe.cov_Obs(4., .1 ** 2, 'cov3') + do *= co1 / co3 + do.tag = {'A': 2} + + o5 = pe.pseudo_Obs(0.8, .1, 'two|r2') + co2 = pe.cov_Obs([1, 2], [[.12, .004], [.004, .02]], 'cov2') + o5 /= co2[0] + o3 /= co2[1] + o5.tag = 2 * otag + testl = [o3, o5] + + arr = np.array([o3, o5]) + 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')]]) + mat[0][1].tag = ['This', 'is', 2, None] + mat[1][0].tag = '{testt}' + mat[1][1].tag = '[tag]' + + tt1 = pe.Obs([np.random.rand(100)], ['t|r1'], idl=[range(2, 202, 2)]) + tt2 = pe.Obs([np.random.rand(100)], ['t|r2'], idl=[range(2, 202, 2)]) + tt3 = pe.Obs([np.random.rand(102)], ['qe']) + + tt = tt1 + tt2 + tt3 + + tt.tag = 'Test Obs: Ä' + + ol = [o4, do, testl, mat, arr, np.array([o]), np.array([tt, tt]), [tt, tt], co1, co2, np.array(co2), co1 / co2[0]] + fname = 'test_rw' + + jsonio.dump_to_json(ol, fname, indent=1, description='[I am a tricky description]') + + rl = jsonio.load_json(fname) + + os.remove(fname + '.json.gz') + + for o, r in zip(ol, rl): + assert np.all(o == r) + + for i in range(len(ol)): + if isinstance(ol[i], pe.Obs): + o = ol[i] - rl[i] + assert(o.is_zero()) + assert(ol[i].tag == rl[i].tag) + or1 = np.ravel(ol[i]) + or2 = np.ravel(rl[i]) + for j in range(len(or1)): + o = or1[j] - or2[j] + assert(o.is_zero()) + + description = {'I': {'Am': {'a': 'nested dictionary!'}}} + jsonio.dump_to_json(ol, fname, indent=0, gz=False, description=description) + + rl = jsonio.load_json(fname, gz=False, full_output=True) + + os.remove(fname + '.json') + + for o, r in zip(ol, rl['obsdata']): + assert np.all(o == r) + + assert(description == rl['description']) + + +def test_json_string_reconstruction(): + my_obs = pe.Obs([np.random.rand(100)], ['name']) + + json_string = pe.input.json.create_json_string(my_obs) + reconstructed_obs1 = pe.input.json.import_json_string(json_string) + assert my_obs == reconstructed_obs1 + + compressed_string = gzip.compress(json_string.encode('utf-8')) + + reconstructed_string = gzip.decompress(compressed_string).decode('utf-8') + reconstructed_obs2 = pe.input.json.import_json_string(reconstructed_string) + + assert reconstructed_string == json_string + assert my_obs == reconstructed_obs2 + + +def test_json_corr_io(): + my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)] + rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list) + + for obs_list in [my_list, rw_list]: + for tag in [None, "test"]: + obs_list[3].tag = tag + for pad in [0, 2]: + for corr_tag in [None, 'my_Corr_tag']: + for prange in [None, [3, 6]]: + for gap in [False, True]: + my_corr = pe.Corr(obs_list, padding=[pad, pad], prange=prange) + my_corr.tag = corr_tag + if gap: + my_corr.content[4] = None + pe.input.json.dump_to_json(my_corr, 'corr') + recover = pe.input.json.load_json('corr') + os.remove('corr.json.gz') + assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]]) + for index, entry in enumerate(my_corr): + if entry is None: + assert recover[index] is None + assert my_corr.tag == recover.tag + assert my_corr.prange == recover.prange + assert my_corr.reweighted == recover.reweighted + + +def test_json_corr_2d_io(): + 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(4)] + + for tag in [None, "test"]: + obs_list[3][0, 1].tag = tag + for padding in [0, 1]: + for prange in [None, [3, 6]]: + my_corr = pe.Corr(obs_list, padding=[padding, padding], prange=prange) + my_corr.tag = tag + pe.input.json.dump_to_json(my_corr, 'corr') + recover = pe.input.json.load_json('corr') + os.remove('corr.json.gz') + 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]]) + for index, entry in enumerate(my_corr): + if entry is None: + assert recover[index] is None + assert my_corr.tag == recover.tag + assert my_corr.prange == recover.prange + + +def test_json_dict_io(): + def check_dict_equality(d1, d2): + def dict_check_obs(d1, d2): + for k, v in d1.items(): + if isinstance(v, dict): + v = dict_check_obs(v, d2[k]) + elif isinstance(v, list) and all([isinstance(o, pe.Obs) for o in v]): + for i in range(len(v)): + assert((v[i] - d2[k][i]).is_zero()) + elif isinstance(v, list): + v = list_check_obs(v, d2[k]) + elif isinstance(v, pe.Obs): + assert((v - d2[k]).is_zero()) + elif isinstance(v, pe.Corr): + for i in range(v.T): + assert((v[i] - d2[k][i]).is_zero()) + elif isinstance(v, np.ndarray): + a1 = np.ravel(v) + a2 = np.ravel(d2[k]) + for i in range(len(a1)): + assert((a1[i] - a2[i]).is_zero()) + + def list_check_obs(l1, l2): + for ei in range(len(l1)): + e = l1[ei] + if isinstance(e, list): + e = list_check_obs(e, l2[ei]) + elif isinstance(e, list) and all([isinstance(o, pe.Obs) for o in e]): + for i in range(len(e)): + assert((e[i] - l2[ei][i]).is_zero()) + elif isinstance(e, dict): + e = dict_check_obs(e, l2[ei]) + elif isinstance(e, pe.Obs): + assert((e - l2[ei]).is_zero()) + elif isinstance(e, pe.Corr): + for i in range(e.T): + assert((e[i] - l2[ei][i]).is_zero()) + elif isinstance(e, np.ndarray): + a1 = np.ravel(e) + a2 = np.ravel(l2[ei]) + for i in range(len(a1)): + assert((a1[i] - a2[i]).is_zero()) + dict_check_obs(d1, d2) + return True + + od = { + 'l': + { + 'a': pe.pseudo_Obs(1, .2, 'testa', samples=10), + 'b': [pe.pseudo_Obs(1.1, .1, 'test', samples=10), pe.pseudo_Obs(1.2, .1, 'test', samples=10), pe.pseudo_Obs(1.3, .1, 'test', samples=10)], + 'c': { + 'd': 1, + 'e': pe.pseudo_Obs(.2, .01, 'teste', samples=10), + 'f': pe.Corr([pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10)]), + 'g': np.reshape(np.asarray([pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10), pe.pseudo_Obs(.1, .01, 'a', samples=10)]), (2, 2)), + } + }, + 's': + { + 'a': 'Infor123', + 'b': ['Some', 'list'], + 'd': pe.pseudo_Obs(.01, .001, 'testd', samples=10) * pe.cov_Obs(1, .01, 'cov1'), + 'se': None, + 'sf': 1.2, + } + } + + fname = 'test_rw' + + desc = 'This is a random description' + + with pytest.raises(Exception): + jsonio.dump_dict_to_json(od, fname, description=desc, reps='|Test') + + jsonio.dump_dict_to_json(od, fname, description=desc, reps='TEST') + nd = jsonio.load_json_dict(fname, full_output=True, reps='TEST') + + with pytest.raises(Exception): + nd = jsonio.load_json_dict(fname, full_output=True) + + jsonio.dump_dict_to_json(od, fname, description=desc) + nd = jsonio.load_json_dict(fname, full_output=True) + assert (desc == nd['description']) + + assert(check_dict_equality(od, nd['obsdata'])) + nd = jsonio.load_json_dict(fname, full_output=False) + assert(check_dict_equality(od, nd)) + + nl = jsonio.load_json(fname, full_output=True) + nl = jsonio.load_json(fname, full_output=False) + + with pytest.raises(Exception): + jsonio.dump_dict_to_json(nl, fname, description=desc) + + od['k'] = 'DICTOBS2' + with pytest.raises(Exception): + jsonio.dump_dict_to_json(od, fname, description=desc) + + od['k'] = ['DICTOBS2'] + with pytest.raises(Exception): + jsonio.dump_dict_to_json(od, fname, description=desc) + + os.remove(fname + '.json.gz') + + +def test_renorm_deriv_of_corr(tmp_path): + c = pe.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)]) + c *= pe.cov_Obs(1., .1, '#ren') + c = c.deriv() + pe.input.json.dump_to_json(c, (tmp_path / 'test').as_posix()) + recover = pe.input.json.load_json((tmp_path / 'test').as_posix()) + assert np.all([o == 0 for o in (c - recover)[1:-1]]) diff --git a/tests/openQCD_in_test.py b/tests/openQCD_in_test.py new file mode 100644 index 00000000..d1dc999d --- /dev/null +++ b/tests/openQCD_in_test.py @@ -0,0 +1,49 @@ +import os +import numpy as np +import pyerrors as pe +import pytest + + +def test_openqcd(): + path = './tests//data/openqcd_test/' + prefix = 'sfqcd' + postfix = '.rwms' + + # sfqcd-1.6: Trajectories instead of confignumbers are printed to file. + rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix) + repname = list(rwfo[0].idl.keys())[0] + assert(rwfo[0].idl[repname] == range(1, 13)) + rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[1], r_stop=[12]) + assert(rwfo[0].idl[repname] == range(1, 13)) + rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[3], r_stop=[8]) + assert(rwfo[0].idl[repname] == range(3, 9)) + rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[2], r_stop=[6]) + assert(rwfo[0].idl[repname] == range(2, 7)) + rwfs = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[1], r_stop=[12], r_step=2) + assert(rwfs[0].idl[repname] == range(1, 12, 2)) + rwfs = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix, r_start=[2], r_stop=[12], r_step=2) + assert(rwfs[0].idl[repname] == range(2, 13, 2)) + rwfo = pe.input.openQCD.read_rwms(path, prefix, version='1.6', postfix=postfix) + assert((rwfo[0].r_values[repname] + rwfo[0].deltas[repname][1]) == (rwfs[0].r_values[repname] + rwfs[0].deltas[repname][0])) + + o = pe.pseudo_Obs(1., .01, repname, samples=12) + pe.reweight(rwfo[0], [o]) + + o = pe.pseudo_Obs(1., .01, repname, samples=6) + pe.reweight(rwfo[0], [o]) + o.idl[repname] = range(2, 13, 2) + pe.reweight(rwfo[0], [o]) + pe.reweight(rwfs[0], [o]) + + files = ['openqcd2r1.ms1.dat'] + names = ['openqcd2|r1'] + + # TM with 2 Hasenbusch factors and 2 sources each + RHMC with one source, openQCD 2.0 + rwfo = pe.input.openQCD.read_rwms(path, prefix, version='2.0', files=files, names=names) + assert(len(rwfo) == 2) + assert(rwfo[0].value == 0.9999974970236312) + assert(rwfo[1].value == 1.184681251089919) + repname = list(rwfo[0].idl.keys())[0] + assert(rwfo[0].idl[repname] == range(1, 10)) + rwfo = pe.input.openQCD.read_rwms(path, prefix, version='2.0', files=files, names=names, r_start=[1], r_stop=[8]) + assert(rwfo[0].idl[repname] == range(1, 9))