Merge pull request #34 from fjosw/feature/json_corr_Nd

Feature/json corr nd

pyerrors/input/json.py

@@ -8,6 +8,7 @@ import platform
 import warnings
 from ..obs import Obs
 from ..covobs import Covobs
+from ..correlators import Corr
 from .. import version as pyerrorsversion
 
 
@@ -173,6 +174,18 @@ def create_json_string(ol, description='', indent=1):
             d['cdata'] = cdata
         return d
 
+    def write_Corr_to_dict(my_corr):
+        front_padding = next(i for i, j in enumerate(my_corr.content) if np.all(j))
+        back_padding_start = front_padding + next((i for i, j in enumerate(my_corr.content[front_padding:]) if not np.all(j)), my_corr.T)
+        dat = write_Array_to_dict(np.array(my_corr.content[front_padding:back_padding_start]))
+        dat['type'] = 'Corr'
+        corr_meta_data = str(front_padding) + '|' + str(my_corr.T - back_padding_start) + '|' + str(my_corr.tag)
+        if 'tag' in dat.keys():
+            dat['tag'].append(corr_meta_data)
+        else:
+            dat['tag'] = [corr_meta_data]
+        return dat
+
     if not isinstance(ol, list):
         ol = [ol]
 
@@ -193,6 +206,10 @@ def create_json_string(ol, description='', indent=1):
             d['obsdata'].append(write_List_to_dict(io))
         elif isinstance(io, np.ndarray):
             d['obsdata'].append(write_Array_to_dict(io))
+        elif isinstance(io, Corr):
+            d['obsdata'].append(write_Corr_to_dict(io))
+        else:
+            raise Exception("Unkown datatype.")
 
     jsonstring = json.dumps(d, indent=indent, cls=my_encoder, ensure_ascii=False)
 
@@ -374,6 +391,22 @@ def import_json_string(json_string, verbose=True, full_output=False):
             ret[-1].tag = taglist[i]
         return np.reshape(ret, layout)
 
+    def get_Corr_from_dict(o):
+        taglist = o.get('tag')
+        corr_meta_data = taglist[-1].split('|')
+        padding_front = int(corr_meta_data[0])
+        padding_back = int(corr_meta_data[1])
+        corr_tag = corr_meta_data[2]
+        tmp_o = o
+        tmp_o['tag'] = taglist[:-1]
+        if len(tmp_o['tag']) == 0:
+            del tmp_o['tag']
+        dat = get_Array_from_dict(tmp_o)
+        my_corr = Corr(list(dat), padding_front=padding_front, padding_back=padding_back)
+        if corr_tag != 'None':
+            my_corr.tag = corr_tag
+        return my_corr
+
     json_dict = json.loads(json_string)
 
     prog = json_dict.get('program', '')
@@ -400,6 +433,10 @@ def import_json_string(json_string, verbose=True, full_output=False):
             ol.append(get_List_from_dict(io))
         elif io['type'] == 'Array':
             ol.append(get_Array_from_dict(io))
+        elif io['type'] == 'Corr':
+            ol.append(get_Corr_from_dict(io))
+        else:
+            raise Exception("Unkown datatype.")
 
     if full_output:
         retd = {}

tests/io_test.py

@@ -89,3 +89,36 @@ def test_json_string_reconstruction():
 
     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 fp in [0, 2]:
+                for bp in [0, 7]:
+                    for corr_tag in [None, 'my_Corr_tag']:
+                        my_corr = pe.Corr(obs_list, padding_front=fp, padding_back=bp)
+                        my_corr.tag = corr_tag
+                        pe.input.json.dump_to_json(my_corr, 'corr')
+                        recover = pe.input.json.load_json('corr')
+                        assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]])
+                        assert my_corr.tag == recover.tag
+                        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(8)]
+
+    for tag in [None, "test"]:
+        obs_list[3][0, 1].tag = tag
+        for padding in [0, 1]:
+            my_corr = pe.Corr(obs_list, padding_front=padding, padding_back=padding)
+            my_corr.tag = tag
+            pe.input.json.dump_to_json(my_corr, 'corr')
+            recover = pe.input.json.load_json('corr')
+            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]])
+            assert my_corr.tag == recover.tag

tests/obs_test.py

@@ -615,7 +615,7 @@ def test_covariance_symmetry():
     cov_ab = pe.covariance(test_obs1, a)
     cov_ba = pe.covariance(a, test_obs1)
     assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
-    assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
+    assert np.abs(cov_ab) < test_obs1.dvalue * a.dvalue * (1 + 10 * np.finfo(np.float64).eps)
 
 
 def test_empty_obs():