def test_Metadata():
md = Metadata()
assert repr(md) == "Metadata({})"
dic = {1: 1}
md.put("dict", dic)
val = md.get("dict")
val[2] = 2
del val
dic[3] = 3
del dic
md["dict"][4] = 4
assert md["dict"] == {1: 1, 2: 2, 3: 3, 4: 4}
md = Metadata({"array": np.array([3, 4])})
md["dict"] = {1: 1, 2: 2}
md["str'i\"ng"] = "str'i\"ng"
md["str'ing"] = "str'ing"
md["double"] = 3.14
md["bool"] = True
md["int"] = 7
md["string"] = "str\0ing"
md["string"] = "str\ning"
md["str\ting"] = "str\ting"
md["str\0ing"] = "str\0ing"
md["str\\0ing"] = "str\\0ing"
md_copy = pickle.loads(pickle.dumps(md))
for i in md:
if i == "array":
assert (md[i] == md_copy[i]).all()
else:
assert md[i] == md_copy[i]
md_copy2 = Metadata(dict(md))
assert not md_copy2.modified()
assert md.modified() == md_copy.modified()
md = Metadata({
"<class 'numpy.ndarray'>": np.array([3, 4]),
"<class 'dict'>": {
1: 1,
2: 2
},
"string": "string",
"double": 3.14,
"bool": True,
"long": 7,
"<class 'bytes'>": b"\xba\xd0\xba\xd0",
"<class 'NoneType'>": None,
})
for i in md:
assert md.type(i) == i
md[b"\xba\xd0"] = b"\xba\xd0"
md_copy = pickle.loads(pickle.dumps(md))
for i in md:
if i == "<class 'numpy.ndarray'>":
assert (md[i] == md_copy[i]).all()
else:
assert md[i] == md_copy[i]
del md["<class 'numpy.ndarray'>"]
md_copy.erase("<class 'numpy.ndarray'>")
assert not "<class 'numpy.ndarray'>" in md
assert not "<class 'numpy.ndarray'>" in md_copy
assert md.keys() == md_copy.keys()
with pytest.raises(TypeError, match="Metadata"):
reversed(md)
md = Metadata({1: 1, 3: 3})
md_copy = Metadata({2: 2, 3: 30})
md += md_copy
assert md.__repr__() == "Metadata({'1': 1, '2': 2, '3': 30})"
# Test with real data
dic = {
"_format": "MSEED",
"arrival.time": 1356901212.242550,
"calib": 1.000000,
"chan": "BHZ",
"delta": 0.025000,
"deltim": -1.000000,
"endtime": 1356904168.544538,
"iphase": "P",
"loc": "",
"mseed": {
"dataquality": "D",
"number_of_records": 36,
"encoding": "STEIM2",
"byteorder": ">",
"record_length": 4096,
"filesize": 726344704,
},
"net": "CI",
"npts": 144000,
"phase": "P",
"sampling_rate": 40.000000,
"site.elev": 0.258000,
"site.lat": 35.126900,
"site.lon": -118.830090,
"site_id": "5fb6a67b37f8eef2f0658e9a",
"sta": "ARV",
"starttime": 1356900568.569538,
}
md = Metadata(dic)
md["mod"] = "mod"
md_copy = pickle.loads(pickle.dumps(md))
for i in md:
assert md[i] == md_copy[i]
assert md.modified() == md_copy.modified()
def test_Metadata():
md = Metadata()
assert repr(md) == 'Metadata({})'
dic = {1: 1}
md.put('dict', dic)
val = md.get('dict')
val[2] = 2
del val
dic[3] = 3
del dic
md['dict'][4] = 4
assert md['dict'] == {1: 1, 2: 2, 3: 3, 4: 4}
md = Metadata({'array': np.array([3, 4])})
md['dict'] = {1: 1, 2: 2}
md['str\'i"ng'] = 'str\'i"ng'
md["str'ing"] = "str'ing"
md['double'] = 3.14
md['bool'] = True
md['int'] = 7
md["string"] = "str\0ing"
md["string"] = "str\ning"
md["str\ting"] = "str\ting"
md["str\0ing"] = "str\0ing"
md["str\\0ing"] = "str\\0ing"
md_copy = pickle.loads(pickle.dumps(md))
for i in md:
if i == 'array':
assert (md[i] == md_copy[i]).all()
else:
assert md[i] == md_copy[i]
md = Metadata({
"<class 'numpy.ndarray'>": np.array([3, 4]),
"<class 'dict'>": {
1: 1,
2: 2
},
'string': 'string',
'double': 3.14,
'bool': True,
'long': 7,
"<class 'bytes'>": b'\xba\xd0\xba\xd0',
"<class 'NoneType'>": None
})
for i in md:
assert md.type(i) == i
md[b'\xba\xd0'] = b'\xba\xd0'
md_copy = pickle.loads(pickle.dumps(md))
for i in md:
if i == "<class 'numpy.ndarray'>":
assert (md[i] == md_copy[i]).all()
else:
assert md[i] == md_copy[i]
del md["<class 'numpy.ndarray'>"]
md_copy.erase("<class 'numpy.ndarray'>")
assert not "<class 'numpy.ndarray'>" in md
assert not "<class 'numpy.ndarray'>" in md_copy
assert md.keys() == md_copy.keys()
with pytest.raises(TypeError, match='Metadata'):
reversed(md)
md = Metadata({1: 1, 3: 3})
md_copy = Metadata({2: 2, 3: 30})
md += md_copy
assert md.__repr__() == "Metadata({'1': 1, '2': 2, '3': 30})"
