test beautification with black --line-length 150

Author: fangq

test/testjd.py

@@ -19,44 +19,42 @@
 import jdata as jd
 import numpy as np
 from collections import OrderedDict
- 
+
+
 class TestModule(unittest.TestCase):
     def test_module(self):
-        data=OrderedDict();
-        data['const']=[2.0, 1, True, False, None, float('nan'), float('-inf')];
-        data['shortarray']=[1,2,3];
-        data['a_complex']=1+2.0j;
-        data['object']=[[[1],[2],[3]],None, False];
-        data['a_typedarray']=np.asarray([9,9,9,9],dtype=np.uint8);
-        data['a_ndarray']=np.arange(1,10,dtype=np.int32).reshape(3,3);
-        data['a_biginteger']=9007199254740991;
-        data['a_map']={
-          float('nan'): 'one',
-          2: 'two',
-          "k": 'three'
-        };
-
-        print('== Original Python native data ==')
-        newdata=data.copy();
-        print(newdata);
-
-        print('== JData-annotated data ==')
-        print(jd.show(jd.encode(newdata),indent=4, default=jd.jsonfilter));
-
-        print('== JData-annotated data exported to JSON with zlib compression ==')
-        newdata=data.copy();
-        print(jd.show(jd.encode(newdata,{'compression':'zlib','base64':True}), indent=4, default=jd.jsonfilter));
-
-        print('== Decoding a JData-encoded data and printed in JSON format ==')
-        newdata=data.copy();
-        print(jd.show(jd.decode(jd.encode(newdata)), indent=4, default=jd.jsonfilter));
-
-        print('== Saving encoded data to test.json ==')
-        jd.save(data,'test.json')
-
-        print('== Loading data from test.json and decode ==')
-        newdata=jd.load('test.json')
-        print(jd.show(newdata, indent=4, default=jd.jsonfilter));
-
-if __name__ == '__main__':
+        data = OrderedDict()
+        data["const"] = [2.0, 1, True, False, None, float("nan"), float("-inf")]
+        data["shortarray"] = [1, 2, 3]
+        data["a_complex"] = 1 + 2.0j
+        data["object"] = [[[1], [2], [3]], None, False]
+        data["a_typedarray"] = np.asarray([9, 9, 9, 9], dtype=np.uint8)
+        data["a_ndarray"] = np.arange(1, 10, dtype=np.int32).reshape(3, 3)
+        data["a_biginteger"] = 9007199254740991
+        data["a_map"] = {float("nan"): "one", 2: "two", "k": "three"}
+
+        print("== Original Python native data ==")
+        newdata = data.copy()
+        print(newdata)
+
+        print("== JData-annotated data ==")
+        print(jd.show(jd.encode(newdata), indent=4, default=jd.jsonfilter))
+
+        print("== JData-annotated data exported to JSON with zlib compression ==")
+        newdata = data.copy()
+        print(jd.show(jd.encode(newdata, {"compression": "zlib", "base64": True}), indent=4, default=jd.jsonfilter))
+
+        print("== Decoding a JData-encoded data and printed in JSON format ==")
+        newdata = data.copy()
+        print(jd.show(jd.decode(jd.encode(newdata)), indent=4, default=jd.jsonfilter))
+
+        print("== Saving encoded data to test.json ==")
+        jd.save(data, "test.json")
+
+        print("== Loading data from test.json and decode ==")
+        newdata = jd.load("test.json")
+        print(jd.show(newdata, indent=4, default=jd.jsonfilter))
+
+
+if __name__ == "__main__":
     unittest.main()