replace _decode with decode (#16)

* introduce `decode` and `decode_obj` and replace `_decode` method. * add docstrings, add black, isort, pylint * cleanup code * fix some cyclic imports * some refactoring and updating docs * poetry update + test version * ensure backwards compatibility * Update README.md * disable cron tests * poetry update
zincware · Oct 4, 2022 · eb61655 · eb61655
1 parent edadb06
commit eb61655
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diff --git a/.github/workflows/pytest.yaml b/.github/workflows/pytest.yaml
@@ -6,8 +6,8 @@ name: pytest
 on:
   push:
   pull_request:
-  schedule:
-    - cron: '14 3 * * 1'  # at 03:14 on Monday.
+#  schedule:
+#    - cron: '14 3 * * 1'  # at 03:14 on Monday.
 
 jobs:
   build:
@@ -22,7 +22,6 @@ jobs:
           - 3.8
         os:
           - ubuntu-latest
-#          - windows-latest
 
     steps:
       - uses: actions/checkout@v2

diff --git a/README.md b/README.md
@@ -77,17 +77,17 @@ class DatetimeConverter(ConverterBase):
     representation = "datetime"
     instance = datetime
 
-    def _encode(self, obj: datetime) -> str:
+    def encode(self, obj: datetime) -> str:
         """Convert the datetime object to str / isoformat"""
         return obj.isoformat()
-    def _decode(self, value: str) -> datetime:
+    def decode(self, value: str) -> datetime:
         """Create datetime object from str / isoformat"""
         return datetime.fromisoformat(value)
 ````
 
 This allows us to use this new serializer:
 ````python
-znjson.register(DatetimeConverter) # we need to register the new converter first
+znjson.config.register(DatetimeConverter) # we need to register the new converter first
 json_string = json.dumps(dt, cls=znjson.ZnEncoder, indent=4)
 json.loads(json_string, cls=znjson.ZnDecoder)
 ````

diff --git a/example/compare_to_jsonpickle.ipynb b/example/compare_to_jsonpickle.ipynb
@@ -19,20 +19,6 @@
    "cell_type": "code",
    "execution_count": 2,
    "outputs": [],
-   "source": [
-    "znjson.register(znjson.converter.NumpyConverterBase64)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [],
    "source": [
     "data1 = {\"a\": np.arange(1000), \"b\": [x for x in range(100)]}\n",
     "data2 = {\"a\": np.random.normal(size=(16, 16, 16)), \"b\": [x / 3 for x in range(100)]}\n",
@@ -44,10 +30,7 @@
     "}"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    }
   },
   {
@@ -56,24 +39,21 @@
     "The following results are not affected by running one timeit before the other."
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "59.6 µs ± 2.02 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
-      "241 µs ± 466 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
-      "1.07 ms ± 14 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
-      "50.7 µs ± 334 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
+      "253 µs ± 53.8 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
+      "1.11 ms ± 128 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
+      "4.25 ms ± 861 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "55.3 µs ± 11 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
      ]
     }
    ],
@@ -84,24 +64,21 @@
     "%timeit json.dumps(data4, cls=znjson.ZnEncoder)"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "199 µs ± 1.32 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
-      "386 µs ± 6.62 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
-      "2.24 ms ± 22.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
-      "200 µs ± 9.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
+      "736 µs ± 72.7 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
+      "1.65 ms ± 174 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
+      "8.57 ms ± 808 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "734 µs ± 114 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -112,24 +89,25 @@
     "%timeit jsonpickle.dumps(data4)"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "{\n",
       "  \"numpy\": {\n",
-      "    \"_type\": \"np.ndarray64\",\n",
-      "    \"value\": \"k05VTVBZAQB2AHsnZGVzY3InOiAnPGk0JywgJ2ZvcnRyYW5fb3JkZXInOiBGYWxzZSwgJ3NoYXBlJzogKDMsKSwgfSAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAoBAAAAAgAAAAMAAAA=\"\n",
+      "    \"_type\": \"np.ndarray_small\",\n",
+      "    \"value\": [\n",
+      "      1,\n",
+      "      2,\n",
+      "      3\n",
+      "    ]\n",
       "  },\n",
       "  \"list\": [\n",
       "    1,\n",
@@ -138,8 +116,12 @@
       "  ],\n",
       "  \"nested\": {\n",
       "    \"numpy\": {\n",
-      "      \"_type\": \"np.ndarray64\",\n",
-      "      \"value\": \"k05VTVBZAQB2AHsnZGVzY3InOiAnPGk0JywgJ2ZvcnRyYW5fb3JkZXInOiBGYWxzZSwgJ3NoYXBlJzogKDMsKSwgfSAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAoBAAAAAgAAAAMAAAA=\"\n",
+      "      \"_type\": \"np.ndarray_small\",\n",
+      "      \"value\": [\n",
+      "        1,\n",
+      "        2,\n",
+      "        3\n",
+      "      ]\n",
       "    },\n",
       "    \"list\": [\n",
       "      1,\n",
@@ -155,15 +137,12 @@
     "print(json.dumps(data4, cls=znjson.ZnEncoder, indent=2))"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "outputs": [
     {
      "name": "stdout",
@@ -291,15 +270,12 @@
     "print(jsonpickle.dumps(data4, indent=2))"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "outputs": [
     {
      "name": "stdout",
@@ -319,15 +295,12 @@
     "print(sys.getsizeof(jsonpickle.dumps(data4)))"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "outputs": [
     {
      "name": "stdout",
@@ -336,7 +309,7 @@
       "5995\n",
       "45467\n",
       "97859\n",
-      "573\n"
+      "219\n"
      ]
     }
    ],
@@ -347,10 +320,7 @@
     "print(sys.getsizeof(json.dumps(data4, cls=znjson.ZnEncoder)))"
    ],
    "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
+    "collapsed": false
    }
   }
  ],
@@ -375,4 +345,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}