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def run(self): model_path = self.arguments[0] module_name, model_name = model_path.rsplit(".", 1) try: module = importlib.import_module(module_name) except ImportError: pass model = getattr(module, model_name, None) if model is None: pass if type(model) != Viewable: pass model_obj = model() model_json = json.dumps( json.loads(serialize_json(model_obj.dump(changed_only=False))), sort_keys=True, indent=2, separators=(',', ': ') ) rst_text = MODEL_TEMPLATE.render( model_path=model_path, model_json=model_json, ) result = ViewList() for line in rst_text.split("\n"): result.append(line, "<bokeh-model>") node = nodes.paragraph() node.document = self.state.document nested_parse_with_titles(self.state, result, node) return node.children
10,202
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10
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class Test(unittest.TestCase): def test_all(self): data = '76 A9 14 89 AB CD EF AB BA AB BA AB BA AB BA AB BA AB BA AB BA AB BA 88 AC'.replace(' ', '').decode('hex') self.assertEquals( list(script.parse(data)), [('UNK_118', None), ('UNK_169', None), ('PUSH', '\x89\xab\xcd\xef\xab\xba\xab\xba\xab\xba\xab\xba\xab\xba\xab\xba\xab\xba\xab\xba'), ('UNK_136', None), ('CHECKSIG', None)], ) self.assertEquals(script.get_sigop_count(data), 1)
10,203
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13
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def _merge_data(appId, ids, group_by, ext): eStatAPI._['appId'] = appId aggregate = request.args.get('aggregate') if request.args.get( 'aggregate') is not None else '' data = eStatAPI.merge_data(ids, group_by, aggregate) eStatAPI.path['tmp_merge'] = eStatAPI.path['tmp'] + '.'.join( [eStatAPI._['appId'], ''.join([l for l in random.choice(eStatAPI.random_str)]), 'csv']) data.to_csv(eStatAPI.path['tmp_merge'], quoting=csv.QUOTE_NONNUMERIC, index=None) tmp_csv = eStatAPI.cmd_line(eStatAPI.build_cmd( ['cat', eStatAPI.path['tmp_merge']])) eStatAPI.cmd_line(eStatAPI.build_cmd(['rm', eStatAPI.path['tmp_merge']])) return eStatAPI.response(eStatAPI.get_output(tmp_csv, ext), ext)
10,204
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14
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def readdate(data): datepos = -1 if data[:5] == 'DATE ': datepos = 0 else: datepos = data.find('\nDATE ') if datepos >= 0: datepos = datepos + 1 if datepos < 0: return None datestr = '' datepos = datepos + 5 while True: if datepos >= len(data): return None d = data[datepos] if d >= '0' and d <= '9': datestr = datestr + d elif d == '\n' or d == '\r': if len(datestr) == 8: return datestr[:4] + '-' + datestr[4:6] + '-' + datestr[6:] return None elif d != ' ' and d != '-': return None datepos = datepos + 1
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"review", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "filediff", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_diff_comment", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "review", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_reply", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "user", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "user", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "get_review_reply_diff_comment_list_url", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "review_reply_diff_comment_list_mimetype", "ctx": {"_type": "Load", "_fields": {}}}}]}}}}], "name": "setup_review_request_child_test", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def setup_review_request_child_test(self, review_request): if not review_request.repository_id: # The group tests don't create a repository by default. review_request.repository = self.create_repository() review_request.save() diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review_request.publish(review_request.submitter) review = self.create_review(review_request, publish=True) self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=self.user) return (get_review_reply_diff_comment_list_url(reply), review_reply_diff_comment_list_mimetype)
10,206
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9
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "user", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "with_local_site", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "local_site_name", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "populate_items", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_review_request", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", 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"_fields": {"id": "reply", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "filediff", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_diff_comment", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "reply_to", "value": {"_type": "Name", "_fields": {"id": "comment", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "items", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}], "test": {"_type": "Name", "_fields": {"id": "populate_items", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [{"_type": "Assign", "_fields": {"value": {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}, "targets": [{"_type": "Name", "_fields": {"id": "items", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}]}}, {"_type": "Return", "_fields": {"value": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "local_site_name", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "get_review_reply_diff_comment_list_url", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "review_reply_diff_comment_list_mimetype", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "items", "ctx": {"_type": "Load", "_fields": {}}}}]}}}}], "name": "setup_basic_get_test", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def setup_basic_get_test(self, user, with_local_site, local_site_name, populate_items): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) if populate_items: items = [ self.create_diff_comment(reply, filediff, reply_to=comment), ] else: items = [] return (get_review_reply_diff_comment_list_url(reply, local_site_name), review_reply_diff_comment_list_mimetype, items)
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8
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "item_rsp", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "comment", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "id"}}, "value": {"_type": "Name", "_fields": {"id": "item_rsp", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "pk", "value": {"_type": "Name", "_fields": {"id": "comment", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "text"}}, "value": {"_type": "Name", "_fields": {"id": "item_rsp", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "text", "value": {"_type": "Name", "_fields": {"id": "comment", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "text_type"}}, "value": {"_type": "Name", "_fields": {"id": "item_rsp", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "markdown"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "rich_text", "value": {"_type": "Name", "_fields": {"id": "comment", "ctx": {"_type": "Load", "_fields": {}}}}}}, "orelse": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "Constant", "_fields": {"kind": null, "value": "text_type"}}, "value": {"_type": "Name", "_fields": {"id": "item_rsp", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "plain"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}]}}], "name": "compare_item", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def compare_item(self, item_rsp, comment): self.assertEqual(item_rsp['id'], comment.pk) self.assertEqual(item_rsp['text'], comment.text) if comment.rich_text: self.assertEqual(item_rsp['text_type'], 'markdown') else: self.assertEqual(item_rsp['text_type'], 'plain')
10,208
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"_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "create_repository", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}, {"_type": "keyword", "_fields": {"arg": "with_local_site", "value": {"_type": "Name", "_fields": {"id": "with_local_site", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "submitter", "value": {"_type": "Name", "_fields": {"id": "user", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "publish", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "review_request", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "review_request", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", 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{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_review", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "user", "value": {"_type": "Name", "_fields": {"id": "user", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "publish", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "review", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "review", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "filediff", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_diff_comment", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "comment", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "review", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_reply", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "user", "value": {"_type": "Name", "_fields": {"id": "user", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "local_site_name", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "get_review_reply_diff_comment_list_url", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "review_reply_diff_comment_item_mimetype", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "reply_to_id"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "text"}}], "values": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "pk", "value": {"_type": "Name", "_fields": {"id": "comment", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "Test comment"}}]}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "comment", "ctx": {"_type": "Load", "_fields": {}}}}]}}]}}}}], "name": "setup_basic_post_test", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def setup_basic_post_test(self, user, with_local_site, local_site_name, post_valid_data): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user, publish=True) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) return (get_review_reply_diff_comment_list_url(reply, local_site_name), review_reply_diff_comment_item_mimetype, { 'reply_to_id': comment.pk, 'text': 'Test comment', }, [reply, comment])
10,209
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8
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def setup_review_request_child_test(self, review_request): if not review_request.repository_id: # The group tests don't create a repository by default. review_request.repository = self.create_repository() review_request.save() diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review_request.publish(review_request.submitter) review = self.create_review(review_request, publish=True) self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=self.user) return (get_review_reply_diff_comment_list_url(reply), review_reply_diff_comment_list_mimetype)
10,210
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8
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def compare_item(self, item_rsp, comment): self.assertEqual(item_rsp['id'], comment.pk) self.assertEqual(item_rsp['text'], comment.text) if comment.rich_text: self.assertEqual(item_rsp['text_type'], 'markdown') else: self.assertEqual(item_rsp['text_type'], 'plain')
10,211
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{"_type": "Constant", "_fields": {"kind": null, "value": true}}}}, {"_type": "keyword", "_fields": {"arg": "with_local_site", "value": {"_type": "Name", "_fields": {"id": "with_local_site", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "submitter", "value": {"_type": "Name", "_fields": {"id": "user", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "publish", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "review_request", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "review_request", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_diffset", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": 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"value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "user", "value": {"_type": "Name", "_fields": {"id": "user", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "keyword", "_fields": {"arg": "publish", "value": {"_type": "Constant", "_fields": {"kind": null, "value": true}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "review", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "review", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "filediff", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_diff_comment", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "comment", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "review", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_reply", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "user", "value": {"_type": "Name", "_fields": {"id": "user", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "filediff", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_diff_comment", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "reply_to", "value": {"_type": "Name", "_fields": {"id": "comment", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "reply_comment", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "pk", "value": {"_type": "Name", "_fields": {"id": "reply_comment", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Name", "_fields": {"id": "local_site_name", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "get_review_reply_diff_comment_item_url", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "reply_comment", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "reply", "ctx": {"_type": "Load", "_fields": {}}}}]}}]}}}}], "name": "setup_basic_delete_test", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def setup_basic_delete_test(self, user, with_local_site, local_site_name): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user, publish=True) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) reply_comment = self.create_diff_comment(reply, filediff, reply_to=comment) return ( get_review_reply_diff_comment_item_url( reply, reply_comment.pk, local_site_name), [reply_comment, reply] )
10,212
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8
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def setup_basic_get_test(self, user, with_local_site, local_site_name): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user, publish=True) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) reply_comment = self.create_diff_comment( reply, filediff, reply_to=comment) return ( get_review_reply_diff_comment_item_url( reply, reply_comment.pk, local_site_name), review_reply_diff_comment_item_mimetype, reply_comment )
10,213
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8
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "user", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "with_local_site", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "local_site_name", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "put_valid_data", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "create_review_request", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", 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{}}, "attr": "pk", "value": {"_type": "Name", "_fields": {"id": "reply_comment", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Name", "_fields": {"id": "local_site_name", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "get_review_reply_diff_comment_item_url", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "review_reply_diff_comment_item_mimetype", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "text"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": "Test comment"}}]}}, {"_type": "Name", "_fields": {"id": "reply_comment", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}]}}}}], "name": "setup_basic_put_test", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], 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def setup_basic_put_test(self, user, with_local_site, local_site_name, put_valid_data): review_request = self.create_review_request( create_repository=True, with_local_site=with_local_site, submitter=user, publish=True) diffset = self.create_diffset(review_request) filediff = self.create_filediff(diffset) review = self.create_review(review_request, user=user, publish=True) comment = self.create_diff_comment(review, filediff) reply = self.create_reply(review, user=user) reply_comment = self.create_diff_comment(reply, filediff, reply_to=comment) return ( get_review_reply_diff_comment_item_url( reply, reply_comment.pk, local_site_name), review_reply_diff_comment_item_mimetype, { 'text': 'Test comment', }, reply_comment, [])
10,214
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class Base10xPipeline(object): def __init__(self, config): self.config = config self.summary_filename = self.config.data_10x.data_10x_cell_summary self.bam_filename = self.config.data_10x.data_10x_bam self.bai_filename = self.config.data_10x.data_10x_bai assert os.path.exists(self.summary_filename), self.summary_filename assert os.path.exists(self.bam_filename), self.bam_filename assert os.path.exists(self.bai_filename), self.bai_filename self.summary_df = pd.read_csv(self.summary_filename, sep=',') self.barcodes = { k: v for (k, v) in self.summary_df[['barcode', 'cell_id']].to_records(index=False) } self.genome = Genome(self.config) assert self.genome is not None @staticmethod def _cell_name(cell_id): cell_name = f"C{cell_id:0>8}" return cell_name
10,215
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def __init__(self, config): self.config = config self.summary_filename = self.config.data_10x.data_10x_cell_summary self.bam_filename = self.config.data_10x.data_10x_bam self.bai_filename = self.config.data_10x.data_10x_bai assert os.path.exists(self.summary_filename), self.summary_filename assert os.path.exists(self.bam_filename), self.bam_filename assert os.path.exists(self.bai_filename), self.bai_filename self.summary_df = pd.read_csv(self.summary_filename, sep=',') self.barcodes = { k: v for (k, v) in self.summary_df[['barcode', 'cell_id']].to_records(index=False) } self.genome = Genome(self.config) assert self.genome is not None
10,216
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def _build_segment_regions(self, segment_len=50_000_000): with pysam.AlignmentFile(self.bam_filename, 'rb') as samfile: assert samfile.check_index(), \ (self.bam_filename, self.bai_filename) print(samfile.get_index_statistics()) sam_stats = samfile.get_index_statistics() chrom_sizes = self.genome.chrom_sizes() segments = [] index = 0 for stat in sam_stats: contig = stat.contig contig_name = contig if contig_name not in chrom_sizes: contig_name = "chr{}".format(contig) if contig_name not in chrom_sizes: continue size = chrom_sizes[contig_name]['size'] count = size // segment_len count = max(count, 1) segment_size = size // count for seg_index in range(count): begin_pos = seg_index * segment_size end_pos = (seg_index + 1) * segment_size - 1 if seg_index + 1 == count: end_pos = size index += 1 segments.append((index, (contig, begin_pos, end_pos))) return segments
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class Extract10xPipeline(Base10xPipeline): def __init__(self, config): super(Extract10xPipeline, self).__init__(config) self.reads_dirname = self.config.reads.reads_dir def _build_segment_regions(self, segment_len=50_000_000): with pysam.AlignmentFile(self.bam_filename, 'rb') as samfile: assert samfile.check_index(), \ (self.bam_filename, self.bai_filename) print(samfile.get_index_statistics()) sam_stats = samfile.get_index_statistics() chrom_sizes = self.genome.chrom_sizes() segments = [] index = 0 for stat in sam_stats: contig = stat.contig contig_name = contig if contig_name not in chrom_sizes: contig_name = "chr{}".format(contig) if contig_name not in chrom_sizes: continue size = chrom_sizes[contig_name]['size'] count = size // segment_len count = max(count, 1) segment_size = size // count for seg_index in range(count): begin_pos = seg_index * segment_size end_pos = (seg_index + 1) * segment_size - 1 if seg_index + 1 == count: end_pos = size index += 1 segments.append((index, (contig, begin_pos, end_pos))) return segments PROGRESS_STEP = 10_000 FLUSH_STEP = 2_000 def _process_segment(self, segment, region): with pysam.AlignmentFile(self.bam_filename, 'rb') as samfile: assert samfile.check_index(), \ (self.bam_filename, self.bai_filename) chrom, begin, end = region mapped = 0 cell_records = defaultdict(lambda: []) for rec in samfile.fetch(chrom, begin, end): if not rec.has_tag('CB'): continue mapped += 1 barcode = rec.get_tag('CB') if barcode not in self.barcodes: continue cell_records[barcode].append(rec) if len(cell_records[barcode]) > self.FLUSH_STEP: if barcode in self.barcodes: self._write_to_fastq( barcode, segment, cell_records[barcode]) cell_records[barcode] = [] for barcode, records in cell_records.items(): if barcode in self.barcodes: self._write_to_fastq(barcode, segment, records) cell_records[barcode] = [] def _cell_segment_filename(self, cell_id, segment): cell_name = self._cell_name(cell_id) segment_name = f"{segment:0>8}" filename = os.path.join( self.reads_dirname, cell_name, f"{cell_name}_{segment_name}{self.config.reads.reads_suffix}" ) return filename def _cell_fragments(self, cell_id): cell_name = self._cell_name(cell_id) pattern = f"{cell_name}_*{self.config.reads.reads_suffix}" pattern = os.path.join( self.reads_dirname, cell_name, pattern ) print(colored( "merging fastq cell files {} ".format( pattern ), "green")) filenames = glob.glob(pattern) return filenames def _write_to_fastq(self, barcode, segment, records): cell = self.barcodes[barcode] filename = self._cell_segment_filename(cell, segment) os.makedirs(os.path.dirname(filename), exist_ok=True) with gzip.open(filename, "at") as outfile: for rec in records: assert barcode == rec.get_tag('CB') outfile.write('@') outfile.write(rec.query_name) outfile.write('\n') outfile.write(rec.query_sequence) outfile.write('\n') outfile.write('+\n') outfile.write(''.join( [chr(ch + 33) for ch in rec.query_qualities])) outfile.write('\n') def _split_once(self, dry_run, segment): segment_index, region = segment contig, begin, end = region print(colored( "processing 10x bam file {} segment {}, {}:{}-{}".format( self.bam_filename, segment_index, contig, begin, end ), "green")) if dry_run: return self._process_segment(segment_index, region) def _merge_cell(self, dry_run, cell_id): filenames = self._cell_fragments(cell_id) if not filenames: print(colored( f"can't find segments for cell {cell_id}", 'red')) return [] cell_name = self._cell_name(cell_id) outfile = os.path.join( self.reads_dirname, f"{cell_name}{self.config.reads.reads_suffix}") command = "cat {} > {}".format( " ".join(filenames), outfile ) print(colored(command, "yellow")) os.system(command) command = "rm -f {}".format(" ".join(filenames)) print(colored(command, "yellow")) os.system(command) cell_segments_dirname = os.path.join( self.reads_dirname, cell_name ) command = "rm -f {}".format(cell_segments_dirname) print(colored(command, "yellow")) os.system(command) return filenames def run(self, dask_client): try: self.config.check_nonempty_workdir(self.reads_dirname) except ValueError: return command = "rm -rf {}/*".format(self.reads_dirname) print(colored(command, 'yellow')) os.system(command) os.makedirs(self.reads_dirname, exist_ok=True) segments = self._build_segment_regions(segment_len=50_000_000) delayed_tasks = dask_client.map( lambda segment: self._split_once(self.config.dry_run, segment), segments ) distributed.wait(delayed_tasks) cells = self.barcodes.values() delayed_tasks = dask_client.map( lambda cell_id: self._merge_cell(self.config.dry_run, cell_id), cells ) distributed.wait(delayed_tasks)
10,218
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11
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{"id": "samfile", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "rec", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "For", "_fields": {"body": [{"_type": "If", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "barcode", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "segment", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "records", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "_write_to_fastq", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "In", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "barcode", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "barcodes", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "orelse": []}}, {"_type": "Assign", "_fields": {"value": {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}, "targets": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "slice": {"_type": "Name", "_fields": {"id": "barcode", "ctx": {"_type": "Load", "_fields": {}}}}, "value": {"_type": "Name", "_fields": {"id": "cell_records", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}], "iter": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "items", "value": {"_type": "Name", "_fields": {"id": "cell_records", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "barcode", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "records", "ctx": {"_type": "Store", "_fields": {}}}}]}}, "type_comment": null}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "bam_filename", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "rb"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "AlignmentFile", "value": {"_type": "Name", "_fields": {"id": "pysam", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": {"_type": "Name", "_fields": {"id": "samfile", "ctx": {"_type": "Store", "_fields": {}}}}}}], "type_comment": null}}], "name": "_process_segment", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _process_segment(self, segment, region): with pysam.AlignmentFile(self.bam_filename, 'rb') as samfile: assert samfile.check_index(), \ (self.bam_filename, self.bai_filename) chrom, begin, end = region mapped = 0 cell_records = defaultdict(lambda: []) for rec in samfile.fetch(chrom, begin, end): if not rec.has_tag('CB'): continue mapped += 1 barcode = rec.get_tag('CB') if barcode not in self.barcodes: continue cell_records[barcode].append(rec) if len(cell_records[barcode]) > self.FLUSH_STEP: if barcode in self.barcodes: self._write_to_fastq( barcode, segment, cell_records[barcode]) cell_records[barcode] = [] for barcode, records in cell_records.items(): if barcode in self.barcodes: self._write_to_fastq(barcode, segment, records) cell_records[barcode] = []
10,219
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10
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def _cell_fragments(self, cell_id): cell_name = self._cell_name(cell_id) pattern = f"{cell_name}_*{self.config.reads.reads_suffix}" pattern = os.path.join( self.reads_dirname, cell_name, pattern ) print(colored( "merging fastq cell files {} ".format( pattern ), "green")) filenames = glob.glob(pattern) return filenames
10,220
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9
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def _split_once(self, dry_run, segment): segment_index, region = segment contig, begin, end = region print(colored( "processing 10x bam file {} segment {}, {}:{}-{}".format( self.bam_filename, segment_index, contig, begin, end ), "green")) if dry_run: return self._process_segment(segment_index, region)
10,221
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12
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"_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "CB"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_tag", "value": {"_type": "Name", "_fields": {"id": "rec", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}]}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "@"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "write", "value": {"_type": "Name", "_fields": {"id": "outfile", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "query_name", "value": {"_type": "Name", "_fields": {"id": "rec", "ctx": {"_type": "Load", "_fields": {}}}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": 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"write", "value": {"_type": "Name", "_fields": {"id": "outfile", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "\n"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "write", "value": {"_type": "Name", "_fields": {"id": "outfile", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "iter": {"_type": "Name", "_fields": {"id": "records", "ctx": {"_type": "Load", "_fields": {}}}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "rec", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "items": [{"_type": "withitem", "_fields": {"context_expr": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "filename", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "at"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "open", "value": {"_type": "Name", "_fields": {"id": "gzip", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "optional_vars": {"_type": "Name", "_fields": {"id": "outfile", "ctx": {"_type": "Store", "_fields": {}}}}}}], "type_comment": null}}], "name": "_write_to_fastq", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _write_to_fastq(self, barcode, segment, records): cell = self.barcodes[barcode] filename = self._cell_segment_filename(cell, segment) os.makedirs(os.path.dirname(filename), exist_ok=True) with gzip.open(filename, "at") as outfile: for rec in records: assert barcode == rec.get_tag('CB') outfile.write('@') outfile.write(rec.query_name) outfile.write('\n') outfile.write(rec.query_sequence) outfile.write('\n') outfile.write('+\n') outfile.write(''.join( [chr(ch + 33) for ch in rec.query_qualities])) outfile.write('\n')
10,222
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def _merge_cell(self, dry_run, cell_id): filenames = self._cell_fragments(cell_id) if not filenames: print(colored( f"can't find segments for cell {cell_id}", 'red')) return [] cell_name = self._cell_name(cell_id) outfile = os.path.join( self.reads_dirname, f"{cell_name}{self.config.reads.reads_suffix}") command = "cat {} > {}".format( " ".join(filenames), outfile ) print(colored(command, "yellow")) os.system(command) command = "rm -f {}".format(" ".join(filenames)) print(colored(command, "yellow")) os.system(command) cell_segments_dirname = os.path.join( self.reads_dirname, cell_name ) command = "rm -f {}".format(cell_segments_dirname) print(colored(command, "yellow")) os.system(command) return filenames
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10
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def run(self, dask_client): try: self.config.check_nonempty_workdir(self.reads_dirname) except ValueError: return command = "rm -rf {}/*".format(self.reads_dirname) print(colored(command, 'yellow')) os.system(command) os.makedirs(self.reads_dirname, exist_ok=True) segments = self._build_segment_regions(segment_len=50_000_000) delayed_tasks = dask_client.map( lambda segment: self._split_once(self.config.dry_run, segment), segments ) distributed.wait(delayed_tasks) cells = self.barcodes.values() delayed_tasks = dask_client.map( lambda cell_id: self._merge_cell(self.config.dry_run, cell_id), cells ) distributed.wait(delayed_tasks)
10,224
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7
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "# Header\n\n<!-- toc -->"}}, "targets": [{"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "\n\n## Table of contents\n\n- [Section](#section)\n - [More](#more)\n - [Even more](#even-more)\n- [Other section](#other-section)\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "<!-- tocstop -->\n\n## Section\n\ntext\n\n### More\n\n### Even more\n\n## Other section\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_basic_toc", "returns": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_basic_toc(self) -> None: header = "# Header\n\n<!-- toc -->" toc = ( "\n\n## Table of contents\n\n" "- [Section](#section)\n" " - [More](#more)\n" " - [Even more](#even-more)\n" "- [Other section](#other-section)\n" "\n" ) body = ( "<!-- tocstop -->\n\n" "## Section\n\n" "text\n\n" "### More\n\n" "### Even more\n\n" "## Other section\n\n" ) before = header + body after = header + toc + body self.assert_exit_code(before, after) self.assert_exit_code(after, after)
10,225
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11
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"_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_basic_toc", "returns": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "# Header\n\n<!-- toc -->"}}, "targets": [{"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "\n\n## Table of contents\n\n- [Section](#section)\n - [More](#more)\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "<!-- tocstop -->\n\n## Section\n\ntext\n\n```\n## Ignored\n```\n### More\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_codeblock_toc", "returns": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "# Header\n\n<!-- toc -->"}}, "targets": [{"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "\n\n## Table of contents\n\n- [Advanced Find & Replace](#advanced-find--replace)\n- [Package as `package`](#package-as-package)\n- [Something _italicized_](#something-italicized)\n- [Something **bolded**](#something-bolded)\n- [Some _nested **chars `here`** now_](#some-nested-chars-here-now)\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "<!-- tocstop -->\n\n## Advanced Find & Replace\n\n## Package as `package`\n\n## Something _italicized_\n\n## Something **bolded**\n\n## Some _nested **chars `here`** now_\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_weird_toc", "returns": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "type_params": [], "type_comment": null, "decorator_list": []}}, {"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "# Header\n\n<!-- toc -->"}}, "targets": [{"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "\n\n## Table of contents\n\n- [Bork](#bork)\n- [Bork](#bork-1)\n- [Bork](#bork-2)\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "<!-- tocstop -->\n\n## Bork\n\n## Bork\n\n## Bork\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_repeating_toc", "returns": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "TestMarkdownToc", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "FileTestCase", "value": {"_type": "Name", "_fields": {"id": "file_test_case", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TestMarkdownToc(file_test_case.FileTestCase): def setUp(self) -> None: self.setup_helper( lambda filename: markdown_toc.main(argv=["bin", filename]) ) def test_wrong_ext(self) -> None: contents = "<!-- toc --><!-- tocstop -->\n" self.assert_exit_code(contents, contents, ext=".py") def test_bad_header_level(self) -> None: contents = "<!-- toc --><!-- tocstop -->\n### Test" self.assert_exit_code(contents, contents, exit_code=1) def test_empty_file(self) -> None: self.assert_exit_code("", "") def test_empty_toc(self) -> None: before = "<!-- toc --><!-- tocstop -->\n" after = "<!-- toc -->\n\n## Table of contents\n\n<!-- tocstop -->\n" self.assert_exit_code(before, after) self.assert_exit_code(after, after) def test_basic_toc(self) -> None: header = "# Header\n\n<!-- toc -->" toc = ( "\n\n## Table of contents\n\n" "- [Section](#section)\n" " - [More](#more)\n" " - [Even more](#even-more)\n" "- [Other section](#other-section)\n" "\n" ) body = ( "<!-- tocstop -->\n\n" "## Section\n\n" "text\n\n" "### More\n\n" "### Even more\n\n" "## Other section\n\n" ) before = header + body after = header + toc + body self.assert_exit_code(before, after) self.assert_exit_code(after, after) def test_codeblock_toc(self) -> None: header = "# Header\n\n<!-- toc -->" toc = ( "\n\n## Table of contents\n\n" "- [Section](#section)\n" " - [More](#more)\n" "\n" ) body = ( "<!-- tocstop -->\n\n" "## Section\n\n" "text\n\n" "```\n" "## Ignored\n" "```\n" "### More\n\n" ) before = header + body after = header + toc + body self.assert_exit_code(before, after) self.assert_exit_code(after, after) def test_weird_toc(self) -> None: header = "# Header\n\n<!-- toc -->" toc = ( "\n\n## Table of contents\n\n" "- [Advanced Find & Replace](#advanced-find--replace)\n" "- [Package as `package`](#package-as-package)\n" "- [Something _italicized_](#something-italicized)\n" "- [Something **bolded**](#something-bolded)\n" "- [Some _nested **chars `here`** now_]" "(#some-nested-chars-here-now)\n" "\n" ) body = ( "<!-- tocstop -->\n\n" "## Advanced Find & Replace\n\n" "## Package as `package`\n\n" "## Something _italicized_\n\n" "## Something **bolded**\n\n" "## Some _nested **chars `here`** now_\n\n" ) before = header + body after = header + toc + body self.assert_exit_code(before, after) self.assert_exit_code(after, after) def test_repeating_toc(self) -> None: header = "# Header\n\n<!-- toc -->" toc = ( "\n\n## Table of contents\n\n" "- [Bork](#bork)\n" "- [Bork](#bork-1)\n" "- [Bork](#bork-2)\n" "\n" ) body = "<!-- tocstop -->\n\n## Bork\n\n## Bork\n\n## Bork\n\n" before = header + body after = header + toc + body self.assert_exit_code(before, after) self.assert_exit_code(after, after)
10,226
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7
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "# Header\n\n<!-- toc -->"}}, "targets": [{"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "\n\n## Table of contents\n\n- [Section](#section)\n - [More](#more)\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "<!-- tocstop -->\n\n## Section\n\ntext\n\n```\n## Ignored\n```\n### More\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_codeblock_toc", "returns": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_codeblock_toc(self) -> None: header = "# Header\n\n<!-- toc -->" toc = ( "\n\n## Table of contents\n\n" "- [Section](#section)\n" " - [More](#more)\n" "\n" ) body = ( "<!-- tocstop -->\n\n" "## Section\n\n" "text\n\n" "```\n" "## Ignored\n" "```\n" "### More\n\n" ) before = header + body after = header + toc + body self.assert_exit_code(before, after) self.assert_exit_code(after, after)
10,227
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7
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "# Header\n\n<!-- toc -->"}}, "targets": [{"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "\n\n## Table of contents\n\n- [Advanced Find & Replace](#advanced-find--replace)\n- [Package as `package`](#package-as-package)\n- [Something _italicized_](#something-italicized)\n- [Something **bolded**](#something-bolded)\n- [Some _nested **chars `here`** now_](#some-nested-chars-here-now)\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "<!-- tocstop -->\n\n## Advanced Find & Replace\n\n## Package as `package`\n\n## Something _italicized_\n\n## Something **bolded**\n\n## Some _nested **chars `here`** now_\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_weird_toc", "returns": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_weird_toc(self) -> None: header = "# Header\n\n<!-- toc -->" toc = ( "\n\n## Table of contents\n\n" "- [Advanced Find & Replace](#advanced-find--replace)\n" "- [Package as `package`](#package-as-package)\n" "- [Something _italicized_](#something-italicized)\n" "- [Something **bolded**](#something-bolded)\n" "- [Some _nested **chars `here`** now_]" "(#some-nested-chars-here-now)\n" "\n" ) body = ( "<!-- tocstop -->\n\n" "## Advanced Find & Replace\n\n" "## Package as `package`\n\n" "## Something _italicized_\n\n" "## Something **bolded**\n\n" "## Some _nested **chars `here`** now_\n\n" ) before = header + body after = header + toc + body self.assert_exit_code(before, after) self.assert_exit_code(after, after)
10,228
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9
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def test_rpc_consumer_isolation(self): class NeverCalled(object): def __getattribute__(*args): assert False, "I should never get called." server = rpc.get_server(messaging.Target(topic='compute', server=CONF.host), endpoints=[NeverCalled()]) server.start()
10,229
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7
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "# Header\n\n<!-- toc -->"}}, "targets": [{"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "\n\n## Table of contents\n\n- [Bork](#bork)\n- [Bork](#bork-1)\n- [Bork](#bork-2)\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "<!-- tocstop -->\n\n## Bork\n\n## Bork\n\n## Bork\n\n"}}, "targets": [{"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Name", "_fields": {"id": "header", "ctx": {"_type": "Load", "_fields": {}}}}, "right": {"_type": "Name", "_fields": {"id": "toc", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Name", "_fields": {"id": "body", "ctx": {"_type": "Load", "_fields": {}}}}}}, "targets": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "before", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "after", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_exit_code", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_repeating_toc", "returns": {"_type": "Constant", "_fields": {"kind": null, "value": null}}, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_repeating_toc(self) -> None: header = "# Header\n\n<!-- toc -->" toc = ( "\n\n## Table of contents\n\n" "- [Bork](#bork)\n" "- [Bork](#bork-1)\n" "- [Bork](#bork-2)\n" "\n" ) body = "<!-- tocstop -->\n\n## Bork\n\n## Bork\n\n## Bork\n\n" before = header + body after = header + toc + body self.assert_exit_code(before, after) self.assert_exit_code(after, after)
10,230
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12
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{"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "top"}}], "values": [{"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "l1"}}], "values": [{"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "l2"}}], "values": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "c"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "a"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "d"}}]}}]}}]}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "observed", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Try", "_fields": {"body": [{"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", 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"_fields": {}}, "attr": "text_type", "value": {"_type": "Name", "_fields": {"id": "six", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIn", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "Matcher: {'top': {'l1': {'l2': ['a', 'b', 'c']}}}"}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "e", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "text_type", "value": {"_type": "Name", "_fields": {"id": "six", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIn", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "e", "type": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}}}], "finalbody": []}}], "name": "test_json_equal_fail_on_inner", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "JsonTestCase", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TestCase", "value": {"_type": "Name", "_fields": {"id": "test", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class JsonTestCase(test.TestCase): def test_json_equal(self): expected = { "employees": [ {"firstName": "Anna", "lastName": "Smith"}, {"firstName": "John", "lastName": "Doe"}, {"firstName": "Peter", "lastName": "Jones"} ], "locations": set(['Boston', 'Mumbai', 'Beijing', 'Perth']) } observed = """{ "employees": [ { "lastName": "Doe", "firstName": "John" }, { "lastName": "Smith", "firstName": "Anna" }, { "lastName": "Jones", "firstName": "Peter" } ], "locations": [ "Perth", "Boston", "Mumbai", "Beijing" ] }""" self.assertJsonEqual(expected, observed) def test_json_equal_fail_on_length(self): expected = { 'top': { 'l1': { 'l2': ['a', 'b', 'c'] } } } observed = { 'top': { 'l1': { 'l2': ['c', 'a', 'b', 'd'] } } } try: self.assertJsonEqual(expected, observed) except Exception as e: # error reported is going to be a cryptic length failure # on the level2 structure. self.assertEqual(e.mismatch.describe(), "3 != 4") self.assertIn( "Matchee: {'top': {'l1': {'l2': ['c', 'a', 'b', 'd']}}}", six.text_type(e)) self.assertIn( "Matcher: {'top': {'l1': {'l2': ['a', 'b', 'c']}}}", six.text_type(e)) else: self.fail("This should have raised a mismatch exception") def test_json_equal_fail_on_inner(self): expected = { 'top': { 'l1': { 'l2': ['a', 'b', 'c'] } } } observed = { 'top': { 'l1': { 'l2': ['c', 'a', 'd'] } } } try: self.assertJsonEqual(expected, observed) except Exception as e: # error reported is going to be a cryptic length failure # on the level2 structure. self.assertEqual(e.mismatch.describe(), "'b' != 'c'") self.assertIn( "Matchee: {'top': {'l1': {'l2': ['c', 'a', 'd']}}}", six.text_type(e)) self.assertIn( "Matcher: {'top': {'l1': {'l2': ['a', 'b', 'c']}}}", six.text_type(e)) else: self.fail("This should have raised a mismatch exception")
10,231
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11
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{"args": [{"_type": "Name", "_fields": {"id": "e", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "text_type", "value": {"_type": "Name", "_fields": {"id": "six", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIn", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "Matcher: {'top': {'l1': {'l2': ['a', 'b', 'c']}}}"}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "e", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "text_type", "value": {"_type": "Name", "_fields": {"id": "six", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIn", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "e", "type": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}}}], "finalbody": []}}], "name": "test_json_equal_fail_on_length", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_json_equal_fail_on_length(self): expected = { 'top': { 'l1': { 'l2': ['a', 'b', 'c'] } } } observed = { 'top': { 'l1': { 'l2': ['c', 'a', 'b', 'd'] } } } try: self.assertJsonEqual(expected, observed) except Exception as e: # error reported is going to be a cryptic length failure # on the level2 structure. self.assertEqual(e.mismatch.describe(), "3 != 4") self.assertIn( "Matchee: {'top': {'l1': {'l2': ['c', 'a', 'b', 'd']}}}", six.text_type(e)) self.assertIn( "Matcher: {'top': {'l1': {'l2': ['a', 'b', 'c']}}}", six.text_type(e)) else: self.fail("This should have raised a mismatch exception")
10,232
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11
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"c"}}]}}]}}]}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "top"}}], "values": [{"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "l1"}}], "values": [{"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "l2"}}], "values": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "c"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "a"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "d"}}]}}]}}]}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "observed", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Try", "_fields": {"body": [{"_type": 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"_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "text_type", "value": {"_type": "Name", "_fields": {"id": "six", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIn", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "Matcher: {'top': {'l1': {'l2': ['a', 'b', 'c']}}}"}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "e", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "text_type", "value": {"_type": "Name", "_fields": {"id": "six", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertIn", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "e", "type": {"_type": "Name", "_fields": {"id": "Exception", "ctx": {"_type": "Load", "_fields": {}}}}}}], "finalbody": []}}], "name": "test_json_equal_fail_on_inner", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_json_equal_fail_on_inner(self): expected = { 'top': { 'l1': { 'l2': ['a', 'b', 'c'] } } } observed = { 'top': { 'l1': { 'l2': ['c', 'a', 'd'] } } } try: self.assertJsonEqual(expected, observed) except Exception as e: # error reported is going to be a cryptic length failure # on the level2 structure. self.assertEqual(e.mismatch.describe(), "'b' != 'c'") self.assertIn( "Matchee: {'top': {'l1': {'l2': ['c', 'a', 'd']}}}", six.text_type(e)) self.assertIn( "Matcher: {'top': {'l1': {'l2': ['a', 'b', 'c']}}}", six.text_type(e)) else: self.fail("This should have raised a mismatch exception")
10,233
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8
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class MatchTypeTestCase(test.TestCase): def test_match_type_simple(self): matcher = test.MatchType(dict) self.assertEqual(matcher, {}) self.assertEqual(matcher, {"hello": "world"}) self.assertEqual(matcher, {"hello": ["world"]}) self.assertNotEqual(matcher, []) self.assertNotEqual(matcher, [{"hello": "world"}]) self.assertNotEqual(matcher, 123) self.assertNotEqual(matcher, "foo") def test_match_type_object(self): class Hello(object): pass class World(object): pass matcher = test.MatchType(Hello) self.assertEqual(matcher, Hello()) self.assertNotEqual(matcher, World()) self.assertNotEqual(matcher, 123) self.assertNotEqual(matcher, "foo")
10,234
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7
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "dict", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "MatchType", "value": {"_type": "Name", "_fields": {"id": "test", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "matcher", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "matcher", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Dict", "_fields": {"keys": [], "values": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "matcher", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "hello"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": "world"}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "matcher", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "hello"}}], "values": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "world"}}]}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "matcher", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertNotEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "matcher", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Dict", "_fields": {"keys": [{"_type": "Constant", "_fields": {"kind": null, "value": "hello"}}], "values": [{"_type": "Constant", "_fields": {"kind": null, "value": "world"}}]}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertNotEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "matcher", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": 123}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertNotEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "matcher", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Constant", "_fields": {"kind": null, "value": "foo"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assertNotEqual", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_match_type_simple", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_match_type_simple(self): matcher = test.MatchType(dict) self.assertEqual(matcher, {}) self.assertEqual(matcher, {"hello": "world"}) self.assertEqual(matcher, {"hello": ["world"]}) self.assertNotEqual(matcher, []) self.assertNotEqual(matcher, [{"hello": "world"}]) self.assertNotEqual(matcher, 123) self.assertNotEqual(matcher, "foo")
10,235
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7
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def test_match_type_object(self): class Hello(object): pass class World(object): pass matcher = test.MatchType(Hello) self.assertEqual(matcher, Hello()) self.assertNotEqual(matcher, World()) self.assertNotEqual(matcher, 123) self.assertNotEqual(matcher, "foo")
10,236
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10
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"Name", "_fields": {"id": "module", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "msg", "value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "e", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "str", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}]}}}}], "name": "e", "type": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "OpenStackCloudException", "value": {"_type": "Name", "_fields": {"id": "shade", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "finalbody": []}}], "name": "main", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def main(): argument_spec = openstack_full_argument_spec( name=dict(required=False, default=None), filters=dict(required=False, type='dict', default=None) ) module = AnsibleModule(argument_spec) shade, cloud = openstack_cloud_from_module(module) try: subnets = cloud.search_subnets(module.params['name'], module.params['filters']) module.exit_json(changed=False, ansible_facts=dict( openstack_subnets=subnets)) except shade.OpenStackCloudException as e: module.fail_json(msg=str(e))
10,237
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"handlers": [{"_type": "ExceptHandler", "_fields": {"body": [{"_type": "Assign", "_fields": {"value": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "magnet:?xt=urn:btih:"}}, "right": {"_type": "Name", "_fields": {"id": "t_hash", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": "&dn="}}}}, "right": {"_type": "Name", "_fields": {"id": "title", "ctx": {"_type": "Load", "_fields": {}}}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": 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"ratio", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}], "name": "seedRatio", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "TORRENTPROJECTProvider", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TorrentProvider", "value": {"_type": "Name", "_fields": {"id": "generic", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TORRENTPROJECTProvider(generic.TorrentProvider): def __init__(self): generic.TorrentProvider.__init__(self, "TorrentProject") self.supportsBacklog = True self.public = True self.ratio = 0 self.urls = {'api': u'https://torrentproject.se/',} self.url = self.urls['api'] self.headers.update({'User-Agent': USER_AGENT}) self.minseed = None self.minleech = None self.cache = TORRENTPROJECTCache(self) def _doSearch(self, search_strings, search_mode='eponly', epcount=0, age=0, epObj=None): results = [] items = {'Season': [], 'Episode': [], 'RSS': []} for mode in search_strings.keys(): # Mode = RSS, Season, Episode logger.log(u"Search Mode: %s" % mode, logger.DEBUG) for search_string in search_strings[mode]: if mode is not 'RSS': logger.log(u"Search string: %s " % search_string, logger.DEBUG) searchURL = self.urls['api'] + "?s=%s&out=json&filter=2101&num=150" % quote_plus(search_string.encode('utf-8')) logger.log(u"Search URL: %s" % searchURL, logger.DEBUG) torrents = self.getURL(searchURL, json=True) if not (torrents and "total_found" in torrents and int(torrents["total_found"]) > 0): logger.log(u"Data returned from provider does not contain any torrents", logger.DEBUG) continue del torrents["total_found"] results = [] for i in torrents: title = torrents[i]["title"] seeders = helpers.tryInt(torrents[i]["seeds"], 1) leechers = helpers.tryInt(torrents[i]["leechs"], 0) if seeders < self.minseed or leechers < self.minleech: if mode is not 'RSS': logger.log(u"Torrent doesn't meet minimum seeds & leechers not selecting : %s" % title, logger.DEBUG) continue t_hash = torrents[i]["torrent_hash"] size = int(torrents[i]["torrent_size"]) try: assert seeders < 10 assert mode is not 'RSS' logger.log(u"Torrent has less than 10 seeds getting dyn trackers: " + title, logger.DEBUG) trackerUrl = self.urls['api'] + "" + t_hash + "/trackers_json" jdata = self.getURL(trackerUrl, json=True) assert jdata is not "maintenance" download_url = "magnet:?xt=urn:btih:" + t_hash + "&dn=" + title + "".join(["&tr=" + s for s in jdata]) except (Exception, AssertionError): download_url = "magnet:?xt=urn:btih:" + t_hash + "&dn=" + title + "&tr=udp://tracker.openbittorrent.com:80&tr=udp://tracker.coppersurfer.tk:6969&tr=udp://open.demonii.com:1337&tr=udp://tracker.leechers-paradise.org:6969&tr=udp://exodus.desync.com:6969" if not all([title, download_url]): continue item = title, download_url, size, seeders, leechers if mode is not 'RSS': logger.log(u"Found result: %s" % title, logger.DEBUG) items[mode].append(item) # For each search mode sort all the items by seeders items[mode].sort(key=lambda tup: tup[3], reverse=True) results += items[mode] return results def seedRatio(self): return self.ratio
10,238
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"Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "keys", "value": {"_type": "Name", "_fields": {"id": "search_strings", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "mode", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Name", "_fields": {"id": "results", "ctx": {"_type": "Load", "_fields": {}}}}}}], "name": "_doSearch", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def _doSearch(self, search_strings, search_mode='eponly', epcount=0, age=0, epObj=None): results = [] items = {'Season': [], 'Episode': [], 'RSS': []} for mode in search_strings.keys(): # Mode = RSS, Season, Episode logger.log(u"Search Mode: %s" % mode, logger.DEBUG) for search_string in search_strings[mode]: if mode is not 'RSS': logger.log(u"Search string: %s " % search_string, logger.DEBUG) searchURL = self.urls['api'] + "?s=%s&out=json&filter=2101&num=150" % quote_plus(search_string.encode('utf-8')) logger.log(u"Search URL: %s" % searchURL, logger.DEBUG) torrents = self.getURL(searchURL, json=True) if not (torrents and "total_found" in torrents and int(torrents["total_found"]) > 0): logger.log(u"Data returned from provider does not contain any torrents", logger.DEBUG) continue del torrents["total_found"] results = [] for i in torrents: title = torrents[i]["title"] seeders = helpers.tryInt(torrents[i]["seeds"], 1) leechers = helpers.tryInt(torrents[i]["leechs"], 0) if seeders < self.minseed or leechers < self.minleech: if mode is not 'RSS': logger.log(u"Torrent doesn't meet minimum seeds & leechers not selecting : %s" % title, logger.DEBUG) continue t_hash = torrents[i]["torrent_hash"] size = int(torrents[i]["torrent_size"]) try: assert seeders < 10 assert mode is not 'RSS' logger.log(u"Torrent has less than 10 seeds getting dyn trackers: " + title, logger.DEBUG) trackerUrl = self.urls['api'] + "" + t_hash + "/trackers_json" jdata = self.getURL(trackerUrl, json=True) assert jdata is not "maintenance" download_url = "magnet:?xt=urn:btih:" + t_hash + "&dn=" + title + "".join(["&tr=" + s for s in jdata]) except (Exception, AssertionError): download_url = "magnet:?xt=urn:btih:" + t_hash + "&dn=" + title + "&tr=udp://tracker.openbittorrent.com:80&tr=udp://tracker.coppersurfer.tk:6969&tr=udp://open.demonii.com:1337&tr=udp://tracker.leechers-paradise.org:6969&tr=udp://exodus.desync.com:6969" if not all([title, download_url]): continue item = title, download_url, size, seeders, leechers if mode is not 'RSS': logger.log(u"Found result: %s" % title, logger.DEBUG) items[mode].append(item) # For each search mode sort all the items by seeders items[mode].sort(key=lambda tup: tup[3], reverse=True) results += items[mode] return results
10,239
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8
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def __init__(self): generic.TorrentProvider.__init__(self, "TorrentProject") self.supportsBacklog = True self.public = True self.ratio = 0 self.urls = {'api': u'https://torrentproject.se/',} self.url = self.urls['api'] self.headers.update({'User-Agent': USER_AGENT}) self.minseed = None self.minleech = None self.cache = TORRENTPROJECTCache(self)
10,240
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class TestTimedeltaIndex(DatetimeLike): _holder = TimedeltaIndex def setup_method(self, method): self.indices = dict(index=tm.makeTimedeltaIndex(10)) self.setup_indices() def create_index(self): return pd.to_timedelta(range(5), unit='d') + pd.offsets.Hour(1) def test_numeric_compat(self): # Dummy method to override super's version; this test is now done # in test_arithmetic.py pass def test_shift(self): pass # this is handled in test_arithmetic.py def test_pickle_compat_construction(self): pass def test_fillna_timedelta(self): # GH 11343 idx = pd.TimedeltaIndex(['1 day', pd.NaT, '3 day']) exp = pd.TimedeltaIndex(['1 day', '2 day', '3 day']) tm.assert_index_equal(idx.fillna(pd.Timedelta('2 day')), exp) exp = pd.TimedeltaIndex(['1 day', '3 hour', '3 day']) idx.fillna(pd.Timedelta('3 hour')) exp = pd.Index( [pd.Timedelta('1 day'), 'x', pd.Timedelta('3 day')], dtype=object) tm.assert_index_equal(idx.fillna('x'), exp) def test_difference_freq(self): # GH14323: Difference of TimedeltaIndex should not preserve frequency index = timedelta_range("0 days", "5 days", freq="D") other = timedelta_range("1 days", "4 days", freq="D") expected = TimedeltaIndex(["0 days", "5 days"], freq=None) idx_diff = index.difference(other) tm.assert_index_equal(idx_diff, expected) tm.assert_attr_equal('freq', idx_diff, expected) other = timedelta_range("2 days", "5 days", freq="D") idx_diff = index.difference(other) expected = TimedeltaIndex(["0 days", "1 days"], freq=None) tm.assert_index_equal(idx_diff, expected) tm.assert_attr_equal('freq', idx_diff, expected) def test_isin(self): index = tm.makeTimedeltaIndex(4) result = index.isin(index) assert result.all() result = index.isin(list(index)) assert result.all() assert_almost_equal(index.isin([index[2], 5]), np.array([False, False, True, False])) def test_factorize(self): idx1 = TimedeltaIndex(['1 day', '1 day', '2 day', '2 day', '3 day', '3 day']) exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp) exp_idx = TimedeltaIndex(['1 day', '2 day', '3 day']) arr, idx = idx1.factorize() tm.assert_numpy_array_equal(arr, exp_arr) tm.assert_index_equal(idx, exp_idx) arr, idx = idx1.factorize(sort=True) tm.assert_numpy_array_equal(arr, exp_arr) tm.assert_index_equal(idx, exp_idx) # freq must be preserved idx3 = timedelta_range('1 day', periods=4, freq='s') exp_arr = np.array([0, 1, 2, 3], dtype=np.intp) arr, idx = idx3.factorize() tm.assert_numpy_array_equal(arr, exp_arr) tm.assert_index_equal(idx, idx3) def test_join_self(self, join_type): index = timedelta_range('1 day', periods=10) joined = index.join(index, how=join_type) tm.assert_index_equal(index, joined) def test_does_not_convert_mixed_integer(self): df = tm.makeCustomDataframe(10, 10, data_gen_f=lambda *args, **kwargs: randn(), r_idx_type='i', c_idx_type='td') str(df) cols = df.columns.join(df.index, how='outer') joined = cols.join(df.columns) assert cols.dtype == np.dtype('O') assert cols.dtype == joined.dtype tm.assert_index_equal(cols, joined) def test_sort_values(self): idx = TimedeltaIndex(['4d', '1d', '2d']) ordered = idx.sort_values() assert ordered.is_monotonic ordered = idx.sort_values(ascending=False) assert ordered[::-1].is_monotonic ordered, dexer = idx.sort_values(return_indexer=True) assert ordered.is_monotonic tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0]), check_dtype=False) ordered, dexer = idx.sort_values(return_indexer=True, ascending=False) assert ordered[::-1].is_monotonic tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1]), check_dtype=False) def test_get_duplicates(self): idx = TimedeltaIndex(['1 day', '2 day', '2 day', '3 day', '3day', '4day']) with tm.assert_produces_warning(FutureWarning): # Deprecated - see GH20239 result = idx.get_duplicates() ex = TimedeltaIndex(['2 day', '3day']) tm.assert_index_equal(result, ex) def test_argmin_argmax(self): idx = TimedeltaIndex(['1 day 00:00:05', '1 day 00:00:01', '1 day 00:00:02']) assert idx.argmin() == 1 assert idx.argmax() == 0 def test_misc_coverage(self): rng = timedelta_range('1 day', periods=5) result = rng.groupby(rng.days) assert isinstance(list(result.values())[0][0], Timedelta) idx = TimedeltaIndex(['3d', '1d', '2d']) assert not idx.equals(list(idx)) non_td = Index(list('abc')) assert not idx.equals(list(non_td)) def test_map(self): # test_map_dictlike generally tests rng = timedelta_range('1 day', periods=10) f = lambda x: x.days result = rng.map(f) exp = Int64Index([f(x) for x in rng]) tm.assert_index_equal(result, exp) def test_pass_TimedeltaIndex_to_index(self): rng = timedelta_range('1 days', '10 days') idx = Index(rng, dtype=object) expected = Index(rng.to_pytimedelta(), dtype=object) tm.assert_numpy_array_equal(idx.values, expected.values) def test_pickle(self): rng = timedelta_range('1 days', periods=10) rng_p = tm.round_trip_pickle(rng) tm.assert_index_equal(rng, rng_p) def test_hash_error(self): index = timedelta_range('1 days', periods=10) with tm.assert_raises_regex(TypeError, "unhashable type: %r" % type(index).__name__): hash(index) def test_append_join_nondatetimeindex(self): rng = timedelta_range('1 days', periods=10) idx = Index(['a', 'b', 'c', 'd']) result = rng.append(idx) assert isinstance(result[0], Timedelta) # it works rng.join(idx, how='outer') def test_append_numpy_bug_1681(self): td = timedelta_range('1 days', '10 days', freq='2D') a = DataFrame() c = DataFrame({'A': 'foo', 'B': td}, index=td) str(c) result = a.append(c) assert (result['B'] == td).all() def test_fields(self): rng = timedelta_range('1 days, 10:11:12.100123456', periods=2, freq='s') tm.assert_index_equal(rng.days, Index([1, 1], dtype='int64')) tm.assert_index_equal( rng.seconds, Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype='int64')) tm.assert_index_equal( rng.microseconds, Index([100 * 1000 + 123, 100 * 1000 + 123], dtype='int64')) tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype='int64')) pytest.raises(AttributeError, lambda: rng.hours) pytest.raises(AttributeError, lambda: rng.minutes) pytest.raises(AttributeError, lambda: rng.milliseconds) # with nat s = Series(rng) s[1] = np.nan tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=[0, 1])) tm.assert_series_equal(s.dt.seconds, Series( [10 * 3600 + 11 * 60 + 12, np.nan], index=[0, 1])) # preserve name (GH15589) rng.name = 'name' assert rng.days.name == 'name' def test_freq_conversion(self): # doc example # series td = Series(date_range('20130101', periods=4)) - \ Series(date_range('20121201', periods=4)) td[2] += timedelta(minutes=5, seconds=3) td[3] = np.nan result = td / np.timedelta64(1, 'D') expected = Series([31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan ]) assert_series_equal(result, expected) result = td.astype('timedelta64[D]') expected = Series([31, 31, 31, np.nan]) assert_series_equal(result, expected) result = td / np.timedelta64(1, 's') expected = Series([31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan]) assert_series_equal(result, expected) result = td.astype('timedelta64[s]') assert_series_equal(result, expected) # tdi td = TimedeltaIndex(td) result = td / np.timedelta64(1, 'D') expected = Index([31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan]) assert_index_equal(result, expected) result = td.astype('timedelta64[D]') expected = Index([31, 31, 31, np.nan]) assert_index_equal(result, expected) result = td / np.timedelta64(1, 's') expected = Index([31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan]) assert_index_equal(result, expected) result = td.astype('timedelta64[s]') assert_index_equal(result, expected)
10,241
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"Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "1 day"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "3 hour"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "3 day"}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "TimedeltaIndex", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "exp", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "3 hour"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Timedelta", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "fillna", "value": {"_type": "Name", "_fields": {"id": "idx", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "1 day"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Timedelta", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Constant", "_fields": {"kind": null, "value": "x"}}, {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "3 day"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Timedelta", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "Index", "value": {"_type": "Name", "_fields": {"id": "pd", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "dtype", "value": {"_type": "Name", "_fields": {"id": "object", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "exp", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "x"}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "fillna", "value": {"_type": "Name", "_fields": {"id": "idx", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, {"_type": "Name", "_fields": {"id": "exp", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_index_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_fillna_timedelta", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_fillna_timedelta(self): # GH 11343 idx = pd.TimedeltaIndex(['1 day', pd.NaT, '3 day']) exp = pd.TimedeltaIndex(['1 day', '2 day', '3 day']) tm.assert_index_equal(idx.fillna(pd.Timedelta('2 day')), exp) exp = pd.TimedeltaIndex(['1 day', '3 hour', '3 day']) idx.fillna(pd.Timedelta('3 hour')) exp = pd.Index( [pd.Timedelta('1 day'), 'x', pd.Timedelta('3 day')], dtype=object) tm.assert_index_equal(idx.fillna('x'), exp)
10,242
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9
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def test_isin(self): index = tm.makeTimedeltaIndex(4) result = index.isin(index) assert result.all() result = index.isin(list(index)) assert result.all() assert_almost_equal(index.isin([index[2], 5]), np.array([False, False, True, False]))
10,243
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7
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{"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": "0 days"}}, {"_type": "Constant", "_fields": {"kind": null, "value": "1 days"}}]}}], "func": {"_type": "Name", "_fields": {"id": "TimedeltaIndex", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "freq", "value": {"_type": "Constant", "_fields": {"kind": null, "value": null}}}}]}}, "targets": [{"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "idx_diff", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_index_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": "freq"}}, {"_type": "Name", "_fields": {"id": "idx_diff", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "expected", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_attr_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_difference_freq", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_difference_freq(self): # GH14323: Difference of TimedeltaIndex should not preserve frequency index = timedelta_range("0 days", "5 days", freq="D") other = timedelta_range("1 days", "4 days", freq="D") expected = TimedeltaIndex(["0 days", "5 days"], freq=None) idx_diff = index.difference(other) tm.assert_index_equal(idx_diff, expected) tm.assert_attr_equal('freq', idx_diff, expected) other = timedelta_range("2 days", "5 days", freq="D") idx_diff = index.difference(other) expected = TimedeltaIndex(["0 days", "1 days"], freq=None) tm.assert_index_equal(idx_diff, expected) tm.assert_attr_equal('freq', idx_diff, expected)
10,244
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9
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def test_does_not_convert_mixed_integer(self): df = tm.makeCustomDataframe(10, 10, data_gen_f=lambda *args, **kwargs: randn(), r_idx_type='i', c_idx_type='td') str(df) cols = df.columns.join(df.index, how='outer') joined = cols.join(df.columns) assert cols.dtype == np.dtype('O') assert cols.dtype == joined.dtype tm.assert_index_equal(cols, joined)
10,245
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"attr": "factorize", "value": {"_type": "Name", "_fields": {"id": "idx3", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}, "targets": [{"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "arr", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "idx", "ctx": {"_type": "Store", "_fields": {}}}}]}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "arr", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "exp_arr", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_numpy_array_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "idx", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "idx3", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_index_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}], "name": "test_factorize", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_factorize(self): idx1 = TimedeltaIndex(['1 day', '1 day', '2 day', '2 day', '3 day', '3 day']) exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp) exp_idx = TimedeltaIndex(['1 day', '2 day', '3 day']) arr, idx = idx1.factorize() tm.assert_numpy_array_equal(arr, exp_arr) tm.assert_index_equal(idx, exp_idx) arr, idx = idx1.factorize(sort=True) tm.assert_numpy_array_equal(arr, exp_arr) tm.assert_index_equal(idx, exp_idx) # freq must be preserved idx3 = timedelta_range('1 day', periods=4, freq='s') exp_arr = np.array([0, 1, 2, 3], dtype=np.intp) arr, idx = idx3.factorize() tm.assert_numpy_array_equal(arr, exp_arr) tm.assert_index_equal(idx, idx3)
10,246
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8
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def test_sort_values(self): idx = TimedeltaIndex(['4d', '1d', '2d']) ordered = idx.sort_values() assert ordered.is_monotonic ordered = idx.sort_values(ascending=False) assert ordered[::-1].is_monotonic ordered, dexer = idx.sort_values(return_indexer=True) assert ordered.is_monotonic tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0]), check_dtype=False) ordered, dexer = idx.sort_values(return_indexer=True, ascending=False) assert ordered[::-1].is_monotonic tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1]), check_dtype=False)
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8
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def test_get_duplicates(self): idx = TimedeltaIndex(['1 day', '2 day', '2 day', '3 day', '3day', '4day']) with tm.assert_produces_warning(FutureWarning): # Deprecated - see GH20239 result = idx.get_duplicates() ex = TimedeltaIndex(['2 day', '3day']) tm.assert_index_equal(result, ex)
10,248
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11
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def test_misc_coverage(self): rng = timedelta_range('1 day', periods=5) result = rng.groupby(rng.days) assert isinstance(list(result.values())[0][0], Timedelta) idx = TimedeltaIndex(['3d', '1d', '2d']) assert not idx.equals(list(idx)) non_td = Index(list('abc')) assert not idx.equals(list(non_td))
10,249
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8
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def test_map(self): # test_map_dictlike generally tests rng = timedelta_range('1 day', periods=10) f = lambda x: x.days result = rng.map(f) exp = Int64Index([f(x) for x in rng]) tm.assert_index_equal(result, exp)
10,250
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8
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def test_pass_TimedeltaIndex_to_index(self): rng = timedelta_range('1 days', '10 days') idx = Index(rng, dtype=object) expected = Index(rng.to_pytimedelta(), dtype=object) tm.assert_numpy_array_equal(idx.values, expected.values)
10,251
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7
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def test_append_join_nondatetimeindex(self): rng = timedelta_range('1 days', periods=10) idx = Index(['a', 'b', 'c', 'd']) result = rng.append(idx) assert isinstance(result[0], Timedelta) # it works rng.join(idx, how='outer')
10,252
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"value": 1}}, "value": {"_type": "Name", "_fields": {"id": "s", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "days", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "dt", "value": {"_type": "Name", "_fields": {"id": "s", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Call", "_fields": {"args": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "nan", "value": {"_type": "Name", "_fields": {"id": "np", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "Series", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "index", "value": {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}, {"_type": "Constant", "_fields": {"kind": null, "value": 1}}]}}}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_series_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "seconds", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "dt", "value": {"_type": "Name", "_fields": {"id": "s", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, {"_type": "Call", "_fields": {"args": [{"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Add", "_fields": {}}, "left": {"_type": "BinOp", "_fields": {"op": {"_type": "Mult", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": 10}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": 3600}}}}, "right": {"_type": "BinOp", "_fields": {"op": {"_type": "Mult", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": 11}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": 60}}}}}}, "right": {"_type": "Constant", "_fields": {"kind": null, "value": 12}}}}, {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "nan", "value": {"_type": "Name", "_fields": {"id": "np", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "Series", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "index", "value": {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": 0}}, {"_type": "Constant", "_fields": {"kind": null, "value": 1}}]}}}}]}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "assert_series_equal", "value": {"_type": "Name", "_fields": {"id": "tm", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Constant", "_fields": {"kind": null, "value": "name"}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "name", "value": {"_type": "Name", "_fields": {"id": "rng", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Assert", "_fields": {"msg": null, "test": {"_type": "Compare", "_fields": {"ops": [{"_type": "Eq", "_fields": {}}], "left": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "name", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "days", "value": {"_type": "Name", "_fields": {"id": "rng", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "comparators": [{"_type": "Constant", "_fields": {"kind": null, "value": "name"}}]}}}}], "name": "test_fields", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_fields(self): rng = timedelta_range('1 days, 10:11:12.100123456', periods=2, freq='s') tm.assert_index_equal(rng.days, Index([1, 1], dtype='int64')) tm.assert_index_equal( rng.seconds, Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype='int64')) tm.assert_index_equal( rng.microseconds, Index([100 * 1000 + 123, 100 * 1000 + 123], dtype='int64')) tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype='int64')) pytest.raises(AttributeError, lambda: rng.hours) pytest.raises(AttributeError, lambda: rng.minutes) pytest.raises(AttributeError, lambda: rng.milliseconds) # with nat s = Series(rng) s[1] = np.nan tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=[0, 1])) tm.assert_series_equal(s.dt.seconds, Series( [10 * 3600 + 11 * 60 + 12, np.nan], index=[0, 1])) # preserve name (GH15589) rng.name = 'name' assert rng.days.name == 'name'
10,253
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9
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def test_append_numpy_bug_1681(self): td = timedelta_range('1 days', '10 days', freq='2D') a = DataFrame() c = DataFrame({'A': 'foo', 'B': td}, index=td) str(c) result = a.append(c) assert (result['B'] == td).all()
10,254
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9
{"_type": "Module", "_fields": {"body": [{"_type": "FunctionDef", "_fields": {"args": {"_type": "arguments", "_fields": {"args": [{"_type": "arg", "_fields": {"arg": "self", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "request", "annotation": null, "type_comment": null}}, {"_type": "arg", "_fields": {"arg": "view", "annotation": null, "type_comment": null}}], "kwarg": null, "vararg": null, "defaults": [], "kwonlyargs": [], "kw_defaults": [], "posonlyargs": []}}, "body": [{"_type": "If", "_fields": {"body": [{"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "is_authenticated", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "user", "value": {"_type": "Name", "_fields": {"id": "request", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}}}], "test": {"_type": "UnaryOp", "_fields": {"op": {"_type": "Not", "_fields": {}}, "operand": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "auth", "value": {"_type": "Name", "_fields": {"id": "request", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "orelse": []}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "method", "value": {"_type": "Name", "_fields": {"id": "request", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "scope_map", "value": {"_type": "Name", "_fields": {"id": "self", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}], "func": {"_type": "Name", "_fields": {"id": "set", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "allowed_scopes", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "get_scopes", "value": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "auth", "value": {"_type": "Name", "_fields": {"id": "request", "ctx": {"_type": "Load", "_fields": {}}}}}}}}, "keywords": []}}, "targets": [{"_type": "Name", "_fields": {"id": "current_scopes", "ctx": {"_type": "Store", "_fields": {}}}}], "type_comment": null}}, {"_type": "Return", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "GeneratorExp", "_fields": {"elt": {"_type": "Compare", "_fields": {"ops": [{"_type": "In", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "s", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Name", "_fields": {"id": "allowed_scopes", "ctx": {"_type": "Load", "_fields": {}}}}]}}, "generators": [{"_type": "comprehension", "_fields": {"ifs": [], "iter": {"_type": "Name", "_fields": {"id": "current_scopes", "ctx": {"_type": "Load", "_fields": {}}}}, "target": {"_type": "Name", "_fields": {"id": "s", "ctx": {"_type": "Store", "_fields": {}}}}, "is_async": 0}}]}}], "func": {"_type": "Name", "_fields": {"id": "any", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "has_permission", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def has_permission(self, request, view): # session-based auth has all scopes for a logged in user if not request.auth: return request.user.is_authenticated() allowed_scopes = set(self.scope_map.get(request.method, [])) current_scopes = request.auth.get_scopes() return any(s in allowed_scopes for s in current_scopes)
10,255
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def test_freq_conversion(self): # doc example # series td = Series(date_range('20130101', periods=4)) - \ Series(date_range('20121201', periods=4)) td[2] += timedelta(minutes=5, seconds=3) td[3] = np.nan result = td / np.timedelta64(1, 'D') expected = Series([31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan ]) assert_series_equal(result, expected) result = td.astype('timedelta64[D]') expected = Series([31, 31, 31, np.nan]) assert_series_equal(result, expected) result = td / np.timedelta64(1, 's') expected = Series([31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan]) assert_series_equal(result, expected) result = td.astype('timedelta64[s]') assert_series_equal(result, expected) # tdi td = TimedeltaIndex(td) result = td / np.timedelta64(1, 'D') expected = Index([31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan]) assert_index_equal(result, expected) result = td.astype('timedelta64[D]') expected = Index([31, 31, 31, np.nan]) assert_index_equal(result, expected) result = td / np.timedelta64(1, 's') expected = Index([31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan]) assert_index_equal(result, expected) result = td.astype('timedelta64[s]') assert_index_equal(result, expected)
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11
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class URLTests(TestCase): def setUp(self): self.client = Client() self.client.login( username=settings.TEST_USERNAME, password=settings.TEST_PASSWORD) def test_urls(self): urls = [ "/date/", "/date/month/", "/date/month/add/", "/date/month/1/", "/date/season/", "/date/season/add/", "/date/season/5/", "/date/weekday/", "/date/weekday/add/", "/date/weekday/1/"] for url in urls: response = self.client.get(url) log.info("%s, %s" % (response.status_code, url)) self.assertEqual(response.status_code, 200)
10,257
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10
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def test_urls(self): urls = [ "/date/", "/date/month/", "/date/month/add/", "/date/month/1/", "/date/season/", "/date/season/add/", "/date/season/5/", "/date/weekday/", "/date/weekday/add/", "/date/weekday/1/"] for url in urls: response = self.client.get(url) log.info("%s, %s" % (response.status_code, url)) self.assertEqual(response.status_code, 200)
10,258
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{"_type": "Load", "_fields": {}}}}, "keywords": []}}, {"_type": "UnaryOp", "_fields": {"op": {"_type": "USub", "_fields": {}}, "operand": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}]}}]}}, {"_type": "BinOp", "_fields": {"op": {"_type": "Mod", "_fields": {}}, "left": {"_type": "Constant", "_fields": {"kind": null, "value": "selectedrealkind(%s): expected %r but got %r"}}, "right": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "selected_real_kind", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "selectedrealkind", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}]}}}}], "func": {"_type": "Name", "_fields": {"id": "assert_", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "iter": {"_type": "Call", "_fields": {"args": [{"_type": "Constant", "_fields": {"kind": null, "value": 20}}], "func": {"_type": "Name", "_fields": {"id": "range", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}, "orelse": [], "target": {"_type": "Name", "_fields": {"id": "i", "ctx": {"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "name": "test_all", "returns": null, "type_params": [], "type_comment": null, "decorator_list": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "slow", "value": {"_type": "Name", "_fields": {"id": "dec", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}], "name": "TestKind", "bases": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "F2PyTest", "value": {"_type": "Name", "_fields": {"id": "util", "ctx": {"_type": "Load", "_fields": {}}}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TestKind(util.F2PyTest): sources = [_path('src', 'kind', 'foo.f90')] @dec.slow def test_all(self): selectedrealkind = self.module.selectedrealkind selectedintkind = self.module.selectedintkind for i in range(40): assert_(selectedintkind(i) in [selected_int_kind(i), -1], 'selectedintkind(%s): expected %r but got %r' % (i, selected_int_kind(i), selectedintkind(i))) for i in range(20): assert_(selectedrealkind(i) in [selected_real_kind(i), -1], 'selectedrealkind(%s): expected %r but got %r' % (i, selected_real_kind(i), selectedrealkind(i)))
10,259
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10
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"type_ignores": []}}
def test_all(self): selectedrealkind = self.module.selectedrealkind selectedintkind = self.module.selectedintkind for i in range(40): assert_(selectedintkind(i) in [selected_int_kind(i), -1], 'selectedintkind(%s): expected %r but got %r' % (i, selected_int_kind(i), selectedintkind(i))) for i in range(20): assert_(selectedrealkind(i) in [selected_real_kind(i), -1], 'selectedrealkind(%s): expected %r but got %r' % (i, selected_real_kind(i), selectedrealkind(i)))
10,260
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class TestShapiro(TestCase): def test_basic(self): x1 = [0.11,7.87,4.61,10.14,7.95,3.14,0.46, 4.43,0.21,4.75,0.71,1.52,3.24, 0.93,0.42,4.97,9.53,4.55,0.47,6.66] w,pw = stats.shapiro(x1) assert_almost_equal(w,0.90047299861907959,6) assert_almost_equal(pw,0.042089745402336121,6) x2 = [1.36,1.14,2.92,2.55,1.46,1.06,5.27,-1.11, 3.48,1.10,0.88,-0.51,1.46,0.52,6.20,1.69, 0.08,3.67,2.81,3.49] w,pw = stats.shapiro(x2) assert_almost_equal(w,0.9590270,6) assert_almost_equal(pw,0.52460,3) def test_bad_arg(self): # Length of x is less than 3. x = [1] assert_raises(ValueError, stats.shapiro, x)
10,261
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7
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def test_basic(self): x1 = [0.11,7.87,4.61,10.14,7.95,3.14,0.46, 4.43,0.21,4.75,0.71,1.52,3.24, 0.93,0.42,4.97,9.53,4.55,0.47,6.66] w,pw = stats.shapiro(x1) assert_almost_equal(w,0.90047299861907959,6) assert_almost_equal(pw,0.042089745402336121,6) x2 = [1.36,1.14,2.92,2.55,1.46,1.06,5.27,-1.11, 3.48,1.10,0.88,-0.51,1.46,0.52,6.20,1.69, 0.08,3.67,2.81,3.49] w,pw = stats.shapiro(x2) assert_almost_equal(w,0.9590270,6) assert_almost_equal(pw,0.52460,3)
10,262
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8
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def test_normal(self): rs = RandomState(1234567890) x1 = rs.standard_exponential(size=50) x2 = rs.standard_normal(size=50) A,crit,sig = stats.anderson(x1) assert_array_less(crit[:-1], A) A,crit,sig = stats.anderson(x2) assert_array_less(A, crit[-2:])
10,263
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class TestAnderson(TestCase): def test_normal(self): rs = RandomState(1234567890) x1 = rs.standard_exponential(size=50) x2 = rs.standard_normal(size=50) A,crit,sig = stats.anderson(x1) assert_array_less(crit[:-1], A) A,crit,sig = stats.anderson(x2) assert_array_less(A, crit[-2:]) def test_expon(self): rs = RandomState(1234567890) x1 = rs.standard_exponential(size=50) x2 = rs.standard_normal(size=50) A,crit,sig = stats.anderson(x1,'expon') assert_array_less(A, crit[-2:]) olderr = np.seterr(all='ignore') try: A,crit,sig = stats.anderson(x2,'expon') finally: np.seterr(**olderr) assert_(A > crit[-1]) def test_bad_arg(self): assert_raises(ValueError, stats.anderson, [1], dist='plate_of_shrimp')
10,264
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{}}, "attr": "seterr", "value": {"_type": "Name", "_fields": {"id": "np", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": null, "value": {"_type": "Name", "_fields": {"id": "olderr", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}}}]}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Compare", "_fields": {"ops": [{"_type": "Gt", "_fields": {}}], "left": {"_type": "Name", "_fields": {"id": "A", "ctx": {"_type": "Load", "_fields": {}}}}, "comparators": [{"_type": "Subscript", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "slice": {"_type": "UnaryOp", "_fields": {"op": {"_type": "USub", "_fields": {}}, "operand": {"_type": "Constant", "_fields": {"kind": null, "value": 1}}}}, "value": {"_type": "Name", "_fields": {"id": "crit", "ctx": {"_type": "Load", "_fields": {}}}}}}]}}], "func": {"_type": "Name", "_fields": {"id": "assert_", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "test_expon", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_expon(self): rs = RandomState(1234567890) x1 = rs.standard_exponential(size=50) x2 = rs.standard_normal(size=50) A,crit,sig = stats.anderson(x1,'expon') assert_array_less(A, crit[-2:]) olderr = np.seterr(all='ignore') try: A,crit,sig = stats.anderson(x2,'expon') finally: np.seterr(**olderr) assert_(A > crit[-1])
10,265
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class TestAndersonKSamp(TestCase): def test_example1a(self): # Example data from Scholz & Stephens (1987), originally # published in Lehmann (1995, Nonparametrics, Statistical # Methods Based on Ranks, p. 309) # Pass a mixture of lists and arrays t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0] t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8]) t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0]) t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8]) assert_warns(UserWarning, stats.anderson_ksamp, (t1, t2, t3, t4), midrank=False) with warnings.catch_warnings(): warnings.filterwarnings('ignore', message='approximate p-value') Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=False) assert_almost_equal(Tk, 4.449, 3) assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459], tm, 4) assert_almost_equal(p, 0.0021, 4) def test_example1b(self): # Example data from Scholz & Stephens (1987), originally # published in Lehmann (1995, Nonparametrics, Statistical # Methods Based on Ranks, p. 309) # Pass arrays t1 = np.array([38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]) t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8]) t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0]) t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8]) with warnings.catch_warnings(): warnings.filterwarnings('ignore', message='approximate p-value') Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=True) assert_almost_equal(Tk, 4.480, 3) assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459], tm, 4) assert_almost_equal(p, 0.0020, 4) def test_example2a(self): # Example data taken from an earlier technical report of # Scholz and Stephens # Pass lists instead of arrays t1 = [194, 15, 41, 29, 33, 181] t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118] t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34] t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29, 118, 25, 156, 310, 76, 26, 44, 23, 62] t5 = [130, 208, 70, 101, 208] t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27] t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33] t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5, 12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95] t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82, 54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24] t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36, 22, 139, 210, 97, 30, 23, 13, 14] t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438] t12 = [50, 254, 5, 283, 35, 12] t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130] t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66, 61, 34] with warnings.catch_warnings(): warnings.filterwarnings('ignore', message='approximate p-value') Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t14), midrank=False) assert_almost_equal(Tk, 3.288, 3) assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009], tm, 4) assert_almost_equal(p, 0.0041, 4) def test_example2b(self): # Example data taken from an earlier technical report of # Scholz and Stephens t1 = [194, 15, 41, 29, 33, 181] t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118] t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34] t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29, 118, 25, 156, 310, 76, 26, 44, 23, 62] t5 = [130, 208, 70, 101, 208] t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27] t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33] t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5, 12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95] t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82, 54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24] t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36, 22, 139, 210, 97, 30, 23, 13, 14] t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438] t12 = [50, 254, 5, 283, 35, 12] t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130] t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66, 61, 34] with warnings.catch_warnings(): warnings.filterwarnings('ignore', message='approximate p-value') Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t14), midrank=True) assert_almost_equal(Tk, 3.294, 3) assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009], tm, 4) assert_almost_equal(p, 0.0041, 4) def test_not_enough_samples(self): assert_raises(ValueError, stats.anderson_ksamp, np.ones(5)) def test_no_distinct_observations(self): assert_raises(ValueError, stats.anderson_ksamp, (np.ones(5), np.ones(5))) def test_empty_sample(self): assert_raises(ValueError, stats.anderson_ksamp, (np.ones(5), []))
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8
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def test_example1b(self): # Example data from Scholz & Stephens (1987), originally # published in Lehmann (1995, Nonparametrics, Statistical # Methods Based on Ranks, p. 309) # Pass arrays t1 = np.array([38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]) t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8]) t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0]) t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8]) with warnings.catch_warnings(): warnings.filterwarnings('ignore', message='approximate p-value') Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=True) assert_almost_equal(Tk, 4.480, 3) assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459], tm, 4) assert_almost_equal(p, 0.0020, 4)
10,267
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def test_example1a(self): # Example data from Scholz & Stephens (1987), originally # published in Lehmann (1995, Nonparametrics, Statistical # Methods Based on Ranks, p. 309) # Pass a mixture of lists and arrays t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0] t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8]) t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0]) t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8]) assert_warns(UserWarning, stats.anderson_ksamp, (t1, t2, t3, t4), midrank=False) with warnings.catch_warnings(): warnings.filterwarnings('ignore', message='approximate p-value') Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=False) assert_almost_equal(Tk, 4.449, 3) assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459], tm, 4) assert_almost_equal(p, 0.0021, 4)
10,268
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def test_example2b(self): # Example data taken from an earlier technical report of # Scholz and Stephens t1 = [194, 15, 41, 29, 33, 181] t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118] t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34] t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29, 118, 25, 156, 310, 76, 26, 44, 23, 62] t5 = [130, 208, 70, 101, 208] t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27] t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33] t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5, 12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95] t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82, 54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24] t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36, 22, 139, 210, 97, 30, 23, 13, 14] t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438] t12 = [50, 254, 5, 283, 35, 12] t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130] t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66, 61, 34] with warnings.catch_warnings(): warnings.filterwarnings('ignore', message='approximate p-value') Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t14), midrank=True) assert_almost_equal(Tk, 3.294, 3) assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009], tm, 4) assert_almost_equal(p, 0.0041, 4)
10,269
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def test_example2a(self): # Example data taken from an earlier technical report of # Scholz and Stephens # Pass lists instead of arrays t1 = [194, 15, 41, 29, 33, 181] t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118] t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34] t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29, 118, 25, 156, 310, 76, 26, 44, 23, 62] t5 = [130, 208, 70, 101, 208] t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27] t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33] t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5, 12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95] t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82, 54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24] t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36, 22, 139, 210, 97, 30, 23, 13, 14] t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438] t12 = [50, 254, 5, 283, 35, 12] t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130] t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66, 61, 34] with warnings.catch_warnings(): warnings.filterwarnings('ignore', message='approximate p-value') Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t14), midrank=False) assert_almost_equal(Tk, 3.288, 3) assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009], tm, 4) assert_almost_equal(p, 0.0041, 4)
10,270
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9
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class TestAnsari(TestCase): def test_small(self): x = [1,2,3,3,4] y = [3,2,6,1,6,1,4,1] W, pval = stats.ansari(x,y) assert_almost_equal(W,23.5,11) assert_almost_equal(pval,0.13499256881897437,11) def test_approx(self): ramsay = np.array((111, 107, 100, 99, 102, 106, 109, 108, 104, 99, 101, 96, 97, 102, 107, 113, 116, 113, 110, 98)) parekh = np.array((107, 108, 106, 98, 105, 103, 110, 105, 104, 100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99)) with warnings.catch_warnings(): warnings.filterwarnings('ignore', message="Ties preclude use of exact statistic.") W, pval = stats.ansari(ramsay, parekh) assert_almost_equal(W,185.5,11) assert_almost_equal(pval,0.18145819972867083,11) def test_exact(self): W,pval = stats.ansari([1,2,3,4],[15,5,20,8,10,12]) assert_almost_equal(W,10.0,11) assert_almost_equal(pval,0.533333333333333333,7) def test_bad_arg(self): assert_raises(ValueError, stats.ansari, [], [1]) assert_raises(ValueError, stats.ansari, [1], [])
10,271
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8
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def test_approx(self): ramsay = np.array((111, 107, 100, 99, 102, 106, 109, 108, 104, 99, 101, 96, 97, 102, 107, 113, 116, 113, 110, 98)) parekh = np.array((107, 108, 106, 98, 105, 103, 110, 105, 104, 100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99)) with warnings.catch_warnings(): warnings.filterwarnings('ignore', message="Ties preclude use of exact statistic.") W, pval = stats.ansari(ramsay, parekh) assert_almost_equal(W,185.5,11) assert_almost_equal(pval,0.18145819972867083,11)
10,272
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8
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class TestBartlett(TestCase): def test_data(self): args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10] T, pval = stats.bartlett(*args) assert_almost_equal(T,20.78587342806484,7) assert_almost_equal(pval,0.0136358632781,7) def test_bad_arg(self): # Too few args raises ValueError. assert_raises(ValueError, stats.bartlett, [1])
10,273
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[{"_type": "Constant", "_fields": {"kind": null, "value": 1}}]}}], "func": {"_type": "Name", "_fields": {"id": "assert_raises", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "test_too_few_args", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "TestLevene", "bases": [{"_type": "Name", "_fields": {"id": "TestCase", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TestLevene(TestCase): def test_data(self): args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10] W, pval = stats.levene(*args) assert_almost_equal(W,1.7059176930008939,7) assert_almost_equal(pval,0.0990829755522,7) def test_trimmed1(self): # Test that center='trimmed' gives the same result as center='mean' # when proportiontocut=0. W1, pval1 = stats.levene(g1, g2, g3, center='mean') W2, pval2 = stats.levene(g1, g2, g3, center='trimmed', proportiontocut=0.0) assert_almost_equal(W1, W2) assert_almost_equal(pval1, pval2) def test_trimmed2(self): x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0] y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0] np.random.seed(1234) x2 = np.random.permutation(x) # Use center='trimmed' W0, pval0 = stats.levene(x, y, center='trimmed', proportiontocut=0.125) W1, pval1 = stats.levene(x2, y, center='trimmed', proportiontocut=0.125) # Trim the data here, and use center='mean' W2, pval2 = stats.levene(x[1:-1], y[1:-1], center='mean') # Result should be the same. assert_almost_equal(W0, W2) assert_almost_equal(W1, W2) assert_almost_equal(pval1, pval2) def test_equal_mean_median(self): x = np.linspace(-1,1,21) np.random.seed(1234) x2 = np.random.permutation(x) y = x**3 W1, pval1 = stats.levene(x, y, center='mean') W2, pval2 = stats.levene(x2, y, center='median') assert_almost_equal(W1, W2) assert_almost_equal(pval1, pval2) def test_bad_keyword(self): x = np.linspace(-1,1,21) assert_raises(TypeError, stats.levene, x, x, portiontocut=0.1) def test_bad_center_value(self): x = np.linspace(-1,1,21) assert_raises(ValueError, stats.levene, x, x, center='trim') def test_too_few_args(self): assert_raises(ValueError, stats.levene, [1])
10,274
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def test_trimmed2(self): x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0] y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0] np.random.seed(1234) x2 = np.random.permutation(x) # Use center='trimmed' W0, pval0 = stats.levene(x, y, center='trimmed', proportiontocut=0.125) W1, pval1 = stats.levene(x2, y, center='trimmed', proportiontocut=0.125) # Trim the data here, and use center='mean' W2, pval2 = stats.levene(x[1:-1], y[1:-1], center='mean') # Result should be the same. assert_almost_equal(W0, W2) assert_almost_equal(W1, W2) assert_almost_equal(pval1, pval2)
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def test_equal_mean_median(self): x = np.linspace(-1,1,21) np.random.seed(1234) x2 = np.random.permutation(x) y = x**3 W1, pval1 = stats.levene(x, y, center='mean') W2, pval2 = stats.levene(x2, y, center='median') assert_almost_equal(W1, W2) assert_almost_equal(pval1, pval2)
10,276
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class TestBinomP(TestCase): def test_data(self): pval = stats.binom_test(100,250) assert_almost_equal(pval,0.0018833009350757682,11) pval = stats.binom_test(201,405) assert_almost_equal(pval,0.92085205962670713,11) pval = stats.binom_test([682,243],p=3.0/4) assert_almost_equal(pval,0.38249155957481695,11) def test_bad_len_x(self): # Length of x must be 1 or 2. assert_raises(ValueError, stats.binom_test, [1,2,3]) def test_bad_n(self): # len(x) is 1, but n is invalid. # Missing n assert_raises(ValueError, stats.binom_test, [100]) # n less than x[0] assert_raises(ValueError, stats.binom_test, [100], n=50) def test_bad_p(self): assert_raises(ValueError, stats.binom_test, [50, 50], p=2.0)
10,277
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"Name", "_fields": {"id": "repeated", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}], "func": {"_type": "Name", "_fields": {"id": "assert_array_equal", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "counts", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}], "func": {"_type": "Name", "_fields": {"id": "assert_array_equal", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "test_empty_result", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "TestFindRepeats", "bases": [{"_type": "Name", "_fields": {"id": "TestCase", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TestFindRepeats(TestCase): def test_basic(self): a = [1,2,3,4,1,2,3,4,1,2,5] res,nums = stats.find_repeats(a) assert_array_equal(res,[1,2,3,4]) assert_array_equal(nums,[3,3,2,2]) def test_empty_result(self): # Check that empty arrays are returned when there are no repeats. a = [10, 20, 50, 30, 40] repeated, counts = stats.find_repeats(a) assert_array_equal(repeated, []) assert_array_equal(counts, [])
10,278
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8
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"Name", "_fields": {"id": "pval1", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "pval2", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "assert_almost_equal", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "test_trimmed2", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_trimmed2(self): x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0] y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0] # Use center='trimmed' Xsq1, pval1 = stats.fligner(x, y, center='trimmed', proportiontocut=0.125) # Trim the data here, and use center='mean' Xsq2, pval2 = stats.fligner(x[1:-1], y[1:-1], center='mean') # Result should be the same. assert_almost_equal(Xsq1, Xsq2) assert_almost_equal(pval1, pval2)
10,279
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{"_type": "Load", "_fields": {}}, "attr": "fligner", "value": {"_type": "Name", "_fields": {"id": "stats", "ctx": {"_type": "Load", "_fields": {}}}}}}, {"_type": "List", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": [{"_type": "Constant", "_fields": {"kind": null, "value": 1}}]}}], "func": {"_type": "Name", "_fields": {"id": "assert_raises", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}], "name": "test_bad_num_args", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "name": "TestFligner", "bases": [{"_type": "Name", "_fields": {"id": "TestCase", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TestFligner(TestCase): def test_data(self): # numbers from R: fligner.test in package stats x1 = np.arange(5) assert_array_almost_equal(stats.fligner(x1,x1**2), (3.2282229927203536, 0.072379187848207877), 11) def test_trimmed1(self): # Test that center='trimmed' gives the same result as center='mean' # when proportiontocut=0. Xsq1, pval1 = stats.fligner(g1, g2, g3, center='mean') Xsq2, pval2 = stats.fligner(g1, g2, g3, center='trimmed', proportiontocut=0.0) assert_almost_equal(Xsq1, Xsq2) assert_almost_equal(pval1, pval2) def test_trimmed2(self): x = [1.2, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0] y = [0.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 200.0] # Use center='trimmed' Xsq1, pval1 = stats.fligner(x, y, center='trimmed', proportiontocut=0.125) # Trim the data here, and use center='mean' Xsq2, pval2 = stats.fligner(x[1:-1], y[1:-1], center='mean') # Result should be the same. assert_almost_equal(Xsq1, Xsq2) assert_almost_equal(pval1, pval2) # The following test looks reasonable at first, but fligner() uses the # function stats.rankdata(), and in one of the cases in this test, # there are ties, while in the other (because of normal rounding # errors) there are not. This difference leads to differences in the # third significant digit of W. # #def test_equal_mean_median(self): # x = np.linspace(-1,1,21) # y = x**3 # W1, pval1 = stats.fligner(x, y, center='mean') # W2, pval2 = stats.fligner(x, y, center='median') # assert_almost_equal(W1, W2) # assert_almost_equal(pval1, pval2) def test_bad_keyword(self): x = np.linspace(-1,1,21) assert_raises(TypeError, stats.fligner, x, x, portiontocut=0.1) def test_bad_center_value(self): x = np.linspace(-1,1,21) assert_raises(ValueError, stats.fligner, x, x, center='trim') def test_bad_num_args(self): # Too few args raises ValueError. assert_raises(ValueError, stats.fligner, [1])
10,280
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8
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def test_mood_order_of_args(self): # z should change sign when the order of arguments changes, pvalue # should not change np.random.seed(1234) x1 = np.random.randn(10, 1) x2 = np.random.randn(15, 1) z1, p1 = stats.mood(x1, x2) z2, p2 = stats.mood(x2, x1) assert_array_almost_equal([z1, p1], [-z2, p2])
10,281
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class TestMood(TestCase): def test_mood(self): # numbers from R: mood.test in package stats x1 = np.arange(5) assert_array_almost_equal(stats.mood(x1, x1**2), (-1.3830857299399906, 0.16663858066771478), 11) def test_mood_order_of_args(self): # z should change sign when the order of arguments changes, pvalue # should not change np.random.seed(1234) x1 = np.random.randn(10, 1) x2 = np.random.randn(15, 1) z1, p1 = stats.mood(x1, x2) z2, p2 = stats.mood(x2, x1) assert_array_almost_equal([z1, p1], [-z2, p2]) def test_mood_with_axis_none(self): #Test with axis = None, compare with results from R x1 = [-0.626453810742332, 0.183643324222082, -0.835628612410047, 1.59528080213779, 0.329507771815361, -0.820468384118015, 0.487429052428485, 0.738324705129217, 0.575781351653492, -0.305388387156356, 1.51178116845085, 0.389843236411431, -0.621240580541804, -2.2146998871775, 1.12493091814311, -0.0449336090152309, -0.0161902630989461, 0.943836210685299, 0.821221195098089, 0.593901321217509] x2 = [-0.896914546624981, 0.184849184646742, 1.58784533120882, -1.13037567424629, -0.0802517565509893, 0.132420284381094, 0.707954729271733, -0.23969802417184, 1.98447393665293, -0.138787012119665, 0.417650750792556, 0.981752777463662, -0.392695355503813, -1.03966897694891, 1.78222896030858, -2.31106908460517, 0.878604580921265, 0.035806718015226, 1.01282869212708, 0.432265154539617, 2.09081920524915, -1.19992581964387, 1.58963820029007, 1.95465164222325, 0.00493777682814261, -2.45170638784613, 0.477237302613617, -0.596558168631403, 0.792203270299649, 0.289636710177348] x1 = np.array(x1) x2 = np.array(x2) x1.shape = (10, 2) x2.shape = (15, 2) assert_array_almost_equal(stats.mood(x1, x2, axis=None), [-1.31716607555, 0.18778296257]) def test_mood_2d(self): # Test if the results of mood test in 2-D case are consistent with the # R result for the same inputs. Numbers from R mood.test(). ny = 5 np.random.seed(1234) x1 = np.random.randn(10, ny) x2 = np.random.randn(15, ny) z_vectest, pval_vectest = stats.mood(x1, x2) for j in range(ny): assert_array_almost_equal([z_vectest[j], pval_vectest[j]], stats.mood(x1[:, j], x2[:, j])) # inverse order of dimensions x1 = x1.transpose() x2 = x2.transpose() z_vectest, pval_vectest = stats.mood(x1, x2, axis=1) for i in range(ny): # check axis handling is self consistent assert_array_almost_equal([z_vectest[i], pval_vectest[i]], stats.mood(x1[i, :], x2[i, :])) def test_mood_3d(self): shape = (10, 5, 6) np.random.seed(1234) x1 = np.random.randn(*shape) x2 = np.random.randn(*shape) for axis in range(3): z_vectest, pval_vectest = stats.mood(x1, x2, axis=axis) # Tests that result for 3-D arrays is equal to that for the # same calculation on a set of 1-D arrays taken from the # 3-D array axes_idx = ([1, 2], [0, 2], [0, 1]) # the two axes != axis for i in range(shape[axes_idx[axis][0]]): for j in range(shape[axes_idx[axis][1]]): if axis == 0: slice1 = x1[:, i, j] slice2 = x2[:, i, j] elif axis == 1: slice1 = x1[i, :, j] slice2 = x2[i, :, j] else: slice1 = x1[i, j, :] slice2 = x2[i, j, :] assert_array_almost_equal([z_vectest[i, j], pval_vectest[i, j]], stats.mood(slice1, slice2)) def test_mood_bad_arg(self): # Raise ValueError when the sum of the lengths of the args is less than 3 assert_raises(ValueError, stats.mood, [1], [])
10,282
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10
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def test_mood_2d(self): # Test if the results of mood test in 2-D case are consistent with the # R result for the same inputs. Numbers from R mood.test(). ny = 5 np.random.seed(1234) x1 = np.random.randn(10, ny) x2 = np.random.randn(15, ny) z_vectest, pval_vectest = stats.mood(x1, x2) for j in range(ny): assert_array_almost_equal([z_vectest[j], pval_vectest[j]], stats.mood(x1[:, j], x2[:, j])) # inverse order of dimensions x1 = x1.transpose() x2 = x2.transpose() z_vectest, pval_vectest = stats.mood(x1, x2, axis=1) for i in range(ny): # check axis handling is self consistent assert_array_almost_equal([z_vectest[i], pval_vectest[i]], stats.mood(x1[i, :], x2[i, :]))
10,283
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"elts": [{"_type": "Constant", "_fields": {"kind": null, "value": 15}}, {"_type": "Constant", "_fields": {"kind": null, "value": 2}}]}}, "targets": [{"_type": "Attribute", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "attr": "shape", "value": {"_type": "Name", "_fields": {"id": "x2", "ctx": {"_type": "Load", "_fields": {}}}}}}], "type_comment": null}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "x1", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "x2", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "mood", "value": {"_type": "Name", "_fields": {"id": "stats", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "axis", "value": {"_type": "Constant", "_fields": {"kind": null, "value": null}}}}]}}, {"_type": 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def test_mood_with_axis_none(self): #Test with axis = None, compare with results from R x1 = [-0.626453810742332, 0.183643324222082, -0.835628612410047, 1.59528080213779, 0.329507771815361, -0.820468384118015, 0.487429052428485, 0.738324705129217, 0.575781351653492, -0.305388387156356, 1.51178116845085, 0.389843236411431, -0.621240580541804, -2.2146998871775, 1.12493091814311, -0.0449336090152309, -0.0161902630989461, 0.943836210685299, 0.821221195098089, 0.593901321217509] x2 = [-0.896914546624981, 0.184849184646742, 1.58784533120882, -1.13037567424629, -0.0802517565509893, 0.132420284381094, 0.707954729271733, -0.23969802417184, 1.98447393665293, -0.138787012119665, 0.417650750792556, 0.981752777463662, -0.392695355503813, -1.03966897694891, 1.78222896030858, -2.31106908460517, 0.878604580921265, 0.035806718015226, 1.01282869212708, 0.432265154539617, 2.09081920524915, -1.19992581964387, 1.58963820029007, 1.95465164222325, 0.00493777682814261, -2.45170638784613, 0.477237302613617, -0.596558168631403, 0.792203270299649, 0.289636710177348] x1 = np.array(x1) x2 = np.array(x2) x1.shape = (10, 2) x2.shape = (15, 2) assert_array_almost_equal(stats.mood(x1, x2, axis=None), [-1.31716607555, 0.18778296257])
10,284
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{"_type": "Store", "_fields": {}}}}, "type_comment": null}}], "name": "test_mood_3d", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_mood_3d(self): shape = (10, 5, 6) np.random.seed(1234) x1 = np.random.randn(*shape) x2 = np.random.randn(*shape) for axis in range(3): z_vectest, pval_vectest = stats.mood(x1, x2, axis=axis) # Tests that result for 3-D arrays is equal to that for the # same calculation on a set of 1-D arrays taken from the # 3-D array axes_idx = ([1, 2], [0, 2], [0, 1]) # the two axes != axis for i in range(shape[axes_idx[axis][0]]): for j in range(shape[axes_idx[axis][1]]): if axis == 0: slice1 = x1[:, i, j] slice2 = x2[:, i, j] elif axis == 1: slice1 = x1[i, :, j] slice2 = x2[i, :, j] else: slice1 = x1[i, j, :] slice2 = x2[i, j, :] assert_array_almost_equal([z_vectest[i, j], pval_vectest[i, j]], stats.mood(slice1, slice2))
10,285
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def test_basic(self): np.random.seed(12345) x = stats.norm.rvs(size=20) osm, osr = stats.probplot(x, fit=False) osm_expected = [-1.8241636, -1.38768012, -1.11829229, -0.91222575, -0.73908135, -0.5857176, -0.44506467, -0.31273668, -0.18568928, -0.06158146, 0.06158146, 0.18568928, 0.31273668, 0.44506467, 0.5857176, 0.73908135, 0.91222575, 1.11829229, 1.38768012, 1.8241636] assert_allclose(osr, np.sort(x)) assert_allclose(osm, osm_expected) res, res_fit = stats.probplot(x, fit=True) res_fit_expected = [1.05361841, 0.31297795, 0.98741609] assert_allclose(res_fit, res_fit_expected)
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{}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "osr1", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "osr2", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "assert_allclose", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, {"_type": "Expr", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "osr1", "ctx": {"_type": "Load", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "osr3", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Name", "_fields": {"id": "assert_allclose", "ctx": {"_type": "Load", "_fields": {}}}}, "keywords": []}}}}, {"_type": "Assign", "_fields": {"value": {"_type": "Call", "_fields": {"args": [{"_type": "Name", "_fields": {"id": "x", "ctx": {"_type": "Load", "_fields": {}}}}], "func": {"_type": "Attribute", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "attr": "probplot", "value": {"_type": "Name", "_fields": {"id": "stats", "ctx": {"_type": "Load", "_fields": {}}}}}}, "keywords": [{"_type": "keyword", "_fields": {"arg": "sparams", "value": {"_type": "Tuple", "_fields": {"ctx": {"_type": "Load", "_fields": {}}, "elts": []}}}}, {"_type": "keyword", "_fields": {"arg": "fit", "value": {"_type": "Constant", "_fields": {"kind": null, "value": false}}}}]}}, "targets": [{"_type": "Tuple", "_fields": {"ctx": {"_type": "Store", "_fields": {}}, "elts": [{"_type": "Name", "_fields": {"id": "osm", "ctx": {"_type": "Store", "_fields": {}}}}, {"_type": "Name", "_fields": {"id": "osr", "ctx": {"_type": "Store", "_fields": {}}}}]}}], "type_comment": null}}], "name": "test_sparams_keyword", "returns": null, "type_params": [], "type_comment": null, "decorator_list": []}}], "type_ignores": []}}
def test_sparams_keyword(self): np.random.seed(123456) x = stats.norm.rvs(size=100) # Check that None, () and 0 (loc=0, for normal distribution) all work # and give the same results osm1, osr1 = stats.probplot(x, sparams=None, fit=False) osm2, osr2 = stats.probplot(x, sparams=0, fit=False) osm3, osr3 = stats.probplot(x, sparams=(), fit=False) assert_allclose(osm1, osm2) assert_allclose(osm1, osm3) assert_allclose(osr1, osr2) assert_allclose(osr1, osr3) # Check giving (loc, scale) params for normal distribution osm, osr = stats.probplot(x, sparams=(), fit=False)
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def test_plot_kwarg(self): np.random.seed(7654321) fig = plt.figure() fig.add_subplot(111) x = stats.t.rvs(3, size=100) res1, fitres1 = stats.probplot(x, plot=plt) plt.close() res2, fitres2 = stats.probplot(x, plot=None) res3 = stats.probplot(x, fit=False, plot=plt) plt.close() res4 = stats.probplot(x, fit=False, plot=None) # Check that results are consistent between combinations of `fit` and # `plot` keywords. assert_(len(res1) == len(res2) == len(res3) == len(res4) == 2) assert_allclose(res1, res2) assert_allclose(res1, res3) assert_allclose(res1, res4) assert_allclose(fitres1, fitres2) # Check that a Matplotlib Axes object is accepted fig = plt.figure() ax = fig.add_subplot(111) stats.probplot(x, fit=False, plot=ax) plt.close()
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9
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def test_moments_normal_distribution(self): np.random.seed(32149) data = np.random.randn(12345) moments = [] for n in [1, 2, 3, 4]: moments.append(stats.kstat(data, n)) expected = [0.011315, 1.017931, 0.05811052, 0.0754134] assert_allclose(moments, expected, rtol=1e-4)
10,289
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10
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class TestKstat(TestCase): # Note: `kstat` still needs review. Statistics Review issue gh-675. def test_moments_normal_distribution(self): np.random.seed(32149) data = np.random.randn(12345) moments = [] for n in [1, 2, 3, 4]: moments.append(stats.kstat(data, n)) expected = [0.011315, 1.017931, 0.05811052, 0.0754134] assert_allclose(moments, expected, rtol=1e-4) def test_kstat_bad_arg(self): # Raise ValueError if n > 4 or n < 1. data = np.arange(10) for n in [0, 4.001]: assert_raises(ValueError, stats.kstat, data, n=n)
10,290
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9
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def test_2d_input(self): # Note: boxcox_llf() was already working with 2-D input (sort of), so # keep it like that. boxcox() doesn't work with 2-D input though, due # to brent() returning a scalar. np.random.seed(54321) x = stats.norm.rvs(size=100, loc=10) lmbda = 1 llf = stats.boxcox_llf(lmbda, x) llf2 = stats.boxcox_llf(lmbda, np.vstack([x, x]).T) assert_allclose([llf, llf], llf2, rtol=1e-12)
10,291
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class TestBoxcox_llf(TestCase): def test_basic(self): np.random.seed(54321) x = stats.norm.rvs(size=10000, loc=10) lmbda = 1 llf = stats.boxcox_llf(lmbda, x) llf_expected = -x.size / 2. * np.log(np.sum(x.std()**2)) assert_allclose(llf, llf_expected) def test_array_like(self): np.random.seed(54321) x = stats.norm.rvs(size=100, loc=10) lmbda = 1 llf = stats.boxcox_llf(lmbda, x) llf2 = stats.boxcox_llf(lmbda, list(x)) assert_allclose(llf, llf2, rtol=1e-12) def test_2d_input(self): # Note: boxcox_llf() was already working with 2-D input (sort of), so # keep it like that. boxcox() doesn't work with 2-D input though, due # to brent() returning a scalar. np.random.seed(54321) x = stats.norm.rvs(size=100, loc=10) lmbda = 1 llf = stats.boxcox_llf(lmbda, x) llf2 = stats.boxcox_llf(lmbda, np.vstack([x, x]).T) assert_allclose([llf, llf], llf2, rtol=1e-12) def test_empty(self): assert_(np.isnan(stats.boxcox_llf(1, [])))
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class TestBoxcox(TestCase): def test_fixed_lmbda(self): np.random.seed(12345) x = stats.loggamma.rvs(5, size=50) + 5 xt = stats.boxcox(x, lmbda=1) assert_allclose(xt, x - 1) xt = stats.boxcox(x, lmbda=-1) assert_allclose(xt, 1 - 1/x) xt = stats.boxcox(x, lmbda=0) assert_allclose(xt, np.log(x)) # Also test that array_like input works xt = stats.boxcox(list(x), lmbda=0) assert_allclose(xt, np.log(x)) def test_lmbda_None(self): np.random.seed(1234567) # Start from normal rv's, do inverse transform to check that # optimization function gets close to the right answer. np.random.seed(1245) lmbda = 2.5 x = stats.norm.rvs(loc=10, size=50000) x_inv = (x * lmbda + 1)**(-lmbda) xt, maxlog = stats.boxcox(x_inv) assert_almost_equal(maxlog, -1 / lmbda, decimal=2) def test_alpha(self): np.random.seed(1234) x = stats.loggamma.rvs(5, size=50) + 5 # Some regular values for alpha, on a small sample size _, _, interval = stats.boxcox(x, alpha=0.75) assert_allclose(interval, [4.004485780226041, 5.138756355035744]) _, _, interval = stats.boxcox(x, alpha=0.05) assert_allclose(interval, [1.2138178554857557, 8.209033272375663]) # Try some extreme values, see we don't hit the N=500 limit x = stats.loggamma.rvs(7, size=500) + 15 _, _, interval = stats.boxcox(x, alpha=0.001) assert_allclose(interval, [0.3988867, 11.40553131]) _, _, interval = stats.boxcox(x, alpha=0.999) assert_allclose(interval, [5.83316246, 5.83735292]) def test_boxcox_bad_arg(self): # Raise ValueError if any data value is negative. x = np.array([-1]) assert_raises(ValueError, stats.boxcox, x) def test_empty(self): assert_(stats.boxcox([]).shape == (0,))
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def test_fixed_lmbda(self): np.random.seed(12345) x = stats.loggamma.rvs(5, size=50) + 5 xt = stats.boxcox(x, lmbda=1) assert_allclose(xt, x - 1) xt = stats.boxcox(x, lmbda=-1) assert_allclose(xt, 1 - 1/x) xt = stats.boxcox(x, lmbda=0) assert_allclose(xt, np.log(x)) # Also test that array_like input works xt = stats.boxcox(list(x), lmbda=0) assert_allclose(xt, np.log(x))
10,294
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9
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def test_alpha(self): np.random.seed(1234) x = stats.loggamma.rvs(5, size=50) + 5 # Some regular values for alpha, on a small sample size _, _, interval = stats.boxcox(x, alpha=0.75) assert_allclose(interval, [4.004485780226041, 5.138756355035744]) _, _, interval = stats.boxcox(x, alpha=0.05) assert_allclose(interval, [1.2138178554857557, 8.209033272375663]) # Try some extreme values, see we don't hit the N=500 limit x = stats.loggamma.rvs(7, size=500) + 15 _, _, interval = stats.boxcox(x, alpha=0.001) assert_allclose(interval, [0.3988867, 11.40553131]) _, _, interval = stats.boxcox(x, alpha=0.999) assert_allclose(interval, [5.83316246, 5.83735292])
10,295
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def test_lmbda_None(self): np.random.seed(1234567) # Start from normal rv's, do inverse transform to check that # optimization function gets close to the right answer. np.random.seed(1245) lmbda = 2.5 x = stats.norm.rvs(loc=10, size=50000) x_inv = (x * lmbda + 1)**(-lmbda) xt, maxlog = stats.boxcox(x_inv) assert_almost_equal(maxlog, -1 / lmbda, decimal=2)
10,296
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10
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"type_comment": null, "decorator_list": []}}], "name": "TestBoxcoxNormmax", "bases": [{"_type": "Name", "_fields": {"id": "TestCase", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TestBoxcoxNormmax(TestCase): def setUp(self): np.random.seed(12345) self.x = stats.loggamma.rvs(5, size=50) + 5 def test_pearsonr(self): maxlog = stats.boxcox_normmax(self.x) assert_allclose(maxlog, 1.804465, rtol=1e-6) def test_mle(self): maxlog = stats.boxcox_normmax(self.x, method='mle') assert_allclose(maxlog, 1.758101, rtol=1e-6) # Check that boxcox() uses 'mle' _, maxlog_boxcox = stats.boxcox(self.x) assert_allclose(maxlog_boxcox, maxlog) def test_all(self): maxlog_all = stats.boxcox_normmax(self.x, method='all') assert_allclose(maxlog_all, [1.804465, 1.758101], rtol=1e-6)
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class TestBoxcoxNormplot(TestCase): def setUp(self): np.random.seed(7654321) self.x = stats.loggamma.rvs(5, size=500) + 5 def test_basic(self): N = 5 lmbdas, ppcc = stats.boxcox_normplot(self.x, -10, 10, N=N) ppcc_expected = [0.57783375, 0.83610988, 0.97524311, 0.99756057, 0.95843297] assert_allclose(lmbdas, np.linspace(-10, 10, num=N)) assert_allclose(ppcc, ppcc_expected) @dec.skipif(not have_matplotlib) def test_plot_kwarg(self): # Check with the matplotlib.pyplot module fig = plt.figure() fig.add_subplot(111) stats.boxcox_normplot(self.x, -20, 20, plot=plt) plt.close() # Check that a Matplotlib Axes object is accepted fig.add_subplot(111) ax = fig.add_subplot(111) stats.boxcox_normplot(self.x, -20, 20, plot=ax) plt.close() def test_invalid_inputs(self): # `lb` has to be larger than `la` assert_raises(ValueError, stats.boxcox_normplot, self.x, 1, 0) # `x` can not contain negative values assert_raises(ValueError, stats.boxcox_normplot, [-1, 1], 0, 1) def test_empty(self): assert_(stats.boxcox_normplot([], 0, 1).size == 0)
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11
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[]}}], "name": "TestCircFuncs", "bases": [{"_type": "Name", "_fields": {"id": "TestCase", "ctx": {"_type": "Load", "_fields": {}}}}], "keywords": [], "type_params": [], "decorator_list": []}}], "type_ignores": []}}
class TestCircFuncs(TestCase): def test_circfuncs(self): x = np.array([355,5,2,359,10,350]) M = stats.circmean(x, high=360) Mval = 0.167690146 assert_allclose(M, Mval, rtol=1e-7) V = stats.circvar(x, high=360) Vval = 42.51955609 assert_allclose(V, Vval, rtol=1e-7) S = stats.circstd(x, high=360) Sval = 6.520702116 assert_allclose(S, Sval, rtol=1e-7) def test_circfuncs_small(self): x = np.array([20,21,22,18,19,20.5,19.2]) M1 = x.mean() M2 = stats.circmean(x, high=360) assert_allclose(M2, M1, rtol=1e-5) V1 = x.var() V2 = stats.circvar(x, high=360) assert_allclose(V2, V1, rtol=1e-4) S1 = x.std() S2 = stats.circstd(x, high=360) assert_allclose(S2, S1, rtol=1e-4) def test_circmean_axis(self): x = np.array([[355,5,2,359,10,350], [351,7,4,352,9,349], [357,9,8,358,4,356]]) M1 = stats.circmean(x, high=360) M2 = stats.circmean(x.ravel(), high=360) assert_allclose(M1, M2, rtol=1e-14) M1 = stats.circmean(x, high=360, axis=1) M2 = [stats.circmean(x[i], high=360) for i in range(x.shape[0])] assert_allclose(M1, M2, rtol=1e-14) M1 = stats.circmean(x, high=360, axis=0) M2 = [stats.circmean(x[:,i], high=360) for i in range(x.shape[1])] assert_allclose(M1, M2, rtol=1e-14) def test_circvar_axis(self): x = np.array([[355,5,2,359,10,350], [351,7,4,352,9,349], [357,9,8,358,4,356]]) V1 = stats.circvar(x, high=360) V2 = stats.circvar(x.ravel(), high=360) assert_allclose(V1, V2, rtol=1e-11) V1 = stats.circvar(x, high=360, axis=1) V2 = [stats.circvar(x[i], high=360) for i in range(x.shape[0])] assert_allclose(V1, V2, rtol=1e-11) V1 = stats.circvar(x, high=360, axis=0) V2 = [stats.circvar(x[:,i], high=360) for i in range(x.shape[1])] assert_allclose(V1, V2, rtol=1e-11) def test_circstd_axis(self): x = np.array([[355,5,2,359,10,350], [351,7,4,352,9,349], [357,9,8,358,4,356]]) S1 = stats.circstd(x, high=360) S2 = stats.circstd(x.ravel(), high=360) assert_allclose(S1, S2, rtol=1e-11) S1 = stats.circstd(x, high=360, axis=1) S2 = [stats.circstd(x[i], high=360) for i in range(x.shape[0])] assert_allclose(S1, S2, rtol=1e-11) S1 = stats.circstd(x, high=360, axis=0) S2 = [stats.circstd(x[:,i], high=360) for i in range(x.shape[1])] assert_allclose(S1, S2, rtol=1e-11) def test_circfuncs_array_like(self): x = [355,5,2,359,10,350] assert_allclose(stats.circmean(x, high=360), 0.167690146, rtol=1e-7) assert_allclose(stats.circvar(x, high=360), 42.51955609, rtol=1e-7) assert_allclose(stats.circstd(x, high=360), 6.520702116, rtol=1e-7) def test_empty(self): assert_(np.isnan(stats.circmean([]))) assert_(np.isnan(stats.circstd([]))) assert_(np.isnan(stats.circvar([])))
10,299
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7
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def test_plot_kwarg(self): # Check with the matplotlib.pyplot module fig = plt.figure() fig.add_subplot(111) stats.boxcox_normplot(self.x, -20, 20, plot=plt) plt.close() # Check that a Matplotlib Axes object is accepted fig.add_subplot(111) ax = fig.add_subplot(111) stats.boxcox_normplot(self.x, -20, 20, plot=ax) plt.close()
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7
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def test_circfuncs(self): x = np.array([355,5,2,359,10,350]) M = stats.circmean(x, high=360) Mval = 0.167690146 assert_allclose(M, Mval, rtol=1e-7) V = stats.circvar(x, high=360) Vval = 42.51955609 assert_allclose(V, Vval, rtol=1e-7) S = stats.circstd(x, high=360) Sval = 6.520702116 assert_allclose(S, Sval, rtol=1e-7)