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d246ab7eddaf6b10c72bd35b5dea63b0229fdb99f19bb48d0587d05cede79f63 | def reset_states(self):
'Resets all of the metric state variables.'
K.batch_set_value([(v, np.zeros((self.num_classes, self.num_classes))) for v in self.variables]) | Resets all of the metric state variables. | core/metrics.py | reset_states | JohnBoxAnn/TSGL-EEGNet | 3 | python | def reset_states(self):
K.batch_set_value([(v, np.zeros((self.num_classes, self.num_classes))) for v in self.variables]) | def reset_states(self):
K.batch_set_value([(v, np.zeros((self.num_classes, self.num_classes))) for v in self.variables])<|docstring|>Resets all of the metric state variables.<|endoftext|> |
fed7dec13073325e51bc79611a65b3b7550c39a5ca0516702c1ec124939d47a6 | def get_examples(mode='train'):
'\n dataset[0][0] examples\n '
examples = {'train': {'id': 242, 'candidates': ['风云人物', '气势汹汹', '予取予求', '乘龙佳婿', '正中下怀', '天方夜谭', '心如刀割'], 'content': '据俄罗斯卫星通讯社3月15日报道,新八国联军#idiom#逼近附近海域,但是军舰却遭岸舰导弹锁定,英承认今非昔比。 最近一段时间,北约多个国家开始频繁进行军事演习,来对其他国家进行威慑。3月12日当天,英国出动了兰开斯特号、威斯敏斯特号...', 'answer': 1}}
return examples[mode] | dataset[0][0] examples | tests/dataset/test_fewclue_chid.py | get_examples | zjjlivein/PaddleNLP | 0 | python | def get_examples(mode='train'):
'\n \n '
examples = {'train': {'id': 242, 'candidates': ['风云人物', '气势汹汹', '予取予求', '乘龙佳婿', '正中下怀', '天方夜谭', '心如刀割'], 'content': '据俄罗斯卫星通讯社3月15日报道,新八国联军#idiom#逼近附近海域,但是军舰却遭岸舰导弹锁定,英承认今非昔比。 最近一段时间,北约多个国家开始频繁进行军事演习,来对其他国家进行威慑。3月12日当天,英国出动了兰开斯特号、威斯敏斯特号...', 'answer': 1}}
return examples[mode] | def get_examples(mode='train'):
'\n \n '
examples = {'train': {'id': 242, 'candidates': ['风云人物', '气势汹汹', '予取予求', '乘龙佳婿', '正中下怀', '天方夜谭', '心如刀割'], 'content': '据俄罗斯卫星通讯社3月15日报道,新八国联军#idiom#逼近附近海域,但是军舰却遭岸舰导弹锁定,英承认今非昔比。 最近一段时间,北约多个国家开始频繁进行军事演习,来对其他国家进行威慑。3月12日当天,英国出动了兰开斯特号、威斯敏斯特号...', 'answer': 1}}
return examples[mode]<|docstring|>dataset[0][0] examples<|endoftext|> |
e3f0712c8fac8b0575db41e360b9f0dcca5be9e3f93e0038060238d4eb55e5ef | def setUp(self):
'\n check input params & datasets all flies\n '
self.config['path_or_read_func'] = 'fewclue'
self.config['name'] = 'chid'
self.config['splits'] = ['train_0', 'train_1', 'train_2', 'train_3', 'train_4', 'train_few_all', 'dev_0', 'dev_1', 'dev_2', 'dev_3', 'dev_4', 'dev_few_all', 'unlabeled', 'test', 'test_public'] | check input params & datasets all flies | tests/dataset/test_fewclue_chid.py | setUp | zjjlivein/PaddleNLP | 0 | python | def setUp(self):
'\n \n '
self.config['path_or_read_func'] = 'fewclue'
self.config['name'] = 'chid'
self.config['splits'] = ['train_0', 'train_1', 'train_2', 'train_3', 'train_4', 'train_few_all', 'dev_0', 'dev_1', 'dev_2', 'dev_3', 'dev_4', 'dev_few_all', 'unlabeled', 'test', 'test_public'] | def setUp(self):
'\n \n '
self.config['path_or_read_func'] = 'fewclue'
self.config['name'] = 'chid'
self.config['splits'] = ['train_0', 'train_1', 'train_2', 'train_3', 'train_4', 'train_few_all', 'dev_0', 'dev_1', 'dev_2', 'dev_3', 'dev_4', 'dev_few_all', 'unlabeled', 'test', 'test_public']<|docstring|>check input params & datasets all flies<|endoftext|> |
4ac22548afa7776ce8001276ce827d2c472b16f2ffb772cfba9fa76d84e6316b | def test_train_set(self):
'\n check train.json length, id, candidates, content, answer\n '
expected_ds_num = 15
expected_len = 42
expected_train = get_examples('train')
ds = load_dataset(**self.config)
self.check_output_equal(len(ds), expected_ds_num)
self.check_output_equal(len(ds[0]), expected_len)
self.check_output_equal(int(expected_train['answer']), ds[0][0]['answer'])
self.check_output_equal(expected_train['candidates'], ds[0][0]['candidates'])
self.check_output_equal(expected_train['content'], ds[0][0]['content'])
self.check_output_equal(expected_train['id'], ds[0][0]['id']) | check train.json length, id, candidates, content, answer | tests/dataset/test_fewclue_chid.py | test_train_set | zjjlivein/PaddleNLP | 0 | python | def test_train_set(self):
'\n \n '
expected_ds_num = 15
expected_len = 42
expected_train = get_examples('train')
ds = load_dataset(**self.config)
self.check_output_equal(len(ds), expected_ds_num)
self.check_output_equal(len(ds[0]), expected_len)
self.check_output_equal(int(expected_train['answer']), ds[0][0]['answer'])
self.check_output_equal(expected_train['candidates'], ds[0][0]['candidates'])
self.check_output_equal(expected_train['content'], ds[0][0]['content'])
self.check_output_equal(expected_train['id'], ds[0][0]['id']) | def test_train_set(self):
'\n \n '
expected_ds_num = 15
expected_len = 42
expected_train = get_examples('train')
ds = load_dataset(**self.config)
self.check_output_equal(len(ds), expected_ds_num)
self.check_output_equal(len(ds[0]), expected_len)
self.check_output_equal(int(expected_train['answer']), ds[0][0]['answer'])
self.check_output_equal(expected_train['candidates'], ds[0][0]['candidates'])
self.check_output_equal(expected_train['content'], ds[0][0]['content'])
self.check_output_equal(expected_train['id'], ds[0][0]['id'])<|docstring|>check train.json length, id, candidates, content, answer<|endoftext|> |
4f4e5d6c9fb51c614f14613eeed46a56d809dd00713a9673116cc10c5bd6ace6 | def fetch_symbols_list(only_a=True):
'获取上市公司代码及简称'
url = 'http://www.cninfo.com.cn/cninfo-new/information/companylist'
response = get_page_response(url)
def _parse(response):
soup = BeautifulSoup(response.text, 'lxml')
tag_as = soup.find_all('a', href=re.compile('companyinfo_n.html'))
res = [(x.text[:6].strip(), x.text[7:].lstrip()) for x in tag_as]
df = pd.DataFrame(res, columns=['code', 'short_name'])
if only_a:
df = df[df.code.str.get(0).str.contains('[0,3,6]')]
return df.set_index('code', drop=True)
return _parse(response) | 获取上市公司代码及简称 | cnswd/websource/juchao.py | fetch_symbols_list | huangzhangfeng/cnswd | 0 | python | def fetch_symbols_list(only_a=True):
url = 'http://www.cninfo.com.cn/cninfo-new/information/companylist'
response = get_page_response(url)
def _parse(response):
soup = BeautifulSoup(response.text, 'lxml')
tag_as = soup.find_all('a', href=re.compile('companyinfo_n.html'))
res = [(x.text[:6].strip(), x.text[7:].lstrip()) for x in tag_as]
df = pd.DataFrame(res, columns=['code', 'short_name'])
if only_a:
df = df[df.code.str.get(0).str.contains('[0,3,6]')]
return df.set_index('code', drop=True)
return _parse(response) | def fetch_symbols_list(only_a=True):
url = 'http://www.cninfo.com.cn/cninfo-new/information/companylist'
response = get_page_response(url)
def _parse(response):
soup = BeautifulSoup(response.text, 'lxml')
tag_as = soup.find_all('a', href=re.compile('companyinfo_n.html'))
res = [(x.text[:6].strip(), x.text[7:].lstrip()) for x in tag_as]
df = pd.DataFrame(res, columns=['code', 'short_name'])
if only_a:
df = df[df.code.str.get(0).str.contains('[0,3,6]')]
return df.set_index('code', drop=True)
return _parse(response)<|docstring|>获取上市公司代码及简称<|endoftext|> |
5f4f0e6c952bc8ba9d11d188124515ad57af438db6b63a78b07183363cf1ac32 | def _mark_changed(old_df, new_df):
'标记股票状态、名称字段的改变'
new_df['changed'] = False
added = old_df.index.difference(new_df.index)
new_df = pd.concat([new_df, old_df.loc[(added, :)]])
for s in new_df.status.unique():
o_index = old_df.query('status == {}'.format(s)).index
n_index = new_df.query('status == {}'.format(s)).index
new_df.loc[(o_index.symmetric_difference(n_index), 'changed')] = True
i_index = new_df.index.intersection(old_df.index)
for i in i_index:
o_name = old_df.loc[(i, 'name')]
n_name = new_df.loc[(i, 'name')]
if (o_name.strip() != n_name.strip()):
new_df.loc[(i, 'changed')] = True | 标记股票状态、名称字段的改变 | cnswd/websource/juchao.py | _mark_changed | huangzhangfeng/cnswd | 0 | python | def _mark_changed(old_df, new_df):
new_df['changed'] = False
added = old_df.index.difference(new_df.index)
new_df = pd.concat([new_df, old_df.loc[(added, :)]])
for s in new_df.status.unique():
o_index = old_df.query('status == {}'.format(s)).index
n_index = new_df.query('status == {}'.format(s)).index
new_df.loc[(o_index.symmetric_difference(n_index), 'changed')] = True
i_index = new_df.index.intersection(old_df.index)
for i in i_index:
o_name = old_df.loc[(i, 'name')]
n_name = new_df.loc[(i, 'name')]
if (o_name.strip() != n_name.strip()):
new_df.loc[(i, 'changed')] = True | def _mark_changed(old_df, new_df):
new_df['changed'] = False
added = old_df.index.difference(new_df.index)
new_df = pd.concat([new_df, old_df.loc[(added, :)]])
for s in new_df.status.unique():
o_index = old_df.query('status == {}'.format(s)).index
n_index = new_df.query('status == {}'.format(s)).index
new_df.loc[(o_index.symmetric_difference(n_index), 'changed')] = True
i_index = new_df.index.intersection(old_df.index)
for i in i_index:
o_name = old_df.loc[(i, 'name')]
n_name = new_df.loc[(i, 'name')]
if (o_name.strip() != n_name.strip()):
new_df.loc[(i, 'changed')] = True<|docstring|>标记股票状态、名称字段的改变<|endoftext|> |
e3bf714d14d52cd9dc5df44c6c3eff68c3475b60f76f699d4ae1cef07f600120 | def get_stock_codes():
'所有主板股票代码（含已经退市）'
p1 = fetch_symbols_list()
p1.rename(columns={'short_name': 'name'}, inplace=True)
p1['status'] = '在市'
p1.sort_index(inplace=True)
p2 = fetch_suspend_stocks()[['seccode', 'secname']].rename(columns={'seccode': 'code', 'secname': 'name'})
p2.set_index('code', drop=True, inplace=True)
p2 = p2[p2.index.str.get(0).str.contains('[0,3,6]')]
p1.loc[(p2.index, 'status')] = '暂停'
p3 = fetch_delisting_stocks()[['name']]
p3 = p3[p3.index.str.get(0).str.contains('[0,3,6]')]
p3['status'] = '退市'
df = pd.concat([p1, p3])
df.sort_index(inplace=True)
return df | 所有主板股票代码（含已经退市） | cnswd/websource/juchao.py | get_stock_codes | huangzhangfeng/cnswd | 0 | python | def get_stock_codes():
p1 = fetch_symbols_list()
p1.rename(columns={'short_name': 'name'}, inplace=True)
p1['status'] = '在市'
p1.sort_index(inplace=True)
p2 = fetch_suspend_stocks()[['seccode', 'secname']].rename(columns={'seccode': 'code', 'secname': 'name'})
p2.set_index('code', drop=True, inplace=True)
p2 = p2[p2.index.str.get(0).str.contains('[0,3,6]')]
p1.loc[(p2.index, 'status')] = '暂停'
p3 = fetch_delisting_stocks()[['name']]
p3 = p3[p3.index.str.get(0).str.contains('[0,3,6]')]
p3['status'] = '退市'
df = pd.concat([p1, p3])
df.sort_index(inplace=True)
return df | def get_stock_codes():
p1 = fetch_symbols_list()
p1.rename(columns={'short_name': 'name'}, inplace=True)
p1['status'] = '在市'
p1.sort_index(inplace=True)
p2 = fetch_suspend_stocks()[['seccode', 'secname']].rename(columns={'seccode': 'code', 'secname': 'name'})
p2.set_index('code', drop=True, inplace=True)
p2 = p2[p2.index.str.get(0).str.contains('[0,3,6]')]
p1.loc[(p2.index, 'status')] = '暂停'
p3 = fetch_delisting_stocks()[['name']]
p3 = p3[p3.index.str.get(0).str.contains('[0,3,6]')]
p3['status'] = '退市'
df = pd.concat([p1, p3])
df.sort_index(inplace=True)
return df<|docstring|>所有主板股票代码（含已经退市）<|endoftext|> |
789a7ce57961ff00cac684fc50a9271fcfd5141c9a4521f9652f47b8a970cd51 | def fetch_suspend_stocks():
'获取暂停上市股票列表'
url_fmt = 'http://www.cninfo.com.cn/cninfo-new/information/suspendlist-1?market={}'
urls = [url_fmt.format(x) for x in ('sh', 'sz')]
datas = [get_page_response(url, method='post').json() for url in urls]
dfs = [pd.DataFrame(d) for d in datas]
df = pd.concat(dfs).iloc[(:, 1:)]
return df.reset_index(drop=True) | 获取暂停上市股票列表 | cnswd/websource/juchao.py | fetch_suspend_stocks | huangzhangfeng/cnswd | 0 | python | def fetch_suspend_stocks():
url_fmt = 'http://www.cninfo.com.cn/cninfo-new/information/suspendlist-1?market={}'
urls = [url_fmt.format(x) for x in ('sh', 'sz')]
datas = [get_page_response(url, method='post').json() for url in urls]
dfs = [pd.DataFrame(d) for d in datas]
df = pd.concat(dfs).iloc[(:, 1:)]
return df.reset_index(drop=True) | def fetch_suspend_stocks():
url_fmt = 'http://www.cninfo.com.cn/cninfo-new/information/suspendlist-1?market={}'
urls = [url_fmt.format(x) for x in ('sh', 'sz')]
datas = [get_page_response(url, method='post').json() for url in urls]
dfs = [pd.DataFrame(d) for d in datas]
df = pd.concat(dfs).iloc[(:, 1:)]
return df.reset_index(drop=True)<|docstring|>获取暂停上市股票列表<|endoftext|> |
cda3a3a614a7c23a7b314b54cd7ef4b2e79eee392e383db352be8dbdbbb6ac02 | def fetch_delisting_stocks():
'获取终止上市股票清单'
url_fmt = 'http://three.cninfo.com.cn/new/information/getDelistingList?market={}'
urls = [url_fmt.format(x) for x in ('sh', 'sz')]
datas = [get_page_response(url, method='post').json() for url in urls]
dfs = [pd.DataFrame(d) for d in datas]
df = pd.concat(dfs)
df = df.rename(columns={'f007d_0007': '转板日期', 'f008d_0007': '终止上市日期', 'r_seccode_0007': '三板证券代码', 'r_secname_0007': '三板证券简称', 'y_seccode_0007': '股票代码', 'y_secname_0007': '股票简称'})
df.set_index('股票代码', drop=True, inplace=True)
return df.applymap(str.strip) | 获取终止上市股票清单 | cnswd/websource/juchao.py | fetch_delisting_stocks | huangzhangfeng/cnswd | 0 | python | def fetch_delisting_stocks():
url_fmt = 'http://three.cninfo.com.cn/new/information/getDelistingList?market={}'
urls = [url_fmt.format(x) for x in ('sh', 'sz')]
datas = [get_page_response(url, method='post').json() for url in urls]
dfs = [pd.DataFrame(d) for d in datas]
df = pd.concat(dfs)
df = df.rename(columns={'f007d_0007': '转板日期', 'f008d_0007': '终止上市日期', 'r_seccode_0007': '三板证券代码', 'r_secname_0007': '三板证券简称', 'y_seccode_0007': '股票代码', 'y_secname_0007': '股票简称'})
df.set_index('股票代码', drop=True, inplace=True)
return df.applymap(str.strip) | def fetch_delisting_stocks():
url_fmt = 'http://three.cninfo.com.cn/new/information/getDelistingList?market={}'
urls = [url_fmt.format(x) for x in ('sh', 'sz')]
datas = [get_page_response(url, method='post').json() for url in urls]
dfs = [pd.DataFrame(d) for d in datas]
df = pd.concat(dfs)
df = df.rename(columns={'f007d_0007': '转板日期', 'f008d_0007': '终止上市日期', 'r_seccode_0007': '三板证券代码', 'r_secname_0007': '三板证券简称', 'y_seccode_0007': '股票代码', 'y_secname_0007': '股票简称'})
df.set_index('股票代码', drop=True, inplace=True)
return df.applymap(str.strip)<|docstring|>获取终止上市股票清单<|endoftext|> |
0571c882a8a6fa9d9dd0e0e035b3311c774e3a834a29d5a18dfb51ff5e7f718b | @friendly_download(30)
def fetch_company_brief_info(stock_code):
'公司简要信息'
url = _get_url(stock_code, 'brief')
r = requests.get(url)
r.encoding = 'gb18030'
df = pd.read_html(r.text, flavor='lxml')[1]
return df | 公司简要信息 | cnswd/websource/juchao.py | fetch_company_brief_info | huangzhangfeng/cnswd | 0 | python | @friendly_download(30)
def fetch_company_brief_info(stock_code):
url = _get_url(stock_code, 'brief')
r = requests.get(url)
r.encoding = 'gb18030'
df = pd.read_html(r.text, flavor='lxml')[1]
return df | @friendly_download(30)
def fetch_company_brief_info(stock_code):
url = _get_url(stock_code, 'brief')
r = requests.get(url)
r.encoding = 'gb18030'
df = pd.read_html(r.text, flavor='lxml')[1]
return df<|docstring|>公司简要信息<|endoftext|> |
56298065b7fc70d907bd418eedefcd0947a70f89b4277b8bd4bc9adf21a93949 | def fetch_issue_info(stock_code):
'发行信息'
url = _get_url(stock_code, 'issue')
page_response = get_page_response(url)
df = pd.read_html(StringIO(page_response.content))[1]
return df | 发行信息 | cnswd/websource/juchao.py | fetch_issue_info | huangzhangfeng/cnswd | 0 | python | def fetch_issue_info(stock_code):
url = _get_url(stock_code, 'issue')
page_response = get_page_response(url)
df = pd.read_html(StringIO(page_response.content))[1]
return df | def fetch_issue_info(stock_code):
url = _get_url(stock_code, 'issue')
page_response = get_page_response(url)
df = pd.read_html(StringIO(page_response.content))[1]
return df<|docstring|>发行信息<|endoftext|> |
714607eee9b090834270d7f4e081384fb8a850931c61266ef39a4b004e16a328 | def fetch_index_info():
'获取指数基本信息'
xl = ['szxl', 'jcxl']
zs = ['gmzs', 'hyzs', 'fgzs', 'ztzs', 'clzs', 'dzzs', 'zhzs', 'jjzs', 'zqzs', 'qyzs']
prod_ = product(xl, zs)
url_fmt = 'http://www.cnindex.com.cn/zstx/{}/{}'
urls = [url_fmt.format(x[0], x[1]) for x in prod_]
dfs = []
@friendly_download(30, max_sleep=1)
def _process(url):
try:
page_response = get_page_response(url)
df = pd.read_html(BytesIO(page_response.content), header=0)[1]
dfs.append(df)
except ConnectFailed:
pass
for url in urls:
_process(url)
data = pd.concat(dfs)
col_names = ['name', 'code', 'base_day', 'base_point', 'launch_day', 'constituents']
data.columns = col_names
def f(x):
return pd.to_datetime(x, format='%Y-%m-%d', errors='coerce')
data['base_day'] = data['base_day'].apply(f)
data['launch_day'] = data['launch_day'].apply(f)
data.set_index('code', drop=True, inplace=True)
return data | 获取指数基本信息 | cnswd/websource/juchao.py | fetch_index_info | huangzhangfeng/cnswd | 0 | python | def fetch_index_info():
xl = ['szxl', 'jcxl']
zs = ['gmzs', 'hyzs', 'fgzs', 'ztzs', 'clzs', 'dzzs', 'zhzs', 'jjzs', 'zqzs', 'qyzs']
prod_ = product(xl, zs)
url_fmt = 'http://www.cnindex.com.cn/zstx/{}/{}'
urls = [url_fmt.format(x[0], x[1]) for x in prod_]
dfs = []
@friendly_download(30, max_sleep=1)
def _process(url):
try:
page_response = get_page_response(url)
df = pd.read_html(BytesIO(page_response.content), header=0)[1]
dfs.append(df)
except ConnectFailed:
pass
for url in urls:
_process(url)
data = pd.concat(dfs)
col_names = ['name', 'code', 'base_day', 'base_point', 'launch_day', 'constituents']
data.columns = col_names
def f(x):
return pd.to_datetime(x, format='%Y-%m-%d', errors='coerce')
data['base_day'] = data['base_day'].apply(f)
data['launch_day'] = data['launch_day'].apply(f)
data.set_index('code', drop=True, inplace=True)
return data | def fetch_index_info():
xl = ['szxl', 'jcxl']
zs = ['gmzs', 'hyzs', 'fgzs', 'ztzs', 'clzs', 'dzzs', 'zhzs', 'jjzs', 'zqzs', 'qyzs']
prod_ = product(xl, zs)
url_fmt = 'http://www.cnindex.com.cn/zstx/{}/{}'
urls = [url_fmt.format(x[0], x[1]) for x in prod_]
dfs = []
@friendly_download(30, max_sleep=1)
def _process(url):
try:
page_response = get_page_response(url)
df = pd.read_html(BytesIO(page_response.content), header=0)[1]
dfs.append(df)
except ConnectFailed:
pass
for url in urls:
_process(url)
data = pd.concat(dfs)
col_names = ['name', 'code', 'base_day', 'base_point', 'launch_day', 'constituents']
data.columns = col_names
def f(x):
return pd.to_datetime(x, format='%Y-%m-%d', errors='coerce')
data['base_day'] = data['base_day'].apply(f)
data['launch_day'] = data['launch_day'].apply(f)
data.set_index('code', drop=True, inplace=True)
return data<|docstring|>获取指数基本信息<|endoftext|> |
bb5173b5ec41cba8d5ca5916983965da6413fc3cde32c8641314aa74056d10a5 | def fetch_adjustment(stock_code):
'\n 提取股票历史分配记录\n 深圳交易所除权基准日与红股上市日一致；上海证券交易所红股上市日\n 一般晚于除权基准日。\n\n 注意：\n 使用除权基准日作为支付日，红股上市日作为生效日；\n '
url = _get_url(stock_code, 'dividend')
page_response = get_page_response(url)
df = pd.read_html(BytesIO(page_response.content), match='分红年度', skiprows=[0])[0]
df.dropna(how='all', inplace=True)
if df.empty:
return df
df.columns = _ADJUSTMENT_FIELDS
data = _parse_ratio_and_amount(df)
data.set_index('effective_date', inplace=True)
data.sort_index(inplace=True)
return data | 提取股票历史分配记录
深圳交易所除权基准日与红股上市日一致；上海证券交易所红股上市日
一般晚于除权基准日。
注意：
使用除权基准日作为支付日，红股上市日作为生效日； | cnswd/websource/juchao.py | fetch_adjustment | huangzhangfeng/cnswd | 0 | python | def fetch_adjustment(stock_code):
'\n 提取股票历史分配记录\n 深圳交易所除权基准日与红股上市日一致；上海证券交易所红股上市日\n 一般晚于除权基准日。\n\n 注意：\n 使用除权基准日作为支付日，红股上市日作为生效日；\n '
url = _get_url(stock_code, 'dividend')
page_response = get_page_response(url)
df = pd.read_html(BytesIO(page_response.content), match='分红年度', skiprows=[0])[0]
df.dropna(how='all', inplace=True)
if df.empty:
return df
df.columns = _ADJUSTMENT_FIELDS
data = _parse_ratio_and_amount(df)
data.set_index('effective_date', inplace=True)
data.sort_index(inplace=True)
return data | def fetch_adjustment(stock_code):
'\n 提取股票历史分配记录\n 深圳交易所除权基准日与红股上市日一致；上海证券交易所红股上市日\n 一般晚于除权基准日。\n\n 注意：\n 使用除权基准日作为支付日，红股上市日作为生效日；\n '
url = _get_url(stock_code, 'dividend')
page_response = get_page_response(url)
df = pd.read_html(BytesIO(page_response.content), match='分红年度', skiprows=[0])[0]
df.dropna(how='all', inplace=True)
if df.empty:
return df
df.columns = _ADJUSTMENT_FIELDS
data = _parse_ratio_and_amount(df)
data.set_index('effective_date', inplace=True)
data.sort_index(inplace=True)
return data<|docstring|>提取股票历史分配记录
深圳交易所除权基准日与红股上市日一致；上海证券交易所红股上市日
一般晚于除权基准日。
注意：
使用除权基准日作为支付日，红股上市日作为生效日；<|endoftext|> |
f4d6123403e5a0024593e23a44840c08bb85f2c348592f34507eab93791e9595 | def _parse_ratio_and_amount(df):
'\n 解析分配比例及分红金额（每股）\n 更改说明：\n 简化处理解析。单纯计算，而不进行合并。\n 待后续查询时，如一天内有二条记录，则合并计算\n '
base = df.scheme.str.extract(_BASE_PATTERN, expand=False)
increase = df.scheme.str.extract(_INCREASE_PATTERN, expand=False)
give = df.scheme.str.extract(_GIVE_PATTERN, expand=False)
dividend = df.scheme.str.extract(_DIVIDEND_PATTERN, expand=False)
increase.fillna(0, inplace=True)
give.fillna(0, inplace=True)
dividend.fillna(0, inplace=True)
ratio = (increase.astype(float).add(give.astype(float)) / base.astype(float))
amount = (dividend.astype(float) / base.astype(float))
def f(x):
return pd.to_datetime(x, format='%Y%m%d', errors='coerce')
data = pd.DataFrame({'ratio': ratio.values, 'amount': amount.values, 'annual': df.annual, 'record_date': df.record_date.apply(f), 'pay_date': df.effective_date.apply(f), 'listing_date': df.listing_date.apply(f)})
data.loc[(data['pay_date'].isnull(), 'pay_date')] = data.loc[(data['pay_date'].isnull(), 'listing_date')]
data.loc[(data['pay_date'].isnull(), 'pay_date')] = data.loc[(data['pay_date'].isnull(), 'record_date')]
data.loc[(data['listing_date'].isnull(), 'listing_date')] = data.loc[(data['listing_date'].isnull(), 'pay_date')]
data['effective_date'] = data['pay_date']
return data | 解析分配比例及分红金额（每股）
更改说明：
简化处理解析。单纯计算，而不进行合并。
待后续查询时，如一天内有二条记录，则合并计算 | cnswd/websource/juchao.py | _parse_ratio_and_amount | huangzhangfeng/cnswd | 0 | python | def _parse_ratio_and_amount(df):
'\n 解析分配比例及分红金额（每股）\n 更改说明：\n 简化处理解析。单纯计算，而不进行合并。\n 待后续查询时，如一天内有二条记录，则合并计算\n '
base = df.scheme.str.extract(_BASE_PATTERN, expand=False)
increase = df.scheme.str.extract(_INCREASE_PATTERN, expand=False)
give = df.scheme.str.extract(_GIVE_PATTERN, expand=False)
dividend = df.scheme.str.extract(_DIVIDEND_PATTERN, expand=False)
increase.fillna(0, inplace=True)
give.fillna(0, inplace=True)
dividend.fillna(0, inplace=True)
ratio = (increase.astype(float).add(give.astype(float)) / base.astype(float))
amount = (dividend.astype(float) / base.astype(float))
def f(x):
return pd.to_datetime(x, format='%Y%m%d', errors='coerce')
data = pd.DataFrame({'ratio': ratio.values, 'amount': amount.values, 'annual': df.annual, 'record_date': df.record_date.apply(f), 'pay_date': df.effective_date.apply(f), 'listing_date': df.listing_date.apply(f)})
data.loc[(data['pay_date'].isnull(), 'pay_date')] = data.loc[(data['pay_date'].isnull(), 'listing_date')]
data.loc[(data['pay_date'].isnull(), 'pay_date')] = data.loc[(data['pay_date'].isnull(), 'record_date')]
data.loc[(data['listing_date'].isnull(), 'listing_date')] = data.loc[(data['listing_date'].isnull(), 'pay_date')]
data['effective_date'] = data['pay_date']
return data | def _parse_ratio_and_amount(df):
'\n 解析分配比例及分红金额（每股）\n 更改说明：\n 简化处理解析。单纯计算，而不进行合并。\n 待后续查询时，如一天内有二条记录，则合并计算\n '
base = df.scheme.str.extract(_BASE_PATTERN, expand=False)
increase = df.scheme.str.extract(_INCREASE_PATTERN, expand=False)
give = df.scheme.str.extract(_GIVE_PATTERN, expand=False)
dividend = df.scheme.str.extract(_DIVIDEND_PATTERN, expand=False)
increase.fillna(0, inplace=True)
give.fillna(0, inplace=True)
dividend.fillna(0, inplace=True)
ratio = (increase.astype(float).add(give.astype(float)) / base.astype(float))
amount = (dividend.astype(float) / base.astype(float))
def f(x):
return pd.to_datetime(x, format='%Y%m%d', errors='coerce')
data = pd.DataFrame({'ratio': ratio.values, 'amount': amount.values, 'annual': df.annual, 'record_date': df.record_date.apply(f), 'pay_date': df.effective_date.apply(f), 'listing_date': df.listing_date.apply(f)})
data.loc[(data['pay_date'].isnull(), 'pay_date')] = data.loc[(data['pay_date'].isnull(), 'listing_date')]
data.loc[(data['pay_date'].isnull(), 'pay_date')] = data.loc[(data['pay_date'].isnull(), 'record_date')]
data.loc[(data['listing_date'].isnull(), 'listing_date')] = data.loc[(data['listing_date'].isnull(), 'pay_date')]
data['effective_date'] = data['pay_date']
return data<|docstring|>解析分配比例及分红金额（每股）
更改说明：
简化处理解析。单纯计算，而不进行合并。
待后续查询时，如一天内有二条记录，则合并计算<|endoftext|> |
cf38fd2f663fe529768ec3e10a1851d389eb60c497d3d0d54f5635a6eb5fd19e | def fetch_announcement_summary():
'获取最近一期公司公告摘要信息\n 用途：\n 1、限定需要更新公司名录；\n 2、限定刷新公司财务报告名录；\n 3、辅助分析\n '
cols = ['announcementTime', 'announcementTitle', 'announcementType', 'announcementTypeName', 'secCode', 'secName']
url_fmt = 'http://www.cninfo.com.cn/cninfo-new/disclosure/{}_summary/?pageNum={}'
markets = ('sse', 'szse')
dfs = []
for m in markets:
for i in range(1, 100):
url = url_fmt.format(m, i)
r = get_page_response(url, 'post')
d = r.json()
df = pd.DataFrame.from_dict(d['announcements'])[cols]
dfs.append(df)
if (not d['hasMore']):
break
data = pd.concat(dfs)
data.reset_index(inplace=True, drop=True)
output = pd.DataFrame({'股票代码': data['secCode'].values, '股票简称': data['secName'].values, '公告时间': data['announcementTime'].apply(pd.Timestamp, unit='ms'), '公告标题': data['announcementTitle'].values, '类别': data['announcementTypeName'].values})
return output | 获取最近一期公司公告摘要信息
用途：
1、限定需要更新公司名录；
2、限定刷新公司财务报告名录；
3、辅助分析 | cnswd/websource/juchao.py | fetch_announcement_summary | huangzhangfeng/cnswd | 0 | python | def fetch_announcement_summary():
'获取最近一期公司公告摘要信息\n 用途：\n 1、限定需要更新公司名录；\n 2、限定刷新公司财务报告名录；\n 3、辅助分析\n '
cols = ['announcementTime', 'announcementTitle', 'announcementType', 'announcementTypeName', 'secCode', 'secName']
url_fmt = 'http://www.cninfo.com.cn/cninfo-new/disclosure/{}_summary/?pageNum={}'
markets = ('sse', 'szse')
dfs = []
for m in markets:
for i in range(1, 100):
url = url_fmt.format(m, i)
r = get_page_response(url, 'post')
d = r.json()
df = pd.DataFrame.from_dict(d['announcements'])[cols]
dfs.append(df)
if (not d['hasMore']):
break
data = pd.concat(dfs)
data.reset_index(inplace=True, drop=True)
output = pd.DataFrame({'股票代码': data['secCode'].values, '股票简称': data['secName'].values, '公告时间': data['announcementTime'].apply(pd.Timestamp, unit='ms'), '公告标题': data['announcementTitle'].values, '类别': data['announcementTypeName'].values})
return output | def fetch_announcement_summary():
'获取最近一期公司公告摘要信息\n 用途：\n 1、限定需要更新公司名录；\n 2、限定刷新公司财务报告名录；\n 3、辅助分析\n '
cols = ['announcementTime', 'announcementTitle', 'announcementType', 'announcementTypeName', 'secCode', 'secName']
url_fmt = 'http://www.cninfo.com.cn/cninfo-new/disclosure/{}_summary/?pageNum={}'
markets = ('sse', 'szse')
dfs = []
for m in markets:
for i in range(1, 100):
url = url_fmt.format(m, i)
r = get_page_response(url, 'post')
d = r.json()
df = pd.DataFrame.from_dict(d['announcements'])[cols]
dfs.append(df)
if (not d['hasMore']):
break
data = pd.concat(dfs)
data.reset_index(inplace=True, drop=True)
output = pd.DataFrame({'股票代码': data['secCode'].values, '股票简称': data['secName'].values, '公告时间': data['announcementTime'].apply(pd.Timestamp, unit='ms'), '公告标题': data['announcementTitle'].values, '类别': data['announcementTypeName'].values})
return output<|docstring|>获取最近一期公司公告摘要信息
用途：
1、限定需要更新公司名录；
2、限定刷新公司财务报告名录；
3、辅助分析<|endoftext|> |
9731de31dc50971dd6115ef6e048e795f57a6bfcf5c715c30f4f16eea87451b8 | def fetch_industry(date_str, department):
'巨潮、证监会行业编码\n\n 异常：\n 如果date_为非交易日，触发值异常\n '
url_fmt = 'http://www.cnindex.com.cn/syl/{}/{}_hsls.html'
url = url_fmt.format(date_str, department)
try:
df = pd.read_html(url)[1].loc[(:, range(2))]
df.columns = ['industry_id', 'name']
return df
except HTTPError:
msg_fmt = "或者当前日期的数据尚未发布，或者日期'{}'并非交易日"
raise ValueError(msg_fmt.format(date_str)) | 巨潮、证监会行业编码
异常：
如果date_为非交易日，触发值异常 | cnswd/websource/juchao.py | fetch_industry | huangzhangfeng/cnswd | 0 | python | def fetch_industry(date_str, department):
'巨潮、证监会行业编码\n\n 异常：\n 如果date_为非交易日，触发值异常\n '
url_fmt = 'http://www.cnindex.com.cn/syl/{}/{}_hsls.html'
url = url_fmt.format(date_str, department)
try:
df = pd.read_html(url)[1].loc[(:, range(2))]
df.columns = ['industry_id', 'name']
return df
except HTTPError:
msg_fmt = "或者当前日期的数据尚未发布，或者日期'{}'并非交易日"
raise ValueError(msg_fmt.format(date_str)) | def fetch_industry(date_str, department):
'巨潮、证监会行业编码\n\n 异常：\n 如果date_为非交易日，触发值异常\n '
url_fmt = 'http://www.cnindex.com.cn/syl/{}/{}_hsls.html'
url = url_fmt.format(date_str, department)
try:
df = pd.read_html(url)[1].loc[(:, range(2))]
df.columns = ['industry_id', 'name']
return df
except HTTPError:
msg_fmt = "或者当前日期的数据尚未发布，或者日期'{}'并非交易日"
raise ValueError(msg_fmt.format(date_str))<|docstring|>巨潮、证监会行业编码
异常：
如果date_为非交易日，触发值异常<|endoftext|> |
2b2255fa7d0222f93294789073bec983122b9ddc1013434ac3af19e9582674e1 | def fetch_industry_stocks(date_, department='cninfo'):
'行业分类股票列表'
logger = logbook.Logger('巨潮行业分类')
msg = '"cninfo"代表国证行业分类,"csrc"代表证监会行业分类'
assert (department in ('cninfo', 'csrc')), msg
a_cols = ['code', 'short_name', 'b_code', 'b_short_name']
b_cols = ['group_code', 'group_name', 'industry_code', 'industry_name']
c_cols = ['a_static_pe', 'a_roll_pe', 'b_static_pe', 'b_roll_pe', 'ab_static_pe', 'ab_roll_pe']
date_str = pd.Timestamp(date_).strftime('%Y-%m-%d')
industry = fetch_industry(date_str, department)
if (department == 'cninfo'):
b_cols = (['sector_code', 'sector_name'] + b_cols)
pat = '.\\d{2}$'
else:
pat = '.$'
col_names = ((a_cols + b_cols) + c_cols)
codes = industry.loc[(industry.industry_id.str.match(pat), 'industry_id')].values
dfs = []
def _process(industry_id):
df = _industry_stocks(industry_id, date_str)
dfs.append(df)
for code in codes:
_process(code)
logger.info('部门：{}，日期：{}，编码:{}'.format(department, date_, code))
res = pd.concat(dfs, ignore_index=True)
res.columns = col_names
res = res.loc[((~ res.code.isnull()), :)]
res.code = res.code.map((lambda x: str(int(x)).zfill(6)))
return res | 行业分类股票列表 | cnswd/websource/juchao.py | fetch_industry_stocks | huangzhangfeng/cnswd | 0 | python | def fetch_industry_stocks(date_, department='cninfo'):
logger = logbook.Logger('巨潮行业分类')
msg = '"cninfo"代表国证行业分类,"csrc"代表证监会行业分类'
assert (department in ('cninfo', 'csrc')), msg
a_cols = ['code', 'short_name', 'b_code', 'b_short_name']
b_cols = ['group_code', 'group_name', 'industry_code', 'industry_name']
c_cols = ['a_static_pe', 'a_roll_pe', 'b_static_pe', 'b_roll_pe', 'ab_static_pe', 'ab_roll_pe']
date_str = pd.Timestamp(date_).strftime('%Y-%m-%d')
industry = fetch_industry(date_str, department)
if (department == 'cninfo'):
b_cols = (['sector_code', 'sector_name'] + b_cols)
pat = '.\\d{2}$'
else:
pat = '.$'
col_names = ((a_cols + b_cols) + c_cols)
codes = industry.loc[(industry.industry_id.str.match(pat), 'industry_id')].values
dfs = []
def _process(industry_id):
df = _industry_stocks(industry_id, date_str)
dfs.append(df)
for code in codes:
_process(code)
logger.info('部门：{}，日期：{}，编码:{}'.format(department, date_, code))
res = pd.concat(dfs, ignore_index=True)
res.columns = col_names
res = res.loc[((~ res.code.isnull()), :)]
res.code = res.code.map((lambda x: str(int(x)).zfill(6)))
return res | def fetch_industry_stocks(date_, department='cninfo'):
logger = logbook.Logger('巨潮行业分类')
msg = '"cninfo"代表国证行业分类,"csrc"代表证监会行业分类'
assert (department in ('cninfo', 'csrc')), msg
a_cols = ['code', 'short_name', 'b_code', 'b_short_name']
b_cols = ['group_code', 'group_name', 'industry_code', 'industry_name']
c_cols = ['a_static_pe', 'a_roll_pe', 'b_static_pe', 'b_roll_pe', 'ab_static_pe', 'ab_roll_pe']
date_str = pd.Timestamp(date_).strftime('%Y-%m-%d')
industry = fetch_industry(date_str, department)
if (department == 'cninfo'):
b_cols = (['sector_code', 'sector_name'] + b_cols)
pat = '.\\d{2}$'
else:
pat = '.$'
col_names = ((a_cols + b_cols) + c_cols)
codes = industry.loc[(industry.industry_id.str.match(pat), 'industry_id')].values
dfs = []
def _process(industry_id):
df = _industry_stocks(industry_id, date_str)
dfs.append(df)
for code in codes:
_process(code)
logger.info('部门：{}，日期：{}，编码:{}'.format(department, date_, code))
res = pd.concat(dfs, ignore_index=True)
res.columns = col_names
res = res.loc[((~ res.code.isnull()), :)]
res.code = res.code.map((lambda x: str(int(x)).zfill(6)))
return res<|docstring|>行业分类股票列表<|endoftext|> |
daf42803d8d2a5fe7105cdd2a6144a0d8d7eb31c3e2564e5d478f5fa8c56f9e3 | def ensure_report_date(date_):
'\n 转换为报告日期\n\n 逻辑\n ----\n 1. 如输入日期为当天，则自动转换为前一个财务报告期；\n 2. 如果为历史日期，输入日期必须为季度报告截止日期\n '
date_ = pd.Timestamp(date_)
if (date_.date() == pd.Timestamp('today').date()):
qe = pd.tseries.offsets.QuarterEnd((- 1))
return qe.apply(date_).date()
else:
if (not date_.is_quarter_end):
raise ValueError('输入日期无效，应为报告截止日期')
return date_ | 转换为报告日期
逻辑
----
1. 如输入日期为当天，则自动转换为前一个财务报告期；
2. 如果为历史日期，输入日期必须为季度报告截止日期 | cnswd/websource/juchao.py | ensure_report_date | huangzhangfeng/cnswd | 0 | python | def ensure_report_date(date_):
'\n 转换为报告日期\n\n 逻辑\n ----\n 1. 如输入日期为当天，则自动转换为前一个财务报告期；\n 2. 如果为历史日期，输入日期必须为季度报告截止日期\n '
date_ = pd.Timestamp(date_)
if (date_.date() == pd.Timestamp('today').date()):
qe = pd.tseries.offsets.QuarterEnd((- 1))
return qe.apply(date_).date()
else:
if (not date_.is_quarter_end):
raise ValueError('输入日期无效，应为报告截止日期')
return date_ | def ensure_report_date(date_):
'\n 转换为报告日期\n\n 逻辑\n ----\n 1. 如输入日期为当天，则自动转换为前一个财务报告期；\n 2. 如果为历史日期，输入日期必须为季度报告截止日期\n '
date_ = pd.Timestamp(date_)
if (date_.date() == pd.Timestamp('today').date()):
qe = pd.tseries.offsets.QuarterEnd((- 1))
return qe.apply(date_).date()
else:
if (not date_.is_quarter_end):
raise ValueError('输入日期无效，应为报告截止日期')
return date_<|docstring|>转换为报告日期
逻辑
----
1. 如输入日期为当天，则自动转换为前一个财务报告期；
2. 如果为历史日期，输入日期必须为季度报告截止日期<|endoftext|> |
0ec5b1cab1375a97e54b578f74ce501a12e4de19e2d293bb7d17936bb35cefad | def fetch_prbookinfos(date_=pd.Timestamp('today')):
'\n 股票财务报告期预约披露时间表\n\n 参数\n ----\n date_ ： 类似日期\n 要抓取预约时间表的报告日期\n 默认为当天，代表当前日期下，上一个应公布财务报告的报告日期\n 除接受当日参数外，其余日期必须为标准财务报告截止日期\n 如2018-03-31,2017-12-31\n\n 备注\n ----\n 连续抓页，须长时间休眠才能正确完成\n 尤其是当出现异常页，再次尝试前，休眠至少3秒\n\n '
date_ = pd.Timestamp(date_)
if (date_ < EARLIEST_DATE):
raise NoDataBefore('日期不得早于{}'.format(EARLIEST_DATE))
url = 'http://three.cninfo.com.cn/new/information/getPrbookInfo'
cols = ['报告期', '首次预约', '第一次变更', '第二次变更', '第三次变更', '实际披露', 'orgId', '股票代码', '股票简称']
report_date = ensure_report_date(date_).strftime('%Y-%m-%d')
markets = _get_markets(date_)
try:
dfs = _fetch_prbookinfos(report_date, url, markets)
except TypeError:
raise NoWebData('网页不存在报告截止日期为{}的预约时间表'.format(report_date))
res = pd.concat(dfs, ignore_index=True)
res.columns = cols
return res | 股票财务报告期预约披露时间表
参数
----
date_ ： 类似日期
要抓取预约时间表的报告日期
默认为当天，代表当前日期下，上一个应公布财务报告的报告日期
除接受当日参数外，其余日期必须为标准财务报告截止日期
如2018-03-31,2017-12-31
备注
----
连续抓页，须长时间休眠才能正确完成
尤其是当出现异常页，再次尝试前，休眠至少3秒 | cnswd/websource/juchao.py | fetch_prbookinfos | huangzhangfeng/cnswd | 0 | python | def fetch_prbookinfos(date_=pd.Timestamp('today')):
'\n 股票财务报告期预约披露时间表\n\n 参数\n ----\n date_ ： 类似日期\n 要抓取预约时间表的报告日期\n 默认为当天，代表当前日期下，上一个应公布财务报告的报告日期\n 除接受当日参数外，其余日期必须为标准财务报告截止日期\n 如2018-03-31,2017-12-31\n\n 备注\n ----\n 连续抓页，须长时间休眠才能正确完成\n 尤其是当出现异常页，再次尝试前，休眠至少3秒\n\n '
date_ = pd.Timestamp(date_)
if (date_ < EARLIEST_DATE):
raise NoDataBefore('日期不得早于{}'.format(EARLIEST_DATE))
url = 'http://three.cninfo.com.cn/new/information/getPrbookInfo'
cols = ['报告期', '首次预约', '第一次变更', '第二次变更', '第三次变更', '实际披露', 'orgId', '股票代码', '股票简称']
report_date = ensure_report_date(date_).strftime('%Y-%m-%d')
markets = _get_markets(date_)
try:
dfs = _fetch_prbookinfos(report_date, url, markets)
except TypeError:
raise NoWebData('网页不存在报告截止日期为{}的预约时间表'.format(report_date))
res = pd.concat(dfs, ignore_index=True)
res.columns = cols
return res | def fetch_prbookinfos(date_=pd.Timestamp('today')):
'\n 股票财务报告期预约披露时间表\n\n 参数\n ----\n date_ ： 类似日期\n 要抓取预约时间表的报告日期\n 默认为当天，代表当前日期下，上一个应公布财务报告的报告日期\n 除接受当日参数外，其余日期必须为标准财务报告截止日期\n 如2018-03-31,2017-12-31\n\n 备注\n ----\n 连续抓页，须长时间休眠才能正确完成\n 尤其是当出现异常页，再次尝试前，休眠至少3秒\n\n '
date_ = pd.Timestamp(date_)
if (date_ < EARLIEST_DATE):
raise NoDataBefore('日期不得早于{}'.format(EARLIEST_DATE))
url = 'http://three.cninfo.com.cn/new/information/getPrbookInfo'
cols = ['报告期', '首次预约', '第一次变更', '第二次变更', '第三次变更', '实际披露', 'orgId', '股票代码', '股票简称']
report_date = ensure_report_date(date_).strftime('%Y-%m-%d')
markets = _get_markets(date_)
try:
dfs = _fetch_prbookinfos(report_date, url, markets)
except TypeError:
raise NoWebData('网页不存在报告截止日期为{}的预约时间表'.format(report_date))
res = pd.concat(dfs, ignore_index=True)
res.columns = cols
return res<|docstring|>股票财务报告期预约披露时间表
参数
----
date_ ： 类似日期
要抓取预约时间表的报告日期
默认为当天，代表当前日期下，上一个应公布财务报告的报告日期
除接受当日参数外，其余日期必须为标准财务报告截止日期
如2018-03-31,2017-12-31
备注
----
连续抓页，须长时间休眠才能正确完成
尤其是当出现异常页，再次尝试前，休眠至少3秒<|endoftext|> |
3c447adfdc8059d4976ed7a21c596e511feca11c8a5701d7a9ba00694c663fe9 | @ms_function
def default_parameter_f(x, y=3):
' default_parameter_f '
z = (x + y)
return z | default_parameter_f | tests/ut/python/pynative_mode/test_parse_method.py | default_parameter_f | limberc/mindspore | 3,200 | python | @ms_function
def (x, y=3):
' '
z = (x + y)
return z | @ms_function
def (x, y=3):
' '
z = (x + y)
return z<|docstring|>default_parameter_f<|endoftext|> |
315de39142b12500d29a7147b04b7a6fa026144cdf215ba2d88c5d4d382ed8a6 | def test_parse_defalut_parameter_case1():
' Test default parameter function call '
log.debug('begin test_parse_defalut_parameter_case1')
ret = default_parameter_f(2)
log.debug('finished test_parse_defalut_parameter_case1, ret = %r', ret) | Test default parameter function call | tests/ut/python/pynative_mode/test_parse_method.py | test_parse_defalut_parameter_case1 | limberc/mindspore | 3,200 | python | def test_parse_defalut_parameter_case1():
' '
log.debug('begin test_parse_defalut_parameter_case1')
ret = default_parameter_f(2)
log.debug('finished test_parse_defalut_parameter_case1, ret = %r', ret) | def test_parse_defalut_parameter_case1():
' '
log.debug('begin test_parse_defalut_parameter_case1')
ret = default_parameter_f(2)
log.debug('finished test_parse_defalut_parameter_case1, ret = %r', ret)<|docstring|>Test default parameter function call<|endoftext|> |
8fe80656f2f776c033cc73a0c03f7801547756d4e7de75ca14a0a67df336fa02 | def get_val_fn(x):
' get_val_fn '
ret = (x + 3)
return ret | get_val_fn | tests/ut/python/pynative_mode/test_parse_method.py | get_val_fn | limberc/mindspore | 3,200 | python | def (x):
' '
ret = (x + 3)
return ret | def (x):
' '
ret = (x + 3)
return ret<|docstring|>get_val_fn<|endoftext|> |
c4206ba59be36827fdb25d862dc52f88c2e404207dd6a2c0f8ffa0c5b9a11f40 | @ms_function
def bool_exp(x, y):
' bool_exp '
return (not (x > y)) | bool_exp | tests/ut/python/pynative_mode/test_parse_method.py | bool_exp | limberc/mindspore | 3,200 | python | @ms_function
def (x, y):
' '
return (not (x > y)) | @ms_function
def (x, y):
' '
return (not (x > y))<|docstring|>bool_exp<|endoftext|> |
002eb9f21e8b07988ac824ba65f9b3415fc15690f8800a34ee56f8bf7d0343b7 | def test_bool_exp():
' test_bool_exp '
bool_exp(1, 2) | test_bool_exp | tests/ut/python/pynative_mode/test_parse_method.py | test_bool_exp | limberc/mindspore | 3,200 | python | def ():
' '
bool_exp(1, 2) | def ():
' '
bool_exp(1, 2)<|docstring|>test_bool_exp<|endoftext|> |
b9748548992ac67309bd05b3b879bf54b31007d073d50eaac6dc803b80f9e53e | @ms_function
def var_parameter_f(x, *args):
' var_parameter_f '
z = (((x + args[0]) + args[1]) + args[2])
return z | var_parameter_f | tests/ut/python/pynative_mode/test_parse_method.py | var_parameter_f | limberc/mindspore | 3,200 | python | @ms_function
def (x, *args):
' '
z = (((x + args[0]) + args[1]) + args[2])
return z | @ms_function
def (x, *args):
' '
z = (((x + args[0]) + args[1]) + args[2])
return z<|docstring|>var_parameter_f<|endoftext|> |
575f844dab9f36fbd2fd3bc4b3dee66a74ce802eb4fbcc408465f00823c98618 | def test_var_parameter_case1():
' test_var_parameter_case1 '
log.debug('start test_var_parameter_case1')
var_parameter_f(1, 2, 3, 4, 5)
log.debug('end test_var_parameter_case1') | test_var_parameter_case1 | tests/ut/python/pynative_mode/test_parse_method.py | test_var_parameter_case1 | limberc/mindspore | 3,200 | python | def ():
' '
log.debug('start ')
var_parameter_f(1, 2, 3, 4, 5)
log.debug('end ') | def ():
' '
log.debug('start ')
var_parameter_f(1, 2, 3, 4, 5)
log.debug('end ')<|docstring|>test_var_parameter_case1<|endoftext|> |
fc338ecbb248be64c56eb92663403e9fc82df3ca09531694040a9dbbfeab12ad | @non_graph_engine
def test_call_method_on_construct():
' test_call_method_on_construct '
log.debug('begin test_call_method_on_construct')
x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2, 3, 4], [1, 1, 2]]).astype(np.int32))
z = np.array([[3, 5, 7], [2, 3, 5]]).astype(np.int32)
net = Net(y)
output = net.construct(x)
result = output.asnumpy()
print(result)
assert np.all((result == z))
log.debug('finished test_call_method_on_construct') | test_call_method_on_construct | tests/ut/python/pynative_mode/test_parse_method.py | test_call_method_on_construct | limberc/mindspore | 3,200 | python | @non_graph_engine
def ():
' '
log.debug('begin ')
x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2, 3, 4], [1, 1, 2]]).astype(np.int32))
z = np.array([[3, 5, 7], [2, 3, 5]]).astype(np.int32)
net = Net(y)
output = net.construct(x)
result = output.asnumpy()
print(result)
assert np.all((result == z))
log.debug('finished ') | @non_graph_engine
def ():
' '
log.debug('begin ')
x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2, 3, 4], [1, 1, 2]]).astype(np.int32))
z = np.array([[3, 5, 7], [2, 3, 5]]).astype(np.int32)
net = Net(y)
output = net.construct(x)
result = output.asnumpy()
print(result)
assert np.all((result == z))
log.debug('finished ')<|docstring|>test_call_method_on_construct<|endoftext|> |
4dcf05b5c6e0504ec94bc0fe772f301098d2851b8192eb45bd853c459bb59d7c | @non_graph_engine
def test_call_other_object_method():
' test_call_other_object_method '
log.debug('begin test_call_other_object_method')
x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2, 3, 4], [1, 1, 2]]).astype(np.int32))
y1 = Tensor(np.array([[5, 4, 5], [1, 1, 2]]).astype(np.int32))
z = np.array([[8, 9, 12], [3, 4, 7]]).astype(np.int32)
net = Net1(y, y1)
with pytest.raises(TypeError):
output = net.construct(x)
result = output.asnumpy()
print(result)
assert np.all((result == z))
log.debug('finished test_call_other_object_method') | test_call_other_object_method | tests/ut/python/pynative_mode/test_parse_method.py | test_call_other_object_method | limberc/mindspore | 3,200 | python | @non_graph_engine
def ():
' '
log.debug('begin ')
x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2, 3, 4], [1, 1, 2]]).astype(np.int32))
y1 = Tensor(np.array([[5, 4, 5], [1, 1, 2]]).astype(np.int32))
z = np.array([[8, 9, 12], [3, 4, 7]]).astype(np.int32)
net = Net1(y, y1)
with pytest.raises(TypeError):
output = net.construct(x)
result = output.asnumpy()
print(result)
assert np.all((result == z))
log.debug('finished ') | @non_graph_engine
def ():
' '
log.debug('begin ')
x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2, 3, 4], [1, 1, 2]]).astype(np.int32))
y1 = Tensor(np.array([[5, 4, 5], [1, 1, 2]]).astype(np.int32))
z = np.array([[8, 9, 12], [3, 4, 7]]).astype(np.int32)
net = Net1(y, y1)
with pytest.raises(TypeError):
output = net.construct(x)
result = output.asnumpy()
print(result)
assert np.all((result == z))
log.debug('finished ')<|docstring|>test_call_other_object_method<|endoftext|> |
cd4090e088820fbe9542a79c6ab7060ab58e9332c402aff5891de5341978054e | @non_graph_engine
def test_call_no_self_other_object_method():
' test_call_no_self_other_object_method '
log.debug('begin test_call_other_object_method')
x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2, 3, 4], [1, 1, 2]]).astype(np.int32))
z = np.array([[6, 9, 12], [3, 4, 7]]).astype(np.int32)
net = Net2(y)
with pytest.raises(TypeError):
output = net.construct(x)
result = output.asnumpy()
print(result)
assert np.all((result == z))
log.debug('finished test_call_other_object_method') | test_call_no_self_other_object_method | tests/ut/python/pynative_mode/test_parse_method.py | test_call_no_self_other_object_method | limberc/mindspore | 3,200 | python | @non_graph_engine
def ():
' '
log.debug('begin test_call_other_object_method')
x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2, 3, 4], [1, 1, 2]]).astype(np.int32))
z = np.array([[6, 9, 12], [3, 4, 7]]).astype(np.int32)
net = Net2(y)
with pytest.raises(TypeError):
output = net.construct(x)
result = output.asnumpy()
print(result)
assert np.all((result == z))
log.debug('finished test_call_other_object_method') | @non_graph_engine
def ():
' '
log.debug('begin test_call_other_object_method')
x = Tensor(np.array([[1, 2, 3], [1, 2, 3]]).astype(np.int32))
y = Tensor(np.array([[2, 3, 4], [1, 1, 2]]).astype(np.int32))
z = np.array([[6, 9, 12], [3, 4, 7]]).astype(np.int32)
net = Net2(y)
with pytest.raises(TypeError):
output = net.construct(x)
result = output.asnumpy()
print(result)
assert np.all((result == z))
log.debug('finished test_call_other_object_method')<|docstring|>test_call_no_self_other_object_method<|endoftext|> |
b476a0ccfe5c131f4d79137651c2f7bc47bfe12717b2a160b07e238663513ee6 | def test_call_no_self_other_object_attr_value():
' test_call_no_self_other_object_attr_value '
return | test_call_no_self_other_object_attr_value | tests/ut/python/pynative_mode/test_parse_method.py | test_call_no_self_other_object_attr_value | limberc/mindspore | 3,200 | python | def ():
' '
return | def ():
' '
return<|docstring|>test_call_no_self_other_object_attr_value<|endoftext|> |
9e6745a0cfbb37a6a1517fad35674e24208b52cc189be310430cbb85d73c2300 | def vararg1(x, y):
' vararg1 '
z = (x + y)
return z | vararg1 | tests/ut/python/pynative_mode/test_parse_method.py | vararg1 | limberc/mindspore | 3,200 | python | def (x, y):
' '
z = (x + y)
return z | def (x, y):
' '
z = (x + y)
return z<|docstring|>vararg1<|endoftext|> |
42fc28190b3e7f74c82cd956890a956060bdac9910ce23ac51fc39c6509a81b0 | def varargs_main(fn):
' varargs_main '
@ms_function
def t1(*args):
return fn(*args)
return t1 | varargs_main | tests/ut/python/pynative_mode/test_parse_method.py | varargs_main | limberc/mindspore | 3,200 | python | def (fn):
' '
@ms_function
def t1(*args):
return fn(*args)
return t1 | def (fn):
' '
@ms_function
def t1(*args):
return fn(*args)
return t1<|docstring|>varargs_main<|endoftext|> |
8e0fe8707c707957b02f5f88c6b0a15cf5153cc4329279de8ae6133d60069736 | def test_var_parameter_case3():
' test_var_parameter_case3 '
log.debug('start test_var_parameter_case3')
ret = varargs_main(vararg1)(1, 2)
log.debug('ret = %r', ret)
log.debug('end test_var_parameter_case3') | test_var_parameter_case3 | tests/ut/python/pynative_mode/test_parse_method.py | test_var_parameter_case3 | limberc/mindspore | 3,200 | python | def ():
' '
log.debug('start ')
ret = varargs_main(vararg1)(1, 2)
log.debug('ret = %r', ret)
log.debug('end ') | def ():
' '
log.debug('start ')
ret = varargs_main(vararg1)(1, 2)
log.debug('ret = %r', ret)
log.debug('end ')<|docstring|>test_var_parameter_case3<|endoftext|> |
40a18b5e013c7cda2fec93465842955df6165d5b8932b9d076059409aadd4aef | @core(tg=True)
def set_flag(x):
' set_flag '
return (x + 1) | set_flag | tests/ut/python/pynative_mode/test_parse_method.py | set_flag | limberc/mindspore | 3,200 | python | @core(tg=True)
def (x):
' '
return (x + 1) | @core(tg=True)
def (x):
' '
return (x + 1)<|docstring|>set_flag<|endoftext|> |
c2c2222fa3ff45b08a3d502a16f8432bc075926064692428ad1ba9403d73f9a0 | @ms_function
def set_test_flag_main(x, y):
' set_test_flag_main '
z = set_flag(x)
z = (z + y)
return z | set_test_flag_main | tests/ut/python/pynative_mode/test_parse_method.py | set_test_flag_main | limberc/mindspore | 3,200 | python | @ms_function
def (x, y):
' '
z = set_flag(x)
z = (z + y)
return z | @ms_function
def (x, y):
' '
z = set_flag(x)
z = (z + y)
return z<|docstring|>set_test_flag_main<|endoftext|> |
88766fd8f8cf43c5ebf563a0a4240fb2ddb57aeb03c8a16690da406ec073d0df | def test_set_flag():
' Test default parameter function call '
log.debug('begin test_set_flag')
ret = set_test_flag_main(2, 3)
log.debug('finished test_set_flag, ret = %r', ret) | Test default parameter function call | tests/ut/python/pynative_mode/test_parse_method.py | test_set_flag | limberc/mindspore | 3,200 | python | def test_set_flag():
' '
log.debug('begin test_set_flag')
ret = set_test_flag_main(2, 3)
log.debug('finished test_set_flag, ret = %r', ret) | def test_set_flag():
' '
log.debug('begin test_set_flag')
ret = set_test_flag_main(2, 3)
log.debug('finished test_set_flag, ret = %r', ret)<|docstring|>Test default parameter function call<|endoftext|> |
7f4632938e7fb1f78df3b3587466c4f2da66cf1f0baa70aa7dd2d74b45a2a6a8 | @ms_function
def invoke_dataclass(x, y):
' invoke_dataclass '
acs = Access(x, y)
return acs.max() | invoke_dataclass | tests/ut/python/pynative_mode/test_parse_method.py | invoke_dataclass | limberc/mindspore | 3,200 | python | @ms_function
def (x, y):
' '
acs = Access(x, y)
return acs.max() | @ms_function
def (x, y):
' '
acs = Access(x, y)
return acs.max()<|docstring|>invoke_dataclass<|endoftext|> |
af6f24db9eb32c7991b17db6b18d77feead4f8811464022269251144e3d8ed1f | def test_access():
' test_access '
invoke_dataclass(1, 2) | test_access | tests/ut/python/pynative_mode/test_parse_method.py | test_access | limberc/mindspore | 3,200 | python | def ():
' '
invoke_dataclass(1, 2) | def ():
' '
invoke_dataclass(1, 2)<|docstring|>test_access<|endoftext|> |
7c60da264701b949344a5f1d0d56ca893f4bfc71f4080945ac78aa564028af85 | @ms_function
def invoke_dataclass2(x, y):
' invoke_dataclass '
acs = Access2(x, y)
return acs.max() | invoke_dataclass | tests/ut/python/pynative_mode/test_parse_method.py | invoke_dataclass2 | limberc/mindspore | 3,200 | python | @ms_function
def 2(x, y):
' '
acs = Access2(x, y)
return acs.max() | @ms_function
def 2(x, y):
' '
acs = Access2(x, y)
return acs.max()<|docstring|>invoke_dataclass<|endoftext|> |
0d53e3a73fbba7bcacafa449be7069104a8c23ff491654a4071b6faa1df0ca3b | def test_access_attr_error():
' test_access '
with pytest.raises(AttributeError):
invoke_dataclass2(2, 1) | test_access | tests/ut/python/pynative_mode/test_parse_method.py | test_access_attr_error | limberc/mindspore | 3,200 | python | def _attr_error():
' '
with pytest.raises(AttributeError):
invoke_dataclass2(2, 1) | def _attr_error():
' '
with pytest.raises(AttributeError):
invoke_dataclass2(2, 1)<|docstring|>test_access<|endoftext|> |
6f212e1099c687eaa2f5210b92d15c3b9731b87c26dc65614b3cf7815de5f36d | def myfunc(x):
' myfunc '
return (x * x) | myfunc | tests/ut/python/pynative_mode/test_parse_method.py | myfunc | limberc/mindspore | 3,200 | python | def (x):
' '
return (x * x) | def (x):
' '
return (x * x)<|docstring|>myfunc<|endoftext|> |
f405a07d68d58eb1b38a1554fccc387b71f7c31b929bbd931cc2703b3338f752 | @ms_function
def ms_infer_for():
' ms_infer_for '
a = 0.0
for x in [1.1, 2.3, 3.3]:
a = (a + x)
return a | ms_infer_for | tests/ut/python/pynative_mode/test_parse_method.py | ms_infer_for | limberc/mindspore | 3,200 | python | @ms_function
def ():
' '
a = 0.0
for x in [1.1, 2.3, 3.3]:
a = (a + x)
return a | @ms_function
def ():
' '
a = 0.0
for x in [1.1, 2.3, 3.3]:
a = (a + x)
return a<|docstring|>ms_infer_for<|endoftext|> |
f698e52221cd63cfd144c72162a16ca046ec87f360eb45d16f9f77054e9ee7e6 | def test_infer_for():
' test_infer_for '
ms_infer_for() | test_infer_for | tests/ut/python/pynative_mode/test_parse_method.py | test_infer_for | limberc/mindspore | 3,200 | python | def ():
' '
ms_infer_for() | def ():
' '
ms_infer_for()<|docstring|>test_infer_for<|endoftext|> |
f73185a4253ea936a29e0da99f53daf631c8073c5ee57a0dd26f121c3573b33b | @ms_function
def ms_infer_for_func(y):
' ms_infer_for_func '
for x in [1.0, 2.0, 3.0]:
y = (myfunc(x) + y)
return y | ms_infer_for_func | tests/ut/python/pynative_mode/test_parse_method.py | ms_infer_for_func | limberc/mindspore | 3,200 | python | @ms_function
def (y):
' '
for x in [1.0, 2.0, 3.0]:
y = (myfunc(x) + y)
return y | @ms_function
def (y):
' '
for x in [1.0, 2.0, 3.0]:
y = (myfunc(x) + y)
return y<|docstring|>ms_infer_for_func<|endoftext|> |
28466c9b65c66e5d765cc252d47dc05f135a5438bcd67128c24c75eb0fc47312 | def test_ms_infer_for_func():
' test_ms_infer_for_func '
ms_infer_for_func(1.0) | test_ms_infer_for_func | tests/ut/python/pynative_mode/test_parse_method.py | test_ms_infer_for_func | limberc/mindspore | 3,200 | python | def ():
' '
ms_infer_for_func(1.0) | def ():
' '
ms_infer_for_func(1.0)<|docstring|>test_ms_infer_for_func<|endoftext|> |
b9ed3993765de513802da781366f99d448ffbce7122e2352818bbf69c1b2ad8e | @ms_function
def add(x, y):
' add '
return (x + y) | add | tests/ut/python/pynative_mode/test_parse_method.py | add | limberc/mindspore | 3,200 | python | @ms_function
def (x, y):
' '
return (x + y) | @ms_function
def (x, y):
' '
return (x + y)<|docstring|>add<|endoftext|> |
b9ce39a2d3581d339e9036fa402198758954f6499600fdd169ef9308ff4aa3f1 | def test_add():
' test_add '
res = add(1, 2.0)
return res | test_add | tests/ut/python/pynative_mode/test_parse_method.py | test_add | limberc/mindspore | 3,200 | python | def ():
' '
res = add(1, 2.0)
return res | def ():
' '
res = add(1, 2.0)
return res<|docstring|>test_add<|endoftext|> |
e04011b29ccc99b60ab25e96d2cca594e3b8fb601f905e722c12cce74d52fd14 | @ms_function
def add_list():
' add_list '
a = [1, 2, 3]
b = (a[1] + a[2])
return b | add_list | tests/ut/python/pynative_mode/test_parse_method.py | add_list | limberc/mindspore | 3,200 | python | @ms_function
def ():
' '
a = [1, 2, 3]
b = (a[1] + a[2])
return b | @ms_function
def ():
' '
a = [1, 2, 3]
b = (a[1] + a[2])
return b<|docstring|>add_list<|endoftext|> |
6c2d3e9a91b770ad1bbbceeff1c6054765931350b3fd1a5a8e94d3fa192ddf54 | def test_list():
' test_list '
return add_list() | test_list | tests/ut/python/pynative_mode/test_parse_method.py | test_list | limberc/mindspore | 3,200 | python | def ():
' '
return add_list() | def ():
' '
return add_list()<|docstring|>test_list<|endoftext|> |
6e88b0a5d03ee7ca36d74baf0a4fbce3f1a07b294709c94b8d350b7b7d5d8f47 | @ms_function
def compare_list_len():
' compare_list_len '
a = [1, 2, 3]
return ms_len(a) | compare_list_len | tests/ut/python/pynative_mode/test_parse_method.py | compare_list_len | limberc/mindspore | 3,200 | python | @ms_function
def ():
' '
a = [1, 2, 3]
return ms_len(a) | @ms_function
def ():
' '
a = [1, 2, 3]
return ms_len(a)<|docstring|>compare_list_len<|endoftext|> |
5a9b4a4125383593db414dd31c2fd08fa1074adf35cd999aaeb3a3028a44aedf | def test_list_len():
' test_list_len '
compare_list_len() | test_list_len | tests/ut/python/pynative_mode/test_parse_method.py | test_list_len | limberc/mindspore | 3,200 | python | def ():
' '
compare_list_len() | def ():
' '
compare_list_len()<|docstring|>test_list_len<|endoftext|> |
e3298fda6a89d09fbd7e918ace88fd4a752106b4968aed7a97075f9a6c4022eb | @ms_function
def add_tuple():
' add_tuple '
a = (1, 2, 3)
b = (a[1] + a[2])
return b | add_tuple | tests/ut/python/pynative_mode/test_parse_method.py | add_tuple | limberc/mindspore | 3,200 | python | @ms_function
def ():
' '
a = (1, 2, 3)
b = (a[1] + a[2])
return b | @ms_function
def ():
' '
a = (1, 2, 3)
b = (a[1] + a[2])
return b<|docstring|>add_tuple<|endoftext|> |
d8d4b3d93732bd867bc8069a4a7ac2023064d5367862e4903101578a5fb45fe3 | def test_tuple():
' test_tuple '
return add_tuple() | test_tuple | tests/ut/python/pynative_mode/test_parse_method.py | test_tuple | limberc/mindspore | 3,200 | python | def ():
' '
return add_tuple() | def ():
' '
return add_tuple()<|docstring|>test_tuple<|endoftext|> |
f61d6f31786e4a280f793155b74794d8b70e3126a020939dd981775ba93d843a | def invoke_func(x):
' invoke_func '
return (x * x) | invoke_func | tests/ut/python/pynative_mode/test_parse_method.py | invoke_func | limberc/mindspore | 3,200 | python | def (x):
' '
return (x * x) | def (x):
' '
return (x * x)<|docstring|>invoke_func<|endoftext|> |
5fd5c54c3f27c4db9c475ff97faa20c6bcb05bacd028494acc3a615a1f79e052 | @ms_function
def tuple_of_node(x, y):
' tuple_of_node '
a = invoke_func(x)
b = invoke_func(y)
c = (a, b)
d = (c[1] * x)
return d | tuple_of_node | tests/ut/python/pynative_mode/test_parse_method.py | tuple_of_node | limberc/mindspore | 3,200 | python | @ms_function
def (x, y):
' '
a = invoke_func(x)
b = invoke_func(y)
c = (a, b)
d = (c[1] * x)
return d | @ms_function
def (x, y):
' '
a = invoke_func(x)
b = invoke_func(y)
c = (a, b)
d = (c[1] * x)
return d<|docstring|>tuple_of_node<|endoftext|> |
88079f0229531247c88f4b2d40835c932732a64860b79d065c5c443517efbbc1 | def test_tuple_node():
' test_tuple_node '
res = tuple_of_node(1, 2)
return res | test_tuple_node | tests/ut/python/pynative_mode/test_parse_method.py | test_tuple_node | limberc/mindspore | 3,200 | python | def ():
' '
res = tuple_of_node(1, 2)
return res | def ():
' '
res = tuple_of_node(1, 2)
return res<|docstring|>test_tuple_node<|endoftext|> |
02fd7488471b1d3deebf3c2caf015b712c9b357eb34126cab86ecd2eae4eca26 | @ms_function
def range_spec(x, y):
' range_spec '
for _ in range(1, 10, 3):
x = (x + 1)
return (x + y) | range_spec | tests/ut/python/pynative_mode/test_parse_method.py | range_spec | limberc/mindspore | 3,200 | python | @ms_function
def (x, y):
' '
for _ in range(1, 10, 3):
x = (x + 1)
return (x + y) | @ms_function
def (x, y):
' '
for _ in range(1, 10, 3):
x = (x + 1)
return (x + y)<|docstring|>range_spec<|endoftext|> |
adb64c43535fe64a78e93f7f82c8c16426eeacb156536c04d73c881211615611 | def test_range():
' test_range '
res = range_spec(10, 10)
return res | test_range | tests/ut/python/pynative_mode/test_parse_method.py | test_range | limberc/mindspore | 3,200 | python | def ():
' '
res = range_spec(10, 10)
return res | def ():
' '
res = range_spec(10, 10)
return res<|docstring|>test_range<|endoftext|> |
a343b86dc9bbb67ab66e40743f08cd306b913a731300431c560ce5b7f98c8089 | def test_expr():
' test const expr '
a = (1, 2)
@constexpr
def tuple_len(x):
assert (len(x) == 2)
tuple_len(a) | test const expr | tests/ut/python/pynative_mode/test_parse_method.py | test_expr | limberc/mindspore | 3,200 | python | def test_expr():
' '
a = (1, 2)
@constexpr
def tuple_len(x):
assert (len(x) == 2)
tuple_len(a) | def test_expr():
' '
a = (1, 2)
@constexpr
def tuple_len(x):
assert (len(x) == 2)
tuple_len(a)<|docstring|>test const expr<|endoftext|> |
5cef5ccc43de65ed11a5f624757dd839a909692b54241939f1d6b194e4f4d7f7 | def test_tuple_to_array():
' test range tuple to array '
range_x = range(10)
res = F.tuple_to_array(range_x)
print(res) | test range tuple to array | tests/ut/python/pynative_mode/test_parse_method.py | test_tuple_to_array | limberc/mindspore | 3,200 | python | def test_tuple_to_array():
' '
range_x = range(10)
res = F.tuple_to_array(range_x)
print(res) | def test_tuple_to_array():
' '
range_x = range(10)
res = F.tuple_to_array(range_x)
print(res)<|docstring|>test range tuple to array<|endoftext|> |
ee1d9c108a8ec3b62edf9268693067cc44b4a107731b284fbd695356a9b2ddc5 | def candidate(self, residuals):
"Given a set of residuals this will return the index of the candidate event and the 'significance' value"
n = len(residuals)
if (n == 0):
return (0, 0)
std = np.std(residuals)
if (std == 0):
return (0, 0)
result = (np.cumsum(residuals) * (1.0 / (std * np.sqrt(n))))
ols_cusum = np.insert(result, 0, 0)
t = np.linspace(0, 1, num=(n + 1))
shape = np.sqrt((t * (1 - t)))
clambda = np.append(np.insert((ols_cusum[1:(- 1)] / shape[1:(- 1)]), 0, 0), 0)
index = np.abs(clambda).argmax()
significance = np.abs(clambda[index])
return (index, significance) | Given a set of residuals this will return the index of the candidate event and the 'significance' value | edinet_baseline_hourly_module/edinet_models/pyEMIS2/analysis/ols_cusum.py | candidate | BeeGroup-cimne/module_edinet | 0 | python | def candidate(self, residuals):
n = len(residuals)
if (n == 0):
return (0, 0)
std = np.std(residuals)
if (std == 0):
return (0, 0)
result = (np.cumsum(residuals) * (1.0 / (std * np.sqrt(n))))
ols_cusum = np.insert(result, 0, 0)
t = np.linspace(0, 1, num=(n + 1))
shape = np.sqrt((t * (1 - t)))
clambda = np.append(np.insert((ols_cusum[1:(- 1)] / shape[1:(- 1)]), 0, 0), 0)
index = np.abs(clambda).argmax()
significance = np.abs(clambda[index])
return (index, significance) | def candidate(self, residuals):
n = len(residuals)
if (n == 0):
return (0, 0)
std = np.std(residuals)
if (std == 0):
return (0, 0)
result = (np.cumsum(residuals) * (1.0 / (std * np.sqrt(n))))
ols_cusum = np.insert(result, 0, 0)
t = np.linspace(0, 1, num=(n + 1))
shape = np.sqrt((t * (1 - t)))
clambda = np.append(np.insert((ols_cusum[1:(- 1)] / shape[1:(- 1)]), 0, 0), 0)
index = np.abs(clambda).argmax()
significance = np.abs(clambda[index])
return (index, significance)<|docstring|>Given a set of residuals this will return the index of the candidate event and the 'significance' value<|endoftext|> |
e614459246ec1a1b4a8a68e19d07aa0ed14e9c88dd6c0fe2f62b5885599a2a1a | def add_event(self, model):
'See if an event can be detected in the model. If so, add it.'
candidates = []
for p in model.periods:
cusum = self.period_CUSUM(p)
if cusum.has_event():
candidates.append(cusum.event)
self.logger.info(('%i candidate events' % len(candidates)))
if (len(candidates) == 0):
return False
winner = sorted(candidates, key=(lambda x: x.significance))[0]
self.logger.info(('winner: %s' % winner.date.strftime('%d/%m/%Y')))
model.add_event(winner)
return True | See if an event can be detected in the model. If so, add it. | edinet_baseline_hourly_module/edinet_models/pyEMIS2/analysis/ols_cusum.py | add_event | BeeGroup-cimne/module_edinet | 0 | python | def add_event(self, model):
candidates = []
for p in model.periods:
cusum = self.period_CUSUM(p)
if cusum.has_event():
candidates.append(cusum.event)
self.logger.info(('%i candidate events' % len(candidates)))
if (len(candidates) == 0):
return False
winner = sorted(candidates, key=(lambda x: x.significance))[0]
self.logger.info(('winner: %s' % winner.date.strftime('%d/%m/%Y')))
model.add_event(winner)
return True | def add_event(self, model):
candidates = []
for p in model.periods:
cusum = self.period_CUSUM(p)
if cusum.has_event():
candidates.append(cusum.event)
self.logger.info(('%i candidate events' % len(candidates)))
if (len(candidates) == 0):
return False
winner = sorted(candidates, key=(lambda x: x.significance))[0]
self.logger.info(('winner: %s' % winner.date.strftime('%d/%m/%Y')))
model.add_event(winner)
return True<|docstring|>See if an event can be detected in the model. If so, add it.<|endoftext|> |
b02037aa79040788ebdd78c1927fc94ed03b132fe28ece78a684e230964bf1e4 | def event_CUSUM(self, model, event_index):
'Return an OLS_CUSUM covering the periods before and after an event'
dates = model.event_dates()
from_indices = (model.data['date'] > dates[event_index])
to_indices = (model.data['date'] <= dates[(event_index + 2)])
data = model.data[(from_indices & to_indices)]
submodel = model.modelFactory(data)
res = submodel.residuals(data)
return OLS_CUSUM(data['date'], res, self.alpha) | Return an OLS_CUSUM covering the periods before and after an event | edinet_baseline_hourly_module/edinet_models/pyEMIS2/analysis/ols_cusum.py | event_CUSUM | BeeGroup-cimne/module_edinet | 0 | python | def event_CUSUM(self, model, event_index):
dates = model.event_dates()
from_indices = (model.data['date'] > dates[event_index])
to_indices = (model.data['date'] <= dates[(event_index + 2)])
data = model.data[(from_indices & to_indices)]
submodel = model.modelFactory(data)
res = submodel.residuals(data)
return OLS_CUSUM(data['date'], res, self.alpha) | def event_CUSUM(self, model, event_index):
dates = model.event_dates()
from_indices = (model.data['date'] > dates[event_index])
to_indices = (model.data['date'] <= dates[(event_index + 2)])
data = model.data[(from_indices & to_indices)]
submodel = model.modelFactory(data)
res = submodel.residuals(data)
return OLS_CUSUM(data['date'], res, self.alpha)<|docstring|>Return an OLS_CUSUM covering the periods before and after an event<|endoftext|> |
a8bf6c7af4e9173e5b28a3419afa3b967966afbe52fdf83f1c8d9931b03100e3 | def create_vmdk(service_instance, datacenter_mo, datastore_path):
'Create vmdk in specific datacenter'
vdm = service_instance.content.virtualDiskManager
task = vdm.CreateVirtualDisk(datastore_path, datacenter_mo, vim.VirtualDiskManager.SeSparseVirtualDiskSpec(diskType='seSparse', adapterType='lsiLogic', capacityKb=((1024 * 1024) * 4)))
pyVim.task.WaitForTask(task)
print("Created VMDK '{}' in Datacenter '{}'".format(datastore_path, datacenter_mo.name))
return task.info.result | Create vmdk in specific datacenter | samples/vsphere/common/vim/vmdk.py | create_vmdk | eoq/vsphere-automation-sdk-python | 589 | python | def create_vmdk(service_instance, datacenter_mo, datastore_path):
vdm = service_instance.content.virtualDiskManager
task = vdm.CreateVirtualDisk(datastore_path, datacenter_mo, vim.VirtualDiskManager.SeSparseVirtualDiskSpec(diskType='seSparse', adapterType='lsiLogic', capacityKb=((1024 * 1024) * 4)))
pyVim.task.WaitForTask(task)
print("Created VMDK '{}' in Datacenter '{}'".format(datastore_path, datacenter_mo.name))
return task.info.result | def create_vmdk(service_instance, datacenter_mo, datastore_path):
vdm = service_instance.content.virtualDiskManager
task = vdm.CreateVirtualDisk(datastore_path, datacenter_mo, vim.VirtualDiskManager.SeSparseVirtualDiskSpec(diskType='seSparse', adapterType='lsiLogic', capacityKb=((1024 * 1024) * 4)))
pyVim.task.WaitForTask(task)
print("Created VMDK '{}' in Datacenter '{}'".format(datastore_path, datacenter_mo.name))
return task.info.result<|docstring|>Create vmdk in specific datacenter<|endoftext|> |
4ceb8a125f3d5135866afb963a394f0efd03e4522c114a3d040142d89811464b | def delete_vmdk(service_instance, datacenter_mo, datastore_path):
'Delete vmdk from specific datastore'
vdm = service_instance.content.virtualDiskManager
task = vdm.DeleteVirtualDisk(datastore_path, datacenter_mo)
pyVim.task.WaitForTask(task) | Delete vmdk from specific datastore | samples/vsphere/common/vim/vmdk.py | delete_vmdk | eoq/vsphere-automation-sdk-python | 589 | python | def delete_vmdk(service_instance, datacenter_mo, datastore_path):
vdm = service_instance.content.virtualDiskManager
task = vdm.DeleteVirtualDisk(datastore_path, datacenter_mo)
pyVim.task.WaitForTask(task) | def delete_vmdk(service_instance, datacenter_mo, datastore_path):
vdm = service_instance.content.virtualDiskManager
task = vdm.DeleteVirtualDisk(datastore_path, datacenter_mo)
pyVim.task.WaitForTask(task)<|docstring|>Delete vmdk from specific datastore<|endoftext|> |
8a34fa095633851c45f2326645baf0e5acef46520499faf4101d0dbd59ac6852 | def detect_vmdk(client, soap_stub, datacenter_name, datastore_name, datastore_path):
'Find vmdk in specific datastore'
datastore_mo = get_datastore_mo(client, soap_stub, datacenter_name, datastore_name)
if (not datastore_mo):
return False
dsfile = datastore_file.File(datastore_mo)
if dsfile.exists(datastore_path):
return True
else:
return False | Find vmdk in specific datastore | samples/vsphere/common/vim/vmdk.py | detect_vmdk | eoq/vsphere-automation-sdk-python | 589 | python | def detect_vmdk(client, soap_stub, datacenter_name, datastore_name, datastore_path):
datastore_mo = get_datastore_mo(client, soap_stub, datacenter_name, datastore_name)
if (not datastore_mo):
return False
dsfile = datastore_file.File(datastore_mo)
if dsfile.exists(datastore_path):
return True
else:
return False | def detect_vmdk(client, soap_stub, datacenter_name, datastore_name, datastore_path):
datastore_mo = get_datastore_mo(client, soap_stub, datacenter_name, datastore_name)
if (not datastore_mo):
return False
dsfile = datastore_file.File(datastore_mo)
if dsfile.exists(datastore_path):
return True
else:
return False<|docstring|>Find vmdk in specific datastore<|endoftext|> |
689d8b6180c1328f72976c1cdbd20467c08571d0a1ddb719694adcdf21770bf5 | async def async_setup_entry(hass, config_entry, async_add_entities):
'Set up from config entry.'
coordinator: DataUpdateCoordinator = hass.data[DOMAIN][config_entry.entry_id]
name = config_entry.data[CONF_NAME]
entities = [SyncThruOnlineSensor(coordinator, name), SyncThruProblemSensor(coordinator, name)]
async_add_entities(entities) | Set up from config entry. | homeassistant/components/syncthru/binary_sensor.py | async_setup_entry | Tommatheussen/core | 11 | python | async def async_setup_entry(hass, config_entry, async_add_entities):
coordinator: DataUpdateCoordinator = hass.data[DOMAIN][config_entry.entry_id]
name = config_entry.data[CONF_NAME]
entities = [SyncThruOnlineSensor(coordinator, name), SyncThruProblemSensor(coordinator, name)]
async_add_entities(entities) | async def async_setup_entry(hass, config_entry, async_add_entities):
coordinator: DataUpdateCoordinator = hass.data[DOMAIN][config_entry.entry_id]
name = config_entry.data[CONF_NAME]
entities = [SyncThruOnlineSensor(coordinator, name), SyncThruProblemSensor(coordinator, name)]
async_add_entities(entities)<|docstring|>Set up from config entry.<|endoftext|> |
a0ba4ef3d5de544de84f774fdc0514f02f272e4ac1fe9392a1390b2b11e3a31a | def __init__(self, coordinator, name):
'Initialize the sensor.'
super().__init__(coordinator)
self.syncthru: SyncThru = coordinator.data
self._name = name
self._id_suffix = '' | Initialize the sensor. | homeassistant/components/syncthru/binary_sensor.py | __init__ | Tommatheussen/core | 11 | python | def __init__(self, coordinator, name):
super().__init__(coordinator)
self.syncthru: SyncThru = coordinator.data
self._name = name
self._id_suffix = | def __init__(self, coordinator, name):
super().__init__(coordinator)
self.syncthru: SyncThru = coordinator.data
self._name = name
self._id_suffix = <|docstring|>Initialize the sensor.<|endoftext|> |
957abd3e8798671bb3b2cb8802e38436ac240fa56cfd6d92719d3f0021603681 | @property
def unique_id(self):
'Return unique ID for the sensor.'
serial = self.syncthru.serial_number()
return (f'{serial}{self._id_suffix}' if serial else None) | Return unique ID for the sensor. | homeassistant/components/syncthru/binary_sensor.py | unique_id | Tommatheussen/core | 11 | python | @property
def unique_id(self):
serial = self.syncthru.serial_number()
return (f'{serial}{self._id_suffix}' if serial else None) | @property
def unique_id(self):
serial = self.syncthru.serial_number()
return (f'{serial}{self._id_suffix}' if serial else None)<|docstring|>Return unique ID for the sensor.<|endoftext|> |
c2acbec88b5ad13d0f458e2f3155e56fd2fabdb29665addbac450039553aa2e4 | @property
def name(self):
'Return the name of the sensor.'
return self._name | Return the name of the sensor. | homeassistant/components/syncthru/binary_sensor.py | name | Tommatheussen/core | 11 | python | @property
def name(self):
return self._name | @property
def name(self):
return self._name<|docstring|>Return the name of the sensor.<|endoftext|> |
632da38c928d1d72dc75af84bb155909101eb17884e272f3022822412f45336c | @property
def device_info(self):
'Return device information.'
return {'identifiers': device_identifiers(self.syncthru)} | Return device information. | homeassistant/components/syncthru/binary_sensor.py | device_info | Tommatheussen/core | 11 | python | @property
def device_info(self):
return {'identifiers': device_identifiers(self.syncthru)} | @property
def device_info(self):
return {'identifiers': device_identifiers(self.syncthru)}<|docstring|>Return device information.<|endoftext|> |
575143fce557e6cf1ad1757beb6ef7140246fb56707a173b4bec3cab83976c6c | def __init__(self, syncthru, name):
'Initialize the sensor.'
super().__init__(syncthru, name)
self._id_suffix = '_online' | Initialize the sensor. | homeassistant/components/syncthru/binary_sensor.py | __init__ | Tommatheussen/core | 11 | python | def __init__(self, syncthru, name):
super().__init__(syncthru, name)
self._id_suffix = '_online' | def __init__(self, syncthru, name):
super().__init__(syncthru, name)
self._id_suffix = '_online'<|docstring|>Initialize the sensor.<|endoftext|> |
73e314fdf4c6a944f3f4bd6e7a98284a0fd81746914add2b9f0099e6466a3d7a | @property
def is_on(self):
'Set the state to whether the printer is online.'
return self.syncthru.is_online() | Set the state to whether the printer is online. | homeassistant/components/syncthru/binary_sensor.py | is_on | Tommatheussen/core | 11 | python | @property
def is_on(self):
return self.syncthru.is_online() | @property
def is_on(self):
return self.syncthru.is_online()<|docstring|>Set the state to whether the printer is online.<|endoftext|> |
8c221d2b4b0ea5a6283c6861af4856a85a2ab98e810675314a4995c3bb5df7b9 | def __init__(self, syncthru, name):
'Initialize the sensor.'
super().__init__(syncthru, name)
self._id_suffix = '_problem' | Initialize the sensor. | homeassistant/components/syncthru/binary_sensor.py | __init__ | Tommatheussen/core | 11 | python | def __init__(self, syncthru, name):
super().__init__(syncthru, name)
self._id_suffix = '_problem' | def __init__(self, syncthru, name):
super().__init__(syncthru, name)
self._id_suffix = '_problem'<|docstring|>Initialize the sensor.<|endoftext|> |
05765d8bb05fd6fc8fa1a550c50077a1c090b411f763e35efa364fc3854febd1 | @property
def is_on(self):
'Set the state to whether there is a problem with the printer.'
return SYNCTHRU_STATE_PROBLEM[self.syncthru.device_status()] | Set the state to whether there is a problem with the printer. | homeassistant/components/syncthru/binary_sensor.py | is_on | Tommatheussen/core | 11 | python | @property
def is_on(self):
return SYNCTHRU_STATE_PROBLEM[self.syncthru.device_status()] | @property
def is_on(self):
return SYNCTHRU_STATE_PROBLEM[self.syncthru.device_status()]<|docstring|>Set the state to whether there is a problem with the printer.<|endoftext|> |
d1df3c77cc5d1c1ef49350fa09206297ef255d42c27c1381d918335ffdd9da98 | def __init__(__self__, resource_name, opts=None, eventhub_name=None, location=None, name=None, namespace_name=None, resource_group_name=None, user_metadata=None, __props__=None, __name__=None, __opts__=None):
"\n Manages a Event Hubs Consumer Group as a nested resource within an Event Hub.\n \n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.\n :param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.\n :param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.\n :param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.\n :param pulumi.Input[str] user_metadata: Specifies the user metadata.\n\n > This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown.\n "
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if (eventhub_name is None):
raise TypeError("Missing required property 'eventhub_name'")
__props__['eventhub_name'] = eventhub_name
__props__['location'] = location
__props__['name'] = name
if (namespace_name is None):
raise TypeError("Missing required property 'namespace_name'")
__props__['namespace_name'] = namespace_name
if (resource_group_name is None):
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['user_metadata'] = user_metadata
super(EventHubConsumerGroup, __self__).__init__('azure:eventhub/eventHubConsumerGroup:EventHubConsumerGroup', resource_name, __props__, opts) | Manages a Event Hubs Consumer Group as a nested resource within an Event Hub.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.
:param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.
:param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.
:param pulumi.Input[str] user_metadata: Specifies the user metadata.
> This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown. | sdk/python/pulumi_azure/eventhub/event_hub_consumer_group.py | __init__ | vijayraavi/pulumi-azure | 0 | python | def __init__(__self__, resource_name, opts=None, eventhub_name=None, location=None, name=None, namespace_name=None, resource_group_name=None, user_metadata=None, __props__=None, __name__=None, __opts__=None):
"\n Manages a Event Hubs Consumer Group as a nested resource within an Event Hub.\n \n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.\n :param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.\n :param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.\n :param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.\n :param pulumi.Input[str] user_metadata: Specifies the user metadata.\n\n > This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown.\n "
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if (eventhub_name is None):
raise TypeError("Missing required property 'eventhub_name'")
__props__['eventhub_name'] = eventhub_name
__props__['location'] = location
__props__['name'] = name
if (namespace_name is None):
raise TypeError("Missing required property 'namespace_name'")
__props__['namespace_name'] = namespace_name
if (resource_group_name is None):
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['user_metadata'] = user_metadata
super(EventHubConsumerGroup, __self__).__init__('azure:eventhub/eventHubConsumerGroup:EventHubConsumerGroup', resource_name, __props__, opts) | def __init__(__self__, resource_name, opts=None, eventhub_name=None, location=None, name=None, namespace_name=None, resource_group_name=None, user_metadata=None, __props__=None, __name__=None, __opts__=None):
"\n Manages a Event Hubs Consumer Group as a nested resource within an Event Hub.\n \n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.\n :param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.\n :param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.\n :param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.\n :param pulumi.Input[str] user_metadata: Specifies the user metadata.\n\n > This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown.\n "
if (__name__ is not None):
warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning)
resource_name = __name__
if (__opts__ is not None):
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if (opts is None):
opts = pulumi.ResourceOptions()
if (not isinstance(opts, pulumi.ResourceOptions)):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if (opts.version is None):
opts.version = utilities.get_version()
if (opts.id is None):
if (__props__ is not None):
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if (eventhub_name is None):
raise TypeError("Missing required property 'eventhub_name'")
__props__['eventhub_name'] = eventhub_name
__props__['location'] = location
__props__['name'] = name
if (namespace_name is None):
raise TypeError("Missing required property 'namespace_name'")
__props__['namespace_name'] = namespace_name
if (resource_group_name is None):
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['user_metadata'] = user_metadata
super(EventHubConsumerGroup, __self__).__init__('azure:eventhub/eventHubConsumerGroup:EventHubConsumerGroup', resource_name, __props__, opts)<|docstring|>Manages a Event Hubs Consumer Group as a nested resource within an Event Hub.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.
:param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.
:param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.
:param pulumi.Input[str] user_metadata: Specifies the user metadata.
> This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown.<|endoftext|> |
afa0524fe440efd4908b0775ec4a5b758ee5e12ad60be1594e8648701475c651 | @staticmethod
def get(resource_name, id, opts=None, eventhub_name=None, location=None, name=None, namespace_name=None, resource_group_name=None, user_metadata=None):
"\n Get an existing EventHubConsumerGroup resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n \n :param str resource_name: The unique name of the resulting resource.\n :param str id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.\n :param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.\n :param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.\n :param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.\n :param pulumi.Input[str] user_metadata: Specifies the user metadata.\n\n > This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['eventhub_name'] = eventhub_name
__props__['location'] = location
__props__['name'] = name
__props__['namespace_name'] = namespace_name
__props__['resource_group_name'] = resource_group_name
__props__['user_metadata'] = user_metadata
return EventHubConsumerGroup(resource_name, opts=opts, __props__=__props__) | Get an existing EventHubConsumerGroup resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param str id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.
:param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.
:param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.
:param pulumi.Input[str] user_metadata: Specifies the user metadata.
> This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown. | sdk/python/pulumi_azure/eventhub/event_hub_consumer_group.py | get | vijayraavi/pulumi-azure | 0 | python | @staticmethod
def get(resource_name, id, opts=None, eventhub_name=None, location=None, name=None, namespace_name=None, resource_group_name=None, user_metadata=None):
"\n Get an existing EventHubConsumerGroup resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n \n :param str resource_name: The unique name of the resulting resource.\n :param str id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.\n :param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.\n :param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.\n :param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.\n :param pulumi.Input[str] user_metadata: Specifies the user metadata.\n\n > This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['eventhub_name'] = eventhub_name
__props__['location'] = location
__props__['name'] = name
__props__['namespace_name'] = namespace_name
__props__['resource_group_name'] = resource_group_name
__props__['user_metadata'] = user_metadata
return EventHubConsumerGroup(resource_name, opts=opts, __props__=__props__) | @staticmethod
def get(resource_name, id, opts=None, eventhub_name=None, location=None, name=None, namespace_name=None, resource_group_name=None, user_metadata=None):
"\n Get an existing EventHubConsumerGroup resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n \n :param str resource_name: The unique name of the resulting resource.\n :param str id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.\n :param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.\n :param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.\n :param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.\n :param pulumi.Input[str] user_metadata: Specifies the user metadata.\n\n > This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown.\n "
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['eventhub_name'] = eventhub_name
__props__['location'] = location
__props__['name'] = name
__props__['namespace_name'] = namespace_name
__props__['resource_group_name'] = resource_group_name
__props__['user_metadata'] = user_metadata
return EventHubConsumerGroup(resource_name, opts=opts, __props__=__props__)<|docstring|>Get an existing EventHubConsumerGroup resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param str id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] eventhub_name: Specifies the name of the EventHub. Changing this forces a new resource to be created.
:param pulumi.Input[str] name: Specifies the name of the EventHub Consumer Group resource. Changing this forces a new resource to be created.
:param pulumi.Input[str] namespace_name: Specifies the name of the grandparent EventHub Namespace. Changing this forces a new resource to be created.
:param pulumi.Input[str] resource_group_name: The name of the resource group in which the EventHub Consumer Group's grandparent Namespace exists. Changing this forces a new resource to be created.
:param pulumi.Input[str] user_metadata: Specifies the user metadata.
> This content is derived from https://github.com/terraform-providers/terraform-provider-azurerm/blob/master/website/docs/r/eventhub_consumer_group_legacy.html.markdown.<|endoftext|> |
b81dd19dda4c5b78409968ea80d92f00901cb25b7ad9393744d299658e664ae5 | def Classify(self, *args):
'\n :param P:\n :type P: gp_Pnt2d\n :param Tol:\n :type Tol: float\n :rtype: TopAbs_State\n\n '
return _IntStart.IntStart_SITopolTool_Classify(self, *args) | :param P:
:type P: gp_Pnt2d
:param Tol:
:type Tol: float
:rtype: TopAbs_State | Lib/site-packages/OCC/IntStart.py | Classify | JWerbrouck/RWTH_M1_Projekt | 0 | python | def Classify(self, *args):
'\n :param P:\n :type P: gp_Pnt2d\n :param Tol:\n :type Tol: float\n :rtype: TopAbs_State\n\n '
return _IntStart.IntStart_SITopolTool_Classify(self, *args) | def Classify(self, *args):
'\n :param P:\n :type P: gp_Pnt2d\n :param Tol:\n :type Tol: float\n :rtype: TopAbs_State\n\n '
return _IntStart.IntStart_SITopolTool_Classify(self, *args)<|docstring|>:param P:
:type P: gp_Pnt2d
:param Tol:
:type Tol: float
:rtype: TopAbs_State<|endoftext|> |
80516eb6a922ab202a11180abf0e564c18d04125ff6bd27c8ec3391532529f7b | def _kill_pointed(self):
'_kill_pointed(IntStart_SITopolTool self)'
return _IntStart.IntStart_SITopolTool__kill_pointed(self) | _kill_pointed(IntStart_SITopolTool self) | Lib/site-packages/OCC/IntStart.py | _kill_pointed | JWerbrouck/RWTH_M1_Projekt | 0 | python | def _kill_pointed(self):
return _IntStart.IntStart_SITopolTool__kill_pointed(self) | def _kill_pointed(self):
return _IntStart.IntStart_SITopolTool__kill_pointed(self)<|docstring|>_kill_pointed(IntStart_SITopolTool self)<|endoftext|> |
becae5d18bca64d9cd035e5284d5cbabf1a3a9a598cfa7cb5722fb7876499f33 | def GetHandle(self):
'GetHandle(IntStart_SITopolTool self) -> Handle_IntStart_SITopolTool'
return _IntStart.IntStart_SITopolTool_GetHandle(self) | GetHandle(IntStart_SITopolTool self) -> Handle_IntStart_SITopolTool | Lib/site-packages/OCC/IntStart.py | GetHandle | JWerbrouck/RWTH_M1_Projekt | 0 | python | def GetHandle(self):
return _IntStart.IntStart_SITopolTool_GetHandle(self) | def GetHandle(self):
return _IntStart.IntStart_SITopolTool_GetHandle(self)<|docstring|>GetHandle(IntStart_SITopolTool self) -> Handle_IntStart_SITopolTool<|endoftext|> |
6767495c163b810279f09e88107019a4204561f8ada551195bdb81c3d539a434 | def merge_templates(self, replacements, separator):
'\n Duplicate template. Creates a copy of the template, does a merge, and separates them by a new paragraph, a new break or a new section break.\n separator must be :\n - page_break : Page Break. \n - column_break : Column Break. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS\n - textWrapping_break : Line Break.\n - continuous_section : Continuous section break. Begins the section on the next paragraph.\n - evenPage_section : evenPage section break. section begins on the next even-numbered page, leaving the next odd page blank if necessary.\n - nextColumn_section : nextColumn section break. section begins on the following column on the page. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS\n - nextPage_section : nextPage section break. section begins on the following page.\n - oddPage_section : oddPage section break. section begins on the next odd-numbered page, leaving the next even page blank if necessary.\n '
valid_separators = {'page_break', 'column_break', 'textWrapping_break', 'continuous_section', 'evenPage_section', 'nextColumn_section', 'nextPage_section', 'oddPage_section'}
if (not (separator in valid_separators)):
raise ValueError('Invalid separator argument')
(type, sepClass) = separator.split('_')
for part in self.parts.values():
root = part.getroot()
tag = root.tag
if ((tag == ('{%(w)s}ftr' % NAMESPACES)) or (tag == ('{%(w)s}hdr' % NAMESPACES))):
continue
if (sepClass == 'section'):
firstSection = root.find('w:body/w:p/w:pPr/w:sectPr', namespaces=NAMESPACES)
if (firstSection == None):
firstSection = root.find('w:body/w:sectPr', namespaces=NAMESPACES)
nextPageSec = deepcopy(firstSection)
for child in nextPageSec:
if (child.tag == ('{%(w)s}type' % NAMESPACES)):
nextPageSec.remove(child)
newType = etree.SubElement(nextPageSec, ('{%(w)s}type' % NAMESPACES))
newType.set(('{%(w)s}val' % NAMESPACES), type)
secRoot = firstSection.getparent()
secRoot.replace(firstSection, nextPageSec)
lastSection = root.find('w:body/w:sectPr', namespaces=NAMESPACES)
mainSection = deepcopy(lastSection)
lsecRoot = lastSection.getparent()
lsecRoot.remove(lastSection)
childrenList = root.findall('w:body/*', namespaces=NAMESPACES)
for child in root:
if (child.tag == ('{%(w)s}body' % NAMESPACES)):
child.clear()
lr = len(replacements)
lc = len(childrenList)
parts = []
for (i, repl) in enumerate(replacements):
for (j, n) in enumerate(childrenList):
element = deepcopy(n)
for child in root:
if (child.tag == ('{%(w)s}body' % NAMESPACES)):
child.append(element)
parts.append(element)
if ((j + 1) == lc):
if ((i + 1) == lr):
child.append(mainSection)
parts.append(mainSection)
elif (sepClass == 'section'):
intSection = deepcopy(mainSection)
p = etree.SubElement(child, ('{%(w)s}p' % NAMESPACES))
pPr = etree.SubElement(p, ('{%(w)s}pPr' % NAMESPACES))
pPr.append(intSection)
parts.append(p)
elif (sepClass == 'break'):
pb = etree.SubElement(child, ('{%(w)s}p' % NAMESPACES))
r = etree.SubElement(pb, ('{%(w)s}r' % NAMESPACES))
nbreak = Element(('{%(w)s}br' % NAMESPACES))
nbreak.attrib[('{%(w)s}type' % NAMESPACES)] = type
r.append(nbreak)
self.merge(parts, **repl) | Duplicate template. Creates a copy of the template, does a merge, and separates them by a new paragraph, a new break or a new section break.
separator must be :
- page_break : Page Break.
- column_break : Column Break. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS
- textWrapping_break : Line Break.
- continuous_section : Continuous section break. Begins the section on the next paragraph.
- evenPage_section : evenPage section break. section begins on the next even-numbered page, leaving the next odd page blank if necessary.
- nextColumn_section : nextColumn section break. section begins on the following column on the page. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS
- nextPage_section : nextPage section break. section begins on the following page.
- oddPage_section : oddPage section break. section begins on the next odd-numbered page, leaving the next even page blank if necessary. | mailmerge.py | merge_templates | danigoland/docx-mailmerge | 2 | python | def merge_templates(self, replacements, separator):
'\n Duplicate template. Creates a copy of the template, does a merge, and separates them by a new paragraph, a new break or a new section break.\n separator must be :\n - page_break : Page Break. \n - column_break : Column Break. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS\n - textWrapping_break : Line Break.\n - continuous_section : Continuous section break. Begins the section on the next paragraph.\n - evenPage_section : evenPage section break. section begins on the next even-numbered page, leaving the next odd page blank if necessary.\n - nextColumn_section : nextColumn section break. section begins on the following column on the page. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS\n - nextPage_section : nextPage section break. section begins on the following page.\n - oddPage_section : oddPage section break. section begins on the next odd-numbered page, leaving the next even page blank if necessary.\n '
valid_separators = {'page_break', 'column_break', 'textWrapping_break', 'continuous_section', 'evenPage_section', 'nextColumn_section', 'nextPage_section', 'oddPage_section'}
if (not (separator in valid_separators)):
raise ValueError('Invalid separator argument')
(type, sepClass) = separator.split('_')
for part in self.parts.values():
root = part.getroot()
tag = root.tag
if ((tag == ('{%(w)s}ftr' % NAMESPACES)) or (tag == ('{%(w)s}hdr' % NAMESPACES))):
continue
if (sepClass == 'section'):
firstSection = root.find('w:body/w:p/w:pPr/w:sectPr', namespaces=NAMESPACES)
if (firstSection == None):
firstSection = root.find('w:body/w:sectPr', namespaces=NAMESPACES)
nextPageSec = deepcopy(firstSection)
for child in nextPageSec:
if (child.tag == ('{%(w)s}type' % NAMESPACES)):
nextPageSec.remove(child)
newType = etree.SubElement(nextPageSec, ('{%(w)s}type' % NAMESPACES))
newType.set(('{%(w)s}val' % NAMESPACES), type)
secRoot = firstSection.getparent()
secRoot.replace(firstSection, nextPageSec)
lastSection = root.find('w:body/w:sectPr', namespaces=NAMESPACES)
mainSection = deepcopy(lastSection)
lsecRoot = lastSection.getparent()
lsecRoot.remove(lastSection)
childrenList = root.findall('w:body/*', namespaces=NAMESPACES)
for child in root:
if (child.tag == ('{%(w)s}body' % NAMESPACES)):
child.clear()
lr = len(replacements)
lc = len(childrenList)
parts = []
for (i, repl) in enumerate(replacements):
for (j, n) in enumerate(childrenList):
element = deepcopy(n)
for child in root:
if (child.tag == ('{%(w)s}body' % NAMESPACES)):
child.append(element)
parts.append(element)
if ((j + 1) == lc):
if ((i + 1) == lr):
child.append(mainSection)
parts.append(mainSection)
elif (sepClass == 'section'):
intSection = deepcopy(mainSection)
p = etree.SubElement(child, ('{%(w)s}p' % NAMESPACES))
pPr = etree.SubElement(p, ('{%(w)s}pPr' % NAMESPACES))
pPr.append(intSection)
parts.append(p)
elif (sepClass == 'break'):
pb = etree.SubElement(child, ('{%(w)s}p' % NAMESPACES))
r = etree.SubElement(pb, ('{%(w)s}r' % NAMESPACES))
nbreak = Element(('{%(w)s}br' % NAMESPACES))
nbreak.attrib[('{%(w)s}type' % NAMESPACES)] = type
r.append(nbreak)
self.merge(parts, **repl) | def merge_templates(self, replacements, separator):
'\n Duplicate template. Creates a copy of the template, does a merge, and separates them by a new paragraph, a new break or a new section break.\n separator must be :\n - page_break : Page Break. \n - column_break : Column Break. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS\n - textWrapping_break : Line Break.\n - continuous_section : Continuous section break. Begins the section on the next paragraph.\n - evenPage_section : evenPage section break. section begins on the next even-numbered page, leaving the next odd page blank if necessary.\n - nextColumn_section : nextColumn section break. section begins on the following column on the page. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS\n - nextPage_section : nextPage section break. section begins on the following page.\n - oddPage_section : oddPage section break. section begins on the next odd-numbered page, leaving the next even page blank if necessary.\n '
valid_separators = {'page_break', 'column_break', 'textWrapping_break', 'continuous_section', 'evenPage_section', 'nextColumn_section', 'nextPage_section', 'oddPage_section'}
if (not (separator in valid_separators)):
raise ValueError('Invalid separator argument')
(type, sepClass) = separator.split('_')
for part in self.parts.values():
root = part.getroot()
tag = root.tag
if ((tag == ('{%(w)s}ftr' % NAMESPACES)) or (tag == ('{%(w)s}hdr' % NAMESPACES))):
continue
if (sepClass == 'section'):
firstSection = root.find('w:body/w:p/w:pPr/w:sectPr', namespaces=NAMESPACES)
if (firstSection == None):
firstSection = root.find('w:body/w:sectPr', namespaces=NAMESPACES)
nextPageSec = deepcopy(firstSection)
for child in nextPageSec:
if (child.tag == ('{%(w)s}type' % NAMESPACES)):
nextPageSec.remove(child)
newType = etree.SubElement(nextPageSec, ('{%(w)s}type' % NAMESPACES))
newType.set(('{%(w)s}val' % NAMESPACES), type)
secRoot = firstSection.getparent()
secRoot.replace(firstSection, nextPageSec)
lastSection = root.find('w:body/w:sectPr', namespaces=NAMESPACES)
mainSection = deepcopy(lastSection)
lsecRoot = lastSection.getparent()
lsecRoot.remove(lastSection)
childrenList = root.findall('w:body/*', namespaces=NAMESPACES)
for child in root:
if (child.tag == ('{%(w)s}body' % NAMESPACES)):
child.clear()
lr = len(replacements)
lc = len(childrenList)
parts = []
for (i, repl) in enumerate(replacements):
for (j, n) in enumerate(childrenList):
element = deepcopy(n)
for child in root:
if (child.tag == ('{%(w)s}body' % NAMESPACES)):
child.append(element)
parts.append(element)
if ((j + 1) == lc):
if ((i + 1) == lr):
child.append(mainSection)
parts.append(mainSection)
elif (sepClass == 'section'):
intSection = deepcopy(mainSection)
p = etree.SubElement(child, ('{%(w)s}p' % NAMESPACES))
pPr = etree.SubElement(p, ('{%(w)s}pPr' % NAMESPACES))
pPr.append(intSection)
parts.append(p)
elif (sepClass == 'break'):
pb = etree.SubElement(child, ('{%(w)s}p' % NAMESPACES))
r = etree.SubElement(pb, ('{%(w)s}r' % NAMESPACES))
nbreak = Element(('{%(w)s}br' % NAMESPACES))
nbreak.attrib[('{%(w)s}type' % NAMESPACES)] = type
r.append(nbreak)
self.merge(parts, **repl)<|docstring|>Duplicate template. Creates a copy of the template, does a merge, and separates them by a new paragraph, a new break or a new section break.
separator must be :
- page_break : Page Break.
- column_break : Column Break. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS
- textWrapping_break : Line Break.
- continuous_section : Continuous section break. Begins the section on the next paragraph.
- evenPage_section : evenPage section break. section begins on the next even-numbered page, leaving the next odd page blank if necessary.
- nextColumn_section : nextColumn section break. section begins on the following column on the page. ONLY HAVE EFFECT IF DOCUMENT HAVE COLUMNS
- nextPage_section : nextPage section break. section begins on the following page.
- oddPage_section : oddPage section break. section begins on the next odd-numbered page, leaving the next even page blank if necessary.<|endoftext|> |
205d31190b7a479f688c7eab63a03508ab1c5b5d3a687e5198c55e5052fd059a | def merge_pages(self, replacements):
'\n Deprecated method.\n '
warnings.warn('merge_pages has been deprecated in favour of merge_templates', category=DeprecationWarning, stacklevel=2)
self.merge_templates(replacements, 'page_break') | Deprecated method. | mailmerge.py | merge_pages | danigoland/docx-mailmerge | 2 | python | def merge_pages(self, replacements):
'\n \n '
warnings.warn('merge_pages has been deprecated in favour of merge_templates', category=DeprecationWarning, stacklevel=2)
self.merge_templates(replacements, 'page_break') | def merge_pages(self, replacements):
'\n \n '
warnings.warn('merge_pages has been deprecated in favour of merge_templates', category=DeprecationWarning, stacklevel=2)
self.merge_templates(replacements, 'page_break')<|docstring|>Deprecated method.<|endoftext|> |
f8b0a2b1405cff14dd1e1fb27c0d410cd92a88d483d01b6fbef39157c14cf78f | def task1():
'\n Pick three ids at random and plot their line graph\n '
size = 3
np.random.seed(1)
plt.ioff()
(train_vectors, train_labels) = get_vector_and_labels(TRAIN_LABELED_FILE)
output_folder = (RESULT_DIR / 'task1')
all_indices = np.arange(32)
np.random.shuffle(all_indices)
random_ids = all_indices[:size]
random_window_indices = np.random.randint(5, size=size)
random_indices = np.multiply(random_ids, random_window_indices)
subset = np.take(train_vectors, random_indices, axis=0)
label_subset = np.take(train_labels, random_indices, axis=0)
for i in range(size):
plt.figure((i + 1))
plt.title('User ID: {}'.format(int(label_subset[i][(- 1)])))
plt.plot(subset[i])
plt.savefig((output_folder / 'vis{}.png'.format((i + 1)))) | Pick three ids at random and plot their line graph | omsignal/task.py | task1 | eeishaan/ift6759-block1 | 0 | python | def task1():
'\n \n '
size = 3
np.random.seed(1)
plt.ioff()
(train_vectors, train_labels) = get_vector_and_labels(TRAIN_LABELED_FILE)
output_folder = (RESULT_DIR / 'task1')
all_indices = np.arange(32)
np.random.shuffle(all_indices)
random_ids = all_indices[:size]
random_window_indices = np.random.randint(5, size=size)
random_indices = np.multiply(random_ids, random_window_indices)
subset = np.take(train_vectors, random_indices, axis=0)
label_subset = np.take(train_labels, random_indices, axis=0)
for i in range(size):
plt.figure((i + 1))
plt.title('User ID: {}'.format(int(label_subset[i][(- 1)])))
plt.plot(subset[i])
plt.savefig((output_folder / 'vis{}.png'.format((i + 1)))) | def task1():
'\n \n '
size = 3
np.random.seed(1)
plt.ioff()
(train_vectors, train_labels) = get_vector_and_labels(TRAIN_LABELED_FILE)
output_folder = (RESULT_DIR / 'task1')
all_indices = np.arange(32)
np.random.shuffle(all_indices)
random_ids = all_indices[:size]
random_window_indices = np.random.randint(5, size=size)
random_indices = np.multiply(random_ids, random_window_indices)
subset = np.take(train_vectors, random_indices, axis=0)
label_subset = np.take(train_labels, random_indices, axis=0)
for i in range(size):
plt.figure((i + 1))
plt.title('User ID: {}'.format(int(label_subset[i][(- 1)])))
plt.plot(subset[i])
plt.savefig((output_folder / 'vis{}.png'.format((i + 1))))<|docstring|>Pick three ids at random and plot their line graph<|endoftext|> |
c9af56c4d17c0b03335a97aec7b84aa4f9054331a7b283dfd5cc808c8581e68b | def batchify(self, obs):
'Convert batch observations `text` and `label` to\n rank 3 tensor `x` and rank 2 tensor `q`, `y`\n '
exs = [ex for ex in obs if ('text' in ex)]
ids = [ex['id'] for ex in obs if ('text' in ex)]
valid_inds = [i for (i, ex) in enumerate(obs) if ('text' in ex)]
if (len(exs) == 0):
return ((None,) * 5)
ms = self.memory_size
xs = [ex['text'].split('\n') for ex in exs]
qs = [self.txt2vec(x.pop()) for x in xs]
parsed_xs = []
for x in xs:
x_mask = [('?' not in s) for s in x]
x = [s for (s, b) in zip(x, x_mask) if b]
if (('labels' in exs[0]) and self.use_random_noise):
parsed_x = []
for s in x:
parsed_x.append(s)
if (random.random() < 0.1):
parsed_x.append('')
x = parsed_x
parsed_xs.append(x[(- ms):])
xs = parsed_xs
xs = [[self.txt2vec(sent) for sent in x] for x in xs]
x_max_len = ms
arr_max_len = max((max((len(arr) for arr in x)) for x in xs))
tensor = xp.zeros((len(xs), x_max_len, arr_max_len)).astype(xp.int32)
for (i, x) in enumerate(xs):
offset = (ms - len(x))
for (j, arr) in enumerate(x):
tensor[(i, (offset + j))][:len(arr)] = arr
x = chainer.Variable(tensor)
if False:
print('\n\nx:', [self.vec2txt(tensor[0][i]) for i in range(len(tensor[0]))])
arr_max_len = max([len(arr) for arr in qs])
tensor = xp.zeros((len(qs), arr_max_len)).astype(xp.int32)
for (j, arr) in enumerate(qs):
tensor[j][:len(arr)] = arr
q = chainer.Variable(tensor)
if False:
print('q:', self.vec2txt(tensor[0]))
y = None
if ('labels' in exs[0]):
ys = [self.txt2vec(' '.join(ex['labels']))[:2] for ex in exs]
tensor = xp.zeros((len(ys), 2)).astype(xp.int32)
for (j, arr) in enumerate(ys):
tensor[j][:len(arr)] = arr
y = chainer.Variable(tensor)
if False:
print('y:', self.vec2txt(tensor[0]))
return (x, q, y, ids, valid_inds) | Convert batch observations `text` and `label` to
rank 3 tensor `x` and rank 2 tensor `q`, `y` | chainer_memn2n/chainer_memn2n.py | batchify | ryonakamura/parlai_agents | 47 | python | def batchify(self, obs):
'Convert batch observations `text` and `label` to\n rank 3 tensor `x` and rank 2 tensor `q`, `y`\n '
exs = [ex for ex in obs if ('text' in ex)]
ids = [ex['id'] for ex in obs if ('text' in ex)]
valid_inds = [i for (i, ex) in enumerate(obs) if ('text' in ex)]
if (len(exs) == 0):
return ((None,) * 5)
ms = self.memory_size
xs = [ex['text'].split('\n') for ex in exs]
qs = [self.txt2vec(x.pop()) for x in xs]
parsed_xs = []
for x in xs:
x_mask = [('?' not in s) for s in x]
x = [s for (s, b) in zip(x, x_mask) if b]
if (('labels' in exs[0]) and self.use_random_noise):
parsed_x = []
for s in x:
parsed_x.append(s)
if (random.random() < 0.1):
parsed_x.append()
x = parsed_x
parsed_xs.append(x[(- ms):])
xs = parsed_xs
xs = [[self.txt2vec(sent) for sent in x] for x in xs]
x_max_len = ms
arr_max_len = max((max((len(arr) for arr in x)) for x in xs))
tensor = xp.zeros((len(xs), x_max_len, arr_max_len)).astype(xp.int32)
for (i, x) in enumerate(xs):
offset = (ms - len(x))
for (j, arr) in enumerate(x):
tensor[(i, (offset + j))][:len(arr)] = arr
x = chainer.Variable(tensor)
if False:
print('\n\nx:', [self.vec2txt(tensor[0][i]) for i in range(len(tensor[0]))])
arr_max_len = max([len(arr) for arr in qs])
tensor = xp.zeros((len(qs), arr_max_len)).astype(xp.int32)
for (j, arr) in enumerate(qs):
tensor[j][:len(arr)] = arr
q = chainer.Variable(tensor)
if False:
print('q:', self.vec2txt(tensor[0]))
y = None
if ('labels' in exs[0]):
ys = [self.txt2vec(' '.join(ex['labels']))[:2] for ex in exs]
tensor = xp.zeros((len(ys), 2)).astype(xp.int32)
for (j, arr) in enumerate(ys):
tensor[j][:len(arr)] = arr
y = chainer.Variable(tensor)
if False:
print('y:', self.vec2txt(tensor[0]))
return (x, q, y, ids, valid_inds) | def batchify(self, obs):
'Convert batch observations `text` and `label` to\n rank 3 tensor `x` and rank 2 tensor `q`, `y`\n '
exs = [ex for ex in obs if ('text' in ex)]
ids = [ex['id'] for ex in obs if ('text' in ex)]
valid_inds = [i for (i, ex) in enumerate(obs) if ('text' in ex)]
if (len(exs) == 0):
return ((None,) * 5)
ms = self.memory_size
xs = [ex['text'].split('\n') for ex in exs]
qs = [self.txt2vec(x.pop()) for x in xs]
parsed_xs = []
for x in xs:
x_mask = [('?' not in s) for s in x]
x = [s for (s, b) in zip(x, x_mask) if b]
if (('labels' in exs[0]) and self.use_random_noise):
parsed_x = []
for s in x:
parsed_x.append(s)
if (random.random() < 0.1):
parsed_x.append()
x = parsed_x
parsed_xs.append(x[(- ms):])
xs = parsed_xs
xs = [[self.txt2vec(sent) for sent in x] for x in xs]
x_max_len = ms
arr_max_len = max((max((len(arr) for arr in x)) for x in xs))
tensor = xp.zeros((len(xs), x_max_len, arr_max_len)).astype(xp.int32)
for (i, x) in enumerate(xs):
offset = (ms - len(x))
for (j, arr) in enumerate(x):
tensor[(i, (offset + j))][:len(arr)] = arr
x = chainer.Variable(tensor)
if False:
print('\n\nx:', [self.vec2txt(tensor[0][i]) for i in range(len(tensor[0]))])
arr_max_len = max([len(arr) for arr in qs])
tensor = xp.zeros((len(qs), arr_max_len)).astype(xp.int32)
for (j, arr) in enumerate(qs):
tensor[j][:len(arr)] = arr
q = chainer.Variable(tensor)
if False:
print('q:', self.vec2txt(tensor[0]))
y = None
if ('labels' in exs[0]):
ys = [self.txt2vec(' '.join(ex['labels']))[:2] for ex in exs]
tensor = xp.zeros((len(ys), 2)).astype(xp.int32)
for (j, arr) in enumerate(ys):
tensor[j][:len(arr)] = arr
y = chainer.Variable(tensor)
if False:
print('y:', self.vec2txt(tensor[0]))
return (x, q, y, ids, valid_inds)<|docstring|>Convert batch observations `text` and `label` to
rank 3 tensor `x` and rank 2 tensor `q`, `y`<|endoftext|> |
01ebe870e8dd9e345a4e67d5b3e0641e41265d3bd39f1b78937b6d45bd8cf4a5 | def sparsemax_loss(X, target, k=None):
'sparsemax loss: sparse alternative to cross-entropy.\n\n Computed using a partial sorting strategy.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n k : int or None\n number of largest elements to partial-sort over. For optimal\n performance, should be slightly bigger than the expected number of\n nonzeros in the solution. If the solution is more than k-sparse,\n this function is recursively called with a 2*k schedule.\n If `None`, full sorting is performed from the beginning.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return SparsemaxLossFunction.apply(X, target, k) | sparsemax loss: sparse alternative to cross-entropy.
Computed using a partial sorting strategy.
Parameters
----------
X : torch.Tensor, shape=(n_samples, n_classes)
The input 2D tensor of predicted scores
target : torch.LongTensor, shape=(n_samples,)
The ground truth labels, 0 <= target < n_classes.
k : int or None
number of largest elements to partial-sort over. For optimal
performance, should be slightly bigger than the expected number of
nonzeros in the solution. If the solution is more than k-sparse,
this function is recursively called with a 2*k schedule.
If `None`, full sorting is performed from the beginning.
Returns
-------
losses, torch.Tensor, shape=(n_samples,)
The loss incurred at each sample. | ludwig/utils/entmax/losses.py | sparsemax_loss | dantreiman/ludwig | 7,739 | python | def sparsemax_loss(X, target, k=None):
'sparsemax loss: sparse alternative to cross-entropy.\n\n Computed using a partial sorting strategy.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n k : int or None\n number of largest elements to partial-sort over. For optimal\n performance, should be slightly bigger than the expected number of\n nonzeros in the solution. If the solution is more than k-sparse,\n this function is recursively called with a 2*k schedule.\n If `None`, full sorting is performed from the beginning.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return SparsemaxLossFunction.apply(X, target, k) | def sparsemax_loss(X, target, k=None):
'sparsemax loss: sparse alternative to cross-entropy.\n\n Computed using a partial sorting strategy.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n k : int or None\n number of largest elements to partial-sort over. For optimal\n performance, should be slightly bigger than the expected number of\n nonzeros in the solution. If the solution is more than k-sparse,\n this function is recursively called with a 2*k schedule.\n If `None`, full sorting is performed from the beginning.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return SparsemaxLossFunction.apply(X, target, k)<|docstring|>sparsemax loss: sparse alternative to cross-entropy.
Computed using a partial sorting strategy.
Parameters
----------
X : torch.Tensor, shape=(n_samples, n_classes)
The input 2D tensor of predicted scores
target : torch.LongTensor, shape=(n_samples,)
The ground truth labels, 0 <= target < n_classes.
k : int or None
number of largest elements to partial-sort over. For optimal
performance, should be slightly bigger than the expected number of
nonzeros in the solution. If the solution is more than k-sparse,
this function is recursively called with a 2*k schedule.
If `None`, full sorting is performed from the beginning.
Returns
-------
losses, torch.Tensor, shape=(n_samples,)
The loss incurred at each sample.<|endoftext|> |
425f2f43714ed0f9d84e6777ba489f1f80afd2ce416ac39f7892544a517dfdc4 | def sparsemax_bisect_loss(X, target, n_iter=50):
'sparsemax loss: sparse alternative to cross-entropy.\n\n Computed using bisection.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n n_iter : int\n Number of bisection iterations. For float32, 24 iterations should\n suffice for machine precision.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return SparsemaxBisectLossFunction.apply(X, target, n_iter) | sparsemax loss: sparse alternative to cross-entropy.
Computed using bisection.
Parameters
----------
X : torch.Tensor, shape=(n_samples, n_classes)
The input 2D tensor of predicted scores
target : torch.LongTensor, shape=(n_samples,)
The ground truth labels, 0 <= target < n_classes.
n_iter : int
Number of bisection iterations. For float32, 24 iterations should
suffice for machine precision.
Returns
-------
losses, torch.Tensor, shape=(n_samples,)
The loss incurred at each sample. | ludwig/utils/entmax/losses.py | sparsemax_bisect_loss | dantreiman/ludwig | 7,739 | python | def sparsemax_bisect_loss(X, target, n_iter=50):
'sparsemax loss: sparse alternative to cross-entropy.\n\n Computed using bisection.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n n_iter : int\n Number of bisection iterations. For float32, 24 iterations should\n suffice for machine precision.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return SparsemaxBisectLossFunction.apply(X, target, n_iter) | def sparsemax_bisect_loss(X, target, n_iter=50):
'sparsemax loss: sparse alternative to cross-entropy.\n\n Computed using bisection.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n n_iter : int\n Number of bisection iterations. For float32, 24 iterations should\n suffice for machine precision.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return SparsemaxBisectLossFunction.apply(X, target, n_iter)<|docstring|>sparsemax loss: sparse alternative to cross-entropy.
Computed using bisection.
Parameters
----------
X : torch.Tensor, shape=(n_samples, n_classes)
The input 2D tensor of predicted scores
target : torch.LongTensor, shape=(n_samples,)
The ground truth labels, 0 <= target < n_classes.
n_iter : int
Number of bisection iterations. For float32, 24 iterations should
suffice for machine precision.
Returns
-------
losses, torch.Tensor, shape=(n_samples,)
The loss incurred at each sample.<|endoftext|> |
e78623aa5dd84b85e212d1f9bcabefc1b9d9753ebb8dedeb64c10244d747843d | def entmax15_loss(X, target, k=None):
'1.5-entmax loss: sparse alternative to cross-entropy\n\n Computed using a partial sorting strategy.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n k : int or None\n number of largest elements to partial-sort over. For optimal\n performance, should be slightly bigger than the expected number of\n nonzeros in the solution. If the solution is more than k-sparse,\n this function is recursively called with a 2*k schedule.\n If `None`, full sorting is performed from the beginning.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return Entmax15LossFunction.apply(X, target, k) | 1.5-entmax loss: sparse alternative to cross-entropy
Computed using a partial sorting strategy.
Parameters
----------
X : torch.Tensor, shape=(n_samples, n_classes)
The input 2D tensor of predicted scores
target : torch.LongTensor, shape=(n_samples,)
The ground truth labels, 0 <= target < n_classes.
k : int or None
number of largest elements to partial-sort over. For optimal
performance, should be slightly bigger than the expected number of
nonzeros in the solution. If the solution is more than k-sparse,
this function is recursively called with a 2*k schedule.
If `None`, full sorting is performed from the beginning.
Returns
-------
losses, torch.Tensor, shape=(n_samples,)
The loss incurred at each sample. | ludwig/utils/entmax/losses.py | entmax15_loss | dantreiman/ludwig | 7,739 | python | def entmax15_loss(X, target, k=None):
'1.5-entmax loss: sparse alternative to cross-entropy\n\n Computed using a partial sorting strategy.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n k : int or None\n number of largest elements to partial-sort over. For optimal\n performance, should be slightly bigger than the expected number of\n nonzeros in the solution. If the solution is more than k-sparse,\n this function is recursively called with a 2*k schedule.\n If `None`, full sorting is performed from the beginning.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return Entmax15LossFunction.apply(X, target, k) | def entmax15_loss(X, target, k=None):
'1.5-entmax loss: sparse alternative to cross-entropy\n\n Computed using a partial sorting strategy.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n k : int or None\n number of largest elements to partial-sort over. For optimal\n performance, should be slightly bigger than the expected number of\n nonzeros in the solution. If the solution is more than k-sparse,\n this function is recursively called with a 2*k schedule.\n If `None`, full sorting is performed from the beginning.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return Entmax15LossFunction.apply(X, target, k)<|docstring|>1.5-entmax loss: sparse alternative to cross-entropy
Computed using a partial sorting strategy.
Parameters
----------
X : torch.Tensor, shape=(n_samples, n_classes)
The input 2D tensor of predicted scores
target : torch.LongTensor, shape=(n_samples,)
The ground truth labels, 0 <= target < n_classes.
k : int or None
number of largest elements to partial-sort over. For optimal
performance, should be slightly bigger than the expected number of
nonzeros in the solution. If the solution is more than k-sparse,
this function is recursively called with a 2*k schedule.
If `None`, full sorting is performed from the beginning.
Returns
-------
losses, torch.Tensor, shape=(n_samples,)
The loss incurred at each sample.<|endoftext|> |
73a0f8f32caebe2b3c29b77e0a5398c83770ff446309ac2b562822d59906e0b5 | def entmax_bisect_loss(X, target, alpha=1.5, n_iter=50):
'alpha-entmax loss: sparse alternative to cross-entropy.\n\n Computed using bisection, supporting arbitrary alpha > 1.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n alpha : float or torch.Tensor\n Tensor of alpha parameters (> 1) to use for each row of X. If scalar\n or python float, the same value is used for all rows. A value of\n alpha=2 corresponds to sparsemax, and alpha=1 would in theory recover\n softmax. For numeric reasons, this algorithm does not work with `alpha=1`:\n if you want softmax, we recommend `torch.nn.softmax`\n\n n_iter : int\n Number of bisection iterations. For float32, 24 iterations should\n suffice for machine precision.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return EntmaxBisectLossFunction.apply(X, target, alpha, n_iter) | alpha-entmax loss: sparse alternative to cross-entropy.
Computed using bisection, supporting arbitrary alpha > 1.
Parameters
----------
X : torch.Tensor, shape=(n_samples, n_classes)
The input 2D tensor of predicted scores
target : torch.LongTensor, shape=(n_samples,)
The ground truth labels, 0 <= target < n_classes.
alpha : float or torch.Tensor
Tensor of alpha parameters (> 1) to use for each row of X. If scalar
or python float, the same value is used for all rows. A value of
alpha=2 corresponds to sparsemax, and alpha=1 would in theory recover
softmax. For numeric reasons, this algorithm does not work with `alpha=1`:
if you want softmax, we recommend `torch.nn.softmax`
n_iter : int
Number of bisection iterations. For float32, 24 iterations should
suffice for machine precision.
Returns
-------
losses, torch.Tensor, shape=(n_samples,)
The loss incurred at each sample. | ludwig/utils/entmax/losses.py | entmax_bisect_loss | dantreiman/ludwig | 7,739 | python | def entmax_bisect_loss(X, target, alpha=1.5, n_iter=50):
'alpha-entmax loss: sparse alternative to cross-entropy.\n\n Computed using bisection, supporting arbitrary alpha > 1.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n alpha : float or torch.Tensor\n Tensor of alpha parameters (> 1) to use for each row of X. If scalar\n or python float, the same value is used for all rows. A value of\n alpha=2 corresponds to sparsemax, and alpha=1 would in theory recover\n softmax. For numeric reasons, this algorithm does not work with `alpha=1`:\n if you want softmax, we recommend `torch.nn.softmax`\n\n n_iter : int\n Number of bisection iterations. For float32, 24 iterations should\n suffice for machine precision.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return EntmaxBisectLossFunction.apply(X, target, alpha, n_iter) | def entmax_bisect_loss(X, target, alpha=1.5, n_iter=50):
'alpha-entmax loss: sparse alternative to cross-entropy.\n\n Computed using bisection, supporting arbitrary alpha > 1.\n\n Parameters\n ----------\n X : torch.Tensor, shape=(n_samples, n_classes)\n The input 2D tensor of predicted scores\n\n target : torch.LongTensor, shape=(n_samples,)\n The ground truth labels, 0 <= target < n_classes.\n\n alpha : float or torch.Tensor\n Tensor of alpha parameters (> 1) to use for each row of X. If scalar\n or python float, the same value is used for all rows. A value of\n alpha=2 corresponds to sparsemax, and alpha=1 would in theory recover\n softmax. For numeric reasons, this algorithm does not work with `alpha=1`:\n if you want softmax, we recommend `torch.nn.softmax`\n\n n_iter : int\n Number of bisection iterations. For float32, 24 iterations should\n suffice for machine precision.\n\n Returns\n -------\n losses, torch.Tensor, shape=(n_samples,)\n The loss incurred at each sample.\n '
return EntmaxBisectLossFunction.apply(X, target, alpha, n_iter)<|docstring|>alpha-entmax loss: sparse alternative to cross-entropy.
Computed using bisection, supporting arbitrary alpha > 1.
Parameters
----------
X : torch.Tensor, shape=(n_samples, n_classes)
The input 2D tensor of predicted scores
target : torch.LongTensor, shape=(n_samples,)
The ground truth labels, 0 <= target < n_classes.
alpha : float or torch.Tensor
Tensor of alpha parameters (> 1) to use for each row of X. If scalar
or python float, the same value is used for all rows. A value of
alpha=2 corresponds to sparsemax, and alpha=1 would in theory recover
softmax. For numeric reasons, this algorithm does not work with `alpha=1`:
if you want softmax, we recommend `torch.nn.softmax`
n_iter : int
Number of bisection iterations. For float32, 24 iterations should
suffice for machine precision.
Returns
-------
losses, torch.Tensor, shape=(n_samples,)
The loss incurred at each sample.<|endoftext|> |
b5edc0d7484a99a8043c001581d43db0adb57c08a47e562124c81e2a9700cc5a | @classmethod
def forward(cls, ctx, X, target, alpha, proj_args):
'X (FloatTensor): n x num_classes target (LongTensor): n, the indices of the target classes.'
assert (X.shape[0] == target.shape[0])
p_star = cls.project(X, alpha, **proj_args)
loss = cls.omega(p_star, alpha)
p_star.scatter_add_(1, target.unsqueeze(1), torch.full_like(p_star, (- 1)))
loss += torch.einsum('ij,ij->i', p_star, X)
ctx.save_for_backward(p_star)
return loss | X (FloatTensor): n x num_classes target (LongTensor): n, the indices of the target classes. | ludwig/utils/entmax/losses.py | forward | dantreiman/ludwig | 7,739 | python | @classmethod
def forward(cls, ctx, X, target, alpha, proj_args):
assert (X.shape[0] == target.shape[0])
p_star = cls.project(X, alpha, **proj_args)
loss = cls.omega(p_star, alpha)
p_star.scatter_add_(1, target.unsqueeze(1), torch.full_like(p_star, (- 1)))
loss += torch.einsum('ij,ij->i', p_star, X)
ctx.save_for_backward(p_star)
return loss | @classmethod
def forward(cls, ctx, X, target, alpha, proj_args):
assert (X.shape[0] == target.shape[0])
p_star = cls.project(X, alpha, **proj_args)
loss = cls.omega(p_star, alpha)
p_star.scatter_add_(1, target.unsqueeze(1), torch.full_like(p_star, (- 1)))
loss += torch.einsum('ij,ij->i', p_star, X)
ctx.save_for_backward(p_star)
return loss<|docstring|>X (FloatTensor): n x num_classes target (LongTensor): n, the indices of the target classes.<|endoftext|> |
c99a300b7a0140e53adf688fe27b382e41e6eeeb1e1f351b137453854688ffad | def data_filename(modname, filename):
'Given the module name, and filename, finds the path to the file from the python sys.modules\n dictionary. Used to access the header dictionary file'
import os, sys
filename = os.path.join(os.path.dirname(sys.modules[modname].__file__), filename)
return filename | Given the module name, and filename, finds the path to the file from the python sys.modules
dictionary. Used to access the header dictionary file | src/dwell/rad/__init__.py | data_filename | eelsirhc/pydwell | 0 | python | def data_filename(modname, filename):
'Given the module name, and filename, finds the path to the file from the python sys.modules\n dictionary. Used to access the header dictionary file'
import os, sys
filename = os.path.join(os.path.dirname(sys.modules[modname].__file__), filename)
return filename | def data_filename(modname, filename):
'Given the module name, and filename, finds the path to the file from the python sys.modules\n dictionary. Used to access the header dictionary file'
import os, sys
filename = os.path.join(os.path.dirname(sys.modules[modname].__file__), filename)
return filename<|docstring|>Given the module name, and filename, finds the path to the file from the python sys.modules
dictionary. Used to access the header dictionary file<|endoftext|> |
261cd7fe86a73aa905244e2c2b6b8d4b79f46f63fed739eaa3e70af0b0044cbe | def has_install(self, pkg_name, pkg_version=None, binary=False, with_dep=False):
"\n pkg_info = self.get_metadata(pkg_name=pkg_name, pkg_version=pkg_version)\n return pkg_info and 'scripts' in pkg_info and any(s in pkg_info['scripts'] for s in self._INSTALL_SCRIPTS)\n "
return True | pkg_info = self.get_metadata(pkg_name=pkg_name, pkg_version=pkg_version)
return pkg_info and 'scripts' in pkg_info and any(s in pkg_info['scripts'] for s in self._INSTALL_SCRIPTS) | src/pm_proxy/npmjs.py | has_install | Yanivmd/maloss | 1 | python | def has_install(self, pkg_name, pkg_version=None, binary=False, with_dep=False):
"\n pkg_info = self.get_metadata(pkg_name=pkg_name, pkg_version=pkg_version)\n return pkg_info and 'scripts' in pkg_info and any(s in pkg_info['scripts'] for s in self._INSTALL_SCRIPTS)\n "
return True | def has_install(self, pkg_name, pkg_version=None, binary=False, with_dep=False):
"\n pkg_info = self.get_metadata(pkg_name=pkg_name, pkg_version=pkg_version)\n return pkg_info and 'scripts' in pkg_info and any(s in pkg_info['scripts'] for s in self._INSTALL_SCRIPTS)\n "
return True<|docstring|>pkg_info = self.get_metadata(pkg_name=pkg_name, pkg_version=pkg_version)
return pkg_info and 'scripts' in pkg_info and any(s in pkg_info['scripts'] for s in self._INSTALL_SCRIPTS)<|endoftext|> |
c1731b2f44aa2498e07b9d8dd9893f2f9c1bb3d305c1f724c0a905d9ea02ddbb | def filesys_decode(path):
'\n Ensure that the given path is decoded,\n NONE when no expected encoding works\n '
if isinstance(path, six.text_type):
return path
fs_enc = (sys.getfilesystemencoding() or 'utf-8')
candidates = (fs_enc, 'utf-8')
for enc in candidates:
try:
return path.decode(enc)
except UnicodeDecodeError:
continue | Ensure that the given path is decoded,
NONE when no expected encoding works | venv/Lib/site-packages/setuptools/unicode_utils.py | filesys_decode | suraj038/TCS_Hospital_Management_System_Case_Studies | 38,667 | python | def filesys_decode(path):
'\n Ensure that the given path is decoded,\n NONE when no expected encoding works\n '
if isinstance(path, six.text_type):
return path
fs_enc = (sys.getfilesystemencoding() or 'utf-8')
candidates = (fs_enc, 'utf-8')
for enc in candidates:
try:
return path.decode(enc)
except UnicodeDecodeError:
continue | def filesys_decode(path):
'\n Ensure that the given path is decoded,\n NONE when no expected encoding works\n '
if isinstance(path, six.text_type):
return path
fs_enc = (sys.getfilesystemencoding() or 'utf-8')
candidates = (fs_enc, 'utf-8')
for enc in candidates:
try:
return path.decode(enc)
except UnicodeDecodeError:
continue<|docstring|>Ensure that the given path is decoded,
NONE when no expected encoding works<|endoftext|> |
5019ca3065935e09a314fdc4ae8ecea5a26f6a7272d44b3a0f477bf4251a0d90 | def try_encode(string, enc):
'turn unicode encoding into a functional routine'
try:
return string.encode(enc)
except UnicodeEncodeError:
return None | turn unicode encoding into a functional routine | venv/Lib/site-packages/setuptools/unicode_utils.py | try_encode | suraj038/TCS_Hospital_Management_System_Case_Studies | 38,667 | python | def try_encode(string, enc):
try:
return string.encode(enc)
except UnicodeEncodeError:
return None | def try_encode(string, enc):
try:
return string.encode(enc)
except UnicodeEncodeError:
return None<|docstring|>turn unicode encoding into a functional routine<|endoftext|> |
9c5e482f90ca15a5af947c3e07a96bb55d24cc664c1f8dc205b6e39c21b39b0d | def sum_earnings(finance_data):
'Validate and process input.'
try:
required_nums = (finance_data.count(',') + 1)
history = list(map(int, finance_data.split(',')))
if (len(history) != required_nums):
raise ValueError
balance = 0
for i in history:
balance += i
if (balance < 0):
balance = 0
return balance
except ValueError:
return 0 | Validate and process input. | python/beginner/sum-earnings_Kushagra-0801.py | sum_earnings | fredbaa/hacktoberithms | 16 | python | def sum_earnings(finance_data):
try:
required_nums = (finance_data.count(',') + 1)
history = list(map(int, finance_data.split(',')))
if (len(history) != required_nums):
raise ValueError
balance = 0
for i in history:
balance += i
if (balance < 0):
balance = 0
return balance
except ValueError:
return 0 | def sum_earnings(finance_data):
try:
required_nums = (finance_data.count(',') + 1)
history = list(map(int, finance_data.split(',')))
if (len(history) != required_nums):
raise ValueError
balance = 0
for i in history:
balance += i
if (balance < 0):
balance = 0
return balance
except ValueError:
return 0<|docstring|>Validate and process input.<|endoftext|> |
06919361e952e6e104d4da031f5922cae267f92cf20e9be41f2bee10ad303317 | def DepotToolsPylint(input_api, output_api):
'Gather all the pylint logic into one place to make it self-contained.'
files_to_check = ['^[^/]*\\.py$', '^testing_support/[^/]*\\.py$', '^tests/[^/]*\\.py$', '^recipe_modules/.*\\.py$']
files_to_skip = list(input_api.DEFAULT_FILES_TO_SKIP)
if os.path.exists('.gitignore'):
with open('.gitignore') as fh:
lines = [l.strip() for l in fh.readlines()]
files_to_skip.extend([fnmatch.translate(l) for l in lines if (l and (not l.startswith('#')))])
if os.path.exists('.git/info/exclude'):
with open('.git/info/exclude') as fh:
lines = [l.strip() for l in fh.readlines()]
files_to_skip.extend([fnmatch.translate(l) for l in lines if (l and (not l.startswith('#')))])
disabled_warnings = ['R0401', 'W0613']
return input_api.canned_checks.GetPylint(input_api, output_api, files_to_check=files_to_check, files_to_skip=files_to_skip, disabled_warnings=disabled_warnings) | Gather all the pylint logic into one place to make it self-contained. | third_party/depot_tools/PRESUBMIT.py | DepotToolsPylint | gengleilei/wee8 | 3 | python | def DepotToolsPylint(input_api, output_api):
files_to_check = ['^[^/]*\\.py$', '^testing_support/[^/]*\\.py$', '^tests/[^/]*\\.py$', '^recipe_modules/.*\\.py$']
files_to_skip = list(input_api.DEFAULT_FILES_TO_SKIP)
if os.path.exists('.gitignore'):
with open('.gitignore') as fh:
lines = [l.strip() for l in fh.readlines()]
files_to_skip.extend([fnmatch.translate(l) for l in lines if (l and (not l.startswith('#')))])
if os.path.exists('.git/info/exclude'):
with open('.git/info/exclude') as fh:
lines = [l.strip() for l in fh.readlines()]
files_to_skip.extend([fnmatch.translate(l) for l in lines if (l and (not l.startswith('#')))])
disabled_warnings = ['R0401', 'W0613']
return input_api.canned_checks.GetPylint(input_api, output_api, files_to_check=files_to_check, files_to_skip=files_to_skip, disabled_warnings=disabled_warnings) | def DepotToolsPylint(input_api, output_api):
files_to_check = ['^[^/]*\\.py$', '^testing_support/[^/]*\\.py$', '^tests/[^/]*\\.py$', '^recipe_modules/.*\\.py$']
files_to_skip = list(input_api.DEFAULT_FILES_TO_SKIP)
if os.path.exists('.gitignore'):
with open('.gitignore') as fh:
lines = [l.strip() for l in fh.readlines()]
files_to_skip.extend([fnmatch.translate(l) for l in lines if (l and (not l.startswith('#')))])
if os.path.exists('.git/info/exclude'):
with open('.git/info/exclude') as fh:
lines = [l.strip() for l in fh.readlines()]
files_to_skip.extend([fnmatch.translate(l) for l in lines if (l and (not l.startswith('#')))])
disabled_warnings = ['R0401', 'W0613']
return input_api.canned_checks.GetPylint(input_api, output_api, files_to_check=files_to_check, files_to_skip=files_to_skip, disabled_warnings=disabled_warnings)<|docstring|>Gather all the pylint logic into one place to make it self-contained.<|endoftext|> |
a9c22c891302665c5da5b63bf4a614cb68480eb8bda1cd112ca912a87dd0ec2b | def find_demand_list_match_str(title):
'模糊查询需求列表\n\n GET /api/project/demand/<str:title>\n '
return jsonify({'msg': 'ok', 'data': list(demand.find_demand_list_match_str(title))}) | 模糊查询需求列表
GET /api/project/demand/<str:title> | api/controller/demand.py | find_demand_list_match_str | preservance717/pms | 27 | python | def find_demand_list_match_str(title):
'模糊查询需求列表\n\n GET /api/project/demand/<str:title>\n '
return jsonify({'msg': 'ok', 'data': list(demand.find_demand_list_match_str(title))}) | def find_demand_list_match_str(title):
'模糊查询需求列表\n\n GET /api/project/demand/<str:title>\n '
return jsonify({'msg': 'ok', 'data': list(demand.find_demand_list_match_str(title))})<|docstring|>模糊查询需求列表
GET /api/project/demand/<str:title><|endoftext|> |
ee7d6d6da22905db2837159ef0308aad77c08d3df044a0f0ae3dcab1c3e8526a | @fresh_jwt_required
def demand_search():
'模糊查询项目需求\n GET /api/demand?title=aaa&projectId=1\n '
return {'data': list(Demand.find().where((Demand.projectId == request.args.get('projectId')), (Demand.title % (('%' + request.args.get('title')) + '%'))))} | 模糊查询项目需求
GET /api/demand?title=aaa&projectId=1 | api/controller/demand.py | demand_search | preservance717/pms | 27 | python | @fresh_jwt_required
def demand_search():
'模糊查询项目需求\n GET /api/demand?title=aaa&projectId=1\n '
return {'data': list(Demand.find().where((Demand.projectId == request.args.get('projectId')), (Demand.title % (('%' + request.args.get('title')) + '%'))))} | @fresh_jwt_required
def demand_search():
'模糊查询项目需求\n GET /api/demand?title=aaa&projectId=1\n '
return {'data': list(Demand.find().where((Demand.projectId == request.args.get('projectId')), (Demand.title % (('%' + request.args.get('title')) + '%'))))}<|docstring|>模糊查询项目需求
GET /api/demand?title=aaa&projectId=1<|endoftext|> |
a99d530586e0e82823edfe03c659ef4ca772b36bbb9ecd407ff4f9f06f23193a | @fresh_jwt_required
def demand_add():
'添加需求\n\n POST /api/project/demand\n '
if (not request.is_json):
return (jsonify({'msg': 'Missing JSON in request'}), 400)
schema = DemandSchema()
(data, errors) = schema.load(request.json)
if errors:
return (jsonify({'msg': errors}), 400)
try:
data = demand.create_demand(request.json)
if (data[1] == False):
return (jsonify({'msg': '需求名称重复'}), 200)
elif (data[1] == True):
return (jsonify({'msg': 'ok', 'data': model_to_dict(data[0])}), 201)
except PermissionDenied:
return jsonify({'msg': 'PermissionDenied'}) | 添加需求
POST /api/project/demand | api/controller/demand.py | demand_add | preservance717/pms | 27 | python | @fresh_jwt_required
def demand_add():
'添加需求\n\n POST /api/project/demand\n '
if (not request.is_json):
return (jsonify({'msg': 'Missing JSON in request'}), 400)
schema = DemandSchema()
(data, errors) = schema.load(request.json)
if errors:
return (jsonify({'msg': errors}), 400)
try:
data = demand.create_demand(request.json)
if (data[1] == False):
return (jsonify({'msg': '需求名称重复'}), 200)
elif (data[1] == True):
return (jsonify({'msg': 'ok', 'data': model_to_dict(data[0])}), 201)
except PermissionDenied:
return jsonify({'msg': 'PermissionDenied'}) | @fresh_jwt_required
def demand_add():
'添加需求\n\n POST /api/project/demand\n '
if (not request.is_json):
return (jsonify({'msg': 'Missing JSON in request'}), 400)
schema = DemandSchema()
(data, errors) = schema.load(request.json)
if errors:
return (jsonify({'msg': errors}), 400)
try:
data = demand.create_demand(request.json)
if (data[1] == False):
return (jsonify({'msg': '需求名称重复'}), 200)
elif (data[1] == True):
return (jsonify({'msg': 'ok', 'data': model_to_dict(data[0])}), 201)
except PermissionDenied:
return jsonify({'msg': 'PermissionDenied'})<|docstring|>添加需求
POST /api/project/demand<|endoftext|> |
ad943b7076bb39476fbc7857821573bcf2ca38d796c9c68785c2384935d8ce41 | @fresh_jwt_required
def demand_info(demand_id):
'获取需求详情\n\n GET /api/project/demand/<int:demand_id>\n '
return jsonify({'msg': 'ok', 'data': demand.demand_detail(demand_id)}) | 获取需求详情
GET /api/project/demand/<int:demand_id> | api/controller/demand.py | demand_info | preservance717/pms | 27 | python | @fresh_jwt_required
def demand_info(demand_id):
'获取需求详情\n\n GET /api/project/demand/<int:demand_id>\n '
return jsonify({'msg': 'ok', 'data': demand.demand_detail(demand_id)}) | @fresh_jwt_required
def demand_info(demand_id):
'获取需求详情\n\n GET /api/project/demand/<int:demand_id>\n '
return jsonify({'msg': 'ok', 'data': demand.demand_detail(demand_id)})<|docstring|>获取需求详情
GET /api/project/demand/<int:demand_id><|endoftext|> |
82721066fecb7a1121b93ab010ed9fb5a7c2b88efea752dbfc51715389916c94 | def demand_update():
'更新需求信息\n\n PUT /api/project/demand\n '
if (not request.json):
abort(400)
if (demand.find_demand_title_by_id(request.json['id']) == request.json['title']):
pass
elif demand.find_one_demand_by_title(request.json['title']):
return jsonify({'msg': '该需求已存在'})
try:
data = demand.update_demands(request.json)
return jsonify({'msg': 'ok', 'data': model_to_dict(data)})
except PermissionDenied:
return jsonify({'msg': 'PermissionDenied'}) | 更新需求信息
PUT /api/project/demand | api/controller/demand.py | demand_update | preservance717/pms | 27 | python | def demand_update():
'更新需求信息\n\n PUT /api/project/demand\n '
if (not request.json):
abort(400)
if (demand.find_demand_title_by_id(request.json['id']) == request.json['title']):
pass
elif demand.find_one_demand_by_title(request.json['title']):
return jsonify({'msg': '该需求已存在'})
try:
data = demand.update_demands(request.json)
return jsonify({'msg': 'ok', 'data': model_to_dict(data)})
except PermissionDenied:
return jsonify({'msg': 'PermissionDenied'}) | def demand_update():
'更新需求信息\n\n PUT /api/project/demand\n '
if (not request.json):
abort(400)
if (demand.find_demand_title_by_id(request.json['id']) == request.json['title']):
pass
elif demand.find_one_demand_by_title(request.json['title']):
return jsonify({'msg': '该需求已存在'})
try:
data = demand.update_demands(request.json)
return jsonify({'msg': 'ok', 'data': model_to_dict(data)})
except PermissionDenied:
return jsonify({'msg': 'PermissionDenied'})<|docstring|>更新需求信息
PUT /api/project/demand<|endoftext|> |
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