body_hash
stringlengths 64
64
| body
stringlengths 23
109k
| docstring
stringlengths 1
57k
| path
stringlengths 4
198
| name
stringlengths 1
115
| repository_name
stringlengths 7
111
| repository_stars
float64 0
191k
| lang
stringclasses 1
value | body_without_docstring
stringlengths 14
108k
| unified
stringlengths 45
133k
|
|---|---|---|---|---|---|---|---|---|---|
14daa325ca4d15df21524a64fff5f095a48b1a11696e8b986ff787c6a4dfad88 | @fresh_jwt_required
def demand_lists():
'获取需求列表\n\n GET /api/demand/list\n '
demand_list = Demand.select().where((Demand.status == 0)).dicts().get()
return list(demand_list) | 获取需求列表
GET /api/demand/list | api/controller/demand.py | demand_lists | preservance717/pms | 27 | python | @fresh_jwt_required
def demand_lists():
'获取需求列表\n\n GET /api/demand/list\n '
demand_list = Demand.select().where((Demand.status == 0)).dicts().get()
return list(demand_list) | @fresh_jwt_required
def demand_lists():
'获取需求列表\n\n GET /api/demand/list\n '
demand_list = Demand.select().where((Demand.status == 0)).dicts().get()
return list(demand_list)<|docstring|>获取需求列表
GET /api/demand/list<|endoftext|> |
10fc10767a0d067800853cd0b943fb158cf68597ce656d5f49fe0f5ea1090b99 | def __init__(self):
'\n Initialize your data structure here.\n '
self.nums = []
self.is_sorted = False | Initialize your data structure here. | Arrays/170. Two Sum III - Data structure design.py | __init__ | thewires2/Leetcode | 1 | python | def __init__(self):
'\n \n '
self.nums = []
self.is_sorted = False | def __init__(self):
'\n \n '
self.nums = []
self.is_sorted = False<|docstring|>Initialize your data structure here.<|endoftext|> |
2931a619bc86badc0c3de693ec58b7695b80a77408971fd2e4f445b60fd24dd1 | def add(self, number):
'\n Add the number to an internal data structure..\n :type number: int\n :rtype: None\n '
self.nums.append(number)
self.is_sorted = False | Add the number to an internal data structure..
:type number: int
:rtype: None | Arrays/170. Two Sum III - Data structure design.py | add | thewires2/Leetcode | 1 | python | def add(self, number):
'\n Add the number to an internal data structure..\n :type number: int\n :rtype: None\n '
self.nums.append(number)
self.is_sorted = False | def add(self, number):
'\n Add the number to an internal data structure..\n :type number: int\n :rtype: None\n '
self.nums.append(number)
self.is_sorted = False<|docstring|>Add the number to an internal data structure..
:type number: int
:rtype: None<|endoftext|> |
46c5c2cae08d4141aad35d57078c7c09e73eb305039ba85f0b9c0d516c3910b2 | def find(self, value):
'\n Find if there exists any pair of numbers which sum is equal to the value.\n :type value: int\n :rtype: bool\n '
if (not self.is_sorted):
self.nums.sort()
self.is_sorted = True
(low, high) = (0, (len(self.nums) - 1))
while (low < high):
currSum = (self.nums[low] + self.nums[high])
if (currSum < value):
low += 1
elif (currSum > value):
high -= 1
else:
return True
return False | Find if there exists any pair of numbers which sum is equal to the value.
:type value: int
:rtype: bool | Arrays/170. Two Sum III - Data structure design.py | find | thewires2/Leetcode | 1 | python | def find(self, value):
'\n Find if there exists any pair of numbers which sum is equal to the value.\n :type value: int\n :rtype: bool\n '
if (not self.is_sorted):
self.nums.sort()
self.is_sorted = True
(low, high) = (0, (len(self.nums) - 1))
while (low < high):
currSum = (self.nums[low] + self.nums[high])
if (currSum < value):
low += 1
elif (currSum > value):
high -= 1
else:
return True
return False | def find(self, value):
'\n Find if there exists any pair of numbers which sum is equal to the value.\n :type value: int\n :rtype: bool\n '
if (not self.is_sorted):
self.nums.sort()
self.is_sorted = True
(low, high) = (0, (len(self.nums) - 1))
while (low < high):
currSum = (self.nums[low] + self.nums[high])
if (currSum < value):
low += 1
elif (currSum > value):
high -= 1
else:
return True
return False<|docstring|>Find if there exists any pair of numbers which sum is equal to the value.
:type value: int
:rtype: bool<|endoftext|> |
d4d3945bc26ec1b76e8991931b20de6551cf06fbef3061488d4f0de42d15a8c4 | def test_basic_init(self):
'TODO.'
task = Task('Runnable', module=__name__)
result = 'result'
for task_result in (TaskResult(task, result, True, None, None), TaskResult(task, result, True, 'msg', None), TaskResult(task, result, True, None, [Task('Runnable', module=__name__)])):
serialized = task_result.dumps()
new_task_result = TaskResult().loads(serialized)
for attr in ('_result', 'status', '_reason', '_uid'):
task_result_attr = getattr(task_result, attr)
new_task_result_attr = getattr(new_task_result, attr)
assert (task_result_attr == new_task_result_attr)
assert (task_result._task._uid == new_task_result._task._uid)
if (task_result.follow is None):
assert (task_result.follow == new_task_result.follow)
else:
assert (len(task_result.follow) == len(new_task_result.follow))
for (idx, task) in enumerate(task_result.follow):
assert (task._uid == new_task_result.follow[idx]._uid) | TODO. | tests/unit/testplan/runners/pools/tasks/test_task_results.py | test_basic_init | Pyifan/testplan | 96 | python | def test_basic_init(self):
task = Task('Runnable', module=__name__)
result = 'result'
for task_result in (TaskResult(task, result, True, None, None), TaskResult(task, result, True, 'msg', None), TaskResult(task, result, True, None, [Task('Runnable', module=__name__)])):
serialized = task_result.dumps()
new_task_result = TaskResult().loads(serialized)
for attr in ('_result', 'status', '_reason', '_uid'):
task_result_attr = getattr(task_result, attr)
new_task_result_attr = getattr(new_task_result, attr)
assert (task_result_attr == new_task_result_attr)
assert (task_result._task._uid == new_task_result._task._uid)
if (task_result.follow is None):
assert (task_result.follow == new_task_result.follow)
else:
assert (len(task_result.follow) == len(new_task_result.follow))
for (idx, task) in enumerate(task_result.follow):
assert (task._uid == new_task_result.follow[idx]._uid) | def test_basic_init(self):
task = Task('Runnable', module=__name__)
result = 'result'
for task_result in (TaskResult(task, result, True, None, None), TaskResult(task, result, True, 'msg', None), TaskResult(task, result, True, None, [Task('Runnable', module=__name__)])):
serialized = task_result.dumps()
new_task_result = TaskResult().loads(serialized)
for attr in ('_result', 'status', '_reason', '_uid'):
task_result_attr = getattr(task_result, attr)
new_task_result_attr = getattr(new_task_result, attr)
assert (task_result_attr == new_task_result_attr)
assert (task_result._task._uid == new_task_result._task._uid)
if (task_result.follow is None):
assert (task_result.follow == new_task_result.follow)
else:
assert (len(task_result.follow) == len(new_task_result.follow))
for (idx, task) in enumerate(task_result.follow):
assert (task._uid == new_task_result.follow[idx]._uid)<|docstring|>TODO.<|endoftext|> |
694528fce879a5881ea58556151d7fb796a70b747d9cb4c17bf2e5944f95a881 | def query_etym_online(query: str, verbose: bool=False) -> str:
'Perform lookup on etymonline.com.'
r = requests.get('https://www.etymonline.com/index.php?search={}'.format(query))
soup = BeautifulSoup(r.content, features='html.parser')
try:
hit = soup.find_all('p')[0].contents
except IndexError:
raise NoResultsFound(query)
s = beautify(hit)
if (s == 'links'):
raise NoResultsFound(query)
return s | Perform lookup on etymonline.com. | etym/utils.py | query_etym_online | conorsch/etym | 13 | python | def query_etym_online(query: str, verbose: bool=False) -> str:
r = requests.get('https://www.etymonline.com/index.php?search={}'.format(query))
soup = BeautifulSoup(r.content, features='html.parser')
try:
hit = soup.find_all('p')[0].contents
except IndexError:
raise NoResultsFound(query)
s = beautify(hit)
if (s == 'links'):
raise NoResultsFound(query)
return s | def query_etym_online(query: str, verbose: bool=False) -> str:
r = requests.get('https://www.etymonline.com/index.php?search={}'.format(query))
soup = BeautifulSoup(r.content, features='html.parser')
try:
hit = soup.find_all('p')[0].contents
except IndexError:
raise NoResultsFound(query)
s = beautify(hit)
if (s == 'links'):
raise NoResultsFound(query)
return s<|docstring|>Perform lookup on etymonline.com.<|endoftext|> |
8610596937d39b56b697d6a1998f06944770174acd05321e81b24dc7f8a96eac | def beautify(soup: BeautifulSoup, rich_terminal: bool=True) -> str:
"\n Cleans up the raw HTML so it's more presentable.\n Parse BeautifulSoup HTML and return prettified string\n "
beautifiedText = str()
for i in soup:
if rich_terminal:
term = Terminal()
span_sub = '{t.italic}\\1{t.normal}'.format(t=term)
strong_sub = '{t.bold}\\1{t.normal}'.format(t=term)
else:
span_sub = '\\1'
strong_sub = '\\1'
i = re.sub('<span class="\\w+">(.+)</span>', span_sub, str(i))
i = re.sub('<strong>(.+)</strong>', strong_sub, str(i))
beautifiedText += (' ' + i)
beautifiedText = re.sub('^\\s+', '', beautifiedText)
beautifiedText = re.sub('\\s{2,}', ' ', beautifiedText)
beautifiedText = re.sub('\\s+([,\\)\\].;:])', '\\g<1>', beautifiedText)
beautifiedText = re.sub('([\\(])\\s+', '\\g<1>', beautifiedText)
return beautifiedText | Cleans up the raw HTML so it's more presentable.
Parse BeautifulSoup HTML and return prettified string | etym/utils.py | beautify | conorsch/etym | 13 | python | def beautify(soup: BeautifulSoup, rich_terminal: bool=True) -> str:
"\n Cleans up the raw HTML so it's more presentable.\n Parse BeautifulSoup HTML and return prettified string\n "
beautifiedText = str()
for i in soup:
if rich_terminal:
term = Terminal()
span_sub = '{t.italic}\\1{t.normal}'.format(t=term)
strong_sub = '{t.bold}\\1{t.normal}'.format(t=term)
else:
span_sub = '\\1'
strong_sub = '\\1'
i = re.sub('<span class="\\w+">(.+)</span>', span_sub, str(i))
i = re.sub('<strong>(.+)</strong>', strong_sub, str(i))
beautifiedText += (' ' + i)
beautifiedText = re.sub('^\\s+', , beautifiedText)
beautifiedText = re.sub('\\s{2,}', ' ', beautifiedText)
beautifiedText = re.sub('\\s+([,\\)\\].;:])', '\\g<1>', beautifiedText)
beautifiedText = re.sub('([\\(])\\s+', '\\g<1>', beautifiedText)
return beautifiedText | def beautify(soup: BeautifulSoup, rich_terminal: bool=True) -> str:
"\n Cleans up the raw HTML so it's more presentable.\n Parse BeautifulSoup HTML and return prettified string\n "
beautifiedText = str()
for i in soup:
if rich_terminal:
term = Terminal()
span_sub = '{t.italic}\\1{t.normal}'.format(t=term)
strong_sub = '{t.bold}\\1{t.normal}'.format(t=term)
else:
span_sub = '\\1'
strong_sub = '\\1'
i = re.sub('<span class="\\w+">(.+)</span>', span_sub, str(i))
i = re.sub('<strong>(.+)</strong>', strong_sub, str(i))
beautifiedText += (' ' + i)
beautifiedText = re.sub('^\\s+', , beautifiedText)
beautifiedText = re.sub('\\s{2,}', ' ', beautifiedText)
beautifiedText = re.sub('\\s+([,\\)\\].;:])', '\\g<1>', beautifiedText)
beautifiedText = re.sub('([\\(])\\s+', '\\g<1>', beautifiedText)
return beautifiedText<|docstring|>Cleans up the raw HTML so it's more presentable.
Parse BeautifulSoup HTML and return prettified string<|endoftext|> |
d08da78a1e3ca50bb813b1bea144eb1a003ffe60f1f467f6d27fb94b67f023e7 | async def set_permissions(self, *, server_permissions: Optional[ServerPermissions]=None, channel_permissions: Optional[ChannelPermissions]=None) -> None:
'Sets the permissions for a role in a server.'
if ((not server_permissions) and (not channel_permissions)):
return
server_value = (server_permissions or self.server_permissions).value
channel_value = (channel_permissions or self.channel_permissions).value
(await self._cache.api.set_role_permissions(self.server.id, self.id, server_value, channel_value)) | Sets the permissions for a role in a server. | pyvolt/models/role.py | set_permissions | Gael-devv/Pyvolt | 0 | python | async def set_permissions(self, *, server_permissions: Optional[ServerPermissions]=None, channel_permissions: Optional[ChannelPermissions]=None) -> None:
if ((not server_permissions) and (not channel_permissions)):
return
server_value = (server_permissions or self.server_permissions).value
channel_value = (channel_permissions or self.channel_permissions).value
(await self._cache.api.set_role_permissions(self.server.id, self.id, server_value, channel_value)) | async def set_permissions(self, *, server_permissions: Optional[ServerPermissions]=None, channel_permissions: Optional[ChannelPermissions]=None) -> None:
if ((not server_permissions) and (not channel_permissions)):
return
server_value = (server_permissions or self.server_permissions).value
channel_value = (channel_permissions or self.channel_permissions).value
(await self._cache.api.set_role_permissions(self.server.id, self.id, server_value, channel_value))<|docstring|>Sets the permissions for a role in a server.<|endoftext|> |
248abaaca361f4ec9825c91fd0a3cd2dc8bbf07858a1e26c5d7fbe25df92ec2d | async def delete(self) -> None:
'Deletes the role'
(await self._cache.api.delete_role(self.server.id, self.id)) | Deletes the role | pyvolt/models/role.py | delete | Gael-devv/Pyvolt | 0 | python | async def delete(self) -> None:
(await self._cache.api.delete_role(self.server.id, self.id)) | async def delete(self) -> None:
(await self._cache.api.delete_role(self.server.id, self.id))<|docstring|>Deletes the role<|endoftext|> |
ba491cd7b38f035707b54dd565d547f70fc3419cf58b9b2f6af2b4e98a24b750 | async def edit(self, **kwargs) -> None:
'Edits the role'
if (kwargs.get('colour', MISSING) == None):
kwargs['remove_colour'] = True
(await self._cache.api.edit_role(self.server.id, self.id, **kwargs)) | Edits the role | pyvolt/models/role.py | edit | Gael-devv/Pyvolt | 0 | python | async def edit(self, **kwargs) -> None:
if (kwargs.get('colour', MISSING) == None):
kwargs['remove_colour'] = True
(await self._cache.api.edit_role(self.server.id, self.id, **kwargs)) | async def edit(self, **kwargs) -> None:
if (kwargs.get('colour', MISSING) == None):
kwargs['remove_colour'] = True
(await self._cache.api.edit_role(self.server.id, self.id, **kwargs))<|docstring|>Edits the role<|endoftext|> |
a21f0beed325b5cb44d2b3621f1d9e2d13aec42e599ef0fd02b5535d9b7b94f4 | def save_admin(session, c_user, fb_dtsg, thread_id, admin_id, add):
'\n Only for group thread.\n\n thread_id: `str`\n\n admin_id: `str` or `list`\n\n add: `bool`. make admin or remove\n '
referer = 'https://www.messenger.com/t/{}'.format(thread_id)
header = Header.create('origin', 'accept-language', 'user-agent', 'content-type', others={'accept-encoding': 'gzip, deflate', 'x-msgr-region': 'ATN', 'cache-control': 'no-cache', 'referer': referer})
payload = {'thread_fbid': thread_id, 'add': add, '__user': c_user, '__a': 1, '__dyn': Payload.DYN, '__req': '1h', '__be': (- 1), '__pc': 'PHASED:messengerdotcom_pkg', '__rev': 4056466, 'fb_dtsg': fb_dtsg, 'jazoest': Payload.JAZOEST}
pamars = {'dpr': 1.5}
if (type(admin_id) is str):
admin_id = [admin_id]
for (i, a_id) in enumerate(admin_id):
payload['admin_ids[{}]'.format(i)] = a_id
try:
r = session.post('https://www.messenger.com/messaging/save_admins/', headers=header, data=payload, params=pamars)
return utils.check_result(r.text, 'You might not be a admin.')
except exceptions.RequestException:
logging.warning('{} fail'.format(save_admin.__name__)) | Only for group thread.
thread_id: `str`
admin_id: `str` or `list`
add: `bool`. make admin or remove | messenger/setting/admin.py | save_admin | brian41005/Python-Messenger-Wrapper | 9 | python | def save_admin(session, c_user, fb_dtsg, thread_id, admin_id, add):
'\n Only for group thread.\n\n thread_id: `str`\n\n admin_id: `str` or `list`\n\n add: `bool`. make admin or remove\n '
referer = 'https://www.messenger.com/t/{}'.format(thread_id)
header = Header.create('origin', 'accept-language', 'user-agent', 'content-type', others={'accept-encoding': 'gzip, deflate', 'x-msgr-region': 'ATN', 'cache-control': 'no-cache', 'referer': referer})
payload = {'thread_fbid': thread_id, 'add': add, '__user': c_user, '__a': 1, '__dyn': Payload.DYN, '__req': '1h', '__be': (- 1), '__pc': 'PHASED:messengerdotcom_pkg', '__rev': 4056466, 'fb_dtsg': fb_dtsg, 'jazoest': Payload.JAZOEST}
pamars = {'dpr': 1.5}
if (type(admin_id) is str):
admin_id = [admin_id]
for (i, a_id) in enumerate(admin_id):
payload['admin_ids[{}]'.format(i)] = a_id
try:
r = session.post('https://www.messenger.com/messaging/save_admins/', headers=header, data=payload, params=pamars)
return utils.check_result(r.text, 'You might not be a admin.')
except exceptions.RequestException:
logging.warning('{} fail'.format(save_admin.__name__)) | def save_admin(session, c_user, fb_dtsg, thread_id, admin_id, add):
'\n Only for group thread.\n\n thread_id: `str`\n\n admin_id: `str` or `list`\n\n add: `bool`. make admin or remove\n '
referer = 'https://www.messenger.com/t/{}'.format(thread_id)
header = Header.create('origin', 'accept-language', 'user-agent', 'content-type', others={'accept-encoding': 'gzip, deflate', 'x-msgr-region': 'ATN', 'cache-control': 'no-cache', 'referer': referer})
payload = {'thread_fbid': thread_id, 'add': add, '__user': c_user, '__a': 1, '__dyn': Payload.DYN, '__req': '1h', '__be': (- 1), '__pc': 'PHASED:messengerdotcom_pkg', '__rev': 4056466, 'fb_dtsg': fb_dtsg, 'jazoest': Payload.JAZOEST}
pamars = {'dpr': 1.5}
if (type(admin_id) is str):
admin_id = [admin_id]
for (i, a_id) in enumerate(admin_id):
payload['admin_ids[{}]'.format(i)] = a_id
try:
r = session.post('https://www.messenger.com/messaging/save_admins/', headers=header, data=payload, params=pamars)
return utils.check_result(r.text, 'You might not be a admin.')
except exceptions.RequestException:
logging.warning('{} fail'.format(save_admin.__name__))<|docstring|>Only for group thread.
thread_id: `str`
admin_id: `str` or `list`
add: `bool`. make admin or remove<|endoftext|> |
54f30b80e263784324da052d4d56cb68a19145cc41f364ad0412dc5c5e56f88c | def make_token(payload, expires_in=600):
'Encode an arbitrary payload into a JWT token.'
secret = current_app.config.get('JWT_SECRET_KEY')
if (secret is None):
raise RuntimeError('application must have JWT_SECRET_KEY set')
return _jwt_token(payload, secret, expires_in=expires_in).decode('ascii') | Encode an arbitrary payload into a JWT token. | cubbie/auth.py | make_token | rjw57/cubbie | 0 | python | def make_token(payload, expires_in=600):
secret = current_app.config.get('JWT_SECRET_KEY')
if (secret is None):
raise RuntimeError('application must have JWT_SECRET_KEY set')
return _jwt_token(payload, secret, expires_in=expires_in).decode('ascii') | def make_token(payload, expires_in=600):
secret = current_app.config.get('JWT_SECRET_KEY')
if (secret is None):
raise RuntimeError('application must have JWT_SECRET_KEY set')
return _jwt_token(payload, secret, expires_in=expires_in).decode('ascii')<|docstring|>Encode an arbitrary payload into a JWT token.<|endoftext|> |
b68ebd38f42be0093d8589336d963811d2400e503cbdf08b833cadce22d0a52b | def make_user_token(user, expires_in=3600):
'Generate an authorization token for the given user. The user must be an\n instance of cubbie.model.User.\n\n '
return make_token(dict(user=user.id), expires_in=expires_in) | Generate an authorization token for the given user. The user must be an
instance of cubbie.model.User. | cubbie/auth.py | make_user_token | rjw57/cubbie | 0 | python | def make_user_token(user, expires_in=3600):
'Generate an authorization token for the given user. The user must be an\n instance of cubbie.model.User.\n\n '
return make_token(dict(user=user.id), expires_in=expires_in) | def make_user_token(user, expires_in=3600):
'Generate an authorization token for the given user. The user must be an\n instance of cubbie.model.User.\n\n '
return make_token(dict(user=user.id), expires_in=expires_in)<|docstring|>Generate an authorization token for the given user. The user must be an
instance of cubbie.model.User.<|endoftext|> |
802f683a07bb4b3d71894232d0611bc0c9208282a6c4803d0b357fa0d4039a9b | def _to_numeric(dt):
'Convert a datetime instance to a numeric date as per JWT spec.'
return int((dt - datetime.datetime.utcfromtimestamp(0)).total_seconds()) | Convert a datetime instance to a numeric date as per JWT spec. | cubbie/auth.py | _to_numeric | rjw57/cubbie | 0 | python | def _to_numeric(dt):
return int((dt - datetime.datetime.utcfromtimestamp(0)).total_seconds()) | def _to_numeric(dt):
return int((dt - datetime.datetime.utcfromtimestamp(0)).total_seconds())<|docstring|>Convert a datetime instance to a numeric date as per JWT spec.<|endoftext|> |
9d35901ab55143a52ba7fdc1ade50d0b692a7e85e5d7632bb5cb016bc758af7c | def _jwt_token(payload, secret, expires_in=30, with_times=True, **kwargs):
"Return a JWT with the given payload. Any remaining keyword args are\n passed to jwt.encode().\n\n If payload is None, an empty payload is used.\n\n If headers is None, no headers are added beyond 'exp' and 'nbf'.\n\n The standard 'exp' and 'nbf' claims are added. In addition, the 'exp' claim\n is added into the header. The 'exp' time is now plus the number of seconds\n specified as expires_in.\n\n Note that if the passed payload has 'exp' and/or 'nbf' claims, these are\n used in preference.\n\n "
if (payload is None):
payload = {}
headers = kwargs.get('headers', {})
if ('headers' in kwargs):
del kwargs['headers']
if (secret is None):
raise ValueError('Bad secret')
if with_times:
ext_payload = dict(exp=_to_numeric((datetime.datetime.utcnow() + datetime.timedelta(seconds=expires_in))), nbf=_to_numeric(datetime.datetime.utcnow()))
ext_payload.update(payload)
payload = ext_payload
if ('exp' not in headers):
headers['exp'] = ext_payload['exp']
return jwt.encode(payload, secret, headers=headers, **kwargs) | Return a JWT with the given payload. Any remaining keyword args are
passed to jwt.encode().
If payload is None, an empty payload is used.
If headers is None, no headers are added beyond 'exp' and 'nbf'.
The standard 'exp' and 'nbf' claims are added. In addition, the 'exp' claim
is added into the header. The 'exp' time is now plus the number of seconds
specified as expires_in.
Note that if the passed payload has 'exp' and/or 'nbf' claims, these are
used in preference. | cubbie/auth.py | _jwt_token | rjw57/cubbie | 0 | python | def _jwt_token(payload, secret, expires_in=30, with_times=True, **kwargs):
"Return a JWT with the given payload. Any remaining keyword args are\n passed to jwt.encode().\n\n If payload is None, an empty payload is used.\n\n If headers is None, no headers are added beyond 'exp' and 'nbf'.\n\n The standard 'exp' and 'nbf' claims are added. In addition, the 'exp' claim\n is added into the header. The 'exp' time is now plus the number of seconds\n specified as expires_in.\n\n Note that if the passed payload has 'exp' and/or 'nbf' claims, these are\n used in preference.\n\n "
if (payload is None):
payload = {}
headers = kwargs.get('headers', {})
if ('headers' in kwargs):
del kwargs['headers']
if (secret is None):
raise ValueError('Bad secret')
if with_times:
ext_payload = dict(exp=_to_numeric((datetime.datetime.utcnow() + datetime.timedelta(seconds=expires_in))), nbf=_to_numeric(datetime.datetime.utcnow()))
ext_payload.update(payload)
payload = ext_payload
if ('exp' not in headers):
headers['exp'] = ext_payload['exp']
return jwt.encode(payload, secret, headers=headers, **kwargs) | def _jwt_token(payload, secret, expires_in=30, with_times=True, **kwargs):
"Return a JWT with the given payload. Any remaining keyword args are\n passed to jwt.encode().\n\n If payload is None, an empty payload is used.\n\n If headers is None, no headers are added beyond 'exp' and 'nbf'.\n\n The standard 'exp' and 'nbf' claims are added. In addition, the 'exp' claim\n is added into the header. The 'exp' time is now plus the number of seconds\n specified as expires_in.\n\n Note that if the passed payload has 'exp' and/or 'nbf' claims, these are\n used in preference.\n\n "
if (payload is None):
payload = {}
headers = kwargs.get('headers', {})
if ('headers' in kwargs):
del kwargs['headers']
if (secret is None):
raise ValueError('Bad secret')
if with_times:
ext_payload = dict(exp=_to_numeric((datetime.datetime.utcnow() + datetime.timedelta(seconds=expires_in))), nbf=_to_numeric(datetime.datetime.utcnow()))
ext_payload.update(payload)
payload = ext_payload
if ('exp' not in headers):
headers['exp'] = ext_payload['exp']
return jwt.encode(payload, secret, headers=headers, **kwargs)<|docstring|>Return a JWT with the given payload. Any remaining keyword args are
passed to jwt.encode().
If payload is None, an empty payload is used.
If headers is None, no headers are added beyond 'exp' and 'nbf'.
The standard 'exp' and 'nbf' claims are added. In addition, the 'exp' claim
is added into the header. The 'exp' time is now plus the number of seconds
specified as expires_in.
Note that if the passed payload has 'exp' and/or 'nbf' claims, these are
used in preference.<|endoftext|> |
fc229a415f0965df869fb50a46d4d7a1a6937c831a122628342bc9df98cad294 | @property
def can_authenticate(self) -> bool:
'\n Test whether the user can authenticate.\n Even if authentication service is configured, user authentication\n may still be optional. In this case the server will publish\n the resources configured to be free for everyone.\n '
return bool(self.authentication.get('Domain')) | Test whether the user can authenticate.
Even if authentication service is configured, user authentication
may still be optional. In this case the server will publish
the resources configured to be free for everyone. | xcube/webapi/context.py | can_authenticate | bcdev/xcube | 0 | python | @property
def can_authenticate(self) -> bool:
'\n Test whether the user can authenticate.\n Even if authentication service is configured, user authentication\n may still be optional. In this case the server will publish\n the resources configured to be free for everyone.\n '
return bool(self.authentication.get('Domain')) | @property
def can_authenticate(self) -> bool:
'\n Test whether the user can authenticate.\n Even if authentication service is configured, user authentication\n may still be optional. In this case the server will publish\n the resources configured to be free for everyone.\n '
return bool(self.authentication.get('Domain'))<|docstring|>Test whether the user can authenticate.
Even if authentication service is configured, user authentication
may still be optional. In this case the server will publish
the resources configured to be free for everyone.<|endoftext|> |
7fe97483e001ede83b6314485d872b21a8ce4f5092133329a54ab2b51361308b | @property
def must_authenticate(self) -> bool:
'\n Test whether the user must authenticate.\n '
return (self.can_authenticate and self.authentication.get('IsRequired', False)) | Test whether the user must authenticate. | xcube/webapi/context.py | must_authenticate | bcdev/xcube | 0 | python | @property
def must_authenticate(self) -> bool:
'\n \n '
return (self.can_authenticate and self.authentication.get('IsRequired', False)) | @property
def must_authenticate(self) -> bool:
'\n \n '
return (self.can_authenticate and self.authentication.get('IsRequired', False))<|docstring|>Test whether the user must authenticate.<|endoftext|> |
8d33091658ca93ee1db2afa1a8577506d517fbf3dfac6d46986cc115862abce3 | def print_table(tablefmt, data_dict={}, record=None):
'\n\treturns colored table output\n\t'
headers = []
table = []
if (not data_dict):
return click.echo('Invalid data !!!')
if data_dict['headers']:
headers = data_dict['headers']
if (not isinstance(headers, list)):
return click.echo('Invalid headers !!!')
headers = [click.style(str(each_element), bold=True, fg='red') for each_element in headers]
if data_dict['table_data']:
table_data = data_dict['table_data']
if (not all((isinstance(each_list, list) for each_list in table_data))):
return click.echo('Invlaid table data !!!')
table = [[click.style(str(each_element), fg='green') for each_element in each_list] for each_list in table_data]
return click.echo(tabulate(table, headers, tablefmt=tablefmt))
click.echo()
return click.echo(('No %s records found for your account' % record)) | returns colored table output | docli/commands/base_request.py | print_table | yspanchal/docli | 39 | python | def print_table(tablefmt, data_dict={}, record=None):
'\n\t\n\t'
headers = []
table = []
if (not data_dict):
return click.echo('Invalid data !!!')
if data_dict['headers']:
headers = data_dict['headers']
if (not isinstance(headers, list)):
return click.echo('Invalid headers !!!')
headers = [click.style(str(each_element), bold=True, fg='red') for each_element in headers]
if data_dict['table_data']:
table_data = data_dict['table_data']
if (not all((isinstance(each_list, list) for each_list in table_data))):
return click.echo('Invlaid table data !!!')
table = [[click.style(str(each_element), fg='green') for each_element in each_list] for each_list in table_data]
return click.echo(tabulate(table, headers, tablefmt=tablefmt))
click.echo()
return click.echo(('No %s records found for your account' % record)) | def print_table(tablefmt, data_dict={}, record=None):
'\n\t\n\t'
headers = []
table = []
if (not data_dict):
return click.echo('Invalid data !!!')
if data_dict['headers']:
headers = data_dict['headers']
if (not isinstance(headers, list)):
return click.echo('Invalid headers !!!')
headers = [click.style(str(each_element), bold=True, fg='red') for each_element in headers]
if data_dict['table_data']:
table_data = data_dict['table_data']
if (not all((isinstance(each_list, list) for each_list in table_data))):
return click.echo('Invlaid table data !!!')
table = [[click.style(str(each_element), fg='green') for each_element in each_list] for each_list in table_data]
return click.echo(tabulate(table, headers, tablefmt=tablefmt))
click.echo()
return click.echo(('No %s records found for your account' % record))<|docstring|>returns colored table output<|endoftext|> |
9714d68fdc20bd229cb03b58a3dc332640b0b9e7f94bb02ee9d83b44feef015c | def explicit_euler(ode_func, y0, x0, step_size, x_end):
"\n Calculate numeric solution at each step to an ODE using Euler's Method\n\n https://en.wikipedia.org/wiki/Euler_method\n\n Arguments:\n ode_func -- The ode as a function of x and y\n y0 -- the initial value for y\n x0 -- the initial value for x\n stepsize -- the increment value for x\n x_end -- the end value for x\n\n >>> # the exact solution is math.exp(x)\n >>> def f(x, y):\n ... return y\n >>> y0 = 1\n >>> y = explicit_euler(f, y0, 0.0, 0.01, 5)\n >>> y[-1]\n 144.77277243257308\n "
N = int(np.ceil(((x_end - x0) / step_size)))
y = np.zeros(((N + 1),))
y[0] = y0
x = x0
for k in range(N):
y[(k + 1)] = (y[k] + (step_size * ode_func(x, y[k])))
x += step_size
return y | Calculate numeric solution at each step to an ODE using Euler's Method
https://en.wikipedia.org/wiki/Euler_method
Arguments:
ode_func -- The ode as a function of x and y
y0 -- the initial value for y
x0 -- the initial value for x
stepsize -- the increment value for x
x_end -- the end value for x
>>> # the exact solution is math.exp(x)
>>> def f(x, y):
... return y
>>> y0 = 1
>>> y = explicit_euler(f, y0, 0.0, 0.01, 5)
>>> y[-1]
144.77277243257308 | maths/explicit_euler.py | explicit_euler | kc8055/Python | 21 | python | def explicit_euler(ode_func, y0, x0, step_size, x_end):
"\n Calculate numeric solution at each step to an ODE using Euler's Method\n\n https://en.wikipedia.org/wiki/Euler_method\n\n Arguments:\n ode_func -- The ode as a function of x and y\n y0 -- the initial value for y\n x0 -- the initial value for x\n stepsize -- the increment value for x\n x_end -- the end value for x\n\n >>> # the exact solution is math.exp(x)\n >>> def f(x, y):\n ... return y\n >>> y0 = 1\n >>> y = explicit_euler(f, y0, 0.0, 0.01, 5)\n >>> y[-1]\n 144.77277243257308\n "
N = int(np.ceil(((x_end - x0) / step_size)))
y = np.zeros(((N + 1),))
y[0] = y0
x = x0
for k in range(N):
y[(k + 1)] = (y[k] + (step_size * ode_func(x, y[k])))
x += step_size
return y | def explicit_euler(ode_func, y0, x0, step_size, x_end):
"\n Calculate numeric solution at each step to an ODE using Euler's Method\n\n https://en.wikipedia.org/wiki/Euler_method\n\n Arguments:\n ode_func -- The ode as a function of x and y\n y0 -- the initial value for y\n x0 -- the initial value for x\n stepsize -- the increment value for x\n x_end -- the end value for x\n\n >>> # the exact solution is math.exp(x)\n >>> def f(x, y):\n ... return y\n >>> y0 = 1\n >>> y = explicit_euler(f, y0, 0.0, 0.01, 5)\n >>> y[-1]\n 144.77277243257308\n "
N = int(np.ceil(((x_end - x0) / step_size)))
y = np.zeros(((N + 1),))
y[0] = y0
x = x0
for k in range(N):
y[(k + 1)] = (y[k] + (step_size * ode_func(x, y[k])))
x += step_size
return y<|docstring|>Calculate numeric solution at each step to an ODE using Euler's Method
https://en.wikipedia.org/wiki/Euler_method
Arguments:
ode_func -- The ode as a function of x and y
y0 -- the initial value for y
x0 -- the initial value for x
stepsize -- the increment value for x
x_end -- the end value for x
>>> # the exact solution is math.exp(x)
>>> def f(x, y):
... return y
>>> y0 = 1
>>> y = explicit_euler(f, y0, 0.0, 0.01, 5)
>>> y[-1]
144.77277243257308<|endoftext|> |
d7913e131b709402a9ec6eaa940cbbf88faf34e43d7ade865336656fad75c70c | def __init__(self, capacity=10.0, aep=2500.0, static_investment=68000.0, price=0.3779, capital_ratio=0.25, working_ratio=0.3, equipment_cost=0.0, equipment_ratio=0.65, loan_rate=0.054, working_rate=0.0435, rate_discount=1.0, income_tax_rate=0.25, build_tax_rate=0.05, vat_rate=0.13, vat_refund_rate=0.5, edu_surcharge_rate=0.05, workers=10, labor_cost=16.0, in_repair_rate=0.005, out_repair_rate=0.015, warranty=5.0, insurance_rate=0.0025, material_quota=10.0, other_quota=30.0, working_quota=30.0, provident_rate=0.1, operate_period=20, build_period=1.0, loan_period=13, grace_period=1, residual_rate=0.04):
'\n 初始化类变量\n '
self.capacity = capacity
self.aep = aep
self.static_investment = static_investment
self.price = price
self.capital_ratio = capital_ratio
self.working_ratio = working_ratio
self.equipment_ratio = equipment_ratio
if (equipment_cost == 0.0):
self.equipment_cost = (self.static_investment * self.equipment_ratio)
else:
self.equipment_cost = equipment_cost
self.loan_rate = loan_rate
self.working_rate = working_rate
self.rate_discount = rate_discount
self.income_tax_rate = income_tax_rate
self.vat_rate = vat_rate
self.vat_refund_rate = vat_refund_rate
self.build_tax_rate = build_tax_rate
self.edu_surcharge_rate = edu_surcharge_rate
self.workers = workers
self.labor_cost = labor_cost
self.in_repair_rate = in_repair_rate
self.out_repair_rate = out_repair_rate
self.warranty = int(warranty)
self.insurance_rate = insurance_rate
self.material_quota = material_quota
self.other_quota = other_quota
self.working_quota = working_quota
self.provident_rate = provident_rate
self.build_period = build_period
self.operate_period = operate_period
self.loan_period = loan_period
self.grace_period = grace_period
self.residual_rate = residual_rate | 初始化类变量 | finance/base.py | __init__ | path2019/Finance | 0 | python | def __init__(self, capacity=10.0, aep=2500.0, static_investment=68000.0, price=0.3779, capital_ratio=0.25, working_ratio=0.3, equipment_cost=0.0, equipment_ratio=0.65, loan_rate=0.054, working_rate=0.0435, rate_discount=1.0, income_tax_rate=0.25, build_tax_rate=0.05, vat_rate=0.13, vat_refund_rate=0.5, edu_surcharge_rate=0.05, workers=10, labor_cost=16.0, in_repair_rate=0.005, out_repair_rate=0.015, warranty=5.0, insurance_rate=0.0025, material_quota=10.0, other_quota=30.0, working_quota=30.0, provident_rate=0.1, operate_period=20, build_period=1.0, loan_period=13, grace_period=1, residual_rate=0.04):
'\n \n '
self.capacity = capacity
self.aep = aep
self.static_investment = static_investment
self.price = price
self.capital_ratio = capital_ratio
self.working_ratio = working_ratio
self.equipment_ratio = equipment_ratio
if (equipment_cost == 0.0):
self.equipment_cost = (self.static_investment * self.equipment_ratio)
else:
self.equipment_cost = equipment_cost
self.loan_rate = loan_rate
self.working_rate = working_rate
self.rate_discount = rate_discount
self.income_tax_rate = income_tax_rate
self.vat_rate = vat_rate
self.vat_refund_rate = vat_refund_rate
self.build_tax_rate = build_tax_rate
self.edu_surcharge_rate = edu_surcharge_rate
self.workers = workers
self.labor_cost = labor_cost
self.in_repair_rate = in_repair_rate
self.out_repair_rate = out_repair_rate
self.warranty = int(warranty)
self.insurance_rate = insurance_rate
self.material_quota = material_quota
self.other_quota = other_quota
self.working_quota = working_quota
self.provident_rate = provident_rate
self.build_period = build_period
self.operate_period = operate_period
self.loan_period = loan_period
self.grace_period = grace_period
self.residual_rate = residual_rate | def __init__(self, capacity=10.0, aep=2500.0, static_investment=68000.0, price=0.3779, capital_ratio=0.25, working_ratio=0.3, equipment_cost=0.0, equipment_ratio=0.65, loan_rate=0.054, working_rate=0.0435, rate_discount=1.0, income_tax_rate=0.25, build_tax_rate=0.05, vat_rate=0.13, vat_refund_rate=0.5, edu_surcharge_rate=0.05, workers=10, labor_cost=16.0, in_repair_rate=0.005, out_repair_rate=0.015, warranty=5.0, insurance_rate=0.0025, material_quota=10.0, other_quota=30.0, working_quota=30.0, provident_rate=0.1, operate_period=20, build_period=1.0, loan_period=13, grace_period=1, residual_rate=0.04):
'\n \n '
self.capacity = capacity
self.aep = aep
self.static_investment = static_investment
self.price = price
self.capital_ratio = capital_ratio
self.working_ratio = working_ratio
self.equipment_ratio = equipment_ratio
if (equipment_cost == 0.0):
self.equipment_cost = (self.static_investment * self.equipment_ratio)
else:
self.equipment_cost = equipment_cost
self.loan_rate = loan_rate
self.working_rate = working_rate
self.rate_discount = rate_discount
self.income_tax_rate = income_tax_rate
self.vat_rate = vat_rate
self.vat_refund_rate = vat_refund_rate
self.build_tax_rate = build_tax_rate
self.edu_surcharge_rate = edu_surcharge_rate
self.workers = workers
self.labor_cost = labor_cost
self.in_repair_rate = in_repair_rate
self.out_repair_rate = out_repair_rate
self.warranty = int(warranty)
self.insurance_rate = insurance_rate
self.material_quota = material_quota
self.other_quota = other_quota
self.working_quota = working_quota
self.provident_rate = provident_rate
self.build_period = build_period
self.operate_period = operate_period
self.loan_period = loan_period
self.grace_period = grace_period
self.residual_rate = residual_rate<|docstring|>初始化类变量<|endoftext|> |
951a449f5cbc5079c5d99e5b143ddf11edab259d484c71cd7e09148a96a7327f | def com_finance(self, mode=False):
'\n 计算类实例所抽象出的项目（边界）的（财务、资本金等）现金流序列。\n\n 输入参数：\n ----------\n mode: bool, default = False\n 财务计算过程结果（表）返回标识，若为True，则将财评过程结果 com_result 返回；\n 若为False，则结果不返回；默认值为 Fasle，即默认不返回财评过程结果。\n\n 返回结果：\n ----------\n 1. 若 mode 为 False：\n (pre_pro_netflow, after_pro_netflow, cap_netflow): (np.array<float>,np.array<float>,np.array<float>)\n 税前财务现金流量、税后财务现金流量和资本金现金流量组成的元表，每个流量序列不含总计值\n 2. 若 mode 为 True:\n (pre_pro_netflow, after_pro_netflow, cap_netflow, com_result): (np.array<float>,np.array<float>,np.array<float>,list)\n 前三个参数同上，com_result 为财评过程数据表，三维列表[表页[表单]]\n\n 备注：\n ----------\n 1. 本方法是类对象的核心算法，会涉及到较大量的有效中间计算结果，需梳理好临时变量，以便能将结果输出；\n 2. 注意临时变量的分类和初始化工作；\n 3. 第一阶段默认建设期为 1 年，运营期（含建设期）为 21 年，后续再行扩充可变建设期和运营期。\n '
build_cells = math.ceil(self.build_period)
row_cells = ((self.operate_period + build_cells) + 1)
total_investment = np.zeros(row_cells)
build_investment = np.zeros(row_cells)
build_interest = np.zeros(row_cells)
working_capital = np.zeros(row_cells)
finance = np.zeros(row_cells)
capital = np.zeros(row_cells)
debt = np.zeros(row_cells)
long_loan = np.zeros(row_cells)
working_loan = np.zeros(row_cells)
material = np.zeros(row_cells)
wage = np.zeros(row_cells)
maintenance = np.zeros(row_cells)
insurance = np.zeros(row_cells)
other_expense = np.zeros(row_cells)
operate_cost = np.zeros(row_cells)
depreciation = np.zeros(row_cells)
amortization = np.zeros(row_cells)
interest = np.zeros(row_cells)
fix_cost = np.zeros(row_cells)
var_cost = np.zeros(row_cells)
total_cost = np.zeros(row_cells)
long_opening = np.zeros(row_cells)
long_return = np.zeros(row_cells)
long_principal = np.zeros(row_cells)
long_interest = np.zeros(row_cells)
long_ending = np.zeros(row_cells)
working_interest = np.zeros(row_cells)
working_principal = np.zeros(row_cells)
working_return = np.zeros(row_cells)
total_return = np.zeros(row_cells)
income = np.zeros(row_cells)
intax_balance = np.zeros(row_cells)
operate_tax = np.zeros(row_cells)
build_tax = np.zeros(row_cells)
edu_surcharge = np.zeros(row_cells)
subside = np.zeros(row_cells)
vat_return = np.zeros(row_cells)
vat_turn = np.zeros(row_cells)
profit = np.zeros(row_cells)
offset_loss = np.zeros(row_cells)
tax_income = np.zeros(row_cells)
income_tax = np.zeros(row_cells)
net_profit = np.zeros(row_cells)
provident = np.zeros(row_cells)
distribute_profit = np.zeros(row_cells)
ebit = np.zeros(row_cells)
pro_inflow = np.zeros(row_cells)
recover_asset = np.zeros(row_cells)
recover_pro_working = np.zeros(row_cells)
pro_outflow = np.zeros(row_cells)
pre_pro_netflow = np.zeros(row_cells)
after_pro_netflow = np.zeros(row_cells)
cap_inflow = np.zeros(row_cells)
recover_cap_working = np.zeros(row_cells)
cap_outflow = np.zeros(row_cells)
cap_netflow = np.zeros(row_cells)
build_investment[1] = self.static_investment
build_interest[1] = (((build_investment[1] * self.loan_rate) * self.rate_discount) / 2)
working_capital[(build_cells + 1)] = (self.capacity * self.working_quota)
build_investment[0] = np.sum(build_investment)
build_interest[0] = np.sum(build_interest)
working_capital[0] = np.sum(working_capital)
total_investment = ((build_investment + build_interest) + working_capital)
capital[1] = (total_investment[1] * self.capital_ratio)
capital[(build_cells + 1)] = (total_investment[(build_cells + 1)] * self.working_ratio)
long_loan[1] = (total_investment[1] - capital[1])
working_loan[(build_cells + 1)] = (total_investment[(build_cells + 1)] - capital[(build_cells + 1)])
capital[0] = np.sum(capital)
long_loan[0] = np.sum(long_loan)
working_loan[0] = np.sum(working_loan)
debt = (total_investment - capital)
finance = (capital + debt)
vat_deduction = ((self.equipment_cost / (1 + self.vat_rate)) * self.vat_rate)
fix_assets = (total_investment[1] - vat_deduction)
output_vat = ((((self.capacity * self.aep) * self.price) * self.vat_rate) / (1 + self.vat_rate))
material[(build_cells + 1):] = (self.capacity * self.material_quota)
wage[(build_cells + 1):] = (self.workers * self.labor_cost)
maintenance[(build_cells + 1):((build_cells + 1) + self.warranty)] = (fix_assets * self.in_repair_rate)
maintenance[((build_cells + 1) + self.warranty):] = (fix_assets * self.out_repair_rate)
insurance[(build_cells + 1):] = (fix_assets * self.insurance_rate)
other_expense[(build_cells + 1):] = (self.capacity * self.other_quota)
depreciation[(build_cells + 1):] = ((fix_assets * (1 - self.residual_rate)) / self.operate_period)
depreciation[0] = np.sum(depreciation)
maintenance[0] = np.sum(maintenance)
wage[0] = np.sum(wage)
insurance[0] = np.sum(insurance)
material[0] = np.sum(material)
other_expense[0] = np.sum(other_expense)
amortization[0] = np.sum(amortization)
var_cost = material
operate_cost = ((((maintenance + wage) + insurance) + material) + other_expense)
long_principal[(build_cells + 1):((build_cells + self.loan_period) + 1)] = (long_loan[1] / self.loan_period)
for i in range(self.loan_period):
long_opening[((build_cells + i) + 1)] = (long_loan[1] - (i * long_principal[(build_cells + i)]))
long_ending[(build_cells + 1):(build_cells + self.loan_period)] = long_opening[(build_cells + 2):((build_cells + self.loan_period) + 1)]
long_interest = ((long_opening * self.loan_rate) * self.rate_discount)
long_interest[0] = np.sum(long_interest)
long_principal[0] = np.sum(long_principal)
long_return = (long_principal + long_interest)
working_interest[(build_cells + 1):] = (working_loan[(build_cells + 1)] * self.working_rate)
working_principal[(row_cells - 1)] = working_loan[(build_cells + 1)]
working_interest[0] = np.sum(working_interest)
working_principal = np.sum(working_principal)
working_return = (working_interest + working_principal)
interest = (long_interest + working_interest)
total_return = (long_return + working_return)
fix_cost = (((((depreciation + maintenance) + wage) + insurance) + interest) + other_expense)
total_cost = (((((((depreciation + maintenance) + wage) + insurance) + material) + amortization) + interest) + other_expense)
income[(build_cells + 1):] = (((self.capacity * self.aep) * self.price) / (1 + self.vat_rate))
income[0] = np.sum(income)
for i in range(self.operate_period):
intax_balance[((build_cells + 1) + i)] = (vat_deduction - (i * output_vat))
if (intax_balance[((build_cells + 1) + i)] <= 0):
build_tax[((build_cells + 1) + i)] = (output_vat * self.build_tax_rate)
edu_surcharge[((build_cells + 1) + i)] = (output_vat * self.edu_surcharge_rate)
elif ((intax_balance[((build_cells + 1) + i)] > 0) and ((vat_deduction - ((i + 1) * output_vat)) <= 0)):
build_tax[((build_cells + 1) + i)] = ((output_vat - intax_balance[((build_cells + 1) + i)]) * self.build_tax_rate)
edu_surcharge[((build_cells + 1) + i)] = ((output_vat - intax_balance[((build_cells + 1) + i)]) * self.edu_surcharge_rate)
else:
build_tax[((build_cells + 1) + i)] = 0
edu_surcharge[((build_cells + 1) + i)] = 0
build_tax[0] = np.sum(build_tax)
edu_surcharge[0] = np.sum(edu_surcharge)
operate_tax = (build_tax + edu_surcharge)
vat_return = ((build_tax * self.vat_refund_rate) / self.build_tax_rate)
for i in range((build_cells + 1), self.operate_period):
if (intax_balance[i] < 0):
vat_turn[i] = 0
elif (intax_balance[i] >= output_vat):
vat_turn[i] = output_vat
else:
vat_turn[i] = intax_balance[i]
vat_turn[0] = np.sum(vat_turn)
subside = (vat_return + vat_turn)
profit = (((income - operate_tax) - total_cost) + vat_return)
tax_income = (profit - offset_loss)
for i in range(self.operate_period):
if (i < 3):
income_tax[((build_cells + 1) + i)] = 0
elif (tax_income[((build_cells + 1) + i)] <= 0):
income_tax[((build_cells + 1) + i)] = 0
elif (i < 6):
income_tax[((build_cells + 1) + i)] = ((tax_income[((build_cells + 1) + i)] * self.income_tax_rate) / 2)
else:
income_tax[((build_cells + 1) + i)] = (tax_income[((build_cells + 1) + i)] * self.income_tax_rate)
income_tax[0] = np.sum(income_tax)
net_profit = (profit - income_tax)
provident = (net_profit * self.provident_rate)
distribute_profit = (net_profit - provident)
ebit = (profit + interest)
recover_asset[(build_cells + self.operate_period)] = (fix_assets * self.residual_rate)
recover_pro_working[(build_cells + self.operate_period)] = working_capital[(build_cells + 1)]
recover_asset[0] = np.sum(recover_asset)
recover_pro_working[0] = np.sum(recover_pro_working)
pro_inflow = (((income + subside) + recover_asset) + recover_pro_working)
pro_outflow = (((build_investment + working_capital) + operate_cost) + operate_tax)
pre_pro_netflow = (pro_inflow - pro_outflow)
after_pro_netflow = (pre_pro_netflow - income_tax)
recover_cap_working[[0, (build_cells + self.operate_period)]] = (working_capital[(build_cells + 1)] * self.working_ratio)
cap_inflow = (((income + subside) + recover_asset) + recover_cap_working)
cap_outflow = (((((capital + long_principal) + interest) + operate_cost) + operate_tax) + income_tax)
cap_netflow = (cap_inflow - cap_outflow)
if (mode == True):
investment_finance = [total_investment[:(build_cells + 2)], build_investment[:(build_cells + 2)], build_interest[:(build_cells + 2)], working_capital[:(build_cells + 2)], finance[:(build_cells + 2)], capital[:(build_cells + 2)], debt[:(build_cells + 2)], long_loan[:(build_cells + 2)], working_loan[:(build_cells + 2)]]
cost_finance = [material, wage, maintenance, insurance, other_expense, operate_cost, depreciation, amortization, interest, total_cost, var_cost, fix_cost]
return_finance = [long_loan, long_opening, long_return, long_principal, long_interest, long_ending, working_loan, working_loan, working_return, working_principal, working_interest, (working_loan - working_principal), (long_loan + working_loan), (long_opening + working_loan), total_return, (long_principal + working_principal), (long_interest + working_interest)]
profit_finance = [income, operate_tax, build_tax, edu_surcharge, total_cost, subside, vat_return, vat_turn, profit, offset_loss, tax_income, income_tax, net_profit, provident, ebit]
pro_flow = [pro_inflow, income, subside, recover_asset, recover_pro_working, pro_outflow, build_investment, working_capital, operate_cost, operate_tax, pre_pro_netflow, income_tax, after_pro_netflow]
cap_flow = [cap_inflow, income, subside, recover_asset, recover_cap_working, cap_outflow, capital, long_return, interest, operate_cost, operate_tax, income_tax, cap_netflow]
com_result = [investment_finance, cost_finance, return_finance, profit_finance, pro_flow, cap_flow]
if mode:
return (pre_pro_netflow[1:], after_pro_netflow[1:], cap_netflow[1:], com_result)
else:
return (pre_pro_netflow[1:], after_pro_netflow[1:], cap_netflow[1:]) | 计算类实例所抽象出的项目（边界）的（财务、资本金等）现金流序列。
输入参数：
----------
mode: bool, default = False
财务计算过程结果（表）返回标识，若为True，则将财评过程结果 com_result 返回；
若为False，则结果不返回；默认值为 Fasle，即默认不返回财评过程结果。
返回结果：
----------
1. 若 mode 为 False：
(pre_pro_netflow, after_pro_netflow, cap_netflow): (np.array<float>,np.array<float>,np.array<float>)
税前财务现金流量、税后财务现金流量和资本金现金流量组成的元表，每个流量序列不含总计值
2. 若 mode 为 True:
(pre_pro_netflow, after_pro_netflow, cap_netflow, com_result): (np.array<float>,np.array<float>,np.array<float>,list)
前三个参数同上，com_result 为财评过程数据表，三维列表[表页[表单]]
备注：
----------
1. 本方法是类对象的核心算法，会涉及到较大量的有效中间计算结果，需梳理好临时变量，以便能将结果输出；
2. 注意临时变量的分类和初始化工作；
3. 第一阶段默认建设期为 1 年，运营期（含建设期）为 21 年，后续再行扩充可变建设期和运营期。 | finance/base.py | com_finance | path2019/Finance | 0 | python | def com_finance(self, mode=False):
'\n 计算类实例所抽象出的项目（边界）的（财务、资本金等）现金流序列。\n\n 输入参数：\n ----------\n mode: bool, default = False\n 财务计算过程结果（表）返回标识，若为True，则将财评过程结果 com_result 返回；\n 若为False，则结果不返回；默认值为 Fasle，即默认不返回财评过程结果。\n\n 返回结果：\n ----------\n 1. 若 mode 为 False：\n (pre_pro_netflow, after_pro_netflow, cap_netflow): (np.array<float>,np.array<float>,np.array<float>)\n 税前财务现金流量、税后财务现金流量和资本金现金流量组成的元表，每个流量序列不含总计值\n 2. 若 mode 为 True:\n (pre_pro_netflow, after_pro_netflow, cap_netflow, com_result): (np.array<float>,np.array<float>,np.array<float>,list)\n 前三个参数同上，com_result 为财评过程数据表，三维列表[表页[表单]]\n\n 备注：\n ----------\n 1. 本方法是类对象的核心算法，会涉及到较大量的有效中间计算结果，需梳理好临时变量，以便能将结果输出；\n 2. 注意临时变量的分类和初始化工作；\n 3. 第一阶段默认建设期为 1 年，运营期（含建设期）为 21 年，后续再行扩充可变建设期和运营期。\n '
build_cells = math.ceil(self.build_period)
row_cells = ((self.operate_period + build_cells) + 1)
total_investment = np.zeros(row_cells)
build_investment = np.zeros(row_cells)
build_interest = np.zeros(row_cells)
working_capital = np.zeros(row_cells)
finance = np.zeros(row_cells)
capital = np.zeros(row_cells)
debt = np.zeros(row_cells)
long_loan = np.zeros(row_cells)
working_loan = np.zeros(row_cells)
material = np.zeros(row_cells)
wage = np.zeros(row_cells)
maintenance = np.zeros(row_cells)
insurance = np.zeros(row_cells)
other_expense = np.zeros(row_cells)
operate_cost = np.zeros(row_cells)
depreciation = np.zeros(row_cells)
amortization = np.zeros(row_cells)
interest = np.zeros(row_cells)
fix_cost = np.zeros(row_cells)
var_cost = np.zeros(row_cells)
total_cost = np.zeros(row_cells)
long_opening = np.zeros(row_cells)
long_return = np.zeros(row_cells)
long_principal = np.zeros(row_cells)
long_interest = np.zeros(row_cells)
long_ending = np.zeros(row_cells)
working_interest = np.zeros(row_cells)
working_principal = np.zeros(row_cells)
working_return = np.zeros(row_cells)
total_return = np.zeros(row_cells)
income = np.zeros(row_cells)
intax_balance = np.zeros(row_cells)
operate_tax = np.zeros(row_cells)
build_tax = np.zeros(row_cells)
edu_surcharge = np.zeros(row_cells)
subside = np.zeros(row_cells)
vat_return = np.zeros(row_cells)
vat_turn = np.zeros(row_cells)
profit = np.zeros(row_cells)
offset_loss = np.zeros(row_cells)
tax_income = np.zeros(row_cells)
income_tax = np.zeros(row_cells)
net_profit = np.zeros(row_cells)
provident = np.zeros(row_cells)
distribute_profit = np.zeros(row_cells)
ebit = np.zeros(row_cells)
pro_inflow = np.zeros(row_cells)
recover_asset = np.zeros(row_cells)
recover_pro_working = np.zeros(row_cells)
pro_outflow = np.zeros(row_cells)
pre_pro_netflow = np.zeros(row_cells)
after_pro_netflow = np.zeros(row_cells)
cap_inflow = np.zeros(row_cells)
recover_cap_working = np.zeros(row_cells)
cap_outflow = np.zeros(row_cells)
cap_netflow = np.zeros(row_cells)
build_investment[1] = self.static_investment
build_interest[1] = (((build_investment[1] * self.loan_rate) * self.rate_discount) / 2)
working_capital[(build_cells + 1)] = (self.capacity * self.working_quota)
build_investment[0] = np.sum(build_investment)
build_interest[0] = np.sum(build_interest)
working_capital[0] = np.sum(working_capital)
total_investment = ((build_investment + build_interest) + working_capital)
capital[1] = (total_investment[1] * self.capital_ratio)
capital[(build_cells + 1)] = (total_investment[(build_cells + 1)] * self.working_ratio)
long_loan[1] = (total_investment[1] - capital[1])
working_loan[(build_cells + 1)] = (total_investment[(build_cells + 1)] - capital[(build_cells + 1)])
capital[0] = np.sum(capital)
long_loan[0] = np.sum(long_loan)
working_loan[0] = np.sum(working_loan)
debt = (total_investment - capital)
finance = (capital + debt)
vat_deduction = ((self.equipment_cost / (1 + self.vat_rate)) * self.vat_rate)
fix_assets = (total_investment[1] - vat_deduction)
output_vat = ((((self.capacity * self.aep) * self.price) * self.vat_rate) / (1 + self.vat_rate))
material[(build_cells + 1):] = (self.capacity * self.material_quota)
wage[(build_cells + 1):] = (self.workers * self.labor_cost)
maintenance[(build_cells + 1):((build_cells + 1) + self.warranty)] = (fix_assets * self.in_repair_rate)
maintenance[((build_cells + 1) + self.warranty):] = (fix_assets * self.out_repair_rate)
insurance[(build_cells + 1):] = (fix_assets * self.insurance_rate)
other_expense[(build_cells + 1):] = (self.capacity * self.other_quota)
depreciation[(build_cells + 1):] = ((fix_assets * (1 - self.residual_rate)) / self.operate_period)
depreciation[0] = np.sum(depreciation)
maintenance[0] = np.sum(maintenance)
wage[0] = np.sum(wage)
insurance[0] = np.sum(insurance)
material[0] = np.sum(material)
other_expense[0] = np.sum(other_expense)
amortization[0] = np.sum(amortization)
var_cost = material
operate_cost = ((((maintenance + wage) + insurance) + material) + other_expense)
long_principal[(build_cells + 1):((build_cells + self.loan_period) + 1)] = (long_loan[1] / self.loan_period)
for i in range(self.loan_period):
long_opening[((build_cells + i) + 1)] = (long_loan[1] - (i * long_principal[(build_cells + i)]))
long_ending[(build_cells + 1):(build_cells + self.loan_period)] = long_opening[(build_cells + 2):((build_cells + self.loan_period) + 1)]
long_interest = ((long_opening * self.loan_rate) * self.rate_discount)
long_interest[0] = np.sum(long_interest)
long_principal[0] = np.sum(long_principal)
long_return = (long_principal + long_interest)
working_interest[(build_cells + 1):] = (working_loan[(build_cells + 1)] * self.working_rate)
working_principal[(row_cells - 1)] = working_loan[(build_cells + 1)]
working_interest[0] = np.sum(working_interest)
working_principal = np.sum(working_principal)
working_return = (working_interest + working_principal)
interest = (long_interest + working_interest)
total_return = (long_return + working_return)
fix_cost = (((((depreciation + maintenance) + wage) + insurance) + interest) + other_expense)
total_cost = (((((((depreciation + maintenance) + wage) + insurance) + material) + amortization) + interest) + other_expense)
income[(build_cells + 1):] = (((self.capacity * self.aep) * self.price) / (1 + self.vat_rate))
income[0] = np.sum(income)
for i in range(self.operate_period):
intax_balance[((build_cells + 1) + i)] = (vat_deduction - (i * output_vat))
if (intax_balance[((build_cells + 1) + i)] <= 0):
build_tax[((build_cells + 1) + i)] = (output_vat * self.build_tax_rate)
edu_surcharge[((build_cells + 1) + i)] = (output_vat * self.edu_surcharge_rate)
elif ((intax_balance[((build_cells + 1) + i)] > 0) and ((vat_deduction - ((i + 1) * output_vat)) <= 0)):
build_tax[((build_cells + 1) + i)] = ((output_vat - intax_balance[((build_cells + 1) + i)]) * self.build_tax_rate)
edu_surcharge[((build_cells + 1) + i)] = ((output_vat - intax_balance[((build_cells + 1) + i)]) * self.edu_surcharge_rate)
else:
build_tax[((build_cells + 1) + i)] = 0
edu_surcharge[((build_cells + 1) + i)] = 0
build_tax[0] = np.sum(build_tax)
edu_surcharge[0] = np.sum(edu_surcharge)
operate_tax = (build_tax + edu_surcharge)
vat_return = ((build_tax * self.vat_refund_rate) / self.build_tax_rate)
for i in range((build_cells + 1), self.operate_period):
if (intax_balance[i] < 0):
vat_turn[i] = 0
elif (intax_balance[i] >= output_vat):
vat_turn[i] = output_vat
else:
vat_turn[i] = intax_balance[i]
vat_turn[0] = np.sum(vat_turn)
subside = (vat_return + vat_turn)
profit = (((income - operate_tax) - total_cost) + vat_return)
tax_income = (profit - offset_loss)
for i in range(self.operate_period):
if (i < 3):
income_tax[((build_cells + 1) + i)] = 0
elif (tax_income[((build_cells + 1) + i)] <= 0):
income_tax[((build_cells + 1) + i)] = 0
elif (i < 6):
income_tax[((build_cells + 1) + i)] = ((tax_income[((build_cells + 1) + i)] * self.income_tax_rate) / 2)
else:
income_tax[((build_cells + 1) + i)] = (tax_income[((build_cells + 1) + i)] * self.income_tax_rate)
income_tax[0] = np.sum(income_tax)
net_profit = (profit - income_tax)
provident = (net_profit * self.provident_rate)
distribute_profit = (net_profit - provident)
ebit = (profit + interest)
recover_asset[(build_cells + self.operate_period)] = (fix_assets * self.residual_rate)
recover_pro_working[(build_cells + self.operate_period)] = working_capital[(build_cells + 1)]
recover_asset[0] = np.sum(recover_asset)
recover_pro_working[0] = np.sum(recover_pro_working)
pro_inflow = (((income + subside) + recover_asset) + recover_pro_working)
pro_outflow = (((build_investment + working_capital) + operate_cost) + operate_tax)
pre_pro_netflow = (pro_inflow - pro_outflow)
after_pro_netflow = (pre_pro_netflow - income_tax)
recover_cap_working[[0, (build_cells + self.operate_period)]] = (working_capital[(build_cells + 1)] * self.working_ratio)
cap_inflow = (((income + subside) + recover_asset) + recover_cap_working)
cap_outflow = (((((capital + long_principal) + interest) + operate_cost) + operate_tax) + income_tax)
cap_netflow = (cap_inflow - cap_outflow)
if (mode == True):
investment_finance = [total_investment[:(build_cells + 2)], build_investment[:(build_cells + 2)], build_interest[:(build_cells + 2)], working_capital[:(build_cells + 2)], finance[:(build_cells + 2)], capital[:(build_cells + 2)], debt[:(build_cells + 2)], long_loan[:(build_cells + 2)], working_loan[:(build_cells + 2)]]
cost_finance = [material, wage, maintenance, insurance, other_expense, operate_cost, depreciation, amortization, interest, total_cost, var_cost, fix_cost]
return_finance = [long_loan, long_opening, long_return, long_principal, long_interest, long_ending, working_loan, working_loan, working_return, working_principal, working_interest, (working_loan - working_principal), (long_loan + working_loan), (long_opening + working_loan), total_return, (long_principal + working_principal), (long_interest + working_interest)]
profit_finance = [income, operate_tax, build_tax, edu_surcharge, total_cost, subside, vat_return, vat_turn, profit, offset_loss, tax_income, income_tax, net_profit, provident, ebit]
pro_flow = [pro_inflow, income, subside, recover_asset, recover_pro_working, pro_outflow, build_investment, working_capital, operate_cost, operate_tax, pre_pro_netflow, income_tax, after_pro_netflow]
cap_flow = [cap_inflow, income, subside, recover_asset, recover_cap_working, cap_outflow, capital, long_return, interest, operate_cost, operate_tax, income_tax, cap_netflow]
com_result = [investment_finance, cost_finance, return_finance, profit_finance, pro_flow, cap_flow]
if mode:
return (pre_pro_netflow[1:], after_pro_netflow[1:], cap_netflow[1:], com_result)
else:
return (pre_pro_netflow[1:], after_pro_netflow[1:], cap_netflow[1:]) | def com_finance(self, mode=False):
'\n 计算类实例所抽象出的项目（边界）的（财务、资本金等）现金流序列。\n\n 输入参数：\n ----------\n mode: bool, default = False\n 财务计算过程结果（表）返回标识，若为True，则将财评过程结果 com_result 返回；\n 若为False，则结果不返回；默认值为 Fasle，即默认不返回财评过程结果。\n\n 返回结果：\n ----------\n 1. 若 mode 为 False：\n (pre_pro_netflow, after_pro_netflow, cap_netflow): (np.array<float>,np.array<float>,np.array<float>)\n 税前财务现金流量、税后财务现金流量和资本金现金流量组成的元表，每个流量序列不含总计值\n 2. 若 mode 为 True:\n (pre_pro_netflow, after_pro_netflow, cap_netflow, com_result): (np.array<float>,np.array<float>,np.array<float>,list)\n 前三个参数同上，com_result 为财评过程数据表，三维列表[表页[表单]]\n\n 备注：\n ----------\n 1. 本方法是类对象的核心算法，会涉及到较大量的有效中间计算结果，需梳理好临时变量，以便能将结果输出；\n 2. 注意临时变量的分类和初始化工作；\n 3. 第一阶段默认建设期为 1 年，运营期（含建设期）为 21 年，后续再行扩充可变建设期和运营期。\n '
build_cells = math.ceil(self.build_period)
row_cells = ((self.operate_period + build_cells) + 1)
total_investment = np.zeros(row_cells)
build_investment = np.zeros(row_cells)
build_interest = np.zeros(row_cells)
working_capital = np.zeros(row_cells)
finance = np.zeros(row_cells)
capital = np.zeros(row_cells)
debt = np.zeros(row_cells)
long_loan = np.zeros(row_cells)
working_loan = np.zeros(row_cells)
material = np.zeros(row_cells)
wage = np.zeros(row_cells)
maintenance = np.zeros(row_cells)
insurance = np.zeros(row_cells)
other_expense = np.zeros(row_cells)
operate_cost = np.zeros(row_cells)
depreciation = np.zeros(row_cells)
amortization = np.zeros(row_cells)
interest = np.zeros(row_cells)
fix_cost = np.zeros(row_cells)
var_cost = np.zeros(row_cells)
total_cost = np.zeros(row_cells)
long_opening = np.zeros(row_cells)
long_return = np.zeros(row_cells)
long_principal = np.zeros(row_cells)
long_interest = np.zeros(row_cells)
long_ending = np.zeros(row_cells)
working_interest = np.zeros(row_cells)
working_principal = np.zeros(row_cells)
working_return = np.zeros(row_cells)
total_return = np.zeros(row_cells)
income = np.zeros(row_cells)
intax_balance = np.zeros(row_cells)
operate_tax = np.zeros(row_cells)
build_tax = np.zeros(row_cells)
edu_surcharge = np.zeros(row_cells)
subside = np.zeros(row_cells)
vat_return = np.zeros(row_cells)
vat_turn = np.zeros(row_cells)
profit = np.zeros(row_cells)
offset_loss = np.zeros(row_cells)
tax_income = np.zeros(row_cells)
income_tax = np.zeros(row_cells)
net_profit = np.zeros(row_cells)
provident = np.zeros(row_cells)
distribute_profit = np.zeros(row_cells)
ebit = np.zeros(row_cells)
pro_inflow = np.zeros(row_cells)
recover_asset = np.zeros(row_cells)
recover_pro_working = np.zeros(row_cells)
pro_outflow = np.zeros(row_cells)
pre_pro_netflow = np.zeros(row_cells)
after_pro_netflow = np.zeros(row_cells)
cap_inflow = np.zeros(row_cells)
recover_cap_working = np.zeros(row_cells)
cap_outflow = np.zeros(row_cells)
cap_netflow = np.zeros(row_cells)
build_investment[1] = self.static_investment
build_interest[1] = (((build_investment[1] * self.loan_rate) * self.rate_discount) / 2)
working_capital[(build_cells + 1)] = (self.capacity * self.working_quota)
build_investment[0] = np.sum(build_investment)
build_interest[0] = np.sum(build_interest)
working_capital[0] = np.sum(working_capital)
total_investment = ((build_investment + build_interest) + working_capital)
capital[1] = (total_investment[1] * self.capital_ratio)
capital[(build_cells + 1)] = (total_investment[(build_cells + 1)] * self.working_ratio)
long_loan[1] = (total_investment[1] - capital[1])
working_loan[(build_cells + 1)] = (total_investment[(build_cells + 1)] - capital[(build_cells + 1)])
capital[0] = np.sum(capital)
long_loan[0] = np.sum(long_loan)
working_loan[0] = np.sum(working_loan)
debt = (total_investment - capital)
finance = (capital + debt)
vat_deduction = ((self.equipment_cost / (1 + self.vat_rate)) * self.vat_rate)
fix_assets = (total_investment[1] - vat_deduction)
output_vat = ((((self.capacity * self.aep) * self.price) * self.vat_rate) / (1 + self.vat_rate))
material[(build_cells + 1):] = (self.capacity * self.material_quota)
wage[(build_cells + 1):] = (self.workers * self.labor_cost)
maintenance[(build_cells + 1):((build_cells + 1) + self.warranty)] = (fix_assets * self.in_repair_rate)
maintenance[((build_cells + 1) + self.warranty):] = (fix_assets * self.out_repair_rate)
insurance[(build_cells + 1):] = (fix_assets * self.insurance_rate)
other_expense[(build_cells + 1):] = (self.capacity * self.other_quota)
depreciation[(build_cells + 1):] = ((fix_assets * (1 - self.residual_rate)) / self.operate_period)
depreciation[0] = np.sum(depreciation)
maintenance[0] = np.sum(maintenance)
wage[0] = np.sum(wage)
insurance[0] = np.sum(insurance)
material[0] = np.sum(material)
other_expense[0] = np.sum(other_expense)
amortization[0] = np.sum(amortization)
var_cost = material
operate_cost = ((((maintenance + wage) + insurance) + material) + other_expense)
long_principal[(build_cells + 1):((build_cells + self.loan_period) + 1)] = (long_loan[1] / self.loan_period)
for i in range(self.loan_period):
long_opening[((build_cells + i) + 1)] = (long_loan[1] - (i * long_principal[(build_cells + i)]))
long_ending[(build_cells + 1):(build_cells + self.loan_period)] = long_opening[(build_cells + 2):((build_cells + self.loan_period) + 1)]
long_interest = ((long_opening * self.loan_rate) * self.rate_discount)
long_interest[0] = np.sum(long_interest)
long_principal[0] = np.sum(long_principal)
long_return = (long_principal + long_interest)
working_interest[(build_cells + 1):] = (working_loan[(build_cells + 1)] * self.working_rate)
working_principal[(row_cells - 1)] = working_loan[(build_cells + 1)]
working_interest[0] = np.sum(working_interest)
working_principal = np.sum(working_principal)
working_return = (working_interest + working_principal)
interest = (long_interest + working_interest)
total_return = (long_return + working_return)
fix_cost = (((((depreciation + maintenance) + wage) + insurance) + interest) + other_expense)
total_cost = (((((((depreciation + maintenance) + wage) + insurance) + material) + amortization) + interest) + other_expense)
income[(build_cells + 1):] = (((self.capacity * self.aep) * self.price) / (1 + self.vat_rate))
income[0] = np.sum(income)
for i in range(self.operate_period):
intax_balance[((build_cells + 1) + i)] = (vat_deduction - (i * output_vat))
if (intax_balance[((build_cells + 1) + i)] <= 0):
build_tax[((build_cells + 1) + i)] = (output_vat * self.build_tax_rate)
edu_surcharge[((build_cells + 1) + i)] = (output_vat * self.edu_surcharge_rate)
elif ((intax_balance[((build_cells + 1) + i)] > 0) and ((vat_deduction - ((i + 1) * output_vat)) <= 0)):
build_tax[((build_cells + 1) + i)] = ((output_vat - intax_balance[((build_cells + 1) + i)]) * self.build_tax_rate)
edu_surcharge[((build_cells + 1) + i)] = ((output_vat - intax_balance[((build_cells + 1) + i)]) * self.edu_surcharge_rate)
else:
build_tax[((build_cells + 1) + i)] = 0
edu_surcharge[((build_cells + 1) + i)] = 0
build_tax[0] = np.sum(build_tax)
edu_surcharge[0] = np.sum(edu_surcharge)
operate_tax = (build_tax + edu_surcharge)
vat_return = ((build_tax * self.vat_refund_rate) / self.build_tax_rate)
for i in range((build_cells + 1), self.operate_period):
if (intax_balance[i] < 0):
vat_turn[i] = 0
elif (intax_balance[i] >= output_vat):
vat_turn[i] = output_vat
else:
vat_turn[i] = intax_balance[i]
vat_turn[0] = np.sum(vat_turn)
subside = (vat_return + vat_turn)
profit = (((income - operate_tax) - total_cost) + vat_return)
tax_income = (profit - offset_loss)
for i in range(self.operate_period):
if (i < 3):
income_tax[((build_cells + 1) + i)] = 0
elif (tax_income[((build_cells + 1) + i)] <= 0):
income_tax[((build_cells + 1) + i)] = 0
elif (i < 6):
income_tax[((build_cells + 1) + i)] = ((tax_income[((build_cells + 1) + i)] * self.income_tax_rate) / 2)
else:
income_tax[((build_cells + 1) + i)] = (tax_income[((build_cells + 1) + i)] * self.income_tax_rate)
income_tax[0] = np.sum(income_tax)
net_profit = (profit - income_tax)
provident = (net_profit * self.provident_rate)
distribute_profit = (net_profit - provident)
ebit = (profit + interest)
recover_asset[(build_cells + self.operate_period)] = (fix_assets * self.residual_rate)
recover_pro_working[(build_cells + self.operate_period)] = working_capital[(build_cells + 1)]
recover_asset[0] = np.sum(recover_asset)
recover_pro_working[0] = np.sum(recover_pro_working)
pro_inflow = (((income + subside) + recover_asset) + recover_pro_working)
pro_outflow = (((build_investment + working_capital) + operate_cost) + operate_tax)
pre_pro_netflow = (pro_inflow - pro_outflow)
after_pro_netflow = (pre_pro_netflow - income_tax)
recover_cap_working[[0, (build_cells + self.operate_period)]] = (working_capital[(build_cells + 1)] * self.working_ratio)
cap_inflow = (((income + subside) + recover_asset) + recover_cap_working)
cap_outflow = (((((capital + long_principal) + interest) + operate_cost) + operate_tax) + income_tax)
cap_netflow = (cap_inflow - cap_outflow)
if (mode == True):
investment_finance = [total_investment[:(build_cells + 2)], build_investment[:(build_cells + 2)], build_interest[:(build_cells + 2)], working_capital[:(build_cells + 2)], finance[:(build_cells + 2)], capital[:(build_cells + 2)], debt[:(build_cells + 2)], long_loan[:(build_cells + 2)], working_loan[:(build_cells + 2)]]
cost_finance = [material, wage, maintenance, insurance, other_expense, operate_cost, depreciation, amortization, interest, total_cost, var_cost, fix_cost]
return_finance = [long_loan, long_opening, long_return, long_principal, long_interest, long_ending, working_loan, working_loan, working_return, working_principal, working_interest, (working_loan - working_principal), (long_loan + working_loan), (long_opening + working_loan), total_return, (long_principal + working_principal), (long_interest + working_interest)]
profit_finance = [income, operate_tax, build_tax, edu_surcharge, total_cost, subside, vat_return, vat_turn, profit, offset_loss, tax_income, income_tax, net_profit, provident, ebit]
pro_flow = [pro_inflow, income, subside, recover_asset, recover_pro_working, pro_outflow, build_investment, working_capital, operate_cost, operate_tax, pre_pro_netflow, income_tax, after_pro_netflow]
cap_flow = [cap_inflow, income, subside, recover_asset, recover_cap_working, cap_outflow, capital, long_return, interest, operate_cost, operate_tax, income_tax, cap_netflow]
com_result = [investment_finance, cost_finance, return_finance, profit_finance, pro_flow, cap_flow]
if mode:
return (pre_pro_netflow[1:], after_pro_netflow[1:], cap_netflow[1:], com_result)
else:
return (pre_pro_netflow[1:], after_pro_netflow[1:], cap_netflow[1:])<|docstring|>计算类实例所抽象出的项目（边界）的（财务、资本金等）现金流序列。
输入参数：
----------
mode: bool, default = False
财务计算过程结果（表）返回标识，若为True，则将财评过程结果 com_result 返回；
若为False，则结果不返回；默认值为 Fasle，即默认不返回财评过程结果。
返回结果：
----------
1. 若 mode 为 False：
(pre_pro_netflow, after_pro_netflow, cap_netflow): (np.array<float>,np.array<float>,np.array<float>)
税前财务现金流量、税后财务现金流量和资本金现金流量组成的元表，每个流量序列不含总计值
2. 若 mode 为 True:
(pre_pro_netflow, after_pro_netflow, cap_netflow, com_result): (np.array<float>,np.array<float>,np.array<float>,list)
前三个参数同上，com_result 为财评过程数据表，三维列表[表页[表单]]
备注：
----------
1. 本方法是类对象的核心算法，会涉及到较大量的有效中间计算结果，需梳理好临时变量，以便能将结果输出；
2. 注意临时变量的分类和初始化工作；
3. 第一阶段默认建设期为 1 年，运营期（含建设期）为 21 年，后续再行扩充可变建设期和运营期。<|endoftext|> |
9b54e5b63475da0e335649ab13e1a31b4da041c458364e57236ac5d1ffe5e1bf | @staticmethod
def com_payback(cash_array):
'\n 根据现金流量数组，计算对应的回收期。\n\n 输入参数：\n -----------\n cash_array: np.array<>\n 现金流量数组，一维 np.array 数组\n \n 返回结果：\n ----------\n payback_period: float\n 与现金流量表对应的项目某种回收期，单位为“年”\n \n 备注：\n ----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意对特殊情况，即项目回收期无限大，即项目无法收回投资的处理。\n\n '
pass | 根据现金流量数组，计算对应的回收期。
输入参数：
-----------
cash_array: np.array<>
现金流量数组，一维 np.array 数组
返回结果：
----------
payback_period: float
与现金流量表对应的项目某种回收期，单位为“年”
备注：
----------
1. 为扩大方法的使用范围，将方法设置为类方法；
2. 第一阶段暂不考虑输入参数无效的检查和处理；
3. 注意对特殊情况，即项目回收期无限大，即项目无法收回投资的处理。 | finance/base.py | com_payback | path2019/Finance | 0 | python | @staticmethod
def com_payback(cash_array):
'\n 根据现金流量数组，计算对应的回收期。\n\n 输入参数：\n -----------\n cash_array: np.array<>\n 现金流量数组，一维 np.array 数组\n \n 返回结果：\n ----------\n payback_period: float\n 与现金流量表对应的项目某种回收期，单位为“年”\n \n 备注：\n ----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意对特殊情况，即项目回收期无限大，即项目无法收回投资的处理。\n\n '
pass | @staticmethod
def com_payback(cash_array):
'\n 根据现金流量数组，计算对应的回收期。\n\n 输入参数：\n -----------\n cash_array: np.array<>\n 现金流量数组，一维 np.array 数组\n \n 返回结果：\n ----------\n payback_period: float\n 与现金流量表对应的项目某种回收期，单位为“年”\n \n 备注：\n ----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意对特殊情况，即项目回收期无限大，即项目无法收回投资的处理。\n\n '
pass<|docstring|>根据现金流量数组，计算对应的回收期。
输入参数：
-----------
cash_array: np.array<>
现金流量数组，一维 np.array 数组
返回结果：
----------
payback_period: float
与现金流量表对应的项目某种回收期，单位为“年”
备注：
----------
1. 为扩大方法的使用范围，将方法设置为类方法；
2. 第一阶段暂不考虑输入参数无效的检查和处理；
3. 注意对特殊情况，即项目回收期无限大，即项目无法收回投资的处理。<|endoftext|> |
b4711a9f955076847b60a07ba0d07e8657c76c731dd97660964c8f7907e87cef | @staticmethod
def com_irr(cash_array):
'\n 根据输入的现金流量数组，计算对应的内部收益率。\n\n 输入参数：\n ----------\n cash_array: np.array<>\n 现金流量数组，一维 np.array 数组\n\n 返回结果：\n ----------\n irr: float\n 现金流量数组所对应的项目某个内部收益率\n\n 备注：\n ----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意极端情况的考虑。\n '
return npf.irr(cash_array) | 根据输入的现金流量数组，计算对应的内部收益率。
输入参数：
----------
cash_array: np.array<>
现金流量数组，一维 np.array 数组
返回结果：
----------
irr: float
现金流量数组所对应的项目某个内部收益率
备注：
----------
1. 为扩大方法的使用范围，将方法设置为类方法；
2. 第一阶段暂不考虑输入参数无效的检查和处理；
3. 注意极端情况的考虑。 | finance/base.py | com_irr | path2019/Finance | 0 | python | @staticmethod
def com_irr(cash_array):
'\n 根据输入的现金流量数组，计算对应的内部收益率。\n\n 输入参数：\n ----------\n cash_array: np.array<>\n 现金流量数组，一维 np.array 数组\n\n 返回结果：\n ----------\n irr: float\n 现金流量数组所对应的项目某个内部收益率\n\n 备注：\n ----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意极端情况的考虑。\n '
return npf.irr(cash_array) | @staticmethod
def com_irr(cash_array):
'\n 根据输入的现金流量数组，计算对应的内部收益率。\n\n 输入参数：\n ----------\n cash_array: np.array<>\n 现金流量数组，一维 np.array 数组\n\n 返回结果：\n ----------\n irr: float\n 现金流量数组所对应的项目某个内部收益率\n\n 备注：\n ----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意极端情况的考虑。\n '
return npf.irr(cash_array)<|docstring|>根据输入的现金流量数组，计算对应的内部收益率。
输入参数：
----------
cash_array: np.array<>
现金流量数组，一维 np.array 数组
返回结果：
----------
irr: float
现金流量数组所对应的项目某个内部收益率
备注：
----------
1. 为扩大方法的使用范围，将方法设置为类方法；
2. 第一阶段暂不考虑输入参数无效的检查和处理；
3. 注意极端情况的考虑。<|endoftext|> |
d1dd126016eb2530d376e394988d6839bb008c257c3603ee3655ca1092608cf5 | @staticmethod
def com_present(cash_array, discount_rate=0.05):
'\n 根据所给的现金流量数组和折现率，计算对应的项目净现值。\n\n 输入参数：\n ----------\n cash_array: np.array<>\n 现金流量数组，一维 np.array 数组\n\n discount_rate: float\n 折现率，默认值为 5 %\n\n 返回结果：\n ----------\n present_value: float\n 与所给的现金流量数组和折现率对应的项目净现值\n \n 备注：\n -----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意特殊情况的考虑。 \n\n '
return np.npv(discount_rate, cash_array) | 根据所给的现金流量数组和折现率，计算对应的项目净现值。
输入参数：
----------
cash_array: np.array<>
现金流量数组，一维 np.array 数组
discount_rate: float
折现率，默认值为 5 %
返回结果：
----------
present_value: float
与所给的现金流量数组和折现率对应的项目净现值
备注：
-----------
1. 为扩大方法的使用范围，将方法设置为类方法；
2. 第一阶段暂不考虑输入参数无效的检查和处理；
3. 注意特殊情况的考虑。 | finance/base.py | com_present | path2019/Finance | 0 | python | @staticmethod
def com_present(cash_array, discount_rate=0.05):
'\n 根据所给的现金流量数组和折现率，计算对应的项目净现值。\n\n 输入参数：\n ----------\n cash_array: np.array<>\n 现金流量数组，一维 np.array 数组\n\n discount_rate: float\n 折现率，默认值为 5 %\n\n 返回结果：\n ----------\n present_value: float\n 与所给的现金流量数组和折现率对应的项目净现值\n \n 备注：\n -----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意特殊情况的考虑。 \n\n '
return np.npv(discount_rate, cash_array) | @staticmethod
def com_present(cash_array, discount_rate=0.05):
'\n 根据所给的现金流量数组和折现率，计算对应的项目净现值。\n\n 输入参数：\n ----------\n cash_array: np.array<>\n 现金流量数组，一维 np.array 数组\n\n discount_rate: float\n 折现率，默认值为 5 %\n\n 返回结果：\n ----------\n present_value: float\n 与所给的现金流量数组和折现率对应的项目净现值\n \n 备注：\n -----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意特殊情况的考虑。 \n\n '
return np.npv(discount_rate, cash_array)<|docstring|>根据所给的现金流量数组和折现率，计算对应的项目净现值。
输入参数：
----------
cash_array: np.array<>
现金流量数组，一维 np.array 数组
discount_rate: float
折现率，默认值为 5 %
返回结果：
----------
present_value: float
与所给的现金流量数组和折现率对应的项目净现值
备注：
-----------
1. 为扩大方法的使用范围，将方法设置为类方法；
2. 第一阶段暂不考虑输入参数无效的检查和处理；
3. 注意特殊情况的考虑。<|endoftext|> |
4eff87cbb75531b897e58094490cb43e81a8daec985dfbf33a46cee80436e132 | @staticmethod
def com_lcoe(cost_array, power, discount_rate):
'\n 根据所给的总费用数组、总发电量和折现率，计算对应的LCOE（平准化度电成本）。\n\n 输入参数：\n ----------\n cost_array: np.array<>\n 项目总费用流量数组，一维 np.array 数组\n\n power: float\n 运营期内总发电量\n\n discount_rate: float\n 折现率\n\n 返回结果：\n ----------\n lcoe: float\n 对应所给总费用流量数组、总发电量和折现率的平准化度电成本\n\n 备注：\n ----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意特殊情况的考虑。 \n\n '
pass | 根据所给的总费用数组、总发电量和折现率，计算对应的LCOE（平准化度电成本）。
输入参数：
----------
cost_array: np.array<>
项目总费用流量数组，一维 np.array 数组
power: float
运营期内总发电量
discount_rate: float
折现率
返回结果：
----------
lcoe: float
对应所给总费用流量数组、总发电量和折现率的平准化度电成本
备注：
----------
1. 为扩大方法的使用范围，将方法设置为类方法；
2. 第一阶段暂不考虑输入参数无效的检查和处理；
3. 注意特殊情况的考虑。 | finance/base.py | com_lcoe | path2019/Finance | 0 | python | @staticmethod
def com_lcoe(cost_array, power, discount_rate):
'\n 根据所给的总费用数组、总发电量和折现率，计算对应的LCOE（平准化度电成本）。\n\n 输入参数：\n ----------\n cost_array: np.array<>\n 项目总费用流量数组，一维 np.array 数组\n\n power: float\n 运营期内总发电量\n\n discount_rate: float\n 折现率\n\n 返回结果：\n ----------\n lcoe: float\n 对应所给总费用流量数组、总发电量和折现率的平准化度电成本\n\n 备注：\n ----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意特殊情况的考虑。 \n\n '
pass | @staticmethod
def com_lcoe(cost_array, power, discount_rate):
'\n 根据所给的总费用数组、总发电量和折现率，计算对应的LCOE（平准化度电成本）。\n\n 输入参数：\n ----------\n cost_array: np.array<>\n 项目总费用流量数组，一维 np.array 数组\n\n power: float\n 运营期内总发电量\n\n discount_rate: float\n 折现率\n\n 返回结果：\n ----------\n lcoe: float\n 对应所给总费用流量数组、总发电量和折现率的平准化度电成本\n\n 备注：\n ----------\n 1. 为扩大方法的使用范围，将方法设置为类方法；\n 2. 第一阶段暂不考虑输入参数无效的检查和处理；\n 3. 注意特殊情况的考虑。 \n\n '
pass<|docstring|>根据所给的总费用数组、总发电量和折现率，计算对应的LCOE（平准化度电成本）。
输入参数：
----------
cost_array: np.array<>
项目总费用流量数组，一维 np.array 数组
power: float
运营期内总发电量
discount_rate: float
折现率
返回结果：
----------
lcoe: float
对应所给总费用流量数组、总发电量和折现率的平准化度电成本
备注：
----------
1. 为扩大方法的使用范围，将方法设置为类方法；
2. 第一阶段暂不考虑输入参数无效的检查和处理；
3. 注意特殊情况的考虑。<|endoftext|> |
730c5d962e7b29e98620785b38fd1b856e9c049251530102517b49ac1f2b16f4 | def load_json_data(json_url):
'\n :param json_url: "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_month.geojson"\n :return: Earthquakes json data\n '
data = requests.get(json_url).json()
return data | :param json_url: "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_month.geojson"
:return: Earthquakes json data | read_json_url_requests.py | load_json_data | arturosolutions/earthquakes | 0 | python | def load_json_data(json_url):
'\n :param json_url: "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_month.geojson"\n :return: Earthquakes json data\n '
data = requests.get(json_url).json()
return data | def load_json_data(json_url):
'\n :param json_url: "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_month.geojson"\n :return: Earthquakes json data\n '
data = requests.get(json_url).json()
return data<|docstring|>:param json_url: "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_month.geojson"
:return: Earthquakes json data<|endoftext|> |
1e8aaf063e0dfb7fd4479ad9adbbd2ee0741cfaf29926d6e4a4ec34a73b39bbd | def get_place_and_magnitude(data):
'\n :param data: Earthquakes json data\n :return: List of places and magnitudes, where magnitude is greater than 1.0.\n '
start = time.time()
data = load_json_data(url)
for dictionary in data['features']:
place = dictionary['properties']['place']
magnitude = dictionary['properties']['mag']
if (magnitude > 1.0):
print(place, '|', magnitude)
end = time.time()
print('Time spent', (end - start), 'seconds') | :param data: Earthquakes json data
:return: List of places and magnitudes, where magnitude is greater than 1.0. | read_json_url_requests.py | get_place_and_magnitude | arturosolutions/earthquakes | 0 | python | def get_place_and_magnitude(data):
'\n :param data: Earthquakes json data\n :return: List of places and magnitudes, where magnitude is greater than 1.0.\n '
start = time.time()
data = load_json_data(url)
for dictionary in data['features']:
place = dictionary['properties']['place']
magnitude = dictionary['properties']['mag']
if (magnitude > 1.0):
print(place, '|', magnitude)
end = time.time()
print('Time spent', (end - start), 'seconds') | def get_place_and_magnitude(data):
'\n :param data: Earthquakes json data\n :return: List of places and magnitudes, where magnitude is greater than 1.0.\n '
start = time.time()
data = load_json_data(url)
for dictionary in data['features']:
place = dictionary['properties']['place']
magnitude = dictionary['properties']['mag']
if (magnitude > 1.0):
print(place, '|', magnitude)
end = time.time()
print('Time spent', (end - start), 'seconds')<|docstring|>:param data: Earthquakes json data
:return: List of places and magnitudes, where magnitude is greater than 1.0.<|endoftext|> |
cd7897d90a29a134cab8991ab1a91a5ce7783001430ac931abc576ea41ac4aae | def do(*l, kernel=None):
'\n Simply create a chain of operations. Useful inside of if statements\n (do (X 0) (X 1))\n '
return do_template.format('\n'.join(l)).split(' ') | Simply create a chain of operations. Useful inside of if statements
(do (X 0) (X 1)) | qurry/libraries/standard_library/constructs/do.py | do | LSaldyt/curry | 11 | python | def do(*l, kernel=None):
'\n Simply create a chain of operations. Useful inside of if statements\n (do (X 0) (X 1))\n '
return do_template.format('\n'.join(l)).split(' ') | def do(*l, kernel=None):
'\n Simply create a chain of operations. Useful inside of if statements\n (do (X 0) (X 1))\n '
return do_template.format('\n'.join(l)).split(' ')<|docstring|>Simply create a chain of operations. Useful inside of if statements
(do (X 0) (X 1))<|endoftext|> |
41996488a883d37a5b334a6f00c7dd6e8fd3538549cacfc03d519a657211794b | def test_sparse(self):
' test sparse fields. '
record = self.env['sparse_fields.test'].create({})
self.assertFalse(record.data)
partner = self.env.ref('base.main_partner')
values = [('boolean', True), ('integer', 42), ('float', 3.14), ('char', 'John'), ('selection', 'two'), ('partner', partner.id)]
for (n, (key, val)) in enumerate(values):
record.write({key: val})
self.assertEqual(record.data, dict(values[:(n + 1)]))
for (key, val) in values[:(- 1)]:
self.assertEqual(record[key], val)
self.assertEqual(record.partner, partner)
for (n, (key, val)) in enumerate(values):
record.write({key: False})
self.assertEqual(record.data, dict(values[(n + 1):]))
names = [name for (name, _) in values]
domain = [('model', '=', 'sparse_fields.test'), ('name', 'in', names)]
fields = self.env['ir.model.fields'].search(domain)
self.assertEqual(len(fields), len(names))
for field in fields:
self.assertEqual(field.serialization_field_id.name, 'data') | test sparse fields. | addons/base_sparse_field/tests/test_sparse_fields.py | test_sparse | SHIVJITH/Odoo_Machine_Test | 0 | python | def test_sparse(self):
' '
record = self.env['sparse_fields.test'].create({})
self.assertFalse(record.data)
partner = self.env.ref('base.main_partner')
values = [('boolean', True), ('integer', 42), ('float', 3.14), ('char', 'John'), ('selection', 'two'), ('partner', partner.id)]
for (n, (key, val)) in enumerate(values):
record.write({key: val})
self.assertEqual(record.data, dict(values[:(n + 1)]))
for (key, val) in values[:(- 1)]:
self.assertEqual(record[key], val)
self.assertEqual(record.partner, partner)
for (n, (key, val)) in enumerate(values):
record.write({key: False})
self.assertEqual(record.data, dict(values[(n + 1):]))
names = [name for (name, _) in values]
domain = [('model', '=', 'sparse_fields.test'), ('name', 'in', names)]
fields = self.env['ir.model.fields'].search(domain)
self.assertEqual(len(fields), len(names))
for field in fields:
self.assertEqual(field.serialization_field_id.name, 'data') | def test_sparse(self):
' '
record = self.env['sparse_fields.test'].create({})
self.assertFalse(record.data)
partner = self.env.ref('base.main_partner')
values = [('boolean', True), ('integer', 42), ('float', 3.14), ('char', 'John'), ('selection', 'two'), ('partner', partner.id)]
for (n, (key, val)) in enumerate(values):
record.write({key: val})
self.assertEqual(record.data, dict(values[:(n + 1)]))
for (key, val) in values[:(- 1)]:
self.assertEqual(record[key], val)
self.assertEqual(record.partner, partner)
for (n, (key, val)) in enumerate(values):
record.write({key: False})
self.assertEqual(record.data, dict(values[(n + 1):]))
names = [name for (name, _) in values]
domain = [('model', '=', 'sparse_fields.test'), ('name', 'in', names)]
fields = self.env['ir.model.fields'].search(domain)
self.assertEqual(len(fields), len(names))
for field in fields:
self.assertEqual(field.serialization_field_id.name, 'data')<|docstring|>test sparse fields.<|endoftext|> |
49197ca777c7b27ef45f8ece0349088b5f5d95632af99259b457581bab9108a1 | def __init__(self, module):
'\n Constructor\n '
if (not HAS_PYSNOW):
module.fail_json(msg=missing_required_lib('pysnow'), exception=PYSNOW_IMP_ERR)
self.module = module
self.params = module.params
self.client_id = self.params['client_id']
self.client_secret = self.params['client_secret']
self.username = self.params['username']
self.password = self.params['password']
self.instance = self.params['instance']
self.session = {'token': None}
self.conn = None | Constructor | ansible/venv/lib/python2.7/site-packages/ansible/module_utils/service_now.py | __init__ | gvashchenkolineate/gvashchenkolineate_infra_trytravis | 17 | python | def __init__(self, module):
'\n \n '
if (not HAS_PYSNOW):
module.fail_json(msg=missing_required_lib('pysnow'), exception=PYSNOW_IMP_ERR)
self.module = module
self.params = module.params
self.client_id = self.params['client_id']
self.client_secret = self.params['client_secret']
self.username = self.params['username']
self.password = self.params['password']
self.instance = self.params['instance']
self.session = {'token': None}
self.conn = None | def __init__(self, module):
'\n \n '
if (not HAS_PYSNOW):
module.fail_json(msg=missing_required_lib('pysnow'), exception=PYSNOW_IMP_ERR)
self.module = module
self.params = module.params
self.client_id = self.params['client_id']
self.client_secret = self.params['client_secret']
self.username = self.params['username']
self.password = self.params['password']
self.instance = self.params['instance']
self.session = {'token': None}
self.conn = None<|docstring|>Constructor<|endoftext|> |
fa40b02a58e78835c8f705f779ae53f7766c0a02589f2390f8da55526e6974e0 | def __init__(self, pubchem=None, drugbank=None, chembl=None, chebi=None, hmdb=None, mychem_info=None):
'CompoundInfoIdentifiers - a model defined in OpenAPI\n\n :param pubchem: The pubchem of this CompoundInfoIdentifiers. # noqa: E501\n :type pubchem: str\n :param drugbank: The drugbank of this CompoundInfoIdentifiers. # noqa: E501\n :type drugbank: str\n :param chembl: The chembl of this CompoundInfoIdentifiers. # noqa: E501\n :type chembl: str\n :param chebi: The chebi of this CompoundInfoIdentifiers. # noqa: E501\n :type chebi: str\n :param hmdb: The hmdb of this CompoundInfoIdentifiers. # noqa: E501\n :type hmdb: str\n :param mychem_info: The mychem_info of this CompoundInfoIdentifiers. # noqa: E501\n :type mychem_info: str\n '
self.openapi_types = {'pubchem': str, 'drugbank': str, 'chembl': str, 'chebi': str, 'hmdb': str, 'mychem_info': str}
self.attribute_map = {'pubchem': 'pubchem', 'drugbank': 'drugbank', 'chembl': 'chembl', 'chebi': 'chebi', 'hmdb': 'hmdb', 'mychem_info': 'mychem_info'}
self._pubchem = pubchem
self._drugbank = drugbank
self._chembl = chembl
self._chebi = chebi
self._hmdb = hmdb
self._mychem_info = mychem_info | CompoundInfoIdentifiers - a model defined in OpenAPI
:param pubchem: The pubchem of this CompoundInfoIdentifiers. # noqa: E501
:type pubchem: str
:param drugbank: The drugbank of this CompoundInfoIdentifiers. # noqa: E501
:type drugbank: str
:param chembl: The chembl of this CompoundInfoIdentifiers. # noqa: E501
:type chembl: str
:param chebi: The chebi of this CompoundInfoIdentifiers. # noqa: E501
:type chebi: str
:param hmdb: The hmdb of this CompoundInfoIdentifiers. # noqa: E501
:type hmdb: str
:param mychem_info: The mychem_info of this CompoundInfoIdentifiers. # noqa: E501
:type mychem_info: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | __init__ | pahmadi8740/molecular-data-provider | 5 | python | def __init__(self, pubchem=None, drugbank=None, chembl=None, chebi=None, hmdb=None, mychem_info=None):
'CompoundInfoIdentifiers - a model defined in OpenAPI\n\n :param pubchem: The pubchem of this CompoundInfoIdentifiers. # noqa: E501\n :type pubchem: str\n :param drugbank: The drugbank of this CompoundInfoIdentifiers. # noqa: E501\n :type drugbank: str\n :param chembl: The chembl of this CompoundInfoIdentifiers. # noqa: E501\n :type chembl: str\n :param chebi: The chebi of this CompoundInfoIdentifiers. # noqa: E501\n :type chebi: str\n :param hmdb: The hmdb of this CompoundInfoIdentifiers. # noqa: E501\n :type hmdb: str\n :param mychem_info: The mychem_info of this CompoundInfoIdentifiers. # noqa: E501\n :type mychem_info: str\n '
self.openapi_types = {'pubchem': str, 'drugbank': str, 'chembl': str, 'chebi': str, 'hmdb': str, 'mychem_info': str}
self.attribute_map = {'pubchem': 'pubchem', 'drugbank': 'drugbank', 'chembl': 'chembl', 'chebi': 'chebi', 'hmdb': 'hmdb', 'mychem_info': 'mychem_info'}
self._pubchem = pubchem
self._drugbank = drugbank
self._chembl = chembl
self._chebi = chebi
self._hmdb = hmdb
self._mychem_info = mychem_info | def __init__(self, pubchem=None, drugbank=None, chembl=None, chebi=None, hmdb=None, mychem_info=None):
'CompoundInfoIdentifiers - a model defined in OpenAPI\n\n :param pubchem: The pubchem of this CompoundInfoIdentifiers. # noqa: E501\n :type pubchem: str\n :param drugbank: The drugbank of this CompoundInfoIdentifiers. # noqa: E501\n :type drugbank: str\n :param chembl: The chembl of this CompoundInfoIdentifiers. # noqa: E501\n :type chembl: str\n :param chebi: The chebi of this CompoundInfoIdentifiers. # noqa: E501\n :type chebi: str\n :param hmdb: The hmdb of this CompoundInfoIdentifiers. # noqa: E501\n :type hmdb: str\n :param mychem_info: The mychem_info of this CompoundInfoIdentifiers. # noqa: E501\n :type mychem_info: str\n '
self.openapi_types = {'pubchem': str, 'drugbank': str, 'chembl': str, 'chebi': str, 'hmdb': str, 'mychem_info': str}
self.attribute_map = {'pubchem': 'pubchem', 'drugbank': 'drugbank', 'chembl': 'chembl', 'chebi': 'chebi', 'hmdb': 'hmdb', 'mychem_info': 'mychem_info'}
self._pubchem = pubchem
self._drugbank = drugbank
self._chembl = chembl
self._chebi = chebi
self._hmdb = hmdb
self._mychem_info = mychem_info<|docstring|>CompoundInfoIdentifiers - a model defined in OpenAPI
:param pubchem: The pubchem of this CompoundInfoIdentifiers. # noqa: E501
:type pubchem: str
:param drugbank: The drugbank of this CompoundInfoIdentifiers. # noqa: E501
:type drugbank: str
:param chembl: The chembl of this CompoundInfoIdentifiers. # noqa: E501
:type chembl: str
:param chebi: The chebi of this CompoundInfoIdentifiers. # noqa: E501
:type chebi: str
:param hmdb: The hmdb of this CompoundInfoIdentifiers. # noqa: E501
:type hmdb: str
:param mychem_info: The mychem_info of this CompoundInfoIdentifiers. # noqa: E501
:type mychem_info: str<|endoftext|> |
b433c0f23523cd4ab7e7a1f88f237183082e8ffa03929b937ef04a0c1ac1516d | @classmethod
def from_dict(cls, dikt) -> 'CompoundInfoIdentifiers':
'Returns the dict as a model\n\n :param dikt: A dict.\n :type: dict\n :return: The compound_info_identifiers of this CompoundInfoIdentifiers. # noqa: E501\n :rtype: CompoundInfoIdentifiers\n '
return util.deserialize_model(dikt, cls) | Returns the dict as a model
:param dikt: A dict.
:type: dict
:return: The compound_info_identifiers of this CompoundInfoIdentifiers. # noqa: E501
:rtype: CompoundInfoIdentifiers | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | from_dict | pahmadi8740/molecular-data-provider | 5 | python | @classmethod
def from_dict(cls, dikt) -> 'CompoundInfoIdentifiers':
'Returns the dict as a model\n\n :param dikt: A dict.\n :type: dict\n :return: The compound_info_identifiers of this CompoundInfoIdentifiers. # noqa: E501\n :rtype: CompoundInfoIdentifiers\n '
return util.deserialize_model(dikt, cls) | @classmethod
def from_dict(cls, dikt) -> 'CompoundInfoIdentifiers':
'Returns the dict as a model\n\n :param dikt: A dict.\n :type: dict\n :return: The compound_info_identifiers of this CompoundInfoIdentifiers. # noqa: E501\n :rtype: CompoundInfoIdentifiers\n '
return util.deserialize_model(dikt, cls)<|docstring|>Returns the dict as a model
:param dikt: A dict.
:type: dict
:return: The compound_info_identifiers of this CompoundInfoIdentifiers. # noqa: E501
:rtype: CompoundInfoIdentifiers<|endoftext|> |
b37aa65400b4a8671c4699f1b798a0cac710e04e216f7c54fc2c64fa8184c81b | @property
def pubchem(self):
'Gets the pubchem of this CompoundInfoIdentifiers.\n\n PubChem CID of the compound (CURIE). # noqa: E501\n\n :return: The pubchem of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._pubchem | Gets the pubchem of this CompoundInfoIdentifiers.
PubChem CID of the compound (CURIE). # noqa: E501
:return: The pubchem of this CompoundInfoIdentifiers.
:rtype: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | pubchem | pahmadi8740/molecular-data-provider | 5 | python | @property
def pubchem(self):
'Gets the pubchem of this CompoundInfoIdentifiers.\n\n PubChem CID of the compound (CURIE). # noqa: E501\n\n :return: The pubchem of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._pubchem | @property
def pubchem(self):
'Gets the pubchem of this CompoundInfoIdentifiers.\n\n PubChem CID of the compound (CURIE). # noqa: E501\n\n :return: The pubchem of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._pubchem<|docstring|>Gets the pubchem of this CompoundInfoIdentifiers.
PubChem CID of the compound (CURIE). # noqa: E501
:return: The pubchem of this CompoundInfoIdentifiers.
:rtype: str<|endoftext|> |
362d03029c91952897faba36dbe457fd72c16f740e0589a5b7233007ddc7cda7 | @pubchem.setter
def pubchem(self, pubchem):
'Sets the pubchem of this CompoundInfoIdentifiers.\n\n PubChem CID of the compound (CURIE). # noqa: E501\n\n :param pubchem: The pubchem of this CompoundInfoIdentifiers.\n :type pubchem: str\n '
self._pubchem = pubchem | Sets the pubchem of this CompoundInfoIdentifiers.
PubChem CID of the compound (CURIE). # noqa: E501
:param pubchem: The pubchem of this CompoundInfoIdentifiers.
:type pubchem: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | pubchem | pahmadi8740/molecular-data-provider | 5 | python | @pubchem.setter
def pubchem(self, pubchem):
'Sets the pubchem of this CompoundInfoIdentifiers.\n\n PubChem CID of the compound (CURIE). # noqa: E501\n\n :param pubchem: The pubchem of this CompoundInfoIdentifiers.\n :type pubchem: str\n '
self._pubchem = pubchem | @pubchem.setter
def pubchem(self, pubchem):
'Sets the pubchem of this CompoundInfoIdentifiers.\n\n PubChem CID of the compound (CURIE). # noqa: E501\n\n :param pubchem: The pubchem of this CompoundInfoIdentifiers.\n :type pubchem: str\n '
self._pubchem = pubchem<|docstring|>Sets the pubchem of this CompoundInfoIdentifiers.
PubChem CID of the compound (CURIE). # noqa: E501
:param pubchem: The pubchem of this CompoundInfoIdentifiers.
:type pubchem: str<|endoftext|> |
24f1c001fe1b322575f266f4cc9cd2325cb1ae51bcd0730e95f1cde5ecc38e89 | @property
def drugbank(self):
'Gets the drugbank of this CompoundInfoIdentifiers.\n\n DrugBank id of the compound (CURIE). # noqa: E501\n\n :return: The drugbank of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._drugbank | Gets the drugbank of this CompoundInfoIdentifiers.
DrugBank id of the compound (CURIE). # noqa: E501
:return: The drugbank of this CompoundInfoIdentifiers.
:rtype: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | drugbank | pahmadi8740/molecular-data-provider | 5 | python | @property
def drugbank(self):
'Gets the drugbank of this CompoundInfoIdentifiers.\n\n DrugBank id of the compound (CURIE). # noqa: E501\n\n :return: The drugbank of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._drugbank | @property
def drugbank(self):
'Gets the drugbank of this CompoundInfoIdentifiers.\n\n DrugBank id of the compound (CURIE). # noqa: E501\n\n :return: The drugbank of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._drugbank<|docstring|>Gets the drugbank of this CompoundInfoIdentifiers.
DrugBank id of the compound (CURIE). # noqa: E501
:return: The drugbank of this CompoundInfoIdentifiers.
:rtype: str<|endoftext|> |
95197a4b1f87b9a1c797793b2e6492e6a527578b7c329d641c5d94f771b01200 | @drugbank.setter
def drugbank(self, drugbank):
'Sets the drugbank of this CompoundInfoIdentifiers.\n\n DrugBank id of the compound (CURIE). # noqa: E501\n\n :param drugbank: The drugbank of this CompoundInfoIdentifiers.\n :type drugbank: str\n '
self._drugbank = drugbank | Sets the drugbank of this CompoundInfoIdentifiers.
DrugBank id of the compound (CURIE). # noqa: E501
:param drugbank: The drugbank of this CompoundInfoIdentifiers.
:type drugbank: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | drugbank | pahmadi8740/molecular-data-provider | 5 | python | @drugbank.setter
def drugbank(self, drugbank):
'Sets the drugbank of this CompoundInfoIdentifiers.\n\n DrugBank id of the compound (CURIE). # noqa: E501\n\n :param drugbank: The drugbank of this CompoundInfoIdentifiers.\n :type drugbank: str\n '
self._drugbank = drugbank | @drugbank.setter
def drugbank(self, drugbank):
'Sets the drugbank of this CompoundInfoIdentifiers.\n\n DrugBank id of the compound (CURIE). # noqa: E501\n\n :param drugbank: The drugbank of this CompoundInfoIdentifiers.\n :type drugbank: str\n '
self._drugbank = drugbank<|docstring|>Sets the drugbank of this CompoundInfoIdentifiers.
DrugBank id of the compound (CURIE). # noqa: E501
:param drugbank: The drugbank of this CompoundInfoIdentifiers.
:type drugbank: str<|endoftext|> |
1641915563b922e9a2c6cc911edc592fc0a089a393bcc85212efee9e903cb90b | @property
def chembl(self):
'Gets the chembl of this CompoundInfoIdentifiers.\n\n ChEMBL id of the compound (CURIE). # noqa: E501\n\n :return: The chembl of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._chembl | Gets the chembl of this CompoundInfoIdentifiers.
ChEMBL id of the compound (CURIE). # noqa: E501
:return: The chembl of this CompoundInfoIdentifiers.
:rtype: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | chembl | pahmadi8740/molecular-data-provider | 5 | python | @property
def chembl(self):
'Gets the chembl of this CompoundInfoIdentifiers.\n\n ChEMBL id of the compound (CURIE). # noqa: E501\n\n :return: The chembl of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._chembl | @property
def chembl(self):
'Gets the chembl of this CompoundInfoIdentifiers.\n\n ChEMBL id of the compound (CURIE). # noqa: E501\n\n :return: The chembl of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._chembl<|docstring|>Gets the chembl of this CompoundInfoIdentifiers.
ChEMBL id of the compound (CURIE). # noqa: E501
:return: The chembl of this CompoundInfoIdentifiers.
:rtype: str<|endoftext|> |
ae0b9b6eb8cdac10200f24f98201d22e67d606284c6cd7c9adda31d8dee96392 | @chembl.setter
def chembl(self, chembl):
'Sets the chembl of this CompoundInfoIdentifiers.\n\n ChEMBL id of the compound (CURIE). # noqa: E501\n\n :param chembl: The chembl of this CompoundInfoIdentifiers.\n :type chembl: str\n '
self._chembl = chembl | Sets the chembl of this CompoundInfoIdentifiers.
ChEMBL id of the compound (CURIE). # noqa: E501
:param chembl: The chembl of this CompoundInfoIdentifiers.
:type chembl: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | chembl | pahmadi8740/molecular-data-provider | 5 | python | @chembl.setter
def chembl(self, chembl):
'Sets the chembl of this CompoundInfoIdentifiers.\n\n ChEMBL id of the compound (CURIE). # noqa: E501\n\n :param chembl: The chembl of this CompoundInfoIdentifiers.\n :type chembl: str\n '
self._chembl = chembl | @chembl.setter
def chembl(self, chembl):
'Sets the chembl of this CompoundInfoIdentifiers.\n\n ChEMBL id of the compound (CURIE). # noqa: E501\n\n :param chembl: The chembl of this CompoundInfoIdentifiers.\n :type chembl: str\n '
self._chembl = chembl<|docstring|>Sets the chembl of this CompoundInfoIdentifiers.
ChEMBL id of the compound (CURIE). # noqa: E501
:param chembl: The chembl of this CompoundInfoIdentifiers.
:type chembl: str<|endoftext|> |
d8a15712b85bc3b8d55c881b9bf00f5ed17c78a1adcfe53e56068f192b99d2b2 | @property
def chebi(self):
'Gets the chebi of this CompoundInfoIdentifiers.\n\n ChEBI id of the compound (CURIE). # noqa: E501\n\n :return: The chebi of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._chebi | Gets the chebi of this CompoundInfoIdentifiers.
ChEBI id of the compound (CURIE). # noqa: E501
:return: The chebi of this CompoundInfoIdentifiers.
:rtype: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | chebi | pahmadi8740/molecular-data-provider | 5 | python | @property
def chebi(self):
'Gets the chebi of this CompoundInfoIdentifiers.\n\n ChEBI id of the compound (CURIE). # noqa: E501\n\n :return: The chebi of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._chebi | @property
def chebi(self):
'Gets the chebi of this CompoundInfoIdentifiers.\n\n ChEBI id of the compound (CURIE). # noqa: E501\n\n :return: The chebi of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._chebi<|docstring|>Gets the chebi of this CompoundInfoIdentifiers.
ChEBI id of the compound (CURIE). # noqa: E501
:return: The chebi of this CompoundInfoIdentifiers.
:rtype: str<|endoftext|> |
4810dda84c55bd577f2956d86eea38897209c77d11c0f8c181d28a2a604223ef | @chebi.setter
def chebi(self, chebi):
'Sets the chebi of this CompoundInfoIdentifiers.\n\n ChEBI id of the compound (CURIE). # noqa: E501\n\n :param chebi: The chebi of this CompoundInfoIdentifiers.\n :type chebi: str\n '
self._chebi = chebi | Sets the chebi of this CompoundInfoIdentifiers.
ChEBI id of the compound (CURIE). # noqa: E501
:param chebi: The chebi of this CompoundInfoIdentifiers.
:type chebi: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | chebi | pahmadi8740/molecular-data-provider | 5 | python | @chebi.setter
def chebi(self, chebi):
'Sets the chebi of this CompoundInfoIdentifiers.\n\n ChEBI id of the compound (CURIE). # noqa: E501\n\n :param chebi: The chebi of this CompoundInfoIdentifiers.\n :type chebi: str\n '
self._chebi = chebi | @chebi.setter
def chebi(self, chebi):
'Sets the chebi of this CompoundInfoIdentifiers.\n\n ChEBI id of the compound (CURIE). # noqa: E501\n\n :param chebi: The chebi of this CompoundInfoIdentifiers.\n :type chebi: str\n '
self._chebi = chebi<|docstring|>Sets the chebi of this CompoundInfoIdentifiers.
ChEBI id of the compound (CURIE). # noqa: E501
:param chebi: The chebi of this CompoundInfoIdentifiers.
:type chebi: str<|endoftext|> |
64f137d5dd14047725ce88133ef39a441af6517a2744f154f579c2820be39f67 | @property
def hmdb(self):
'Gets the hmdb of this CompoundInfoIdentifiers.\n\n HMDB id of the compound (CURIE). # noqa: E501\n\n :return: The hmdb of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._hmdb | Gets the hmdb of this CompoundInfoIdentifiers.
HMDB id of the compound (CURIE). # noqa: E501
:return: The hmdb of this CompoundInfoIdentifiers.
:rtype: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | hmdb | pahmadi8740/molecular-data-provider | 5 | python | @property
def hmdb(self):
'Gets the hmdb of this CompoundInfoIdentifiers.\n\n HMDB id of the compound (CURIE). # noqa: E501\n\n :return: The hmdb of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._hmdb | @property
def hmdb(self):
'Gets the hmdb of this CompoundInfoIdentifiers.\n\n HMDB id of the compound (CURIE). # noqa: E501\n\n :return: The hmdb of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._hmdb<|docstring|>Gets the hmdb of this CompoundInfoIdentifiers.
HMDB id of the compound (CURIE). # noqa: E501
:return: The hmdb of this CompoundInfoIdentifiers.
:rtype: str<|endoftext|> |
f59a46754dff88b93a716ccdf7197b46d2e1917677e294caea52ee89cb40a8a3 | @hmdb.setter
def hmdb(self, hmdb):
'Sets the hmdb of this CompoundInfoIdentifiers.\n\n HMDB id of the compound (CURIE). # noqa: E501\n\n :param hmdb: The hmdb of this CompoundInfoIdentifiers.\n :type hmdb: str\n '
self._hmdb = hmdb | Sets the hmdb of this CompoundInfoIdentifiers.
HMDB id of the compound (CURIE). # noqa: E501
:param hmdb: The hmdb of this CompoundInfoIdentifiers.
:type hmdb: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | hmdb | pahmadi8740/molecular-data-provider | 5 | python | @hmdb.setter
def hmdb(self, hmdb):
'Sets the hmdb of this CompoundInfoIdentifiers.\n\n HMDB id of the compound (CURIE). # noqa: E501\n\n :param hmdb: The hmdb of this CompoundInfoIdentifiers.\n :type hmdb: str\n '
self._hmdb = hmdb | @hmdb.setter
def hmdb(self, hmdb):
'Sets the hmdb of this CompoundInfoIdentifiers.\n\n HMDB id of the compound (CURIE). # noqa: E501\n\n :param hmdb: The hmdb of this CompoundInfoIdentifiers.\n :type hmdb: str\n '
self._hmdb = hmdb<|docstring|>Sets the hmdb of this CompoundInfoIdentifiers.
HMDB id of the compound (CURIE). # noqa: E501
:param hmdb: The hmdb of this CompoundInfoIdentifiers.
:type hmdb: str<|endoftext|> |
1505cdd13332004072372b06f7e5bef753ef0cf3135b35df733c070d94a2ac5f | @property
def mychem_info(self):
'Gets the mychem_info of this CompoundInfoIdentifiers.\n\n myChem.info id of the compound. # noqa: E501\n\n :return: The mychem_info of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._mychem_info | Gets the mychem_info of this CompoundInfoIdentifiers.
myChem.info id of the compound. # noqa: E501
:return: The mychem_info of this CompoundInfoIdentifiers.
:rtype: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | mychem_info | pahmadi8740/molecular-data-provider | 5 | python | @property
def mychem_info(self):
'Gets the mychem_info of this CompoundInfoIdentifiers.\n\n myChem.info id of the compound. # noqa: E501\n\n :return: The mychem_info of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._mychem_info | @property
def mychem_info(self):
'Gets the mychem_info of this CompoundInfoIdentifiers.\n\n myChem.info id of the compound. # noqa: E501\n\n :return: The mychem_info of this CompoundInfoIdentifiers.\n :rtype: str\n '
return self._mychem_info<|docstring|>Gets the mychem_info of this CompoundInfoIdentifiers.
myChem.info id of the compound. # noqa: E501
:return: The mychem_info of this CompoundInfoIdentifiers.
:rtype: str<|endoftext|> |
0f241f364025177caca63913b17cd38e97a9c554c53272cee3e33d650d2e76e8 | @mychem_info.setter
def mychem_info(self, mychem_info):
'Sets the mychem_info of this CompoundInfoIdentifiers.\n\n myChem.info id of the compound. # noqa: E501\n\n :param mychem_info: The mychem_info of this CompoundInfoIdentifiers.\n :type mychem_info: str\n '
self._mychem_info = mychem_info | Sets the mychem_info of this CompoundInfoIdentifiers.
myChem.info id of the compound. # noqa: E501
:param mychem_info: The mychem_info of this CompoundInfoIdentifiers.
:type mychem_info: str | transformers/pubchem/python-flask-server/openapi_server/models/compound_info_identifiers.py | mychem_info | pahmadi8740/molecular-data-provider | 5 | python | @mychem_info.setter
def mychem_info(self, mychem_info):
'Sets the mychem_info of this CompoundInfoIdentifiers.\n\n myChem.info id of the compound. # noqa: E501\n\n :param mychem_info: The mychem_info of this CompoundInfoIdentifiers.\n :type mychem_info: str\n '
self._mychem_info = mychem_info | @mychem_info.setter
def mychem_info(self, mychem_info):
'Sets the mychem_info of this CompoundInfoIdentifiers.\n\n myChem.info id of the compound. # noqa: E501\n\n :param mychem_info: The mychem_info of this CompoundInfoIdentifiers.\n :type mychem_info: str\n '
self._mychem_info = mychem_info<|docstring|>Sets the mychem_info of this CompoundInfoIdentifiers.
myChem.info id of the compound. # noqa: E501
:param mychem_info: The mychem_info of this CompoundInfoIdentifiers.
:type mychem_info: str<|endoftext|> |
393112421766e64935e057a7ebe91854e6140266c290294953650368454e0348 | def __init__(self, env):
'\n envs: list of gym environments to run in subprocesses\n '
self.env = env
self.remotes = [0] | envs: list of gym environments to run in subprocesses | common/FakeVecEnv.py | __init__ | neverdie88/collective-planning | 1 | python | def __init__(self, env):
'\n \n '
self.env = env
self.remotes = [0] | def __init__(self, env):
'\n \n '
self.env = env
self.remotes = [0]<|docstring|>envs: list of gym environments to run in subprocesses<|endoftext|> |
415b17435a6cd23000ee3e0d3c99121de6fe7810b5e394741135318fc59699e9 | def check_params(self):
'\n Validate some of the parameters.\n '
if (not (((self.xM_size * self.yN_size) % self.N) == 0)):
raise ValueError | Validate some of the parameters. | src/fourier_transform/algorithm_parameters.py | check_params | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def check_params(self):
'\n \n '
if (not (((self.xM_size * self.yN_size) % self.N) == 0)):
raise ValueError | def check_params(self):
'\n \n '
if (not (((self.xM_size * self.yN_size) % self.N) == 0)):
raise ValueError<|docstring|>Validate some of the parameters.<|endoftext|> |
940e0938623a9f8bf87ec7097afde10054dc2de52f4e9471873738b057fbcb3d | def calculate_facet_off(self):
'\n Calculate facet offset array\n '
facet_off = (self.yB_size * numpy.arange(self.nfacet))
return facet_off | Calculate facet offset array | src/fourier_transform/algorithm_parameters.py | calculate_facet_off | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def calculate_facet_off(self):
'\n \n '
facet_off = (self.yB_size * numpy.arange(self.nfacet))
return facet_off | def calculate_facet_off(self):
'\n \n '
facet_off = (self.yB_size * numpy.arange(self.nfacet))
return facet_off<|docstring|>Calculate facet offset array<|endoftext|> |
f48c879af7376b43242903b15603de1706c67f62df417073be8810b9e99f53fa | def calculate_subgrid_off(self):
'\n Calculate subgrid offset array\n '
subgrid_off = ((self.xA_size * numpy.arange(self.nsubgrid)) + self.Nx)
return subgrid_off | Calculate subgrid offset array | src/fourier_transform/algorithm_parameters.py | calculate_subgrid_off | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def calculate_subgrid_off(self):
'\n \n '
subgrid_off = ((self.xA_size * numpy.arange(self.nsubgrid)) + self.Nx)
return subgrid_off | def calculate_subgrid_off(self):
'\n \n '
subgrid_off = ((self.xA_size * numpy.arange(self.nsubgrid)) + self.Nx)
return subgrid_off<|docstring|>Calculate subgrid offset array<|endoftext|> |
881fb19f3f246b2e43f28159b4211041526f7883af2cc60b52e3b8d604c5431e | def _generate_mask(self, mask_size, offsets):
'\n Determine the appropriate masks for cutting out subgrids/facets.\n For each offset in offsets, a mask is generated of size mask_size.\n The mask is centred around the specific offset.\n\n :param mask_size: size of the required mask (xA_size or yB_size)\n :param offsets: array of subgrid or facet offsets\n (subgrid_off or facet_off)\n\n :return: mask (subgrid_A or facet_B)\n '
mask = numpy.zeros((len(offsets), mask_size), dtype=int)
border = ((offsets + numpy.hstack([offsets[1:], [(self.N + offsets[0])]])) // 2)
for (i, offset) in enumerate(offsets):
left = (((border[(i - 1)] - offset) + (mask_size // 2)) % self.N)
right = ((border[i] - offset) + (mask_size // 2))
if ((not (left >= 0)) and (right <= mask_size)):
raise ValueError('Mask size not large enough to cover subgrids / facets!')
mask[(i, left:right)] = 1
return mask | Determine the appropriate masks for cutting out subgrids/facets.
For each offset in offsets, a mask is generated of size mask_size.
The mask is centred around the specific offset.
:param mask_size: size of the required mask (xA_size or yB_size)
:param offsets: array of subgrid or facet offsets
(subgrid_off or facet_off)
:return: mask (subgrid_A or facet_B) | src/fourier_transform/algorithm_parameters.py | _generate_mask | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def _generate_mask(self, mask_size, offsets):
'\n Determine the appropriate masks for cutting out subgrids/facets.\n For each offset in offsets, a mask is generated of size mask_size.\n The mask is centred around the specific offset.\n\n :param mask_size: size of the required mask (xA_size or yB_size)\n :param offsets: array of subgrid or facet offsets\n (subgrid_off or facet_off)\n\n :return: mask (subgrid_A or facet_B)\n '
mask = numpy.zeros((len(offsets), mask_size), dtype=int)
border = ((offsets + numpy.hstack([offsets[1:], [(self.N + offsets[0])]])) // 2)
for (i, offset) in enumerate(offsets):
left = (((border[(i - 1)] - offset) + (mask_size // 2)) % self.N)
right = ((border[i] - offset) + (mask_size // 2))
if ((not (left >= 0)) and (right <= mask_size)):
raise ValueError('Mask size not large enough to cover subgrids / facets!')
mask[(i, left:right)] = 1
return mask | def _generate_mask(self, mask_size, offsets):
'\n Determine the appropriate masks for cutting out subgrids/facets.\n For each offset in offsets, a mask is generated of size mask_size.\n The mask is centred around the specific offset.\n\n :param mask_size: size of the required mask (xA_size or yB_size)\n :param offsets: array of subgrid or facet offsets\n (subgrid_off or facet_off)\n\n :return: mask (subgrid_A or facet_B)\n '
mask = numpy.zeros((len(offsets), mask_size), dtype=int)
border = ((offsets + numpy.hstack([offsets[1:], [(self.N + offsets[0])]])) // 2)
for (i, offset) in enumerate(offsets):
left = (((border[(i - 1)] - offset) + (mask_size // 2)) % self.N)
right = ((border[i] - offset) + (mask_size // 2))
if ((not (left >= 0)) and (right <= mask_size)):
raise ValueError('Mask size not large enough to cover subgrids / facets!')
mask[(i, left:right)] = 1
return mask<|docstring|>Determine the appropriate masks for cutting out subgrids/facets.
For each offset in offsets, a mask is generated of size mask_size.
The mask is centred around the specific offset.
:param mask_size: size of the required mask (xA_size or yB_size)
:param offsets: array of subgrid or facet offsets
(subgrid_off or facet_off)
:return: mask (subgrid_A or facet_B)<|endoftext|> |
99102ca93893d0dc9e72473f3c3dfda8d60c7afb836d038c882b7617b92a0bab | def calculate_facet_B(self):
'\n Calculate facet mask\n '
facet_B = self._generate_mask(self.yB_size, self.facet_off)
return facet_B | Calculate facet mask | src/fourier_transform/algorithm_parameters.py | calculate_facet_B | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def calculate_facet_B(self):
'\n \n '
facet_B = self._generate_mask(self.yB_size, self.facet_off)
return facet_B | def calculate_facet_B(self):
'\n \n '
facet_B = self._generate_mask(self.yB_size, self.facet_off)
return facet_B<|docstring|>Calculate facet mask<|endoftext|> |
1a290f454d13b84a100b1f95b859a69163297667804b929ca322b9120bc3efb4 | def calculate_subgrid_A(self):
'\n Calculate subgrid mask\n '
subgrid_A = self._generate_mask(self.xA_size, self.subgrid_off)
return subgrid_A | Calculate subgrid mask | src/fourier_transform/algorithm_parameters.py | calculate_subgrid_A | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def calculate_subgrid_A(self):
'\n \n '
subgrid_A = self._generate_mask(self.xA_size, self.subgrid_off)
return subgrid_A | def calculate_subgrid_A(self):
'\n \n '
subgrid_A = self._generate_mask(self.xA_size, self.subgrid_off)
return subgrid_A<|docstring|>Calculate subgrid mask<|endoftext|> |
c3bdb95a4242af31d6d708668343641539945c2e398488e7c60e2d460f768411 | def calculate_Fb(self):
'\n Calculate the Fourier transform of grid correction function\n '
Fb = (1 / extract_mid(self.pswf, self.yB_size, axis=0))
return Fb | Calculate the Fourier transform of grid correction function | src/fourier_transform/algorithm_parameters.py | calculate_Fb | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def calculate_Fb(self):
'\n \n '
Fb = (1 / extract_mid(self.pswf, self.yB_size, axis=0))
return Fb | def calculate_Fb(self):
'\n \n '
Fb = (1 / extract_mid(self.pswf, self.yB_size, axis=0))
return Fb<|docstring|>Calculate the Fourier transform of grid correction function<|endoftext|> |
598f895b02e34385a3d750215f90b537b2648efc5aac65c04beaede98c8d5787 | def calculate_Fn(self):
'\n Calculate the Fourier transform of gridding function\n '
Fn = self.pswf[((self.yN_size // 2) % int((self.N / self.xM_size)))::int((self.N / self.xM_size))]
return Fn | Calculate the Fourier transform of gridding function | src/fourier_transform/algorithm_parameters.py | calculate_Fn | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def calculate_Fn(self):
'\n \n '
Fn = self.pswf[((self.yN_size // 2) % int((self.N / self.xM_size)))::int((self.N / self.xM_size))]
return Fn | def calculate_Fn(self):
'\n \n '
Fn = self.pswf[((self.yN_size // 2) % int((self.N / self.xM_size)))::int((self.N / self.xM_size))]
return Fn<|docstring|>Calculate the Fourier transform of gridding function<|endoftext|> |
c11a7d8fc4b4c8ffb09af334aaa0c3b9ed91f7e9b843692fb8721ef44abb303d | def calculate_facet_m0_trunc(self):
'\n Calculate the mask truncated to a facet (image space)\n '
temp_facet_m0_trunc = (self.pswf * numpy.sinc((((coordinates(self.yN_size) * self.xM_size) / self.N) * self.yN_size)))
facet_m0_trunc = (((self.xM_size * self.yP_size) / self.N) * extract_mid(ifft(pad_mid(temp_facet_m0_trunc, self.yP_size, axis=0), axis=0), self.xMxN_yP_size, axis=0).real)
return facet_m0_trunc | Calculate the mask truncated to a facet (image space) | src/fourier_transform/algorithm_parameters.py | calculate_facet_m0_trunc | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def calculate_facet_m0_trunc(self):
'\n \n '
temp_facet_m0_trunc = (self.pswf * numpy.sinc((((coordinates(self.yN_size) * self.xM_size) / self.N) * self.yN_size)))
facet_m0_trunc = (((self.xM_size * self.yP_size) / self.N) * extract_mid(ifft(pad_mid(temp_facet_m0_trunc, self.yP_size, axis=0), axis=0), self.xMxN_yP_size, axis=0).real)
return facet_m0_trunc | def calculate_facet_m0_trunc(self):
'\n \n '
temp_facet_m0_trunc = (self.pswf * numpy.sinc((((coordinates(self.yN_size) * self.xM_size) / self.N) * self.yN_size)))
facet_m0_trunc = (((self.xM_size * self.yP_size) / self.N) * extract_mid(ifft(pad_mid(temp_facet_m0_trunc, self.yP_size, axis=0), axis=0), self.xMxN_yP_size, axis=0).real)
return facet_m0_trunc<|docstring|>Calculate the mask truncated to a facet (image space)<|endoftext|> |
80d0666e2482a8353e1256b79b044d2fbfbfb40fe90f954915bc2920612974b3 | def calculate_pswf(self):
'\n Calculate 1D PSWF (prolate-spheroidal wave function) at the\n full required resolution (facet size)\n\n See also: VLA Scientific Memoranda 129, 131, 132\n '
alpha = 0
pswf = scipy.special.pro_ang1(alpha, alpha, ((numpy.pi * self.W) / 2), (2 * coordinates(self.yN_size)))[0]
pswf[0] = 0
pswf = pswf.real
pswf /= numpy.prod(numpy.arange(((2 * alpha) - 1), 0, (- 2), dtype=float))
return pswf | Calculate 1D PSWF (prolate-spheroidal wave function) at the
full required resolution (facet size)
See also: VLA Scientific Memoranda 129, 131, 132 | src/fourier_transform/algorithm_parameters.py | calculate_pswf | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | def calculate_pswf(self):
'\n Calculate 1D PSWF (prolate-spheroidal wave function) at the\n full required resolution (facet size)\n\n See also: VLA Scientific Memoranda 129, 131, 132\n '
alpha = 0
pswf = scipy.special.pro_ang1(alpha, alpha, ((numpy.pi * self.W) / 2), (2 * coordinates(self.yN_size)))[0]
pswf[0] = 0
pswf = pswf.real
pswf /= numpy.prod(numpy.arange(((2 * alpha) - 1), 0, (- 2), dtype=float))
return pswf | def calculate_pswf(self):
'\n Calculate 1D PSWF (prolate-spheroidal wave function) at the\n full required resolution (facet size)\n\n See also: VLA Scientific Memoranda 129, 131, 132\n '
alpha = 0
pswf = scipy.special.pro_ang1(alpha, alpha, ((numpy.pi * self.W) / 2), (2 * coordinates(self.yN_size)))[0]
pswf[0] = 0
pswf = pswf.real
pswf /= numpy.prod(numpy.arange(((2 * alpha) - 1), 0, (- 2), dtype=float))
return pswf<|docstring|>Calculate 1D PSWF (prolate-spheroidal wave function) at the
full required resolution (facet size)
See also: VLA Scientific Memoranda 129, 131, 132<|endoftext|> |
26beb9ea2ba5756773551c718209885a8da24cd3878795f21f353e1985fc2275 | @dask_wrapper
def prepare_facet(self, facet, axis, Fb, **kwargs):
'\n Calculate the inverse FFT of a padded facet element multiplied by Fb\n (Fb: Fourier transform of grid correction function)\n\n :param facet: single facet element\n :param axis: axis along which operations are performed (0 or 1)\n :param Fb: Fourier transform of grid correction function\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: TODO: BF? prepared facet\n '
BF = pad_mid((facet * broadcast(Fb, len(facet.shape), axis)), self.yP_size, axis)
BF = ifft(BF, axis)
return BF | Calculate the inverse FFT of a padded facet element multiplied by Fb
(Fb: Fourier transform of grid correction function)
:param facet: single facet element
:param axis: axis along which operations are performed (0 or 1)
:param Fb: Fourier transform of grid correction function
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: TODO: BF? prepared facet | src/fourier_transform/algorithm_parameters.py | prepare_facet | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | @dask_wrapper
def prepare_facet(self, facet, axis, Fb, **kwargs):
'\n Calculate the inverse FFT of a padded facet element multiplied by Fb\n (Fb: Fourier transform of grid correction function)\n\n :param facet: single facet element\n :param axis: axis along which operations are performed (0 or 1)\n :param Fb: Fourier transform of grid correction function\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: TODO: BF? prepared facet\n '
BF = pad_mid((facet * broadcast(Fb, len(facet.shape), axis)), self.yP_size, axis)
BF = ifft(BF, axis)
return BF | @dask_wrapper
def prepare_facet(self, facet, axis, Fb, **kwargs):
'\n Calculate the inverse FFT of a padded facet element multiplied by Fb\n (Fb: Fourier transform of grid correction function)\n\n :param facet: single facet element\n :param axis: axis along which operations are performed (0 or 1)\n :param Fb: Fourier transform of grid correction function\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: TODO: BF? prepared facet\n '
BF = pad_mid((facet * broadcast(Fb, len(facet.shape), axis)), self.yP_size, axis)
BF = ifft(BF, axis)
return BF<|docstring|>Calculate the inverse FFT of a padded facet element multiplied by Fb
(Fb: Fourier transform of grid correction function)
:param facet: single facet element
:param axis: axis along which operations are performed (0 or 1)
:param Fb: Fourier transform of grid correction function
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: TODO: BF? prepared facet<|endoftext|> |
73c8549d516c9b1ad937dc9a82fafd2b9b979e0a8b9e0939aacb8712539a05b7 | @dask_wrapper
def extract_facet_contrib_to_subgrid(self, BF, axis, subgrid_off_elem, facet_m0_trunc, Fn, **kwargs):
'\n Extract the facet contribution to a subgrid.\n\n :param BF: TODO: ? prepared facet\n :param axis: axis along which the operations are performed (0 or 1)\n :param subgrid_off_elem: single subgrid offset element\n :param facet_m0_trunc: mask truncated to a facet (image space)\n :param Fn: Fourier transform of gridding function\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: contribution of facet to subgrid\n '
dims = len(BF.shape)
BF_mid = extract_mid(numpy.roll(BF, (((- subgrid_off_elem) * self.yP_size) // self.N), axis), self.xMxN_yP_size, axis)
MBF = (broadcast(facet_m0_trunc, dims, axis) * BF_mid)
MBF_sum = numpy.array(extract_mid(MBF, self.xM_yP_size, axis))
xN_yP_size = (self.xMxN_yP_size - self.xM_yP_size)
slc1 = create_slice(slice(None), slice((xN_yP_size // 2)), dims, axis)
slc2 = create_slice(slice(None), slice(((- xN_yP_size) // 2), None), dims, axis)
MBF_sum[slc1] += MBF[slc2]
MBF_sum[slc2] += MBF[slc1]
return (broadcast(Fn, len(BF.shape), axis) * extract_mid(fft(MBF_sum, axis), self.xM_yN_size, axis)) | Extract the facet contribution to a subgrid.
:param BF: TODO: ? prepared facet
:param axis: axis along which the operations are performed (0 or 1)
:param subgrid_off_elem: single subgrid offset element
:param facet_m0_trunc: mask truncated to a facet (image space)
:param Fn: Fourier transform of gridding function
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: contribution of facet to subgrid | src/fourier_transform/algorithm_parameters.py | extract_facet_contrib_to_subgrid | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | @dask_wrapper
def extract_facet_contrib_to_subgrid(self, BF, axis, subgrid_off_elem, facet_m0_trunc, Fn, **kwargs):
'\n Extract the facet contribution to a subgrid.\n\n :param BF: TODO: ? prepared facet\n :param axis: axis along which the operations are performed (0 or 1)\n :param subgrid_off_elem: single subgrid offset element\n :param facet_m0_trunc: mask truncated to a facet (image space)\n :param Fn: Fourier transform of gridding function\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: contribution of facet to subgrid\n '
dims = len(BF.shape)
BF_mid = extract_mid(numpy.roll(BF, (((- subgrid_off_elem) * self.yP_size) // self.N), axis), self.xMxN_yP_size, axis)
MBF = (broadcast(facet_m0_trunc, dims, axis) * BF_mid)
MBF_sum = numpy.array(extract_mid(MBF, self.xM_yP_size, axis))
xN_yP_size = (self.xMxN_yP_size - self.xM_yP_size)
slc1 = create_slice(slice(None), slice((xN_yP_size // 2)), dims, axis)
slc2 = create_slice(slice(None), slice(((- xN_yP_size) // 2), None), dims, axis)
MBF_sum[slc1] += MBF[slc2]
MBF_sum[slc2] += MBF[slc1]
return (broadcast(Fn, len(BF.shape), axis) * extract_mid(fft(MBF_sum, axis), self.xM_yN_size, axis)) | @dask_wrapper
def extract_facet_contrib_to_subgrid(self, BF, axis, subgrid_off_elem, facet_m0_trunc, Fn, **kwargs):
'\n Extract the facet contribution to a subgrid.\n\n :param BF: TODO: ? prepared facet\n :param axis: axis along which the operations are performed (0 or 1)\n :param subgrid_off_elem: single subgrid offset element\n :param facet_m0_trunc: mask truncated to a facet (image space)\n :param Fn: Fourier transform of gridding function\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: contribution of facet to subgrid\n '
dims = len(BF.shape)
BF_mid = extract_mid(numpy.roll(BF, (((- subgrid_off_elem) * self.yP_size) // self.N), axis), self.xMxN_yP_size, axis)
MBF = (broadcast(facet_m0_trunc, dims, axis) * BF_mid)
MBF_sum = numpy.array(extract_mid(MBF, self.xM_yP_size, axis))
xN_yP_size = (self.xMxN_yP_size - self.xM_yP_size)
slc1 = create_slice(slice(None), slice((xN_yP_size // 2)), dims, axis)
slc2 = create_slice(slice(None), slice(((- xN_yP_size) // 2), None), dims, axis)
MBF_sum[slc1] += MBF[slc2]
MBF_sum[slc2] += MBF[slc1]
return (broadcast(Fn, len(BF.shape), axis) * extract_mid(fft(MBF_sum, axis), self.xM_yN_size, axis))<|docstring|>Extract the facet contribution to a subgrid.
:param BF: TODO: ? prepared facet
:param axis: axis along which the operations are performed (0 or 1)
:param subgrid_off_elem: single subgrid offset element
:param facet_m0_trunc: mask truncated to a facet (image space)
:param Fn: Fourier transform of gridding function
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: contribution of facet to subgrid<|endoftext|> |
004ef272ab22b3c484046f9aaa04575ff372cd7bb496b32e9c13cfe68ffcee57 | @dask_wrapper
def add_facet_contribution(self, facet_contrib, facet_off_elem, axis, **kwargs):
'\n Further transforms facet contributions, which then will be summed up.\n\n :param facet_contrib: array-chunk of individual facet contributions\n :param facet_off_elem: facet offset for the facet_contrib array chunk\n :param axis: axis along which the operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: TODO??\n '
return numpy.roll(pad_mid(facet_contrib, self.xM_size, axis), ((facet_off_elem * self.xM_size) // self.N), axis=axis) | Further transforms facet contributions, which then will be summed up.
:param facet_contrib: array-chunk of individual facet contributions
:param facet_off_elem: facet offset for the facet_contrib array chunk
:param axis: axis along which the operations are performed (0 or 1)
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: TODO?? | src/fourier_transform/algorithm_parameters.py | add_facet_contribution | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | @dask_wrapper
def add_facet_contribution(self, facet_contrib, facet_off_elem, axis, **kwargs):
'\n Further transforms facet contributions, which then will be summed up.\n\n :param facet_contrib: array-chunk of individual facet contributions\n :param facet_off_elem: facet offset for the facet_contrib array chunk\n :param axis: axis along which the operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: TODO??\n '
return numpy.roll(pad_mid(facet_contrib, self.xM_size, axis), ((facet_off_elem * self.xM_size) // self.N), axis=axis) | @dask_wrapper
def add_facet_contribution(self, facet_contrib, facet_off_elem, axis, **kwargs):
'\n Further transforms facet contributions, which then will be summed up.\n\n :param facet_contrib: array-chunk of individual facet contributions\n :param facet_off_elem: facet offset for the facet_contrib array chunk\n :param axis: axis along which the operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: TODO??\n '
return numpy.roll(pad_mid(facet_contrib, self.xM_size, axis), ((facet_off_elem * self.xM_size) // self.N), axis=axis)<|docstring|>Further transforms facet contributions, which then will be summed up.
:param facet_contrib: array-chunk of individual facet contributions
:param facet_off_elem: facet offset for the facet_contrib array chunk
:param axis: axis along which the operations are performed (0 or 1)
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: TODO??<|endoftext|> |
be1ae30a00ae3ec20ab35d6087237b2702cc4a6ea99148a81826119ddb620d3e | @dask_wrapper
def finish_subgrid(self, summed_facets, subgrid_mask1, subgrid_mask2, **kwargs):
'\n Obtain finished subgrid.\n Operation performed for both axis\n (only works on 2D arrays in its current form).\n\n :param summed_facets: summed facets contributing to thins subgrid\n :param subgrid_mask1: ith subgrid mask element\n :param subgrid_mask2: (i+1)th subgrid mask element\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: approximate subgrid element\n '
tmp = extract_mid(extract_mid(ifft(ifft(summed_facets, axis=0), axis=1), self.xA_size, axis=0), self.xA_size, axis=1)
approx_subgrid = (tmp * numpy.outer(subgrid_mask1, subgrid_mask2))
return approx_subgrid | Obtain finished subgrid.
Operation performed for both axis
(only works on 2D arrays in its current form).
:param summed_facets: summed facets contributing to thins subgrid
:param subgrid_mask1: ith subgrid mask element
:param subgrid_mask2: (i+1)th subgrid mask element
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: approximate subgrid element | src/fourier_transform/algorithm_parameters.py | finish_subgrid | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | @dask_wrapper
def finish_subgrid(self, summed_facets, subgrid_mask1, subgrid_mask2, **kwargs):
'\n Obtain finished subgrid.\n Operation performed for both axis\n (only works on 2D arrays in its current form).\n\n :param summed_facets: summed facets contributing to thins subgrid\n :param subgrid_mask1: ith subgrid mask element\n :param subgrid_mask2: (i+1)th subgrid mask element\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: approximate subgrid element\n '
tmp = extract_mid(extract_mid(ifft(ifft(summed_facets, axis=0), axis=1), self.xA_size, axis=0), self.xA_size, axis=1)
approx_subgrid = (tmp * numpy.outer(subgrid_mask1, subgrid_mask2))
return approx_subgrid | @dask_wrapper
def finish_subgrid(self, summed_facets, subgrid_mask1, subgrid_mask2, **kwargs):
'\n Obtain finished subgrid.\n Operation performed for both axis\n (only works on 2D arrays in its current form).\n\n :param summed_facets: summed facets contributing to thins subgrid\n :param subgrid_mask1: ith subgrid mask element\n :param subgrid_mask2: (i+1)th subgrid mask element\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: approximate subgrid element\n '
tmp = extract_mid(extract_mid(ifft(ifft(summed_facets, axis=0), axis=1), self.xA_size, axis=0), self.xA_size, axis=1)
approx_subgrid = (tmp * numpy.outer(subgrid_mask1, subgrid_mask2))
return approx_subgrid<|docstring|>Obtain finished subgrid.
Operation performed for both axis
(only works on 2D arrays in its current form).
:param summed_facets: summed facets contributing to thins subgrid
:param subgrid_mask1: ith subgrid mask element
:param subgrid_mask2: (i+1)th subgrid mask element
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: approximate subgrid element<|endoftext|> |
225e638fcfe77b178d7b03e67072be83afb3f12ea9ea02439d532efb55fa411d | @dask_wrapper
def prepare_subgrid(self, subgrid, **kwargs):
'\n Calculate the FFT of a padded subgrid element.\n No reason to do this per-axis, so always do it for both axis.\n (Note: it will only work for 2D subgrid arrays)\n\n :param subgrid: single subgrid array element\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: TODO: the FS ??? term\n '
padded = pad_mid(pad_mid(subgrid, self.xM_size, axis=0), self.xM_size, axis=1)
fftd = fft(fft(padded, axis=0), axis=1)
return fftd | Calculate the FFT of a padded subgrid element.
No reason to do this per-axis, so always do it for both axis.
(Note: it will only work for 2D subgrid arrays)
:param subgrid: single subgrid array element
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: TODO: the FS ??? term | src/fourier_transform/algorithm_parameters.py | prepare_subgrid | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | @dask_wrapper
def prepare_subgrid(self, subgrid, **kwargs):
'\n Calculate the FFT of a padded subgrid element.\n No reason to do this per-axis, so always do it for both axis.\n (Note: it will only work for 2D subgrid arrays)\n\n :param subgrid: single subgrid array element\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: TODO: the FS ??? term\n '
padded = pad_mid(pad_mid(subgrid, self.xM_size, axis=0), self.xM_size, axis=1)
fftd = fft(fft(padded, axis=0), axis=1)
return fftd | @dask_wrapper
def prepare_subgrid(self, subgrid, **kwargs):
'\n Calculate the FFT of a padded subgrid element.\n No reason to do this per-axis, so always do it for both axis.\n (Note: it will only work for 2D subgrid arrays)\n\n :param subgrid: single subgrid array element\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: TODO: the FS ??? term\n '
padded = pad_mid(pad_mid(subgrid, self.xM_size, axis=0), self.xM_size, axis=1)
fftd = fft(fft(padded, axis=0), axis=1)
return fftd<|docstring|>Calculate the FFT of a padded subgrid element.
No reason to do this per-axis, so always do it for both axis.
(Note: it will only work for 2D subgrid arrays)
:param subgrid: single subgrid array element
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: TODO: the FS ??? term<|endoftext|> |
52a654166f28d4beb57005009b414f02af353505243029efd7ec1188aa745b52 | @dask_wrapper
def extract_subgrid_contrib_to_facet(self, FSi, facet_off_elem, Fn, axis, **kwargs):
'\n Extract contribution of subgrid to a facet.\n\n :param Fsi: TODO???\n :param facet_off_elem: single facet offset element\n :param Fn: Fourier transform of gridding function\n :param axis: axis along which the operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: Contribution of subgrid to facet\n\n '
return (broadcast(Fn, len(FSi.shape), axis) * extract_mid(numpy.roll(FSi, (((- facet_off_elem) * self.xM_size) // self.N), axis), self.xM_yN_size, axis)) | Extract contribution of subgrid to a facet.
:param Fsi: TODO???
:param facet_off_elem: single facet offset element
:param Fn: Fourier transform of gridding function
:param axis: axis along which the operations are performed (0 or 1)
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: Contribution of subgrid to facet | src/fourier_transform/algorithm_parameters.py | extract_subgrid_contrib_to_facet | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | @dask_wrapper
def extract_subgrid_contrib_to_facet(self, FSi, facet_off_elem, Fn, axis, **kwargs):
'\n Extract contribution of subgrid to a facet.\n\n :param Fsi: TODO???\n :param facet_off_elem: single facet offset element\n :param Fn: Fourier transform of gridding function\n :param axis: axis along which the operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: Contribution of subgrid to facet\n\n '
return (broadcast(Fn, len(FSi.shape), axis) * extract_mid(numpy.roll(FSi, (((- facet_off_elem) * self.xM_size) // self.N), axis), self.xM_yN_size, axis)) | @dask_wrapper
def extract_subgrid_contrib_to_facet(self, FSi, facet_off_elem, Fn, axis, **kwargs):
'\n Extract contribution of subgrid to a facet.\n\n :param Fsi: TODO???\n :param facet_off_elem: single facet offset element\n :param Fn: Fourier transform of gridding function\n :param axis: axis along which the operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: Contribution of subgrid to facet\n\n '
return (broadcast(Fn, len(FSi.shape), axis) * extract_mid(numpy.roll(FSi, (((- facet_off_elem) * self.xM_size) // self.N), axis), self.xM_yN_size, axis))<|docstring|>Extract contribution of subgrid to a facet.
:param Fsi: TODO???
:param facet_off_elem: single facet offset element
:param Fn: Fourier transform of gridding function
:param axis: axis along which the operations are performed (0 or 1)
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: Contribution of subgrid to facet<|endoftext|> |
1fd9deb738e3a8db22769909df0ee09d45f35283443ad67d6fe4c4727d5cf8b4 | @dask_wrapper
def add_subgrid_contribution(self, dims, NjSi, subgrid_off_elem, facet_m0_trunc, axis, **kwargs):
'\n Further transform subgrid contributions, which are then summed up.\n\n :param dims: length of tuple to be produced by create_slice\n (i.e. number of dimensions); int\n :param NjSi: TODO\n :param subgrid_off_elem: single subgrid offset element\n :param facet_m0_trunc: mask truncated to a facet (image space)\n :param axis: axis along which operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return summed subgrid contributions\n\n '
xN_yP_size = (self.xMxN_yP_size - self.xM_yP_size)
NjSi_mid = ifft(pad_mid(NjSi, self.xM_yP_size, axis), axis)
NjSi_temp = pad_mid(NjSi_mid, self.xMxN_yP_size, axis)
slc1 = create_slice(slice(None), slice((xN_yP_size // 2)), dims, axis)
slc2 = create_slice(slice(None), slice(((- xN_yP_size) // 2), None), dims, axis)
NjSi_temp[slc1] = NjSi_mid[slc2]
NjSi_temp[slc2] = NjSi_mid[slc1]
NjSi_temp = (NjSi_temp * broadcast(facet_m0_trunc, len(NjSi.shape), axis))
return numpy.roll(pad_mid(NjSi_temp, self.yP_size, axis), ((subgrid_off_elem * self.yP_size) // self.N), axis=axis) | Further transform subgrid contributions, which are then summed up.
:param dims: length of tuple to be produced by create_slice
(i.e. number of dimensions); int
:param NjSi: TODO
:param subgrid_off_elem: single subgrid offset element
:param facet_m0_trunc: mask truncated to a facet (image space)
:param axis: axis along which operations are performed (0 or 1)
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return summed subgrid contributions | src/fourier_transform/algorithm_parameters.py | add_subgrid_contribution | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | @dask_wrapper
def add_subgrid_contribution(self, dims, NjSi, subgrid_off_elem, facet_m0_trunc, axis, **kwargs):
'\n Further transform subgrid contributions, which are then summed up.\n\n :param dims: length of tuple to be produced by create_slice\n (i.e. number of dimensions); int\n :param NjSi: TODO\n :param subgrid_off_elem: single subgrid offset element\n :param facet_m0_trunc: mask truncated to a facet (image space)\n :param axis: axis along which operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return summed subgrid contributions\n\n '
xN_yP_size = (self.xMxN_yP_size - self.xM_yP_size)
NjSi_mid = ifft(pad_mid(NjSi, self.xM_yP_size, axis), axis)
NjSi_temp = pad_mid(NjSi_mid, self.xMxN_yP_size, axis)
slc1 = create_slice(slice(None), slice((xN_yP_size // 2)), dims, axis)
slc2 = create_slice(slice(None), slice(((- xN_yP_size) // 2), None), dims, axis)
NjSi_temp[slc1] = NjSi_mid[slc2]
NjSi_temp[slc2] = NjSi_mid[slc1]
NjSi_temp = (NjSi_temp * broadcast(facet_m0_trunc, len(NjSi.shape), axis))
return numpy.roll(pad_mid(NjSi_temp, self.yP_size, axis), ((subgrid_off_elem * self.yP_size) // self.N), axis=axis) | @dask_wrapper
def add_subgrid_contribution(self, dims, NjSi, subgrid_off_elem, facet_m0_trunc, axis, **kwargs):
'\n Further transform subgrid contributions, which are then summed up.\n\n :param dims: length of tuple to be produced by create_slice\n (i.e. number of dimensions); int\n :param NjSi: TODO\n :param subgrid_off_elem: single subgrid offset element\n :param facet_m0_trunc: mask truncated to a facet (image space)\n :param axis: axis along which operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return summed subgrid contributions\n\n '
xN_yP_size = (self.xMxN_yP_size - self.xM_yP_size)
NjSi_mid = ifft(pad_mid(NjSi, self.xM_yP_size, axis), axis)
NjSi_temp = pad_mid(NjSi_mid, self.xMxN_yP_size, axis)
slc1 = create_slice(slice(None), slice((xN_yP_size // 2)), dims, axis)
slc2 = create_slice(slice(None), slice(((- xN_yP_size) // 2), None), dims, axis)
NjSi_temp[slc1] = NjSi_mid[slc2]
NjSi_temp[slc2] = NjSi_mid[slc1]
NjSi_temp = (NjSi_temp * broadcast(facet_m0_trunc, len(NjSi.shape), axis))
return numpy.roll(pad_mid(NjSi_temp, self.yP_size, axis), ((subgrid_off_elem * self.yP_size) // self.N), axis=axis)<|docstring|>Further transform subgrid contributions, which are then summed up.
:param dims: length of tuple to be produced by create_slice
(i.e. number of dimensions); int
:param NjSi: TODO
:param subgrid_off_elem: single subgrid offset element
:param facet_m0_trunc: mask truncated to a facet (image space)
:param axis: axis along which operations are performed (0 or 1)
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return summed subgrid contributions<|endoftext|> |
353a8fc8af5f6480dd7321c6b6a38bdd01ac2f48e81d94e33ecacff05e4cc2a6 | @dask_wrapper
def finish_facet(self, MiNjSi_sum, facet_B_mask_elem, Fb, axis, **kwargs):
'\n Obtain finished facet.\n\n It extracts from the padded facet (obtained from subgrid via FFT)\n the true-sized facet and multiplies with masked Fb.\n (Fb: Fourier transform of grid correction function)\n\n :param MiNjSi_sum: sum of subgrid contributions to a facet\n :param facet_B_mask_elem: a facet mask element\n :param Fb: Fourier transform of grid correction function\n :param axis: axis along which operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: finished (approximate) facet element\n '
return (extract_mid(fft(MiNjSi_sum, axis), self.yB_size, axis) * broadcast((Fb * facet_B_mask_elem), len(MiNjSi_sum.shape), axis)) | Obtain finished facet.
It extracts from the padded facet (obtained from subgrid via FFT)
the true-sized facet and multiplies with masked Fb.
(Fb: Fourier transform of grid correction function)
:param MiNjSi_sum: sum of subgrid contributions to a facet
:param facet_B_mask_elem: a facet mask element
:param Fb: Fourier transform of grid correction function
:param axis: axis along which operations are performed (0 or 1)
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: finished (approximate) facet element | src/fourier_transform/algorithm_parameters.py | finish_facet | ska-telescope/ska-sdp-distributed-fourier-transform | 0 | python | @dask_wrapper
def finish_facet(self, MiNjSi_sum, facet_B_mask_elem, Fb, axis, **kwargs):
'\n Obtain finished facet.\n\n It extracts from the padded facet (obtained from subgrid via FFT)\n the true-sized facet and multiplies with masked Fb.\n (Fb: Fourier transform of grid correction function)\n\n :param MiNjSi_sum: sum of subgrid contributions to a facet\n :param facet_B_mask_elem: a facet mask element\n :param Fb: Fourier transform of grid correction function\n :param axis: axis along which operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: finished (approximate) facet element\n '
return (extract_mid(fft(MiNjSi_sum, axis), self.yB_size, axis) * broadcast((Fb * facet_B_mask_elem), len(MiNjSi_sum.shape), axis)) | @dask_wrapper
def finish_facet(self, MiNjSi_sum, facet_B_mask_elem, Fb, axis, **kwargs):
'\n Obtain finished facet.\n\n It extracts from the padded facet (obtained from subgrid via FFT)\n the true-sized facet and multiplies with masked Fb.\n (Fb: Fourier transform of grid correction function)\n\n :param MiNjSi_sum: sum of subgrid contributions to a facet\n :param facet_B_mask_elem: a facet mask element\n :param Fb: Fourier transform of grid correction function\n :param axis: axis along which operations are performed (0 or 1)\n :param kwargs: needs to contain the following if dask is used:\n use_dask: True\n nout: <number of function outputs> --> 1\n\n :return: finished (approximate) facet element\n '
return (extract_mid(fft(MiNjSi_sum, axis), self.yB_size, axis) * broadcast((Fb * facet_B_mask_elem), len(MiNjSi_sum.shape), axis))<|docstring|>Obtain finished facet.
It extracts from the padded facet (obtained from subgrid via FFT)
the true-sized facet and multiplies with masked Fb.
(Fb: Fourier transform of grid correction function)
:param MiNjSi_sum: sum of subgrid contributions to a facet
:param facet_B_mask_elem: a facet mask element
:param Fb: Fourier transform of grid correction function
:param axis: axis along which operations are performed (0 or 1)
:param kwargs: needs to contain the following if dask is used:
use_dask: True
nout: <number of function outputs> --> 1
:return: finished (approximate) facet element<|endoftext|> |
69e5f1de0c843cf53840668087329980137c69ddff55dde0508f218e0d8ba0a6 | def setUp(self):
'Set up the test environment.'
self.backend = bluepy.BluepyBackend() | Set up the test environment. | test/integration_tests/test_bluepy_backend.py | setUp | onkelbeh/miflora | 274 | python | def setUp(self):
self.backend = bluepy.BluepyBackend() | def setUp(self):
self.backend = bluepy.BluepyBackend()<|docstring|>Set up the test environment.<|endoftext|> |
9c805c8d8b6e57135ceb77c09cee3579145292ad074c5cbb134115d03a31b481 | def test_scan(self):
'Test scanning for devices.\n\n Note: fore the test to pass, there must be at least one BTLE device\n near by.\n '
devices = bluepy.BluepyBackend.scan_for_devices(5)
self.assertGreater(len(devices), 0)
for device in devices:
self.assertIsNotNone(device) | Test scanning for devices.
Note: fore the test to pass, there must be at least one BTLE device
near by. | test/integration_tests/test_bluepy_backend.py | test_scan | onkelbeh/miflora | 274 | python | def test_scan(self):
'Test scanning for devices.\n\n Note: fore the test to pass, there must be at least one BTLE device\n near by.\n '
devices = bluepy.BluepyBackend.scan_for_devices(5)
self.assertGreater(len(devices), 0)
for device in devices:
self.assertIsNotNone(device) | def test_scan(self):
'Test scanning for devices.\n\n Note: fore the test to pass, there must be at least one BTLE device\n near by.\n '
devices = bluepy.BluepyBackend.scan_for_devices(5)
self.assertGreater(len(devices), 0)
for device in devices:
self.assertIsNotNone(device)<|docstring|>Test scanning for devices.
Note: fore the test to pass, there must be at least one BTLE device
near by.<|endoftext|> |
36bad2f50466050bbb726a54445802854210ec677329597c07dff32812e64f03 | def test_invalid_mac_exception(self):
'Test writing data to handle of the sensor.'
bluepy.RETRY_LIMIT = 1
with self.assertRaises(BluetoothBackendException):
self.backend.connect(TEST_MAC)
self.backend.read_handle(HANDLE_READ_NAME)
bluepy.RETRY_LIMIT = 3 | Test writing data to handle of the sensor. | test/integration_tests/test_bluepy_backend.py | test_invalid_mac_exception | onkelbeh/miflora | 274 | python | def test_invalid_mac_exception(self):
bluepy.RETRY_LIMIT = 1
with self.assertRaises(BluetoothBackendException):
self.backend.connect(TEST_MAC)
self.backend.read_handle(HANDLE_READ_NAME)
bluepy.RETRY_LIMIT = 3 | def test_invalid_mac_exception(self):
bluepy.RETRY_LIMIT = 1
with self.assertRaises(BluetoothBackendException):
self.backend.connect(TEST_MAC)
self.backend.read_handle(HANDLE_READ_NAME)
bluepy.RETRY_LIMIT = 3<|docstring|>Test writing data to handle of the sensor.<|endoftext|> |
e5d5807a074d1c85be105757967ef887f3a21feefd0e9508e613b7da3da59042 | def list2df(self, data):
'Convert string list to dataframe'
cols = data[0].split('\t')
array = []
for line in data[1:]:
array.append(line.split('\t'))
df = pd.DataFrame(np.array(array), columns=cols)
return df | Convert string list to dataframe | quartet_rnaseq_report/modules/rnaseq_performance_assessment/performance_assessment.py | list2df | chinese-quartet/quartet-rnaseq-report | 1 | python | def list2df(self, data):
cols = data[0].split('\t')
array = []
for line in data[1:]:
array.append(line.split('\t'))
df = pd.DataFrame(np.array(array), columns=cols)
return df | def list2df(self, data):
cols = data[0].split('\t')
array = []
for line in data[1:]:
array.append(line.split('\t'))
df = pd.DataFrame(np.array(array), columns=cols)
return df<|docstring|>Convert string list to dataframe<|endoftext|> |
09bb1cefc0da14353a2edb4f6683f8b371e1076f6bf5bf1bf72e036b47f32834 | def draw_point(self, point, color=Color.cyan()):
'\n :type point: Vector\n :type color: Color\n :return: None\n '
if ((0 <= point.x < self.width) and (0 <= point.y < self.height)):
self.put_pixel(point.x, point.y, color) | :type point: Vector
:type color: Color
:return: None | canvas.py | draw_point | loucq123/rasterizer | 79 | python | def draw_point(self, point, color=Color.cyan()):
'\n :type point: Vector\n :type color: Color\n :return: None\n '
if ((0 <= point.x < self.width) and (0 <= point.y < self.height)):
self.put_pixel(point.x, point.y, color) | def draw_point(self, point, color=Color.cyan()):
'\n :type point: Vector\n :type color: Color\n :return: None\n '
if ((0 <= point.x < self.width) and (0 <= point.y < self.height)):
self.put_pixel(point.x, point.y, color)<|docstring|>:type point: Vector
:type color: Color
:return: None<|endoftext|> |
aa6ab13634eb375a6e2450195c5bb7afc27fcff7f1645a608456d8c991eff6cf | def draw_line(self, p1, p2):
'\n :type p1: Vector\n :type p2: Vector\n '
(x1, y1, x2, y2) = [int(i) for i in [p1.x, p1.y, p2.x, p2.y]]
dx = (x2 - x1)
dy = (y2 - y1)
if (abs(dx) > abs(dy)):
(xmin, xmax) = sorted([x1, x2])
ratio = (0 if (dx == 0) else (dy / dx))
for x in range(xmin, xmax):
y = (y1 + ((x - x1) * ratio))
self.draw_point(Vector(x, y))
else:
(ymin, ymax) = sorted([y1, y2])
ratio = (0 if (dy == 0) else (dx / dy))
for y in range(ymin, ymax):
x = (x1 + ((y - y1) * ratio))
self.draw_point(Vector(x, y)) | :type p1: Vector
:type p2: Vector | canvas.py | draw_line | loucq123/rasterizer | 79 | python | def draw_line(self, p1, p2):
'\n :type p1: Vector\n :type p2: Vector\n '
(x1, y1, x2, y2) = [int(i) for i in [p1.x, p1.y, p2.x, p2.y]]
dx = (x2 - x1)
dy = (y2 - y1)
if (abs(dx) > abs(dy)):
(xmin, xmax) = sorted([x1, x2])
ratio = (0 if (dx == 0) else (dy / dx))
for x in range(xmin, xmax):
y = (y1 + ((x - x1) * ratio))
self.draw_point(Vector(x, y))
else:
(ymin, ymax) = sorted([y1, y2])
ratio = (0 if (dy == 0) else (dx / dy))
for y in range(ymin, ymax):
x = (x1 + ((y - y1) * ratio))
self.draw_point(Vector(x, y)) | def draw_line(self, p1, p2):
'\n :type p1: Vector\n :type p2: Vector\n '
(x1, y1, x2, y2) = [int(i) for i in [p1.x, p1.y, p2.x, p2.y]]
dx = (x2 - x1)
dy = (y2 - y1)
if (abs(dx) > abs(dy)):
(xmin, xmax) = sorted([x1, x2])
ratio = (0 if (dx == 0) else (dy / dx))
for x in range(xmin, xmax):
y = (y1 + ((x - x1) * ratio))
self.draw_point(Vector(x, y))
else:
(ymin, ymax) = sorted([y1, y2])
ratio = (0 if (dy == 0) else (dx / dy))
for y in range(ymin, ymax):
x = (x1 + ((y - y1) * ratio))
self.draw_point(Vector(x, y))<|docstring|>:type p1: Vector
:type p2: Vector<|endoftext|> |
2b8740b9f4f18d89332204460d66f906f818c1239eeb51062c7a2490c769a26e | def draw_triangle(self, v1, v2, v3):
'\n :type v1: Vertex\n :type v2: Vertex\n :type v3: Vertex\n '
(a, b, c) = sorted([v1, v2, v3], key=(lambda k: k.position.y))
middle_factor = 0
if ((c.position.y - a.position.y) != 0):
middle_factor = ((b.position.y - a.position.y) / (c.position.y - a.position.y))
middle = interpolate(a, c, middle_factor)
start_y = int(a.position.y)
end_y = int(b.position.y)
for y in range(start_y, (end_y + 1)):
factor = (((y - start_y) / (end_y - start_y)) if (end_y != start_y) else 0)
va = interpolate(a, b, factor)
vb = interpolate(a, middle, factor)
self.draw_scanline(va, vb, y)
start_y = int(b.position.y)
end_y = int(c.position.y)
for y in range(start_y, (end_y + 1)):
factor = (((y - start_y) / (end_y - start_y)) if (end_y != start_y) else 0)
va = interpolate(b, c, factor)
vb = interpolate(middle, c, factor)
self.draw_scanline(va, vb, y) | :type v1: Vertex
:type v2: Vertex
:type v3: Vertex | canvas.py | draw_triangle | loucq123/rasterizer | 79 | python | def draw_triangle(self, v1, v2, v3):
'\n :type v1: Vertex\n :type v2: Vertex\n :type v3: Vertex\n '
(a, b, c) = sorted([v1, v2, v3], key=(lambda k: k.position.y))
middle_factor = 0
if ((c.position.y - a.position.y) != 0):
middle_factor = ((b.position.y - a.position.y) / (c.position.y - a.position.y))
middle = interpolate(a, c, middle_factor)
start_y = int(a.position.y)
end_y = int(b.position.y)
for y in range(start_y, (end_y + 1)):
factor = (((y - start_y) / (end_y - start_y)) if (end_y != start_y) else 0)
va = interpolate(a, b, factor)
vb = interpolate(a, middle, factor)
self.draw_scanline(va, vb, y)
start_y = int(b.position.y)
end_y = int(c.position.y)
for y in range(start_y, (end_y + 1)):
factor = (((y - start_y) / (end_y - start_y)) if (end_y != start_y) else 0)
va = interpolate(b, c, factor)
vb = interpolate(middle, c, factor)
self.draw_scanline(va, vb, y) | def draw_triangle(self, v1, v2, v3):
'\n :type v1: Vertex\n :type v2: Vertex\n :type v3: Vertex\n '
(a, b, c) = sorted([v1, v2, v3], key=(lambda k: k.position.y))
middle_factor = 0
if ((c.position.y - a.position.y) != 0):
middle_factor = ((b.position.y - a.position.y) / (c.position.y - a.position.y))
middle = interpolate(a, c, middle_factor)
start_y = int(a.position.y)
end_y = int(b.position.y)
for y in range(start_y, (end_y + 1)):
factor = (((y - start_y) / (end_y - start_y)) if (end_y != start_y) else 0)
va = interpolate(a, b, factor)
vb = interpolate(a, middle, factor)
self.draw_scanline(va, vb, y)
start_y = int(b.position.y)
end_y = int(c.position.y)
for y in range(start_y, (end_y + 1)):
factor = (((y - start_y) / (end_y - start_y)) if (end_y != start_y) else 0)
va = interpolate(b, c, factor)
vb = interpolate(middle, c, factor)
self.draw_scanline(va, vb, y)<|docstring|>:type v1: Vertex
:type v2: Vertex
:type v3: Vertex<|endoftext|> |
550ddd7c1979ae781d270216f16505f618a287a557f62edf8439c98073e41627 | def test_xy_to_CCT_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition.\n '
np.testing.assert_allclose(xy_to_CCT_CIE_D(np.array([0.382343625, 0.383766261015578]), {'method': 'Nelder-Mead'}), 4000, rtol=1e-07, atol=1e-07)
np.testing.assert_allclose(xy_to_CCT_CIE_D(np.array([0.30535743148688, 0.321646345474552]), {'method': 'Nelder-Mead'}), 7000, rtol=1e-07, atol=1e-07)
np.testing.assert_allclose(xy_to_CCT_CIE_D(np.array([0.24985367, 0.254799464210944]), {'method': 'Nelder-Mead'}), 25000, rtol=1e-07, atol=1e-07) | Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition. | colour/temperature/tests/test_cie_d.py | test_xy_to_CCT_CIE_D | trevorandersen/colour | 6 | python | def test_xy_to_CCT_CIE_D(self):
'\n \n '
np.testing.assert_allclose(xy_to_CCT_CIE_D(np.array([0.382343625, 0.383766261015578]), {'method': 'Nelder-Mead'}), 4000, rtol=1e-07, atol=1e-07)
np.testing.assert_allclose(xy_to_CCT_CIE_D(np.array([0.30535743148688, 0.321646345474552]), {'method': 'Nelder-Mead'}), 7000, rtol=1e-07, atol=1e-07)
np.testing.assert_allclose(xy_to_CCT_CIE_D(np.array([0.24985367, 0.254799464210944]), {'method': 'Nelder-Mead'}), 25000, rtol=1e-07, atol=1e-07) | def test_xy_to_CCT_CIE_D(self):
'\n \n '
np.testing.assert_allclose(xy_to_CCT_CIE_D(np.array([0.382343625, 0.383766261015578]), {'method': 'Nelder-Mead'}), 4000, rtol=1e-07, atol=1e-07)
np.testing.assert_allclose(xy_to_CCT_CIE_D(np.array([0.30535743148688, 0.321646345474552]), {'method': 'Nelder-Mead'}), 7000, rtol=1e-07, atol=1e-07)
np.testing.assert_allclose(xy_to_CCT_CIE_D(np.array([0.24985367, 0.254799464210944]), {'method': 'Nelder-Mead'}), 25000, rtol=1e-07, atol=1e-07)<|docstring|>Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition.<|endoftext|> |
0097f2b4d642db6eb950d42ed89116e4a16ea26e86fa030bb537e00cd5d29cc9 | def test_n_dimensional_xy_to_CCT_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition\n n-dimensional arrays support.\n '
xy = np.array([0.382343625, 0.383766261015578])
CCT = xy_to_CCT_CIE_D(xy)
xy = np.tile(xy, (6, 1))
CCT = np.tile(CCT, 6)
np.testing.assert_almost_equal(xy_to_CCT_CIE_D(xy), CCT, decimal=7)
xy = np.reshape(xy, (2, 3, 2))
CCT = np.reshape(CCT, (2, 3))
np.testing.assert_almost_equal(xy_to_CCT_CIE_D(xy), CCT, decimal=7) | Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition
n-dimensional arrays support. | colour/temperature/tests/test_cie_d.py | test_n_dimensional_xy_to_CCT_CIE_D | trevorandersen/colour | 6 | python | def test_n_dimensional_xy_to_CCT_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition\n n-dimensional arrays support.\n '
xy = np.array([0.382343625, 0.383766261015578])
CCT = xy_to_CCT_CIE_D(xy)
xy = np.tile(xy, (6, 1))
CCT = np.tile(CCT, 6)
np.testing.assert_almost_equal(xy_to_CCT_CIE_D(xy), CCT, decimal=7)
xy = np.reshape(xy, (2, 3, 2))
CCT = np.reshape(CCT, (2, 3))
np.testing.assert_almost_equal(xy_to_CCT_CIE_D(xy), CCT, decimal=7) | def test_n_dimensional_xy_to_CCT_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition\n n-dimensional arrays support.\n '
xy = np.array([0.382343625, 0.383766261015578])
CCT = xy_to_CCT_CIE_D(xy)
xy = np.tile(xy, (6, 1))
CCT = np.tile(CCT, 6)
np.testing.assert_almost_equal(xy_to_CCT_CIE_D(xy), CCT, decimal=7)
xy = np.reshape(xy, (2, 3, 2))
CCT = np.reshape(CCT, (2, 3))
np.testing.assert_almost_equal(xy_to_CCT_CIE_D(xy), CCT, decimal=7)<|docstring|>Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition
n-dimensional arrays support.<|endoftext|> |
a83406968293d3fab8e529fbd1319c1875fc11d73dcc8f2e302d4a9724425a16 | @ignore_numpy_errors
def test_nan_xy_to_CCT_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition nan\n support.\n '
cases = [(- 1.0), 0.0, 1.0, (- np.inf), np.inf, np.nan]
cases = set(permutations((cases * 3), r=2))
for case in cases:
xy_to_CCT_CIE_D(case) | Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition nan
support. | colour/temperature/tests/test_cie_d.py | test_nan_xy_to_CCT_CIE_D | trevorandersen/colour | 6 | python | @ignore_numpy_errors
def test_nan_xy_to_CCT_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition nan\n support.\n '
cases = [(- 1.0), 0.0, 1.0, (- np.inf), np.inf, np.nan]
cases = set(permutations((cases * 3), r=2))
for case in cases:
xy_to_CCT_CIE_D(case) | @ignore_numpy_errors
def test_nan_xy_to_CCT_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition nan\n support.\n '
cases = [(- 1.0), 0.0, 1.0, (- np.inf), np.inf, np.nan]
cases = set(permutations((cases * 3), r=2))
for case in cases:
xy_to_CCT_CIE_D(case)<|docstring|>Tests :func:`colour.temperature.cie_d.xy_to_CCT_CIE_D` definition nan
support.<|endoftext|> |
5eab51c5ddc06260ef9ce292238038b515809b491041b90a2826859696118c6a | def test_CCT_to_xy_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition.\n '
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(4000), np.array([0.382343625, 0.383766261015578]), decimal=7)
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(7000), np.array([0.30535743148688, 0.321646345474552]), decimal=7)
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(25000), np.array([0.24985367, 0.254799464210944]), decimal=7) | Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition. | colour/temperature/tests/test_cie_d.py | test_CCT_to_xy_CIE_D | trevorandersen/colour | 6 | python | def test_CCT_to_xy_CIE_D(self):
'\n \n '
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(4000), np.array([0.382343625, 0.383766261015578]), decimal=7)
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(7000), np.array([0.30535743148688, 0.321646345474552]), decimal=7)
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(25000), np.array([0.24985367, 0.254799464210944]), decimal=7) | def test_CCT_to_xy_CIE_D(self):
'\n \n '
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(4000), np.array([0.382343625, 0.383766261015578]), decimal=7)
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(7000), np.array([0.30535743148688, 0.321646345474552]), decimal=7)
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(25000), np.array([0.24985367, 0.254799464210944]), decimal=7)<|docstring|>Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition.<|endoftext|> |
03bd0928f9a7b34e7d96616c4adccd7b25913ff95ffb02b95a1529e360cab0a3 | def test_n_dimensional_CCT_to_xy_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition\n n-dimensional arrays support.\n '
CCT = 4000
xy = CCT_to_xy_CIE_D(CCT)
CCT = np.tile(CCT, 6)
xy = np.tile(xy, (6, 1))
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(CCT), xy, decimal=7)
CCT = np.reshape(CCT, (2, 3))
xy = np.reshape(xy, (2, 3, 2))
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(CCT), xy, decimal=7) | Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition
n-dimensional arrays support. | colour/temperature/tests/test_cie_d.py | test_n_dimensional_CCT_to_xy_CIE_D | trevorandersen/colour | 6 | python | def test_n_dimensional_CCT_to_xy_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition\n n-dimensional arrays support.\n '
CCT = 4000
xy = CCT_to_xy_CIE_D(CCT)
CCT = np.tile(CCT, 6)
xy = np.tile(xy, (6, 1))
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(CCT), xy, decimal=7)
CCT = np.reshape(CCT, (2, 3))
xy = np.reshape(xy, (2, 3, 2))
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(CCT), xy, decimal=7) | def test_n_dimensional_CCT_to_xy_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition\n n-dimensional arrays support.\n '
CCT = 4000
xy = CCT_to_xy_CIE_D(CCT)
CCT = np.tile(CCT, 6)
xy = np.tile(xy, (6, 1))
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(CCT), xy, decimal=7)
CCT = np.reshape(CCT, (2, 3))
xy = np.reshape(xy, (2, 3, 2))
np.testing.assert_almost_equal(CCT_to_xy_CIE_D(CCT), xy, decimal=7)<|docstring|>Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition
n-dimensional arrays support.<|endoftext|> |
fe1f5ca408caf68bc77db05eeb40e64450140c06fb52fd44632d9e8facc51215 | @ignore_numpy_errors
def test_nan_CCT_to_xy_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition\n nan support.\n '
cases = [(- 1.0), 0.0, 1.0, (- np.inf), np.inf, np.nan]
cases = set(permutations((cases * 3), r=2))
for case in cases:
CCT_to_xy_CIE_D(case) | Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition
nan support. | colour/temperature/tests/test_cie_d.py | test_nan_CCT_to_xy_CIE_D | trevorandersen/colour | 6 | python | @ignore_numpy_errors
def test_nan_CCT_to_xy_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition\n nan support.\n '
cases = [(- 1.0), 0.0, 1.0, (- np.inf), np.inf, np.nan]
cases = set(permutations((cases * 3), r=2))
for case in cases:
CCT_to_xy_CIE_D(case) | @ignore_numpy_errors
def test_nan_CCT_to_xy_CIE_D(self):
'\n Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition\n nan support.\n '
cases = [(- 1.0), 0.0, 1.0, (- np.inf), np.inf, np.nan]
cases = set(permutations((cases * 3), r=2))
for case in cases:
CCT_to_xy_CIE_D(case)<|docstring|>Tests :func:`colour.temperature.cie_d.CCT_to_xy_CIE_D` definition
nan support.<|endoftext|> |
7f8f3ca69a5afd3e9e3765d2dc69e2f1a62e8faf9f52a115f4689c40ea697b0c | def get_array_of_dicescores(seg):
'computes the array of dicescores for the given segmentation,\n it is assumed, that the label of interest is 1 and all other labels are 0.\n More in detail : For every two consecutive slices, the dice score is computed\n throughout the image. In the case of both images being black (no label 1), the dicescore \n is set to one.\n\n Args:\n seg (torch.Tensor): the segmentation\n\n Returns (ndarray): array of dicescores (one for every two consecutive slices in the comp)\n '
shape = np.shape(seg)
nr_slices = shape[0]
arr_of_dicescores = np.array([])
first_slide = seg[(0, :, :)]
first_ones = torch.sum(first_slide)
for i in range((nr_slices - 1)):
second_slide = seg[((i + 1), :, :)]
second_ones = torch.sum(second_slide)
intersection = torch.dot(torch.flatten(first_slide), torch.flatten(second_slide))
if (not ((first_ones + second_ones) == 0)):
dice_score = ((2 * intersection) / (first_ones + second_ones))
else:
dice_score = 1
if (not (dice_score == 1)):
arr_of_dicescores = np.append(arr_of_dicescores, np.array([dice_score]))
first_slide = second_slide
first_ones = second_ones
return arr_of_dicescores | computes the array of dicescores for the given segmentation,
it is assumed, that the label of interest is 1 and all other labels are 0.
More in detail : For every two consecutive slices, the dice score is computed
throughout the image. In the case of both images being black (no label 1), the dicescore
is set to one.
Args:
seg (torch.Tensor): the segmentation
Returns (ndarray): array of dicescores (one for every two consecutive slices in the comp) | mp/utils/feature_extractor.py | get_array_of_dicescores | MECLabTUDA/Seg_QA | 0 | python | def get_array_of_dicescores(seg):
'computes the array of dicescores for the given segmentation,\n it is assumed, that the label of interest is 1 and all other labels are 0.\n More in detail : For every two consecutive slices, the dice score is computed\n throughout the image. In the case of both images being black (no label 1), the dicescore \n is set to one.\n\n Args:\n seg (torch.Tensor): the segmentation\n\n Returns (ndarray): array of dicescores (one for every two consecutive slices in the comp)\n '
shape = np.shape(seg)
nr_slices = shape[0]
arr_of_dicescores = np.array([])
first_slide = seg[(0, :, :)]
first_ones = torch.sum(first_slide)
for i in range((nr_slices - 1)):
second_slide = seg[((i + 1), :, :)]
second_ones = torch.sum(second_slide)
intersection = torch.dot(torch.flatten(first_slide), torch.flatten(second_slide))
if (not ((first_ones + second_ones) == 0)):
dice_score = ((2 * intersection) / (first_ones + second_ones))
else:
dice_score = 1
if (not (dice_score == 1)):
arr_of_dicescores = np.append(arr_of_dicescores, np.array([dice_score]))
first_slide = second_slide
first_ones = second_ones
return arr_of_dicescores | def get_array_of_dicescores(seg):
'computes the array of dicescores for the given segmentation,\n it is assumed, that the label of interest is 1 and all other labels are 0.\n More in detail : For every two consecutive slices, the dice score is computed\n throughout the image. In the case of both images being black (no label 1), the dicescore \n is set to one.\n\n Args:\n seg (torch.Tensor): the segmentation\n\n Returns (ndarray): array of dicescores (one for every two consecutive slices in the comp)\n '
shape = np.shape(seg)
nr_slices = shape[0]
arr_of_dicescores = np.array([])
first_slide = seg[(0, :, :)]
first_ones = torch.sum(first_slide)
for i in range((nr_slices - 1)):
second_slide = seg[((i + 1), :, :)]
second_ones = torch.sum(second_slide)
intersection = torch.dot(torch.flatten(first_slide), torch.flatten(second_slide))
if (not ((first_ones + second_ones) == 0)):
dice_score = ((2 * intersection) / (first_ones + second_ones))
else:
dice_score = 1
if (not (dice_score == 1)):
arr_of_dicescores = np.append(arr_of_dicescores, np.array([dice_score]))
first_slide = second_slide
first_ones = second_ones
return arr_of_dicescores<|docstring|>computes the array of dicescores for the given segmentation,
it is assumed, that the label of interest is 1 and all other labels are 0.
More in detail : For every two consecutive slices, the dice score is computed
throughout the image. In the case of both images being black (no label 1), the dicescore
is set to one.
Args:
seg (torch.Tensor): the segmentation
Returns (ndarray): array of dicescores (one for every two consecutive slices in the comp)<|endoftext|> |
30ac912bebfb40f76cf35ea09cfc6fc669ff07e3085ab0888c214075a9f3979d | def get_dice_averages(img, seg, props):
'Computes the average dice score for a connected component of the \n given img-seg pair. \n STILL COMPUTED BUT NOT USED ANYMORE: Also computes the average differences between the dice scores \n and computes that average, because it was observed, that in bad artificial bad segmentations,\n these dice scores had a more rough graph, then good segmentations, thus it is used as feature.\n \n Args: \n img (torch.Tensor): the image\n seg (torch.Tensor): its segmentation\n props (dict[str->object]): a regionprops dictionary, c.f. skimage-> regionprops\n \n Returns (list(floats)): a list of two values, the avg dice score and the avg dice score difference'
(min_row, min_col, min_sl, max_row, max_col, max_sl) = props.bbox
cut_seg = seg[(min_row:max_row, min_col:max_col, min_sl:max_sl)]
arr_of_dicescores = get_array_of_dicescores(cut_seg)
dice_avg_value = np.average(arr_of_dicescores)
if (len(arr_of_dicescores) < 10):
dice_diff_avg_value = 1
else:
dice_diff = np.diff(arr_of_dicescores)
dice_diff_abs = np.absolute(dice_diff)
dice_diff_avg_value = np.average(dice_diff_abs)
return [dice_avg_value, dice_diff_avg_value] | Computes the average dice score for a connected component of the
given img-seg pair.
STILL COMPUTED BUT NOT USED ANYMORE: Also computes the average differences between the dice scores
and computes that average, because it was observed, that in bad artificial bad segmentations,
these dice scores had a more rough graph, then good segmentations, thus it is used as feature.
Args:
img (torch.Tensor): the image
seg (torch.Tensor): its segmentation
props (dict[str->object]): a regionprops dictionary, c.f. skimage-> regionprops
Returns (list(floats)): a list of two values, the avg dice score and the avg dice score difference | mp/utils/feature_extractor.py | get_dice_averages | MECLabTUDA/Seg_QA | 0 | python | def get_dice_averages(img, seg, props):
'Computes the average dice score for a connected component of the \n given img-seg pair. \n STILL COMPUTED BUT NOT USED ANYMORE: Also computes the average differences between the dice scores \n and computes that average, because it was observed, that in bad artificial bad segmentations,\n these dice scores had a more rough graph, then good segmentations, thus it is used as feature.\n \n Args: \n img (torch.Tensor): the image\n seg (torch.Tensor): its segmentation\n props (dict[str->object]): a regionprops dictionary, c.f. skimage-> regionprops\n \n Returns (list(floats)): a list of two values, the avg dice score and the avg dice score difference'
(min_row, min_col, min_sl, max_row, max_col, max_sl) = props.bbox
cut_seg = seg[(min_row:max_row, min_col:max_col, min_sl:max_sl)]
arr_of_dicescores = get_array_of_dicescores(cut_seg)
dice_avg_value = np.average(arr_of_dicescores)
if (len(arr_of_dicescores) < 10):
dice_diff_avg_value = 1
else:
dice_diff = np.diff(arr_of_dicescores)
dice_diff_abs = np.absolute(dice_diff)
dice_diff_avg_value = np.average(dice_diff_abs)
return [dice_avg_value, dice_diff_avg_value] | def get_dice_averages(img, seg, props):
'Computes the average dice score for a connected component of the \n given img-seg pair. \n STILL COMPUTED BUT NOT USED ANYMORE: Also computes the average differences between the dice scores \n and computes that average, because it was observed, that in bad artificial bad segmentations,\n these dice scores had a more rough graph, then good segmentations, thus it is used as feature.\n \n Args: \n img (torch.Tensor): the image\n seg (torch.Tensor): its segmentation\n props (dict[str->object]): a regionprops dictionary, c.f. skimage-> regionprops\n \n Returns (list(floats)): a list of two values, the avg dice score and the avg dice score difference'
(min_row, min_col, min_sl, max_row, max_col, max_sl) = props.bbox
cut_seg = seg[(min_row:max_row, min_col:max_col, min_sl:max_sl)]
arr_of_dicescores = get_array_of_dicescores(cut_seg)
dice_avg_value = np.average(arr_of_dicescores)
if (len(arr_of_dicescores) < 10):
dice_diff_avg_value = 1
else:
dice_diff = np.diff(arr_of_dicescores)
dice_diff_abs = np.absolute(dice_diff)
dice_diff_avg_value = np.average(dice_diff_abs)
return [dice_avg_value, dice_diff_avg_value]<|docstring|>Computes the average dice score for a connected component of the
given img-seg pair.
STILL COMPUTED BUT NOT USED ANYMORE: Also computes the average differences between the dice scores
and computes that average, because it was observed, that in bad artificial bad segmentations,
these dice scores had a more rough graph, then good segmentations, thus it is used as feature.
Args:
img (torch.Tensor): the image
seg (torch.Tensor): its segmentation
props (dict[str->object]): a regionprops dictionary, c.f. skimage-> regionprops
Returns (list(floats)): a list of two values, the avg dice score and the avg dice score difference<|endoftext|> |
f535450725fe91cc028b460447365e0c79beadf8851c25cd4e9f39481f896829 | def mean_var_big_comp(img, seg):
' Computes the mean and variance of the united intensity values \n of the 4 biggest connected components in the image\n Args:\n img(ndarray or torch tensor): the image \n seg(ndarray or torch tensor): a segmentation of any tissue in the image \n \n Returns(float,float): the mean and variance of the sampled intensity values'
(labeled_image, _) = label(seg, return_num=True)
props = regionprops(labeled_image)
props = sorted(props, reverse=True, key=(lambda dict: dict['area']))
ints = sample_intensities(img, seg, props[0], number=5000)
dens = GaussianMixture(n_components=1).fit(np.reshape(ints, newshape=((- 1), 1)))
mean = dens.means_[(0, 0)]
var = dens.covariances_[(0, 0, 0)]
return (mean, var) | Computes the mean and variance of the united intensity values
of the 4 biggest connected components in the image
Args:
img(ndarray or torch tensor): the image
seg(ndarray or torch tensor): a segmentation of any tissue in the image
Returns(float,float): the mean and variance of the sampled intensity values | mp/utils/feature_extractor.py | mean_var_big_comp | MECLabTUDA/Seg_QA | 0 | python | def mean_var_big_comp(img, seg):
' Computes the mean and variance of the united intensity values \n of the 4 biggest connected components in the image\n Args:\n img(ndarray or torch tensor): the image \n seg(ndarray or torch tensor): a segmentation of any tissue in the image \n \n Returns(float,float): the mean and variance of the sampled intensity values'
(labeled_image, _) = label(seg, return_num=True)
props = regionprops(labeled_image)
props = sorted(props, reverse=True, key=(lambda dict: dict['area']))
ints = sample_intensities(img, seg, props[0], number=5000)
dens = GaussianMixture(n_components=1).fit(np.reshape(ints, newshape=((- 1), 1)))
mean = dens.means_[(0, 0)]
var = dens.covariances_[(0, 0, 0)]
return (mean, var) | def mean_var_big_comp(img, seg):
' Computes the mean and variance of the united intensity values \n of the 4 biggest connected components in the image\n Args:\n img(ndarray or torch tensor): the image \n seg(ndarray or torch tensor): a segmentation of any tissue in the image \n \n Returns(float,float): the mean and variance of the sampled intensity values'
(labeled_image, _) = label(seg, return_num=True)
props = regionprops(labeled_image)
props = sorted(props, reverse=True, key=(lambda dict: dict['area']))
ints = sample_intensities(img, seg, props[0], number=5000)
dens = GaussianMixture(n_components=1).fit(np.reshape(ints, newshape=((- 1), 1)))
mean = dens.means_[(0, 0)]
var = dens.covariances_[(0, 0, 0)]
return (mean, var)<|docstring|>Computes the mean and variance of the united intensity values
of the 4 biggest connected components in the image
Args:
img(ndarray or torch tensor): the image
seg(ndarray or torch tensor): a segmentation of any tissue in the image
Returns(float,float): the mean and variance of the sampled intensity values<|endoftext|> |
40f18c9087cd06167f56333272a12c0d1cc9c19d8138fac17e0300aa1df623c4 | def get_feature(self, feature, img, seg, lung_seg):
'Extracts the given feature for the given img-seg pair\n\n Args: \n feature (str): The feature to be extracted \n img (ndarray): the image \n seg (ndarray): The corresponding mask\n lung_seg (ndarray): The segmentation of the lung in the image \n\n Returns (object): depending on the feature: \n dice_scores -> (ndarray with two entries): array with two entries, the dice averages and dice_diff averages \n connected_components -> (integer): The number of connected components\n '
component_iterator = Component_Iterator(img, seg)
original_threshhold = component_iterator.threshold
if (feature == 'dice_scores'):
dice_metrices = component_iterator.iterate(get_dice_averages)
if (not dice_metrices):
print('Image only has very small components')
component_iterator.threshold = 0
dice_metrices = component_iterator.iterate(get_dice_averages)
component_iterator.threshold = original_threshhold
if (not dice_metrices):
print('Image has no usable components, no reliable computations can be made for dice')
return 1
dice_metrices = np.array(dice_metrices)
dice_metrices = np.mean(dice_metrices, 0)
return dice_metrices[0]
if (feature == 'connected_components'):
(_, number_components) = label(seg, return_num=True, connectivity=3)
return number_components
if (feature == 'gauss_params'):
(mean, _) = mean_var_big_comp(img, seg)
return mean
if (feature == 'seg_in_lung'):
dice_seg_lung = segmentation_in_lung(seg, lung_seg)
return float(dice_seg_lung) | Extracts the given feature for the given img-seg pair
Args:
feature (str): The feature to be extracted
img (ndarray): the image
seg (ndarray): The corresponding mask
lung_seg (ndarray): The segmentation of the lung in the image
Returns (object): depending on the feature:
dice_scores -> (ndarray with two entries): array with two entries, the dice averages and dice_diff averages
connected_components -> (integer): The number of connected components | mp/utils/feature_extractor.py | get_feature | MECLabTUDA/Seg_QA | 0 | python | def get_feature(self, feature, img, seg, lung_seg):
'Extracts the given feature for the given img-seg pair\n\n Args: \n feature (str): The feature to be extracted \n img (ndarray): the image \n seg (ndarray): The corresponding mask\n lung_seg (ndarray): The segmentation of the lung in the image \n\n Returns (object): depending on the feature: \n dice_scores -> (ndarray with two entries): array with two entries, the dice averages and dice_diff averages \n connected_components -> (integer): The number of connected components\n '
component_iterator = Component_Iterator(img, seg)
original_threshhold = component_iterator.threshold
if (feature == 'dice_scores'):
dice_metrices = component_iterator.iterate(get_dice_averages)
if (not dice_metrices):
print('Image only has very small components')
component_iterator.threshold = 0
dice_metrices = component_iterator.iterate(get_dice_averages)
component_iterator.threshold = original_threshhold
if (not dice_metrices):
print('Image has no usable components, no reliable computations can be made for dice')
return 1
dice_metrices = np.array(dice_metrices)
dice_metrices = np.mean(dice_metrices, 0)
return dice_metrices[0]
if (feature == 'connected_components'):
(_, number_components) = label(seg, return_num=True, connectivity=3)
return number_components
if (feature == 'gauss_params'):
(mean, _) = mean_var_big_comp(img, seg)
return mean
if (feature == 'seg_in_lung'):
dice_seg_lung = segmentation_in_lung(seg, lung_seg)
return float(dice_seg_lung) | def get_feature(self, feature, img, seg, lung_seg):
'Extracts the given feature for the given img-seg pair\n\n Args: \n feature (str): The feature to be extracted \n img (ndarray): the image \n seg (ndarray): The corresponding mask\n lung_seg (ndarray): The segmentation of the lung in the image \n\n Returns (object): depending on the feature: \n dice_scores -> (ndarray with two entries): array with two entries, the dice averages and dice_diff averages \n connected_components -> (integer): The number of connected components\n '
component_iterator = Component_Iterator(img, seg)
original_threshhold = component_iterator.threshold
if (feature == 'dice_scores'):
dice_metrices = component_iterator.iterate(get_dice_averages)
if (not dice_metrices):
print('Image only has very small components')
component_iterator.threshold = 0
dice_metrices = component_iterator.iterate(get_dice_averages)
component_iterator.threshold = original_threshhold
if (not dice_metrices):
print('Image has no usable components, no reliable computations can be made for dice')
return 1
dice_metrices = np.array(dice_metrices)
dice_metrices = np.mean(dice_metrices, 0)
return dice_metrices[0]
if (feature == 'connected_components'):
(_, number_components) = label(seg, return_num=True, connectivity=3)
return number_components
if (feature == 'gauss_params'):
(mean, _) = mean_var_big_comp(img, seg)
return mean
if (feature == 'seg_in_lung'):
dice_seg_lung = segmentation_in_lung(seg, lung_seg)
return float(dice_seg_lung)<|docstring|>Extracts the given feature for the given img-seg pair
Args:
feature (str): The feature to be extracted
img (ndarray): the image
seg (ndarray): The corresponding mask
lung_seg (ndarray): The segmentation of the lung in the image
Returns (object): depending on the feature:
dice_scores -> (ndarray with two entries): array with two entries, the dice averages and dice_diff averages
connected_components -> (integer): The number of connected components<|endoftext|> |
7649ac640dae70f442dc87dba57f18168ebabd18e772deb638d7500d23f2704c | def compute_features_id(self, id, features='all'):
'Computes all features for the img-seg and img-pred pairs (if existing)\n and saves them in the preprocessed_dir/.../id/...\n Args:\n id (str): the id of the patient to compute the features for\n features (str or list(str)): either all or a list of features to compute\n '
if (features == 'all'):
features = self.features
if (not (os.environ['INFERENCE_OR_TRAIN'] == 'train')):
id_path = os.path.join(os.environ['PREPROCESSED_WORKFLOW_DIR'], os.environ['PREPROCESSED_OPERATOR_OUT_SCALED_DIR'], id)
else:
id_path = os.path.join(os.environ['PREPROCESSED_WORKFLOW_DIR'], os.environ['PREPROCESSED_OPERATOR_OUT_SCALED_DIR_TRAIN'], id)
img_path = os.path.join(id_path, 'img', 'img.nii.gz')
lung_seg_path = os.path.join(id_path, 'lung_seg', 'lung_seg.nii.gz')
all_pred_path = os.path.join(id_path, 'pred')
if os.path.exists(all_pred_path):
for model in os.listdir(all_pred_path):
mask_path_short = os.path.join(id_path, 'pred', model)
self.save_feat_dict_from_paths(img_path, mask_path_short, lung_seg_path, features)
seg_path_short = os.path.join(id_path, 'seg')
seg_path = os.path.join(id_path, 'seg', '001.nii.gz')
img = torch.tensor(torchio.Image(img_path, type=torchio.INTENSITY).numpy())[0]
seg = torch.tensor(torchio.Image(seg_path, type=torchio.LABEL).numpy())[0]
lung_seg = torch.tensor(torchio.Image(lung_seg_path, type=torchio.LABEL).numpy())[0]
feature_save_path = os.path.join(seg_path_short, 'features.json')
if os.path.exists(feature_save_path):
with open(feature_save_path) as file:
feat_dict = json.load(file)
else:
feat_dict = {}
for feat in features:
feat_dict[feat] = self.get_feature(feat, img, seg, lung_seg)
with open(feature_save_path, 'w') as f:
json.dump(feat_dict, f) | Computes all features for the img-seg and img-pred pairs (if existing)
and saves them in the preprocessed_dir/.../id/...
Args:
id (str): the id of the patient to compute the features for
features (str or list(str)): either all or a list of features to compute | mp/utils/feature_extractor.py | compute_features_id | MECLabTUDA/Seg_QA | 0 | python | def compute_features_id(self, id, features='all'):
'Computes all features for the img-seg and img-pred pairs (if existing)\n and saves them in the preprocessed_dir/.../id/...\n Args:\n id (str): the id of the patient to compute the features for\n features (str or list(str)): either all or a list of features to compute\n '
if (features == 'all'):
features = self.features
if (not (os.environ['INFERENCE_OR_TRAIN'] == 'train')):
id_path = os.path.join(os.environ['PREPROCESSED_WORKFLOW_DIR'], os.environ['PREPROCESSED_OPERATOR_OUT_SCALED_DIR'], id)
else:
id_path = os.path.join(os.environ['PREPROCESSED_WORKFLOW_DIR'], os.environ['PREPROCESSED_OPERATOR_OUT_SCALED_DIR_TRAIN'], id)
img_path = os.path.join(id_path, 'img', 'img.nii.gz')
lung_seg_path = os.path.join(id_path, 'lung_seg', 'lung_seg.nii.gz')
all_pred_path = os.path.join(id_path, 'pred')
if os.path.exists(all_pred_path):
for model in os.listdir(all_pred_path):
mask_path_short = os.path.join(id_path, 'pred', model)
self.save_feat_dict_from_paths(img_path, mask_path_short, lung_seg_path, features)
seg_path_short = os.path.join(id_path, 'seg')
seg_path = os.path.join(id_path, 'seg', '001.nii.gz')
img = torch.tensor(torchio.Image(img_path, type=torchio.INTENSITY).numpy())[0]
seg = torch.tensor(torchio.Image(seg_path, type=torchio.LABEL).numpy())[0]
lung_seg = torch.tensor(torchio.Image(lung_seg_path, type=torchio.LABEL).numpy())[0]
feature_save_path = os.path.join(seg_path_short, 'features.json')
if os.path.exists(feature_save_path):
with open(feature_save_path) as file:
feat_dict = json.load(file)
else:
feat_dict = {}
for feat in features:
feat_dict[feat] = self.get_feature(feat, img, seg, lung_seg)
with open(feature_save_path, 'w') as f:
json.dump(feat_dict, f) | def compute_features_id(self, id, features='all'):
'Computes all features for the img-seg and img-pred pairs (if existing)\n and saves them in the preprocessed_dir/.../id/...\n Args:\n id (str): the id of the patient to compute the features for\n features (str or list(str)): either all or a list of features to compute\n '
if (features == 'all'):
features = self.features
if (not (os.environ['INFERENCE_OR_TRAIN'] == 'train')):
id_path = os.path.join(os.environ['PREPROCESSED_WORKFLOW_DIR'], os.environ['PREPROCESSED_OPERATOR_OUT_SCALED_DIR'], id)
else:
id_path = os.path.join(os.environ['PREPROCESSED_WORKFLOW_DIR'], os.environ['PREPROCESSED_OPERATOR_OUT_SCALED_DIR_TRAIN'], id)
img_path = os.path.join(id_path, 'img', 'img.nii.gz')
lung_seg_path = os.path.join(id_path, 'lung_seg', 'lung_seg.nii.gz')
all_pred_path = os.path.join(id_path, 'pred')
if os.path.exists(all_pred_path):
for model in os.listdir(all_pred_path):
mask_path_short = os.path.join(id_path, 'pred', model)
self.save_feat_dict_from_paths(img_path, mask_path_short, lung_seg_path, features)
seg_path_short = os.path.join(id_path, 'seg')
seg_path = os.path.join(id_path, 'seg', '001.nii.gz')
img = torch.tensor(torchio.Image(img_path, type=torchio.INTENSITY).numpy())[0]
seg = torch.tensor(torchio.Image(seg_path, type=torchio.LABEL).numpy())[0]
lung_seg = torch.tensor(torchio.Image(lung_seg_path, type=torchio.LABEL).numpy())[0]
feature_save_path = os.path.join(seg_path_short, 'features.json')
if os.path.exists(feature_save_path):
with open(feature_save_path) as file:
feat_dict = json.load(file)
else:
feat_dict = {}
for feat in features:
feat_dict[feat] = self.get_feature(feat, img, seg, lung_seg)
with open(feature_save_path, 'w') as f:
json.dump(feat_dict, f)<|docstring|>Computes all features for the img-seg and img-pred pairs (if existing)
and saves them in the preprocessed_dir/.../id/...
Args:
id (str): the id of the patient to compute the features for
features (str or list(str)): either all or a list of features to compute<|endoftext|> |
2c5c58edd4c557569f8dc5101f6b97734adb9cf5816ead9a9ab2f9d35b9ba816 | def save_feat_dict_from_paths(self, img_path, mask_path_short, lung_seg_path, features):
'computes and saves the feature dict for a given img-pred pair \n is a utility function for compute_features_id\n\n Args: \n img_path (str): The path to the image \n mask_path_short (str): the path to the folder containing the seg mask\n and where the feature dict is to be saved \n lung_seg_path (str): The path to the segmentation of the lung \n features (list(str)): a list of strings for the features to compute \n '
mask_path = os.path.join(mask_path_short, 'pred.nii.gz')
img = torch.tensor(torchio.Image(img_path, type=torchio.INTENSITY).numpy())[0]
mask = torch.tensor(torchio.Image(mask_path, type=torchio.LABEL).numpy())[0]
lung_seg = torch.tensor(torchio.Image(lung_seg_path, type=torchio.LABEL).numpy())[0]
feature_save_path = os.path.join(mask_path_short, 'features.json')
if os.path.exists(feature_save_path):
with open(feature_save_path) as file:
feat_dict = json.load(file)
else:
feat_dict = {}
for feat in features:
feat_dict[feat] = self.get_feature(feat, img, mask, lung_seg)
with open(feature_save_path, 'w') as f:
json.dump(feat_dict, f) | computes and saves the feature dict for a given img-pred pair
is a utility function for compute_features_id
Args:
img_path (str): The path to the image
mask_path_short (str): the path to the folder containing the seg mask
and where the feature dict is to be saved
lung_seg_path (str): The path to the segmentation of the lung
features (list(str)): a list of strings for the features to compute | mp/utils/feature_extractor.py | save_feat_dict_from_paths | MECLabTUDA/Seg_QA | 0 | python | def save_feat_dict_from_paths(self, img_path, mask_path_short, lung_seg_path, features):
'computes and saves the feature dict for a given img-pred pair \n is a utility function for compute_features_id\n\n Args: \n img_path (str): The path to the image \n mask_path_short (str): the path to the folder containing the seg mask\n and where the feature dict is to be saved \n lung_seg_path (str): The path to the segmentation of the lung \n features (list(str)): a list of strings for the features to compute \n '
mask_path = os.path.join(mask_path_short, 'pred.nii.gz')
img = torch.tensor(torchio.Image(img_path, type=torchio.INTENSITY).numpy())[0]
mask = torch.tensor(torchio.Image(mask_path, type=torchio.LABEL).numpy())[0]
lung_seg = torch.tensor(torchio.Image(lung_seg_path, type=torchio.LABEL).numpy())[0]
feature_save_path = os.path.join(mask_path_short, 'features.json')
if os.path.exists(feature_save_path):
with open(feature_save_path) as file:
feat_dict = json.load(file)
else:
feat_dict = {}
for feat in features:
feat_dict[feat] = self.get_feature(feat, img, mask, lung_seg)
with open(feature_save_path, 'w') as f:
json.dump(feat_dict, f) | def save_feat_dict_from_paths(self, img_path, mask_path_short, lung_seg_path, features):
'computes and saves the feature dict for a given img-pred pair \n is a utility function for compute_features_id\n\n Args: \n img_path (str): The path to the image \n mask_path_short (str): the path to the folder containing the seg mask\n and where the feature dict is to be saved \n lung_seg_path (str): The path to the segmentation of the lung \n features (list(str)): a list of strings for the features to compute \n '
mask_path = os.path.join(mask_path_short, 'pred.nii.gz')
img = torch.tensor(torchio.Image(img_path, type=torchio.INTENSITY).numpy())[0]
mask = torch.tensor(torchio.Image(mask_path, type=torchio.LABEL).numpy())[0]
lung_seg = torch.tensor(torchio.Image(lung_seg_path, type=torchio.LABEL).numpy())[0]
feature_save_path = os.path.join(mask_path_short, 'features.json')
if os.path.exists(feature_save_path):
with open(feature_save_path) as file:
feat_dict = json.load(file)
else:
feat_dict = {}
for feat in features:
feat_dict[feat] = self.get_feature(feat, img, mask, lung_seg)
with open(feature_save_path, 'w') as f:
json.dump(feat_dict, f)<|docstring|>computes and saves the feature dict for a given img-pred pair
is a utility function for compute_features_id
Args:
img_path (str): The path to the image
mask_path_short (str): the path to the folder containing the seg mask
and where the feature dict is to be saved
lung_seg_path (str): The path to the segmentation of the lung
features (list(str)): a list of strings for the features to compute<|endoftext|> |
96f484d482e351f62a31e752a357e693d0c1fc30e6120adde1440a2cbff072f8 | def collect_train_data(self):
'goes through the train directory and collects all the feature vectors and labels\n \n Returns: (ndarray,ndarray) the features and labels '
if (os.environ['INFERENCE_OR_TRAIN'] == 'train'):
all_features = []
labels = []
path = os.path.join(os.environ['PREPROCESSED_WORKFLOW_DIR'], os.environ['PREPROCESSED_OPERATOR_OUT_SCALED_DIR_TRAIN'])
for id in os.listdir(path):
all_pred_path = os.path.join(path, id, 'pred')
if os.path.exists(all_pred_path):
for model in os.listdir(all_pred_path):
pred_path = os.path.join(all_pred_path, model)
feature_path = os.path.join(pred_path, 'features.json')
label_path = os.path.join(pred_path, 'dice_score.json')
feature_vec = self.read_feature_vector(feature_path)
label = self.read_prediction_label(label_path)
if np.isnan(np.sum(np.array(feature_vec))):
pass
else:
all_features.append(feature_vec)
labels.append(label)
else:
print('This method is only for train time')
RuntimeError
return (np.array(all_features), np.array(labels)) | goes through the train directory and collects all the feature vectors and labels
Returns: (ndarray,ndarray) the features and labels | mp/utils/feature_extractor.py | collect_train_data | MECLabTUDA/Seg_QA | 0 | python | def collect_train_data(self):
'goes through the train directory and collects all the feature vectors and labels\n \n Returns: (ndarray,ndarray) the features and labels '
if (os.environ['INFERENCE_OR_TRAIN'] == 'train'):
all_features = []
labels = []
path = os.path.join(os.environ['PREPROCESSED_WORKFLOW_DIR'], os.environ['PREPROCESSED_OPERATOR_OUT_SCALED_DIR_TRAIN'])
for id in os.listdir(path):
all_pred_path = os.path.join(path, id, 'pred')
if os.path.exists(all_pred_path):
for model in os.listdir(all_pred_path):
pred_path = os.path.join(all_pred_path, model)
feature_path = os.path.join(pred_path, 'features.json')
label_path = os.path.join(pred_path, 'dice_score.json')
feature_vec = self.read_feature_vector(feature_path)
label = self.read_prediction_label(label_path)
if np.isnan(np.sum(np.array(feature_vec))):
pass
else:
all_features.append(feature_vec)
labels.append(label)
else:
print('This method is only for train time')
RuntimeError
return (np.array(all_features), np.array(labels)) | def collect_train_data(self):
'goes through the train directory and collects all the feature vectors and labels\n \n Returns: (ndarray,ndarray) the features and labels '
if (os.environ['INFERENCE_OR_TRAIN'] == 'train'):
all_features = []
labels = []
path = os.path.join(os.environ['PREPROCESSED_WORKFLOW_DIR'], os.environ['PREPROCESSED_OPERATOR_OUT_SCALED_DIR_TRAIN'])
for id in os.listdir(path):
all_pred_path = os.path.join(path, id, 'pred')
if os.path.exists(all_pred_path):
for model in os.listdir(all_pred_path):
pred_path = os.path.join(all_pred_path, model)
feature_path = os.path.join(pred_path, 'features.json')
label_path = os.path.join(pred_path, 'dice_score.json')
feature_vec = self.read_feature_vector(feature_path)
label = self.read_prediction_label(label_path)
if np.isnan(np.sum(np.array(feature_vec))):
pass
else:
all_features.append(feature_vec)
labels.append(label)
else:
print('This method is only for train time')
RuntimeError
return (np.array(all_features), np.array(labels))<|docstring|>goes through the train directory and collects all the feature vectors and labels
Returns: (ndarray,ndarray) the features and labels<|endoftext|> |
f241ddf152562aa805cea03275f11d0a014671b95d16da97ff5ca737983ccded | def __init__(self):
'\n NodesLnnHardwareNode - a model defined in Swagger\n\n :param dict swaggerTypes: The key is attribute name\n and the value is attribute type.\n :param dict attributeMap: The key is attribute name\n and the value is json key in definition.\n '
self.swagger_types = {'chassis': 'str', 'chassis_code': 'str', 'chassis_count': 'str', '_class': 'str', 'configuration_id': 'str', 'cpu': 'str', 'disk_controller': 'str', 'disk_expander': 'str', 'family_code': 'str', 'flash_drive': 'str', 'generation_code': 'str', 'hwgen': 'str', 'id': 'int', 'imb_version': 'str', 'infiniband': 'str', 'lcd_version': 'str', 'lnn': 'int', 'motherboard': 'str', 'net_interfaces': 'str', 'nvram': 'str', 'powersupplies': 'list[str]', 'processor': 'str', 'product': 'str', 'ram': 'int', 'serial_number': 'str', 'series': 'str', 'storage_class': 'str'}
self.attribute_map = {'chassis': 'chassis', 'chassis_code': 'chassis_code', 'chassis_count': 'chassis_count', '_class': 'class', 'configuration_id': 'configuration_id', 'cpu': 'cpu', 'disk_controller': 'disk_controller', 'disk_expander': 'disk_expander', 'family_code': 'family_code', 'flash_drive': 'flash_drive', 'generation_code': 'generation_code', 'hwgen': 'hwgen', 'id': 'id', 'imb_version': 'imb_version', 'infiniband': 'infiniband', 'lcd_version': 'lcd_version', 'lnn': 'lnn', 'motherboard': 'motherboard', 'net_interfaces': 'net_interfaces', 'nvram': 'nvram', 'powersupplies': 'powersupplies', 'processor': 'processor', 'product': 'product', 'ram': 'ram', 'serial_number': 'serial_number', 'series': 'series', 'storage_class': 'storage_class'}
self._chassis = None
self._chassis_code = None
self._chassis_count = None
self.__class = None
self._configuration_id = None
self._cpu = None
self._disk_controller = None
self._disk_expander = None
self._family_code = None
self._flash_drive = None
self._generation_code = None
self._hwgen = None
self._id = None
self._imb_version = None
self._infiniband = None
self._lcd_version = None
self._lnn = None
self._motherboard = None
self._net_interfaces = None
self._nvram = None
self._powersupplies = None
self._processor = None
self._product = None
self._ram = None
self._serial_number = None
self._series = None
self._storage_class = None | NodesLnnHardwareNode - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition. | isi_sdk/models/nodes_lnn_hardware_node.py | __init__ | Atomicology/isilon_sdk_python | 0 | python | def __init__(self):
'\n NodesLnnHardwareNode - a model defined in Swagger\n\n :param dict swaggerTypes: The key is attribute name\n and the value is attribute type.\n :param dict attributeMap: The key is attribute name\n and the value is json key in definition.\n '
self.swagger_types = {'chassis': 'str', 'chassis_code': 'str', 'chassis_count': 'str', '_class': 'str', 'configuration_id': 'str', 'cpu': 'str', 'disk_controller': 'str', 'disk_expander': 'str', 'family_code': 'str', 'flash_drive': 'str', 'generation_code': 'str', 'hwgen': 'str', 'id': 'int', 'imb_version': 'str', 'infiniband': 'str', 'lcd_version': 'str', 'lnn': 'int', 'motherboard': 'str', 'net_interfaces': 'str', 'nvram': 'str', 'powersupplies': 'list[str]', 'processor': 'str', 'product': 'str', 'ram': 'int', 'serial_number': 'str', 'series': 'str', 'storage_class': 'str'}
self.attribute_map = {'chassis': 'chassis', 'chassis_code': 'chassis_code', 'chassis_count': 'chassis_count', '_class': 'class', 'configuration_id': 'configuration_id', 'cpu': 'cpu', 'disk_controller': 'disk_controller', 'disk_expander': 'disk_expander', 'family_code': 'family_code', 'flash_drive': 'flash_drive', 'generation_code': 'generation_code', 'hwgen': 'hwgen', 'id': 'id', 'imb_version': 'imb_version', 'infiniband': 'infiniband', 'lcd_version': 'lcd_version', 'lnn': 'lnn', 'motherboard': 'motherboard', 'net_interfaces': 'net_interfaces', 'nvram': 'nvram', 'powersupplies': 'powersupplies', 'processor': 'processor', 'product': 'product', 'ram': 'ram', 'serial_number': 'serial_number', 'series': 'series', 'storage_class': 'storage_class'}
self._chassis = None
self._chassis_code = None
self._chassis_count = None
self.__class = None
self._configuration_id = None
self._cpu = None
self._disk_controller = None
self._disk_expander = None
self._family_code = None
self._flash_drive = None
self._generation_code = None
self._hwgen = None
self._id = None
self._imb_version = None
self._infiniband = None
self._lcd_version = None
self._lnn = None
self._motherboard = None
self._net_interfaces = None
self._nvram = None
self._powersupplies = None
self._processor = None
self._product = None
self._ram = None
self._serial_number = None
self._series = None
self._storage_class = None | def __init__(self):
'\n NodesLnnHardwareNode - a model defined in Swagger\n\n :param dict swaggerTypes: The key is attribute name\n and the value is attribute type.\n :param dict attributeMap: The key is attribute name\n and the value is json key in definition.\n '
self.swagger_types = {'chassis': 'str', 'chassis_code': 'str', 'chassis_count': 'str', '_class': 'str', 'configuration_id': 'str', 'cpu': 'str', 'disk_controller': 'str', 'disk_expander': 'str', 'family_code': 'str', 'flash_drive': 'str', 'generation_code': 'str', 'hwgen': 'str', 'id': 'int', 'imb_version': 'str', 'infiniband': 'str', 'lcd_version': 'str', 'lnn': 'int', 'motherboard': 'str', 'net_interfaces': 'str', 'nvram': 'str', 'powersupplies': 'list[str]', 'processor': 'str', 'product': 'str', 'ram': 'int', 'serial_number': 'str', 'series': 'str', 'storage_class': 'str'}
self.attribute_map = {'chassis': 'chassis', 'chassis_code': 'chassis_code', 'chassis_count': 'chassis_count', '_class': 'class', 'configuration_id': 'configuration_id', 'cpu': 'cpu', 'disk_controller': 'disk_controller', 'disk_expander': 'disk_expander', 'family_code': 'family_code', 'flash_drive': 'flash_drive', 'generation_code': 'generation_code', 'hwgen': 'hwgen', 'id': 'id', 'imb_version': 'imb_version', 'infiniband': 'infiniband', 'lcd_version': 'lcd_version', 'lnn': 'lnn', 'motherboard': 'motherboard', 'net_interfaces': 'net_interfaces', 'nvram': 'nvram', 'powersupplies': 'powersupplies', 'processor': 'processor', 'product': 'product', 'ram': 'ram', 'serial_number': 'serial_number', 'series': 'series', 'storage_class': 'storage_class'}
self._chassis = None
self._chassis_code = None
self._chassis_count = None
self.__class = None
self._configuration_id = None
self._cpu = None
self._disk_controller = None
self._disk_expander = None
self._family_code = None
self._flash_drive = None
self._generation_code = None
self._hwgen = None
self._id = None
self._imb_version = None
self._infiniband = None
self._lcd_version = None
self._lnn = None
self._motherboard = None
self._net_interfaces = None
self._nvram = None
self._powersupplies = None
self._processor = None
self._product = None
self._ram = None
self._serial_number = None
self._series = None
self._storage_class = None<|docstring|>NodesLnnHardwareNode - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.<|endoftext|> |
830de4536ac309c4e3830c97e8e58de3ed982bc2c47a7958949fcd9811fe4d64 | @property
def chassis(self):
"\n Gets the chassis of this NodesLnnHardwareNode.\n Name of this node's chassis.\n\n :return: The chassis of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._chassis | Gets the chassis of this NodesLnnHardwareNode.
Name of this node's chassis.
:return: The chassis of this NodesLnnHardwareNode.
:rtype: str | isi_sdk/models/nodes_lnn_hardware_node.py | chassis | Atomicology/isilon_sdk_python | 0 | python | @property
def chassis(self):
"\n Gets the chassis of this NodesLnnHardwareNode.\n Name of this node's chassis.\n\n :return: The chassis of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._chassis | @property
def chassis(self):
"\n Gets the chassis of this NodesLnnHardwareNode.\n Name of this node's chassis.\n\n :return: The chassis of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._chassis<|docstring|>Gets the chassis of this NodesLnnHardwareNode.
Name of this node's chassis.
:return: The chassis of this NodesLnnHardwareNode.
:rtype: str<|endoftext|> |
007de8c16090edc70de9c13169746fba9286b8f1566b324b381f0b7a90849836 | @chassis.setter
def chassis(self, chassis):
"\n Sets the chassis of this NodesLnnHardwareNode.\n Name of this node's chassis.\n\n :param chassis: The chassis of this NodesLnnHardwareNode.\n :type: str\n "
self._chassis = chassis | Sets the chassis of this NodesLnnHardwareNode.
Name of this node's chassis.
:param chassis: The chassis of this NodesLnnHardwareNode.
:type: str | isi_sdk/models/nodes_lnn_hardware_node.py | chassis | Atomicology/isilon_sdk_python | 0 | python | @chassis.setter
def chassis(self, chassis):
"\n Sets the chassis of this NodesLnnHardwareNode.\n Name of this node's chassis.\n\n :param chassis: The chassis of this NodesLnnHardwareNode.\n :type: str\n "
self._chassis = chassis | @chassis.setter
def chassis(self, chassis):
"\n Sets the chassis of this NodesLnnHardwareNode.\n Name of this node's chassis.\n\n :param chassis: The chassis of this NodesLnnHardwareNode.\n :type: str\n "
self._chassis = chassis<|docstring|>Sets the chassis of this NodesLnnHardwareNode.
Name of this node's chassis.
:param chassis: The chassis of this NodesLnnHardwareNode.
:type: str<|endoftext|> |
1da7ffec5d6f750c1c6d69ccfba81d467afc94ac564a62fc11b379eb4b7604ac | @property
def chassis_code(self):
'\n Gets the chassis_code of this NodesLnnHardwareNode.\n Chassis code of this node (1U, 2U, etc.).\n\n :return: The chassis_code of this NodesLnnHardwareNode.\n :rtype: str\n '
return self._chassis_code | Gets the chassis_code of this NodesLnnHardwareNode.
Chassis code of this node (1U, 2U, etc.).
:return: The chassis_code of this NodesLnnHardwareNode.
:rtype: str | isi_sdk/models/nodes_lnn_hardware_node.py | chassis_code | Atomicology/isilon_sdk_python | 0 | python | @property
def chassis_code(self):
'\n Gets the chassis_code of this NodesLnnHardwareNode.\n Chassis code of this node (1U, 2U, etc.).\n\n :return: The chassis_code of this NodesLnnHardwareNode.\n :rtype: str\n '
return self._chassis_code | @property
def chassis_code(self):
'\n Gets the chassis_code of this NodesLnnHardwareNode.\n Chassis code of this node (1U, 2U, etc.).\n\n :return: The chassis_code of this NodesLnnHardwareNode.\n :rtype: str\n '
return self._chassis_code<|docstring|>Gets the chassis_code of this NodesLnnHardwareNode.
Chassis code of this node (1U, 2U, etc.).
:return: The chassis_code of this NodesLnnHardwareNode.
:rtype: str<|endoftext|> |
07e703f31cf79d43761aa3b3f82a2c798e53a991a3c54d5e39257b3902225768 | @chassis_code.setter
def chassis_code(self, chassis_code):
'\n Sets the chassis_code of this NodesLnnHardwareNode.\n Chassis code of this node (1U, 2U, etc.).\n\n :param chassis_code: The chassis_code of this NodesLnnHardwareNode.\n :type: str\n '
self._chassis_code = chassis_code | Sets the chassis_code of this NodesLnnHardwareNode.
Chassis code of this node (1U, 2U, etc.).
:param chassis_code: The chassis_code of this NodesLnnHardwareNode.
:type: str | isi_sdk/models/nodes_lnn_hardware_node.py | chassis_code | Atomicology/isilon_sdk_python | 0 | python | @chassis_code.setter
def chassis_code(self, chassis_code):
'\n Sets the chassis_code of this NodesLnnHardwareNode.\n Chassis code of this node (1U, 2U, etc.).\n\n :param chassis_code: The chassis_code of this NodesLnnHardwareNode.\n :type: str\n '
self._chassis_code = chassis_code | @chassis_code.setter
def chassis_code(self, chassis_code):
'\n Sets the chassis_code of this NodesLnnHardwareNode.\n Chassis code of this node (1U, 2U, etc.).\n\n :param chassis_code: The chassis_code of this NodesLnnHardwareNode.\n :type: str\n '
self._chassis_code = chassis_code<|docstring|>Sets the chassis_code of this NodesLnnHardwareNode.
Chassis code of this node (1U, 2U, etc.).
:param chassis_code: The chassis_code of this NodesLnnHardwareNode.
:type: str<|endoftext|> |
125c984a172065a06a008437dbf5342eced9c2a817e5757a65237b4cd530acbf | @property
def chassis_count(self):
'\n Gets the chassis_count of this NodesLnnHardwareNode.\n Number of chassis making up this node.\n\n :return: The chassis_count of this NodesLnnHardwareNode.\n :rtype: str\n '
return self._chassis_count | Gets the chassis_count of this NodesLnnHardwareNode.
Number of chassis making up this node.
:return: The chassis_count of this NodesLnnHardwareNode.
:rtype: str | isi_sdk/models/nodes_lnn_hardware_node.py | chassis_count | Atomicology/isilon_sdk_python | 0 | python | @property
def chassis_count(self):
'\n Gets the chassis_count of this NodesLnnHardwareNode.\n Number of chassis making up this node.\n\n :return: The chassis_count of this NodesLnnHardwareNode.\n :rtype: str\n '
return self._chassis_count | @property
def chassis_count(self):
'\n Gets the chassis_count of this NodesLnnHardwareNode.\n Number of chassis making up this node.\n\n :return: The chassis_count of this NodesLnnHardwareNode.\n :rtype: str\n '
return self._chassis_count<|docstring|>Gets the chassis_count of this NodesLnnHardwareNode.
Number of chassis making up this node.
:return: The chassis_count of this NodesLnnHardwareNode.
:rtype: str<|endoftext|> |
7d59a36440348ea9c592feb332abe355a2b1b2c60e8159bb059b12a1b2380885 | @chassis_count.setter
def chassis_count(self, chassis_count):
'\n Sets the chassis_count of this NodesLnnHardwareNode.\n Number of chassis making up this node.\n\n :param chassis_count: The chassis_count of this NodesLnnHardwareNode.\n :type: str\n '
self._chassis_count = chassis_count | Sets the chassis_count of this NodesLnnHardwareNode.
Number of chassis making up this node.
:param chassis_count: The chassis_count of this NodesLnnHardwareNode.
:type: str | isi_sdk/models/nodes_lnn_hardware_node.py | chassis_count | Atomicology/isilon_sdk_python | 0 | python | @chassis_count.setter
def chassis_count(self, chassis_count):
'\n Sets the chassis_count of this NodesLnnHardwareNode.\n Number of chassis making up this node.\n\n :param chassis_count: The chassis_count of this NodesLnnHardwareNode.\n :type: str\n '
self._chassis_count = chassis_count | @chassis_count.setter
def chassis_count(self, chassis_count):
'\n Sets the chassis_count of this NodesLnnHardwareNode.\n Number of chassis making up this node.\n\n :param chassis_count: The chassis_count of this NodesLnnHardwareNode.\n :type: str\n '
self._chassis_count = chassis_count<|docstring|>Sets the chassis_count of this NodesLnnHardwareNode.
Number of chassis making up this node.
:param chassis_count: The chassis_count of this NodesLnnHardwareNode.
:type: str<|endoftext|> |
27a1b63cd54c8474a3af0ab5988a29dd3fc1ef546fe9a7c1bff2ac30e0ab8139 | @property
def _class(self):
'\n Gets the _class of this NodesLnnHardwareNode.\n Class of this node (storage, accelerator, etc.).\n\n :return: The _class of this NodesLnnHardwareNode.\n :rtype: str\n '
return self.__class | Gets the _class of this NodesLnnHardwareNode.
Class of this node (storage, accelerator, etc.).
:return: The _class of this NodesLnnHardwareNode.
:rtype: str | isi_sdk/models/nodes_lnn_hardware_node.py | _class | Atomicology/isilon_sdk_python | 0 | python | @property
def _class(self):
'\n Gets the _class of this NodesLnnHardwareNode.\n Class of this node (storage, accelerator, etc.).\n\n :return: The _class of this NodesLnnHardwareNode.\n :rtype: str\n '
return self.__class | @property
def _class(self):
'\n Gets the _class of this NodesLnnHardwareNode.\n Class of this node (storage, accelerator, etc.).\n\n :return: The _class of this NodesLnnHardwareNode.\n :rtype: str\n '
return self.__class<|docstring|>Gets the _class of this NodesLnnHardwareNode.
Class of this node (storage, accelerator, etc.).
:return: The _class of this NodesLnnHardwareNode.
:rtype: str<|endoftext|> |
7491beaa6778eabb65f31338f1e66bc601935874557f4f7a6e6a4c96a1082ddf | @_class.setter
def _class(self, _class):
'\n Sets the _class of this NodesLnnHardwareNode.\n Class of this node (storage, accelerator, etc.).\n\n :param _class: The _class of this NodesLnnHardwareNode.\n :type: str\n '
self.__class = _class | Sets the _class of this NodesLnnHardwareNode.
Class of this node (storage, accelerator, etc.).
:param _class: The _class of this NodesLnnHardwareNode.
:type: str | isi_sdk/models/nodes_lnn_hardware_node.py | _class | Atomicology/isilon_sdk_python | 0 | python | @_class.setter
def _class(self, _class):
'\n Sets the _class of this NodesLnnHardwareNode.\n Class of this node (storage, accelerator, etc.).\n\n :param _class: The _class of this NodesLnnHardwareNode.\n :type: str\n '
self.__class = _class | @_class.setter
def _class(self, _class):
'\n Sets the _class of this NodesLnnHardwareNode.\n Class of this node (storage, accelerator, etc.).\n\n :param _class: The _class of this NodesLnnHardwareNode.\n :type: str\n '
self.__class = _class<|docstring|>Sets the _class of this NodesLnnHardwareNode.
Class of this node (storage, accelerator, etc.).
:param _class: The _class of this NodesLnnHardwareNode.
:type: str<|endoftext|> |
d6471136c8c68afbdea64376a44f19a9a8aa1226aaa1dce5c3de6b7632cf2533 | @property
def configuration_id(self):
'\n Gets the configuration_id of this NodesLnnHardwareNode.\n Node configuration ID.\n\n :return: The configuration_id of this NodesLnnHardwareNode.\n :rtype: str\n '
return self._configuration_id | Gets the configuration_id of this NodesLnnHardwareNode.
Node configuration ID.
:return: The configuration_id of this NodesLnnHardwareNode.
:rtype: str | isi_sdk/models/nodes_lnn_hardware_node.py | configuration_id | Atomicology/isilon_sdk_python | 0 | python | @property
def configuration_id(self):
'\n Gets the configuration_id of this NodesLnnHardwareNode.\n Node configuration ID.\n\n :return: The configuration_id of this NodesLnnHardwareNode.\n :rtype: str\n '
return self._configuration_id | @property
def configuration_id(self):
'\n Gets the configuration_id of this NodesLnnHardwareNode.\n Node configuration ID.\n\n :return: The configuration_id of this NodesLnnHardwareNode.\n :rtype: str\n '
return self._configuration_id<|docstring|>Gets the configuration_id of this NodesLnnHardwareNode.
Node configuration ID.
:return: The configuration_id of this NodesLnnHardwareNode.
:rtype: str<|endoftext|> |
e17c4f21390ac4afa374b7f5a3362408da640fb735595316a520511a553d4b3c | @configuration_id.setter
def configuration_id(self, configuration_id):
'\n Sets the configuration_id of this NodesLnnHardwareNode.\n Node configuration ID.\n\n :param configuration_id: The configuration_id of this NodesLnnHardwareNode.\n :type: str\n '
self._configuration_id = configuration_id | Sets the configuration_id of this NodesLnnHardwareNode.
Node configuration ID.
:param configuration_id: The configuration_id of this NodesLnnHardwareNode.
:type: str | isi_sdk/models/nodes_lnn_hardware_node.py | configuration_id | Atomicology/isilon_sdk_python | 0 | python | @configuration_id.setter
def configuration_id(self, configuration_id):
'\n Sets the configuration_id of this NodesLnnHardwareNode.\n Node configuration ID.\n\n :param configuration_id: The configuration_id of this NodesLnnHardwareNode.\n :type: str\n '
self._configuration_id = configuration_id | @configuration_id.setter
def configuration_id(self, configuration_id):
'\n Sets the configuration_id of this NodesLnnHardwareNode.\n Node configuration ID.\n\n :param configuration_id: The configuration_id of this NodesLnnHardwareNode.\n :type: str\n '
self._configuration_id = configuration_id<|docstring|>Sets the configuration_id of this NodesLnnHardwareNode.
Node configuration ID.
:param configuration_id: The configuration_id of this NodesLnnHardwareNode.
:type: str<|endoftext|> |
dfa93e9155e2d1369a09eb2bab8c74640bbee043d74171488c25b1d46adcab58 | @property
def cpu(self):
"\n Gets the cpu of this NodesLnnHardwareNode.\n Manufacturer and model of this node's CPU.\n\n :return: The cpu of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._cpu | Gets the cpu of this NodesLnnHardwareNode.
Manufacturer and model of this node's CPU.
:return: The cpu of this NodesLnnHardwareNode.
:rtype: str | isi_sdk/models/nodes_lnn_hardware_node.py | cpu | Atomicology/isilon_sdk_python | 0 | python | @property
def cpu(self):
"\n Gets the cpu of this NodesLnnHardwareNode.\n Manufacturer and model of this node's CPU.\n\n :return: The cpu of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._cpu | @property
def cpu(self):
"\n Gets the cpu of this NodesLnnHardwareNode.\n Manufacturer and model of this node's CPU.\n\n :return: The cpu of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._cpu<|docstring|>Gets the cpu of this NodesLnnHardwareNode.
Manufacturer and model of this node's CPU.
:return: The cpu of this NodesLnnHardwareNode.
:rtype: str<|endoftext|> |
7dbf1ee9cd1dd2b3e987b1313161d278376dbe5927c37e2ff94414d0dbde9c28 | @cpu.setter
def cpu(self, cpu):
"\n Sets the cpu of this NodesLnnHardwareNode.\n Manufacturer and model of this node's CPU.\n\n :param cpu: The cpu of this NodesLnnHardwareNode.\n :type: str\n "
self._cpu = cpu | Sets the cpu of this NodesLnnHardwareNode.
Manufacturer and model of this node's CPU.
:param cpu: The cpu of this NodesLnnHardwareNode.
:type: str | isi_sdk/models/nodes_lnn_hardware_node.py | cpu | Atomicology/isilon_sdk_python | 0 | python | @cpu.setter
def cpu(self, cpu):
"\n Sets the cpu of this NodesLnnHardwareNode.\n Manufacturer and model of this node's CPU.\n\n :param cpu: The cpu of this NodesLnnHardwareNode.\n :type: str\n "
self._cpu = cpu | @cpu.setter
def cpu(self, cpu):
"\n Sets the cpu of this NodesLnnHardwareNode.\n Manufacturer and model of this node's CPU.\n\n :param cpu: The cpu of this NodesLnnHardwareNode.\n :type: str\n "
self._cpu = cpu<|docstring|>Sets the cpu of this NodesLnnHardwareNode.
Manufacturer and model of this node's CPU.
:param cpu: The cpu of this NodesLnnHardwareNode.
:type: str<|endoftext|> |
74547b4a948af4a6c47f504e440f554220589186b21c8e43bffba177b013a20f | @property
def disk_controller(self):
"\n Gets the disk_controller of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk controller.\n\n :return: The disk_controller of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._disk_controller | Gets the disk_controller of this NodesLnnHardwareNode.
Manufacturer and model of this node's disk controller.
:return: The disk_controller of this NodesLnnHardwareNode.
:rtype: str | isi_sdk/models/nodes_lnn_hardware_node.py | disk_controller | Atomicology/isilon_sdk_python | 0 | python | @property
def disk_controller(self):
"\n Gets the disk_controller of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk controller.\n\n :return: The disk_controller of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._disk_controller | @property
def disk_controller(self):
"\n Gets the disk_controller of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk controller.\n\n :return: The disk_controller of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._disk_controller<|docstring|>Gets the disk_controller of this NodesLnnHardwareNode.
Manufacturer and model of this node's disk controller.
:return: The disk_controller of this NodesLnnHardwareNode.
:rtype: str<|endoftext|> |
cf91cc3025a265161b17c07d67cec0f6b00f442731e59dba0ff13c57aefa137b | @disk_controller.setter
def disk_controller(self, disk_controller):
"\n Sets the disk_controller of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk controller.\n\n :param disk_controller: The disk_controller of this NodesLnnHardwareNode.\n :type: str\n "
self._disk_controller = disk_controller | Sets the disk_controller of this NodesLnnHardwareNode.
Manufacturer and model of this node's disk controller.
:param disk_controller: The disk_controller of this NodesLnnHardwareNode.
:type: str | isi_sdk/models/nodes_lnn_hardware_node.py | disk_controller | Atomicology/isilon_sdk_python | 0 | python | @disk_controller.setter
def disk_controller(self, disk_controller):
"\n Sets the disk_controller of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk controller.\n\n :param disk_controller: The disk_controller of this NodesLnnHardwareNode.\n :type: str\n "
self._disk_controller = disk_controller | @disk_controller.setter
def disk_controller(self, disk_controller):
"\n Sets the disk_controller of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk controller.\n\n :param disk_controller: The disk_controller of this NodesLnnHardwareNode.\n :type: str\n "
self._disk_controller = disk_controller<|docstring|>Sets the disk_controller of this NodesLnnHardwareNode.
Manufacturer and model of this node's disk controller.
:param disk_controller: The disk_controller of this NodesLnnHardwareNode.
:type: str<|endoftext|> |
0e75102941c04ed8f633567cf95108db2f8f51e653fc9659bb5d070e035fb55c | @property
def disk_expander(self):
"\n Gets the disk_expander of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk expander.\n\n :return: The disk_expander of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._disk_expander | Gets the disk_expander of this NodesLnnHardwareNode.
Manufacturer and model of this node's disk expander.
:return: The disk_expander of this NodesLnnHardwareNode.
:rtype: str | isi_sdk/models/nodes_lnn_hardware_node.py | disk_expander | Atomicology/isilon_sdk_python | 0 | python | @property
def disk_expander(self):
"\n Gets the disk_expander of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk expander.\n\n :return: The disk_expander of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._disk_expander | @property
def disk_expander(self):
"\n Gets the disk_expander of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk expander.\n\n :return: The disk_expander of this NodesLnnHardwareNode.\n :rtype: str\n "
return self._disk_expander<|docstring|>Gets the disk_expander of this NodesLnnHardwareNode.
Manufacturer and model of this node's disk expander.
:return: The disk_expander of this NodesLnnHardwareNode.
:rtype: str<|endoftext|> |
4984d724a28aae6630a6054880d76842d4c949f01594ffd65ef7807b6db8a557 | @disk_expander.setter
def disk_expander(self, disk_expander):
"\n Sets the disk_expander of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk expander.\n\n :param disk_expander: The disk_expander of this NodesLnnHardwareNode.\n :type: str\n "
self._disk_expander = disk_expander | Sets the disk_expander of this NodesLnnHardwareNode.
Manufacturer and model of this node's disk expander.
:param disk_expander: The disk_expander of this NodesLnnHardwareNode.
:type: str | isi_sdk/models/nodes_lnn_hardware_node.py | disk_expander | Atomicology/isilon_sdk_python | 0 | python | @disk_expander.setter
def disk_expander(self, disk_expander):
"\n Sets the disk_expander of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk expander.\n\n :param disk_expander: The disk_expander of this NodesLnnHardwareNode.\n :type: str\n "
self._disk_expander = disk_expander | @disk_expander.setter
def disk_expander(self, disk_expander):
"\n Sets the disk_expander of this NodesLnnHardwareNode.\n Manufacturer and model of this node's disk expander.\n\n :param disk_expander: The disk_expander of this NodesLnnHardwareNode.\n :type: str\n "
self._disk_expander = disk_expander<|docstring|>Sets the disk_expander of this NodesLnnHardwareNode.
Manufacturer and model of this node's disk expander.
:param disk_expander: The disk_expander of this NodesLnnHardwareNode.
:type: str<|endoftext|> |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.