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f7167dafa438abb972d4d287944bf016ae2c3bf7
| 15,655
|
py
|
Python
|
MuJoCo/modules/utils.py
|
mosesnah-shared/whip-project-targeting
|
7f47598635f027e2cb05ad33b66ed67627d20329
|
[
"BSD-3-Clause"
] | null | null | null |
MuJoCo/modules/utils.py
|
mosesnah-shared/whip-project-targeting
|
7f47598635f027e2cb05ad33b66ed67627d20329
|
[
"BSD-3-Clause"
] | null | null | null |
MuJoCo/modules/utils.py
|
mosesnah-shared/whip-project-targeting
|
7f47598635f027e2cb05ad33b66ed67627d20329
|
[
"BSD-3-Clause"
] | null | null | null |
# [Built-in modules]
import os
import re
import sys
import shutil
import time, datetime
import math as myMath
import glob
# [3rd party modules]
import cv2
import numpy as np
import xml.etree.ElementTree as ET
import sympy as sp
from sympy.utilities.lambdify import lambdify, implemented_function
from scipy.special import lambertw
from scipy.integrate import quad
from scipy.spatial.transform import Rotation as R
# [Local modules]
from modules.constants import Constants
class MyVideo:
"""
Description
----------
Arguments
---------
Returns
-------
"""
def __init__( self, vid_dir = None, height = 1440, width = 850, fps = 60 ):
# self.height = height
# self.width = width
self.height = 2880
self.width = 1800
self.vid_dir = vid_dir if not None else "."
self.fps = fps
fourcc = cv2.VideoWriter_fourcc( *'MP4V' ) # 4-character code of codec used to compress the frames.
# For example, VideoWriter::fourcc('P','I','M','1') is a MPEG-1 codec,
# VideoWriter::fourcc('M','J','P','G') is a motion-jpeg codec etc.
# List of codes can be obtained at Video Codecs by FOURCC page.
# self.outVideo = cv2.VideoWriter( self.vid_dir + "/video.mp4", fourcc, fps, ( self.height, self.width ) )
self.outVideo = cv2.VideoWriter( self.vid_dir + "/video.mp4", fourcc, fps, ( self.height//2, self.width//2 ) )
def write( self, myViewer ):
data = myViewer.read_pixels( self.height, self.width, depth = False ) # Get the pixel from the render screen
data = cv2.cvtColor( data, cv2.COLOR_BGR2RGB )
# data = cv2.resize( data,( self.height, self.width ) )
data = cv2.resize( data,( self.height//2, self.width//2 ) )
self.outVideo.write( np.flip( data, axis = 0 ) )
def release( self ):
self.outVideo.release()
def length_elem2elem( mjModel, mjData, elem_name1, elem_name2 ):
type1 = get_elem_type( mjModel, elem_name1 )
type2 = get_elem_type( mjModel, elem_name2 )
# The euclidean distance between two elements, calling using "get_geom_xpos" or "get_site_xpos" or "get_body_xpos" methods
return np.linalg.norm( getattr( mjData, "get_" + type1 + "_" + "xpos" )( elem_name1 )
- getattr( mjData, "get_" + type2 + "_" + "xpos" )( elem_name2 ) , ord = 2 )
def get_elem_type( mjModel, elem_name ):
"""
The naming convention of our mujoco simulation is "{elem}_name", where elem = [geom, site, body]
The string before the first underbar '_' describes the elem(ent) of the model.
This function parses the string and returns the first string (i.e., the element of the model)
"""
return elem_name.split( '_' )[ 0 ] # Parse and get the first string before "_"
def get_property( mjModel, elem_name, prop_name ):
# Get the property of the name
# The name of the elements start with "XXXX_", hence getting the string before the underbar.
type = get_elem_type( mjModel, elem_name )
for idx, s in enumerate( getattr( mjModel, type + "_" + "names" ) ): # run through the list of "geom_names" or "body_names"
if elem_name == s:
tmp = getattr( mjModel, type + "_" + prop_name )
return tmp[ idx ]
# If couldn't match in list, raise error
raise NameError( 'Cannot find geom_name with {0} in list, please check'.format( elem_name ) )
def snake2camel( s ):
"""
Switch string s from snake_form_naming to CamelCase
"""
return ''.join( word.title() for word in s.split( '_' ) )
def camel2snake( s ):
"""
Switch string s from CamelCase to snake_form_naming
[REF] https://stackoverflow.com/questions/1175208/elegant-python-function-to-convert-camelcase-to-snake-case
"""
re.sub( r'(?<!^)(?=[A-Z])', '_', s ).lower()
def clear_dir( dir ):
""" Cleaning up the contents in the directory """
def args_cleanup( args, s ):
"""
Description
-----------
Clean-up the substring s for keys in args
Arguments
---------
args: The dictionary to be parsed
s : Substring to be discarded. e.g. s = '--', then "--record" --> "record"
"""
if not isinstance( args, dict ) or not isinstance( s, str ):
raise ValueError( "Wrong input type. args should be type dict and s should be type str. {0:} and {1:} are rather given".format(
type( args ), type( str ) ) )
for old_key in list( args ) :
new_key = old_key.replace( s, '' )
args[ new_key ] = args.pop( old_key )
return args
def rot2quat( rot ):
# Taking the SO(3) matrix as an input and return the quaternion
return quat
def euler2quaternion( euler_angs ):
"""
Description
-----------
This code is directly from the following reference
[REF] https://computergraphics.stackexchange.com/questions/8195/how-to-convert-euler-angles-to-quaternions-and-get-the-same-euler-angles-back-fr
Converting a R4 quaternion vector (w, x, y, z) to Euler Angle (Roll, Pitch, Yaw)
Arguments
---------
[NAME] [TYPE] [DESCRIPTION]
(1) yaw, pitch, roll The euler angles of the given quaternion vector.
[OUTPUTS]
-----------
[NAME] [TYPE] [DESCRIPTION]
(1) quatVec List The quaternion vector, ordered in w, x, y and z
"""
yaw, pitch, roll = euler_angs[ : ]
cy = np.cos( yaw * 0.5 )
sy = np.sin( yaw * 0.5 )
cp = np.cos( pitch * 0.5 )
sp = np.sin( pitch * 0.5 )
cr = np.cos( roll * 0.5 )
sr = np.sin( roll * 0.5 )
w = cr * cp * cy + sr * sp * sy;
x = sr * cp * cy - cr * sp * sy;
y = cr * sp * cy + sr * cp * sy;
z = cr * cp * sy - sr * sp * cy;
return w,x,y,z
def quaternion2euler( quatVec ): # Inputting quaternion matrix and outputing the yaw, pitch, roll of the euler angle.
"""
Description
-----------
Converting a R4 quaternion vector (w, x, y, z) to Euler Angle (Roll, Pitch, Yaw)
This code is directly from the following reference
[REF] https://computergraphics.stackexchange.com/questions/8195/how-to-convert-euler-angles-to-quaternions-and-get-the-same-euler-angles-back-fr
Arguments
---------
[NAME] [TYPE] [DESCRIPTION]
(1) quatVec List The quaternion vector, ordered in w, x, y and z
Outputs
--------
[NAME] [TYPE] [DESCRIPTION]
(1) yaw, pitch, roll The euler angles of the given quaternion vector.
"""
if len( quatVec ) != 4:
raise ValueError( "Wrong size of input argument. Given size is [{0:d}] while it should be 4".format(
len( quatVec ) ) )
w, x, y ,z = quatVec[:]
t0 = + 2.0 * ( w * x + y * z )
t1 = + 1.0 - 2.0 * ( x * x + y * y )
roll = myMath.atan2( t0, t1 )
t2 = + 2.0 * ( w * y - z * x )
t2 = + 1.0 if t2 > +1.0 else t2
t2 = - 1.0 if t2 < -1.0 else t2
pitch = myMath.asin( t2 )
t3 = + 2.0 * ( w * z + x * y )
t4 = + 1.0 - 2.0 * ( y * y + z * z )
yaw = myMath.atan2( t3, t4 )
return yaw, pitch, roll
def str2bool( s ):
"""
Description:
----------
Converting an input string to a boolean
Arguments:
----------
[NAME] [TYPE] [DESCRIPTION]
(1) s dict, str The string which
Returns:
----------
True/False depending on the given input strin gv
"""
if isinstance( s, dict ):
for key, _ in s.items():
s[ key ] = str2bool( s[ key ] )
else:
return v.lower() in ( "yes", "true", "t", "1" )
def str2float( s ):
"""
Description:
----------
Converting an input string to a float arraay
Arguments:
----------
[NAME] [TYPE] [DESCRIPTION]
(1) s str The string which will be parsed to float array
Returns:
----------
The parsed float array
"""
if not isinstance( s, str ):
raise ValueError( "Input argument should be string, but {} is given".format( type( s ) ) )
return [ float( i ) for i in re.findall( r"[-+]?\d*\.\d+|[-+]?\d+", s ) ]
def my_mkdir( ):
dir = Constants.TMP_DIR # Temporarily saving at tmp
dir += datetime.datetime.now().strftime( "%Y%m%d_%H%M%S/" ) # Appending the date when this directory is called.
if not os.path.exists( dir ): # If directory not exist
os.makedirs( dir, exist_ok = True ) # mkdir -p functionality via exist_ok
return dir
def my_mvdir( from_dir, to_dir ):
shutil.move( from_dir , to_dir )
def my_rmdir( dir ):
if not isinstance( dir, str ):
raise ValueError( "Input directory should be a str, {} is given".format( type ( dir ) ) )
try:
shutil.rmtree( dir )
except:
print( "{0:s} Doesn't exist, hence cannot remove the directory".format( dir ) )
print( "Erasing Directory [{0:s}]".format( dir ) )
def my_print( **kwargs ):
"""
Description:
----------
** double asterisk means giving the argument as dictionary
By using double asterisk "kwargs" as input argument,
Arguments:
----------
Returns:
----------
"""
prec = kwargs[ "prec" ] if "prec" in kwargs else 5
f = kwargs[ "file" ] if "file" in kwargs else sys.stdout # If there is a keyword called "file" then use that as our standard output
tmpMaxLen = len( max( kwargs.keys( ), key = len ) ) # Getting the maximum length of a string list
for args in kwargs:
if 'file' == args.lower( ):
# Ignore the file's value, since it should not be added to the "output.txt" log file.
continue
print( "[{1:{0}s}]:".format( tmpMaxLen, args ), end = ' ', file = f ) # Printing out the name of the array
# {1:{0}s} Enables to set a variable as format length.
tmpData = kwargs[ args ]
if isinstance( tmpData, ( float, int ) ):
tmpPrint = "{2:{1}.{0}f}".format( prec, prec + 2, tmpData )
elif isinstance( tmpData, list ):
tmpPrint = np.array2string( np.array( tmpData ).flatten(), precision = prec, separator = ',' )
elif isinstance( tmpData, np.ndarray ):
tmpPrint = np.array2string( tmpData.flatten() , precision = prec, separator = ',' )
elif isinstance( tmpData, str ):
tmpPrint = tmpData
elif tmpData is None:
tmpPrint = "None"
else:
raise ValueError( "CHECK INPUT")
print( tmpPrint, file = f )
def solve_eq_posture( q0 ):
q1_0 = q0[ 0 ]
q2_0 = q0[ 1 ]
q3_0 = q0[ 2 ]
q4_0 = q0[ 3 ]
q1 = sp.Symbol( 'q1' )
q2 = sp.Symbol( 'q2' )
q3 = sp.Symbol( 'q3' )
q4 = sp.Symbol( 'q4' )
eqn1 = 0.52444712807465876380774716380984*sp.cos(q2)*sp.sin(q1) - 0.12721953522735995889547666592989*sp.cos(q1)*sp.sin(q2) - 0.05501625493258266441642945210333*sp.sin(q4)*(sp.sin(q1)*sp.sin(q3) + sp.cos(q1)*sp.cos(q3)*sp.sin(q2)) - 0.063807174539763700238381716189906*sp.cos(q1)*sp.cos(q2)*sp.sin(q4) - 0.042749427781976545581699156173272*sp.cos(q1)*sp.cos(q4)*sp.sin(q2) + 0.1762293392050615636890142923221*sp.cos(q2)*sp.cos(q4)*sp.sin(q1) + 0.1762293392050615636890142923221*sp.cos(q1)*sp.cos(q3)*sp.sin(q4) - 0.063807174539763700238381716189906*sp.cos(q3)*sp.cos(q4)*sp.sin(q1) + 0.042749427781976545581699156173272*sp.cos(q1)*sp.cos(q2)*sp.sin(q3)*sp.sin(q4) + 0.063807174539763700238381716189906*sp.cos(q1)*sp.cos(q4)*sp.sin(q2)*sp.sin(q3) + 0.1762293392050615636890142923221*sp.sin(q1)*sp.sin(q2)*sp.sin(q3)*sp.sin(q4) + q1 - q1_0
eqn2 = 0.1966778910733553153988850681344*sp.cos(q1)*sp.sin(q2) - 0.12721953522735995889547666592989*sp.cos(q2)*sp.sin(q1) + 0.020788410744410568131712579997838*sp.sin(q4)*(sp.sin(q1)*sp.sin(q3) + sp.cos(q1)*sp.cos(q3)*sp.sin(q2)) + 0.015478241093474287559672575298464*sp.cos(q1)*sp.cos(q2)*sp.sin(q4) + 0.066089435759419945526360606891103*sp.cos(q1)*sp.cos(q4)*sp.sin(q2) - 0.042749427781976545581699156173272*sp.cos(q2)*sp.cos(q4)*sp.sin(q1) - 0.042749427781976545581699156173272*sp.cos(q1)*sp.cos(q3)*sp.sin(q4) + 0.015478241093474287559672575298464*sp.cos(q3)*sp.cos(q4)*sp.sin(q1) - 0.066089435759419945526360606891103*sp.cos(q1)*sp.cos(q2)*sp.sin(q3)*sp.sin(q4) - 0.015478241093474287559672575298464*sp.cos(q1)*sp.cos(q4)*sp.sin(q2)*sp.sin(q3) - 0.042749427781976545581699156173272*sp.sin(q1)*sp.sin(q2)*sp.sin(q3)*sp.sin(q4) + q2 - q2_0
eqn3 = 0.1637248203220158515591720060911*sp.cos(q2)*sp.sin(q1) - 0.061864967327922570916598488111049*sp.cos(q1)*sp.sin(q2) - 0.083555731966853175052278857037891*sp.sin(q4)*(sp.sin(q1)*sp.sin(q3) + sp.cos(q1)*sp.cos(q3)*sp.sin(q2)) - 0.019919678510073035582195188908372*sp.cos(q1)*sp.cos(q2)*sp.sin(q4) - 0.020788410744410568131712579997838*sp.cos(q1)*sp.cos(q4)*sp.sin(q2) + 0.05501625493258266441642945210333*sp.cos(q2)*sp.cos(q4)*sp.sin(q1) + 0.05501625493258266441642945210333*sp.cos(q1)*sp.cos(q3)*sp.sin(q4) - 0.019919678510073035582195188908372*sp.cos(q3)*sp.cos(q4)*sp.sin(q1) + 0.020788410744410568131712579997838*sp.cos(q1)*sp.cos(q2)*sp.sin(q3)*sp.sin(q4) + 0.019919678510073035582195188908372*sp.cos(q1)*sp.cos(q4)*sp.sin(q2)*sp.sin(q3) + 0.05501625493258266441642945210333*sp.sin(q1)*sp.sin(q2)*sp.sin(q3)*sp.sin(q4) + q3 - q3_0
eqn4 = 0.046062245513354471704303705337225*sp.cos(q1)*sp.sin(q2) - 0.18988602913048024944941971625667*sp.cos(q2)*sp.sin(q1) + 0.019919678510073035582195188908372*sp.sin(q4)*(sp.sin(q1)*sp.sin(q3) + sp.cos(q1)*sp.cos(q3)*sp.sin(q2)) + 0.10117159250577656415259752975544*sp.cos(q1)*sp.cos(q2)*sp.sin(q4) + 0.015478241093474287559672575298464*sp.cos(q1)*sp.cos(q4)*sp.sin(q2) - 0.063807174539763700238381716189906*sp.cos(q2)*sp.cos(q4)*sp.sin(q1) - 0.063807174539763700238381716189906*sp.cos(q1)*sp.cos(q3)*sp.sin(q4) + 0.10117159250577656415259752975544*sp.cos(q3)*sp.cos(q4)*sp.sin(q1) - 0.015478241093474287559672575298464*sp.cos(q1)*sp.cos(q2)*sp.sin(q3)*sp.sin(q4) - 0.10117159250577656415259752975544*sp.cos(q1)*sp.cos(q4)*sp.sin(q2)*sp.sin(q3) - 0.063807174539763700238381716189906*sp.sin(q1)*sp.sin(q2)*sp.sin(q3)*sp.sin(q4) + q4 - q4_0
sol = sp.solvers.nsolve( ( eqn1, eqn2, eqn3, eqn4 ), ( q1, q2, q3, q4 ), q0 )
sol = np.array( sol )
return np.array( [ sol[ 0 ][ 0 ], sol[ 1 ][ 0 ], sol[ 2 ][ 0 ], sol[ 3 ][ 0 ] ] )
if __name__ == '__main__':
pass
| 42.196765
| 846
| 0.570808
|
import os
import re
import sys
import shutil
import time, datetime
import math as myMath
import glob
import cv2
import numpy as np
import xml.etree.ElementTree as ET
import sympy as sp
from sympy.utilities.lambdify import lambdify, implemented_function
from scipy.special import lambertw
from scipy.integrate import quad
from scipy.spatial.transform import Rotation as R
from modules.constants import Constants
class MyVideo:
def __init__( self, vid_dir = None, height = 1440, width = 850, fps = 60 ):
self.height = 2880
self.width = 1800
self.vid_dir = vid_dir if not None else "."
self.fps = fps
fourcc = cv2.VideoWriter_fourcc( *'MP4V' )
self.outVideo = cv2.VideoWriter( self.vid_dir + "/video.mp4", fourcc, fps, ( self.height//2, self.width//2 ) )
def write( self, myViewer ):
data = myViewer.read_pixels( self.height, self.width, depth = False )
data = cv2.cvtColor( data, cv2.COLOR_BGR2RGB )
data = cv2.resize( data,( self.height//2, self.width//2 ) )
self.outVideo.write( np.flip( data, axis = 0 ) )
def release( self ):
self.outVideo.release()
def length_elem2elem( mjModel, mjData, elem_name1, elem_name2 ):
type1 = get_elem_type( mjModel, elem_name1 )
type2 = get_elem_type( mjModel, elem_name2 )
return np.linalg.norm( getattr( mjData, "get_" + type1 + "_" + "xpos" )( elem_name1 )
- getattr( mjData, "get_" + type2 + "_" + "xpos" )( elem_name2 ) , ord = 2 )
def get_elem_type( mjModel, elem_name ):
return elem_name.split( '_' )[ 0 ]
def get_property( mjModel, elem_name, prop_name ):
type = get_elem_type( mjModel, elem_name )
for idx, s in enumerate( getattr( mjModel, type + "_" + "names" ) ):
if elem_name == s:
tmp = getattr( mjModel, type + "_" + prop_name )
return tmp[ idx ]
raise NameError( 'Cannot find geom_name with {0} in list, please check'.format( elem_name ) )
def snake2camel( s ):
return ''.join( word.title() for word in s.split( '_' ) )
def camel2snake( s ):
re.sub( r'(?<!^)(?=[A-Z])', '_', s ).lower()
def clear_dir( dir ):
def args_cleanup( args, s ):
if not isinstance( args, dict ) or not isinstance( s, str ):
raise ValueError( "Wrong input type. args should be type dict and s should be type str. {0:} and {1:} are rather given".format(
type( args ), type( str ) ) )
for old_key in list( args ) :
new_key = old_key.replace( s, '' )
args[ new_key ] = args.pop( old_key )
return args
def rot2quat( rot ):
# Taking the SO(3) matrix as an input and return the quaternion
return quat
def euler2quaternion( euler_angs ):
yaw, pitch, roll = euler_angs[ : ]
cy = np.cos( yaw * 0.5 )
sy = np.sin( yaw * 0.5 )
cp = np.cos( pitch * 0.5 )
sp = np.sin( pitch * 0.5 )
cr = np.cos( roll * 0.5 )
sr = np.sin( roll * 0.5 )
w = cr * cp * cy + sr * sp * sy;
x = sr * cp * cy - cr * sp * sy;
y = cr * sp * cy + sr * cp * sy;
z = cr * cp * sy - sr * sp * cy;
return w,x,y,z
def quaternion2euler( quatVec ): # Inputting quaternion matrix and outputing the yaw, pitch, roll of the euler angle.
if len( quatVec ) != 4:
raise ValueError( "Wrong size of input argument. Given size is [{0:d}] while it should be 4".format(
len( quatVec ) ) )
w, x, y ,z = quatVec[:]
t0 = + 2.0 * ( w * x + y * z )
t1 = + 1.0 - 2.0 * ( x * x + y * y )
roll = myMath.atan2( t0, t1 )
t2 = + 2.0 * ( w * y - z * x )
t2 = + 1.0 if t2 > +1.0 else t2
t2 = - 1.0 if t2 < -1.0 else t2
pitch = myMath.asin( t2 )
t3 = + 2.0 * ( w * z + x * y )
t4 = + 1.0 - 2.0 * ( y * y + z * z )
yaw = myMath.atan2( t3, t4 )
return yaw, pitch, roll
def str2bool( s ):
if isinstance( s, dict ):
for key, _ in s.items():
s[ key ] = str2bool( s[ key ] )
else:
return v.lower() in ( "yes", "true", "t", "1" )
def str2float( s ):
if not isinstance( s, str ):
raise ValueError( "Input argument should be string, but {} is given".format( type( s ) ) )
return [ float( i ) for i in re.findall( r"[-+]?\d*\.\d+|[-+]?\d+", s ) ]
def my_mkdir( ):
dir = Constants.TMP_DIR # Temporarily saving at tmp
dir += datetime.datetime.now().strftime( "%Y%m%d_%H%M%S/" ) # Appending the date when this directory is called.
if not os.path.exists( dir ): # If directory not exist
os.makedirs( dir, exist_ok = True ) # mkdir -p functionality via exist_ok
return dir
def my_mvdir( from_dir, to_dir ):
shutil.move( from_dir , to_dir )
def my_rmdir( dir ):
if not isinstance( dir, str ):
raise ValueError( "Input directory should be a str, {} is given".format( type ( dir ) ) )
try:
shutil.rmtree( dir )
except:
print( "{0:s} Doesn't exist, hence cannot remove the directory".format( dir ) )
print( "Erasing Directory [{0:s}]".format( dir ) )
def my_print( **kwargs ):
prec = kwargs[ "prec" ] if "prec" in kwargs else 5
f = kwargs[ "file" ] if "file" in kwargs else sys.stdout
tmpMaxLen = len( max( kwargs.keys( ), key = len ) )
for args in kwargs:
if 'file' == args.lower( ):
continue
print( "[{1:{0}s}]:".format( tmpMaxLen, args ), end = ' ', file = f ) # Printing out the name of the array
# {1:{0}s} Enables to set a variable as format length.
tmpData = kwargs[ args ]
if isinstance( tmpData, ( float, int ) ):
tmpPrint = "{2:{1}.{0}f}".format( prec, prec + 2, tmpData )
elif isinstance( tmpData, list ):
tmpPrint = np.array2string( np.array( tmpData ).flatten(), precision = prec, separator = ',' )
elif isinstance( tmpData, np.ndarray ):
tmpPrint = np.array2string( tmpData.flatten() , precision = prec, separator = ',' )
elif isinstance( tmpData, str ):
tmpPrint = tmpData
elif tmpData is None:
tmpPrint = "None"
else:
raise ValueError( "CHECK INPUT")
print( tmpPrint, file = f )
def solve_eq_posture( q0 ):
q1_0 = q0[ 0 ]
q2_0 = q0[ 1 ]
q3_0 = q0[ 2 ]
q4_0 = q0[ 3 ]
q1 = sp.Symbol( 'q1' )
q2 = sp.Symbol( 'q2' )
q3 = sp.Symbol( 'q3' )
q4 = sp.Symbol( 'q4' )
eqn1 = 0.52444712807465876380774716380984*sp.cos(q2)*sp.sin(q1) - 0.12721953522735995889547666592989*sp.cos(q1)*sp.sin(q2) - 0.05501625493258266441642945210333*sp.sin(q4)*(sp.sin(q1)*sp.sin(q3) + sp.cos(q1)*sp.cos(q3)*sp.sin(q2)) - 0.063807174539763700238381716189906*sp.cos(q1)*sp.cos(q2)*sp.sin(q4) - 0.042749427781976545581699156173272*sp.cos(q1)*sp.cos(q4)*sp.sin(q2) + 0.1762293392050615636890142923221*sp.cos(q2)*sp.cos(q4)*sp.sin(q1) + 0.1762293392050615636890142923221*sp.cos(q1)*sp.cos(q3)*sp.sin(q4) - 0.063807174539763700238381716189906*sp.cos(q3)*sp.cos(q4)*sp.sin(q1) + 0.042749427781976545581699156173272*sp.cos(q1)*sp.cos(q2)*sp.sin(q3)*sp.sin(q4) + 0.063807174539763700238381716189906*sp.cos(q1)*sp.cos(q4)*sp.sin(q2)*sp.sin(q3) + 0.1762293392050615636890142923221*sp.sin(q1)*sp.sin(q2)*sp.sin(q3)*sp.sin(q4) + q1 - q1_0
eqn2 = 0.1966778910733553153988850681344*sp.cos(q1)*sp.sin(q2) - 0.12721953522735995889547666592989*sp.cos(q2)*sp.sin(q1) + 0.020788410744410568131712579997838*sp.sin(q4)*(sp.sin(q1)*sp.sin(q3) + sp.cos(q1)*sp.cos(q3)*sp.sin(q2)) + 0.015478241093474287559672575298464*sp.cos(q1)*sp.cos(q2)*sp.sin(q4) + 0.066089435759419945526360606891103*sp.cos(q1)*sp.cos(q4)*sp.sin(q2) - 0.042749427781976545581699156173272*sp.cos(q2)*sp.cos(q4)*sp.sin(q1) - 0.042749427781976545581699156173272*sp.cos(q1)*sp.cos(q3)*sp.sin(q4) + 0.015478241093474287559672575298464*sp.cos(q3)*sp.cos(q4)*sp.sin(q1) - 0.066089435759419945526360606891103*sp.cos(q1)*sp.cos(q2)*sp.sin(q3)*sp.sin(q4) - 0.015478241093474287559672575298464*sp.cos(q1)*sp.cos(q4)*sp.sin(q2)*sp.sin(q3) - 0.042749427781976545581699156173272*sp.sin(q1)*sp.sin(q2)*sp.sin(q3)*sp.sin(q4) + q2 - q2_0
eqn3 = 0.1637248203220158515591720060911*sp.cos(q2)*sp.sin(q1) - 0.061864967327922570916598488111049*sp.cos(q1)*sp.sin(q2) - 0.083555731966853175052278857037891*sp.sin(q4)*(sp.sin(q1)*sp.sin(q3) + sp.cos(q1)*sp.cos(q3)*sp.sin(q2)) - 0.019919678510073035582195188908372*sp.cos(q1)*sp.cos(q2)*sp.sin(q4) - 0.020788410744410568131712579997838*sp.cos(q1)*sp.cos(q4)*sp.sin(q2) + 0.05501625493258266441642945210333*sp.cos(q2)*sp.cos(q4)*sp.sin(q1) + 0.05501625493258266441642945210333*sp.cos(q1)*sp.cos(q3)*sp.sin(q4) - 0.019919678510073035582195188908372*sp.cos(q3)*sp.cos(q4)*sp.sin(q1) + 0.020788410744410568131712579997838*sp.cos(q1)*sp.cos(q2)*sp.sin(q3)*sp.sin(q4) + 0.019919678510073035582195188908372*sp.cos(q1)*sp.cos(q4)*sp.sin(q2)*sp.sin(q3) + 0.05501625493258266441642945210333*sp.sin(q1)*sp.sin(q2)*sp.sin(q3)*sp.sin(q4) + q3 - q3_0
eqn4 = 0.046062245513354471704303705337225*sp.cos(q1)*sp.sin(q2) - 0.18988602913048024944941971625667*sp.cos(q2)*sp.sin(q1) + 0.019919678510073035582195188908372*sp.sin(q4)*(sp.sin(q1)*sp.sin(q3) + sp.cos(q1)*sp.cos(q3)*sp.sin(q2)) + 0.10117159250577656415259752975544*sp.cos(q1)*sp.cos(q2)*sp.sin(q4) + 0.015478241093474287559672575298464*sp.cos(q1)*sp.cos(q4)*sp.sin(q2) - 0.063807174539763700238381716189906*sp.cos(q2)*sp.cos(q4)*sp.sin(q1) - 0.063807174539763700238381716189906*sp.cos(q1)*sp.cos(q3)*sp.sin(q4) + 0.10117159250577656415259752975544*sp.cos(q3)*sp.cos(q4)*sp.sin(q1) - 0.015478241093474287559672575298464*sp.cos(q1)*sp.cos(q2)*sp.sin(q3)*sp.sin(q4) - 0.10117159250577656415259752975544*sp.cos(q1)*sp.cos(q4)*sp.sin(q2)*sp.sin(q3) - 0.063807174539763700238381716189906*sp.sin(q1)*sp.sin(q2)*sp.sin(q3)*sp.sin(q4) + q4 - q4_0
sol = sp.solvers.nsolve( ( eqn1, eqn2, eqn3, eqn4 ), ( q1, q2, q3, q4 ), q0 )
sol = np.array( sol )
return np.array( [ sol[ 0 ][ 0 ], sol[ 1 ][ 0 ], sol[ 2 ][ 0 ], sol[ 3 ][ 0 ] ] )
if __name__ == '__main__':
pass
| true
| true
|
f7167e61cefc29e05e76cf5f35782284b46a20aa
| 4,682
|
py
|
Python
|
sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/aio/operations/_operations.py
|
ankitarorabit/azure-sdk-for-python
|
dd90281cbad9400f8080754a5ef2f56791a5a88f
|
[
"MIT"
] | 3
|
2020-06-23T02:25:27.000Z
|
2021-09-07T18:48:11.000Z
|
sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/aio/operations/_operations.py
|
ankitarorabit/azure-sdk-for-python
|
dd90281cbad9400f8080754a5ef2f56791a5a88f
|
[
"MIT"
] | 510
|
2019-07-17T16:11:19.000Z
|
2021-08-02T08:38:32.000Z
|
sdk/netapp/azure-mgmt-netapp/azure/mgmt/netapp/aio/operations/_operations.py
|
ankitarorabit/azure-sdk-for-python
|
dd90281cbad9400f8080754a5ef2f56791a5a88f
|
[
"MIT"
] | 5
|
2019-09-04T12:51:37.000Z
|
2020-09-16T07:28:40.000Z
|
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from azure.mgmt.core.exceptions import ARMErrorFormat
from ... import models as _models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class Operations:
"""Operations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.mgmt.netapp.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
def list(
self,
**kwargs: Any
) -> AsyncIterable["_models.OperationListResult"]:
"""Describes the Resource Provider.
Lists all of the available Microsoft.NetApp Rest API operations.
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either OperationListResult or the result of cls(response)
:rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.netapp.models.OperationListResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.OperationListResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-02-01"
accept = "application/json"
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list.metadata['url'] # type: ignore
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {} # type: Dict[str, Any]
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('OperationListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(
get_next, extract_data
)
list.metadata = {'url': '/providers/Microsoft.NetApp/operations'} # type: ignore
| 43.757009
| 133
| 0.65912
|
from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from azure.mgmt.core.exceptions import ARMErrorFormat
from ... import models as _models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class Operations:
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
def list(
self,
**kwargs: Any
) -> AsyncIterable["_models.OperationListResult"]:
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2021-02-01"
accept = "application/json"
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
url = self.list.metadata['url']
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('OperationListResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(
get_next, extract_data
)
list.metadata = {'url': '/providers/Microsoft.NetApp/operations'}
| true
| true
|
f7167ea0d6c0f9e11b4a88a19c5af9d2c9a92a69
| 17,093
|
py
|
Python
|
intersight/model/niatelemetry_supervisor_module_details.py
|
CiscoDevNet/intersight-python
|
04b721f37c3044646a91c185c7259edfb991557a
|
[
"Apache-2.0"
] | 5
|
2021-12-16T15:13:32.000Z
|
2022-03-29T16:09:54.000Z
|
intersight/model/niatelemetry_supervisor_module_details.py
|
CiscoDevNet/intersight-python
|
04b721f37c3044646a91c185c7259edfb991557a
|
[
"Apache-2.0"
] | 4
|
2022-01-25T19:05:51.000Z
|
2022-03-29T20:18:37.000Z
|
intersight/model/niatelemetry_supervisor_module_details.py
|
CiscoDevNet/intersight-python
|
04b721f37c3044646a91c185c7259edfb991557a
|
[
"Apache-2.0"
] | 2
|
2020-07-07T15:01:08.000Z
|
2022-01-31T04:27:35.000Z
|
"""
Cisco Intersight
Cisco Intersight is a management platform delivered as a service with embedded analytics for your Cisco and 3rd party IT infrastructure. This platform offers an intelligent level of management that enables IT organizations to analyze, simplify, and automate their environments in more advanced ways than the prior generations of tools. Cisco Intersight provides an integrated and intuitive management experience for resources in the traditional data center as well as at the edge. With flexible deployment options to address complex security needs, getting started with Intersight is quick and easy. Cisco Intersight has deep integration with Cisco UCS and HyperFlex systems allowing for remote deployment, configuration, and ongoing maintenance. The model-based deployment works for a single system in a remote location or hundreds of systems in a data center and enables rapid, standardized configuration and deployment. It also streamlines maintaining those systems whether you are working with small or very large configurations. The Intersight OpenAPI document defines the complete set of properties that are returned in the HTTP response. From that perspective, a client can expect that no additional properties are returned, unless these properties are explicitly defined in the OpenAPI document. However, when a client uses an older version of the Intersight OpenAPI document, the server may send additional properties because the software is more recent than the client. In that case, the client may receive properties that it does not know about. Some generated SDKs perform a strict validation of the HTTP response body against the OpenAPI document. # noqa: E501
The version of the OpenAPI document: 1.0.9-4950
Contact: intersight@cisco.com
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
from intersight.model_utils import ( # noqa: F401
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
)
def lazy_import():
from intersight.model.asset_device_registration_relationship import AssetDeviceRegistrationRelationship
from intersight.model.display_names import DisplayNames
from intersight.model.mo_base_mo import MoBaseMo
from intersight.model.mo_base_mo_relationship import MoBaseMoRelationship
from intersight.model.mo_tag import MoTag
from intersight.model.mo_version_context import MoVersionContext
from intersight.model.niatelemetry_supervisor_module_details_all_of import NiatelemetrySupervisorModuleDetailsAllOf
globals()['AssetDeviceRegistrationRelationship'] = AssetDeviceRegistrationRelationship
globals()['DisplayNames'] = DisplayNames
globals()['MoBaseMo'] = MoBaseMo
globals()['MoBaseMoRelationship'] = MoBaseMoRelationship
globals()['MoTag'] = MoTag
globals()['MoVersionContext'] = MoVersionContext
globals()['NiatelemetrySupervisorModuleDetailsAllOf'] = NiatelemetrySupervisorModuleDetailsAllOf
class NiatelemetrySupervisorModuleDetails(ModelComposed):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
('class_id',): {
'NIATELEMETRY.SUPERVISORMODULEDETAILS': "niatelemetry.SupervisorModuleDetails",
},
('object_type',): {
'NIATELEMETRY.SUPERVISORMODULEDETAILS': "niatelemetry.SupervisorModuleDetails",
},
}
validations = {
}
@cached_property
def additional_properties_type():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
"""
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
lazy_import()
return {
'class_id': (str,), # noqa: E501
'object_type': (str,), # noqa: E501
'dn': (str,), # noqa: E501
'hw_ver': (str,), # noqa: E501
'model': (str,), # noqa: E501
'record_type': (str,), # noqa: E501
'record_version': (str,), # noqa: E501
'serial': (str,), # noqa: E501
'site_name': (str,), # noqa: E501
'registered_device': (AssetDeviceRegistrationRelationship,), # noqa: E501
'account_moid': (str,), # noqa: E501
'create_time': (datetime,), # noqa: E501
'domain_group_moid': (str,), # noqa: E501
'mod_time': (datetime,), # noqa: E501
'moid': (str,), # noqa: E501
'owners': ([str], none_type,), # noqa: E501
'shared_scope': (str,), # noqa: E501
'tags': ([MoTag], none_type,), # noqa: E501
'version_context': (MoVersionContext,), # noqa: E501
'ancestors': ([MoBaseMoRelationship], none_type,), # noqa: E501
'parent': (MoBaseMoRelationship,), # noqa: E501
'permission_resources': ([MoBaseMoRelationship], none_type,), # noqa: E501
'display_names': (DisplayNames,), # noqa: E501
}
@cached_property
def discriminator():
val = {
}
if not val:
return None
return {'class_id': val}
attribute_map = {
'class_id': 'ClassId', # noqa: E501
'object_type': 'ObjectType', # noqa: E501
'dn': 'Dn', # noqa: E501
'hw_ver': 'HwVer', # noqa: E501
'model': 'Model', # noqa: E501
'record_type': 'RecordType', # noqa: E501
'record_version': 'RecordVersion', # noqa: E501
'serial': 'Serial', # noqa: E501
'site_name': 'SiteName', # noqa: E501
'registered_device': 'RegisteredDevice', # noqa: E501
'account_moid': 'AccountMoid', # noqa: E501
'create_time': 'CreateTime', # noqa: E501
'domain_group_moid': 'DomainGroupMoid', # noqa: E501
'mod_time': 'ModTime', # noqa: E501
'moid': 'Moid', # noqa: E501
'owners': 'Owners', # noqa: E501
'shared_scope': 'SharedScope', # noqa: E501
'tags': 'Tags', # noqa: E501
'version_context': 'VersionContext', # noqa: E501
'ancestors': 'Ancestors', # noqa: E501
'parent': 'Parent', # noqa: E501
'permission_resources': 'PermissionResources', # noqa: E501
'display_names': 'DisplayNames', # noqa: E501
}
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
'_composed_instances',
'_var_name_to_model_instances',
'_additional_properties_model_instances',
])
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs): # noqa: E501
"""NiatelemetrySupervisorModuleDetails - a model defined in OpenAPI
Args:
Keyword Args:
class_id (str): The fully-qualified name of the instantiated, concrete type. This property is used as a discriminator to identify the type of the payload when marshaling and unmarshaling data.. defaults to "niatelemetry.SupervisorModuleDetails", must be one of ["niatelemetry.SupervisorModuleDetails", ] # noqa: E501
object_type (str): The fully-qualified name of the instantiated, concrete type. The value should be the same as the 'ClassId' property.. defaults to "niatelemetry.SupervisorModuleDetails", must be one of ["niatelemetry.SupervisorModuleDetails", ] # noqa: E501
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
dn (str): Dn of the supervisor module in APIC.. [optional] # noqa: E501
hw_ver (str): Hardware version of supervisor module.. [optional] # noqa: E501
model (str): Model of the supervisor module.. [optional] # noqa: E501
record_type (str): Type of record DCNM / APIC / SE. This determines the type of platform where inventory was collected.. [optional] # noqa: E501
record_version (str): Version of record being pushed. This determines what was the API version for data available from the device.. [optional] # noqa: E501
serial (str): Serial number of the supervisor module.. [optional] # noqa: E501
site_name (str): Name of the APIC site from which this data is being collected.. [optional] # noqa: E501
registered_device (AssetDeviceRegistrationRelationship): [optional] # noqa: E501
account_moid (str): The Account ID for this managed object.. [optional] # noqa: E501
create_time (datetime): The time when this managed object was created.. [optional] # noqa: E501
domain_group_moid (str): The DomainGroup ID for this managed object.. [optional] # noqa: E501
mod_time (datetime): The time when this managed object was last modified.. [optional] # noqa: E501
moid (str): The unique identifier of this Managed Object instance.. [optional] # noqa: E501
owners ([str], none_type): [optional] # noqa: E501
shared_scope (str): Intersight provides pre-built workflows, tasks and policies to end users through global catalogs. Objects that are made available through global catalogs are said to have a 'shared' ownership. Shared objects are either made globally available to all end users or restricted to end users based on their license entitlement. Users can use this property to differentiate the scope (global or a specific license tier) to which a shared MO belongs.. [optional] # noqa: E501
tags ([MoTag], none_type): [optional] # noqa: E501
version_context (MoVersionContext): [optional] # noqa: E501
ancestors ([MoBaseMoRelationship], none_type): An array of relationships to moBaseMo resources.. [optional] # noqa: E501
parent (MoBaseMoRelationship): [optional] # noqa: E501
permission_resources ([MoBaseMoRelationship], none_type): An array of relationships to moBaseMo resources.. [optional] # noqa: E501
display_names (DisplayNames): [optional] # noqa: E501
"""
class_id = kwargs.get('class_id', "niatelemetry.SupervisorModuleDetails")
object_type = kwargs.get('object_type', "niatelemetry.SupervisorModuleDetails")
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
constant_args = {
'_check_type': _check_type,
'_path_to_item': _path_to_item,
'_spec_property_naming': _spec_property_naming,
'_configuration': _configuration,
'_visited_composed_classes': self._visited_composed_classes,
}
required_args = {
'class_id': class_id,
'object_type': object_type,
}
model_args = {}
model_args.update(required_args)
model_args.update(kwargs)
composed_info = validate_get_composed_info(
constant_args, model_args, self)
self._composed_instances = composed_info[0]
self._var_name_to_model_instances = composed_info[1]
self._additional_properties_model_instances = composed_info[2]
unused_args = composed_info[3]
for var_name, var_value in required_args.items():
setattr(self, var_name, var_value)
for var_name, var_value in kwargs.items():
if var_name in unused_args and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
not self._additional_properties_model_instances:
# discard variable.
continue
setattr(self, var_name, var_value)
@cached_property
def _composed_schemas():
# we need this here to make our import statements work
# we must store _composed_schemas in here so the code is only run
# when we invoke this method. If we kept this at the class
# level we would get an error beause the class level
# code would be run when this module is imported, and these composed
# classes don't exist yet because their module has not finished
# loading
lazy_import()
return {
'anyOf': [
],
'allOf': [
MoBaseMo,
NiatelemetrySupervisorModuleDetailsAllOf,
],
'oneOf': [
],
}
| 54.091772
| 1,678
| 0.639677
|
import re
import sys
from intersight.model_utils import (
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
)
def lazy_import():
from intersight.model.asset_device_registration_relationship import AssetDeviceRegistrationRelationship
from intersight.model.display_names import DisplayNames
from intersight.model.mo_base_mo import MoBaseMo
from intersight.model.mo_base_mo_relationship import MoBaseMoRelationship
from intersight.model.mo_tag import MoTag
from intersight.model.mo_version_context import MoVersionContext
from intersight.model.niatelemetry_supervisor_module_details_all_of import NiatelemetrySupervisorModuleDetailsAllOf
globals()['AssetDeviceRegistrationRelationship'] = AssetDeviceRegistrationRelationship
globals()['DisplayNames'] = DisplayNames
globals()['MoBaseMo'] = MoBaseMo
globals()['MoBaseMoRelationship'] = MoBaseMoRelationship
globals()['MoTag'] = MoTag
globals()['MoVersionContext'] = MoVersionContext
globals()['NiatelemetrySupervisorModuleDetailsAllOf'] = NiatelemetrySupervisorModuleDetailsAllOf
class NiatelemetrySupervisorModuleDetails(ModelComposed):
allowed_values = {
('class_id',): {
'NIATELEMETRY.SUPERVISORMODULEDETAILS': "niatelemetry.SupervisorModuleDetails",
},
('object_type',): {
'NIATELEMETRY.SUPERVISORMODULEDETAILS': "niatelemetry.SupervisorModuleDetails",
},
}
validations = {
}
@cached_property
def additional_properties_type():
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type,)
_nullable = False
@cached_property
def openapi_types():
lazy_import()
return {
'class_id': (str,),
'object_type': (str,),
'dn': (str,),
'hw_ver': (str,),
'model': (str,),
'record_type': (str,),
'record_version': (str,),
'serial': (str,),
'site_name': (str,),
'registered_device': (AssetDeviceRegistrationRelationship,),
'account_moid': (str,),
'create_time': (datetime,),
'domain_group_moid': (str,),
'mod_time': (datetime,),
'moid': (str,),
'owners': ([str], none_type,),
'shared_scope': (str,),
'tags': ([MoTag], none_type,),
'version_context': (MoVersionContext,),
'ancestors': ([MoBaseMoRelationship], none_type,),
'parent': (MoBaseMoRelationship,),
'permission_resources': ([MoBaseMoRelationship], none_type,),
'display_names': (DisplayNames,),
}
@cached_property
def discriminator():
val = {
}
if not val:
return None
return {'class_id': val}
attribute_map = {
'class_id': 'ClassId',
'object_type': 'ObjectType',
'dn': 'Dn',
'hw_ver': 'HwVer',
'model': 'Model',
'record_type': 'RecordType',
'record_version': 'RecordVersion',
'serial': 'Serial',
'site_name': 'SiteName',
'registered_device': 'RegisteredDevice',
'account_moid': 'AccountMoid',
'create_time': 'CreateTime',
'domain_group_moid': 'DomainGroupMoid',
'mod_time': 'ModTime',
'moid': 'Moid',
'owners': 'Owners',
'shared_scope': 'SharedScope',
'tags': 'Tags',
'version_context': 'VersionContext',
'ancestors': 'Ancestors',
'parent': 'Parent',
'permission_resources': 'PermissionResources',
'display_names': 'DisplayNames',
}
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
'_composed_instances',
'_var_name_to_model_instances',
'_additional_properties_model_instances',
])
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs):
class_id = kwargs.get('class_id', "niatelemetry.SupervisorModuleDetails")
object_type = kwargs.get('object_type', "niatelemetry.SupervisorModuleDetails")
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
constant_args = {
'_check_type': _check_type,
'_path_to_item': _path_to_item,
'_spec_property_naming': _spec_property_naming,
'_configuration': _configuration,
'_visited_composed_classes': self._visited_composed_classes,
}
required_args = {
'class_id': class_id,
'object_type': object_type,
}
model_args = {}
model_args.update(required_args)
model_args.update(kwargs)
composed_info = validate_get_composed_info(
constant_args, model_args, self)
self._composed_instances = composed_info[0]
self._var_name_to_model_instances = composed_info[1]
self._additional_properties_model_instances = composed_info[2]
unused_args = composed_info[3]
for var_name, var_value in required_args.items():
setattr(self, var_name, var_value)
for var_name, var_value in kwargs.items():
if var_name in unused_args and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
not self._additional_properties_model_instances:
continue
setattr(self, var_name, var_value)
@cached_property
def _composed_schemas():
# loading
lazy_import()
return {
'anyOf': [
],
'allOf': [
MoBaseMo,
NiatelemetrySupervisorModuleDetailsAllOf,
],
'oneOf': [
],
}
| true
| true
|
f7167ec21a6a129bda61142db392f78aae4d6960
| 271
|
py
|
Python
|
tests/database.py
|
tophat/ormar-postgres-extensions
|
88fcab5a73bc89090739c38f063191d8473957a5
|
[
"Apache-2.0"
] | 6
|
2021-08-02T22:24:34.000Z
|
2022-03-21T08:59:22.000Z
|
tests/database.py
|
tophat/ormar-postgres-extensions
|
88fcab5a73bc89090739c38f063191d8473957a5
|
[
"Apache-2.0"
] | 14
|
2021-08-03T00:03:55.000Z
|
2022-02-17T02:10:08.000Z
|
tests/database.py
|
tophat/ormar-postgres-extensions
|
88fcab5a73bc89090739c38f063191d8473957a5
|
[
"Apache-2.0"
] | 2
|
2022-01-28T20:10:12.000Z
|
2022-02-09T16:49:01.000Z
|
import databases
import sqlalchemy
DB_HOST = "localhost"
DB_NAME = "TEST_DATABASE"
DATABASE_URL = databases.DatabaseURL(
f"postgres://DEV_USER:DEV_PASSWORD@{DB_HOST}:5432/{DB_NAME}"
)
database = databases.Database(str(DATABASE_URL))
metadata = sqlalchemy.MetaData()
| 24.636364
| 64
| 0.782288
|
import databases
import sqlalchemy
DB_HOST = "localhost"
DB_NAME = "TEST_DATABASE"
DATABASE_URL = databases.DatabaseURL(
f"postgres://DEV_USER:DEV_PASSWORD@{DB_HOST}:5432/{DB_NAME}"
)
database = databases.Database(str(DATABASE_URL))
metadata = sqlalchemy.MetaData()
| true
| true
|
f71680e22b485ad498455c52808137f6caab7e0c
| 910
|
py
|
Python
|
gammapy/stats/tests/test_significance.py
|
qpiel/gammapy
|
cfb976909e63f4d5d578e1495245c0baad69482b
|
[
"BSD-3-Clause"
] | null | null | null |
gammapy/stats/tests/test_significance.py
|
qpiel/gammapy
|
cfb976909e63f4d5d578e1495245c0baad69482b
|
[
"BSD-3-Clause"
] | 1
|
2020-10-29T19:55:46.000Z
|
2020-10-29T19:55:46.000Z
|
gammapy/stats/tests/test_significance.py
|
qpiel/gammapy
|
cfb976909e63f4d5d578e1495245c0baad69482b
|
[
"BSD-3-Clause"
] | null | null | null |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import absolute_import, division, print_function, unicode_literals
from numpy.testing import assert_allclose
from ...stats import (
significance_to_probability_normal,
probability_to_significance_normal,
probability_to_significance_normal_limit,
significance_to_probability_normal_limit,
)
def test_significance_to_probability_normal():
significance = 5
p = significance_to_probability_normal(significance)
assert_allclose(p, 2.8665157187919328e-07)
s = probability_to_significance_normal(p)
assert_allclose(s, significance)
def test_significance_to_probability_normal_limit():
significance = 5
p = significance_to_probability_normal_limit(significance)
assert_allclose(p, 2.792513e-07)
s = probability_to_significance_normal_limit(p)
assert_allclose(s, significance)
| 32.5
| 82
| 0.806593
|
from __future__ import absolute_import, division, print_function, unicode_literals
from numpy.testing import assert_allclose
from ...stats import (
significance_to_probability_normal,
probability_to_significance_normal,
probability_to_significance_normal_limit,
significance_to_probability_normal_limit,
)
def test_significance_to_probability_normal():
significance = 5
p = significance_to_probability_normal(significance)
assert_allclose(p, 2.8665157187919328e-07)
s = probability_to_significance_normal(p)
assert_allclose(s, significance)
def test_significance_to_probability_normal_limit():
significance = 5
p = significance_to_probability_normal_limit(significance)
assert_allclose(p, 2.792513e-07)
s = probability_to_significance_normal_limit(p)
assert_allclose(s, significance)
| true
| true
|
f716815fb86995f906bd4284b72bbcf1851ecedc
| 9,243
|
py
|
Python
|
ros_pkg/dock/scripts/dock_generator.py
|
introlab/securbot
|
0652ddf3e2dbcf0bb6ffcf76898749b67e443327
|
[
"Apache-2.0"
] | 20
|
2019-03-13T13:37:51.000Z
|
2022-01-25T16:56:35.000Z
|
ros_pkg/dock/scripts/dock_generator.py
|
introlab/securbot
|
0652ddf3e2dbcf0bb6ffcf76898749b67e443327
|
[
"Apache-2.0"
] | 15
|
2019-02-27T20:29:34.000Z
|
2020-08-24T19:44:20.000Z
|
ros_pkg/dock/scripts/dock_generator.py
|
introlab/securbot
|
0652ddf3e2dbcf0bb6ffcf76898749b67e443327
|
[
"Apache-2.0"
] | 13
|
2019-07-31T00:47:49.000Z
|
2021-04-15T01:33:02.000Z
|
#!/usr/bin/env python
"""Provide docking motivation layer node."""
import rospy
import json
import math
from enum import Enum
from threading import Lock
import tf
from tf.transformations import euler_from_quaternion
from std_msgs.msg import Bool
from std_msgs.msg import Empty
from geometry_msgs.msg import PoseStamped
from sensor_msgs.msg import BatteryState
from hbba_msgs.msg import Desire
from hbba_msgs.msg import Intention
from hbba_msgs.srv import AddDesires
from hbba_msgs.srv import RemoveDesires
class DockState(Enum):
"""Define dockig process states."""
idle = 0 # No dock required
approaching = 1 # Driving to dock approach goal
docking = 2 # Docking to station
docked = 3 # Docked at station and dock required
class DockGenerator:
"""Generate dock desires according to robot and dock state."""
def force_callback(self, msg):
"""Receive force docking command from electron."""
self.lock.acquire()
self.force_docking = msg.data
self.last_force = rospy.Time.now()
self.lock.release()
def battery_callback(self, msg):
"""Receive battery level from battery board."""
self.lock.acquire()
if msg.percentage < self.min_percent:
self.low_battery = True
if msg.percentage > self.charge_percent:
self.low_battery = False
self.lock.release()
def collision_callback(self, msg):
"""Receive collision events from collision detector."""
self.lock.acquire()
if self.state == DockState.docking:
self.finish_docking()
self.lock.release()
def intention_callback(self, msg):
"""Monitor current robot intention from hbba."""
self.lock.acquire()
self.approach_active = self.approach_desire.id in msg.desires
if self.state == DockState.docking:
if self.loiter_desire.id in msg.desires:
self.state = DockState.docked
elif self.dock_desire.id not in msg.desires:
self.restart_docking()
if self.state == DockState.docked:
if self.loiter_desire.id not in msg.desires:
self.stop_docking()
self.lock.release()
def approach_callback(self, msg):
"""Set approach goal."""
self.lock.acquire()
self.approach_goal['frame_id'] = msg.header.frame_id
self.approach_goal['x'] = msg.pose.position.x
self.approach_goal['y'] = msg.pose.position.y
quat = (
msg.pose.orientation.w,
msg.pose.orientation.x,
msg.pose.orientation.y,
msg.pose.orientation.z
)
euler = euler_from_quaternion(quat)
self.approach_goal['t'] = euler[2]
self.lock.release()
def start_docking(self):
"""Initialize docking sequence."""
self.add_desire([self.dock_desire])
self.restart_docking()
def restart_docking(self):
"""Restart approach after interuption."""
self.approach_desire.params = json.dumps(self.approach_goal)
self.add_desire([self.approach_desire])
self.state = DockState.approaching
def finish_approach(self):
"""End approach phase."""
self.rem_desire([self.approach_desire.id])
self.state = DockState.docking
def finish_docking(self):
"""End docking phase."""
self.add_desire([self.loiter_desire])
self.rem_desire([self.dock_desire.id])
def stop_docking(self):
"""Cancel docking process."""
if self.state == DockState.approaching:
# both dock and approach must be removed
self.rem_desire([
self.dock_desire.id,
self.approach_desire.id
])
elif self.state == DockState.docking:
# only dock desire must be removed
self.rem_desire([self.dock_desire.id])
elif self.state == DockState.docked:
# loiter desire must be removed
self.rem_desire([self.loiter_desire.id])
self.state = DockState.idle
def run(self):
"""Update desires and check if robot reached approach goal."""
rospy.Subscriber('force_docking', Bool, self.force_callback)
rospy.Subscriber('collision', Empty, self.collision_callback)
rospy.Subscriber('battery_state', BatteryState, self.battery_callback)
rospy.Subscriber('approach_goal', PoseStamped, self.approach_callback)
rospy.Subscriber('hbba/intention', Intention, self.intention_callback)
tf_listener = tf.TransformListener()
rate = rospy.Rate(1)
while not rospy.is_shutdown():
rate.sleep()
self.lock.acquire()
# Check if force is expired
now = rospy.Time.now()
timeout = rospy.Duration(self.force_timeout)
if now - self.last_force > timeout:
self.force_docking = False
# Check if we need to start docking
if (self.force_docking or self.low_battery)\
and self.state == DockState.idle:
self.start_docking()
self.lock.release()
continue
# Check if we need to stop docking
if not self.force_docking\
and not self.low_battery\
and self.state != DockState.idle:
self.stop_docking()
self.lock.release()
continue
# We are not approaching we cancel tf lookup
if self.state != DockState.approaching or not self.approach_active:
self.lock.release()
continue
# Lookup robot pose in map
try:
pos, quat = tf_listener.lookupTransform(
self.map_frame,
self.robot_frame,
rospy.Time())
except (tf.Exception):
self.lock.release()
continue
# Check if we reached our approach goal
dx = pos[0] - self.approach_goal['x']
dy = pos[1] - self.approach_goal['y']
lin_dst = math.sqrt(dx*dx + dy*dy)
angles = euler_from_quaternion(quat)
yaw_dst = angles[2] - self.approach_goal['t']
if lin_dst < self.appr_lin_tol and abs(yaw_dst) < self.appr_yaw_tol:
self.finish_approach()
self.lock.release()
def __init__(self):
"""Initialize dock generator node."""
self.lock = Lock()
self.low_battery = False
self.force_docking = False
self.approach_active = False
self.state = DockState.idle
self.last_force = rospy.Time()
rospy.init_node('dock_generator')
rospy.loginfo('waiting for add desire to be availble')
rospy.wait_for_service('/hbba/add_desires')
self.add_desire = rospy.ServiceProxy(
'/hbba/add_desires',
AddDesires)
rospy.loginfo('add desire registered')
rospy.loginfo('waiting for remove desire to be availbe')
rospy.wait_for_service('/hbba/remove_desires')
self.rem_desire = rospy.ServiceProxy(
'/hbba/remove_desires',
RemoveDesires)
rospy.loginfo('remove desire registered')
self.map_frame = rospy.get_param('~map_frame', 'map')
self.robot_frame = rospy.get_param('~robot_frame', 'base_footprint')
self.appr_lin_tol = rospy.get_param('~appr_lin_tol', 0.40)
self.appr_yaw_tol = rospy.get_param('~appr_yaw_tol', 0.1)
self.min_percent = rospy.get_param('~min_percent', 30)
self.charge_percent = rospy.get_param('~charge_percent', 80)
self.undock_time = rospy.get_param('~undock_time', 4.0)
self.force_timeout = rospy.get_param('~force_timeout', 10.0)
self.approach_goal = dict()
self.approach_goal['x'] = rospy.get_param('approach/x', 0.0)
self.approach_goal['y'] = rospy.get_param('approach/y', 0.0)
self.approach_goal['t'] = rospy.get_param('approach/t', 0.0)
self.approach_goal['frame_id'] = self.map_frame
self.approach_desire = Desire()
self.approach_desire.id = 'dock_approach'
self.approach_desire.type = 'GoTo'
self.approach_desire.intensity = 1
self.approach_desire.utility = 1
self.approach_desire.security = False
self.dock_desire = Desire()
self.dock_desire.id = 'dock_dock'
self.dock_desire.type = 'Dock'
self.dock_desire.intensity = 1
self.dock_desire.utility = 1
self.dock_desire.security = False
self.loiter_desire = Desire()
self.loiter_desire.id = 'dock_charge'
self.loiter_desire.type = 'Loiter'
self.loiter_desire.intensity = 1
self.loiter_desire.utility = 1
self.loiter_desire.security = False
params = dict()
params['t'] = self.undock_time
self.loiter_desire.params = json.dumps(params)
def node():
"""Run the docking motivation node."""
generator_node = DockGenerator()
generator_node.run()
if __name__ == '__main__':
node()
| 33.48913
| 80
| 0.612896
|
import rospy
import json
import math
from enum import Enum
from threading import Lock
import tf
from tf.transformations import euler_from_quaternion
from std_msgs.msg import Bool
from std_msgs.msg import Empty
from geometry_msgs.msg import PoseStamped
from sensor_msgs.msg import BatteryState
from hbba_msgs.msg import Desire
from hbba_msgs.msg import Intention
from hbba_msgs.srv import AddDesires
from hbba_msgs.srv import RemoveDesires
class DockState(Enum):
idle = 0
approaching = 1
docking = 2
docked = 3
class DockGenerator:
def force_callback(self, msg):
self.lock.acquire()
self.force_docking = msg.data
self.last_force = rospy.Time.now()
self.lock.release()
def battery_callback(self, msg):
self.lock.acquire()
if msg.percentage < self.min_percent:
self.low_battery = True
if msg.percentage > self.charge_percent:
self.low_battery = False
self.lock.release()
def collision_callback(self, msg):
self.lock.acquire()
if self.state == DockState.docking:
self.finish_docking()
self.lock.release()
def intention_callback(self, msg):
self.lock.acquire()
self.approach_active = self.approach_desire.id in msg.desires
if self.state == DockState.docking:
if self.loiter_desire.id in msg.desires:
self.state = DockState.docked
elif self.dock_desire.id not in msg.desires:
self.restart_docking()
if self.state == DockState.docked:
if self.loiter_desire.id not in msg.desires:
self.stop_docking()
self.lock.release()
def approach_callback(self, msg):
self.lock.acquire()
self.approach_goal['frame_id'] = msg.header.frame_id
self.approach_goal['x'] = msg.pose.position.x
self.approach_goal['y'] = msg.pose.position.y
quat = (
msg.pose.orientation.w,
msg.pose.orientation.x,
msg.pose.orientation.y,
msg.pose.orientation.z
)
euler = euler_from_quaternion(quat)
self.approach_goal['t'] = euler[2]
self.lock.release()
def start_docking(self):
self.add_desire([self.dock_desire])
self.restart_docking()
def restart_docking(self):
self.approach_desire.params = json.dumps(self.approach_goal)
self.add_desire([self.approach_desire])
self.state = DockState.approaching
def finish_approach(self):
self.rem_desire([self.approach_desire.id])
self.state = DockState.docking
def finish_docking(self):
self.add_desire([self.loiter_desire])
self.rem_desire([self.dock_desire.id])
def stop_docking(self):
if self.state == DockState.approaching:
self.rem_desire([
self.dock_desire.id,
self.approach_desire.id
])
elif self.state == DockState.docking:
self.rem_desire([self.dock_desire.id])
elif self.state == DockState.docked:
self.rem_desire([self.loiter_desire.id])
self.state = DockState.idle
def run(self):
rospy.Subscriber('force_docking', Bool, self.force_callback)
rospy.Subscriber('collision', Empty, self.collision_callback)
rospy.Subscriber('battery_state', BatteryState, self.battery_callback)
rospy.Subscriber('approach_goal', PoseStamped, self.approach_callback)
rospy.Subscriber('hbba/intention', Intention, self.intention_callback)
tf_listener = tf.TransformListener()
rate = rospy.Rate(1)
while not rospy.is_shutdown():
rate.sleep()
self.lock.acquire()
now = rospy.Time.now()
timeout = rospy.Duration(self.force_timeout)
if now - self.last_force > timeout:
self.force_docking = False
if (self.force_docking or self.low_battery)\
and self.state == DockState.idle:
self.start_docking()
self.lock.release()
continue
if not self.force_docking\
and not self.low_battery\
and self.state != DockState.idle:
self.stop_docking()
self.lock.release()
continue
if self.state != DockState.approaching or not self.approach_active:
self.lock.release()
continue
try:
pos, quat = tf_listener.lookupTransform(
self.map_frame,
self.robot_frame,
rospy.Time())
except (tf.Exception):
self.lock.release()
continue
dx = pos[0] - self.approach_goal['x']
dy = pos[1] - self.approach_goal['y']
lin_dst = math.sqrt(dx*dx + dy*dy)
angles = euler_from_quaternion(quat)
yaw_dst = angles[2] - self.approach_goal['t']
if lin_dst < self.appr_lin_tol and abs(yaw_dst) < self.appr_yaw_tol:
self.finish_approach()
self.lock.release()
def __init__(self):
self.lock = Lock()
self.low_battery = False
self.force_docking = False
self.approach_active = False
self.state = DockState.idle
self.last_force = rospy.Time()
rospy.init_node('dock_generator')
rospy.loginfo('waiting for add desire to be availble')
rospy.wait_for_service('/hbba/add_desires')
self.add_desire = rospy.ServiceProxy(
'/hbba/add_desires',
AddDesires)
rospy.loginfo('add desire registered')
rospy.loginfo('waiting for remove desire to be availbe')
rospy.wait_for_service('/hbba/remove_desires')
self.rem_desire = rospy.ServiceProxy(
'/hbba/remove_desires',
RemoveDesires)
rospy.loginfo('remove desire registered')
self.map_frame = rospy.get_param('~map_frame', 'map')
self.robot_frame = rospy.get_param('~robot_frame', 'base_footprint')
self.appr_lin_tol = rospy.get_param('~appr_lin_tol', 0.40)
self.appr_yaw_tol = rospy.get_param('~appr_yaw_tol', 0.1)
self.min_percent = rospy.get_param('~min_percent', 30)
self.charge_percent = rospy.get_param('~charge_percent', 80)
self.undock_time = rospy.get_param('~undock_time', 4.0)
self.force_timeout = rospy.get_param('~force_timeout', 10.0)
self.approach_goal = dict()
self.approach_goal['x'] = rospy.get_param('approach/x', 0.0)
self.approach_goal['y'] = rospy.get_param('approach/y', 0.0)
self.approach_goal['t'] = rospy.get_param('approach/t', 0.0)
self.approach_goal['frame_id'] = self.map_frame
self.approach_desire = Desire()
self.approach_desire.id = 'dock_approach'
self.approach_desire.type = 'GoTo'
self.approach_desire.intensity = 1
self.approach_desire.utility = 1
self.approach_desire.security = False
self.dock_desire = Desire()
self.dock_desire.id = 'dock_dock'
self.dock_desire.type = 'Dock'
self.dock_desire.intensity = 1
self.dock_desire.utility = 1
self.dock_desire.security = False
self.loiter_desire = Desire()
self.loiter_desire.id = 'dock_charge'
self.loiter_desire.type = 'Loiter'
self.loiter_desire.intensity = 1
self.loiter_desire.utility = 1
self.loiter_desire.security = False
params = dict()
params['t'] = self.undock_time
self.loiter_desire.params = json.dumps(params)
def node():
generator_node = DockGenerator()
generator_node.run()
if __name__ == '__main__':
node()
| true
| true
|
f716825448eb20eaa87e600968288d77efb47ddf
| 14,659
|
py
|
Python
|
dataReader.py
|
tsfw/yolov3
|
bf6d03d9a84a0ac1e94bcc4f9a026f7d32dfbdab
|
[
"Apache-2.0"
] | null | null | null |
dataReader.py
|
tsfw/yolov3
|
bf6d03d9a84a0ac1e94bcc4f9a026f7d32dfbdab
|
[
"Apache-2.0"
] | null | null | null |
dataReader.py
|
tsfw/yolov3
|
bf6d03d9a84a0ac1e94bcc4f9a026f7d32dfbdab
|
[
"Apache-2.0"
] | null | null | null |
import os
import config
import json
import tensorflow as tf
import numpy as np
from collections import defaultdict
class Reader:
def __init__(self, mode, data_dir, anchors_path, num_classes, tfrecord_num = 12, input_shape = 416, max_boxes = 20):
"""
Introduction
------------
构造函数
Parameters
----------
data_dir: 文件路径
mode: 数据集模式
anchors: 数据集聚类得到的anchor
num_classes: 数据集图片类别数量
input_shape: 图像输入模型的大小
max_boxes: 每张图片最大的box数量
jitter: 随机长宽比系数
hue: 调整hsv颜色空间系数
sat: 调整饱和度系数
cont: 调整对比度系数
bri: 调整亮度系数
"""
self.data_dir = data_dir
self.input_shape = input_shape
self.max_boxes = max_boxes
self.mode = mode
self.annotations_file = {'train' : config.train_annotations_file, 'val' : config.val_annotations_file}
self.data_file = {'train': config.train_data_file, 'val': config.val_data_file}
self.anchors_path = anchors_path
self.anchors = self._get_anchors()
self.num_classes = num_classes
file_pattern = self.data_dir + "/*" + self.mode + '.tfrecords'
self.TfrecordFile = tf.gfile.Glob(file_pattern)
self.class_names = self._get_class(config.classes_path)
if len(self.TfrecordFile) == 0:
self.convert_to_tfrecord(self.data_dir, tfrecord_num)
def _get_anchors(self):
"""
Introduction
------------
获取anchors
Returns
-------
anchors: anchor数组
"""
anchors_path = os.path.expanduser(self.anchors_path)
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape(-1, 2)
def _get_class(self, classes_path):
"""
Introduction
------------
获取类别名字
Returns
-------
class_names: coco数据集类别对应的名字
"""
classes_path = os.path.expanduser(classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def Preprocess_true_boxes(self, true_boxes):
"""
Introduction
------------
对训练数据的ground truth box进行预处理
Parameters
----------
true_boxes: ground truth box 形状为[boxes, 5], x_min, y_min, x_max, y_max, class_id
"""
num_layers = len(self.anchors) // 3
anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
true_boxes = np.array(true_boxes, dtype='float32')
input_shape = np.array([self.input_shape, self.input_shape], dtype='int32')
boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2.
boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
true_boxes[..., 0:2] = boxes_xy / input_shape[::-1]
true_boxes[..., 2:4] = boxes_wh / input_shape[::-1]
grid_shapes = [input_shape // 32, input_shape // 16, input_shape // 8]
y_true = [np.zeros((grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]), 5 + self.num_classes), dtype='float32') for l in range(num_layers)]
# 这里扩充维度是为了后面应用广播计算每个图中所有box的anchor互相之间的iou
anchors = np.expand_dims(self.anchors, 0)
anchors_max = anchors / 2.
anchors_min = -anchors_max
# 因为之前对box做了padding, 因此需要去除全0行
valid_mask = boxes_wh[..., 0] > 0
wh = boxes_wh[valid_mask]
# 为了应用广播扩充维度
wh = np.expand_dims(wh, -2)
# wh 的shape为[box_num, 1, 2]
boxes_max = wh / 2.
boxes_min = -boxes_max
intersect_min = np.maximum(boxes_min, anchors_min)
intersect_max = np.minimum(boxes_max, anchors_max)
intersect_wh = np.maximum(intersect_max - intersect_min, 0.)
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
box_area = wh[..., 0] * wh[..., 1]
anchor_area = anchors[..., 0] * anchors[..., 1]
iou = intersect_area / (box_area + anchor_area - intersect_area)
# 找出和ground truth box的iou最大的anchor box, 然后将对应不同比例的负责该ground turth box 的位置置为ground truth box坐标
best_anchor = np.argmax(iou, axis = -1)
for t, n in enumerate(best_anchor):
for l in range(num_layers):
if n in anchor_mask[l]:
i = np.floor(true_boxes[t, 0] * grid_shapes[l][1]).astype('int32')
j = np.floor(true_boxes[t, 1] * grid_shapes[l][0]).astype('int32')
k = anchor_mask[l].index(n)
c = true_boxes[t, 4].astype('int32')
y_true[l][j, i, k, 0:4] = true_boxes[t, 0:4]
y_true[l][j, i, k, 4] = 1.
y_true[l][j, i, k, 5 + c] = 1.
return y_true[0], y_true[1], y_true[2]
def read_annotations(self):
"""
Introduction
------------
读取COCO数据集图片路径和对应的标注
Parameters
----------
data_file: 文件路径
"""
image_data = []
boxes_data = []
name_box_id = defaultdict(list)
with open(self.annotations_file[self.mode], encoding='utf-8') as file:
data = json.load(file)
annotations = data['annotations']
for ant in annotations:
id = ant['image_id']
name = os.path.join(self.data_file[self.mode], '%012d.jpg' % id)
cat = ant['category_id']
if cat >= 1 and cat <= 11:
cat = cat - 1
elif cat >= 13 and cat <= 25:
cat = cat - 2
elif cat >= 27 and cat <= 28:
cat = cat - 3
elif cat >= 31 and cat <= 44:
cat = cat - 5
elif cat >= 46 and cat <= 65:
cat = cat - 6
elif cat == 67:
cat = cat - 7
elif cat == 70:
cat = cat - 9
elif cat >= 72 and cat <= 82:
cat = cat - 10
elif cat >= 84 and cat <= 90:
cat = cat - 11
name_box_id[name].append([ant['bbox'], cat])
for key in name_box_id.keys():
boxes = []
image_data.append(key)
box_infos = name_box_id[key]
for info in box_infos:
x_min = info[0][0]
y_min = info[0][1]
x_max = x_min + info[0][2]
y_max = y_min + info[0][3]
boxes.append(np.array([x_min, y_min, x_max, y_max, info[1]]))
boxes_data.append(np.array(boxes))
return image_data, boxes_data
def convert_to_tfrecord(self, tfrecord_path, num_tfrecords):
"""
Introduction
------------
将图片和boxes数据存储为tfRecord
Parameters
----------
tfrecord_path: tfrecord文件存储路径
num_tfrecords: 分成多少个tfrecord
"""
image_data, boxes_data = self.read_annotations()
images_num = int(len(image_data) / num_tfrecords)
for index_records in range(num_tfrecords):
output_file = os.path.join(tfrecord_path, str(index_records) + '_' + self.mode + '.tfrecords')
with tf.python_io.TFRecordWriter(output_file) as record_writer:
for index in range(index_records * images_num, (index_records + 1) * images_num):
with tf.gfile.FastGFile(image_data[index], 'rb') as file:
image = file.read()
xmin, xmax, ymin, ymax, label = [], [], [], [], []
for box in boxes_data[index]:
xmin.append(box[0])
ymin.append(box[1])
xmax.append(box[2])
ymax.append(box[3])
label.append(box[4])
example = tf.train.Example(features = tf.train.Features(
feature = {
'image/encoded' : tf.train.Feature(bytes_list = tf.train.BytesList(value = [image])),
'image/object/bbox/xmin' : tf.train.Feature(float_list = tf.train.FloatList(value = xmin)),
'image/object/bbox/xmax': tf.train.Feature(float_list = tf.train.FloatList(value = xmax)),
'image/object/bbox/ymin': tf.train.Feature(float_list = tf.train.FloatList(value = ymin)),
'image/object/bbox/ymax': tf.train.Feature(float_list = tf.train.FloatList(value = ymax)),
'image/object/bbox/label': tf.train.Feature(float_list = tf.train.FloatList(value = label)),
}
))
record_writer.write(example.SerializeToString())
if index % 1000 == 0:
print('Processed {} of {} images'.format(index + 1, len(image_data)))
def parser(self, serialized_example):
"""
Introduction
------------
解析tfRecord数据
Parameters
----------
serialized_example: 序列化的每条数据
"""
features = tf.parse_single_example(
serialized_example,
features = {
'image/encoded' : tf.FixedLenFeature([], dtype = tf.string),
'image/object/bbox/xmin' : tf.VarLenFeature(dtype = tf.float32),
'image/object/bbox/xmax': tf.VarLenFeature(dtype = tf.float32),
'image/object/bbox/ymin': tf.VarLenFeature(dtype = tf.float32),
'image/object/bbox/ymax': tf.VarLenFeature(dtype = tf.float32),
'image/object/bbox/label': tf.VarLenFeature(dtype = tf.float32)
}
)
image = tf.image.decode_jpeg(features['image/encoded'], channels = 3)
image = tf.image.convert_image_dtype(image, tf.uint8)
xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, axis = 0)
ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, axis = 0)
xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, axis = 0)
ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, axis = 0)
label = tf.expand_dims(features['image/object/bbox/label'].values, axis = 0)
bbox = tf.concat(axis = 0, values = [xmin, ymin, xmax, ymax, label])
bbox = tf.transpose(bbox, [1, 0])
image, bbox = self.Preprocess(image, bbox)
bbox_true_13, bbox_true_26, bbox_true_52 = tf.py_func(self.Preprocess_true_boxes, [bbox], [tf.float32, tf.float32, tf.float32])
return image, bbox, bbox_true_13, bbox_true_26, bbox_true_52
def Preprocess(self, image, bbox):
"""
Introduction
------------
对图片进行预处理，增强数据集
Parameters
----------
image: tensorflow解析的图片
bbox: 图片中对应的box坐标
"""
image_width, image_high = tf.cast(tf.shape(image)[1], tf.float32), tf.cast(tf.shape(image)[0], tf.float32)
input_width = tf.cast(self.input_shape, tf.float32)
input_high = tf.cast(self.input_shape, tf.float32)
new_high = image_high * tf.minimum(input_width / image_width, input_high / image_high)
new_width = image_width * tf.minimum(input_width / image_width, input_high / image_high)
# 将图片按照固定长宽比进行padding缩放
dx = (input_width - new_width) / 2
dy = (input_high - new_high) / 2
image = tf.image.resize_images(image, [tf.cast(new_high, tf.int32), tf.cast(new_width, tf.int32)], method = tf.image.ResizeMethod.BICUBIC)
new_image = tf.image.pad_to_bounding_box(image, tf.cast(dy, tf.int32), tf.cast(dx, tf.int32), tf.cast(input_high, tf.int32), tf.cast(input_width, tf.int32))
image_ones = tf.ones_like(image)
image_ones_padded = tf.image.pad_to_bounding_box(image_ones, tf.cast(dy, tf.int32), tf.cast(dx, tf.int32), tf.cast(input_high, tf.int32), tf.cast(input_width, tf.int32))
image_color_padded = (1 - image_ones_padded) * 128
image = image_color_padded + new_image
# 矫正bbox坐标
xmin, ymin, xmax, ymax, label = tf.split(value = bbox, num_or_size_splits=5, axis = 1)
xmin = xmin * new_width / image_width + dx
xmax = xmax * new_width / image_width + dx
ymin = ymin * new_high / image_high + dy
ymax = ymax * new_high / image_high + dy
bbox = tf.concat([xmin, ymin, xmax, ymax, label], 1)
if self.mode == 'train':
# 随机左右翻转图片
def _flip_left_right_boxes(boxes):
xmin, ymin, xmax, ymax, label = tf.split(value = boxes, num_or_size_splits = 5, axis = 1)
flipped_xmin = tf.subtract(input_width, xmax)
flipped_xmax = tf.subtract(input_width, xmin)
flipped_boxes = tf.concat([flipped_xmin, ymin, flipped_xmax, ymax, label], 1)
return flipped_boxes
flip_left_right = tf.greater(tf.random_uniform([], dtype = tf.float32, minval = 0, maxval = 1), 0.5)
image = tf.cond(flip_left_right, lambda : tf.image.flip_left_right(image), lambda : image)
bbox = tf.cond(flip_left_right, lambda: _flip_left_right_boxes(bbox), lambda: bbox)
# 将图片归一化到0和1之间
image = image / 255.
image = tf.clip_by_value(image, clip_value_min = 0.0, clip_value_max = 1.0)
bbox = tf.clip_by_value(bbox, clip_value_min = 0, clip_value_max = input_width - 1)
bbox = tf.cond(tf.greater(tf.shape(bbox)[0], config.max_boxes), lambda: bbox[:config.max_boxes], lambda: tf.pad(bbox, paddings = [[0, config.max_boxes - tf.shape(bbox)[0]], [0, 0]], mode = 'CONSTANT'))
return image, bbox
def build_dataset(self, batch_size):
"""
Introduction
------------
建立数据集dataset
Parameters
----------
batch_size: batch大小
Return
------
dataset: 返回tensorflow的dataset
"""
dataset = tf.data.TFRecordDataset(filenames = self.TfrecordFile)
dataset = dataset.map(self.parser, num_parallel_calls = 10)
if self.mode == 'train':
dataset = dataset.repeat().shuffle(9000).batch(batch_size).prefetch(batch_size)
else:
dataset = dataset.repeat().batch(batch_size).prefetch(batch_size)
return dataset
| 44.966258
| 209
| 0.548469
|
import os
import config
import json
import tensorflow as tf
import numpy as np
from collections import defaultdict
class Reader:
def __init__(self, mode, data_dir, anchors_path, num_classes, tfrecord_num = 12, input_shape = 416, max_boxes = 20):
self.data_dir = data_dir
self.input_shape = input_shape
self.max_boxes = max_boxes
self.mode = mode
self.annotations_file = {'train' : config.train_annotations_file, 'val' : config.val_annotations_file}
self.data_file = {'train': config.train_data_file, 'val': config.val_data_file}
self.anchors_path = anchors_path
self.anchors = self._get_anchors()
self.num_classes = num_classes
file_pattern = self.data_dir + "/*" + self.mode + '.tfrecords'
self.TfrecordFile = tf.gfile.Glob(file_pattern)
self.class_names = self._get_class(config.classes_path)
if len(self.TfrecordFile) == 0:
self.convert_to_tfrecord(self.data_dir, tfrecord_num)
def _get_anchors(self):
anchors_path = os.path.expanduser(self.anchors_path)
with open(anchors_path) as f:
anchors = f.readline()
anchors = [float(x) for x in anchors.split(',')]
return np.array(anchors).reshape(-1, 2)
def _get_class(self, classes_path):
classes_path = os.path.expanduser(classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def Preprocess_true_boxes(self, true_boxes):
num_layers = len(self.anchors) // 3
anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
true_boxes = np.array(true_boxes, dtype='float32')
input_shape = np.array([self.input_shape, self.input_shape], dtype='int32')
boxes_xy = (true_boxes[..., 0:2] + true_boxes[..., 2:4]) // 2.
boxes_wh = true_boxes[..., 2:4] - true_boxes[..., 0:2]
true_boxes[..., 0:2] = boxes_xy / input_shape[::-1]
true_boxes[..., 2:4] = boxes_wh / input_shape[::-1]
grid_shapes = [input_shape // 32, input_shape // 16, input_shape // 8]
y_true = [np.zeros((grid_shapes[l][0], grid_shapes[l][1], len(anchor_mask[l]), 5 + self.num_classes), dtype='float32') for l in range(num_layers)]
anchors = np.expand_dims(self.anchors, 0)
anchors_max = anchors / 2.
anchors_min = -anchors_max
valid_mask = boxes_wh[..., 0] > 0
wh = boxes_wh[valid_mask]
wh = np.expand_dims(wh, -2)
boxes_max = wh / 2.
boxes_min = -boxes_max
intersect_min = np.maximum(boxes_min, anchors_min)
intersect_max = np.minimum(boxes_max, anchors_max)
intersect_wh = np.maximum(intersect_max - intersect_min, 0.)
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
box_area = wh[..., 0] * wh[..., 1]
anchor_area = anchors[..., 0] * anchors[..., 1]
iou = intersect_area / (box_area + anchor_area - intersect_area)
best_anchor = np.argmax(iou, axis = -1)
for t, n in enumerate(best_anchor):
for l in range(num_layers):
if n in anchor_mask[l]:
i = np.floor(true_boxes[t, 0] * grid_shapes[l][1]).astype('int32')
j = np.floor(true_boxes[t, 1] * grid_shapes[l][0]).astype('int32')
k = anchor_mask[l].index(n)
c = true_boxes[t, 4].astype('int32')
y_true[l][j, i, k, 0:4] = true_boxes[t, 0:4]
y_true[l][j, i, k, 4] = 1.
y_true[l][j, i, k, 5 + c] = 1.
return y_true[0], y_true[1], y_true[2]
def read_annotations(self):
image_data = []
boxes_data = []
name_box_id = defaultdict(list)
with open(self.annotations_file[self.mode], encoding='utf-8') as file:
data = json.load(file)
annotations = data['annotations']
for ant in annotations:
id = ant['image_id']
name = os.path.join(self.data_file[self.mode], '%012d.jpg' % id)
cat = ant['category_id']
if cat >= 1 and cat <= 11:
cat = cat - 1
elif cat >= 13 and cat <= 25:
cat = cat - 2
elif cat >= 27 and cat <= 28:
cat = cat - 3
elif cat >= 31 and cat <= 44:
cat = cat - 5
elif cat >= 46 and cat <= 65:
cat = cat - 6
elif cat == 67:
cat = cat - 7
elif cat == 70:
cat = cat - 9
elif cat >= 72 and cat <= 82:
cat = cat - 10
elif cat >= 84 and cat <= 90:
cat = cat - 11
name_box_id[name].append([ant['bbox'], cat])
for key in name_box_id.keys():
boxes = []
image_data.append(key)
box_infos = name_box_id[key]
for info in box_infos:
x_min = info[0][0]
y_min = info[0][1]
x_max = x_min + info[0][2]
y_max = y_min + info[0][3]
boxes.append(np.array([x_min, y_min, x_max, y_max, info[1]]))
boxes_data.append(np.array(boxes))
return image_data, boxes_data
def convert_to_tfrecord(self, tfrecord_path, num_tfrecords):
image_data, boxes_data = self.read_annotations()
images_num = int(len(image_data) / num_tfrecords)
for index_records in range(num_tfrecords):
output_file = os.path.join(tfrecord_path, str(index_records) + '_' + self.mode + '.tfrecords')
with tf.python_io.TFRecordWriter(output_file) as record_writer:
for index in range(index_records * images_num, (index_records + 1) * images_num):
with tf.gfile.FastGFile(image_data[index], 'rb') as file:
image = file.read()
xmin, xmax, ymin, ymax, label = [], [], [], [], []
for box in boxes_data[index]:
xmin.append(box[0])
ymin.append(box[1])
xmax.append(box[2])
ymax.append(box[3])
label.append(box[4])
example = tf.train.Example(features = tf.train.Features(
feature = {
'image/encoded' : tf.train.Feature(bytes_list = tf.train.BytesList(value = [image])),
'image/object/bbox/xmin' : tf.train.Feature(float_list = tf.train.FloatList(value = xmin)),
'image/object/bbox/xmax': tf.train.Feature(float_list = tf.train.FloatList(value = xmax)),
'image/object/bbox/ymin': tf.train.Feature(float_list = tf.train.FloatList(value = ymin)),
'image/object/bbox/ymax': tf.train.Feature(float_list = tf.train.FloatList(value = ymax)),
'image/object/bbox/label': tf.train.Feature(float_list = tf.train.FloatList(value = label)),
}
))
record_writer.write(example.SerializeToString())
if index % 1000 == 0:
print('Processed {} of {} images'.format(index + 1, len(image_data)))
def parser(self, serialized_example):
features = tf.parse_single_example(
serialized_example,
features = {
'image/encoded' : tf.FixedLenFeature([], dtype = tf.string),
'image/object/bbox/xmin' : tf.VarLenFeature(dtype = tf.float32),
'image/object/bbox/xmax': tf.VarLenFeature(dtype = tf.float32),
'image/object/bbox/ymin': tf.VarLenFeature(dtype = tf.float32),
'image/object/bbox/ymax': tf.VarLenFeature(dtype = tf.float32),
'image/object/bbox/label': tf.VarLenFeature(dtype = tf.float32)
}
)
image = tf.image.decode_jpeg(features['image/encoded'], channels = 3)
image = tf.image.convert_image_dtype(image, tf.uint8)
xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, axis = 0)
ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, axis = 0)
xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, axis = 0)
ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, axis = 0)
label = tf.expand_dims(features['image/object/bbox/label'].values, axis = 0)
bbox = tf.concat(axis = 0, values = [xmin, ymin, xmax, ymax, label])
bbox = tf.transpose(bbox, [1, 0])
image, bbox = self.Preprocess(image, bbox)
bbox_true_13, bbox_true_26, bbox_true_52 = tf.py_func(self.Preprocess_true_boxes, [bbox], [tf.float32, tf.float32, tf.float32])
return image, bbox, bbox_true_13, bbox_true_26, bbox_true_52
def Preprocess(self, image, bbox):
image_width, image_high = tf.cast(tf.shape(image)[1], tf.float32), tf.cast(tf.shape(image)[0], tf.float32)
input_width = tf.cast(self.input_shape, tf.float32)
input_high = tf.cast(self.input_shape, tf.float32)
new_high = image_high * tf.minimum(input_width / image_width, input_high / image_high)
new_width = image_width * tf.minimum(input_width / image_width, input_high / image_high)
dx = (input_width - new_width) / 2
dy = (input_high - new_high) / 2
image = tf.image.resize_images(image, [tf.cast(new_high, tf.int32), tf.cast(new_width, tf.int32)], method = tf.image.ResizeMethod.BICUBIC)
new_image = tf.image.pad_to_bounding_box(image, tf.cast(dy, tf.int32), tf.cast(dx, tf.int32), tf.cast(input_high, tf.int32), tf.cast(input_width, tf.int32))
image_ones = tf.ones_like(image)
image_ones_padded = tf.image.pad_to_bounding_box(image_ones, tf.cast(dy, tf.int32), tf.cast(dx, tf.int32), tf.cast(input_high, tf.int32), tf.cast(input_width, tf.int32))
image_color_padded = (1 - image_ones_padded) * 128
image = image_color_padded + new_image
xmin, ymin, xmax, ymax, label = tf.split(value = bbox, num_or_size_splits=5, axis = 1)
xmin = xmin * new_width / image_width + dx
xmax = xmax * new_width / image_width + dx
ymin = ymin * new_high / image_high + dy
ymax = ymax * new_high / image_high + dy
bbox = tf.concat([xmin, ymin, xmax, ymax, label], 1)
if self.mode == 'train':
def _flip_left_right_boxes(boxes):
xmin, ymin, xmax, ymax, label = tf.split(value = boxes, num_or_size_splits = 5, axis = 1)
flipped_xmin = tf.subtract(input_width, xmax)
flipped_xmax = tf.subtract(input_width, xmin)
flipped_boxes = tf.concat([flipped_xmin, ymin, flipped_xmax, ymax, label], 1)
return flipped_boxes
flip_left_right = tf.greater(tf.random_uniform([], dtype = tf.float32, minval = 0, maxval = 1), 0.5)
image = tf.cond(flip_left_right, lambda : tf.image.flip_left_right(image), lambda : image)
bbox = tf.cond(flip_left_right, lambda: _flip_left_right_boxes(bbox), lambda: bbox)
image = image / 255.
image = tf.clip_by_value(image, clip_value_min = 0.0, clip_value_max = 1.0)
bbox = tf.clip_by_value(bbox, clip_value_min = 0, clip_value_max = input_width - 1)
bbox = tf.cond(tf.greater(tf.shape(bbox)[0], config.max_boxes), lambda: bbox[:config.max_boxes], lambda: tf.pad(bbox, paddings = [[0, config.max_boxes - tf.shape(bbox)[0]], [0, 0]], mode = 'CONSTANT'))
return image, bbox
def build_dataset(self, batch_size):
dataset = tf.data.TFRecordDataset(filenames = self.TfrecordFile)
dataset = dataset.map(self.parser, num_parallel_calls = 10)
if self.mode == 'train':
dataset = dataset.repeat().shuffle(9000).batch(batch_size).prefetch(batch_size)
else:
dataset = dataset.repeat().batch(batch_size).prefetch(batch_size)
return dataset
| true
| true
|
f71684275afc20018793ca67d49712a2693a0850
| 10,212
|
py
|
Python
|
a3c_master_sewak.py
|
sebtac/MLxE
|
93baa6b7c9fd14e54abd7199e868fb828e9a7c52
|
[
"Apache-2.0"
] | 1
|
2020-12-15T17:19:33.000Z
|
2020-12-15T17:19:33.000Z
|
a3c_master_sewak.py
|
sebtac/MLxE
|
93baa6b7c9fd14e54abd7199e868fb828e9a7c52
|
[
"Apache-2.0"
] | null | null | null |
a3c_master_sewak.py
|
sebtac/MLxE
|
93baa6b7c9fd14e54abd7199e868fb828e9a7c52
|
[
"Apache-2.0"
] | null | null | null |
""" A3C in Code - Centralized/ Gobal Network Parameter Server/ Controller
Based On:
A3C Code as in the book Deep Reinforcement Learning, Chapter 12.
Runtime: Python 3.6.5
Dependencies: numpy, matplotlib, tensorflow (/ tensorflow-gpu), gym
DocStrings: GoogleStyle
Author : Mohit Sewak (p20150023@goa-bits-pilani.ac.in)
Inspired from: A3C implementation on TensorFLow official github repository (Tensorflow/models/research)
**********************************************************************
Adjusted by Seabstian Taciak as part of develeopment of MLxE Architecture
@author: sebtac
@contact: https://www.linkedin.com/in/sebastian-taciak-5893861/
"""
# SET BEFORE RUNNIG
# AGENT TYPE
# 0 - Sewak Base Agent (Fixed)
# 1 - Sewak DNN Adjusted
# 2 - Sewak "Task" Modified
# 3 - Sewak ISTB (Iterative, Synchronous Thread Based)
Agent_Type = 3
learning_rate = 0.0001
import multiprocessing
cores = multiprocessing.cpu_count() # DEFAULT SETTING
#cores = 1 # FOR DEBUGGING
# GENERAL IMPORTS
import sys
sys.path.append(r'C:\Users\surface\Documents\Python\RL\MLxE\Mohit Sewak RL\Mohit12_A3C')
import time
import winsound
import logging
import os
import numpy as np
import matplotlib.pyplot as plt
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
# DEEP LEARING and ENVIRONEMENT RELATER IMPORTS
import tensorflow as tf
import tensorflow_addons as tfa # ST for DNN Adjustment
import gym
# CUSTOM SEWAK's MODULES with OPTIONAL SEBTAC ADJUSTMENTS
from experience_replay_sewak import SimpleListBasedMemory
if Agent_Type == 0:
from actorcritic_model_sewak import ActorCriticModel as ACModel # For Sewak Fixed version
from a3c_worker_sewak_base import A3C_Worker # the intial Sewak's implementation with fixes of the Policy_Loss Calcultion
elif Agent_Type == 1:
from actorcritic_model_sewak import ActorCriticModel_Dimond as ACModel
from a3c_worker_sewak_DNN_Adjusted import A3C_Worker
elif Agent_Type == 2:
from actorcritic_model_sewak import ActorCriticModel_Dimond as ACModel
from a3c_worker_sewak_Task_Modifications import A3C_Worker
elif Agent_Type == 3:
from actorcritic_model_sewak import ActorCriticModel_DoubleDimond as ACModel
from a3c_worker_sewak_ISTB import A3C_Worker
# SEWAK's Implementation Fix
"""
- Policy Loss Calcualtion
- Using actual play in example generation (was random)
"""
# DNN Adjustments
"""
- Adding monotonic decrease in Learing Rate relative to the number of episodes run with:
self.alpha_power = 0.998
self.alpha_limit = 0.000001
- Modifying the Model to: common_network_size=[128,256,128], policy_network_size=[64,128,64], value_network_size=[64,128,64]
- Changing the Optimizer to RectifiedAdam -- requaires tensorflow_addons
- Changing Gamma coeffcient to 0.97
"""
# Task Specific Modifications
"""
- Modified state representation with addition of 5th parameter representing the squared distance of the cart from the center of the plane
- Adverse Initial Position
- Negative Reward: -10.0 (originally 0.0)
- Monotonically Decreasing Discount Factor (Gamma Coefficent)
- Goal Specific Reward for cart being close to center of the pland and the pole being close to vertical
"""
class A3C_Master():
"""A3C Master
Centralized Master class of A3C used for hosting the global network parameters and spawning the agents.
Args:
env_name (str): Name of a valid gym environment
model_dir (str): Directory for saving the model during training, and loading the same while playing
learning_rate (float): The learning rate (alpha) for the optimizer
Examples:
agent = A3C_Master()
agent.train()
agent.play()
"""
def __init__(self, Agent_Type=Agent_Type, env_name='CartPole-v0', model_dir="models", learning_rate=learning_rate): #ST 0.001 for Fixed, 0.0001 otherwise
self.env_name = env_name
self.model_dir = model_dir
self.alpha = learning_rate
if not os.path.exists(model_dir):
os.makedirs(model_dir)
self.env = gym.make(self.env_name)
self.action_size = self.env.action_space.n
if Agent_Type <= 1:
self.state_size = self.env.observation_space.shape[0] # For None TaH imlementations
elif Agent_Type == 2:
self.state_size = self.env.observation_space.shape[0] + 1 # ST for TaH implementation
elif Agent_Type == 3:
self.state_size = self.env.observation_space.shape[0] + 1 # ST for TaH implementation
if Agent_Type == 0:
self.optimizer = tf.keras.optimizers.Adam(self.alpha)
else:
self.optimizer = tfa.optimizers.RectifiedAdam(self.alpha) # ST DNN Adjustment
logger.debug("StateSize:{}, ActionSize:{}".format(self.state_size, self.action_size))
self.master_model = ACModel(self.action_size)
self.master_model(tf.convert_to_tensor(np.random.random((1, self.state_size)), dtype=tf.float32))
def train(self, cores):
"""Train the A3C agent
Main function to train the A3C agent after instantiation.
This method uses the number of processor cores to spawns as many Workers. The workers are spawned as
multiple parallel threads instead of multiple parallel processes. Being a threaded execution, the workers
share memory and hence can write directly into the shared global variables.
A more optimal, completely asynchronous implementation could be to spawn the workers as different processes
using a task queue or multiprocessing. In case if this is adopted, then the shared variables need to made
accessible in the distributed environment.
"""
a3c_workers = [A3C_Worker(self.master_model,
self.optimizer,
i,
self.env_name,
self.model_dir,
workers_num = cores,
learning_rate = learning_rate)
for i in range(cores)]
for i, worker in enumerate(a3c_workers):
logger.info("Starting worker {}".format(i))
worker.start()
[worker.join() for worker in a3c_workers]
self.plot_training_statistics()
def play(self):
"""Play the environment using a trained agent
This function opens a (graphical) window that will play a trained agent. The function will try to retrieve
the model saved in the model_dir with filename formatted to contain the associated env_name.
If the model is not found, then the function will first call the train function to start the training.
"""
env = self.env.unwrapped
state = env.reset()
model = self.master_model
model_path = os.path.join(self.model_dir, 'model_{}.h5'.format(self.env_name))
if not os.path.exists(model_path):
logger.info('A3CMaster: No model found at {}, starting fresh training before playing!'.format(model_path))
self.train()
logger.info('A3CMaster: Playing env, Loading model from: {}'.format(model_path))
print("Model Path:", model_path)
#model.load_weights(model_path)
done = False
step_counter = 0
reward_sum = 0
try:
while not done:
env.render(mode='rgb_array')
policy, value = model(tf.convert_to_tensor(state[None, :], dtype=tf.float32))
policy = tf.nn.softmax(policy)
action = np.argmax(policy)
state, reward, done, _ = env.step(action)
reward_sum += reward
logger.info("{}. Reward: {}, action: {}".format(step_counter, reward_sum, action))
step_counter += 1
except KeyboardInterrupt:
print("Received Keyboard Interrupt. Shutting down.")
finally:
env.close()
def plot_training_statistics(self, training_statistics=None):
"""Plot training statistics
This function plot the training statistics like the steps, rewards, discounted_rewards, and loss in each
of the training episode.
"""
training_statistics = A3C_Worker.global_shared_training_stats if training_statistics is None \
else training_statistics
all_episodes = []
all_steps = []
all_rewards = []
all_discounted_rewards = []
all_losses = []
for stats in training_statistics:
worker, episode, steps, reward, discounted_rewards, loss = stats
all_episodes.append(episode)
all_steps.append(steps)
all_rewards.append(reward)
all_discounted_rewards.append(discounted_rewards)
all_losses.append(loss)
self._make_double_axis_plot(all_episodes, all_steps, all_rewards)
self._make_double_axis_plot(all_episodes,all_discounted_rewards,all_losses, label_y1="Discounted Reward",
label_y2="Loss", color_y1="cyan", color_y2="black")
np.savetxt('run.csv', all_steps, delimiter=',', fmt='%d')
@staticmethod
def _make_double_axis_plot(data_x, data_y1, data_y2, x_label='Episodes (e)', label_y1='Steps To Episode Completion',
label_y2='Reward in each Episode', color_y1="red", color_y2="blue"):
"""Internal helper function for plotting dual axis plots
"""
fig, ax1 = plt.subplots()
ax1.set_xlabel(x_label)
ax1.set_ylabel(label_y1, color=color_y1)
ax1.plot(data_x, data_y1, color=color_y1)
ax2 = ax1.twinx()
ax2.set_ylabel(label_y2, color=color_y2)
ax2.plot(data_x, data_y2, color=color_y2)
fig.tight_layout()
plt.show()
if __name__ == "__main__":
"""Main function for testing the A3C Master code's implementation
"""
agent = A3C_Master(Agent_Type=Agent_Type)
agent.train(cores)
#agent.play()
for i in range(10):
winsound.Beep(500,500)
| 39.890625
| 158
| 0.665785
|
Agent_Type = 3
learning_rate = 0.0001
import multiprocessing
cores = multiprocessing.cpu_count()
ys.path.append(r'C:\Users\surface\Documents\Python\RL\MLxE\Mohit Sewak RL\Mohit12_A3C')
import time
import winsound
import logging
import os
import numpy as np
import matplotlib.pyplot as plt
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
import tensorflow as tf
import tensorflow_addons as tfa
import gym
from experience_replay_sewak import SimpleListBasedMemory
if Agent_Type == 0:
from actorcritic_model_sewak import ActorCriticModel as ACModel # For Sewak Fixed version
from a3c_worker_sewak_base import A3C_Worker # the intial Sewak's implementation with fixes of the Policy_Loss Calcultion
elif Agent_Type == 1:
from actorcritic_model_sewak import ActorCriticModel_Dimond as ACModel
from a3c_worker_sewak_DNN_Adjusted import A3C_Worker
elif Agent_Type == 2:
from actorcritic_model_sewak import ActorCriticModel_Dimond as ACModel
from a3c_worker_sewak_Task_Modifications import A3C_Worker
elif Agent_Type == 3:
from actorcritic_model_sewak import ActorCriticModel_DoubleDimond as ACModel
from a3c_worker_sewak_ISTB import A3C_Worker
# DNN Adjustments
# Task Specific Modifications
class A3C_Master():
def __init__(self, Agent_Type=Agent_Type, env_name='CartPole-v0', model_dir="models", learning_rate=learning_rate): #ST 0.001 for Fixed, 0.0001 otherwise
self.env_name = env_name
self.model_dir = model_dir
self.alpha = learning_rate
if not os.path.exists(model_dir):
os.makedirs(model_dir)
self.env = gym.make(self.env_name)
self.action_size = self.env.action_space.n
if Agent_Type <= 1:
self.state_size = self.env.observation_space.shape[0] # For None TaH imlementations
elif Agent_Type == 2:
self.state_size = self.env.observation_space.shape[0] + 1 # ST for TaH implementation
elif Agent_Type == 3:
self.state_size = self.env.observation_space.shape[0] + 1 # ST for TaH implementation
if Agent_Type == 0:
self.optimizer = tf.keras.optimizers.Adam(self.alpha)
else:
self.optimizer = tfa.optimizers.RectifiedAdam(self.alpha) # ST DNN Adjustment
logger.debug("StateSize:{}, ActionSize:{}".format(self.state_size, self.action_size))
self.master_model = ACModel(self.action_size)
self.master_model(tf.convert_to_tensor(np.random.random((1, self.state_size)), dtype=tf.float32))
def train(self, cores):
a3c_workers = [A3C_Worker(self.master_model,
self.optimizer,
i,
self.env_name,
self.model_dir,
workers_num = cores,
learning_rate = learning_rate)
for i in range(cores)]
for i, worker in enumerate(a3c_workers):
logger.info("Starting worker {}".format(i))
worker.start()
[worker.join() for worker in a3c_workers]
self.plot_training_statistics()
def play(self):
env = self.env.unwrapped
state = env.reset()
model = self.master_model
model_path = os.path.join(self.model_dir, 'model_{}.h5'.format(self.env_name))
if not os.path.exists(model_path):
logger.info('A3CMaster: No model found at {}, starting fresh training before playing!'.format(model_path))
self.train()
logger.info('A3CMaster: Playing env, Loading model from: {}'.format(model_path))
print("Model Path:", model_path)
#model.load_weights(model_path)
done = False
step_counter = 0
reward_sum = 0
try:
while not done:
env.render(mode='rgb_array')
policy, value = model(tf.convert_to_tensor(state[None, :], dtype=tf.float32))
policy = tf.nn.softmax(policy)
action = np.argmax(policy)
state, reward, done, _ = env.step(action)
reward_sum += reward
logger.info("{}. Reward: {}, action: {}".format(step_counter, reward_sum, action))
step_counter += 1
except KeyboardInterrupt:
print("Received Keyboard Interrupt. Shutting down.")
finally:
env.close()
def plot_training_statistics(self, training_statistics=None):
training_statistics = A3C_Worker.global_shared_training_stats if training_statistics is None \
else training_statistics
all_episodes = []
all_steps = []
all_rewards = []
all_discounted_rewards = []
all_losses = []
for stats in training_statistics:
worker, episode, steps, reward, discounted_rewards, loss = stats
all_episodes.append(episode)
all_steps.append(steps)
all_rewards.append(reward)
all_discounted_rewards.append(discounted_rewards)
all_losses.append(loss)
self._make_double_axis_plot(all_episodes, all_steps, all_rewards)
self._make_double_axis_plot(all_episodes,all_discounted_rewards,all_losses, label_y1="Discounted Reward",
label_y2="Loss", color_y1="cyan", color_y2="black")
np.savetxt('run.csv', all_steps, delimiter=',', fmt='%d')
@staticmethod
def _make_double_axis_plot(data_x, data_y1, data_y2, x_label='Episodes (e)', label_y1='Steps To Episode Completion',
label_y2='Reward in each Episode', color_y1="red", color_y2="blue"):
fig, ax1 = plt.subplots()
ax1.set_xlabel(x_label)
ax1.set_ylabel(label_y1, color=color_y1)
ax1.plot(data_x, data_y1, color=color_y1)
ax2 = ax1.twinx()
ax2.set_ylabel(label_y2, color=color_y2)
ax2.plot(data_x, data_y2, color=color_y2)
fig.tight_layout()
plt.show()
if __name__ == "__main__":
agent = A3C_Master(Agent_Type=Agent_Type)
agent.train(cores)
#agent.play()
for i in range(10):
winsound.Beep(500,500)
| true
| true
|
f716847b7e7f2b74e68a21c9fff18732128dc20b
| 1,196
|
py
|
Python
|
examples/cdv/plttraj.py
|
geflaspohler/deep-OTD
|
0daec276669776952b5142149007175b8a3c4d87
|
[
"MIT"
] | 1
|
2020-07-18T02:00:50.000Z
|
2020-07-18T02:00:50.000Z
|
examples/cdv/plttraj.py
|
geflaspohler/deep-OTD
|
0daec276669776952b5142149007175b8a3c4d87
|
[
"MIT"
] | null | null | null |
examples/cdv/plttraj.py
|
geflaspohler/deep-OTD
|
0daec276669776952b5142149007175b8a3c4d87
|
[
"MIT"
] | 3
|
2019-11-28T04:15:59.000Z
|
2020-03-27T16:15:36.000Z
|
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.size'] = 9
ndim = 6
data = np.genfromtxt('dOTD_tst1.out')
xticks = [900, 1100, 1300]
yticks = [[0.7, 0.8, 0.9, 1],
[-0.2, 0, 0.2, 0.4],
[-0.5, 0, 0.5],
[-1, -0.5, 0],
[-0.5, 0, 0.5],
[-0.5, 0, 0.5, 1]]
def latexify(ticklabels):
"""Manually set LaTeX format for tick labels."""
return [r"$" + str(label) + "$" for label in ticklabels]
for ii in range(ndim):
fig = plt.figure(figsize=(2.2,1.3), constrained_layout=True)
fig.set_constrained_layout_pads(w_pad=0, h_pad=0)
ax = plt.axes()
plt.plot(data[:,0], data[:,ii+1], 'k-', linewidth=0.75)
plt.xlabel('$t$')
plt.ylabel('$z_{' + str(ii+1) + '}$')
plt.xlim(xticks[0], xticks[-1])
plt.ylim(yticks[ii][0], yticks[ii][-1])
ax.set_xticks(xticks)
ax.set_yticks(yticks[ii])
ax.set_xticklabels(latexify(xticks))
ax.set_yticklabels(latexify(yticks[ii]))
ax.yaxis.set_label_coords(-0.2, 0.5)
ax.tick_params(direction='in', length=2)
plt.savefig('traj' + str(ii+1) + '.pdf')
| 29.170732
| 64
| 0.591137
|
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.size'] = 9
ndim = 6
data = np.genfromtxt('dOTD_tst1.out')
xticks = [900, 1100, 1300]
yticks = [[0.7, 0.8, 0.9, 1],
[-0.2, 0, 0.2, 0.4],
[-0.5, 0, 0.5],
[-1, -0.5, 0],
[-0.5, 0, 0.5],
[-0.5, 0, 0.5, 1]]
def latexify(ticklabels):
return [r"$" + str(label) + "$" for label in ticklabels]
for ii in range(ndim):
fig = plt.figure(figsize=(2.2,1.3), constrained_layout=True)
fig.set_constrained_layout_pads(w_pad=0, h_pad=0)
ax = plt.axes()
plt.plot(data[:,0], data[:,ii+1], 'k-', linewidth=0.75)
plt.xlabel('$t$')
plt.ylabel('$z_{' + str(ii+1) + '}$')
plt.xlim(xticks[0], xticks[-1])
plt.ylim(yticks[ii][0], yticks[ii][-1])
ax.set_xticks(xticks)
ax.set_yticks(yticks[ii])
ax.set_xticklabels(latexify(xticks))
ax.set_yticklabels(latexify(yticks[ii]))
ax.yaxis.set_label_coords(-0.2, 0.5)
ax.tick_params(direction='in', length=2)
plt.savefig('traj' + str(ii+1) + '.pdf')
| true
| true
|
f7168651a64c6d1d8e833e8659ea210c7750b724
| 662
|
py
|
Python
|
exercicio55Corrigido.py
|
adrianomdantas/Exercicios-Python
|
ef5025a186615258aec0cf35ed839fe49577d983
|
[
"MIT"
] | null | null | null |
exercicio55Corrigido.py
|
adrianomdantas/Exercicios-Python
|
ef5025a186615258aec0cf35ed839fe49577d983
|
[
"MIT"
] | null | null | null |
exercicio55Corrigido.py
|
adrianomdantas/Exercicios-Python
|
ef5025a186615258aec0cf35ed839fe49577d983
|
[
"MIT"
] | null | null | null |
'''maior = 0
menor = 0
for p in range(1, 6):
peso = float(input('digite o {}o peso '.format(p)))
if p == 1:
menor = peso
maior = peso
else:
if peso >= maior:
maior = peso
if peso < menor:
menor = peso
print('O maior peso registrado foi {:.1f}kg \nO menor peso registrado foi {:.1f}kg'.format(maior, menor))
'''
lst=[] #lista vazia
for c in range(1, 6):
peso=float(input('Peso da {}ª pessoa: '.format(c)))
lst+=[peso] #adc os valores de peso na lista
print('')
print('O Maior peso foi:', max(lst)) #maximo valor da lista
print('O Menor peso foi:', min(lst)) #minimo valor da lista
| 28.782609
| 105
| 0.575529
|
lst=[]
for c in range(1, 6):
peso=float(input('Peso da {}ª pessoa: '.format(c)))
lst+=[peso]
print('')
print('O Maior peso foi:', max(lst))
print('O Menor peso foi:', min(lst))
| true
| true
|
f71686608991387d6cf7844d049a4fbe7531d303
| 1,071
|
py
|
Python
|
learning/set-background-image-v2.py
|
CrtomirJuren/pyfirmata-arduino
|
020873972d119955e80387f44bcc193ad0b460a6
|
[
"MIT"
] | null | null | null |
learning/set-background-image-v2.py
|
CrtomirJuren/pyfirmata-arduino
|
020873972d119955e80387f44bcc193ad0b460a6
|
[
"MIT"
] | null | null | null |
learning/set-background-image-v2.py
|
CrtomirJuren/pyfirmata-arduino
|
020873972d119955e80387f44bcc193ad0b460a6
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 25 15:21:34 2021
@author: crtjur
"""
import tkinter as tk
from PIL import Image, ImageTk
root = tk.Tk()
root.title("Title")
root.geometry("280x350")
root.configure(background="black")
class Example(tk.Frame):
def __init__(self, master, *pargs):
tk.Frame.__init__(self, master, *pargs)
self.image = Image.open("diagram-v2.png")
self.img_copy= self.image.copy()
self.background_image = ImageTk.PhotoImage(self.image)
self.background = tk.Label(self, image=self.background_image)
self.background.pack(fill= tk.BOTH, expand=True) #,
self.background.bind('<Configure>', self._resize_image)
def _resize_image(self,event):
new_width = event.width
new_height = event.height
self.image = self.img_copy.resize((new_width, new_height))
self.background_image = ImageTk.PhotoImage(self.image)
self.background.configure(image = self.background_image)
e = Example(root)
e.pack(fill=tk.BOTH, expand=True)
root.mainloop()
| 24.340909
| 69
| 0.674136
|
import tkinter as tk
from PIL import Image, ImageTk
root = tk.Tk()
root.title("Title")
root.geometry("280x350")
root.configure(background="black")
class Example(tk.Frame):
def __init__(self, master, *pargs):
tk.Frame.__init__(self, master, *pargs)
self.image = Image.open("diagram-v2.png")
self.img_copy= self.image.copy()
self.background_image = ImageTk.PhotoImage(self.image)
self.background = tk.Label(self, image=self.background_image)
self.background.pack(fill= tk.BOTH, expand=True)
self.background.bind('<Configure>', self._resize_image)
def _resize_image(self,event):
new_width = event.width
new_height = event.height
self.image = self.img_copy.resize((new_width, new_height))
self.background_image = ImageTk.PhotoImage(self.image)
self.background.configure(image = self.background_image)
e = Example(root)
e.pack(fill=tk.BOTH, expand=True)
root.mainloop()
| true
| true
|
f716868e337d9c56b7761e3167f53df728c5c6c1
| 4,185
|
py
|
Python
|
tools/train.py
|
nikhilrayaprolu/food-version2
|
5fe558ba96be34e52e48ee60bdde2298245a769e
|
[
"Apache-2.0"
] | null | null | null |
tools/train.py
|
nikhilrayaprolu/food-version2
|
5fe558ba96be34e52e48ee60bdde2298245a769e
|
[
"Apache-2.0"
] | null | null | null |
tools/train.py
|
nikhilrayaprolu/food-version2
|
5fe558ba96be34e52e48ee60bdde2298245a769e
|
[
"Apache-2.0"
] | 1
|
2020-12-22T08:38:56.000Z
|
2020-12-22T08:38:56.000Z
|
from __future__ import division
import argparse
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import Config
from mmcv.runner import init_dist
from mmdet import __version__
from mmdet.apis import set_random_seed, train_detector
from mmdet.datasets import build_dataset
from mmdet.models import build_detector
from mmdet.utils import get_root_logger
import warnings
warnings.filterwarnings("ignore")
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
parser.add_argument('config', help='train config file path')
parser.add_argument('--work_dir', help='the dir to save logs and models')
parser.add_argument(
'--resume_from', help='the checkpoint file to resume from')
parser.add_argument(
'--validate',
action='store_true',
help='whether to evaluate the checkpoint during training')
parser.add_argument(
'--gpus',
type=int,
default=1,
help='number of gpus to use '
'(only applicable to non-distributed training)')
parser.add_argument('--seed', type=int, default=None, help='random seed')
parser.add_argument(
'--deterministic',
action='store_true',
help='whether to set deterministic options for CUDNN backend.')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument(
'--autoscale-lr',
action='store_true',
help='automatically scale lr with the number of gpus')
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if args.autoscale_lr:
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
# create work_dir
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
# init the logger before other steps
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, '{}.log'.format(timestamp))
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
# log some basic info
logger.info('Distributed training: {}'.format(distributed))
logger.info('MMDetection Version: {}'.format(__version__))
logger.info('Config:\n{}'.format(cfg.text))
# set random seeds
if args.seed is not None:
logger.info('Set random seed to {}, deterministic: {}'.format(
args.seed, args.deterministic))
set_random_seed(args.seed, deterministic=args.deterministic)
model = build_detector(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
datasets.append(build_dataset(cfg.data.val))
if cfg.checkpoint_config is not None:
# save mmdet version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
mmdet_version=__version__,
config=cfg.text,
CLASSES=datasets[0].CLASSES)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_detector(
model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
timestamp=timestamp)
if __name__ == '__main__':
main()
| 32.952756
| 77
| 0.6681
|
from __future__ import division
import argparse
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import Config
from mmcv.runner import init_dist
from mmdet import __version__
from mmdet.apis import set_random_seed, train_detector
from mmdet.datasets import build_dataset
from mmdet.models import build_detector
from mmdet.utils import get_root_logger
import warnings
warnings.filterwarnings("ignore")
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
parser.add_argument('config', help='train config file path')
parser.add_argument('--work_dir', help='the dir to save logs and models')
parser.add_argument(
'--resume_from', help='the checkpoint file to resume from')
parser.add_argument(
'--validate',
action='store_true',
help='whether to evaluate the checkpoint during training')
parser.add_argument(
'--gpus',
type=int,
default=1,
help='number of gpus to use '
'(only applicable to non-distributed training)')
parser.add_argument('--seed', type=int, default=None, help='random seed')
parser.add_argument(
'--deterministic',
action='store_true',
help='whether to set deterministic options for CUDNN backend.')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument(
'--autoscale-lr',
action='store_true',
help='automatically scale lr with the number of gpus')
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
cfg.gpus = args.gpus
if args.autoscale_lr:
cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(cfg.work_dir, '{}.log'.format(timestamp))
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
logger.info('MMDetection Version: {}'.format(__version__))
logger.info('Config:\n{}'.format(cfg.text))
if args.seed is not None:
logger.info('Set random seed to {}, deterministic: {}'.format(
args.seed, args.deterministic))
set_random_seed(args.seed, deterministic=args.deterministic)
model = build_detector(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
datasets.append(build_dataset(cfg.data.val))
if cfg.checkpoint_config is not None:
cfg.checkpoint_config.meta = dict(
mmdet_version=__version__,
config=cfg.text,
CLASSES=datasets[0].CLASSES)
model.CLASSES = datasets[0].CLASSES
train_detector(
model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
timestamp=timestamp)
if __name__ == '__main__':
main()
| true
| true
|
f71687e21d8ea9780570ef161a23f33fdc061d2f
| 2,837
|
py
|
Python
|
samples/basic/executor/models/cisco-ios-xr/Cisco-IOS-XR-snmp-test-trap-act/nc-execute-xr-snmp-test-trap-act-115-ydk.py
|
maccioni/ydk-py-samples
|
d1758694bef97327c5477e65649326c7595ce499
|
[
"Apache-2.0"
] | 1
|
2021-07-08T14:02:12.000Z
|
2021-07-08T14:02:12.000Z
|
samples/basic/executor/models/cisco-ios-xr/Cisco-IOS-XR-snmp-test-trap-act/nc-execute-xr-snmp-test-trap-act-115-ydk.py
|
maccioni/ydk-py-samples
|
d1758694bef97327c5477e65649326c7595ce499
|
[
"Apache-2.0"
] | null | null | null |
samples/basic/executor/models/cisco-ios-xr/Cisco-IOS-XR-snmp-test-trap-act/nc-execute-xr-snmp-test-trap-act-115-ydk.py
|
maccioni/ydk-py-samples
|
d1758694bef97327c5477e65649326c7595ce499
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
#
# Copyright 2016 Cisco Systems, Inc.
#
# 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.
#
"""
Execute RPC for model Cisco-IOS-XR-snmp-test-trap-act.
usage: nc-execute-xr-snmp-test-trap-act-115-ydk.py [-h] [-v] device
positional arguments:
device NETCONF device (ssh://user:password@host:port)
optional arguments:
-h, --help show this help message and exit
-v, --verbose print debugging messages
"""
from argparse import ArgumentParser
from urlparse import urlparse
from ydk.services import ExecutorService
from ydk.providers import NetconfServiceProvider
from ydk.models.cisco_ios_xr import Cisco_IOS_XR_snmp_test_trap_act \
as xr_snmp_test_trap_act
import logging
def prepare_sonet_line_status_rpc(sonet_line_status_rpc):
"""Add RPC input data to sonet_line_status_rpc object."""
pass
if __name__ == "__main__":
"""Execute main program."""
parser = ArgumentParser()
parser.add_argument("-v", "--verbose", help="print debugging messages",
action="store_true")
parser.add_argument("device",
help="NETCONF device (ssh://user:password@host:port)")
args = parser.parse_args()
device = urlparse(args.device)
# log debug messages if verbose argument specified
if args.verbose:
logger = logging.getLogger("ydk")
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter(("%(asctime)s - %(name)s - "
"%(levelname)s - %(message)s"))
handler.setFormatter(formatter)
logger.addHandler(handler)
# create NETCONF provider
provider = NetconfServiceProvider(address=device.hostname,
port=device.port,
username=device.username,
password=device.password,
protocol=device.scheme)
# create executor service
executor = ExecutorService()
sonet_line_status_rpc = xr_snmp_test_trap_act.SonetLineStatusRpc() # create object
prepare_sonet_line_status_rpc(sonet_line_status_rpc) # add RPC input
# execute RPC on NETCONF device
# executor.execute_rpc(provider, sonet_line_status_rpc)
exit()
# End of script
| 34.180723
| 87
| 0.673599
|
from argparse import ArgumentParser
from urlparse import urlparse
from ydk.services import ExecutorService
from ydk.providers import NetconfServiceProvider
from ydk.models.cisco_ios_xr import Cisco_IOS_XR_snmp_test_trap_act \
as xr_snmp_test_trap_act
import logging
def prepare_sonet_line_status_rpc(sonet_line_status_rpc):
pass
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("-v", "--verbose", help="print debugging messages",
action="store_true")
parser.add_argument("device",
help="NETCONF device (ssh://user:password@host:port)")
args = parser.parse_args()
device = urlparse(args.device)
if args.verbose:
logger = logging.getLogger("ydk")
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter(("%(asctime)s - %(name)s - "
"%(levelname)s - %(message)s"))
handler.setFormatter(formatter)
logger.addHandler(handler)
provider = NetconfServiceProvider(address=device.hostname,
port=device.port,
username=device.username,
password=device.password,
protocol=device.scheme)
executor = ExecutorService()
sonet_line_status_rpc = xr_snmp_test_trap_act.SonetLineStatusRpc()
prepare_sonet_line_status_rpc(sonet_line_status_rpc)
exit()
| true
| true
|
f7168881dda3037f1831c4695a51ad6b12964680
| 963
|
py
|
Python
|
membership/forms.py
|
carpentries/membershipdb
|
9f6270cea7251c490686926f041e21a929218e6c
|
[
"MIT"
] | 3
|
2018-05-31T16:09:52.000Z
|
2018-09-30T21:35:06.000Z
|
membership/forms.py
|
carpentries/membershipdb
|
9f6270cea7251c490686926f041e21a929218e6c
|
[
"MIT"
] | 5
|
2018-06-19T21:51:16.000Z
|
2020-01-19T20:18:48.000Z
|
membership/forms.py
|
carpentries/membershipdb
|
9f6270cea7251c490686926f041e21a929218e6c
|
[
"MIT"
] | 1
|
2020-07-07T03:23:34.000Z
|
2020-07-07T03:23:34.000Z
|
"""Membership forms module
"""
from django.forms import ModelForm
from .models import Note, Term, Organization, Contact, Membership
class NoteForm(ModelForm):
"""Note Form
"""
class Meta:
model = Note
fields = ['title', 'content', 'date_time']
class TermForm(ModelForm):
"""Term Form
"""
class Meta:
model = Term
fields = ['mem_type', 'n_workshops',
'n_instructors', 'reserve',
'inh_trainer', 'local_train',
'publicize', 'recruit',
'coordinate']
class OrganizationForm(ModelForm):
"""Organization Form
"""
class Meta:
model = Organization
fields = []
class ContactForm(ModelForm):
"""Contact Form
"""
class Meta:
model = Contact
fields = []
class MembershipForm(ModelForm):
"""Membership Form
"""
class Meta:
model = Membership
fields = []
| 19.26
| 65
| 0.553479
|
from django.forms import ModelForm
from .models import Note, Term, Organization, Contact, Membership
class NoteForm(ModelForm):
class Meta:
model = Note
fields = ['title', 'content', 'date_time']
class TermForm(ModelForm):
class Meta:
model = Term
fields = ['mem_type', 'n_workshops',
'n_instructors', 'reserve',
'inh_trainer', 'local_train',
'publicize', 'recruit',
'coordinate']
class OrganizationForm(ModelForm):
class Meta:
model = Organization
fields = []
class ContactForm(ModelForm):
class Meta:
model = Contact
fields = []
class MembershipForm(ModelForm):
class Meta:
model = Membership
fields = []
| true
| true
|
f71688edd94f6768ac57dec0eba3a526f1529856
| 28,910
|
py
|
Python
|
sdk/python/pulumi_azure_native/insights/v20150501/component.py
|
polivbr/pulumi-azure-native
|
09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/insights/v20150501/component.py
|
polivbr/pulumi-azure-native
|
09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7
|
[
"Apache-2.0"
] | null | null | null |
sdk/python/pulumi_azure_native/insights/v20150501/component.py
|
polivbr/pulumi-azure-native
|
09571f3bf6bdc4f3621aabefd1ba6c0d4ecfb0e7
|
[
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from . import outputs
from ._enums import *
__all__ = ['ComponentArgs', 'Component']
@pulumi.input_type
class ComponentArgs:
def __init__(__self__, *,
application_type: pulumi.Input[Union[str, 'ApplicationType']],
kind: pulumi.Input[str],
resource_group_name: pulumi.Input[str],
disable_ip_masking: Optional[pulumi.Input[bool]] = None,
flow_type: Optional[pulumi.Input[Union[str, 'FlowType']]] = None,
hockey_app_id: Optional[pulumi.Input[str]] = None,
immediate_purge_data_on30_days: Optional[pulumi.Input[bool]] = None,
ingestion_mode: Optional[pulumi.Input[Union[str, 'IngestionMode']]] = None,
location: Optional[pulumi.Input[str]] = None,
request_source: Optional[pulumi.Input[Union[str, 'RequestSource']]] = None,
resource_name: Optional[pulumi.Input[str]] = None,
retention_in_days: Optional[pulumi.Input[int]] = None,
sampling_percentage: Optional[pulumi.Input[float]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None):
"""
The set of arguments for constructing a Component resource.
:param pulumi.Input[Union[str, 'ApplicationType']] application_type: Type of application being monitored.
:param pulumi.Input[str] kind: The kind of application that this component refers to, used to customize UI. This value is a freeform string, values should typically be one of the following: web, ios, other, store, java, phone.
:param pulumi.Input[str] resource_group_name: The name of the resource group. The name is case insensitive.
:param pulumi.Input[bool] disable_ip_masking: Disable IP masking.
:param pulumi.Input[Union[str, 'FlowType']] flow_type: Used by the Application Insights system to determine what kind of flow this component was created by. This is to be set to 'Bluefield' when creating/updating a component via the REST API.
:param pulumi.Input[str] hockey_app_id: The unique application ID created when a new application is added to HockeyApp, used for communications with HockeyApp.
:param pulumi.Input[bool] immediate_purge_data_on30_days: Purge data immediately after 30 days.
:param pulumi.Input[Union[str, 'IngestionMode']] ingestion_mode: Indicates the flow of the ingestion.
:param pulumi.Input[str] location: Resource location
:param pulumi.Input[Union[str, 'RequestSource']] request_source: Describes what tool created this Application Insights component. Customers using this API should set this to the default 'rest'.
:param pulumi.Input[str] resource_name: The name of the Application Insights component resource.
:param pulumi.Input[int] retention_in_days: Retention period in days.
:param pulumi.Input[float] sampling_percentage: Percentage of the data produced by the application being monitored that is being sampled for Application Insights telemetry.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags
"""
if application_type is None:
application_type = 'web'
pulumi.set(__self__, "application_type", application_type)
pulumi.set(__self__, "kind", kind)
pulumi.set(__self__, "resource_group_name", resource_group_name)
if disable_ip_masking is not None:
pulumi.set(__self__, "disable_ip_masking", disable_ip_masking)
if flow_type is None:
flow_type = 'Bluefield'
if flow_type is not None:
pulumi.set(__self__, "flow_type", flow_type)
if hockey_app_id is not None:
pulumi.set(__self__, "hockey_app_id", hockey_app_id)
if immediate_purge_data_on30_days is not None:
pulumi.set(__self__, "immediate_purge_data_on30_days", immediate_purge_data_on30_days)
if ingestion_mode is None:
ingestion_mode = 'ApplicationInsights'
if ingestion_mode is not None:
pulumi.set(__self__, "ingestion_mode", ingestion_mode)
if location is not None:
pulumi.set(__self__, "location", location)
if request_source is None:
request_source = 'rest'
if request_source is not None:
pulumi.set(__self__, "request_source", request_source)
if resource_name is not None:
pulumi.set(__self__, "resource_name", resource_name)
if retention_in_days is None:
retention_in_days = 90
if retention_in_days is not None:
pulumi.set(__self__, "retention_in_days", retention_in_days)
if sampling_percentage is not None:
pulumi.set(__self__, "sampling_percentage", sampling_percentage)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter(name="applicationType")
def application_type(self) -> pulumi.Input[Union[str, 'ApplicationType']]:
"""
Type of application being monitored.
"""
return pulumi.get(self, "application_type")
@application_type.setter
def application_type(self, value: pulumi.Input[Union[str, 'ApplicationType']]):
pulumi.set(self, "application_type", value)
@property
@pulumi.getter
def kind(self) -> pulumi.Input[str]:
"""
The kind of application that this component refers to, used to customize UI. This value is a freeform string, values should typically be one of the following: web, ios, other, store, java, phone.
"""
return pulumi.get(self, "kind")
@kind.setter
def kind(self, value: pulumi.Input[str]):
pulumi.set(self, "kind", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Input[str]:
"""
The name of the resource group. The name is case insensitive.
"""
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: pulumi.Input[str]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter(name="disableIpMasking")
def disable_ip_masking(self) -> Optional[pulumi.Input[bool]]:
"""
Disable IP masking.
"""
return pulumi.get(self, "disable_ip_masking")
@disable_ip_masking.setter
def disable_ip_masking(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "disable_ip_masking", value)
@property
@pulumi.getter(name="flowType")
def flow_type(self) -> Optional[pulumi.Input[Union[str, 'FlowType']]]:
"""
Used by the Application Insights system to determine what kind of flow this component was created by. This is to be set to 'Bluefield' when creating/updating a component via the REST API.
"""
return pulumi.get(self, "flow_type")
@flow_type.setter
def flow_type(self, value: Optional[pulumi.Input[Union[str, 'FlowType']]]):
pulumi.set(self, "flow_type", value)
@property
@pulumi.getter(name="hockeyAppId")
def hockey_app_id(self) -> Optional[pulumi.Input[str]]:
"""
The unique application ID created when a new application is added to HockeyApp, used for communications with HockeyApp.
"""
return pulumi.get(self, "hockey_app_id")
@hockey_app_id.setter
def hockey_app_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "hockey_app_id", value)
@property
@pulumi.getter(name="immediatePurgeDataOn30Days")
def immediate_purge_data_on30_days(self) -> Optional[pulumi.Input[bool]]:
"""
Purge data immediately after 30 days.
"""
return pulumi.get(self, "immediate_purge_data_on30_days")
@immediate_purge_data_on30_days.setter
def immediate_purge_data_on30_days(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "immediate_purge_data_on30_days", value)
@property
@pulumi.getter(name="ingestionMode")
def ingestion_mode(self) -> Optional[pulumi.Input[Union[str, 'IngestionMode']]]:
"""
Indicates the flow of the ingestion.
"""
return pulumi.get(self, "ingestion_mode")
@ingestion_mode.setter
def ingestion_mode(self, value: Optional[pulumi.Input[Union[str, 'IngestionMode']]]):
pulumi.set(self, "ingestion_mode", value)
@property
@pulumi.getter
def location(self) -> Optional[pulumi.Input[str]]:
"""
Resource location
"""
return pulumi.get(self, "location")
@location.setter
def location(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "location", value)
@property
@pulumi.getter(name="requestSource")
def request_source(self) -> Optional[pulumi.Input[Union[str, 'RequestSource']]]:
"""
Describes what tool created this Application Insights component. Customers using this API should set this to the default 'rest'.
"""
return pulumi.get(self, "request_source")
@request_source.setter
def request_source(self, value: Optional[pulumi.Input[Union[str, 'RequestSource']]]):
pulumi.set(self, "request_source", value)
@property
@pulumi.getter(name="resourceName")
def resource_name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the Application Insights component resource.
"""
return pulumi.get(self, "resource_name")
@resource_name.setter
def resource_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "resource_name", value)
@property
@pulumi.getter(name="retentionInDays")
def retention_in_days(self) -> Optional[pulumi.Input[int]]:
"""
Retention period in days.
"""
return pulumi.get(self, "retention_in_days")
@retention_in_days.setter
def retention_in_days(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "retention_in_days", value)
@property
@pulumi.getter(name="samplingPercentage")
def sampling_percentage(self) -> Optional[pulumi.Input[float]]:
"""
Percentage of the data produced by the application being monitored that is being sampled for Application Insights telemetry.
"""
return pulumi.get(self, "sampling_percentage")
@sampling_percentage.setter
def sampling_percentage(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "sampling_percentage", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
"""
Resource tags
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
class Component(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
application_type: Optional[pulumi.Input[Union[str, 'ApplicationType']]] = None,
disable_ip_masking: Optional[pulumi.Input[bool]] = None,
flow_type: Optional[pulumi.Input[Union[str, 'FlowType']]] = None,
hockey_app_id: Optional[pulumi.Input[str]] = None,
immediate_purge_data_on30_days: Optional[pulumi.Input[bool]] = None,
ingestion_mode: Optional[pulumi.Input[Union[str, 'IngestionMode']]] = None,
kind: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
request_source: Optional[pulumi.Input[Union[str, 'RequestSource']]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
resource_name_: Optional[pulumi.Input[str]] = None,
retention_in_days: Optional[pulumi.Input[int]] = None,
sampling_percentage: Optional[pulumi.Input[float]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
"""
An Application Insights component definition.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[Union[str, 'ApplicationType']] application_type: Type of application being monitored.
:param pulumi.Input[bool] disable_ip_masking: Disable IP masking.
:param pulumi.Input[Union[str, 'FlowType']] flow_type: Used by the Application Insights system to determine what kind of flow this component was created by. This is to be set to 'Bluefield' when creating/updating a component via the REST API.
:param pulumi.Input[str] hockey_app_id: The unique application ID created when a new application is added to HockeyApp, used for communications with HockeyApp.
:param pulumi.Input[bool] immediate_purge_data_on30_days: Purge data immediately after 30 days.
:param pulumi.Input[Union[str, 'IngestionMode']] ingestion_mode: Indicates the flow of the ingestion.
:param pulumi.Input[str] kind: The kind of application that this component refers to, used to customize UI. This value is a freeform string, values should typically be one of the following: web, ios, other, store, java, phone.
:param pulumi.Input[str] location: Resource location
:param pulumi.Input[Union[str, 'RequestSource']] request_source: Describes what tool created this Application Insights component. Customers using this API should set this to the default 'rest'.
:param pulumi.Input[str] resource_group_name: The name of the resource group. The name is case insensitive.
:param pulumi.Input[str] resource_name_: The name of the Application Insights component resource.
:param pulumi.Input[int] retention_in_days: Retention period in days.
:param pulumi.Input[float] sampling_percentage: Percentage of the data produced by the application being monitored that is being sampled for Application Insights telemetry.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: ComponentArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
An Application Insights component definition.
:param str resource_name: The name of the resource.
:param ComponentArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ComponentArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
application_type: Optional[pulumi.Input[Union[str, 'ApplicationType']]] = None,
disable_ip_masking: Optional[pulumi.Input[bool]] = None,
flow_type: Optional[pulumi.Input[Union[str, 'FlowType']]] = None,
hockey_app_id: Optional[pulumi.Input[str]] = None,
immediate_purge_data_on30_days: Optional[pulumi.Input[bool]] = None,
ingestion_mode: Optional[pulumi.Input[Union[str, 'IngestionMode']]] = None,
kind: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
request_source: Optional[pulumi.Input[Union[str, 'RequestSource']]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
resource_name_: Optional[pulumi.Input[str]] = None,
retention_in_days: Optional[pulumi.Input[int]] = None,
sampling_percentage: Optional[pulumi.Input[float]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ComponentArgs.__new__(ComponentArgs)
if application_type is None:
application_type = 'web'
if application_type is None and not opts.urn:
raise TypeError("Missing required property 'application_type'")
__props__.__dict__["application_type"] = application_type
__props__.__dict__["disable_ip_masking"] = disable_ip_masking
if flow_type is None:
flow_type = 'Bluefield'
__props__.__dict__["flow_type"] = flow_type
__props__.__dict__["hockey_app_id"] = hockey_app_id
__props__.__dict__["immediate_purge_data_on30_days"] = immediate_purge_data_on30_days
if ingestion_mode is None:
ingestion_mode = 'ApplicationInsights'
__props__.__dict__["ingestion_mode"] = ingestion_mode
if kind is None and not opts.urn:
raise TypeError("Missing required property 'kind'")
__props__.__dict__["kind"] = kind
__props__.__dict__["location"] = location
if request_source is None:
request_source = 'rest'
__props__.__dict__["request_source"] = request_source
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__.__dict__["resource_group_name"] = resource_group_name
__props__.__dict__["resource_name"] = resource_name_
if retention_in_days is None:
retention_in_days = 90
__props__.__dict__["retention_in_days"] = retention_in_days
__props__.__dict__["sampling_percentage"] = sampling_percentage
__props__.__dict__["tags"] = tags
__props__.__dict__["app_id"] = None
__props__.__dict__["application_id"] = None
__props__.__dict__["connection_string"] = None
__props__.__dict__["creation_date"] = None
__props__.__dict__["hockey_app_token"] = None
__props__.__dict__["instrumentation_key"] = None
__props__.__dict__["name"] = None
__props__.__dict__["private_link_scoped_resources"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["tenant_id"] = None
__props__.__dict__["type"] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:insights/v20150501:Component"), pulumi.Alias(type_="azure-native:insights:Component"), pulumi.Alias(type_="azure-nextgen:insights:Component"), pulumi.Alias(type_="azure-native:insights/v20180501preview:Component"), pulumi.Alias(type_="azure-nextgen:insights/v20180501preview:Component"), pulumi.Alias(type_="azure-native:insights/v20200202:Component"), pulumi.Alias(type_="azure-nextgen:insights/v20200202:Component"), pulumi.Alias(type_="azure-native:insights/v20200202preview:Component"), pulumi.Alias(type_="azure-nextgen:insights/v20200202preview:Component")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(Component, __self__).__init__(
'azure-native:insights/v20150501:Component',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'Component':
"""
Get an existing Component resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = ComponentArgs.__new__(ComponentArgs)
__props__.__dict__["app_id"] = None
__props__.__dict__["application_id"] = None
__props__.__dict__["application_type"] = None
__props__.__dict__["connection_string"] = None
__props__.__dict__["creation_date"] = None
__props__.__dict__["disable_ip_masking"] = None
__props__.__dict__["flow_type"] = None
__props__.__dict__["hockey_app_id"] = None
__props__.__dict__["hockey_app_token"] = None
__props__.__dict__["immediate_purge_data_on30_days"] = None
__props__.__dict__["ingestion_mode"] = None
__props__.__dict__["instrumentation_key"] = None
__props__.__dict__["kind"] = None
__props__.__dict__["location"] = None
__props__.__dict__["name"] = None
__props__.__dict__["private_link_scoped_resources"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["request_source"] = None
__props__.__dict__["retention_in_days"] = None
__props__.__dict__["sampling_percentage"] = None
__props__.__dict__["tags"] = None
__props__.__dict__["tenant_id"] = None
__props__.__dict__["type"] = None
return Component(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="appId")
def app_id(self) -> pulumi.Output[str]:
"""
Application Insights Unique ID for your Application.
"""
return pulumi.get(self, "app_id")
@property
@pulumi.getter(name="applicationId")
def application_id(self) -> pulumi.Output[str]:
"""
The unique ID of your application. This field mirrors the 'Name' field and cannot be changed.
"""
return pulumi.get(self, "application_id")
@property
@pulumi.getter(name="applicationType")
def application_type(self) -> pulumi.Output[str]:
"""
Type of application being monitored.
"""
return pulumi.get(self, "application_type")
@property
@pulumi.getter(name="connectionString")
def connection_string(self) -> pulumi.Output[str]:
"""
Application Insights component connection string.
"""
return pulumi.get(self, "connection_string")
@property
@pulumi.getter(name="creationDate")
def creation_date(self) -> pulumi.Output[str]:
"""
Creation Date for the Application Insights component, in ISO 8601 format.
"""
return pulumi.get(self, "creation_date")
@property
@pulumi.getter(name="disableIpMasking")
def disable_ip_masking(self) -> pulumi.Output[Optional[bool]]:
"""
Disable IP masking.
"""
return pulumi.get(self, "disable_ip_masking")
@property
@pulumi.getter(name="flowType")
def flow_type(self) -> pulumi.Output[Optional[str]]:
"""
Used by the Application Insights system to determine what kind of flow this component was created by. This is to be set to 'Bluefield' when creating/updating a component via the REST API.
"""
return pulumi.get(self, "flow_type")
@property
@pulumi.getter(name="hockeyAppId")
def hockey_app_id(self) -> pulumi.Output[Optional[str]]:
"""
The unique application ID created when a new application is added to HockeyApp, used for communications with HockeyApp.
"""
return pulumi.get(self, "hockey_app_id")
@property
@pulumi.getter(name="hockeyAppToken")
def hockey_app_token(self) -> pulumi.Output[str]:
"""
Token used to authenticate communications with between Application Insights and HockeyApp.
"""
return pulumi.get(self, "hockey_app_token")
@property
@pulumi.getter(name="immediatePurgeDataOn30Days")
def immediate_purge_data_on30_days(self) -> pulumi.Output[Optional[bool]]:
"""
Purge data immediately after 30 days.
"""
return pulumi.get(self, "immediate_purge_data_on30_days")
@property
@pulumi.getter(name="ingestionMode")
def ingestion_mode(self) -> pulumi.Output[Optional[str]]:
"""
Indicates the flow of the ingestion.
"""
return pulumi.get(self, "ingestion_mode")
@property
@pulumi.getter(name="instrumentationKey")
def instrumentation_key(self) -> pulumi.Output[str]:
"""
Application Insights Instrumentation key. A read-only value that applications can use to identify the destination for all telemetry sent to Azure Application Insights. This value will be supplied upon construction of each new Application Insights component.
"""
return pulumi.get(self, "instrumentation_key")
@property
@pulumi.getter
def kind(self) -> pulumi.Output[str]:
"""
The kind of application that this component refers to, used to customize UI. This value is a freeform string, values should typically be one of the following: web, ios, other, store, java, phone.
"""
return pulumi.get(self, "kind")
@property
@pulumi.getter
def location(self) -> pulumi.Output[str]:
"""
Resource location
"""
return pulumi.get(self, "location")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Azure resource name
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="privateLinkScopedResources")
def private_link_scoped_resources(self) -> pulumi.Output[Sequence['outputs.PrivateLinkScopedResourceResponse']]:
"""
List of linked private link scope resources.
"""
return pulumi.get(self, "private_link_scoped_resources")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
"""
Current state of this component: whether or not is has been provisioned within the resource group it is defined. Users cannot change this value but are able to read from it. Values will include Succeeded, Deploying, Canceled, and Failed.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="requestSource")
def request_source(self) -> pulumi.Output[Optional[str]]:
"""
Describes what tool created this Application Insights component. Customers using this API should set this to the default 'rest'.
"""
return pulumi.get(self, "request_source")
@property
@pulumi.getter(name="retentionInDays")
def retention_in_days(self) -> pulumi.Output[Optional[int]]:
"""
Retention period in days.
"""
return pulumi.get(self, "retention_in_days")
@property
@pulumi.getter(name="samplingPercentage")
def sampling_percentage(self) -> pulumi.Output[Optional[float]]:
"""
Percentage of the data produced by the application being monitored that is being sampled for Application Insights telemetry.
"""
return pulumi.get(self, "sampling_percentage")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
"""
Resource tags
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter(name="tenantId")
def tenant_id(self) -> pulumi.Output[str]:
"""
Azure Tenant Id.
"""
return pulumi.get(self, "tenant_id")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
Azure resource type
"""
return pulumi.get(self, "type")
| 46.779935
| 651
| 0.661397
|
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from ... import _utilities
from . import outputs
from ._enums import *
__all__ = ['ComponentArgs', 'Component']
@pulumi.input_type
class ComponentArgs:
def __init__(__self__, *,
application_type: pulumi.Input[Union[str, 'ApplicationType']],
kind: pulumi.Input[str],
resource_group_name: pulumi.Input[str],
disable_ip_masking: Optional[pulumi.Input[bool]] = None,
flow_type: Optional[pulumi.Input[Union[str, 'FlowType']]] = None,
hockey_app_id: Optional[pulumi.Input[str]] = None,
immediate_purge_data_on30_days: Optional[pulumi.Input[bool]] = None,
ingestion_mode: Optional[pulumi.Input[Union[str, 'IngestionMode']]] = None,
location: Optional[pulumi.Input[str]] = None,
request_source: Optional[pulumi.Input[Union[str, 'RequestSource']]] = None,
resource_name: Optional[pulumi.Input[str]] = None,
retention_in_days: Optional[pulumi.Input[int]] = None,
sampling_percentage: Optional[pulumi.Input[float]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None):
if application_type is None:
application_type = 'web'
pulumi.set(__self__, "application_type", application_type)
pulumi.set(__self__, "kind", kind)
pulumi.set(__self__, "resource_group_name", resource_group_name)
if disable_ip_masking is not None:
pulumi.set(__self__, "disable_ip_masking", disable_ip_masking)
if flow_type is None:
flow_type = 'Bluefield'
if flow_type is not None:
pulumi.set(__self__, "flow_type", flow_type)
if hockey_app_id is not None:
pulumi.set(__self__, "hockey_app_id", hockey_app_id)
if immediate_purge_data_on30_days is not None:
pulumi.set(__self__, "immediate_purge_data_on30_days", immediate_purge_data_on30_days)
if ingestion_mode is None:
ingestion_mode = 'ApplicationInsights'
if ingestion_mode is not None:
pulumi.set(__self__, "ingestion_mode", ingestion_mode)
if location is not None:
pulumi.set(__self__, "location", location)
if request_source is None:
request_source = 'rest'
if request_source is not None:
pulumi.set(__self__, "request_source", request_source)
if resource_name is not None:
pulumi.set(__self__, "resource_name", resource_name)
if retention_in_days is None:
retention_in_days = 90
if retention_in_days is not None:
pulumi.set(__self__, "retention_in_days", retention_in_days)
if sampling_percentage is not None:
pulumi.set(__self__, "sampling_percentage", sampling_percentage)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter(name="applicationType")
def application_type(self) -> pulumi.Input[Union[str, 'ApplicationType']]:
return pulumi.get(self, "application_type")
@application_type.setter
def application_type(self, value: pulumi.Input[Union[str, 'ApplicationType']]):
pulumi.set(self, "application_type", value)
@property
@pulumi.getter
def kind(self) -> pulumi.Input[str]:
return pulumi.get(self, "kind")
@kind.setter
def kind(self, value: pulumi.Input[str]):
pulumi.set(self, "kind", value)
@property
@pulumi.getter(name="resourceGroupName")
def resource_group_name(self) -> pulumi.Input[str]:
return pulumi.get(self, "resource_group_name")
@resource_group_name.setter
def resource_group_name(self, value: pulumi.Input[str]):
pulumi.set(self, "resource_group_name", value)
@property
@pulumi.getter(name="disableIpMasking")
def disable_ip_masking(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "disable_ip_masking")
@disable_ip_masking.setter
def disable_ip_masking(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "disable_ip_masking", value)
@property
@pulumi.getter(name="flowType")
def flow_type(self) -> Optional[pulumi.Input[Union[str, 'FlowType']]]:
return pulumi.get(self, "flow_type")
@flow_type.setter
def flow_type(self, value: Optional[pulumi.Input[Union[str, 'FlowType']]]):
pulumi.set(self, "flow_type", value)
@property
@pulumi.getter(name="hockeyAppId")
def hockey_app_id(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "hockey_app_id")
@hockey_app_id.setter
def hockey_app_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "hockey_app_id", value)
@property
@pulumi.getter(name="immediatePurgeDataOn30Days")
def immediate_purge_data_on30_days(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "immediate_purge_data_on30_days")
@immediate_purge_data_on30_days.setter
def immediate_purge_data_on30_days(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "immediate_purge_data_on30_days", value)
@property
@pulumi.getter(name="ingestionMode")
def ingestion_mode(self) -> Optional[pulumi.Input[Union[str, 'IngestionMode']]]:
return pulumi.get(self, "ingestion_mode")
@ingestion_mode.setter
def ingestion_mode(self, value: Optional[pulumi.Input[Union[str, 'IngestionMode']]]):
pulumi.set(self, "ingestion_mode", value)
@property
@pulumi.getter
def location(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "location")
@location.setter
def location(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "location", value)
@property
@pulumi.getter(name="requestSource")
def request_source(self) -> Optional[pulumi.Input[Union[str, 'RequestSource']]]:
return pulumi.get(self, "request_source")
@request_source.setter
def request_source(self, value: Optional[pulumi.Input[Union[str, 'RequestSource']]]):
pulumi.set(self, "request_source", value)
@property
@pulumi.getter(name="resourceName")
def resource_name(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "resource_name")
@resource_name.setter
def resource_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "resource_name", value)
@property
@pulumi.getter(name="retentionInDays")
def retention_in_days(self) -> Optional[pulumi.Input[int]]:
return pulumi.get(self, "retention_in_days")
@retention_in_days.setter
def retention_in_days(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "retention_in_days", value)
@property
@pulumi.getter(name="samplingPercentage")
def sampling_percentage(self) -> Optional[pulumi.Input[float]]:
return pulumi.get(self, "sampling_percentage")
@sampling_percentage.setter
def sampling_percentage(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "sampling_percentage", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
class Component(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
application_type: Optional[pulumi.Input[Union[str, 'ApplicationType']]] = None,
disable_ip_masking: Optional[pulumi.Input[bool]] = None,
flow_type: Optional[pulumi.Input[Union[str, 'FlowType']]] = None,
hockey_app_id: Optional[pulumi.Input[str]] = None,
immediate_purge_data_on30_days: Optional[pulumi.Input[bool]] = None,
ingestion_mode: Optional[pulumi.Input[Union[str, 'IngestionMode']]] = None,
kind: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
request_source: Optional[pulumi.Input[Union[str, 'RequestSource']]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
resource_name_: Optional[pulumi.Input[str]] = None,
retention_in_days: Optional[pulumi.Input[int]] = None,
sampling_percentage: Optional[pulumi.Input[float]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
...
@overload
def __init__(__self__,
resource_name: str,
args: ComponentArgs,
opts: Optional[pulumi.ResourceOptions] = None):
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ComponentArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
application_type: Optional[pulumi.Input[Union[str, 'ApplicationType']]] = None,
disable_ip_masking: Optional[pulumi.Input[bool]] = None,
flow_type: Optional[pulumi.Input[Union[str, 'FlowType']]] = None,
hockey_app_id: Optional[pulumi.Input[str]] = None,
immediate_purge_data_on30_days: Optional[pulumi.Input[bool]] = None,
ingestion_mode: Optional[pulumi.Input[Union[str, 'IngestionMode']]] = None,
kind: Optional[pulumi.Input[str]] = None,
location: Optional[pulumi.Input[str]] = None,
request_source: Optional[pulumi.Input[Union[str, 'RequestSource']]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
resource_name_: Optional[pulumi.Input[str]] = None,
retention_in_days: Optional[pulumi.Input[int]] = None,
sampling_percentage: Optional[pulumi.Input[float]] = None,
tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ComponentArgs.__new__(ComponentArgs)
if application_type is None:
application_type = 'web'
if application_type is None and not opts.urn:
raise TypeError("Missing required property 'application_type'")
__props__.__dict__["application_type"] = application_type
__props__.__dict__["disable_ip_masking"] = disable_ip_masking
if flow_type is None:
flow_type = 'Bluefield'
__props__.__dict__["flow_type"] = flow_type
__props__.__dict__["hockey_app_id"] = hockey_app_id
__props__.__dict__["immediate_purge_data_on30_days"] = immediate_purge_data_on30_days
if ingestion_mode is None:
ingestion_mode = 'ApplicationInsights'
__props__.__dict__["ingestion_mode"] = ingestion_mode
if kind is None and not opts.urn:
raise TypeError("Missing required property 'kind'")
__props__.__dict__["kind"] = kind
__props__.__dict__["location"] = location
if request_source is None:
request_source = 'rest'
__props__.__dict__["request_source"] = request_source
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__.__dict__["resource_group_name"] = resource_group_name
__props__.__dict__["resource_name"] = resource_name_
if retention_in_days is None:
retention_in_days = 90
__props__.__dict__["retention_in_days"] = retention_in_days
__props__.__dict__["sampling_percentage"] = sampling_percentage
__props__.__dict__["tags"] = tags
__props__.__dict__["app_id"] = None
__props__.__dict__["application_id"] = None
__props__.__dict__["connection_string"] = None
__props__.__dict__["creation_date"] = None
__props__.__dict__["hockey_app_token"] = None
__props__.__dict__["instrumentation_key"] = None
__props__.__dict__["name"] = None
__props__.__dict__["private_link_scoped_resources"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["tenant_id"] = None
__props__.__dict__["type"] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:insights/v20150501:Component"), pulumi.Alias(type_="azure-native:insights:Component"), pulumi.Alias(type_="azure-nextgen:insights:Component"), pulumi.Alias(type_="azure-native:insights/v20180501preview:Component"), pulumi.Alias(type_="azure-nextgen:insights/v20180501preview:Component"), pulumi.Alias(type_="azure-native:insights/v20200202:Component"), pulumi.Alias(type_="azure-nextgen:insights/v20200202:Component"), pulumi.Alias(type_="azure-native:insights/v20200202preview:Component"), pulumi.Alias(type_="azure-nextgen:insights/v20200202preview:Component")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(Component, __self__).__init__(
'azure-native:insights/v20150501:Component',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'Component':
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = ComponentArgs.__new__(ComponentArgs)
__props__.__dict__["app_id"] = None
__props__.__dict__["application_id"] = None
__props__.__dict__["application_type"] = None
__props__.__dict__["connection_string"] = None
__props__.__dict__["creation_date"] = None
__props__.__dict__["disable_ip_masking"] = None
__props__.__dict__["flow_type"] = None
__props__.__dict__["hockey_app_id"] = None
__props__.__dict__["hockey_app_token"] = None
__props__.__dict__["immediate_purge_data_on30_days"] = None
__props__.__dict__["ingestion_mode"] = None
__props__.__dict__["instrumentation_key"] = None
__props__.__dict__["kind"] = None
__props__.__dict__["location"] = None
__props__.__dict__["name"] = None
__props__.__dict__["private_link_scoped_resources"] = None
__props__.__dict__["provisioning_state"] = None
__props__.__dict__["request_source"] = None
__props__.__dict__["retention_in_days"] = None
__props__.__dict__["sampling_percentage"] = None
__props__.__dict__["tags"] = None
__props__.__dict__["tenant_id"] = None
__props__.__dict__["type"] = None
return Component(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="appId")
def app_id(self) -> pulumi.Output[str]:
return pulumi.get(self, "app_id")
@property
@pulumi.getter(name="applicationId")
def application_id(self) -> pulumi.Output[str]:
return pulumi.get(self, "application_id")
@property
@pulumi.getter(name="applicationType")
def application_type(self) -> pulumi.Output[str]:
return pulumi.get(self, "application_type")
@property
@pulumi.getter(name="connectionString")
def connection_string(self) -> pulumi.Output[str]:
return pulumi.get(self, "connection_string")
@property
@pulumi.getter(name="creationDate")
def creation_date(self) -> pulumi.Output[str]:
return pulumi.get(self, "creation_date")
@property
@pulumi.getter(name="disableIpMasking")
def disable_ip_masking(self) -> pulumi.Output[Optional[bool]]:
return pulumi.get(self, "disable_ip_masking")
@property
@pulumi.getter(name="flowType")
def flow_type(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "flow_type")
@property
@pulumi.getter(name="hockeyAppId")
def hockey_app_id(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "hockey_app_id")
@property
@pulumi.getter(name="hockeyAppToken")
def hockey_app_token(self) -> pulumi.Output[str]:
return pulumi.get(self, "hockey_app_token")
@property
@pulumi.getter(name="immediatePurgeDataOn30Days")
def immediate_purge_data_on30_days(self) -> pulumi.Output[Optional[bool]]:
return pulumi.get(self, "immediate_purge_data_on30_days")
@property
@pulumi.getter(name="ingestionMode")
def ingestion_mode(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "ingestion_mode")
@property
@pulumi.getter(name="instrumentationKey")
def instrumentation_key(self) -> pulumi.Output[str]:
return pulumi.get(self, "instrumentation_key")
@property
@pulumi.getter
def kind(self) -> pulumi.Output[str]:
return pulumi.get(self, "kind")
@property
@pulumi.getter
def location(self) -> pulumi.Output[str]:
return pulumi.get(self, "location")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
return pulumi.get(self, "name")
@property
@pulumi.getter(name="privateLinkScopedResources")
def private_link_scoped_resources(self) -> pulumi.Output[Sequence['outputs.PrivateLinkScopedResourceResponse']]:
return pulumi.get(self, "private_link_scoped_resources")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="requestSource")
def request_source(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "request_source")
@property
@pulumi.getter(name="retentionInDays")
def retention_in_days(self) -> pulumi.Output[Optional[int]]:
return pulumi.get(self, "retention_in_days")
@property
@pulumi.getter(name="samplingPercentage")
def sampling_percentage(self) -> pulumi.Output[Optional[float]]:
return pulumi.get(self, "sampling_percentage")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]:
return pulumi.get(self, "tags")
@property
@pulumi.getter(name="tenantId")
def tenant_id(self) -> pulumi.Output[str]:
return pulumi.get(self, "tenant_id")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
return pulumi.get(self, "type")
| true
| true
|
f7168a4422b050f7d1300a0db6bbaf4282e7bd75
| 1,953
|
py
|
Python
|
jungle/code/sorting.py
|
nate-russell/Jungle
|
114d744ed66fec11b8d5e62444253892a7ffa5cd
|
[
"MIT"
] | null | null | null |
jungle/code/sorting.py
|
nate-russell/Jungle
|
114d744ed66fec11b8d5e62444253892a7ffa5cd
|
[
"MIT"
] | 4
|
2017-12-28T02:07:33.000Z
|
2018-01-04T06:38:43.000Z
|
jungle/code/sorting.py
|
nate-russell/Jungle
|
114d744ed66fec11b8d5e62444253892a7ffa5cd
|
[
"MIT"
] | null | null | null |
'''
Sorting Examples for showcasing and developing Jungle features
'''
import inspect
from jungle import JungleExperiment, JungleProfiler
import numpy as np
print('Finished Loading Modules')
class Sorting_Prototype:
print('\n---Test Sort N---')
@JungleExperiment(reps=1, n=[100, 500])
def test_sort_n(self, n=100, seed=1234):
''' Test sorting an iterable of size n with a random distribution '''
# make data to sort with random distribution
np.random.seed(seed)
list_2_sort = list(np.random.randn(n))
@JungleProfiler()
def sort_n(l):
sorted_list = self.sort(l)
return sorted_list
# Sort and check sort status
sorted_list, _ = sort_n(list_2_sort)
sort_status = all(sorted_list[i] <= sorted_list[i + 1] for i in range(len(sorted_list) - 1))
return sort_status
print('\n---Test Block Sort---')
@JungleExperiment(reps=1, n_blocks=[2, 4], block_size=[50, 100])
@JungleProfiler()
def test_block_random_sort(self, n_blocks=4, block_size=100):
print('n_blocks: %s' % n_blocks)
print('block_size: %s' % block_size)
return 'something'
class NP_QuickSort(Sorting_Prototype):
def sort(self, l):
return np.sort(l, kind='quicksort')
class NP_MergeSort(Sorting_Prototype):
def sort(self, l):
return np.sort(l, kind='mergesort')
class NP_HeapSort(Sorting_Prototype):
def sort(self, l):
return np.sort(l, kind='heapsort')
if __name__ == '__main__':
print('\n__main__\n')
print('\n---Starting Call #1---')
m1 = NP_QuickSort()
jc1 = m1.test_sort_n()
print('\n---Starting Call #2---')
m2 = NP_MergeSort()
jc2 = m2.test_sort_n()
print('\n---Starting Call #3---')
m1 = NP_QuickSort()
jc1 = m1.test_block_random_sort()
print('\n---Starting Call #4---')
m2 = NP_MergeSort()
jc2 = m2.test_block_random_sort()
| 24.721519
| 100
| 0.63236
|
import inspect
from jungle import JungleExperiment, JungleProfiler
import numpy as np
print('Finished Loading Modules')
class Sorting_Prototype:
print('\n---Test Sort N---')
@JungleExperiment(reps=1, n=[100, 500])
def test_sort_n(self, n=100, seed=1234):
np.random.seed(seed)
list_2_sort = list(np.random.randn(n))
@JungleProfiler()
def sort_n(l):
sorted_list = self.sort(l)
return sorted_list
sorted_list, _ = sort_n(list_2_sort)
sort_status = all(sorted_list[i] <= sorted_list[i + 1] for i in range(len(sorted_list) - 1))
return sort_status
print('\n---Test Block Sort---')
@JungleExperiment(reps=1, n_blocks=[2, 4], block_size=[50, 100])
@JungleProfiler()
def test_block_random_sort(self, n_blocks=4, block_size=100):
print('n_blocks: %s' % n_blocks)
print('block_size: %s' % block_size)
return 'something'
class NP_QuickSort(Sorting_Prototype):
def sort(self, l):
return np.sort(l, kind='quicksort')
class NP_MergeSort(Sorting_Prototype):
def sort(self, l):
return np.sort(l, kind='mergesort')
class NP_HeapSort(Sorting_Prototype):
def sort(self, l):
return np.sort(l, kind='heapsort')
if __name__ == '__main__':
print('\n__main__\n')
print('\n---Starting Call #1---')
m1 = NP_QuickSort()
jc1 = m1.test_sort_n()
print('\n---Starting Call #2---')
m2 = NP_MergeSort()
jc2 = m2.test_sort_n()
print('\n---Starting Call #3---')
m1 = NP_QuickSort()
jc1 = m1.test_block_random_sort()
print('\n---Starting Call #4---')
m2 = NP_MergeSort()
jc2 = m2.test_block_random_sort()
| true
| true
|
f7168a45d1129bfe9f7ee66ee3c14b06f2b75b19
| 432
|
py
|
Python
|
exercicios/ex052.py
|
MaikolSantos/curso-em-video-python3
|
3a1ab2761b8a0f98e128083a7b0e50b19a75b7bf
|
[
"MIT"
] | null | null | null |
exercicios/ex052.py
|
MaikolSantos/curso-em-video-python3
|
3a1ab2761b8a0f98e128083a7b0e50b19a75b7bf
|
[
"MIT"
] | null | null | null |
exercicios/ex052.py
|
MaikolSantos/curso-em-video-python3
|
3a1ab2761b8a0f98e128083a7b0e50b19a75b7bf
|
[
"MIT"
] | null | null | null |
n = int(input('Digite um número inteiro: '))
cont = 0
for c in range(1, n + 1):
if n % c == 0:
print('\033[034m{}\033[m'.format(c), end=' ')
cont += 1
else:
print('\033[031m{}\033[m'.format(c), end=' ')
print('\nO número {} foi divisível {} vezes '.format(n, cont))
if cont == 2:
print('Portanto, o número {} é PRIMO'.format(n))
else:
print('Portanto, o número {} NÃO é PRIMO'.format(n))
| 24
| 62
| 0.546296
|
n = int(input('Digite um número inteiro: '))
cont = 0
for c in range(1, n + 1):
if n % c == 0:
print('\033[034m{}\033[m'.format(c), end=' ')
cont += 1
else:
print('\033[031m{}\033[m'.format(c), end=' ')
print('\nO número {} foi divisível {} vezes '.format(n, cont))
if cont == 2:
print('Portanto, o número {} é PRIMO'.format(n))
else:
print('Portanto, o número {} NÃO é PRIMO'.format(n))
| true
| true
|
f7168a487b6bc455015981782fb336330a0490cd
| 26,905
|
py
|
Python
|
src/sage/rings/polynomial/flatten.py
|
nikmihale/sage
|
e2dcdeeabb578c37bcf0361c0be3079315e9252c
|
[
"BSL-1.0"
] | null | null | null |
src/sage/rings/polynomial/flatten.py
|
nikmihale/sage
|
e2dcdeeabb578c37bcf0361c0be3079315e9252c
|
[
"BSL-1.0"
] | null | null | null |
src/sage/rings/polynomial/flatten.py
|
nikmihale/sage
|
e2dcdeeabb578c37bcf0361c0be3079315e9252c
|
[
"BSL-1.0"
] | null | null | null |
# -*- coding: utf-8 -*-
r"""
Class to flatten polynomial rings over polynomial ring
For example ``QQ['a','b'],['x','y']`` flattens to ``QQ['a','b','x','y']``.
EXAMPLES::
sage: R = QQ['x']['y']['s','t']['X']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: phi = FlatteningMorphism(R); phi
Flattening morphism:
From: Univariate Polynomial Ring in X over Multivariate Polynomial Ring in s, t over Univariate Polynomial Ring in y over Univariate Polynomial Ring in x over Rational Field
To: Multivariate Polynomial Ring in x, y, s, t, X over Rational Field
sage: phi('x*y*s + t*X').parent()
Multivariate Polynomial Ring in x, y, s, t, X over Rational Field
Authors:
Vincent Delecroix, Ben Hutz (July 2016): initial implementation
"""
# ****************************************************************************
# Copyright (C) 2016
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
# https://www.gnu.org/licenses/
# ****************************************************************************
from __future__ import absolute_import, print_function
import itertools
from sage.categories.homset import Homset
from sage.categories.morphism import Morphism
from sage.misc.cachefunc import cached_method
from .polynomial_ring_constructor import PolynomialRing
from .polynomial_ring import is_PolynomialRing
from .multi_polynomial_ring_base import is_MPolynomialRing
from sage.rings.fraction_field import is_FractionField
from sage.rings.fraction_field_element import FractionFieldElement
from sage.rings.polynomial.polydict import ETuple
class FlatteningMorphism(Morphism):
r"""
EXAMPLES::
sage: R = QQ['a','b']['x','y','z']['t1','t2']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: f = FlatteningMorphism(R)
sage: f.codomain()
Multivariate Polynomial Ring in a, b, x, y, z, t1, t2 over Rational Field
sage: p = R('(a+b)*x + (a^2-b)*t2*(z+y)')
sage: p
((a^2 - b)*y + (a^2 - b)*z)*t2 + (a + b)*x
sage: f(p)
a^2*y*t2 + a^2*z*t2 - b*y*t2 - b*z*t2 + a*x + b*x
sage: f(p).parent()
Multivariate Polynomial Ring in a, b, x, y, z, t1, t2 over Rational Field
Also works when univariate polynomial ring are involved::
sage: R = QQ['x']['y']['s','t']['X']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: f = FlatteningMorphism(R)
sage: f.codomain()
Multivariate Polynomial Ring in x, y, s, t, X over Rational Field
sage: p = R('((x^2 + 1) + (x+2)*y + x*y^3)*(s+t) + x*y*X')
sage: p
x*y*X + (x*y^3 + (x + 2)*y + x^2 + 1)*s + (x*y^3 + (x + 2)*y + x^2 + 1)*t
sage: f(p)
x*y^3*s + x*y^3*t + x^2*s + x*y*s + x^2*t + x*y*t + x*y*X + 2*y*s + 2*y*t + s + t
sage: f(p).parent()
Multivariate Polynomial Ring in x, y, s, t, X over Rational Field
"""
def __init__(self, domain):
"""
The Python constructor
EXAMPLES::
sage: R = ZZ['a', 'b', 'c']['x', 'y', 'z']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: FlatteningMorphism(R)
Flattening morphism:
From: Multivariate Polynomial Ring in x, y, z over Multivariate Polynomial Ring in a, b, c over Integer Ring
To: Multivariate Polynomial Ring in a, b, c, x, y, z over Integer Ring
::
sage: R = ZZ['a']['b']['c']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: FlatteningMorphism(R)
Flattening morphism:
From: Univariate Polynomial Ring in c over Univariate Polynomial Ring in b over Univariate Polynomial Ring in a over Integer Ring
To: Multivariate Polynomial Ring in a, b, c over Integer Ring
::
sage: R = ZZ['a']['a','b']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: FlatteningMorphism(R)
Flattening morphism:
From: Multivariate Polynomial Ring in a, b over Univariate Polynomial Ring in a over Integer Ring
To: Multivariate Polynomial Ring in a, a0, b over Integer Ring
::
sage: K.<v> = NumberField(x^3 - 2)
sage: R = K['x','y']['a','b']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: f = FlatteningMorphism(R)
sage: f(R('v*a*x^2 + b^2 + 1/v*y'))
(v)*x^2*a + b^2 + (1/2*v^2)*y
::
sage: R = QQbar['x','y']['a','b']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: f = FlatteningMorphism(R)
sage: f(R('QQbar(sqrt(2))*a*x^2 + b^2 + QQbar(I)*y'))
1.414213562373095?*x^2*a + b^2 + I*y
::
sage: R.<z> = PolynomialRing(QQbar,1)
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: f = FlatteningMorphism(R)
sage: f.domain(), f.codomain()
(Multivariate Polynomial Ring in z over Algebraic Field,
Multivariate Polynomial Ring in z over Algebraic Field)
::
sage: R.<z> = PolynomialRing(QQbar)
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: f = FlatteningMorphism(R)
sage: f.domain(), f.codomain()
(Univariate Polynomial Ring in z over Algebraic Field,
Univariate Polynomial Ring in z over Algebraic Field)
TESTS::
sage: Pol = QQ['x']['x0']['x']
sage: fl = FlatteningMorphism(Pol)
sage: fl
Flattening morphism:
From: Univariate Polynomial Ring in x over Univariate Polynomial Ring in x0 over Univariate Polynomial Ring in x over Rational Field
To: Multivariate Polynomial Ring in x, x0, x1 over Rational Field
sage: p = Pol([[[1,2],[3,4]],[[5,6],[7,8]]])
sage: fl.section()(fl(p)) == p
True
"""
if not is_PolynomialRing(domain) and not is_MPolynomialRing(domain):
raise ValueError("domain should be a polynomial ring")
ring = domain
variables = []
intermediate_rings = []
while is_PolynomialRing(ring) or is_MPolynomialRing(ring):
intermediate_rings.append(ring)
v = ring.variable_names()
variables.extend(reversed(v))
ring = ring.base_ring()
self._intermediate_rings = intermediate_rings
variables.reverse()
for i, a in enumerate(variables):
if a in variables[:i]:
for index in itertools.count():
b = a + str(index)
if b not in variables: # not just variables[:i]!
break
variables[i] = b
if is_MPolynomialRing(domain):
codomain = PolynomialRing(ring, variables, len(variables))
else:
codomain = PolynomialRing(ring, variables)
hom = Homset(domain, codomain, base=ring, check=False)
Morphism.__init__(self, hom)
self._repr_type_str = 'Flattening'
def _call_(self, p):
r"""
Evaluate a flattening morphism.
EXAMPLES::
sage: R = QQ['a','b','c']['x','y','z']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: h = FlatteningMorphism(R)('2*a*x + b*z'); h
2*a*x + b*z
sage: h.parent()
Multivariate Polynomial Ring in a, b, c, x, y, z over Rational Field
TESTS::
sage: R = QQ['x']['y']['s','t']
sage: p = R('s*x + y*t + x^2*s + 1 + t')
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: f = FlatteningMorphism(R)
sage: f._call_(p)
x^2*s + x*s + y*t + t + 1
"""
# If we are just specializing a univariate polynomial, then
# the flattening morphism is the identity
if self.codomain().ngens() == 1:
return p
p = {(): p}
for ring in self._intermediate_rings:
new_p = {}
if is_PolynomialRing(ring):
for mon, pp in p.items():
assert pp.parent() is ring
for i, j in pp.dict().items():
new_p[(i,)+(mon)] = j
elif is_MPolynomialRing(ring):
for mon, pp in p.items():
assert pp.parent() is ring
for mmon, q in pp.dict().items():
new_p[tuple(mmon)+mon] = q
else:
raise RuntimeError
p = new_p
return self.codomain()(p, check=False)
@cached_method
def section(self):
"""
Inverse of this flattening morphism.
EXAMPLES::
sage: R = QQ['a','b','c']['x','y','z']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: h = FlatteningMorphism(R)
sage: h.section()
Unflattening morphism:
From: Multivariate Polynomial Ring in a, b, c, x, y, z over Rational Field
To: Multivariate Polynomial Ring in x, y, z over Multivariate Polynomial Ring in a, b, c over Rational Field
::
sage: R = ZZ['a']['b']['c']
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: FlatteningMorphism(R).section()
Unflattening morphism:
From: Multivariate Polynomial Ring in a, b, c over Integer Ring
To: Univariate Polynomial Ring in c over Univariate Polynomial Ring in b over Univariate Polynomial Ring in a over Integer Ring
"""
return UnflatteningMorphism(self.codomain(), self.domain())
class UnflatteningMorphism(Morphism):
r"""
Inverses for :class:`FlatteningMorphism`
EXAMPLES::
sage: R = QQ['c','x','y','z']
sage: S = QQ['c']['x','y','z']
sage: from sage.rings.polynomial.flatten import UnflatteningMorphism
sage: f = UnflatteningMorphism(R, S)
sage: g = f(R('x^2 + c*y^2 - z^2'));g
x^2 + c*y^2 - z^2
sage: g.parent()
Multivariate Polynomial Ring in x, y, z over Univariate Polynomial Ring in c over Rational Field
::
sage: R = QQ['a','b', 'x','y']
sage: S = QQ['a','b']['x','y']
sage: from sage.rings.polynomial.flatten import UnflatteningMorphism
sage: UnflatteningMorphism(R, S)
Unflattening morphism:
From: Multivariate Polynomial Ring in a, b, x, y over Rational Field
To: Multivariate Polynomial Ring in x, y over Multivariate Polynomial Ring in a, b over Rational Field
"""
def __init__(self, domain, codomain):
"""
The Python constructor
EXAMPLES::
sage: R = QQ['x']['y']['s','t']['X']
sage: p = R.random_element()
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: f = FlatteningMorphism(R)
sage: g = f.section()
sage: g(f(p)) == p
True
::
sage: R = QQ['a','b','x','y']
sage: S = ZZ['a','b']['x','z']
sage: from sage.rings.polynomial.flatten import UnflatteningMorphism
sage: UnflatteningMorphism(R, S)
Traceback (most recent call last):
...
ValueError: rings must have same base ring
::
sage: R = QQ['a','b','x','y']
sage: S = QQ['a','b']['x','z','w']
sage: from sage.rings.polynomial.flatten import UnflatteningMorphism
sage: UnflatteningMorphism(R, S)
Traceback (most recent call last):
...
ValueError: rings must have the same number of variables
"""
if not is_MPolynomialRing(domain):
raise ValueError("domain should be a multivariate polynomial ring")
if not is_PolynomialRing(codomain) and not is_MPolynomialRing(codomain):
raise ValueError("codomain should be a polynomial ring")
ring = codomain
intermediate_rings = []
while True:
is_polynomial_ring = is_PolynomialRing(ring)
if not (is_polynomial_ring or is_MPolynomialRing(ring)):
break
intermediate_rings.append((ring, is_polynomial_ring))
ring = ring.base_ring()
if domain.base_ring() != intermediate_rings[-1][0].base_ring():
raise ValueError("rings must have same base ring")
if domain.ngens() != sum([R.ngens() for R, _ in intermediate_rings]):
raise ValueError("rings must have the same number of variables")
self._intermediate_rings = intermediate_rings
hom = Homset(domain, codomain, base=ring, check=False)
Morphism.__init__(self, hom)
self._repr_type_str = 'Unflattening'
def _call_(self, p):
"""
Evaluate an unflattening morphism.
TESTS::
sage: from sage.rings.polynomial.flatten import FlatteningMorphism
sage: for R in [ZZ['x']['y']['a,b,c'], GF(4)['x','y']['a','b'],
....: AA['x']['a','b']['y'], QQbar['a1','a2']['t']['X','Y']]:
....: f = FlatteningMorphism(R)
....: g = f.section()
....: for _ in range(10):
....: p = R.random_element()
....: assert p == g(f(p))
....: z = R.zero()
....: assert z == g(f(z))
"""
index = [0]
for R, _ in reversed(self._intermediate_rings):
index.append(index[-1] + len(R.gens()))
newpol = [{} for _ in self._intermediate_rings]
expo = sorted(p.exponents(), key=lambda e: tuple(reversed(e)))
for i in range(len(expo)):
cur_exp = expo[i]
for l in range(len(self._intermediate_rings)):
R, univariate = self._intermediate_rings[-1 - l]
idx = index[l + 1]
sub_exp = (cur_exp[index[l]] if univariate
else cur_exp[index[l]:idx])
if l == 0:
newpol[l][sub_exp] = p[cur_exp]
else:
newpol[l][sub_exp] = newpol[l - 1]
newpol[l - 1] = {}
if (i == len(expo) - 1 or expo[i + 1][idx:] != cur_exp[idx:]):
newpol[l] = R(newpol[l], check=False)
else:
break
return R(newpol[-1], check=False)
class SpecializationMorphism(Morphism):
r"""
Morphisms to specialize parameters in (stacked) polynomial rings
EXAMPLES::
sage: R.<c> = PolynomialRing(QQ)
sage: S.<x,y,z> = PolynomialRing(R)
sage: D = dict({c:1})
sage: from sage.rings.polynomial.flatten import SpecializationMorphism
sage: f = SpecializationMorphism(S, D)
sage: g = f(x^2 + c*y^2 - z^2); g
x^2 + y^2 - z^2
sage: g.parent()
Multivariate Polynomial Ring in x, y, z over Rational Field
::
sage: R.<c> = PolynomialRing(QQ)
sage: S.<z> = PolynomialRing(R)
sage: from sage.rings.polynomial.flatten import SpecializationMorphism
sage: xi = SpecializationMorphism(S, {c:0}); xi
Specialization morphism:
From: Univariate Polynomial Ring in z over Univariate Polynomial Ring in c over Rational Field
To: Univariate Polynomial Ring in z over Rational Field
sage: xi(z^2+c)
z^2
::
sage: R1.<u,v> = PolynomialRing(QQ)
sage: R2.<a,b,c> = PolynomialRing(R1)
sage: S.<x,y,z> = PolynomialRing(R2)
sage: D = dict({a:1, b:2, x:0, u:1})
sage: from sage.rings.polynomial.flatten import SpecializationMorphism
sage: xi = SpecializationMorphism(S, D); xi
Specialization morphism:
From: Multivariate Polynomial Ring in x, y, z over Multivariate Polynomial Ring in a, b, c over Multivariate Polynomial Ring in u, v over Rational Field
To: Multivariate Polynomial Ring in y, z over Univariate Polynomial Ring in c over Univariate Polynomial Ring in v over Rational Field
sage: xi(a*(x*z+y^2)*u+b*v*u*(x*z+y^2)*y^2*c+c*y^2*z^2)
2*v*c*y^4 + c*y^2*z^2 + y^2
"""
def __init__(self, domain, D):
"""
The Python constructor
EXAMPLES::
sage: S.<x,y> = PolynomialRing(QQ)
sage: D = dict({x:1})
sage: from sage.rings.polynomial.flatten import SpecializationMorphism
sage: phi = SpecializationMorphism(S, D); phi
Specialization morphism:
From: Multivariate Polynomial Ring in x, y over Rational Field
To: Univariate Polynomial Ring in y over Rational Field
sage: phi(x^2 + y^2)
y^2 + 1
::
sage: R.<a,b,c> = PolynomialRing(ZZ)
sage: S.<x,y,z> = PolynomialRing(R)
sage: from sage.rings.polynomial.flatten import SpecializationMorphism
sage: xi = SpecializationMorphism(S, {a:1/2})
Traceback (most recent call last):
...
TypeError: no conversion of this rational to integer
The following was fixed in :trac:`23811`::
sage: R.<c> = RR[]
sage: P.<z> = AffineSpace(R, 1)
sage: H = End(P)
sage: f = H([z^2 + c])
sage: f.specialization({c:1})
Scheme endomorphism of Affine Space of dimension 1 over Real Field with 53 bits of precision
Defn: Defined on coordinates by sending (z) to
(z^2 + 1.00000000000000)
"""
if not is_PolynomialRing(domain) and not is_MPolynomialRing(domain):
raise TypeError("domain should be a polynomial ring")
# use only the generators that are in the stack somewhere,
# and ignore the rest
all_gens = domain.gens_dict_recursive()
new_D = {}
for gen in D:
if str(gen) in all_gens:
new_D[gen] = D[gen]
D = new_D
# _sub_specialization is a specialization morphism (recursive)
# which is applied to the base Fraction field, or None if it's
# any other base ring
self._sub_specialization = None
# We use this composition where "flat" is a flattened
# polynomial ring.
#
# phi D psi
# domain → flat → flat → R
# │ │ │
# └─────────┴───────────────┘
# _flattening_morph _eval_morph
# = phi = psi ∘ D
phi = FlatteningMorphism(domain)
flat = phi.codomain()
base = flat.base_ring()
# Change domain of D to "flat" and ensure that the values lie
# in the base ring.
D = {phi(k): base(D[k]) for k in D}
# Construct unflattened codomain R
new_vars = []
R = domain
while is_PolynomialRing(R) or is_MPolynomialRing(R) or is_FractionField(R):
if is_FractionField(R):
# We've hit base_ring, so set _sub_specialization and exit the loop
field_over = R.base()
applicable_vars = {key: val for key, val in D.items()
if key not in flat.gens()}
# If there are any variables in D to set in _sub_specialization
if applicable_vars:
# Coerce the generators to be in the right ring
# This un-does changing the domain of D to be in the flat base ring
tmp = {}
for var, val in applicable_vars.items():
for gstr, gen in field_over.gens_dict_recursive().items():
if str(var) == gstr:
tmp[gen] = val
break
else:
# Should have been caught earlier
raise NameError("argument " + str(var) + " is not a generator anywhere in the polynomial tower")
applicable_vars = tmp
self._sub_specialization = FractionSpecializationMorphism(R, applicable_vars)
break
# We're still in the polynomials, so keep track of the tower
old = R.gens()
new = [t for t in old if t not in D]
force_multivariate = ((len(old) == 1) and is_MPolynomialRing(R))
new_vars.append((new, force_multivariate, old))
R = R.base_ring()
if self._sub_specialization:
# The sub_specialization range will be different
# if it applied some variables from D
R = self._sub_specialization.codomain().fraction_field()
# Construct unflattening map psi (only defined on the variables
# of "flat" which are not involved in D)
psi = dict()
# Reconstruct the proper domain of this morphism
# based on the sub_specialization domains
new_domain = R
for new, force_multivariate, old in reversed(new_vars):
if self._sub_specialization:
if force_multivariate:
new_domain = PolynomialRing(new_domain, old, len(old))
else:
new_domain = PolynomialRing(new_domain, old)
if not new:
continue
var_names = [str(var) for var in new]
if force_multivariate:
R = PolynomialRing(R, var_names, len(var_names))
else:
R = PolynomialRing(R, var_names)
# Map variables in "new" to R
psi.update(zip([phi(w) for w in new], R.gens()))
# Fix domain of eval_morph
# (note: phi's domain is correct)
if self._sub_specialization:
phi_prime = FlatteningMorphism(new_domain)
flat_old = flat
flat = phi_prime.codomain()
base_prime = flat.base_ring()
D = {phi(k): base_prime(D[k]) for k in D}
else:
# The bottom of our tower has not changed
def flat_old(x):
return x
# Compose D with psi
vals = []
for t in flat.gens():
if t in D:
vals.append(R.coerce(D[t]))
else:
# Make sure keys are in the old domain
# or else they won't match exactly
vals.append(psi[flat_old(t)])
self._flattening_morph = phi
self._eval_morph = flat.hom(vals, R)
self._repr_type_str = 'Specialization'
Morphism.__init__(self, domain, R)
def _call_(self, p):
"""
Evaluate a specialization morphism.
EXAMPLES::
sage: R.<a,b,c> = PolynomialRing(ZZ)
sage: S.<x,y,z> = PolynomialRing(R)
sage: D = dict({a:1, b:2, c:3})
sage: from sage.rings.polynomial.flatten import SpecializationMorphism
sage: xi = SpecializationMorphism(S, D)
sage: xi(a*x + b*y + c*z)
x + 2*y + 3*z
"""
flat = self._flattening_morph(p)
if self._sub_specialization is not None:
# The base_ring should be a fraction field, so
# apply _sub_specialization to each coefficient
# in the flattened polynomial
tmp = {}
for exponent, coefficient in flat.dict().items():
# Fix the type of exponent from (a,) to a
# (necessary for R(tmp) later)
if isinstance(exponent, ETuple) and len(exponent) == 1:
exponent = exponent[0]
# Coefficient should be a fraction
tmp[exponent] = self._sub_specialization._call_(coefficient)
# tmp's parent should be the same construction as flat
# but over _sub_specialization's codomain
ring_constructor = flat.parent().construction()[0]
fraction_type = self._sub_specialization.codomain()
R = ring_constructor(fraction_type)
flat = R(tmp)
return self._eval_morph(flat)
class FractionSpecializationMorphism(Morphism):
"""
A specialization morphism for fraction fields over (stacked) polynomial rings
"""
def __init__(self, domain, D):
"""
Initialize the morphism with a domain and dictionary of specializations
EXAMPLES::
sage: R.<a,c> = QQ[]
sage: S.<x,y> = R[]
sage: from sage.rings.polynomial.flatten import FractionSpecializationMorphism
sage: phi = FractionSpecializationMorphism(Frac(S), {c:3})
sage: phi
Fraction Specialization morphism:
From: Fraction Field of Multivariate Polynomial Ring in x, y over Multivariate Polynomial Ring in a, c over Rational Field
To: Fraction Field of Multivariate Polynomial Ring in x, y over Univariate Polynomial Ring in a over Rational Field
"""
if not is_FractionField(domain):
raise TypeError("domain must be a fraction field")
self._specialization = SpecializationMorphism(domain.base(), D)
self._repr_type_str = 'Fraction Specialization'
Morphism.__init__(self, domain, self._specialization.codomain().fraction_field())
def _call_(self, p):
"""
Evaluate a fraction specialization morphism
EXAMPLES::
sage: R.<a,b,c> = QQ[]
sage: S.<x,y,z> = R[]
sage: from sage.rings.polynomial.flatten import FractionSpecializationMorphism
sage: phi = FractionSpecializationMorphism(Frac(S), {a:3, b:2, c:-2})
sage: spec = phi((a*x + b*y) / (c*z))
sage: spec
(3*x + 2*y)/(-2*z)
sage: spec.parent()
Fraction Field of Multivariate Polynomial Ring in x, y, z over Rational Field
"""
if not isinstance(p, FractionFieldElement):
raise TypeError("p must be a fraction field element")
numerator = self._specialization._call_(p.numerator())
denominator = self._specialization._call_(p.denominator())
return numerator / denominator
| 39.918398
| 179
| 0.55551
|
from __future__ import absolute_import, print_function
import itertools
from sage.categories.homset import Homset
from sage.categories.morphism import Morphism
from sage.misc.cachefunc import cached_method
from .polynomial_ring_constructor import PolynomialRing
from .polynomial_ring import is_PolynomialRing
from .multi_polynomial_ring_base import is_MPolynomialRing
from sage.rings.fraction_field import is_FractionField
from sage.rings.fraction_field_element import FractionFieldElement
from sage.rings.polynomial.polydict import ETuple
class FlatteningMorphism(Morphism):
def __init__(self, domain):
if not is_PolynomialRing(domain) and not is_MPolynomialRing(domain):
raise ValueError("domain should be a polynomial ring")
ring = domain
variables = []
intermediate_rings = []
while is_PolynomialRing(ring) or is_MPolynomialRing(ring):
intermediate_rings.append(ring)
v = ring.variable_names()
variables.extend(reversed(v))
ring = ring.base_ring()
self._intermediate_rings = intermediate_rings
variables.reverse()
for i, a in enumerate(variables):
if a in variables[:i]:
for index in itertools.count():
b = a + str(index)
if b not in variables:
break
variables[i] = b
if is_MPolynomialRing(domain):
codomain = PolynomialRing(ring, variables, len(variables))
else:
codomain = PolynomialRing(ring, variables)
hom = Homset(domain, codomain, base=ring, check=False)
Morphism.__init__(self, hom)
self._repr_type_str = 'Flattening'
def _call_(self, p):
if self.codomain().ngens() == 1:
return p
p = {(): p}
for ring in self._intermediate_rings:
new_p = {}
if is_PolynomialRing(ring):
for mon, pp in p.items():
assert pp.parent() is ring
for i, j in pp.dict().items():
new_p[(i,)+(mon)] = j
elif is_MPolynomialRing(ring):
for mon, pp in p.items():
assert pp.parent() is ring
for mmon, q in pp.dict().items():
new_p[tuple(mmon)+mon] = q
else:
raise RuntimeError
p = new_p
return self.codomain()(p, check=False)
@cached_method
def section(self):
return UnflatteningMorphism(self.codomain(), self.domain())
class UnflatteningMorphism(Morphism):
def __init__(self, domain, codomain):
if not is_MPolynomialRing(domain):
raise ValueError("domain should be a multivariate polynomial ring")
if not is_PolynomialRing(codomain) and not is_MPolynomialRing(codomain):
raise ValueError("codomain should be a polynomial ring")
ring = codomain
intermediate_rings = []
while True:
is_polynomial_ring = is_PolynomialRing(ring)
if not (is_polynomial_ring or is_MPolynomialRing(ring)):
break
intermediate_rings.append((ring, is_polynomial_ring))
ring = ring.base_ring()
if domain.base_ring() != intermediate_rings[-1][0].base_ring():
raise ValueError("rings must have same base ring")
if domain.ngens() != sum([R.ngens() for R, _ in intermediate_rings]):
raise ValueError("rings must have the same number of variables")
self._intermediate_rings = intermediate_rings
hom = Homset(domain, codomain, base=ring, check=False)
Morphism.__init__(self, hom)
self._repr_type_str = 'Unflattening'
def _call_(self, p):
index = [0]
for R, _ in reversed(self._intermediate_rings):
index.append(index[-1] + len(R.gens()))
newpol = [{} for _ in self._intermediate_rings]
expo = sorted(p.exponents(), key=lambda e: tuple(reversed(e)))
for i in range(len(expo)):
cur_exp = expo[i]
for l in range(len(self._intermediate_rings)):
R, univariate = self._intermediate_rings[-1 - l]
idx = index[l + 1]
sub_exp = (cur_exp[index[l]] if univariate
else cur_exp[index[l]:idx])
if l == 0:
newpol[l][sub_exp] = p[cur_exp]
else:
newpol[l][sub_exp] = newpol[l - 1]
newpol[l - 1] = {}
if (i == len(expo) - 1 or expo[i + 1][idx:] != cur_exp[idx:]):
newpol[l] = R(newpol[l], check=False)
else:
break
return R(newpol[-1], check=False)
class SpecializationMorphism(Morphism):
def __init__(self, domain, D):
if not is_PolynomialRing(domain) and not is_MPolynomialRing(domain):
raise TypeError("domain should be a polynomial ring")
all_gens = domain.gens_dict_recursive()
new_D = {}
for gen in D:
if str(gen) in all_gens:
new_D[gen] = D[gen]
D = new_D
# any other base ring
self._sub_specialization = None
# We use this composition where "flat" is a flattened
# polynomial ring.
#
# phi D psi
# domain → flat → flat → R
# │ │ │
# └─────────┴───────────────┘
# _flattening_morph _eval_morph
# = phi = psi ∘ D
phi = FlatteningMorphism(domain)
flat = phi.codomain()
base = flat.base_ring()
# Change domain of D to "flat" and ensure that the values lie
# in the base ring.
D = {phi(k): base(D[k]) for k in D}
# Construct unflattened codomain R
new_vars = []
R = domain
while is_PolynomialRing(R) or is_MPolynomialRing(R) or is_FractionField(R):
if is_FractionField(R):
# We've hit base_ring, so set _sub_specialization and exit the loop
field_over = R.base()
applicable_vars = {key: val for key, val in D.items()
if key not in flat.gens()}
if applicable_vars:
tmp = {}
for var, val in applicable_vars.items():
for gstr, gen in field_over.gens_dict_recursive().items():
if str(var) == gstr:
tmp[gen] = val
break
else:
raise NameError("argument " + str(var) + " is not a generator anywhere in the polynomial tower")
applicable_vars = tmp
self._sub_specialization = FractionSpecializationMorphism(R, applicable_vars)
break
old = R.gens()
new = [t for t in old if t not in D]
force_multivariate = ((len(old) == 1) and is_MPolynomialRing(R))
new_vars.append((new, force_multivariate, old))
R = R.base_ring()
if self._sub_specialization:
# The sub_specialization range will be different
# if it applied some variables from D
R = self._sub_specialization.codomain().fraction_field()
# Construct unflattening map psi (only defined on the variables
# of "flat" which are not involved in D)
psi = dict()
# Reconstruct the proper domain of this morphism
# based on the sub_specialization domains
new_domain = R
for new, force_multivariate, old in reversed(new_vars):
if self._sub_specialization:
if force_multivariate:
new_domain = PolynomialRing(new_domain, old, len(old))
else:
new_domain = PolynomialRing(new_domain, old)
if not new:
continue
var_names = [str(var) for var in new]
if force_multivariate:
R = PolynomialRing(R, var_names, len(var_names))
else:
R = PolynomialRing(R, var_names)
# Map variables in "new" to R
psi.update(zip([phi(w) for w in new], R.gens()))
# Fix domain of eval_morph
# (note: phi's domain is correct)
if self._sub_specialization:
phi_prime = FlatteningMorphism(new_domain)
flat_old = flat
flat = phi_prime.codomain()
base_prime = flat.base_ring()
D = {phi(k): base_prime(D[k]) for k in D}
else:
def flat_old(x):
return x
vals = []
for t in flat.gens():
if t in D:
vals.append(R.coerce(D[t]))
else:
vals.append(psi[flat_old(t)])
self._flattening_morph = phi
self._eval_morph = flat.hom(vals, R)
self._repr_type_str = 'Specialization'
Morphism.__init__(self, domain, R)
def _call_(self, p):
flat = self._flattening_morph(p)
if self._sub_specialization is not None:
# The base_ring should be a fraction field, so
# apply _sub_specialization to each coefficient
# in the flattened polynomial
tmp = {}
for exponent, coefficient in flat.dict().items():
# Fix the type of exponent from (a,) to a
# (necessary for R(tmp) later)
if isinstance(exponent, ETuple) and len(exponent) == 1:
exponent = exponent[0]
# Coefficient should be a fraction
tmp[exponent] = self._sub_specialization._call_(coefficient)
# tmp's parent should be the same construction as flat
ring_constructor = flat.parent().construction()[0]
fraction_type = self._sub_specialization.codomain()
R = ring_constructor(fraction_type)
flat = R(tmp)
return self._eval_morph(flat)
class FractionSpecializationMorphism(Morphism):
def __init__(self, domain, D):
if not is_FractionField(domain):
raise TypeError("domain must be a fraction field")
self._specialization = SpecializationMorphism(domain.base(), D)
self._repr_type_str = 'Fraction Specialization'
Morphism.__init__(self, domain, self._specialization.codomain().fraction_field())
def _call_(self, p):
if not isinstance(p, FractionFieldElement):
raise TypeError("p must be a fraction field element")
numerator = self._specialization._call_(p.numerator())
denominator = self._specialization._call_(p.denominator())
return numerator / denominator
| true
| true
|
f7168ba8849334a57d1b394944e515d207126631
| 19,617
|
py
|
Python
|
main.py
|
gmathez/Project_ADA_2018_Bruttin_Mathez_Petitpierre
|
e237300b3d9fb966b0eb747dd66816cc6cfc11b3
|
[
"Apache-2.0"
] | 1
|
2018-12-01T12:17:58.000Z
|
2018-12-01T12:17:58.000Z
|
main.py
|
gmathez/Project_ADA_2018_Bruttin_Mathez_Petitpierre
|
e237300b3d9fb966b0eb747dd66816cc6cfc11b3
|
[
"Apache-2.0"
] | null | null | null |
main.py
|
gmathez/Project_ADA_2018_Bruttin_Mathez_Petitpierre
|
e237300b3d9fb966b0eb747dd66816cc6cfc11b3
|
[
"Apache-2.0"
] | null | null | null |
# Import kivy tools
from kivy.app import App
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.gridlayout import GridLayout
from kivy.uix.recycleboxlayout import RecycleBoxLayout
from kivy.uix.label import Label
from kivy.uix.button import Button
from kivy.uix.checkbox import CheckBox
from kivy.uix.spinner import Spinner
from kivy.uix.recycleview import RecycleView
from kivy.uix.recycleview.views import RecycleDataViewBehavior
from kivy.uix.behaviors import FocusBehavior
from kivy.uix.recycleview.layout import LayoutSelectionBehavior
from kivy.properties import BooleanProperty, ObjectProperty
from kivy.uix.screenmanager import ScreenManager, Screen
from kivy.lang import Builder
# Import the kv files
Builder.load_file('./src/rv.kv')
Builder.load_file('./src/screenhome.kv')
Builder.load_file('./src/screenprofile.kv')
Builder.load_file('./src/screensettings.kv')
Builder.load_file('./src/screenproduct.kv')
Builder.load_file('./src/screenquantities.kv')
Builder.load_file('./src/screenfinal.kv')
Builder.load_file('./src/manager.kv')
# Other imports
import pandas as pd
import re
from Algo_main import algo # Import the algorithm for NutriScore computation
class SelectableRecycleBoxLayout(FocusBehavior, LayoutSelectionBehavior,
RecycleBoxLayout):
''' Add selection and focus behaviour to the view '''
pass
class SelectableGrid(RecycleDataViewBehavior, GridLayout):
''' Add selection support to the Label '''
index = None
selected = BooleanProperty(False)
selectable = BooleanProperty(True)
def refresh_view_attrs(self, rv, index, data):
''' Catch and handle the view changes '''
self.index = index
self.ids['id_label1'].text = data['label1']['text']
self.ids['id_label2'].text = data['label2']['text']
self.ids['id_label3'].text = data['label3']['text']
return super(SelectableGrid, self).refresh_view_attrs(
rv, index, data)
def on_touch_down(self, touch):
''' Add selection on touch down '''
if super(SelectableGrid, self).on_touch_down(touch):
return True
if self.collide_point(*touch.pos) and self.selectable:
return self.parent.select_with_touch(self.index, touch)
def apply_selection(self, rv, index, is_selected):
''' Respond to the selection of items '''
self.selected = is_selected
class SelectableQuantity(RecycleDataViewBehavior, GridLayout):
''' Add selection support to the Label '''
index = None
selected = BooleanProperty(False)
selectable = BooleanProperty(True)
def refresh_view_attrs(self, rv, index, data):
''' Catch and handle the view changes '''
self.index = index
self.ids['id_label1'].text = data['label1']['text']
self.ids['id_label2'].text = data['label2']['text']
self.ids['id_label3'].text = data['label3']['text']
return super(SelectableQuantity, self).refresh_view_attrs(
rv, index, data)
class RV(RecycleView):
''' Class for the RecycleView Controller '''
def __init__(self, **kwargs):
super(RV, self).__init__(**kwargs)
def upload(self, query, active):
''' Search data according to the user input '''
# Reset data
self.data = []
# Check if the Raw Food CheckBox is active or not
if active:
self.parent.parent.getSelection('API', query, True)
self.data = [{'label1': {'text': 'API'}, 'label2': {'text': query}, 'label3': {'text': 'Add/Remove'}}]
else:
isinside = allTrue
for item in query.split(): # Split the query in keywords
isinside = isinside & \
(DF['product_name'].str.contains(item, case=False) | \
DF['Brands'].str.contains(item, case=False))
if any(isinside):
selection = DF[isinside] # Select products to display
for row in selection.itertuples(): # Iterate through the columns of DF
d = {'label1': {'text': str(row[0])}, \
'label2': {'text': str(row[1])},
'label3': {'text': str(row[-1])}} # barcode, product_name, brand
self.data.append(d)
else:
isinside = DF.index.str.contains(query, case=False) # Search for Barcode
if any(isinside):
selection = DF[isinside]
for row in selection.itertuples():
d = {'label1': {'text': str(row[0])}, \
'label2': {'text': str(row[1])},
'label3': {'text': str(row[-1])}} # barcode, product_name, brand
self.data.append(d)
else:
# In case no product is found
self.data = [{'label1': {'text': ''}, \
'label2': {'text': 'No product found'}, 'label3': {'text': ''}}]
def getQuantities(self, dict):
''' Gather data for display on Quantities Screen '''
self.data = []
code = dict['code']
product_name = dict['product_name']
quantity = dict['quantity']
for index in range(len(code)):
d = {'label1': {'text': code[index]}, 'label2': {'text': product_name[index]}, \
'label3': {'text': quantity[index]}}
self.data.append(d)
class ScreenHome(Screen):
''' Class for the Home Screen. No variables or functions needed for this screen '''
pass
class ScreenProfile(Screen):
''' Class for the Profile Screen '''
def updateDF(self):
global DF
DF = pd.read_csv('https://drive.google.com/uc?export=download&id=1aLUh1UoQcS9lBa6oVRln-DuskxK5uK3y', \
index_col=[0], low_memory = False)
DF.to_csv('./data/OpenFoodFacts_final.csv.gz', compression='gzip')
self.ids['update'].text = 'Updated'
self.ids['update'].background_color = (0,1,0,1)
def update(self):
self.ids['update'].text = 'Updating'
self.ids['update'].background_color = (50/255,164/255,206/255,1)
class ScreenSettings(Screen):
''' Class for the Settings Screen '''
settings = {'rec': True,'name': '', 'surname': '', 'age': 0, 'sex': True, 'weight': 0, \
'email': '', 'activity': 0, 'days': 0}
id_profile = -999
def resetForm(self):
''' Reset the indicators of invalid input '''
self.ids.sex.color = (1,1,1,1)
self.ids.activity.color = (1,1,1,1)
self.ids.age.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.weight.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.days.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.email.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.name.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.surname.hint_text_color = (0.5, 0.5, 0.5, 1.0)
def setForm(self, id_profile):
self.id_profile = id_profile
self.settings = {'rec': True,'name': '', 'surname': '', 'age': 0, 'sex': True, 'weight': 0, \
'email': '', 'activity': 0, 'days': 0}
if int(self.id_profile) >= 0:
self.ids.name.text = str(profile_list.iloc[self.id_profile]['name'])
self.ids.surname.text= str(profile_list.iloc[self.id_profile]['surname'])
self.ids.age.text = str(profile_list.iloc[self.id_profile]['age'])
if bool(profile_list.iloc[self.id_profile]['sex']):
self.ids.male.active = True
self.ids.female.active = False
else:
self.ids.male.active = False
self.ids.female.active = True
self.ids.weight.text = str(profile_list.iloc[self.id_profile]['weight'])
self.ids.email.text = str(profile_list.iloc[self.id_profile]['email'])
self.ids.days.text = str(profile_list.iloc[self.id_profile]['days'])
if int(profile_list.iloc[self.id_profile]['activity']) == 1.8:
self.ids.seated.active = False
self.ids.both.active = False
self.ids.standing.active = True
elif int(profile_list.iloc[self.id_profile]['activity']) == 1.6:
self.ids.seated.active = False
self.ids.both.active = True
self.ids.standing.active = False
else:
self.ids.seated.active = True
self.ids.both.active = False
self.ids.standing.active = False
elif int(self.id_profile) == -999:
self.ids.name.text = ''
self.ids.surname.text = ''
self.ids.age.text = ''
self.ids.male.active = False
self.ids.female.active = False
self.ids.email.text = ''
self.ids.weight.text = ''
self.ids.seated.active = False
self.ids.both.active = False
self.ids.standing.active = False
self.ids.days.text = ''
else:
self.changeScreen(False)
def changeScreen(self, valid):
''' Handle the validity of the inputs and the change of current screen '''
if valid:
self.resetForm()
# Check name validity
if self.ids.name.text.strip() == '':
self.ids.name.hint_text_color = (1,0,0,1)
return False
# Check surname validity
elif self.ids.surname.text.strip() == '':
self.ids.surname.hint_text_color = (1,0,0,1)
return False
# Check age validity
elif self.ids.age.text.strip() == '' or int(self.ids.age.text) <= 0 or \
int(self.ids.age.text) >= 120:
self.ids.age.text = ''
self.ids.age.hint_text_color = (1,0,0,1)
return False
# Check sex validity
elif not(self.ids.male.active or self.ids.female.active):
self.ids.sex.color = (1,0,0,1)
return False
# Check email validity
elif not re.match(r"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)", self.ids.email.text):
self.ids.email.text = ''
self.ids.email.hint_text_color = (1,0,0,1)
return False
# Check weight validity
elif self.ids.weight.text.strip() == '' or int(self.ids.weight.text) <= 0:
self.ids.weight.text = ''
self.ids.weight.hint_text_color = (1,0,0,1)
return False
# Check activity validity
elif not(self.ids.seated.active or self.ids.both.active or self.ids.standing.active):
self.ids.activity.color = (1,0,0,1)
return False
# Check days validity
elif self.ids.days.text.strip() == '' or int(self.ids.days.text) <= 0:
self.ids.days.text = ''
self.ids.days.hint_text_color = (1,0,0,1)
return False
else: # Validation of the form and reset
self.settings['rec'] = True
self.settings['name'] = self.ids.name.text
self.settings['surname'] = self.ids.surname.text
self.settings['age'] = int(self.ids.age.text)
self.settings['weight'] = int(self.ids.weight.text)
self.settings['email'] = self.ids.email.text
self.settings['days'] = int(self.ids.days.text)
self.settings['sex'] = self.ids.male.active
if self.ids.seated.active:
self.settings['activity'] = 1.4
if self.ids.both.active:
self.settings['activity'] = 1.6
if self.ids.standing.active:
self.settings['activity'] = 1.8
self.resetForm()
else: # If the user pass the settings screen
self.settings['rec'] = False
self.manager.setSettings(self.settings, self.id_profile)
# Change the current screen
self.manager.current = 'Product Screen'
class ScreenProduct(Screen):
''' Class for the Product Screen '''
temp_dict = {'code':'', 'product_name': ''}
def getSelection(self, text1, text2, state):
# Select or deselect temporarly a product
if state:
self.temp_dict['code'] = text1
self.temp_dict['product_name'] = text2
else:
self.temp_dict['code'] = ''
self.temp_dict['product_name'] = ''
class ScreenQuantities(Screen):
''' Class for the Quantities Screen '''
temp_dict = {'code': [], 'product_name': [], 'quantity': [], 'color': []}
def initQuantity(self, data):
''' Initialize the dictionary of the products '''
if self.temp_dict['quantity'] == []:
self.temp_dict = data
self.ids.rv.getQuantities(data)
def updateQuantity(self, index, text1, text2, text3):
''' Store the quantities input by the user '''
l = len(self.temp_dict['quantity'])
if text3 == '' or text3 == '-' or int(text3) < 0:
text3 = '0'
if index < l:
self.temp_dict['code'][index] = text1
self.temp_dict['product_name'][index] = text2
self.temp_dict['quantity'][index] = text3
# Append the list of quantities if needed
else:
temp = ['0' for i in range(index-l)]
self.temp_dict['code'] = self.temp_dict['code'] + temp + [text1]
self.temp_dict['product_name'] = self.temp_dict['product_name'] + temp + [text2]
self.temp_dict['quantity'] = self.temp_dict['quantity'] + temp + [text3]
# Update the data displayed
self.initQuantity(self.temp_dict)
class ScreenFinal(Screen):
''' Class for the Final Screen. No variables or functions needed for this screen '''
pass
class Manager(ScreenManager):
''' Class for the Manager Controller. Store main data '''
selected_products = {'code': [], 'product_name': [], 'quantity': []}
settings = {'Rec': True, 'Name': '', 'Surname': '', 'Email': '', 'Age': 0, 'Sex': True, 'Pal': 0, \
'Weight': 0, 'Day': 0}
def getProfiles(self):
self.ids.screen_profile.ids.profile_spinner.values = \
[str(index + 1) + ' : ' + str(profile_list['name'][index]) + ' ' + str(profile_list['surname'][index]) \
for index in profile_list.index]
def toSettings(self, text):
if text == 'new':
id_profile = -999
elif text == 'pass':
id_profile = -1000
else:
items = text.split()
id_profile = items[0].strip()
id_profile = int(id_profile) - 1
self.ids.screen_settings.setForm(id_profile)
if id_profile != -1000:
self.current = 'Settings Screen'
def addProduct(self):
''' Add product to main storage '''
item1 = self.ids.screen_product.temp_dict['code']
item2 = self.ids.screen_product.temp_dict['product_name']
if item1 != '' and item2 != '':
self.selected_products['code'].append(item1)
self.selected_products['product_name'].append(item2)
self.selected_products['quantity'].append('0')
def deleteProduct(self):
''' Remove product of main storage '''
item1 = self.ids.screen_product.temp_dict['code']
item2 = self.ids.screen_product.temp_dict['product_name']
if item1 in self.selected_products['code'] and item2 in self.selected_products['product_name']:
self.selected_products['code'].remove(item1)
self.selected_products['product_name'].remove(item2)
self.selected_products['quantity'].pop()
def getQuantities(self, data):
''' Add quantities to main storage '''
self.selected_products['quantity'] = data['quantity']
l = len(self.selected_products['quantity'])
for item in range(l):
if self.selected_products['quantity'][item] == '':
self.selected_products['quantity'][item] = '0'
self.current = 'Final Screen'
def setSettings(self, data, new):
''' Add settings to main storage '''
self.settings['Rec'] = data['rec']
self.settings['Name'] = data['name']
self.settings['Surname'] = data['surname']
self.settings['Email'] = data['email']
self.settings['Pal'] = data['activity']
self.settings['Weight'] = data['weight']
self.settings['Day'] = data['days']
self.settings['Sex'] = data['sex']
self.settings['Age'] = data['age']
update = True
if new == -999:
temp_df = pd.DataFrame.from_dict({'index': [len(profile_list)], \
'name': [data['name']], 'surname': [data['surname']], \
'age': [data['age']], 'sex': [data['sex']], 'email': [data['email']], \
'weight': [data['weight']], \
'activity': [data['activity']], 'days': [data['days']]}).set_index('index')
new_profile_list = pd.concat([profile_list, temp_df])
elif new == -1000:
update = False
else:
temp_df = pd.DataFrame.from_dict({'name': [data['name']], 'surname': [data['surname']], \
'age': [data['age']], 'sex': [data['sex']], 'email': [data['email']], 'weight': [data['weight']], \
'activity': [data['activity']], 'days': [data['days']]})
new_profile_list= profile_list
new_profile_list.iloc[new] = temp_df.iloc[0]
if update:
new_profile_list.to_csv('./data/profile.csv', sep=';')
def computation(self):
''' Call algo for computation of NutriScore and recommendation. Display results '''
dict_product = {'Product': [], 'API': []}
for index in range(len(self.selected_products['code'])):
# Separation of API and OpenFoodFacts data
if str(self.selected_products['code'][index]) == 'API':
dict_product['API'].append((str(self.selected_products[
'product_name'][index]), int(self.selected_products['quantity'][index])))
else:
dict_product['Product'].append((str(self.selected_products[
'code'][index]), int(self.selected_products['quantity'][index])))
# Run the algorithm to get the recommendation to print on-screen
text_app_beverages, text_app_nonbeverages = algo(dict_product, self.settings, DF)
self.ids.screen_final.ids.beverages.text = text_app_beverages
self.ids.screen_final.ids.non_beverages.text = text_app_nonbeverages
class NutriScoreApp(App):
''' Main class of the App '''
def build(self):
''' Import the database for the whole application '''
global DF, allTrue, profile_list
try:
DF = pd.read_csv('./data/OpenFoodFacts_final.csv.gz', low_memory=False, index_col = [0])
allTrue = DF['product_name'].str.contains('', case=False) # True Vector of length len(DF)
profile_list = pd.read_csv('./data/profile.csv', sep=';', index_col=[0])
except:
print('Fatal error: files missing')
return Manager()
if __name__ == '__main__':
NutriScoreApp().run()
| 39.470825
| 116
| 0.568181
|
from kivy.app import App
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.gridlayout import GridLayout
from kivy.uix.recycleboxlayout import RecycleBoxLayout
from kivy.uix.label import Label
from kivy.uix.button import Button
from kivy.uix.checkbox import CheckBox
from kivy.uix.spinner import Spinner
from kivy.uix.recycleview import RecycleView
from kivy.uix.recycleview.views import RecycleDataViewBehavior
from kivy.uix.behaviors import FocusBehavior
from kivy.uix.recycleview.layout import LayoutSelectionBehavior
from kivy.properties import BooleanProperty, ObjectProperty
from kivy.uix.screenmanager import ScreenManager, Screen
from kivy.lang import Builder
Builder.load_file('./src/rv.kv')
Builder.load_file('./src/screenhome.kv')
Builder.load_file('./src/screenprofile.kv')
Builder.load_file('./src/screensettings.kv')
Builder.load_file('./src/screenproduct.kv')
Builder.load_file('./src/screenquantities.kv')
Builder.load_file('./src/screenfinal.kv')
Builder.load_file('./src/manager.kv')
import pandas as pd
import re
from Algo_main import algo
class SelectableRecycleBoxLayout(FocusBehavior, LayoutSelectionBehavior,
RecycleBoxLayout):
pass
class SelectableGrid(RecycleDataViewBehavior, GridLayout):
index = None
selected = BooleanProperty(False)
selectable = BooleanProperty(True)
def refresh_view_attrs(self, rv, index, data):
self.index = index
self.ids['id_label1'].text = data['label1']['text']
self.ids['id_label2'].text = data['label2']['text']
self.ids['id_label3'].text = data['label3']['text']
return super(SelectableGrid, self).refresh_view_attrs(
rv, index, data)
def on_touch_down(self, touch):
if super(SelectableGrid, self).on_touch_down(touch):
return True
if self.collide_point(*touch.pos) and self.selectable:
return self.parent.select_with_touch(self.index, touch)
def apply_selection(self, rv, index, is_selected):
self.selected = is_selected
class SelectableQuantity(RecycleDataViewBehavior, GridLayout):
index = None
selected = BooleanProperty(False)
selectable = BooleanProperty(True)
def refresh_view_attrs(self, rv, index, data):
self.index = index
self.ids['id_label1'].text = data['label1']['text']
self.ids['id_label2'].text = data['label2']['text']
self.ids['id_label3'].text = data['label3']['text']
return super(SelectableQuantity, self).refresh_view_attrs(
rv, index, data)
class RV(RecycleView):
def __init__(self, **kwargs):
super(RV, self).__init__(**kwargs)
def upload(self, query, active):
self.data = []
if active:
self.parent.parent.getSelection('API', query, True)
self.data = [{'label1': {'text': 'API'}, 'label2': {'text': query}, 'label3': {'text': 'Add/Remove'}}]
else:
isinside = allTrue
for item in query.split():
isinside = isinside & \
(DF['product_name'].str.contains(item, case=False) | \
DF['Brands'].str.contains(item, case=False))
if any(isinside):
selection = DF[isinside]
for row in selection.itertuples():
d = {'label1': {'text': str(row[0])}, \
'label2': {'text': str(row[1])},
'label3': {'text': str(row[-1])}}
self.data.append(d)
else:
isinside = DF.index.str.contains(query, case=False)
if any(isinside):
selection = DF[isinside]
for row in selection.itertuples():
d = {'label1': {'text': str(row[0])}, \
'label2': {'text': str(row[1])},
'label3': {'text': str(row[-1])}}
self.data.append(d)
else:
self.data = [{'label1': {'text': ''}, \
'label2': {'text': 'No product found'}, 'label3': {'text': ''}}]
def getQuantities(self, dict):
self.data = []
code = dict['code']
product_name = dict['product_name']
quantity = dict['quantity']
for index in range(len(code)):
d = {'label1': {'text': code[index]}, 'label2': {'text': product_name[index]}, \
'label3': {'text': quantity[index]}}
self.data.append(d)
class ScreenHome(Screen):
pass
class ScreenProfile(Screen):
def updateDF(self):
global DF
DF = pd.read_csv('https://drive.google.com/uc?export=download&id=1aLUh1UoQcS9lBa6oVRln-DuskxK5uK3y', \
index_col=[0], low_memory = False)
DF.to_csv('./data/OpenFoodFacts_final.csv.gz', compression='gzip')
self.ids['update'].text = 'Updated'
self.ids['update'].background_color = (0,1,0,1)
def update(self):
self.ids['update'].text = 'Updating'
self.ids['update'].background_color = (50/255,164/255,206/255,1)
class ScreenSettings(Screen):
settings = {'rec': True,'name': '', 'surname': '', 'age': 0, 'sex': True, 'weight': 0, \
'email': '', 'activity': 0, 'days': 0}
id_profile = -999
def resetForm(self):
self.ids.sex.color = (1,1,1,1)
self.ids.activity.color = (1,1,1,1)
self.ids.age.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.weight.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.days.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.email.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.name.hint_text_color = (0.5, 0.5, 0.5, 1.0)
self.ids.surname.hint_text_color = (0.5, 0.5, 0.5, 1.0)
def setForm(self, id_profile):
self.id_profile = id_profile
self.settings = {'rec': True,'name': '', 'surname': '', 'age': 0, 'sex': True, 'weight': 0, \
'email': '', 'activity': 0, 'days': 0}
if int(self.id_profile) >= 0:
self.ids.name.text = str(profile_list.iloc[self.id_profile]['name'])
self.ids.surname.text= str(profile_list.iloc[self.id_profile]['surname'])
self.ids.age.text = str(profile_list.iloc[self.id_profile]['age'])
if bool(profile_list.iloc[self.id_profile]['sex']):
self.ids.male.active = True
self.ids.female.active = False
else:
self.ids.male.active = False
self.ids.female.active = True
self.ids.weight.text = str(profile_list.iloc[self.id_profile]['weight'])
self.ids.email.text = str(profile_list.iloc[self.id_profile]['email'])
self.ids.days.text = str(profile_list.iloc[self.id_profile]['days'])
if int(profile_list.iloc[self.id_profile]['activity']) == 1.8:
self.ids.seated.active = False
self.ids.both.active = False
self.ids.standing.active = True
elif int(profile_list.iloc[self.id_profile]['activity']) == 1.6:
self.ids.seated.active = False
self.ids.both.active = True
self.ids.standing.active = False
else:
self.ids.seated.active = True
self.ids.both.active = False
self.ids.standing.active = False
elif int(self.id_profile) == -999:
self.ids.name.text = ''
self.ids.surname.text = ''
self.ids.age.text = ''
self.ids.male.active = False
self.ids.female.active = False
self.ids.email.text = ''
self.ids.weight.text = ''
self.ids.seated.active = False
self.ids.both.active = False
self.ids.standing.active = False
self.ids.days.text = ''
else:
self.changeScreen(False)
def changeScreen(self, valid):
if valid:
self.resetForm()
if self.ids.name.text.strip() == '':
self.ids.name.hint_text_color = (1,0,0,1)
return False
elif self.ids.surname.text.strip() == '':
self.ids.surname.hint_text_color = (1,0,0,1)
return False
elif self.ids.age.text.strip() == '' or int(self.ids.age.text) <= 0 or \
int(self.ids.age.text) >= 120:
self.ids.age.text = ''
self.ids.age.hint_text_color = (1,0,0,1)
return False
elif not(self.ids.male.active or self.ids.female.active):
self.ids.sex.color = (1,0,0,1)
return False
elif not re.match(r"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$)", self.ids.email.text):
self.ids.email.text = ''
self.ids.email.hint_text_color = (1,0,0,1)
return False
elif self.ids.weight.text.strip() == '' or int(self.ids.weight.text) <= 0:
self.ids.weight.text = ''
self.ids.weight.hint_text_color = (1,0,0,1)
return False
elif not(self.ids.seated.active or self.ids.both.active or self.ids.standing.active):
self.ids.activity.color = (1,0,0,1)
return False
elif self.ids.days.text.strip() == '' or int(self.ids.days.text) <= 0:
self.ids.days.text = ''
self.ids.days.hint_text_color = (1,0,0,1)
return False
else:
self.settings['rec'] = True
self.settings['name'] = self.ids.name.text
self.settings['surname'] = self.ids.surname.text
self.settings['age'] = int(self.ids.age.text)
self.settings['weight'] = int(self.ids.weight.text)
self.settings['email'] = self.ids.email.text
self.settings['days'] = int(self.ids.days.text)
self.settings['sex'] = self.ids.male.active
if self.ids.seated.active:
self.settings['activity'] = 1.4
if self.ids.both.active:
self.settings['activity'] = 1.6
if self.ids.standing.active:
self.settings['activity'] = 1.8
self.resetForm()
else:
self.settings['rec'] = False
self.manager.setSettings(self.settings, self.id_profile)
self.manager.current = 'Product Screen'
class ScreenProduct(Screen):
temp_dict = {'code':'', 'product_name': ''}
def getSelection(self, text1, text2, state):
if state:
self.temp_dict['code'] = text1
self.temp_dict['product_name'] = text2
else:
self.temp_dict['code'] = ''
self.temp_dict['product_name'] = ''
class ScreenQuantities(Screen):
temp_dict = {'code': [], 'product_name': [], 'quantity': [], 'color': []}
def initQuantity(self, data):
if self.temp_dict['quantity'] == []:
self.temp_dict = data
self.ids.rv.getQuantities(data)
def updateQuantity(self, index, text1, text2, text3):
l = len(self.temp_dict['quantity'])
if text3 == '' or text3 == '-' or int(text3) < 0:
text3 = '0'
if index < l:
self.temp_dict['code'][index] = text1
self.temp_dict['product_name'][index] = text2
self.temp_dict['quantity'][index] = text3
else:
temp = ['0' for i in range(index-l)]
self.temp_dict['code'] = self.temp_dict['code'] + temp + [text1]
self.temp_dict['product_name'] = self.temp_dict['product_name'] + temp + [text2]
self.temp_dict['quantity'] = self.temp_dict['quantity'] + temp + [text3]
self.initQuantity(self.temp_dict)
class ScreenFinal(Screen):
pass
class Manager(ScreenManager):
selected_products = {'code': [], 'product_name': [], 'quantity': []}
settings = {'Rec': True, 'Name': '', 'Surname': '', 'Email': '', 'Age': 0, 'Sex': True, 'Pal': 0, \
'Weight': 0, 'Day': 0}
def getProfiles(self):
self.ids.screen_profile.ids.profile_spinner.values = \
[str(index + 1) + ' : ' + str(profile_list['name'][index]) + ' ' + str(profile_list['surname'][index]) \
for index in profile_list.index]
def toSettings(self, text):
if text == 'new':
id_profile = -999
elif text == 'pass':
id_profile = -1000
else:
items = text.split()
id_profile = items[0].strip()
id_profile = int(id_profile) - 1
self.ids.screen_settings.setForm(id_profile)
if id_profile != -1000:
self.current = 'Settings Screen'
def addProduct(self):
item1 = self.ids.screen_product.temp_dict['code']
item2 = self.ids.screen_product.temp_dict['product_name']
if item1 != '' and item2 != '':
self.selected_products['code'].append(item1)
self.selected_products['product_name'].append(item2)
self.selected_products['quantity'].append('0')
def deleteProduct(self):
item1 = self.ids.screen_product.temp_dict['code']
item2 = self.ids.screen_product.temp_dict['product_name']
if item1 in self.selected_products['code'] and item2 in self.selected_products['product_name']:
self.selected_products['code'].remove(item1)
self.selected_products['product_name'].remove(item2)
self.selected_products['quantity'].pop()
def getQuantities(self, data):
self.selected_products['quantity'] = data['quantity']
l = len(self.selected_products['quantity'])
for item in range(l):
if self.selected_products['quantity'][item] == '':
self.selected_products['quantity'][item] = '0'
self.current = 'Final Screen'
def setSettings(self, data, new):
self.settings['Rec'] = data['rec']
self.settings['Name'] = data['name']
self.settings['Surname'] = data['surname']
self.settings['Email'] = data['email']
self.settings['Pal'] = data['activity']
self.settings['Weight'] = data['weight']
self.settings['Day'] = data['days']
self.settings['Sex'] = data['sex']
self.settings['Age'] = data['age']
update = True
if new == -999:
temp_df = pd.DataFrame.from_dict({'index': [len(profile_list)], \
'name': [data['name']], 'surname': [data['surname']], \
'age': [data['age']], 'sex': [data['sex']], 'email': [data['email']], \
'weight': [data['weight']], \
'activity': [data['activity']], 'days': [data['days']]}).set_index('index')
new_profile_list = pd.concat([profile_list, temp_df])
elif new == -1000:
update = False
else:
temp_df = pd.DataFrame.from_dict({'name': [data['name']], 'surname': [data['surname']], \
'age': [data['age']], 'sex': [data['sex']], 'email': [data['email']], 'weight': [data['weight']], \
'activity': [data['activity']], 'days': [data['days']]})
new_profile_list= profile_list
new_profile_list.iloc[new] = temp_df.iloc[0]
if update:
new_profile_list.to_csv('./data/profile.csv', sep=';')
def computation(self):
dict_product = {'Product': [], 'API': []}
for index in range(len(self.selected_products['code'])):
if str(self.selected_products['code'][index]) == 'API':
dict_product['API'].append((str(self.selected_products[
'product_name'][index]), int(self.selected_products['quantity'][index])))
else:
dict_product['Product'].append((str(self.selected_products[
'code'][index]), int(self.selected_products['quantity'][index])))
text_app_beverages, text_app_nonbeverages = algo(dict_product, self.settings, DF)
self.ids.screen_final.ids.beverages.text = text_app_beverages
self.ids.screen_final.ids.non_beverages.text = text_app_nonbeverages
class NutriScoreApp(App):
def build(self):
global DF, allTrue, profile_list
try:
DF = pd.read_csv('./data/OpenFoodFacts_final.csv.gz', low_memory=False, index_col = [0])
allTrue = DF['product_name'].str.contains('', case=False)
profile_list = pd.read_csv('./data/profile.csv', sep=';', index_col=[0])
except:
print('Fatal error: files missing')
return Manager()
if __name__ == '__main__':
NutriScoreApp().run()
| true
| true
|
f7168bb426ad7afb8dbd70d8b80b3ec7a14dbc4b
| 28,177
|
py
|
Python
|
sppas/sppas/src/ui/phoenix/page_files/associate.py
|
mirfan899/MTTS
|
3167b65f576abcc27a8767d24c274a04712bd948
|
[
"MIT"
] | null | null | null |
sppas/sppas/src/ui/phoenix/page_files/associate.py
|
mirfan899/MTTS
|
3167b65f576abcc27a8767d24c274a04712bd948
|
[
"MIT"
] | null | null | null |
sppas/sppas/src/ui/phoenix/page_files/associate.py
|
mirfan899/MTTS
|
3167b65f576abcc27a8767d24c274a04712bd948
|
[
"MIT"
] | null | null | null |
"""
..
---------------------------------------------------------------------
___ __ __ __ ___
/ | \ | \ | \ / the automatic
\__ |__/ |__/ |___| \__ annotation and
\ | | | | \ analysis
___/ | | | | ___/ of speech
http://www.sppas.org/
Use of this software is governed by the GNU Public License, version 3.
SPPAS is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
SPPAS is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with SPPAS. If not, see <http://www.gnu.org/licenses/>.
This banner notice must not be removed.
---------------------------------------------------------------------
ui.phoenix.page_files.associate.py
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Actions to associate files and references of the catalogue.
"""
import wx
import logging
from sppas import sppasTypeError
from sppas import sg
from sppas.src.config import ui_translation
from sppas.src.files import FileData
from sppas.src.files import States
from sppas.src.files import sppasFileDataFilters
from ..dialogs import Information, Error
from ..windows import sppasStaticText, sppasTextCtrl
from ..windows import sppasPanel
from ..windows import sppasDialog
from ..windows import sppasToolbar
from ..windows import BitmapTextButton, CheckButton
from ..windows import sppasRadioBoxPanel
from ..main_events import DataChangedEvent
from .filesutils import IdentifierTextValidator
# ---------------------------------------------------------------------------
MSG_HEADER_FILTER = ui_translation.gettext("Checking files")
MSG_NB_CHECKED = "{:d} files were matching the given filters and were checked."
MSG_NO_CHECKED = "None of the files is matching the given filters."
ASS_ACT_CHECK_ERROR = "Files can't be filtered due to the following" \
" error:\n{!s:s}"
# ---------------------------------------------------------------------------
class AssociatePanel(sppasPanel):
"""Panel with tools to associate files and references of the catalogue.
:author: Brigitte Bigi
:organization: Laboratoire Parole et Langage, Aix-en-Provence, France
:contact: develop@sppas.org
:license: GPL, v3
:copyright: Copyright (C) 2011-2019 Brigitte Bigi
"""
def __init__(self, parent, name=wx.PanelNameStr):
super(AssociatePanel, self).__init__(
parent,
id=wx.ID_ANY,
pos=wx.DefaultPosition,
size=wx.DefaultSize,
style=wx.BORDER_NONE | wx.TAB_TRAVERSAL | wx.WANTS_CHARS | wx.NO_FULL_REPAINT_ON_RESIZE | wx.CLIP_CHILDREN,
name=name)
# The data this page is working on
self.__data = FileData()
# State of the button to check all or none of the filenames
self._checkall = False
# Construct the panel
self._create_content()
self._setup_events()
self.Layout()
# ------------------------------------------------------------------------
def set_data(self, data):
"""Assign new data to this panel.
:param data: (FileData)
"""
if isinstance(data, FileData) is False:
raise sppasTypeError("FileData", type(data))
logging.debug('New data to set in the associate panel. '
'Id={:s}'.format(data.id))
self.__data = data
# ------------------------------------------------------------------------
# Private methods to construct the panel.
# ------------------------------------------------------------------------
def _create_content(self):
"""Create the main content."""
filtr = self.__create_button("check_filter")
check = self.__create_button("checklist")
link = self.__create_button("link_add")
unlink = self.__create_button("link_del")
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.AddStretchSpacer(4)
sizer.Add(filtr, 1, wx.TOP | wx.ALIGN_CENTRE, 0)
sizer.Add(check, 1, wx.TOP | wx.ALIGN_CENTRE, 0)
sizer.AddStretchSpacer(2)
sizer.Add(link, 1, wx.BOTTOM | wx.ALIGN_CENTRE, 0)
sizer.Add(unlink, 1, wx.BOTTOM | wx.ALIGN_CENTRE, 0)
sizer.AddStretchSpacer(4)
self.SetMinSize(wx.Size(sppasPanel.fix_size(32), -1))
self.SetSizer(sizer)
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
def __create_button(self, icon, label=None):
btn = BitmapTextButton(self, name=icon, label=label)
btn.FocusStyle = wx.PENSTYLE_SOLID
btn.FocusWidth = 3
btn.FocusColour = wx.Colour(128, 128, 196, 128) # violet
btn.LabelPosition = wx.BOTTOM
btn.Spacing = 4
btn.BorderWidth = 0
btn.BitmapColour = self.GetForegroundColour()
btn.SetMinSize(wx.Size(sppasPanel.fix_size(24),
sppasPanel.fix_size(24)))
return btn
# -----------------------------------------------------------------------
# Events management
# -----------------------------------------------------------------------
def _setup_events(self):
"""Associate a handler function with the events.
It means that when an event occurs then the process handler function
will be called.
"""
# The user pressed a key of its keyboard
self.Bind(wx.EVT_KEY_DOWN, self._process_key_event)
# The user clicked (LeftDown - LeftUp) an action button
self.Bind(wx.EVT_BUTTON, self._process_action)
# ------------------------------------------------------------------------
def notify(self):
"""Send the EVT_DATA_CHANGED to the parent."""
if self.GetParent() is not None:
self.__data.set_state(States().CHECKED)
evt = DataChangedEvent(data=self.__data)
evt.SetEventObject(self)
wx.PostEvent(self.GetParent(), evt)
# ------------------------------------------------------------------------
# Callbacks to events
# ------------------------------------------------------------------------
def _process_key_event(self, event):
"""Respond to a keypress event."""
key_code = event.GetKeyCode()
logging.debug('Associate panel received a key event. key_code={:d}'.format(key_code))
logging.debug('Key event skipped by the associate panel.')
event.Skip()
# ------------------------------------------------------------------------
def _process_action(self, event):
"""Respond to an association event."""
name = event.GetButtonObj().GetName()
if name == "check_filter":
self.check_filter()
elif name == "checklist":
self.check_all()
elif name == "link_add":
self.add_links()
elif name == "link_del":
self.delete_links()
event.Skip()
# ------------------------------------------------------------------------
# GUI methods to perform actions on the data
# ------------------------------------------------------------------------
def check_filter(self):
"""Check filenames matching the user-defined filters."""
dlg = sppasFilesFilterDialog(self)
response = dlg.ShowModal()
if response != wx.ID_CANCEL:
data_filters = dlg.get_filters()
if len(data_filters) > 0:
wx.BeginBusyCursor()
try:
data_set = self.__process_filter(data_filters, dlg.match_all)
if len(data_set) == 0:
Information(MSG_NO_CHECKED)
else:
# Uncheck all files (except the locked ones!) and all references
self.__data.set_object_state(States().UNUSED)
roots = list()
# Check files of the filtered data_set
for fn in data_set:
self.__data.set_object_state(States().CHECKED, fn)
root = self.__data.get_parent(fn)
if root not in roots:
roots.append(root)
Information(MSG_NB_CHECKED.format(len(data_set)))
# Check references matching the checked files
for fr in roots:
for ref in fr.get_references():
ref.set_state(States().CHECKED)
self.notify()
wx.EndBusyCursor()
except Exception as e:
wx.EndBusyCursor()
Error(ASS_ACT_CHECK_ERROR.format(str(e)), "Check error")
dlg.Destroy()
# ------------------------------------------------------------------------
def __process_filter(self, data_filters, match_all=True):
"""Perform the filter process.
:param data_filters: list of tuples with (filter name, function name, values)
:param match_all: (bool)
"""
logging.info("Check files matching the following: ")
logging.info(" >>> filter = sppasFileDataFilters()")
f = sppasFileDataFilters(self.__data)
data_sets = list()
for d in data_filters:
if len(d) != 3:
logging.error("Bad data format: {:s}".format(str(d)))
continue
# the method to be uses by Compare
method = d[0]
# the function to be applied
fct = d[1]
if method == "att":
# identifier:value are separated by a ":" but a tuple is needed
values = tuple(d[2].split(":"))
logging.info(" >>> filter.{:s}({:s}={!s:s})".format(method, fct, str(values)))
data_set = getattr(f, method)(**{fct: values})
# a little bit of doc:
# - getattr() returns the value of the named attributed of object:
# it returns f.tag if called like getattr(f, "tag")
# - func(**{'x': '3'}) is equivalent to func(x='3')
else:
# all the possible values are separated by commas
values = d[2].split(",")
logging.info(" >>> filter.{:s}({:s}={!s:s})".format(method, fct, values[0]))
data_set = getattr(f, method)(**{fct: values[0]})
# Apply "or" between each data_set matching a value
for i in range(1, len(values)):
v = values[i].strip()
data_set = data_set | getattr(f, method)(**{fct: v})
logging.info(" >>> | filter.{:s}({:s}={!s:s})".format(method, fct, v))
data_sets.append(data_set)
# no filename is matching
if len(data_sets) == 0:
return list()
# Merge results (apply '&' or '|' on the resulting data sets)
data_set = data_sets[0]
if match_all is True:
for i in range(1, len(data_sets)):
data_set = data_set & data_sets[i]
if len(data_set) == 0:
# no need to go further...
return list()
else:
for i in range(1, len(data_sets)):
data_set = data_set | data_sets[i]
return data_set
# ------------------------------------------------------------------------
def check_all(self):
"""Check all or any of the filenames and references."""
# reverse the current state
self._checkall = not self._checkall
# ask the data to change their state
if self._checkall is True:
state = States().CHECKED
else:
state = States().UNUSED
self.__data.set_object_state(state)
# update the view of checked references & checked files
self.notify()
# ------------------------------------------------------------------------
def add_links(self):
"""Associate checked filenames with checked references."""
associed = self.__data.associate()
if associed > 0:
self.notify()
# ------------------------------------------------------------------------
def delete_links(self):
"""Dissociate checked filenames with checked references."""
dissocied = self.__data.dissociate()
if dissocied > 0:
self.notify()
# ---------------------------------------------------------------------------
class sppasFilesFilterDialog(sppasDialog):
"""Dialog to get filters to check files and references.
:author: Brigitte Bigi
:organization: Laboratoire Parole et Langage, Aix-en-Provence, France
:contact: develop@sppas.org
:license: GPL, v3
:copyright: Copyright (C) 2011-2019 Brigitte Bigi
"""
def __init__(self, parent):
"""Create a files filter dialog.
:param parent: (wx.Window)
"""
super(sppasFilesFilterDialog, self).__init__(
parent=parent,
title='{:s} Files selection'.format(sg.__name__),
style=wx.DEFAULT_FRAME_STYLE)
self.match_all = True
self.CreateHeader(title="Define filters to check files",
icon_name="check_filter")
self._create_content()
self._create_buttons()
self.Bind(wx.EVT_BUTTON, self._process_event)
self.SetSize(wx.Size(480, 320))
self.LayoutComponents()
self.CenterOnParent()
self.FadeIn(deltaN=-8)
# -----------------------------------------------------------------------
# Public methods
# -----------------------------------------------------------------------
def get_filters(self):
"""Return a list of (filter, function, values)."""
filters = list()
for i in range(self.listctrl.GetItemCount()):
filter_name = self.listctrl.GetValue(i, 0)
fct_name = self.listctrl.GetValue(i, 1)
values = self.listctrl.GetValue(i, 2)
filters.append((filter_name, fct_name, values))
return filters
# -----------------------------------------------------------------------
# Methods to construct the GUI
# -----------------------------------------------------------------------
def _create_content(self):
"""Create the content of the message dialog."""
panel = sppasPanel(self, name="content")
tb = self.__create_toolbar(panel)
self.listctrl = wx.dataview.DataViewListCtrl(panel, wx.ID_ANY)
self.listctrl.AppendTextColumn("filter", width=80)
self.listctrl.AppendTextColumn("function", width=90)
self.listctrl.AppendTextColumn("value", width=120)
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(tb, proportion=0, flag=wx.EXPAND, border=0)
sizer.Add(self.listctrl, proportion=1, flag=wx.EXPAND | wx.LEFT | wx.RIGHT, border=5)
panel.SetSizer(sizer)
self.SetMinSize(wx.Size(320, 200))
panel.SetAutoLayout(True)
self.SetContent(panel)
# -----------------------------------------------------------------------
def __create_toolbar(self, parent):
"""Create the toolbar."""
tb = sppasToolbar(parent)
tb.set_focus_color(wx.Colour(196, 196, 96, 128))
tb.AddTextButton("filter_path", "+ Path")
tb.AddTextButton("filter_name", "+ Name")
tb.AddTextButton("filter_ext", "+ Type")
tb.AddTextButton("filter_ref", "+ Ref.")
tb.AddTextButton("filter_att", "+ Value")
tb.AddSpacer()
#tb.AddTextButton(None, "- Remove")
return tb
# -----------------------------------------------------------------------
def _create_buttons(self):
"""Create the buttons and bind events."""
panel = sppasPanel(self, name="actions")
# panel.SetMinSize(wx.Size(-1, wx.GetApp().settings.action_height))
sizer = wx.BoxSizer(wx.HORIZONTAL)
# Create the buttons
cancel_btn = self.__create_action_button(panel, "Cancel", "cancel")
apply_or_btn = self.__create_action_button(panel, "Apply - OR", "apply")
apply_and_btn = self.__create_action_button(panel, "Apply - AND", "ok")
apply_and_btn.SetFocus()
sizer.Add(cancel_btn, 1, wx.ALL | wx.EXPAND, 0)
sizer.Add(self.VertLine(parent=panel), 0, wx.ALL | wx.EXPAND, 0)
sizer.Add(apply_or_btn, 1, wx.ALL | wx.EXPAND, 0)
sizer.Add(self.VertLine(parent=panel), 0, wx.ALL | wx.EXPAND, 0)
sizer.Add(apply_and_btn, 1, wx.ALL | wx.EXPAND, 0)
panel.SetSizer(sizer)
self.SetActions(panel)
# -----------------------------------------------------------------------
def __create_action_button(self, parent, text, icon):
btn = BitmapTextButton(parent, label=text, name=icon)
btn.LabelPosition = wx.RIGHT
btn.Spacing = sppasDialog.fix_size(12)
btn.BorderWidth = 0
btn.BitmapColour = self.GetForegroundColour()
btn.SetMinSize(wx.Size(sppasDialog.fix_size(32),
sppasDialog.fix_size(32)))
return btn
# ------------------------------------------------------------------------
# Callback to events
# ------------------------------------------------------------------------
def _process_event(self, event):
"""Process any kind of events.
:param event: (wx.Event)
"""
event_obj = event.GetEventObject()
event_name = event_obj.GetName()
if event_name == "filter_path":
self.__append_filter("path")
elif event_name == "filter_name":
self.__append_filter("name")
elif event_name == "filter_ext":
self.__append_filter("extension")
elif event_name == "filter_ref":
self.__append_filter("ref")
elif event_name == "filter_att":
dlg = sppasAttributeFilterDialog(self)
response = dlg.ShowModal()
if response == wx.ID_OK:
# Name of the method in sppasFileDataFilters,
# Name of the function and its value
f = dlg.get_data()
v = f[1].split(':')
if len(v[0].strip()) > 1 and len(v[1].strip()) > 0:
self.listctrl.AppendItem(["att", f[0], f[1].strip()])
else:
logging.error("Invalid input string for identifier or value.")
dlg.Destroy()
elif event_name == "cancel":
self.SetReturnCode(wx.ID_CANCEL)
self.Close()
elif event_name == "apply":
self.match_all = False
self.EndModal(wx.ID_APPLY)
elif event_name == "ok":
self.match_all = True
self.EndModal(wx.ID_OK)
else:
event.Skip()
# ------------------------------------------------------------------------
def __append_filter(self, fct):
dlg = sppasStringFilterDialog(self)
response = dlg.ShowModal()
if response == wx.ID_OK:
# Name of the method in sppasFileDataFilters,
# Name of the function and its value
f = dlg.get_data()
if len(f[1].strip()) > 0:
self.listctrl.AppendItem([fct, f[0], f[1].strip()])
else:
logging.error("Empty input pattern.")
dlg.Destroy()
# ---------------------------------------------------------------------------
class sppasStringFilterDialog(sppasDialog):
"""Dialog to get a filter on a string.
:author: Brigitte Bigi
:organization: Laboratoire Parole et Langage, Aix-en-Provence, France
:contact: develop@sppas.org
:license: GPL, v3
:copyright: Copyright (C) 2011-2019 Brigitte Bigi
"""
choices = (
("exact", "exact"),
("contains", "contains"),
("starts with", "startswith"),
("ends with", "endswith"),
("match (regexp)", "regexp"),
("not exact", "exact"),
("not contains", "contains"),
("not starts with", "startswith"),
("not ends with", "endswith"),
("not match", "regexp")
)
def __init__(self, parent):
"""Create a string filter dialog.
:param parent: (wx.Window)
"""
super(sppasStringFilterDialog, self).__init__(
parent=parent,
title='{:s} filter'.format(sg.__name__),
style=wx.DEFAULT_FRAME_STYLE)
self._create_content()
self.CreateActions([wx.ID_CANCEL, wx.ID_OK])
self.SetSize(wx.Size(380, 320))
self.LayoutComponents()
self.CenterOnParent()
# -----------------------------------------------------------------------
def get_data(self):
"""Return the data defined by the user.
Returns: (tuple) with:
- function (str): one of the methods in Compare
- values (list): patterns to find separated by commas
"""
idx = self.radiobox.GetSelection()
label = self.radiobox.GetStringSelection()
given_fct = self.choices[idx][1]
# Fill the resulting dict
prepend_fct = ""
if given_fct != "regexp":
# prepend "not_" if reverse
if "not" in label:
prepend_fct += "not_"
# prepend "i" if case-insensitive
if self.checkbox.GetValue() is False:
prepend_fct += "i"
return prepend_fct+given_fct, self.text.GetValue()
# -----------------------------------------------------------------------
# Methods to construct the GUI
# -----------------------------------------------------------------------
def _create_content(self):
"""Create the content of the message dialog."""
panel = sppasPanel(self, name="content")
label = sppasStaticText(panel, label="Search for pattern(s): ")
self.text = sppasTextCtrl(panel, value="")
choices = [row[0] for row in self.choices]
self.radiobox = sppasRadioBoxPanel(
panel,
choices=choices,
majorDimension=2,
style=wx.RA_SPECIFY_COLS)
self.radiobox.SetSelection(1)
self.checkbox = CheckButton(panel, label="Case sensitive")
self.checkbox.SetValue(False)
# Layout
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(label, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.text, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.radiobox, 1, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.checkbox, 0, flag=wx.EXPAND | wx.ALL, border=4)
panel.SetSizer(sizer)
panel.SetMinSize((240, 160))
panel.SetAutoLayout(True)
self.SetContent(panel)
# ---------------------------------------------------------------------------
class sppasAttributeFilterDialog(sppasDialog):
"""Dialog to get a filter on an attribute.
:author: Brigitte Bigi
:organization: Laboratoire Parole et Langage, Aix-en-Provence, France
:contact: develop@sppas.org
:license: GPL, v3
:copyright: Copyright (C) 2011-2019 Brigitte Bigi
"""
choices = (
("exact", "exact"),
("contains", "contains"),
("starts with", "startswith"),
("ends with", "endswith"),
("match (regexp)", "regexp"),
("not exact", "exact"),
("not contains", "contains"),
("not starts with", "startswith"),
("not ends with", "endswith"),
("not match", "regexp"),
("equal", "equal"),
("greater than", "gt"),
("greater or equal", "ge"),
("lower than", "lt"),
("lower or equal", "le")
)
def __init__(self, parent):
"""Create an attribute filter dialog.
:param parent: (wx.Window)
"""
super(sppasAttributeFilterDialog, self).__init__(
parent=parent,
title='{:s} filter'.format(sg.__name__),
style=wx.DEFAULT_FRAME_STYLE)
self._create_content()
self.CreateActions([wx.ID_CANCEL, wx.ID_OK])
self.SetMinSize(wx.Size(sppasDialog.fix_size(420),
sppasDialog.fix_size(320)))
self.LayoutComponents()
self.CenterOnParent()
# ------------------------------------------------------------------------
def get_data(self):
"""Return the data defined by the user.
Returns: (tuple) with:
- function (str): one of the methods in Compare
- values (list): attribute to find as identifier, value
"""
idx = self.radiobox.GetSelection()
label = self.radiobox.GetStringSelection()
given_fct = self.choices[idx][1]
# Fill the resulting dict
prepend_fct = ""
if idx < 10 and given_fct != "regexp":
# prepend "not_" if reverse
if "not" in label:
prepend_fct += "not_"
# prepend "i" if case-insensitive
if self.checkbox.GetValue() is False:
prepend_fct += "i"
return prepend_fct + given_fct, \
self.text_ident.GetValue() + ":" + self.text_value.GetValue()
# -----------------------------------------------------------------------
# Methods to construct the GUI
# -----------------------------------------------------------------------
def _create_content(self):
"""Create the content of the message dialog."""
panel = sppasPanel(self, name="content")
label = sppasStaticText(panel, label="Identifier: ")
self.text_ident = sppasTextCtrl(
panel,
value="",
validator=IdentifierTextValidator())
choices = [row[0] for row in sppasAttributeFilterDialog.choices]
self.radiobox = sppasRadioBoxPanel(
panel,
choices=choices,
majorDimension=3,
style=wx.RA_SPECIFY_COLS)
self.radiobox.SetSelection(1)
self.radiobox.Bind(wx.EVT_RADIOBOX, self._on_radiobox_checked)
self.checkbox = CheckButton(panel, label="Case sensitive")
self.checkbox.SetValue(False)
self.text_value = sppasTextCtrl(panel, value="")
# Layout
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(label, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.text_ident, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.radiobox, 1, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.text_value, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.checkbox, 0, flag=wx.EXPAND | wx.ALL, border=4)
panel.SetSizer(sizer)
panel.SetMinSize((240, 160))
panel.SetAutoLayout(True)
self.SetContent(panel)
def _on_radiobox_checked(self, event):
value = self.radiobox.GetStringSelection()
if value in sppasAttributeFilterDialog.choices[10:]:
self.checkbox.SetValue(False)
self.checkbox.Enable(False)
else:
self.checkbox.Enable(True)
| 35.894268
| 119
| 0.509245
|
import wx
import logging
from sppas import sppasTypeError
from sppas import sg
from sppas.src.config import ui_translation
from sppas.src.files import FileData
from sppas.src.files import States
from sppas.src.files import sppasFileDataFilters
from ..dialogs import Information, Error
from ..windows import sppasStaticText, sppasTextCtrl
from ..windows import sppasPanel
from ..windows import sppasDialog
from ..windows import sppasToolbar
from ..windows import BitmapTextButton, CheckButton
from ..windows import sppasRadioBoxPanel
from ..main_events import DataChangedEvent
from .filesutils import IdentifierTextValidator
MSG_HEADER_FILTER = ui_translation.gettext("Checking files")
MSG_NB_CHECKED = "{:d} files were matching the given filters and were checked."
MSG_NO_CHECKED = "None of the files is matching the given filters."
ASS_ACT_CHECK_ERROR = "Files can't be filtered due to the following" \
" error:\n{!s:s}"
# ---------------------------------------------------------------------------
class AssociatePanel(sppasPanel):
def __init__(self, parent, name=wx.PanelNameStr):
super(AssociatePanel, self).__init__(
parent,
id=wx.ID_ANY,
pos=wx.DefaultPosition,
size=wx.DefaultSize,
style=wx.BORDER_NONE | wx.TAB_TRAVERSAL | wx.WANTS_CHARS | wx.NO_FULL_REPAINT_ON_RESIZE | wx.CLIP_CHILDREN,
name=name)
# The data this page is working on
self.__data = FileData()
# State of the button to check all or none of the filenames
self._checkall = False
# Construct the panel
self._create_content()
self._setup_events()
self.Layout()
# ------------------------------------------------------------------------
def set_data(self, data):
if isinstance(data, FileData) is False:
raise sppasTypeError("FileData", type(data))
logging.debug('New data to set in the associate panel. '
'Id={:s}'.format(data.id))
self.__data = data
# ------------------------------------------------------------------------
# Private methods to construct the panel.
# ------------------------------------------------------------------------
def _create_content(self):
filtr = self.__create_button("check_filter")
check = self.__create_button("checklist")
link = self.__create_button("link_add")
unlink = self.__create_button("link_del")
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.AddStretchSpacer(4)
sizer.Add(filtr, 1, wx.TOP | wx.ALIGN_CENTRE, 0)
sizer.Add(check, 1, wx.TOP | wx.ALIGN_CENTRE, 0)
sizer.AddStretchSpacer(2)
sizer.Add(link, 1, wx.BOTTOM | wx.ALIGN_CENTRE, 0)
sizer.Add(unlink, 1, wx.BOTTOM | wx.ALIGN_CENTRE, 0)
sizer.AddStretchSpacer(4)
self.SetMinSize(wx.Size(sppasPanel.fix_size(32), -1))
self.SetSizer(sizer)
# ------------------------------------------------------------------------
# ------------------------------------------------------------------------
def __create_button(self, icon, label=None):
btn = BitmapTextButton(self, name=icon, label=label)
btn.FocusStyle = wx.PENSTYLE_SOLID
btn.FocusWidth = 3
btn.FocusColour = wx.Colour(128, 128, 196, 128) # violet
btn.LabelPosition = wx.BOTTOM
btn.Spacing = 4
btn.BorderWidth = 0
btn.BitmapColour = self.GetForegroundColour()
btn.SetMinSize(wx.Size(sppasPanel.fix_size(24),
sppasPanel.fix_size(24)))
return btn
# -----------------------------------------------------------------------
# Events management
# -----------------------------------------------------------------------
def _setup_events(self):
# The user pressed a key of its keyboard
self.Bind(wx.EVT_KEY_DOWN, self._process_key_event)
# The user clicked (LeftDown - LeftUp) an action button
self.Bind(wx.EVT_BUTTON, self._process_action)
# ------------------------------------------------------------------------
def notify(self):
if self.GetParent() is not None:
self.__data.set_state(States().CHECKED)
evt = DataChangedEvent(data=self.__data)
evt.SetEventObject(self)
wx.PostEvent(self.GetParent(), evt)
# ------------------------------------------------------------------------
# Callbacks to events
# ------------------------------------------------------------------------
def _process_key_event(self, event):
key_code = event.GetKeyCode()
logging.debug('Associate panel received a key event. key_code={:d}'.format(key_code))
logging.debug('Key event skipped by the associate panel.')
event.Skip()
# ------------------------------------------------------------------------
def _process_action(self, event):
name = event.GetButtonObj().GetName()
if name == "check_filter":
self.check_filter()
elif name == "checklist":
self.check_all()
elif name == "link_add":
self.add_links()
elif name == "link_del":
self.delete_links()
event.Skip()
# ------------------------------------------------------------------------
# GUI methods to perform actions on the data
# ------------------------------------------------------------------------
def check_filter(self):
dlg = sppasFilesFilterDialog(self)
response = dlg.ShowModal()
if response != wx.ID_CANCEL:
data_filters = dlg.get_filters()
if len(data_filters) > 0:
wx.BeginBusyCursor()
try:
data_set = self.__process_filter(data_filters, dlg.match_all)
if len(data_set) == 0:
Information(MSG_NO_CHECKED)
else:
# Uncheck all files (except the locked ones!) and all references
self.__data.set_object_state(States().UNUSED)
roots = list()
# Check files of the filtered data_set
for fn in data_set:
self.__data.set_object_state(States().CHECKED, fn)
root = self.__data.get_parent(fn)
if root not in roots:
roots.append(root)
Information(MSG_NB_CHECKED.format(len(data_set)))
# Check references matching the checked files
for fr in roots:
for ref in fr.get_references():
ref.set_state(States().CHECKED)
self.notify()
wx.EndBusyCursor()
except Exception as e:
wx.EndBusyCursor()
Error(ASS_ACT_CHECK_ERROR.format(str(e)), "Check error")
dlg.Destroy()
# ------------------------------------------------------------------------
def __process_filter(self, data_filters, match_all=True):
logging.info("Check files matching the following: ")
logging.info(" >>> filter = sppasFileDataFilters()")
f = sppasFileDataFilters(self.__data)
data_sets = list()
for d in data_filters:
if len(d) != 3:
logging.error("Bad data format: {:s}".format(str(d)))
continue
# the method to be uses by Compare
method = d[0]
# the function to be applied
fct = d[1]
if method == "att":
# identifier:value are separated by a ":" but a tuple is needed
values = tuple(d[2].split(":"))
logging.info(" >>> filter.{:s}({:s}={!s:s})".format(method, fct, str(values)))
data_set = getattr(f, method)(**{fct: values})
# a little bit of doc:
# - getattr() returns the value of the named attributed of object:
# it returns f.tag if called like getattr(f, "tag")
# - func(**{'x': '3'}) is equivalent to func(x='3')
else:
# all the possible values are separated by commas
values = d[2].split(",")
logging.info(" >>> filter.{:s}({:s}={!s:s})".format(method, fct, values[0]))
data_set = getattr(f, method)(**{fct: values[0]})
# Apply "or" between each data_set matching a value
for i in range(1, len(values)):
v = values[i].strip()
data_set = data_set | getattr(f, method)(**{fct: v})
logging.info(" >>> | filter.{:s}({:s}={!s:s})".format(method, fct, v))
data_sets.append(data_set)
# no filename is matching
if len(data_sets) == 0:
return list()
# Merge results (apply '&' or '|' on the resulting data sets)
data_set = data_sets[0]
if match_all is True:
for i in range(1, len(data_sets)):
data_set = data_set & data_sets[i]
if len(data_set) == 0:
# no need to go further...
return list()
else:
for i in range(1, len(data_sets)):
data_set = data_set | data_sets[i]
return data_set
# ------------------------------------------------------------------------
def check_all(self):
# reverse the current state
self._checkall = not self._checkall
# ask the data to change their state
if self._checkall is True:
state = States().CHECKED
else:
state = States().UNUSED
self.__data.set_object_state(state)
# update the view of checked references & checked files
self.notify()
# ------------------------------------------------------------------------
def add_links(self):
associed = self.__data.associate()
if associed > 0:
self.notify()
# ------------------------------------------------------------------------
def delete_links(self):
dissocied = self.__data.dissociate()
if dissocied > 0:
self.notify()
# ---------------------------------------------------------------------------
class sppasFilesFilterDialog(sppasDialog):
def __init__(self, parent):
super(sppasFilesFilterDialog, self).__init__(
parent=parent,
title='{:s} Files selection'.format(sg.__name__),
style=wx.DEFAULT_FRAME_STYLE)
self.match_all = True
self.CreateHeader(title="Define filters to check files",
icon_name="check_filter")
self._create_content()
self._create_buttons()
self.Bind(wx.EVT_BUTTON, self._process_event)
self.SetSize(wx.Size(480, 320))
self.LayoutComponents()
self.CenterOnParent()
self.FadeIn(deltaN=-8)
# -----------------------------------------------------------------------
# Public methods
# -----------------------------------------------------------------------
def get_filters(self):
filters = list()
for i in range(self.listctrl.GetItemCount()):
filter_name = self.listctrl.GetValue(i, 0)
fct_name = self.listctrl.GetValue(i, 1)
values = self.listctrl.GetValue(i, 2)
filters.append((filter_name, fct_name, values))
return filters
# -----------------------------------------------------------------------
# Methods to construct the GUI
# -----------------------------------------------------------------------
def _create_content(self):
panel = sppasPanel(self, name="content")
tb = self.__create_toolbar(panel)
self.listctrl = wx.dataview.DataViewListCtrl(panel, wx.ID_ANY)
self.listctrl.AppendTextColumn("filter", width=80)
self.listctrl.AppendTextColumn("function", width=90)
self.listctrl.AppendTextColumn("value", width=120)
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(tb, proportion=0, flag=wx.EXPAND, border=0)
sizer.Add(self.listctrl, proportion=1, flag=wx.EXPAND | wx.LEFT | wx.RIGHT, border=5)
panel.SetSizer(sizer)
self.SetMinSize(wx.Size(320, 200))
panel.SetAutoLayout(True)
self.SetContent(panel)
# -----------------------------------------------------------------------
def __create_toolbar(self, parent):
tb = sppasToolbar(parent)
tb.set_focus_color(wx.Colour(196, 196, 96, 128))
tb.AddTextButton("filter_path", "+ Path")
tb.AddTextButton("filter_name", "+ Name")
tb.AddTextButton("filter_ext", "+ Type")
tb.AddTextButton("filter_ref", "+ Ref.")
tb.AddTextButton("filter_att", "+ Value")
tb.AddSpacer()
#tb.AddTextButton(None, "- Remove")
return tb
# -----------------------------------------------------------------------
def _create_buttons(self):
panel = sppasPanel(self, name="actions")
# panel.SetMinSize(wx.Size(-1, wx.GetApp().settings.action_height))
sizer = wx.BoxSizer(wx.HORIZONTAL)
# Create the buttons
cancel_btn = self.__create_action_button(panel, "Cancel", "cancel")
apply_or_btn = self.__create_action_button(panel, "Apply - OR", "apply")
apply_and_btn = self.__create_action_button(panel, "Apply - AND", "ok")
apply_and_btn.SetFocus()
sizer.Add(cancel_btn, 1, wx.ALL | wx.EXPAND, 0)
sizer.Add(self.VertLine(parent=panel), 0, wx.ALL | wx.EXPAND, 0)
sizer.Add(apply_or_btn, 1, wx.ALL | wx.EXPAND, 0)
sizer.Add(self.VertLine(parent=panel), 0, wx.ALL | wx.EXPAND, 0)
sizer.Add(apply_and_btn, 1, wx.ALL | wx.EXPAND, 0)
panel.SetSizer(sizer)
self.SetActions(panel)
# -----------------------------------------------------------------------
def __create_action_button(self, parent, text, icon):
btn = BitmapTextButton(parent, label=text, name=icon)
btn.LabelPosition = wx.RIGHT
btn.Spacing = sppasDialog.fix_size(12)
btn.BorderWidth = 0
btn.BitmapColour = self.GetForegroundColour()
btn.SetMinSize(wx.Size(sppasDialog.fix_size(32),
sppasDialog.fix_size(32)))
return btn
# ------------------------------------------------------------------------
# Callback to events
# ------------------------------------------------------------------------
def _process_event(self, event):
event_obj = event.GetEventObject()
event_name = event_obj.GetName()
if event_name == "filter_path":
self.__append_filter("path")
elif event_name == "filter_name":
self.__append_filter("name")
elif event_name == "filter_ext":
self.__append_filter("extension")
elif event_name == "filter_ref":
self.__append_filter("ref")
elif event_name == "filter_att":
dlg = sppasAttributeFilterDialog(self)
response = dlg.ShowModal()
if response == wx.ID_OK:
# Name of the method in sppasFileDataFilters,
# Name of the function and its value
f = dlg.get_data()
v = f[1].split(':')
if len(v[0].strip()) > 1 and len(v[1].strip()) > 0:
self.listctrl.AppendItem(["att", f[0], f[1].strip()])
else:
logging.error("Invalid input string for identifier or value.")
dlg.Destroy()
elif event_name == "cancel":
self.SetReturnCode(wx.ID_CANCEL)
self.Close()
elif event_name == "apply":
self.match_all = False
self.EndModal(wx.ID_APPLY)
elif event_name == "ok":
self.match_all = True
self.EndModal(wx.ID_OK)
else:
event.Skip()
# ------------------------------------------------------------------------
def __append_filter(self, fct):
dlg = sppasStringFilterDialog(self)
response = dlg.ShowModal()
if response == wx.ID_OK:
# Name of the method in sppasFileDataFilters,
# Name of the function and its value
f = dlg.get_data()
if len(f[1].strip()) > 0:
self.listctrl.AppendItem([fct, f[0], f[1].strip()])
else:
logging.error("Empty input pattern.")
dlg.Destroy()
# ---------------------------------------------------------------------------
class sppasStringFilterDialog(sppasDialog):
choices = (
("exact", "exact"),
("contains", "contains"),
("starts with", "startswith"),
("ends with", "endswith"),
("match (regexp)", "regexp"),
("not exact", "exact"),
("not contains", "contains"),
("not starts with", "startswith"),
("not ends with", "endswith"),
("not match", "regexp")
)
def __init__(self, parent):
super(sppasStringFilterDialog, self).__init__(
parent=parent,
title='{:s} filter'.format(sg.__name__),
style=wx.DEFAULT_FRAME_STYLE)
self._create_content()
self.CreateActions([wx.ID_CANCEL, wx.ID_OK])
self.SetSize(wx.Size(380, 320))
self.LayoutComponents()
self.CenterOnParent()
# -----------------------------------------------------------------------
def get_data(self):
idx = self.radiobox.GetSelection()
label = self.radiobox.GetStringSelection()
given_fct = self.choices[idx][1]
# Fill the resulting dict
prepend_fct = ""
if given_fct != "regexp":
# prepend "not_" if reverse
if "not" in label:
prepend_fct += "not_"
# prepend "i" if case-insensitive
if self.checkbox.GetValue() is False:
prepend_fct += "i"
return prepend_fct+given_fct, self.text.GetValue()
# -----------------------------------------------------------------------
# Methods to construct the GUI
# -----------------------------------------------------------------------
def _create_content(self):
panel = sppasPanel(self, name="content")
label = sppasStaticText(panel, label="Search for pattern(s): ")
self.text = sppasTextCtrl(panel, value="")
choices = [row[0] for row in self.choices]
self.radiobox = sppasRadioBoxPanel(
panel,
choices=choices,
majorDimension=2,
style=wx.RA_SPECIFY_COLS)
self.radiobox.SetSelection(1)
self.checkbox = CheckButton(panel, label="Case sensitive")
self.checkbox.SetValue(False)
# Layout
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(label, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.text, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.radiobox, 1, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.checkbox, 0, flag=wx.EXPAND | wx.ALL, border=4)
panel.SetSizer(sizer)
panel.SetMinSize((240, 160))
panel.SetAutoLayout(True)
self.SetContent(panel)
# ---------------------------------------------------------------------------
class sppasAttributeFilterDialog(sppasDialog):
choices = (
("exact", "exact"),
("contains", "contains"),
("starts with", "startswith"),
("ends with", "endswith"),
("match (regexp)", "regexp"),
("not exact", "exact"),
("not contains", "contains"),
("not starts with", "startswith"),
("not ends with", "endswith"),
("not match", "regexp"),
("equal", "equal"),
("greater than", "gt"),
("greater or equal", "ge"),
("lower than", "lt"),
("lower or equal", "le")
)
def __init__(self, parent):
super(sppasAttributeFilterDialog, self).__init__(
parent=parent,
title='{:s} filter'.format(sg.__name__),
style=wx.DEFAULT_FRAME_STYLE)
self._create_content()
self.CreateActions([wx.ID_CANCEL, wx.ID_OK])
self.SetMinSize(wx.Size(sppasDialog.fix_size(420),
sppasDialog.fix_size(320)))
self.LayoutComponents()
self.CenterOnParent()
# ------------------------------------------------------------------------
def get_data(self):
idx = self.radiobox.GetSelection()
label = self.radiobox.GetStringSelection()
given_fct = self.choices[idx][1]
# Fill the resulting dict
prepend_fct = ""
if idx < 10 and given_fct != "regexp":
# prepend "not_" if reverse
if "not" in label:
prepend_fct += "not_"
# prepend "i" if case-insensitive
if self.checkbox.GetValue() is False:
prepend_fct += "i"
return prepend_fct + given_fct, \
self.text_ident.GetValue() + ":" + self.text_value.GetValue()
# -----------------------------------------------------------------------
# Methods to construct the GUI
# -----------------------------------------------------------------------
def _create_content(self):
panel = sppasPanel(self, name="content")
label = sppasStaticText(panel, label="Identifier: ")
self.text_ident = sppasTextCtrl(
panel,
value="",
validator=IdentifierTextValidator())
choices = [row[0] for row in sppasAttributeFilterDialog.choices]
self.radiobox = sppasRadioBoxPanel(
panel,
choices=choices,
majorDimension=3,
style=wx.RA_SPECIFY_COLS)
self.radiobox.SetSelection(1)
self.radiobox.Bind(wx.EVT_RADIOBOX, self._on_radiobox_checked)
self.checkbox = CheckButton(panel, label="Case sensitive")
self.checkbox.SetValue(False)
self.text_value = sppasTextCtrl(panel, value="")
# Layout
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(label, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.text_ident, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.radiobox, 1, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.text_value, 0, flag=wx.EXPAND | wx.ALL, border=4)
sizer.Add(self.checkbox, 0, flag=wx.EXPAND | wx.ALL, border=4)
panel.SetSizer(sizer)
panel.SetMinSize((240, 160))
panel.SetAutoLayout(True)
self.SetContent(panel)
def _on_radiobox_checked(self, event):
value = self.radiobox.GetStringSelection()
if value in sppasAttributeFilterDialog.choices[10:]:
self.checkbox.SetValue(False)
self.checkbox.Enable(False)
else:
self.checkbox.Enable(True)
| true
| true
|
f7168c18be9b4f3d5729f54a2173850564c1755b
| 617
|
py
|
Python
|
src/DoorControl_manual.py
|
oulkaid/MoSIG-SystemDesign-WCET
|
bbe640ebbad7a372e4bd826a3334a69f3aca28e7
|
[
"MIT"
] | null | null | null |
src/DoorControl_manual.py
|
oulkaid/MoSIG-SystemDesign-WCET
|
bbe640ebbad7a372e4bd826a3334a69f3aca28e7
|
[
"MIT"
] | null | null | null |
src/DoorControl_manual.py
|
oulkaid/MoSIG-SystemDesign-WCET
|
bbe640ebbad7a372e4bd826a3334a69f3aca28e7
|
[
"MIT"
] | null | null | null |
start_addr = []
next_addr = []
start_addr.append(['8478'])
next_addr.append(['84a4','8498'])
start_addr.append(['8498'])
next_addr.append(['84b0','84a4'])
start_addr.append(['84a4'])
next_addr.append(['84b0'])
start_addr.append(['84b0'])
next_addr.append(['84e0','84d4'])
start_addr.append(['84d4'])
next_addr.append(['84ec','84e0'])
start_addr.append(['84e0'])
next_addr.append(['84ec'])
start_addr.append(['84ec'])
next_addr.append(['8514','84fc'])
for index in range(len(start_addr)):
print("BB " + str(index+1) + ": Start@ = " + str(start_addr[index]) + " , Next@ = " + str(next_addr[index]))
| 20.566667
| 117
| 0.648298
|
start_addr = []
next_addr = []
start_addr.append(['8478'])
next_addr.append(['84a4','8498'])
start_addr.append(['8498'])
next_addr.append(['84b0','84a4'])
start_addr.append(['84a4'])
next_addr.append(['84b0'])
start_addr.append(['84b0'])
next_addr.append(['84e0','84d4'])
start_addr.append(['84d4'])
next_addr.append(['84ec','84e0'])
start_addr.append(['84e0'])
next_addr.append(['84ec'])
start_addr.append(['84ec'])
next_addr.append(['8514','84fc'])
for index in range(len(start_addr)):
print("BB " + str(index+1) + ": Start@ = " + str(start_addr[index]) + " , Next@ = " + str(next_addr[index]))
| true
| true
|
f7168c43af7fe79343018a0288432cf66f89741a
| 1,124
|
py
|
Python
|
app/auth/views.py
|
0xff-dev/SutAcmDRA
|
f3e744c19f66f930deb3f921b75d5f1189354b41
|
[
"MIT"
] | 2
|
2018-07-16T16:14:32.000Z
|
2018-07-23T06:48:29.000Z
|
app/auth/views.py
|
0xff-dev/SutAcmDRA
|
f3e744c19f66f930deb3f921b75d5f1189354b41
|
[
"MIT"
] | 3
|
2021-06-08T19:23:40.000Z
|
2021-12-13T19:56:54.000Z
|
app/auth/views.py
|
stevenshuang/SutAcmDRA
|
f3e744c19f66f930deb3f921b75d5f1189354b41
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# coding=utf-8
from flask import render_template, request
from flask import redirect, url_for, flash
from flask_login import login_user
from flask_login import login_required
from flask_login import logout_user
from . import auth
from ..models import User
from .. import logger
@auth.route('/login', methods=['GET', 'POST'])
def login():
if request.method == 'GET':
return render_template('login.html')
else:
user_name = request.form.get('username')
passwd = request.form.get('password')
try:
user = User.query.filter_by(hdoj_username=user_name).first()
except Exception as e:
logger.info('用户不存在')
if user is not None and user.verify_password(passwd):
login_user(user)
return redirect(
request.args.get('next') or
url_for('main.index', )
)
flash('Invald Username Or Password')
@auth.route('/logout')
@login_required
def logout():
logout_user()
flash('You have been logged out')
return redirect(url_for('main.index'))
| 28.1
| 72
| 0.6379
|
from flask import render_template, request
from flask import redirect, url_for, flash
from flask_login import login_user
from flask_login import login_required
from flask_login import logout_user
from . import auth
from ..models import User
from .. import logger
@auth.route('/login', methods=['GET', 'POST'])
def login():
if request.method == 'GET':
return render_template('login.html')
else:
user_name = request.form.get('username')
passwd = request.form.get('password')
try:
user = User.query.filter_by(hdoj_username=user_name).first()
except Exception as e:
logger.info('用户不存在')
if user is not None and user.verify_password(passwd):
login_user(user)
return redirect(
request.args.get('next') or
url_for('main.index', )
)
flash('Invald Username Or Password')
@auth.route('/logout')
@login_required
def logout():
logout_user()
flash('You have been logged out')
return redirect(url_for('main.index'))
| true
| true
|
f7168e80f5e8ed5edf7f7288316f9a3d0d8192e3
| 5,675
|
py
|
Python
|
nimiqclient/models/block.py
|
rraallvv/python-client
|
65d0c3f835ed8ce3ba6bfa2565cac61f7da6b748
|
[
"Apache-2.0"
] | 4
|
2020-11-03T21:13:13.000Z
|
2022-01-18T08:40:27.000Z
|
nimiqclient/models/block.py
|
rraallvv/python-client
|
65d0c3f835ed8ce3ba6bfa2565cac61f7da6b748
|
[
"Apache-2.0"
] | 1
|
2020-08-09T21:36:02.000Z
|
2020-08-09T21:36:02.000Z
|
nimiqclient/models/block.py
|
rraallvv/python-client
|
65d0c3f835ed8ce3ba6bfa2565cac61f7da6b748
|
[
"Apache-2.0"
] | 1
|
2020-08-03T01:05:44.000Z
|
2020-08-03T01:05:44.000Z
|
__all__ = ["Block", "BlockTemplateHeader", "BlockTemplateBody", "BlockTemplate"]
from .transaction import Transaction
class Block:
"""
Block returned by the server.
:param number: Height of the block.
:type number: int
:param hash: Hex-encoded 32-byte hash of the block.
:type hash: str
:param pow: Hex-encoded 32-byte Proof-of-Work hash of the block.
:type pow: str
:param parentHash: Hex-encoded 32-byte hash of the predecessor block.
:type parentHash: str
:param nonce: The nonce of the block used to fulfill the Proof-of-Work.
:type nonce: int
:param bodyHash: Hex-encoded 32-byte hash of the block body Merkle root.
:type bodyHash: str
:param accountsHash: Hex-encoded 32-byte hash of the accounts tree root.
:type accountsHash: str
:param difficulty: Block difficulty, encoded as decimal number in string.
:type difficulty: str
:param timestamp: UNIX timestamp of the block.
:type timestamp: int
:param confirmations: Number of confirmations for this transaction (number of blocks on top of the block where this transaction was in).
:type confirmations: int
:param miner: Hex-encoded 20 byte address of the miner of the block.
:type miner: str
:param minerAddress: User friendly address (NQ-address) of the miner of the block.
:type minerAddress: str
:param extraData: Hex-encoded value of the extra data field, maximum of 255 bytes.
:type extraData: str
:param size: Block size in byte.
:type size: int
:param transactions: List of transactions. Either represented by the transaction hash or a Transaction object.
:type transactions: list of (Transaction or str)
"""
def __init__(
self,
number,
hash,
pow,
parentHash,
nonce,
bodyHash,
accountsHash,
difficulty,
timestamp,
confirmations,
miner,
minerAddress,
extraData,
size,
transactions,
):
self.number = number
self.hash = hash
self.pow = pow
self.parentHash = parentHash
self.nonce = nonce
self.bodyHash = bodyHash
self.accountsHash = accountsHash
self.difficulty = difficulty
self.timestamp = timestamp
self.confirmations = confirmations
self.miner = miner
self.minerAddress = minerAddress
self.extraData = extraData
self.size = size
for index, transaction in enumerate(transactions):
tt = type(transaction)
if tt is not str and tt is not Transaction:
if tt is dict:
transactions[index] = Transaction(**transaction)
else:
from ..nimiq_client import InternalErrorException
raise InternalErrorException(
"Couldn't parse Transaction {0}".format(transaction)
)
self.transactions = transactions
class BlockTemplateHeader:
"""
Block template header returned by the server.
:param version: Version in block header.
:type version: int
:param prevHash: 32-byte hex-encoded hash of the previous block.
:type prevHash: str
:param interlinkHash: 32-byte hex-encoded hash of the interlink.
:type interlinkHash: str
:param accountsHash: 32-byte hex-encoded hash of the accounts tree.
:type accountsHash: str
:param nBits: Compact form of the hash target for this block.
:type nBits: int
:param height: Height of the block in the block chain (also known as block number).
:type height: int
"""
def __init__(self, version, prevHash, interlinkHash, accountsHash, nBits, height):
self.version = version
self.prevHash = prevHash
self.interlinkHash = interlinkHash
self.accountsHash = accountsHash
self.nBits = nBits
self.height = height
class BlockTemplateBody:
"""
Block template body returned by the server.
:param hash: 32-byte hex-encoded hash of the block body.
:type hash: str
:param minerAddr: 20-byte hex-encoded miner address.
:type minerAddr: str
:param extraData: Hex-encoded value of the extra data field.
:type extraData: str
:param transactions: List of hex-encoded transactions for this block.
:type transactions: str
:param prunedAccounts: List of hex-encoded pruned accounts for this block.
:type prunedAccounts: str
:param merkleHashes: List of hex-encoded hashes that verify the path of the miner address in the merkle tree. This can be used to change the miner address easily.
:type merkleHashes: str
"""
def __init__(
self, hash, minerAddr, extraData, transactions, prunedAccounts, merkleHashes
):
self.hash = hash
self.minerAddr = minerAddr
self.extraData = extraData
self.transactions = transactions
self.prunedAccounts = prunedAccounts
self.merkleHashes = merkleHashes
class BlockTemplate:
"""
Block template returned by the server.
:param header: Block template header returned by the server.
:type header: BlockTemplateHeader
:param interlink: Hex-encoded interlink.
:type interlink: str
:param body: Block template body returned by the server.
:type body: BlockTemplateBody
:param target: Compact form of the hash target to submit a block to this client.
:type target: int
"""
def __init__(self, header, interlink, body, target):
self.header = header
self.interlink = interlink
self.body = body
self.target = target
| 34.815951
| 166
| 0.66185
|
__all__ = ["Block", "BlockTemplateHeader", "BlockTemplateBody", "BlockTemplate"]
from .transaction import Transaction
class Block:
def __init__(
self,
number,
hash,
pow,
parentHash,
nonce,
bodyHash,
accountsHash,
difficulty,
timestamp,
confirmations,
miner,
minerAddress,
extraData,
size,
transactions,
):
self.number = number
self.hash = hash
self.pow = pow
self.parentHash = parentHash
self.nonce = nonce
self.bodyHash = bodyHash
self.accountsHash = accountsHash
self.difficulty = difficulty
self.timestamp = timestamp
self.confirmations = confirmations
self.miner = miner
self.minerAddress = minerAddress
self.extraData = extraData
self.size = size
for index, transaction in enumerate(transactions):
tt = type(transaction)
if tt is not str and tt is not Transaction:
if tt is dict:
transactions[index] = Transaction(**transaction)
else:
from ..nimiq_client import InternalErrorException
raise InternalErrorException(
"Couldn't parse Transaction {0}".format(transaction)
)
self.transactions = transactions
class BlockTemplateHeader:
def __init__(self, version, prevHash, interlinkHash, accountsHash, nBits, height):
self.version = version
self.prevHash = prevHash
self.interlinkHash = interlinkHash
self.accountsHash = accountsHash
self.nBits = nBits
self.height = height
class BlockTemplateBody:
def __init__(
self, hash, minerAddr, extraData, transactions, prunedAccounts, merkleHashes
):
self.hash = hash
self.minerAddr = minerAddr
self.extraData = extraData
self.transactions = transactions
self.prunedAccounts = prunedAccounts
self.merkleHashes = merkleHashes
class BlockTemplate:
def __init__(self, header, interlink, body, target):
self.header = header
self.interlink = interlink
self.body = body
self.target = target
| true
| true
|
f7168f6333b407972ae83a3eb73553f078b2cd44
| 7,304
|
py
|
Python
|
src/sage/combinat/kazhdan_lusztig.py
|
saraedum/sage-renamed
|
d2da67b14da2ad766a5906425d60d43a3b3e1270
|
[
"BSL-1.0"
] | null | null | null |
src/sage/combinat/kazhdan_lusztig.py
|
saraedum/sage-renamed
|
d2da67b14da2ad766a5906425d60d43a3b3e1270
|
[
"BSL-1.0"
] | null | null | null |
src/sage/combinat/kazhdan_lusztig.py
|
saraedum/sage-renamed
|
d2da67b14da2ad766a5906425d60d43a3b3e1270
|
[
"BSL-1.0"
] | null | null | null |
r"""
Kazhdan-Lusztig Polynomials
AUTHORS:
- Daniel Bump (2008): initial version
- Alan J.X. Guo (2014-03-18): ``R_tilde()`` method.
"""
#*****************************************************************************
# Copyright (C) 2008 Daniel Bump <bump at match.stanford.edu>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
# http://www.gnu.org/licenses/
#*****************************************************************************
from __future__ import absolute_import, print_function, division
from sage.rings.polynomial.polynomial_element import is_Polynomial
from sage.misc.cachefunc import cached_method
from sage.rings.polynomial.laurent_polynomial import LaurentPolynomial
from sage.structure.sage_object import SageObject
from sage.structure.unique_representation import UniqueRepresentation
class KazhdanLusztigPolynomial(UniqueRepresentation, SageObject):
"""
A Kazhdan-Lusztig polynomial.
INPUT:
- ``W`` -- a Weyl Group
- ``q`` -- an indeterminate
OPTIONAL:
- ``trace`` -- if ``True``, then this displays the trace: the intermediate
results. This is instructive and fun.
The parent of ``q`` may be a :class:`PolynomialRing` or a
:class:`LaurentPolynomialRing`.
REFERENCES:
.. [KL79] \D. Kazhdan and G. Lusztig. *Representations of Coxeter
groups and Hecke algebras*. Invent. Math. **53** (1979).
no. 2, 165--184. :doi:`10.1007/BF01390031` :mathscinet:`MR0560412`
.. [Dy93] \M. J. Dyer. *Hecke algebras and shellings of Bruhat
intervals*. Compositio Mathematica, 1993, 89(1): 91-115.
.. [BB05] \A. Bjorner, F. Brenti. *Combinatorics of Coxeter
groups*. New York: Springer, 2005.
EXAMPLES::
sage: W = WeylGroup("B3",prefix="s")
sage: [s1,s2,s3] = W.simple_reflections()
sage: R.<q> = LaurentPolynomialRing(QQ)
sage: KL = KazhdanLusztigPolynomial(W,q)
sage: KL.P(s2,s3*s2*s3*s1*s2)
1 + q
A faster implementation (using the optional package Coxeter 3) is given by::
sage: W = CoxeterGroup(['B', 3], implementation='coxeter3') # optional - coxeter3
sage: W.kazhdan_lusztig_polynomial([2], [3,2,3,1,2]) # optional - coxeter3
q + 1
"""
def __init__(self, W, q, trace=False):
"""
Initialize ``self``.
EXAMPLES::
sage: W = WeylGroup("B3",prefix="s")
sage: R.<q> = LaurentPolynomialRing(QQ)
sage: KL = KazhdanLusztigPolynomial(W,q)
sage: TestSuite(KL).run()
"""
self._coxeter_group = W
self._q = q
self._trace = trace
self._one = W.one()
self._base_ring = q.parent()
if is_Polynomial(q):
self._base_ring_type = "polynomial"
elif isinstance(q, LaurentPolynomial):
self._base_ring_type = "laurent"
else:
self._base_ring_type = "unknown"
@cached_method
def R(self, x, y):
"""
Return the Kazhdan-Lusztig `R` polynomial.
INPUT:
- ``x``, ``y`` -- elements of the underlying Coxeter group
EXAMPLES::
sage: R.<q>=QQ[]
sage: W = WeylGroup("A2", prefix="s")
sage: [s1,s2]=W.simple_reflections()
sage: KL = KazhdanLusztigPolynomial(W, q)
sage: [KL.R(x,s2*s1) for x in [1,s1,s2,s1*s2]]
[q^2 - 2*q + 1, q - 1, q - 1, 0]
"""
if x == 1:
x = self._one
if y == 1:
y = self._one
if x == y:
return self._base_ring.one()
if not x.bruhat_le(y):
return self._base_ring.zero()
if y.length() == 0:
if x.length() == 0:
return self._base_ring.one()
else:
return self._base_ring.zero()
s = self._coxeter_group.simple_reflection(y.first_descent(side="left"))
if (s*x).length() < x.length():
ret = self.R(s*x,s*y)
if self._trace:
print(" R(%s,%s)=%s" % (x, y, ret))
return ret
else:
ret = (self._q-1)*self.R(s*x,y)+self._q*self.R(s*x,s*y)
if self._trace:
print(" R(%s,%s)=%s" % (x, y, ret))
return ret
@cached_method
def R_tilde(self, x, y):
r"""
Return the Kazhdan-Lusztig `\tilde{R}` polynomial.
Information about the `\tilde{R}` polynomials can be found in
[Dy93]_ and [BB05]_.
INPUT:
- ``x``, ``y`` -- elements of the underlying Coxeter group
EXAMPLES::
sage: R.<q> = QQ[]
sage: W = WeylGroup("A2", prefix="s")
sage: [s1,s2] = W.simple_reflections()
sage: KL = KazhdanLusztigPolynomial(W, q)
sage: [KL.R_tilde(x,s2*s1) for x in [1,s1,s2,s1*s2]]
[q^2, q, q, 0]
"""
if x == 1:
x = self._one
if y == 1:
y = self._one
if not x.bruhat_le(y):
return self._base_ring.zero()
if x == y:
return self._base_ring.one()
s = self._coxeter_group.simple_reflection(y.first_descent(side="right"))
if (x * s).length() < x.length():
ret = self.R_tilde(x * s, y * s)
if self._trace:
print(" R_tilde(%s,%s)=%s" % (x, y, ret))
return ret
else:
ret = self.R_tilde(x * s, y * s) + self._q * self.R_tilde(x, y * s)
if self._trace:
print(" R_tilde(%s,%s)=%s" % (x, y, ret))
return ret
@cached_method
def P(self, x, y):
"""
Return the Kazhdan-Lusztig `P` polynomial.
If the rank is large, this runs slowly at first but speeds up
as you do repeated calculations due to the caching.
INPUT:
- ``x``, ``y`` -- elements of the underlying Coxeter group
.. SEEALSO::
:mod:`~sage.libs.coxeter3.coxeter_group.CoxeterGroup.kazhdan_lusztig_polynomial`
for a faster implementation using Fokko Ducloux's Coxeter3 C++ library.
EXAMPLES::
sage: R.<q> = QQ[]
sage: W = WeylGroup("A3", prefix="s")
sage: [s1,s2,s3] = W.simple_reflections()
sage: KL = KazhdanLusztigPolynomial(W, q)
sage: KL.P(s2,s2*s1*s3*s2)
q + 1
"""
if x == 1:
x = self._one
if y == 1:
y = self._one
if x == y:
return self._base_ring.one()
if not x.bruhat_le(y):
return self._base_ring.zero()
if y.length() == 0:
if x.length() == 0:
return self._base_ring.one()
else:
return self._base_ring.zero()
p = sum(-self.R(x, t) * self.P(t, y)
for t in self._coxeter_group.bruhat_interval(x, y) if t != x)
tr = (y.length() - x.length() + 1) // 2
ret = p.truncate(tr)
if self._trace:
print(" P({},{})={}".format(x, y, ret))
return ret
| 32.035088
| 92
| 0.529299
|
from __future__ import absolute_import, print_function, division
from sage.rings.polynomial.polynomial_element import is_Polynomial
from sage.misc.cachefunc import cached_method
from sage.rings.polynomial.laurent_polynomial import LaurentPolynomial
from sage.structure.sage_object import SageObject
from sage.structure.unique_representation import UniqueRepresentation
class KazhdanLusztigPolynomial(UniqueRepresentation, SageObject):
def __init__(self, W, q, trace=False):
self._coxeter_group = W
self._q = q
self._trace = trace
self._one = W.one()
self._base_ring = q.parent()
if is_Polynomial(q):
self._base_ring_type = "polynomial"
elif isinstance(q, LaurentPolynomial):
self._base_ring_type = "laurent"
else:
self._base_ring_type = "unknown"
@cached_method
def R(self, x, y):
if x == 1:
x = self._one
if y == 1:
y = self._one
if x == y:
return self._base_ring.one()
if not x.bruhat_le(y):
return self._base_ring.zero()
if y.length() == 0:
if x.length() == 0:
return self._base_ring.one()
else:
return self._base_ring.zero()
s = self._coxeter_group.simple_reflection(y.first_descent(side="left"))
if (s*x).length() < x.length():
ret = self.R(s*x,s*y)
if self._trace:
print(" R(%s,%s)=%s" % (x, y, ret))
return ret
else:
ret = (self._q-1)*self.R(s*x,y)+self._q*self.R(s*x,s*y)
if self._trace:
print(" R(%s,%s)=%s" % (x, y, ret))
return ret
@cached_method
def R_tilde(self, x, y):
if x == 1:
x = self._one
if y == 1:
y = self._one
if not x.bruhat_le(y):
return self._base_ring.zero()
if x == y:
return self._base_ring.one()
s = self._coxeter_group.simple_reflection(y.first_descent(side="right"))
if (x * s).length() < x.length():
ret = self.R_tilde(x * s, y * s)
if self._trace:
print(" R_tilde(%s,%s)=%s" % (x, y, ret))
return ret
else:
ret = self.R_tilde(x * s, y * s) + self._q * self.R_tilde(x, y * s)
if self._trace:
print(" R_tilde(%s,%s)=%s" % (x, y, ret))
return ret
@cached_method
def P(self, x, y):
if x == 1:
x = self._one
if y == 1:
y = self._one
if x == y:
return self._base_ring.one()
if not x.bruhat_le(y):
return self._base_ring.zero()
if y.length() == 0:
if x.length() == 0:
return self._base_ring.one()
else:
return self._base_ring.zero()
p = sum(-self.R(x, t) * self.P(t, y)
for t in self._coxeter_group.bruhat_interval(x, y) if t != x)
tr = (y.length() - x.length() + 1) // 2
ret = p.truncate(tr)
if self._trace:
print(" P({},{})={}".format(x, y, ret))
return ret
| true
| true
|
f716900640c20a86067bea4f88ec8a40a962d91a
| 1,771
|
py
|
Python
|
server/apps/widgets/serializers/widget.py
|
Sergey-x/SibdevPractice
|
12e3f7e704af93911a0c4a66aad60733d2121e15
|
[
"MIT"
] | null | null | null |
server/apps/widgets/serializers/widget.py
|
Sergey-x/SibdevPractice
|
12e3f7e704af93911a0c4a66aad60733d2121e15
|
[
"MIT"
] | null | null | null |
server/apps/widgets/serializers/widget.py
|
Sergey-x/SibdevPractice
|
12e3f7e704af93911a0c4a66aad60733d2121e15
|
[
"MIT"
] | null | null | null |
from datetime import timedelta
from rest_framework import serializers
from ..models.widget import Widget
class BaseWidgetSerializer(serializers.ModelSerializer):
"""
This is the base serializer class for Widget model.
Other widget serializers must be inherited from it.
"""
class Meta:
model = Widget
fields = (
'id',
'category',
'limit',
'duration',
'criteria',
'color',
'creation_date',
)
class CreateWidgetSerializer(BaseWidgetSerializer):
color = serializers.CharField(max_length=10)
def validate_duration(self, value: timedelta):
"""
Check that duration is acceptable quantity of days.
"""
if value.days not in Widget.VALID_DURATION:
raise serializers.ValidationError(
f"You can chose only this day-periods {Widget.VALID_DURATION}."
)
return timedelta(days=value.days)
def validate_color(self, value):
"""
Check that color specified in hex correctly.
"""
if value.startswith('0x'):
return value[2:]
return value
def create(self, validated_data):
validated_data['owner'] = self.context['request'].user
return Widget.objects.create(**validated_data)
class DestroyWidgetSerializer(BaseWidgetSerializer):
pass
class ListWidgetSerializer(BaseWidgetSerializer):
class Meta(BaseWidgetSerializer.Meta):
fields = (
'id',
'category',
'limit',
'duration',
'criteria',
'color',
'creation_date',
'ending_date',
'sum',
'owner',
)
| 24.943662
| 79
| 0.579898
|
from datetime import timedelta
from rest_framework import serializers
from ..models.widget import Widget
class BaseWidgetSerializer(serializers.ModelSerializer):
class Meta:
model = Widget
fields = (
'id',
'category',
'limit',
'duration',
'criteria',
'color',
'creation_date',
)
class CreateWidgetSerializer(BaseWidgetSerializer):
color = serializers.CharField(max_length=10)
def validate_duration(self, value: timedelta):
if value.days not in Widget.VALID_DURATION:
raise serializers.ValidationError(
f"You can chose only this day-periods {Widget.VALID_DURATION}."
)
return timedelta(days=value.days)
def validate_color(self, value):
if value.startswith('0x'):
return value[2:]
return value
def create(self, validated_data):
validated_data['owner'] = self.context['request'].user
return Widget.objects.create(**validated_data)
class DestroyWidgetSerializer(BaseWidgetSerializer):
pass
class ListWidgetSerializer(BaseWidgetSerializer):
class Meta(BaseWidgetSerializer.Meta):
fields = (
'id',
'category',
'limit',
'duration',
'criteria',
'color',
'creation_date',
'ending_date',
'sum',
'owner',
)
| true
| true
|
f716900a4d34d8a8bb3af91bb18c52ab94dfe78e
| 2,563
|
py
|
Python
|
src/server.py
|
alamminsalo/lua-webshop
|
2f0f1123a9d693103c4dbaef02836777e779844c
|
[
"MIT"
] | 1
|
2016-09-12T16:16:33.000Z
|
2016-09-12T16:16:33.000Z
|
src/server.py
|
alamminsalo/py-webshop
|
2f0f1123a9d693103c4dbaef02836777e779844c
|
[
"MIT"
] | null | null | null |
src/server.py
|
alamminsalo/py-webshop
|
2f0f1123a9d693103c4dbaef02836777e779844c
|
[
"MIT"
] | null | null | null |
# Restfull api using falcon framework
from wsgiref import simple_server
import falcon
import json
#Resource endpoints import
from cartsResource import *
from productsResource import *
# Check that client has application/json in Accept header
# and Content-Type, if request has body
class RequireJSON(object):
def process_request(self, req, resp):
if not req.client_accepts_json:
raise falcon.HTTPNotAcceptable(
'This API only supports responses encoded as JSON.',
href='http://docs.examples.com/api/json')
if req.method in ('POST', 'PUT'):
if 'application/json' not in req.content_type:
raise falcon.HTTPUnsupportedMediaType(
'This API only supports requests encoded as JSON.',
href='http://docs.examples.com/api/json')
#JSON builder func for both incoming and outgoing messages
class JSONBuilder(object):
def process_request(self, req, resp):
if req.content_length in (None, 0):
# Nothing to do
return
body = req.stream.read()
if not body:
raise falcon.HTTPBadRequest('Empty request body',
'A valid JSON document is required.')
try:
req.context['doc'] = json.loads(body.decode('utf-8'))
except (ValueError, UnicodeDecodeError):
raise falcon.HTTPError(falcon.HTTP_753,
'Malformed JSON',
'Could not decode the request body. The '
'JSON was incorrect or not encoded as '
'UTF-8.')
def process_response(self, req, resp, resource):
if 'result' not in req.context:
return
resp.body = json.dumps(req.context['result'])
api = falcon.API(middleware=[
RequireJSON(),
JSONBuilder()
])
#Add api endpoints
products = ProductsResource()
api.add_route('/api/products', products)
product = ProductResource()
api.add_route('/api/products/{productId}', product)
shopcart = ShopCartResource()
api.add_route('/api/shopcarts/{userId}', shopcart)
shopcartProducts = ShopCartProductsResource()
api.add_route('/api/shopcarts/{userId}/products', shopcartProducts)
#Start the server
if __name__ == '__main__':
print("Staring server in 127.0.0.1:8000")
httpd = simple_server.make_server('127.0.0.1', 8000, api)
httpd.serve_forever()
| 29.802326
| 81
| 0.60359
|
from wsgiref import simple_server
import falcon
import json
from cartsResource import *
from productsResource import *
class RequireJSON(object):
def process_request(self, req, resp):
if not req.client_accepts_json:
raise falcon.HTTPNotAcceptable(
'This API only supports responses encoded as JSON.',
href='http://docs.examples.com/api/json')
if req.method in ('POST', 'PUT'):
if 'application/json' not in req.content_type:
raise falcon.HTTPUnsupportedMediaType(
'This API only supports requests encoded as JSON.',
href='http://docs.examples.com/api/json')
class JSONBuilder(object):
def process_request(self, req, resp):
if req.content_length in (None, 0):
return
body = req.stream.read()
if not body:
raise falcon.HTTPBadRequest('Empty request body',
'A valid JSON document is required.')
try:
req.context['doc'] = json.loads(body.decode('utf-8'))
except (ValueError, UnicodeDecodeError):
raise falcon.HTTPError(falcon.HTTP_753,
'Malformed JSON',
'Could not decode the request body. The '
'JSON was incorrect or not encoded as '
'UTF-8.')
def process_response(self, req, resp, resource):
if 'result' not in req.context:
return
resp.body = json.dumps(req.context['result'])
api = falcon.API(middleware=[
RequireJSON(),
JSONBuilder()
])
products = ProductsResource()
api.add_route('/api/products', products)
product = ProductResource()
api.add_route('/api/products/{productId}', product)
shopcart = ShopCartResource()
api.add_route('/api/shopcarts/{userId}', shopcart)
shopcartProducts = ShopCartProductsResource()
api.add_route('/api/shopcarts/{userId}/products', shopcartProducts)
if __name__ == '__main__':
print("Staring server in 127.0.0.1:8000")
httpd = simple_server.make_server('127.0.0.1', 8000, api)
httpd.serve_forever()
| true
| true
|
f7169146ec3baeb91915b19f26b91545909663f8
| 149
|
py
|
Python
|
foundation/migrate.py
|
shreyashah115/foundation
|
42f19d23cfda77bae533f4884aecc15b3cd07f14
|
[
"MIT"
] | null | null | null |
foundation/migrate.py
|
shreyashah115/foundation
|
42f19d23cfda77bae533f4884aecc15b3cd07f14
|
[
"MIT"
] | null | null | null |
foundation/migrate.py
|
shreyashah115/foundation
|
42f19d23cfda77bae533f4884aecc15b3cd07f14
|
[
"MIT"
] | null | null | null |
import frappe
from frappe.database import Database
from markdown2 import markdown
from frappe.utils import validate_email_add
def migrate():
pass
| 16.555556
| 43
| 0.832215
|
import frappe
from frappe.database import Database
from markdown2 import markdown
from frappe.utils import validate_email_add
def migrate():
pass
| true
| true
|
f716914b86089ef9a2f697d9a1dfeb32bd90f95f
| 599
|
py
|
Python
|
HelloWorld/Models/admin.py
|
xautshuanglong/PythonWebApp
|
68ae417121740e4115f0e56c12a2a4831df30fc1
|
[
"MIT"
] | null | null | null |
HelloWorld/Models/admin.py
|
xautshuanglong/PythonWebApp
|
68ae417121740e4115f0e56c12a2a4831df30fc1
|
[
"MIT"
] | null | null | null |
HelloWorld/Models/admin.py
|
xautshuanglong/PythonWebApp
|
68ae417121740e4115f0e56c12a2a4831df30fc1
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from Models.models import UserInfo, Question, Choice
admin.site.register(UserInfo)
# admin.site.register(Question)
# admin.site.register(Choice)
class ChoiceInline(admin.StackedInline):
model = Choice
extra = 3
class QuestionAdmin(admin.ModelAdmin):
fieldsets = [
(None, {'fields': ['question_text']}),
('Date information', {'fields': ['pub_date'], 'classes':['collapse']}),
]
inlines = [ChoiceInline]
list_display = ('question_text', 'pub_date')
admin.site.register(Question, QuestionAdmin)
| 23.038462
| 80
| 0.661102
|
from django.contrib import admin
from Models.models import UserInfo, Question, Choice
admin.site.register(UserInfo)
class ChoiceInline(admin.StackedInline):
model = Choice
extra = 3
class QuestionAdmin(admin.ModelAdmin):
fieldsets = [
(None, {'fields': ['question_text']}),
('Date information', {'fields': ['pub_date'], 'classes':['collapse']}),
]
inlines = [ChoiceInline]
list_display = ('question_text', 'pub_date')
admin.site.register(Question, QuestionAdmin)
| true
| true
|
f71693818cb726573e4d1f282b8d61fe8a87df6b
| 678
|
py
|
Python
|
resource-timing/resources/fake_responses.py
|
shs96c/web-platform-tests
|
61acad6dd9bb99d32340eb41f5146de64f542359
|
[
"BSD-3-Clause"
] | 1
|
2022-03-19T09:43:35.000Z
|
2022-03-19T09:43:35.000Z
|
resource-timing/resources/fake_responses.py
|
shs96c/web-platform-tests
|
61acad6dd9bb99d32340eb41f5146de64f542359
|
[
"BSD-3-Clause"
] | 1
|
2021-12-13T19:49:45.000Z
|
2021-12-13T19:49:45.000Z
|
resource-timing/resources/fake_responses.py
|
shs96c/web-platform-tests
|
61acad6dd9bb99d32340eb41f5146de64f542359
|
[
"BSD-3-Clause"
] | 1
|
2021-04-06T20:06:58.000Z
|
2021-04-06T20:06:58.000Z
|
# /xhr/resources/conditional.py -- to fake a 304 response
def main(request, response):
tag = request.GET.first("tag", None)
match = request.headers.get("If-None-Match", None)
date = request.GET.first("date", "")
modified = request.headers.get("If-Modified-Since", None)
if tag:
response.headers.set("ETag", '"%s"' % tag)
elif date:
response.headers.set("Last-Modified", date)
if ((match is not None and match == tag) or
(modified is not None and modified == date)):
response.status = (304, "SUPERCOOL")
return ""
else:
response.headers.set("Content-Type", "text/plain")
return "MAYBE NOT"
| 35.684211
| 61
| 0.613569
|
def main(request, response):
tag = request.GET.first("tag", None)
match = request.headers.get("If-None-Match", None)
date = request.GET.first("date", "")
modified = request.headers.get("If-Modified-Since", None)
if tag:
response.headers.set("ETag", '"%s"' % tag)
elif date:
response.headers.set("Last-Modified", date)
if ((match is not None and match == tag) or
(modified is not None and modified == date)):
response.status = (304, "SUPERCOOL")
return ""
else:
response.headers.set("Content-Type", "text/plain")
return "MAYBE NOT"
| true
| true
|
f71695c7f499b72a9e1e239c8622fe0294a9c60f
| 3,752
|
py
|
Python
|
personal/Ervin/run_knn_collaborative_item.py
|
edervishaj/spotify-recsys-challenge
|
4077201ac7e4ed9da433bd10a92c183614182437
|
[
"Apache-2.0"
] | 3
|
2018-10-12T20:19:57.000Z
|
2019-12-11T01:11:38.000Z
|
personal/Ervin/run_knn_collaborative_item.py
|
kiminh/spotify-recsys-challenge
|
5e7844a77ce3c26658400f161d2d74d682f30e69
|
[
"Apache-2.0"
] | null | null | null |
personal/Ervin/run_knn_collaborative_item.py
|
kiminh/spotify-recsys-challenge
|
5e7844a77ce3c26658400f161d2d74d682f30e69
|
[
"Apache-2.0"
] | 4
|
2018-10-27T20:30:18.000Z
|
2020-10-14T07:43:27.000Z
|
from utils.datareader import Datareader
from utils.evaluator import Evaluator
from utils.submitter import Submitter
from utils.post_processing import eurm_to_recommendation_list_submission
from utils.post_processing import eurm_to_recommendation_list
from utils.pre_processing import norm_l1_row, norm_max_row, norm_max_col
from recommenders.knn_collaborative_item import Knn_collaborative_item
import recommenders.similarity.similarity as sm
import scipy.sparse as sps
import sys
import numpy as np
from personal.Ervin.other_similarity import position_similarity
'''
This file contains just an example on how to run the algorithm.
The parameter used are just the result of a first research of the optimum value.
To run this file just set the parameter at the start of the main function or set from console as argv parameter.
As argv you can even set mode of execution (online, offline) and the name of the result file
'''
if __name__ == '__main__':
### Select execution mode: 'offline', 'online' ###
mode = "offline"
name = "CFitem"
knn = 200
topk = 750
if len(sys.argv) > 1:
mode = sys.argv[1]
name = sys.argv[2]
knn = int(sys.argv[3])
topk = int(sys.argv[4])
complete_name = mode+"_"+name+"_knn="+str(knn)+"_topk="+str(topk)
if mode == "offline":
"""Test Set"""
#Data initialization
dr = Datareader(verbose=True, mode=mode, only_load=True)
#Evaluetor initialization
ev = Evaluator(dr)
#Recommender algorithm initialization
rec = Knn_collaborative_item()
#Getting for the recommender algorithm
urm = dr.get_urm()
urm.data = np.ones(len(urm.data))
position_urm = dr.get_position_matrix(position_type='last')
pos_urm = position_urm.T.tocoo().tocsr()
pid = dr.get_test_pids()
#Fitting data
rec.fit(urm, pid)
#Computing similarity/model
rec.compute_model(top_k= knn, sm_type=sm.TVERSKY, shrink=200, alpha=0.1, beta=1, binary=True, verbose=True)
rec.model = rec.model.tocsr()
rec.model.eliminate_zeros()
# rec.model = norm_max_row(rec.model)
print('Initial model has {:2} data'.format(len(rec.model.data)))
print('[ Updating the model ]')
rec.model = position_similarity(rec.model, pos_urm, knn=knn, verbose=True)
rec.model.eliminate_zeros()
print('New model has {:2} data'.format(len(rec.model.data)))
#Computing ratings
rec.compute_rating(top_k=topk,verbose=True, small=True, remove_seed=False)
#evaluation and saving
sps.save_npz(complete_name+".npz", rec.eurm)
ev.evaluate(recommendation_list=eurm_to_recommendation_list(rec.eurm, datareader=dr, remove_seed=True),
name=name, old_mode=False)
if mode == "online":
"""Submission"""
#Data initialization
dr = Datareader(verbose=True, mode=mode, only_load=False)
#Recommender algorithm initialization
rec = Knn_collaborative_item()
#Submitter initialization
sb = Submitter(dr)
#Getting for the recommender algorithm
urm = dr.get_urm()
pid = dr.get_test_pids()
#Fitting data
rec.fit(urm, pid)
#Computing similarity/model
rec.compute_model(top_k=knn, sm_type=sm.TVERSKY,shrink=200, alpha=0.1, beta=1, binary=True, verbose=True)
#Computing ratings
rec.compute_rating(top_k=topk, verbose=True, small=True)
#submission
sps.save_npz(complete_name+".npz", rec.eurm)
sb.submit(recommendation_list=eurm_to_recommendation_list_submission(rec.eurm), name=name, track="main", verify=True, gzipped=False)
| 32.912281
| 140
| 0.676972
|
from utils.datareader import Datareader
from utils.evaluator import Evaluator
from utils.submitter import Submitter
from utils.post_processing import eurm_to_recommendation_list_submission
from utils.post_processing import eurm_to_recommendation_list
from utils.pre_processing import norm_l1_row, norm_max_row, norm_max_col
from recommenders.knn_collaborative_item import Knn_collaborative_item
import recommenders.similarity.similarity as sm
import scipy.sparse as sps
import sys
import numpy as np
from personal.Ervin.other_similarity import position_similarity
if __name__ == '__main__':
mode = sys.argv[1]
name = sys.argv[2]
knn = int(sys.argv[3])
topk = int(sys.argv[4])
complete_name = mode+"_"+name+"_knn="+str(knn)+"_topk="+str(topk)
if mode == "offline":
dr = Datareader(verbose=True, mode=mode, only_load=True)
ev = Evaluator(dr)
rec = Knn_collaborative_item()
urm = dr.get_urm()
urm.data = np.ones(len(urm.data))
position_urm = dr.get_position_matrix(position_type='last')
pos_urm = position_urm.T.tocoo().tocsr()
pid = dr.get_test_pids()
rec.fit(urm, pid)
rec.compute_model(top_k= knn, sm_type=sm.TVERSKY, shrink=200, alpha=0.1, beta=1, binary=True, verbose=True)
rec.model = rec.model.tocsr()
rec.model.eliminate_zeros()
print('Initial model has {:2} data'.format(len(rec.model.data)))
print('[ Updating the model ]')
rec.model = position_similarity(rec.model, pos_urm, knn=knn, verbose=True)
rec.model.eliminate_zeros()
print('New model has {:2} data'.format(len(rec.model.data)))
rec.compute_rating(top_k=topk,verbose=True, small=True, remove_seed=False)
sps.save_npz(complete_name+".npz", rec.eurm)
ev.evaluate(recommendation_list=eurm_to_recommendation_list(rec.eurm, datareader=dr, remove_seed=True),
name=name, old_mode=False)
if mode == "online":
dr = Datareader(verbose=True, mode=mode, only_load=False)
rec = Knn_collaborative_item()
sb = Submitter(dr)
urm = dr.get_urm()
pid = dr.get_test_pids()
rec.fit(urm, pid)
rec.compute_model(top_k=knn, sm_type=sm.TVERSKY,shrink=200, alpha=0.1, beta=1, binary=True, verbose=True)
rec.compute_rating(top_k=topk, verbose=True, small=True)
sps.save_npz(complete_name+".npz", rec.eurm)
sb.submit(recommendation_list=eurm_to_recommendation_list_submission(rec.eurm), name=name, track="main", verify=True, gzipped=False)
| true
| true
|
f71695f6ec0e1e0a59e525bb30d5fb132322b04b
| 564
|
py
|
Python
|
wagtail/core/migrations/0036_populate_page_last_published_at.py
|
brownaa/wagtail
|
c97bc56c6822eb1b6589d5c33e07f71acfc48845
|
[
"BSD-3-Clause"
] | 8,851
|
2016-12-09T19:01:45.000Z
|
2022-03-31T04:45:06.000Z
|
wagtail/core/migrations/0036_populate_page_last_published_at.py
|
brownaa/wagtail
|
c97bc56c6822eb1b6589d5c33e07f71acfc48845
|
[
"BSD-3-Clause"
] | 5,197
|
2016-12-09T19:24:37.000Z
|
2022-03-31T22:17:55.000Z
|
wagtail/core/migrations/0036_populate_page_last_published_at.py
|
brownaa/wagtail
|
c97bc56c6822eb1b6589d5c33e07f71acfc48845
|
[
"BSD-3-Clause"
] | 2,548
|
2016-12-09T18:16:55.000Z
|
2022-03-31T21:34:38.000Z
|
# -*- coding: utf-8 -*-
# Generated by Django 1.11.1 on 2017-06-01 11:03
from django.db import migrations
from django.db.models import F
def forwards_func(apps, schema_editor):
Page = apps.get_model("wagtailcore", "Page")
Page.objects.filter(has_unpublished_changes=False).update(last_published_at=F('latest_revision_created_at'))
class Migration(migrations.Migration):
dependencies = [
('wagtailcore', '0035_page_last_published_at'),
]
operations = [
migrations.RunPython(forwards_func, migrations.RunPython.noop),
]
| 26.857143
| 112
| 0.719858
|
from django.db import migrations
from django.db.models import F
def forwards_func(apps, schema_editor):
Page = apps.get_model("wagtailcore", "Page")
Page.objects.filter(has_unpublished_changes=False).update(last_published_at=F('latest_revision_created_at'))
class Migration(migrations.Migration):
dependencies = [
('wagtailcore', '0035_page_last_published_at'),
]
operations = [
migrations.RunPython(forwards_func, migrations.RunPython.noop),
]
| true
| true
|
f71696347edb214d95d4dd5dbb9b50d0df5285c4
| 5,287
|
py
|
Python
|
integration-testing/test/test_multiple_deploys.py
|
ArturGajowy/rchain
|
7785a9195a4863a89f8aa22743e245c3e5f7940c
|
[
"Apache-2.0"
] | null | null | null |
integration-testing/test/test_multiple_deploys.py
|
ArturGajowy/rchain
|
7785a9195a4863a89f8aa22743e245c3e5f7940c
|
[
"Apache-2.0"
] | null | null | null |
integration-testing/test/test_multiple_deploys.py
|
ArturGajowy/rchain
|
7785a9195a4863a89f8aa22743e245c3e5f7940c
|
[
"Apache-2.0"
] | 1
|
2018-09-12T10:26:23.000Z
|
2018-09-12T10:26:23.000Z
|
import logging
import contextlib
import threading
from typing import (
TYPE_CHECKING,
Generator,
)
import pytest
import conftest
from rnode_testing.common import TestingContext
from rnode_testing.rnode import (
docker_network_with_started_bootstrap,
started_peer,
)
from rnode_testing.wait import (
wait_for_blocks_count_at_least,
wait_for_approved_block_received_handler_state,
)
if TYPE_CHECKING:
from _pytest.fixtures import SubRequest
from docker.client import DockerClient
from rnode_testing.rnode import Node
class DeployThread(threading.Thread):
def __init__(self, name, node, contract, count):
threading.Thread.__init__(self)
self.name = name
self.node = node
self.contract = contract
self.count = count
logging.info(f"Setup thread - {self.contract} to node {self.name}, amount {count}.")
def run(self):
for i in range(self.count):
logging.info(f"[{self.name}]-[{i}] Will deploy {self.contract}.")
d = self.node.deploy(self.contract)
logging.info(f"[{self.name}]-[{i}] Deploy {self.contract}: {d}")
p = self.node.propose()
logging.info(f"[{self.name}]-[{i}] Proposed {self.contract}: {p}")
s = self.node.show_blocks_with_depth(1)
logging.info(f"[{self.name}]-[{i}] Show blocks: {s}")
BOOTSTRAP_NODE_KEYS = conftest.KeyPair(private_key='80366db5fbb8dad7946f27037422715e4176dda41d582224db87b6c3b783d709', public_key='1cd8bf79a2c1bd0afa160f6cdfeb8597257e48135c9bf5e4823f2875a1492c97')
BONDED_VALIDATOR_KEY_1 = conftest.KeyPair(private_key='120d42175739387af0264921bb117e4c4c05fbe2ce5410031e8b158c6e414bb5', public_key='02ab69930f74b931209df3ce54e3993674ab3e7c98f715608a5e74048b332821')
BONDED_VALIDATOR_KEY_2 = conftest.KeyPair(private_key='120d42175739387af0264921bb117e4c4c05fbe2ce5410031e8b158c6e414bb5', public_key='02ab69930f74b931209df3ce54e3993674ab3e7c98f715608a5e74048b332821')
BONDED_VALIDATOR_KEY_3 = conftest.KeyPair(private_key='120d42175739387af0264921bb117e4c4c05fbe2ce5410031e8b158c6e414bb5', public_key='02ab69930f74b931209df3ce54e3993674ab3e7c98f715608a5e74048b332821')
@contextlib.contextmanager
def started_bonded_validator(context: TestingContext, bootstrap_node: "Node", no, key_pair) -> Generator["Node", None, None]:
with started_peer(
context=context,
network=bootstrap_node.network,
name='bonded-validator-' + str(no),
bootstrap=bootstrap_node,
key_pair=key_pair,
) as bonded_validator:
wait_for_approved_block_received_handler_state(bonded_validator, context.node_startup_timeout)
yield bonded_validator
@pytest.mark.xfail
def test_multiple_deploys_at_once(command_line_options_fixture, docker_client_fixture) -> None:
contract_path = '/opt/docker/examples/hello_world_again.rho'
peers_keypairs = [BONDED_VALIDATOR_KEY_1, BONDED_VALIDATOR_KEY_2, BONDED_VALIDATOR_KEY_3]
with conftest.testing_context(command_line_options_fixture, docker_client_fixture, bootstrap_keypair=BOOTSTRAP_NODE_KEYS, peers_keypairs=peers_keypairs) as context:
with docker_network_with_started_bootstrap(context=context) as bootstrap_node:
with started_bonded_validator(context, bootstrap_node, 1, BONDED_VALIDATOR_KEY_1) as no1:
with started_bonded_validator(context, bootstrap_node, 2, BONDED_VALIDATOR_KEY_2) as no2:
with started_bonded_validator(context, bootstrap_node, 3, BONDED_VALIDATOR_KEY_3) as no3:
deploy1 = DeployThread("node1", no1, contract_path, 1)
deploy1.start()
expected_blocks_count = 1
max_retrieved_blocks = 1
wait_for_blocks_count_at_least(
no1,
expected_blocks_count,
max_retrieved_blocks,
expected_blocks_count * 10,
)
deploy2 = DeployThread("node2", no2, contract_path, 3)
deploy2.start()
deploy3 = DeployThread("node3", no3, contract_path, 3)
deploy3.start()
expected_blocks_count = 7
max_retrieved_blocks = 7
wait_for_blocks_count_at_least(
no1,
expected_blocks_count,
max_retrieved_blocks,
480
)
wait_for_blocks_count_at_least(
no2,
expected_blocks_count,
max_retrieved_blocks,
expected_blocks_count * 10,
)
wait_for_blocks_count_at_least(
no3,
expected_blocks_count,
max_retrieved_blocks,
expected_blocks_count * 10,
)
deploy1.join()
deploy2.join()
deploy3.join()
| 44.058333
| 200
| 0.635521
|
import logging
import contextlib
import threading
from typing import (
TYPE_CHECKING,
Generator,
)
import pytest
import conftest
from rnode_testing.common import TestingContext
from rnode_testing.rnode import (
docker_network_with_started_bootstrap,
started_peer,
)
from rnode_testing.wait import (
wait_for_blocks_count_at_least,
wait_for_approved_block_received_handler_state,
)
if TYPE_CHECKING:
from _pytest.fixtures import SubRequest
from docker.client import DockerClient
from rnode_testing.rnode import Node
class DeployThread(threading.Thread):
def __init__(self, name, node, contract, count):
threading.Thread.__init__(self)
self.name = name
self.node = node
self.contract = contract
self.count = count
logging.info(f"Setup thread - {self.contract} to node {self.name}, amount {count}.")
def run(self):
for i in range(self.count):
logging.info(f"[{self.name}]-[{i}] Will deploy {self.contract}.")
d = self.node.deploy(self.contract)
logging.info(f"[{self.name}]-[{i}] Deploy {self.contract}: {d}")
p = self.node.propose()
logging.info(f"[{self.name}]-[{i}] Proposed {self.contract}: {p}")
s = self.node.show_blocks_with_depth(1)
logging.info(f"[{self.name}]-[{i}] Show blocks: {s}")
BOOTSTRAP_NODE_KEYS = conftest.KeyPair(private_key='80366db5fbb8dad7946f27037422715e4176dda41d582224db87b6c3b783d709', public_key='1cd8bf79a2c1bd0afa160f6cdfeb8597257e48135c9bf5e4823f2875a1492c97')
BONDED_VALIDATOR_KEY_1 = conftest.KeyPair(private_key='120d42175739387af0264921bb117e4c4c05fbe2ce5410031e8b158c6e414bb5', public_key='02ab69930f74b931209df3ce54e3993674ab3e7c98f715608a5e74048b332821')
BONDED_VALIDATOR_KEY_2 = conftest.KeyPair(private_key='120d42175739387af0264921bb117e4c4c05fbe2ce5410031e8b158c6e414bb5', public_key='02ab69930f74b931209df3ce54e3993674ab3e7c98f715608a5e74048b332821')
BONDED_VALIDATOR_KEY_3 = conftest.KeyPair(private_key='120d42175739387af0264921bb117e4c4c05fbe2ce5410031e8b158c6e414bb5', public_key='02ab69930f74b931209df3ce54e3993674ab3e7c98f715608a5e74048b332821')
@contextlib.contextmanager
def started_bonded_validator(context: TestingContext, bootstrap_node: "Node", no, key_pair) -> Generator["Node", None, None]:
with started_peer(
context=context,
network=bootstrap_node.network,
name='bonded-validator-' + str(no),
bootstrap=bootstrap_node,
key_pair=key_pair,
) as bonded_validator:
wait_for_approved_block_received_handler_state(bonded_validator, context.node_startup_timeout)
yield bonded_validator
@pytest.mark.xfail
def test_multiple_deploys_at_once(command_line_options_fixture, docker_client_fixture) -> None:
contract_path = '/opt/docker/examples/hello_world_again.rho'
peers_keypairs = [BONDED_VALIDATOR_KEY_1, BONDED_VALIDATOR_KEY_2, BONDED_VALIDATOR_KEY_3]
with conftest.testing_context(command_line_options_fixture, docker_client_fixture, bootstrap_keypair=BOOTSTRAP_NODE_KEYS, peers_keypairs=peers_keypairs) as context:
with docker_network_with_started_bootstrap(context=context) as bootstrap_node:
with started_bonded_validator(context, bootstrap_node, 1, BONDED_VALIDATOR_KEY_1) as no1:
with started_bonded_validator(context, bootstrap_node, 2, BONDED_VALIDATOR_KEY_2) as no2:
with started_bonded_validator(context, bootstrap_node, 3, BONDED_VALIDATOR_KEY_3) as no3:
deploy1 = DeployThread("node1", no1, contract_path, 1)
deploy1.start()
expected_blocks_count = 1
max_retrieved_blocks = 1
wait_for_blocks_count_at_least(
no1,
expected_blocks_count,
max_retrieved_blocks,
expected_blocks_count * 10,
)
deploy2 = DeployThread("node2", no2, contract_path, 3)
deploy2.start()
deploy3 = DeployThread("node3", no3, contract_path, 3)
deploy3.start()
expected_blocks_count = 7
max_retrieved_blocks = 7
wait_for_blocks_count_at_least(
no1,
expected_blocks_count,
max_retrieved_blocks,
480
)
wait_for_blocks_count_at_least(
no2,
expected_blocks_count,
max_retrieved_blocks,
expected_blocks_count * 10,
)
wait_for_blocks_count_at_least(
no3,
expected_blocks_count,
max_retrieved_blocks,
expected_blocks_count * 10,
)
deploy1.join()
deploy2.join()
deploy3.join()
| true
| true
|
f716972ac65d7ac3dcdcd16d28ba7fac85854f2f
| 4,791
|
py
|
Python
|
estonian_learner/verb.py
|
natter1/estonian_learner
|
da7837f0d64f4c1f6a212a9c473252c4b834699a
|
[
"MIT"
] | null | null | null |
estonian_learner/verb.py
|
natter1/estonian_learner
|
da7837f0d64f4c1f6a212a9c473252c4b834699a
|
[
"MIT"
] | null | null | null |
estonian_learner/verb.py
|
natter1/estonian_learner
|
da7837f0d64f4c1f6a212a9c473252c4b834699a
|
[
"MIT"
] | null | null | null |
"""
@author: Nathanael Jöhrmann
"""
import json
import textwrap
class Conjugations:
def __init__(self):
self.person = {}
self.negative = ["", ""]
self.passive = ["", ""]
self.passive_negative = ["", ""]
@property
def summary(self) -> str:
result = ""
sep = " "
for i in range(1, 7):
try:
result += sep.join([f"{i}P", self.person[str(i)][0], self.person[i][1]]) + "\n"
except KeyError: # needed, if data where put to json and back (int becomes str)
result += sep.join([f"{i}P", self.person[str(i)][0], self.person[str(i)][1]]) + "\n"
result += sep.join(["negative", self.negative[0], self.negative[1]]) + "\n"
result += sep.join(["passive", self.passive[0], self.passive[1]]) + "\n"
result += sep.join(["passive negative", self.passive_negative[0], self.passive_negative[1]]) + "\n"
return result
# def add_person(self, original, translation, index):
# self.person[index] = original
# self.person_translation[index] = translation
def to_json(self) -> dict:
"""
Returns a jsonified dict containing the data of self.
:return: dict
"""
my_dict = {"person": self.person,
"negative": self.negative,
"passive": self.passive,
"passive_negative": self.passive_negative
}
result = json.loads(json.dumps(my_dict, indent=2, ensure_ascii=False))
# print((my_dict))
# print(result)
# return my_dict # todo: test if this works with SQLAlchemy
return result
def from_json(self, _json: dict) -> None:
self.person = _json["person"]
self.negative = _json["negative"]
self.passive = _json["passive"]
self.passive_negative = _json["passive_negative"]
class Verb:
def __init__(self):
self.infinitive_ma = ("", "")
self.infinitive_da = ("", "")
self.past_active_participle = ("", "")
self.past_passive_participle = ("", "")
self.present = Conjugations()
self.conditional_mood = Conjugations()
self.imperative_mood = Conjugations()
self.imperative_negative_mood = Conjugations()
self.perfect = Conjugations()
self.past = Conjugations()
self.plusperfect = Conjugations()
self.conditional_perfect_mood = Conjugations()
self.quotative = Conjugations()
self.quotative_perfect = Conjugations()
self.jussive = Conjugations()
self.jussive_perfect = Conjugations()
self.other = {}
self.usage_info = ""
self.audio = None
@property
def summary(self) -> str:
result = textwrap.dedent(f"""\
---------------------------------------------------------
Usage info:
{self.usage_info}\n
Infinitive (-ma -da translation):
{self.infinitive_ma[0]} {self.infinitive_da[0]} {self.infinitive_ma[1]}\n
Past active participle:
{self.past_active_participle[0]} {self.past_active_participle[1]}\n
Past passive participle:
{self.past_passive_participle[0]} {self.past_passive_participle[1]}
""")
result += "\nPast passive participle\n"
result += self.past_passive_participle[0] + " " + self.past_passive_participle[1] + "\n"
result += "\nPresent tense\n"
result += self.present.summary
result += "\nConditional mood\n"
result += self.conditional_mood.summary
result += "\nImperative mood\n"
result += self.imperative_mood.summary
result += "\nImperative negative mood\n"
result += self.imperative_negative_mood.summary
result += "\nPerfect tense\n"
result += self.perfect.summary
result += "\nPast tense\n"
result += self.past.summary
result += "\nPlusperfect tense\n"
result += self.plusperfect.summary
result += "\nConditional perfect mood\n"
result += self.conditional_perfect_mood.summary
result += "\nQuotative tense\n"
result += self.quotative.summary
result += "\nQuotative perfect tense\n"
result += self.quotative_perfect.summary
result += "\nJussive tense\n"
result += self.jussive.summary
result += "\nJussive perfect tense\n"
result += self.jussive_perfect.summary
result += "\nOther\n"
for key in self.other:
result += key + " " + self.other[key][0] + " " + self.other[key][1] + "\n"
result += "---------------------------------------------------------\n"
return result
| 33.041379
| 107
| 0.557921
|
import json
import textwrap
class Conjugations:
def __init__(self):
self.person = {}
self.negative = ["", ""]
self.passive = ["", ""]
self.passive_negative = ["", ""]
@property
def summary(self) -> str:
result = ""
sep = " "
for i in range(1, 7):
try:
result += sep.join([f"{i}P", self.person[str(i)][0], self.person[i][1]]) + "\n"
except KeyError:
result += sep.join([f"{i}P", self.person[str(i)][0], self.person[str(i)][1]]) + "\n"
result += sep.join(["negative", self.negative[0], self.negative[1]]) + "\n"
result += sep.join(["passive", self.passive[0], self.passive[1]]) + "\n"
result += sep.join(["passive negative", self.passive_negative[0], self.passive_negative[1]]) + "\n"
return result
def to_json(self) -> dict:
my_dict = {"person": self.person,
"negative": self.negative,
"passive": self.passive,
"passive_negative": self.passive_negative
}
result = json.loads(json.dumps(my_dict, indent=2, ensure_ascii=False))
self, _json: dict) -> None:
self.person = _json["person"]
self.negative = _json["negative"]
self.passive = _json["passive"]
self.passive_negative = _json["passive_negative"]
class Verb:
def __init__(self):
self.infinitive_ma = ("", "")
self.infinitive_da = ("", "")
self.past_active_participle = ("", "")
self.past_passive_participle = ("", "")
self.present = Conjugations()
self.conditional_mood = Conjugations()
self.imperative_mood = Conjugations()
self.imperative_negative_mood = Conjugations()
self.perfect = Conjugations()
self.past = Conjugations()
self.plusperfect = Conjugations()
self.conditional_perfect_mood = Conjugations()
self.quotative = Conjugations()
self.quotative_perfect = Conjugations()
self.jussive = Conjugations()
self.jussive_perfect = Conjugations()
self.other = {}
self.usage_info = ""
self.audio = None
@property
def summary(self) -> str:
result = textwrap.dedent(f"""\
---------------------------------------------------------
Usage info:
{self.usage_info}\n
Infinitive (-ma -da translation):
{self.infinitive_ma[0]} {self.infinitive_da[0]} {self.infinitive_ma[1]}\n
Past active participle:
{self.past_active_participle[0]} {self.past_active_participle[1]}\n
Past passive participle:
{self.past_passive_participle[0]} {self.past_passive_participle[1]}
""")
result += "\nPast passive participle\n"
result += self.past_passive_participle[0] + " " + self.past_passive_participle[1] + "\n"
result += "\nPresent tense\n"
result += self.present.summary
result += "\nConditional mood\n"
result += self.conditional_mood.summary
result += "\nImperative mood\n"
result += self.imperative_mood.summary
result += "\nImperative negative mood\n"
result += self.imperative_negative_mood.summary
result += "\nPerfect tense\n"
result += self.perfect.summary
result += "\nPast tense\n"
result += self.past.summary
result += "\nPlusperfect tense\n"
result += self.plusperfect.summary
result += "\nConditional perfect mood\n"
result += self.conditional_perfect_mood.summary
result += "\nQuotative tense\n"
result += self.quotative.summary
result += "\nQuotative perfect tense\n"
result += self.quotative_perfect.summary
result += "\nJussive tense\n"
result += self.jussive.summary
result += "\nJussive perfect tense\n"
result += self.jussive_perfect.summary
result += "\nOther\n"
for key in self.other:
result += key + " " + self.other[key][0] + " " + self.other[key][1] + "\n"
result += "---------------------------------------------------------\n"
return result
| true
| true
|
f71697bced2a9b09c787e7d1ea296be902ceb742
| 2,498
|
py
|
Python
|
src/cobald/daemon/runners/asyncio_runner.py
|
thoto/cobald
|
27f7a0b5208383e8a7a386f358009a433084908e
|
[
"MIT"
] | 7
|
2019-06-11T12:57:10.000Z
|
2019-10-07T17:46:41.000Z
|
src/cobald/daemon/runners/asyncio_runner.py
|
thoto/cobald
|
27f7a0b5208383e8a7a386f358009a433084908e
|
[
"MIT"
] | 76
|
2019-03-01T08:24:08.000Z
|
2022-03-24T20:37:23.000Z
|
src/cobald/daemon/runners/asyncio_runner.py
|
thoto/cobald
|
27f7a0b5208383e8a7a386f358009a433084908e
|
[
"MIT"
] | 8
|
2019-06-27T13:06:12.000Z
|
2022-02-15T15:27:58.000Z
|
import asyncio
from functools import partial
from .base_runner import BaseRunner
from .async_tools import raise_return, AsyncExecution
class AsyncioRunner(BaseRunner):
"""Runner for coroutines with :py:mod:`asyncio`"""
flavour = asyncio
def __init__(self):
super().__init__()
self.event_loop = asyncio.new_event_loop()
self._tasks = set()
def register_payload(self, payload):
super().register_payload(partial(raise_return, payload))
def run_payload(self, payload):
execution = AsyncExecution(payload)
super().register_payload(execution.coroutine)
return execution.wait()
def _run(self):
asyncio.set_event_loop(self.event_loop)
self.event_loop.run_until_complete(self._run_payloads())
async def _run_payloads(self):
"""Async component of _run"""
delay = 0.0
try:
while self.running.is_set():
await self._start_payloads()
await self._reap_payloads()
await asyncio.sleep(delay)
delay = min(delay + 0.1, 1.0)
except Exception:
await self._cancel_payloads()
raise
async def _start_payloads(self):
"""Start all queued payloads"""
with self._lock:
for coroutine in self._payloads:
task = self.event_loop.create_task(coroutine())
self._tasks.add(task)
self._payloads.clear()
await asyncio.sleep(0)
async def _reap_payloads(self):
"""Clean up all finished payloads"""
for task in self._tasks.copy():
if task.done():
self._tasks.remove(task)
if task.exception() is not None:
raise task.exception()
await asyncio.sleep(0)
async def _cancel_payloads(self):
"""Cancel all remaining payloads"""
for task in self._tasks:
task.cancel()
await asyncio.sleep(0)
for task in self._tasks:
while not task.done():
await asyncio.sleep(0.1)
task.cancel()
def stop(self):
if not self.running.wait(0.2):
return
self._logger.debug("runner disabled: %s", self)
with self._lock:
self.running.clear()
for task in self._tasks:
task.cancel()
self._stopped.wait()
self.event_loop.stop()
self.event_loop.close()
| 30.463415
| 64
| 0.583667
|
import asyncio
from functools import partial
from .base_runner import BaseRunner
from .async_tools import raise_return, AsyncExecution
class AsyncioRunner(BaseRunner):
flavour = asyncio
def __init__(self):
super().__init__()
self.event_loop = asyncio.new_event_loop()
self._tasks = set()
def register_payload(self, payload):
super().register_payload(partial(raise_return, payload))
def run_payload(self, payload):
execution = AsyncExecution(payload)
super().register_payload(execution.coroutine)
return execution.wait()
def _run(self):
asyncio.set_event_loop(self.event_loop)
self.event_loop.run_until_complete(self._run_payloads())
async def _run_payloads(self):
delay = 0.0
try:
while self.running.is_set():
await self._start_payloads()
await self._reap_payloads()
await asyncio.sleep(delay)
delay = min(delay + 0.1, 1.0)
except Exception:
await self._cancel_payloads()
raise
async def _start_payloads(self):
with self._lock:
for coroutine in self._payloads:
task = self.event_loop.create_task(coroutine())
self._tasks.add(task)
self._payloads.clear()
await asyncio.sleep(0)
async def _reap_payloads(self):
for task in self._tasks.copy():
if task.done():
self._tasks.remove(task)
if task.exception() is not None:
raise task.exception()
await asyncio.sleep(0)
async def _cancel_payloads(self):
for task in self._tasks:
task.cancel()
await asyncio.sleep(0)
for task in self._tasks:
while not task.done():
await asyncio.sleep(0.1)
task.cancel()
def stop(self):
if not self.running.wait(0.2):
return
self._logger.debug("runner disabled: %s", self)
with self._lock:
self.running.clear()
for task in self._tasks:
task.cancel()
self._stopped.wait()
self.event_loop.stop()
self.event_loop.close()
| true
| true
|
f71697bcf73bda98b7059ee2d3fd8ac5e69857ec
| 2,881
|
py
|
Python
|
tests/test_fuzzy_completion.py
|
vijayraavi/mssql-cli
|
bc16073e371314b970479c2830266ff24d63bd16
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_fuzzy_completion.py
|
vijayraavi/mssql-cli
|
bc16073e371314b970479c2830266ff24d63bd16
|
[
"BSD-3-Clause"
] | null | null | null |
tests/test_fuzzy_completion.py
|
vijayraavi/mssql-cli
|
bc16073e371314b970479c2830266ff24d63bd16
|
[
"BSD-3-Clause"
] | null | null | null |
from __future__ import unicode_literals
import pytest
@pytest.fixture
def completer():
import mssqlcli.mssqlcompleter as mssqlcompleter
return mssqlcompleter.MssqlCompleter()
def test_ranking_ignores_identifier_quotes(completer):
"""When calculating result rank, identifier quotes should be ignored.
The result ranking algorithm ignores identifier quotes. Without this
correction, the match "user", which Postgres requires to be quoted
since it is also a reserved word, would incorrectly fall below the
match user_action because the literal quotation marks in "user"
alter the position of the match.
This test checks that the fuzzy ranking algorithm correctly ignores
quotation marks when computing match ranks.
"""
text = 'user'
collection = ['user_action', '"user"']
matches = completer.find_matches(text, collection)
assert len(matches) == 2
def test_ranking_based_on_shortest_match(completer):
"""Fuzzy result rank should be based on shortest match.
Result ranking in fuzzy searching is partially based on the length
of matches: shorter matches are considered more relevant than
longer ones. When searching for the text 'user', the length
component of the match 'user_group' could be either 4 ('user') or
7 ('user_gr').
This test checks that the fuzzy ranking algorithm uses the shorter
match when calculating result rank.
"""
text = 'user'
collection = ['api_user', 'user_group']
matches = completer.find_matches(text, collection)
assert matches[1].priority > matches[0].priority
@pytest.mark.parametrize('collection', [
['user_action', 'user'],
['user_group', 'user'],
['user_group', 'user_action'],
])
def test_should_break_ties_using_lexical_order(completer, collection):
"""Fuzzy result rank should use lexical order to break ties.
When fuzzy matching, if multiple matches have the same match length and
start position, present them in lexical (rather than arbitrary) order. For
example, if we have tables 'user', 'user_action', and 'user_group', a
search for the text 'user' should present these tables in this order.
The input collections to this test are out of order; each run checks that
the search text 'user' results in the input tables being reordered
lexically.
"""
text = 'user'
matches = completer.find_matches(text, collection)
assert matches[1].priority > matches[0].priority
def test_matching_should_be_case_insensitive(completer):
"""Fuzzy matching should keep matches even if letter casing doesn't match.
This test checks that variations of the text which have different casing
are still matched.
"""
text = 'foo'
collection = ['Foo', 'FOO', 'fOO']
matches = completer.find_matches(text, collection)
assert len(matches) == 3
| 32.370787
| 78
| 0.72579
|
from __future__ import unicode_literals
import pytest
@pytest.fixture
def completer():
import mssqlcli.mssqlcompleter as mssqlcompleter
return mssqlcompleter.MssqlCompleter()
def test_ranking_ignores_identifier_quotes(completer):
text = 'user'
collection = ['user_action', '"user"']
matches = completer.find_matches(text, collection)
assert len(matches) == 2
def test_ranking_based_on_shortest_match(completer):
text = 'user'
collection = ['api_user', 'user_group']
matches = completer.find_matches(text, collection)
assert matches[1].priority > matches[0].priority
@pytest.mark.parametrize('collection', [
['user_action', 'user'],
['user_group', 'user'],
['user_group', 'user_action'],
])
def test_should_break_ties_using_lexical_order(completer, collection):
text = 'user'
matches = completer.find_matches(text, collection)
assert matches[1].priority > matches[0].priority
def test_matching_should_be_case_insensitive(completer):
text = 'foo'
collection = ['Foo', 'FOO', 'fOO']
matches = completer.find_matches(text, collection)
assert len(matches) == 3
| true
| true
|
f71697d88f48702950eaef9afcbbc278acd2b761
| 606
|
py
|
Python
|
codewars/7 kyu/string-ends-with.py
|
sirken/coding-practice
|
9c5e23b2c24f525a89a5e1d15ce3aec3ad1a01ab
|
[
"MIT"
] | null | null | null |
codewars/7 kyu/string-ends-with.py
|
sirken/coding-practice
|
9c5e23b2c24f525a89a5e1d15ce3aec3ad1a01ab
|
[
"MIT"
] | null | null | null |
codewars/7 kyu/string-ends-with.py
|
sirken/coding-practice
|
9c5e23b2c24f525a89a5e1d15ce3aec3ad1a01ab
|
[
"MIT"
] | null | null | null |
from Test import Test, Test as test
'''
Complete the solution so that it returns true if the first argument(string) passed in ends with the 2nd argument (also a string).
Examples:
solution('abc', 'bc') # returns true
solution('abc', 'd') # returns false
'''
def solution(string, ending):
return True if string[-len(ending):] == ending or len(ending) == 0 else False
# Top solution
def solution(string, ending):
return string.endswith(ending)
test.assert_equals(solution('abcde', 'cde'), True)
test.assert_equals(solution('abcde', 'abc'), False)
test.assert_equals(solution('abcde', ''), True)
| 28.857143
| 129
| 0.714521
|
from Test import Test, Test as test
def solution(string, ending):
return True if string[-len(ending):] == ending or len(ending) == 0 else False
def solution(string, ending):
return string.endswith(ending)
test.assert_equals(solution('abcde', 'cde'), True)
test.assert_equals(solution('abcde', 'abc'), False)
test.assert_equals(solution('abcde', ''), True)
| true
| true
|
f7169805436b5cf887fbf1c99fd59f5c2e43d93c
| 7,209
|
py
|
Python
|
ros/src/styx/bridge.py
|
Valentinkvn/Udacity-Full-Autonomous-Vehicle-Project
|
b1313345a09f84c122a91c1145230fe69da0d20f
|
[
"MIT"
] | null | null | null |
ros/src/styx/bridge.py
|
Valentinkvn/Udacity-Full-Autonomous-Vehicle-Project
|
b1313345a09f84c122a91c1145230fe69da0d20f
|
[
"MIT"
] | 6
|
2021-02-22T08:44:36.000Z
|
2022-03-12T00:13:46.000Z
|
ros/src/styx/bridge.py
|
Valentinkvn/Udacity-Full-Autonomous-Vehicle-Project
|
b1313345a09f84c122a91c1145230fe69da0d20f
|
[
"MIT"
] | null | null | null |
import rospy
import tf
from geometry_msgs.msg import PoseStamped, Quaternion, TwistStamped
from dbw_mkz_msgs.msg import SteeringReport, ThrottleCmd, BrakeCmd, SteeringCmd
from std_msgs.msg import Float32 as Float
from std_msgs.msg import Bool
from sensor_msgs.msg import PointCloud2
from sensor_msgs.msg import Image
import sensor_msgs.point_cloud2 as pcl2
from std_msgs.msg import Header
from cv_bridge import CvBridge, CvBridgeError
from styx_msgs.msg import TrafficLight, TrafficLightArray, Lane
import numpy as np
from PIL import Image as PIL_Image
from io import BytesIO
import base64
import math
TYPE = {
'bool': Bool,
'float': Float,
'pose': PoseStamped,
'pcl': PointCloud2,
'twist': TwistStamped,
'steer': SteeringReport,
'trafficlights': TrafficLightArray,
'steer_cmd': SteeringCmd,
'brake_cmd': BrakeCmd,
'throttle_cmd': ThrottleCmd,
'path_draw': Lane,
'image':Image
}
NUM_IMAGES_TO_SKIP = 2
class Bridge(object):
def __init__(self, conf, server):
rospy.init_node('styx_server')
self.server = server
self.vel = 0.
self.yaw = None
self.angular_vel = 0.
self.bridge = CvBridge()
self.img_count = 0
self.callbacks = {
'/vehicle/steering_cmd': self.callback_steering,
'/vehicle/throttle_cmd': self.callback_throttle,
'/vehicle/brake_cmd': self.callback_brake,
'/final_waypoints': self.callback_path
}
self.subscribers = [rospy.Subscriber(e.topic, TYPE[e.type], self.callbacks[e.topic])
for e in conf.subscribers]
self.publishers = {e.name: rospy.Publisher(e.topic, TYPE[e.type], queue_size=1)
for e in conf.publishers}
def create_light(self, x, y, z, yaw, state):
light = TrafficLight()
light.header = Header()
light.header.stamp = rospy.Time.now()
light.header.frame_id = '/world'
light.pose = self.create_pose(x, y, z, yaw)
light.state = state
return light
def create_pose(self, x, y, z, yaw=0.):
pose = PoseStamped()
pose.header = Header()
pose.header.stamp = rospy.Time.now()
pose.header.frame_id = '/world'
pose.pose.position.x = x
pose.pose.position.y = y
pose.pose.position.z = z
q = tf.transformations.quaternion_from_euler(0., 0., math.pi * yaw/180.)
pose.pose.orientation = Quaternion(*q)
return pose
def create_float(self, val):
fl = Float()
fl.data = val
return fl
def create_twist(self, velocity, angular):
tw = TwistStamped()
tw.twist.linear.x = velocity
tw.twist.angular.z = angular
return tw
def create_steer(self, val):
st = SteeringReport()
st.steering_wheel_angle_cmd = val * math.pi/180.
st.enabled = True
st.speed = self.vel
return st
def calc_angular(self, yaw):
angular_vel = 0.
if self.yaw is not None:
angular_vel = (yaw - self.yaw)/(rospy.get_time() - self.prev_time)
self.yaw = yaw
self.prev_time = rospy.get_time()
return angular_vel
def create_point_cloud_message(self, pts):
header = Header()
header.stamp = rospy.Time.now()
header.frame_id = '/world'
cloud_message = pcl2.create_cloud_xyz32(header, pts)
return cloud_message
def broadcast_transform(self, name, position, orientation):
br = tf.TransformBroadcaster()
br.sendTransform(position,
orientation,
rospy.Time.now(),
name,
"world")
def publish_odometry(self, data):
pose = self.create_pose(data['x'], data['y'], data['z'], data['yaw'])
position = (data['x'], data['y'], data['z'])
orientation = tf.transformations.quaternion_from_euler(0, 0, math.pi * data['yaw']/180.)
self.broadcast_transform("base_link", position, orientation)
self.publishers['current_pose'].publish(pose)
self.vel = data['velocity']* 0.44704
self.angular = self.calc_angular(data['yaw'] * math.pi/180.)
self.publishers['current_velocity'].publish(self.create_twist(self.vel, self.angular))
def publish_controls(self, data):
steering, throttle, brake = data['steering_angle'], data['throttle'], data['brake']
self.publishers['steering_report'].publish(self.create_steer(steering))
self.publishers['throttle_report'].publish(self.create_float(throttle))
self.publishers['brake_report'].publish(self.create_float(brake))
def publish_obstacles(self, data):
for obs in data['obstacles']:
pose = self.create_pose(obs[0], obs[1], obs[2])
self.publishers['obstacle'].publish(pose)
header = Header()
header.stamp = rospy.Time.now()
header.frame_id = '/world'
cloud = pcl2.create_cloud_xyz32(header, data['obstacles'])
self.publishers['obstacle_points'].publish(cloud)
def publish_lidar(self, data):
self.publishers['lidar'].publish(self.create_point_cloud_message(zip(data['lidar_x'], data['lidar_y'], data['lidar_z'])))
def publish_traffic(self, data):
x, y, z = data['light_pos_x'], data['light_pos_y'], data['light_pos_z'],
yaw = [math.atan2(dy, dx) for dx, dy in zip(data['light_pos_dx'], data['light_pos_dy'])]
status = data['light_state']
lights = TrafficLightArray()
header = Header()
header.stamp = rospy.Time.now()
header.frame_id = '/world'
lights.lights = [self.create_light(*e) for e in zip(x, y, z, yaw, status)]
self.publishers['trafficlights'].publish(lights)
def publish_dbw_status(self, data):
self.publishers['dbw_status'].publish(Bool(data))
def publish_camera(self, data):
self.img_count += 1
if self.img_count >= NUM_IMAGES_TO_SKIP:
# rospy.logwarn("Publish camera data")
imgString = data["image"]
image = PIL_Image.open(BytesIO(base64.b64decode(imgString)))
image_array = np.asarray(image)
image_message = self.bridge.cv2_to_imgmsg(image_array, encoding="rgb8")
self.publishers['image'].publish(image_message)
self.img_count = 0
def callback_steering(self, data):
self.server('steer', data={'steering_angle': str(data.steering_wheel_angle_cmd)})
def callback_throttle(self, data):
self.server('throttle', data={'throttle': str(data.pedal_cmd)})
def callback_brake(self, data):
self.server('brake', data={'brake': str(data.pedal_cmd)})
def callback_path(self, data):
x_values = []
y_values = []
z_values = []
for waypoint in data.waypoints:
x = waypoint.pose.pose.position.x
y = waypoint.pose.pose.position.y
z = waypoint.pose.pose.position.z+0.5
x_values.append(x)
y_values.append(y)
z_values.append(z)
self.server('drawline', data={'next_x': x_values, 'next_y': y_values, 'next_z': z_values})
| 34.004717
| 129
| 0.627688
|
import rospy
import tf
from geometry_msgs.msg import PoseStamped, Quaternion, TwistStamped
from dbw_mkz_msgs.msg import SteeringReport, ThrottleCmd, BrakeCmd, SteeringCmd
from std_msgs.msg import Float32 as Float
from std_msgs.msg import Bool
from sensor_msgs.msg import PointCloud2
from sensor_msgs.msg import Image
import sensor_msgs.point_cloud2 as pcl2
from std_msgs.msg import Header
from cv_bridge import CvBridge, CvBridgeError
from styx_msgs.msg import TrafficLight, TrafficLightArray, Lane
import numpy as np
from PIL import Image as PIL_Image
from io import BytesIO
import base64
import math
TYPE = {
'bool': Bool,
'float': Float,
'pose': PoseStamped,
'pcl': PointCloud2,
'twist': TwistStamped,
'steer': SteeringReport,
'trafficlights': TrafficLightArray,
'steer_cmd': SteeringCmd,
'brake_cmd': BrakeCmd,
'throttle_cmd': ThrottleCmd,
'path_draw': Lane,
'image':Image
}
NUM_IMAGES_TO_SKIP = 2
class Bridge(object):
def __init__(self, conf, server):
rospy.init_node('styx_server')
self.server = server
self.vel = 0.
self.yaw = None
self.angular_vel = 0.
self.bridge = CvBridge()
self.img_count = 0
self.callbacks = {
'/vehicle/steering_cmd': self.callback_steering,
'/vehicle/throttle_cmd': self.callback_throttle,
'/vehicle/brake_cmd': self.callback_brake,
'/final_waypoints': self.callback_path
}
self.subscribers = [rospy.Subscriber(e.topic, TYPE[e.type], self.callbacks[e.topic])
for e in conf.subscribers]
self.publishers = {e.name: rospy.Publisher(e.topic, TYPE[e.type], queue_size=1)
for e in conf.publishers}
def create_light(self, x, y, z, yaw, state):
light = TrafficLight()
light.header = Header()
light.header.stamp = rospy.Time.now()
light.header.frame_id = '/world'
light.pose = self.create_pose(x, y, z, yaw)
light.state = state
return light
def create_pose(self, x, y, z, yaw=0.):
pose = PoseStamped()
pose.header = Header()
pose.header.stamp = rospy.Time.now()
pose.header.frame_id = '/world'
pose.pose.position.x = x
pose.pose.position.y = y
pose.pose.position.z = z
q = tf.transformations.quaternion_from_euler(0., 0., math.pi * yaw/180.)
pose.pose.orientation = Quaternion(*q)
return pose
def create_float(self, val):
fl = Float()
fl.data = val
return fl
def create_twist(self, velocity, angular):
tw = TwistStamped()
tw.twist.linear.x = velocity
tw.twist.angular.z = angular
return tw
def create_steer(self, val):
st = SteeringReport()
st.steering_wheel_angle_cmd = val * math.pi/180.
st.enabled = True
st.speed = self.vel
return st
def calc_angular(self, yaw):
angular_vel = 0.
if self.yaw is not None:
angular_vel = (yaw - self.yaw)/(rospy.get_time() - self.prev_time)
self.yaw = yaw
self.prev_time = rospy.get_time()
return angular_vel
def create_point_cloud_message(self, pts):
header = Header()
header.stamp = rospy.Time.now()
header.frame_id = '/world'
cloud_message = pcl2.create_cloud_xyz32(header, pts)
return cloud_message
def broadcast_transform(self, name, position, orientation):
br = tf.TransformBroadcaster()
br.sendTransform(position,
orientation,
rospy.Time.now(),
name,
"world")
def publish_odometry(self, data):
pose = self.create_pose(data['x'], data['y'], data['z'], data['yaw'])
position = (data['x'], data['y'], data['z'])
orientation = tf.transformations.quaternion_from_euler(0, 0, math.pi * data['yaw']/180.)
self.broadcast_transform("base_link", position, orientation)
self.publishers['current_pose'].publish(pose)
self.vel = data['velocity']* 0.44704
self.angular = self.calc_angular(data['yaw'] * math.pi/180.)
self.publishers['current_velocity'].publish(self.create_twist(self.vel, self.angular))
def publish_controls(self, data):
steering, throttle, brake = data['steering_angle'], data['throttle'], data['brake']
self.publishers['steering_report'].publish(self.create_steer(steering))
self.publishers['throttle_report'].publish(self.create_float(throttle))
self.publishers['brake_report'].publish(self.create_float(brake))
def publish_obstacles(self, data):
for obs in data['obstacles']:
pose = self.create_pose(obs[0], obs[1], obs[2])
self.publishers['obstacle'].publish(pose)
header = Header()
header.stamp = rospy.Time.now()
header.frame_id = '/world'
cloud = pcl2.create_cloud_xyz32(header, data['obstacles'])
self.publishers['obstacle_points'].publish(cloud)
def publish_lidar(self, data):
self.publishers['lidar'].publish(self.create_point_cloud_message(zip(data['lidar_x'], data['lidar_y'], data['lidar_z'])))
def publish_traffic(self, data):
x, y, z = data['light_pos_x'], data['light_pos_y'], data['light_pos_z'],
yaw = [math.atan2(dy, dx) for dx, dy in zip(data['light_pos_dx'], data['light_pos_dy'])]
status = data['light_state']
lights = TrafficLightArray()
header = Header()
header.stamp = rospy.Time.now()
header.frame_id = '/world'
lights.lights = [self.create_light(*e) for e in zip(x, y, z, yaw, status)]
self.publishers['trafficlights'].publish(lights)
def publish_dbw_status(self, data):
self.publishers['dbw_status'].publish(Bool(data))
def publish_camera(self, data):
self.img_count += 1
if self.img_count >= NUM_IMAGES_TO_SKIP:
imgString = data["image"]
image = PIL_Image.open(BytesIO(base64.b64decode(imgString)))
image_array = np.asarray(image)
image_message = self.bridge.cv2_to_imgmsg(image_array, encoding="rgb8")
self.publishers['image'].publish(image_message)
self.img_count = 0
def callback_steering(self, data):
self.server('steer', data={'steering_angle': str(data.steering_wheel_angle_cmd)})
def callback_throttle(self, data):
self.server('throttle', data={'throttle': str(data.pedal_cmd)})
def callback_brake(self, data):
self.server('brake', data={'brake': str(data.pedal_cmd)})
def callback_path(self, data):
x_values = []
y_values = []
z_values = []
for waypoint in data.waypoints:
x = waypoint.pose.pose.position.x
y = waypoint.pose.pose.position.y
z = waypoint.pose.pose.position.z+0.5
x_values.append(x)
y_values.append(y)
z_values.append(z)
self.server('drawline', data={'next_x': x_values, 'next_y': y_values, 'next_z': z_values})
| true
| true
|
f716984bca20b513662e2e393027677207b388b1
| 1,954
|
py
|
Python
|
mfr2.py
|
HeegyuKim/face_recognition
|
d96d2c94225e49d3dd8f2cae4444d35d5c88d13b
|
[
"MIT"
] | null | null | null |
mfr2.py
|
HeegyuKim/face_recognition
|
d96d2c94225e49d3dd8f2cae4444d35d5c88d13b
|
[
"MIT"
] | null | null | null |
mfr2.py
|
HeegyuKim/face_recognition
|
d96d2c94225e49d3dd8f2cae4444d35d5c88d13b
|
[
"MIT"
] | null | null | null |
import os
import shutil
import os
from glob import glob
import pandas as pd
import random
from collections import defaultdict
from PIL import Image
from torch.utils.data import Dataset, DataLoader
def get_all_images(dir):
types = ["jpeg", "jpg", "png"]
files = []
for t in types:
path = os.path.join(dir, "**", "*." + t)
files.extend(glob(path))
return files
def casia(dir):
files = get_all_images(dir)
users = defaultdict(set)
rows = []
for file in files:
user = file.split("/")[-2]
users[user].add(file)
rows.append({
"image": file,
"id": user
})
df = pd.DataFrame(rows)
positives = []
negatives = []
for user, files in users.items():
if len(files) <= 1:
continue
samples = random.sample(files, 2)
positives.append({
"image1": samples[0],
"image2": samples[1],
"id1": user,
"id2": user,
"label": 1
})
user_ids = list(users.keys())
for i in range(0, len(user_ids), 2):
if i == len(user_ids) - 1:
continue
id1, id2 = user_ids[i], user_ids[i + 1]
files1, files2 = users[id1], users[id2]
if len(files1) < 2 or len(files2) < 2:
break
samples1, samples2 = random.sample(files1, 2), random.sample(files2, 2)
for j in range(2):
negatives.append({
"image1": samples1[j],
"image2": samples2[j],
"id1": id1,
"id2": id2,
"label": -1
})
test_set = pd.DataFrame(positives + negatives)
return df, test_set
# trainset, testset = casia("train/")
# trainset.to_csv("train.csv", index=False)
# testset.to_csv("train_eval.csv", index=False)
for file in glob("dataset/validation/**/*.png", recursive=True):
tokens = file.split("/")
filename = tokens[-1]
id = tokens[-3]
dst = f"mfeval/{id}/{filename}"
os.makedirs(os.path.abspath(os.path.dirname(dst)), exist_ok=True)
shutil.copyfile(file, dst)
| 22.45977
| 75
| 0.592119
|
import os
import shutil
import os
from glob import glob
import pandas as pd
import random
from collections import defaultdict
from PIL import Image
from torch.utils.data import Dataset, DataLoader
def get_all_images(dir):
types = ["jpeg", "jpg", "png"]
files = []
for t in types:
path = os.path.join(dir, "**", "*." + t)
files.extend(glob(path))
return files
def casia(dir):
files = get_all_images(dir)
users = defaultdict(set)
rows = []
for file in files:
user = file.split("/")[-2]
users[user].add(file)
rows.append({
"image": file,
"id": user
})
df = pd.DataFrame(rows)
positives = []
negatives = []
for user, files in users.items():
if len(files) <= 1:
continue
samples = random.sample(files, 2)
positives.append({
"image1": samples[0],
"image2": samples[1],
"id1": user,
"id2": user,
"label": 1
})
user_ids = list(users.keys())
for i in range(0, len(user_ids), 2):
if i == len(user_ids) - 1:
continue
id1, id2 = user_ids[i], user_ids[i + 1]
files1, files2 = users[id1], users[id2]
if len(files1) < 2 or len(files2) < 2:
break
samples1, samples2 = random.sample(files1, 2), random.sample(files2, 2)
for j in range(2):
negatives.append({
"image1": samples1[j],
"image2": samples2[j],
"id1": id1,
"id2": id2,
"label": -1
})
test_set = pd.DataFrame(positives + negatives)
return df, test_set
for file in glob("dataset/validation/**/*.png", recursive=True):
tokens = file.split("/")
filename = tokens[-1]
id = tokens[-3]
dst = f"mfeval/{id}/{filename}"
os.makedirs(os.path.abspath(os.path.dirname(dst)), exist_ok=True)
shutil.copyfile(file, dst)
| true
| true
|
f716985e68c4ca47db77d1c2d95da2329e93bfc9
| 1,483
|
py
|
Python
|
official/nlp/modeling/networks/__init__.py
|
akineeic/models
|
2912042352009c9993dc05403624100bfe42d9c1
|
[
"Apache-2.0"
] | 15
|
2018-08-15T19:29:39.000Z
|
2021-11-05T02:14:59.000Z
|
official/nlp/modeling/networks/__init__.py
|
yangxl-2014-fe/models
|
11ea5237818e791a5717716d5413977f4c4db1e3
|
[
"Apache-2.0"
] | 5
|
2020-10-01T09:02:34.000Z
|
2021-02-21T12:50:11.000Z
|
official/nlp/modeling/networks/__init__.py
|
yangxl-2014-fe/models
|
11ea5237818e791a5717716d5413977f4c4db1e3
|
[
"Apache-2.0"
] | 8
|
2019-06-06T20:37:15.000Z
|
2022-03-04T13:54:38.000Z
|
# Copyright 2019 The TensorFlow 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.
# ==============================================================================
"""Networks package definition."""
from official.nlp.modeling.networks.albert_encoder import AlbertEncoder
from official.nlp.modeling.networks.bert_encoder import BertEncoder
from official.nlp.modeling.networks.classification import Classification
from official.nlp.modeling.networks.encoder_scaffold import EncoderScaffold
from official.nlp.modeling.networks.mobile_bert_encoder import MobileBERTEncoder
from official.nlp.modeling.networks.packed_sequence_embedding import PackedSequenceEmbedding
from official.nlp.modeling.networks.span_labeling import SpanLabeling
from official.nlp.modeling.networks.span_labeling import XLNetSpanLabeling
from official.nlp.modeling.networks.xlnet_base import XLNetBase
# Backward compatibility. The modules are deprecated.
TransformerEncoder = BertEncoder
| 54.925926
| 92
| 0.784895
|
from official.nlp.modeling.networks.albert_encoder import AlbertEncoder
from official.nlp.modeling.networks.bert_encoder import BertEncoder
from official.nlp.modeling.networks.classification import Classification
from official.nlp.modeling.networks.encoder_scaffold import EncoderScaffold
from official.nlp.modeling.networks.mobile_bert_encoder import MobileBERTEncoder
from official.nlp.modeling.networks.packed_sequence_embedding import PackedSequenceEmbedding
from official.nlp.modeling.networks.span_labeling import SpanLabeling
from official.nlp.modeling.networks.span_labeling import XLNetSpanLabeling
from official.nlp.modeling.networks.xlnet_base import XLNetBase
TransformerEncoder = BertEncoder
| true
| true
|
f71698db8367f39cb85d20a59c8f5d11cc1b4ccc
| 4,301
|
py
|
Python
|
coding_problems/move_zeros.py
|
NescobarAlopLop/miscellaneous
|
8e33cb34ddc54dad233d2418d4a90a96ce3c393e
|
[
"MIT"
] | null | null | null |
coding_problems/move_zeros.py
|
NescobarAlopLop/miscellaneous
|
8e33cb34ddc54dad233d2418d4a90a96ce3c393e
|
[
"MIT"
] | null | null | null |
coding_problems/move_zeros.py
|
NescobarAlopLop/miscellaneous
|
8e33cb34ddc54dad233d2418d4a90a96ce3c393e
|
[
"MIT"
] | null | null | null |
"""
Given an array nums, write a function to move all 0's to the end of it while maintaining the relative order of the
non-zero elements.
Example:
Input: [0,1,0,3,12]
Output: [1,3,12,0,0]
Note:
You must do this in-place without making a copy of the array.
Minimize the total number of operations.
"""
from unittest import TestCase
class Solution:
@staticmethod
def get_next(nums, start, zero=True):
for idx in range(start, len(nums)):
if zero and nums[idx] == 0:
return idx
if not zero and nums[idx] != zero:
return idx
return len(nums)
def moveZeroes(self, nums):
"""
:type nums: List[int]
:rtype: void Do not return anything, modify nums in-place instead.
"""
z = self.get_next(nums, 0, zero=True)
n = self.get_next(nums, 0, zero=False)
while n < len(nums):
if n > z and nums[n] != 0:
nums[z], nums[n] = nums[n], nums[z]
z = self.get_next(nums, z, zero=True)
n = self.get_next(nums, n, zero=False)
continue
else:
n += 1
class TestSolution(TestCase):
sol = Solution()
def test_0(self):
nums = [0, 1, 2, 3, 4]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1, 2, 3, 4, 0], nums)
def test_1(self):
nums = [0, 1, 0, 2, 0, 3, 0, 1]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1, 2, 3, 1, 0, 0, 0, 0], nums)
def test_3(self):
nums = [0,1,0,3,12]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1, 3, 12, 0, 0], nums)
def test_4(self):
nums = [1]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1], nums)
def test_5(self):
nums = [1, -2, 0, 0, 3, 0, 4, 0]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1, -2, 3, 4, 0, 0, 0, 0], nums)
def test_6(self):
nums = [-959151711,623836953,209446690,-1950418142,1339915067,-733626417,481171539,-2125997010,-1225423476,1462109565,147434687,-1800073781,-1431212205,-450443973,50097298,753533734,-747189404,-2070885638,0,-1484353894,-340296594,-2133744570,619639811,-1626162038,669689561,0,112220218,502447212,-787793179,0,-726846372,-1611013491,204107194,1605165582,-566891128,2082852116,0,532995238,-1502590712,0,2136989777,-2031153343,371398938,-1907397429,342796391,609166045,-2007448660,-1096076344,-323570318,0,-2082980371,2129956379,-243553361,-1549960929,1502383415,0,-1394618779,694799815,78595689,-1439173023,-1416578800,685225786,-333502212,-1181308536,-380569313,772035354,0,-915266376,663709718,1443496021,-777017729,-883300731,-387828385,1907473488,-725483724,-972961871,-1255712537,383120918,1383877998,1722751914,0,-1156050682,1952527902,-560244497,1304305692,1173974542,-1313227247,-201476579,-298899493,-1828496581,-1724396350,1933643204,1531804925,1728655262,-955565449,0,-69843702,-461760848,268336768,1446130876]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([-959151711,623836953,209446690,-1950418142,1339915067,-733626417,481171539,-2125997010,-1225423476,1462109565,147434687,-1800073781,-1431212205,-450443973,50097298,753533734,-747189404,-2070885638,-1484353894,-340296594,-2133744570,619639811,-1626162038,669689561,112220218,502447212,-787793179,-726846372,-1611013491,204107194,1605165582,-566891128,2082852116,532995238,-1502590712,2136989777,-2031153343,371398938,-1907397429,342796391,609166045,-2007448660,-1096076344,-323570318,-2082980371,2129956379,-243553361,-1549960929,1502383415,-1394618779,694799815,78595689,-1439173023,-1416578800,685225786,-333502212,-1181308536,-380569313,772035354,-915266376,663709718,1443496021,-777017729,-883300731,-387828385,1907473488,-725483724,-972961871,-1255712537,383120918,1383877998,1722751914,-1156050682,1952527902,-560244497,1304305692,1173974542,-1313227247,-201476579,-298899493,-1828496581,-1724396350,1933643204,1531804925,1728655262,-955565449,-69843702,-461760848,268336768,1446130876,0,0,0,0,0,0,0,0,0,0], nums)
| 52.45122
| 1,044
| 0.681702
|
from unittest import TestCase
class Solution:
@staticmethod
def get_next(nums, start, zero=True):
for idx in range(start, len(nums)):
if zero and nums[idx] == 0:
return idx
if not zero and nums[idx] != zero:
return idx
return len(nums)
def moveZeroes(self, nums):
z = self.get_next(nums, 0, zero=True)
n = self.get_next(nums, 0, zero=False)
while n < len(nums):
if n > z and nums[n] != 0:
nums[z], nums[n] = nums[n], nums[z]
z = self.get_next(nums, z, zero=True)
n = self.get_next(nums, n, zero=False)
continue
else:
n += 1
class TestSolution(TestCase):
sol = Solution()
def test_0(self):
nums = [0, 1, 2, 3, 4]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1, 2, 3, 4, 0], nums)
def test_1(self):
nums = [0, 1, 0, 2, 0, 3, 0, 1]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1, 2, 3, 1, 0, 0, 0, 0], nums)
def test_3(self):
nums = [0,1,0,3,12]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1, 3, 12, 0, 0], nums)
def test_4(self):
nums = [1]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1], nums)
def test_5(self):
nums = [1, -2, 0, 0, 3, 0, 4, 0]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([1, -2, 3, 4, 0, 0, 0, 0], nums)
def test_6(self):
nums = [-959151711,623836953,209446690,-1950418142,1339915067,-733626417,481171539,-2125997010,-1225423476,1462109565,147434687,-1800073781,-1431212205,-450443973,50097298,753533734,-747189404,-2070885638,0,-1484353894,-340296594,-2133744570,619639811,-1626162038,669689561,0,112220218,502447212,-787793179,0,-726846372,-1611013491,204107194,1605165582,-566891128,2082852116,0,532995238,-1502590712,0,2136989777,-2031153343,371398938,-1907397429,342796391,609166045,-2007448660,-1096076344,-323570318,0,-2082980371,2129956379,-243553361,-1549960929,1502383415,0,-1394618779,694799815,78595689,-1439173023,-1416578800,685225786,-333502212,-1181308536,-380569313,772035354,0,-915266376,663709718,1443496021,-777017729,-883300731,-387828385,1907473488,-725483724,-972961871,-1255712537,383120918,1383877998,1722751914,0,-1156050682,1952527902,-560244497,1304305692,1173974542,-1313227247,-201476579,-298899493,-1828496581,-1724396350,1933643204,1531804925,1728655262,-955565449,0,-69843702,-461760848,268336768,1446130876]
self.sol.moveZeroes(nums)
print("result: {}".format(nums))
self.assertEqual([-959151711,623836953,209446690,-1950418142,1339915067,-733626417,481171539,-2125997010,-1225423476,1462109565,147434687,-1800073781,-1431212205,-450443973,50097298,753533734,-747189404,-2070885638,-1484353894,-340296594,-2133744570,619639811,-1626162038,669689561,112220218,502447212,-787793179,-726846372,-1611013491,204107194,1605165582,-566891128,2082852116,532995238,-1502590712,2136989777,-2031153343,371398938,-1907397429,342796391,609166045,-2007448660,-1096076344,-323570318,-2082980371,2129956379,-243553361,-1549960929,1502383415,-1394618779,694799815,78595689,-1439173023,-1416578800,685225786,-333502212,-1181308536,-380569313,772035354,-915266376,663709718,1443496021,-777017729,-883300731,-387828385,1907473488,-725483724,-972961871,-1255712537,383120918,1383877998,1722751914,-1156050682,1952527902,-560244497,1304305692,1173974542,-1313227247,-201476579,-298899493,-1828496581,-1724396350,1933643204,1531804925,1728655262,-955565449,-69843702,-461760848,268336768,1446130876,0,0,0,0,0,0,0,0,0,0], nums)
| true
| true
|
f71699ccf4bde15c74f7288f21e09a3602fee877
| 4,755
|
py
|
Python
|
events/yukicon2015/management/commands/setup_yukicon2015.py
|
jlaunonen/turska
|
fc6ec4e0ae50a823e931152ce8835098b96f5966
|
[
"CC-BY-3.0"
] | null | null | null |
events/yukicon2015/management/commands/setup_yukicon2015.py
|
jlaunonen/turska
|
fc6ec4e0ae50a823e931152ce8835098b96f5966
|
[
"CC-BY-3.0"
] | null | null | null |
events/yukicon2015/management/commands/setup_yukicon2015.py
|
jlaunonen/turska
|
fc6ec4e0ae50a823e931152ce8835098b96f5966
|
[
"CC-BY-3.0"
] | null | null | null |
# encoding: utf-8
from datetime import datetime, timedelta
from django.conf import settings
from django.core.management.base import BaseCommand
from django.utils.timezone import now
from dateutil.tz import tzlocal
from core.utils import slugify
class Setup(object):
def setup(self, test=False):
self.test = test
self.tz = tzlocal()
self.setup_core()
self.setup_tickets()
def setup_core(self):
from core.models import Venue, Event
self.venue, unused = Venue.objects.get_or_create(name='Espoon kulttuurikeskus', defaults=dict(
name_inessive='Espoon kulttuurikeskuksessa',
))
self.event, unused = Event.objects.get_or_create(slug='yukicon2015', defaults=dict(
name='Yukicon 2.0',
name_genitive='Yukicon 2.0 -tapahtuman',
name_illative='Yukicon 2.0 -tapahtumaan',
name_inessive='Yukicon 2.0 -tapahtumassa',
homepage_url='http://www.yukicon.fi',
organization_name='Yukitea ry',
organization_url='http://www.yukicon.fi',
start_time=datetime(2015, 1, 10, 10, 0, tzinfo=self.tz),
end_time=datetime(2015, 1, 11, 18, 0, tzinfo=self.tz),
venue=self.venue,
))
def setup_tickets(self):
from tickets.models import TicketsEventMeta, LimitGroup, Product
tickets_admin_group, = TicketsEventMeta.get_or_create_groups(self.event, ['admins'])
defaults = dict(
admin_group=tickets_admin_group,
due_days=14,
shipping_and_handling_cents=0,
reference_number_template="2015{:05d}",
contact_email='Yukicon <yukicon@yukicon.fi>',
plain_contact_email='yukicon@yukicon.fi',
ticket_free_text=u"Tämä on sähköinen lippusi Yukicon 2.0 -tapahtumaan. Sähköinen lippu vaihdetaan rannekkeeseen\n"
u"lipunvaihtopisteessä saapuessasi tapahtumaan. Voit tulostaa tämän lipun tai näyttää sen\n"
u"älypuhelimen tai tablettitietokoneen näytöltä. Mikäli kumpikaan näistä ei ole mahdollista, ota ylös\n"
u"kunkin viivakoodin alla oleva neljästä tai viidestä sanasta koostuva sanakoodi ja ilmoita se\n"
u"lipunvaihtopisteessä.\n\n"
u"Tervetuloa Yukiconiin!",
front_page_text=u"<h2>Tervetuloa ostamaan pääsylippuja Yukicon 2.0 -tapahtumaan!</h2>"
u"<p>Liput maksetaan suomalaisilla verkkopankkitunnuksilla heti tilauksen yhteydessä.</p>"
u"<p>Lue lisää tapahtumasta <a href='http://www.yukicon.fi'>Yukiconin kotisivuilta</a>.</p>",
)
if self.test:
t = now()
defaults.update(
ticket_sales_starts=t - timedelta(days=60),
ticket_sales_ends=t + timedelta(days=60),
)
else:
defaults.update(
ticket_sales_starts=datetime(2014, 11, 20, 18, 0, tzinfo=self.tz),
ticket_sales_ends=datetime(2015, 1, 11, 18, 0, tzinfo=self.tz),
)
meta, unused = TicketsEventMeta.objects.get_or_create(event=self.event, defaults=defaults)
def limit_group(description, limit):
limit_group, unused = LimitGroup.objects.get_or_create(
event=self.event,
description=description,
defaults=dict(limit=limit),
)
return limit_group
def ordering():
ordering.counter += 10
return ordering.counter
ordering.counter = 0
for product_info in [
dict(
name=u'Yukicon 2015 -pääsylippu',
description=u'Lippu kattaa koko viikonlopun. Maksettuasi sinulle lähetetään PDF-lippu antamaasi sähköpostiin, jota vastaan saat rannekkeen tapahtuman ovelta.',
limit_groups=[
limit_group('Pääsyliput', 1450),
],
price_cents=1700,
requires_shipping=False,
electronic_ticket=True,
available=True,
ordering=ordering(),
),
]:
name = product_info.pop('name')
limit_groups = product_info.pop('limit_groups')
product, unused = Product.objects.get_or_create(
event=self.event,
name=name,
defaults=product_info
)
if not product.limit_groups.exists():
product.limit_groups = limit_groups
product.save()
class Command(BaseCommand):
args = ''
help = 'Setup yukicon2015 specific stuff'
def handle(self, *args, **opts):
Setup().setup(test=settings.DEBUG)
| 38.04
| 175
| 0.606519
|
from datetime import datetime, timedelta
from django.conf import settings
from django.core.management.base import BaseCommand
from django.utils.timezone import now
from dateutil.tz import tzlocal
from core.utils import slugify
class Setup(object):
def setup(self, test=False):
self.test = test
self.tz = tzlocal()
self.setup_core()
self.setup_tickets()
def setup_core(self):
from core.models import Venue, Event
self.venue, unused = Venue.objects.get_or_create(name='Espoon kulttuurikeskus', defaults=dict(
name_inessive='Espoon kulttuurikeskuksessa',
))
self.event, unused = Event.objects.get_or_create(slug='yukicon2015', defaults=dict(
name='Yukicon 2.0',
name_genitive='Yukicon 2.0 -tapahtuman',
name_illative='Yukicon 2.0 -tapahtumaan',
name_inessive='Yukicon 2.0 -tapahtumassa',
homepage_url='http://www.yukicon.fi',
organization_name='Yukitea ry',
organization_url='http://www.yukicon.fi',
start_time=datetime(2015, 1, 10, 10, 0, tzinfo=self.tz),
end_time=datetime(2015, 1, 11, 18, 0, tzinfo=self.tz),
venue=self.venue,
))
def setup_tickets(self):
from tickets.models import TicketsEventMeta, LimitGroup, Product
tickets_admin_group, = TicketsEventMeta.get_or_create_groups(self.event, ['admins'])
defaults = dict(
admin_group=tickets_admin_group,
due_days=14,
shipping_and_handling_cents=0,
reference_number_template="2015{:05d}",
contact_email='Yukicon <yukicon@yukicon.fi>',
plain_contact_email='yukicon@yukicon.fi',
ticket_free_text=u"Tämä on sähköinen lippusi Yukicon 2.0 -tapahtumaan. Sähköinen lippu vaihdetaan rannekkeeseen\n"
u"lipunvaihtopisteessä saapuessasi tapahtumaan. Voit tulostaa tämän lipun tai näyttää sen\n"
u"älypuhelimen tai tablettitietokoneen näytöltä. Mikäli kumpikaan näistä ei ole mahdollista, ota ylös\n"
u"kunkin viivakoodin alla oleva neljästä tai viidestä sanasta koostuva sanakoodi ja ilmoita se\n"
u"lipunvaihtopisteessä.\n\n"
u"Tervetuloa Yukiconiin!",
front_page_text=u"<h2>Tervetuloa ostamaan pääsylippuja Yukicon 2.0 -tapahtumaan!</h2>"
u"<p>Liput maksetaan suomalaisilla verkkopankkitunnuksilla heti tilauksen yhteydessä.</p>"
u"<p>Lue lisää tapahtumasta <a href='http://www.yukicon.fi'>Yukiconin kotisivuilta</a>.</p>",
)
if self.test:
t = now()
defaults.update(
ticket_sales_starts=t - timedelta(days=60),
ticket_sales_ends=t + timedelta(days=60),
)
else:
defaults.update(
ticket_sales_starts=datetime(2014, 11, 20, 18, 0, tzinfo=self.tz),
ticket_sales_ends=datetime(2015, 1, 11, 18, 0, tzinfo=self.tz),
)
meta, unused = TicketsEventMeta.objects.get_or_create(event=self.event, defaults=defaults)
def limit_group(description, limit):
limit_group, unused = LimitGroup.objects.get_or_create(
event=self.event,
description=description,
defaults=dict(limit=limit),
)
return limit_group
def ordering():
ordering.counter += 10
return ordering.counter
ordering.counter = 0
for product_info in [
dict(
name=u'Yukicon 2015 -pääsylippu',
description=u'Lippu kattaa koko viikonlopun. Maksettuasi sinulle lähetetään PDF-lippu antamaasi sähköpostiin, jota vastaan saat rannekkeen tapahtuman ovelta.',
limit_groups=[
limit_group('Pääsyliput', 1450),
],
price_cents=1700,
requires_shipping=False,
electronic_ticket=True,
available=True,
ordering=ordering(),
),
]:
name = product_info.pop('name')
limit_groups = product_info.pop('limit_groups')
product, unused = Product.objects.get_or_create(
event=self.event,
name=name,
defaults=product_info
)
if not product.limit_groups.exists():
product.limit_groups = limit_groups
product.save()
class Command(BaseCommand):
args = ''
help = 'Setup yukicon2015 specific stuff'
def handle(self, *args, **opts):
Setup().setup(test=settings.DEBUG)
| true
| true
|
f7169a3697dd8cdb379fbd71762594e9c77e9d4a
| 5,784
|
py
|
Python
|
emerge.py
|
puiterwijk/dnf-plugins-emerge
|
fc5640a374fb9fbc5eb6c749e1f6f32617dd9532
|
[
"MIT"
] | 2
|
2018-02-27T23:25:36.000Z
|
2018-03-01T09:18:47.000Z
|
emerge.py
|
puiterwijk/dnf-plugins-emerge
|
fc5640a374fb9fbc5eb6c749e1f6f32617dd9532
|
[
"MIT"
] | null | null | null |
emerge.py
|
puiterwijk/dnf-plugins-emerge
|
fc5640a374fb9fbc5eb6c749e1f6f32617dd9532
|
[
"MIT"
] | null | null | null |
import dnf
import dnf.cli
from glob import glob
import logging
import threading
import tempfile
import subprocess
import shutil
import os
logger = logging.getLogger('dnf')
class ErrorThread(threading.Thread):
_my_exception = None
def run(self, *args):
try:
self._run(*self._args)
except Exception as ex:
self._my_exception = ex
class BuildThread(ErrorThread):
@property
def branch(self):
return 'master'
@property
def template_mock_config(self):
return '/etc/mock/fedora-rawhide-x86_64.cfg'
def _run(self, workdir, pkg):
pkgdir = os.path.join(workdir, pkg)
# Grab sources
logger.info('Grabbing sources')
subprocess.run(['fedpkg', 'clone', '--anonymous', '--branch', self.branch, 'rpms/%s' % pkg, pkgdir],
check=True)
# Generate mockconfig
logger.info('Generating mock config')
mock_config = os.path.join(workdir, '_mockconfig', 'emerge-%s.cfg' % pkg)
with open(self.template_mock_config, 'r') as template:
with open(mock_config, 'w') as out:
out.write("config_opts['basedir'] = '%s'\n" % (os.path.join(workdir, '_mockroots')))
for line in template.readlines():
if "config_opts['root']" in line:
out.write("config_opts['root'] = 'emerge-%s'\n" % pkg)
else:
out.write(line)
# Run mockbuild
logger.info('Building')
subprocess.run(['fedpkg', 'mockbuild', '--root', mock_config, '--no-clean-all'], check=True, cwd=pkgdir)
@dnf.plugin.register_command
class EmergeCommand(dnf.cli.Command):
aliases = ['emerge']
workdir = None
def configure(self):
self.cli.demands.available_repos = True
self.cli.demands.sack_activation = True
self.cli.demands.root_user = True
self.cli.demands.resolving = True
@staticmethod
def set_argparser(parser):
parser.add_argument('package', nargs='+', metavar='package',
help='Package to emerge')
parser.add_argument('--workdir')
parser.add_argument('--skip-build', action='store_true')
parser.add_argument('--skip-clean', action='store_true')
def run_transaction(self):
self._rmworkdir()
def _rmworkdir(self):
if self.workdir and not self.opts.workdir and not self.opts.skip_clean:
shutil.rmtree(self.workdir)
def run(self):
try:
self._run()
except:
self._rmworkdir()
raise
def _run(self):
q = self.base.sack.query()
pkgs = self.base.sack.query().available().filter(name=self.opts.package).latest().run()
if not pkgs:
raise dnf.exceptions.Error('no package matched')
to_build_install = {}
for pkg in pkgs:
if pkg.source_name in to_build_install:
to_build_install[pkg.source_name].add(pkg.name)
else:
to_build_install[pkg.source_name] = set([pkg.name])
logger.info('Building/installing: %s' % to_build_install)
if self.opts.workdir:
self.workdir = self.opts.workdir
else:
self.workdir = tempfile.TemporaryDirectory(prefix='dnf-emerge-').name
logger.debug('Workdir: %s', self.workdir)
self._build(self.workdir, to_build_install)
pkgs = self._find_packages(self.workdir, to_build_install)
err_pkgs = []
for pkg in self.base.add_remote_rpms(pkgs):
try:
self.base.package_install(pkg)
except dnf.exceptions.MarkingError:
logger.info('Unable to install %s' % self.base.output.term.bold(pkg.location))
err_pkgs.append(pkg)
if len(err_pkgs) != 0 and strict:
raise dnf.exceptions.PackagesNotAvailableError(
'Unable to find a match', packages=err_pkgs)
@staticmethod
def _is_wanted_file(fname, haystack):
for needle in haystack:
if fname.endswith('.src.rpm'):
continue
if not fname.startswith(needle + '-'):
continue
rest = fname[len(needle)+1:].split('-')
if len(rest) > 2:
continue
if not rest[0][0].isdigit():
continue
return True
return False
def _find_packages(self, workdir, to_build_install):
to_install = []
for source, binaries in to_build_install.items():
sourcedir = os.path.join(workdir, source, 'results_%s' % source, '*', '*', '*.rpm')
for fpath in glob(sourcedir):
fname = os.path.basename(fpath)
if self._is_wanted_file(fname, binaries):
to_install.append(fpath)
logger.info('Marking for installation: %s', to_install)
return to_install
def _build(self, workdir, to_build_install):
if self.opts.skip_build:
logger.error('Skipping build per request')
return
os.makedirs(os.path.join(workdir, '_mockconfig'))
os.makedirs(os.path.join(workdir, '_mockroots'))
buildthreads = []
for pkg in to_build_install.keys():
bthread = BuildThread(name='emerge-build-%s' % pkg, args=(workdir, pkg))
buildthreads.append(bthread)
bthread.start()
logger.info('All builds started, waiting for them to finish...')
for bthread in buildthreads:
bthread.join()
if bthread._my_exception:
raise bthread._my_exception
logger.info('All builds finished')
| 32.494382
| 112
| 0.584198
|
import dnf
import dnf.cli
from glob import glob
import logging
import threading
import tempfile
import subprocess
import shutil
import os
logger = logging.getLogger('dnf')
class ErrorThread(threading.Thread):
_my_exception = None
def run(self, *args):
try:
self._run(*self._args)
except Exception as ex:
self._my_exception = ex
class BuildThread(ErrorThread):
@property
def branch(self):
return 'master'
@property
def template_mock_config(self):
return '/etc/mock/fedora-rawhide-x86_64.cfg'
def _run(self, workdir, pkg):
pkgdir = os.path.join(workdir, pkg)
logger.info('Grabbing sources')
subprocess.run(['fedpkg', 'clone', '--anonymous', '--branch', self.branch, 'rpms/%s' % pkg, pkgdir],
check=True)
logger.info('Generating mock config')
mock_config = os.path.join(workdir, '_mockconfig', 'emerge-%s.cfg' % pkg)
with open(self.template_mock_config, 'r') as template:
with open(mock_config, 'w') as out:
out.write("config_opts['basedir'] = '%s'\n" % (os.path.join(workdir, '_mockroots')))
for line in template.readlines():
if "config_opts['root']" in line:
out.write("config_opts['root'] = 'emerge-%s'\n" % pkg)
else:
out.write(line)
logger.info('Building')
subprocess.run(['fedpkg', 'mockbuild', '--root', mock_config, '--no-clean-all'], check=True, cwd=pkgdir)
@dnf.plugin.register_command
class EmergeCommand(dnf.cli.Command):
aliases = ['emerge']
workdir = None
def configure(self):
self.cli.demands.available_repos = True
self.cli.demands.sack_activation = True
self.cli.demands.root_user = True
self.cli.demands.resolving = True
@staticmethod
def set_argparser(parser):
parser.add_argument('package', nargs='+', metavar='package',
help='Package to emerge')
parser.add_argument('--workdir')
parser.add_argument('--skip-build', action='store_true')
parser.add_argument('--skip-clean', action='store_true')
def run_transaction(self):
self._rmworkdir()
def _rmworkdir(self):
if self.workdir and not self.opts.workdir and not self.opts.skip_clean:
shutil.rmtree(self.workdir)
def run(self):
try:
self._run()
except:
self._rmworkdir()
raise
def _run(self):
q = self.base.sack.query()
pkgs = self.base.sack.query().available().filter(name=self.opts.package).latest().run()
if not pkgs:
raise dnf.exceptions.Error('no package matched')
to_build_install = {}
for pkg in pkgs:
if pkg.source_name in to_build_install:
to_build_install[pkg.source_name].add(pkg.name)
else:
to_build_install[pkg.source_name] = set([pkg.name])
logger.info('Building/installing: %s' % to_build_install)
if self.opts.workdir:
self.workdir = self.opts.workdir
else:
self.workdir = tempfile.TemporaryDirectory(prefix='dnf-emerge-').name
logger.debug('Workdir: %s', self.workdir)
self._build(self.workdir, to_build_install)
pkgs = self._find_packages(self.workdir, to_build_install)
err_pkgs = []
for pkg in self.base.add_remote_rpms(pkgs):
try:
self.base.package_install(pkg)
except dnf.exceptions.MarkingError:
logger.info('Unable to install %s' % self.base.output.term.bold(pkg.location))
err_pkgs.append(pkg)
if len(err_pkgs) != 0 and strict:
raise dnf.exceptions.PackagesNotAvailableError(
'Unable to find a match', packages=err_pkgs)
@staticmethod
def _is_wanted_file(fname, haystack):
for needle in haystack:
if fname.endswith('.src.rpm'):
continue
if not fname.startswith(needle + '-'):
continue
rest = fname[len(needle)+1:].split('-')
if len(rest) > 2:
continue
if not rest[0][0].isdigit():
continue
return True
return False
def _find_packages(self, workdir, to_build_install):
to_install = []
for source, binaries in to_build_install.items():
sourcedir = os.path.join(workdir, source, 'results_%s' % source, '*', '*', '*.rpm')
for fpath in glob(sourcedir):
fname = os.path.basename(fpath)
if self._is_wanted_file(fname, binaries):
to_install.append(fpath)
logger.info('Marking for installation: %s', to_install)
return to_install
def _build(self, workdir, to_build_install):
if self.opts.skip_build:
logger.error('Skipping build per request')
return
os.makedirs(os.path.join(workdir, '_mockconfig'))
os.makedirs(os.path.join(workdir, '_mockroots'))
buildthreads = []
for pkg in to_build_install.keys():
bthread = BuildThread(name='emerge-build-%s' % pkg, args=(workdir, pkg))
buildthreads.append(bthread)
bthread.start()
logger.info('All builds started, waiting for them to finish...')
for bthread in buildthreads:
bthread.join()
if bthread._my_exception:
raise bthread._my_exception
logger.info('All builds finished')
| true
| true
|
f7169acccf677a901455907c655d4e12841b5f55
| 11,443
|
py
|
Python
|
tf_agents/experimental/examples/sac/haarnoja18/sac_train_eval.py
|
cmarlin/agents
|
1729e06f42237b34dab8bd9d8c01980c2d2b391c
|
[
"Apache-2.0"
] | 1
|
2021-04-19T02:28:24.000Z
|
2021-04-19T02:28:24.000Z
|
tf_agents/experimental/examples/sac/haarnoja18/sac_train_eval.py
|
cmarlin/agents
|
1729e06f42237b34dab8bd9d8c01980c2d2b391c
|
[
"Apache-2.0"
] | null | null | null |
tf_agents/experimental/examples/sac/haarnoja18/sac_train_eval.py
|
cmarlin/agents
|
1729e06f42237b34dab8bd9d8c01980c2d2b391c
|
[
"Apache-2.0"
] | null | null | null |
# coding=utf-8
# Copyright 2020 The TF-Agents Authors.
#
# 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.
# Lint as: python3
r"""Train and Eval SAC.
All hyperparameters come from the SAC paper
https://arxiv.org/pdf/1812.05905.pdf
"""
import functools
import os
from absl import app
from absl import flags
from absl import logging
import gin
import reverb
import tensorflow as tf
from tf_agents.agents.sac import sac_agent
from tf_agents.agents.sac import tanh_normal_projection_network
from tf_agents.environments import suite_mujoco
from tf_agents.keras_layers import inner_reshape
from tf_agents.metrics import py_metrics
from tf_agents.networks import nest_map
from tf_agents.networks import sequential
from tf_agents.policies import greedy_policy
from tf_agents.policies import py_tf_eager_policy
from tf_agents.policies import random_py_policy
from tf_agents.replay_buffers import reverb_replay_buffer
from tf_agents.replay_buffers import reverb_utils
from tf_agents.train import actor
from tf_agents.train import learner
from tf_agents.train import triggers
from tf_agents.train.utils import spec_utils
from tf_agents.train.utils import train_utils
FLAGS = flags.FLAGS
flags.DEFINE_string('root_dir', os.getenv('TEST_UNDECLARED_OUTPUTS_DIR'),
'Root directory for writing logs/summaries/checkpoints.')
flags.DEFINE_integer(
'reverb_port', None,
'Port for reverb server, if None, use a randomly chosen unused port.')
flags.DEFINE_integer('num_iterations', 3000000,
'Total number train/eval iterations to perform.')
flags.DEFINE_integer(
'eval_interval', 10000,
'Number of train steps between evaluations. Set to 0 to skip.')
flags.DEFINE_multi_string('gin_file', None, 'Paths to the gin-config files.')
flags.DEFINE_multi_string('gin_bindings', None, 'Gin binding parameters.')
dense = functools.partial(
tf.keras.layers.Dense,
activation=tf.keras.activations.relu,
kernel_initializer='glorot_uniform')
def create_fc_network(layer_units):
return sequential.Sequential([dense(num_units) for num_units in layer_units])
def create_identity_layer():
return tf.keras.layers.Lambda(lambda x: x)
def create_sequential_critic_network(obs_fc_layer_units,
action_fc_layer_units,
joint_fc_layer_units):
"""Create a sequential critic network."""
# Split the inputs into observations and actions.
def split_inputs(inputs):
return {'observation': inputs[0], 'action': inputs[1]}
# Create an observation network.
obs_network = (create_fc_network(obs_fc_layer_units) if obs_fc_layer_units
else create_identity_layer())
# Create an action network.
action_network = (create_fc_network(action_fc_layer_units)
if action_fc_layer_units else create_identity_layer())
# Create a joint network.
joint_network = (create_fc_network(joint_fc_layer_units)
if joint_fc_layer_units else create_identity_layer())
# Final layer.
value_layer = tf.keras.layers.Dense(1, kernel_initializer='glorot_uniform')
return sequential.Sequential([
tf.keras.layers.Lambda(split_inputs),
nest_map.NestMap({
'observation': obs_network,
'action': action_network
}),
nest_map.NestFlatten(),
tf.keras.layers.Concatenate(),
joint_network,
value_layer,
inner_reshape.InnerReshape(current_shape=[1], new_shape=[])
], name='sequential_critic')
class _TanhNormalProjectionNetworkWrapper(
tanh_normal_projection_network.TanhNormalProjectionNetwork):
"""Wrapper to pass predefined `outer_rank` to underlying projection net."""
def __init__(self, sample_spec, predefined_outer_rank=1):
super(_TanhNormalProjectionNetworkWrapper, self).__init__(sample_spec)
self.predefined_outer_rank = predefined_outer_rank
def call(self, inputs, network_state=(), **kwargs):
kwargs['outer_rank'] = self.predefined_outer_rank
if 'step_type' in kwargs:
del kwargs['step_type']
return super(_TanhNormalProjectionNetworkWrapper,
self).call(inputs, **kwargs)
def create_sequential_actor_network(actor_fc_layers, action_tensor_spec):
"""Create a sequential actor network."""
def tile_as_nest(non_nested_output):
return tf.nest.map_structure(lambda _: non_nested_output,
action_tensor_spec)
return sequential.Sequential(
[dense(num_units) for num_units in actor_fc_layers] +
[tf.keras.layers.Lambda(tile_as_nest)] + [
nest_map.NestMap(
tf.nest.map_structure(_TanhNormalProjectionNetworkWrapper,
action_tensor_spec))
])
@gin.configurable
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
# Training params
initial_collect_steps=10000,
num_iterations=3200000,
actor_fc_layers=(256, 256),
critic_obs_fc_layers=None,
critic_action_fc_layers=None,
critic_joint_fc_layers=(256, 256),
# Agent params
batch_size=256,
actor_learning_rate=3e-4,
critic_learning_rate=3e-4,
alpha_learning_rate=3e-4,
gamma=0.99,
target_update_tau=0.005,
target_update_period=1,
reward_scale_factor=0.1,
# Replay params
reverb_port=None,
replay_capacity=1000000,
# Others
policy_save_interval=10000,
replay_buffer_save_interval=100000,
eval_interval=10000,
eval_episodes=30,
debug_summaries=False,
summarize_grads_and_vars=False):
"""Trains and evaluates SAC."""
logging.info('Training SAC on: %s', env_name)
collect_env = suite_mujoco.load(env_name)
eval_env = suite_mujoco.load(env_name)
_, action_tensor_spec, time_step_tensor_spec = (
spec_utils.get_tensor_specs(collect_env))
train_step = train_utils.create_train_step()
actor_net = create_sequential_actor_network(
actor_fc_layers=actor_fc_layers, action_tensor_spec=action_tensor_spec)
critic_net = create_sequential_critic_network(
obs_fc_layer_units=critic_obs_fc_layers,
action_fc_layer_units=critic_action_fc_layers,
joint_fc_layer_units=critic_joint_fc_layers)
agent = sac_agent.SacAgent(
time_step_tensor_spec,
action_tensor_spec,
actor_network=actor_net,
critic_network=critic_net,
actor_optimizer=tf.keras.optimizers.Adam(
learning_rate=actor_learning_rate),
critic_optimizer=tf.keras.optimizers.Adam(
learning_rate=critic_learning_rate),
alpha_optimizer=tf.keras.optimizers.Adam(
learning_rate=alpha_learning_rate),
target_update_tau=target_update_tau,
target_update_period=target_update_period,
td_errors_loss_fn=tf.math.squared_difference,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=None,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=train_step)
agent.initialize()
table_name = 'uniform_table'
table = reverb.Table(
table_name,
max_size=replay_capacity,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1))
reverb_checkpoint_dir = os.path.join(root_dir, learner.TRAIN_DIR,
learner.REPLAY_BUFFER_CHECKPOINT_DIR)
reverb_checkpointer = reverb.platform.checkpointers_lib.DefaultCheckpointer(
path=reverb_checkpoint_dir)
reverb_server = reverb.Server([table],
port=reverb_port,
checkpointer=reverb_checkpointer)
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=2,
table_name=table_name,
local_server=reverb_server)
rb_observer = reverb_utils.ReverbAddTrajectoryObserver(
reverb_replay.py_client,
table_name,
sequence_length=2,
stride_length=1)
dataset = reverb_replay.as_dataset(
sample_batch_size=batch_size, num_steps=2).prefetch(50)
experience_dataset_fn = lambda: dataset
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
env_step_metric = py_metrics.EnvironmentSteps()
learning_triggers = [
triggers.PolicySavedModelTrigger(
saved_model_dir,
agent,
train_step,
interval=policy_save_interval,
metadata_metrics={triggers.ENV_STEP_METADATA_KEY: env_step_metric}),
triggers.ReverbCheckpointTrigger(
train_step,
interval=replay_buffer_save_interval,
reverb_client=reverb_replay.py_client),
# TODO(b/165023684): Add SIGTERM handler to checkpoint before preemption.
triggers.StepPerSecondLogTrigger(train_step, interval=1000),
]
agent_learner = learner.Learner(
root_dir,
train_step,
agent,
experience_dataset_fn,
triggers=learning_triggers)
random_policy = random_py_policy.RandomPyPolicy(
collect_env.time_step_spec(), collect_env.action_spec())
initial_collect_actor = actor.Actor(
collect_env,
random_policy,
train_step,
steps_per_run=initial_collect_steps,
observers=[rb_observer])
logging.info('Doing initial collect.')
initial_collect_actor.run()
tf_collect_policy = agent.collect_policy
collect_policy = py_tf_eager_policy.PyTFEagerPolicy(
tf_collect_policy, use_tf_function=True)
collect_actor = actor.Actor(
collect_env,
collect_policy,
train_step,
steps_per_run=1,
metrics=actor.collect_metrics(10),
summary_dir=os.path.join(root_dir, learner.TRAIN_DIR),
observers=[rb_observer, env_step_metric])
tf_greedy_policy = greedy_policy.GreedyPolicy(agent.policy)
eval_greedy_policy = py_tf_eager_policy.PyTFEagerPolicy(
tf_greedy_policy, use_tf_function=True)
eval_actor = actor.Actor(
eval_env,
eval_greedy_policy,
train_step,
episodes_per_run=eval_episodes,
metrics=actor.eval_metrics(eval_episodes),
summary_dir=os.path.join(root_dir, 'eval'),
)
if eval_interval:
logging.info('Evaluating.')
eval_actor.run_and_log()
logging.info('Training.')
for _ in range(num_iterations):
collect_actor.run()
agent_learner.run(iterations=1)
if eval_interval and agent_learner.train_step_numpy % eval_interval == 0:
logging.info('Evaluating.')
eval_actor.run_and_log()
rb_observer.close()
reverb_server.stop()
def main(_):
logging.set_verbosity(logging.INFO)
tf.compat.v1.enable_v2_behavior()
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_bindings)
train_eval(
FLAGS.root_dir,
num_iterations=FLAGS.num_iterations,
reverb_port=FLAGS.reverb_port,
eval_interval=FLAGS.eval_interval)
if __name__ == '__main__':
flags.mark_flag_as_required('root_dir')
app.run(main)
| 33.361516
| 79
| 0.733811
|
import functools
import os
from absl import app
from absl import flags
from absl import logging
import gin
import reverb
import tensorflow as tf
from tf_agents.agents.sac import sac_agent
from tf_agents.agents.sac import tanh_normal_projection_network
from tf_agents.environments import suite_mujoco
from tf_agents.keras_layers import inner_reshape
from tf_agents.metrics import py_metrics
from tf_agents.networks import nest_map
from tf_agents.networks import sequential
from tf_agents.policies import greedy_policy
from tf_agents.policies import py_tf_eager_policy
from tf_agents.policies import random_py_policy
from tf_agents.replay_buffers import reverb_replay_buffer
from tf_agents.replay_buffers import reverb_utils
from tf_agents.train import actor
from tf_agents.train import learner
from tf_agents.train import triggers
from tf_agents.train.utils import spec_utils
from tf_agents.train.utils import train_utils
FLAGS = flags.FLAGS
flags.DEFINE_string('root_dir', os.getenv('TEST_UNDECLARED_OUTPUTS_DIR'),
'Root directory for writing logs/summaries/checkpoints.')
flags.DEFINE_integer(
'reverb_port', None,
'Port for reverb server, if None, use a randomly chosen unused port.')
flags.DEFINE_integer('num_iterations', 3000000,
'Total number train/eval iterations to perform.')
flags.DEFINE_integer(
'eval_interval', 10000,
'Number of train steps between evaluations. Set to 0 to skip.')
flags.DEFINE_multi_string('gin_file', None, 'Paths to the gin-config files.')
flags.DEFINE_multi_string('gin_bindings', None, 'Gin binding parameters.')
dense = functools.partial(
tf.keras.layers.Dense,
activation=tf.keras.activations.relu,
kernel_initializer='glorot_uniform')
def create_fc_network(layer_units):
return sequential.Sequential([dense(num_units) for num_units in layer_units])
def create_identity_layer():
return tf.keras.layers.Lambda(lambda x: x)
def create_sequential_critic_network(obs_fc_layer_units,
action_fc_layer_units,
joint_fc_layer_units):
def split_inputs(inputs):
return {'observation': inputs[0], 'action': inputs[1]}
obs_network = (create_fc_network(obs_fc_layer_units) if obs_fc_layer_units
else create_identity_layer())
action_network = (create_fc_network(action_fc_layer_units)
if action_fc_layer_units else create_identity_layer())
joint_network = (create_fc_network(joint_fc_layer_units)
if joint_fc_layer_units else create_identity_layer())
value_layer = tf.keras.layers.Dense(1, kernel_initializer='glorot_uniform')
return sequential.Sequential([
tf.keras.layers.Lambda(split_inputs),
nest_map.NestMap({
'observation': obs_network,
'action': action_network
}),
nest_map.NestFlatten(),
tf.keras.layers.Concatenate(),
joint_network,
value_layer,
inner_reshape.InnerReshape(current_shape=[1], new_shape=[])
], name='sequential_critic')
class _TanhNormalProjectionNetworkWrapper(
tanh_normal_projection_network.TanhNormalProjectionNetwork):
def __init__(self, sample_spec, predefined_outer_rank=1):
super(_TanhNormalProjectionNetworkWrapper, self).__init__(sample_spec)
self.predefined_outer_rank = predefined_outer_rank
def call(self, inputs, network_state=(), **kwargs):
kwargs['outer_rank'] = self.predefined_outer_rank
if 'step_type' in kwargs:
del kwargs['step_type']
return super(_TanhNormalProjectionNetworkWrapper,
self).call(inputs, **kwargs)
def create_sequential_actor_network(actor_fc_layers, action_tensor_spec):
def tile_as_nest(non_nested_output):
return tf.nest.map_structure(lambda _: non_nested_output,
action_tensor_spec)
return sequential.Sequential(
[dense(num_units) for num_units in actor_fc_layers] +
[tf.keras.layers.Lambda(tile_as_nest)] + [
nest_map.NestMap(
tf.nest.map_structure(_TanhNormalProjectionNetworkWrapper,
action_tensor_spec))
])
@gin.configurable
def train_eval(
root_dir,
env_name='HalfCheetah-v2',
initial_collect_steps=10000,
num_iterations=3200000,
actor_fc_layers=(256, 256),
critic_obs_fc_layers=None,
critic_action_fc_layers=None,
critic_joint_fc_layers=(256, 256),
batch_size=256,
actor_learning_rate=3e-4,
critic_learning_rate=3e-4,
alpha_learning_rate=3e-4,
gamma=0.99,
target_update_tau=0.005,
target_update_period=1,
reward_scale_factor=0.1,
reverb_port=None,
replay_capacity=1000000,
policy_save_interval=10000,
replay_buffer_save_interval=100000,
eval_interval=10000,
eval_episodes=30,
debug_summaries=False,
summarize_grads_and_vars=False):
logging.info('Training SAC on: %s', env_name)
collect_env = suite_mujoco.load(env_name)
eval_env = suite_mujoco.load(env_name)
_, action_tensor_spec, time_step_tensor_spec = (
spec_utils.get_tensor_specs(collect_env))
train_step = train_utils.create_train_step()
actor_net = create_sequential_actor_network(
actor_fc_layers=actor_fc_layers, action_tensor_spec=action_tensor_spec)
critic_net = create_sequential_critic_network(
obs_fc_layer_units=critic_obs_fc_layers,
action_fc_layer_units=critic_action_fc_layers,
joint_fc_layer_units=critic_joint_fc_layers)
agent = sac_agent.SacAgent(
time_step_tensor_spec,
action_tensor_spec,
actor_network=actor_net,
critic_network=critic_net,
actor_optimizer=tf.keras.optimizers.Adam(
learning_rate=actor_learning_rate),
critic_optimizer=tf.keras.optimizers.Adam(
learning_rate=critic_learning_rate),
alpha_optimizer=tf.keras.optimizers.Adam(
learning_rate=alpha_learning_rate),
target_update_tau=target_update_tau,
target_update_period=target_update_period,
td_errors_loss_fn=tf.math.squared_difference,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=None,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=train_step)
agent.initialize()
table_name = 'uniform_table'
table = reverb.Table(
table_name,
max_size=replay_capacity,
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
rate_limiter=reverb.rate_limiters.MinSize(1))
reverb_checkpoint_dir = os.path.join(root_dir, learner.TRAIN_DIR,
learner.REPLAY_BUFFER_CHECKPOINT_DIR)
reverb_checkpointer = reverb.platform.checkpointers_lib.DefaultCheckpointer(
path=reverb_checkpoint_dir)
reverb_server = reverb.Server([table],
port=reverb_port,
checkpointer=reverb_checkpointer)
reverb_replay = reverb_replay_buffer.ReverbReplayBuffer(
agent.collect_data_spec,
sequence_length=2,
table_name=table_name,
local_server=reverb_server)
rb_observer = reverb_utils.ReverbAddTrajectoryObserver(
reverb_replay.py_client,
table_name,
sequence_length=2,
stride_length=1)
dataset = reverb_replay.as_dataset(
sample_batch_size=batch_size, num_steps=2).prefetch(50)
experience_dataset_fn = lambda: dataset
saved_model_dir = os.path.join(root_dir, learner.POLICY_SAVED_MODEL_DIR)
env_step_metric = py_metrics.EnvironmentSteps()
learning_triggers = [
triggers.PolicySavedModelTrigger(
saved_model_dir,
agent,
train_step,
interval=policy_save_interval,
metadata_metrics={triggers.ENV_STEP_METADATA_KEY: env_step_metric}),
triggers.ReverbCheckpointTrigger(
train_step,
interval=replay_buffer_save_interval,
reverb_client=reverb_replay.py_client),
triggers.StepPerSecondLogTrigger(train_step, interval=1000),
]
agent_learner = learner.Learner(
root_dir,
train_step,
agent,
experience_dataset_fn,
triggers=learning_triggers)
random_policy = random_py_policy.RandomPyPolicy(
collect_env.time_step_spec(), collect_env.action_spec())
initial_collect_actor = actor.Actor(
collect_env,
random_policy,
train_step,
steps_per_run=initial_collect_steps,
observers=[rb_observer])
logging.info('Doing initial collect.')
initial_collect_actor.run()
tf_collect_policy = agent.collect_policy
collect_policy = py_tf_eager_policy.PyTFEagerPolicy(
tf_collect_policy, use_tf_function=True)
collect_actor = actor.Actor(
collect_env,
collect_policy,
train_step,
steps_per_run=1,
metrics=actor.collect_metrics(10),
summary_dir=os.path.join(root_dir, learner.TRAIN_DIR),
observers=[rb_observer, env_step_metric])
tf_greedy_policy = greedy_policy.GreedyPolicy(agent.policy)
eval_greedy_policy = py_tf_eager_policy.PyTFEagerPolicy(
tf_greedy_policy, use_tf_function=True)
eval_actor = actor.Actor(
eval_env,
eval_greedy_policy,
train_step,
episodes_per_run=eval_episodes,
metrics=actor.eval_metrics(eval_episodes),
summary_dir=os.path.join(root_dir, 'eval'),
)
if eval_interval:
logging.info('Evaluating.')
eval_actor.run_and_log()
logging.info('Training.')
for _ in range(num_iterations):
collect_actor.run()
agent_learner.run(iterations=1)
if eval_interval and agent_learner.train_step_numpy % eval_interval == 0:
logging.info('Evaluating.')
eval_actor.run_and_log()
rb_observer.close()
reverb_server.stop()
def main(_):
logging.set_verbosity(logging.INFO)
tf.compat.v1.enable_v2_behavior()
gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_bindings)
train_eval(
FLAGS.root_dir,
num_iterations=FLAGS.num_iterations,
reverb_port=FLAGS.reverb_port,
eval_interval=FLAGS.eval_interval)
if __name__ == '__main__':
flags.mark_flag_as_required('root_dir')
app.run(main)
| true
| true
|
f7169b0b69005cd6f9c943ebfaa084ff31332512
| 1,045
|
py
|
Python
|
parameter_tutorial_py/setup.py
|
JaehyunShim/ros2_tutorial_py
|
ed94477341ae6053dbd126fe5092b5cbf44ffa89
|
[
"Apache-2.0"
] | null | null | null |
parameter_tutorial_py/setup.py
|
JaehyunShim/ros2_tutorial_py
|
ed94477341ae6053dbd126fe5092b5cbf44ffa89
|
[
"Apache-2.0"
] | null | null | null |
parameter_tutorial_py/setup.py
|
JaehyunShim/ros2_tutorial_py
|
ed94477341ae6053dbd126fe5092b5cbf44ffa89
|
[
"Apache-2.0"
] | null | null | null |
from glob import glob
import os
from setuptools import setup
package_name = 'parameter_tutorial_py'
setup(
name=package_name,
version='0.0.0',
packages=[package_name],
data_files=[
('share/ament_index/resource_index/packages',
['resource/' + package_name]),
('share/' + package_name, ['package.xml']),
(os.path.join('share', package_name), glob('launch/*.launch.py')),
# (os.path.join('share', package_name), glob('param/*')),
('share/' + package_name, ['param/param.yaml']),
# ('share/' + package_name, ['param']),
],
install_requires=['setuptools'],
zip_safe=True,
maintainer='Jaehyun Shim',
maintainer_email='jhshim@robotis.com',
description='ROS 2 packages for parameter_tutorial_py',
license='Apache 2.0',
author='Jaehyun Shim',
author_email='jhshim@robotis.com',
tests_require=['pytest'],
entry_points={
'console_scripts': [
'parameter = parameter_tutorial_py.parameter:main'
],
},
)
| 29.027778
| 74
| 0.622967
|
from glob import glob
import os
from setuptools import setup
package_name = 'parameter_tutorial_py'
setup(
name=package_name,
version='0.0.0',
packages=[package_name],
data_files=[
('share/ament_index/resource_index/packages',
['resource/' + package_name]),
('share/' + package_name, ['package.xml']),
(os.path.join('share', package_name), glob('launch/*.launch.py')),
('share/' + package_name, ['param/param.yaml']),
],
install_requires=['setuptools'],
zip_safe=True,
maintainer='Jaehyun Shim',
maintainer_email='jhshim@robotis.com',
description='ROS 2 packages for parameter_tutorial_py',
license='Apache 2.0',
author='Jaehyun Shim',
author_email='jhshim@robotis.com',
tests_require=['pytest'],
entry_points={
'console_scripts': [
'parameter = parameter_tutorial_py.parameter:main'
],
},
)
| true
| true
|
f7169bb6a32f8e404c1c0086cbe414701694aecd
| 2,198
|
py
|
Python
|
ros/src/twist_controller/twist_controller.py
|
ysavchenko/carnd-capstone
|
682eb7ed52153c667f63e7c7eb46f13584469888
|
[
"MIT"
] | null | null | null |
ros/src/twist_controller/twist_controller.py
|
ysavchenko/carnd-capstone
|
682eb7ed52153c667f63e7c7eb46f13584469888
|
[
"MIT"
] | null | null | null |
ros/src/twist_controller/twist_controller.py
|
ysavchenko/carnd-capstone
|
682eb7ed52153c667f63e7c7eb46f13584469888
|
[
"MIT"
] | null | null | null |
from yaw_controller import YawController
from pid import PID
from lowpass import LowPassFilter
import rospy
GAS_DENSITY = 2.858
ONE_MPH = 0.44704
class Controller(object):
def __init__(self, vehicle_mass, wheel_radius, decel_limit):
self.yaw_controller = None
self.throttle_controller = None
self.velocity_filter = LowPassFilter(.5, .02)
self.vehicle_mass = vehicle_mass
self.wheel_radius = wheel_radius
self.decel_limit = decel_limit
self.last_time = rospy.get_time()
def init_yaw(self, wheel_base, steer_ratio, min_speed, max_lat_accel, max_steer_angle):
self.yaw_controller = YawController(
wheel_base,
steer_ratio,
min_speed,
max_lat_accel,
max_steer_angle
)
def init_throttle(self, kp, ki, kd, min_throttle, max_throttle):
self.throttle_controller = PID(kp, ki, kd, min_throttle, max_throttle)
def control(self, target_linear_velocity, target_angular_velocity, current_linear_velocity):
if self.yaw_controller is None or self.throttle_controller is None:
return 0., 0., 0.
current_linear_velocity = self.velocity_filter.filt(current_linear_velocity)
steering = self.yaw_controller.get_steering(target_linear_velocity, target_angular_velocity, current_linear_velocity)
delta_velocity = target_linear_velocity - current_linear_velocity
current_time = rospy.get_time()
delta_time = current_time - self.last_time
self.last_time = current_time
throttle = self.throttle_controller.step(delta_velocity, delta_time)
brake = 0.
if target_linear_velocity == 0. and current_linear_velocity < 0.1:
# Full stop
throttle = 0.
brake = 400
elif throttle < .1 and delta_velocity < 0:
# Slow deceleration
throttle = 0.
deceleration = max(delta_velocity, self.decel_limit)
brake = abs(deceleration) * self.vehicle_mass * self.wheel_radius
return throttle, brake, steering
def reset(self):
self.throttle_controller.reset()
| 33.815385
| 125
| 0.674249
|
from yaw_controller import YawController
from pid import PID
from lowpass import LowPassFilter
import rospy
GAS_DENSITY = 2.858
ONE_MPH = 0.44704
class Controller(object):
def __init__(self, vehicle_mass, wheel_radius, decel_limit):
self.yaw_controller = None
self.throttle_controller = None
self.velocity_filter = LowPassFilter(.5, .02)
self.vehicle_mass = vehicle_mass
self.wheel_radius = wheel_radius
self.decel_limit = decel_limit
self.last_time = rospy.get_time()
def init_yaw(self, wheel_base, steer_ratio, min_speed, max_lat_accel, max_steer_angle):
self.yaw_controller = YawController(
wheel_base,
steer_ratio,
min_speed,
max_lat_accel,
max_steer_angle
)
def init_throttle(self, kp, ki, kd, min_throttle, max_throttle):
self.throttle_controller = PID(kp, ki, kd, min_throttle, max_throttle)
def control(self, target_linear_velocity, target_angular_velocity, current_linear_velocity):
if self.yaw_controller is None or self.throttle_controller is None:
return 0., 0., 0.
current_linear_velocity = self.velocity_filter.filt(current_linear_velocity)
steering = self.yaw_controller.get_steering(target_linear_velocity, target_angular_velocity, current_linear_velocity)
delta_velocity = target_linear_velocity - current_linear_velocity
current_time = rospy.get_time()
delta_time = current_time - self.last_time
self.last_time = current_time
throttle = self.throttle_controller.step(delta_velocity, delta_time)
brake = 0.
if target_linear_velocity == 0. and current_linear_velocity < 0.1:
throttle = 0.
brake = 400
elif throttle < .1 and delta_velocity < 0:
throttle = 0.
deceleration = max(delta_velocity, self.decel_limit)
brake = abs(deceleration) * self.vehicle_mass * self.wheel_radius
return throttle, brake, steering
def reset(self):
self.throttle_controller.reset()
| true
| true
|
f7169e22e550d5a34ea898f7d6792737a32dc834
| 2,050
|
py
|
Python
|
pyJCal.py
|
Geeknux/jalali-calendar-widget
|
034d6895cead2059825a931294ef9ffce7436caa
|
[
"MIT"
] | 1
|
2015-09-01T03:57:05.000Z
|
2015-09-01T03:57:05.000Z
|
pyJCal.py
|
Geeknux/jalali-calendar-widget
|
034d6895cead2059825a931294ef9ffce7436caa
|
[
"MIT"
] | 1
|
2015-09-01T03:56:35.000Z
|
2015-09-01T03:56:35.000Z
|
pyJCal.py
|
Geeknux/jalali-calendar-widget
|
034d6895cead2059825a931294ef9ffce7436caa
|
[
"MIT"
] | null | null | null |
#! /usr/bin/python2
# coding=utf-8
class pyJCal:
def __init__(self):
pass
def div(self, a, b):
return a / b
def gregorian_to_jalali(self, g_y, g_m, g_d):
"""
this function returns result of converting ye gregorian date to jalali
"""
g_days_in_month = (31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)
j_days_in_month = (31, 31, 31, 31, 31, 31, 30, 30, 30, 30, 30, 29)
gy = g_y - 1600
gm = g_m - 1
gd = g_d - 1
g_day_no = 365 * gy + self.div(gy+3, 4) - self.div(gy+99, 100) + self.div(gy+399, 400)
i = 0
while i < gm:
g_day_no += g_days_in_month[i]
i += 1
if(gm > 1 and ((gy % 4 == 0 and gy % 100 != 0) or (gy % 400 == 0))):
g_day_no += 1
g_day_no += gd
j_day_no = g_day_no - 79
j_np = self.div(j_day_no, 12053)
j_day_no = j_day_no % 12053
jy = 979 + 33 * j_np + 4 * self.div(j_day_no, 1461)
j_day_no %= 1461
if j_day_no >= 365:
jy += self.div(j_day_no - 1, 365)
j_day_no = (j_day_no - 1) % 365
i = 0
while (i < 11 and j_day_no >= j_days_in_month[i]):
j_day_no -= j_days_in_month[i]
i += 1
jm = i + 1
jd = j_day_no + 1
return (jy, jm, jd)
def MonthName(self, _m_num):
_m_num = int(_m_num)
if _m_num == 1:
return 'فروردین'
elif _m_num == 2:
return 'اردیبهشت'
elif _m_num == 3:
return 'خرداد'
elif _m_num == 4:
return 'تیر'
elif _m_num == 5:
return 'مرداد'
elif _m_num == 6:
return 'شهریور'
elif _m_num == 7:
return 'مهر'
elif _m_num == 8:
return 'آبان'
elif _m_num == 9:
return 'آذر'
elif _m_num == 10:
return 'دی'
elif _m_num == 11:
return 'بهمن'
elif _m_num == 12:
return 'اسفند'
else:
return False
def WeekDayName(self, _d):
if _d == 'Saturday':
return 'شنبه'
elif _d == 'Sunday':
return 'یکشنبه'
elif _d == 'Monday':
return 'دوشنبه'
elif _d == 'Tuesday':
return 'سه شنبه'
elif _d == 'Wednesday':
return 'چهارشنبه'
elif _d == 'Thursday':
return 'پنجشنبه'
elif _d == 'Friday':
return 'جمعه'
else:
return _d
| 19.711538
| 88
| 0.57122
|
class pyJCal:
def __init__(self):
pass
def div(self, a, b):
return a / b
def gregorian_to_jalali(self, g_y, g_m, g_d):
g_days_in_month = (31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)
j_days_in_month = (31, 31, 31, 31, 31, 31, 30, 30, 30, 30, 30, 29)
gy = g_y - 1600
gm = g_m - 1
gd = g_d - 1
g_day_no = 365 * gy + self.div(gy+3, 4) - self.div(gy+99, 100) + self.div(gy+399, 400)
i = 0
while i < gm:
g_day_no += g_days_in_month[i]
i += 1
if(gm > 1 and ((gy % 4 == 0 and gy % 100 != 0) or (gy % 400 == 0))):
g_day_no += 1
g_day_no += gd
j_day_no = g_day_no - 79
j_np = self.div(j_day_no, 12053)
j_day_no = j_day_no % 12053
jy = 979 + 33 * j_np + 4 * self.div(j_day_no, 1461)
j_day_no %= 1461
if j_day_no >= 365:
jy += self.div(j_day_no - 1, 365)
j_day_no = (j_day_no - 1) % 365
i = 0
while (i < 11 and j_day_no >= j_days_in_month[i]):
j_day_no -= j_days_in_month[i]
i += 1
jm = i + 1
jd = j_day_no + 1
return (jy, jm, jd)
def MonthName(self, _m_num):
_m_num = int(_m_num)
if _m_num == 1:
return 'فروردین'
elif _m_num == 2:
return 'اردیبهشت'
elif _m_num == 3:
return 'خرداد'
elif _m_num == 4:
return 'تیر'
elif _m_num == 5:
return 'مرداد'
elif _m_num == 6:
return 'شهریور'
elif _m_num == 7:
return 'مهر'
elif _m_num == 8:
return 'آبان'
elif _m_num == 9:
return 'آذر'
elif _m_num == 10:
return 'دی'
elif _m_num == 11:
return 'بهمن'
elif _m_num == 12:
return 'اسفند'
else:
return False
def WeekDayName(self, _d):
if _d == 'Saturday':
return 'شنبه'
elif _d == 'Sunday':
return 'یکشنبه'
elif _d == 'Monday':
return 'دوشنبه'
elif _d == 'Tuesday':
return 'سه شنبه'
elif _d == 'Wednesday':
return 'چهارشنبه'
elif _d == 'Thursday':
return 'پنجشنبه'
elif _d == 'Friday':
return 'جمعه'
else:
return _d
| true
| true
|
f7169ea9d45728fbd3a7a434bf6954f50de55d91
| 8,833
|
py
|
Python
|
source/conf.py
|
abhivishal/website
|
5debb3bc7da05a018996a7fbf7d140ad8b91a66e
|
[
"Apache-2.0"
] | 40
|
2015-07-24T14:11:25.000Z
|
2021-08-02T14:25:09.000Z
|
source/conf.py
|
abhivishal/website
|
5debb3bc7da05a018996a7fbf7d140ad8b91a66e
|
[
"Apache-2.0"
] | 79
|
2015-01-07T20:46:42.000Z
|
2021-06-08T19:02:48.000Z
|
source/conf.py
|
abhivishal/website
|
5debb3bc7da05a018996a7fbf7d140ad8b91a66e
|
[
"Apache-2.0"
] | 41
|
2015-01-29T00:10:00.000Z
|
2022-03-20T02:55:45.000Z
|
# -*- coding: utf-8 -*-
#
# OSU DevOps BootCamp documentation build configuration file, created by
# sphinx-quickstart on Tue Oct 15 12:20:17 2013.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys, os
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.todo',]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'OSU DevOps BootCamp'
copyright = u'2013-2017, Oregon State Unviersity'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0.0.1'
# The full version, including alpha/beta/rc tags.
release = '0.0.1'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = []
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Hieroglyph Congfig -------------------------------------------------------
# When autoslides is True, Hieroglyph will generate slides from the document
# sections. If autoslides is set to False, only generate slides from the slide
# directive.
# This can be overridden on a per-document basis using the slideconf directive.
if not os.environ.get('READTHEDOCS', None):
extensions += ['hieroglyph',]
autoslides = True
slide_theme = 'single-level'
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
html_context = {
'display_github': True,
'github_user': 'devopsbootcamp',
'github_repo': 'website',
'github_version': 'master',
'conf_py_path': '/source/',
'source_suffix': '.rst'
}
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
html_style = 'styles.css'
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'OSUDevOpsBootCampdoc'
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index', 'OSUDevOpsBootCamp.tex', u'OSU DevOps BootCamp Documentation',
u'OSU OSL & OSU LUG', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'osudevopsbootcamp', u'OSU DevOps BootCamp Documentation',
[u'OSU OSL & OSU LUG'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output ------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'OSUDevOpsBootCamp', u'OSU DevOps BootCamp Documentation',
u'OSU OSL & OSU LUG', 'OSUDevOpsBootCamp', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
slide_theme_options = {
'custom_css': 'custom.css',
'custom_js': 'ga.js',
}
if not os.environ.get('READTHEDOCS', None):
import sphinx_rtd_theme
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
| 32.355311
| 81
| 0.709498
|
import sys, os
extensions = ['sphinx.ext.todo',]
templates_path = ['_templates']
source_suffix = '.rst'
master_doc = 'index'
project = u'OSU DevOps BootCamp'
copyright = u'2013-2017, Oregon State Unviersity'
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0.0.1'
# The full version, including alpha/beta/rc tags.
release = '0.0.1'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = []
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Hieroglyph Congfig -------------------------------------------------------
# When autoslides is True, Hieroglyph will generate slides from the document
# sections. If autoslides is set to False, only generate slides from the slide
# directive.
# This can be overridden on a per-document basis using the slideconf directive.
if not os.environ.get('READTHEDOCS', None):
extensions += ['hieroglyph',]
autoslides = True
slide_theme = 'single-level'
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'default'
html_context = {
'display_github': True,
'github_user': 'devopsbootcamp',
'github_repo': 'website',
'github_version': 'master',
'conf_py_path': '/source/',
'source_suffix': '.rst'
}
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
html_style = 'styles.css'
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'OSUDevOpsBootCampdoc'
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index', 'OSUDevOpsBootCamp.tex', u'OSU DevOps BootCamp Documentation',
u'OSU OSL & OSU LUG', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'osudevopsbootcamp', u'OSU DevOps BootCamp Documentation',
[u'OSU OSL & OSU LUG'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output ------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'OSUDevOpsBootCamp', u'OSU DevOps BootCamp Documentation',
u'OSU OSL & OSU LUG', 'OSUDevOpsBootCamp', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
slide_theme_options = {
'custom_css': 'custom.css',
'custom_js': 'ga.js',
}
if not os.environ.get('READTHEDOCS', None):
import sphinx_rtd_theme
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
| true
| true
|
f7169eeeb7b39a8b509cb6a7cc0f4ab643571126
| 331
|
py
|
Python
|
api/app/api/endpoints/bets.py
|
franloza/apiestas
|
46978f58858b1d3822b2f80eb8948009944aa005
|
[
"MIT"
] | 53
|
2019-11-19T03:02:06.000Z
|
2022-01-13T15:29:38.000Z
|
api/app/api/endpoints/bets.py
|
franloza/apiestas
|
46978f58858b1d3822b2f80eb8948009944aa005
|
[
"MIT"
] | 3
|
2019-12-27T22:49:11.000Z
|
2021-03-31T18:59:36.000Z
|
api/app/api/endpoints/bets.py
|
franloza/apiestas
|
46978f58858b1d3822b2f80eb8948009944aa005
|
[
"MIT"
] | 5
|
2021-04-25T13:02:48.000Z
|
2022-02-16T13:57:21.000Z
|
from fastapi import APIRouter, Depends
from ...api.dependencies.bets import get_bet_by_slug_from_path
from ...models.bets import Bet
router = APIRouter()
@router.get(
'/{slug}',
response_model=Bet,
name="bets:get-bet"
)
async def get_bet(bet=Depends(get_bet_by_slug_from_path)) -> Bet:
return Bet(**bet.dict())
| 20.6875
| 65
| 0.719033
|
from fastapi import APIRouter, Depends
from ...api.dependencies.bets import get_bet_by_slug_from_path
from ...models.bets import Bet
router = APIRouter()
@router.get(
'/{slug}',
response_model=Bet,
name="bets:get-bet"
)
async def get_bet(bet=Depends(get_bet_by_slug_from_path)) -> Bet:
return Bet(**bet.dict())
| true
| true
|
f7169fa15e8d91d107ba43a0da61b69e67f59f1d
| 34,678
|
py
|
Python
|
networking_sfc/tests/unit/extensions/test_flowclassifier.py
|
huiweics/networking-sfc
|
e7675aa1a31769d55740c025a39644cc6e1ca8dd
|
[
"Apache-2.0"
] | null | null | null |
networking_sfc/tests/unit/extensions/test_flowclassifier.py
|
huiweics/networking-sfc
|
e7675aa1a31769d55740c025a39644cc6e1ca8dd
|
[
"Apache-2.0"
] | null | null | null |
networking_sfc/tests/unit/extensions/test_flowclassifier.py
|
huiweics/networking-sfc
|
e7675aa1a31769d55740c025a39644cc6e1ca8dd
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2015 Futurewei. 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 copy
import mock
from neutron.api.v2 import resource as api_res_log
from neutron import manager
from neutron.notifiers import nova as nova_log
from neutron.tests.unit.api.v2 import test_base as test_api_v2
from neutron.tests.unit.extensions import base as test_api_v2_extension
from neutron_lib import constants as const
from oslo_config import cfg
from oslo_utils import uuidutils
from webob import exc
import webtest
from networking_sfc.extensions import flowclassifier as fc_ext
_uuid = uuidutils.generate_uuid
_get_path = test_api_v2._get_path
FLOW_CLASSIFIER_PATH = (fc_ext.FLOW_CLASSIFIER_PREFIX[1:] + '/' +
fc_ext.FLOW_CLASSIFIER_EXT + 's')
class FlowClassifierExtensionTestCase(
test_api_v2_extension.ExtensionTestCase
):
fmt = 'json'
def setUp(self):
self._mock_unnecessary_logging()
super(FlowClassifierExtensionTestCase, self).setUp()
self.setup_extension(
'networking_sfc.extensions.flowclassifier.'
'FlowClassifierPluginBase',
fc_ext.FLOW_CLASSIFIER_EXT,
fc_ext.Flowclassifier,
fc_ext.FLOW_CLASSIFIER_PREFIX[1:],
plural_mappings={}
)
def _mock_unnecessary_logging(self):
mock_log_cfg_p = mock.patch.object(cfg, 'LOG')
self.mock_log_cfg = mock_log_cfg_p.start()
mock_log_manager_p = mock.patch.object(manager, 'LOG')
self.mock_log_manager = mock_log_manager_p.start()
mock_log_nova_p = mock.patch.object(nova_log, 'LOG')
self.mock_log_nova = mock_log_nova_p.start()
mock_log_api_res_log_p = mock.patch.object(api_res_log, 'LOG')
self.mock_log_api_res_log = mock_log_api_res_log_p.start()
def _get_expected_flow_classifier(self, data):
source_port_range_min = data['flow_classifier'].get(
'source_port_range_min')
if source_port_range_min is not None:
source_port_range_min = int(source_port_range_min)
source_port_range_max = data['flow_classifier'].get(
'source_port_range_max')
if source_port_range_max is not None:
source_port_range_max = int(source_port_range_max)
destination_port_range_min = data['flow_classifier'].get(
'destination_port_range_min')
if destination_port_range_min is not None:
destination_port_range_min = int(destination_port_range_min)
destination_port_range_max = data['flow_classifier'].get(
'destination_port_range_max')
if destination_port_range_max is not None:
destination_port_range_max = int(destination_port_range_max)
return {'flow_classifier': {
'name': data['flow_classifier'].get('name') or '',
'description': data['flow_classifier'].get('description') or '',
'tenant_id': data['flow_classifier']['tenant_id'],
'project_id': data['flow_classifier']['project_id'],
'source_port_range_min': source_port_range_min,
'source_port_range_max': source_port_range_max,
'destination_port_range_min': destination_port_range_min,
'destination_port_range_max': destination_port_range_max,
'l7_parameters': data['flow_classifier'].get(
'l7_parameters') or {},
'destination_ip_prefix': data['flow_classifier'].get(
'destination_ip_prefix'),
'source_ip_prefix': data['flow_classifier'].get(
'source_ip_prefix'),
'logical_source_port': data['flow_classifier'].get(
'logical_source_port'),
'logical_destination_port': data['flow_classifier'].get(
'logical_destination_port'),
'ethertype': data['flow_classifier'].get(
'ethertype') or 'IPv4',
'protocol': data['flow_classifier'].get(
'protocol')
}}
def test_create_flow_classifier(self):
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_source_port_range(self):
for source_port_range_min in [None, 100, '100']:
for source_port_range_max in [None, 200, '200']:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'source_port_range_min': source_port_range_min,
'source_port_range_max': source_port_range_max,
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_destination_port_range(self):
for destination_port_range_min in [None, 100, '100']:
for destination_port_range_max in [None, 200, '200']:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'destination_port_range_min': destination_port_range_min,
'destination_port_range_max': destination_port_range_max,
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_source_ip_prefix(self):
for logical_source_ip_prefix in [
None, '10.0.0.0/8'
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'source_ip_prefix': logical_source_ip_prefix,
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_destination_ip_prefix(self):
for logical_destination_ip_prefix in [
None, '10.0.0.0/8'
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'destination_ip_prefix': logical_destination_ip_prefix,
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(res.status_int, exc.HTTPCreated.code)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_logical_source_port(self):
for logical_source_port in [
None, _uuid()
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': logical_source_port,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_logical_destination_port(self):
for logical_destination_port in [
None, _uuid()
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_destination_port': logical_destination_port,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_l7_parameters(self):
for l7_parameters in [None, {}]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
'l7_parameters': l7_parameters
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_ethertype(self):
for ethertype in [None, 'IPv4', 'IPv6']:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
'ethertype': ethertype
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_protocol(self):
for protocol in [
None, const.PROTO_NAME_TCP, const.PROTO_NAME_UDP,
const.PROTO_NAME_ICMP
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
'protocol': protocol
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_all_fields(self):
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'name': 'test1',
'description': 'desc',
'tenant_id': tenant_id, 'project_id': tenant_id,
'source_port_range_min': 100,
'source_port_range_max': 200,
'destination_port_range_min': 100,
'destination_port_range_max': 200,
'l7_parameters': {},
'destination_ip_prefix': '10.0.0.0/8',
'source_ip_prefix': '10.0.0.0/8',
'logical_source_port': _uuid(),
'logical_destination_port': _uuid(),
'ethertype': None,
'protocol': None
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_invalid_l7_parameters(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'l7_parameters': {'abc': 'def'},
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_invalid_protocol(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'protocol': 'unknown',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_invalid_ethertype(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'ethertype': 'unknown',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_port_small(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'source_port_range_min': -1,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_port_large(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'source_port_range_min': 65536,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_ip_prefix_no_cidr(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'source_ip_prefix': '10.0.0.0',
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_ip_prefix_invalid_cidr(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'source_ip_prefix': '10.0.0.0/33',
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_port_id_nouuid(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': 'unknown',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_list(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
return_value = [{
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}]
instance = self.plugin.return_value
instance.get_flow_classifiers.return_value = return_value
res = self.api.get(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt))
instance.get_flow_classifiers.assert_called_with(
mock.ANY,
fields=mock.ANY,
filters=mock.ANY
)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifiers', res)
self.assertEqual(return_value, res['flow_classifiers'])
def test_flow_classifier_list_all_fields(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
return_value = [{
'name': 'abc',
'description': 'abc',
'ethertype': 'IPv4',
'protocol': const.PROTO_NAME_TCP,
'source_ip_prefix': '10.0.0.0/8',
'destination_ip_prefix': '10.0.0.0/8',
'source_port_range_min': 100,
'source_port_range_max': 200,
'destination_port_range_min': 100,
'destination_port_range_max': 200,
'logical_source_port': _uuid(),
'logical_destination_port': _uuid(),
'l7_parameters': {},
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}]
instance = self.plugin.return_value
instance.get_flow_classifiers.return_value = return_value
res = self.api.get(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt))
instance.get_flow_classifiers.assert_called_with(
mock.ANY,
fields=mock.ANY,
filters=mock.ANY
)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifiers', res)
self.assertEqual(return_value, res['flow_classifiers'])
def test_flow_classifier_list_unknown_fields(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
return_value = [{
'logical_source_port': _uuid(),
'new_key': 'value',
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}]
expected_return = copy.copy(return_value)
for item in expected_return:
del item['new_key']
instance = self.plugin.return_value
instance.get_flow_classifiers.return_value = return_value
res = self.api.get(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt))
instance.get_flow_classifiers.assert_called_with(
mock.ANY,
fields=mock.ANY,
filters=mock.ANY
)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifiers', res)
self.assertEqual(expected_return, res['flow_classifiers'])
def test_flow_classifier_get(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
return_value = {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}
instance = self.plugin.return_value
instance.get_flow_classifier.return_value = return_value
res = self.api.get(
_get_path(
FLOW_CLASSIFIER_PATH,
id=flowclassifier_id, fmt=self.fmt
)
)
instance.get_flow_classifier.assert_called_with(
mock.ANY,
flowclassifier_id,
fields=mock.ANY
)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_flow_classifier_update(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
update_data = {'flow_classifier': {
'name': 'new_name',
'description': 'new_desc',
}}
return_value = {
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}
instance = self.plugin.return_value
instance.update_flow_classifier.return_value = return_value
res = self.api.put(
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(update_data))
instance.update_flow_classifier.assert_called_with(
mock.ANY, flowclassifier_id,
flow_classifier=update_data)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_flow_classifier_update_source_port_range_min(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'source_port_range_min': 100,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_source_port_range_max(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'source_port_range_max': 100,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_destination_port_range_min(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'destination_port_range_min': 100,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_destination_port_range_max(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'destination_port_range_max': 100,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_source_ip_prefix(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'source_ip_prefix': '10.0.0.0/8',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_destination_ip_prefix(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'destination_ip_prefix': '10.0.0.0/8',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_logical_source_port(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_logical_destination_port(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'logical_destination_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_ethertype(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'ethertype': None,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_protocol(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'protococol': None,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_l7_parameters(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'l7_parameters': {},
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_delete(self):
self._test_entity_delete('flow_classifier')
| 41.630252
| 78
| 0.603639
|
import copy
import mock
from neutron.api.v2 import resource as api_res_log
from neutron import manager
from neutron.notifiers import nova as nova_log
from neutron.tests.unit.api.v2 import test_base as test_api_v2
from neutron.tests.unit.extensions import base as test_api_v2_extension
from neutron_lib import constants as const
from oslo_config import cfg
from oslo_utils import uuidutils
from webob import exc
import webtest
from networking_sfc.extensions import flowclassifier as fc_ext
_uuid = uuidutils.generate_uuid
_get_path = test_api_v2._get_path
FLOW_CLASSIFIER_PATH = (fc_ext.FLOW_CLASSIFIER_PREFIX[1:] + '/' +
fc_ext.FLOW_CLASSIFIER_EXT + 's')
class FlowClassifierExtensionTestCase(
test_api_v2_extension.ExtensionTestCase
):
fmt = 'json'
def setUp(self):
self._mock_unnecessary_logging()
super(FlowClassifierExtensionTestCase, self).setUp()
self.setup_extension(
'networking_sfc.extensions.flowclassifier.'
'FlowClassifierPluginBase',
fc_ext.FLOW_CLASSIFIER_EXT,
fc_ext.Flowclassifier,
fc_ext.FLOW_CLASSIFIER_PREFIX[1:],
plural_mappings={}
)
def _mock_unnecessary_logging(self):
mock_log_cfg_p = mock.patch.object(cfg, 'LOG')
self.mock_log_cfg = mock_log_cfg_p.start()
mock_log_manager_p = mock.patch.object(manager, 'LOG')
self.mock_log_manager = mock_log_manager_p.start()
mock_log_nova_p = mock.patch.object(nova_log, 'LOG')
self.mock_log_nova = mock_log_nova_p.start()
mock_log_api_res_log_p = mock.patch.object(api_res_log, 'LOG')
self.mock_log_api_res_log = mock_log_api_res_log_p.start()
def _get_expected_flow_classifier(self, data):
source_port_range_min = data['flow_classifier'].get(
'source_port_range_min')
if source_port_range_min is not None:
source_port_range_min = int(source_port_range_min)
source_port_range_max = data['flow_classifier'].get(
'source_port_range_max')
if source_port_range_max is not None:
source_port_range_max = int(source_port_range_max)
destination_port_range_min = data['flow_classifier'].get(
'destination_port_range_min')
if destination_port_range_min is not None:
destination_port_range_min = int(destination_port_range_min)
destination_port_range_max = data['flow_classifier'].get(
'destination_port_range_max')
if destination_port_range_max is not None:
destination_port_range_max = int(destination_port_range_max)
return {'flow_classifier': {
'name': data['flow_classifier'].get('name') or '',
'description': data['flow_classifier'].get('description') or '',
'tenant_id': data['flow_classifier']['tenant_id'],
'project_id': data['flow_classifier']['project_id'],
'source_port_range_min': source_port_range_min,
'source_port_range_max': source_port_range_max,
'destination_port_range_min': destination_port_range_min,
'destination_port_range_max': destination_port_range_max,
'l7_parameters': data['flow_classifier'].get(
'l7_parameters') or {},
'destination_ip_prefix': data['flow_classifier'].get(
'destination_ip_prefix'),
'source_ip_prefix': data['flow_classifier'].get(
'source_ip_prefix'),
'logical_source_port': data['flow_classifier'].get(
'logical_source_port'),
'logical_destination_port': data['flow_classifier'].get(
'logical_destination_port'),
'ethertype': data['flow_classifier'].get(
'ethertype') or 'IPv4',
'protocol': data['flow_classifier'].get(
'protocol')
}}
def test_create_flow_classifier(self):
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_source_port_range(self):
for source_port_range_min in [None, 100, '100']:
for source_port_range_max in [None, 200, '200']:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'source_port_range_min': source_port_range_min,
'source_port_range_max': source_port_range_max,
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_destination_port_range(self):
for destination_port_range_min in [None, 100, '100']:
for destination_port_range_max in [None, 200, '200']:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'destination_port_range_min': destination_port_range_min,
'destination_port_range_max': destination_port_range_max,
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_source_ip_prefix(self):
for logical_source_ip_prefix in [
None, '10.0.0.0/8'
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'source_ip_prefix': logical_source_ip_prefix,
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_destination_ip_prefix(self):
for logical_destination_ip_prefix in [
None, '10.0.0.0/8'
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'destination_ip_prefix': logical_destination_ip_prefix,
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(res.status_int, exc.HTTPCreated.code)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_logical_source_port(self):
for logical_source_port in [
None, _uuid()
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': logical_source_port,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_logical_destination_port(self):
for logical_destination_port in [
None, _uuid()
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_destination_port': logical_destination_port,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_l7_parameters(self):
for l7_parameters in [None, {}]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
'l7_parameters': l7_parameters
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_ethertype(self):
for ethertype in [None, 'IPv4', 'IPv6']:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
'ethertype': ethertype
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_protocol(self):
for protocol in [
None, const.PROTO_NAME_TCP, const.PROTO_NAME_UDP,
const.PROTO_NAME_ICMP
]:
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
'protocol': protocol
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_all_fields(self):
flowclassifier_id = _uuid()
tenant_id = _uuid()
data = {'flow_classifier': {
'name': 'test1',
'description': 'desc',
'tenant_id': tenant_id, 'project_id': tenant_id,
'source_port_range_min': 100,
'source_port_range_max': 200,
'destination_port_range_min': 100,
'destination_port_range_max': 200,
'l7_parameters': {},
'destination_ip_prefix': '10.0.0.0/8',
'source_ip_prefix': '10.0.0.0/8',
'logical_source_port': _uuid(),
'logical_destination_port': _uuid(),
'ethertype': None,
'protocol': None
}}
expected_data = self._get_expected_flow_classifier(data)
return_value = copy.copy(expected_data['flow_classifier'])
return_value.update({'id': flowclassifier_id})
instance = self.plugin.return_value
instance.create_flow_classifier.return_value = return_value
res = self.api.post(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
instance.create_flow_classifier.assert_called_with(
mock.ANY,
flow_classifier=expected_data)
self.assertEqual(exc.HTTPCreated.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_create_flow_classifier_invalid_l7_parameters(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'l7_parameters': {'abc': 'def'},
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_invalid_protocol(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'protocol': 'unknown',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_invalid_ethertype(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'ethertype': 'unknown',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_port_small(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'source_port_range_min': -1,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_port_large(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'source_port_range_min': 65536,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_ip_prefix_no_cidr(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'source_ip_prefix': '10.0.0.0',
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_ip_prefix_invalid_cidr(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'source_ip_prefix': '10.0.0.0/33',
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_create_flow_classifier_port_id_nouuid(self):
tenant_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': 'unknown',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.post,
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_list(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
return_value = [{
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}]
instance = self.plugin.return_value
instance.get_flow_classifiers.return_value = return_value
res = self.api.get(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt))
instance.get_flow_classifiers.assert_called_with(
mock.ANY,
fields=mock.ANY,
filters=mock.ANY
)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifiers', res)
self.assertEqual(return_value, res['flow_classifiers'])
def test_flow_classifier_list_all_fields(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
return_value = [{
'name': 'abc',
'description': 'abc',
'ethertype': 'IPv4',
'protocol': const.PROTO_NAME_TCP,
'source_ip_prefix': '10.0.0.0/8',
'destination_ip_prefix': '10.0.0.0/8',
'source_port_range_min': 100,
'source_port_range_max': 200,
'destination_port_range_min': 100,
'destination_port_range_max': 200,
'logical_source_port': _uuid(),
'logical_destination_port': _uuid(),
'l7_parameters': {},
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}]
instance = self.plugin.return_value
instance.get_flow_classifiers.return_value = return_value
res = self.api.get(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt))
instance.get_flow_classifiers.assert_called_with(
mock.ANY,
fields=mock.ANY,
filters=mock.ANY
)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifiers', res)
self.assertEqual(return_value, res['flow_classifiers'])
def test_flow_classifier_list_unknown_fields(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
return_value = [{
'logical_source_port': _uuid(),
'new_key': 'value',
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}]
expected_return = copy.copy(return_value)
for item in expected_return:
del item['new_key']
instance = self.plugin.return_value
instance.get_flow_classifiers.return_value = return_value
res = self.api.get(
_get_path(FLOW_CLASSIFIER_PATH, fmt=self.fmt))
instance.get_flow_classifiers.assert_called_with(
mock.ANY,
fields=mock.ANY,
filters=mock.ANY
)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifiers', res)
self.assertEqual(expected_return, res['flow_classifiers'])
def test_flow_classifier_get(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
return_value = {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}
instance = self.plugin.return_value
instance.get_flow_classifier.return_value = return_value
res = self.api.get(
_get_path(
FLOW_CLASSIFIER_PATH,
id=flowclassifier_id, fmt=self.fmt
)
)
instance.get_flow_classifier.assert_called_with(
mock.ANY,
flowclassifier_id,
fields=mock.ANY
)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_flow_classifier_update(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
update_data = {'flow_classifier': {
'name': 'new_name',
'description': 'new_desc',
}}
return_value = {
'tenant_id': tenant_id, 'project_id': tenant_id,
'id': flowclassifier_id
}
instance = self.plugin.return_value
instance.update_flow_classifier.return_value = return_value
res = self.api.put(
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(update_data))
instance.update_flow_classifier.assert_called_with(
mock.ANY, flowclassifier_id,
flow_classifier=update_data)
self.assertEqual(exc.HTTPOk.code, res.status_int)
res = self.deserialize(res)
self.assertIn('flow_classifier', res)
self.assertEqual(return_value, res['flow_classifier'])
def test_flow_classifier_update_source_port_range_min(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'source_port_range_min': 100,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_source_port_range_max(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'source_port_range_max': 100,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_destination_port_range_min(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'destination_port_range_min': 100,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_destination_port_range_max(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'destination_port_range_max': 100,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_source_ip_prefix(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'source_ip_prefix': '10.0.0.0/8',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_destination_ip_prefix(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'destination_ip_prefix': '10.0.0.0/8',
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_logical_source_port(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'logical_source_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_logical_destination_port(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'logical_destination_port': _uuid(),
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_ethertype(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'ethertype': None,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_protocol(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'protococol': None,
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_update_l7_parameters(self):
tenant_id = _uuid()
flowclassifier_id = _uuid()
data = {'flow_classifier': {
'l7_parameters': {},
'tenant_id': tenant_id, 'project_id': tenant_id,
}}
self.assertRaises(
webtest.app.AppError,
self.api.put,
_get_path(FLOW_CLASSIFIER_PATH, id=flowclassifier_id,
fmt=self.fmt),
self.serialize(data),
content_type='application/%s' % self.fmt)
def test_flow_classifier_delete(self):
self._test_entity_delete('flow_classifier')
| true
| true
|
f716a0394e3827031c5c048b941822b24f227531
| 7,848
|
py
|
Python
|
research/compression/entropy_coder/core/entropy_coder_train.py
|
jdavidagudelo/tensorflow-models
|
6f019beec73b01861363bf717706e27f4210b979
|
[
"Apache-2.0"
] | 1
|
2021-05-17T01:42:29.000Z
|
2021-05-17T01:42:29.000Z
|
research/compression/entropy_coder/core/entropy_coder_train.py
|
jdavidagudelo/tensorflow-models
|
6f019beec73b01861363bf717706e27f4210b979
|
[
"Apache-2.0"
] | null | null | null |
research/compression/entropy_coder/core/entropy_coder_train.py
|
jdavidagudelo/tensorflow-models
|
6f019beec73b01861363bf717706e27f4210b979
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2017 The TensorFlow 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.
# ==============================================================================
"""Train an entropy coder model."""
import time
import tensorflow as tf
from research.compression.entropy_coder.core import code_loader
from research.compression.entropy_coder.core import config_helper
# pylint: enable=unused-import
from research.compression.entropy_coder.model import model_factory
FLAGS = tf.app.flags.FLAGS
# Hardware resources configuration.
tf.app.flags.DEFINE_string('master', '',
"""Name of the TensorFlow master to use.""")
tf.app.flags.DEFINE_string('train_dir', None,
"""Directory where to write event logs.""")
tf.app.flags.DEFINE_integer('task', None,
"""Task id of the replica running the training.""")
tf.app.flags.DEFINE_integer('ps_tasks', 0, """Number of tasks in the ps job.
If 0 no ps job is used.""")
# Model selection and configuration.
tf.app.flags.DEFINE_string('model', None, """Underlying encoder model.""")
tf.app.flags.DEFINE_string('model_config', None,
"""Model config protobuf given as text file.""")
# Training data and parameters configuration.
tf.app.flags.DEFINE_string('input_config', None,
"""Path to the training input config file.""")
tf.app.flags.DEFINE_string('train_config', None,
"""Path to the training experiment config file.""")
def train():
if FLAGS.train_dir is None:
raise ValueError('Parameter train_dir must be provided')
if FLAGS.task is None:
raise ValueError('Parameter task must be provided')
if FLAGS.model is None:
raise ValueError('Parameter model must be provided')
input_config_string = config_helper.GetConfigString(FLAGS.input_config)
input_config = config_helper.InputConfig(input_config_string)
# Training parameters.
train_config_string = config_helper.GetConfigString(FLAGS.train_config)
train_config = config_helper.TrainConfig(train_config_string)
batch_size = train_config.batch_size
initial_learning_rate = train_config.learning_rate
decay_rate = train_config.decay_rate
samples_per_decay = train_config.samples_per_decay
# Parameters for learning-rate decay.
# The formula is decay_rate ** floor(steps / decay_steps).
decay_steps = samples_per_decay / batch_size
decay_steps = max(decay_steps, 1)
first_code = code_loader.ReadFirstCode(input_config.data)
first_code_height = (
first_code.features.feature['code_shape'].int64_list.value[0])
first_code_width = (
first_code.features.feature['code_shape'].int64_list.value[1])
max_bit_depth = (
first_code.features.feature['code_shape'].int64_list.value[2])
print('Maximum code depth: {}'.format(max_bit_depth))
with tf.Graph().as_default():
ps_ops = ["Variable", "VariableV2", "AutoReloadVariable", "VarHandleOp"]
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks,
ps_ops=ps_ops)):
codes = code_loader.LoadBinaryCode(
input_config=input_config,
batch_size=batch_size)
if input_config.unique_code_size:
print('Input code size: {} x {}'.format(first_code_height,
first_code_width))
codes.set_shape(
[batch_size, first_code_height, first_code_width, max_bit_depth])
else:
codes.set_shape([batch_size, None, None, max_bit_depth])
codes_effective_shape = tf.shape(codes)
global_step = tf.contrib.framework.create_global_step()
# Apply learning-rate decay.
learning_rate = tf.train.exponential_decay(
learning_rate=initial_learning_rate,
global_step=global_step,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=True)
tf.summary.scalar('Learning Rate', learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
epsilon=1.0)
# Create the entropy coder model.
model = model_factory.GetModelRegistry().CreateModel(FLAGS.model)
model_config_string = config_helper.GetConfigString(FLAGS.model_config)
model.Initialize(global_step, optimizer, model_config_string)
model.BuildGraph(codes)
summary_op = tf.summary.merge_all()
# Verify that the model can actually be trained.
if model.train_op is None:
raise ValueError('Input model {} is not trainable'.format(FLAGS.model))
# We disable the summary thread run by Supervisor class by passing
# summary_op=None. We still pass save_summaries_secs because it is used by
# the global step counter thread.
is_chief = (FLAGS.task == 0)
sv = tf.train.Supervisor(logdir=FLAGS.train_dir,
is_chief=is_chief,
global_step=global_step,
# saver=model.saver,
summary_op=None,
save_summaries_secs=120,
save_model_secs=600,
recovery_wait_secs=30)
sess = sv.PrepareSession(FLAGS.master)
sv.StartQueueRunners(sess)
step = sess.run(global_step)
print('Trainer initial step: {}.'.format(step))
# Once everything has been setup properly, save the configs.
if is_chief:
config_helper.SaveConfig(FLAGS.train_dir, 'input_config.json',
input_config_string)
config_helper.SaveConfig(FLAGS.train_dir, 'model_config.json',
model_config_string)
config_helper.SaveConfig(FLAGS.train_dir, 'train_config.json',
train_config_string)
# Train the model.
next_summary_time = time.time()
while not sv.ShouldStop():
feed_dict = None
# Once in a while, update the summaries on the chief worker.
if is_chief and next_summary_time < time.time():
summary_str = sess.run(summary_op, feed_dict=feed_dict)
sv.SummaryComputed(sess, summary_str)
next_summary_time = time.time() + sv.save_summaries_secs
else:
tf_tensors = {
'train': model.train_op,
'code_length': model.average_code_length
}
np_tensors = sess.run(tf_tensors, feed_dict=feed_dict)
print(np_tensors['code_length'])
sv.Stop()
def main(argv=None): # pylint: disable=unused-argument
train()
if __name__ == '__main__':
tf.app.run()
| 43.120879
| 87
| 0.605377
|
import time
import tensorflow as tf
from research.compression.entropy_coder.core import code_loader
from research.compression.entropy_coder.core import config_helper
from research.compression.entropy_coder.model import model_factory
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('master', '',
"""Name of the TensorFlow master to use.""")
tf.app.flags.DEFINE_string('train_dir', None,
"""Directory where to write event logs.""")
tf.app.flags.DEFINE_integer('task', None,
"""Task id of the replica running the training.""")
tf.app.flags.DEFINE_integer('ps_tasks', 0, """Number of tasks in the ps job.
If 0 no ps job is used.""")
tf.app.flags.DEFINE_string('model', None, """Underlying encoder model.""")
tf.app.flags.DEFINE_string('model_config', None,
"""Model config protobuf given as text file.""")
tf.app.flags.DEFINE_string('input_config', None,
"""Path to the training input config file.""")
tf.app.flags.DEFINE_string('train_config', None,
"""Path to the training experiment config file.""")
def train():
if FLAGS.train_dir is None:
raise ValueError('Parameter train_dir must be provided')
if FLAGS.task is None:
raise ValueError('Parameter task must be provided')
if FLAGS.model is None:
raise ValueError('Parameter model must be provided')
input_config_string = config_helper.GetConfigString(FLAGS.input_config)
input_config = config_helper.InputConfig(input_config_string)
train_config_string = config_helper.GetConfigString(FLAGS.train_config)
train_config = config_helper.TrainConfig(train_config_string)
batch_size = train_config.batch_size
initial_learning_rate = train_config.learning_rate
decay_rate = train_config.decay_rate
samples_per_decay = train_config.samples_per_decay
decay_steps = samples_per_decay / batch_size
decay_steps = max(decay_steps, 1)
first_code = code_loader.ReadFirstCode(input_config.data)
first_code_height = (
first_code.features.feature['code_shape'].int64_list.value[0])
first_code_width = (
first_code.features.feature['code_shape'].int64_list.value[1])
max_bit_depth = (
first_code.features.feature['code_shape'].int64_list.value[2])
print('Maximum code depth: {}'.format(max_bit_depth))
with tf.Graph().as_default():
ps_ops = ["Variable", "VariableV2", "AutoReloadVariable", "VarHandleOp"]
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks,
ps_ops=ps_ops)):
codes = code_loader.LoadBinaryCode(
input_config=input_config,
batch_size=batch_size)
if input_config.unique_code_size:
print('Input code size: {} x {}'.format(first_code_height,
first_code_width))
codes.set_shape(
[batch_size, first_code_height, first_code_width, max_bit_depth])
else:
codes.set_shape([batch_size, None, None, max_bit_depth])
codes_effective_shape = tf.shape(codes)
global_step = tf.contrib.framework.create_global_step()
learning_rate = tf.train.exponential_decay(
learning_rate=initial_learning_rate,
global_step=global_step,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=True)
tf.summary.scalar('Learning Rate', learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
epsilon=1.0)
model = model_factory.GetModelRegistry().CreateModel(FLAGS.model)
model_config_string = config_helper.GetConfigString(FLAGS.model_config)
model.Initialize(global_step, optimizer, model_config_string)
model.BuildGraph(codes)
summary_op = tf.summary.merge_all()
if model.train_op is None:
raise ValueError('Input model {} is not trainable'.format(FLAGS.model))
is_chief = (FLAGS.task == 0)
sv = tf.train.Supervisor(logdir=FLAGS.train_dir,
is_chief=is_chief,
global_step=global_step,
summary_op=None,
save_summaries_secs=120,
save_model_secs=600,
recovery_wait_secs=30)
sess = sv.PrepareSession(FLAGS.master)
sv.StartQueueRunners(sess)
step = sess.run(global_step)
print('Trainer initial step: {}.'.format(step))
if is_chief:
config_helper.SaveConfig(FLAGS.train_dir, 'input_config.json',
input_config_string)
config_helper.SaveConfig(FLAGS.train_dir, 'model_config.json',
model_config_string)
config_helper.SaveConfig(FLAGS.train_dir, 'train_config.json',
train_config_string)
next_summary_time = time.time()
while not sv.ShouldStop():
feed_dict = None
if is_chief and next_summary_time < time.time():
summary_str = sess.run(summary_op, feed_dict=feed_dict)
sv.SummaryComputed(sess, summary_str)
next_summary_time = time.time() + sv.save_summaries_secs
else:
tf_tensors = {
'train': model.train_op,
'code_length': model.average_code_length
}
np_tensors = sess.run(tf_tensors, feed_dict=feed_dict)
print(np_tensors['code_length'])
sv.Stop()
def main(argv=None):
train()
if __name__ == '__main__':
tf.app.run()
| true
| true
|
f716a0a3d08f1b810ae639fdca8086b153407a06
| 260,784
|
py
|
Python
|
instances/passenger_demand/pas-20210422-1717-int14000000000000001e/48.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210422-1717-int14000000000000001e/48.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210422-1717-int14000000000000001e/48.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 26645
passenger_arriving = (
(9, 10, 5, 5, 3, 2, 2, 3, 3, 1, 1, 0, 0, 6, 9, 0, 8, 12, 3, 4, 1, 0, 2, 2, 2, 0), # 0
(5, 10, 9, 11, 6, 2, 0, 5, 1, 1, 1, 0, 0, 11, 5, 5, 6, 6, 1, 2, 2, 1, 4, 0, 0, 0), # 1
(7, 9, 3, 3, 3, 3, 3, 5, 4, 4, 2, 0, 0, 9, 6, 5, 7, 9, 1, 5, 4, 2, 2, 2, 2, 0), # 2
(5, 10, 11, 9, 12, 3, 6, 6, 4, 1, 0, 0, 0, 6, 10, 2, 8, 8, 8, 2, 2, 3, 2, 2, 0, 0), # 3
(11, 9, 6, 8, 7, 3, 4, 5, 4, 1, 0, 0, 0, 8, 12, 3, 5, 10, 3, 0, 4, 1, 1, 0, 3, 0), # 4
(11, 9, 9, 13, 3, 3, 7, 7, 4, 2, 0, 2, 0, 7, 13, 7, 8, 9, 5, 5, 1, 2, 5, 1, 1, 0), # 5
(12, 13, 8, 8, 8, 4, 1, 4, 3, 4, 3, 2, 0, 9, 8, 7, 9, 6, 2, 7, 3, 6, 3, 0, 1, 0), # 6
(12, 8, 8, 11, 13, 3, 3, 1, 4, 1, 1, 0, 0, 12, 7, 11, 4, 9, 4, 3, 2, 3, 2, 1, 1, 0), # 7
(14, 11, 17, 10, 8, 3, 3, 2, 3, 1, 3, 0, 0, 11, 7, 9, 6, 11, 2, 7, 4, 1, 3, 1, 0, 0), # 8
(11, 10, 7, 9, 8, 8, 7, 5, 5, 0, 3, 3, 0, 17, 12, 9, 6, 10, 9, 4, 2, 3, 2, 4, 1, 0), # 9
(10, 12, 11, 10, 7, 3, 6, 3, 7, 3, 2, 2, 0, 18, 13, 12, 9, 12, 6, 2, 3, 6, 2, 3, 2, 0), # 10
(15, 11, 10, 11, 12, 7, 4, 3, 3, 1, 3, 2, 0, 10, 5, 13, 8, 12, 6, 6, 3, 1, 1, 4, 1, 0), # 11
(11, 16, 10, 11, 9, 2, 7, 4, 3, 3, 1, 2, 0, 17, 8, 9, 5, 8, 5, 5, 0, 6, 4, 1, 1, 0), # 12
(11, 17, 10, 10, 6, 7, 4, 2, 6, 1, 3, 1, 0, 14, 12, 4, 3, 5, 6, 4, 5, 3, 7, 3, 1, 0), # 13
(12, 21, 12, 12, 11, 7, 4, 4, 4, 3, 5, 0, 0, 10, 12, 6, 7, 12, 6, 7, 1, 4, 4, 0, 0, 0), # 14
(6, 12, 9, 21, 12, 3, 3, 4, 6, 2, 1, 2, 0, 9, 19, 8, 3, 6, 7, 7, 6, 8, 0, 0, 2, 0), # 15
(7, 12, 10, 13, 12, 7, 5, 4, 4, 3, 2, 3, 0, 12, 8, 4, 6, 14, 10, 4, 2, 4, 4, 1, 1, 0), # 16
(15, 15, 18, 15, 7, 9, 4, 3, 5, 6, 3, 1, 0, 12, 10, 7, 11, 10, 6, 2, 5, 7, 3, 1, 0, 0), # 17
(14, 14, 16, 17, 15, 4, 6, 4, 4, 1, 2, 1, 0, 16, 17, 7, 4, 7, 7, 9, 5, 6, 4, 2, 2, 0), # 18
(23, 16, 12, 13, 6, 6, 6, 3, 7, 0, 1, 1, 0, 17, 18, 11, 11, 15, 13, 6, 3, 5, 6, 4, 2, 0), # 19
(12, 12, 13, 13, 10, 7, 6, 4, 6, 2, 2, 1, 0, 10, 17, 13, 7, 11, 7, 5, 5, 6, 5, 4, 1, 0), # 20
(16, 18, 12, 13, 8, 6, 2, 6, 11, 2, 1, 1, 0, 7, 12, 10, 10, 16, 4, 4, 7, 4, 3, 1, 2, 0), # 21
(14, 13, 8, 11, 7, 7, 2, 3, 9, 4, 2, 0, 0, 18, 9, 6, 12, 12, 7, 3, 3, 3, 8, 0, 3, 0), # 22
(16, 9, 10, 8, 14, 9, 6, 2, 9, 3, 2, 1, 0, 16, 21, 9, 4, 5, 7, 6, 6, 1, 4, 1, 1, 0), # 23
(11, 13, 11, 10, 6, 4, 10, 6, 6, 1, 5, 1, 0, 18, 17, 16, 3, 12, 13, 8, 2, 4, 1, 4, 1, 0), # 24
(11, 16, 17, 7, 12, 5, 8, 5, 6, 2, 0, 2, 0, 10, 14, 10, 7, 20, 5, 12, 4, 3, 3, 3, 2, 0), # 25
(18, 15, 18, 8, 12, 4, 14, 2, 7, 1, 1, 1, 0, 9, 10, 10, 9, 16, 3, 4, 2, 4, 4, 1, 1, 0), # 26
(9, 9, 8, 9, 5, 7, 11, 5, 8, 2, 3, 1, 0, 10, 13, 9, 8, 12, 13, 3, 1, 3, 4, 1, 1, 0), # 27
(18, 14, 17, 16, 17, 2, 7, 7, 7, 7, 2, 2, 0, 20, 13, 12, 8, 7, 1, 4, 2, 6, 1, 2, 2, 0), # 28
(16, 16, 9, 15, 6, 4, 4, 7, 5, 3, 8, 1, 0, 12, 14, 11, 9, 11, 7, 2, 7, 2, 7, 6, 0, 0), # 29
(13, 12, 17, 14, 12, 7, 9, 4, 2, 1, 3, 0, 0, 22, 14, 9, 9, 20, 6, 9, 3, 11, 3, 0, 2, 0), # 30
(17, 9, 10, 22, 14, 6, 4, 2, 3, 5, 3, 0, 0, 19, 13, 9, 7, 15, 3, 2, 3, 5, 2, 1, 1, 0), # 31
(12, 14, 16, 12, 11, 11, 5, 5, 6, 1, 2, 1, 0, 15, 12, 13, 7, 8, 13, 4, 5, 4, 7, 1, 2, 0), # 32
(11, 16, 15, 17, 3, 7, 1, 6, 6, 1, 4, 1, 0, 18, 10, 9, 8, 14, 2, 3, 3, 5, 8, 1, 1, 0), # 33
(8, 11, 12, 15, 18, 9, 4, 6, 5, 2, 4, 0, 0, 13, 13, 5, 4, 11, 7, 6, 5, 2, 2, 1, 0, 0), # 34
(23, 16, 11, 5, 13, 3, 4, 4, 4, 3, 3, 1, 0, 13, 12, 8, 6, 9, 7, 4, 5, 6, 6, 6, 1, 0), # 35
(13, 12, 17, 16, 14, 3, 4, 4, 5, 3, 1, 0, 0, 11, 5, 6, 11, 6, 9, 6, 3, 6, 5, 1, 1, 0), # 36
(15, 9, 13, 12, 8, 5, 9, 8, 8, 1, 1, 1, 0, 22, 18, 11, 7, 14, 11, 8, 2, 5, 7, 3, 0, 0), # 37
(15, 14, 16, 13, 8, 4, 4, 4, 4, 6, 1, 1, 0, 13, 8, 16, 3, 4, 7, 7, 3, 7, 6, 2, 1, 0), # 38
(16, 17, 8, 14, 9, 4, 4, 3, 6, 0, 4, 0, 0, 17, 15, 11, 7, 13, 6, 4, 3, 5, 6, 0, 0, 0), # 39
(11, 13, 11, 7, 9, 3, 1, 6, 8, 3, 3, 0, 0, 15, 7, 6, 12, 11, 6, 5, 7, 5, 6, 3, 1, 0), # 40
(15, 11, 11, 7, 10, 5, 6, 3, 8, 5, 1, 1, 0, 9, 9, 13, 7, 9, 12, 6, 3, 3, 3, 3, 1, 0), # 41
(21, 12, 14, 12, 7, 0, 4, 5, 4, 0, 0, 2, 0, 22, 14, 7, 4, 14, 13, 6, 5, 7, 6, 1, 0, 0), # 42
(17, 16, 8, 12, 13, 1, 7, 4, 6, 1, 2, 0, 0, 15, 5, 9, 8, 13, 4, 9, 4, 1, 3, 2, 1, 0), # 43
(11, 17, 16, 10, 7, 5, 7, 4, 4, 3, 1, 4, 0, 18, 14, 8, 8, 15, 7, 5, 7, 4, 4, 0, 3, 0), # 44
(12, 14, 12, 14, 10, 4, 4, 4, 3, 2, 1, 2, 0, 13, 13, 13, 8, 6, 5, 5, 2, 4, 2, 4, 1, 0), # 45
(18, 21, 10, 16, 12, 4, 4, 6, 6, 3, 1, 0, 0, 8, 5, 7, 7, 13, 8, 5, 4, 10, 2, 0, 3, 0), # 46
(7, 10, 11, 16, 9, 8, 5, 3, 8, 0, 3, 1, 0, 20, 17, 8, 3, 7, 12, 6, 4, 7, 4, 2, 3, 0), # 47
(19, 12, 8, 5, 7, 4, 3, 3, 5, 3, 1, 1, 0, 9, 7, 12, 11, 19, 9, 8, 4, 8, 2, 3, 1, 0), # 48
(9, 7, 14, 20, 17, 2, 7, 3, 4, 3, 4, 3, 0, 13, 11, 10, 9, 13, 7, 4, 3, 3, 6, 1, 1, 0), # 49
(15, 21, 13, 12, 7, 6, 7, 4, 4, 1, 1, 2, 0, 18, 12, 11, 8, 13, 10, 7, 2, 4, 6, 3, 2, 0), # 50
(11, 9, 13, 15, 9, 2, 6, 2, 4, 7, 1, 2, 0, 21, 18, 9, 9, 14, 5, 5, 1, 5, 3, 3, 3, 0), # 51
(15, 15, 11, 10, 8, 4, 3, 5, 6, 1, 1, 1, 0, 7, 7, 12, 11, 14, 5, 5, 2, 4, 0, 1, 3, 0), # 52
(11, 11, 8, 13, 9, 4, 5, 5, 2, 3, 3, 1, 0, 13, 15, 8, 4, 16, 5, 6, 6, 5, 3, 2, 1, 0), # 53
(17, 18, 6, 15, 9, 4, 5, 6, 10, 4, 2, 2, 0, 21, 17, 14, 8, 16, 6, 4, 6, 6, 3, 1, 1, 0), # 54
(10, 12, 18, 10, 11, 7, 1, 7, 7, 3, 1, 0, 0, 14, 7, 15, 7, 7, 7, 5, 3, 1, 10, 0, 0, 0), # 55
(6, 12, 13, 14, 15, 2, 4, 4, 4, 1, 3, 1, 0, 21, 16, 11, 6, 15, 4, 7, 7, 5, 4, 4, 1, 0), # 56
(6, 16, 19, 17, 4, 4, 7, 6, 8, 2, 0, 0, 0, 19, 14, 6, 2, 13, 10, 6, 4, 4, 0, 2, 1, 0), # 57
(15, 13, 15, 19, 8, 3, 6, 6, 5, 0, 2, 2, 0, 14, 9, 8, 6, 14, 3, 9, 6, 7, 4, 4, 1, 0), # 58
(13, 4, 10, 14, 7, 4, 7, 3, 7, 6, 0, 2, 0, 10, 12, 9, 10, 11, 6, 3, 5, 7, 4, 2, 0, 0), # 59
(14, 24, 6, 17, 14, 6, 4, 1, 1, 2, 1, 3, 0, 14, 10, 12, 12, 19, 5, 9, 1, 6, 6, 1, 0, 0), # 60
(13, 20, 9, 10, 11, 7, 5, 9, 9, 1, 1, 2, 0, 16, 12, 11, 7, 16, 13, 3, 6, 6, 4, 0, 1, 0), # 61
(17, 7, 12, 12, 8, 5, 5, 4, 6, 3, 1, 0, 0, 13, 8, 10, 10, 11, 4, 4, 5, 3, 6, 4, 3, 0), # 62
(8, 14, 19, 12, 9, 5, 5, 5, 2, 0, 7, 0, 0, 12, 13, 3, 7, 10, 5, 1, 4, 3, 1, 1, 0, 0), # 63
(16, 10, 11, 8, 12, 2, 7, 8, 5, 1, 4, 0, 0, 14, 16, 8, 11, 14, 7, 5, 4, 8, 4, 1, 1, 0), # 64
(7, 13, 15, 14, 9, 2, 4, 4, 3, 1, 2, 0, 0, 16, 12, 19, 4, 13, 6, 5, 1, 11, 4, 1, 1, 0), # 65
(18, 14, 11, 11, 11, 2, 0, 5, 6, 6, 1, 1, 0, 15, 10, 7, 8, 14, 10, 2, 2, 5, 4, 0, 1, 0), # 66
(15, 17, 9, 12, 15, 4, 7, 7, 8, 0, 2, 3, 0, 17, 10, 6, 11, 9, 4, 12, 2, 1, 8, 3, 0, 0), # 67
(10, 11, 6, 11, 12, 4, 7, 4, 8, 2, 2, 1, 0, 10, 8, 9, 7, 11, 3, 8, 3, 4, 4, 5, 0, 0), # 68
(14, 10, 9, 16, 6, 4, 8, 7, 3, 1, 0, 4, 0, 12, 10, 9, 5, 12, 7, 9, 3, 4, 4, 2, 0, 0), # 69
(19, 11, 8, 18, 13, 6, 7, 4, 4, 0, 5, 1, 0, 16, 8, 10, 6, 11, 6, 6, 2, 6, 4, 1, 2, 0), # 70
(17, 6, 13, 11, 15, 9, 2, 1, 9, 4, 2, 0, 0, 11, 10, 8, 4, 7, 5, 9, 4, 6, 5, 0, 0, 0), # 71
(15, 9, 19, 17, 10, 5, 9, 6, 7, 3, 1, 0, 0, 16, 11, 12, 17, 9, 2, 8, 7, 7, 4, 2, 3, 0), # 72
(17, 11, 11, 15, 9, 6, 3, 7, 9, 2, 2, 0, 0, 16, 9, 5, 5, 16, 6, 7, 5, 2, 1, 2, 0, 0), # 73
(9, 8, 8, 14, 12, 11, 3, 2, 3, 3, 0, 0, 0, 18, 17, 13, 4, 13, 5, 5, 1, 5, 4, 4, 0, 0), # 74
(11, 13, 14, 11, 13, 7, 6, 3, 7, 3, 2, 1, 0, 14, 14, 9, 9, 7, 5, 6, 1, 8, 4, 3, 1, 0), # 75
(19, 12, 16, 10, 11, 5, 8, 3, 3, 6, 1, 0, 0, 7, 12, 10, 8, 19, 9, 7, 2, 5, 6, 4, 0, 0), # 76
(10, 8, 14, 12, 13, 5, 9, 5, 5, 1, 3, 0, 0, 18, 21, 9, 11, 6, 2, 2, 4, 8, 2, 3, 1, 0), # 77
(12, 14, 6, 17, 15, 6, 4, 4, 6, 2, 2, 0, 0, 11, 12, 9, 5, 11, 6, 4, 2, 5, 7, 0, 0, 0), # 78
(12, 8, 10, 13, 10, 6, 4, 6, 6, 3, 2, 0, 0, 16, 7, 7, 8, 5, 4, 5, 6, 9, 2, 1, 0, 0), # 79
(17, 17, 12, 9, 15, 8, 2, 3, 5, 1, 1, 1, 0, 10, 15, 13, 5, 13, 4, 6, 4, 7, 3, 1, 0, 0), # 80
(13, 11, 12, 8, 12, 6, 8, 6, 8, 2, 3, 0, 0, 19, 9, 12, 10, 12, 4, 5, 2, 5, 1, 1, 1, 0), # 81
(13, 13, 7, 15, 11, 7, 6, 6, 5, 2, 0, 7, 0, 18, 14, 7, 12, 7, 3, 3, 8, 3, 6, 3, 2, 0), # 82
(12, 6, 13, 6, 6, 5, 8, 3, 6, 4, 2, 0, 0, 17, 9, 8, 8, 12, 7, 5, 3, 7, 4, 2, 0, 0), # 83
(11, 13, 14, 16, 11, 7, 8, 7, 6, 2, 1, 1, 0, 7, 16, 8, 5, 5, 3, 6, 4, 5, 3, 2, 0, 0), # 84
(12, 14, 21, 14, 14, 7, 6, 3, 8, 4, 1, 1, 0, 9, 15, 10, 2, 13, 3, 6, 4, 6, 3, 3, 0, 0), # 85
(16, 11, 11, 11, 15, 4, 4, 4, 6, 1, 2, 1, 0, 11, 7, 8, 11, 10, 8, 3, 3, 5, 8, 2, 0, 0), # 86
(17, 7, 11, 12, 6, 3, 4, 2, 5, 2, 1, 0, 0, 11, 16, 8, 10, 7, 8, 5, 6, 7, 9, 1, 0, 0), # 87
(11, 17, 10, 9, 10, 6, 6, 2, 3, 4, 5, 0, 0, 21, 12, 9, 10, 13, 1, 2, 5, 6, 5, 2, 1, 0), # 88
(15, 9, 14, 15, 7, 4, 4, 5, 4, 1, 3, 1, 0, 18, 14, 9, 4, 9, 6, 9, 4, 5, 4, 2, 0, 0), # 89
(13, 8, 9, 11, 11, 9, 7, 2, 3, 2, 0, 0, 0, 13, 13, 7, 3, 6, 9, 4, 4, 6, 1, 3, 1, 0), # 90
(18, 13, 7, 14, 9, 4, 4, 0, 8, 2, 2, 0, 0, 14, 10, 11, 5, 7, 4, 10, 3, 2, 2, 5, 2, 0), # 91
(12, 12, 7, 13, 13, 7, 1, 8, 5, 4, 5, 1, 0, 14, 17, 8, 8, 11, 4, 5, 3, 5, 5, 2, 1, 0), # 92
(11, 5, 12, 12, 4, 4, 3, 4, 10, 3, 1, 0, 0, 13, 10, 10, 6, 21, 6, 4, 3, 2, 3, 2, 1, 0), # 93
(14, 13, 12, 13, 13, 2, 4, 7, 3, 2, 2, 1, 0, 12, 13, 4, 7, 15, 6, 5, 1, 6, 5, 0, 0, 0), # 94
(9, 19, 11, 11, 7, 5, 2, 4, 3, 4, 0, 3, 0, 12, 17, 7, 11, 11, 6, 5, 2, 3, 3, 0, 2, 0), # 95
(9, 8, 14, 10, 7, 6, 8, 7, 9, 3, 1, 2, 0, 10, 10, 9, 8, 7, 6, 3, 9, 9, 5, 5, 0, 0), # 96
(13, 10, 8, 14, 10, 4, 2, 10, 5, 2, 2, 3, 0, 11, 3, 8, 8, 12, 7, 5, 1, 8, 1, 2, 1, 0), # 97
(17, 8, 12, 12, 12, 7, 4, 2, 4, 4, 0, 2, 0, 8, 9, 5, 6, 10, 5, 3, 1, 7, 4, 4, 2, 0), # 98
(14, 10, 11, 15, 12, 4, 5, 3, 4, 1, 0, 2, 0, 14, 13, 13, 7, 9, 4, 4, 0, 2, 6, 4, 2, 0), # 99
(9, 9, 11, 10, 11, 5, 2, 5, 8, 1, 0, 5, 0, 12, 8, 12, 8, 12, 2, 7, 3, 10, 4, 4, 1, 0), # 100
(16, 11, 10, 7, 12, 3, 2, 3, 6, 1, 1, 1, 0, 13, 12, 4, 5, 10, 9, 6, 3, 6, 4, 2, 0, 0), # 101
(17, 12, 8, 14, 5, 6, 5, 5, 4, 2, 1, 0, 0, 15, 7, 5, 12, 9, 6, 2, 5, 3, 7, 3, 3, 0), # 102
(14, 12, 8, 12, 8, 5, 4, 5, 7, 2, 1, 0, 0, 20, 14, 10, 8, 6, 4, 4, 2, 8, 3, 0, 1, 0), # 103
(14, 6, 11, 14, 11, 4, 4, 5, 5, 3, 1, 1, 0, 10, 6, 14, 6, 8, 9, 4, 5, 2, 3, 1, 0, 0), # 104
(16, 11, 9, 13, 12, 5, 8, 5, 8, 2, 2, 0, 0, 17, 10, 15, 8, 10, 3, 6, 1, 6, 4, 3, 0, 0), # 105
(8, 12, 13, 10, 6, 5, 5, 2, 8, 2, 1, 1, 0, 19, 14, 7, 4, 12, 4, 3, 4, 5, 3, 1, 1, 0), # 106
(11, 12, 16, 5, 3, 9, 3, 2, 7, 2, 0, 2, 0, 15, 13, 3, 3, 8, 5, 7, 4, 6, 4, 3, 1, 0), # 107
(12, 9, 7, 9, 7, 5, 4, 3, 2, 1, 2, 3, 0, 24, 10, 11, 9, 9, 5, 6, 2, 6, 4, 1, 4, 0), # 108
(10, 15, 14, 10, 7, 7, 9, 5, 8, 2, 1, 2, 0, 17, 9, 6, 5, 9, 9, 3, 5, 9, 1, 3, 0, 0), # 109
(15, 7, 10, 8, 8, 4, 4, 4, 5, 0, 0, 0, 0, 13, 9, 13, 9, 9, 6, 5, 5, 5, 4, 0, 2, 0), # 110
(19, 13, 9, 13, 16, 2, 2, 2, 9, 4, 1, 0, 0, 8, 8, 11, 4, 10, 1, 5, 2, 6, 2, 3, 0, 0), # 111
(15, 18, 10, 14, 4, 2, 3, 2, 9, 0, 1, 0, 0, 12, 10, 6, 11, 7, 2, 2, 3, 10, 3, 2, 0, 0), # 112
(9, 7, 13, 17, 5, 2, 0, 1, 8, 1, 3, 1, 0, 18, 9, 11, 6, 9, 11, 2, 2, 5, 5, 4, 1, 0), # 113
(14, 4, 12, 10, 8, 7, 2, 2, 6, 2, 2, 0, 0, 13, 11, 9, 6, 10, 1, 6, 3, 4, 3, 2, 1, 0), # 114
(15, 7, 9, 13, 10, 3, 7, 1, 4, 0, 2, 2, 0, 11, 11, 15, 4, 12, 4, 3, 3, 4, 5, 4, 3, 0), # 115
(4, 14, 12, 13, 12, 6, 1, 6, 4, 0, 0, 1, 0, 11, 9, 9, 4, 7, 11, 3, 2, 4, 5, 1, 1, 0), # 116
(15, 9, 13, 10, 9, 6, 4, 3, 9, 5, 1, 2, 0, 10, 10, 9, 9, 12, 1, 3, 4, 5, 2, 1, 0, 0), # 117
(10, 10, 13, 17, 10, 6, 3, 3, 4, 1, 2, 2, 0, 12, 15, 10, 9, 5, 4, 3, 3, 6, 4, 0, 1, 0), # 118
(5, 8, 8, 7, 11, 3, 3, 5, 5, 2, 4, 1, 0, 10, 9, 11, 3, 11, 7, 3, 3, 3, 2, 4, 3, 0), # 119
(9, 9, 9, 9, 11, 4, 4, 2, 5, 3, 1, 0, 0, 11, 12, 7, 8, 8, 8, 3, 3, 8, 2, 4, 2, 0), # 120
(10, 15, 17, 15, 15, 5, 4, 4, 10, 3, 3, 0, 0, 17, 9, 6, 7, 4, 4, 2, 3, 4, 5, 4, 0, 0), # 121
(23, 10, 9, 8, 11, 4, 2, 3, 9, 1, 2, 0, 0, 17, 12, 6, 7, 8, 5, 2, 3, 8, 3, 3, 0, 0), # 122
(16, 5, 5, 14, 10, 3, 4, 1, 4, 3, 2, 1, 0, 17, 11, 11, 4, 9, 3, 5, 5, 3, 2, 4, 1, 0), # 123
(10, 19, 10, 15, 5, 6, 5, 2, 5, 2, 0, 0, 0, 7, 11, 6, 6, 10, 6, 2, 4, 7, 3, 0, 1, 0), # 124
(7, 8, 9, 6, 11, 4, 5, 2, 4, 3, 1, 0, 0, 13, 11, 11, 10, 11, 6, 7, 4, 5, 2, 4, 0, 0), # 125
(15, 13, 6, 12, 4, 5, 6, 2, 0, 3, 1, 1, 0, 19, 6, 6, 6, 8, 4, 4, 3, 4, 3, 1, 1, 0), # 126
(7, 8, 9, 11, 12, 4, 4, 2, 6, 2, 0, 0, 0, 8, 7, 4, 3, 9, 5, 2, 2, 6, 1, 5, 1, 0), # 127
(15, 12, 9, 9, 4, 7, 4, 2, 8, 5, 1, 2, 0, 11, 5, 9, 5, 11, 3, 4, 3, 3, 3, 4, 1, 0), # 128
(13, 9, 14, 9, 10, 5, 4, 1, 6, 1, 1, 1, 0, 14, 10, 6, 5, 10, 9, 5, 2, 1, 8, 0, 0, 0), # 129
(8, 12, 10, 11, 11, 5, 2, 6, 5, 1, 2, 1, 0, 8, 5, 5, 6, 9, 3, 7, 1, 2, 2, 3, 0, 0), # 130
(5, 6, 19, 7, 5, 2, 1, 4, 6, 1, 0, 0, 0, 9, 10, 9, 5, 8, 7, 3, 4, 3, 1, 2, 0, 0), # 131
(12, 10, 10, 5, 10, 6, 4, 7, 2, 1, 1, 0, 0, 17, 9, 5, 10, 13, 3, 2, 3, 1, 4, 7, 1, 0), # 132
(6, 10, 12, 11, 5, 4, 4, 4, 3, 1, 2, 3, 0, 9, 11, 6, 4, 14, 4, 6, 4, 10, 5, 3, 1, 0), # 133
(15, 8, 11, 16, 9, 5, 2, 3, 7, 2, 1, 1, 0, 11, 5, 12, 5, 12, 1, 5, 5, 5, 4, 2, 0, 0), # 134
(12, 11, 13, 13, 13, 2, 0, 2, 4, 2, 3, 0, 0, 10, 11, 7, 5, 15, 7, 6, 2, 3, 3, 1, 3, 0), # 135
(17, 7, 13, 15, 7, 6, 5, 2, 5, 1, 0, 1, 0, 10, 7, 8, 4, 14, 4, 4, 4, 5, 1, 2, 0, 0), # 136
(10, 11, 12, 12, 12, 5, 2, 3, 2, 2, 2, 0, 0, 18, 11, 8, 6, 12, 4, 7, 2, 6, 3, 3, 1, 0), # 137
(17, 4, 5, 12, 11, 3, 7, 2, 6, 6, 0, 1, 0, 11, 11, 6, 5, 8, 2, 3, 3, 7, 6, 3, 1, 0), # 138
(16, 12, 12, 8, 8, 6, 6, 3, 4, 1, 3, 1, 0, 13, 8, 8, 7, 9, 8, 2, 4, 4, 4, 5, 1, 0), # 139
(13, 9, 5, 14, 8, 5, 3, 5, 6, 2, 3, 1, 0, 6, 9, 7, 3, 11, 6, 5, 7, 5, 6, 3, 1, 0), # 140
(16, 6, 11, 8, 7, 6, 6, 2, 6, 0, 1, 1, 0, 14, 13, 8, 7, 10, 2, 4, 1, 5, 2, 1, 0, 0), # 141
(7, 5, 9, 11, 8, 5, 2, 6, 5, 2, 0, 2, 0, 12, 5, 4, 2, 10, 6, 5, 3, 3, 4, 0, 3, 0), # 142
(17, 14, 13, 10, 15, 4, 5, 5, 7, 0, 3, 4, 0, 13, 10, 6, 6, 15, 5, 6, 3, 2, 5, 1, 1, 0), # 143
(11, 11, 15, 13, 5, 4, 6, 6, 1, 4, 5, 0, 0, 11, 11, 7, 4, 11, 4, 4, 8, 7, 3, 1, 0, 0), # 144
(6, 13, 11, 4, 7, 6, 2, 7, 7, 1, 1, 1, 0, 9, 7, 13, 5, 8, 11, 6, 8, 5, 5, 2, 3, 0), # 145
(15, 12, 8, 10, 9, 5, 4, 5, 4, 0, 1, 1, 0, 11, 9, 13, 5, 11, 4, 5, 2, 3, 3, 1, 0, 0), # 146
(12, 12, 9, 8, 7, 4, 2, 5, 2, 1, 2, 1, 0, 16, 12, 6, 5, 9, 4, 3, 3, 4, 3, 2, 2, 0), # 147
(10, 9, 15, 12, 8, 7, 6, 6, 4, 2, 1, 2, 0, 14, 8, 9, 6, 8, 4, 4, 3, 2, 4, 2, 0, 0), # 148
(18, 13, 6, 15, 12, 4, 6, 2, 5, 4, 2, 0, 0, 10, 7, 8, 5, 8, 2, 3, 4, 7, 2, 3, 2, 0), # 149
(18, 3, 10, 10, 4, 5, 2, 3, 2, 1, 2, 0, 0, 9, 11, 4, 5, 18, 3, 7, 4, 6, 3, 2, 2, 0), # 150
(9, 5, 4, 3, 7, 5, 3, 3, 5, 0, 3, 0, 0, 10, 12, 8, 6, 10, 5, 1, 1, 1, 3, 1, 1, 0), # 151
(9, 3, 9, 12, 8, 3, 1, 1, 4, 3, 1, 0, 0, 14, 6, 3, 4, 9, 4, 3, 7, 3, 5, 0, 0, 0), # 152
(7, 5, 7, 12, 5, 6, 3, 3, 4, 3, 2, 0, 0, 10, 11, 5, 8, 9, 5, 2, 4, 7, 3, 2, 1, 0), # 153
(7, 10, 8, 8, 8, 3, 3, 0, 2, 2, 1, 1, 0, 9, 11, 10, 7, 14, 4, 4, 2, 3, 2, 1, 2, 0), # 154
(17, 6, 10, 13, 6, 1, 5, 2, 1, 0, 1, 0, 0, 14, 3, 5, 3, 7, 3, 2, 4, 5, 4, 1, 0, 0), # 155
(4, 5, 4, 8, 4, 6, 3, 4, 3, 0, 1, 1, 0, 10, 9, 5, 4, 7, 10, 3, 7, 4, 5, 1, 0, 0), # 156
(3, 7, 6, 5, 14, 6, 3, 1, 2, 2, 1, 2, 0, 13, 8, 6, 5, 11, 2, 4, 3, 3, 5, 4, 0, 0), # 157
(9, 5, 16, 8, 8, 4, 6, 4, 7, 3, 2, 0, 0, 5, 9, 1, 8, 12, 5, 4, 2, 3, 1, 4, 0, 0), # 158
(7, 6, 10, 5, 8, 5, 4, 5, 6, 1, 1, 0, 0, 6, 13, 7, 6, 7, 6, 3, 4, 4, 1, 1, 0, 0), # 159
(13, 5, 12, 5, 6, 4, 1, 5, 2, 2, 2, 0, 0, 11, 7, 6, 5, 7, 4, 1, 5, 5, 1, 1, 0, 0), # 160
(11, 6, 9, 7, 4, 2, 1, 6, 7, 2, 0, 0, 0, 6, 11, 7, 3, 6, 7, 7, 3, 6, 3, 2, 1, 0), # 161
(12, 10, 5, 6, 9, 6, 2, 5, 7, 1, 1, 0, 0, 10, 5, 10, 4, 13, 3, 1, 1, 7, 2, 3, 0, 0), # 162
(9, 6, 12, 8, 4, 3, 2, 3, 7, 2, 3, 0, 0, 12, 6, 7, 1, 5, 2, 3, 6, 3, 4, 2, 0, 0), # 163
(9, 5, 7, 9, 8, 3, 2, 3, 6, 1, 0, 0, 0, 9, 5, 4, 2, 8, 3, 3, 4, 5, 3, 1, 1, 0), # 164
(11, 8, 12, 7, 5, 4, 6, 1, 6, 3, 1, 1, 0, 6, 9, 3, 1, 4, 5, 2, 3, 2, 4, 0, 0, 0), # 165
(5, 3, 13, 9, 4, 2, 1, 4, 5, 1, 1, 0, 0, 12, 5, 5, 5, 9, 6, 2, 1, 2, 4, 1, 0, 0), # 166
(6, 7, 8, 8, 4, 3, 6, 5, 6, 1, 3, 0, 0, 13, 6, 10, 5, 5, 4, 1, 3, 5, 1, 0, 1, 0), # 167
(9, 5, 11, 11, 5, 3, 3, 3, 2, 2, 1, 2, 0, 8, 8, 6, 3, 8, 3, 3, 1, 1, 2, 1, 0, 0), # 168
(5, 5, 8, 10, 3, 2, 5, 3, 4, 0, 2, 0, 0, 13, 6, 4, 2, 6, 2, 2, 3, 0, 2, 3, 0, 0), # 169
(11, 5, 5, 6, 5, 2, 3, 3, 3, 0, 2, 1, 0, 9, 6, 9, 6, 7, 3, 3, 4, 6, 1, 2, 2, 0), # 170
(10, 4, 5, 4, 11, 4, 1, 4, 2, 1, 1, 1, 0, 7, 6, 11, 4, 13, 7, 0, 0, 2, 4, 0, 0, 0), # 171
(9, 2, 4, 6, 3, 5, 1, 1, 2, 3, 2, 0, 0, 4, 5, 4, 3, 8, 2, 2, 3, 1, 1, 3, 0, 0), # 172
(8, 6, 2, 4, 6, 3, 2, 0, 2, 1, 0, 0, 0, 8, 7, 9, 5, 4, 1, 3, 2, 3, 6, 0, 1, 0), # 173
(6, 5, 6, 6, 7, 3, 2, 0, 2, 2, 0, 0, 0, 8, 6, 3, 2, 6, 4, 0, 2, 4, 2, 0, 0, 0), # 174
(8, 3, 5, 7, 5, 1, 2, 0, 2, 2, 3, 1, 0, 6, 3, 1, 5, 5, 1, 0, 3, 2, 2, 2, 2, 0), # 175
(5, 5, 4, 7, 8, 4, 2, 1, 2, 1, 1, 2, 0, 5, 0, 2, 2, 5, 3, 1, 1, 2, 3, 0, 0, 0), # 176
(11, 4, 9, 6, 5, 3, 3, 2, 1, 2, 0, 1, 0, 8, 1, 4, 3, 5, 1, 3, 0, 3, 1, 0, 0, 0), # 177
(5, 4, 4, 3, 6, 2, 1, 3, 5, 1, 0, 2, 0, 9, 1, 1, 2, 4, 0, 2, 0, 5, 2, 2, 1, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(7.029211809720476, 7.735403983570434, 7.29579652145751, 8.700534883408807, 7.776559850653457, 4.394116904852274, 5.804449861523481, 6.514446642171193, 8.52613868703521, 5.541221021731318, 5.887371229439844, 6.857081109628643, 7.117432297609708), # 0
(7.496058012827964, 8.246084971802663, 7.777485227862214, 9.275201954587263, 8.291486472463932, 4.684377017659578, 6.187256517769172, 6.943319212067992, 9.089143456866074, 5.90657296918801, 6.2763345903385845, 7.309703325140097, 7.587708306415797), # 1
(7.9614122125716245, 8.754739239247371, 8.257259199766379, 9.847582786530712, 8.804548163249642, 4.9734791603174235, 6.568545911144986, 7.370475347066188, 9.64990152962857, 6.270479285028765, 6.663752408286839, 7.760525712874277, 8.056110759493567), # 2
(8.423460910405188, 9.259348702711026, 8.733215217047796, 10.415406970544904, 9.313726346402664, 5.260276871619158, 6.946805098307138, 7.79422162049231, 10.206189225289531, 6.631495777796654, 7.0480877765583365, 8.207759958902646, 8.520781928755916), # 3
(8.880390607782374, 9.757895279000085, 9.203450059584252, 10.976404097935598, 9.81700244531509, 5.543623690358135, 7.320521135911843, 8.212864605672882, 10.75578286381579, 6.988178256034751, 7.4278037884268056, 8.64961774929667, 8.979864086115745), # 4
(9.330387806156915, 10.248360884921025, 9.666060507253526, 11.528303760008551, 10.312357883378994, 5.822373155327701, 7.688181080615314, 8.62471087593443, 11.296458765174183, 7.339082528286129, 7.801363537165986, 9.084310770127807, 9.43149950348596), # 5
(9.771639006982534, 10.728727437280302, 10.119143339933412, 12.068835548069513, 10.79777408398646, 6.09537880532121, 8.048271989073768, 9.028067004603484, 11.825993249331543, 7.682764403093862, 8.167230116049597, 9.510050707467531, 9.87383045277945), # 6
(10.202330711712957, 11.196976852884385, 10.56079533750169, 12.595729053424249, 11.271232470529577, 6.36149417913201, 8.39928091794342, 9.421239565006573, 12.342162636254702, 8.017779689001022, 8.523866618351377, 9.925049247387301, 10.304999205909127), # 7
(10.62064942180191, 11.651091048539739, 10.989113279836156, 13.1067138673785, 11.730714466400421, 6.619572815553446, 8.739694923880478, 9.802535130470215, 12.842743245910489, 8.342684194550685, 8.86973613734505, 10.327518075958585, 10.723148034787885), # 8
(11.02478163870312, 12.089051941052832, 11.402193946814586, 13.599519581238038, 12.174201494991074, 6.868468253378878, 9.068001063541168, 10.170260274320949, 13.325511398265744, 8.65603372828592, 9.20330176630435, 10.71566887925284, 11.126419211328628), # 9
(11.412913863870306, 12.508841447230123, 11.798134118314776, 14.071875786308604, 12.599674979693622, 7.107034031401651, 9.382686393581697, 10.522721569885295, 13.7882434132873, 8.956384098749801, 9.523026598503003, 11.087713343341534, 11.512955007444255), # 10
(11.783232598757209, 12.90844148387809, 12.175030574214501, 14.521512073895957, 13.005116343900148, 7.334123688415116, 9.682237970658283, 10.85822559048978, 14.228715610941991, 9.242291114485408, 9.82737372721475, 11.441863154296136, 11.880897695047656), # 11
(12.133924344817538, 13.285833967803178, 12.530980094391557, 14.946158035305858, 13.38850701100273, 7.5485907632126175, 9.965142851427137, 11.17507890946093, 14.644704311196652, 9.512310584035802, 10.114806245713309, 11.776329998188096, 12.22838954605175), # 12
(12.463175603505027, 13.639000815811869, 12.864079458723728, 15.343543261844063, 13.747828404393443, 7.749288794587514, 10.22988809254448, 11.471588100125276, 15.033985834018106, 9.764998315944066, 10.383787247272418, 12.08932556108889, 12.55357283236943), # 13
(12.769172876273403, 13.965923944710624, 13.172425447088806, 15.71139734481631, 14.081061947464386, 7.935071321333148, 10.474960750666526, 11.746059735809345, 15.39433649937319, 9.998910118753269, 10.6327798251658, 12.379061529069986, 12.85458982591359), # 14
(13.050102664576398, 14.264585271305906, 13.45411483936456, 16.047449875528383, 14.386189063607633, 8.104791882242878, 10.698847882449478, 11.99680038983966, 15.723532627228748, 10.212601801006487, 10.860247072667189, 12.64374958820284, 13.129582798597134), # 15
(13.30415146986772, 14.532966712404187, 13.707244415428796, 16.349430445286004, 14.661191176215267, 8.257304016110044, 10.900036544549568, 12.222116635542745, 16.019350537551603, 10.404629171246796, 11.06465208305032, 12.881601424558916, 13.376694022332964), # 16
(13.529505793601107, 14.769050184811926, 13.929910955159293, 16.61506864539496, 14.904049708679375, 8.391461261728, 11.077013793622996, 12.420315046245145, 16.27956655030858, 10.573548038017254, 11.24445794958892, 13.090828724209679, 13.594065769033982), # 17
(13.724352137230287, 14.970817605335585, 14.120211238433834, 16.842094067160993, 15.112746084392025, 8.506117157890104, 11.228266686325993, 12.589702195273366, 16.501956985466535, 10.717914209860952, 11.398127765556712, 13.269643173226603, 13.779840310613086), # 18
(13.88687700220898, 15.136250890781643, 14.27624204513021, 17.02823630188984, 15.285261726745313, 8.600125243389693, 11.352282279314753, 12.728584655953943, 16.68429816299229, 10.83628349532096, 11.52412462422743, 13.416256457681136, 13.932159918983176), # 19
(14.015266889990915, 15.263331957956549, 14.396100155126206, 17.171224940887296, 15.419578059131322, 8.672339057020126, 11.44754762924551, 12.835269001613405, 16.82436640285268, 10.927211702940342, 11.62091161887481, 13.528880263644748, 14.049166866057154), # 20
(14.107708302029813, 15.350042723666784, 14.477882348299607, 17.26878957545908, 15.513676504942126, 8.72161213757475, 11.512549792774463, 12.908061805578273, 16.91993802501453, 10.989254641262178, 11.686951842772585, 13.60572627718891, 14.12900342374791), # 21
(14.162387739779412, 15.394365104718803, 14.5196854045282, 17.31865979691097, 15.565538487569807, 8.746798023846914, 11.54577582655784, 12.945269641175082, 16.968789349444684, 11.02096811882954, 11.720708389194478, 13.645006184385087, 14.16981186396836), # 22
(14.182550708679697, 15.39961303155007, 14.524892455418383, 17.324903137860087, 15.578824878445637, 8.75, 11.549725603163076, 12.949291358024693, 16.974896728395063, 11.024709181527207, 11.724941252026436, 13.649856607224509, 14.175), # 23
(14.197417378247815, 15.396551851851854, 14.524040740740743, 17.324134722222226, 15.586350659060795, 8.75, 11.547555337690634, 12.943700000000002, 16.974078333333335, 11.02241086419753, 11.724474410774413, 13.648720987654322, 14.175), # 24
(14.211970122296213, 15.390517832647463, 14.522359396433473, 17.322614454732513, 15.593710923832306, 8.75, 11.543278463648836, 12.932716049382718, 16.97246141975309, 11.01788637402835, 11.723548759196907, 13.646479195244629, 14.175), # 25
(14.226207826667249, 15.381603155006863, 14.519871467764064, 17.320359619341563, 15.600905415789548, 8.75, 11.53696140563221, 12.916546913580248, 16.97006672839506, 11.011210992226795, 11.722172677391198, 13.643161957018751, 14.175), # 26
(14.240129377203292, 15.3699, 14.5166, 17.3173875, 15.607933877961901, 8.75, 11.528670588235297, 12.895400000000002, 16.966915, 11.00246, 11.720354545454546, 13.638800000000003, 14.175), # 27
(14.253733659746702, 15.355500548696845, 14.51256803840878, 17.313715380658437, 15.614796053378763, 8.75, 11.518472436052612, 12.869482716049385, 16.963026975308644, 10.9917086785551, 11.718102743484225, 13.633424051211708, 14.175), # 28
(14.26701956013985, 15.338496982167355, 14.50779862825789, 17.30936054526749, 15.62149168506951, 8.75, 11.506433373678693, 12.839002469135803, 16.95842339506173, 10.979032309099225, 11.715425651577503, 13.627064837677183, 14.175), # 29
(14.279985964225098, 15.318981481481483, 14.502314814814815, 17.30434027777778, 15.628020516063533, 8.75, 11.492619825708061, 12.804166666666665, 16.953125, 10.964506172839508, 11.71233164983165, 13.619753086419752, 14.175), # 30
(14.292631757844802, 15.297046227709194, 14.496139643347053, 17.29867186213992, 15.634382289390214, 8.75, 11.477098216735257, 12.765182716049384, 16.947152530864198, 10.948205550983083, 11.708829118343933, 13.611519524462738, 14.175), # 31
(14.304955826841338, 15.27278340192044, 14.489296159122084, 17.29237258230453, 15.640576748078935, 8.75, 11.4599349713548, 12.72225802469136, 16.940526728395064, 10.930205724737084, 11.704926437211622, 13.602394878829449, 14.175), # 32
(14.316957057057056, 15.246285185185185, 14.481807407407409, 17.28545972222222, 15.646603635159089, 8.75, 11.441196514161222, 12.675600000000001, 16.933268333333334, 10.910581975308643, 11.700631986531986, 13.59240987654321, 14.175), # 33
(14.328634334334335, 15.217643758573388, 14.473696433470508, 17.27795056584362, 15.652462693660054, 8.75, 11.420949269749054, 12.625416049382716, 16.925398086419758, 10.889409583904893, 11.695954146402293, 13.581595244627344, 14.175), # 34
(14.339986544515531, 15.186951303155007, 14.464986282578877, 17.26986239711934, 15.65815366661122, 8.75, 11.399259662712824, 12.571913580246914, 16.916936728395065, 10.866763831732968, 11.690901296919815, 13.569981710105168, 14.175), # 35
(14.35101257344301, 15.1543, 14.455700000000002, 17.2612125, 15.663676297041972, 8.75, 11.37619411764706, 12.515300000000002, 16.907905, 10.84272, 11.685481818181819, 13.557600000000003, 14.175), # 36
(14.361711306959135, 15.119782030178326, 14.445860631001374, 17.252018158436215, 15.669030327981691, 8.75, 11.351819059146292, 12.455782716049384, 16.89832364197531, 10.817353369913125, 11.679704090285574, 13.544480841335163, 14.175), # 37
(14.372081630906267, 15.083489574759948, 14.43549122085048, 17.242296656378603, 15.674215502459768, 8.75, 11.326200911805053, 12.393569135802473, 16.88821339506173, 10.790739222679472, 11.673576493328346, 13.530654961133976, 14.175), # 38
(14.382122431126781, 15.045514814814815, 14.424614814814818, 17.232065277777778, 15.679231563505585, 8.75, 11.299406100217867, 12.328866666666666, 16.877595000000003, 10.762952839506175, 11.667107407407409, 13.516153086419752, 14.175), # 39
(14.39183259346303, 15.005949931412895, 14.413254458161866, 17.221341306584364, 15.684078254148528, 8.75, 11.271501048979264, 12.261882716049385, 16.866489197530868, 10.734069501600368, 11.660305212620028, 13.501005944215823, 14.175), # 40
(14.40121100375738, 14.964887105624143, 14.401433196159124, 17.210142026748972, 15.688755317417984, 8.75, 11.242552182683774, 12.192824691358027, 16.85491672839506, 10.704164490169182, 11.653178289063476, 13.485244261545498, 14.175), # 41
(14.410256547852201, 14.922418518518521, 14.389174074074077, 17.198484722222226, 15.693262496343333, 8.75, 11.212625925925927, 12.121900000000002, 16.842898333333338, 10.673313086419753, 11.645735016835017, 13.4688987654321, 14.175), # 42
(14.418968111589852, 14.878636351165984, 14.376500137174213, 17.186386676954736, 15.697599533953966, 8.75, 11.181788703300251, 12.049316049382718, 16.83045475308642, 10.641590571559215, 11.637983776031925, 13.452000182898951, 14.175), # 43
(14.427344580812699, 14.83363278463649, 14.363434430727025, 17.173865174897124, 15.701766173279264, 8.75, 11.150106939401276, 11.975280246913583, 16.817606728395063, 10.609072226794698, 11.629932946751465, 13.434579240969367, 14.175), # 44
(14.435384841363105, 14.787500000000001, 14.350000000000001, 17.160937500000003, 15.705762157348616, 8.75, 11.11764705882353, 11.9, 16.804375, 10.575833333333335, 11.62159090909091, 13.416666666666666, 14.175), # 45
(14.443087779083434, 14.740330178326476, 14.336219890260631, 17.147620936213993, 15.709587229191404, 8.75, 11.084475486161544, 11.823682716049385, 16.790780308641974, 10.541949172382258, 11.612966043147525, 13.398293187014175, 14.175), # 46
(14.45045227981605, 14.692215500685872, 14.322117146776408, 17.133932767489714, 15.713241131837016, 8.75, 11.050658646009847, 11.746535802469136, 16.776843395061732, 10.507495025148607, 11.604066729018582, 13.37948952903521, 14.175), # 47
(14.457477229403315, 14.64324814814815, 14.307714814814817, 17.11989027777778, 15.716723608314837, 8.75, 11.016262962962964, 11.668766666666668, 16.762585, 10.472546172839506, 11.594901346801347, 13.360286419753088, 14.175), # 48
(14.464161513687602, 14.593520301783265, 14.29303593964335, 17.10551075102881, 15.720034401654251, 8.75, 10.981354861615428, 11.590582716049383, 16.748025864197533, 10.437177896662096, 11.585478276593093, 13.340714586191131, 14.175), # 49
(14.470504018511264, 14.543124142661183, 14.278103566529495, 17.090811471193415, 15.723173254884642, 8.75, 10.94600076656177, 11.512191358024692, 16.73318672839506, 10.401465477823503, 11.575805898491085, 13.32080475537266, 14.175), # 50
(14.476503629716676, 14.492151851851853, 14.262940740740742, 17.075809722222225, 15.726139911035398, 8.75, 10.910267102396515, 11.433800000000002, 16.718088333333338, 10.365484197530865, 11.565892592592595, 13.30058765432099, 14.175), # 51
(14.482159233146191, 14.440695610425243, 14.247570507544584, 17.060522788065846, 15.728934113135901, 8.75, 10.874220293714194, 11.355616049382716, 16.70275141975309, 10.329309336991313, 11.555746738994888, 13.280094010059445, 14.175), # 52
(14.487469714642183, 14.388847599451307, 14.232015912208508, 17.0449679526749, 15.731555604215542, 8.75, 10.837926765109337, 11.277846913580248, 16.687196728395065, 10.293016177411982, 11.545376717795238, 13.259354549611341, 14.175), # 53
(14.492433960047004, 14.336700000000002, 14.2163, 17.0291625, 15.734004127303704, 8.75, 10.801452941176471, 11.2007, 16.671445000000002, 10.256680000000001, 11.534790909090908, 13.2384, 14.175), # 54
(14.497050855203032, 14.284344993141291, 14.200445816186559, 17.01312371399177, 15.736279425429768, 8.75, 10.764865246510128, 11.124382716049384, 16.655516975308643, 10.220376085962506, 11.523997692979176, 13.217261088248744, 14.175), # 55
(14.501319285952622, 14.231874759945132, 14.184476406035667, 16.996868878600825, 15.738381241623124, 8.75, 10.728230105704835, 11.049102469135804, 16.63943339506173, 10.184179716506632, 11.513005449557303, 13.195968541380887, 14.175), # 56
(14.505238138138138, 14.179381481481483, 14.168414814814819, 16.98041527777778, 15.740309318913155, 8.75, 10.69161394335512, 10.975066666666669, 16.623215000000002, 10.148166172839508, 11.50182255892256, 13.174553086419753, 14.175), # 57
(14.508806297601952, 14.126957338820304, 14.152284087791497, 16.96378019547325, 15.742063400329245, 8.75, 10.655083184055517, 10.902482716049382, 16.606882530864198, 10.112410736168268, 11.490457401172218, 13.153045450388662, 14.175), # 58
(14.51202265018642, 14.07469451303155, 14.136107270233198, 16.946980915637862, 15.743643228900785, 8.75, 10.61870425240055, 10.83155802469136, 16.590456728395065, 10.076988687700048, 11.478918356403542, 13.131476360310929, 14.175), # 59
(14.51488608173391, 14.022685185185187, 14.119907407407407, 16.930034722222224, 15.745048547657152, 8.75, 10.582543572984749, 10.762500000000001, 16.573958333333337, 10.041975308641977, 11.467213804713806, 13.109876543209879, 14.175), # 60
(14.517395478086781, 13.971021536351168, 14.10370754458162, 16.912958899176957, 15.746279099627737, 8.75, 10.546667570402647, 10.695516049382718, 16.557408086419755, 10.00744588020119, 11.455352126200275, 13.088276726108827, 14.175), # 61
(14.519549725087407, 13.919795747599453, 14.087530727023323, 16.89577073045268, 15.74733462784193, 8.75, 10.51114266924877, 10.630813580246915, 16.540826728395064, 9.973475683584821, 11.44334170096022, 13.066707636031095, 14.175), # 62
(14.521347708578144, 13.869100000000001, 14.071400000000002, 16.878487500000002, 15.7482148753291, 8.75, 10.476035294117647, 10.568600000000002, 16.524235, 9.94014, 11.43119090909091, 13.045200000000001, 14.175), # 63
(14.522788314401359, 13.819026474622772, 14.05533840877915, 16.86112649176955, 15.74891958511865, 8.75, 10.44141186960381, 10.509082716049384, 16.50765364197531, 9.907514110653864, 11.41890813068961, 13.023784545038868, 14.175), # 64
(14.523870428399414, 13.769667352537724, 14.03936899862826, 16.843704989711934, 15.749448500239955, 8.75, 10.407338820301785, 10.45246913580247, 16.49110339506173, 9.875673296753543, 11.4065017458536, 13.00249199817101, 14.175), # 65
(14.524592936414676, 13.721114814814818, 14.023514814814817, 16.826240277777778, 15.749801363722403, 8.75, 10.373882570806101, 10.398966666666668, 16.474605000000004, 9.844692839506173, 11.393980134680135, 12.981353086419755, 14.175), # 66
(14.524954724289511, 13.673461042524005, 14.00779890260631, 16.808749639917696, 15.749977918595382, 8.75, 10.341109545711289, 10.348782716049385, 16.458179197530864, 9.814648020118886, 11.381351677266494, 12.960398536808412, 14.175), # 67
(14.524708260273156, 13.626548095048452, 13.99216832990398, 16.7910984366613, 15.749829137416285, 8.74983761621704, 10.308921272761506, 10.301681390032009, 16.44172298811157, 9.785468618306034, 11.368400383956526, 12.939542030659641, 14.174825210048013), # 68
(14.522398389694043, 13.578943727598569, 13.976183796296295, 16.772396920289854, 15.748474945533768, 8.748553909465022, 10.27637545388526, 10.25513827160494, 16.424516975308645, 9.756328946986201, 11.35380797448166, 12.918106562703056, 14.17344039351852), # 69
(14.517840102582454, 13.5304294437807, 13.95977580589849, 16.752521973966722, 15.74579903978052, 8.746025758268557, 10.243324188385918, 10.208733424782809, 16.40646404892547, 9.727087334247829, 11.337408441136512, 12.895991865809934, 14.170705268347055), # 70
(14.511097524900102, 13.481034236028144, 13.942950120027435, 16.731502905260335, 15.74183531025579, 8.742294131992075, 10.209782323354585, 10.162482213077277, 16.387591095107457, 9.697744503079695, 11.319262319097408, 12.873214112097802, 14.166655842764062), # 71
(14.502234782608697, 13.430787096774193, 13.9257125, 16.709369021739132, 15.736617647058825, 8.737400000000001, 10.175764705882354, 10.1164, 16.367925000000003, 9.668301176470589, 11.299430143540672, 12.849789473684211, 14.161328125), # 72
(14.491316001669949, 13.379717018452144, 13.90806870713306, 16.686149630971553, 15.730179940288872, 8.73138433165676, 10.141286183060329, 10.070502149062644, 16.347492649748517, 9.63875807740929, 11.277972449642624, 12.825734122686688, 14.154758123285324), # 73
(14.478405308045566, 13.32785299349529, 13.890024502743485, 16.661874040526033, 15.722556080045187, 8.72428809632678, 10.106361601979613, 10.024804023776863, 16.3263209304984, 9.609115928884586, 11.254949772579598, 12.801064231222776, 14.146981845850483), # 74
(14.463566827697262, 13.275224014336917, 13.871585648148148, 16.636571557971017, 15.713779956427018, 8.716152263374488, 10.0710058097313, 9.979320987654322, 16.30443672839506, 9.579375453885259, 11.23042264752791, 12.775795971410007, 14.138035300925928), # 75
(14.44686468658675, 13.22185907341033, 13.852757904663925, 16.610271490874936, 15.703885459533609, 8.707017802164305, 10.035233653406493, 9.934068404206677, 16.281866929583906, 9.549537375400092, 11.20445160966389, 12.749945515365916, 14.127954496742113), # 76
(14.428363010675731, 13.167787163148816, 13.833547033607681, 16.583003146806227, 15.692906479464213, 8.696925682060662, 9.999059980096293, 9.88906163694559, 16.258638420210335, 9.519602416417872, 11.177097194163862, 12.723529035208049, 14.116775441529496), # 77
(14.408125925925928, 13.113037275985667, 13.813958796296298, 16.554795833333333, 15.680876906318085, 8.685916872427983, 9.962499636891796, 9.844316049382718, 16.23477808641975, 9.489571299927379, 11.148419936204148, 12.696562703053933, 14.10453414351852), # 78
(14.386217558299041, 13.057638404354178, 13.793998954046641, 16.525678858024694, 15.667830630194468, 8.674032342630696, 9.925567470884102, 9.799847005029722, 16.210312814357568, 9.4594447489174, 11.118480370961072, 12.669062691021107, 14.091266610939643), # 79
(14.362702033756786, 13.001619540687642, 13.773673268175584, 16.495681528448742, 15.653801541192612, 8.661313062033226, 9.888278329164315, 9.755669867398264, 16.185269490169183, 9.429223486376719, 11.087339033610965, 12.64104517122711, 14.07700885202332), # 80
(14.337643478260873, 12.945009677419357, 13.752987500000001, 16.464833152173917, 15.638823529411765, 8.6478, 9.85064705882353, 9.711800000000002, 16.159675, 9.398908235294119, 11.055056459330146, 12.612526315789475, 14.061796875), # 81
(14.311106017773009, 12.887837806982612, 13.731947410836765, 16.433163036768654, 15.622930484951183, 8.633534125895444, 9.812688506952853, 9.668252766346594, 16.133556229995428, 9.368499718658382, 11.02169318329494, 12.583522296825743, 14.045666688100141), # 82
(14.283153778254908, 12.8301329218107, 13.710558762002744, 16.400700489801395, 15.606156297910111, 8.618556409083983, 9.774417520643375, 9.625043529949703, 16.10694006630087, 9.337998659458297, 10.987309740681672, 12.554049286453447, 14.028654299554185), # 83
(14.253850885668278, 12.77192401433692, 13.688827314814816, 16.36747481884058, 15.588534858387801, 8.602907818930042, 9.735848946986202, 9.582187654320988, 16.07985339506173, 9.307405780682645, 10.951966666666667, 12.524123456790125, 14.010795717592593), # 84
(14.223261465974833, 12.713240076994557, 13.666758830589849, 16.333515331454645, 15.5701000564835, 8.58662932479805, 9.696997633072435, 9.53970050297211, 16.05232310242341, 9.276721805320209, 10.915724496426252, 12.493760979953313, 13.992126950445819), # 85
(14.191449645136279, 12.654110102216913, 13.644359070644722, 16.298851335212028, 15.550885782296458, 8.569761896052432, 9.65787842599317, 9.497597439414724, 16.024376074531325, 9.245947456359774, 10.878643765136749, 12.462978028060553, 13.97268400634431), # 86
(14.15847954911433, 12.594563082437277, 13.621633796296296, 16.26351213768116, 15.53092592592593, 8.552346502057613, 9.618506172839506, 9.455893827160494, 15.996039197530868, 9.215083456790124, 10.840785007974482, 12.43179077322937, 13.95250289351852), # 87
(14.124415303870702, 12.534628010088941, 13.598588768861456, 16.22752704643049, 15.510254377471155, 8.534424112178023, 9.578895720702548, 9.414605029721079, 15.967339357567447, 9.184130529600042, 10.802208760115779, 12.400215387577312, 13.931619620198905), # 88
(14.089321035367092, 12.474333877605204, 13.575229749657066, 16.19092536902845, 15.488905027031391, 8.516035695778085, 9.539061916673392, 9.37374641060814, 15.938303440786468, 9.153089397778317, 10.762975556736963, 12.36826804322191, 13.910070194615912), # 89
(14.053260869565218, 12.413709677419357, 13.551562500000001, 16.153736413043482, 15.466911764705886, 8.497222222222224, 9.499019607843138, 9.333333333333334, 15.908958333333336, 9.121960784313726, 10.723145933014354, 12.335964912280703, 13.887890625), # 90
(14.016298932426789, 12.352784401964689, 13.527592781207133, 16.11598948604402, 15.444308480593882, 8.478024660874867, 9.458783641302887, 9.293381161408323, 15.879330921353455, 9.090745412195057, 10.682780424124285, 12.303322166871226, 13.865116919581618), # 91
(13.978499349913523, 12.2915870436745, 13.503326354595337, 16.0777138955985, 15.421129064794641, 8.458483981100443, 9.418368864143739, 9.253905258344766, 15.84944809099223, 9.059444004411093, 10.641939565243074, 12.270355979111017, 13.841785086591221), # 92
(13.939926247987117, 12.230146594982081, 13.478768981481483, 16.038938949275366, 15.397407407407409, 8.438641152263374, 9.37779012345679, 9.214920987654322, 15.819336728395063, 9.028057283950616, 10.600683891547051, 12.23708252111761, 13.81793113425926), # 93
(13.900643752609293, 12.168492048320722, 13.453926423182445, 15.999693954643051, 15.37317739853143, 8.418537143728091, 9.337062266333147, 9.176443712848654, 15.789023719707364, 8.996585973802416, 10.559073938212535, 12.203517965008546, 13.793591070816188), # 94
(13.860715989741754, 12.106652396123724, 13.42880444101509, 15.960008219269996, 15.34847292826596, 8.398212924859017, 9.296200139863902, 9.138488797439416, 15.758535951074533, 8.96503079695527, 10.517170240415854, 12.169678482901354, 13.768800904492457), # 95
(13.820207085346219, 12.044656630824377, 13.403408796296299, 15.91991105072464, 15.32332788671024, 8.377709465020576, 9.25521859114016, 9.101071604938273, 15.727900308641976, 8.933392476397968, 10.475033333333334, 12.135580246913582, 13.74359664351852), # 96
(13.779181165384388, 11.98253374485597, 13.377745250342937, 15.879431756575416, 15.297776163963531, 8.357067733577198, 9.21413246725302, 9.064207498856883, 15.6971436785551, 8.901671735119288, 10.432723752141296, 12.101239429162758, 13.718014296124831), # 97
(13.737702355817978, 11.9203127306518, 13.35181956447188, 15.83859964439077, 15.271851650125074, 8.336328699893311, 9.17295661529358, 9.027911842706905, 15.666292946959304, 8.86986929610802, 10.390302032016068, 12.066672201766417, 13.69208987054184), # 98
(13.695834782608697, 11.858022580645162, 13.325637500000003, 15.797444021739132, 15.24558823529412, 8.315533333333335, 9.131705882352943, 8.9922, 15.635375000000002, 8.83798588235294, 10.347828708133973, 12.031894736842107, 13.665859375000002), # 99
(13.653642571718258, 11.795692287269347, 13.29920481824417, 15.755994196188944, 15.21901980956992, 8.294722603261699, 9.090395115522204, 8.957087334247829, 15.60441672382259, 8.806022216842843, 10.305364315671335, 11.996923206507354, 13.639358817729768), # 100
(13.611189849108369, 11.733350842957654, 13.272527280521263, 15.714279475308645, 15.192180263051725, 8.273937479042829, 9.049039161892468, 8.922589208962048, 15.573445004572475, 8.773979022566504, 10.262969389804478, 11.961773782879694, 13.612624206961591), # 101
(13.568540740740744, 11.67102724014337, 13.245610648148148, 15.67232916666667, 15.165103485838781, 8.253218930041154, 9.00765286855483, 8.888720987654322, 15.542486728395062, 8.741857022512711, 10.22070446570973, 11.926462638076675, 13.585691550925928), # 102
(13.525759372577088, 11.60875047125979, 13.218460682441702, 15.630172577831457, 15.137823368030341, 8.232607925621096, 8.966251082600394, 8.855498033836307, 15.511568781435757, 8.709656939670245, 10.178630078563414, 11.891005944215824, 13.558596857853223), # 103
(13.482909870579116, 11.546549528740211, 13.191083144718794, 15.587839016371445, 15.110373799725652, 8.212145435147082, 8.924848651120257, 8.822935711019662, 15.480718049839965, 8.677379497027893, 10.13680676354185, 11.855419873414677, 13.53137613597394), # 104
(13.440056360708535, 11.484453405017922, 13.163483796296298, 15.545357789855073, 15.082788671023966, 8.19187242798354, 8.883460421205521, 8.79104938271605, 15.449961419753087, 8.64502541757444, 10.095295055821373, 11.819720597790775, 13.50406539351852), # 105
(13.39726296892706, 11.42249109252622, 13.135668398491084, 15.50275820585078, 15.055101872024531, 8.171829873494895, 8.842101239947283, 8.759854412437129, 15.41932577732053, 8.612595424298663, 10.054155490578298, 11.783924289461654, 13.476700638717421), # 106
(13.3545938211964, 11.360691583698395, 13.10764271262003, 15.460069571927, 15.027347292826596, 8.152058741045574, 8.800785954436646, 8.72936616369456, 15.388838008687703, 8.580090240189355, 10.013448602988953, 11.748047120544847, 13.449317879801098), # 107
(13.312113043478263, 11.299083870967744, 13.079412500000002, 15.417321195652177, 14.999558823529412, 8.132600000000002, 8.759529411764706, 8.699600000000002, 15.358525000000002, 8.547510588235296, 9.973234928229665, 11.712105263157897, 13.421953125000002), # 108
(13.26988476173436, 11.237696946767558, 13.050983521947876, 15.374542384594738, 14.97177035423223, 8.113494619722603, 8.718346459022568, 8.670571284865114, 15.328413637402836, 8.514857191425268, 9.933575001476758, 11.676114889418335, 13.394642382544584), # 109
(13.227973101926404, 11.176559803531132, 13.022361539780524, 15.331762446323136, 14.944015775034297, 8.094783569577809, 8.677251943301325, 8.642295381801555, 15.29853080704161, 8.482130772748057, 9.894529357906551, 11.640092171443701, 13.367421660665297), # 110
(13.186442190016104, 11.11570143369176, 12.993552314814819, 15.2890106884058, 14.91632897603486, 8.076507818930043, 8.636260711692085, 8.614787654320988, 15.26890339506173, 8.449332055192448, 9.856158532695375, 11.60405328135153, 13.340326967592594), # 111
(13.14535615196517, 11.055150829682729, 12.96456160836763, 15.246316418411165, 14.888743847333174, 8.05870833714373, 8.595387611285942, 8.588063465935072, 15.239558287608595, 8.416461761747223, 9.818523061019553, 11.568014391259355, 13.313394311556928), # 112
(13.104705913184263, 10.995038066300333, 12.935464959552897, 15.203767435488858, 14.861245952243188, 8.04141767690032, 8.554736349119478, 8.562193596292849, 15.21059793576207, 8.383626631257822, 9.781693468614014, 11.5320701111062, 13.286621461180511), # 113
(13.064073257060091, 10.935956056935751, 12.906663945030267, 15.161705189788272, 14.833550696392859, 8.024596451941862, 8.514825491774811, 8.537495763307168, 15.182466649998286, 8.351441235077896, 9.745742071958476, 11.496677040958165, 13.25978557982405), # 114
(13.023338864205595, 10.877926078156266, 12.878175705790246, 15.120118307254492, 14.805570749044042, 8.008200917498272, 8.475683510268187, 8.513963715990194, 15.155174970136306, 8.319955459183308, 9.710616315997932, 11.461852615582393, 13.232809284324528), # 115
(12.982451822532688, 10.820863593808383, 12.849945065977423, 15.078932610372966, 14.777263936937292, 7.992192428201937, 8.43724674453905, 8.491532438058591, 15.128653874918964, 8.289110701829367, 9.676248303780074, 11.427532476482286, 13.205650163658248), # 116
(12.941361219953283, 10.76468406773861, 12.82191684973638, 15.038073921629142, 14.748588086813156, 7.976532338685248, 8.399451534526854, 8.47013691322902, 15.102834343089086, 8.258848361271381, 9.642570138352598, 11.39365226516125, 13.178265806801516), # 117
(12.900016144379297, 10.709302963793455, 12.794035881211714, 14.997468063508467, 14.71950102541218, 7.9611820035805945, 8.362234220171041, 8.449712125218136, 15.07764735338951, 8.229109835764664, 9.609513922763194, 11.36014762312269, 13.150613802730636), # 118
(12.858365683722639, 10.654635745819421, 12.766246984548014, 14.95704085849639, 14.689960579474912, 7.946102777520366, 8.325531141411059, 8.430193057742605, 15.053023884563062, 8.199836523564521, 9.577011760059559, 11.326954191870009, 13.122651740421906), # 119
(12.816358925895228, 10.600597877663022, 12.738494983889867, 14.916718129078353, 14.659924575741897, 7.931256015136952, 8.289278638186355, 8.41151469451908, 15.028894915352582, 8.170969822926269, 9.544995753289383, 11.294007612906617, 13.094337208851638), # 120
(12.773944958808976, 10.547104823170763, 12.710724703381864, 14.876425697739808, 14.629350840953688, 7.9166030710627435, 8.253413050436373, 8.39361201926423, 15.0051914245009, 8.142451132105215, 9.513398005500363, 11.261243527735912, 13.065627796996127), # 121
(12.731072870375797, 10.494072046189146, 12.682880967168597, 14.836089386966199, 14.598197201850828, 7.902105299930128, 8.217870718100565, 8.376420015694709, 14.981844390750846, 8.11422184935667, 9.482150619740192, 11.228597577861303, 13.036481093831679), # 122
(12.687691748507607, 10.441415010564684, 12.65490859939465, 14.795635019242972, 14.56642148517387, 7.887724056371495, 8.182587981118376, 8.359873667527177, 14.958784792845258, 8.086223372935942, 9.451185699056563, 11.19600540478619, 13.0068546883346), # 123
(12.643750681116316, 10.389049180143882, 12.62675242420462, 14.754988417055582, 14.533981517663353, 7.873420695019235, 8.147501179429248, 8.343907958478297, 14.935943609526962, 8.058397101098347, 9.420435346497168, 11.163402650013985, 12.976706169481197), # 124
(12.599198756113843, 10.33689001877325, 12.598357265743093, 14.714075402889465, 14.500835126059833, 7.859156570505739, 8.112546652972636, 8.328457872264728, 14.913251819538791, 8.030684432099187, 9.389831665109703, 11.130724955048088, 12.94599312624776), # 125
(12.553985061412101, 10.284852990299292, 12.56966794815466, 14.672821799230077, 14.466940137103851, 7.844893037463395, 8.077660741687978, 8.31345839260313, 14.890640401623585, 8.00302676419378, 9.359306757941859, 11.097907961391908, 12.91467314761061), # 126
(12.508058684923006, 10.232853558568515, 12.540629295583907, 14.63115342856286, 14.432254377535958, 7.830591450524592, 8.042779785514732, 8.298844503210164, 14.86804033452417, 7.975365495637434, 9.32879272804133, 11.064887310548842, 12.88270382254604), # 127
(12.461368714558466, 10.18080718742743, 12.51118613217543, 14.588996113373266, 14.396735674096707, 7.816213164321722, 8.007840124392336, 8.284551187802489, 14.845382596983379, 7.947642024685458, 9.298221678455814, 11.031598644022305, 12.850042740030352), # 128
(12.413864238230394, 10.128629340722538, 12.481283282073816, 14.546275676146736, 14.360341853526638, 7.801719533487173, 7.972778098260239, 8.270513430096765, 14.822598167744045, 7.919797749593164, 9.267525712233, 10.997977603315691, 12.816647489039854), # 129
(12.365494343850713, 10.076235482300353, 12.450865569423652, 14.502917939368722, 14.3230307425663, 7.7870719126533325, 7.937530047057888, 8.256666213809652, 14.799618025549002, 7.89177406861586, 9.236636932420582, 10.963959829932413, 12.78247565855085), # 130
(12.316208119331334, 10.023541076007378, 12.419877818369534, 14.458848725524668, 14.284760167956243, 7.772231656452593, 7.902032310724733, 8.24294452265781, 14.776373149141081, 7.86351238000886, 9.205487442066255, 10.929480965375875, 12.747484837539638), # 131
(12.265954652584163, 9.970461585690122, 12.388264853056045, 14.413993857100023, 14.245487956437017, 7.757160119517344, 7.8662212292002165, 8.229283340357902, 14.752794517263117, 7.834954082027471, 9.17400934421771, 10.894476651149478, 12.711632614982527), # 132
(12.21468303152113, 9.91691247519509, 12.355971497627777, 14.368279156580234, 14.205171934749162, 7.741818656479974, 7.830033142423786, 8.215617650626585, 14.728813108657938, 7.806040572927006, 9.142134741922645, 10.85888252875663, 12.674876579855821), # 133
(12.162342344054133, 9.862809208368793, 12.322942576229327, 14.321630446450746, 14.163769929633231, 7.726168621972872, 7.79340439033489, 8.201882437180522, 14.704359902068381, 7.776713250962773, 9.109795738228751, 10.822634239700733, 12.637174321135817), # 134
(12.108881678095097, 9.808067249057736, 12.289122913005274, 14.273973549197011, 14.12123976782977, 7.710171370628429, 7.756271312872975, 8.18801268373637, 14.679365876237274, 7.746913514390087, 9.07692443618372, 10.785667425485194, 12.59848342779883), # 135
(12.05425012155593, 9.752602061108423, 12.254457332100213, 14.225234287304469, 14.077539276079325, 7.693788257079036, 7.718570249977489, 8.173943374010788, 14.65376200990745, 7.716582761464252, 9.043452938835248, 10.747917727613418, 12.558761488821151), # 136
(11.998396762348548, 9.696329108367367, 12.218890657658735, 14.175338483258576, 14.032626281122448, 7.6769806359570785, 7.6802375415878785, 8.159609491720442, 14.627479281821747, 7.685662390440583, 9.009313349231029, 10.709320787588808, 12.517966093179089), # 137
(11.941270688384867, 9.639163854681073, 12.182367713825425, 14.12421195954477, 13.986458609699687, 7.6597098618949495, 7.6412095276435865, 8.144946020581987, 14.600448670722995, 7.654093799574386, 8.974437770418753, 10.66981224691477, 12.476054829848946), # 138
(11.882820987576796, 9.581021763896047, 12.144833324744877, 14.071780538648504, 13.938994088551583, 7.641937289525037, 7.601422548084064, 8.129887944312085, 14.572601155354022, 7.621818387120976, 8.938758305446116, 10.62932774709471, 12.432985287807028), # 139
(11.822996747836257, 9.521818299858795, 12.106232314561684, 14.017970043055223, 13.890190544418692, 7.623624273479732, 7.560812942848756, 8.114370246627395, 14.543867714457667, 7.588777551335661, 8.902207057360812, 10.58780292963203, 12.38871505602964), # 140
(11.761747057075162, 9.46146892641583, 12.066509507420426, 13.962706295250376, 13.840005804041555, 7.604732168391422, 7.519317051877113, 8.09832791124458, 14.514179326776754, 7.554912690473753, 8.864716129210535, 10.545173436030137, 12.34320172349308), # 141
(11.69902100320542, 9.399889107413653, 12.0256097274657, 13.90591511771941, 13.788397694160723, 7.585222328892499, 7.476871215108577, 8.081695921880296, 14.48346697105412, 7.52016520279056, 8.826217624042977, 10.501374907792433, 12.296402879173653), # 142
(11.634767674138946, 9.336994306698774, 11.983477798842097, 13.847522332947767, 13.735324041516742, 7.56505610961535, 7.4334117724825965, 8.064409262251205, 14.451661626032607, 7.484476486541395, 8.786643644905832, 10.456342986422326, 12.248276112047666), # 143
(11.56893615778766, 9.2726999881177, 11.9400585456942, 13.787453763420901, 13.680742672850162, 7.544194865192366, 7.3888750639386185, 8.04640291607397, 14.418694270455035, 7.4477879399815645, 8.745926294846791, 10.41001331342322, 12.198779011091421), # 144
(11.501475542063469, 9.20692161551694, 11.895296792166606, 13.725635231624254, 13.624611414901528, 7.5225999502559375, 7.343197429416091, 8.027611867065247, 14.384495883064238, 7.410040961366383, 8.703997676913554, 10.36232153029852, 12.14786916528122), # 145
(11.432334914878291, 9.139574652742999, 11.849137362403903, 13.661992560043277, 13.566888094411391, 7.500232719438453, 7.2963152088544625, 8.007971098941699, 14.34899744260305, 7.37117694895116, 8.660789894153808, 10.313203278551628, 12.095504163593366), # 146
(11.361463364144042, 9.070574563642383, 11.801525080550675, 13.596451571163414, 13.507530538120294, 7.477054527372301, 7.2481647421931745, 7.987415595419982, 14.312129927814308, 7.331137300991204, 8.616235049615252, 10.262594199685955, 12.041641595004167), # 147
(11.288809977772631, 8.999836812061604, 11.752404770751518, 13.528938087470117, 13.446496572768787, 7.453026728689875, 7.198682369371678, 7.965880340216761, 14.273824317440841, 7.289863415741826, 8.570265246345576, 10.210429935204898, 11.986239048489919), # 148
(11.214323843675977, 8.927276861847163, 11.701721257151021, 13.459377931448826, 13.38374402509742, 7.42811067802356, 7.147804430329418, 7.943300317048694, 14.234011590225474, 7.247296691458339, 8.522812587392474, 10.156646126611868, 11.929254113026934), # 149
(11.137954049765991, 8.852810176845571, 11.649419363893772, 13.387696925584994, 13.319230721846738, 7.402267730005749, 7.0954672650058415, 7.91961050963244, 14.192622724911054, 7.2033785263960475, 8.473809175803641, 10.101178415410269, 11.870644377591507), # 150
(11.059649683954586, 8.776352220903336, 11.59544391512436, 13.313820892364063, 13.252914489757288, 7.375459239268828, 7.041607213340397, 7.8947459016846615, 14.149588700240406, 7.15805031881027, 8.423187114626767, 10.043962443103501, 11.810367431159946), # 151
(10.979359834153682, 8.697818457866962, 11.539739734987382, 13.237675654271488, 13.184753155569618, 7.34764656044519, 6.986160615272531, 7.8686414769220185, 14.10484049495636, 7.11125346695631, 8.37087850690955, 9.984933851194974, 11.748380862708558), # 152
(10.897033588275185, 8.61712435158296, 11.482251647627416, 13.159187033792707, 13.11470454602428, 7.318791048167222, 6.929063810741687, 7.841232219061167, 14.058309087801755, 7.062929369089481, 8.316815455699683, 9.92402828118809, 11.68464226121364), # 153
(10.81262003423102, 8.534185365897834, 11.422924477189063, 13.078280853413174, 13.042726487861813, 7.288854057067317, 6.87025313968732, 7.8124531118187726, 14.009925457519413, 7.013019423465095, 8.260930064044857, 9.861181374586256, 11.6191092156515), # 154
(10.72606825993309, 8.448916964658093, 11.361703047816906, 12.99488293561833, 12.968776807822776, 7.257796941777861, 6.809664942048866, 7.782239138911491, 13.95962058285218, 6.9614650283384565, 8.203154434992767, 9.796328772892876, 11.551739314998438), # 155
(10.637327353293314, 8.361234611710243, 11.298532183655539, 12.908919102893627, 12.892813332647707, 7.225581056931246, 6.74723555776578, 7.750525284055986, 13.907325442542877, 6.9082075819648825, 8.143420671591107, 9.729406117611353, 11.48249014823076), # 156
(10.546346402223609, 8.271053770900794, 11.233356708849547, 12.820315177724513, 12.81479388907716, 7.19216775715986, 6.6829013267775075, 7.717246530968915, 13.852971015334345, 6.853188482599679, 8.08166087688757, 9.660349050245092, 11.411319304324769), # 157
(10.450553324967336, 8.176634369081162, 11.163028735463298, 12.725677414311741, 12.731153548219398, 7.155434266843955, 6.615149409299001, 7.680115733289122, 13.792326928238738, 6.794712282807602, 8.01583405355452, 9.586639389872076, 11.335080203181485), # 158
(10.335201473769764, 8.06829144743927, 11.069432945764184, 12.605568022303835, 12.62126783369428, 7.103165507209945, 6.535497868740003, 7.626098945870136, 13.700998165711002, 6.723193391738244, 7.934383709866593, 9.493907533156353, 11.235598705688274), # 159
(10.198820932866035, 7.945135419957, 10.950689341138245, 12.458008514572404, 12.482988183885514, 7.034077814466758, 6.443141247737298, 7.553838865338286, 13.576395318120113, 6.637687912608051, 7.8361633120533565, 9.380702728442985, 11.110988852451014), # 160
(10.042510876420344, 7.8079692153126565, 10.808065760674433, 12.28440150525942, 12.317750373994958, 6.94900813819844, 6.338754024409627, 7.464240746353693, 13.420161673798626, 6.5389214704393135, 7.7220383164395905, 9.248074456470599, 10.962523662746737), # 161
(9.8673704785969, 7.657595762184535, 10.642830043461695, 12.086149608506858, 12.126990179224487, 6.848793427989039, 6.223010676875733, 7.358209843576484, 13.233940521079093, 6.427619690254325, 7.592874179350069, 9.09707219797781, 10.791476155852466), # 162
(9.674498913559898, 7.494817989250934, 10.456250028588983, 11.864655438456708, 11.912143374775964, 6.734270633422602, 6.096585683254362, 7.2366514116667755, 13.019375148294069, 6.304508197075376, 7.449536357109572, 8.928745433703247, 10.599119351045232), # 163
(9.464995355473539, 7.320438825190149, 10.249593555145248, 11.621321609250947, 11.674645735851264, 6.606276704083181, 5.960153521664253, 7.100470705284697, 12.778108843776113, 6.170312615924756, 7.292890306042875, 8.744143644385526, 10.386726267602059), # 164
(9.239958978502024, 7.135261198680485, 10.024128462219437, 11.357550735031554, 11.415933037652254, 6.465648589554821, 5.814388670224151, 6.950572979090365, 12.511784895857772, 6.02575857182476, 7.123801482474756, 8.544316310763268, 10.155569924799979), # 165
(9.000488956809557, 6.940088038400237, 9.7811225889005, 11.074745429940503, 11.137441055380801, 6.313223239421572, 5.659965607052801, 6.787863487743908, 12.222046592871603, 5.871571689797677, 6.943135342729992, 8.330312913575103, 9.906923341916015), # 166
(8.747684464560333, 6.735722273027703, 9.521843774277388, 10.774308308119782, 10.840605564238773, 6.149837603267482, 5.497558810268945, 6.613247485905448, 11.91053722315016, 5.7084775948658, 6.751757343133359, 8.103182933559642, 9.642059538227196), # 167
(8.482644675918554, 6.52296683124118, 9.247559857439049, 10.457641983711365, 10.526862339428039, 5.9763286306765995, 5.327842757991326, 6.427630228235103, 11.578900075025999, 5.5372019120514215, 6.550532940009634, 7.863975851455517, 9.362251533010546), # 168
(8.206468765048422, 6.302624641718972, 8.959538677474432, 10.126149070857236, 10.197647156150468, 5.793533271232973, 5.151491928338689, 6.231916969393004, 11.228778436831673, 5.358470266376831, 6.3403275896835956, 7.613741148001342, 9.0687723455431), # 169
(7.9202559061141375, 6.0754986331393726, 8.659048073472489, 9.781232183699368, 9.854395789607928, 5.60228847452065, 4.9691807994297745, 6.027012964039266, 10.861815596899735, 5.173008282864322, 6.122006748480023, 7.353528303935743, 8.762894995101878), # 170
(7.6251052732799005, 5.842391734180682, 8.34735588452217, 9.424293936379751, 9.498544015002288, 5.403431190123678, 4.781583849383328, 5.813823466834017, 10.47965484356274, 4.981541586536184, 5.896435872723688, 7.0843867999973416, 8.445892500963913), # 171
(7.322116040709912, 5.604106873521197, 8.025729949712423, 9.056736943040356, 9.131527607535416, 5.197798367626108, 4.5893755563180925, 5.593253732437379, 10.083939465153241, 4.784795802414712, 5.664480418739371, 6.80736611692476, 8.119037882406225), # 172
(7.012387382568372, 5.3614469798392195, 7.695438108132197, 8.679963817823166, 8.754782342409182, 4.9862269566119855, 4.39323039835281, 5.366209015509473, 9.676312750003792, 4.583496555522195, 5.427005842851849, 6.523515735456615, 7.783604158705848), # 173
(6.697018473019482, 5.115214981813045, 7.357748198870443, 8.295377174870158, 8.369743994825454, 4.76955390666536, 4.193822853606226, 5.133594570710425, 9.25841798644695, 4.3783694708809255, 5.1848776013858995, 6.233885136331535, 7.440864349139807), # 174
(6.377108486227438, 4.866213808120973, 7.013928061016112, 7.904379628323315, 7.977848339986097, 4.54861616737028, 3.9918274001970815, 4.896315652700355, 8.831898462815268, 4.170140173513194, 4.938961150666297, 5.939523800288141, 7.092091472985131), # 175
(6.053756596356447, 4.615246387441302, 6.66524553365815, 7.508373792324615, 7.580531153092983, 4.324250688310793, 3.787918516244121, 4.655277516139389, 8.3983974674413, 3.959534288441294, 4.690121947017822, 5.641481208065051, 6.738558549518844), # 176
(5.7280619775707065, 4.363115648452332, 6.3129684558855095, 7.108762281016037, 7.179228209347984, 4.097294419070949, 3.582770679866088, 4.411385415687646, 7.959558288657599, 3.7472774406875144, 4.43922544676525, 5.340806840400891, 6.381538598017975), # 177
(5.401123804034416, 4.11062451983236, 5.95836466678714, 6.7069477085395635, 6.775375283952959, 3.8685843092347962, 3.3770583691817246, 4.165544606005252, 7.51702421479672, 3.5340952552741505, 4.187137106233358, 5.038550178034279, 6.022304637759553), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(9, 10, 5, 5, 3, 2, 2, 3, 3, 1, 1, 0, 0, 6, 9, 0, 8, 12, 3, 4, 1, 0, 2, 2, 2, 0), # 0
(14, 20, 14, 16, 9, 4, 2, 8, 4, 2, 2, 0, 0, 17, 14, 5, 14, 18, 4, 6, 3, 1, 6, 2, 2, 0), # 1
(21, 29, 17, 19, 12, 7, 5, 13, 8, 6, 4, 0, 0, 26, 20, 10, 21, 27, 5, 11, 7, 3, 8, 4, 4, 0), # 2
(26, 39, 28, 28, 24, 10, 11, 19, 12, 7, 4, 0, 0, 32, 30, 12, 29, 35, 13, 13, 9, 6, 10, 6, 4, 0), # 3
(37, 48, 34, 36, 31, 13, 15, 24, 16, 8, 4, 0, 0, 40, 42, 15, 34, 45, 16, 13, 13, 7, 11, 6, 7, 0), # 4
(48, 57, 43, 49, 34, 16, 22, 31, 20, 10, 4, 2, 0, 47, 55, 22, 42, 54, 21, 18, 14, 9, 16, 7, 8, 0), # 5
(60, 70, 51, 57, 42, 20, 23, 35, 23, 14, 7, 4, 0, 56, 63, 29, 51, 60, 23, 25, 17, 15, 19, 7, 9, 0), # 6
(72, 78, 59, 68, 55, 23, 26, 36, 27, 15, 8, 4, 0, 68, 70, 40, 55, 69, 27, 28, 19, 18, 21, 8, 10, 0), # 7
(86, 89, 76, 78, 63, 26, 29, 38, 30, 16, 11, 4, 0, 79, 77, 49, 61, 80, 29, 35, 23, 19, 24, 9, 10, 0), # 8
(97, 99, 83, 87, 71, 34, 36, 43, 35, 16, 14, 7, 0, 96, 89, 58, 67, 90, 38, 39, 25, 22, 26, 13, 11, 0), # 9
(107, 111, 94, 97, 78, 37, 42, 46, 42, 19, 16, 9, 0, 114, 102, 70, 76, 102, 44, 41, 28, 28, 28, 16, 13, 0), # 10
(122, 122, 104, 108, 90, 44, 46, 49, 45, 20, 19, 11, 0, 124, 107, 83, 84, 114, 50, 47, 31, 29, 29, 20, 14, 0), # 11
(133, 138, 114, 119, 99, 46, 53, 53, 48, 23, 20, 13, 0, 141, 115, 92, 89, 122, 55, 52, 31, 35, 33, 21, 15, 0), # 12
(144, 155, 124, 129, 105, 53, 57, 55, 54, 24, 23, 14, 0, 155, 127, 96, 92, 127, 61, 56, 36, 38, 40, 24, 16, 0), # 13
(156, 176, 136, 141, 116, 60, 61, 59, 58, 27, 28, 14, 0, 165, 139, 102, 99, 139, 67, 63, 37, 42, 44, 24, 16, 0), # 14
(162, 188, 145, 162, 128, 63, 64, 63, 64, 29, 29, 16, 0, 174, 158, 110, 102, 145, 74, 70, 43, 50, 44, 24, 18, 0), # 15
(169, 200, 155, 175, 140, 70, 69, 67, 68, 32, 31, 19, 0, 186, 166, 114, 108, 159, 84, 74, 45, 54, 48, 25, 19, 0), # 16
(184, 215, 173, 190, 147, 79, 73, 70, 73, 38, 34, 20, 0, 198, 176, 121, 119, 169, 90, 76, 50, 61, 51, 26, 19, 0), # 17
(198, 229, 189, 207, 162, 83, 79, 74, 77, 39, 36, 21, 0, 214, 193, 128, 123, 176, 97, 85, 55, 67, 55, 28, 21, 0), # 18
(221, 245, 201, 220, 168, 89, 85, 77, 84, 39, 37, 22, 0, 231, 211, 139, 134, 191, 110, 91, 58, 72, 61, 32, 23, 0), # 19
(233, 257, 214, 233, 178, 96, 91, 81, 90, 41, 39, 23, 0, 241, 228, 152, 141, 202, 117, 96, 63, 78, 66, 36, 24, 0), # 20
(249, 275, 226, 246, 186, 102, 93, 87, 101, 43, 40, 24, 0, 248, 240, 162, 151, 218, 121, 100, 70, 82, 69, 37, 26, 0), # 21
(263, 288, 234, 257, 193, 109, 95, 90, 110, 47, 42, 24, 0, 266, 249, 168, 163, 230, 128, 103, 73, 85, 77, 37, 29, 0), # 22
(279, 297, 244, 265, 207, 118, 101, 92, 119, 50, 44, 25, 0, 282, 270, 177, 167, 235, 135, 109, 79, 86, 81, 38, 30, 0), # 23
(290, 310, 255, 275, 213, 122, 111, 98, 125, 51, 49, 26, 0, 300, 287, 193, 170, 247, 148, 117, 81, 90, 82, 42, 31, 0), # 24
(301, 326, 272, 282, 225, 127, 119, 103, 131, 53, 49, 28, 0, 310, 301, 203, 177, 267, 153, 129, 85, 93, 85, 45, 33, 0), # 25
(319, 341, 290, 290, 237, 131, 133, 105, 138, 54, 50, 29, 0, 319, 311, 213, 186, 283, 156, 133, 87, 97, 89, 46, 34, 0), # 26
(328, 350, 298, 299, 242, 138, 144, 110, 146, 56, 53, 30, 0, 329, 324, 222, 194, 295, 169, 136, 88, 100, 93, 47, 35, 0), # 27
(346, 364, 315, 315, 259, 140, 151, 117, 153, 63, 55, 32, 0, 349, 337, 234, 202, 302, 170, 140, 90, 106, 94, 49, 37, 0), # 28
(362, 380, 324, 330, 265, 144, 155, 124, 158, 66, 63, 33, 0, 361, 351, 245, 211, 313, 177, 142, 97, 108, 101, 55, 37, 0), # 29
(375, 392, 341, 344, 277, 151, 164, 128, 160, 67, 66, 33, 0, 383, 365, 254, 220, 333, 183, 151, 100, 119, 104, 55, 39, 0), # 30
(392, 401, 351, 366, 291, 157, 168, 130, 163, 72, 69, 33, 0, 402, 378, 263, 227, 348, 186, 153, 103, 124, 106, 56, 40, 0), # 31
(404, 415, 367, 378, 302, 168, 173, 135, 169, 73, 71, 34, 0, 417, 390, 276, 234, 356, 199, 157, 108, 128, 113, 57, 42, 0), # 32
(415, 431, 382, 395, 305, 175, 174, 141, 175, 74, 75, 35, 0, 435, 400, 285, 242, 370, 201, 160, 111, 133, 121, 58, 43, 0), # 33
(423, 442, 394, 410, 323, 184, 178, 147, 180, 76, 79, 35, 0, 448, 413, 290, 246, 381, 208, 166, 116, 135, 123, 59, 43, 0), # 34
(446, 458, 405, 415, 336, 187, 182, 151, 184, 79, 82, 36, 0, 461, 425, 298, 252, 390, 215, 170, 121, 141, 129, 65, 44, 0), # 35
(459, 470, 422, 431, 350, 190, 186, 155, 189, 82, 83, 36, 0, 472, 430, 304, 263, 396, 224, 176, 124, 147, 134, 66, 45, 0), # 36
(474, 479, 435, 443, 358, 195, 195, 163, 197, 83, 84, 37, 0, 494, 448, 315, 270, 410, 235, 184, 126, 152, 141, 69, 45, 0), # 37
(489, 493, 451, 456, 366, 199, 199, 167, 201, 89, 85, 38, 0, 507, 456, 331, 273, 414, 242, 191, 129, 159, 147, 71, 46, 0), # 38
(505, 510, 459, 470, 375, 203, 203, 170, 207, 89, 89, 38, 0, 524, 471, 342, 280, 427, 248, 195, 132, 164, 153, 71, 46, 0), # 39
(516, 523, 470, 477, 384, 206, 204, 176, 215, 92, 92, 38, 0, 539, 478, 348, 292, 438, 254, 200, 139, 169, 159, 74, 47, 0), # 40
(531, 534, 481, 484, 394, 211, 210, 179, 223, 97, 93, 39, 0, 548, 487, 361, 299, 447, 266, 206, 142, 172, 162, 77, 48, 0), # 41
(552, 546, 495, 496, 401, 211, 214, 184, 227, 97, 93, 41, 0, 570, 501, 368, 303, 461, 279, 212, 147, 179, 168, 78, 48, 0), # 42
(569, 562, 503, 508, 414, 212, 221, 188, 233, 98, 95, 41, 0, 585, 506, 377, 311, 474, 283, 221, 151, 180, 171, 80, 49, 0), # 43
(580, 579, 519, 518, 421, 217, 228, 192, 237, 101, 96, 45, 0, 603, 520, 385, 319, 489, 290, 226, 158, 184, 175, 80, 52, 0), # 44
(592, 593, 531, 532, 431, 221, 232, 196, 240, 103, 97, 47, 0, 616, 533, 398, 327, 495, 295, 231, 160, 188, 177, 84, 53, 0), # 45
(610, 614, 541, 548, 443, 225, 236, 202, 246, 106, 98, 47, 0, 624, 538, 405, 334, 508, 303, 236, 164, 198, 179, 84, 56, 0), # 46
(617, 624, 552, 564, 452, 233, 241, 205, 254, 106, 101, 48, 0, 644, 555, 413, 337, 515, 315, 242, 168, 205, 183, 86, 59, 0), # 47
(636, 636, 560, 569, 459, 237, 244, 208, 259, 109, 102, 49, 0, 653, 562, 425, 348, 534, 324, 250, 172, 213, 185, 89, 60, 0), # 48
(645, 643, 574, 589, 476, 239, 251, 211, 263, 112, 106, 52, 0, 666, 573, 435, 357, 547, 331, 254, 175, 216, 191, 90, 61, 0), # 49
(660, 664, 587, 601, 483, 245, 258, 215, 267, 113, 107, 54, 0, 684, 585, 446, 365, 560, 341, 261, 177, 220, 197, 93, 63, 0), # 50
(671, 673, 600, 616, 492, 247, 264, 217, 271, 120, 108, 56, 0, 705, 603, 455, 374, 574, 346, 266, 178, 225, 200, 96, 66, 0), # 51
(686, 688, 611, 626, 500, 251, 267, 222, 277, 121, 109, 57, 0, 712, 610, 467, 385, 588, 351, 271, 180, 229, 200, 97, 69, 0), # 52
(697, 699, 619, 639, 509, 255, 272, 227, 279, 124, 112, 58, 0, 725, 625, 475, 389, 604, 356, 277, 186, 234, 203, 99, 70, 0), # 53
(714, 717, 625, 654, 518, 259, 277, 233, 289, 128, 114, 60, 0, 746, 642, 489, 397, 620, 362, 281, 192, 240, 206, 100, 71, 0), # 54
(724, 729, 643, 664, 529, 266, 278, 240, 296, 131, 115, 60, 0, 760, 649, 504, 404, 627, 369, 286, 195, 241, 216, 100, 71, 0), # 55
(730, 741, 656, 678, 544, 268, 282, 244, 300, 132, 118, 61, 0, 781, 665, 515, 410, 642, 373, 293, 202, 246, 220, 104, 72, 0), # 56
(736, 757, 675, 695, 548, 272, 289, 250, 308, 134, 118, 61, 0, 800, 679, 521, 412, 655, 383, 299, 206, 250, 220, 106, 73, 0), # 57
(751, 770, 690, 714, 556, 275, 295, 256, 313, 134, 120, 63, 0, 814, 688, 529, 418, 669, 386, 308, 212, 257, 224, 110, 74, 0), # 58
(764, 774, 700, 728, 563, 279, 302, 259, 320, 140, 120, 65, 0, 824, 700, 538, 428, 680, 392, 311, 217, 264, 228, 112, 74, 0), # 59
(778, 798, 706, 745, 577, 285, 306, 260, 321, 142, 121, 68, 0, 838, 710, 550, 440, 699, 397, 320, 218, 270, 234, 113, 74, 0), # 60
(791, 818, 715, 755, 588, 292, 311, 269, 330, 143, 122, 70, 0, 854, 722, 561, 447, 715, 410, 323, 224, 276, 238, 113, 75, 0), # 61
(808, 825, 727, 767, 596, 297, 316, 273, 336, 146, 123, 70, 0, 867, 730, 571, 457, 726, 414, 327, 229, 279, 244, 117, 78, 0), # 62
(816, 839, 746, 779, 605, 302, 321, 278, 338, 146, 130, 70, 0, 879, 743, 574, 464, 736, 419, 328, 233, 282, 245, 118, 78, 0), # 63
(832, 849, 757, 787, 617, 304, 328, 286, 343, 147, 134, 70, 0, 893, 759, 582, 475, 750, 426, 333, 237, 290, 249, 119, 79, 0), # 64
(839, 862, 772, 801, 626, 306, 332, 290, 346, 148, 136, 70, 0, 909, 771, 601, 479, 763, 432, 338, 238, 301, 253, 120, 80, 0), # 65
(857, 876, 783, 812, 637, 308, 332, 295, 352, 154, 137, 71, 0, 924, 781, 608, 487, 777, 442, 340, 240, 306, 257, 120, 81, 0), # 66
(872, 893, 792, 824, 652, 312, 339, 302, 360, 154, 139, 74, 0, 941, 791, 614, 498, 786, 446, 352, 242, 307, 265, 123, 81, 0), # 67
(882, 904, 798, 835, 664, 316, 346, 306, 368, 156, 141, 75, 0, 951, 799, 623, 505, 797, 449, 360, 245, 311, 269, 128, 81, 0), # 68
(896, 914, 807, 851, 670, 320, 354, 313, 371, 157, 141, 79, 0, 963, 809, 632, 510, 809, 456, 369, 248, 315, 273, 130, 81, 0), # 69
(915, 925, 815, 869, 683, 326, 361, 317, 375, 157, 146, 80, 0, 979, 817, 642, 516, 820, 462, 375, 250, 321, 277, 131, 83, 0), # 70
(932, 931, 828, 880, 698, 335, 363, 318, 384, 161, 148, 80, 0, 990, 827, 650, 520, 827, 467, 384, 254, 327, 282, 131, 83, 0), # 71
(947, 940, 847, 897, 708, 340, 372, 324, 391, 164, 149, 80, 0, 1006, 838, 662, 537, 836, 469, 392, 261, 334, 286, 133, 86, 0), # 72
(964, 951, 858, 912, 717, 346, 375, 331, 400, 166, 151, 80, 0, 1022, 847, 667, 542, 852, 475, 399, 266, 336, 287, 135, 86, 0), # 73
(973, 959, 866, 926, 729, 357, 378, 333, 403, 169, 151, 80, 0, 1040, 864, 680, 546, 865, 480, 404, 267, 341, 291, 139, 86, 0), # 74
(984, 972, 880, 937, 742, 364, 384, 336, 410, 172, 153, 81, 0, 1054, 878, 689, 555, 872, 485, 410, 268, 349, 295, 142, 87, 0), # 75
(1003, 984, 896, 947, 753, 369, 392, 339, 413, 178, 154, 81, 0, 1061, 890, 699, 563, 891, 494, 417, 270, 354, 301, 146, 87, 0), # 76
(1013, 992, 910, 959, 766, 374, 401, 344, 418, 179, 157, 81, 0, 1079, 911, 708, 574, 897, 496, 419, 274, 362, 303, 149, 88, 0), # 77
(1025, 1006, 916, 976, 781, 380, 405, 348, 424, 181, 159, 81, 0, 1090, 923, 717, 579, 908, 502, 423, 276, 367, 310, 149, 88, 0), # 78
(1037, 1014, 926, 989, 791, 386, 409, 354, 430, 184, 161, 81, 0, 1106, 930, 724, 587, 913, 506, 428, 282, 376, 312, 150, 88, 0), # 79
(1054, 1031, 938, 998, 806, 394, 411, 357, 435, 185, 162, 82, 0, 1116, 945, 737, 592, 926, 510, 434, 286, 383, 315, 151, 88, 0), # 80
(1067, 1042, 950, 1006, 818, 400, 419, 363, 443, 187, 165, 82, 0, 1135, 954, 749, 602, 938, 514, 439, 288, 388, 316, 152, 89, 0), # 81
(1080, 1055, 957, 1021, 829, 407, 425, 369, 448, 189, 165, 89, 0, 1153, 968, 756, 614, 945, 517, 442, 296, 391, 322, 155, 91, 0), # 82
(1092, 1061, 970, 1027, 835, 412, 433, 372, 454, 193, 167, 89, 0, 1170, 977, 764, 622, 957, 524, 447, 299, 398, 326, 157, 91, 0), # 83
(1103, 1074, 984, 1043, 846, 419, 441, 379, 460, 195, 168, 90, 0, 1177, 993, 772, 627, 962, 527, 453, 303, 403, 329, 159, 91, 0), # 84
(1115, 1088, 1005, 1057, 860, 426, 447, 382, 468, 199, 169, 91, 0, 1186, 1008, 782, 629, 975, 530, 459, 307, 409, 332, 162, 91, 0), # 85
(1131, 1099, 1016, 1068, 875, 430, 451, 386, 474, 200, 171, 92, 0, 1197, 1015, 790, 640, 985, 538, 462, 310, 414, 340, 164, 91, 0), # 86
(1148, 1106, 1027, 1080, 881, 433, 455, 388, 479, 202, 172, 92, 0, 1208, 1031, 798, 650, 992, 546, 467, 316, 421, 349, 165, 91, 0), # 87
(1159, 1123, 1037, 1089, 891, 439, 461, 390, 482, 206, 177, 92, 0, 1229, 1043, 807, 660, 1005, 547, 469, 321, 427, 354, 167, 92, 0), # 88
(1174, 1132, 1051, 1104, 898, 443, 465, 395, 486, 207, 180, 93, 0, 1247, 1057, 816, 664, 1014, 553, 478, 325, 432, 358, 169, 92, 0), # 89
(1187, 1140, 1060, 1115, 909, 452, 472, 397, 489, 209, 180, 93, 0, 1260, 1070, 823, 667, 1020, 562, 482, 329, 438, 359, 172, 93, 0), # 90
(1205, 1153, 1067, 1129, 918, 456, 476, 397, 497, 211, 182, 93, 0, 1274, 1080, 834, 672, 1027, 566, 492, 332, 440, 361, 177, 95, 0), # 91
(1217, 1165, 1074, 1142, 931, 463, 477, 405, 502, 215, 187, 94, 0, 1288, 1097, 842, 680, 1038, 570, 497, 335, 445, 366, 179, 96, 0), # 92
(1228, 1170, 1086, 1154, 935, 467, 480, 409, 512, 218, 188, 94, 0, 1301, 1107, 852, 686, 1059, 576, 501, 338, 447, 369, 181, 97, 0), # 93
(1242, 1183, 1098, 1167, 948, 469, 484, 416, 515, 220, 190, 95, 0, 1313, 1120, 856, 693, 1074, 582, 506, 339, 453, 374, 181, 97, 0), # 94
(1251, 1202, 1109, 1178, 955, 474, 486, 420, 518, 224, 190, 98, 0, 1325, 1137, 863, 704, 1085, 588, 511, 341, 456, 377, 181, 99, 0), # 95
(1260, 1210, 1123, 1188, 962, 480, 494, 427, 527, 227, 191, 100, 0, 1335, 1147, 872, 712, 1092, 594, 514, 350, 465, 382, 186, 99, 0), # 96
(1273, 1220, 1131, 1202, 972, 484, 496, 437, 532, 229, 193, 103, 0, 1346, 1150, 880, 720, 1104, 601, 519, 351, 473, 383, 188, 100, 0), # 97
(1290, 1228, 1143, 1214, 984, 491, 500, 439, 536, 233, 193, 105, 0, 1354, 1159, 885, 726, 1114, 606, 522, 352, 480, 387, 192, 102, 0), # 98
(1304, 1238, 1154, 1229, 996, 495, 505, 442, 540, 234, 193, 107, 0, 1368, 1172, 898, 733, 1123, 610, 526, 352, 482, 393, 196, 104, 0), # 99
(1313, 1247, 1165, 1239, 1007, 500, 507, 447, 548, 235, 193, 112, 0, 1380, 1180, 910, 741, 1135, 612, 533, 355, 492, 397, 200, 105, 0), # 100
(1329, 1258, 1175, 1246, 1019, 503, 509, 450, 554, 236, 194, 113, 0, 1393, 1192, 914, 746, 1145, 621, 539, 358, 498, 401, 202, 105, 0), # 101
(1346, 1270, 1183, 1260, 1024, 509, 514, 455, 558, 238, 195, 113, 0, 1408, 1199, 919, 758, 1154, 627, 541, 363, 501, 408, 205, 108, 0), # 102
(1360, 1282, 1191, 1272, 1032, 514, 518, 460, 565, 240, 196, 113, 0, 1428, 1213, 929, 766, 1160, 631, 545, 365, 509, 411, 205, 109, 0), # 103
(1374, 1288, 1202, 1286, 1043, 518, 522, 465, 570, 243, 197, 114, 0, 1438, 1219, 943, 772, 1168, 640, 549, 370, 511, 414, 206, 109, 0), # 104
(1390, 1299, 1211, 1299, 1055, 523, 530, 470, 578, 245, 199, 114, 0, 1455, 1229, 958, 780, 1178, 643, 555, 371, 517, 418, 209, 109, 0), # 105
(1398, 1311, 1224, 1309, 1061, 528, 535, 472, 586, 247, 200, 115, 0, 1474, 1243, 965, 784, 1190, 647, 558, 375, 522, 421, 210, 110, 0), # 106
(1409, 1323, 1240, 1314, 1064, 537, 538, 474, 593, 249, 200, 117, 0, 1489, 1256, 968, 787, 1198, 652, 565, 379, 528, 425, 213, 111, 0), # 107
(1421, 1332, 1247, 1323, 1071, 542, 542, 477, 595, 250, 202, 120, 0, 1513, 1266, 979, 796, 1207, 657, 571, 381, 534, 429, 214, 115, 0), # 108
(1431, 1347, 1261, 1333, 1078, 549, 551, 482, 603, 252, 203, 122, 0, 1530, 1275, 985, 801, 1216, 666, 574, 386, 543, 430, 217, 115, 0), # 109
(1446, 1354, 1271, 1341, 1086, 553, 555, 486, 608, 252, 203, 122, 0, 1543, 1284, 998, 810, 1225, 672, 579, 391, 548, 434, 217, 117, 0), # 110
(1465, 1367, 1280, 1354, 1102, 555, 557, 488, 617, 256, 204, 122, 0, 1551, 1292, 1009, 814, 1235, 673, 584, 393, 554, 436, 220, 117, 0), # 111
(1480, 1385, 1290, 1368, 1106, 557, 560, 490, 626, 256, 205, 122, 0, 1563, 1302, 1015, 825, 1242, 675, 586, 396, 564, 439, 222, 117, 0), # 112
(1489, 1392, 1303, 1385, 1111, 559, 560, 491, 634, 257, 208, 123, 0, 1581, 1311, 1026, 831, 1251, 686, 588, 398, 569, 444, 226, 118, 0), # 113
(1503, 1396, 1315, 1395, 1119, 566, 562, 493, 640, 259, 210, 123, 0, 1594, 1322, 1035, 837, 1261, 687, 594, 401, 573, 447, 228, 119, 0), # 114
(1518, 1403, 1324, 1408, 1129, 569, 569, 494, 644, 259, 212, 125, 0, 1605, 1333, 1050, 841, 1273, 691, 597, 404, 577, 452, 232, 122, 0), # 115
(1522, 1417, 1336, 1421, 1141, 575, 570, 500, 648, 259, 212, 126, 0, 1616, 1342, 1059, 845, 1280, 702, 600, 406, 581, 457, 233, 123, 0), # 116
(1537, 1426, 1349, 1431, 1150, 581, 574, 503, 657, 264, 213, 128, 0, 1626, 1352, 1068, 854, 1292, 703, 603, 410, 586, 459, 234, 123, 0), # 117
(1547, 1436, 1362, 1448, 1160, 587, 577, 506, 661, 265, 215, 130, 0, 1638, 1367, 1078, 863, 1297, 707, 606, 413, 592, 463, 234, 124, 0), # 118
(1552, 1444, 1370, 1455, 1171, 590, 580, 511, 666, 267, 219, 131, 0, 1648, 1376, 1089, 866, 1308, 714, 609, 416, 595, 465, 238, 127, 0), # 119
(1561, 1453, 1379, 1464, 1182, 594, 584, 513, 671, 270, 220, 131, 0, 1659, 1388, 1096, 874, 1316, 722, 612, 419, 603, 467, 242, 129, 0), # 120
(1571, 1468, 1396, 1479, 1197, 599, 588, 517, 681, 273, 223, 131, 0, 1676, 1397, 1102, 881, 1320, 726, 614, 422, 607, 472, 246, 129, 0), # 121
(1594, 1478, 1405, 1487, 1208, 603, 590, 520, 690, 274, 225, 131, 0, 1693, 1409, 1108, 888, 1328, 731, 616, 425, 615, 475, 249, 129, 0), # 122
(1610, 1483, 1410, 1501, 1218, 606, 594, 521, 694, 277, 227, 132, 0, 1710, 1420, 1119, 892, 1337, 734, 621, 430, 618, 477, 253, 130, 0), # 123
(1620, 1502, 1420, 1516, 1223, 612, 599, 523, 699, 279, 227, 132, 0, 1717, 1431, 1125, 898, 1347, 740, 623, 434, 625, 480, 253, 131, 0), # 124
(1627, 1510, 1429, 1522, 1234, 616, 604, 525, 703, 282, 228, 132, 0, 1730, 1442, 1136, 908, 1358, 746, 630, 438, 630, 482, 257, 131, 0), # 125
(1642, 1523, 1435, 1534, 1238, 621, 610, 527, 703, 285, 229, 133, 0, 1749, 1448, 1142, 914, 1366, 750, 634, 441, 634, 485, 258, 132, 0), # 126
(1649, 1531, 1444, 1545, 1250, 625, 614, 529, 709, 287, 229, 133, 0, 1757, 1455, 1146, 917, 1375, 755, 636, 443, 640, 486, 263, 133, 0), # 127
(1664, 1543, 1453, 1554, 1254, 632, 618, 531, 717, 292, 230, 135, 0, 1768, 1460, 1155, 922, 1386, 758, 640, 446, 643, 489, 267, 134, 0), # 128
(1677, 1552, 1467, 1563, 1264, 637, 622, 532, 723, 293, 231, 136, 0, 1782, 1470, 1161, 927, 1396, 767, 645, 448, 644, 497, 267, 134, 0), # 129
(1685, 1564, 1477, 1574, 1275, 642, 624, 538, 728, 294, 233, 137, 0, 1790, 1475, 1166, 933, 1405, 770, 652, 449, 646, 499, 270, 134, 0), # 130
(1690, 1570, 1496, 1581, 1280, 644, 625, 542, 734, 295, 233, 137, 0, 1799, 1485, 1175, 938, 1413, 777, 655, 453, 649, 500, 272, 134, 0), # 131
(1702, 1580, 1506, 1586, 1290, 650, 629, 549, 736, 296, 234, 137, 0, 1816, 1494, 1180, 948, 1426, 780, 657, 456, 650, 504, 279, 135, 0), # 132
(1708, 1590, 1518, 1597, 1295, 654, 633, 553, 739, 297, 236, 140, 0, 1825, 1505, 1186, 952, 1440, 784, 663, 460, 660, 509, 282, 136, 0), # 133
(1723, 1598, 1529, 1613, 1304, 659, 635, 556, 746, 299, 237, 141, 0, 1836, 1510, 1198, 957, 1452, 785, 668, 465, 665, 513, 284, 136, 0), # 134
(1735, 1609, 1542, 1626, 1317, 661, 635, 558, 750, 301, 240, 141, 0, 1846, 1521, 1205, 962, 1467, 792, 674, 467, 668, 516, 285, 139, 0), # 135
(1752, 1616, 1555, 1641, 1324, 667, 640, 560, 755, 302, 240, 142, 0, 1856, 1528, 1213, 966, 1481, 796, 678, 471, 673, 517, 287, 139, 0), # 136
(1762, 1627, 1567, 1653, 1336, 672, 642, 563, 757, 304, 242, 142, 0, 1874, 1539, 1221, 972, 1493, 800, 685, 473, 679, 520, 290, 140, 0), # 137
(1779, 1631, 1572, 1665, 1347, 675, 649, 565, 763, 310, 242, 143, 0, 1885, 1550, 1227, 977, 1501, 802, 688, 476, 686, 526, 293, 141, 0), # 138
(1795, 1643, 1584, 1673, 1355, 681, 655, 568, 767, 311, 245, 144, 0, 1898, 1558, 1235, 984, 1510, 810, 690, 480, 690, 530, 298, 142, 0), # 139
(1808, 1652, 1589, 1687, 1363, 686, 658, 573, 773, 313, 248, 145, 0, 1904, 1567, 1242, 987, 1521, 816, 695, 487, 695, 536, 301, 143, 0), # 140
(1824, 1658, 1600, 1695, 1370, 692, 664, 575, 779, 313, 249, 146, 0, 1918, 1580, 1250, 994, 1531, 818, 699, 488, 700, 538, 302, 143, 0), # 141
(1831, 1663, 1609, 1706, 1378, 697, 666, 581, 784, 315, 249, 148, 0, 1930, 1585, 1254, 996, 1541, 824, 704, 491, 703, 542, 302, 146, 0), # 142
(1848, 1677, 1622, 1716, 1393, 701, 671, 586, 791, 315, 252, 152, 0, 1943, 1595, 1260, 1002, 1556, 829, 710, 494, 705, 547, 303, 147, 0), # 143
(1859, 1688, 1637, 1729, 1398, 705, 677, 592, 792, 319, 257, 152, 0, 1954, 1606, 1267, 1006, 1567, 833, 714, 502, 712, 550, 304, 147, 0), # 144
(1865, 1701, 1648, 1733, 1405, 711, 679, 599, 799, 320, 258, 153, 0, 1963, 1613, 1280, 1011, 1575, 844, 720, 510, 717, 555, 306, 150, 0), # 145
(1880, 1713, 1656, 1743, 1414, 716, 683, 604, 803, 320, 259, 154, 0, 1974, 1622, 1293, 1016, 1586, 848, 725, 512, 720, 558, 307, 150, 0), # 146
(1892, 1725, 1665, 1751, 1421, 720, 685, 609, 805, 321, 261, 155, 0, 1990, 1634, 1299, 1021, 1595, 852, 728, 515, 724, 561, 309, 152, 0), # 147
(1902, 1734, 1680, 1763, 1429, 727, 691, 615, 809, 323, 262, 157, 0, 2004, 1642, 1308, 1027, 1603, 856, 732, 518, 726, 565, 311, 152, 0), # 148
(1920, 1747, 1686, 1778, 1441, 731, 697, 617, 814, 327, 264, 157, 0, 2014, 1649, 1316, 1032, 1611, 858, 735, 522, 733, 567, 314, 154, 0), # 149
(1938, 1750, 1696, 1788, 1445, 736, 699, 620, 816, 328, 266, 157, 0, 2023, 1660, 1320, 1037, 1629, 861, 742, 526, 739, 570, 316, 156, 0), # 150
(1947, 1755, 1700, 1791, 1452, 741, 702, 623, 821, 328, 269, 157, 0, 2033, 1672, 1328, 1043, 1639, 866, 743, 527, 740, 573, 317, 157, 0), # 151
(1956, 1758, 1709, 1803, 1460, 744, 703, 624, 825, 331, 270, 157, 0, 2047, 1678, 1331, 1047, 1648, 870, 746, 534, 743, 578, 317, 157, 0), # 152
(1963, 1763, 1716, 1815, 1465, 750, 706, 627, 829, 334, 272, 157, 0, 2057, 1689, 1336, 1055, 1657, 875, 748, 538, 750, 581, 319, 158, 0), # 153
(1970, 1773, 1724, 1823, 1473, 753, 709, 627, 831, 336, 273, 158, 0, 2066, 1700, 1346, 1062, 1671, 879, 752, 540, 753, 583, 320, 160, 0), # 154
(1987, 1779, 1734, 1836, 1479, 754, 714, 629, 832, 336, 274, 158, 0, 2080, 1703, 1351, 1065, 1678, 882, 754, 544, 758, 587, 321, 160, 0), # 155
(1991, 1784, 1738, 1844, 1483, 760, 717, 633, 835, 336, 275, 159, 0, 2090, 1712, 1356, 1069, 1685, 892, 757, 551, 762, 592, 322, 160, 0), # 156
(1994, 1791, 1744, 1849, 1497, 766, 720, 634, 837, 338, 276, 161, 0, 2103, 1720, 1362, 1074, 1696, 894, 761, 554, 765, 597, 326, 160, 0), # 157
(2003, 1796, 1760, 1857, 1505, 770, 726, 638, 844, 341, 278, 161, 0, 2108, 1729, 1363, 1082, 1708, 899, 765, 556, 768, 598, 330, 160, 0), # 158
(2010, 1802, 1770, 1862, 1513, 775, 730, 643, 850, 342, 279, 161, 0, 2114, 1742, 1370, 1088, 1715, 905, 768, 560, 772, 599, 331, 160, 0), # 159
(2023, 1807, 1782, 1867, 1519, 779, 731, 648, 852, 344, 281, 161, 0, 2125, 1749, 1376, 1093, 1722, 909, 769, 565, 777, 600, 332, 160, 0), # 160
(2034, 1813, 1791, 1874, 1523, 781, 732, 654, 859, 346, 281, 161, 0, 2131, 1760, 1383, 1096, 1728, 916, 776, 568, 783, 603, 334, 161, 0), # 161
(2046, 1823, 1796, 1880, 1532, 787, 734, 659, 866, 347, 282, 161, 0, 2141, 1765, 1393, 1100, 1741, 919, 777, 569, 790, 605, 337, 161, 0), # 162
(2055, 1829, 1808, 1888, 1536, 790, 736, 662, 873, 349, 285, 161, 0, 2153, 1771, 1400, 1101, 1746, 921, 780, 575, 793, 609, 339, 161, 0), # 163
(2064, 1834, 1815, 1897, 1544, 793, 738, 665, 879, 350, 285, 161, 0, 2162, 1776, 1404, 1103, 1754, 924, 783, 579, 798, 612, 340, 162, 0), # 164
(2075, 1842, 1827, 1904, 1549, 797, 744, 666, 885, 353, 286, 162, 0, 2168, 1785, 1407, 1104, 1758, 929, 785, 582, 800, 616, 340, 162, 0), # 165
(2080, 1845, 1840, 1913, 1553, 799, 745, 670, 890, 354, 287, 162, 0, 2180, 1790, 1412, 1109, 1767, 935, 787, 583, 802, 620, 341, 162, 0), # 166
(2086, 1852, 1848, 1921, 1557, 802, 751, 675, 896, 355, 290, 162, 0, 2193, 1796, 1422, 1114, 1772, 939, 788, 586, 807, 621, 341, 163, 0), # 167
(2095, 1857, 1859, 1932, 1562, 805, 754, 678, 898, 357, 291, 164, 0, 2201, 1804, 1428, 1117, 1780, 942, 791, 587, 808, 623, 342, 163, 0), # 168
(2100, 1862, 1867, 1942, 1565, 807, 759, 681, 902, 357, 293, 164, 0, 2214, 1810, 1432, 1119, 1786, 944, 793, 590, 808, 625, 345, 163, 0), # 169
(2111, 1867, 1872, 1948, 1570, 809, 762, 684, 905, 357, 295, 165, 0, 2223, 1816, 1441, 1125, 1793, 947, 796, 594, 814, 626, 347, 165, 0), # 170
(2121, 1871, 1877, 1952, 1581, 813, 763, 688, 907, 358, 296, 166, 0, 2230, 1822, 1452, 1129, 1806, 954, 796, 594, 816, 630, 347, 165, 0), # 171
(2130, 1873, 1881, 1958, 1584, 818, 764, 689, 909, 361, 298, 166, 0, 2234, 1827, 1456, 1132, 1814, 956, 798, 597, 817, 631, 350, 165, 0), # 172
(2138, 1879, 1883, 1962, 1590, 821, 766, 689, 911, 362, 298, 166, 0, 2242, 1834, 1465, 1137, 1818, 957, 801, 599, 820, 637, 350, 166, 0), # 173
(2144, 1884, 1889, 1968, 1597, 824, 768, 689, 913, 364, 298, 166, 0, 2250, 1840, 1468, 1139, 1824, 961, 801, 601, 824, 639, 350, 166, 0), # 174
(2152, 1887, 1894, 1975, 1602, 825, 770, 689, 915, 366, 301, 167, 0, 2256, 1843, 1469, 1144, 1829, 962, 801, 604, 826, 641, 352, 168, 0), # 175
(2157, 1892, 1898, 1982, 1610, 829, 772, 690, 917, 367, 302, 169, 0, 2261, 1843, 1471, 1146, 1834, 965, 802, 605, 828, 644, 352, 168, 0), # 176
(2168, 1896, 1907, 1988, 1615, 832, 775, 692, 918, 369, 302, 170, 0, 2269, 1844, 1475, 1149, 1839, 966, 805, 605, 831, 645, 352, 168, 0), # 177
(2173, 1900, 1911, 1991, 1621, 834, 776, 695, 923, 370, 302, 172, 0, 2278, 1845, 1476, 1151, 1843, 966, 807, 605, 836, 647, 354, 169, 0), # 178
(2173, 1900, 1911, 1991, 1621, 834, 776, 695, 923, 370, 302, 172, 0, 2278, 1845, 1476, 1151, 1843, 966, 807, 605, 836, 647, 354, 169, 0), # 179
)
passenger_arriving_rate = (
(7.029211809720476, 7.090786984939564, 6.079830434547925, 6.525401162556605, 5.184373233768971, 2.563234861163827, 2.9022249307617405, 2.7143527675713304, 2.8420462290117365, 1.3853052554328298, 0.9812285382399741, 0.571423425802387, 0.0, 7.117432297609708, 6.285657683826256, 4.90614269119987, 4.155915766298489, 5.684092458023473, 3.8000938745998627, 2.9022249307617405, 1.8308820436884476, 2.5921866168844856, 2.175133720852202, 1.2159660869095852, 0.6446169986308695, 0.0), # 0
(7.496058012827964, 7.558911224152441, 6.4812376898851785, 6.956401465940448, 5.527657648309288, 2.7325532603014207, 3.093628258884586, 2.893049671694997, 3.0297144856220246, 1.4766432422970026, 1.0460557650564308, 0.6091419437616749, 0.0, 7.587708306415797, 6.700561381378422, 5.230278825282154, 4.429929726891007, 6.059428971244049, 4.050269540372995, 3.093628258884586, 1.9518237573581576, 2.763828824154644, 2.3188004886468163, 1.2962475379770357, 0.687173747650222, 0.0), # 1
(7.9614122125716245, 8.025177635976757, 6.881049333138649, 7.385687089898034, 5.869698775499761, 2.9011961768518306, 3.284272955572493, 3.071031394610912, 3.2166338432095234, 1.5676198212571917, 1.1106254013811399, 0.6467104760728565, 0.0, 8.056110759493567, 7.113815236801421, 5.553127006905699, 4.702859463771574, 6.433267686419047, 4.2994439524552766, 3.284272955572493, 2.0722829834655934, 2.9348493877498805, 2.4618956966326784, 1.37620986662773, 0.7295616032706144, 0.0), # 2
(8.423460910405188, 8.487736310818441, 7.277679347539831, 7.811555227908678, 6.209150897601775, 3.0684948417778424, 3.473402549153569, 3.2475923418717962, 3.4020630750965104, 1.657873944449164, 1.1746812960930562, 0.6839799965752206, 0.0, 8.520781928755916, 7.523779962327425, 5.873406480465281, 4.97362183334749, 6.804126150193021, 4.5466292786205145, 3.473402549153569, 2.191782029841316, 3.1045754488008876, 2.6038517426362264, 1.455535869507966, 0.7716123918925856, 0.0), # 3
(8.880390607782374, 8.94473733908341, 7.669541716320211, 8.232303073451698, 6.5446682968767265, 3.233780486042246, 3.6602605679559215, 3.4220269190303676, 3.585260954605263, 1.7470445640086882, 1.2379672980711345, 0.7208014791080559, 0.0, 8.979864086115745, 7.928816270188614, 6.189836490355671, 5.241133692026064, 7.170521909210526, 4.790837686642515, 3.6602605679559215, 2.30984320431589, 3.2723341484383632, 2.7441010244839, 1.5339083432640421, 0.8131579399166738, 0.0), # 4
(9.330387806156915, 9.394330811177607, 8.055050422711272, 8.646227820006413, 6.874905255585995, 3.396384340607826, 3.844090540307657, 3.593629531639346, 3.765486255058061, 1.8347706320715327, 1.300227256194331, 0.7570258975106506, 0.0, 9.43149950348596, 8.327284872617156, 6.501136280971655, 5.504311896214597, 7.530972510116122, 5.031081344295084, 3.844090540307657, 2.4259888147198754, 3.4374526277929975, 2.8820759400021383, 1.6110100845422546, 0.8540300737434189, 0.0), # 5
(9.771639006982534, 9.834666817506942, 8.43261944994451, 9.051626661052135, 7.198516055990973, 3.5556376364373725, 4.024135994536884, 3.7616945852514516, 3.9419977497771805, 1.920691100773466, 1.3612050193415997, 0.7925042256222944, 0.0, 9.87383045277945, 8.717546481845236, 6.806025096707997, 5.762073302320396, 7.883995499554361, 5.266372419352033, 4.024135994536884, 2.5397411688838374, 3.5992580279954867, 3.017208887017379, 1.6865238899889023, 0.8940606197733586, 0.0), # 6
(10.202330711712957, 10.263895448477353, 8.800662781251408, 9.446796790068186, 7.514154980353052, 3.710871604493673, 4.19964045897171, 3.9255164854194056, 4.1140542120849, 2.004444922250256, 1.4206444363918964, 0.8270874372822752, 0.0, 10.304999205909127, 9.097961810105026, 7.103222181959481, 6.013334766750766, 8.2281084241698, 5.495723079587168, 4.19964045897171, 2.6506225746383376, 3.757077490176526, 3.148932263356063, 1.7601325562502819, 0.9330814044070321, 0.0), # 7
(10.62064942180191, 10.68016679449476, 9.157594399863463, 9.830035400533875, 7.820476310933614, 3.8614174757395103, 4.369847461940239, 4.0843896376959234, 4.280914415303496, 2.0856710486376717, 1.4782893562241752, 0.8606265063298821, 0.0, 10.723148034787885, 9.466891569628702, 7.391446781120876, 6.257013145913014, 8.561828830606991, 5.718145492774292, 4.369847461940239, 2.758155339813936, 3.910238155466807, 3.276678466844626, 1.831518879972693, 0.9709242540449783, 0.0), # 8
(11.02478163870312, 11.081630945965095, 9.501828289012156, 10.199639685928528, 8.116134329994049, 4.006606481137679, 4.534000531770584, 4.237608447633729, 4.441837132755248, 2.1640084320714803, 1.5338836277173917, 0.8929724066044035, 0.0, 11.126419211328628, 9.822696472648436, 7.669418138586958, 6.49202529621444, 8.883674265510496, 5.932651826687221, 4.534000531770584, 2.861861772241199, 4.058067164997024, 3.3998798953095104, 1.9003656578024313, 1.0074209950877362, 0.0), # 9
(11.412913863870306, 11.46643799329428, 9.83177843192898, 10.553906839731454, 8.399783319795748, 4.145769851650964, 4.691343196790848, 4.38446732078554, 4.596081137762433, 2.2390960246874507, 1.5871710997505006, 0.923976111945128, 0.0, 11.512955007444255, 10.163737231396405, 7.935855498752503, 6.717288074062351, 9.192162275524867, 6.138254249099756, 4.691343196790848, 2.961264179750688, 4.199891659897874, 3.517968946577152, 1.9663556863857963, 1.0424034539358438, 0.0), # 10
(11.783232598757209, 11.832738026888249, 10.145858811845418, 10.891134055421968, 8.670077562600099, 4.278238818242151, 4.841118985329142, 4.524260662704076, 4.7429052036473305, 2.3105727786213524, 1.6378956212024585, 0.9534885961913449, 0.0, 11.880897695047656, 10.488374558104791, 8.189478106012292, 6.931718335864056, 9.485810407294661, 6.333964927785706, 4.841118985329142, 3.055884870172965, 4.3350387813000495, 3.63037801847399, 2.0291717623690837, 1.075703456989841, 0.0), # 11
(12.133924344817538, 12.178681137152912, 10.442483411992965, 11.209618526479394, 8.925671340668487, 4.403344611874027, 4.9825714257135685, 4.656282878942054, 4.881568103732217, 2.378077646008951, 1.6858010409522184, 0.9813608331823415, 0.0, 12.22838954605175, 10.794969165005755, 8.429005204761092, 7.134232938026852, 9.763136207464434, 6.518796030518876, 4.9825714257135685, 3.1452461513385908, 4.462835670334243, 3.7365395088264655, 2.0884966823985933, 1.107152830650265, 0.0), # 12
(12.463175603505027, 12.502417414494213, 10.720066215603106, 11.507657446383048, 9.165218936262296, 4.520418463509383, 5.11494404627224, 4.779828375052198, 5.011328611339368, 2.441249578986017, 1.7306312078787365, 1.0074437967574077, 0.0, 12.55357283236943, 11.08188176433148, 8.653156039393682, 7.323748736958049, 10.022657222678736, 6.691759725073078, 5.11494404627224, 3.228870331078131, 4.582609468131148, 3.8358858154610167, 2.1440132431206216, 1.136583401317656, 0.0), # 13
(12.769172876273403, 12.802096949318072, 10.977021205907338, 11.783548008612232, 9.387374631642924, 4.6287916041110035, 5.237480375333263, 4.894191556587227, 5.131445499791063, 2.4997275296883177, 1.7721299708609668, 1.0315884607558323, 0.0, 12.85458982591359, 11.347473068314153, 8.860649854304834, 7.499182589064952, 10.262890999582126, 6.8518681792221185, 5.237480375333263, 3.306279717222145, 4.693687315821462, 3.9278493362040785, 2.195404241181468, 1.1638269953925522, 0.0), # 14
(13.050102664576398, 13.075869832030413, 11.211762366137135, 12.035587406646286, 9.590792709071755, 4.72779526464168, 5.349423941224739, 4.998666829099858, 5.241177542409583, 2.5531504502516222, 1.810041178777865, 1.0536457990169035, 0.0, 13.129582798597134, 11.590103789185937, 9.050205893889325, 7.659451350754866, 10.482355084819165, 6.998133560739801, 5.349423941224739, 3.3769966176011996, 4.795396354535877, 4.0118624688820965, 2.242352473227427, 1.1887154392754924, 0.0), # 15
(13.30415146986772, 13.321886153037171, 11.422703679523998, 12.262072833964503, 9.774127450810177, 4.816760676064193, 5.450018272274784, 5.092548598142811, 5.339783512517201, 2.6011572928116995, 1.8441086805083868, 1.0734667853799098, 0.0, 13.376694022332964, 11.808134639179006, 9.220543402541933, 7.803471878435097, 10.679567025034402, 7.1295680373999355, 5.450018272274784, 3.440543340045852, 4.887063725405088, 4.087357611321502, 2.2845407359047996, 1.2110805593670158, 0.0), # 16
(13.529505793601107, 13.538296002744264, 11.608259129299412, 12.46130148404622, 9.936033139119584, 4.895019069341334, 5.538506896811498, 5.17513126926881, 5.426522183436193, 2.643387009504314, 1.874076324931487, 1.09090239368414, 0.0, 13.594065769033982, 11.999926330525538, 9.370381624657433, 7.9301610285129405, 10.853044366872385, 7.245183776976335, 5.538506896811498, 3.496442192386667, 4.968016569559792, 4.153767161348741, 2.3216518258598824, 1.2307541820676606, 0.0), # 17
(13.724352137230287, 13.723249471557619, 11.766842698694862, 12.631570550370744, 10.07516405626135, 4.961901675435895, 5.6141333431629965, 5.245709248030569, 5.500652328488845, 2.6794785524652385, 1.8996879609261188, 1.1058035977688838, 0.0, 13.779840310613086, 12.163839575457718, 9.498439804630594, 8.038435657395715, 11.00130465697769, 7.343992947242797, 5.6141333431629965, 3.5442154824542103, 5.037582028130675, 4.210523516790249, 2.3533685397389728, 1.2475681337779656, 0.0), # 18
(13.88687700220898, 13.874896649883173, 11.896868370941842, 12.77117722641738, 10.190174484496875, 5.0167397253106545, 5.676141139657377, 5.30357693998081, 5.561432720997431, 2.7090708738302403, 1.9206874373712384, 1.1180213714734282, 0.0, 13.932159918983176, 12.298235086207708, 9.603437186856192, 8.12721262149072, 11.122865441994861, 7.425007715973134, 5.676141139657377, 3.5833855180790386, 5.095087242248438, 4.257059075472461, 2.379373674188369, 1.2613542408984704, 0.0), # 19
(14.015266889990915, 13.991387628126835, 11.996750129271838, 12.87841870566547, 10.279718706087547, 5.058864449928407, 5.723773814622755, 5.348028750672253, 5.608122134284226, 2.731802925735086, 1.936818603145802, 1.1274066886370624, 0.0, 14.049166866057154, 12.401473575007685, 9.68409301572901, 8.195408777205257, 11.216244268568452, 7.487240250941153, 5.723773814622755, 3.6134746070917196, 5.139859353043773, 4.292806235221825, 2.399350025854368, 1.2719443298297126, 0.0), # 20
(14.107708302029813, 14.070872496694552, 12.064901956916339, 12.951592181594311, 10.34245100329475, 5.087607080251938, 5.756274896387231, 5.378359085657614, 5.63997934167151, 2.747313660315545, 1.9478253071287643, 1.133810523099076, 0.0, 14.12900342374791, 12.471915754089835, 9.739126535643821, 8.241940980946634, 11.27995868334302, 7.529702719920659, 5.756274896387231, 3.634005057322813, 5.171225501647375, 4.317197393864771, 2.412980391383268, 1.279170226972232, 0.0), # 21
(14.162387739779412, 14.111501345992236, 12.099737837106835, 12.988994847683228, 10.377025658379871, 5.102298847244033, 5.77288791327892, 5.393862350489618, 5.656263116481561, 2.7552420297073854, 1.9534513981990798, 1.1370838486987573, 0.0, 14.16981186396836, 12.50792233568633, 9.7672569909954, 8.265726089122154, 11.312526232963123, 7.551407290685465, 5.77288791327892, 3.644499176602881, 5.188512829189936, 4.329664949227744, 2.419947567421367, 1.282863758726567, 0.0), # 22
(14.182550708679697, 14.116311945587563, 12.104077046181986, 12.993677353395064, 10.385883252297091, 5.104166666666667, 5.774862801581538, 5.395538065843622, 5.658298909465021, 2.7561772953818022, 1.9541568753377396, 1.1374880506020426, 0.0, 14.175, 12.512368556622466, 9.770784376688697, 8.268531886145405, 11.316597818930042, 7.553753292181072, 5.774862801581538, 3.6458333333333335, 5.192941626148546, 4.331225784465023, 2.4208154092363974, 1.283301085962506, 0.0), # 23
(14.197417378247815, 14.113505864197531, 12.10336728395062, 12.99310104166667, 10.390900439373862, 5.104166666666667, 5.773777668845317, 5.393208333333334, 5.658026111111111, 2.755602716049383, 1.9540790684624023, 1.1373934156378602, 0.0, 14.175, 12.51132757201646, 9.77039534231201, 8.266808148148147, 11.316052222222222, 7.550491666666668, 5.773777668845317, 3.6458333333333335, 5.195450219686931, 4.331033680555557, 2.4206734567901242, 1.2830459876543212, 0.0), # 24
(14.211970122296213, 14.10797467992684, 12.101966163694561, 12.991960841049384, 10.39580728255487, 5.104166666666667, 5.771639231824418, 5.388631687242799, 5.657487139917696, 2.754471593507088, 1.9539247931994848, 1.1372065996037193, 0.0, 14.175, 12.509272595640908, 9.769623965997424, 8.263414780521263, 11.314974279835392, 7.544084362139919, 5.771639231824418, 3.6458333333333335, 5.197903641277435, 4.330653613683129, 2.4203932327389124, 1.2825431527206221, 0.0), # 25
(14.226207826667249, 14.099802892089624, 12.099892889803387, 12.990269714506173, 10.400603610526364, 5.104166666666667, 5.768480702816105, 5.381894547325103, 5.65668890946502, 2.7528027480566992, 1.9536954462318665, 1.136930163084896, 0.0, 14.175, 12.506231793933855, 9.768477231159332, 8.258408244170097, 11.31337781893004, 7.534652366255146, 5.768480702816105, 3.6458333333333335, 5.200301805263182, 4.330089904835392, 2.4199785779606775, 1.2818002629172387, 0.0), # 26
(14.240129377203292, 14.089075, 12.097166666666668, 12.988040625, 10.405289251974601, 5.104166666666667, 5.7643352941176484, 5.3730833333333345, 5.655638333333333, 2.7506150000000003, 1.9533924242424245, 1.1365666666666672, 0.0, 14.175, 12.502233333333336, 9.766962121212122, 8.251845, 11.311276666666666, 7.5223166666666685, 5.7643352941176484, 3.6458333333333335, 5.2026446259873005, 4.329346875000001, 2.4194333333333335, 1.280825, 0.0), # 27
(14.253733659746702, 14.075875502972108, 12.093806698673983, 12.985286535493827, 10.40986403558584, 5.104166666666667, 5.759236218026306, 5.362284465020577, 5.654342325102881, 2.7479271696387753, 1.9530171239140377, 1.1361186709343092, 0.0, 14.175, 12.4973053802774, 9.765085619570188, 8.243781508916324, 11.308684650205763, 7.507198251028808, 5.759236218026306, 3.6458333333333335, 5.20493201779292, 4.32842884516461, 2.418761339734797, 1.2796250457247373, 0.0), # 28
(14.26701956013985, 14.060288900320074, 12.089832190214908, 12.982020408950618, 10.41432779004634, 5.104166666666667, 5.753216686839346, 5.349584362139918, 5.652807798353909, 2.7447580772748066, 1.952570941929584, 1.1355887364730988, 0.0, 14.175, 12.491476101204084, 9.76285470964792, 8.234274231824418, 11.305615596707819, 7.489418106995886, 5.753216686839346, 3.6458333333333335, 5.20716389502317, 4.327340136316874, 2.4179664380429817, 1.2782080818472796, 0.0), # 29
(14.279985964225098, 14.042399691358026, 12.085262345679013, 12.978255208333334, 10.418680344042354, 5.104166666666667, 5.746309912854031, 5.335069444444444, 5.651041666666666, 2.7411265432098775, 1.952055274971942, 1.1349794238683129, 0.0, 14.175, 12.48477366255144, 9.760276374859709, 8.223379629629632, 11.302083333333332, 7.469097222222222, 5.746309912854031, 3.6458333333333335, 5.209340172021177, 4.326085069444446, 2.4170524691358026, 1.276581790123457, 0.0), # 30
(14.292631757844802, 14.022292375400093, 12.080116369455878, 12.97400389660494, 10.422921526260142, 5.104166666666667, 5.7385491083676285, 5.318826131687244, 5.649050843621399, 2.737051387745771, 1.9514715197239891, 1.1342932937052284, 0.0, 14.175, 12.477226230757509, 9.757357598619945, 8.211154163237312, 11.298101687242799, 7.4463565843621415, 5.7385491083676285, 3.6458333333333335, 5.211460763130071, 4.324667965534981, 2.416023273891176, 1.2747538523090995, 0.0), # 31
(14.304955826841338, 14.000051451760402, 12.07441346593507, 12.969279436728398, 10.427051165385956, 5.104166666666667, 5.7299674856774, 5.3009408436214, 5.646842242798354, 2.7325514311842714, 1.950821072868604, 1.1335329065691209, 0.0, 14.175, 12.468861972260328, 9.754105364343019, 8.197654293552812, 11.293684485596708, 7.421317181069961, 5.7299674856774, 3.6458333333333335, 5.213525582692978, 4.3230931455761334, 2.4148826931870144, 1.272731950160037, 0.0), # 32
(14.316957057057056, 13.975761419753086, 12.068172839506175, 12.964094791666666, 10.431069090106059, 5.104166666666667, 5.720598257080611, 5.2815, 5.644422777777778, 2.7276454938271613, 1.9501053310886647, 1.1327008230452675, 0.0, 14.175, 12.459709053497942, 9.750526655443322, 8.182936481481482, 11.288845555555556, 7.394100000000001, 5.720598257080611, 3.6458333333333335, 5.215534545053029, 4.321364930555556, 2.413634567901235, 1.2705237654320989, 0.0), # 33
(14.328634334334335, 13.949506778692271, 12.061413694558757, 12.958462924382715, 10.434975129106702, 5.104166666666667, 5.710474634874527, 5.260590020576132, 5.641799362139919, 2.7223523959762237, 1.9493256910670491, 1.1317996037189455, 0.0, 14.175, 12.449795640908398, 9.746628455335244, 8.16705718792867, 11.283598724279837, 7.3648260288065845, 5.710474634874527, 3.6458333333333335, 5.217487564553351, 4.319487641460906, 2.4122827389117516, 1.2681369798811157, 0.0), # 34
(14.339986544515531, 13.92137202789209, 12.054155235482398, 12.952396797839505, 10.438769111074146, 5.104166666666667, 5.699629831356412, 5.238297325102881, 5.638978909465021, 2.7166909579332423, 1.9484835494866362, 1.1308318091754308, 0.0, 14.175, 12.439149900929737, 9.74241774743318, 8.150072873799726, 11.277957818930043, 7.333616255144034, 5.699629831356412, 3.6458333333333335, 5.219384555537073, 4.317465599279836, 2.41083104709648, 1.2655792752629174, 0.0), # 35
(14.35101257344301, 13.891441666666665, 12.04641666666667, 12.945909375, 10.442450864694647, 5.104166666666667, 5.68809705882353, 5.214708333333334, 5.635968333333333, 2.7106800000000004, 1.9475803030303034, 1.1298000000000004, 0.0, 14.175, 12.427800000000001, 9.737901515151515, 8.13204, 11.271936666666665, 7.300591666666668, 5.68809705882353, 3.6458333333333335, 5.221225432347324, 4.315303125000001, 2.409283333333334, 1.2628583333333334, 0.0), # 36
(14.361711306959135, 13.859800194330132, 12.038217192501145, 12.939013618827161, 10.44602021865446, 5.104166666666667, 5.675909529573146, 5.189909465020577, 5.632774547325103, 2.7043383424782816, 1.9466173483809293, 1.1287067367779304, 0.0, 14.175, 12.415774104557233, 9.733086741904645, 8.113015027434844, 11.265549094650206, 7.265873251028808, 5.675909529573146, 3.6458333333333335, 5.22301010932723, 4.313004539609055, 2.407643438500229, 1.259981835848194, 0.0), # 37
(14.372081630906267, 13.826532110196618, 12.029576017375401, 12.931722492283953, 10.449477001639845, 5.104166666666667, 5.663100455902526, 5.1639871399176975, 5.629404465020576, 2.6976848056698683, 1.9455960822213911, 1.1275545800944982, 0.0, 14.175, 12.403100381039478, 9.727980411106955, 8.093054417009604, 11.258808930041152, 7.229581995884776, 5.663100455902526, 3.6458333333333335, 5.224738500819923, 4.3105741640946516, 2.40591520347508, 1.2569574645633292, 0.0), # 38
(14.382122431126781, 13.791721913580247, 12.020512345679016, 12.924048958333334, 10.452821042337057, 5.104166666666667, 5.649703050108934, 5.137027777777778, 5.625865000000001, 2.690738209876544, 1.9445179012345684, 1.1263460905349796, 0.0, 14.175, 12.389806995884772, 9.722589506172842, 8.07221462962963, 11.251730000000002, 7.191838888888889, 5.649703050108934, 3.6458333333333335, 5.226410521168528, 4.308016319444445, 2.4041024691358035, 1.253792901234568, 0.0), # 39
(14.39183259346303, 13.755454103795152, 12.011045381801555, 12.916005979938273, 10.45605216943235, 5.104166666666667, 5.635750524489632, 5.1091177983539104, 5.622163065843623, 2.6835173754000925, 1.943384202103338, 1.125083828684652, 0.0, 14.175, 12.375922115531171, 9.71692101051669, 8.050552126200277, 11.244326131687245, 7.1527649176954755, 5.635750524489632, 3.6458333333333335, 5.228026084716175, 4.305335326646092, 2.4022090763603114, 1.2504958276177414, 0.0), # 40
(14.40121100375738, 13.717813180155463, 12.001194330132604, 12.90760652006173, 10.459170211611989, 5.104166666666667, 5.621276091341887, 5.080343621399178, 5.618305576131687, 2.676041122542296, 1.9421963815105796, 1.1237703551287916, 0.0, 14.175, 12.361473906416705, 9.710981907552897, 8.028123367626886, 11.236611152263373, 7.112481069958849, 5.621276091341887, 3.6458333333333335, 5.229585105805994, 4.302535506687244, 2.400238866026521, 1.2470739254686787, 0.0), # 41
(14.410256547852201, 13.678883641975311, 11.990978395061731, 12.89886354166667, 10.462174997562222, 5.104166666666667, 5.6063129629629636, 5.050791666666668, 5.614299444444446, 2.668328271604939, 1.9409558361391697, 1.122408230452675, 0.0, 14.175, 12.346490534979424, 9.704779180695848, 8.004984814814815, 11.228598888888891, 7.071108333333335, 5.6063129629629636, 3.6458333333333335, 5.231087498781111, 4.299621180555557, 2.3981956790123466, 1.2435348765432102, 0.0), # 42
(14.418968111589852, 13.638749988568819, 11.980416780978512, 12.889790007716051, 10.46506635596931, 5.104166666666667, 5.5908943516501255, 5.020548353909466, 5.61015158436214, 2.660397642889804, 1.9396639626719878, 1.1210000152415793, 0.0, 14.175, 12.331000167657372, 9.698319813359937, 7.981192928669412, 11.22030316872428, 7.0287676954732525, 5.5908943516501255, 3.6458333333333335, 5.232533177984655, 4.296596669238685, 2.3960833561957027, 1.2398863625971654, 0.0), # 43
(14.427344580812699, 13.597496719250115, 11.969528692272522, 12.880398881172843, 10.467844115519508, 5.104166666666667, 5.575053469700638, 4.98970010288066, 5.605868909465021, 2.652268056698675, 1.938322157791911, 1.1195482700807806, 0.0, 14.175, 12.315030970888586, 9.691610788959554, 7.9568041700960235, 11.211737818930041, 6.985580144032924, 5.575053469700638, 3.6458333333333335, 5.233922057759754, 4.293466293724282, 2.3939057384545044, 1.2361360653863744, 0.0), # 44
(14.435384841363105, 13.555208333333335, 11.958333333333336, 12.870703125000002, 10.470508104899077, 5.104166666666667, 5.558823529411765, 4.958333333333334, 5.601458333333333, 2.6439583333333343, 1.9369318181818187, 1.1180555555555556, 0.0, 14.175, 12.29861111111111, 9.684659090909092, 7.931875000000002, 11.202916666666667, 6.941666666666667, 5.558823529411765, 3.6458333333333335, 5.235254052449538, 4.290234375000002, 2.391666666666667, 1.232291666666667, 0.0), # 45
(14.443087779083434, 13.511969330132603, 11.946849908550526, 12.860715702160494, 10.47305815279427, 5.104166666666667, 5.542237743080772, 4.926534465020577, 5.596926769547324, 2.635487293095565, 1.9354943405245877, 1.1165244322511814, 0.0, 14.175, 12.281768754762993, 9.677471702622938, 7.906461879286693, 11.193853539094649, 6.897148251028808, 5.542237743080772, 3.6458333333333335, 5.236529076397135, 4.286905234053499, 2.3893699817101055, 1.228360848193873, 0.0), # 46
(14.45045227981605, 13.46786420896205, 11.935097622313673, 12.850449575617287, 10.475494087891343, 5.104166666666667, 5.525329323004923, 4.894389917695474, 5.592281131687244, 2.6268737562871523, 1.9340111215030973, 1.1149574607529342, 0.0, 14.175, 12.264532068282275, 9.670055607515485, 7.880621268861455, 11.184562263374488, 6.852145884773663, 5.525329323004923, 3.6458333333333335, 5.237747043945672, 4.283483191872429, 2.387019524462735, 1.2243512917238228, 0.0), # 47
(14.457477229403315, 13.422977469135803, 11.923095679012349, 12.839917708333335, 10.477815738876558, 5.104166666666667, 5.508131481481482, 4.861986111111112, 5.587528333333333, 2.618136543209877, 1.9324835578002246, 1.1133572016460909, 0.0, 14.175, 12.246929218106997, 9.662417789001124, 7.854409629629629, 11.175056666666666, 6.806780555555557, 5.508131481481482, 3.6458333333333335, 5.238907869438279, 4.279972569444446, 2.38461913580247, 1.2202706790123459, 0.0), # 48
(14.464161513687602, 13.377393609967992, 11.910863283036125, 12.829133063271607, 10.480022934436168, 5.104166666666667, 5.490677430807714, 4.829409465020577, 5.582675288065844, 2.6092944741655244, 1.930913046098849, 1.1117262155159278, 0.0, 14.175, 12.228988370675204, 9.654565230494246, 7.827883422496572, 11.165350576131688, 6.761173251028807, 5.490677430807714, 3.6458333333333335, 5.240011467218084, 4.276377687757203, 2.382172656607225, 1.2161266918152722, 0.0), # 49
(14.470504018511264, 13.33119713077275, 11.89841963877458, 12.81810860339506, 10.482115503256427, 5.104166666666667, 5.473000383280885, 4.796746399176955, 5.57772890946502, 2.6003663694558763, 1.9293009830818477, 1.1100670629477218, 0.0, 14.175, 12.210737692424937, 9.646504915409238, 7.8010991083676275, 11.15545781893004, 6.715444958847738, 5.473000383280885, 3.6458333333333335, 5.2410577516282135, 4.272702867798355, 2.379683927754916, 1.211927011888432, 0.0), # 50
(14.476503629716676, 13.284472530864198, 11.885783950617286, 12.806857291666669, 10.484093274023598, 5.104166666666667, 5.455133551198258, 4.764083333333335, 5.572696111111112, 2.5913710493827167, 1.9276487654320995, 1.1083823045267494, 0.0, 14.175, 12.192205349794241, 9.638243827160496, 7.774113148148149, 11.145392222222224, 6.669716666666668, 5.455133551198258, 3.6458333333333335, 5.242046637011799, 4.268952430555557, 2.377156790123457, 1.2076793209876546, 0.0), # 51
(14.482159233146191, 13.237304309556471, 11.87297542295382, 12.795392091049385, 10.485956075423934, 5.104166666666667, 5.437110146857097, 4.731506687242798, 5.567583806584363, 2.582327334247829, 1.9259577898324816, 1.1066745008382872, 0.0, 14.175, 12.173419509221157, 9.629788949162407, 7.746982002743485, 11.135167613168726, 6.624109362139918, 5.437110146857097, 3.6458333333333335, 5.242978037711967, 4.265130697016462, 2.3745950845907644, 1.2033913008687704, 0.0), # 52
(14.487469714642183, 13.189776966163697, 11.860013260173757, 12.783725964506175, 10.487703736143693, 5.104166666666667, 5.418963382554669, 4.699102880658437, 5.5623989094650215, 2.573254044352996, 1.9242294529658732, 1.104946212467612, 0.0, 14.175, 12.15440833714373, 9.621147264829364, 7.719762133058986, 11.124797818930043, 6.578744032921811, 5.418963382554669, 3.6458333333333335, 5.243851868071847, 4.261241988168726, 2.3720026520347517, 1.199070633287609, 0.0), # 53
(14.492433960047004, 13.141975000000002, 11.846916666666667, 12.771871875000002, 10.489336084869135, 5.104166666666667, 5.400726470588236, 4.6669583333333335, 5.557148333333334, 2.5641700000000007, 1.9224651515151516, 1.1032000000000002, 0.0, 14.175, 12.1352, 9.612325757575757, 7.69251, 11.114296666666668, 6.533741666666667, 5.400726470588236, 3.6458333333333335, 5.244668042434568, 4.257290625000001, 2.369383333333334, 1.1947250000000003, 0.0), # 54
(14.497050855203032, 13.093982910379516, 11.833704846822133, 12.759842785493827, 10.490852950286511, 5.104166666666667, 5.382432623255064, 4.6351594650205765, 5.551838991769547, 2.555094021490627, 1.9206662821631961, 1.101438424020729, 0.0, 14.175, 12.115822664228014, 9.603331410815981, 7.66528206447188, 11.103677983539095, 6.4892232510288075, 5.382432623255064, 3.6458333333333335, 5.2454264751432556, 4.253280928497944, 2.3667409693644266, 1.1903620827617745, 0.0), # 55
(14.501319285952622, 13.045885196616371, 11.820397005029724, 12.74765165895062, 10.492254161082082, 5.104166666666667, 5.3641150528524175, 4.603792695473252, 5.5464777983539095, 2.5460449291266585, 1.918834241592884, 1.099664045115074, 0.0, 14.175, 12.096304496265812, 9.59417120796442, 7.638134787379974, 11.092955596707819, 6.445309773662553, 5.3641150528524175, 3.6458333333333335, 5.246127080541041, 4.249217219650207, 2.3640794010059447, 1.1859895633287612, 0.0), # 56
(14.505238138138138, 12.997766358024693, 11.807012345679016, 12.735311458333335, 10.493539545942102, 5.104166666666667, 5.34580697167756, 4.572944444444445, 5.541071666666667, 2.5370415432098774, 1.9169704264870937, 1.097879423868313, 0.0, 14.175, 12.076673662551439, 9.584852132435467, 7.61112462962963, 11.082143333333335, 6.402122222222224, 5.34580697167756, 3.6458333333333335, 5.246769772971051, 4.245103819444446, 2.3614024691358035, 1.1816151234567904, 0.0), # 57
(14.508806297601952, 12.949710893918612, 11.79357007315958, 12.72283514660494, 10.494708933552829, 5.104166666666667, 5.3275415920277585, 4.5427011316872425, 5.535627510288066, 2.5281026840420675, 1.9150762335287033, 1.096087120865722, 0.0, 14.175, 12.05695832952294, 9.575381167643515, 7.584308052126201, 11.071255020576132, 6.35978158436214, 5.3275415920277585, 3.6458333333333335, 5.2473544667764145, 4.240945048868314, 2.3587140146319165, 1.1772464449016922, 0.0), # 58
(14.51202265018642, 12.901803303612255, 11.780089391860999, 12.710235686728396, 10.495762152600523, 5.104166666666667, 5.309352126200275, 4.513149176954733, 5.530152242798355, 2.5192471719250125, 1.9131530594005905, 1.0942896966925775, 0.0, 14.175, 12.037186663618352, 9.565765297002951, 7.557741515775036, 11.06030448559671, 6.3184088477366265, 5.309352126200275, 3.6458333333333335, 5.247881076300262, 4.2367452289094665, 2.3560178783722, 1.172891209419296, 0.0), # 59
(14.51488608173391, 12.854128086419754, 11.76658950617284, 12.697526041666668, 10.496699031771435, 5.104166666666667, 5.291271786492374, 4.484375000000001, 5.524652777777779, 2.5104938271604946, 1.9112023007856345, 1.0924897119341568, 0.0, 14.175, 12.017386831275722, 9.556011503928172, 7.5314814814814826, 11.049305555555557, 6.278125000000001, 5.291271786492374, 3.6458333333333335, 5.248349515885717, 4.232508680555557, 2.353317901234568, 1.1685570987654323, 0.0), # 60
(14.517395478086781, 12.806769741655238, 11.753089620484685, 12.684719174382717, 10.497519399751823, 5.104166666666667, 5.273333785201324, 4.4564650205761325, 5.519136028806585, 2.501861470050298, 1.9092253543667126, 1.0906897271757356, 0.0, 14.175, 11.997586998933091, 9.546126771833563, 7.5055844101508935, 11.03827205761317, 6.2390510288065855, 5.273333785201324, 3.6458333333333335, 5.248759699875912, 4.22823972479424, 2.350617924096937, 1.1642517946959308, 0.0), # 61
(14.519549725087407, 12.759812768632832, 11.739608939186102, 12.671828047839508, 10.498223085227952, 5.104166666666667, 5.255571334624385, 4.429505658436215, 5.513608909465021, 2.4933689208962058, 1.9072236168267036, 1.0888923030025914, 0.0, 14.175, 11.977815333028504, 9.536118084133516, 7.4801067626886155, 11.027217818930042, 6.201307921810701, 5.255571334624385, 3.6458333333333335, 5.249111542613976, 4.2239426826131705, 2.3479217878372207, 1.1599829789666212, 0.0), # 62
(14.521347708578144, 12.713341666666667, 11.72616666666667, 12.658865625, 10.498809916886067, 5.104166666666667, 5.238017647058824, 4.4035833333333345, 5.508078333333334, 2.4850350000000003, 1.9051984848484853, 1.0871000000000002, 0.0, 14.175, 11.9581, 9.525992424242425, 7.455105, 11.016156666666667, 6.165016666666668, 5.238017647058824, 3.6458333333333335, 5.249404958443034, 4.219621875000001, 2.345233333333334, 1.1557583333333337, 0.0), # 63
(14.522788314401359, 12.667440935070873, 11.712782007315958, 12.645844868827162, 10.499279723412432, 5.104166666666667, 5.220705934801905, 4.378784465020577, 5.50255121399177, 2.4768785276634664, 1.9031513551149353, 1.0853153787532392, 0.0, 14.175, 11.938469166285628, 9.515756775574676, 7.430635582990398, 11.00510242798354, 6.130298251028808, 5.220705934801905, 3.6458333333333335, 5.249639861706216, 4.215281622942388, 2.342556401463192, 1.151585539551898, 0.0), # 64
(14.523870428399414, 12.62219507315958, 11.69947416552355, 12.63277874228395, 10.499632333493302, 5.104166666666667, 5.2036694101508925, 4.35519547325103, 5.497034465020577, 2.4689183241883863, 1.9010836243089335, 1.0835409998475842, 0.0, 14.175, 11.918950998323425, 9.505418121544666, 7.406754972565158, 10.994068930041154, 6.097273662551442, 5.2036694101508925, 3.6458333333333335, 5.249816166746651, 4.2109262474279845, 2.3398948331047102, 1.1474722793781438, 0.0), # 65
(14.524592936414676, 12.577688580246916, 11.686262345679015, 12.619680208333333, 10.499867575814935, 5.104166666666667, 5.1869412854030505, 4.332902777777779, 5.491535000000001, 2.4611732098765438, 1.898996689113356, 1.0817794238683132, 0.0, 14.175, 11.899573662551441, 9.49498344556678, 7.38351962962963, 10.983070000000001, 6.06606388888889, 5.1869412854030505, 3.6458333333333335, 5.249933787907468, 4.206560069444445, 2.337252469135803, 1.1434262345679016, 0.0), # 66
(14.524954724289511, 12.534005955647004, 11.673165752171926, 12.606562229938273, 10.499985279063587, 5.104166666666667, 5.1705547728556445, 4.311992798353911, 5.486059732510288, 2.453662005029722, 1.8968919462110825, 1.0800332114007012, 0.0, 14.175, 11.88036532540771, 9.484459731055413, 7.360986015089164, 10.972119465020576, 6.036789917695475, 5.1705547728556445, 3.6458333333333335, 5.2499926395317935, 4.202187409979425, 2.3346331504343856, 1.1394550868770006, 0.0), # 67
(14.524708260273156, 12.491002420461081, 11.660140274919984, 12.593323827495976, 10.499886091610856, 5.104071942793273, 5.154460636380753, 4.292367245846671, 5.480574329370524, 2.446367154576509, 1.894733397326088, 1.078295169221637, 0.0, 14.174825210048013, 11.861246861438005, 9.47366698663044, 7.339101463729525, 10.961148658741047, 6.009314144185339, 5.154460636380753, 3.6457656734237665, 5.249943045805428, 4.197774609165326, 2.3320280549839967, 1.135545674587371, 0.0), # 68
(14.522398389694043, 12.44736508363202, 11.646819830246914, 12.579297690217391, 10.498983297022512, 5.1033231138545965, 5.13818772694263, 4.272974279835392, 5.474838991769548, 2.439082236746551, 1.8923013290802768, 1.0765088802252547, 0.0, 14.17344039351852, 11.8415976824778, 9.461506645401384, 7.317246710239651, 10.949677983539097, 5.982163991769549, 5.13818772694263, 3.6452307956104257, 5.249491648511256, 4.193099230072464, 2.329363966049383, 1.1315786439665476, 0.0), # 69
(14.517840102582454, 12.402893656798973, 11.633146504915409, 12.564391480475042, 10.49719935985368, 5.101848358989992, 5.121662094192959, 4.253638926992837, 5.468821349641823, 2.4317718335619576, 1.8895680735227522, 1.0746659888174948, 0.0, 14.170705268347055, 11.82132587699244, 9.447840367613761, 7.295315500685872, 10.937642699283646, 5.955094497789972, 5.121662094192959, 3.6441773992785653, 5.24859967992684, 4.188130493491681, 2.326629300983082, 1.127535786981725, 0.0), # 70
(14.511097524900102, 12.357614716359132, 11.619125100022863, 12.548627178945251, 10.49455687350386, 5.0996715769953775, 5.104891161677292, 4.234367588782199, 5.462530365035819, 2.4244361257699243, 1.8865437198495683, 1.072767842674817, 0.0, 14.166655842764062, 11.800446269422984, 9.43271859924784, 7.273308377309771, 10.925060730071637, 5.928114624295079, 5.104891161677292, 3.642622554996698, 5.24727843675193, 4.182875726315085, 2.323825020004573, 1.1234195196690122, 0.0), # 71
(14.502234782608697, 12.311554838709677, 11.604760416666666, 12.532026766304348, 10.49107843137255, 5.096816666666667, 5.087882352941177, 4.215166666666667, 5.4559750000000005, 2.4170752941176477, 1.8832383572567788, 1.0708157894736845, 0.0, 14.161328125, 11.778973684210527, 9.416191786283894, 7.251225882352942, 10.911950000000001, 5.901233333333334, 5.087882352941177, 3.6405833333333337, 5.245539215686275, 4.177342255434784, 2.3209520833333337, 1.1192322580645162, 0.0), # 72
(14.491316001669949, 12.264740600247798, 11.590057255944217, 12.514612223228664, 10.486786626859248, 5.0933075267997765, 5.070643091530164, 4.196042562109436, 5.4491642165828384, 2.409689519352323, 1.8796620749404376, 1.0688111768905575, 0.0, 14.154758123285324, 11.75692294579613, 9.398310374702186, 7.229068558056968, 10.898328433165677, 5.8744595869532095, 5.070643091530164, 3.638076804856983, 5.243393313429624, 4.171537407742889, 2.3180114511888434, 1.1149764182043456, 0.0), # 73
(14.478405308045566, 12.21719857737068, 11.575020418952905, 12.496405530394526, 10.481704053363458, 5.089168056190623, 5.053180800989806, 4.177001676573693, 5.4421069768328, 2.402278982221147, 1.8758249620965999, 1.0667553526018982, 0.0, 14.146981845850483, 11.734308878620878, 9.379124810482999, 7.20683694666344, 10.8842139536656, 5.84780234720317, 5.053180800989806, 3.635120040136159, 5.240852026681729, 4.165468510131509, 2.315004083790581, 1.1106544161246077, 0.0), # 74
(14.463566827697262, 12.168955346475506, 11.559654706790123, 12.477428668478263, 10.475853304284678, 5.084422153635118, 5.03550290486565, 4.158050411522635, 5.434812242798353, 2.394843863471315, 1.8717371079213185, 1.0646496642841674, 0.0, 14.138035300925928, 11.711146307125839, 9.358685539606592, 7.184531590413944, 10.869624485596706, 5.821270576131688, 5.03550290486565, 3.63173010973937, 5.237926652142339, 4.159142889492755, 2.311930941358025, 1.10626866786141, 0.0), # 75
(14.44686468658675, 12.12003748395947, 11.543964920553272, 12.457703618156202, 10.469256973022405, 5.079093717929179, 5.017616826703247, 4.139195168419449, 5.427288976527969, 2.3873843438500235, 1.8674086016106486, 1.0624954596138265, 0.0, 14.127954496742113, 11.68745005575209, 9.337043008053241, 7.162153031550069, 10.854577953055937, 5.794873235787229, 5.017616826703247, 3.6279240842351275, 5.234628486511203, 4.152567872718735, 2.3087929841106543, 1.101821589450861, 0.0), # 76
(14.428363010675731, 12.070471566219748, 11.527955861339734, 12.43725236010467, 10.461937652976141, 5.07320664786872, 4.9995299900481465, 4.120442348727329, 5.4195461400701115, 2.3799006041044684, 1.8628495323606438, 1.0602940862673376, 0.0, 14.116775441529496, 11.663234948940712, 9.314247661803218, 7.139701812313404, 10.839092280140223, 5.768619288218261, 4.9995299900481465, 3.623719034191943, 5.230968826488071, 4.145750786701558, 2.305591172267947, 1.0973155969290682, 0.0), # 77
(14.408125925925928, 12.020284169653527, 11.511632330246915, 12.416096875000001, 10.45391793754539, 5.066784842249657, 4.981249818445898, 4.101798353909466, 5.41159269547325, 2.372392824981845, 1.8580699893673582, 1.0580468919211612, 0.0, 14.10453414351852, 11.638515811132772, 9.29034994683679, 7.1171784749455345, 10.8231853909465, 5.742517695473253, 4.981249818445898, 3.6191320301783265, 5.226958968772695, 4.138698958333334, 2.3023264660493834, 1.092753106332139, 0.0), # 78
(14.386217558299041, 11.969501870657995, 11.494999128372202, 12.394259143518521, 10.445220420129644, 5.0598521998679065, 4.962783735442051, 4.0832695854290515, 5.403437604785855, 2.3648611872293506, 1.8530800618268455, 1.0557552242517592, 0.0, 14.091266610939643, 11.613307466769347, 9.265400309134227, 7.094583561688051, 10.80687520957171, 5.716577419600672, 4.962783735442051, 3.61418014276279, 5.222610210064822, 4.131419714506174, 2.2989998256744406, 1.0881365336961817, 0.0), # 79
(14.362702033756786, 11.918151245630337, 11.478061056812987, 12.371761146336556, 10.435867694128408, 5.052432619519382, 4.9441391645821575, 4.064862444749277, 5.395089830056394, 2.35730587159418, 1.847889838935161, 1.0534204309355928, 0.0, 14.07700885202332, 11.587624740291517, 9.239449194675805, 7.071917614782539, 10.790179660112788, 5.690807422648988, 4.9441391645821575, 3.6088804425138443, 5.217933847064204, 4.123920382112186, 2.2956122113625974, 1.0834682950573036, 0.0), # 80
(14.337643478260873, 11.866258870967743, 11.460822916666668, 12.348624864130437, 10.425882352941176, 5.04455, 4.925323529411765, 4.046583333333334, 5.386558333333333, 2.34972705882353, 1.8425094098883579, 1.0510438596491232, 0.0, 14.061796875, 11.561482456140352, 9.212547049441788, 7.049181176470589, 10.773116666666667, 5.665216666666669, 4.925323529411765, 3.60325, 5.212941176470588, 4.11620828804348, 2.2921645833333337, 1.0787508064516131, 0.0), # 81
(14.311106017773009, 11.813851323067393, 11.443289509030638, 12.32487227757649, 10.415286989967456, 5.036228240105676, 4.906344253476426, 4.0284386526444145, 5.3778520766651425, 2.342124929664596, 1.83694886388249, 1.048626858068812, 0.0, 14.045666688100141, 11.53489543875693, 9.18474431941245, 7.026374788993786, 10.755704153330285, 5.63981411370218, 4.906344253476426, 3.5973058857897686, 5.207643494983728, 4.1082907591921645, 2.2886579018061277, 1.0739864839152178, 0.0), # 82
(14.283153778254908, 11.760955178326475, 11.425465635002288, 12.300525367351046, 10.40410419860674, 5.027491238632323, 4.887208760321688, 4.01043480414571, 5.368980022100289, 2.3344996648645746, 1.8312182901136123, 1.0461707738711208, 0.0, 14.028654299554185, 11.507878512582325, 9.156091450568061, 7.0034989945937225, 10.737960044200578, 5.614608725803994, 4.887208760321688, 3.5910651704516594, 5.20205209930337, 4.1001751224503495, 2.2850931270004575, 1.0691777434842251, 0.0), # 83
(14.253850885668278, 11.707597013142175, 11.407356095679013, 12.275606114130436, 10.392356572258533, 5.0183628943758585, 4.867924473493101, 3.9925781893004118, 5.359951131687243, 2.3268514451706617, 1.825327777777778, 1.0436769547325107, 0.0, 14.010795717592593, 11.480446502057614, 9.12663888888889, 6.980554335511984, 10.719902263374486, 5.589609465020577, 4.867924473493101, 3.5845449245541845, 5.196178286129267, 4.091868704710146, 2.281471219135803, 1.0643270011947434, 0.0), # 84
(14.223261465974833, 11.653803403911677, 11.388965692158209, 12.250136498590983, 10.380066704322333, 5.008867106132196, 4.8484988165362175, 3.974875209571713, 5.35077436747447, 2.3191804513300527, 1.8192874160710422, 1.041146748329443, 0.0, 13.992126950445819, 11.452614231623869, 9.09643708035521, 6.957541353990157, 10.70154873494894, 5.564825293400398, 4.8484988165362175, 3.577762218665854, 5.190033352161167, 4.083378832863662, 2.2777931384316417, 1.05943667308288, 0.0), # 85
(14.191449645136279, 11.59960092703217, 11.370299225537268, 12.224138501409021, 10.367257188197637, 4.999027772697253, 4.828939212996585, 3.9573322664228017, 5.341458691510441, 2.311486864089944, 1.8131072941894584, 1.0385815023383795, 0.0, 13.97268400634431, 11.424396525722173, 9.065536470947292, 6.934460592269831, 10.682917383020882, 5.540265172991923, 4.828939212996585, 3.57073412335518, 5.183628594098819, 4.074712833803008, 2.274059845107454, 1.0545091751847429, 0.0), # 86
(14.15847954911433, 11.545016158900838, 11.35136149691358, 12.19763410326087, 10.353950617283953, 4.988868792866941, 4.809253086419753, 3.939955761316873, 5.332013065843622, 2.3037708641975314, 1.8067975013290805, 1.035982564435781, 0.0, 13.95250289351852, 11.39580820879359, 9.033987506645403, 6.9113125925925925, 10.664026131687244, 5.515938065843622, 4.809253086419753, 3.563477709190672, 5.1769753086419765, 4.065878034420291, 2.2702722993827162, 1.0495469235364399, 0.0), # 87
(14.124415303870702, 11.490075675914863, 11.332157307384547, 12.170645284822868, 10.340169584980769, 4.97841406543718, 4.789447860351274, 3.9227520957171165, 5.322446452522482, 2.296032632400011, 1.8003681266859632, 1.0333512822981095, 0.0, 13.931619620198905, 11.366864105279202, 9.001840633429817, 6.888097897200032, 10.644892905044964, 5.491852934003963, 4.789447860351274, 3.556010046740843, 5.1700847924903846, 4.056881761607624, 2.2664314614769094, 1.0445523341740786, 0.0), # 88
(14.089321035367092, 11.434806054471437, 11.312691458047555, 12.143194026771337, 10.325936684687594, 4.967687489203883, 4.769530958336696, 3.905727671086725, 5.312767813595489, 2.2882723494445796, 1.7938292594561607, 1.030689003601826, 0.0, 13.910070194615912, 11.337579039620083, 8.969146297280803, 6.864817048333737, 10.625535627190978, 5.4680187395214155, 4.769530958336696, 3.548348206574202, 5.162968342343797, 4.047731342257113, 2.2625382916095114, 1.0395278231337672, 0.0), # 89
(14.053260869565218, 11.379233870967743, 11.292968750000002, 12.115302309782612, 10.311274509803923, 4.956712962962964, 4.749509803921569, 3.8888888888888893, 5.302986111111112, 2.280490196078432, 1.787190988835726, 1.027997076023392, 0.0, 13.887890625, 11.30796783625731, 8.93595494417863, 6.841470588235294, 10.605972222222224, 5.4444444444444455, 4.749509803921569, 3.54050925925926, 5.155637254901961, 4.0384341032608715, 2.2585937500000006, 1.0344758064516133, 0.0), # 90
(14.016298932426789, 11.323385701800964, 11.272993984339278, 12.086992114533015, 10.296205653729254, 4.945514385510339, 4.729391820651443, 3.8722421505868017, 5.293110307117818, 2.2726863530487647, 1.7804634040207143, 1.025276847239269, 0.0, 13.865116919581618, 11.278045319631957, 8.902317020103572, 6.818059059146293, 10.586220614235636, 5.4211390108215225, 4.729391820651443, 3.5325102753645283, 5.148102826864627, 4.0289973715110055, 2.254598796867856, 1.0293987001637241, 0.0), # 91
(13.978499349913523, 11.267288123368292, 11.252771962162782, 12.058285421698875, 10.280752709863094, 4.934115655641925, 4.709184432071869, 3.8557938576436523, 5.2831493636640765, 2.2648610011027737, 1.7736565942071794, 1.0225296649259181, 0.0, 13.841785086591221, 11.247826314185097, 8.868282971035896, 6.79458300330832, 10.566298727328153, 5.398111400701113, 4.709184432071869, 3.524368325458518, 5.140376354931547, 4.019428473899626, 2.2505543924325564, 1.0242989203062085, 0.0), # 92
(13.939926247987117, 11.210967712066907, 11.232307484567903, 12.029204211956525, 10.264938271604938, 4.9225406721536356, 4.688895061728395, 3.839550411522634, 5.273112242798354, 2.2570143209876545, 1.7667806485911755, 1.019756876759801, 0.0, 13.81793113425926, 11.217325644357809, 8.833903242955877, 6.771042962962962, 10.546224485596708, 5.375370576131688, 4.688895061728395, 3.5161004801097393, 5.132469135802469, 4.009734737318842, 2.246461496913581, 1.0191788829151736, 0.0), # 93
(13.900643752609293, 11.154451044293994, 11.211605352652038, 11.999770465982289, 10.248784932354287, 4.910813333841387, 4.6685311331665735, 3.8235182136869392, 5.263007906569121, 2.2491464934506045, 1.7598456563687561, 1.016959830417379, 0.0, 13.793591070816188, 11.186558134591166, 8.79922828184378, 6.747439480351812, 10.526015813138242, 5.3529254991617155, 4.6685311331665735, 3.5077238098867047, 5.124392466177143, 3.9999234886607637, 2.2423210705304077, 1.014041004026727, 0.0), # 94
(13.860715989741754, 11.097764696446747, 11.190670367512576, 11.970006164452498, 10.232315285510639, 4.898957539501094, 4.648100069931951, 3.807703665599757, 5.252845317024844, 2.241257699238818, 1.752861706735976, 1.014139873575113, 0.0, 13.768800904492457, 11.155538609326241, 8.764308533679879, 6.723773097716453, 10.505690634049689, 5.33078513183966, 4.648100069931951, 3.499255385357924, 5.1161576427553195, 3.9900020548175, 2.2381340735025153, 1.0088876996769771, 0.0), # 95
(13.820207085346219, 11.040935244922345, 11.169507330246915, 11.93993328804348, 10.215551924473493, 4.88699718792867, 4.62760929557008, 3.7921131687242804, 5.242633436213992, 2.2333481190994924, 1.7458388888888892, 1.0112983539094653, 0.0, 13.74359664351852, 11.124281893004117, 8.729194444444445, 6.700044357298475, 10.485266872427983, 5.3089584362139925, 4.62760929557008, 3.490712277091907, 5.1077759622367465, 3.9799777626811608, 2.2339014660493834, 1.0037213859020315, 0.0), # 96
(13.779181165384388, 10.983989266117973, 11.148121041952448, 11.909573817431562, 10.198517442642354, 4.8749561779200326, 4.60706623362651, 3.7767531245237014, 5.2323812261850335, 2.2254179337798226, 1.7387872920235496, 1.0084366190968967, 0.0, 13.718014296124831, 11.09280281006586, 8.693936460117747, 6.676253801339467, 10.464762452370067, 5.287454374333182, 4.60706623362651, 3.482111555657166, 5.099258721321177, 3.969857939143855, 2.2296242083904896, 0.9985444787379977, 0.0), # 97
(13.737702355817978, 10.926953336430817, 11.126516303726566, 11.878949733293078, 10.181234433416716, 4.862858408271099, 4.58647830764679, 3.7616299344612103, 5.222097648986434, 2.2174673240270053, 1.7317170053360116, 1.0055560168138682, 0.0, 13.69208987054184, 11.06111618495255, 8.658585026680058, 6.652401972081014, 10.444195297972868, 5.266281908245695, 4.58647830764679, 3.4734702916222133, 5.090617216708358, 3.9596499110976935, 2.2253032607453136, 0.9933593942209834, 0.0), # 98
(13.695834782608697, 10.869854032258065, 11.10469791666667, 11.848083016304349, 10.163725490196079, 4.850727777777779, 4.5658529411764714, 3.7467500000000005, 5.211791666666667, 2.2094964705882356, 1.724638118022329, 1.0026578947368423, 0.0, 13.665859375000002, 11.029236842105265, 8.623190590111644, 6.628489411764706, 10.423583333333333, 5.245450000000001, 4.5658529411764714, 3.4648055555555564, 5.081862745098039, 3.949361005434784, 2.220939583333334, 0.988168548387097, 0.0), # 99
(13.653642571718258, 10.8127179299969, 11.082670681870143, 11.816995647141708, 10.146013206379946, 4.8385881852359915, 4.545197557761102, 3.732119722603262, 5.201472241274196, 2.201505554210711, 1.717560719278556, 0.9997436005422796, 0.0, 13.639358817729768, 10.997179605965075, 8.58780359639278, 6.6045166626321326, 10.402944482548392, 5.224967611644567, 4.545197557761102, 3.456134418025708, 5.073006603189973, 3.938998549047237, 2.2165341363740287, 0.9829743572724456, 0.0), # 100
(13.611189849108369, 10.755571606044516, 11.060439400434387, 11.785709606481484, 10.128120175367815, 4.82646352944165, 4.524519580946234, 3.7177455037341867, 5.191148334857491, 2.1934947556416264, 1.7104948983007466, 0.9968144819066413, 0.0, 13.612624206961591, 10.964959300973053, 8.552474491503732, 6.580484266924878, 10.382296669714982, 5.204843705227861, 4.524519580946234, 3.4474739496011786, 5.064060087683908, 3.928569868827162, 2.2120878800868775, 0.977779236913138, 0.0), # 101
(13.568540740740744, 10.698441636798089, 11.038008873456791, 11.754246875000002, 10.110068990559187, 4.814377709190674, 4.503826434277415, 3.7036337448559675, 5.180828909465021, 2.1854642556281783, 1.7034507442849551, 0.9938718865063897, 0.0, 13.585691550925928, 10.932590751570284, 8.517253721424776, 6.556392766884533, 10.361657818930041, 5.185087242798355, 4.503826434277415, 3.438841220850481, 5.055034495279593, 3.918082291666668, 2.207601774691358, 0.972585603345281, 0.0), # 102
(13.525759372577088, 10.641354598654807, 11.015383902034753, 11.722629433373593, 10.09188224535356, 4.802354623278973, 4.483125541300197, 3.689790847431795, 5.170522927145252, 2.1774142349175616, 1.696438346427236, 0.9909171620179854, 0.0, 13.558596857853223, 10.900088782197837, 8.482191732136178, 6.532242704752683, 10.341045854290504, 5.1657071864045125, 4.483125541300197, 3.4302533023421233, 5.04594112267678, 3.907543144457865, 2.2030767804069504, 0.9673958726049827, 0.0), # 103
(13.482909870579116, 10.58433706801186, 10.992569287265662, 11.690879262278584, 10.073582533150434, 4.790418170502465, 4.462424325560129, 3.6762232129248593, 5.160239349946655, 2.1693448742569736, 1.689467793923642, 0.9879516561178898, 0.0, 13.53137613597394, 10.867468217296787, 8.447338969618208, 6.50803462277092, 10.32047869989331, 5.146712498094804, 4.462424325560129, 3.421727264644618, 5.036791266575217, 3.896959754092862, 2.1985138574531327, 0.9622124607283511, 0.0), # 104
(13.440056360708535, 10.527415621266428, 10.969569830246915, 11.659018342391304, 10.05519244734931, 4.778592249657065, 4.441730210602761, 3.662937242798354, 5.1499871399176955, 2.1612563543936103, 1.682549175970229, 0.9849767164825647, 0.0, 13.50406539351852, 10.83474388130821, 8.412745879851144, 6.48376906318083, 10.299974279835391, 5.128112139917696, 4.441730210602761, 3.4132801783264752, 5.027596223674655, 3.886339447463769, 2.1939139660493834, 0.9570377837514936, 0.0), # 105
(13.39726296892706, 10.470616834815702, 10.946390332075904, 11.627068654388085, 10.036734581349688, 4.766900759538689, 4.4210506199736415, 3.6499393385154706, 5.139775259106843, 2.153148856074666, 1.67569258176305, 0.9819936907884712, 0.0, 13.476700638717421, 10.801930598673183, 8.378462908815248, 6.459446568223997, 10.279550518213686, 5.109915073921659, 4.4210506199736415, 3.4049291139562063, 5.018367290674844, 3.875689551462696, 2.189278066415181, 0.9518742577105185, 0.0), # 106
(13.3545938211964, 10.413967285056863, 10.923035593850026, 11.59505217894525, 10.018231528551063, 4.755367598943252, 4.400392977218323, 3.6372359015394005, 5.129612669562567, 2.145022560047339, 1.6689081004981592, 0.9790039267120707, 0.0, 13.449317879801098, 10.769043193832776, 8.344540502490794, 6.435067680142016, 10.259225339125134, 5.092130262155161, 4.400392977218323, 3.3966911421023225, 5.009115764275531, 3.865017392981751, 2.1846071187700056, 0.9467242986415331, 0.0), # 107
(13.312113043478263, 10.357493548387097, 10.899510416666669, 11.562990896739132, 9.999705882352941, 4.744016666666668, 4.379764705882353, 3.6248333333333345, 5.119508333333334, 2.1368776470588244, 1.662205821371611, 0.9760087719298248, 0.0, 13.421953125000002, 10.736096491228071, 8.311029106858054, 6.4106329411764715, 10.239016666666668, 5.074766666666668, 4.379764705882353, 3.3885833333333344, 4.999852941176471, 3.854330298913045, 2.179902083333334, 0.9415903225806455, 0.0), # 108
(13.26988476173436, 10.301222201203595, 10.87581960162323, 11.530906788446053, 9.98118023615482, 4.732871861504853, 4.359173229511284, 3.612738035360464, 5.109471212467612, 2.1287142978563174, 1.6555958335794598, 0.9730095741181947, 0.0, 13.394642382544584, 10.70310531530014, 8.277979167897298, 6.386142893568951, 10.218942424935223, 5.05783324950465, 4.359173229511284, 3.3806227582177515, 4.99059011807741, 3.8436355961486854, 2.1751639203246462, 0.9364747455639633, 0.0), # 109
(13.227973101926404, 10.245179819903537, 10.851967949817103, 11.498821834742351, 9.962677183356197, 4.721957082253722, 4.3386259716506625, 3.6009564090839814, 5.099510269013869, 2.1205326931870148, 1.6490882263177586, 0.9700076809536419, 0.0, 13.367421660665297, 10.670084490490058, 8.245441131588793, 6.361598079561043, 10.199020538027739, 5.041338972717574, 4.3386259716506625, 3.372826487324087, 4.981338591678099, 3.832940611580785, 2.170393589963421, 0.9313799836275944, 0.0), # 110
(13.186442190016104, 10.189392980884113, 10.827960262345682, 11.46675801630435, 9.944219317356573, 4.711296227709192, 4.318130355846042, 3.5894948559670787, 5.089634465020577, 2.1123330137981124, 1.6426930887825626, 0.9670044401126275, 0.0, 13.340326967592594, 10.6370488412389, 8.213465443912813, 6.336999041394336, 10.179268930041154, 5.02529279835391, 4.318130355846042, 3.3652115912208513, 4.972109658678287, 3.8222526721014507, 2.1655920524691368, 0.9263084528076467, 0.0), # 111
(13.14535615196517, 10.133888260542502, 10.803801340306359, 11.434737313808373, 9.925829231555449, 4.700913196667176, 4.297693805642971, 3.5783597774729468, 5.079852762536198, 2.1041154404368063, 1.6364205101699256, 0.9640011992716131, 0.0, 13.313394311556928, 10.604013191987741, 8.182102550849628, 6.312346321310418, 10.159705525072397, 5.0097036884621255, 4.297693805642971, 3.357795140476554, 4.962914615777724, 3.8115791046027923, 2.160760268061272, 0.9212625691402275, 0.0), # 112
(13.104705913184263, 10.078784894108638, 10.779554132960747, 11.402825576616644, 9.907497301495457, 4.690826978191853, 4.277368174559739, 3.5675806651220205, 5.07019931192069, 2.095906657814456, 1.6302822447690024, 0.9610058425921835, 0.0, 13.286621461180511, 10.571064268514016, 8.151411223845011, 6.287719973443367, 10.14039862384138, 4.9946129311708285, 4.277368174559739, 3.3505906987084666, 4.953748650747729, 3.8009418588722155, 2.15591082659215, 0.9162531721916946, 0.0), # 113
(13.064073257060091, 10.024626385524439, 10.755553287525224, 11.371278892341204, 9.88903379759524, 4.681014596966087, 4.257412745887406, 3.557289901377987, 5.060822216666095, 2.0878603087694745, 1.6242903453264128, 0.9580564200798471, 0.0, 13.25978557982405, 10.538620620878318, 8.121451726632063, 6.263580926308422, 10.12164443333219, 4.980205861929182, 4.257412745887406, 3.3435818549757763, 4.94451689879762, 3.790426297447069, 2.1511106575050447, 0.9113296714113127, 0.0), # 114
(13.023338864205595, 9.97143223830991, 10.731813088158539, 11.340088730440868, 9.870380499362694, 4.671450535207326, 4.2378417551340934, 3.547484881662581, 5.051724990045435, 2.0799888647958276, 1.6184360526663222, 0.9551543846318662, 0.0, 13.232809284324528, 10.506698230950526, 8.09218026333161, 6.239966594387481, 10.10344998009087, 4.966478834327614, 4.2378417551340934, 3.336750382290947, 4.935190249681347, 3.780029576813624, 2.146362617631708, 0.9064938398463556, 0.0), # 115
(12.982451822532688, 9.919124960991017, 10.708287554981187, 11.309199457779725, 9.851509291291528, 4.662112249784464, 4.218623372269525, 3.5381385158577467, 5.042884624972988, 2.072277675457342, 1.6127080506300124, 0.9522943730401906, 0.0, 13.205650163658248, 10.475238103442095, 8.063540253150062, 6.216833026372026, 10.085769249945976, 4.953393922200846, 4.218623372269525, 3.330080178417474, 4.925754645645764, 3.7697331525932425, 2.1416575109962372, 0.9017386328173653, 0.0), # 116
(12.941361219953283, 9.867627062093726, 10.68493070811365, 11.278555441221856, 9.832392057875436, 4.652977197566394, 4.199725767263427, 3.529223713845425, 5.034278114363028, 2.0647120903178457, 1.6070950230587664, 0.949471022096771, 0.0, 13.178265806801516, 10.44418124306448, 8.035475115293831, 6.1941362709535355, 10.068556228726056, 4.940913199383595, 4.199725767263427, 3.3235551411188533, 4.916196028937718, 3.7595184804072863, 2.1369861416227303, 0.8970570056448843, 0.0), # 117
(12.900016144379297, 9.816861050144, 10.66169656767643, 11.248101047631351, 9.81300068360812, 4.644022835422014, 4.181117110085521, 3.5207133855075567, 5.025882451129837, 2.0572774589411664, 1.6015856537938657, 0.9466789685935577, 0.0, 13.150613802730636, 10.413468654529133, 8.007928268969328, 6.171832376823498, 10.051764902259674, 4.92899873971058, 4.181117110085521, 3.317159168158581, 4.90650034180406, 3.7493670158771177, 2.132339313535286, 0.8924419136494547, 0.0), # 118
(12.858365683722639, 9.766749433667803, 10.638539153790012, 11.217780643872292, 9.793307052983273, 4.635226620220214, 4.162765570705529, 3.512580440726085, 5.017674628187687, 2.0499591308911307, 1.5961686266765933, 0.9439128493225009, 0.0, 13.122651740421906, 10.383041342547507, 7.980843133382966, 6.149877392673391, 10.035349256375374, 4.91761261701652, 4.162765570705529, 3.310876157300153, 4.896653526491637, 3.7392602146240983, 2.1277078307580024, 0.8878863121516185, 0.0), # 119
(12.816358925895228, 9.717214721191104, 10.61541248657489, 11.187538596808764, 9.773283050494598, 4.626566008829889, 4.144639319093177, 3.5047977893829505, 5.009631638450861, 2.0427424557315677, 1.5908326255482306, 0.9411673010755515, 0.0, 13.094337208851638, 10.352840311831065, 7.954163127741153, 6.128227367194702, 10.019263276901722, 4.906716905136131, 4.144639319093177, 3.3046900063070637, 4.886641525247299, 3.729179532269589, 2.1230824973149782, 0.8833831564719186, 0.0), # 120
(12.773944958808976, 9.668179421239865, 10.592270586151553, 11.157319273304857, 9.75290056063579, 4.618018458119934, 4.126706525218187, 3.4973383413600962, 5.001730474833633, 2.035612783026304, 1.5855663342500608, 0.9384369606446594, 0.0, 13.065627796996127, 10.322806567091252, 7.927831671250303, 6.106838349078911, 10.003460949667266, 4.8962736779041345, 4.126706525218187, 3.29858461294281, 4.876450280317895, 3.719106424434953, 2.118454117230311, 0.878925401930897, 0.0), # 121
(12.731072870375797, 9.61956604234005, 10.569067472640498, 11.127067040224649, 9.732131467900551, 4.609561424959241, 4.108935359050283, 3.490175006539462, 4.993948130250281, 2.0285554623391677, 1.5803584366233656, 0.9357164648217753, 0.0, 13.036481093831679, 10.292881113039527, 7.901792183116827, 6.085666387017502, 9.987896260500563, 4.886245009155247, 4.108935359050283, 3.2925438749708866, 4.8660657339502755, 3.7090223467415506, 2.1138134945280997, 0.8745060038490956, 0.0), # 122
(12.687691748507607, 9.571297093017627, 10.54575716616221, 11.09672626443223, 9.71094765678258, 4.601172366216706, 4.091293990559188, 3.4832806948029904, 4.986261597615085, 2.021555843233986, 1.5751976165094272, 0.9330004503988493, 0.0, 13.0068546883346, 10.263004954387341, 7.875988082547136, 6.064667529701957, 9.97252319523017, 4.876592972724187, 4.091293990559188, 3.28655169015479, 4.85547382839129, 3.698908754810744, 2.109151433232442, 0.8701179175470571, 0.0), # 123
(12.643750681116316, 9.523295081798558, 10.522293686837184, 11.066241312791686, 9.689321011775569, 4.592828738761221, 4.073750589714624, 3.476628316032624, 4.97864786984232, 2.014599275274587, 1.5700725577495283, 0.9302835541678323, 0.0, 12.976706169481197, 10.233119095846153, 7.85036278874764, 6.04379782582376, 9.95729573968464, 4.8672796424456735, 4.073750589714624, 3.280591956258015, 4.844660505887784, 3.6887471042638964, 2.104458737367437, 0.8657540983453236, 0.0), # 124
(12.599198756113843, 9.475482517208812, 10.498631054785912, 11.0355565521671, 9.667223417373222, 4.584507999461682, 4.056273326486318, 3.4701907801103036, 4.971083939846263, 2.0076711080247973, 1.5649719441849508, 0.927560412920674, 0.0, 12.94599312624776, 10.203164542127412, 7.824859720924753, 6.023013324074391, 9.942167879692526, 4.858267092154425, 4.056273326486318, 3.2746485710440583, 4.833611708686611, 3.678518850722367, 2.0997262109571824, 0.8614075015644376, 0.0), # 125
(12.553985061412101, 9.427781907774351, 10.474723290128884, 11.004616349422557, 9.644626758069233, 4.5761876051869805, 4.038830370843989, 3.463940996917971, 4.963546800541195, 2.0007566910484456, 1.5598844596569765, 0.9248256634493257, 0.0, 12.91467314761061, 10.173082297942582, 7.799422298284883, 6.002270073145335, 9.92709360108239, 4.849517395685159, 4.038830370843989, 3.268705432276415, 4.822313379034616, 3.66820544980752, 2.094944658025777, 0.8570710825249411, 0.0), # 126
(12.508058684923006, 9.380115762021138, 10.450524412986589, 10.973365071422144, 9.621502918357304, 4.567845012806012, 4.021389892757366, 3.4578518763375685, 4.95601344484139, 1.993841373909359, 1.5547987880068885, 0.9220739425457369, 0.0, 12.88270382254604, 10.142813368003106, 7.773993940034442, 5.981524121728076, 9.91202688968278, 4.8409926268725965, 4.021389892757366, 3.26274643771858, 4.810751459178652, 3.6577883571407157, 2.090104882597318, 0.8527377965473764, 0.0), # 127
(12.461368714558466, 9.332406588475143, 10.425988443479525, 10.941747085029949, 9.597823782731137, 4.5594576791876715, 4.003920062196168, 3.451896328251037, 4.948460865661126, 1.986910506171365, 1.5497036130759692, 0.9192998870018588, 0.0, 12.850042740030352, 10.112298757020445, 7.748518065379845, 5.960731518514094, 9.896921731322252, 4.832654859551452, 4.003920062196168, 3.2567554851340508, 4.798911891365568, 3.6472490283433174, 2.085197688695905, 0.8484005989522859, 0.0), # 128
(12.413864238230394, 9.284576895662326, 10.401069401728181, 10.909706757110053, 9.573561235684425, 4.551003061200851, 3.9863890491301195, 3.446047262540319, 4.9408660559146815, 1.9799494373982915, 1.5445876187055003, 0.916498133609641, 0.0, 12.816647489039854, 10.08147946970605, 7.7229380935275005, 5.939848312194873, 9.881732111829363, 4.824466167556446, 3.9863890491301195, 3.250716472286322, 4.786780617842212, 3.636568919036685, 2.0802138803456365, 0.8440524450602116, 0.0), # 129
(12.365494343850713, 9.236549192108656, 10.375721307853043, 10.877188454526541, 9.548687161710866, 4.542458615714445, 3.968765023528944, 3.440277589087355, 4.933206008516334, 1.9729435171539655, 1.539439488736764, 0.9136633191610346, 0.0, 12.78247565855085, 10.050296510771378, 7.697197443683819, 5.9188305514618955, 9.866412017032667, 4.816388624722297, 3.968765023528944, 3.244613296938889, 4.774343580855433, 3.6257294848421813, 2.075144261570609, 0.8396862901916962, 0.0), # 130
(12.316208119331334, 9.188245986340096, 10.349898181974611, 10.8441365441435, 9.523173445304161, 4.533801799597346, 3.9510161553623666, 3.4345602177740875, 4.92545771638036, 1.9658780950022154, 1.5342479070110426, 0.9107900804479897, 0.0, 12.747484837539638, 10.018690884927885, 7.671239535055213, 5.897634285006645, 9.85091543276072, 4.808384304883723, 3.9510161553623666, 3.238429856855247, 4.761586722652081, 3.614712181381168, 2.0699796363949226, 0.8352950896672816, 0.0), # 131
(12.265954652584163, 9.139589786882611, 10.32355404421337, 10.810495392825016, 9.49699197095801, 4.525010069718451, 3.9331106146001082, 3.4288680584824593, 4.917598172421039, 1.9587385205068681, 1.5290015573696185, 0.9078730542624567, 0.0, 12.711632614982527, 9.986603596887022, 7.645007786848092, 5.876215561520603, 9.835196344842078, 4.800415281875443, 3.9331106146001082, 3.2321500497988938, 4.748495985479005, 3.6034984642750065, 2.0647108088426744, 0.8308717988075103, 0.0), # 132
(12.21468303152113, 9.090503102262165, 10.296642914689816, 10.776209367435175, 9.470114623166108, 4.516060882946651, 3.915016571211893, 3.4231740210944106, 4.909604369552646, 1.9515101432317519, 1.5236891236537742, 0.904906877396386, 0.0, 12.674876579855821, 9.953975651360244, 7.618445618268871, 5.854530429695254, 9.819208739105292, 4.792443629532175, 3.915016571211893, 3.2257577735333225, 4.735057311583054, 3.5920697891450595, 2.059328582937963, 0.8264093729329243, 0.0), # 133
(12.162342344054133, 9.040908441004726, 10.26911881352444, 10.741222834838059, 9.442513286422153, 4.5069316961508425, 3.896702195167445, 3.4174510154918845, 4.90145330068946, 1.9441783127406937, 1.518299289704792, 0.9018861866417278, 0.0, 12.637174321135817, 9.920748053059004, 7.5914964485239596, 5.83253493822208, 9.80290660137892, 4.784431421688638, 3.896702195167445, 3.21923692582203, 4.721256643211077, 3.5804076116126873, 2.053823762704888, 0.8219007673640661, 0.0), # 134
(12.108881678095097, 8.990728311636257, 10.24093576083773, 10.705480161897759, 9.414159845219846, 4.4975999661999175, 3.8781356564364877, 3.4116719515568206, 4.893121958745757, 1.9367283785975222, 1.5128207393639534, 0.898805618790433, 0.0, 12.59848342779883, 9.88686180669476, 7.5641036968197675, 5.810185135792565, 9.786243917491515, 4.776340732179549, 3.8781356564364877, 3.212571404428512, 4.707079922609923, 3.5684933872992537, 2.048187152167546, 0.817338937421478, 0.0), # 135
(12.05425012155593, 8.93988522268272, 10.212047776750177, 10.668925715478352, 9.385026184052883, 4.488043149962771, 3.8592851249887445, 3.4058097391711617, 4.884587336635816, 1.9291456903660635, 1.5072421564725416, 0.8956598106344515, 0.0, 12.558761488821151, 9.852257916978965, 7.536210782362707, 5.787437071098189, 9.769174673271632, 4.768133634839627, 3.8592851249887445, 3.205745107116265, 4.6925130920264415, 3.556308571826118, 2.042409555350036, 0.812716838425702, 0.0), # 136
(11.998396762348548, 8.888301682670086, 10.18240888138228, 10.631503862443932, 9.355084187414965, 4.478238704308296, 3.8401187707939393, 3.399837288216851, 4.875826427273916, 1.9214155976101461, 1.5015522248718383, 0.8924433989657341, 0.0, 12.517966093179089, 9.816877388623073, 7.507761124359191, 5.764246792830437, 9.751652854547832, 4.759772203503592, 3.8401187707939393, 3.1987419316487826, 4.6775420937074825, 3.543834620814645, 2.036481776276456, 0.8080274256972807, 0.0), # 137
(11.941270688384867, 8.835900200124316, 10.15197309485452, 10.593158969658578, 9.32430573979979, 4.4681640861053875, 3.8206047638217933, 3.393727508575828, 4.8668162235743315, 1.913523449893597, 1.4957396284031257, 0.889151020576231, 0.0, 12.476054829848946, 9.78066122633854, 7.478698142015627, 5.740570349680789, 9.733632447148663, 4.751218512006159, 3.8206047638217933, 3.1915457757895624, 4.662152869899895, 3.5310529898861933, 2.0303946189709046, 0.8032636545567561, 0.0), # 138
(11.882820987576796, 8.782603283571376, 10.120694437287398, 10.553835403986378, 9.292662725701055, 4.457796752222938, 3.800711274042032, 3.3874533101300353, 4.85753371845134, 1.9054545967802445, 1.4897930509076862, 0.8857773122578926, 0.0, 12.432985287807028, 9.743550434836816, 7.448965254538431, 5.716363790340733, 9.71506743690268, 4.742434634182049, 3.800711274042032, 3.184140537302099, 4.646331362850527, 3.517945134662127, 2.0241388874574797, 0.7984184803246707, 0.0), # 139
(11.822996747836257, 8.72833344153723, 10.088526928801404, 10.513477532291418, 9.26012702961246, 4.447114159529844, 3.780406471424378, 3.3809876027614147, 4.847955904819222, 1.8971943878339157, 1.4837011762268022, 0.8823169108026693, 0.0, 12.38871505602964, 9.70548601882936, 7.41850588113401, 5.691583163501746, 9.695911809638444, 4.733382643865981, 3.780406471424378, 3.176510113949888, 4.63006351480623, 3.5044925107638067, 2.017705385760281, 0.7934848583215663, 0.0), # 140
(11.761747057075162, 8.673013182547843, 10.055424589517022, 10.472029721437782, 9.226670536027703, 4.436093764894997, 3.7596585259385567, 3.374303296351908, 4.838059775592251, 1.8887281726184386, 1.477452688201756, 0.8787644530025115, 0.0, 12.34320172349308, 9.666408983027624, 7.38726344100878, 5.6661845178553145, 9.676119551184502, 4.724024614892672, 3.7596585259385567, 3.168638403496426, 4.613335268013851, 3.490676573812595, 2.0110849179034047, 0.7884557438679859, 0.0), # 141
(11.69902100320542, 8.616565015129181, 10.02134143955475, 10.429436338289557, 9.192265129440482, 4.424713025187291, 3.7384356075542886, 3.367373300783457, 4.827822323684707, 1.8800413006976404, 1.4710362706738296, 0.8751145756493696, 0.0, 12.296402879173653, 9.626260332143064, 7.355181353369148, 5.64012390209292, 9.655644647369414, 4.71432262109684, 3.7384356075542886, 3.160509303705208, 4.596132564720241, 3.4764787794298533, 2.0042682879109504, 0.7833240922844712, 0.0), # 142
(11.634767674138946, 8.558911447807208, 9.986231499035082, 10.385641749710825, 9.156882694344494, 4.412949397275621, 3.7167058862412983, 3.360170525938002, 4.817220542010869, 1.871119121635349, 1.4644406074843055, 0.8713619155351939, 0.0, 12.248276112047666, 9.584981070887132, 7.322203037421526, 5.6133573649060455, 9.634441084021738, 4.704238736313203, 3.7167058862412983, 3.1521067123397293, 4.578441347172247, 3.4618805832369426, 1.9972462998070164, 0.7780828588915646, 0.0), # 143
(11.56893615778766, 8.499974989107892, 9.950048788078501, 10.340590322565676, 9.12049511523344, 4.400780338028881, 3.6944375319693092, 3.3526678816974873, 4.806231423485011, 1.8619469849953916, 1.4576543824744654, 0.867501109451935, 0.0, 12.198779011091421, 9.542512203971285, 7.288271912372326, 5.585840954986173, 9.612462846970022, 4.693735034376482, 3.6944375319693092, 3.1434145271634857, 4.56024755761672, 3.446863440855226, 1.9900097576157, 0.7727249990098085, 0.0), # 144
(11.501475542063469, 8.439678147557194, 9.912747326805505, 10.294226423718191, 9.083074276601018, 4.388183304315964, 3.6715987147080456, 3.344838277943853, 4.794831961021412, 1.8525102403415963, 1.4506662794855925, 0.8635267941915434, 0.0, 12.14786916528122, 9.498794736106976, 7.253331397427962, 5.557530721024787, 9.589663922042824, 4.682773589121394, 3.6715987147080456, 3.1344166459399743, 4.541537138300509, 3.4314088079060645, 1.9825494653611013, 0.7672434679597451, 0.0), # 145
(11.432334914878291, 8.377943431681082, 9.874281135336586, 10.246494420032459, 9.044592062940927, 4.375135753005765, 3.6481576044272312, 3.336654624559041, 4.782999147534349, 1.8427942372377903, 1.4434649823589683, 0.8594336065459691, 0.0, 12.095504163593366, 9.453769672005658, 7.21732491179484, 5.52838271171337, 9.565998295068699, 4.671316474382658, 3.6481576044272312, 3.125096966432689, 4.522296031470463, 3.41549814001082, 1.9748562270673173, 0.7616312210619166, 0.0), # 146
(11.361463364144042, 8.314693350005518, 9.83460423379223, 10.19733867837256, 9.005020358746862, 4.361615140967176, 3.6240823710965873, 3.3280898314249927, 4.770709975938102, 1.8327843252478015, 1.4360391749358754, 0.855216183307163, 0.0, 12.041641595004167, 9.407378016378791, 7.180195874679377, 5.498352975743403, 9.541419951876204, 4.65932576399499, 3.6240823710965873, 3.1154393864051255, 4.502510179373431, 3.3991128927908543, 1.966920846758446, 0.7558812136368653, 0.0), # 147
(11.288809977772631, 8.24985041105647, 9.793670642292932, 10.146703565602587, 8.964331048512523, 4.347598925069094, 3.599341184685839, 3.3191168084236504, 4.757941439146947, 1.822465853935457, 1.428377541057596, 0.8508691612670749, 0.0, 11.986239048489919, 9.359560773937822, 7.141887705287981, 5.4673975618063695, 9.515882878293894, 4.646763531793111, 3.599341184685839, 3.105427803620781, 4.482165524256262, 3.38223452186753, 1.9587341284585866, 0.7499864010051337, 0.0), # 148
(11.214323843675977, 8.1833371233599, 9.751434380959186, 10.094533448586619, 8.922496016731612, 4.33306456218041, 3.573902215164709, 3.3097084654369557, 4.744670530075158, 1.8118241728645852, 1.4204687645654126, 0.8463871772176558, 0.0, 11.929254113026934, 9.310258949394212, 7.102343822827062, 5.4354725185937545, 9.489341060150316, 4.6335918516117385, 3.573902215164709, 3.09504611584315, 4.461248008365806, 3.3648444828622073, 1.950286876191837, 0.7439397384872637, 0.0), # 149
(11.137954049765991, 8.115075995441773, 9.707849469911476, 10.040772694188746, 8.879487147897825, 4.317989509170021, 3.5477336325029207, 3.29983771234685, 4.730874241637018, 1.8008446315990123, 1.412301529300607, 0.8417648679508558, 0.0, 11.870644377591507, 9.259413547459413, 7.061507646503035, 5.402533894797036, 9.461748483274036, 4.61977279728559, 3.5477336325029207, 3.084278220835729, 4.439743573948912, 3.3469242313962493, 1.9415698939822956, 0.7377341814037977, 0.0), # 150
(11.059649683954586, 8.044989535828057, 9.6628699292703, 9.985365669273047, 8.835276326504857, 4.302351222906816, 3.5208036066701984, 3.2894774590352758, 4.716529566746802, 1.789512579702568, 1.4038645191044614, 0.8369968702586252, 0.0, 11.810367431159946, 9.206965572844876, 7.019322595522306, 5.368537739107703, 9.433059133493604, 4.605268442649386, 3.5208036066701984, 3.0731080163620117, 4.417638163252429, 3.3284552230910167, 1.9325739858540603, 0.731362685075278, 0.0), # 151
(10.979359834153682, 7.973000253044715, 9.616449779156152, 9.928256740703617, 8.789835437046412, 4.286127160259694, 3.4930803076362653, 3.2786006153841747, 4.701613498318786, 1.7778133667390779, 1.3951464178182584, 0.8320778209329146, 0.0, 11.748380862708558, 9.15285603026206, 6.975732089091292, 5.333440100217232, 9.403226996637573, 4.590040861537845, 3.4930803076362653, 3.061519400185496, 4.394917718523206, 3.309418913567873, 1.9232899558312306, 0.7248182048222469, 0.0), # 152
(10.897033588275185, 7.899030655617714, 9.568543039689514, 9.86939027534453, 8.743136364016186, 4.269294778097547, 3.4645319053708437, 3.2671800912754865, 4.686103029267251, 1.7657323422723707, 1.3861359092832806, 0.8270023567656742, 0.0, 11.68464226121364, 9.097025924422415, 6.930679546416402, 5.297197026817111, 9.372206058534502, 4.574052127785681, 3.4645319053708437, 3.049496270069676, 4.371568182008093, 3.2897967584481775, 1.9137086079379029, 0.7180936959652467, 0.0), # 153
(10.81262003423102, 7.823003252073014, 9.519103730990887, 9.80871064005988, 8.695150991907875, 4.251831533289268, 3.43512656984366, 3.2551887965911552, 4.6699751525064706, 1.7532548558662742, 1.3768216773408095, 0.8217651145488547, 0.0, 11.6191092156515, 9.0394162600374, 6.884108386704048, 5.259764567598821, 9.339950305012941, 4.557264315227617, 3.43512656984366, 3.037022523778049, 4.347575495953937, 3.2695702133532945, 1.9038207461981775, 0.7111821138248196, 0.0), # 154
(10.72606825993309, 7.744840550936584, 9.468085873180756, 9.746162201713748, 8.645851205215184, 4.233714882703753, 3.404832471024433, 3.2425996412131215, 4.653206860950727, 1.7403662570846146, 1.3671924058321279, 0.8163607310744064, 0.0, 11.551739314998438, 8.97996804181847, 6.8359620291606396, 5.221098771253843, 9.306413721901453, 4.53963949769837, 3.404832471024433, 3.0240820590741087, 4.322925602607592, 3.2487207339045834, 1.8936171746361512, 0.7040764137215078, 0.0), # 155
(10.637327353293314, 7.664465060734389, 9.415443486379615, 9.68168932717022, 8.595208888431804, 4.214922283209894, 3.37361777888289, 3.2293855350233276, 4.635775147514292, 1.727051895491221, 1.357236778598518, 0.8107838431342794, 0.0, 11.48249014823076, 8.918622274477073, 6.7861838929925895, 5.181155686473662, 9.271550295028584, 4.521139749032659, 3.37361777888289, 3.0106587737213526, 4.297604444215902, 3.2272297757234076, 1.8830886972759233, 0.6967695509758537, 0.0), # 156
(10.546346402223609, 7.581799289992394, 9.361130590707957, 9.615236383293386, 8.543195926051439, 4.195431191676585, 3.3414506633887537, 3.215519387903715, 4.6176570051114485, 1.7132971206499201, 1.3469434794812618, 0.8050290875204243, 0.0, 11.411319304324769, 8.855319962724668, 6.734717397406309, 5.1398913619497595, 9.235314010222897, 4.501727143065201, 3.3414506633887537, 2.996736565483275, 4.2715979630257195, 3.205078794431129, 1.8722261181415913, 0.6892544809083996, 0.0), # 157
(10.450553324967336, 7.495248171657732, 9.302523946219415, 9.544258060733807, 8.48743569881293, 4.174003322325641, 3.3075747046495003, 3.200048222203801, 4.597442309412912, 1.698678070701901, 1.335972342259087, 0.7988866158226731, 0.0, 11.335080203181485, 8.787752774049402, 6.679861711295434, 5.096034212105701, 9.194884618825824, 4.480067511085322, 3.3075747046495003, 2.9814309445183147, 4.243717849406465, 3.1814193535779363, 1.8605047892438833, 0.6813861974234302, 0.0), # 158
(10.335201473769764, 7.395933826819331, 9.224527454803487, 9.454176016727876, 8.414178555796186, 4.143513212539135, 3.2677489343700015, 3.17754122744589, 4.566999388570334, 1.6807983479345614, 1.3223972849777657, 0.7911589610963629, 0.0, 11.235598705688274, 8.70274857205999, 6.611986424888827, 5.042395043803683, 9.133998777140668, 4.448557718424246, 3.2677489343700015, 2.9596522946708106, 4.207089277898093, 3.1513920055759597, 1.8449054909606977, 0.6723576206199392, 0.0), # 159
(10.198820932866035, 7.28304080162725, 9.125574450948537, 9.343506385929302, 8.321992122590341, 4.103212058438943, 3.221570623868649, 3.147432860557619, 4.525465106040038, 1.6594219781520132, 1.3060272186755595, 0.7817252273702489, 0.0, 11.110988852451014, 8.598977501072737, 6.530136093377798, 4.978265934456038, 9.050930212080075, 4.406406004780667, 3.221570623868649, 2.9308657560278157, 4.160996061295171, 3.114502128643102, 1.8251148901897079, 0.6620946183297501, 0.0), # 160
(10.042510876420344, 7.1573051140366015, 9.006721467228694, 9.213301128944565, 8.211833582663305, 4.053588080615757, 3.1693770122048135, 3.1101003109807053, 4.473387224599541, 1.6347303676098288, 1.2870063860732652, 0.77067287137255, 0.0, 10.962523662746737, 8.477401585098049, 6.435031930366326, 4.904191102829485, 8.946774449199083, 4.354140435372988, 3.1693770122048135, 2.8954200575826836, 4.105916791331652, 3.071100376314856, 1.801344293445739, 0.6506641012760548, 0.0), # 161
(9.8673704785969, 7.01946278200249, 8.86902503621808, 9.064612206380144, 8.08466011948299, 3.9951294996602726, 3.1115053384378664, 3.0659207681568685, 4.411313507026364, 1.6069049225635816, 1.2654790298916783, 0.7580893498314843, 0.0, 10.791476155852466, 8.338982848146326, 6.3273951494583915, 4.820714767690744, 8.822627014052728, 4.292289075419616, 3.1115053384378664, 2.8536639283287664, 4.042330059741495, 3.0215374021267154, 1.773805007243616, 0.6381329801820447, 0.0), # 162
(9.674498913559898, 6.870249823480022, 8.71354169049082, 8.898491578842531, 7.941428916517308, 3.928324536163185, 3.048292841627181, 3.015271421527823, 4.339791716098023, 1.5761270492688444, 1.2415893928515955, 0.7440621194752707, 0.0, 10.599119351045232, 8.184683314227977, 6.207946964257977, 4.728381147806532, 8.679583432196045, 4.221379990138953, 3.048292841627181, 2.8059460972594175, 3.970714458258654, 2.9661638596141775, 1.742708338098164, 0.6245681657709112, 0.0), # 163
(9.464995355473539, 6.710402256424303, 8.54132796262104, 8.71599120693821, 7.783097157234176, 3.853661410715189, 2.9800767608321266, 2.9585294605352903, 4.259369614592037, 1.5425781539811894, 1.2154817176738126, 0.7286786370321272, 0.0, 10.386726267602059, 8.015465007353399, 6.077408588369063, 4.627734461943566, 8.518739229184074, 4.141941244749407, 2.9800767608321266, 2.752615293367992, 3.891548578617088, 2.905330402312737, 1.7082655925242083, 0.6100365687658459, 0.0), # 164
(9.239958978502024, 6.5406560987904445, 8.353440385182864, 8.518163051273666, 7.610622025101502, 3.771628343906979, 2.9071943351120755, 2.8960720746209856, 4.1705949652859235, 1.5064396429561904, 1.1873002470791263, 0.7120263592302724, 0.0, 10.155569924799979, 7.832289951532995, 5.936501235395631, 4.51931892886857, 8.341189930571847, 4.05450090446938, 2.9071943351120755, 2.694020245647842, 3.805311012550751, 2.839387683757889, 1.670688077036573, 0.5946050998900405, 0.0), # 165
(9.000488956809557, 6.361747368533551, 8.150935490750417, 8.306059072455376, 7.4249607035872005, 3.682713556329251, 2.8299828035264003, 2.8282764532266285, 4.074015530957201, 1.4678929224494195, 1.157189223788332, 0.6941927427979253, 0.0, 9.906923341916015, 7.636120170777177, 5.78594611894166, 4.403678767348258, 8.148031061914402, 3.95958703451728, 2.8299828035264003, 2.630509683092322, 3.7124803517936003, 2.768686357485126, 1.6301870981500834, 0.5783406698666865, 0.0), # 166
(8.747684464560333, 6.174412083608727, 7.934869811897824, 8.080731231089835, 7.2270703761591815, 3.5874052685726983, 2.7487794051344725, 2.7555197857939366, 3.9701790743833865, 1.4271193987164503, 1.1252928905222266, 0.6752652444633036, 0.0, 9.642059538227196, 7.427917689096338, 5.626464452611132, 4.28135819614935, 7.940358148766773, 3.8577277001115116, 2.7487794051344725, 2.562432334694784, 3.6135351880795907, 2.693577077029946, 1.5869739623795647, 0.5613101894189753, 0.0), # 167
(8.482644675918554, 5.979386261971081, 7.706299881199207, 7.843231487783524, 7.017908226285359, 3.4861917012280164, 2.663921378995663, 2.6781792617646265, 3.8596333583419993, 1.3843004780128556, 1.0917554900016058, 0.6553313209546264, 0.0, 9.362251533010546, 7.20864453050089, 5.458777450008029, 4.152901434038566, 7.7192667166839986, 3.7494509664704774, 2.663921378995663, 2.490136929448583, 3.5089541131426794, 2.614410495927842, 1.5412599762398416, 0.5435805692700985, 0.0), # 168
(8.206468765048422, 5.777405921575724, 7.466282231228694, 7.594611803142927, 6.798431437433646, 3.3795610748859013, 2.5757459641693443, 2.5966320705804184, 3.7429261456105576, 1.339617566594208, 1.0567212649472661, 0.6344784290001119, 0.0, 9.0687723455431, 6.9792627190012295, 5.28360632473633, 4.018852699782624, 7.485852291221115, 3.635284898812586, 2.5757459641693443, 2.413972196347072, 3.399215718716823, 2.5315372677143095, 1.493256446245739, 0.5252187201432478, 0.0), # 169
(7.9202559061141375, 5.569207080377758, 7.215873394560408, 7.335924137774526, 6.569597193071951, 3.268001610137046, 2.4845903997148873, 2.5112554016830275, 3.620605198966578, 1.2932520707160806, 1.020334458080004, 0.6127940253279787, 0.0, 8.762894995101878, 6.740734278607764, 5.101672290400019, 3.879756212148241, 7.241210397933156, 3.5157575623562387, 2.4845903997148873, 2.3342868643836043, 3.2847985965359756, 2.4453080459248424, 1.4431746789120816, 0.5062915527616144, 0.0), # 170
(7.6251052732799005, 5.355525756332291, 6.956129903768475, 7.068220452284813, 6.3323626766681915, 3.152001527572146, 2.390791924691664, 2.4224264445141737, 3.4932182811875796, 1.2453853966340462, 0.9827393121206148, 0.5903655666664452, 0.0, 8.445892500963913, 6.494021233330896, 4.913696560603074, 3.736156189902138, 6.986436562375159, 3.3913970223198433, 2.390791924691664, 2.2514296625515327, 3.1661813383340958, 2.356073484094938, 1.391225980753695, 0.4868659778483902, 0.0), # 171
(7.322116040709912, 5.137097967394431, 6.688108291427019, 6.792552707280267, 6.087685071690277, 3.0320490477818964, 2.2946877781590462, 2.3305223885155746, 3.3613131550510804, 1.1961989506036783, 0.9440800697898953, 0.56728050974373, 0.0, 8.119037882406225, 6.24008560718103, 4.720400348949476, 3.588596851811034, 6.722626310102161, 3.2627313439218044, 2.2946877781590462, 2.165749319844212, 3.0438425358451386, 2.2641842357600894, 1.337621658285404, 0.4670089061267665, 0.0), # 172
(7.012387382568372, 4.914659731519285, 6.412865090110164, 6.509972863367375, 5.836521561606121, 2.9086323913569916, 2.196615199176405, 2.235920423128947, 3.225437583334597, 1.145874138880549, 0.9045009738086416, 0.5436263112880514, 0.0, 7.783604158705848, 5.979889424168563, 4.522504869043208, 3.437622416641646, 6.450875166669194, 3.130288592380526, 2.196615199176405, 2.077594565254994, 2.9182607808030605, 2.169990954455792, 1.282573018022033, 0.446787248319935, 0.0), # 173
(6.697018473019482, 4.6889470666619575, 6.131456832392036, 6.221532881152618, 5.579829329883635, 2.7822397788881266, 2.096911426803113, 2.1389977377960108, 3.08613932881565, 1.0945923677202316, 0.8641462668976501, 0.519490428027628, 0.0, 7.440864349139807, 5.7143947083039075, 4.32073133448825, 3.283777103160694, 6.1722786576313, 2.994596832914415, 2.096911426803113, 1.9873141277772333, 2.7899146649418176, 2.07384429371754, 1.2262913664784072, 0.42626791515108714, 0.0), # 174
(6.377108486227438, 4.460695990777558, 5.84494005084676, 5.928284721242486, 5.318565559990731, 2.653359430965997, 1.9959137000985407, 2.040131521958481, 2.943966154271756, 1.0425350433782987, 0.8231601917777163, 0.49496031669067847, 0.0, 7.092091472985131, 5.444563483597462, 4.115800958888581, 3.1276051301348957, 5.887932308543512, 2.8561841307418736, 1.9959137000985407, 1.8952567364042836, 2.6592827799953653, 1.9760949070808291, 1.1689880101693522, 0.40551781734341447, 0.0), # 175
(6.053756596356447, 4.230642521821194, 5.554371278048459, 5.631280344243462, 5.053687435395322, 2.5224795681812964, 1.8939592581220606, 1.9396989650580787, 2.7994658224804327, 0.9898835721103237, 0.781686991169637, 0.470123434005421, 0.0, 6.738558549518844, 5.17135777405963, 3.9084349558481852, 2.9696507163309707, 5.5989316449608655, 2.71557855108131, 1.8939592581220606, 1.8017711201294973, 2.526843717697661, 1.8770934480811543, 1.1108742556096918, 0.38460386562010856, 0.0), # 176
(5.7280619775707065, 3.9995226777479713, 5.260807046571258, 5.331571710762027, 4.786152139565322, 2.3900884111247205, 1.791385339933044, 1.8380772565365193, 2.6531860962191995, 0.9368193601718788, 0.7398709077942084, 0.4450672367000743, 0.0, 6.381538598017975, 4.895739603700816, 3.699354538971042, 2.8104580805156356, 5.306372192438399, 2.5733081591511273, 1.791385339933044, 1.707206007946229, 2.393076069782661, 1.7771905702540096, 1.0521614093142517, 0.3635929707043611, 0.0), # 177
(5.401123804034416, 3.7680724765129963, 4.9653038889892835, 5.030210781404673, 4.516916855968639, 2.2566741803869648, 1.6885291845908623, 1.7356435858355217, 2.505674738265573, 0.8835238138185378, 0.6978561843722264, 0.41987918150285664, 0.0, 6.022304637759553, 4.618670996531422, 3.489280921861132, 2.6505714414556127, 5.011349476531146, 2.4299010201697304, 1.6885291845908623, 1.611910128847832, 2.2584584279843196, 1.6767369271348913, 0.9930607777978567, 0.34255204331936334, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 157
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 158
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 159
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 160
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 161
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 163
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 164
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 165
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
47, # 1
)
| 278.913369
| 492
| 0.77175
|
numPassengers = 26645
passenger_arriving = (
(9, 10, 5, 5, 3, 2, 2, 3, 3, 1, 1, 0, 0, 6, 9, 0, 8, 12, 3, 4, 1, 0, 2, 2, 2, 0),
(5, 10, 9, 11, 6, 2, 0, 5, 1, 1, 1, 0, 0, 11, 5, 5, 6, 6, 1, 2, 2, 1, 4, 0, 0, 0),
(7, 9, 3, 3, 3, 3, 3, 5, 4, 4, 2, 0, 0, 9, 6, 5, 7, 9, 1, 5, 4, 2, 2, 2, 2, 0),
(5, 10, 11, 9, 12, 3, 6, 6, 4, 1, 0, 0, 0, 6, 10, 2, 8, 8, 8, 2, 2, 3, 2, 2, 0, 0),
(11, 9, 6, 8, 7, 3, 4, 5, 4, 1, 0, 0, 0, 8, 12, 3, 5, 10, 3, 0, 4, 1, 1, 0, 3, 0),
(11, 9, 9, 13, 3, 3, 7, 7, 4, 2, 0, 2, 0, 7, 13, 7, 8, 9, 5, 5, 1, 2, 5, 1, 1, 0),
(12, 13, 8, 8, 8, 4, 1, 4, 3, 4, 3, 2, 0, 9, 8, 7, 9, 6, 2, 7, 3, 6, 3, 0, 1, 0),
(12, 8, 8, 11, 13, 3, 3, 1, 4, 1, 1, 0, 0, 12, 7, 11, 4, 9, 4, 3, 2, 3, 2, 1, 1, 0),
(14, 11, 17, 10, 8, 3, 3, 2, 3, 1, 3, 0, 0, 11, 7, 9, 6, 11, 2, 7, 4, 1, 3, 1, 0, 0),
(11, 10, 7, 9, 8, 8, 7, 5, 5, 0, 3, 3, 0, 17, 12, 9, 6, 10, 9, 4, 2, 3, 2, 4, 1, 0),
(10, 12, 11, 10, 7, 3, 6, 3, 7, 3, 2, 2, 0, 18, 13, 12, 9, 12, 6, 2, 3, 6, 2, 3, 2, 0),
(15, 11, 10, 11, 12, 7, 4, 3, 3, 1, 3, 2, 0, 10, 5, 13, 8, 12, 6, 6, 3, 1, 1, 4, 1, 0),
(11, 16, 10, 11, 9, 2, 7, 4, 3, 3, 1, 2, 0, 17, 8, 9, 5, 8, 5, 5, 0, 6, 4, 1, 1, 0),
(11, 17, 10, 10, 6, 7, 4, 2, 6, 1, 3, 1, 0, 14, 12, 4, 3, 5, 6, 4, 5, 3, 7, 3, 1, 0),
(12, 21, 12, 12, 11, 7, 4, 4, 4, 3, 5, 0, 0, 10, 12, 6, 7, 12, 6, 7, 1, 4, 4, 0, 0, 0),
(6, 12, 9, 21, 12, 3, 3, 4, 6, 2, 1, 2, 0, 9, 19, 8, 3, 6, 7, 7, 6, 8, 0, 0, 2, 0),
(7, 12, 10, 13, 12, 7, 5, 4, 4, 3, 2, 3, 0, 12, 8, 4, 6, 14, 10, 4, 2, 4, 4, 1, 1, 0),
(15, 15, 18, 15, 7, 9, 4, 3, 5, 6, 3, 1, 0, 12, 10, 7, 11, 10, 6, 2, 5, 7, 3, 1, 0, 0),
(14, 14, 16, 17, 15, 4, 6, 4, 4, 1, 2, 1, 0, 16, 17, 7, 4, 7, 7, 9, 5, 6, 4, 2, 2, 0),
(23, 16, 12, 13, 6, 6, 6, 3, 7, 0, 1, 1, 0, 17, 18, 11, 11, 15, 13, 6, 3, 5, 6, 4, 2, 0),
(12, 12, 13, 13, 10, 7, 6, 4, 6, 2, 2, 1, 0, 10, 17, 13, 7, 11, 7, 5, 5, 6, 5, 4, 1, 0),
(16, 18, 12, 13, 8, 6, 2, 6, 11, 2, 1, 1, 0, 7, 12, 10, 10, 16, 4, 4, 7, 4, 3, 1, 2, 0),
(14, 13, 8, 11, 7, 7, 2, 3, 9, 4, 2, 0, 0, 18, 9, 6, 12, 12, 7, 3, 3, 3, 8, 0, 3, 0),
(16, 9, 10, 8, 14, 9, 6, 2, 9, 3, 2, 1, 0, 16, 21, 9, 4, 5, 7, 6, 6, 1, 4, 1, 1, 0),
(11, 13, 11, 10, 6, 4, 10, 6, 6, 1, 5, 1, 0, 18, 17, 16, 3, 12, 13, 8, 2, 4, 1, 4, 1, 0),
(11, 16, 17, 7, 12, 5, 8, 5, 6, 2, 0, 2, 0, 10, 14, 10, 7, 20, 5, 12, 4, 3, 3, 3, 2, 0),
(18, 15, 18, 8, 12, 4, 14, 2, 7, 1, 1, 1, 0, 9, 10, 10, 9, 16, 3, 4, 2, 4, 4, 1, 1, 0),
(9, 9, 8, 9, 5, 7, 11, 5, 8, 2, 3, 1, 0, 10, 13, 9, 8, 12, 13, 3, 1, 3, 4, 1, 1, 0),
(18, 14, 17, 16, 17, 2, 7, 7, 7, 7, 2, 2, 0, 20, 13, 12, 8, 7, 1, 4, 2, 6, 1, 2, 2, 0),
(16, 16, 9, 15, 6, 4, 4, 7, 5, 3, 8, 1, 0, 12, 14, 11, 9, 11, 7, 2, 7, 2, 7, 6, 0, 0),
(13, 12, 17, 14, 12, 7, 9, 4, 2, 1, 3, 0, 0, 22, 14, 9, 9, 20, 6, 9, 3, 11, 3, 0, 2, 0),
(17, 9, 10, 22, 14, 6, 4, 2, 3, 5, 3, 0, 0, 19, 13, 9, 7, 15, 3, 2, 3, 5, 2, 1, 1, 0),
(12, 14, 16, 12, 11, 11, 5, 5, 6, 1, 2, 1, 0, 15, 12, 13, 7, 8, 13, 4, 5, 4, 7, 1, 2, 0),
(11, 16, 15, 17, 3, 7, 1, 6, 6, 1, 4, 1, 0, 18, 10, 9, 8, 14, 2, 3, 3, 5, 8, 1, 1, 0),
(8, 11, 12, 15, 18, 9, 4, 6, 5, 2, 4, 0, 0, 13, 13, 5, 4, 11, 7, 6, 5, 2, 2, 1, 0, 0),
(23, 16, 11, 5, 13, 3, 4, 4, 4, 3, 3, 1, 0, 13, 12, 8, 6, 9, 7, 4, 5, 6, 6, 6, 1, 0),
(13, 12, 17, 16, 14, 3, 4, 4, 5, 3, 1, 0, 0, 11, 5, 6, 11, 6, 9, 6, 3, 6, 5, 1, 1, 0),
(15, 9, 13, 12, 8, 5, 9, 8, 8, 1, 1, 1, 0, 22, 18, 11, 7, 14, 11, 8, 2, 5, 7, 3, 0, 0),
(15, 14, 16, 13, 8, 4, 4, 4, 4, 6, 1, 1, 0, 13, 8, 16, 3, 4, 7, 7, 3, 7, 6, 2, 1, 0),
(16, 17, 8, 14, 9, 4, 4, 3, 6, 0, 4, 0, 0, 17, 15, 11, 7, 13, 6, 4, 3, 5, 6, 0, 0, 0),
(11, 13, 11, 7, 9, 3, 1, 6, 8, 3, 3, 0, 0, 15, 7, 6, 12, 11, 6, 5, 7, 5, 6, 3, 1, 0),
(15, 11, 11, 7, 10, 5, 6, 3, 8, 5, 1, 1, 0, 9, 9, 13, 7, 9, 12, 6, 3, 3, 3, 3, 1, 0),
(21, 12, 14, 12, 7, 0, 4, 5, 4, 0, 0, 2, 0, 22, 14, 7, 4, 14, 13, 6, 5, 7, 6, 1, 0, 0),
(17, 16, 8, 12, 13, 1, 7, 4, 6, 1, 2, 0, 0, 15, 5, 9, 8, 13, 4, 9, 4, 1, 3, 2, 1, 0),
(11, 17, 16, 10, 7, 5, 7, 4, 4, 3, 1, 4, 0, 18, 14, 8, 8, 15, 7, 5, 7, 4, 4, 0, 3, 0),
(12, 14, 12, 14, 10, 4, 4, 4, 3, 2, 1, 2, 0, 13, 13, 13, 8, 6, 5, 5, 2, 4, 2, 4, 1, 0),
(18, 21, 10, 16, 12, 4, 4, 6, 6, 3, 1, 0, 0, 8, 5, 7, 7, 13, 8, 5, 4, 10, 2, 0, 3, 0),
(7, 10, 11, 16, 9, 8, 5, 3, 8, 0, 3, 1, 0, 20, 17, 8, 3, 7, 12, 6, 4, 7, 4, 2, 3, 0),
(19, 12, 8, 5, 7, 4, 3, 3, 5, 3, 1, 1, 0, 9, 7, 12, 11, 19, 9, 8, 4, 8, 2, 3, 1, 0),
(9, 7, 14, 20, 17, 2, 7, 3, 4, 3, 4, 3, 0, 13, 11, 10, 9, 13, 7, 4, 3, 3, 6, 1, 1, 0),
(15, 21, 13, 12, 7, 6, 7, 4, 4, 1, 1, 2, 0, 18, 12, 11, 8, 13, 10, 7, 2, 4, 6, 3, 2, 0),
(11, 9, 13, 15, 9, 2, 6, 2, 4, 7, 1, 2, 0, 21, 18, 9, 9, 14, 5, 5, 1, 5, 3, 3, 3, 0),
(15, 15, 11, 10, 8, 4, 3, 5, 6, 1, 1, 1, 0, 7, 7, 12, 11, 14, 5, 5, 2, 4, 0, 1, 3, 0),
(11, 11, 8, 13, 9, 4, 5, 5, 2, 3, 3, 1, 0, 13, 15, 8, 4, 16, 5, 6, 6, 5, 3, 2, 1, 0),
(17, 18, 6, 15, 9, 4, 5, 6, 10, 4, 2, 2, 0, 21, 17, 14, 8, 16, 6, 4, 6, 6, 3, 1, 1, 0),
(10, 12, 18, 10, 11, 7, 1, 7, 7, 3, 1, 0, 0, 14, 7, 15, 7, 7, 7, 5, 3, 1, 10, 0, 0, 0),
(6, 12, 13, 14, 15, 2, 4, 4, 4, 1, 3, 1, 0, 21, 16, 11, 6, 15, 4, 7, 7, 5, 4, 4, 1, 0),
(6, 16, 19, 17, 4, 4, 7, 6, 8, 2, 0, 0, 0, 19, 14, 6, 2, 13, 10, 6, 4, 4, 0, 2, 1, 0),
(15, 13, 15, 19, 8, 3, 6, 6, 5, 0, 2, 2, 0, 14, 9, 8, 6, 14, 3, 9, 6, 7, 4, 4, 1, 0),
(13, 4, 10, 14, 7, 4, 7, 3, 7, 6, 0, 2, 0, 10, 12, 9, 10, 11, 6, 3, 5, 7, 4, 2, 0, 0),
(14, 24, 6, 17, 14, 6, 4, 1, 1, 2, 1, 3, 0, 14, 10, 12, 12, 19, 5, 9, 1, 6, 6, 1, 0, 0),
(13, 20, 9, 10, 11, 7, 5, 9, 9, 1, 1, 2, 0, 16, 12, 11, 7, 16, 13, 3, 6, 6, 4, 0, 1, 0),
(17, 7, 12, 12, 8, 5, 5, 4, 6, 3, 1, 0, 0, 13, 8, 10, 10, 11, 4, 4, 5, 3, 6, 4, 3, 0),
(8, 14, 19, 12, 9, 5, 5, 5, 2, 0, 7, 0, 0, 12, 13, 3, 7, 10, 5, 1, 4, 3, 1, 1, 0, 0),
(16, 10, 11, 8, 12, 2, 7, 8, 5, 1, 4, 0, 0, 14, 16, 8, 11, 14, 7, 5, 4, 8, 4, 1, 1, 0),
(7, 13, 15, 14, 9, 2, 4, 4, 3, 1, 2, 0, 0, 16, 12, 19, 4, 13, 6, 5, 1, 11, 4, 1, 1, 0),
(18, 14, 11, 11, 11, 2, 0, 5, 6, 6, 1, 1, 0, 15, 10, 7, 8, 14, 10, 2, 2, 5, 4, 0, 1, 0),
(15, 17, 9, 12, 15, 4, 7, 7, 8, 0, 2, 3, 0, 17, 10, 6, 11, 9, 4, 12, 2, 1, 8, 3, 0, 0),
(10, 11, 6, 11, 12, 4, 7, 4, 8, 2, 2, 1, 0, 10, 8, 9, 7, 11, 3, 8, 3, 4, 4, 5, 0, 0),
(14, 10, 9, 16, 6, 4, 8, 7, 3, 1, 0, 4, 0, 12, 10, 9, 5, 12, 7, 9, 3, 4, 4, 2, 0, 0),
(19, 11, 8, 18, 13, 6, 7, 4, 4, 0, 5, 1, 0, 16, 8, 10, 6, 11, 6, 6, 2, 6, 4, 1, 2, 0),
(17, 6, 13, 11, 15, 9, 2, 1, 9, 4, 2, 0, 0, 11, 10, 8, 4, 7, 5, 9, 4, 6, 5, 0, 0, 0),
(15, 9, 19, 17, 10, 5, 9, 6, 7, 3, 1, 0, 0, 16, 11, 12, 17, 9, 2, 8, 7, 7, 4, 2, 3, 0),
(17, 11, 11, 15, 9, 6, 3, 7, 9, 2, 2, 0, 0, 16, 9, 5, 5, 16, 6, 7, 5, 2, 1, 2, 0, 0),
(9, 8, 8, 14, 12, 11, 3, 2, 3, 3, 0, 0, 0, 18, 17, 13, 4, 13, 5, 5, 1, 5, 4, 4, 0, 0),
(11, 13, 14, 11, 13, 7, 6, 3, 7, 3, 2, 1, 0, 14, 14, 9, 9, 7, 5, 6, 1, 8, 4, 3, 1, 0),
(19, 12, 16, 10, 11, 5, 8, 3, 3, 6, 1, 0, 0, 7, 12, 10, 8, 19, 9, 7, 2, 5, 6, 4, 0, 0),
(10, 8, 14, 12, 13, 5, 9, 5, 5, 1, 3, 0, 0, 18, 21, 9, 11, 6, 2, 2, 4, 8, 2, 3, 1, 0),
(12, 14, 6, 17, 15, 6, 4, 4, 6, 2, 2, 0, 0, 11, 12, 9, 5, 11, 6, 4, 2, 5, 7, 0, 0, 0),
(12, 8, 10, 13, 10, 6, 4, 6, 6, 3, 2, 0, 0, 16, 7, 7, 8, 5, 4, 5, 6, 9, 2, 1, 0, 0),
(17, 17, 12, 9, 15, 8, 2, 3, 5, 1, 1, 1, 0, 10, 15, 13, 5, 13, 4, 6, 4, 7, 3, 1, 0, 0),
(13, 11, 12, 8, 12, 6, 8, 6, 8, 2, 3, 0, 0, 19, 9, 12, 10, 12, 4, 5, 2, 5, 1, 1, 1, 0),
(13, 13, 7, 15, 11, 7, 6, 6, 5, 2, 0, 7, 0, 18, 14, 7, 12, 7, 3, 3, 8, 3, 6, 3, 2, 0),
(12, 6, 13, 6, 6, 5, 8, 3, 6, 4, 2, 0, 0, 17, 9, 8, 8, 12, 7, 5, 3, 7, 4, 2, 0, 0),
(11, 13, 14, 16, 11, 7, 8, 7, 6, 2, 1, 1, 0, 7, 16, 8, 5, 5, 3, 6, 4, 5, 3, 2, 0, 0),
(12, 14, 21, 14, 14, 7, 6, 3, 8, 4, 1, 1, 0, 9, 15, 10, 2, 13, 3, 6, 4, 6, 3, 3, 0, 0),
(16, 11, 11, 11, 15, 4, 4, 4, 6, 1, 2, 1, 0, 11, 7, 8, 11, 10, 8, 3, 3, 5, 8, 2, 0, 0),
(17, 7, 11, 12, 6, 3, 4, 2, 5, 2, 1, 0, 0, 11, 16, 8, 10, 7, 8, 5, 6, 7, 9, 1, 0, 0),
(11, 17, 10, 9, 10, 6, 6, 2, 3, 4, 5, 0, 0, 21, 12, 9, 10, 13, 1, 2, 5, 6, 5, 2, 1, 0),
(15, 9, 14, 15, 7, 4, 4, 5, 4, 1, 3, 1, 0, 18, 14, 9, 4, 9, 6, 9, 4, 5, 4, 2, 0, 0),
(13, 8, 9, 11, 11, 9, 7, 2, 3, 2, 0, 0, 0, 13, 13, 7, 3, 6, 9, 4, 4, 6, 1, 3, 1, 0),
(18, 13, 7, 14, 9, 4, 4, 0, 8, 2, 2, 0, 0, 14, 10, 11, 5, 7, 4, 10, 3, 2, 2, 5, 2, 0),
(12, 12, 7, 13, 13, 7, 1, 8, 5, 4, 5, 1, 0, 14, 17, 8, 8, 11, 4, 5, 3, 5, 5, 2, 1, 0),
(11, 5, 12, 12, 4, 4, 3, 4, 10, 3, 1, 0, 0, 13, 10, 10, 6, 21, 6, 4, 3, 2, 3, 2, 1, 0),
(14, 13, 12, 13, 13, 2, 4, 7, 3, 2, 2, 1, 0, 12, 13, 4, 7, 15, 6, 5, 1, 6, 5, 0, 0, 0),
(9, 19, 11, 11, 7, 5, 2, 4, 3, 4, 0, 3, 0, 12, 17, 7, 11, 11, 6, 5, 2, 3, 3, 0, 2, 0),
(9, 8, 14, 10, 7, 6, 8, 7, 9, 3, 1, 2, 0, 10, 10, 9, 8, 7, 6, 3, 9, 9, 5, 5, 0, 0),
(13, 10, 8, 14, 10, 4, 2, 10, 5, 2, 2, 3, 0, 11, 3, 8, 8, 12, 7, 5, 1, 8, 1, 2, 1, 0),
(17, 8, 12, 12, 12, 7, 4, 2, 4, 4, 0, 2, 0, 8, 9, 5, 6, 10, 5, 3, 1, 7, 4, 4, 2, 0),
(14, 10, 11, 15, 12, 4, 5, 3, 4, 1, 0, 2, 0, 14, 13, 13, 7, 9, 4, 4, 0, 2, 6, 4, 2, 0),
(9, 9, 11, 10, 11, 5, 2, 5, 8, 1, 0, 5, 0, 12, 8, 12, 8, 12, 2, 7, 3, 10, 4, 4, 1, 0),
(16, 11, 10, 7, 12, 3, 2, 3, 6, 1, 1, 1, 0, 13, 12, 4, 5, 10, 9, 6, 3, 6, 4, 2, 0, 0),
(17, 12, 8, 14, 5, 6, 5, 5, 4, 2, 1, 0, 0, 15, 7, 5, 12, 9, 6, 2, 5, 3, 7, 3, 3, 0),
(14, 12, 8, 12, 8, 5, 4, 5, 7, 2, 1, 0, 0, 20, 14, 10, 8, 6, 4, 4, 2, 8, 3, 0, 1, 0),
(14, 6, 11, 14, 11, 4, 4, 5, 5, 3, 1, 1, 0, 10, 6, 14, 6, 8, 9, 4, 5, 2, 3, 1, 0, 0),
(16, 11, 9, 13, 12, 5, 8, 5, 8, 2, 2, 0, 0, 17, 10, 15, 8, 10, 3, 6, 1, 6, 4, 3, 0, 0),
(8, 12, 13, 10, 6, 5, 5, 2, 8, 2, 1, 1, 0, 19, 14, 7, 4, 12, 4, 3, 4, 5, 3, 1, 1, 0),
(11, 12, 16, 5, 3, 9, 3, 2, 7, 2, 0, 2, 0, 15, 13, 3, 3, 8, 5, 7, 4, 6, 4, 3, 1, 0),
(12, 9, 7, 9, 7, 5, 4, 3, 2, 1, 2, 3, 0, 24, 10, 11, 9, 9, 5, 6, 2, 6, 4, 1, 4, 0),
(10, 15, 14, 10, 7, 7, 9, 5, 8, 2, 1, 2, 0, 17, 9, 6, 5, 9, 9, 3, 5, 9, 1, 3, 0, 0),
(15, 7, 10, 8, 8, 4, 4, 4, 5, 0, 0, 0, 0, 13, 9, 13, 9, 9, 6, 5, 5, 5, 4, 0, 2, 0),
(19, 13, 9, 13, 16, 2, 2, 2, 9, 4, 1, 0, 0, 8, 8, 11, 4, 10, 1, 5, 2, 6, 2, 3, 0, 0),
(15, 18, 10, 14, 4, 2, 3, 2, 9, 0, 1, 0, 0, 12, 10, 6, 11, 7, 2, 2, 3, 10, 3, 2, 0, 0),
(9, 7, 13, 17, 5, 2, 0, 1, 8, 1, 3, 1, 0, 18, 9, 11, 6, 9, 11, 2, 2, 5, 5, 4, 1, 0),
(14, 4, 12, 10, 8, 7, 2, 2, 6, 2, 2, 0, 0, 13, 11, 9, 6, 10, 1, 6, 3, 4, 3, 2, 1, 0),
(15, 7, 9, 13, 10, 3, 7, 1, 4, 0, 2, 2, 0, 11, 11, 15, 4, 12, 4, 3, 3, 4, 5, 4, 3, 0),
(4, 14, 12, 13, 12, 6, 1, 6, 4, 0, 0, 1, 0, 11, 9, 9, 4, 7, 11, 3, 2, 4, 5, 1, 1, 0),
(15, 9, 13, 10, 9, 6, 4, 3, 9, 5, 1, 2, 0, 10, 10, 9, 9, 12, 1, 3, 4, 5, 2, 1, 0, 0),
(10, 10, 13, 17, 10, 6, 3, 3, 4, 1, 2, 2, 0, 12, 15, 10, 9, 5, 4, 3, 3, 6, 4, 0, 1, 0),
(5, 8, 8, 7, 11, 3, 3, 5, 5, 2, 4, 1, 0, 10, 9, 11, 3, 11, 7, 3, 3, 3, 2, 4, 3, 0),
(9, 9, 9, 9, 11, 4, 4, 2, 5, 3, 1, 0, 0, 11, 12, 7, 8, 8, 8, 3, 3, 8, 2, 4, 2, 0),
(10, 15, 17, 15, 15, 5, 4, 4, 10, 3, 3, 0, 0, 17, 9, 6, 7, 4, 4, 2, 3, 4, 5, 4, 0, 0),
(23, 10, 9, 8, 11, 4, 2, 3, 9, 1, 2, 0, 0, 17, 12, 6, 7, 8, 5, 2, 3, 8, 3, 3, 0, 0),
(16, 5, 5, 14, 10, 3, 4, 1, 4, 3, 2, 1, 0, 17, 11, 11, 4, 9, 3, 5, 5, 3, 2, 4, 1, 0),
(10, 19, 10, 15, 5, 6, 5, 2, 5, 2, 0, 0, 0, 7, 11, 6, 6, 10, 6, 2, 4, 7, 3, 0, 1, 0),
(7, 8, 9, 6, 11, 4, 5, 2, 4, 3, 1, 0, 0, 13, 11, 11, 10, 11, 6, 7, 4, 5, 2, 4, 0, 0),
(15, 13, 6, 12, 4, 5, 6, 2, 0, 3, 1, 1, 0, 19, 6, 6, 6, 8, 4, 4, 3, 4, 3, 1, 1, 0),
(7, 8, 9, 11, 12, 4, 4, 2, 6, 2, 0, 0, 0, 8, 7, 4, 3, 9, 5, 2, 2, 6, 1, 5, 1, 0),
(15, 12, 9, 9, 4, 7, 4, 2, 8, 5, 1, 2, 0, 11, 5, 9, 5, 11, 3, 4, 3, 3, 3, 4, 1, 0),
(13, 9, 14, 9, 10, 5, 4, 1, 6, 1, 1, 1, 0, 14, 10, 6, 5, 10, 9, 5, 2, 1, 8, 0, 0, 0),
(8, 12, 10, 11, 11, 5, 2, 6, 5, 1, 2, 1, 0, 8, 5, 5, 6, 9, 3, 7, 1, 2, 2, 3, 0, 0),
(5, 6, 19, 7, 5, 2, 1, 4, 6, 1, 0, 0, 0, 9, 10, 9, 5, 8, 7, 3, 4, 3, 1, 2, 0, 0),
(12, 10, 10, 5, 10, 6, 4, 7, 2, 1, 1, 0, 0, 17, 9, 5, 10, 13, 3, 2, 3, 1, 4, 7, 1, 0),
(6, 10, 12, 11, 5, 4, 4, 4, 3, 1, 2, 3, 0, 9, 11, 6, 4, 14, 4, 6, 4, 10, 5, 3, 1, 0),
(15, 8, 11, 16, 9, 5, 2, 3, 7, 2, 1, 1, 0, 11, 5, 12, 5, 12, 1, 5, 5, 5, 4, 2, 0, 0),
(12, 11, 13, 13, 13, 2, 0, 2, 4, 2, 3, 0, 0, 10, 11, 7, 5, 15, 7, 6, 2, 3, 3, 1, 3, 0),
(17, 7, 13, 15, 7, 6, 5, 2, 5, 1, 0, 1, 0, 10, 7, 8, 4, 14, 4, 4, 4, 5, 1, 2, 0, 0),
(10, 11, 12, 12, 12, 5, 2, 3, 2, 2, 2, 0, 0, 18, 11, 8, 6, 12, 4, 7, 2, 6, 3, 3, 1, 0),
(17, 4, 5, 12, 11, 3, 7, 2, 6, 6, 0, 1, 0, 11, 11, 6, 5, 8, 2, 3, 3, 7, 6, 3, 1, 0),
(16, 12, 12, 8, 8, 6, 6, 3, 4, 1, 3, 1, 0, 13, 8, 8, 7, 9, 8, 2, 4, 4, 4, 5, 1, 0),
(13, 9, 5, 14, 8, 5, 3, 5, 6, 2, 3, 1, 0, 6, 9, 7, 3, 11, 6, 5, 7, 5, 6, 3, 1, 0),
(16, 6, 11, 8, 7, 6, 6, 2, 6, 0, 1, 1, 0, 14, 13, 8, 7, 10, 2, 4, 1, 5, 2, 1, 0, 0),
(7, 5, 9, 11, 8, 5, 2, 6, 5, 2, 0, 2, 0, 12, 5, 4, 2, 10, 6, 5, 3, 3, 4, 0, 3, 0),
(17, 14, 13, 10, 15, 4, 5, 5, 7, 0, 3, 4, 0, 13, 10, 6, 6, 15, 5, 6, 3, 2, 5, 1, 1, 0),
(11, 11, 15, 13, 5, 4, 6, 6, 1, 4, 5, 0, 0, 11, 11, 7, 4, 11, 4, 4, 8, 7, 3, 1, 0, 0),
(6, 13, 11, 4, 7, 6, 2, 7, 7, 1, 1, 1, 0, 9, 7, 13, 5, 8, 11, 6, 8, 5, 5, 2, 3, 0),
(15, 12, 8, 10, 9, 5, 4, 5, 4, 0, 1, 1, 0, 11, 9, 13, 5, 11, 4, 5, 2, 3, 3, 1, 0, 0),
(12, 12, 9, 8, 7, 4, 2, 5, 2, 1, 2, 1, 0, 16, 12, 6, 5, 9, 4, 3, 3, 4, 3, 2, 2, 0),
(10, 9, 15, 12, 8, 7, 6, 6, 4, 2, 1, 2, 0, 14, 8, 9, 6, 8, 4, 4, 3, 2, 4, 2, 0, 0),
(18, 13, 6, 15, 12, 4, 6, 2, 5, 4, 2, 0, 0, 10, 7, 8, 5, 8, 2, 3, 4, 7, 2, 3, 2, 0),
(18, 3, 10, 10, 4, 5, 2, 3, 2, 1, 2, 0, 0, 9, 11, 4, 5, 18, 3, 7, 4, 6, 3, 2, 2, 0),
(9, 5, 4, 3, 7, 5, 3, 3, 5, 0, 3, 0, 0, 10, 12, 8, 6, 10, 5, 1, 1, 1, 3, 1, 1, 0),
(9, 3, 9, 12, 8, 3, 1, 1, 4, 3, 1, 0, 0, 14, 6, 3, 4, 9, 4, 3, 7, 3, 5, 0, 0, 0),
(7, 5, 7, 12, 5, 6, 3, 3, 4, 3, 2, 0, 0, 10, 11, 5, 8, 9, 5, 2, 4, 7, 3, 2, 1, 0),
(7, 10, 8, 8, 8, 3, 3, 0, 2, 2, 1, 1, 0, 9, 11, 10, 7, 14, 4, 4, 2, 3, 2, 1, 2, 0),
(17, 6, 10, 13, 6, 1, 5, 2, 1, 0, 1, 0, 0, 14, 3, 5, 3, 7, 3, 2, 4, 5, 4, 1, 0, 0),
(4, 5, 4, 8, 4, 6, 3, 4, 3, 0, 1, 1, 0, 10, 9, 5, 4, 7, 10, 3, 7, 4, 5, 1, 0, 0),
(3, 7, 6, 5, 14, 6, 3, 1, 2, 2, 1, 2, 0, 13, 8, 6, 5, 11, 2, 4, 3, 3, 5, 4, 0, 0),
(9, 5, 16, 8, 8, 4, 6, 4, 7, 3, 2, 0, 0, 5, 9, 1, 8, 12, 5, 4, 2, 3, 1, 4, 0, 0),
(7, 6, 10, 5, 8, 5, 4, 5, 6, 1, 1, 0, 0, 6, 13, 7, 6, 7, 6, 3, 4, 4, 1, 1, 0, 0),
(13, 5, 12, 5, 6, 4, 1, 5, 2, 2, 2, 0, 0, 11, 7, 6, 5, 7, 4, 1, 5, 5, 1, 1, 0, 0),
(11, 6, 9, 7, 4, 2, 1, 6, 7, 2, 0, 0, 0, 6, 11, 7, 3, 6, 7, 7, 3, 6, 3, 2, 1, 0),
(12, 10, 5, 6, 9, 6, 2, 5, 7, 1, 1, 0, 0, 10, 5, 10, 4, 13, 3, 1, 1, 7, 2, 3, 0, 0),
(9, 6, 12, 8, 4, 3, 2, 3, 7, 2, 3, 0, 0, 12, 6, 7, 1, 5, 2, 3, 6, 3, 4, 2, 0, 0),
(9, 5, 7, 9, 8, 3, 2, 3, 6, 1, 0, 0, 0, 9, 5, 4, 2, 8, 3, 3, 4, 5, 3, 1, 1, 0),
(11, 8, 12, 7, 5, 4, 6, 1, 6, 3, 1, 1, 0, 6, 9, 3, 1, 4, 5, 2, 3, 2, 4, 0, 0, 0),
(5, 3, 13, 9, 4, 2, 1, 4, 5, 1, 1, 0, 0, 12, 5, 5, 5, 9, 6, 2, 1, 2, 4, 1, 0, 0),
(6, 7, 8, 8, 4, 3, 6, 5, 6, 1, 3, 0, 0, 13, 6, 10, 5, 5, 4, 1, 3, 5, 1, 0, 1, 0),
(9, 5, 11, 11, 5, 3, 3, 3, 2, 2, 1, 2, 0, 8, 8, 6, 3, 8, 3, 3, 1, 1, 2, 1, 0, 0),
(5, 5, 8, 10, 3, 2, 5, 3, 4, 0, 2, 0, 0, 13, 6, 4, 2, 6, 2, 2, 3, 0, 2, 3, 0, 0),
(11, 5, 5, 6, 5, 2, 3, 3, 3, 0, 2, 1, 0, 9, 6, 9, 6, 7, 3, 3, 4, 6, 1, 2, 2, 0),
(10, 4, 5, 4, 11, 4, 1, 4, 2, 1, 1, 1, 0, 7, 6, 11, 4, 13, 7, 0, 0, 2, 4, 0, 0, 0),
(9, 2, 4, 6, 3, 5, 1, 1, 2, 3, 2, 0, 0, 4, 5, 4, 3, 8, 2, 2, 3, 1, 1, 3, 0, 0),
(8, 6, 2, 4, 6, 3, 2, 0, 2, 1, 0, 0, 0, 8, 7, 9, 5, 4, 1, 3, 2, 3, 6, 0, 1, 0),
(6, 5, 6, 6, 7, 3, 2, 0, 2, 2, 0, 0, 0, 8, 6, 3, 2, 6, 4, 0, 2, 4, 2, 0, 0, 0),
(8, 3, 5, 7, 5, 1, 2, 0, 2, 2, 3, 1, 0, 6, 3, 1, 5, 5, 1, 0, 3, 2, 2, 2, 2, 0),
(5, 5, 4, 7, 8, 4, 2, 1, 2, 1, 1, 2, 0, 5, 0, 2, 2, 5, 3, 1, 1, 2, 3, 0, 0, 0),
(11, 4, 9, 6, 5, 3, 3, 2, 1, 2, 0, 1, 0, 8, 1, 4, 3, 5, 1, 3, 0, 3, 1, 0, 0, 0),
(5, 4, 4, 3, 6, 2, 1, 3, 5, 1, 0, 2, 0, 9, 1, 1, 2, 4, 0, 2, 0, 5, 2, 2, 1, 0),
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
)
station_arriving_intensity = (
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(12.162342344054133, 9.862809208368793, 12.322942576229327, 14.321630446450746, 14.163769929633231, 7.726168621972872, 7.79340439033489, 8.201882437180522, 14.704359902068381, 7.776713250962773, 9.109795738228751, 10.822634239700733, 12.637174321135817),
(12.108881678095097, 9.808067249057736, 12.289122913005274, 14.273973549197011, 14.12123976782977, 7.710171370628429, 7.756271312872975, 8.18801268373637, 14.679365876237274, 7.746913514390087, 9.07692443618372, 10.785667425485194, 12.59848342779883),
(12.05425012155593, 9.752602061108423, 12.254457332100213, 14.225234287304469, 14.077539276079325, 7.693788257079036, 7.718570249977489, 8.173943374010788, 14.65376200990745, 7.716582761464252, 9.043452938835248, 10.747917727613418, 12.558761488821151),
(11.998396762348548, 9.696329108367367, 12.218890657658735, 14.175338483258576, 14.032626281122448, 7.6769806359570785, 7.6802375415878785, 8.159609491720442, 14.627479281821747, 7.685662390440583, 9.009313349231029, 10.709320787588808, 12.517966093179089),
(11.941270688384867, 9.639163854681073, 12.182367713825425, 14.12421195954477, 13.986458609699687, 7.6597098618949495, 7.6412095276435865, 8.144946020581987, 14.600448670722995, 7.654093799574386, 8.974437770418753, 10.66981224691477, 12.476054829848946),
(11.882820987576796, 9.581021763896047, 12.144833324744877, 14.071780538648504, 13.938994088551583, 7.641937289525037, 7.601422548084064, 8.129887944312085, 14.572601155354022, 7.621818387120976, 8.938758305446116, 10.62932774709471, 12.432985287807028),
(11.822996747836257, 9.521818299858795, 12.106232314561684, 14.017970043055223, 13.890190544418692, 7.623624273479732, 7.560812942848756, 8.114370246627395, 14.543867714457667, 7.588777551335661, 8.902207057360812, 10.58780292963203, 12.38871505602964),
(11.761747057075162, 9.46146892641583, 12.066509507420426, 13.962706295250376, 13.840005804041555, 7.604732168391422, 7.519317051877113, 8.09832791124458, 14.514179326776754, 7.554912690473753, 8.864716129210535, 10.545173436030137, 12.34320172349308),
(11.69902100320542, 9.399889107413653, 12.0256097274657, 13.90591511771941, 13.788397694160723, 7.585222328892499, 7.476871215108577, 8.081695921880296, 14.48346697105412, 7.52016520279056, 8.826217624042977, 10.501374907792433, 12.296402879173653),
(11.634767674138946, 9.336994306698774, 11.983477798842097, 13.847522332947767, 13.735324041516742, 7.56505610961535, 7.4334117724825965, 8.064409262251205, 14.451661626032607, 7.484476486541395, 8.786643644905832, 10.456342986422326, 12.248276112047666),
(11.56893615778766, 9.2726999881177, 11.9400585456942, 13.787453763420901, 13.680742672850162, 7.544194865192366, 7.3888750639386185, 8.04640291607397, 14.418694270455035, 7.4477879399815645, 8.745926294846791, 10.41001331342322, 12.198779011091421),
(11.501475542063469, 9.20692161551694, 11.895296792166606, 13.725635231624254, 13.624611414901528, 7.5225999502559375, 7.343197429416091, 8.027611867065247, 14.384495883064238, 7.410040961366383, 8.703997676913554, 10.36232153029852, 12.14786916528122),
(11.432334914878291, 9.139574652742999, 11.849137362403903, 13.661992560043277, 13.566888094411391, 7.500232719438453, 7.2963152088544625, 8.007971098941699, 14.34899744260305, 7.37117694895116, 8.660789894153808, 10.313203278551628, 12.095504163593366),
(11.361463364144042, 9.070574563642383, 11.801525080550675, 13.596451571163414, 13.507530538120294, 7.477054527372301, 7.2481647421931745, 7.987415595419982, 14.312129927814308, 7.331137300991204, 8.616235049615252, 10.262594199685955, 12.041641595004167),
(11.288809977772631, 8.999836812061604, 11.752404770751518, 13.528938087470117, 13.446496572768787, 7.453026728689875, 7.198682369371678, 7.965880340216761, 14.273824317440841, 7.289863415741826, 8.570265246345576, 10.210429935204898, 11.986239048489919),
(11.214323843675977, 8.927276861847163, 11.701721257151021, 13.459377931448826, 13.38374402509742, 7.42811067802356, 7.147804430329418, 7.943300317048694, 14.234011590225474, 7.247296691458339, 8.522812587392474, 10.156646126611868, 11.929254113026934),
(11.137954049765991, 8.852810176845571, 11.649419363893772, 13.387696925584994, 13.319230721846738, 7.402267730005749, 7.0954672650058415, 7.91961050963244, 14.192622724911054, 7.2033785263960475, 8.473809175803641, 10.101178415410269, 11.870644377591507),
(11.059649683954586, 8.776352220903336, 11.59544391512436, 13.313820892364063, 13.252914489757288, 7.375459239268828, 7.041607213340397, 7.8947459016846615, 14.149588700240406, 7.15805031881027, 8.423187114626767, 10.043962443103501, 11.810367431159946),
(10.979359834153682, 8.697818457866962, 11.539739734987382, 13.237675654271488, 13.184753155569618, 7.34764656044519, 6.986160615272531, 7.8686414769220185, 14.10484049495636, 7.11125346695631, 8.37087850690955, 9.984933851194974, 11.748380862708558),
(10.897033588275185, 8.61712435158296, 11.482251647627416, 13.159187033792707, 13.11470454602428, 7.318791048167222, 6.929063810741687, 7.841232219061167, 14.058309087801755, 7.062929369089481, 8.316815455699683, 9.92402828118809, 11.68464226121364),
(10.81262003423102, 8.534185365897834, 11.422924477189063, 13.078280853413174, 13.042726487861813, 7.288854057067317, 6.87025313968732, 7.8124531118187726, 14.009925457519413, 7.013019423465095, 8.260930064044857, 9.861181374586256, 11.6191092156515),
(10.72606825993309, 8.448916964658093, 11.361703047816906, 12.99488293561833, 12.968776807822776, 7.257796941777861, 6.809664942048866, 7.782239138911491, 13.95962058285218, 6.9614650283384565, 8.203154434992767, 9.796328772892876, 11.551739314998438),
(10.637327353293314, 8.361234611710243, 11.298532183655539, 12.908919102893627, 12.892813332647707, 7.225581056931246, 6.74723555776578, 7.750525284055986, 13.907325442542877, 6.9082075819648825, 8.143420671591107, 9.729406117611353, 11.48249014823076),
(10.546346402223609, 8.271053770900794, 11.233356708849547, 12.820315177724513, 12.81479388907716, 7.19216775715986, 6.6829013267775075, 7.717246530968915, 13.852971015334345, 6.853188482599679, 8.08166087688757, 9.660349050245092, 11.411319304324769),
(10.450553324967336, 8.176634369081162, 11.163028735463298, 12.725677414311741, 12.731153548219398, 7.155434266843955, 6.615149409299001, 7.680115733289122, 13.792326928238738, 6.794712282807602, 8.01583405355452, 9.586639389872076, 11.335080203181485),
(10.335201473769764, 8.06829144743927, 11.069432945764184, 12.605568022303835, 12.62126783369428, 7.103165507209945, 6.535497868740003, 7.626098945870136, 13.700998165711002, 6.723193391738244, 7.934383709866593, 9.493907533156353, 11.235598705688274),
(10.198820932866035, 7.945135419957, 10.950689341138245, 12.458008514572404, 12.482988183885514, 7.034077814466758, 6.443141247737298, 7.553838865338286, 13.576395318120113, 6.637687912608051, 7.8361633120533565, 9.380702728442985, 11.110988852451014),
(10.042510876420344, 7.8079692153126565, 10.808065760674433, 12.28440150525942, 12.317750373994958, 6.94900813819844, 6.338754024409627, 7.464240746353693, 13.420161673798626, 6.5389214704393135, 7.7220383164395905, 9.248074456470599, 10.962523662746737),
(9.8673704785969, 7.657595762184535, 10.642830043461695, 12.086149608506858, 12.126990179224487, 6.848793427989039, 6.223010676875733, 7.358209843576484, 13.233940521079093, 6.427619690254325, 7.592874179350069, 9.09707219797781, 10.791476155852466),
(9.674498913559898, 7.494817989250934, 10.456250028588983, 11.864655438456708, 11.912143374775964, 6.734270633422602, 6.096585683254362, 7.2366514116667755, 13.019375148294069, 6.304508197075376, 7.449536357109572, 8.928745433703247, 10.599119351045232),
(9.464995355473539, 7.320438825190149, 10.249593555145248, 11.621321609250947, 11.674645735851264, 6.606276704083181, 5.960153521664253, 7.100470705284697, 12.778108843776113, 6.170312615924756, 7.292890306042875, 8.744143644385526, 10.386726267602059),
(9.239958978502024, 7.135261198680485, 10.024128462219437, 11.357550735031554, 11.415933037652254, 6.465648589554821, 5.814388670224151, 6.950572979090365, 12.511784895857772, 6.02575857182476, 7.123801482474756, 8.544316310763268, 10.155569924799979),
(9.000488956809557, 6.940088038400237, 9.7811225889005, 11.074745429940503, 11.137441055380801, 6.313223239421572, 5.659965607052801, 6.787863487743908, 12.222046592871603, 5.871571689797677, 6.943135342729992, 8.330312913575103, 9.906923341916015),
(8.747684464560333, 6.735722273027703, 9.521843774277388, 10.774308308119782, 10.840605564238773, 6.149837603267482, 5.497558810268945, 6.613247485905448, 11.91053722315016, 5.7084775948658, 6.751757343133359, 8.103182933559642, 9.642059538227196),
(8.482644675918554, 6.52296683124118, 9.247559857439049, 10.457641983711365, 10.526862339428039, 5.9763286306765995, 5.327842757991326, 6.427630228235103, 11.578900075025999, 5.5372019120514215, 6.550532940009634, 7.863975851455517, 9.362251533010546),
(8.206468765048422, 6.302624641718972, 8.959538677474432, 10.126149070857236, 10.197647156150468, 5.793533271232973, 5.151491928338689, 6.231916969393004, 11.228778436831673, 5.358470266376831, 6.3403275896835956, 7.613741148001342, 9.0687723455431),
(7.9202559061141375, 6.0754986331393726, 8.659048073472489, 9.781232183699368, 9.854395789607928, 5.60228847452065, 4.9691807994297745, 6.027012964039266, 10.861815596899735, 5.173008282864322, 6.122006748480023, 7.353528303935743, 8.762894995101878),
(7.6251052732799005, 5.842391734180682, 8.34735588452217, 9.424293936379751, 9.498544015002288, 5.403431190123678, 4.781583849383328, 5.813823466834017, 10.47965484356274, 4.981541586536184, 5.896435872723688, 7.0843867999973416, 8.445892500963913),
(7.322116040709912, 5.604106873521197, 8.025729949712423, 9.056736943040356, 9.131527607535416, 5.197798367626108, 4.5893755563180925, 5.593253732437379, 10.083939465153241, 4.784795802414712, 5.664480418739371, 6.80736611692476, 8.119037882406225),
(7.012387382568372, 5.3614469798392195, 7.695438108132197, 8.679963817823166, 8.754782342409182, 4.9862269566119855, 4.39323039835281, 5.366209015509473, 9.676312750003792, 4.583496555522195, 5.427005842851849, 6.523515735456615, 7.783604158705848),
(6.697018473019482, 5.115214981813045, 7.357748198870443, 8.295377174870158, 8.369743994825454, 4.76955390666536, 4.193822853606226, 5.133594570710425, 9.25841798644695, 4.3783694708809255, 5.1848776013858995, 6.233885136331535, 7.440864349139807),
(6.377108486227438, 4.866213808120973, 7.013928061016112, 7.904379628323315, 7.977848339986097, 4.54861616737028, 3.9918274001970815, 4.896315652700355, 8.831898462815268, 4.170140173513194, 4.938961150666297, 5.939523800288141, 7.092091472985131),
(6.053756596356447, 4.615246387441302, 6.66524553365815, 7.508373792324615, 7.580531153092983, 4.324250688310793, 3.787918516244121, 4.655277516139389, 8.3983974674413, 3.959534288441294, 4.690121947017822, 5.641481208065051, 6.738558549518844),
(5.7280619775707065, 4.363115648452332, 6.3129684558855095, 7.108762281016037, 7.179228209347984, 4.097294419070949, 3.582770679866088, 4.411385415687646, 7.959558288657599, 3.7472774406875144, 4.43922544676525, 5.340806840400891, 6.381538598017975),
(5.401123804034416, 4.11062451983236, 5.95836466678714, 6.7069477085395635, 6.775375283952959, 3.8685843092347962, 3.3770583691817246, 4.165544606005252, 7.51702421479672, 3.5340952552741505, 4.187137106233358, 5.038550178034279, 6.022304637759553),
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
)
passenger_arriving_acc = (
(9, 10, 5, 5, 3, 2, 2, 3, 3, 1, 1, 0, 0, 6, 9, 0, 8, 12, 3, 4, 1, 0, 2, 2, 2, 0),
(14, 20, 14, 16, 9, 4, 2, 8, 4, 2, 2, 0, 0, 17, 14, 5, 14, 18, 4, 6, 3, 1, 6, 2, 2, 0),
(21, 29, 17, 19, 12, 7, 5, 13, 8, 6, 4, 0, 0, 26, 20, 10, 21, 27, 5, 11, 7, 3, 8, 4, 4, 0),
(26, 39, 28, 28, 24, 10, 11, 19, 12, 7, 4, 0, 0, 32, 30, 12, 29, 35, 13, 13, 9, 6, 10, 6, 4, 0),
(37, 48, 34, 36, 31, 13, 15, 24, 16, 8, 4, 0, 0, 40, 42, 15, 34, 45, 16, 13, 13, 7, 11, 6, 7, 0),
(48, 57, 43, 49, 34, 16, 22, 31, 20, 10, 4, 2, 0, 47, 55, 22, 42, 54, 21, 18, 14, 9, 16, 7, 8, 0),
(60, 70, 51, 57, 42, 20, 23, 35, 23, 14, 7, 4, 0, 56, 63, 29, 51, 60, 23, 25, 17, 15, 19, 7, 9, 0),
(72, 78, 59, 68, 55, 23, 26, 36, 27, 15, 8, 4, 0, 68, 70, 40, 55, 69, 27, 28, 19, 18, 21, 8, 10, 0),
(86, 89, 76, 78, 63, 26, 29, 38, 30, 16, 11, 4, 0, 79, 77, 49, 61, 80, 29, 35, 23, 19, 24, 9, 10, 0),
(97, 99, 83, 87, 71, 34, 36, 43, 35, 16, 14, 7, 0, 96, 89, 58, 67, 90, 38, 39, 25, 22, 26, 13, 11, 0),
(107, 111, 94, 97, 78, 37, 42, 46, 42, 19, 16, 9, 0, 114, 102, 70, 76, 102, 44, 41, 28, 28, 28, 16, 13, 0),
(122, 122, 104, 108, 90, 44, 46, 49, 45, 20, 19, 11, 0, 124, 107, 83, 84, 114, 50, 47, 31, 29, 29, 20, 14, 0),
(133, 138, 114, 119, 99, 46, 53, 53, 48, 23, 20, 13, 0, 141, 115, 92, 89, 122, 55, 52, 31, 35, 33, 21, 15, 0),
(144, 155, 124, 129, 105, 53, 57, 55, 54, 24, 23, 14, 0, 155, 127, 96, 92, 127, 61, 56, 36, 38, 40, 24, 16, 0),
(156, 176, 136, 141, 116, 60, 61, 59, 58, 27, 28, 14, 0, 165, 139, 102, 99, 139, 67, 63, 37, 42, 44, 24, 16, 0),
(162, 188, 145, 162, 128, 63, 64, 63, 64, 29, 29, 16, 0, 174, 158, 110, 102, 145, 74, 70, 43, 50, 44, 24, 18, 0),
(169, 200, 155, 175, 140, 70, 69, 67, 68, 32, 31, 19, 0, 186, 166, 114, 108, 159, 84, 74, 45, 54, 48, 25, 19, 0),
(184, 215, 173, 190, 147, 79, 73, 70, 73, 38, 34, 20, 0, 198, 176, 121, 119, 169, 90, 76, 50, 61, 51, 26, 19, 0),
(198, 229, 189, 207, 162, 83, 79, 74, 77, 39, 36, 21, 0, 214, 193, 128, 123, 176, 97, 85, 55, 67, 55, 28, 21, 0),
(221, 245, 201, 220, 168, 89, 85, 77, 84, 39, 37, 22, 0, 231, 211, 139, 134, 191, 110, 91, 58, 72, 61, 32, 23, 0),
(233, 257, 214, 233, 178, 96, 91, 81, 90, 41, 39, 23, 0, 241, 228, 152, 141, 202, 117, 96, 63, 78, 66, 36, 24, 0),
(249, 275, 226, 246, 186, 102, 93, 87, 101, 43, 40, 24, 0, 248, 240, 162, 151, 218, 121, 100, 70, 82, 69, 37, 26, 0),
(263, 288, 234, 257, 193, 109, 95, 90, 110, 47, 42, 24, 0, 266, 249, 168, 163, 230, 128, 103, 73, 85, 77, 37, 29, 0),
(279, 297, 244, 265, 207, 118, 101, 92, 119, 50, 44, 25, 0, 282, 270, 177, 167, 235, 135, 109, 79, 86, 81, 38, 30, 0),
(290, 310, 255, 275, 213, 122, 111, 98, 125, 51, 49, 26, 0, 300, 287, 193, 170, 247, 148, 117, 81, 90, 82, 42, 31, 0),
(301, 326, 272, 282, 225, 127, 119, 103, 131, 53, 49, 28, 0, 310, 301, 203, 177, 267, 153, 129, 85, 93, 85, 45, 33, 0),
(319, 341, 290, 290, 237, 131, 133, 105, 138, 54, 50, 29, 0, 319, 311, 213, 186, 283, 156, 133, 87, 97, 89, 46, 34, 0),
(328, 350, 298, 299, 242, 138, 144, 110, 146, 56, 53, 30, 0, 329, 324, 222, 194, 295, 169, 136, 88, 100, 93, 47, 35, 0),
(346, 364, 315, 315, 259, 140, 151, 117, 153, 63, 55, 32, 0, 349, 337, 234, 202, 302, 170, 140, 90, 106, 94, 49, 37, 0),
(362, 380, 324, 330, 265, 144, 155, 124, 158, 66, 63, 33, 0, 361, 351, 245, 211, 313, 177, 142, 97, 108, 101, 55, 37, 0),
(375, 392, 341, 344, 277, 151, 164, 128, 160, 67, 66, 33, 0, 383, 365, 254, 220, 333, 183, 151, 100, 119, 104, 55, 39, 0),
(392, 401, 351, 366, 291, 157, 168, 130, 163, 72, 69, 33, 0, 402, 378, 263, 227, 348, 186, 153, 103, 124, 106, 56, 40, 0),
(404, 415, 367, 378, 302, 168, 173, 135, 169, 73, 71, 34, 0, 417, 390, 276, 234, 356, 199, 157, 108, 128, 113, 57, 42, 0),
(415, 431, 382, 395, 305, 175, 174, 141, 175, 74, 75, 35, 0, 435, 400, 285, 242, 370, 201, 160, 111, 133, 121, 58, 43, 0),
(423, 442, 394, 410, 323, 184, 178, 147, 180, 76, 79, 35, 0, 448, 413, 290, 246, 381, 208, 166, 116, 135, 123, 59, 43, 0),
(446, 458, 405, 415, 336, 187, 182, 151, 184, 79, 82, 36, 0, 461, 425, 298, 252, 390, 215, 170, 121, 141, 129, 65, 44, 0),
(459, 470, 422, 431, 350, 190, 186, 155, 189, 82, 83, 36, 0, 472, 430, 304, 263, 396, 224, 176, 124, 147, 134, 66, 45, 0),
(474, 479, 435, 443, 358, 195, 195, 163, 197, 83, 84, 37, 0, 494, 448, 315, 270, 410, 235, 184, 126, 152, 141, 69, 45, 0),
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(1880, 1713, 1656, 1743, 1414, 716, 683, 604, 803, 320, 259, 154, 0, 1974, 1622, 1293, 1016, 1586, 848, 725, 512, 720, 558, 307, 150, 0),
(1892, 1725, 1665, 1751, 1421, 720, 685, 609, 805, 321, 261, 155, 0, 1990, 1634, 1299, 1021, 1595, 852, 728, 515, 724, 561, 309, 152, 0),
(1902, 1734, 1680, 1763, 1429, 727, 691, 615, 809, 323, 262, 157, 0, 2004, 1642, 1308, 1027, 1603, 856, 732, 518, 726, 565, 311, 152, 0),
(1920, 1747, 1686, 1778, 1441, 731, 697, 617, 814, 327, 264, 157, 0, 2014, 1649, 1316, 1032, 1611, 858, 735, 522, 733, 567, 314, 154, 0),
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(1991, 1784, 1738, 1844, 1483, 760, 717, 633, 835, 336, 275, 159, 0, 2090, 1712, 1356, 1069, 1685, 892, 757, 551, 762, 592, 322, 160, 0),
(1994, 1791, 1744, 1849, 1497, 766, 720, 634, 837, 338, 276, 161, 0, 2103, 1720, 1362, 1074, 1696, 894, 761, 554, 765, 597, 326, 160, 0),
(2003, 1796, 1760, 1857, 1505, 770, 726, 638, 844, 341, 278, 161, 0, 2108, 1729, 1363, 1082, 1708, 899, 765, 556, 768, 598, 330, 160, 0),
(2010, 1802, 1770, 1862, 1513, 775, 730, 643, 850, 342, 279, 161, 0, 2114, 1742, 1370, 1088, 1715, 905, 768, 560, 772, 599, 331, 160, 0),
(2023, 1807, 1782, 1867, 1519, 779, 731, 648, 852, 344, 281, 161, 0, 2125, 1749, 1376, 1093, 1722, 909, 769, 565, 777, 600, 332, 160, 0),
(2034, 1813, 1791, 1874, 1523, 781, 732, 654, 859, 346, 281, 161, 0, 2131, 1760, 1383, 1096, 1728, 916, 776, 568, 783, 603, 334, 161, 0),
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passenger_arriving_rate = (
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)
entropy = 8991598675325360468762009371570610170
child_seed_index = (
1,
47,
)
| true
| true
|
f716a1a5f632f06075f249c2221bcd21beac3b38
| 657
|
py
|
Python
|
odc_gee/setup.py
|
admariner/data_cube_notebooks
|
984a84b2f92114040e36a533d3f476dcf384695e
|
[
"Apache-2.0"
] | null | null | null |
odc_gee/setup.py
|
admariner/data_cube_notebooks
|
984a84b2f92114040e36a533d3f476dcf384695e
|
[
"Apache-2.0"
] | null | null | null |
odc_gee/setup.py
|
admariner/data_cube_notebooks
|
984a84b2f92114040e36a533d3f476dcf384695e
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
from setuptools import setup, find_packages
setup(name='odc-gee',
version='2.24',
description='Google Earth Engine indexing tools for Open Data Cube',
author='Andrew Lubawy',
author_email='andrew.m.lubawy@ama-inc.com',
install_requires=[
"google-auth>=1.11.0,<=1.32.0"
"click-plugins>=1.1.1",
"click>=7.1.2",
"datacube>=1.8.3",
"earthengine-api>=0.1.24",
"numpy>=1.18.4",
"rasterio>=1.1.8",
"google-api-core==1.31.2"
],
packages=find_packages(),
scripts=['scripts/index_gee', 'scripts/new_product'],)
| 29.863636
| 74
| 0.564688
|
from setuptools import setup, find_packages
setup(name='odc-gee',
version='2.24',
description='Google Earth Engine indexing tools for Open Data Cube',
author='Andrew Lubawy',
author_email='andrew.m.lubawy@ama-inc.com',
install_requires=[
"google-auth>=1.11.0,<=1.32.0"
"click-plugins>=1.1.1",
"click>=7.1.2",
"datacube>=1.8.3",
"earthengine-api>=0.1.24",
"numpy>=1.18.4",
"rasterio>=1.1.8",
"google-api-core==1.31.2"
],
packages=find_packages(),
scripts=['scripts/index_gee', 'scripts/new_product'],)
| true
| true
|
f716a22900c092bb89fd4f6f98a63d9202ab5429
| 7,980
|
py
|
Python
|
client/deploy_kafka.py
|
jzmq/minos
|
510b6e30758f4900a72fee1a5e6258bdc7c83f17
|
[
"Apache-2.0"
] | 365
|
2015-01-26T13:56:42.000Z
|
2022-03-28T06:36:31.000Z
|
client/deploy_kafka.py
|
jzmq/minos
|
510b6e30758f4900a72fee1a5e6258bdc7c83f17
|
[
"Apache-2.0"
] | 3
|
2015-12-29T07:44:24.000Z
|
2021-03-18T06:13:07.000Z
|
client/deploy_kafka.py
|
jzmq/minos
|
510b6e30758f4900a72fee1a5e6258bdc7c83f17
|
[
"Apache-2.0"
] | 135
|
2015-01-31T00:46:51.000Z
|
2022-03-03T06:31:09.000Z
|
#!/usr/bin/env python
import argparse
import os
import parallel_deploy
import service_config
import subprocess
import sys
import urlparse
import deploy_utils
from log import Log
ALL_JOBS = ["kafka", "kafkascribe"]
def _get_kafka_service_config(args):
args.kafka_config = deploy_utils.get_service_config(args)
def generate_configs(args, job_name, host_id, instance_id):
kafka_cfg_dict = args.kafka_config.configuration.generated_files["kafka.cfg"]
hosts = args.kafka_config.jobs[job_name].hosts
kafka_cfg_dict["broker.id"] = deploy_utils.get_task_id(hosts, host_id, instance_id)
kafka_cfg = deploy_utils.generate_properties_file(args, kafka_cfg_dict)
kafka_scribe_cfg_dict = args.kafka_config.configuration.generated_files["kafka-scribe.cfg"]
kafka_job = args.kafka_config.jobs["kafka"]
kafka_scribe_cfg_dict["metadata.broker.list"] = ",".join(
service_config.get_job_host_port_list(kafka_job))
kafka_scribe_cfg = deploy_utils.generate_properties_file(args, kafka_scribe_cfg_dict)
config_files = {
"kafka.cfg": kafka_cfg,
"kafka-scribe.cfg": kafka_scribe_cfg,
}
config_files.update(args.kafka_config.configuration.raw_files)
return config_files
def generate_run_scripts_params(args, host, job_name, host_id, instance_id):
job = args.kafka_config.jobs[job_name]
supervisor_client = deploy_utils.get_supervisor_client(host,
"kafka", args.kafka_config.cluster.name, job_name, instance_id=instance_id)
artifact_and_version = "kafka-" + args.kafka_config.cluster.version
jar_dirs = "$package_dir/*"
log_level = deploy_utils.get_service_log_level(args, args.kafka_config)
params = job.get_arguments(args, args.kafka_config.cluster, args.kafka_config.jobs,
args.kafka_config.arguments_dict, job_name, host_id, instance_id)
script_dict = {
"artifact": artifact_and_version,
"job_name": job_name,
"jar_dirs": jar_dirs,
"run_dir": supervisor_client.get_run_dir(),
"params": params,
}
return script_dict
def generate_start_script(args, host, job_name, host_id, instance_id):
script_params = generate_run_scripts_params(args, host, job_name, host_id, instance_id)
return deploy_utils.create_run_script(
"%s/start.sh.tmpl" % deploy_utils.get_template_dir(),
script_params)
def install(args):
_get_kafka_service_config(args)
deploy_utils.install_service(args, "kafka", args.kafka_config, "kafka")
def cleanup_job(args, host, job_name, host_id, instance_id, cleanup_token, active):
deploy_utils.cleanup_job("kafka", args.kafka_config,
host, job_name, instance_id, cleanup_token)
def cleanup(args):
_get_kafka_service_config(args)
cleanup_token = deploy_utils.confirm_cleanup(args,
"kafka", args.kafka_config)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,
'cleanup', cleanup_token=cleanup_token)
parallel_deploy.start_deploy_threads(cleanup_job, task_list)
def bootstrap_job(args, host, job_name, host_id, instance_id, cleanup_token, active):
# parse the service_config according to the instance_id
args.kafka_config.parse_generated_config_files(args, job_name, host_id, instance_id)
deploy_utils.bootstrap_job(args, "kafka", "kafka",
args.kafka_config, host, job_name, instance_id, cleanup_token, '0')
start_job(args, host, job_name, host_id, instance_id)
def bootstrap(args):
_get_kafka_service_config(args)
cleanup_token = deploy_utils.confirm_bootstrap("kafka", args.kafka_config)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,
'bootstrap', cleanup_token=cleanup_token)
parallel_deploy.start_deploy_threads(bootstrap_job, task_list)
def start_job(args, host, job_name, host_id, instance_id, is_wait=False):
if is_wait:
deploy_utils.wait_for_job_stopping("kafka",
args.kafka_config.cluster.name, job_name, host, instance_id)
# parse the service_config according to the instance_id
args.kafka_config.parse_generated_config_files(args, job_name, host_id, instance_id)
config_files = generate_configs(args, job_name, host_id, instance_id)
start_script = generate_start_script(args, host, job_name, host_id, instance_id)
http_url = deploy_utils.get_http_service_uri(host,
args.kafka_config.jobs[job_name].base_port, instance_id)
deploy_utils.start_job(args, "kafka", "kafka", args.kafka_config,
host, job_name, instance_id, start_script, http_url, **config_files)
def start(args):
if not args.skip_confirm:
deploy_utils.confirm_start(args)
_get_kafka_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'start')
parallel_deploy.start_deploy_threads(start_job, task_list)
def stop_job(args, host, job_name, instance_id):
deploy_utils.stop_job("kafka", args.kafka_config, host, job_name, instance_id)
def stop(args):
if not args.skip_confirm:
deploy_utils.confirm_stop(args)
_get_kafka_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'stop')
parallel_deploy.start_deploy_threads(stop_job, task_list)
def restart(args):
if not args.skip_confirm:
deploy_utils.confirm_restart(args)
_get_kafka_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'stop')
parallel_deploy.start_deploy_threads(stop_job, task_list)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,
'start', is_wait=True)
parallel_deploy.start_deploy_threads(start_job, task_list)
def show_job(args, host, job_name, instance_id):
deploy_utils.show_job("kafka", args.kafka_config, host, job_name, instance_id)
def show(args):
_get_kafka_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'show')
parallel_deploy.start_deploy_threads(show_job, task_list)
def run_shell(args):
Log.print_critical("'shell' command is not supported!")
def pack(args):
Log.print_critical("'pack' command is not supported!")
def rolling_update(args):
if not args.job:
Log.print_critical("You must specify the job name to do rolling update")
_get_kafka_service_config(args)
job_name = args.job[0]
if not args.skip_confirm:
deploy_utils.confirm_action(args, "rolling_update")
Log.print_info("Rolling updating %s" % job_name)
hosts = args.kafka_config.jobs[job_name].hosts
wait_time = 0
args.task_map = deploy_utils.parse_args_host_and_task(args, hosts)
for host_id in args.task_map.keys() or hosts.iterkeys():
for instance_id in args.task_map.get(host_id) or range(hosts[host_id].instance_num):
instance_id = -1 if not deploy_utils.is_multiple_instances(host_id, hosts) else instance_id
deploy_utils.confirm_rolling_update(host_id, instance_id, wait_time)
stop_job(args, hosts[host_id].ip, job_name, instance_id)
deploy_utils.wait_for_job_stopping("kafka",
args.kafka_config.cluster.name, job_name, hosts[host_id].ip, instance_id)
start_job(args, hosts[host_id].ip, job_name, host_id, instance_id)
deploy_utils.wait_for_job_starting("kafka",
args.kafka_config.cluster.name, job_name, hosts[host_id].ip, instance_id)
wait_time = args.time_interval
Log.print_success("Rolling updating %s success" % job_name)
if __name__ == '__main__':
test()
| 38.550725
| 97
| 0.773308
|
import argparse
import os
import parallel_deploy
import service_config
import subprocess
import sys
import urlparse
import deploy_utils
from log import Log
ALL_JOBS = ["kafka", "kafkascribe"]
def _get_kafka_service_config(args):
args.kafka_config = deploy_utils.get_service_config(args)
def generate_configs(args, job_name, host_id, instance_id):
kafka_cfg_dict = args.kafka_config.configuration.generated_files["kafka.cfg"]
hosts = args.kafka_config.jobs[job_name].hosts
kafka_cfg_dict["broker.id"] = deploy_utils.get_task_id(hosts, host_id, instance_id)
kafka_cfg = deploy_utils.generate_properties_file(args, kafka_cfg_dict)
kafka_scribe_cfg_dict = args.kafka_config.configuration.generated_files["kafka-scribe.cfg"]
kafka_job = args.kafka_config.jobs["kafka"]
kafka_scribe_cfg_dict["metadata.broker.list"] = ",".join(
service_config.get_job_host_port_list(kafka_job))
kafka_scribe_cfg = deploy_utils.generate_properties_file(args, kafka_scribe_cfg_dict)
config_files = {
"kafka.cfg": kafka_cfg,
"kafka-scribe.cfg": kafka_scribe_cfg,
}
config_files.update(args.kafka_config.configuration.raw_files)
return config_files
def generate_run_scripts_params(args, host, job_name, host_id, instance_id):
job = args.kafka_config.jobs[job_name]
supervisor_client = deploy_utils.get_supervisor_client(host,
"kafka", args.kafka_config.cluster.name, job_name, instance_id=instance_id)
artifact_and_version = "kafka-" + args.kafka_config.cluster.version
jar_dirs = "$package_dir/*"
log_level = deploy_utils.get_service_log_level(args, args.kafka_config)
params = job.get_arguments(args, args.kafka_config.cluster, args.kafka_config.jobs,
args.kafka_config.arguments_dict, job_name, host_id, instance_id)
script_dict = {
"artifact": artifact_and_version,
"job_name": job_name,
"jar_dirs": jar_dirs,
"run_dir": supervisor_client.get_run_dir(),
"params": params,
}
return script_dict
def generate_start_script(args, host, job_name, host_id, instance_id):
script_params = generate_run_scripts_params(args, host, job_name, host_id, instance_id)
return deploy_utils.create_run_script(
"%s/start.sh.tmpl" % deploy_utils.get_template_dir(),
script_params)
def install(args):
_get_kafka_service_config(args)
deploy_utils.install_service(args, "kafka", args.kafka_config, "kafka")
def cleanup_job(args, host, job_name, host_id, instance_id, cleanup_token, active):
deploy_utils.cleanup_job("kafka", args.kafka_config,
host, job_name, instance_id, cleanup_token)
def cleanup(args):
_get_kafka_service_config(args)
cleanup_token = deploy_utils.confirm_cleanup(args,
"kafka", args.kafka_config)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,
'cleanup', cleanup_token=cleanup_token)
parallel_deploy.start_deploy_threads(cleanup_job, task_list)
def bootstrap_job(args, host, job_name, host_id, instance_id, cleanup_token, active):
args.kafka_config.parse_generated_config_files(args, job_name, host_id, instance_id)
deploy_utils.bootstrap_job(args, "kafka", "kafka",
args.kafka_config, host, job_name, instance_id, cleanup_token, '0')
start_job(args, host, job_name, host_id, instance_id)
def bootstrap(args):
_get_kafka_service_config(args)
cleanup_token = deploy_utils.confirm_bootstrap("kafka", args.kafka_config)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,
'bootstrap', cleanup_token=cleanup_token)
parallel_deploy.start_deploy_threads(bootstrap_job, task_list)
def start_job(args, host, job_name, host_id, instance_id, is_wait=False):
if is_wait:
deploy_utils.wait_for_job_stopping("kafka",
args.kafka_config.cluster.name, job_name, host, instance_id)
args.kafka_config.parse_generated_config_files(args, job_name, host_id, instance_id)
config_files = generate_configs(args, job_name, host_id, instance_id)
start_script = generate_start_script(args, host, job_name, host_id, instance_id)
http_url = deploy_utils.get_http_service_uri(host,
args.kafka_config.jobs[job_name].base_port, instance_id)
deploy_utils.start_job(args, "kafka", "kafka", args.kafka_config,
host, job_name, instance_id, start_script, http_url, **config_files)
def start(args):
if not args.skip_confirm:
deploy_utils.confirm_start(args)
_get_kafka_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'start')
parallel_deploy.start_deploy_threads(start_job, task_list)
def stop_job(args, host, job_name, instance_id):
deploy_utils.stop_job("kafka", args.kafka_config, host, job_name, instance_id)
def stop(args):
if not args.skip_confirm:
deploy_utils.confirm_stop(args)
_get_kafka_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'stop')
parallel_deploy.start_deploy_threads(stop_job, task_list)
def restart(args):
if not args.skip_confirm:
deploy_utils.confirm_restart(args)
_get_kafka_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'stop')
parallel_deploy.start_deploy_threads(stop_job, task_list)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name,
'start', is_wait=True)
parallel_deploy.start_deploy_threads(start_job, task_list)
def show_job(args, host, job_name, instance_id):
deploy_utils.show_job("kafka", args.kafka_config, host, job_name, instance_id)
def show(args):
_get_kafka_service_config(args)
for job_name in args.job or ALL_JOBS:
hosts = args.kafka_config.jobs[job_name].hosts
task_list = deploy_utils.schedule_task_for_threads(args, hosts, job_name, 'show')
parallel_deploy.start_deploy_threads(show_job, task_list)
def run_shell(args):
Log.print_critical("'shell' command is not supported!")
def pack(args):
Log.print_critical("'pack' command is not supported!")
def rolling_update(args):
if not args.job:
Log.print_critical("You must specify the job name to do rolling update")
_get_kafka_service_config(args)
job_name = args.job[0]
if not args.skip_confirm:
deploy_utils.confirm_action(args, "rolling_update")
Log.print_info("Rolling updating %s" % job_name)
hosts = args.kafka_config.jobs[job_name].hosts
wait_time = 0
args.task_map = deploy_utils.parse_args_host_and_task(args, hosts)
for host_id in args.task_map.keys() or hosts.iterkeys():
for instance_id in args.task_map.get(host_id) or range(hosts[host_id].instance_num):
instance_id = -1 if not deploy_utils.is_multiple_instances(host_id, hosts) else instance_id
deploy_utils.confirm_rolling_update(host_id, instance_id, wait_time)
stop_job(args, hosts[host_id].ip, job_name, instance_id)
deploy_utils.wait_for_job_stopping("kafka",
args.kafka_config.cluster.name, job_name, hosts[host_id].ip, instance_id)
start_job(args, hosts[host_id].ip, job_name, host_id, instance_id)
deploy_utils.wait_for_job_starting("kafka",
args.kafka_config.cluster.name, job_name, hosts[host_id].ip, instance_id)
wait_time = args.time_interval
Log.print_success("Rolling updating %s success" % job_name)
if __name__ == '__main__':
test()
| true
| true
|
f716a2954dca50e0e1254eb1298f66d92f7eacbe
| 2,317
|
py
|
Python
|
scripts/sanity_chk/scl.py
|
cinlyooi-intel/zephyr
|
193fb971c24f827464982307b5b3bb34de0fa98d
|
[
"Apache-2.0"
] | null | null | null |
scripts/sanity_chk/scl.py
|
cinlyooi-intel/zephyr
|
193fb971c24f827464982307b5b3bb34de0fa98d
|
[
"Apache-2.0"
] | null | null | null |
scripts/sanity_chk/scl.py
|
cinlyooi-intel/zephyr
|
193fb971c24f827464982307b5b3bb34de0fa98d
|
[
"Apache-2.0"
] | null | null | null |
#! /usr/bin/python
#
# Zephyr's Sanity Check library
#
# Set of code that other projects can also import to do things on
# Zephyr's sanity check testcases.
import logging
import yaml
log = logging.getLogger("scl")
#
#
def yaml_load(filename):
"""
Safely load a YAML document
Follows recomendations from
https://security.openstack.org/guidelines/dg_avoid-dangerous-input-parsing-libraries.html.
:param str filename: filename to load
:raises yaml.scanner: On YAML scan issues
:raises: any other exception on file access erors
:return: dictionary representing the YAML document
"""
try:
with open(filename, 'r') as f:
return yaml.safe_load(f)
except yaml.scanner.ScannerError as e: # For errors parsing schema.yaml
mark = e.problem_mark
cmark = e.context_mark
log.error("%s:%d:%d: error: %s (note %s context @%s:%d:%d %s)",
mark.name, mark.line, mark.column, e.problem,
e.note, cmark.name, cmark.line, cmark.column, e.context)
raise
# If pykwalify is installed, then the validate functionw ill work --
# otherwise, it is a stub and we'd warn about it.
try:
import pykwalify.core
# Don't print error messages yourself, let us do it
logging.getLogger("pykwalify.core").setLevel(50)
def _yaml_validate(data, schema):
if not schema:
return
c = pykwalify.core.Core(source_data = data, schema_data = schema)
c.validate(raise_exception = True)
except ImportError as e:
log.warning("can't import pykwalify; won't validate YAML (%s)", e)
def _yaml_validate(data, schema):
pass
def yaml_load_verify(filename, schema):
"""
Safely load a testcase/sample yaml document and validate it
against the YAML schema, returing in case of success the YAML data.
:param str filename: name of the file to load and process
:param dict schema: loaded YAML schema (can load with :func:`yaml_load`)
# 'document.yaml' contains a single YAML document.
:raises yaml.scanner.ScannerError: on YAML parsing error
:raises pykwalify.errors.SchemaError: on Schema violation error
"""
# 'document.yaml' contains a single YAML document.
y = yaml_load(filename)
_yaml_validate(y, schema)
return y
| 32.180556
| 94
| 0.678032
|
#
# Set of code that other projects can also import to do things on
# Zephyr's sanity check testcases.
import logging
import yaml
log = logging.getLogger("scl")
def yaml_load(filename):
try:
with open(filename, 'r') as f:
return yaml.safe_load(f)
except yaml.scanner.ScannerError as e:
mark = e.problem_mark
cmark = e.context_mark
log.error("%s:%d:%d: error: %s (note %s context @%s:%d:%d %s)",
mark.name, mark.line, mark.column, e.problem,
e.note, cmark.name, cmark.line, cmark.column, e.context)
raise
try:
import pykwalify.core
# Don't print error messages yourself, let us do it
logging.getLogger("pykwalify.core").setLevel(50)
def _yaml_validate(data, schema):
if not schema:
return
c = pykwalify.core.Core(source_data = data, schema_data = schema)
c.validate(raise_exception = True)
except ImportError as e:
log.warning("can't import pykwalify; won't validate YAML (%s)", e)
def _yaml_validate(data, schema):
pass
def yaml_load_verify(filename, schema):
y = yaml_load(filename)
_yaml_validate(y, schema)
return y
| true
| true
|
f716a3a44cd54534078de417bc370bb62502718a
| 68,239
|
py
|
Python
|
pandas/tests/groupby/test_groupby.py
|
gurukiran07/pandas
|
67c9385787c4d854b75a6f2c04fdf6886fdb1a06
|
[
"PSF-2.0",
"Apache-2.0",
"BSD-3-Clause-No-Nuclear-License-2014",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | 2
|
2017-12-14T19:50:52.000Z
|
2020-04-07T16:47:23.000Z
|
pandas/tests/groupby/test_groupby.py
|
zoehuang7/pandas
|
3cce96f515917170ea9bce731ffcc913750464b8
|
[
"PSF-2.0",
"Apache-2.0",
"BSD-3-Clause-No-Nuclear-License-2014",
"MIT",
"MIT-0",
"ECL-2.0",
"BSD-3-Clause"
] | null | null | null |
pandas/tests/groupby/test_groupby.py
|
zoehuang7/pandas
|
3cce96f515917170ea9bce731ffcc913750464b8
|
[
"PSF-2.0",
"Apache-2.0",
"BSD-3-Clause-No-Nuclear-License-2014",
"MIT",
"MIT-0",
"ECL-2.0",
"BSD-3-Clause"
] | 1
|
2018-01-26T08:33:54.000Z
|
2018-01-26T08:33:54.000Z
|
from datetime import datetime
from decimal import Decimal
from io import StringIO
import numpy as np
import pytest
from pandas.compat import IS64
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Grouper,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
read_csv,
to_datetime,
)
import pandas._testing as tm
from pandas.core.base import SpecificationError
import pandas.core.common as com
def test_repr():
# GH18203
result = repr(Grouper(key="A", level="B"))
expected = "Grouper(key='A', level='B', axis=0, sort=False)"
assert result == expected
@pytest.mark.parametrize("dtype", ["int64", "int32", "float64", "float32"])
def test_basic(dtype):
data = Series(np.arange(9) // 3, index=np.arange(9), dtype=dtype)
index = np.arange(9)
np.random.shuffle(index)
data = data.reindex(index)
grouped = data.groupby(lambda x: x // 3)
for k, v in grouped:
assert len(v) == 3
agged = grouped.aggregate(np.mean)
assert agged[1] == 1
tm.assert_series_equal(agged, grouped.agg(np.mean)) # shorthand
tm.assert_series_equal(agged, grouped.mean())
tm.assert_series_equal(grouped.agg(np.sum), grouped.sum())
expected = grouped.apply(lambda x: x * x.sum())
transformed = grouped.transform(lambda x: x * x.sum())
assert transformed[7] == 12
tm.assert_series_equal(transformed, expected)
value_grouped = data.groupby(data)
tm.assert_series_equal(
value_grouped.aggregate(np.mean), agged, check_index_type=False
)
# complex agg
agged = grouped.aggregate([np.mean, np.std])
msg = r"nested renamer is not supported"
with pytest.raises(SpecificationError, match=msg):
grouped.aggregate({"one": np.mean, "two": np.std})
group_constants = {0: 10, 1: 20, 2: 30}
agged = grouped.agg(lambda x: group_constants[x.name] + x.mean())
assert agged[1] == 21
# corner cases
msg = "Must produce aggregated value"
# exception raised is type Exception
with pytest.raises(Exception, match=msg):
grouped.aggregate(lambda x: x * 2)
def test_groupby_nonobject_dtype(mframe, df_mixed_floats):
key = mframe.index.codes[0]
grouped = mframe.groupby(key)
result = grouped.sum()
expected = mframe.groupby(key.astype("O")).sum()
tm.assert_frame_equal(result, expected)
# GH 3911, mixed frame non-conversion
df = df_mixed_floats.copy()
df["value"] = range(len(df))
def max_value(group):
return group.loc[group["value"].idxmax()]
applied = df.groupby("A").apply(max_value)
result = applied.dtypes
expected = df.dtypes
tm.assert_series_equal(result, expected)
def test_groupby_return_type():
# GH2893, return a reduced type
df1 = DataFrame(
[
{"val1": 1, "val2": 20},
{"val1": 1, "val2": 19},
{"val1": 2, "val2": 27},
{"val1": 2, "val2": 12},
]
)
def func(dataf):
return dataf["val2"] - dataf["val2"].mean()
with tm.assert_produces_warning(FutureWarning):
result = df1.groupby("val1", squeeze=True).apply(func)
assert isinstance(result, Series)
df2 = DataFrame(
[
{"val1": 1, "val2": 20},
{"val1": 1, "val2": 19},
{"val1": 1, "val2": 27},
{"val1": 1, "val2": 12},
]
)
def func(dataf):
return dataf["val2"] - dataf["val2"].mean()
with tm.assert_produces_warning(FutureWarning):
result = df2.groupby("val1", squeeze=True).apply(func)
assert isinstance(result, Series)
# GH3596, return a consistent type (regression in 0.11 from 0.10.1)
df = DataFrame([[1, 1], [1, 1]], columns=["X", "Y"])
with tm.assert_produces_warning(FutureWarning):
result = df.groupby("X", squeeze=False).count()
assert isinstance(result, DataFrame)
def test_inconsistent_return_type():
# GH5592
# inconsistent return type
df = DataFrame(
{
"A": ["Tiger", "Tiger", "Tiger", "Lamb", "Lamb", "Pony", "Pony"],
"B": Series(np.arange(7), dtype="int64"),
"C": date_range("20130101", periods=7),
}
)
def f(grp):
return grp.iloc[0]
expected = df.groupby("A").first()[["B"]]
result = df.groupby("A").apply(f)[["B"]]
tm.assert_frame_equal(result, expected)
def f(grp):
if grp.name == "Tiger":
return None
return grp.iloc[0]
result = df.groupby("A").apply(f)[["B"]]
e = expected.copy()
e.loc["Tiger"] = np.nan
tm.assert_frame_equal(result, e)
def f(grp):
if grp.name == "Pony":
return None
return grp.iloc[0]
result = df.groupby("A").apply(f)[["B"]]
e = expected.copy()
e.loc["Pony"] = np.nan
tm.assert_frame_equal(result, e)
# 5592 revisited, with datetimes
def f(grp):
if grp.name == "Pony":
return None
return grp.iloc[0]
result = df.groupby("A").apply(f)[["C"]]
e = df.groupby("A").first()[["C"]]
e.loc["Pony"] = pd.NaT
tm.assert_frame_equal(result, e)
# scalar outputs
def f(grp):
if grp.name == "Pony":
return None
return grp.iloc[0].loc["C"]
result = df.groupby("A").apply(f)
e = df.groupby("A").first()["C"].copy()
e.loc["Pony"] = np.nan
e.name = None
tm.assert_series_equal(result, e)
def test_pass_args_kwargs(ts, tsframe):
def f(x, q=None, axis=0):
return np.percentile(x, q, axis=axis)
g = lambda x: np.percentile(x, 80, axis=0)
# Series
ts_grouped = ts.groupby(lambda x: x.month)
agg_result = ts_grouped.agg(np.percentile, 80, axis=0)
apply_result = ts_grouped.apply(np.percentile, 80, axis=0)
trans_result = ts_grouped.transform(np.percentile, 80, axis=0)
agg_expected = ts_grouped.quantile(0.8)
trans_expected = ts_grouped.transform(g)
tm.assert_series_equal(apply_result, agg_expected)
tm.assert_series_equal(agg_result, agg_expected)
tm.assert_series_equal(trans_result, trans_expected)
agg_result = ts_grouped.agg(f, q=80)
apply_result = ts_grouped.apply(f, q=80)
trans_result = ts_grouped.transform(f, q=80)
tm.assert_series_equal(agg_result, agg_expected)
tm.assert_series_equal(apply_result, agg_expected)
tm.assert_series_equal(trans_result, trans_expected)
# DataFrame
df_grouped = tsframe.groupby(lambda x: x.month)
agg_result = df_grouped.agg(np.percentile, 80, axis=0)
apply_result = df_grouped.apply(DataFrame.quantile, 0.8)
expected = df_grouped.quantile(0.8)
tm.assert_frame_equal(apply_result, expected, check_names=False)
tm.assert_frame_equal(agg_result, expected)
agg_result = df_grouped.agg(f, q=80)
apply_result = df_grouped.apply(DataFrame.quantile, q=0.8)
tm.assert_frame_equal(agg_result, expected)
tm.assert_frame_equal(apply_result, expected, check_names=False)
def test_len():
df = tm.makeTimeDataFrame()
grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day])
assert len(grouped) == len(df)
grouped = df.groupby([lambda x: x.year, lambda x: x.month])
expected = len({(x.year, x.month) for x in df.index})
assert len(grouped) == expected
# issue 11016
df = DataFrame({"a": [np.nan] * 3, "b": [1, 2, 3]})
assert len(df.groupby("a")) == 0
assert len(df.groupby("b")) == 3
assert len(df.groupby(["a", "b"])) == 3
def test_basic_regression():
# regression
result = Series([1.0 * x for x in list(range(1, 10)) * 10])
data = np.random.random(1100) * 10.0
groupings = Series(data)
grouped = result.groupby(groupings)
grouped.mean()
@pytest.mark.parametrize(
"dtype", ["float64", "float32", "int64", "int32", "int16", "int8"]
)
def test_with_na_groups(dtype):
index = Index(np.arange(10))
values = Series(np.ones(10), index, dtype=dtype)
labels = Series(
[np.nan, "foo", "bar", "bar", np.nan, np.nan, "bar", "bar", np.nan, "foo"],
index=index,
)
# this SHOULD be an int
grouped = values.groupby(labels)
agged = grouped.agg(len)
expected = Series([4, 2], index=["bar", "foo"])
tm.assert_series_equal(agged, expected, check_dtype=False)
# assert issubclass(agged.dtype.type, np.integer)
# explicitly return a float from my function
def f(x):
return float(len(x))
agged = grouped.agg(f)
expected = Series([4.0, 2.0], index=["bar", "foo"])
tm.assert_series_equal(agged, expected)
def test_indices_concatenation_order():
# GH 2808
def f1(x):
y = x[(x.b % 2) == 1] ** 2
if y.empty:
multiindex = MultiIndex(levels=[[]] * 2, codes=[[]] * 2, names=["b", "c"])
res = DataFrame(columns=["a"], index=multiindex)
return res
else:
y = y.set_index(["b", "c"])
return y
def f2(x):
y = x[(x.b % 2) == 1] ** 2
if y.empty:
return DataFrame()
else:
y = y.set_index(["b", "c"])
return y
def f3(x):
y = x[(x.b % 2) == 1] ** 2
if y.empty:
multiindex = MultiIndex(
levels=[[]] * 2, codes=[[]] * 2, names=["foo", "bar"]
)
res = DataFrame(columns=["a", "b"], index=multiindex)
return res
else:
return y
df = DataFrame({"a": [1, 2, 2, 2], "b": range(4), "c": range(5, 9)})
df2 = DataFrame({"a": [3, 2, 2, 2], "b": range(4), "c": range(5, 9)})
# correct result
result1 = df.groupby("a").apply(f1)
result2 = df2.groupby("a").apply(f1)
tm.assert_frame_equal(result1, result2)
# should fail (not the same number of levels)
msg = "Cannot concat indices that do not have the same number of levels"
with pytest.raises(AssertionError, match=msg):
df.groupby("a").apply(f2)
with pytest.raises(AssertionError, match=msg):
df2.groupby("a").apply(f2)
# should fail (incorrect shape)
with pytest.raises(AssertionError, match=msg):
df.groupby("a").apply(f3)
with pytest.raises(AssertionError, match=msg):
df2.groupby("a").apply(f3)
def test_attr_wrapper(ts):
grouped = ts.groupby(lambda x: x.weekday())
result = grouped.std()
expected = grouped.agg(lambda x: np.std(x, ddof=1))
tm.assert_series_equal(result, expected)
# this is pretty cool
result = grouped.describe()
expected = {name: gp.describe() for name, gp in grouped}
expected = DataFrame(expected).T
tm.assert_frame_equal(result, expected)
# get attribute
result = grouped.dtype
expected = grouped.agg(lambda x: x.dtype)
tm.assert_series_equal(result, expected)
# make sure raises error
msg = "'SeriesGroupBy' object has no attribute 'foo'"
with pytest.raises(AttributeError, match=msg):
getattr(grouped, "foo")
def test_frame_groupby(tsframe):
grouped = tsframe.groupby(lambda x: x.weekday())
# aggregate
aggregated = grouped.aggregate(np.mean)
assert len(aggregated) == 5
assert len(aggregated.columns) == 4
# by string
tscopy = tsframe.copy()
tscopy["weekday"] = [x.weekday() for x in tscopy.index]
stragged = tscopy.groupby("weekday").aggregate(np.mean)
tm.assert_frame_equal(stragged, aggregated, check_names=False)
# transform
grouped = tsframe.head(30).groupby(lambda x: x.weekday())
transformed = grouped.transform(lambda x: x - x.mean())
assert len(transformed) == 30
assert len(transformed.columns) == 4
# transform propagate
transformed = grouped.transform(lambda x: x.mean())
for name, group in grouped:
mean = group.mean()
for idx in group.index:
tm.assert_series_equal(transformed.xs(idx), mean, check_names=False)
# iterate
for weekday, group in grouped:
assert group.index[0].weekday() == weekday
# groups / group_indices
groups = grouped.groups
indices = grouped.indices
for k, v in groups.items():
samething = tsframe.index.take(indices[k])
assert (samething == v).all()
def test_frame_groupby_columns(tsframe):
mapping = {"A": 0, "B": 0, "C": 1, "D": 1}
grouped = tsframe.groupby(mapping, axis=1)
# aggregate
aggregated = grouped.aggregate(np.mean)
assert len(aggregated) == len(tsframe)
assert len(aggregated.columns) == 2
# transform
tf = lambda x: x - x.mean()
groupedT = tsframe.T.groupby(mapping, axis=0)
tm.assert_frame_equal(groupedT.transform(tf).T, grouped.transform(tf))
# iterate
for k, v in grouped:
assert len(v.columns) == 2
def test_frame_set_name_single(df):
grouped = df.groupby("A")
result = grouped.mean()
assert result.index.name == "A"
result = df.groupby("A", as_index=False).mean()
assert result.index.name != "A"
result = grouped.agg(np.mean)
assert result.index.name == "A"
result = grouped.agg({"C": np.mean, "D": np.std})
assert result.index.name == "A"
result = grouped["C"].mean()
assert result.index.name == "A"
result = grouped["C"].agg(np.mean)
assert result.index.name == "A"
result = grouped["C"].agg([np.mean, np.std])
assert result.index.name == "A"
msg = r"nested renamer is not supported"
with pytest.raises(SpecificationError, match=msg):
grouped["C"].agg({"foo": np.mean, "bar": np.std})
def test_multi_func(df):
col1 = df["A"]
col2 = df["B"]
grouped = df.groupby([col1.get, col2.get])
agged = grouped.mean()
expected = df.groupby(["A", "B"]).mean()
# TODO groupby get drops names
tm.assert_frame_equal(
agged.loc[:, ["C", "D"]], expected.loc[:, ["C", "D"]], check_names=False
)
# some "groups" with no data
df = DataFrame(
{
"v1": np.random.randn(6),
"v2": np.random.randn(6),
"k1": np.array(["b", "b", "b", "a", "a", "a"]),
"k2": np.array(["1", "1", "1", "2", "2", "2"]),
},
index=["one", "two", "three", "four", "five", "six"],
)
# only verify that it works for now
grouped = df.groupby(["k1", "k2"])
grouped.agg(np.sum)
def test_multi_key_multiple_functions(df):
grouped = df.groupby(["A", "B"])["C"]
agged = grouped.agg([np.mean, np.std])
expected = DataFrame({"mean": grouped.agg(np.mean), "std": grouped.agg(np.std)})
tm.assert_frame_equal(agged, expected)
def test_frame_multi_key_function_list():
data = DataFrame(
{
"A": [
"foo",
"foo",
"foo",
"foo",
"bar",
"bar",
"bar",
"bar",
"foo",
"foo",
"foo",
],
"B": [
"one",
"one",
"one",
"two",
"one",
"one",
"one",
"two",
"two",
"two",
"one",
],
"C": [
"dull",
"dull",
"shiny",
"dull",
"dull",
"shiny",
"shiny",
"dull",
"shiny",
"shiny",
"shiny",
],
"D": np.random.randn(11),
"E": np.random.randn(11),
"F": np.random.randn(11),
}
)
grouped = data.groupby(["A", "B"])
funcs = [np.mean, np.std]
agged = grouped.agg(funcs)
expected = pd.concat(
[grouped["D"].agg(funcs), grouped["E"].agg(funcs), grouped["F"].agg(funcs)],
keys=["D", "E", "F"],
axis=1,
)
assert isinstance(agged.index, MultiIndex)
assert isinstance(expected.index, MultiIndex)
tm.assert_frame_equal(agged, expected)
@pytest.mark.parametrize("op", [lambda x: x.sum(), lambda x: x.mean()])
def test_groupby_multiple_columns(df, op):
data = df
grouped = data.groupby(["A", "B"])
result1 = op(grouped)
keys = []
values = []
for n1, gp1 in data.groupby("A"):
for n2, gp2 in gp1.groupby("B"):
keys.append((n1, n2))
values.append(op(gp2.loc[:, ["C", "D"]]))
mi = MultiIndex.from_tuples(keys, names=["A", "B"])
expected = pd.concat(values, axis=1).T
expected.index = mi
# a little bit crude
for col in ["C", "D"]:
result_col = op(grouped[col])
pivoted = result1[col]
exp = expected[col]
tm.assert_series_equal(result_col, exp)
tm.assert_series_equal(pivoted, exp)
# test single series works the same
result = data["C"].groupby([data["A"], data["B"]]).mean()
expected = data.groupby(["A", "B"]).mean()["C"]
tm.assert_series_equal(result, expected)
def test_as_index_select_column():
# GH 5764
df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"])
result = df.groupby("A", as_index=False)["B"].get_group(1)
expected = Series([2, 4], name="B")
tm.assert_series_equal(result, expected)
result = df.groupby("A", as_index=False)["B"].apply(lambda x: x.cumsum())
expected = Series(
[2, 6, 6], name="B", index=MultiIndex.from_tuples([(0, 0), (0, 1), (1, 2)])
)
tm.assert_series_equal(result, expected)
def test_groupby_as_index_select_column_sum_empty_df():
# GH 35246
df = DataFrame(columns=["A", "B", "C"])
left = df.groupby(by="A", as_index=False)["B"].sum()
assert type(left) is DataFrame
assert left.to_dict() == {"A": {}, "B": {}}
def test_groupby_as_index_agg(df):
grouped = df.groupby("A", as_index=False)
# single-key
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
result2 = grouped.agg({"C": np.mean, "D": np.sum})
expected2 = grouped.mean()
expected2["D"] = grouped.sum()["D"]
tm.assert_frame_equal(result2, expected2)
grouped = df.groupby("A", as_index=True)
msg = r"nested renamer is not supported"
with pytest.raises(SpecificationError, match=msg):
grouped["C"].agg({"Q": np.sum})
# multi-key
grouped = df.groupby(["A", "B"], as_index=False)
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
result2 = grouped.agg({"C": np.mean, "D": np.sum})
expected2 = grouped.mean()
expected2["D"] = grouped.sum()["D"]
tm.assert_frame_equal(result2, expected2)
expected3 = grouped["C"].sum()
expected3 = DataFrame(expected3).rename(columns={"C": "Q"})
result3 = grouped["C"].agg({"Q": np.sum})
tm.assert_frame_equal(result3, expected3)
# GH7115 & GH8112 & GH8582
df = DataFrame(np.random.randint(0, 100, (50, 3)), columns=["jim", "joe", "jolie"])
ts = Series(np.random.randint(5, 10, 50), name="jim")
gr = df.groupby(ts)
gr.nth(0) # invokes set_selection_from_grouper internally
tm.assert_frame_equal(gr.apply(sum), df.groupby(ts).apply(sum))
for attr in ["mean", "max", "count", "idxmax", "cumsum", "all"]:
gr = df.groupby(ts, as_index=False)
left = getattr(gr, attr)()
gr = df.groupby(ts.values, as_index=True)
right = getattr(gr, attr)().reset_index(drop=True)
tm.assert_frame_equal(left, right)
def test_ops_not_as_index(reduction_func):
# GH 10355, 21090
# Using as_index=False should not modify grouped column
if reduction_func in ("corrwith",):
pytest.skip("Test not applicable")
if reduction_func in ("nth", "ngroup"):
pytest.skip("Skip until behavior is determined (GH #5755)")
df = DataFrame(np.random.randint(0, 5, size=(100, 2)), columns=["a", "b"])
expected = getattr(df.groupby("a"), reduction_func)()
if reduction_func == "size":
expected = expected.rename("size")
expected = expected.reset_index()
g = df.groupby("a", as_index=False)
result = getattr(g, reduction_func)()
tm.assert_frame_equal(result, expected)
result = g.agg(reduction_func)
tm.assert_frame_equal(result, expected)
result = getattr(g["b"], reduction_func)()
tm.assert_frame_equal(result, expected)
result = g["b"].agg(reduction_func)
tm.assert_frame_equal(result, expected)
def test_as_index_series_return_frame(df):
grouped = df.groupby("A", as_index=False)
grouped2 = df.groupby(["A", "B"], as_index=False)
result = grouped["C"].agg(np.sum)
expected = grouped.agg(np.sum).loc[:, ["A", "C"]]
assert isinstance(result, DataFrame)
tm.assert_frame_equal(result, expected)
result2 = grouped2["C"].agg(np.sum)
expected2 = grouped2.agg(np.sum).loc[:, ["A", "B", "C"]]
assert isinstance(result2, DataFrame)
tm.assert_frame_equal(result2, expected2)
result = grouped["C"].sum()
expected = grouped.sum().loc[:, ["A", "C"]]
assert isinstance(result, DataFrame)
tm.assert_frame_equal(result, expected)
result2 = grouped2["C"].sum()
expected2 = grouped2.sum().loc[:, ["A", "B", "C"]]
assert isinstance(result2, DataFrame)
tm.assert_frame_equal(result2, expected2)
def test_as_index_series_column_slice_raises(df):
# GH15072
grouped = df.groupby("A", as_index=False)
msg = r"Column\(s\) C already selected"
with pytest.raises(IndexError, match=msg):
grouped["C"].__getitem__("D")
def test_groupby_as_index_cython(df):
data = df
# single-key
grouped = data.groupby("A", as_index=False)
result = grouped.mean()
expected = data.groupby(["A"]).mean()
expected.insert(0, "A", expected.index)
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
# multi-key
grouped = data.groupby(["A", "B"], as_index=False)
result = grouped.mean()
expected = data.groupby(["A", "B"]).mean()
arrays = list(zip(*expected.index.values))
expected.insert(0, "A", arrays[0])
expected.insert(1, "B", arrays[1])
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
def test_groupby_as_index_series_scalar(df):
grouped = df.groupby(["A", "B"], as_index=False)
# GH #421
result = grouped["C"].agg(len)
expected = grouped.agg(len).loc[:, ["A", "B", "C"]]
tm.assert_frame_equal(result, expected)
def test_groupby_as_index_corner(df, ts):
msg = "as_index=False only valid with DataFrame"
with pytest.raises(TypeError, match=msg):
ts.groupby(lambda x: x.weekday(), as_index=False)
msg = "as_index=False only valid for axis=0"
with pytest.raises(ValueError, match=msg):
df.groupby(lambda x: x.lower(), as_index=False, axis=1)
def test_groupby_multiple_key(df):
df = tm.makeTimeDataFrame()
grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day])
agged = grouped.sum()
tm.assert_almost_equal(df.values, agged.values)
grouped = df.T.groupby(
[lambda x: x.year, lambda x: x.month, lambda x: x.day], axis=1
)
agged = grouped.agg(lambda x: x.sum())
tm.assert_index_equal(agged.index, df.columns)
tm.assert_almost_equal(df.T.values, agged.values)
agged = grouped.agg(lambda x: x.sum())
tm.assert_almost_equal(df.T.values, agged.values)
def test_groupby_multi_corner(df):
# test that having an all-NA column doesn't mess you up
df = df.copy()
df["bad"] = np.nan
agged = df.groupby(["A", "B"]).mean()
expected = df.groupby(["A", "B"]).mean()
expected["bad"] = np.nan
tm.assert_frame_equal(agged, expected)
def test_omit_nuisance(df):
grouped = df.groupby("A")
result = grouped.mean()
expected = df.loc[:, ["A", "C", "D"]].groupby("A").mean()
tm.assert_frame_equal(result, expected)
agged = grouped.agg(np.mean)
exp = grouped.mean()
tm.assert_frame_equal(agged, exp)
df = df.loc[:, ["A", "C", "D"]]
df["E"] = datetime.now()
grouped = df.groupby("A")
result = grouped.agg(np.sum)
expected = grouped.sum()
tm.assert_frame_equal(result, expected)
# won't work with axis = 1
grouped = df.groupby({"A": 0, "C": 0, "D": 1, "E": 1}, axis=1)
msg = "'DatetimeArray' does not implement reduction 'sum'"
with pytest.raises(TypeError, match=msg):
grouped.agg(lambda x: x.sum(0, numeric_only=False))
def test_omit_nuisance_sem(df):
# GH 38774 - sem should work with nuisance columns
grouped = df.groupby("A")
result = grouped.sem()
expected = df.loc[:, ["A", "C", "D"]].groupby("A").sem()
tm.assert_frame_equal(result, expected)
def test_omit_nuisance_python_multiple(three_group):
grouped = three_group.groupby(["A", "B"])
agged = grouped.agg(np.mean)
exp = grouped.mean()
tm.assert_frame_equal(agged, exp)
def test_empty_groups_corner(mframe):
# handle empty groups
df = DataFrame(
{
"k1": np.array(["b", "b", "b", "a", "a", "a"]),
"k2": np.array(["1", "1", "1", "2", "2", "2"]),
"k3": ["foo", "bar"] * 3,
"v1": np.random.randn(6),
"v2": np.random.randn(6),
}
)
grouped = df.groupby(["k1", "k2"])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
grouped = mframe[3:5].groupby(level=0)
agged = grouped.apply(lambda x: x.mean())
agged_A = grouped["A"].apply(np.mean)
tm.assert_series_equal(agged["A"], agged_A)
assert agged.index.name == "first"
def test_nonsense_func():
df = DataFrame([0])
msg = r"unsupported operand type\(s\) for \+: 'int' and 'str'"
with pytest.raises(TypeError, match=msg):
df.groupby(lambda x: x + "foo")
def test_wrap_aggregated_output_multindex(mframe):
df = mframe.T
df["baz", "two"] = "peekaboo"
keys = [np.array([0, 0, 1]), np.array([0, 0, 1])]
agged = df.groupby(keys).agg(np.mean)
assert isinstance(agged.columns, MultiIndex)
def aggfun(ser):
if ser.name == ("foo", "one"):
raise TypeError
else:
return ser.sum()
agged2 = df.groupby(keys).aggregate(aggfun)
assert len(agged2.columns) + 1 == len(df.columns)
def test_groupby_level_apply(mframe):
result = mframe.groupby(level=0).count()
assert result.index.name == "first"
result = mframe.groupby(level=1).count()
assert result.index.name == "second"
result = mframe["A"].groupby(level=0).count()
assert result.index.name == "first"
def test_groupby_level_mapper(mframe):
deleveled = mframe.reset_index()
mapper0 = {"foo": 0, "bar": 0, "baz": 1, "qux": 1}
mapper1 = {"one": 0, "two": 0, "three": 1}
result0 = mframe.groupby(mapper0, level=0).sum()
result1 = mframe.groupby(mapper1, level=1).sum()
mapped_level0 = np.array([mapper0.get(x) for x in deleveled["first"]])
mapped_level1 = np.array([mapper1.get(x) for x in deleveled["second"]])
expected0 = mframe.groupby(mapped_level0).sum()
expected1 = mframe.groupby(mapped_level1).sum()
expected0.index.name, expected1.index.name = "first", "second"
tm.assert_frame_equal(result0, expected0)
tm.assert_frame_equal(result1, expected1)
def test_groupby_level_nonmulti():
# GH 1313, GH 13901
s = Series([1, 2, 3, 10, 4, 5, 20, 6], Index([1, 2, 3, 1, 4, 5, 2, 6], name="foo"))
expected = Series([11, 22, 3, 4, 5, 6], Index(range(1, 7), name="foo"))
result = s.groupby(level=0).sum()
tm.assert_series_equal(result, expected)
result = s.groupby(level=[0]).sum()
tm.assert_series_equal(result, expected)
result = s.groupby(level=-1).sum()
tm.assert_series_equal(result, expected)
result = s.groupby(level=[-1]).sum()
tm.assert_series_equal(result, expected)
msg = "level > 0 or level < -1 only valid with MultiIndex"
with pytest.raises(ValueError, match=msg):
s.groupby(level=1)
with pytest.raises(ValueError, match=msg):
s.groupby(level=-2)
msg = "No group keys passed!"
with pytest.raises(ValueError, match=msg):
s.groupby(level=[])
msg = "multiple levels only valid with MultiIndex"
with pytest.raises(ValueError, match=msg):
s.groupby(level=[0, 0])
with pytest.raises(ValueError, match=msg):
s.groupby(level=[0, 1])
msg = "level > 0 or level < -1 only valid with MultiIndex"
with pytest.raises(ValueError, match=msg):
s.groupby(level=[1])
def test_groupby_complex():
# GH 12902
a = Series(data=np.arange(4) * (1 + 2j), index=[0, 0, 1, 1])
expected = Series((1 + 2j, 5 + 10j))
result = a.groupby(level=0).sum()
tm.assert_series_equal(result, expected)
with tm.assert_produces_warning(FutureWarning):
result = a.sum(level=0)
tm.assert_series_equal(result, expected)
def test_groupby_series_indexed_differently():
s1 = Series(
[5.0, -9.0, 4.0, 100.0, -5.0, 55.0, 6.7],
index=Index(["a", "b", "c", "d", "e", "f", "g"]),
)
s2 = Series(
[1.0, 1.0, 4.0, 5.0, 5.0, 7.0], index=Index(["a", "b", "d", "f", "g", "h"])
)
grouped = s1.groupby(s2)
agged = grouped.mean()
exp = s1.groupby(s2.reindex(s1.index).get).mean()
tm.assert_series_equal(agged, exp)
def test_groupby_with_hier_columns():
tuples = list(
zip(
*[
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
)
)
index = MultiIndex.from_tuples(tuples)
columns = MultiIndex.from_tuples(
[("A", "cat"), ("B", "dog"), ("B", "cat"), ("A", "dog")]
)
df = DataFrame(np.random.randn(8, 4), index=index, columns=columns)
result = df.groupby(level=0).mean()
tm.assert_index_equal(result.columns, columns)
result = df.groupby(level=0, axis=1).mean()
tm.assert_index_equal(result.index, df.index)
result = df.groupby(level=0).agg(np.mean)
tm.assert_index_equal(result.columns, columns)
result = df.groupby(level=0).apply(lambda x: x.mean())
tm.assert_index_equal(result.columns, columns)
result = df.groupby(level=0, axis=1).agg(lambda x: x.mean(1))
tm.assert_index_equal(result.columns, Index(["A", "B"]))
tm.assert_index_equal(result.index, df.index)
# add a nuisance column
sorted_columns, _ = columns.sortlevel(0)
df["A", "foo"] = "bar"
result = df.groupby(level=0).mean()
tm.assert_index_equal(result.columns, df.columns[:-1])
def test_grouping_ndarray(df):
grouped = df.groupby(df["A"].values)
result = grouped.sum()
expected = df.groupby("A").sum()
tm.assert_frame_equal(
result, expected, check_names=False
) # Note: no names when grouping by value
def test_groupby_wrong_multi_labels():
data = """index,foo,bar,baz,spam,data
0,foo1,bar1,baz1,spam2,20
1,foo1,bar2,baz1,spam3,30
2,foo2,bar2,baz1,spam2,40
3,foo1,bar1,baz2,spam1,50
4,foo3,bar1,baz2,spam1,60"""
data = read_csv(StringIO(data), index_col=0)
grouped = data.groupby(["foo", "bar", "baz", "spam"])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
def test_groupby_series_with_name(df):
result = df.groupby(df["A"]).mean()
result2 = df.groupby(df["A"], as_index=False).mean()
assert result.index.name == "A"
assert "A" in result2
result = df.groupby([df["A"], df["B"]]).mean()
result2 = df.groupby([df["A"], df["B"]], as_index=False).mean()
assert result.index.names == ("A", "B")
assert "A" in result2
assert "B" in result2
def test_seriesgroupby_name_attr(df):
# GH 6265
result = df.groupby("A")["C"]
assert result.count().name == "C"
assert result.mean().name == "C"
testFunc = lambda x: np.sum(x) * 2
assert result.agg(testFunc).name == "C"
def test_consistency_name():
# GH 12363
df = DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "two", "two", "two", "one", "two"],
"C": np.random.randn(8) + 1.0,
"D": np.arange(8),
}
)
expected = df.groupby(["A"]).B.count()
result = df.B.groupby(df.A).count()
tm.assert_series_equal(result, expected)
def test_groupby_name_propagation(df):
# GH 6124
def summarize(df, name=None):
return Series({"count": 1, "mean": 2, "omissions": 3}, name=name)
def summarize_random_name(df):
# Provide a different name for each Series. In this case, groupby
# should not attempt to propagate the Series name since they are
# inconsistent.
return Series({"count": 1, "mean": 2, "omissions": 3}, name=df.iloc[0]["A"])
metrics = df.groupby("A").apply(summarize)
assert metrics.columns.name is None
metrics = df.groupby("A").apply(summarize, "metrics")
assert metrics.columns.name == "metrics"
metrics = df.groupby("A").apply(summarize_random_name)
assert metrics.columns.name is None
def test_groupby_nonstring_columns():
df = DataFrame([np.arange(10) for x in range(10)])
grouped = df.groupby(0)
result = grouped.mean()
expected = df.groupby(df[0]).mean()
tm.assert_frame_equal(result, expected)
def test_groupby_mixed_type_columns():
# GH 13432, unorderable types in py3
df = DataFrame([[0, 1, 2]], columns=["A", "B", 0])
expected = DataFrame([[1, 2]], columns=["B", 0], index=Index([0], name="A"))
result = df.groupby("A").first()
tm.assert_frame_equal(result, expected)
result = df.groupby("A").sum()
tm.assert_frame_equal(result, expected)
# TODO: Ensure warning isn't emitted in the first place
@pytest.mark.filterwarnings("ignore:Mean of:RuntimeWarning")
def test_cython_grouper_series_bug_noncontig():
arr = np.empty((100, 100))
arr.fill(np.nan)
obj = Series(arr[:, 0])
inds = np.tile(range(10), 10)
result = obj.groupby(inds).agg(Series.median)
assert result.isna().all()
def test_series_grouper_noncontig_index():
index = Index(tm.rands_array(10, 100))
values = Series(np.random.randn(50), index=index[::2])
labels = np.random.randint(0, 5, 50)
# it works!
grouped = values.groupby(labels)
# accessing the index elements causes segfault
f = lambda x: len(set(map(id, x.index)))
grouped.agg(f)
def test_convert_objects_leave_decimal_alone():
s = Series(range(5))
labels = np.array(["a", "b", "c", "d", "e"], dtype="O")
def convert_fast(x):
return Decimal(str(x.mean()))
def convert_force_pure(x):
# base will be length 0
assert len(x.values.base) > 0
return Decimal(str(x.mean()))
grouped = s.groupby(labels)
result = grouped.agg(convert_fast)
assert result.dtype == np.object_
assert isinstance(result[0], Decimal)
result = grouped.agg(convert_force_pure)
assert result.dtype == np.object_
assert isinstance(result[0], Decimal)
def test_groupby_dtype_inference_empty():
# GH 6733
df = DataFrame({"x": [], "range": np.arange(0, dtype="int64")})
assert df["x"].dtype == np.float64
result = df.groupby("x").first()
exp_index = Index([], name="x", dtype=np.float64)
expected = DataFrame({"range": Series([], index=exp_index, dtype="int64")})
tm.assert_frame_equal(result, expected, by_blocks=True)
def test_groupby_unit64_float_conversion():
# GH: 30859 groupby converts unit64 to floats sometimes
df = DataFrame({"first": [1], "second": [1], "value": [16148277970000000000]})
result = df.groupby(["first", "second"])["value"].max()
expected = Series(
[16148277970000000000],
MultiIndex.from_product([[1], [1]], names=["first", "second"]),
name="value",
)
tm.assert_series_equal(result, expected)
def test_groupby_list_infer_array_like(df):
result = df.groupby(list(df["A"])).mean()
expected = df.groupby(df["A"]).mean()
tm.assert_frame_equal(result, expected, check_names=False)
with pytest.raises(KeyError, match=r"^'foo'$"):
df.groupby(list(df["A"][:-1]))
# pathological case of ambiguity
df = DataFrame({"foo": [0, 1], "bar": [3, 4], "val": np.random.randn(2)})
result = df.groupby(["foo", "bar"]).mean()
expected = df.groupby([df["foo"], df["bar"]]).mean()[["val"]]
def test_groupby_keys_same_size_as_index():
# GH 11185
freq = "s"
index = date_range(
start=Timestamp("2015-09-29T11:34:44-0700"), periods=2, freq=freq
)
df = DataFrame([["A", 10], ["B", 15]], columns=["metric", "values"], index=index)
result = df.groupby([Grouper(level=0, freq=freq), "metric"]).mean()
expected = df.set_index([df.index, "metric"])
tm.assert_frame_equal(result, expected)
def test_groupby_one_row():
# GH 11741
msg = r"^'Z'$"
df1 = DataFrame(np.random.randn(1, 4), columns=list("ABCD"))
with pytest.raises(KeyError, match=msg):
df1.groupby("Z")
df2 = DataFrame(np.random.randn(2, 4), columns=list("ABCD"))
with pytest.raises(KeyError, match=msg):
df2.groupby("Z")
def test_groupby_nat_exclude():
# GH 6992
df = DataFrame(
{
"values": np.random.randn(8),
"dt": [
np.nan,
Timestamp("2013-01-01"),
np.nan,
Timestamp("2013-02-01"),
np.nan,
Timestamp("2013-02-01"),
np.nan,
Timestamp("2013-01-01"),
],
"str": [np.nan, "a", np.nan, "a", np.nan, "a", np.nan, "b"],
}
)
grouped = df.groupby("dt")
expected = [Index([1, 7]), Index([3, 5])]
keys = sorted(grouped.groups.keys())
assert len(keys) == 2
for k, e in zip(keys, expected):
# grouped.groups keys are np.datetime64 with system tz
# not to be affected by tz, only compare values
tm.assert_index_equal(grouped.groups[k], e)
# confirm obj is not filtered
tm.assert_frame_equal(grouped.grouper.groupings[0].obj, df)
assert grouped.ngroups == 2
expected = {
Timestamp("2013-01-01 00:00:00"): np.array([1, 7], dtype=np.intp),
Timestamp("2013-02-01 00:00:00"): np.array([3, 5], dtype=np.intp),
}
for k in grouped.indices:
tm.assert_numpy_array_equal(grouped.indices[k], expected[k])
tm.assert_frame_equal(grouped.get_group(Timestamp("2013-01-01")), df.iloc[[1, 7]])
tm.assert_frame_equal(grouped.get_group(Timestamp("2013-02-01")), df.iloc[[3, 5]])
with pytest.raises(KeyError, match=r"^NaT$"):
grouped.get_group(pd.NaT)
nan_df = DataFrame(
{"nan": [np.nan, np.nan, np.nan], "nat": [pd.NaT, pd.NaT, pd.NaT]}
)
assert nan_df["nan"].dtype == "float64"
assert nan_df["nat"].dtype == "datetime64[ns]"
for key in ["nan", "nat"]:
grouped = nan_df.groupby(key)
assert grouped.groups == {}
assert grouped.ngroups == 0
assert grouped.indices == {}
with pytest.raises(KeyError, match=r"^nan$"):
grouped.get_group(np.nan)
with pytest.raises(KeyError, match=r"^NaT$"):
grouped.get_group(pd.NaT)
def test_groupby_two_group_keys_all_nan():
# GH #36842: Grouping over two group keys shouldn't raise an error
df = DataFrame({"a": [np.nan, np.nan], "b": [np.nan, np.nan], "c": [1, 2]})
result = df.groupby(["a", "b"]).indices
assert result == {}
def test_groupby_2d_malformed():
d = DataFrame(index=range(2))
d["group"] = ["g1", "g2"]
d["zeros"] = [0, 0]
d["ones"] = [1, 1]
d["label"] = ["l1", "l2"]
tmp = d.groupby(["group"]).mean()
res_values = np.array([[0, 1], [0, 1]], dtype=np.int64)
tm.assert_index_equal(tmp.columns, Index(["zeros", "ones"]))
tm.assert_numpy_array_equal(tmp.values, res_values)
def test_int32_overflow():
B = np.concatenate((np.arange(10000), np.arange(10000), np.arange(5000)))
A = np.arange(25000)
df = DataFrame({"A": A, "B": B, "C": A, "D": B, "E": np.random.randn(25000)})
left = df.groupby(["A", "B", "C", "D"]).sum()
right = df.groupby(["D", "C", "B", "A"]).sum()
assert len(left) == len(right)
def test_groupby_sort_multi():
df = DataFrame(
{
"a": ["foo", "bar", "baz"],
"b": [3, 2, 1],
"c": [0, 1, 2],
"d": np.random.randn(3),
}
)
tups = [tuple(row) for row in df[["a", "b", "c"]].values]
tups = com.asarray_tuplesafe(tups)
result = df.groupby(["a", "b", "c"], sort=True).sum()
tm.assert_numpy_array_equal(result.index.values, tups[[1, 2, 0]])
tups = [tuple(row) for row in df[["c", "a", "b"]].values]
tups = com.asarray_tuplesafe(tups)
result = df.groupby(["c", "a", "b"], sort=True).sum()
tm.assert_numpy_array_equal(result.index.values, tups)
tups = [tuple(x) for x in df[["b", "c", "a"]].values]
tups = com.asarray_tuplesafe(tups)
result = df.groupby(["b", "c", "a"], sort=True).sum()
tm.assert_numpy_array_equal(result.index.values, tups[[2, 1, 0]])
df = DataFrame(
{"a": [0, 1, 2, 0, 1, 2], "b": [0, 0, 0, 1, 1, 1], "d": np.random.randn(6)}
)
grouped = df.groupby(["a", "b"])["d"]
result = grouped.sum()
def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
tups = [tuple(row) for row in df[keys].values]
tups = com.asarray_tuplesafe(tups)
expected = f(df.groupby(tups)[field])
for k, v in expected.items():
assert result[k] == v
_check_groupby(df, result, ["a", "b"], "d")
def test_dont_clobber_name_column():
df = DataFrame(
{"key": ["a", "a", "a", "b", "b", "b"], "name": ["foo", "bar", "baz"] * 2}
)
result = df.groupby("key").apply(lambda x: x)
tm.assert_frame_equal(result, df)
def test_skip_group_keys():
tsf = tm.makeTimeDataFrame()
grouped = tsf.groupby(lambda x: x.month, group_keys=False)
result = grouped.apply(lambda x: x.sort_values(by="A")[:3])
pieces = [group.sort_values(by="A")[:3] for key, group in grouped]
expected = pd.concat(pieces)
tm.assert_frame_equal(result, expected)
grouped = tsf["A"].groupby(lambda x: x.month, group_keys=False)
result = grouped.apply(lambda x: x.sort_values()[:3])
pieces = [group.sort_values()[:3] for key, group in grouped]
expected = pd.concat(pieces)
tm.assert_series_equal(result, expected)
def test_no_nonsense_name(float_frame):
# GH #995
s = float_frame["C"].copy()
s.name = None
result = s.groupby(float_frame["A"]).agg(np.sum)
assert result.name is None
def test_multifunc_sum_bug():
# GH #1065
x = DataFrame(np.arange(9).reshape(3, 3))
x["test"] = 0
x["fl"] = [1.3, 1.5, 1.6]
grouped = x.groupby("test")
result = grouped.agg({"fl": "sum", 2: "size"})
assert result["fl"].dtype == np.float64
def test_handle_dict_return_value(df):
def f(group):
return {"max": group.max(), "min": group.min()}
def g(group):
return Series({"max": group.max(), "min": group.min()})
result = df.groupby("A")["C"].apply(f)
expected = df.groupby("A")["C"].apply(g)
assert isinstance(result, Series)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("grouper", ["A", ["A", "B"]])
def test_set_group_name(df, grouper):
def f(group):
assert group.name is not None
return group
def freduce(group):
assert group.name is not None
return group.sum()
def foo(x):
return freduce(x)
grouped = df.groupby(grouper)
# make sure all these work
grouped.apply(f)
grouped.aggregate(freduce)
grouped.aggregate({"C": freduce, "D": freduce})
grouped.transform(f)
grouped["C"].apply(f)
grouped["C"].aggregate(freduce)
grouped["C"].aggregate([freduce, foo])
grouped["C"].transform(f)
def test_group_name_available_in_inference_pass():
# gh-15062
df = DataFrame({"a": [0, 0, 1, 1, 2, 2], "b": np.arange(6)})
names = []
def f(group):
names.append(group.name)
return group.copy()
df.groupby("a", sort=False, group_keys=False).apply(f)
expected_names = [0, 1, 2]
assert names == expected_names
def test_no_dummy_key_names(df):
# see gh-1291
result = df.groupby(df["A"].values).sum()
assert result.index.name is None
result = df.groupby([df["A"].values, df["B"].values]).sum()
assert result.index.names == (None, None)
def test_groupby_sort_multiindex_series():
# series multiindex groupby sort argument was not being passed through
# _compress_group_index
# GH 9444
index = MultiIndex(
levels=[[1, 2], [1, 2]],
codes=[[0, 0, 0, 0, 1, 1], [1, 1, 0, 0, 0, 0]],
names=["a", "b"],
)
mseries = Series([0, 1, 2, 3, 4, 5], index=index)
index = MultiIndex(
levels=[[1, 2], [1, 2]], codes=[[0, 0, 1], [1, 0, 0]], names=["a", "b"]
)
mseries_result = Series([0, 2, 4], index=index)
result = mseries.groupby(level=["a", "b"], sort=False).first()
tm.assert_series_equal(result, mseries_result)
result = mseries.groupby(level=["a", "b"], sort=True).first()
tm.assert_series_equal(result, mseries_result.sort_index())
def test_groupby_reindex_inside_function():
periods = 1000
ind = date_range(start="2012/1/1", freq="5min", periods=periods)
df = DataFrame({"high": np.arange(periods), "low": np.arange(periods)}, index=ind)
def agg_before(func, fix=False):
"""
Run an aggregate func on the subset of data.
"""
def _func(data):
d = data.loc[data.index.map(lambda x: x.hour < 11)].dropna()
if fix:
data[data.index[0]]
if len(d) == 0:
return None
return func(d)
return _func
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
closure_bad = grouped.agg({"high": agg_before(np.max)})
closure_good = grouped.agg({"high": agg_before(np.max, True)})
tm.assert_frame_equal(closure_bad, closure_good)
def test_groupby_multiindex_missing_pair():
# GH9049
df = DataFrame(
{
"group1": ["a", "a", "a", "b"],
"group2": ["c", "c", "d", "c"],
"value": [1, 1, 1, 5],
}
)
df = df.set_index(["group1", "group2"])
df_grouped = df.groupby(level=["group1", "group2"], sort=True)
res = df_grouped.agg("sum")
idx = MultiIndex.from_tuples(
[("a", "c"), ("a", "d"), ("b", "c")], names=["group1", "group2"]
)
exp = DataFrame([[2], [1], [5]], index=idx, columns=["value"])
tm.assert_frame_equal(res, exp)
def test_groupby_multiindex_not_lexsorted():
# GH 11640
# define the lexsorted version
lexsorted_mi = MultiIndex.from_tuples(
[("a", ""), ("b1", "c1"), ("b2", "c2")], names=["b", "c"]
)
lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi)
assert lexsorted_df.columns._is_lexsorted()
# define the non-lexsorted version
not_lexsorted_df = DataFrame(
columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]]
)
not_lexsorted_df = not_lexsorted_df.pivot_table(
index="a", columns=["b", "c"], values="d"
)
not_lexsorted_df = not_lexsorted_df.reset_index()
assert not not_lexsorted_df.columns._is_lexsorted()
# compare the results
tm.assert_frame_equal(lexsorted_df, not_lexsorted_df)
expected = lexsorted_df.groupby("a").mean()
with tm.assert_produces_warning(PerformanceWarning):
result = not_lexsorted_df.groupby("a").mean()
tm.assert_frame_equal(expected, result)
# a transforming function should work regardless of sort
# GH 14776
df = DataFrame(
{"x": ["a", "a", "b", "a"], "y": [1, 1, 2, 2], "z": [1, 2, 3, 4]}
).set_index(["x", "y"])
assert not df.index._is_lexsorted()
for level in [0, 1, [0, 1]]:
for sort in [False, True]:
result = df.groupby(level=level, sort=sort).apply(DataFrame.drop_duplicates)
expected = df
tm.assert_frame_equal(expected, result)
result = (
df.sort_index()
.groupby(level=level, sort=sort)
.apply(DataFrame.drop_duplicates)
)
expected = df.sort_index()
tm.assert_frame_equal(expected, result)
def test_index_label_overlaps_location():
# checking we don't have any label/location confusion in the
# wake of GH5375
df = DataFrame(list("ABCDE"), index=[2, 0, 2, 1, 1])
g = df.groupby(list("ababb"))
actual = g.filter(lambda x: len(x) > 2)
expected = df.iloc[[1, 3, 4]]
tm.assert_frame_equal(actual, expected)
ser = df[0]
g = ser.groupby(list("ababb"))
actual = g.filter(lambda x: len(x) > 2)
expected = ser.take([1, 3, 4])
tm.assert_series_equal(actual, expected)
# ... and again, with a generic Index of floats
df.index = df.index.astype(float)
g = df.groupby(list("ababb"))
actual = g.filter(lambda x: len(x) > 2)
expected = df.iloc[[1, 3, 4]]
tm.assert_frame_equal(actual, expected)
ser = df[0]
g = ser.groupby(list("ababb"))
actual = g.filter(lambda x: len(x) > 2)
expected = ser.take([1, 3, 4])
tm.assert_series_equal(actual, expected)
def test_transform_doesnt_clobber_ints():
# GH 7972
n = 6
x = np.arange(n)
df = DataFrame({"a": x // 2, "b": 2.0 * x, "c": 3.0 * x})
df2 = DataFrame({"a": x // 2 * 1.0, "b": 2.0 * x, "c": 3.0 * x})
gb = df.groupby("a")
result = gb.transform("mean")
gb2 = df2.groupby("a")
expected = gb2.transform("mean")
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"sort_column",
["ints", "floats", "strings", ["ints", "floats"], ["ints", "strings"]],
)
@pytest.mark.parametrize(
"group_column", ["int_groups", "string_groups", ["int_groups", "string_groups"]]
)
def test_groupby_preserves_sort(sort_column, group_column):
# Test to ensure that groupby always preserves sort order of original
# object. Issue #8588 and #9651
df = DataFrame(
{
"int_groups": [3, 1, 0, 1, 0, 3, 3, 3],
"string_groups": ["z", "a", "z", "a", "a", "g", "g", "g"],
"ints": [8, 7, 4, 5, 2, 9, 1, 1],
"floats": [2.3, 5.3, 6.2, -2.4, 2.2, 1.1, 1.1, 5],
"strings": ["z", "d", "a", "e", "word", "word2", "42", "47"],
}
)
# Try sorting on different types and with different group types
df = df.sort_values(by=sort_column)
g = df.groupby(group_column)
def test_sort(x):
tm.assert_frame_equal(x, x.sort_values(by=sort_column))
g.apply(test_sort)
def test_pivot_table_values_key_error():
# This test is designed to replicate the error in issue #14938
df = DataFrame(
{
"eventDate": date_range(datetime.today(), periods=20, freq="M").tolist(),
"thename": range(0, 20),
}
)
df["year"] = df.set_index("eventDate").index.year
df["month"] = df.set_index("eventDate").index.month
with pytest.raises(KeyError, match="'badname'"):
df.reset_index().pivot_table(
index="year", columns="month", values="badname", aggfunc="count"
)
@pytest.mark.parametrize("columns", ["C", ["C"]])
@pytest.mark.parametrize("keys", [["A"], ["A", "B"]])
@pytest.mark.parametrize(
"values",
[
[True],
[0],
[0.0],
["a"],
Categorical([0]),
[to_datetime(0)],
date_range(0, 1, 1, tz="US/Eastern"),
pd.array([0], dtype="Int64"),
pd.array([0], dtype="Float64"),
pd.array([False], dtype="boolean"),
],
)
@pytest.mark.parametrize("method", ["attr", "agg", "apply"])
@pytest.mark.parametrize(
"op", ["idxmax", "idxmin", "mad", "min", "max", "sum", "prod", "skew"]
)
def test_empty_groupby(columns, keys, values, method, op, request):
# GH8093 & GH26411
if isinstance(values, Categorical) and len(keys) == 1 and method == "apply":
mark = pytest.mark.xfail(raises=TypeError, match="'str' object is not callable")
request.node.add_marker(mark)
elif (
isinstance(values, Categorical)
and len(keys) == 1
and op in ["idxmax", "idxmin"]
):
mark = pytest.mark.xfail(
raises=ValueError, match="attempt to get arg(min|max) of an empty sequence"
)
request.node.add_marker(mark)
elif (
isinstance(values, Categorical)
and len(keys) == 1
and not isinstance(columns, list)
):
mark = pytest.mark.xfail(
raises=TypeError, match="'Categorical' does not implement"
)
request.node.add_marker(mark)
elif (
isinstance(values, Categorical)
and len(keys) == 1
and op in ["mad", "min", "max", "sum", "prod", "skew"]
):
mark = pytest.mark.xfail(
raises=AssertionError, match="(DataFrame|Series) are different"
)
request.node.add_marker(mark)
elif (
isinstance(values, Categorical)
and len(keys) == 2
and op in ["min", "max", "sum"]
and method != "apply"
):
mark = pytest.mark.xfail(
raises=AssertionError, match="(DataFrame|Series) are different"
)
request.node.add_marker(mark)
elif (
isinstance(values, pd.core.arrays.BooleanArray)
and op in ["sum", "prod"]
and method != "apply"
):
mark = pytest.mark.xfail(
raises=AssertionError, match="(DataFrame|Series) are different"
)
request.node.add_marker(mark)
override_dtype = None
if isinstance(values[0], bool) and op in ("prod", "sum") and method != "apply":
# sum/product of bools is an integer
override_dtype = "int64"
df = DataFrame({"A": values, "B": values, "C": values}, columns=list("ABC"))
if hasattr(values, "dtype"):
# check that we did the construction right
assert (df.dtypes == values.dtype).all()
df = df.iloc[:0]
gb = df.groupby(keys)[columns]
if method == "attr":
result = getattr(gb, op)()
else:
result = getattr(gb, method)(op)
expected = df.set_index(keys)[columns]
if override_dtype is not None:
expected = expected.astype(override_dtype)
if len(keys) == 1:
expected.index.name = keys[0]
tm.assert_equal(result, expected)
def test_tuple_as_grouping():
# https://github.com/pandas-dev/pandas/issues/18314
df = DataFrame(
{
("a", "b"): [1, 1, 1, 1],
"a": [2, 2, 2, 2],
"b": [2, 2, 2, 2],
"c": [1, 1, 1, 1],
}
)
with pytest.raises(KeyError, match=r"('a', 'b')"):
df[["a", "b", "c"]].groupby(("a", "b"))
result = df.groupby(("a", "b"))["c"].sum()
expected = Series([4], name="c", index=Index([1], name=("a", "b")))
tm.assert_series_equal(result, expected)
def test_tuple_correct_keyerror():
# https://github.com/pandas-dev/pandas/issues/18798
df = DataFrame(1, index=range(3), columns=MultiIndex.from_product([[1, 2], [3, 4]]))
with pytest.raises(KeyError, match=r"^\(7, 8\)$"):
df.groupby((7, 8)).mean()
def test_groupby_agg_ohlc_non_first():
# GH 21716
df = DataFrame(
[[1], [1]],
columns=["foo"],
index=date_range("2018-01-01", periods=2, freq="D"),
)
expected = DataFrame(
[[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]],
columns=MultiIndex.from_tuples(
(
("foo", "sum", "foo"),
("foo", "ohlc", "open"),
("foo", "ohlc", "high"),
("foo", "ohlc", "low"),
("foo", "ohlc", "close"),
)
),
index=date_range("2018-01-01", periods=2, freq="D"),
)
result = df.groupby(Grouper(freq="D")).agg(["sum", "ohlc"])
tm.assert_frame_equal(result, expected)
def test_groupby_multiindex_nat():
# GH 9236
values = [
(pd.NaT, "a"),
(datetime(2012, 1, 2), "a"),
(datetime(2012, 1, 2), "b"),
(datetime(2012, 1, 3), "a"),
]
mi = MultiIndex.from_tuples(values, names=["date", None])
ser = Series([3, 2, 2.5, 4], index=mi)
result = ser.groupby(level=1).mean()
expected = Series([3.0, 2.5], index=["a", "b"])
tm.assert_series_equal(result, expected)
def test_groupby_empty_list_raises():
# GH 5289
values = zip(range(10), range(10))
df = DataFrame(values, columns=["apple", "b"])
msg = "Grouper and axis must be same length"
with pytest.raises(ValueError, match=msg):
df.groupby([[]])
def test_groupby_multiindex_series_keys_len_equal_group_axis():
# GH 25704
index_array = [["x", "x"], ["a", "b"], ["k", "k"]]
index_names = ["first", "second", "third"]
ri = MultiIndex.from_arrays(index_array, names=index_names)
s = Series(data=[1, 2], index=ri)
result = s.groupby(["first", "third"]).sum()
index_array = [["x"], ["k"]]
index_names = ["first", "third"]
ei = MultiIndex.from_arrays(index_array, names=index_names)
expected = Series([3], index=ei)
tm.assert_series_equal(result, expected)
def test_groupby_groups_in_BaseGrouper():
# GH 26326
# Test if DataFrame grouped with a pandas.Grouper has correct groups
mi = MultiIndex.from_product([["A", "B"], ["C", "D"]], names=["alpha", "beta"])
df = DataFrame({"foo": [1, 2, 1, 2], "bar": [1, 2, 3, 4]}, index=mi)
result = df.groupby([Grouper(level="alpha"), "beta"])
expected = df.groupby(["alpha", "beta"])
assert result.groups == expected.groups
result = df.groupby(["beta", Grouper(level="alpha")])
expected = df.groupby(["beta", "alpha"])
assert result.groups == expected.groups
@pytest.mark.parametrize("group_name", ["x", ["x"]])
def test_groupby_axis_1(group_name):
# GH 27614
df = DataFrame(
np.arange(12).reshape(3, 4), index=[0, 1, 0], columns=[10, 20, 10, 20]
)
df.index.name = "y"
df.columns.name = "x"
results = df.groupby(group_name, axis=1).sum()
expected = df.T.groupby(group_name).sum().T
tm.assert_frame_equal(results, expected)
# test on MI column
iterables = [["bar", "baz", "foo"], ["one", "two"]]
mi = MultiIndex.from_product(iterables=iterables, names=["x", "x1"])
df = DataFrame(np.arange(18).reshape(3, 6), index=[0, 1, 0], columns=mi)
results = df.groupby(group_name, axis=1).sum()
expected = df.T.groupby(group_name).sum().T
tm.assert_frame_equal(results, expected)
@pytest.mark.parametrize(
"op, expected",
[
(
"shift",
{
"time": [
None,
None,
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
None,
None,
]
},
),
(
"bfill",
{
"time": [
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
]
},
),
(
"ffill",
{
"time": [
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
]
},
),
],
)
def test_shift_bfill_ffill_tz(tz_naive_fixture, op, expected):
# GH19995, GH27992: Check that timezone does not drop in shift, bfill, and ffill
tz = tz_naive_fixture
data = {
"id": ["A", "B", "A", "B", "A", "B"],
"time": [
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
None,
None,
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
],
}
df = DataFrame(data).assign(time=lambda x: x.time.dt.tz_localize(tz))
grouped = df.groupby("id")
result = getattr(grouped, op)()
expected = DataFrame(expected).assign(time=lambda x: x.time.dt.tz_localize(tz))
tm.assert_frame_equal(result, expected)
def test_groupby_only_none_group():
# see GH21624
# this was crashing with "ValueError: Length of passed values is 1, index implies 0"
df = DataFrame({"g": [None], "x": 1})
actual = df.groupby("g")["x"].transform("sum")
expected = Series([np.nan], name="x")
tm.assert_series_equal(actual, expected)
def test_groupby_duplicate_index():
# GH#29189 the groupby call here used to raise
ser = Series([2, 5, 6, 8], index=[2.0, 4.0, 4.0, 5.0])
gb = ser.groupby(level=0)
result = gb.mean()
expected = Series([2, 5.5, 8], index=[2.0, 4.0, 5.0])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"idx", [Index(["a", "a"]), MultiIndex.from_tuples((("a", "a"), ("a", "a")))]
)
@pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
def test_dup_labels_output_shape(groupby_func, idx):
if groupby_func in {"size", "ngroup", "cumcount"}:
pytest.skip("Not applicable")
df = DataFrame([[1, 1]], columns=idx)
grp_by = df.groupby([0])
args = []
if groupby_func in {"fillna", "nth"}:
args.append(0)
elif groupby_func == "corrwith":
args.append(df)
elif groupby_func == "tshift":
df.index = [Timestamp("today")]
args.extend([1, "D"])
result = getattr(grp_by, groupby_func)(*args)
assert result.shape == (1, 2)
tm.assert_index_equal(result.columns, idx)
def test_groupby_crash_on_nunique(axis):
# Fix following 30253
df = DataFrame({("A", "B"): [1, 2], ("A", "C"): [1, 3], ("D", "B"): [0, 0]})
axis_number = df._get_axis_number(axis)
if not axis_number:
df = df.T
result = df.groupby(axis=axis_number, level=0).nunique()
expected = DataFrame({"A": [1, 2], "D": [1, 1]})
if not axis_number:
expected = expected.T
tm.assert_frame_equal(result, expected)
# same thing, but empty columns
gb = df[[]].groupby(axis=axis_number, level=0)
res = gb.nunique()
exp = expected[[]]
tm.assert_frame_equal(res, exp)
def test_groupby_list_level():
# GH 9790
expected = DataFrame(np.arange(0, 9).reshape(3, 3))
result = expected.groupby(level=[0]).mean()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"max_seq_items, expected",
[
(5, "{0: [0], 1: [1], 2: [2], 3: [3], 4: [4]}"),
(4, "{0: [0], 1: [1], 2: [2], 3: [3], ...}"),
(1, "{0: [0], ...}"),
],
)
def test_groups_repr_truncates(max_seq_items, expected):
# GH 1135
df = DataFrame(np.random.randn(5, 1))
df["a"] = df.index
with pd.option_context("display.max_seq_items", max_seq_items):
result = df.groupby("a").groups.__repr__()
assert result == expected
result = df.groupby(np.array(df.a)).groups.__repr__()
assert result == expected
def test_group_on_two_row_multiindex_returns_one_tuple_key():
# GH 18451
df = DataFrame([{"a": 1, "b": 2, "c": 99}, {"a": 1, "b": 2, "c": 88}])
df = df.set_index(["a", "b"])
grp = df.groupby(["a", "b"])
result = grp.indices
expected = {(1, 2): np.array([0, 1], dtype=np.int64)}
assert len(result) == 1
key = (1, 2)
assert (result[key] == expected[key]).all()
@pytest.mark.parametrize(
"klass, attr, value",
[
(DataFrame, "level", "a"),
(DataFrame, "as_index", False),
(DataFrame, "sort", False),
(DataFrame, "group_keys", False),
(DataFrame, "squeeze", True),
(DataFrame, "observed", True),
(DataFrame, "dropna", False),
pytest.param(
Series,
"axis",
1,
marks=pytest.mark.xfail(
reason="GH 35443: Attribute currently not passed on to series"
),
),
(Series, "level", "a"),
(Series, "as_index", False),
(Series, "sort", False),
(Series, "group_keys", False),
(Series, "squeeze", True),
(Series, "observed", True),
(Series, "dropna", False),
],
)
@pytest.mark.filterwarnings(
"ignore:The `squeeze` parameter is deprecated:FutureWarning"
)
def test_subsetting_columns_keeps_attrs(klass, attr, value):
# GH 9959 - When subsetting columns, don't drop attributes
df = DataFrame({"a": [1], "b": [2], "c": [3]})
if attr != "axis":
df = df.set_index("a")
expected = df.groupby("a", **{attr: value})
result = expected[["b"]] if klass is DataFrame else expected["b"]
assert getattr(result, attr) == getattr(expected, attr)
def test_subsetting_columns_axis_1():
# GH 37725
g = DataFrame({"A": [1], "B": [2], "C": [3]}).groupby([0, 0, 1], axis=1)
match = "Cannot subset columns when using axis=1"
with pytest.raises(ValueError, match=match):
g[["A", "B"]].sum()
@pytest.mark.parametrize("func", ["sum", "any", "shift"])
def test_groupby_column_index_name_lost(func):
# GH: 29764 groupby loses index sometimes
expected = Index(["a"], name="idx")
df = DataFrame([[1]], columns=expected)
df_grouped = df.groupby([1])
result = getattr(df_grouped, func)().columns
tm.assert_index_equal(result, expected)
def test_groupby_duplicate_columns():
# GH: 31735
df = DataFrame(
{"A": ["f", "e", "g", "h"], "B": ["a", "b", "c", "d"], "C": [1, 2, 3, 4]}
).astype(object)
df.columns = ["A", "B", "B"]
result = df.groupby([0, 0, 0, 0]).min()
expected = DataFrame([["e", "a", 1]], columns=["A", "B", "B"])
tm.assert_frame_equal(result, expected)
def test_groupby_series_with_tuple_name():
# GH 37755
ser = Series([1, 2, 3, 4], index=[1, 1, 2, 2], name=("a", "a"))
ser.index.name = ("b", "b")
result = ser.groupby(level=0).last()
expected = Series([2, 4], index=[1, 2], name=("a", "a"))
expected.index.name = ("b", "b")
tm.assert_series_equal(result, expected)
@pytest.mark.xfail(not IS64, reason="GH#38778: fail on 32-bit system")
@pytest.mark.parametrize(
"func, values", [("sum", [97.0, 98.0]), ("mean", [24.25, 24.5])]
)
def test_groupby_numerical_stability_sum_mean(func, values):
# GH#38778
data = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15]
df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data})
result = getattr(df.groupby("group"), func)()
expected = DataFrame({"a": values, "b": values}, index=Index([1, 2], name="group"))
tm.assert_frame_equal(result, expected)
@pytest.mark.xfail(not IS64, reason="GH#38778: fail on 32-bit system")
def test_groupby_numerical_stability_cumsum():
# GH#38934
data = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15]
df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data})
result = df.groupby("group").cumsum()
exp_data = (
[1e16] * 2 + [1e16 + 96, 1e16 + 98] + [5e15 + 97, 5e15 + 98] + [97.0, 98.0]
)
expected = DataFrame({"a": exp_data, "b": exp_data})
tm.assert_frame_equal(result, expected, check_exact=True)
def test_groupby_mean_duplicate_index(rand_series_with_duplicate_datetimeindex):
dups = rand_series_with_duplicate_datetimeindex
result = dups.groupby(level=0).mean()
expected = dups.groupby(dups.index).mean()
tm.assert_series_equal(result, expected)
| 30.504694
| 88
| 0.587201
|
from datetime import datetime
from decimal import Decimal
from io import StringIO
import numpy as np
import pytest
from pandas.compat import IS64
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Grouper,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
read_csv,
to_datetime,
)
import pandas._testing as tm
from pandas.core.base import SpecificationError
import pandas.core.common as com
def test_repr():
result = repr(Grouper(key="A", level="B"))
expected = "Grouper(key='A', level='B', axis=0, sort=False)"
assert result == expected
@pytest.mark.parametrize("dtype", ["int64", "int32", "float64", "float32"])
def test_basic(dtype):
data = Series(np.arange(9) // 3, index=np.arange(9), dtype=dtype)
index = np.arange(9)
np.random.shuffle(index)
data = data.reindex(index)
grouped = data.groupby(lambda x: x // 3)
for k, v in grouped:
assert len(v) == 3
agged = grouped.aggregate(np.mean)
assert agged[1] == 1
tm.assert_series_equal(agged, grouped.agg(np.mean))
tm.assert_series_equal(agged, grouped.mean())
tm.assert_series_equal(grouped.agg(np.sum), grouped.sum())
expected = grouped.apply(lambda x: x * x.sum())
transformed = grouped.transform(lambda x: x * x.sum())
assert transformed[7] == 12
tm.assert_series_equal(transformed, expected)
value_grouped = data.groupby(data)
tm.assert_series_equal(
value_grouped.aggregate(np.mean), agged, check_index_type=False
)
agged = grouped.aggregate([np.mean, np.std])
msg = r"nested renamer is not supported"
with pytest.raises(SpecificationError, match=msg):
grouped.aggregate({"one": np.mean, "two": np.std})
group_constants = {0: 10, 1: 20, 2: 30}
agged = grouped.agg(lambda x: group_constants[x.name] + x.mean())
assert agged[1] == 21
msg = "Must produce aggregated value"
with pytest.raises(Exception, match=msg):
grouped.aggregate(lambda x: x * 2)
def test_groupby_nonobject_dtype(mframe, df_mixed_floats):
key = mframe.index.codes[0]
grouped = mframe.groupby(key)
result = grouped.sum()
expected = mframe.groupby(key.astype("O")).sum()
tm.assert_frame_equal(result, expected)
df = df_mixed_floats.copy()
df["value"] = range(len(df))
def max_value(group):
return group.loc[group["value"].idxmax()]
applied = df.groupby("A").apply(max_value)
result = applied.dtypes
expected = df.dtypes
tm.assert_series_equal(result, expected)
def test_groupby_return_type():
df1 = DataFrame(
[
{"val1": 1, "val2": 20},
{"val1": 1, "val2": 19},
{"val1": 2, "val2": 27},
{"val1": 2, "val2": 12},
]
)
def func(dataf):
return dataf["val2"] - dataf["val2"].mean()
with tm.assert_produces_warning(FutureWarning):
result = df1.groupby("val1", squeeze=True).apply(func)
assert isinstance(result, Series)
df2 = DataFrame(
[
{"val1": 1, "val2": 20},
{"val1": 1, "val2": 19},
{"val1": 1, "val2": 27},
{"val1": 1, "val2": 12},
]
)
def func(dataf):
return dataf["val2"] - dataf["val2"].mean()
with tm.assert_produces_warning(FutureWarning):
result = df2.groupby("val1", squeeze=True).apply(func)
assert isinstance(result, Series)
df = DataFrame([[1, 1], [1, 1]], columns=["X", "Y"])
with tm.assert_produces_warning(FutureWarning):
result = df.groupby("X", squeeze=False).count()
assert isinstance(result, DataFrame)
def test_inconsistent_return_type():
df = DataFrame(
{
"A": ["Tiger", "Tiger", "Tiger", "Lamb", "Lamb", "Pony", "Pony"],
"B": Series(np.arange(7), dtype="int64"),
"C": date_range("20130101", periods=7),
}
)
def f(grp):
return grp.iloc[0]
expected = df.groupby("A").first()[["B"]]
result = df.groupby("A").apply(f)[["B"]]
tm.assert_frame_equal(result, expected)
def f(grp):
if grp.name == "Tiger":
return None
return grp.iloc[0]
result = df.groupby("A").apply(f)[["B"]]
e = expected.copy()
e.loc["Tiger"] = np.nan
tm.assert_frame_equal(result, e)
def f(grp):
if grp.name == "Pony":
return None
return grp.iloc[0]
result = df.groupby("A").apply(f)[["B"]]
e = expected.copy()
e.loc["Pony"] = np.nan
tm.assert_frame_equal(result, e)
def f(grp):
if grp.name == "Pony":
return None
return grp.iloc[0]
result = df.groupby("A").apply(f)[["C"]]
e = df.groupby("A").first()[["C"]]
e.loc["Pony"] = pd.NaT
tm.assert_frame_equal(result, e)
def f(grp):
if grp.name == "Pony":
return None
return grp.iloc[0].loc["C"]
result = df.groupby("A").apply(f)
e = df.groupby("A").first()["C"].copy()
e.loc["Pony"] = np.nan
e.name = None
tm.assert_series_equal(result, e)
def test_pass_args_kwargs(ts, tsframe):
def f(x, q=None, axis=0):
return np.percentile(x, q, axis=axis)
g = lambda x: np.percentile(x, 80, axis=0)
ts_grouped = ts.groupby(lambda x: x.month)
agg_result = ts_grouped.agg(np.percentile, 80, axis=0)
apply_result = ts_grouped.apply(np.percentile, 80, axis=0)
trans_result = ts_grouped.transform(np.percentile, 80, axis=0)
agg_expected = ts_grouped.quantile(0.8)
trans_expected = ts_grouped.transform(g)
tm.assert_series_equal(apply_result, agg_expected)
tm.assert_series_equal(agg_result, agg_expected)
tm.assert_series_equal(trans_result, trans_expected)
agg_result = ts_grouped.agg(f, q=80)
apply_result = ts_grouped.apply(f, q=80)
trans_result = ts_grouped.transform(f, q=80)
tm.assert_series_equal(agg_result, agg_expected)
tm.assert_series_equal(apply_result, agg_expected)
tm.assert_series_equal(trans_result, trans_expected)
df_grouped = tsframe.groupby(lambda x: x.month)
agg_result = df_grouped.agg(np.percentile, 80, axis=0)
apply_result = df_grouped.apply(DataFrame.quantile, 0.8)
expected = df_grouped.quantile(0.8)
tm.assert_frame_equal(apply_result, expected, check_names=False)
tm.assert_frame_equal(agg_result, expected)
agg_result = df_grouped.agg(f, q=80)
apply_result = df_grouped.apply(DataFrame.quantile, q=0.8)
tm.assert_frame_equal(agg_result, expected)
tm.assert_frame_equal(apply_result, expected, check_names=False)
def test_len():
df = tm.makeTimeDataFrame()
grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day])
assert len(grouped) == len(df)
grouped = df.groupby([lambda x: x.year, lambda x: x.month])
expected = len({(x.year, x.month) for x in df.index})
assert len(grouped) == expected
df = DataFrame({"a": [np.nan] * 3, "b": [1, 2, 3]})
assert len(df.groupby("a")) == 0
assert len(df.groupby("b")) == 3
assert len(df.groupby(["a", "b"])) == 3
def test_basic_regression():
result = Series([1.0 * x for x in list(range(1, 10)) * 10])
data = np.random.random(1100) * 10.0
groupings = Series(data)
grouped = result.groupby(groupings)
grouped.mean()
@pytest.mark.parametrize(
"dtype", ["float64", "float32", "int64", "int32", "int16", "int8"]
)
def test_with_na_groups(dtype):
index = Index(np.arange(10))
values = Series(np.ones(10), index, dtype=dtype)
labels = Series(
[np.nan, "foo", "bar", "bar", np.nan, np.nan, "bar", "bar", np.nan, "foo"],
index=index,
)
grouped = values.groupby(labels)
agged = grouped.agg(len)
expected = Series([4, 2], index=["bar", "foo"])
tm.assert_series_equal(agged, expected, check_dtype=False)
def f(x):
return float(len(x))
agged = grouped.agg(f)
expected = Series([4.0, 2.0], index=["bar", "foo"])
tm.assert_series_equal(agged, expected)
def test_indices_concatenation_order():
def f1(x):
y = x[(x.b % 2) == 1] ** 2
if y.empty:
multiindex = MultiIndex(levels=[[]] * 2, codes=[[]] * 2, names=["b", "c"])
res = DataFrame(columns=["a"], index=multiindex)
return res
else:
y = y.set_index(["b", "c"])
return y
def f2(x):
y = x[(x.b % 2) == 1] ** 2
if y.empty:
return DataFrame()
else:
y = y.set_index(["b", "c"])
return y
def f3(x):
y = x[(x.b % 2) == 1] ** 2
if y.empty:
multiindex = MultiIndex(
levels=[[]] * 2, codes=[[]] * 2, names=["foo", "bar"]
)
res = DataFrame(columns=["a", "b"], index=multiindex)
return res
else:
return y
df = DataFrame({"a": [1, 2, 2, 2], "b": range(4), "c": range(5, 9)})
df2 = DataFrame({"a": [3, 2, 2, 2], "b": range(4), "c": range(5, 9)})
result1 = df.groupby("a").apply(f1)
result2 = df2.groupby("a").apply(f1)
tm.assert_frame_equal(result1, result2)
msg = "Cannot concat indices that do not have the same number of levels"
with pytest.raises(AssertionError, match=msg):
df.groupby("a").apply(f2)
with pytest.raises(AssertionError, match=msg):
df2.groupby("a").apply(f2)
with pytest.raises(AssertionError, match=msg):
df.groupby("a").apply(f3)
with pytest.raises(AssertionError, match=msg):
df2.groupby("a").apply(f3)
def test_attr_wrapper(ts):
grouped = ts.groupby(lambda x: x.weekday())
result = grouped.std()
expected = grouped.agg(lambda x: np.std(x, ddof=1))
tm.assert_series_equal(result, expected)
result = grouped.describe()
expected = {name: gp.describe() for name, gp in grouped}
expected = DataFrame(expected).T
tm.assert_frame_equal(result, expected)
result = grouped.dtype
expected = grouped.agg(lambda x: x.dtype)
tm.assert_series_equal(result, expected)
msg = "'SeriesGroupBy' object has no attribute 'foo'"
with pytest.raises(AttributeError, match=msg):
getattr(grouped, "foo")
def test_frame_groupby(tsframe):
grouped = tsframe.groupby(lambda x: x.weekday())
aggregated = grouped.aggregate(np.mean)
assert len(aggregated) == 5
assert len(aggregated.columns) == 4
tscopy = tsframe.copy()
tscopy["weekday"] = [x.weekday() for x in tscopy.index]
stragged = tscopy.groupby("weekday").aggregate(np.mean)
tm.assert_frame_equal(stragged, aggregated, check_names=False)
grouped = tsframe.head(30).groupby(lambda x: x.weekday())
transformed = grouped.transform(lambda x: x - x.mean())
assert len(transformed) == 30
assert len(transformed.columns) == 4
transformed = grouped.transform(lambda x: x.mean())
for name, group in grouped:
mean = group.mean()
for idx in group.index:
tm.assert_series_equal(transformed.xs(idx), mean, check_names=False)
for weekday, group in grouped:
assert group.index[0].weekday() == weekday
groups = grouped.groups
indices = grouped.indices
for k, v in groups.items():
samething = tsframe.index.take(indices[k])
assert (samething == v).all()
def test_frame_groupby_columns(tsframe):
mapping = {"A": 0, "B": 0, "C": 1, "D": 1}
grouped = tsframe.groupby(mapping, axis=1)
aggregated = grouped.aggregate(np.mean)
assert len(aggregated) == len(tsframe)
assert len(aggregated.columns) == 2
tf = lambda x: x - x.mean()
groupedT = tsframe.T.groupby(mapping, axis=0)
tm.assert_frame_equal(groupedT.transform(tf).T, grouped.transform(tf))
for k, v in grouped:
assert len(v.columns) == 2
def test_frame_set_name_single(df):
grouped = df.groupby("A")
result = grouped.mean()
assert result.index.name == "A"
result = df.groupby("A", as_index=False).mean()
assert result.index.name != "A"
result = grouped.agg(np.mean)
assert result.index.name == "A"
result = grouped.agg({"C": np.mean, "D": np.std})
assert result.index.name == "A"
result = grouped["C"].mean()
assert result.index.name == "A"
result = grouped["C"].agg(np.mean)
assert result.index.name == "A"
result = grouped["C"].agg([np.mean, np.std])
assert result.index.name == "A"
msg = r"nested renamer is not supported"
with pytest.raises(SpecificationError, match=msg):
grouped["C"].agg({"foo": np.mean, "bar": np.std})
def test_multi_func(df):
col1 = df["A"]
col2 = df["B"]
grouped = df.groupby([col1.get, col2.get])
agged = grouped.mean()
expected = df.groupby(["A", "B"]).mean()
tm.assert_frame_equal(
agged.loc[:, ["C", "D"]], expected.loc[:, ["C", "D"]], check_names=False
)
df = DataFrame(
{
"v1": np.random.randn(6),
"v2": np.random.randn(6),
"k1": np.array(["b", "b", "b", "a", "a", "a"]),
"k2": np.array(["1", "1", "1", "2", "2", "2"]),
},
index=["one", "two", "three", "four", "five", "six"],
)
grouped = df.groupby(["k1", "k2"])
grouped.agg(np.sum)
def test_multi_key_multiple_functions(df):
grouped = df.groupby(["A", "B"])["C"]
agged = grouped.agg([np.mean, np.std])
expected = DataFrame({"mean": grouped.agg(np.mean), "std": grouped.agg(np.std)})
tm.assert_frame_equal(agged, expected)
def test_frame_multi_key_function_list():
data = DataFrame(
{
"A": [
"foo",
"foo",
"foo",
"foo",
"bar",
"bar",
"bar",
"bar",
"foo",
"foo",
"foo",
],
"B": [
"one",
"one",
"one",
"two",
"one",
"one",
"one",
"two",
"two",
"two",
"one",
],
"C": [
"dull",
"dull",
"shiny",
"dull",
"dull",
"shiny",
"shiny",
"dull",
"shiny",
"shiny",
"shiny",
],
"D": np.random.randn(11),
"E": np.random.randn(11),
"F": np.random.randn(11),
}
)
grouped = data.groupby(["A", "B"])
funcs = [np.mean, np.std]
agged = grouped.agg(funcs)
expected = pd.concat(
[grouped["D"].agg(funcs), grouped["E"].agg(funcs), grouped["F"].agg(funcs)],
keys=["D", "E", "F"],
axis=1,
)
assert isinstance(agged.index, MultiIndex)
assert isinstance(expected.index, MultiIndex)
tm.assert_frame_equal(agged, expected)
@pytest.mark.parametrize("op", [lambda x: x.sum(), lambda x: x.mean()])
def test_groupby_multiple_columns(df, op):
data = df
grouped = data.groupby(["A", "B"])
result1 = op(grouped)
keys = []
values = []
for n1, gp1 in data.groupby("A"):
for n2, gp2 in gp1.groupby("B"):
keys.append((n1, n2))
values.append(op(gp2.loc[:, ["C", "D"]]))
mi = MultiIndex.from_tuples(keys, names=["A", "B"])
expected = pd.concat(values, axis=1).T
expected.index = mi
for col in ["C", "D"]:
result_col = op(grouped[col])
pivoted = result1[col]
exp = expected[col]
tm.assert_series_equal(result_col, exp)
tm.assert_series_equal(pivoted, exp)
result = data["C"].groupby([data["A"], data["B"]]).mean()
expected = data.groupby(["A", "B"]).mean()["C"]
tm.assert_series_equal(result, expected)
def test_as_index_select_column():
df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"])
result = df.groupby("A", as_index=False)["B"].get_group(1)
expected = Series([2, 4], name="B")
tm.assert_series_equal(result, expected)
result = df.groupby("A", as_index=False)["B"].apply(lambda x: x.cumsum())
expected = Series(
[2, 6, 6], name="B", index=MultiIndex.from_tuples([(0, 0), (0, 1), (1, 2)])
)
tm.assert_series_equal(result, expected)
def test_groupby_as_index_select_column_sum_empty_df():
df = DataFrame(columns=["A", "B", "C"])
left = df.groupby(by="A", as_index=False)["B"].sum()
assert type(left) is DataFrame
assert left.to_dict() == {"A": {}, "B": {}}
def test_groupby_as_index_agg(df):
grouped = df.groupby("A", as_index=False)
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
result2 = grouped.agg({"C": np.mean, "D": np.sum})
expected2 = grouped.mean()
expected2["D"] = grouped.sum()["D"]
tm.assert_frame_equal(result2, expected2)
grouped = df.groupby("A", as_index=True)
msg = r"nested renamer is not supported"
with pytest.raises(SpecificationError, match=msg):
grouped["C"].agg({"Q": np.sum})
grouped = df.groupby(["A", "B"], as_index=False)
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
result2 = grouped.agg({"C": np.mean, "D": np.sum})
expected2 = grouped.mean()
expected2["D"] = grouped.sum()["D"]
tm.assert_frame_equal(result2, expected2)
expected3 = grouped["C"].sum()
expected3 = DataFrame(expected3).rename(columns={"C": "Q"})
result3 = grouped["C"].agg({"Q": np.sum})
tm.assert_frame_equal(result3, expected3)
df = DataFrame(np.random.randint(0, 100, (50, 3)), columns=["jim", "joe", "jolie"])
ts = Series(np.random.randint(5, 10, 50), name="jim")
gr = df.groupby(ts)
gr.nth(0)
tm.assert_frame_equal(gr.apply(sum), df.groupby(ts).apply(sum))
for attr in ["mean", "max", "count", "idxmax", "cumsum", "all"]:
gr = df.groupby(ts, as_index=False)
left = getattr(gr, attr)()
gr = df.groupby(ts.values, as_index=True)
right = getattr(gr, attr)().reset_index(drop=True)
tm.assert_frame_equal(left, right)
def test_ops_not_as_index(reduction_func):
if reduction_func in ("corrwith",):
pytest.skip("Test not applicable")
if reduction_func in ("nth", "ngroup"):
pytest.skip("Skip until behavior is determined (GH #5755)")
df = DataFrame(np.random.randint(0, 5, size=(100, 2)), columns=["a", "b"])
expected = getattr(df.groupby("a"), reduction_func)()
if reduction_func == "size":
expected = expected.rename("size")
expected = expected.reset_index()
g = df.groupby("a", as_index=False)
result = getattr(g, reduction_func)()
tm.assert_frame_equal(result, expected)
result = g.agg(reduction_func)
tm.assert_frame_equal(result, expected)
result = getattr(g["b"], reduction_func)()
tm.assert_frame_equal(result, expected)
result = g["b"].agg(reduction_func)
tm.assert_frame_equal(result, expected)
def test_as_index_series_return_frame(df):
grouped = df.groupby("A", as_index=False)
grouped2 = df.groupby(["A", "B"], as_index=False)
result = grouped["C"].agg(np.sum)
expected = grouped.agg(np.sum).loc[:, ["A", "C"]]
assert isinstance(result, DataFrame)
tm.assert_frame_equal(result, expected)
result2 = grouped2["C"].agg(np.sum)
expected2 = grouped2.agg(np.sum).loc[:, ["A", "B", "C"]]
assert isinstance(result2, DataFrame)
tm.assert_frame_equal(result2, expected2)
result = grouped["C"].sum()
expected = grouped.sum().loc[:, ["A", "C"]]
assert isinstance(result, DataFrame)
tm.assert_frame_equal(result, expected)
result2 = grouped2["C"].sum()
expected2 = grouped2.sum().loc[:, ["A", "B", "C"]]
assert isinstance(result2, DataFrame)
tm.assert_frame_equal(result2, expected2)
def test_as_index_series_column_slice_raises(df):
grouped = df.groupby("A", as_index=False)
msg = r"Column\(s\) C already selected"
with pytest.raises(IndexError, match=msg):
grouped["C"].__getitem__("D")
def test_groupby_as_index_cython(df):
data = df
grouped = data.groupby("A", as_index=False)
result = grouped.mean()
expected = data.groupby(["A"]).mean()
expected.insert(0, "A", expected.index)
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
grouped = data.groupby(["A", "B"], as_index=False)
result = grouped.mean()
expected = data.groupby(["A", "B"]).mean()
arrays = list(zip(*expected.index.values))
expected.insert(0, "A", arrays[0])
expected.insert(1, "B", arrays[1])
expected.index = np.arange(len(expected))
tm.assert_frame_equal(result, expected)
def test_groupby_as_index_series_scalar(df):
grouped = df.groupby(["A", "B"], as_index=False)
result = grouped["C"].agg(len)
expected = grouped.agg(len).loc[:, ["A", "B", "C"]]
tm.assert_frame_equal(result, expected)
def test_groupby_as_index_corner(df, ts):
msg = "as_index=False only valid with DataFrame"
with pytest.raises(TypeError, match=msg):
ts.groupby(lambda x: x.weekday(), as_index=False)
msg = "as_index=False only valid for axis=0"
with pytest.raises(ValueError, match=msg):
df.groupby(lambda x: x.lower(), as_index=False, axis=1)
def test_groupby_multiple_key(df):
df = tm.makeTimeDataFrame()
grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day])
agged = grouped.sum()
tm.assert_almost_equal(df.values, agged.values)
grouped = df.T.groupby(
[lambda x: x.year, lambda x: x.month, lambda x: x.day], axis=1
)
agged = grouped.agg(lambda x: x.sum())
tm.assert_index_equal(agged.index, df.columns)
tm.assert_almost_equal(df.T.values, agged.values)
agged = grouped.agg(lambda x: x.sum())
tm.assert_almost_equal(df.T.values, agged.values)
def test_groupby_multi_corner(df):
df = df.copy()
df["bad"] = np.nan
agged = df.groupby(["A", "B"]).mean()
expected = df.groupby(["A", "B"]).mean()
expected["bad"] = np.nan
tm.assert_frame_equal(agged, expected)
def test_omit_nuisance(df):
grouped = df.groupby("A")
result = grouped.mean()
expected = df.loc[:, ["A", "C", "D"]].groupby("A").mean()
tm.assert_frame_equal(result, expected)
agged = grouped.agg(np.mean)
exp = grouped.mean()
tm.assert_frame_equal(agged, exp)
df = df.loc[:, ["A", "C", "D"]]
df["E"] = datetime.now()
grouped = df.groupby("A")
result = grouped.agg(np.sum)
expected = grouped.sum()
tm.assert_frame_equal(result, expected)
# won't work with axis = 1
grouped = df.groupby({"A": 0, "C": 0, "D": 1, "E": 1}, axis=1)
msg = "'DatetimeArray' does not implement reduction 'sum'"
with pytest.raises(TypeError, match=msg):
grouped.agg(lambda x: x.sum(0, numeric_only=False))
def test_omit_nuisance_sem(df):
grouped = df.groupby("A")
result = grouped.sem()
expected = df.loc[:, ["A", "C", "D"]].groupby("A").sem()
tm.assert_frame_equal(result, expected)
def test_omit_nuisance_python_multiple(three_group):
grouped = three_group.groupby(["A", "B"])
agged = grouped.agg(np.mean)
exp = grouped.mean()
tm.assert_frame_equal(agged, exp)
def test_empty_groups_corner(mframe):
df = DataFrame(
{
"k1": np.array(["b", "b", "b", "a", "a", "a"]),
"k2": np.array(["1", "1", "1", "2", "2", "2"]),
"k3": ["foo", "bar"] * 3,
"v1": np.random.randn(6),
"v2": np.random.randn(6),
}
)
grouped = df.groupby(["k1", "k2"])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
grouped = mframe[3:5].groupby(level=0)
agged = grouped.apply(lambda x: x.mean())
agged_A = grouped["A"].apply(np.mean)
tm.assert_series_equal(agged["A"], agged_A)
assert agged.index.name == "first"
def test_nonsense_func():
df = DataFrame([0])
msg = r"unsupported operand type\(s\) for \+: 'int' and 'str'"
with pytest.raises(TypeError, match=msg):
df.groupby(lambda x: x + "foo")
def test_wrap_aggregated_output_multindex(mframe):
df = mframe.T
df["baz", "two"] = "peekaboo"
keys = [np.array([0, 0, 1]), np.array([0, 0, 1])]
agged = df.groupby(keys).agg(np.mean)
assert isinstance(agged.columns, MultiIndex)
def aggfun(ser):
if ser.name == ("foo", "one"):
raise TypeError
else:
return ser.sum()
agged2 = df.groupby(keys).aggregate(aggfun)
assert len(agged2.columns) + 1 == len(df.columns)
def test_groupby_level_apply(mframe):
result = mframe.groupby(level=0).count()
assert result.index.name == "first"
result = mframe.groupby(level=1).count()
assert result.index.name == "second"
result = mframe["A"].groupby(level=0).count()
assert result.index.name == "first"
def test_groupby_level_mapper(mframe):
deleveled = mframe.reset_index()
mapper0 = {"foo": 0, "bar": 0, "baz": 1, "qux": 1}
mapper1 = {"one": 0, "two": 0, "three": 1}
result0 = mframe.groupby(mapper0, level=0).sum()
result1 = mframe.groupby(mapper1, level=1).sum()
mapped_level0 = np.array([mapper0.get(x) for x in deleveled["first"]])
mapped_level1 = np.array([mapper1.get(x) for x in deleveled["second"]])
expected0 = mframe.groupby(mapped_level0).sum()
expected1 = mframe.groupby(mapped_level1).sum()
expected0.index.name, expected1.index.name = "first", "second"
tm.assert_frame_equal(result0, expected0)
tm.assert_frame_equal(result1, expected1)
def test_groupby_level_nonmulti():
s = Series([1, 2, 3, 10, 4, 5, 20, 6], Index([1, 2, 3, 1, 4, 5, 2, 6], name="foo"))
expected = Series([11, 22, 3, 4, 5, 6], Index(range(1, 7), name="foo"))
result = s.groupby(level=0).sum()
tm.assert_series_equal(result, expected)
result = s.groupby(level=[0]).sum()
tm.assert_series_equal(result, expected)
result = s.groupby(level=-1).sum()
tm.assert_series_equal(result, expected)
result = s.groupby(level=[-1]).sum()
tm.assert_series_equal(result, expected)
msg = "level > 0 or level < -1 only valid with MultiIndex"
with pytest.raises(ValueError, match=msg):
s.groupby(level=1)
with pytest.raises(ValueError, match=msg):
s.groupby(level=-2)
msg = "No group keys passed!"
with pytest.raises(ValueError, match=msg):
s.groupby(level=[])
msg = "multiple levels only valid with MultiIndex"
with pytest.raises(ValueError, match=msg):
s.groupby(level=[0, 0])
with pytest.raises(ValueError, match=msg):
s.groupby(level=[0, 1])
msg = "level > 0 or level < -1 only valid with MultiIndex"
with pytest.raises(ValueError, match=msg):
s.groupby(level=[1])
def test_groupby_complex():
a = Series(data=np.arange(4) * (1 + 2j), index=[0, 0, 1, 1])
expected = Series((1 + 2j, 5 + 10j))
result = a.groupby(level=0).sum()
tm.assert_series_equal(result, expected)
with tm.assert_produces_warning(FutureWarning):
result = a.sum(level=0)
tm.assert_series_equal(result, expected)
def test_groupby_series_indexed_differently():
s1 = Series(
[5.0, -9.0, 4.0, 100.0, -5.0, 55.0, 6.7],
index=Index(["a", "b", "c", "d", "e", "f", "g"]),
)
s2 = Series(
[1.0, 1.0, 4.0, 5.0, 5.0, 7.0], index=Index(["a", "b", "d", "f", "g", "h"])
)
grouped = s1.groupby(s2)
agged = grouped.mean()
exp = s1.groupby(s2.reindex(s1.index).get).mean()
tm.assert_series_equal(agged, exp)
def test_groupby_with_hier_columns():
tuples = list(
zip(
*[
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
)
)
index = MultiIndex.from_tuples(tuples)
columns = MultiIndex.from_tuples(
[("A", "cat"), ("B", "dog"), ("B", "cat"), ("A", "dog")]
)
df = DataFrame(np.random.randn(8, 4), index=index, columns=columns)
result = df.groupby(level=0).mean()
tm.assert_index_equal(result.columns, columns)
result = df.groupby(level=0, axis=1).mean()
tm.assert_index_equal(result.index, df.index)
result = df.groupby(level=0).agg(np.mean)
tm.assert_index_equal(result.columns, columns)
result = df.groupby(level=0).apply(lambda x: x.mean())
tm.assert_index_equal(result.columns, columns)
result = df.groupby(level=0, axis=1).agg(lambda x: x.mean(1))
tm.assert_index_equal(result.columns, Index(["A", "B"]))
tm.assert_index_equal(result.index, df.index)
sorted_columns, _ = columns.sortlevel(0)
df["A", "foo"] = "bar"
result = df.groupby(level=0).mean()
tm.assert_index_equal(result.columns, df.columns[:-1])
def test_grouping_ndarray(df):
grouped = df.groupby(df["A"].values)
result = grouped.sum()
expected = df.groupby("A").sum()
tm.assert_frame_equal(
result, expected, check_names=False
)
def test_groupby_wrong_multi_labels():
data = """index,foo,bar,baz,spam,data
0,foo1,bar1,baz1,spam2,20
1,foo1,bar2,baz1,spam3,30
2,foo2,bar2,baz1,spam2,40
3,foo1,bar1,baz2,spam1,50
4,foo3,bar1,baz2,spam1,60"""
data = read_csv(StringIO(data), index_col=0)
grouped = data.groupby(["foo", "bar", "baz", "spam"])
result = grouped.agg(np.mean)
expected = grouped.mean()
tm.assert_frame_equal(result, expected)
def test_groupby_series_with_name(df):
result = df.groupby(df["A"]).mean()
result2 = df.groupby(df["A"], as_index=False).mean()
assert result.index.name == "A"
assert "A" in result2
result = df.groupby([df["A"], df["B"]]).mean()
result2 = df.groupby([df["A"], df["B"]], as_index=False).mean()
assert result.index.names == ("A", "B")
assert "A" in result2
assert "B" in result2
def test_seriesgroupby_name_attr(df):
result = df.groupby("A")["C"]
assert result.count().name == "C"
assert result.mean().name == "C"
testFunc = lambda x: np.sum(x) * 2
assert result.agg(testFunc).name == "C"
def test_consistency_name():
df = DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "two", "two", "two", "one", "two"],
"C": np.random.randn(8) + 1.0,
"D": np.arange(8),
}
)
expected = df.groupby(["A"]).B.count()
result = df.B.groupby(df.A).count()
tm.assert_series_equal(result, expected)
def test_groupby_name_propagation(df):
def summarize(df, name=None):
return Series({"count": 1, "mean": 2, "omissions": 3}, name=name)
def summarize_random_name(df):
return Series({"count": 1, "mean": 2, "omissions": 3}, name=df.iloc[0]["A"])
metrics = df.groupby("A").apply(summarize)
assert metrics.columns.name is None
metrics = df.groupby("A").apply(summarize, "metrics")
assert metrics.columns.name == "metrics"
metrics = df.groupby("A").apply(summarize_random_name)
assert metrics.columns.name is None
def test_groupby_nonstring_columns():
df = DataFrame([np.arange(10) for x in range(10)])
grouped = df.groupby(0)
result = grouped.mean()
expected = df.groupby(df[0]).mean()
tm.assert_frame_equal(result, expected)
def test_groupby_mixed_type_columns():
df = DataFrame([[0, 1, 2]], columns=["A", "B", 0])
expected = DataFrame([[1, 2]], columns=["B", 0], index=Index([0], name="A"))
result = df.groupby("A").first()
tm.assert_frame_equal(result, expected)
result = df.groupby("A").sum()
tm.assert_frame_equal(result, expected)
@pytest.mark.filterwarnings("ignore:Mean of:RuntimeWarning")
def test_cython_grouper_series_bug_noncontig():
arr = np.empty((100, 100))
arr.fill(np.nan)
obj = Series(arr[:, 0])
inds = np.tile(range(10), 10)
result = obj.groupby(inds).agg(Series.median)
assert result.isna().all()
def test_series_grouper_noncontig_index():
index = Index(tm.rands_array(10, 100))
values = Series(np.random.randn(50), index=index[::2])
labels = np.random.randint(0, 5, 50)
# it works!
grouped = values.groupby(labels)
# accessing the index elements causes segfault
f = lambda x: len(set(map(id, x.index)))
grouped.agg(f)
def test_convert_objects_leave_decimal_alone():
s = Series(range(5))
labels = np.array(["a", "b", "c", "d", "e"], dtype="O")
def convert_fast(x):
return Decimal(str(x.mean()))
def convert_force_pure(x):
# base will be length 0
assert len(x.values.base) > 0
return Decimal(str(x.mean()))
grouped = s.groupby(labels)
result = grouped.agg(convert_fast)
assert result.dtype == np.object_
assert isinstance(result[0], Decimal)
result = grouped.agg(convert_force_pure)
assert result.dtype == np.object_
assert isinstance(result[0], Decimal)
def test_groupby_dtype_inference_empty():
# GH 6733
df = DataFrame({"x": [], "range": np.arange(0, dtype="int64")})
assert df["x"].dtype == np.float64
result = df.groupby("x").first()
exp_index = Index([], name="x", dtype=np.float64)
expected = DataFrame({"range": Series([], index=exp_index, dtype="int64")})
tm.assert_frame_equal(result, expected, by_blocks=True)
def test_groupby_unit64_float_conversion():
# GH: 30859 groupby converts unit64 to floats sometimes
df = DataFrame({"first": [1], "second": [1], "value": [16148277970000000000]})
result = df.groupby(["first", "second"])["value"].max()
expected = Series(
[16148277970000000000],
MultiIndex.from_product([[1], [1]], names=["first", "second"]),
name="value",
)
tm.assert_series_equal(result, expected)
def test_groupby_list_infer_array_like(df):
result = df.groupby(list(df["A"])).mean()
expected = df.groupby(df["A"]).mean()
tm.assert_frame_equal(result, expected, check_names=False)
with pytest.raises(KeyError, match=r"^'foo'$"):
df.groupby(list(df["A"][:-1]))
# pathological case of ambiguity
df = DataFrame({"foo": [0, 1], "bar": [3, 4], "val": np.random.randn(2)})
result = df.groupby(["foo", "bar"]).mean()
expected = df.groupby([df["foo"], df["bar"]]).mean()[["val"]]
def test_groupby_keys_same_size_as_index():
# GH 11185
freq = "s"
index = date_range(
start=Timestamp("2015-09-29T11:34:44-0700"), periods=2, freq=freq
)
df = DataFrame([["A", 10], ["B", 15]], columns=["metric", "values"], index=index)
result = df.groupby([Grouper(level=0, freq=freq), "metric"]).mean()
expected = df.set_index([df.index, "metric"])
tm.assert_frame_equal(result, expected)
def test_groupby_one_row():
# GH 11741
msg = r"^'Z'$"
df1 = DataFrame(np.random.randn(1, 4), columns=list("ABCD"))
with pytest.raises(KeyError, match=msg):
df1.groupby("Z")
df2 = DataFrame(np.random.randn(2, 4), columns=list("ABCD"))
with pytest.raises(KeyError, match=msg):
df2.groupby("Z")
def test_groupby_nat_exclude():
# GH 6992
df = DataFrame(
{
"values": np.random.randn(8),
"dt": [
np.nan,
Timestamp("2013-01-01"),
np.nan,
Timestamp("2013-02-01"),
np.nan,
Timestamp("2013-02-01"),
np.nan,
Timestamp("2013-01-01"),
],
"str": [np.nan, "a", np.nan, "a", np.nan, "a", np.nan, "b"],
}
)
grouped = df.groupby("dt")
expected = [Index([1, 7]), Index([3, 5])]
keys = sorted(grouped.groups.keys())
assert len(keys) == 2
for k, e in zip(keys, expected):
# grouped.groups keys are np.datetime64 with system tz
# not to be affected by tz, only compare values
tm.assert_index_equal(grouped.groups[k], e)
# confirm obj is not filtered
tm.assert_frame_equal(grouped.grouper.groupings[0].obj, df)
assert grouped.ngroups == 2
expected = {
Timestamp("2013-01-01 00:00:00"): np.array([1, 7], dtype=np.intp),
Timestamp("2013-02-01 00:00:00"): np.array([3, 5], dtype=np.intp),
}
for k in grouped.indices:
tm.assert_numpy_array_equal(grouped.indices[k], expected[k])
tm.assert_frame_equal(grouped.get_group(Timestamp("2013-01-01")), df.iloc[[1, 7]])
tm.assert_frame_equal(grouped.get_group(Timestamp("2013-02-01")), df.iloc[[3, 5]])
with pytest.raises(KeyError, match=r"^NaT$"):
grouped.get_group(pd.NaT)
nan_df = DataFrame(
{"nan": [np.nan, np.nan, np.nan], "nat": [pd.NaT, pd.NaT, pd.NaT]}
)
assert nan_df["nan"].dtype == "float64"
assert nan_df["nat"].dtype == "datetime64[ns]"
for key in ["nan", "nat"]:
grouped = nan_df.groupby(key)
assert grouped.groups == {}
assert grouped.ngroups == 0
assert grouped.indices == {}
with pytest.raises(KeyError, match=r"^nan$"):
grouped.get_group(np.nan)
with pytest.raises(KeyError, match=r"^NaT$"):
grouped.get_group(pd.NaT)
def test_groupby_two_group_keys_all_nan():
# GH #36842: Grouping over two group keys shouldn't raise an error
df = DataFrame({"a": [np.nan, np.nan], "b": [np.nan, np.nan], "c": [1, 2]})
result = df.groupby(["a", "b"]).indices
assert result == {}
def test_groupby_2d_malformed():
d = DataFrame(index=range(2))
d["group"] = ["g1", "g2"]
d["zeros"] = [0, 0]
d["ones"] = [1, 1]
d["label"] = ["l1", "l2"]
tmp = d.groupby(["group"]).mean()
res_values = np.array([[0, 1], [0, 1]], dtype=np.int64)
tm.assert_index_equal(tmp.columns, Index(["zeros", "ones"]))
tm.assert_numpy_array_equal(tmp.values, res_values)
def test_int32_overflow():
B = np.concatenate((np.arange(10000), np.arange(10000), np.arange(5000)))
A = np.arange(25000)
df = DataFrame({"A": A, "B": B, "C": A, "D": B, "E": np.random.randn(25000)})
left = df.groupby(["A", "B", "C", "D"]).sum()
right = df.groupby(["D", "C", "B", "A"]).sum()
assert len(left) == len(right)
def test_groupby_sort_multi():
df = DataFrame(
{
"a": ["foo", "bar", "baz"],
"b": [3, 2, 1],
"c": [0, 1, 2],
"d": np.random.randn(3),
}
)
tups = [tuple(row) for row in df[["a", "b", "c"]].values]
tups = com.asarray_tuplesafe(tups)
result = df.groupby(["a", "b", "c"], sort=True).sum()
tm.assert_numpy_array_equal(result.index.values, tups[[1, 2, 0]])
tups = [tuple(row) for row in df[["c", "a", "b"]].values]
tups = com.asarray_tuplesafe(tups)
result = df.groupby(["c", "a", "b"], sort=True).sum()
tm.assert_numpy_array_equal(result.index.values, tups)
tups = [tuple(x) for x in df[["b", "c", "a"]].values]
tups = com.asarray_tuplesafe(tups)
result = df.groupby(["b", "c", "a"], sort=True).sum()
tm.assert_numpy_array_equal(result.index.values, tups[[2, 1, 0]])
df = DataFrame(
{"a": [0, 1, 2, 0, 1, 2], "b": [0, 0, 0, 1, 1, 1], "d": np.random.randn(6)}
)
grouped = df.groupby(["a", "b"])["d"]
result = grouped.sum()
def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
tups = [tuple(row) for row in df[keys].values]
tups = com.asarray_tuplesafe(tups)
expected = f(df.groupby(tups)[field])
for k, v in expected.items():
assert result[k] == v
_check_groupby(df, result, ["a", "b"], "d")
def test_dont_clobber_name_column():
df = DataFrame(
{"key": ["a", "a", "a", "b", "b", "b"], "name": ["foo", "bar", "baz"] * 2}
)
result = df.groupby("key").apply(lambda x: x)
tm.assert_frame_equal(result, df)
def test_skip_group_keys():
tsf = tm.makeTimeDataFrame()
grouped = tsf.groupby(lambda x: x.month, group_keys=False)
result = grouped.apply(lambda x: x.sort_values(by="A")[:3])
pieces = [group.sort_values(by="A")[:3] for key, group in grouped]
expected = pd.concat(pieces)
tm.assert_frame_equal(result, expected)
grouped = tsf["A"].groupby(lambda x: x.month, group_keys=False)
result = grouped.apply(lambda x: x.sort_values()[:3])
pieces = [group.sort_values()[:3] for key, group in grouped]
expected = pd.concat(pieces)
tm.assert_series_equal(result, expected)
def test_no_nonsense_name(float_frame):
s = float_frame["C"].copy()
s.name = None
result = s.groupby(float_frame["A"]).agg(np.sum)
assert result.name is None
def test_multifunc_sum_bug():
x = DataFrame(np.arange(9).reshape(3, 3))
x["test"] = 0
x["fl"] = [1.3, 1.5, 1.6]
grouped = x.groupby("test")
result = grouped.agg({"fl": "sum", 2: "size"})
assert result["fl"].dtype == np.float64
def test_handle_dict_return_value(df):
def f(group):
return {"max": group.max(), "min": group.min()}
def g(group):
return Series({"max": group.max(), "min": group.min()})
result = df.groupby("A")["C"].apply(f)
expected = df.groupby("A")["C"].apply(g)
assert isinstance(result, Series)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("grouper", ["A", ["A", "B"]])
def test_set_group_name(df, grouper):
def f(group):
assert group.name is not None
return group
def freduce(group):
assert group.name is not None
return group.sum()
def foo(x):
return freduce(x)
grouped = df.groupby(grouper)
grouped.apply(f)
grouped.aggregate(freduce)
grouped.aggregate({"C": freduce, "D": freduce})
grouped.transform(f)
grouped["C"].apply(f)
grouped["C"].aggregate(freduce)
grouped["C"].aggregate([freduce, foo])
grouped["C"].transform(f)
def test_group_name_available_in_inference_pass():
df = DataFrame({"a": [0, 0, 1, 1, 2, 2], "b": np.arange(6)})
names = []
def f(group):
names.append(group.name)
return group.copy()
df.groupby("a", sort=False, group_keys=False).apply(f)
expected_names = [0, 1, 2]
assert names == expected_names
def test_no_dummy_key_names(df):
result = df.groupby(df["A"].values).sum()
assert result.index.name is None
result = df.groupby([df["A"].values, df["B"].values]).sum()
assert result.index.names == (None, None)
def test_groupby_sort_multiindex_series():
index = MultiIndex(
levels=[[1, 2], [1, 2]],
codes=[[0, 0, 0, 0, 1, 1], [1, 1, 0, 0, 0, 0]],
names=["a", "b"],
)
mseries = Series([0, 1, 2, 3, 4, 5], index=index)
index = MultiIndex(
levels=[[1, 2], [1, 2]], codes=[[0, 0, 1], [1, 0, 0]], names=["a", "b"]
)
mseries_result = Series([0, 2, 4], index=index)
result = mseries.groupby(level=["a", "b"], sort=False).first()
tm.assert_series_equal(result, mseries_result)
result = mseries.groupby(level=["a", "b"], sort=True).first()
tm.assert_series_equal(result, mseries_result.sort_index())
def test_groupby_reindex_inside_function():
periods = 1000
ind = date_range(start="2012/1/1", freq="5min", periods=periods)
df = DataFrame({"high": np.arange(periods), "low": np.arange(periods)}, index=ind)
def agg_before(func, fix=False):
def _func(data):
d = data.loc[data.index.map(lambda x: x.hour < 11)].dropna()
if fix:
data[data.index[0]]
if len(d) == 0:
return None
return func(d)
return _func
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
closure_bad = grouped.agg({"high": agg_before(np.max)})
closure_good = grouped.agg({"high": agg_before(np.max, True)})
tm.assert_frame_equal(closure_bad, closure_good)
def test_groupby_multiindex_missing_pair():
df = DataFrame(
{
"group1": ["a", "a", "a", "b"],
"group2": ["c", "c", "d", "c"],
"value": [1, 1, 1, 5],
}
)
df = df.set_index(["group1", "group2"])
df_grouped = df.groupby(level=["group1", "group2"], sort=True)
res = df_grouped.agg("sum")
idx = MultiIndex.from_tuples(
[("a", "c"), ("a", "d"), ("b", "c")], names=["group1", "group2"]
)
exp = DataFrame([[2], [1], [5]], index=idx, columns=["value"])
tm.assert_frame_equal(res, exp)
def test_groupby_multiindex_not_lexsorted():
lexsorted_mi = MultiIndex.from_tuples(
[("a", ""), ("b1", "c1"), ("b2", "c2")], names=["b", "c"]
)
lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi)
assert lexsorted_df.columns._is_lexsorted()
not_lexsorted_df = DataFrame(
columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]]
)
not_lexsorted_df = not_lexsorted_df.pivot_table(
index="a", columns=["b", "c"], values="d"
)
not_lexsorted_df = not_lexsorted_df.reset_index()
assert not not_lexsorted_df.columns._is_lexsorted()
tm.assert_frame_equal(lexsorted_df, not_lexsorted_df)
expected = lexsorted_df.groupby("a").mean()
with tm.assert_produces_warning(PerformanceWarning):
result = not_lexsorted_df.groupby("a").mean()
tm.assert_frame_equal(expected, result)
df = DataFrame(
{"x": ["a", "a", "b", "a"], "y": [1, 1, 2, 2], "z": [1, 2, 3, 4]}
).set_index(["x", "y"])
assert not df.index._is_lexsorted()
for level in [0, 1, [0, 1]]:
for sort in [False, True]:
result = df.groupby(level=level, sort=sort).apply(DataFrame.drop_duplicates)
expected = df
tm.assert_frame_equal(expected, result)
result = (
df.sort_index()
.groupby(level=level, sort=sort)
.apply(DataFrame.drop_duplicates)
)
expected = df.sort_index()
tm.assert_frame_equal(expected, result)
def test_index_label_overlaps_location():
# wake of GH5375
df = DataFrame(list("ABCDE"), index=[2, 0, 2, 1, 1])
g = df.groupby(list("ababb"))
actual = g.filter(lambda x: len(x) > 2)
expected = df.iloc[[1, 3, 4]]
tm.assert_frame_equal(actual, expected)
ser = df[0]
g = ser.groupby(list("ababb"))
actual = g.filter(lambda x: len(x) > 2)
expected = ser.take([1, 3, 4])
tm.assert_series_equal(actual, expected)
# ... and again, with a generic Index of floats
df.index = df.index.astype(float)
g = df.groupby(list("ababb"))
actual = g.filter(lambda x: len(x) > 2)
expected = df.iloc[[1, 3, 4]]
tm.assert_frame_equal(actual, expected)
ser = df[0]
g = ser.groupby(list("ababb"))
actual = g.filter(lambda x: len(x) > 2)
expected = ser.take([1, 3, 4])
tm.assert_series_equal(actual, expected)
def test_transform_doesnt_clobber_ints():
# GH 7972
n = 6
x = np.arange(n)
df = DataFrame({"a": x // 2, "b": 2.0 * x, "c": 3.0 * x})
df2 = DataFrame({"a": x // 2 * 1.0, "b": 2.0 * x, "c": 3.0 * x})
gb = df.groupby("a")
result = gb.transform("mean")
gb2 = df2.groupby("a")
expected = gb2.transform("mean")
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"sort_column",
["ints", "floats", "strings", ["ints", "floats"], ["ints", "strings"]],
)
@pytest.mark.parametrize(
"group_column", ["int_groups", "string_groups", ["int_groups", "string_groups"]]
)
def test_groupby_preserves_sort(sort_column, group_column):
# Test to ensure that groupby always preserves sort order of original
# object. Issue #8588 and #9651
df = DataFrame(
{
"int_groups": [3, 1, 0, 1, 0, 3, 3, 3],
"string_groups": ["z", "a", "z", "a", "a", "g", "g", "g"],
"ints": [8, 7, 4, 5, 2, 9, 1, 1],
"floats": [2.3, 5.3, 6.2, -2.4, 2.2, 1.1, 1.1, 5],
"strings": ["z", "d", "a", "e", "word", "word2", "42", "47"],
}
)
# Try sorting on different types and with different group types
df = df.sort_values(by=sort_column)
g = df.groupby(group_column)
def test_sort(x):
tm.assert_frame_equal(x, x.sort_values(by=sort_column))
g.apply(test_sort)
def test_pivot_table_values_key_error():
# This test is designed to replicate the error in issue #14938
df = DataFrame(
{
"eventDate": date_range(datetime.today(), periods=20, freq="M").tolist(),
"thename": range(0, 20),
}
)
df["year"] = df.set_index("eventDate").index.year
df["month"] = df.set_index("eventDate").index.month
with pytest.raises(KeyError, match="'badname'"):
df.reset_index().pivot_table(
index="year", columns="month", values="badname", aggfunc="count"
)
@pytest.mark.parametrize("columns", ["C", ["C"]])
@pytest.mark.parametrize("keys", [["A"], ["A", "B"]])
@pytest.mark.parametrize(
"values",
[
[True],
[0],
[0.0],
["a"],
Categorical([0]),
[to_datetime(0)],
date_range(0, 1, 1, tz="US/Eastern"),
pd.array([0], dtype="Int64"),
pd.array([0], dtype="Float64"),
pd.array([False], dtype="boolean"),
],
)
@pytest.mark.parametrize("method", ["attr", "agg", "apply"])
@pytest.mark.parametrize(
"op", ["idxmax", "idxmin", "mad", "min", "max", "sum", "prod", "skew"]
)
def test_empty_groupby(columns, keys, values, method, op, request):
# GH8093 & GH26411
if isinstance(values, Categorical) and len(keys) == 1 and method == "apply":
mark = pytest.mark.xfail(raises=TypeError, match="'str' object is not callable")
request.node.add_marker(mark)
elif (
isinstance(values, Categorical)
and len(keys) == 1
and op in ["idxmax", "idxmin"]
):
mark = pytest.mark.xfail(
raises=ValueError, match="attempt to get arg(min|max) of an empty sequence"
)
request.node.add_marker(mark)
elif (
isinstance(values, Categorical)
and len(keys) == 1
and not isinstance(columns, list)
):
mark = pytest.mark.xfail(
raises=TypeError, match="'Categorical' does not implement"
)
request.node.add_marker(mark)
elif (
isinstance(values, Categorical)
and len(keys) == 1
and op in ["mad", "min", "max", "sum", "prod", "skew"]
):
mark = pytest.mark.xfail(
raises=AssertionError, match="(DataFrame|Series) are different"
)
request.node.add_marker(mark)
elif (
isinstance(values, Categorical)
and len(keys) == 2
and op in ["min", "max", "sum"]
and method != "apply"
):
mark = pytest.mark.xfail(
raises=AssertionError, match="(DataFrame|Series) are different"
)
request.node.add_marker(mark)
elif (
isinstance(values, pd.core.arrays.BooleanArray)
and op in ["sum", "prod"]
and method != "apply"
):
mark = pytest.mark.xfail(
raises=AssertionError, match="(DataFrame|Series) are different"
)
request.node.add_marker(mark)
override_dtype = None
if isinstance(values[0], bool) and op in ("prod", "sum") and method != "apply":
# sum/product of bools is an integer
override_dtype = "int64"
df = DataFrame({"A": values, "B": values, "C": values}, columns=list("ABC"))
if hasattr(values, "dtype"):
# check that we did the construction right
assert (df.dtypes == values.dtype).all()
df = df.iloc[:0]
gb = df.groupby(keys)[columns]
if method == "attr":
result = getattr(gb, op)()
else:
result = getattr(gb, method)(op)
expected = df.set_index(keys)[columns]
if override_dtype is not None:
expected = expected.astype(override_dtype)
if len(keys) == 1:
expected.index.name = keys[0]
tm.assert_equal(result, expected)
def test_tuple_as_grouping():
# https://github.com/pandas-dev/pandas/issues/18314
df = DataFrame(
{
("a", "b"): [1, 1, 1, 1],
"a": [2, 2, 2, 2],
"b": [2, 2, 2, 2],
"c": [1, 1, 1, 1],
}
)
with pytest.raises(KeyError, match=r"('a', 'b')"):
df[["a", "b", "c"]].groupby(("a", "b"))
result = df.groupby(("a", "b"))["c"].sum()
expected = Series([4], name="c", index=Index([1], name=("a", "b")))
tm.assert_series_equal(result, expected)
def test_tuple_correct_keyerror():
# https://github.com/pandas-dev/pandas/issues/18798
df = DataFrame(1, index=range(3), columns=MultiIndex.from_product([[1, 2], [3, 4]]))
with pytest.raises(KeyError, match=r"^\(7, 8\)$"):
df.groupby((7, 8)).mean()
def test_groupby_agg_ohlc_non_first():
# GH 21716
df = DataFrame(
[[1], [1]],
columns=["foo"],
index=date_range("2018-01-01", periods=2, freq="D"),
)
expected = DataFrame(
[[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]],
columns=MultiIndex.from_tuples(
(
("foo", "sum", "foo"),
("foo", "ohlc", "open"),
("foo", "ohlc", "high"),
("foo", "ohlc", "low"),
("foo", "ohlc", "close"),
)
),
index=date_range("2018-01-01", periods=2, freq="D"),
)
result = df.groupby(Grouper(freq="D")).agg(["sum", "ohlc"])
tm.assert_frame_equal(result, expected)
def test_groupby_multiindex_nat():
# GH 9236
values = [
(pd.NaT, "a"),
(datetime(2012, 1, 2), "a"),
(datetime(2012, 1, 2), "b"),
(datetime(2012, 1, 3), "a"),
]
mi = MultiIndex.from_tuples(values, names=["date", None])
ser = Series([3, 2, 2.5, 4], index=mi)
result = ser.groupby(level=1).mean()
expected = Series([3.0, 2.5], index=["a", "b"])
tm.assert_series_equal(result, expected)
def test_groupby_empty_list_raises():
# GH 5289
values = zip(range(10), range(10))
df = DataFrame(values, columns=["apple", "b"])
msg = "Grouper and axis must be same length"
with pytest.raises(ValueError, match=msg):
df.groupby([[]])
def test_groupby_multiindex_series_keys_len_equal_group_axis():
# GH 25704
index_array = [["x", "x"], ["a", "b"], ["k", "k"]]
index_names = ["first", "second", "third"]
ri = MultiIndex.from_arrays(index_array, names=index_names)
s = Series(data=[1, 2], index=ri)
result = s.groupby(["first", "third"]).sum()
index_array = [["x"], ["k"]]
index_names = ["first", "third"]
ei = MultiIndex.from_arrays(index_array, names=index_names)
expected = Series([3], index=ei)
tm.assert_series_equal(result, expected)
def test_groupby_groups_in_BaseGrouper():
# GH 26326
# Test if DataFrame grouped with a pandas.Grouper has correct groups
mi = MultiIndex.from_product([["A", "B"], ["C", "D"]], names=["alpha", "beta"])
df = DataFrame({"foo": [1, 2, 1, 2], "bar": [1, 2, 3, 4]}, index=mi)
result = df.groupby([Grouper(level="alpha"), "beta"])
expected = df.groupby(["alpha", "beta"])
assert result.groups == expected.groups
result = df.groupby(["beta", Grouper(level="alpha")])
expected = df.groupby(["beta", "alpha"])
assert result.groups == expected.groups
@pytest.mark.parametrize("group_name", ["x", ["x"]])
def test_groupby_axis_1(group_name):
# GH 27614
df = DataFrame(
np.arange(12).reshape(3, 4), index=[0, 1, 0], columns=[10, 20, 10, 20]
)
df.index.name = "y"
df.columns.name = "x"
results = df.groupby(group_name, axis=1).sum()
expected = df.T.groupby(group_name).sum().T
tm.assert_frame_equal(results, expected)
# test on MI column
iterables = [["bar", "baz", "foo"], ["one", "two"]]
mi = MultiIndex.from_product(iterables=iterables, names=["x", "x1"])
df = DataFrame(np.arange(18).reshape(3, 6), index=[0, 1, 0], columns=mi)
results = df.groupby(group_name, axis=1).sum()
expected = df.T.groupby(group_name).sum().T
tm.assert_frame_equal(results, expected)
@pytest.mark.parametrize(
"op, expected",
[
(
"shift",
{
"time": [
None,
None,
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
None,
None,
]
},
),
(
"bfill",
{
"time": [
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
]
},
),
(
"ffill",
{
"time": [
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
]
},
),
],
)
def test_shift_bfill_ffill_tz(tz_naive_fixture, op, expected):
# GH19995, GH27992: Check that timezone does not drop in shift, bfill, and ffill
tz = tz_naive_fixture
data = {
"id": ["A", "B", "A", "B", "A", "B"],
"time": [
Timestamp("2019-01-01 12:00:00"),
Timestamp("2019-01-01 12:30:00"),
None,
None,
Timestamp("2019-01-01 14:00:00"),
Timestamp("2019-01-01 14:30:00"),
],
}
df = DataFrame(data).assign(time=lambda x: x.time.dt.tz_localize(tz))
grouped = df.groupby("id")
result = getattr(grouped, op)()
expected = DataFrame(expected).assign(time=lambda x: x.time.dt.tz_localize(tz))
tm.assert_frame_equal(result, expected)
def test_groupby_only_none_group():
# see GH21624
# this was crashing with "ValueError: Length of passed values is 1, index implies 0"
df = DataFrame({"g": [None], "x": 1})
actual = df.groupby("g")["x"].transform("sum")
expected = Series([np.nan], name="x")
tm.assert_series_equal(actual, expected)
def test_groupby_duplicate_index():
# GH#29189 the groupby call here used to raise
ser = Series([2, 5, 6, 8], index=[2.0, 4.0, 4.0, 5.0])
gb = ser.groupby(level=0)
result = gb.mean()
expected = Series([2, 5.5, 8], index=[2.0, 4.0, 5.0])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"idx", [Index(["a", "a"]), MultiIndex.from_tuples((("a", "a"), ("a", "a")))]
)
@pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
def test_dup_labels_output_shape(groupby_func, idx):
if groupby_func in {"size", "ngroup", "cumcount"}:
pytest.skip("Not applicable")
df = DataFrame([[1, 1]], columns=idx)
grp_by = df.groupby([0])
args = []
if groupby_func in {"fillna", "nth"}:
args.append(0)
elif groupby_func == "corrwith":
args.append(df)
elif groupby_func == "tshift":
df.index = [Timestamp("today")]
args.extend([1, "D"])
result = getattr(grp_by, groupby_func)(*args)
assert result.shape == (1, 2)
tm.assert_index_equal(result.columns, idx)
def test_groupby_crash_on_nunique(axis):
# Fix following 30253
df = DataFrame({("A", "B"): [1, 2], ("A", "C"): [1, 3], ("D", "B"): [0, 0]})
axis_number = df._get_axis_number(axis)
if not axis_number:
df = df.T
result = df.groupby(axis=axis_number, level=0).nunique()
expected = DataFrame({"A": [1, 2], "D": [1, 1]})
if not axis_number:
expected = expected.T
tm.assert_frame_equal(result, expected)
# same thing, but empty columns
gb = df[[]].groupby(axis=axis_number, level=0)
res = gb.nunique()
exp = expected[[]]
tm.assert_frame_equal(res, exp)
def test_groupby_list_level():
# GH 9790
expected = DataFrame(np.arange(0, 9).reshape(3, 3))
result = expected.groupby(level=[0]).mean()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"max_seq_items, expected",
[
(5, "{0: [0], 1: [1], 2: [2], 3: [3], 4: [4]}"),
(4, "{0: [0], 1: [1], 2: [2], 3: [3], ...}"),
(1, "{0: [0], ...}"),
],
)
def test_groups_repr_truncates(max_seq_items, expected):
# GH 1135
df = DataFrame(np.random.randn(5, 1))
df["a"] = df.index
with pd.option_context("display.max_seq_items", max_seq_items):
result = df.groupby("a").groups.__repr__()
assert result == expected
result = df.groupby(np.array(df.a)).groups.__repr__()
assert result == expected
def test_group_on_two_row_multiindex_returns_one_tuple_key():
# GH 18451
df = DataFrame([{"a": 1, "b": 2, "c": 99}, {"a": 1, "b": 2, "c": 88}])
df = df.set_index(["a", "b"])
grp = df.groupby(["a", "b"])
result = grp.indices
expected = {(1, 2): np.array([0, 1], dtype=np.int64)}
assert len(result) == 1
key = (1, 2)
assert (result[key] == expected[key]).all()
@pytest.mark.parametrize(
"klass, attr, value",
[
(DataFrame, "level", "a"),
(DataFrame, "as_index", False),
(DataFrame, "sort", False),
(DataFrame, "group_keys", False),
(DataFrame, "squeeze", True),
(DataFrame, "observed", True),
(DataFrame, "dropna", False),
pytest.param(
Series,
"axis",
1,
marks=pytest.mark.xfail(
reason="GH 35443: Attribute currently not passed on to series"
),
),
(Series, "level", "a"),
(Series, "as_index", False),
(Series, "sort", False),
(Series, "group_keys", False),
(Series, "squeeze", True),
(Series, "observed", True),
(Series, "dropna", False),
],
)
@pytest.mark.filterwarnings(
"ignore:The `squeeze` parameter is deprecated:FutureWarning"
)
def test_subsetting_columns_keeps_attrs(klass, attr, value):
# GH 9959 - When subsetting columns, don't drop attributes
df = DataFrame({"a": [1], "b": [2], "c": [3]})
if attr != "axis":
df = df.set_index("a")
expected = df.groupby("a", **{attr: value})
result = expected[["b"]] if klass is DataFrame else expected["b"]
assert getattr(result, attr) == getattr(expected, attr)
def test_subsetting_columns_axis_1():
g = DataFrame({"A": [1], "B": [2], "C": [3]}).groupby([0, 0, 1], axis=1)
match = "Cannot subset columns when using axis=1"
with pytest.raises(ValueError, match=match):
g[["A", "B"]].sum()
@pytest.mark.parametrize("func", ["sum", "any", "shift"])
def test_groupby_column_index_name_lost(func):
expected = Index(["a"], name="idx")
df = DataFrame([[1]], columns=expected)
df_grouped = df.groupby([1])
result = getattr(df_grouped, func)().columns
tm.assert_index_equal(result, expected)
def test_groupby_duplicate_columns():
df = DataFrame(
{"A": ["f", "e", "g", "h"], "B": ["a", "b", "c", "d"], "C": [1, 2, 3, 4]}
).astype(object)
df.columns = ["A", "B", "B"]
result = df.groupby([0, 0, 0, 0]).min()
expected = DataFrame([["e", "a", 1]], columns=["A", "B", "B"])
tm.assert_frame_equal(result, expected)
def test_groupby_series_with_tuple_name():
ser = Series([1, 2, 3, 4], index=[1, 1, 2, 2], name=("a", "a"))
ser.index.name = ("b", "b")
result = ser.groupby(level=0).last()
expected = Series([2, 4], index=[1, 2], name=("a", "a"))
expected.index.name = ("b", "b")
tm.assert_series_equal(result, expected)
@pytest.mark.xfail(not IS64, reason="GH#38778: fail on 32-bit system")
@pytest.mark.parametrize(
"func, values", [("sum", [97.0, 98.0]), ("mean", [24.25, 24.5])]
)
def test_groupby_numerical_stability_sum_mean(func, values):
ata = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15]
df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data})
result = getattr(df.groupby("group"), func)()
expected = DataFrame({"a": values, "b": values}, index=Index([1, 2], name="group"))
tm.assert_frame_equal(result, expected)
@pytest.mark.xfail(not IS64, reason="GH#38778: fail on 32-bit system")
def test_groupby_numerical_stability_cumsum():
ata = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15]
df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data})
result = df.groupby("group").cumsum()
exp_data = (
[1e16] * 2 + [1e16 + 96, 1e16 + 98] + [5e15 + 97, 5e15 + 98] + [97.0, 98.0]
)
expected = DataFrame({"a": exp_data, "b": exp_data})
tm.assert_frame_equal(result, expected, check_exact=True)
def test_groupby_mean_duplicate_index(rand_series_with_duplicate_datetimeindex):
dups = rand_series_with_duplicate_datetimeindex
result = dups.groupby(level=0).mean()
expected = dups.groupby(dups.index).mean()
tm.assert_series_equal(result, expected)
| true
| true
|
f716a3b6932792541e61b438e6424ea8e3b6dd6f
| 5,536
|
py
|
Python
|
src/modules/site/base/views/tools/heritability.py
|
AndersenLab/CAENDR
|
ce4cdb74db736db8226ffc90988959b71b0d5ff5
|
[
"MIT"
] | 3
|
2022-02-09T07:04:37.000Z
|
2022-03-11T02:46:35.000Z
|
src/modules/site/base/views/tools/heritability.py
|
AndersenLab/CAENDR
|
ce4cdb74db736db8226ffc90988959b71b0d5ff5
|
[
"MIT"
] | 4
|
2022-01-28T22:28:08.000Z
|
2022-02-11T21:47:15.000Z
|
src/modules/site/base/views/tools/heritability.py
|
AndersenLab/CAENDR
|
ce4cdb74db736db8226ffc90988959b71b0d5ff5
|
[
"MIT"
] | 1
|
2022-01-11T03:39:02.000Z
|
2022-01-11T03:39:02.000Z
|
import io
import pandas as pd
import json
from flask import (flash,
request,
redirect,
url_for,
jsonify,
render_template,
Blueprint,
abort)
from logzero import logger
from datetime import datetime
from base.forms import HeritabilityForm
from base.utils.auth import jwt_required, admin_required, get_jwt, get_current_user
from caendr.api.strain import get_strains
from caendr.services.heritability_report import get_all_heritability_results, get_user_heritability_results, create_new_heritability_report, get_heritability_report
from caendr.utils.data import unique_id, convert_data_table_to_tsv, get_object_hash
from caendr.services.cloud.storage import get_blob, generate_blob_url
# ================== #
# heritability #
# ================== #
# Tools blueprint
heritability_bp = Blueprint('heritability',
__name__)
@heritability_bp.route('/heritability')
def heritability():
title = "Heritability Calculator"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
form = HeritabilityForm()
hide_form = True
strain_list = []
return render_template('tools/heritability/submit.html', **locals())
@heritability_bp.route('/heritability/create', methods=["GET"])
@jwt_required()
def heritability_create():
""" This endpoint is used to create a heritability job. """
title = "Heritability Calculator"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
jwt_csrf_token = (get_jwt() or {}).get("csrf")
form = HeritabilityForm()
strain_data = get_strains()
strain_list = []
for x in strain_data:
strain_list.append(x.strain)
hide_form = False
id = unique_id()
return render_template('tools/heritability/submit.html', **locals())
@heritability_bp.route("/heritability/all-results")
@admin_required()
def heritability_all_results():
title = "All Heritability Results"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
user = get_current_user()
items = get_all_heritability_results()
return render_template('tools/heritability/list-all.html', **locals())
@heritability_bp.route("/heritability/my-results")
@jwt_required()
def heritability_user_results():
title = "My Heritability Results"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
user = get_current_user()
items = get_user_heritability_results(user.name)
return render_template('tools/heritability/list-user.html', **locals())
@heritability_bp.route('/heritability/submit', methods=["POST"])
@jwt_required()
def submit_h2():
user = get_current_user()
label = request.values['label']
columns = ["AssayNumber", "Strain", "TraitName", "Replicate", "Value"]
# Extract table data
data = json.loads(request.values['table_data'])
data = [x for x in data[1:] if x[0] is not None]
trait = data[0][2]
data_tsv = convert_data_table_to_tsv(data, columns)
# Generate an ID for the data based on its hash
data_hash = get_object_hash(data, length=32)
logger.debug(data_hash)
id = unique_id()
try:
h = create_new_heritability_report(id, user.name, label, data_hash, trait, data_tsv)
except Exception as ex:
if str(type(ex).__name__) == 'DuplicateDataError':
flash('It looks like you submitted that data already - redirecting to your list of Heritability Reports', 'danger')
return jsonify({'duplicate': True,
'data_hash': data_hash,
'id': id})
if str(type(ex).__name__) == 'CachedDataError':
flash('It looks like that data has already been submitted - redirecting to the saved results', 'danger')
return jsonify({'cached': True,
'data_hash': data_hash,
'id': id})
return jsonify({'started': True,
'data_hash': data_hash,
'id': id})
# TODO: Move this into a separate service
@heritability_bp.route("/heritability/h2/<id>")
@jwt_required()
def heritability_result(id):
title = "Heritability Results"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
user = get_current_user()
hr = get_heritability_report(id)
ready = False
data_url = generate_blob_url(hr.get_bucket_name(), hr.get_data_blob_path())
if (not hr._exists) or (hr.username != user.name):
flash('You do not have access to that report', 'danger')
abort(401)
data_hash = hr.data_hash
data_blob = hr.get_data_blob_path()
result_blob = hr.get_result_blob_path()
data = get_blob(hr.get_bucket_name(), hr.get_data_blob_path())
result = get_blob(hr.get_bucket_name(), hr.get_result_blob_path())
if data is None:
return abort(404, description="Heritability report not found")
data = data.download_as_string().decode('utf-8')
data = pd.read_csv(io.StringIO(data), sep="\t")
data['AssayNumber'] = data['AssayNumber'].astype(str)
data['label'] = data.apply(lambda x: f"{x['AssayNumber']}: {x['Value']}", 1)
data = data.to_dict('records')
trait = data[0]['TraitName']
# Get trait and set title
subtitle = trait
if result:
hr.status = 'COMPLETE'
hr.save()
result = result.download_as_string().decode('utf-8')
result = pd.read_csv(io.StringIO(result), sep="\t")
result = result.to_dict('records')[0]
fnam=datetime.today().strftime('%Y%m%d.')+trait
ready = True
return render_template("tools/heritability/view.html", **locals())
| 33.756098
| 164
| 0.684429
|
import io
import pandas as pd
import json
from flask import (flash,
request,
redirect,
url_for,
jsonify,
render_template,
Blueprint,
abort)
from logzero import logger
from datetime import datetime
from base.forms import HeritabilityForm
from base.utils.auth import jwt_required, admin_required, get_jwt, get_current_user
from caendr.api.strain import get_strains
from caendr.services.heritability_report import get_all_heritability_results, get_user_heritability_results, create_new_heritability_report, get_heritability_report
from caendr.utils.data import unique_id, convert_data_table_to_tsv, get_object_hash
from caendr.services.cloud.storage import get_blob, generate_blob_url
heritability_bp = Blueprint('heritability',
__name__)
@heritability_bp.route('/heritability')
def heritability():
title = "Heritability Calculator"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
form = HeritabilityForm()
hide_form = True
strain_list = []
return render_template('tools/heritability/submit.html', **locals())
@heritability_bp.route('/heritability/create', methods=["GET"])
@jwt_required()
def heritability_create():
title = "Heritability Calculator"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
jwt_csrf_token = (get_jwt() or {}).get("csrf")
form = HeritabilityForm()
strain_data = get_strains()
strain_list = []
for x in strain_data:
strain_list.append(x.strain)
hide_form = False
id = unique_id()
return render_template('tools/heritability/submit.html', **locals())
@heritability_bp.route("/heritability/all-results")
@admin_required()
def heritability_all_results():
title = "All Heritability Results"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
user = get_current_user()
items = get_all_heritability_results()
return render_template('tools/heritability/list-all.html', **locals())
@heritability_bp.route("/heritability/my-results")
@jwt_required()
def heritability_user_results():
title = "My Heritability Results"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
user = get_current_user()
items = get_user_heritability_results(user.name)
return render_template('tools/heritability/list-user.html', **locals())
@heritability_bp.route('/heritability/submit', methods=["POST"])
@jwt_required()
def submit_h2():
user = get_current_user()
label = request.values['label']
columns = ["AssayNumber", "Strain", "TraitName", "Replicate", "Value"]
data = json.loads(request.values['table_data'])
data = [x for x in data[1:] if x[0] is not None]
trait = data[0][2]
data_tsv = convert_data_table_to_tsv(data, columns)
data_hash = get_object_hash(data, length=32)
logger.debug(data_hash)
id = unique_id()
try:
h = create_new_heritability_report(id, user.name, label, data_hash, trait, data_tsv)
except Exception as ex:
if str(type(ex).__name__) == 'DuplicateDataError':
flash('It looks like you submitted that data already - redirecting to your list of Heritability Reports', 'danger')
return jsonify({'duplicate': True,
'data_hash': data_hash,
'id': id})
if str(type(ex).__name__) == 'CachedDataError':
flash('It looks like that data has already been submitted - redirecting to the saved results', 'danger')
return jsonify({'cached': True,
'data_hash': data_hash,
'id': id})
return jsonify({'started': True,
'data_hash': data_hash,
'id': id})
@heritability_bp.route("/heritability/h2/<id>")
@jwt_required()
def heritability_result(id):
title = "Heritability Results"
alt_parent_breadcrumb = {"title": "Tools", "url": url_for('tools.tools')}
user = get_current_user()
hr = get_heritability_report(id)
ready = False
data_url = generate_blob_url(hr.get_bucket_name(), hr.get_data_blob_path())
if (not hr._exists) or (hr.username != user.name):
flash('You do not have access to that report', 'danger')
abort(401)
data_hash = hr.data_hash
data_blob = hr.get_data_blob_path()
result_blob = hr.get_result_blob_path()
data = get_blob(hr.get_bucket_name(), hr.get_data_blob_path())
result = get_blob(hr.get_bucket_name(), hr.get_result_blob_path())
if data is None:
return abort(404, description="Heritability report not found")
data = data.download_as_string().decode('utf-8')
data = pd.read_csv(io.StringIO(data), sep="\t")
data['AssayNumber'] = data['AssayNumber'].astype(str)
data['label'] = data.apply(lambda x: f"{x['AssayNumber']}: {x['Value']}", 1)
data = data.to_dict('records')
trait = data[0]['TraitName']
subtitle = trait
if result:
hr.status = 'COMPLETE'
hr.save()
result = result.download_as_string().decode('utf-8')
result = pd.read_csv(io.StringIO(result), sep="\t")
result = result.to_dict('records')[0]
fnam=datetime.today().strftime('%Y%m%d.')+trait
ready = True
return render_template("tools/heritability/view.html", **locals())
| true
| true
|
f716a488f4da02f72691c1194f86e83d967d3e2b
| 45,335
|
py
|
Python
|
test/test_sort_and_select.py
|
kbrose/pytorch
|
fc0b8e60337ae46b90ed5d2f6d1f623f0f8d6581
|
[
"Intel"
] | null | null | null |
test/test_sort_and_select.py
|
kbrose/pytorch
|
fc0b8e60337ae46b90ed5d2f6d1f623f0f8d6581
|
[
"Intel"
] | null | null | null |
test/test_sort_and_select.py
|
kbrose/pytorch
|
fc0b8e60337ae46b90ed5d2f6d1f623f0f8d6581
|
[
"Intel"
] | null | null | null |
import torch
import numpy as np
import random
from torch._six import nan
from itertools import permutations, product
from torch.testing import all_types, all_types_and
from torch.testing._internal.common_utils import \
(TEST_WITH_ROCM, TestCase, run_tests, make_tensor, slowTest)
from torch.testing._internal.common_device_type import \
(instantiate_device_type_tests, dtypes, onlyOnCPUAndCUDA,
skipCUDAIfRocm, onlyCUDA, dtypesIfCUDA, dtypesIfCPU, onlyCPU, largeTensorTest)
# TODO: remove this
SIZE = 100
class TestSortAndSelect(TestCase):
def assertIsOrdered(self, order, x, mxx, ixx, task):
SIZE = x.size(1)
if order == 'descending':
def check_order(a, b):
# `a != a` because we put NaNs
# at the end of ascending sorted lists,
# and the beginning of descending ones.
return ((a != a) | (a >= b)).all().item()
elif order == 'ascending':
def check_order(a, b):
# see above
return ((b != b) | (a <= b)).all().item()
else:
error('unknown order "{}", must be "ascending" or "descending"'.format(order))
are_ordered = True
for k in range(1, SIZE):
self.assertTrue(check_order(mxx[:, k - 1], mxx[:, k]),
'torch.sort ({}) values unordered for {}'.format(order, task))
seen = set()
indicesCorrect = True
size0 = x.size(0)
size = x.size(x.dim() - 1)
x = x.tolist()
mxx = mxx.tolist()
ixx = ixx.tolist()
for k in range(size0):
seen.clear()
for j in range(size):
self.assertEqual(x[k][ixx[k][j]], mxx[k][j],
msg='torch.sort ({}) indices wrong for {}'.format(order, task))
seen.add(ixx[k][j])
self.assertEqual(len(seen), size)
def test_sort(self, device):
# on CUDA 2048 vs >2048 have different code path for the dim being sorted
for SIZE in (4, 2049):
x = torch.rand(4, SIZE, device=device)
res1val, res1ind = torch.sort(x)
# Test inplace
y = x.clone()
y_inds = torch.tensor((), dtype=torch.int64, device=device)
torch.sort(y, out=(y, y_inds))
x_vals, x_inds = torch.sort(x)
self.assertEqual(x_vals, y)
self.assertEqual(x_inds, y_inds)
# Test use of result tensor
res2val = torch.tensor((), device=device)
res2ind = torch.tensor((), device=device, dtype=torch.long)
torch.sort(x, out=(res2val, res2ind))
self.assertEqual(res1val, res2val, atol=0, rtol=0)
self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
self.assertEqual(torch.argsort(x), res1ind)
self.assertEqual(x.argsort(), res1ind)
# Test sorting of random numbers
self.assertIsOrdered('ascending', x, res2val, res2ind, 'random')
# Test simple sort
self.assertEqual(
torch.sort(torch.tensor((50, 40, 30, 20, 10), device=device))[0],
torch.tensor((10, 20, 30, 40, 50), device=device),
atol=0, rtol=0
)
# Test that we still have proper sorting with duplicate keys
x = torch.floor(torch.rand(4, SIZE, device=device) * 10)
torch.sort(x, out=(res2val, res2ind))
self.assertIsOrdered('ascending', x, res2val, res2ind, 'random with duplicate keys')
# DESCENDING SORT
x = torch.rand(4, SIZE, device=device)
res1val, res1ind = torch.sort(x, x.dim() - 1, True)
# Test use of result tensor
res2val = torch.tensor((), device=device)
res2ind = torch.tensor((), device=device, dtype=torch.long)
torch.sort(x, x.dim() - 1, True, out=(res2val, res2ind))
self.assertEqual(res1val, res2val, atol=0, rtol=0)
self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
self.assertEqual(torch.argsort(x, x.dim() - 1, True), res1ind)
self.assertEqual(x.argsort(x.dim() - 1, True), res1ind)
# Test sorting of random numbers
self.assertIsOrdered('descending', x, res2val, res2ind, 'random')
# Test simple sort task
self.assertEqual(
torch.sort(torch.tensor((10, 20, 30, 40, 50), device=device), 0, True)[0],
torch.tensor((50, 40, 30, 20, 10), device=device),
atol=0, rtol=0
)
# Test that we still have proper sorting with duplicate keys
self.assertIsOrdered('descending', x, res2val, res2ind, 'random with duplicate keys')
# Test sorting with NaNs
x = torch.rand(4, SIZE, device=device)
x[1][2] = float('NaN')
x[3][0] = float('NaN')
torch.sort(x, out=(res2val, res2ind))
self.assertIsOrdered('ascending', x, res2val, res2ind,
'random with NaNs')
torch.sort(x, out=(res2val, res2ind), descending=True)
self.assertIsOrdered('descending', x, res2val, res2ind,
'random with NaNs')
# FIXME: remove torch.bool from unsupported types once support is added for cub sort
@dtypes(*set(torch.testing.get_all_dtypes()) - {torch.bool, torch.complex64, torch.complex128})
def test_stable_sort(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
sizes = (100, 1000, 10000)
for ncopies in sizes:
x = torch.tensor([0, 1] * ncopies, dtype=dtype, device=device)
_, idx = x.sort(stable=True)
self.assertEqual(
idx[:ncopies],
torch.arange(start=0, end=2 * ncopies, step=2, device=device)
)
self.assertEqual(
idx[ncopies:],
torch.arange(start=1, end=2 * ncopies, step=2, device=device)
)
@onlyCUDA
@dtypes(torch.uint8)
@largeTensorTest('200GB') # Unfortunately 80GB A100 is not large enough
def test_sort_large(self, device, dtype):
t0 = torch.randperm(8192, device=device).to(dtype)
t = t0.view(1, 8192).expand(2 ** 18 + 1, -1).contiguous()
v, i = t.sort()
del t
iv, im = i.var_mean(dim=0)
del i
vv, vm = v.var_mean(dim=0)
del v
self.assertEqual(vv, torch.zeros_like(vv))
self.assertEqual(iv, torch.zeros_like(iv))
self.assertEqual(vm, torch.arange(255, dtype=dtype, device=device))
self.assertEqual(im, t0.sort().indices)
def _test_sort_discontiguous(self, device, dtype):
# on CUDA 2048 vs >2048 have different code path for the dim being sorted
sizes = (5, 7, 2049)
for shape in permutations(sizes):
for perm in permutations((0, 1, 2)):
for dim in range(3):
t = torch.randn(shape, device=device, dtype=dtype).permute(perm)
r1 = t.sort(dim=dim)
r2 = t.contiguous().sort(dim=dim)
self.assertEqual(r1, r2)
n = t.size(dim)
# assert ordered
self.assertTrue((r1.values.narrow(dim, 1, n - 1) >= r1.values.narrow(dim, 0, n - 1)).all())
# assert that different segments does not mix, which can easily happen
# if the stride is not handled correctly
self.assertTrue((t.unsqueeze(-1).transpose(dim, -1) == r1.values.unsqueeze(-1)).any(dim=dim).any(dim=-1).all())
# assert stride is preserved
if self.device_type == 'cuda':
# FIXME: this behavior should be true for all cases, not
# just the one specified in if condition
self.assertEqual(r1.values.stride(), t.stride())
self.assertEqual(r1.indices.stride(), t.stride())
@onlyCUDA
@dtypes(torch.float32)
def test_sort_discontiguous(self, device, dtype):
self._test_sort_discontiguous(device, dtype)
@slowTest # this test is slow on CPU, but not on CUDA
@onlyCPU
@dtypes(torch.float32)
def test_sort_discontiguous_slow(self, device, dtype):
self._test_sort_discontiguous(device, dtype)
# FIXME: remove torch.bool from unsupported types once support is added for cub sort
@dtypes(*set(torch.testing.get_all_dtypes()) - {torch.bool, torch.complex64, torch.complex128})
def test_stable_sort_against_numpy(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
if dtype in torch.testing.floating_types_and(torch.float16, torch.bfloat16):
inf = float('inf')
neg_inf = -float('inf')
nan = float('nan')
else:
if dtype != torch.bool:
# no torch.iinfo support for torch.bool
inf = torch.iinfo(dtype).max
neg_inf = torch.iinfo(dtype).min
else:
inf = True
neg_inf = ~inf
# no nan for integral types, we use inf instead for simplicity
nan = inf
def generate_samples():
from itertools import chain, combinations
for sizes in [(1025,), (10000,)]:
size = sizes[0]
# binary strings
yield (torch.tensor([0, 1] * size, dtype=dtype, device=device), 0)
if self.device_type == 'cuda':
return
yield (torch.tensor([0, 1] * 100, dtype=dtype, device=device), 0)
def repeated_index_fill(t, dim, idxs, vals):
res = t
for idx, val in zip(idxs, vals):
res = res.index_fill(dim, idx, val)
return res
for sizes in [(1, 10), (10, 1), (10, 10), (10, 10, 10)]:
size = min(*sizes)
x = (torch.randn(*sizes, device=device) * size).to(dtype)
yield (x, 0)
# Generate tensors which are being filled at random locations
# with values from the non-empty subsets of the set (inf, neg_inf, nan)
# for each dimension.
n_fill_vals = 3 # cardinality of (inf, neg_inf, nan)
for dim in range(len(sizes)):
idxs = (torch.randint(high=size, size=(size // 10,)) for i in range(n_fill_vals))
vals = (inf, neg_inf, nan)
subsets = chain.from_iterable(combinations(list(zip(idxs, vals)), r)
for r in range(1, n_fill_vals + 1))
for subset in subsets:
idxs_subset, vals_subset = zip(*subset)
yield (repeated_index_fill(x, dim, idxs_subset, vals_subset), dim)
for sample, dim in generate_samples():
_, idx_torch = sample.sort(dim=dim, stable=True)
if dtype is torch.bfloat16:
sample_numpy = sample.float().cpu().numpy()
else:
sample_numpy = sample.cpu().numpy()
idx_numpy = np.argsort(sample_numpy, axis=dim, kind='stable')
self.assertEqual(idx_torch, idx_numpy)
@dtypes(*(torch.testing.get_all_int_dtypes() + torch.testing.get_all_fp_dtypes()))
def test_msort(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
def test(shape):
tensor = make_tensor(shape, device, dtype, low=-9, high=9)
if tensor.size() != torch.Size([]):
if dtype is torch.bfloat16:
expected = torch.from_numpy(np.msort(tensor.float().cpu().numpy())).bfloat16()
else:
expected = torch.from_numpy(np.msort(tensor.cpu().numpy()))
else:
expected = tensor # numpy.msort() does not support empty shapes tensor
result = torch.msort(tensor)
self.assertEqual(result, expected)
out = torch.empty_like(result)
torch.msort(tensor, out=out)
self.assertEqual(out, expected)
shapes = (
[],
[0, ],
[20, ],
[1, 20],
[30, 30],
[10, 20, 30]
)
for shape in shapes:
test(shape)
def test_topk(self, device):
def topKViaSort(t, k, dim, dir):
sorted, indices = t.sort(dim, dir)
return sorted.narrow(dim, 0, k), indices.narrow(dim, 0, k)
def compareTensors(t, res1, ind1, res2, ind2, dim):
# Values should be exactly equivalent
self.assertEqual(res1, res2, atol=0, rtol=0)
# Indices might differ based on the implementation, since there is
# no guarantee of the relative order of selection
if not ind1.eq(ind2).all():
# To verify that the indices represent equivalent elements,
# gather from the input using the topk indices and compare against
# the sort indices
vals = t.gather(dim, ind2)
self.assertEqual(res1, vals, atol=0, rtol=0)
def compare(t, k, dim, dir):
topKVal, topKInd = t.topk(k, dim, dir, True)
sortKVal, sortKInd = topKViaSort(t, k, dim, dir)
compareTensors(t, sortKVal, sortKInd, topKVal, topKInd, dim)
t = torch.rand(random.randint(1, SIZE),
random.randint(1, SIZE),
random.randint(1, SIZE), device=device)
for _kTries in range(3):
for _dimTries in range(3):
for transpose in (True, False):
for dir in (True, False):
testTensor = t
if transpose:
dim1 = random.randrange(t.ndimension())
dim2 = dim1
while dim1 == dim2:
dim2 = random.randrange(t.ndimension())
testTensor = t.transpose(dim1, dim2)
dim = random.randrange(testTensor.ndimension())
k = random.randint(1, testTensor.size(dim))
compare(testTensor, k, dim, dir)
def test_topk_arguments(self, device):
q = torch.randn(10, 2, 10, device=device)
# Make sure True isn't mistakenly taken as the 2nd dimension (interpreted as 1)
self.assertRaises(TypeError, lambda: q.topk(4, True))
@skipCUDAIfRocm
def test_unique_dim(self, device):
self.assertFalse(hasattr(torch, 'unique_dim'))
def run_test(device, dtype):
x = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]],
[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]],
dtype=dtype,
device=device)
x_empty = torch.empty(5, 0, dtype=dtype, device=device)
x_ill_formed_empty = torch.empty(5, 0, 0, dtype=dtype, device=device)
x_ill_formed_empty_another = torch.empty(5, 0, 5, dtype=dtype, device=device)
expected_unique_dim0 = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]],
dtype=dtype,
device=device)
expected_inverse_dim0 = torch.tensor([0, 0])
expected_counts_dim0 = torch.tensor([2])
expected_unique_dim1 = torch.tensor([[[0., 1.],
[1., 1.],
[2., 1.]],
[[0., 1.],
[1., 1.],
[2., 1.]]],
dtype=dtype,
device=device)
expected_unique_dim1_bool = torch.tensor([[[False, True], [True, True]],
[[False, True], [True, True]]],
dtype=torch.bool,
device=device)
expected_inverse_dim1 = torch.tensor([1, 0, 2, 0])
expected_inverse_dim1_bool = torch.tensor([1, 0, 1, 0])
expected_counts_dim1 = torch.tensor([2, 1, 1])
expected_counts_dim1_bool = torch.tensor([2, 2])
expected_unique_dim2 = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]],
[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]],
dtype=dtype,
device=device)
expected_inverse_dim2 = torch.tensor([0, 1])
expected_counts_dim2 = torch.tensor([1, 1])
expected_unique_empty = torch.tensor([], dtype=dtype, device=device)
expected_inverse_empty = torch.tensor([], dtype=torch.long, device=device)
expected_counts_empty = torch.tensor([], dtype=torch.long, device=device)
# dim0
x_unique = torch.unique(x, dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
x_unique, x_inverse = torch.unique(
x,
return_inverse=True,
dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_inverse_dim0, x_inverse)
x_unique, x_counts = torch.unique(
x,
return_inverse=False,
return_counts=True,
dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_counts_dim0, x_counts)
x_unique, x_inverse, x_counts = torch.unique(
x,
return_inverse=True,
return_counts=True,
dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_inverse_dim0, x_inverse)
self.assertEqual(expected_counts_dim0, x_counts)
# dim1
x_unique = torch.unique(x, dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
else:
self.assertEqual(expected_unique_dim1, x_unique)
x_unique, x_inverse = torch.unique(
x,
return_inverse=True,
dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
self.assertEqual(expected_inverse_dim1_bool, x_inverse)
else:
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_inverse_dim1, x_inverse)
x_unique, x_counts = torch.unique(
x,
return_inverse=False,
return_counts=True,
dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
self.assertEqual(expected_counts_dim1_bool, x_counts)
else:
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_counts_dim1, x_counts)
x_unique, x_inverse, x_counts = torch.unique(
x,
return_inverse=True,
return_counts=True,
dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
self.assertEqual(expected_inverse_dim1_bool, x_inverse)
self.assertEqual(expected_counts_dim1_bool, x_counts)
else:
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_inverse_dim1, x_inverse)
self.assertEqual(expected_counts_dim1, x_counts)
# dim2
x_unique = torch.unique(x, dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
x_unique, x_inverse = torch.unique(
x,
return_inverse=True,
dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_inverse_dim2, x_inverse)
x_unique, x_counts = torch.unique(
x,
return_inverse=False,
return_counts=True,
dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_counts_dim2, x_counts)
x_unique, x_inverse, x_counts = torch.unique(
x,
return_inverse=True,
return_counts=True,
dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_inverse_dim2, x_inverse)
self.assertEqual(expected_counts_dim2, x_counts)
# test empty tensor
x_unique, x_inverse, x_counts = torch.unique(
x_empty,
return_inverse=True,
return_counts=True,
dim=1)
self.assertEqual(expected_unique_empty, x_unique)
self.assertEqual(expected_inverse_empty, x_inverse)
self.assertEqual(expected_counts_empty, x_counts)
# test not a well formed tensor
# Checking for runtime error, as this is the expected behaviour
with self.assertRaises(RuntimeError):
torch.unique(
x_ill_formed_empty,
return_inverse=True,
return_counts=True,
dim=1)
# test along dim2
with self.assertRaises(RuntimeError):
torch.unique(
x_ill_formed_empty_another,
return_inverse=True,
return_counts=True,
dim=2)
# test consecutive version
y = torch.tensor(
[[0, 1],
[0, 1],
[0, 1],
[1, 2],
[1, 2],
[3, 4],
[0, 1],
[0, 1],
[3, 4],
[1, 2]],
dtype=dtype,
device=device
)
expected_y_unique = torch.tensor(
[[0, 1],
[1, 2],
[3, 4],
[0, 1],
[3, 4],
[1, 2]],
dtype=dtype,
device=device
)
expected_y_inverse = torch.tensor([0, 0, 0, 1, 1, 2, 3, 3, 4, 5], dtype=torch.int64, device=device)
expected_y_counts = torch.tensor([3, 2, 1, 2, 1, 1], dtype=torch.int64, device=device)
expected_y_inverse_bool = torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 3, 3], dtype=torch.int64, device=device)
expected_y_counts_bool = torch.tensor([3, 3, 2, 2], dtype=torch.int64, device=device)
y_unique, y_inverse, y_counts = torch.unique_consecutive(y, return_inverse=True, return_counts=True, dim=0)
if x.dtype == torch.bool:
self.assertEqual(expected_y_inverse_bool, y_inverse)
self.assertEqual(expected_y_counts_bool, y_counts)
else:
self.assertEqual(expected_y_inverse, y_inverse)
self.assertEqual(expected_y_counts, y_counts)
run_test(device, torch.float)
run_test(device, torch.double)
run_test(device, torch.long)
run_test(device, torch.uint8)
run_test(device, torch.bool)
@onlyCUDA
def test_topk_noncontiguous_gpu(self, device):
t = torch.randn(20, device=device)[::2]
top1, idx1 = t.topk(5)
top2, idx2 = t.contiguous().topk(5)
self.assertEqual(top1, top2)
self.assertEqual(idx1, idx2)
def _test_topk_dtype(self, device, dtype, integral, size):
if integral:
a = torch.randint(torch.iinfo(dtype).min, torch.iinfo(dtype).max,
size=(size,), dtype=dtype, device=device)
else:
a = torch.randn(size=(size,), dtype=dtype, device=device)
sort_topk = a.sort()[0][-(size // 2):].flip(0)
topk = a.topk(size // 2)
self.assertEqual(sort_topk, topk[0]) # check values
self.assertEqual(sort_topk, a[topk[1]]) # check indices
@dtypes(torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64)
def test_topk_integral(self, device, dtype):
small = 10
large = 4096
for curr_size in (small, large):
self._test_topk_dtype(device, dtype, True, curr_size)
@onlyCUDA
@dtypes(torch.bfloat16)
@skipCUDAIfRocm
def test_topk_bfloat16(self, device, dtype):
small = 10
large = 8192
for curr_size in (small, large):
self._test_topk_dtype(device, dtype, False, curr_size)
@dtypesIfCUDA(*torch.testing.get_all_fp_dtypes())
@dtypes(torch.float, torch.double, torch.bfloat16)
def test_topk_nonfinite(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
x = torch.tensor([float('nan'), float('inf'), 1e4, 0, -1e4, -float('inf')], device=device, dtype=dtype)
val, idx = x.topk(4)
expect = torch.tensor([float('nan'), float('inf'), 1e4, 0], device=device, dtype=dtype)
self.assertEqual(val, expect)
self.assertEqual(idx, [0, 1, 2, 3])
val, idx = x.topk(4, largest=False)
expect = torch.tensor([-float('inf'), -1e4, 0, 1e4], device=device, dtype=dtype)
self.assertEqual(val, expect)
self.assertEqual(idx, [5, 4, 3, 2])
def test_topk_4d(self, device):
x = torch.ones(2, 3072, 2, 2, device=device)
x[:, 1, :, :] *= 2.
x[:, 10, :, :] *= 1.5
val, ind = torch.topk(x, k=2, dim=1)
expected_ind = torch.ones(2, 2, 2, 2, dtype=torch.long, device=device)
expected_ind[:, 1, :, :] = 10
expected_val = torch.ones(2, 2, 2, 2, device=device)
expected_val[:, 0, :, :] *= 2.
expected_val[:, 1, :, :] *= 1.5
self.assertEqual(val, expected_val, atol=0, rtol=0)
self.assertEqual(ind, expected_ind, atol=0, rtol=0)
@onlyOnCPUAndCUDA
@dtypesIfCUDA(*(torch.testing.get_all_dtypes(include_complex=False,
include_bool=False,
include_half=False,
include_bfloat16=True)))
@dtypes(*(torch.testing.get_all_dtypes(include_complex=False, include_bool=False, include_half=False, include_bfloat16=False)))
def test_topk_zero(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
# https://github.com/pytorch/pytorch/issues/49205
t = torch.rand(2, 2, device=device).to(dtype=dtype)
val, idx = torch.topk(t, k=0, largest=False)
self.assertEqual(val.size(), torch.Size([2, 0]))
self.assertEqual(idx.size(), torch.Size([2, 0]))
def _test_unique_scalar_empty(self, dtype, device, f):
# test scalar
x = torch.tensor(0, dtype=dtype, device=device)
unique, inverse, counts = f(x, return_inverse=True, return_counts=True)
expected_unique = torch.tensor([0], dtype=dtype, device=device)
expected_inverse = torch.tensor(0, device=device)
expected_counts = torch.tensor([1], device=device)
self.assertEqual(unique, expected_unique)
self.assertEqual(inverse, expected_inverse)
self.assertEqual(counts, expected_counts)
# test zero sized tensor
x = torch.zeros((0, 0, 3), dtype=dtype, device=device)
unique, inverse, counts = f(x, return_inverse=True, return_counts=True)
expected_unique = torch.tensor([], dtype=dtype, device=device)
expected_inverse = torch.empty((0, 0, 3), dtype=torch.long, device=device)
expected_counts = torch.tensor([], dtype=torch.long, device=device)
self.assertEqual(unique, expected_unique)
self.assertEqual(inverse, expected_inverse)
self.assertEqual(counts, expected_counts)
def _test_unique_with_expects(self, device, dtype, f, x, expected_unique, expected_inverse, expected_counts, additional_shape):
def ensure_tuple(x):
if isinstance(x, torch.Tensor):
return (x,)
return x
for return_inverse in [True, False]:
for return_counts in [True, False]:
# test with expected
ret = ensure_tuple(f(x, return_inverse=return_inverse, return_counts=return_counts))
self.assertEqual(len(ret), 1 + int(return_inverse) + int(return_counts))
self.assertEqual(expected_unique, ret[0])
if return_inverse:
self.assertEqual(expected_inverse, ret[1])
if return_counts:
count_index = 1 + int(return_inverse)
self.assertEqual(expected_counts, ret[count_index])
# tests per-element unique on a higher rank tensor.
y = x.view(additional_shape)
y_unique, y_inverse, y_counts = f(y, return_inverse=True, return_counts=True)
self.assertEqual(expected_unique, y_unique)
self.assertEqual(expected_inverse.view(additional_shape), y_inverse)
self.assertEqual(expected_counts, y_counts)
@dtypesIfCPU(*set(torch.testing.get_all_dtypes()) - {torch.complex64, torch.complex128})
@dtypes(*set(torch.testing.get_all_dtypes()) - {torch.bfloat16, torch.complex64, torch.complex128})
def test_unique(self, device, dtype):
if dtype is torch.half and self.device_type == 'cpu':
return # CPU does not have half support
def ensure_tuple(x):
if isinstance(x, torch.Tensor):
return (x,)
return x
if dtype is torch.bool:
x = torch.tensor([True, False, False, False, True, False, True, False], dtype=torch.bool, device=device)
expected_unique = torch.tensor([False, True], dtype=torch.bool, device=device)
expected_inverse = torch.tensor([1, 0, 0, 0, 1, 0, 1, 0], dtype=torch.long, device=device)
expected_counts = torch.tensor([5, 3], dtype=torch.long, device=device)
else:
x = torch.tensor([1, 2, 3, 2, 8, 5, 2, 3], dtype=dtype, device=device)
expected_unique = torch.tensor([1, 2, 3, 5, 8], dtype=dtype, device=device)
expected_inverse = torch.tensor([0, 1, 2, 1, 4, 3, 1, 2], device=device)
expected_counts = torch.tensor([1, 3, 2, 1, 1], device=device)
# test sorted unique
fs = (
lambda x, **kwargs: torch.unique(x, sorted=True, **kwargs),
lambda x, **kwargs: x.unique(sorted=True, **kwargs),
)
x_sliced = torch.empty(x.size(0) * 2, dtype=dtype, device=device)[::2].copy_(x)
xs = (x, x_sliced)
for f, x in product(fs, xs):
self._test_unique_with_expects(device, dtype, f, x, expected_unique, expected_inverse, expected_counts, (2, 2, 2))
self._test_unique_scalar_empty(dtype, device, f)
# test unsorted unique
fs = (
lambda x, **kwargs: torch.unique(x, sorted=False, **kwargs),
lambda x, **kwargs: x.unique(sorted=False, **kwargs)
)
for f, x in product(fs, xs):
self._test_unique_scalar_empty(dtype, device, f)
for return_inverse, return_counts in product((True, False), repeat=2):
ret = ensure_tuple(f(x, return_inverse=return_inverse, return_counts=return_counts))
self.assertEqual(len(ret), 1 + int(return_inverse) + int(return_counts))
x_list = x.tolist()
x_unique_list = ret[0].tolist()
self.assertEqual(expected_unique.tolist(), sorted(x_unique_list))
if return_inverse:
x_inverse_list = ret[1].tolist()
for i, j in enumerate(x_inverse_list):
self.assertEqual(x_list[i], x_unique_list[j])
if return_counts:
count_index = 1 + int(return_inverse)
x_counts_list = ret[count_index].tolist()
for i, j in zip(x_unique_list, x_counts_list):
count = 0
for k in x_list:
if k == i:
count += 1
self.assertEqual(j, count)
@dtypesIfCPU(*set(torch.testing.get_all_dtypes()) - {torch.complex64, torch.complex128})
@dtypes(*set(torch.testing.get_all_dtypes()) - {torch.bfloat16, torch.complex64, torch.complex128})
def test_unique_consecutive(self, device, dtype):
if dtype is torch.half and self.device_type == 'cpu':
return # CPU does not have half support
if dtype is torch.bool:
x = torch.tensor([True, False, False, False, True, True, False, False, False], dtype=torch.bool, device=device)
expected_unique = torch.tensor([True, False, True, False], dtype=torch.bool, device=device)
expected_inverse = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 3], dtype=torch.long, device=device)
expected_counts = torch.tensor([1, 3, 2, 3], dtype=torch.long, device=device)
else:
x = torch.tensor([1, 2, 2, 2, 5, 5, 2, 2, 3], dtype=dtype, device=device)
expected_unique = torch.tensor([1, 2, 5, 2, 3], dtype=dtype, device=device)
expected_inverse = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 4], device=device)
expected_counts = torch.tensor([1, 3, 2, 2, 1], device=device)
for f in [torch.unique_consecutive, lambda x, **kwargs: x.unique_consecutive(**kwargs)]:
self._test_unique_with_expects(device, dtype, f, x, expected_unique, expected_inverse, expected_counts, (3, 3))
self._test_unique_scalar_empty(dtype, device, f)
@dtypes(torch.double)
def test_kthvalue(self, device, dtype):
SIZE = 50
x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
x0 = x.clone()
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
res2val, res2ind = torch.sort(x)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
# test use of result tensors
k = random.randint(1, SIZE)
res1val = torch.tensor([], dtype=dtype, device=device)
res1ind = torch.tensor([], dtype=torch.long, device=device)
torch.kthvalue(x, k, keepdim=False, out=(res1val, res1ind))
res2val, res2ind = torch.sort(x)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
# test non-default dim
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(x, k, 0, keepdim=False)
res2val, res2ind = torch.sort(x, 0)
self.assertEqual(res1val, res2val[k - 1], atol=0, rtol=0)
self.assertEqual(res1ind, res2ind[k - 1], atol=0, rtol=0)
# non-contiguous
y = x.narrow(1, 0, 1)
y0 = y.contiguous()
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(y, k)
res2val, res2ind = torch.kthvalue(y0, k)
self.assertEqual(res1val, res2val, atol=0, rtol=0)
self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
# non-contiguous [Reference: https://github.com/pytorch/pytorch/issues/45721]
non_contig_t = torch.tensor([0, -1, 1, -2, 2], dtype=dtype, device=device)[::2]
expected_val, expected_ind = non_contig_t.contiguous().kthvalue(2)
non_contig_cpu_t = non_contig_t.cpu()
expected_val_cpu, expected_ind_cpu = non_contig_cpu_t.kthvalue(2)
out_val, out_ind = non_contig_t.kthvalue(2)
self.assertEqual(expected_val, out_val, atol=0, rtol=0)
self.assertEqual(expected_ind, out_ind, atol=0, rtol=0)
self.assertEqual(expected_val_cpu, out_val, atol=0, rtol=0)
self.assertEqual(expected_ind_cpu, out_ind, atol=0, rtol=0)
# check that the input wasn't modified
self.assertEqual(x, x0, atol=0, rtol=0)
# simple test case (with repetitions)
y = torch.tensor((3., 5, 4, 1, 1, 5), dtype=dtype, device=device)
self.assertEqual(torch.kthvalue(y, 3)[0], 3, atol=0, rtol=0)
self.assertEqual(torch.kthvalue(y, 2)[0], 1, atol=0, rtol=0)
# simple test case (with NaN)
SIZE = 50
x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
x[torch.arange(SIZE), :, torch.randint(50, (50,))] = nan
ks = [random.randint(1, SIZE), 1, SIZE, SIZE - 1]
res2val, res2ind = torch.sort(x)
for k in ks:
res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
# test overlapping output
@dtypes(torch.double)
@onlyOnCPUAndCUDA # Fails on XLA
def test_kthvalue_overlap(self, device, dtype):
S = 10
k = 5
a = torch.randn(S, device=device)
indices = torch.empty((), device=device, dtype=torch.long)
with self.assertRaisesRegex(RuntimeError, "unsupported operation:"):
torch.kthvalue(a, k, out=(a, indices))
@dtypes(torch.float)
@onlyOnCPUAndCUDA # Fails on XLA
def test_kthvalue_scalar(self, device, dtype):
# Test scalar input (test case from https://github.com/pytorch/pytorch/issues/30818)
# Tests that passing a scalar tensor or 1D tensor with 1 element work either way
res = torch.tensor(2, device=device, dtype=dtype).kthvalue(1)
ref = torch.tensor([2], device=device, dtype=dtype).kthvalue(1)
self.assertEqual(res[0], ref[0].squeeze())
self.assertEqual(res[1], ref[1].squeeze())
@dtypes(*all_types())
@dtypesIfCUDA(*all_types_and(torch.half))
def test_isin(self, device, dtype):
def assert_isin_equal(a, b):
# Compare to the numpy reference implementation.
x = torch.isin(a, b)
a = a.cpu().numpy() if torch.is_tensor(a) else np.array(a)
b = b.cpu().numpy() if torch.is_tensor(b) else np.array(b)
y = np.isin(a, b)
self.assertEqual(x, y)
# multi-dim tensor, multi-dim tensor
a = torch.arange(24, device=device, dtype=dtype).reshape([2, 3, 4])
b = torch.tensor([[10, 20, 30], [0, 1, 3], [11, 22, 33]], device=device, dtype=dtype)
assert_isin_equal(a, b)
# zero-dim tensor
zero_d = torch.tensor(3, device=device, dtype=dtype)
assert_isin_equal(zero_d, b)
assert_isin_equal(a, zero_d)
assert_isin_equal(zero_d, zero_d)
# empty tensor
empty = torch.tensor([], device=device, dtype=dtype)
assert_isin_equal(empty, b)
assert_isin_equal(a, empty)
assert_isin_equal(empty, empty)
# scalar
assert_isin_equal(a, 6)
assert_isin_equal(5, b)
def define_expected(lst, invert=False):
expected = torch.tensor(lst, device=device)
if invert:
expected = expected.logical_not()
return expected
# Adapted from numpy's in1d tests
for mult in [1, 10]:
for invert in [False, True]:
a = torch.tensor([5, 7, 1, 2], device=device, dtype=dtype)
b = torch.tensor([2, 4, 3, 1, 5] * mult, device=device, dtype=dtype)
ec = define_expected([True, False, True, True], invert=invert)
c = torch.isin(a, b, assume_unique=True, invert=invert)
self.assertEqual(c, ec)
a[0] = 8
ec = define_expected([False, False, True, True], invert=invert)
c = torch.isin(a, b, assume_unique=True, invert=invert)
self.assertEqual(c, ec)
a[0], a[3] = 4, 8
ec = define_expected([True, False, True, False], invert=invert)
c = torch.isin(a, b, assume_unique=True, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5], device=device, dtype=dtype)
b = torch.tensor([2, 3, 4] * mult, device=device, dtype=dtype)
ec = define_expected([False, True, False, True, True, True, True, True, True,
False, True, False, False, False], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
b = torch.tensor([2, 3, 4] * mult + [5, 5, 4] * mult, device=device, dtype=dtype)
ec = define_expected([True, True, True, True, True, True, True, True, True, True,
True, False, True, True], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 7, 1, 2], device=device, dtype=dtype)
b = torch.tensor([2, 4, 3, 1, 5] * mult, device=device, dtype=dtype)
ec = define_expected([True, False, True, True], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 7, 1, 1, 2], device=device, dtype=dtype)
b = torch.tensor([2, 4, 3, 3, 1, 5] * mult, device=device, dtype=dtype)
ec = define_expected([True, False, True, True, True], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 5], device=device, dtype=dtype)
b = torch.tensor([2, 2] * mult, device=device, dtype=dtype)
ec = define_expected([False, False], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
# multi-dimensional input case using sort-based algo
for assume_unique in [False, True]:
a = torch.arange(6, device=device, dtype=dtype).reshape([2, 3])
b = torch.arange(3, 30, device=device, dtype=dtype)
ec = define_expected([[False, False, False], [True, True, True]], invert=invert)
c = torch.isin(a, b, invert=invert, assume_unique=assume_unique)
self.assertEqual(c, ec)
def test_isin_different_dtypes(self, device):
supported_types = all_types() if device == 'cpu' else all_types_and(torch.half)
for mult in [1, 10]:
for assume_unique in [False, True]:
for dtype1, dtype2 in product(supported_types, supported_types):
a = torch.tensor([1, 2, 3], device=device, dtype=dtype1)
b = torch.tensor([3, 4, 5] * mult, device=device, dtype=dtype2)
ec = torch.tensor([False, False, True], device=device)
c = torch.isin(a, b, assume_unique=assume_unique)
self.assertEqual(c, ec)
@onlyCUDA
@dtypes(*all_types())
def test_isin_different_devices(self, device, dtype):
a = torch.arange(6, device=device, dtype=dtype).reshape([2, 3])
b = torch.arange(3, 30, device='cpu', dtype=dtype)
with self.assertRaises(RuntimeError):
torch.isin(a, b)
c = torch.arange(6, device='cpu', dtype=dtype).reshape([2, 3])
d = torch.arange(3, 30, device=device, dtype=dtype)
with self.assertRaises(RuntimeError):
torch.isin(c, d)
instantiate_device_type_tests(TestSortAndSelect, globals())
if __name__ == '__main__':
run_tests()
| 45.28971
| 131
| 0.543179
|
import torch
import numpy as np
import random
from torch._six import nan
from itertools import permutations, product
from torch.testing import all_types, all_types_and
from torch.testing._internal.common_utils import \
(TEST_WITH_ROCM, TestCase, run_tests, make_tensor, slowTest)
from torch.testing._internal.common_device_type import \
(instantiate_device_type_tests, dtypes, onlyOnCPUAndCUDA,
skipCUDAIfRocm, onlyCUDA, dtypesIfCUDA, dtypesIfCPU, onlyCPU, largeTensorTest)
SIZE = 100
class TestSortAndSelect(TestCase):
def assertIsOrdered(self, order, x, mxx, ixx, task):
SIZE = x.size(1)
if order == 'descending':
def check_order(a, b):
return ((a != a) | (a >= b)).all().item()
elif order == 'ascending':
def check_order(a, b):
return ((b != b) | (a <= b)).all().item()
else:
error('unknown order "{}", must be "ascending" or "descending"'.format(order))
are_ordered = True
for k in range(1, SIZE):
self.assertTrue(check_order(mxx[:, k - 1], mxx[:, k]),
'torch.sort ({}) values unordered for {}'.format(order, task))
seen = set()
indicesCorrect = True
size0 = x.size(0)
size = x.size(x.dim() - 1)
x = x.tolist()
mxx = mxx.tolist()
ixx = ixx.tolist()
for k in range(size0):
seen.clear()
for j in range(size):
self.assertEqual(x[k][ixx[k][j]], mxx[k][j],
msg='torch.sort ({}) indices wrong for {}'.format(order, task))
seen.add(ixx[k][j])
self.assertEqual(len(seen), size)
def test_sort(self, device):
for SIZE in (4, 2049):
x = torch.rand(4, SIZE, device=device)
res1val, res1ind = torch.sort(x)
y = x.clone()
y_inds = torch.tensor((), dtype=torch.int64, device=device)
torch.sort(y, out=(y, y_inds))
x_vals, x_inds = torch.sort(x)
self.assertEqual(x_vals, y)
self.assertEqual(x_inds, y_inds)
res2val = torch.tensor((), device=device)
res2ind = torch.tensor((), device=device, dtype=torch.long)
torch.sort(x, out=(res2val, res2ind))
self.assertEqual(res1val, res2val, atol=0, rtol=0)
self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
self.assertEqual(torch.argsort(x), res1ind)
self.assertEqual(x.argsort(), res1ind)
self.assertIsOrdered('ascending', x, res2val, res2ind, 'random')
self.assertEqual(
torch.sort(torch.tensor((50, 40, 30, 20, 10), device=device))[0],
torch.tensor((10, 20, 30, 40, 50), device=device),
atol=0, rtol=0
)
x = torch.floor(torch.rand(4, SIZE, device=device) * 10)
torch.sort(x, out=(res2val, res2ind))
self.assertIsOrdered('ascending', x, res2val, res2ind, 'random with duplicate keys')
x = torch.rand(4, SIZE, device=device)
res1val, res1ind = torch.sort(x, x.dim() - 1, True)
res2val = torch.tensor((), device=device)
res2ind = torch.tensor((), device=device, dtype=torch.long)
torch.sort(x, x.dim() - 1, True, out=(res2val, res2ind))
self.assertEqual(res1val, res2val, atol=0, rtol=0)
self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
self.assertEqual(torch.argsort(x, x.dim() - 1, True), res1ind)
self.assertEqual(x.argsort(x.dim() - 1, True), res1ind)
self.assertIsOrdered('descending', x, res2val, res2ind, 'random')
self.assertEqual(
torch.sort(torch.tensor((10, 20, 30, 40, 50), device=device), 0, True)[0],
torch.tensor((50, 40, 30, 20, 10), device=device),
atol=0, rtol=0
)
self.assertIsOrdered('descending', x, res2val, res2ind, 'random with duplicate keys')
x = torch.rand(4, SIZE, device=device)
x[1][2] = float('NaN')
x[3][0] = float('NaN')
torch.sort(x, out=(res2val, res2ind))
self.assertIsOrdered('ascending', x, res2val, res2ind,
'random with NaNs')
torch.sort(x, out=(res2val, res2ind), descending=True)
self.assertIsOrdered('descending', x, res2val, res2ind,
'random with NaNs')
@dtypes(*set(torch.testing.get_all_dtypes()) - {torch.bool, torch.complex64, torch.complex128})
def test_stable_sort(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
sizes = (100, 1000, 10000)
for ncopies in sizes:
x = torch.tensor([0, 1] * ncopies, dtype=dtype, device=device)
_, idx = x.sort(stable=True)
self.assertEqual(
idx[:ncopies],
torch.arange(start=0, end=2 * ncopies, step=2, device=device)
)
self.assertEqual(
idx[ncopies:],
torch.arange(start=1, end=2 * ncopies, step=2, device=device)
)
@onlyCUDA
@dtypes(torch.uint8)
@largeTensorTest('200GB')
def test_sort_large(self, device, dtype):
t0 = torch.randperm(8192, device=device).to(dtype)
t = t0.view(1, 8192).expand(2 ** 18 + 1, -1).contiguous()
v, i = t.sort()
del t
iv, im = i.var_mean(dim=0)
del i
vv, vm = v.var_mean(dim=0)
del v
self.assertEqual(vv, torch.zeros_like(vv))
self.assertEqual(iv, torch.zeros_like(iv))
self.assertEqual(vm, torch.arange(255, dtype=dtype, device=device))
self.assertEqual(im, t0.sort().indices)
def _test_sort_discontiguous(self, device, dtype):
sizes = (5, 7, 2049)
for shape in permutations(sizes):
for perm in permutations((0, 1, 2)):
for dim in range(3):
t = torch.randn(shape, device=device, dtype=dtype).permute(perm)
r1 = t.sort(dim=dim)
r2 = t.contiguous().sort(dim=dim)
self.assertEqual(r1, r2)
n = t.size(dim)
self.assertTrue((r1.values.narrow(dim, 1, n - 1) >= r1.values.narrow(dim, 0, n - 1)).all())
self.assertTrue((t.unsqueeze(-1).transpose(dim, -1) == r1.values.unsqueeze(-1)).any(dim=dim).any(dim=-1).all())
if self.device_type == 'cuda':
self.assertEqual(r1.values.stride(), t.stride())
self.assertEqual(r1.indices.stride(), t.stride())
@onlyCUDA
@dtypes(torch.float32)
def test_sort_discontiguous(self, device, dtype):
self._test_sort_discontiguous(device, dtype)
@slowTest
@onlyCPU
@dtypes(torch.float32)
def test_sort_discontiguous_slow(self, device, dtype):
self._test_sort_discontiguous(device, dtype)
@dtypes(*set(torch.testing.get_all_dtypes()) - {torch.bool, torch.complex64, torch.complex128})
def test_stable_sort_against_numpy(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
if dtype in torch.testing.floating_types_and(torch.float16, torch.bfloat16):
inf = float('inf')
neg_inf = -float('inf')
nan = float('nan')
else:
if dtype != torch.bool:
inf = torch.iinfo(dtype).max
neg_inf = torch.iinfo(dtype).min
else:
inf = True
neg_inf = ~inf
nan = inf
def generate_samples():
from itertools import chain, combinations
for sizes in [(1025,), (10000,)]:
size = sizes[0]
yield (torch.tensor([0, 1] * size, dtype=dtype, device=device), 0)
if self.device_type == 'cuda':
return
yield (torch.tensor([0, 1] * 100, dtype=dtype, device=device), 0)
def repeated_index_fill(t, dim, idxs, vals):
res = t
for idx, val in zip(idxs, vals):
res = res.index_fill(dim, idx, val)
return res
for sizes in [(1, 10), (10, 1), (10, 10), (10, 10, 10)]:
size = min(*sizes)
x = (torch.randn(*sizes, device=device) * size).to(dtype)
yield (x, 0)
n_fill_vals = 3
for dim in range(len(sizes)):
idxs = (torch.randint(high=size, size=(size // 10,)) for i in range(n_fill_vals))
vals = (inf, neg_inf, nan)
subsets = chain.from_iterable(combinations(list(zip(idxs, vals)), r)
for r in range(1, n_fill_vals + 1))
for subset in subsets:
idxs_subset, vals_subset = zip(*subset)
yield (repeated_index_fill(x, dim, idxs_subset, vals_subset), dim)
for sample, dim in generate_samples():
_, idx_torch = sample.sort(dim=dim, stable=True)
if dtype is torch.bfloat16:
sample_numpy = sample.float().cpu().numpy()
else:
sample_numpy = sample.cpu().numpy()
idx_numpy = np.argsort(sample_numpy, axis=dim, kind='stable')
self.assertEqual(idx_torch, idx_numpy)
@dtypes(*(torch.testing.get_all_int_dtypes() + torch.testing.get_all_fp_dtypes()))
def test_msort(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
def test(shape):
tensor = make_tensor(shape, device, dtype, low=-9, high=9)
if tensor.size() != torch.Size([]):
if dtype is torch.bfloat16:
expected = torch.from_numpy(np.msort(tensor.float().cpu().numpy())).bfloat16()
else:
expected = torch.from_numpy(np.msort(tensor.cpu().numpy()))
else:
expected = tensor
result = torch.msort(tensor)
self.assertEqual(result, expected)
out = torch.empty_like(result)
torch.msort(tensor, out=out)
self.assertEqual(out, expected)
shapes = (
[],
[0, ],
[20, ],
[1, 20],
[30, 30],
[10, 20, 30]
)
for shape in shapes:
test(shape)
def test_topk(self, device):
def topKViaSort(t, k, dim, dir):
sorted, indices = t.sort(dim, dir)
return sorted.narrow(dim, 0, k), indices.narrow(dim, 0, k)
def compareTensors(t, res1, ind1, res2, ind2, dim):
self.assertEqual(res1, res2, atol=0, rtol=0)
if not ind1.eq(ind2).all():
vals = t.gather(dim, ind2)
self.assertEqual(res1, vals, atol=0, rtol=0)
def compare(t, k, dim, dir):
topKVal, topKInd = t.topk(k, dim, dir, True)
sortKVal, sortKInd = topKViaSort(t, k, dim, dir)
compareTensors(t, sortKVal, sortKInd, topKVal, topKInd, dim)
t = torch.rand(random.randint(1, SIZE),
random.randint(1, SIZE),
random.randint(1, SIZE), device=device)
for _kTries in range(3):
for _dimTries in range(3):
for transpose in (True, False):
for dir in (True, False):
testTensor = t
if transpose:
dim1 = random.randrange(t.ndimension())
dim2 = dim1
while dim1 == dim2:
dim2 = random.randrange(t.ndimension())
testTensor = t.transpose(dim1, dim2)
dim = random.randrange(testTensor.ndimension())
k = random.randint(1, testTensor.size(dim))
compare(testTensor, k, dim, dir)
def test_topk_arguments(self, device):
q = torch.randn(10, 2, 10, device=device)
self.assertRaises(TypeError, lambda: q.topk(4, True))
@skipCUDAIfRocm
def test_unique_dim(self, device):
self.assertFalse(hasattr(torch, 'unique_dim'))
def run_test(device, dtype):
x = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]],
[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]],
dtype=dtype,
device=device)
x_empty = torch.empty(5, 0, dtype=dtype, device=device)
x_ill_formed_empty = torch.empty(5, 0, 0, dtype=dtype, device=device)
x_ill_formed_empty_another = torch.empty(5, 0, 5, dtype=dtype, device=device)
expected_unique_dim0 = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]],
dtype=dtype,
device=device)
expected_inverse_dim0 = torch.tensor([0, 0])
expected_counts_dim0 = torch.tensor([2])
expected_unique_dim1 = torch.tensor([[[0., 1.],
[1., 1.],
[2., 1.]],
[[0., 1.],
[1., 1.],
[2., 1.]]],
dtype=dtype,
device=device)
expected_unique_dim1_bool = torch.tensor([[[False, True], [True, True]],
[[False, True], [True, True]]],
dtype=torch.bool,
device=device)
expected_inverse_dim1 = torch.tensor([1, 0, 2, 0])
expected_inverse_dim1_bool = torch.tensor([1, 0, 1, 0])
expected_counts_dim1 = torch.tensor([2, 1, 1])
expected_counts_dim1_bool = torch.tensor([2, 2])
expected_unique_dim2 = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]],
[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]],
dtype=dtype,
device=device)
expected_inverse_dim2 = torch.tensor([0, 1])
expected_counts_dim2 = torch.tensor([1, 1])
expected_unique_empty = torch.tensor([], dtype=dtype, device=device)
expected_inverse_empty = torch.tensor([], dtype=torch.long, device=device)
expected_counts_empty = torch.tensor([], dtype=torch.long, device=device)
# dim0
x_unique = torch.unique(x, dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
x_unique, x_inverse = torch.unique(
x,
return_inverse=True,
dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_inverse_dim0, x_inverse)
x_unique, x_counts = torch.unique(
x,
return_inverse=False,
return_counts=True,
dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_counts_dim0, x_counts)
x_unique, x_inverse, x_counts = torch.unique(
x,
return_inverse=True,
return_counts=True,
dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_inverse_dim0, x_inverse)
self.assertEqual(expected_counts_dim0, x_counts)
# dim1
x_unique = torch.unique(x, dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
else:
self.assertEqual(expected_unique_dim1, x_unique)
x_unique, x_inverse = torch.unique(
x,
return_inverse=True,
dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
self.assertEqual(expected_inverse_dim1_bool, x_inverse)
else:
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_inverse_dim1, x_inverse)
x_unique, x_counts = torch.unique(
x,
return_inverse=False,
return_counts=True,
dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
self.assertEqual(expected_counts_dim1_bool, x_counts)
else:
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_counts_dim1, x_counts)
x_unique, x_inverse, x_counts = torch.unique(
x,
return_inverse=True,
return_counts=True,
dim=1)
if x.dtype == torch.bool:
self.assertEqual(expected_unique_dim1_bool, x_unique)
self.assertEqual(expected_inverse_dim1_bool, x_inverse)
self.assertEqual(expected_counts_dim1_bool, x_counts)
else:
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_inverse_dim1, x_inverse)
self.assertEqual(expected_counts_dim1, x_counts)
# dim2
x_unique = torch.unique(x, dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
x_unique, x_inverse = torch.unique(
x,
return_inverse=True,
dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_inverse_dim2, x_inverse)
x_unique, x_counts = torch.unique(
x,
return_inverse=False,
return_counts=True,
dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_counts_dim2, x_counts)
x_unique, x_inverse, x_counts = torch.unique(
x,
return_inverse=True,
return_counts=True,
dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_inverse_dim2, x_inverse)
self.assertEqual(expected_counts_dim2, x_counts)
# test empty tensor
x_unique, x_inverse, x_counts = torch.unique(
x_empty,
return_inverse=True,
return_counts=True,
dim=1)
self.assertEqual(expected_unique_empty, x_unique)
self.assertEqual(expected_inverse_empty, x_inverse)
self.assertEqual(expected_counts_empty, x_counts)
# test not a well formed tensor
# Checking for runtime error, as this is the expected behaviour
with self.assertRaises(RuntimeError):
torch.unique(
x_ill_formed_empty,
return_inverse=True,
return_counts=True,
dim=1)
# test along dim2
with self.assertRaises(RuntimeError):
torch.unique(
x_ill_formed_empty_another,
return_inverse=True,
return_counts=True,
dim=2)
# test consecutive version
y = torch.tensor(
[[0, 1],
[0, 1],
[0, 1],
[1, 2],
[1, 2],
[3, 4],
[0, 1],
[0, 1],
[3, 4],
[1, 2]],
dtype=dtype,
device=device
)
expected_y_unique = torch.tensor(
[[0, 1],
[1, 2],
[3, 4],
[0, 1],
[3, 4],
[1, 2]],
dtype=dtype,
device=device
)
expected_y_inverse = torch.tensor([0, 0, 0, 1, 1, 2, 3, 3, 4, 5], dtype=torch.int64, device=device)
expected_y_counts = torch.tensor([3, 2, 1, 2, 1, 1], dtype=torch.int64, device=device)
expected_y_inverse_bool = torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 3, 3], dtype=torch.int64, device=device)
expected_y_counts_bool = torch.tensor([3, 3, 2, 2], dtype=torch.int64, device=device)
y_unique, y_inverse, y_counts = torch.unique_consecutive(y, return_inverse=True, return_counts=True, dim=0)
if x.dtype == torch.bool:
self.assertEqual(expected_y_inverse_bool, y_inverse)
self.assertEqual(expected_y_counts_bool, y_counts)
else:
self.assertEqual(expected_y_inverse, y_inverse)
self.assertEqual(expected_y_counts, y_counts)
run_test(device, torch.float)
run_test(device, torch.double)
run_test(device, torch.long)
run_test(device, torch.uint8)
run_test(device, torch.bool)
@onlyCUDA
def test_topk_noncontiguous_gpu(self, device):
t = torch.randn(20, device=device)[::2]
top1, idx1 = t.topk(5)
top2, idx2 = t.contiguous().topk(5)
self.assertEqual(top1, top2)
self.assertEqual(idx1, idx2)
def _test_topk_dtype(self, device, dtype, integral, size):
if integral:
a = torch.randint(torch.iinfo(dtype).min, torch.iinfo(dtype).max,
size=(size,), dtype=dtype, device=device)
else:
a = torch.randn(size=(size,), dtype=dtype, device=device)
sort_topk = a.sort()[0][-(size // 2):].flip(0)
topk = a.topk(size // 2)
self.assertEqual(sort_topk, topk[0]) # check values
self.assertEqual(sort_topk, a[topk[1]]) # check indices
@dtypes(torch.int8, torch.uint8, torch.int16, torch.int32, torch.int64)
def test_topk_integral(self, device, dtype):
small = 10
large = 4096
for curr_size in (small, large):
self._test_topk_dtype(device, dtype, True, curr_size)
@onlyCUDA
@dtypes(torch.bfloat16)
@skipCUDAIfRocm
def test_topk_bfloat16(self, device, dtype):
small = 10
large = 8192
for curr_size in (small, large):
self._test_topk_dtype(device, dtype, False, curr_size)
@dtypesIfCUDA(*torch.testing.get_all_fp_dtypes())
@dtypes(torch.float, torch.double, torch.bfloat16)
def test_topk_nonfinite(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
x = torch.tensor([float('nan'), float('inf'), 1e4, 0, -1e4, -float('inf')], device=device, dtype=dtype)
val, idx = x.topk(4)
expect = torch.tensor([float('nan'), float('inf'), 1e4, 0], device=device, dtype=dtype)
self.assertEqual(val, expect)
self.assertEqual(idx, [0, 1, 2, 3])
val, idx = x.topk(4, largest=False)
expect = torch.tensor([-float('inf'), -1e4, 0, 1e4], device=device, dtype=dtype)
self.assertEqual(val, expect)
self.assertEqual(idx, [5, 4, 3, 2])
def test_topk_4d(self, device):
x = torch.ones(2, 3072, 2, 2, device=device)
x[:, 1, :, :] *= 2.
x[:, 10, :, :] *= 1.5
val, ind = torch.topk(x, k=2, dim=1)
expected_ind = torch.ones(2, 2, 2, 2, dtype=torch.long, device=device)
expected_ind[:, 1, :, :] = 10
expected_val = torch.ones(2, 2, 2, 2, device=device)
expected_val[:, 0, :, :] *= 2.
expected_val[:, 1, :, :] *= 1.5
self.assertEqual(val, expected_val, atol=0, rtol=0)
self.assertEqual(ind, expected_ind, atol=0, rtol=0)
@onlyOnCPUAndCUDA
@dtypesIfCUDA(*(torch.testing.get_all_dtypes(include_complex=False,
include_bool=False,
include_half=False,
include_bfloat16=True)))
@dtypes(*(torch.testing.get_all_dtypes(include_complex=False, include_bool=False, include_half=False, include_bfloat16=False)))
def test_topk_zero(self, device, dtype):
if TEST_WITH_ROCM and dtype == torch.bfloat16:
return
# https://github.com/pytorch/pytorch/issues/49205
t = torch.rand(2, 2, device=device).to(dtype=dtype)
val, idx = torch.topk(t, k=0, largest=False)
self.assertEqual(val.size(), torch.Size([2, 0]))
self.assertEqual(idx.size(), torch.Size([2, 0]))
def _test_unique_scalar_empty(self, dtype, device, f):
# test scalar
x = torch.tensor(0, dtype=dtype, device=device)
unique, inverse, counts = f(x, return_inverse=True, return_counts=True)
expected_unique = torch.tensor([0], dtype=dtype, device=device)
expected_inverse = torch.tensor(0, device=device)
expected_counts = torch.tensor([1], device=device)
self.assertEqual(unique, expected_unique)
self.assertEqual(inverse, expected_inverse)
self.assertEqual(counts, expected_counts)
# test zero sized tensor
x = torch.zeros((0, 0, 3), dtype=dtype, device=device)
unique, inverse, counts = f(x, return_inverse=True, return_counts=True)
expected_unique = torch.tensor([], dtype=dtype, device=device)
expected_inverse = torch.empty((0, 0, 3), dtype=torch.long, device=device)
expected_counts = torch.tensor([], dtype=torch.long, device=device)
self.assertEqual(unique, expected_unique)
self.assertEqual(inverse, expected_inverse)
self.assertEqual(counts, expected_counts)
def _test_unique_with_expects(self, device, dtype, f, x, expected_unique, expected_inverse, expected_counts, additional_shape):
def ensure_tuple(x):
if isinstance(x, torch.Tensor):
return (x,)
return x
for return_inverse in [True, False]:
for return_counts in [True, False]:
# test with expected
ret = ensure_tuple(f(x, return_inverse=return_inverse, return_counts=return_counts))
self.assertEqual(len(ret), 1 + int(return_inverse) + int(return_counts))
self.assertEqual(expected_unique, ret[0])
if return_inverse:
self.assertEqual(expected_inverse, ret[1])
if return_counts:
count_index = 1 + int(return_inverse)
self.assertEqual(expected_counts, ret[count_index])
# tests per-element unique on a higher rank tensor.
y = x.view(additional_shape)
y_unique, y_inverse, y_counts = f(y, return_inverse=True, return_counts=True)
self.assertEqual(expected_unique, y_unique)
self.assertEqual(expected_inverse.view(additional_shape), y_inverse)
self.assertEqual(expected_counts, y_counts)
@dtypesIfCPU(*set(torch.testing.get_all_dtypes()) - {torch.complex64, torch.complex128})
@dtypes(*set(torch.testing.get_all_dtypes()) - {torch.bfloat16, torch.complex64, torch.complex128})
def test_unique(self, device, dtype):
if dtype is torch.half and self.device_type == 'cpu':
return # CPU does not have half support
def ensure_tuple(x):
if isinstance(x, torch.Tensor):
return (x,)
return x
if dtype is torch.bool:
x = torch.tensor([True, False, False, False, True, False, True, False], dtype=torch.bool, device=device)
expected_unique = torch.tensor([False, True], dtype=torch.bool, device=device)
expected_inverse = torch.tensor([1, 0, 0, 0, 1, 0, 1, 0], dtype=torch.long, device=device)
expected_counts = torch.tensor([5, 3], dtype=torch.long, device=device)
else:
x = torch.tensor([1, 2, 3, 2, 8, 5, 2, 3], dtype=dtype, device=device)
expected_unique = torch.tensor([1, 2, 3, 5, 8], dtype=dtype, device=device)
expected_inverse = torch.tensor([0, 1, 2, 1, 4, 3, 1, 2], device=device)
expected_counts = torch.tensor([1, 3, 2, 1, 1], device=device)
# test sorted unique
fs = (
lambda x, **kwargs: torch.unique(x, sorted=True, **kwargs),
lambda x, **kwargs: x.unique(sorted=True, **kwargs),
)
x_sliced = torch.empty(x.size(0) * 2, dtype=dtype, device=device)[::2].copy_(x)
xs = (x, x_sliced)
for f, x in product(fs, xs):
self._test_unique_with_expects(device, dtype, f, x, expected_unique, expected_inverse, expected_counts, (2, 2, 2))
self._test_unique_scalar_empty(dtype, device, f)
# test unsorted unique
fs = (
lambda x, **kwargs: torch.unique(x, sorted=False, **kwargs),
lambda x, **kwargs: x.unique(sorted=False, **kwargs)
)
for f, x in product(fs, xs):
self._test_unique_scalar_empty(dtype, device, f)
for return_inverse, return_counts in product((True, False), repeat=2):
ret = ensure_tuple(f(x, return_inverse=return_inverse, return_counts=return_counts))
self.assertEqual(len(ret), 1 + int(return_inverse) + int(return_counts))
x_list = x.tolist()
x_unique_list = ret[0].tolist()
self.assertEqual(expected_unique.tolist(), sorted(x_unique_list))
if return_inverse:
x_inverse_list = ret[1].tolist()
for i, j in enumerate(x_inverse_list):
self.assertEqual(x_list[i], x_unique_list[j])
if return_counts:
count_index = 1 + int(return_inverse)
x_counts_list = ret[count_index].tolist()
for i, j in zip(x_unique_list, x_counts_list):
count = 0
for k in x_list:
if k == i:
count += 1
self.assertEqual(j, count)
@dtypesIfCPU(*set(torch.testing.get_all_dtypes()) - {torch.complex64, torch.complex128})
@dtypes(*set(torch.testing.get_all_dtypes()) - {torch.bfloat16, torch.complex64, torch.complex128})
def test_unique_consecutive(self, device, dtype):
if dtype is torch.half and self.device_type == 'cpu':
return # CPU does not have half support
if dtype is torch.bool:
x = torch.tensor([True, False, False, False, True, True, False, False, False], dtype=torch.bool, device=device)
expected_unique = torch.tensor([True, False, True, False], dtype=torch.bool, device=device)
expected_inverse = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 3], dtype=torch.long, device=device)
expected_counts = torch.tensor([1, 3, 2, 3], dtype=torch.long, device=device)
else:
x = torch.tensor([1, 2, 2, 2, 5, 5, 2, 2, 3], dtype=dtype, device=device)
expected_unique = torch.tensor([1, 2, 5, 2, 3], dtype=dtype, device=device)
expected_inverse = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 4], device=device)
expected_counts = torch.tensor([1, 3, 2, 2, 1], device=device)
for f in [torch.unique_consecutive, lambda x, **kwargs: x.unique_consecutive(**kwargs)]:
self._test_unique_with_expects(device, dtype, f, x, expected_unique, expected_inverse, expected_counts, (3, 3))
self._test_unique_scalar_empty(dtype, device, f)
@dtypes(torch.double)
def test_kthvalue(self, device, dtype):
SIZE = 50
x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
x0 = x.clone()
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
res2val, res2ind = torch.sort(x)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
# test use of result tensors
k = random.randint(1, SIZE)
res1val = torch.tensor([], dtype=dtype, device=device)
res1ind = torch.tensor([], dtype=torch.long, device=device)
torch.kthvalue(x, k, keepdim=False, out=(res1val, res1ind))
res2val, res2ind = torch.sort(x)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
# test non-default dim
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(x, k, 0, keepdim=False)
res2val, res2ind = torch.sort(x, 0)
self.assertEqual(res1val, res2val[k - 1], atol=0, rtol=0)
self.assertEqual(res1ind, res2ind[k - 1], atol=0, rtol=0)
# non-contiguous
y = x.narrow(1, 0, 1)
y0 = y.contiguous()
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(y, k)
res2val, res2ind = torch.kthvalue(y0, k)
self.assertEqual(res1val, res2val, atol=0, rtol=0)
self.assertEqual(res1ind, res2ind, atol=0, rtol=0)
# non-contiguous [Reference: https://github.com/pytorch/pytorch/issues/45721]
non_contig_t = torch.tensor([0, -1, 1, -2, 2], dtype=dtype, device=device)[::2]
expected_val, expected_ind = non_contig_t.contiguous().kthvalue(2)
non_contig_cpu_t = non_contig_t.cpu()
expected_val_cpu, expected_ind_cpu = non_contig_cpu_t.kthvalue(2)
out_val, out_ind = non_contig_t.kthvalue(2)
self.assertEqual(expected_val, out_val, atol=0, rtol=0)
self.assertEqual(expected_ind, out_ind, atol=0, rtol=0)
self.assertEqual(expected_val_cpu, out_val, atol=0, rtol=0)
self.assertEqual(expected_ind_cpu, out_ind, atol=0, rtol=0)
# check that the input wasn't modified
self.assertEqual(x, x0, atol=0, rtol=0)
y = torch.tensor((3., 5, 4, 1, 1, 5), dtype=dtype, device=device)
self.assertEqual(torch.kthvalue(y, 3)[0], 3, atol=0, rtol=0)
self.assertEqual(torch.kthvalue(y, 2)[0], 1, atol=0, rtol=0)
SIZE = 50
x = torch.rand(SIZE, SIZE, SIZE, dtype=dtype, device=device)
x[torch.arange(SIZE), :, torch.randint(50, (50,))] = nan
ks = [random.randint(1, SIZE), 1, SIZE, SIZE - 1]
res2val, res2ind = torch.sort(x)
for k in ks:
res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], atol=0, rtol=0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], atol=0, rtol=0)
@dtypes(torch.double)
@onlyOnCPUAndCUDA
def test_kthvalue_overlap(self, device, dtype):
S = 10
k = 5
a = torch.randn(S, device=device)
indices = torch.empty((), device=device, dtype=torch.long)
with self.assertRaisesRegex(RuntimeError, "unsupported operation:"):
torch.kthvalue(a, k, out=(a, indices))
@dtypes(torch.float)
@onlyOnCPUAndCUDA
def test_kthvalue_scalar(self, device, dtype):
res = torch.tensor(2, device=device, dtype=dtype).kthvalue(1)
ref = torch.tensor([2], device=device, dtype=dtype).kthvalue(1)
self.assertEqual(res[0], ref[0].squeeze())
self.assertEqual(res[1], ref[1].squeeze())
@dtypes(*all_types())
@dtypesIfCUDA(*all_types_and(torch.half))
def test_isin(self, device, dtype):
def assert_isin_equal(a, b):
x = torch.isin(a, b)
a = a.cpu().numpy() if torch.is_tensor(a) else np.array(a)
b = b.cpu().numpy() if torch.is_tensor(b) else np.array(b)
y = np.isin(a, b)
self.assertEqual(x, y)
a = torch.arange(24, device=device, dtype=dtype).reshape([2, 3, 4])
b = torch.tensor([[10, 20, 30], [0, 1, 3], [11, 22, 33]], device=device, dtype=dtype)
assert_isin_equal(a, b)
zero_d = torch.tensor(3, device=device, dtype=dtype)
assert_isin_equal(zero_d, b)
assert_isin_equal(a, zero_d)
assert_isin_equal(zero_d, zero_d)
empty = torch.tensor([], device=device, dtype=dtype)
assert_isin_equal(empty, b)
assert_isin_equal(a, empty)
assert_isin_equal(empty, empty)
assert_isin_equal(a, 6)
assert_isin_equal(5, b)
def define_expected(lst, invert=False):
expected = torch.tensor(lst, device=device)
if invert:
expected = expected.logical_not()
return expected
for mult in [1, 10]:
for invert in [False, True]:
a = torch.tensor([5, 7, 1, 2], device=device, dtype=dtype)
b = torch.tensor([2, 4, 3, 1, 5] * mult, device=device, dtype=dtype)
ec = define_expected([True, False, True, True], invert=invert)
c = torch.isin(a, b, assume_unique=True, invert=invert)
self.assertEqual(c, ec)
a[0] = 8
ec = define_expected([False, False, True, True], invert=invert)
c = torch.isin(a, b, assume_unique=True, invert=invert)
self.assertEqual(c, ec)
a[0], a[3] = 4, 8
ec = define_expected([True, False, True, False], invert=invert)
c = torch.isin(a, b, assume_unique=True, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 4, 5, 3, 4, 4, 3, 4, 3, 5, 2, 1, 5, 5], device=device, dtype=dtype)
b = torch.tensor([2, 3, 4] * mult, device=device, dtype=dtype)
ec = define_expected([False, True, False, True, True, True, True, True, True,
False, True, False, False, False], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
b = torch.tensor([2, 3, 4] * mult + [5, 5, 4] * mult, device=device, dtype=dtype)
ec = define_expected([True, True, True, True, True, True, True, True, True, True,
True, False, True, True], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 7, 1, 2], device=device, dtype=dtype)
b = torch.tensor([2, 4, 3, 1, 5] * mult, device=device, dtype=dtype)
ec = define_expected([True, False, True, True], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 7, 1, 1, 2], device=device, dtype=dtype)
b = torch.tensor([2, 4, 3, 3, 1, 5] * mult, device=device, dtype=dtype)
ec = define_expected([True, False, True, True, True], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
a = torch.tensor([5, 5], device=device, dtype=dtype)
b = torch.tensor([2, 2] * mult, device=device, dtype=dtype)
ec = define_expected([False, False], invert=invert)
c = torch.isin(a, b, invert=invert)
self.assertEqual(c, ec)
# multi-dimensional input case using sort-based algo
for assume_unique in [False, True]:
a = torch.arange(6, device=device, dtype=dtype).reshape([2, 3])
b = torch.arange(3, 30, device=device, dtype=dtype)
ec = define_expected([[False, False, False], [True, True, True]], invert=invert)
c = torch.isin(a, b, invert=invert, assume_unique=assume_unique)
self.assertEqual(c, ec)
def test_isin_different_dtypes(self, device):
supported_types = all_types() if device == 'cpu' else all_types_and(torch.half)
for mult in [1, 10]:
for assume_unique in [False, True]:
for dtype1, dtype2 in product(supported_types, supported_types):
a = torch.tensor([1, 2, 3], device=device, dtype=dtype1)
b = torch.tensor([3, 4, 5] * mult, device=device, dtype=dtype2)
ec = torch.tensor([False, False, True], device=device)
c = torch.isin(a, b, assume_unique=assume_unique)
self.assertEqual(c, ec)
@onlyCUDA
@dtypes(*all_types())
def test_isin_different_devices(self, device, dtype):
a = torch.arange(6, device=device, dtype=dtype).reshape([2, 3])
b = torch.arange(3, 30, device='cpu', dtype=dtype)
with self.assertRaises(RuntimeError):
torch.isin(a, b)
c = torch.arange(6, device='cpu', dtype=dtype).reshape([2, 3])
d = torch.arange(3, 30, device=device, dtype=dtype)
with self.assertRaises(RuntimeError):
torch.isin(c, d)
instantiate_device_type_tests(TestSortAndSelect, globals())
if __name__ == '__main__':
run_tests()
| true
| true
|
f716a4e754c3e3f35917e5279cd691b574a49a9b
| 798
|
py
|
Python
|
tests/test_api_v1_services_sshd_stop.py
|
pincher95/pfsense-api
|
001a4b8a1ec39138668d6d92b3c9d0c89a7f1b45
|
[
"Apache-2.0"
] | null | null | null |
tests/test_api_v1_services_sshd_stop.py
|
pincher95/pfsense-api
|
001a4b8a1ec39138668d6d92b3c9d0c89a7f1b45
|
[
"Apache-2.0"
] | null | null | null |
tests/test_api_v1_services_sshd_stop.py
|
pincher95/pfsense-api
|
001a4b8a1ec39138668d6d92b3c9d0c89a7f1b45
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2022 Jared Hendrickson
#
# 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 e2e_test_framework
class APIE2ETestServicesSSHdStop(e2e_test_framework.APIE2ETest):
uri = "/api/v1/services/sshd/stop"
post_tests = [{"name": "Stop the SSHd service"}]
APIE2ETestServicesSSHdStop()
| 33.25
| 74
| 0.764411
|
import e2e_test_framework
class APIE2ETestServicesSSHdStop(e2e_test_framework.APIE2ETest):
uri = "/api/v1/services/sshd/stop"
post_tests = [{"name": "Stop the SSHd service"}]
APIE2ETestServicesSSHdStop()
| true
| true
|
f716a628b677fa7e5aaed86bc610566d88632de5
| 1,598
|
py
|
Python
|
mySpider/spiders/wangyi.py
|
songw831/mySpider
|
04312701e891e14ba7e470c3f2c0aa9997074096
|
[
"MIT"
] | null | null | null |
mySpider/spiders/wangyi.py
|
songw831/mySpider
|
04312701e891e14ba7e470c3f2c0aa9997074096
|
[
"MIT"
] | null | null | null |
mySpider/spiders/wangyi.py
|
songw831/mySpider
|
04312701e891e14ba7e470c3f2c0aa9997074096
|
[
"MIT"
] | null | null | null |
import scrapy
from selenium import webdriver
from mySpider.items import MyspiderItem
class FundSpider(scrapy.Spider):
name = 'wangyi'
#allowed_domains = ['www.xxx.com']
start_urls = ['http://news.163.com/']
modules_url = [] #存放五个版块的url
def __init__(self):
self.bro = webdriver.Chrome(executable_path='D:\PyCharm\mySpider\chromedriver.exe')
def parse(self, response):
li_list = response.xpath('//*[@id="index2016_wrap"]/div[1]/div[2]/div[2]/div[2]/div[2]/div/ul')
alist = [3,4,6,7,8]
for index in alist:
module_url = li_list[index].xpath('./a/@href').extract_first()
self.modules_url.append(module_url)
#依次对每一个版块进行请求
for url in self.module_url:
yield scrapy.Request(url, callback= self.parse_module)
def parse_module(self, response):
div_list = response.xpath('/html/body/div/div[3]/div[4]/div[1]/div[1]/div/ul/li/div/div')
for div in div_list:
title = div.xpath('./div/div[1]/h3/a/text()').extract_fisrt()
new_detail_url = div.xpath('./div/div[1]/h3/a/@href').extract_first()
item = MyspiderItem()
item.title = title
yield scrapy.Request(url=new_detail_url, callback=self.parse_detail, meta={'item':item})
def parse_detail(self,response):
content = response.xpath('//*[@id="content"]/div[2]//text()').extract()
content = ''.join(content)
item = response.meta('item')
item['content'] = content
yield item
def closed(self, spider):
self.bro.quit()
| 38.97561
| 103
| 0.61577
|
import scrapy
from selenium import webdriver
from mySpider.items import MyspiderItem
class FundSpider(scrapy.Spider):
name = 'wangyi'
start_urls = ['http://news.163.com/']
modules_url = []
def __init__(self):
self.bro = webdriver.Chrome(executable_path='D:\PyCharm\mySpider\chromedriver.exe')
def parse(self, response):
li_list = response.xpath('//*[@id="index2016_wrap"]/div[1]/div[2]/div[2]/div[2]/div[2]/div/ul')
alist = [3,4,6,7,8]
for index in alist:
module_url = li_list[index].xpath('./a/@href').extract_first()
self.modules_url.append(module_url)
for url in self.module_url:
yield scrapy.Request(url, callback= self.parse_module)
def parse_module(self, response):
div_list = response.xpath('/html/body/div/div[3]/div[4]/div[1]/div[1]/div/ul/li/div/div')
for div in div_list:
title = div.xpath('./div/div[1]/h3/a/text()').extract_fisrt()
new_detail_url = div.xpath('./div/div[1]/h3/a/@href').extract_first()
item = MyspiderItem()
item.title = title
yield scrapy.Request(url=new_detail_url, callback=self.parse_detail, meta={'item':item})
def parse_detail(self,response):
content = response.xpath('//*[@id="content"]/div[2]//text()').extract()
content = ''.join(content)
item = response.meta('item')
item['content'] = content
yield item
def closed(self, spider):
self.bro.quit()
| true
| true
|
f716a6346eb2c4bb4c555f3cc80875549f7a88fc
| 1,780
|
py
|
Python
|
pandas_ta/performance/log_return.py
|
yssource/pandas-ta
|
0f975320684a91db3c04f6ea3dd739177dcb65aa
|
[
"MIT"
] | 2
|
2021-03-30T01:23:14.000Z
|
2021-04-02T18:04:51.000Z
|
pandas_ta/performance/log_return.py
|
lukaszbinden/pandas-ta
|
98478f8bf049a4c8748d6f3c795f4f335ced05ca
|
[
"MIT"
] | null | null | null |
pandas_ta/performance/log_return.py
|
lukaszbinden/pandas-ta
|
98478f8bf049a4c8748d6f3c795f4f335ced05ca
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from numpy import log as nplog
from pandas_ta.utils import get_offset, verify_series
def log_return(close, length=None, cumulative=False, offset=None, **kwargs):
"""Indicator: Log Return"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 1
offset = get_offset(offset)
# Calculate Result
log_return = nplog(close).diff(periods=length)
if cumulative:
log_return = log_return.cumsum()
# Offset
if offset != 0:
log_return = log_return.shift(offset)
# Handle fills
if "fillna" in kwargs:
log_return.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
log_return.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
log_return.name = f"{'CUM' if cumulative else ''}LOGRET_{length}"
log_return.category = "performance"
return log_return
log_return.__doc__ = \
"""Log Return
Calculates the logarithmic return of a Series.
See also: help(df.ta.log_return) for additional **kwargs a valid 'df'.
Sources:
https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe
Calculation:
Default Inputs:
length=1, cumulative=False
LOGRET = log( close.diff(periods=length) )
CUMLOGRET = LOGRET.cumsum() if cumulative
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 20
cumulative (bool): If True, returns the cumulative returns. Default: False
offset (int): How many periods to offset the result. Default: 0
Kwargs:
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.Series: New feature generated.
"""
| 27.8125
| 88
| 0.690449
|
from numpy import log as nplog
from pandas_ta.utils import get_offset, verify_series
def log_return(close, length=None, cumulative=False, offset=None, **kwargs):
close = verify_series(close)
length = int(length) if length and length > 0 else 1
offset = get_offset(offset)
log_return = nplog(close).diff(periods=length)
if cumulative:
log_return = log_return.cumsum()
if offset != 0:
log_return = log_return.shift(offset)
if "fillna" in kwargs:
log_return.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
log_return.fillna(method=kwargs["fill_method"], inplace=True)
log_return.name = f"{'CUM' if cumulative else ''}LOGRET_{length}"
log_return.category = "performance"
return log_return
log_return.__doc__ = \
"""Log Return
Calculates the logarithmic return of a Series.
See also: help(df.ta.log_return) for additional **kwargs a valid 'df'.
Sources:
https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe
Calculation:
Default Inputs:
length=1, cumulative=False
LOGRET = log( close.diff(periods=length) )
CUMLOGRET = LOGRET.cumsum() if cumulative
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 20
cumulative (bool): If True, returns the cumulative returns. Default: False
offset (int): How many periods to offset the result. Default: 0
Kwargs:
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.Series: New feature generated.
"""
| true
| true
|
f716a738e6208427b79ad0f628bfff8e87f2f337
| 12,558
|
py
|
Python
|
recipe_modules/file/resources/fileutil.py
|
engeg/recipes-py
|
9dac536b55887262b4ce846f3db7a7f596542e5e
|
[
"Apache-2.0"
] | null | null | null |
recipe_modules/file/resources/fileutil.py
|
engeg/recipes-py
|
9dac536b55887262b4ce846f3db7a7f596542e5e
|
[
"Apache-2.0"
] | null | null | null |
recipe_modules/file/resources/fileutil.py
|
engeg/recipes-py
|
9dac536b55887262b4ce846f3db7a7f596542e5e
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
# Copyright 2017 The LUCI Authors. All rights reserved.
# Use of this source code is governed under the Apache License, Version 2.0
# that can be found in the LICENSE file.
"""Utility exporting basic filesystem operations.
This file was cut from "scripts/common/chromium_utils.py" at:
91310531c31fa645256b4fb5d44b460c42b3e151
"""
from __future__ import print_function
import argparse
import errno
import fnmatch
import glob
import itertools
import json
import os
import shutil
import subprocess
import sys
import tempfile
import time
def _LocateFiles(pattern, root):
"""Yeilds files matching pattern found in root and its subdirectories.
An exception is thrown if root doesn't exist."""
for path, _, files in os.walk(os.path.abspath(root)):
for filename in fnmatch.filter(files, pattern):
yield os.path.join(path, filename)
def _RmGlob(file_wildcard, root):
"""Removes files matching 'file_wildcard' in root and its subdirectories, if
any exists.
An exception is thrown if root doesn't exist."""
for item in _LocateFiles(file_wildcard, root):
try:
os.remove(item)
except OSError, e:
if e.errno != errno.ENOENT:
raise
def _RmContents(path):
if os.path.exists(path):
os.chmod(path, 0770)
for p in (os.path.join(path, x) for x in os.listdir(path)):
if os.path.isdir(p):
_RmTree(p)
else:
os.unlink(p)
def _RmTree(path):
"""Recursively removes a directory, even if it's marked read-only.
Remove the directory located at path, if it exists.
shutil.rmtree() doesn't work on Windows if any of the files or directories
are read-only, which svn repositories and some .svn files are. We need to
be able to force the files to be writable (i.e., deletable) as we traverse
the tree.
Even with all this, Windows still sometimes fails to delete a file, citing
a permission error (maybe something to do with antivirus scans or disk
indexing). The best suggestion any of the user forums had was to wait a
bit and try again, so we do that too. It's hand-waving, but sometimes it
works. :/
"""
if not os.path.exists(path):
print('WARNING: Failed to find %s during rmtree. Ignoring.\n' % path)
return
if sys.platform == 'win32':
# Give up and use cmd.exe's rd command.
cmd = ['cmd.exe', '/c', 'rd', '/q', '/s', os.path.normcase(path)]
for _ in xrange(3):
print('RemoveDirectory running %s' % (' '.join(cmd)))
if not subprocess.call(cmd):
break
print(' Failed')
time.sleep(3)
return
# If we call "rmtree" on a file, just delete it.
if not os.path.isdir(path):
os.remove(path)
return
def RemoveWithRetry_non_win(rmfunc, path):
if os.path.islink(path):
return os.remove(path)
return rmfunc(path)
remove_with_retry = RemoveWithRetry_non_win
def RmTreeOnError(function, path, excinfo):
r"""This works around a problem whereby python 2.x on Windows has no ability
to check for symbolic links. os.path.islink always returns False. But
shutil.rmtree will fail if invoked on a symbolic link whose target was
deleted before the link. E.g., reproduce like this:
> mkdir test
> mkdir test\1
> mklink /D test\current test\1
> python -c "import chromium_utils; chromium_utils.RemoveDirectory('test')"
To avoid this issue, we pass this error-handling function to rmtree. If
we see the exact sort of failure, we ignore it. All other failures we re-
raise.
"""
exception_type = excinfo[0]
exception_value = excinfo[1]
# If shutil.rmtree encounters a symbolic link on Windows, os.listdir will
# fail with a WindowsError exception with an ENOENT errno (i.e., file not
# found). We'll ignore that error. Note that WindowsError is not defined
# for non-Windows platforms, so we use OSError (of which it is a subclass)
# to avoid lint complaints about an undefined global on non-Windows
# platforms.
if (function is os.listdir) and issubclass(exception_type, OSError):
if exception_value.errno == errno.ENOENT:
# File does not exist, and we're trying to delete, so we can ignore the
# failure.
print('WARNING: Failed to list %s during rmtree. Ignoring.\n' % path)
else:
raise
else:
raise
for root, dirs, files in os.walk(path, topdown=False):
# For POSIX: making the directory writable guarantees removability.
# Windows will ignore the non-read-only bits in the chmod value.
os.chmod(root, 0770)
for name in files:
remove_with_retry(os.remove, os.path.join(root, name))
for name in dirs:
remove_with_retry(lambda p: shutil.rmtree(p, onerror=RmTreeOnError),
os.path.join(root, name))
remove_with_retry(os.rmdir, path)
def _EnsureDir(mode, dest):
if not os.path.isdir(dest):
if os.path.exists(dest):
raise OSError(errno.EEXIST, os.strerror(errno.EEXIST))
os.makedirs(dest, mode)
def _Glob(base, pattern):
cwd = os.getcwd()
try:
os.chdir(base)
hits = glob.glob(pattern)
if hits:
print('\n'.join(sorted(hits)))
finally:
os.chdir(cwd)
def _Remove(path):
try:
os.remove(path)
except OSError as e:
if e.errno != errno.ENOENT:
raise
def _Truncate(path, size_mb):
with open(path, 'w') as f:
f.truncate(size_mb * 1024 * 1024)
def _FlattenSingleDirectories(path):
assert os.path.isabs(path), 'nonabs path: %r' % (path,)
assert os.path.isdir(path), 'nondir path: %r' % (path,)
first_single_dir = None
print('flattening single directories in %r' % (path,))
for root, dirs, files in os.walk(path):
# if it's a single dir, we keep walking
if len(dirs) == 1 and not files:
if not first_single_dir:
first_single_dir = os.path.join(path, dirs[0])
continue
# otherwise we found some stuff!
if not first_single_dir:
# if we didn't find a first_single_dir, we're still in the base directory
# and don't have anything to do.
print('contents appears already flattened')
return 0
print('found contents at: %r' % (os.path.relpath(root, path),))
# first move the first_single_dir out of the way, in case there's
# a file/folder we need to move that has a conflicting name.
tmpname = tempfile.mktemp(dir=path)
print('moving root folder out of the way: %r -> %r' % (first_single_dir, tmpname))
os.rename(first_single_dir, tmpname)
for name in itertools.chain(dirs, files):
fullname = os.path.join(root, name).replace(first_single_dir, tmpname)
to = os.path.join(path, name)
print('mv %r %r' % (fullname, to))
os.rename(fullname, to)
print('moved %d dirs and %d files' % (len(dirs), len(files)))
print('rm -rf %r' % (tmpname,))
shutil.rmtree(tmpname)
return 0
def main(args):
parser = argparse.ArgumentParser()
parser.add_argument('--json-output', required=True,
type=argparse.FileType('w'),
help="path to JSON output file")
subparsers = parser.add_subparsers()
# Subcommand: rmtree
subparser = subparsers.add_parser('rmtree',
help='Recursively remove a directory.')
subparser.add_argument('source', help='A path to remove.')
subparser.set_defaults(func=lambda opts: _RmTree(opts.source))
# Subcommand: rmcontents
subparser = subparsers.add_parser('rmcontents',
help='Recursively remove the contents of a directory.')
subparser.add_argument('source', help='The target directory.')
subparser.set_defaults(func=lambda opts: _RmContents(opts.source))
# Subcommand: rmwildcard
subparser = subparsers.add_parser('rmglob',
help='Recursively remove the contents of a directory.')
subparser.add_argument('root', help='The directory to search through.')
subparser.add_argument('wildcard', help='The wildcard expression to remove.')
subparser.set_defaults(func=lambda opts:
_RmGlob(opts.wildcard, opts.root))
# Subcommand: copy
subparser = subparsers.add_parser('copy',
help='Copy one file to another. Behaves like shutil.copy().')
subparser.add_argument('source', help='The file to copy.')
subparser.add_argument('dest', help='The destination to copy to.')
subparser.set_defaults(func=lambda opts: shutil.copy(opts.source, opts.dest))
# Subcommand: copytree
subparser = subparsers.add_parser('copytree',
help='Recursively copy a file tree. Behaves like shutil.copytree().')
subparser.add_argument('--symlinks', action='store_true',
help='Copy symlinks as symlinks.')
subparser.add_argument('source', help='The directory to copy.')
subparser.add_argument('dest', help='The destination directory to copy to.')
subparser.set_defaults(
func=lambda opts: shutil.copytree(opts.source, opts.dest, opts.symlinks))
# Subcommand: move
subparser = subparsers.add_parser('move',
help='Moves/renames a file. Behaves like shutil.move().')
subparser.add_argument('source', help='The item to move.')
subparser.add_argument('dest', help='The destination name.')
subparser.set_defaults(
func=lambda opts: shutil.move(opts.source, opts.dest))
# Subcommand: glob
subparser = subparsers.add_parser('glob',
help='Prints a list of absolute paths with match the pattern.')
subparser.add_argument('base', help='The directory to glob in.')
subparser.add_argument('pattern', help='The glob patern to expand.')
subparser.set_defaults(func=lambda opts: _Glob(opts.base, opts.pattern))
# Subcommand: remove
subparser = subparsers.add_parser('remove',
help='Remove a file')
subparser.add_argument('source', help='The file to remove.')
subparser.set_defaults(func=lambda opts: _Remove(opts.source))
# Subcommand: listdir
subparser = subparsers.add_parser('listdir',
help='Print all entries in the given folder to stdout.')
subparser.add_argument('source', help='The dir to list.')
subparser.set_defaults(
func=lambda opts: print('\n'.join(sorted(os.listdir(opts.source))), end=''))
# Subcommand: ensure-directory
subparser = subparsers.add_parser('ensure-directory',
help='Ensures that the given path is a directory.')
subparser.add_argument('--mode', help='The octal mode of the directory.',
type=lambda s: int(s, 8))
subparser.add_argument('dest', help='The dir to ensure.')
subparser.set_defaults(func=lambda opts: _EnsureDir(opts.mode, opts.dest))
# Subcommand: filesizes
subparser = subparsers.add_parser('filesizes',
help='Prints a list for sizes in bytes (1 per line) for each given file')
subparser.add_argument('file', nargs='+', help='Path to a file')
subparser.set_defaults(
func=lambda opts: print('\n'.join(str(os.stat(f).st_size)
for f in opts.file)))
# Subcommand: filesizes
subparser = subparsers.add_parser('symlink',
help='Creates a symlink. Behaves like os.symlink.')
subparser.add_argument('source', help='The thing to link to.')
subparser.add_argument('link', help='The link to create.')
subparser.set_defaults(
func=lambda opts: os.symlink(opts.source, opts.link))
# Subcommand: truncate
subparser = subparsers.add_parser(
'truncate', help='Creates an empty file with specified size.')
subparser.add_argument('path', help='The path to the file.')
subparser.add_argument('size_mb', help='The size of the file in megabytes.',
type=int)
subparser.set_defaults(func=lambda opts: _Truncate(opts.path, opts.size_mb))
# Subcommand: flatten_single_directories
subparser = subparsers.add_parser(
'flatten_single_directories',
help=('Moves contents of single/dir/with/contents to the top level '
'directory.'))
subparser.add_argument('path', help='The path to flatten from.')
subparser.set_defaults(func=lambda opts: _FlattenSingleDirectories(opts.path))
# Parse arguments.
opts = parser.parse_args(args)
# Actually do the thing.
data = {
'ok': False,
'errno_name': '',
'message': '',
}
try:
opts.func(opts)
data['ok'] = True
except OSError as e:
data['errno_name'] = errno.errorcode[e.errno]
data['message'] = str(e)
except shutil.Error as e:
data['message'] = e.message
except Exception as e:
data['message'] = 'UNKNOWN: %s' % e
with opts.json_output:
json.dump(data, opts.json_output)
return 0
if __name__ == '__main__':
sys.exit(main(sys.argv[1:]))
| 35.078212
| 86
| 0.686495
|
"""Utility exporting basic filesystem operations.
This file was cut from "scripts/common/chromium_utils.py" at:
91310531c31fa645256b4fb5d44b460c42b3e151
"""
from __future__ import print_function
import argparse
import errno
import fnmatch
import glob
import itertools
import json
import os
import shutil
import subprocess
import sys
import tempfile
import time
def _LocateFiles(pattern, root):
"""Yeilds files matching pattern found in root and its subdirectories.
An exception is thrown if root doesn't exist."""
for path, _, files in os.walk(os.path.abspath(root)):
for filename in fnmatch.filter(files, pattern):
yield os.path.join(path, filename)
def _RmGlob(file_wildcard, root):
"""Removes files matching 'file_wildcard' in root and its subdirectories, if
any exists.
An exception is thrown if root doesn't exist."""
for item in _LocateFiles(file_wildcard, root):
try:
os.remove(item)
except OSError, e:
if e.errno != errno.ENOENT:
raise
def _RmContents(path):
if os.path.exists(path):
os.chmod(path, 0770)
for p in (os.path.join(path, x) for x in os.listdir(path)):
if os.path.isdir(p):
_RmTree(p)
else:
os.unlink(p)
def _RmTree(path):
"""Recursively removes a directory, even if it's marked read-only.
Remove the directory located at path, if it exists.
shutil.rmtree() doesn't work on Windows if any of the files or directories
are read-only, which svn repositories and some .svn files are. We need to
be able to force the files to be writable (i.e., deletable) as we traverse
the tree.
Even with all this, Windows still sometimes fails to delete a file, citing
a permission error (maybe something to do with antivirus scans or disk
indexing). The best suggestion any of the user forums had was to wait a
bit and try again, so we do that too. It's hand-waving, but sometimes it
works. :/
"""
if not os.path.exists(path):
print('WARNING: Failed to find %s during rmtree. Ignoring.\n' % path)
return
if sys.platform == 'win32':
# Give up and use cmd.exe's rd command.
cmd = ['cmd.exe', '/c', 'rd', '/q', '/s', os.path.normcase(path)]
for _ in xrange(3):
print('RemoveDirectory running %s' % (' '.join(cmd)))
if not subprocess.call(cmd):
break
print(' Failed')
time.sleep(3)
return
if not os.path.isdir(path):
os.remove(path)
return
def RemoveWithRetry_non_win(rmfunc, path):
if os.path.islink(path):
return os.remove(path)
return rmfunc(path)
remove_with_retry = RemoveWithRetry_non_win
def RmTreeOnError(function, path, excinfo):
r"""This works around a problem whereby python 2.x on Windows has no ability
to check for symbolic links. os.path.islink always returns False. But
shutil.rmtree will fail if invoked on a symbolic link whose target was
deleted before the link. E.g., reproduce like this:
> mkdir test
> mkdir test\1
> mklink /D test\current test\1
> python -c "import chromium_utils; chromium_utils.RemoveDirectory('test')"
To avoid this issue, we pass this error-handling function to rmtree. If
we see the exact sort of failure, we ignore it. All other failures we re-
raise.
"""
exception_type = excinfo[0]
exception_value = excinfo[1]
# for non-Windows platforms, so we use OSError (of which it is a subclass)
# to avoid lint complaints about an undefined global on non-Windows
# platforms.
if (function is os.listdir) and issubclass(exception_type, OSError):
if exception_value.errno == errno.ENOENT:
# File does not exist, and we're trying to delete, so we can ignore the
print('WARNING: Failed to list %s during rmtree. Ignoring.\n' % path)
else:
raise
else:
raise
for root, dirs, files in os.walk(path, topdown=False):
os.chmod(root, 0770)
for name in files:
remove_with_retry(os.remove, os.path.join(root, name))
for name in dirs:
remove_with_retry(lambda p: shutil.rmtree(p, onerror=RmTreeOnError),
os.path.join(root, name))
remove_with_retry(os.rmdir, path)
def _EnsureDir(mode, dest):
if not os.path.isdir(dest):
if os.path.exists(dest):
raise OSError(errno.EEXIST, os.strerror(errno.EEXIST))
os.makedirs(dest, mode)
def _Glob(base, pattern):
cwd = os.getcwd()
try:
os.chdir(base)
hits = glob.glob(pattern)
if hits:
print('\n'.join(sorted(hits)))
finally:
os.chdir(cwd)
def _Remove(path):
try:
os.remove(path)
except OSError as e:
if e.errno != errno.ENOENT:
raise
def _Truncate(path, size_mb):
with open(path, 'w') as f:
f.truncate(size_mb * 1024 * 1024)
def _FlattenSingleDirectories(path):
assert os.path.isabs(path), 'nonabs path: %r' % (path,)
assert os.path.isdir(path), 'nondir path: %r' % (path,)
first_single_dir = None
print('flattening single directories in %r' % (path,))
for root, dirs, files in os.walk(path):
if len(dirs) == 1 and not files:
if not first_single_dir:
first_single_dir = os.path.join(path, dirs[0])
continue
# otherwise we found some stuff!
if not first_single_dir:
# if we didn't find a first_single_dir, we're still in the base directory
# and don't have anything to do.
print('contents appears already flattened')
return 0
print('found contents at: %r' % (os.path.relpath(root, path),))
# a file/folder we need to move that has a conflicting name.
tmpname = tempfile.mktemp(dir=path)
print('moving root folder out of the way: %r -> %r' % (first_single_dir, tmpname))
os.rename(first_single_dir, tmpname)
for name in itertools.chain(dirs, files):
fullname = os.path.join(root, name).replace(first_single_dir, tmpname)
to = os.path.join(path, name)
print('mv %r %r' % (fullname, to))
os.rename(fullname, to)
print('moved %d dirs and %d files' % (len(dirs), len(files)))
print('rm -rf %r' % (tmpname,))
shutil.rmtree(tmpname)
return 0
def main(args):
parser = argparse.ArgumentParser()
parser.add_argument('--json-output', required=True,
type=argparse.FileType('w'),
help="path to JSON output file")
subparsers = parser.add_subparsers()
# Subcommand: rmtree
subparser = subparsers.add_parser('rmtree',
help='Recursively remove a directory.')
subparser.add_argument('source', help='A path to remove.')
subparser.set_defaults(func=lambda opts: _RmTree(opts.source))
# Subcommand: rmcontents
subparser = subparsers.add_parser('rmcontents',
help='Recursively remove the contents of a directory.')
subparser.add_argument('source', help='The target directory.')
subparser.set_defaults(func=lambda opts: _RmContents(opts.source))
# Subcommand: rmwildcard
subparser = subparsers.add_parser('rmglob',
help='Recursively remove the contents of a directory.')
subparser.add_argument('root', help='The directory to search through.')
subparser.add_argument('wildcard', help='The wildcard expression to remove.')
subparser.set_defaults(func=lambda opts:
_RmGlob(opts.wildcard, opts.root))
# Subcommand: copy
subparser = subparsers.add_parser('copy',
help='Copy one file to another. Behaves like shutil.copy().')
subparser.add_argument('source', help='The file to copy.')
subparser.add_argument('dest', help='The destination to copy to.')
subparser.set_defaults(func=lambda opts: shutil.copy(opts.source, opts.dest))
# Subcommand: copytree
subparser = subparsers.add_parser('copytree',
help='Recursively copy a file tree. Behaves like shutil.copytree().')
subparser.add_argument('--symlinks', action='store_true',
help='Copy symlinks as symlinks.')
subparser.add_argument('source', help='The directory to copy.')
subparser.add_argument('dest', help='The destination directory to copy to.')
subparser.set_defaults(
func=lambda opts: shutil.copytree(opts.source, opts.dest, opts.symlinks))
# Subcommand: move
subparser = subparsers.add_parser('move',
help='Moves/renames a file. Behaves like shutil.move().')
subparser.add_argument('source', help='The item to move.')
subparser.add_argument('dest', help='The destination name.')
subparser.set_defaults(
func=lambda opts: shutil.move(opts.source, opts.dest))
# Subcommand: glob
subparser = subparsers.add_parser('glob',
help='Prints a list of absolute paths with match the pattern.')
subparser.add_argument('base', help='The directory to glob in.')
subparser.add_argument('pattern', help='The glob patern to expand.')
subparser.set_defaults(func=lambda opts: _Glob(opts.base, opts.pattern))
# Subcommand: remove
subparser = subparsers.add_parser('remove',
help='Remove a file')
subparser.add_argument('source', help='The file to remove.')
subparser.set_defaults(func=lambda opts: _Remove(opts.source))
# Subcommand: listdir
subparser = subparsers.add_parser('listdir',
help='Print all entries in the given folder to stdout.')
subparser.add_argument('source', help='The dir to list.')
subparser.set_defaults(
func=lambda opts: print('\n'.join(sorted(os.listdir(opts.source))), end=''))
# Subcommand: ensure-directory
subparser = subparsers.add_parser('ensure-directory',
help='Ensures that the given path is a directory.')
subparser.add_argument('--mode', help='The octal mode of the directory.',
type=lambda s: int(s, 8))
subparser.add_argument('dest', help='The dir to ensure.')
subparser.set_defaults(func=lambda opts: _EnsureDir(opts.mode, opts.dest))
# Subcommand: filesizes
subparser = subparsers.add_parser('filesizes',
help='Prints a list for sizes in bytes (1 per line) for each given file')
subparser.add_argument('file', nargs='+', help='Path to a file')
subparser.set_defaults(
func=lambda opts: print('\n'.join(str(os.stat(f).st_size)
for f in opts.file)))
# Subcommand: filesizes
subparser = subparsers.add_parser('symlink',
help='Creates a symlink. Behaves like os.symlink.')
subparser.add_argument('source', help='The thing to link to.')
subparser.add_argument('link', help='The link to create.')
subparser.set_defaults(
func=lambda opts: os.symlink(opts.source, opts.link))
# Subcommand: truncate
subparser = subparsers.add_parser(
'truncate', help='Creates an empty file with specified size.')
subparser.add_argument('path', help='The path to the file.')
subparser.add_argument('size_mb', help='The size of the file in megabytes.',
type=int)
subparser.set_defaults(func=lambda opts: _Truncate(opts.path, opts.size_mb))
# Subcommand: flatten_single_directories
subparser = subparsers.add_parser(
'flatten_single_directories',
help=('Moves contents of single/dir/with/contents to the top level '
'directory.'))
subparser.add_argument('path', help='The path to flatten from.')
subparser.set_defaults(func=lambda opts: _FlattenSingleDirectories(opts.path))
# Parse arguments.
opts = parser.parse_args(args)
# Actually do the thing.
data = {
'ok': False,
'errno_name': '',
'message': '',
}
try:
opts.func(opts)
data['ok'] = True
except OSError as e:
data['errno_name'] = errno.errorcode[e.errno]
data['message'] = str(e)
except shutil.Error as e:
data['message'] = e.message
except Exception as e:
data['message'] = 'UNKNOWN: %s' % e
with opts.json_output:
json.dump(data, opts.json_output)
return 0
if __name__ == '__main__':
sys.exit(main(sys.argv[1:]))
| false
| true
|
f716a9d014c49a805a816c403771d17f603d78f9
| 5,119
|
py
|
Python
|
resolwe_bio/tools/basespace_download.py
|
JureZmrzlikar/resolwe-bio
|
54cde9b293abebad2db0564c9fefa33d6d2fe835
|
[
"Apache-2.0"
] | null | null | null |
resolwe_bio/tools/basespace_download.py
|
JureZmrzlikar/resolwe-bio
|
54cde9b293abebad2db0564c9fefa33d6d2fe835
|
[
"Apache-2.0"
] | null | null | null |
resolwe_bio/tools/basespace_download.py
|
JureZmrzlikar/resolwe-bio
|
54cde9b293abebad2db0564c9fefa33d6d2fe835
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python3
"""Tool to download files from BaseSpace."""
import sys
import traceback
import atexit
import argparse
import requests
class BaseSpaceDownloadError(Exception):
"""BaseSpace download error."""
pass
def main():
"""Entry point."""
session = requests.Session()
atexit.register(on_exit, session)
parser = argparse.ArgumentParser(description='Download file from Illumina BaseSpace.')
parser.add_argument('--file-id',
dest='file_ids',
action='append',
required=True,
help="BaseSpace file ID. This argument can be repeated to specify multiple files.")
parser.add_argument('--access-token-secret-path',
dest='access_token_secret_path',
required=True,
help="BaseSpace access token secret path.")
parser.add_argument('--output',
dest='output',
type=str,
choices=['full', 'filename'],
default='full',
help="Sets what is printed to standard output. "
"Argument 'full' outputs everything, "
"argument 'filename' outputs only file names of downloaded files.")
parser.add_argument('--verbose',
dest='verbose',
action='store_true',
default=False,
help="Print detailed exception information when error occurs. "
"Output argument had no effect on this argument.")
args = parser.parse_args()
try:
file_ids = args.file_ids
access_token = get_token_from_secret_file(args.access_token_secret_path)
headers = {'x-access-token': access_token}
for file_id in file_ids:
file_name = get_file_name(session, file_id, headers)
download_file(session, file_id, file_name, headers)
output(args.output, 'filename={}'.format(file_name))
except:
if args.verbose:
traceback.print_exc()
else:
print("An error occurred while processing the Basespace download request. Use --verbose to see details.")
sys.exit(1)
def on_exit(session):
"""Clean up function called on exit."""
session.close()
def output(output_option, value):
"""
Print to standard output.
This function should be used instead of ``print`` function. Printing errors is exempted
and can be printed without using this function.
"""
if output_option == 'full':
print(value)
elif output_option == 'filename':
if value.startswith('filename='):
print(value[len('filename='):])
else:
print("Internal error: output argument {} handling not implemented".format(output_option))
sys.exit(1)
def get_token_from_secret_file(secret_file_path):
"""Read secret file to obtain access token."""
try:
with open(secret_file_path, 'r') as f:
return f.readline()
except FileNotFoundError:
raise BaseSpaceDownloadError('Secret file not found')
except PermissionError:
raise BaseSpaceDownloadError('No permissions to read secret file')
def make_get_request(session, url, headers, stream=False):
"""Make a get request."""
response = session.get(url, headers=headers, stream=stream)
if response.status_code == 401:
raise BaseSpaceDownloadError('Authentication failed on URL {}'.format(url))
elif response.status_code == 404:
raise BaseSpaceDownloadError('BaseSpace file {} not found'.format(url))
return response
def get_basespace_api_url():
"""Get base BaseSpace API URL."""
return 'https://api.basespace.illumina.com/v1pre3'
def get_basespace_api_file_url(file_id):
"""Get BaseSpace API file URL."""
return '{}/files/{}'.format(get_basespace_api_url(), file_id)
def get_basespace_api_file_content_url(file_id):
"""Get BaseSpace API file contents URL."""
return '{}/content'.format(get_basespace_api_file_url(file_id))
def get_file_name(session, file_id, request_headers):
"""Get file name."""
response = make_get_request(session, get_basespace_api_file_url(file_id), request_headers)
return response.json()['Response']['Name']
def download_file(session, file_id, file_name, request_headers):
"""Download BaseSpace file."""
response = make_get_request(session, get_basespace_api_file_content_url(file_id), request_headers, True)
try:
with open(file_name, 'wb') as f:
for chunk in response.iter_content(chunk_size=1024):
f.write(chunk)
except FileNotFoundError:
raise BaseSpaceDownloadError('Could not save file to {}, due to directory not being found'.format(file_name))
except PermissionError:
raise BaseSpaceDownloadError('Could not save file to {}, due to insufficient permissions'.format(file_name))
if __name__ == "__main__":
main()
| 34.126667
| 117
| 0.632936
|
import sys
import traceback
import atexit
import argparse
import requests
class BaseSpaceDownloadError(Exception):
pass
def main():
session = requests.Session()
atexit.register(on_exit, session)
parser = argparse.ArgumentParser(description='Download file from Illumina BaseSpace.')
parser.add_argument('--file-id',
dest='file_ids',
action='append',
required=True,
help="BaseSpace file ID. This argument can be repeated to specify multiple files.")
parser.add_argument('--access-token-secret-path',
dest='access_token_secret_path',
required=True,
help="BaseSpace access token secret path.")
parser.add_argument('--output',
dest='output',
type=str,
choices=['full', 'filename'],
default='full',
help="Sets what is printed to standard output. "
"Argument 'full' outputs everything, "
"argument 'filename' outputs only file names of downloaded files.")
parser.add_argument('--verbose',
dest='verbose',
action='store_true',
default=False,
help="Print detailed exception information when error occurs. "
"Output argument had no effect on this argument.")
args = parser.parse_args()
try:
file_ids = args.file_ids
access_token = get_token_from_secret_file(args.access_token_secret_path)
headers = {'x-access-token': access_token}
for file_id in file_ids:
file_name = get_file_name(session, file_id, headers)
download_file(session, file_id, file_name, headers)
output(args.output, 'filename={}'.format(file_name))
except:
if args.verbose:
traceback.print_exc()
else:
print("An error occurred while processing the Basespace download request. Use --verbose to see details.")
sys.exit(1)
def on_exit(session):
session.close()
def output(output_option, value):
if output_option == 'full':
print(value)
elif output_option == 'filename':
if value.startswith('filename='):
print(value[len('filename='):])
else:
print("Internal error: output argument {} handling not implemented".format(output_option))
sys.exit(1)
def get_token_from_secret_file(secret_file_path):
try:
with open(secret_file_path, 'r') as f:
return f.readline()
except FileNotFoundError:
raise BaseSpaceDownloadError('Secret file not found')
except PermissionError:
raise BaseSpaceDownloadError('No permissions to read secret file')
def make_get_request(session, url, headers, stream=False):
response = session.get(url, headers=headers, stream=stream)
if response.status_code == 401:
raise BaseSpaceDownloadError('Authentication failed on URL {}'.format(url))
elif response.status_code == 404:
raise BaseSpaceDownloadError('BaseSpace file {} not found'.format(url))
return response
def get_basespace_api_url():
return 'https://api.basespace.illumina.com/v1pre3'
def get_basespace_api_file_url(file_id):
return '{}/files/{}'.format(get_basespace_api_url(), file_id)
def get_basespace_api_file_content_url(file_id):
return '{}/content'.format(get_basespace_api_file_url(file_id))
def get_file_name(session, file_id, request_headers):
response = make_get_request(session, get_basespace_api_file_url(file_id), request_headers)
return response.json()['Response']['Name']
def download_file(session, file_id, file_name, request_headers):
response = make_get_request(session, get_basespace_api_file_content_url(file_id), request_headers, True)
try:
with open(file_name, 'wb') as f:
for chunk in response.iter_content(chunk_size=1024):
f.write(chunk)
except FileNotFoundError:
raise BaseSpaceDownloadError('Could not save file to {}, due to directory not being found'.format(file_name))
except PermissionError:
raise BaseSpaceDownloadError('Could not save file to {}, due to insufficient permissions'.format(file_name))
if __name__ == "__main__":
main()
| true
| true
|
f716aa1fd41279e8e408620b6f55302402277111
| 26,185
|
py
|
Python
|
tests/test_sdb.py
|
peppelinux/pyoidc
|
2e751ed84039259a2b138148eae204c877518950
|
[
"Apache-2.0"
] | 1
|
2020-09-30T13:08:14.000Z
|
2020-09-30T13:08:14.000Z
|
tests/test_sdb.py
|
peppelinux/pyoidc
|
2e751ed84039259a2b138148eae204c877518950
|
[
"Apache-2.0"
] | null | null | null |
tests/test_sdb.py
|
peppelinux/pyoidc
|
2e751ed84039259a2b138148eae204c877518950
|
[
"Apache-2.0"
] | null | null | null |
import base64
import datetime
import hashlib
import hmac
import json
import random
import time
from unittest import TestCase
import pytest
from freezegun import freeze_time
from oic.oic.message import AuthorizationRequest
from oic.oic.message import OpenIDRequest
from oic.utils.sdb import AccessCodeUsed
from oic.utils.sdb import AuthnEvent
from oic.utils.sdb import Crypt
from oic.utils.sdb import DefaultToken
from oic.utils.sdb import DictRefreshDB
from oic.utils.sdb import DictSessionBackend
from oic.utils.sdb import ExpiredToken
from oic.utils.sdb import WrongTokenType
from oic.utils.sdb import create_session_db
__author__ = "rohe0002"
AREQ = AuthorizationRequest(
response_type="code",
client_id="client1",
redirect_uri="http://example.com/authz",
scope=["openid"],
state="state000",
)
AREQN = AuthorizationRequest(
response_type="code",
client_id="client1",
redirect_uri="http://example.com/authz",
scope=["openid"],
state="state000",
nonce="something",
)
AREQO = AuthorizationRequest(
response_type="code",
client_id="client1",
redirect_uri="http://example.com/authz",
scope=["openid", "offlien_access"],
prompt="consent",
state="state000",
)
OIDR = OpenIDRequest(
response_type="code",
client_id="client1",
redirect_uri="http://example.com/authz",
scope=["openid"],
state="state000",
)
def _eq(l1, l2):
return set(l1) == set(l2)
class TestAuthnEvent(object):
"""Tests for AuthnEvent class."""
def test_from_json(self):
dic = {"uid": "uid", "salt": "salt", "authn_time": 1000, "valid_until": 1500}
ae = AuthnEvent.from_json(json.dumps(dic))
assert ae.uid == "uid"
assert ae.salt == "salt"
assert ae.authn_time == 1000
assert ae.valid_until == 1500
def test_to_json(self):
ae = AuthnEvent("uid", "salt", authn_time=1000, valid_until=1500)
json_repr = ae.to_json()
assert json.loads(json_repr) == {
"uid": "uid",
"salt": "salt",
"authn_time": 1000,
"valid_until": 1500,
"authn_info": None,
}
class TestDictRefreshDB(object):
@pytest.fixture(autouse=True)
def create_rdb(self):
self.rdb = DictRefreshDB()
def test_verify_token(self):
token = self.rdb.create_token(
"client1", "uid", "openid", "sub1", "authzreq", "sid"
)
assert self.rdb.verify_token("client1", token)
assert self.rdb.verify_token("client2", token) is False
def test_revoke_token(self):
token = self.rdb.create_token(
"client1", "uid", "openid", "sub1", "authzreq", "sid"
)
self.rdb.remove(token)
assert self.rdb.verify_token("client1", token) is False
with pytest.raises(KeyError):
self.rdb.get(token)
def test_get_token(self):
with pytest.raises(KeyError):
self.rdb.get("token")
token = self.rdb.create_token(
"client1", "uid", ["openid"], "sub1", "authzreq", "sid"
)
assert self.rdb.get(token) == {
"client_id": "client1",
"sub": "sub1",
"scope": ["openid"],
"uid": "uid",
"authzreq": "authzreq",
"sid": "sid",
}
class TestToken(object):
@pytest.fixture(autouse=True)
def create_token(self):
self.token = DefaultToken("secret", "password", lifetime=60)
def test_token(self):
sid = self.token.key(areq=AREQ)
assert len(sid) == 56
def test_new_token(self):
sid = self.token.key(areq=AREQ)
assert len(sid) == 56
self.token(sid=sid, ttype="T")
assert len(sid) == 56
sid2 = self.token.key(areq=AREQ, user="jones")
assert len(sid2) == 56
assert sid != sid2
def test_type_and_key(self):
sid = self.token.key(areq=AREQ)
code = self.token(sid=sid)
part = self.token.type_and_key(code)
assert part[0] == "A"
assert part[1] == sid
def test_expired_fresh(self):
factory = DefaultToken("secret", "password", lifetime=60)
token = factory(sid="abc", ttype="T")
assert factory.is_expired(token) is False
def test_expired_stale(self):
initial_datetime = datetime.datetime(2018, 2, 5, 10, 0, 0, 0)
final_datetime = datetime.datetime(2018, 2, 5, 10, 1, 0, 0)
factory = DefaultToken("secret", "password", lifetime=2)
with freeze_time(initial_datetime) as frozen:
token = factory(sid="abc", ttype="T")
frozen.move_to(final_datetime)
assert factory.is_expired(token) is True
def test_expired_when(self):
factory = DefaultToken("secret", "password", lifetime=2)
token = factory(sid="abc", ttype="T")
when = time.time() + 5 # 5 seconds from now
assert factory.is_expired(token, when=when) is True
class TestSessionBackend(TestCase):
"""Unittests for SessionBackend - using the DictSessionBackend."""
def setUp(self):
self.backend = DictSessionBackend()
def test_setitem(self):
self.backend["key"] = "value"
self.assertEqual(self.backend.storage["key"], "value")
self.backend["key"] = "new_value"
self.assertEqual(self.backend.storage["key"], "new_value")
def test_getitem(self):
self.backend.storage = {"key": "value"}
self.assertEqual(self.backend["key"], "value")
with self.assertRaises(KeyError):
self.backend["missing"]
def test_delitem(self):
self.backend.storage = {"key": "value"}
del self.backend["key"]
self.assertEqual(self.backend.storage, {})
def test_contains(self):
self.backend["key"] = "value"
self.assertTrue("key" in self.backend)
self.assertFalse("missing" in self.backend)
def test_get_by_sub(self):
self.backend.storage = {"session_id": {"sub": "my_sub"}}
self.assertEqual(set(self.backend.get_by_sub("my_sub")), {"session_id"})
self.assertEqual(set(self.backend.get_by_sub("missing")), set())
def test_get_by_sub_multiple(self):
self.backend.storage = {
"session_id1": {"sub": "my_sub"},
"session_id2": {"sub": "my_sub"},
}
self.assertEqual(
set(self.backend.get_by_sub("my_sub")), {"session_id1", "session_id2"}
)
def test_get_by_uid(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {"session_id": {"authn_event": aevent}}
self.assertEqual(set(self.backend.get_by_uid("my_uid")), {"session_id"})
self.assertEqual(set(self.backend.get_by_uid("missing")), set())
def test_get_by_uid_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {"authn_event": aevent1},
"session_id2": {"authn_event": aevent2},
}
self.assertEqual(
set(self.backend.get_by_uid("my_uid")), {"session_id1", "session_id2"}
)
def test_get_client_ids_for_uid(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id": {"authn_event": aevent, "client_id": "my_client"}
}
self.assertEqual(
set(self.backend.get_client_ids_for_uid("my_uid")), {"my_client"}
)
self.assertEqual(set(self.backend.get_client_ids_for_uid("missing")), set())
def test_get_client_ids_for_uid_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {"authn_event": aevent1, "client_id": "my_client"},
"session_id2": {"authn_event": aevent2, "client_id": "my_other"},
}
self.assertEqual(
set(self.backend.get_client_ids_for_uid("my_uid")),
{"my_client", "my_other"},
)
def test_get_verified_logout(self):
aevent1 = AuthnEvent("my_uid1", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid2", "some_salt").to_json()
self.backend.storage = {
"session_id": {
"authn_event": aevent1,
"verified_logout": "verification key",
},
"session_id2": {"authn_event": aevent2},
}
self.assertEqual(
self.backend.get_verified_logout("my_uid1"), "verification key"
)
self.assertIsNone(self.backend.get_verified_logout("my_uid2"))
self.assertIsNone(self.backend.get_verified_logout("missing"))
def test_get_verified_logout_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {
"authn_event": aevent1,
"verified_logout": "verification key",
},
"session_id2": {
"authn_event": aevent2,
"verified_logout": "verification key",
},
}
self.assertEqual(self.backend.get_verified_logout("my_uid"), "verification key")
def test_get_token_ids(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id": {"authn_event": aevent, "id_token": "Id token"}
}
self.assertEqual(set(self.backend.get_token_ids("my_uid")), {"Id token"})
self.assertEqual(set(self.backend.get_token_ids("missing")), set())
def test_get_token_ids_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {"authn_event": aevent1, "id_token": "Id token 1"},
"session_id2": {"authn_event": aevent2, "id_token": "Id token 2"},
}
self.assertEqual(
set(self.backend.get_token_ids("my_uid")), {"Id token 1", "Id token 2"}
)
def test_is_revoke_uid_false(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {"session_id": {"authn_event": aevent, "revoked": False}}
self.assertFalse(self.backend.is_revoke_uid("my_uid"))
def test_is_revoke_uid_true(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {"session_id": {"authn_event": aevent, "revoked": True}}
self.assertTrue(self.backend.is_revoke_uid("my_uid"))
def test_is_revoke_uid_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {"authn_event": aevent1, "revoked": True},
"session_id2": {"authn_event": aevent2, "revoked": False},
}
self.assertTrue(self.backend.is_revoke_uid("my_uid"))
class TestSessionDB(object):
@pytest.fixture(autouse=True)
def create_sdb(self, session_db_factory):
self.sdb = session_db_factory("https://example.com/")
def test_create_authz_session(self):
ae = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
self.sdb.do_sub(sid, "client_salt")
info = self.sdb[sid]
assert info["oauth_state"] == "authz"
def test_create_authz_session_without_nonce(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
info = self.sdb[sid]
assert info["oauth_state"] == "authz"
def test_create_authz_session_with_nonce(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQN)
info = self.sdb[sid]
assert info["nonce"] == "something"
def test_create_authz_session_with_id_token(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQN, id_token="id_token")
info = self.sdb[sid]
assert info["id_token"] == "id_token"
def test_create_authz_session_with_oidreq(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQN, oidreq=OIDR)
info = self.sdb[sid]
assert "id_token" not in info
assert "oidreq" in info
def test_create_authz_session_with_sector_id(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQN, oidreq=OIDR)
self.sdb.do_sub(sid, "client_salt", "http://example.com/si.jwt", "pairwise")
info_1 = self.sdb[sid].copy()
assert "id_token" not in info_1
assert "oidreq" in info_1
assert info_1["sub"] != "sub"
self.sdb.do_sub(sid, "client_salt", "http://example.net/si.jwt", "pairwise")
info_2 = self.sdb[sid]
assert info_2["sub"] != "sub"
assert info_2["sub"] != info_1["sub"]
def test_upgrade_to_token(self):
ae1 = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae1, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
_dict = self.sdb.upgrade_to_token(grant)
print(_dict.keys())
assert _eq(
list(_dict.keys()),
[
"authn_event",
"code",
"authzreq",
"revoked",
"access_token",
"token_type",
"state",
"redirect_uri",
"code_used",
"client_id",
"scope",
"oauth_state",
"access_token_scope",
"sub",
"response_type",
],
)
# can't update again
with pytest.raises(AccessCodeUsed):
self.sdb.upgrade_to_token(grant)
self.sdb.upgrade_to_token(_dict["access_token"])
def test_upgrade_to_token_refresh(self):
ae1 = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae1, AREQO)
self.sdb.do_sub(sid, ae1.salt)
grant = self.sdb[sid]["code"]
_dict = self.sdb.upgrade_to_token(grant, issue_refresh=True)
print(_dict.keys())
assert _eq(
_dict.keys(),
[
"authn_event",
"code",
"authzreq",
"revoked",
"access_token",
"response_type",
"token_type",
"state",
"redirect_uri",
"code_used",
"client_id",
"scope",
"oauth_state",
"access_token_scope",
"refresh_token",
"sub",
],
)
# can't update again
with pytest.raises(AccessCodeUsed):
self.sdb.upgrade_to_token(grant)
self.sdb.upgrade_to_token(_dict["access_token"])
def test_upgrade_to_token_with_id_token_and_oidreq(self):
ae2 = AuthnEvent("another_user_id", "salt")
sid = self.sdb.create_authz_session(ae2, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
_dict = self.sdb.upgrade_to_token(grant, id_token="id_token", oidreq=OIDR)
print(_dict.keys())
assert _eq(
list(_dict.keys()),
[
"authn_event",
"code",
"authzreq",
"revoked",
"oidreq",
"access_token",
"id_token",
"response_type",
"token_type",
"state",
"redirect_uri",
"code_used",
"client_id",
"scope",
"oauth_state",
"access_token_scope",
"sub",
],
)
assert _dict["id_token"] == "id_token"
assert isinstance(_dict["oidreq"], OpenIDRequest)
def test_refresh_token(self):
ae = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
dict1 = self.sdb.upgrade_to_token(grant, issue_refresh=True).copy()
rtoken = dict1["refresh_token"]
dict2 = self.sdb.refresh_token(rtoken, AREQ["client_id"])
assert dict1["access_token"] != dict2["access_token"]
with pytest.raises(WrongTokenType):
self.sdb.refresh_token(dict2["access_token"], AREQ["client_id"])
def test_refresh_token_cleared_session(self):
ae = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
dict1 = self.sdb.upgrade_to_token(grant, issue_refresh=True)
ac1 = dict1["access_token"]
# Purge the SessionDB
self.sdb._db = {}
rtoken = dict1["refresh_token"]
dict2 = self.sdb.refresh_token(rtoken, AREQ["client_id"])
assert ac1 != dict2["access_token"]
assert self.sdb.is_valid(dict2["access_token"])
def test_is_valid(self):
ae1 = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae1, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
assert self.sdb.is_valid(grant)
sinfo = self.sdb.upgrade_to_token(grant, issue_refresh=True)
assert not self.sdb.is_valid(grant)
access_token = sinfo["access_token"]
assert self.sdb.access_token.valid(access_token)
refresh_token = sinfo["refresh_token"]
sinfo = self.sdb.refresh_token(refresh_token, AREQ["client_id"])
access_token2 = sinfo["access_token"]
assert self.sdb.is_valid(access_token2)
# The old access code should be invalid
try:
self.sdb.is_valid(access_token)
except KeyError:
pass
def test_valid_grant(self):
ae = AuthnEvent("another:user", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
grant = self.sdb[sid]["code"]
assert self.sdb.is_valid(grant)
def test_revoke_token(self):
ae1 = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae1, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
tokens = self.sdb.upgrade_to_token(grant, issue_refresh=True)
access_token = tokens["access_token"]
refresh_token = tokens["refresh_token"]
assert self.sdb.is_valid(access_token)
self.sdb.revoke_token(access_token)
assert not self.sdb.is_valid(access_token)
sinfo = self.sdb.refresh_token(refresh_token, AREQ["client_id"])
access_token = sinfo["access_token"]
assert self.sdb.is_valid(access_token)
self.sdb.revoke_refresh_token(refresh_token)
assert not self.sdb.is_valid(refresh_token)
try:
self.sdb.refresh_token(refresh_token, AREQ["client_id"])
except ExpiredToken:
pass
assert self.sdb.is_valid(access_token)
ae2 = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae2, AREQ)
grant = self.sdb[sid]["code"]
self.sdb.revoke_token(grant)
assert not self.sdb.is_valid(grant)
def test_revoke_all_tokens(self):
ae1 = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae1, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
tokens = self.sdb.upgrade_to_token(grant, issue_refresh=True)
access_token = tokens["access_token"]
refresh_token = tokens["refresh_token"]
self.sdb.revoke_all_tokens(access_token)
assert not self.sdb.is_valid(access_token)
assert not self.sdb.is_valid(refresh_token)
def test_sub_to_authn_event(self):
ae = AuthnEvent("sub", "salt", time_stamp=time.time())
sid = self.sdb.create_authz_session(ae, AREQ)
sub = self.sdb.do_sub(sid, "client_salt")
# given the sub find out whether the authn event is still valid
sids = self.sdb.get_sids_by_sub(sub)
ae = self.sdb[sids[0]]["authn_event"]
assert AuthnEvent.from_json(ae).valid()
def test_do_sub_deterministic(self):
ae = AuthnEvent("tester", "random_value")
sid = self.sdb.create_authz_session(ae, AREQ)
self.sdb.do_sub(sid, "other_random_value")
info = self.sdb[sid]
assert (
info["sub"]
== "179670cdee6375c48e577317b2abd7d5cd26a5cdb1cfb7ef84af3d703c71d013"
)
self.sdb.do_sub(
sid,
"other_random_value",
sector_id="http://example.com",
subject_type="pairwise",
)
info2 = self.sdb[sid]
assert (
info2["sub"]
== "aaa50d80f8780cf1c4beb39e8e126556292f5091b9e39596424fefa2b99d9c53"
)
self.sdb.do_sub(
sid,
"another_random_value",
sector_id="http://other.example.com",
subject_type="pairwise",
)
info2 = self.sdb[sid]
assert (
info2["sub"]
== "62fb630e29f0d41b88e049ac0ef49a9c3ac5418c029d6e4f5417df7e9443976b"
)
def test_get_authentication_event_dict(self):
self.sdb._db["123"] = {}
self.sdb._db["123"]["authn_event"] = {
"uid": "uid",
"salt": "salt",
"authn_time": 1000,
"valid_until": 1500,
}
ae = self.sdb.get_authentication_event("123")
assert ae.uid == "uid"
assert ae.salt == "salt"
assert ae.authn_time == 1000
assert ae.valid_until == 1500
def test_get_authentication_event_json(self):
self.sdb._db["123"] = {}
self.sdb._db["123"]["authn_event"] = json.dumps(
{"uid": "uid", "salt": "salt", "authn_time": 1000, "valid_until": 1500}
)
ae = self.sdb.get_authentication_event("123")
assert ae.uid == "uid"
assert ae.salt == "salt"
assert ae.authn_time == 1000
assert ae.valid_until == 1500
def test_get_sids_from_uid_distributed(self):
db = DictSessionBackend()
sdb1 = create_session_db("https://example.com/1", "secret", "password", db=db)
sdb2 = create_session_db("https://example.com/2", "secret", "password", db=db)
ae = AuthnEvent("sub", "salt", time_stamp=time.time())
sid1 = sdb1.create_authz_session(ae, AREQ)
sdb1.do_sub(sid1, "salt")
sid2 = sdb2.create_authz_session(ae, AREQ)
sdb2.do_sub(sid2, "salt")
sdb1sids = sdb1.get_sids_from_uid("sub")
sdb2sids = sdb2.get_sids_from_uid("sub")
assert sdb1sids == sdb2sids
def test_get_client_ids_for_uid(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"}),
"client_id": "my_client",
}
assert self.sdb.get_client_ids_for_uid("my_uid") == ["my_client"]
def test_get_verify_logout(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"}),
"verified_logout": "something",
}
assert self.sdb.get_verify_logout("my_uid") == "something"
def test_set_verify_logout(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"})
}
self.sdb.set_verify_logout("my_uid")
assert self.sdb.get_verify_logout("my_uid") is not None
def test_set_verify_logout_multiple(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"})
}
self.sdb._db["321"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"})
}
self.sdb.set_verify_logout("my_uid")
assert self.sdb.get_verify_logout("my_uid") is not None
assert (
self.sdb._db["123"]["verified_logout"]
== self.sdb._db["321"]["verified_logout"]
)
def test_get_token_ids(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"}),
"id_token": "Id token",
}
assert set(self.sdb.get_token_ids("my_uid")) == {"Id token"}
def test_get_is_revoke_uid(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"}),
"revoked": True,
}
assert self.sdb.is_revoke_uid("my_uid")
def test_revoke_uid(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"})
}
self.sdb.revoke_uid("my_uid")
assert self.sdb.is_revoke_uid("my_uid")
class TestCrypt(object):
@pytest.fixture(autouse=True)
def create_crypt(self):
self.crypt = Crypt("4-amino-1H-pyrimidine-2-one")
def test_encrypt_decrypt(self):
ctext = self.crypt.encrypt("Cytosine")
plain = self.crypt.decrypt(ctext).decode("utf-8")
assert plain == "Cytosine "
ctext = self.crypt.encrypt("cytidinetriphosp")
plain = self.crypt.decrypt(ctext).decode("utf-8")
assert plain == "cytidinetriphosp"
def test_crypt_with_b64(self):
db = {}
msg = "secret{}{}".format(time.time(), random.random())
csum = hmac.new(msg.encode("utf-8"), digestmod=hashlib.sha224)
txt = csum.digest() # 28 bytes long, 224 bits
db[txt] = "foobar"
txt = txt + b"aces" # another 4 bytes
ctext = self.crypt.encrypt(txt)
onthewire = base64.b64encode(ctext)
plain = self.crypt.decrypt(base64.b64decode(onthewire))
assert plain.endswith(b"aces")
assert db[plain[:-4]] == "foobar"
| 34.228758
| 88
| 0.586901
|
import base64
import datetime
import hashlib
import hmac
import json
import random
import time
from unittest import TestCase
import pytest
from freezegun import freeze_time
from oic.oic.message import AuthorizationRequest
from oic.oic.message import OpenIDRequest
from oic.utils.sdb import AccessCodeUsed
from oic.utils.sdb import AuthnEvent
from oic.utils.sdb import Crypt
from oic.utils.sdb import DefaultToken
from oic.utils.sdb import DictRefreshDB
from oic.utils.sdb import DictSessionBackend
from oic.utils.sdb import ExpiredToken
from oic.utils.sdb import WrongTokenType
from oic.utils.sdb import create_session_db
__author__ = "rohe0002"
AREQ = AuthorizationRequest(
response_type="code",
client_id="client1",
redirect_uri="http://example.com/authz",
scope=["openid"],
state="state000",
)
AREQN = AuthorizationRequest(
response_type="code",
client_id="client1",
redirect_uri="http://example.com/authz",
scope=["openid"],
state="state000",
nonce="something",
)
AREQO = AuthorizationRequest(
response_type="code",
client_id="client1",
redirect_uri="http://example.com/authz",
scope=["openid", "offlien_access"],
prompt="consent",
state="state000",
)
OIDR = OpenIDRequest(
response_type="code",
client_id="client1",
redirect_uri="http://example.com/authz",
scope=["openid"],
state="state000",
)
def _eq(l1, l2):
return set(l1) == set(l2)
class TestAuthnEvent(object):
def test_from_json(self):
dic = {"uid": "uid", "salt": "salt", "authn_time": 1000, "valid_until": 1500}
ae = AuthnEvent.from_json(json.dumps(dic))
assert ae.uid == "uid"
assert ae.salt == "salt"
assert ae.authn_time == 1000
assert ae.valid_until == 1500
def test_to_json(self):
ae = AuthnEvent("uid", "salt", authn_time=1000, valid_until=1500)
json_repr = ae.to_json()
assert json.loads(json_repr) == {
"uid": "uid",
"salt": "salt",
"authn_time": 1000,
"valid_until": 1500,
"authn_info": None,
}
class TestDictRefreshDB(object):
@pytest.fixture(autouse=True)
def create_rdb(self):
self.rdb = DictRefreshDB()
def test_verify_token(self):
token = self.rdb.create_token(
"client1", "uid", "openid", "sub1", "authzreq", "sid"
)
assert self.rdb.verify_token("client1", token)
assert self.rdb.verify_token("client2", token) is False
def test_revoke_token(self):
token = self.rdb.create_token(
"client1", "uid", "openid", "sub1", "authzreq", "sid"
)
self.rdb.remove(token)
assert self.rdb.verify_token("client1", token) is False
with pytest.raises(KeyError):
self.rdb.get(token)
def test_get_token(self):
with pytest.raises(KeyError):
self.rdb.get("token")
token = self.rdb.create_token(
"client1", "uid", ["openid"], "sub1", "authzreq", "sid"
)
assert self.rdb.get(token) == {
"client_id": "client1",
"sub": "sub1",
"scope": ["openid"],
"uid": "uid",
"authzreq": "authzreq",
"sid": "sid",
}
class TestToken(object):
@pytest.fixture(autouse=True)
def create_token(self):
self.token = DefaultToken("secret", "password", lifetime=60)
def test_token(self):
sid = self.token.key(areq=AREQ)
assert len(sid) == 56
def test_new_token(self):
sid = self.token.key(areq=AREQ)
assert len(sid) == 56
self.token(sid=sid, ttype="T")
assert len(sid) == 56
sid2 = self.token.key(areq=AREQ, user="jones")
assert len(sid2) == 56
assert sid != sid2
def test_type_and_key(self):
sid = self.token.key(areq=AREQ)
code = self.token(sid=sid)
part = self.token.type_and_key(code)
assert part[0] == "A"
assert part[1] == sid
def test_expired_fresh(self):
factory = DefaultToken("secret", "password", lifetime=60)
token = factory(sid="abc", ttype="T")
assert factory.is_expired(token) is False
def test_expired_stale(self):
initial_datetime = datetime.datetime(2018, 2, 5, 10, 0, 0, 0)
final_datetime = datetime.datetime(2018, 2, 5, 10, 1, 0, 0)
factory = DefaultToken("secret", "password", lifetime=2)
with freeze_time(initial_datetime) as frozen:
token = factory(sid="abc", ttype="T")
frozen.move_to(final_datetime)
assert factory.is_expired(token) is True
def test_expired_when(self):
factory = DefaultToken("secret", "password", lifetime=2)
token = factory(sid="abc", ttype="T")
when = time.time() + 5
assert factory.is_expired(token, when=when) is True
class TestSessionBackend(TestCase):
def setUp(self):
self.backend = DictSessionBackend()
def test_setitem(self):
self.backend["key"] = "value"
self.assertEqual(self.backend.storage["key"], "value")
self.backend["key"] = "new_value"
self.assertEqual(self.backend.storage["key"], "new_value")
def test_getitem(self):
self.backend.storage = {"key": "value"}
self.assertEqual(self.backend["key"], "value")
with self.assertRaises(KeyError):
self.backend["missing"]
def test_delitem(self):
self.backend.storage = {"key": "value"}
del self.backend["key"]
self.assertEqual(self.backend.storage, {})
def test_contains(self):
self.backend["key"] = "value"
self.assertTrue("key" in self.backend)
self.assertFalse("missing" in self.backend)
def test_get_by_sub(self):
self.backend.storage = {"session_id": {"sub": "my_sub"}}
self.assertEqual(set(self.backend.get_by_sub("my_sub")), {"session_id"})
self.assertEqual(set(self.backend.get_by_sub("missing")), set())
def test_get_by_sub_multiple(self):
self.backend.storage = {
"session_id1": {"sub": "my_sub"},
"session_id2": {"sub": "my_sub"},
}
self.assertEqual(
set(self.backend.get_by_sub("my_sub")), {"session_id1", "session_id2"}
)
def test_get_by_uid(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {"session_id": {"authn_event": aevent}}
self.assertEqual(set(self.backend.get_by_uid("my_uid")), {"session_id"})
self.assertEqual(set(self.backend.get_by_uid("missing")), set())
def test_get_by_uid_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {"authn_event": aevent1},
"session_id2": {"authn_event": aevent2},
}
self.assertEqual(
set(self.backend.get_by_uid("my_uid")), {"session_id1", "session_id2"}
)
def test_get_client_ids_for_uid(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id": {"authn_event": aevent, "client_id": "my_client"}
}
self.assertEqual(
set(self.backend.get_client_ids_for_uid("my_uid")), {"my_client"}
)
self.assertEqual(set(self.backend.get_client_ids_for_uid("missing")), set())
def test_get_client_ids_for_uid_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {"authn_event": aevent1, "client_id": "my_client"},
"session_id2": {"authn_event": aevent2, "client_id": "my_other"},
}
self.assertEqual(
set(self.backend.get_client_ids_for_uid("my_uid")),
{"my_client", "my_other"},
)
def test_get_verified_logout(self):
aevent1 = AuthnEvent("my_uid1", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid2", "some_salt").to_json()
self.backend.storage = {
"session_id": {
"authn_event": aevent1,
"verified_logout": "verification key",
},
"session_id2": {"authn_event": aevent2},
}
self.assertEqual(
self.backend.get_verified_logout("my_uid1"), "verification key"
)
self.assertIsNone(self.backend.get_verified_logout("my_uid2"))
self.assertIsNone(self.backend.get_verified_logout("missing"))
def test_get_verified_logout_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {
"authn_event": aevent1,
"verified_logout": "verification key",
},
"session_id2": {
"authn_event": aevent2,
"verified_logout": "verification key",
},
}
self.assertEqual(self.backend.get_verified_logout("my_uid"), "verification key")
def test_get_token_ids(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id": {"authn_event": aevent, "id_token": "Id token"}
}
self.assertEqual(set(self.backend.get_token_ids("my_uid")), {"Id token"})
self.assertEqual(set(self.backend.get_token_ids("missing")), set())
def test_get_token_ids_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {"authn_event": aevent1, "id_token": "Id token 1"},
"session_id2": {"authn_event": aevent2, "id_token": "Id token 2"},
}
self.assertEqual(
set(self.backend.get_token_ids("my_uid")), {"Id token 1", "Id token 2"}
)
def test_is_revoke_uid_false(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {"session_id": {"authn_event": aevent, "revoked": False}}
self.assertFalse(self.backend.is_revoke_uid("my_uid"))
def test_is_revoke_uid_true(self):
aevent = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {"session_id": {"authn_event": aevent, "revoked": True}}
self.assertTrue(self.backend.is_revoke_uid("my_uid"))
def test_is_revoke_uid_multiple(self):
aevent1 = AuthnEvent("my_uid", "some_salt").to_json()
aevent2 = AuthnEvent("my_uid", "some_salt").to_json()
self.backend.storage = {
"session_id1": {"authn_event": aevent1, "revoked": True},
"session_id2": {"authn_event": aevent2, "revoked": False},
}
self.assertTrue(self.backend.is_revoke_uid("my_uid"))
class TestSessionDB(object):
@pytest.fixture(autouse=True)
def create_sdb(self, session_db_factory):
self.sdb = session_db_factory("https://example.com/")
def test_create_authz_session(self):
ae = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
self.sdb.do_sub(sid, "client_salt")
info = self.sdb[sid]
assert info["oauth_state"] == "authz"
def test_create_authz_session_without_nonce(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
info = self.sdb[sid]
assert info["oauth_state"] == "authz"
def test_create_authz_session_with_nonce(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQN)
info = self.sdb[sid]
assert info["nonce"] == "something"
def test_create_authz_session_with_id_token(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQN, id_token="id_token")
info = self.sdb[sid]
assert info["id_token"] == "id_token"
def test_create_authz_session_with_oidreq(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQN, oidreq=OIDR)
info = self.sdb[sid]
assert "id_token" not in info
assert "oidreq" in info
def test_create_authz_session_with_sector_id(self):
ae = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae, AREQN, oidreq=OIDR)
self.sdb.do_sub(sid, "client_salt", "http://example.com/si.jwt", "pairwise")
info_1 = self.sdb[sid].copy()
assert "id_token" not in info_1
assert "oidreq" in info_1
assert info_1["sub"] != "sub"
self.sdb.do_sub(sid, "client_salt", "http://example.net/si.jwt", "pairwise")
info_2 = self.sdb[sid]
assert info_2["sub"] != "sub"
assert info_2["sub"] != info_1["sub"]
def test_upgrade_to_token(self):
ae1 = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae1, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
_dict = self.sdb.upgrade_to_token(grant)
print(_dict.keys())
assert _eq(
list(_dict.keys()),
[
"authn_event",
"code",
"authzreq",
"revoked",
"access_token",
"token_type",
"state",
"redirect_uri",
"code_used",
"client_id",
"scope",
"oauth_state",
"access_token_scope",
"sub",
"response_type",
],
)
with pytest.raises(AccessCodeUsed):
self.sdb.upgrade_to_token(grant)
self.sdb.upgrade_to_token(_dict["access_token"])
def test_upgrade_to_token_refresh(self):
ae1 = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae1, AREQO)
self.sdb.do_sub(sid, ae1.salt)
grant = self.sdb[sid]["code"]
_dict = self.sdb.upgrade_to_token(grant, issue_refresh=True)
print(_dict.keys())
assert _eq(
_dict.keys(),
[
"authn_event",
"code",
"authzreq",
"revoked",
"access_token",
"response_type",
"token_type",
"state",
"redirect_uri",
"code_used",
"client_id",
"scope",
"oauth_state",
"access_token_scope",
"refresh_token",
"sub",
],
)
# can't update again
with pytest.raises(AccessCodeUsed):
self.sdb.upgrade_to_token(grant)
self.sdb.upgrade_to_token(_dict["access_token"])
def test_upgrade_to_token_with_id_token_and_oidreq(self):
ae2 = AuthnEvent("another_user_id", "salt")
sid = self.sdb.create_authz_session(ae2, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
_dict = self.sdb.upgrade_to_token(grant, id_token="id_token", oidreq=OIDR)
print(_dict.keys())
assert _eq(
list(_dict.keys()),
[
"authn_event",
"code",
"authzreq",
"revoked",
"oidreq",
"access_token",
"id_token",
"response_type",
"token_type",
"state",
"redirect_uri",
"code_used",
"client_id",
"scope",
"oauth_state",
"access_token_scope",
"sub",
],
)
assert _dict["id_token"] == "id_token"
assert isinstance(_dict["oidreq"], OpenIDRequest)
def test_refresh_token(self):
ae = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
dict1 = self.sdb.upgrade_to_token(grant, issue_refresh=True).copy()
rtoken = dict1["refresh_token"]
dict2 = self.sdb.refresh_token(rtoken, AREQ["client_id"])
assert dict1["access_token"] != dict2["access_token"]
with pytest.raises(WrongTokenType):
self.sdb.refresh_token(dict2["access_token"], AREQ["client_id"])
def test_refresh_token_cleared_session(self):
ae = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
dict1 = self.sdb.upgrade_to_token(grant, issue_refresh=True)
ac1 = dict1["access_token"]
self.sdb._db = {}
rtoken = dict1["refresh_token"]
dict2 = self.sdb.refresh_token(rtoken, AREQ["client_id"])
assert ac1 != dict2["access_token"]
assert self.sdb.is_valid(dict2["access_token"])
def test_is_valid(self):
ae1 = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae1, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
assert self.sdb.is_valid(grant)
sinfo = self.sdb.upgrade_to_token(grant, issue_refresh=True)
assert not self.sdb.is_valid(grant)
access_token = sinfo["access_token"]
assert self.sdb.access_token.valid(access_token)
refresh_token = sinfo["refresh_token"]
sinfo = self.sdb.refresh_token(refresh_token, AREQ["client_id"])
access_token2 = sinfo["access_token"]
assert self.sdb.is_valid(access_token2)
try:
self.sdb.is_valid(access_token)
except KeyError:
pass
def test_valid_grant(self):
ae = AuthnEvent("another:user", "salt")
sid = self.sdb.create_authz_session(ae, AREQ)
grant = self.sdb[sid]["code"]
assert self.sdb.is_valid(grant)
def test_revoke_token(self):
ae1 = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae1, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
tokens = self.sdb.upgrade_to_token(grant, issue_refresh=True)
access_token = tokens["access_token"]
refresh_token = tokens["refresh_token"]
assert self.sdb.is_valid(access_token)
self.sdb.revoke_token(access_token)
assert not self.sdb.is_valid(access_token)
sinfo = self.sdb.refresh_token(refresh_token, AREQ["client_id"])
access_token = sinfo["access_token"]
assert self.sdb.is_valid(access_token)
self.sdb.revoke_refresh_token(refresh_token)
assert not self.sdb.is_valid(refresh_token)
try:
self.sdb.refresh_token(refresh_token, AREQ["client_id"])
except ExpiredToken:
pass
assert self.sdb.is_valid(access_token)
ae2 = AuthnEvent("sub", "salt")
sid = self.sdb.create_authz_session(ae2, AREQ)
grant = self.sdb[sid]["code"]
self.sdb.revoke_token(grant)
assert not self.sdb.is_valid(grant)
def test_revoke_all_tokens(self):
ae1 = AuthnEvent("uid", "salt")
sid = self.sdb.create_authz_session(ae1, AREQ)
self.sdb[sid]["sub"] = "sub"
grant = self.sdb[sid]["code"]
tokens = self.sdb.upgrade_to_token(grant, issue_refresh=True)
access_token = tokens["access_token"]
refresh_token = tokens["refresh_token"]
self.sdb.revoke_all_tokens(access_token)
assert not self.sdb.is_valid(access_token)
assert not self.sdb.is_valid(refresh_token)
def test_sub_to_authn_event(self):
ae = AuthnEvent("sub", "salt", time_stamp=time.time())
sid = self.sdb.create_authz_session(ae, AREQ)
sub = self.sdb.do_sub(sid, "client_salt")
sids = self.sdb.get_sids_by_sub(sub)
ae = self.sdb[sids[0]]["authn_event"]
assert AuthnEvent.from_json(ae).valid()
def test_do_sub_deterministic(self):
ae = AuthnEvent("tester", "random_value")
sid = self.sdb.create_authz_session(ae, AREQ)
self.sdb.do_sub(sid, "other_random_value")
info = self.sdb[sid]
assert (
info["sub"]
== "179670cdee6375c48e577317b2abd7d5cd26a5cdb1cfb7ef84af3d703c71d013"
)
self.sdb.do_sub(
sid,
"other_random_value",
sector_id="http://example.com",
subject_type="pairwise",
)
info2 = self.sdb[sid]
assert (
info2["sub"]
== "aaa50d80f8780cf1c4beb39e8e126556292f5091b9e39596424fefa2b99d9c53"
)
self.sdb.do_sub(
sid,
"another_random_value",
sector_id="http://other.example.com",
subject_type="pairwise",
)
info2 = self.sdb[sid]
assert (
info2["sub"]
== "62fb630e29f0d41b88e049ac0ef49a9c3ac5418c029d6e4f5417df7e9443976b"
)
def test_get_authentication_event_dict(self):
self.sdb._db["123"] = {}
self.sdb._db["123"]["authn_event"] = {
"uid": "uid",
"salt": "salt",
"authn_time": 1000,
"valid_until": 1500,
}
ae = self.sdb.get_authentication_event("123")
assert ae.uid == "uid"
assert ae.salt == "salt"
assert ae.authn_time == 1000
assert ae.valid_until == 1500
def test_get_authentication_event_json(self):
self.sdb._db["123"] = {}
self.sdb._db["123"]["authn_event"] = json.dumps(
{"uid": "uid", "salt": "salt", "authn_time": 1000, "valid_until": 1500}
)
ae = self.sdb.get_authentication_event("123")
assert ae.uid == "uid"
assert ae.salt == "salt"
assert ae.authn_time == 1000
assert ae.valid_until == 1500
def test_get_sids_from_uid_distributed(self):
db = DictSessionBackend()
sdb1 = create_session_db("https://example.com/1", "secret", "password", db=db)
sdb2 = create_session_db("https://example.com/2", "secret", "password", db=db)
ae = AuthnEvent("sub", "salt", time_stamp=time.time())
sid1 = sdb1.create_authz_session(ae, AREQ)
sdb1.do_sub(sid1, "salt")
sid2 = sdb2.create_authz_session(ae, AREQ)
sdb2.do_sub(sid2, "salt")
sdb1sids = sdb1.get_sids_from_uid("sub")
sdb2sids = sdb2.get_sids_from_uid("sub")
assert sdb1sids == sdb2sids
def test_get_client_ids_for_uid(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"}),
"client_id": "my_client",
}
assert self.sdb.get_client_ids_for_uid("my_uid") == ["my_client"]
def test_get_verify_logout(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"}),
"verified_logout": "something",
}
assert self.sdb.get_verify_logout("my_uid") == "something"
def test_set_verify_logout(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"})
}
self.sdb.set_verify_logout("my_uid")
assert self.sdb.get_verify_logout("my_uid") is not None
def test_set_verify_logout_multiple(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"})
}
self.sdb._db["321"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"})
}
self.sdb.set_verify_logout("my_uid")
assert self.sdb.get_verify_logout("my_uid") is not None
assert (
self.sdb._db["123"]["verified_logout"]
== self.sdb._db["321"]["verified_logout"]
)
def test_get_token_ids(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"}),
"id_token": "Id token",
}
assert set(self.sdb.get_token_ids("my_uid")) == {"Id token"}
def test_get_is_revoke_uid(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"}),
"revoked": True,
}
assert self.sdb.is_revoke_uid("my_uid")
def test_revoke_uid(self):
self.sdb._db["123"] = {
"authn_event": json.dumps({"uid": "my_uid", "salt": "salt"})
}
self.sdb.revoke_uid("my_uid")
assert self.sdb.is_revoke_uid("my_uid")
class TestCrypt(object):
@pytest.fixture(autouse=True)
def create_crypt(self):
self.crypt = Crypt("4-amino-1H-pyrimidine-2-one")
def test_encrypt_decrypt(self):
ctext = self.crypt.encrypt("Cytosine")
plain = self.crypt.decrypt(ctext).decode("utf-8")
assert plain == "Cytosine "
ctext = self.crypt.encrypt("cytidinetriphosp")
plain = self.crypt.decrypt(ctext).decode("utf-8")
assert plain == "cytidinetriphosp"
def test_crypt_with_b64(self):
db = {}
msg = "secret{}{}".format(time.time(), random.random())
csum = hmac.new(msg.encode("utf-8"), digestmod=hashlib.sha224)
txt = csum.digest()
db[txt] = "foobar"
txt = txt + b"aces"
ctext = self.crypt.encrypt(txt)
onthewire = base64.b64encode(ctext)
plain = self.crypt.decrypt(base64.b64decode(onthewire))
assert plain.endswith(b"aces")
assert db[plain[:-4]] == "foobar"
| true
| true
|
f716aa23ec2670b3b6679f13ad0859e223f380ff
| 309
|
py
|
Python
|
dataset/proxies.py
|
unknown/reddit-aita
|
557fd67120e529db8b014b74a71e3b926b0ed528
|
[
"MIT"
] | null | null | null |
dataset/proxies.py
|
unknown/reddit-aita
|
557fd67120e529db8b014b74a71e3b926b0ed528
|
[
"MIT"
] | null | null | null |
dataset/proxies.py
|
unknown/reddit-aita
|
557fd67120e529db8b014b74a71e3b926b0ed528
|
[
"MIT"
] | null | null | null |
import random
proxy_list = [
'http://p.webshare.io:19999'
]
def random_proxy():
i = random.randint(0, len(proxy_list) - 1)
p = {
'http': proxy_list[i]
}
return p
def remove_proxy(proxy):
proxy_list.remove(proxy)
print(f'Removed {proxy}-- {len(proxy_list)} proxies left')
| 19.3125
| 62
| 0.621359
|
import random
proxy_list = [
'http://p.webshare.io:19999'
]
def random_proxy():
i = random.randint(0, len(proxy_list) - 1)
p = {
'http': proxy_list[i]
}
return p
def remove_proxy(proxy):
proxy_list.remove(proxy)
print(f'Removed {proxy}-- {len(proxy_list)} proxies left')
| true
| true
|
f716ab6b8551b5a9bf03bfb3d99382d22fe6e166
| 30,230
|
py
|
Python
|
Scripts/simulation/interactions/utils/creation.py
|
velocist/TS4CheatsInfo
|
b59ea7e5f4bd01d3b3bd7603843d525a9c179867
|
[
"Apache-2.0"
] | null | null | null |
Scripts/simulation/interactions/utils/creation.py
|
velocist/TS4CheatsInfo
|
b59ea7e5f4bd01d3b3bd7603843d525a9c179867
|
[
"Apache-2.0"
] | null | null | null |
Scripts/simulation/interactions/utils/creation.py
|
velocist/TS4CheatsInfo
|
b59ea7e5f4bd01d3b3bd7603843d525a9c179867
|
[
"Apache-2.0"
] | null | null | null |
# uncompyle6 version 3.7.4
# Python bytecode 3.7 (3394)
# Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)]
# Embedded file name: T:\InGame\Gameplay\Scripts\Server\interactions\utils\creation.py
# Compiled at: 2019-12-03 21:37:40
# Size of source mod 2**32: 42011 bytes
from animation.animation_utils import flush_all_animations
from carry.carry_elements import enter_carry_while_holding
from element_utils import build_critical_section
from event_testing.resolver import SingleSimResolver
from filters.sim_template import TunableSimTemplate
from filters.tunable import TunableSimFilter
from interactions import ParticipantType, ParticipantTypeActorTargetSim, ParticipantTypeSingleSim, ParticipantTypeSingle
from interactions.interaction_finisher import FinishingType
from interactions.utils.interaction_elements import XevtTriggeredElement
from objects import VisibilityState
from objects.object_creation import ObjectCreationMixin
from objects.slots import RuntimeSlot
from sims.genealogy_tracker import genealogy_caching, FamilyRelationshipIndex
from sims.pregnancy.pregnancy_tracker import PregnancyTracker
from sims.sim_info_lod import SimInfoLODLevel
from sims.sim_spawner import SimSpawner, SimCreator
from sims4.tuning.geometric import TunableDistanceSquared
from sims4.tuning.tunable import TunableList, OptionalTunable, Tunable, TunableEnumEntry, TunableVariant, TunableFactory, TunableReference, HasTunableSingletonFactory, AutoFactoryInit
from singletons import EMPTY_SET, DEFAULT
from tag import Tag
from venues.venue_constants import NPCSummoningPurpose
from world.spawn_actions import TunableSpawnActionVariant
import element_utils, id_generator, interactions, services, sims.ghost, sims4.log, sims4.math, sims4.telemetry, telemetry_helper
logger = sims4.log.Logger('Creation')
TELEMETRY_GROUP_OBJECT = 'OBJC'
TELEMETRY_HOOK_OBJECT_CREATE_BSCEXTRA = 'CRBE'
TELEMETRY_FIELD_OBJECT_INTERACTION = 'intr'
TELEMETRY_FIELD_OBJECT_DEFINITION = 'objc'
writer = sims4.telemetry.TelemetryWriter(TELEMETRY_GROUP_OBJECT)
class ObjectCreationElement(XevtTriggeredElement, ObjectCreationMixin):
FACTORY_TUNABLES = {'cancel_on_destroy':Tunable(description='\n If checked, the interaction will be canceled if object is destroyed\n due to placement failure or if destroy on placement failure is\n unchecked and the fallback fails.\n ',
tunable_type=bool,
default=True),
'transient':Tunable(description='\n If checked, the created object will be destroyed when the interaction ends.\n ',
tunable_type=bool,
default=False),
'set_to_invisible':Tunable(description='\n If checked, the created object will be set to invisible when the \n interaction ends.\n ',
tunable_type=bool,
default=False)}
def __init__(self, interaction, *args, sequence=(), **kwargs):
(super().__init__)(interaction, *args, sequence=sequence, **kwargs)
self._definition_cache = None
self._placement_failed = False
self.initialize_helper(interaction.get_resolver())
if self.transient:
self.require_claim = True
@property
def definition(self):
if self._definition_cache is None:
self._definition_cache = super().definition
return self._definition_cache
@property
def placement_failed(self):
return self._placement_failed
def create_object_in_sequence(self):
self._place_object(self._object_helper.object)
if self._placement_failed:
if self.cancel_on_destroy:
self.interaction.cancel((FinishingType.FAILED_TESTS), cancel_reason_msg='Cannot place object')
return False
return True
if not self.transient:
self._object_helper.claim()
if self.set_to_invisible:
self._object_helper.object.visibility = VisibilityState(False)
with telemetry_helper.begin_hook(writer, TELEMETRY_HOOK_OBJECT_CREATE_BSCEXTRA) as (hook):
hook.write_enum(TELEMETRY_FIELD_OBJECT_INTERACTION, self.interaction.guid64)
hook.write_guid(TELEMETRY_FIELD_OBJECT_DEFINITION, self._object_helper.object.definition.id)
return True
def _setup_created_object(self, created_object):
self.interaction.object_create_helper = self._object_helper
super()._setup_created_object(created_object)
def _place_object(self, created_object):
place_object = super()._place_object(created_object)
if not place_object:
self._placement_failed = True
return place_object
def _build_outer_elements(self, sequence):
def set_carry_target(_):
self.interaction.track = DEFAULT
self.interaction.map_create_target(self.interaction.created_target)
def enter_carry(timeline):
result = yield from element_utils.run_child(timeline, enter_carry_while_holding((self.interaction), obj=(self.interaction.created_target),
carry_track_override=(self.location.carry_track_override),
owning_affordance=None,
sequence=(build_critical_section(sequence, flush_all_animations))))
return result
if False:
yield None
location_type = getattr(self.location, 'location', None)
if location_type == self.CARRY:
return self._object_helper.create(set_carry_target, enter_carry)
return self._object_helper.create(sequence)
def _do_behavior(self):
self.create_object_in_sequence()
class SimCreationElement(XevtTriggeredElement):
class _ActiveHouseholdFactory(TunableFactory):
@staticmethod
def factory(_):
return services.active_household()
FACTORY_TYPE = factory
class _ParticipantHouseholdFactory(TunableFactory):
@staticmethod
def factory(interaction, participant):
sim = interaction.get_participant(participant)
if sim is None:
logger.error('_ParticipantHouseholdFactory: {} does not have participant {}', interaction,
participant,
owner='jjacobson')
return
return sim.household
FACTORY_TYPE = factory
def __init__(self, *args, **kwargs):
(super().__init__)(participant=TunableEnumEntry(description='\n The participant that will have their household used to put the\n sim into.\n ',
tunable_type=ParticipantTypeActorTargetSim,
default=(ParticipantTypeActorTargetSim.Actor)), **kwargs)
class _NoHousheoldFactory(TunableFactory):
@staticmethod
def factory(_):
pass
FACTORY_TYPE = factory
class _HiddenHouseholdFactory(TunableFactory):
@staticmethod
def factory(_):
household = services.household_manager().create_household(services.get_first_client().account)
household.set_to_hidden(family_funds=0)
return household
FACTORY_TYPE = factory
class _BaseSimInfoSource(HasTunableSingletonFactory, AutoFactoryInit):
def get_sim_infos_and_positions(self, resolver):
raise NotImplementedError('Attempting to use the _BaseSimInfoSource base class, use sub-classes instead.')
def _try_add_sim_info_to_household(self, sim_info, resolver, household, skip_household_check=False):
if household is not None:
if skip_household_check or household is not sim_info.household:
if not household.can_add_sim_info(sim_info):
logger.warn('create_sim_from_sim_info element on the interaction: {} could not add a new sim to the tuned household.', resolver.interaction)
return False
if sim_info.household is not household:
sim_info.household.remove_sim_info(sim_info)
household.add_sim_info_to_household(sim_info)
return True
def do_pre_spawn_behavior(self, sim_info, resolver, household):
self._try_add_sim_info_to_household(sim_info, resolver, household)
def do_post_spawn_behavior(self, sim_info, resolver, client_manager):
client = client_manager.get_client_by_household_id(sim_info.household_id)
if client is not None:
client.add_selectable_sim_info(sim_info)
class _TargetedObjectResurrection(_BaseSimInfoSource):
FACTORY_TUNABLES = {'participant':TunableEnumEntry(description='\n The participant of the interaction against whom any relationship\n and genealogy tunables are applied.\n ',
tunable_type=ParticipantType,
default=ParticipantType.Actor),
'sim_info_subject':TunableEnumEntry(description='\n The subject from which the Sim Info used to create the new Sim\n should be fetched.\n ',
tunable_type=ParticipantType,
default=ParticipantType.Object),
'resurrect':Tunable(description='\n If checked, all Ghost traits are removed from the created Sim\n and its death type is cleared.\n \n If unchecked, this is a simple spawn operation.\n ',
tunable_type=bool,
default=True)}
def get_sim_infos_and_positions(self, resolver, household):
use_fgl = True
stored_sim_info_object = resolver.get_participant(self.sim_info_subject)
if stored_sim_info_object is None:
return ()
sim_info = stored_sim_info_object.get_stored_sim_info()
if sim_info is None:
return ()
return (
(
sim_info, stored_sim_info_object.position, None, use_fgl),)
def do_pre_spawn_behavior(self, sim_info, resolver, household):
super().do_pre_spawn_behavior(sim_info, resolver, household)
if self.resurrect:
sims.ghost.Ghost.remove_ghost_from_sim(sim_info)
class _MassObjectResurrection(_BaseSimInfoSource):
FACTORY_TUNABLES = {'participant':TunableEnumEntry(description='\n The participant of the interaction that will have sims resurrected\n around their position.\n ',
tunable_type=ParticipantType,
default=ParticipantType.Actor),
'radius':TunableDistanceSquared(description='\n The distance around a participant that will resurrect all of the\n dead sim objects.\n ',
default=1),
'tag':TunableEnumEntry(description='\n Tag the delineates an object that we want to resurrect sims\n from.\n ',
tunable_type=Tag,
default=Tag.INVALID)}
def get_sim_infos_and_positions(self, resolver, household):
use_fgl = True
sim_infos_and_positions = []
participant = resolver.get_participant(self.participant)
position = participant.position
for obj in services.object_manager().get_objects_with_tag_gen(self.tag):
obj_position = obj.position
distance_from_pos = obj_position - position
if distance_from_pos.magnitude_squared() > self.radius:
continue
sim_info = obj.get_stored_sim_info()
if sim_info is None:
continue
sim_infos_and_positions.append((sim_info, obj_position, None, use_fgl))
return tuple(sim_infos_and_positions)
def do_pre_spawn_behavior(self, sim_info, resolver, household):
super().do_pre_spawn_behavior(sim_info, resolver, household)
sims.ghost.Ghost.remove_ghost_from_sim(sim_info)
class _SlotSpawningSimInfoSource(_BaseSimInfoSource):
class _SlotByName(HasTunableSingletonFactory, AutoFactoryInit):
FACTORY_TUNABLES = {'slot_name': Tunable(description='\n The exact name of a slot on the parent object.\n ',
tunable_type=str,
default='_ctnm_')}
def get_slot_type_and_hash(self):
return (
None, sims4.hash_util.hash32(self.slot_name))
class _SlotByType(HasTunableSingletonFactory, AutoFactoryInit):
FACTORY_TUNABLES = {'slot_type': TunableReference(description='\n A particular slot type in which the should spawn.\n ',
manager=(services.get_instance_manager(sims4.resources.Types.SLOT_TYPE)))}
def get_slot_type_and_hash(self):
return (
self.slot_type, None)
FACTORY_TUNABLES = {'participant':TunableEnumEntry(description='\n The participant that is a sim that will be cloned\n Note: MUST be a sim. Use create object - clone object for non-sim objects.\n ',
tunable_type=ParticipantTypeSingleSim,
default=ParticipantTypeSingleSim.Actor),
'sim_spawn_slot':TunableVariant(description="\n The slot on the parent object where the sim should spawn. This\n may be either the exact name of a bone on the parent object or a\n slot type, in which case the first empty slot of the specified type\n will be used. If None is chosen, then the sim will at or near\n the interaction target's location.\n ",
by_name=_SlotByName.TunableFactory(),
by_type=_SlotByType.TunableFactory()),
'spawn_location_participant':TunableEnumEntry(description='\n The participant used for finding where to spawn the Sim. Typically you want to leave this as object.\n \n Special cases include:\n - For self-interactions, Object will resolve to None. This can be set to Actor if you want to spawn\n near the Sim running the interaction.\n ',
tunable_type=ParticipantTypeSingle,
default=ParticipantTypeSingle.Object)}
def __init__(self, sim_spawn_slot=None, **kwargs):
(super().__init__)(sim_spawn_slot=sim_spawn_slot, **kwargs)
self._slot_type = None
self._bone_name_hash = None
if sim_spawn_slot is not None:
self._slot_type, self._bone_name_hash = sim_spawn_slot.get_slot_type_and_hash()
def _get_position_and_location(self, spawning_object, resolver):
position, location = (None, None)
if self._slot_type is not None:
for runtime_slot in spawning_object.get_runtime_slots_gen(slot_types={self._slot_type}, bone_name_hash=(self._bone_name_hash)):
location = runtime_slot.location
else:
if self._bone_name_hash is not None:
runtime_slot = RuntimeSlot(spawning_object, self._bone_name_hash, EMPTY_SET)
if runtime_slot is not None:
location = runtime_slot.location
else:
location = spawning_object.location
if location is not None:
location = sims4.math.Location((location.world_transform), (spawning_object.routing_surface), slot_hash=(location.slot_hash))
position = location.transform.translation
return (position, location)
def _get_spawning_object(self, resolver):
spawning_object = resolver.get_participant(self.spawn_location_participant)
if spawning_object.is_sim:
spawning_object = spawning_object.get_sim_instance()
return spawning_object
class _CloneSimInfoSource(_SlotSpawningSimInfoSource):
FACTORY_TUNABLES = {'force_fgl': Tunable(description="\n Normally, FGL will only be invoked if no spawning position is found. Use this tunable to force\n FGL to run. e.g. Cloning spell uses caster Sim's position as a spawning position. In that case,\n we still want to force FGL so the clone spawns near that Sim rather than directly on top of the Sim. \n ",
tunable_type=bool,
default=False)}
def _ensure_parental_lineage_exists(self, source_sim_info, clone_sim_info):
with genealogy_caching():
if any(source_sim_info.genealogy.get_parent_sim_ids_gen()):
return
mom_id = id_generator.generate_object_id()
source_sim_info.genealogy.set_family_relation(FamilyRelationshipIndex.MOTHER, mom_id)
clone_sim_info.genealogy.set_family_relation(FamilyRelationshipIndex.MOTHER, mom_id)
sim_info_manager = services.sim_info_manager()
for child_sim_id in source_sim_info.genealogy.get_children_sim_ids_gen():
child_sim_info = sim_info_manager.get(child_sim_id)
if child_sim_info is not None:
grandparent_relation = FamilyRelationshipIndex.MOTHERS_MOM if source_sim_info.is_female else FamilyRelationshipIndex.FATHERS_MOM
child_sim_info.genealogy.set_family_relation(grandparent_relation, mom_id)
def _create_clone_sim_info(self, source_sim_info, resolver, household):
sim_creator = SimCreator(gender=(source_sim_info.gender), age=(source_sim_info.age),
first_name=(SimSpawner.get_random_first_name(source_sim_info.gender, source_sim_info.species)),
last_name=(source_sim_info._base.last_name),
traits=(source_sim_info.trait_tracker.equipped_traits))
sim_info_list, _ = SimSpawner.create_sim_infos((sim_creator,), household=household,
account=(source_sim_info.account),
generate_deterministic_sim=True,
creation_source='cloning',
skip_adding_to_household=True)
clone_sim_info = sim_info_list[0]
source_sim_proto = source_sim_info.save_sim(for_cloning=True)
clone_sim_id = clone_sim_info.sim_id
clone_first_name = clone_sim_info._base.first_name
clone_last_name = clone_sim_info._base.last_name
clone_breed_name = clone_sim_info._base.breed_name
clone_first_name_key = clone_sim_info._base.first_name_key
clone_last_name_key = clone_sim_info._base.last_name_key
clone_full_name_key = clone_sim_info._base.full_name_key
clone_breed_name_key = clone_sim_info._base.breed_name_key
clone_sim_info.load_sim_info(source_sim_proto, is_clone=True, default_lod=(SimInfoLODLevel.FULL))
clone_sim_info.sim_id = clone_sim_id
clone_sim_info._base.first_name = clone_first_name
clone_sim_info._base.last_name = clone_last_name
clone_sim_info._base.breed_name = clone_breed_name
clone_sim_info._base.first_name_key = clone_first_name_key
clone_sim_info._base.last_name_key = clone_last_name_key
clone_sim_info._base.full_name_key = clone_full_name_key
clone_sim_info._base.breed_name_key = clone_breed_name_key
clone_sim_info._household_id = household.id
if not self._try_add_sim_info_to_household(clone_sim_info, resolver, household, skip_household_check=True):
return
source_trait_tracker = source_sim_info.trait_tracker
clone_trait_tracker = clone_sim_info.trait_tracker
for trait in clone_trait_tracker.personality_traits:
if not source_trait_tracker.has_trait(trait):
clone_sim_info.remove_trait(trait)
for trait in clone_trait_tracker.gender_option_traits:
if not source_trait_tracker.has_trait(trait):
clone_sim_info.remove_trait(trait)
correct_aspiration_trait = clone_sim_info.primary_aspiration.primary_trait
for trait in tuple(clone_trait_tracker.aspiration_traits):
if trait is not correct_aspiration_trait:
clone_sim_info.remove_trait(trait)
source_sim_info.relationship_tracker.create_relationship(clone_sim_info.sim_id)
source_sim_info.relationship_tracker.add_relationship_score(clone_sim_info.sim_id, 1)
self._ensure_parental_lineage_exists(source_sim_info, clone_sim_info)
services.sim_info_manager().set_default_genealogy(sim_infos=(clone_sim_info,))
clone_sim_info.set_default_data()
clone_sim_info.save_sim()
household.save_data()
if not household.is_active_household:
clone_sim_info.request_lod(SimInfoLODLevel.BASE)
clone_sim_info.resend_physical_attributes()
clone_sim_info.relationship_tracker.clean_and_send_remaining_relationship_info()
return clone_sim_info
def do_pre_spawn_behavior(self, sim_info, resolver, household):
pass
def get_sim_infos_and_positions(self, resolver, household):
use_fgl = False
sim_info = resolver.get_participant(self.participant)
clone_sim_info = self._create_clone_sim_info(sim_info, resolver, household)
if clone_sim_info is None:
return ()
position, location = (None, None)
spawning_object = self._get_spawning_object(resolver)
if spawning_object is not None:
position, location = self._get_position_and_location(spawning_object, resolver)
use_fgl = self.force_fgl or position is None
return ((clone_sim_info, position, location, use_fgl),)
def do_post_spawn_behavior(self, sim_info, resolver, client_manager):
super().do_post_spawn_behavior(sim_info, resolver, client_manager)
sim_info.commodity_tracker.set_all_commodities_to_best_value(visible_only=True)
class _SimFilterSimInfoSource(_SlotSpawningSimInfoSource):
FACTORY_TUNABLES = {'filter': TunableSimFilter.TunableReference(description='\n Sim filter that is used to create or find a Sim that matches\n this filter request.\n ')}
def get_sim_filter_gsi_name(self):
return str(self)
def get_sim_infos_and_positions(self, resolver, household):
use_fgl = True
sim_info = resolver.get_participant(self.participant)
filter_result = services.sim_filter_service().submit_matching_filter(sim_filter=(self.filter), requesting_sim_info=sim_info,
allow_yielding=False,
gsi_source_fn=(self.get_sim_filter_gsi_name))
if not filter_result:
return ()
position, location = (None, None)
spawning_object = self._get_spawning_object(resolver)
if spawning_object is not None:
position, location = self._get_position_and_location(spawning_object, resolver)
use_fgl = position is None
return ((filter_result[0].sim_info, position, location, use_fgl),)
class _SimTemplateSimInfoSource(_SlotSpawningSimInfoSource):
FACTORY_TUNABLES = {'template': TunableSimTemplate.TunableReference(description='\n The template to use.\n ')}
def get_sim_infos_and_positions(self, resolver, household):
sim_creator = self.template.sim_creator
sim_info_list, _ = SimSpawner.create_sim_infos((sim_creator,), sim_name_type=(sim_creator.sim_name_type),
household=household)
self.template.add_template_data_to_sim((sim_info_list[0]), sim_creator=sim_creator)
position, location = (None, None)
spawning_object = self._get_spawning_object(resolver)
if spawning_object is not None:
position, location = self._get_position_and_location(spawning_object, resolver)
use_fgl = position is None
return ((sim_info_list[0], position, location, use_fgl),)
class _GenalogySetAsChild(HasTunableSingletonFactory):
def __call__(self, actor_sim_info, created_sim_info):
created_sim_info.last_name = SimSpawner.get_last_name(actor_sim_info.last_name, created_sim_info.gender, created_sim_info.species)
parent_a = actor_sim_info
parent_b = services.sim_info_manager().get(parent_a.spouse_sim_id)
created_sim_info.relationship_tracker.destroy_all_relationships()
for relation in FamilyRelationshipIndex:
relation_id = created_sim_info.get_relation(relation)
relation_info = services.sim_info_manager().get(relation_id)
if relation_info is not None:
created_sim_info.genealogy.remove_family_link(relation)
family_relation = relation_info.genealogy.get_family_relationship_bit(created_sim_info.sim_id)
relation_info.genealogy.clear_family_relation(family_relation)
relation_info.relationship_tracker.destroy_relationship(created_sim_info.sim_id)
created_sim_info.genealogy.clear_family_relation(relation)
PregnancyTracker.initialize_sim_info(created_sim_info, parent_a, parent_b)
FACTORY_TUNABLES = {'sim_info_source':TunableVariant(description='\n The source of the sim_info and position data for the sims to be\n created.\n ',
targeted=_TargetedObjectResurrection.TunableFactory(),
mass_object=_MassObjectResurrection.TunableFactory(),
clone_a_sim=_CloneSimInfoSource.TunableFactory(),
sim_filter=_SimFilterSimInfoSource.TunableFactory(),
sim_template=_SimTemplateSimInfoSource.TunableFactory(),
default='targeted'),
'household_option':TunableVariant(description='\n The household that the created sim should be put into.\n ',
active_household=_ActiveHouseholdFactory(),
participant_household=_ParticipantHouseholdFactory(),
no_household=_NoHousheoldFactory(),
hidden_household=_HiddenHouseholdFactory(),
default='participant_household'),
'spawn_action':TunableSpawnActionVariant(description='\n Define the methods to show the Sim after spawning on the lot. This\n defaults to fading the Sim in, but can be a specific interaction or\n an animation.\n '),
'relationship_bits_to_add':TunableList(description='\n A list of relationship bits to add between the source sim\n and the created sim.\n ',
tunable=TunableReference(manager=(services.get_instance_manager(sims4.resources.Types.RELATIONSHIP_BIT)))),
'set_summoning_purpose':OptionalTunable(description="\n If enabled this will trigger the summon NPC situation depending\n on the summoning purpose type set. This should be tuned when\n we create Sims and don't add them into the active household.\n ",
tunable=TunableEnumEntry(description='\n The purpose that is used to summon the sim to the lot. \n Defined in venue tuning.\n ',
tunable_type=NPCSummoningPurpose,
default=(NPCSummoningPurpose.DEFAULT))),
'set_genealogy':TunableVariant(description='\n Genealogy option to set on the created Sim. \n Example: Setting a child of a family.\n ',
set_as_child=_GenalogySetAsChild.TunableFactory(),
locked_args={'no_action': None},
default='no_action'),
'pre_spawn_loot':TunableList(description='\n List of loot actions to apply to the created sim info before it is\n spawned.\n ',
tunable=TunableReference(manager=(services.get_instance_manager(sims4.resources.Types.ACTION)),
class_restrictions=('LootActions', )))}
def _apply_relationship_bits(self, actor_sim_info, created_sim_info):
for rel_bit in self.relationship_bits_to_add:
actor_sim_info.relationship_tracker.add_relationship_bit((created_sim_info.sim_id), rel_bit, force_add=True)
def _do_behavior(self):
resolver = self.interaction.get_resolver()
target_participant = resolver.get_participant(self.sim_info_source.participant)
household = self.household_option(self.interaction)
client_manager = services.client_manager()
for sim_info, position, location, use_fgl in self.sim_info_source.get_sim_infos_and_positions(resolver, household):
if target_participant is not None:
self._apply_relationship_bits(target_participant, sim_info)
else:
single_sim_resolver = SingleSimResolver(sim_info)
for loot in self.pre_spawn_loot:
loot.apply_to_resolver(single_sim_resolver)
self.sim_info_source.do_pre_spawn_behavior(sim_info, resolver, household)
SimSpawner.spawn_sim(sim_info, position, spawn_action=(self.spawn_action), sim_location=location, use_fgl=use_fgl)
if self.set_summoning_purpose is not None:
services.current_zone().venue_service.active_venue.summon_npcs((sim_info,), self.set_summoning_purpose)
if self.set_genealogy is not None and target_participant is not None:
self.set_genealogy(target_participant, sim_info)
self.sim_info_source.do_post_spawn_behavior(sim_info, resolver, client_manager)
return True
| 58.927875
| 452
| 0.673139
|
from animation.animation_utils import flush_all_animations
from carry.carry_elements import enter_carry_while_holding
from element_utils import build_critical_section
from event_testing.resolver import SingleSimResolver
from filters.sim_template import TunableSimTemplate
from filters.tunable import TunableSimFilter
from interactions import ParticipantType, ParticipantTypeActorTargetSim, ParticipantTypeSingleSim, ParticipantTypeSingle
from interactions.interaction_finisher import FinishingType
from interactions.utils.interaction_elements import XevtTriggeredElement
from objects import VisibilityState
from objects.object_creation import ObjectCreationMixin
from objects.slots import RuntimeSlot
from sims.genealogy_tracker import genealogy_caching, FamilyRelationshipIndex
from sims.pregnancy.pregnancy_tracker import PregnancyTracker
from sims.sim_info_lod import SimInfoLODLevel
from sims.sim_spawner import SimSpawner, SimCreator
from sims4.tuning.geometric import TunableDistanceSquared
from sims4.tuning.tunable import TunableList, OptionalTunable, Tunable, TunableEnumEntry, TunableVariant, TunableFactory, TunableReference, HasTunableSingletonFactory, AutoFactoryInit
from singletons import EMPTY_SET, DEFAULT
from tag import Tag
from venues.venue_constants import NPCSummoningPurpose
from world.spawn_actions import TunableSpawnActionVariant
import element_utils, id_generator, interactions, services, sims.ghost, sims4.log, sims4.math, sims4.telemetry, telemetry_helper
logger = sims4.log.Logger('Creation')
TELEMETRY_GROUP_OBJECT = 'OBJC'
TELEMETRY_HOOK_OBJECT_CREATE_BSCEXTRA = 'CRBE'
TELEMETRY_FIELD_OBJECT_INTERACTION = 'intr'
TELEMETRY_FIELD_OBJECT_DEFINITION = 'objc'
writer = sims4.telemetry.TelemetryWriter(TELEMETRY_GROUP_OBJECT)
class ObjectCreationElement(XevtTriggeredElement, ObjectCreationMixin):
FACTORY_TUNABLES = {'cancel_on_destroy':Tunable(description='\n If checked, the interaction will be canceled if object is destroyed\n due to placement failure or if destroy on placement failure is\n unchecked and the fallback fails.\n ',
tunable_type=bool,
default=True),
'transient':Tunable(description='\n If checked, the created object will be destroyed when the interaction ends.\n ',
tunable_type=bool,
default=False),
'set_to_invisible':Tunable(description='\n If checked, the created object will be set to invisible when the \n interaction ends.\n ',
tunable_type=bool,
default=False)}
def __init__(self, interaction, *args, sequence=(), **kwargs):
(super().__init__)(interaction, *args, sequence=sequence, **kwargs)
self._definition_cache = None
self._placement_failed = False
self.initialize_helper(interaction.get_resolver())
if self.transient:
self.require_claim = True
@property
def definition(self):
if self._definition_cache is None:
self._definition_cache = super().definition
return self._definition_cache
@property
def placement_failed(self):
return self._placement_failed
def create_object_in_sequence(self):
self._place_object(self._object_helper.object)
if self._placement_failed:
if self.cancel_on_destroy:
self.interaction.cancel((FinishingType.FAILED_TESTS), cancel_reason_msg='Cannot place object')
return False
return True
if not self.transient:
self._object_helper.claim()
if self.set_to_invisible:
self._object_helper.object.visibility = VisibilityState(False)
with telemetry_helper.begin_hook(writer, TELEMETRY_HOOK_OBJECT_CREATE_BSCEXTRA) as (hook):
hook.write_enum(TELEMETRY_FIELD_OBJECT_INTERACTION, self.interaction.guid64)
hook.write_guid(TELEMETRY_FIELD_OBJECT_DEFINITION, self._object_helper.object.definition.id)
return True
def _setup_created_object(self, created_object):
self.interaction.object_create_helper = self._object_helper
super()._setup_created_object(created_object)
def _place_object(self, created_object):
place_object = super()._place_object(created_object)
if not place_object:
self._placement_failed = True
return place_object
def _build_outer_elements(self, sequence):
def set_carry_target(_):
self.interaction.track = DEFAULT
self.interaction.map_create_target(self.interaction.created_target)
def enter_carry(timeline):
result = yield from element_utils.run_child(timeline, enter_carry_while_holding((self.interaction), obj=(self.interaction.created_target),
carry_track_override=(self.location.carry_track_override),
owning_affordance=None,
sequence=(build_critical_section(sequence, flush_all_animations))))
return result
if False:
yield None
location_type = getattr(self.location, 'location', None)
if location_type == self.CARRY:
return self._object_helper.create(set_carry_target, enter_carry)
return self._object_helper.create(sequence)
def _do_behavior(self):
self.create_object_in_sequence()
class SimCreationElement(XevtTriggeredElement):
class _ActiveHouseholdFactory(TunableFactory):
@staticmethod
def factory(_):
return services.active_household()
FACTORY_TYPE = factory
class _ParticipantHouseholdFactory(TunableFactory):
@staticmethod
def factory(interaction, participant):
sim = interaction.get_participant(participant)
if sim is None:
logger.error('_ParticipantHouseholdFactory: {} does not have participant {}', interaction,
participant,
owner='jjacobson')
return
return sim.household
FACTORY_TYPE = factory
def __init__(self, *args, **kwargs):
(super().__init__)(participant=TunableEnumEntry(description='\n The participant that will have their household used to put the\n sim into.\n ',
tunable_type=ParticipantTypeActorTargetSim,
default=(ParticipantTypeActorTargetSim.Actor)), **kwargs)
class _NoHousheoldFactory(TunableFactory):
@staticmethod
def factory(_):
pass
FACTORY_TYPE = factory
class _HiddenHouseholdFactory(TunableFactory):
@staticmethod
def factory(_):
household = services.household_manager().create_household(services.get_first_client().account)
household.set_to_hidden(family_funds=0)
return household
FACTORY_TYPE = factory
class _BaseSimInfoSource(HasTunableSingletonFactory, AutoFactoryInit):
def get_sim_infos_and_positions(self, resolver):
raise NotImplementedError('Attempting to use the _BaseSimInfoSource base class, use sub-classes instead.')
def _try_add_sim_info_to_household(self, sim_info, resolver, household, skip_household_check=False):
if household is not None:
if skip_household_check or household is not sim_info.household:
if not household.can_add_sim_info(sim_info):
logger.warn('create_sim_from_sim_info element on the interaction: {} could not add a new sim to the tuned household.', resolver.interaction)
return False
if sim_info.household is not household:
sim_info.household.remove_sim_info(sim_info)
household.add_sim_info_to_household(sim_info)
return True
def do_pre_spawn_behavior(self, sim_info, resolver, household):
self._try_add_sim_info_to_household(sim_info, resolver, household)
def do_post_spawn_behavior(self, sim_info, resolver, client_manager):
client = client_manager.get_client_by_household_id(sim_info.household_id)
if client is not None:
client.add_selectable_sim_info(sim_info)
class _TargetedObjectResurrection(_BaseSimInfoSource):
FACTORY_TUNABLES = {'participant':TunableEnumEntry(description='\n The participant of the interaction against whom any relationship\n and genealogy tunables are applied.\n ',
tunable_type=ParticipantType,
default=ParticipantType.Actor),
'sim_info_subject':TunableEnumEntry(description='\n The subject from which the Sim Info used to create the new Sim\n should be fetched.\n ',
tunable_type=ParticipantType,
default=ParticipantType.Object),
'resurrect':Tunable(description='\n If checked, all Ghost traits are removed from the created Sim\n and its death type is cleared.\n \n If unchecked, this is a simple spawn operation.\n ',
tunable_type=bool,
default=True)}
def get_sim_infos_and_positions(self, resolver, household):
use_fgl = True
stored_sim_info_object = resolver.get_participant(self.sim_info_subject)
if stored_sim_info_object is None:
return ()
sim_info = stored_sim_info_object.get_stored_sim_info()
if sim_info is None:
return ()
return (
(
sim_info, stored_sim_info_object.position, None, use_fgl),)
def do_pre_spawn_behavior(self, sim_info, resolver, household):
super().do_pre_spawn_behavior(sim_info, resolver, household)
if self.resurrect:
sims.ghost.Ghost.remove_ghost_from_sim(sim_info)
class _MassObjectResurrection(_BaseSimInfoSource):
FACTORY_TUNABLES = {'participant':TunableEnumEntry(description='\n The participant of the interaction that will have sims resurrected\n around their position.\n ',
tunable_type=ParticipantType,
default=ParticipantType.Actor),
'radius':TunableDistanceSquared(description='\n The distance around a participant that will resurrect all of the\n dead sim objects.\n ',
default=1),
'tag':TunableEnumEntry(description='\n Tag the delineates an object that we want to resurrect sims\n from.\n ',
tunable_type=Tag,
default=Tag.INVALID)}
def get_sim_infos_and_positions(self, resolver, household):
use_fgl = True
sim_infos_and_positions = []
participant = resolver.get_participant(self.participant)
position = participant.position
for obj in services.object_manager().get_objects_with_tag_gen(self.tag):
obj_position = obj.position
distance_from_pos = obj_position - position
if distance_from_pos.magnitude_squared() > self.radius:
continue
sim_info = obj.get_stored_sim_info()
if sim_info is None:
continue
sim_infos_and_positions.append((sim_info, obj_position, None, use_fgl))
return tuple(sim_infos_and_positions)
def do_pre_spawn_behavior(self, sim_info, resolver, household):
super().do_pre_spawn_behavior(sim_info, resolver, household)
sims.ghost.Ghost.remove_ghost_from_sim(sim_info)
class _SlotSpawningSimInfoSource(_BaseSimInfoSource):
class _SlotByName(HasTunableSingletonFactory, AutoFactoryInit):
FACTORY_TUNABLES = {'slot_name': Tunable(description='\n The exact name of a slot on the parent object.\n ',
tunable_type=str,
default='_ctnm_')}
def get_slot_type_and_hash(self):
return (
None, sims4.hash_util.hash32(self.slot_name))
class _SlotByType(HasTunableSingletonFactory, AutoFactoryInit):
FACTORY_TUNABLES = {'slot_type': TunableReference(description='\n A particular slot type in which the should spawn.\n ',
manager=(services.get_instance_manager(sims4.resources.Types.SLOT_TYPE)))}
def get_slot_type_and_hash(self):
return (
self.slot_type, None)
FACTORY_TUNABLES = {'participant':TunableEnumEntry(description='\n The participant that is a sim that will be cloned\n Note: MUST be a sim. Use create object - clone object for non-sim objects.\n ',
tunable_type=ParticipantTypeSingleSim,
default=ParticipantTypeSingleSim.Actor),
'sim_spawn_slot':TunableVariant(description="\n The slot on the parent object where the sim should spawn. This\n may be either the exact name of a bone on the parent object or a\n slot type, in which case the first empty slot of the specified type\n will be used. If None is chosen, then the sim will at or near\n the interaction target's location.\n ",
by_name=_SlotByName.TunableFactory(),
by_type=_SlotByType.TunableFactory()),
'spawn_location_participant':TunableEnumEntry(description='\n The participant used for finding where to spawn the Sim. Typically you want to leave this as object.\n \n Special cases include:\n - For self-interactions, Object will resolve to None. This can be set to Actor if you want to spawn\n near the Sim running the interaction.\n ',
tunable_type=ParticipantTypeSingle,
default=ParticipantTypeSingle.Object)}
def __init__(self, sim_spawn_slot=None, **kwargs):
(super().__init__)(sim_spawn_slot=sim_spawn_slot, **kwargs)
self._slot_type = None
self._bone_name_hash = None
if sim_spawn_slot is not None:
self._slot_type, self._bone_name_hash = sim_spawn_slot.get_slot_type_and_hash()
def _get_position_and_location(self, spawning_object, resolver):
position, location = (None, None)
if self._slot_type is not None:
for runtime_slot in spawning_object.get_runtime_slots_gen(slot_types={self._slot_type}, bone_name_hash=(self._bone_name_hash)):
location = runtime_slot.location
else:
if self._bone_name_hash is not None:
runtime_slot = RuntimeSlot(spawning_object, self._bone_name_hash, EMPTY_SET)
if runtime_slot is not None:
location = runtime_slot.location
else:
location = spawning_object.location
if location is not None:
location = sims4.math.Location((location.world_transform), (spawning_object.routing_surface), slot_hash=(location.slot_hash))
position = location.transform.translation
return (position, location)
def _get_spawning_object(self, resolver):
spawning_object = resolver.get_participant(self.spawn_location_participant)
if spawning_object.is_sim:
spawning_object = spawning_object.get_sim_instance()
return spawning_object
class _CloneSimInfoSource(_SlotSpawningSimInfoSource):
FACTORY_TUNABLES = {'force_fgl': Tunable(description="\n Normally, FGL will only be invoked if no spawning position is found. Use this tunable to force\n FGL to run. e.g. Cloning spell uses caster Sim's position as a spawning position. In that case,\n we still want to force FGL so the clone spawns near that Sim rather than directly on top of the Sim. \n ",
tunable_type=bool,
default=False)}
def _ensure_parental_lineage_exists(self, source_sim_info, clone_sim_info):
with genealogy_caching():
if any(source_sim_info.genealogy.get_parent_sim_ids_gen()):
return
mom_id = id_generator.generate_object_id()
source_sim_info.genealogy.set_family_relation(FamilyRelationshipIndex.MOTHER, mom_id)
clone_sim_info.genealogy.set_family_relation(FamilyRelationshipIndex.MOTHER, mom_id)
sim_info_manager = services.sim_info_manager()
for child_sim_id in source_sim_info.genealogy.get_children_sim_ids_gen():
child_sim_info = sim_info_manager.get(child_sim_id)
if child_sim_info is not None:
grandparent_relation = FamilyRelationshipIndex.MOTHERS_MOM if source_sim_info.is_female else FamilyRelationshipIndex.FATHERS_MOM
child_sim_info.genealogy.set_family_relation(grandparent_relation, mom_id)
def _create_clone_sim_info(self, source_sim_info, resolver, household):
sim_creator = SimCreator(gender=(source_sim_info.gender), age=(source_sim_info.age),
first_name=(SimSpawner.get_random_first_name(source_sim_info.gender, source_sim_info.species)),
last_name=(source_sim_info._base.last_name),
traits=(source_sim_info.trait_tracker.equipped_traits))
sim_info_list, _ = SimSpawner.create_sim_infos((sim_creator,), household=household,
account=(source_sim_info.account),
generate_deterministic_sim=True,
creation_source='cloning',
skip_adding_to_household=True)
clone_sim_info = sim_info_list[0]
source_sim_proto = source_sim_info.save_sim(for_cloning=True)
clone_sim_id = clone_sim_info.sim_id
clone_first_name = clone_sim_info._base.first_name
clone_last_name = clone_sim_info._base.last_name
clone_breed_name = clone_sim_info._base.breed_name
clone_first_name_key = clone_sim_info._base.first_name_key
clone_last_name_key = clone_sim_info._base.last_name_key
clone_full_name_key = clone_sim_info._base.full_name_key
clone_breed_name_key = clone_sim_info._base.breed_name_key
clone_sim_info.load_sim_info(source_sim_proto, is_clone=True, default_lod=(SimInfoLODLevel.FULL))
clone_sim_info.sim_id = clone_sim_id
clone_sim_info._base.first_name = clone_first_name
clone_sim_info._base.last_name = clone_last_name
clone_sim_info._base.breed_name = clone_breed_name
clone_sim_info._base.first_name_key = clone_first_name_key
clone_sim_info._base.last_name_key = clone_last_name_key
clone_sim_info._base.full_name_key = clone_full_name_key
clone_sim_info._base.breed_name_key = clone_breed_name_key
clone_sim_info._household_id = household.id
if not self._try_add_sim_info_to_household(clone_sim_info, resolver, household, skip_household_check=True):
return
source_trait_tracker = source_sim_info.trait_tracker
clone_trait_tracker = clone_sim_info.trait_tracker
for trait in clone_trait_tracker.personality_traits:
if not source_trait_tracker.has_trait(trait):
clone_sim_info.remove_trait(trait)
for trait in clone_trait_tracker.gender_option_traits:
if not source_trait_tracker.has_trait(trait):
clone_sim_info.remove_trait(trait)
correct_aspiration_trait = clone_sim_info.primary_aspiration.primary_trait
for trait in tuple(clone_trait_tracker.aspiration_traits):
if trait is not correct_aspiration_trait:
clone_sim_info.remove_trait(trait)
source_sim_info.relationship_tracker.create_relationship(clone_sim_info.sim_id)
source_sim_info.relationship_tracker.add_relationship_score(clone_sim_info.sim_id, 1)
self._ensure_parental_lineage_exists(source_sim_info, clone_sim_info)
services.sim_info_manager().set_default_genealogy(sim_infos=(clone_sim_info,))
clone_sim_info.set_default_data()
clone_sim_info.save_sim()
household.save_data()
if not household.is_active_household:
clone_sim_info.request_lod(SimInfoLODLevel.BASE)
clone_sim_info.resend_physical_attributes()
clone_sim_info.relationship_tracker.clean_and_send_remaining_relationship_info()
return clone_sim_info
def do_pre_spawn_behavior(self, sim_info, resolver, household):
pass
def get_sim_infos_and_positions(self, resolver, household):
use_fgl = False
sim_info = resolver.get_participant(self.participant)
clone_sim_info = self._create_clone_sim_info(sim_info, resolver, household)
if clone_sim_info is None:
return ()
position, location = (None, None)
spawning_object = self._get_spawning_object(resolver)
if spawning_object is not None:
position, location = self._get_position_and_location(spawning_object, resolver)
use_fgl = self.force_fgl or position is None
return ((clone_sim_info, position, location, use_fgl),)
def do_post_spawn_behavior(self, sim_info, resolver, client_manager):
super().do_post_spawn_behavior(sim_info, resolver, client_manager)
sim_info.commodity_tracker.set_all_commodities_to_best_value(visible_only=True)
class _SimFilterSimInfoSource(_SlotSpawningSimInfoSource):
FACTORY_TUNABLES = {'filter': TunableSimFilter.TunableReference(description='\n Sim filter that is used to create or find a Sim that matches\n this filter request.\n ')}
def get_sim_filter_gsi_name(self):
return str(self)
def get_sim_infos_and_positions(self, resolver, household):
use_fgl = True
sim_info = resolver.get_participant(self.participant)
filter_result = services.sim_filter_service().submit_matching_filter(sim_filter=(self.filter), requesting_sim_info=sim_info,
allow_yielding=False,
gsi_source_fn=(self.get_sim_filter_gsi_name))
if not filter_result:
return ()
position, location = (None, None)
spawning_object = self._get_spawning_object(resolver)
if spawning_object is not None:
position, location = self._get_position_and_location(spawning_object, resolver)
use_fgl = position is None
return ((filter_result[0].sim_info, position, location, use_fgl),)
class _SimTemplateSimInfoSource(_SlotSpawningSimInfoSource):
FACTORY_TUNABLES = {'template': TunableSimTemplate.TunableReference(description='\n The template to use.\n ')}
def get_sim_infos_and_positions(self, resolver, household):
sim_creator = self.template.sim_creator
sim_info_list, _ = SimSpawner.create_sim_infos((sim_creator,), sim_name_type=(sim_creator.sim_name_type),
household=household)
self.template.add_template_data_to_sim((sim_info_list[0]), sim_creator=sim_creator)
position, location = (None, None)
spawning_object = self._get_spawning_object(resolver)
if spawning_object is not None:
position, location = self._get_position_and_location(spawning_object, resolver)
use_fgl = position is None
return ((sim_info_list[0], position, location, use_fgl),)
class _GenalogySetAsChild(HasTunableSingletonFactory):
def __call__(self, actor_sim_info, created_sim_info):
created_sim_info.last_name = SimSpawner.get_last_name(actor_sim_info.last_name, created_sim_info.gender, created_sim_info.species)
parent_a = actor_sim_info
parent_b = services.sim_info_manager().get(parent_a.spouse_sim_id)
created_sim_info.relationship_tracker.destroy_all_relationships()
for relation in FamilyRelationshipIndex:
relation_id = created_sim_info.get_relation(relation)
relation_info = services.sim_info_manager().get(relation_id)
if relation_info is not None:
created_sim_info.genealogy.remove_family_link(relation)
family_relation = relation_info.genealogy.get_family_relationship_bit(created_sim_info.sim_id)
relation_info.genealogy.clear_family_relation(family_relation)
relation_info.relationship_tracker.destroy_relationship(created_sim_info.sim_id)
created_sim_info.genealogy.clear_family_relation(relation)
PregnancyTracker.initialize_sim_info(created_sim_info, parent_a, parent_b)
FACTORY_TUNABLES = {'sim_info_source':TunableVariant(description='\n The source of the sim_info and position data for the sims to be\n created.\n ',
targeted=_TargetedObjectResurrection.TunableFactory(),
mass_object=_MassObjectResurrection.TunableFactory(),
clone_a_sim=_CloneSimInfoSource.TunableFactory(),
sim_filter=_SimFilterSimInfoSource.TunableFactory(),
sim_template=_SimTemplateSimInfoSource.TunableFactory(),
default='targeted'),
'household_option':TunableVariant(description='\n The household that the created sim should be put into.\n ',
active_household=_ActiveHouseholdFactory(),
participant_household=_ParticipantHouseholdFactory(),
no_household=_NoHousheoldFactory(),
hidden_household=_HiddenHouseholdFactory(),
default='participant_household'),
'spawn_action':TunableSpawnActionVariant(description='\n Define the methods to show the Sim after spawning on the lot. This\n defaults to fading the Sim in, but can be a specific interaction or\n an animation.\n '),
'relationship_bits_to_add':TunableList(description='\n A list of relationship bits to add between the source sim\n and the created sim.\n ',
tunable=TunableReference(manager=(services.get_instance_manager(sims4.resources.Types.RELATIONSHIP_BIT)))),
'set_summoning_purpose':OptionalTunable(description="\n If enabled this will trigger the summon NPC situation depending\n on the summoning purpose type set. This should be tuned when\n we create Sims and don't add them into the active household.\n ",
tunable=TunableEnumEntry(description='\n The purpose that is used to summon the sim to the lot. \n Defined in venue tuning.\n ',
tunable_type=NPCSummoningPurpose,
default=(NPCSummoningPurpose.DEFAULT))),
'set_genealogy':TunableVariant(description='\n Genealogy option to set on the created Sim. \n Example: Setting a child of a family.\n ',
set_as_child=_GenalogySetAsChild.TunableFactory(),
locked_args={'no_action': None},
default='no_action'),
'pre_spawn_loot':TunableList(description='\n List of loot actions to apply to the created sim info before it is\n spawned.\n ',
tunable=TunableReference(manager=(services.get_instance_manager(sims4.resources.Types.ACTION)),
class_restrictions=('LootActions', )))}
def _apply_relationship_bits(self, actor_sim_info, created_sim_info):
for rel_bit in self.relationship_bits_to_add:
actor_sim_info.relationship_tracker.add_relationship_bit((created_sim_info.sim_id), rel_bit, force_add=True)
def _do_behavior(self):
resolver = self.interaction.get_resolver()
target_participant = resolver.get_participant(self.sim_info_source.participant)
household = self.household_option(self.interaction)
client_manager = services.client_manager()
for sim_info, position, location, use_fgl in self.sim_info_source.get_sim_infos_and_positions(resolver, household):
if target_participant is not None:
self._apply_relationship_bits(target_participant, sim_info)
else:
single_sim_resolver = SingleSimResolver(sim_info)
for loot in self.pre_spawn_loot:
loot.apply_to_resolver(single_sim_resolver)
self.sim_info_source.do_pre_spawn_behavior(sim_info, resolver, household)
SimSpawner.spawn_sim(sim_info, position, spawn_action=(self.spawn_action), sim_location=location, use_fgl=use_fgl)
if self.set_summoning_purpose is not None:
services.current_zone().venue_service.active_venue.summon_npcs((sim_info,), self.set_summoning_purpose)
if self.set_genealogy is not None and target_participant is not None:
self.set_genealogy(target_participant, sim_info)
self.sim_info_source.do_post_spawn_behavior(sim_info, resolver, client_manager)
return True
| true
| true
|
f716ac0e3959c71c4d5608a0af8d5faae868b677
| 93
|
py
|
Python
|
src/bot.py
|
amirsafiee/template_telegram_bot
|
2dfaba93de6ff6066a8b2ac64e1ee95be19c1548
|
[
"MIT"
] | null | null | null |
src/bot.py
|
amirsafiee/template_telegram_bot
|
2dfaba93de6ff6066a8b2ac64e1ee95be19c1548
|
[
"MIT"
] | null | null | null |
src/bot.py
|
amirsafiee/template_telegram_bot
|
2dfaba93de6ff6066a8b2ac64e1ee95be19c1548
|
[
"MIT"
] | 1
|
2022-01-24T12:59:19.000Z
|
2022-01-24T12:59:19.000Z
|
import os
import telebot
bot = telebot.TeleBot(os.environ['BOT_TOKEN'], parse_mode='HTML')
| 15.5
| 65
| 0.752688
|
import os
import telebot
bot = telebot.TeleBot(os.environ['BOT_TOKEN'], parse_mode='HTML')
| true
| true
|
f716ac52c89aa1029f08962d2e19db4bbf190274
| 796
|
py
|
Python
|
util/plaintext.py
|
seounghwan-oh/homomorphic_encryption
|
be700505547b81671c37026e55c4eefbd44dcaae
|
[
"MIT"
] | 25
|
2020-11-06T13:54:33.000Z
|
2022-03-18T18:53:37.000Z
|
util/plaintext.py
|
seounghwan-oh/homomorphic_encryption
|
be700505547b81671c37026e55c4eefbd44dcaae
|
[
"MIT"
] | 1
|
2021-04-04T17:49:00.000Z
|
2021-04-05T13:46:21.000Z
|
util/plaintext.py
|
seounghwan-oh/homomorphic_encryption
|
be700505547b81671c37026e55c4eefbd44dcaae
|
[
"MIT"
] | 6
|
2021-04-04T17:26:09.000Z
|
2022-03-28T19:26:29.000Z
|
"""A module to keep track of a plaintext."""
class Plaintext:
"""An instance of a plaintext.
This is a wrapper class for a plaintext, which consists
of one polynomial.
Attributes:
poly (Polynomial): Plaintext polynomial.
scaling_factor (float): Scaling factor.
"""
def __init__(self, poly, scaling_factor=None):
"""Sets plaintext to given polynomial.
Args:
poly (Polynomial): Plaintext polynomial.
scaling_factor (float): Scaling factor.
"""
self.poly = poly
self.scaling_factor = scaling_factor
def __str__(self):
"""Represents plaintext as a readable string.
Returns:
A string which represents the Plaintext.
"""
return str(self.poly)
| 25.677419
| 59
| 0.614322
|
class Plaintext:
def __init__(self, poly, scaling_factor=None):
self.poly = poly
self.scaling_factor = scaling_factor
def __str__(self):
return str(self.poly)
| true
| true
|
f716acb54b4962beca238b19ba8258573c3ffe65
| 766
|
py
|
Python
|
tst/util/chain_utils.py
|
TST-Group-BE/flax-blockchain
|
ed850df4f28ef4b6f71c175c8b6d07d27f7b3cd5
|
[
"Apache-2.0"
] | null | null | null |
tst/util/chain_utils.py
|
TST-Group-BE/flax-blockchain
|
ed850df4f28ef4b6f71c175c8b6d07d27f7b3cd5
|
[
"Apache-2.0"
] | null | null | null |
tst/util/chain_utils.py
|
TST-Group-BE/flax-blockchain
|
ed850df4f28ef4b6f71c175c8b6d07d27f7b3cd5
|
[
"Apache-2.0"
] | null | null | null |
from typing import List
from tst.types.blockchain_format.coin import Coin
from tst.types.blockchain_format.program import SerializedProgram
from tst.types.blockchain_format.sized_bytes import bytes32
from tst.util.condition_tools import (
conditions_dict_for_solution,
created_outputs_for_conditions_dict,
)
def additions_for_solution(
coin_name: bytes32, puzzle_reveal: SerializedProgram, solution: SerializedProgram, max_cost: int
) -> List[Coin]:
"""
Checks the conditions created by CoinSolution and returns the list of all coins created
"""
err, dic, cost = conditions_dict_for_solution(puzzle_reveal, solution, max_cost)
if err or dic is None:
return []
return created_outputs_for_conditions_dict(dic, coin_name)
| 34.818182
| 100
| 0.784595
|
from typing import List
from tst.types.blockchain_format.coin import Coin
from tst.types.blockchain_format.program import SerializedProgram
from tst.types.blockchain_format.sized_bytes import bytes32
from tst.util.condition_tools import (
conditions_dict_for_solution,
created_outputs_for_conditions_dict,
)
def additions_for_solution(
coin_name: bytes32, puzzle_reveal: SerializedProgram, solution: SerializedProgram, max_cost: int
) -> List[Coin]:
err, dic, cost = conditions_dict_for_solution(puzzle_reveal, solution, max_cost)
if err or dic is None:
return []
return created_outputs_for_conditions_dict(dic, coin_name)
| true
| true
|
f716ada0abc16a0476ced0d0b57cf8389076e57f
| 7,206
|
py
|
Python
|
ABA_PY/CHK_2D_postp_part_A_v4.4.py
|
SunilAnandatheertha/ABAPYMAT
|
48d4d178de38c1f3c4510ad7f06fe1647ae6227c
|
[
"BSD-3-Clause"
] | 1
|
2021-01-13T14:06:34.000Z
|
2021-01-13T14:06:34.000Z
|
ABA_PY/CHK_2D_postp_part_A_v4.4.py
|
SunilAnandatheertha/ABAPYMAT
|
48d4d178de38c1f3c4510ad7f06fe1647ae6227c
|
[
"BSD-3-Clause"
] | 5
|
2020-12-21T14:51:57.000Z
|
2021-01-21T13:42:44.000Z
|
ABA_PY/CHK_2D_postp_part_A_v4.4.py
|
SunilAnandatheertha/ABAPYMAT-PXTAL-2D
|
48d4d178de38c1f3c4510ad7f06fe1647ae6227c
|
[
"BSD-3-Clause"
] | 1
|
2022-02-25T20:03:18.000Z
|
2022-02-25T20:03:18.000Z
|
"""
COMPLETE DESCRIPTION HERE
"""
#-----------------------------------------------------------------
#1. Seperate this file for calibration models
# MAKE THE PYTHON PRE, MATLAB AND PYTHON POST and SPYDER pipelines ready for calibrations
# Set up the required folders
# Set up the excel file where to store the file numbers and model numbers
#2. Seperate this file for residual stress induce model
#3. Seperate this file for residual stress induce and relaxation model
#-----------------------------------------------------------------
# ANYTHING TO AID DEVELOPEMENT GOES HERE
# Snippet: to get strain atv centroids and SOLUTION DEPENDENT VARIABLES AT THE CENTROIDAL POSITIONS
# Strain = lastFrame.fieldOutputs['LE'].getSubset(region=polyI)
# Strain = myOdb.steps['Step-1'].frames[1].fieldOutputs['LE' ].getSubset(region=polyI, position=CENTROID)
# p01 = myOdb.steps['Step-1'].frames[1].fieldOutputs['SDV7' ].getSubset(region=polyI, position=CENTROID)
#-----------------------------------------------------------------
# Compatibility listing
# 1. CPS4, CPS4R
#-----------------------------------------------------------------
from abaqus import *
from abaqusConstants import *
from caeModules import *
from driverUtils import executeOnCaeStartup
import os
import visualization
import time
import numpy as np
#-----------------------------------------------------------------
executeOnCaeStartup()
Mdb()
#-----------------------------------------------------------------
# model and basic element dimensions
# GET USER REQUIREMENTS FOR UPPER CAPPING AND LOWER CAPPING THE CONTOUR DISPLAY LEGEND LEVELS
from abaqus import getInputs
fields = (('Model_origin_x:', '0'),
('Model_origin_y:', '0'),
('Model_enddim_x:', '100'),
('Model_enddim_y:', '6'),
('Model_enddim_z:', '1'),
)
Model_origin_x, Model_origin_y, Model_enddim_x,\
Model_enddim_y, Model_enddim_z, \
= getInputs(fields = fields, label = 'Specify Checkerboard model dimsnions:', dialogTitle = 'Keep origin at (0, 0) for now', )
Model_origin_x = float(Model_origin_x)
Model_origin_y = float(Model_origin_y)
Model_enddim_x = float(Model_enddim_x)
Model_enddim_y = float(Model_enddim_y)
Model_enddim_z = float(Model_enddim_z)
del fields
#-----------------------------------------------------------------
# Acquire level 0 solution metadata
odbinfo = (('Location', 'B'),
('Calibration iteration number(as: 00n, 0mn, mno', '009'),
('ODB_FileName (enter without the .odb):', 'Loc_B_009'),
('# of frames:', '16'),
('# of grains along x:', '24'),
('# of grains along y:', '4'),
('Element factor used:', '1'),
('SolutionInstance_metadata_Num (keep unchanged for now)', '1'),
('SolutionInstance_folder_path', 'C:\\Users\\anandats\\OneDrive - Coventry University\\coventry-thesis\\Chapter7\\ABAQUS_CAL_DATA_FILES\\LocationB\\'),
)
Cal_Location, Calib_Iteration_Num, This_ODB_FILENAME, NumOfFrames, NumGrains_X,\
NumGrains_Y, Elem_Factor_Used, SolInst_metadata_Num, SolInst_folder_path\
= getInputs(fields = odbinfo, label = 'Specify details of solution file', dialogTitle = 'Level 0 solution metadata', )
del odbinfo
MODEL_INFORMATION = {1:['ODBfilename', This_ODB_FILENAME, 'frames', NumOfFrames, 'Ngx', NumGrains_X, 'Ngy', NumGrains_Y, 'ElemFacUSED', Elem_Factor_Used],
2:[SolInst_folder_path],}
# only enter odd number IDs for ODB_ID, i.e. only line number containing meta-data and not folder address
ODB_ID = 1
ODB_FileName = MODEL_INFORMATION[ODB_ID][1]
TotalNumFrames = int(MODEL_INFORMATION[ODB_ID][3])
NumPartitions_x = int(MODEL_INFORMATION[ODB_ID][5])
NumPartitions_y = int(MODEL_INFORMATION[ODB_ID][7])
factorUSED = float(MODEL_INFORMATION[ODB_ID][9])
ElementSize = (Model_enddim_y/NumPartitions_y)/factorUSED
# frame incerements needed
# texture id value
# Elements per grain value
# elemebnt type value
frincr = 1
TEXIDVALUE = '02'
EPGValue = '003'
ElementType = 'CPS4'
#-----------------------------------------------------------------
# generate variable values needed to re-create element set names
Num_DatumPlanes_x = NumPartitions_x - 1
Num_DatumPlanes_y = NumPartitions_y - 1
#--------------------------------------------------------
# should the elemental results at centrouids be extracted? If extracted, they will be written to file
Extract_S11_ELCEN = 0
Extract_S22_ELCEN = 0
Extract_S12_ELCEN = 0
#--------------------------------------------------------
# defining filenames
import random
RandomNumber_START_MATLAB_OUTPUT = str(random.randint(10, 99))
RandomNumber_END_MATLAB_OUTPUT = str(random.randint(10, 99))
#--------------------------------------------------------
# GET USER REQUIREMENTS FOR UPPER CAPPING AND LOWER CAPPING THE CONTOUR DISPLAY LEGEND LEVELS
from abaqus import getInputs
fields = (('S11_contour_label_max_MPa:', '+0500'),
('S11_contour_label_min_MPa:', '+0000'),
('S22_contour_label_max_MPa:', '+0100'),
('S22_contour_label_min_MPa:', '-0100'),
('S12_contour_label_max_MPa:', '+0050'),
('S12_contour_label_min_MPa:', '-0050'),
)
S11_contour_label_max_MPa, S11_contour_label_min_MPa,\
S22_contour_label_max_MPa, S22_contour_label_min_MPa,\
S12_contour_label_max_MPa, S12_contour_label_min_MPa, \
= getInputs(fields = fields, label = 'Specify UPPER AND LOWER CAPPING LEVELS FOR CONTOUR LEGEND:', dialogTitle = 'Legend limits: STRESS', )
S11_contour_label_max_MPa = float(S11_contour_label_max_MPa)
S11_contour_label_min_MPa = float(S11_contour_label_min_MPa)
S22_contour_label_max_MPa = float(S22_contour_label_max_MPa)
S22_contour_label_min_MPa = float(S22_contour_label_min_MPa)
S12_contour_label_max_MPa = float(S12_contour_label_max_MPa)
S12_contour_label_min_MPa = float(S12_contour_label_min_MPa)
del fields
#--------------------------------------------------------
# PRINT FLAGS TO SPECIFY WHTHER IMAGES ARE TO BE PRINTED TO PNG FILES
fields = (('Print_S11_Contours_File:', '0'), ('Print_S22_Contours_File:', '0'),
('Print_S12_Contours_File:', '0'),
)
Print_S11_Contours_File, Print_S22_Contours_File,\
Print_S12_Contours_File,\
= getInputs(fields = fields, label = 'Enter 1(print) and 0(dont print)', dialogTitle = 'Set print to .png file requirements', )
Print_S11_Contours_File = float(Print_S11_Contours_File)
del fields
#-----------------------------------------------------------------
# VIEWPORT - 1
VP_num = 1
VP_name = 'Viewport: ' + str(VP_num)
#VP_ODB_PathName = 'C:/Temp/CalibrationModels/Cal_100ng/'
VP_ODB_PathName = MODEL_INFORMATION[ODB_ID+1][0]
#VP_ODB_FileName = ODB_FileName + '.odb'
VP_ODB_FileName = MODEL_INFORMATION[ODB_ID][1]
VP_ODB_FullPathName = VP_ODB_PathName + VP_ODB_FileName + '.odb'
VP_UpGraded_ODB_FullPathName = VP_ODB_PathName + VP_ODB_FileName + '_UpGraded' + '.odb'
MVPport = session.Viewport(name = VP_name, origin = (0.0, 0.0), width = 150, height = 100)
SESS_VP = session.viewports[VP_name]
SESS_VP.makeCurrent()
SESS_VP.maximize()
SESS_VP.partDisplay.geometryOptions.setValues(referenceRepresentation = ON)
SESS_VP.setValues(displayedObject = None)
import os.path
import odbAccess
import visualization
import abaqus
| 44.481481
| 156
| 0.664585
|
from abaqus import *
from abaqusConstants import *
from caeModules import *
from driverUtils import executeOnCaeStartup
import os
import visualization
import time
import numpy as np
executeOnCaeStartup()
Mdb()
from abaqus import getInputs
fields = (('Model_origin_x:', '0'),
('Model_origin_y:', '0'),
('Model_enddim_x:', '100'),
('Model_enddim_y:', '6'),
('Model_enddim_z:', '1'),
)
Model_origin_x, Model_origin_y, Model_enddim_x,\
Model_enddim_y, Model_enddim_z, \
= getInputs(fields = fields, label = 'Specify Checkerboard model dimsnions:', dialogTitle = 'Keep origin at (0, 0) for now', )
Model_origin_x = float(Model_origin_x)
Model_origin_y = float(Model_origin_y)
Model_enddim_x = float(Model_enddim_x)
Model_enddim_y = float(Model_enddim_y)
Model_enddim_z = float(Model_enddim_z)
del fields
odbinfo = (('Location', 'B'),
('Calibration iteration number(as: 00n, 0mn, mno', '009'),
('ODB_FileName (enter without the .odb):', 'Loc_B_009'),
('# of frames:', '16'),
('# of grains along x:', '24'),
('# of grains along y:', '4'),
('Element factor used:', '1'),
('SolutionInstance_metadata_Num (keep unchanged for now)', '1'),
('SolutionInstance_folder_path', 'C:\\Users\\anandats\\OneDrive - Coventry University\\coventry-thesis\\Chapter7\\ABAQUS_CAL_DATA_FILES\\LocationB\\'),
)
Cal_Location, Calib_Iteration_Num, This_ODB_FILENAME, NumOfFrames, NumGrains_X,\
NumGrains_Y, Elem_Factor_Used, SolInst_metadata_Num, SolInst_folder_path\
= getInputs(fields = odbinfo, label = 'Specify details of solution file', dialogTitle = 'Level 0 solution metadata', )
del odbinfo
MODEL_INFORMATION = {1:['ODBfilename', This_ODB_FILENAME, 'frames', NumOfFrames, 'Ngx', NumGrains_X, 'Ngy', NumGrains_Y, 'ElemFacUSED', Elem_Factor_Used],
2:[SolInst_folder_path],}
ODB_ID = 1
ODB_FileName = MODEL_INFORMATION[ODB_ID][1]
TotalNumFrames = int(MODEL_INFORMATION[ODB_ID][3])
NumPartitions_x = int(MODEL_INFORMATION[ODB_ID][5])
NumPartitions_y = int(MODEL_INFORMATION[ODB_ID][7])
factorUSED = float(MODEL_INFORMATION[ODB_ID][9])
ElementSize = (Model_enddim_y/NumPartitions_y)/factorUSED
frincr = 1
TEXIDVALUE = '02'
EPGValue = '003'
ElementType = 'CPS4'
Num_DatumPlanes_x = NumPartitions_x - 1
Num_DatumPlanes_y = NumPartitions_y - 1
Extract_S11_ELCEN = 0
Extract_S22_ELCEN = 0
Extract_S12_ELCEN = 0
import random
RandomNumber_START_MATLAB_OUTPUT = str(random.randint(10, 99))
RandomNumber_END_MATLAB_OUTPUT = str(random.randint(10, 99))
from abaqus import getInputs
fields = (('S11_contour_label_max_MPa:', '+0500'),
('S11_contour_label_min_MPa:', '+0000'),
('S22_contour_label_max_MPa:', '+0100'),
('S22_contour_label_min_MPa:', '-0100'),
('S12_contour_label_max_MPa:', '+0050'),
('S12_contour_label_min_MPa:', '-0050'),
)
S11_contour_label_max_MPa, S11_contour_label_min_MPa,\
S22_contour_label_max_MPa, S22_contour_label_min_MPa,\
S12_contour_label_max_MPa, S12_contour_label_min_MPa, \
= getInputs(fields = fields, label = 'Specify UPPER AND LOWER CAPPING LEVELS FOR CONTOUR LEGEND:', dialogTitle = 'Legend limits: STRESS', )
S11_contour_label_max_MPa = float(S11_contour_label_max_MPa)
S11_contour_label_min_MPa = float(S11_contour_label_min_MPa)
S22_contour_label_max_MPa = float(S22_contour_label_max_MPa)
S22_contour_label_min_MPa = float(S22_contour_label_min_MPa)
S12_contour_label_max_MPa = float(S12_contour_label_max_MPa)
S12_contour_label_min_MPa = float(S12_contour_label_min_MPa)
del fields
fields = (('Print_S11_Contours_File:', '0'), ('Print_S22_Contours_File:', '0'),
('Print_S12_Contours_File:', '0'),
)
Print_S11_Contours_File, Print_S22_Contours_File,\
Print_S12_Contours_File,\
= getInputs(fields = fields, label = 'Enter 1(print) and 0(dont print)', dialogTitle = 'Set print to .png file requirements', )
Print_S11_Contours_File = float(Print_S11_Contours_File)
del fields
VP_num = 1
VP_name = 'Viewport: ' + str(VP_num)
VP_ODB_PathName = MODEL_INFORMATION[ODB_ID+1][0]
VP_ODB_FileName = MODEL_INFORMATION[ODB_ID][1]
VP_ODB_FullPathName = VP_ODB_PathName + VP_ODB_FileName + '.odb'
VP_UpGraded_ODB_FullPathName = VP_ODB_PathName + VP_ODB_FileName + '_UpGraded' + '.odb'
MVPport = session.Viewport(name = VP_name, origin = (0.0, 0.0), width = 150, height = 100)
SESS_VP = session.viewports[VP_name]
SESS_VP.makeCurrent()
SESS_VP.maximize()
SESS_VP.partDisplay.geometryOptions.setValues(referenceRepresentation = ON)
SESS_VP.setValues(displayedObject = None)
import os.path
import odbAccess
import visualization
import abaqus
| true
| true
|
f716adfe3834f0888422f74ed1401389c19dd141
| 21,995
|
py
|
Python
|
sdk/network/azure-mgmt-network/azure/mgmt/network/v2018_06_01/aio/operations/_vpn_connections_operations.py
|
rsdoherty/azure-sdk-for-python
|
6bba5326677468e6660845a703686327178bb7b1
|
[
"MIT"
] | 3
|
2020-06-23T02:25:27.000Z
|
2021-09-07T18:48:11.000Z
|
sdk/network/azure-mgmt-network/azure/mgmt/network/v2018_06_01/aio/operations/_vpn_connections_operations.py
|
rsdoherty/azure-sdk-for-python
|
6bba5326677468e6660845a703686327178bb7b1
|
[
"MIT"
] | 510
|
2019-07-17T16:11:19.000Z
|
2021-08-02T08:38:32.000Z
|
sdk/network/azure-mgmt-network/azure/mgmt/network/v2018_06_01/aio/operations/_vpn_connections_operations.py
|
rsdoherty/azure-sdk-for-python
|
6bba5326677468e6660845a703686327178bb7b1
|
[
"MIT"
] | 5
|
2019-09-04T12:51:37.000Z
|
2020-09-16T07:28:40.000Z
|
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union
import warnings
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
from azure.mgmt.core.exceptions import ARMErrorFormat
from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
from ... import models as _models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class VpnConnectionsOperations:
"""VpnConnectionsOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.mgmt.network.v2018_06_01.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
async def get(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
**kwargs
) -> "_models.VpnConnection":
"""Retrieves the details of a vpn connection.
:param resource_group_name: The resource group name of the VpnGateway.
:type resource_group_name: str
:param gateway_name: The name of the gateway.
:type gateway_name: str
:param connection_name: The name of the vpn connection.
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: VpnConnection, or the result of cls(response)
:rtype: ~azure.mgmt.network.v2018_06_01.models.VpnConnection
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.VpnConnection"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-06-01"
accept = "application/json"
# Construct URL
url = self.get.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize('VpnConnection', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'} # type: ignore
async def _create_or_update_initial(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
vpn_connection_parameters: "_models.VpnConnection",
**kwargs
) -> "_models.VpnConnection":
cls = kwargs.pop('cls', None) # type: ClsType["_models.VpnConnection"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-06-01"
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
# Construct URL
url = self._create_or_update_initial.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(vpn_connection_parameters, 'VpnConnection')
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if response.status_code == 200:
deserialized = self._deserialize('VpnConnection', pipeline_response)
if response.status_code == 201:
deserialized = self._deserialize('VpnConnection', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'} # type: ignore
async def begin_create_or_update(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
vpn_connection_parameters: "_models.VpnConnection",
**kwargs
) -> AsyncLROPoller["_models.VpnConnection"]:
"""Creates a vpn connection to a scalable vpn gateway if it doesn't exist else updates the
existing connection.
:param resource_group_name: The resource group name of the VpnGateway.
:type resource_group_name: str
:param gateway_name: The name of the gateway.
:type gateway_name: str
:param connection_name: The name of the connection.
:type connection_name: str
:param vpn_connection_parameters: Parameters supplied to create or Update a VPN Connection.
:type vpn_connection_parameters: ~azure.mgmt.network.v2018_06_01.models.VpnConnection
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: Pass in True if you'd like the AsyncARMPolling polling method,
False for no polling, or your own initialized polling object for a personal polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either VpnConnection or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[~azure.mgmt.network.v2018_06_01.models.VpnConnection]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType["_models.VpnConnection"]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._create_or_update_initial(
resource_group_name=resource_group_name,
gateway_name=gateway_name,
connection_name=connection_name,
vpn_connection_parameters=vpn_connection_parameters,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('VpnConnection', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'} # type: ignore
async def _delete_initial(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
**kwargs
) -> None:
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-06-01"
accept = "application/json"
# Construct URL
url = self._delete_initial.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.delete(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'} # type: ignore
async def begin_delete(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
**kwargs
) -> AsyncLROPoller[None]:
"""Deletes a vpn connection.
:param resource_group_name: The resource group name of the VpnGateway.
:type resource_group_name: str
:param gateway_name: The name of the gateway.
:type gateway_name: str
:param connection_name: The name of the connection.
:type connection_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: Pass in True if you'd like the AsyncARMPolling polling method,
False for no polling, or your own initialized polling object for a personal polling strategy.
:paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present.
:return: An instance of AsyncLROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.AsyncLROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError:
"""
polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod]
cls = kwargs.pop('cls', None) # type: ClsType[None]
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None) # type: Optional[str]
if cont_token is None:
raw_result = await self._delete_initial(
resource_group_name=resource_group_name,
gateway_name=gateway_name,
connection_name=connection_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'} # type: ignore
def list_by_vpn_gateway(
self,
resource_group_name: str,
gateway_name: str,
**kwargs
) -> AsyncIterable["_models.ListVpnConnectionsResult"]:
"""Retrieves all vpn connections for a particular virtual wan vpn gateway.
:param resource_group_name: The resource group name of the VpnGateway.
:type resource_group_name: str
:param gateway_name: The name of the gateway.
:type gateway_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either ListVpnConnectionsResult or the result of cls(response)
:rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.network.v2018_06_01.models.ListVpnConnectionsResult]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.ListVpnConnectionsResult"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-06-01"
accept = "application/json"
def prepare_request(next_link=None):
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
# Construct URL
url = self.list_by_vpn_gateway.metadata['url'] # type: ignore
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {} # type: Dict[str, Any]
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('ListVpnConnectionsResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
error = self._deserialize.failsafe_deserialize(_models.Error, response)
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(
get_next, extract_data
)
list_by_vpn_gateway.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections'} # type: ignore
| 50.447248
| 220
| 0.672926
|
from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union
import warnings
from azure.core.async_paging import AsyncItemPaged, AsyncList
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod
from azure.mgmt.core.exceptions import ARMErrorFormat
from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling
from ... import models as _models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class VpnConnectionsOperations:
models = _models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
async def get(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
**kwargs
) -> "_models.VpnConnection":
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-06-01"
accept = "application/json"
url = self.get.metadata['url']
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize('VpnConnection', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'}
async def _create_or_update_initial(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
vpn_connection_parameters: "_models.VpnConnection",
**kwargs
) -> "_models.VpnConnection":
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-06-01"
content_type = kwargs.pop("content_type", "application/json")
accept = "application/json"
url = self._create_or_update_initial.metadata['url']
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
header_parameters = {}
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {}
body_content = self._serialize.body(vpn_connection_parameters, 'VpnConnection')
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if response.status_code == 200:
deserialized = self._deserialize('VpnConnection', pipeline_response)
if response.status_code == 201:
deserialized = self._deserialize('VpnConnection', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'}
async def begin_create_or_update(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
vpn_connection_parameters: "_models.VpnConnection",
**kwargs
) -> AsyncLROPoller["_models.VpnConnection"]:
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None)
if cont_token is None:
raw_result = await self._create_or_update_initial(
resource_group_name=resource_group_name,
gateway_name=gateway_name,
connection_name=connection_name,
vpn_connection_parameters=vpn_connection_parameters,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize('VpnConnection', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'}
async def _delete_initial(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
**kwargs
) -> None:
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-06-01"
accept = "application/json"
url = self._delete_initial.metadata['url']
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
header_parameters = {}
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.delete(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200, 202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.Error, response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if cls:
return cls(pipeline_response, None, {})
_delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'}
async def begin_delete(
self,
resource_group_name: str,
gateway_name: str,
connection_name: str,
**kwargs
) -> AsyncLROPoller[None]:
polling = kwargs.pop('polling', True)
cls = kwargs.pop('cls', None)
lro_delay = kwargs.pop(
'polling_interval',
self._config.polling_interval
)
cont_token = kwargs.pop('continuation_token', None)
if cont_token is None:
raw_result = await self._delete_initial(
resource_group_name=resource_group_name,
gateway_name=gateway_name,
connection_name=connection_name,
cls=lambda x,y,z: x,
**kwargs
)
kwargs.pop('error_map', None)
kwargs.pop('content_type', None)
def get_long_running_output(pipeline_response):
if cls:
return cls(pipeline_response, None, {})
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
'connectionName': self._serialize.url("connection_name", connection_name, 'str'),
}
if polling is True: polling_method = AsyncARMPolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
elif polling is False: polling_method = AsyncNoPolling()
else: polling_method = polling
if cont_token:
return AsyncLROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output
)
else:
return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections/{connectionName}'}
def list_by_vpn_gateway(
self,
resource_group_name: str,
gateway_name: str,
**kwargs
) -> AsyncIterable["_models.ListVpnConnectionsResult"]:
cls = kwargs.pop('cls', None)
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
api_version = "2018-06-01"
accept = "application/json"
def prepare_request(next_link=None):
header_parameters = {}
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
if not next_link:
url = self.list_by_vpn_gateway.metadata['url']
path_format_arguments = {
'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'),
'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'),
'gatewayName': self._serialize.url("gateway_name", gateway_name, 'str'),
}
url = self._client.format_url(url, **path_format_arguments)
query_parameters = {}
query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str')
request = self._client.get(url, query_parameters, header_parameters)
else:
url = next_link
query_parameters = {}
request = self._client.get(url, query_parameters, header_parameters)
return request
async def extract_data(pipeline_response):
deserialized = self._deserialize('ListVpnConnectionsResult', pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, AsyncList(list_of_elem)
async def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
error = self._deserialize.failsafe_deserialize(_models.Error, response)
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
return pipeline_response
return AsyncItemPaged(
get_next, extract_data
)
list_by_vpn_gateway.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Network/vpnGateways/{gatewayName}/vpnConnections'}
| true
| true
|
f716ae18f7020711ec64646291e557f4c140c538
| 51
|
py
|
Python
|
temp.py
|
Ziggareto/hackerrank_python
|
02875e9677d74f397d75577b8f3b38b584f31506
|
[
"MIT"
] | null | null | null |
temp.py
|
Ziggareto/hackerrank_python
|
02875e9677d74f397d75577b8f3b38b584f31506
|
[
"MIT"
] | null | null | null |
temp.py
|
Ziggareto/hackerrank_python
|
02875e9677d74f397d75577b8f3b38b584f31506
|
[
"MIT"
] | null | null | null |
if __name__ == '__main__':
print('hi there!!')
| 25.5
| 27
| 0.588235
|
if __name__ == '__main__':
print('hi there!!')
| true
| true
|
f716ae2b03112cbb59f032b586e1bf0f10e6bd85
| 11,973
|
py
|
Python
|
cbt/views.py
|
belloshehu/multiple-choice-questions
|
abfb7ac8cc24bc3f9ee34e9505bc6c6944786ac0
|
[
"MIT"
] | null | null | null |
cbt/views.py
|
belloshehu/multiple-choice-questions
|
abfb7ac8cc24bc3f9ee34e9505bc6c6944786ac0
|
[
"MIT"
] | 2
|
2020-09-03T21:48:33.000Z
|
2020-09-22T08:51:14.000Z
|
cbt/views.py
|
belloshehu/multiple-choice-questions
|
abfb7ac8cc24bc3f9ee34e9505bc6c6944786ac0
|
[
"MIT"
] | null | null | null |
from django.shortcuts import (
render,
redirect,
reverse,
get_object_or_404,
get_list_or_404,
)
from django.db.models import Q
from django.urls import reverse_lazy
from django.contrib.auth import authenticate, login, logout
from django.contrib import messages
from cbt.forms import (
IndividualAssessmentForm,
InstitutionForm, InstitutionAssessmentForm
)
from multiple_choices.forms import UserRegistration, UserLogin
from .models import (
InstitutionAssessment,
IndividualAssessment,
Institution
)
from account.forms import UserLoginForm, UserCreationForm
from choice.models import IndividualChoice, InstitutionChoice
from question.models import IndividualQuestion, InstitutionQuestion
from django.core.mail import send_mail
from django.conf import settings
from django.contrib.auth.models import User
from django.contrib.auth.forms import PasswordResetForm
from django.http import HttpResponse
from django.template.loader import render_to_string
from django.utils.http import urlsafe_base64_encode
from django.contrib.auth.tokens import default_token_generator
from django.utils.encoding import force_bytes
from django.views.generic import(
CreateView,
DeleteView,
DetailView,
ListView,
UpdateView,
TemplateView,
View
)
from django.contrib.auth.mixins import LoginRequiredMixin
# Create your views here.
def home(request):
return render (request, 'cbt/home.html', {'form':UserLoginForm})
def cbt_type(request):
'''Renders Assessment types template'''
return render (request, 'cbt/assessment_types.html')
class AssessmentHelpView(TemplateView):
template_name = 'cbt/partials/assessment_help.html'
###############
# Institution Assessment type CRUD, list and details views:
##############
class InstitutionAssessmentCreateView(LoginRequiredMixin ,CreateView):
''' View to create Assessment by organisation. '''
model = InstitutionAssessment
form_class = InstitutionAssessmentForm
template_name = 'cbt/institution/assessment_form.html'
success_url = reverse_lazy('cbt:institution-assessment-list')
def form_valid(self, form):
form.instance.user = self.request.user
return super().form_valid(form)
class InstitutionAssessmentListView(LoginRequiredMixin, ListView):
model = InstitutionAssessment
template_name = 'cbt/institution/assessment_list.html'
context_object_name = 'assessments'
class InstitutionAssessmentDetailView(LoginRequiredMixin, DetailView):
model = InstitutionAssessment
template_name = 'cbt/institution/assessment_detail.html'
context_object_name = 'assessment'
def get_context_data(self, *args, **kwargs):
context = super().get_context_data(*args, **kwargs)
try:
context['choices'] = InstitutionChoice.objects.all()
context['questions'] = InstitutionQuestion.objects.filter(
assessment_id=self.kwargs.get('pk')
)
except InstitutionChoice.DoesNotExist:
context['choices'] = None
except InstitutionQuestion.DoesNotExist:
context['questions'] = None
return context
class InstitutionAssessmentUpdateView(LoginRequiredMixin, UpdateView):
model = InstitutionAssessment
form_class = InstitutionAssessmentForm
template_name = 'cbt/institution/assessment_update_form.html'
success_url = reverse_lazy('cbt:institution-assessment-list')
context_object_name = 'assessment'
class InstitutionAssessmentDeleteView(LoginRequiredMixin, DeleteView):
model = InstitutionAssessment
form_class = InstitutionAssessmentForm
template_name = 'cbt/institution/assessment_delete_confirm.html'
success_url = reverse_lazy('cbt:institution-assessment-list')
context_object_name = 'assessment'
# ####################
# Individual assessment CRUD, details and list views:
######################
class IndividualAssessmentCreateView(LoginRequiredMixin ,CreateView):
''' View to create Assessment by individuals. '''
model = IndividualAssessment
form_class = IndividualAssessmentForm
template_name = 'cbt/individual/individual_assessment.html'
success_url = reverse_lazy('cbt:individual-assessment-list')
def form_valid(self, form):
form.instance.user = self.request.user
return super().form_valid(form)
class IndividualAssessmentListView(LoginRequiredMixin, ListView):
model = IndividualAssessment
template_name = 'cbt/individual/individual_assessment_list.html'
context_object_name = 'assessments'
def get_queryset(self):
try:
queryset = IndividualAssessment.objects.filter(
user=self.request.user
)
except IndividualAssessment.DoesNotExist:
pass
return queryset
class IndividualAssessmentDetailView(LoginRequiredMixin, DetailView):
model = IndividualAssessment
template_name = 'cbt/individual/individual_assessment_detail.html'
context_object_name = 'assessment'
def get_context_data(self, *args, **kwargs):
context = super().get_context_data(*args, **kwargs)
try:
context['choices'] = IndividualChoice.objects.filter(
question__assessment__user=self.request.user
)
context['questions'] = IndividualQuestion.objects.filter(
assessment_id=self.kwargs.get('pk'),
assessment__user=self.request.user
)
except IndividualChoice.DoesNotExist:
context['choices'] = None
except IndividualQuestion.DoesNotExist:
context['questions'] = None
return context
def get_question_with_passage(self):
Q1 = Q(passage__title=None)
Q2 = Q(passage__body=None)
Q3 = Q(passage__no_of_questions=None)
question_with_passage = None
try:
question_with_passage = IndividualQuestion.objects.filter(
Q1&Q2&Q3
)
except IndividualQuestion.DoesNotExist:
pass
return question_with_passage
class IndividualAssessmentDeleteView(LoginRequiredMixin, DeleteView):
model = IndividualAssessment
form_class = IndividualAssessmentForm
template_name = 'cbt/individual/assessment_confirm_delete.html'
success_url = reverse_lazy('cbt:individual-assessment-list')
context_object_name = 'assessment'
class IndividualAssessmentUpdateView(LoginRequiredMixin, UpdateView):
model = IndividualAssessment
form_class = IndividualAssessmentForm
template_name = 'cbt/individual/assessment_update_form.html'
success_url = reverse_lazy('cbt:individual-assessment-list')
context_object_name = 'assessment'
#================================
# Sample Assessments
#===============================
class SampleAssessmentListView(LoginRequiredMixin, ListView):
model = IndividualAssessment
template_name = 'cbt/sample/sample_list.html'
context_object_name = 'assessments'
def get_queryset(self):
try:
queryset = IndividualAssessment.objects.filter(
user__is_superuser=True,
is_sample=True
)
except IndividualAssessment.DoesNotExist:
pass
return queryset
#=========================
# Institution CRUD, list and details views
#==========================
class InstitutionListView(LoginRequiredMixin, ListView):
model = Institution
template_name = 'cbt/institution_list.html'
context_object_name = 'institutions'
def get_queryset(self):
try:
queryset = Institution.objects.filter(
user=self.request.user
)
except Institution.DoesNotExist:
pass
return queryset
class InstitutionDetailView(LoginRequiredMixin, DetailView):
pass
class InstitutionDeleteView(LoginRequiredMixin, DeleteView):
model = Institution
template_name = 'cbt/institution_confirm_delete.html'
success_url = reverse_lazy('cbt:institution-list')
class InstitutionUpdateView(LoginRequiredMixin, UpdateView):
model = Institution
form_class = InstitutionForm
template_name = 'cbt/institution_update_form.html'
success_url = reverse_lazy('cbt:institution-list')
class InstitutionCreateView(LoginRequiredMixin, CreateView):
''' View to create instance of Institution.'''
model = Institution
form_class = InstitutionForm
template_name = 'cbt/institution_form.html'
success_url = reverse_lazy('cbt:institution-list')
def form_valid(self, form):
form.instance.user = self.request.user
return super().form_valid(form)
def user_login(request):
form = UserLogin()
if request.method == 'POST':
user = authenticate(username=request.POST['username'], password=request.POST['password'])
if user:
login(request, user)
return redirect('cbt:cbt_list')
messages.error(request, 'Login credentials error!')
return redirect(reverse('cbt:login'))
return render(request, 'cbt/login.html', {'form':form} )
def user_signup(request):
form = UserRegistration()
if request.method == 'POST':
form = UserRegistration(request.POST)
if form.is_valid:
if User.objects.filter(email=request.POST['email']).exists():
messages.error(request, 'Username is already taken')
return render(request, 'cbt/signup.html', {'form':form})
form.save()
user_detail = request.POST
email_subject = 'Welcome to CBTMaker'
message = f'''Hi {user_detail.get('username')},
\n Thank you for registering with CBTMaker.
\n\n Enjoy CBTMaker. \n\n CBTMaker team.'''
email_sender = settings.EMAIL_HOST_USER
recipient_list = [user_detail.get('email')]
send_mail(email_subject, message, email_sender, recipient_list)
print('Email sent')
return redirect('cbt:home')
else:
return redirect(reverse('cbt:signup'))
return render(request, 'cbt/signup.html', {'form':form})
def user_logout(request):
logout(request)
return redirect('cbt:home')
def password_reset(request):
''' View for resetting user's password. '''
if request.method == 'POST':
password_reset_form = PasswordResetForm(request.POST)
if password_reset_form.is_valid:
email = request.POST['email']
associated_users = User.objects.filter(email=email)
if associated_users.exists():
for user in associated_users:
subject = "Password Reset Requested"
email_template_name = "cbt/password/password_email.txt"
c = {
"email":user.email,
'domain':'127.0.0.1:8000',
'site_name': 'Website',
"uid": urlsafe_base64_encode(force_bytes(user.pk)),
"user": user,
'token': default_token_generator.make_token(user),
'protocol': 'http',
}
email = render_to_string(email_template_name, c)
try:
send_mail(subject, email, 'admin@example.com' , [user.email], fail_silently=False)
except BadHeaderError:
return HttpResponse('Invalid header found.')
return redirect('password_reset_done')
password_reset_form = PasswordResetForm()
context = {'password_reset_form':password_reset_form}
return render(request, 'cbt/password/password_reset.html', context)
| 35.633929
| 106
| 0.664662
|
from django.shortcuts import (
render,
redirect,
reverse,
get_object_or_404,
get_list_or_404,
)
from django.db.models import Q
from django.urls import reverse_lazy
from django.contrib.auth import authenticate, login, logout
from django.contrib import messages
from cbt.forms import (
IndividualAssessmentForm,
InstitutionForm, InstitutionAssessmentForm
)
from multiple_choices.forms import UserRegistration, UserLogin
from .models import (
InstitutionAssessment,
IndividualAssessment,
Institution
)
from account.forms import UserLoginForm, UserCreationForm
from choice.models import IndividualChoice, InstitutionChoice
from question.models import IndividualQuestion, InstitutionQuestion
from django.core.mail import send_mail
from django.conf import settings
from django.contrib.auth.models import User
from django.contrib.auth.forms import PasswordResetForm
from django.http import HttpResponse
from django.template.loader import render_to_string
from django.utils.http import urlsafe_base64_encode
from django.contrib.auth.tokens import default_token_generator
from django.utils.encoding import force_bytes
from django.views.generic import(
CreateView,
DeleteView,
DetailView,
ListView,
UpdateView,
TemplateView,
View
)
from django.contrib.auth.mixins import LoginRequiredMixin
def home(request):
return render (request, 'cbt/home.html', {'form':UserLoginForm})
def cbt_type(request):
return render (request, 'cbt/assessment_types.html')
class AssessmentHelpView(TemplateView):
template_name = 'cbt/partials/assessment_help.html'
ment_form.html'
success_url = reverse_lazy('cbt:institution-assessment-list')
def form_valid(self, form):
form.instance.user = self.request.user
return super().form_valid(form)
class InstitutionAssessmentListView(LoginRequiredMixin, ListView):
model = InstitutionAssessment
template_name = 'cbt/institution/assessment_list.html'
context_object_name = 'assessments'
class InstitutionAssessmentDetailView(LoginRequiredMixin, DetailView):
model = InstitutionAssessment
template_name = 'cbt/institution/assessment_detail.html'
context_object_name = 'assessment'
def get_context_data(self, *args, **kwargs):
context = super().get_context_data(*args, **kwargs)
try:
context['choices'] = InstitutionChoice.objects.all()
context['questions'] = InstitutionQuestion.objects.filter(
assessment_id=self.kwargs.get('pk')
)
except InstitutionChoice.DoesNotExist:
context['choices'] = None
except InstitutionQuestion.DoesNotExist:
context['questions'] = None
return context
class InstitutionAssessmentUpdateView(LoginRequiredMixin, UpdateView):
model = InstitutionAssessment
form_class = InstitutionAssessmentForm
template_name = 'cbt/institution/assessment_update_form.html'
success_url = reverse_lazy('cbt:institution-assessment-list')
context_object_name = 'assessment'
class InstitutionAssessmentDeleteView(LoginRequiredMixin, DeleteView):
model = InstitutionAssessment
form_class = InstitutionAssessmentForm
template_name = 'cbt/institution/assessment_delete_confirm.html'
success_url = reverse_lazy('cbt:institution-assessment-list')
context_object_name = 'assessment'
equiredMixin, ListView):
model = IndividualAssessment
template_name = 'cbt/individual/individual_assessment_list.html'
context_object_name = 'assessments'
def get_queryset(self):
try:
queryset = IndividualAssessment.objects.filter(
user=self.request.user
)
except IndividualAssessment.DoesNotExist:
pass
return queryset
class IndividualAssessmentDetailView(LoginRequiredMixin, DetailView):
model = IndividualAssessment
template_name = 'cbt/individual/individual_assessment_detail.html'
context_object_name = 'assessment'
def get_context_data(self, *args, **kwargs):
context = super().get_context_data(*args, **kwargs)
try:
context['choices'] = IndividualChoice.objects.filter(
question__assessment__user=self.request.user
)
context['questions'] = IndividualQuestion.objects.filter(
assessment_id=self.kwargs.get('pk'),
assessment__user=self.request.user
)
except IndividualChoice.DoesNotExist:
context['choices'] = None
except IndividualQuestion.DoesNotExist:
context['questions'] = None
return context
def get_question_with_passage(self):
Q1 = Q(passage__title=None)
Q2 = Q(passage__body=None)
Q3 = Q(passage__no_of_questions=None)
question_with_passage = None
try:
question_with_passage = IndividualQuestion.objects.filter(
Q1&Q2&Q3
)
except IndividualQuestion.DoesNotExist:
pass
return question_with_passage
class IndividualAssessmentDeleteView(LoginRequiredMixin, DeleteView):
model = IndividualAssessment
form_class = IndividualAssessmentForm
template_name = 'cbt/individual/assessment_confirm_delete.html'
success_url = reverse_lazy('cbt:individual-assessment-list')
context_object_name = 'assessment'
class IndividualAssessmentUpdateView(LoginRequiredMixin, UpdateView):
model = IndividualAssessment
form_class = IndividualAssessmentForm
template_name = 'cbt/individual/assessment_update_form.html'
success_url = reverse_lazy('cbt:individual-assessment-list')
context_object_name = 'assessment'
class SampleAssessmentListView(LoginRequiredMixin, ListView):
model = IndividualAssessment
template_name = 'cbt/sample/sample_list.html'
context_object_name = 'assessments'
def get_queryset(self):
try:
queryset = IndividualAssessment.objects.filter(
user__is_superuser=True,
is_sample=True
)
except IndividualAssessment.DoesNotExist:
pass
return queryset
class InstitutionListView(LoginRequiredMixin, ListView):
model = Institution
template_name = 'cbt/institution_list.html'
context_object_name = 'institutions'
def get_queryset(self):
try:
queryset = Institution.objects.filter(
user=self.request.user
)
except Institution.DoesNotExist:
pass
return queryset
class InstitutionDetailView(LoginRequiredMixin, DetailView):
pass
class InstitutionDeleteView(LoginRequiredMixin, DeleteView):
model = Institution
template_name = 'cbt/institution_confirm_delete.html'
success_url = reverse_lazy('cbt:institution-list')
class InstitutionUpdateView(LoginRequiredMixin, UpdateView):
model = Institution
form_class = InstitutionForm
template_name = 'cbt/institution_update_form.html'
success_url = reverse_lazy('cbt:institution-list')
class InstitutionCreateView(LoginRequiredMixin, CreateView):
model = Institution
form_class = InstitutionForm
template_name = 'cbt/institution_form.html'
success_url = reverse_lazy('cbt:institution-list')
def form_valid(self, form):
form.instance.user = self.request.user
return super().form_valid(form)
def user_login(request):
form = UserLogin()
if request.method == 'POST':
user = authenticate(username=request.POST['username'], password=request.POST['password'])
if user:
login(request, user)
return redirect('cbt:cbt_list')
messages.error(request, 'Login credentials error!')
return redirect(reverse('cbt:login'))
return render(request, 'cbt/login.html', {'form':form} )
def user_signup(request):
form = UserRegistration()
if request.method == 'POST':
form = UserRegistration(request.POST)
if form.is_valid:
if User.objects.filter(email=request.POST['email']).exists():
messages.error(request, 'Username is already taken')
return render(request, 'cbt/signup.html', {'form':form})
form.save()
user_detail = request.POST
email_subject = 'Welcome to CBTMaker'
message = f'''Hi {user_detail.get('username')},
\n Thank you for registering with CBTMaker.
\n\n Enjoy CBTMaker. \n\n CBTMaker team.'''
email_sender = settings.EMAIL_HOST_USER
recipient_list = [user_detail.get('email')]
send_mail(email_subject, message, email_sender, recipient_list)
print('Email sent')
return redirect('cbt:home')
else:
return redirect(reverse('cbt:signup'))
return render(request, 'cbt/signup.html', {'form':form})
def user_logout(request):
logout(request)
return redirect('cbt:home')
def password_reset(request):
if request.method == 'POST':
password_reset_form = PasswordResetForm(request.POST)
if password_reset_form.is_valid:
email = request.POST['email']
associated_users = User.objects.filter(email=email)
if associated_users.exists():
for user in associated_users:
subject = "Password Reset Requested"
email_template_name = "cbt/password/password_email.txt"
c = {
"email":user.email,
'domain':'127.0.0.1:8000',
'site_name': 'Website',
"uid": urlsafe_base64_encode(force_bytes(user.pk)),
"user": user,
'token': default_token_generator.make_token(user),
'protocol': 'http',
}
email = render_to_string(email_template_name, c)
try:
send_mail(subject, email, 'admin@example.com' , [user.email], fail_silently=False)
except BadHeaderError:
return HttpResponse('Invalid header found.')
return redirect('password_reset_done')
password_reset_form = PasswordResetForm()
context = {'password_reset_form':password_reset_form}
return render(request, 'cbt/password/password_reset.html', context)
| true
| true
|
f716af95472fe7d7e24b2543ce0d6861fcc6249f
| 6,087
|
py
|
Python
|
src/main/resources/rally/RallyClient.py
|
xebialabs-community/xlr-rally-plugin
|
d513cf8253457ee540dd3fc714891f33f302ea77
|
[
"MIT"
] | null | null | null |
src/main/resources/rally/RallyClient.py
|
xebialabs-community/xlr-rally-plugin
|
d513cf8253457ee540dd3fc714891f33f302ea77
|
[
"MIT"
] | 8
|
2015-06-02T10:55:04.000Z
|
2020-11-19T12:47:47.000Z
|
src/main/resources/rally/RallyClient.py
|
xebialabs-community/xlr-rally-plugin
|
d513cf8253457ee540dd3fc714891f33f302ea77
|
[
"MIT"
] | 4
|
2015-06-02T09:53:43.000Z
|
2020-02-13T04:38:19.000Z
|
#
# Copyright 2021 XEBIALABS
#
# 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 ast
import os
import logging
from pyral import Rally
from xlrelease.CredentialsFallback import CredentialsFallback
from java.net import URI
import requests
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
class RallyClient(object):
def __init__(self, rally_server, username, password, oauth_key):
print "Initializing RallyClient\n"
self.rally_server = rally_server
rally_url = self.rally_server['url']
credentials = CredentialsFallback(self.rally_server, username, password).getCredentials()
self.rest_api = None
self.configure_proxy()
self.rest_api = Rally(URI(rally_url), credentials['username'], credentials['password'],
apikey=oauth_key if oauth_key else self.rally_server['oAuthKey'], verify_ssl_cert=False)
@staticmethod
def create_client(rally_server, username, password, oauth_key):
print "Executing create_client() in RallyClient class in RallyClient.py\n"
return RallyClient(rally_server, username, password, oauth_key)
def configure_proxy(self):
if self.rally_server['proxyHost']:
os.environ["HTTP_PROXY"] = "http://%s:%s" % (self.rally_server['proxyHost'], self.rally_server['proxyPort'])
os.environ["HTTPS_PROXY"] = "https://%s:%s" % (self.rally_server['proxyHost'], self.rally_server['proxyPort'])
if self.rally_server['proxyUsername']:
os.environ["HTTP_PROXY"] = "http://%s:%s@%s:%s" % (
self.rally_server['proxyUsername'], self.rally_server['proxyPassword'],
self.rally_server['proxyHost'],
self.rally_server['proxyPort'])
os.environ["HTTPS_PROXY"] = "https://%s:%s@%s:%s" % (
self.rally_server['proxyUsername'], self.rally_server['proxyPassword'],
self.rally_server['proxyHost'],
self.rally_server['proxyPort'])
def lookup_item_by_formatted_id(self, workspace, project, type, formatted_id):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
response = self.rest_api.get(type, fetch="ObjectID", query="FormattedID = %s" % formatted_id)
if not response.errors:
print("Total results: %d\n" % response.resultCount)
result = response.next()
return result.ObjectID
else:
print("The following errors occurred: ")
for err in response.errors:
print("\t" + err)
return None
def create_sub_item(self, workspace, project, properties, user_story_formatted_id, user_story_type, property_type,
item_type):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
story_ref = self.lookup_item_by_formatted_id(workspace, project, user_story_type, user_story_formatted_id)
property_dict = dict(ast.literal_eval(properties))
property_dict[property_type] = story_ref
return self.create_item(workspace, project, property_dict, item_type)
def create_item(self, workspace, project, properties, item_type):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
logging.debug("About to create item in rally")
item_create_response = self.rest_api.put(item_type, properties)
logging.debug("Create Item response: %s" % (item_create_response.details()))
print "Executed successful on Rally"
return item_create_response.FormattedID
def update_item(self, workspace, project, properties, item_formatted_id, item_type):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
item_object_id = self.lookup_item_by_formatted_id(workspace, project, item_type, item_formatted_id)
property_dict = dict(ast.literal_eval(properties))
property_dict["ObjectID"] = item_object_id
update_response = self.rest_api.post(item_type, property_dict)
print "Executed successful on Rally"
return update_response.FormattedID
def query(self, workspace, project, item_type, query, fetch="True", rollupdata=False):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
response = self.rest_api.get(item_type, fetch=fetch, query=query, projectScopeDown=rollupdata)
if not response.errors:
print("Total results: %d\n" % response.resultCount)
return response
else:
print("The following errors occurred: ")
for err in response.errors:
print("\t" + err)
return None
def get_user_object_id(self, owner_username=None, owner_name=None):
response = self.rest_api.getUserInfo(username=owner_username, name=owner_name)
return response[0].ObjectID
| 50.305785
| 462
| 0.693116
|
import ast
import os
import logging
from pyral import Rally
from xlrelease.CredentialsFallback import CredentialsFallback
from java.net import URI
import requests
from requests.packages.urllib3.exceptions import InsecureRequestWarning
requests.packages.urllib3.disable_warnings(InsecureRequestWarning)
class RallyClient(object):
def __init__(self, rally_server, username, password, oauth_key):
print "Initializing RallyClient\n"
self.rally_server = rally_server
rally_url = self.rally_server['url']
credentials = CredentialsFallback(self.rally_server, username, password).getCredentials()
self.rest_api = None
self.configure_proxy()
self.rest_api = Rally(URI(rally_url), credentials['username'], credentials['password'],
apikey=oauth_key if oauth_key else self.rally_server['oAuthKey'], verify_ssl_cert=False)
@staticmethod
def create_client(rally_server, username, password, oauth_key):
print "Executing create_client() in RallyClient class in RallyClient.py\n"
return RallyClient(rally_server, username, password, oauth_key)
def configure_proxy(self):
if self.rally_server['proxyHost']:
os.environ["HTTP_PROXY"] = "http://%s:%s" % (self.rally_server['proxyHost'], self.rally_server['proxyPort'])
os.environ["HTTPS_PROXY"] = "https://%s:%s" % (self.rally_server['proxyHost'], self.rally_server['proxyPort'])
if self.rally_server['proxyUsername']:
os.environ["HTTP_PROXY"] = "http://%s:%s@%s:%s" % (
self.rally_server['proxyUsername'], self.rally_server['proxyPassword'],
self.rally_server['proxyHost'],
self.rally_server['proxyPort'])
os.environ["HTTPS_PROXY"] = "https://%s:%s@%s:%s" % (
self.rally_server['proxyUsername'], self.rally_server['proxyPassword'],
self.rally_server['proxyHost'],
self.rally_server['proxyPort'])
def lookup_item_by_formatted_id(self, workspace, project, type, formatted_id):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
response = self.rest_api.get(type, fetch="ObjectID", query="FormattedID = %s" % formatted_id)
if not response.errors:
print("Total results: %d\n" % response.resultCount)
result = response.next()
return result.ObjectID
else:
print("The following errors occurred: ")
for err in response.errors:
print("\t" + err)
return None
def create_sub_item(self, workspace, project, properties, user_story_formatted_id, user_story_type, property_type,
item_type):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
story_ref = self.lookup_item_by_formatted_id(workspace, project, user_story_type, user_story_formatted_id)
property_dict = dict(ast.literal_eval(properties))
property_dict[property_type] = story_ref
return self.create_item(workspace, project, property_dict, item_type)
def create_item(self, workspace, project, properties, item_type):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
logging.debug("About to create item in rally")
item_create_response = self.rest_api.put(item_type, properties)
logging.debug("Create Item response: %s" % (item_create_response.details()))
print "Executed successful on Rally"
return item_create_response.FormattedID
def update_item(self, workspace, project, properties, item_formatted_id, item_type):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
item_object_id = self.lookup_item_by_formatted_id(workspace, project, item_type, item_formatted_id)
property_dict = dict(ast.literal_eval(properties))
property_dict["ObjectID"] = item_object_id
update_response = self.rest_api.post(item_type, property_dict)
print "Executed successful on Rally"
return update_response.FormattedID
def query(self, workspace, project, item_type, query, fetch="True", rollupdata=False):
self.rest_api.setWorkspace(workspace)
self.rest_api.setProject(project)
response = self.rest_api.get(item_type, fetch=fetch, query=query, projectScopeDown=rollupdata)
if not response.errors:
print("Total results: %d\n" % response.resultCount)
return response
else:
print("The following errors occurred: ")
for err in response.errors:
print("\t" + err)
return None
def get_user_object_id(self, owner_username=None, owner_name=None):
response = self.rest_api.getUserInfo(username=owner_username, name=owner_name)
return response[0].ObjectID
| false
| true
|
f716af9d8b3eb607fff52cdc80c5efef400bad64
| 664
|
py
|
Python
|
exp/alto/tools/create_terminals_bi.py
|
Hollo1996/4lang
|
d167110f619e652a5cce723d211946baeae077ea
|
[
"MIT"
] | 20
|
2016-03-01T07:34:17.000Z
|
2021-09-06T11:08:11.000Z
|
exp/alto/tools/create_terminals_bi.py
|
Hollo1996/4lang
|
d167110f619e652a5cce723d211946baeae077ea
|
[
"MIT"
] | 103
|
2015-02-03T13:34:55.000Z
|
2020-07-13T11:21:22.000Z
|
exp/alto/tools/create_terminals_bi.py
|
Hollo1996/4lang
|
d167110f619e652a5cce723d211946baeae077ea
|
[
"MIT"
] | 14
|
2015-02-03T09:00:17.000Z
|
2021-12-15T11:26:30.000Z
|
#!/usr/bin/env python
import sys
from hunmisc.corpustools.tsv_tools import sentence_iterator
from common import sanitize_word
TEMPLATE = ('{0} -> {1}_{0}\n[graph] "({1}<root> / {1})"\n' +
'[fourlang] "({1}<root> / {1})"\n')
def main():
seen = set()
with open(sys.argv[1]) as stream:
for sentence in sentence_iterator(stream, comment_tag='#'):
for tok in sentence:
word = sanitize_word(tok[1])
pos = tok[3]
if (word, pos) not in seen:
print(TEMPLATE.format(pos, word))
seen.add((word, pos))
if __name__ == "__main__":
main()
| 24.592593
| 67
| 0.536145
|
import sys
from hunmisc.corpustools.tsv_tools import sentence_iterator
from common import sanitize_word
TEMPLATE = ('{0} -> {1}_{0}\n[graph] "({1}<root> / {1})"\n' +
'[fourlang] "({1}<root> / {1})"\n')
def main():
seen = set()
with open(sys.argv[1]) as stream:
for sentence in sentence_iterator(stream, comment_tag='#'):
for tok in sentence:
word = sanitize_word(tok[1])
pos = tok[3]
if (word, pos) not in seen:
print(TEMPLATE.format(pos, word))
seen.add((word, pos))
if __name__ == "__main__":
main()
| true
| true
|
f716afd528fd4bac2c2ee7f4d45b47e27b40ca16
| 1,143
|
py
|
Python
|
HDPython/tests/test_axi_fifo.py
|
HardwareDesignWithPython/HDPython
|
aade03aaa092b1684fa12bffd17674cf1c45f5ac
|
[
"MIT"
] | null | null | null |
HDPython/tests/test_axi_fifo.py
|
HardwareDesignWithPython/HDPython
|
aade03aaa092b1684fa12bffd17674cf1c45f5ac
|
[
"MIT"
] | null | null | null |
HDPython/tests/test_axi_fifo.py
|
HardwareDesignWithPython/HDPython
|
aade03aaa092b1684fa12bffd17674cf1c45f5ac
|
[
"MIT"
] | 1
|
2021-10-20T20:08:16.000Z
|
2021-10-20T20:08:16.000Z
|
from HDPython import *
from HDPython.examples import *
from .helpers import Folders_isSame, vhdl_conversion, do_simulation,printf
from HDPython.test_handler import add_test
class test_bench_axi_fifo(v_entity):
def __init__(self):
super().__init__()
self.architecture()
def architecture(self):
clkgen = clk_generator()
maxCount = v_slv(32,20)
pipe1 = rollingCounter(clkgen.clk,maxCount) \
| axiFifo(clkgen.clk) \
| axiFifo(clkgen.clk, depth = 5) \
| axiPrint(clkgen.clk)
end_architecture()
@do_simulation
def test_bench_axi_fifo_sim(OutputPath, f= None):
tb = test_bench_axi_fifo()
return tb
def test_test_bench_axi_fifo_sim():
return test_bench_axi_fifo_sim("tests/axi_fifo_sim/")
add_test("axi_fifo_sim", test_test_bench_axi_fifo_sim)
@vhdl_conversion
def test_bench_axi_fifo_2vhdl(OutputPath, f= None):
tb = test_bench_axi_fifo()
return tb
def test_test_bench_axi_fifo_2vhdl():
return test_bench_axi_fifo_2vhdl("tests/axi_fifo/")
add_test("axi_fifo_2vhdl", test_test_bench_axi_fifo_2vhdl)
| 23.326531
| 74
| 0.707787
|
from HDPython import *
from HDPython.examples import *
from .helpers import Folders_isSame, vhdl_conversion, do_simulation,printf
from HDPython.test_handler import add_test
class test_bench_axi_fifo(v_entity):
def __init__(self):
super().__init__()
self.architecture()
def architecture(self):
clkgen = clk_generator()
maxCount = v_slv(32,20)
pipe1 = rollingCounter(clkgen.clk,maxCount) \
| axiFifo(clkgen.clk) \
| axiFifo(clkgen.clk, depth = 5) \
| axiPrint(clkgen.clk)
end_architecture()
@do_simulation
def test_bench_axi_fifo_sim(OutputPath, f= None):
tb = test_bench_axi_fifo()
return tb
def test_test_bench_axi_fifo_sim():
return test_bench_axi_fifo_sim("tests/axi_fifo_sim/")
add_test("axi_fifo_sim", test_test_bench_axi_fifo_sim)
@vhdl_conversion
def test_bench_axi_fifo_2vhdl(OutputPath, f= None):
tb = test_bench_axi_fifo()
return tb
def test_test_bench_axi_fifo_2vhdl():
return test_bench_axi_fifo_2vhdl("tests/axi_fifo/")
add_test("axi_fifo_2vhdl", test_test_bench_axi_fifo_2vhdl)
| true
| true
|
f716b0186a220c17477a6aa6ebacf35273a20db8
| 1,161
|
py
|
Python
|
test/geocoders/geonames.py
|
navidata/geopy
|
2c8e441cfb1a813fb2ab34fd41386204ad18f872
|
[
"MIT"
] | null | null | null |
test/geocoders/geonames.py
|
navidata/geopy
|
2c8e441cfb1a813fb2ab34fd41386204ad18f872
|
[
"MIT"
] | null | null | null |
test/geocoders/geonames.py
|
navidata/geopy
|
2c8e441cfb1a813fb2ab34fd41386204ad18f872
|
[
"MIT"
] | null | null | null |
# -*- coding: UTF-8 -*-
import unittest
from geopy.compat import u
from geopy.geocoders import GeoNames
from test.geocoders.util import GeocoderTestBase, env
@unittest.skipUnless( # pylint: disable=R0904,C0111
bool(env.get('GEONAMES_USERNAME')),
"No GEONAMES_USERNAME env variable set"
)
class GeoNamesTestCase(GeocoderTestBase):
@classmethod
def setUpClass(cls):
cls.delta = 0.04
def test_unicode_name(self):
"""
GeoNames.geocode unicode
"""
# work around ConfigurationError raised in GeoNames init
self.geocoder = GeoNames(username=env['GEONAMES_USERNAME'])
self.geocode_run(
{"query": "Mount Everest, Nepal"},
{"latitude": 27.987, "longitude": 86.925},
)
def test_reverse(self):
"""
GeoNames.reverse
"""
# work around ConfigurationError raised in GeoNames init
self.geocoder = GeoNames(username=env['GEONAMES_USERNAME'])
self.reverse_run(
{"query": "40.75376406311989, -73.98489005863667"},
{"latitude": 40.75376406311989, "longitude": -73.98489005863667},
)
| 29.025
| 77
| 0.634798
|
import unittest
from geopy.compat import u
from geopy.geocoders import GeoNames
from test.geocoders.util import GeocoderTestBase, env
@unittest.skipUnless(
bool(env.get('GEONAMES_USERNAME')),
"No GEONAMES_USERNAME env variable set"
)
class GeoNamesTestCase(GeocoderTestBase):
@classmethod
def setUpClass(cls):
cls.delta = 0.04
def test_unicode_name(self):
self.geocoder = GeoNames(username=env['GEONAMES_USERNAME'])
self.geocode_run(
{"query": "Mount Everest, Nepal"},
{"latitude": 27.987, "longitude": 86.925},
)
def test_reverse(self):
self.geocoder = GeoNames(username=env['GEONAMES_USERNAME'])
self.reverse_run(
{"query": "40.75376406311989, -73.98489005863667"},
{"latitude": 40.75376406311989, "longitude": -73.98489005863667},
)
| true
| true
|
f716b2f64bf5f1efd19c1fea7576ec774a9a9154
| 532
|
py
|
Python
|
School/Average mark.py
|
Bamgm14/My-Random-Work
|
b9678a3a84dd8ff00efd638890cff76eb6967c1b
|
[
"MIT"
] | null | null | null |
School/Average mark.py
|
Bamgm14/My-Random-Work
|
b9678a3a84dd8ff00efd638890cff76eb6967c1b
|
[
"MIT"
] | null | null | null |
School/Average mark.py
|
Bamgm14/My-Random-Work
|
b9678a3a84dd8ff00efd638890cff76eb6967c1b
|
[
"MIT"
] | null | null | null |
#To accepts marks in 5 subjects and displays the total and average mark
#Above 90% Grade A*
#90 - 80 % Grade A
#70 – 80 % Grade B
#60 – 70 % Grade C
#Less than 60 Grade D
a=float(input("Enter Mark(1):"))
b=float(input("Enter Mark(2):"))
c=float(input("Enter Mark(3):"))
d=float(input("Enter Mark(4):"))
e=float(input("Enter Mark(5):"))
f=(a+b+c+d+e)/5
if f>=90:
print ("A+")
elif f<90 and f>=80:
print ("A")
elif f<80 and f>=70:
print ("B")
elif f<70 and f>=60:
print ("C")
else:
print ("D")
| 22.166667
| 71
| 0.577068
|
a=float(input("Enter Mark(1):"))
b=float(input("Enter Mark(2):"))
c=float(input("Enter Mark(3):"))
d=float(input("Enter Mark(4):"))
e=float(input("Enter Mark(5):"))
f=(a+b+c+d+e)/5
if f>=90:
print ("A+")
elif f<90 and f>=80:
print ("A")
elif f<80 and f>=70:
print ("B")
elif f<70 and f>=60:
print ("C")
else:
print ("D")
| true
| true
|
f716b6b7fe18e32ba821b081861b3329d11d7a78
| 1,125
|
py
|
Python
|
csvObject/csvWriter.py
|
sbaker-dev/csvObject
|
e31668c9b71284c7e7f6516e61c9617ad7abb7b1
|
[
"MIT"
] | null | null | null |
csvObject/csvWriter.py
|
sbaker-dev/csvObject
|
e31668c9b71284c7e7f6516e61c9617ad7abb7b1
|
[
"MIT"
] | null | null | null |
csvObject/csvWriter.py
|
sbaker-dev/csvObject
|
e31668c9b71284c7e7f6516e61c9617ad7abb7b1
|
[
"MIT"
] | null | null | null |
import csv
def write_csv(write_out_path, name, headers, rows_to_write):
"""
Purpose
-------
This writes out a csv file of row data with an optional header. If you don't want a header, pass None to headers
Parameters
----------
:param name: The file name
:type name: str
:param write_out_path: The write directory
:type write_out_path: str
:param headers: The headers for the columns you want to write
:type headers: list
:param rows_to_write: A list of row data to write, each columns row should be an individual element of a list.
:type rows_to_write: list
:return: Nothing, just write out the file to the specified directory named the specified name
:rtype: None
"""
if type(rows_to_write[0]) != list:
rows_to_write = [[row] for row in rows_to_write]
with open(f"{write_out_path}/{name}.csv", "w", newline="", encoding="utf-8") as csv_reader:
csv_writer = csv.writer(csv_reader)
if len(headers) > 0:
csv_writer.writerow(headers)
for row in rows_to_write:
csv_writer.writerow(row)
| 28.846154
| 116
| 0.657778
|
import csv
def write_csv(write_out_path, name, headers, rows_to_write):
if type(rows_to_write[0]) != list:
rows_to_write = [[row] for row in rows_to_write]
with open(f"{write_out_path}/{name}.csv", "w", newline="", encoding="utf-8") as csv_reader:
csv_writer = csv.writer(csv_reader)
if len(headers) > 0:
csv_writer.writerow(headers)
for row in rows_to_write:
csv_writer.writerow(row)
| true
| true
|
f716b763cbd4d44af6bf17e00908e21772e85af9
| 851
|
py
|
Python
|
service/test_service.py
|
theBraindonor/la-parking-tickets
|
9537900c54c0fb4a5ca27d2828621b9b8a5ede73
|
[
"CC-BY-4.0"
] | null | null | null |
service/test_service.py
|
theBraindonor/la-parking-tickets
|
9537900c54c0fb4a5ca27d2828621b9b8a5ede73
|
[
"CC-BY-4.0"
] | 9
|
2020-03-24T16:55:25.000Z
|
2022-02-17T21:56:35.000Z
|
service/test_service.py
|
theBraindonor/la-parking-tickets
|
9537900c54c0fb4a5ca27d2828621b9b8a5ede73
|
[
"CC-BY-4.0"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Test the ticket model being serviced on localhost:8080
"""
__author__ = "John Hoff"
__email__ = "john.hoff@braindonor.net"
__copyright__ = "Copyright 2019, John Hoff"
__license__ = "Creative Commons Attribution-ShareAlike 4.0 International License"
__version__ = "1.0.0"
import json
import requests
from utility import use_project_path
from model import load_sample_data_frame
if __name__ == '__main__':
use_project_path()
for index, row in load_sample_data_frame().iterrows():
print(row.to_json())
headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
response = requests.post('http://127.0.0.1:8080/ticketPrediction', data=row.to_json(), headers=headers)
print(json.loads(response.text))
if index > 100:
break
| 26.59375
| 111
| 0.692127
|
__author__ = "John Hoff"
__email__ = "john.hoff@braindonor.net"
__copyright__ = "Copyright 2019, John Hoff"
__license__ = "Creative Commons Attribution-ShareAlike 4.0 International License"
__version__ = "1.0.0"
import json
import requests
from utility import use_project_path
from model import load_sample_data_frame
if __name__ == '__main__':
use_project_path()
for index, row in load_sample_data_frame().iterrows():
print(row.to_json())
headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
response = requests.post('http://127.0.0.1:8080/ticketPrediction', data=row.to_json(), headers=headers)
print(json.loads(response.text))
if index > 100:
break
| true
| true
|
f716b8d03170ced406cf21f6d4db40052bbb480d
| 2,560
|
py
|
Python
|
notebooks/shared/nbconvert/exporters/script.py
|
leonbett/debuggingbook
|
ae1fa940c306160429232fbc93a7a7f14b44efb7
|
[
"MIT"
] | 728
|
2018-09-21T03:51:04.000Z
|
2022-03-28T09:35:04.000Z
|
notebooks/shared/nbconvert/exporters/script.py
|
leonbett/debuggingbook
|
ae1fa940c306160429232fbc93a7a7f14b44efb7
|
[
"MIT"
] | 103
|
2018-09-02T12:26:32.000Z
|
2022-02-09T07:19:08.000Z
|
notebooks/shared/nbconvert/exporters/script.py
|
leonbett/debuggingbook
|
ae1fa940c306160429232fbc93a7a7f14b44efb7
|
[
"MIT"
] | 157
|
2018-09-02T08:00:50.000Z
|
2022-03-27T22:04:50.000Z
|
"""Generic script exporter class for any kernel language"""
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
import entrypoints
from .templateexporter import TemplateExporter
from traitlets import Dict, default
from .base import get_exporter
class ScriptExporter(TemplateExporter):
# Caches of already looked-up and instantiated exporters for delegation:
_exporters = Dict()
_lang_exporters = Dict()
@default('template_file')
def _template_file_default(self):
return 'script.tpl'
def _get_language_exporter(self, lang_name):
"""Find an exporter for the language name from notebook metadata.
Uses the nbconvert.exporters.script group of entry points.
Returns None if no exporter is found.
"""
if lang_name not in self._lang_exporters:
try:
Exporter = entrypoints.get_single(
'nbconvert.exporters.script', lang_name).load()
except entrypoints.NoSuchEntryPoint:
self._lang_exporters[lang_name] = None
else:
self._lang_exporters[lang_name] = Exporter(parent=self)
return self._lang_exporters[lang_name]
def from_notebook_node(self, nb, resources=None, **kw):
langinfo = nb.metadata.get('language_info', {})
# delegate to custom exporter, if specified
exporter_name = langinfo.get('nbconvert_exporter')
if exporter_name and exporter_name != 'script':
self.log.debug("Loading script exporter: %s", exporter_name)
if exporter_name not in self._exporters:
Exporter = get_exporter(exporter_name)
self._exporters[exporter_name] = Exporter(parent=self)
exporter = self._exporters[exporter_name]
return exporter.from_notebook_node(nb, resources, **kw)
# Look up a script exporter for this notebook's language
lang_name = langinfo.get('name')
if lang_name:
self.log.debug("Using script exporter for language: %s", lang_name)
exporter = self._get_language_exporter(lang_name)
if exporter is not None:
return exporter.from_notebook_node(nb, resources, **kw)
# Fall back to plain script export
self.file_extension = langinfo.get('file_extension', '.txt')
self.output_mimetype = langinfo.get('mimetype', 'text/plain')
return super(ScriptExporter, self).from_notebook_node(nb, resources, **kw)
| 40.634921
| 82
| 0.664844
|
import entrypoints
from .templateexporter import TemplateExporter
from traitlets import Dict, default
from .base import get_exporter
class ScriptExporter(TemplateExporter):
_exporters = Dict()
_lang_exporters = Dict()
@default('template_file')
def _template_file_default(self):
return 'script.tpl'
def _get_language_exporter(self, lang_name):
if lang_name not in self._lang_exporters:
try:
Exporter = entrypoints.get_single(
'nbconvert.exporters.script', lang_name).load()
except entrypoints.NoSuchEntryPoint:
self._lang_exporters[lang_name] = None
else:
self._lang_exporters[lang_name] = Exporter(parent=self)
return self._lang_exporters[lang_name]
def from_notebook_node(self, nb, resources=None, **kw):
langinfo = nb.metadata.get('language_info', {})
exporter_name = langinfo.get('nbconvert_exporter')
if exporter_name and exporter_name != 'script':
self.log.debug("Loading script exporter: %s", exporter_name)
if exporter_name not in self._exporters:
Exporter = get_exporter(exporter_name)
self._exporters[exporter_name] = Exporter(parent=self)
exporter = self._exporters[exporter_name]
return exporter.from_notebook_node(nb, resources, **kw)
lang_name = langinfo.get('name')
if lang_name:
self.log.debug("Using script exporter for language: %s", lang_name)
exporter = self._get_language_exporter(lang_name)
if exporter is not None:
return exporter.from_notebook_node(nb, resources, **kw)
# Fall back to plain script export
self.file_extension = langinfo.get('file_extension', '.txt')
self.output_mimetype = langinfo.get('mimetype', 'text/plain')
return super(ScriptExporter, self).from_notebook_node(nb, resources, **kw)
| true
| true
|
f716ba644f454c2eb8166dff1191bf0ce61c89b2
| 598
|
py
|
Python
|
derg/forms.py
|
mihail4216/myter
|
4ed1e8abc3f57595858347b21c86a9a10b3ff4a4
|
[
"MIT"
] | null | null | null |
derg/forms.py
|
mihail4216/myter
|
4ed1e8abc3f57595858347b21c86a9a10b3ff4a4
|
[
"MIT"
] | null | null | null |
derg/forms.py
|
mihail4216/myter
|
4ed1e8abc3f57595858347b21c86a9a10b3ff4a4
|
[
"MIT"
] | null | null | null |
# -*- coding:utf-8 -*-
from django import forms
# class username(forms.Form):
# usernames = forms.CharField(max_length=100)
class LoginForm(forms.Form):
""" Лучше пользоваться формой и объявлять ее тут, тут можно менять сам тип поля, тест, число, визивик повесить на поле
В самом html этого не сможем сделать, но там можно как вначале ты делал так объявлять, для простых форм
"""
username = forms.CharField(label=u'Имя пользователя')
password = forms.CharField(label=u'Пароль', widget=forms.PasswordInput())
class LogoutForm(forms.Form):
logout = forms.BooleanField
| 35.176471
| 122
| 0.727425
|
from django import forms
class LoginForm(forms.Form):
username = forms.CharField(label=u'Имя пользователя')
password = forms.CharField(label=u'Пароль', widget=forms.PasswordInput())
class LogoutForm(forms.Form):
logout = forms.BooleanField
| true
| true
|
f716ba93e72c41d843f4c01e2abc5c7f8996c487
| 148
|
py
|
Python
|
petstagram/common/urls.py
|
DimAntDim/SoftUni_Petstagram_Workshop
|
b4d6da5fa0d19de4b434046d0b7c73a40c8343b5
|
[
"MIT"
] | 1
|
2021-06-14T19:50:52.000Z
|
2021-06-14T19:50:52.000Z
|
petstagram/common/urls.py
|
ArifRasim/Petstagram
|
dc754ecc2ee7184563b26d2ba3f795c2fc767b93
|
[
"MIT"
] | 1
|
2021-08-09T16:31:13.000Z
|
2021-08-09T16:31:13.000Z
|
petstagram/common/urls.py
|
ArifRasim/Petstagram
|
dc754ecc2ee7184563b26d2ba3f795c2fc767b93
|
[
"MIT"
] | 1
|
2022-03-15T13:50:30.000Z
|
2022-03-15T13:50:30.000Z
|
from django.urls import path
from petstagram.common.views import LandingPage
urlpatterns = [
path('', LandingPage.as_view(), name='index'),
]
| 18.5
| 50
| 0.72973
|
from django.urls import path
from petstagram.common.views import LandingPage
urlpatterns = [
path('', LandingPage.as_view(), name='index'),
]
| true
| true
|
f716bbce2341f6be93b2694916cecbdef85fba95
| 1,261
|
py
|
Python
|
demo/filepicker_demo/migrations/0002_auto_20150323_1549.py
|
aaronang/filepicker-django
|
9de61e9184ae93db9b260764cc2f45a38cb48400
|
[
"MIT"
] | 15
|
2015-03-25T14:00:16.000Z
|
2021-04-15T17:47:02.000Z
|
demo/filepicker_demo/migrations/0002_auto_20150323_1549.py
|
aaronang/filepicker-django
|
9de61e9184ae93db9b260764cc2f45a38cb48400
|
[
"MIT"
] | 3
|
2015-07-14T08:33:37.000Z
|
2018-12-15T12:58:52.000Z
|
demo/filepicker_demo/migrations/0002_auto_20150323_1549.py
|
Ink/django-filepicker
|
9de61e9184ae93db9b260764cc2f45a38cb48400
|
[
"MIT"
] | 5
|
2015-07-14T13:30:38.000Z
|
2018-09-30T19:56:29.000Z
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
import django_filepicker.models
class Migration(migrations.Migration):
dependencies = [
('filepicker_demo', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='BasicFilesModel',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('text', models.CharField(max_length=64)),
],
options={
},
bases=(models.Model,),
),
migrations.CreateModel(
name='FileModel',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('fpfile', django_filepicker.models.FPFileField(upload_to=b'uploads')),
('fpurl', models.URLField(max_length=255, null=True, blank=True)),
('mid', models.ForeignKey(to='filepicker_demo.BasicFilesModel')),
],
options={
},
bases=(models.Model,),
),
migrations.DeleteModel(
name='TestModel',
),
]
| 30.756098
| 114
| 0.552736
|
from __future__ import unicode_literals
from django.db import models, migrations
import django_filepicker.models
class Migration(migrations.Migration):
dependencies = [
('filepicker_demo', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='BasicFilesModel',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('text', models.CharField(max_length=64)),
],
options={
},
bases=(models.Model,),
),
migrations.CreateModel(
name='FileModel',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('fpfile', django_filepicker.models.FPFileField(upload_to=b'uploads')),
('fpurl', models.URLField(max_length=255, null=True, blank=True)),
('mid', models.ForeignKey(to='filepicker_demo.BasicFilesModel')),
],
options={
},
bases=(models.Model,),
),
migrations.DeleteModel(
name='TestModel',
),
]
| true
| true
|
f716bc49f322c1fc8a1504767c31a6b027dcd198
| 583
|
py
|
Python
|
utils/pbtxt.py
|
Sarmyt/masters_python_code
|
800d22624e9a9c4ae023f1c5ef40bd0efee5366b
|
[
"MIT"
] | null | null | null |
utils/pbtxt.py
|
Sarmyt/masters_python_code
|
800d22624e9a9c4ae023f1c5ef40bd0efee5366b
|
[
"MIT"
] | null | null | null |
utils/pbtxt.py
|
Sarmyt/masters_python_code
|
800d22624e9a9c4ae023f1c5ef40bd0efee5366b
|
[
"MIT"
] | 1
|
2021-06-08T18:02:53.000Z
|
2021-06-08T18:02:53.000Z
|
from object_detection.protos.string_int_label_map_pb2 import StringIntLabelMap, StringIntLabelMapItem
from google.protobuf import text_format
def convert_classes(classes, start=1):
msg = StringIntLabelMap()
for id, name in enumerate(classes, start=start):
msg.item.append(StringIntLabelMapItem(id=id, name=name))
text = str(text_format.MessageToBytes(msg, as_utf8=True), 'utf-8')
return text
if __name__ == '__main__':
txt = convert_classes(['Agent'])
print(txt)
with open('label_map.pbtxt', 'w') as f:
f.write(txt)
| 30.684211
| 102
| 0.698113
|
from object_detection.protos.string_int_label_map_pb2 import StringIntLabelMap, StringIntLabelMapItem
from google.protobuf import text_format
def convert_classes(classes, start=1):
msg = StringIntLabelMap()
for id, name in enumerate(classes, start=start):
msg.item.append(StringIntLabelMapItem(id=id, name=name))
text = str(text_format.MessageToBytes(msg, as_utf8=True), 'utf-8')
return text
if __name__ == '__main__':
txt = convert_classes(['Agent'])
print(txt)
with open('label_map.pbtxt', 'w') as f:
f.write(txt)
| true
| true
|
f716bc57ce244e895a0fbf1e2a341ecb30e07e9c
| 199
|
py
|
Python
|
profiles/apps.py
|
javokhirbek1999/pet-finder-rest-api
|
67e926ad7b9aa4cb03a35f69e5a52b48dc776c62
|
[
"PostgreSQL",
"Unlicense"
] | 1
|
2021-08-22T22:44:41.000Z
|
2021-08-22T22:44:41.000Z
|
profiles/apps.py
|
javokhirbek1999/pet-finder-rest-api
|
67e926ad7b9aa4cb03a35f69e5a52b48dc776c62
|
[
"PostgreSQL",
"Unlicense"
] | null | null | null |
profiles/apps.py
|
javokhirbek1999/pet-finder-rest-api
|
67e926ad7b9aa4cb03a35f69e5a52b48dc776c62
|
[
"PostgreSQL",
"Unlicense"
] | null | null | null |
from django.apps import AppConfig
class ProfilesConfig(AppConfig):
default_auto_field = 'django.db.models.BigAutoField'
name = 'profiles'
def ready(self):
from . import signals
| 22.111111
| 56
| 0.713568
|
from django.apps import AppConfig
class ProfilesConfig(AppConfig):
default_auto_field = 'django.db.models.BigAutoField'
name = 'profiles'
def ready(self):
from . import signals
| true
| true
|
f716bc7d927d8f7d83db1221db13eba13a2be4a7
| 958
|
py
|
Python
|
assets/Point3D.py
|
sanils2002/PYTHON-CODES
|
607fadc2cba4b185a5529bd101faefa08f4c3469
|
[
"MIT"
] | null | null | null |
assets/Point3D.py
|
sanils2002/PYTHON-CODES
|
607fadc2cba4b185a5529bd101faefa08f4c3469
|
[
"MIT"
] | null | null | null |
assets/Point3D.py
|
sanils2002/PYTHON-CODES
|
607fadc2cba4b185a5529bd101faefa08f4c3469
|
[
"MIT"
] | null | null | null |
#Define a Point3D class that inherits from object
#Inside the Point3D class, define an __init__() function that accepts self, x, y, and z, and assigns these numbers to the member variables self.x, self.y, self.z
#Define a __repr__() method that returns "(%d, %d, %d)" % (self.x, self.y, self.z). This tells Python to represent this object in the following format: (x, y, z).
#Outside the class definition, create a variable named my_point containing a new instance of Point3D with x=1, y=2, and z=3.
#Finally, print my_point.
import os
from time import sleep
def screen_clear():
if os.name == 'posix':
_ = os.system('clear')
else:
_ = os.system('cls')
sleep(1)
screen_clear()
class Point3D(object):
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def __repr__(self):
return "(%d, %d, %d)" % (self.x, self.y, self.z)
my_point = Point3D(1,2,3)
print(my_point)
| 34.214286
| 166
| 0.64405
|
import os
from time import sleep
def screen_clear():
if os.name == 'posix':
_ = os.system('clear')
else:
_ = os.system('cls')
sleep(1)
screen_clear()
class Point3D(object):
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def __repr__(self):
return "(%d, %d, %d)" % (self.x, self.y, self.z)
my_point = Point3D(1,2,3)
print(my_point)
| true
| true
|
f716bd1d23d98952f326deff4604828c2677d9b6
| 2,934
|
py
|
Python
|
src/crawl_data/crawl_data/spiders/XinjiangSpider.py
|
SmartDataLab/Policy_crawler
|
fb9fcb7ab701dfb98606afe9f7260f2f2e857506
|
[
"MIT"
] | 3
|
2020-05-06T06:11:46.000Z
|
2020-05-24T15:07:22.000Z
|
src/crawl_data/crawl_data/spiders/XinjiangSpider.py
|
SmartDataLab/Policy_crawler
|
fb9fcb7ab701dfb98606afe9f7260f2f2e857506
|
[
"MIT"
] | 2
|
2020-04-02T14:14:28.000Z
|
2020-04-27T12:45:48.000Z
|
src/crawl_data/crawl_data/spiders/XinjiangSpider.py
|
SmartDataLab/Policy_crawler
|
fb9fcb7ab701dfb98606afe9f7260f2f2e857506
|
[
"MIT"
] | 2
|
2020-04-04T09:32:07.000Z
|
2020-07-07T09:54:23.000Z
|
import scrapy
import pickle
import os
import ast
from urllib import parse
from scrapy.selector import Selector
class XinjiangSpider(scrapy.Spider):
name = "Xinjiang"
if not os.path.exists('../../data/HTML_pk/%s' % name):
os.makedirs('../../data/HTML_pk/%s' % name)
if not os.path.exists('../../data/text/%s' % name):
os.makedirs('../../data/text/%s' % name)
def start_requests(self):
total_page = 34
# total_page = 3
url_base = 'http://www.xinjiang.gov.cn/xinjiang/gfxwj/zfxxgk_gknrz{0}.shtml'
for i in range(total_page):
page = '_'+ str(i+1) if i > 0 else ''
yield scrapy.Request(url=url_base.format(page), callback=self.parse)
def parse(self,response):
detail_page_links = []
for dd in response.css('div.gknr_list dd'):
url = response.urljoin(dd.css('a::attr(href)').get())
UID = url.split('/')[-1][:-6]
if '?' not in UID:
detail_page_links.append(url)
yield {
'UID': UID,
'title': dd.css('a::attr(title)').get(),
'date': dd.css('span::text').get(),
'FileNumber':None,
'text length':0,
'url': url,
'crawl state':'half'
}
yield from response.follow_all(detail_page_links, callback = self.parse_content)
def parse_content(self, response):
UID = response.url.split('/')[-1][:-6]
doc_info_dict = {}
for li in response.css('ul.clearfix li'):
tmp_l = li.css('*::text').getall()
if len(tmp_l) == 2:
doc_info_dict[tmp_l[0]] = tmp_l[1]
else:
tmp_l = tmp_l[0].split('：')
if len(tmp_l) == 2:
doc_info_dict[tmp_l[0]] = tmp_l[1]
File_num = None
if '发文字号' in doc_info_dict.keys():
File_num = doc_info_dict['发文字号']
paragraph_list = response.css('div.gknbxq_detail p *::text').getall()
attachment_link = response.css('div.ewebeditor_doc img::attr(src)').getall()
if len(paragraph_list) == 0:
paragraph_list = response.css('p *::text').getall()
length = len(''.join(paragraph_list))
if length > 0:
state = 'full'
with open('../../data/HTML_pk/%s/%s.pkl' % (self.name,UID), 'wb') as f:
pickle.dump(response.text,f)
with open('../../data/text/%s/%s.txt' % (self.name,UID), 'w') as f:
f.write('\n'.join(paragraph_list))
else:
state = 'empty'
return {
'UID': UID,
'FileNumber':File_num,
'mainText': paragraph_list,
'attachment_link': attachment_link,
'doc_info_dict':doc_info_dict,
'crawl state':state,
'text length':length,
}
| 38.605263
| 92
| 0.519087
|
import scrapy
import pickle
import os
import ast
from urllib import parse
from scrapy.selector import Selector
class XinjiangSpider(scrapy.Spider):
name = "Xinjiang"
if not os.path.exists('../../data/HTML_pk/%s' % name):
os.makedirs('../../data/HTML_pk/%s' % name)
if not os.path.exists('../../data/text/%s' % name):
os.makedirs('../../data/text/%s' % name)
def start_requests(self):
total_page = 34
url_base = 'http://www.xinjiang.gov.cn/xinjiang/gfxwj/zfxxgk_gknrz{0}.shtml'
for i in range(total_page):
page = '_'+ str(i+1) if i > 0 else ''
yield scrapy.Request(url=url_base.format(page), callback=self.parse)
def parse(self,response):
detail_page_links = []
for dd in response.css('div.gknr_list dd'):
url = response.urljoin(dd.css('a::attr(href)').get())
UID = url.split('/')[-1][:-6]
if '?' not in UID:
detail_page_links.append(url)
yield {
'UID': UID,
'title': dd.css('a::attr(title)').get(),
'date': dd.css('span::text').get(),
'FileNumber':None,
'text length':0,
'url': url,
'crawl state':'half'
}
yield from response.follow_all(detail_page_links, callback = self.parse_content)
def parse_content(self, response):
UID = response.url.split('/')[-1][:-6]
doc_info_dict = {}
for li in response.css('ul.clearfix li'):
tmp_l = li.css('*::text').getall()
if len(tmp_l) == 2:
doc_info_dict[tmp_l[0]] = tmp_l[1]
else:
tmp_l = tmp_l[0].split('：')
if len(tmp_l) == 2:
doc_info_dict[tmp_l[0]] = tmp_l[1]
File_num = None
if '发文字号' in doc_info_dict.keys():
File_num = doc_info_dict['发文字号']
paragraph_list = response.css('div.gknbxq_detail p *::text').getall()
attachment_link = response.css('div.ewebeditor_doc img::attr(src)').getall()
if len(paragraph_list) == 0:
paragraph_list = response.css('p *::text').getall()
length = len(''.join(paragraph_list))
if length > 0:
state = 'full'
with open('../../data/HTML_pk/%s/%s.pkl' % (self.name,UID), 'wb') as f:
pickle.dump(response.text,f)
with open('../../data/text/%s/%s.txt' % (self.name,UID), 'w') as f:
f.write('\n'.join(paragraph_list))
else:
state = 'empty'
return {
'UID': UID,
'FileNumber':File_num,
'mainText': paragraph_list,
'attachment_link': attachment_link,
'doc_info_dict':doc_info_dict,
'crawl state':state,
'text length':length,
}
| true
| true
|
f716bf95e7d6fbcfe61fecbdb7066fd00f5a91e2
| 62,475
|
py
|
Python
|
gamdist/gamdist.py
|
rwilson4/gamdist_new
|
b5d41f7c55fd96399edd1a14e6e259b525495dfd
|
[
"Apache-2.0"
] | null | null | null |
gamdist/gamdist.py
|
rwilson4/gamdist_new
|
b5d41f7c55fd96399edd1a14e6e259b525495dfd
|
[
"Apache-2.0"
] | null | null | null |
gamdist/gamdist.py
|
rwilson4/gamdist_new
|
b5d41f7c55fd96399edd1a14e6e259b525495dfd
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2017 Match Group, 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
#
# 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.
#
# Passing untrusted user input may have unintended consequences. Not
# designed to consume input from unknown sources (i.e., the public
# internet).
#
# This file has been modified from the original release by Match Group
# LLC. A description of changes may be found in the change log
# accompanying this source code.
import sys
import pickle
import multiprocessing as mp
import numpy as np
import scipy.special as special
import scipy.stats as stats
import scipy.linalg as linalg
from matplotlib import pyplot as plt
from .feature import _Feature
from .categorical_feature import _CategoricalFeature
from .linear_feature import _LinearFeature
from .spline_feature import _SplineFeature
import proximal_operators as po
# To do:
# - Hierarchical models
# - Piecewise constant fits, total variation regularization
# - Monotone constraint
# - Implement overdispersion for Poisson family
# - Implement Multinomial, Proportional Hazards
# - Implement outlier detection
# - AICc, BIC, R-squared estimate
# - Confidence intervals on mu, predictions (probably need to use Bootstrap but can
# do so intelligently)
# - Confidence intervals on model parameters, p-values
# - Group lasso penalty (l2 norm -- not squared -- or l_\infty norm on f_j(x_j; p_j))
# - Interactions
# - Runtime optimization (Cython)
# - Fit in parallel
# - Residuals
# - Compute different types of residuals (Sec 3.1.7 of [GAMr])
# - Plot residuals against mean response, variance, predictor, unused predictor
# - QQ plot of residuals
#
# Done:
# - Implement Gaussian, Binomial, Poisson, Gamma, Inv Gaussian,
# - Plot splines
# - Deviance (on training set and test set), AIC, Dispersion, GCV, UBRE
# - Write documentation
# - Check implementation of Gamma dispersion
# - Implement probit, complementary log-log links.
# - Implement Binomial models for covariate classes
# - Constrain spline to have mean prediction 0 over the data
# - Save and load properly
# - Implement overdispersion for Binomial family
FAMILIES = ['normal',
'binomial',
'poisson',
'gamma',
'exponential',
'inverse_gaussian'
]
LINKS = ['identity',
'logistic',
'probit',
'complementary_log_log',
'log',
'reciprocal',
'reciprocal_squared'
]
FAMILIES_WITH_KNOWN_DISPERSIONS = {'binomial': 1,
'poisson': 1
}
CANONICAL_LINKS = {'normal': 'identity',
'binomial': 'logistic',
'poisson': 'log',
'gamma': 'reciprocal',
'inverse_gaussian': 'reciprocal_squared'
}
# Non-canonical but common link/family combinations include:
# Binomial: probit and complementary log-log
# Gamma: identity and log
def _plot_convergence(prim_res, prim_tol, dual_res, dual_tol, dev):
"""Plot convergence progress.
We deem the algorithm to have converged when the prime and dual
residuals are smaller than tolerances which are themselves computed
based on the data as in [ADMM]. Some analysts prefer to claim
convergence when changes to the deviance (a measure of goodness of
fit). Thus we plot that as well. Specifically, we plot, on a log
scale, dev - dev_final, where dev_final is the deviance of the final
model. We add 1e-10 just to avoid taking the logarithm of zero, which
is completely arbitrary but makes the plot look acceptable.
Parameters
----------
prim_res : array
Array of prime residuals after each iteration.
prim_tol : array
Array of prime tolerances after each iteration.
dual_res : array
Array of dual residuals after each iteration.
dual_tol : array
Array of dual tolerances after each iteration.
dev : array
Array of deviances after each iteration
Returns
-------
(nothing)
"""
fig = plt.figure(figsize=(12., 10.))
ax = fig.add_subplot(211)
ax.plot(range(len(prim_res)), prim_res, 'b-', label='Primal Residual')
ax.plot(range(len(prim_tol)), prim_tol, 'b--', label='Primal Tolerance')
ax.plot(range(len(dual_res)), dual_res, 'r-', label='Dual Residual')
ax.plot(range(len(dual_tol)), dual_tol, 'r--', label='Dual Tolerance')
ax.set_yscale('log')
plt.xlabel('Iteration', fontsize=24)
plt.ylabel('Residual', fontsize=24)
plt.legend(fontsize=24, loc=3)
ax = fig.add_subplot(212)
ax.plot(range(len(dev)), (dev - dev[-1]) + 1e-10, 'b-', label='Deviance')
ax.set_yscale('log')
plt.xlabel('Iteration', fontsize=24)
plt.ylabel('Deviance Suboptimality', fontsize=24)
plt.gcf().subplots_adjust(bottom=0.1)
plt.gcf().subplots_adjust(left=0.1)
plt.show()
def _feature_wrapper(f):
"""Wrapper for feature optimization.
This is a wrapper for use with multi-threaded versions.
Unfortunately Python threads are *terrible*, so this doesn't
actually get used.
Parameters
------
f : list
Array of inputs. f[0] is the name of the feature. f[1]
is the feature object itself. f[2] is N * fpumz (the
vector input to the feature during optimization). f[3]
is the ADMM parameter, rho.
Returns
-------
name : str
The name of the feature. (The same as the input.)
f_j : array
The array of fitted values returned by the feature.
"""
return f[0], f[1].optimize(f[2], f[3])
def _gamma_dispersion(dof, dev, num_obs):
"""Gamma dispersion.
This function estimates the dispersion of a Gamma family with p
degrees of freedom and deviance D, and n observations. The
dispersion nu is that number satisfying
2*n * (log nu - psi(nu)) - p / nu = D
We use Newton's method with a learning rate to solve this nonlinear
equation.
Parameters
----------
dof : float
Degrees of freedom
dev : float
Deviance
num_obs : int
Number of observations
Returns
-------
nu : float
Estimated dispersion
"""
beta = 0.1
tol = 1e-6
max_its = 100
nu = 1.
for i in range(max_its):
num = 2. * num_obs * (np.log(nu) - special.psi(nu)) - dof / nu - dev
denom = 2. * num_obs * (1. / nu - special.polygamma(1, nu)) + dof / (nu * nu)
dnu = num / denom
nu -= dnu * beta
if abs(dnu) < tol:
return nu
else:
raise ValueError('Could not estimate gamma dispersion.')
class GAM:
def __init__(self, family=None, link=None, dispersion=None,
estimate_overdispersion=False, name=None,
load_from_file=None):
"""Generalized Additive Model
This is the constructor for a Generalized Additive Model.
References
----------
[glmnet] glmnet (R package):
https://cran.r-project.org/web/packages/glmnet/index.html
This is the standard package for GAMs in R and was written by people
much smarter than I am!
[pygam] pygam (Python package): https://github.com/dswah/pyGAM
This is a library in Python that does basically the same thing as this
script, but in a different way (not using ADMM).
[GLM] Generalized Linear Models by McCullagh and Nelder
The standard text on GLMs.
[GAM] Generalized Additive Models; by Hastie and Tibshirani
The book by the folks who invented GAMs.
[ESL] The Elements of Statistical Learning; by Hastie, Tibshirani, and
Friedman. Covers a lot more than just GAMs.
[GAMr] Generalized Additive Models: an Introduction with R; by Wood.
Covers more implementation details than [GAM].
[ADMM] Distributed Optimization and Statistical Learning via the Alternating
Direction Method of Multipliers; by Boyd, Parikh, Chu, Peleato, and
Eckstein. A mouthful, a work of genius.
[GAMADMM] A Distributed Algorithm for Fitting Generalized Additive Models;
by Chu, Keshavarz, and Boyd
Forms the basis of our approach, the inspiration for this package!
Parameters
----------
family : str or None (default None)
Family of the model. Currently supported families include:
'normal' (for continuous responses),
'binomial' (for binary responses),
'poisson' (for counts),
'gamma' (still in progress),
'inverse_gaussian' (still in progress).
Not currently supported families that could be supported
include Multinomial models (ordinal and nominal) and
proportional hazards models. Required unless loading an
existing model from file (see load_from_file).
link : str or None (optional)
Link function associated with the model. Supported link
functions include:
Link Canonical For Family
'identity' 'normal'
'logistic' 'binomial'
'log' 'poisson'
'reciprocal' 'gamma'
'reciprocal_squared' 'inverse_gaussian'
Other links worth supporting include probit, log-log
and complementary log-log link functions. If not
specified, the canonical link will be used, but non-
canonical links are still permitted. Certain link/family
combinations result in a non-convex problem and
convergence is not guaranteed.
dispersion : float or None (optional)
Dispersion parameter associated with the model. Certain
families (binomial, poisson) have dispersion independent
of the data. Specifying the dispersion for these families
does nothing. In other instances, the dispersion is
typically unknown and must be estimated from the data.
If the dispersion is known, it can be specified here which
will reduce the uncertainty of the model.
estimate_overdispersion : boolean (optional)
Flag specifying whether to estimate over-dispersion for
Binomial and Poisson (not yet implemented) families. Is
only possible when covariate classes are present and have
at least modest size. See [GLM, S4.5] for
details. Defaults to False.
name : str or None (optional)
Name for model, to be used in plots and in saving files.
load_from_file : str or None (optional)
This module uses an iterative approach to fitting models.
For complicated models with lots of data, each iteration
can take a long time (though the number of iterations is
typically less than 100). If the user wishes to pause
after the end of an iteration, they can pick up where
the left off by saving results (see the save_flag in .fit)
and loading them to start the next iterations. Specifying
this option supercedes all other parameters.
Returns
-------
mdl : Generalized Additive Model object
"""
if load_from_file is not None:
self._load(load_from_file)
return
if family is None:
raise ValueError('Family not specified.')
elif family not in FAMILIES:
raise ValueError('{} family not supported'.format(family))
elif family == 'exponential':
# Exponential is a special case of Gamma with a dispersion of 1.
self._family = 'gamma'
dispersion = 1.
else:
self._family = family
if link is None:
self._link = CANONICAL_LINKS[family]
elif link in LINKS:
self._link = link
else:
raise ValueError('{} link not supported'.format(link))
if dispersion is not None:
self._known_dispersion = True
self._dispersion = dispersion
elif (self._family in FAMILIES_WITH_KNOWN_DISPERSIONS.keys()
and not estimate_overdispersion):
self._known_dispersion = True
self._dispersion = FAMILIES_WITH_KNOWN_DISPERSIONS[self._family]
else:
self._known_dispersion = False
if self._link == 'identity':
self._eval_link = lambda x: x
self._eval_inv_link = lambda x: x
elif self._link == 'logistic':
self._eval_link = lambda x: np.log( x / (1. - x) )
self._eval_inv_link = lambda x: np.exp(x) / (1 + np.exp(x))
elif self._link == 'probit':
# Inverse CDF of the Gaussian distribution
self._eval_link = lambda x: stats.norm.ppf(x)
self._eval_inv_link = lambda x: stats.norm.cdf(x)
elif self._link == 'complementary_log_log':
self._eval_link = lambda x: np.log(-np.log(1. - x))
self._eval_inv_link = lambda x: 1. - np.exp(-np.exp(x))
elif self._link == 'log':
self._eval_link = lambda x: np.log(x)
self._eval_inv_link = lambda x: np.exp(x)
elif self._link == 'reciprocal':
self._eval_link = lambda x: 1. / x
self._eval_inv_link = lambda x: 1. / x
elif self._link == 'reciprocal_squared':
self._eval_link = lambda x: 1. / (x * x)
self._eval_inv_link = lambda x: 1. / np.sqrt(x)
self._estimate_overdispersion = estimate_overdispersion
self._features = {}
self._offset = 0.0
self._num_features = 0
self._fitted = False
self._name = name
def _save(self):
"""Save state.
Save the model to file to make predictions later, or continue
a fitting session.
"""
mv = {}
mv['family'] = self._family
mv['link'] = self._link
mv['known_dispersion'] = self._known_dispersion
if self._known_dispersion:
mv['dispersion'] = self._dispersion
mv['estimate_overdispersion'] = self._estimate_overdispersion
mv['offset'] = self._offset
mv['num_features'] = self._num_features
mv['fitted'] = self._fitted
mv['name'] = self._name
features = {}
for name, feature in self._features.iteritems():
features[name] = {'type': feature.__type__,
'filename': feature._filename
}
mv['features'] = features
# mv['rho'] = self._rho
mv['num_obs'] = self._num_obs
mv['y'] = self._y
mv['weights'] = self._weights
mv['has_covariate_classes'] = self._has_covariate_classes
if self._has_covariate_classes:
mv['covariate_class_sizes'] = self._covariate_class_sizes
mv['f_bar'] = self.f_bar
mv['z_bar'] = self.z_bar
mv['u'] = self.u
mv['prim_res'] = self.prim_res
mv['dual_res'] = self.dual_res
mv['prim_tol'] = self.prim_tol
mv['dual_tol'] = self.dual_tol
mv['dev'] = self.dev
filename = '{0:s}_model.pckl'.format(self._name)
f = open(filename, 'w')
pickle.dump(mv, f)
f.close()
def _load(self, filename):
"""Load state.
Load a model from file to make predictions.
"""
f = open(filename)
mv = pickle.load(f)
f.close()
self._filename = filename
self._family = mv['family']
self._link = mv['link']
self._known_dispersion = mv['known_dispersion']
if self._known_dispersion:
self._dispersion = mv['dispersion']
self._estimate_overdispersion = mv['estimate_overdispersion']
self._offset = mv['offset']
self._num_features = mv['num_features']
self._fitted = mv['fitted']
self._name = mv['name']
self._features = {}
features = mv['features']
for (name, feature) in features.iteritems():
if feature['type'] == 'categorical':
self._features[name] = _CategoricalFeature(load_from_file=feature['filename'])
elif feature['type'] == 'linear':
self._features[name] = _LinearFeature(load_from_file=feature['filename'])
elif feature['type'] == 'spline':
self._features[name] = _SplineFeature(load_from_file=feature['filename'])
else:
raise ValueError('Invalid feature type')
# self._rho = mv['rho']
self._num_obs = mv['num_obs']
self._y = mv['y']
self._weights = mv['weights']
self._has_covariate_classes = mv['has_covariate_classes']
if self._has_covariate_classes:
self._covariate_class_sizes = mv['covariate_class_sizes']
self.f_bar = mv['f_bar']
self.z_bar = mv['z_bar']
self.u = mv['u']
self.prim_res = mv['prim_res']
self.dual_res = mv['dual_res']
self.prim_tol = mv['prim_tol']
self.dual_tol = mv['dual_tol']
self.dev = mv['dev']
if self._link == 'identity':
self._eval_link = lambda x: x
self._eval_inv_link = lambda x: x
elif self._link == 'logistic':
self._eval_link = lambda x: np.log( x / (1. - x) )
self._eval_inv_link = lambda x: np.exp(x) / (1 + np.exp(x))
elif self._link == 'probit':
# Inverse CDF of the Gaussian distribution
self._eval_link = lambda x: stats.norm.ppf(x)
self._eval_inv_link = lambda x: stats.norm.cdf(x)
elif self._link == 'complementary_log_log':
self._eval_link = lambda x: np.log(-np.log(1. - x))
self._eval_inv_link = lambda x: 1. - np.exp(-np.exp(x))
elif self._link == 'log':
self._eval_link = lambda x: np.log(x)
self._eval_inv_link = lambda x: np.exp(x)
elif self._link == 'reciprocal':
self._eval_link = lambda x: 1. / x
self._eval_inv_link = lambda x: 1. / x
elif self._link == 'reciprocal_squared':
self._eval_link = lambda x: 1. / (x * x)
self._eval_inv_link = lambda x: 1. / np.sqrt(x)
def add_feature(self, name, type, transform=None, rel_dof=None, regularization=None):
"""Add a feature
Add a feature to a Generalized Additive Model. (An implicit
constant feature is always included, representing the overall
average response.)
Parameters
----------
name : str
Name for feature. Used internally to keep track of
features and is also used when saving files and in
plots.
type : str
Type of feature. Currently supported options include:
'categorical' (for categorical variables)
'linear' (for variables with a linear contribution
to the response)
'spline' (for variables with a potentially nonlinear
contribution to the response).
Other types of features worth supporting include
piecewise constant functions and monotonic functions.
Those might end up being regularization terms.
transform : function or None
Optional transform applied to feature data, saving
the user from repetitive boilerplate code. Any function
may be used; it is applied to data provided during fitting
and prediction. Common options might include np.log, np.log1p,
or np.sqrt. The user may wish to start with a base feature
like 'age' and use derived features 'age_linear', 'age_quadratic'
to permit quadratic models for that feature, with potentially
different regularization applied to each.
rel_dof : float or None
Relative degrees of freedom. Applicable only to spline features.
The degrees of freedom associated with a spline represent how
"wiggly" it is allowed to be. A spline with two degrees of freedom
is just a line. (Actually, since these features are constrained
to have zero mean response over the data, linear features
only have one degree of freedom.) The relative degrees of freedom
are used to specify the baseline smoothing parameter (lambda)
associated with a feature. When the model is fit to data, the user
can specify an overall smoothing parameter applied to all features
to alter the amount of regularization in the entire model. Thus
the actual degrees of freedom will vary based on the amount of
smoothing. The idea is that the analyst may wish to permit some
features to be more wiggly than others. By default, all
splines have 4 relative degrees of freedom.
Regularization of any feature effectively reduces the degrees of
freedom, and so this term is potentially applicable, but that is
not yet supported.
regularization : dictionary or None
Dictionary specifying the regularization applied to this feature.
Different types of features support different types of regularization.
Splines implicitly only support regularization of the wiggliness
via a C2 smoothness penalty. That is controlled via the rel_dof.
Other features have more diverse options described in their own
documentation.
Returns
-------
(nothing)
"""
if type == 'categorical':
f = _CategoricalFeature(name, regularization=regularization)
elif type == 'linear':
f = _LinearFeature(name, transform, regularization=regularization)
elif type == 'spline':
f = _SplineFeature(name, transform, rel_dof)
else:
raise ValueError('Features of type {} not supported.'.format(type))
self._features[name] = f
self._num_features += 1
def fit(self, X, y, covariate_class_sizes=None, weights=None,
optimizer='admm', smoothing=1., save_flag=False,
verbose=False, plot_convergence=False, max_its=100):
"""Fit a Generalized Additive Model to data.
Note regarding binomial families: many data sets include
multiple observations having identical features. For example,
imagine a data set with features 'gender', and 'country' and
binary response indicating whether the person died (morbid but
common in biostatistics). The data might look like this:
gender country patients survivors
M USA 50 48
F USA 70 65
M CAN 40 38
F CAN 45 43
This still describes a binomial family, but in a more compact
format than specifying each individual user. We eventually
want to support this more compact format, but we do not
currently! In this context, it is important to check for
over-dispersion (see [GLM]), and I need to learn more first.
In the current implementation, we assume that there is no
over-dispersion, and that the number of users having the
same set of features is small.
Parameters
----------
X : pandas dataframe
Dataframe of features. The column names must correspond
to the names of features added to the model. X may have
extra columns corresponding to features not included in
the model; these are simply ignored. Where applicable,
the data should be "pre-transformation", since this code
will apply any transformations specified in .add_feature.
y : array
Response. Depending on the model family, the response
may need to be in a particular form (for example, for
a binomial family, the y's should be either 0 or 1),
but this is not checked anywhere!
covariate_class_sizes : array or None.
If observations are grouped into covariance classes, the
size of those classes should be listed in this input.
w : array
Weights applied to each observation. This is effectively
specifying the dispersion of each observation.
optimizer : string
We use the Alternating Direction Method of Multipliers
('admm') to fit the model. We may eventually support more
methods, but right now this option does nothing.
smoothing : float
Smoothing to apply to entire model, used in conjunction
with other regularization parameters. That is, whatever
regularization is used for the various features, is
scaled by this term, allowing the user to set the overall
smoothing by Cross Validation or whatever they like. This
allows the user to specify different regularization for
each feature, while still permitting a one-dimensional
family of models corresponding to different amounts of
regularization. Defaults to 1., leaving the regularization
as specified in .add_feature().
save_flag : boolean
Specifies whether to save intermediate results after each
iteration. Useful for complicated models with massive
data sets that take a while to fit. If the system crashes
during the fit, the analyst can pick up where they left
off instead of starting from scratch. Defaults to False.
verbose : boolean
Specifies whether to print mildly useful information to
the screen during the fit. Defaults to False.
plot_convergence : boolean
Specifies whether to plot the convergence graph at the
end. (I suspect only Convex Optimization nerds like me
want to see this.) Defaults to False.
max_its : integer
Maximum number of iterations. Defaults to 100.
Returns
-------
(nothing)
"""
if save_flag and self._name is None:
msg = 'Cannot save a GAM with no name.'
msg += ' Specify name when instantiating model.'
raise ValueError(msg)
if len(X) != len(y):
raise ValueError('Inconsistent number of observations in X and y.')
num_threads = 1
self._rho = 0.1
eps_abs = 1e-3
eps_rel = 1e-3
# Note that X may include columns that do not correspond to features in our model
# (for example, if the user is experimenting with leaving out features to assess
# importance). Thus, the real number of features is self._num_features, not
# num_features as in the next line.
self._num_obs, num_features = X.shape
self._y = y.flatten()
self._weights = weights
if covariate_class_sizes is not None:
self._has_covariate_classes = True
self._covariate_class_sizes = covariate_class_sizes
mean_response = float(np.sum(self._y)) / np.sum(self._covariate_class_sizes)
self._offset = self._eval_link(mean_response)
else:
self._has_covariate_classes = False
self._covariate_class_sizes = None
self._offset = self._eval_link(np.mean(self._y))
fj = {}
for name, feature in self._features.iteritems():
feature.initialize(X[name].values, smoothing=smoothing,
covariate_class_sizes=self._covariate_class_sizes,
save_flag=save_flag, save_prefix=self._name)
fj[name] = np.zeros(self._num_obs)
self.f_bar = np.full((self._num_obs,), self._offset / self._num_features)
self.z_bar = np.zeros(self._num_obs)
self.u = np.zeros(self._num_obs)
self.prim_res = []
self.dual_res = []
self.prim_tol = []
self.dual_tol = []
self.dev = []
z_new = np.zeros(self._num_obs)
if num_threads > 1:
p = mp.Pool(num_threads)
else:
p = None
for i in range(max_its):
if verbose:
print 'Iteration {0:d}'.format(i)
print 'Optimizing primal variables'
fpumz = self._num_features * (self.f_bar + self.u - self.z_bar)
fj_new = {}
f_new = np.full((self._num_obs,), self._offset)
if False: #num_threads > 1:
# Getting python to run a for loop in parallel
# might as well be impossible :-(
args = [(i, self._features[i], fpumz, self._rho) for i in self._features.keys()]
results = p.map(_feature_wrapper, args)
for i in results:
fj_new[i[0]] = i[1]
f_new += i[1]
else:
for name, feature in self._features.iteritems():
if verbose:
print 'Optimizing {0:s}'.format(name)
fj_new[name] = feature.optimize(fpumz, self._rho)
f_new += fj_new[name]
f_new /= self._num_features
if verbose:
print 'Optimizing dual variables'
z_new = self._optimize(self.u + f_new, self._num_features, p)
self.u += f_new - z_new
prim_res = np.sqrt(self._num_features) * linalg.norm(f_new - z_new)
dual_res = 0.0
norm_ax = 0.0
norm_bz = 0.0
norm_aty = 0.0
num_params = 0
for name, feature in self._features.iteritems():
dr = ((fj_new[name] - fj[name])
+ (z_new - self.z_bar)
- (f_new - self.f_bar))
dual_res += dr.dot(dr)
norm_ax += fj_new[name].dot(fj_new[name])
zik = fj_new[name] + z_new - f_new
norm_bz += zik.dot(zik)
norm_aty += feature.compute_dual_tol(self.u)
num_params += feature.num_params()
dual_res = self._rho * np.sqrt(dual_res)
norm_ax = np.sqrt(norm_ax)
norm_bz = np.sqrt(norm_bz)
norm_aty = np.sqrt(norm_aty)
self.f_bar = f_new
fj = fj_new
self.z_bar = z_new
if self._has_covariate_classes:
sccs = np.sum(self._covariate_class_sizes)
prim_tol = (np.sqrt(sccs * self._num_features) * eps_abs
+ eps_rel * np.max([norm_ax, norm_bz]))
else:
prim_tol = (np.sqrt(self._num_obs * self._num_features) * eps_abs
+ eps_rel * np.max([norm_ax, norm_bz]))
dual_tol = np.sqrt(num_params) * eps_abs + eps_rel * norm_aty
self.prim_res.append(prim_res)
self.dual_res.append(dual_res)
self.prim_tol.append(prim_tol)
self.dual_tol.append(dual_tol)
self.dev.append(self.deviance())
if prim_res < prim_tol and dual_res < dual_tol:
if verbose:
print 'Fit converged'
break
else:
if verbose:
print 'Fit did not converge'
if num_threads > 1:
p.close()
p.join()
self._fitted = True
if save_flag:
self._save()
if plot_convergence:
_plot_convergence(self.prim_res, self.prim_tol, self.dual_res,
self.dual_tol, self.dev)
def _optimize(self, upf, N, p=None):
"""Optimize \bar{z}.
Solves the optimization problem:
minimize L(N*z) + \rho/2 * \| N*z - N*u - N*\bar{f} \|_2^2
where z is the variable, N is the number of features, u is the scaled
dual variable, \bar{f} is the average feature response, and L is
the likelihood function which is different depending on the
family and link function. This is accomplished via a proximal
operator, as discussed in [GAMADMM]:
prox_\mu(v) := argmin_x L(x) + \mu/2 * \| x - v \|_2^2
I strongly believe that paper contains a typo in this equation, so we
return (1. / N) * prox_\mu (N * (u + \bar{f}) with \mu = \rho instead
of \mu = \rho / N as in [GAMADMM]. When implemented as in the paper,
convergence was much slower, but it did still converge.
Certain combinations of family and link function result in proximal
operators with closed form solutions, making this step *very* fast
(e.g. 3 flops per observation).
Parameters
----------
upf : array
Vector representing u + \bar{f}
N : integer
Number of features.
p : Multiprocessing Pool (optional)
If multiple threads are available, massive data sets may
benefit from solving this optimization problem in parallel.
It is up to the individual functions to decide whether to
actually do this.
Returns
-------
z : array
Result of the above optimization problem.
"""
prox = None
if self._family == 'normal':
if self._link == 'identity':
prox = po._prox_normal_identity
else:
prox = po._prox_normal
elif self._family == 'binomial':
if self._link == 'logistic':
prox = po._prox_binomial_logit
else:
prox = po._prox_binomial
if self._has_covariate_classes:
return (1. / N) * prox(N*upf, self._rho, self._y,
self._covariate_class_sizes,
self._weights, self._eval_inv_link, p=p)
elif self._family == 'poisson':
if self._link == 'log':
prox = po._prox_poisson_log
else:
prox = po._prox_poisson
elif self._family == 'gamma':
if self._link == 'reciprocal':
prox = po._prox_gamma_reciprocal
else:
prox = po._prox_gamma
elif self._family == 'inverse_gaussian':
if self._link == 'reciprocal_squared':
prox = po._prox_inv_gaussian_reciprocal_squared
else:
prox = po._prox_inv_gaussian
else:
msg = 'Family {0:s} and Link Function {1:s} not (yet) supported.'
raise ValueError(msg.format(self._family, self._link))
return (1. / N) * prox(N*upf, self._rho, self._y, w=self._weights,
inv_link=self._eval_inv_link, p=p)
def predict(self, X):
"""Apply fitted model to features.
Parameters
----------
X : pandas dataframe
Data for which we wish to predict the response. The
column names must correspond to the names of the
features used to fit the model. X may have extra
columns corresponding to features not in the model;
these are simply ignored. Where applicable, the data
should be "pre-transformation", since this code will
apply any transformations specified while defining
the model.
Returns
-------
mu : array
Predicted mean response for each data point.
"""
if not self._fitted:
raise AttributeError('Model not yet fit.')
num_points, m = X.shape
eta = np.full((num_points,), self._offset)
for name, feature in self._features.iteritems():
eta += feature.predict(X[name].values)
return self._eval_inv_link(eta)
def confidence_intervals(self, X, prediction=False, width=0.95):
"""Confidence intervals on predictions.
NOT YET IMPLEMENTED
There are two notions of confidence intervals that are
appropriate. The first is a confidence interval on mu,
the mean response. This follows from the uncertainty
associated with the fit model. The second is a confidence
interval on observations of this model. The distinction
is best understood by example. For a Gaussian family,
the model might be a perfect fit to the data, and we
may have billions of observations, so we know mu perfectly.
Confidence intervals on the mean response would be very
small. But the response is Gaussian with a non-zero
variance, so observations will in general still be spread
around the mean response. A confidence interval on the
prediction would be larger.
Now consider a binomial family. The estimated mean response
will be some number between 0 and 1, and we can estimate
a confidence interval for that mean. But the observed
response is always either 0 or 1, so it doesn't make sense
to talk about a confidence interval on the prediction
(except in some pedantic sense perhaps).
Note that if we are making multiple predictions, it makes
sense to talk about a "global" set of confidence intervals.
Such a set has the property that *all* predictions fall
within their intervals with specified probability. This
function does not compute global confidence intervals!
Instead each confidence interval is computed "in vacuo".
Parameters
----------
X : pandas dataframe
Data for which we wish to predict the response. The
column names must correspond to the names of the
features used to fit the model. X may have extra
columns corresponding to features not in the model;
these are simply ignored. Where applicable, the data
should be "pre-transformation", since this code will
apply any transformations specified while defining
the model.
prediction : boolean
Specifies whether to return a confidence interval
on the mean response or on the predicted response.
(See above.) Defaults to False, leading to a
confidence interval on the mean response.
width : float between 0 and 1
Desired confidence width. Defaults to 0.95.
Returns
-------
mu : (n x 2) array
Lower and upper bounds on the confidence interval
associated with each prediction.
"""
pass
def plot(self, name, true_fn=None):
"""Plot the component of the modelf for a particular feature.
Parameters
----------
name : str
Name of feature (must be a feature in the model).
true_fn : function or None (optional)
Function representing the "true" relationship
between the feature and the response.
Returns
-------
(nothing)
"""
self._features[name]._plot(true_fn=true_fn)
def deviance(self, X=None, y=None, covariate_class_sizes=None, w=None):
"""Deviance
This function works in one of two ways:
Firstly, it computes the deviance of the model, defined as
2 * \phi * (\ell(y; y) - \ell(\mu; y))
where \phi is the dispersion (which is only in this equation
to cancel out the denominator of the log-likelihood),
\ell(y; y) is the log-likelihood of the model that fits the
data perfectly, and \ell(\mu; y) is the log-likelihood of the
fitted model on the data used to fit the model. This is
the quantity we minimize when fitting the model.
Secondly, it computes the deviance of the model on arbitrary
data sets. This can be used in conjunction with Cross Validation
to choose the smoothing parameter by minimizing the deviance
on the hold-out set.
Parameters
----------
X : pandas dataframe (optional)
Dataframe of features. The column names must correspond
to the names of features added to the model. (See .predict()).
Only applicable for the second use case described above.
y : array (optional)
Response. Only applicable for the second use case.
covariate_class_sizes : array (optional)
Array of covariate class sizes.
w : array (optional)
Weights for observations. Only applicable for the second
use case, but optional even then.
Returns
-------
D : float
The deviance of the model.
"""
if X is None or y is None:
y = self._y
mu = self._eval_inv_link(self._num_features * self.f_bar)
w = self._weights
if self._has_covariate_classes:
m = self._covariate_class_sizes
else:
m = 1.
else:
mu = self.predict(X)
if covariate_class_sizes is None:
m = covariate_class_sizes
else:
m = 1.
if self._family == 'normal':
y_minus_mu = y - mu
if w is None:
return y_minus_mu.dot(y_minus_mu)
else:
return w.dot(y_minus_mu * y_minus_mu)
elif self._family == 'binomial':
if w is None:
return -2. * np.sum( y * np.log(mu) + (m - y) * np.log1p(-mu) )
else:
return -2. * w.dot( y * np.log(mu) + (m - y) * np.log1p(-mu) )
elif self._family == 'poisson':
if w is None:
return 2. * np.sum(y * np.log(y / mu) - (y - mu))
else:
return 2. * w.dot(y * np.log(y / mu) - (y - mu))
elif self._family == 'gamma':
if w is None:
return 2. * np.sum(-1. * np.log(y / mu) + (y - mu) / mu)
else:
return 2. * w.dot(-1. * np.log(y / mu) + (y - mu) / mu)
elif self._family == 'inverse_gaussian':
if w is None:
return np.sum( (y - mu) * (y - mu) / (mu * mu * y) )
else:
return w.dot( (y - mu) * (y - mu) / (mu * mu * y) )
def dispersion(self, formula='deviance'):
"""Dispersion
Returns the dispersion associated with the model. Depending on
the model family and whether the dispersion was specified by
the user, the dispersion may or may not be known a
priori. This function will estimate this parameter when
appropriate.
There are different ways of estimating this parameter that may
be appropriate for different kinds of families. The current
implementation is based on the deviance, as in Eqn 3.10 on
p. 110 of GAMr. As discussed in that section, this tends not
to work well for Poisson data (with overdispersion) when the
mean response is small. Alternatives are offered in that
section, but I have not yet implemented them. This is not
terribly relevant for the current implementation since
overdispersion is not supported! (When overdispersion is not
present, the dispersion of the Poisson is exactly 1.)
My eventual hope is to understand the appropriate methods for
all the different circumstances and have intelligent defaults
that can be overridden by opinionated users.
Parameters
----------
formula : str
Formula for the dispersion. Options include:
'deviance' (default)
'pearson'
'fletcher'
"""
if self._family == 'normal':
if self._known_dispersion:
return self._dispersion
else:
sigma2 = self.deviance() / (self._num_obs - self.dof())
return sigma2
elif self._family == 'binomial':
if self._known_dispersion:
return self._dispersion
elif self._estimate_overdispersion:
return self._binomial_overdispersion()
else:
return 1.
elif self._family == 'poisson':
return 1.
elif self._family == 'gamma':
if self._known_dispersion:
return self._dispersion
else:
return _gamma_dispersion(self.dof(), self.deviance(), self._num_obs)
# This equation is a first-order approximation valid when nu is
# large (see Section 8.3.6 of [GLM])
#Dbar = self.deviance() / self._num_obs
#return Dbar * (6. + Dbar) / (6. + 2. * Dbar)
elif self._family == 'inverse_gaussian':
if self._known_dispersion:
return self._dispersion
else:
sigma2 = self.deviance() / (self._num_obs - self.dof())
return sigma2
def _binomial_overdispersion(self, formula=None):
"""Over-Dispersion
Parameters
----------
formula : str
Which formula to use, either 'replication' or
'pearson'. See Notes.
Returns
-------
sigma2 : float
Estimate of over-dispersion. This is also saved as the
self._dispersion parameter so we only calculate this once
regardless of how many times this function is called.
Notes
-----
When using covariate classes, the observed variance may exceed
the baseline for the family due to clustering in the
population. See GLM for motivation. That text gives two
methodologies for estimating over-dispersion. When there are
no covariate classes (multiple observations with identical
features), estimating over-dispersion is not possible.
The most reliable assessment of over-dispersion is only
possible when there is replication amongst the covariate
classes. This is best illustrated through example. Suppose we
have data on patients from two hospitals as shown in the table
below. Note that there are 3 rows corresponding to Men in
hospital 1. These entries could of course be pooled to give
the total patients and survivors for this covariate class, but
because they have not, it permits us to estimate
over-dispersion more reliably.
Gender Hospital Patients Survivors
M 1 30 15
M 1 40 19
M 1 35 15
F 1 10 8
M 2 10 3
M 2 18 6
F 2 40 30
Because we are building a model based on gender and hospital
alone, we are assuming that all three entries are drawn from
the same binomial distribution. We could actually test that
hypothesis using, for example, Welch's t-Test. If the result
indicates a significant departure from the null hypothesis,
there must be some (unobserved) explanation for different
survival rates. Perhaps the repeated entries correspond to
different doctors, with some doctors being more effective than
others. Or perhaps the multiple entries refer to different
time periods, like before and after a new treatment was
instituted. Regardless, we can quantify the additional
variance and use it to make (hopefully) more accurate
confidence intervals.
When replication is present, we take the following approach,
per GLM. Suppose a particular covariate class (e.g. Gender=M,
Hospital=1) has r replicates. Across all r replicates,
determine the observed success rate, pi. In our example, we
have 105 patients and 49 survivors, for a total survival rate
of pi = 0.47. Next we compute the variance on r-1 DOF:
1 r (y_j - m_j * pi)^2
s^2 = --- \sum ------------------
r-1 j=1 m_j pi * (1 - pi)
where y_j is the number of successes in the jth replicate, m_j
is the number of trials in the jth replicate, and s^2 is
estimated variance. Per GLM, this is an unbiased estimate of
the dispersion parameter. Filling in our specific numbers, we
get s^2 = 0.17, indicating under-dispersion. (Important note:
these are made up numbers, so there is actually more
consistency in the data than would be exhibited from a true
binomial model. Over-dispersion is more common than
under-dispersion.)
Each covariate class with replication can be used to derive an
estimate of the dispersion parameter. If we expect the
dispersion to be independent of the covariate classes (which
may or may not be true), we can pool these estimates, weighted
by the degree of replication. If the kth covariate class has
r_k replicates and dispersion estimate s_k^2, the overall
estimate of dispersion is:
\sum_k (r_k - 1) * s_k^2
s^2 = -------------------------
\sum_k (r_k - 1)
Another important note: the above formula is *not* present in
GLM. That text just says to pool the estimates, but does not
specify how. This approach makes sense to me, but that doesn't
make it correct!
When replication is not present, or even if the degree of
replication is small, the above methodology breaks
down. Instead, GLM advocates the use of a Pearson-residual
based approach. If pi_j is the model prediction for the jth
covariate class, then we estimate dispersion as:
1 (y_j - m_j * pi_j)^2
s^2 = ----- \sum -----------------------
n - p j m_j * pi_j * (1 - pi_j)
This is similar to the replicate-based formula, but we are
using the model prediction for pi_j instead of the pooled
observations, and we are using the n-p as the error DOF
instead of the number of replicates. This methodology still
breaks down when the sizes of the covariate classes, m_j, are
small.
In order to use the replicate-based formula, there must be at
least one covariate class exhibiting replication, and the
degree of replication must be at least two. If these
conditions are not met, and the user dictates that we use the
replicate-based formula, we simply ignore that directive and
use the Pearson-based approach. (It might be best to issue a
warning in this case, but we do not do that.)
If this function is called without specifying which
methodology to use, we use the following criteria in assessing
whether there is enough replication to use the first
approach. First, there must be at least two covariate classes
exhibiting replication. Second, the degree of replication of
the most-replicated covariate class must be at least
3. Finally, the total replication degrees of freedom must be
at least 10. For example, in the example data set above, there
are two covariate classes exhibiting replication: Males in
Hospital 1, and Males in Hospital 2, with 3 and 2 degrees of
replication, respectively. The degree of replication of the
most-replicate covariate class is therefore equal to 3. The
degrees of freedom are (2-1) + (3-1) = 3, which is below the
threshold of 10. We would therefore use the Pearson-based
formula in this case.
These criteria are completely arbitrary! I need to do more
research to determine the appropriate criteria.
"""
if not self._has_covariate_classes:
return 1.
min_cc_replicates = 1
min_replication = 2
des_cc_replicates = 2
des_replication = 3
des_replication_dof = 10
# Determine degree of replication
#
# To use the replication formula, we need at least one
# covariate class with replication, and that covariate class
# needs replication of at least 2. It might make sense to use
# a more stringent set of criteria, but this is enough for
# now.
#
# The way we decide whether two observations have the same
# covariate class is by encoding the covariate class by an
# index. Each categorical feature has already indexed each
# category by an internal integer between 0 and n_k - 1, where
# n_k is the number of categories of the kth feature. (None of
# this is applicable unless all the features are categorical.
#
# We use these internal indices along with the numbers of
# categories in conjunction with the numpy ravel_multi_index
# function to map a tuple of category indices into a single
# integer between 0 and the the product of all category sizes
# (minus 1).
#
# We need to take care to loop over the features in a
# consistent order, so we create the fnames array just to give
# an arbitrary but consistent ordering.
r = {}
covariate_class = np.zeros((self._num_obs,))
fnames = self._features.keys()
for i in range(self._num_obs):
multi_index = []
dims = []
for fname in fnames:
cindex, csize = self._features[fname].category_index(i)
multi_index.append(cindex)
dims.append(csize)
cci = np.ravel_multi_index(multi_index, dims)
covariate_class[i] = cci
r[cci] = r.get(cci, 0) + 1
num_cc_with_replicates = 0
max_replication = 0
replication_dof = 0
for j in r.values():
if j > 1:
num_cc_with_replicates += 1
replication_dof += j - 1
if j > max_replication:
max_replication = j
if ((num_cc_with_replicates >= min_cc_replicates
and max_replication >= min_replication)):
has_replication = True
else:
has_replication = False
if ((num_cc_with_replicates >= des_cc_replicates
and max_replication >= des_replication
and replication_dof >= des_replication_dof)):
has_desired_replication = True
else:
has_desired_replication = False
if formula is None:
if has_desired_replication:
formula = 'replication'
else:
formula = 'pearson'
if has_replication and formula == 'replication':
trials = {}
successes = {}
# Initial loop to pool trials/successes.
for i in range(self._num_obs):
cci = covariate_class[i]
trials[cci] = trials.get(cci, 0) + self._covariate_class_sizes[i]
successes[cci] = successes.get(cci, 0) + self._y[i]
# Final loop to compute dispersion
s2 = 0.
for i in range(self._num_obs):
cci = covariate_class[i]
pi = float(successes[cci]) / trials[cci]
num = self._y[i] - self._covariate_class_sizes[i] * pi
denom = self._covariate_class_sizes[i] * pi * (1 - pi)
s2 += num * num / denom
# Divide by the error DOF
s2 /= replication_dof
self._known_dispersion = True
self._dispersion = s2
return s2
else:
mu = self._eval_inv_link(self._num_features * self.f_bar)
m = self._covariate_class_sizes
bl_var = np.multiply(mu, 1. - mu)
res = self._y - np.multiply(m, mu)
num = np.multiply(res, res)
denom = np.multiply(m, bl_var)
n_minus_p = self._num_obs - self.dof()
s2 = np.sum(np.divide(num, denom)) / n_minus_p
self._known_dispersion = True
self._dispersion = s2
return s2
def dof(self):
"""Degrees of Freedom
Returns the degrees of freedom associated with this model.
Simply adds up the degrees of freedom associated with each
feature.
"""
dof = 1. # Affine factor
for name, feature in self._features.iteritems():
dof += feature.dof()
return dof
def aic(self):
"""Akaike Information Criterion
Returns the AIC for the fitted model, useful for choosing
smoothing parameters. The AIC we compute is actually off
by a constant factor, making it easier to compute without
detracting from its role in model selection.
Different authors seem to throw in multiplicative or additive
factors willy-nilly since it doesn't affect model selection.
"""
p = self.dof()
if not self._known_dispersion:
# If we are estimating the dispersion, we need to
# add one to the DOF.
p += 1
# Note that the deviance is twice the dispersion times the
# log-likelihood, so no factor of two required there.
return self.deviance() / self.dispersion() + 2. * p
# return (self.deviance() / self._num_obs
# + 2. * p * self.dispersion() / self._num_obs)
def aicc(self):
# Eqn 6.32 on p. 304 of [GAMr]
pass
def ubre(self, gamma=1.0):
"""Un-Biased Risk Estimator
Returns the Un-Biased Risk Estimator as discussed in Sections
6.2.1 and 6.2.5 of [GAMr]. This can be used for choosing the
smoothing parameter when the dispersion is known.
As discussed in Section 6.2.5 of [GAMr], sometimes it is helpful
to force smoother fits by exaggerating the effective degrees of
freedom. In that case, a value of gamma > 1. may be desirable.
"""
return self.deviance() + 2. * gamma * self.dispersion() * self.dof()
def gcv(self, gamma=1.0):
"""Generalized Cross Validation
This function returns the Generalized Cross Validation (GCV)
score, which can be used for choosing the smoothing parameter
when the dispersion is unknown.
As discussed in Section 6.2.5 of [GAMr], sometimes it is helpful
to force smoother fits by exaggerating the effective degrees of
freedom. In that case, a value of gamma > 1. may be desirable.
"""
denom = self._num_obs - gamma * self.dof()
return self._num_obs * self.deviance() / (denom * denom)
def summary(self):
"""Print summary statistics associated with fitted model.
Prints statistics for the overall model, as well as for
each individual feature (see the __str__() function in
each feature type for details about what is printed
there).
For the overall model, the following are printed:
phi: Estimated dispersion parameter. Omitted
if specified or if it is known for the
Family (e.g. Poisson).
edof: Estimated degrees of freedom.
Deviance: The difference between the log-likelihood of
the model that fits the data perfectly and
that of the fitted model, times twice the
dispersion.
AIC: Akaike Information Criterion.
AICc: AIC with correction for finite data sets.
UBRE: Unbiased Risk Estimator (if dispersion is known).
GCV: Generalized Cross Validation (if dispersion is estimated).
For more details on these parameters, see the documentation
in the corresponding functions. It may also be helpful to
include an R^2 value where appropriate, and perhaps a p-value
for the model against the null model having just the affine
term. It would also be nice to have confidence intervals
at least on the estimated dispersion parameter.
"""
print 'Model Statistics'
print '----------------'
if not self._known_dispersion:
print 'phi: {0:0.06g}'.format(self.dispersion())
print 'edof: {0:0.0f}'.format(self.dof())
print 'Deviance: {0:0.06g}'.format(self.deviance())
print 'AIC: {0:0.06g}'.format(self.aic())
#print 'AICc: {0:0.06g}'.format(aicc)
if self._known_dispersion:
print 'UBRE: {0:0.06g}'.format(self.ubre())
else:
print 'GCV: {0:0.06g}'.format(self.gcv())
print ''
print 'Features'
print '--------'
for name, feature in self._features.iteritems():
print feature.__str__()
| 41.429045
| 96
| 0.595534
|
import sys
import pickle
import multiprocessing as mp
import numpy as np
import scipy.special as special
import scipy.stats as stats
import scipy.linalg as linalg
from matplotlib import pyplot as plt
from .feature import _Feature
from .categorical_feature import _CategoricalFeature
from .linear_feature import _LinearFeature
from .spline_feature import _SplineFeature
import proximal_operators as po
FAMILIES = ['normal',
'binomial',
'poisson',
'gamma',
'exponential',
'inverse_gaussian'
]
LINKS = ['identity',
'logistic',
'probit',
'complementary_log_log',
'log',
'reciprocal',
'reciprocal_squared'
]
FAMILIES_WITH_KNOWN_DISPERSIONS = {'binomial': 1,
'poisson': 1
}
CANONICAL_LINKS = {'normal': 'identity',
'binomial': 'logistic',
'poisson': 'log',
'gamma': 'reciprocal',
'inverse_gaussian': 'reciprocal_squared'
}
def _plot_convergence(prim_res, prim_tol, dual_res, dual_tol, dev):
"""Plot convergence progress.
We deem the algorithm to have converged when the prime and dual
residuals are smaller than tolerances which are themselves computed
based on the data as in [ADMM]. Some analysts prefer to claim
convergence when changes to the deviance (a measure of goodness of
fit). Thus we plot that as well. Specifically, we plot, on a log
scale, dev - dev_final, where dev_final is the deviance of the final
model. We add 1e-10 just to avoid taking the logarithm of zero, which
is completely arbitrary but makes the plot look acceptable.
Parameters
----------
prim_res : array
Array of prime residuals after each iteration.
prim_tol : array
Array of prime tolerances after each iteration.
dual_res : array
Array of dual residuals after each iteration.
dual_tol : array
Array of dual tolerances after each iteration.
dev : array
Array of deviances after each iteration
Returns
-------
(nothing)
"""
fig = plt.figure(figsize=(12., 10.))
ax = fig.add_subplot(211)
ax.plot(range(len(prim_res)), prim_res, 'b-', label='Primal Residual')
ax.plot(range(len(prim_tol)), prim_tol, 'b--', label='Primal Tolerance')
ax.plot(range(len(dual_res)), dual_res, 'r-', label='Dual Residual')
ax.plot(range(len(dual_tol)), dual_tol, 'r--', label='Dual Tolerance')
ax.set_yscale('log')
plt.xlabel('Iteration', fontsize=24)
plt.ylabel('Residual', fontsize=24)
plt.legend(fontsize=24, loc=3)
ax = fig.add_subplot(212)
ax.plot(range(len(dev)), (dev - dev[-1]) + 1e-10, 'b-', label='Deviance')
ax.set_yscale('log')
plt.xlabel('Iteration', fontsize=24)
plt.ylabel('Deviance Suboptimality', fontsize=24)
plt.gcf().subplots_adjust(bottom=0.1)
plt.gcf().subplots_adjust(left=0.1)
plt.show()
def _feature_wrapper(f):
"""Wrapper for feature optimization.
This is a wrapper for use with multi-threaded versions.
Unfortunately Python threads are *terrible*, so this doesn't
actually get used.
Parameters
------
f : list
Array of inputs. f[0] is the name of the feature. f[1]
is the feature object itself. f[2] is N * fpumz (the
vector input to the feature during optimization). f[3]
is the ADMM parameter, rho.
Returns
-------
name : str
The name of the feature. (The same as the input.)
f_j : array
The array of fitted values returned by the feature.
"""
return f[0], f[1].optimize(f[2], f[3])
def _gamma_dispersion(dof, dev, num_obs):
"""Gamma dispersion.
This function estimates the dispersion of a Gamma family with p
degrees of freedom and deviance D, and n observations. The
dispersion nu is that number satisfying
2*n * (log nu - psi(nu)) - p / nu = D
We use Newton's method with a learning rate to solve this nonlinear
equation.
Parameters
----------
dof : float
Degrees of freedom
dev : float
Deviance
num_obs : int
Number of observations
Returns
-------
nu : float
Estimated dispersion
"""
beta = 0.1
tol = 1e-6
max_its = 100
nu = 1.
for i in range(max_its):
num = 2. * num_obs * (np.log(nu) - special.psi(nu)) - dof / nu - dev
denom = 2. * num_obs * (1. / nu - special.polygamma(1, nu)) + dof / (nu * nu)
dnu = num / denom
nu -= dnu * beta
if abs(dnu) < tol:
return nu
else:
raise ValueError('Could not estimate gamma dispersion.')
class GAM:
def __init__(self, family=None, link=None, dispersion=None,
estimate_overdispersion=False, name=None,
load_from_file=None):
"""Generalized Additive Model
This is the constructor for a Generalized Additive Model.
References
----------
[glmnet] glmnet (R package):
https://cran.r-project.org/web/packages/glmnet/index.html
This is the standard package for GAMs in R and was written by people
much smarter than I am!
[pygam] pygam (Python package): https://github.com/dswah/pyGAM
This is a library in Python that does basically the same thing as this
script, but in a different way (not using ADMM).
[GLM] Generalized Linear Models by McCullagh and Nelder
The standard text on GLMs.
[GAM] Generalized Additive Models; by Hastie and Tibshirani
The book by the folks who invented GAMs.
[ESL] The Elements of Statistical Learning; by Hastie, Tibshirani, and
Friedman. Covers a lot more than just GAMs.
[GAMr] Generalized Additive Models: an Introduction with R; by Wood.
Covers more implementation details than [GAM].
[ADMM] Distributed Optimization and Statistical Learning via the Alternating
Direction Method of Multipliers; by Boyd, Parikh, Chu, Peleato, and
Eckstein. A mouthful, a work of genius.
[GAMADMM] A Distributed Algorithm for Fitting Generalized Additive Models;
by Chu, Keshavarz, and Boyd
Forms the basis of our approach, the inspiration for this package!
Parameters
----------
family : str or None (default None)
Family of the model. Currently supported families include:
'normal' (for continuous responses),
'binomial' (for binary responses),
'poisson' (for counts),
'gamma' (still in progress),
'inverse_gaussian' (still in progress).
Not currently supported families that could be supported
include Multinomial models (ordinal and nominal) and
proportional hazards models. Required unless loading an
existing model from file (see load_from_file).
link : str or None (optional)
Link function associated with the model. Supported link
functions include:
Link Canonical For Family
'identity' 'normal'
'logistic' 'binomial'
'log' 'poisson'
'reciprocal' 'gamma'
'reciprocal_squared' 'inverse_gaussian'
Other links worth supporting include probit, log-log
and complementary log-log link functions. If not
specified, the canonical link will be used, but non-
canonical links are still permitted. Certain link/family
combinations result in a non-convex problem and
convergence is not guaranteed.
dispersion : float or None (optional)
Dispersion parameter associated with the model. Certain
families (binomial, poisson) have dispersion independent
of the data. Specifying the dispersion for these families
does nothing. In other instances, the dispersion is
typically unknown and must be estimated from the data.
If the dispersion is known, it can be specified here which
will reduce the uncertainty of the model.
estimate_overdispersion : boolean (optional)
Flag specifying whether to estimate over-dispersion for
Binomial and Poisson (not yet implemented) families. Is
only possible when covariate classes are present and have
at least modest size. See [GLM, S4.5] for
details. Defaults to False.
name : str or None (optional)
Name for model, to be used in plots and in saving files.
load_from_file : str or None (optional)
This module uses an iterative approach to fitting models.
For complicated models with lots of data, each iteration
can take a long time (though the number of iterations is
typically less than 100). If the user wishes to pause
after the end of an iteration, they can pick up where
the left off by saving results (see the save_flag in .fit)
and loading them to start the next iterations. Specifying
this option supercedes all other parameters.
Returns
-------
mdl : Generalized Additive Model object
"""
if load_from_file is not None:
self._load(load_from_file)
return
if family is None:
raise ValueError('Family not specified.')
elif family not in FAMILIES:
raise ValueError('{} family not supported'.format(family))
elif family == 'exponential':
self._family = 'gamma'
dispersion = 1.
else:
self._family = family
if link is None:
self._link = CANONICAL_LINKS[family]
elif link in LINKS:
self._link = link
else:
raise ValueError('{} link not supported'.format(link))
if dispersion is not None:
self._known_dispersion = True
self._dispersion = dispersion
elif (self._family in FAMILIES_WITH_KNOWN_DISPERSIONS.keys()
and not estimate_overdispersion):
self._known_dispersion = True
self._dispersion = FAMILIES_WITH_KNOWN_DISPERSIONS[self._family]
else:
self._known_dispersion = False
if self._link == 'identity':
self._eval_link = lambda x: x
self._eval_inv_link = lambda x: x
elif self._link == 'logistic':
self._eval_link = lambda x: np.log( x / (1. - x) )
self._eval_inv_link = lambda x: np.exp(x) / (1 + np.exp(x))
elif self._link == 'probit':
self._eval_link = lambda x: stats.norm.ppf(x)
self._eval_inv_link = lambda x: stats.norm.cdf(x)
elif self._link == 'complementary_log_log':
self._eval_link = lambda x: np.log(-np.log(1. - x))
self._eval_inv_link = lambda x: 1. - np.exp(-np.exp(x))
elif self._link == 'log':
self._eval_link = lambda x: np.log(x)
self._eval_inv_link = lambda x: np.exp(x)
elif self._link == 'reciprocal':
self._eval_link = lambda x: 1. / x
self._eval_inv_link = lambda x: 1. / x
elif self._link == 'reciprocal_squared':
self._eval_link = lambda x: 1. / (x * x)
self._eval_inv_link = lambda x: 1. / np.sqrt(x)
self._estimate_overdispersion = estimate_overdispersion
self._features = {}
self._offset = 0.0
self._num_features = 0
self._fitted = False
self._name = name
def _save(self):
"""Save state.
Save the model to file to make predictions later, or continue
a fitting session.
"""
mv = {}
mv['family'] = self._family
mv['link'] = self._link
mv['known_dispersion'] = self._known_dispersion
if self._known_dispersion:
mv['dispersion'] = self._dispersion
mv['estimate_overdispersion'] = self._estimate_overdispersion
mv['offset'] = self._offset
mv['num_features'] = self._num_features
mv['fitted'] = self._fitted
mv['name'] = self._name
features = {}
for name, feature in self._features.iteritems():
features[name] = {'type': feature.__type__,
'filename': feature._filename
}
mv['features'] = features
mv['num_obs'] = self._num_obs
mv['y'] = self._y
mv['weights'] = self._weights
mv['has_covariate_classes'] = self._has_covariate_classes
if self._has_covariate_classes:
mv['covariate_class_sizes'] = self._covariate_class_sizes
mv['f_bar'] = self.f_bar
mv['z_bar'] = self.z_bar
mv['u'] = self.u
mv['prim_res'] = self.prim_res
mv['dual_res'] = self.dual_res
mv['prim_tol'] = self.prim_tol
mv['dual_tol'] = self.dual_tol
mv['dev'] = self.dev
filename = '{0:s}_model.pckl'.format(self._name)
f = open(filename, 'w')
pickle.dump(mv, f)
f.close()
def _load(self, filename):
"""Load state.
Load a model from file to make predictions.
"""
f = open(filename)
mv = pickle.load(f)
f.close()
self._filename = filename
self._family = mv['family']
self._link = mv['link']
self._known_dispersion = mv['known_dispersion']
if self._known_dispersion:
self._dispersion = mv['dispersion']
self._estimate_overdispersion = mv['estimate_overdispersion']
self._offset = mv['offset']
self._num_features = mv['num_features']
self._fitted = mv['fitted']
self._name = mv['name']
self._features = {}
features = mv['features']
for (name, feature) in features.iteritems():
if feature['type'] == 'categorical':
self._features[name] = _CategoricalFeature(load_from_file=feature['filename'])
elif feature['type'] == 'linear':
self._features[name] = _LinearFeature(load_from_file=feature['filename'])
elif feature['type'] == 'spline':
self._features[name] = _SplineFeature(load_from_file=feature['filename'])
else:
raise ValueError('Invalid feature type')
self._num_obs = mv['num_obs']
self._y = mv['y']
self._weights = mv['weights']
self._has_covariate_classes = mv['has_covariate_classes']
if self._has_covariate_classes:
self._covariate_class_sizes = mv['covariate_class_sizes']
self.f_bar = mv['f_bar']
self.z_bar = mv['z_bar']
self.u = mv['u']
self.prim_res = mv['prim_res']
self.dual_res = mv['dual_res']
self.prim_tol = mv['prim_tol']
self.dual_tol = mv['dual_tol']
self.dev = mv['dev']
if self._link == 'identity':
self._eval_link = lambda x: x
self._eval_inv_link = lambda x: x
elif self._link == 'logistic':
self._eval_link = lambda x: np.log( x / (1. - x) )
self._eval_inv_link = lambda x: np.exp(x) / (1 + np.exp(x))
elif self._link == 'probit':
self._eval_link = lambda x: stats.norm.ppf(x)
self._eval_inv_link = lambda x: stats.norm.cdf(x)
elif self._link == 'complementary_log_log':
self._eval_link = lambda x: np.log(-np.log(1. - x))
self._eval_inv_link = lambda x: 1. - np.exp(-np.exp(x))
elif self._link == 'log':
self._eval_link = lambda x: np.log(x)
self._eval_inv_link = lambda x: np.exp(x)
elif self._link == 'reciprocal':
self._eval_link = lambda x: 1. / x
self._eval_inv_link = lambda x: 1. / x
elif self._link == 'reciprocal_squared':
self._eval_link = lambda x: 1. / (x * x)
self._eval_inv_link = lambda x: 1. / np.sqrt(x)
def add_feature(self, name, type, transform=None, rel_dof=None, regularization=None):
"""Add a feature
Add a feature to a Generalized Additive Model. (An implicit
constant feature is always included, representing the overall
average response.)
Parameters
----------
name : str
Name for feature. Used internally to keep track of
features and is also used when saving files and in
plots.
type : str
Type of feature. Currently supported options include:
'categorical' (for categorical variables)
'linear' (for variables with a linear contribution
to the response)
'spline' (for variables with a potentially nonlinear
contribution to the response).
Other types of features worth supporting include
piecewise constant functions and monotonic functions.
Those might end up being regularization terms.
transform : function or None
Optional transform applied to feature data, saving
the user from repetitive boilerplate code. Any function
may be used; it is applied to data provided during fitting
and prediction. Common options might include np.log, np.log1p,
or np.sqrt. The user may wish to start with a base feature
like 'age' and use derived features 'age_linear', 'age_quadratic'
to permit quadratic models for that feature, with potentially
different regularization applied to each.
rel_dof : float or None
Relative degrees of freedom. Applicable only to spline features.
The degrees of freedom associated with a spline represent how
"wiggly" it is allowed to be. A spline with two degrees of freedom
is just a line. (Actually, since these features are constrained
to have zero mean response over the data, linear features
only have one degree of freedom.) The relative degrees of freedom
are used to specify the baseline smoothing parameter (lambda)
associated with a feature. When the model is fit to data, the user
can specify an overall smoothing parameter applied to all features
to alter the amount of regularization in the entire model. Thus
the actual degrees of freedom will vary based on the amount of
smoothing. The idea is that the analyst may wish to permit some
features to be more wiggly than others. By default, all
splines have 4 relative degrees of freedom.
Regularization of any feature effectively reduces the degrees of
freedom, and so this term is potentially applicable, but that is
not yet supported.
regularization : dictionary or None
Dictionary specifying the regularization applied to this feature.
Different types of features support different types of regularization.
Splines implicitly only support regularization of the wiggliness
via a C2 smoothness penalty. That is controlled via the rel_dof.
Other features have more diverse options described in their own
documentation.
Returns
-------
(nothing)
"""
if type == 'categorical':
f = _CategoricalFeature(name, regularization=regularization)
elif type == 'linear':
f = _LinearFeature(name, transform, regularization=regularization)
elif type == 'spline':
f = _SplineFeature(name, transform, rel_dof)
else:
raise ValueError('Features of type {} not supported.'.format(type))
self._features[name] = f
self._num_features += 1
def fit(self, X, y, covariate_class_sizes=None, weights=None,
optimizer='admm', smoothing=1., save_flag=False,
verbose=False, plot_convergence=False, max_its=100):
"""Fit a Generalized Additive Model to data.
Note regarding binomial families: many data sets include
multiple observations having identical features. For example,
imagine a data set with features 'gender', and 'country' and
binary response indicating whether the person died (morbid but
common in biostatistics). The data might look like this:
gender country patients survivors
M USA 50 48
F USA 70 65
M CAN 40 38
F CAN 45 43
This still describes a binomial family, but in a more compact
format than specifying each individual user. We eventually
want to support this more compact format, but we do not
currently! In this context, it is important to check for
over-dispersion (see [GLM]), and I need to learn more first.
In the current implementation, we assume that there is no
over-dispersion, and that the number of users having the
same set of features is small.
Parameters
----------
X : pandas dataframe
Dataframe of features. The column names must correspond
to the names of features added to the model. X may have
extra columns corresponding to features not included in
the model; these are simply ignored. Where applicable,
the data should be "pre-transformation", since this code
will apply any transformations specified in .add_feature.
y : array
Response. Depending on the model family, the response
may need to be in a particular form (for example, for
a binomial family, the y's should be either 0 or 1),
but this is not checked anywhere!
covariate_class_sizes : array or None.
If observations are grouped into covariance classes, the
size of those classes should be listed in this input.
w : array
Weights applied to each observation. This is effectively
specifying the dispersion of each observation.
optimizer : string
We use the Alternating Direction Method of Multipliers
('admm') to fit the model. We may eventually support more
methods, but right now this option does nothing.
smoothing : float
Smoothing to apply to entire model, used in conjunction
with other regularization parameters. That is, whatever
regularization is used for the various features, is
scaled by this term, allowing the user to set the overall
smoothing by Cross Validation or whatever they like. This
allows the user to specify different regularization for
each feature, while still permitting a one-dimensional
family of models corresponding to different amounts of
regularization. Defaults to 1., leaving the regularization
as specified in .add_feature().
save_flag : boolean
Specifies whether to save intermediate results after each
iteration. Useful for complicated models with massive
data sets that take a while to fit. If the system crashes
during the fit, the analyst can pick up where they left
off instead of starting from scratch. Defaults to False.
verbose : boolean
Specifies whether to print mildly useful information to
the screen during the fit. Defaults to False.
plot_convergence : boolean
Specifies whether to plot the convergence graph at the
end. (I suspect only Convex Optimization nerds like me
want to see this.) Defaults to False.
max_its : integer
Maximum number of iterations. Defaults to 100.
Returns
-------
(nothing)
"""
if save_flag and self._name is None:
msg = 'Cannot save a GAM with no name.'
msg += ' Specify name when instantiating model.'
raise ValueError(msg)
if len(X) != len(y):
raise ValueError('Inconsistent number of observations in X and y.')
num_threads = 1
self._rho = 0.1
eps_abs = 1e-3
eps_rel = 1e-3
# Note that X may include columns that do not correspond to features in our model
# (for example, if the user is experimenting with leaving out features to assess
# importance). Thus, the real number of features is self._num_features, not
# num_features as in the next line.
self._num_obs, num_features = X.shape
self._y = y.flatten()
self._weights = weights
if covariate_class_sizes is not None:
self._has_covariate_classes = True
self._covariate_class_sizes = covariate_class_sizes
mean_response = float(np.sum(self._y)) / np.sum(self._covariate_class_sizes)
self._offset = self._eval_link(mean_response)
else:
self._has_covariate_classes = False
self._covariate_class_sizes = None
self._offset = self._eval_link(np.mean(self._y))
fj = {}
for name, feature in self._features.iteritems():
feature.initialize(X[name].values, smoothing=smoothing,
covariate_class_sizes=self._covariate_class_sizes,
save_flag=save_flag, save_prefix=self._name)
fj[name] = np.zeros(self._num_obs)
self.f_bar = np.full((self._num_obs,), self._offset / self._num_features)
self.z_bar = np.zeros(self._num_obs)
self.u = np.zeros(self._num_obs)
self.prim_res = []
self.dual_res = []
self.prim_tol = []
self.dual_tol = []
self.dev = []
z_new = np.zeros(self._num_obs)
if num_threads > 1:
p = mp.Pool(num_threads)
else:
p = None
for i in range(max_its):
if verbose:
print 'Iteration {0:d}'.format(i)
print 'Optimizing primal variables'
fpumz = self._num_features * (self.f_bar + self.u - self.z_bar)
fj_new = {}
f_new = np.full((self._num_obs,), self._offset)
if False: #num_threads > 1:
# Getting python to run a for loop in parallel
# might as well be impossible :-(
args = [(i, self._features[i], fpumz, self._rho) for i in self._features.keys()]
results = p.map(_feature_wrapper, args)
for i in results:
fj_new[i[0]] = i[1]
f_new += i[1]
else:
for name, feature in self._features.iteritems():
if verbose:
print 'Optimizing {0:s}'.format(name)
fj_new[name] = feature.optimize(fpumz, self._rho)
f_new += fj_new[name]
f_new /= self._num_features
if verbose:
print 'Optimizing dual variables'
z_new = self._optimize(self.u + f_new, self._num_features, p)
self.u += f_new - z_new
prim_res = np.sqrt(self._num_features) * linalg.norm(f_new - z_new)
dual_res = 0.0
norm_ax = 0.0
norm_bz = 0.0
norm_aty = 0.0
num_params = 0
for name, feature in self._features.iteritems():
dr = ((fj_new[name] - fj[name])
+ (z_new - self.z_bar)
- (f_new - self.f_bar))
dual_res += dr.dot(dr)
norm_ax += fj_new[name].dot(fj_new[name])
zik = fj_new[name] + z_new - f_new
norm_bz += zik.dot(zik)
norm_aty += feature.compute_dual_tol(self.u)
num_params += feature.num_params()
dual_res = self._rho * np.sqrt(dual_res)
norm_ax = np.sqrt(norm_ax)
norm_bz = np.sqrt(norm_bz)
norm_aty = np.sqrt(norm_aty)
self.f_bar = f_new
fj = fj_new
self.z_bar = z_new
if self._has_covariate_classes:
sccs = np.sum(self._covariate_class_sizes)
prim_tol = (np.sqrt(sccs * self._num_features) * eps_abs
+ eps_rel * np.max([norm_ax, norm_bz]))
else:
prim_tol = (np.sqrt(self._num_obs * self._num_features) * eps_abs
+ eps_rel * np.max([norm_ax, norm_bz]))
dual_tol = np.sqrt(num_params) * eps_abs + eps_rel * norm_aty
self.prim_res.append(prim_res)
self.dual_res.append(dual_res)
self.prim_tol.append(prim_tol)
self.dual_tol.append(dual_tol)
self.dev.append(self.deviance())
if prim_res < prim_tol and dual_res < dual_tol:
if verbose:
print 'Fit converged'
break
else:
if verbose:
print 'Fit did not converge'
if num_threads > 1:
p.close()
p.join()
self._fitted = True
if save_flag:
self._save()
if plot_convergence:
_plot_convergence(self.prim_res, self.prim_tol, self.dual_res,
self.dual_tol, self.dev)
def _optimize(self, upf, N, p=None):
"""Optimize \bar{z}.
Solves the optimization problem:
minimize L(N*z) + \rho/2 * \| N*z - N*u - N*\bar{f} \|_2^2
where z is the variable, N is the number of features, u is the scaled
dual variable, \bar{f} is the average feature response, and L is
the likelihood function which is different depending on the
family and link function. This is accomplished via a proximal
operator, as discussed in [GAMADMM]:
prox_\mu(v) := argmin_x L(x) + \mu/2 * \| x - v \|_2^2
I strongly believe that paper contains a typo in this equation, so we
return (1. / N) * prox_\mu (N * (u + \bar{f}) with \mu = \rho instead
of \mu = \rho / N as in [GAMADMM]. When implemented as in the paper,
convergence was much slower, but it did still converge.
Certain combinations of family and link function result in proximal
operators with closed form solutions, making this step *very* fast
(e.g. 3 flops per observation).
Parameters
----------
upf : array
Vector representing u + \bar{f}
N : integer
Number of features.
p : Multiprocessing Pool (optional)
If multiple threads are available, massive data sets may
benefit from solving this optimization problem in parallel.
It is up to the individual functions to decide whether to
actually do this.
Returns
-------
z : array
Result of the above optimization problem.
"""
prox = None
if self._family == 'normal':
if self._link == 'identity':
prox = po._prox_normal_identity
else:
prox = po._prox_normal
elif self._family == 'binomial':
if self._link == 'logistic':
prox = po._prox_binomial_logit
else:
prox = po._prox_binomial
if self._has_covariate_classes:
return (1. / N) * prox(N*upf, self._rho, self._y,
self._covariate_class_sizes,
self._weights, self._eval_inv_link, p=p)
elif self._family == 'poisson':
if self._link == 'log':
prox = po._prox_poisson_log
else:
prox = po._prox_poisson
elif self._family == 'gamma':
if self._link == 'reciprocal':
prox = po._prox_gamma_reciprocal
else:
prox = po._prox_gamma
elif self._family == 'inverse_gaussian':
if self._link == 'reciprocal_squared':
prox = po._prox_inv_gaussian_reciprocal_squared
else:
prox = po._prox_inv_gaussian
else:
msg = 'Family {0:s} and Link Function {1:s} not (yet) supported.'
raise ValueError(msg.format(self._family, self._link))
return (1. / N) * prox(N*upf, self._rho, self._y, w=self._weights,
inv_link=self._eval_inv_link, p=p)
def predict(self, X):
"""Apply fitted model to features.
Parameters
----------
X : pandas dataframe
Data for which we wish to predict the response. The
column names must correspond to the names of the
features used to fit the model. X may have extra
columns corresponding to features not in the model;
these are simply ignored. Where applicable, the data
should be "pre-transformation", since this code will
apply any transformations specified while defining
the model.
Returns
-------
mu : array
Predicted mean response for each data point.
"""
if not self._fitted:
raise AttributeError('Model not yet fit.')
num_points, m = X.shape
eta = np.full((num_points,), self._offset)
for name, feature in self._features.iteritems():
eta += feature.predict(X[name].values)
return self._eval_inv_link(eta)
def confidence_intervals(self, X, prediction=False, width=0.95):
"""Confidence intervals on predictions.
NOT YET IMPLEMENTED
There are two notions of confidence intervals that are
appropriate. The first is a confidence interval on mu,
the mean response. This follows from the uncertainty
associated with the fit model. The second is a confidence
interval on observations of this model. The distinction
is best understood by example. For a Gaussian family,
the model might be a perfect fit to the data, and we
may have billions of observations, so we know mu perfectly.
Confidence intervals on the mean response would be very
small. But the response is Gaussian with a non-zero
variance, so observations will in general still be spread
around the mean response. A confidence interval on the
prediction would be larger.
Now consider a binomial family. The estimated mean response
will be some number between 0 and 1, and we can estimate
a confidence interval for that mean. But the observed
response is always either 0 or 1, so it doesn't make sense
to talk about a confidence interval on the prediction
(except in some pedantic sense perhaps).
Note that if we are making multiple predictions, it makes
sense to talk about a "global" set of confidence intervals.
Such a set has the property that *all* predictions fall
within their intervals with specified probability. This
function does not compute global confidence intervals!
Instead each confidence interval is computed "in vacuo".
Parameters
----------
X : pandas dataframe
Data for which we wish to predict the response. The
column names must correspond to the names of the
features used to fit the model. X may have extra
columns corresponding to features not in the model;
these are simply ignored. Where applicable, the data
should be "pre-transformation", since this code will
apply any transformations specified while defining
the model.
prediction : boolean
Specifies whether to return a confidence interval
on the mean response or on the predicted response.
(See above.) Defaults to False, leading to a
confidence interval on the mean response.
width : float between 0 and 1
Desired confidence width. Defaults to 0.95.
Returns
-------
mu : (n x 2) array
Lower and upper bounds on the confidence interval
associated with each prediction.
"""
pass
def plot(self, name, true_fn=None):
"""Plot the component of the modelf for a particular feature.
Parameters
----------
name : str
Name of feature (must be a feature in the model).
true_fn : function or None (optional)
Function representing the "true" relationship
between the feature and the response.
Returns
-------
(nothing)
"""
self._features[name]._plot(true_fn=true_fn)
def deviance(self, X=None, y=None, covariate_class_sizes=None, w=None):
"""Deviance
This function works in one of two ways:
Firstly, it computes the deviance of the model, defined as
2 * \phi * (\ell(y; y) - \ell(\mu; y))
where \phi is the dispersion (which is only in this equation
to cancel out the denominator of the log-likelihood),
\ell(y; y) is the log-likelihood of the model that fits the
data perfectly, and \ell(\mu; y) is the log-likelihood of the
fitted model on the data used to fit the model. This is
the quantity we minimize when fitting the model.
Secondly, it computes the deviance of the model on arbitrary
data sets. This can be used in conjunction with Cross Validation
to choose the smoothing parameter by minimizing the deviance
on the hold-out set.
Parameters
----------
X : pandas dataframe (optional)
Dataframe of features. The column names must correspond
to the names of features added to the model. (See .predict()).
Only applicable for the second use case described above.
y : array (optional)
Response. Only applicable for the second use case.
covariate_class_sizes : array (optional)
Array of covariate class sizes.
w : array (optional)
Weights for observations. Only applicable for the second
use case, but optional even then.
Returns
-------
D : float
The deviance of the model.
"""
if X is None or y is None:
y = self._y
mu = self._eval_inv_link(self._num_features * self.f_bar)
w = self._weights
if self._has_covariate_classes:
m = self._covariate_class_sizes
else:
m = 1.
else:
mu = self.predict(X)
if covariate_class_sizes is None:
m = covariate_class_sizes
else:
m = 1.
if self._family == 'normal':
y_minus_mu = y - mu
if w is None:
return y_minus_mu.dot(y_minus_mu)
else:
return w.dot(y_minus_mu * y_minus_mu)
elif self._family == 'binomial':
if w is None:
return -2. * np.sum( y * np.log(mu) + (m - y) * np.log1p(-mu) )
else:
return -2. * w.dot( y * np.log(mu) + (m - y) * np.log1p(-mu) )
elif self._family == 'poisson':
if w is None:
return 2. * np.sum(y * np.log(y / mu) - (y - mu))
else:
return 2. * w.dot(y * np.log(y / mu) - (y - mu))
elif self._family == 'gamma':
if w is None:
return 2. * np.sum(-1. * np.log(y / mu) + (y - mu) / mu)
else:
return 2. * w.dot(-1. * np.log(y / mu) + (y - mu) / mu)
elif self._family == 'inverse_gaussian':
if w is None:
return np.sum( (y - mu) * (y - mu) / (mu * mu * y) )
else:
return w.dot( (y - mu) * (y - mu) / (mu * mu * y) )
def dispersion(self, formula='deviance'):
"""Dispersion
Returns the dispersion associated with the model. Depending on
the model family and whether the dispersion was specified by
the user, the dispersion may or may not be known a
priori. This function will estimate this parameter when
appropriate.
There are different ways of estimating this parameter that may
be appropriate for different kinds of families. The current
implementation is based on the deviance, as in Eqn 3.10 on
p. 110 of GAMr. As discussed in that section, this tends not
to work well for Poisson data (with overdispersion) when the
mean response is small. Alternatives are offered in that
section, but I have not yet implemented them. This is not
terribly relevant for the current implementation since
overdispersion is not supported! (When overdispersion is not
present, the dispersion of the Poisson is exactly 1.)
My eventual hope is to understand the appropriate methods for
all the different circumstances and have intelligent defaults
that can be overridden by opinionated users.
Parameters
----------
formula : str
Formula for the dispersion. Options include:
'deviance' (default)
'pearson'
'fletcher'
"""
if self._family == 'normal':
if self._known_dispersion:
return self._dispersion
else:
sigma2 = self.deviance() / (self._num_obs - self.dof())
return sigma2
elif self._family == 'binomial':
if self._known_dispersion:
return self._dispersion
elif self._estimate_overdispersion:
return self._binomial_overdispersion()
else:
return 1.
elif self._family == 'poisson':
return 1.
elif self._family == 'gamma':
if self._known_dispersion:
return self._dispersion
else:
return _gamma_dispersion(self.dof(), self.deviance(), self._num_obs)
elif self._family == 'inverse_gaussian':
if self._known_dispersion:
return self._dispersion
else:
sigma2 = self.deviance() / (self._num_obs - self.dof())
return sigma2
def _binomial_overdispersion(self, formula=None):
"""Over-Dispersion
Parameters
----------
formula : str
Which formula to use, either 'replication' or
'pearson'. See Notes.
Returns
-------
sigma2 : float
Estimate of over-dispersion. This is also saved as the
self._dispersion parameter so we only calculate this once
regardless of how many times this function is called.
Notes
-----
When using covariate classes, the observed variance may exceed
the baseline for the family due to clustering in the
population. See GLM for motivation. That text gives two
methodologies for estimating over-dispersion. When there are
no covariate classes (multiple observations with identical
features), estimating over-dispersion is not possible.
The most reliable assessment of over-dispersion is only
possible when there is replication amongst the covariate
classes. This is best illustrated through example. Suppose we
have data on patients from two hospitals as shown in the table
below. Note that there are 3 rows corresponding to Men in
hospital 1. These entries could of course be pooled to give
the total patients and survivors for this covariate class, but
because they have not, it permits us to estimate
over-dispersion more reliably.
Gender Hospital Patients Survivors
M 1 30 15
M 1 40 19
M 1 35 15
F 1 10 8
M 2 10 3
M 2 18 6
F 2 40 30
Because we are building a model based on gender and hospital
alone, we are assuming that all three entries are drawn from
the same binomial distribution. We could actually test that
hypothesis using, for example, Welch's t-Test. If the result
indicates a significant departure from the null hypothesis,
there must be some (unobserved) explanation for different
survival rates. Perhaps the repeated entries correspond to
different doctors, with some doctors being more effective than
others. Or perhaps the multiple entries refer to different
time periods, like before and after a new treatment was
instituted. Regardless, we can quantify the additional
variance and use it to make (hopefully) more accurate
confidence intervals.
When replication is present, we take the following approach,
per GLM. Suppose a particular covariate class (e.g. Gender=M,
Hospital=1) has r replicates. Across all r replicates,
determine the observed success rate, pi. In our example, we
have 105 patients and 49 survivors, for a total survival rate
of pi = 0.47. Next we compute the variance on r-1 DOF:
1 r (y_j - m_j * pi)^2
s^2 = --- \sum ------------------
r-1 j=1 m_j pi * (1 - pi)
where y_j is the number of successes in the jth replicate, m_j
is the number of trials in the jth replicate, and s^2 is
estimated variance. Per GLM, this is an unbiased estimate of
the dispersion parameter. Filling in our specific numbers, we
get s^2 = 0.17, indicating under-dispersion. (Important note:
these are made up numbers, so there is actually more
consistency in the data than would be exhibited from a true
binomial model. Over-dispersion is more common than
under-dispersion.)
Each covariate class with replication can be used to derive an
estimate of the dispersion parameter. If we expect the
dispersion to be independent of the covariate classes (which
may or may not be true), we can pool these estimates, weighted
by the degree of replication. If the kth covariate class has
r_k replicates and dispersion estimate s_k^2, the overall
estimate of dispersion is:
\sum_k (r_k - 1) * s_k^2
s^2 = -------------------------
\sum_k (r_k - 1)
Another important note: the above formula is *not* present in
GLM. That text just says to pool the estimates, but does not
specify how. This approach makes sense to me, but that doesn't
make it correct!
When replication is not present, or even if the degree of
replication is small, the above methodology breaks
down. Instead, GLM advocates the use of a Pearson-residual
based approach. If pi_j is the model prediction for the jth
covariate class, then we estimate dispersion as:
1 (y_j - m_j * pi_j)^2
s^2 = ----- \sum -----------------------
n - p j m_j * pi_j * (1 - pi_j)
This is similar to the replicate-based formula, but we are
using the model prediction for pi_j instead of the pooled
observations, and we are using the n-p as the error DOF
instead of the number of replicates. This methodology still
breaks down when the sizes of the covariate classes, m_j, are
small.
In order to use the replicate-based formula, there must be at
least one covariate class exhibiting replication, and the
degree of replication must be at least two. If these
conditions are not met, and the user dictates that we use the
replicate-based formula, we simply ignore that directive and
use the Pearson-based approach. (It might be best to issue a
warning in this case, but we do not do that.)
If this function is called without specifying which
methodology to use, we use the following criteria in assessing
whether there is enough replication to use the first
approach. First, there must be at least two covariate classes
exhibiting replication. Second, the degree of replication of
the most-replicated covariate class must be at least
3. Finally, the total replication degrees of freedom must be
at least 10. For example, in the example data set above, there
are two covariate classes exhibiting replication: Males in
Hospital 1, and Males in Hospital 2, with 3 and 2 degrees of
replication, respectively. The degree of replication of the
most-replicate covariate class is therefore equal to 3. The
degrees of freedom are (2-1) + (3-1) = 3, which is below the
threshold of 10. We would therefore use the Pearson-based
formula in this case.
These criteria are completely arbitrary! I need to do more
research to determine the appropriate criteria.
"""
if not self._has_covariate_classes:
return 1.
min_cc_replicates = 1
min_replication = 2
des_cc_replicates = 2
des_replication = 3
des_replication_dof = 10
r = {}
covariate_class = np.zeros((self._num_obs,))
fnames = self._features.keys()
for i in range(self._num_obs):
multi_index = []
dims = []
for fname in fnames:
cindex, csize = self._features[fname].category_index(i)
multi_index.append(cindex)
dims.append(csize)
cci = np.ravel_multi_index(multi_index, dims)
covariate_class[i] = cci
r[cci] = r.get(cci, 0) + 1
num_cc_with_replicates = 0
max_replication = 0
replication_dof = 0
for j in r.values():
if j > 1:
num_cc_with_replicates += 1
replication_dof += j - 1
if j > max_replication:
max_replication = j
if ((num_cc_with_replicates >= min_cc_replicates
and max_replication >= min_replication)):
has_replication = True
else:
has_replication = False
if ((num_cc_with_replicates >= des_cc_replicates
and max_replication >= des_replication
and replication_dof >= des_replication_dof)):
has_desired_replication = True
else:
has_desired_replication = False
if formula is None:
if has_desired_replication:
formula = 'replication'
else:
formula = 'pearson'
if has_replication and formula == 'replication':
trials = {}
successes = {}
for i in range(self._num_obs):
cci = covariate_class[i]
trials[cci] = trials.get(cci, 0) + self._covariate_class_sizes[i]
successes[cci] = successes.get(cci, 0) + self._y[i]
s2 = 0.
for i in range(self._num_obs):
cci = covariate_class[i]
pi = float(successes[cci]) / trials[cci]
num = self._y[i] - self._covariate_class_sizes[i] * pi
denom = self._covariate_class_sizes[i] * pi * (1 - pi)
s2 += num * num / denom
s2 /= replication_dof
self._known_dispersion = True
self._dispersion = s2
return s2
else:
mu = self._eval_inv_link(self._num_features * self.f_bar)
m = self._covariate_class_sizes
bl_var = np.multiply(mu, 1. - mu)
res = self._y - np.multiply(m, mu)
num = np.multiply(res, res)
denom = np.multiply(m, bl_var)
n_minus_p = self._num_obs - self.dof()
s2 = np.sum(np.divide(num, denom)) / n_minus_p
self._known_dispersion = True
self._dispersion = s2
return s2
def dof(self):
"""Degrees of Freedom
Returns the degrees of freedom associated with this model.
Simply adds up the degrees of freedom associated with each
feature.
"""
dof = 1.
for name, feature in self._features.iteritems():
dof += feature.dof()
return dof
def aic(self):
"""Akaike Information Criterion
Returns the AIC for the fitted model, useful for choosing
smoothing parameters. The AIC we compute is actually off
by a constant factor, making it easier to compute without
detracting from its role in model selection.
Different authors seem to throw in multiplicative or additive
factors willy-nilly since it doesn't affect model selection.
"""
p = self.dof()
if not self._known_dispersion:
# If we are estimating the dispersion, we need to
# add one to the DOF.
p += 1
# Note that the deviance is twice the dispersion times the
# log-likelihood, so no factor of two required there.
return self.deviance() / self.dispersion() + 2. * p
# return (self.deviance() / self._num_obs
# + 2. * p * self.dispersion() / self._num_obs)
def aicc(self):
# Eqn 6.32 on p. 304 of [GAMr]
pass
def ubre(self, gamma=1.0):
"""Un-Biased Risk Estimator
Returns the Un-Biased Risk Estimator as discussed in Sections
6.2.1 and 6.2.5 of [GAMr]. This can be used for choosing the
smoothing parameter when the dispersion is known.
As discussed in Section 6.2.5 of [GAMr], sometimes it is helpful
to force smoother fits by exaggerating the effective degrees of
freedom. In that case, a value of gamma > 1. may be desirable.
"""
return self.deviance() + 2. * gamma * self.dispersion() * self.dof()
def gcv(self, gamma=1.0):
"""Generalized Cross Validation
This function returns the Generalized Cross Validation (GCV)
score, which can be used for choosing the smoothing parameter
when the dispersion is unknown.
As discussed in Section 6.2.5 of [GAMr], sometimes it is helpful
to force smoother fits by exaggerating the effective degrees of
freedom. In that case, a value of gamma > 1. may be desirable.
"""
denom = self._num_obs - gamma * self.dof()
return self._num_obs * self.deviance() / (denom * denom)
def summary(self):
"""Print summary statistics associated with fitted model.
Prints statistics for the overall model, as well as for
each individual feature (see the __str__() function in
each feature type for details about what is printed
there).
For the overall model, the following are printed:
phi: Estimated dispersion parameter. Omitted
if specified or if it is known for the
Family (e.g. Poisson).
edof: Estimated degrees of freedom.
Deviance: The difference between the log-likelihood of
the model that fits the data perfectly and
that of the fitted model, times twice the
dispersion.
AIC: Akaike Information Criterion.
AICc: AIC with correction for finite data sets.
UBRE: Unbiased Risk Estimator (if dispersion is known).
GCV: Generalized Cross Validation (if dispersion is estimated).
For more details on these parameters, see the documentation
in the corresponding functions. It may also be helpful to
include an R^2 value where appropriate, and perhaps a p-value
for the model against the null model having just the affine
term. It would also be nice to have confidence intervals
at least on the estimated dispersion parameter.
"""
print 'Model Statistics'
print '----------------'
if not self._known_dispersion:
print 'phi: {0:0.06g}'.format(self.dispersion())
print 'edof: {0:0.0f}'.format(self.dof())
print 'Deviance: {0:0.06g}'.format(self.deviance())
print 'AIC: {0:0.06g}'.format(self.aic())
#print 'AICc: {0:0.06g}'.format(aicc)
if self._known_dispersion:
print 'UBRE: {0:0.06g}'.format(self.ubre())
else:
print 'GCV: {0:0.06g}'.format(self.gcv())
print ''
print 'Features'
print '--------'
for name, feature in self._features.iteritems():
print feature.__str__()
| false
| true
|
f716bfb88f86b0dedbf9b46d2a9ed40caaf4e047
| 405
|
py
|
Python
|
tests/benchmarkstt/test_modules.py
|
ioannisNoukakis/benchmarkstt
|
41074c9b89632e8d9ff8e0ee72187211052bfb04
|
[
"MIT"
] | 1
|
2019-02-01T10:37:12.000Z
|
2019-02-01T10:37:12.000Z
|
tests/benchmarkstt/test_modules.py
|
ioannisNoukakis/benchmarkstt
|
41074c9b89632e8d9ff8e0ee72187211052bfb04
|
[
"MIT"
] | null | null | null |
tests/benchmarkstt/test_modules.py
|
ioannisNoukakis/benchmarkstt
|
41074c9b89632e8d9ff8e0ee72187211052bfb04
|
[
"MIT"
] | null | null | null |
from benchmarkstt.modules import Modules
from benchmarkstt.normalization import cli
def test_module():
modules = Modules('cli')
assert modules['normalization'] is cli
assert modules.normalization is cli
for k, v in modules:
assert modules[k] is v
assert getattr(modules, k) is v
keys = modules.keys()
assert type(keys) is list
assert 'normalization' in keys
| 25.3125
| 42
| 0.696296
|
from benchmarkstt.modules import Modules
from benchmarkstt.normalization import cli
def test_module():
modules = Modules('cli')
assert modules['normalization'] is cli
assert modules.normalization is cli
for k, v in modules:
assert modules[k] is v
assert getattr(modules, k) is v
keys = modules.keys()
assert type(keys) is list
assert 'normalization' in keys
| true
| true
|
f716c03e06bd0f761318cae39eb26bd38855049d
| 3,328
|
py
|
Python
|
src/sst/elements/merlin/interfaces/pymerlin-interface.py
|
vjleung/sst-elements
|
b2d4a41f1cd152ac96c9eca54000980a26a757d3
|
[
"BSD-3-Clause"
] | 2
|
2019-06-10T15:32:03.000Z
|
2019-06-11T14:17:32.000Z
|
src/sst/elements/merlin/interfaces/pymerlin-interface.py
|
plavin/sst-elements
|
a84c63fa024782383272fb32ca24eb668f25b1c7
|
[
"BSD-3-Clause"
] | null | null | null |
src/sst/elements/merlin/interfaces/pymerlin-interface.py
|
plavin/sst-elements
|
a84c63fa024782383272fb32ca24eb668f25b1c7
|
[
"BSD-3-Clause"
] | 1
|
2019-09-24T13:41:56.000Z
|
2019-09-24T13:41:56.000Z
|
#!/usr/bin/env python
#
# Copyright 2009-2020 NTESS. Under the terms
# of Contract DE-NA0003525 with NTESS, the U.S.
# Government retains certain rights in this software.
#
# Copyright (c) 2009-2020, NTESS
# All rights reserved.
#
# Portions are copyright of other developers:
# See the file CONTRIBUTORS.TXT in the top level directory
# the distribution for more information.
#
# This file is part of the SST software package. For license
# information, see the LICENSE file in the top level directory of the
# distribution.
import sst
from sst.merlin.base import *
class LinkControl(NetworkInterface):
def __init__(self):
NetworkInterface.__init__(self)
self._declareParams("params",["link_bw","input_buf_size","output_buf_size","vn_remap"])
self._subscribeToPlatformParamSet("network_interface")
# returns subcomp, port_name
def build(self,comp,slot,slot_num,job_id,job_size,logical_nid,use_nid_remap = False):
sub = comp.setSubComponent(slot,"merlin.linkcontrol",slot_num)
self._applyStatisticsSettings(sub)
sub.addParams(self._getGroupParams("params"))
sub.addParam("job_id",job_id)
sub.addParam("job_size",job_size)
sub.addParam("use_nid_remap",use_nid_remap)
sub.addParam("logical_nid",logical_nid)
return sub,"rtr_port"
class ReorderLinkControl(NetworkInterface):
def __init__(self):
NetworkInterface.__init__(self)
self._declareClassVariables(["network_interface"])
self._setCallbackOnWrite("network_interface",self._network_interface_callback)
self.network_interface = PlatformDefinition.getPlatformDefinedClassInstance("reorderlinkcontrol_network_interface")
if not self.network_interface:
self.network_interface = LinkControl()
# This is just a default, can be overwritten
self._unlockVariable("network_interface")
def _network_interface_callback(self, variable_name, value):
if not value: return
self._lockVariable("network_interface")
self._setPassthroughTarget(value)
def setNetworkInterface(self,interface):
self.network_interface = interface
def build(self,comp,slot,slot_num,job_id,job_size,nid,use_nid_map = False):
sub = comp.setSubComponent(slot,"merlin.reorderlinkcontrol",slot_num)
#self._applyStatisticsSettings(sub)
#sub.addParams(self._params)
return self.network_interface.build(sub,"networkIF",0,job_id,job_size,nid,use_nid_map)
# Functions to enable statistics
def enableAllStatistics(self,stat_params,apply_to_children=False):
# no stats of our own, simply pass to network interface
if self.network_interface:
self.network_interface.enableAllStatistics(stat_params,apply_to_children)
def enableStatistics(self,stats,stat_params,apply_to_children=False):
# no stats of our own, simply pass to network interface
if self.network_interface:
self.network_interface.enableStatistics(stats,stat_params,apply_to_children)
def setStatisticLoadLevel(self,level,apply_to_children=False):
# no stats of our own, simply pass to network interface
if self.network_interface:
self.network_intrface.setStatisticLoadLevel(level,apply_to_children)
| 41.08642
| 123
| 0.735577
|
import sst
from sst.merlin.base import *
class LinkControl(NetworkInterface):
def __init__(self):
NetworkInterface.__init__(self)
self._declareParams("params",["link_bw","input_buf_size","output_buf_size","vn_remap"])
self._subscribeToPlatformParamSet("network_interface")
def build(self,comp,slot,slot_num,job_id,job_size,logical_nid,use_nid_remap = False):
sub = comp.setSubComponent(slot,"merlin.linkcontrol",slot_num)
self._applyStatisticsSettings(sub)
sub.addParams(self._getGroupParams("params"))
sub.addParam("job_id",job_id)
sub.addParam("job_size",job_size)
sub.addParam("use_nid_remap",use_nid_remap)
sub.addParam("logical_nid",logical_nid)
return sub,"rtr_port"
class ReorderLinkControl(NetworkInterface):
def __init__(self):
NetworkInterface.__init__(self)
self._declareClassVariables(["network_interface"])
self._setCallbackOnWrite("network_interface",self._network_interface_callback)
self.network_interface = PlatformDefinition.getPlatformDefinedClassInstance("reorderlinkcontrol_network_interface")
if not self.network_interface:
self.network_interface = LinkControl()
self._unlockVariable("network_interface")
def _network_interface_callback(self, variable_name, value):
if not value: return
self._lockVariable("network_interface")
self._setPassthroughTarget(value)
def setNetworkInterface(self,interface):
self.network_interface = interface
def build(self,comp,slot,slot_num,job_id,job_size,nid,use_nid_map = False):
sub = comp.setSubComponent(slot,"merlin.reorderlinkcontrol",slot_num)
return self.network_interface.build(sub,"networkIF",0,job_id,job_size,nid,use_nid_map)
def enableAllStatistics(self,stat_params,apply_to_children=False):
if self.network_interface:
self.network_interface.enableAllStatistics(stat_params,apply_to_children)
def enableStatistics(self,stats,stat_params,apply_to_children=False):
if self.network_interface:
self.network_interface.enableStatistics(stats,stat_params,apply_to_children)
def setStatisticLoadLevel(self,level,apply_to_children=False):
if self.network_interface:
self.network_intrface.setStatisticLoadLevel(level,apply_to_children)
| true
| true
|
f716c359878a902aee90be96fdffab16acbcdd2b
| 110,391
|
py
|
Python
|
articles/inversion.py
|
Solara570/demo-solara
|
3ce6df1fd68089c427bbd46fb0857e8b76428ca6
|
[
"MIT"
] | 79
|
2017-09-25T04:42:05.000Z
|
2022-03-24T06:10:56.000Z
|
articles/inversion.py
|
Solara570/demo-solara
|
3ce6df1fd68089c427bbd46fb0857e8b76428ca6
|
[
"MIT"
] | 1
|
2018-04-13T14:12:00.000Z
|
2018-04-13T14:12:00.000Z
|
articles/inversion.py
|
Solara570/demo-solara
|
3ce6df1fd68089c427bbd46fb0857e8b76428ca6
|
[
"MIT"
] | 13
|
2017-09-29T03:20:20.000Z
|
2022-03-07T13:18:16.000Z
|
#coding=utf-8
################################################################################################
# A 3-part series on circle inversion, Descartes' theorem along with its variants, and more! #
# #
# Part 1: An Introduction to Circle Inversion - https://zhuanlan.zhihu.com/p/86644341 #
# Part 2: Four Circles & Descartes' Theorem (1) - https://zhuanlan.zhihu.com/p/105819963 #
# Part 3: Four Circles & Descartes' Theorem (2) - https://zhuanlan.zhihu.com/p/106874090 #
################################################################################################
import numpy as np
import itertools as it
from manimlib.constants import *
from manimlib.utils.color import *
from manimlib.utils.space_ops import *
from manimlib.utils.simple_functions import *
from manimlib.animation.composition import AnimationGroup
from manimlib.animation.creation import ShowCreation, Write, DrawBorderThenFill
from manimlib.animation.fading import FadeOut, FadeInFromDown
from manimlib.animation.transform import Transform, ReplacementTransform, MoveToTarget, ApplyMethod
from manimlib.mobject.mobject import Mobject
from manimlib.mobject.coordinate_systems import Axes, NumberPlane, ThreeDAxes
from manimlib.mobject.geometry import Circle, Line, Dot, SmallDot, Square, Polygon, RegularPolygon, \
Arrow, Sector, Vector
from manimlib.mobject.numbers import DecimalNumber
from manimlib.mobject.value_tracker import ValueTracker
from manimlib.mobject.shape_matchers import BackgroundRectangle, SurroundingRectangle
from manimlib.mobject.three_dimensions import Sphere
from manimlib.mobject.svg.brace import Brace
from manimlib.mobject.svg.tex_mobject import TexMobject, TextMobject
from manimlib.mobject.types.vectorized_mobject import VMobject, VGroup, VectorizedPoint, DashedVMobject
from manimlib.scene.scene import Scene
from manimlib.scene.three_d_scene import ThreeDScene
from short.apollonian_gasket import calc_centers_by_radii, calc_new_agc_info, AGCircle, \
ApollonianGasket, ApollonianGasketScene
from short.ford_circles import get_coprime_numers_by_denom, get_stroke_width_by_height, \
AssembledFraction, ZoomInOnFordCircles
#####
## Constants
MAX_NORM = 1e2
CB_DARK = "#825201"
CB_LIGHT = "#B69B4C"
#####
## General Methods
def complex_inversion(z, z0, r):
return z0 + np.conjugate(r**2 / (z-z0))
def R3_inversion(point, inv_center, radius):
z = R3_to_complex(point)
z0 = R3_to_complex(inv_center)
w = complex_inversion(z, z0, radius)
return complex_to_R3(w)
def inversion(point, inv_center, radius):
# Just a rename
return R3_inversion(point, inv_center, radius)
def is_close_in_R3(p1, p2, thres = 1e-6):
"""Check if two points are close in R^3."""
return np.linalg.norm(p1 - p2) < thres
def is_close(z1, z2, thres = 1e-6):
"""Check if two complex numbers are close to each other."""
return np.abs(z1 - z2) < thres
def get_tangent_point(c1, c2, thres = 1e-4):
"""Return the tangency point of circles 'c1' and 'c2'."""
p1 = c1.get_center()
p2 = c2.get_center()
r1 = c1.get_height() / 2
r2 = c2.get_height() / 2
d = get_norm(p2 - p1)
if is_close(d, r1-r2, thres):
return p1 + r1*normalize(p2-p1)
elif is_close(d, r2-r1, thres):
return p2 + r2*normalize(p1-p2)
elif is_close(d, r1+r2, thres):
return (r1*p2+r2*p1) / (r1+r2)
else:
raise Exception("These two circles aren't tangent.")
def get_para_and_perp_components(point, lp1, lp2):
v = lp2 - point
v0 = lp2 - lp1
v_para = fdiv(np.dot(v, v0), np.dot(v0, v0)) * v0
v_perp = v - v_para
return v_para, v_perp
def distance_to_the_line(point, lp1, lp2):
"""Return the distance from 'point' to the line given by 'lp1' and 'lp2'."""
v_para, v_perp = get_para_and_perp_components(point, lp1, lp2)
return np.linalg.norm(v_perp)
def is_on_the_line(point, lp1, lp2, thres = 1e-6):
"""Check if 'point' is on the line given by two points 'lp1' and 'lp2'."""
return is_close(distance_to_the_line(point, lp1, lp2), thres)
def get_random_vector(max_step):
"""Return a random vector with a maximum length of 'max_step'."""
return max_step*np.random.random() * rotate_vector(RIGHT, TAU*np.random.random())
def get_nearest_int(num):
return int(np.round(num, 0))
def solve_quadratic_equation(a, b, c):
delta = b**2 - 4*a*c
x1 = (-b-np.sqrt(delta)) /(2*a)
x2 = (-b+np.sqrt(delta)) /(2*a)
print(a, b, c, x1, x2)
return x1, x2
def get_next_terms(k1, k2, k3):
"""Return two adjacent terms in the loxodromic sequence."""
b = -2*(k1+k2+k3)
c = 2*(k1**2+k2**2+k3**2) - (k1+k2+k3)**2
return list(map(get_nearest_int, solve_quadratic_equation(1, b, c)))
def get_sequence_string(arr):
arr_copy = list(map(str, arr))
arr_copy.insert(0, "...")
arr_copy.append("...")
return ", ".join(arr_copy)
#####
## Mobjects
class FineCircle(Circle):
CONFIG = {
# In manim, circles are approximated by multiple cubic Beziers,
# so it's necessary to increase the number of components for
# high-precision calculations.
"num_components": 100,
}
class ExtendedLine(Line):
def __init__(self, sp, ep, n = 10, **kwargs):
unit_vec = normalize(ep - sp)
new_sp = sp - n * unit_vec
new_ep = ep + n * unit_vec
Line.__init__(self, new_sp, new_ep, **kwargs)
class DotLabel(VMobject):
CONFIG = {
"position" : UP,
"label_buff" : 0.25,
}
def __init__(self, label_text, dot, **kwargs):
VMobject.__init__(self, **kwargs)
self.dot = dot
label = TexMobject(label_text, **kwargs)
if self.position is not None:
label.add_updater(
lambda l: l.next_to(self.dot.get_center(), self.position, buff = self.label_buff)
)
self.add(label)
def set_label(self, label):
label.next_to(self.dot.get_center())
class TwoDotsSegment(Line):
def __init__(self, dot_1, dot_2, **kwargs):
self.dot_1 = dot_1
self.dot_2 = dot_2
sp, ep = self.get_dots_centers()
Line.__init__(self, start = sp, end = ep, **kwargs)
self.add_updater(self.set_start_and_end)
def get_dots_centers(self):
return self.dot_1.get_center(), self.dot_2.get_center()
def set_start_and_end(self, line_mob):
sp, ep = self.get_dots_centers()
line_mob.put_start_and_end_on(sp, ep)
class LengthLabel(DecimalNumber):
CONFIG = {
"num_decimal_places" : 3,
"label_height" : 0.3,
"label_buff" : 0.3,
"offset" : 0,
"is_on_opposite_side" : False,
}
def __init__(self, line_mob, **kwargs):
DecimalNumber.__init__(self, **kwargs)
self.line_mob = line_mob
self.add_updater(self.set_label)
def set_label(self, label):
label.set_value(self.line_mob.get_length())
label.set_height(self.label_height)
label.rotate(self.line_mob.get_angle())
side_factor = -1 if self.is_on_opposite_side else 1
label.move_to(
self.line_mob.get_center() \
+ self.line_mob.get_vector() / 2 * self.offset \
+ side_factor * rotate_vector(self.line_mob.get_unit_vector(), PI/2) * self.label_buff
)
def set_offset(self, offset):
self.offset = offset
return self
def switch_side(self):
self.is_on_opposite_side = not self.is_on_opposite_side
return self
class ManyDotsPolygon(VMobject):
def __init__(self, *dots, **kwargs):
VMobject.__init__(self, **kwargs)
self.dots = dots
dots_centers = self.get_dots_centers()
polygon = Polygon(*dots_centers, **kwargs)
polygon.add_updater(self.set_vertices)
self.add(polygon)
def get_dots_centers(self):
return [dot.get_center() for dot in self.dots]
def set_vertices(self, polygon_mob):
vertices = self.get_dots_centers()
polygon_mob.set_points_as_corners([*vertices, vertices[0]])
class AngleIndicator(VMobject):
CONFIG = {
"color" : RED,
"radius" : 0.2,
"fill_opacity" : 0.6,
"is_minor_arc" : True,
}
def __init__(self, dot_A, dot_C, dot_B, **kwargs):
VMobject.__init__(self, **kwargs)
self.dot_A = dot_A
self.dot_C = dot_C
self.dot_B = dot_B
sector = Sector()
sector.add_updater(self.set_sector)
self.add(sector)
self.sector = sector
def get_point_center(self, point_or_mob):
if isinstance(point_or_mob, Mobject):
return point_or_mob.get_center()
else:
return point_or_mob
def get_point_centers(self):
return tuple(map(self.get_point_center, [self.dot_A, self.dot_C, self.dot_B]))
def set_sector(self, mob):
pt_A, pt_C, pt_B = self.get_point_centers()
start_angle, angle = self.get_angles()
outer_radius = min([self.radius, get_norm(pt_C - pt_A)/2, get_norm(pt_C - pt_B)/2])
new_sector = Sector(
start_angle = start_angle, angle = angle, outer_radius = outer_radius,
color = self.color, fill_opacity = self.fill_opacity, stroke_width = 0
)
new_sector.move_arc_center_to(self.get_point_center(self.dot_C))
mob.become(new_sector)
def get_angles(self):
pt_A, pt_C, pt_B = self.get_point_centers()
start_angle = angle_of_vector(pt_A - pt_C)
end_angle = angle_of_vector(pt_B - pt_C)
angle = (end_angle - start_angle) % TAU
if self.is_minor_arc and angle > PI:
angle -= TAU
return start_angle, angle
class RightAngleIndicator(VMobject):
CONFIG = {
"color" : WHITE,
"side_length" : 0.2,
"line_width" : 1,
"square_opacity" : 0.5,
}
def __init__(self, dot_A, dot_C, dot_B, **kwargs):
VMobject.__init__(self, **kwargs)
self.dot_A = dot_A
self.dot_C = dot_C
self.dot_B = dot_B
line = VMobject(stroke_width = self.line_width, fill_opacity = 0)
square = VMobject(stroke_width = 0, fill_color = self.color, fill_opacity = self.square_opacity)
line.add_updater(self.set_line)
square.add_updater(self.set_square)
self.add(square, line)
self.line = line
self.square = square
def get_point_center(self, point_or_mob):
if isinstance(point_or_mob, Mobject):
return point_or_mob.get_center()
else:
return point_or_mob
def get_point_centers(self):
return tuple(map(self.get_point_center, [self.dot_A, self.dot_C, self.dot_B]))
def get_norm_vectors(self):
pt_A, pt_C, pt_B = self.get_point_centers()
norm_vec_CA = normalize(pt_A - pt_C)
norm_vec_CB = normalize(pt_B - pt_C)
return norm_vec_CA, norm_vec_CB
def get_corner_points(self):
pt_A, pt_C, pt_B = self.get_point_centers()
norm_vec_CA, norm_vec_CB = self.get_norm_vectors()
side_length = min([self.side_length, get_norm(pt_A - pt_C)/2, get_norm(pt_B - pt_C)/2])
return (
pt_C,
pt_C + norm_vec_CA * side_length,
pt_C + norm_vec_CA * side_length + norm_vec_CB * side_length,
pt_C + norm_vec_CB * side_length
)
def set_line(self, line_mob):
p, q, r, s = self.get_corner_points()
line_mob.set_points_as_corners([q, r, s])
def set_square(self, square_mob):
p, q, r, s = self.get_corner_points()
square_mob.set_points_as_corners([p, q, r, s, p])
class InversedDot(VMobject):
CONFIG = {
"color" : PINK,
"stroke_width" : 3,
"fill_opacity" : 1,
"is_hollow" : True,
"center_color" : BLACK,
}
def __init__(self, orig_dot, circle, **kwargs):
self.orig_dot = orig_dot
self.circle = circle
VMobject.__init__(self, **kwargs)
def generate_points(self):
if self.is_hollow:
self.fill_color = self.center_color
else:
self.fill_color = self.color
self.stroke_width = 0
inv_dot = Dot(ORIGIN, color = self.color)
self.inv_dot = inv_dot
self.add(inv_dot)
self.add_updater_to_inversed_dot()
def add_updater_to_inversed_dot(self):
self.inv_dot.add_updater(self.move_inversed_dot)
def move_inversed_dot(self, inv_dot):
point = self.orig_dot.get_center()
inv_center = self.circle.get_center()
radius = self.circle.get_height() / 2.
if is_close_in_R3(point, inv_center):
pass
else:
inv_dot.move_to(inversion(point, inv_center, radius))
class InversedVMobject(VMobject):
CONFIG = {
"is_analytical" : True,
"match_original_style" : False,
"use_dashed_vmob" : True,
"dashed_vmob_config": {
"num_dashes" : 50,
"positive_space_ratio" : 0.6,
},
}
def __init__(self, orig_vmob, circle, **kwargs):
VMobject.__init__(self, **kwargs)
self.orig_vmob = orig_vmob
self.circle = circle
self.orig_vmob_type = "Others"
self.initialize_orig_vmob_type()
self.add_updater_to_inversed_vmobject()
def add_updater_to_inversed_vmobject(self):
self.add_updater(self.set_inversed_vmobject)
def initialize_orig_vmob_type(self):
if isinstance(self.orig_vmob, Line):
self.orig_vmob_type = "Line"
elif isinstance(self.orig_vmob, Circle):
self.orig_vmob_type = "Circle"
else:
self.orig_vmob_type = "Others"
def set_orig_vmob_type(self, orig_vmob_type):
self.orig_vmob_type = orig_vmob_type
def set_inversed_vmobject(self, inv_vmob):
inv_center = self.circle.get_center()
radius = self.circle.get_height() / 2.
if self.is_analytical and self.orig_vmob_type == "Line":
# If it's a line...
lp1, lp2 = self.orig_vmob.get_start_and_end()
if is_on_the_line(inv_center, lp1, lp2):
# If it's a line passing through the inversion center,
# then the inversion is just the line itself.
temp_vmob = ExtendedLine(lp1, lp2)
else:
# If it's a line NOT through the inversion center,
# then the inversion is a circle passing through the inversion center.
v_para, v_perp = get_para_and_perp_components(inv_center, lp1, lp2)
d = distance_to_the_line(inv_center, lp1, lp2)
# d = np.linalg.norm(v_perp)
inv_vmob_radius = fdiv(radius**2, 2*d)
closepoint = inv_center + v_perp
inv_vmob_closepoint = inversion(closepoint, inv_center, radius)
inv_vmob_center = (inv_center + inv_vmob_closepoint) / 2.
temp_vmob = FineCircle(radius = inv_vmob_radius)
temp_vmob.move_to(inv_vmob_center)
elif self.is_analytical and self.orig_vmob_type == "Circle":
# If it's a circle...
orig_vmob_center = self.orig_vmob.get_center()
orig_vmob_radius = self.orig_vmob.get_height() / 2.
center_vec = orig_vmob_center - inv_center
d = get_norm(center_vec)
if is_close(orig_vmob_radius, d):
# If it's a circle passing through the inversion center,
# then the inversion is a line perps to the line through the circle centers.
foot = inv_center + fdiv(radius**2, 2*d) * normalize(center_vec)
lp1 = foot + rotate_vector(center_vec, PI/2)
lp2 = foot + rotate_vector(center_vec, -PI/2)
temp_vmob = ExtendedLine(lp1, lp2)
else:
# If it's a circle NOT through the inversion center,
# then the inversion is a circle NOT through the inversion center.
dp1 = orig_vmob_center - orig_vmob_radius * normalize(center_vec)
dp2 = orig_vmob_center + orig_vmob_radius * normalize(center_vec)
inv_dp1 = inversion(dp1, inv_center, radius)
inv_dp2 = inversion(dp2, inv_center, radius)
inv_vmob_radius = get_norm(inv_dp2 - inv_dp1) / 2.
inv_vmob_center = (inv_dp2 + inv_dp1) / 2.
temp_vmob = FineCircle(radius = inv_vmob_radius)
temp_vmob.move_to(inv_vmob_center)
else:
temp_vmob = self.orig_vmob.copy()
temp_vmob.apply_function(lambda p: inversion(p, inv_center, radius))
if self.use_dashed_vmob:
temp_vmob = DashedVMobject(temp_vmob, **self.dashed_vmob_config)
inv_vmob.become(temp_vmob)
if self.match_original_style:
inv_vmob.match_style(self.orig_vmob)
class FourCirclesNormalForm(VMobject):
CONFIG = {
"circle_colors" : [MAROON_B, RED, GREEN, BLUE],
"r" : 1.2,
"l" : 9,
"use_dashed_vmob" : True,
"dashed_vmob_config" : {
"num_dashes" : 30,
"positive_space_ratio" : 0.6,
}
}
def __init__(self, **kwargs):
VMobject.__init__(self, **kwargs)
c1 = Circle(radius = self.r, **kwargs).shift(self.r*LEFT)
c2 = Circle(radius = self.r, **kwargs).shift(self.r*RIGHT)
c3 = Line(self.l*LEFT, self.l*RIGHT, **kwargs).shift(self.r*DOWN)
c4 = Line(self.l*LEFT, self.l*RIGHT, **kwargs).shift(self.r*UP)
for mob, color in zip([c1, c2, c3, c4], self.circle_colors):
mob.set_color(color)
if self.use_dashed_vmob:
self.add(DashedVMobject(mob, **self.dashed_vmob_config))
else:
self.add(mob)
class DescartesFourCircles(VMobject):
CONFIG = {
"outer_circle_index" : None,
"orig_circle_color" : BLUE,
"new_circle_color" : YELLOW,
"show_new_circles" : True,
"show_new_circles_centers" : False,
}
def __init__(self, ccdot1, ccdot2, ccdot3, **kwargs):
self.ccdot1 = ccdot1
self.ccdot2 = ccdot2
self.ccdot3 = ccdot3
VMobject.__init__(self, **kwargs)
self.add_orig_circles()
self.add_orig_circles_updaters()
self.generate_new_circles()
if self.show_new_circles:
self.add_new_circles()
if self.show_new_circles_centers:
self.add_new_circles_centers()
def add_orig_circles(self):
self.c1, self.c2, self.c3 = self.cs = VGroup(*[
Circle(arc_center = cc, radius = r, color = self.orig_circle_color)
for cc, r in zip(self.get_orig_circle_centers(), self.calc_radii_by_centers())
])
self.add(self.cs)
def add_orig_circles_updaters(self):
def get_center(k):
return self.get_orig_circle_centers()[k]
def get_abs_radius(k):
return np.abs(self.calc_radii_by_centers()[k])
# Since enumerate() won't work here (seriously?),
# I have to use a much more direct approach - list them all.
self.c1.add_updater(lambda c: c.move_to(get_center(0)))
self.c1.add_updater(lambda c: c.set_height(2*get_abs_radius(0)))
self.c2.add_updater(lambda c: c.move_to(get_center(1)))
self.c2.add_updater(lambda c: c.set_height(2*get_abs_radius(1)))
self.c3.add_updater(lambda c: c.move_to(get_center(2)))
self.c3.add_updater(lambda c: c.set_height(2*get_abs_radius(2)))
def get_orig_circles(self):
return self.cs
def get_orig_circle_centers(self):
return [dot.get_center() for dot in (self.ccdot1, self.ccdot2, self.ccdot3)]
def get_orig_circle_radii(self):
return self.calc_radii_by_centers()
def get_orig_circle_curvatures(self):
return [fdiv(1, radius) for radius in self.calc_radii_by_centers()]
def calc_radii_by_centers(self):
p1, p2, p3 = self.get_orig_circle_centers()
d12 = get_norm(p2 - p1)
d23 = get_norm(p3 - p2)
d13 = get_norm(p3 - p1)
sum_r = (d12 + d23 + d13) / 2.
if self.outer_circle_index == 1:
# If circle 1 contains other two circles...
return [-sum_r, sum_r-d12, sum_r-d13]
elif self.outer_circle_index == 2:
# If circle 2 contains other two circles...
return [sum_r-d12, -sum_r, sum_r-d23]
elif self.outer_circle_index == 3:
# If circle 3 contains other two circles...
return [sum_r-d13, sum_r-d23, -sum_r]
else:
return [sum_r-d23, sum_r-d13, sum_r-d12]
def generate_new_circles(self):
self.c4_1, self.c4_2 = self.new_circles = VGroup(*[
Circle(arc_center = new_cc, radius = new_r, color = self.new_circle_color)
for new_cc, new_r in self.calc_new_circles_centers_and_radii()
])
self.generate_new_circles_centers()
self.add_new_circles_updaters()
def calc_new_circles_centers_and_radii(self):
k1, k2, k3 = self.get_orig_circle_curvatures()
z1, z2, z3 = map(R3_to_complex, self.get_orig_circle_centers())
# Calculate the curvatures of new circles
sum_k = k1 + k2 + k3
sum_k2 = k1**2 + k2**2 + k3**2
sum_k_cycle_prod = k1*k2 + k2*k3 + k3*k1
b = (-2)*sum_k
c = sum_k2 - 2*sum_k_cycle_prod
delta = b**2 - 4*c
k4_1 = (-b + np.sqrt(delta)) / 2
k4_2 = (-b - np.sqrt(delta)) / 2
# Calculate the centers of new circles
# arxiv.org/abs/math/0101066v1 - Eqn 2.3
sum_kz = k1*z1 + k2*z2 + k3*z3
sum_k2z = k1**2 * z1 + k2**2 * z2 + k3**2 * z3
coeff_1 = (sum_k - k4_1) * k4_1
const_1 = 2 * sum_k2z - (sum_k + k4_1) * sum_kz
z4_1 = const_1 / coeff_1
coeff_2 = (sum_k - k4_2) * k4_2
const_2 = 2 * sum_k2z - (sum_k + k4_2) * sum_kz
z4_2 = const_2 / coeff_2
return [[complex_to_R3(z4_1), fdiv(1, k4_1)], [complex_to_R3(z4_2), fdiv(1, k4_2)]]
def generate_new_circles_centers(self):
ccdot4_1 = Dot(color = self.new_circle_color)
ccdot4_1.add_updater(lambda m: m.move_to(self.c4_1.get_center()))
ccdot4_2 = Dot(color = self.new_circle_color)
ccdot4_2.add_updater(lambda m: m.move_to(self.c4_2.get_center()))
self.ccdot4_1 = ccdot4_1
self.ccdot4_2 = ccdot4_2
def add_new_circles_updaters(self):
def get_new_center(k):
return self.calc_new_circles_centers_and_radii()[k][0]
def get_abs_new_radius(k):
return np.abs(self.calc_new_circles_centers_and_radii()[k][1])
# Since enumerate() won't work here (seriously?),
# I have to use a much more direct approach - list them all.
self.c4_1.add_updater(lambda c: c.move_to(get_new_center(0)))
self.c4_1.add_updater(lambda c: c.set_height(2*get_abs_new_radius(0)))
self.c4_2.add_updater(lambda c: c.move_to(get_new_center(1)))
self.c4_2.add_updater(lambda c: c.set_height(2*get_abs_new_radius(1)))
def add_new_circles(self):
if not hasattr(self, "new_circles"):
self.new_circles = generate_new_circles()
self.add(self.new_circles)
def get_new_circles(self):
if not hasattr(self, "new_circles"):
self.new_circles = generate_new_circles()
return self.new_circles
def add_new_circles_centers(self):
self.add(self.ccdot4_1, self.ccdot4_2)
def remove_new_circles_center(self):
self.remove(self.ccdot4_1, self.ccdot4_2)
#####
## Inversion Introduction Scenes
class ConceptsInInversion(Scene):
CONFIG = {
"color_circle" : YELLOW,
"color_radius" : RED,
"color_P" : WHITE,
}
def construct(self):
self.add_backgrounds()
self.move_around_point_P()
def add_backgrounds(self):
circle_O = Circle(radius = 3.5, color = self.color_circle)
circle_O.shift(3*LEFT)
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle_O.get_bottom(), UP)
dot_O = Dot(circle_O.get_center(), color = self.color_circle)
label_O = DotLabel("O", dot_O, color = self.color_circle, position = DOWN)
remark_O = TextMobject("反演中心", color = self.color_circle)
remark_O.next_to(label_O, LEFT, buff = 0.15)
radius = Line(circle_O.get_center(), circle_O.get_left())
label_radius = TexMobject("R").scale(0.8)
remark_radius = TextMobject("反演幂").scale(0.8)
brace_radius = Brace(radius, UP)
brace_radius.put_at_tip(label_radius)
remark_radius.next_to(label_radius, LEFT, buff = 0.15)
group_radius = VGroup(radius, label_radius, brace_radius, remark_radius)
group_radius.set_color(self.color_radius)
group_radius.rotate(-PI/12, about_point = dot_O.get_center())
def_inversion = TextMobject("反演变换：$P \\mapsto P'$")
rlt_inversion = TexMobject("|OP| \\times |OP'|=", "R^2")
rlt_inversion.next_to(def_inversion, DOWN, aligned_edge = RIGHT)
rlt_inversion[-1].set_color(self.color_radius)
remarks = VGroup(def_inversion, rlt_inversion)
remarks.to_corner(DR)
dot_P = Dot(LEFT, color = self.color_P)
label_P = DotLabel("P", dot_P, color = self.color_P, position = DL, label_buff = 0.2)
dot_Pi = InversedDot(dot_P, circle_O, color = self.color_P)
label_Pi = DotLabel("P'", dot_Pi, color = self.color_P, position = DR, label_buff = 0.2)
line_OP = TwoDotsSegment(dot_O, dot_P, stroke_width = 2)
line_OPi = TwoDotsSegment(dot_O, dot_Pi, stroke_width = 2)
self.add(remarks)
self.add(group_radius)
self.add(circle_O, dot_O, label_O, remark_O, remark_circle)
self.add(dot_P, dot_Pi, label_P, label_Pi, line_OP, line_OPi)
self.circle_O = circle_O
self.dot_P = dot_P
def move_around_point_P(self):
self.dot_P.save_state()
for dx, dy in [(-0.2, 0.3), (0.1, -0.4), (4, 0.3), (1, 1)]:
vec = np.array([dx, dy, 0])
self.play(self.dot_P.shift, vec, run_time = 1)
self.wait()
self.play(self.dot_P.move_to, self.circle_O.get_right())
self.wait()
self.play(self.dot_P.restore, run_time = 1)
self.wait()
class InversionExamples(Scene):
CONFIG = {
"color_circle" : YELLOW,
}
def construct(self):
circle_O = Circle(radius = 3.5, color = self.color_circle)
circle_O.shift(3*LEFT)
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle_O.get_bottom(), UP)
dot_O = Dot(circle_O.get_center(), color = self.color_circle)
label_O = DotLabel("O", dot_O, color = self.color_circle, position = DOWN)
init_shape = Square(side_length = 1.2, color = BLUE).rotate(TAU/13)
init_shape.next_to(circle_O.get_right(), LEFT, buff = 0.5)
init_shape.save_state()
inv_shape = InversedVMobject(init_shape, circle_O, use_dashed_vmob = False)
new_shapes = [
RegularPolygon(n = 6, start_angle = PI/7, color = PINK).scale(0.8),
TexMobject("42", color = RED).scale(2.5).rotate(-PI/9),
TexMobject("\\pi", color = MAROON_B).scale(5).rotate(PI/15),
]
self.add(circle_O, remark_circle, dot_O, label_O)
self.add(init_shape, inv_shape)
for new_shape in new_shapes:
# new_shape.set_color(BLUE)
new_shape.next_to(circle_O.get_right(), LEFT, buff = 0.6)
self.play(Transform(init_shape, new_shape), run_time = 1)
self.wait()
init_shape.generate_target()
init_shape.target.become(new_shape)
init_shape.target.shift(get_random_vector(0.5))
random_angle = 0.5*np.random.random()
init_shape.target.rotate(random_angle)
self.play(MoveToTarget(init_shape, path_arc = random_angle, run_time = 1)),
self.wait()
self.play(ApplyMethod(init_shape.restore))
self.wait()
class LineToLineInversion(Scene):
CONFIG = {
"color_circle" : YELLOW,
"color_orig" : BLUE,
"color_inv" : RED,
}
def construct(self):
self.add_backgrounds()
self.show_line_to_line_inversion()
def add_backgrounds(self):
circle_O = Circle(radius = 2.5, color = self.color_circle)
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle_O.get_bottom(), UP)
dot_O = Dot(circle_O.get_center(), color = self.color_circle)
label_O = DotLabel("O", dot_O, color = self.color_circle, position = DOWN)
conclusion = TextMobject("经过反演中心的直线", "$\\mapsto$", "经过反演中心的直线")
conclusion.scale(0.8)
conclusion[0].set_color(self.color_orig)
conclusion[2].set_color(self.color_inv)
conclusion.to_corner(DR)
self.add(circle_O, remark_circle, dot_O, label_O)
self.add(conclusion)
self.circle_O = circle_O
def show_line_to_line_inversion(self):
angle_tracker = ValueTracker(-PI/11)
position_tracker = ValueTracker(1.4)
angle_tracker.save_state()
position_tracker.save_state()
orig_line = ExtendedLine(LEFT, RIGHT, color = self.color_orig, stroke_width = 8)
orig_line.add_updater(lambda m: m.rotate(angle_tracker.get_value() - m.get_angle()))
inv_line = ExtendedLine(LEFT, RIGHT, color = self.color_inv, stroke_width = 4)
inv_line.add_updater(lambda m: m.rotate(angle_tracker.get_value() - m.get_angle()))
dot_P = Dot(color = self.color_orig)
dot_P.add_updater(
lambda m: m.move_to(
position_tracker.get_value() * rotate_vector(RIGHT, angle_tracker.get_value())
)
)
dot_Pi = InversedDot(dot_P, self.circle_O, is_hollow = False, color = self.color_inv)
label_P = DotLabel("P", dot_P, position = DOWN, color = self.color_orig)
label_Pi = DotLabel("P'", dot_Pi, position = DOWN, color = self.color_inv)
def get_lb():
return LEFT_SIDE + UP * LEFT_SIDE[0] * np.tan(angle_tracker.get_value())
def get_rb():
return RIGHT_SIDE + UP * RIGHT_SIDE[0] * np.tan(angle_tracker.get_value())
def is_oolb(m):
return m.get_right()[0] < LEFT_SIDE[0]
def is_oorb(m):
return m.get_left()[0] > RIGHT_SIDE[0]
oolb_arrow = Arrow(ORIGIN, LEFT, color = self.color_inv).scale(2)
oolb_arrow.add_updater(lambda m: m.set_angle(angle_tracker.get_value() + PI))
oolb_arrow.add_updater(lambda m: m.next_to(get_lb(), DOWN, aligned_edge = LEFT, buff = 0.2))
oorb_arrow = Arrow(ORIGIN, RIGHT, color = self.color_inv).scale(2)
oorb_arrow.add_updater(lambda m: m.set_angle(angle_tracker.get_value()))
oorb_arrow.add_updater(lambda m: m.next_to(get_rb(), DOWN, aligned_edge = RIGHT, buff = 0.2))
oolb_label = TexMobject("P'", color = self.color_inv, background_stroke_width = 0)
oolb_label.add_updater(lambda m: m.next_to(oolb_arrow, DOWN, buff = 0.2))
oorb_label = TexMobject("P'", color = self.color_inv, background_stroke_width = 0)
oorb_label.add_updater(lambda m: m.next_to(oorb_arrow, DOWN, buff = 0.2))
oolb_group = VGroup(oolb_arrow, oolb_label)
oorb_group = VGroup(oorb_arrow, oorb_label)
oolb_group.add_updater(lambda m: m.set_fill(opacity = 1 if is_oolb(label_Pi) else 0))
oolb_group.add_updater(lambda m: m.set_stroke(opacity = 1 if is_oolb(label_Pi) else 0))
oorb_group.add_updater(lambda m: m.set_fill(opacity = 1 if is_oorb(label_Pi) else 0))
oorb_group.add_updater(lambda m: m.set_stroke(opacity = 1 if is_oorb(label_Pi) else 0))
self.add(orig_line, inv_line, dot_P, dot_Pi, label_P, label_Pi)
self.add(oolb_group, oorb_group)
for d_position, d_angle in [(2, 0), (1, PI/10), (-5, 0), (-3, -PI/7), (4, PI/11)]:
self.play(
ApplyMethod(position_tracker.increment_value, d_position),
ApplyMethod(angle_tracker.increment_value, d_angle),
run_time = 2,
)
self.wait()
self.play(
ApplyMethod(angle_tracker.restore),
ApplyMethod(position_tracker.restore),
run_time = 2,
)
self.wait()
class LineToCircleInversion(Scene):
CONFIG = {
"color_circle" : YELLOW,
"color_orig" : BLUE,
"color_inv" : RED,
"line_config" : {
"stroke_width" : 2,
"color" : WHITE,
},
}
def construct(self):
self.add_backgrounds()
self.add_shapes()
self.show_line_to_circle_inversion()
def add_backgrounds(self):
circle_O = Circle(radius = 3, color = self.color_circle)
circle_O.shift(3*LEFT+0.5*UP)
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle_O.get_bottom(), UP)
dot_O = Dot(circle_O.get_center(), color = self.color_circle)
label_O = DotLabel("O", dot_O, color = self.color_circle, position = DOWN)
conclusion1 = TextMobject("不经过反演中心的直线", "$\\mapsto$", "经过反演中心的圆")
conclusion1[0].set_color(self.color_orig)
conclusion1[-1].set_color(self.color_inv)
conclusion2 = TextMobject("经过反演中心的圆", "$\\mapsto$", "不经过反演中心的直线")
conclusion2[0].set_color(self.color_inv)
conclusion2[-1].set_color(self.color_orig)
conclusions = VGroup(conclusion1, conclusion2)
for c in conclusions:
c.scale(0.8)
conclusions.arrange_submobjects(DOWN, index_of_submobject_to_align = 1)
conclusions.to_corner(DR)
bg_rect = BackgroundRectangle(conclusions)
self.add(circle_O, remark_circle)
self.add_foreground_mobjects(dot_O, label_O, bg_rect, conclusions)
self.dot_O = dot_O
self.circle_O = circle_O
self.conclusions = conclusions
self.bg_rect = bg_rect
def add_shapes(self):
position_tracker = ValueTracker(2)
line_angle_tracker = ValueTracker(PI*9/19)
circle_angle_tracker = ValueTracker(PI/5)
line = ExtendedLine(LEFT, RIGHT, color = self.color_orig)
line.add_updater(lambda m: m.move_to(position_tracker.get_value() * RIGHT))
line.add_updater(lambda m: m.rotate(line_angle_tracker.get_value() - m.get_angle()))
inv_line = InversedVMobject(line, self.circle_O, use_dashed_vmob = False, color = self.color_inv)
inv_line_center = SmallDot(color = self.color_inv)
inv_line_center.add_updater(lambda m: m.move_to(inv_line.get_center()))
dot_Ai = Dot(color = self.color_inv)
dot_Ai.add_updater(
lambda m: m.move_to(inv_line.get_center() * 2 - self.circle_O.get_center())
)
dot_Pi = Dot(color = self.color_inv)
dot_Pi.add_updater(
lambda m: m.move_to(
inv_line.get_center() \
+ rotate_vector(
inv_line.get_center() - self.circle_O.get_center(),
circle_angle_tracker.get_value()
)
)
)
dot_P = InversedDot(dot_Pi, self.circle_O, is_hollow = False, color = self.color_orig)
dot_A = InversedDot(dot_Ai, self.circle_O, is_hollow = False, color = self.color_orig)
line_OA, line_OAi, line_OP, line_OPi, line_AP, line_AiPi = aux_lines = VGroup(*[
TwoDotsSegment(pt_1, pt_2, **self.line_config)
for pt_1, pt_2 in [
(self.dot_O, dot_A), (self.dot_O, dot_Ai),
(self.dot_O, dot_P), (self.dot_O, dot_Pi),
(dot_A, dot_P), (dot_Ai, dot_Pi)
]
])
ai_AiOPi = AngleIndicator(dot_Ai, self.dot_O, dot_Pi, color = MAROON_B, radius = 0.8)
rtai_OAP = RightAngleIndicator(self.dot_O, dot_A, dot_P)
rtai_OPiAi = RightAngleIndicator(self.dot_O, dot_Pi, dot_Ai)
label_P = TexMobject("P", color = self.color_orig)
label_Pi = TexMobject("P'", color = self.color_inv)
label_A = TexMobject("A", color = self.color_orig)
label_Ai = TexMobject("A'", color = self.color_inv)
label_A.add_updater(
lambda m: m.move_to(
dot_A.get_center() + 0.3 * normalize(dot_A.get_center() - self.dot_O.get_center())
)
)
label_P.add_updater(
lambda m: m.move_to(
dot_P.get_center() + 0.3 * normalize(dot_A.get_center() - self.dot_O.get_center())
)
)
label_Ai.add_updater(
lambda m: m.move_to(
dot_Ai.get_center() + 0.4 * rotate_vector(
normalize(dot_Ai.get_center() - inv_line_center.get_center()), -PI/4
)
)
)
label_Pi.add_updater(
lambda m: m.move_to(
dot_Pi.get_center() + 0.4 * normalize(dot_Pi.get_center() - inv_line_center.get_center())
)
)
def get_ub():
return line.get_center() + TOP + RIGHT * TOP[1] / np.tan(line_angle_tracker.get_value())
def get_bb():
return line.get_center() + BOTTOM + RIGHT * BOTTOM[1] / np.tan(line_angle_tracker.get_value())
def is_ooub(m):
return m.get_bottom()[1] > TOP[1]
def is_oobb(m):
return m.get_top()[1] < BOTTOM[1]
ooub_arrow = Arrow(ORIGIN, LEFT, color = self.color_orig).scale(2)
ooub_arrow.add_updater(lambda m: m.set_angle(line_angle_tracker.get_value()))
ooub_arrow.add_updater(lambda m: m.next_to(get_ub(), RIGHT, aligned_edge = TOP, buff = 0.2))
oobb_arrow = Arrow(ORIGIN, RIGHT, color = self.color_orig).scale(2)
oobb_arrow.add_updater(lambda m: m.set_angle(line_angle_tracker.get_value() + PI))
oobb_arrow.add_updater(lambda m: m.next_to(get_bb(), RIGHT, aligned_edge = BOTTOM, buff = 0.2))
oolb_label = TexMobject("P", color = self.color_orig, background_stroke_width = 0)
oolb_label.add_updater(lambda m: m.next_to(ooub_arrow, RIGHT, buff = 0.2))
oorb_label = TexMobject("P", color = self.color_orig, background_stroke_width = 0)
oorb_label.add_updater(lambda m: m.next_to(oobb_arrow, RIGHT, buff = 0.2))
ooub_group = VGroup(ooub_arrow, oolb_label)
oobb_group = VGroup(oobb_arrow, oorb_label)
ooub_group.add_updater(lambda m: m.set_fill(opacity = 1 if is_ooub(label_P) else 0))
ooub_group.add_updater(lambda m: m.set_stroke(opacity = 1 if is_ooub(label_P) else 0))
oobb_group.add_updater(lambda m: m.set_fill(opacity = 1 if is_oobb(label_P) else 0))
oobb_group.add_updater(lambda m: m.set_stroke(opacity = 1 if is_oobb(label_P) else 0))
self.add(line, inv_line)
self.add(dot_A, dot_P, dot_Ai, dot_Pi)
self.add(label_P, label_Pi, label_A, label_Ai)
self.add(aux_lines)
self.add(ai_AiOPi, rtai_OAP, rtai_OPiAi)
self.add(ooub_group, oobb_group)
self.position_tracker = position_tracker
self.line_angle_tracker = line_angle_tracker
self.circle_angle_tracker = circle_angle_tracker
def show_line_to_circle_inversion(self):
play_args = [
[0, PI/12, 0, 2],
[0, 0, PI*7/5, 4],
[-2, PI/8, -PI/5, 3],
[0, 0, PI*19/10, 6],
[1.5, -PI/7, PI*2/5, 4],
]
restore_arg = [
-sum([arg[k] for arg in play_args])
for k in range(len(play_args[0]))
]
restore_arg[1] = (restore_arg[1] + PI) % (2*PI) - PI
restore_arg[2] = (restore_arg[2] + PI) % (2*PI) - PI
restore_arg[-1] = 3
play_args.append(restore_arg)
for d_center, d_line_angle, d_circle_angle, run_time in play_args:
self.play(
ApplyMethod(self.position_tracker.increment_value, d_center),
ApplyMethod(self.line_angle_tracker.increment_value, d_line_angle),
ApplyMethod(self.circle_angle_tracker.increment_value, d_circle_angle),
run_time = run_time,
)
self.wait()
class InversionCreateSimilarTriangles(Scene):
CONFIG = {
"random_seed" : 5+7-0,
"num_of_nudges" : 5,
"max_step" : 1,
"color_A" : RED,
"color_B" : BLUE,
"color_combined" : MAROON_B,
"color_circle": YELLOW,
}
def construct(self):
self.add_remark()
self.show_figure_animation()
def add_remark(self):
cond_1 = TexMobject("{|OP|", "\\over", "|OQ|}", "=", "{|OQ'|", "\\over", "|OP'|}")
cond_2 = TexMobject("\\angle POQ", "=", "\\angle Q'OP'")
conds = VGroup(cond_1, cond_2)
conds.arrange_submobjects(DOWN, buff = 0.5)
conds_rect = SurroundingRectangle(conds, color = WHITE)
arrow = TexMobject("\\Downarrow")
arrow.next_to(conds_rect, DOWN)
concl = TexMobject("\\triangle OPQ", "\\sim", "\\triangle OQ'P'")
concl.next_to(arrow, DOWN)
for mob in (cond_1[0], cond_1[2], concl[0]):
mob.set_color(self.color_A)
for mob in (cond_1[-1], cond_1[-3], concl[-1]):
mob.set_color(self.color_B)
for mob in (cond_2[0], cond_2[-1]):
mob.set_color(self.color_combined)
remark = VGroup(conds, conds_rect, arrow, concl)
remark.to_corner(DR)
self.add(remark)
def show_figure_animation(self):
circle = Circle(radius = 3, color = self.color_circle)
circle.move_to(3.5*LEFT)
dot_O = Dot(color = self.color_combined)
dot_O.add_updater(lambda m: m.move_to(circle.get_center()))
dot_P = Dot(point = 1.2*UP+LEFT, color = self.color_A)
dot_Q = Dot(point = 0.5*DOWN+1.9*LEFT, color = self.color_A)
dot_Pi = InversedDot(dot_P, circle, is_hollow = False, color = self.color_B)
dot_Qi = InversedDot(dot_Q, circle, is_hollow = False, color = self.color_B)
triangle_OPQ = ManyDotsPolygon(
dot_O, dot_P, dot_Q, color = self.color_A,
stroke_width = 5, fill_opacity = 0.4
)
triangle_OPiQi = ManyDotsPolygon(
dot_O, dot_Pi, dot_Qi, color = self.color_B,
stroke_width = 2, fill_opacity = 0.3
)
label_O, label_P, label_Pi, label_Q, label_Qi = (
DotLabel(
text, dot, color = color, position = position,
background_stroke_width = 5,
).scale(0.8)
for text, dot, color, position in zip(
["O", "P", "P'", "Q", "Q'"],
[dot_O, dot_P, dot_Pi, dot_Q, dot_Qi],
[self.color_combined, self.color_A, self.color_B, self.color_A, self.color_B],
[LEFT, UP, UP, DOWN, DOWN]
)
)
self.add(dot_O, dot_P, dot_Q, dot_Pi, dot_Qi)
self.add(circle, triangle_OPQ, triangle_OPiQi)
self.add(label_O, label_P, label_Pi, label_Q, label_Qi)
dot_P.save_state()
dot_Q.save_state()
for k in range(self.num_of_nudges):
nudge_P = get_random_vector(self.max_step)
nudge_Q = get_random_vector(self.max_step)
self.play(
ApplyMethod(dot_P.shift, nudge_P),
ApplyMethod(dot_Q.shift, nudge_Q),
run_time = 2
)
self.wait()
self.play(dot_P.restore, dot_Q.restore, run_time = 2)
self.wait()
class CircleToCircleInversionProof(Scene):
CONFIG = {
"color_O" : YELLOW,
"color_A" : RED,
"color_B" : BLUE,
"color_combined" : MAROON_B,
"label_buff" : 0.1,
"label_scaling_factor" : 0.75,
"line_config" : {
"stroke_width" : 2,
"color" : WHITE,
},
}
def construct(self):
self.add_backgrounds()
self.show_left_and_right_points()
self.show_random_point()
self.show_similar_triangles()
self.show_complementary_property()
self.show_inversion_result()
def add_backgrounds(self):
circle_O = Circle(radius = 3.2, color = self.color_O)
circle_O.shift(3.5*LEFT)
dot_O = Dot(circle_O.get_center(), color = self.color_O)
remark_O = TextMobject("反演圆", color = YELLOW)
remark_O.next_to(circle_O.get_bottom(), UP, buff = 0.4)
circle_C = Circle(radius = 0.8, stroke_width = 2)
circle_C.next_to(circle_O.get_right(), LEFT, buff = 0.5)
dot_C = Dot(circle_C.get_center())
label_O, label_C = (
DotLabel(
text, dot, color = color, position = DOWN, label_buff = self.label_buff
).scale(self.label_scaling_factor)
for text, dot, color in zip(["O", "C"], [dot_O, dot_C], [self.color_O, WHITE])
)
for orig_mob in (circle_C, dot_C, label_C):
orig_mob.set_sheen_direction(RIGHT)
orig_mob.set_color([self.color_A, self.color_B])
inv_circle_template = InversedVMobject(circle_C, circle_O, use_dashed_vmob = False)
inv_circle = Circle(radius = inv_circle_template.get_width()/2)
inv_circle.move_to(inv_circle_template.get_center())
inv_circle.set_sheen_direction(LEFT)
inv_circle.set_color([self.color_A, self.color_B])
self.add(circle_O, dot_O, circle_C, dot_C)
self.add(label_O, label_C)
self.add(remark_O)
self.wait()
self.circle_O = circle_O
self.dot_O = dot_O
self.remark_O = remark_O
self.circle_C = circle_C
self.dot_C = dot_C
self.inv_circle = inv_circle
def show_left_and_right_points(self):
dot_A = Dot(color = self.color_A)
dot_A.move_to(self.circle_C.get_left())
dot_B = Dot(color = self.color_B)
dot_B.move_to(self.circle_C.get_right())
dot_Ai = InversedDot(dot_A, self.circle_O, is_hollow = False, color = self.color_A)
dot_Bi = InversedDot(dot_B, self.circle_O, is_hollow = False, color = self.color_B)
dot_Q = Dot((dot_Ai.get_center() + dot_Bi.get_center()) / 2)
line_OB = Line(self.dot_O.get_center(), dot_B.get_center(), **self.line_config)
line_OAi = Line(self.dot_O.get_center(), dot_Ai.get_center(), **self.line_config)
label_A, label_Ai, label_B, label_Bi = (
DotLabel(
text, dot, color = color, position = position, label_buff = self.label_buff
).scale(self.label_scaling_factor)
for text, dot, color, position in zip(
["A", "A'", "B", "B'"],
[dot_A, dot_Ai, dot_B, dot_Bi],
[self.color_A, self.color_A, self.color_B, self.color_B],
[DL, DR, DR, DL]
)
)
remark_AB = TextMobject("圆心连线 \\\\ 的交点...").scale(0.6)
remark_AB.next_to(VGroup(dot_A, dot_B), DOWN, buff = 1)
arrows_AB = VGroup(*[
Arrow(remark_AB.get_critical_point(direction), dot, buff = 0.1)
for direction, dot in zip([UL, UR], [dot_A, dot_B])
])
remark_AiBi = TextMobject("...以及它们的反点").scale(0.8)
remark_AiBi.next_to(VGroup(dot_Ai, dot_Bi), DOWN, buff = 1)
arrows_AiBi = VGroup(*[
Arrow(remark_AiBi.get_critical_point(direction), dot, buff = 0.1)
for direction, dot in zip([UR, UL], [dot_Ai, dot_Bi])
])
self.play(ShowCreation(line_OB))
self.play(Write(dot_A), Write(dot_B), Write(label_A), Write(label_B))
self.wait()
self.play(Write(remark_AB), ShowCreation(arrows_AB))
self.wait()
self.play(
ReplacementTransform(dot_A.deepcopy(), dot_Ai),
ReplacementTransform(dot_B.deepcopy(), dot_Bi),
)
self.play(Write(label_Ai), Write(label_Bi))
self.wait()
self.play(
ReplacementTransform(remark_AB, remark_AiBi),
ReplacementTransform(arrows_AB, arrows_AiBi)
)
self.play(ReplacementTransform(line_OB, line_OAi))
self.play(FadeOut(VGroup(remark_AiBi, arrows_AiBi)))
self.wait()
self.dot_A = dot_A
self.dot_Ai = dot_Ai
self.dot_B = dot_B
self.dot_Bi = dot_Bi
self.dot_Q = dot_Q
self.line_OAi = line_OAi
self.dots_AB = VGroup(dot_A, dot_Ai, dot_B, dot_Bi)
self.labels_AB = VGroup(label_A, label_Ai, label_B, label_Bi)
def show_random_point(self):
angle_tracker = ValueTracker(PI/3)
dot_P = Dot()
dot_P.add_updater(
lambda m: m.move_to(
self.circle_C.point_at_angle(angle_tracker.get_value() % TAU)
)
)
dot_P.add_updater(
lambda m: m.set_color(
interpolate_color(
self.color_A, self.color_B,
(dot_P.get_center()[0] - self.dot_A.get_center()[0]) / (self.dot_B.get_center()[0] - self.dot_A.get_center()[0])
)
)
)
label_P = DotLabel("P", dot_P, position = None)
label_P.scale(0.8)
label_P.add_updater(lambda m: m.set_color(dot_P.get_color()))
label_P.add_updater(
lambda m: m.move_to(dot_P.get_center() * 1.4 - self.dot_C.get_center() * 0.4)
)
arrow_P = Vector(DR, buff = 0, color = WHITE).scale(0.5)
arrow_P.add_updater(lambda m: m.next_to(dot_P, UL, buff = 0.1))
remark_P = TextMobject("圆上任意一点...").scale(0.75)
remark_P.add_updater(lambda m: m.next_to(arrow_P, UL, buff = 0.1))
dot_Pi = InversedDot(dot_P, self.circle_O, is_hollow = False)
dot_Pi.add_updater(lambda m: m.set_color(dot_P.get_color()))
label_Pi = DotLabel("P'", dot_Pi, position = None)
label_Pi.scale(0.8)
label_Pi.add_updater(lambda m: m.set_color(dot_Pi.get_color()))
label_Pi.add_updater(
lambda m: m.move_to(dot_Pi.get_center() * 1.1 - self.inv_circle.get_center() * 0.1)
)
arrow_Pi = Vector(DL, buff = 0, color = WHITE).scale(0.5)
arrow_Pi.add_updater(lambda m: m.next_to(dot_Pi, UR, buff = 0.1))
remark_Pi = TextMobject("...以及它的反点").scale(0.75)
remark_Pi.add_updater(lambda m: m.next_to(arrow_Pi, UR, buff = 0.1))
line_OP, line_OPi, line_AP, line_AiPi, line_BP, line_BiPi = aux_lines = VGroup(*[
TwoDotsSegment(pt_1, pt_2, **self.line_config)
for pt_1, pt_2 in [
(self.dot_O, dot_P), (self.dot_O, dot_Pi), (self.dot_A, dot_P),
(self.dot_Ai, dot_Pi), (self.dot_B, dot_P), (self.dot_Bi, dot_Pi)
]
])
rtai_APB = RightAngleIndicator(self.dot_A, dot_P, self.dot_B)
rtai_BiPiAi = RightAngleIndicator(self.dot_Bi, dot_Pi, self.dot_Ai, side_length = 0.5)
self.play(Write(dot_P), Write(label_P))
self.play(ShowCreation(arrow_P), Write(remark_P))
self.play(Write(line_AP), Write(line_BP))
self.play(ShowCreation(rtai_APB))
self.wait()
self.play(ReplacementTransform(dot_P.deepcopy(), dot_Pi))
self.play(Write(label_Pi))
self.play(
ReplacementTransform(arrow_P.deepcopy(), arrow_Pi),
ReplacementTransform(remark_P.deepcopy(), remark_Pi),
)
self.play(angle_tracker.increment_value, PI/6, run_time = 2)
self.play(FadeOut(VGroup(arrow_P, remark_P, arrow_Pi, remark_Pi)))
self.wait()
self.play(Write(VGroup(line_OP, line_OPi, line_AiPi, line_BiPi)))
self.wait()
self.dot_P = dot_P
self.dot_Pi = dot_Pi
self.rtai_APB = rtai_APB
self.rtai_BiPiAi = rtai_BiPiAi
self.angle_tracker = angle_tracker
self.aux_lines = aux_lines
self.dots_P = VGroup(dot_P, dot_Pi)
self.labels_P = VGroup(label_P, label_Pi)
self.rtais = VGroup(self.rtai_APB, self.rtai_BiPiAi)
def show_similar_triangles(self):
ai_OAP = AngleIndicator(self.dot_O, self.dot_A, self.dot_P, radius = 0.3, color = self.color_A)
ai_OBP = AngleIndicator(self.dot_O, self.dot_B, self.dot_P, radius = 0.4, color = self.color_B)
ai_OPiAi = AngleIndicator(self.dot_O, self.dot_Pi, self.dot_Ai, radius = 0.3, color = self.color_A)
ai_OPiBi = AngleIndicator(self.dot_O, self.dot_Pi, self.dot_Bi, radius = 0.4, color = self.color_B)
triangle_OAP, triangle_OPiAi, triangle_OBP, triangle_OPiBi = [
ManyDotsPolygon(
pt_1, pt_2, pt_3, color = self.color_combined,
stroke_width = 0, fill_opacity = 0.4
)
for pt_1, pt_2, pt_3 in (
(self.dot_O, self.dot_A, self.dot_P),
(self.dot_O, self.dot_Pi, self.dot_Ai),
(self.dot_O, self.dot_B, self.dot_P),
(self.dot_O, self.dot_Pi, self.dot_Bi),
)
]
remark_sim_A = TexMobject("\\triangle OAP", "\\sim", "\\triangle OP'A'")
remark_sim_B = TexMobject("\\triangle OBP", "\\sim", "\\triangle OP'B'")
remark_arrow = TexMobject("\\Downarrow")
remark_angle_A = TexMobject("\\angle OAP", "=", "\\angle OP'A'")
remark_angle_B = TexMobject("\\angle OBP", "=", "\\angle OP'B'")
remarks_A = VGroup(remark_sim_A, remark_arrow, remark_angle_A)
remarks_B = VGroup(remark_sim_B, remark_arrow, remark_angle_B)
remarks_A.arrange_submobjects(DOWN)
remarks_A.next_to(self.dot_Q, DOWN, buff = 1)
remark_sim_B.move_to(remark_sim_A.get_center())
remark_angle_B.move_to(remark_angle_A.get_center())
for remark, color in ([remark_sim_A, self.color_combined], [remark_sim_B, self.color_combined], \
[remark_angle_A, self.color_A], [remark_angle_B, self.color_B]):
remark[0].set_color(color)
remark[-1].set_color(color)
self.play(Write(remark_sim_A))
self.play(FadeInFromDown(VGroup(remark_arrow, remark_angle_A)))
self.wait()
self.play(ShowCreation(triangle_OAP), ShowCreation(ai_OAP))
self.wait()
self.play(
ReplacementTransform(triangle_OAP, triangle_OPiAi),
ReplacementTransform(ai_OAP.deepcopy(), ai_OPiAi),
)
self.play(FadeOut(triangle_OPiAi))
self.wait()
self.play(ReplacementTransform(remarks_A, remarks_B))
self.wait()
self.play(ShowCreation(triangle_OBP), ShowCreation(ai_OBP))
self.wait()
self.play(
ReplacementTransform(triangle_OBP, triangle_OPiBi),
ReplacementTransform(ai_OBP.deepcopy(), ai_OPiBi),
)
self.play(FadeOut(remarks_B), FadeOut(triangle_OPiBi))
self.wait()
self.ai_OAP = ai_OAP
self.ai_OBP = ai_OBP
self.ai_OPiAi = ai_OPiAi
self.ai_OPiBi = ai_OPiBi
self.ais = VGroup(ai_OAP, ai_OBP, ai_OPiAi, ai_OPiBi)
def show_complementary_property(self):
ai_OAP_copy = self.ai_OAP.deepcopy()
ai_OBP_copy = self.ai_OBP.deepcopy()
rtai_APB_copy = self.rtai_APB.deepcopy()
for ai_copy in (ai_OAP_copy, ai_OBP_copy, rtai_APB_copy):
ai_copy.clear_updaters()
comp_prop = VGroup(ai_OAP_copy, TexMobject("="), ai_OBP_copy, TexMobject("+"), rtai_APB_copy)
comp_prop.arrange_submobjects(RIGHT)
comp_prop.scale(1.2)
comp_prop.next_to(self.circle_O.get_top(), DOWN, buff = 1)
self.play(
ReplacementTransform(self.ai_OAP.deepcopy(), ai_OAP_copy),
ReplacementTransform(self.ai_OBP.deepcopy(), ai_OBP_copy),
ReplacementTransform(self.rtai_APB.deepcopy(), rtai_APB_copy),
)
self.play(Write(comp_prop[1]), Write(comp_prop[3]))
self.wait()
self.play(ReplacementTransform(rtai_APB_copy.deepcopy(), self.rtai_BiPiAi))
self.wait()
for ai in self.ais:
ai.clear_updaters()
self.play(
FadeOut(comp_prop),
FadeOut(self.ais),
FadeOut(self.labels_AB), FadeOut(self.labels_P),
)
self.wait()
def show_inversion_result(self):
inv_circle_copy = self.inv_circle.deepcopy()
self.play(self.angle_tracker.set_value, PI, run_time = 2)
self.wait()
def update_inv_circle(inv_circle):
angle = self.angle_tracker.get_value()
if (angle <= -PI) or (angle > PI):
alpha = 1
else:
QPi = self.dot_Pi.get_center() - self.dot_Q.get_center()
QAi = self.dot_Ai.get_center() - self.dot_Q.get_center()
theta = angle_between(QPi, QAi)
if self.dot_Pi.get_center()[1] < self.dot_Q.get_center()[1]:
theta = 2*PI - theta
alpha = theta / (2*PI)
inv_circle.become(inv_circle_copy.get_subcurve(0, alpha))
self.inv_circle.add_updater(update_inv_circle)
self.add(self.inv_circle)
self.play(
ApplyMethod(self.angle_tracker.increment_value, -2*PI),
run_time = 5,
)
self.inv_circle.clear_updaters()
for line in self.aux_lines:
line.clear_updaters()
self.play(
FadeOut(self.dots_AB), FadeOut(self.dots_P), FadeOut(self.rtais),
FadeOut(self.line_OAi), FadeOut(self.aux_lines)
)
self.wait()
color_template = Square(
stroke_width = 0, fill_opacity = 1, fill_color = [self.color_A, self.color_B]
)
conclusion = TextMobject("不经过反演中心的圆", "$\\mapsto$", "不经过反演中心的圆")
conclusion.scale(0.8)
conclusion[0].set_color_by_gradient(self.color_A, self.color_B)
conclusion[2].set_color_by_gradient(self.color_B, self.color_A)
conclusion.to_corner(DR)
self.play(Write(conclusion))
self.wait(3)
self.play(FadeOut(conclusion), FadeOut(self.inv_circle))
self.wait()
class ConcentricPropertyDoesNotHold(Scene):
def setup(self):
N = 8
self.circle_radii = [0.9-0.1*k for k in range(N)]
self.dot_radii = [0.08-0.005*k for k in range(N)]
self.circle_colors = color_gradient([BLUE, GREEN, RED], N)
def construct(self):
orig_circles = VGroup(*[
Circle(radius = radius, stroke_width = 1.5,color = color)
for radius, color in zip(self.circle_radii, self.circle_colors)]
)
orig_circles.shift(2*LEFT+0.5*DOWN)
orig_circles_centers = VGroup(*[
Dot(circle.get_center(), radius = radius, color = color)
for circle, radius, color in zip(orig_circles, self.dot_radii, self.circle_colors)
])
# Dot(orig_circles.get_center())
circle = Circle(radius = 3, color = YELLOW)
circle.shift(3.8*LEFT+0.5*DOWN)
circle_center = Dot(circle.get_center(), color = YELLOW)
inv_circles = VGroup(*[
InversedVMobject(orig_circle, circle).clear_updaters().set_color(color)
for orig_circle, color in zip(orig_circles, self.circle_colors)
])
inv_circles_centers = VGroup(*[
Dot(inv_circle.get_center(), color = color)
for inv_circle, color in zip(inv_circles, self.circle_colors)
])
circle_text = TextMobject("反演圆", color = YELLOW)
circle_text.next_to(circle.get_bottom(), UP, buff = 0.4)
orig_circles_text = TextMobject("同心的圆", color = WHITE)
orig_circles_text.next_to(orig_circles, UP)
orig_circles_text.to_edge(UP, buff = 0.4)
inv_circles_text = TextMobject("不同心的像", color = WHITE)
inv_circles_text.next_to(inv_circles, UP)
inv_circles_text.to_edge(UP, buff = 0.4)
arrow = Arrow(orig_circles_text.get_right(), inv_circles_text.get_left())
self.add(circle, circle_center)
self.add(orig_circles, orig_circles_centers)
self.add(inv_circles, inv_circles_centers)
self.add(circle_text, orig_circles_text, inv_circles_text, arrow)
self.wait()
class DemonstratePtolemyInequality(Scene):
CONFIG = {
"R" : 2.7,
"angle_A" : -PI*2/3,
"angle_B" : PI*4/5,
"angle_D" : -PI/5,
"radius_C" : 3.2,
"angle_C" : PI/5,
}
def construct(self):
radius_tracker = ValueTracker(self.radius_C)
angle_tracker = ValueTracker(self.angle_C)
circle = Circle(radius = self.R, color = WHITE, stroke_width = 1)
circle.shift(DOWN)
dashed_circle = DashedVMobject(circle, num_dashes = 100, positive_space_ratio = 0.5)
dot_A, dot_B, dot_C, dot_D = dots = VGroup(*[
Dot(circle.point_at_angle(angle % TAU), color = WHITE)
for angle in (self.angle_A, self.angle_B, self.angle_C, self.angle_D)
])
dot_C.add_updater(
lambda m: m.move_to(
circle.get_center() + radius_tracker.get_value() * \
rotate_vector(RIGHT, angle_tracker.get_value())
)
)
dot_labels = VGroup(*[
DotLabel(text, dot, position = position, label_buff = 0.1)
for text, dot, position in zip(
["A", "B", "C", "D"], dots, [DL, UL, UR, DR]
)
])
lines = VGroup(*[
TwoDotsSegment(dot_1, dot_2)
for dot_1, dot_2 in (
[dot_B, dot_A], [dot_A, dot_C], [dot_A, dot_D],
[dot_B, dot_C], [dot_B, dot_D], [dot_C, dot_D],
)
])
length_labels = VGroup(*[LengthLabel(line) for line in lines])
length_labels[0].switch_side()
length_labels[2].switch_side()
length_labels[1].set_offset(-0.4)
length_labels[-2].set_offset(-0.4)
def get_sums():
AB, AC, AD, BC, BD, CD = [line.get_length() for line in lines]
sum_lhs = AB * CD + AD * BC
sum_rhs = AC * BD
return sum_lhs, sum_rhs
relation_eq = TexMobject(
"|AB| \\cdot |CD| + |AD| \\cdot |BC|", "=", "|AC| \\cdot |BD|",
background_stroke_width = 0,
)
relation_neq = TexMobject(
"|AB| \\cdot |CD| + |AD| \\cdot |BC|", ">", "|AC| \\cdot |BD|",
background_stroke_width = 0,
)
relation_eq[1].set_color(GREEN)
relation_neq[1].set_color(RED)
relation_eq.to_edge(UP, buff = 1.2)
for eq_mob, neq_mob in zip(relation_eq, relation_neq):
neq_mob.move_to(eq_mob.get_center())
lhs, eq_sign, rhs = relation_eq
neq_sign = relation_neq[1]
label_lhs = DecimalNumber(num_decimal_places = 4, show_ellipsis = True)
label_rhs = DecimalNumber(num_decimal_places = 4, show_ellipsis = True)
label_lhs.add_updater(lambda m: m.set_value(get_sums()[0]))
label_rhs.add_updater(lambda m: m.set_value(get_sums()[1]))
brace_lhs = Brace(lhs, UP, buff = 0.1)
brace_rhs = Brace(rhs, UP, buff = 0.1)
brace_lhs.put_at_tip(label_lhs)
brace_rhs.put_at_tip(label_rhs)
def get_indication_color(thres = 1e-2):
return GREEN if is_close(radius_tracker.get_value(), self.R, thres = thres) else RED
def get_indication_opacity(thres = 1e-2):
return 0 if is_close(radius_tracker.get_value(), self.R, thres = thres) else 1
figure_group = VGroup(dashed_circle, dots, lines, length_labels, dot_labels)
figure_group.add_updater(lambda m: m.set_color(get_indication_color()))
relation_group = VGroup(lhs, eq_sign, rhs, neq_sign, brace_lhs, brace_rhs, label_lhs, label_rhs)
label_lhs.add_updater(lambda m: m.set_color(get_indication_color()))
label_rhs.add_updater(lambda m: m.set_color(get_indication_color()))
eq_sign.add_updater(lambda m: m.set_opacity(1 - get_indication_opacity()))
neq_sign.add_updater(lambda m: m.set_opacity(get_indication_opacity()))
self.add(figure_group)
self.add(relation_group)
deltas = [
(0.5, -0.1), (0, -0.4), (-1, 0.3), (0, 0.4),
(-1, 0), (0.3, -0.2), (0.7, -0.3),
]
radius_tracker.save_state()
angle_tracker.save_state()
for d_radius, d_angle in deltas:
self.play(
ApplyMethod(radius_tracker.increment_value, d_radius),
ApplyMethod(angle_tracker.increment_value, d_angle),
run_time = 2,
)
self.wait()
self.play(
ApplyMethod(radius_tracker.restore),
ApplyMethod(angle_tracker.restore),
run_time = 2,
)
self.wait()
class PtolemyInversionFigure(Scene):
CONFIG = {
"R" : 3.8,
"r" : 1.3,
"angle_A" : PI,
"angle_B" : PI/3,
"angle_C" : -PI/9,
"angle_D" : -PI*2/7,
"color_circle" : YELLOW,
"color_ABD" : BLUE,
}
def construct(self):
circle_ABD = Circle(radius = self.r, color = self.color_ABD, stroke_width = 3)
circle_ABD.shift(0.2*LEFT)
dot_A, dot_B, dot_C, dot_D = dots = VGroup(*[
Dot(circle_ABD.point_at_angle(angle % TAU), color = WHITE)
for angle in (self.angle_A, self.angle_B, self.angle_C, self.angle_D)
])
dot_A.set_color(self.color_circle)
dot_C.shift(0.4*RIGHT)
circle = Circle(radius = self.R, color = self.color_circle, stroke_width = 5)
circle.move_to(dot_A.get_center())
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle.get_bottom(), UP)
label_A, label_B, label_C, label_D = dot_labels = VGroup(*[
DotLabel(text, dot, position = position, label_buff = 0.2)
for text, dot, position in zip(
["A", "B", "C", "D"], dots, [DL, UP, DOWN, DOWN]
)
])
label_A.set_color(self.color_circle)
dot_Bi, dot_Ci, dot_Di = inv_dots = VGroup(*[
InversedDot(dot, circle, is_hollow = False, color = WHITE)
for dot in (dot_B, dot_C, dot_D)
])
label_Bi, label_Ci, label_Di = inv_dot_labels = VGroup(*[
DotLabel(text, dot, position = RIGHT, label_buff = 0.2)
for text, dot in zip(["B'", "C'", "D'"], [dot_Bi, dot_Ci, dot_Di])
])
lines = VGroup(*[
TwoDotsSegment(dot_1, dot_2, stroke_width = 1)
for dot_1, dot_2 in (
[dot_A, dot_B], [dot_A, dot_C], [dot_A, dot_D],
[dot_B, dot_C], [dot_B, dot_D], [dot_C, dot_D],
[dot_A, dot_Bi], [dot_A, dot_Ci], [dot_A, dot_Di],
[dot_Bi, dot_Ci], [dot_Bi, dot_Di], [dot_Ci, dot_Di],
)
])
inv_circle_ABD = InversedVMobject(circle_ABD, circle, use_dashed_vmob = False)
inv_circle_ABD.add_updater(lambda m: m.set_color(self.color_ABD))
inv_circle_ABD.add_updater(lambda m: m.set_stroke(width = 2))
self.add(circle, remark_circle, circle_ABD, inv_circle_ABD)
self.add(dots, dot_labels, inv_dots, inv_dot_labels, lines)
self.add()
self.wait()
#####
## Inversion Advanced P1 Scenes
class KissingCirclesPuzzle(Scene):
def construct(self):
self.show_figure()
self.show_question()
def show_figure(self):
type_text_1 = TextMobject("外切-外切-外切")
type_text_2 = TextMobject("内切-内切-外切")
type_text_1.move_to(LEFT_SIDE/2)
type_text_2.move_to(RIGHT_SIDE/2)
type_text_1.to_edge(DOWN)
type_text_2.to_edge(DOWN)
dot_l1, dot_l2, dot_l3 = dots_l = VGroup(*[
VectorizedPoint(np.array([coords[0], coords[1], 0]), color = BLUE)
for coords in [(-3.9, 1.5), (-4.9, 0.0), (-2.8, -1.0)]
])
dot_r1, dot_r2, dot_r3 = dots_r = VGroup(*[
VectorizedPoint(np.array([coords[0], coords[1], 0]), color = BLUE)
for coords in [(4.6, 0.3), (3.9, 0.6), (3.5, 1.6)]
])
dfc_l = DescartesFourCircles(*dots_l, show_new_circles = False)
dfc_r = DescartesFourCircles(*dots_r, show_new_circles = False, outer_circle_index = 2)
for dfc in [dfc_l, dfc_r]:
for mob in dfc.get_orig_circles():
mob.set_stroke(width = 2, color = BLUE)
self.add(type_text_1, type_text_2)
self.add(dfc_l, dfc_r)
self.dfc_l = dfc_l
self.dfc_r = dfc_r
self.dots_l = dots_l
self.dots_r = dots_r
def show_question(self):
question = TextMobject("能否添加第四个圆，使之与其他三个圆都相切？")
question.to_edge(UP, buff = 0.2)
self.add(question)
self.wait()
class KissingCirclesSimplified(Scene):
def construct(self):
line1 = ExtendedLine(UL, UR)
line2 = ExtendedLine(DL, DR)
center_circle = Circle(radius = 1)
figure_group = VGroup(line1, line2, center_circle)
for mob in figure_group:
mob.set_stroke(width = 2, color = BLUE)
question = TextMobject("能否添加第四个“圆”，使之与其他三个“圆”都相切？")
question.next_to(figure_group, UP, buff = 0.5)
group = VGroup(question, figure_group)
group.move_to(ORIGIN)
self.add(group)
self.wait()
class KissingCirclesSimplifiedAnswer(Scene):
def construct(self):
line1 = ExtendedLine(UL, UR, stroke_width = 2, color = BLUE)
line2 = ExtendedLine(DL, DR, stroke_width = 2, color = BLUE)
center_circle = Circle(radius = 1, stroke_width = 2, color = BLUE)
new_circles = VGroup(*[
Circle(radius = 1, color = color, fill_opacity = 0.1, stroke_width = 5) \
.next_to(center_circle, direction, buff = 0)
for direction, color in zip([LEFT, RIGHT], [RED, ORANGE])
])
numbers = VGroup(*[
TexMobject(f"{num}", color = circle.get_color()).move_to(circle.get_center())
for num, circle in zip(["1", "2"], new_circles)
])
group = VGroup(line1, line2, center_circle, new_circles, numbers)
group.move_to(ORIGIN)
self.add(group)
self.wait()
class KissingCirclesSimplifiedExplanation(Scene):
CONFIG = {
"dashed_vmob_config" : {
"num_dashes" : 30,
"positive_space_ratio" : 0.6,
},
"line_colors" : [GREEN, BLUE],
"center_color" : MAROON_B,
"circle_colors" : [RED, ORANGE],
}
def construct(self):
self.add_backgrounds()
self.show_process()
def add_backgrounds(self):
N = 5
line1 = Line(UP + N*LEFT, UP + N*RIGHT, stroke_width = 2, color = self.line_colors[0])
line2 = Line(DOWN + N*LEFT, DOWN + N*RIGHT, stroke_width = 2, color = self.line_colors[1])
center_circle = FineCircle(radius = 1, stroke_width = 2, color = self.center_color)
new_circle1 = FineCircle(radius = 1, stroke_width = 5, color = self.circle_colors[0])
new_circle1.next_to(center_circle, LEFT, buff = 0)
new_circle2 = FineCircle(radius = 1, stroke_width = 5, color = self.circle_colors[1])
new_circle2.next_to(center_circle, RIGHT, buff = 0)
inv_old_group = VGroup(line1, line2, center_circle)
inv_new_group = VGroup(new_circle1, new_circle2)
inv_group = VGroup(inv_old_group, inv_new_group)
inv_group.rotate(-PI*2/5)
inv_group.shift(3*RIGHT)
circle = FineCircle(radius = 3.5, color = YELLOW)
circle.shift(2*LEFT)
circle_center = Dot(circle.get_center(), color = YELLOW)
remark_circle = TextMobject("反演圆", color = YELLOW)
remark_circle.next_to(circle.get_bottom(), UP)
remark_center = VGroup(*[
Arrow(DL, UR, color = YELLOW, buff = 0).scale(0.3),
TextMobject("反演中心", color = YELLOW).scale(0.8),
])
remark_center.arrange_submobjects(DL, buff = 0)
remark_center.next_to(circle_center, DL, buff = 0.1)
orig_old_group = VGroup(*[
InversedVMobject(mob, circle, use_dashed_vmob = False, match_original_style = True)
for mob in inv_old_group
])
orig_new_group = VGroup(*[
InversedVMobject(mob, circle, use_dashed_vmob = False, match_original_style = True)
for mob in inv_new_group
])
for mob in orig_old_group:
mob.clear_updaters()
mob.set_stroke(width = 2)
for mob in orig_new_group:
mob.clear_updaters()
mob.set_stroke(width = 5)
mob.set_fill(opacity = 0.1)
self.add(orig_old_group)
self.add(circle, circle_center, remark_circle, remark_center)
self.circle = circle
self.inv_old_group = inv_old_group
self.inv_new_group = inv_new_group
self.orig_old_group = orig_old_group
self.orig_new_group = orig_new_group
def show_process(self):
dashed_inv_old_group = VGroup(*[
DashedVMobject(mob, **self.dashed_vmob_config)
for mob in self.inv_old_group
])
dashed_inv_new_group = VGroup(*[
DashedVMobject(mob, **self.dashed_vmob_config)
for mob in self.inv_new_group
])
self.play(ShowCreation(dashed_inv_old_group, lag_ratio = 0.05), run_time = 3)
self.wait()
dashed_copys = VGroup(*[dashed_inv_old_group[-1].deepcopy() for k in range(2)])
dashed_copys.generate_target()
for mob_copy, mob_template in zip(dashed_copys.target, dashed_inv_new_group):
mob_copy.match_style(mob_template)
mob_copy.move_to(mob_template.get_center())
self.play(MoveToTarget(dashed_copys), run_time = 3)
self.remove(dashed_copys)
self.add(dashed_inv_new_group)
self.wait()
self.play(DrawBorderThenFill(self.orig_new_group), run_time = 3)
self.wait(2)
self.play(
FadeOut(dashed_inv_new_group),
FadeOut(dashed_inv_old_group),
FadeOut(self.orig_new_group),
)
self.wait()
class DifferentTangentTypesWithSameConclusion(KissingCirclesPuzzle):
CONFIG = {
"random_seed" : 570,
"num_of_nudges" : 5,
"max_step" : 0.5,
"color_1" : ORANGE,
"color_2" : RED,
}
def construct(self):
super().show_figure()
self.dots_l.save_state()
self.dots_r.save_state()
for dfc in [self.dfc_l, self.dfc_r]:
dfc.add_new_circles()
dfc.get_orig_circles().set_stroke(width = 2)
c4_1, c4_2 = dfc.get_new_circles()
c4_1.set_color(self.color_1)
c4_2.set_color(self.color_2)
self.add(self.dfc_l, self.dfc_r)
for k in range(self.num_of_nudges):
for dot in it.chain(self.dots_l, self.dots_r):
dot.generate_target()
dot.target.shift(get_random_vector(self.max_step))
anims = AnimationGroup(*[
MoveToTarget(dot, path_arc = PI/3., run_time = 1.5)
for dot in it.chain(self.dots_l, self.dots_r)
], run_time = 2)
self.play(anims)
self.wait()
self.play(self.dots_l.restore, self.dots_r.restore, run_time = 1.5)
class LineToCircleInversionRevisited(LineToCircleInversion):
def construct(self):
super().construct()
self.remove_conclusions()
self.add_explanation()
def remove_conclusions(self):
self.remove(self.bg_rect)
self.remove(self.conclusions)
def add_explanation(self):
radius = Line(
self.circle_O.get_left(), self.circle_O.get_center(),
color = self.color_circle, stroke_width = 1,
)
radius_text = TexMobject("R", color = self.color_circle)
radius_text.next_to(radius, UP, buff = 0.1)
radius_group = VGroup(radius, radius_text)
radius_group.rotate(-PI/12, about_point = self.circle_O.get_center())
remark_length = TexMobject("|OA| = d", "\\Downarrow", "|OA'| = \dfrac{R^2}{d}")
remark_length.arrange_submobjects(DOWN)
remark_length.scale(1.2)
remark_length[0].set_color(self.color_orig)
remark_length[-1].set_color(self.color_inv)
remark_length.to_edge(RIGHT)
self.add(radius_group, remark_length)
self.wait()
class CircleToCircleInversionRevisited(CircleToCircleInversionProof):
def construct(self):
super().add_backgrounds()
super().show_left_and_right_points()
super().show_random_point()
super().show_similar_triangles()
self.arrange_elements()
self.add_explanation()
def arrange_elements(self):
self.angle_tracker.set_value(PI/3)
self.remove(self.remark_O)
self.remove(self.ai_OAP, self.ai_OBP, self.ai_OPiAi, self.ai_OPiBi)
self.add(self.inv_circle)
self.add(self.dots_P, self.labels_P)
self.add(self.dots_AB, self.labels_AB)
self.add(self.aux_lines, self.rtais)
dot_I = Dot(self.inv_circle.get_center())
label_I = DotLabel("I", dot_I, position = DOWN, label_buff = 0.15).scale(0.8)
for mob in (dot_I, label_I):
mob.set_sheen_direction(RIGHT)
mob.set_color([self.color_B, self.color_A])
remark_I = TextMobject("反形的圆心（并非$C$的反点！）")
remark_I.scale(0.5)
remark_I.next_to(label_I, DOWN, buff = 0.1)
self.add(dot_I, label_I, remark_I)
def add_explanation(self):
for circle, color, text, angle in zip(
[self.circle_O, self.circle_C], [self.color_O, MAROON_B],
["R", "r"], [-PI/12, PI/3]
):
radius = Line(
circle.get_left(), circle.get_center(),
color = color, stroke_width = 1,
)
radius_text = TexMobject(text, color = color)
radius_text.next_to(radius, UP, buff = 0.1)
radius_group = VGroup(radius, radius_text)
radius_group.rotate(angle, about_point = circle.get_center())
self.add(radius_group)
remark_length_A = TexMobject("|OA| = d-r", "\\Rightarrow", "|OA'| = \dfrac{R^2}{d-r}")
remark_length_B = TexMobject("|OB| = d+r", "\\Rightarrow", "|OB'| = \dfrac{R^2}{d+r}")
remark_length_A[0].set_color(self.color_A)
remark_length_A[-1].set_color(self.color_A)
remark_length_B[0].set_color(self.color_B)
remark_length_B[-1].set_color(self.color_B)
length_group = VGroup(remark_length_A, remark_length_B)
length_group.arrange_submobjects(DOWN, buff = 0.4)
brace = Brace(length_group, RIGHT)
arrow = TexMobject("\\Rightarrow")
remarks = VGroup(
TexMobject("|A'B'| = \\dfrac{2 R^2 r}{|d^2-r^2|}"),
TexMobject("|OI| = \\dfrac{R^2 d}{|d^2-r^2|}")
)
remarks.arrange_submobjects(DOWN, aligned_edge = LEFT)
remarks.set_color(MAROON_B)
result_group = VGroup(brace, arrow, remarks)
result_group.arrange_submobjects(RIGHT)
result_group.next_to(length_group, RIGHT)
remark_group = VGroup(length_group, result_group)
remark_group.center().to_edge(DOWN, buff = 0.2)
bg_rect = BackgroundRectangle(remark_group, fill_opacity = 0.9)
self.add(bg_rect, remark_group)
self.wait()
class DescartesTheoremExamples(Scene):
CONFIG = {
"circle_colors" : [MAROON_B, RED, GREEN, BLUE],
"curvs_outer" : [3, 6, 7, 34],
"curvs_inner" : [10, 15, 19, -6],
}
def setup(self):
self.text_color_map = dict(
zip(["{k_1}", "{k_2}", "{k_3}", "{k_4}"], self.circle_colors)
)
def construct(self):
self.add_title()
self.add_outer_dfc()
self.add_inner_dfc()
def add_title(self):
title = TexMobject(
"\\left(", "{k_1}", "+", "{k_2}", "+", "{k_3}", "+", "{k_4}", "\\right) ^2",
"= 2 \\left(", "{k_1}","^2 +","{k_2}","^2 +","{k_3}","^2 +","{k_4}","^2", "\\right)"
)
title.set_color_by_tex_to_color_map(self.text_color_map)
title.scale(1.2)
title.to_edge(UP, buff = 0.2)
self.add(title)
def add_outer_dfc(self):
r1, r2, r3, r4 = [1./curv for curv in self.curvs_outer]
p1, p2, p3 = [
VectorizedPoint(center)
for center in calc_centers_by_radii(r1, r2, r3, init_angle = PI*2/3)
]
outer_dfc = DescartesFourCircles(p1, p2, p3, show_new_circles = False)
c1, c2, c3 = outer_dfc.get_orig_circles()
c4 = outer_dfc.get_new_circles()[0]
outer_circles = VGroup(c1, c2, c3, c4)
outer_circles.clear_updaters()
outer_circles.set_height(5.5)
outer_circles.to_corner(DL)
texts = VGroup(*[
TexMobject(f"k_{num}", "=", f"{curv}") \
.scale(0.8) \
.move_to(circle.get_center())
for num, curv, circle in zip(range(1, 5), self.curvs_outer, outer_circles)
])
for circle, text, color in zip(outer_circles, texts, self.circle_colors):
circle.set_color(color)
text.set_color(color)
texts[-1].shift(2.5*RIGHT+1.2*UP)
arrow = Arrow(
texts[-1].get_bottom(), outer_circles[-1].get_right(),
path_arc = -PI*2/3, buff = 0.1,
).set_color(self.circle_colors[-1])
outer_group = VGroup(outer_circles, texts, arrow)
self.add(outer_group)
def add_inner_dfc(self):
r1, r2, r3, r4 = [1./curv for curv in self.curvs_inner]
p1, p2, p3 = [
VectorizedPoint(center)
for center in calc_centers_by_radii(r1, r2, r3, init_angle = -PI/7)
]
inner_dfc = DescartesFourCircles(p1, p2, p3, show_new_circles = False)
c1, c2, c3 = inner_dfc.get_orig_circles()
c4 = inner_dfc.get_new_circles()[1]
inner_circles = VGroup(c1, c2, c3, c4)
inner_circles.clear_updaters()
inner_circles.set_height(5.5)
inner_circles.to_corner(DR)
inner_texts = VGroup(*[
TexMobject(f"k_{num}", "=", f"{curv}") \
.scale(0.8) \
.move_to(circle.get_center())
for num, curv, circle in zip(range(1, 5), self.curvs_inner, inner_circles)
])
for circle, text, color in zip(inner_circles, inner_texts, self.circle_colors):
circle.set_color(color)
text.set_color(color)
inner_texts[-1].shift(2.8*LEFT+2.7*UP)
inner_arrow = Arrow(
inner_texts[-1].get_critical_point(DOWN),
inner_texts[-1].get_critical_point(DOWN)+0.7*DR,
buff = 0.1,
).set_color(self.circle_colors[-1])
inner_group = VGroup(inner_circles, inner_texts, inner_arrow)
self.add(inner_group)
self.wait()
self.inner_circles = inner_circles
self.inner_texts = inner_texts
self.inner_arrow = inner_arrow
class DFCInversionProofP1(DescartesTheoremExamples):
CONFIG = {
"remark_scale_text" : "示意图，图像并非真实比例",
"orig_label_texts" : ["C_1", "C_2", "C_3", "C_4"],
"inv_label_texts" : ["C_1'", "C_2'", "C_3'", "C_4'"],
}
def construct(self):
super().add_inner_dfc()
self.arrange_elements()
self.add_labels()
self.add_inversion_center()
self.add_mapsto_symbol()
self.add_not_to_scale_remark()
self.wait()
def arrange_elements(self):
self.remove(self.inner_texts, self.inner_arrow)
self.inner_circles.center().shift(4*UP)
normal_form = FourCirclesNormalForm()
normal_form.shift(4*DOWN)
self.add(normal_form)
self.normal_form = normal_form
def add_labels(self):
orig_labels = VGroup()
for n, (circle, text) in enumerate(zip(self.inner_circles, self.orig_label_texts)):
label = TexMobject(text).scale(1.2)
label.set_color(circle.get_color())
label.move_to(circle.get_center())
orig_labels.add(label)
inv_labels = VGroup()
for n, (circle, text) in enumerate(zip(self.normal_form, self.inv_label_texts)):
label = TexMobject(text).scale(1.2)
label.set_color(circle.get_color())
label.move_to(circle.get_center())
inv_labels.add(label)
c1, c2, c3, c4 = self.inner_circles
l1, l2, l3, l4 = orig_labels
c1i, c2i, c3i, c4i = self.normal_form
l1i, l2i, l3i, l4i = inv_labels
l4.next_to(c4.get_bottom(), UP, buff = 0.3)
l3i.next_to(c3i, DOWN).to_edge(RIGHT)
l4i.next_to(c4i, UP).to_edge(RIGHT)
self.add(orig_labels, inv_labels)
self.orig_labels = orig_labels
self.inv_labels = inv_labels
def add_inversion_center(self):
c1, c2, c3, c4 = self.inner_circles
inv_center = get_tangent_point(c3, c4)
dot_O = Dot(inv_center, color = YELLOW)
label_O = TexMobject("O", color = YELLOW).next_to(dot_O, UP)
remark_O = TextMobject("反演中心", color = YELLOW)
remark_O.next_to(dot_O, RIGHT, buff = 1.5)
arrow_O = Arrow(remark_O.get_left(), dot_O.get_right(), color = YELLOW, buff = 0.2)
orig_center_group = VGroup(dot_O, label_O, remark_O, arrow_O)
inv_dot_O = VectorizedPoint()
inv_dot_O.next_to(self.normal_form[-1], UP, buff = 1.4)
inv_dot_O.shift(2*RIGHT)
inv_center_group = orig_center_group.deepcopy()
inv_center_group.shift(inv_dot_O.get_center() - dot_O.get_center())
self.add(orig_center_group, inv_center_group)
self.orig_center_group = orig_center_group
self.inv_center_group = inv_center_group
def add_mapsto_symbol(self):
mapsto = TexMobject("\\mapsto")
mapsto.rotate(-PI/2)
mapsto.scale(2.5)
mapsto.next_to(self.inner_circles, DOWN)
remark_mapsto = TextMobject("反演变换")
remark_mapsto.next_to(mapsto, LEFT)
self.add(mapsto, remark_mapsto)
def add_not_to_scale_remark(self):
remark_scale = TextMobject("（" + self.remark_scale_text + "）")
remark_scale.scale(0.75)
remark_scale.next_to(6.5*DL, RIGHT, buff = 0)
self.add(remark_scale)
class DFCInversionProofP2(DFCInversionProofP1):
CONFIG = {
"remark_scale_text" : "示意图，反演圆未标出，且图像并非真实比例",
"inv_label_texts" : ["C_1'", "C_2'", "C_3':y=-1", "C_4':y=1"],
"inv_center_coord_text" : "(x_0, y_0) \\, (y_0>1)",
"circle_center_coord_texts" : ["(-1,0)", "(1,0)"],
}
def construct(self):
super().construct()
self.change_center_remarks()
self.add_coord_system()
self.change_inv_labels()
self.wait()
def change_center_remarks(self):
for center_group in (self.orig_center_group, self.inv_center_group):
dot, label, remark, arrow = center_group
self.remove(remark, arrow)
if center_group is self.inv_center_group:
coord = TexMobject(self.inv_center_coord_text)
coord.next_to(dot, RIGHT)
coord.set_color(dot.get_color())
self.add(coord)
def add_coord_system(self):
c1, c2, c3, c4 = self.normal_form
center_point = (c1.get_center() + c2.get_center()) / 2
unit_size = c1.get_height()/2
coord_system = Axes(
center_point = center_point,
number_line_config = {"unit_size" : unit_size},
y_min = -1.8, y_max = 2.8,
)
self.add(coord_system)
self.coord_system = coord_system
def change_inv_labels(self):
l1i, l2i, l3i, l4i = self.inv_labels
for label, x_coord, coord_text in zip([l1i, l2i], [-1, 1], self.circle_center_coord_texts):
center = self.coord_system.c2p(x_coord, 0)
label.next_to(center, UP)
dot_i = Dot(center, radius = 0.1).set_color(label.get_color())
coord_i = TexMobject(coord_text).set_color(label.get_color()).next_to(center, DOWN)
self.add(dot_i, coord_i)
#####
## Inversion Advanced P2 Scenes
class ApollonianGasketConstruction(ApollonianGasketScene):
CONFIG = {
"max_iter" : 8,
"curvatures" : [2, 2, 3],
"init_angle" : 0,
"curv_thres" : 30000,
"ag_config": {
"agc_config" : {
"radius_thres" : 1e-3,
"circle_color" : BLUE,
"label_color" : WHITE,
},
},
"color_curr" : YELLOW,
"wait_time" : 2,
}
def construct(self):
r1, r2, r3 = [1./curv for curv in self.curvatures]
p1, p2, p3 = calc_centers_by_radii(r1, r2, r3, init_angle = self.init_angle)
agc1 = AGCircle(p1, r1, parents = None, **self.ag_config["agc_config"])
agc2 = AGCircle(p2, r2, parents = None, **self.ag_config["agc_config"])
agc3 = AGCircle(p3, r3, parents = None, **self.ag_config["agc_config"])
remark = TextMobject("（圆内数字为该圆的曲率）")
remark.scale(0.75).to_corner(DL)
self.add(remark)
for k in range(self.max_iter):
agcs_copy = [agc.deepcopy() for agc in (agc1, agc2, agc3)]
ag = ApollonianGasket(
*agcs_copy, num_iter = k,
curv_thres = self.curv_thres, **self.ag_config
)
iter_num = VGroup(
TextMobject("迭代次数："), TexMobject(f"{k}")
).arrange_submobjects(RIGHT).scale(1.5)
iter_num.to_edge(LEFT, buff = 1)
ag.scale(3.8)
ag.shift(np.array([0, 3.8, 0]) - ag.get_top() + 3*RIGHT)
VGroup(*ag.agc_list[-1]).set_color(self.color_curr)
self.add(ag, iter_num)
self.wait(self.wait_time)
if k != self.max_iter-1:
self.remove(ag, iter_num)
class ApollonianGasketExample1(Scene):
CONFIG = {
"max_iter" : 20,
"curvatures" : [3, 6, 7],
"curvature_texts" : [-2, 3, 6, 7],
"init_angle" : 0,
"curv_thres" : 4000,
"ag_config": {
"agc_config" : {
"radius_thres" : 1e-3,
"circle_color" : BLUE,
"label_color" : WHITE,
},
},
"ag_scaling_factor" : 5.2,
}
def construct(self):
r1, r2, r3 = [1./curv for curv in self.curvatures]
p1, p2, p3 = calc_centers_by_radii(r1, r2, r3, init_angle = self.init_angle)
agc1 = AGCircle(p1, r1, parents = None, **self.ag_config["agc_config"])
agc2 = AGCircle(p2, r2, parents = None, **self.ag_config["agc_config"])
agc3 = AGCircle(p3, r3, parents = None, **self.ag_config["agc_config"])
ag_seed = ApollonianGasket(
*[agc.deepcopy() for agc in (agc1, agc2, agc3)],
num_iter = 0, curv_thres = self.curv_thres, **self.ag_config
)
ag_result = ApollonianGasket(
*[agc.deepcopy() for agc in (agc1, agc2, agc3)],
num_iter = self.max_iter, curv_thres = self.curv_thres, **self.ag_config
)
ag_seed_center = ag_seed[0][0].get_right()
ag_result_center = ag_result[0][0].get_right()
arrow = Arrow(LEFT, RIGHT)
figure_group = VGroup(ag_seed, ag_result, arrow)
for ag, center, direction in zip(
[ag_seed, ag_result], [ag_seed_center, ag_result_center], [4*LEFT, 4*RIGHT]):
ag.scale(self.ag_scaling_factor)
ag.shift(direction - center)
figure_group.shift(DOWN)
k1, k2, k3, k4 = list(map(str, self.curvature_texts))
title = TexMobject(
f"({k1}+{k2}+{k3}+{k4})^2 = 2\\left[({k1})^2+{k2}^2+{k3}^2+{k4}^2 \\right]"
)
title.set_width(13)
title.set_color(YELLOW)
title.to_edge(UP)
self.add(figure_group, title)
self.wait()
class ApollonianGasketExample2(ApollonianGasketExample1):
CONFIG = {
"max_iter" : 20,
"curvatures" : [5, 8, 12],
"curvature_texts" : [-3, 5, 8, 12],
"curv_thres" : 5000,
"ag_config": {
"agc_config" : {
"radius_thres" : 5e-4,
"circle_color" : BLUE,
"label_color" : WHITE,
},
},
"ag_scaling_factor" : 8,
}
class LoxodromicSpiralInTangentCircles(Scene):
CONFIG = {
"max_iter" : 20,
"agc_config" : {
"radius_thres" : 1,
"circle_color" : BLUE,
"label_color" : WHITE,
},
"curve_config" : {
"color" : YELLOW,
"stroke_width" : 2,
},
"alpha" : 0.6,
"dashed_line_config" : {
"color" : GREY,
"stroke_width" : 0.5,
"num_dashes" : 200,
"positive_space_ratio" : 0.6,
}
}
def construct(self):
self.generate_circles()
self.generate_curves()
self.generate_labels()
self.generate_lines()
self.add_elements()
self.zooming_in()
def generate_circles(self):
agcm2 = AGCircle(2/3.*UP, 1/3., **self.agc_config)
agcm1 = AGCircle(RIGHT/2, 1/2., **self.agc_config)
agczr = AGCircle(ORIGIN, -1, **self.agc_config)
agcp1 = AGCircle(LEFT/2, 1/2., **self.agc_config)
agcp2 = AGCircle(2/3.*DOWN, 1/3., **self.agc_config)
agc_list = [agcm2, agcm1, agczr, agcp1, agcp2]
for n in range(self.max_iter):
A, B, C, known_agc = agc_list[:4]
agc_m_k, agc_m_c = calc_new_agc_info(A, B, C, known_agc = known_agc)
agc_m = AGCircle(agc_m_c, 1./agc_m_k, parents = (A, B, C), **self.agc_config)
known_agc, C, B, A = agc_list[-4:]
agc_p_k, agc_p_c = calc_new_agc_info(C, B, A, known_agc = known_agc)
agc_p = AGCircle(agc_p_c, 1./agc_p_k, parents = (C, B, A), **self.agc_config)
agc_list.insert(0, agc_m)
agc_list.append(agc_p)
agc_group = VGroup(*agc_list)
agc_group.set_height(7.8)
self.agc_list = agc_list
self.agc_group = agc_group
def generate_curves(self):
agc_ps = self.agc_list[-self.max_iter-4:]
agc_ps_points = []
loxo_curve_p_solid = VMobject(**self.curve_config)
for k in range(len(agc_ps)-2):
if k != 0:
c1, c2, c3 = agc_ps[k], agc_ps[k+1], agc_ps[k+2]
pt1 = get_tangent_point(c1, c2)
pt2 = get_tangent_point(c2, c3)
p = c2.get_center()
if k != 1:
agc_ps_points.extend(
[pt1, p*(1-self.alpha)+pt1*self.alpha, p*(1-self.alpha)+pt2*self.alpha, pt2]
)
else:
agc_ps_points.extend(
[pt1, p*0.7+pt1*0.3, p*0.6+pt2*0.4, pt2]
)
else:
c1, c2 = agc_ps[1], agc_ps[2]
pt = get_tangent_point(c1, c2)
agc_ps_points.extend([8*LEFT, 7*LEFT, 6*LEFT, pt])
loxo_curve_p_solid.append_points(agc_ps_points)
loxo_curve_m_solid = loxo_curve_p_solid.deepcopy()
loxo_curve_m_solid.rotate(PI, about_point = self.agc_group.get_center())
self.loxo_curve_p_solid = loxo_curve_p_solid
self.loxo_curve_m_solid = loxo_curve_m_solid
def generate_labels(self):
labels = VGroup(*[
TexMobject("C_{%d}" % num, background_stroke_width = 0)
for num in range(-self.max_iter-2, self.max_iter+3)
])
for label, circle in zip(labels, self.agc_group):
label.set_height(circle.get_height()*0.15)
label.move_to(circle.get_center())
label_c0 = labels[self.max_iter+2]
label_c0.set_height(0.8)
label_c0.next_to(self.agc_group.get_critical_point(UL), DR, buff = 0.1)
self.labels = labels
def generate_lines(self):
agc_ps = self.agc_list[-self.max_iter-2:]
line_p_solid = VMobject(**self.dashed_line_config)
line_p_solid_corners = [8*LEFT]
for circle in agc_ps:
line_p_solid_corners.append(circle.get_center())
line_p_solid.set_points_as_corners(line_p_solid_corners)
line_m_solid = line_p_solid.deepcopy()
line_m_solid.rotate(PI, about_point = self.agc_group.get_center())
self.line_p_solid = line_p_solid
self.line_m_solid = line_m_solid
def add_elements(self):
figure = VGroup(
self.agc_group, self.loxo_curve_p_solid, self.loxo_curve_m_solid,
self.line_p_solid, self.line_m_solid, self.labels,
)
self.add(figure)
self.figure = figure
def zooming_in(self):
self.figure.save_state()
self.wait(0.5)
self.play(
ApplyMethod(self.figure.shift, -self.agc_group[-1].get_center()),
run_time = 2,
)
self.wait()
for k in range(10):
self.play(
ApplyMethod(self.figure.scale, 2.5, {"about_point" : self.agc_group[-1].get_center()}),
run_time = 2,
)
self.wait()
self.play(self.figure.restore, run_time = 15)
self.wait(2)
class ShowFordCircles(ZoomInOnFordCircles):
CONFIG = {
"q_max" : 30,
}
def construct(self):
self.setup_axes()
self.setup_circles_and_labels()
self.add_remarks()
self.first_zoom_in()
self.wait()
def first_zoom_in(self):
self.zoom_in_on(1/2., 6)
def add_remarks(self):
nl_text = TextMobject("数轴")
nl_arrow = Arrow(ORIGIN, UP).match_height(nl_text)
nl_remark = VGroup(nl_arrow, nl_text)
nl_remark.scale(0.8)
nl_remark.set_color(LIGHT_GREY)
nl_remark.arrange_submobjects(RIGHT, buff = 0.1)
nl_remark.next_to(self.axes.coords_to_point(0, 0), DOWN, buff = 0.1)
nl_remark.to_edge(LEFT, buff = 0.15)
frac_remark = TextMobject("圆内分数为圆心横坐标")
frac_remark.scale(0.6)
frac_remark.to_corner(DL, buff = 0.15)
self.add(nl_remark, frac_remark)
class ShowFordCirclesDetails(ShowFordCircles):
CONFIG = {
"q_max" : 100,
}
def construct(self):
super().construct()
self.further_zoom_in()
def setup_circles_and_labels(self):
circles = VGroup()
labels = VGroup()
for q in range(1, self.q_max+1):
for p in get_coprime_numers_by_denom(q):
if (q <= 40) or (0.6 <= p/q <= 0.8):
circle = self.generate_circle_by_fraction(p, q)
circle.add_updater(
lambda m: m.set_stroke(width = get_stroke_width_by_height(m.get_height()))
)
label = AssembledFraction(p, q)
label.set_height(circle.get_height() * self.label_height_factor)
label.move_to(circle.get_center())
circles.add(circle)
labels.add(label)
self.add(circles, labels)
self.circles = circles
self.labels = labels
def further_zoom_in(self):
self.acl = VGroup(self.axes, self.circles, self.labels)
self.acl.save_state()
self.wait(0.5)
self.play_zooming_animation(1/np.sqrt(2), 9, run_time = 5)
self.wait()
self.play_zooming_animation(0.73, 5, run_time = 4)
self.wait()
self.play_zooming_animation(0.74, 5, run_time = 4)
self.wait()
self.play(self.acl.restore, run_time = 5)
self.wait(2)
class ProveFordCirclesPropertiesP1(Scene):
CONFIG = {
"c1_frac" : [2, 3],
"c2_frac" : [3, 4],
"c3_frac" : [5, 7],
"circle_config" : {"stroke_color" : BLUE, "stroke_width" : 2,},
"line_config" : {"stroke_color" : GREY, "stroke_width" : 2,},
"aux_line_config" : {"stroke_color" : GREY, "stroke_width" : 0.8,},
"polygon_config" : {"fill_color" : GREY, "fill_opacity" : 0.4, "stroke_width" : 0,},
}
def setup(self):
a, b = self.c1_frac
c, d = self.c2_frac
p, q = self.c3_frac
r1 = 1/(2*b**2)
r2 = 1/(2*d**2)
r3 = 1/(2*q**2)
c1_center = a/b*RIGHT + r1*UP
c2_center = c/d*RIGHT + r2*UP
c3_center = p/q*RIGHT + r3*UP
c1 = Circle(arc_center = c1_center, radius = r1, **self.circle_config)
c2 = Circle(arc_center = c2_center, radius = r2, **self.circle_config)
c3 = Circle(arc_center = c3_center, radius = r3, **self.circle_config)
c1_dot = SmallDot(color = GREY)
c1_dot.add_updater(lambda m: m.move_to(c1.get_center()))
c2_dot = SmallDot(color = GREY)
c2_dot.add_updater(lambda m: m.move_to(c2.get_center()))
c3_dot = SmallDot(color = GREY)
c3_dot.add_updater(lambda m: m.move_to(c3.get_center()))
line = Line(
2*c1.get_bottom()-c2.get_bottom(),
2*c2.get_bottom()-c1.get_bottom(),
**self.line_config
)
VGroup(c1, c2, c3, line).set_height(6).center().to_edge(UP)
aux_line_1 = Line(c1.get_center(), c1.get_bottom(), **self.aux_line_config)
aux_line_2 = Line(c2.get_center(), c2.get_bottom(), **self.aux_line_config)
aux_line_3 = Line(c1.get_center(), c2.get_center(), **self.aux_line_config)
aux_line_4 = Line(c1.get_bottom(), c2.get_bottom(), **self.aux_line_config) \
.shift(c2.get_height()/2*UP)
polygon = Polygon(
c1.get_center(), c2.get_center(), aux_line_4.get_start_and_end()[0],
**self.polygon_config,
)
l1 = TexMobject("\\dfrac{a}{b}").next_to(c1, DOWN)
l2 = TexMobject("\\dfrac{c}{d}").next_to(c2, DOWN)
l3 = TexMobject("\\dfrac{a+c}{b+d}").next_to(c3, DOWN)
self.orig_group = VGroup(c1, c2, line, c1_dot, c2_dot, l1, l2)
self.aux_group = VGroup(aux_line_1, aux_line_2, aux_line_3, aux_line_4, polygon)
self.new_group = VGroup(c3, c3_dot, l3)
def construct(self):
self.add(self.orig_group, self.aux_group)
self.wait()
class ProveFordCirclesPropertiesP2(ProveFordCirclesPropertiesP1):
def construct(self):
self.add(self.orig_group, self.new_group)
self.wait()
class ShowFordCirclesFareySum(ZoomInOnFordCircles):
pass
# A rename, that's it.
class DFCInversionProofP3(DFCInversionProofP2):
CONFIG = {
"remark_scale_text" : "示意图，反演圆未标出，且图像并非真实比例",
"inv_label_texts" : ["C_1'", "C_2'", "C_3':\\mathrm{Im}(z)=-1", "C_4':\\mathrm{Im}(z)=1"],
"inv_center_coord_text" : "z_0 = x_0+iy_0\\, (y_0>1)",
"circle_center_coord_texts" : ["-1", "1"],
}
def construct(self):
super().construct()
self.wait()
def add_coord_system(self):
c1, c2, c3, c4 = self.normal_form
center_point = (c1.get_center() + c2.get_center()) / 2
unit_size = c1.get_height()/2
coord_system = NumberPlane(
center_point = center_point,
number_line_config = {"unit_size" : unit_size},
y_min = -3, y_max = 3,
background_line_style = {
"stroke_color" : GREY,
"stroke_width" : 1.5,
"stroke_opacity" : 0.8,
},
)
aux_coord_system = Axes(
center_point = center_point,
number_line_config = {"unit_size" : unit_size},
y_min = -3, y_max = 3,
stroke_opacity = 0.8,
)
self.add(coord_system, aux_coord_system)
self.coord_system = coord_system
class NormalFormIn3D(ThreeDScene):
CONFIG = {
"axis_unit_size" : 1.5,
"axis_min" : -1.5,
"axis_max" : 2.8,
"resolution" : (60, 120),
"plane_colors" : [GREEN, BLUE],
"sphere_colors" : [MAROON_B, RED, PINK],
}
def construct(self):
self.add_3d_stuff()
self.add_2d_stuff()
def add_3d_stuff(self):
self.set_camera_orientation(theta = 70 * DEGREES, phi = 50 * DEGREES)
axes = ThreeDAxes(
x_min = self.axis_min, x_max = self.axis_max,
y_min = self.axis_min, y_max = self.axis_max,
z_min = self.axis_min, z_max = self.axis_max,
number_line_config = {"unit_size" : self.axis_unit_size},
)
sphere_centers = [
axis.number_to_point(1)
for axis in [axes.x_axis, axes.y_axis, axes.z_axis]
]
radius = 1/np.sqrt(2) * self.axis_unit_size
sphere_dots = VGroup(*[
Sphere(
radius = 0.08, resolution = self.resolution,
fill_opacity = 1, stroke_width = 0,
).move_to(sphere_center).set_color(color)
for sphere_center, color in zip(sphere_centers, self.sphere_colors)
])
spheres = VGroup(*[
Sphere(
radius = radius, resolution = self.resolution,
fill_opacity = 0.6, stroke_width = 0.5,
).move_to(sphere_center).set_color(color)
for sphere_center, color in zip(sphere_centers, self.sphere_colors)
])
planes = VGroup(*[
VGroup(*[
Square(
side_length = 1, fill_opacity = fill_opacity,
stroke_color = GREY, stroke_width = 0.3, stroke_opacity = 0.2,
)
for k in range(n**2)
]).arrange_in_grid(n, n, buff = 0) \
.apply_matrix(z_to_vector([1, 1, 1])) \
.move_to(np.average(sphere_centers)) \
.shift(radius * normalize(direction)) \
.set_color(color)
for n, fill_opacity, direction, color in zip(
[7, 8], [0.2, 0.3], [np.ones(3), -np.ones(3)], self.plane_colors,
)
])
figure_group = VGroup(axes, planes, sphere_dots, spheres)
figure_group.shift(RIGHT*2+0.5*OUT)
self.add(figure_group)
self.add(axes)
self.add(planes)
self.add(sphere_dots, spheres)
def add_2d_stuff(self):
sphere_remarks = VGroup(*[
TextMobject(
"球：圆心为" + f"$({int(x)},{int(y)},{int(z)})$" + \
"，半径为" + "$\\dfrac{1}{\\sqrt{2}}$"
).set_color(color)
for (x, y, z), color in zip([RIGHT, UP, OUT], self.sphere_colors)
]).arrange_submobjects(DOWN)
plane_remarks = VGroup(*[
TexMobject(
"\\text{平面：}" + "x+y+z=1" + sign + "\\dfrac{\\sqrt{3}}{\\sqrt{2}"
).set_color(color)
for sign, color in zip(["+", "-"], self.plane_colors)
]).arrange_submobjects(DOWN)
remarks = VGroup(sphere_remarks, plane_remarks)
remarks.arrange_submobjects(DOWN, aligned_edge = LEFT)
remarks.scale(0.8)
remarks.to_corner(DR)
self.add_fixed_in_frame_mobjects(remarks)
self.wait()
#####
## Banner
class Banner_Intro(Scene):
CONFIG = {
"circle_color" : YELLOW,
"text_color" : BLUE,
"inv_text_color" : BLUE,
"circle_center" : 0.8*UP,
"circle_radius" : 3,
"grid_side_length" : 0.5,
"x_range" : 300,
"y_range" : 300,
"dist_thres" : 300,
}
def construct(self):
circle = Circle(color = self.circle_color, radius = self.circle_radius, stroke_width = 5)
circle.move_to(self.circle_center)
dot = SmallDot(self.circle_center, color = self.circle_color)
text = TextMobject("Inversion", color = self.text_color, background_stroke_width = 3)
text.rotate(PI/2.)
text.move_to(0.4*RIGHT)
text.apply_complex_function(np.exp)
text.rotate(-PI/2.)
text.scale(1.5)
text.move_to(0.9*DOWN)
inv_text = InversedVMobject(text, circle, use_dashed_vmob = False)
inv_text.suspend_updating()
inv_text.set_background_stroke(color = "#303030", width = 3)
inv_text.set_stroke(width = 0)
inv_text.set_fill(color = self.inv_text_color, opacity = 0.5)
grid = VGroup(*[
Square(
side_length = self.grid_side_length,
stroke_width = 0, fill_opacity = 0.3,
fill_color = CB_DARK if (i+j)%2==0 else CB_LIGHT
).move_to(self.circle_center + (i*RIGHT+j*UP)*self.grid_side_length)
for i in range(-self.x_range, self.x_range+1, 1)
for j in range(-self.y_range, self.y_range+1, 1)
if np.sqrt(i**2+j**2) * self.grid_side_length < self.dist_thres
])
for square in grid:
if is_close_in_R3(square.get_center(), self.circle_center):
grid.remove(square)
inv_grid = InversedVMobject(grid, circle, use_dashed_vmob = False)
self.add(inv_grid, circle, dot, text, inv_text)
self.wait()
class Banner_AdvancedP1(ApollonianGasketScene):
CONFIG = {
"curvatures" : [570, 968, 1112],
"init_angle" : PI/7,
"num_iter" : 20,
"curv_thres" : 1e6,
"ag_config" : {
"agc_config" : {
"radius_thres" : 5e-6,
"circle_color" : YELLOW,
"label_color" : WHITE,
},
},
"part_text" : "上篇",
}
def construct(self):
super().construct()
ag = self.ag
ag.set_height(7)
circle_myst = ag.agc_list[0][0]
label_myst = circle_myst.label
label_question = TexMobject("???")
label_question.match_height(label_myst)
label_question.move_to(label_myst)
self.remove(label_myst)
self.add(label_question)
part = TextMobject(self.part_text)
part.to_corner(DR)
self.add(part)
class Banner_AdvancedP2(Banner_AdvancedP1):
CONFIG = {
"part_text" : "下篇",
}
| 40.915864
| 132
| 0.599125
|
= sp - n * unit_vec
new_ep = ep + n * unit_vec
Line.__init__(self, new_sp, new_ep, **kwargs)
class DotLabel(VMobject):
CONFIG = {
"position" : UP,
"label_buff" : 0.25,
}
def __init__(self, label_text, dot, **kwargs):
VMobject.__init__(self, **kwargs)
self.dot = dot
label = TexMobject(label_text, **kwargs)
if self.position is not None:
label.add_updater(
lambda l: l.next_to(self.dot.get_center(), self.position, buff = self.label_buff)
)
self.add(label)
def set_label(self, label):
label.next_to(self.dot.get_center())
class TwoDotsSegment(Line):
def __init__(self, dot_1, dot_2, **kwargs):
self.dot_1 = dot_1
self.dot_2 = dot_2
sp, ep = self.get_dots_centers()
Line.__init__(self, start = sp, end = ep, **kwargs)
self.add_updater(self.set_start_and_end)
def get_dots_centers(self):
return self.dot_1.get_center(), self.dot_2.get_center()
def set_start_and_end(self, line_mob):
sp, ep = self.get_dots_centers()
line_mob.put_start_and_end_on(sp, ep)
class LengthLabel(DecimalNumber):
CONFIG = {
"num_decimal_places" : 3,
"label_height" : 0.3,
"label_buff" : 0.3,
"offset" : 0,
"is_on_opposite_side" : False,
}
def __init__(self, line_mob, **kwargs):
DecimalNumber.__init__(self, **kwargs)
self.line_mob = line_mob
self.add_updater(self.set_label)
def set_label(self, label):
label.set_value(self.line_mob.get_length())
label.set_height(self.label_height)
label.rotate(self.line_mob.get_angle())
side_factor = -1 if self.is_on_opposite_side else 1
label.move_to(
self.line_mob.get_center() \
+ self.line_mob.get_vector() / 2 * self.offset \
+ side_factor * rotate_vector(self.line_mob.get_unit_vector(), PI/2) * self.label_buff
)
def set_offset(self, offset):
self.offset = offset
return self
def switch_side(self):
self.is_on_opposite_side = not self.is_on_opposite_side
return self
class ManyDotsPolygon(VMobject):
def __init__(self, *dots, **kwargs):
VMobject.__init__(self, **kwargs)
self.dots = dots
dots_centers = self.get_dots_centers()
polygon = Polygon(*dots_centers, **kwargs)
polygon.add_updater(self.set_vertices)
self.add(polygon)
def get_dots_centers(self):
return [dot.get_center() for dot in self.dots]
def set_vertices(self, polygon_mob):
vertices = self.get_dots_centers()
polygon_mob.set_points_as_corners([*vertices, vertices[0]])
class AngleIndicator(VMobject):
CONFIG = {
"color" : RED,
"radius" : 0.2,
"fill_opacity" : 0.6,
"is_minor_arc" : True,
}
def __init__(self, dot_A, dot_C, dot_B, **kwargs):
VMobject.__init__(self, **kwargs)
self.dot_A = dot_A
self.dot_C = dot_C
self.dot_B = dot_B
sector = Sector()
sector.add_updater(self.set_sector)
self.add(sector)
self.sector = sector
def get_point_center(self, point_or_mob):
if isinstance(point_or_mob, Mobject):
return point_or_mob.get_center()
else:
return point_or_mob
def get_point_centers(self):
return tuple(map(self.get_point_center, [self.dot_A, self.dot_C, self.dot_B]))
def set_sector(self, mob):
pt_A, pt_C, pt_B = self.get_point_centers()
start_angle, angle = self.get_angles()
outer_radius = min([self.radius, get_norm(pt_C - pt_A)/2, get_norm(pt_C - pt_B)/2])
new_sector = Sector(
start_angle = start_angle, angle = angle, outer_radius = outer_radius,
color = self.color, fill_opacity = self.fill_opacity, stroke_width = 0
)
new_sector.move_arc_center_to(self.get_point_center(self.dot_C))
mob.become(new_sector)
def get_angles(self):
pt_A, pt_C, pt_B = self.get_point_centers()
start_angle = angle_of_vector(pt_A - pt_C)
end_angle = angle_of_vector(pt_B - pt_C)
angle = (end_angle - start_angle) % TAU
if self.is_minor_arc and angle > PI:
angle -= TAU
return start_angle, angle
class RightAngleIndicator(VMobject):
CONFIG = {
"color" : WHITE,
"side_length" : 0.2,
"line_width" : 1,
"square_opacity" : 0.5,
}
def __init__(self, dot_A, dot_C, dot_B, **kwargs):
VMobject.__init__(self, **kwargs)
self.dot_A = dot_A
self.dot_C = dot_C
self.dot_B = dot_B
line = VMobject(stroke_width = self.line_width, fill_opacity = 0)
square = VMobject(stroke_width = 0, fill_color = self.color, fill_opacity = self.square_opacity)
line.add_updater(self.set_line)
square.add_updater(self.set_square)
self.add(square, line)
self.line = line
self.square = square
def get_point_center(self, point_or_mob):
if isinstance(point_or_mob, Mobject):
return point_or_mob.get_center()
else:
return point_or_mob
def get_point_centers(self):
return tuple(map(self.get_point_center, [self.dot_A, self.dot_C, self.dot_B]))
def get_norm_vectors(self):
pt_A, pt_C, pt_B = self.get_point_centers()
norm_vec_CA = normalize(pt_A - pt_C)
norm_vec_CB = normalize(pt_B - pt_C)
return norm_vec_CA, norm_vec_CB
def get_corner_points(self):
pt_A, pt_C, pt_B = self.get_point_centers()
norm_vec_CA, norm_vec_CB = self.get_norm_vectors()
side_length = min([self.side_length, get_norm(pt_A - pt_C)/2, get_norm(pt_B - pt_C)/2])
return (
pt_C,
pt_C + norm_vec_CA * side_length,
pt_C + norm_vec_CA * side_length + norm_vec_CB * side_length,
pt_C + norm_vec_CB * side_length
)
def set_line(self, line_mob):
p, q, r, s = self.get_corner_points()
line_mob.set_points_as_corners([q, r, s])
def set_square(self, square_mob):
p, q, r, s = self.get_corner_points()
square_mob.set_points_as_corners([p, q, r, s, p])
class InversedDot(VMobject):
CONFIG = {
"color" : PINK,
"stroke_width" : 3,
"fill_opacity" : 1,
"is_hollow" : True,
"center_color" : BLACK,
}
def __init__(self, orig_dot, circle, **kwargs):
self.orig_dot = orig_dot
self.circle = circle
VMobject.__init__(self, **kwargs)
def generate_points(self):
if self.is_hollow:
self.fill_color = self.center_color
else:
self.fill_color = self.color
self.stroke_width = 0
inv_dot = Dot(ORIGIN, color = self.color)
self.inv_dot = inv_dot
self.add(inv_dot)
self.add_updater_to_inversed_dot()
def add_updater_to_inversed_dot(self):
self.inv_dot.add_updater(self.move_inversed_dot)
def move_inversed_dot(self, inv_dot):
point = self.orig_dot.get_center()
inv_center = self.circle.get_center()
radius = self.circle.get_height() / 2.
if is_close_in_R3(point, inv_center):
pass
else:
inv_dot.move_to(inversion(point, inv_center, radius))
class InversedVMobject(VMobject):
CONFIG = {
"is_analytical" : True,
"match_original_style" : False,
"use_dashed_vmob" : True,
"dashed_vmob_config": {
"num_dashes" : 50,
"positive_space_ratio" : 0.6,
},
}
def __init__(self, orig_vmob, circle, **kwargs):
VMobject.__init__(self, **kwargs)
self.orig_vmob = orig_vmob
self.circle = circle
self.orig_vmob_type = "Others"
self.initialize_orig_vmob_type()
self.add_updater_to_inversed_vmobject()
def add_updater_to_inversed_vmobject(self):
self.add_updater(self.set_inversed_vmobject)
def initialize_orig_vmob_type(self):
if isinstance(self.orig_vmob, Line):
self.orig_vmob_type = "Line"
elif isinstance(self.orig_vmob, Circle):
self.orig_vmob_type = "Circle"
else:
self.orig_vmob_type = "Others"
def set_orig_vmob_type(self, orig_vmob_type):
self.orig_vmob_type = orig_vmob_type
def set_inversed_vmobject(self, inv_vmob):
inv_center = self.circle.get_center()
radius = self.circle.get_height() / 2.
if self.is_analytical and self.orig_vmob_type == "Line":
# If it's a line...
lp1, lp2 = self.orig_vmob.get_start_and_end()
if is_on_the_line(inv_center, lp1, lp2):
# then the inversion is just the line itself.
temp_vmob = ExtendedLine(lp1, lp2)
else:
# If it's a line NOT through the inversion center,
v_para, v_perp = get_para_and_perp_components(inv_center, lp1, lp2)
d = distance_to_the_line(inv_center, lp1, lp2)
inv_vmob_radius = fdiv(radius**2, 2*d)
closepoint = inv_center + v_perp
inv_vmob_closepoint = inversion(closepoint, inv_center, radius)
inv_vmob_center = (inv_center + inv_vmob_closepoint) / 2.
temp_vmob = FineCircle(radius = inv_vmob_radius)
temp_vmob.move_to(inv_vmob_center)
elif self.is_analytical and self.orig_vmob_type == "Circle":
orig_vmob_center = self.orig_vmob.get_center()
orig_vmob_radius = self.orig_vmob.get_height() / 2.
center_vec = orig_vmob_center - inv_center
d = get_norm(center_vec)
if is_close(orig_vmob_radius, d):
# If it's a circle passing through the inversion center,
foot = inv_center + fdiv(radius**2, 2*d) * normalize(center_vec)
lp1 = foot + rotate_vector(center_vec, PI/2)
lp2 = foot + rotate_vector(center_vec, -PI/2)
temp_vmob = ExtendedLine(lp1, lp2)
else:
# then the inversion is a circle NOT through the inversion center.
dp1 = orig_vmob_center - orig_vmob_radius * normalize(center_vec)
dp2 = orig_vmob_center + orig_vmob_radius * normalize(center_vec)
inv_dp1 = inversion(dp1, inv_center, radius)
inv_dp2 = inversion(dp2, inv_center, radius)
inv_vmob_radius = get_norm(inv_dp2 - inv_dp1) / 2.
inv_vmob_center = (inv_dp2 + inv_dp1) / 2.
temp_vmob = FineCircle(radius = inv_vmob_radius)
temp_vmob.move_to(inv_vmob_center)
else:
temp_vmob = self.orig_vmob.copy()
temp_vmob.apply_function(lambda p: inversion(p, inv_center, radius))
if self.use_dashed_vmob:
temp_vmob = DashedVMobject(temp_vmob, **self.dashed_vmob_config)
inv_vmob.become(temp_vmob)
if self.match_original_style:
inv_vmob.match_style(self.orig_vmob)
class FourCirclesNormalForm(VMobject):
CONFIG = {
"circle_colors" : [MAROON_B, RED, GREEN, BLUE],
"r" : 1.2,
"l" : 9,
"use_dashed_vmob" : True,
"dashed_vmob_config" : {
"num_dashes" : 30,
"positive_space_ratio" : 0.6,
}
}
def __init__(self, **kwargs):
VMobject.__init__(self, **kwargs)
c1 = Circle(radius = self.r, **kwargs).shift(self.r*LEFT)
c2 = Circle(radius = self.r, **kwargs).shift(self.r*RIGHT)
c3 = Line(self.l*LEFT, self.l*RIGHT, **kwargs).shift(self.r*DOWN)
c4 = Line(self.l*LEFT, self.l*RIGHT, **kwargs).shift(self.r*UP)
for mob, color in zip([c1, c2, c3, c4], self.circle_colors):
mob.set_color(color)
if self.use_dashed_vmob:
self.add(DashedVMobject(mob, **self.dashed_vmob_config))
else:
self.add(mob)
class DescartesFourCircles(VMobject):
CONFIG = {
"outer_circle_index" : None,
"orig_circle_color" : BLUE,
"new_circle_color" : YELLOW,
"show_new_circles" : True,
"show_new_circles_centers" : False,
}
def __init__(self, ccdot1, ccdot2, ccdot3, **kwargs):
self.ccdot1 = ccdot1
self.ccdot2 = ccdot2
self.ccdot3 = ccdot3
VMobject.__init__(self, **kwargs)
self.add_orig_circles()
self.add_orig_circles_updaters()
self.generate_new_circles()
if self.show_new_circles:
self.add_new_circles()
if self.show_new_circles_centers:
self.add_new_circles_centers()
def add_orig_circles(self):
self.c1, self.c2, self.c3 = self.cs = VGroup(*[
Circle(arc_center = cc, radius = r, color = self.orig_circle_color)
for cc, r in zip(self.get_orig_circle_centers(), self.calc_radii_by_centers())
])
self.add(self.cs)
def add_orig_circles_updaters(self):
def get_center(k):
return self.get_orig_circle_centers()[k]
def get_abs_radius(k):
return np.abs(self.calc_radii_by_centers()[k])
# Since enumerate() won't work here (seriously?),
self.c1.add_updater(lambda c: c.move_to(get_center(0)))
self.c1.add_updater(lambda c: c.set_height(2*get_abs_radius(0)))
self.c2.add_updater(lambda c: c.move_to(get_center(1)))
self.c2.add_updater(lambda c: c.set_height(2*get_abs_radius(1)))
self.c3.add_updater(lambda c: c.move_to(get_center(2)))
self.c3.add_updater(lambda c: c.set_height(2*get_abs_radius(2)))
def get_orig_circles(self):
return self.cs
def get_orig_circle_centers(self):
return [dot.get_center() for dot in (self.ccdot1, self.ccdot2, self.ccdot3)]
def get_orig_circle_radii(self):
return self.calc_radii_by_centers()
def get_orig_circle_curvatures(self):
return [fdiv(1, radius) for radius in self.calc_radii_by_centers()]
def calc_radii_by_centers(self):
p1, p2, p3 = self.get_orig_circle_centers()
d12 = get_norm(p2 - p1)
d23 = get_norm(p3 - p2)
d13 = get_norm(p3 - p1)
sum_r = (d12 + d23 + d13) / 2.
if self.outer_circle_index == 1:
return [-sum_r, sum_r-d12, sum_r-d13]
elif self.outer_circle_index == 2:
return [sum_r-d12, -sum_r, sum_r-d23]
elif self.outer_circle_index == 3:
return [sum_r-d13, sum_r-d23, -sum_r]
else:
return [sum_r-d23, sum_r-d13, sum_r-d12]
def generate_new_circles(self):
self.c4_1, self.c4_2 = self.new_circles = VGroup(*[
Circle(arc_center = new_cc, radius = new_r, color = self.new_circle_color)
for new_cc, new_r in self.calc_new_circles_centers_and_radii()
])
self.generate_new_circles_centers()
self.add_new_circles_updaters()
def calc_new_circles_centers_and_radii(self):
k1, k2, k3 = self.get_orig_circle_curvatures()
z1, z2, z3 = map(R3_to_complex, self.get_orig_circle_centers())
sum_k = k1 + k2 + k3
sum_k2 = k1**2 + k2**2 + k3**2
sum_k_cycle_prod = k1*k2 + k2*k3 + k3*k1
b = (-2)*sum_k
c = sum_k2 - 2*sum_k_cycle_prod
delta = b**2 - 4*c
k4_1 = (-b + np.sqrt(delta)) / 2
k4_2 = (-b - np.sqrt(delta)) / 2
sum_kz = k1*z1 + k2*z2 + k3*z3
sum_k2z = k1**2 * z1 + k2**2 * z2 + k3**2 * z3
coeff_1 = (sum_k - k4_1) * k4_1
const_1 = 2 * sum_k2z - (sum_k + k4_1) * sum_kz
z4_1 = const_1 / coeff_1
coeff_2 = (sum_k - k4_2) * k4_2
const_2 = 2 * sum_k2z - (sum_k + k4_2) * sum_kz
z4_2 = const_2 / coeff_2
return [[complex_to_R3(z4_1), fdiv(1, k4_1)], [complex_to_R3(z4_2), fdiv(1, k4_2)]]
def generate_new_circles_centers(self):
ccdot4_1 = Dot(color = self.new_circle_color)
ccdot4_1.add_updater(lambda m: m.move_to(self.c4_1.get_center()))
ccdot4_2 = Dot(color = self.new_circle_color)
ccdot4_2.add_updater(lambda m: m.move_to(self.c4_2.get_center()))
self.ccdot4_1 = ccdot4_1
self.ccdot4_2 = ccdot4_2
def add_new_circles_updaters(self):
def get_new_center(k):
return self.calc_new_circles_centers_and_radii()[k][0]
def get_abs_new_radius(k):
return np.abs(self.calc_new_circles_centers_and_radii()[k][1])
# I have to use a much more direct approach - list them all.
self.c4_1.add_updater(lambda c: c.move_to(get_new_center(0)))
self.c4_1.add_updater(lambda c: c.set_height(2*get_abs_new_radius(0)))
self.c4_2.add_updater(lambda c: c.move_to(get_new_center(1)))
self.c4_2.add_updater(lambda c: c.set_height(2*get_abs_new_radius(1)))
def add_new_circles(self):
if not hasattr(self, "new_circles"):
self.new_circles = generate_new_circles()
self.add(self.new_circles)
def get_new_circles(self):
if not hasattr(self, "new_circles"):
self.new_circles = generate_new_circles()
return self.new_circles
def add_new_circles_centers(self):
self.add(self.ccdot4_1, self.ccdot4_2)
def remove_new_circles_center(self):
self.remove(self.ccdot4_1, self.ccdot4_2)
#####
## Inversion Introduction Scenes
class ConceptsInInversion(Scene):
CONFIG = {
"color_circle" : YELLOW,
"color_radius" : RED,
"color_P" : WHITE,
}
def construct(self):
self.add_backgrounds()
self.move_around_point_P()
def add_backgrounds(self):
circle_O = Circle(radius = 3.5, color = self.color_circle)
circle_O.shift(3*LEFT)
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle_O.get_bottom(), UP)
dot_O = Dot(circle_O.get_center(), color = self.color_circle)
label_O = DotLabel("O", dot_O, color = self.color_circle, position = DOWN)
remark_O = TextMobject("反演中心", color = self.color_circle)
remark_O.next_to(label_O, LEFT, buff = 0.15)
radius = Line(circle_O.get_center(), circle_O.get_left())
label_radius = TexMobject("R").scale(0.8)
remark_radius = TextMobject("反演幂").scale(0.8)
brace_radius = Brace(radius, UP)
brace_radius.put_at_tip(label_radius)
remark_radius.next_to(label_radius, LEFT, buff = 0.15)
group_radius = VGroup(radius, label_radius, brace_radius, remark_radius)
group_radius.set_color(self.color_radius)
group_radius.rotate(-PI/12, about_point = dot_O.get_center())
def_inversion = TextMobject("反演变换：$P \\mapsto P'$")
rlt_inversion = TexMobject("|OP| \\times |OP'|=", "R^2")
rlt_inversion.next_to(def_inversion, DOWN, aligned_edge = RIGHT)
rlt_inversion[-1].set_color(self.color_radius)
remarks = VGroup(def_inversion, rlt_inversion)
remarks.to_corner(DR)
dot_P = Dot(LEFT, color = self.color_P)
label_P = DotLabel("P", dot_P, color = self.color_P, position = DL, label_buff = 0.2)
dot_Pi = InversedDot(dot_P, circle_O, color = self.color_P)
label_Pi = DotLabel("P'", dot_Pi, color = self.color_P, position = DR, label_buff = 0.2)
line_OP = TwoDotsSegment(dot_O, dot_P, stroke_width = 2)
line_OPi = TwoDotsSegment(dot_O, dot_Pi, stroke_width = 2)
self.add(remarks)
self.add(group_radius)
self.add(circle_O, dot_O, label_O, remark_O, remark_circle)
self.add(dot_P, dot_Pi, label_P, label_Pi, line_OP, line_OPi)
self.circle_O = circle_O
self.dot_P = dot_P
def move_around_point_P(self):
self.dot_P.save_state()
for dx, dy in [(-0.2, 0.3), (0.1, -0.4), (4, 0.3), (1, 1)]:
vec = np.array([dx, dy, 0])
self.play(self.dot_P.shift, vec, run_time = 1)
self.wait()
self.play(self.dot_P.move_to, self.circle_O.get_right())
self.wait()
self.play(self.dot_P.restore, run_time = 1)
self.wait()
class InversionExamples(Scene):
CONFIG = {
"color_circle" : YELLOW,
}
def construct(self):
circle_O = Circle(radius = 3.5, color = self.color_circle)
circle_O.shift(3*LEFT)
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle_O.get_bottom(), UP)
dot_O = Dot(circle_O.get_center(), color = self.color_circle)
label_O = DotLabel("O", dot_O, color = self.color_circle, position = DOWN)
init_shape = Square(side_length = 1.2, color = BLUE).rotate(TAU/13)
init_shape.next_to(circle_O.get_right(), LEFT, buff = 0.5)
init_shape.save_state()
inv_shape = InversedVMobject(init_shape, circle_O, use_dashed_vmob = False)
new_shapes = [
RegularPolygon(n = 6, start_angle = PI/7, color = PINK).scale(0.8),
TexMobject("42", color = RED).scale(2.5).rotate(-PI/9),
TexMobject("\\pi", color = MAROON_B).scale(5).rotate(PI/15),
]
self.add(circle_O, remark_circle, dot_O, label_O)
self.add(init_shape, inv_shape)
for new_shape in new_shapes:
new_shape.next_to(circle_O.get_right(), LEFT, buff = 0.6)
self.play(Transform(init_shape, new_shape), run_time = 1)
self.wait()
init_shape.generate_target()
init_shape.target.become(new_shape)
init_shape.target.shift(get_random_vector(0.5))
random_angle = 0.5*np.random.random()
init_shape.target.rotate(random_angle)
self.play(MoveToTarget(init_shape, path_arc = random_angle, run_time = 1)),
self.wait()
self.play(ApplyMethod(init_shape.restore))
self.wait()
class LineToLineInversion(Scene):
CONFIG = {
"color_circle" : YELLOW,
"color_orig" : BLUE,
"color_inv" : RED,
}
def construct(self):
self.add_backgrounds()
self.show_line_to_line_inversion()
def add_backgrounds(self):
circle_O = Circle(radius = 2.5, color = self.color_circle)
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle_O.get_bottom(), UP)
dot_O = Dot(circle_O.get_center(), color = self.color_circle)
label_O = DotLabel("O", dot_O, color = self.color_circle, position = DOWN)
conclusion = TextMobject("经过反演中心的直线", "$\\mapsto$", "经过反演中心的直线")
conclusion.scale(0.8)
conclusion[0].set_color(self.color_orig)
conclusion[2].set_color(self.color_inv)
conclusion.to_corner(DR)
self.add(circle_O, remark_circle, dot_O, label_O)
self.add(conclusion)
self.circle_O = circle_O
def show_line_to_line_inversion(self):
angle_tracker = ValueTracker(-PI/11)
position_tracker = ValueTracker(1.4)
angle_tracker.save_state()
position_tracker.save_state()
orig_line = ExtendedLine(LEFT, RIGHT, color = self.color_orig, stroke_width = 8)
orig_line.add_updater(lambda m: m.rotate(angle_tracker.get_value() - m.get_angle()))
inv_line = ExtendedLine(LEFT, RIGHT, color = self.color_inv, stroke_width = 4)
inv_line.add_updater(lambda m: m.rotate(angle_tracker.get_value() - m.get_angle()))
dot_P = Dot(color = self.color_orig)
dot_P.add_updater(
lambda m: m.move_to(
position_tracker.get_value() * rotate_vector(RIGHT, angle_tracker.get_value())
)
)
dot_Pi = InversedDot(dot_P, self.circle_O, is_hollow = False, color = self.color_inv)
label_P = DotLabel("P", dot_P, position = DOWN, color = self.color_orig)
label_Pi = DotLabel("P'", dot_Pi, position = DOWN, color = self.color_inv)
def get_lb():
return LEFT_SIDE + UP * LEFT_SIDE[0] * np.tan(angle_tracker.get_value())
def get_rb():
return RIGHT_SIDE + UP * RIGHT_SIDE[0] * np.tan(angle_tracker.get_value())
def is_oolb(m):
return m.get_right()[0] < LEFT_SIDE[0]
def is_oorb(m):
return m.get_left()[0] > RIGHT_SIDE[0]
oolb_arrow = Arrow(ORIGIN, LEFT, color = self.color_inv).scale(2)
oolb_arrow.add_updater(lambda m: m.set_angle(angle_tracker.get_value() + PI))
oolb_arrow.add_updater(lambda m: m.next_to(get_lb(), DOWN, aligned_edge = LEFT, buff = 0.2))
oorb_arrow = Arrow(ORIGIN, RIGHT, color = self.color_inv).scale(2)
oorb_arrow.add_updater(lambda m: m.set_angle(angle_tracker.get_value()))
oorb_arrow.add_updater(lambda m: m.next_to(get_rb(), DOWN, aligned_edge = RIGHT, buff = 0.2))
oolb_label = TexMobject("P'", color = self.color_inv, background_stroke_width = 0)
oolb_label.add_updater(lambda m: m.next_to(oolb_arrow, DOWN, buff = 0.2))
oorb_label = TexMobject("P'", color = self.color_inv, background_stroke_width = 0)
oorb_label.add_updater(lambda m: m.next_to(oorb_arrow, DOWN, buff = 0.2))
oolb_group = VGroup(oolb_arrow, oolb_label)
oorb_group = VGroup(oorb_arrow, oorb_label)
oolb_group.add_updater(lambda m: m.set_fill(opacity = 1 if is_oolb(label_Pi) else 0))
oolb_group.add_updater(lambda m: m.set_stroke(opacity = 1 if is_oolb(label_Pi) else 0))
oorb_group.add_updater(lambda m: m.set_fill(opacity = 1 if is_oorb(label_Pi) else 0))
oorb_group.add_updater(lambda m: m.set_stroke(opacity = 1 if is_oorb(label_Pi) else 0))
self.add(orig_line, inv_line, dot_P, dot_Pi, label_P, label_Pi)
self.add(oolb_group, oorb_group)
for d_position, d_angle in [(2, 0), (1, PI/10), (-5, 0), (-3, -PI/7), (4, PI/11)]:
self.play(
ApplyMethod(position_tracker.increment_value, d_position),
ApplyMethod(angle_tracker.increment_value, d_angle),
run_time = 2,
)
self.wait()
self.play(
ApplyMethod(angle_tracker.restore),
ApplyMethod(position_tracker.restore),
run_time = 2,
)
self.wait()
class LineToCircleInversion(Scene):
CONFIG = {
"color_circle" : YELLOW,
"color_orig" : BLUE,
"color_inv" : RED,
"line_config" : {
"stroke_width" : 2,
"color" : WHITE,
},
}
def construct(self):
self.add_backgrounds()
self.add_shapes()
self.show_line_to_circle_inversion()
def add_backgrounds(self):
circle_O = Circle(radius = 3, color = self.color_circle)
circle_O.shift(3*LEFT+0.5*UP)
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle_O.get_bottom(), UP)
dot_O = Dot(circle_O.get_center(), color = self.color_circle)
label_O = DotLabel("O", dot_O, color = self.color_circle, position = DOWN)
conclusion1 = TextMobject("不经过反演中心的直线", "$\\mapsto$", "经过反演中心的圆")
conclusion1[0].set_color(self.color_orig)
conclusion1[-1].set_color(self.color_inv)
conclusion2 = TextMobject("经过反演中心的圆", "$\\mapsto$", "不经过反演中心的直线")
conclusion2[0].set_color(self.color_inv)
conclusion2[-1].set_color(self.color_orig)
conclusions = VGroup(conclusion1, conclusion2)
for c in conclusions:
c.scale(0.8)
conclusions.arrange_submobjects(DOWN, index_of_submobject_to_align = 1)
conclusions.to_corner(DR)
bg_rect = BackgroundRectangle(conclusions)
self.add(circle_O, remark_circle)
self.add_foreground_mobjects(dot_O, label_O, bg_rect, conclusions)
self.dot_O = dot_O
self.circle_O = circle_O
self.conclusions = conclusions
self.bg_rect = bg_rect
def add_shapes(self):
position_tracker = ValueTracker(2)
line_angle_tracker = ValueTracker(PI*9/19)
circle_angle_tracker = ValueTracker(PI/5)
line = ExtendedLine(LEFT, RIGHT, color = self.color_orig)
line.add_updater(lambda m: m.move_to(position_tracker.get_value() * RIGHT))
line.add_updater(lambda m: m.rotate(line_angle_tracker.get_value() - m.get_angle()))
inv_line = InversedVMobject(line, self.circle_O, use_dashed_vmob = False, color = self.color_inv)
inv_line_center = SmallDot(color = self.color_inv)
inv_line_center.add_updater(lambda m: m.move_to(inv_line.get_center()))
dot_Ai = Dot(color = self.color_inv)
dot_Ai.add_updater(
lambda m: m.move_to(inv_line.get_center() * 2 - self.circle_O.get_center())
)
dot_Pi = Dot(color = self.color_inv)
dot_Pi.add_updater(
lambda m: m.move_to(
inv_line.get_center() \
+ rotate_vector(
inv_line.get_center() - self.circle_O.get_center(),
circle_angle_tracker.get_value()
)
)
)
dot_P = InversedDot(dot_Pi, self.circle_O, is_hollow = False, color = self.color_orig)
dot_A = InversedDot(dot_Ai, self.circle_O, is_hollow = False, color = self.color_orig)
line_OA, line_OAi, line_OP, line_OPi, line_AP, line_AiPi = aux_lines = VGroup(*[
TwoDotsSegment(pt_1, pt_2, **self.line_config)
for pt_1, pt_2 in [
(self.dot_O, dot_A), (self.dot_O, dot_Ai),
(self.dot_O, dot_P), (self.dot_O, dot_Pi),
(dot_A, dot_P), (dot_Ai, dot_Pi)
]
])
ai_AiOPi = AngleIndicator(dot_Ai, self.dot_O, dot_Pi, color = MAROON_B, radius = 0.8)
rtai_OAP = RightAngleIndicator(self.dot_O, dot_A, dot_P)
rtai_OPiAi = RightAngleIndicator(self.dot_O, dot_Pi, dot_Ai)
label_P = TexMobject("P", color = self.color_orig)
label_Pi = TexMobject("P'", color = self.color_inv)
label_A = TexMobject("A", color = self.color_orig)
label_Ai = TexMobject("A'", color = self.color_inv)
label_A.add_updater(
lambda m: m.move_to(
dot_A.get_center() + 0.3 * normalize(dot_A.get_center() - self.dot_O.get_center())
)
)
label_P.add_updater(
lambda m: m.move_to(
dot_P.get_center() + 0.3 * normalize(dot_A.get_center() - self.dot_O.get_center())
)
)
label_Ai.add_updater(
lambda m: m.move_to(
dot_Ai.get_center() + 0.4 * rotate_vector(
normalize(dot_Ai.get_center() - inv_line_center.get_center()), -PI/4
)
)
)
label_Pi.add_updater(
lambda m: m.move_to(
dot_Pi.get_center() + 0.4 * normalize(dot_Pi.get_center() - inv_line_center.get_center())
)
)
def get_ub():
return line.get_center() + TOP + RIGHT * TOP[1] / np.tan(line_angle_tracker.get_value())
def get_bb():
return line.get_center() + BOTTOM + RIGHT * BOTTOM[1] / np.tan(line_angle_tracker.get_value())
def is_ooub(m):
return m.get_bottom()[1] > TOP[1]
def is_oobb(m):
return m.get_top()[1] < BOTTOM[1]
ooub_arrow = Arrow(ORIGIN, LEFT, color = self.color_orig).scale(2)
ooub_arrow.add_updater(lambda m: m.set_angle(line_angle_tracker.get_value()))
ooub_arrow.add_updater(lambda m: m.next_to(get_ub(), RIGHT, aligned_edge = TOP, buff = 0.2))
oobb_arrow = Arrow(ORIGIN, RIGHT, color = self.color_orig).scale(2)
oobb_arrow.add_updater(lambda m: m.set_angle(line_angle_tracker.get_value() + PI))
oobb_arrow.add_updater(lambda m: m.next_to(get_bb(), RIGHT, aligned_edge = BOTTOM, buff = 0.2))
oolb_label = TexMobject("P", color = self.color_orig, background_stroke_width = 0)
oolb_label.add_updater(lambda m: m.next_to(ooub_arrow, RIGHT, buff = 0.2))
oorb_label = TexMobject("P", color = self.color_orig, background_stroke_width = 0)
oorb_label.add_updater(lambda m: m.next_to(oobb_arrow, RIGHT, buff = 0.2))
ooub_group = VGroup(ooub_arrow, oolb_label)
oobb_group = VGroup(oobb_arrow, oorb_label)
ooub_group.add_updater(lambda m: m.set_fill(opacity = 1 if is_ooub(label_P) else 0))
ooub_group.add_updater(lambda m: m.set_stroke(opacity = 1 if is_ooub(label_P) else 0))
oobb_group.add_updater(lambda m: m.set_fill(opacity = 1 if is_oobb(label_P) else 0))
oobb_group.add_updater(lambda m: m.set_stroke(opacity = 1 if is_oobb(label_P) else 0))
self.add(line, inv_line)
self.add(dot_A, dot_P, dot_Ai, dot_Pi)
self.add(label_P, label_Pi, label_A, label_Ai)
self.add(aux_lines)
self.add(ai_AiOPi, rtai_OAP, rtai_OPiAi)
self.add(ooub_group, oobb_group)
self.position_tracker = position_tracker
self.line_angle_tracker = line_angle_tracker
self.circle_angle_tracker = circle_angle_tracker
def show_line_to_circle_inversion(self):
play_args = [
[0, PI/12, 0, 2],
[0, 0, PI*7/5, 4],
[-2, PI/8, -PI/5, 3],
[0, 0, PI*19/10, 6],
[1.5, -PI/7, PI*2/5, 4],
]
restore_arg = [
-sum([arg[k] for arg in play_args])
for k in range(len(play_args[0]))
]
restore_arg[1] = (restore_arg[1] + PI) % (2*PI) - PI
restore_arg[2] = (restore_arg[2] + PI) % (2*PI) - PI
restore_arg[-1] = 3
play_args.append(restore_arg)
for d_center, d_line_angle, d_circle_angle, run_time in play_args:
self.play(
ApplyMethod(self.position_tracker.increment_value, d_center),
ApplyMethod(self.line_angle_tracker.increment_value, d_line_angle),
ApplyMethod(self.circle_angle_tracker.increment_value, d_circle_angle),
run_time = run_time,
)
self.wait()
class InversionCreateSimilarTriangles(Scene):
CONFIG = {
"random_seed" : 5+7-0,
"num_of_nudges" : 5,
"max_step" : 1,
"color_A" : RED,
"color_B" : BLUE,
"color_combined" : MAROON_B,
"color_circle": YELLOW,
}
def construct(self):
self.add_remark()
self.show_figure_animation()
def add_remark(self):
cond_1 = TexMobject("{|OP|", "\\over", "|OQ|}", "=", "{|OQ'|", "\\over", "|OP'|}")
cond_2 = TexMobject("\\angle POQ", "=", "\\angle Q'OP'")
conds = VGroup(cond_1, cond_2)
conds.arrange_submobjects(DOWN, buff = 0.5)
conds_rect = SurroundingRectangle(conds, color = WHITE)
arrow = TexMobject("\\Downarrow")
arrow.next_to(conds_rect, DOWN)
concl = TexMobject("\\triangle OPQ", "\\sim", "\\triangle OQ'P'")
concl.next_to(arrow, DOWN)
for mob in (cond_1[0], cond_1[2], concl[0]):
mob.set_color(self.color_A)
for mob in (cond_1[-1], cond_1[-3], concl[-1]):
mob.set_color(self.color_B)
for mob in (cond_2[0], cond_2[-1]):
mob.set_color(self.color_combined)
remark = VGroup(conds, conds_rect, arrow, concl)
remark.to_corner(DR)
self.add(remark)
def show_figure_animation(self):
circle = Circle(radius = 3, color = self.color_circle)
circle.move_to(3.5*LEFT)
dot_O = Dot(color = self.color_combined)
dot_O.add_updater(lambda m: m.move_to(circle.get_center()))
dot_P = Dot(point = 1.2*UP+LEFT, color = self.color_A)
dot_Q = Dot(point = 0.5*DOWN+1.9*LEFT, color = self.color_A)
dot_Pi = InversedDot(dot_P, circle, is_hollow = False, color = self.color_B)
dot_Qi = InversedDot(dot_Q, circle, is_hollow = False, color = self.color_B)
triangle_OPQ = ManyDotsPolygon(
dot_O, dot_P, dot_Q, color = self.color_A,
stroke_width = 5, fill_opacity = 0.4
)
triangle_OPiQi = ManyDotsPolygon(
dot_O, dot_Pi, dot_Qi, color = self.color_B,
stroke_width = 2, fill_opacity = 0.3
)
label_O, label_P, label_Pi, label_Q, label_Qi = (
DotLabel(
text, dot, color = color, position = position,
background_stroke_width = 5,
).scale(0.8)
for text, dot, color, position in zip(
["O", "P", "P'", "Q", "Q'"],
[dot_O, dot_P, dot_Pi, dot_Q, dot_Qi],
[self.color_combined, self.color_A, self.color_B, self.color_A, self.color_B],
[LEFT, UP, UP, DOWN, DOWN]
)
)
self.add(dot_O, dot_P, dot_Q, dot_Pi, dot_Qi)
self.add(circle, triangle_OPQ, triangle_OPiQi)
self.add(label_O, label_P, label_Pi, label_Q, label_Qi)
dot_P.save_state()
dot_Q.save_state()
for k in range(self.num_of_nudges):
nudge_P = get_random_vector(self.max_step)
nudge_Q = get_random_vector(self.max_step)
self.play(
ApplyMethod(dot_P.shift, nudge_P),
ApplyMethod(dot_Q.shift, nudge_Q),
run_time = 2
)
self.wait()
self.play(dot_P.restore, dot_Q.restore, run_time = 2)
self.wait()
class CircleToCircleInversionProof(Scene):
CONFIG = {
"color_O" : YELLOW,
"color_A" : RED,
"color_B" : BLUE,
"color_combined" : MAROON_B,
"label_buff" : 0.1,
"label_scaling_factor" : 0.75,
"line_config" : {
"stroke_width" : 2,
"color" : WHITE,
},
}
def construct(self):
self.add_backgrounds()
self.show_left_and_right_points()
self.show_random_point()
self.show_similar_triangles()
self.show_complementary_property()
self.show_inversion_result()
def add_backgrounds(self):
circle_O = Circle(radius = 3.2, color = self.color_O)
circle_O.shift(3.5*LEFT)
dot_O = Dot(circle_O.get_center(), color = self.color_O)
remark_O = TextMobject("反演圆", color = YELLOW)
remark_O.next_to(circle_O.get_bottom(), UP, buff = 0.4)
circle_C = Circle(radius = 0.8, stroke_width = 2)
circle_C.next_to(circle_O.get_right(), LEFT, buff = 0.5)
dot_C = Dot(circle_C.get_center())
label_O, label_C = (
DotLabel(
text, dot, color = color, position = DOWN, label_buff = self.label_buff
).scale(self.label_scaling_factor)
for text, dot, color in zip(["O", "C"], [dot_O, dot_C], [self.color_O, WHITE])
)
for orig_mob in (circle_C, dot_C, label_C):
orig_mob.set_sheen_direction(RIGHT)
orig_mob.set_color([self.color_A, self.color_B])
inv_circle_template = InversedVMobject(circle_C, circle_O, use_dashed_vmob = False)
inv_circle = Circle(radius = inv_circle_template.get_width()/2)
inv_circle.move_to(inv_circle_template.get_center())
inv_circle.set_sheen_direction(LEFT)
inv_circle.set_color([self.color_A, self.color_B])
self.add(circle_O, dot_O, circle_C, dot_C)
self.add(label_O, label_C)
self.add(remark_O)
self.wait()
self.circle_O = circle_O
self.dot_O = dot_O
self.remark_O = remark_O
self.circle_C = circle_C
self.dot_C = dot_C
self.inv_circle = inv_circle
def show_left_and_right_points(self):
dot_A = Dot(color = self.color_A)
dot_A.move_to(self.circle_C.get_left())
dot_B = Dot(color = self.color_B)
dot_B.move_to(self.circle_C.get_right())
dot_Ai = InversedDot(dot_A, self.circle_O, is_hollow = False, color = self.color_A)
dot_Bi = InversedDot(dot_B, self.circle_O, is_hollow = False, color = self.color_B)
dot_Q = Dot((dot_Ai.get_center() + dot_Bi.get_center()) / 2)
line_OB = Line(self.dot_O.get_center(), dot_B.get_center(), **self.line_config)
line_OAi = Line(self.dot_O.get_center(), dot_Ai.get_center(), **self.line_config)
label_A, label_Ai, label_B, label_Bi = (
DotLabel(
text, dot, color = color, position = position, label_buff = self.label_buff
).scale(self.label_scaling_factor)
for text, dot, color, position in zip(
["A", "A'", "B", "B'"],
[dot_A, dot_Ai, dot_B, dot_Bi],
[self.color_A, self.color_A, self.color_B, self.color_B],
[DL, DR, DR, DL]
)
)
remark_AB = TextMobject("圆心连线 \\\\ 的交点...").scale(0.6)
remark_AB.next_to(VGroup(dot_A, dot_B), DOWN, buff = 1)
arrows_AB = VGroup(*[
Arrow(remark_AB.get_critical_point(direction), dot, buff = 0.1)
for direction, dot in zip([UL, UR], [dot_A, dot_B])
])
remark_AiBi = TextMobject("...以及它们的反点").scale(0.8)
remark_AiBi.next_to(VGroup(dot_Ai, dot_Bi), DOWN, buff = 1)
arrows_AiBi = VGroup(*[
Arrow(remark_AiBi.get_critical_point(direction), dot, buff = 0.1)
for direction, dot in zip([UR, UL], [dot_Ai, dot_Bi])
])
self.play(ShowCreation(line_OB))
self.play(Write(dot_A), Write(dot_B), Write(label_A), Write(label_B))
self.wait()
self.play(Write(remark_AB), ShowCreation(arrows_AB))
self.wait()
self.play(
ReplacementTransform(dot_A.deepcopy(), dot_Ai),
ReplacementTransform(dot_B.deepcopy(), dot_Bi),
)
self.play(Write(label_Ai), Write(label_Bi))
self.wait()
self.play(
ReplacementTransform(remark_AB, remark_AiBi),
ReplacementTransform(arrows_AB, arrows_AiBi)
)
self.play(ReplacementTransform(line_OB, line_OAi))
self.play(FadeOut(VGroup(remark_AiBi, arrows_AiBi)))
self.wait()
self.dot_A = dot_A
self.dot_Ai = dot_Ai
self.dot_B = dot_B
self.dot_Bi = dot_Bi
self.dot_Q = dot_Q
self.line_OAi = line_OAi
self.dots_AB = VGroup(dot_A, dot_Ai, dot_B, dot_Bi)
self.labels_AB = VGroup(label_A, label_Ai, label_B, label_Bi)
def show_random_point(self):
angle_tracker = ValueTracker(PI/3)
dot_P = Dot()
dot_P.add_updater(
lambda m: m.move_to(
self.circle_C.point_at_angle(angle_tracker.get_value() % TAU)
)
)
dot_P.add_updater(
lambda m: m.set_color(
interpolate_color(
self.color_A, self.color_B,
(dot_P.get_center()[0] - self.dot_A.get_center()[0]) / (self.dot_B.get_center()[0] - self.dot_A.get_center()[0])
)
)
)
label_P = DotLabel("P", dot_P, position = None)
label_P.scale(0.8)
label_P.add_updater(lambda m: m.set_color(dot_P.get_color()))
label_P.add_updater(
lambda m: m.move_to(dot_P.get_center() * 1.4 - self.dot_C.get_center() * 0.4)
)
arrow_P = Vector(DR, buff = 0, color = WHITE).scale(0.5)
arrow_P.add_updater(lambda m: m.next_to(dot_P, UL, buff = 0.1))
remark_P = TextMobject("圆上任意一点...").scale(0.75)
remark_P.add_updater(lambda m: m.next_to(arrow_P, UL, buff = 0.1))
dot_Pi = InversedDot(dot_P, self.circle_O, is_hollow = False)
dot_Pi.add_updater(lambda m: m.set_color(dot_P.get_color()))
label_Pi = DotLabel("P'", dot_Pi, position = None)
label_Pi.scale(0.8)
label_Pi.add_updater(lambda m: m.set_color(dot_Pi.get_color()))
label_Pi.add_updater(
lambda m: m.move_to(dot_Pi.get_center() * 1.1 - self.inv_circle.get_center() * 0.1)
)
arrow_Pi = Vector(DL, buff = 0, color = WHITE).scale(0.5)
arrow_Pi.add_updater(lambda m: m.next_to(dot_Pi, UR, buff = 0.1))
remark_Pi = TextMobject("...以及它的反点").scale(0.75)
remark_Pi.add_updater(lambda m: m.next_to(arrow_Pi, UR, buff = 0.1))
line_OP, line_OPi, line_AP, line_AiPi, line_BP, line_BiPi = aux_lines = VGroup(*[
TwoDotsSegment(pt_1, pt_2, **self.line_config)
for pt_1, pt_2 in [
(self.dot_O, dot_P), (self.dot_O, dot_Pi), (self.dot_A, dot_P),
(self.dot_Ai, dot_Pi), (self.dot_B, dot_P), (self.dot_Bi, dot_Pi)
]
])
rtai_APB = RightAngleIndicator(self.dot_A, dot_P, self.dot_B)
rtai_BiPiAi = RightAngleIndicator(self.dot_Bi, dot_Pi, self.dot_Ai, side_length = 0.5)
self.play(Write(dot_P), Write(label_P))
self.play(ShowCreation(arrow_P), Write(remark_P))
self.play(Write(line_AP), Write(line_BP))
self.play(ShowCreation(rtai_APB))
self.wait()
self.play(ReplacementTransform(dot_P.deepcopy(), dot_Pi))
self.play(Write(label_Pi))
self.play(
ReplacementTransform(arrow_P.deepcopy(), arrow_Pi),
ReplacementTransform(remark_P.deepcopy(), remark_Pi),
)
self.play(angle_tracker.increment_value, PI/6, run_time = 2)
self.play(FadeOut(VGroup(arrow_P, remark_P, arrow_Pi, remark_Pi)))
self.wait()
self.play(Write(VGroup(line_OP, line_OPi, line_AiPi, line_BiPi)))
self.wait()
self.dot_P = dot_P
self.dot_Pi = dot_Pi
self.rtai_APB = rtai_APB
self.rtai_BiPiAi = rtai_BiPiAi
self.angle_tracker = angle_tracker
self.aux_lines = aux_lines
self.dots_P = VGroup(dot_P, dot_Pi)
self.labels_P = VGroup(label_P, label_Pi)
self.rtais = VGroup(self.rtai_APB, self.rtai_BiPiAi)
def show_similar_triangles(self):
ai_OAP = AngleIndicator(self.dot_O, self.dot_A, self.dot_P, radius = 0.3, color = self.color_A)
ai_OBP = AngleIndicator(self.dot_O, self.dot_B, self.dot_P, radius = 0.4, color = self.color_B)
ai_OPiAi = AngleIndicator(self.dot_O, self.dot_Pi, self.dot_Ai, radius = 0.3, color = self.color_A)
ai_OPiBi = AngleIndicator(self.dot_O, self.dot_Pi, self.dot_Bi, radius = 0.4, color = self.color_B)
triangle_OAP, triangle_OPiAi, triangle_OBP, triangle_OPiBi = [
ManyDotsPolygon(
pt_1, pt_2, pt_3, color = self.color_combined,
stroke_width = 0, fill_opacity = 0.4
)
for pt_1, pt_2, pt_3 in (
(self.dot_O, self.dot_A, self.dot_P),
(self.dot_O, self.dot_Pi, self.dot_Ai),
(self.dot_O, self.dot_B, self.dot_P),
(self.dot_O, self.dot_Pi, self.dot_Bi),
)
]
remark_sim_A = TexMobject("\\triangle OAP", "\\sim", "\\triangle OP'A'")
remark_sim_B = TexMobject("\\triangle OBP", "\\sim", "\\triangle OP'B'")
remark_arrow = TexMobject("\\Downarrow")
remark_angle_A = TexMobject("\\angle OAP", "=", "\\angle OP'A'")
remark_angle_B = TexMobject("\\angle OBP", "=", "\\angle OP'B'")
remarks_A = VGroup(remark_sim_A, remark_arrow, remark_angle_A)
remarks_B = VGroup(remark_sim_B, remark_arrow, remark_angle_B)
remarks_A.arrange_submobjects(DOWN)
remarks_A.next_to(self.dot_Q, DOWN, buff = 1)
remark_sim_B.move_to(remark_sim_A.get_center())
remark_angle_B.move_to(remark_angle_A.get_center())
for remark, color in ([remark_sim_A, self.color_combined], [remark_sim_B, self.color_combined], \
[remark_angle_A, self.color_A], [remark_angle_B, self.color_B]):
remark[0].set_color(color)
remark[-1].set_color(color)
self.play(Write(remark_sim_A))
self.play(FadeInFromDown(VGroup(remark_arrow, remark_angle_A)))
self.wait()
self.play(ShowCreation(triangle_OAP), ShowCreation(ai_OAP))
self.wait()
self.play(
ReplacementTransform(triangle_OAP, triangle_OPiAi),
ReplacementTransform(ai_OAP.deepcopy(), ai_OPiAi),
)
self.play(FadeOut(triangle_OPiAi))
self.wait()
self.play(ReplacementTransform(remarks_A, remarks_B))
self.wait()
self.play(ShowCreation(triangle_OBP), ShowCreation(ai_OBP))
self.wait()
self.play(
ReplacementTransform(triangle_OBP, triangle_OPiBi),
ReplacementTransform(ai_OBP.deepcopy(), ai_OPiBi),
)
self.play(FadeOut(remarks_B), FadeOut(triangle_OPiBi))
self.wait()
self.ai_OAP = ai_OAP
self.ai_OBP = ai_OBP
self.ai_OPiAi = ai_OPiAi
self.ai_OPiBi = ai_OPiBi
self.ais = VGroup(ai_OAP, ai_OBP, ai_OPiAi, ai_OPiBi)
def show_complementary_property(self):
ai_OAP_copy = self.ai_OAP.deepcopy()
ai_OBP_copy = self.ai_OBP.deepcopy()
rtai_APB_copy = self.rtai_APB.deepcopy()
for ai_copy in (ai_OAP_copy, ai_OBP_copy, rtai_APB_copy):
ai_copy.clear_updaters()
comp_prop = VGroup(ai_OAP_copy, TexMobject("="), ai_OBP_copy, TexMobject("+"), rtai_APB_copy)
comp_prop.arrange_submobjects(RIGHT)
comp_prop.scale(1.2)
comp_prop.next_to(self.circle_O.get_top(), DOWN, buff = 1)
self.play(
ReplacementTransform(self.ai_OAP.deepcopy(), ai_OAP_copy),
ReplacementTransform(self.ai_OBP.deepcopy(), ai_OBP_copy),
ReplacementTransform(self.rtai_APB.deepcopy(), rtai_APB_copy),
)
self.play(Write(comp_prop[1]), Write(comp_prop[3]))
self.wait()
self.play(ReplacementTransform(rtai_APB_copy.deepcopy(), self.rtai_BiPiAi))
self.wait()
for ai in self.ais:
ai.clear_updaters()
self.play(
FadeOut(comp_prop),
FadeOut(self.ais),
FadeOut(self.labels_AB), FadeOut(self.labels_P),
)
self.wait()
def show_inversion_result(self):
inv_circle_copy = self.inv_circle.deepcopy()
self.play(self.angle_tracker.set_value, PI, run_time = 2)
self.wait()
def update_inv_circle(inv_circle):
angle = self.angle_tracker.get_value()
if (angle <= -PI) or (angle > PI):
alpha = 1
else:
QPi = self.dot_Pi.get_center() - self.dot_Q.get_center()
QAi = self.dot_Ai.get_center() - self.dot_Q.get_center()
theta = angle_between(QPi, QAi)
if self.dot_Pi.get_center()[1] < self.dot_Q.get_center()[1]:
theta = 2*PI - theta
alpha = theta / (2*PI)
inv_circle.become(inv_circle_copy.get_subcurve(0, alpha))
self.inv_circle.add_updater(update_inv_circle)
self.add(self.inv_circle)
self.play(
ApplyMethod(self.angle_tracker.increment_value, -2*PI),
run_time = 5,
)
self.inv_circle.clear_updaters()
for line in self.aux_lines:
line.clear_updaters()
self.play(
FadeOut(self.dots_AB), FadeOut(self.dots_P), FadeOut(self.rtais),
FadeOut(self.line_OAi), FadeOut(self.aux_lines)
)
self.wait()
color_template = Square(
stroke_width = 0, fill_opacity = 1, fill_color = [self.color_A, self.color_B]
)
conclusion = TextMobject("不经过反演中心的圆", "$\\mapsto$", "不经过反演中心的圆")
conclusion.scale(0.8)
conclusion[0].set_color_by_gradient(self.color_A, self.color_B)
conclusion[2].set_color_by_gradient(self.color_B, self.color_A)
conclusion.to_corner(DR)
self.play(Write(conclusion))
self.wait(3)
self.play(FadeOut(conclusion), FadeOut(self.inv_circle))
self.wait()
class ConcentricPropertyDoesNotHold(Scene):
def setup(self):
N = 8
self.circle_radii = [0.9-0.1*k for k in range(N)]
self.dot_radii = [0.08-0.005*k for k in range(N)]
self.circle_colors = color_gradient([BLUE, GREEN, RED], N)
def construct(self):
orig_circles = VGroup(*[
Circle(radius = radius, stroke_width = 1.5,color = color)
for radius, color in zip(self.circle_radii, self.circle_colors)]
)
orig_circles.shift(2*LEFT+0.5*DOWN)
orig_circles_centers = VGroup(*[
Dot(circle.get_center(), radius = radius, color = color)
for circle, radius, color in zip(orig_circles, self.dot_radii, self.circle_colors)
])
circle = Circle(radius = 3, color = YELLOW)
circle.shift(3.8*LEFT+0.5*DOWN)
circle_center = Dot(circle.get_center(), color = YELLOW)
inv_circles = VGroup(*[
InversedVMobject(orig_circle, circle).clear_updaters().set_color(color)
for orig_circle, color in zip(orig_circles, self.circle_colors)
])
inv_circles_centers = VGroup(*[
Dot(inv_circle.get_center(), color = color)
for inv_circle, color in zip(inv_circles, self.circle_colors)
])
circle_text = TextMobject("反演圆", color = YELLOW)
circle_text.next_to(circle.get_bottom(), UP, buff = 0.4)
orig_circles_text = TextMobject("同心的圆", color = WHITE)
orig_circles_text.next_to(orig_circles, UP)
orig_circles_text.to_edge(UP, buff = 0.4)
inv_circles_text = TextMobject("不同心的像", color = WHITE)
inv_circles_text.next_to(inv_circles, UP)
inv_circles_text.to_edge(UP, buff = 0.4)
arrow = Arrow(orig_circles_text.get_right(), inv_circles_text.get_left())
self.add(circle, circle_center)
self.add(orig_circles, orig_circles_centers)
self.add(inv_circles, inv_circles_centers)
self.add(circle_text, orig_circles_text, inv_circles_text, arrow)
self.wait()
class DemonstratePtolemyInequality(Scene):
CONFIG = {
"R" : 2.7,
"angle_A" : -PI*2/3,
"angle_B" : PI*4/5,
"angle_D" : -PI/5,
"radius_C" : 3.2,
"angle_C" : PI/5,
}
def construct(self):
radius_tracker = ValueTracker(self.radius_C)
angle_tracker = ValueTracker(self.angle_C)
circle = Circle(radius = self.R, color = WHITE, stroke_width = 1)
circle.shift(DOWN)
dashed_circle = DashedVMobject(circle, num_dashes = 100, positive_space_ratio = 0.5)
dot_A, dot_B, dot_C, dot_D = dots = VGroup(*[
Dot(circle.point_at_angle(angle % TAU), color = WHITE)
for angle in (self.angle_A, self.angle_B, self.angle_C, self.angle_D)
])
dot_C.add_updater(
lambda m: m.move_to(
circle.get_center() + radius_tracker.get_value() * \
rotate_vector(RIGHT, angle_tracker.get_value())
)
)
dot_labels = VGroup(*[
DotLabel(text, dot, position = position, label_buff = 0.1)
for text, dot, position in zip(
["A", "B", "C", "D"], dots, [DL, UL, UR, DR]
)
])
lines = VGroup(*[
TwoDotsSegment(dot_1, dot_2)
for dot_1, dot_2 in (
[dot_B, dot_A], [dot_A, dot_C], [dot_A, dot_D],
[dot_B, dot_C], [dot_B, dot_D], [dot_C, dot_D],
)
])
length_labels = VGroup(*[LengthLabel(line) for line in lines])
length_labels[0].switch_side()
length_labels[2].switch_side()
length_labels[1].set_offset(-0.4)
length_labels[-2].set_offset(-0.4)
def get_sums():
AB, AC, AD, BC, BD, CD = [line.get_length() for line in lines]
sum_lhs = AB * CD + AD * BC
sum_rhs = AC * BD
return sum_lhs, sum_rhs
relation_eq = TexMobject(
"|AB| \\cdot |CD| + |AD| \\cdot |BC|", "=", "|AC| \\cdot |BD|",
background_stroke_width = 0,
)
relation_neq = TexMobject(
"|AB| \\cdot |CD| + |AD| \\cdot |BC|", ">", "|AC| \\cdot |BD|",
background_stroke_width = 0,
)
relation_eq[1].set_color(GREEN)
relation_neq[1].set_color(RED)
relation_eq.to_edge(UP, buff = 1.2)
for eq_mob, neq_mob in zip(relation_eq, relation_neq):
neq_mob.move_to(eq_mob.get_center())
lhs, eq_sign, rhs = relation_eq
neq_sign = relation_neq[1]
label_lhs = DecimalNumber(num_decimal_places = 4, show_ellipsis = True)
label_rhs = DecimalNumber(num_decimal_places = 4, show_ellipsis = True)
label_lhs.add_updater(lambda m: m.set_value(get_sums()[0]))
label_rhs.add_updater(lambda m: m.set_value(get_sums()[1]))
brace_lhs = Brace(lhs, UP, buff = 0.1)
brace_rhs = Brace(rhs, UP, buff = 0.1)
brace_lhs.put_at_tip(label_lhs)
brace_rhs.put_at_tip(label_rhs)
def get_indication_color(thres = 1e-2):
return GREEN if is_close(radius_tracker.get_value(), self.R, thres = thres) else RED
def get_indication_opacity(thres = 1e-2):
return 0 if is_close(radius_tracker.get_value(), self.R, thres = thres) else 1
figure_group = VGroup(dashed_circle, dots, lines, length_labels, dot_labels)
figure_group.add_updater(lambda m: m.set_color(get_indication_color()))
relation_group = VGroup(lhs, eq_sign, rhs, neq_sign, brace_lhs, brace_rhs, label_lhs, label_rhs)
label_lhs.add_updater(lambda m: m.set_color(get_indication_color()))
label_rhs.add_updater(lambda m: m.set_color(get_indication_color()))
eq_sign.add_updater(lambda m: m.set_opacity(1 - get_indication_opacity()))
neq_sign.add_updater(lambda m: m.set_opacity(get_indication_opacity()))
self.add(figure_group)
self.add(relation_group)
deltas = [
(0.5, -0.1), (0, -0.4), (-1, 0.3), (0, 0.4),
(-1, 0), (0.3, -0.2), (0.7, -0.3),
]
radius_tracker.save_state()
angle_tracker.save_state()
for d_radius, d_angle in deltas:
self.play(
ApplyMethod(radius_tracker.increment_value, d_radius),
ApplyMethod(angle_tracker.increment_value, d_angle),
run_time = 2,
)
self.wait()
self.play(
ApplyMethod(radius_tracker.restore),
ApplyMethod(angle_tracker.restore),
run_time = 2,
)
self.wait()
class PtolemyInversionFigure(Scene):
CONFIG = {
"R" : 3.8,
"r" : 1.3,
"angle_A" : PI,
"angle_B" : PI/3,
"angle_C" : -PI/9,
"angle_D" : -PI*2/7,
"color_circle" : YELLOW,
"color_ABD" : BLUE,
}
def construct(self):
circle_ABD = Circle(radius = self.r, color = self.color_ABD, stroke_width = 3)
circle_ABD.shift(0.2*LEFT)
dot_A, dot_B, dot_C, dot_D = dots = VGroup(*[
Dot(circle_ABD.point_at_angle(angle % TAU), color = WHITE)
for angle in (self.angle_A, self.angle_B, self.angle_C, self.angle_D)
])
dot_A.set_color(self.color_circle)
dot_C.shift(0.4*RIGHT)
circle = Circle(radius = self.R, color = self.color_circle, stroke_width = 5)
circle.move_to(dot_A.get_center())
remark_circle = TextMobject("反演圆", color = self.color_circle)
remark_circle.next_to(circle.get_bottom(), UP)
label_A, label_B, label_C, label_D = dot_labels = VGroup(*[
DotLabel(text, dot, position = position, label_buff = 0.2)
for text, dot, position in zip(
["A", "B", "C", "D"], dots, [DL, UP, DOWN, DOWN]
)
])
label_A.set_color(self.color_circle)
dot_Bi, dot_Ci, dot_Di = inv_dots = VGroup(*[
InversedDot(dot, circle, is_hollow = False, color = WHITE)
for dot in (dot_B, dot_C, dot_D)
])
label_Bi, label_Ci, label_Di = inv_dot_labels = VGroup(*[
DotLabel(text, dot, position = RIGHT, label_buff = 0.2)
for text, dot in zip(["B'", "C'", "D'"], [dot_Bi, dot_Ci, dot_Di])
])
lines = VGroup(*[
TwoDotsSegment(dot_1, dot_2, stroke_width = 1)
for dot_1, dot_2 in (
[dot_A, dot_B], [dot_A, dot_C], [dot_A, dot_D],
[dot_B, dot_C], [dot_B, dot_D], [dot_C, dot_D],
[dot_A, dot_Bi], [dot_A, dot_Ci], [dot_A, dot_Di],
[dot_Bi, dot_Ci], [dot_Bi, dot_Di], [dot_Ci, dot_Di],
)
])
inv_circle_ABD = InversedVMobject(circle_ABD, circle, use_dashed_vmob = False)
inv_circle_ABD.add_updater(lambda m: m.set_color(self.color_ABD))
inv_circle_ABD.add_updater(lambda m: m.set_stroke(width = 2))
self.add(circle, remark_circle, circle_ABD, inv_circle_ABD)
self.add(dots, dot_labels, inv_dots, inv_dot_labels, lines)
self.add()
self.wait()
#####
## Inversion Advanced P1 Scenes
class KissingCirclesPuzzle(Scene):
def construct(self):
self.show_figure()
self.show_question()
def show_figure(self):
type_text_1 = TextMobject("外切-外切-外切")
type_text_2 = TextMobject("内切-内切-外切")
type_text_1.move_to(LEFT_SIDE/2)
type_text_2.move_to(RIGHT_SIDE/2)
type_text_1.to_edge(DOWN)
type_text_2.to_edge(DOWN)
dot_l1, dot_l2, dot_l3 = dots_l = VGroup(*[
VectorizedPoint(np.array([coords[0], coords[1], 0]), color = BLUE)
for coords in [(-3.9, 1.5), (-4.9, 0.0), (-2.8, -1.0)]
])
dot_r1, dot_r2, dot_r3 = dots_r = VGroup(*[
VectorizedPoint(np.array([coords[0], coords[1], 0]), color = BLUE)
for coords in [(4.6, 0.3), (3.9, 0.6), (3.5, 1.6)]
])
dfc_l = DescartesFourCircles(*dots_l, show_new_circles = False)
dfc_r = DescartesFourCircles(*dots_r, show_new_circles = False, outer_circle_index = 2)
for dfc in [dfc_l, dfc_r]:
for mob in dfc.get_orig_circles():
mob.set_stroke(width = 2, color = BLUE)
self.add(type_text_1, type_text_2)
self.add(dfc_l, dfc_r)
self.dfc_l = dfc_l
self.dfc_r = dfc_r
self.dots_l = dots_l
self.dots_r = dots_r
def show_question(self):
question = TextMobject("能否添加第四个圆，使之与其他三个圆都相切？")
question.to_edge(UP, buff = 0.2)
self.add(question)
self.wait()
class KissingCirclesSimplified(Scene):
def construct(self):
line1 = ExtendedLine(UL, UR)
line2 = ExtendedLine(DL, DR)
center_circle = Circle(radius = 1)
figure_group = VGroup(line1, line2, center_circle)
for mob in figure_group:
mob.set_stroke(width = 2, color = BLUE)
question = TextMobject("能否添加第四个“圆”，使之与其他三个“圆”都相切？")
question.next_to(figure_group, UP, buff = 0.5)
group = VGroup(question, figure_group)
group.move_to(ORIGIN)
self.add(group)
self.wait()
class KissingCirclesSimplifiedAnswer(Scene):
def construct(self):
line1 = ExtendedLine(UL, UR, stroke_width = 2, color = BLUE)
line2 = ExtendedLine(DL, DR, stroke_width = 2, color = BLUE)
center_circle = Circle(radius = 1, stroke_width = 2, color = BLUE)
new_circles = VGroup(*[
Circle(radius = 1, color = color, fill_opacity = 0.1, stroke_width = 5) \
.next_to(center_circle, direction, buff = 0)
for direction, color in zip([LEFT, RIGHT], [RED, ORANGE])
])
numbers = VGroup(*[
TexMobject(f"{num}", color = circle.get_color()).move_to(circle.get_center())
for num, circle in zip(["1", "2"], new_circles)
])
group = VGroup(line1, line2, center_circle, new_circles, numbers)
group.move_to(ORIGIN)
self.add(group)
self.wait()
class KissingCirclesSimplifiedExplanation(Scene):
CONFIG = {
"dashed_vmob_config" : {
"num_dashes" : 30,
"positive_space_ratio" : 0.6,
},
"line_colors" : [GREEN, BLUE],
"center_color" : MAROON_B,
"circle_colors" : [RED, ORANGE],
}
def construct(self):
self.add_backgrounds()
self.show_process()
def add_backgrounds(self):
N = 5
line1 = Line(UP + N*LEFT, UP + N*RIGHT, stroke_width = 2, color = self.line_colors[0])
line2 = Line(DOWN + N*LEFT, DOWN + N*RIGHT, stroke_width = 2, color = self.line_colors[1])
center_circle = FineCircle(radius = 1, stroke_width = 2, color = self.center_color)
new_circle1 = FineCircle(radius = 1, stroke_width = 5, color = self.circle_colors[0])
new_circle1.next_to(center_circle, LEFT, buff = 0)
new_circle2 = FineCircle(radius = 1, stroke_width = 5, color = self.circle_colors[1])
new_circle2.next_to(center_circle, RIGHT, buff = 0)
inv_old_group = VGroup(line1, line2, center_circle)
inv_new_group = VGroup(new_circle1, new_circle2)
inv_group = VGroup(inv_old_group, inv_new_group)
inv_group.rotate(-PI*2/5)
inv_group.shift(3*RIGHT)
circle = FineCircle(radius = 3.5, color = YELLOW)
circle.shift(2*LEFT)
circle_center = Dot(circle.get_center(), color = YELLOW)
remark_circle = TextMobject("反演圆", color = YELLOW)
remark_circle.next_to(circle.get_bottom(), UP)
remark_center = VGroup(*[
Arrow(DL, UR, color = YELLOW, buff = 0).scale(0.3),
TextMobject("反演中心", color = YELLOW).scale(0.8),
])
remark_center.arrange_submobjects(DL, buff = 0)
remark_center.next_to(circle_center, DL, buff = 0.1)
orig_old_group = VGroup(*[
InversedVMobject(mob, circle, use_dashed_vmob = False, match_original_style = True)
for mob in inv_old_group
])
orig_new_group = VGroup(*[
InversedVMobject(mob, circle, use_dashed_vmob = False, match_original_style = True)
for mob in inv_new_group
])
for mob in orig_old_group:
mob.clear_updaters()
mob.set_stroke(width = 2)
for mob in orig_new_group:
mob.clear_updaters()
mob.set_stroke(width = 5)
mob.set_fill(opacity = 0.1)
self.add(orig_old_group)
self.add(circle, circle_center, remark_circle, remark_center)
self.circle = circle
self.inv_old_group = inv_old_group
self.inv_new_group = inv_new_group
self.orig_old_group = orig_old_group
self.orig_new_group = orig_new_group
def show_process(self):
dashed_inv_old_group = VGroup(*[
DashedVMobject(mob, **self.dashed_vmob_config)
for mob in self.inv_old_group
])
dashed_inv_new_group = VGroup(*[
DashedVMobject(mob, **self.dashed_vmob_config)
for mob in self.inv_new_group
])
self.play(ShowCreation(dashed_inv_old_group, lag_ratio = 0.05), run_time = 3)
self.wait()
dashed_copys = VGroup(*[dashed_inv_old_group[-1].deepcopy() for k in range(2)])
dashed_copys.generate_target()
for mob_copy, mob_template in zip(dashed_copys.target, dashed_inv_new_group):
mob_copy.match_style(mob_template)
mob_copy.move_to(mob_template.get_center())
self.play(MoveToTarget(dashed_copys), run_time = 3)
self.remove(dashed_copys)
self.add(dashed_inv_new_group)
self.wait()
self.play(DrawBorderThenFill(self.orig_new_group), run_time = 3)
self.wait(2)
self.play(
FadeOut(dashed_inv_new_group),
FadeOut(dashed_inv_old_group),
FadeOut(self.orig_new_group),
)
self.wait()
class DifferentTangentTypesWithSameConclusion(KissingCirclesPuzzle):
CONFIG = {
"random_seed" : 570,
"num_of_nudges" : 5,
"max_step" : 0.5,
"color_1" : ORANGE,
"color_2" : RED,
}
def construct(self):
super().show_figure()
self.dots_l.save_state()
self.dots_r.save_state()
for dfc in [self.dfc_l, self.dfc_r]:
dfc.add_new_circles()
dfc.get_orig_circles().set_stroke(width = 2)
c4_1, c4_2 = dfc.get_new_circles()
c4_1.set_color(self.color_1)
c4_2.set_color(self.color_2)
self.add(self.dfc_l, self.dfc_r)
for k in range(self.num_of_nudges):
for dot in it.chain(self.dots_l, self.dots_r):
dot.generate_target()
dot.target.shift(get_random_vector(self.max_step))
anims = AnimationGroup(*[
MoveToTarget(dot, path_arc = PI/3., run_time = 1.5)
for dot in it.chain(self.dots_l, self.dots_r)
], run_time = 2)
self.play(anims)
self.wait()
self.play(self.dots_l.restore, self.dots_r.restore, run_time = 1.5)
class LineToCircleInversionRevisited(LineToCircleInversion):
def construct(self):
super().construct()
self.remove_conclusions()
self.add_explanation()
def remove_conclusions(self):
self.remove(self.bg_rect)
self.remove(self.conclusions)
def add_explanation(self):
radius = Line(
self.circle_O.get_left(), self.circle_O.get_center(),
color = self.color_circle, stroke_width = 1,
)
radius_text = TexMobject("R", color = self.color_circle)
radius_text.next_to(radius, UP, buff = 0.1)
radius_group = VGroup(radius, radius_text)
radius_group.rotate(-PI/12, about_point = self.circle_O.get_center())
remark_length = TexMobject("|OA| = d", "\\Downarrow", "|OA'| = \dfrac{R^2}{d}")
remark_length.arrange_submobjects(DOWN)
remark_length.scale(1.2)
remark_length[0].set_color(self.color_orig)
remark_length[-1].set_color(self.color_inv)
remark_length.to_edge(RIGHT)
self.add(radius_group, remark_length)
self.wait()
class CircleToCircleInversionRevisited(CircleToCircleInversionProof):
def construct(self):
super().add_backgrounds()
super().show_left_and_right_points()
super().show_random_point()
super().show_similar_triangles()
self.arrange_elements()
self.add_explanation()
def arrange_elements(self):
self.angle_tracker.set_value(PI/3)
self.remove(self.remark_O)
self.remove(self.ai_OAP, self.ai_OBP, self.ai_OPiAi, self.ai_OPiBi)
self.add(self.inv_circle)
self.add(self.dots_P, self.labels_P)
self.add(self.dots_AB, self.labels_AB)
self.add(self.aux_lines, self.rtais)
dot_I = Dot(self.inv_circle.get_center())
label_I = DotLabel("I", dot_I, position = DOWN, label_buff = 0.15).scale(0.8)
for mob in (dot_I, label_I):
mob.set_sheen_direction(RIGHT)
mob.set_color([self.color_B, self.color_A])
remark_I = TextMobject("反形的圆心（并非$C$的反点！）")
remark_I.scale(0.5)
remark_I.next_to(label_I, DOWN, buff = 0.1)
self.add(dot_I, label_I, remark_I)
def add_explanation(self):
for circle, color, text, angle in zip(
[self.circle_O, self.circle_C], [self.color_O, MAROON_B],
["R", "r"], [-PI/12, PI/3]
):
radius = Line(
circle.get_left(), circle.get_center(),
color = color, stroke_width = 1,
)
radius_text = TexMobject(text, color = color)
radius_text.next_to(radius, UP, buff = 0.1)
radius_group = VGroup(radius, radius_text)
radius_group.rotate(angle, about_point = circle.get_center())
self.add(radius_group)
remark_length_A = TexMobject("|OA| = d-r", "\\Rightarrow", "|OA'| = \dfrac{R^2}{d-r}")
remark_length_B = TexMobject("|OB| = d+r", "\\Rightarrow", "|OB'| = \dfrac{R^2}{d+r}")
remark_length_A[0].set_color(self.color_A)
remark_length_A[-1].set_color(self.color_A)
remark_length_B[0].set_color(self.color_B)
remark_length_B[-1].set_color(self.color_B)
length_group = VGroup(remark_length_A, remark_length_B)
length_group.arrange_submobjects(DOWN, buff = 0.4)
brace = Brace(length_group, RIGHT)
arrow = TexMobject("\\Rightarrow")
remarks = VGroup(
TexMobject("|A'B'| = \\dfrac{2 R^2 r}{|d^2-r^2|}"),
TexMobject("|OI| = \\dfrac{R^2 d}{|d^2-r^2|}")
)
remarks.arrange_submobjects(DOWN, aligned_edge = LEFT)
remarks.set_color(MAROON_B)
result_group = VGroup(brace, arrow, remarks)
result_group.arrange_submobjects(RIGHT)
result_group.next_to(length_group, RIGHT)
remark_group = VGroup(length_group, result_group)
remark_group.center().to_edge(DOWN, buff = 0.2)
bg_rect = BackgroundRectangle(remark_group, fill_opacity = 0.9)
self.add(bg_rect, remark_group)
self.wait()
class DescartesTheoremExamples(Scene):
CONFIG = {
"circle_colors" : [MAROON_B, RED, GREEN, BLUE],
"curvs_outer" : [3, 6, 7, 34],
"curvs_inner" : [10, 15, 19, -6],
}
def setup(self):
self.text_color_map = dict(
zip(["{k_1}", "{k_2}", "{k_3}", "{k_4}"], self.circle_colors)
)
def construct(self):
self.add_title()
self.add_outer_dfc()
self.add_inner_dfc()
def add_title(self):
title = TexMobject(
"\\left(", "{k_1}", "+", "{k_2}", "+", "{k_3}", "+", "{k_4}", "\\right) ^2",
"= 2 \\left(", "{k_1}","^2 +","{k_2}","^2 +","{k_3}","^2 +","{k_4}","^2", "\\right)"
)
title.set_color_by_tex_to_color_map(self.text_color_map)
title.scale(1.2)
title.to_edge(UP, buff = 0.2)
self.add(title)
def add_outer_dfc(self):
r1, r2, r3, r4 = [1./curv for curv in self.curvs_outer]
p1, p2, p3 = [
VectorizedPoint(center)
for center in calc_centers_by_radii(r1, r2, r3, init_angle = PI*2/3)
]
outer_dfc = DescartesFourCircles(p1, p2, p3, show_new_circles = False)
c1, c2, c3 = outer_dfc.get_orig_circles()
c4 = outer_dfc.get_new_circles()[0]
outer_circles = VGroup(c1, c2, c3, c4)
outer_circles.clear_updaters()
outer_circles.set_height(5.5)
outer_circles.to_corner(DL)
texts = VGroup(*[
TexMobject(f"k_{num}", "=", f"{curv}") \
.scale(0.8) \
.move_to(circle.get_center())
for num, curv, circle in zip(range(1, 5), self.curvs_outer, outer_circles)
])
for circle, text, color in zip(outer_circles, texts, self.circle_colors):
circle.set_color(color)
text.set_color(color)
texts[-1].shift(2.5*RIGHT+1.2*UP)
arrow = Arrow(
texts[-1].get_bottom(), outer_circles[-1].get_right(),
path_arc = -PI*2/3, buff = 0.1,
).set_color(self.circle_colors[-1])
outer_group = VGroup(outer_circles, texts, arrow)
self.add(outer_group)
def add_inner_dfc(self):
r1, r2, r3, r4 = [1./curv for curv in self.curvs_inner]
p1, p2, p3 = [
VectorizedPoint(center)
for center in calc_centers_by_radii(r1, r2, r3, init_angle = -PI/7)
]
inner_dfc = DescartesFourCircles(p1, p2, p3, show_new_circles = False)
c1, c2, c3 = inner_dfc.get_orig_circles()
c4 = inner_dfc.get_new_circles()[1]
inner_circles = VGroup(c1, c2, c3, c4)
inner_circles.clear_updaters()
inner_circles.set_height(5.5)
inner_circles.to_corner(DR)
inner_texts = VGroup(*[
TexMobject(f"k_{num}", "=", f"{curv}") \
.scale(0.8) \
.move_to(circle.get_center())
for num, curv, circle in zip(range(1, 5), self.curvs_inner, inner_circles)
])
for circle, text, color in zip(inner_circles, inner_texts, self.circle_colors):
circle.set_color(color)
text.set_color(color)
inner_texts[-1].shift(2.8*LEFT+2.7*UP)
inner_arrow = Arrow(
inner_texts[-1].get_critical_point(DOWN),
inner_texts[-1].get_critical_point(DOWN)+0.7*DR,
buff = 0.1,
).set_color(self.circle_colors[-1])
inner_group = VGroup(inner_circles, inner_texts, inner_arrow)
self.add(inner_group)
self.wait()
self.inner_circles = inner_circles
self.inner_texts = inner_texts
self.inner_arrow = inner_arrow
class DFCInversionProofP1(DescartesTheoremExamples):
CONFIG = {
"remark_scale_text" : "示意图，图像并非真实比例",
"orig_label_texts" : ["C_1", "C_2", "C_3", "C_4"],
"inv_label_texts" : ["C_1'", "C_2'", "C_3'", "C_4'"],
}
def construct(self):
super().add_inner_dfc()
self.arrange_elements()
self.add_labels()
self.add_inversion_center()
self.add_mapsto_symbol()
self.add_not_to_scale_remark()
self.wait()
def arrange_elements(self):
self.remove(self.inner_texts, self.inner_arrow)
self.inner_circles.center().shift(4*UP)
normal_form = FourCirclesNormalForm()
normal_form.shift(4*DOWN)
self.add(normal_form)
self.normal_form = normal_form
def add_labels(self):
orig_labels = VGroup()
for n, (circle, text) in enumerate(zip(self.inner_circles, self.orig_label_texts)):
label = TexMobject(text).scale(1.2)
label.set_color(circle.get_color())
label.move_to(circle.get_center())
orig_labels.add(label)
inv_labels = VGroup()
for n, (circle, text) in enumerate(zip(self.normal_form, self.inv_label_texts)):
label = TexMobject(text).scale(1.2)
label.set_color(circle.get_color())
label.move_to(circle.get_center())
inv_labels.add(label)
c1, c2, c3, c4 = self.inner_circles
l1, l2, l3, l4 = orig_labels
c1i, c2i, c3i, c4i = self.normal_form
l1i, l2i, l3i, l4i = inv_labels
l4.next_to(c4.get_bottom(), UP, buff = 0.3)
l3i.next_to(c3i, DOWN).to_edge(RIGHT)
l4i.next_to(c4i, UP).to_edge(RIGHT)
self.add(orig_labels, inv_labels)
self.orig_labels = orig_labels
self.inv_labels = inv_labels
def add_inversion_center(self):
c1, c2, c3, c4 = self.inner_circles
inv_center = get_tangent_point(c3, c4)
dot_O = Dot(inv_center, color = YELLOW)
label_O = TexMobject("O", color = YELLOW).next_to(dot_O, UP)
remark_O = TextMobject("反演中心", color = YELLOW)
remark_O.next_to(dot_O, RIGHT, buff = 1.5)
arrow_O = Arrow(remark_O.get_left(), dot_O.get_right(), color = YELLOW, buff = 0.2)
orig_center_group = VGroup(dot_O, label_O, remark_O, arrow_O)
inv_dot_O = VectorizedPoint()
inv_dot_O.next_to(self.normal_form[-1], UP, buff = 1.4)
inv_dot_O.shift(2*RIGHT)
inv_center_group = orig_center_group.deepcopy()
inv_center_group.shift(inv_dot_O.get_center() - dot_O.get_center())
self.add(orig_center_group, inv_center_group)
self.orig_center_group = orig_center_group
self.inv_center_group = inv_center_group
def add_mapsto_symbol(self):
mapsto = TexMobject("\\mapsto")
mapsto.rotate(-PI/2)
mapsto.scale(2.5)
mapsto.next_to(self.inner_circles, DOWN)
remark_mapsto = TextMobject("反演变换")
remark_mapsto.next_to(mapsto, LEFT)
self.add(mapsto, remark_mapsto)
def add_not_to_scale_remark(self):
remark_scale = TextMobject("（" + self.remark_scale_text + "）")
remark_scale.scale(0.75)
remark_scale.next_to(6.5*DL, RIGHT, buff = 0)
self.add(remark_scale)
class DFCInversionProofP2(DFCInversionProofP1):
CONFIG = {
"remark_scale_text" : "示意图，反演圆未标出，且图像并非真实比例",
"inv_label_texts" : ["C_1'", "C_2'", "C_3':y=-1", "C_4':y=1"],
"inv_center_coord_text" : "(x_0, y_0) \\, (y_0>1)",
"circle_center_coord_texts" : ["(-1,0)", "(1,0)"],
}
def construct(self):
super().construct()
self.change_center_remarks()
self.add_coord_system()
self.change_inv_labels()
self.wait()
def change_center_remarks(self):
for center_group in (self.orig_center_group, self.inv_center_group):
dot, label, remark, arrow = center_group
self.remove(remark, arrow)
if center_group is self.inv_center_group:
coord = TexMobject(self.inv_center_coord_text)
coord.next_to(dot, RIGHT)
coord.set_color(dot.get_color())
self.add(coord)
def add_coord_system(self):
c1, c2, c3, c4 = self.normal_form
center_point = (c1.get_center() + c2.get_center()) / 2
unit_size = c1.get_height()/2
coord_system = Axes(
center_point = center_point,
number_line_config = {"unit_size" : unit_size},
y_min = -1.8, y_max = 2.8,
)
self.add(coord_system)
self.coord_system = coord_system
def change_inv_labels(self):
l1i, l2i, l3i, l4i = self.inv_labels
for label, x_coord, coord_text in zip([l1i, l2i], [-1, 1], self.circle_center_coord_texts):
center = self.coord_system.c2p(x_coord, 0)
label.next_to(center, UP)
dot_i = Dot(center, radius = 0.1).set_color(label.get_color())
coord_i = TexMobject(coord_text).set_color(label.get_color()).next_to(center, DOWN)
self.add(dot_i, coord_i)
llonianGasketScene):
CONFIG = {
"max_iter" : 8,
"curvatures" : [2, 2, 3],
"init_angle" : 0,
"curv_thres" : 30000,
"ag_config": {
"agc_config" : {
"radius_thres" : 1e-3,
"circle_color" : BLUE,
"label_color" : WHITE,
},
},
"color_curr" : YELLOW,
"wait_time" : 2,
}
def construct(self):
r1, r2, r3 = [1./curv for curv in self.curvatures]
p1, p2, p3 = calc_centers_by_radii(r1, r2, r3, init_angle = self.init_angle)
agc1 = AGCircle(p1, r1, parents = None, **self.ag_config["agc_config"])
agc2 = AGCircle(p2, r2, parents = None, **self.ag_config["agc_config"])
agc3 = AGCircle(p3, r3, parents = None, **self.ag_config["agc_config"])
remark = TextMobject("（圆内数字为该圆的曲率）")
remark.scale(0.75).to_corner(DL)
self.add(remark)
for k in range(self.max_iter):
agcs_copy = [agc.deepcopy() for agc in (agc1, agc2, agc3)]
ag = ApollonianGasket(
*agcs_copy, num_iter = k,
curv_thres = self.curv_thres, **self.ag_config
)
iter_num = VGroup(
TextMobject("迭代次数："), TexMobject(f"{k}")
).arrange_submobjects(RIGHT).scale(1.5)
iter_num.to_edge(LEFT, buff = 1)
ag.scale(3.8)
ag.shift(np.array([0, 3.8, 0]) - ag.get_top() + 3*RIGHT)
VGroup(*ag.agc_list[-1]).set_color(self.color_curr)
self.add(ag, iter_num)
self.wait(self.wait_time)
if k != self.max_iter-1:
self.remove(ag, iter_num)
class ApollonianGasketExample1(Scene):
CONFIG = {
"max_iter" : 20,
"curvatures" : [3, 6, 7],
"curvature_texts" : [-2, 3, 6, 7],
"init_angle" : 0,
"curv_thres" : 4000,
"ag_config": {
"agc_config" : {
"radius_thres" : 1e-3,
"circle_color" : BLUE,
"label_color" : WHITE,
},
},
"ag_scaling_factor" : 5.2,
}
def construct(self):
r1, r2, r3 = [1./curv for curv in self.curvatures]
p1, p2, p3 = calc_centers_by_radii(r1, r2, r3, init_angle = self.init_angle)
agc1 = AGCircle(p1, r1, parents = None, **self.ag_config["agc_config"])
agc2 = AGCircle(p2, r2, parents = None, **self.ag_config["agc_config"])
agc3 = AGCircle(p3, r3, parents = None, **self.ag_config["agc_config"])
ag_seed = ApollonianGasket(
*[agc.deepcopy() for agc in (agc1, agc2, agc3)],
num_iter = 0, curv_thres = self.curv_thres, **self.ag_config
)
ag_result = ApollonianGasket(
*[agc.deepcopy() for agc in (agc1, agc2, agc3)],
num_iter = self.max_iter, curv_thres = self.curv_thres, **self.ag_config
)
ag_seed_center = ag_seed[0][0].get_right()
ag_result_center = ag_result[0][0].get_right()
arrow = Arrow(LEFT, RIGHT)
figure_group = VGroup(ag_seed, ag_result, arrow)
for ag, center, direction in zip(
[ag_seed, ag_result], [ag_seed_center, ag_result_center], [4*LEFT, 4*RIGHT]):
ag.scale(self.ag_scaling_factor)
ag.shift(direction - center)
figure_group.shift(DOWN)
k1, k2, k3, k4 = list(map(str, self.curvature_texts))
title = TexMobject(
f"({k1}+{k2}+{k3}+{k4})^2 = 2\\left[({k1})^2+{k2}^2+{k3}^2+{k4}^2 \\right]"
)
title.set_width(13)
title.set_color(YELLOW)
title.to_edge(UP)
self.add(figure_group, title)
self.wait()
class ApollonianGasketExample2(ApollonianGasketExample1):
CONFIG = {
"max_iter" : 20,
"curvatures" : [5, 8, 12],
"curvature_texts" : [-3, 5, 8, 12],
"curv_thres" : 5000,
"ag_config": {
"agc_config" : {
"radius_thres" : 5e-4,
"circle_color" : BLUE,
"label_color" : WHITE,
},
},
"ag_scaling_factor" : 8,
}
class LoxodromicSpiralInTangentCircles(Scene):
CONFIG = {
"max_iter" : 20,
"agc_config" : {
"radius_thres" : 1,
"circle_color" : BLUE,
"label_color" : WHITE,
},
"curve_config" : {
"color" : YELLOW,
"stroke_width" : 2,
},
"alpha" : 0.6,
"dashed_line_config" : {
"color" : GREY,
"stroke_width" : 0.5,
"num_dashes" : 200,
"positive_space_ratio" : 0.6,
}
}
def construct(self):
self.generate_circles()
self.generate_curves()
self.generate_labels()
self.generate_lines()
self.add_elements()
self.zooming_in()
def generate_circles(self):
agcm2 = AGCircle(2/3.*UP, 1/3., **self.agc_config)
agcm1 = AGCircle(RIGHT/2, 1/2., **self.agc_config)
agczr = AGCircle(ORIGIN, -1, **self.agc_config)
agcp1 = AGCircle(LEFT/2, 1/2., **self.agc_config)
agcp2 = AGCircle(2/3.*DOWN, 1/3., **self.agc_config)
agc_list = [agcm2, agcm1, agczr, agcp1, agcp2]
for n in range(self.max_iter):
A, B, C, known_agc = agc_list[:4]
agc_m_k, agc_m_c = calc_new_agc_info(A, B, C, known_agc = known_agc)
agc_m = AGCircle(agc_m_c, 1./agc_m_k, parents = (A, B, C), **self.agc_config)
known_agc, C, B, A = agc_list[-4:]
agc_p_k, agc_p_c = calc_new_agc_info(C, B, A, known_agc = known_agc)
agc_p = AGCircle(agc_p_c, 1./agc_p_k, parents = (C, B, A), **self.agc_config)
agc_list.insert(0, agc_m)
agc_list.append(agc_p)
agc_group = VGroup(*agc_list)
agc_group.set_height(7.8)
self.agc_list = agc_list
self.agc_group = agc_group
def generate_curves(self):
agc_ps = self.agc_list[-self.max_iter-4:]
agc_ps_points = []
loxo_curve_p_solid = VMobject(**self.curve_config)
for k in range(len(agc_ps)-2):
if k != 0:
c1, c2, c3 = agc_ps[k], agc_ps[k+1], agc_ps[k+2]
pt1 = get_tangent_point(c1, c2)
pt2 = get_tangent_point(c2, c3)
p = c2.get_center()
if k != 1:
agc_ps_points.extend(
[pt1, p*(1-self.alpha)+pt1*self.alpha, p*(1-self.alpha)+pt2*self.alpha, pt2]
)
else:
agc_ps_points.extend(
[pt1, p*0.7+pt1*0.3, p*0.6+pt2*0.4, pt2]
)
else:
c1, c2 = agc_ps[1], agc_ps[2]
pt = get_tangent_point(c1, c2)
agc_ps_points.extend([8*LEFT, 7*LEFT, 6*LEFT, pt])
loxo_curve_p_solid.append_points(agc_ps_points)
loxo_curve_m_solid = loxo_curve_p_solid.deepcopy()
loxo_curve_m_solid.rotate(PI, about_point = self.agc_group.get_center())
self.loxo_curve_p_solid = loxo_curve_p_solid
self.loxo_curve_m_solid = loxo_curve_m_solid
def generate_labels(self):
labels = VGroup(*[
TexMobject("C_{%d}" % num, background_stroke_width = 0)
for num in range(-self.max_iter-2, self.max_iter+3)
])
for label, circle in zip(labels, self.agc_group):
label.set_height(circle.get_height()*0.15)
label.move_to(circle.get_center())
label_c0 = labels[self.max_iter+2]
label_c0.set_height(0.8)
label_c0.next_to(self.agc_group.get_critical_point(UL), DR, buff = 0.1)
self.labels = labels
def generate_lines(self):
agc_ps = self.agc_list[-self.max_iter-2:]
line_p_solid = VMobject(**self.dashed_line_config)
line_p_solid_corners = [8*LEFT]
for circle in agc_ps:
line_p_solid_corners.append(circle.get_center())
line_p_solid.set_points_as_corners(line_p_solid_corners)
line_m_solid = line_p_solid.deepcopy()
line_m_solid.rotate(PI, about_point = self.agc_group.get_center())
self.line_p_solid = line_p_solid
self.line_m_solid = line_m_solid
def add_elements(self):
figure = VGroup(
self.agc_group, self.loxo_curve_p_solid, self.loxo_curve_m_solid,
self.line_p_solid, self.line_m_solid, self.labels,
)
self.add(figure)
self.figure = figure
def zooming_in(self):
self.figure.save_state()
self.wait(0.5)
self.play(
ApplyMethod(self.figure.shift, -self.agc_group[-1].get_center()),
run_time = 2,
)
self.wait()
for k in range(10):
self.play(
ApplyMethod(self.figure.scale, 2.5, {"about_point" : self.agc_group[-1].get_center()}),
run_time = 2,
)
self.wait()
self.play(self.figure.restore, run_time = 15)
self.wait(2)
class ShowFordCircles(ZoomInOnFordCircles):
CONFIG = {
"q_max" : 30,
}
def construct(self):
self.setup_axes()
self.setup_circles_and_labels()
self.add_remarks()
self.first_zoom_in()
self.wait()
def first_zoom_in(self):
self.zoom_in_on(1/2., 6)
def add_remarks(self):
nl_text = TextMobject("数轴")
nl_arrow = Arrow(ORIGIN, UP).match_height(nl_text)
nl_remark = VGroup(nl_arrow, nl_text)
nl_remark.scale(0.8)
nl_remark.set_color(LIGHT_GREY)
nl_remark.arrange_submobjects(RIGHT, buff = 0.1)
nl_remark.next_to(self.axes.coords_to_point(0, 0), DOWN, buff = 0.1)
nl_remark.to_edge(LEFT, buff = 0.15)
frac_remark = TextMobject("圆内分数为圆心横坐标")
frac_remark.scale(0.6)
frac_remark.to_corner(DL, buff = 0.15)
self.add(nl_remark, frac_remark)
class ShowFordCirclesDetails(ShowFordCircles):
CONFIG = {
"q_max" : 100,
}
def construct(self):
super().construct()
self.further_zoom_in()
def setup_circles_and_labels(self):
circles = VGroup()
labels = VGroup()
for q in range(1, self.q_max+1):
for p in get_coprime_numers_by_denom(q):
if (q <= 40) or (0.6 <= p/q <= 0.8):
circle = self.generate_circle_by_fraction(p, q)
circle.add_updater(
lambda m: m.set_stroke(width = get_stroke_width_by_height(m.get_height()))
)
label = AssembledFraction(p, q)
label.set_height(circle.get_height() * self.label_height_factor)
label.move_to(circle.get_center())
circles.add(circle)
labels.add(label)
self.add(circles, labels)
self.circles = circles
self.labels = labels
def further_zoom_in(self):
self.acl = VGroup(self.axes, self.circles, self.labels)
self.acl.save_state()
self.wait(0.5)
self.play_zooming_animation(1/np.sqrt(2), 9, run_time = 5)
self.wait()
self.play_zooming_animation(0.73, 5, run_time = 4)
self.wait()
self.play_zooming_animation(0.74, 5, run_time = 4)
self.wait()
self.play(self.acl.restore, run_time = 5)
self.wait(2)
class ProveFordCirclesPropertiesP1(Scene):
CONFIG = {
"c1_frac" : [2, 3],
"c2_frac" : [3, 4],
"c3_frac" : [5, 7],
"circle_config" : {"stroke_color" : BLUE, "stroke_width" : 2,},
"line_config" : {"stroke_color" : GREY, "stroke_width" : 2,},
"aux_line_config" : {"stroke_color" : GREY, "stroke_width" : 0.8,},
"polygon_config" : {"fill_color" : GREY, "fill_opacity" : 0.4, "stroke_width" : 0,},
}
def setup(self):
a, b = self.c1_frac
c, d = self.c2_frac
p, q = self.c3_frac
r1 = 1/(2*b**2)
r2 = 1/(2*d**2)
r3 = 1/(2*q**2)
c1_center = a/b*RIGHT + r1*UP
c2_center = c/d*RIGHT + r2*UP
c3_center = p/q*RIGHT + r3*UP
c1 = Circle(arc_center = c1_center, radius = r1, **self.circle_config)
c2 = Circle(arc_center = c2_center, radius = r2, **self.circle_config)
c3 = Circle(arc_center = c3_center, radius = r3, **self.circle_config)
c1_dot = SmallDot(color = GREY)
c1_dot.add_updater(lambda m: m.move_to(c1.get_center()))
c2_dot = SmallDot(color = GREY)
c2_dot.add_updater(lambda m: m.move_to(c2.get_center()))
c3_dot = SmallDot(color = GREY)
c3_dot.add_updater(lambda m: m.move_to(c3.get_center()))
line = Line(
2*c1.get_bottom()-c2.get_bottom(),
2*c2.get_bottom()-c1.get_bottom(),
**self.line_config
)
VGroup(c1, c2, c3, line).set_height(6).center().to_edge(UP)
aux_line_1 = Line(c1.get_center(), c1.get_bottom(), **self.aux_line_config)
aux_line_2 = Line(c2.get_center(), c2.get_bottom(), **self.aux_line_config)
aux_line_3 = Line(c1.get_center(), c2.get_center(), **self.aux_line_config)
aux_line_4 = Line(c1.get_bottom(), c2.get_bottom(), **self.aux_line_config) \
.shift(c2.get_height()/2*UP)
polygon = Polygon(
c1.get_center(), c2.get_center(), aux_line_4.get_start_and_end()[0],
**self.polygon_config,
)
l1 = TexMobject("\\dfrac{a}{b}").next_to(c1, DOWN)
l2 = TexMobject("\\dfrac{c}{d}").next_to(c2, DOWN)
l3 = TexMobject("\\dfrac{a+c}{b+d}").next_to(c3, DOWN)
self.orig_group = VGroup(c1, c2, line, c1_dot, c2_dot, l1, l2)
self.aux_group = VGroup(aux_line_1, aux_line_2, aux_line_3, aux_line_4, polygon)
self.new_group = VGroup(c3, c3_dot, l3)
def construct(self):
self.add(self.orig_group, self.aux_group)
self.wait()
class ProveFordCirclesPropertiesP2(ProveFordCirclesPropertiesP1):
def construct(self):
self.add(self.orig_group, self.new_group)
self.wait()
class ShowFordCirclesFareySum(ZoomInOnFordCircles):
pass
class DFCInversionProofP3(DFCInversionProofP2):
CONFIG = {
"remark_scale_text" : "示意图，反演圆未标出，且图像并非真实比例",
"inv_label_texts" : ["C_1'", "C_2'", "C_3':\\mathrm{Im}(z)=-1", "C_4':\\mathrm{Im}(z)=1"],
"inv_center_coord_text" : "z_0 = x_0+iy_0\\, (y_0>1)",
"circle_center_coord_texts" : ["-1", "1"],
}
def construct(self):
super().construct()
self.wait()
def add_coord_system(self):
c1, c2, c3, c4 = self.normal_form
center_point = (c1.get_center() + c2.get_center()) / 2
unit_size = c1.get_height()/2
coord_system = NumberPlane(
center_point = center_point,
number_line_config = {"unit_size" : unit_size},
y_min = -3, y_max = 3,
background_line_style = {
"stroke_color" : GREY,
"stroke_width" : 1.5,
"stroke_opacity" : 0.8,
},
)
aux_coord_system = Axes(
center_point = center_point,
number_line_config = {"unit_size" : unit_size},
y_min = -3, y_max = 3,
stroke_opacity = 0.8,
)
self.add(coord_system, aux_coord_system)
self.coord_system = coord_system
class NormalFormIn3D(ThreeDScene):
CONFIG = {
"axis_unit_size" : 1.5,
"axis_min" : -1.5,
"axis_max" : 2.8,
"resolution" : (60, 120),
"plane_colors" : [GREEN, BLUE],
"sphere_colors" : [MAROON_B, RED, PINK],
}
def construct(self):
self.add_3d_stuff()
self.add_2d_stuff()
def add_3d_stuff(self):
self.set_camera_orientation(theta = 70 * DEGREES, phi = 50 * DEGREES)
axes = ThreeDAxes(
x_min = self.axis_min, x_max = self.axis_max,
y_min = self.axis_min, y_max = self.axis_max,
z_min = self.axis_min, z_max = self.axis_max,
number_line_config = {"unit_size" : self.axis_unit_size},
)
sphere_centers = [
axis.number_to_point(1)
for axis in [axes.x_axis, axes.y_axis, axes.z_axis]
]
radius = 1/np.sqrt(2) * self.axis_unit_size
sphere_dots = VGroup(*[
Sphere(
radius = 0.08, resolution = self.resolution,
fill_opacity = 1, stroke_width = 0,
).move_to(sphere_center).set_color(color)
for sphere_center, color in zip(sphere_centers, self.sphere_colors)
])
spheres = VGroup(*[
Sphere(
radius = radius, resolution = self.resolution,
fill_opacity = 0.6, stroke_width = 0.5,
).move_to(sphere_center).set_color(color)
for sphere_center, color in zip(sphere_centers, self.sphere_colors)
])
planes = VGroup(*[
VGroup(*[
Square(
side_length = 1, fill_opacity = fill_opacity,
stroke_color = GREY, stroke_width = 0.3, stroke_opacity = 0.2,
)
for k in range(n**2)
]).arrange_in_grid(n, n, buff = 0) \
.apply_matrix(z_to_vector([1, 1, 1])) \
.move_to(np.average(sphere_centers)) \
.shift(radius * normalize(direction)) \
.set_color(color)
for n, fill_opacity, direction, color in zip(
[7, 8], [0.2, 0.3], [np.ones(3), -np.ones(3)], self.plane_colors,
)
])
figure_group = VGroup(axes, planes, sphere_dots, spheres)
figure_group.shift(RIGHT*2+0.5*OUT)
self.add(figure_group)
self.add(axes)
self.add(planes)
self.add(sphere_dots, spheres)
def add_2d_stuff(self):
sphere_remarks = VGroup(*[
TextMobject(
"球：圆心为" + f"$({int(x)},{int(y)},{int(z)})$" + \
"，半径为" + "$\\dfrac{1}{\\sqrt{2}}$"
).set_color(color)
for (x, y, z), color in zip([RIGHT, UP, OUT], self.sphere_colors)
]).arrange_submobjects(DOWN)
plane_remarks = VGroup(*[
TexMobject(
"\\text{平面：}" + "x+y+z=1" + sign + "\\dfrac{\\sqrt{3}}{\\sqrt{2}"
).set_color(color)
for sign, color in zip(["+", "-"], self.plane_colors)
]).arrange_submobjects(DOWN)
remarks = VGroup(sphere_remarks, plane_remarks)
remarks.arrange_submobjects(DOWN, aligned_edge = LEFT)
remarks.scale(0.8)
remarks.to_corner(DR)
self.add_fixed_in_frame_mobjects(remarks)
self.wait()
#####
## Banner
class Banner_Intro(Scene):
CONFIG = {
"circle_color" : YELLOW,
"text_color" : BLUE,
"inv_text_color" : BLUE,
"circle_center" : 0.8*UP,
"circle_radius" : 3,
"grid_side_length" : 0.5,
"x_range" : 300,
"y_range" : 300,
"dist_thres" : 300,
}
def construct(self):
circle = Circle(color = self.circle_color, radius = self.circle_radius, stroke_width = 5)
circle.move_to(self.circle_center)
dot = SmallDot(self.circle_center, color = self.circle_color)
text = TextMobject("Inversion", color = self.text_color, background_stroke_width = 3)
text.rotate(PI/2.)
text.move_to(0.4*RIGHT)
text.apply_complex_function(np.exp)
text.rotate(-PI/2.)
text.scale(1.5)
text.move_to(0.9*DOWN)
inv_text = InversedVMobject(text, circle, use_dashed_vmob = False)
inv_text.suspend_updating()
inv_text.set_background_stroke(color = "#303030", width = 3)
inv_text.set_stroke(width = 0)
inv_text.set_fill(color = self.inv_text_color, opacity = 0.5)
grid = VGroup(*[
Square(
side_length = self.grid_side_length,
stroke_width = 0, fill_opacity = 0.3,
fill_color = CB_DARK if (i+j)%2==0 else CB_LIGHT
).move_to(self.circle_center + (i*RIGHT+j*UP)*self.grid_side_length)
for i in range(-self.x_range, self.x_range+1, 1)
for j in range(-self.y_range, self.y_range+1, 1)
if np.sqrt(i**2+j**2) * self.grid_side_length < self.dist_thres
])
for square in grid:
if is_close_in_R3(square.get_center(), self.circle_center):
grid.remove(square)
inv_grid = InversedVMobject(grid, circle, use_dashed_vmob = False)
self.add(inv_grid, circle, dot, text, inv_text)
self.wait()
class Banner_AdvancedP1(ApollonianGasketScene):
CONFIG = {
"curvatures" : [570, 968, 1112],
"init_angle" : PI/7,
"num_iter" : 20,
"curv_thres" : 1e6,
"ag_config" : {
"agc_config" : {
"radius_thres" : 5e-6,
"circle_color" : YELLOW,
"label_color" : WHITE,
},
},
"part_text" : "上篇",
}
def construct(self):
super().construct()
ag = self.ag
ag.set_height(7)
circle_myst = ag.agc_list[0][0]
label_myst = circle_myst.label
label_question = TexMobject("???")
label_question.match_height(label_myst)
label_question.move_to(label_myst)
self.remove(label_myst)
self.add(label_question)
part = TextMobject(self.part_text)
part.to_corner(DR)
self.add(part)
class Banner_AdvancedP2(Banner_AdvancedP1):
CONFIG = {
"part_text" : "下篇",
}
| true
| true
|
f716c43082d102e192f1af6ae1893777487ae14c
| 11,372
|
py
|
Python
|
smartcab/environment.py
|
BhanuPrakashNani/physics-simulation
|
7d7ad4bff654f4ad80dbc6a7ab254489d623658f
|
[
"MIT"
] | 7
|
2018-12-07T14:25:15.000Z
|
2021-04-07T22:14:49.000Z
|
smartcab/environment.py
|
BhanuPrakashNani/physics-simulation
|
7d7ad4bff654f4ad80dbc6a7ab254489d623658f
|
[
"MIT"
] | 9
|
2018-12-07T18:11:29.000Z
|
2018-12-22T09:39:39.000Z
|
smartcab/environment.py
|
BhanuPrakashNani/physics-simulation
|
7d7ad4bff654f4ad80dbc6a7ab254489d623658f
|
[
"MIT"
] | 22
|
2018-12-06T16:35:34.000Z
|
2019-01-26T13:08:14.000Z
|
import time,random
from collections import OrderedDict
from simulator import Simulator
class TrafficLight(object):
"""A traffic light that switches periodically."""
valid_states = [True, False] # True = NS open, False = EW open
def __init__(self, state=None, period=None):
self.state = state if state is not None else random.choice(self.valid_states)
self.period = period if period is not None else random.choice([3, 4, 5])
self.last_updated = 0
def reset(self):
self.last_updated = 0
def update(self, t):
if t - self.last_updated >= self.period:
self.state = not self.state # assuming state is boolean
self.last_updated = t
class Environment(object):
"""Environment within which all agents operate."""
valid_actions = [None, 'forward', 'left', 'right']
valid_inputs = {'light': TrafficLight.valid_states, 'oncoming': valid_actions, 'left': valid_actions, 'right': valid_actions}
valid_headings = [(1, 0), (0, -1), (-1, 0), (0, 1)] # ENWS
hard_time_limit = -100 # even if enforce_deadline is False, end trial when deadline reaches this value (to avoid deadlocks)
def __init__(self):
self.done = False
self.t = 0
self.agent_states = OrderedDict()
self.status_text = ""
# Road network
self.grid_size = (8, 6) # (cols, rows)
self.bounds = (1, 1, self.grid_size[0], self.grid_size[1])
self.block_size = 100
self.intersections = OrderedDict()
self.roads = []
for x in xrange(self.bounds[0], self.bounds[2] + 1):
for y in xrange(self.bounds[1], self.bounds[3] + 1):
self.intersections[(x, y)] = TrafficLight() # a traffic light at each intersection
for a in self.intersections:
for b in self.intersections:
if a == b:
continue
if (abs(a[0] - b[0]) + abs(a[1] - b[1])) == 1: # L1 distance = 1
self.roads.append((a, b))
# Dummy agents
self.num_dummies = 3 # no. of dummy agents
for i in xrange(self.num_dummies):
self.create_agent(DummyAgent)
# Primary agent
self.primary_agent = None # to be set explicitly
self.enforce_deadline = False
def create_agent(self, agent_class, *args, **kwargs):
agent = agent_class(self, *args, **kwargs)
self.agent_states[agent] = {'location': random.choice(self.intersections.keys()), 'heading': (0, 1)}
return agent
def set_primary_agent(self, agent, enforce_deadline=False):
self.primary_agent = agent
self.enforce_deadline = enforce_deadline
def reset(self):
self.done = False
self.t = 0
# Reset traffic lights
for traffic_light in self.intersections.itervalues():
traffic_light.reset()
# Pick a start and a destination
start = random.choice(self.intersections.keys())
destination = random.choice(self.intersections.keys())
# Ensure starting location and destination are not too close
while self.compute_dist(start, destination) < 4:
start = random.choice(self.intersections.keys())
destination = random.choice(self.intersections.keys())
start_heading = random.choice(self.valid_headings)
deadline = self.compute_dist(start, destination) * 5
print "Environment.reset(): Trial set up with start = {}, destination = {}, deadline = {}".format(start, destination, deadline)
# Initialize agent(s)
for agent in self.agent_states.iterkeys():
self.agent_states[agent] = {
'location': start if agent is self.primary_agent else random.choice(self.intersections.keys()),
'heading': start_heading if agent is self.primary_agent else random.choice(self.valid_headings),
'destination': destination if agent is self.primary_agent else None,
'deadline': deadline if agent is self.primary_agent else None}
agent.reset(destination=(destination if agent is self.primary_agent else None))
def step(self):
#print "Environment.step(): t = {}".format(self.t) # [debug]
# Update traffic lights
for intersection, traffic_light in self.intersections.iteritems():
traffic_light.update(self.t)
# Update agents
for agent in self.agent_states.iterkeys():
agent.update(self.t)
self.t += 1
if self.primary_agent is not None:
agent_deadline = self.agent_states[self.primary_agent]['deadline']
if agent_deadline <= self.hard_time_limit:
self.done = True
print "Environment.step(): Primary agent hit hard time limit ({})! Trial aborted.".format(self.hard_time_limit)
elif self.enforce_deadline and agent_deadline <= 0:
self.done = True
print "Environment.step(): Primary agent ran out of time! Trial aborted."
self.agent_states[self.primary_agent]['deadline'] = agent_deadline - 1
def sense(self, agent):
assert agent in self.agent_states, "Unknown agent!"
state = self.agent_states[agent]
location = state['location']
heading = state['heading']
light = 'green' if (self.intersections[location].state and heading[1] != 0) or ((not self.intersections[location].state) and heading[0] != 0) else 'red'
# Populate oncoming, left, right
oncoming = None
left = None
right = None
for other_agent, other_state in self.agent_states.iteritems():
if agent == other_agent or location != other_state['location'] or (heading[0] == other_state['heading'][0] and heading[1] == other_state['heading'][1]):
continue
other_heading = other_agent.get_next_waypoint()
if (heading[0] * other_state['heading'][0] + heading[1] * other_state['heading'][1]) == -1:
if oncoming != 'left': # we don't want to override oncoming == 'left'
oncoming = other_heading
elif (heading[1] == other_state['heading'][0] and -heading[0] == other_state['heading'][1]):
if right != 'forward' and right != 'left': # we don't want to override right == 'forward or 'left'
right = other_heading
else:
if left != 'forward': # we don't want to override left == 'forward'
left = other_heading
return {'light': light, 'oncoming': oncoming, 'left': left, 'right': right} # TODO: make this a namedtuple
def get_deadline(self, agent):
return self.agent_states[agent]['deadline'] if agent is self.primary_agent else None
def act(self, agent, action):
assert agent in self.agent_states, "Unknown agent!"
assert action in self.valid_actions, "Invalid action!"
state = self.agent_states[agent]
location = state['location']
heading = state['heading']
light = 'green' if (self.intersections[location].state and heading[1] != 0) or ((not self.intersections[location].state) and heading[0] != 0) else 'red'
sense = self.sense(agent)
# Move agent if within bounds and obeys traffic rules
reward = 0 # reward/penalty
move_okay = True
if action == 'forward':
if light != 'green':
move_okay = False
elif action == 'left':
if light == 'green' and (sense['oncoming'] == None or sense['oncoming'] == 'left'):
heading = (heading[1], -heading[0])
else:
move_okay = False
elif action == 'right':
if light == 'green' or sense['left'] != 'straight':
heading = (-heading[1], heading[0])
else:
move_okay = False
if move_okay:
# Valid move (could be null)
if action is not None:
# Valid non-null move
location = ((location[0] + heading[0] - self.bounds[0]) % (self.bounds[2] - self.bounds[0] + 1) + self.bounds[0],
(location[1] + heading[1] - self.bounds[1]) % (self.bounds[3] - self.bounds[1] + 1) + self.bounds[1]) # wrap-around
#if self.bounds[0] <= location[0] <= self.bounds[2] and self.bounds[1] <= location[1] <= self.bounds[3]: # bounded
state['location'] = location
state['heading'] = heading
reward = 2.0 if action == agent.get_next_waypoint() else -0.5 # valid, but is it correct? (as per waypoint)
else:
# Valid null move
reward = 0.0
else:
# Invalid move
reward = -1.0
if agent is self.primary_agent:
if state['location'] == state['destination']:
if state['deadline'] >= 0:
reward += 10 # bonus
self.done = True
print "Environment.act(): Primary agent has reached destination!" # [debug]
self.status_text = "state: {}\naction: {}\nreward: {}".format(agent.get_state(), action, reward)
#print "Environment.act() [POST]: location: {}, heading: {}, action: {}, reward: {}".format(location, heading, action, reward) # [debug]
return reward
def compute_dist(self, a, b):
"""L1 distance between two points."""
return abs(b[0] - a[0]) + abs(b[1] - a[1])
class Agent(object):
"""Base class for all agents."""
def __init__(self, env):
self.env = env
self.state = None
self.next_waypoint = None
self.color = 'cyan'
def reset(self, destination=None):
pass
def update(self, t):
pass
def get_state(self):
return self.state
def get_next_waypoint(self):
return self.next_waypoint
class DummyAgent(Agent):
color_choices = ['blue', 'cyan', 'magenta', 'orange']
def __init__(self, env):
super(DummyAgent, self).__init__(env) # sets self.env = env, state = None, next_waypoint = None, and a default color
self.next_waypoint = random.choice(Environment.valid_actions[1:])
self.color = random.choice(self.color_choices)
def update(self, t):
inputs = self.env.sense(self)
action_okay = True
if self.next_waypoint == 'right':
if inputs['light'] == 'red' and inputs['left'] == 'forward':
action_okay = False
elif self.next_waypoint == 'forward':
if inputs['light'] == 'red':
action_okay = False
elif self.next_waypoint == 'left':
if inputs['light'] == 'red' or (inputs['oncoming'] == 'forward' or inputs['oncoming'] == 'right'):
action_okay = False
action = None
if action_okay:
action = self.next_waypoint
self.next_waypoint = random.choice(Environment.valid_actions[1:])
reward = self.env.act(self, action)
#print "DummyAgent.update(): t = {}, inputs = {}, action = {}, reward = {}".format(t, inputs, action, reward) # [debug]
#print "DummyAgent.update(): next_waypoint = {}".format(self.next_waypoint) # [debug]
| 42.59176
| 164
| 0.587671
|
import time,random
from collections import OrderedDict
from simulator import Simulator
class TrafficLight(object):
"""A traffic light that switches periodically."""
valid_states = [True, False]
def __init__(self, state=None, period=None):
self.state = state if state is not None else random.choice(self.valid_states)
self.period = period if period is not None else random.choice([3, 4, 5])
self.last_updated = 0
def reset(self):
self.last_updated = 0
def update(self, t):
if t - self.last_updated >= self.period:
self.state = not self.state
self.last_updated = t
class Environment(object):
"""Environment within which all agents operate."""
valid_actions = [None, 'forward', 'left', 'right']
valid_inputs = {'light': TrafficLight.valid_states, 'oncoming': valid_actions, 'left': valid_actions, 'right': valid_actions}
valid_headings = [(1, 0), (0, -1), (-1, 0), (0, 1)]
hard_time_limit = -100
def __init__(self):
self.done = False
self.t = 0
self.agent_states = OrderedDict()
self.status_text = ""
self.grid_size = (8, 6)
self.bounds = (1, 1, self.grid_size[0], self.grid_size[1])
self.block_size = 100
self.intersections = OrderedDict()
self.roads = []
for x in xrange(self.bounds[0], self.bounds[2] + 1):
for y in xrange(self.bounds[1], self.bounds[3] + 1):
self.intersections[(x, y)] = TrafficLight()
for a in self.intersections:
for b in self.intersections:
if a == b:
continue
if (abs(a[0] - b[0]) + abs(a[1] - b[1])) == 1:
self.roads.append((a, b))
self.num_dummies = 3
for i in xrange(self.num_dummies):
self.create_agent(DummyAgent)
self.primary_agent = None
self.enforce_deadline = False
def create_agent(self, agent_class, *args, **kwargs):
agent = agent_class(self, *args, **kwargs)
self.agent_states[agent] = {'location': random.choice(self.intersections.keys()), 'heading': (0, 1)}
return agent
def set_primary_agent(self, agent, enforce_deadline=False):
self.primary_agent = agent
self.enforce_deadline = enforce_deadline
def reset(self):
self.done = False
self.t = 0
for traffic_light in self.intersections.itervalues():
traffic_light.reset()
start = random.choice(self.intersections.keys())
destination = random.choice(self.intersections.keys())
while self.compute_dist(start, destination) < 4:
start = random.choice(self.intersections.keys())
destination = random.choice(self.intersections.keys())
start_heading = random.choice(self.valid_headings)
deadline = self.compute_dist(start, destination) * 5
print "Environment.reset(): Trial set up with start = {}, destination = {}, deadline = {}".format(start, destination, deadline)
for agent in self.agent_states.iterkeys():
self.agent_states[agent] = {
'location': start if agent is self.primary_agent else random.choice(self.intersections.keys()),
'heading': start_heading if agent is self.primary_agent else random.choice(self.valid_headings),
'destination': destination if agent is self.primary_agent else None,
'deadline': deadline if agent is self.primary_agent else None}
agent.reset(destination=(destination if agent is self.primary_agent else None))
def step(self):
for intersection, traffic_light in self.intersections.iteritems():
traffic_light.update(self.t)
for agent in self.agent_states.iterkeys():
agent.update(self.t)
self.t += 1
if self.primary_agent is not None:
agent_deadline = self.agent_states[self.primary_agent]['deadline']
if agent_deadline <= self.hard_time_limit:
self.done = True
print "Environment.step(): Primary agent hit hard time limit ({})! Trial aborted.".format(self.hard_time_limit)
elif self.enforce_deadline and agent_deadline <= 0:
self.done = True
print "Environment.step(): Primary agent ran out of time! Trial aborted."
self.agent_states[self.primary_agent]['deadline'] = agent_deadline - 1
def sense(self, agent):
assert agent in self.agent_states, "Unknown agent!"
state = self.agent_states[agent]
location = state['location']
heading = state['heading']
light = 'green' if (self.intersections[location].state and heading[1] != 0) or ((not self.intersections[location].state) and heading[0] != 0) else 'red'
oncoming = None
left = None
right = None
for other_agent, other_state in self.agent_states.iteritems():
if agent == other_agent or location != other_state['location'] or (heading[0] == other_state['heading'][0] and heading[1] == other_state['heading'][1]):
continue
other_heading = other_agent.get_next_waypoint()
if (heading[0] * other_state['heading'][0] + heading[1] * other_state['heading'][1]) == -1:
if oncoming != 'left':
oncoming = other_heading
elif (heading[1] == other_state['heading'][0] and -heading[0] == other_state['heading'][1]):
if right != 'forward' and right != 'left': # we don't want to override right == 'forward or 'left'
right = other_heading
else:
if left != 'forward': # we don't want to override left == 'forward'
left = other_heading
return {'light': light, 'oncoming': oncoming, 'left': left, 'right': right}
def get_deadline(self, agent):
return self.agent_states[agent]['deadline'] if agent is self.primary_agent else None
def act(self, agent, action):
assert agent in self.agent_states, "Unknown agent!"
assert action in self.valid_actions, "Invalid action!"
state = self.agent_states[agent]
location = state['location']
heading = state['heading']
light = 'green' if (self.intersections[location].state and heading[1] != 0) or ((not self.intersections[location].state) and heading[0] != 0) else 'red'
sense = self.sense(agent)
reward = 0
move_okay = True
if action == 'forward':
if light != 'green':
move_okay = False
elif action == 'left':
if light == 'green' and (sense['oncoming'] == None or sense['oncoming'] == 'left'):
heading = (heading[1], -heading[0])
else:
move_okay = False
elif action == 'right':
if light == 'green' or sense['left'] != 'straight':
heading = (-heading[1], heading[0])
else:
move_okay = False
if move_okay:
if action is not None:
location = ((location[0] + heading[0] - self.bounds[0]) % (self.bounds[2] - self.bounds[0] + 1) + self.bounds[0],
(location[1] + heading[1] - self.bounds[1]) % (self.bounds[3] - self.bounds[1] + 1) + self.bounds[1])
state['location'] = location
state['heading'] = heading
reward = 2.0 if action == agent.get_next_waypoint() else -0.5
else:
reward = 0.0
else:
reward = -1.0
if agent is self.primary_agent:
if state['location'] == state['destination']:
if state['deadline'] >= 0:
reward += 10
self.done = True
print "Environment.act(): Primary agent has reached destination!"
self.status_text = "state: {}\naction: {}\nreward: {}".format(agent.get_state(), action, reward)
return reward
def compute_dist(self, a, b):
"""L1 distance between two points."""
return abs(b[0] - a[0]) + abs(b[1] - a[1])
class Agent(object):
"""Base class for all agents."""
def __init__(self, env):
self.env = env
self.state = None
self.next_waypoint = None
self.color = 'cyan'
def reset(self, destination=None):
pass
def update(self, t):
pass
def get_state(self):
return self.state
def get_next_waypoint(self):
return self.next_waypoint
class DummyAgent(Agent):
color_choices = ['blue', 'cyan', 'magenta', 'orange']
def __init__(self, env):
super(DummyAgent, self).__init__(env)
self.next_waypoint = random.choice(Environment.valid_actions[1:])
self.color = random.choice(self.color_choices)
def update(self, t):
inputs = self.env.sense(self)
action_okay = True
if self.next_waypoint == 'right':
if inputs['light'] == 'red' and inputs['left'] == 'forward':
action_okay = False
elif self.next_waypoint == 'forward':
if inputs['light'] == 'red':
action_okay = False
elif self.next_waypoint == 'left':
if inputs['light'] == 'red' or (inputs['oncoming'] == 'forward' or inputs['oncoming'] == 'right'):
action_okay = False
action = None
if action_okay:
action = self.next_waypoint
self.next_waypoint = random.choice(Environment.valid_actions[1:])
reward = self.env.act(self, action)
| false
| true
|
f716c4d714c5addfce5044ca823eb0d4556612cc
| 518
|
py
|
Python
|
test/autest/gold_tests/smoke/smoke.test.py
|
SolidWallOfCode/txn-box
|
d92269be8bb8989bdaa96048757a01a0d3f6ba6d
|
[
"Apache-2.0"
] | 7
|
2019-10-11T23:53:01.000Z
|
2021-09-15T01:56:50.000Z
|
test/autest/gold_tests/smoke/smoke.test.py
|
SolidWallOfCode/txn-box
|
d92269be8bb8989bdaa96048757a01a0d3f6ba6d
|
[
"Apache-2.0"
] | 13
|
2019-08-07T16:03:51.000Z
|
2022-03-24T19:01:33.000Z
|
test/autest/gold_tests/smoke/smoke.test.py
|
SolidWallOfCode/txn-box
|
d92269be8bb8989bdaa96048757a01a0d3f6ba6d
|
[
"Apache-2.0"
] | 5
|
2019-07-24T15:59:02.000Z
|
2021-06-23T10:02:47.000Z
|
# @file
#
# Copyright 2020, Verizon Media
# SPDX-License-Identifier: Apache-2.0
#
'''
Basic smoke tests.
'''
Test.Summary = '''
Test basic functions and directives.
'''
Test.TxnBoxTestAndRun("Smoke Test", "smoke.replay.yaml", config_path='Auto', config_key="meta.txn_box.global"
,remap=[('http://example.one/3', 'http://example.one/3', ('--key=meta.txn_box.remap-1', 'smoke.replay.yaml'))
,('http://example.one', 'http://example.one')
]
)
| 28.777778
| 125
| 0.57722
|
Test.Summary = '''
Test basic functions and directives.
'''
Test.TxnBoxTestAndRun("Smoke Test", "smoke.replay.yaml", config_path='Auto', config_key="meta.txn_box.global"
,remap=[('http://example.one/3', 'http://example.one/3', ('--key=meta.txn_box.remap-1', 'smoke.replay.yaml'))
,('http://example.one', 'http://example.one')
]
)
| true
| true
|
f716c4e4e1ba4e92ac11bebaf53ac8c7e09eae5b
| 209
|
py
|
Python
|
treat/__init__.py
|
tjlaboss/tasty_treat
|
5a137b49c6648eda6500025de8bab9c8dcc78d45
|
[
"MIT"
] | 3
|
2019-03-04T22:52:07.000Z
|
2022-01-23T12:28:58.000Z
|
treat/__init__.py
|
tjlaboss/tasty_treat
|
5a137b49c6648eda6500025de8bab9c8dcc78d45
|
[
"MIT"
] | 3
|
2021-07-23T17:30:35.000Z
|
2021-09-17T16:25:57.000Z
|
treat/__init__.py
|
tjlaboss/tasty_treat
|
5a137b49c6648eda6500025de8bab9c8dcc78d45
|
[
"MIT"
] | null | null | null |
from . import constants
from . import argparse
from . import elements
from . import materials
from . import mesh
from . import moc
from .treat_lattice import TreatLattice
from .core_builder import CoreBuilder
| 23.222222
| 39
| 0.808612
|
from . import constants
from . import argparse
from . import elements
from . import materials
from . import mesh
from . import moc
from .treat_lattice import TreatLattice
from .core_builder import CoreBuilder
| true
| true
|
f716c526012bd7a8136418de119de0fdce82deb1
| 2,884
|
py
|
Python
|
mswh/comm/tests/test_sql.py
|
hannesb0/MSWH
|
ce214f26369106c124052638e93cc38fbd58cc91
|
[
"BSD-3-Clause-LBNL"
] | 5
|
2019-05-23T00:54:33.000Z
|
2021-06-01T18:06:49.000Z
|
mswh/comm/tests/test_sql.py
|
hannesb0/MSWH
|
ce214f26369106c124052638e93cc38fbd58cc91
|
[
"BSD-3-Clause-LBNL"
] | 36
|
2019-05-22T23:02:35.000Z
|
2021-04-04T21:24:17.000Z
|
mswh/comm/tests/test_sql.py
|
hannesb0/MSWH
|
ce214f26369106c124052638e93cc38fbd58cc91
|
[
"BSD-3-Clause-LBNL"
] | 14
|
2019-08-25T01:27:40.000Z
|
2021-11-17T19:25:02.000Z
|
import logging
import os
import unittest
from mswh.comm.sql import Sql
import pandas as pd
logging.basicConfig(level=logging.DEBUG)
# has setUpClass method, thus run the test on the entire class
class SqlTests(unittest.TestCase):
"""Tests the db-python read-write capabilities."""
@classmethod
def setUpClass(cls):
"""Initiates the sqlite db engine
for the test db file.
"""
test_db_name = "test.db"
test_db_fulpath = os.path.join(os.path.dirname(__file__), test_db_name)
cls.test_db_fulpath = test_db_fulpath
print(test_db_fulpath)
# create test db if it does not exist
if not os.path.exists(test_db_fulpath):
os.system("touch " + test_db_fulpath)
cls.sql_api = Sql(test_db_fulpath)
# example dict to write to db
cls.df = pd.DataFrame(
data=[["a", 1], ["b", 2]], columns=["comp", "cost"]
)
# example dict to write to db as table
cls.dict = {"k1": [12, 13, 14], "k2": ["a", "b", "c"]}
# example csv data
cls.path_to_csv = os.path.join(os.path.dirname(__file__), "table.csv")
# sql code to execute
cls.raw_sql = """CREATE TABLE sys_components
(
Component TEXT NOT NULL ,
Function TEXT NOT NULL ,
PRIMARY KEY (Component)
);"""
@classmethod
def tearDownClass(cls):
"""Clean up for any reinitiation of the test,
but keep the result. Any new run will overwrite
the result.
"""
store_db_name = "test_done.db"
# close the test db
cls.sql_api.db.close()
store_db_fulpath = os.path.join(
os.path.dirname(__file__), store_db_name
)
# rename file, overwrite if exists
if os.path.exists(store_db_fulpath):
os.remove(store_db_fulpath)
os.rename(cls.test_db_fulpath, store_db_fulpath)
def test_a_pd2table(self):
"""Tests write pandas dataframe to
db as a table.
"""
self.sql_api.pd2table(self.df, "pd2table")
def test_b_csv2table(self):
"""Tests write csv file to
db as a table.
"""
self.sql_api.csv2table(self.path_to_csv, "csv2table")
def test_c_table2pd(self):
"""Reads a single table from db as a pd.df"""
df = self.sql_api.table2pd("pd2table")
self.assertTrue((df == self.df).all().all())
def test_d_commit(self):
"""Use sql to write to db (e.g. create, alter)"""
self.assertTrue(self.sql_api.commit(self.raw_sql))
def test_e_tables2dict(self):
"""Read all tables from db into a dictionary
of dataframes.
"""
data = self.sql_api.tables2dict()
self.assertEqual(data["pd2table"].iloc[1, 1], 2)
| 29.428571
| 80
| 0.587379
|
import logging
import os
import unittest
from mswh.comm.sql import Sql
import pandas as pd
logging.basicConfig(level=logging.DEBUG)
class SqlTests(unittest.TestCase):
@classmethod
def setUpClass(cls):
test_db_name = "test.db"
test_db_fulpath = os.path.join(os.path.dirname(__file__), test_db_name)
cls.test_db_fulpath = test_db_fulpath
print(test_db_fulpath)
if not os.path.exists(test_db_fulpath):
os.system("touch " + test_db_fulpath)
cls.sql_api = Sql(test_db_fulpath)
cls.df = pd.DataFrame(
data=[["a", 1], ["b", 2]], columns=["comp", "cost"]
)
cls.dict = {"k1": [12, 13, 14], "k2": ["a", "b", "c"]}
cls.path_to_csv = os.path.join(os.path.dirname(__file__), "table.csv")
cls.raw_sql = """CREATE TABLE sys_components
(
Component TEXT NOT NULL ,
Function TEXT NOT NULL ,
PRIMARY KEY (Component)
);"""
@classmethod
def tearDownClass(cls):
store_db_name = "test_done.db"
cls.sql_api.db.close()
store_db_fulpath = os.path.join(
os.path.dirname(__file__), store_db_name
)
if os.path.exists(store_db_fulpath):
os.remove(store_db_fulpath)
os.rename(cls.test_db_fulpath, store_db_fulpath)
def test_a_pd2table(self):
self.sql_api.pd2table(self.df, "pd2table")
def test_b_csv2table(self):
self.sql_api.csv2table(self.path_to_csv, "csv2table")
def test_c_table2pd(self):
df = self.sql_api.table2pd("pd2table")
self.assertTrue((df == self.df).all().all())
def test_d_commit(self):
self.assertTrue(self.sql_api.commit(self.raw_sql))
def test_e_tables2dict(self):
data = self.sql_api.tables2dict()
self.assertEqual(data["pd2table"].iloc[1, 1], 2)
| true
| true
|
f716c53c15f21f36710f5ff9f2db87e1f34e3965
| 2,442
|
py
|
Python
|
2022-TR-Crystallography/Simulations-2.py
|
JMSkelton/Linkage-Isomer-JMAK-Kinetics
|
6ad7626ea9447855121a9b8ef6eb10efb93db300
|
[
"MIT"
] | null | null | null |
2022-TR-Crystallography/Simulations-2.py
|
JMSkelton/Linkage-Isomer-JMAK-Kinetics
|
6ad7626ea9447855121a9b8ef6eb10efb93db300
|
[
"MIT"
] | null | null | null |
2022-TR-Crystallography/Simulations-2.py
|
JMSkelton/Linkage-Isomer-JMAK-Kinetics
|
6ad7626ea9447855121a9b8ef6eb10efb93db300
|
[
"MIT"
] | null | null | null |
# Simulations-2.py
import glob
import math
import yaml
from KineticAnalysis.NumericalSimulator import NumericalSimulator
PPEqThreshold = 1.0e-4
if __name__ == "__main__":
# Read fits to time-resolved datasets.
tr_data_sets = { }
for f in glob.glob(r"TimeResolved-*.yaml"):
with open(f, 'rb') as input_file:
input_yaml = yaml.load(
input_file, Loader = yaml.CLoader
)
tr_data_sets[input_yaml['label']] = (
input_yaml['t_cyc'],
input_yaml['t_exc'],
input_yaml['temp'],
(input_yaml['data_t'], input_yaml['data_a']),
(input_yaml['alpha_bg'], input_yaml['k_exc'], input_yaml['k_dec']),
input_yaml['rms']
)
# For each dataset, determine the maximum excited-state population given the fitted k_exc/k_dec.
for label, (t_cyc, t_exc, _, _, (_, k_exc, k_dec), _) in tr_data_sets.items():
simulator = NumericalSimulator(
excN = 1.0, excK = k_exc, decN = 1.0, decK = k_dec
)
# 1. Determine the excitation level achieved with the set t_cyc/t_exc.
a_0, a_max = 0.0, 0.0
while True:
simulator.InitialiseTrajectory(a_0)
simulator.SetExcitation(True)
simulator.RunTrajectory(t_exc)
_, a_sim = simulator.GetTrajectory()
a_max = a_sim[-1]
simulator.SetExcitation(False)
simulator.RunTrajectory(t_cyc - t_exc)
_, a_sim = simulator.GetTrajectory()
if math.fabs(a_sim[-1] - a_0) < PPEqThreshold:
break
a_0 = a_sim[-1]
# 2. Determine the steady-state (maximum) excitation level.
simulator.SetExcitation(True)
simulator.InitialiseTrajectory(0.0)
a_max_sim = 0.0
while True:
simulator.RunTrajectory(1.0)
_, a_sim = simulator.GetTrajectory()
if math.fabs(a_sim[-1] - a_max_sim) < PPEqThreshold:
break
a_max_sim = a_sim[-1]
print("{0}: a_max = {1:.3f}, theoretical = {2:.3f}".format(label, a_max, a_max_sim))
print("")
| 28.729412
| 100
| 0.514742
|
import glob
import math
import yaml
from KineticAnalysis.NumericalSimulator import NumericalSimulator
PPEqThreshold = 1.0e-4
if __name__ == "__main__":
tr_data_sets = { }
for f in glob.glob(r"TimeResolved-*.yaml"):
with open(f, 'rb') as input_file:
input_yaml = yaml.load(
input_file, Loader = yaml.CLoader
)
tr_data_sets[input_yaml['label']] = (
input_yaml['t_cyc'],
input_yaml['t_exc'],
input_yaml['temp'],
(input_yaml['data_t'], input_yaml['data_a']),
(input_yaml['alpha_bg'], input_yaml['k_exc'], input_yaml['k_dec']),
input_yaml['rms']
)
for label, (t_cyc, t_exc, _, _, (_, k_exc, k_dec), _) in tr_data_sets.items():
simulator = NumericalSimulator(
excN = 1.0, excK = k_exc, decN = 1.0, decK = k_dec
)
a_0, a_max = 0.0, 0.0
while True:
simulator.InitialiseTrajectory(a_0)
simulator.SetExcitation(True)
simulator.RunTrajectory(t_exc)
_, a_sim = simulator.GetTrajectory()
a_max = a_sim[-1]
simulator.SetExcitation(False)
simulator.RunTrajectory(t_cyc - t_exc)
_, a_sim = simulator.GetTrajectory()
if math.fabs(a_sim[-1] - a_0) < PPEqThreshold:
break
a_0 = a_sim[-1]
simulator.SetExcitation(True)
simulator.InitialiseTrajectory(0.0)
a_max_sim = 0.0
while True:
simulator.RunTrajectory(1.0)
_, a_sim = simulator.GetTrajectory()
if math.fabs(a_sim[-1] - a_max_sim) < PPEqThreshold:
break
a_max_sim = a_sim[-1]
print("{0}: a_max = {1:.3f}, theoretical = {2:.3f}".format(label, a_max, a_max_sim))
print("")
| true
| true
|
f716c5621fcdf0c3aada36d496dec8b1fe35c8cc
| 9,045
|
py
|
Python
|
GUI-II/web_scrap.py
|
DulceWRLD/College
|
9b94868514f461c97121d72ea0855f72ca95e798
|
[
"MIT"
] | 2
|
2021-08-21T01:25:50.000Z
|
2021-12-10T06:51:46.000Z
|
GUI-II/web_scrap.py
|
DulceWRLD/College
|
9b94868514f461c97121d72ea0855f72ca95e798
|
[
"MIT"
] | null | null | null |
GUI-II/web_scrap.py
|
DulceWRLD/College
|
9b94868514f461c97121d72ea0855f72ca95e798
|
[
"MIT"
] | 6
|
2021-03-14T22:21:23.000Z
|
2022-03-29T15:30:58.000Z
|
###########################################################################################
# Created by Jason Downing #
# Some code originally found at this Stackoverflow Post: #
# https://stackoverflow.com/questions/18966368/python-beautifulsoup-scrape-tables #
# Also this page as well: #
# http://www.pythonforbeginners.com/python-on-the-web/web-scraping-with-beautifulsoup/ #
# #
# Copyright 2016 Jason Downing # #
# MIT LICENSED - DO WHATEVER YOU WANT WITH THIS FILE. #
###########################################################################################
# To setup urllib2 / bs4 (BeautifulSoup)
# Follow this URL: http://linuxconfig.org/how-to-install-python3-beautiful-soup-environment-on-debian-linux
# and run this command: pip install requests
import json
import requests
from bs4 import BeautifulSoup
# 6 URLs to scrap for lift / trail data.
# Order is: Waterville Valley, Cannon Mt, Bretton Woods, Loon Mt & Cranmore Mt
urls = ["http://www.waterville.com/ski-ride/snow-report.html",
"http://cannonmt.com/trail-lift-report.html",
"http://brettonwoods.com/alpine_trails/trail_report#top",
"http://www.loonmtn.com/explore/snow-conditions/trail-lift-report",
"http://www.cranmore.com/winter/snow-grooming-report",
"http://www.patspeak.com/snow_report.php"]
mountains = ["Waterville Valley", "Cannon Mt", "Bretton Woods",
"Loon Mt", "Cranmore Mt", "Pats Peak"]
# global JSON object to write only once.
JSON_trails = {}
# Waterville Valley
def waterville():
print ("DONE\n")
open_trails = []
closed_trails = []
# Get the page, then grab just the text and use BeautifulSoup to work some magic on it.
page = requests.get(urls[0])
data = page.text
soup = BeautifulSoup(data, "lxml")
# Get an entire div.
ski_data = soup.findAll('div', {'class' : 'tabset_content'})
# Let's get all open trails.
for each_div in soup.findAll('li', {'class' : 'open'}):
open_trails.append(each_div.text)
# Also all closed trails.
for each_div in soup.findAll('li', {'class' : 'closed'}):
closed_trails.append(each_div.text)
# Dump to trails object.
JSON_trails['waterville_open'] = open_trails
JSON_trails['waterville_closed'] = closed_trails
# Cannon Mt
def cannon():
print ("DONE\n")
trail_list = []
trail_status = []
open_trails = []
closed_trails = []
# Get the page, then grab just the text and use BeautifulSoup to work some magic on it.
page = requests.get(urls[1])
data = page.text
soup = BeautifulSoup(data, "lxml")
# Get lift status
# From stackoverflow:
# https://stackoverflow.com/questions/13074586/extracting-selected-columns-from-a-table-using-beautifulsoup
tables = soup.find('table') # change this for consistent code.
rows = tables.findAll('tr')
for cells in rows:
cell = cells.findAll('td')
trail_list.append(cell[0].text)
trail_status.append(cell[1].text)
# Get trail status
# THIS TRICK COMES FROM STACKOVERFLOW!
# https://stackoverflow.com/questions/14095511/beautifulsoup-in-python-getting-the-n-th-tag-of-a-type
tables = soup.findAll('table')[1]
rows = tables.findAll('tr')
for cells in rows:
if (len(cells) == 4):
cell = cells.findAll('td')
trail_list.append(cell[0].text)
trail_status.append(cell[1].text)
# # Print for debugging purposes.
# print ("Trails: \n")
# for trail in trail_list:
# print (trail)
# print ("Status: \n")
# for status in trail_status:
# print (status)
# Now let's figure out open / closed status for trails!
list_length = len(trail_list)
for a in range(list_length):
if (trail_status[a] == 'Open'):
open_trails.append(trail_list[a])
else:
closed_trails.append(trail_list[a])
# Dump to trails object.
JSON_trails['cannon_open'] = open_trails
JSON_trails['cannon_closed'] = closed_trails
# Bretton Woods
def bretton_woods():
print ("DONE\n")
trail_list = [] # List of all the trails, in order on the page.
trail_status = [] # List of trail status, in order on the page.
open_trails = [] # All the open trails or lifts
closed_trails = [] # All the closed trails or lifts
open_src = '/images/icons/open-sm.png'
# Get the page, then grab just the text and use BeautifulSoup to work some magic on it.
page = requests.get(urls[2])
data = page.text
soup = BeautifulSoup(data, "lxml")
# Get an entire div.
ski_data = soup.findAll('div', {'id' : 'trail-content'})
# Using this Stackoverflow post to figure out how to get the text I need.
# https://stackoverflow.com/questions/13202087/beautiful-soup-find-children-for-particular-div
for tag in ski_data:
tab = tag.findAll('div', {'class': 'trails-report'})
for tag2 in tab:
trail_list.append(tag2.text) # This gets all the trails by name.
# Now to get trail conditions
tab = tag.findAll('div', {'class': 'condition'})
for img in tab:
img_src = img.findAll('img')[0].get('src') # This gets the trail status (by image source)
trail_status.append(img_src)
# Now let's figure out open / closed status for trails!
list_length = len(trail_list)
for a in range(list_length):
if (trail_status[a] == open_src):
open_trails.append(trail_list[a])
else:
closed_trails.append(trail_list[a])
# Dump to trails object.
JSON_trails['bretton_woods_open'] = open_trails
JSON_trails['bretton_woods_closed'] = closed_trails
# Loon Mt
def loon():
trail_list = [] # List of all the trails, in order on the page.
trail_status = [] # List of trail status, in order on the page.
open_trails = [] # All the open trails or lifts
closed_trails = [] # All the closed trails or lifts
# Get the page, then grab just the text and use BeautifulSoup to work some magic on it.
page = requests.get(urls[3])
data = page.text
soup = BeautifulSoup(data, "lxml")
lifts = soup.findAll("table", {"class": "lift-status"})
titles_html = []
open_src = "/assets/prebuilt/img/template/small-green-checkmark.png"
closed_src = "/assets/prebuilt/img/template/small-red-x.png"
# Get all the td's on the page so we can go through and find the names / trail status.
for td in soup.findAll("td"):
titles_html += td
# Let's get all the img's so we can find open / closed trails and lifts.
for lift in lifts:
# Get all the trail names.
for td in lift.findAll('td'):
#print (td.getText())
trail_list.append(td.getText().strip())
# Get all the trail status'
img_src = lift.findAll('img') # Get all img's.
# See if we found an image
if len(img_src):
# We did, so only keep the relevant images.
if (img_src[0].get('src') == open_src or img_src[0].get('src') == closed_src):
# Append to our trail status list.
trail_status.append(img_src)
# See what list of trails we got.
# for trail in trail_list:
# print (trail)
# See what list of status we got.
for status in trail_status:
print (status)
print ("length of names: ")
print (len(trail_list))
print ("length of status: ")
print (len(trail_status))
# Now that we have a list of status and trails, let's put them together.
list_length = len(trail_list)
for a in range(list_length):
if (trail_status[a] == open_src):
open_trails.append(trail_list[a])
else:
closed_trails.append(trail_list[a])
# Dump to trails object.
JSON_trails['loon_open'] = open_trails
JSON_trails['loon_closed'] = closed_trails
# Cranmore Mt
def cranmore():
print ("NOT DONE.\n")
open_trails = []
closed_trails = []
# Dump to trails object.
JSON_trails['cranmore_open'] = open_trails
JSON_trails['cranmore_closed'] = closed_trails
def pats_peak():
print ("NOT DONE.\n")
open_trails = []
closed_trails = []
# Dump to trails object.
JSON_trails['pats_peak_open'] = open_trails
JSON_trails['pats_peak_closed'] = closed_trails
# Main loop for data gathering
for num in range(0, len(urls)):
print (mountains[num] + " lift / trail conditions")
print ("Current URL to check: " + urls[num] + "\n")
if (num == 0):
print ("Hello.")
#waterville()
if (num == 1):
print ("Hello.")
#cannon()
if (num == 2):
print ("Hello.")
#bretton_woods()
if (num == 3):
loon()
if (num == 4):
print ("Hello.")
#cranmore()
if (num == 5):
print ("Hello.")
#pats_peak()
# Dump to JSON file now.
# Stackoverflow post this is from:
# https://stackoverflow.com/questions/16267767/python-writing-json-to-file
with open("json/ski.json", "w") as outfile:
json.dump(JSON_trails, outfile, indent = 4)
| 31.297578
| 109
| 0.629519
| true
| true
|
|
f716c5911528e44ebe67f4a2d8cb1ca1d4e1243b
| 4,273
|
py
|
Python
|
codalab/rest/chats.py
|
millerjohnp/codalab-worksheets
|
d6fc37864e7a8966380fc9d73865b10e434d6678
|
[
"Apache-2.0"
] | null | null | null |
codalab/rest/chats.py
|
millerjohnp/codalab-worksheets
|
d6fc37864e7a8966380fc9d73865b10e434d6678
|
[
"Apache-2.0"
] | null | null | null |
codalab/rest/chats.py
|
millerjohnp/codalab-worksheets
|
d6fc37864e7a8966380fc9d73865b10e434d6678
|
[
"Apache-2.0"
] | 1
|
2020-03-13T08:16:17.000Z
|
2020-03-13T08:16:17.000Z
|
"""
Chatbox API
"""
import os
from bottle import get, local, post, request
import yaml
from codalab.objects.chat_box_qa import ChatBoxQA
from codalab.server.authenticated_plugin import AuthenticatedPlugin
@get('/chats', apply=AuthenticatedPlugin())
def get_chat_box():
"""
Return a list of chats that the current user has had
"""
query = {'user_id': request.user.user_id}
return {
'chats': local.model.get_chat_log_info(query),
'root_user_id': local.model.root_user_id,
'system_user_id': local.model.system_user_id,
}
@post('/chats', apply=AuthenticatedPlugin())
def post_chat_box():
"""
Add the chat to the log.
Return an auto response, if the chat is directed to the system.
Otherwise, return an updated chat list of the sender.
"""
recipient_user_id = request.POST.get('recipientUserId', None)
message = request.POST.get('message', None)
worksheet_uuid = request.POST.get('worksheetId', -1)
bundle_uuid = request.POST.get('bundleId', -1)
info = {
'sender_user_id': request.user.user_id,
'recipient_user_id': recipient_user_id,
'message': message,
'worksheet_uuid': worksheet_uuid,
'bundle_uuid': bundle_uuid,
}
chats = add_chat_log_info(info)
return {'chats': chats}
# @get('/faqs')
def get_faq():
"""
Return a list of FAQ items, each of the following format:
'0': {
'question': 'how can I upload / add a bundle?'
'answer': {
'response': 'You can do cl upload or click Update Bundle.',
'command': 'cl upload <file_path>'
}
}
Currently disabled. Needs further work.
"""
file_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '../objects/chat_box_qa.yaml'
)
with open(file_path, 'r') as stream:
content = yaml.safe_load(stream)
return {'faq': content}
def add_chat_log_info(query_info):
"""
Add the given chat into the database.
|query_info| encapsulates all the information of one chat
Example: query_info = {
'sender_user_id': 1,
'recipient_user_id': 2,
'message': 'Hello this is my message',
'worksheet_uuid': 0x508cf51e546742beba97ed9a69329838, // the worksheet the user is browsing when he/she sends this message
'bundle_uuid': 0x8e66b11ecbda42e2a1f544627acf1418, // the bundle the user is browsing when he/she sends this message
}
Return an auto response, if the chat is directed to the system.
Otherwise, return an updated chat list of the sender.
"""
updated_data = local.model.add_chat_log_info(query_info)
if query_info.get('recipient_user_id') != local.model.system_user_id:
return updated_data
else:
message = query_info.get('message')
worksheet_uuid = query_info.get('worksheet_uuid')
bundle_uuid = query_info.get('bundle_uuid')
bot_response = format_message_response(
ChatBoxQA.answer(message, worksheet_uuid, bundle_uuid)
)
info = {
'sender_user_id': local.model.system_user_id,
'recipient_user_id': request.user.user_id,
'message': bot_response,
'worksheet_uuid': worksheet_uuid,
'bundle_uuid': bundle_uuid,
}
local.model.add_chat_log_info(info)
return bot_response
def format_message_response(params):
"""
Format automatic response
|params| is None if the system can't process the user's message
or is not confident enough to give a response.
Otherwise, |params| is a triple that consists of
the question that the system is trying to answer,
the response it has for that question, and the recommended command to run.
Return the automatic response that will be sent back to the user's chat box.
"""
if params is None:
return 'Thank you for your question. Our staff will get back to you as soon as we can.'
else:
question, response, command = params
result = 'This is the question we are trying to answer: ' + question + '\n'
result += response + '\n'
result += 'You can try to run the following command: \n'
result += command
return result
| 34.739837
| 132
| 0.656681
|
import os
from bottle import get, local, post, request
import yaml
from codalab.objects.chat_box_qa import ChatBoxQA
from codalab.server.authenticated_plugin import AuthenticatedPlugin
@get('/chats', apply=AuthenticatedPlugin())
def get_chat_box():
query = {'user_id': request.user.user_id}
return {
'chats': local.model.get_chat_log_info(query),
'root_user_id': local.model.root_user_id,
'system_user_id': local.model.system_user_id,
}
@post('/chats', apply=AuthenticatedPlugin())
def post_chat_box():
recipient_user_id = request.POST.get('recipientUserId', None)
message = request.POST.get('message', None)
worksheet_uuid = request.POST.get('worksheetId', -1)
bundle_uuid = request.POST.get('bundleId', -1)
info = {
'sender_user_id': request.user.user_id,
'recipient_user_id': recipient_user_id,
'message': message,
'worksheet_uuid': worksheet_uuid,
'bundle_uuid': bundle_uuid,
}
chats = add_chat_log_info(info)
return {'chats': chats}
def get_faq():
file_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '../objects/chat_box_qa.yaml'
)
with open(file_path, 'r') as stream:
content = yaml.safe_load(stream)
return {'faq': content}
def add_chat_log_info(query_info):
updated_data = local.model.add_chat_log_info(query_info)
if query_info.get('recipient_user_id') != local.model.system_user_id:
return updated_data
else:
message = query_info.get('message')
worksheet_uuid = query_info.get('worksheet_uuid')
bundle_uuid = query_info.get('bundle_uuid')
bot_response = format_message_response(
ChatBoxQA.answer(message, worksheet_uuid, bundle_uuid)
)
info = {
'sender_user_id': local.model.system_user_id,
'recipient_user_id': request.user.user_id,
'message': bot_response,
'worksheet_uuid': worksheet_uuid,
'bundle_uuid': bundle_uuid,
}
local.model.add_chat_log_info(info)
return bot_response
def format_message_response(params):
if params is None:
return 'Thank you for your question. Our staff will get back to you as soon as we can.'
else:
question, response, command = params
result = 'This is the question we are trying to answer: ' + question + '\n'
result += response + '\n'
result += 'You can try to run the following command: \n'
result += command
return result
| true
| true
|
f716c5deb4bfae59b106289a4f4dd8d17a07d48e
| 12,462
|
py
|
Python
|
testing/test_ldclient_evaluation.py
|
gangeli/python-server-sdk
|
3095315fd53c3bf723b0f16b0c18acadef4dfb3e
|
[
"Apache-2.0"
] | null | null | null |
testing/test_ldclient_evaluation.py
|
gangeli/python-server-sdk
|
3095315fd53c3bf723b0f16b0c18acadef4dfb3e
|
[
"Apache-2.0"
] | null | null | null |
testing/test_ldclient_evaluation.py
|
gangeli/python-server-sdk
|
3095315fd53c3bf723b0f16b0c18acadef4dfb3e
|
[
"Apache-2.0"
] | null | null | null |
import pytest
import json
import time
from ldclient.client import LDClient, Config
from ldclient.feature_store import InMemoryFeatureStore
from ldclient.flag import EvaluationDetail
from ldclient.interfaces import FeatureStore
from ldclient.versioned_data_kind import FEATURES
from testing.stub_util import MockEventProcessor, MockUpdateProcessor
from testing.test_ldclient import make_off_flag_with_value
user = { 'key': 'userkey' }
flag1 = {
'key': 'key1',
'version': 100,
'on': False,
'offVariation': 0,
'variations': [ 'value1' ],
'trackEvents': False
}
flag2 = {
'key': 'key2',
'version': 200,
'on': False,
'offVariation': 1,
'variations': [ 'x', 'value2' ],
'trackEvents': True,
'debugEventsUntilDate': 1000
}
class ErroringFeatureStore(FeatureStore):
def get(self, kind, key, callback=lambda x: x):
raise NotImplementedError()
def all(self, kind, callback=lambda x: x):
raise NotImplementedError()
def upsert(self, kind, item):
pass
def delete(self, key, version):
pass
def init(self, data):
pass
@property
def initialized(self):
return True
def make_client(store):
return LDClient(config=Config(sdk_key='SDK_KEY',
base_uri='http://test',
event_processor_class=MockEventProcessor,
update_processor_class=MockUpdateProcessor,
feature_store=store))
def get_log_lines(caplog, level):
loglines = caplog.records
if callable(loglines):
# records() is a function in older versions of the caplog plugin
loglines = loglines()
return [line.message for line in loglines if line.levelname == level]
def test_variation_for_existing_feature():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
assert 'value' == client.variation('feature.key', user, default='default')
def test_variation_for_unknown_feature():
store = InMemoryFeatureStore()
client = make_client(store)
assert 'default' == client.variation('feature.key', user, default='default')
def test_variation_when_user_is_none():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
assert 'default' == client.variation('feature.key', None, default='default')
def test_variation_when_user_has_no_key():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
assert 'default' == client.variation('feature.key', { }, default='default')
def test_variation_for_flag_that_evaluates_to_none():
empty_flag = {
'key': 'feature.key',
'on': False,
'offVariation': None
}
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': empty_flag}})
client = make_client(store)
assert 'default' == client.variation('feature.key', user, default='default')
def test_variation_detail_for_existing_feature():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
expected = EvaluationDetail('value', 0, {'kind': 'OFF'})
assert expected == client.variation_detail('feature.key', user, default='default')
def test_variation_detail_for_unknown_feature():
store = InMemoryFeatureStore()
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'ERROR', 'errorKind': 'FLAG_NOT_FOUND'})
assert expected == client.variation_detail('feature.key', user, default='default')
def test_variation_detail_when_user_is_none():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'ERROR', 'errorKind': 'USER_NOT_SPECIFIED'})
assert expected == client.variation_detail('feature.key', None, default='default')
def test_variation_detail_when_user_has_no_key():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'ERROR', 'errorKind': 'USER_NOT_SPECIFIED'})
assert expected == client.variation_detail('feature.key', { }, default='default')
def test_variation_detail_for_flag_that_evaluates_to_none():
empty_flag = {
'key': 'feature.key',
'on': False,
'offVariation': None
}
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': empty_flag}})
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'OFF'})
actual = client.variation_detail('feature.key', user, default='default')
assert expected == actual
assert actual.is_default_value() == True
def test_variation_when_feature_store_throws_error(caplog):
store = ErroringFeatureStore()
client = make_client(store)
assert client.variation('feature.key', { "key": "user" }, default='default') == 'default'
errlog = get_log_lines(caplog, 'ERROR')
assert errlog == [ 'Unexpected error while retrieving feature flag "feature.key": NotImplementedError()' ]
def test_variation_detail_when_feature_store_throws_error(caplog):
store = ErroringFeatureStore()
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'ERROR', 'errorKind': 'EXCEPTION'})
actual = client.variation_detail('feature.key', { "key": "user" }, default='default')
assert expected == actual
assert actual.is_default_value() == True
errlog = get_log_lines(caplog, 'ERROR')
assert errlog == [ 'Unexpected error while retrieving feature flag "feature.key": NotImplementedError()' ]
def test_all_flags_returns_values():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
result = client.all_flags(user)
assert result == { 'key1': 'value1', 'key2': 'value2' }
def test_all_flags_returns_none_if_user_is_none():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
result = client.all_flags(None)
assert result is None
def test_all_flags_returns_none_if_user_has_no_key():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
result = client.all_flags({ })
assert result is None
def test_all_flags_returns_none_if_feature_store_throws_error(caplog):
store = ErroringFeatureStore()
client = make_client(store)
assert client.all_flags({ "key": "user" }) is None
errlog = get_log_lines(caplog, 'ERROR')
assert errlog == [ 'Unable to read flags for all_flag_state: NotImplementedError()' ]
def test_all_flags_state_returns_state():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
state = client.all_flags_state(user)
assert state.valid == True
result = state.to_json_dict()
assert result == {
'key1': 'value1',
'key2': 'value2',
'$flagsState': {
'key1': {
'variation': 0,
'version': 100
},
'key2': {
'variation': 1,
'version': 200,
'trackEvents': True,
'debugEventsUntilDate': 1000
}
},
'$valid': True
}
def test_all_flags_state_returns_state_with_reasons():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
state = client.all_flags_state(user, with_reasons=True)
assert state.valid == True
result = state.to_json_dict()
assert result == {
'key1': 'value1',
'key2': 'value2',
'$flagsState': {
'key1': {
'variation': 0,
'version': 100,
'reason': {'kind': 'OFF'}
},
'key2': {
'variation': 1,
'version': 200,
'trackEvents': True,
'debugEventsUntilDate': 1000,
'reason': {'kind': 'OFF'}
}
},
'$valid': True
}
def test_all_flags_state_can_be_filtered_for_client_side_flags():
flag1 = {
'key': 'server-side-1',
'on': False,
'offVariation': 0,
'variations': [ 'a' ],
'clientSide': False
}
flag2 = {
'key': 'server-side-2',
'on': False,
'offVariation': 0,
'variations': [ 'b' ],
'clientSide': False
}
flag3 = {
'key': 'client-side-1',
'on': False,
'offVariation': 0,
'variations': [ 'value1' ],
'clientSide': True
}
flag4 = {
'key': 'client-side-2',
'on': False,
'offVariation': 0,
'variations': [ 'value2' ],
'clientSide': True
}
store = InMemoryFeatureStore()
store.init({ FEATURES: { flag1['key']: flag1, flag2['key']: flag2, flag3['key']: flag3, flag4['key']: flag4 } })
client = make_client(store)
state = client.all_flags_state(user, client_side_only=True)
assert state.valid == True
values = state.to_values_map()
assert values == { 'client-side-1': 'value1', 'client-side-2': 'value2' }
def test_all_flags_state_can_omit_details_for_untracked_flags():
future_time = (time.time() * 1000) + 100000
flag1 = {
'key': 'key1',
'version': 100,
'on': False,
'offVariation': 0,
'variations': [ 'value1' ],
'trackEvents': False
}
flag2 = {
'key': 'key2',
'version': 200,
'on': False,
'offVariation': 1,
'variations': [ 'x', 'value2' ],
'trackEvents': True
}
flag3 = {
'key': 'key3',
'version': 300,
'on': False,
'offVariation': 1,
'variations': [ 'x', 'value3' ],
'debugEventsUntilDate': future_time
}
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2, 'key3': flag3 } })
client = make_client(store)
state = client.all_flags_state(user, with_reasons=True, details_only_for_tracked_flags=True)
assert state.valid == True
result = state.to_json_dict()
assert result == {
'key1': 'value1',
'key2': 'value2',
'key3': 'value3',
'$flagsState': {
'key1': {
'variation': 0
},
'key2': {
'variation': 1,
'version': 200,
'trackEvents': True,
'reason': {'kind': 'OFF'}
},
'key3': {
'variation': 1,
'version': 300,
'debugEventsUntilDate': future_time,
'reason': {'kind': 'OFF'}
}
},
'$valid': True
}
def test_all_flags_state_returns_empty_state_if_user_is_none():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
state = client.all_flags_state(None)
assert state.valid == False
def test_all_flags_state_returns_empty_state_if_user_has_no_key():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
state = client.all_flags_state({ })
assert state.valid == False
def test_all_flags_returns_empty_state_if_feature_store_throws_error(caplog):
store = ErroringFeatureStore()
client = make_client(store)
state = client.all_flags_state({ "key": "user" })
assert state.valid == False
errlog = get_log_lines(caplog, 'ERROR')
assert errlog == [ 'Unable to read flags for all_flag_state: NotImplementedError()' ]
| 34.520776
| 116
| 0.620847
|
import pytest
import json
import time
from ldclient.client import LDClient, Config
from ldclient.feature_store import InMemoryFeatureStore
from ldclient.flag import EvaluationDetail
from ldclient.interfaces import FeatureStore
from ldclient.versioned_data_kind import FEATURES
from testing.stub_util import MockEventProcessor, MockUpdateProcessor
from testing.test_ldclient import make_off_flag_with_value
user = { 'key': 'userkey' }
flag1 = {
'key': 'key1',
'version': 100,
'on': False,
'offVariation': 0,
'variations': [ 'value1' ],
'trackEvents': False
}
flag2 = {
'key': 'key2',
'version': 200,
'on': False,
'offVariation': 1,
'variations': [ 'x', 'value2' ],
'trackEvents': True,
'debugEventsUntilDate': 1000
}
class ErroringFeatureStore(FeatureStore):
def get(self, kind, key, callback=lambda x: x):
raise NotImplementedError()
def all(self, kind, callback=lambda x: x):
raise NotImplementedError()
def upsert(self, kind, item):
pass
def delete(self, key, version):
pass
def init(self, data):
pass
@property
def initialized(self):
return True
def make_client(store):
return LDClient(config=Config(sdk_key='SDK_KEY',
base_uri='http://test',
event_processor_class=MockEventProcessor,
update_processor_class=MockUpdateProcessor,
feature_store=store))
def get_log_lines(caplog, level):
loglines = caplog.records
if callable(loglines):
loglines = loglines()
return [line.message for line in loglines if line.levelname == level]
def test_variation_for_existing_feature():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
assert 'value' == client.variation('feature.key', user, default='default')
def test_variation_for_unknown_feature():
store = InMemoryFeatureStore()
client = make_client(store)
assert 'default' == client.variation('feature.key', user, default='default')
def test_variation_when_user_is_none():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
assert 'default' == client.variation('feature.key', None, default='default')
def test_variation_when_user_has_no_key():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
assert 'default' == client.variation('feature.key', { }, default='default')
def test_variation_for_flag_that_evaluates_to_none():
empty_flag = {
'key': 'feature.key',
'on': False,
'offVariation': None
}
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': empty_flag}})
client = make_client(store)
assert 'default' == client.variation('feature.key', user, default='default')
def test_variation_detail_for_existing_feature():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
expected = EvaluationDetail('value', 0, {'kind': 'OFF'})
assert expected == client.variation_detail('feature.key', user, default='default')
def test_variation_detail_for_unknown_feature():
store = InMemoryFeatureStore()
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'ERROR', 'errorKind': 'FLAG_NOT_FOUND'})
assert expected == client.variation_detail('feature.key', user, default='default')
def test_variation_detail_when_user_is_none():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'ERROR', 'errorKind': 'USER_NOT_SPECIFIED'})
assert expected == client.variation_detail('feature.key', None, default='default')
def test_variation_detail_when_user_has_no_key():
feature = make_off_flag_with_value('feature.key', 'value')
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': feature}})
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'ERROR', 'errorKind': 'USER_NOT_SPECIFIED'})
assert expected == client.variation_detail('feature.key', { }, default='default')
def test_variation_detail_for_flag_that_evaluates_to_none():
empty_flag = {
'key': 'feature.key',
'on': False,
'offVariation': None
}
store = InMemoryFeatureStore()
store.init({FEATURES: {'feature.key': empty_flag}})
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'OFF'})
actual = client.variation_detail('feature.key', user, default='default')
assert expected == actual
assert actual.is_default_value() == True
def test_variation_when_feature_store_throws_error(caplog):
store = ErroringFeatureStore()
client = make_client(store)
assert client.variation('feature.key', { "key": "user" }, default='default') == 'default'
errlog = get_log_lines(caplog, 'ERROR')
assert errlog == [ 'Unexpected error while retrieving feature flag "feature.key": NotImplementedError()' ]
def test_variation_detail_when_feature_store_throws_error(caplog):
store = ErroringFeatureStore()
client = make_client(store)
expected = EvaluationDetail('default', None, {'kind': 'ERROR', 'errorKind': 'EXCEPTION'})
actual = client.variation_detail('feature.key', { "key": "user" }, default='default')
assert expected == actual
assert actual.is_default_value() == True
errlog = get_log_lines(caplog, 'ERROR')
assert errlog == [ 'Unexpected error while retrieving feature flag "feature.key": NotImplementedError()' ]
def test_all_flags_returns_values():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
result = client.all_flags(user)
assert result == { 'key1': 'value1', 'key2': 'value2' }
def test_all_flags_returns_none_if_user_is_none():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
result = client.all_flags(None)
assert result is None
def test_all_flags_returns_none_if_user_has_no_key():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
result = client.all_flags({ })
assert result is None
def test_all_flags_returns_none_if_feature_store_throws_error(caplog):
store = ErroringFeatureStore()
client = make_client(store)
assert client.all_flags({ "key": "user" }) is None
errlog = get_log_lines(caplog, 'ERROR')
assert errlog == [ 'Unable to read flags for all_flag_state: NotImplementedError()' ]
def test_all_flags_state_returns_state():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
state = client.all_flags_state(user)
assert state.valid == True
result = state.to_json_dict()
assert result == {
'key1': 'value1',
'key2': 'value2',
'$flagsState': {
'key1': {
'variation': 0,
'version': 100
},
'key2': {
'variation': 1,
'version': 200,
'trackEvents': True,
'debugEventsUntilDate': 1000
}
},
'$valid': True
}
def test_all_flags_state_returns_state_with_reasons():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
state = client.all_flags_state(user, with_reasons=True)
assert state.valid == True
result = state.to_json_dict()
assert result == {
'key1': 'value1',
'key2': 'value2',
'$flagsState': {
'key1': {
'variation': 0,
'version': 100,
'reason': {'kind': 'OFF'}
},
'key2': {
'variation': 1,
'version': 200,
'trackEvents': True,
'debugEventsUntilDate': 1000,
'reason': {'kind': 'OFF'}
}
},
'$valid': True
}
def test_all_flags_state_can_be_filtered_for_client_side_flags():
flag1 = {
'key': 'server-side-1',
'on': False,
'offVariation': 0,
'variations': [ 'a' ],
'clientSide': False
}
flag2 = {
'key': 'server-side-2',
'on': False,
'offVariation': 0,
'variations': [ 'b' ],
'clientSide': False
}
flag3 = {
'key': 'client-side-1',
'on': False,
'offVariation': 0,
'variations': [ 'value1' ],
'clientSide': True
}
flag4 = {
'key': 'client-side-2',
'on': False,
'offVariation': 0,
'variations': [ 'value2' ],
'clientSide': True
}
store = InMemoryFeatureStore()
store.init({ FEATURES: { flag1['key']: flag1, flag2['key']: flag2, flag3['key']: flag3, flag4['key']: flag4 } })
client = make_client(store)
state = client.all_flags_state(user, client_side_only=True)
assert state.valid == True
values = state.to_values_map()
assert values == { 'client-side-1': 'value1', 'client-side-2': 'value2' }
def test_all_flags_state_can_omit_details_for_untracked_flags():
future_time = (time.time() * 1000) + 100000
flag1 = {
'key': 'key1',
'version': 100,
'on': False,
'offVariation': 0,
'variations': [ 'value1' ],
'trackEvents': False
}
flag2 = {
'key': 'key2',
'version': 200,
'on': False,
'offVariation': 1,
'variations': [ 'x', 'value2' ],
'trackEvents': True
}
flag3 = {
'key': 'key3',
'version': 300,
'on': False,
'offVariation': 1,
'variations': [ 'x', 'value3' ],
'debugEventsUntilDate': future_time
}
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2, 'key3': flag3 } })
client = make_client(store)
state = client.all_flags_state(user, with_reasons=True, details_only_for_tracked_flags=True)
assert state.valid == True
result = state.to_json_dict()
assert result == {
'key1': 'value1',
'key2': 'value2',
'key3': 'value3',
'$flagsState': {
'key1': {
'variation': 0
},
'key2': {
'variation': 1,
'version': 200,
'trackEvents': True,
'reason': {'kind': 'OFF'}
},
'key3': {
'variation': 1,
'version': 300,
'debugEventsUntilDate': future_time,
'reason': {'kind': 'OFF'}
}
},
'$valid': True
}
def test_all_flags_state_returns_empty_state_if_user_is_none():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
state = client.all_flags_state(None)
assert state.valid == False
def test_all_flags_state_returns_empty_state_if_user_has_no_key():
store = InMemoryFeatureStore()
store.init({ FEATURES: { 'key1': flag1, 'key2': flag2 } })
client = make_client(store)
state = client.all_flags_state({ })
assert state.valid == False
def test_all_flags_returns_empty_state_if_feature_store_throws_error(caplog):
store = ErroringFeatureStore()
client = make_client(store)
state = client.all_flags_state({ "key": "user" })
assert state.valid == False
errlog = get_log_lines(caplog, 'ERROR')
assert errlog == [ 'Unable to read flags for all_flag_state: NotImplementedError()' ]
| true
| true
|
f716c6d4dbe952ad73298aca3af754a81ad523a5
| 1,666
|
py
|
Python
|
step6_orchestration/telegram_publisher/publisher.py
|
osmya/pydatanlp
|
4162221224d2ad67078949691b0cfc4222731acd
|
[
"Apache-2.0"
] | 19
|
2019-07-01T13:31:45.000Z
|
2021-08-02T10:46:59.000Z
|
step6_orchestration/telegram_publisher/publisher.py
|
osmya/pydatanlp
|
4162221224d2ad67078949691b0cfc4222731acd
|
[
"Apache-2.0"
] | 1
|
2019-07-11T15:15:18.000Z
|
2019-07-20T16:41:36.000Z
|
step6_orchestration/telegram_publisher/publisher.py
|
osmya/pydatanlp
|
4162221224d2ad67078949691b0cfc4222731acd
|
[
"Apache-2.0"
] | 15
|
2019-07-11T21:26:06.000Z
|
2021-08-04T09:04:59.000Z
|
from aiogram import Bot, Dispatcher, executor, types
from aiogram.utils.exceptions import CantParseEntities
from dotenv import load_dotenv, find_dotenv
from signal import signal, SIGINT
from tqdm import tqdm
from os import getenv
import sys
import fire
import uvloop
import redis
load_dotenv(find_dotenv('.telegram'))
uvloop.install()
REDIS_HOST = getenv('REDIS_URL', 'localhost')
channel_id = getenv('MY_TELEGRAM_NUMBER')
async def push_update(content, bot):
try:
return await bot.send_message(
channel_id, content, parse_mode='Markdown')
except CantParseEntities:
return await bot.send_message(channel_id, content)
async def listen(source):
r_conn = redis.Redis(REDIS_HOST)
p = r_conn.pubsub(ignore_subscribe_messages=True)
p.subscribe(source)
for message in tqdm(p.listen()):
yield message['data']
async def subscribe_and_listen(bot, channel_name='processed'):
async for message in listen(channel_name):
await push_update(message, bot)
def main():
fire.Fire(TelegramPublisher)
class TelegramPublisher:
def publish(self, channel_name='processed'):
signal(SIGINT, interrupt_handler)
try:
loop = uvloop.new_event_loop()
bot = Bot(token=getenv('TELEGRAM_KEY'), loop=loop)
task = loop.create_task(subscribe_and_listen(bot, channel_name))
loop.run_until_complete(task)
finally:
task.cancel()
loop.run_until_complete(bot.close())
loop.close()
def interrupt_handler(signal, frame):
print('\nYou pressed Ctrl+C!')
sys.exit(0)
if __name__ == "__main__":
main()
| 27.311475
| 76
| 0.697479
|
from aiogram import Bot, Dispatcher, executor, types
from aiogram.utils.exceptions import CantParseEntities
from dotenv import load_dotenv, find_dotenv
from signal import signal, SIGINT
from tqdm import tqdm
from os import getenv
import sys
import fire
import uvloop
import redis
load_dotenv(find_dotenv('.telegram'))
uvloop.install()
REDIS_HOST = getenv('REDIS_URL', 'localhost')
channel_id = getenv('MY_TELEGRAM_NUMBER')
async def push_update(content, bot):
try:
return await bot.send_message(
channel_id, content, parse_mode='Markdown')
except CantParseEntities:
return await bot.send_message(channel_id, content)
async def listen(source):
r_conn = redis.Redis(REDIS_HOST)
p = r_conn.pubsub(ignore_subscribe_messages=True)
p.subscribe(source)
for message in tqdm(p.listen()):
yield message['data']
async def subscribe_and_listen(bot, channel_name='processed'):
async for message in listen(channel_name):
await push_update(message, bot)
def main():
fire.Fire(TelegramPublisher)
class TelegramPublisher:
def publish(self, channel_name='processed'):
signal(SIGINT, interrupt_handler)
try:
loop = uvloop.new_event_loop()
bot = Bot(token=getenv('TELEGRAM_KEY'), loop=loop)
task = loop.create_task(subscribe_and_listen(bot, channel_name))
loop.run_until_complete(task)
finally:
task.cancel()
loop.run_until_complete(bot.close())
loop.close()
def interrupt_handler(signal, frame):
print('\nYou pressed Ctrl+C!')
sys.exit(0)
if __name__ == "__main__":
main()
| true
| true
|
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