xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

ad6caaf895bc1263240d8f9376ba437ced2dd6f3

216

py

Python

source/auxiliary/other_utilities.py

JoZimmer/ParOptBeam

50d15d8d822a2718f2932807e06c4a7e02f866a3

[ "BSD-3-Clause" ]

1

2021-04-09T14:08:20.000Z

source/auxiliary/other_utilities.py

JoZimmer/ParOptBeam

50d15d8d822a2718f2932807e06c4a7e02f866a3

[ "BSD-3-Clause" ]

2

2021-04-28T15:05:01.000Z

2021-11-10T15:12:56.000Z

source/auxiliary/other_utilities.py

JoZimmer/ParOptBeam

50d15d8d822a2718f2932807e06c4a7e02f866a3

[ "BSD-3-Clause" ]

2

2021-02-01T08:49:45.000Z

2021-08-10T02:07:36.000Z

from os.path import sep as os_sep def get_adjusted_path_string(path_string): for separator in ['\\\\', '\\', '/', '//']: path_string = path_string.replace(separator, os_sep) return path_string[:]

21.6

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ad7112c3738411a5a1f2089e56459f416d494862

17,160

py

Python

tests/diag/test_ccsd.py

fevangelista/pyWicked

9bc0e13f6e45c86222ea95fdadf1cb66eb59862f

[ "MIT" ]

null

tests/diag/test_ccsd.py

fevangelista/pyWicked

9bc0e13f6e45c86222ea95fdadf1cb66eb59862f

[ "MIT" ]

null

tests/diag/test_ccsd.py

fevangelista/pyWicked

9bc0e13f6e45c86222ea95fdadf1cb66eb59862f

[ "MIT" ]

null

import wicked as w def print_comparison(val, val2): print(f"Result: {val}") print(f"Test: {val2}") def compare_expressions(test, ref): test_expr = w.Expression() ref_expr = w.Expression() for s in ref: ref_expr += w.string_to_expr(s) for eq in test: test_expr += eq.rhs_expression() print_comparison(test_expr, ref_expr) assert test_expr == ref_expr def initialize(): w.reset_space() w.add_space("o", "fermion", "occupied", ["i", "j", "k", "l", "m", "n"]) w.add_space("v", "fermion", "unoccupied", ["a", "b", "c", "d", "e", "f"]) def test_energy1(): """CCSD Energy <F T1> (1)""" initialize() T1 = w.op("t", ["v+ o"]) Fov = w.op("f", ["o+ v"]) wt = w.WickTheorem() val = wt.contract(w.rational(1), Fov @ T1, 0, 0) val2 = w.expression("f^{v_0}_{o_0} t^{o_0}_{v_0}") print_comparison(val, val2) assert val == val2 def test_energy2(): """CCSD Energy <V T2> (2)""" initialize() T2 = w.op("t", ["v+ v+ o o"]) Voovv = w.op("v", ["o+ o+ v v"]) wt = w.WickTheorem() val = wt.contract(w.rational(1), Voovv @ T2, 0, 0) val2 = w.expression("1/4 t^{o_0,o_1}_{v_0,v_1} v^{v_0,v_1}_{o_0,o_1}") print_comparison(val, val2) assert val == val2 def test_energy3(): """CCSD Energy 1/2 <V T1 T1> (3)""" initialize() T1 = w.op("t", ["v+ o"]) Voovv = w.op("v", ["o+ o+ v v"]) wt = w.WickTheorem() val = wt.contract(w.rational(1, 2), Voovv @ T1 @ T1, 0, 0) val2 = w.expression("1/2 t^{o0}_{v0} t^{o1}_{v1} v^{v0,v1}_{o0,o1}") print_comparison(val, val2) assert val == val2 def test_r1_1(): """CCSD T1 Residual Fov (1)""" initialize() Fvo = w.op("f", ["v+ o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), Fvo, 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("f^{o0}_{v0}").canonicalize() # print(val[0].rhs_term()) print_comparison(val, val2) assert val == val2 def test_r1_2(): """CCSD T1 Residual [Fvv,T1] (2)""" initialize() T1 = w.op("t", ["v+ o"]) Fvv = w.op("f", ["v+ v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), Fvv @ T1, 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("f^{v1}_{v0} t^{o0}_{v1}") print_comparison(val, val2) assert val == val2 def test_r1_3(): """CCSD T1 Residual [Foo,T1] (3)""" initialize() T1 = w.op("t", ["v+ o"]) Foo = w.op("f", ["o+ o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), Foo @ T1, 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("-1 f^{o0}_{o1} t^{o1}_{v0}") print_comparison(val, val2) assert val == val2 def test_r1_4(): """CCSD T1 Residual [Vovov,T1] (4)""" initialize() T1 = w.op("t", ["v+ o"]) Vovov = w.op("v", ["o+ v+ v o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), Vovov @ T1, 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("-1 t^{o1}_{v1} v^{o0,v1}_{o1,v0}") print_comparison(val, val2) assert val == val2 def test_r1_5(): """CCSD T1 Residual [Fvo,T2] (5)""" initialize() T2 = w.op("t", ["v+ v+ o o"]) Fov = w.op("f", ["o+ v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), Fov @ T2, 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("1 f^{v1}_{o1} t^{o0,o1}_{v0,v1}") print_comparison(val, val2) assert val == val2 def test_r1_6(): """CCSD T1 Residual [Vovvv,T2] (6)""" initialize() T2 = w.op("t", ["v+ v+ o o"]) Vovvv = w.op("v", ["o+ v+ v v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), Vovvv @ T2, 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("-1/2 t^{o0,o1}_{v1,v2} v^{v1,v2}_{o1,v0}") print_comparison(val, val2) assert val == val2 def test_r1_7(): """CCSD T1 Residual [Vooov,T2] (7)""" initialize() T2 = w.op("t", ["v+ v+ o o"]) Vooov = w.op("v", ["o+ o+ v o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), Vooov @ T2, 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("-1/2 t^{o1,o2}_{v0,v1} v^{o0,v1}_{o1,o2}") print_comparison(val, val2) assert val == val2 def test_r1_8(): """CCSD T1 Residual 1/2 [[Fov,T1],T1] (8)""" initialize() T1 = w.op("t", ["v+ o"]) Fov = w.op("f", ["o+ v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 2), w.commutator(w.commutator(Fov, T1), T1), 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("-1 f^{v1}_{o1} t^{o1}_{v0} t^{o0}_{v1}") print_comparison(val, val2) assert val == val2 def test_r1_9(): """CCSD T1 Residual 1/2 [[Vooov,T1],T1] (9)""" initialize() T1 = w.op("t", ["v+ o"]) Vooov = w.op("v", ["o+ o+ v o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 2), w.commutator(w.commutator(Vooov, T1), T1), 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("-1 t^{o1}_{v0} t^{o2}_{v1} v^{o0,v1}_{o1,o2}") print_comparison(val, val2) assert val == val2 def test_r1_10(): """CCSD T1 Residual 1/2 [[Vovvv,T1],T1] (10)""" initialize() T1 = w.op("t", ["v+ o"]) Vovvv = w.op("v", ["o+ v+ v v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 2), w.commutator(w.commutator(Vovvv, T1), T1), 2, 2) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("-1 t^{o0}_{v1} t^{o1}_{v2} v^{v1,v2}_{o1,v0}") print_comparison(val, val2) assert val == val2 def test_r1_11(): """CCSD T1 Residual 1/6 [[[Voovv,T1],T1],T1] (11)""" initialize() T1 = w.op("t", ["v+ o"]) Voovv = w.op("v", ["o+ o+ v v"]) wt = w.WickTheorem() sum = wt.contract( w.rational(1, 6), w.commutator(w.commutator(w.commutator(Voovv, T1), T1), T1), 2, 2, ) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val2 = w.expression("-1 t^{o1}_{v0} t^{o0}_{v1} t^{o2}_{v2} v^{v1,v2}_{o1,o2}") print_comparison(val, val2) assert val == val2 def test_r1_12_14(): """CCSD T1 Residual [[Voovv,T1],T2] (12-14)""" initialize() T1 = w.op("t", ["v+ o"]) T2 = w.op("t", ["v+ v+ o o"]) Voovv = w.op("v", ["o+ o+ v v"]) wt = w.WickTheorem() sum = wt.contract( w.rational(1), w.commutator(w.commutator(Voovv, T1), T2), 2, 2, ) val = sum.to_manybody_equation("r")["o|v"][0].rhs_expression() val += sum.to_manybody_equation("r")["o|v"][1].rhs_expression() val += sum.to_manybody_equation("r")["o|v"][2].rhs_expression() val2 = ( w.expression("1 t^{o1}_{v1} t^{o0,o2}_{v0,v2} v^{v1,v2}_{o1,o2}") + w.expression("-1/2 t^{o0}_{v1} t^{o1,o2}_{v0,v2} v^{v1,v2}_{o1,o2}") + w.expression("-1/2 t^{o1}_{v0} t^{o0,o2}_{v1,v2} v^{v1,v2}_{o1,o2}") ) print_comparison(val, val2) assert val == val2 def test_r2_1(): """CCSD T2 Residual Vvvoo (1)""" Vvvoo = w.op("v", ["v+ v+ o o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), Vvvoo, 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("1/4 v^{o0,o1}_{v0,v1}") print_comparison(val, val2) assert val == val2 def test_r2_2(): """CCSD T2 Residual [Fvv,T2] (2)""" T2 = w.op("t", ["v+ v+ o o"]) Fvv = w.op("f", ["v+ v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), w.commutator(Fvv, T2), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("-1/2 f^{v2}_{v0} t^{o0,o1}_{v1,v2}") print_comparison(val, val2) assert val == val2 def test_r2_3(): """CCSD T2 Residual [Foo,T2] (3)""" T2 = w.op("t", ["v+ v+ o o"]) Foo = w.op("f", ["o+ o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), w.commutator(Foo, T2), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("1/2 f^{o0}_{o2} t^{o1,o2}_{v0,v1}") print_comparison(val, val2) assert val == val2 def test_r2_4(): """CCSD T2 Residual [Voooo,T2] (4)""" T2 = w.op("t", ["v+ v+ o o"]) Voooo = w.op("v", ["o+ o+ o o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), w.commutator(Voooo, T2), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("1/8 t^{o2,o3}_{v0,v1} v^{o0,o1}_{o2,o3}") print_comparison(val, val2) assert val == val2 def test_r2_5(): """CCSD T2 Residual [Vvvvv,T2] (5)""" T2 = w.op("t", ["v+ v+ o o"]) Vvvvv = w.op("v", ["v+ v+ v v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), w.commutator(Vvvvv, T2), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("1/8 t^{o0,o1}_{v2,v3} v^{v2,v3}_{v0,v1}") print_comparison(val, val2) assert val == val2 def test_r2_6(): """CCSD T2 Residual [Vovov,T2] (6)""" T2 = w.op("t", ["v+ v+ o o"]) Vovov = w.op("v", ["o+ v+ v o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), w.commutator(Vovov, T2), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("- t^{o0,o2}_{v0,v2} v^{o1,v2}_{o2,v1}") print_comparison(val, val2) assert val == val2 def test_r2_7(): """CCSD T2 Residual [Vvvov,T1] (7)""" T1 = w.op("t", ["v+ o"]) Vvvov = w.op("v", ["v+ v+ v o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), w.commutator(Vvvov, T1), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("-1/2 t^{o0}_{v2} v^{o1,v2}_{v0,v1}") print_comparison(val, val2) assert val == val2 def test_r2_8(): """CCSD T2 Residual [Vovoo,T1] (8)""" T1 = w.op("t", ["v+ o"]) Vovoo = w.op("v", ["o+ v+ o o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1), w.commutator(Vovoo, T1), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("-1/2 t^{o2}_{v0} v^{o0,o1}_{o2,v1}") print_comparison(val, val2) assert val == val2 def test_r2_9_12(): """CCSD T2 Residual 1/2 [[Voovv,T2],T2] (9-12)""" T2 = w.op("t", ["v+ v+ o o"]) Voovv = w.op("v", ["o+ o+ v v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 2), w.commutator(w.commutator(Voovv, T2), T2), 4, 4) compare_expressions( sum.to_manybody_equation("r")["oo|vv"], [ "1/2 t^{o0,o2}_{v0,v2} t^{o1,o3}_{v1,v3} v^{v2,v3}_{o2,o3}", "-1/4 t^{o0,o1}_{v0,v2} t^{o2,o3}_{v1,v3} v^{v2,v3}_{o2,o3}", "1/16 t^{o2,o3}_{v0,v1} t^{o0,o1}_{v2,v3} v^{v2,v3}_{o2,o3}", "-1/4 t^{o0,o2}_{v0,v1} t^{o1,o3}_{v2,v3} v^{v2,v3}_{o2,o3}", ], ) def test_r2_13(): """CCSD T2 Residual 1/2 [[Voooo,T1],T1] (13)""" T1 = w.op("t", ["v+ o"]) Voooo = w.op("v", ["o+ o+ o o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 2), w.commutator(w.commutator(Voooo, T1), T1), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("1/4 t^{o2}_{v0} t^{o3}_{v1} v^{o0,o1}_{o2,o3}") print_comparison(val, val2) assert val == val2 def test_r2_14(): """CCSD T2 Residual 1/2 [[Vvvvv,T1],T1] (14)""" T1 = w.op("t", ["v+ o"]) Vvvvv = w.op("v", ["v+ v+ v v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 2), w.commutator(w.commutator(Vvvvv, T1), T1), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("1/4 t^{o0}_{v2} t^{o1}_{v3} v^{v2,v3}_{v0,v1}") print_comparison(val, val2) assert val == val2 def test_r2_15(): """CCSD T2 Residual 1/2 [[Vovov,T1],T1] (15)""" T1 = w.op("t", ["v+ o"]) Vovov = w.op("v", ["o+ v+ v o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 2), w.commutator(w.commutator(Vovov, T1), T1), 4, 4) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("t^{o2}_{v0} t^{o0}_{v2} v^{o1,v2}_{o2,v1}") print_comparison(val, val2) assert val == val2 def test_r2_16_17(): """CCSD T2 Residual [[Fov,T1],T2] (16-17)""" T1 = w.op("t", ["v+ o"]) T2 = w.op("t", ["v+ v+ o o"]) Fov = w.op("f", ["o+ v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 1), w.commutator(w.commutator(Fov, T1), T2), 4, 4) compare_expressions( sum.to_manybody_equation("r")["oo|vv"], [ "1/2 f^{v2}_{o2} t^{o0}_{v2} t^{o1,o2}_{v0,v1}", "1/2 f^{v2}_{o2} t^{o2}_{v0} t^{o0,o1}_{v1,v2}", ], ) def test_r2_18_21_22(): """CCSD T2 Residual [[Vooov,T1],T2] (18,21,22)""" T1 = w.op("t", ["v+ o"]) T2 = w.op("t", ["v+ v+ o o"]) Vooov = w.op("v", ["o+ o+ v o"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 1), w.commutator(w.commutator(Vooov, T1), T2), 4, 4) compare_expressions( sum.to_manybody_equation("r")["oo|vv"], [ "1/2 t^{o2}_{v2} t^{o0,o3}_{v0,v1} v^{o1,v2}_{o2,o3}", "-1/4 t^{o0}_{v2} t^{o2,o3}_{v0,v1} v^{o1,v2}_{o2,o3}", "t^{o2}_{v0} t^{o0,o3}_{v1,v2} v^{o1,v2}_{o2,o3}", ], ) def test_r2_19_20_23(): """CCSD T2 Residual [[Vovvv,T1],T2] (19,20,23)""" T1 = w.op("t", ["v+ o"]) T2 = w.op("t", ["v+ v+ o o"]) Vovvv = w.op("v", ["o+ v+ v v"]) wt = w.WickTheorem() sum = wt.contract(w.rational(1, 1), w.commutator(w.commutator(Vovvv, T1), T2), 4, 4) compare_expressions( sum.to_manybody_equation("r")["oo|vv"], [ "1/2 t^{o2}_{v2} t^{o0,o1}_{v0,v3} v^{v2,v3}_{o2,v1}", "t^{o0}_{v2} t^{o1,o2}_{v0,v3} v^{v2,v3}_{o2,v1}", "-1/4 t^{o2}_{v0} t^{o0,o1}_{v2,v3} v^{v2,v3}_{o2,v1}", ], ) def test_r2_24(): """CCSD T2 Residual 1/6 [[[Vovvv,T1],T1],T1] (24)""" T1 = w.op("t", ["v+ o"]) Vovvv = w.op("v", ["o+ v+ v v"]) wt = w.WickTheorem() sum = wt.contract( w.rational(1, 6), w.commutator(w.commutator(w.commutator(Vovvv, T1), T1), T1), 4, 4, ) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("-1/2 t^{o2}_{v0} t^{o0}_{v2} t^{o1}_{v3} v^{v2,v3}_{o2,v1}") print_comparison(val, val2) assert val == val2 def test_r2_25(): """CCSD T2 Residual 1/6 [[[Vooov,T1],T1],T1] (25)""" T1 = w.op("t", ["v+ o"]) Vooov = w.op("v", ["o+ o+ v o"]) wt = w.WickTheorem() sum = wt.contract( w.rational(1, 6), w.commutator(w.commutator(w.commutator(Vooov, T1), T1), T1), 4, 4, ) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression("-1/2 t^{o2}_{v0} t^{o3}_{v1} t^{o0}_{v2} v^{o1,v2}_{o2,o3}") print_comparison(val, val2) assert val == val2 def test_r2_26_30(): """CCSD T2 Residual [[[Voovv,T1],T1],T2] (26-30)""" T1 = w.op("t", ["v+ o"]) T2 = w.op("t", ["v+ v+ o o"]) Voovv = w.op("v", ["o+ o+ v v"]) wt = w.WickTheorem() sum = wt.contract( w.rational(1, 2), w.commutator(w.commutator(w.commutator(Voovv, T1), T1), T2), 4, 4, ) compare_expressions( sum.to_manybody_equation("r")["oo|vv"], [ "-1/2 t^{o0}_{v2} t^{o2}_{v3} t^{o1,o3}_{v0,v1} v^{v2,v3}_{o2,o3}", "1/8 t^{o0}_{v2} t^{o1}_{v3} t^{o2,o3}_{v0,v1} v^{v2,v3}_{o2,o3}", "-1/2 t^{o2}_{v0} t^{o3}_{v2} t^{o0,o1}_{v1,v3} v^{v2,v3}_{o2,o3}", "-1 t^{o2}_{v0} t^{o0}_{v2} t^{o1,o3}_{v1,v3} v^{v2,v3}_{o2,o3}", "1/8 t^{o2}_{v0} t^{o3}_{v1} t^{o0,o1}_{v2,v3} v^{v2,v3}_{o2,o3}", ], ) def test_r2_31(): """CCSD T2 Residual 1/24 [[[[Voovv,T1],T1],T1],T1] (31)""" T1 = w.op("t", ["v+ o"]) Voovv = w.op("v", ["o+ o+ v v"]) wt = w.WickTheorem() sum = wt.contract( w.rational(1, 24), w.commutator(w.commutator(w.commutator(w.commutator(Voovv, T1), T1), T1), T1), 4, 4, ) val = sum.to_manybody_equation("r")["oo|vv"][0].rhs_expression() val2 = w.expression( "1/4 t^{o2}_{v0} t^{o3}_{v1} t^{o0}_{v2} t^{o1}_{v3} v^{v2,v3}_{o2,o3}" ) print_comparison(val, val2) assert val == val2 if __name__ == "__main__": test_energy1() test_energy2() test_energy3() test_r1_1() test_r1_2() test_r1_3() test_r1_4() test_r1_5() test_r1_6() test_r1_7() test_r1_8() test_r1_9() test_r1_10() test_r1_11() test_r1_12_14() test_r2_1() test_r2_2() test_r2_3() test_r2_4() test_r2_5() test_r2_6() test_r2_7() test_r2_8() test_r2_9_12() test_r2_13() test_r2_14() test_r2_15() test_r2_16_17() test_r2_18_21_22() test_r2_19_20_23() test_r2_24() test_r2_25() test_r2_26_30() test_r2_31()

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17,160

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null

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a8e92779935c13faae8293404567f0278d30ae7e

138

py

Python

scripts/reactor/autogen_ludiquest2.py

hsienjan/SideQuest-Server

3e88debaf45615b759d999255908f99a15283695

[ "MIT" ]

null

scripts/reactor/autogen_ludiquest2.py

hsienjan/SideQuest-Server

3e88debaf45615b759d999255908f99a15283695

[ "MIT" ]

null

scripts/reactor/autogen_ludiquest2.py

hsienjan/SideQuest-Server

3e88debaf45615b759d999255908f99a15283695

[ "MIT" ]

null

# ParentID: 2202002 # Character field ID when accessed: 220020000 # ObjectID: 1000016 # Object Position X: -228 # Object Position Y: -198

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null

0

1

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6

a8ec8c2dda24550165672ed485cf1f2b5ef00950

30

py

Python

python/orcreader/__init__.py

nqbao/python-orc-reader

c4d6a06851b12a309f485ef208c0d84e80b22f8b

[ "BSD-3-Clause" ]

15

2016-07-04T17:05:31.000Z

2020-06-28T02:15:49.000Z

python/orcreader/__init__.py

nqbao/python-orc-reader

c4d6a06851b12a309f485ef208c0d84e80b22f8b

[ "BSD-3-Clause" ]

3

2017-05-15T06:01:18.000Z

2018-04-18T21:14:17.000Z

python/orcreader/__init__.py

nqbao/python-orc-reader

c4d6a06851b12a309f485ef208c0d84e80b22f8b

[ "BSD-3-Clause" ]

6

2017-01-23T23:47:52.000Z

2018-11-01T17:43:40.000Z

from .reader import OrcReader

15

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6

a8fbf8ce338a6262976301ea199c5ca131183f5f

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py

Python

reloader/__init__.py

gerardroche/AutomaticPackageReloader

e90c22a50f6bfb195394cc6eedab0e7977a0011d

[ "MIT" ]

30

2017-03-05T12:28:31.000Z

2022-03-23T11:32:23.000Z

reloader/__init__.py

gerardroche/AutomaticPackageReloader

e90c22a50f6bfb195394cc6eedab0e7977a0011d

[ "MIT" ]

34

2017-03-14T05:59:58.000Z

2021-08-24T16:25:05.000Z

reloader/__init__.py

randy3k/PackageReloader

1255fcb0bc8effb66956e2240c42b7ae10615860

[ "MIT" ]

16

2017-03-09T12:03:21.000Z

2019-10-18T08:19:37.000Z

from .reloader import reload_package, load_dummy

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6

d10113d3430723c74844cb610a13bb918fa54c11

46

py

Python

bunruija/modules/__init__.py

tma15/bunruija

64a5c993a06e9de75f8f382cc4b817f91965223f

[ "MIT" ]

4

2020-12-22T11:12:35.000Z

2021-12-15T13:30:02.000Z

bunruija/modules/__init__.py

tma15/bunruija

64a5c993a06e9de75f8f382cc4b817f91965223f

[ "MIT" ]

4

2021-01-16T07:34:22.000Z

2021-08-14T06:56:07.000Z

bunruija/modules/__init__.py

tma15/bunruija

64a5c993a06e9de75f8f382cc4b817f91965223f

[ "MIT" ]

null

from .static_embedding import StaticEmbedding

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45

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null

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1

0

1

0

1

0

6

d10682fb60ec99068c4dbd7e5b8bac0fee1bcbce

32

py

Python

dag_executor/Executor/__init__.py

GennadiiTurutin/dag_executor

ddc7eab1e0e98753309e245247ac00e465e52ec1

[ "MIT" ]

null

dag_executor/Executor/__init__.py

GennadiiTurutin/dag_executor

ddc7eab1e0e98753309e245247ac00e465e52ec1

[ "MIT" ]

null

dag_executor/Executor/__init__.py

GennadiiTurutin/dag_executor

ddc7eab1e0e98753309e245247ac00e465e52ec1

[ "MIT" ]

null

from .executor import Executor

16

31

0.8125

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null

0

1

0

1

0

1

0

6

d154ae1217c3ae34783bb85b3da68ecf82e62291

223

py

Python

app/__init__.py

cca/libraries_syllabus_notifications

0d42c96ca6fd777e501024bb986418e8897b3dbc

[ "ECL-2.0" ]

null

app/__init__.py

cca/libraries_syllabus_notifications

0d42c96ca6fd777e501024bb986418e8897b3dbc

[ "ECL-2.0" ]

5

2016-01-02T20:12:21.000Z

2022-01-21T20:31:39.000Z

app/__init__.py

cca/libraries_syllabus_notifications

0d42c96ca6fd777e501024bb986418e8897b3dbc

[ "ECL-2.0" ]

null

# @TODO we want to "from .app import main" so the test suite can import the # main() function but if we do that then app.py throws errors when importing # from config.py & its other dependencies from .has_syllabus import *

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1

0

1

0

6

d1720a3f200947c6d598c557c5d06099c334bc22

11,905

py

Python

chatbrick/brick/icn.py

BluehackRano/cb-wh

ecf11100ad83df71eac9d56f6abbd59ceeda9d83

[ "MIT" ]

null

chatbrick/brick/icn.py

BluehackRano/cb-wh

ecf11100ad83df71eac9d56f6abbd59ceeda9d83

[ "MIT" ]

null

chatbrick/brick/icn.py

BluehackRano/cb-wh

ecf11100ad83df71eac9d56f6abbd59ceeda9d83

[ "MIT" ]

1

2019-03-05T06:50:11.000Z

import logging import blueforge.apis.telegram as tg import requests from blueforge.apis.facebook import Message, ImageAttachment, QuickReply, QuickReplyTextItem, TemplateAttachment, \ GenericTemplate, Element, PostBackButton from chatbrick.util import get_items_from_xml, UNKNOWN_ERROR_MSG import time logger = logging.getLogger(__name__) BRICK_DEFAULT_IMAGE = 'https://www.chatbrick.io/api/static/brick/img_brick_13_001.png' GATE_INFO = { '0': '원할', '1': '보통', '2': '혼잡', '3': '매우혼잡', '9': '종료' } class Icn(object): def __init__(self, fb, brick_db): self.brick_db = brick_db self.fb = fb async def facebook(self, command): if command == 'get_started': # send_message = [ # Message( # attachment=ImageAttachment( # url=BRICK_DEFAULT_IMAGE # ) # ), # Message( # text='인천국제공항공사에서 제공하는 "출국장 대기인원 조회 서비스"에요.' # ), # Message( # attachment=TemplateAttachment( # payload=GenericTemplate( # elements=[ # Element( # image_url='https://www.chatbrick.io/api/static/brick/img_brick_13_002.png', # title='제 1여객터미널', # subtitle='제 1여객터미널의 게이트별 대기인원을 알려드려요.', # buttons=[ # PostBackButton( # title='1여객터미널 조회', # payload='brick|icn|1' # ) # ] # ), # Element( # image_url='https://www.chatbrick.io/api/static/brick/img_brick_13_002.png', # title='제 2여객터미널', # subtitle='제 2여객터미널의 게이트별 대기인원을 알려드려요.', # buttons=[ # PostBackButton( # title='2여객터미널 조회', # payload='brick|icn|2' # ) # ] # ) # ] # ) # ) # ) # ] send_message = [ Message( attachment=TemplateAttachment( payload=GenericTemplate( elements=[ Element(image_url=BRICK_DEFAULT_IMAGE, title='출국장 대기인원 조회 서비스', subtitle='인천국제공항공사에서 제공하는 "출국장 대기인원 조회 서비스"에요.') ] ) ) ), Message( attachment=TemplateAttachment( payload=GenericTemplate( elements=[ Element( image_url='https://www.chatbrick.io/api/static/brick/img_brick_13_002.png', title='제 1여객터미널', subtitle='제 1여객터미널의 게이트별 대기인원을 알려드려요.', buttons=[ PostBackButton( title='1여객터미널 조회', payload='brick|icn|1' ) ] ), Element( image_url='https://www.chatbrick.io/api/static/brick/img_brick_13_002.png', title='제 2여객터미널', subtitle='제 2여객터미널의 게이트별 대기인원을 알려드려요.', buttons=[ PostBackButton( title='2여객터미널 조회', payload='brick|icn|2' ) ] ) ] ) ) ) ] await self.fb.send_messages(send_message) await self.brick_db.save() elif command == '1' or command == '2': input_data = await self.brick_db.get() res = requests.get( url='http://openapi.airport.kr/openapi/service/StatusOfDepartures/getDeparturesCongestion?serviceKey=%s&terno=%s' % ( input_data['data']['api_key'], command), headers={ 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'}) items = get_items_from_xml(res) if type(items) is dict: if items.get('code', '00') == '99' or items.get('code', '00') == '30': send_message = [ Message( text='chatbrick 홈페이지에 올바르지 않은 API key를 입력했어요. 다시 한번 확인해주세요.', ) ] else: send_message = [ Message( text=UNKNOWN_ERROR_MSG ) ] else: if command == '1': the_other = '2' else: the_other = '1' raw_data = items[0] sending_message = '제 {terno} 여객터미널\n조회날짜 : {cgtdt}\n조회시간 : {cgthm}'.format(**raw_data) if command == '1': sending_message += '\n2번 출국장: %s명 (%s)' % (raw_data['gateinfo1'], GATE_INFO[raw_data['gate1']]) sending_message += '\n3번 출국장: %s명 (%s)' % (raw_data['gateinfo2'], GATE_INFO[raw_data['gate2']]) sending_message += '\n4번 출국장: %s명 (%s)' % (raw_data['gateinfo3'], GATE_INFO[raw_data['gate3']]) sending_message += '\n5번 출국장: %s명 (%s)' % (raw_data['gateinfo4'], GATE_INFO[raw_data['gate4']]) elif command == '2': sending_message += '\n1번 출국장: %s명 (%s)' % (raw_data['gateinfo1'], GATE_INFO[raw_data['gate1']]) sending_message += '\n2번 출국장: %s명 (%s)' % (raw_data['gateinfo2'], GATE_INFO[raw_data['gate2']]) send_message = [ Message( text=sending_message, quick_replies=QuickReply( quick_reply_items=[ QuickReplyTextItem( title='새로고침', payload='brick|icn|%s' % command ), QuickReplyTextItem( title='제%s여객터미널 조회' % the_other, payload='brick|icn|%s' % the_other ) ] ) ) ] await self.fb.send_messages(send_message) return None async def telegram(self, command): if command == 'get_started': send_message = [ tg.SendPhoto( photo=BRICK_DEFAULT_IMAGE ), tg.SendMessage( text='인천국제공항공사에서 제공하는 "출국장 대기인원 조회 서비스"에요.', reply_markup=tg.MarkUpContainer( inline_keyboard=[ [ tg.CallbackButton( text='제1여객터미널', callback_data='BRICK|icn|1' ), tg.CallbackButton( text='제2여객터미널', callback_data='BRICK|icn|2' ) ] ] ) ) ] await self.fb.send_messages(send_message) await self.brick_db.save() elif command == '1' or command == '2': input_data = await self.brick_db.get() res = requests.get( url='http://openapi.airport.kr/openapi/service/StatusOfDepartures/getDeparturesCongestion?serviceKey=%s&terno=%s' % ( input_data['data']['api_key'], command), headers={ 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'}) items = get_items_from_xml(res) if type(items) is dict: if items.get('code', '00') == '99' or items.get('code', '00') == '30': send_message = [ tg.SendMessage( text='chatbrick 홈페이지에 올바르지 않은 API key를 입력했어요. 다시 한번 확인해주세요.', ) ] else: send_message = [ tg.SendMessage( text=UNKNOWN_ERROR_MSG ) ] else: if command == '1': the_other = '2' else: the_other = '1' raw_data = items[0] sending_message = '*제 {terno} 여객터미널*\n조회날짜 : {cgtdt}\n조회시간 : {cgthm}'.format(**raw_data) if command == '1': sending_message += '\n2번 출국장: %s명 (%s)' % (raw_data['gateinfo1'], GATE_INFO[raw_data['gate1']]) sending_message += '\n3번 출국장: %s명 (%s)' % (raw_data['gateinfo2'], GATE_INFO[raw_data['gate2']]) sending_message += '\n4번 출국장: %s명 (%s)' % (raw_data['gateinfo3'], GATE_INFO[raw_data['gate3']]) sending_message += '\n5번 출국장: %s명 (%s)' % (raw_data['gateinfo4'], GATE_INFO[raw_data['gate4']]) elif command == '2': sending_message += '\n1번 출국장: %s명 (%s)' % (raw_data['gateinfo1'], GATE_INFO[raw_data['gate1']]) sending_message += '\n2번 출국장: %s명 (%s)' % (raw_data['gateinfo2'], GATE_INFO[raw_data['gate2']]) send_message = [ tg.SendMessage( text=sending_message, parse_mode='Markdown', reply_markup=tg.MarkUpContainer( inline_keyboard=[ [ tg.CallbackButton( text='새로고침', callback_data='BRICK|icn|%s' % command ) ], [ tg.CallbackButton( text='제%s여객터미널 조회' % the_other, callback_data='BRICK|icn|%s' % the_other ) ] ] ) ) ] await self.fb.send_messages(send_message) return None

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0

1

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1

0

null

0

6

66f4c17269c6170a21fe09050ef187d175632d22

3,384

py

Python

compiler/parser/expression_models/comparison.py

Fire-Script/FireScript

8103b9bafe68163c8018aae2e760b6ad50310595

[ "MIT" ]

2

2021-12-31T02:23:13.000Z

2022-01-13T09:59:52.000Z

compiler/parser/expression_models/comparison.py

classPythonAddike/FireScript

8103b9bafe68163c8018aae2e760b6ad50310595

[ "MIT" ]

1

2021-12-31T13:24:07.000Z

compiler/parser/expression_models/comparison.py

classPythonAddike/FireScript

8103b9bafe68163c8018aae2e760b6ad50310595

[ "MIT" ]

3

2021-12-31T12:08:23.000Z

2022-01-02T12:00:57.000Z

from compiler.bytecode.opcodes import OpCodes from compiler.errors.errors import FTypeError from compiler.parser.expressions import Expression from typing import List, Dict class EqualToExp(Expression): """ Syntax: (= arg1 arg2) Argument Types: Any Return Type: Bool Check if two objects are equal """ def __init__(self, line: int, *args: "Expression"): self.line = line self.lval = args[0] self.rval = args[1] def eval(self, variables: Dict[str, int]) -> List[List[str]]: return self.rval.eval(variables) + self.lval.eval(variables) + [[OpCodes.COMPARE, "0"]] def load_type(self, variables: Dict[str, str]) -> Dict[str, str]: variables = self.lval.load_type(variables) variables = self.rval.load_type(variables) if self.lval.value_type != self.rval.value_type: FTypeError( self.line, f"Cannot compare objects of type {self.lval.value_type} and {self.rval.value_type}!" ).raise_error() self._value_type = "Bool" return variables @classmethod def keyword(cls) -> str: return "=" @classmethod def num_args(cls) -> int: return 2 class GreaterThanExp(EqualToExp): """ Syntax: (> arg1 arg2) Argument Types: Integer | Float Return Type: Bool Check if arg1 > arg2 """ def load_type(self, variables: Dict[str, str]) -> Dict[str, str]: variables = self.lval.load_type(variables) variables = self.rval.load_type(variables) if self.lval.value_type != self.rval.value_type: FTypeError( self.line, f"Cannot compare objects of type {self.lval.value_type} and {self.rval.value_type}!" ).raise_error() if self.lval.value_type not in ["Integer", "Float"]: FTypeError( self.line, f"Cannot compare objects of type {self.lval.value_type}!" ).raise_error() self._value_type = "Bool" return variables def eval(self, variables: Dict[str, int]) -> List[List[str]]: return self.rval.eval(variables) + self.lval.eval(variables) + [[OpCodes.COMPARE, "1"]] @classmethod def keyword(cls) -> str: return ">" class LessThanExp(GreaterThanExp): """ Syntax: (< arg1 arg2) Argument Types: Integer | Float Return Type: Bool Check if arg1 < arg2 """ def __init__(self, line: int, *args: "Expression"): self.line = line self.lval = args[1] self.rval = args[0] @classmethod def keyword(cls) -> str: return "<" class GreaterThanOrEqualExp(GreaterThanExp): """ Syntax: (>= arg1 arg2) Argument Types: Integer | Float Return Type: Bool Check if arg1 >= arg2 """ def eval(self, variables: Dict[str, int]) -> List[List[str]]: return self.rval.eval(variables) + self.lval.eval(variables) + [[OpCodes.COMPARE, "2"]] @classmethod def keyword(cls) -> str: return ">" # First identifier will be `>` @classmethod def num_args(cls) -> int: return 3 # 1 argument for the `=` class LessThanOrEqualExp(GreaterThanOrEqualExp): def __init__(self, line: int, *args: "Expression"): self.line = line self.lval = args[1] self.rval = args[0]

27.737705

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0

0.010591

0.274527

3,384

121

101

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0

null

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1

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1

0

null

0

1

0

6

0f1a8a8fdf277ed1542030735b29d945045c82ec

40

py

Python

tools/RNN/rnn_quantizer/tensorflow/tf_nndct/utils/__init__.py

hito0512/Vitis-AI

996459fb96cb077ed2f7e789d515893b1cccbc95

[ "Apache-2.0" ]

1

2021-04-01T06:38:48.000Z

tools/RNN/rnn_quantizer/tensorflow/tf_nndct/utils/__init__.py

hito0512/Vitis-AI

996459fb96cb077ed2f7e789d515893b1cccbc95

[ "Apache-2.0" ]

null

tools/RNN/rnn_quantizer/tensorflow/tf_nndct/utils/__init__.py

hito0512/Vitis-AI

996459fb96cb077ed2f7e789d515893b1cccbc95

[ "Apache-2.0" ]

null

from nndct_shared.utils import registry

20

39

0.875

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null

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0

1

0

1

0

6

0f8b3458e256dcb554d3948811ed8426a35f7e37

2,067

py

Python

tests/test_entrez.py

ckrusemd/meta-analysis-tool

31685973f767c198952df4b87813a9c8345554b6

[ "BSD-3-Clause" ]

null

tests/test_entrez.py

ckrusemd/meta-analysis-tool

31685973f767c198952df4b87813a9c8345554b6

[ "BSD-3-Clause" ]

null

tests/test_entrez.py

ckrusemd/meta-analysis-tool

31685973f767c198952df4b87813a9c8345554b6

[ "BSD-3-Clause" ]

null

import requests from loguru import logger def test_entrez_query(): json_ = { "query": "kruse eiken vestergaard", "email": "XXX@YYY.com" } response = requests.post("http://api:8080/entrez/query", json = json_) assert response.status_code == 200 assert response.json() logger.info( response.json() ) def test_entrez_summary_single(): json_ = { "uid": "28197643", "email": "XXX@YYY.com" } response = requests.post("http://api:8080/entrez/summary/single", json = json_) assert response.status_code == 200 assert response.json() logger.info( response.json() ) def test_entrez_summary_list(): json_ = { "uid_list": ["28197643","29679305","27848006"], "email": "XXX@YYY.com" } response = requests.post("http://api:8080/entrez/summary/list", json = json_) assert response.status_code == 200 assert response.json() logger.info( response.json() ) def test_entrez_abstract_single(): json_ = { "uid": "28197643", "email": "XXX@YYY.com" } response = requests.post("http://api:8080/entrez/abstract/single", json = json_) assert response.status_code == 200 assert response.json() logger.info( response.json() ) def test_entrez_abstract_list(): json_ = { "uid_list": ["28197643","29679305","27848006"], "email": "XXX@YYY.com" } response = requests.post("http://api:8080/entrez/abstract/list", json = json_) assert response.status_code == 200 assert response.json() logger.info( response.json() ) def test_entrez_elink_single(): json_ = { "uid": "28197643", "email": "XXX@YYY.com" } response = requests.post("http://api:8080/entrez/elink/single", json = json_) assert response.status_code == 200 assert response.json() logger.info( response.json() ) def test_entrez_elink_list(): json_ = { "uid_list": ["28197643","29679305","27848006"], "email": "XXX@YYY.com" } response = requests.post("http://api:8080/entrez/elink/list", json = json_) assert response.status_code == 200 assert response.json() logger.info( response.json() )

39.75

86

0.669086

260

2,067

5.15

0.138462

0.146378

0.067961

0.073189

0.933532

0

0.083912

0.164006

2,067

52

87

39.75

0.690972

0

0.613636

0

0.247218

0

0.318182

1

0.159091

false

0

0.045455

0

0.204545

0

null

0

1

0

null

0

6

7e2848ed83aa47cbc6a16174581fc8a9c100f6b0

79,206

py

Python

xanalysis_groundstate_paper_figs.py

kseetharam/genPolaron

b4eb05c595f1dc7151aa564f56fcfbdeded570c5

[ "MIT" ]

null

xanalysis_groundstate_paper_figs.py

kseetharam/genPolaron

b4eb05c595f1dc7151aa564f56fcfbdeded570c5

[ "MIT" ]

null

xanalysis_groundstate_paper_figs.py

kseetharam/genPolaron

b4eb05c595f1dc7151aa564f56fcfbdeded570c5

[ "MIT" ]

null

import numpy as np import pandas as pd import xarray as xr import matplotlib import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from matplotlib.lines import Line2D import matplotlib.colors as colors from matplotlib.animation import writers from matplotlib.patches import ConnectionPatch import matplotlib.image as mpimg import os import itertools import pf_dynamic_cart as pfc import pf_dynamic_sph as pfs import Grid from scipy import interpolate from timeit import default_timer as timer import scipy.stats as ss import colors as col if __name__ == "__main__": # # Initialization # matplotlib.rcParams.update({'font.size': 12, 'text.usetex': True}) mpegWriter = writers['ffmpeg'](fps=0.75, bitrate=1800) matplotlib.rcParams.update({'font.size': 16, 'font.family': 'Times New Roman', 'text.usetex': True, 'mathtext.fontset': 'dejavuserif'}) axl = matplotlib.rcParams['axes.linewidth'] # ---- INITIALIZE GRIDS ---- (Lx, Ly, Lz) = (21, 21, 21) (dx, dy, dz) = (0.375, 0.375, 0.375) # (Lx, Ly, Lz) = (105, 105, 105) # (dx, dy, dz) = (0.375, 0.375, 0.375) NGridPoints_cart = (1 + 2 * Lx / dx) * (1 + 2 * Ly / dy) * (1 + 2 * Lz / dz) # Toggle parameters toggleDict = {'Dynamics': 'imaginary', 'Interaction': 'on', 'Grid': 'spherical', 'Coupling': 'twophonon', 'IRcuts': 'false', 'ReducedInterp': 'false', 'kGrid_ext': 'false'} # ---- SET OUTPUT DATA FOLDER ---- datapath = '/Users/kis/Dropbox/VariationalResearch/HarvardOdyssey/genPol_data/NGridPoints_{:.2E}/massRatio={:.1f}'.format(NGridPoints_cart, 1) animpath = '/Users/kis/Dropbox/VariationalResearch/DataAnalysis/figs' if toggleDict['Dynamics'] == 'real': innerdatapath = datapath + '/redyn' animpath = animpath + '/rdyn' elif toggleDict['Dynamics'] == 'imaginary': innerdatapath = datapath + '/imdyn' animpath = animpath + '/idyn' if toggleDict['Grid'] == 'cartesian': innerdatapath = innerdatapath + '_cart' elif toggleDict['Grid'] == 'spherical': innerdatapath = innerdatapath + '_spherical' if toggleDict['Coupling'] == 'frohlich': innerdatapath = innerdatapath + '_froh' animpath = animpath + '_frohlich' elif toggleDict['Coupling'] == 'twophonon': innerdatapath = innerdatapath animpath = animpath + '_twophonon' if toggleDict['IRcuts'] == 'true': innerdatapath = innerdatapath + '_IRcuts' elif toggleDict['IRcuts'] == 'false': innerdatapath = innerdatapath print(innerdatapath) # figdatapath = '/Users/kis/Dropbox/Apps/Overleaf/Quantum Cherenkov Transition in Bose Polaron Systems/figures/figdump' figdatapath = '/Users/kis/Dropbox/Apps/Overleaf/Cherenkov Polaron Paper pt1/figures/figdump' innerdatapath_cart = innerdatapath[0:-10] + '_cart' # # Analysis of Total Dataset base02 = col.base02.ashexstring() base2 = col.base2.ashexstring() red = col.red.ashexstring() green = col.green.ashexstring() cyan = col.cyan.ashexstring() blue = col.blue.ashexstring() violet = col.violet.ashexstring() aIBi = -2 # qds = xr.open_dataset(innerdatapath + '/quench_Dataset.nc') # qds_aIBi = qds.sel(aIBi=aIBi) qds = xr.open_dataset(innerdatapath + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) qds_aIBi = qds PVals = qds['P'].values tVals = qds['t'].values n0 = qds.attrs['n0'] gBB = qds.attrs['gBB'] mI = qds.attrs['mI'] mB = qds.attrs['mB'] nu = np.sqrt(n0 * gBB / mB) aBB = (mB / (4 * np.pi)) * gBB xi = (8 * np.pi * n0 * aBB)**(-1 / 2) print(qds.attrs['k_mag_cutoff'] * xi) aIBi_Vals = np.array([-12.5, -10.0, -9.0, -8.0, -7.0, -5.0, -3.5, -2.0, -1.0, -0.75, -0.5, -0.1]) # used by many plots (spherical) # # # # FIG SCHEMATIC - POLARON GRAPHIC + BOGO DISPERSION + POLARON DISPERSION # matplotlib.rcParams.update({'font.size': 12}) # labelsize = 13 # legendsize = 12 # fig1 = plt.figure(constrained_layout=False) # # gs1 = fig1.add_gridspec(nrows=1, ncols=1, bottom=0.1, top=0.95, left=0.05, right=0.2) # # gs2 = fig1.add_gridspec(nrows=1, ncols=1, bottom=0.15, top=0.91, left=0.32, right=0.58) # # gs3 = fig1.add_gridspec(nrows=1, ncols=1, bottom=0.15, top=0.91, left=0.7, right=0.97) # gs2 = fig1.add_gridspec(nrows=1, ncols=1, bottom=0.15, top=0.91, left=0.08, right=0.45) # gs3 = fig1.add_gridspec(nrows=1, ncols=1, bottom=0.15, top=0.91, left=0.57, right=0.97) # # ax_pol = fig1.add_subplot(gs1[0], frame_on=False); ax_pol.get_xaxis().set_visible(False); ax_pol.get_yaxis().set_visible(False) # ax_bogo = fig1.add_subplot(gs2[0]) # ax_gsE = fig1.add_subplot(gs3[0]) # # fig1.text(0.01, 0.95, '(a)', fontsize=labelsize) # # fig1.text(0.24, 0.95, '(b)', fontsize=labelsize) # # fig1.text(0.65, 0.95, '(c)', fontsize=labelsize) # fig1.text(0.01, 0.95, '(a)', fontsize=labelsize) # fig1.text(0.52, 0.95, '(b)', fontsize=labelsize) # fig1.set_size_inches(7.8, 3.5) # # # POLARON GRAPHIC # # polimg = mpimg.imread('images/PolaronGraphic.png') # # imgplot = ax_pol.imshow(polimg) # # BOGOLIUBOV DISPERSION (SPHERICAL) # kgrid = Grid.Grid("SPHERICAL_2D"); kgrid.initArray_premade('k', qds.coords['k'].values); kgrid.initArray_premade('th', qds.coords['th'].values) # kVals = kgrid.getArray('k') # wk_Vals = pfs.omegak(kVals, mB, n0, gBB) # mask = (wk_Vals < 2) * (wk_Vals > 0) # ax_bogo.plot(kVals[mask], wk_Vals[mask], 'k-', label='') # ax_bogo.plot(kVals[mask], nu * kVals[mask], color=red, linestyle='--', label=r'$c|\mathbf{k}|$') # ax_bogo.set_xlabel(r'$|\mathbf{k}|$', fontsize=labelsize) # ax_bogo.set_ylabel(r'$\omega_{|\mathbf{k}|}$', fontsize=labelsize) # ax_bogo.set_xlim([0 - 0.09, np.max(kVals[mask]) + 0.09]) # ax_bogo.xaxis.set_major_locator(plt.MaxNLocator(2)) # ax_bogo.set_ylim([0 - 0.09, 2 + 0.09]) # ax_bogo.yaxis.set_major_locator(plt.MaxNLocator(3)) # ax_bogo.legend(loc=2, fontsize=legendsize) # # # GROUND STATE ENERGY (SPHERICAL) # aIBi = -5 # print(aIBi * xi) # qds_aIBi = xr.open_dataset(innerdatapath + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # PVals = qds_aIBi['P'].values # CSAmp_ds = qds_aIBi['Real_CSAmp'] + 1j * qds_aIBi['Imag_CSAmp'] # kgrid = Grid.Grid("SPHERICAL_2D"); kgrid.initArray_premade('k', CSAmp_ds.coords['k'].values); kgrid.initArray_premade('th', CSAmp_ds.coords['th'].values) # Energy_Vals = np.zeros((PVals.size, tVals.size)) # for Pind, P in enumerate(PVals): # for tind, t in enumerate(tVals): # CSAmp = CSAmp_ds.sel(P=P, t=t).values # Energy_Vals[Pind, tind] = pfs.Energy(CSAmp, kgrid, P, aIBi, mI, mB, n0, gBB) # Energy_Vals_inf = Energy_Vals[:, -1] # Einf_tck = interpolate.splrep(PVals, Energy_Vals_inf, s=0) # # Pinf_Vals = np.linspace(np.min(PVals), np.max(PVals), 5 * PVals.size) # Pinf_Vals = np.linspace(0, np.max(PVals), 5 * PVals.size) # Einf_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=0) # Einf_1stderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=1) # Einf_2ndderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=2) # sound_mask = np.abs(Einf_2ndderiv_Vals) <= 5e-3 # Einf_sound = Einf_Vals[sound_mask] # Pinf_sound = Pinf_Vals[sound_mask] # [vsound, vs_const] = np.polyfit(Pinf_sound, Einf_sound, deg=1) # ms_mask = Pinf_Vals <= 0.5 # Einf_1stderiv_ms = Einf_1stderiv_Vals[ms_mask] # Pinf_ms = Pinf_Vals[ms_mask] # [ms, ms_const] = np.polyfit(Pinf_ms, Einf_1stderiv_ms, deg=1) # mask = (Pinf_Vals / (mI * nu)) < 2.2 # Ecrit = Einf_Vals[np.argmin(np.gradient(Einf_2ndderiv_Vals)) - 0] # ax_gsE.plot(Pinf_Vals[mask][1:-1] / (mI * nu), Einf_Vals[mask][1:-1] / np.abs(Ecrit), 'k-') # ax_gsE.set_xlabel(r'$P/(m_{I}c)$', fontsize=labelsize) # ax_gsE.set_ylabel(r'$E/E_{\rm crit}$', fontsize=labelsize) # # ymin = -2.1 / np.abs(Ecrit); ymax = -1 / np.abs(Ecrit) # ymin = -1.3; ymax = -0.6 # # ax_gsE.set_ylim([ymin, ymax]); ax_gsE.set_xlim([0, 2.2]) # ax_gsE.set_ylim([ymin - 0.02, ymax + 0.02]); ax_gsE.set_xlim([-0.05, 2.2 + 0.05]) # ax_gsE.yaxis.set_major_locator(plt.MaxNLocator(2)) # Pcrit = Pinf_Vals[np.argmin(np.gradient(Einf_2ndderiv_Vals)) - 0] # ax_gsE.axvline(x=Pcrit / (mI * nu), ymin=0.03, ymax=0.975, linestyle=':', color=green, lw=2) # ax_bogo.tick_params(direction='in', right=True, top=True) # ax_gsE.tick_params(direction='in', right=True, top=True) # ax_bogo.set_title('BEC without Impurity') # ax_gsE.set_title('BEC with Impurity') # subVals = np.linspace(0, Pcrit / (mI * nu), 100) # supVals = np.linspace(Pcrit / (mI * nu), np.max(Pinf_Vals[mask] / (mI * nu)), 100) # ax_gsE.fill_between(supVals, ymin, ymax, facecolor=base2, alpha=0.75) # ax_gsE.fill_between(subVals, ymin, ymax, facecolor=base02, alpha=0.3) # font = {'family': 'serif', 'color': 'black', 'size': legendsize} # sfont = {'family': 'serif', 'color': 'black', 'size': legendsize - 1} # ax_gsE.text(0.16, -1.3 / np.abs(Ecrit), 'Polaron', fontdict=font) # ax_gsE.text(0.16, -1.4 / np.abs(Ecrit), '(quadratic)', fontdict=sfont) # ax_gsE.text(1.3, -1.8 / np.abs(Ecrit), 'Cherenkov', fontdict=font) # ax_gsE.text(1.3, -1.9 / np.abs(Ecrit), '(linear)', fontdict=sfont) # # ax_gsE.margins(1.05, 1.05) # fig1.savefig(figdatapath + '/FigSchematic.pdf') # # # # FIG 1 - PHASE DIAGRAM + DISTRIBUTION PLOTS - LETTER # matplotlib.rcParams['axes.linewidth'] = 0.5 * axl # matplotlib.rcParams.update({'font.size': 12}) # labelsize = 13 # legendsize = 12 # fig1 = plt.figure(constrained_layout=False) # gs1 = fig1.add_gridspec(nrows=1, ncols=1, bottom=0.13, top=0.94, left=0.08, right=0.55) # # gs2 = fig1.add_gridspec(nrows=2, ncols=1, bottom=0.13, top=0.94, left=0.67, right=0.99, height_ratios=[1, 1], hspace=0.2) # for ground state impurity distributions # gs2 = fig1.add_gridspec(nrows=2, ncols=1, bottom=0.13, top=0.94, left=0.63, right=0.915, height_ratios=[1, 1], hspace=0.2) # for dynamical real space density distributions # gs3 = fig1.add_gridspec(nrows=1, ncols=1, bottom=0.13, top=0.94, left=0.93, right=0.945); ax_colorbar = fig1.add_subplot(gs3[0]) # for dynamical real space density distributions # ax_PD = fig1.add_subplot(gs1[0]) # ax_supDist = fig1.add_subplot(gs2[0]) # ax_subDist = fig1.add_subplot(gs2[1]) # fig1.text(0.01, 0.95, '(a)', fontsize=labelsize) # fig1.text(0.575, 0.95, '(b)', fontsize=labelsize) # fig1.text(0.575, 0.52, '(c)', fontsize=labelsize) # fig1.set_size_inches(7.8, 4.5) # # PHASE DIAGRAM (SPHERICAL) # Pcrit = np.zeros(aIBi_Vals.size) # ms_Vals = np.zeros(aIBi_Vals.size) # for aind, aIBi in enumerate(aIBi_Vals): # qds_aIBi = xr.open_dataset(innerdatapath + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # CSAmp_ds = qds_aIBi['Real_CSAmp'] + 1j * qds_aIBi['Imag_CSAmp'] # kgrid = Grid.Grid("SPHERICAL_2D"); kgrid.initArray_premade('k', CSAmp_ds.coords['k'].values); kgrid.initArray_premade('th', CSAmp_ds.coords['th'].values) # Energy_Vals_inf = np.zeros(PVals.size) # for Pind, P in enumerate(PVals): # CSAmp = CSAmp_ds.sel(P=P).isel(t=-1).values # Energy_Vals_inf[Pind] = pfs.Energy(CSAmp, kgrid, P, aIBi, mI, mB, n0, gBB) # Einf_tck = interpolate.splrep(PVals, Energy_Vals_inf, s=0) # Pinf_Vals = np.linspace(np.min(PVals), np.max(PVals), 2 * PVals.size) # Einf_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=0) # Einf_1stderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=1) # Einf_2ndderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=2) # # Pcrit[aind] = Pinf_Vals[np.argwhere(Einf_2ndderiv_Vals < 0)[-2][0] + 3] # Pcrit[aind] = Pinf_Vals[np.argmin(np.gradient(Einf_2ndderiv_Vals)) - 0] # there is a little bit of fudging with the -3 here so that aIBi=-10 gives me Pcrit/(mI*c) = 1 -> I can also just generate data for weaker interactions and see if it's better # ms_mask = Pinf_Vals < 0.3 # Einf_1stderiv_ms = Einf_1stderiv_Vals[ms_mask] # Pinf_ms = Pinf_Vals[ms_mask] # [ms_Vals[aind], ms_const] = np.polyfit(Pinf_ms, Einf_1stderiv_ms, deg=1) # Pcrit_norm = Pcrit / (mI * nu) # Pcrit_tck = interpolate.splrep(aIBi_Vals, Pcrit_norm, s=0, k=3) # aIBi_interpVals = np.linspace(np.min(aIBi_Vals), np.max(aIBi_Vals), 5 * aIBi_Vals.size) # Pcrit_interpVals = 1 * interpolate.splev(aIBi_interpVals, Pcrit_tck, der=0) # print(Pcrit_norm) # print(Pcrit_norm[1], Pcrit_norm[5], Pcrit_norm[-5]) # massEnhancement_Vals = (1 / ms_Vals) / mI # mE_tck = interpolate.splrep(aIBi_Vals, massEnhancement_Vals, s=0) # aIBi_interpVals = np.linspace(np.min(aIBi_Vals), np.max(aIBi_Vals), 5 * aIBi_Vals.size) # mE_interpVals = 1 * interpolate.splev(aIBi_interpVals, mE_tck, der=0) # # scalefac = 1.0 # scalefac = 0.95 # just to align weakly interacting case slightly to 1 (it's pretty much there, would just need higher resolution data) # Pcrit_norm = scalefac * Pcrit_norm # Pcrit_interpVals = scalefac * Pcrit_interpVals # # xmin = np.min(aIBi_interpVals / xi); xmax = 1.01 * np.max(aIBi_interpVals / xi) # # ymin = 0; ymax = 1.01 * np.max(Pcrit_interpVals) # xmin = -11.45; xmax = 0.25 # ymin = -0.1; ymax = 4.0 # font = {'family': 'serif', 'color': 'black', 'size': legendsize} # sfont = {'family': 'serif', 'color': 'black', 'size': legendsize - 1} # ax_PD.plot(aIBi_Vals * xi, Pcrit_norm, marker='s', linestyle='None', mec='k', mfc='None', ms=5) # ax_PD.plot(aIBi_interpVals * xi, Pcrit_interpVals, 'k-') # # f1 = interpolate.interp1d(aIBi_Vals, Pcrit_norm, kind='cubic') # # ax_PD.plot(aIBi_interpVals, f1(aIBi_interpVals), 'k-') # ax_PD.set_xlabel(r'$a_{\rm IB}^{-1}/\xi^{-1}$', fontsize=labelsize) # ax_PD.set_ylabel(r'Total Momentum $P/(m_{I}c)$', fontsize=labelsize) # ax_PD.set_xlim([xmin, xmax]); ax_PD.set_ylim([ymin, ymax]) # ax_PD.fill_between(aIBi_interpVals * xi, Pcrit_interpVals, ymax - 0.1, facecolor=base2, alpha=0.75) # ax_PD.fill_between(aIBi_interpVals * xi, ymin + 0.1, Pcrit_interpVals, facecolor=base02, alpha=0.3) # # ax_PD.text(-3.2, ymin + 0.155 * (ymax - ymin), 'Polaron', fontdict=font) # # ax_PD.text(-3.1, ymin + 0.08 * (ymax - ymin), '(' + r'$Z>0$' + ')', fontdict=sfont) # ax_PD.text(-10.5, ymin + 0.155 * (ymax - ymin), 'Subsonic', fontdict=font) # # ax_PD.text(-10.5, ymin + 0.155 * (ymax - ymin), 'Polaron', fontdict=font) # ax_PD.text(-10.2, ymin + 0.08 * (ymax - ymin), r'$Z>0$', fontdict=sfont) # ax_PD.text(-10.5, ymin + 0.86 * (ymax - ymin), 'Cherenkov', fontdict=font) # ax_PD.text(-10.2, ymin + 0.785 * (ymax - ymin), r'$Z=0$', fontdict=sfont) # # ax_PD.text(-5.7, ymin + 0.5 * (ymax - ymin), 'Dynamical', fontdict=font, color=red) # # ax_PD.text(-5.6, ymin + 0.44 * (ymax - ymin), 'Transition', fontdict=font, color=red) # # # POLARON EFFECTIVE MASS (SPHERICAL) # # ax_PD.plot(aIBi_Vals * xi, massEnhancement_Vals, color='#ba9e88', marker='D', linestyle='None', markerfacecolor='None', mew=1, ms=5) # ax_PD.plot(aIBi_interpVals * xi, mE_interpVals, color='k', linestyle='dashed') # # CONNECTING LINES TO DISTRIBUTION FUNCTIONS # supDist_coords = [-5.0 * xi, 3.0] # is [aIBi/xi, P/(mI*c)] # subDist_coords = [-5.0 * xi, 0.5] # is [aIBi/xi, P/(mI*c)] # ax_PD.plot(supDist_coords[0], supDist_coords[1], linestyle='', marker='8', mec='k', mfc='k', ms=10) # ax_PD.plot(subDist_coords[0], subDist_coords[1], linestyle='', marker='8', mec='k', mfc='k', ms=10) # # # For ground state impurity distributions # # con_sup = ConnectionPatch(xyA=(supDist_coords[0], supDist_coords[1]), xyB=(0, 0.49), coordsA="data", coordsB="data", axesA=ax_PD, axesB=ax_supDist, color='k', linestyle='dotted', lw=0.5) # # con_sub = ConnectionPatch(xyA=(subDist_coords[0], subDist_coords[1]), xyB=(0, 0.34), coordsA="data", coordsB="data", axesA=ax_PD, axesB=ax_subDist, color='k', linestyle='dotted', lw=0.5) # # For dynamical real space density distributions # con_sup = ConnectionPatch(xyA=(supDist_coords[0], supDist_coords[1]), xyB=(0, -7), coordsA="data", coordsB="data", axesA=ax_PD, axesB=ax_supDist, color='k', linestyle='dotted', lw=0.5) # con_sub = ConnectionPatch(xyA=(subDist_coords[0], subDist_coords[1]), xyB=(0, -25), coordsA="data", coordsB="data", axesA=ax_PD, axesB=ax_subDist, color='k', linestyle='dotted', lw=0.5) # ax_PD.add_artist(con_sup) # ax_PD.add_artist(con_sub) # # # GROUND STATE IMPURITY DISTRIBUTION (CARTESIAN) # # # GaussianBroadening = True; sigma = 0.0168 # # GaussianBroadening = True; sigma = 0.02 # # incoh_color = green # # delta_color = base02 # # def GPDF(xVals, mean, stdev): # # return (1 / (stdev * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((xVals - mean) / stdev)**2) # # # return (1 / (1 * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((xVals - mean) / stdev)**2) # # aIBi = -5 # # qds_aIBi = xr.open_dataset(innerdatapath_cart + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # # PVals = qds_aIBi['P'].values # # nPIm_FWHM_indices = [] # # nPIm_distPeak_index = np.zeros(PVals.size, dtype=int) # # nPIm_FWHM_Vals = np.zeros(PVals.size) # # nPIm_distPeak_Vals = np.zeros(PVals.size) # # nPIm_deltaPeak_Vals = np.zeros(PVals.size) # # nPIm_Tot_Vals = np.zeros(PVals.size) # # nPIm_Vec = np.empty(PVals.size, dtype=np.object) # # PIm_Vec = np.empty(PVals.size, dtype=np.object) # # for ind, P in enumerate(PVals): # # qds_nPIm_inf = qds_aIBi['nPI_mag'].sel(P=P).isel(t=-1).dropna('PI_mag') # # PIm_Vals = qds_nPIm_inf.coords['PI_mag'].values # # dPIm = PIm_Vals[1] - PIm_Vals[0] # # nPIm_Vec[ind] = qds_nPIm_inf.values # # PIm_Vec[ind] = PIm_Vals # # # # Calculate nPIm(t=inf) normalization # # nPIm_Tot_Vals[ind] = np.sum(qds_nPIm_inf.values * dPIm) + qds_aIBi.sel(P=P).isel(t=-1)['mom_deltapeak'].values # # # Calculate FWHM, distribution peak, and delta peak # # nPIm_FWHM_Vals[ind] = pfc.FWHM(PIm_Vals, qds_nPIm_inf.values) # # nPIm_distPeak_Vals[ind] = np.max(qds_nPIm_inf.values) # # nPIm_deltaPeak_Vals[ind] = qds_aIBi.sel(P=P).isel(t=-1)['mom_deltapeak'].values # # D = qds_nPIm_inf.values - np.max(qds_nPIm_inf.values) / 2 # # indices = np.where(D > 0)[0] # # nPIm_FWHM_indices.append((indices[0], indices[-1])) # # nPIm_distPeak_index[ind] = np.argmax(qds_nPIm_inf.values) # # Pnorm = PVals / (mI * nu) # # Pratio_sup = 3.0; Pind_sup = np.abs(Pnorm - Pratio_sup).argmin() # # Pratio_sub = 0.5; Pind_sub = np.abs(Pnorm - Pratio_sub).argmin() # # print(Pnorm[Pind_sup], Pnorm[Pind_sub]) # # print(nPIm_deltaPeak_Vals[Pind_sup], nPIm_deltaPeak_Vals[Pind_sub]) # # ax_supDist.plot(PIm_Vec[Pind_sup] / (mI * nu), nPIm_Vec[Pind_sup], color=incoh_color, lw=1.0, label='Incoherent Part') # # ax_supDist.set_xlim([-0.01, 5]) # # ax_supDist.set_ylim([0, 1.05]) # # ax_supDist.set_ylabel(r'$n_{|\mathbf{P}_{\rm imp}|}$', fontsize=labelsize) # # # ax_supDist.set_xlabel(r'$|\vec{P_{I}}|/(m_{I}c)$', fontsize=labelsize) # # ax_supDist.fill_between(PIm_Vec[Pind_sup] / (mI * nu), np.zeros(PIm_Vals.size), nPIm_Vec[Pind_sup], facecolor=incoh_color, alpha=0.25) # # if GaussianBroadening: # # Pnorm_sup = PVals[Pind_sup] / (mI * nu) # # deltaPeak_sup = nPIm_deltaPeak_Vals[Pind_sup] # # PIm_norm_sup = PIm_Vec[Pind_sup] / (mI * nu) # # delta_GB_sup = deltaPeak_sup * GPDF(PIm_norm_sup, Pnorm_sup, sigma) # # # ax_supDist.plot(PIm_norm_sup, delta_GB_sup, linestyle='-', color=delta_color, linewidth=1, label=r'$\delta$-Peak') # # ax_supDist.plot(PIm_norm_sup, delta_GB_sup, linestyle='-', color=delta_color, linewidth=1.0, label='') # # ax_supDist.fill_between(PIm_norm_sup, np.zeros(PIm_norm_sup.size), delta_GB_sup, facecolor=delta_color, alpha=0.25) # # else: # # ax_supDist.plot((PVals[Pind_sup] / (mI * nu)) * np.ones(PIm_Vals.size), np.linspace(0, nPIm_deltaPeak_Vals[Pind_sup], PIm_Vals.size), linestyle='-', color=delta_color, linewidth=1.5, label='Delta Peak (Z-factor)') # # ax_supDist.legend(loc=1, fontsize=legendsize, frameon=False) # # ax_subDist.plot(PIm_Vec[Pind_sub] / (mI * nu), nPIm_Vec[Pind_sub], color=incoh_color, lw=1.0, label='Incoherent Part') # # # ax_subDist.set_xlim([-0.01, np.max(PIm_Vec[Pind_sub] / (mI*nu))]) # # ax_subDist.set_xlim([-0.01, 5]) # # ax_subDist.set_ylim([0, 1.05]) # # ax_subDist.set_ylabel(r'$n_{|\mathbf{P}_{\rm imp}|}$', fontsize=labelsize) # # ax_subDist.set_xlabel(r'$|\mathbf{P}_{\rm imp}|/(m_{I}c)$', fontsize=labelsize) # # ax_subDist.fill_between(PIm_Vec[Pind_sub] / (mI * nu), np.zeros(PIm_Vals.size), nPIm_Vec[Pind_sub], facecolor=incoh_color, alpha=0.25) # # if GaussianBroadening: # # Pnorm_sub = PVals[Pind_sub] / (mI * nu) # # deltaPeak_sub = nPIm_deltaPeak_Vals[Pind_sub] # # PIm_norm_sub = PIm_Vec[Pind_sub] / (mI * nu) # # delta_GB_sub = deltaPeak_sub * GPDF(PIm_norm_sub, Pnorm_sub, sigma) # # print(np.trapz(delta_GB_sub, PIm_norm_sub)) # # # ax_subDist.plot(PIm_norm_sub, delta_GB_sub, linestyle='-', color=delta_color, linewidth=1.0, label=r'$\delta$-Peak') # # # ax_subDist.fill_between(PIm_norm_sub, np.zeros(PIm_norm_sub.size), delta_GB_sub, facecolor=delta_color, alpha=0.25) # # ax_subDist.axvline(x=Pnorm_sub - 0.05, linestyle='-', color=delta_color, lw=1) # # ax_subDist.axvline(x=Pnorm_sub + 0.05, linestyle='-', color=delta_color, lw=1) # # else: # # ax_subDist.plot((PVals[Pind_sub] / (mI * nu)) * np.ones(PIm_Vals.size), np.linspace(0, nPIm_deltaPeak_Vals[Pind_sub], PIm_Vals.size), linestyle='-', color=delta_color, linewidth=1, label='Delta Peak (Z-factor)') # # ax_subDist.legend(loc=1, fontsize=legendsize, frameon=False) # # print(deltaPeak_sub, deltaPeak_sup) # # ax_PD.tick_params(direction='in', right=True, top=True) # # ax_subDist.tick_params(direction='in', right=True, top=True) # # ax_supDist.tick_params(direction='in', right=True, top=True) # # ax_supDist.xaxis.set_ticklabels([]) # # GAS DENSITY REAL SPACE DISTRIBUTION (CARTESIAN INTERPOLATION) # interpdatapath = '/Users/kis/Dropbox/VariationalResearch/HarvardOdyssey/genPol_data/NGridPoints_1.11E+08_resRat_0.50/massRatio=1.0/redyn_spherical/interp' # cmap = 'afmhot' # avmin = 1e-5; avmax = 1e-1 # aIBi = -5 # Pratio_sup = 3.0 # Pratio_sub = 0.52 # tratio = 39.99 # nu = 0.7926654595212022 # xi = 0.8920620580763856 # tscale = xi / nu # linDimMajor, linDimMinor = (10, 10) # interp_ds_sup = xr.open_dataset(interpdatapath + '/InterpDat_P_{:.2f}_aIBi_{:.2f}_t_{:.2f}_lDM_{:.2f}_lDm_{:.2f}.nc'.format(Pratio_sup * nu, aIBi, tratio * tscale, linDimMajor, linDimMinor)) # interp_ds_sub = xr.open_dataset(interpdatapath + '/InterpDat_P_{:.2f}_aIBi_{:.2f}_t_{:.2f}_lDM_{:.2f}_lDm_{:.2f}.nc'.format(Pratio_sub * nu, aIBi, tratio * tscale, linDimMajor, linDimMinor)) # n0 = interp_ds_sup.attrs['n0']; gBB = interp_ds_sup.attrs['gBB']; mI = interp_ds_sup.attrs['mI']; mB = interp_ds_sup.attrs['mB'] # nu = np.sqrt(n0 * gBB / mB) # mc = mI * nu # aBB = (mB / (4 * np.pi)) * gBB # xi = (8 * np.pi * n0 * aBB)**(-1 / 2) # tscale = xi / nu # P_sup = interp_ds_sup.attrs['P']; Pratio_sup = P_sup / mc # P_sub = interp_ds_sub.attrs['P']; Pratio_sub = P_sub / mc # xL = interp_ds_sup['x'].values; yL = interp_ds_sup['y'].values; zL = interp_ds_sup['z'].values # xLg, zLg = np.meshgrid(xL, zL, indexing='ij') # dx = xL[1] - xL[0]; dy = yL[1] - yL[0]; dz = zL[1] - zL[0] # na_xz_int_sup = interp_ds_sup['na_xz_int'].values; na_xz_int_norm_sup = na_xz_int_sup / (np.sum(na_xz_int_sup) * dx * dz) # na_xz_int_sub = interp_ds_sub['na_xz_int'].values; na_xz_int_norm_sub = na_xz_int_sub / (np.sum(na_xz_int_sub) * dx * dz) # quad_sup = ax_supDist.pcolormesh(zLg / xi, xLg / xi, na_xz_int_norm_sup, norm=colors.LogNorm(vmin=avmin, vmax=avmax), cmap=cmap, rasterized=True) # # ax_supDist.text(0.57, 0.85, r'$t/(\xi c^{-1})$' + ': {:.1f}'.format(tratio), transform=ax_supDist.transAxes, color='w', fontsize=legendsize - 1) # ax_supDist.set_ylabel(r'$x/\xi$', labelpad=-10, fontsize=labelsize) # quad_sub = ax_subDist.pcolormesh(zLg / xi, xLg / xi, na_xz_int_norm_sub, norm=colors.LogNorm(vmin=avmin, vmax=avmax), cmap=cmap, rasterized=True) # # ax_subDist.text(0.57, 0.85, r'$t/(\xi c^{-1})$' + ': {:.1f}'.format(tratio), transform=ax_subDist.transAxes, color='w', fontsize=legendsize - 1) # ax_subDist.set_xlabel(r'$z/\xi$', fontsize=labelsize) # ax_subDist.set_ylabel(r'$x/\xi$', labelpad=-10, fontsize=labelsize) # fig1.colorbar(quad_sup, cax=ax_colorbar, extend='both') # ax_PD.tick_params(direction='in', right=True, top=True) # ax_subDist.tick_params(direction='in', right=True, top=True) # ax_supDist.tick_params(direction='in', right=True, top=True) # ax_supDist.xaxis.set_ticklabels([]) # # # # DPT # qds = xr.open_dataset('/Users/kis/Dropbox/VariationalResearch/HarvardOdyssey/genPol_data/NGridPoints_1.11E+08_resRat_0.50/massRatio=1.0_noCSAmp/redyn_spherical' + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # tVals = qds['t'].values # DynOvExp_NegMask = False # DynOvExp_Cut = False # cut = 1e-4 # consecDetection = True # consecSamples = 10 # def powerfunc(t, a, b): # return b * t**(-1 * a) # tmin = 90 # tmax = 100 # tfVals = tVals[(tVals <= tmax) * (tVals >= tmin)] # rollwin = 1 # colorList = ['red', '#7e1e9c', 'green', 'orange', '#60460f', 'blue', 'magenta'] # lineList = ['solid', 'dashed', 'dotted', '-.'] # aIBi_des = np.array([-10.0, -5.0, -3.5, -2.5, -2.0, -1.75]) # massRat_des = np.array([1.0]) # datapath = '/Users/kis/Dropbox/VariationalResearch/HarvardOdyssey/genPol_data/NGridPoints_1.11E+08_resRat_0.50/massRatio=1.0_noCSAmp' # Pcrit_da = xr.DataArray(np.full(aIBi_des.size, np.nan, dtype=float), coords=[aIBi_des], dims=['aIBi']) # for inda, aIBi in enumerate(aIBi_des): # mds = xr.open_dataset(datapath + '/redyn_spherical/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # Plen = mds.coords['P'].values.size # Pstart_ind = 0 # PVals = mds.coords['P'].values[Pstart_ind:Plen] # n0 = mds.attrs['n0'] # gBB = mds.attrs['gBB'] # mI = mds.attrs['mI'] # mB = mds.attrs['mB'] # nu = np.sqrt(n0 * gBB / mB) # vI0_Vals = (PVals - mds.isel(t=0, P=np.arange(Pstart_ind, Plen))['Pph'].values) / mI # mds_ts = mds.sel(t=tfVals) # DynOv_Exponents = np.zeros(PVals.size) # DynOv_Constants = np.zeros(PVals.size) # for indP, P in enumerate(PVals): # DynOv_raw = np.abs(mds_ts.isel(P=indP)['Real_DynOv'].values + 1j * mds_ts.isel(P=indP)['Imag_DynOv'].values).real.astype(float) # DynOv_ds = xr.DataArray(DynOv_raw, coords=[tfVals], dims=['t']) # # DynOv_ds = DynOv_ds.rolling(t=rollwin, center=True).mean().dropna('t') # DynOv_Vals = DynOv_ds.values # tDynOvc_Vals = DynOv_ds['t'].values # S_slope, S_intercept, S_rvalue, S_pvalue, S_stderr = ss.linregress(np.log(tDynOvc_Vals), np.log(DynOv_Vals)) # DynOv_Exponents[indP] = -1 * S_slope # DynOv_Constants[indP] = np.exp(S_intercept) # if DynOvExp_NegMask: # DynOv_Exponents[DynOv_Exponents < 0] = 0 # if DynOvExp_Cut: # DynOv_Exponents[np.abs(DynOv_Exponents) < cut] = 0 # if consecDetection: # crit_ind = 0 # for indE, exp in enumerate(DynOv_Exponents): # if indE > DynOv_Exponents.size - consecDetection: # break # expSlice = DynOv_Exponents[indE:(indE + consecSamples)] # if np.all(expSlice > 0): # crit_ind = indE # break # DynOv_Exponents[0:crit_ind] = 0 # Pcrit_da[inda] = PVals[crit_ind] / (mI * nu) # DynOvf_Vals = powerfunc(1e1000, DynOv_Exponents, DynOv_Constants) # ax_PD.plot(aIBi_des * xi, Pcrit_da.values, linestyle='None', marker='D', mec=red, mfc=red, mew=2, ms=5) # print(aIBi_des) # print(Pcrit_da.values) # fig1.savefig(figdatapath + '/Fig1_Letter.pdf') # # fig1.savefig(figdatapath + '/Fig1_Letter.jpg', quality=100) # matplotlib.rcParams['axes.linewidth'] = axl # # # # FIG 2 - LETTER # matplotlib.rcParams.update({'font.size': 12}) # labelsize = 13 # legendsize = 12 # fig2 = plt.figure(constrained_layout=False) # gs1 = fig2.add_gridspec(nrows=2, ncols=1, bottom=0.23, top=0.95, left=0.12, right=0.48, hspace=0.1) # gs2 = fig2.add_gridspec(nrows=2, ncols=1, bottom=0.23, top=0.95, left=0.61, right=0.98, hspace=0.1) # ax_gsZ = fig2.add_subplot(gs1[1]) # ax_gsVel = fig2.add_subplot(gs1[0]) # ax_dynS = fig2.add_subplot(gs2[1]) # ax_dynVel = fig2.add_subplot(gs2[0]) # fig2.text(0.02, 0.95, '(a)', fontsize=labelsize) # fig2.text(0.02, 0.55, '(b)', fontsize=labelsize) # fig2.text(0.52, 0.95, '(c)', fontsize=labelsize) # fig2.text(0.52, 0.55, '(d)', fontsize=labelsize) # # colorList = ['red', 'green', 'blue'] # colorList = [red, green, blue] # # colorList = ['red', '#7e1e9c', 'green', 'orange', '#60460f', 'blue', 'magenta'] # lineList = ['solid', 'dashed', 'dotted', '-.'] # dyndatapath = '/Users/kis/Dropbox/VariationalResearch/HarvardOdyssey/genPol_data/NGridPoints_1.11E+08_resRat_0.50/massRatio=1.0_noCSAmp/redyn_spherical' # # ax_GSE1.set_ylim([0, 1.2 * np.max(Einf_1stderiv_Vals / np.abs(Ecrit))]) # # aIBi_des = np.array([-10.0, -5.0, -3.5, -2.0, -1.0]) # # aIBi_Vals = np.array([-10.0, -5.0, -3.5, -2.0]) # used by many plots (spherical) # aIBi_Vals = np.array([-10.0, -3.5, -2.0]) # used by many plots (spherical) # # # POLARON SOUND VELOCITY (SPHERICAL) # # Check to see if linear part of polaron (total system) energy spectrum has slope equal to sound velocity # vsound_Vals = np.zeros(aIBi_Vals.size) # vI_Vals = np.zeros(aIBi_Vals.size) # for aind, aIBi in enumerate(aIBi_Vals): # qds = xr.open_dataset(innerdatapath + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # qds_aIBi = qds.isel(t=-1) # ZVals = np.exp(-1 * qds_aIBi['Nph'].values) # CSAmp_ds = qds_aIBi['Real_CSAmp'] + 1j * qds_aIBi['Imag_CSAmp'] # kgrid = Grid.Grid("SPHERICAL_2D"); kgrid.initArray_premade('k', CSAmp_ds.coords['k'].values); kgrid.initArray_premade('th', CSAmp_ds.coords['th'].values) # Energy_Vals_inf = np.zeros(PVals.size) # PI_Vals = np.zeros(PVals.size) # for Pind, P in enumerate(PVals): # CSAmp = CSAmp_ds.sel(P=P).values # Energy_Vals_inf[Pind] = pfs.Energy(CSAmp, kgrid, P, aIBi, mI, mB, n0, gBB) # PI_Vals[Pind] = P - qds_aIBi.sel(P=P)['Pph'].values # Einf_tck = interpolate.splrep(PVals, Energy_Vals_inf, s=0) # Pinf_Vals = np.linspace(np.min(PVals), np.max(PVals), 2 * PVals.size) # Einf_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=0) # Einf_1stderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=1) # Einf_1stderiv_Vals_subsamp = 1 * interpolate.splev(PVals, Einf_tck, der=1) # xmask = (PVals / (mI * nu)) <= 4 # ax_gsZ.plot(PVals[xmask] / (mI * nu), ZVals[xmask], color=colorList[aind], linestyle='solid', marker='D', ms=4) # # ax_gsVel.plot(Pinf_Vals / (mI * nu), Einf_1stderiv_Vals / nu, color=colorList[aind], linestyle='solid', marker='D', ms=4) # ax_gsVel.plot(PVals[xmask] / (mI * nu), Einf_1stderiv_Vals_subsamp[xmask] / nu, color=colorList[aind], linestyle='solid', marker='D', ms=4) # ax_gsVel.plot(Pinf_Vals / (mI * nu), np.ones(Pinf_Vals.size), 'k:') # ax_gsZ.set_xlabel(r'$P/(m_{I}c)$', fontsize=13) # ax_gsVel.set_ylabel(r'$v_{\rm pol}/c$', fontsize=13) # ax_gsZ.set_ylabel(r'$Z$', fontsize=13) # # DYN S(t) AND VELOCITY # qds = xr.open_dataset('/Users/kis/Dropbox/VariationalResearch/HarvardOdyssey/genPol_data/NGridPoints_1.11E+08_resRat_0.50/massRatio=1.0_noCSAmp/redyn_spherical' + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # tVals = qds['t'].values # mc = mI * nu # DynOvData_roll = False # DynOvData_rollwin = 2 # PimpData_roll = False # PimpData_rollwin = 2 # DynOvExp_roll = False # DynOvExp_rollwin = 2 # DynOvExp_NegMask = False # DynOvExp_Cut = False # cut = 1e-4 # consecDetection = True # consecSamples = 10 # flattenAboveC = True # # aIBi_des = np.array([-10.0, -5.0, -3.5, -2.5, -2.0, -1.75]) # Pnorm = PVals / mc # tmin = 90; tmax = 100 # tfVals = tVals[(tVals <= tmax) * (tVals >= tmin)] # def powerfunc(t, a, b): # return b * t**(-1 * a) # Pcrit_da = xr.DataArray(np.full(aIBi_Vals.size, np.nan, dtype=float), coords=[aIBi_Vals], dims=['aIBi']) # for inda, aIBi in enumerate(aIBi_Vals): # qds_aIBi = xr.open_dataset(dyndatapath + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # # print(qds_aIBi['t'].values) # qds_aIBi_ts = qds_aIBi.sel(t=tfVals) # PVals = qds_aIBi['P'].values # Pnorm = PVals / mc # DynOv_Exponents = np.zeros(PVals.size) # DynOv_Cov = np.full(PVals.size, np.nan) # vImp_Exponents = np.zeros(PVals.size) # vImp_Cov = np.full(PVals.size, np.nan) # Plen = PVals.size # Pstart_ind = 0 # vI0_Vals = (PVals - qds_aIBi.isel(t=0, P=np.arange(Pstart_ind, Plen))['Pph'].values) / mI # DynOv_Exponents = np.zeros(PVals.size) # DynOv_Constants = np.zeros(PVals.size) # vImp_Exponents = np.zeros(PVals.size) # vImp_Constants = np.zeros(PVals.size) # DynOv_Rvalues = np.zeros(PVals.size) # DynOv_Pvalues = np.zeros(PVals.size) # DynOv_stderr = np.zeros(PVals.size) # DynOv_tstat = np.zeros(PVals.size) # DynOv_logAve = np.zeros(PVals.size) # for indP, P in enumerate(PVals): # DynOv_raw = np.abs(qds_aIBi_ts.isel(P=indP)['Real_DynOv'].values + 1j * qds_aIBi_ts.isel(P=indP)['Imag_DynOv'].values).real.astype(float) # DynOv_ds = xr.DataArray(DynOv_raw, coords=[tfVals], dims=['t']) # Pph_ds = xr.DataArray(qds_aIBi_ts.isel(P=indP)['Pph'].values, coords=[tfVals], dims=['t']) # if DynOvData_roll: # DynOv_ds = DynOv_ds.rolling(t=DynOvData_rollwin, center=True).mean().dropna('t') # if PimpData_roll: # Pph_ds = Pph_ds.rolling(t=PimpData_rollwin, center=True).mean().dropna('t') # DynOv_Vals = DynOv_ds.values # tDynOv_Vals = DynOv_ds['t'].values # vImpc_Vals = (P - Pph_ds.values) / mI - nu # tvImpc_Vals = Pph_ds['t'].values # S_slope, S_intercept, S_rvalue, S_pvalue, S_stderr = ss.linregress(np.log(tDynOv_Vals), np.log(DynOv_Vals)) # DynOv_Exponents[indP] = -1 * S_slope # DynOv_Constants[indP] = np.exp(S_intercept) # DynOv_Rvalues[indP] = S_rvalue # DynOv_Pvalues[indP] = S_pvalue # DynOv_stderr[indP] = S_stderr # DynOv_tstat[indP] = S_slope / S_stderr # DynOv_logAve[indP] = np.average(np.log(DynOv_Vals)) # # if (-1 * S_slope) < 0: # # DynOv_Exponents[indP] = 0 # if vImpc_Vals[-1] < 0: # vImp_Exponents[indP] = 0 # vImp_Constants[indP] = vImpc_Vals[-1] # else: # vI_slope, vI_intercept, vI_rvalue, vI_pvalue, vI_stderr = ss.linregress(np.log(tvImpc_Vals), np.log(vImpc_Vals)) # vImp_Exponents[indP] = -1 * vI_slope # vImp_Constants[indP] = np.exp(vI_intercept) # if (-1 * vI_slope) < 0: # vImp_Exponents[indP] = 0 # DynOvExponents_da = xr.DataArray(DynOv_Exponents, coords=[PVals], dims=['P']) # if DynOvExp_roll: # DynOvExponents_da = DynOvExponents_da.rolling(P=DynOvExp_rollwin, center=True).mean().dropna('P') # if DynOvExp_NegMask: # ExpMask = DynOvExponents_da.values < 0 # DynOvExponents_da[ExpMask] = 0 # if DynOvExp_Cut: # ExpMask = np.abs(DynOvExponents_da.values) < cut # DynOvExponents_da[ExpMask] = 0 # DynOv_Exponents = DynOvExponents_da.values # if consecDetection: # crit_ind = 0 # for indE, exp in enumerate(DynOv_Exponents): # if indE > DynOv_Exponents.size - consecDetection: # break # expSlice = DynOv_Exponents[indE:(indE + consecSamples)] # if np.all(expSlice > 0): # crit_ind = indE # break # DynOvExponents_da[0:crit_ind] = 0 # DynOv_Exponents = DynOvExponents_da.values # Pnorm_dynov = DynOvExponents_da['P'].values / mc # DynOvf_Vals = powerfunc(1e1000, DynOv_Exponents, DynOv_Constants) # Pcrit_da[inda] = PVals[crit_ind] / (mI * nu) # vIf_Vals = nu + powerfunc(1e1000, vImp_Exponents, vImp_Constants) # if flattenAboveC: # vIf_Vals[vIf_Vals > nu] = nu # xmask = (vI0_Vals / nu) <= 4 # ax_dynS.plot(vI0_Vals[xmask] / nu, DynOvf_Vals[xmask], color=colorList[inda], linestyle='solid', marker='D', ms=4) # ax_dynVel.plot(vI0_Vals[xmask] / nu, vIf_Vals[xmask] / nu, label='{:.2f}'.format(aIBi * xi), color=colorList[inda], linestyle='solid', marker='D', ms=4) # ax_dynS.set_ylabel(r'$S(t_{\infty})$', fontsize=13) # ax_dynVel.plot(vI0_Vals / nu, np.ones(vI0_Vals.size), 'k:') # ax_dynS.set_xlabel(r'$v_{\rm imp}(t_{0})/c$', fontsize=13) # ax_dynVel.set_ylabel(r'$v_{\rm imp}(t_{\infty})/c$', fontsize=13) # ax_dynS.tick_params(which='both', direction='in', right=True, top=True) # ax_dynVel.tick_params(which='both', direction='in', right=True, top=True) # # GENERAL # handles, labels = ax_dynVel.get_legend_handles_labels() # plt.rcParams['legend.title_fontsize'] = 13 # # fig2.legend(handles, labels, title=r'$a_{\rm IB}^{-1}/\xi^{-1}$', ncol=aIBi_Vals.size, loc='lower center', bbox_to_anchor=(0.55, 0.001), fontsize=12) # fig2.legend(handles, labels, title=r'$a_{\rm IB}^{-1}/\xi^{-1}$', ncol=aIBi_Vals.size, loc='lower center', bbox_to_anchor=(0.55, 0.001), fontsize=12) # ax_gsVel.xaxis.set_ticklabels([]) # ax_dynVel.xaxis.set_ticklabels([]) # # ax_gsVel.set_xticks([0.0, 1.0, 2.0]) # ax_gsZ.tick_params(direction='in', right=True, top=True) # ax_gsVel.tick_params(direction='in', right=True, top=True) # ax_dynS.tick_params(direction='in', right=True, top=True) # ax_dynVel.tick_params(direction='in', right=True, top=True) # ax_gsZ.set_xlim([0, 4.14]); ax_gsZ.set_ylim([-0.05, 1.1]) # ax_gsVel.set_xlim([0, 4.14]); ax_gsVel.set_ylim([-0.05, 1.2]) # ax_dynS.set_xlim([-0.05, 4.14]); ax_dynS.set_ylim([-0.05, 1.1]) # ax_dynVel.set_xlim([-0.05, 4.14]); ax_dynVel.set_ylim([-0.05, 1.2]) # fig2.set_size_inches(7.8, 5.2) # fig2.savefig(figdatapath + '/Fig2_Letter.pdf') # # # # # # ############################################################################################################################# # # # # # # FIG SM1 - LETTER # # # # # ############################################################################################################################# # matplotlib.rcParams.update({'font.size': 12}) # labelsize = 13 # legendsize = 12 # figSM1 = plt.figure(constrained_layout=False) # gs1 = figSM1.add_gridspec(nrows=1, ncols=1, bottom=0.18, top=0.93, left=0.08, right=0.31) # gs2 = figSM1.add_gridspec(nrows=1, ncols=1, bottom=0.18, top=0.93, left=0.37, right=0.60) # gs3 = figSM1.add_gridspec(nrows=1, ncols=1, bottom=0.18, top=0.93, left=0.70, right=0.94) # ax_subDist = figSM1.add_subplot(gs1[0]) # ax_supDist = figSM1.add_subplot(gs2[0]) # ax_distChar = figSM1.add_subplot(gs3[0]) # # figSM1.set_size_inches(7.8, 3.5) # figSM1.set_size_inches(7.8, 2.5) # figSM1.text(0.005, 0.93, '(a)', fontsize=labelsize) # figSM1.text(0.325, 0.93, '(b)', fontsize=labelsize) # figSM1.text(0.62, 0.93, '(c)', fontsize=labelsize) # # GROUND STATE IMPURITY DISTRIBUTION (CARTESIAN) # # GaussianBroadening = True; sigma = 0.0168 # GaussianBroadening = True; sigma = 0.02 # incoh_color = green # delta_color = base02 # def GPDF(xVals, mean, stdev): # return (1 / (stdev * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((xVals - mean) / stdev)**2) # # return (1 / (1 * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((xVals - mean) / stdev)**2) # aIBi = -5 # qds_aIBi = xr.open_dataset(innerdatapath_cart + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # PVals = qds_aIBi['P'].values # print('Interaction: {0}'.format(aIBi * xi)) # nPIm_FWHM_indices = [] # nPIm_distPeak_index = np.zeros(PVals.size, dtype=int) # nPIm_FWHM_Vals = np.zeros(PVals.size) # nPIm_distPeak_Vals = np.zeros(PVals.size) # nPIm_deltaPeak_Vals = np.zeros(PVals.size) # nPIm_Tot_Vals = np.zeros(PVals.size) # nPIm_Vec = np.empty(PVals.size, dtype=np.object) # PIm_Vec = np.empty(PVals.size, dtype=np.object) # for ind, P in enumerate(PVals): # qds_nPIm_inf = qds_aIBi['nPI_mag'].sel(P=P).isel(t=-1).dropna('PI_mag') # PIm_Vals = qds_nPIm_inf.coords['PI_mag'].values # dPIm = PIm_Vals[1] - PIm_Vals[0] # nPIm_Vec[ind] = qds_nPIm_inf.values # PIm_Vec[ind] = PIm_Vals # # # Calculate nPIm(t=inf) normalization # nPIm_Tot_Vals[ind] = np.sum(qds_nPIm_inf.values * dPIm) + qds_aIBi.sel(P=P).isel(t=-1)['mom_deltapeak'].values # # Calculate FWHM, distribution peak, and delta peak # nPIm_FWHM_Vals[ind] = pfc.FWHM(PIm_Vals, qds_nPIm_inf.values) # nPIm_distPeak_Vals[ind] = np.max(qds_nPIm_inf.values) # nPIm_deltaPeak_Vals[ind] = qds_aIBi.sel(P=P).isel(t=-1)['mom_deltapeak'].values # D = qds_nPIm_inf.values - np.max(qds_nPIm_inf.values) / 2 # indices = np.where(D > 0)[0] # nPIm_FWHM_indices.append((indices[0], indices[-1])) # nPIm_distPeak_index[ind] = np.argmax(qds_nPIm_inf.values) # Pnorm = PVals / (mI * nu) # Pratio_sup = 3.0; Pind_sup = np.abs(Pnorm - Pratio_sup).argmin() # Pratio_sub = 0.5; Pind_sub = np.abs(Pnorm - Pratio_sub).argmin() # print(Pnorm[Pind_sup], Pnorm[Pind_sub]) # print(nPIm_deltaPeak_Vals[Pind_sup], nPIm_deltaPeak_Vals[Pind_sub]) # ax_supDist.plot(PIm_Vec[Pind_sup] / (mI * nu), nPIm_Vec[Pind_sup], color=incoh_color, lw=1.0, label='Incoherent Part') # ax_supDist.set_xlim([-0.01, 5]) # ax_supDist.set_ylim([0, 1.05]) # # ax_supDist.set_ylabel(r'$n_{|\mathbf{P}_{\rm imp}|}$', fontsize=labelsize) # ax_supDist.set_xlabel(r'$|\mathbf{P}_{\rm imp}|/(m_{I}c)$', fontsize=labelsize) # ax_supDist.fill_between(PIm_Vec[Pind_sup] / (mI * nu), np.zeros(PIm_Vals.size), nPIm_Vec[Pind_sup], facecolor=incoh_color, alpha=0.25) # if GaussianBroadening: # Pnorm_sup = PVals[Pind_sup] / (mI * nu) # deltaPeak_sup = nPIm_deltaPeak_Vals[Pind_sup] # PIm_norm_sup = PIm_Vec[Pind_sup] / (mI * nu) # delta_GB_sup = deltaPeak_sup * GPDF(PIm_norm_sup, Pnorm_sup, sigma) # # ax_supDist.plot(PIm_norm_sup, delta_GB_sup, linestyle='-', color=delta_color, linewidth=1, label=r'$\delta$-Peak') # ax_supDist.plot(PIm_norm_sup, delta_GB_sup, linestyle='-', color=delta_color, linewidth=1.0, label='') # ax_supDist.fill_between(PIm_norm_sup, np.zeros(PIm_norm_sup.size), delta_GB_sup, facecolor=delta_color, alpha=0.25) # else: # ax_supDist.plot((PVals[Pind_sup] / (mI * nu)) * np.ones(PIm_Vals.size), np.linspace(0, nPIm_deltaPeak_Vals[Pind_sup], PIm_Vals.size), linestyle='-', color=delta_color, linewidth=1.5, label='Delta Peak (Z-factor)') # ax_supDist.legend(loc=1, fontsize=legendsize - 3, frameon=False) # ax_subDist.plot(PIm_Vec[Pind_sub] / (mI * nu), nPIm_Vec[Pind_sub], color=incoh_color, lw=1.0, label='Incoherent Part') # # ax_subDist.set_xlim([-0.01, np.max(PIm_Vec[Pind_sub] / (mI*nu))]) # ax_subDist.set_xlim([-0.01, 5]) # ax_subDist.set_ylim([0, 1.05]) # ax_subDist.set_ylabel(r'$n_{|\mathbf{P}_{\rm imp}|}$', fontsize=labelsize) # ax_subDist.set_xlabel(r'$|\mathbf{P}_{\rm imp}|/(m_{I}c)$', fontsize=labelsize) # ax_subDist.fill_between(PIm_Vec[Pind_sub] / (mI * nu), np.zeros(PIm_Vals.size), nPIm_Vec[Pind_sub], facecolor=incoh_color, alpha=0.25) # if GaussianBroadening: # Pnorm_sub = PVals[Pind_sub] / (mI * nu) # deltaPeak_sub = nPIm_deltaPeak_Vals[Pind_sub] # PIm_norm_sub = PIm_Vec[Pind_sub] / (mI * nu) # delta_GB_sub = deltaPeak_sub * GPDF(PIm_norm_sub, Pnorm_sub, sigma) # print(np.trapz(delta_GB_sub, PIm_norm_sub)) # ax_subDist.plot(PIm_norm_sub, delta_GB_sub, linestyle='-', color=delta_color, linewidth=1.0, label=r'$\delta$-Peak') # ax_subDist.fill_between(PIm_norm_sub, np.zeros(PIm_norm_sub.size), delta_GB_sub, facecolor=delta_color, alpha=0.25) # # ax_subDist.axvline(x=Pnorm_sub - 0.05, linestyle='-', color=delta_color, lw=1) # # ax_subDist.axvline(x=Pnorm_sub + 0.05, linestyle='-', color=delta_color, lw=1) # else: # ax_subDist.plot((PVals[Pind_sub] / (mI * nu)) * np.ones(PIm_Vals.size), np.linspace(0, nPIm_deltaPeak_Vals[Pind_sub], PIm_Vals.size), linestyle='-', color=delta_color, linewidth=1, label='Delta Peak (Z-factor)') # ax_subDist.legend(loc=1, fontsize=legendsize - 3, frameon=False) # print(deltaPeak_sub, deltaPeak_sup) # ax_subDist.set_yticks([0.0, 0.5, 1.0]) # ax_supDist.set_yticks([0.0, 0.5, 1.0]) # ax_subDist.tick_params(direction='in', right=True, top=True) # ax_supDist.tick_params(direction='in', right=True, top=True) # ax_supDist.yaxis.set_ticklabels([]) # ax_distChar2 = ax_distChar.twinx() # ax_distChar.tick_params(axis='y', labelcolor=delta_color, direction='in') # ax_distChar2.tick_params(axis='y', labelcolor=incoh_color, direction='in') # ax_distChar.tick_params(direction='in', top=True) # ax_distChar.plot(PVals / (mI * nu), nPIm_deltaPeak_Vals, linestyle='-', color=delta_color, alpha=0.75) # ax_distChar2.plot(PVals / (mI * nu), nPIm_FWHM_Vals, linestyle='-', color=incoh_color) # ax_distChar.set_xlim([-0.01, 5]) # ax_distChar.set_xlabel(r'$P/(m_{I}c)$', fontsize=labelsize) # ax_distChar.set_ylim([-0.05, 1.05]) # ax_distChar.set_yticks([0.0, 0.5, 1.0]) # ax_distChar2.set_ylim([-0.05, 2.05]) # ax_distChar2.set_yticks([0.0, 1.0, 2.0]) # ax_distChar.set_ylabel(r'$\delta$-Peak Magnitude', fontsize=labelsize, color=delta_color, alpha=0.75) # ax_distChar2.set_ylabel('Incoherent Part FWHM', rotation=270, labelpad=17, fontsize=labelsize, color=incoh_color) # figSM1.savefig(figdatapath + '/FigSM1_Letter.pdf') # # # # # # ############################################################################################################################# # # # # # # FIG SM2 - LETTER # # # # # ############################################################################################################################# # matplotlib.rcParams.update({'font.size': 12}) # labelsize = 13 # legendsize = 12 # figSM2 = plt.figure(constrained_layout=False) # gs1 = figSM2.add_gridspec(nrows=1, ncols=1, bottom=0.16, top=0.93, left=0.1, right=0.45) # gs2 = figSM2.add_gridspec(nrows=1, ncols=1, bottom=0.16, top=0.93, left=0.6, right=0.98) # ax_supDist = figSM2.add_subplot(gs2[0]) # ax_subDist = figSM2.add_subplot(gs1[0]) # figSM2.set_size_inches(7.8, 3.5) # figSM2.text(0.01, 0.94, '(a)', fontsize=labelsize) # figSM2.text(0.5, 0.94, '(b)', fontsize=labelsize) # interpdatapath = '/Users/kis/Dropbox/VariationalResearch/HarvardOdyssey/genPol_data/NGridPoints_1.11E+08_resRat_0.50/massRatio=1.0/redyn_spherical/interp' # zlim = 20 # aIBi = -5 # Pratio_sup = 3.0 # Pratio_sub = 0.52 # tratio = 39.99 # nu = 0.7926654595212022 # xi = 0.8920620580763856 # tscale = xi / nu # linDimMajor, linDimMinor = (10, 10) # interp_ds_sup = xr.open_dataset(interpdatapath + '/InterpDat_P_{:.2f}_aIBi_{:.2f}_t_{:.2f}_lDM_{:.2f}_lDm_{:.2f}.nc'.format(Pratio_sup * nu, aIBi, tratio * tscale, linDimMajor, linDimMinor)) # interp_ds_sub = xr.open_dataset(interpdatapath + '/InterpDat_P_{:.2f}_aIBi_{:.2f}_t_{:.2f}_lDM_{:.2f}_lDm_{:.2f}.nc'.format(Pratio_sub * nu, aIBi, tratio * tscale, linDimMajor, linDimMinor)) # n0 = interp_ds_sup.attrs['n0']; gBB = interp_ds_sup.attrs['gBB']; mI = interp_ds_sup.attrs['mI']; mB = interp_ds_sup.attrs['mB'] # nu = np.sqrt(n0 * gBB / mB) # mc = mI * nu # aBB = (mB / (4 * np.pi)) * gBB # xi = (8 * np.pi * n0 * aBB)**(-1 / 2) # tscale = xi / nu # P_sup = interp_ds_sup.attrs['P']; Pratio_sup = P_sup / mc # P_sub = interp_ds_sub.attrs['P']; Pratio_sub = P_sub / mc # xL = interp_ds_sup['x'].values; yL = interp_ds_sup['y'].values; zL = interp_ds_sup['z'].values # dx = xL[1] - xL[0]; dy = yL[1] - yL[0]; dz = zL[1] - zL[0] # na_xz_int_sup = interp_ds_sup['na_xz_int'].values; na_xz_int_norm_sup = na_xz_int_sup / (np.sum(na_xz_int_sup) * dx * dz) # na_xz_int_sub = interp_ds_sub['na_xz_int'].values; na_xz_int_norm_sub = na_xz_int_sub / (np.sum(na_xz_int_sub) * dx * dz) # na_z_int_sup = np.sum(na_xz_int_norm_sup, axis=0) * dx # na_z_int_sub = np.sum(na_xz_int_norm_sub, axis=0) * dx # ax_supDist.plot(zL / xi, na_z_int_sup, color=red, linestyle='-') # ax_supDist.set_xlim([-1 * zlim, zlim]) # ax_supDist.set_xlabel(r'$z/\xi$', fontsize=labelsize) # ax_supDist.set_ylabel(r'$n_{a}(z)$', fontsize=labelsize) # ax_supDist.set_ylim([0, 0.12]) # ax_subDist.plot(zL / xi, na_z_int_sub, color=red, linestyle='-') # ax_subDist.set_xlim([-1 * zlim, zlim]) # ax_subDist.set_xlabel(r'$z/\xi$', fontsize=labelsize) # ax_subDist.set_ylabel(r'$n_{a}(z)$', fontsize=labelsize) # ax_subDist.set_ylim([0, 0.12]) # ax_supDist.tick_params(direction='in', right=True, top=True) # ax_subDist.tick_params(direction='in', right=True, top=True) # from matplotlib.patches import Rectangle # rect = Rectangle((0.2, 0.0135), 8.3, 0.05, linestyle='dashed', facecolor='None', edgecolor='k') # ax_supDist.add_patch(rect) # figSM2.savefig(figdatapath + '/FigSM2_Letter.pdf') # # # # # # ############################################################################################################################# # # # # # # FIG SM3 - LETTER # # # # # ############################################################################################################################# # # # # # # ############################################################################################################################# # # # # # # OLD FIGS # # # # # ############################################################################################################################# # # # # FIG 1 (OLD) - POLARON GRAPHIC + BOGO DISPERSION + PHASE DIAGRAM + DISTRIBUTION PLOTS # matplotlib.rcParams.update({'font.size': 12}) # labelsize = 13 # legendsize = 12 # fig1 = plt.figure(constrained_layout=False) # gs1 = fig1.add_gridspec(nrows=2, ncols=1, bottom=0.55, top=0.95, left=0.12, right=0.35, height_ratios=[1, 1]) # gs2 = fig1.add_gridspec(nrows=1, ncols=1, bottom=0.55, top=0.95, left=0.5, right=0.98) # gs3 = fig1.add_gridspec(nrows=1, ncols=2, bottom=0.08, top=0.4, left=0.12, right=0.96, wspace=0.3) # ax_pol = fig1.add_subplot(gs1[0], frame_on=False); ax_pol.get_xaxis().set_visible(False); ax_pol.get_yaxis().set_visible(False) # ax_bogo = fig1.add_subplot(gs1[1]) # ax_PD = fig1.add_subplot(gs2[0]) # ax_supDist = fig1.add_subplot(gs3[0]) # ax_subDist = fig1.add_subplot(gs3[1]) # fig1.text(0.01, 0.97, '(a)', fontsize=labelsize) # fig1.text(0.01, 0.75, '(b)', fontsize=labelsize) # fig1.text(0.43, 0.97, '(c)', fontsize=labelsize) # fig1.text(0.01, 0.42, '(d)', fontsize=labelsize) # fig1.text(0.51, 0.42, '(e)', fontsize=labelsize) # # POLARON GRAPHIC # polimg = mpimg.imread('images/PolaronGraphic.png') # imgplot = ax_pol.imshow(polimg) # # BOGOLIUBOV DISPERSION (SPHERICAL) # kgrid = Grid.Grid("SPHERICAL_2D"); kgrid.initArray_premade('k', qds.coords['k'].values); kgrid.initArray_premade('th', qds.coords['th'].values) # kVals = kgrid.getArray('k') # wk_Vals = pfs.omegak(kVals, mB, n0, gBB) # ax_bogo.plot(kVals, wk_Vals, 'k-', label='') # ax_bogo.plot(kVals, nu * kVals, 'b--', label=r'$c|k|$') # ax_bogo.set_xlabel(r'$|k|$', fontsize=labelsize) # ax_bogo.set_ylabel(r'$\omega_{|k|}$', fontsize=labelsize) # ax_bogo.set_xlim([0, 2]) # ax_bogo.xaxis.set_major_locator(plt.MaxNLocator(2)) # ax_bogo.set_ylim([0, 3]) # ax_bogo.yaxis.set_major_locator(plt.MaxNLocator(3)) # ax_bogo.legend(loc=2, fontsize=legendsize) # # PHASE DIAGRAM (SPHERICAL) # Pcrit = np.zeros(aIBi_Vals.size) # for aind, aIBi in enumerate(aIBi_Vals): # qds_aIBi = xr.open_dataset(innerdatapath + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # CSAmp_ds = qds_aIBi['Real_CSAmp'] + 1j * qds_aIBi['Imag_CSAmp'] # kgrid = Grid.Grid("SPHERICAL_2D"); kgrid.initArray_premade('k', CSAmp_ds.coords['k'].values); kgrid.initArray_premade('th', CSAmp_ds.coords['th'].values) # Energy_Vals_inf = np.zeros(PVals.size) # for Pind, P in enumerate(PVals): # CSAmp = CSAmp_ds.sel(P=P).isel(t=-1).values # Energy_Vals_inf[Pind] = pfs.Energy(CSAmp, kgrid, P, aIBi, mI, mB, n0, gBB) # Einf_tck = interpolate.splrep(PVals, Energy_Vals_inf, s=0) # Pinf_Vals = np.linspace(np.min(PVals), np.max(PVals), 2 * PVals.size) # Einf_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=0) # Einf_2ndderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=2) # # Pcrit[aind] = Pinf_Vals[np.argwhere(Einf_2ndderiv_Vals < 0)[-2][0] + 3] # Pcrit[aind] = Pinf_Vals[np.argmin(np.gradient(Einf_2ndderiv_Vals)) - 0] # there is a little bit of fudging with the -3 here so that aIBi=-10 gives me Pcrit/(mI*c) = 1 -> I can also just generate data for weaker interactions and see if it's better # Pcrit_norm = Pcrit / (mI * nu) # Pcrit_tck = interpolate.splrep(aIBi_Vals, Pcrit_norm, s=0, k=3) # aIBi_interpVals = np.linspace(np.min(aIBi_Vals), np.max(aIBi_Vals), 5 * aIBi_Vals.size) # Pcrit_interpVals = 1 * interpolate.splev(aIBi_interpVals, Pcrit_tck, der=0) # print(Pcrit_norm) # print(Pcrit_norm[1], Pcrit_norm[5], Pcrit_norm[-5]) # scalefac = 1.0 # # scalefac = 0.95 # just to align weakly interacting case slightly to 1 (it's pretty much there, would just need higher resolution data) # Pcrit_norm = scalefac * Pcrit_norm # Pcrit_interpVals = scalefac * Pcrit_interpVals # xmin = np.min(aIBi_interpVals / xi) # xmax = 1.01 * np.max(aIBi_interpVals / xi) # ymin = 0 # ymax = 1.01 * np.max(Pcrit_interpVals) # font = {'family': 'serif', 'color': 'black', 'size': legendsize} # sfont = {'family': 'serif', 'color': 'black', 'size': legendsize - 1} # ax_PD.plot(aIBi_Vals / xi, Pcrit_norm, 'kx') # ax_PD.plot(aIBi_interpVals / xi, Pcrit_interpVals, 'k-') # # f1 = interpolate.interp1d(aIBi_Vals, Pcrit_norm, kind='cubic') # # ax_PD.plot(aIBi_interpVals, f1(aIBi_interpVals), 'k-') # ax_PD.set_xlabel(r'$a_{IB}^{-1}$ [$\xi$]', fontsize=labelsize) # ax_PD.set_ylabel(r'Total Momentum $P$ [$m_{I}c$]', fontsize=labelsize) # ax_PD.set_xlim([xmin, xmax]) # ax_PD.set_ylim([ymin, ymax]) # ax_PD.fill_between(aIBi_interpVals / xi, Pcrit_interpVals, ymax, facecolor='b', alpha=0.25) # ax_PD.fill_between(aIBi_interpVals / xi, ymin, Pcrit_interpVals, facecolor='g', alpha=0.25) # ax_PD.text(-3.2, ymin + 0.175 * (ymax - ymin), 'Polaron', fontdict=font) # ax_PD.text(-3.1, ymin + 0.1 * (ymax - ymin), '(' + r'$Z>0$' + ')', fontdict=sfont) # # ax_PD.text(-6.5, ymin + 0.6 * (ymax - ymin), 'Cherenkov', fontdict=font) # # ax_PD.text(-6.35, ymin + 0.525 * (ymax - ymin), '(' + r'$Z=0$' + ')', fontdict=sfont) # ax_PD.text(-12.8, ymin + 0.86 * (ymax - ymin), 'Cherenkov', fontdict=font) # ax_PD.text(-12.65, ymin + 0.785 * (ymax - ymin), '(' + r'$Z=0$' + ')', fontdict=sfont) # supDist_coords = [-5.0 / xi, 3.0] # is [aIBi/xi, P/(mI*c)] # subDist_coords = [-5.0 / xi, 0.5] # is [aIBi/xi, P/(mI*c)] # ax_PD.plot(supDist_coords[0], supDist_coords[1], linestyle='', marker='8', mec='#8f1402', mfc='#8f1402', ms=10) # ax_PD.plot(subDist_coords[0], subDist_coords[1], linestyle='', marker='8', mec='#8f1402', mfc='#8f1402', ms=10) # # IMPURITY DISTRIBUTION (CARTESIAN) # GaussianBroadening = True; sigma = 0.1 # incoh_color = '#8f1402' # delta_color = '#bf9005' # def GPDF(xVals, mean, stdev): # return (1 / (stdev * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((xVals - mean) / stdev)**2) # # return (1 / (1 * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((xVals - mean) / stdev)**2) # aIBi = -5 # qds_aIBi = xr.open_dataset(innerdatapath_cart + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # PVals = qds_aIBi['P'].values # nPIm_FWHM_indices = [] # nPIm_distPeak_index = np.zeros(PVals.size, dtype=int) # nPIm_FWHM_Vals = np.zeros(PVals.size) # nPIm_distPeak_Vals = np.zeros(PVals.size) # nPIm_deltaPeak_Vals = np.zeros(PVals.size) # nPIm_Tot_Vals = np.zeros(PVals.size) # nPIm_Vec = np.empty(PVals.size, dtype=np.object) # PIm_Vec = np.empty(PVals.size, dtype=np.object) # for ind, P in enumerate(PVals): # qds_nPIm_inf = qds_aIBi['nPI_mag'].sel(P=P).isel(t=-1).dropna('PI_mag') # PIm_Vals = qds_nPIm_inf.coords['PI_mag'].values # dPIm = PIm_Vals[1] - PIm_Vals[0] # nPIm_Vec[ind] = qds_nPIm_inf.values # PIm_Vec[ind] = PIm_Vals # # # Calculate nPIm(t=inf) normalization # nPIm_Tot_Vals[ind] = np.sum(qds_nPIm_inf.values * dPIm) + qds_aIBi.sel(P=P).isel(t=-1)['mom_deltapeak'].values # # Calculate FWHM, distribution peak, and delta peak # nPIm_FWHM_Vals[ind] = pfc.FWHM(PIm_Vals, qds_nPIm_inf.values) # nPIm_distPeak_Vals[ind] = np.max(qds_nPIm_inf.values) # nPIm_deltaPeak_Vals[ind] = qds_aIBi.sel(P=P).isel(t=-1)['mom_deltapeak'].values # D = qds_nPIm_inf.values - np.max(qds_nPIm_inf.values) / 2 # indices = np.where(D > 0)[0] # nPIm_FWHM_indices.append((indices[0], indices[-1])) # nPIm_distPeak_index[ind] = np.argmax(qds_nPIm_inf.values) # Pnorm = PVals / (mI * nu) # Pratio_sup = 3.0; Pind_sup = np.abs(Pnorm - Pratio_sup).argmin() # Pratio_sub = 0.5; Pind_sub = np.abs(Pnorm - Pratio_sub).argmin() # print(Pnorm[Pind_sup], Pnorm[Pind_sub]) # print(nPIm_deltaPeak_Vals[Pind_sup], nPIm_deltaPeak_Vals[Pind_sub]) # ax_supDist.plot(PIm_Vec[Pind_sup] / (mI * nu), nPIm_Vec[Pind_sup], color=incoh_color, lw=0.5, label='Incoherent Part') # ax_supDist.set_xlim([-0.01, 10]) # ax_supDist.set_ylim([0, 1.05]) # ax_supDist.set_ylabel(r'$n_{|\vec{P_{I}}|}$', fontsize=labelsize) # ax_supDist.set_xlabel(r'$|\vec{P_{I}}|/(m_{I}c)$', fontsize=labelsize) # ax_supDist.fill_between(PIm_Vec[Pind_sup] / (mI * nu), np.zeros(PIm_Vals.size), nPIm_Vec[Pind_sup], facecolor=incoh_color, alpha=0.25) # if GaussianBroadening: # Pnorm_sup = PVals[Pind_sup] / (mI * nu) # deltaPeak_sup = nPIm_deltaPeak_Vals[Pind_sup] # PIm_norm_sup = PIm_Vec[Pind_sup] / (mI * nu) # delta_GB_sup = deltaPeak_sup * GPDF(PIm_norm_sup, Pnorm_sup, sigma) # # ax_supDist.plot(PIm_norm_sup, delta_GB_sup, linestyle='-', color=delta_color, linewidth=1, label=r'$\delta$-Peak') # ax_supDist.plot(PIm_norm_sup, delta_GB_sup, linestyle='-', color=delta_color, linewidth=1, label='') # ax_supDist.fill_between(PIm_norm_sup, np.zeros(PIm_norm_sup.size), delta_GB_sup, facecolor=delta_color, alpha=0.25) # else: # ax_supDist.plot((PVals[Pind_sup] / (mI * nu)) * np.ones(PIm_Vals.size), np.linspace(0, nPIm_deltaPeak_Vals[Pind_sup], PIm_Vals.size), linestyle='-', color=delta_color, linewidth=1, label='Delta Peak (Z-factor)') # ax_supDist.legend(loc=1, fontsize=legendsize) # ax_subDist.plot(PIm_Vec[Pind_sub] / (mI * nu), nPIm_Vec[Pind_sub], color=incoh_color, lw=0.5, label='Incoherent Part') # # ax_subDist.set_xlim([-0.01, np.max(PIm_Vec[Pind_sub] / (mI*nu))]) # ax_subDist.set_xlim([-0.01, 10]) # ax_subDist.set_ylim([0, 1.05]) # ax_subDist.set_ylabel(r'$n_{|\vec{P_{I}}|}$', fontsize=labelsize) # ax_subDist.set_xlabel(r'$|\vec{P_{I}}|/(m_{I}c)$', fontsize=labelsize) # ax_subDist.fill_between(PIm_Vec[Pind_sub] / (mI * nu), np.zeros(PIm_Vals.size), nPIm_Vec[Pind_sub], facecolor=incoh_color, alpha=0.25) # if GaussianBroadening: # Pnorm_sub = PVals[Pind_sub] / (mI * nu) # deltaPeak_sub = nPIm_deltaPeak_Vals[Pind_sub] # PIm_norm_sub = PIm_Vec[Pind_sub] / (mI * nu) # delta_GB_sub = deltaPeak_sub * GPDF(PIm_norm_sub, Pnorm_sub, sigma) # ax_subDist.plot(PIm_norm_sub, delta_GB_sub, linestyle='-', color=delta_color, linewidth=1, label=r'$\delta$-Peak') # ax_subDist.fill_between(PIm_norm_sub, np.zeros(PIm_norm_sub.size), delta_GB_sub, facecolor=delta_color, alpha=0.25) # else: # ax_subDist.plot((PVals[Pind_sub] / (mI * nu)) * np.ones(PIm_Vals.size), np.linspace(0, nPIm_deltaPeak_Vals[Pind_sub], PIm_Vals.size), linestyle='-', color=delta_color, linewidth=1, label='Delta Peak (Z-factor)') # ax_subDist.legend(loc=1, fontsize=legendsize) # fig1.set_size_inches(7.8, 9) # fig1.savefig(figdatapath + '/Fig1.pdf') # # # # FIG 2 - ENERGY DERIVATIVES + SOUND VELOCITY + EFFECTIVE MASS # matplotlib.rcParams.update({'font.size': 12}) # labelsize = 13 # legendsize = 12 # fig2 = plt.figure(constrained_layout=False) # # gs1 = fig2.add_gridspec(nrows=3, ncols=1, bottom=0.12, top=0.925, left=0.12, right=0.40, hspace=1.0) # # gs2 = fig2.add_gridspec(nrows=2, ncols=1, bottom=0.12, top=0.925, left=0.58, right=0.98, hspace=0.7) # gs1 = fig2.add_gridspec(nrows=3, ncols=1, bottom=0.12, top=0.95, left=0.12, right=0.40, hspace=0.2) # gs2 = fig2.add_gridspec(nrows=2, ncols=1, bottom=0.12, top=0.95, left=0.58, right=0.98, hspace=0.1) # ax_GSE0 = fig2.add_subplot(gs1[0]) # ax_GSE1 = fig2.add_subplot(gs1[1]) # ax_GSE2 = fig2.add_subplot(gs1[2]) # ax_Vel = fig2.add_subplot(gs2[0]) # ax_Mass = fig2.add_subplot(gs2[1]) # # fig2.text(0.01, 0.95, '(a)', fontsize=labelsize) # # fig2.text(0.01, 0.65, '(b)', fontsize=labelsize) # # fig2.text(0.01, 0.32, '(c)', fontsize=labelsize) # # fig2.text(0.47, 0.95, '(d)', fontsize=labelsize) # # fig2.text(0.47, 0.47, '(e)', fontsize=labelsize) # fig2.text(0.01, 0.95, '(a)', fontsize=labelsize) # fig2.text(0.01, 0.65, '(b)', fontsize=labelsize) # fig2.text(0.01, 0.37, '(c)', fontsize=labelsize) # fig2.text(0.47, 0.95, '(d)', fontsize=labelsize) # fig2.text(0.47, 0.52, '(e)', fontsize=labelsize) # # # ENERGY DERIVATIVES (SPHERICAL) # aIBi = -5 # qds_aIBi = xr.open_dataset(innerdatapath + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # PVals = qds_aIBi['P'].values # print(aIBi * xi) # CSAmp_ds = qds_aIBi['Real_CSAmp'] + 1j * qds_aIBi['Imag_CSAmp'] # kgrid = Grid.Grid("SPHERICAL_2D"); kgrid.initArray_premade('k', CSAmp_ds.coords['k'].values); kgrid.initArray_premade('th', CSAmp_ds.coords['th'].values) # Energy_Vals = np.zeros((PVals.size, tVals.size)) # for Pind, P in enumerate(PVals): # for tind, t in enumerate(tVals): # CSAmp = CSAmp_ds.sel(P=P, t=t).values # Energy_Vals[Pind, tind] = pfs.Energy(CSAmp, kgrid, P, aIBi, mI, mB, n0, gBB) # Energy_Vals_inf = Energy_Vals[:, -1] # Einf_tck = interpolate.splrep(PVals, Energy_Vals_inf, s=0) # Pinf_Vals = np.linspace(np.min(PVals), np.max(PVals), 5 * PVals.size) # Einf_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=0) # Einf_1stderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=1) # Einf_2ndderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=2) # sound_mask = np.abs(Einf_2ndderiv_Vals) <= 5e-3 # Einf_sound = Einf_Vals[sound_mask] # Pinf_sound = Pinf_Vals[sound_mask] # [vsound, vs_const] = np.polyfit(Pinf_sound, Einf_sound, deg=1) # ms_mask = Pinf_Vals <= 0.5 # Einf_1stderiv_ms = Einf_1stderiv_Vals[ms_mask] # Pinf_ms = Pinf_Vals[ms_mask] # [ms, ms_const] = np.polyfit(Pinf_ms, Einf_1stderiv_ms, deg=1) # Ecrit = Einf_Vals[np.argmin(np.gradient(Einf_2ndderiv_Vals))] # # ax_GSE0.plot(Pinf_Vals / (mI * nu), Einf_Vals / np.abs(Ecrit), 'k-', lw=1.5) # ax_GSE0.plot(Pinf_Vals[::2] / (mI * nu), Einf_Vals[::2] / np.abs(Ecrit), 'ko', ms=6) # # ax_GSE0.set_title('Ground State Energy (' + r'$a_{IB}^{-1}=$' + '{0})'.format(aIBi)) # # ax_GSE0.set_xlabel(r'$P$ [$m_{I}c$]', fontsize=labelsize) # ax_GSE0.set_ylabel(r'$E$', fontsize=labelsize) # ax_GSE0.set_ylim([1.1 * np.min(Einf_Vals / np.abs(Ecrit)), -0.5 / np.abs(Ecrit)]) # ax_GSE0.set_xlim([0, 2.0]) # # ax_GSE1.plot(Pinf_Vals / (mI * nu), Einf_1stderiv_Vals / np.abs(Ecrit), 'k-', lw=1.5) # ax_GSE1.plot(Pinf_Vals[::2] / (mI * nu), Einf_1stderiv_Vals[::2] / np.abs(Ecrit), 'ko', ms=6) # # ax_GSE1.set_title('First Derivative of Energy') # # ax_GSE1.set_xlabel(r'$P$ [$m_{I}c$]', fontsize=labelsize) # ax_GSE1.set_ylabel(r'$dE/dP$', fontsize=labelsize) # ax_GSE1.plot(Pinf_Vals / (mI * nu), vsound * np.ones(Pinf_Vals.size) / np.abs(Ecrit), color=red, linestyle='--', linewidth=2.0) # ax_GSE1.set_ylim([0, 1.2 * np.max(Einf_1stderiv_Vals / np.abs(Ecrit))]) # ax_GSE1.set_xlim([0, 2.0]) # # ax_GSE2.plot(Pinf_Vals / (mI * nu), Einf_2ndderiv_Vals / np.abs(Ecrit), 'k-', lw=1.5) # ax_GSE2.plot(Pinf_Vals[::2] / (mI * nu), Einf_2ndderiv_Vals[::2] / np.abs(Ecrit), 'ko', ms=6) # # ax_GSE2.set_title('Second Derivative of Energy') # ax_GSE2.set_xlabel(r'$P/(m_{I}c)$', fontsize=labelsize) # ax_GSE2.set_ylabel(r'$d^{2}E/dP^{2}$', fontsize=labelsize) # ax_GSE2.plot(Pinf_Vals / (mI * nu), ms * np.ones(Pinf_Vals.size) / np.abs(Ecrit), color=blue, linestyle='--', linewidth=2.0) # ax_GSE2.set_ylim([-.12, 1.2 * np.max(Einf_2ndderiv_Vals / np.abs(Ecrit))]) # ax_GSE2.set_xlim([0, 2.0]) # # including a Pcrit line # Pcrit = Pinf_Vals[np.argmin(np.gradient(Einf_2ndderiv_Vals)) - 0] # # Pcrit_2 = Pinf_Vals[sound_mask][0]; print(Pcrit, Pcrit_2) # ax_GSE0.axvline(x=Pcrit / (mI * nu), linestyle=':', color=green, lw=2) # ax_GSE1.axvline(x=Pcrit / (mI * nu), linestyle=':', color=green, lw=2) # ax_GSE2.axvline(x=Pcrit / (mI * nu), linestyle=':', color=green, lw=2) # # # POLARON SOUND VELOCITY (SPHERICAL) # # Check to see if linear part of polaron (total system) energy spectrum has slope equal to sound velocity # vsound_Vals = np.zeros(aIBi_Vals.size) # vI_Vals = np.zeros(aIBi_Vals.size) # for aind, aIBi in enumerate(aIBi_Vals): # qds = xr.open_dataset(innerdatapath + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # qds_aIBi = qds.isel(t=-1) # CSAmp_ds = qds_aIBi['Real_CSAmp'] + 1j * qds_aIBi['Imag_CSAmp'] # kgrid = Grid.Grid("SPHERICAL_2D"); kgrid.initArray_premade('k', CSAmp_ds.coords['k'].values); kgrid.initArray_premade('th', CSAmp_ds.coords['th'].values) # Energy_Vals_inf = np.zeros(PVals.size) # PI_Vals = np.zeros(PVals.size) # for Pind, P in enumerate(PVals): # CSAmp = CSAmp_ds.sel(P=P).values # Energy_Vals_inf[Pind] = pfs.Energy(CSAmp, kgrid, P, aIBi, mI, mB, n0, gBB) # PI_Vals[Pind] = P - qds_aIBi.sel(P=P)['Pph'].values # Einf_tck = interpolate.splrep(PVals, Energy_Vals_inf, s=0) # Pinf_Vals = np.linspace(np.min(PVals), np.max(PVals), 2 * PVals.size) # Einf_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=0) # Einf_2ndderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=2) # sound_mask = np.abs(Einf_2ndderiv_Vals) <= 5e-3 # Einf_sound = Einf_Vals[sound_mask] # Pinf_sound = Pinf_Vals[sound_mask] # [vsound_Vals[aind], vs_const] = np.polyfit(Pinf_sound, Einf_sound, deg=1) # vI_inf_tck = interpolate.splrep(PVals, PI_Vals / mI, s=0) # vI_inf_Vals = 1 * interpolate.splev(Pinf_Vals, vI_inf_tck, der=0) # vI_Vals[aind] = np.polyfit(Pinf_sound, vI_inf_Vals[sound_mask], deg=0) # print(vsound_Vals) # print(100 * (vsound_Vals - nu) / nu) # ax_Vel.plot(aIBi_Vals * xi, vsound_Vals / nu, linestyle='None', mec=red, mfc=red, marker='x', mew=1, ms=10, label='Polaron') # ax_Vel.plot(aIBi_Vals * xi, vI_Vals / nu, 'ko', mew=1, ms=10, markerfacecolor='none', label='Impurity') # ax_Vel.plot(aIBi_Vals * xi, np.ones(aIBi_Vals.size), color='grey', linestyle='dashdot', linewidth=2.0, label='$c$') # ax_Vel.set_ylim([0.5, 1.25]) # # ax_Vel.set_ylim([0.8, 1.25]) # ax_Vel.legend(loc=(0.25, 0.1), fontsize=legendsize) # # ax_Vel.set_xlabel(r'$a_{IB}^{-1}$ [$\xi$]', fontsize=labelsize) # ax_Vel.set_ylabel(r'Velocity', fontsize=labelsize) # # # POLARON EFFECTIVE MASS (SPHERICAL) # ms_Vals = np.zeros(aIBi_Vals.size) # for aind, aIBi in enumerate(aIBi_Vals): # qds = xr.open_dataset(innerdatapath + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # qds_aIBi = qds.isel(t=-1) # CSAmp_ds = qds_aIBi['Real_CSAmp'] + 1j * qds_aIBi['Imag_CSAmp'] # kgrid = Grid.Grid("SPHERICAL_2D"); kgrid.initArray_premade('k', CSAmp_ds.coords['k'].values); kgrid.initArray_premade('th', CSAmp_ds.coords['th'].values) # Energy_Vals_inf = np.zeros(PVals.size) # PI_Vals = np.zeros(PVals.size) # for Pind, P in enumerate(PVals): # CSAmp = CSAmp_ds.sel(P=P).values # Energy_Vals_inf[Pind] = pfs.Energy(CSAmp, kgrid, P, aIBi, mI, mB, n0, gBB) # PI_Vals[Pind] = P - qds_aIBi.sel(P=P)['Pph'].values # Einf_tck = interpolate.splrep(PVals, Energy_Vals_inf, s=0) # Pinf_Vals = np.linspace(np.min(PVals), np.max(PVals), 2 * PVals.size) # Einf_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=0) # Einf_1stderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=1) # Einf_2ndderiv_Vals = 1 * interpolate.splev(Pinf_Vals, Einf_tck, der=2) # ms_mask = Pinf_Vals < 0.3 # Einf_1stderiv_ms = Einf_1stderiv_Vals[ms_mask] # Pinf_ms = Pinf_Vals[ms_mask] # [ms_Vals[aind], ms_const] = np.polyfit(Pinf_ms, Einf_1stderiv_ms, deg=1) # massEnhancement_Vals = (1 / ms_Vals) / mI # mE_tck = interpolate.splrep(aIBi_Vals, massEnhancement_Vals, s=0) # aIBi_interpVals = np.linspace(np.min(aIBi_Vals), np.max(aIBi_Vals), 5 * aIBi_Vals.size) # mE_interpVals = 1 * interpolate.splev(aIBi_interpVals, mE_tck, der=0) # ax_Mass.plot(aIBi_Vals * xi, massEnhancement_Vals, linestyle='None', marker='D', mec=blue, mfc=blue, mew=1, ms=5) # ax_Mass.plot(aIBi_interpVals * xi, mE_interpVals, color=blue, linestyle='-') # ax_Mass.set_xlabel(r'$a_{\rm IB}^{-1}/\xi^{-1}$', fontsize=labelsize) # # ax_Mass.set_ylabel(r'$\frac{m^{*}}{m_{I}} = \frac{1}{m_{I}}\frac{\partial^{2} E}{\partial P^{2}}$') # ax_Mass.set_ylabel(r'Effective Mass', fontsize=labelsize) # ax_GSE0.xaxis.set_ticklabels([]) # ax_GSE1.xaxis.set_ticklabels([]) # ax_Vel.xaxis.set_ticklabels([]) # ax_GSE0.set_xticks([0.0, 1.0, 2.0]) # ax_GSE1.set_xticks([0.0, 1.0, 2.0]) # ax_GSE2.set_xticks([0.0, 1.0, 2.0]) # ax_GSE0.tick_params(direction='in', right=True, top=True) # ax_GSE1.tick_params(direction='in', right=True, top=True) # ax_GSE2.tick_params(direction='in', right=True, top=True) # ax_Vel.tick_params(direction='in', right=True, top=True) # ax_Mass.tick_params(direction='in', right=True, top=True) # vel_coords = [2, vsound / np.abs(Ecrit)] # effM_coords = [2, ms / np.abs(Ecrit)] # con_vel = ConnectionPatch(xyA=(vel_coords[0], vel_coords[1]), xyB=(-11, 1.0), coordsA="data", coordsB="data", axesA=ax_GSE1, axesB=ax_Vel, color=red, linestyle='dashed', lw=0.5) # con_effM = ConnectionPatch(xyA=(effM_coords[0], effM_coords[1]), xyB=(-11, 1.92), coordsA="data", coordsB="data", axesA=ax_GSE2, axesB=ax_Mass, color=blue, linestyle='dashed', lw=0.5) # ax_GSE1.add_artist(con_vel) # ax_GSE2.add_artist(con_effM) # fig2.set_size_inches(7.8, 5.0) # fig2.savefig(figdatapath + '/Fig2.pdf') # # FIG 3 - IMPURITY DISTRIBUTION WITH CHARACTERIZATION (CARTESIAN) # matplotlib.rcParams.update({'font.size': 12}) # labelsize = 13 # legendsize = 12 # GaussianBroadening = True; sigma = 0.1 # incoh_color = green # delta_color = base02 # fwhm_color = red # def GPDF(xVals, mean, stdev): # return (1 / (stdev * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((xVals - mean) / stdev)**2) # # return (1 / (1 * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((xVals - mean) / stdev)**2) # aIBi = -2 # print('int: {0}'.format(aIBi * xi)) # qds_aIBi = xr.open_dataset(innerdatapath_cart + '/quench_Dataset_aIBi_{:.2f}.nc'.format(aIBi)) # PVals = qds_aIBi['P'].values # nPIm_FWHM_indices = [] # nPIm_distPeak_index = np.zeros(PVals.size, dtype=int) # nPIm_FWHM_Vals = np.zeros(PVals.size) # nPIm_distPeak_Vals = np.zeros(PVals.size) # nPIm_deltaPeak_Vals = np.zeros(PVals.size) # nPIm_Tot_Vals = np.zeros(PVals.size) # nPIm_Vec = np.empty(PVals.size, dtype=np.object) # PIm_Vec = np.empty(PVals.size, dtype=np.object) # for ind, P in enumerate(PVals): # qds_nPIm_inf = qds_aIBi['nPI_mag'].sel(P=P).isel(t=-1).dropna('PI_mag') # PIm_Vals = qds_nPIm_inf.coords['PI_mag'].values # dPIm = PIm_Vals[1] - PIm_Vals[0] # # # Plot nPIm(t=inf) # # qds_nPIm_inf.plot(ax=ax, label='P: {:.1f}'.format(P)) # nPIm_Vec[ind] = qds_nPIm_inf.values # PIm_Vec[ind] = PIm_Vals # # # Calculate nPIm(t=inf) normalization # nPIm_Tot_Vals[ind] = np.sum(qds_nPIm_inf.values * dPIm) + qds_aIBi.sel(P=P).isel(t=-1)['mom_deltapeak'].values # # Calculate FWHM, distribution peak, and delta peak # nPIm_FWHM_Vals[ind] = pfc.FWHM(PIm_Vals, qds_nPIm_inf.values) # nPIm_distPeak_Vals[ind] = np.max(qds_nPIm_inf.values) # nPIm_deltaPeak_Vals[ind] = qds_aIBi.sel(P=P).isel(t=-1)['mom_deltapeak'].values # D = qds_nPIm_inf.values - np.max(qds_nPIm_inf.values) / 2 # indices = np.where(D > 0)[0] # nPIm_FWHM_indices.append((indices[0], indices[-1])) # nPIm_distPeak_index[ind] = np.argmax(qds_nPIm_inf.values) # Pratio = 1.4 # Pnorm = PVals / (mI * nu) # Pind = np.abs(Pnorm - Pratio).argmin() # print(Pnorm[Pind], aIBi / xi) # print(nPIm_deltaPeak_Vals[Pind]) # fig3, axes3 = plt.subplots(nrows=1, ncols=3) # ind_s, ind_f = nPIm_FWHM_indices[Pind] # ind_f = ind_f - 1 # this is just to make the FWHM marker on the plot look a little cleaner # axes3[0].plot(PIm_Vec[Pind] / (mI * nu), nPIm_Vec[Pind], color=incoh_color, lw=1.0, label='Incoherent Part') # axes3[0].set_xlim([-0.01, 10]) # axes3[0].set_ylim([0, 1.05]) # axes3[0].set_ylabel(r'$n_{|\mathbf{P}_{\rm imp}|}$', fontsize=labelsize) # axes3[0].set_xlabel(r'$|\mathbf{P}_{\rm imp}|/(m_{I}c)$', fontsize=labelsize) # axes3[0].fill_between(PIm_Vec[Pind] / (mI * nu), np.zeros(PIm_Vals.size), nPIm_Vec[Pind], facecolor=incoh_color, alpha=0.25) # if GaussianBroadening: # Pnorm = PVals[Pind] / (mI * nu) # deltaPeak = nPIm_deltaPeak_Vals[Pind] # PIm_norm = PIm_Vec[Pind] / (mI * nu) # delta_GB = deltaPeak * GPDF(PIm_norm, Pnorm, sigma) # axes3[0].plot(PIm_norm, delta_GB, linestyle='-', color=delta_color, linewidth=1, label='Delta Peak') # axes3[0].fill_between(PIm_norm, np.zeros(PIm_norm.size), delta_GB, facecolor=delta_color, alpha=0.25) # else: # axes3[0].plot((PVals[Pind] / (mI * nu)) * np.ones(PIm_Vals.size), np.linspace(0, nPIm_deltaPeak_Vals[Pind], PIm_Vals.size), linestyle='-', color=delta_color, linewidth=1, label='Delta Peak Weight (Z-factor)') # # axes3[0].legend(loc=1, fontsize=legendsize) # axes3[0].plot(np.linspace(PIm_Vec[Pind][ind_s] / (mI * nu), PIm_Vec[Pind][ind_f] / (mI * nu), 100), nPIm_Vec[Pind][ind_s] * np.ones(100), linestyle='-', color=fwhm_color, linewidth=2.0, label='Incoherent Part FWHM') # axes3[0].plot(np.linspace(PIm_Vec[Pind][ind_s] / (mI * nu), PIm_Vec[Pind][ind_f] / (mI * nu), 2), nPIm_Vec[Pind][ind_s] * np.ones(2), marker='D', color=fwhm_color, mew=0.5, ms=4, label='') # axes3[1].plot(PVals / (mI * nu), nPIm_deltaPeak_Vals, linestyle='-', color=delta_color) # axes3[1].set_xlabel(r'$P/(m_{I}c)$', fontsize=labelsize) # axes3[1].set_ylabel(r'Quasiparticle Residue ($Z$)', fontsize=labelsize) # axes3[2].plot(PVals / (mI * nu), nPIm_FWHM_Vals, linestyle='-', color=fwhm_color) # axes3[2].set_xlabel(r'$P/(m_{I}c)$', fontsize=labelsize) # axes3[2].set_ylabel('Incoherent Part FWHM', fontsize=labelsize) # axes3[2].set_ylim([0.5, 2.5]) # axes3[2].yaxis.set_major_locator(plt.MaxNLocator(4)) # axes3[0].tick_params(direction='in', right=True, top=True) # axes3[1].tick_params(direction='in', right=True, top=True) # axes3[2].tick_params(direction='in', right=True, top=True) # fig3.text(0.01, 0.95, '(a)', fontsize=labelsize) # fig3.text(0.33, 0.95, '(b)', fontsize=labelsize) # fig3.text(0.66, 0.95, '(c)', fontsize=labelsize) # fig3.subplots_adjust(left=0.1, bottom=0.17, top=0.91, right=0.98, wspace=0.6) # fig3.set_size_inches(7.8, 3.5) # fig3.savefig(figdatapath + '/Fig3.pdf') # # plt.show()

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null

0

6

7e3819a044cb8584161c27f50d05ce5285d87d92

84

py

Python

org.beerware.empty.app/assets/axp.py

pmp-p/projects

e958ef2c6d89d0d818086d4c88668d46044ace14

[ "MIT" ]

null

org.beerware.empty.app/assets/axp.py

pmp-p/projects

e958ef2c6d89d0d818086d4c88668d46044ace14

[ "MIT" ]

3

2020-11-01T18:54:24.000Z

2020-11-15T03:59:34.000Z

org.beerware.empty.app/assets/axp.py

pmp-p/projects

e958ef2c6d89d0d818086d4c88668d46044ace14

[ "MIT" ]

null

from android import * print(widget.Button) from android.widget import TextView

9.333333

35

0.77381

11

84

5.909091

0.636364

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0

0.166667

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true

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0

null

0

1

0

1

0

1

0

6

7e3f28a79344a9233183603246e2d32fc400d428

141

py

Python

holobot/extensions/crypto/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

1

2021-05-24T00:17:46.000Z

holobot/extensions/crypto/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

41

2021-03-24T22:50:09.000Z

2021-12-17T12:15:13.000Z

holobot/extensions/crypto/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

null

from .alert_manager_interface import AlertManagerInterface from .alert_manager import AlertManager from .crypto_updater import CryptoUpdater

35.25

58

0.893617

16

141

7.625

0.625

0.147541

0.262295

0

0.085106

141

3

59

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0.945736

0

1

0

true

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1

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1

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null

0

1

0

1

0

1

0

6

7e95766c342499bfa038fc67107c66bd872341ac

24

py

Python

src/models/__init__.py

WorqHat/Stanford-MRnet-Challenge

0a8a7438f55503307d5b6b0ddaaa3c50fa4d692b

[ "MIT" ]

1

2021-06-27T18:22:57.000Z

src/models/__init__.py

WorqHat/Stanford-MRnet-Challenge

0a8a7438f55503307d5b6b0ddaaa3c50fa4d692b

[ "MIT" ]

null

src/models/__init__.py

WorqHat/Stanford-MRnet-Challenge

0a8a7438f55503307d5b6b0ddaaa3c50fa4d692b

[ "MIT" ]

2

2020-05-28T07:30:39.000Z

2021-06-27T18:22:59.000Z

from .MRnet import MRnet

24

0.833333

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24

5

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true

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null

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1

0

1

0

1

0

6

7ec9f217b2ef0eef366902d1d8043f6411d178a9

143

py

Python

_longname.py

michaelshumshum/kahoot-annoyer

a4f35e6ac6e54390815164fc9d81e28d8c301057

[ "MIT" ]

4

2021-02-20T10:55:33.000Z

2021-09-25T06:20:16.000Z

_longname.py

michaelshumshum/kahoot-annoyer

a4f35e6ac6e54390815164fc9d81e28d8c301057

[ "MIT" ]

7

2021-01-07T13:57:21.000Z

2021-09-30T06:28:15.000Z

_longname.py

michaelshumshum/kahoot-annoyer

a4f35e6ac6e54390815164fc9d81e28d8c301057

[ "MIT" ]

5

2021-01-19T12:27:49.000Z

2021-09-23T12:29:44.000Z

from random import randint def longname(): return ''.join(chr(randint(0,143859)) for i in range(10000)).encode('utf-8','ignore').decode()

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143

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null

0

1

0

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0

6

7edaeae2fc38a800bb95865424b3192bce1ad4c5

96

py

Python

cubes/query/__init__.py

digitalsatori/cubes

140133e8c2e3f2ff60631cc3ebc9966d16c1655e

[ "MIT" ]

1,020

2015-01-02T03:05:26.000Z

2022-02-12T18:48:51.000Z

cubes/query/__init__.py

digitalsatori/cubes

140133e8c2e3f2ff60631cc3ebc9966d16c1655e

[ "MIT" ]

259

2015-01-02T22:35:14.000Z

2021-09-02T04:20:41.000Z

cubes/query/__init__.py

digitalsatori/cubes

140133e8c2e3f2ff60631cc3ebc9966d16c1655e

[ "MIT" ]

288

2015-01-08T00:42:26.000Z

2022-03-31T17:25:10.000Z

from .browser import * from .cells import * from .computation import * from .statutils import *

19.2

26

0.75

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96

6

0.5

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0.166667

96

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null

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1

0

1

0

6

7d17c5d4335dc7c37aaf77acd240d8436fc7dcc4

69

py

Python

sample/core.py

trs319843/mypackage

cdcefaac5635805a577c26bea8e3437dc3f7e049

[ "MIT" ]

null

sample/core.py

trs319843/mypackage

cdcefaac5635805a577c26bea8e3437dc3f7e049

[ "MIT" ]

null

sample/core.py

trs319843/mypackage

cdcefaac5635805a577c26bea8e3437dc3f7e049

[ "MIT" ]

null

# sample\core.py def run_core(): print("In pycharm run_core")

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1

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6

ada57bfdb85d2618a08b453ff23946dc9309abc3

54,652

py

Python

tests/main/views/test_agreements.py

uk-gov-mirror/alphagov.digitalmarketplace-api

5a1db63691d0c4a435714837196ab6914badaf62

[ "MIT" ]

25

2015-01-14T10:45:13.000Z

2021-05-26T17:21:41.000Z

tests/main/views/test_agreements.py

uk-gov-mirror/alphagov.digitalmarketplace-api

5a1db63691d0c4a435714837196ab6914badaf62

[ "MIT" ]

641

2015-01-15T11:10:50.000Z

2021-06-15T22:18:42.000Z

tests/main/views/test_agreements.py

uk-gov-mirror/alphagov.digitalmarketplace-api

5a1db63691d0c4a435714837196ab6914badaf62

[ "MIT" ]

22

2015-06-13T15:37:45.000Z

2021-08-19T23:40:49.000Z

import json from datetime import datetime from freezegun import freeze_time from app.models import AuditEvent, db, Framework, FrameworkAgreement, User from tests.helpers import fixture_params from tests.bases import BaseApplicationTest class BaseFrameworkAgreementTest(BaseApplicationTest): def create_agreement(self, supplier_framework, **framework_agreement_kwargs): framework = Framework.query.filter(Framework.slug == supplier_framework['frameworkSlug']).first() agreement = FrameworkAgreement( supplier_id=supplier_framework['supplierId'], framework_id=framework.id, **framework_agreement_kwargs) db.session.add(agreement) db.session.commit() return agreement.id class TestCreateFrameworkAgreement(BaseApplicationTest): def post_create_agreement(self, supplier_id=None, framework_slug=None): agreement_data = {} if supplier_id: agreement_data['supplierId'] = supplier_id if framework_slug: agreement_data['frameworkSlug'] = framework_slug return self.client.post( '/agreements', data=json.dumps( { 'updated_by': 'interested@example.com', 'agreement': agreement_data }), content_type='application/json') def test_can_create_framework_agreement(self, supplier_framework): res = self.post_create_agreement( supplier_id=supplier_framework['supplierId'], framework_slug=supplier_framework['frameworkSlug'] ) assert res.status_code == 201 res_agreement_json = json.loads(res.get_data(as_text=True))['agreement'] assert res_agreement_json['id'] > 0 assert res_agreement_json['supplierId'] == supplier_framework['supplierId'] assert res_agreement_json['frameworkSlug'] == supplier_framework['frameworkSlug'] assert res_agreement_json['status'] == 'draft' res2 = self.client.get('/agreements/{}'.format(res_agreement_json['id'])) assert res2.status_code == 200 assert json.loads(res2.get_data(as_text=True))['agreement'] == res_agreement_json def test_create_framework_agreement_makes_an_audit_event(self, supplier_framework): res = self.post_create_agreement( supplier_id=supplier_framework['supplierId'], framework_slug=supplier_framework['frameworkSlug'] ) assert res.status_code == 201 agreement_id = json.loads(res.get_data(as_text=True))['agreement']['id'] agreement = FrameworkAgreement.query.filter( FrameworkAgreement.id == agreement_id ).first() audit = AuditEvent.query.filter( AuditEvent.object == agreement ).first() assert audit.type == "create_agreement" assert audit.user == "interested@example.com" assert audit.data == { 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'] } def test_404_if_creating_framework_agreement_with_no_supplier_framework(self, supplier_framework): res = self.post_create_agreement( supplier_id=7, framework_slug='dos' ) assert res.status_code == 404 assert json.loads(res.get_data(as_text=True))['error'] == "supplier_id '7' is not on framework 'dos'" @fixture_params('supplier_framework', {'on_framework': False}) def test_404_if_creating_framework_agreement_with_supplier_framework_not_on_framework(self, supplier_framework): res = self.post_create_agreement( supplier_id=supplier_framework['supplierId'], framework_slug=supplier_framework['frameworkSlug'] ) assert res.status_code == 404 assert ( json.loads(res.get_data(as_text=True))['error'] == "supplier_id '{}' is not on framework '{}'".format( supplier_framework['supplierId'], supplier_framework['frameworkSlug'] ) ) def test_can_not_create_framework_agreement_if_no_supplier_id_provided(self, supplier_framework): res = self.post_create_agreement( framework_slug=supplier_framework['frameworkSlug'] ) assert res.status_code == 400 assert ( json.loads(res.get_data(as_text=True))['error'] == "Invalid JSON must have 'supplierId' keys" ) def test_can_not_create_framework_agreement_if_no_framework_slug_provided(self, supplier_framework): res = self.post_create_agreement( supplier_id=supplier_framework['supplierId'] ) assert res.status_code == 400 assert ( json.loads(res.get_data(as_text=True))['error'] == "Invalid JSON must have 'frameworkSlug' keys" ) class TestGetFrameworkAgreement(BaseFrameworkAgreementTest): def test_it_gets_a_newly_created_framework_agreement_by_id(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework ) res = self.client.get('/agreements/{}'.format(agreement_id)) assert res.status_code == 200 assert json.loads(res.get_data(as_text=True))['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'draft' } def test_it_returns_a_framework_agreement_with_details_only(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_details={'details': 'here'} ) res = self.client.get('/agreements/{}'.format(agreement_id)) assert res.status_code == 200 assert json.loads(res.get_data(as_text=True))['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'signedAgreementDetails': {'details': 'here'}, 'status': 'draft' } def test_it_gets_a_signed_framework_agreement_by_id(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1), signed_agreement_details={'details': 'here'}, signed_agreement_path='path' ) res = self.client.get('/agreements/{}'.format(agreement_id)) assert res.status_code == 200 assert json.loads(res.get_data(as_text=True))['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'signed', 'signedAgreementDetails': {'details': 'here'}, 'signedAgreementPath': 'path', 'signedAgreementReturnedAt': '2016-10-01T01:01:01.000000Z', } def test_it_gets_an_on_hold_framework_agreement_by_id(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1), signed_agreement_details={'details': 'here'}, signed_agreement_path='path', signed_agreement_put_on_hold_at=datetime(2016, 11, 1, 1, 1, 1), ) res = self.client.get('/agreements/{}'.format(agreement_id)) assert res.status_code == 200 assert json.loads(res.get_data(as_text=True))['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'on-hold', 'signedAgreementDetails': {'details': 'here'}, 'signedAgreementPath': 'path', 'signedAgreementReturnedAt': '2016-10-01T01:01:01.000000Z', 'signedAgreementPutOnHoldAt': '2016-11-01T01:01:01.000000Z', } def test_it_gets_an_approved_framework_agreement_by_id(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1), signed_agreement_details={'details': 'here'}, signed_agreement_path='path', countersigned_agreement_details={'countersigneddetails': 'here'}, countersigned_agreement_returned_at=datetime(2016, 11, 1, 1, 1, 1), ) res = self.client.get('/agreements/{}'.format(agreement_id)) assert res.status_code == 200 assert json.loads(res.get_data(as_text=True))['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'approved', 'signedAgreementDetails': {'details': 'here'}, 'signedAgreementPath': 'path', 'signedAgreementReturnedAt': '2016-10-01T01:01:01.000000Z', 'countersignedAgreementDetails': {'countersigneddetails': 'here'}, 'countersignedAgreementReturnedAt': '2016-11-01T01:01:01.000000Z', } def test_it_gets_a_countersigned_framework_agreement_by_id(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1), signed_agreement_details={'details': 'here'}, signed_agreement_path='path', countersigned_agreement_details={'countersigneddetails': 'here'}, countersigned_agreement_returned_at=datetime(2016, 11, 1, 1, 1, 1), countersigned_agreement_path='path' ) res = self.client.get('/agreements/{}'.format(agreement_id)) assert res.status_code == 200 assert json.loads(res.get_data(as_text=True))['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'countersigned', 'signedAgreementDetails': {'details': 'here'}, 'signedAgreementPath': 'path', 'signedAgreementReturnedAt': '2016-10-01T01:01:01.000000Z', 'countersignedAgreementDetails': {'countersigneddetails': 'here'}, 'countersignedAgreementReturnedAt': '2016-11-01T01:01:01.000000Z', 'countersignedAgreementPath': 'path' } def test_it_gets_a_countersigned_and_uploaded_framework_agreement_by_id(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1), signed_agreement_details={'details': 'here'}, signed_agreement_path='path', countersigned_agreement_details={'countersigneddetails': 'here'}, countersigned_agreement_returned_at=datetime(2016, 11, 1, 1, 1, 1), countersigned_agreement_path='/example.pdf' ) res = self.client.get('/agreements/{}'.format(agreement_id)) assert res.status_code == 200 assert json.loads(res.get_data(as_text=True))['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'countersigned', 'signedAgreementDetails': {'details': 'here'}, 'signedAgreementPath': 'path', 'signedAgreementReturnedAt': '2016-10-01T01:01:01.000000Z', 'countersignedAgreementDetails': {'countersigneddetails': 'here'}, 'countersignedAgreementReturnedAt': '2016-11-01T01:01:01.000000Z', 'countersignedAgreementPath': '/example.pdf' } class TestUpdateFrameworkAgreement(BaseFrameworkAgreementTest): def post_agreement_update(self, agreement_id, agreement): return self.client.post( '/agreements/{}'.format(agreement_id), data=json.dumps( { 'updated_by': 'interested@example.com', 'agreement': agreement }), content_type='application/json') @fixture_params('live_example_framework', {'framework_agreement_details': None}) def test_cant_set_agreement_details_for_framework_without_agreement_version(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.client.post( '/agreements/{}'.format(agreement_id), data=json.dumps( { 'updated_by': 'interested@example.com', 'agreement': { 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', } } }), content_type='application/json') assert res.status_code == 400 assert ( json.loads(res.get_data(as_text=True))['error'] == "Can not update signedAgreementDetails for a framework agreement without a frameworkAgreementVersion" ) @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_update_framework_agreement_details(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, { 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', } }) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) expected_agreement_json = { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'draft', 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', } } assert data['agreement'] == expected_agreement_json res2 = self.client.get('/agreements/{}'.format(agreement_id)) assert res2.status_code == 200 assert json.loads(res2.get_data(as_text=True))['agreement'] == expected_agreement_json @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_update_signed_agreement_path(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, { 'signedAgreementPath': '/example.pdf' }) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) expected_agreement_json = { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'draft', 'signedAgreementPath': '/example.pdf' } assert data['agreement'] == expected_agreement_json res2 = self.client.get('/agreements/{}'.format(agreement_id)) assert res2.status_code == 200 assert json.loads(res2.get_data(as_text=True))['agreement'] == expected_agreement_json @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_update_signed_agreement_details_and_signed_agreement_path(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, { 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', }, 'signedAgreementPath': '/example.pdf' }) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) expected_agreement_json = { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'draft', 'signedAgreementPath': '/example.pdf', 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', } } assert data['agreement'] == expected_agreement_json res2 = self.client.get('/agreements/{}'.format(agreement_id)) assert res2.status_code == 200 assert json.loads(res2.get_data(as_text=True))['agreement'] == expected_agreement_json @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_audit_event_created_when_updating_framework_agreement(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, { 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', }, 'signedAgreementPath': '/example.pdf' }) assert res.status_code == 200 agreement = FrameworkAgreement.query.filter( FrameworkAgreement.id == agreement_id ).first() audit = AuditEvent.query.filter( AuditEvent.object == agreement ).first() assert audit.type == "update_agreement" assert audit.user == "interested@example.com" assert audit.data == { 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'update': { 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', }, 'signedAgreementPath': '/example.pdf' } } @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_not_set_framework_agreement_version_directly(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, { 'frameworkAgreementVersion': 'v23.4' }) assert res.status_code == 400 assert json.loads(res.get_data(as_text=True)) == { 'error': "Invalid JSON should not have 'frameworkAgreementVersion' keys" } @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_agreement_returned_at_timestamp_cannot_be_set(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, { 'signedAgreementReturnedAt': '2013-13-13T00:00:00.000000Z' }) assert res.status_code == 400 assert json.loads(res.get_data(as_text=True)) == { 'error': "Invalid JSON should not have 'signedAgreementReturnedAt' keys" } @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_400_cannot_update_signed_agreement(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework, signed_agreement_returned_at=datetime.utcnow()) res = self.post_agreement_update(agreement_id, { 'signedAgreementPath': '/example.pdf' }) assert res.status_code == 400 assert json.loads(res.get_data(as_text=True)) == { 'error': 'Can not update signedAgreementDetails or signedAgreementPath if agreement has been signed' } @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_400_if_unknown_field_present_in_update_json(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, { 'signedRandomKey': 'banana' }) assert res.status_code == 400 assert json.loads(res.get_data(as_text=True)) == { 'error': "Invalid JSON should not have 'signedRandomKey' keys" } @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_400_if_unknown_field_present_in_signed_agreement_details(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, { 'signedAgreementDetails': { 'signerName': 'name', 'randomKey': 'value', } }) assert res.status_code == 400 data = json.loads(res.get_data(as_text=True)) # split assertions into keyphrases due to nested unicode string in python 2 strings_we_expect_in_the_error_message = [ 'Additional properties are not allowed', 'randomKey', 'was unexpected'] for error_string in strings_we_expect_in_the_error_message: assert error_string in data['error']['_form'][0] @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_200_if_signed_agreement_details_is_empty_object(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, {'signedAgreementDetails': {}}) assert res.status_code == 200 @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_400_if_signed_agreement_details_contains_empty_strings_as_values(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.post_agreement_update(agreement_id, { 'signedAgreementDetails': { 'signerName': '', 'signerRole': '', } }) assert res.status_code == 400 assert json.loads(res.get_data(as_text=True)) == { 'error': {'signerName': 'answer_required', 'signerRole': 'answer_required'} } @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_update_countersigned_agreement_path_for_framework_with_agreement_version(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_details={ 'signerName': 'name', 'signerRole': 'role', }, signed_agreement_path='path/file.pdf', signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1), countersigned_agreement_returned_at=datetime(2016, 11, 1, 1, 1, 1) ) res = self.post_agreement_update(agreement_id, { 'countersignedAgreementPath': 'countersigned/file.jpg' }) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) expected_agreement_json = { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'countersigned', 'signedAgreementPath': 'path/file.pdf', 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', }, 'signedAgreementReturnedAt': '2016-10-01T01:01:01.000000Z', 'countersignedAgreementReturnedAt': '2016-11-01T01:01:01.000000Z', 'countersignedAgreementPath': 'countersigned/file.jpg' } assert data['agreement'] == expected_agreement_json res2 = self.client.get('/agreements/{}'.format(agreement_id)) assert res2.status_code == 200 assert json.loads(res2.get_data(as_text=True))['agreement'] == expected_agreement_json @fixture_params('live_example_framework', {'framework_agreement_details': None}) def test_can_update_countersigned_agreement_path_for_framework_without_agreement_version(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_path='path/file.pdf', signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1), countersigned_agreement_returned_at=datetime(2016, 11, 1, 1, 1, 1) ) res = self.post_agreement_update(agreement_id, { 'countersignedAgreementPath': 'countersigned/file.jpg' }) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) expected_agreement_json = { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'countersigned', 'signedAgreementPath': 'path/file.pdf', 'signedAgreementReturnedAt': '2016-10-01T01:01:01.000000Z', 'countersignedAgreementReturnedAt': '2016-11-01T01:01:01.000000Z', 'countersignedAgreementPath': 'countersigned/file.jpg' } assert data['agreement'] == expected_agreement_json res2 = self.client.get('/agreements/{}'.format(agreement_id)) assert res2.status_code == 200 assert json.loads(res2.get_data(as_text=True))['agreement'] == expected_agreement_json @fixture_params( 'live_example_framework', { 'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}, 'slug': 'g-cloud-11', } ) def test_cannot_update_countersigned_agreement_path_if_agreement_has_not_been_approved(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_path='path/file.pdf', signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1) ) res = self.post_agreement_update(agreement_id, { 'countersignedAgreementPath': 'countersigned/file.jpg' }) assert res.status_code == 400 assert json.loads(res.get_data(as_text=True)) == { 'error': 'Can not update countersignedAgreementPath if agreement has not been approved for countersigning' } @fixture_params( 'live_example_framework', { 'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}, 'slug': 'g-cloud-12', } ) def test_can_update_countersigned_agreement_path_without_approval_for_esignature_framework( self, supplier_framework ): agreement_id = self.create_agreement( supplier_framework, signed_agreement_path='path/file.pdf', signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1) ) res = self.post_agreement_update(agreement_id, { 'countersignedAgreementPath': 'countersigned/file.jpg' }) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) expected_agreement_json = { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'countersigned', 'signedAgreementPath': 'path/file.pdf', 'signedAgreementReturnedAt': '2016-10-01T01:01:01.000000Z', 'countersignedAgreementPath': 'countersigned/file.jpg' } assert data['agreement'] == expected_agreement_json res2 = self.client.get('/agreements/{}'.format(agreement_id)) assert res2.status_code == 200 assert json.loads(res2.get_data(as_text=True))['agreement'] == expected_agreement_json def test_can_unset_countersigned_agreement_path(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_path='path/file.pdf', signed_agreement_returned_at=datetime(2016, 10, 1, 1, 1, 1), countersigned_agreement_returned_at=datetime(2016, 11, 1, 1, 1, 1), countersigned_agreement_path='countersigned/that/bad/boy.pdf' ) res = self.post_agreement_update(agreement_id, { 'countersignedAgreementPath': None }) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) expected_agreement_json = { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'approved', 'signedAgreementPath': 'path/file.pdf', 'signedAgreementReturnedAt': '2016-10-01T01:01:01.000000Z', 'countersignedAgreementReturnedAt': '2016-11-01T01:01:01.000000Z' } assert data['agreement'] == expected_agreement_json res2 = self.client.get('/agreements/{}'.format(agreement_id)) assert res2.status_code == 200 assert json.loads(res2.get_data(as_text=True))['agreement'] == expected_agreement_json @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) class TestSignFrameworkAgreementThatHasFrameworkAgreementVersion(BaseFrameworkAgreementTest): def sign_agreement(self, agreement_id, agreement): return self.client.post( '/agreements/{}/sign'.format(agreement_id), data=json.dumps( { 'updated_by': 'interested@example.com', 'agreement': agreement }), content_type='application/json') def test_can_sign_framework_agreement(self, user_role_supplier, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_details={'signerName': 'name', 'signerRole': 'role'}, signed_agreement_path='/example.pdf' ) with freeze_time('2016-12-12'): res = self.sign_agreement(agreement_id, {'signedAgreementDetails': {'uploaderUserId': 1}}) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) assert data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'signed', 'signedAgreementPath': '/example.pdf', 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', 'uploaderUserId': user_role_supplier, 'frameworkAgreementVersion': 'v1.0' }, 'signedAgreementReturnedAt': '2016-12-12T00:00:00.000000Z' } def test_signing_framework_agreement_produces_audit_event(self, user_role_supplier, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_details={'signerName': 'name', 'signerRole': 'role'}, signed_agreement_path='/example.pdf' ) res = self.sign_agreement(agreement_id, {'signedAgreementDetails': {'uploaderUserId': user_role_supplier}}) assert res.status_code == 200 agreement = FrameworkAgreement.query.filter( FrameworkAgreement.id == agreement_id ).first() audit = AuditEvent.query.filter( AuditEvent.object == agreement ).first() assert audit.type == "sign_agreement" assert audit.user == "interested@example.com" assert audit.data == { 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'update': {'signedAgreementDetails': {'uploaderUserId': user_role_supplier}} } def test_can_re_sign_framework_agreement(self, user_role_supplier, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_details={ 'signerName': 'name', 'signerRole': 'role', 'uploaderUserId': 2, 'frameworkAgreementVersion': 'v1.0' }, signed_agreement_path='/example.pdf', signed_agreement_returned_at=datetime.utcnow() ) with freeze_time('2016-12-12'): res = self.sign_agreement(agreement_id, {'signedAgreementDetails': {'uploaderUserId': user_role_supplier}}) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) assert data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'signed', 'signedAgreementPath': '/example.pdf', 'signedAgreementDetails': { 'signerName': 'name', 'signerRole': 'role', 'uploaderUserId': 1, 'frameworkAgreementVersion': 'v1.0' }, 'signedAgreementReturnedAt': '2016-12-12T00:00:00.000000Z' } def test_can_not_sign_framework_agreement_that_has_no_signer_name(self, user_role_supplier, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_details={'signerRole': 'role'}, signed_agreement_path='/example.pdf' ) res = self.sign_agreement(agreement_id, {'signedAgreementDetails': {'uploaderUserId': user_role_supplier}}) assert res.status_code == 400 assert ( json.loads(res.get_data(as_text=True))['error'] == {'signerName': 'answer_required'}) def test_can_not_sign_framework_agreement_that_has_no_signer_role(self, user_role_supplier, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_details={'signerName': 'name'}, signed_agreement_path='/example.pdf' ) res = self.sign_agreement(agreement_id, {'signedAgreementDetails': {'uploaderUserId': user_role_supplier}}) assert res.status_code == 400 assert ( json.loads(res.get_data(as_text=True))['error'] == {'signerRole': 'answer_required'}) def test_400_if_user_signing_framework_agreement_does_not_exist(self, user_role_supplier, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_details={'signerName': 'name', 'signerRole': 'role'}, signed_agreement_path='/example.pdf' ) # The user_role_supplier fixture sets up user with ID 1; there is no user with ID 20 res = self.sign_agreement(agreement_id, {'signedAgreementDetails': {'uploaderUserId': 20}}) assert res.status_code == 400 assert ( json.loads(res.get_data(as_text=True))['error'] == "No user found with id '20'") # Frameworks prior to G-Cloud 8 do not have framework_agreement_version set, and signing these stores only the timestamp class TestSignFrameworkAgreementThatHasNoFrameworkAgreementVersion(BaseFrameworkAgreementTest): def sign_agreement(self, agreement_id): return self.client.post( '/agreements/{}/sign'.format(agreement_id), data=json.dumps( { 'updated_by': 'interested@example.com' }), content_type='application/json') def test_can_sign_framework_agreement(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) with freeze_time('2016-12-12'): res = self.sign_agreement(agreement_id) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) assert data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'signed', 'signedAgreementReturnedAt': '2016-12-12T00:00:00.000000Z' } def test_signing_framework_agreement_produces_audit_event(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.sign_agreement(agreement_id) assert res.status_code == 200 agreement = FrameworkAgreement.query.filter( FrameworkAgreement.id == agreement_id ).first() audit = AuditEvent.query.filter( AuditEvent.object == agreement ).first() assert audit.type == "sign_agreement" assert audit.user == "interested@example.com" assert audit.data == { 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], } def test_can_re_sign_framework_agreement(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime.utcnow() ) with freeze_time('2016-12-12'): res = self.sign_agreement(agreement_id) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) assert data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'signed', 'signedAgreementReturnedAt': '2016-12-12T00:00:00.000000Z' } class TestPutFrameworkAgreementOnHold(BaseFrameworkAgreementTest): def put_framework_agreement_on_hold(self, agreement_id): return self.client.post( '/agreements/{}/on-hold'.format(agreement_id), data=json.dumps( { 'updated_by': 'interested@example.com' }), content_type='application/json') @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_put_framework_agreement_on_hold(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1), ) with freeze_time('2016-12-12'): res = self.put_framework_agreement_on_hold(agreement_id) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) assert data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'on-hold', 'signedAgreementReturnedAt': '2016-10-01T00:00:00.000000Z', 'signedAgreementPutOnHoldAt': '2016-12-12T00:00:00.000000Z' } agreement = FrameworkAgreement.query.filter( FrameworkAgreement.id == agreement_id ).first() audit = AuditEvent.query.filter( AuditEvent.object == agreement ).first() assert audit.type == "update_agreement" assert audit.user == "interested@example.com" assert audit.data == { 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'on-hold' } @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_not_put_unsigned_framework_agreement_on_hold(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.put_framework_agreement_on_hold(agreement_id) assert res.status_code == 400 error_message = json.loads(res.get_data(as_text=True))['error'] assert error_message == "Framework agreement must have status 'signed' to be put on hold" @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_not_put_countersigned_framework_agreement_on_hold(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 9, 1), countersigned_agreement_returned_at=datetime(2016, 10, 1) ) res = self.put_framework_agreement_on_hold(agreement_id) assert res.status_code == 400 error_message = json.loads(res.get_data(as_text=True))['error'] assert error_message == "Framework agreement must have status 'signed' to be put on hold" def test_can_not_put_framework_agreement_on_hold_that_has_no_framework_agreement_version(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1) ) res = self.put_framework_agreement_on_hold(agreement_id) assert res.status_code == 400 error_message = json.loads(res.get_data(as_text=True))['error'] assert error_message == "Framework agreement must have a 'frameworkAgreementVersion' to be put on hold" class TestApproveFrameworkAgreement(BaseFrameworkAgreementTest): def approve_framework_agreement(self, agreement_id): return self.client.post( '/agreements/{}/approve'.format(agreement_id), data=json.dumps( { 'updated_by': 'chris@example.com', 'agreement': {'userId': '1234'} }), content_type='application/json') def unapprove_framework_agreement(self, agreement_id): return self.client.post( '/agreements/{}/approve'.format(agreement_id), data=json.dumps( { 'updated_by': 'made-a-whoopsie@example.com', 'agreement': {'userId': '1234', 'unapprove': True} }), content_type='application/json') @fixture_params( 'live_example_framework', { 'framework_agreement_details': { 'frameworkAgreementVersion': 'v1.0', 'countersignerName': 'The Boss', 'countersignerRole': 'Director of Strings' } } ) def test_can_approve_signed_framework_agreement(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1), ) with freeze_time('2016-12-12'): res = self.approve_framework_agreement(agreement_id) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) assert data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'approved', 'signedAgreementReturnedAt': '2016-10-01T00:00:00.000000Z', 'countersignedAgreementReturnedAt': '2016-12-12T00:00:00.000000Z', 'countersignedAgreementDetails': { 'countersignerName': 'The Boss', 'countersignerRole': 'Director of Strings', 'approvedByUserId': '1234' } } agreement = FrameworkAgreement.query.filter( FrameworkAgreement.id == agreement_id ).first() audit = AuditEvent.query.filter( AuditEvent.object == agreement ).first() assert audit.type == "countersign_agreement" assert audit.user == "chris@example.com" assert audit.data == { 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'approved' } @fixture_params( 'live_example_framework', { 'framework_agreement_details': { 'frameworkAgreementVersion': 'v1.0', 'countersignerName': 'The Boss', 'countersignerRole': 'Director of Strings' } } ) def test_can_approve_on_hold_framework_agreement(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1), ) with freeze_time('2016-10-02'): on_hold_res = self.client.post( '/agreements/{}/on-hold'.format(agreement_id), data=json.dumps( { 'updated_by': 'interested@example.com' }), content_type='application/json') assert on_hold_res.status_code == 200 on_hold_data = json.loads(on_hold_res.get_data(as_text=True))['agreement'] assert on_hold_data['status'] == 'on-hold' with freeze_time('2016-10-03'): res = self.approve_framework_agreement(agreement_id) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) assert 'signedAgreementPutOnHoldAt' not in data['agreement'] assert data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'approved', 'signedAgreementReturnedAt': '2016-10-01T00:00:00.000000Z', 'countersignedAgreementReturnedAt': '2016-10-03T00:00:00.000000Z', 'countersignedAgreementDetails': { 'countersignerName': 'The Boss', 'countersignerRole': 'Director of Strings', 'approvedByUserId': '1234' } } @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_not_approve_unsigned_framework_agreement(self, supplier_framework): agreement_id = self.create_agreement(supplier_framework) res = self.approve_framework_agreement(agreement_id) assert res.status_code == 400 error_message = json.loads(res.get_data(as_text=True))['error'] assert error_message == "Framework agreement must have status 'signed' or 'on hold' to be countersigned" def test_can_approve_framework_agreement_that_has_no_framework_agreement_version(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1) ) with freeze_time('2016-10-03'): res = self.approve_framework_agreement(agreement_id) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) assert data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'approved', 'signedAgreementReturnedAt': '2016-10-01T00:00:00.000000Z', 'countersignedAgreementReturnedAt': '2016-10-03T00:00:00.000000Z', 'countersignedAgreementDetails': {'approvedByUserId': '1234'} } @fixture_params('live_example_framework', {'framework_agreement_details': {'frameworkAgreementVersion': 'v1.0'}}) def test_can_approve_framework_agreement_with_agreement_version_but_no_name_or_role(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1) ) with freeze_time('2016-10-03'): res = self.approve_framework_agreement(agreement_id) assert res.status_code == 200 data = json.loads(res.get_data(as_text=True)) assert data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'approved', 'signedAgreementReturnedAt': '2016-10-01T00:00:00.000000Z', 'countersignedAgreementReturnedAt': '2016-10-03T00:00:00.000000Z', 'countersignedAgreementDetails': {'approvedByUserId': '1234'} } @fixture_params( 'live_example_framework', { 'framework_agreement_details': { 'frameworkAgreementVersion': 'v1.0', 'countersignerName': 'The Boss', 'countersignerRole': 'Director of Strings' } } ) def test_serialized_supplier_framework_contains_updater_details_after_approval(self, supplier_framework): user = User( id=1234, name='Chris', email_address='chris@crowncommercial.gov.uk', password='password', active=True, created_at=datetime.now(), password_changed_at=datetime.now(), role='admin-ccs-sourcing' ) db.session.add(user) db.session.commit() agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1), signed_agreement_details={}, countersigned_agreement_details={ "countersignerRole": "Director of Strings", "approvedByUserId": 1234, "countersignerName": "The Boss" }, countersigned_agreement_returned_at=datetime.now() ) agreement = FrameworkAgreement.query.filter(FrameworkAgreement.id == agreement_id).first() supplier_framework = agreement.supplier_framework.serialize(with_users=True) assert supplier_framework['countersignedDetails']['approvedByUserName'] == 'Chris' assert supplier_framework['countersignedDetails']['approvedByUserEmail'] == 'chris@crowncommercial.gov.uk' def test_can_unapprove_approved_agreement(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1) ) with freeze_time('2016-12-12'): res1 = self.approve_framework_agreement(agreement_id) agreement_before_unapprove_data = json.loads(res1.get_data(as_text=True)) # Check that the agreement is definitely approved assert agreement_before_unapprove_data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'approved', 'signedAgreementReturnedAt': '2016-10-01T00:00:00.000000Z', 'countersignedAgreementReturnedAt': '2016-12-12T00:00:00.000000Z', 'countersignedAgreementDetails': {'approvedByUserId': '1234'} } res2 = self.unapprove_framework_agreement(agreement_id) assert res2.status_code == 200 unapproved_agreement_data = json.loads(res2.get_data(as_text=True)) # Check that status is reverted to 'signed' and countersigned info has been removed assert unapproved_agreement_data['agreement'] == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'signed', 'signedAgreementReturnedAt': '2016-10-01T00:00:00.000000Z', } agreement = FrameworkAgreement.query.filter( FrameworkAgreement.id == agreement_id ).first() # Get the most recent audit event and check it is the "unapprove" event audit = AuditEvent.query.filter( AuditEvent.object == agreement ).order_by(AuditEvent.created_at.desc()).first() assert audit.type == "countersign_agreement" assert audit.user == "made-a-whoopsie@example.com" assert audit.data == { 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'unapproved' } def test_can_not_unapprove_countersigned_agreement(self, supplier_framework): agreement_id = self.create_agreement( supplier_framework, signed_agreement_returned_at=datetime(2016, 10, 1), countersigned_agreement_returned_at=datetime(2016, 10, 2), countersigned_agreement_path='/path/to/countersigned/document' ) res1 = self.client.get('/agreements/{}'.format(agreement_id)) data1 = json.loads(res1.get_data(as_text=True))['agreement'] assert data1['status'] == 'countersigned' res2 = self.unapprove_framework_agreement(agreement_id) data2 = json.loads(res2.get_data(as_text=True)) assert res2.status_code == 400 assert data2['error'] == "Framework agreement must have status 'approved' to be unapproved" # Check that status has not been changed res3 = self.client.get('/agreements/{}'.format(agreement_id)) data3 = json.loads(res3.get_data(as_text=True))['agreement'] assert data3 == { 'id': agreement_id, 'supplierId': supplier_framework['supplierId'], 'frameworkSlug': supplier_framework['frameworkSlug'], 'status': 'countersigned', 'signedAgreementReturnedAt': '2016-10-01T00:00:00.000000Z', 'countersignedAgreementReturnedAt': '2016-10-02T00:00:00.000000Z', 'countersignedAgreementPath': '/path/to/countersigned/document' }

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null

0

1

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null

0

6

bc21fb987995424da0c4a8da3583644d72bd8f6a

3,326

py

Python

test_prime_numbers.py

zekedran/python_tdd_ci_tutorial

6a318ce5f26953dd44ca49da73815de4a2634fe5

[ "MIT" ]

null

test_prime_numbers.py

zekedran/python_tdd_ci_tutorial

6a318ce5f26953dd44ca49da73815de4a2634fe5

[ "MIT" ]

null

test_prime_numbers.py

zekedran/python_tdd_ci_tutorial

6a318ce5f26953dd44ca49da73815de4a2634fe5

[ "MIT" ]

null

from prime_numbers import is_prime def test_is_prime(): assert is_prime(-1) is False assert is_prime(0) is False assert is_prime(4) is False assert is_prime(6) is False assert is_prime(8) is False assert is_prime(9) is False assert is_prime(10) is False assert is_prime(12) is False assert is_prime(14) is False assert is_prime(15) is False assert is_prime(16) is False assert is_prime(18) is False assert is_prime(20) is False assert is_prime(21) is False assert is_prime(22) is False assert is_prime(24) is False assert is_prime(25) is False assert is_prime(26) is False assert is_prime(27) is False assert is_prime(28) is False assert is_prime(30) is False assert is_prime(32) is False assert is_prime(33) is False assert is_prime(34) is False assert is_prime(35) is False assert is_prime(36) is False assert is_prime(38) is False assert is_prime(39) is False assert is_prime(40) is False assert is_prime(42) is False assert is_prime(44) is False assert is_prime(45) is False assert is_prime(46) is False assert is_prime(48) is False assert is_prime(49) is False assert is_prime(50) is False assert is_prime(51) is False assert is_prime(52) is False assert is_prime(54) is False assert is_prime(55) is False assert is_prime(56) is False assert is_prime(57) is False assert is_prime(58) is False assert is_prime(60) is False assert is_prime(62) is False assert is_prime(63) is False assert is_prime(64) is False assert is_prime(65) is False assert is_prime(66) is False assert is_prime(68) is False assert is_prime(69) is False assert is_prime(70) is False assert is_prime(72) is False assert is_prime(74) is False assert is_prime(75) is False assert is_prime(76) is False assert is_prime(77) is False assert is_prime(78) is False assert is_prime(80) is False assert is_prime(81) is False assert is_prime(82) is False assert is_prime(84) is False assert is_prime(85) is False assert is_prime(86) is False assert is_prime(87) is False assert is_prime(88) is False assert is_prime(90) is False assert is_prime(91) is False assert is_prime(92) is False assert is_prime(93) is False assert is_prime(94) is False assert is_prime(95) is False assert is_prime(96) is False assert is_prime(98) is False assert is_prime(99) is False assert is_prime(2) is True assert is_prime(3) is True assert is_prime(5) is True assert is_prime(7) is True assert is_prime(11) is True assert is_prime(13) is True assert is_prime(17) is True assert is_prime(19) is True assert is_prime(23) is True assert is_prime(29) is True assert is_prime(31) is True assert is_prime(37) is True assert is_prime(41) is True assert is_prime(43) is True assert is_prime(47) is True assert is_prime(53) is True assert is_prime(59) is True assert is_prime(61) is True assert is_prime(67) is True assert is_prime(71) is True assert is_prime(73) is True assert is_prime(79) is True assert is_prime(83) is True assert is_prime(89) is True assert is_prime(97) is True

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null

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1

0

1

0

6

bc22939116e8ef6b4d13b61eab3aac762d3ff398

7,386

py

Python

client/tests/test_up_args.py

gefyrahq/gefyra

0bc205b4b01100c640081ead671bdb195761299b

[ "Apache-2.0" ]

41

2022-03-24T15:45:56.000Z

2022-03-31T08:07:19.000Z

client/tests/test_up_args.py

Schille/gefyra

43abd17b8ed5867a26266b5e7a5d6f9edfebab4a

[ "Apache-2.0" ]

23

2021-12-02T10:29:09.000Z

2022-03-17T18:10:57.000Z

client/tests/test_up_args.py

Schille/gefyra

43abd17b8ed5867a26266b5e7a5d6f9edfebab4a

[ "Apache-2.0" ]

3

2022-02-18T21:16:10.000Z

2022-03-09T22:46:28.000Z

from gefyra.__main__ import up_parser, up_command from gefyra.configuration import ClientConfiguration, __VERSION__ REGISTRY_URL = "my-reg.io/gefyra" QUAY_REGISTRY_URL = "quay.io/gefyra" STOWAWAY_LATEST = "my-reg.io/gefyra/stowaway:latest" CARGO_LATEST = "my-reg.io/gefyra/cargo:latest" OPERATOR_LATEST = "my-reg.io/gefyra/operator:latest" CARRIER_LATEST = "my-reg.io/gefyra/carrier:latest" KUBE_CONFIG = "~/.kube/config" def test_parse_registry_a(): args = up_parser.parse_args(["--registry", REGISTRY_URL]) configuration = ClientConfiguration(registry_url=args.registry) assert configuration.REGISTRY_URL == REGISTRY_URL def test_parse_registry_b(): args = up_parser.parse_args(["--registry", "my-reg.io/gefyra/"]) configuration = ClientConfiguration(registry_url=args.registry) assert configuration.REGISTRY_URL, REGISTRY_URL args = up_parser.parse_args(["-r", "my-reg.io/gefyra/"]) configuration = ClientConfiguration(registry_url=args.registry) assert configuration.REGISTRY_URL == REGISTRY_URL def test_parse_no_registry(): args = up_parser.parse_args() configuration = ClientConfiguration(registry_url=args.registry) assert configuration.REGISTRY_URL == QUAY_REGISTRY_URL def test_parse_no_stowaway_image(): args = up_parser.parse_args() configuration = ClientConfiguration(stowaway_image_url=args.stowaway) assert configuration.STOWAWAY_IMAGE == f"quay.io/gefyra/stowaway:{__VERSION__}" def test_parse_no_carrier_image(): args = up_parser.parse_args() configuration = ClientConfiguration(carrier_image_url=args.carrier) assert configuration.CARRIER_IMAGE == f"quay.io/gefyra/carrier:{__VERSION__}" def test_parse_no_operator_image(): args = up_parser.parse_args() configuration = ClientConfiguration(operator_image_url=args.operator) assert configuration.OPERATOR_IMAGE == f"quay.io/gefyra/operator:{__VERSION__}" def test_parse_no_cargo_image(): args = up_parser.parse_args() configuration = ClientConfiguration(cargo_image_url=args.cargo) assert configuration.CARGO_IMAGE == f"quay.io/gefyra/cargo:{__VERSION__}" def test_parse_stowaway_image(): args = up_parser.parse_args(["--stowaway", STOWAWAY_LATEST]) configuration = ClientConfiguration(stowaway_image_url=args.stowaway) assert configuration.STOWAWAY_IMAGE == STOWAWAY_LATEST args = up_parser.parse_args(["-s", STOWAWAY_LATEST]) configuration = ClientConfiguration(stowaway_image_url=args.stowaway) assert configuration.STOWAWAY_IMAGE == STOWAWAY_LATEST args = up_parser.parse_args(["-s", STOWAWAY_LATEST, "-r", QUAY_REGISTRY_URL]) configuration = ClientConfiguration( registry_url=args.registry, stowaway_image_url=args.stowaway ) assert configuration.STOWAWAY_IMAGE == STOWAWAY_LATEST def test_parse_cargo_image(): args = up_parser.parse_args(["--cargo", CARGO_LATEST]) configuration = ClientConfiguration(cargo_image_url=args.cargo) assert configuration.CARGO_IMAGE == CARGO_LATEST args = up_parser.parse_args(["-a", CARGO_LATEST]) configuration = ClientConfiguration(cargo_image_url=args.cargo) assert configuration.CARGO_IMAGE == CARGO_LATEST args = up_parser.parse_args(["-a", CARGO_LATEST, "-r", QUAY_REGISTRY_URL]) configuration = ClientConfiguration( registry_url=args.registry, cargo_image_url=args.cargo ) assert configuration.CARGO_IMAGE == CARGO_LATEST def test_parse_operator_image(): args = up_parser.parse_args(["--operator", OPERATOR_LATEST]) configuration = ClientConfiguration(operator_image_url=args.operator) assert configuration.OPERATOR_IMAGE == OPERATOR_LATEST args = up_parser.parse_args(["-o", OPERATOR_LATEST]) configuration = ClientConfiguration(operator_image_url=args.operator) assert configuration.OPERATOR_IMAGE == OPERATOR_LATEST args = up_parser.parse_args(["-o", OPERATOR_LATEST, "-r", QUAY_REGISTRY_URL]) configuration = ClientConfiguration( registry_url=args.registry, operator_image_url=args.operator ) assert configuration.OPERATOR_IMAGE == OPERATOR_LATEST def test_parse_carrier_image(): args = up_parser.parse_args(["--carrier", CARRIER_LATEST]) configuration = ClientConfiguration(carrier_image_url=args.carrier) assert configuration.CARRIER_IMAGE == CARRIER_LATEST args = up_parser.parse_args(["-c", CARRIER_LATEST]) configuration = ClientConfiguration(carrier_image_url=args.carrier) assert configuration.CARRIER_IMAGE == CARRIER_LATEST args = up_parser.parse_args(["-c", CARRIER_LATEST, "-r", QUAY_REGISTRY_URL]) configuration = ClientConfiguration( registry_url=args.registry, carrier_image_url=args.carrier ) assert configuration.CARRIER_IMAGE == CARRIER_LATEST def test_parse_combination_a(): args = up_parser.parse_args(["-c", CARRIER_LATEST]) configuration = ClientConfiguration( registry_url=args.registry, stowaway_image_url=args.stowaway, operator_image_url=args.operator, cargo_image_url=args.cargo, carrier_image_url=args.carrier, ) assert configuration.REGISTRY_URL == QUAY_REGISTRY_URL assert configuration.OPERATOR_IMAGE == f"quay.io/gefyra/operator:{__VERSION__}" assert configuration.CARRIER_IMAGE == CARRIER_LATEST def test_parse_combination_b(): args = up_parser.parse_args(["-r", REGISTRY_URL]) configuration = ClientConfiguration( registry_url=args.registry, stowaway_image_url=args.stowaway, operator_image_url=args.operator, cargo_image_url=args.cargo, carrier_image_url=args.carrier, ) assert configuration.REGISTRY_URL == REGISTRY_URL assert configuration.OPERATOR_IMAGE == f"my-reg.io/gefyra/operator:{__VERSION__}" assert configuration.CARRIER_IMAGE == f"my-reg.io/gefyra/carrier:{__VERSION__}" def test_parse_combination_c(): args = up_parser.parse_args( ["-r", REGISTRY_URL, "-c", "quay.io/gefyra/carrier:latest"] ) configuration = ClientConfiguration( registry_url=args.registry, stowaway_image_url=args.stowaway, operator_image_url=args.operator, cargo_image_url=args.cargo, carrier_image_url=args.carrier, ) assert configuration.REGISTRY_URL == REGISTRY_URL assert configuration.OPERATOR_IMAGE == f"my-reg.io/gefyra/operator:{__VERSION__}" assert configuration.CARRIER_IMAGE == "quay.io/gefyra/carrier:latest" def test_parse_endpoint(): args = up_parser.parse_args(["-e", "10.30.34.25:31820"]) configuration = ClientConfiguration( cargo_endpoint=args.endpoint, registry_url=args.registry, stowaway_image_url=args.stowaway, operator_image_url=args.operator, cargo_image_url=args.cargo, carrier_image_url=args.carrier, ) assert configuration.CARGO_ENDPOINT == "10.30.34.25:31820" def test_parse_up_fct(monkeypatch): monkeypatch.setattr("gefyra.api.up", lambda config: True) args = up_parser.parse_args(["-e", "10.30.34.25:31820"]) up_command(args) def test_parse_up_kube_conf(): configuration = ClientConfiguration(kube_config_file=KUBE_CONFIG) assert configuration.KUBE_CONFIG_FILE == KUBE_CONFIG def test_parse_up_no_kube_conf(): configuration = ClientConfiguration() assert configuration.KUBE_CONFIG_FILE is None

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0

0.136054

0

null

0

1

0

null

0

6

bc32a3234dd90ad352aba5adb527d79b901adc29

303

py

Python

examples/fixture.no_background/features/steps/use_steplib_behave4cmd.py

wombat70/behave

c54493b0531795d946ac6754bfc643248cf3056a

[ "BSD-2-Clause" ]

13

2019-10-03T19:15:14.000Z

2019-10-16T02:01:57.000Z

examples/fixture.no_background/features/steps/use_steplib_behave4cmd.py

wombat70/behave

c54493b0531795d946ac6754bfc643248cf3056a

[ "BSD-2-Clause" ]

2

2020-03-21T22:37:54.000Z

2021-10-04T17:14:14.000Z

examples/fixture.no_background/features/steps/use_steplib_behave4cmd.py

fluendo/behave

eeffde083456dcf1a0ea9b6139b32091970118c0

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- """ Use behave4cmd0 step library (predecessor of behave4cmd). """ from __future__ import absolute_import # -- REGISTER-STEPS FROM STEP-LIBRARY: # import behave4cmd0.__all_steps__ # import behave4cmd0.failing_steps import behave4cmd0.passing_steps import behave4cmd0.note_steps

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1

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1

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6

70ae466b7e7db5a0d09e04c20a68c88f305a4aac

77

py

Python

build_start.py

sanman00/SpongeSkills

7a54e424071a21a55a84479e0177ed7ebacc9c31

[ "MIT" ]

null

build_start.py

sanman00/SpongeSkills

7a54e424071a21a55a84479e0177ed7ebacc9c31

[ "MIT" ]

4

2017-05-05T15:51:09.000Z

2017-05-08T17:18:27.000Z

build_start.py

sanman00/SpongeSkills

7a54e424071a21a55a84479e0177ed7ebacc9c31

[ "MIT" ]

null

from build import replace_text, version replace_text("@{version}", version)

19.25

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0.779221

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0.37931

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0.103896

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null

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0

1

0

null

0

1

0

1

0

6

70cae01a3bbf35c083ab9e6723658c85595554ce

983

py

Python

problems/test_ic_1_stock_prices.py

gregdferrell/algo

974ae25b028d49bcb7ded6655a7e11dcf6aa221d

[ "MIT" ]

null

problems/test_ic_1_stock_prices.py

gregdferrell/algo

974ae25b028d49bcb7ded6655a7e11dcf6aa221d

[ "MIT" ]

null

problems/test_ic_1_stock_prices.py

gregdferrell/algo

974ae25b028d49bcb7ded6655a7e11dcf6aa221d

[ "MIT" ]

null

from .ic_1_stock_prices import stock_prices_1_brute_force, stock_prices_2_greedy def test_stock_price_algorithms_lose(): stock_prices = [10, 9, 7] assert stock_prices_1_brute_force(stock_prices) == -1 assert stock_prices_2_greedy(stock_prices) == -1 def test_stock_price_algorithms_no_gain(): stock_prices = [2, 2, 1, 1] assert stock_prices_1_brute_force(stock_prices) == 0 assert stock_prices_2_greedy(stock_prices) == 0 def test_stock_price_algorithms_gain_1(): stock_prices = [1, 2, 3, 4, 5] assert stock_prices_1_brute_force(stock_prices) == 4 assert stock_prices_2_greedy(stock_prices) == 4 def test_stock_price_algorithms_gain_2(): stock_prices = [10, 7, 5, 8, 11, 9] assert stock_prices_1_brute_force(stock_prices) == 6 assert stock_prices_2_greedy(stock_prices) == 6 def test_stock_price_algorithms_gain_3(): stock_prices = [9, 8, 10, 7, 11, 6, 12] assert stock_prices_1_brute_force(stock_prices) == 6 assert stock_prices_2_greedy(stock_prices) == 6

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31

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false

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null

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1

0

1

0

6

70ecc3bb253029a64b550829a901f26d30203690

31

py

Python

arxivtimes_indicator/server/__init__.py

chakki-works/arXivTimesIndicator

501413571dc0024b5a9d0bc2e9f17f805de92690

[ "Apache-2.0" ]

34

2017-08-02T07:01:13.000Z

2019-01-06T10:35:57.000Z

arxivtimes_indicator/server/__init__.py

arXivTimes/arXivTimesIndicator

501413571dc0024b5a9d0bc2e9f17f805de92690

[ "Apache-2.0" ]

4

2017-08-09T06:47:26.000Z

2017-10-13T01:47:01.000Z

arxivtimes_indicator/server/__init__.py

arXivTimes/arXivTimesIndicator

501413571dc0024b5a9d0bc2e9f17f805de92690

[ "Apache-2.0" ]

2

2020-10-21T12:31:02.000Z

2021-11-05T05:26:15.000Z

from .server import Application

31

0.870968

4

31

6.75

1

0

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1

31

0.964286

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1

0

true

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null

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1

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1

0

1

0

6

cb0d83bca9047c82889cf5995c31524e13285dd4

39

py

Python

perses/samplers/__init__.py

schallerdavid/perses

58bd6e626e027879e136f56e175683893e016f8c

[ "MIT" ]

99

2016-01-19T18:10:37.000Z

2022-03-26T02:43:08.000Z

perses/samplers/__init__.py

schallerdavid/perses

58bd6e626e027879e136f56e175683893e016f8c

[ "MIT" ]

878

2015-09-18T19:25:30.000Z

2022-03-31T02:33:04.000Z

perses/samplers/__init__.py

schallerdavid/perses

58bd6e626e027879e136f56e175683893e016f8c

[ "MIT" ]

30

2015-09-21T15:26:35.000Z

2022-01-10T20:07:24.000Z

from perses.samplers.samplers import *

19.5

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null

0

1

0

1

0

1

0

6

cb207174c9db151c0797349e4b3c26422a817936

5,416

py

Python

test_nlp_util.py

kenttw/2021-bitbrain-shopee

f63975babc17f03718209432013e325b6eb5e39e

[ "Apache-2.0" ]

null

test_nlp_util.py

kenttw/2021-bitbrain-shopee

f63975babc17f03718209432013e325b6eb5e39e

[ "Apache-2.0" ]

null

test_nlp_util.py

kenttw/2021-bitbrain-shopee

f63975babc17f03718209432013e325b6eb5e39e

[ "Apache-2.0" ]

null

import nlp_util def testGC(): raw_address = 'isn s.h. & rekan, somba opu 76' result = nlp_util.genCC(raw_address) print(result) assert result def test_getPair(): # label, raw = "hanief sembilan mtr -h", "kuripan hanief semb mtr -h, gajah mada, 58112" # print(getPair(label, raw)) raw = " ".join(['a', 'b', 'c', 'd']) label = " ".join(['ba', '-', 'cc']) assert nlp_util.get_fuzzy_pairs(raw, label) == [1, [('ba', 'b'), ('-', None), ('cc', 'c')]] raw = " ".join(['b', '-', 'c', 'd']) label = " ".join(['ba', 'cc']) assert nlp_util.get_fuzzy_pairs(raw, label) == [0, [('ba', 'b'), ('cc', 'c')]] raw = ['a', 'b', 'c', 'd'] label = ['ba', 'cc'] assert nlp_util.get_fuzzy_pairs(raw, label) == [1, [('ba', 'b'), ('cc', 'c')]] def test_get_range_kent(): label,raw = "hanief sembilan mtr -h", "kuripan hanief semb mtr -h, gajah mada, 58112" result = nlp_util.get_range_kent(label,raw) print(result) assert raw[result[0]:result[1]] == 'hanief semb mtr -h' def test_get_bio_tagging_range(): print("") # case 1: poi,street,raw = "toko bb kids", "raya samb gede", "xxx raya. sa-mb gede, 299 toko bb k&ids yyy", p_start,p_end,s_start,s_end = nlp_util.get_bio_tagging_range(raw,street,poi) print("case1") print("POI==>", raw[p_start:p_end]) assert raw[p_start:p_end] == 'toko bb k&ids' print("Street==>", raw[s_start:s_end]) assert raw[s_start:s_end] =='raya. sa-mb gede' # case 2: poi,street,raw = "toko bb kids", "raya samb gede", " toko bb kids, raya samb gede, 299", p_start,p_end,s_start,s_end = nlp_util.get_bio_tagging_range(raw,street,poi) print("case2") print("POI==>", raw[p_start:p_end]) print("Street==>", raw[s_start:s_end]) # case 3: poi,street,raw = "toko bb kids", "raya samb gede", "aaa toko bb kids, raya samb gede, 299", p_start,p_end,s_start,s_end = nlp_util.get_bio_tagging_range(raw,street,poi) print("case3") print("POI==>", raw[p_start:p_end]) print("Street==>", raw[s_start:s_end]) poi,street,raw = "tahu jontor bung tomo ", "bung tomo", "tahu jon bung tomo bung tomo, sungai keledang samarinda seberang" p_start,p_end,s_start,s_end = nlp_util.get_bio_tagging_range(raw,street,poi) print("case4") print("POI==>", raw[p_start:p_end]) print("Street==>", raw[s_start:s_end]) poi,street,raw = '',"citra yuda iv peru depok", "raya. sa-mb gede, 299 toko bb k&ids yyy" p_start,p_end,s_start,s_end = nlp_util.get_bio_tagging_range(raw,street,poi) print("case5") print("POI==>", raw[p_start:p_end]) print("Street==>", raw[s_start:s_end]) print("") # case 6: poi,street,raw = "toko bb kids", "raya samb gede", "xxx raya. sa-mb gede, 299 toko bb k&ids yyy", p_start,p_end,s_start,s_end = nlp_util.get_bio_tagging_range(raw,street,'') print("case6") assert p_start == None print("Street==>", raw[s_start:s_end]) assert raw[s_start:s_end] =='raya. sa-mb gede' print("") # case 7: poi,street,raw = "toko bb kids", "raya samb gede", "xxx raya. sa-mb gede, 299 toko bb k&ids yyy", p_start,p_end,s_start,s_end = nlp_util.get_bio_tagging_range(raw,'',poi) print("case7") print("POI==>", raw[p_start:p_end]) assert raw[p_start:p_end] == 'toko bb k&ids' assert s_start==None poi, street, raw = 'sd neg 12 anggrek', '', 'sd negeri 12 anggrek' p_start,p_end,s_start,s_end = nlp_util.get_bio_tagging_range(raw,street,poi) print("case8") print("POI==>", raw[p_start:p_end]) # print("Street==>", raw[s_start:s_end]) assert s_start == None def test_get_bio_tagging_string(): print("") # case 1: poi, street, raw = "toko bb kids", "raya samb gede", "xxx raya. sa-mb gede, 299 toko bb k&ids yyy", BIO = nlp_util.get_bio_tagging_string(raw, street, poi) print(BIO) print("") poi,street,raw = None,"citra yuda iv peru depok", "raya. sa-mb gede, 299 toko bb k&ids yyy" BIO = nlp_util.get_bio_tagging_string(raw, street, poi) print(BIO) poi,street,raw = "lapangan futsal sukaluyu", "tang", "xxx raya. sa-mb gede, 299 toko bb k&ids yyy" BIO = nlp_util.get_bio_tagging_string(raw, street, poi) print(BIO) poi,street,raw = "lapangan futsal sukaluyu", "tang", "xxx raya. sa-mb gede, 299 toko bb k&ids yyy" BIO = nlp_util.get_bio_tagging_string(raw, None, poi) print(BIO) poi,street,raw = "lapangan futsal sukaluyu", "tang", "xxx raya. sa-mb gede, 299 toko bb k&ids yyy" BIO = nlp_util.get_bio_tagging_string(raw, None, None) print(BIO) def test_find_sub_list(): print("") poi,street,raw = "tahu jontor bung tomo ", "bung tomo", "tahu jon bung tomo bung tomo, sungai keledang samarinda seberang" p_start, p_end, s_start, s_end = nlp_util.get_bio_tagging_range(raw, street, poi) text_splits = nlp_util.prepare_text(raw) #['tahu', 'jon', 'bung', 'tomo', 'bung', 'tomo', ',', 'sungai', 'keledang', 'samarinda', 'seberang'] p_splits = nlp_util.prepare_text(raw[p_start:p_end]) start, end = nlp_util.find_sub_list(p_splits, text_splits) s_splits = nlp_util.prepare_text(raw[s_start:s_end]) start_2, end_2 = nlp_util.find_sub_list(s_splits, text_splits, (start,end)) set1 = set(range(start, end)) set2 = set(range(start_2, end_2)) print(start,end,start_2,end_2) assert len(set1.intersection(set2))==0

37.351724

145

0.64051

906

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0

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5,416

145

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0.35

0

null

0

1

0

null

0

6

cb321935a3fe0e9c8f187816524cd352184e7a6b

629

py

Python

day_1/day1.py

mickeelm/aoc2019

7fd532d2237e1cf0686c9b331a2b97515ee94c03

[ "Unlicense" ]

1

2021-02-02T08:32:36.000Z

day_1/day1.py

mickeelm/aoc2019

7fd532d2237e1cf0686c9b331a2b97515ee94c03

[ "Unlicense" ]

null

day_1/day1.py

mickeelm/aoc2019

7fd532d2237e1cf0686c9b331a2b97515ee94c03

[ "Unlicense" ]

null

def fuel_required_single_module(mass): fuel = int(mass / 3) - 2 return fuel if fuel > 0 else 0 def fuel_required_multiple_modules(masses): total_fuel = 0 for mass in masses: total_fuel += fuel_required_single_module(mass) return total_fuel def recursive_fuel_required_single_module(mass): total_fuel = 0 while mass := fuel_required_single_module(mass): total_fuel += mass return total_fuel def recursive_fuel_required_multiple_modules(masses): total_fuel = 0 for mass in masses: total_fuel += recursive_fuel_required_single_module(mass) return total_fuel

25.16

65

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629

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0.211765

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null

0

1

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null

0

1

0

6

cb4d4cf11ac360c8c881c5f2263fff2c5582e3f9

299

py

Python

Cracking the Coding Interview/ctci-solutions-master/ch-06-math-and-logic-puzzles/07-the-apocalypse.py

nikku1234/Code-Practise

94eb6680ea36efd10856c377000219285f77e5a4

[ "Apache-2.0" ]

9

2020-07-02T06:06:17.000Z

2022-02-26T11:08:09.000Z

Cracking the Coding Interview/ctci-solutions-master/ch-06-math-and-logic-puzzles/07-the-apocalypse.py

nikku1234/Code-Practise

94eb6680ea36efd10856c377000219285f77e5a4

[ "Apache-2.0" ]

1

2021-11-04T17:26:36.000Z

Cracking the Coding Interview/ctci-solutions-master/ch-06-math-and-logic-puzzles/07-the-apocalypse.py

nikku1234/Code-Practise

94eb6680ea36efd10856c377000219285f77e5a4

[ "Apache-2.0" ]

8

2021-01-31T10:31:12.000Z

2022-03-13T09:15:55.000Z

# What will the gender ratio be after every family stops having children after # after they have a girl and not until then. def birth_ratio(): # Everytime a child is born, there is a 0.5 chance of the baby being male # and 0.5 chance of the baby being a girl. So the ratio is 1:1. return 1

33.222222

78

0.725753

59

299

3.661017

0.627119

0.046296

0.074074

0.092593

0.203704

0

0.030303

0.227425

299

8

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0

1

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true

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null

0

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1

0

null

0

1

0

1

0

6

cb6279ec0b7ded0ba4cb59e073b1c143b3a2125e

2,197

py

Python

tests/test_contributors.py

chfw/gease

dc5e69840c368dab15e0929824a7c952bd7074c3

[ "MIT" ]

1

2019-01-12T19:39:35.000Z

tests/test_contributors.py

chfw/gease

dc5e69840c368dab15e0929824a7c952bd7074c3

[ "MIT" ]

10

2017-10-20T07:24:49.000Z

2020-10-18T11:37:51.000Z

tests/test_contributors.py

chfw/gease

dc5e69840c368dab15e0929824a7c952bd7074c3

[ "MIT" ]

null

from mock import MagicMock, patch from nose.tools import eq_ from gease.contributors import EndPoint from gease.exceptions import NoGeaseConfigFound class TestPublish: @patch("gease.contributors.get_token") @patch("gease.contributors.Api.get_public_api") def test_all_contributors(self, fake_api, get_token): get_token.side_effect = [NoGeaseConfigFound] sample_reply = [ {"login": "howdy", "url": "https://api.github.com/users/howdy"} ] fake_api.return_value = MagicMock( get=MagicMock( side_effect=[ sample_reply, {"name": "hello world", "html_url": ""}, ] ) ) repo = EndPoint("test", "repo") contributors = repo.get_all_contributors() eq_( contributors, [{"name": "hello world", "html_url": ""}], ) @patch("gease.contributors.get_token") @patch("gease.contributors.Api.get_public_api") def test_private_api(self, fake_api, get_token): get_token.side_effect = [NoGeaseConfigFound] sample_reply = [ {"login": "howdy", "url": "https://api.github.com/users/howdy"} ] fake_api.return_value = MagicMock( get=MagicMock( side_effect=[sample_reply, {"name": None, "html_url": ""}] ) ) repo = EndPoint("test", "repo") contributors = repo.get_all_contributors() eq_( contributors, [{"name": "howdy", "html_url": ""}], ) @patch("gease.contributors.get_token") @patch("gease.contributors.Api.get_api") def test_no_names(self, fake_api, _): sample_reply = [ {"login": "howdy", "url": "https://api.github.com/users/howdy"} ] fake_api.return_value = MagicMock( get=MagicMock( side_effect=[sample_reply, {"name": None, "html_url": ""}] ) ) repo = EndPoint("test", "repo") contributors = repo.get_all_contributors() eq_( contributors, [{"name": "howdy", "html_url": ""}], )

30.09589

75

0.549841

217

2,197

5.313364

0.211982

0.103209

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0.065048

0.830009

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0.311789

2,197

72

76

30.513889

0.762566

0

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0

0.208011

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0

1

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false

0

0.065574

0

0.131148

0

null

0

1

0

null

0

6

cb8d02b22bb2917353763bb8532feaa16e03096a

202

py

Python

Server/Python/src/dbs/dao/MySQL/File/MgrtList.py

vkuznet/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

8

2015-08-14T04:01:32.000Z

2021-06-03T00:56:42.000Z

Server/Python/src/dbs/dao/MySQL/File/MgrtList.py

yuyiguo/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

162

2015-01-07T21:34:47.000Z

2021-10-13T09:42:41.000Z

Server/Python/src/dbs/dao/MySQL/File/MgrtList.py

yuyiguo/DBS

14df8bbe8ee8f874fe423399b18afef911fe78c7

[ "Apache-2.0" ]

16

2015-01-22T15:27:29.000Z

2021-04-28T09:23:28.000Z

#!/usr/bin/env python """ This module provides File.MgrtList data access object. """ from dbs.dao.Oracle.File.MgrtList import MgrtList as OraFileMgrtList class MgrtList(OraFileMgrtList): pass

20.2

68

0.752475

26

202

5.846154

0.807692

0.157895

0

0.148515

202

9

69

22.444444

0.883721

0.371287

0

1

0

true

0.333333

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0.666667

0

1

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null

0

1

0

null

0

1

0

1

0

6

cbd3598d91ff793b9cf6d9fdbee232e97be7c505

35,768

py

Python

morphological_analysis_v4.py

rivernuthead/DoD_analysis

b06219d4026e89a9b9f1e8939010a63612750c80

[ "MIT" ]

null

morphological_analysis_v4.py

rivernuthead/DoD_analysis

b06219d4026e89a9b9f1e8939010a63612750c80

[ "MIT" ]

null

morphological_analysis_v4.py

rivernuthead/DoD_analysis

b06219d4026e89a9b9f1e8939010a63612750c80

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed May 25 16:02:58 2022 @author: erri """ import os import numpy as np import math from morph_quantities_func_v2 import morph_quantities import matplotlib.pyplot as plt # SINGLE RUN NAME run = 'q07_1' DoD_name = 'DoD_s1-s0_filt_nozero_rst.txt' # Step between surveys DoD_delta = 1 # Base length in terms of columns. If the windows dimensions are channel width # multiples, the windows_length_base is 12 columns windows_length_base = 12 window_mode = 1 ''' windows_mode: 0 = fixed windows (all the channel) 1 = expanding window 2 = floating fixed windows (WxW, Wx2W, Wx3W, ...) without overlapping 3 = floating fixed windows (WxW, Wx2W, Wx3W, ...) with overlapping ''' plot_mode = 2 ''' plot_mode: 1 = only summary plot 2 = all single DoD plot ''' # Parameters # Survey pixel dimension px_x = 50 # [mm] px_y = 5 # [mm] W = 0.6 # Width [m] d50 = 0.001 NaN = -999 # setup working directory and DEM's name home_dir = os.getcwd() # Source DoDs folder DoDs_folder = os.path.join(home_dir, 'DoDs', 'DoD_'+run) DoDs_name_array = [] # List the file's name of the DoDs with step of delta_step for f in sorted(os.listdir(DoDs_folder)): if f.endswith('_filt_nozero_rst.txt') and f.startswith('DoD_'): delta = eval(f[5]) - eval(f[8]) if delta == DoD_delta: DoDs_name_array = np.append(DoDs_name_array, f) else: pass # Initialize overall arrays dep_vol_w_array_all = [] sco_vol_w_array_all = [] # Loop over the DoDs with step of delta_step for f in DoDs_name_array: DoD_name = f print(f) DoD_path = os.path.join(DoDs_folder,DoD_name) DoD_filt_nozero = np.loadtxt(DoD_path, delimiter='\t') # DoD length DoD_length = DoD_filt_nozero.shape[1]*px_x/1000 # DoD length [m] dim_x = DoD_filt_nozero.shape[1] # Initialize array # Define total volume matrix, Deposition matrix and Scour matrix DoD_vol = np.where(np.isnan(DoD_filt_nozero), 0, DoD_filt_nozero) # Total volume matrix DoD_vol = np.where(DoD_vol==NaN, 0, DoD_vol) dep_DoD = (DoD_vol>0)*DoD_vol # DoD of only deposition data sco_DoD = (DoD_vol<0)*DoD_vol # DoD of only scour data # Active pixel matrix: act_px_matrix = np.where(DoD_vol!=0, 1, 0) # Active pixel matrix, both scour and deposition act_px_matrix_dep = np.where(dep_DoD != 0, 1, 0) # Active deposition matrix act_px_matrix_sco = np.where(sco_DoD != 0, 1, 0) # Active scour matrix # Initialize array for each window dimension ################################################################### # MOVING WINDOWS ANALYSIS ################################################################### array = DoD_filt_nozero W=windows_length_base mean_array_tot = [] std_array_tot= [] window_boundary = np.array([0,0]) x_data_tot=[] tot_vol_array=[] # Tot volume tot_vol_mean_array=[] tot_vol_std_array=[] sum_vol_array=[] # Sum of scour and deposition volume dep_vol_array=[] # Deposition volume sco_vol_array=[] # Scour volume morph_act_area_array=[] # Total active area array morph_act_area_dep_array=[] # Deposition active area array morph_act_area_sco_array=[] # Active active area array act_width_mean_array=[] # Total active width mean array act_width_mean_dep_array=[] # Deposition active width mean array act_width_mean_sco_array=[] # Scour active width mean array if window_mode == 1: # With overlapping for w in range(1, int(math.floor(array.shape[1]/W))+1): # W*w is the dimension of every possible window # Initialize arrays that stock data for each window position x_data=[] tot_vol_w_array = [] sum_vol_w_array = [] dep_vol_w_array = [] sco_vol_w_array =[] morph_act_area_w_array = [] morph_act_area_dep_w_array = [] morph_act_area_sco_w_array = [] act_width_mean_w_array = [] act_width_mean_dep_w_array = [] act_width_mean_sco_w_array = [] act_thickness_w_array = [] act_thickness_dep_w_array = [] act_thickness_sco_w_array = [] for i in range(0, array.shape[1]+1): if i+w*W <= array.shape[1]: window = array[:, i:W*w+i] boundary = np.array([i,W*w+i]) window_boundary = np.vstack((window_boundary, boundary)) x_data=np.append(x_data, w) # Calculate morphological quantities tot_vol, sum_vol, dep_vol, sco_vol, morph_act_area, morph_act_area_dep, morph_act_area_sco, act_width_mean, act_width_mean_dep, act_width_mean_sco, act_thickness, act_thickness_dep, act_thickness_sco = morph_quantities(window) # Append single data to array # For each window position the calculated parameters will be appended to _array tot_vol_w_array=np.append(tot_vol_w_array, tot_vol) sum_vol_w_array=np.append(sum_vol_w_array, sum_vol) dep_vol_w_array=np.append(dep_vol_w_array, dep_vol) sco_vol_w_array=np.append(sco_vol_w_array, sco_vol) morph_act_area_w_array=np.append(morph_act_area_w_array, morph_act_area) morph_act_area_dep_w_array=np.append(morph_act_area_dep_w_array, morph_act_area_dep) morph_act_area_sco_w_array=np.append(morph_act_area_sco_w_array, morph_act_area_sco) act_width_mean_w_array=np.append(act_width_mean_w_array, act_width_mean) act_width_mean_dep_w_array=np.append(act_width_mean_dep_w_array, act_width_mean_dep) act_width_mean_sco_w_array=np.append(act_width_mean_sco_w_array, act_width_mean_sco) act_thickness_w_array=np.append(act_thickness_w_array, act_thickness) act_thickness_dep_w_array=np.append(act_thickness_dep_w_array, act_thickness_dep) act_thickness_sco_w_array=np.append(act_thickness_sco_w_array, act_thickness_sco) # For each window dimension w*W, x_data_tot=np.append(x_data_tot, np.nanmean(x_data)) # Append one value of x_data tot_vol_mean_array=np.append(tot_vol_mean_array, np.nanmean(tot_vol_w_array)) # Append the tot_vol_array mean tot_vol_std_array=np.append(tot_vol_std_array, np.nanstd(tot_vol_w_array)) # Append the tot_vol_array mean # sum_vol_array= # dep_vol_array= # sco_vol_array= # morph_act_area_array= # morph_act_area_dep_array= # morph_act_area_sco_array= # act_width_mean_array= # act_width_mean_dep_array= # act_width_mean_sco_array= # Slice window boundaries array to delete [0,0] when initialized window_boundary = window_boundary[1,:] if window_mode == 2: # Without overlapping for w in range(1, int(math.floor(array.shape[1]/W))+1): # W*w is the dimension of every possible window mean_array = [] std_array= [] x_data=[] for i in range(0, array.shape[1]+1): if W*w*(i+1) <= array.shape[1]: window = array[:, W*w*i:W*w*(i+1)] boundary = np.array([W*w*i,W*w*(i+1)]) window_boundary = np.vstack((window_boundary, boundary)) mean = np.nanmean(window) std = np.nanstd(window) mean_array = np.append(mean_array, mean) std_array = np.append(std_array, std) x_data=np.append(x_data, w) mean_array_tot = np.append(mean_array_tot, np.nanmean(mean_array)) std_array_tot= np.append(std_array_tot, np.nanstd(std_array)) #TODO check this x_data_tot=np.append(x_data_tot, np.nanmean(x_data)) # Slice window boundaries array to delete [0,0] when initialized window_boundary = window_boundary[1,:] if window_mode == 3: # Increasing window dimension keeping still the upstream cross section mean_array = [] std_array= [] x_data=[] for i in range(0, array.shape[1]+1): if W*(i+1) <= array.shape[1]: window = array[:, 0:W*(i+1)] boundary = np.array([0,W*(i+1)]) window_boundary = np.vstack((window_boundary, boundary)) mean = np.nanmean(window) std = np.nanstd(window) mean_array = np.append(mean_array, mean) std_array = np.append(std_array, std) x_data=np.append(x_data, i) mean_array_tot = np.append(mean_array_tot, np.nanmean(mean_array)) std_array_tot= np.append(std_array_tot, np.nanstd(std_array)) #TODO check this x_data_tot=np.append(x_data_tot, np.nanmean(x_data)) # Slice window boundaries array to delete [0,0] when initialized window_boundary = window_boundary[1,:] # # TODO Go on with this section # if windows_mode == 1: # # Define x_data for plots # x_data = np.linspace(W,dim_x,math.floor(DoD_length/W))*px_x/1e03 # for n in range(1,math.floor(DoD_length/W)+1): # w_cols = n*round(W/(px_x/1000)) # Window analysis length in number of columns # w_len = round(n*W,1) # Window analysis lenght im meter [m] # # Define total volume matrix, Deposition matrix and Scour matrix # DoD_vol_w = DoD_vol[:,0:w_cols] # Total volume matrix # dep_DoD_w = dep_DoD[:,0:w_cols] # DoD of only deposition data # sco_DoD_w = sco_DoD[:,0:w_cols] # DoD of only scour data # # Define active pixel matrix # act_px_matrix_w = act_px_matrix[:,0:w_cols] # Active pixel matrix, both scour and deposition # act_px_matrix_dep_w = act_px_matrix_dep[:,0:w_cols] # Active deposition matrix # act_px_matrix_sco_w = act_px_matrix_sco[:,0:w_cols] # Active scour matrix # # Calculate principal quantities: # # Volumes # tot_vol_w = np.sum(DoD_vol_w)*px_x*px_y/(W*w_len*d50*1e09)# Total volume as V/(L*W*d50) [-] considering negative sign for scour # sum_vol_w = np.sum(np.abs(DoD_vol_w))*px_x*px_y/(W*w_len*d50*1e09) # Sum of scour and deposition volume as V/(L*W*d50) [-] # dep_vol_w = np.sum(dep_DoD_w)*px_x*px_y/(W*w_len*d50*1e09) # Deposition volume as V/(L*W*d50) [-] # sco_vol_w = np.sum(sco_DoD_w)*px_x*px_y/(W*w_len*d50*1e09) # Scour volume as V/(L*W*d50) [-] # # Areas: # morph_act_area_w = np.count_nonzero(act_px_matrix_w)*px_x*px_y/(W*w_len*1e06) # Active area both in terms of scour and deposition as A/(W*L) [-] # morph_act_area_dep_w = np.count_nonzero(act_px_matrix_dep_w)*px_x*px_y/(W*w_len*1e06) # Active deposition area as A/(W*L) [-] # morph_act_area_sco_w = np.count_nonzero(act_px_matrix_sco_w)*px_x*px_y/(W*w_len*1e06) # Active scour area as A/(W*L) [-] # # Widths: # act_width_mean_w = np.count_nonzero(act_px_matrix_w)*px_x*px_y/(W*w_len*1e06) # Total mean active width [%] - Wact/W # act_width_mean_dep_w = np.count_nonzero(act_px_matrix_dep_w)*px_x*px_y/(W*w_len*1e06) # Deposition mean active width [%] - Wact/W # act_width_mean_sco_w = np.count_nonzero(act_px_matrix_sco_w)*px_x*px_y/(W*w_len*1e06) # Scour mean active width [%] - Wact/W # # Thicknesses: # act_thickness_w = sum_vol_w/morph_act_area_w*(d50*1e03) # Total active thickness (abs(V_sco) + V_dep)/act_area [mm] # act_thickness_dep_w = dep_vol_w/morph_act_area_dep_w*(d50*1e03) # Deposition active thickness V_dep/act_area [mm] # act_thickness_sco_w = sco_vol_w/act_width_mean_sco_w*(d50*1e03) # Scour active thickness V_sco/act_area [mm] # # Append all values in arrays # tot_vol_w_array = np.append(tot_vol_w_array, tot_vol_w) # sum_vol_w_array = np.append(sum_vol_w_array, sum_vol_w) # dep_vol_w_array = np.append(dep_vol_w_array, dep_vol_w) # sco_vol_w_array = np.append(sco_vol_w_array, sco_vol_w) # morph_act_area_w_array = np.append(morph_act_area_w_array, morph_act_area_w) # morph_act_area_dep_w_array = np.append(morph_act_area_dep_w_array, morph_act_area_dep_w) # morph_act_area_sco_w_array = np.append(morph_act_area_sco_w_array, morph_act_area_sco_w) # act_width_mean_w_array = np.append(act_width_mean_w_array, act_width_mean_w) # act_width_mean_dep_w_array = np.append(act_width_mean_dep_w_array, act_width_mean_dep_w) # act_width_mean_sco_w_array = np.append(act_width_mean_sco_w_array, act_width_mean_sco_w) # act_thickness_w_array = np.append(act_thickness_w_array, act_thickness_w) # act_thickness_dep_w_array = np.append(act_thickness_dep_w_array, act_thickness_dep_w) # act_thickness_sco_w_array = np.append(act_thickness_sco_w_array, act_thickness_sco_w) # if plot_mode ==2: # # Plots # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, dep_vol_w_array, '-', c='brown') # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Deposition volumes V/(W*L*d50) [-]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, sco_vol_w_array, '-', c='brown') # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Scour volumes V/(W*L*d50) [-]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, act_width_mean_w_array, '-', c='brown') # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Active width actW/W [-]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, act_thickness_w_array, '-', c='brown') # axs.set_title(run) # axs.set_xlabel('Longitudinal coordinate [m]') # axs.set_ylabel('Active thickness [mm]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # # Fixed window without overlapping # if windows_mode == 2: # # Calculate the number of suitable windows in the channel length # c_array = [] # W_cols = int(W/px_x*1e03) # for i in range(1, round(dim_x/W_cols)): # c = math.floor(dim_x/(W_cols*i)) # if c*W_cols*i<=dim_x: # c_array = np.append(c_array, c) # else: # pass # # Define the components of the slicing operation (exclude the first one) # f_cols_array = [0,0] # x_data = [] # X data for the plot # n = 0 # Initialize variable count # for m in range(0,len(c_array)): # # m is the window dimension in columns # n+=1 # for i in range(1,(math.floor(dim_x/(W_cols*(m+1)))+1)): # f_cols = [round(W_cols*(m+1)*(i-1), 1), round(W_cols*(m+1)*(i),1)] # f_cols_array = np.vstack((f_cols_array, f_cols)) # x_data = np.append(x_data, n) # x_data = (x_data)*W # # Resize f_cols_array # f_cols_array = f_cols_array[1:] # for p in range(0, f_cols_array.shape[0]): # Loop over all the available window # w_len = (f_cols_array[p,1] - f_cols_array[p,0])*px_x/1e03 # Define the window lwgth # # Define total volume matrix, Deposition matrix and Scour matrix # DoD_vol_w = DoD_vol[:, f_cols_array[p,0]:f_cols_array[p,1]] # Total volume matrix # dep_DoD_w = dep_DoD[:, f_cols_array[p,0]:f_cols_array[p,1]] # DoD of only deposition data # sco_DoD_w = sco_DoD[:, f_cols_array[p,0]:f_cols_array[p,1]] # DoD of only scour data # # Define active pixel matrix # act_px_matrix_w = act_px_matrix[:, f_cols_array[p,0]:f_cols_array[p,1]] # Active pixel matrix, both scour and deposition # act_px_matrix_dep_w = act_px_matrix_dep[:, f_cols_array[p,0]:f_cols_array[p,1]] # Active deposition matrix # act_px_matrix_sco_w = act_px_matrix_sco[:, f_cols_array[p,0]:f_cols_array[p,1]] # Active scour matrix # # Calculate principal quantities: # # Volumes # tot_vol_w = np.sum(DoD_vol_w)*px_x*px_y/(W*w_len*d50*1e09)# Total volume as V/(L*W*d50) [-] considering negative sign for scour # sum_vol_w = np.sum(np.abs(DoD_vol_w))*px_x*px_y/(W*w_len*d50*1e09) # Sum of scour and deposition volume as V/(L*W*d50) [-] # dep_vol_w = np.sum(dep_DoD_w)*px_x*px_y/(W*w_len*d50*1e09) # Deposition volume as V/(L*W*d50) [-] # sco_vol_w = np.sum(sco_DoD_w)*px_x*px_y/(W*w_len*d50*1e09) # Scour volume as V/(L*W*d50) [-] # # Areas: # morph_act_area_w = np.count_nonzero(act_px_matrix_w)*px_x*px_y/(W*w_len*1e06) # Active area both in terms of scour and deposition as A/(W*L) [-] # morph_act_area_dep_w = np.count_nonzero(act_px_matrix_dep_w)*px_x*px_y/(W*w_len*1e06) # Active deposition area as A/(W*L) [-] # morph_act_area_sco_w = np.count_nonzero(act_px_matrix_sco_w)*px_x*px_y/(W*w_len*1e06) # Active scour area as A/(W*L) [-] # # Widths: # act_width_mean_w = np.count_nonzero(act_px_matrix_w)*px_x*px_y/(W*w_len*1e06) # Total mean active width [%] - Wact/W # act_width_mean_dep_w = np.count_nonzero(act_px_matrix_dep_w)*px_x*px_y/(W*w_len*1e06) # Deposition mean active width [%] - Wact/W # act_width_mean_sco_w = np.count_nonzero(act_px_matrix_sco_w)*px_x*px_y/(W*w_len*1e06) # Scour mean active width [%] - Wact/W # # Thicknesses: # act_thickness_w = sum_vol_w/morph_act_area_w*(d50*1e03) # Total active thickness (abs(V_sco) + V_dep)/act_area [mm] # act_thickness_dep_w = dep_vol_w/morph_act_area_dep_w*(d50*1e03) # Deposition active thickness V_dep/act_area [mm] # act_thickness_sco_w = sco_vol_w/act_width_mean_sco_w*(d50*1e03) # Scour active thickness V_sco/act_area [mm] # # Append all values in arrays # tot_vol_w_array = np.append(tot_vol_w_array, tot_vol_w) # sum_vol_w_array = np.append(sum_vol_w_array, sum_vol_w) # dep_vol_w_array = np.append(dep_vol_w_array, dep_vol_w) # sco_vol_w_array = np.append(sco_vol_w_array, sco_vol_w) # morph_act_area_w_array = np.append(morph_act_area_w_array, morph_act_area_w) # morph_act_area_dep_w_array = np.append(morph_act_area_dep_w_array, morph_act_area_dep_w) # morph_act_area_sco_w_array = np.append(morph_act_area_sco_w_array, morph_act_area_sco_w) # act_width_mean_w_array = np.append(act_width_mean_w_array, act_width_mean_w) # act_width_mean_dep_w_array = np.append(act_width_mean_dep_w_array, act_width_mean_dep_w) # act_width_mean_sco_w_array = np.append(act_width_mean_sco_w_array, act_width_mean_sco_w) # act_thickness_w_array = np.append(act_thickness_w_array, act_thickness_w) # act_thickness_dep_w_array = np.append(act_thickness_dep_w_array, act_thickness_dep_w) # act_thickness_sco_w_array = np.append(act_thickness_sco_w_array, act_thickness_sco_w) # if plot_mode ==2: # # Plots # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, dep_vol_w_array, 'o', c='brown') # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Deposition volumes V/(W*L*d50) [-]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, sco_vol_w_array, 'o', c='brown') # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Scour volumes V/(W*L*d50) [-]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, act_width_mean_w_array, 'o', c='brown') # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Active width actW/W [-]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, act_thickness_w_array, 'o', c='brown') # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Active thickness [mm]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # # Fixed window with overlapping # if windows_mode == 3: # # Calculate the number of suitable windows in the channel length # c_array = [] # W_cols = int(W/px_x*1e03) # Minimum windows length WxW dimension in columns # for i in range(1, math.floor(dim_x/W_cols)+1): # per each windows analysis WxWi # c = dim_x - W_cols*i # c_array = np.append(c_array, c) # Contains the number of windows for each dimension WxW*i # else: # pass # f_cols_array = [0,0] # x_data = [] # n = 0 # for m in range(1,int(dim_x/W_cols)+1): # w_length = m*W_cols # Analysis windows length # # print(w_length) # n+=1 # for i in range(0,dim_x): # i is the lower limit of the analysis window # low_lim = i # Analisys window lower limit # upp_lim = i + w_length # Analisys window upper limit # if upp_lim<=dim_x: # # print(low_lim, upp_lim) # # print(i+w_length) # f_cols = [low_lim, upp_lim] # Lower and upper boundary of the analysis window # f_cols_array = np.vstack((f_cols_array, f_cols)) # x_data = np.append(x_data, n) # else: # pass # x_data = x_data*W # # Resize f_cols_array # f_cols_array = f_cols_array[1:] # for p in range(0, f_cols_array.shape[0]): # w_len = (f_cols_array[p,1] - f_cols_array[p,0])*px_x/1e03 # Define the window length # # print() # # print(f_cols_array[p,:]) # # print(w_len) # # Define total volume matrix, Deposition matrix and Scour matrix # DoD_vol_w = DoD_vol[:, f_cols_array[p,0]:f_cols_array[p,1]] # Total volume matrix # dep_DoD_w = dep_DoD[:, f_cols_array[p,0]:f_cols_array[p,1]] # DoD of only deposition data # sco_DoD_w = sco_DoD[:, f_cols_array[p,0]:f_cols_array[p,1]] # DoD of only scour data # # Define active pixel matrix # act_px_matrix_w = act_px_matrix[:, f_cols_array[p,0]:f_cols_array[p,1]] # Active pixel matrix, both scour and deposition # act_px_matrix_dep_w = act_px_matrix_dep[:, f_cols_array[p,0]:f_cols_array[p,1]] # Active deposition matrix # act_px_matrix_sco_w = act_px_matrix_sco[:, f_cols_array[p,0]:f_cols_array[p,1]] # Active scour matrix # # Calculate principal quantities: # # Volumes # tot_vol_w = np.sum(DoD_vol_w)*px_x*px_y/(W*w_len*d50*1e09)# Total volume as V/(L*W*d50) [-] considering negative sign for scour # sum_vol_w = np.sum(np.abs(DoD_vol_w))*px_x*px_y/(W*w_len*d50*1e09) # Sum of scour and deposition volume as V/(L*W*d50) [-] # dep_vol_w = np.sum(dep_DoD_w)*px_x*px_y/(W*w_len*d50*1e09) # Deposition volume as V/(L*W*d50) [-] # sco_vol_w = np.sum(sco_DoD_w)*px_x*px_y/(W*w_len*d50*1e09) # Scour volume as V/(L*W*d50) [-] # # Areas: # morph_act_area_w = np.count_nonzero(act_px_matrix_w)*px_x*px_y/(W*w_len*1e06) # Active area both in terms of scour and deposition as A/(W*L) [-] # morph_act_area_dep_w = np.count_nonzero(act_px_matrix_dep_w)*px_x*px_y/(W*w_len*1e06) # Active deposition area as A/(W*L) [-] # morph_act_area_sco_w = np.count_nonzero(act_px_matrix_sco_w)*px_x*px_y/(W*w_len*1e06) # Active scour area as A/(W*L) [-] # # Widths: # act_width_mean_w = np.count_nonzero(act_px_matrix_w)*px_x*px_y/(W*w_len*1e06) # Total mean active width [%] - Wact/W # act_width_mean_dep_w = np.count_nonzero(act_px_matrix_dep_w)*px_x*px_y/(W*w_len*1e06) # Deposition mean active width [%] - Wact/W # act_width_mean_sco_w = np.count_nonzero(act_px_matrix_sco_w)*px_x*px_y/(W*w_len*1e06) # Scour mean active width [%] - Wact/W # # Thicknesses: # act_thickness_w = sum_vol_w/morph_act_area_w*(d50*1e03) # Total active thickness (abs(V_sco) + V_dep)/act_area [mm] # act_thickness_dep_w = dep_vol_w/morph_act_area_dep_w*(d50*1e03) # Deposition active thickness V_dep/act_area [mm] # act_thickness_sco_w = sco_vol_w/act_width_mean_sco_w*(d50*1e03) # Scour active thickness V_sco/act_area [mm] # # Append all values in arrays # tot_vol_w_array = np.append(tot_vol_w_array, tot_vol_w) # sum_vol_w_array = np.append(sum_vol_w_array, sum_vol_w) # dep_vol_w_array = np.append(dep_vol_w_array, dep_vol_w) # sco_vol_w_array = np.append(sco_vol_w_array, sco_vol_w) # morph_act_area_w_array = np.append(morph_act_area_w_array, morph_act_area_w) # morph_act_area_dep_w_array = np.append(morph_act_area_dep_w_array, morph_act_area_dep_w) # morph_act_area_sco_w_array = np.append(morph_act_area_sco_w_array, morph_act_area_sco_w) # act_width_mean_w_array = np.append(act_width_mean_w_array, act_width_mean_w) # act_width_mean_dep_w_array = np.append(act_width_mean_dep_w_array, act_width_mean_dep_w) # act_width_mean_sco_w_array = np.append(act_width_mean_sco_w_array, act_width_mean_sco_w) # act_thickness_w_array = np.append(act_thickness_w_array, act_thickness_w) # act_thickness_dep_w_array = np.append(act_thickness_dep_w_array, act_thickness_dep_w) # act_thickness_sco_w_array = np.append(act_thickness_sco_w_array, act_thickness_sco_w) # if plot_mode ==2: # # Plots # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, dep_vol_w_array, 'o', c='brown', markersize=0.1) # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Deposition volumes V/(W*L*d50) [-]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, sco_vol_w_array, 'o', c='brown', markersize=0.1) # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Scour volumes V/(W*L*d50) [-]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, act_width_mean_w_array, 'o', c='brown', markersize=0.1) # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Active width actW/W [-]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # axs.plot(x_data, act_thickness_w_array, 'o', c='brown', markersize=0.1) # axs.set_title(run) # axs.set_xlabel('Window analysis length [m]') # axs.set_ylabel('Active thickness [mm]') # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # plt.show() # if f == DoDs_name_array[0]: # dep_vol_w_array_all = np.transpose(np.array(dep_vol_w_array)) # sco_vol_w_array_all = np.transpose(np.array(sco_vol_w_array)) # else: # pass # dep_vol_w_array_all = np.vstack((dep_vol_w_array_all,dep_vol_w_array)) # dep_vol_mean = np.mean(dep_vol_w_array_all, axis=0) # dep_vol_std = np.std(dep_vol_w_array_all, axis=0) # sco_vol_w_array_all = np.vstack((sco_vol_w_array_all,sco_vol_w_array)) # sco_vol_mean = np.mean(sco_vol_w_array_all, axis=0) # sco_vol_std = np.std(sco_vol_w_array_all, axis=0) # if windows_mode==2: # # Loop to define the windows to clusterize data # array = [0] # num=0 # for n in range(0,len(c_array)): # num += c_array[n] # array = np.append(array, num) # Clusterize window dimension # dep_vol_mean = [] # sco_vol_mean = [] # dep_vol_std = [] # sco_vol_std = [] # x_data_full = x_data # x_data = [] # for n in range(0, len(array)-1): # x_data = np.append(x_data, x_data_full[int(array[n])]) # for n in f_cols_array: # dep_vol_mean = np.append(dep_vol_mean, np.mean(dep_vol_w_array_all[:,int(array[n]):int(array[n+1])])) # sco_vol_mean = np.append(sco_vol_mean, np.mean(sco_vol_w_array_all[:,int(array[n]):int(array[n+1])])) # dep_vol_std = np.append(dep_vol_std, np.std(dep_vol_w_array_all[:,int(array[n]):int(array[n+1])])) # sco_vol_std = np.append(sco_vol_std, np.std(sco_vol_w_array_all[:,int(array[n]):int(array[n+1])])) # # To finish # if windows_mode == 3: # # Loop to define the windows to clusterize data # array = [0] # num=0 # for n in range(0,len(c_array)): # num += c_array[n] # array = np.append(array, num) # Clusterize window dimension # dep_vol_mean = [] # sco_vol_mean = [] # dep_vol_std = [] # sco_vol_std = [] # x_data_full = x_data # x_data = [] # for n in range(0, len(array)-1): # # low_lim = int(f_cols_array[n,0]) # # upp_lim = int(f_cols_array[n,1]) # x_data = np.append(x_data, round(x_data_full[int(array[n])+n],1)) # # dep_vol_mean = np.append(dep_vol_mean, np.mean(dep_vol_w_array_all[:,low_lim:upp_lim])) # # sco_vol_mean = np.append(sco_vol_mean, np.mean(sco_vol_w_array_all[:,low_lim:upp_lim])) # # dep_vol_std = np.append(dep_vol_std, np.std(dep_vol_w_array_all[:,low_lim:upp_lim])) # # sco_vol_std = np.append(sco_vol_std, np.std(sco_vol_w_array_all[:,low_lim:upp_lim])) # dep_vol_mean = np.append(dep_vol_mean, np.mean(dep_vol_w_array_all[:,int(array[n]):int(array[n+1])])) # sco_vol_mean = np.append(sco_vol_mean, np.mean(sco_vol_w_array_all[:,int(array[n]):int(array[n+1])])) # dep_vol_std = np.append(dep_vol_std, np.std(dep_vol_w_array_all[:,int(array[n]):int(array[n+1])])) # sco_vol_std = np.append(sco_vol_std, np.std(sco_vol_w_array_all[:,int(array[n]):int(array[n+1])])) # # print(int(array[n]),int(array[n+1])) # # TODO To finish # fig3, axs = plt.subplots(2,1,dpi=80, figsize=(10,6), sharex=True, tight_layout=True) # fig3.suptitle(run + ' - Volume') # axs[0].errorbar(x_data, sco_vol_mean, sco_vol_std, linestyle='--', marker='^', color='red') # # axs[0].set_ylim(bottom=0) # axs[0].set_title('Scour') # # axs[0].set_xlabel() # axs[0].set_ylabel('Scour volume V/(L*W*d50) [-]') # axs[1].errorbar(x_data, dep_vol_mean, dep_vol_std, linestyle='--', marker='^', color='blue') # axs[1].set_ylim(bottom=0) # axs[1].set_title('Deposition') # axs[1].set_xlabel('Analysis window length [m]') # axs[1].set_ylabel('Deposition volume V/(L*W*d50) [-]') # # plt.savefig(os.path.join(plot_dir, run +'dep_scour.png'), dpi=200) # plt.show() # # # Plots # # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # # axs.plot(x_data, dep_vol_w_array, 'o', c='brown') # # axs.set_title(run) # # axs.set_xlabel('Longitudinal coordinate [m]') # # axs.set_ylabel('Deposition volumes V/(W*L*d50) [-]') # # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # # plt.show() # # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # # axs.plot(x_data, sco_vol_w_array, 'o', c='brown') # # axs.set_title(run) # # axs.set_xlabel('Longitudinal coordinate [m]') # # axs.set_ylabel('Scour volumes V/(W*L*d50) [-]') # # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # # plt.show() # # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # # axs.plot(x_data, act_width_mean_w_array, 'o', c='brown') # # axs.set_title(run) # # axs.set_xlabel('Longitudinal coordinate [m]') # # axs.set_ylabel('Active width actW/W [-]') # # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # # plt.show() # # fig1, axs = plt.subplots(1,1,dpi=200, sharex=True, tight_layout=True) # # axs.plot(x_data, act_thickness_w_array, 'o', c='brown') # # axs.set_title(run) # # axs.set_xlabel('Longitudinal coordinate [m]') # # axs.set_ylabel('Active thickness [mm]') # # # plt.savefig(os.path.join(plot_dir, run +'_morphW_interp.png'), dpi=200) # # plt.show()

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py

Python

build/lib/zorb/models/__init__.py

varunranga/zorb

ffad98d15c3200eafc1b10c68860ce34ebf78f62

[ "MIT" ]

3

2021-05-13T16:28:39.000Z

2022-02-18T23:10:35.000Z

src/zorb/models/__init__.py

varunranga/zorb

ffad98d15c3200eafc1b10c68860ce34ebf78f62

[ "MIT" ]

null

src/zorb/models/__init__.py

varunranga/zorb

ffad98d15c3200eafc1b10c68860ce34ebf78f62

[ "MIT" ]

null

from .Sequential import Sequential

17.5

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py

Python

tests/test_nestedprops.py

atviriduomenys/spinta

77a10e201f8cdc63143fce7996fd0898acb1ff58

[ "MIT" ]

2

2019-03-14T06:41:14.000Z

2019-03-26T11:48:14.000Z

tests/test_nestedprops.py

sirex/spinta

77a10e201f8cdc63143fce7996fd0898acb1ff58

[ "MIT" ]

44

2019-04-05T15:52:45.000Z

2022-03-30T07:41:33.000Z

tests/test_nestedprops.py

sirex/spinta

77a10e201f8cdc63143fce7996fd0898acb1ff58

[ "MIT" ]

1

2019-04-01T09:54:27.000Z

import pytest @pytest.mark.models( 'backends/mongo/report', 'backends/postgres/report', ) def test_update_object(model, app): app.authmodel(model, ['insert', 'patch', 'getone']) resp = app.post(f'/{model}', json={ 'status': 'ok', 'sync': { 'sync_revision': '1', 'sync_resources': [ { 'sync_id': '2', 'sync_source': 'report' } ] } }) assert resp.status_code == 201, resp.json() id_ = resp.json()['_id'] rev = resp.json()['_revision'] resp = app.patch(f'/{model}/{id_}', json={ '_revision': rev, 'sync': { 'sync_revision': '3' } }) assert resp.status_code == 200, resp.json() rev = resp.json()['_revision'] resp = app.get(f'/{model}/{id_}') assert resp.status_code == 200, resp.json() assert resp.json()['sync'] == { 'sync_revision': '3', 'sync_resources': [ { 'sync_id': '2', 'sync_source': 'report' } ] } @pytest.mark.models( 'backends/mongo/report', 'backends/postgres/report', ) def test_update_object_array(model, app): app.authmodel(model, ['insert', 'patch', 'getone']) resp = app.post(f'/{model}', json={ 'status': 'ok', 'sync': { 'sync_revision': '1', 'sync_resources': [ { 'sync_id': '2', 'sync_source': 'report' } ] } }) assert resp.status_code == 201, resp.json() id_ = resp.json()['_id'] rev = resp.json()['_revision'] resp = app.patch(f'/{model}/{id_}', json={ '_revision': rev, 'sync': { 'sync_resources': [{ 'sync_id': '3', 'sync_source': 'troper' }], } }) assert resp.status_code == 200, resp.json() rev = resp.json()['_revision'] resp = app.get(f'/{model}/{id_}') assert resp.status_code == 200, resp.json() assert resp.json()['sync'] == { 'sync_revision': '1', 'sync_resources': [ { 'sync_id': '3', 'sync_source': 'troper' } ] }

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6

1dfcb4d77e3deb4901ba246b595206ea283bb0d6

136,009

py

Python

src_data/community_health_metrics.py

marcmiquel/WDO

7d8d8e912f8dbacb2cdc0f6fd5c26370b8310cbb

[ "MIT" ]

3

2020-12-21T06:06:16.000Z

2021-08-28T12:52:07.000Z

src_data/community_health_metrics.py

marcmiquel/WDO

7d8d8e912f8dbacb2cdc0f6fd5c26370b8310cbb

[ "MIT" ]

1

2021-01-27T19:33:20.000Z

src_data/community_health_metrics.py

marcmiquel/WDO

7d8d8e912f8dbacb2cdc0f6fd5c26370b8310cbb

[ "MIT" ]

null

# -*- coding: utf-8 -*- # script import wikilanguages_utils from wikilanguages_utils import * # time import time import datetime from dateutil import relativedelta import calendar # system import os import sys import shutil import re import random import operator # databases import MySQLdb as mdb, MySQLdb.cursors as mdb_cursors import sqlite3 # files import gzip import zipfile import bz2 import json import csv import codecs # requests and others import requests import urllib import webbrowser import reverse_geocoder as rg import numpy as np from random import shuffle # data import pandas as pd import gc # https://stats.wikimedia.org/#/all-projects # https://meta.wikimedia.org/wiki/List_of_Wikipedias/ca # https://meta.wikimedia.org/wiki/Research:Metrics#Volume_of_contribution # https://meta.wikimedia.org/wiki/Research:Wikistats_metrics/Active_editors community_health_metrics_db = 'community_health_metrics.db' # MAIN def main(): create_community_health_metrics_db() for languagecode in wikilanguagecodes: # wikilanguagecodes print (languagecode) editor_metrics_dump_iterator(languagecode) # it fills the database cawiki_editors, cawiki_editor_metrics print ('dump iterator done.\n') # # input('') # editor_metrics_db_iterator(languagecode) # it fills the database cawiki_editor_metrics # print ('database iterator done.\n') # print ('hell yeh') # input('') # community_metrics_db_iterator(languagecode) # it fills the database cawiki_community_metrics # input('') print ('done') ### # export_community_health_metrics_csv(languagecode) # it fills the database cawiki_editor_metrics # editor_metrics_content_diversity(languagecode) # editor_metrics_multilingual(languagecode) ################################################################ # FUNCTIONS def create_community_health_metrics_db(): conn = sqlite3.connect(databases_path + community_health_metrics_db); cursor = conn.cursor() for languagecode in wikilanguagecodes: table_name = languagecode+'wiki_editors' try: cursor.execute("DROP TABLE "+table_name+";") except: pass query = ("CREATE TABLE IF NOT EXISTS "+table_name+" (user_id integer, user_name text, bot text, user_flags text, highest_flag text, highest_flag_year_month text, gender text, primarybinary integer, primarylang text, primarybinary_ecount integer, totallangs_ecount integer, numberlangs integer, registration_date, year_month_registration, first_edit_timestamp text, year_month_first_edit text, year_first_edit text, lustrum_first_edit text, survived60d text, last_edit_timestamp text, year_last_edit text, lifetime_days integer, editing_days integer, percent_editing_days real, days_since_last_edit integer, seconds_between_last_two_edits integer, PRIMARY KEY (user_id, user_name))") cursor.execute(query) table_name = languagecode+'wiki_editor_metrics' try: cursor.execute("DROP TABLE "+table_name+";") except: pass query = ("CREATE TABLE IF NOT EXISTS "+table_name+" (user_id integer, user_name text, abs_value real, rel_value real, metric_name text, year_month text, timestamp text, PRIMARY KEY (user_id, metric_name, year_month, timestamp))") cursor.execute(query) table_name = languagecode+'wiki_community_metrics' try: cursor.execute("DROP TABLE "+table_name+";") except: pass query = ("CREATE TABLE IF NOT EXISTS "+table_name+" (year_month text, topic text, m1 text, m1_calculation text, m1_value text, m2 text, m2_calculation text, m2_value text, m1_count float, m2_count float, PRIMARY KEY (topic, m1, m1_calculation, m1_value, m2, m2_calculation, m2_value))") cursor.execute(query) table_name = languagecode+'wiki_article_metrics' try: cursor.execute("DROP TABLE "+table_name+";") except: pass query = ("CREATE TABLE IF NOT EXISTS "+table_name+" (qitem text, page_id integer, page_title text, abs_value real, rel_value, metric_name text, year_month text, PRIMARY KEY (metric_name, year_month))") cursor.execute(query) ### ---- ### table_name = languagecode+'wiki_editor_content_metrics' try: cursor.execute("DROP TABLE "+table_name+";") except: pass query = ("CREATE TABLE IF NOT EXISTS "+table_name+" (user_id integer, user_name text, content_type text, abs_value real, rel_value real, year_month text, PRIMARY KEY (user_name, user_id, content_type))") cursor.execute(query) conn.commit() def get_mediawiki_paths(languagecode): cym = cycle_year_month d_paths = [] print ('/public/dumps/public/other/mediawiki_history/'+cym) if os.path.isdir('/public/dumps/public/other/mediawiki_history/'+cym)==False: cym = datetime.datetime.strptime(cym,'%Y-%m')-dateutil.relativedelta.relativedelta(months=1) cym = cym.strftime('%Y-%m') print ('/public/dumps/public/other/mediawiki_history/'+cym) dumps_path = '/public/dumps/public/other/mediawiki_history/'+cym+'/'+languagecode+'wiki/'+cym+'.'+languagecode+'wiki.all-time.tsv.bz2' if os.path.isfile(dumps_path): print ('one all-time file.') d_paths.append(dumps_path) else: print ('multiple files.') for year in range (1999, 2025): dumps_path = '/public/dumps/public/other/mediawiki_history/'+cym+'/'+languagecode+'wiki/'+cym+'.'+languagecode+'wiki.'+str(year)+'.tsv.bz2' if os.path.isfile(dumps_path): d_paths.append(dumps_path) if len(d_paths) == 0: for year in range(1999, 2025): # months for month in range(0, 13): if month > 9: dumps_path = '/public/dumps/public/other/mediawiki_history/'+cym+'/'+languagecode+'wiki/'+cym+'.'+languagecode+'wiki.'+str(year)+'-'+str(month)+'.tsv.bz2' else: dumps_path = '/public/dumps/public/other/mediawiki_history/'+cym+'/'+languagecode+'wiki/'+cym+'.'+languagecode+'wiki.'+str(year)+'-0'+str(month)+'.tsv.bz2' if os.path.isfile(dumps_path) == True: d_paths.append(dumps_path) print(len(d_paths)) print (d_paths) return d_paths, cym def editor_metrics_dump_iterator(languagecode): functionstartTime = time.time() function_name = 'editor_metrics_dump_iterator '+languagecode print (function_name) d_paths, cym = get_mediawiki_paths(languagecode) if (len(d_paths)==0): print ('dump error. this language has no mediawiki_history dump: '+languagecode) # wikilanguages_utils.send_email_toolaccount('dump error at script '+script_name, dumps_path) # quit() conn = sqlite3.connect(databases_path + community_health_metrics_db); cursor = conn.cursor() user_id_user_name_dict = {} user_id_bot_dict = {} user_id_user_groups_dict = {} editor_first_edit_timestamp = {} editor_registration_date = {} editor_last_edit_timestamp = {} editor_seconds_since_last_edit = {} editor_user_group_dict = {} editor_user_group_dict_timestamp = {} # for the survival part survived_dict = {} survival_measures = [] user_id_edit_count = {} editor_user_page_edit_count = {} editor_user_page_talk_page_edit_count = {} # for the monthly part editor_monthly_namespace0_edits = {} editor_monthly_namespace1_edits = {} editor_monthly_namespace2_edits = {} editor_monthly_namespace3_edits = {} editor_monthly_namespace4_edits = {} editor_monthly_namespace5_edits = {} editor_monthly_namespace6_edits = {} editor_monthly_namespace7_edits = {} editor_monthly_namespace8_edits = {} editor_monthly_namespace9_edits = {} editor_monthly_namespace10_edits = {} editor_monthly_namespace11_edits = {} editor_monthly_namespace12_edits = {} editor_monthly_namespace13_edits = {} editor_monthly_namespace14_edits = {} editor_monthly_namespace15_edits = {} editor_monthly_user_page_edit_count = {} editor_monthly_user_page_talk_page_edit_count = {} editor_monthly_edits = {} editor_monthly_seconds_between_edits = {} editor_monthly_editing_days = {} editor_monthly_created_articles = {} editor_monthly_deleted_articles = {} editor_monthly_moved_articles = {} editor_monthly_undeleted_articles = {} editor_monthly_accounts_created = {} editor_monthly_users_renamed = {} editor_monthly_autoblocks = {} editor_monthly_edits_reverted = {} editor_monthly_reverts_made = {} last_year_month = 0 first_date = datetime.datetime.strptime('2001-01-01 01:15:15','%Y-%m-%d %H:%M:%S') for dump_path in d_paths: print('\n'+dump_path) iterTime = time.time() dump_in = bz2.open(dump_path, 'r') line = 'something' line = dump_in.readline() while line != '': # print ('*') # print (line) # print (seconds_since_last_edit) # print ('*') # input('') line = dump_in.readline() line = line.rstrip().decode('utf-8')[:-1] values = line.split('\t') if len(values)==1: continue event_entity = values[1] event_type = values[2] event_user_id = values[5] try: int(event_user_id) except: continue event_user_text = values[7] if event_user_text != '': user_id_user_name_dict[event_user_id] = event_user_text else: continue try: editor_last_edit = editor_last_edit_timestamp[event_user_id] last_edit_date_dt = datetime.datetime.strptime(editor_last_edit[:len(editor_last_edit)-2],'%Y-%m-%d %H:%M:%S') last_edit_year_month_day = datetime.datetime.strptime(last_edit_date_dt.strftime('%Y-%m-%d'),'%Y-%m-%d') except: last_edit_year_month_day = '' event_timestamp = values[3] event_timestamp_dt = datetime.datetime.strptime(event_timestamp[:len(event_timestamp)-2],'%Y-%m-%d %H:%M:%S') editor_last_edit_timestamp[event_user_id] = event_timestamp event_user_groups = values[11] if event_user_groups != '': user_id_user_groups_dict[event_user_id] = event_user_groups page_namespace = values[28] if event_entity == 'revision': revision_is_identity_reverted = values[64] # són edits que seran reverted en el futur. if revision_is_identity_reverted == 'true': try: editor_monthly_edits_reverted[event_user_id] = editor_monthly_edits_reverted[event_user_id]+1 except: editor_monthly_edits_reverted[event_user_id] = 1 # print ('made',revision_is_identity_reverted, values) # input('') revision_is_identity_revert = values[67] # són edits que revert un altre edit if revision_is_identity_revert == 'true': try: editor_monthly_reverts_made[event_user_id] = editor_monthly_reverts_made[event_user_id]+1 except: editor_monthly_reverts_made[event_user_id] = 1 # print ('received',revision_is_identity_revert, values) # input('') elif event_entity == 'page' and page_namespace == '0': if event_type == 'create': try: editor_monthly_created_articles[event_user_id] = editor_monthly_created_articles[event_user_id]+1 except: editor_monthly_created_articles[event_user_id] = 1 elif event_type == 'delete': try: editor_monthly_deleted_articles[event_user_id] = editor_monthly_deleted_articles[event_user_id]+1 except: editor_monthly_deleted_articles[event_user_id] = 1 elif event_type == 'move': try: editor_monthly_moved_articles[event_user_id] = editor_monthly_moved_articles[event_user_id]+1 except: editor_monthly_moved_articles[event_user_id] = 1 elif event_type == 'restore': try: editor_monthly_undeleted_articles[event_user_id] = editor_monthly_undeleted_articles[event_user_id]+1 except: editor_monthly_undeleted_articles[event_user_id] = 1 elif event_entity == 'user': user_text = str(values[38]) # this is target of the event if event_type == 'create' and event_user_text != user_text: try: editor_monthly_accounts_created[event_user_id] = editor_monthly_accounts_created[event_user_id]+1 except: editor_monthly_accounts_created[event_user_id] = 1 elif event_type == 'rename': try: editor_monthly_users_renamed[event_user_id] = editor_monthly_users_renamed[event_user_id]+1 except: editor_monthly_users_renamed[event_user_id] = 1 elif event_type == 'altergroups': user_id = values[36] user_group = values[41] cur_ug = '' if user_group != '' and user_group != None: try: cur_ug = editor_user_group_dict[user_id] if len(cur_ug) < len(user_group): change = user_group.replace(cur_ug,'').strip(',') metric_name = 'granted_flag' else: change = cur_ug.replace(user_group,'').strip(',') metric_name = 'removed_flag' # this is only for the case that one flag is removed by another editor. when an editor removes him/herself the flag, it does not appear here. except: change = user_group metric_name = 'granted_flag' # change (what is new + o -); # user_group (what is he has after the change); # cur_ug (what he had right before); # values[42] (what he'll have in the future and in the end) # input('') editor_user_group_dict[user_id] = user_group if change != '': # user_text = values[38] # print (user_id, user_text, ' - ', change, ' - ', user_group, ' - ', cur_ug ,' - ', values[42], ' - ', metric_name, event_timestamp) # print ('\n',event_type, event_entity, event_user_text, cur_ug, event_user_groups,'\n',line) if ',' in change: change_ = change.split(',') event_timestamp2 = event_timestamp[:len(event_timestamp)-2] editor_user_group_dict_timestamp[user_id,event_timestamp] = [metric_name, change_[0], cur_ug] editor_user_group_dict_timestamp[user_id,event_timestamp2] = [metric_name, change_[1], cur_ug] else: editor_user_group_dict_timestamp[user_id,event_timestamp] = [metric_name, change, cur_ug] elif event_type == 'alterblocks': try: editor_monthly_autoblocks[event_user_id] = editor_monthly_autoblocks[event_user_id]+1 except: editor_monthly_autoblocks[event_user_id] = 1 event_is_bot_by = values[13] if event_is_bot_by != '': user_id_bot_dict[event_user_id] = event_is_bot_by # print (event_user_text, event_is_bot_by) event_user_is_anonymous = values[17] if event_user_is_anonymous == True or event_user_id == '': continue event_user_registration_date = values[18] if event_user_id not in editor_registration_date: if event_user_registration_date != '': editor_registration_date[event_user_id] = event_user_registration_date ####### --------- # MONTHLY EDITS COUNTER try: editor_monthly_edits[event_user_id] = editor_monthly_edits[event_user_id]+1 except: editor_monthly_edits[event_user_id] = 1 # MONTHLY NAMESPACES EDIT COUNTER if page_namespace == '0': try: editor_monthly_namespace0_edits[event_user_id] = editor_monthly_namespace0_edits[event_user_id]+1 except: editor_monthly_namespace0_edits[event_user_id] = 1 elif page_namespace == '1': try: editor_monthly_namespace1_edits[event_user_id] = editor_monthly_namespace1_edits[event_user_id]+1 except: editor_monthly_namespace1_edits[event_user_id] = 1 elif page_namespace == '2': try: editor_monthly_namespace2_edits[event_user_id] = editor_monthly_namespace2_edits[event_user_id]+1 except: editor_monthly_namespace2_edits[event_user_id] = 1 elif page_namespace == '3': try: editor_monthly_namespace3_edits[event_user_id] = editor_monthly_namespace3_edits[event_user_id]+1 except: editor_monthly_namespace3_edits[event_user_id] = 1 elif page_namespace == '4': try: editor_monthly_namespace4_edits[event_user_id] = editor_monthly_namespace4_edits[event_user_id]+1 except: editor_monthly_namespace4_edits[event_user_id] = 1 elif page_namespace == '5': try: editor_monthly_namespace5_edits[event_user_id] = editor_monthly_namespace5_edits[event_user_id]+1 except: editor_monthly_namespace5_edits[event_user_id] = 1 elif page_namespace == '6': try: editor_monthly_namespace6_edits[event_user_id] = editor_monthly_namespace6_edits[event_user_id]+1 except: editor_monthly_namespace6_edits[event_user_id] = 1 elif page_namespace == '7': try: editor_monthly_namespace7_edits[event_user_id] = editor_monthly_namespace7_edits[event_user_id]+1 except: editor_monthly_namespace7_edits[event_user_id] = 1 elif page_namespace == '8': try: editor_monthly_namespace8_edits[event_user_id] = editor_monthly_namespace8_edits[event_user_id]+1 except: editor_monthly_namespace8_edits[event_user_id] = 1 elif page_namespace == '9': try: editor_monthly_namespace9_edits[event_user_id] = editor_monthly_namespace9_edits[event_user_id]+1 except: editor_monthly_namespace9_edits[event_user_id] = 1 elif page_namespace == '10': try: editor_monthly_namespace10_edits[event_user_id] = editor_monthly_namespace10_edits[event_user_id]+1 except: editor_monthly_namespace10_edits[event_user_id] = 1 elif page_namespace == '11': try: editor_monthly_namespace11_edits[event_user_id] = editor_monthly_namespace11_edits[event_user_id]+1 except: editor_monthly_namespace11_edits[event_user_id] = 1 elif page_namespace == '12': try: editor_monthly_namespace12_edits[event_user_id] = editor_monthly_namespace12_edits[event_user_id]+1 except: editor_monthly_namespace12_edits[event_user_id] = 1 elif page_namespace == '13': try: editor_monthly_namespace13_edits[event_user_id] = editor_monthly_namespace13_edits[event_user_id]+1 except: editor_monthly_namespace13_edits[event_user_id] = 1 elif page_namespace == '14': try: editor_monthly_namespace14_edits[event_user_id] = editor_monthly_namespace14_edits[event_user_id]+1 except: editor_monthly_namespace14_edits[event_user_id] = 1 elif page_namespace == '15': try: editor_monthly_namespace15_edits[event_user_id] = editor_monthly_namespace15_edits[event_user_id]+1 except: editor_monthly_namespace15_edits[event_user_id] = 1 # MONTHLY USER PAGE/USER PAGE TALK PAGE EDIT COUNTER page_title = values[25] if event_user_text == page_title and page_namespace == '2': try: editor_monthly_user_page_edit_count[event_user_id] = editor_monthly_user_page_edit_count[event_user_id]+1 except: editor_monthly_user_page_edit_count[event_user_id] = 1 if event_user_text == page_title and page_namespace == '3': try: editor_monthly_user_page_talk_page_edit_count[event_user_id] = editor_monthly_user_page_talk_page_edit_count[event_user_id]+1 except: editor_monthly_user_page_talk_page_edit_count[event_user_id] = 1 # MONTHLY AVERAGE SECONDS BETWEEN EDITS COUNTER seconds_since_last_edit = values[22] if seconds_since_last_edit != None and seconds_since_last_edit != '': seconds_since_last_edit = int(seconds_since_last_edit) editor_seconds_since_last_edit[event_user_id] = seconds_since_last_edit if seconds_since_last_edit != None and seconds_since_last_edit != '': if event_user_id != '' and event_user_id != 0: try: editor_monthly_seconds_between_edits[event_user_id].append(seconds_since_last_edit) except: editor_monthly_seconds_between_edits[event_user_id] = [seconds_since_last_edit] # COUNTING DAYS current_year_month_day = datetime.datetime.strptime(event_timestamp_dt.strftime('%Y-%m-%d'),'%Y-%m-%d') if current_year_month_day != last_edit_year_month_day: try: editor_monthly_editing_days[event_user_id]+=1 except: editor_monthly_editing_days[event_user_id]=1 #######--------- --------- --------- --------- --------- --------- # CHECK MONTH CHANGE AND INSERT MONTHLY EDITS/NAMESPACES EDITS/SECONDS current_year_month = datetime.datetime.strptime(event_timestamp_dt.strftime('%Y-%m'),'%Y-%m') if last_year_month != current_year_month and last_year_month != 0: lym = last_year_month.strftime('%Y-%m') print (current_year_month, lym, cym) lym_sp = lym.split('-') ly = lym_sp[0] lm = lym_sp[1] lym_days = calendar.monthrange(int(ly),int(lm))[1] monthly_articles = [] monthly_user_actions = [] monthly_reverts = [] monthly_edits = [] monthly_seconds = [] namespaces = [] for user_id, edits in editor_monthly_created_articles.items(): monthly_articles.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_created_articles', lym, '')) for user_id, edits in editor_monthly_deleted_articles.items(): monthly_articles.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_deleted_articles', lym, '')) for user_id, edits in editor_monthly_moved_articles.items(): monthly_articles.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_moved_articles', lym, '')) for user_id, edits in editor_monthly_undeleted_articles.items(): monthly_articles.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_undeleted_articles', lym, '')) for user_id, edits in editor_monthly_accounts_created.items(): monthly_user_actions.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_accounts_created', lym, '')) for user_id, edits in editor_monthly_users_renamed.items(): monthly_user_actions.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_users_renamed', lym, '')) for user_id, edits in editor_monthly_autoblocks.items(): monthly_user_actions.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_autoblocks', lym, '')) for user_id, edits in editor_monthly_edits_reverted.items(): monthly_user_actions.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_reverted', lym, '')) for user_id, edits in editor_monthly_reverts_made.items(): monthly_user_actions.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_reverts_made', lym, '')) for user_id, edits in editor_monthly_edits.items(): monthly_edits.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits', lym, '')) for user_id, seconds_list in editor_monthly_seconds_between_edits.items(): if seconds_list == None: continue elif len(seconds_list) > 1: average_seconds = np.mean(seconds_list) monthly_seconds.append((user_id, user_id_user_name_dict[user_id], average_seconds, None, 'monthly_average_seconds_between_edits', lym, '')) for user_id, edits in editor_monthly_namespace0_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns0_main', lym, '')) except: pass for user_id, edits in editor_monthly_namespace1_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns1_talk', lym, '')) except: pass for user_id, edits in editor_monthly_namespace2_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns2_user', lym, '')) except: pass for user_id, edits in editor_monthly_namespace3_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns3_user_talk', lym, '')) except: pass for user_id, edits in editor_monthly_namespace4_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns4_project', lym, '')) except: pass for user_id, edits in editor_monthly_namespace5_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns5_project_talk', lym, '')) except: pass for user_id, edits in editor_monthly_namespace6_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns6_file', lym, '')) except: pass for user_id, edits in editor_monthly_namespace7_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns7_file_talk', lym, '')) except: pass for user_id, edits in editor_monthly_namespace8_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns8_mediawiki', lym, '')) except: pass for user_id, edits in editor_monthly_namespace9_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns9_mediawiki_talk', lym, '')) except: pass for user_id, edits in editor_monthly_namespace10_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns10_template', lym, '')) except: pass for user_id, edits in editor_monthly_namespace11_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns11_template_talk', lym, '')) except: pass for user_id, edits in editor_monthly_namespace12_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns12_help', lym, '')) except: pass for user_id, edits in editor_monthly_namespace13_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns13_help_talk', lym, '')) except: pass for user_id, edits in editor_monthly_namespace14_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns14_category', lym, '')) except: pass for user_id, edits in editor_monthly_namespace15_edits.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_edits_ns15_category_talk', lym, '')) except: pass for user_id, edits in editor_monthly_user_page_edit_count.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_user_page_edit_count', lym, '')) except: pass for user_id, edits in editor_monthly_user_page_talk_page_edit_count.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], edits, None, 'monthly_user_page_talk_page_edit_count', lym, '')) except: pass for user_id, days in editor_monthly_editing_days.items(): try: namespaces.append((user_id, user_id_user_name_dict[user_id], days, 100*(days/lym_days), 'monthly_editing_days', lym, '')) except: pass for key, data in editor_user_group_dict_timestamp.items(): user_id = key[0] timestamp = key[1] metric_name = data[0] flags = data[1] old_flags = data[2] try: if metric_name == 'removed_flag': namespaces.append((user_id, user_id_user_name_dict[user_id], old_flags, None, metric_name, lym, timestamp)) # print ((user_id, user_id_user_name_dict[user_id], old_flags, None, metric_name, lym, timestamp)) else: namespaces.append((user_id, user_id_user_name_dict[user_id], flags, None, metric_name, lym, timestamp)) # print ((user_id, user_id_user_name_dict[user_id], flags, None, metric_name, lym, timestamp)) except: pass query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month, timestamp) VALUES (?,?,?,?,?,?,?);' cursor.executemany(query,monthly_articles) cursor.executemany(query,monthly_edits) cursor.executemany(query,monthly_reverts) cursor.executemany(query,monthly_user_actions) cursor.executemany(query,namespaces) cursor.executemany(query,monthly_seconds) conn.commit() monthly_articles = [] monthly_user_actions = [] monthly_reverts = [] monthly_edits = [] monthly_seconds = [] namespaces = [] editor_monthly_created_articles = {} editor_monthly_deleted_articles = {} editor_monthly_moved_articles = {} editor_monthly_undeleted_articles = {} editor_monthly_accounts_created = {} editor_monthly_users_renamed = {} editor_monthly_autoblocks = {} editor_monthly_edits_reverted = {} editor_monthly_reverts_made = {} editor_monthly_namespace0_edits = {} editor_monthly_namespace1_edits = {} editor_monthly_namespace2_edits = {} editor_monthly_namespace3_edits = {} editor_monthly_namespace4_edits = {} editor_monthly_namespace5_edits = {} editor_monthly_namespace6_edits = {} editor_monthly_namespace7_edits = {} editor_monthly_namespace8_edits = {} editor_monthly_namespace9_edits = {} editor_monthly_namespace10_edits = {} editor_monthly_namespace11_edits = {} editor_monthly_namespace12_edits = {} editor_monthly_namespace13_edits = {} editor_monthly_namespace14_edits = {} editor_monthly_namespace15_edits = {} editor_monthly_edits = {} editor_monthly_seconds_between_edits = {} editor_monthly_user_page_edit_count = {} editor_monthly_user_page_talk_page_edit_count = {} editor_monthly_editing_days = {} editor_user_group_dict_timestamp = {} last_year_month = current_year_month ####### --------- # SURVIVAL MEASURES event_user_first_edit_timestamp = values[20] if event_user_id not in editor_first_edit_timestamp: editor_first_edit_timestamp[event_user_id] = event_user_first_edit_timestamp if event_user_first_edit_timestamp == '' or event_user_first_edit_timestamp == None: event_user_first_edit_timestamp = editor_first_edit_timestamp[event_user_id] if event_user_first_edit_timestamp != '' and event_user_id not in survived_dict: event_user_first_edit_timestamp_dt = datetime.datetime.strptime(event_user_first_edit_timestamp[:len(event_user_first_edit_timestamp)-2],'%Y-%m-%d %H:%M:%S') # thresholds first_edit_timestamp_1day_dt = (event_user_first_edit_timestamp_dt + relativedelta.relativedelta(days=1)) first_edit_timestamp_7days_dt = (event_user_first_edit_timestamp_dt + relativedelta.relativedelta(days=7)) first_edit_timestamp_1months_dt = (event_user_first_edit_timestamp_dt + relativedelta.relativedelta(months=1)) first_edit_timestamp_2months_dt = (event_user_first_edit_timestamp_dt + relativedelta.relativedelta(months=2)) try: ec = user_id_edit_count[event_user_id] except: ec = 1 # at 1 day if event_timestamp_dt >= first_edit_timestamp_1day_dt: survival_measures.append((event_user_id, event_user_text, ec, None, 'edit_count_24h', first_edit_timestamp_1day_dt.strftime('%Y-%m'),first_edit_timestamp_1day_dt.strftime('%Y-%m-%d %H:%M:%S'))) if event_user_id in editor_user_page_edit_count: survival_measures.append((event_user_id, event_user_text, editor_user_page_edit_count[event_user_id], None, 'user_page_edit_count_24h', first_edit_timestamp_1day_dt.strftime('%Y-%m'),first_edit_timestamp_1day_dt.strftime('%Y-%m-%d %H:%M:%S'))) if event_user_id in editor_user_page_talk_page_edit_count: survival_measures.append((event_user_id, event_user_text, editor_user_page_talk_page_edit_count[event_user_id], None, 'user_page_talk_page_edit_count_24h', first_edit_timestamp_1day_dt.strftime('%Y-%m'),first_edit_timestamp_1day_dt.strftime('%Y-%m-%d %H:%M:%S'))) # at 7 days if event_timestamp_dt >= first_edit_timestamp_7days_dt: survival_measures.append((event_user_id, event_user_text, ec, None, 'edit_count_7d', first_edit_timestamp_7days_dt.strftime('%Y-%m'),first_edit_timestamp_7days_dt.strftime('%Y-%m-%d %H:%M:%S'))) # at 1 month if event_timestamp_dt >= first_edit_timestamp_1months_dt: survival_measures.append((event_user_id, event_user_text, ec, None, 'edit_count_30d', first_edit_timestamp_1months_dt.strftime('%Y-%m'),first_edit_timestamp_1months_dt.strftime('%Y-%m-%d %H:%M:%S'))) if event_user_id in editor_user_page_edit_count: survival_measures.append((event_user_id, event_user_text, editor_user_page_edit_count[event_user_id], None, 'user_page_edit_count_1month', first_edit_timestamp_1day_dt.strftime('%Y-%m'),first_edit_timestamp_1day_dt.strftime('%Y-%m-%d %H:%M:%S'))) if event_user_id in editor_user_page_talk_page_edit_count: survival_measures.append((event_user_id, event_user_text, editor_user_page_talk_page_edit_count[event_user_id], None, 'user_page_talk_page_edit_count_1month', first_edit_timestamp_1day_dt.strftime('%Y-%m'),first_edit_timestamp_1day_dt.strftime('%Y-%m-%d %H:%M:%S'))) # at 2 months if event_timestamp_dt >= first_edit_timestamp_2months_dt: survival_measures.append((event_user_id, event_user_text, ec, None, 'edit_count_60d', first_edit_timestamp_2months_dt.strftime('%Y-%m'),first_edit_timestamp_2months_dt.strftime('%Y-%m-%d %H:%M:%S'))) survived_dict[event_user_id]=event_user_text try: del user_id_edit_count[event_user_id] except: pass try: del editor_user_page_talk_page_edit_count[event_user_id] except: pass try: del editor_user_page_edit_count[event_user_id] except: pass # USER PAGE EDIT COUNT, ADD ONE MORE EDIT. if event_user_id not in survived_dict: if event_user_text == page_title and page_namespace == '2': try: editor_user_page_edit_count[event_user_id] = editor_user_page_edit_count[event_user_id]+1 except: editor_user_page_edit_count[event_user_id] = 1 if event_user_text == page_title and page_namespace == '3': try: editor_user_page_talk_page_edit_count[event_user_id] = editor_user_page_talk_page_edit_count[event_user_id]+1 except: editor_user_page_talk_page_edit_count[event_user_id] = 1 # EDIT COUNT, ADD ONE MORE EDIT. event_user_revision_count = values[21] if event_user_revision_count != '': user_id_edit_count[event_user_id] = event_user_revision_count elif event_user_id in user_id_edit_count: user_id_edit_count[event_user_id] = int(user_id_edit_count[event_user_id]) + 1 else: user_id_edit_count[event_user_id] = 1 ####### --------- # SURVIVAL MEASURES INSERT query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month, timestamp) VALUES (?,?,?,?,?,?,?);' cursor.executemany(query,survival_measures) conn.commit() survival_measures = [] # MONTHLY EDITS/SECONDS INSERT (LAST ROUND) lym = last_year_month.strftime('%Y-%m') if lym != cym: monthly_edits = [] for event_user_id, edits in editor_monthly_edits.items(): monthly_edits.append((event_user_id, user_id_user_name_dict[event_user_id], edits, None, 'monthly_edits', lym, '')) query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month, timestamp) VALUES (?,?,?,?,?,?,?);' cursor.executemany(query,monthly_edits) conn.commit() editor_monthly_edits = {} monthly_edits = [] monthly_seconds = [] for event_user_id, seconds_list in editor_monthly_seconds_between_edits.items(): if seconds_list == None: continue elif len(seconds_list) > 1: average_seconds = np.mean(seconds_list) monthly_seconds.append((event_user_id, user_id_user_name_dict[event_user_id], average_seconds, None, 'monthly_average_seconds_between_edits', lym, '')) query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month, timestamp) VALUES (?,?,?,?,?,?,?);' cursor.executemany(query,monthly_seconds) conn.commit() editor_monthly_seconds_between_edits = {} monthly_seconds = [] # USER CHARACTERISTICS INSERT user_characteristics1 = [] user_characteristics2 = [] for user_id, user_name in user_id_user_name_dict.items(): try: user_flags = user_id_user_groups_dict[user_id] except: user_flags = '' try: bot = user_id_bot_dict[user_id] except: bot = 'editor' if user_id in survived_dict: survived60d = '1' else: survived60d = '0' try: registration_date = editor_registration_date[user_id] except: registration_date = '' if registration_date == '': # THIS IS SOMETHING WE "ASSUME" BECAUSE THERE ARE MANY ACCOUNTS WITHOUT A REGISTRATION DATE. try: registration_date = editor_first_edit_timestamp[user_id] except: registration_date = '' if registration_date != '': year_month_registration = datetime.datetime.strptime(registration_date[:len(registration_date)-2],'%Y-%m-%d %H:%M:%S').strftime('%Y-%m') else: year_month_registration = '' try: fe = editor_first_edit_timestamp[user_id] except: fe = '' try: le = editor_last_edit_timestamp[user_id] year_last_edit = datetime.datetime.strptime(le[:len(le)-2],'%Y-%m-%d %H:%M:%S').strftime('%Y') except: le = '' year_last_edit if fe != '': year_month = datetime.datetime.strptime(fe[:len(fe)-2],'%Y-%m-%d %H:%M:%S').strftime('%Y-%m') year_first_edit = datetime.datetime.strptime(fe[:len(fe)-2],'%Y-%m-%d %H:%M:%S').strftime('%Y') if int(year_first_edit) >= 2001 < 2006: lustrum_first_edit = '2001-2005' if int(year_first_edit) >= 2006 < 2011: lustrum_first_edit = '2006-2010' if int(year_first_edit) >= 2011 < 2016: lustrum_first_edit = '2011-2015' if int(year_first_edit) >= 2016 < 2021: lustrum_first_edit = '2016-2020' if int(year_first_edit) >= 2020 < 2026: lustrum_first_edit = '2021-2025' fe_d = datetime.datetime.strptime(fe[:len(fe)-2],'%Y-%m-%d %H:%M:%S') else: year_month = '' year_first_edit = '' lustrum_first_edit = '' fe_d = '' if le != '': le_d = datetime.datetime.strptime(le[:len(le)-2],'%Y-%m-%d %H:%M:%S') days_since_last_edit = (event_timestamp_dt - le_d).days else: le_d = '' days_since_last_edit = '' if fe != '' and le != '': lifetime_days = (le_d - fe_d).days else: lifetime_days = 0 try: se = editor_seconds_since_last_edit[user_id] except: se = '' user_characteristics1.append((user_id, user_name, registration_date, year_month_registration, fe, year_month, year_first_edit, lustrum_first_edit, survived60d)) user_characteristics2.append((bot, user_flags, le, year_last_edit, lifetime_days, days_since_last_edit, se, user_id, user_name)) query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editors (user_id, user_name, registration_date, year_month_registration, first_edit_timestamp, year_month_first_edit, year_first_edit, lustrum_first_edit, survived60d) VALUES (?,?,?,?,?,?,?,?,?);' cursor.executemany(query,user_characteristics1) query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editors (bot, user_flags, last_edit_timestamp, year_last_edit, lifetime_days, days_since_last_edit, seconds_between_last_two_edits, user_id, user_name) VALUES (?,?,?,?,?,?,?,?,?);' cursor.executemany(query,user_characteristics2) query = 'UPDATE '+languagecode+'wiki_editors SET bot = ?, user_flags = ?, last_edit_timestamp = ?, year_last_edit = ?, lifetime_days = ?, days_since_last_edit = ?, seconds_between_last_two_edits = ? WHERE user_id = ? AND user_name = ?;' cursor.executemany(query,user_characteristics2) conn.commit() print (len(user_characteristics1),len(user_characteristics2)) user_characteristics1 = [] user_characteristics2 = [] # insert or ignore + update user_id_bot_dict = {} user_id_user_groups_dict = {} editor_last_edit_timestamp = {} editor_seconds_since_last_edit = {} # insert or ignore editor_first_edit_timestamp = {} editor_registration_date = {} # END OF THE DUMP!!!! print ('end of the dump.') print ('*') print (str(datetime.timedelta(seconds=time.time() - iterTime))) # AGGREGATED METRICS (EDIT COUNTS) monthly_aggregated_metrics = {'monthly_edits':'edit_count', 'monthly_user_page_edit_count': 'edit_count_editor_user_page', 'monthly_user_page_talk_page_edit_count': 'edit_count_editor_user_page_talk_page', 'monthly_edits_ns0_main':'edit_count_ns0_main', 'monthly_edits_ns1_talk':'edit_count_ns1_talk', 'monthly_edits_ns2_user':'edit_count_ns2_user', 'monthly_edits_ns3_user_talk': 'edit_count_ns3_user_talk', 'monthly_edits_ns4_project':'edit_count_ns4_project', 'monthly_edits_ns5_project_talk': 'edit_count_ns5_project_talk', 'monthly_edits_ns6_file': 'edit_count_edits_ns6_file', 'monthly_edits_ns7_file_talk':'edit_count_ns7_file_talk', 'monthly_edits_ns8_mediawiki': 'edit_count_ns8_mediawiki', 'monthly_edits_ns9_mediawiki_talk': 'edit_count_ns9_mediawiki_talk', 'monthly_edits_ns10_template':'edit_count_ns10_template', 'monthly_edits_ns11_template_talk':'edit_count_ns11_template_talk', 'monthly_edits_ns12_help':'edit_count_ns12_help','monthly_edits_ns13_help_talk':'edit_count_ns13_help_talk','monthly_edits_ns14_category':'edit_count_ns14_category','monthly_edits_ns15_category_talk':'edit_count_ns15_category_talk','monthly_created_articles':'created_articles_count','monthly_deleted_articles':'deleted_articles_count','monthly_moved_articles':'moved_articles_count','monthly_undeleted_articles':'undeleted_articles_count','monthly_accounts_created':'created_accounts_count','monthly_users_renamed':'users_renamed_count','monthly_autoblocks':'autoblocks_count','monthly_edits_reverted':'edits_reverted_count','monthly_reverts_made':'reverts_made_count'} conn2 = sqlite3.connect(databases_path + community_health_metrics_db); cursor2 = conn2.cursor() for monthly_metric_name, metric_name in monthly_aggregated_metrics.items(): edit_counts = [] query = 'SELECT user_id, user_name, SUM(abs_value) FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "'+monthly_metric_name+'" GROUP BY 2;' for row in cursor.execute(query): edit_counts.append((row[0],row[1],row[2],metric_name,lym)) if metric_name == 'edit_count': ec = row[2] bin_v = '' if ec > 1 and ec <= 100: bin_v = '1-100' if ec > 100 and ec <= 500: bin_v = '101-500' if ec > 500 and ec <= 1000: bin_v = '501-1000' if ec > 1000 and ec <= 5000: bin_v = '1001-5000' if ec > 5000 and ec <= 10000: bin_v = '5001-10000' if ec > 10000 and ec <= 50000: bin_v = '10001-50000' if ec > 50000 and ec <= 100000: bin_v = '50001-100000' if ec > 100000 and ec <= 500000: bin_v = '100001-500000' if ec > 500000 and ec <= 1000000: bin_v = '500001-1000000' if ec > 1000000: bin_v = '1000001+' if bin_v != '': edit_counts.append((row[0],row[1],bin_v,'edit_count_bin',lym)) query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, metric_name, year_month) VALUES (?,?,?,?,?);'; cursor2.executemany(query,edit_counts) conn2.commit() edit_counts = [] query = 'SELECT user_id, user_name, AVG(abs_value) FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "monthly_edits" GROUP BY 2;' for row in cursor.execute(query): ec = row[2] bin_v = '' if ec > 1 and ec <= 5: bin_v = '1-5' if ec > 5 and ec <= 10: bin_v = '6-10' if ec > 10 and ec <= 100: bin_v = '11-100' if ec > 100 and ec <= 500: bin_v = '101-500' if ec > 500 and ec <= 1000: bin_v = '501-1000' if ec > 1000 and ec <= 5000: bin_v = '1001-5000' if ec > 5000: bin_v = '5001+' if bin_v != '': edit_counts.append((row[0],row[1],bin_v,'monthly_edit_count_bin',lym)) query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, metric_name, year_month) VALUES (?,?,?,?,?);'; cursor2.executemany(query,edit_counts) conn2.commit() edit_counts = [] # print ('stop monthly edit count'); input('stop'); # FLAGS UPDATE # Getting the highest flag conn = sqlite3.connect(databases_path + community_health_metrics_db); cursor = conn.cursor() query = 'SELECT user_flags, count(user_id) FROM '+languagecode+'wiki_editors WHERE user_flags != "" GROUP BY 1;' flags_count_dict = {} for row in cursor.execute(query): flags = row[0] count = row[1] if ',' in flags: fs = flags.split(',') for x in fs: try: flags_count_dict[x]+=count except: flags_count_dict[x]=1 else: try: flags_count_dict[flags]+=count except: flags_count_dict[flags]=1 print ('Number of editors for each flag') print (flags_count_dict) # print ('in') # input('') flag_ranks = { 'confirmed':1,'ipblock-exempt':1, 'filemover':2,'accountcreator':2,'autopatrolled':2,'reviewer':2,'autoreviewer':2,'rollbacker':2,'abusefilter':2,'abusefilter-ehlper':2,'interface-admin':2,'eventcoordinator':2,'extendedconfirmed':2,'extendedmover':2, 'filemover':2, 'massmessage-sender':2, 'patroller':2, 'researcher':2, 'templateeditor':2, 'sysop':3,'bureaucrat':3.5, 'checkuser':4,'oversight':4.5, 'steward':5.5, 'import':5, 'founder':6 } query = 'SELECT user_id, user_flags, user_name FROM '+languagecode+'wiki_editors WHERE user_flags != "";' params = [] user_id_flag = {} for row in cursor.execute(query): user_id = row[0] user_flags = row[1] user_name = row[2] highest_rank = {} highest_count = {} if ',' in user_flags: uf = user_flags.split(',') for x in uf: if x in flag_ranks and 'bot' not in x: val = flag_ranks[x] highest_rank[x] = val if len(highest_rank) > 1: maxval = max(highest_rank.values()) highest_rank = {key:val for key, val in highest_rank.items() if val == maxval} # we are choosing the flag of highest rank. if len(highest_rank)>1: for x in highest_rank.keys(): val = flags_count_dict[x] highest_count[x] = val maxval = max(highest_count.values()) highest_count = {key:val for key, val in highest_count.items() if val == maxval} # we are choosing the flag that exists more in the community. f = list(highest_count.keys())[0] params.append((f, user_id, user_name)) user_id_flag[user_id]=f else: f = list(highest_rank.keys())[0] params.append((f, user_id, user_name)) user_id_flag[user_id]=f else: if user_flags in flag_ranks and 'bot' not in user_flags: params.append((user_flags, user_id, user_name)) user_id_flag[user_id]=user_flags query = 'UPDATE '+languagecode+'wiki_editors SET highest_flag = ? WHERE user_id = ? AND user_name = ?;' cursor.executemany(query,params) conn.commit() print ('Updated the editors table with highest flag') # let's update the highest_flag_year_month query = 'SELECT year_month, user_id, user_name, abs_value FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "granted_flag";' params2 = [] conn = sqlite3.connect(databases_path + community_health_metrics_db); cursor = conn.cursor() for row in cursor.execute(query): year_month=row[0] user_id=row[1] user_name=row[2] flag = row[3] try: ex_flag = user_id_flag[user_id] except: continue # print ((ex_flag, flag,year_month,user_id,user_name)) if ex_flag in flag: # print ((ex_flag, flag,year_month,user_id,user_name)) params2.append((year_month,user_id,user_name)) # print (params2) query = 'UPDATE '+languagecode+'wiki_editors SET highest_flag_year_month = ? WHERE user_id = ? AND user_name = ?;' cursor.executemany(query,params2) conn.commit() print ('Updated the editors table with the year month they obtained the highest flag.') # print(list(highest_flag.values()).count('bureaucrat')) # print ('stop highest flag year month'); input('stop'); # If an editor has been granted the 'bot' flag, even if it has been taken away, it must be a flag. query = 'SELECT user_id, user_name FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "granted_flag" AND abs_value LIKE "%bot";' params = [] for row in cursor.execute(query): username = row[1] if 'bot' in username: bottype = 'name,group' else: bottype = 'group' params.append((bottype,row[0],username)) query = 'UPDATE '+languagecode+'wiki_editors SET bot = ? WHERE user_id = ? AND user_name = ?;' cursor.executemany(query,params) conn.commit() print ('Updated the table with the bots from flag.') def gender(languagecode): functionstartTime = time.time() function_name = 'gender '+languagecode print (function_name) conn = sqlite3.connect(databases_path + community_health_metrics_db); cursor = conn.cursor() gender_params = [] query = 'SELECT up_value, user_name, up_user FROM user INNER JOIN user_properties ON user_id = up_user WHERE up_property = "gender";' mysql_con_read = wikilanguages_utils.establish_mysql_connection_read(languagecode); mysql_cur_read = mysql_con_read.cursor() mysql_cur_read.execute(query) rows = mysql_cur_read.fetchall() for row in rows: gender_params.append((row[0], row[1], row[2])) if len(gender_params) % 10000 == 0: query = 'UPDATE '+languagecode+'wiki_editors SET gender = ? WHERE user_id = ? AND user_name = ?;' cursor.executemany(query,user_characteristics2) conn.commit() gender_params = [] duration = str(datetime.timedelta(seconds=time.time() - functionstartTime)) print(languagecode+' '+ function_name+' '+ duration) # gender(languagecode) duration = str(datetime.timedelta(seconds=time.time() - functionstartTime)) print(languagecode+' '+ function_name+' '+ duration) def editor_metrics_db_iterator(languagecode): functionstartTime = time.time() function_name = 'editor_metrics_db_iterator '+languagecode print (function_name) d_paths, cym = get_mediawiki_paths(languagecode) cycle_year_month = cym print (cycle_year_month) conn = sqlite3.connect(databases_path + community_health_metrics_db); cursor = conn.cursor() # MONTHLY EDITS LOOP query = 'SELECT abs_value, year_month, user_id, user_name FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "monthly_edits" ORDER BY user_name, year_month' # AND user_name in ("Toniher","Marcmiquel","Barcelona","TaronjaSatsuma","Kippelboy") # print (query) user_count = 0 old_user_id = '' old_edits = None expected_year_month_dt = '' # parameters = [] # editors_edits_baseline_parameters = [] active_months = 0 active_months_row = 0 total_months = 0 max_active_months_row = 0 inactivity_periods = 0 inactive_months = 0 max_inactive_months_row = 0 total_edits = [] edits_increase_decrease = 0 try: os.remove(databases_path +'temporary_editor_metrics.txt') except: pass edfile2 = open(databases_path+'temporary_editor_metrics.txt', "w") for row in cursor.execute(query): edits=row[0] current_year_month = row[1] cur_user_id = row[2] cur_user_name = row[3] if cur_user_id != old_user_id and old_user_id != '': user_count += 1 cycle_year_month_dt = datetime.datetime.strptime(cycle_year_month,'%Y-%m') months_since_last_edit = (cycle_year_month_dt.year - current_year_month_dt.year) * 12 + cycle_year_month_dt.month - current_year_month_dt.month if months_since_last_edit < 0: months_since_last_edit = 0 if months_since_last_edit > 0: edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(months_since_last_edit)+'\t'+" "+'\t'+"months_since_last_edit"+'\t'+old_year_month+'\t'+" "+'\n') if months_since_last_edit > max_inactive_months_row: edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(months_since_last_edit)+'\t'+" "+'\t'+"max_inactive_months_row"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(1)+'\t'+" "+'\t'+"over_past_max_inactive_months_row"+'\t'+cycle_year_month+'\t'+" "+'\n') else: edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(max_inactive_months_row)+'\t'+" "+'\t'+"max_inactive_months_row"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(inactivity_periods)+'\t'+" "+'\t'+"inactivity_periods"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(active_months)+'\t'+" "+'\t'+"active_months"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(max_active_months_row)+'\t'+" "+'\t'+"max_active_months_row"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(total_months)+'\t'+" "+'\t'+"total_months"+'\t'+old_year_month+'\t'+" "+'\n') active_months = 0 total_months = 0 active_months_row = 0 max_active_months_row = 0 inactivity_periods = 0 inactive_months = 0 max_inactive_months_row = 0 total_edits = [] old_edits = None current_year_month_dt = datetime.datetime.strptime(current_year_month,'%Y-%m') # here there is a change of month # if the month is not the expected one if expected_year_month_dt != current_year_month_dt and expected_year_month_dt != '' and old_user_id == cur_user_id: inactivity_periods += 1 while expected_year_month_dt < current_year_month_dt: # print (expected_year_month_dt, current_year_month_dt) inactive_months = inactive_months + 1 expected_year_month_dt = (expected_year_month_dt + relativedelta.relativedelta(months=1)) total_months = total_months + 1 if inactive_months > max_inactive_months_row: max_inactive_months_row = inactive_months if active_months_row > max_active_months_row: max_active_months_row = active_months_row edfile2.write(str(cur_user_id)+'\t'+cur_user_name+'\t'+str(inactive_months)+'\t'+" "+'\t'+"inactive_months_row"+'\t'+current_year_month+'\t'+" "+'\n') active_months_row = 1 inactive_months = 0 edits_increase_decrease = 1 edfile2.write(str(cur_user_id)+'\t'+cur_user_name+'\t'+str(edits_increase_decrease)+'\t'+" "+'\t'+"monthly_edits_increasing_decreasing"+'\t'+current_year_month+'\t'+" "+'\n') else: active_months_row = active_months_row + 1 if active_months_row > 1: edfile2.write(str(cur_user_id)+'\t'+cur_user_name+'\t'+str(active_months_row)+'\t'+" "+'\t'+"active_months_row"+'\t'+current_year_month+'\t'+" "+'\n') if active_months_row > max_active_months_row: max_active_months_row = active_months_row if inactive_months == 0 and total_months == 0: edfile2.write(str(cur_user_id)+'\t'+cur_user_name+'\t'+str(-1)+'\t'+" "+'\t'+"inactive_months_row"+'\t'+current_year_month+'\t'+" "+'\n') else: edfile2.write(str(cur_user_id)+'\t'+cur_user_name+'\t'+str(inactive_months)+'\t'+" "+'\t'+"inactive_months_row"+'\t'+current_year_month+'\t'+" "+'\n') if old_edits != None: if old_edits > edits: if edits_increase_decrease <= 0: edits_increase_decrease = edits_increase_decrease - 1 else: edits_increase_decrease = -1 elif old_edits < edits: if edits_increase_decrease >= 0: edits_increase_decrease = edits_increase_decrease + 1 else: edits_increase_decrease = 1 else: edits_increase_decrease = 0 edfile2.write(str(cur_user_id)+'\t'+cur_user_name+'\t'+str(edits_increase_decrease)+'\t'+" "+'\t'+"monthly_edits_increasing_decreasing"+'\t'+current_year_month+'\t'+" "+'\n') else: edits_increase_decrease = 1 if total_edits != []: median_total_edits = np.median(total_edits) edfile2.write(str(cur_user_id)+'\t'+cur_user_name+'\t'+str((100*edits/median_total_edits - 100))+'\t'+" "+'\t'+"monthly_edits_to_baseline"+'\t'+current_year_month+'\t'+" "+'\n') # if cur_user_name == '-Erick-': # print (str(cur_user_id)+'\t'+cur_user_name+','+str((100*edits/median_total_edits - 100))+'\t'+" "+'\t'+"monthly_edits_to_baseline"+'\t'+current_year_month+'\n') total_edits.append(edits) old_edits = edits total_months = total_months + 1 active_months = active_months + 1 old_year_month = current_year_month expected_year_month_dt = (datetime.datetime.strptime(old_year_month,'%Y-%m') + relativedelta.relativedelta(months=1)) old_user_id = cur_user_id old_user_name = cur_user_name # print ('# update: ',old_user_id, old_user_name, active_months, max_active_months_row, max_inactive_months_row, total_months) # input('') cycle_year_month_dt = datetime.datetime.strptime(cycle_year_month,'%Y-%m') if current_year_month_dt == None: print ('The table is empty. ERROR.') months_since_last_edit = (cycle_year_month_dt.year - current_year_month_dt.year) * 12 + cycle_year_month_dt.month - current_year_month_dt.month if months_since_last_edit < 0: months_since_last_edit = 0 if months_since_last_edit > 0: edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(months_since_last_edit)+'\t'+" "+'\t'+"months_since_last_edit"+'\t'+old_year_month+'\t'+" "+'\n') if months_since_last_edit > max_inactive_months_row: edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(months_since_last_edit)+'\t'+" "+'\t'+"max_inactive_months_row"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(1)+'\t'+" "+'\t'+"over_past_max_inactive_months_row"+'\t'+cycle_year_month+'\t'+" "+'\n') else: edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(max_inactive_months_row)+'\t'+" "+'\t'+"max_inactive_months_row"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(inactivity_periods)+'\t'+" "+'\t'+"inactivity_periods"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(active_months)+'\t'+" "+'\t'+"active_months"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(max_active_months_row)+'\t'+" "+'\t'+"max_active_months_row"+'\t'+old_year_month+'\t'+" "+'\n') edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(total_months)+'\t'+" "+'\t'+"total_months"+'\t'+old_year_month+'\t'+" "+'\n') conn = sqlite3.connect(databases_path + community_health_metrics_db); cursor = conn.cursor() a_file = open(databases_path+"temporary_editor_metrics.txt") editors_metrics_parameters = csv.reader(a_file, delimiter="\t", quotechar = '|') # edfile2 = open(databases_path+'temporary_editor_metrics.txt', "r") # editors_metrics_parameters = [] # while True: # user_count+=1 # line = edfile2.readline() # char = line.strip().split('\t') # # print (char) # try: # metric_name = char[4] # # print (metric_name) # if metric_name != '': editors_metrics_parameters.append((char[0],char[1],char[2],char[3],metric_name,char[5])) # except: # pass # if user_count % 100000 == 0: # query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month) VALUES (?,?,?,?,?,?);' # cursor.executemany(query,editors_metrics_parameters) # # print (len(editors_metrics_parameters)) # conn.commit() # editors_metrics_parameters = [] # if not line: break query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month, timestamp) VALUES (?,?,?,?,?,?,?);' cursor.executemany(query,editors_metrics_parameters) conn.commit() os.remove(databases_path +'temporary_editor_metrics.txt') editors_metrics_parameters = [] print ('done with the monthly edits.') conn = sqlite3.connect(databases_path + community_health_metrics_db); cursor = conn.cursor() conn2 = sqlite3.connect(databases_path + community_health_metrics_db); cursor2 = conn2.cursor() # MONTHLY EDITING DAYS LOOP query = 'SELECT abs_value, year_month, user_id, user_name FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "monthly_editing_days" ORDER BY user_id, year_month' # print (query) user_count = 0 old_user_id = '' expected_year_month_dt = '' editing_days = [] sum_editing_days = 0 try: os.remove(databases_path +'temporary_editors.txt') except: pass try: os.remove(databases_path +'temporary_editor_metrics.txt') except: pass edfile = open(databases_path+'temporary_editors.txt', "w") edfile2 = open(databases_path+'temporary_editor_metrics.txt', "w") for row in cursor.execute(query): monthly_editing_days=row[0] current_year_month = row[1] cur_user_id = row[2] cur_user_name = row[3] # print (row) if cur_user_id != old_user_id and old_user_id != '': user_count += 1 sum_editing_days = int(sum(editing_days)) edfile.write(str(sum_editing_days)+'\t'+str(old_user_id)+'\t'+old_user_name+'\n') if editing_days != []: median_editing_days = np.median(editing_days) if median_editing_days == 0: value = 0 else: value = (100*monthly_editing_days/median_editing_days - 100) edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(value)+'\t'+" "+'\t'+"monthly_editing_days_to_baseline"+'\t'+current_year_month+'\n') sum_editing_days = 0 editing_days = [] current_year_month_dt = datetime.datetime.strptime(current_year_month,'%Y-%m') if expected_year_month_dt != current_year_month_dt and expected_year_month_dt != '' and old_user_id == cur_user_id: while expected_year_month_dt < current_year_month_dt: editing_days.append(0) expected_year_month_dt = (expected_year_month_dt + relativedelta.relativedelta(months=1)) editing_days.append(monthly_editing_days) old_year_month = current_year_month expected_year_month_dt = (datetime.datetime.strptime(old_year_month,'%Y-%m') + relativedelta.relativedelta(months=1)) old_user_id = cur_user_id old_user_name = cur_user_name # print ('out of the loop') # print (user_count) # last row percent baseline if editing_days != []: median_editing_days = np.median(editing_days) if median_editing_days == 0: value = 0 else: value = (100*monthly_editing_days/median_editing_days - 100) edfile2.write(str(old_user_id)+'\t'+old_user_name+'\t'+str(value)+'\t'+" "+'\t'+"monthly_editing_days_to_baseline"+'\t'+current_year_month+'\n') sum_editing_days = sum(editing_days) edfile.write(str(sum_editing_days)+','+str(old_user_id)+','+old_user_name+'\n') # BASELINE MEASURES # edfile = open(databases_path+'temporary_editor_metrics.txt', "r") # editors_metrics_parameters = [] a_file = open(databases_path+"temporary_editor_metrics.txt") editors_metrics_parameters = csv.reader(a_file, delimiter="\t", quotechar = '|') # while True: # user_count+=1 # line = edfile.readline() # char = line.strip().split('\t') # try: # metric_name = char[4] # if metric_name != '': editors_metrics_parameters.append((char[0],char[1],char[2],char[3],metric_name,char[5])) # except: # pass # if user_count % 100000 == 0: # query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month) VALUES (?,?,?,?,?,?);' # cursor2.executemany(query,editors_metrics_parameters) # conn2.commit() # editors_metrics_parameters = [] # if not line: break query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month) VALUES (?,?,?,?,?,?);' cursor2.executemany(query,editors_metrics_parameters) conn2.commit() os.remove(databases_path +'temporary_editor_metrics.txt') # EDITING DAYS # sum # edfile = open(databases_path+'temporary_editors.txt', "r") # editors_characteristics_parameters = [] a_file = open(databases_path+"temporary_editors.txt") editors_characteristics_parameters = csv.reader(a_file, delimiter="\t", quotechar = '|') # while True: # user_count+=1 # line = edfile.readline() # char = line.strip().split('\t') # try: # editors_characteristics_parameters.append((char[0],char[1],char[2])) # except: # pass # if user_count % 100000 == 0: # query = 'UPDATE '+languagecode+'wiki_editors SET editing_days = ? WHERE user_id = ? AND user_name = ?;' # cursor2.executemany(query,editors_characteristics_parameters) # conn2.commit() # editors_characteristics_parameters = [] # if not line: break query = 'UPDATE '+languagecode+'wiki_editors SET editing_days = ? WHERE user_id = ? AND user_name = ?;' cursor2.executemany(query,editors_characteristics_parameters) conn2.commit() os.remove(databases_path +'temporary_editors.txt') editors_characteristics_parameters = [] # percent query = 'UPDATE '+languagecode+'wiki_editors SET percent_editing_days = (100*editing_days/lifetime_days);' cursor.execute(query) conn.commit() print ('done with the monthly editing days.') duration = str(datetime.timedelta(seconds=time.time() - functionstartTime)) #### --------- --------- --------- --------- --------- --------- --------- --------- --------- # # OVER PAST MAX INACTIVE MONTHS ROW # query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month, timestamp) SELECT i1.user_id, i1.user_name, (i1.abs_value - i2.abs_value), i1.rel_value, "over_past_max_inactive_months_row", i2.year_month, i2.timestamp FROM '+languagecode+'wiki_editor_metrics i1 INNER JOIN '+languagecode+'wiki_editor_metrics i2 ON i1.user_id = i2.user_id WHERE i1.metric_name = "max_inactive_months_row" AND i2.metric_name = "months_since_last_edit";' # cursor.execute(query) # conn.commit() # OVER EDIT BIN AVERAGE PAST MAX INACTIVE MONTHS ROW edit_bin_average_past_max_inactive_months_row = {} query = 'SELECT i2.abs_value, AVG(i1.abs_value) FROM '+languagecode+'wiki_editor_metrics i1 INNER JOIN '+languagecode+'wiki_editor_metrics i2 ON i1.user_id = i2.user_id WHERE i1.metric_name = "max_inactive_months_row" AND i2.metric_name = "edit_count_bin" GROUP BY i2.abs_value;'; for row in cursor.execute(query): edit_bin_average_past_max_inactive_months_row[row[0]]=row[1] for edit_bin, average in edit_bin_average_past_max_inactive_months_row.items(): query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month, timestamp) SELECT i1.user_id, i1.user_name, (? - i2.abs_value), i1.rel_value, "over_edit_bin_average_past_max_inactive_months_row", i2.year_month, i2.timestamp FROM '+languagecode+'wiki_editor_metrics i1 INNER JOIN '+languagecode+'wiki_editor_metrics i2 ON i1.user_id = i2.user_id WHERE i1.metric_name = "edit_count_bin" AND i1.abs_value = ? AND i2.metric_name = "months_since_last_edit";' cursor.execute(query,(average, edit_bin)) conn.commit() # OVER MONTHLY EDIT BIN AVERAGE PAST MAX INACTIVE MONTHS ROW edit_bin_average_past_max_inactive_months_row = {} query = 'SELECT i2.abs_value, AVG(i1.abs_value) FROM '+languagecode+'wiki_editor_metrics i1 INNER JOIN '+languagecode+'wiki_editor_metrics i2 ON i1.user_id = i2.user_id WHERE i1.metric_name = "max_inactive_months_row" AND i2.metric_name = "monthly_edit_count_bin" GROUP BY i2.abs_value;'; for row in cursor.execute(query): edit_bin_average_past_max_inactive_months_row[row[0]]=row[1] for edit_bin, average in edit_bin_average_past_max_inactive_months_row.items(): query = 'INSERT OR IGNORE INTO '+languagecode+'wiki_editor_metrics (user_id, user_name, abs_value, rel_value, metric_name, year_month, timestamp) SELECT i1.user_id, i1.user_name, (? - i2.abs_value), i1.rel_value, "over_monthly_edit_bin_average_past_max_inactive_months_row", i2.year_month, i2.timestamp FROM '+languagecode+'wiki_editor_metrics i1 INNER JOIN '+languagecode+'wiki_editor_metrics i2 ON i1.user_id = i2.user_id WHERE i1.metric_name = "monthly_edit_count_bin" AND i1.abs_value = ? AND i2.metric_name = "months_since_last_edit";' cursor.execute(query,(average, edit_bin)) conn.commit() duration = str(datetime.timedelta(seconds=time.time() - functionstartTime)) print(languagecode+' '+ function_name+' '+ duration) def community_metrics_db_iterator(languagecode): functionstartTime = time.time() function_name = 'community_metrics_db_iterator '+languagecode print (function_name) conn = sqlite3.connect(databases_path + community_health_metrics_db); cursor = conn.cursor() d_paths, cym = get_mediawiki_paths(languagecode) cycle_year_month = cym print (cycle_year_month) query_cm = 'INSERT OR IGNORE INTO '+languagecode+'wiki_community_metrics (year_month, topic, m1, m1_calculation, m1_value, m2, m2_calculation, m2_value, m1_count, m2_count) VALUES (?,?,?,?,?,?,?,?,?,?);' def participation(): # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 parameters = [] edit_bins_count = {} query = 'SELECT count(user_id), abs_value, year_month FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "edit_count_bin" GROUP by abs_value;' for row in cursor.execute(query): m1_count = row[0] m1_value = row[1] year_month = row[2] edit_bins_count[m1_value] = m1_count parameters.append((year_month, 'editor_participation', 'total_edits', 'bin', m1_value, None, None, None, m1_count, None)) cursor.executemany(query_cm,parameters) conn.commit() # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 monthly_edits threshold 5 parameters = [] query = 'SELECT count(e1.user_id), e1.abs_value, e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" AND e2.metric_name = "monthly_edits" AND e2.abs_value >= 5 GROUP BY e1.abs_value ORDER BY e1.abs_value DESC;' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] year_month = row[2] m1_count = edit_bins_count[m1_value] parameters.append((year_month, 'editor_participation', 'total_edits', 'bin', m1_value, 'monthly_edits', 'threshold', 5, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 year_first_edit bin 2001-2021 parameters = [] query = 'SELECT count(e1.user_id), e1.abs_value, e2.year_first_edit, e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" GROUP by e1.abs_value, e2.year_first_edit;' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m2_value = row[2] m1_count = edit_bins_count[m1_value] year_month = row[3] parameters.append((year_month, 'editor_participation', 'total_edits', 'bin', m1_value, 'year_first_edit', 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 lustrum_first_edit bin 2001, 2006, 2011, 2016, 2021 parameters = [] query = 'SELECT count(e1.user_id), e1.abs_value, e2.lustrum_first_edit, e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" GROUP by e1.abs_value, e2.lustrum_first_edit;' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m2_value = row[2] m1_count = edit_bins_count[m1_value] year_month = row[3] parameters.append((year_month, 'editor_participation', 'total_edits', 'bin', m1_value, 'lustrum_first_edit', 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() editing_days = {(1,100):'1-100',(101,500):'101-500', (501,1000):'501-1000', (1001,1500):'1001-1500', (1501,2500):'1501-2500', (2501,3500):'2501-3500', (3501,4500):'3501-4500', (4501,5500):'4501-5500', (5501,6500):'5501-6500', (6501,7500):'6501-7500'} percent_editing_days = {(0,10):'0-10',(11,20):'11-20',(21,30):'21-30',(31,40):'31-40',(41,50):'41-50',(51,60):'51-60',(61,70):'61-70',(71,80):'71-80',(81,90):'81-90',(91,100):'91-100'} active_months = {(0,0):'0', (217, 228): '217-228', (301, 312): '301-312', (277, 288): '277-288', (25, 36): '25-36', (241, 252): '241-252', (109, 120): '109-120', (85, 96): '85-96', (61, 72): '61-72', (205, 216): '205-216', (289, 300): '289-300', (193, 204): '193-204', (73, 84): '73-84', (49, 60): '49-60', (37, 48): '37-48', (265, 276): '265-276', (181, 192): '181-192', (145, 156): '145-156', (13, 24): '13-24', (253, 264): '253-264', (133, 144): '133-144', (1, 12): '1-12', (121, 132): '121-132', (169, 180): '169-180', (157, 168): '157-168', (229, 240): '229-240', (97, 108): '97-108'} bin_dicts = {'editing_days':editing_days, 'percent_editing_days':percent_editing_days, 'active_months':active_months} # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 editing_days bin 0_100, 100_500, 500_1000, 1000-1500, 1500-2500, 2500-3500, 3500-5000, 5000-6500, 6500-7500… # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 percent_editing_days bin 1-10 to 100 # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 active_months bin 1-10, 10-20, 30-40,... to 150 parameters = [] for variable_name, bin_dict in bin_dicts.items(): for interval, label in bin_dict.items(): query = 'SELECT count(e1.user_id), e1.abs_value, e1.year_month, "'+label+'" FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" AND e2.'+variable_name+' BETWEEN '+str(interval[0])+' AND '+str(interval[1])+' GROUP by e1.abs_value;' m2_count = row[0] m1_value = row[1] m2_value = label year_month = row[2] m1_count = edit_bins_count[m1_value] parameters.append((year_month, 'editor_participation', 'total_edits', 'bin', m1_value, variable_name, 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 flag name sysop, autopatrolled, bureaucrat, etc. parameters = [] query = 'SELECT count(e1.user_id), e1.abs_value, e2.highest_flag, e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" GROUP by e1.abs_value, e2.highest_flag;' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m2_value = row[2] year_month = row[3] m1_count = edit_bins_count[m1_value] parameters.append((year_month, 'editor_participation', 'total_edits', 'bin', m1_value, 'highest_flag', 'name', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() print ('editor_participation') def flags(): # flags granted_flag name sysop, autopatrolled, bureaucrat, etc. # flags removed_flag name sysop, autopatrolled, bureaucrat, etc. for variablef in ['granted_flag','removed_flag']: parameters = [] year_month = cycle_year_month query = 'SELECT count(user_id), abs_value, year_month FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "'+variablef+'" AND abs_value != "bot" GROUP BY year_month, abs_value;' for row in cursor.execute(query): m1_count = row[0] m1_value = row[1] year_month = row[2] parameters.append((year_month, 'editor_flags', 'highest_flag', 'name', m1_value, None, None, None, m1_count, None)) cursor.executemany(query_cm,parameters) conn.commit() # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. parameters = [] highest_flag_count = {} year_month = cycle_year_month query = 'SELECT count(user_id), highest_flag FROM '+languagecode+'wiki_editors GROUP by highest_flag;' for row in cursor.execute(query): m1_count = row[0] m1_value = row[1] highest_flag_count[m1_value] = m1_count parameters.append((year_month, 'editor_flags', 'highest_flag', 'name', m1_value, None, None, None, m1_count, None)) cursor.executemany(query_cm,parameters) conn.commit() # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. monthly_edits threshold 5 parameters = [] query = 'SELECT count(e1.user_id), e1.highest_flag, e2.year_month FROM '+languagecode+'wiki_editors e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e2.metric_name = "monthly_edits" AND e2.abs_value >= 5 GROUP BY e1.highest_flag, e2.year_month ORDER BY e1.highest_flag, e2.year_month ASC;' for row in cursor.execute(query): m1_count = highest_flag_count[m1_value] m2_count = row[0] m1_value = row[1] year_month = row[2] parameters.append((year_month, 'editor_flags', 'highest_flag', 'name', m1_value, 'monthly_edits', 'threshold', 5, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. highest_flag_year_month bin 2001-2021 x: g1, y: g2 (last year_month) # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. year_first_edit bin 2001-2021 # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. lustrum_first_edit bin 2001, 2006, 2011, 2016, 2021 m2s = ['highest_flag_year_month', 'year_first_edit','lustrum_first_edit'] for g2 in m2s: parameters = [] query = 'SELECT count(user_id), highest_flag, '+g2+' FROM '+languagecode+'wiki_editors GROUP BY highest_flag, '+g2 year_month = cycle_year_month for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m2_value = row[2] m1_count = highest_flag_count[m1_value] parameters.append((year_month, 'editor_flags', 'highest_flag', 'name', m1_value, g2, 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() editing_days = {(1,100):'1-100',(101,500):'101-500', (501,1000):'501-1000', (1001,1500):'1001-1500', (1501,2500):'1501-2500', (2501,3500):'2501-3500', (3501,4500):'3501-4500', (4501,5500):'4501-5500', (5501,6500):'5501-6500', (6501,7500):'6501-7500'} percent_editing_days = {(0,10):'0-10',(11,20):'11-20',(21,30):'21-30',(31,40):'31-40',(41,50):'41-50',(51,60):'51-60',(61,70):'61-70',(71,80):'71-80',(81,90):'81-90',(91,100):'91-100'} active_months = {(0,0):'0', (217, 228): '217-228', (301, 312): '301-312', (277, 288): '277-288', (25, 36): '25-36', (241, 252): '241-252', (109, 120): '109-120', (85, 96): '85-96', (61, 72): '61-72', (205, 216): '205-216', (289, 300): '289-300', (193, 204): '193-204', (73, 84): '73-84', (49, 60): '49-60', (37, 48): '37-48', (265, 276): '265-276', (181, 192): '181-192', (145, 156): '145-156', (13, 24): '13-24', (253, 264): '253-264', (133, 144): '133-144', (1, 12): '1-12', (121, 132): '121-132', (169, 180): '169-180', (157, 168): '157-168', (229, 240): '229-240', (97, 108): '97-108'} bin_dicts = {'editing_days':editing_days, 'percent_editing_days':percent_editing_days} # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. editing_days bin 1-100, 100-200, etc. # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. percent_editing_days bin 1-10 to 100 for variable_name, bin_dict in bin_dicts.items(): parameters = [] for interval, label in bin_dict.items(): query = 'SELECT count(e1.user_id), e1.abs_value, e1.year_month, "'+label+'" FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" AND e2.'+variable_name+' BETWEEN '+str(interval[0])+' AND '+str(interval[1])+' GROUP by e1.abs_value;' m2_count = row[0] m1_value = row[1] m2_value = label year_month = row[2] m1_count = highest_flag_count[m1_value] parameters.append((year_month, 'editor_flags', 'highest_flag', 'name', m1_value, variable_name, 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. active_months bin 1-10, 10-20, 30-40,... to 150 for interval, label in active_months.items(): query = 'SELECT count(e1.user_id), e1.abs_value, e1.year_month, "'+label+'" FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" AND e2.metric_name = "active_months" AND e2.abs_value BETWEEN '+str(interval[0])+' AND '+str(interval[0])+' GROUP by e1.abs_value;' m2_count = row[0] m1_value = row[1] m2_value = label year_month = row[2] m1_count = highest_flag_count[m1_value] parameters.append((year_month, 'editor_flags', 'highest_flag', 'name', m1_value, "active_months", 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 parameters = [] query = 'SELECT count(e1.user_id), e1.highest_flag, e2.abs_value, e2.year_month FROM '+languagecode+'wiki_editors e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e2.metric_name = "edit_count_bin" GROUP BY e1.highest_flag, e2.abs_value;' for row in cursor.execute(query): m1_count = highest_flag_count[m1_value] m2_count = row[0] m1_value = row[1] m2_value = row[2] year_month = row[3] parameters.append((year_month, 'editor_flags', 'highest_flag', 'name', m1_value, 'total_edits', 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() print ('editor_flags') # ACTIVE CONTRIBUTORS (THRESHOLD, NO BINS) # active_editors, active_editors_5, active_editors_10, active_editors_50, active_editors_100, active_editors_500, active_editors_1000 def active_editors(): # active_editors monthly_edits threshold 1, 5, 10, 50, 100, 500, 1000 active_editors_5_year_month = {} values = [1,5,10,50,100,500,1000,5000] parameters = [] for v in values: query = 'SELECT count(distinct user_id), year_month FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "monthly_edits" AND abs_value >= '+str(v)+' GROUP BY year_month ORDER BY year_month' for row in cursor.execute(query): # print (row) m1_count=row[0]; year_month=row[1] if year_month == '': continue parameters.append((year_month, 'active_editors', 'monthly_edits', 'threshold', v, None, None, None, m1_count, None)) if v == 5: active_editors_5_year_month[year_month] = m1_count cursor.executemany(query_cm,parameters) conn.commit() # active_editors monthly_edits bin 1, 5, 10, 50, 100, 500, 1000 parameters = [] values = [1,5,10,50,100,500,1000,5000] for x in range(0,len(values)): v = values[x] if x < len(values)-1: w = values[x+1] query = 'SELECT count(distinct user_id), year_month FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "monthly_edits" AND abs_value >= '+str(v)+' AND abs_value < '+str(w)+' GROUP BY year_month ORDER BY year_month' w = w - 1 else: w = 'inf' query = 'SELECT count(distinct user_id), year_month FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "monthly_edits" AND abs_value >= '+str(v)+' GROUP BY year_month ORDER BY year_month' # print (query) for row in cursor.execute(query): # print (row) m1_count=row[0]; year_month=row[1] if year_month == '': continue parameters.append((year_month, 'active_editors', 'monthly_edits', 'bin', str(v)+'_'+str(w) , None, None, None, m1_count, None)) cursor.executemany(query_cm,parameters) conn.commit() # active_editors monthly_edits threshold 5 year_first_edit bin 2001-2021 query = 'SELECT count(e1.user_id), e1.year_month, e2.year_first_edit FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 on e1.user_id = e2.user_id WHERE e1.metric_name = "monthly_edits" AND e1.abs_value >= 5 GROUP BY e1.year_month, e2.year_first_edit;' parameters = [] for row in cursor.execute(query): # print (row) m2_count=row[0]; year_month=row[1] year_first_edit=row[2] if year_month == '': continue parameters.append((year_month, 'active_editors', 'monthly_edits', 'threshold', 5, 'year_first_edit', 'bin', year_first_edit, active_editors_5_year_month[year_month], m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # active_editors monthly_edits threshold 5 lustrum_first_edit bin 2001, 2006, 2011, 2016, 2021 query = 'SELECT count(e1.user_id), e1.year_month, e2.lustrum_first_edit FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 on e1.user_id = e2.user_id WHERE e1.metric_name = "monthly_edits" AND e1.abs_value >= 5 GROUP BY e1.year_month, e2.lustrum_first_edit;' parameters = [] for row in cursor.execute(query): # print (row) m2_count=row[0]; year_month=row[1] lustrum_first_edit=row[2] if year_month == '': continue parameters.append((year_month, 'active_editors', 'monthly_edits', 'threshold', 5, 'lustrum_first_edit', 'bin', lustrum_first_edit, active_editors_5_year_month[year_month], m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # active_editors monthly_edits threshold 5 active_months bin 1-10, 10-20, 30-40,... to 150 active_months = {(0,0):'0', (217, 228): '217-228', (301, 312): '301-312', (277, 288): '277-288', (25, 36): '25-36', (241, 252): '241-252', (109, 120): '109-120', (85, 96): '85-96', (61, 72): '61-72', (205, 216): '205-216', (289, 300): '289-300', (193, 204): '193-204', (73, 84): '73-84', (49, 60): '49-60', (37, 48): '37-48', (265, 276): '265-276', (181, 192): '181-192', (145, 156): '145-156', (13, 24): '13-24', (253, 264): '253-264', (133, 144): '133-144', (1, 12): '1-12', (121, 132): '121-132', (169, 180): '169-180', (157, 168): '157-168', (229, 240): '229-240', (97, 108): '97-108'} parameters = [] for interval, label in active_months.items(): query = 'SELECT count(e1.user_id), e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "monthly_edits" AND e1.abs_value >= 5 AND e2.metric_name = "active_months" AND e2.abs_value BETWEEN '+str(interval[0])+' AND '+str(interval[1])+' GROUP by e1.year_month, e1.abs_value;' for row in cursor.execute(query): m2_count = row[0] year_month = row[1] m2_value = label m1_count = active_editors_5_year_month[year_month] parameters.append((year_month, 'active_editors', 'monthly_edits', 'threshold', 5, "active_months", 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # active_editors monthly_edits threshold 5 active_months_row bin 2, 3, 4, 5, … # active_editors monthly_edits threshold 5 max_active_months_row bin 2, 3, 4, 5, … # active_editors monthly_edits threshold 5 inactive_months_row bin -1, 0, 1, 2, 3, 4, 5, … 12, … # active_editors monthly_edits threshold 5 max_inactive_months_row bin 2, 3, 4, 5, … m2s = ['inactivity_periods','active_months_row', 'inactive_months_row','max_active_months_row','max_inactive_months_row', 'monthly_edits_increasing_decreasing'] for m2 in m2s: parameters = [] query = 'SELECT count(e1.user_id), e2.abs_value, e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "monthly_edits" AND e1.abs_value >= 5 AND e2.metric_name = "'+m2+'" GROUP BY e1.year_month, e2.abs_value;' for row in cursor.execute(query): m2_count = row[0] m2_value = row[1] year_month = row[2] m1_count = active_editors_5_year_month[year_month] parameters.append((year_month, 'active_editors', 'monthly_edits', 'threshold', 5, m2, 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # active_editors monthly_edits threshold 5 total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 parameters = [] query = 'SELECT count(e1.user_id), e2.abs_value, e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "monthly_edits" AND e1.abs_value >= 5 AND e2.metric_name = "edit_count_bin" GROUP BY e2.abs_value;' for row in cursor.execute(query): m2_count = row[0] m2_value = row[1] year_month = row[2] m1_count = active_editors_5_year_month[year_month] parameters.append((year_month, 'active_editors', 'monthly_edits', 'threshold', 5, 'total_edits', 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # active_editors monthly_edits threshold 5 flag name sysop, autopatrolled, bureaucrat, etc. parameters = [] query = 'SELECT count(e1.user_id), e2.highest_flag, e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "monthly_edits" AND e1.abs_value >= 5 GROUP by e1.abs_value, e2.highest_flag;' for row in cursor.execute(query): m2_count = row[0] m2_value = row[1] year_month = row[2] m1_count = active_editors_5_year_month[year_month] parameters.append((year_month, 'active_editors', 'monthly_edits', 'threshold', 5, 'highest_flag', 'name', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # active_editors monthly_edits threshold 5 monthly_editing_days bin 1-10 to 100 parameters = [] query = 'SELECT count(e1.user_id), e2.abs_value, e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "monthly_edits" AND e1.abs_value >= 5 AND e2.metric_name = "monthly_editing_days" GROUP BY e1.year_month, e2.abs_value;' for row in cursor.execute(query): m2_count = row[0] m2_value = row[1] year_month = row[2] m1_count = active_editors_5_year_month[year_month] parameters.append((year_month, 'active_editors', 'monthly_edits', 'threshold', 5, 'monthly_editing_days', 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() print ('active_editors') def retention(): # monthly_registered_first_edit parameters = [] registered_baseline = {} query = 'SELECT count(distinct user_id), year_month_registration FROM '+languagecode+'wiki_editors GROUP BY 2 ORDER BY 2 ASC;' for row in cursor.execute(query): value=row[0]; year_month=row[1] if year_month == '': continue try: registered_baseline[year_month] = int(value) except: pass parameters.append((year_month, 'editor_retention', 'register', 'threshold', 1, None, None, None, value, None)) retention_baseline = {} query = 'SELECT count(distinct user_id), year_month_first_edit FROM '+languagecode+'wiki_editors GROUP BY 2 ORDER BY 2 ASC;' for row in cursor.execute(query): value=row[0]; year_month=row[1] if year_month == '': continue try: retention_baseline[year_month] = int(value) except: pass parameters.append((year_month, 'editor_retention', 'first_edit', 'threshold', 1, None, None, None, value, None)) try: m1_count = registered_baseline[year_month] except: m1_count = 0 parameters.append((year_month, 'editor_retention', 'register', 'threshold', 1, 'first_edit', 'threshold', 1, m1_count, value)) cursor.executemany(query_cm,parameters) conn.commit() parameters = [] queries_retention_dict = {} # RETENTION # number of editors who edited at least once 24h after the first edit queries_retention_dict['24h'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "edit_count_24h" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' # number of editors who edited at least once 7 days after the first edit queries_retention_dict['7d'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "edit_count_7d" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' # number of editors who edited at least once 30 days after the first edit queries_retention_dict['30d'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "edit_count_30d" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' # number of editors who edited at least once 60 days after the first edit queries_retention_dict['60d'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "edit_count_60d" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' # number of editors who edited at least once 365 days after the first edit queries_retention_dict['365d'] = 'SELECT count(distinct user_id), year_month_first_edit FROM '+languagecode+'wiki_editors WHERE lifetime_days >= 365 GROUP BY 2 ORDER BY 1;' # number of editors who edited at least once 730 days after the first edit queries_retention_dict['730d'] = 'SELECT count(distinct user_id), year_month_first_edit FROM '+languagecode+'wiki_editors WHERE lifetime_days >= 730 GROUP BY 2 ORDER BY 1;' for metric_name, query in queries_retention_dict.items(): for row in cursor.execute(query): value=row[0]; year_month=row[1] if year_month == '': continue try: m1_count = retention_baseline[year_month] except: m1_count = 0 parameters.append((year_month, 'editor_retention', 'first_edit', 'threshold', 1, 'edited_after_time', 'threshold', metric_name, m1_count, value)) try: m1_count = registered_baseline[year_month] except: m1_count = 0 parameters.append((year_month, 'editor_retention', 'register', 'threshold', 1, 'edited_after_time', 'threshold', metric_name, m1_count, value)) cursor.executemany(query_cm,parameters) conn.commit() parameters = [] queries_retention_dict = {} # USER PAGES # number of editors who edited their user_page at least once during the first 24h after their first edit queries_retention_dict['editors_edited_user_page_d24h_afe'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "user_page_edit_count_24h" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' # number of editors who edited their user_page at least once during the first 30 days after their first edit queries_retention_dict['editors_edited_user_page_d30d_afe'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "user_page_edit_count_1month" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' # number of editors who edited their user_page at least once queries_retention_dict['editors_edited_user_page_afe'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "monthly_edits_ns2_user" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' for metric_name, query in queries_retention_dict.items(): for row in cursor.execute(query): value=row[0]; year_month=row[1] if year_month == '': continue try: m1_count = retention_baseline[year_month] except: m1_count = 0 parameters.append((year_month, 'editor_retention', 'first_edit', 'threshold', 1, 'edited_user_page_after_time', 'threshold', metric_name, m1_count, value)) try: m1_count = registered_baseline[year_month] except: m1_count = 0 parameters.append((year_month, 'editor_retention', 'register', 'threshold', 1, 'edited_user_page_after_time', 'threshold', metric_name, m1_count, value)) cursor.executemany(query_cm,parameters) conn.commit() parameters = [] queries_retention_dict = {} # USER PAGE TALK PAGE # number of editors who edited their user_page_talk_page at least once during the first 24h after their first edit queries_retention_dict['editors_edited_user_page_talk_page_d24h_afe'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "user_page_talk_page_edit_count_24h" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' # number of editors who edited their user_page_talk_page at least once during the first 30 daysafter their first edit queries_retention_dict['editors_edited_user_page_talk_page_d30d_afe'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "user_page_talk_page_edit_count_1month" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' # number of editors who edited their user_page_talk_page at least once after the first edit queries_retention_dict['editors_edited_user_page_talk_page_afe'] = 'SELECT count(distinct ch.user_id), ch.year_month_first_edit FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "monthly_edits_ns3_user_talk" AND ce.abs_value > 0 GROUP BY 2 ORDER BY 2 ASC;' for metric_name, query in queries_retention_dict.items(): for row in cursor.execute(query): value=row[0]; year_month=row[1] if year_month == '': continue try: m1_count = retention_baseline[year_month] except: m1_count = 0 parameters.append((year_month, 'editor_retention', 'first_edit', 'threshold', 1, 'edited_user_page_talk_page_after_time', 'threshold', metric_name, m1_count, value)) try: m1_count = registered_baseline[year_month] except: m1_count = 0 parameters.append((year_month, 'editor_retention', 'register', 'threshold', 1, 'edited_user_page_talk_page_after_time', 'threshold', metric_name, m1_count, value)) cursor.executemany(query_cm,parameters) conn.commit() print ('editor_retention') def drop_off(): year_month = cycle_year_month lustrum_first_edit_dict = {} query = 'SELECT count(user_id), lustrum_first_edit FROM '+languagecode+'wiki_editors WHERE lustrum_first_edit != "" GROUP BY lustrum_first_edit;' for row in cursor.execute(query): lustrum_first_edit_dict[row[1]]=row[0] year_first_edit_dict = {} query = 'SELECT count(user_id), year_first_edit FROM '+languagecode+'wiki_editors WHERE year_first_edit != "" GROUP BY year_first_edit;' for row in cursor.execute(query): year_first_edit_dict[row[1]]=row[0] edit_bins_count = {} query = 'SELECT count(user_id), abs_value, year_month FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "edit_count_bin" GROUP by abs_value;' for row in cursor.execute(query): edit_bins_count[row[1]] = row[0] highest_flag_dict = {} query = 'SELECT count(user_id), highest_flag FROM '+languagecode+'wiki_editors WHERE highest_flag != "" GROUP BY highest_flag;' for row in cursor.execute(query): highest_flag_dict[row[1]]=row[0] # registered_editors lustrum_first_edit bin 2001, 2006, 2011, 2016, 2020 year_last_edit bin 2001-2021 (180 days since last edit) parameters = [] query = 'SELECT count(user_id), lustrum_first_edit, year_last_edit FROM '+languagecode+'wiki_editors WHERE lustrum_first_edit != "" AND days_since_last_edit >= 180 GROUP BY lustrum_first_edit, year_last_edit ORDER BY lustrum_first_edit, year_last_edit;' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m2_value = row[2] parameters.append((year_month, 'editor_drop_off', 'lustrum_first_edit', 'bin', m1_value, 'year_last_edit', 'bin', m2_value, lustrum_first_edit_dict[m1_value], m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # registered_editors year_first_edit bin 2001-2021 year_last_edit bin 2001-2021 (180 days since last edit) parameters = [] query = 'SELECT count(user_id), year_first_edit, year_last_edit FROM '+languagecode+'wiki_editors WHERE year_first_edit != "" AND days_since_last_edit >= 180 GROUP BY year_first_edit, year_last_edit ORDER BY year_first_edit, year_last_edit;' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m2_value = row[2] parameters.append((year_month, 'editor_drop_off', 'year_first_edit', 'bin', m1_value, 'year_last_edit', 'bin', m2_value, year_first_edit_dict[m1_value], m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 year_last_edit bin 2001-2021 parameters = [] query = 'SELECT count(e1.user_id), e1.abs_value, e2.year_last_edit, e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" GROUP by e1.abs_value, e2.year_last_edit;' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m2_value = row[2] m1_count = edit_bins_count[m1_value] year_month = row[3] parameters.append((year_month, 'editor_drop_off', 'total_edits', 'bin', m1_value, 'year_last_edit', 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 over_past_max_inactive_months_row threshold # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 over_edit_bin_average_past_max_inactive_months_row threshold m2s = ['over_past_max_inactive_months_row','over_edit_bin_average_past_max_inactive_months_row','over_monthly_edit_bin_average_past_max_inactive_months_row'] for m2 in m2s: parameters = [] query = 'SELECT count(e1.user_id), e1.abs_value FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" AND e2.metric_name = "'+m2+'" AND e2.abs_value > 0 GROUP by e1.abs_value;' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m1_count = edit_bins_count[m1_value] parameters.append((year_month, 'editor_drop_off', 'total_edits', 'bin', m1_value, m2, 'threshold', 0, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # registered_editors lustrum_first_edit bin 2001, 2006, 2011, 2016, 2020 over_past_max_inactive_months_row threshold > 0 # registered_editors year_first_edit bin 2001-2021 over_edit_bin_average_past_max_inactive_months_row threshold > 0 # registered_editors lustrum_first_edit bin 2001, 2006, 2011, 2016, 2020 over_edit_bin_average_past_max_inactive_months_row threshold > 0 # registered_editors year_first_edit bin 2001-2021 over_past_max_inactive_months_row threshold > 0 m1s = ['year_first_edit','lustrum_first_edit'] m2s = ['over_past_max_inactive_months_row','over_edit_bin_average_past_max_inactive_months_row','over_monthly_edit_bin_average_past_max_inactive_months_row'] for m1 in m1s: for m2 in m2s: parameters = [] query = 'SELECT count(e1.user_id), e1.'+m1+' FROM '+languagecode+'wiki_editors e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e2.metric_name = "'+m2+'" AND e2.abs_value > 0 GROUP by e1.'+m1+';' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] if m1 == 'year_first_edit': try: m1_count = year_first_edit_dict[m1_value] except: m1_count = 0 elif m1 == 'lustrum_first_edit': try: m1_count = lustrum_first_edit[m1_value] except: m1_count = 0 parameters.append((year_month, 'editor_drop_off', m1, 'bin', m1_value, m2, 'threshold', 0, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # participative_editors total_edits bin 0_100, 100_500, 500_1000, 1000_5000, 5000_10000, 10000_50000, 50000_100000, 100000_500000, 500000_1000000, 1000000_1000000000000 days_since_last_edit bin 60, 120, 180. days_since_last_edit = 60 while days_since_last_edit <= 1095: # 20 years = 7200 days parameters = [] next_value_days_since_last_edit = days_since_last_edit + 60 if next_value_days_since_last_edit < 1095: query = 'SELECT count(e1.user_id), e1.abs_value FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" AND days_since_last_edit BETWEEN '+str(days_since_last_edit)+' AND '+str(next_value_days_since_last_edit)+' GROUP by e1.abs_value;' else: query = 'SELECT count(e1.user_id), e1.abs_value FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "edit_count_bin" AND days_since_last_edit > '+str(days_since_last_edit)+' GROUP by e1.abs_value;' for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m1_count = edit_bins_count[m1_value] m2_value = days_since_last_edit parameters.append((year_month, 'editor_drop_off', 'total_edits', 'bin', m1_value, 'days_since_last_edit', 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() days_since_last_edit = next_value_days_since_last_edit # flags highest_flag name sysop, autopatrolled, bureaucrat, etc. year_last_edit bin 2001-2021 (180 days inactive since calculation) parameters = [] query = 'SELECT count(user_id), highest_flag, year_last_edit FROM '+languagecode+'wiki_editors WHERE days_since_last_edit >= 180 GROUP BY year_last_edit, highest_flag' year_month = cycle_year_month for row in cursor.execute(query): m2_count = row[0] m1_value = row[1] m2_value = row[2] try: m1_count = highest_flag_dict[m1_value] except: m1_count = 0 parameters.append((year_month, 'editor_drop_off', 'highest_flag', 'name', m1_value, 'year_last_edit', 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() # active_editors monthly_edits threshold 5 monthly_edits_to_baseline bin 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 # active_editors monthly_edits threshold 5 monthly_editing_days_to_baseline bin 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 active_editors_5_year_month = {} query = 'SELECT count(distinct user_id), year_month FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "monthly_edits" AND abs_value >= '+str(5)+' GROUP BY year_month ORDER BY year_month' for row in cursor.execute(query): active_editors_5_year_month[row[1]] = row[0] m2s = ['monthly_edits_to_baseline','monthly_editing_days_to_baseline'] for m2 in m2s: query = 'SELECT count(e1.user_id), e1.year_month, e2.abs_value FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "monthly_edits" AND e1.abs_value >= 5 AND e2.metric_name = "'+m2+'" GROUP by e1.year_month, e2.abs_value;' for row in cursor.execute(query): m2_count = row[0] year_month = row[1] m2_value = row[2] m1_count = active_editors_5_year_month[year_month] parameters.append((year_month, 'editor_drop_off', 'monthly_edits', 'bin', 5, m2, 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() print ('editor_drop_off') def actions(): year_month = cycle_year_month # monthly_edits monthly_edits sum main, monthly_edits_ns0_main, etc. m1s = ['monthly_editing_days','monthly_edits','monthly_edits_ns0_main','monthly_edits_ns10_template','monthly_edits_ns11_template_talk','monthly_edits_ns12_help','monthly_edits_ns13_help_talk','monthly_edits_ns14_category','monthly_edits_ns15_category_talk','monthly_edits_ns1_talk','monthly_edits_ns2_user','monthly_edits_ns3_user_talk','monthly_edits_ns4_project','monthly_edits_ns5_project_talk','monthly_edits_ns6_file','monthly_edits_ns7_file_talk','monthly_edits_ns8_mediawiki','monthly_edits_ns9_mediawiki_talk'] parameters = [] sum_monthly_edits = {} for m1 in m1s: query = 'SELECT SUM(abs_value), year_month FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "'+m1+'" GROUP BY year_month ORDER BY year_month' for row in cursor.execute(query): m1_count = row[0] sum_monthly_edits[m1,row[1]] = m1_count parameters.append((year_month, 'editor_actions', 'monthly_edits', 'sum', m1, None, None, None, m1_count, None)) cursor.executemany(query_cm,parameters) conn.commit() # edits m2s = ['lustrum_first_edit','year_first_edit','year_last_edit','highest_flag'] for m2 in m2s: for m1 in m1s: if m2 == 'year_last_edit': query = 'SELECT SUM(e1.abs_value), e2.'+m2+', e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE metric_name = "'+m1+'" AND days_since_last_edit >= 180 GROUP BY e1.year_month, e2.'+m2+' ORDER BY e1.year_month, e2.'+m2 else: query = 'SELECT SUM(e1.abs_value), e2.'+m2+', e1.year_month FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editors e2 ON e1.user_id = e2.user_id WHERE metric_name = "'+m1+'" GROUP BY e1.year_month, e2.'+m2+' ORDER BY e1.year_month, e2.'+m2 # print (m1, m2) # print (query) # input('') for row in cursor.execute(query): m2_value = row[1] m2_count = row[0] year_month = row[2] m1_count = sum_monthly_edits[m1,year_month] parameters.append((year_month, 'editor_actions', 'monthly_edits', 'sum', m1, m2, 'bin', m2_value, m1_count, m2_count)) # print (len(parameters)) cursor.executemany(query_cm,parameters) conn.commit() # monthly_edits sum main, monthly_edits_ns0_main, etc. active_months bin 1-10, 10-20, 30-40,... to 150 active_months = {(1,100):'0', (217, 228): '217-228', (301, 312): '301-312', (277, 288): '277-288', (25, 36): '25-36', (241, 252): '241-252', (109, 120): '109-120', (85, 96): '85-96', (61, 72): '61-72', (205, 216): '205-216', (289, 300): '289-300', (193, 204): '193-204', (73, 84): '73-84', (49, 60): '49-60', (37, 48): '37-48', (265, 276): '265-276', (181, 192): '181-192', (145, 156): '145-156', (13, 24): '13-24', (253, 264): '253-264', (133, 144): '133-144', (1, 12): '1-12', (121, 132): '121-132', (169, 180): '169-180', (157, 168): '157-168', (229, 240): '229-240', (97, 108): '97-108'} year_months = set() parameters = [] for m1 in m1s: for interval, label in active_months.items(): query = 'SELECT SUM(e1.abs_value), e1.year_month, "'+label+'" FROM '+languagecode+'wiki_editor_metrics e1 INNER JOIN '+languagecode+'wiki_editor_metrics e2 ON e1.user_id = e2.user_id WHERE e1.metric_name = "'+m1+'" AND e2.metric_name = "active_months" AND e2.abs_value BETWEEN '+str(interval[0])+' AND '+str(interval[1])+' GROUP by e1.year_month, e1.abs_value;' for row in cursor.execute(query): m2_value = row[2] m2_count = row[0] year_month = row[1] m1_count = sum_monthly_edits[m1,year_month] year_months.add(year_month) parameters.append((year_month, 'editor_actions', 'monthly_edits', 'sum', m1, "active_months", 'bin', m2_value, m1_count, m2_count)) cursor.executemany(query_cm,parameters) conn.commit() """ # GINI def gini_calculation(x): # (Warning: This is a concise implementation, but it is O(n**2) # in time and memory, where n = len(x). *Don't* pass in huge # samples!) # Mean absolute difference mad = np.abs(np.subtract.outer(x, x)).mean() # Relative mean absolute difference rmad = mad/np.mean(x) # Gini coefficient g = 0.5 * rmad return g parameters = [] ym = sorted(list(year_months)) for year_month in ym: query = 'SELECT ce.abs_value FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "monthly_edits" AND year_month="'+year_month+'" AND ch.bot = "editor" AND ce.abs_value > 0;' query = 'SELECT abs_value FROM '+languagecode+'wiki_editor_metrics WHERE metric_name = "monthly_edits" AND year_month="'+year_month+'"' values = [] for row in cursor.execute(query): values.append(row[0]); v = gini_calculation(values) parameters.append((year_month, 'monthly_edits', 'monthly_edits', 'gini', 'monthly_edits', None, None, None, v, None)) # query = 'SELECT ce.abs_value FROM '+languagecode+'wiki_editors ch INNER JOIN '+languagecode+'wiki_editor_metrics ce ON ch.user_id = ce.user_id WHERE ce.metric_name = "edit_count" AND ch.bot = "editor" AND ce.abs_value > 0;' # values = [] # for row in cursor.execute(query): values.append(row[0]); # v = gini(values) # print (v) # parameters.append((v, 'gini_edits', year_month)) # parameters.append((year_month, 'monthly_edits', 'monthly_edits', 'gini', 'monthly_edits', None, None, None, v, None)) cursor.executemany(query_cm,parameters) conn.commit() """ print ('editor_actions') # participation() # flags() # active_editors() # retention() drop_off() # actions() duration = str(datetime.timedelta(seconds=time.time() - functionstartTime)) print(languagecode+' '+ function_name+' '+ duration) """ def editor_metrics_social(languagecode): pass Iteració sencera a MediaWiki history Mètriques mensuals. Iterar pel mes que anem. Consulta a cada mes als registrats d'aquell mes o dos abans. Comprovar quants d'aquests s'hi interactua I fer els comptadors Cal guardar les últimes edicions de tots els usuaris per comprovar si hi ha interacció. Fetes Interactions newcomers_user_page_talk_page_edits Interactions newcomers_article_talk_page_edits Interactions newcomer_count Interactions survivors_count (aquesta sempre hi haurà un decalatge de dos mesos) -> quants dels newcomers amb qui has interactuat sobreviuen. Interaccions rebudes User talk pages Article talk pages Hipòtesi. Quan els editors estan a punt de fer drop off... Deixen abans d'interactuar amb newcomers. Hipòtesi. Quan els editors deixen d'interactuar amb ells... Estan més a prop del drop off. def editor_metrics_multilingual(languagecode): print('') # * wiki_editors # (user_id integer, user_name text, bot text, user_flags text, primarybinary, primarylang text, primarybinary_ecount, totallangs_ecount, numberlangs integer) # FUNCTION # multilingualism: això cal una funció que passi per les diferents bases de dades i creï aquesta def editor_metrics_content_diversity(languagecode): print('') # https://stackoverflow.com/questions/28816330/sqlite-insert-if-not-exist-else-increase-integer-value # PER NO GUARDAR-HO TOT EN MEMÒRIA. FER L'INSERT DELS CCC EDITATS A CADA ARXIU. # * wiki_editor_content_metrics # (user_id integer, user_name text, content_type text, value real) # FUNCTION # això cal una funció que corri el mediawiki history amb aquest objectiu havent preseleccionat editors també. functionstartTime = time.time() function_name = 'editor_metrics_content_diversity '+languagecode print (function_name) print (languagecode) d_paths = get_mediawiki_paths(languagecode) if (len(d_paths)==0): print ('dump error. this language has no mediawiki_history dump: '+languagecode) # wikilanguages_utils.send_email_toolaccount('dump error at script '+script_name, dumps_path) # quit() for dump_path in d_paths: print(dump_path) iterTime = time.time() dump_in = bz2.open(dump_path, 'r') line = dump_in.readline() line = line.rstrip().decode('utf-8')[:-1] values = line.split(' ') parameters = [] editors_params = [] iter = 0 while line != '': # iter += 1 # if iter % 1000000 == 0: print (str(iter/1000000)+' million lines.') line = dump_in.readline() line = line.rstrip().decode('utf-8')[:-1] values = line.split('\t') if len(values)==1: continue page_id = values[23] page_title = values[25] page_namespace = int(values[28]) edit_count = values[34] Pel tema Edits A Ccc Diccionari de diccionaris amb el què va editant cada editor cada mes. Més ràpid pel hash. dict_editors {} dict_CCC_per_editor {} Els Edits mensuals a cada CCC? els anem col·locant a una bbdd, que pot ser la mateixa o una altra. Després sumar l'acumulat final i ja està. S'esborren els mensuals... Ja que és massa contingut. """ ####################################################################################### class Logger_out(object): # this prints both the output to a file and to the terminal screen. def __init__(self): self.terminal = sys.stdout self.log = open("community_health_metrics2.out", "w") def write(self, message): self.terminal.write(message) self.log.write(message) def flush(self): pass class Logger_err(object): # this prints both the output to a file and to the terminal screen. def __init__(self): self.terminal = sys.stdout self.log = open("community_health_metrics.err", "w") def write(self, message): self.terminal.write(message) self.log.write(message) def flush(self): pass ### MAIN: if __name__ == '__main__': sys.stdout = Logger_out() sys.stderr = Logger_err() startTime = time.time() cycle_year_month = (datetime.date.today() - relativedelta.relativedelta(months=1)).strftime('%Y-%m') territories = wikilanguages_utils.load_wikipedia_languages_territories_mapping() languages = wikilanguages_utils.load_wiki_projects_information(); wikilanguagecodes = sorted(languages.index.tolist()) print ('checking languages Replicas databases and deleting those without one...') # Verify/Remove all languages without a replica database for a in wikilanguagecodes: if wikilanguages_utils.establish_mysql_connection_read(a)==None: wikilanguagecodes.remove(a) print (wikilanguagecodes) # wikilanguagecodes = ['eu','it'] wikilanguagecodes = ['gl','eu','oc'] # wikilanguagecodes = ['ca'] wikilanguagecodes = ['es','fr','it'] wikilanguagecodes = ['ca','eu','es','fr','it'] wikilanguagecodes = ['oc','gl','is','ca','eu'] print ('* Starting the COMMUNITY HEALTH METRICS '+cycle_year_month+' at this exact time: ' + str(datetime.datetime.now().strftime("%m/%d/%Y, %H:%M:%S"))) main() finishTime = time.time() print ('* Done with the COMMUNITY HEALTH METRICS completed successfuly after: ' + str(datetime.timedelta(seconds=finishTime - startTime))) wikilanguages_utils.finish_email(startTime,'community_health_metrics.out', 'COMMUNITY HEALTH METRICS')

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null

0

6

1dfdf1dc9d560dd375ad6fcdf3b6923ca0b72a86

36

py

Python

core/parsers/__init__.py

andrewisakov/taximaster

b92f06894bbc3414086ec77f1c918a3c0f085241

[ "MIT" ]

null

core/parsers/__init__.py

andrewisakov/taximaster

b92f06894bbc3414086ec77f1c918a3c0f085241

[ "MIT" ]

null

core/parsers/__init__.py

andrewisakov/taximaster

b92f06894bbc3414086ec77f1c918a3c0f085241

[ "MIT" ]

null

from .parsers import request_parser

18

35

0.861111

5

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null

0

1

0

1

0

1

0

6

380c11403c1a76b0d531997f5e39af651f1a3bb9

31

py

Python

djshed/__init__.py

carthage-college/django-djshed

122dc2f9dd582fd943915b6268d4e90dc84993ca

[ "BSD-3-Clause" ]

null

djshed/__init__.py

carthage-college/django-djshed

122dc2f9dd582fd943915b6268d4e90dc84993ca

[ "BSD-3-Clause" ]

9

2020-03-04T16:04:21.000Z

2022-02-14T17:34:07.000Z

djshed/__init__.py

carthage-college/django-djshed

122dc2f9dd582fd943915b6268d4e90dc84993ca

[ "BSD-3-Clause" ]

null

from djshed.constants import *

15.5

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null

0

1

0

1

0

1

0

6

382cc8f8f5ea8df980757417230fc969f24fd71f

110

py

Python

pyfantasy/__init__.py

markwhat1/pyfantasy

318a7afc97c7bf6ba978ff8bb8c4c58f8ea0d420

[ "MIT" ]

null

pyfantasy/__init__.py

markwhat1/pyfantasy

318a7afc97c7bf6ba978ff8bb8c4c58f8ea0d420

[ "MIT" ]

null

pyfantasy/__init__.py

markwhat1/pyfantasy

318a7afc97c7bf6ba978ff8bb8c4c58f8ea0d420

[ "MIT" ]

null

from .pyfantasy import Connection from .pyfantasy import League, Team, Player from .yahoo_oauth import OAuth2

27.5

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0.827273

15

110

6

0.666667

0.288889

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0.127273

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null

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6

69b4c48501471d49ec2d4fd4b79c4ecc8adb3282

204

py

Python

util/data/gen/BloonsTD6.exe.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/BloonsTD6.exe.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/BloonsTD6.exe.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

symbols = [] exports = [{'type': 'function', 'name': 'AmdPowerXpressRequestHighPerformance', 'address': '0x7ff66dd14004'}, {'type': 'function', 'name': 'NvOptimusEnablement', 'address': '0x7ff66dd14000'}]

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1

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null

0

6

0e148b2e22f2b41098a22593e27a07d48422bed6

163

py

Python

website_multi_company/__init__.py

RL-OtherApps/website-addons

b0903daefa492c298084542de2c99f1ab13cd4b4

[ "MIT" ]

1

2020-03-01T03:04:21.000Z

website_multi_company/__init__.py

RL-OtherApps/website-addons

b0903daefa492c298084542de2c99f1ab13cd4b4

[ "MIT" ]

null

website_multi_company/__init__.py

RL-OtherApps/website-addons

b0903daefa492c298084542de2c99f1ab13cd4b4

[ "MIT" ]

null

from . import models def post_load(): # use post_load to avoid overriding _get_search_domain when this module is not installed from . import controllers

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6

38985d959385d3a2057ea5f7a76d2853b1c2d13c

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py

Python

Model&Data/tf-VAEGAN/main.py

LiangjunFeng/Generative-Any-Shot-Learning

693c4ab92f2eb04cc453c870782710a982f98e80

[ "Apache-2.0" ]

null

Model&Data/tf-VAEGAN/main.py

LiangjunFeng/Generative-Any-Shot-Learning

693c4ab92f2eb04cc453c870782710a982f98e80

[ "Apache-2.0" ]

null

Model&Data/tf-VAEGAN/main.py

LiangjunFeng/Generative-Any-Shot-Learning

693c4ab92f2eb04cc453c870782710a982f98e80

[ "Apache-2.0" ]

null

import argparse from train_images import run # generalized ZSL # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA1 --few_train False --num_shots 0 --generalized True > awa1.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset SUN --few_train False --num_shots 0 --generalized True > sun.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset CUB --few_train False --num_shots 0 --generalized True > cub.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset FLO --few_train False --num_shots 0 --generalized True > flo.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 0 --generalized True > awa2.log 2>&1 & # naive feature # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 0 --generalized True --image_embedding res101_naive > awa2.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset SUN --few_train False --num_shots 0 --generalized True --image_embedding res101_naive > sun.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset CUB --few_train False --num_shots 0 --generalized True --image_embedding res101_naive > cub.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset FLO --few_train False --num_shots 0 --generalized True --image_embedding res101_naive > flo.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset aPY --few_train False --num_shots 0 --generalized True --image_embedding res101_naive > apy.log 2>&1 & # finetue feature # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 0 --generalized True --image_embedding res101_finetune > awa2.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset SUN --few_train False --num_shots 0 --generalized True --image_embedding res101_finetune > sun.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset CUB --few_train False --num_shots 0 --generalized True --image_embedding res101_finetune > cub.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset FLO --few_train False --num_shots 0 --generalized True --image_embedding res101_finetune > flo.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset aPY --few_train False --num_shots 0 --generalized True --image_embedding res101_finetune > apy.log 2>&1 & # reg feature # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train False --num_shots 0 --generalized True --image_embedding res101_reg > flo.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset CUB --few_train False --num_shots 0 --generalized True --image_embedding res101_reg > cub.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset SUN --few_train False --num_shots 0 --generalized True --image_embedding res101_reg > sun.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 0 --generalized True --image_embedding res101_reg > awa2.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset aPY --few_train False --num_shots 0 --generalized True --image_embedding res101_reg > apy.log 2>&1 & # few shot # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train False --num_shots 1 --generalized True --image_embedding res101_reg > flo0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset FLO --few_train False --num_shots 5 --generalized True --image_embedding res101_reg > flo1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset FLO --few_train False --num_shots 10 --generalized True --image_embedding res101_reg > flo2.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset FLO --few_train False --num_shots 20 --generalized True --image_embedding res101_reg > flo3.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train True --num_shots 1 --generalized True --image_embedding res101_naive > flo0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset FLO --few_train True --num_shots 5 --generalized True --image_embedding res101_naive > flo1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset FLO --few_train True --num_shots 10 --generalized True --image_embedding res101_naive > flo2.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset FLO --few_train True --num_shots 20 --generalized True --image_embedding res101_naive > flo3.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset CUB --few_train False --num_shots 1 --generalized True --image_embedding res101_reg > cub0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset CUB --few_train False --num_shots 5 --generalized True --image_embedding res101_reg > cub1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset CUB --few_train False --num_shots 10 --generalized True --image_embedding res101_reg > cub2.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset CUB --few_train False --num_shots 20 --generalized True --image_embedding res101_reg > cub3.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset CUB --few_train True --num_shots 1 --generalized True --image_embedding res101_naive > cub0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset CUB --few_train True --num_shots 5 --generalized True --image_embedding res101_naive > cub1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset CUB --few_train True --num_shots 10 --generalized True --image_embedding res101_naive > cub2.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset CUB --few_train True --num_shots 20 --generalized True --image_embedding res101_naive > cub3.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset SUN --few_train False --num_shots 1 --generalized True --image_embedding res101_reg > sun0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset SUN --few_train False --num_shots 5 --generalized True --image_embedding res101_reg > sun1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset SUN --few_train False --num_shots 10 --generalized True --image_embedding res101_reg > sun2.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset SUN --few_train True --num_shots 1 --generalized True --image_embedding res101 > sun0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset SUN --few_train True --num_shots 5 --generalized True --image_embedding res101 > sun1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset SUN --few_train True --num_shots 10 --generalized True --image_embedding res101 > sun2.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 1 --generalized True --image_embedding res101_naive > awa20.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 5 --generalized True --image_embedding res101_naive > awa21.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 10 --generalized True --image_embedding res101_naive > awa22.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 20 --generalized True --image_embedding res101_naive > awa23.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA2 --few_train True --num_shots 1 --generalized True --image_embedding res101_naive > awa20.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset AWA2 --few_train True --num_shots 5 --generalized True --image_embedding res101_naive > awa21.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset AWA2 --few_train True --num_shots 10 --generalized True --image_embedding res101_naive > awa22.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset AWA2 --few_train True --num_shots 20 --generalized True --image_embedding res101_naive > awa23.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA1 --few_train False --num_shots 1 --generalized True --image_embedding res101 > awa10.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset AWA1 --few_train False --num_shots 5 --generalized True --image_embedding res101 > awa11.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset AWA1 --few_train False --num_shots 10 --generalized True --image_embedding res101 > awa12.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset AWA1 --few_train False --num_shots 20 --generalized True --image_embedding res101 > awa13.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA1 --few_train True --num_shots 1 --generalized True --image_embedding res101 > awa10.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset AWA1 --few_train True --num_shots 5 --generalized True --image_embedding res101 > awa11.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset AWA1 --few_train True --num_shots 10 --generalized True --image_embedding res101 > awa12.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset AWA1 --few_train True --num_shots 20 --generalized True --image_embedding res101 > awa13.log 2>&1 & # few shot # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train False --num_shots 1 --generalized True --image_embedding res101_reg > flo0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset FLO --few_train False --num_shots 5 --generalized True --image_embedding res101_reg > flo1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset FLO --few_train False --num_shots 10 --generalized True --image_embedding res101_reg > flo2.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset FLO --few_train False --num_shots 20 --generalized True --image_embedding res101_reg > flo3.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train True --num_shots 1 --generalized True --image_embedding res101_naive > flo0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset FLO --few_train True --num_shots 5 --generalized True --image_embedding res101_naive > flo1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset FLO --few_train True --num_shots 10 --generalized True --image_embedding res101_naive > flo2.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset FLO --few_train True --num_shots 20 --generalized True --image_embedding res101_naive > flo3.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset CUB --few_train False --num_shots 1 --generalized True --image_embedding res101_reg > cub0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset CUB --few_train False --num_shots 5 --generalized True --image_embedding res101_reg > cub1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset CUB --few_train False --num_shots 10 --generalized True --image_embedding res101_reg > cub2.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset CUB --few_train False --num_shots 20 --generalized True --image_embedding res101_reg > cub3.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset CUB --few_train True --num_shots 1 --generalized True --image_embedding res101_naive > cub0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset CUB --few_train True --num_shots 5 --generalized True --image_embedding res101_naive > cub1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset CUB --few_train True --num_shots 10 --generalized True --image_embedding res101_naive > cub2.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset CUB --few_train True --num_shots 20 --generalized True --image_embedding res101_naive > cub3.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset SUN --few_train False --num_shots 1 --generalized True --image_embedding res101_reg > sun0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset SUN --few_train False --num_shots 5 --generalized True --image_embedding res101_reg > sun1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset SUN --few_train False --num_shots 10 --generalized True --image_embedding res101_reg > sun2.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset SUN --few_train True --num_shots 1 --generalized True --image_embedding res101 > sun0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset SUN --few_train True --num_shots 5 --generalized True --image_embedding res101 > sun1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset SUN --few_train True --num_shots 10 --generalized True --image_embedding res101 > sun2.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 1 --generalized True --image_embedding res101_naive > awa20.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 5 --generalized True --image_embedding res101_naive > awa21.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 10 --generalized True --image_embedding res101_naive > awa22.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset AWA2 --few_train False --num_shots 20 --generalized True --image_embedding res101_naive > awa23.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA2 --few_train True --num_shots 1 --generalized True --image_embedding res101_naive > awa20.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset AWA2 --few_train True --num_shots 5 --generalized True --image_embedding res101_naive > awa21.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset AWA2 --few_train True --num_shots 10 --generalized True --image_embedding res101_naive > awa22.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset AWA2 --few_train True --num_shots 20 --generalized True --image_embedding res101_naive > awa23.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA1 --few_train False --num_shots 1 --generalized True --image_embedding res101 > awa10.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset AWA1 --few_train False --num_shots 5 --generalized True --image_embedding res101 > awa11.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset AWA1 --few_train False --num_shots 10 --generalized True --image_embedding res101 > awa12.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset AWA1 --few_train False --num_shots 20 --generalized True --image_embedding res101 > awa13.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset AWA1 --few_train True --num_shots 1 --generalized True --image_embedding res101 > awa10.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset AWA1 --few_train True --num_shots 5 --generalized True --image_embedding res101 > awa11.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset AWA1 --few_train True --num_shots 10 --generalized True --image_embedding res101 > awa12.log 2>&1 & # CUDA_VISIBLE_DEVICES=3 nohup python -u main.py --dataset AWA1 --few_train True --num_shots 20 --generalized True --image_embedding res101 > awa13.log 2>&1 & # reg feature + att # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train False --num_shots 0 --generalized True --image_embedding res101_reg --class_embedding att > flo0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset FLO --few_train False --num_shots 0 --generalized True --image_embedding res101_reg --class_embedding att_naive > flo1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset FLO --few_train False --num_shots 0 --generalized True --image_embedding res101_reg --class_embedding att_GRU > flo2.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train False --num_shots 0 --generalized True --image_embedding res101_reg --class_embedding att_GRU_biased > flo3.log 2>&1 & # few shot + class # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train False --num_shots 1 --generalized True --image_embedding res101_reg --class_embedding att_GRU_biased > flo0.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset FLO --few_train False --num_shots 5 --generalized True --image_embedding res101_reg --class_embedding att_GRU_biased > flo1.log 2>&1 & # CUDA_VISIBLE_DEVICES=2 nohup python -u main.py --dataset FLO --few_train False --num_shots 10 --generalized True --image_embedding res101_reg --class_embedding att_GRU_biased > flo2.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train False --num_shots 20 --generalized True --image_embedding res101_reg --class_embedding att_GRU_biased > flo3.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train True --num_shots 1 --generalized True --image_embedding res101_naive --class_embedding att_GRU_biased > flo0.log 2>&1 & # CUDA_VISIBLE_DEVICES=0 nohup python -u main.py --dataset FLO --few_train True --num_shots 5 --generalized True --image_embedding res101_naive --class_embedding att_GRU_biased > flo1.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset FLO --few_train True --num_shots 10 --generalized True --image_embedding res101_naive --class_embedding att_GRU_biased > flo2.log 2>&1 & # CUDA_VISIBLE_DEVICES=1 nohup python -u main.py --dataset FLO --few_train True --num_shots 20 --generalized True --image_embedding res101_naive --class_embedding att_GRU_biased > flo3.log 2>&1 & def str2bool(v): if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') parser = argparse.ArgumentParser() parser.add_argument('--dataset', default='FLO', help='FLO') parser.add_argument('--few_train', default = False, type = str2bool, help='use few train samples') parser.add_argument('--num_shots', type=int, default=5, help='the number of shots, if few_train, then num_shots is for train classes, else for test classes') parser.add_argument('--generalized', default=False, type = str2bool, help='enable generalized zero-shot learning') parser.add_argument('--image_embedding', default='res101', help='res101') parser.add_argument('--class_embedding', default='att', help='att') args = parser.parse_args() class myArgs(): def __init__(self, args): self.dataset = args.dataset self.few_train = args.few_train self.num_shots = args.num_shots self.generalized = args.generalized self.image_embedding = args.image_embedding self.class_embedding = args.class_embedding self.dataroot = "../data" self.syn_num = 100; self.preprocessing = False; self.standardization = False; self.workers = 8 self.batch_size = 64; self.resSize = 2048; self.attSize = 1024; self.nz = 312; self.ngh = 4096 self.ndh = 1024; self.nepoch = 2000; self.critic_iter = 5; self.lambda1 = 10; self.lambda2 = 10 self.lr = 0.001; self.feed_lr = 0.0001; self.dec_lr = 0.0001; self.classifier_lr = 0.001 self.beta1 = 0.5; self.cuda = True; self.encoded_noise = False; self.manualSeed = 0 self.nclass_all = 200; self.validation = False; self.encoder_layer_sizes = [8192, 4096] self.decoder_layer_sizes = [4096, 8192]; self.gammaD = 1000; self.gammaG = 1000 self.gammaG_D2 = 1000; self.gammaD2 = 1000; self.latent_size = 312; self.conditional = True self.a1 = 1.0; self.a2 = 1.0; self.recons_weight = 1.0; self.feedback_loop = 2 self.freeze_dec = False if self.dataset in ["AWA1", "AWA2"]: self.gammaD = 10; self.gammaG = 10; self.encoded_noise = True self.manualSeed = 9182; self.preprocessing = True; self.cuda = True self.nepoch = 120; self.syn_num = 1800; self.ngh = 4096; self.ndh = 4096 self.lambda1 = 10; self.critic_iter = 5; self.nclass_all = 50; self.batch_size = 64; self.nz = 85 self.latent_size = 85; self.attSize=85; self.resSize = 2048; self.lr = 0.00001; self.classifier_lr = 0.001; self.recons_weight = 0.1; self.freeze_dec = True; self.feed_lr = 0.0001; self.dec_lr = 0.0001; self.feedback_loop = 2; self.a1 = 0.01; self.a2 = 0.01 elif self.dataset == "CUB": self.gammaD = 10; self.gammaG = 10; self.manualSeed = 3483; self.encoded_noise = True; self.preprocessing = True self.cuda = True; self.nepoch = 300; self.ngh = 4096 self.ndh = 4096; self.lr = 0.0001; self.classifier_lr = 0.001; self.lambda1 = 10; self.critic_iter = 5 self.nclass_all = 200; self.batch_size = 64; self.nz = 312; self.latent_size = 312; self.attSize = 312 self.resSize = 2048; self.syn_num = 300; self.recons_weight = 0.01; self.a1 = 1; self.a2 = 1 self.feed_lr = 0.00001; self.dec_lr = 0.0001; self.feedback_loop = 2 elif self.dataset == "FLO": self.gammaD = 10; self.gammaG = 10; self.nclass_all = 102; self.latent_size = 1024; self.manualSeed = 806 self.syn_num = 1200; self.preprocessing = True; self.nepoch = 500 self.ngh = 4096; self.ndh = 4096; self.lambda1 = 10; self.critic_iter = 5; self.batch_size = 64 self.nz = 1024; self.attSize = 1024; self.resSize = 2048; self.lr = 0.0001; self.classifier_lr = 0.001 self.cuda = True; self.recons_weight = 0.01; self.feedback_loop = 2 self.feed_lr = 0.00001; self.a1 = 0.5; self.a2 = 0.5; self.dec_lr = 0.0001 elif self.dataset == "SUN": self.gammaD = 1; self.gammaG = 1; self.manualSeed = 4115; self.encoded_noise = True; self.preprocessing = True self.cuda = True; self.nepoch = 400 self.ngh = 4096; self.ndh = 4096; self.lambda1 = 10; self.critic_iter = 5; self.batch_size = 64 self.nz = 102; self.latent_size = 102; self.attSize = 102; self.lr = 0.001; self.classifier_lr = 0.0005 self.syn_num = 400; self.nclass_all = 717; self.recons_weight = 0.01; self.a1 = 0.1; self.a2 = 0.01 self.feedback_loop = 2; self.feed_lr = 0.0001 if self.image_embedding == "res101_reg": self.self.lr = 0.0001; self.classifier_lr = 0.0001; self.recons_weight = 0.0001 elif self.dataset == "aPY": self.gammaD = 10; self.gammaG = 10; self.nclass_all = 32; self.latent_size = 1024; self.manualSeed = 806 self.syn_num = 1200; self.preprocessing = True; self.nepoch = 500 self.ngh = 4096; self.ndh = 4096; self.lambda1 = 10; self.critic_iter = 5; self.batch_size = 64 self.nz = 64; self.attSize = 64; self.resSize = 2048; self.lr = 0.0001; self.classifier_lr = 0.001 self.cuda = True; self.recons_weight = 0.01; self.feedback_loop = 2 self.feed_lr = 0.00001; self.a1 = 0.5; self.a2 = 0.5; self.dec_lr = 0.0001 opt = myArgs(args) opt.lambda2 = opt.lambda1 opt.encoder_layer_sizes[0] = opt.resSize opt.decoder_layer_sizes[-1] = opt.resSize opt.latent_size = opt.attSize print("lr: ", opt.lr, "classifier_lr: ", opt.classifier_lr, "recons_weight: ", opt.recons_weight, "a1: ", opt.a1, opt.a2, "a2: ", "feed_lr: ", opt.feed_lr) run(opt)

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647

py

Python

tests/expectations/cat-hs-x-cat-date-smoothed-col-pct-w3.py

Crunch-io/crunch-cube

80986d5b2106c774f05176fb6c6a5ea0d840f09d

[ "MIT" ]

3

2021-01-22T20:42:31.000Z

2021-06-02T17:53:19.000Z

tests/expectations/cat-hs-x-cat-date-smoothed-col-pct-w3.py

Crunch-io/crunch-cube

80986d5b2106c774f05176fb6c6a5ea0d840f09d

[ "MIT" ]

331

2017-11-13T22:41:56.000Z

2021-12-02T21:59:43.000Z

tests/expectations/cat-hs-x-cat-date-smoothed-col-pct-w3.py

Crunch-io/crunch-cube

80986d5b2106c774f05176fb6c6a5ea0d840f09d

[ "MIT" ]

1

2021-02-19T02:49:00.000Z

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Python

ahmed-package/__init__.py

gokcelahmed/ahmed-package

a6c34fb2d85105ad33063b840c84f70ff7a0aa4d

[ "MIT" ]

null

ahmed-package/__init__.py

gokcelahmed/ahmed-package

a6c34fb2d85105ad33063b840c84f70ff7a0aa4d

[ "MIT" ]

null

ahmed-package/__init__.py

gokcelahmed/ahmed-package

a6c34fb2d85105ad33063b840c84f70ff7a0aa4d

[ "MIT" ]

null

from ahmed-package.functions import printName

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py

Python

fixture/__init__.py

hippa777/python_training

568c12e1a21c3c7eb40a1af25a9db83690a1b26d

[ "Apache-2.0" ]

null

fixture/__init__.py

hippa777/python_training

568c12e1a21c3c7eb40a1af25a9db83690a1b26d

[ "Apache-2.0" ]

null

fixture/__init__.py

hippa777/python_training

568c12e1a21c3c7eb40a1af25a9db83690a1b26d

[ "Apache-2.0" ]

null

from .contact_helper import ContactHelper

41

0.902439

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41

7.2

1

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1

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6

2acb3aee2e0f5e506188c20bf399d1690f3f87a1

35

py

Python

webapp/journal_plugins/example_plugin/models.py

TheCDC/journal_app

18c84acfc7b996329d34c3bdd54348cdfdd55252

[ "MIT" ]

2

2018-03-08T16:21:45.000Z

2018-10-22T02:13:22.000Z

webapp/journal_plugins/example_plugin/models.py

TheCDC/journal_app

18c84acfc7b996329d34c3bdd54348cdfdd55252

[ "MIT" ]

3

2018-05-25T04:21:09.000Z

2020-02-10T00:46:37.000Z

webapp/journal_plugins/example_plugin/models.py

TheCDC/journal_app

18c84acfc7b996329d34c3bdd54348cdfdd55252

[ "MIT" ]

null

from webapp.extensions import db

8.75

32

0.8

5

35

5.6

1

0

0.171429

35

3

33

11.666667

0.965517

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true

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null

0

1

0

1

0

1

0

6

2d83d5ac7ec09bb8e96ad46997e6e2b1b848abb9

231

py

Python

hcap_utils/contrib/material/views/__init__.py

fabiommendes/capacidade_hospitalar

4f675b574573eb3f51e6be8a927ea230bf2712c7

[ "MIT" ]

null

hcap_utils/contrib/material/views/__init__.py

fabiommendes/capacidade_hospitalar

4f675b574573eb3f51e6be8a927ea230bf2712c7

[ "MIT" ]

31

2020-04-11T13:38:17.000Z

2021-09-22T18:51:11.000Z

hcap_utils/contrib/material/views/__init__.py

fabiommendes/capacidade_hospitalar

4f675b574573eb3f51e6be8a927ea230bf2712c7

[ "MIT" ]

1

2020-04-08T17:04:39.000Z

from .create_model_view import CreateModelView from .delete_model_view import DeleteModelView from .detail_model_view import DetailModelView from .list_model_view import ListModelView from .update_model_view import UpdateModelView

38.5

46

0.891775

30

231

6.533333

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0.229592

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0

0.08658

231

5

47

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null

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1

0

1

0

1

0

6

2db96537947c81899effbd609c7adfa473931eeb

33

py

Python

datasets/__init__.py

Masterchef365/pvcnn

db13331a46f672e74e7b5bde60e7bf30d445cd2d

[ "MIT" ]

477

2019-12-10T01:03:43.000Z

2022-03-28T14:10:08.000Z

datasets/__init__.py

chaomath/pvcnn

8f07316611067e9a0e2df8b35e4a729a03e0806b

[ "MIT" ]

57

2019-12-10T10:14:26.000Z

2022-03-26T04:59:43.000Z

datasets/__init__.py

chaomath/pvcnn

8f07316611067e9a0e2df8b35e4a729a03e0806b

[ "MIT" ]

126

2019-12-10T07:59:50.000Z

2022-03-12T07:21:19.000Z

from datasets.s3dis import S3DIS

16.5

32

0.848485

5

33

5.6

0.8

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0.068966

0.121212

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1

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true

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null

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1

0

null

0

1

0

1

0

1

0

6

2dc6b64392d41e06755147486243708c0cbcc454

2,362

py

Python

venv/src/pages/forms.py

ddelgadoJS/ProyectoWeb

f899c910bf16a79d5c3498bc6e8aa6b741fb56e1

[ "MIT" ]

1

2019-10-28T03:44:38.000Z

venv/src/pages/forms.py

ddelgadoJS/ProyectoWeb

f899c910bf16a79d5c3498bc6e8aa6b741fb56e1

[ "MIT" ]

null

venv/src/pages/forms.py

ddelgadoJS/ProyectoWeb

f899c910bf16a79d5c3498bc6e8aa6b741fb56e1

[ "MIT" ]

null

from django import forms from django.contrib.auth.models import User from django.contrib.auth.forms import UserCreationForm from .models import * class EmpresaCreateForm(forms.ModelForm): class Meta: model = Empresa fields = [ 'nombre', 'description', 'direccion', 'horario', 'telefono', 'correo', 'serv_origen', 'serv_destino', 'latitud', 'longitud' ] class EmpresaUpdateForm(forms.ModelForm): class Meta: model = Empresa fields = [ 'nombre', 'description', 'direccion', 'horario', 'telefono', 'correo', 'serv_origen', 'serv_destino', 'latitud', 'longitud' ] class UsuarioUpdateForm(forms.ModelForm): class Meta: model = User fields = [ 'username', 'email', 'first_name', 'last_name' ] class RutaCreateForm(forms.ModelForm): class Meta: model = Ruta fields = [ 'empresa', 'nombre', 'description', 'costo', 'horario', 'duracion_viaje', 'inclusivo', 'origen_latitud', 'origen_longitud', 'destino_latitud', 'destino_longitud' ] class RutaUpdateForm(forms.ModelForm): class Meta: model = Ruta fields = [ 'empresa', 'nombre', 'description', 'costo', 'horario', 'duracion_viaje', 'inclusivo', 'origen_latitud', 'origen_longitud', 'destino_latitud', 'destino_longitud' ] class ParadaCreateForm(forms.ModelForm): class Meta: model = Parada fields = [ 'ruta', 'nombre', 'description', 'horario', 'latitud', 'longitud' ] class ParadaUpdateForm(forms.ModelForm): class Meta: model = Parada fields = [ 'ruta', 'nombre', 'description', 'horario', 'latitud', 'longitud' ]

22.495238

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0.453006

158

2,362

6.670886

0.278481

0.092979

0.126186

0.152751

0.726755

0.70019

0

0.446232

2,362

105

55

22.495238

0.80581

0

0.762887

0

0.222598

0

1

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false

0

0.041237

0

0.185567

0

null

0

1

0

1

0

null

0

6

2dcaf1c764e04a5631433a84042c46eeee80f0f9

28

py

Python

losses/__init__.py

xieqk/SEF

2163a159933d8d9ecad5ff9341cfa626f662a778

[ "MIT" ]

null

losses/__init__.py

xieqk/SEF

2163a159933d8d9ecad5ff9341cfa626f662a778

[ "MIT" ]

null

losses/__init__.py

xieqk/SEF

2163a159933d8d9ecad5ff9341cfa626f662a778

[ "MIT" ]

null

from .ranking import Triplet

28

0.857143

4

28

6

1

0

0.107143

28

1

28

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

930100236a4359a4387dd68192ff211e5a4519df

149

py

Python

roona/roona/doctype/roona_app_setting/test_roona_app_setting.py

mohsinalimat/roona

b24336d9d56eb443a883131afffd9091c9a66add

[ "MIT" ]

1

2021-08-28T04:24:00.000Z

roona/roona/doctype/roona_app_setting/test_roona_app_setting.py

mohsinalimat/roona

b24336d9d56eb443a883131afffd9091c9a66add

[ "MIT" ]

null

roona/roona/doctype/roona_app_setting/test_roona_app_setting.py

mohsinalimat/roona

b24336d9d56eb443a883131afffd9091c9a66add

[ "MIT" ]

null

16.555556

45

0.785235

18

149

6.5

0.888889

0

0.03125

0.14094

149

8

46

18.625

0.882813

0.483221

0

1

0

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

6

930766e7b8ffd5a77cf2414fe0de6b57a69af041

48

py

Python

levelpy/async/__init__.py

rch/levelpy

59e546854b3d478f3467bd573eb5f6b4da62d239

[ "MIT" ]

4

2015-11-06T22:50:22.000Z

2020-05-31T14:49:58.000Z

levelpy/async/__init__.py

rch/levelpy

59e546854b3d478f3467bd573eb5f6b4da62d239

[ "MIT" ]

null

levelpy/async/__init__.py

rch/levelpy

59e546854b3d478f3467bd573eb5f6b4da62d239

[ "MIT" ]

3

2017-01-25T22:26:40.000Z

2021-03-24T07:49:33.000Z

# # levelpy/async/__init__.py # import asyncio

8

27

0.729167

6

48

5.166667

1

0

0.145833

48

5

28

9.6

0.756098

0.520833

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

935fa4a7ed04d049f63c5a737cdba5df5fb7e88c

25

py

Python

src/engines/train/__init__.py

cr3ux53c/DenseNet-Tensorflow2

208143bf4086c407e524e01cd945fd3b0741b48d

[ "MIT" ]

60

2020-07-08T02:39:06.000Z

2022-03-28T14:26:34.000Z

src/engines/train/__init__.py

cr3ux53c/DenseNet-Tensorflow2

208143bf4086c407e524e01cd945fd3b0741b48d

[ "MIT" ]

28

2019-08-13T22:20:46.000Z

2020-02-17T19:27:32.000Z

src/engines/train/__init__.py

cr3ux53c/DenseNet-Tensorflow2

208143bf4086c407e524e01cd945fd3b0741b48d

[ "MIT" ]

18

2020-08-26T02:06:32.000Z

2022-03-22T03:04:40.000Z

from .train import train

12.5

24

0.8

4

25

5

0.75

0

0.16

25

1

25

0.952381

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9361e059240f0fd25cbcad6c1466201786c8b48a

164

py

Python

genie_core/services/KeyBoardService.py

JereMIbq1995/genie-core

f87b6de56749630a46200298f7021047e854c8a3

[ "MIT" ]

null

genie_core/services/KeyBoardService.py

JereMIbq1995/genie-core

f87b6de56749630a46200298f7021047e854c8a3

[ "MIT" ]

null

genie_core/services/KeyBoardService.py

JereMIbq1995/genie-core

f87b6de56749630a46200298f7021047e854c8a3

[ "MIT" ]

null

class KeyBoardService(): def __init__(self): pass def is_key_pressed(self, *keys): pass def is_key_released(self, *key): pass

16.4

36

0.591463

20

164

4.45

0.55

0.157303

0.202247

0.269663

0

0.310976

164

10

37

16.4

0.787611

0

0.428571

0

1

0.428571

false

0.428571

0

0.571429

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

fa97149416601cbc0c317503ce2c46c344e58e58

121

py

Python

src/connections/_sqlalchemy.py

Freonius/tranquillity

bb190b4a8facf643d5018a710100b3ff45d6d640

[ "MIT" ]

null

src/connections/_sqlalchemy.py

Freonius/tranquillity

bb190b4a8facf643d5018a710100b3ff45d6d640

[ "MIT" ]

20

2021-12-31T15:28:20.000Z

2022-02-15T18:24:16.000Z

src/connections/_sqlalchemy.py

Freonius/tranquillity

bb190b4a8facf643d5018a710100b3ff45d6d640

[ "MIT" ]

null

from sqlalchemy.engine import Engine, Connection from .__interface import IConnection class Sql(IConnection): pass

17.285714

48

0.801653

14

121

6.785714

0.714286

0

0.14876

121

6

49

20.166667

0.92233

0

1

0

true

0.25

0.5

0

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

6

faa59189fcb0db287ca016ded409105514d4c263

15,742

py

Python

tests/test_fluids_bw92.py

trhallam/digirock

05b1199d741a384345a4930605be97369c9ec270

[ "MIT" ]

null

tests/test_fluids_bw92.py

trhallam/digirock

05b1199d741a384345a4930605be97369c9ec270

[ "MIT" ]

2

2022-02-28T08:51:53.000Z

2022-02-28T13:24:33.000Z

tests/test_fluids_bw92.py

trhallam/digirock

05b1199d741a384345a4930605be97369c9ec270

[ "MIT" ]

null

"""Test functions for pem.fluid.bw92 module """ import pytest from pytest import approx from _pytest.fixtures import SubRequest from hypothesis import given, settings, strategies as st import numpy as np import digirock.fluids.bw92 as bw92 from .strategies import n_varshp_arrays @pytest.fixture(scope="module") def tol(): return { "rel": 0.05, # relative testing tolerance in percent "abs": 0.00001, # absolute testing tolerance } def test_GAS_R(): assert bw92.GAS_R == 8.31441 # p (MPa), t (degC) @pytest.mark.parametrize( "args,ans", (((10 * 1e6, 273.15), 0.00045422), ((50 * 1e6, 373.15), 0.00010747)) ) @given(data=st.data()) def test_gas_vmol(args, ans, data, tol): (test_p, test_t), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_vmol(test_t, test_p) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # p (MPa), t (degC), m (methane molecular weight) @pytest.mark.parametrize( "args,ans", ( ((10 * 1e6, 273.15, 16.04), 35313.5783218), ((50 * 1e6, 373.15, 16.04), 149248.08786351), ), ) @given(data=st.data()) def test_gas_density(args, ans, data, tol): (test_p, test_t, test_m), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_density(test_m, test_t, test_p) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # p (MPa), t (degC) @pytest.mark.parametrize( "args,ans", (((np.r_[10 * 1e6, 50 * 1e6], 273.15), 2e-08),), ) def test_gas_isotherm_comp(args, ans, tol): v1, v2 = bw92.gas_vmol(args[1], args[0]) assert bw92.gas_isotherm_comp(v1, v2, args[0][0], args[0][1]) == approx(ans) # t (degC), m (methane molecular weight), @pytest.mark.parametrize( "args,ans", ( ((273.15, 16.04), 16.8278695), ((373.15, 16.04), 18.30335126), ), ) @given(data=st.data()) def test_gas_isotherm_vp(args, ans, data, tol): (test_t, test_m), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_isotherm_vp(test_m, test_t) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # G spec grav @pytest.mark.parametrize( "args,ans", (((0.56,), 4.665312),), # methane ) @given(data=st.data()) def test_gas_pseudocrit_pres(args, ans, data, tol): (test_G,), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_pseudocrit_pres(test_G) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # fmt: off # p (MPa), G spec grav @pytest.mark.parametrize( "args,ans", ( ((10 * 1e6, 0.56,), 2143479.36429546,), ((50 * 1e6, 0.56), 10717396.82147732), ), ) # fmt: on @given(data=st.data()) def test_gas_pseudored_pres(args, ans, data, tol): ( test_p, test_G, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_pseudored_pres(test_p, test_G) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # G spec grav @pytest.mark.parametrize( "args,ans", (((0.56,), 190.34),), # methane ) @given(data=st.data()) def test_gas_pseudocrit_temp(args, ans, data, tol): (test_G,), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_pseudocrit_temp(test_G) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # t (degC), G (spec grav) # fmt: off @pytest.mark.parametrize( "args,ans", ( ((273.15, 0.56,), 2.87012714,), ((373.15, 0.56), 3.39550278), ), ) # fmt: on @given(data=st.data()) def test_gas_pseudored_temp(args, ans, data, tol): ( test_t, test_G, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_pseudored_temp(test_t, test_G) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # p (MPa), t (degC), G (spec grav) @pytest.mark.parametrize( "args,ans", ( ((10 * 1e6, 273.15, 0.56), 0.5289487894), ((50 * 1e6, 373.15, 0.56), 0.46664469), ), ) @given(data=st.data()) def test_gas_oga_density(args, ans, data, tol): ( test_p, test_t, test_G, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_oga_density(test_t, test_p, test_G) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape def test_gas_oga_density_warning(): with pytest.warns(UserWarning): bw92.gas_oga_density(4.5, 4.5, 1) # p (MPa), t (degC), G (spec grav) @pytest.mark.parametrize( "args,ans", ( ((10 * 1e6, 273.15, 0.56), 673174274.6197122), ((50 * 1e6, 373.15, 0.56), 1.87375111e10), ), ) @given(data=st.data()) def test_gas_adiabatic_bulkmod(args, ans, data, tol): ( test_p, test_t, test_G, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_adiabatic_bulkmod(test_t, test_p, test_G) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # p (MPa), t (degC), G (spec grav) @pytest.mark.parametrize( "args,ans", ( ((10 * 1e6, 273.15, 0.56), 0.0204339351378), ((50 * 1e6, 373.15, 0.56), 0.03011878), ), ) @given(data=st.data()) def test_gas_adiabatic_viscosity(args, ans, data, tol): ( test_p, test_t, test_G, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.gas_adiabatic_viscosity(test_t, test_p / 1e6, test_G) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # p (MPa), rho (g/cc) @pytest.mark.parametrize( "args,ans", ( ((10 * 1e6, 0.8), 0.8068623025), ((10 * 1e6, 0.9), 0.90521056), ((50 * 1e6, 0.8), 0.83179781), ((50 * 1e6, 0.9), 0.92477031), ), ) @given(data=st.data()) def test_oil_isothermal_density(args, ans, data, tol): ( test_p, test_rho, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_isothermal_density(test_rho, test_p / 1e6) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # p (MPa), rho (g/cc), t (degC) @pytest.mark.parametrize( "args,ans", ( ((10 * 1e6, 0.8, 273.15), 0.63475419), ((10 * 1e6, 0.9, 273.15), 0.71212423), ((50 * 1e6, 0.8, 273.15), 0.65437082), ((50 * 1e6, 0.9, 273.15), 0.72751178), ((10 * 1e6, 0.8, 373.15), 0.57827437), ((10 * 1e6, 0.9, 373.15), 0.6487601), ((50 * 1e6, 0.8, 373.15), 0.59614553), ((50 * 1e6, 0.9, 373.15), 0.65437082), ), ) @given(data=st.data()) def test_oil_density(args, ans, data, tol): ( test_p, test_rho, test_t ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_density(test_rho, test_p / 1e6, test_t) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # rho (g/cc), G (spec_grav), rg (L/L), t (degC) @pytest.mark.parametrize( "args,ans", ( ((0.8, 0.56, 120, 273.15), 1.57823582), ) ) @given(data=st.data()) def test_oil_fvf(args, ans, data, tol): ( test_rho, test_G, test_rg, test_t, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_fvf(test_rho, test_G, test_rg, test_t) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # rho (g/cc), G (spec_grav), p (MPa), t (degC) @pytest.mark.parametrize( "args,ans", (((0.8, 0.6, 50, 100), 415.709664),) ) @given(data=st.data()) def test_oil_rg_rho(args, ans, data, tol): ( test_rho, test_G, test_p, test_t, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_rg(test_rho, test_G, test_p, test_t, mode="rho") assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # rho (g/cc), G (spec_grav), p (MPa), t (degC) @pytest.mark.parametrize( "args,ans", (((45, 0.6, 50, 100), 415.709664),) ) @given(data=st.data()) def test_oil_rg_api(args, ans, data, tol): ( test_rho, test_G, test_p, test_t, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_rg(test_rho, test_G, test_p, test_t, mode="api") assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape def test_oil_rg_bad_mode(): with pytest.raises(ValueError): assert bw92.oil_rg(1, 1, 1, 1, mode="bad_mode") # rho0, g, rg, b0 @pytest.mark.parametrize( "args,ans", ( ((0.8, 0.6, 50, 1.1), 0.76), ((0.9, 0.6, 70, 1.1), 0.864), ((0.9, 0.6, 70, 0.0), 0.0) ), ) @given(data=st.data()) def test_oil_rho_sat(args, ans, data, tol): ( test_rho0, test_g, test_rg, test_b0, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_rho_sat(test_rho0, test_g, test_rg, test_b0) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # rho0, rg, b0 @pytest.mark.parametrize( "args,ans", ( ((0.8, 50, 1.1), 0.69264069264), ((0.9, 70, 1.1), 0.764655904), ((0.9, 70, 0.0), 0.0)), ) @given(data=st.data()) def test_oil_rho_pseudo(args, ans, data, tol): ( test_rho0, test_rg, test_b0, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_rho_pseudo(test_rho0, test_rg, test_b0) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # rho0,p,t,g,rg,b0 @pytest.mark.parametrize( "args,ans", ( ((0.8, 50, 100, 0.6, 120), 1101.21832685), ), ) @given(data=st.data()) def test_oil_velocity_nobo(args, ans, data, tol): ( test_rho0, test_p, test_t, test_g, test_rg, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_velocity(test_rho0, test_p, test_t, test_g, test_rg, b0=None) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # rho0,p,t,g,rg,b0 @pytest.mark.parametrize( "args,ans", ( ((0.8, 50, 100, 0.6, 120), 1206.74469093), ), ) @given(data=st.data()) def test_oil_velocity_bo(args, ans, data, tol): ( test_rho0, test_p, test_t, test_g, test_rg, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_velocity(test_rho0, test_p, test_t, test_g, test_rg, b0=np.r_[1.1]) assert np.allclose(test, ans, rtol=tol["rel"]) # assert np.squeeze(test).shape == result_shape # rho, vp @pytest.mark.parametrize( "args,ans", ( ((0.8, 1200), 1.152), ), ) @given(data=st.data()) def test_oil_bulkmod(args, ans, data, tol): ( test_rho, test_vp, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.oil_bulkmod(test_rho, test_vp) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # p (MPa), t (degC) @pytest.mark.parametrize( "args,ans", (((10 * 1e6, 273.15), 1063.23709), ((50 * 1e6, 373.15), 847.72401465)) ) @given(data=st.data()) @settings(deadline=None) # due to njit def test_wat_velocity_pure(args, ans, data, tol): ( test_p, test_t, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.wat_velocity_pure(test_t, test_p / 1e6) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # p (MPa), t (degC), sal (ppm) @pytest.mark.parametrize( "args,ans", (((10 * 1e6, 273.15, 32000), 1095.70072), ((50 * 1e6, 373.15, 150000), 980.48475247)) ) @given(data=st.data()) @settings(deadline=None) # due to njit def test_wat_velocity_brine(args, ans, data, tol): ( test_p, test_t, test_sal ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.wat_velocity_brine(test_t, test_p / 1e6, test_sal / 1e6) assert np.allclose(test, ans, rtol=tol["rel"]) # p (MPa), t (degC) @pytest.mark.parametrize( "args,ans", (((10 * 1e6, 273.15), 0.77622433), ((50 * 1e6, 373.15), 0.66363597)) ) @given(data=st.data()) def test_wat_density_pure(args, ans, data, tol): ( test_p, test_t, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.wat_density_pure(test_t, test_p / 1e6) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # p (MPa), t (degC), sal (ppm) @pytest.mark.parametrize( "args,ans", (((10 * 1e6, 273.15, 32000), 0.80405636), ((50 * 1e6, 373.15, 150000), 0.79606398)) ) @given(data=st.data()) def test_wat_density_brine(args, ans, data, tol): ( test_p, test_t, test_sal ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.wat_density_brine(test_t, test_p / 1e6, test_sal / 1e6) assert np.allclose(test, ans, rtol=tol["rel"]) # p (MPa), t (degC), sal (ppm) @pytest.mark.parametrize( "args,ans", (((10 * 1e6, 273.15, 32000), None), ((50 * 1e6, 373.15, 150000), None)) ) def test_wat_salinity_brine(args, ans): ( test_p, test_t, test_sal ) = args test_den = bw92.wat_density_brine(test_t, test_p / 1e6, test_sal / 1e6) test = bw92.wat_salinity_brine(test_t, test_p / 1e6, test_den) * 1e6 assert test == approx(test_sal, abs=250) # rho (g/cc), v (m/s) @pytest.mark.parametrize( "args,ans", (((1.0, 1300), 1.69), ((1.1, 1450), 2.31275)) ) @given(data=st.data()) def test_wat_bulkmod(args, ans, data, tol): ( test_rho, test_vp, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.wat_bulkmod(test_rho, test_vp) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # rho (g/cc), v (vfrac), ... @pytest.mark.parametrize( "args,ans", ( ((2, 0.5, 2, 0.5), 2), ((2, 0.5, 1, 0.5), 1.5), ((2, 0.3, 2, 0.3, 2,), 2), ((2, 0.5, 1, 0.5, 2,), 1.5), ) ) @given(data=st.data()) def test_mixed_density(args, ans, data, tol): args, result_shape = data.draw(n_varshp_arrays(args)) test = bw92.mixed_density(*args) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # rho (g/cc), v (m/s) @pytest.mark.parametrize( "args,ans", (((1.0, 1300), 1.69), ((1.1, 1450), 2.31275)) ) @given(data=st.data()) def test_bulkmod(args, ans, data, tol): ( test_rho, test_vp, ), result_shape = data.draw(n_varshp_arrays(args)) test = bw92.bulkmod(test_rho, test_vp) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape # k (GPa), v (vfrac), ... @pytest.mark.parametrize( "args,ans", ( ((2, 0.5, 2, 0.5), 2), ((2, 0.5, 1, 0.5), 1.3333), ((2, 0.3, 2, 0.3, 2,), 2), ((2, 0.5, 1, 0.5, 2,), 1.3333), ) ) @given(data=st.data()) def test_woods_bulkmod(args, ans, data, tol): args, result_shape = data.draw(n_varshp_arrays(args)) test = bw92.woods_bulkmod(*args) assert np.allclose(test, ans, rtol=tol["rel"]) assert test.shape == result_shape

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fab73276314f39860cdf6b1e49f429594d065a0f

14,376

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Python

codes/models/modules/sftmd_arch.py

Mark-sloan/IKC

f70af607e9434931c22e4971469aaed7683a22a3

[ "Apache-2.0" ]

1

2020-02-27T09:27:17.000Z

codes/models/modules/sftmd_arch.py

Mark-sloan/IKC

f70af607e9434931c22e4971469aaed7683a22a3

[ "Apache-2.0" ]

null

codes/models/modules/sftmd_arch.py

Mark-sloan/IKC

f70af607e9434931c22e4971469aaed7683a22a3

[ "Apache-2.0" ]

null

''' architecture for sftmd ''' import torch import torch.nn as nn import torch.nn.functional as F class Predictor(nn.Module): def __init__(self, input_channel=3, code_len=10, ndf=64, use_bias=True): super(Predictor, self).__init__() self.ConvNet = nn.Sequential(*[ nn.Conv2d(input_channel, ndf, kernel_size=5, stride=1, padding=2), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=1, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=1, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=2, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=1, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, code_len, kernel_size=5, stride=1, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), ]) # self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) self.globalPooling = nn.AdaptiveAvgPool2d((1,1)) def forward(self, input): conv = self.ConvNet(input) flat = self.globalPooling(conv) return flat.view(flat.size()[:2]) # torch size: [B, code_len] class Corrector(nn.Module): def __init__(self, input_channel=3, code_len=10, ndf=64, use_bias=True): super(Corrector, self).__init__() self.ConvNet = nn.Sequential(*[ nn.Conv2d(input_channel, ndf, kernel_size=5, stride=1, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=2, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=1, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=2, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=1, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=1, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, ndf, kernel_size=5, stride=1, padding=2, bias=use_bias), nn.LeakyReLU(0.2, True), ]) self.code_dense = nn.Sequential(*[ nn.Linear(code_len, ndf, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Linear(ndf, ndf, bias=use_bias), nn.LeakyReLU(0.2, True), ]) self.global_dense = nn.Sequential(*[ nn.Conv2d(ndf * 2, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf * 2, ndf, kernel_size=1, stride=1, padding=0, bias=use_bias), nn.LeakyReLU(0.2, True), nn.Conv2d(ndf, code_len, kernel_size=1, stride=1, padding=0, bias=use_bias), ]) self.ndf = ndf self.globalPooling = nn.AdaptiveAvgPool2d([1, 1]) def forward(self, input, code, res=False): conv_input = self.ConvNet(input) B, C_f, H_f, W_f = conv_input.size() # LR_size conv_code = self.code_dense(code).view((B, self.ndf, 1, 1)).expand((B, self.ndf, H_f, W_f)) # h_stretch conv_mid = torch.cat((conv_input, conv_code), dim=1) code_res = self.global_dense(conv_mid) # Delta_h_p flat = self.globalPooling(code_res) Delta_h_p = flat.view(flat.size()[:2]) if res: return Delta_h_p else: return Delta_h_p + code class SFT_Layer(nn.Module): def __init__(self, ndf=64, para=10): super(SFT_Layer, self).__init__() self.mul_conv1 = nn.Conv2d(para + ndf, 32, kernel_size=3, stride=1, padding=1) self.mul_leaky = nn.LeakyReLU(0.2) self.mul_conv2 = nn.Conv2d(32, ndf, kernel_size=3, stride=1, padding=1) self.add_conv1 = nn.Conv2d(para + ndf, 32, kernel_size=3, stride=1, padding=1) self.add_leaky = nn.LeakyReLU(0.2) self.add_conv2 = nn.Conv2d(32, ndf, kernel_size=3, stride=1, padding=1) def forward(self, feature_maps, para_maps): cat_input = torch.cat((feature_maps, para_maps), dim=1) mul = F.sigmoid(self.mul_conv2(self.mul_leaky(self.mul_conv1(cat_input)))) add = self.add_conv2(self.add_leaky(self.add_conv1(cat_input))) return feature_maps * mul + add class SFT_Residual_Block(nn.Module): def __init__(self, ndf=64, para=10): super(SFT_Residual_Block, self).__init__() self.sft1 = SFT_Layer(ndf=ndf, para=para) self.sft2 = SFT_Layer(ndf=ndf, para=para) self.conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) def forward(self, feature_maps, para_maps): fea1 = F.relu(self.sft1(feature_maps, para_maps)) fea2 = F.relu(self.sft2(self.conv1(fea1), para_maps)) fea3 = self.conv2(fea2) return torch.add(feature_maps, fea3) class SFTMD(nn.Module): def __init__(self, input_channel=3, input_para=10, scale=4, min=0.0, max=1.0, residuals=16): super(SFTMD, self).__init__() self.min = min self.max = max self.para = input_para self.num_blocks = residuals self.conv1 = nn.Conv2d(input_channel, 64, 3, stride=1, padding=1) self.relu_conv1 = nn.LeakyReLU(0.2) self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1) self.relu_conv2 = nn.LeakyReLU(0.2) self.conv3 = nn.Conv2d(64, 64, 3, stride=1, padding=1) sft_branch = [] for i in range(residuals): sft_branch.append(SFT_Residual_Block()) self.sft_branch = nn.Sequential(*sft_branch) for i in range(residuals): self.add_module('SFT-residual' + str(i + 1), SFT_Residual_Block(ndf=64, para=input_para)) self.sft = SFT_Layer(ndf=64, para=input_para) self.conv_mid = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) if scale == 4: self.upscale = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=64 * scale, kernel_size=3, stride=1, padding=1, bias=True), nn.PixelShuffle(scale // 2), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(in_channels=64, out_channels=64 * scale, kernel_size=3, stride=1, padding=1, bias=True), nn.PixelShuffle(scale // 2), nn.LeakyReLU(0.2, inplace=True), ) else: self.upscale = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=64*scale**2, kernel_size=3, stride=1, padding=1, bias=True), nn.PixelShuffle(scale), nn.LeakyReLU(0.2, inplace=True), ) self.conv_output = nn.Conv2d(in_channels=64, out_channels=input_channel, kernel_size=9, stride=1, padding=4, bias=True) def forward(self, input, ker_code): B, C, H, W = input.size() # I_LR batch B_h, C_h = ker_code.size() # Batch, Len=10 ker_code_exp = ker_code.view((B_h, C_h, 1, 1)).expand((B_h, C_h, H, W)) #kernel_map stretch fea_bef = self.conv3(self.relu_conv2(self.conv2(self.relu_conv1(self.conv1(input))))) fea_in = fea_bef for i in range(self.num_blocks): fea_in = self.__getattr__('SFT-residual' + str(i + 1))(fea_in, ker_code_exp) fea_mid = fea_in #fea_in = self.sft_branch((fea_in, ker_code_exp)) fea_add = torch.add(fea_mid, fea_bef) fea = self.upscale(self.conv_mid(self.sft(fea_add, ker_code_exp))) out = self.conv_output(fea) return torch.clamp(out, min=self.min, max=self.max) class Residual_Block(nn.Module): def __init__(self): super(Residual_Block, self).__init__() self.conv1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) self.conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) self.conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) def forward(self, input): fea = input fea1 = self.lrelu(self.conv1(fea)) fea2 = self.lrelu(self.conv2(fea1)) fea3 = self.lrelu(self.conv3(fea2)) fea4 = self.conv4(fea3) output = input + fea4 return output class SRResNet(nn.Module): def __init__(self, input_channel=3, input_para=32, scale=4, min=0.0, max=1.0, residuals=16): super(SRResNet, self).__init__() self.min = min self.max = max self.para = input_para self.num_blocks = residuals self.conv1 = nn.Conv2d(input_channel, 64, 3, stride=1, padding=1) self.relu_conv1 = nn.LeakyReLU(0.2) self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1) self.relu_conv2 = nn.LeakyReLU(0.2) self.conv3 = nn.Conv2d(64, 64, 3, stride=1, padding=1) sft_branch = [] for i in range(residuals): sft_branch.append(Residual_Block()) self.sft_branch = nn.Sequential(*sft_branch) #for i in range(residuals): # self.add_module('SFT-residual' + str(i + 1), SFT_Residual_Block(ndf=64, para=input_para)) #self.sft = SFT_Layer(ndf=64, para=input_para) self.mul_conv1 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1) self.mul_leaky = nn.LeakyReLU(0.2) self.mul_conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) self.conv_mid = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=True) self.upscale = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=64*scale**2, kernel_size=3, stride=1, padding=1, bias=True), nn.PixelShuffle(scale), nn.LeakyReLU(0.2, inplace=True), ) self.conv_output = nn.Conv2d(in_channels=64, out_channels=input_channel, kernel_size=9, stride=1, padding=4, bias=True) def forward(self, input): #B, C, H, W = input.size() # I_LR batch #B_h, C_h = ker_code.size() # Batch, Len=32 #ker_code_exp = ker_code.view((B_h, C_h, 1, 1)).expand((B_h, C_h, H, W)) #kernel_map stretch fea_bef = self.conv3(self.relu_conv2(self.conv2(self.relu_conv1(self.conv1(input))))) fea_in = fea_bef fea_mid = self.sft_branch(fea_in) fea_add = torch.add(fea_mid, fea_bef) fea = self.upscale(self.conv_mid(self.mul_conv2(self.mul_leaky(self.mul_conv1(fea_add))))) out = self.conv_output(fea) return torch.clamp(out, min=self.min, max=self.max) class SFTMD_DEMO(nn.Module): def __init__(self, input_channel=3, input_para=10, scala=4, min=0.0, max=1.0, residuals=16): super(SFTMD_DEMO, self).__init__() self.min = min self.max = max self.para = input_para self.reses = residuals self.conv1 = nn.Conv2d(input_channel + input_para, 64, 3, stride=1, padding=1) self.relu_conv1 = nn.LeakyReLU(0.2) self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1) self.relu_conv2 = nn.LeakyReLU(0.2) self.conv3 = nn.Conv2d(64, 64, 3, stride=1, padding=1) for i in range(residuals): self.add_module('SFT-residual' + str(i + 1), SFT_Residual_Block(ndf=64, para=input_para)) self.sft_mid = SFT_Layer(ndf=64, para=input_para) self.conv_mid = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False) self.scala = scala if scala == 4: self.upscale = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False), nn.PixelShuffle(2), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(in_channels=64, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False), nn.PixelShuffle(2), nn.LeakyReLU(0.2, inplace=True), ) elif scala == 3: self.upscale = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=64*9, kernel_size=3, stride=1, padding=1, bias=False), nn.PixelShuffle(3), nn.LeakyReLU(0.2, inplace=True), ) elif scala == 2: self.upscale = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False), nn.PixelShuffle(2), nn.LeakyReLU(0.2, inplace=True), ) else: self.upscale = nn.Sequential( nn.Conv2d(in_channels=64, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False), nn.PixelShuffle(2), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(in_channels=64, out_channels=256, kernel_size=3, stride=1, padding=1, bias=False), nn.PixelShuffle(2), nn.LeakyReLU(0.2, inplace=True), ) self.conv_output = nn.Conv2d(in_channels=64, out_channels=input_channel, kernel_size=9, stride=1, padding=4, bias=False) def forward(self, input, code, clip=False): B, C, H, W = input.size() B, C_l = code.size() code_exp = code.view((B, C_l, 1, 1)).expand((B, C_l, H, W)) input_cat = torch.cat([input, code_exp], dim=1) before_res = self.conv3(self.relu_conv2(self.conv2(self.relu_conv1(self.conv1(input_cat))))) res = before_res for i in range(self.reses): res = self.__getattr__('SFT-residual' + str(i + 1))(res, code_exp) mid = self.sft_mid(res, code_exp) mid = F.relu(mid) mid = self.conv_mid(mid) befor_up = torch.add(before_res, mid) uped = self.upscale(befor_up) out = self.conv_output(uped) return torch.clamp(out, min=self.min, max=self.max) if clip else out

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null

0

6

8789474f74a671f6ddfeccf4cb104d024e738101

32

py

Python

poetry/console/commands/cache/__init__.py

hongquan/poetry

d12f6421b1c34067e3968ddec2d821ae7f316af7

[ "MIT" ]

1

2020-12-22T12:51:11.000Z

poetry/console/commands/cache/__init__.py

hongquan/poetry

d12f6421b1c34067e3968ddec2d821ae7f316af7

[ "MIT" ]

null

poetry/console/commands/cache/__init__.py

hongquan/poetry

d12f6421b1c34067e3968ddec2d821ae7f316af7

[ "MIT" ]

null

from .cache import CacheCommand

16

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6

359e4a1d1a339ad0c2dbb1f04272dbcd884b9fc0

163

py

Python

HSTB/shared/settings.py

noaa-ocs-hydrography/shared

d2004e803c708dffa43d09d3ffea4e4045811b28

[ "CC0-1.0" ]

null

HSTB/shared/settings.py

noaa-ocs-hydrography/shared

d2004e803c708dffa43d09d3ffea4e4045811b28

[ "CC0-1.0" ]

null

HSTB/shared/settings.py

noaa-ocs-hydrography/shared

d2004e803c708dffa43d09d3ffea4e4045811b28

[ "CC0-1.0" ]

null

from sys import platform if 'win' in platform: from .winreg import * elif 'linux' in platform: from posixreg import * import posixreg posixreg.__init__()

18.111111

25

0.730061

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163

5.227273

0.545455

0.173913

0.243478

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0.196319

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0

1

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1

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6

35d72c0843e71e8c73c42625fa71b7593cdf2c85

192

py

Python

KSFGHAction/__init__.py

KOLANICH-GHActions/KSFGHAction.py

e1d54ae0043d93d8b190f8e758b978bf8e779c51

[ "Unlicense" ]

null

KSFGHAction/__init__.py

KOLANICH-GHActions/KSFGHAction.py

e1d54ae0043d93d8b190f8e758b978bf8e779c51

[ "Unlicense" ]

3

2019-12-23T22:58:40.000Z

2019-12-25T11:20:13.000Z

KSFGHAction/__init__.py

KOLANICH/KSFGHAction.py

e1d54ae0043d93d8b190f8e758b978bf8e779c51

[ "Unlicense" ]

null

#!/usr/bin/env python3 import typing from .utils import ClassDictMeta from .issueParser import * from .linter import * from miniGHAPI.GitHubAPI import * from miniGHAPI.GHActionsEnv import *

19.2

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6

ea230ac82e5bb4386e749cb1be502c5bdba91b6d

76

py

Python

dotfiles/common/.gdbinit.d/eigen.py

HaoZeke/Dotfiles

f4ac24b0d7e08d87b1f402af67e463c528b1b69d

[ "Unlicense" ]

14

2018-10-29T18:54:25.000Z

2021-12-21T00:22:52.000Z

dotfiles/common/.gdbinit.d/eigen.py

HaoZeke/Dotfiles

f4ac24b0d7e08d87b1f402af67e463c528b1b69d

[ "Unlicense" ]

1

2018-08-20T17:41:10.000Z

2018-08-20T17:42:23.000Z

dotfiles/common/.gdbinit.d/eigen.py

HaoZeke/Dotfiles

f4ac24b0d7e08d87b1f402af67e463c528b1b69d

[ "Unlicense" ]

3

2018-08-20T17:36:29.000Z

2021-01-23T05:18:30.000Z

# Eigen pretty printer __import__('eigengdb').register_eigen_printers(None)

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1

0

1

0

1

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6

ea37e30aaa000862cd2a845865431af3031ecbc6

188

py

Python

src/plugins/hk_reporter/platform/__init__.py

panda361/nonebot-hk-reporter

b94f6cc31844e9307e355fc81f387ea42501a014

[ "MIT" ]

null

src/plugins/hk_reporter/platform/__init__.py

panda361/nonebot-hk-reporter

b94f6cc31844e9307e355fc81f387ea42501a014

[ "MIT" ]

null

src/plugins/hk_reporter/platform/__init__.py

panda361/nonebot-hk-reporter

b94f6cc31844e9307e355fc81f387ea42501a014

[ "MIT" ]

null

from .bilibili import Bilibili from .rss import Rss from .weibo import Weibo from .utils import check_sub_target from .platform import PlatformNoTarget from .utils import platform_manager

26.857143

38

0.840426

27

188

5.740741

0.444444

0.116129

0.193548

0

0.12766

188

6

39

31.333333

0.945122

0

1

0

true

0

1

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1

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1

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null

0

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null

0

1

0

1

0

1

0

6

ea4f94dc62eb152286ddb45f21d8cbc92ac2d88f

1,697

py

Python

sample_data/make_sample_data.py

mhernan88/reshape_tools

e08de72629079457f4194a8a14dbb8641b5b0a13

[ "MIT" ]

null

sample_data/make_sample_data.py

mhernan88/reshape_tools

e08de72629079457f4194a8a14dbb8641b5b0a13

[ "MIT" ]

null

sample_data/make_sample_data.py

mhernan88/reshape_tools

e08de72629079457f4194a8a14dbb8641b5b0a13

[ "MIT" ]

null

import pandas as pd from datetime import datetime def sample_data1() -> pd.DataFrame: times = [ datetime(year=2020, month=11, day=1), datetime(year=2020, month=11, day=2), datetime(year=2020, month=11, day=3), datetime(year=2020, month=11, day=4), datetime(year=2020, month=11, day=5), datetime(year=2020, month=12, day=6), datetime(year=2020, month=12, day=7), datetime(year=2020, month=12, day=8), datetime(year=2020, month=12, day=9), datetime(year=2020, month=12, day=10), ] val1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] val2 = [20, 40, 60, 80, 100, 120, 140, 160, 180, 200] out = pd.DataFrame({"times": times, "val1": val1, "val2": val2}) out["times"] = (out["times"] - datetime(1970, 1, 1)).dt.total_seconds() return out def sample_data2() -> pd.DataFrame: times = [ datetime(year=2020, month=11, day=1, hour=8), datetime(year=2020, month=11, day=1, hour=9), datetime(year=2020, month=11, day=1, hour=10), datetime(year=2020, month=11, day=1, hour=11), datetime(year=2020, month=11, day=1, hour=12), datetime(year=2020, month=11, day=2, hour=8), datetime(year=2020, month=11, day=2, hour=9), datetime(year=2020, month=11, day=2, hour=10), datetime(year=2020, month=11, day=2, hour=11), datetime(year=2020, month=11, day=2, hour=12), ] val1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] val2 = [20, 40, 60, 80, 100, 120, 140, 160, 180, 200] out = pd.DataFrame({"times": times, "val1": val1, "val2": val2}) out["times"] = (out["times"] - datetime(1970, 1, 1)).dt.total_seconds() return out

37.711111

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0

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0.234532

1,697

44

76

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0.552733

0

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0.157895

0

null

1

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null

0

6

ea93a4863758bc25492b7cc4b0d277a031dd4e69

427

py

Python

Python/Tutorial - 3/check.py

JC2295/FCC_Tutorial_Projects

990e1221b2177acb9e4db0264adab518620404a0

[ "MIT" ]

null

Python/Tutorial - 3/check.py

JC2295/FCC_Tutorial_Projects

990e1221b2177acb9e4db0264adab518620404a0

[ "MIT" ]

null

Python/Tutorial - 3/check.py

JC2295/FCC_Tutorial_Projects

990e1221b2177acb9e4db0264adab518620404a0

[ "MIT" ]

null

x = float(input("Enter Number: ")) if(x % 2) == 0 and x > 0: print("The number you entered is positive and even.") elif(x % 2) == 0 and x < 0: print("The number you entered is negative and even.") elif(x % 2) != 0 and x > 0: print("The number you entered is positive and odd.") elif(x % 2) != 0 and x < 0: print("The number you entered is negative and odd.") else: print("Please enter a non zero number.")

32.846154

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77

427

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0.045455

0.090909

0.757576

0

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0.236534

427

12

58

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0.773006

0

0.512881

0

1

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false

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0

null

0

1

0

null

0

1

0

6

577688ba8ec27db2c2499b1d093cb54fc4a94c4c

71

py

Python

Server/app/schema/utils/__init__.py

Team-SeeTo/SeeTo-Backend

19990cd6f4895e773eaa504f7b7a07ddbb5856e5

[ "Apache-2.0" ]

4

2018-06-18T06:50:12.000Z

2018-11-15T00:08:24.000Z

Server/app/schema/utils/__init__.py

Team-SeeTo/SeeTo-Backend

19990cd6f4895e773eaa504f7b7a07ddbb5856e5

[ "Apache-2.0" ]

null

Server/app/schema/utils/__init__.py

Team-SeeTo/SeeTo-Backend

19990cd6f4895e773eaa504f7b7a07ddbb5856e5

[ "Apache-2.0" ]

null

from .activity_logger import idea_activity_logger, todo_activity_logger

71

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0.6

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71

1

71

0.895522

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null

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1

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0

1

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6

57a3a8a709c11a11c55c20bbf889cce49b9d06df

223

py

Python

adjutant-plugin/mfa_views/models.py

catalyst-cloud/adjutant-mfa

d9e99dd006a895ca33b840e7d92bc5107ec4ba8d

[ "Apache-2.0" ]

2

2018-02-15T12:56:05.000Z

2018-03-10T09:45:08.000Z

adjutant-plugin/mfa_views/models.py

catalyst-cloud/adjutant-mfa

d9e99dd006a895ca33b840e7d92bc5107ec4ba8d

[ "Apache-2.0" ]

1

2018-05-24T23:08:17.000Z

adjutant-plugin/mfa_views/models.py

catalyst-cloud/adjutant-mfa

d9e99dd006a895ca33b840e7d92bc5107ec4ba8d

[ "Apache-2.0" ]

3

2018-02-09T03:27:43.000Z

2018-07-02T10:45:35.000Z

from adjutant.api.v1.models import register_taskview_class from mfa_views import views register_taskview_class(r'^openstack/edit-mfa/?$', views.EditMFA) register_taskview_class(r'^openstack/users/?$', views.UserListMFA)

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null

0

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1

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6

57bd96ec29c38dc1f3e044540afbb8ecbffccd50

106

py

Python

dogpile/__init__.py

dhellmann/dogpile.cache

03cbee8b9a812a78a37fe1d54edafe848f696737

[ "MIT" ]

null

dogpile/__init__.py

dhellmann/dogpile.cache

03cbee8b9a812a78a37fe1d54edafe848f696737

[ "MIT" ]

null

dogpile/__init__.py

dhellmann/dogpile.cache

03cbee8b9a812a78a37fe1d54edafe848f696737

[ "MIT" ]

null

__version__ = '0.9.3' from .lock import Lock # noqa from .lock import NeedRegenerationException # noqa

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106

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4

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null

0

1

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6

17f4968f43abac4e2306cd72253ba000d15a0329

49

py

Python

bitswap/block_storage/__init__.py

VladislavSufyanov/py-bitswap

875d15944e485c33b16af9965f24c1d85cb34c55

[ "MIT" ]

null

bitswap/block_storage/__init__.py

VladislavSufyanov/py-bitswap

875d15944e485c33b16af9965f24c1d85cb34c55

[ "MIT" ]

null

bitswap/block_storage/__init__.py

VladislavSufyanov/py-bitswap

875d15944e485c33b16af9965f24c1d85cb34c55

[ "MIT" ]

null

from .base_block_storage import BaseBlockStorage

24.5

48

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1

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1

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6

aa177fa6cb0e8f56b34cd0aa79227c8c06214fe1

85

py

Python

src/lib/__init__.py

nekoffski/bachelor-thesis

23f350e2c5e0a184620bf66f851be6e94df7cdbb

[ "MIT" ]

2

2021-08-09T09:00:34.000Z

2021-08-20T09:31:00.000Z

src/lib/__init__.py

nekoffski/bachelor-thesis

23f350e2c5e0a184620bf66f851be6e94df7cdbb

[ "MIT" ]

null

src/lib/__init__.py

nekoffski/bachelor-thesis

23f350e2c5e0a184620bf66f851be6e94df7cdbb

[ "MIT" ]

null

from .cvision import * from .models import * from .net import * from .util import *

14.166667

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85

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null

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0

1

0

1

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6

aa1c025ad6114c52c33a5057e8bad9a15f3be813

10,790

py

Python

yemek.py

raydingoz/cerrahapp

81a242dcf20d525f44a54f025414e9e8775caa37

[ "MIT" ]

2

2017-12-14T10:30:45.000Z

2022-01-11T19:19:33.000Z

yemek.py

raydingoz/cerrahapp

81a242dcf20d525f44a54f025414e9e8775caa37

[ "MIT" ]

null

yemek.py

raydingoz/cerrahapp

81a242dcf20d525f44a54f025414e9e8775caa37

[ "MIT" ]

null

##bu python kodu, selenium ve chromedriver ile çalışmakta, siteyi normal kullanıcı gibi ziyaret edip, gerekli verileri parse ediyor from selenium import webdriver from selenium.webdriver.chrome.options import Options import os from bs4 import BeautifulSoup import time, datetime import json import requests import sys import ftplib chrome_options = Options() chrome_options.add_argument("--headless") chrome_options.add_argument("--window-size=1920x1080") chrome_driver = os.getcwd() +"\\chromedriver.exe" browser = webdriver.Chrome(chrome_options=chrome_options, executable_path=chrome_driver) #replace with .Firefox(), or with the browser of your choice url = "http://sks.istanbulc.edu.tr/tr/yemeklistesi" browser.get(url) #navigate to the page time.sleep(10) kah_buton = browser.find_element_by_xpath('//*[@id="4E00590053005F006D004C00500035005500720059003100"]/div/div/div[2]/ul/li[1]') ogle_buton = browser.find_element_by_xpath('//*[@id="4E00590053005F006D004C00500035005500720059003100"]/div/div/div[2]/ul/li[2]') #aksam_buton = browser.find_element_by_xpath('//*[@id="4E00590053005F006D004C00500035005500720059003100"]/div/div/div[2]/ul/li[3]') vegan_buton = browser.find_element_by_xpath('//*[@id="4E00590053005F006D004C00500035005500720059003100"]/div/div/div[2]/ul/li[6]') kumanya_buton = browser.find_element_by_xpath('//*[@id="4E00590053005F006D004C00500035005500720059003100"]/div/div/div[2]/ul/li[4]') son = {} son["yemek_liste"] = [] def kah_json_olustur(): time.sleep(5) kah = browser.find_element_by_id("tab-kahvalti") bs = BeautifulSoup(kah.get_attribute('innerHTML'), "lxml") bs2 = bs.find_all('table') js = [] for h in bs2: b = h.find_all('tr') try: yemek1 = b[1].text.split('\n')[2] except: yemek1="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek2 = b[1].text.split('\n')[3] except: yemek2="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek3 = b[1].text.split('\n')[4] except: yemek3="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek4 = b[1].text.split('\n')[5] except: yemek4="---" print("Oops!", sys.exc_info()[0], "occured.") try: calori = b[2].text.replace("\n", "") except: calori="---" print("Oops!", sys.exc_info()[0], "occured.") if yemek1 == "": yemek1 = "---" if yemek2 == "": yemek2 = "---" if yemek3 == "": yemek3 = "---" if yemek4 == "": yemek4 = "---" dt = datetime.datetime.strptime(b[0].text.replace("\n", ""), '%d.%m.%Y') dt = dt.strftime('%Y-%m-%d %H:%M:%S') ta = {"tarih": dt,"ogun":"Kahvaltı","yemek1":yemek1,"yemek2":yemek2,"yemek3":yemek3,"yemek4":yemek4,"calori":calori } son["yemek_liste"].append(ta) def ogle_json_olustur(): time.sleep(5) kah = browser.find_element_by_id("tab-ogle") bs = BeautifulSoup(kah.get_attribute('innerHTML'), "lxml") bs2 = bs.find_all('table') js = [] for h in bs2: b = h.find_all('tr') try: yemek1 = b[1].text.split('\n')[1] except: yemek1="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek2 = b[1].text.split('\n')[2] except: yemek2="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek3 = b[1].text.split('\n')[3] except: yemek3="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek4 = b[1].text.split('\n')[4] except: yemek4="---" print("Oops!", sys.exc_info()[0], "occured.") try: calori = b[2].text.replace("\n", "") except: calori="---" print("Oops!", sys.exc_info()[0], "occured.") dt = datetime.datetime.strptime(b[0].text.replace("\n", ""), '%d.%m.%Y') dt = dt.strftime('%Y-%m-%d %H:%M:%S') if yemek1 == "": yemek1 = "---" if yemek2 == "": yemek2 = "---" if yemek3 == "": yemek3 = "---" if yemek4 == "": yemek4 = "---" ta = {"tarih": dt,"ogun":"Öğle Yemeği","yemek1":yemek1,"yemek2":yemek2,"yemek3":yemek3,"yemek4":yemek4,"calori":calori} son["yemek_liste"].append(ta) def aksam_json_olustur(): time.sleep(5) kah = browser.find_element_by_id("tab-ogle") bs = BeautifulSoup(kah.get_attribute('innerHTML'), "lxml") bs2 = bs.find_all('table') js = [] for h in bs2: b = h.find_all('tr') try: yemek1 = b[1].text.split('\n')[1] except: yemek1="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek2 = b[1].text.split('\n')[2] except: yemek2="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek3 = b[1].text.split('\n')[3] except: yemek3="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek4 = b[1].text.split('\n')[4] except: yemek4="---" print("Oops!", sys.exc_info()[0], "occured.") try: calori = b[2].text.replace("\n", "") except: calori="---" print("Oops!", sys.exc_info()[0], "occured.") if yemek1 == "": yemek1 = "---" if yemek2 == "": yemek2 = "---" if yemek3 == "": yemek3 = "---" if yemek4 == "": yemek4 = "---" dt = datetime.datetime.strptime(b[0].text.replace("\n", ""), '%d.%m.%Y') dt = dt.strftime('%Y-%m-%d %H:%M:%S') ta = {"tarih": dt,"ogun":"Akşam Yemeği","yemek1":yemek1,"yemek2":yemek2,"yemek3":yemek3,"yemek4":yemek4,"calori": calori} son["yemek_liste"].append(ta) def vegan_json_olustur(): time.sleep(5) kah = browser.find_element_by_id("tab-vegan") bs = BeautifulSoup(kah.get_attribute('innerHTML'), "lxml") bs2 = bs.find_all('table') for h in bs2: b = h.find_all('tr') try: yemek1 = b[1].text.split('\n')[1] except: yemek1="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek2 = b[1].text.split('\n')[2] except: yemek2="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek3 = b[1].text.split('\n')[3] except: yemek3="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek4 = b[1].text.split('\n')[4] except: yemek4="---" print("Oops!", sys.exc_info()[0], "occured.") try: calori = b[2].text.replace("\n", "") except: calori="---" print("Oops!", sys.exc_info()[0], "occured.") if yemek1 == "": yemek1 = "---" if yemek2 == "": yemek2 = "---" if yemek3 == "": yemek3 = "---" if yemek4 == "": yemek4 = "---" dt = datetime.datetime.strptime(b[0].text.replace("\n", ""), '%d.%m.%Y') dt = dt.strftime('%Y-%m-%d %H:%M:%S') ta = {"tarih": dt,"ogun":"Vegan","yemek1":yemek1,"yemek2":yemek2,"yemek3":yemek3,"yemek4":yemek4,"calori": calori} son["yemek_liste"].append(ta) def kumanya_json_olustur(): time.sleep(5) kah = browser.find_element_by_id("tab-kumanya") bs = BeautifulSoup(kah.get_attribute('innerHTML'), "lxml") bs2 = bs.find_all('table') for h in bs2: b = h.find_all('tr') try: yemek1 = b[1].text.split('\n')[1] except: yemek1="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek2 = b[1].text.split('\n')[2] except: yemek2="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek3 = b[1].text.split('\n')[3] except: yemek3="---" print("Oops!", sys.exc_info()[0], "occured.") try: yemek4 = b[1].text.split('\n')[4] except: yemek4="---" print("Oops!", sys.exc_info()[0], "occured.") try: calori = b[2].text.replace("\n", "") except: calori="---" print("Oops!", sys.exc_info()[0], "occured.") if yemek1 == "": yemek1 = "---" if yemek2 == "": yemek2 = "---" if yemek3 == "": yemek3 = "---" if yemek4 == "": yemek4 = "---" dt = datetime.datetime.strptime(b[0].text.replace("\n", ""), '%d.%m.%Y') dt = dt.strftime('%Y-%m-%d %H:%M:%S') ta = {"tarih": dt,"ogun":"Öğle Yemeği","yemek1":yemek1,"yemek2":yemek2,"yemek3":yemek3,"yemek4":yemek4,"calori": calori} son["yemek_liste"].append(ta) def dosya_olsutur(): with open('yemek.json', 'w') as outfile: json.dump(son, outfile) def mysql_isleri(): requests.get("*****") def ftp_yukle(): print("----------------") print(" ") print("ftp deneniyor...") import ftplib ftp = ftplib.FTP() host = "****" port = 21 ftp.connect(host, port) print(ftp.getwelcome()) File2Send = "yemek.json" Output_Directory = "//****//" try: print("Giriş Yapılıyor...") ftp.login("****", "****") time.sleep(6) mysql_isleri() print("Başarılı") except Exception as e: print(e) try: file = open('yemek.json', 'rb') # file to send ftp.storbinary('STOR yemek.json', file) # send the file except Exception as e: print(e) ftp.quit() print(" ") print("----------------") def sonuc(): dosya_olsutur() ftp_yukle() try: kah_buton.click() kah_json_olustur() except: print("Kahvaltı oluşturalamadı", sys.exc_info()[0]) try: ogle_buton.click() ogle_json_olustur() except: print("Öğle oluşturalamadı", sys.exc_info()[0]) try: ogle_buton.click() aksam_json_olustur() except: print("Akşam oluşturalamadı", sys.exc_info()[0]) try: vegan_buton.click() vegan_json_olustur() except: print("Vegan oluşturalamadı", sys.exc_info()[0]) try: kumanya_buton.click() kumanya_json_olustur() except: print("Kumanya oluşturalamadı", sys.exc_info()[0]) browser.close() print(json.dumps(son)) print("-------------") sonuc() time.sleep(5) mysql_isleri() print("-----Güncelleme Bitti----")

29.80663

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0.510843

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4.299521

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0.055731

0.061304

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0.696823

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0

null

0

1

0

1

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null

0

6

aa23ed79c757b361839b4005aa702d809d5ea5a0

43

py

Python

Very Easy/hello.py

Maverick-cmd/Python-Practice

4dc3f1eb5d633e20057052531cfc6e04772bc061

[ "MIT" ]

null

Very Easy/hello.py

Maverick-cmd/Python-Practice

4dc3f1eb5d633e20057052531cfc6e04772bc061

[ "MIT" ]

null

Very Easy/hello.py

Maverick-cmd/Python-Practice

4dc3f1eb5d633e20057052531cfc6e04772bc061

[ "MIT" ]

null

def hello(): return "hello edabit.com"

14.333333

29

0.651163

6

43

4.666667

0.833333

0

0.209302

43

2

30

21.5

0.823529

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0.372093

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1

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true

0

0.5

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1

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null

0

1

0

null

0

1

0

1

0

6

aa282f1ac7388b3707ced3a023f5c1233c6cf501

156

py

Python

tests/middlewares/__init__.py

caputomarcos/mongorest

57d6b28d75e18afed5cef7160522958153b5be15

[ "BSD-3-Clause" ]

16

2015-04-18T02:51:09.000Z

2020-12-15T18:05:16.000Z

tests/middlewares/__init__.py

caputomarcos/mongorest

57d6b28d75e18afed5cef7160522958153b5be15

[ "BSD-3-Clause" ]

8

2015-11-24T23:06:03.000Z

2016-07-21T17:57:59.000Z

tests/middlewares/__init__.py

caputomarcos/mongorest

57d6b28d75e18afed5cef7160522958153b5be15

[ "BSD-3-Clause" ]

2

2015-12-04T13:45:32.000Z

2016-06-11T13:44:53.000Z

# -*- encoding: UTF-8 -*- from __future__ import absolute_import, unicode_literals from .authentication_middleware import * from .cors_middleware import *

26

56

0.788462

18

156

6.388889

0.666667

0.278261

0

0.007299

0.121795

156

5

57

31.2

0.832117

0.147436

0

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true

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0

null

0

1

0

1

0

1

0

6

a4ea59238b7b0825e3fc20672e4667b0dd688bec

165

py

Python

fnss/traffic/__init__.py

brucespang/fnss

8e1d95744347afa77383092e6f144980d84e222d

[ "BSD-2-Clause" ]

114

2015-01-19T14:15:07.000Z

2022-02-22T01:47:19.000Z

fnss/traffic/__init__.py

brucespang/fnss

8e1d95744347afa77383092e6f144980d84e222d

[ "BSD-2-Clause" ]

15

2016-02-11T09:09:02.000Z

2021-04-05T12:57:09.000Z

fnss/traffic/__init__.py

brucespang/fnss

8e1d95744347afa77383092e6f144980d84e222d

[ "BSD-2-Clause" ]

36

2015-02-08T12:28:04.000Z

2021-11-19T06:08:17.000Z

"""Tools for creating and manipulating event schedules and traffic matrices""" from fnss.traffic.eventscheduling import * from fnss.traffic.trafficmatrices import *

41.25

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0.818182

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6.75

0.7

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0

0.109091

165

3

79

55

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0

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null

0

1

0

1

0

1

0

6

354a546386259b039381e2eef2517c0b0f0d4c22

150

py

Python

pyomt5/api/__init__.py

paulorodriguesxv/pyomt5

9287395f9f72b049c945e625e3b75c491ae50407

[ "MIT" ]

8

2019-09-06T02:44:04.000Z

2021-07-08T04:10:11.000Z

pyomt5/api/__init__.py

dausech/pyomt5

691dbf7b9732728425e57a7b9055d971838c5c4d

[ "MIT" ]

null

pyomt5/api/__init__.py

dausech/pyomt5

691dbf7b9732728425e57a7b9055d971838c5c4d

[ "MIT" ]

2

2019-09-10T16:41:16.000Z

2020-10-14T13:49:33.000Z

from .metatradercom import (MetatraderCom, ConnectionTimeoutError, DataNotFoundError) from .timeframe import MT5TimeFrame

37.5

66

0.706667

10

150

10.6

0.7

0

0.008929

0.253333

150

3

67

50

0.9375

0

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0

true

0

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null

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1

0

1

0

1

0

6

103fc50711d0b0de84f01f2724705c3318868eda

273

py

Python

src/aijack/attack/inversion/__init__.py

luoshenseeker/AIJack

4e871a5b3beb4b7c976d38060d6956efcebf880d

[ "MIT" ]

1

2022-03-17T21:17:44.000Z

src/aijack/attack/inversion/__init__.py

luoshenseeker/AIJack

4e871a5b3beb4b7c976d38060d6956efcebf880d

[ "MIT" ]

null

src/aijack/attack/inversion/__init__.py

luoshenseeker/AIJack

4e871a5b3beb4b7c976d38060d6956efcebf880d

[ "MIT" ]

1

2022-03-17T21:17:46.000Z

from .gan_attack import GAN_Attack # noqa: F401 from .generator_attack import Generator_Attack # noqa: F401 from .gradientinversion import GradientInversion_Attack # noqa: F401 from .mi_face import MI_FACE # noqa: F401 from .utils import DataRepExtractor # noqa: F401

45.5

69

0.798535

37

273

5.702703

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0.189573

0.227488

0.255924

0

0.064378

0.14652

273

5

70

54.6

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0

1

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0

null

0

1

0

1

0

1

0

6

1071230a1f43893cad26a92ac7143ffa8a21a932

47

py

Python

Lesson5/srez.py

shinkai-tester/python_beginner

a934328c9a50241cc3f02a423060e16aab53b425

[ "Apache-2.0" ]

2

2021-06-01T13:24:04.000Z

2021-06-01T13:27:47.000Z

Lesson5/srez.py

shinkai-tester/python_beginner

a934328c9a50241cc3f02a423060e16aab53b425

[ "Apache-2.0" ]

null

Lesson5/srez.py

shinkai-tester/python_beginner

a934328c9a50241cc3f02a423060e16aab53b425

[ "Apache-2.0" ]

null

a = [1, 2, 3, 4, 5] b = a[:3] print(b) print(a)

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null

0

1

0

6

10a46c5b25f3ebf58bddf11dc9dfc17dcf31469d

8,540

py

Python

tests/ocr/test_suggestion_medication_administration.py

lifeomic/phc-sdk-py

51709c1c2f129a3fbe336a44e4d501ae0249859e

[ "MIT" ]

1

2020-07-22T12:46:58.000Z

tests/ocr/test_suggestion_medication_administration.py

lifeomic/phc-sdk-py

51709c1c2f129a3fbe336a44e4d501ae0249859e

[ "MIT" ]

54

2019-10-09T16:19:04.000Z

2022-01-19T20:28:59.000Z

tests/ocr/test_suggestion_medication_administration.py

lifeomic/phc-sdk-py

51709c1c2f129a3fbe336a44e4d501ae0249859e

[ "MIT" ]

2

2019-10-30T19:54:43.000Z

2020-12-03T18:57:15.000Z

import pandas as pd from phc.easy.ocr.suggestion import (expand_array_column, expand_medication_administrations, frame_for_type) sample = expand_array_column( pd.DataFrame( [ { "suggestions": [ { "id": "728e79cd-6cd2-421f-9e38-3181200c301", "condition": { "conditionCode": [], "onsetDate": [], "abatementDate": [], "bodySite": [], }, "observation": {}, "medicationAdministration": { "medicationCode": [ { "value": { "system": "http://www.nlm.nih.gov/research/umls/rxnorm", "code": "3640", "display": "doxycycline", }, "dataSource": {"source": "comprehend"}, "confidence": 0.996650755405426, "sourceText": { "text": "doxycycline", "location": { "startIndex": 11, "endIndex": 22, }, }, } ], "date": [], "endDate": [], "status": [ { "value": "unknown", "dataSource": {"source": "comprehend"}, "confidence": 0.9, }, { "value": "completed", "dataSource": {"source": "comprehend"}, "confidence": 0.9, }, { "value": "in-progress", "dataSource": {"source": "comprehend"}, "confidence": 0.9, }, ], "dosage": [ { "value": { "id": "0", "strength": None, "dosage": None, "duration": None, "form": None, "frequencey": None, "rate": None, "route": "po", }, "dataSource": {"source": "comprehend"}, "confidence": 0.996650755405426, "sourceText": { "text": "po", "location": { "startIndex": 23, "endIndex": 25, }, }, } ], }, } ], "anchorDate": "2021-02-24T12:58:32.058Z", "version": 4, "suggestionId": "00022-00007-00001", } ] ), key="suggestions", ) def test_medication_administration_expansion(): df = expand_medication_administrations( frame_for_type(sample, "medicationAdministration") ) pd.testing.assert_frame_equal( df, pd.DataFrame( [ { "anchorDate": "2021-02-24T12:58:32.058Z", "version": 4, "suggestionId": "00022-00007-00001", "id": "728e79cd-6cd2-421f-9e38-3181200c301", "status_value": "unknown", "status_confidence": 0.9, "status_dataSource_source": "comprehend", "dosage_confidence": 0.996650755405426, "dosage_dataSource_source": "comprehend", "dosage_value_id": "0", "dosage_value_strength": None, "dosage_value_dosage": None, "dosage_value_duration": None, "dosage_value_form": None, "dosage_value_frequencey": None, "dosage_value_rate": None, "dosage_value_route": "po", "code_confidence": 0.996650755405426, "code_dataSource_source": "comprehend", "code_value_system": "http://www.nlm.nih.gov/research/umls/rxnorm", "code_value_code": "3640", "code_value_display": "doxycycline", "dosage_sourceText": "po", "code_sourceText": "doxycycline", "type": "medicationAdministration", }, { "anchorDate": "2021-02-24T12:58:32.058Z", "version": 4, "suggestionId": "00022-00007-00001", "id": "728e79cd-6cd2-421f-9e38-3181200c301", "status_value": "completed", "status_confidence": 0.9, "status_dataSource_source": "comprehend", "dosage_confidence": 0.996650755405426, "dosage_dataSource_source": "comprehend", "dosage_value_id": "0", "dosage_value_strength": None, "dosage_value_dosage": None, "dosage_value_duration": None, "dosage_value_form": None, "dosage_value_frequencey": None, "dosage_value_rate": None, "dosage_value_route": "po", "code_confidence": 0.996650755405426, "code_dataSource_source": "comprehend", "code_value_system": "http://www.nlm.nih.gov/research/umls/rxnorm", "code_value_code": "3640", "code_value_display": "doxycycline", "dosage_sourceText": "po", "code_sourceText": "doxycycline", "type": "medicationAdministration", }, { "anchorDate": "2021-02-24T12:58:32.058Z", "version": 4, "suggestionId": "00022-00007-00001", "id": "728e79cd-6cd2-421f-9e38-3181200c301", "status_value": "in-progress", "status_confidence": 0.9, "status_dataSource_source": "comprehend", "dosage_confidence": 0.996650755405426, "dosage_dataSource_source": "comprehend", "dosage_value_id": "0", "dosage_value_strength": None, "dosage_value_dosage": None, "dosage_value_duration": None, "dosage_value_form": None, "dosage_value_frequencey": None, "dosage_value_rate": None, "dosage_value_route": "po", "code_confidence": 0.996650755405426, "code_dataSource_source": "comprehend", "code_value_system": "http://www.nlm.nih.gov/research/umls/rxnorm", "code_value_code": "3640", "code_value_display": "doxycycline", "dosage_sourceText": "po", "code_sourceText": "doxycycline", "type": "medicationAdministration", }, ] ), )

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null

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10ae75ed502594e92857e84f0c648a364231335c

158

py

Python

ISA/Util/__init__.py

tumido/FIT-VUT-projects

0e23c38a11d0aee55921e15b8865256efddefc53

[ "BSD-2-Clause" ]

null

ISA/Util/__init__.py

tumido/FIT-VUT-projects

0e23c38a11d0aee55921e15b8865256efddefc53

[ "BSD-2-Clause" ]

null

ISA/Util/__init__.py

tumido/FIT-VUT-projects

0e23c38a11d0aee55921e15b8865256efddefc53

[ "BSD-2-Clause" ]

3

2015-05-16T00:29:59.000Z

2021-02-03T00:31:16.000Z

from .Announce import get_announce, announce_to_txt from .Torrent import get_torrent_file, parse_torrent_file from .Tracker import get_peerlist, save_peerlist

52.666667

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1

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1

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6

52b9eddf6573e3f16266c439e6b72ad18cdb718c

24

py

Python

__init__.py

yuta-hi/chainer_extensions

f0dbc898623c25251aa0820bfda8417e9edced6a

[ "MIT" ]

1

2020-01-23T10:15:53.000Z

__init__.py

yuta-hi/chainer_extensions

f0dbc898623c25251aa0820bfda8417e9edced6a

[ "MIT" ]

null

__init__.py

yuta-hi/chainer_extensions

f0dbc898623c25251aa0820bfda8417e9edced6a

[ "MIT" ]

null

from . import extensions

24

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null

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1

0

1

0

6

eaae12f421ef051bdaca1bac1d8f9528a09f72e1

4,036

py

Python

tests/nn/softmax_test.py

kbrodt/tor4

d09740b746c534e67a72f492c7c03654f5888a46

[ "MIT" ]

null

tests/nn/softmax_test.py

kbrodt/tor4

d09740b746c534e67a72f492c7c03654f5888a46

[ "MIT" ]

null

tests/nn/softmax_test.py

kbrodt/tor4

d09740b746c534e67a72f492c7c03654f5888a46

[ "MIT" ]

null

import numpy as np import tor4 import tor4.nn as nn def test_softmax(): a = tor4.tensor(data=[0, 0, 0.0]) a_sm = nn.functional.softmax(a, dim=0) assert not a_sm.requires_grad assert a_sm.tolist() == [1 / 3, 1 / 3, 1 / 3] def test_softmax2(): a = tor4.tensor(data=[[0, 0, 0], [0, 0, 0.0]]) a_sm0 = nn.functional.softmax(a, dim=0) a_sm1 = nn.functional.softmax(a, dim=1) assert not a_sm0.requires_grad assert a_sm0.tolist() == [[1 / 2, 1 / 2, 1 / 2], [1 / 2, 1 / 2, 1 / 2]] assert not a_sm1.requires_grad assert a_sm1.tolist() == [[1 / 3, 1 / 3, 1 / 3], [1 / 3, 1 / 3, 1 / 3]] def test_softmax_backward(): a = tor4.tensor(data=[0, 0, 0.0], requires_grad=True) a_sm = nn.functional.softmax(a, dim=-1) a_sm.backward(tor4.tensor([1, 1, 1.0])) assert a_sm.requires_grad assert a_sm.tolist() == [1 / 3, 1 / 3, 1 / 3] assert a.grad.tolist() == [0, 0, 0] def test_softmax_backward2(): a = tor4.tensor(data=[0, 0, 0.0], requires_grad=True) a_sm = nn.functional.softmax(a, dim=-1) a_sm.backward(tor4.tensor([0, 1, -1.0])) assert a_sm.requires_grad assert a_sm.tolist() == [1 / 3, 1 / 3, 1 / 3] assert a.grad.tolist() == [0, 1 / 3, -1 / 3] def test_softmax2d_backward(): a = tor4.tensor(data=[[0, 1, -1.0], [1, -2, 3]], requires_grad=True) a_sm = nn.functional.softmax(a, dim=-1) a_sm.backward(tor4.tensor([[1, 1, 1.0], [1, 1, 1]])) assert a_sm.requires_grad assert np.allclose( a_sm.tolist(), [[0.2447, 0.6652, 0.09], [0.1185, 0.0059, 0.8756]], atol=1e-4, rtol=1e-4, ) assert np.allclose(a.grad.tolist(), [[0, 0, 0], [0, 0, 0]]) def test_softmax2d_backward2(): a = tor4.tensor(data=[[0, 1, -1.0], [1, -2, 3]], requires_grad=True) a_sm = nn.functional.softmax(a, dim=0) a_sm.backward(tor4.tensor([[1, 1, 1.0], [1, 1, 1]])) assert a_sm.requires_grad assert np.allclose( a_sm.tolist(), [[0.2689, 0.9526, 0.018], [0.7311, 0.0474, 0.982]], atol=1e-4, rtol=1e-4, ) assert np.allclose(a.grad.tolist(), [[0, 0, 0], [0, 0, 0]]) def test_softmax2d_backward3(): a = tor4.tensor(data=[[0, 1, -1.0], [1, -2, 3]], requires_grad=True) a_sm = nn.functional.softmax(a, dim=-1) a_sm.backward(tor4.tensor([[0, -1, 1.0], [2, 0, -1]])) assert a_sm.requires_grad assert np.allclose( a_sm.tolist(), [[0.2447, 0.6652, 0.09], [0.1185, 0.0059, 0.8756]], atol=1e-4, rtol=1e-4, ) assert np.allclose( a.grad.tolist(), [[0.1408, -0.2826, 0.1418], [0.3127, 0.0038, -0.3164]], atol=1e-4, rtol=1e-4, ) def test_softmax2d_backward4(): a = tor4.tensor(data=[[0, 1, -1.0], [1, -2, 3]], requires_grad=True) a_sm = nn.functional.softmax(a, dim=0) a_sm.backward(tor4.tensor([[-5, 3, 0.0], [0, 0, 1]])) assert a_sm.requires_grad assert np.allclose( a_sm.tolist(), [[0.2689, 0.9526, 0.018], [0.7311, 0.0474, 0.982]], atol=1e-4, rtol=1e-4, ) assert np.allclose( a.grad.tolist(), [[-0.9831, 0.1355, -0.0177], [0.9831, -0.1355, 0.0177]], atol=1e-4, rtol=1e-4, ) def test_softmax3d_backward(): a = tor4.tensor( data=[[[0, 1, -1.0], [1, -2, 3]], [[1, 4, -2], [0, 0, -3]]], requires_grad=True, ) a_sm = nn.functional.softmax(a, dim=1) a_sm.backward(tor4.tensor([[[-5, 3, 0.0], [0, 0, 1]], [[3, 0, -3], [1, 2, 3]]])) assert a_sm.requires_grad assert np.allclose( a_sm.tolist(), [ [[0.2689, 0.9526, 0.018], [0.7311, 0.0474, 0.982]], [[0.7311, 0.982, 0.7311], [0.2689, 0.018, 0.2689]], ], atol=1e-4, rtol=1e-4, ) assert np.allclose( a.grad.tolist(), [ [[-0.9831, 0.1355, -0.0177], [0.9831, -0.1355, 0.0177]], [[0.3932, -0.0353, -1.1797], [-0.3932, 0.0353, 1.1797]], ], atol=1e-4, rtol=1e-4, )

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null

0

1

0

null

0

6

eacbdadce424c8674d1462fffb1cb970c545200c

2,506

py

Python

emails/utils.py

jmhubbard/quote_of_the_day_custom_user

27024b2953c1c94fd2970563c3ab31ad444912b6

[ "Unlicense" ]

1

2020-11-25T04:57:16.000Z

emails/utils.py

jmhubbard/quote_of_the_day_custom_user

27024b2953c1c94fd2970563c3ab31ad444912b6

[ "Unlicense" ]

null

emails/utils.py

jmhubbard/quote_of_the_day_custom_user

27024b2953c1c94fd2970563c3ab31ad444912b6

[ "Unlicense" ]

null

import os from django.core.mail import send_mail from django.urls import reverse from django.contrib.sites.models import Site from django.template.loader import render_to_string def email_all_users_an_email(user, showlist): #Gets the current domain name domain = Site.objects.get_current().domain # reverse a url in a view to get the path after the domain path = reverse('login') url = 'http://{domain}{path}'.format(domain=domain, path=path) context = { "unsubscribe_uri": url, "showlist": showlist, } message_text = render_to_string("emails/email_all_users.txt", context=context) message_html = render_to_string("emails/email_all_users.html", context=context) return send_mail( "New Shows Added", message_text, os.getenv("EMAIL_HOST_USER"), [user], fail_silently=False, html_message=message_html, ) def email_test(user, message): send_mail( 'Quote test', message, os.getenv("EMAIL_HOST_USER"), [user], fail_silently=False, ) def email_daily_tv_quote(quote, user): #Gets the current domain name domain = Site.objects.get_current().domain # reverse a url in a view to get the path after the domain path = reverse('login') url = 'http://{domain}{path}'.format(domain=domain, path=path) context = { "unsubscribe_uri": url, "quote": quote, } message_text = render_to_string("emails/tv_email.txt", context=context) message_html = render_to_string("emails/tv_email.html", context=context) return send_mail( "Quote Of The Day", message_text, os.getenv("EMAIL_HOST_USER"), [user], fail_silently=False, html_message=message_html, ) def email_daily_movie_quote(quote, user): #Gets the current domain name domain = Site.objects.get_current().domain # reverse a url in a view to get the path after the domain path = reverse('login') url = 'http://{domain}{path}'.format(domain=domain, path=path) context = { "unsubscribe_uri": url, "quote": quote, } message_text = render_to_string("emails/movie_email.txt", context=context) message_html = render_to_string("emails/movie_email.html", context=context) return send_mail( "Quote Of The Day", message_text, os.getenv("EMAIL_HOST_USER"), [user], fail_silently=False, html_message=message_html, )

30.560976

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null

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null

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6

eadcff9016e474eb3c8f61f0826e29c9a4160b3b

159

py

Python

accounts/admin.py

kamel2700/Build-a-team-for-Startup

3955eb5a990e27f100981b7186f3593f7b821128

[ "MIT" ]

null

accounts/admin.py

kamel2700/Build-a-team-for-Startup

3955eb5a990e27f100981b7186f3593f7b821128

[ "MIT" ]

null

accounts/admin.py

kamel2700/Build-a-team-for-Startup

3955eb5a990e27f100981b7186f3593f7b821128

[ "MIT" ]

null

from django.contrib import admin from accounts.models import * admin.site.register(UserProfile) admin.site.register(ProjectPage) admin.site.register(Comment)

22.714286

32

0.830189

21

159

6.285714

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0.204545

0.386364

0

0.075472

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6

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eaea558a7bb60d7e0462270ff2a5a8f1d8c33727

248

py

Python

tests/python-reference/bool/bool-isinstance.py

jpolitz/lambda-py-paper

746ef63fc1123714b4adaf78119028afbea7bd76

[ "Apache-2.0" ]

25

2015-04-16T04:31:49.000Z

2022-03-10T15:53:28.000Z

tests/python-reference/bool/bool-isinstance.py

jpolitz/lambda-py-paper

746ef63fc1123714b4adaf78119028afbea7bd76

[ "Apache-2.0" ]

1

2018-11-21T22:40:02.000Z

2018-11-26T17:53:11.000Z

tests/python-reference/bool/bool-isinstance.py

jpolitz/lambda-py-paper

746ef63fc1123714b4adaf78119028afbea7bd76

[ "Apache-2.0" ]

1

2021-03-26T03:36:19.000Z

___assertIs(isinstance(True, bool), True) ___assertIs(isinstance(False, bool), True) ___assertIs(isinstance(True, int), True) ___assertIs(isinstance(False, int), True) ___assertIs(isinstance(1, bool), False) ___assertIs(isinstance(0, bool), False)

35.428571

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5.866667

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6

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1

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null

0

1

0

1

0

6

eaff872f4834bf2846f82c2e9bc383d651070c30

129

py

Python

starfish/image/_segmentation/__init__.py

ttung/starfish

1bd8abf55a335620e4b20abb041f478334714081

[ "MIT" ]

null

starfish/image/_segmentation/__init__.py

ttung/starfish

1bd8abf55a335620e4b20abb041f478334714081

[ "MIT" ]

null

starfish/image/_segmentation/__init__.py

ttung/starfish

1bd8abf55a335620e4b20abb041f478334714081

[ "MIT" ]

null

from starfish.pipeline import import_all_submodules from ._base import Segmentation import_all_submodules(__file__, __package__)

32.25

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0.883721

16

129

6.3125

0.625

0.178218

0.376238

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0.077519

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3

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0

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null

0

1

0

1

0

1

0

6

d81f398ba332a8ddc4a369fda6218312310af6ed

33

py

Python

test/regression/features/imports/fromImport.py

ppelleti/berp

30925288376a6464695341445688be64ac6b2600

[ "BSD-3-Clause" ]

90

2015-02-03T23:56:30.000Z

2022-02-10T03:55:32.000Z

test/regression/features/imports/fromImport.py

ppelleti/berp

30925288376a6464695341445688be64ac6b2600

[ "BSD-3-Clause" ]

4

2015-04-01T13:49:13.000Z

2019-07-09T19:28:56.000Z

test/regression/features/imports/fromImport.py

bjpop/berp

30925288376a6464695341445688be64ac6b2600

[ "BSD-3-Clause" ]

8

2015-04-25T03:47:52.000Z

2019-07-27T06:33:56.000Z

from DefinesX import x print(x)

8.25

22

0.757576

6

33

4.166667

0.833333

0

0.181818

33

3

23

11

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true

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1

0

null

0

1

0

1

0

1

0

6

dc36fd31dfeeb75d7050190c4caf81b68761883e

8,634

py

Python

megumin/modulos/admin/mute.py

davitudoplugins1234/WhiterKang

f4779d2c440849fa97e7014cd856f885b0abbc87

[ "MIT" ]

2

2022-02-01T17:55:44.000Z

2022-03-27T17:21:55.000Z

megumin/modulos/admin/mute.py

davitudoplugins1234/WhiterKang

f4779d2c440849fa97e7014cd856f885b0abbc87

[ "MIT" ]

null

megumin/modulos/admin/mute.py

davitudoplugins1234/WhiterKang

f4779d2c440849fa97e7014cd856f885b0abbc87

[ "MIT" ]

null

import asyncio from pyrogram import filters from pyrogram.errors import PeerIdInvalid, UserIdInvalid, UsernameInvalid from pyrogram.types import ChatPermissions, Message from megumin import megux from megumin.utils import ( check_bot_rights, check_rights, extract_time, is_admin, is_dev, is_self, sed_sticker, get_collection, ) @megux.on_message(filters.command("mute", prefixes=["/", "!"])) async def _mute_user(_, message: Message): DISABLED = get_collection(f"DISABLED {message.chat.id}") query = "mute" off = await DISABLED.find_one({"_cmd": query}) if off: return chat_id = message.chat.id if not await check_rights(chat_id, message.from_user.id, "can_restrict_members"): await message.reply("Você não tem direitos suficientes para silenciar usuários") return cmd = len(message.text) replied = message.reply_to_message reason = "" if replied: id_ = replied.from_user.id if cmd > 5: _, reason = message.text.split(maxsplit=1) elif cmd > 5: _, args = message.text.split(maxsplit=1) if " " in args: id_, reason = args.split(" ", maxsplit=1) else: id_ = args else: await message.reply("`Nenhum User_id válido ou mensagem especificada.`") return try: user = await megux.get_users(id_) user_id = user.id mention = user.mention except (UsernameInvalid, PeerIdInvalid, UserIdInvalid): await message.reply( "`User_id ou nome de usuário inválido, tente novamente com informações válidas ⚠`" ) return if await is_self(user_id): await message.reply("Eu não vou mutar!") return if is_dev(user_id): await message.reply("Porque eu iria mutar meu desenvolvedor? Isso me parece uma idéia muito idiota.") return if is_admin(chat_id, user_id): await message.reply("Porque eu iria mutar um(a) administrador(a)? Isso me parece uma idéia bem idiota.") return if not await check_rights(chat_id, megux.me.id, "can_restrict_members"): await message.reply("Não posso restringir as pessoas aqui! Certifique-se de que sou administrador e de que posso adicionar novos administradores.") await sed_sticker(message) return sent = await message.reply("`Mutando Usuário...`") try: await megux.restrict_chat_member(chat_id, user_id, ChatPermissions()) await asyncio.sleep(1) await sent.edit( f"{mention} está silenciado(mutado) em {message.chat.title}\n" f"Motivo: `{reason or None}`" ) except Exception as e_f: await sent.edit(f"`Algo deu errado 🤔`\n\n**ERROR**: `{e_f}`") @megux.on_message(filters.command("tmute", prefixes=["/", "!"])) async def _tmute_user(_, message: Message): DISABLED = get_collection(f"DISABLED {message.chat.id}") query = "tmute" off = await DISABLED.find_one({"_cmd": query}) if off: return chat_id = message.chat.id if not await check_rights(chat_id, message.from_user.id, "can_restrict_members"): await message.reply("Você não tem direitos suficientes para silenciar usuários") return cmd = len(message.text) replied = message.reply_to_message if replied: id_ = replied.from_user.id if cmd <= 6: await message.reply("__Você deve especificar um tempo após o comando. Por exemplo:__ /tmute 7d.") return _, args = message.text.split(maxsplit=1) elif cmd > 6: _, text = message.text.split(maxsplit=1) if " " in text: id_, args = text.split(" ", maxsplit=1) else: await message.reply("__Você deve especificar um tempo após o comando. Por exemplo:__ **/tmute 7d.**") else: await message.reply("`Nenhum User_id válido ou mensagem especificada.`") return if " " in args: split = args.split(None, 1) time_val = split[0].lower() reason = split[1] else: time_val = args reason = "" time_ = await extract_time(message, time_val) if not time_: return try: user = await megux.get_users(id_) user_id = user.id mention = user.mention except (UsernameInvalid, PeerIdInvalid, UserIdInvalid): await message.reply( "`User_id ou nome de usuário inválido, tente novamente com informações válidas ⚠`" ) return if await is_self(user_id): await message.reply("Eu não vou mutar!") return if is_dev(user_id): await message.reply("Porque eu iria mutar meu desenvolvedor? Isso me parece uma idéia muito idiota.") return if is_admin(chat_id, user_id): await message.reply("Porque eu iria mutar um(a) administrador(a)? Isso me parece uma idéia bem idiota.") return if not await check_rights(chat_id, megux.me.id, "can_restrict_members"): await message.reply("Não posso restringir as pessoas aqui! Certifique-se de que sou administrador e de que posso adicionar novos administradores.") await sed_sticker(message) return sent = await message.reply("`Mutando usuário...`") try: await megux.restrict_chat_member(chat_id, user_id, ChatPermissions(), time_) await asyncio.sleep(1) await sent.edit( f"{mention} está silenciado(mutado) por {time_val} em {message.chat.title}\n" f"Motivo: `{reason or None}`" ) except Exception as e_f: # pylint: disable=broad-except await sent.edit(f"`Algo deu errado 🤔`\n\n**ERROR**: `{e_f}`") @megux.on_message(filters.command("unmute", prefixes=["/", "!"])) async def _unmute_user(_, message: Message): DISABLED = get_collection(f"DISABLED {message.chat.id}") query = "unmute" off = await DISABLED.find_one({"_cmd": query}) if off: return chat_id = message.chat.id if not await check_rights(chat_id, message.from_user.id, "can_restrict_members"): await message.reply("Você não tem direitos suficientes para silenciar usuários") return replied = message.reply_to_message if replied: id_ = replied.from_user.id elif len(message.text) > 7: _, id_ = message.text.split(maxsplit=1) else: await message.reply("`Nenhum User_id válido ou mensagem especificada.`") return try: user = (await megux.get_users(id_)) mention = user.mention user_id = user.id except (UsernameInvalid, PeerIdInvalid, UserIdInvalid): await message.reply( "`User_id ou nome de usuário inválido, tente novamente com informações válidas ⚠`" ) return if await is_self(user_id): return if is_admin(chat_id, user_id): await message.reply("Este usuario é administrador(a), ele não precisa ser desmutado(a).") return if not await check_rights(chat_id, megux.me.id, "can_restrict_members"): await message.reply("Não posso restringir as pessoas aqui! Certifique-se de que sou administrador e de que posso adicionar novos administradores.") await sed_sticker(message) return sent = await message.reply("Desmutando Usuário...") try: await megux.unban_chat_member(chat_id, user_id) await sent.edit(f"Ok, {mention} já pode começar a falar novamente em {message.chat.title}!") except Exception as e_f: await sent.edit(f"`Algo deu errado!` 🤔\n\n**ERROR:** `{e_f}`") @megux.on_message(filters.command("muteme", prefixes=["/", "!"])) async def muteme_(_, message: Message): DISABLED = get_collection(f"DISABLED {message.chat.id}") query = "muteme" off = await DISABLED.find_one({"_cmd": query}) if off: return chat_id = message.chat.id user_id = message.from_user.id if is_admin(chat_id, user_id): await message.reply("Por que eu mutaria um(a) administrador(a)? Parece uma ideia bem idiota.") return else: try: if not await check_rights(chat_id, megux.me.id, "can_restrict_members"): await message.reply("Não posso restringir as pessoas aqui! Certifique-se de que sou administrador e de que posso adicionar novos administradores.") return await message.reply("Sem Problemas, Mutado!") await megux.restrict_chat_member(chat_id, user_id, ChatPermissions()) except Exception as e: await message.reply(f"**ERRO:**\n{e}")

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0

null

0

1

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null

0

6

dc49d0efef00a025032ec403a6bd5a00b82ee89b

140

py

Python

python/module/calc/example.py

wjiec/packages

4ccaf8f717265a1f8a9af533f9a998b935efb32a

[ "MIT" ]

null

python/module/calc/example.py

wjiec/packages

4ccaf8f717265a1f8a9af533f9a998b935efb32a

[ "MIT" ]

1

2016-09-15T07:06:15.000Z

python/module/calc/example.py

wjiec/packages

4ccaf8f717265a1f8a9af533f9a998b935efb32a

[ "MIT" ]

null

#!/usr/bin/python35 import calc from calc import mult print(calc.add(1, 2)) print(calc.dec(2, 3)) print(calc.div(1, 2)) print(mult(2, 3))

14

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dc52b025b8a7aecbe2b78cacb4452f154781595d

88

py

Python

catkin_ws/src/00-infrastructure/easy_logs/include/easy_logs/cli/__init__.py

yxiao1996/dev

e2181233aaa3d16c472b792b58fc4863983825bd

[ "CC-BY-2.0" ]

2

2018-06-25T02:51:25.000Z

2018-06-25T02:51:27.000Z

catkin_ws/src/00-infrastructure/easy_logs/include/easy_logs/cli/__init__.py

yxiao1996/dev

e2181233aaa3d16c472b792b58fc4863983825bd

[ "CC-BY-2.0" ]

null

catkin_ws/src/00-infrastructure/easy_logs/include/easy_logs/cli/__init__.py

yxiao1996/dev

e2181233aaa3d16c472b792b58fc4863983825bd

[ "CC-BY-2.0" ]

2

2018-09-04T06:44:21.000Z

2018-10-15T02:30:50.000Z

from .easy_logs_summary_imp import * from .dropbox_links import * from .require import *

29.333333

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0.806818

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0.298507

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0.125

88

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6

dc7d2a5bf7773798fe0cb25e532c00e8fdc1e5bb

148

py

Python

utils/postprocessing/__init__.py

bdvllrs/misinformation-detection-tensor-embeddings

eb43e55fb7d4317f4ca0d5c1db8191be3d543716

[ "MIT" ]

7

2020-03-18T03:40:48.000Z

2021-12-29T11:04:53.000Z

utils/postprocessing/__init__.py

bdvllrs/misinformation-detection-tensor-embeddings

eb43e55fb7d4317f4ca0d5c1db8191be3d543716

[ "MIT" ]

null

utils/postprocessing/__init__.py

bdvllrs/misinformation-detection-tensor-embeddings

eb43e55fb7d4317f4ca0d5c1db8191be3d543716

[ "MIT" ]

3

2019-09-30T05:41:59.000Z

2020-12-03T19:49:10.000Z

from utils.postprocessing.PostProcessing import PostProcessing from utils.postprocessing.SelectLabelsPostprocessor import SelectLabelsPostprocessor

49.333333

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11.333333

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0

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null

0

1

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1

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6

dcbbfc2a1126bf01e035f0cedab6dd794f1ffb72

48

py

Python

user_orders/models/__init__.py

Vitamal/shop

facf04da00b8b674f2d8024aca4dae272a0c3de8

[ "MIT" ]

null

user_orders/models/__init__.py

Vitamal/shop

facf04da00b8b674f2d8024aca4dae272a0c3de8

[ "MIT" ]

null

user_orders/models/__init__.py

Vitamal/shop

facf04da00b8b674f2d8024aca4dae272a0c3de8

[ "MIT" ]

null

from .order import Order from .user import User

16

24

0.791667

8

48

4.75

0.5

0

0.166667

48

2

25

24

0.95

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0

null

0

1

0

1

0

1

0

6

f4da84214e93efb0d8a87ad63fc141e1f58417ac

166

py

Python

maguey/tests/test_systems_api.py

andrewmagill/maguey

54efb60a5cab432cf5a3f1cbdaae0d1ffd1f3763

[ "MIT" ]

null

maguey/tests/test_systems_api.py

andrewmagill/maguey

54efb60a5cab432cf5a3f1cbdaae0d1ffd1f3763

[ "MIT" ]

null

maguey/tests/test_systems_api.py

andrewmagill/maguey

54efb60a5cab432cf5a3f1cbdaae0d1ffd1f3763

[ "MIT" ]

null

from unittest import TestCase import maguey class TestSystems(TestCase): def test_add_system(self): pass def test_delete_system(self): pass

16.6

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166

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0

0.240964

166

9

34

18.444444

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0

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false

0.285714

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1

0

1

0

1

0

6

f4e13e171e02dd82b6bb13fe5bf5609262caa599

4,284

py

Python

tests/test_compare.py

grst/jupytext

0bbcd61297d8ee75b4f0329e2617acbbade3eb10

[ "MIT" ]

null

tests/test_compare.py

grst/jupytext

0bbcd61297d8ee75b4f0329e2617acbbade3eb10

[ "MIT" ]

null

tests/test_compare.py

grst/jupytext

0bbcd61297d8ee75b4f0329e2617acbbade3eb10

[ "MIT" ]

null

import pytest from nbformat.v4.nbbase import new_notebook, \ new_markdown_cell, new_code_cell, new_raw_cell import jupytext from jupytext.compare import compare_notebooks, \ test_round_trip_conversion as round_trip_conversion jupytext.file_format_version.FILE_FORMAT_VERSION = {} def test_raise_on_different_metadata(): ref = new_notebook(metadata={'main_language': 'python'}, cells=[new_markdown_cell('Cell one')]) test = new_notebook(metadata={'main_language': 'R'}, cells=[new_markdown_cell('Cell one')]) with pytest.raises(AssertionError): compare_notebooks(ref, test) def test_raise_on_different_cell_count(): ref = new_notebook(cells=[new_markdown_cell('Cell one'), new_code_cell('Cell two')]) test = new_notebook(cells=[new_markdown_cell('Cell one')]) with pytest.raises(AssertionError): compare_notebooks(ref, test) def test_raise_on_different_cell_type(): ref = new_notebook(cells=[new_markdown_cell('Cell one'), new_code_cell('Cell two')]) test = new_notebook(cells=[new_markdown_cell('Cell one'), new_raw_cell('Cell two')]) with pytest.raises(AssertionError): compare_notebooks(ref, test) def test_raise_on_different_cell_content(): ref = new_notebook(cells=[new_markdown_cell('Cell one'), new_code_cell('Cell two')]) test = new_notebook(cells=[new_markdown_cell('Cell one'), new_code_cell('Modified cell two')]) with pytest.raises(AssertionError): compare_notebooks(ref, test) def test_raise_on_split_markdown_cell(): ref = new_notebook(cells=[new_markdown_cell('Cell one\n\n\nsecond line')]) test = new_notebook(cells=[new_markdown_cell('Cell one'), new_markdown_cell('second line')]) with pytest.raises(AssertionError): compare_notebooks(ref, test) def test_raise_on_incomplete_markdown_cell(): ref = new_notebook(cells=[new_markdown_cell('Cell one\n\n\nsecond line')]) test = new_notebook(cells=[new_markdown_cell('Cell one')]) with pytest.raises(AssertionError): compare_notebooks(ref, test, allow_split_markdown=True) def test_dont_raise_on_split_markdown_cell(): ref = new_notebook(cells=[new_markdown_cell('Cell one\n\n\nsecond line')]) test = new_notebook(cells=[new_markdown_cell('Cell one'), new_markdown_cell('second line')]) compare_notebooks(ref, test, allow_split_markdown=True) def test_raise_on_different_cell_metadata(): ref = new_notebook(cells=[new_code_cell('1+1')]) test = new_notebook( cells=[new_code_cell('1+1', metadata={'metakey': 'value'})]) with pytest.raises(AssertionError): compare_notebooks(ref, test) def test_dont_raise_on_different_outputs(): ref = new_notebook(cells=[new_code_cell('1+1')]) test = new_notebook(cells=[new_code_cell('1+1', outputs=[ { "data": { "text/plain": [ "2" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ])]) compare_notebooks(ref, test) def test_raise_on_different_outputs(): ref = new_notebook(cells=[new_code_cell('1+1')]) test = new_notebook(cells=[new_code_cell('1+1', outputs=[ { "data": { "text/plain": [ "2" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ])]) with pytest.raises(AssertionError): compare_notebooks(ref, test, test_outputs=True) def test_test_round_trip_conversion(): notebook = new_notebook(cells=[new_code_cell('1+1', outputs=[ { "data": { "text/plain": [ "2" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ])], metadata={'main_language': 'python'}) round_trip_conversion(notebook, '.py', test_outputs=True)

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0.148515

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null

0

1

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null

0

6

52408c78b60c7bc5958da33b916643304a15847c

20,554

py

Python

monk/tf_keras_1/finetune/level_11_optimizers_main.py

Sanskar329/monk_v1

51a497a925ec1fb2c8fef1d51245ea7040a5a65a

[ "Apache-2.0" ]

7

2020-07-26T08:37:29.000Z

2020-10-30T10:23:11.000Z

monk/tf_keras_1/finetune/level_11_optimizers_main.py

mursalfk/monk_v1

62f34a52f242772186ffff7e56764e958fbcd920

[ "Apache-2.0" ]

null

monk/tf_keras_1/finetune/level_11_optimizers_main.py

mursalfk/monk_v1

62f34a52f242772186ffff7e56764e958fbcd920

[ "Apache-2.0" ]

null

from tf_keras_1.finetune.imports import * from system.imports import * from tf_keras_1.finetune.level_10_schedulers_main import prototype_schedulers class prototype_optimizers(prototype_schedulers): ''' Main class for all optimizers in expert mode Args: verbose (int): Set verbosity levels 0 - Print Nothing 1 - Print desired details ''' @accepts("self", verbose=int, post_trace=False) #@TraceFunction(trace_args=True, trace_rv=True) def __init__(self, verbose=1): super().__init__(verbose=verbose); ############################################################################################################################################### @warning_checks(None, ["lt", 1], momentum=["lt", 1.5], weight_decay=["lt", 0.01], momentum_dampening_rate=None, clipnorm=None, clipvalue=None, post_trace=False) @error_checks(None, ["gt", 0], momentum=["gte", 0], weight_decay=["gte", 0], momentum_dampening_rate=None, clipnorm=None, clipvalue=None, post_trace=False) @accepts("self", [int, float], momentum=[int, float], weight_decay=[int, float], momentum_dampening_rate=[int, float], clipnorm=[int, float], clipvalue=[int, float], post_trace=False) #@TraceFunction(trace_args=True, trace_rv=True) def optimizer_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0): ''' Select stochastic gradient descent optimizer Args: learning_rate (float): Initial base learning rate momentum (float): Momentum value for driving the weights towards minima weight_decay (float): Value for regularizing weights post every update momentum_dampening_rate (float): Reduction rate for momentum clipnorm (float): Gradient clipping factor clipvalue (float): Value for clipping Returns: None ''' self.system_dict = sgd(self.system_dict, learning_rate, momentum=momentum, weight_decay=weight_decay, momentum_dampening_rate=momentum_dampening_rate, clipnorm=clipnorm, clipvalue=clipvalue); self.custom_print("Optimizer"); self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"])); self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"])); self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"])); self.custom_print(""); ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk"); self.custom_print(""); ConstraintWarning("ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk"); self.custom_print(""); ############################################################################################################################################### ############################################################################################################################################### @warning_checks(None, ["lt", 1], momentum=["lt", 1.5], weight_decay=["lt", 0.01], momentum_dampening_rate=None, clipnorm=None, clipvalue=None, post_trace=False) @error_checks(None, ["gt", 0], momentum=["gte", 0], weight_decay=["gte", 0], momentum_dampening_rate=None, clipnorm=None, clipvalue=None, post_trace=False) @accepts("self", [int, float], momentum=[int, float], weight_decay=[int, float], momentum_dampening_rate=[int, float], clipnorm=[int, float], clipvalue=[int, float], post_trace=False) #@TraceFunction(trace_args=True, trace_rv=True) def optimizer_nesterov_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0): ''' Select stochastic gradient descent optimizer with nesterov acceleration Args: learning_rate (float): Initial base learning rate momentum (float): Momentum value for driving the weights towards minima weight_decay (float): Value for regularizing weights post every update momentum_dampening_rate (float): Reduction rate for momentum clipnorm (float): Gradient clipping factor clipvalue (float): Value for clipping Returns: None ''' self.system_dict = nesterov_sgd(self.system_dict, learning_rate, momentum=momentum, weight_decay=weight_decay, momentum_dampening_rate=momentum_dampening_rate, clipnorm=clipnorm, clipvalue=clipvalue); self.custom_print("Optimizer"); self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"])); self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"])); self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"])); self.custom_print(""); ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk"); self.custom_print(""); ConstraintWarning("ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk"); self.custom_print(""); ############################################################################################################################################### ############################################################################################################################################### @warning_checks(None, ["lt", 1], decay_rate=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01], clipnorm=None, clipvalue=None, post_trace=None) @error_checks(None, ["gt", 0], decay_rate=["gt", 0], epsilon=["gte", 0], weight_decay=["gte", 0], clipnorm=None, clipvalue=None, post_trace=False) @accepts("self", [int, float], decay_rate=[int, float], epsilon=[int, float], weight_decay=[int, float], clipnorm=[int, float], clipvalue=[int, float], post_trace=False) #@TraceFunction(trace_args=True, trace_rv=True) def optimizer_rmsprop(self, learning_rate, decay_rate=0.99, epsilon=1e-08, weight_decay=0, clipnorm=0.0, clipvalue=0.0): ''' Select root mean score prop optimizer Args: learning_rate (float): Initial base learning rate decay_rate (float): A decay factor of moving average over past squared gradient. epsilon (float): A value to avoid division by zero weight_decay (float): Value for regularizing weights post every update clipnorm (float): Gradient clipping factor clipvalue (float): Value for clipping Returns: None ''' self.system_dict = rmsprop(self.system_dict , learning_rate, decay_rate=decay_rate, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue); self.custom_print("Optimizer"); self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"])); self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"])); self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"])); self.custom_print(""); ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk"); self.custom_print(""); ############################################################################################################################################### ############################################################################################################################################### @warning_checks(None, ["lt, 1"], beta1=["lt", 1], beta2=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01], amsgrad=None, clipnorm=None, clipvalue=None, post_trace=False) @error_checks(None, ["gt", 0], beta1=["gte", 0], beta2=["gte", 0], epssilon=["gte", 0], weight_decay=["gte", 0], amsgrad=None, clipnorm=None, clipvalue=None, post_trace=False) @accepts("self", [int, float], beta1=[int, float], beta2=[int, float], epsilon=[int, float], weight_decay=[int, float], amsgrad=bool, clipnorm=[int, float], clipvalue=[int, float], post_trace=False) #@TraceFunction(trace_args=True, trace_rv=True) def optimizer_adam(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False, clipnorm=0.0, clipvalue=0.0): ''' Select ADAM optimizer Args: learning_rate (float): Initial base learning rate beta1 (float): Exponential decay rate for first momentum estimates beta2 (float): Exponential decay rate for first second estimates weight_decay (float): Value for regularizing weights post every update amsgrad (bool): If True, AMSGrad variant of this algorithm is used epsilon (float): A value to avoid division by zero clipnorm (float): Gradient clipping factor clipvalue (float): Value for clipping Returns: None ''' self.system_dict = adam(self.system_dict, learning_rate, beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, amsgrad=amsgrad, clipnorm=clipnorm, clipvalue=clipvalue); self.custom_print("Optimizer"); self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"])); self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"])); self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"])); self.custom_print(""); ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk"); self.custom_print(""); ConstraintWarning("ArgumentWarning: amsgrad is active only for keras and pytorch in current version of Monk"); self.custom_print(""); ############################################################################################################################################### ############################################################################################################################################### @warning_checks(None, ["lt, 1"], beta1=["lt", 1], beta2=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01], amsgrad=None, momentum_decay=None, clipnorm=None, clipvalue=None, post_trace=False) @error_checks(None, ["gt", 0], beta1=["gte", 0], beta2=["gte", 0], epssilon=["gte", 0], weight_decay=["gte", 0], amsgrad=None, momentum_decay=None, clipnorm=None, clipvalue=None, post_trace=False) @accepts("self", [int, float], beta1=[int, float], beta2=[int, float], epsilon=[int, float], weight_decay=[int, float], amsgrad=bool, momentum_decay=[int, float], clipnorm=[int, float], clipvalue=[int, float], post_trace=False) #@TraceFunction(trace_args=True, trace_rv=True) def optimizer_nesterov_adam(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False, momentum_decay=0.004, clipnorm=0.0, clipvalue=0.0): ''' Select ADAM optimizer with nesterov momentum acceleration Args: learning_rate (float): Initial base learning rate beta1 (float): Exponential decay rate for first momentum estimates beta2 (float): Exponential decay rate for first second estimates weight_decay (float): Value for regularizing weights post every update amsgrad (bool): If True, AMSGrad variant of this algorithm is used epsilon (float): A value to avoid division by zero clipnorm (float): Gradient clipping factor clipvalue (float): Value for clipping Returns: None ''' self.system_dict = nesterov_adam(self.system_dict, learning_rate, beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, amsgrad=amsgrad, momentum_decay=momentum_decay, clipnorm=clipnorm, clipvalue=clipvalue); self.custom_print("Optimizer"); self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"])); self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"])); self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"])); self.custom_print(""); ConstraintWarning("OptimizerWarning: nesterov adam is active only for keras and gluon in current version of Monk"); self.custom_print(""); ConstraintWarning("ArgumentWarning: amsgrad is inactive in current version of Monk"); self.custom_print(""); ############################################################################################################################################### ############################################################################################################################################### @warning_checks(None, ["lt", 1], beta1=["lt", 1], beta2=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01], clipnorm=None, clipvalue=None, post_trace=False) @error_checks(None, ["gt", 0], beta1=["gte", 0], beta2=["gte", 0], epsilon=["gte", 0], weight_decay=["gte", 0], clipnorm=None, clipvalue=None, post_trace=False) @accepts("self", [int, float], beta1=[int, float], beta2=[int, float], epsilon=[int, float], weight_decay=[int, float], clipnorm=[int, float], clipvalue=[int, float], post_trace=False) #@TraceFunction(trace_args=True, trace_rv=True) def optimizer_adamax(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, clipnorm=0.0, clipvalue=0.0): ''' Select Adamax optimizer Args: learning_rate (float): Initial base learning rate beta1 (float): Exponential decay rate for first momentum estimates beta2 (float): Exponential decay rate for first second estimates weight_decay (float): Value for regularizing weights post every update epsilon (float): A value to avoid division by zero clipnorm (float): Gradient clipping factor clipvalue (float): Value for clipping Returns: None ''' self.system_dict = adamax(self.system_dict, learning_rate, beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue); self.custom_print("Optimizer"); self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"])); self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"])); self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"])); self.custom_print(""); ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk"); self.custom_print(""); ############################################################################################################################################### ############################################################################################################################################### @warning_checks(None, ["lt", 1], rho=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01], clipnorm=None, clipvalue=None, post_trace=False) @error_checks(None, ["gt", 0], rho=["gt", 0], epsilon=["gte", 0], weight_decay=["gte", 0], clipnorm=None, clipvalue=None, post_trace=False) @accepts("self", [int, float], rho=[int, float], epsilon=[int, float], weight_decay=[int, float], clipnorm=[int, float], clipvalue=[int, float], post_trace=False) #@TraceFunction(trace_args=True, trace_rv=True) def optimizer_adadelta(self, learning_rate, rho=0.9, epsilon=1e-06, weight_decay=0, clipnorm=0.0, clipvalue=0.0): ''' Select Adadelta optimizer Args: learning_rate (float): Initial base learning rate rho (float): Exponential decay rate for momentum estimates weight_decay (float): Value for regularizing weights post every update epsilon (float): A value to avoid division by zero clipnorm (float): Gradient clipping factor clipvalue (float): Value for clipping Returns: None ''' self.system_dict = adadelta(self.system_dict, learning_rate, rho=rho, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue); self.custom_print("Optimizer"); self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"])); self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"])); self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"])); self.custom_print(""); ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk"); self.custom_print(""); ############################################################################################################################################### ############################################################################################################################################### @warning_checks(None, ["lt", 1], learning_rate_decay=None, weight_decay=["lt", 0.01], epsilon=None, clipnorm=None, clipvalue=None, post_trace=False) @error_checks(None, ["gt", 0], learning_rate_decay=None, weight_decay=["gte", 0], epsilon=None, clipnorm=None, clipvalue=None, post_trace=False) @accepts("self", [int, float], learning_rate_decay=[int, float], weight_decay=[int, float], epsilon=[int, float], clipnorm=[int, float], clipvalue=[int, float], post_trace=False) #@TraceFunction(trace_args=True, trace_rv=True) def optimizer_adagrad(self, learning_rate, learning_rate_decay=0, weight_decay=0, epsilon=1e-08, clipnorm=0.0, clipvalue=0.0): ''' Select Adagrad optimizer Args: learning_rate (float): Initial base learning rate learning_rate_decay (float): Learning rate decay factor weight_decay (float): Value for regularizing weights post every update epsilon (float): A value to avoid division by zero clipnorm (float): Gradient clipping factor clipvalue (float): Value for clipping Returns: None ''' self.system_dict = adagrad(self.system_dict, learning_rate, learning_rate_decay=learning_rate_decay, weight_decay=weight_decay, epsilon=epsilon, clipnorm=clipnorm, clipvalue=clipvalue); self.custom_print("Optimizer"); self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"])); self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"])); self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"])); self.custom_print(""); ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk"); self.custom_print(""); ConstraintWarning("ArgumentWarning: learning_rate_decay is active only for pytorch in current version of Monk"); self.custom_print(""); ###############################################################################################################################################

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0.926924

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0.865768

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6

bfde0530e9195e24f4af021d544ddf9f69b16e9f

38,456

py

Python

tests/integration/resources_permissions/test_epics_resources.py

aavcc/taiga-openshift

7c33284573ceed38f755b8159ad83f3f68d2f7cb

[ "MIT" ]

null

tests/integration/resources_permissions/test_epics_resources.py

aavcc/taiga-openshift

7c33284573ceed38f755b8159ad83f3f68d2f7cb

[ "MIT" ]

12

2019-11-25T14:08:32.000Z

2021-06-24T10:35:51.000Z

tests/integration/resources_permissions/test_epics_resources.py

threefoldtech/Threefold-Circles

cbc433796b25cf7af9a295af65d665a4a279e2d6

[ "Apache-2.0" ]

1

2018-06-07T10:58:15.000Z

# -*- coding: utf-8 -*- # Copyright (C) 2014-2017 Andrey Antukh <niwi@niwi.nz> # Copyright (C) 2014-2017 Jesús Espino <jespinog@gmail.com> # Copyright (C) 2014-2017 David Barragán <bameda@dbarragan.com> # Copyright (C) 2014-2017 Alejandro Alonso <alejandro.alonso@kaleidos.net> # Copyright (C) 2014-2017 Anler Hernández <hello@anler.me> # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # This program 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 Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. import uuid from django.core.urlresolvers import reverse from taiga.base.utils import json from taiga.projects import choices as project_choices from taiga.projects.models import Project from taiga.projects.epics.serializers import EpicSerializer from taiga.projects.epics.models import Epic from taiga.projects.epics.utils import attach_extra_info as attach_epic_extra_info from taiga.projects.utils import attach_extra_info as attach_project_extra_info from taiga.permissions.choices import MEMBERS_PERMISSIONS, ANON_PERMISSIONS from taiga.projects.occ import OCCResourceMixin from tests import factories as f from tests.utils import helper_test_http_method, reconnect_signals from taiga.projects.votes.services import add_vote from taiga.projects.notifications.services import add_watcher from unittest import mock import pytest pytestmark = pytest.mark.django_db def setup_function(function): reconnect_signals() @pytest.fixture def data(): m = type("Models", (object,), {}) m.registered_user = f.UserFactory.create() m.project_member_with_perms = f.UserFactory.create() m.project_member_without_perms = f.UserFactory.create() m.project_owner = f.UserFactory.create() m.other_user = f.UserFactory.create() m.public_project = f.ProjectFactory(is_private=False, anon_permissions=list(map(lambda x: x[0], ANON_PERMISSIONS)), public_permissions=list(map(lambda x: x[0], ANON_PERMISSIONS)) + ["comment_epic"], owner=m.project_owner, epics_csv_uuid=uuid.uuid4().hex) m.public_project = attach_project_extra_info(Project.objects.all()).get(id=m.public_project.id) m.private_project1 = f.ProjectFactory(is_private=True, anon_permissions=list(map(lambda x: x[0], ANON_PERMISSIONS)), public_permissions=list(map(lambda x: x[0], ANON_PERMISSIONS)), owner=m.project_owner, epics_csv_uuid=uuid.uuid4().hex) m.private_project1 = attach_project_extra_info(Project.objects.all()).get(id=m.private_project1.id) m.private_project2 = f.ProjectFactory(is_private=True, anon_permissions=[], public_permissions=[], owner=m.project_owner, epics_csv_uuid=uuid.uuid4().hex) m.private_project2 = attach_project_extra_info(Project.objects.all()).get(id=m.private_project2.id) m.blocked_project = f.ProjectFactory(is_private=True, anon_permissions=[], public_permissions=[], owner=m.project_owner, epics_csv_uuid=uuid.uuid4().hex, blocked_code=project_choices.BLOCKED_BY_STAFF) m.blocked_project = attach_project_extra_info(Project.objects.all()).get(id=m.blocked_project.id) m.public_membership = f.MembershipFactory( project=m.public_project, user=m.project_member_with_perms, role__project=m.public_project, role__permissions=list(map(lambda x: x[0], MEMBERS_PERMISSIONS))) m.private_membership1 = f.MembershipFactory( project=m.private_project1, user=m.project_member_with_perms, role__project=m.private_project1, role__permissions=list(map(lambda x: x[0], MEMBERS_PERMISSIONS))) f.MembershipFactory( project=m.private_project1, user=m.project_member_without_perms, role__project=m.private_project1, role__permissions=[]) m.private_membership2 = f.MembershipFactory( project=m.private_project2, user=m.project_member_with_perms, role__project=m.private_project2, role__permissions=list(map(lambda x: x[0], MEMBERS_PERMISSIONS))) f.MembershipFactory( project=m.private_project2, user=m.project_member_without_perms, role__project=m.private_project2, role__permissions=[]) m.blocked_membership = f.MembershipFactory( project=m.blocked_project, user=m.project_member_with_perms, role__project=m.blocked_project, role__permissions=list(map(lambda x: x[0], MEMBERS_PERMISSIONS))) f.MembershipFactory(project=m.blocked_project, user=m.project_member_without_perms, role__project=m.blocked_project, role__permissions=[]) f.MembershipFactory(project=m.public_project, user=m.project_owner, is_admin=True) f.MembershipFactory(project=m.private_project1, user=m.project_owner, is_admin=True) f.MembershipFactory(project=m.private_project2, user=m.project_owner, is_admin=True) f.MembershipFactory(project=m.blocked_project, user=m.project_owner, is_admin=True) m.public_epic = f.EpicFactory(project=m.public_project, status__project=m.public_project) m.public_epic = attach_epic_extra_info(Epic.objects.all()).get(id=m.public_epic.id) m.private_epic1 = f.EpicFactory(project=m.private_project1, status__project=m.private_project1) m.private_epic1 = attach_epic_extra_info(Epic.objects.all()).get(id=m.private_epic1.id) m.private_epic2 = f.EpicFactory(project=m.private_project2, status__project=m.private_project2) m.private_epic2 = attach_epic_extra_info(Epic.objects.all()).get(id=m.private_epic2.id) m.blocked_epic = f.EpicFactory(project=m.blocked_project, status__project=m.blocked_project) m.blocked_epic = attach_epic_extra_info(Epic.objects.all()).get(id=m.blocked_epic.id) m.public_us = f.UserStoryFactory(project=m.public_project) m.private_us1 = f.UserStoryFactory(project=m.private_project1) m.private_us2 = f.UserStoryFactory(project=m.private_project2) m.blocked_us = f.UserStoryFactory(project=m.blocked_project) m.public_related_us = f.RelatedUserStory(epic=m.public_epic, user_story=m.public_us) m.private_related_us1 = f.RelatedUserStory(epic=m.private_epic1, user_story=m.private_us1) m.private_related_us2 = f.RelatedUserStory(epic=m.private_epic2, user_story=m.private_us2) m.blocked_related_us = f.RelatedUserStory(epic=m.blocked_epic, user_story=m.blocked_us) m.public_project.default_epic_status = m.public_epic.status m.public_project.save() m.private_project1.default_epic_status = m.private_epic1.status m.private_project1.save() m.private_project2.default_epic_status = m.private_epic2.status m.private_project2.save() m.blocked_project.default_epic_status = m.blocked_epic.status m.blocked_project.save() return m def test_epic_list(client, data): url = reverse('epics-list') response = client.get(url) epics_data = json.loads(response.content.decode('utf-8')) assert len(epics_data) == 2 assert response.status_code == 200 client.login(data.registered_user) response = client.get(url) epics_data = json.loads(response.content.decode('utf-8')) assert len(epics_data) == 2 assert response.status_code == 200 client.login(data.project_member_with_perms) response = client.get(url) epics_data = json.loads(response.content.decode('utf-8')) assert len(epics_data) == 4 assert response.status_code == 200 client.login(data.project_owner) response = client.get(url) epics_data = json.loads(response.content.decode('utf-8')) assert len(epics_data) == 4 assert response.status_code == 200 def test_epic_retrieve(client, data): public_url = reverse('epics-detail', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-detail', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-detail', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-detail', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'get', public_url, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url1, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url2, None, users) assert results == [401, 403, 403, 200, 200] results = helper_test_http_method(client, 'get', blocked_url, None, users) assert results == [401, 403, 403, 200, 200] def test_epic_create(client, data): url = reverse('epics-list') users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] create_data = json.dumps({ "subject": "test", "ref": 1, "project": data.public_project.pk, "status": data.public_project.epic_statuses.all()[0].pk, }) results = helper_test_http_method(client, 'post', url, create_data, users) assert results == [401, 403, 403, 201, 201] create_data = json.dumps({ "subject": "test", "ref": 2, "project": data.private_project1.pk, "status": data.private_project1.epic_statuses.all()[0].pk, }) results = helper_test_http_method(client, 'post', url, create_data, users) assert results == [401, 403, 403, 201, 201] create_data = json.dumps({ "subject": "test", "ref": 3, "project": data.private_project2.pk, "status": data.private_project2.epic_statuses.all()[0].pk, }) results = helper_test_http_method(client, 'post', url, create_data, users) assert results == [401, 403, 403, 201, 201] create_data = json.dumps({ "subject": "test", "ref": 3, "project": data.blocked_project.pk, "status": data.blocked_project.epic_statuses.all()[0].pk, }) results = helper_test_http_method(client, 'post', url, create_data, users) assert results == [401, 403, 403, 451, 451] def test_epic_put_update(client, data): public_url = reverse('epics-detail', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-detail', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-detail', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-detail', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] with mock.patch.object(OCCResourceMixin, "_validate_and_update_version"): epic_data = EpicSerializer(data.public_epic).data epic_data["subject"] = "test" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', public_url, epic_data, users) assert results == [401, 403, 403, 200, 200] epic_data = EpicSerializer(data.private_epic1).data epic_data["subject"] = "test" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', private_url1, epic_data, users) assert results == [401, 403, 403, 200, 200] epic_data = EpicSerializer(data.private_epic2).data epic_data["subject"] = "test" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', private_url2, epic_data, users) assert results == [401, 403, 403, 200, 200] epic_data = EpicSerializer(data.blocked_epic).data epic_data["subject"] = "test" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', blocked_url, epic_data, users) assert results == [401, 403, 403, 451, 451] def test_epic_put_comment(client, data): public_url = reverse('epics-detail', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-detail', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-detail', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-detail', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] with mock.patch.object(OCCResourceMixin, "_validate_and_update_version"): epic_data = EpicSerializer(data.public_epic).data epic_data["comment"] = "test comment" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', public_url, epic_data, users) assert results == [401, 403, 403, 200, 200] epic_data = EpicSerializer(data.private_epic1).data epic_data["comment"] = "test comment" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', private_url1, epic_data, users) assert results == [401, 403, 403, 200, 200] epic_data = EpicSerializer(data.private_epic2).data epic_data["comment"] = "test comment" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', private_url2, epic_data, users) assert results == [401, 403, 403, 200, 200] epic_data = EpicSerializer(data.blocked_epic).data epic_data["comment"] = "test comment" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', blocked_url, epic_data, users) assert results == [401, 403, 403, 451, 451] def test_epic_put_update_and_comment(client, data): public_url = reverse('epics-detail', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-detail', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-detail', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-detail', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] with mock.patch.object(OCCResourceMixin, "_validate_and_update_version"): epic_data = EpicSerializer(data.public_epic).data epic_data["subject"] = "test" epic_data["comment"] = "test comment" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', public_url, epic_data, users) assert results == [401, 403, 403, 200, 200] epic_data = EpicSerializer(data.private_epic1).data epic_data["subject"] = "test" epic_data["comment"] = "test comment" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', private_url1, epic_data, users) assert results == [401, 403, 403, 200, 200] epic_data = EpicSerializer(data.private_epic2).data epic_data["subject"] = "test" epic_data["comment"] = "test comment" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', private_url2, epic_data, users) assert results == [401, 403, 403, 200, 200] epic_data = EpicSerializer(data.blocked_epic).data epic_data["subject"] = "test" epic_data["comment"] = "test comment" epic_data = json.dumps(epic_data) results = helper_test_http_method(client, 'put', blocked_url, epic_data, users) assert results == [401, 403, 403, 451, 451] def test_epic_put_update_with_project_change(client): user1 = f.UserFactory.create() user2 = f.UserFactory.create() user3 = f.UserFactory.create() user4 = f.UserFactory.create() project1 = f.ProjectFactory() project2 = f.ProjectFactory() epic_status1 = f.EpicStatusFactory.create(project=project1) epic_status2 = f.EpicStatusFactory.create(project=project2) project1.default_epic_status = epic_status1 project2.default_epic_status = epic_status2 project1.save() project2.save() project1 = attach_project_extra_info(Project.objects.all()).get(id=project1.id) project2 = attach_project_extra_info(Project.objects.all()).get(id=project2.id) f.MembershipFactory(project=project1, user=user1, role__project=project1, role__permissions=list(map(lambda x: x[0], MEMBERS_PERMISSIONS))) f.MembershipFactory(project=project2, user=user1, role__project=project2, role__permissions=list(map(lambda x: x[0], MEMBERS_PERMISSIONS))) f.MembershipFactory(project=project1, user=user2, role__project=project1, role__permissions=list(map(lambda x: x[0], MEMBERS_PERMISSIONS))) f.MembershipFactory(project=project2, user=user3, role__project=project2, role__permissions=list(map(lambda x: x[0], MEMBERS_PERMISSIONS))) epic = f.EpicFactory.create(project=project1) epic = attach_epic_extra_info(Epic.objects.all()).get(id=epic.id) url = reverse('epics-detail', kwargs={"pk": epic.pk}) # Test user with permissions in both projects client.login(user1) epic_data = EpicSerializer(epic).data epic_data["project"] = project2.id epic_data = json.dumps(epic_data) response = client.put(url, data=epic_data, content_type="application/json") assert response.status_code == 200 epic.project = project1 epic.save() # Test user with permissions in only origin project client.login(user2) epic_data = EpicSerializer(epic).data epic_data["project"] = project2.id epic_data = json.dumps(epic_data) response = client.put(url, data=epic_data, content_type="application/json") assert response.status_code == 403 epic.project = project1 epic.save() # Test user with permissions in only destionation project client.login(user3) epic_data = EpicSerializer(epic).data epic_data["project"] = project2.id epic_data = json.dumps(epic_data) response = client.put(url, data=epic_data, content_type="application/json") assert response.status_code == 403 epic.project = project1 epic.save() # Test user without permissions in the projects client.login(user4) epic_data = EpicSerializer(epic).data epic_data["project"] = project2.id epic_data = json.dumps(epic_data) response = client.put(url, data=epic_data, content_type="application/json") assert response.status_code == 403 epic.project = project1 epic.save() def test_epic_patch_update(client, data): public_url = reverse('epics-detail', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-detail', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-detail', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-detail', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] with mock.patch.object(OCCResourceMixin, "_validate_and_update_version"): patch_data = json.dumps({"subject": "test", "version": data.public_epic.version}) results = helper_test_http_method(client, 'patch', public_url, patch_data, users) assert results == [401, 403, 403, 200, 200] patch_data = json.dumps({"subject": "test", "version": data.private_epic1.version}) results = helper_test_http_method(client, 'patch', private_url1, patch_data, users) assert results == [401, 403, 403, 200, 200] patch_data = json.dumps({"subject": "test", "version": data.private_epic2.version}) results = helper_test_http_method(client, 'patch', private_url2, patch_data, users) assert results == [401, 403, 403, 200, 200] patch_data = json.dumps({"subject": "test", "version": data.blocked_epic.version}) results = helper_test_http_method(client, 'patch', blocked_url, patch_data, users) assert results == [401, 403, 403, 451, 451] def test_epic_patch_comment(client, data): public_url = reverse('epics-detail', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-detail', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-detail', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-detail', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] with mock.patch.object(OCCResourceMixin, "_validate_and_update_version"): patch_data = json.dumps({"comment": "test comment", "version": data.public_epic.version}) results = helper_test_http_method(client, 'patch', public_url, patch_data, users) assert results == [401, 200, 200, 200, 200] patch_data = json.dumps({"comment": "test comment", "version": data.private_epic1.version}) results = helper_test_http_method(client, 'patch', private_url1, patch_data, users) assert results == [401, 403, 403, 200, 200] patch_data = json.dumps({"comment": "test comment", "version": data.private_epic2.version}) results = helper_test_http_method(client, 'patch', private_url2, patch_data, users) assert results == [401, 403, 403, 200, 200] patch_data = json.dumps({"comment": "test comment", "version": data.blocked_epic.version}) results = helper_test_http_method(client, 'patch', blocked_url, patch_data, users) assert results == [401, 403, 403, 451, 451] def test_epic_patch_update_and_comment(client, data): public_url = reverse('epics-detail', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-detail', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-detail', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-detail', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] with mock.patch.object(OCCResourceMixin, "_validate_and_update_version"): patch_data = json.dumps({ "subject": "test", "comment": "test comment", "version": data.public_epic.version }) results = helper_test_http_method(client, 'patch', public_url, patch_data, users) assert results == [401, 403, 403, 200, 200] patch_data = json.dumps({ "subject": "test", "comment": "test comment", "version": data.private_epic1.version }) results = helper_test_http_method(client, 'patch', private_url1, patch_data, users) assert results == [401, 403, 403, 200, 200] patch_data = json.dumps({ "subject": "test", "comment": "test comment", "version": data.private_epic2.version }) results = helper_test_http_method(client, 'patch', private_url2, patch_data, users) assert results == [401, 403, 403, 200, 200] patch_data = json.dumps({ "subject": "test", "comment": "test comment", "version": data.blocked_epic.version }) results = helper_test_http_method(client, 'patch', blocked_url, patch_data, users) assert results == [401, 403, 403, 451, 451] def test_epic_delete(client, data): public_url = reverse('epics-detail', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-detail', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-detail', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-detail', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, ] results = helper_test_http_method(client, 'delete', public_url, None, users) assert results == [401, 403, 403, 204] results = helper_test_http_method(client, 'delete', private_url1, None, users) assert results == [401, 403, 403, 204] results = helper_test_http_method(client, 'delete', private_url2, None, users) assert results == [401, 403, 403, 204] results = helper_test_http_method(client, 'delete', blocked_url, None, users) assert results == [401, 403, 403, 451] def test_epic_action_bulk_create(client, data): url = reverse('epics-bulk-create') users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] bulk_data = json.dumps({ "bulk_epics": "test1\ntest2", "project_id": data.public_epic.project.pk, }) results = helper_test_http_method(client, 'post', url, bulk_data, users) assert results == [401, 403, 403, 200, 200] bulk_data = json.dumps({ "bulk_epics": "test1\ntest2", "project_id": data.private_epic1.project.pk, }) results = helper_test_http_method(client, 'post', url, bulk_data, users) assert results == [401, 403, 403, 200, 200] bulk_data = json.dumps({ "bulk_epics": "test1\ntest2", "project_id": data.private_epic2.project.pk, }) results = helper_test_http_method(client, 'post', url, bulk_data, users) assert results == [401, 403, 403, 200, 200] bulk_data = json.dumps({ "bulk_epics": "test1\ntest2", "project_id": data.blocked_epic.project.pk, }) results = helper_test_http_method(client, 'post', url, bulk_data, users) assert results == [401, 403, 403, 451, 451] def test_epic_action_upvote(client, data): public_url = reverse('epics-upvote', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-upvote', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-upvote', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-upvote', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'post', public_url, "", users) assert results == [401, 200, 200, 200, 200] results = helper_test_http_method(client, 'post', private_url1, "", users) assert results == [401, 200, 200, 200, 200] results = helper_test_http_method(client, 'post', private_url2, "", users) assert results == [404, 404, 404, 200, 200] results = helper_test_http_method(client, 'post', blocked_url, "", users) assert results == [404, 404, 404, 451, 451] def test_epic_action_downvote(client, data): public_url = reverse('epics-downvote', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-downvote', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-downvote', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-downvote', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'post', public_url, "", users) assert results == [401, 200, 200, 200, 200] results = helper_test_http_method(client, 'post', private_url1, "", users) assert results == [401, 200, 200, 200, 200] results = helper_test_http_method(client, 'post', private_url2, "", users) assert results == [404, 404, 404, 200, 200] results = helper_test_http_method(client, 'post', blocked_url, "", users) assert results == [404, 404, 404, 451, 451] def test_epic_voters_list(client, data): public_url = reverse('epic-voters-list', kwargs={"resource_id": data.public_epic.pk}) private_url1 = reverse('epic-voters-list', kwargs={"resource_id": data.private_epic1.pk}) private_url2 = reverse('epic-voters-list', kwargs={"resource_id": data.private_epic2.pk}) blocked_url = reverse('epic-voters-list', kwargs={"resource_id": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'get', public_url, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url1, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url2, None, users) assert results == [401, 403, 403, 200, 200] results = helper_test_http_method(client, 'get', blocked_url, None, users) assert results == [401, 403, 403, 200, 200] def test_epic_voters_retrieve(client, data): add_vote(data.public_epic, data.project_owner) public_url = reverse('epic-voters-detail', kwargs={"resource_id": data.public_epic.pk, "pk": data.project_owner.pk}) add_vote(data.private_epic1, data.project_owner) private_url1 = reverse('epic-voters-detail', kwargs={"resource_id": data.private_epic1.pk, "pk": data.project_owner.pk}) add_vote(data.private_epic2, data.project_owner) private_url2 = reverse('epic-voters-detail', kwargs={"resource_id": data.private_epic2.pk, "pk": data.project_owner.pk}) add_vote(data.blocked_epic, data.project_owner) blocked_url = reverse('epic-voters-detail', kwargs={"resource_id": data.blocked_epic.pk, "pk": data.project_owner.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'get', public_url, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url1, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url2, None, users) assert results == [401, 403, 403, 200, 200] results = helper_test_http_method(client, 'get', blocked_url, None, users) assert results == [401, 403, 403, 200, 200] def test_epic_action_watch(client, data): public_url = reverse('epics-watch', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-watch', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-watch', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-watch', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'post', public_url, "", users) assert results == [401, 200, 200, 200, 200] results = helper_test_http_method(client, 'post', private_url1, "", users) assert results == [401, 200, 200, 200, 200] results = helper_test_http_method(client, 'post', private_url2, "", users) assert results == [404, 404, 404, 200, 200] results = helper_test_http_method(client, 'post', blocked_url, "", users) assert results == [404, 404, 404, 451, 451] def test_epic_action_unwatch(client, data): public_url = reverse('epics-unwatch', kwargs={"pk": data.public_epic.pk}) private_url1 = reverse('epics-unwatch', kwargs={"pk": data.private_epic1.pk}) private_url2 = reverse('epics-unwatch', kwargs={"pk": data.private_epic2.pk}) blocked_url = reverse('epics-unwatch', kwargs={"pk": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'post', public_url, "", users) assert results == [401, 200, 200, 200, 200] results = helper_test_http_method(client, 'post', private_url1, "", users) assert results == [401, 200, 200, 200, 200] results = helper_test_http_method(client, 'post', private_url2, "", users) assert results == [404, 404, 404, 200, 200] results = helper_test_http_method(client, 'post', blocked_url, "", users) assert results == [404, 404, 404, 451, 451] def test_epic_watchers_list(client, data): public_url = reverse('epic-watchers-list', kwargs={"resource_id": data.public_epic.pk}) private_url1 = reverse('epic-watchers-list', kwargs={"resource_id": data.private_epic1.pk}) private_url2 = reverse('epic-watchers-list', kwargs={"resource_id": data.private_epic2.pk}) blocked_url = reverse('epic-watchers-list', kwargs={"resource_id": data.blocked_epic.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'get', public_url, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url1, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url2, None, users) assert results == [401, 403, 403, 200, 200] results = helper_test_http_method(client, 'get', blocked_url, None, users) assert results == [401, 403, 403, 200, 200] def test_epic_watchers_retrieve(client, data): add_watcher(data.public_epic, data.project_owner) public_url = reverse('epic-watchers-detail', kwargs={"resource_id": data.public_epic.pk, "pk": data.project_owner.pk}) add_watcher(data.private_epic1, data.project_owner) private_url1 = reverse('epic-watchers-detail', kwargs={"resource_id": data.private_epic1.pk, "pk": data.project_owner.pk}) add_watcher(data.private_epic2, data.project_owner) private_url2 = reverse('epic-watchers-detail', kwargs={"resource_id": data.private_epic2.pk, "pk": data.project_owner.pk}) add_watcher(data.blocked_epic, data.project_owner) blocked_url = reverse('epic-watchers-detail', kwargs={"resource_id": data.blocked_epic.pk, "pk": data.project_owner.pk}) users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'get', public_url, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url1, None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', private_url2, None, users) assert results == [401, 403, 403, 200, 200] results = helper_test_http_method(client, 'get', blocked_url, None, users) assert results == [401, 403, 403, 200, 200] def test_epics_csv(client, data): url = reverse('epics-csv') csv_public_uuid = data.public_project.epics_csv_uuid csv_private1_uuid = data.private_project1.epics_csv_uuid csv_private2_uuid = data.private_project1.epics_csv_uuid csv_blocked_uuid = data.blocked_project.epics_csv_uuid users = [ None, data.registered_user, data.project_member_without_perms, data.project_member_with_perms, data.project_owner ] results = helper_test_http_method(client, 'get', "{}?uuid={}".format(url, csv_public_uuid), None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', "{}?uuid={}".format(url, csv_private1_uuid), None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', "{}?uuid={}".format(url, csv_private2_uuid), None, users) assert results == [200, 200, 200, 200, 200] results = helper_test_http_method(client, 'get', "{}?uuid={}".format(url, csv_blocked_uuid), None, users) assert results == [200, 200, 200, 200, 200]

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0.661145

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38,456

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null

0

6

bfe43a1f982ed9fd7da447c90684b4c6aa5f68e2

48

py

Python

src/deepke/name_entity_re/__init__.py

johncolezhang/DeepKE

ea4552ec42cb003a835f00fc14fb454f9a9a7183

[ "MIT" ]

710

2021-08-01T16:43:59.000Z

2022-03-31T08:39:17.000Z

src/deepke/name_entity_re/__init__.py

johncolezhang/DeepKE

ea4552ec42cb003a835f00fc14fb454f9a9a7183

[ "MIT" ]

66

2019-06-09T12:14:31.000Z

2021-07-27T05:54:35.000Z

src/deepke/name_entity_re/__init__.py

johncolezhang/DeepKE

ea4552ec42cb003a835f00fc14fb454f9a9a7183

[ "MIT" ]

183

2018-09-07T06:57:13.000Z

2021-08-01T08:50:15.000Z

from .standard import * from .few_shot import *

16

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48

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null

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1

0

1

0

1

0

6

8757b04fd4b2fb6a8af17658469898c40069ccbc

4,776

py

Python

tests/package/aeronpy/archive_test.py

welly87/aeron-python

300a4344dd7f1526aeafb3a23fe5c85fb3313ad1

[ "Apache-2.0" ]

9

2018-11-16T03:06:22.000Z

2022-03-13T19:14:15.000Z

tests/package/aeronpy/archive_test.py

welly87/aeron-python

300a4344dd7f1526aeafb3a23fe5c85fb3313ad1

[ "Apache-2.0" ]

1

2021-11-10T12:43:09.000Z

tests/package/aeronpy/archive_test.py

welly87/aeron-python

300a4344dd7f1526aeafb3a23fe5c85fb3313ad1

[ "Apache-2.0" ]

7

2019-01-21T13:52:03.000Z

2022-03-08T21:09:06.000Z

import os from hamcrest import * from pytest import fixture from tempfile import _get_candidate_names as temp_dir_candidates, tempdir from time import sleep from aeronpy import Archive from aeronpy.driver import archiving_media_driver @fixture() def aeron_directory(): temp_dirs = temp_dir_candidates() where = os.path.join(tempdir, next(temp_dirs)) where_archive = os.path.join(tempdir, next(temp_dirs)) with archiving_media_driver.launch(aeron_directory_name=where, archive_directory_name=where_archive): yield where @fixture() def config_file(): here, _ = os.path.split(__file__) return os.path.join(here, 'archive.properties') def test__archive_create(aeron_directory): archive = Archive(aeron_dir=aeron_directory) assert_that(archive, is_not(None)) def test__archive_create__with_config(aeron_directory, config_file): archive = Archive(config_file=config_file, aeron_dir=aeron_directory) assert_that(archive, is_not(None)) def test__archive_add_recorded_publication(aeron_directory): archive = Archive(aeron_dir=aeron_directory, aeron_archive_dir=aeron_directory) recording = archive.find_last('aeron:ipc', 5000) assert_that(recording, is_(None)) publication = archive.add_recorded_publication('aeron:ipc', 5000) sleep(0.5) recording = archive.find_last('aeron:ipc', 5000) assert_that(recording, is_not(None)) assert_that(recording.id, is_(equal_to(0))) result = publication.offer(b'abc') assert_that(result, is_(greater_than(0))) sleep(0.5) assert_that(recording.position, is_(equal_to(result))) def test__archive_add_recorded_exclusive_publication(aeron_directory): archive = Archive(aeron_dir=aeron_directory, aeron_archive_dir=aeron_directory) recording = archive.find_last('aeron:ipc', 5000) assert_that(recording, is_(None)) publication = archive.add_recorded_exclusive_publication('aeron:ipc', 5000) sleep(0.5) recording = archive.find_last('aeron:ipc', 5000) assert_that(recording, is_not(None)) assert_that(recording.id, is_(equal_to(0))) result = publication.offer(b'abc') assert_that(result, is_(greater_than(0))) sleep(0.5) assert_that(recording.position, is_(equal_to(result))) def test__recording_find(aeron_directory): archive = Archive(aeron_dir=aeron_directory, aeron_archive_dir=aeron_directory) publication = archive.add_recorded_publication('aeron:ipc', 5000) sleep(0.5) recording = archive.find(0) assert_that(recording, is_not(None)) assert_that(recording.position, is_(equal_to(0))) def test__recording_replay(aeron_directory): archive = Archive(aeron_dir=aeron_directory, aeron_archive_dir=aeron_directory) publication = archive.add_recorded_publication('aeron:ipc', 5000) offer_result = publication.offer(b'abc') assert_that(offer_result, is_(greater_than(0))) offer_result = publication.offer(b'def') assert_that(offer_result, is_(greater_than(0))) sleep(0.5) recording = archive.find_last('aeron:ipc', 5000) subscription = recording.replay('aeron:ipc', 6000) assert_that(archive.find_last('aeron:ipc', 6000), is_(None)) replayed = list() subscription.poll(lambda data: replayed.append(bytes(data))) assert_that(replayed, has_length(2)) assert_that(replayed, has_items(equal_to(b'abc'), equal_to(b'def'))) def test__recording_replay__from_position(aeron_directory): archive = Archive(aeron_dir=aeron_directory, aeron_archive_dir=aeron_directory) publication = archive.add_recorded_publication('aeron:ipc', 5000) offer_result = publication.offer(b'abc') assert_that(offer_result, is_(greater_than(0))) offer_result = publication.offer(b'def') assert_that(offer_result, is_(greater_than(0))) sleep(0.5) recording = archive.find_last('aeron:ipc', 5000) subscription = recording.replay('aeron:ipc', 6000, 64) assert_that(archive.find_last('aeron:ipc', 6000), is_(None)) replayed = list() subscription.poll(lambda data: replayed.append(bytes(data))) assert_that(replayed, has_length(1)) assert_that(replayed, has_items(equal_to(b'def'))) def test__recording_replay__from_position__not_aligned(aeron_directory): archive = Archive(aeron_dir=aeron_directory, aeron_archive_dir=aeron_directory) publication = archive.add_recorded_publication('aeron:ipc', 5000) offer_result = publication.offer(b'abc') assert_that(offer_result, is_(greater_than(0))) offer_result = publication.offer(b'def') assert_that(offer_result, is_(greater_than(0))) sleep(0.5) recording = archive.find_last('aeron:ipc', 5000) assert_that(calling(recording.replay).with_args('aeron:ipc', 6000, 50), raises(RuntimeError))

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105

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0.809425

0.806165

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0

null

0

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0

null

0

6

876886b10c5d7283b41479e686ebe38301aec6fb

477

py

Python

examples/src/python/example/tensorflow_custom_op/zero_out_custom_op.py

anthonyjpratti/pants

d98e53af6ddd877861231bce8343f8204da0a9d1

[ "Apache-2.0" ]

1

2020-08-26T03:30:31.000Z

examples/src/python/example/tensorflow_custom_op/zero_out_custom_op.py

wisechengyi/SCL-16273

18ed1fb4212879150ae5b292137d058894051fc6

[ "Apache-2.0" ]

1

2020-01-21T16:34:02.000Z

examples/src/python/example/tensorflow_custom_op/zero_out_custom_op.py

wisechengyi/SCL-16273

18ed1fb4212879150ae5b292137d058894051fc6

[ "Apache-2.0" ]

null

# Copyright 2018 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). import tensorflow as tf # TODO: It would be great if we could maintain the example.tensorflow_custom_op package prefix for # this python_dist()! from wrap_lib.wrap_zero_out_op import zero_out_op_lib_path # We make this a function in order to lazily load the op library. def zero_out_module(): return tf.load_op_library(zero_out_op_lib_path)

36.692308

98

0.802935

82

477

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0.076923

0.074176

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0.087912

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0.140461

477

12

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true

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null

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0

1

0

1

0

6

876d3c5c12899518f4cf30525e0379bbda345c36

35,248

py

Python

src/import_dbf/migrations/0001_initial.py

iplweb/django-bpp

85f183a99d8d5027ae4772efac1e4a9f21675849

[ "BSD-3-Clause" ]

1

2017-04-27T19:50:02.000Z

src/import_dbf/migrations/0001_initial.py

mpasternak/django-bpp

434338821d5ad1aaee598f6327151aba0af66f5e

[ "BSD-3-Clause" ]

41

2019-11-07T00:07:02.000Z

2022-02-27T22:09:39.000Z

src/import_dbf/migrations/0001_initial.py

iplweb/bpp

f027415cc3faf1ca79082bf7bacd4be35b1a6fdf

[ "BSD-3-Clause" ]

null

# Generated by Django 2.1.13 on 2019-11-03 14:05 import django.contrib.postgres.fields.jsonb from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ('contenttypes', '0002_remove_content_type_name'), ] operations = [ migrations.CreateModel( name='Aut', fields=[ ('idt_aut', models.TextField(primary_key=True, serialize=False)), ('imiona', models.TextField(blank=True, null=True)), ('nazwisko', models.TextField(blank=True, null=True)), ('ref', models.TextField(blank=True, null=True)), ('kad_nr', models.TextField(blank=True, null=True)), ('tel', models.TextField(blank=True, null=True)), ('email', models.TextField(blank=True, null=True)), ('www', models.TextField(blank=True, null=True)), ('imiona_bz', models.TextField(blank=True, null=True)), ('nazwisk_bz', models.TextField(blank=True, null=True)), ('tytul', models.TextField(blank=True, null=True)), ('stanowisko', models.TextField(blank=True, null=True)), ('prac_od', models.TextField(blank=True, null=True)), ('dat_zwol', models.TextField(blank=True, null=True)), ('fg', models.TextField(blank=True, null=True)), ('dop', models.TextField(blank=True, null=True)), ('nr_ewid', models.TextField(blank=True, null=True)), ('kad_s_jed', models.TextField(blank=True, null=True)), ('pbn_id', models.TextField(blank=True, null=True)), ('res_id', models.TextField(blank=True, null=True)), ('scop_id', models.TextField(blank=True, null=True)), ('orcid_id', models.TextField(blank=True, null=True)), ('exp_id', models.TextField(blank=True, null=True)), ('polon_id', models.TextField(blank=True, null=True)), ('usos_id', models.TextField(blank=True, null=True)), ('udf_id', models.TextField(blank=True, null=True)), ('control', models.TextField(blank=True, null=True)), ('uwagi', models.TextField(blank=True, null=True)), ('graf', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowany autor', 'verbose_name_plural': 'zaimportowani autorzy', 'db_table': 'import_dbf_aut', 'ordering': ('nazwisko', 'imiona'), 'managed': False, }, ), migrations.CreateModel( name='B_A', fields=[ ('id', models.IntegerField(primary_key=True, serialize=False)), ('lp', models.TextField(blank=True, null=True)), ('wspz', models.TextField(blank=True, null=True)), ('pkt_dod', models.TextField(blank=True, null=True)), ('wspz2', models.TextField(blank=True, null=True)), ('pkt2_dod', models.TextField(blank=True, null=True)), ('afiliacja', models.TextField(blank=True, null=True)), ('odp', models.TextField(blank=True, null=True)), ('study_ga', models.TextField(blank=True, null=True)), ('tytul', models.TextField(blank=True, null=True)), ('stanowisko', models.TextField(blank=True, null=True)), ('uwagi', models.TextField(blank=True, null=True)), ('object_id', models.PositiveIntegerField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_b_a', 'ordering': ('idt__tytul_or_s', 'lp'), 'managed': False, }, ), migrations.CreateModel( name='B_B', fields=[ ('idt', models.TextField(primary_key=True, serialize=False)), ('lp', models.TextField(blank=True, null=True)), ('idt_bazy', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_b_b', 'managed': False, }, ), migrations.CreateModel( name='B_E', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('idt', models.IntegerField()), ('lp', models.TextField(blank=True, null=True)), ('idt_eng', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_b_e', 'managed': False, }, ), migrations.CreateModel( name='B_L', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('idt', models.IntegerField()), ('idt_l', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_b_l', 'managed': False, }, ), migrations.CreateModel( name='B_N', fields=[ ('idt', models.TextField(primary_key=True, serialize=False)), ('lp', models.TextField(blank=True, null=True)), ('idt_pbn', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_b_n', 'managed': False, }, ), migrations.CreateModel( name='B_P', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('idt', models.IntegerField()), ('lp', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_b_p', 'managed': False, }, ), migrations.CreateModel( name='B_U', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('comm', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowane dane OA rekordu', 'verbose_name_plural': 'zaimportowane dane OA rekordow', 'db_table': 'import_dbf_b_u', 'ordering': ('idt', 'comm'), 'managed': False, }, ), migrations.CreateModel( name='Dys', fields=[ ('orcid_id', models.TextField(primary_key=True, serialize=False)), ('a_n', models.TextField(blank=True, null=True)), ('a_w_etatu', models.TextField(blank=True, null=True)), ('a_dysc_1', models.TextField(blank=True, null=True)), ('a_dysc_2', models.TextField(blank=True, null=True)), ('a_dysc_1_e', models.TextField(blank=True, null=True)), ('a_dysc_2_e', models.TextField(blank=True, null=True)), ('b_n', models.TextField(blank=True, null=True)), ('b_w_etatu', models.TextField(blank=True, null=True)), ('b_dysc_1', models.TextField(blank=True, null=True)), ('b_dysc_2', models.TextField(blank=True, null=True)), ('b_dysc_1_e', models.TextField(blank=True, null=True)), ('b_dysc_2_e', models.TextField(blank=True, null=True)), ('c_n', models.TextField(blank=True, null=True)), ('c_w_etatu', models.TextField(blank=True, null=True)), ('c_dysc_1', models.TextField(blank=True, null=True)), ('c_dysc_2', models.TextField(blank=True, null=True)), ('c_dysc_1_e', models.TextField(blank=True, null=True)), ('c_dysc_2_e', models.TextField(blank=True, null=True)), ('d_n', models.TextField(blank=True, null=True)), ('d_w_etatu', models.TextField(blank=True, null=True)), ('d_dysc_1', models.TextField(blank=True, null=True)), ('d_dysc_2', models.TextField(blank=True, null=True)), ('d_dysc_1_e', models.TextField(blank=True, null=True)), ('d_dysc_2_e', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowana dyscyplina pracownika', 'verbose_name_plural': 'zaimportowane dyscypliny pracowników', 'db_table': 'import_dbf_dys', 'managed': False, }, ), migrations.CreateModel( name='Ext', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('cont', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_ext', 'managed': False, }, ), migrations.CreateModel( name='Ixb', fields=[ ('idt_bazy', models.TextField(primary_key=True, serialize=False)), ('baza', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowana baza', 'verbose_name_plural': 'zaimportowane bazy', 'db_table': 'import_dbf_ixb', 'managed': False, }, ), migrations.CreateModel( name='Ixe', fields=[ ('idt_eng', models.TextField(primary_key=True, serialize=False)), ('haslo', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowane hasło naukowe', 'verbose_name_plural': 'zaimportowane hasła naukowe', 'db_table': 'import_dbf_ixe', 'managed': False, }, ), migrations.CreateModel( name='Ixn', fields=[ ('idt_pbn', models.TextField(blank=True, primary_key=True, serialize=False)), ('pbn', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowany identyfikator PBN', 'verbose_name_plural': 'zaimportowane identyfikatory PBN', 'db_table': 'import_dbf_ixn', 'managed': False, }, ), migrations.CreateModel( name='Ixp', fields=[ ('idt_pol', models.TextField(primary_key=True, serialize=False)), ('haslo', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_ixp', 'managed': False, }, ), migrations.CreateModel( name='J_H', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('rok', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowany rekord historii jednostek', 'verbose_name_plural': 'zaimportowane rekordy historii jednostek', 'db_table': 'import_dbf_j_h', 'managed': False, }, ), migrations.CreateModel( name='Jed', fields=[ ('idt_jed', models.TextField(primary_key=True, serialize=False)), ('nr', models.TextField(blank=True, null=True)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ('wyd_skrot', models.TextField(blank=True, null=True)), ('sort', models.TextField(blank=True, null=True)), ('to_print', models.TextField(blank=True, null=True)), ('to_print2', models.TextField(blank=True, null=True)), ('to_print3', models.TextField(blank=True, null=True)), ('to_print4', models.TextField(blank=True, null=True)), ('to_print5', models.TextField(blank=True, null=True)), ('email', models.TextField(blank=True, null=True)), ('www', models.TextField(blank=True, null=True)), ('id_u', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_jed', 'managed': False, }, ), migrations.CreateModel( name='Jer', fields=[ ('nr', models.TextField(primary_key=True, serialize=False)), ('od_roku', models.TextField(blank=True, null=True)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ('wyd_skrot', models.TextField(blank=True, null=True)), ('id_u', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_jer', 'managed': False, }, ), migrations.CreateModel( name='Jez', fields=[ ('skrot', models.TextField(primary_key=True, serialize=False)), ('nazwa', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowany język', 'verbose_name_plural': 'zaimportowane języki', 'db_table': 'import_dbf_jez', 'managed': False, }, ), migrations.CreateModel( name='Kad', fields=[ ('nr', models.TextField(primary_key=True, serialize=False)), ('na', models.TextField(blank=True, null=True)), ('im1', models.TextField(blank=True, null=True)), ('im2', models.TextField(blank=True, null=True)), ('s_jed', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_kad', 'managed': False, }, ), migrations.CreateModel( name='Kbn', fields=[ ('idt_kbn', models.TextField(primary_key=True, serialize=False)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ('to_print', models.TextField(blank=True, null=True)), ('to_print2', models.TextField(blank=True, null=True)), ('to_print3', models.TextField(blank=True, null=True)), ('to_print4', models.TextField(blank=True, null=True)), ('to_print5', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowany typ KBN', 'verbose_name_plural': 'zaimportowane typy KBN', 'db_table': 'import_dbf_kbn', 'managed': False, }, ), migrations.CreateModel( name='Kbr', fields=[ ('idt_kbr', models.TextField(primary_key=True, serialize=False)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ('to_print', models.TextField(blank=True, null=True)), ('to_print2', models.TextField(blank=True, null=True)), ('to_print3', models.TextField(blank=True, null=True)), ('to_print4', models.TextField(blank=True, null=True)), ('to_print5', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowany rekord KBR', 'verbose_name_plural': 'zaimportowane rekordy KBR', 'db_table': 'import_dbf_kbr', 'managed': False, }, ), migrations.CreateModel( name='Ldy', fields=[ ('id', models.TextField(primary_key=True, serialize=False)), ('dziedzina', models.TextField(blank=True, null=True)), ('dyscyplina', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowana dziedzina', 'verbose_name_plural': 'zaimportowane dziedziny', 'db_table': 'import_dbf_ldy', 'managed': False, }, ), migrations.CreateModel( name='Lis', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('rok', models.TextField(blank=True, null=True)), ('kategoria', models.TextField(blank=True, null=True)), ('numer', models.TextField(blank=True, null=True)), ('tytul', models.TextField(blank=True, null=True)), ('issn', models.TextField(blank=True, null=True)), ('eissn', models.TextField(blank=True, null=True)), ('punkty', models.TextField(blank=True, null=True)), ('sobowtor', models.TextField(blank=True, null=True)), ('errissn', models.TextField(blank=True, null=True)), ('dblissn', models.TextField(blank=True, null=True)), ('dbltitul', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowana lista wydawców', 'verbose_name_plural': 'zaimportowane listy wydawców', 'db_table': 'import_dbf_lis', 'managed': False, }, ), migrations.CreateModel( name='Loc', fields=[ ('ident', models.TextField(primary_key=True, serialize=False)), ('ext', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_loc', 'managed': False, }, ), migrations.CreateModel( name='Pba', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('idt', models.TextField(blank=True, null=True)), ('idt_pbn', models.TextField(blank=True, null=True)), ('wyd_skrot', models.TextField(blank=True, null=True)), ('date', models.TextField(blank=True, null=True)), ('category', models.TextField(blank=True, null=True)), ('details', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_pba', 'managed': False, }, ), migrations.CreateModel( name='Pbb', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('rep_f_name', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_pbb', 'managed': False, }, ), migrations.CreateModel( name='Pbc', fields=[ ('idt', models.TextField(primary_key=True, serialize=False)), ('wyd_skrot', models.TextField(blank=True, null=True)), ('date', models.TextField(blank=True, null=True)), ('category', models.TextField(blank=True, null=True)), ('details', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_pbc', 'managed': False, }, ), migrations.CreateModel( name='Pbd', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('rep_f_name', models.TextField(blank=True, null=True)), ('field_ignore_me', models.TextField(blank=True, db_column='_ignore_me', null=True)), ], options={ 'db_table': 'import_dbf_pbd', 'managed': False, }, ), migrations.CreateModel( name='Poz', fields=[ ('id', models.IntegerField(primary_key=True, serialize=False)), ('kod_opisu', models.TextField(blank=True, null=True)), ('lp', models.PositiveSmallIntegerField()), ('tresc', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowany opis rekordu', 'verbose_name_plural': 'zaimportowane opisy rekordow', 'db_table': 'import_dbf_poz', 'ordering': ('idt', 'kod_opisu', 'lp'), 'managed': False, }, ), migrations.CreateModel( name='Pub', fields=[ ('idt_pub', models.TextField(primary_key=True, serialize=False)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ('to_print', models.TextField(blank=True, null=True)), ('to_print2', models.TextField(blank=True, null=True)), ('to_print3', models.TextField(blank=True, null=True)), ('to_print4', models.TextField(blank=True, null=True)), ('to_print5', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowany charakter publikacji', 'verbose_name_plural': 'zaimportowane charaktery publikacji', 'db_table': 'import_dbf_pub', 'managed': False, }, ), migrations.CreateModel( name='Rtf', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('idt', models.TextField(blank=True, null=True)), ('lp', models.TextField(blank=True, null=True)), ('len', models.TextField(blank=True, null=True)), ('rtf', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_rtf', 'managed': False, }, ), migrations.CreateModel( name='S_B', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('idt_sci', models.TextField(blank=True, null=True)), ('cit', models.TextField(blank=True, null=True)), ('doi', models.TextField(blank=True, null=True)), ('del_field', models.TextField(blank=True, db_column='del', null=True)), ('redaktor', models.TextField(blank=True, null=True)), ('dat_akt', models.TextField(blank=True, null=True)), ('autocyt', models.TextField(blank=True, null=True)), ('ut', models.TextField(blank=True, null=True)), ('ut0', models.TextField(blank=True, null=True)), ('uwagi', models.TextField(blank=True, null=True)), ('field_ignore_me', models.TextField(blank=True, db_column='_ignore_me', null=True)), ], options={ 'db_table': 'import_dbf_s_b', 'managed': False, }, ), migrations.CreateModel( name='Sci', fields=[ ('idt_sci', models.TextField(primary_key=True, serialize=False)), ('au', models.TextField(blank=True, null=True)), ('ti', models.TextField(blank=True, null=True)), ('src', models.TextField(blank=True, null=True)), ('ye', models.TextField(blank=True, null=True)), ('cont', models.TextField(blank=True, null=True)), ('ut', models.TextField(blank=True, null=True)), ('field_ignore_me', models.TextField(blank=True, db_column='_ignore_me', null=True)), ], options={ 'db_table': 'import_dbf_sci', 'managed': False, }, ), migrations.CreateModel( name='Ses', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('redaktor', models.TextField(blank=True, null=True)), ('file', models.TextField(blank=True, null=True)), ('login_t', models.TextField(blank=True, null=True)), ('logout_t', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_ses', 'managed': False, }, ), migrations.CreateModel( name='Sys', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('ver', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowana wersja bazy', 'verbose_name_plural': 'zaimportowane wersje bazy', 'db_table': 'import_dbf_sys', 'managed': False, }, ), migrations.CreateModel( name='Usi', fields=[ ('idt_usi', models.IntegerField(primary_key=True, serialize=False)), ('usm_f', models.TextField(blank=True, null=True)), ('usm_sf', models.TextField(blank=True, null=True)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowane źródło', 'verbose_name_plural': 'zaimportowane źródła', 'db_table': 'import_dbf_usi', 'managed': False, }, ), migrations.CreateModel( name='Wsx', fields=[ ('idt_wsx', models.TextField(primary_key=True, serialize=False)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ('wsp', models.TextField(blank=True, null=True)), ('field_ignore_me', models.TextField(blank=True, db_column='_ignore_me', null=True)), ], options={ 'db_table': 'import_dbf_wsx', 'managed': False, }, ), migrations.CreateModel( name='Wsy', fields=[ ('idt_wsy', models.TextField(primary_key=True, serialize=False)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ('wsp', models.TextField(blank=True, null=True)), ('field_ignore_me', models.TextField(blank=True, db_column='_ignore_me', null=True)), ], options={ 'db_table': 'import_dbf_wsy', 'managed': False, }, ), migrations.CreateModel( name='Wx2', fields=[ ('idt_wsx', models.TextField(primary_key=True, serialize=False)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ('wsp', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'import_dbf_wx2', 'managed': False, }, ), migrations.CreateModel( name='Wyd', fields=[ ('idt_wyd', models.TextField(primary_key=True, serialize=False)), ('skrot', models.TextField(blank=True, null=True)), ('nazwa', models.TextField(blank=True, null=True)), ], options={ 'verbose_name': 'zaimportowany wydział', 'verbose_name_plural': 'zaimportowane wydzialy', 'db_table': 'import_dbf_wyd', 'managed': False, }, ), migrations.CreateModel( name='Bib', fields=[ ('idt', models.IntegerField(primary_key=True, serialize=False)), ('tytul_or', models.TextField(blank=True, null=True)), ('title', models.TextField(blank=True, null=True)), ('zrodlo', models.TextField(blank=True, null=True)), ('szczegoly', models.TextField(blank=True, null=True)), ('uwagi', models.TextField(blank=True, null=True)), ('charakter', models.TextField(blank=True, null=True)), ('impact', models.TextField(blank=True, null=True)), ('redakcja', models.TextField(blank=True, null=True)), ('status', models.TextField(blank=True, null=True)), ('rok', models.TextField(blank=True, null=True)), ('sort', models.TextField(blank=True, null=True)), ('sort2', models.TextField(blank=True, null=True)), ('export', models.TextField(blank=True, null=True)), ('import_field', models.TextField(blank=True, db_column='import', null=True)), ('naz_imie', models.TextField(blank=True, null=True)), ('redaktor', models.TextField(blank=True, null=True)), ('redaktor0', models.TextField(blank=True, null=True)), ('tytul_or_s', models.TextField(blank=True, null=True)), ('title_s', models.TextField(blank=True, null=True)), ('zrodlo_s', models.TextField(blank=True, null=True)), ('szczegol_s', models.TextField(blank=True, null=True)), ('mem_fi_ext', models.TextField(blank=True, null=True)), ('dat_akt', models.TextField(blank=True, null=True)), ('kbn', models.TextField(blank=True, null=True)), ('kbr', models.TextField(blank=True, null=True)), ('afiliowana', models.TextField(blank=True, null=True)), ('recenzowan', models.TextField(blank=True, null=True)), ('jezyk', models.TextField(blank=True, null=True)), ('jezyk2', models.TextField(blank=True, null=True)), ('punkty_kbn', models.TextField(blank=True, db_column='pk', null=True)), ('x_skrot', models.TextField(blank=True, null=True)), ('wspx', models.TextField(blank=True, null=True)), ('x2_skrot', models.TextField(blank=True, null=True)), ('wspx2', models.TextField(blank=True, null=True)), ('y_skrot', models.TextField(blank=True, null=True)), ('wspy', models.TextField(blank=True, null=True)), ('wspq', models.TextField(blank=True, null=True)), ('ic', models.TextField(blank=True, null=True)), ('rok_inv', models.TextField(blank=True, null=True)), ('link', models.TextField(blank=True, null=True)), ('lf', models.TextField(blank=True, null=True)), ('rok_punkt', models.TextField(blank=True, null=True)), ('form', models.TextField(blank=True, null=True)), ('k_z', models.TextField(blank=True, null=True)), ('uwagi2', models.TextField(blank=True, null=True)), ('dat_utw', models.TextField(blank=True, null=True)), ('pun_wl', models.TextField(blank=True, null=True)), ('study_gr', models.TextField(blank=True, null=True)), ('sort_fixed', models.TextField(blank=True, null=True)), ('zaznacz_field', models.TextField(blank=True, db_column='zaznacz_', null=True)), ('idt2', models.TextField(blank=True, null=True)), ('pun_max', models.TextField(blank=True, null=True)), ('pun_erih', models.TextField(blank=True, null=True)), ('kwartyl', models.TextField(blank=True, null=True)), ('issn', models.TextField(blank=True, null=True)), ('eissn', models.TextField(blank=True, null=True)), ('wok_id', models.TextField(blank=True, null=True)), ('sco_id', models.TextField(blank=True, null=True)), ('mnsw_fixed', models.TextField(blank=True, null=True)), ('liczba_aut', models.TextField(blank=True, null=True)), ('pro_p_wydz', models.TextField(blank=True, null=True)), ('snip', models.TextField(blank=True, null=True)), ('sjr', models.TextField(blank=True, null=True)), ('cites', models.TextField(blank=True, null=True)), ('if5', models.TextField(blank=True, null=True)), ('lis_numer', models.TextField(blank=True, null=True)), ('object_id', models.PositiveIntegerField(blank=True, null=True)), ('analyzed', models.BooleanField(default=False)), ('content_type', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.DO_NOTHING, to='contenttypes.ContentType')), ], options={ 'verbose_name': 'zaimportowany rekord bibliografi', 'verbose_name_plural': 'zaimportowane rekordy bibliografi', 'db_table': 'import_dbf_bib', }, ), migrations.CreateModel( name='Bib_Desc', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('elem_id', models.PositiveSmallIntegerField(db_index=True)), ('value', django.contrib.postgres.fields.jsonb.JSONField()), ('source', models.CharField(max_length=10)), ('idt', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='import_dbf.Bib')), ], options={ 'ordering': ('idt', 'source'), }, ), ]

47.06008

154

0.513958

3,356

35,248

5.254768

0.097139

0.241565

0.297136

0.356564

0.852679

0.766033

0.63856

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0.504848

0.447576

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0.33582

35,248

748

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null

1

0

1

0

null

0

6

5e5ffe2a9f4736427871c3d92473b21c233a6146

2,273

py

Python

tests/contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/test_michelson_coding_KT1Ki9.py

juztin/pytezos-1

7e608ff599d934bdcf129e47db43dbdb8fef9027

[ "MIT" ]

1

2021-05-20T16:52:08.000Z

tests/contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/test_michelson_coding_KT1Ki9.py

juztin/pytezos-1

7e608ff599d934bdcf129e47db43dbdb8fef9027

[ "MIT" ]

1

2020-12-30T16:44:56.000Z

tests/contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/test_michelson_coding_KT1Ki9.py

tqtezos/pytezos

a4ac0b022d35d4c9f3062609d8ce09d584b5faa8

[ "MIT" ]

1

2022-03-20T19:01:00.000Z

from unittest import TestCase from tests import get_data from pytezos.michelson.micheline import michelson_to_micheline from pytezos.michelson.formatter import micheline_to_michelson class MichelsonCodingTestKT1Ki9(TestCase): def setUp(self): self.maxDiff = None def test_michelson_parse_code_KT1Ki9(self): expected = get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/code_KT1Ki9.json') actual = michelson_to_micheline(get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/code_KT1Ki9.tz')) self.assertEqual(expected, actual) def test_michelson_format_code_KT1Ki9(self): expected = get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/code_KT1Ki9.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/code_KT1Ki9.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_code_KT1Ki9(self): expected = get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/code_KT1Ki9.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual) def test_michelson_parse_storage_KT1Ki9(self): expected = get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/storage_KT1Ki9.json') actual = michelson_to_micheline(get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/storage_KT1Ki9.tz')) self.assertEqual(expected, actual) def test_michelson_format_storage_KT1Ki9(self): expected = get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/storage_KT1Ki9.tz') actual = micheline_to_michelson(get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/storage_KT1Ki9.json'), inline=True) self.assertEqual(expected, actual) def test_michelson_inverse_storage_KT1Ki9(self): expected = get_data( path='contracts/KT1Ki9hCRhWERgvVvXvVnFR3ruwM9sR5eLAN/storage_KT1Ki9.json') actual = michelson_to_micheline(micheline_to_michelson(expected)) self.assertEqual(expected, actual)

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87

0.736472

222

2,273

7.247748

0.162162

0.047856

0.068365

0.124301

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0.835301

0.821628

0

0.045652

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2,273

53

88

42.886792

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0

0.55814

0

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0

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false

0

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0

0.27907

0

null

0

1

0

null

0

6

5e8667c8bd08530e3798b28c3658e7a0a6e72243

258,420

py

Python

instances/passenger_demand/pas-20210422-1717-int1/69.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int1/69.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int1/69.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 19216 passenger_arriving = ( (7, 4, 4, 3, 2, 1, 4, 2, 4, 2, 0, 0, 0, 7, 5, 8, 3, 7, 1, 3, 0, 1, 2, 1, 1, 0), # 0 (4, 7, 2, 3, 3, 1, 2, 5, 2, 1, 1, 0, 0, 6, 4, 4, 3, 2, 1, 5, 1, 1, 1, 0, 1, 0), # 1 (6, 4, 5, 6, 2, 1, 2, 4, 1, 1, 0, 0, 0, 11, 3, 6, 7, 7, 0, 3, 2, 1, 2, 0, 0, 0), # 2 (6, 3, 6, 7, 6, 1, 3, 3, 1, 2, 1, 0, 0, 7, 11, 8, 2, 4, 3, 0, 0, 2, 2, 0, 0, 0), # 3 (11, 5, 8, 3, 6, 1, 2, 2, 2, 0, 0, 0, 0, 6, 9, 5, 1, 5, 1, 2, 1, 1, 1, 0, 0, 0), # 4 (3, 5, 7, 7, 5, 2, 5, 3, 3, 2, 3, 0, 0, 9, 10, 3, 2, 7, 2, 1, 0, 3, 1, 1, 2, 0), # 5 (6, 4, 5, 5, 6, 2, 1, 3, 4, 1, 0, 2, 0, 7, 6, 2, 4, 4, 1, 3, 1, 3, 5, 0, 2, 0), # 6 (5, 8, 6, 4, 6, 0, 6, 4, 2, 1, 2, 0, 0, 9, 6, 2, 5, 3, 0, 1, 1, 3, 2, 2, 0, 0), # 7 (6, 3, 2, 11, 4, 2, 7, 4, 2, 1, 1, 0, 0, 4, 4, 4, 1, 4, 5, 1, 2, 2, 2, 4, 0, 0), # 8 (7, 5, 8, 5, 10, 0, 7, 2, 4, 2, 1, 1, 0, 8, 3, 4, 3, 8, 4, 1, 4, 4, 3, 2, 1, 0), # 9 (3, 4, 12, 6, 6, 4, 4, 0, 2, 1, 0, 0, 0, 15, 3, 9, 3, 0, 4, 2, 2, 4, 3, 2, 1, 0), # 10 (9, 11, 8, 8, 4, 3, 2, 0, 3, 2, 0, 1, 0, 6, 12, 7, 4, 7, 2, 5, 4, 3, 3, 2, 1, 0), # 11 (8, 3, 6, 9, 10, 2, 9, 2, 4, 1, 1, 1, 0, 6, 5, 7, 1, 5, 4, 3, 2, 4, 1, 0, 2, 0), # 12 (5, 11, 12, 7, 6, 3, 5, 4, 3, 2, 1, 3, 0, 16, 6, 8, 8, 8, 1, 2, 4, 2, 2, 2, 1, 0), # 13 (17, 12, 8, 14, 4, 2, 2, 5, 4, 0, 2, 1, 0, 11, 4, 9, 6, 10, 1, 2, 2, 4, 1, 1, 0, 0), # 14 (12, 11, 13, 8, 13, 2, 5, 3, 4, 0, 0, 0, 0, 16, 7, 2, 8, 6, 4, 4, 4, 3, 2, 1, 1, 0), # 15 (7, 5, 4, 7, 9, 1, 5, 4, 4, 4, 1, 1, 0, 11, 10, 4, 7, 8, 7, 5, 1, 3, 4, 0, 0, 0), # 16 (11, 7, 5, 6, 6, 6, 4, 1, 6, 0, 1, 1, 0, 9, 9, 6, 6, 7, 5, 2, 1, 1, 3, 0, 0, 0), # 17 (9, 12, 9, 3, 7, 4, 3, 2, 3, 2, 1, 2, 0, 12, 9, 6, 3, 7, 8, 4, 4, 5, 2, 2, 2, 0), # 18 (11, 15, 6, 8, 7, 6, 5, 6, 2, 1, 0, 1, 0, 11, 11, 3, 4, 9, 4, 6, 4, 4, 2, 3, 1, 0), # 19 (7, 11, 6, 10, 5, 4, 3, 2, 3, 2, 1, 0, 0, 7, 8, 8, 1, 11, 8, 6, 3, 4, 4, 3, 0, 0), # 20 (10, 9, 10, 11, 7, 3, 4, 3, 3, 1, 3, 2, 0, 10, 17, 12, 11, 6, 3, 2, 2, 5, 4, 2, 1, 0), # 21 (14, 8, 8, 11, 4, 6, 3, 3, 4, 1, 1, 0, 0, 11, 5, 6, 6, 15, 3, 1, 4, 5, 2, 1, 0, 0), # 22 (12, 17, 8, 14, 10, 2, 4, 2, 4, 3, 0, 1, 0, 11, 12, 7, 5, 9, 5, 2, 2, 1, 5, 2, 3, 0), # 23 (7, 12, 17, 16, 5, 3, 6, 4, 6, 1, 2, 0, 0, 13, 4, 5, 4, 14, 2, 3, 6, 5, 1, 1, 0, 0), # 24 (10, 11, 9, 7, 4, 6, 2, 1, 5, 3, 0, 3, 0, 5, 9, 8, 6, 8, 3, 4, 4, 3, 1, 4, 1, 0), # 25 (11, 7, 8, 8, 9, 5, 2, 5, 7, 3, 1, 1, 0, 10, 5, 11, 3, 11, 3, 7, 2, 3, 3, 3, 2, 0), # 26 (11, 8, 6, 7, 6, 5, 8, 0, 7, 0, 0, 0, 0, 11, 15, 8, 3, 9, 8, 5, 1, 4, 1, 1, 2, 0), # 27 (6, 15, 10, 15, 9, 2, 4, 5, 4, 4, 0, 0, 0, 16, 9, 7, 6, 7, 1, 6, 0, 7, 3, 0, 4, 0), # 28 (15, 3, 13, 9, 9, 6, 3, 3, 3, 1, 1, 0, 0, 6, 6, 8, 7, 13, 6, 5, 5, 5, 4, 0, 1, 0), # 29 (17, 12, 13, 7, 8, 3, 4, 3, 3, 1, 2, 1, 0, 14, 13, 3, 7, 11, 6, 6, 3, 6, 1, 2, 2, 0), # 30 (17, 10, 3, 9, 4, 3, 5, 2, 5, 3, 1, 1, 0, 12, 10, 4, 7, 12, 7, 5, 1, 3, 4, 1, 2, 0), # 31 (8, 10, 7, 8, 12, 1, 5, 4, 3, 0, 0, 3, 0, 8, 18, 7, 1, 13, 7, 6, 1, 3, 1, 1, 1, 0), # 32 (7, 5, 10, 8, 7, 4, 4, 8, 8, 5, 1, 0, 0, 8, 8, 5, 2, 4, 3, 6, 2, 4, 1, 3, 1, 0), # 33 (5, 10, 12, 11, 9, 7, 4, 4, 2, 1, 0, 1, 0, 9, 11, 6, 6, 11, 6, 5, 0, 1, 2, 2, 2, 0), # 34 (13, 11, 5, 8, 7, 4, 4, 4, 7, 2, 1, 0, 0, 13, 10, 5, 5, 11, 7, 3, 0, 1, 3, 0, 0, 0), # 35 (10, 12, 5, 7, 5, 5, 2, 6, 4, 2, 1, 3, 0, 10, 12, 15, 7, 11, 5, 6, 2, 2, 2, 0, 0, 0), # 36 (11, 11, 13, 9, 9, 4, 1, 1, 4, 2, 1, 1, 0, 13, 12, 6, 3, 7, 6, 5, 1, 4, 2, 2, 0, 0), # 37 (4, 7, 9, 5, 6, 3, 6, 1, 3, 2, 1, 1, 0, 13, 7, 4, 5, 14, 1, 4, 2, 1, 1, 1, 0, 0), # 38 (12, 8, 9, 11, 11, 4, 6, 8, 5, 5, 2, 1, 0, 6, 10, 6, 4, 11, 8, 6, 0, 8, 4, 4, 2, 0), # 39 (12, 14, 10, 9, 6, 2, 5, 0, 4, 2, 0, 2, 0, 10, 8, 5, 4, 7, 6, 3, 1, 3, 6, 1, 2, 0), # 40 (10, 13, 11, 10, 9, 5, 1, 5, 6, 4, 1, 0, 0, 11, 5, 7, 6, 8, 4, 4, 1, 4, 1, 1, 1, 0), # 41 (12, 9, 17, 9, 6, 6, 1, 3, 3, 3, 2, 0, 0, 13, 4, 7, 4, 4, 6, 3, 4, 2, 1, 2, 2, 0), # 42 (3, 14, 13, 6, 11, 5, 4, 3, 3, 2, 0, 3, 0, 13, 6, 8, 4, 6, 7, 9, 3, 2, 2, 3, 0, 0), # 43 (10, 12, 12, 8, 10, 3, 3, 10, 4, 2, 1, 1, 0, 8, 12, 10, 5, 9, 5, 6, 2, 5, 4, 2, 1, 0), # 44 (11, 11, 16, 8, 8, 2, 11, 5, 6, 1, 2, 2, 0, 10, 12, 7, 10, 8, 1, 6, 4, 4, 2, 2, 1, 0), # 45 (9, 10, 12, 14, 12, 5, 3, 2, 2, 1, 4, 0, 0, 12, 9, 8, 6, 13, 6, 3, 1, 4, 3, 3, 0, 0), # 46 (7, 8, 8, 4, 5, 2, 2, 2, 4, 4, 2, 0, 0, 17, 10, 9, 7, 9, 6, 3, 5, 4, 7, 5, 0, 0), # 47 (12, 8, 6, 14, 6, 1, 4, 2, 3, 0, 2, 0, 0, 19, 9, 7, 3, 7, 3, 4, 3, 3, 4, 1, 0, 0), # 48 (11, 8, 7, 8, 12, 5, 3, 2, 9, 1, 2, 0, 0, 14, 11, 8, 2, 4, 7, 3, 3, 7, 2, 1, 0, 0), # 49 (10, 10, 4, 7, 5, 3, 2, 1, 4, 2, 1, 0, 0, 14, 11, 3, 8, 5, 3, 7, 5, 3, 1, 1, 1, 0), # 50 (14, 11, 8, 14, 10, 3, 1, 2, 6, 1, 2, 0, 0, 12, 6, 2, 4, 7, 1, 5, 3, 5, 3, 1, 2, 0), # 51 (5, 16, 8, 10, 9, 6, 5, 7, 1, 3, 1, 2, 0, 14, 12, 4, 6, 8, 3, 3, 3, 5, 8, 2, 0, 0), # 52 (6, 9, 4, 13, 8, 2, 6, 4, 2, 1, 3, 2, 0, 13, 6, 6, 5, 15, 6, 4, 2, 5, 4, 0, 3, 0), # 53 (15, 15, 15, 12, 12, 2, 0, 3, 6, 0, 4, 1, 0, 10, 12, 8, 6, 5, 5, 4, 2, 6, 2, 2, 0, 0), # 54 (11, 8, 8, 6, 9, 4, 4, 1, 2, 4, 1, 1, 0, 3, 14, 5, 3, 7, 4, 4, 1, 1, 3, 3, 1, 0), # 55 (5, 10, 5, 12, 10, 3, 5, 5, 2, 1, 0, 0, 0, 10, 7, 11, 6, 8, 2, 6, 3, 1, 4, 6, 0, 0), # 56 (9, 11, 10, 13, 7, 4, 4, 2, 6, 3, 0, 1, 0, 11, 7, 9, 5, 7, 1, 6, 3, 1, 8, 1, 0, 0), # 57 (13, 12, 11, 6, 9, 1, 1, 5, 3, 4, 4, 0, 0, 14, 10, 9, 10, 10, 2, 6, 3, 3, 3, 1, 1, 0), # 58 (11, 11, 15, 9, 9, 5, 1, 3, 7, 3, 0, 2, 0, 11, 5, 7, 3, 6, 4, 3, 3, 2, 1, 1, 2, 0), # 59 (7, 12, 10, 11, 11, 4, 2, 5, 5, 0, 2, 0, 0, 5, 13, 13, 3, 11, 7, 6, 3, 2, 3, 2, 0, 0), # 60 (11, 5, 10, 12, 8, 1, 2, 0, 2, 1, 1, 0, 0, 4, 7, 4, 5, 13, 5, 3, 7, 3, 4, 2, 2, 0), # 61 (6, 4, 5, 10, 13, 1, 6, 3, 7, 1, 2, 2, 0, 9, 10, 3, 3, 8, 3, 6, 4, 8, 0, 0, 0, 0), # 62 (11, 11, 9, 5, 8, 3, 4, 2, 8, 4, 0, 1, 0, 12, 12, 11, 5, 8, 1, 5, 6, 7, 4, 1, 0, 0), # 63 (8, 11, 12, 7, 16, 3, 6, 4, 3, 1, 1, 2, 0, 14, 4, 6, 6, 7, 3, 4, 3, 2, 4, 1, 1, 0), # 64 (7, 11, 10, 10, 6, 5, 8, 1, 3, 2, 1, 2, 0, 12, 7, 8, 3, 11, 6, 4, 1, 5, 2, 1, 0, 0), # 65 (15, 6, 5, 11, 10, 2, 4, 7, 2, 1, 2, 1, 0, 14, 11, 7, 4, 9, 6, 4, 2, 2, 5, 4, 0, 0), # 66 (4, 7, 4, 10, 5, 4, 0, 4, 4, 3, 2, 0, 0, 14, 5, 11, 4, 6, 4, 5, 4, 3, 3, 2, 2, 0), # 67 (12, 10, 5, 8, 4, 1, 1, 5, 9, 2, 1, 1, 0, 13, 12, 4, 7, 8, 4, 4, 2, 3, 3, 2, 1, 0), # 68 (8, 9, 11, 8, 10, 4, 3, 5, 3, 1, 3, 1, 0, 11, 8, 8, 7, 8, 3, 1, 1, 1, 3, 3, 0, 0), # 69 (9, 6, 3, 11, 9, 3, 5, 2, 2, 3, 4, 1, 0, 11, 5, 7, 5, 9, 5, 4, 4, 5, 2, 0, 0, 0), # 70 (4, 7, 5, 5, 6, 5, 1, 3, 0, 4, 1, 0, 0, 9, 9, 9, 3, 10, 5, 5, 2, 5, 1, 3, 1, 0), # 71 (11, 6, 8, 11, 12, 3, 5, 1, 6, 4, 1, 2, 0, 16, 9, 4, 7, 8, 4, 9, 2, 5, 1, 2, 2, 0), # 72 (10, 10, 10, 5, 11, 5, 3, 3, 4, 0, 2, 1, 0, 15, 3, 8, 6, 8, 2, 6, 1, 4, 1, 0, 2, 0), # 73 (14, 8, 9, 8, 7, 6, 1, 1, 2, 1, 2, 0, 0, 13, 4, 4, 7, 5, 3, 9, 8, 5, 4, 1, 0, 0), # 74 (10, 11, 11, 9, 10, 1, 3, 2, 5, 1, 1, 0, 0, 14, 13, 7, 4, 8, 4, 4, 2, 7, 2, 3, 0, 0), # 75 (13, 10, 10, 12, 7, 6, 3, 3, 4, 3, 0, 1, 0, 12, 12, 9, 8, 4, 3, 3, 6, 2, 3, 2, 1, 0), # 76 (9, 7, 10, 7, 10, 5, 2, 4, 5, 2, 0, 2, 0, 11, 5, 6, 8, 12, 5, 6, 1, 3, 4, 1, 1, 0), # 77 (5, 7, 8, 5, 12, 2, 5, 1, 4, 1, 0, 1, 0, 8, 13, 6, 3, 6, 2, 4, 3, 3, 10, 3, 0, 0), # 78 (13, 11, 10, 3, 9, 4, 5, 3, 3, 0, 0, 0, 0, 10, 12, 8, 7, 5, 1, 4, 7, 5, 4, 0, 3, 0), # 79 (9, 9, 11, 11, 8, 1, 2, 1, 4, 4, 2, 1, 0, 10, 7, 2, 7, 8, 4, 3, 1, 3, 2, 2, 0, 0), # 80 (13, 4, 5, 11, 3, 3, 6, 4, 5, 0, 3, 0, 0, 8, 10, 3, 9, 4, 7, 3, 3, 4, 5, 0, 1, 0), # 81 (13, 9, 8, 4, 5, 3, 5, 3, 4, 1, 2, 1, 0, 12, 5, 7, 2, 7, 5, 0, 0, 2, 0, 2, 1, 0), # 82 (7, 9, 4, 5, 5, 2, 1, 2, 3, 1, 1, 0, 0, 10, 7, 5, 2, 9, 3, 1, 2, 3, 5, 1, 1, 0), # 83 (12, 6, 9, 7, 7, 6, 4, 2, 4, 1, 1, 0, 0, 10, 8, 3, 3, 6, 5, 4, 3, 1, 4, 2, 0, 0), # 84 (10, 10, 3, 7, 10, 4, 5, 3, 1, 1, 1, 2, 0, 9, 13, 3, 5, 5, 5, 3, 4, 5, 3, 1, 0, 0), # 85 (7, 10, 13, 11, 8, 3, 2, 2, 1, 3, 1, 0, 0, 5, 11, 7, 2, 6, 4, 5, 1, 3, 3, 1, 1, 0), # 86 (13, 10, 13, 12, 15, 4, 8, 2, 6, 0, 0, 1, 0, 9, 7, 6, 2, 9, 7, 8, 1, 6, 6, 2, 1, 0), # 87 (6, 10, 7, 12, 4, 4, 2, 2, 3, 4, 3, 1, 0, 11, 7, 9, 0, 2, 7, 4, 2, 4, 3, 0, 1, 0), # 88 (14, 10, 7, 11, 7, 2, 1, 1, 2, 1, 2, 1, 0, 6, 8, 5, 3, 9, 4, 5, 0, 4, 1, 3, 2, 0), # 89 (10, 6, 6, 13, 7, 2, 3, 2, 5, 1, 2, 1, 0, 11, 12, 7, 6, 9, 4, 5, 2, 1, 4, 0, 0, 0), # 90 (14, 8, 8, 7, 5, 3, 6, 1, 4, 3, 1, 0, 0, 12, 5, 4, 4, 9, 1, 5, 4, 0, 5, 3, 0, 0), # 91 (14, 7, 7, 7, 10, 6, 5, 1, 2, 1, 4, 0, 0, 10, 6, 5, 5, 11, 5, 2, 0, 5, 4, 0, 0, 0), # 92 (13, 10, 12, 7, 8, 3, 3, 3, 5, 1, 0, 1, 0, 14, 9, 8, 2, 12, 5, 3, 2, 3, 3, 5, 0, 0), # 93 (4, 4, 9, 7, 9, 3, 1, 3, 1, 1, 2, 0, 0, 12, 9, 3, 5, 10, 4, 3, 3, 2, 2, 2, 0, 0), # 94 (5, 9, 12, 10, 15, 4, 4, 6, 2, 2, 2, 0, 0, 13, 11, 0, 7, 7, 3, 1, 5, 3, 2, 0, 1, 0), # 95 (2, 6, 13, 9, 5, 2, 4, 5, 2, 0, 3, 0, 0, 12, 8, 3, 6, 7, 2, 3, 4, 3, 5, 3, 0, 0), # 96 (8, 6, 5, 6, 8, 4, 1, 2, 2, 0, 1, 1, 0, 9, 7, 4, 4, 7, 1, 4, 1, 2, 3, 1, 0, 0), # 97 (12, 12, 8, 7, 5, 2, 4, 5, 8, 0, 2, 1, 0, 10, 9, 5, 5, 9, 0, 4, 2, 6, 3, 1, 1, 0), # 98 (8, 9, 10, 6, 13, 7, 5, 4, 5, 2, 1, 3, 0, 12, 7, 7, 6, 6, 2, 3, 2, 8, 3, 4, 0, 0), # 99 (10, 12, 11, 4, 12, 3, 3, 4, 3, 2, 1, 0, 0, 8, 8, 4, 3, 5, 7, 2, 5, 5, 1, 1, 0, 0), # 100 (9, 8, 6, 4, 6, 1, 4, 3, 3, 3, 1, 1, 0, 11, 9, 4, 10, 11, 2, 1, 2, 3, 4, 2, 0, 0), # 101 (11, 8, 10, 10, 4, 5, 3, 0, 5, 2, 0, 0, 0, 13, 8, 3, 7, 10, 3, 1, 2, 6, 3, 2, 1, 0), # 102 (6, 9, 9, 7, 5, 5, 2, 2, 5, 0, 2, 2, 0, 14, 11, 5, 3, 8, 0, 2, 3, 5, 1, 0, 2, 0), # 103 (12, 8, 7, 5, 8, 4, 2, 2, 3, 3, 0, 0, 0, 9, 6, 5, 3, 7, 4, 0, 3, 3, 3, 2, 1, 0), # 104 (9, 6, 8, 14, 6, 4, 2, 2, 1, 1, 0, 1, 0, 6, 3, 6, 2, 5, 4, 6, 2, 1, 4, 2, 3, 0), # 105 (8, 13, 9, 7, 12, 6, 5, 4, 5, 1, 3, 0, 0, 13, 9, 7, 5, 6, 3, 1, 3, 3, 1, 1, 0, 0), # 106 (8, 8, 13, 8, 11, 0, 1, 4, 6, 1, 0, 0, 0, 9, 10, 4, 6, 5, 2, 3, 1, 1, 1, 0, 0, 0), # 107 (10, 8, 10, 13, 9, 5, 4, 1, 5, 1, 1, 0, 0, 9, 8, 4, 7, 10, 4, 0, 2, 3, 4, 0, 1, 0), # 108 (7, 8, 5, 7, 11, 7, 5, 4, 7, 1, 0, 1, 0, 14, 8, 8, 6, 10, 3, 1, 1, 3, 4, 1, 1, 0), # 109 (14, 8, 8, 7, 8, 2, 4, 1, 4, 2, 0, 0, 0, 10, 7, 2, 2, 4, 6, 2, 2, 2, 2, 3, 0, 0), # 110 (7, 13, 11, 4, 10, 2, 0, 0, 2, 1, 0, 0, 0, 13, 5, 6, 3, 3, 0, 4, 1, 0, 4, 2, 1, 0), # 111 (13, 5, 11, 8, 5, 4, 1, 2, 2, 2, 2, 0, 0, 7, 6, 10, 5, 7, 2, 3, 2, 2, 2, 0, 1, 0), # 112 (11, 8, 6, 6, 4, 6, 3, 2, 6, 0, 1, 1, 0, 2, 13, 5, 1, 13, 4, 5, 2, 2, 1, 1, 1, 0), # 113 (9, 9, 9, 8, 8, 6, 2, 3, 4, 2, 1, 2, 0, 7, 4, 9, 8, 7, 2, 2, 1, 3, 0, 3, 0, 0), # 114 (12, 10, 9, 8, 9, 4, 0, 0, 4, 0, 0, 0, 0, 6, 8, 1, 3, 7, 4, 1, 2, 3, 2, 2, 1, 0), # 115 (8, 10, 7, 10, 5, 7, 0, 3, 1, 1, 2, 1, 0, 14, 7, 2, 5, 3, 4, 6, 1, 3, 3, 1, 1, 0), # 116 (8, 13, 5, 10, 9, 0, 3, 0, 5, 1, 0, 1, 0, 10, 5, 6, 3, 5, 1, 0, 1, 4, 3, 2, 0, 0), # 117 (9, 6, 6, 13, 15, 3, 0, 7, 3, 1, 2, 0, 0, 7, 10, 5, 5, 6, 0, 2, 0, 2, 2, 1, 2, 0), # 118 (12, 8, 9, 9, 9, 5, 3, 3, 2, 1, 0, 3, 0, 10, 6, 3, 1, 8, 1, 4, 6, 2, 0, 1, 0, 0), # 119 (8, 7, 9, 7, 11, 3, 6, 1, 6, 2, 0, 1, 0, 6, 4, 4, 8, 3, 1, 3, 1, 1, 2, 0, 1, 0), # 120 (8, 6, 11, 6, 10, 6, 4, 1, 4, 3, 0, 2, 0, 5, 9, 6, 7, 8, 2, 3, 3, 5, 4, 0, 1, 0), # 121 (10, 4, 8, 11, 8, 5, 6, 7, 2, 0, 1, 0, 0, 5, 7, 7, 3, 8, 5, 3, 5, 2, 3, 1, 0, 0), # 122 (4, 11, 8, 2, 10, 2, 2, 2, 6, 1, 2, 0, 0, 7, 10, 8, 4, 6, 6, 4, 3, 3, 6, 1, 0, 0), # 123 (9, 4, 10, 11, 6, 4, 2, 0, 2, 2, 0, 0, 0, 10, 10, 6, 2, 10, 7, 2, 1, 3, 2, 1, 0, 0), # 124 (12, 10, 10, 3, 8, 5, 3, 2, 3, 2, 0, 3, 0, 8, 8, 3, 2, 9, 3, 3, 1, 4, 5, 0, 0, 0), # 125 (5, 4, 16, 6, 11, 3, 1, 2, 4, 1, 0, 1, 0, 11, 8, 7, 3, 10, 2, 9, 1, 3, 1, 0, 0, 0), # 126 (15, 8, 5, 8, 7, 2, 1, 4, 3, 1, 2, 0, 0, 9, 7, 2, 7, 7, 3, 2, 2, 3, 1, 1, 1, 0), # 127 (6, 9, 11, 7, 9, 8, 1, 2, 5, 2, 1, 0, 0, 13, 6, 8, 6, 5, 1, 1, 3, 5, 4, 1, 0, 0), # 128 (6, 6, 4, 10, 8, 7, 0, 2, 3, 0, 2, 0, 0, 9, 10, 3, 3, 4, 2, 3, 1, 2, 3, 3, 2, 0), # 129 (18, 5, 8, 10, 7, 3, 3, 5, 1, 2, 1, 0, 0, 12, 7, 6, 5, 4, 2, 4, 2, 4, 2, 1, 0, 0), # 130 (6, 10, 8, 12, 9, 3, 2, 2, 4, 2, 3, 1, 0, 9, 10, 5, 1, 4, 2, 4, 3, 5, 1, 4, 2, 0), # 131 (10, 6, 6, 5, 8, 3, 6, 1, 5, 3, 1, 0, 0, 12, 10, 3, 2, 11, 1, 4, 1, 5, 3, 0, 0, 0), # 132 (11, 7, 8, 3, 10, 4, 2, 2, 4, 1, 1, 1, 0, 11, 9, 4, 1, 5, 4, 3, 2, 3, 4, 2, 2, 0), # 133 (6, 10, 5, 7, 8, 2, 2, 3, 1, 3, 2, 1, 0, 9, 9, 3, 5, 6, 5, 3, 2, 4, 1, 2, 0, 0), # 134 (8, 6, 7, 7, 9, 2, 2, 4, 4, 0, 0, 0, 0, 12, 4, 8, 7, 8, 3, 3, 3, 4, 1, 2, 0, 0), # 135 (7, 8, 2, 11, 5, 1, 5, 2, 8, 0, 1, 2, 0, 5, 5, 9, 2, 5, 0, 4, 4, 7, 2, 2, 3, 0), # 136 (10, 9, 6, 6, 7, 5, 2, 5, 1, 2, 1, 1, 0, 7, 5, 3, 3, 15, 0, 3, 0, 3, 3, 1, 1, 0), # 137 (7, 7, 7, 5, 9, 4, 4, 1, 2, 0, 2, 0, 0, 4, 4, 8, 3, 6, 5, 8, 3, 4, 4, 3, 1, 0), # 138 (11, 5, 6, 6, 5, 3, 4, 3, 1, 0, 1, 0, 0, 11, 7, 5, 4, 4, 1, 3, 1, 4, 2, 1, 3, 0), # 139 (17, 9, 10, 8, 3, 3, 3, 1, 3, 1, 1, 0, 0, 10, 4, 3, 4, 8, 7, 4, 1, 4, 1, 1, 2, 0), # 140 (9, 6, 8, 1, 6, 1, 1, 0, 3, 2, 3, 0, 0, 7, 6, 4, 4, 6, 2, 2, 0, 0, 2, 3, 0, 0), # 141 (7, 6, 9, 8, 13, 4, 2, 6, 5, 2, 1, 0, 0, 7, 9, 6, 3, 11, 5, 5, 2, 6, 2, 0, 0, 0), # 142 (4, 6, 9, 10, 7, 3, 2, 0, 1, 0, 2, 0, 0, 6, 4, 6, 3, 3, 2, 2, 4, 1, 2, 0, 2, 0), # 143 (4, 6, 15, 8, 8, 5, 3, 2, 4, 0, 2, 2, 0, 6, 10, 3, 2, 7, 2, 1, 1, 2, 3, 3, 0, 0), # 144 (6, 5, 6, 6, 11, 3, 3, 0, 2, 1, 1, 1, 0, 9, 4, 3, 3, 6, 2, 1, 1, 0, 3, 1, 0, 0), # 145 (10, 5, 5, 10, 6, 2, 1, 1, 2, 1, 1, 0, 0, 11, 4, 7, 4, 9, 2, 1, 0, 8, 3, 3, 0, 0), # 146 (6, 5, 6, 5, 6, 2, 3, 2, 2, 1, 2, 1, 0, 10, 7, 9, 3, 8, 2, 2, 3, 3, 2, 1, 0, 0), # 147 (7, 4, 11, 10, 5, 1, 2, 2, 2, 0, 0, 0, 0, 11, 10, 2, 6, 6, 2, 1, 3, 6, 2, 0, 0, 0), # 148 (3, 6, 6, 8, 6, 3, 3, 5, 1, 0, 1, 0, 0, 8, 9, 8, 8, 6, 7, 2, 1, 2, 0, 3, 2, 0), # 149 (10, 8, 13, 10, 2, 3, 3, 2, 5, 1, 3, 0, 0, 5, 3, 5, 5, 6, 1, 1, 3, 4, 0, 2, 0, 0), # 150 (7, 2, 8, 12, 7, 5, 5, 4, 2, 4, 1, 1, 0, 8, 6, 7, 1, 8, 4, 0, 3, 2, 0, 0, 0, 0), # 151 (10, 7, 6, 6, 7, 1, 0, 5, 1, 2, 0, 2, 0, 10, 4, 5, 3, 8, 4, 2, 4, 1, 2, 3, 0, 0), # 152 (4, 9, 7, 1, 10, 4, 2, 3, 4, 1, 0, 1, 0, 12, 3, 6, 2, 5, 3, 5, 1, 3, 7, 0, 0, 0), # 153 (7, 8, 10, 6, 4, 1, 3, 2, 6, 3, 1, 1, 0, 7, 7, 1, 3, 10, 5, 1, 2, 3, 4, 3, 0, 0), # 154 (7, 6, 6, 7, 3, 6, 3, 3, 3, 2, 1, 1, 0, 7, 8, 5, 4, 5, 3, 0, 1, 3, 1, 1, 0, 0), # 155 (6, 5, 7, 3, 5, 4, 1, 5, 4, 3, 0, 0, 0, 9, 11, 1, 6, 11, 5, 4, 3, 3, 2, 3, 1, 0), # 156 (11, 5, 6, 15, 9, 4, 1, 1, 3, 0, 1, 0, 0, 6, 4, 7, 4, 14, 7, 4, 1, 3, 2, 2, 0, 0), # 157 (11, 4, 5, 4, 2, 5, 2, 1, 6, 0, 0, 0, 0, 11, 5, 6, 6, 4, 2, 2, 4, 2, 2, 1, 0, 0), # 158 (9, 2, 11, 7, 3, 0, 3, 1, 0, 0, 1, 0, 0, 4, 4, 6, 1, 6, 1, 4, 2, 1, 4, 0, 0, 0), # 159 (10, 9, 7, 5, 7, 4, 4, 0, 4, 3, 0, 0, 0, 4, 4, 6, 0, 5, 5, 2, 1, 5, 4, 0, 2, 0), # 160 (8, 12, 11, 12, 4, 3, 2, 5, 1, 2, 0, 0, 0, 7, 1, 3, 5, 9, 3, 1, 2, 3, 1, 2, 0, 0), # 161 (3, 4, 6, 7, 3, 3, 2, 0, 1, 1, 0, 0, 0, 9, 5, 2, 2, 4, 1, 2, 2, 2, 2, 0, 0, 0), # 162 (13, 7, 7, 10, 8, 3, 1, 6, 4, 0, 0, 0, 0, 9, 3, 4, 1, 6, 1, 2, 1, 7, 1, 2, 0, 0), # 163 (12, 5, 2, 5, 7, 4, 1, 1, 3, 1, 2, 1, 0, 8, 3, 7, 3, 8, 1, 2, 2, 2, 4, 0, 0, 0), # 164 (4, 6, 6, 8, 4, 1, 4, 3, 2, 1, 1, 1, 0, 9, 3, 2, 2, 9, 4, 1, 0, 4, 4, 1, 1, 0), # 165 (9, 4, 3, 5, 6, 1, 2, 4, 1, 1, 0, 0, 0, 10, 3, 4, 3, 8, 0, 0, 1, 4, 3, 2, 2, 0), # 166 (4, 3, 8, 2, 9, 1, 2, 1, 3, 0, 1, 0, 0, 5, 3, 4, 3, 7, 3, 3, 1, 2, 1, 4, 0, 0), # 167 (7, 3, 9, 9, 2, 5, 5, 1, 2, 1, 0, 0, 0, 6, 6, 2, 2, 6, 3, 1, 2, 2, 1, 1, 0, 0), # 168 (3, 4, 6, 4, 4, 5, 1, 2, 4, 0, 1, 0, 0, 8, 8, 3, 4, 5, 2, 2, 1, 4, 1, 2, 0, 0), # 169 (5, 5, 3, 5, 2, 5, 2, 0, 1, 2, 1, 1, 0, 4, 3, 5, 2, 1, 0, 2, 3, 2, 1, 0, 1, 0), # 170 (8, 4, 2, 8, 4, 3, 3, 2, 4, 1, 1, 0, 0, 7, 6, 3, 3, 6, 0, 0, 2, 3, 1, 0, 0, 0), # 171 (5, 4, 8, 4, 5, 0, 3, 0, 6, 0, 0, 1, 0, 4, 8, 2, 2, 2, 1, 1, 1, 3, 1, 1, 1, 0), # 172 (6, 2, 2, 5, 3, 0, 1, 1, 3, 1, 0, 0, 0, 7, 6, 2, 6, 3, 4, 1, 2, 1, 0, 0, 0, 0), # 173 (2, 9, 2, 5, 3, 2, 1, 1, 2, 2, 0, 0, 0, 5, 4, 2, 0, 3, 2, 1, 0, 3, 1, 1, 0, 0), # 174 (7, 6, 3, 2, 5, 3, 0, 3, 1, 1, 0, 0, 0, 2, 3, 3, 4, 5, 3, 3, 0, 4, 3, 0, 0, 0), # 175 (6, 4, 7, 1, 4, 3, 1, 1, 0, 0, 1, 0, 0, 5, 4, 3, 2, 4, 1, 1, 0, 0, 3, 0, 0, 0), # 176 (4, 1, 4, 6, 2, 1, 0, 0, 3, 1, 0, 1, 0, 5, 2, 3, 2, 8, 1, 1, 1, 3, 1, 1, 2, 0), # 177 (6, 2, 5, 1, 1, 1, 1, 0, 2, 1, 1, 0, 0, 4, 0, 4, 1, 4, 0, 1, 1, 4, 3, 0, 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), # 179 ) station_arriving_intensity = ( (5.020865578371768, 5.525288559693166, 5.211283229612507, 6.214667773863432, 5.554685607609612, 3.1386549320373387, 4.146035615373915, 4.653176172979423, 6.090099062168007, 3.9580150155223697, 4.205265163885603, 4.897915078306173, 5.083880212578363), # 0 (5.354327152019974, 5.890060694144759, 5.555346591330152, 6.625144253276616, 5.922490337474237, 3.3459835840425556, 4.419468941263694, 4.959513722905708, 6.492245326332909, 4.21898069227715, 4.483096135956131, 5.221216660814354, 5.419791647439855), # 1 (5.686723008979731, 6.253385170890979, 5.8980422855474135, 7.033987704664794, 6.288962973749744, 3.5524851145124448, 4.691818507960704, 5.264625247904419, 6.892786806877549, 4.478913775020546, 4.759823148776313, 5.543232652053055, 5.75436482820969), # 2 (6.016757793146562, 6.613820501936447, 6.238010869319854, 7.439576407532074, 6.652661676001902, 3.757340622585113, 4.962003641647955, 5.567301157494507, 7.290135160921093, 4.736782698426181, 5.0343484118273825, 5.862685684930461, 6.086272806254225), # 3 (6.343136148415981, 6.9699251992857745, 6.573892899703036, 7.840288641382569, 7.012144603796492, 3.9597312073986677, 5.2289436685084585, 5.866331861194915, 7.682702045582707, 4.991555897167679, 5.305574134590575, 6.178298392354764, 6.414188632939817), # 4 (6.66456271868351, 7.320257774943588, 6.9043289337525175, 8.234502685720393, 7.36596991669928, 4.158837968091214, 5.491557914725224, 6.160507768524592, 8.068899117981559, 5.242201805918663, 5.572402526547132, 6.488793407234148, 6.736785359632827), # 5 (6.979742147844666, 7.663376740914501, 7.227959528523866, 8.620596820049652, 7.712695774276043, 4.353842003800864, 5.7487657064812625, 6.4486192890024885, 8.447138035236815, 5.487688859352758, 5.833735797178282, 6.792893362476808, 7.052736037699606), # 6 (7.2873790797949685, 7.997840609203132, 7.543425241072635, 8.996949323874462, 8.050880336092554, 4.543924413665721, 5.999486369959585, 6.729456832147552, 8.815830454467644, 5.726985492143586, 6.088476155965268, 7.089320890990929, 7.360713718506519), # 7 (7.586178158429934, 8.322207891814099, 7.849366628454396, 9.361938476698928, 8.379081761714586, 4.7282662968238895, 6.2426392313431975, 7.001810807478725, 9.173388032793206, 5.959060138964774, 6.335525812389321, 7.376798625684702, 7.659391453419917), # 8 (7.874844027645085, 8.635037100752022, 8.144424247724704, 9.713942558027169, 8.69585821070791, 4.906048752413484, 6.47714361681512, 7.264471624514963, 9.518222427332674, 6.182881234489941, 6.573786975931678, 7.654049199466313, 7.947442293806162), # 9 (8.152081331335932, 8.934886748021516, 8.427238655939124, 10.051339847363288, 8.9997678426383, 5.076452879572607, 6.701918852558355, 7.516229692775211, 9.848745295205214, 6.397417213392714, 6.802161856073574, 7.919795245243952, 8.22353929103161), # 10 (8.416594713398005, 9.220315345627206, 8.696450410153215, 10.372508624211397, 9.289368817071534, 5.238659777439368, 6.915884264755916, 7.7558754217784145, 10.163368293529993, 6.601636510346719, 7.019552662296249, 8.17275939592581, 8.486355496462611), # 11 (8.667088817726812, 9.489881405573698, 8.95070006742254, 10.675827168075612, 9.563219293573377, 5.391850545151869, 7.1179591795908115, 7.982199221043521, 10.460503079426179, 6.794507560025572, 7.224861604080934, 8.411664284420068, 8.734563961465534), # 12 (8.902268288217876, 9.74214343986562, 9.188628184802662, 10.959673758460044, 9.819877431709601, 5.5352062818482235, 7.307062923246056, 8.193991500089481, 10.738561310012932, 6.974998797102904, 7.416990890908869, 8.63523254363492, 8.966837737406735), # 13 (9.120837768766716, 9.975659960507588, 9.408875319349146, 11.222426674868792, 10.05790139104599, 5.667908086666534, 7.482114821904661, 8.390042668435246, 10.995954642409421, 7.142078656252334, 7.594842732261284, 8.84218680647856, 9.181849875652563), # 14 (9.321501903268855, 10.188989479504217, 9.610082028117542, 11.462464196805985, 10.275849331148308, 5.789137058744912, 7.642034201749626, 8.569143135599756, 11.23109473373482, 7.29471557214749, 7.757319337619419, 9.031249705859171, 9.37827342756938), # 15 (9.5029653356198, 10.380690508860132, 9.790888868163425, 11.678164603775716, 10.472279411582333, 5.898074297221459, 7.785740388963976, 8.73008331110196, 11.442393241108286, 7.431877979461996, 7.9033229164645125, 9.20114387468494, 9.554781444523545), # 16 (9.663932709715075, 10.549321560579946, 9.949936396542352, 11.867906175282112, 10.645749791913838, 5.993900901234285, 7.9121527097307105, 8.871653604460818, 11.628261821648984, 7.552534312869467, 8.031755678277799, 9.350591945864055, 9.710046977881415), # 17 (9.803108669450204, 10.693441146668274, 10.08586517030988, 12.030067190829278, 10.794818631708589, 6.075797969921503, 8.020190490232851, 8.99264442519526, 11.787112132476096, 7.6556530070435365, 8.141519832540508, 9.478316552304715, 9.842743079009345), # 18 (9.919197858720699, 10.811607779129744, 10.197315746521578, 12.163025929921314, 10.918044090532366, 6.142946602421208, 8.108773056653394, 9.091846182824245, 11.917355830708779, 7.740202496657828, 8.231517588733878, 9.583040326915096, 9.951542799273696), # 19 (10.010904921422082, 10.902379969968962, 10.282928682233003, 12.265160672062354, 11.013984327950944, 6.194527897871518, 8.176819735175362, 9.168049286866717, 12.017404573466198, 7.805151216385958, 8.30065115633915, 9.66348590260339, 10.035119190040824), # 20 (10.076934501449866, 10.964316231190558, 10.341344534499719, 12.334849696756486, 11.081197503530088, 6.229722955410535, 8.223249851981759, 9.220044146841623, 12.085670017867521, 7.849467600901555, 8.34782274483756, 9.718375912277793, 10.092145302677078), # 21 (10.115991242699579, 10.995975074799144, 10.371203860377285, 12.370471283507836, 11.118241776835575, 6.247712874176367, 8.2469827332556, 9.246621172267915, 12.120563821031915, 7.872120084878242, 8.37193456371034, 9.74643298884649, 10.121294188548827), # 22 (10.13039336334264, 10.999723593964335, 10.374923182441702, 12.374930812757203, 11.127732056032597, 6.25, 8.249804002259339, 9.249493827160494, 12.124926234567901, 7.874792272519433, 8.37495803716174, 9.749897576588934, 10.125), # 23 (10.141012413034153, 10.997537037037038, 10.374314814814815, 12.374381944444446, 11.133107613614852, 6.25, 8.248253812636166, 9.2455, 12.124341666666666, 7.87315061728395, 8.37462457912458, 9.749086419753086, 10.125), # 24 (10.15140723021158, 10.993227023319616, 10.373113854595337, 12.373296039094651, 11.138364945594503, 6.25, 8.24519890260631, 9.237654320987655, 12.123186728395062, 7.869918838591678, 8.373963399426362, 9.747485139460448, 10.125), # 25 (10.161577019048034, 10.986859396433472, 10.371336762688616, 12.37168544238683, 11.143503868421105, 6.25, 8.240686718308721, 9.226104938271606, 12.1214762345679, 7.865150708733425, 8.372980483850855, 9.745115683584821, 10.125), # 26 (10.171520983716636, 10.978499999999999, 10.369, 12.369562499999999, 11.148524198544214, 6.25, 8.234764705882354, 9.211, 12.119225, 7.858899999999999, 8.371681818181818, 9.742, 10.125), # 27 (10.181238328390501, 10.968214677640603, 10.366120027434842, 12.366939557613168, 11.153425752413401, 6.25, 8.22748031146615, 9.192487654320988, 12.116447839506172, 7.851220484682213, 8.370073388203018, 9.73816003657979, 10.125), # 28 (10.19072825724275, 10.95606927297668, 10.362713305898492, 12.36382896090535, 11.15820834647822, 6.25, 8.218880981199066, 9.170716049382715, 12.113159567901235, 7.842165935070874, 8.368161179698216, 9.733617741197987, 10.125), # 29 (10.199989974446497, 10.94212962962963, 10.358796296296296, 12.360243055555555, 11.162871797188236, 6.25, 8.209014161220043, 9.145833333333332, 12.109375, 7.83179012345679, 8.365951178451178, 9.728395061728394, 10.125), # 30 (10.209022684174858, 10.926461591220852, 10.354385459533608, 12.356194187242798, 11.167415920993008, 6.25, 8.19792729766804, 9.117987654320988, 12.105108950617284, 7.820146822130773, 8.363449370245666, 9.722513946044812, 10.125), # 31 (10.217825590600954, 10.909131001371742, 10.349497256515773, 12.35169470164609, 11.171840534342095, 6.25, 8.185667836681999, 9.087327160493828, 12.100376234567902, 7.807289803383631, 8.360661740865444, 9.715996342021034, 10.125), # 32 (10.226397897897897, 10.890203703703703, 10.344148148148149, 12.346756944444444, 11.176145453685063, 6.25, 8.172283224400871, 9.054, 12.095191666666667, 7.793272839506173, 8.357594276094275, 9.708864197530863, 10.125), # 33 (10.23473881023881, 10.869745541838133, 10.338354595336076, 12.341393261316872, 11.180330495471466, 6.25, 8.15782090696361, 9.018154320987653, 12.089570061728397, 7.778149702789209, 8.354252961715924, 9.701139460448102, 10.125), # 34 (10.242847531796807, 10.847822359396433, 10.332133058984912, 12.335615997942385, 11.18439547615087, 6.25, 8.142328330509159, 8.979938271604938, 12.083526234567902, 7.761974165523548, 8.350643783514153, 9.692844078646548, 10.125), # 35 (10.250723266745005, 10.824499999999999, 10.3255, 12.3294375, 11.188340212172836, 6.25, 8.12585294117647, 8.9395, 12.077074999999999, 7.7448, 8.346772727272727, 9.684000000000001, 10.125), # 36 (10.258365219256524, 10.799844307270233, 10.318471879286694, 12.322870113168724, 11.192164519986921, 6.25, 8.108442185104494, 8.896987654320988, 12.070231172839506, 7.726680978509374, 8.34264577877541, 9.674629172382259, 10.125), # 37 (10.265772593504476, 10.773921124828533, 10.311065157750342, 12.315926183127573, 11.19586821604269, 6.25, 8.09014350843218, 8.85254938271605, 12.063009567901235, 7.707670873342479, 8.33826892380596, 9.664753543667125, 10.125), # 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143 (8.263525826991184, 6.623357134369786, 8.528613246924428, 9.848181259586356, 9.771959052035829, 5.388710617994547, 5.277767902813299, 5.747430654338549, 10.29906733603931, 5.31984852855826, 6.247090210604851, 7.435723795302299, 8.713413579351014), # 144 (8.215339672902477, 6.576372582512099, 8.496640565833289, 9.804025165445895, 9.731865296358233, 5.3732856787542405, 5.245141021011493, 5.734008476475176, 10.274639916474454, 5.292886400975988, 6.217141197795395, 7.401658235927513, 8.6770494037723), # 145 (8.16595351062735, 6.528267609102142, 8.463669544574216, 9.758566114316626, 9.690634353150992, 5.35730908531318, 5.21165372061033, 5.719979356386927, 10.249283887573606, 5.2651263921079705, 6.186278495824149, 7.3665737703940195, 8.639645831138118), # 146 (8.1153309743886, 6.47898183117313, 8.42966077182191, 9.71175112225958, 9.648236098657351, 5.340753233837358, 5.177260530137981, 5.705296853871415, 10.22294994843879, 5.236526643565146, 6.154453606868036, 7.3304244283471105, 8.601172567860118), # 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151 (7.842399881538343, 6.212727469904973, 8.242671239276701, 9.455482610193918, 9.417680825406869, 5.2483189717465635, 4.9901147251946645, 5.620458197801441, 10.07488606782597, 5.079466762111649, 5.979198933506821, 7.132095607996409, 8.391700616220398), # 152 (7.78359542019656, 6.155088822559256, 8.201608319733868, 9.399419309851933, 9.367646104303056, 5.2277078915480155, 4.949331293386919, 5.600880156472262, 10.041649348429823, 5.044949549349629, 5.940582468356916, 7.088591629420064, 8.346173043724027), # 153 (7.723300024450729, 6.095846689927024, 8.159231769420758, 9.34162918100941, 9.31623320561558, 5.206324326476654, 4.907323671205228, 5.580323651299123, 10.007089612513866, 5.009299588189353, 5.900664331460612, 7.043700981847325, 8.299363725465357), # 154 (7.6614773285236355, 6.034940689041495, 8.115502177012075, 9.282059239727378, 9.263412005587696, 5.184140672698471, 4.864046387177761, 5.558742242079636, 9.971157559180128, 4.972475020241754, 5.859396024994833, 6.997377694923482, 8.251242367856026), # 155 (7.598090966638081, 5.972310436935888, 8.070380131182526, 9.220656502066875, 9.209152380462648, 5.161129326379461, 4.8194539698327, 5.5360894886114185, 9.933803887530626, 4.934433987117773, 5.816729051136504, 6.949575798293822, 8.201778677307685), # 156 (7.533104573016862, 5.907895550643423, 8.023826220606818, 9.157367984088937, 9.153424206483685, 5.137262683685614, 4.773500947698219, 5.512318950692082, 9.894979296667389, 4.895134630428341, 5.772614912062549, 6.900249321603637, 8.150942360231976), # 157 (7.464680946405239, 5.840453120772258, 7.973591953902355, 9.089769581651243, 9.093681105870997, 5.11102447631711, 4.725106720927857, 5.485796952349372, 9.851662091599097, 4.8533659162911436, 5.7255957525389425, 6.847599564194339, 8.096485859415345), # 158 (7.382286766978402, 5.763065319599478, 7.906737818402988, 9.003977158788453, 9.015191309781628, 5.073689648007103, 4.668212763385716, 5.4472135327643825, 9.786427261222144, 4.802280994098745, 5.667416935618994, 6.781362523683108, 8.025427646920194), # 159 (7.284872094904309, 5.675096728540714, 7.821920957955888, 8.89857751040886, 8.916420131346795, 5.024341296047684, 4.602243748383784, 5.3955991895273465, 9.697425227228651, 4.741205651862893, 5.59725950860954, 6.700501948887847, 7.93642060889358), # 160 (7.17322205458596, 5.577120868080469, 7.720046971910309, 8.774572503756728, 8.798393124282113, 4.963577241570314, 4.527681446006876, 5.33160053310978, 9.585829766999018, 4.6706581931709374, 5.515741654599707, 6.605767468907571, 7.830374044819097), # 161 (7.048121770426357, 5.469711258703239, 7.602021459615496, 8.632964006076326, 8.662135842303204, 4.891995305706455, 4.445007626339809, 5.255864173983202, 9.452814657913637, 4.5911569216102315, 5.42348155667862, 6.497908712841293, 7.708197254180333), # 162 (6.9103563668284975, 5.353441420893524, 7.468750020420702, 8.474753884611934, 8.508673839125688, 4.810193309587572, 4.354704059467401, 5.169036722619125, 9.299553677352906, 4.503220140768125, 5.321097397935408, 6.3776753097880325, 7.570799536460879), # 163 (6.760710968195384, 5.228884875135821, 7.321138253675176, 8.300944006607818, 8.339032668465189, 4.718769074345129, 4.257252515474466, 5.071764789489069, 9.127220602697223, 4.407366154231968, 5.209207361459196, 6.245816888846803, 7.419090191144328), # 164 (6.599970698930017, 5.096615141914632, 7.160091758728169, 8.112536239308252, 8.154237884037324, 4.618320421110586, 4.153134764445822, 4.964694985064546, 8.93698921132698, 4.3041132655891134, 5.088429630339111, 6.10308307911662, 7.25397851771427), # 165 (6.428920683435397, 4.957205741714454, 6.9865161349289275, 7.910532449957501, 7.955315039557714, 4.509445171015408, 4.042832576466286, 4.848473919817077, 8.730033280622573, 4.193979778426912, 4.959382387664279, 5.950223509696501, 7.0763738156542955), # 166 (6.248346046114523, 4.811230195019787, 6.801316981626704, 7.695934505799843, 7.74328968874198, 4.392741145191058, 3.9268277216206746, 4.723748204218176, 8.5075265879644, 4.077483996332714, 4.822683816523827, 5.7879878096854585, 6.887185384447996), # 167 (6.059031911370395, 4.659262022315128, 6.605399898170748, 7.469744274079546, 7.519187385305742, 4.268806164768999, 3.805601969993804, 4.5911644487393595, 8.270642910732855, 3.955144222893872, 4.678952100006881, 5.617125608182511, 6.6873225235789615), # 168 (5.861763403606015, 4.501874744084979, 6.399670483910309, 7.232963622040883, 7.28403368296462, 4.138238050880695, 3.6796370916704917, 4.451369263852145, 8.020556026308338, 3.8274787616977366, 4.528805421202568, 5.438386534286672, 6.477694532530785), # 169 (5.657325647224384, 4.339641880813837, 6.185034338194635, 6.98659441692812, 7.038854135434233, 4.001634624657607, 3.549414856735553, 4.305009260028047, 7.7584397120712385, 3.6950059163316578, 4.372861963200016, 5.252520217096959, 6.259210710787055), # 170 (5.4465037666285, 4.173136952986201, 5.962397060372978, 6.731638525985535, 6.784674296430206, 3.8595937072311983, 3.4154170352738054, 4.152731047738583, 7.485467745401956, 3.5582439903829886, 4.211739909088348, 5.060276285712386, 6.032780357831365), # 171 (5.230082886221365, 4.002933481086569, 5.7326642497945866, 6.4690978164573965, 6.5225197196681535, 3.7127131197329337, 3.2781253973700655, 3.9951812374552707, 7.202813903680886, 3.41771128743908, 4.046057441956694, 4.862404369231971, 5.799312773147303), # 172 (5.00884813040598, 3.8296049855994423, 5.4967415058087115, 6.1999741555879755, 6.253415958863702, 3.5615906832942748, 3.1380217131091497, 3.8330064396496235, 6.911651964288422, 3.2739261110872815, 3.8764327448941778, 4.659654096754725, 5.5597172562184625), # 173 (4.783584623585344, 3.653724987009318, 5.2555344277646014, 5.9252694106215404, 5.978388567732466, 3.406824219046685, 2.9955877525758754, 3.6668532647931604, 6.613155704604964, 3.1274067649149466, 3.7034840009899277, 4.452775097379668, 5.314903106528433), # 174 (4.555077490162455, 3.4758670058006946, 5.009948615011508, 5.645985448802367, 5.698463099990069, 3.2490115481216284, 2.851305285855058, 3.497368323357396, 6.308498902010905, 2.9786715525094243, 3.5278293933330693, 4.242517000205814, 5.0657796235608075), # 175 (4.324111854540319, 3.296604562458073, 4.760889666898678, 5.363124137374725, 5.41466510935213, 3.0887504916505666, 2.705656083031515, 3.325198225813849, 5.998855333886642, 2.828238777458067, 3.35008710501273, 4.029629434332179, 4.813256106799174), # 176 (4.0914728411219325, 3.1165111774659513, 4.5092631827753635, 5.077687343582883, 5.128020149534273, 2.9266388707649633, 2.5591219141900625, 3.1509895826340326, 5.68539877761257, 2.6766267433482245, 3.1708753191180357, 3.8148620288577786, 4.5582418557271245), # 177 (3.8579455743102966, 2.9361603713088282, 4.255974761990814, 4.790676934671116, 4.8395537742521135, 2.7632745065962827, 2.4121845494155174, 2.9753890042894655, 5.3693030105690855, 2.52435375376725, 2.9908122187381125, 3.598964412881627, 4.301646169828252), # 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 = ( (7, 4, 4, 3, 2, 1, 4, 2, 4, 2, 0, 0, 0, 7, 5, 8, 3, 7, 1, 3, 0, 1, 2, 1, 1, 0), # 0 (11, 11, 6, 6, 5, 2, 6, 7, 6, 3, 1, 0, 0, 13, 9, 12, 6, 9, 2, 8, 1, 2, 3, 1, 2, 0), # 1 (17, 15, 11, 12, 7, 3, 8, 11, 7, 4, 1, 0, 0, 24, 12, 18, 13, 16, 2, 11, 3, 3, 5, 1, 2, 0), # 2 (23, 18, 17, 19, 13, 4, 11, 14, 8, 6, 2, 0, 0, 31, 23, 26, 15, 20, 5, 11, 3, 5, 7, 1, 2, 0), # 3 (34, 23, 25, 22, 19, 5, 13, 16, 10, 6, 2, 0, 0, 37, 32, 31, 16, 25, 6, 13, 4, 6, 8, 1, 2, 0), # 4 (37, 28, 32, 29, 24, 7, 18, 19, 13, 8, 5, 0, 0, 46, 42, 34, 18, 32, 8, 14, 4, 9, 9, 2, 4, 0), # 5 (43, 32, 37, 34, 30, 9, 19, 22, 17, 9, 5, 2, 0, 53, 48, 36, 22, 36, 9, 17, 5, 12, 14, 2, 6, 0), # 6 (48, 40, 43, 38, 36, 9, 25, 26, 19, 10, 7, 2, 0, 62, 54, 38, 27, 39, 9, 18, 6, 15, 16, 4, 6, 0), # 7 (54, 43, 45, 49, 40, 11, 32, 30, 21, 11, 8, 2, 0, 66, 58, 42, 28, 43, 14, 19, 8, 17, 18, 8, 6, 0), # 8 (61, 48, 53, 54, 50, 11, 39, 32, 25, 13, 9, 3, 0, 74, 61, 46, 31, 51, 18, 20, 12, 21, 21, 10, 7, 0), # 9 (64, 52, 65, 60, 56, 15, 43, 32, 27, 14, 9, 3, 0, 89, 64, 55, 34, 51, 22, 22, 14, 25, 24, 12, 8, 0), # 10 (73, 63, 73, 68, 60, 18, 45, 32, 30, 16, 9, 4, 0, 95, 76, 62, 38, 58, 24, 27, 18, 28, 27, 14, 9, 0), # 11 (81, 66, 79, 77, 70, 20, 54, 34, 34, 17, 10, 5, 0, 101, 81, 69, 39, 63, 28, 30, 20, 32, 28, 14, 11, 0), # 12 (86, 77, 91, 84, 76, 23, 59, 38, 37, 19, 11, 8, 0, 117, 87, 77, 47, 71, 29, 32, 24, 34, 30, 16, 12, 0), # 13 (103, 89, 99, 98, 80, 25, 61, 43, 41, 19, 13, 9, 0, 128, 91, 86, 53, 81, 30, 34, 26, 38, 31, 17, 12, 0), # 14 (115, 100, 112, 106, 93, 27, 66, 46, 45, 19, 13, 9, 0, 144, 98, 88, 61, 87, 34, 38, 30, 41, 33, 18, 13, 0), # 15 (122, 105, 116, 113, 102, 28, 71, 50, 49, 23, 14, 10, 0, 155, 108, 92, 68, 95, 41, 43, 31, 44, 37, 18, 13, 0), # 16 (133, 112, 121, 119, 108, 34, 75, 51, 55, 23, 15, 11, 0, 164, 117, 98, 74, 102, 46, 45, 32, 45, 40, 18, 13, 0), # 17 (142, 124, 130, 122, 115, 38, 78, 53, 58, 25, 16, 13, 0, 176, 126, 104, 77, 109, 54, 49, 36, 50, 42, 20, 15, 0), # 18 (153, 139, 136, 130, 122, 44, 83, 59, 60, 26, 16, 14, 0, 187, 137, 107, 81, 118, 58, 55, 40, 54, 44, 23, 16, 0), # 19 (160, 150, 142, 140, 127, 48, 86, 61, 63, 28, 17, 14, 0, 194, 145, 115, 82, 129, 66, 61, 43, 58, 48, 26, 16, 0), # 20 (170, 159, 152, 151, 134, 51, 90, 64, 66, 29, 20, 16, 0, 204, 162, 127, 93, 135, 69, 63, 45, 63, 52, 28, 17, 0), # 21 (184, 167, 160, 162, 138, 57, 93, 67, 70, 30, 21, 16, 0, 215, 167, 133, 99, 150, 72, 64, 49, 68, 54, 29, 17, 0), # 22 (196, 184, 168, 176, 148, 59, 97, 69, 74, 33, 21, 17, 0, 226, 179, 140, 104, 159, 77, 66, 51, 69, 59, 31, 20, 0), # 23 (203, 196, 185, 192, 153, 62, 103, 73, 80, 34, 23, 17, 0, 239, 183, 145, 108, 173, 79, 69, 57, 74, 60, 32, 20, 0), # 24 (213, 207, 194, 199, 157, 68, 105, 74, 85, 37, 23, 20, 0, 244, 192, 153, 114, 181, 82, 73, 61, 77, 61, 36, 21, 0), # 25 (224, 214, 202, 207, 166, 73, 107, 79, 92, 40, 24, 21, 0, 254, 197, 164, 117, 192, 85, 80, 63, 80, 64, 39, 23, 0), # 26 (235, 222, 208, 214, 172, 78, 115, 79, 99, 40, 24, 21, 0, 265, 212, 172, 120, 201, 93, 85, 64, 84, 65, 40, 25, 0), # 27 (241, 237, 218, 229, 181, 80, 119, 84, 103, 44, 24, 21, 0, 281, 221, 179, 126, 208, 94, 91, 64, 91, 68, 40, 29, 0), # 28 (256, 240, 231, 238, 190, 86, 122, 87, 106, 45, 25, 21, 0, 287, 227, 187, 133, 221, 100, 96, 69, 96, 72, 40, 30, 0), # 29 (273, 252, 244, 245, 198, 89, 126, 90, 109, 46, 27, 22, 0, 301, 240, 190, 140, 232, 106, 102, 72, 102, 73, 42, 32, 0), # 30 (290, 262, 247, 254, 202, 92, 131, 92, 114, 49, 28, 23, 0, 313, 250, 194, 147, 244, 113, 107, 73, 105, 77, 43, 34, 0), # 31 (298, 272, 254, 262, 214, 93, 136, 96, 117, 49, 28, 26, 0, 321, 268, 201, 148, 257, 120, 113, 74, 108, 78, 44, 35, 0), # 32 (305, 277, 264, 270, 221, 97, 140, 104, 125, 54, 29, 26, 0, 329, 276, 206, 150, 261, 123, 119, 76, 112, 79, 47, 36, 0), # 33 (310, 287, 276, 281, 230, 104, 144, 108, 127, 55, 29, 27, 0, 338, 287, 212, 156, 272, 129, 124, 76, 113, 81, 49, 38, 0), # 34 (323, 298, 281, 289, 237, 108, 148, 112, 134, 57, 30, 27, 0, 351, 297, 217, 161, 283, 136, 127, 76, 114, 84, 49, 38, 0), # 35 (333, 310, 286, 296, 242, 113, 150, 118, 138, 59, 31, 30, 0, 361, 309, 232, 168, 294, 141, 133, 78, 116, 86, 49, 38, 0), # 36 (344, 321, 299, 305, 251, 117, 151, 119, 142, 61, 32, 31, 0, 374, 321, 238, 171, 301, 147, 138, 79, 120, 88, 51, 38, 0), # 37 (348, 328, 308, 310, 257, 120, 157, 120, 145, 63, 33, 32, 0, 387, 328, 242, 176, 315, 148, 142, 81, 121, 89, 52, 38, 0), # 38 (360, 336, 317, 321, 268, 124, 163, 128, 150, 68, 35, 33, 0, 393, 338, 248, 180, 326, 156, 148, 81, 129, 93, 56, 40, 0), # 39 (372, 350, 327, 330, 274, 126, 168, 128, 154, 70, 35, 35, 0, 403, 346, 253, 184, 333, 162, 151, 82, 132, 99, 57, 42, 0), # 40 (382, 363, 338, 340, 283, 131, 169, 133, 160, 74, 36, 35, 0, 414, 351, 260, 190, 341, 166, 155, 83, 136, 100, 58, 43, 0), # 41 (394, 372, 355, 349, 289, 137, 170, 136, 163, 77, 38, 35, 0, 427, 355, 267, 194, 345, 172, 158, 87, 138, 101, 60, 45, 0), # 42 (397, 386, 368, 355, 300, 142, 174, 139, 166, 79, 38, 38, 0, 440, 361, 275, 198, 351, 179, 167, 90, 140, 103, 63, 45, 0), # 43 (407, 398, 380, 363, 310, 145, 177, 149, 170, 81, 39, 39, 0, 448, 373, 285, 203, 360, 184, 173, 92, 145, 107, 65, 46, 0), # 44 (418, 409, 396, 371, 318, 147, 188, 154, 176, 82, 41, 41, 0, 458, 385, 292, 213, 368, 185, 179, 96, 149, 109, 67, 47, 0), # 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173 (1561, 1404, 1422, 1395, 1307, 583, 555, 479, 628, 265, 193, 122, 0, 1697, 1342, 985, 756, 1302, 601, 588, 392, 581, 460, 260, 130, 0), # 174 (1568, 1410, 1425, 1397, 1312, 586, 555, 482, 629, 266, 193, 122, 0, 1699, 1345, 988, 760, 1307, 604, 591, 392, 585, 463, 260, 130, 0), # 175 (1574, 1414, 1432, 1398, 1316, 589, 556, 483, 629, 266, 194, 122, 0, 1704, 1349, 991, 762, 1311, 605, 592, 392, 585, 466, 260, 130, 0), # 176 (1578, 1415, 1436, 1404, 1318, 590, 556, 483, 632, 267, 194, 123, 0, 1709, 1351, 994, 764, 1319, 606, 593, 393, 588, 467, 261, 132, 0), # 177 (1584, 1417, 1441, 1405, 1319, 591, 557, 483, 634, 268, 195, 123, 0, 1713, 1351, 998, 765, 1323, 606, 594, 394, 592, 470, 261, 132, 0), # 178 (1584, 1417, 1441, 1405, 1319, 591, 557, 483, 634, 268, 195, 123, 0, 1713, 1351, 998, 765, 1323, 606, 594, 394, 592, 470, 261, 132, 0), # 179 ) passenger_arriving_rate = ( (5.020865578371768, 5.064847846385402, 4.342736024677089, 4.661000830397574, 3.7031237384064077, 1.8308820436884476, 2.0730178076869574, 1.938823405408093, 2.030033020722669, 0.9895037538805926, 0.7008775273142672, 0.4081595898588478, 0.0, 5.083880212578363, 4.489755488447325, 3.5043876365713356, 2.968511261641777, 4.060066041445338, 2.7143527675713304, 2.0730178076869574, 1.3077728883488913, 1.8515618692032039, 1.5536669434658585, 0.8685472049354179, 0.4604407133077639, 0.0), # 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6 (7.2873790797949685, 7.331353891769537, 6.286187700893863, 6.747711992905847, 5.367253557395036, 2.650622574638337, 2.9997431849797924, 2.8039403467281465, 2.9386101514892147, 1.4317463730358968, 1.0147460259942116, 0.5907767409159108, 0.0, 7.360713718506519, 6.498544150075018, 5.073730129971057, 4.2952391191076895, 5.877220302978429, 3.9255164854194056, 2.9997431849797924, 1.8933018390273837, 2.683626778697518, 2.249237330968616, 1.2572375401787725, 0.6664867174335943, 0.0), # 7 (7.586178158429934, 7.628690567496257, 6.54113885704533, 7.021453857524196, 5.586054507809724, 2.7581553398139356, 3.1213196156715988, 2.917421169782802, 3.0577960109310682, 1.4897650347411937, 1.0559209687315536, 0.6147332188070586, 0.0, 7.659391453419917, 6.762065406877643, 5.279604843657768, 4.469295104223581, 6.1155920218621365, 4.084389637695923, 3.1213196156715988, 1.970110957009954, 2.793027253904862, 2.3404846191747324, 1.3082277714090662, 0.6935173243178416, 0.0), # 8 (7.874844027645085, 7.915450675689353, 6.787020206437253, 7.285456918520376, 5.797238807138606, 2.861861772241199, 3.23857180840756, 3.0268631768812346, 3.1727408091108913, 1.5457203086224858, 1.0956311626552797, 0.6378374332888596, 0.0, 7.947442293806162, 7.016211766177453, 5.478155813276398, 4.637160925867456, 6.345481618221783, 4.237608447633728, 3.23857180840756, 2.044186980172285, 2.898619403569303, 2.4284856395067926, 1.3574040412874508, 0.7195864250626686, 0.0), # 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12 (8.902268288217876, 8.93029815321015, 7.657190154002218, 8.219755318845033, 6.546584954473067, 3.2288703310781304, 3.653531461623028, 3.414163125037284, 3.579520436670977, 1.7437496992757264, 1.2361651484848115, 0.7196027119695768, 0.0, 8.966837737406735, 7.915629831665344, 6.180825742424058, 5.2312490978271775, 7.159040873341954, 4.7798283750521975, 3.653531461623028, 2.306335950770093, 3.2732924772365335, 2.7399184396150114, 1.5314380308004438, 0.8118452866554684, 0.0), # 13 (9.120837768766716, 9.144354963798623, 7.840729432790956, 8.416820006151594, 6.705267594030659, 3.306279717222145, 3.7410574109523305, 3.4958511118480193, 3.6653182141364735, 1.785519664063084, 1.2658071220435476, 0.7368489005398801, 0.0, 9.181849875652563, 8.10533790593868, 6.329035610217737, 5.3565589921892505, 7.330636428272947, 4.894191556587227, 3.7410574109523305, 2.3616283694443894, 3.3526337970153297, 2.8056066687171985, 1.5681458865581912, 0.8313049967089657, 0.0), # 14 (9.321501903268855, 9.339907022878865, 8.008401690097953, 8.59684814760449, 6.850566220765538, 3.376996617601199, 3.821017100874813, 3.5704763064998986, 3.743698244578273, 1.823678893036873, 1.2928865562699035, 0.752604142154931, 0.0, 9.37827342756938, 8.27864556370424, 6.464432781349516, 5.471036679110618, 7.487396489156546, 4.998666829099858, 3.821017100874813, 2.4121404411437135, 3.425283110382769, 2.865616049201497, 1.6016803380195905, 0.8490824566253515, 0.0), # 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18 (9.919197858720699, 9.910640464202265, 8.497763122101317, 9.122269447440985, 7.2786960603549105, 3.5833855180790386, 4.054386528326697, 3.7882692428434357, 3.9724519435695926, 1.9350506241644574, 1.3719195981223131, 0.7985866939095915, 0.0, 9.951542799273696, 8.784453633005505, 6.859597990611565, 5.80515187249337, 7.944903887139185, 5.30357693998081, 4.054386528326697, 2.55956108434217, 3.6393480301774552, 3.0407564824803295, 1.6995526244202632, 0.9009673149274788, 0.0), # 19 (10.010904921422082, 9.993848305804882, 8.569107235194169, 9.198870504046766, 7.342656218633962, 3.613474607091719, 4.088409867587681, 3.8200205361944657, 4.005801524488732, 1.95128780409649, 1.3834418593898585, 0.805290491883616, 0.0, 10.035119190040824, 8.858195410719775, 6.9172092969492915, 5.853863412289469, 8.011603048977465, 5.348028750672252, 4.088409867587681, 2.5810532907797996, 3.671328109316981, 3.0662901680155894, 1.713821447038834, 0.9085316641640803, 0.0), # 20 (10.076934501449866, 10.050623211924679, 8.6177871120831, 9.251137272567364, 7.387465002353392, 3.6340050573228124, 4.1116249259908795, 3.84168506118401, 4.028556672622507, 1.9623669002253892, 1.39130379080626, 0.8098646593564828, 0.0, 10.092145302677078, 8.90851125292131, 6.9565189540313, 5.887100700676166, 8.057113345245014, 5.378359085657614, 4.1116249259908795, 2.5957178980877234, 3.693732501176696, 3.0837124241891223, 1.72355742241662, 0.91369301926588, 0.0), # 21 (10.115991242699579, 10.079643818565883, 8.642669883647738, 9.277853462630876, 7.41216118455705, 3.644499176602881, 4.1234913666278, 3.852758821778298, 4.040187940343971, 1.968030021219561, 1.3953224272850568, 0.8122027490705409, 0.0, 10.121294188548827, 8.934230239775948, 6.976612136425284, 5.904090063658682, 8.080375880687942, 5.393862350489617, 4.1234913666278, 2.6032136975734863, 3.706080592278525, 3.09261782087696, 1.7285339767295478, 0.9163312562332622, 0.0), # 22 (10.13039336334264, 10.083079961133974, 8.645769318701419, 9.281198109567903, 7.418488037355065, 3.6458333333333335, 4.124902001129669, 3.8539557613168727, 4.0416420781893, 1.9686980681298587, 1.3958263395269568, 0.8124914647157445, 0.0, 10.125, 8.93740611187319, 6.9791316976347835, 5.906094204389575, 8.0832841563786, 5.395538065843622, 4.124902001129669, 2.604166666666667, 3.7092440186775324, 3.0937327031893016, 1.729153863740284, 0.9166436328303613, 0.0), # 23 (10.141012413034153, 10.08107561728395, 8.645262345679013, 9.280786458333335, 7.422071742409901, 3.6458333333333335, 4.124126906318083, 3.852291666666667, 4.041447222222222, 1.968287654320988, 1.39577076318743, 0.8124238683127573, 0.0, 10.125, 8.936662551440328, 6.978853815937151, 5.904862962962962, 8.082894444444443, 5.393208333333334, 4.124126906318083, 2.604166666666667, 3.7110358712049507, 3.0935954861111123, 1.7290524691358027, 0.9164614197530866, 0.0), # 24 (10.15140723021158, 10.077124771376313, 8.644261545496114, 9.279972029320987, 7.4255766303963355, 3.6458333333333335, 4.122599451303155, 3.8490226337448563, 4.041062242798354, 1.96747970964792, 1.3956605665710604, 0.8122904282883707, 0.0, 10.125, 8.935194711172077, 6.978302832855302, 5.902439128943758, 8.082124485596708, 5.388631687242799, 4.122599451303155, 2.604166666666667, 3.7127883151981678, 3.0933240097736636, 1.728852309099223, 0.9161022519433014, 0.0), # 25 (10.161577019048034, 10.071287780064015, 8.642780635573846, 9.278764081790122, 7.429002578947403, 3.6458333333333335, 4.120343359154361, 3.8442103909465026, 4.0404920781893, 1.9662876771833566, 1.3954967473084758, 0.8120929736320684, 0.0, 10.125, 8.933022709952752, 6.977483736542379, 5.898863031550069, 8.0809841563786, 5.381894547325103, 4.120343359154361, 2.604166666666667, 3.7145012894737013, 3.0929213605967085, 1.7285561271147696, 0.915571616369456, 0.0), # 26 (10.171520983716636, 10.063624999999998, 8.640833333333333, 9.277171874999999, 7.432349465696142, 3.6458333333333335, 4.117382352941177, 3.837916666666667, 4.039741666666666, 1.9647250000000003, 1.3952803030303031, 0.8118333333333335, 0.0, 10.125, 8.930166666666667, 6.976401515151515, 5.894175, 8.079483333333332, 5.373083333333334, 4.117382352941177, 2.604166666666667, 3.716174732848071, 3.0923906250000006, 1.7281666666666669, 0.914875, 0.0), # 27 (10.181238328390501, 10.054196787837219, 8.638433356195703, 9.275204668209877, 7.4356171682756, 3.6458333333333335, 4.113740155733075, 3.830203189300412, 4.038815946502057, 1.9628051211705537, 1.3950122313671698, 0.8115133363816492, 0.0, 10.125, 8.926646700198141, 6.9750611568358485, 5.88841536351166, 8.077631893004114, 5.3622844650205765, 4.113740155733075, 2.604166666666667, 3.7178085841378, 3.091734889403293, 1.7276866712391405, 0.9140178898033837, 0.0), # 28 (10.19072825724275, 10.043063500228623, 8.635594421582077, 9.272871720679012, 7.438805564318813, 3.6458333333333335, 4.109440490599533, 3.821131687242798, 4.037719855967078, 1.9605414837677189, 1.3946935299497027, 0.811134811766499, 0.0, 10.125, 8.922482929431489, 6.973467649748514, 5.881624451303155, 8.075439711934155, 5.349584362139917, 4.109440490599533, 2.604166666666667, 3.7194027821594067, 3.0909572402263383, 1.7271188843164156, 0.9130057727480568, 0.0), # 29 (10.199989974446497, 10.03028549382716, 8.63233024691358, 9.270182291666666, 7.441914531458824, 3.6458333333333335, 4.104507080610022, 3.8107638888888884, 4.036458333333333, 1.957947530864198, 1.39432519640853, 0.8106995884773662, 0.0, 10.125, 8.917695473251028, 6.9716259820426485, 5.873842592592593, 8.072916666666666, 5.335069444444444, 4.104507080610022, 2.604166666666667, 3.720957265729412, 3.0900607638888897, 1.7264660493827162, 0.9118441358024693, 0.0), # 30 (10.209022684174858, 10.01592312528578, 8.62865454961134, 9.267145640432098, 7.444943947328672, 3.6458333333333335, 4.09896364883402, 3.799161522633745, 4.035036316872428, 1.9550367055326936, 1.3939082283742779, 0.8102094955037343, 0.0, 10.125, 8.912304450541077, 6.969541141871389, 5.865110116598079, 8.070072633744855, 5.318826131687243, 4.09896364883402, 2.604166666666667, 3.722471973664336, 3.0890485468107003, 1.7257309099222682, 0.910538465935071, 0.0), # 31 (10.217825590600954, 10.00003675125743, 8.624581047096479, 9.263771026234568, 7.447893689561397, 3.6458333333333335, 4.092833918340999, 3.7863863168724285, 4.033458744855967, 1.951822450845908, 1.3934436234775742, 0.8096663618350862, 0.0, 10.125, 8.906329980185948, 6.96721811738787, 5.8554673525377225, 8.066917489711933, 5.3009408436214, 4.092833918340999, 2.604166666666667, 3.7239468447806985, 3.0879236754115236, 1.7249162094192958, 0.909094250114312, 0.0), # 32 (10.226397897897897, 9.98268672839506, 8.620123456790123, 9.260067708333333, 7.450763635790041, 3.6458333333333335, 4.086141612200436, 3.7725000000000004, 4.031730555555555, 1.9483182098765437, 1.392932379349046, 0.8090720164609053, 0.0, 10.125, 8.899792181069957, 6.96466189674523, 5.84495462962963, 8.06346111111111, 5.2815, 4.086141612200436, 2.604166666666667, 3.7253818178950207, 3.086689236111112, 1.724024691358025, 0.9075169753086421, 0.0), # 33 (10.23473881023881, 9.963933413351622, 8.615295496113397, 9.256044945987654, 7.453553663647644, 3.6458333333333335, 4.078910453481805, 3.7575643004115222, 4.029856687242798, 1.9445374256973027, 1.3923754936193207, 0.8084282883706753, 0.0, 10.125, 8.892711172077426, 6.961877468096604, 5.833612277091907, 8.059713374485597, 5.260590020576132, 4.078910453481805, 2.604166666666667, 3.726776831823822, 3.085348315329219, 1.7230590992226795, 0.9058121284865113, 0.0), # 34 (10.242847531796807, 9.943837162780063, 8.610110882487428, 9.25171199845679, 7.456263650767246, 3.6458333333333335, 4.071164165254579, 3.741640946502058, 4.0278420781893, 1.9404935413808875, 1.3917739639190256, 0.807737006553879, 0.0, 10.125, 8.88510707209267, 6.958869819595128, 5.821480624142661, 8.0556841563786, 5.238297325102881, 4.071164165254579, 2.604166666666667, 3.728131825383623, 3.0839039994855972, 1.7220221764974855, 0.9039851966163696, 0.0), # 35 (10.250723266745005, 9.922458333333331, 8.604583333333334, 9.247078125, 7.45889347478189, 3.6458333333333335, 4.062926470588235, 3.724791666666667, 4.025691666666666, 1.9362000000000004, 1.391128787878788, 0.8070000000000002, 0.0, 10.125, 8.877, 6.95564393939394, 5.8086, 8.051383333333332, 5.214708333333334, 4.062926470588235, 2.604166666666667, 3.729446737390945, 3.0823593750000007, 1.7209166666666669, 0.9020416666666666, 0.0), # 36 (10.258365219256524, 9.89985728166438, 8.598726566072246, 9.242152584876543, 7.4614430133246135, 3.6458333333333335, 4.054221092552247, 3.707078189300412, 4.023410390946502, 1.931670244627344, 1.3904409631292352, 0.8062190976985216, 0.0, 10.125, 8.868410074683737, 6.952204815646175, 5.79501073388203, 8.046820781893004, 5.189909465020577, 4.054221092552247, 2.604166666666667, 3.7307215066623067, 3.080717528292182, 1.7197453132144491, 0.8999870256058529, 0.0), # 37 (10.265772593504476, 9.876094364426155, 8.592554298125286, 9.23694463734568, 7.46391214402846, 3.6458333333333335, 4.04507175421609, 3.6885622427983544, 4.021003189300411, 1.92691771833562, 1.3897114873009937, 0.8053961286389272, 0.0, 10.125, 8.859357415028198, 6.948557436504967, 5.780753155006859, 8.042006378600822, 5.163987139917697, 4.04507175421609, 2.604166666666667, 3.73195607201423, 3.078981545781894, 1.7185108596250571, 0.8978267604023779, 0.0), # 38 (10.272944593661986, 9.851229938271604, 8.586080246913582, 9.231463541666667, 7.466300744526468, 3.6458333333333335, 4.035502178649238, 3.6693055555555554, 4.0184750000000005, 1.9219558641975314, 1.3889413580246914, 0.8045329218106996, 0.0, 10.125, 8.849862139917693, 6.944706790123457, 5.765867592592593, 8.036950000000001, 5.137027777777778, 4.035502178649238, 2.604166666666667, 3.733150372263234, 3.07715451388889, 1.7172160493827164, 0.8955663580246914, 0.0), # 39 (10.279880423902163, 9.82532435985368, 8.579318129858253, 9.225718557098766, 7.468608692451679, 3.6458333333333335, 4.025536088921165, 3.649369855967079, 4.015830761316872, 1.9167981252857802, 1.3881315729309558, 0.8036313062033228, 0.0, 10.125, 8.83994436823655, 6.940657864654778, 5.750394375857339, 8.031661522633744, 5.1091177983539104, 4.025536088921165, 2.604166666666667, 3.7343043462258394, 3.0752395190329227, 1.7158636259716507, 0.8932113054412438, 0.0), # 40 (10.286579288398128, 9.79843798582533, 8.57228166438043, 9.219718942901235, 7.4708358654371345, 3.6458333333333335, 4.015197208101347, 3.628816872427984, 4.0130754115226335, 1.9114579446730684, 1.3872831296504138, 0.8026931108062796, 0.0, 10.125, 8.829624218869075, 6.936415648252069, 5.734373834019204, 8.026150823045267, 5.0803436213991775, 4.015197208101347, 2.604166666666667, 3.7354179327185673, 3.073239647633746, 1.7144563328760862, 0.8907670896204848, 0.0), # 41 (10.293040391323, 9.770631172839506, 8.564984567901236, 9.213473958333335, 7.472982141115872, 3.6458333333333335, 4.004509259259259, 3.6077083333333335, 4.010213888888889, 1.9059487654320992, 1.3863970258136926, 0.8017201646090536, 0.0, 10.125, 8.818921810699589, 6.931985129068463, 5.717846296296297, 8.020427777777778, 5.050791666666667, 4.004509259259259, 2.604166666666667, 3.736491070557936, 3.0711579861111122, 1.7129969135802474, 0.8882391975308643, 0.0), # 42 (10.299262936849892, 9.741964277549155, 8.557440557841794, 9.206992862654321, 7.475047397120935, 3.6458333333333335, 3.993495965464375, 3.58610596707819, 4.007251131687243, 1.9002840306355744, 1.3854742590514195, 0.800714296601128, 0.0, 10.125, 8.807857262612407, 6.927371295257098, 5.700852091906722, 8.014502263374485, 5.020548353909466, 3.993495965464375, 2.604166666666667, 3.7375236985604676, 3.0689976208847747, 1.7114881115683587, 0.8856331161408324, 0.0), # 43 (10.305246129151927, 9.712497656607225, 8.549663351623229, 9.200284915123458, 7.477031511085363, 3.6458333333333335, 3.9821810497861696, 3.564071502057614, 4.0041920781893, 1.8944771833561962, 1.3845158269942222, 0.7996773357719861, 0.0, 10.125, 8.796450693491845, 6.92257913497111, 5.683431550068587, 8.0083841563786, 4.98970010288066, 3.9821810497861696, 2.604166666666667, 3.7385157555426813, 3.0667616383744867, 1.709932670324646, 0.8829543324188387, 0.0), # 44 (10.310989172402216, 9.682291666666666, 8.541666666666668, 9.193359375, 7.478934360642197, 3.6458333333333335, 3.9705882352941178, 3.541666666666667, 4.001041666666666, 1.8885416666666672, 1.3835227272727273, 0.798611111111111, 0.0, 10.125, 8.784722222222221, 6.917613636363637, 5.665625, 8.002083333333331, 4.958333333333334, 3.9705882352941178, 2.604166666666667, 3.7394671803210984, 3.064453125000001, 1.7083333333333335, 0.8802083333333335, 0.0), # 45 (10.31649127077388, 9.65140666438043, 8.533464220393233, 9.186225501543209, 7.480755823424477, 3.6458333333333335, 3.958741245057694, 3.518953189300412, 3.997804835390946, 1.8824909236396894, 1.3824959575175624, 0.7975174516079867, 0.0, 10.125, 8.772691967687852, 6.912479787587812, 5.647472770919067, 7.995609670781892, 4.926534465020577, 3.958741245057694, 2.604166666666667, 3.7403779117122387, 3.062075167181071, 1.7066928440786466, 0.8774006058527665, 0.0), # 46 (10.321751628440035, 9.619903006401461, 8.525069730224052, 9.178892554012345, 7.482495777065244, 3.6458333333333335, 3.9466638021463734, 3.4959927983539094, 3.994486522633745, 1.8763383973479657, 1.3814365153593549, 0.7963981862520958, 0.0, 10.125, 8.760380048773053, 6.9071825767967745, 5.629015192043896, 7.98897304526749, 4.894389917695474, 3.9466638021463734, 2.604166666666667, 3.741247888532622, 3.0596308513374493, 1.7050139460448106, 0.8745366369455876, 0.0), # 47 (10.326769449573796, 9.587841049382716, 8.516496913580248, 9.171369791666667, 7.48415409919754, 3.6458333333333335, 3.9343796296296296, 3.4728472222222226, 3.9910916666666667, 1.8700975308641978, 1.3803453984287317, 0.7952551440329219, 0.0, 10.125, 8.74780658436214, 6.901726992143659, 5.610292592592592, 7.982183333333333, 4.861986111111112, 3.9343796296296296, 2.604166666666667, 3.74207704959877, 3.05712326388889, 1.7032993827160496, 0.871621913580247, 0.0), # 48 (10.331543938348286, 9.555281149977136, 8.507759487882945, 9.163666473765433, 7.485730667454405, 3.6458333333333335, 3.9219124505769383, 3.4495781893004116, 3.987625205761317, 1.8637817672610888, 1.3792236043563206, 0.7940901539399483, 0.0, 10.125, 8.73499169333943, 6.896118021781603, 5.5913453017832655, 7.975250411522634, 4.829409465020577, 3.9219124505769383, 2.604166666666667, 3.7428653337272024, 3.054555491255145, 1.7015518975765893, 0.8686619227251944, 0.0), # 49 (10.336074298936616, 9.522283664837678, 8.49887117055327, 9.155791859567902, 7.4872253594688765, 3.6458333333333335, 3.909285988057775, 3.4262474279835393, 3.9840920781893, 1.85740454961134, 1.3780721307727481, 0.7929050449626583, 0.0, 10.125, 8.72195549458924, 6.89036065386374, 5.572213648834019, 7.9681841563786, 4.796746399176955, 3.909285988057775, 2.604166666666667, 3.7436126797344382, 3.051930619855968, 1.6997742341106543, 0.86566215134888, 0.0), # 50 (10.34035973551191, 9.488908950617283, 8.489845679012346, 9.147755208333333, 7.488638052873998, 3.6458333333333335, 3.896523965141612, 3.4029166666666666, 3.9804972222222226, 1.8509793209876546, 1.3768919753086422, 0.7917016460905352, 0.0, 10.125, 8.708718106995885, 6.884459876543211, 5.552937962962963, 7.960994444444445, 4.764083333333334, 3.896523965141612, 2.604166666666667, 3.744319026436999, 3.049251736111112, 1.6979691358024693, 0.8626280864197532, 0.0), # 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114 (9.302384903003995, 7.122451598792792, 7.665580777256098, 8.100063378886334, 7.050271785259067, 3.3367503822909463, 3.027029825095781, 2.533917772616129, 3.6083749928895963, 1.4857063319970194, 1.1560257519045158, 0.6822531318799043, 0.0, 9.452006631660376, 7.5047844506789465, 5.7801287595225785, 4.457118995991058, 7.216749985779193, 3.5474848816625806, 3.027029825095781, 2.3833931302078186, 3.5251358926295335, 2.700021126295445, 1.5331161554512198, 0.647495599890254, 0.0), # 115 (9.273179873237634, 7.0850892578507265, 7.648776824986561, 8.077999612699802, 7.036792350922519, 3.330080178417474, 3.0133024087639466, 2.5272417970412473, 3.6020604464092765, 1.480198339612387, 1.1519343218785802, 0.6802102664572789, 0.0, 9.43260725975589, 7.482312931030067, 5.7596716093929015, 4.44059501883716, 7.204120892818553, 3.5381385158577463, 3.0133024087639466, 2.3786286988696244, 3.5183961754612594, 2.6926665375666015, 1.5297553649973124, 0.6440990234409752, 0.0), # 116 (9.243829442823772, 7.04830504435266, 7.632093362938321, 8.056111029444182, 7.02313718419674, 3.323555141118853, 2.9998041194738763, 2.5208740813181603, 3.5959129388307343, 1.4747943502270324, 1.1479250164705472, 0.6781935872119792, 0.0, 9.413047004858225, 7.46012945933177, 5.739625082352736, 4.424383050681096, 7.1918258776614685, 3.5292237138454245, 2.9998041194738763, 2.3739679579420376, 3.51156859209837, 2.6853703431480613, 1.5264186725876645, 0.6407550040320601, 0.0), # 117 (9.214297245985211, 7.0120436072457135, 7.615497548340306, 8.03435789116525, 7.009286202577227, 3.317159168158581, 2.9865122214896576, 2.51479527536254, 3.5899160365213114, 1.46948389924369, 1.143989752709904, 0.6761992632811126, 0.0, 9.393295573379024, 7.438191896092237, 5.71994876354952, 4.40845169773107, 7.179832073042623, 3.5207133855075567, 2.9865122214896576, 2.369399405827558, 3.5046431012886137, 2.678119297055084, 1.5230995096680613, 0.6374585097496104, 0.0), # 118 (9.184546916944742, 6.976249595477001, 7.598956538421437, 8.012700459908778, 6.99521932355948, 3.3108761573001524, 2.973403979075378, 2.5089860290900607, 3.5840533058483475, 1.4642565220650932, 1.1401204476261382, 0.6742234638017862, 0.0, 9.373322671729932, 7.416458101819647, 5.70060223813069, 4.392769566195279, 7.168106611696695, 3.5125804407260848, 2.973403979075378, 2.3649115409286803, 3.49760966177974, 2.670900153302927, 1.5197913076842873, 0.6342045086797276, 0.0), # 119 (9.154542089925162, 6.940867657993644, 7.582437490410635, 7.991098997720545, 6.980916464638998, 3.304690006307063, 2.9604566564951265, 2.5034269924163928, 3.578308313179186, 1.4591017540939766, 1.136309018248736, 0.6722623579111081, 0.0, 9.353098006322597, 7.394885937022188, 5.68154509124368, 4.377305262281929, 7.156616626358372, 3.50479778938295, 2.9604566564951265, 2.360492861647902, 3.490458232319499, 2.663699665906849, 1.516487498082127, 0.6309879689085133, 0.0), # 120 (9.124246399149268, 6.90584244374276, 7.565907561536823, 7.969513766646325, 6.966357543311279, 3.29858461294281, 2.94764751801299, 2.4980988152572112, 3.572664624881166, 1.4540091307330743, 1.1325473816071863, 0.6703121147461852, 0.0, 9.33259128356866, 7.373433262208036, 5.662736908035931, 4.362027392199222, 7.145329249762332, 3.497338341360096, 2.94764751801299, 2.356131866387721, 3.4831787716556395, 2.656504588882109, 1.5131815123073646, 0.6278038585220692, 0.0), # 121 (9.093623478839854, 6.871118601671464, 7.549333909028926, 7.947905028731892, 6.951522477071823, 3.292543874970886, 2.9349538278930587, 2.492982147528187, 3.5671058073216297, 1.4489681873851195, 1.1288274547309753, 0.6683689034441251, 0.0, 9.31177220987977, 7.352057937885375, 5.644137273654876, 4.346904562155357, 7.1342116146432595, 3.490175006539462, 2.9349538278930587, 2.351817053550633, 3.4757612385359113, 2.6493016762439643, 1.5098667818057854, 0.6246471456064968, 0.0), # 122 (9.062636963219719, 6.836640780726876, 7.532683690115864, 7.92623304602302, 6.936391183416127, 3.28655169015479, 2.9223528503994194, 2.4880576391449933, 3.5616154268679177, 1.443968459452847, 1.1251411546495909, 0.6664288931420351, 0.0, 9.290610491667572, 7.330717824562385, 5.625705773247954, 4.33190537835854, 7.123230853735835, 3.4832806948029904, 2.9223528503994194, 2.3475369215391355, 3.4681955917080636, 2.642077682007674, 1.5065367380231727, 0.621512798247898, 0.0), # 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162 (6.9103563668284975, 4.90732130248573, 6.223958350350585, 6.35606541345895, 5.672449226083792, 2.8059460972594175, 2.1773520297337003, 2.153765301091302, 3.0998512257843016, 1.1258050351920315, 0.8868495663225682, 0.5314729424823361, 0.0, 7.570799536460879, 5.846202367305696, 4.43424783161284, 3.3774151055760937, 6.199702451568603, 3.015271421527823, 2.1773520297337003, 2.0042472123281554, 2.836224613041896, 2.118688471152984, 1.2447916700701172, 0.4461201184077937, 0.0), # 163 (6.760710968195384, 4.793144468874502, 6.100948544729314, 6.225708004955863, 5.559355112310126, 2.752615293367992, 2.128626257737233, 2.113235328953779, 3.0424068675657407, 1.1018415385579923, 0.8682012269098661, 0.5204847407372336, 0.0, 7.419090191144328, 5.725332148109569, 4.34100613454933, 3.305524615673976, 6.0848137351314815, 2.9585294605352903, 2.128626257737233, 1.9661537809771372, 2.779677556155063, 2.075236001651955, 1.2201897089458629, 0.43574040626131844, 0.0), # 164 (6.599970698930017, 4.671897213421746, 5.966743132273474, 6.084402179481189, 5.436158589358215, 2.694020245647842, 2.076567382222911, 2.068622910443561, 2.9789964037756596, 1.0760283163972786, 0.8480716050565187, 0.5085902565930517, 0.0, 7.25397851771427, 5.594492822523568, 4.2403580252825925, 3.2280849491918353, 5.957992807551319, 2.8960720746209856, 2.076567382222911, 1.9243001754627442, 2.7180792946791077, 2.0281340598270634, 1.1933486264546949, 0.42471792849288603, 0.0), # 165 (6.428920683435397, 4.54410526323825, 5.82209677910744, 5.932899337468126, 5.3035433597051425, 2.630509683092322, 2.021416288233143, 2.020197466590449, 2.9100110935408576, 1.0484949446067282, 0.8265637312773799, 0.49585195914137514, 0.0, 7.0763738156542955, 5.454371550555126, 4.1328186563869, 3.145484833820184, 5.820022187081715, 2.8282764532266285, 2.021416288233143, 1.8789354879230868, 2.6517716798525712, 1.9776331124893758, 1.1644193558214881, 0.41310047847620457, 0.0), # 166 (6.248346046114523, 4.410294345434805, 5.667764151355587, 5.771950879349882, 5.1621931258279865, 2.562432334694784, 1.9634138608103373, 1.9682284184242402, 2.835842195988133, 1.0193709990831787, 0.8037806360873045, 0.48233231747378824, 0.0, 6.887185384447996, 5.30565549221167, 4.0189031804365225, 3.058112997249536, 5.671684391976266, 2.755519785793936, 1.9634138608103373, 1.8303088104962744, 2.5810965629139933, 1.9239836264499612, 1.1335528302711175, 0.4009358495849823, 0.0), # 167 (6.059031911370395, 4.270990187122201, 5.50449991514229, 5.60230820555966, 5.012791590203827, 2.490136929448583, 1.902800984996902, 1.9129851869747332, 2.7568809702442847, 0.9887860557234682, 0.7798253500011468, 0.468093800681876, 0.0, 6.6873225235789615, 5.149031807500635, 3.8991267500057343, 2.9663581671704042, 5.513761940488569, 2.6781792617646265, 1.902800984996902, 1.7786692353204163, 2.5063957951019136, 1.867436068519887, 1.100899983028458, 0.3882718351929274, 0.0), # 168 (5.861763403606015, 4.1267185154112305, 5.333058736591924, 5.4247227165306615, 4.856022455309747, 2.413972196347072, 1.8398185458352458, 1.8547371932717271, 2.6735186754361124, 0.9568696904244344, 0.7548009035337614, 0.45319887785722274, 0.0, 6.477694532530785, 4.985187656429449, 3.774004517668807, 2.8706090712733023, 5.347037350872225, 2.596632070580418, 1.8398185458352458, 1.724265854533623, 2.4280112276548733, 1.808240905510221, 1.066611747318385, 0.3751562286737483, 0.0), # 169 (5.657325647224384, 3.978005057412684, 5.154195281828863, 5.23994581269609, 4.692569423622822, 2.334286864383604, 1.7747074283677764, 1.7937538583450197, 2.5861465706904125, 0.9237514790829147, 0.7288103272000027, 0.4377100180914133, 0.0, 6.259210710787055, 4.814810199005545, 3.6440516360000137, 2.7712544372487433, 5.172293141380825, 2.5112554016830275, 1.7747074283677764, 1.6673477602740028, 2.346284711811411, 1.7466486042320304, 1.0308390563657726, 0.36163682340115316, 0.0), # 170 (5.4465037666285, 3.82537554023735, 4.968664216977482, 5.048728894489152, 4.523116197620137, 2.2514296625515327, 1.7077085176369027, 1.7303046032244096, 2.495155915133985, 0.8895609975957474, 0.7019566515147247, 0.4216896904760322, 0.0, 6.032780357831365, 4.638586595236354, 3.509783257573624, 2.6686829927872413, 4.99031183026797, 2.4224264445141737, 1.7077085176369027, 1.6081640446796661, 2.2615580988100685, 1.6829096314963843, 0.9937328433954964, 0.3477614127488501, 0.0), # 171 (5.230082886221365, 3.6693556909960217, 4.777220208162156, 4.851823362343048, 4.348346479778769, 2.1657493198442115, 1.6390626986850327, 1.664658848939696, 2.4009379678936282, 0.8544278218597702, 0.6743429069927823, 0.4052003641026643, 0.0, 5.799312773147303, 4.457204005129307, 3.3717145349639117, 2.56328346557931, 4.8018759357872565, 2.3305223885155746, 1.6390626986850327, 1.5469637998887225, 2.1741732398893845, 1.6172744541143496, 0.9554440416324312, 0.3335777900905475, 0.0), # 172 (5.00884813040598, 3.510471236799489, 4.58061792150726, 4.649980616690982, 4.168943972575801, 2.077594565254994, 1.5690108565545748, 1.5970860165206766, 2.303883988096141, 0.8184815277718206, 0.6460721241490297, 0.3883045080628938, 0.0, 5.5597172562184625, 4.271349588691831, 3.2303606207451483, 2.4554445833154612, 4.607767976192282, 2.235920423128947, 1.5690108565545748, 1.483996118039281, 2.0844719862879004, 1.5499935388969943, 0.916123584301452, 0.31913374879995354, 0.0), # 173 (4.783584623585344, 3.349247904758541, 4.3796120231371685, 4.443952057966156, 3.9855923784883105, 1.987314127777233, 1.4977938762879377, 1.5278555269971503, 2.204385234868321, 0.7818516912287369, 0.6172473334983214, 0.37106459144830567, 0.0, 5.314903106528433, 4.081710505931362, 3.0862366674916064, 2.34555507368621, 4.408770469736642, 2.1389977377960103, 1.4977938762879377, 1.4195100912694523, 1.9927961892441552, 1.4813173526553853, 0.8759224046274336, 0.3044770822507765, 0.0), # 174 (4.555077490162455, 3.18621142198397, 4.174957179176257, 4.2344890866017755, 3.7989753999933793, 1.8952567364042834, 1.425652642927529, 1.457236801398915, 2.102832967336968, 0.7446678881273562, 0.5879715655555117, 0.35354308335048457, 0.0, 5.0657796235608075, 3.8889739168553294, 2.939857827777558, 2.234003664382068, 4.205665934673936, 2.040131521958481, 1.425652642927529, 1.3537548117173452, 1.8994876999966896, 1.411496362200592, 0.8349914358352515, 0.28965558381672457, 0.0), # 175 (4.324111854540319, 3.0218875155865668, 3.9674080557488987, 4.0223431030310435, 3.609776739568087, 1.8017711201294973, 1.3528280415157574, 1.3854992607557703, 1.9996184446288805, 0.7070596943645169, 0.558347850835455, 0.33580245286101496, 0.0, 4.813256106799174, 3.693826981471164, 2.791739254177275, 2.1211790830935504, 3.999236889257761, 1.9396989650580787, 1.3528280415157574, 1.2869793715210696, 1.8048883697840434, 1.3407810343436815, 0.7934816111497798, 0.2747170468715061, 0.0), # 176 (4.0914728411219325, 2.856801912677122, 3.7577193189794698, 3.808265507687162, 3.4186800996895155, 1.7072060079462288, 1.2795609570950313, 1.3129123260975137, 1.8951329258708567, 0.6691566858370562, 0.528479219853006, 0.3179051690714816, 0.0, 4.5582418557271245, 3.496956859786297, 2.6423960992650297, 2.0074700575111684, 3.7902658517417134, 1.838077256536519, 1.2795609570950313, 1.2194328628187348, 1.7093400498447577, 1.269421835895721, 0.751543863795894, 0.25970926478882933, 0.0), # 177 (3.8579455743102966, 2.6914803403664256, 3.5466456349923448, 3.593007701003337, 3.226369182834742, 1.6119101288478317, 1.2060922747077587, 1.239745418453944, 1.7897676701896952, 0.6310884384418126, 0.49846870312301883, 0.299913701073469, 0.0, 4.301646169828252, 3.299050711808158, 2.4923435156150937, 1.8932653153254375, 3.5795353403793904, 1.7356435858355217, 1.2060922747077587, 1.1513643777484512, 1.613184591417371, 1.1976692336677792, 0.7093291269984691, 0.24468003094240237, 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), # 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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), # 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153 (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), # 154 (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), # 155 (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), # 156 (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 68, # 1 )

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