xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

d7093ac2b6ac97131c820a26750b835e609e90d2

156

py

Python

core/co_security.py

ykiveish/mks_dashnoard

5a989200d61c43ae6c414edd712c5763b56e7c27

[ "Apache-2.0" ]

null

core/co_security.py

ykiveish/mks_dashnoard

5a989200d61c43ae6c414edd712c5763b56e7c27

[ "Apache-2.0" ]

null

core/co_security.py

ykiveish/mks_dashnoard

5a989200d61c43ae6c414edd712c5763b56e7c27

[ "Apache-2.0" ]

null

import hashlib class Hashes(): def __init__(self): pass def GetHashMd5(self, data): md5Obj = hashlib.md5(data.encode()) return md5Obj.hexdigest()

17.333333

37

0.711538

20

156

5.35

0.75

0

0.030534

0.160256

156

9

38

17.333333

0.78626

0

1

0.285714

false

0.142857

0

0.714286

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d7275ed5f3ac56b8a4d3f3b714e11d36b780b35d

24,710

py

Python

CofDMansionBuilder.py

MissH0pe/VtR-AI-ST

41600cb283a50c5d4c4813bf171d4c2ccbfb9636

[ "MIT" ]

null

CofDMansionBuilder.py

MissH0pe/VtR-AI-ST

41600cb283a50c5d4c4813bf171d4c2ccbfb9636

[ "MIT" ]

null

CofDMansionBuilder.py

MissH0pe/VtR-AI-ST

41600cb283a50c5d4c4813bf171d4c2ccbfb9636

[ "MIT" ]

null

from PIL import Image, ImageDraw, ImageFont import random #hallway is 2*img.size[1]/24 #doorway is 2*img.size[1]/24 #doors are 350 px each way #upperroom text height n1o1d4/2 #middleroom text height (n29o1d48-(n1o1d4+2*n1o1d24))/2+(n1o1d4+2*n1o1d24) #lowerroom text height (n15o1d16-(n29o1d48+2*n1o1d24))/2+(n29o1d48+2*n1o1d24) img = Image.new('RGB', (3072, 2048), color = (255, 255, 255)) draw = ImageDraw.Draw(img) font = ImageFont.truetype("arial.ttf", 50) lefthall = (img.size[0]/2)-(img.size[1]/24) righthall = (img.size[0]/2)+(img.size[1]/24) n11o1d16 = 11*img.size[1]/16 n15o1d16 = 15*img.size[1]/16 n3o0d8 = 3*img.size[0]/8 n5o0d8 = 5*img.size[0]/8 n1o1d4 = img.size[1]/4 n1p4o0d48 = 1.4*(img.size[0]/48) n3o0d48 = 3*img.size[0]/48 n5o0d48 = 5*img.size[0]/48 n11o0d48 = 11*img.size[0]/48 n13o0d48 = 13*img.size[0]/48 n19o0d48 = 19*img.size[0]/48 n21o0d48 = 21*img.size[0]/48 n27o0d48 = 27*img.size[0]/48 n29o0d48 = 29*img.size[0]/48 n35o0d48 = 35*img.size[0]/48 n37o0d48 = 37*img.size[0]/48 n43o0d48 = 43*img.size[0]/48 n45o0d48 = 45*img.size[0]/48 n29o1d48 = 29*img.size[1]/48 n1o1d3 = img.size[1]/3 n1o1d24 = img.size[1]/24 middleroomtextheight = (n29o1d48-(n1o1d4+2*n1o1d24))/2+(n1o1d4+2*n1o1d24) lowerroomtextheight = (n15o1d16-(n29o1d48+2*n1o1d24))/2+(n29o1d48+2*n1o1d24) mb = "ur2" #can be ur1 ur2 ur3 ur4 ur5 ur6 lr1 lr2 lr3 lr4 cr = "ur3" #can be ur1 ur2 ur3 ur4 ur5 ur6 mr1 mr2 mr3 mr4 mr5 mr6 lr1 lr2 lr3 lr4 darray = [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True] warray = [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True] numur = [random.randint(0,3), random.randint(0,3), random.randint(0,3), random.randint(0,3), random.randint(0,3), random.randint(0,3)] nummr = [random.randint(0,3), random.randint(0,3), random.randint(0,3), random.randint(0,3), random.randint(0,3), random.randint(0,3)] numlr = [random.randint(0,3), random.randint(0,3), random.randint(0,3), random.randint(0,3)] #beginning of exterior draw.line((0, 0, img.size[0], 0), fill=256, width=25) draw.line((0, 0, 0, n15o1d16), fill=256, width=25) draw.line((img.size[0], 0, img.size[0], n15o1d16), fill=256, width=25) draw.line((0, n15o1d16, n3o0d8, n15o1d16), fill=256, width=13) draw.line((n5o0d8, n15o1d16, img.size[0], n15o1d16), fill=256, width=13) draw.line((n3o0d8, n15o1d16, n3o0d8, img.size[1]), fill=256, width=13) draw.line((n5o0d8, n15o1d16, n5o0d8, img.size[1]), fill=256, width=13) #beginning of front door draw.line((n3o0d8, img.size[1], 15*img.size[0]/32, img.size[1]), fill=256, width=25) draw.line((17*img.size[0]/32, img.size[1], n5o0d8, img.size[1]), fill=256, width=25) #end of front door #end of exterior #beginning of entryway draw.line((n3o0d8, n11o1d16, n3o0d8, img.size[1]), fill=256, width=13) draw.line((n5o0d8, n11o1d16, n5o0d8, img.size[1]), fill=256, width=13) #entryway doorway draw.line((n3o0d8, n11o1d16, 15*img.size[0]/32, n11o1d16), fill=256, width=13) draw.line((17*img.size[0]/32, n11o1d16, n5o0d8, n11o1d16), fill=256, width=13) #foyer draw.text((img.size[0]/2-65, 27*img.size[1]/32), "Foyer", fill=256, font=font) #end of entryway #beginning of upper set of rooms draw.line((0, n1o1d4, n3o0d48, n1o1d4), fill=256, width=13) draw.line((n5o0d48, n1o1d4, img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((img.size[0]/6, 0, img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((img.size[0]/6, n1o1d4, n11o0d48, n1o1d4), fill=256, width=13) draw.line((n13o0d48, n1o1d4, 2*img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((2*img.size[0]/6, 0, 2*img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((2*img.size[0]/6, n1o1d4, n19o0d48, n1o1d4), fill=256, width=13) draw.line((n21o0d48, n1o1d4, 3*img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((3*img.size[0]/6, 0, 3*img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((3*img.size[0]/6, n1o1d4, n27o0d48, n1o1d4), fill=256, width=13) draw.line((n29o0d48, n1o1d4, 4*img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((4*img.size[0]/6, 0, 4*img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((4*img.size[0]/6, n1o1d4, n35o0d48, n1o1d4), fill=256, width=13) draw.line((n37o0d48, n1o1d4, 5*img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((5*img.size[0]/6, 0, 5*img.size[0]/6, n1o1d4), fill=256, width=13) draw.line((5*img.size[0]/6, n1o1d4, n43o0d48, n1o1d4), fill=256, width=13) draw.line((n45o0d48, n1o1d4, img.size[0], n1o1d4), fill=256, width=13) #beginning of doors1-6 if darray[0]: draw.line((n3o0d48, n1o1d4, n5o0d48, n1o1d4+n1p4o0d48), fill=256, width=13) if darray[1]: draw.line((n11o0d48, n1o1d4, n13o0d48, n1o1d4+n1p4o0d48), fill=256, width=13) if darray[2]: draw.line((n19o0d48, n1o1d4, n21o0d48, n1o1d4+n1p4o0d48), fill=256, width=13) if darray[3]: draw.line((n27o0d48, n1o1d4, n29o0d48, n1o1d4+n1p4o0d48), fill=256, width=13) if darray[4]: draw.line((n35o0d48, n1o1d4, n37o0d48, n1o1d4+n1p4o0d48), fill=256, width=13) if darray[5]: draw.line((n43o0d48, n1o1d4, n45o0d48, n1o1d4+n1p4o0d48), fill=256, width=13) #end of doors1-6 #beginning of walls1-6 if warray[0]: draw.line((n3o0d48, n1o1d4, n5o0d48, n1o1d4), fill=256, width=13) if warray[1]: draw.line((n11o0d48, n1o1d4, n13o0d48, n1o1d4), fill=256, width=13) if warray[2]: draw.line((n19o0d48, n1o1d4, n21o0d48, n1o1d4), fill=256, width=13) if warray[3]: draw.line((n27o0d48, n1o1d4, n29o0d48, n1o1d4), fill=256, width=13) if warray[4]: draw.line((n35o0d48, n1o1d4, n37o0d48, n1o1d4), fill=256, width=13) if warray[5]: draw.line((n43o0d48, n1o1d4, n45o0d48, n1o1d4), fill=256, width=13) #end of walls1-6 #r1 if mb == "ur1": draw.text((75, n1o1d4/2), "Master Bedroom", fill=256, font=font) elif cr == "ur1": draw.text((100, n1o1d4/2), "Common Room", fill=256, font=font) else: if numur[0] == 0: draw.text((150, n1o1d4/2), "Bedroom", fill=256, font=font) elif numur[0] == 1: draw.text((130, n1o1d4/2), "Coffinroom", fill=256, font=font) elif numur[0] == 2: draw.text((100, n1o1d4/2), "Common Room", fill=256, font=font) elif numur[0] ==3: draw.text((140, n1o1d4/2), "Washroom", fill=256, font=font) #r2 if mb == "ur2": draw.text((75+img.size[0]/6, n1o1d4/2), "Master Bedroom", fill=256, font=font) elif cr == "ur2": draw.text((100+img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) else: if numur[1] == 0: draw.text((150+img.size[0]/6, n1o1d4/2), "Bedroom", fill=256, font=font) elif numur[1] == 1: draw.text((130+img.size[0]/6, n1o1d4/2), "Coffinroom", fill=256, font=font) elif numur[1] == 2: draw.text((100+img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) elif numur[1] ==3: draw.text((140+img.size[0]/6, n1o1d4/2), "Washroom", fill=256, font=font) #r3 if mb == "ur3": draw.text((75+2*img.size[0]/6, n1o1d4/2), "Master Bedroom", fill=256, font=font) elif cr == "ur3": draw.text((100+2*img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) else: if numur[2] == 0: draw.text((150+2*img.size[0]/6, n1o1d4/2), "Bedroom", fill=256, font=font) elif numur[2] == 1: draw.text((130+2*img.size[0]/6, n1o1d4/2), "Coffinroom", fill=256, font=font) elif numur[2] == 2: draw.text((100+2*img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) elif numur[2] == 3: draw.text((140+2*img.size[0]/6, n1o1d4/2), "Washroom", fill=256, font=font) #r4 if mb == "ur4": draw.text((75+3*img.size[0]/6, n1o1d4/2), "Master Bedroom", fill=256, font=font) elif cr == "ur4": draw.text((100+3*img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) else: if numur[3] == 0: draw.text((150+3*img.size[0]/6, n1o1d4/2), "Bedroom", fill=256, font=font) elif numur[3] == 1: draw.text((130+3*img.size[0]/6, n1o1d4/2), "Coffinroom", fill=256, font=font) elif numur[3] == 2: draw.text((100+3*img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) elif numur[3] ==3: draw.text((140+3*img.size[0]/6, n1o1d4/2), "Washroom", fill=256, font=font) #r5 if mb == "ur5": draw.text((75+4*img.size[0]/6, n1o1d4/2), "Master Bedroom", fill=256, font=font) elif cr == "ur5": draw.text((100+4*img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) else: if numur[4] == 0: draw.text((150+4*img.size[0]/6, n1o1d4/2), "Bedroom", fill=256, font=font) elif numur[4] == 1: draw.text((130+4*img.size[0]/6, n1o1d4/2), "Coffinroom", fill=256, font=font) elif numur[4] == 2: draw.text((100+4*img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) elif numur[4] ==3: draw.text((140+4*img.size[0]/6, n1o1d4/2), "Washroom", fill=256, font=font) #r6 if mb == "ur6": draw.text((75+5*img.size[0]/6, n1o1d4/2), "Master Bedroom", fill=256, font=font) elif cr == "ur6": draw.text((100+5*img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) else: if numur[5] == 0: draw.text((150+5*img.size[0]/6, n1o1d4/2), "Bedroom", fill=256, font=font) elif numur[5] == 1: draw.text((130+5*img.size[0]/6, n1o1d4/2), "Coffinroom", fill=256, font=font) elif numur[5] == 2: draw.text((100+5*img.size[0]/6, n1o1d4/2), "Common Room", fill=256, font=font) elif numur[6] ==3: draw.text((140+5*img.size[0]/6, n1o1d4/2), "Washroom", fill=256, font=font) #end of upper set of rooms #beginning of middle set of rooms it goes upper doorframe lower doorframe divider repeat draw.line((0, n1o1d3, ((lefthall)/6)-(n1o1d24), n1o1d3), fill=256, width=13) draw.line((((lefthall)/6)+(n1o1d24), n1o1d3, (lefthall)/3, n1o1d3), fill=256, width=13) draw.line((0, n29o1d48, ((lefthall)/6)-(n1o1d24), n29o1d48), fill=256, width=13) draw.line((((lefthall)/6)+(n1o1d24), n29o1d48, (lefthall)/3, n29o1d48), fill=256, width=13) draw.line(((lefthall)/3, n1o1d3, (lefthall)/3, n29o1d48), fill=256, width=13) draw.line((lefthall/3, n1o1d3, (lefthall/2)-(n1o1d24), n1o1d3), fill=256, width=13) draw.line((((lefthall)/2)+(n1o1d24), n1o1d3, 2*(lefthall)/3, n1o1d3), fill=256, width=13) draw.line((lefthall/3, n29o1d48, (lefthall/2)-(n1o1d24), n29o1d48), fill=256, width=13) draw.line((((lefthall)/2)+(n1o1d24), n29o1d48, 2*(lefthall)/3, n29o1d48), fill=256, width=13) draw.line((2*(lefthall)/3, n1o1d3, 2*(lefthall)/3, n29o1d48), fill=256, width=13) draw.line((2*lefthall/3, n1o1d3, (5*lefthall/6)-(n1o1d24), n1o1d3), fill=256, width=13) draw.line(((5*(lefthall)/6)+(n1o1d24), n1o1d3, (lefthall), n1o1d3), fill=256, width=13) draw.line((2*lefthall/3, n29o1d48, (5*lefthall/6)-(n1o1d24), n29o1d48), fill=256, width=13) draw.line(((5*(lefthall)/6)+(n1o1d24), n29o1d48, (lefthall), n29o1d48), fill=256, width=13) draw.line((righthall, n1o1d3, ((lefthall)/6)-(n1o1d24)+(righthall), n1o1d3), fill=256, width=13) draw.line((((lefthall)/6)+(n1o1d24)+(righthall), n1o1d3, (lefthall/3)+(righthall), n1o1d3), fill=256, width=13) draw.line((righthall, n29o1d48, ((lefthall)/6)-(n1o1d24)+(righthall), n29o1d48), fill=256, width=13) draw.line((((lefthall)/6)+(n1o1d24)+(righthall), n29o1d48, (lefthall/3)+(righthall), n29o1d48), fill=256, width=13) draw.line((((lefthall)/3)+(righthall), n1o1d3, ((lefthall)/3)+(righthall), n29o1d48), fill=256, width=13) draw.line((lefthall/3+(righthall), n1o1d3, (lefthall/2)-(n1o1d24)+(righthall), n1o1d3), fill=256, width=13) draw.line((((lefthall)/2)+(n1o1d24)+(righthall), n1o1d3, 2*(lefthall)/3+(righthall), n1o1d3), fill=256, width=13) draw.line((lefthall/3+(righthall), n29o1d48, (lefthall/2)-(n1o1d24)+(righthall), n29o1d48), fill=256, width=13) draw.line((((lefthall)/2)+(n1o1d24)+(righthall), n29o1d48, 2*(lefthall)/3+(righthall), n29o1d48), fill=256, width=13) draw.line(((2*(lefthall)/3)+(righthall), n1o1d3, (2*(lefthall)/3)+(righthall), n29o1d48), fill=256, width=13) draw.line((2*lefthall/3+(righthall), n1o1d3, (5*lefthall/6)-(n1o1d24)+(righthall), n1o1d3), fill=256, width=13) draw.line(((5*(lefthall)/6)+(n1o1d24)+(righthall), n1o1d3, (lefthall)+(righthall), n1o1d3), fill=256, width=13) draw.line((2*lefthall/3+(righthall), n29o1d48, (5*lefthall/6)-(n1o1d24)+(righthall), n29o1d48), fill=256, width=13) draw.line(((5*(lefthall)/6)+(n1o1d24)+(righthall), n29o1d48, (lefthall)+(righthall), n29o1d48), fill=256, width=13) #beginning of upper doors1-6 if darray[6]: draw.line((((lefthall)/6)-(n1o1d24), n1o1d3, ((lefthall)/6)+(n1o1d24), n1o1d3-n1p4o0d48), fill=256, width=13) if darray[7]: draw.line(((lefthall/2)-(n1o1d24), n1o1d3, (lefthall/2)+(n1o1d24), n1o1d3-n1p4o0d48), fill=256, width=13) if darray[8]: draw.line(((5*lefthall/6)-(n1o1d24), n1o1d3, (5*lefthall/6)+(n1o1d24), n1o1d3-n1p4o0d48), fill=256, width=13) if darray[9]: draw.line((((lefthall)/6)-(n1o1d24)+(righthall), n1o1d3, ((lefthall)/6)+(n1o1d24)+(righthall), n1o1d3-n1p4o0d48), fill=256, width=13) if darray[10]: draw.line(((lefthall/2)-(n1o1d24)+(righthall), n1o1d3, (lefthall/2)+(n1o1d24)+(righthall), n1o1d3-n1p4o0d48), fill=256, width=13) if darray[11]: draw.line(((5*lefthall/6)-(n1o1d24)+(righthall), n1o1d3, (5*lefthall/6)+(n1o1d24)+(righthall), n1o1d3-n1p4o0d48), fill=256, width=13) #end of upper doors1-6 #beginning of upper walls1-6 if warray[6]: draw.line((((lefthall)/6)-(n1o1d24), n1o1d3, ((lefthall)/6)+(n1o1d24), n1o1d3), fill=256, width=13) if warray[7]: draw.line(((lefthall/2)-(n1o1d24), n1o1d3, (lefthall/2)+(n1o1d24), n1o1d3), fill=256, width=13) if warray[8]: draw.line(((5*lefthall/6)-(n1o1d24), n1o1d3, (5*lefthall/6)+(n1o1d24), n1o1d3), fill=256, width=13) if warray[9]: draw.line((((lefthall)/6)-(n1o1d24)+(righthall), n1o1d3, ((lefthall)/6)+(n1o1d24)+(righthall), n1o1d3), fill=256, width=13) if warray[10]: draw.line(((lefthall/2)-(n1o1d24)+(righthall), n1o1d3, (lefthall/2)+(n1o1d24)+(righthall), n1o1d3), fill=256, width=13) if warray[11]: draw.line(((5*lefthall/6)-(n1o1d24)+(righthall), n1o1d3, (5*lefthall/6)+(n1o1d24)+(righthall), n1o1d3), fill=256, width=13) #end of upper walls1-6 #beginning of lower doors1-6 if darray[12]: draw.line((((lefthall)/6)-(n1o1d24), n29o1d48, ((lefthall)/6)+(n1o1d24), n29o1d48+n1p4o0d48), fill=256, width=13) if darray[13]: draw.line(((lefthall/2)-(n1o1d24), n29o1d48, (lefthall/2)+(n1o1d24), n29o1d48+n1p4o0d48), fill=256, width=13) if darray[14]: draw.line(((5*lefthall/6)-(n1o1d24), n29o1d48, (5*lefthall/6)+(n1o1d24), n29o1d48+n1p4o0d48), fill=256, width=13) if darray[15]: draw.line((((lefthall)/6)-(n1o1d24)+(righthall), n29o1d48, ((lefthall)/6)+(n1o1d24)+(righthall), n29o1d48+n1p4o0d48), fill=256, width=13) if darray[16]: draw.line(((lefthall/2)-(n1o1d24)+(righthall), n29o1d48, (lefthall/2)+(n1o1d24)+(righthall), n29o1d48+n1p4o0d48), fill=256, width=13) if darray[17]: draw.line(((5*lefthall/6)-(n1o1d24)+(righthall), n29o1d48, (5*lefthall/6)+(n1o1d24)+(righthall), n29o1d48+n1p4o0d48), fill=256, width=13) #end of lower doors1-6 #beginning of lower walls1-6 if warray[12]: draw.line((((lefthall)/6)-(n1o1d24), n29o1d48, ((lefthall)/6)+(n1o1d24), n29o1d48), fill=256, width=13) if warray[13]: draw.line(((lefthall/2)-(n1o1d24), n29o1d48, (lefthall/2)+(n1o1d24), n29o1d48), fill=256, width=13) if warray[14]: draw.line(((5*lefthall/6)-(n1o1d24), n29o1d48, (5*lefthall/6)+(n1o1d24), n29o1d48), fill=256, width=13) if warray[15]: draw.line((((lefthall)/6)-(n1o1d24)+(righthall), n29o1d48, ((lefthall)/6)+(n1o1d24)+(righthall), n29o1d48), fill=256, width=13) if warray[16]: draw.line(((lefthall/2)-(n1o1d24)+(righthall), n29o1d48, (lefthall/2)+(n1o1d24)+(righthall), n29o1d48), fill=256, width=13) if warray[17]: draw.line(((5*lefthall/6)-(n1o1d24)+(righthall), n29o1d48, (5*lefthall/6)+(n1o1d24)+(righthall), n29o1d48), fill=256, width=13) #end of lower walls1-6 #r1 if cr == "mr1": draw.text((50, middleroomtextheight), "Common Room", fill=256, font=font) else: if nummr[0] == 0: draw.text((75, middleroomtextheight), "Bedroom", fill=256, font=font) elif nummr[0] == 1: draw.text((65, middleroomtextheight), "Coffinroom", fill=256, font=font) elif nummr[0] == 2: draw.text((50, middleroomtextheight), "Common Room", fill=256, font=font) elif nummr[0] == 3: draw.text((70, middleroomtextheight), "Washroom", fill=256, font=font) #r2 if cr == "mr2": draw.text((75+img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) else: if nummr[1] == 0: draw.text((100+img.size[0]/6, middleroomtextheight), "Bedroom", fill=256, font=font) elif nummr[1] == 1: draw.text((100+img.size[0]/6, middleroomtextheight), "Coffinroom", fill=256, font=font) elif nummr[1] == 2: draw.text((75+img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) elif nummr[1] == 3: draw.text((105+img.size[0]/6, middleroomtextheight), "Washroom", fill=256, font=font) #r3 if cr == "mr3": draw.text((30+2*img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) else: if nummr[2] == 0: draw.text((80+2*img.size[0]/6, middleroomtextheight), "Bedroom", fill=256, font=font) elif nummr[2] == 1: draw.text((75+2*img.size[0]/6, middleroomtextheight), "Coffinroom", fill=256, font=font) elif nummr[2] == 2: draw.text((30+2*img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) elif nummr[2] == 3: draw.text((80+2*img.size[0]/6, middleroomtextheight), "Washroom", fill=256, font=font) #r4 if cr == "mr4": draw.text((125+3*img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) else: if nummr[3] == 0: draw.text((175+3*img.size[0]/6, middleroomtextheight), "Bedroom", fill=256, font=font) elif nummr[3] == 1: draw.text((150+3*img.size[0]/6, middleroomtextheight), "Coffinroom", fill=256, font=font) elif nummr[3] == 2: draw.text((125+3*img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) elif nummr[3] == 3: draw.text((160+3*img.size[0]/6, middleroomtextheight), "Washroom", fill=256, font=font) #r5 if cr == "mr5": draw.text((100+4*img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) else: if nummr[4] == 0: draw.text((150+4*img.size[0]/6, middleroomtextheight), "Bedroom", fill=256, font=font) elif nummr[4] == 1: draw.text((130+4*img.size[0]/6, middleroomtextheight), "Coffinroom", fill=256, font=font) elif nummr[4] == 2: draw.text((100+4*img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) elif nummr[4] == 3: draw.text((140+4*img.size[0]/6, middleroomtextheight), "Washroom", fill=256, font=font) #r6 if cr == "mr6": draw.text((100+5*img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) else: if nummr[5] == 0: draw.text((150+5*img.size[0]/6, middleroomtextheight), "Bedroom", fill=256, font=font) elif nummr[5] == 1: draw.text((130+5*img.size[0]/6, middleroomtextheight), "Coffinroom", fill=256, font=font) elif nummr[5] == 2: draw.text((100+5*img.size[0]/6, middleroomtextheight), "Common Room", fill=256, font=font) elif nummr[5] == 3: draw.text((140+5*img.size[0]/6, middleroomtextheight), "Washroom", fill=256, font=font) #end of middle set of rooms #beginning of lower set of rooms draw.line((0, n11o1d16, img.size[0]/16, n11o1d16), fill=256, width=13) draw.line((2*img.size[0]/16, n11o1d16, 3*img.size[0]/16, n11o1d16), fill=256, width=13) draw.line((3*img.size[0]/16, n11o1d16, 3*img.size[0]/16, n15o1d16), fill=256, width=13) draw.line((3*img.size[0]/16, n11o1d16, 4*img.size[0]/16, n11o1d16), fill=256, width=13) draw.line((5*img.size[0]/16, n11o1d16, n3o0d8, n11o1d16), fill=256, width=13) draw.line((10*img.size[0]/16, n11o1d16, 11*img.size[0]/16, n11o1d16), fill=256, width=13) draw.line((12*img.size[0]/16, n11o1d16, 13*img.size[0]/16, n11o1d16), fill=256, width=13) draw.line((13*img.size[0]/16, n11o1d16, 13*img.size[0]/16, n15o1d16), fill=256, width=13) draw.line((13*img.size[0]/16, n11o1d16, 14*img.size[0]/16, n11o1d16), fill=256, width=13) draw.line((15*img.size[0]/16, n11o1d16, img.size[0], n11o1d16), fill=256, width=13) #beginning of doors1-4 if darray[18]: draw.line((img.size[0]/16, n11o1d16, 2*img.size[0]/16, n11o1d16-n1p4o0d48), fill=256, width=13) if darray[19]: draw.line((4*img.size[0]/16, n11o1d16, 5*img.size[0]/16, n11o1d16-n1p4o0d48), fill=256, width=13) if darray[20]: draw.line((11*img.size[0]/16, n11o1d16, 12*img.size[0]/16, n11o1d16-n1p4o0d48), fill=256, width=13) if darray[21]: draw.line((14*img.size[0]/16, n11o1d16, 15*img.size[0]/16, n11o1d16-n1p4o0d48), fill=256, width=13) #end of doors1-4 #beginning of walls1-4 if warray[18]: draw.line((img.size[0]/16, n11o1d16, 2*img.size[0]/16, n11o1d16), fill=256, width=13) if warray[19]: draw.line((4*img.size[0]/16, n11o1d16, 5*img.size[0]/16, n11o1d16), fill=256, width=13) if warray[20]: draw.line((11*img.size[0]/16, n11o1d16, 12*img.size[0]/16, n11o1d16), fill=256, width=13) if warray[21]: draw.line((14*img.size[0]/16, n11o1d16, 15*img.size[0]/16, n11o1d16), fill=256, width=13) #end of walls1-4 #r1 if mb == "lr1": draw.text((75, lowerroomtextheight), "Master Bedroom", fill=256, font=font) elif cr == "lr1": draw.text((100, lowerroomtextheight), "Common Room", fill=256, font=font) else: if numlr[0] == 0: draw.text((150, lowerroomtextheight), "Bedroom", fill=256, font=font) elif numlr[0] == 1: draw.text((130, lowerroomtextheight), "Coffinroom", fill=256, font=font) elif numlr[0] == 2: draw.text((100, lowerroomtextheight), "Common Room", fill=256, font=font) elif numlr[0] == 3: draw.text((140, lowerroomtextheight), "Washroom", fill=256, font=font) #r2 if mb == "lr2": draw.text((125+img.size[0]/6, lowerroomtextheight), "Master Bedroom", fill=256, font=font) elif cr == "lr2": draw.text((150+img.size[0]/6, lowerroomtextheight), "Common Room", fill=256, font=font) else: if numlr[1] == 0: draw.text((200+img.size[0]/6, lowerroomtextheight), "Bedroom", fill=256, font=font) elif numlr[1] == 1: draw.text((180+img.size[0]/6, lowerroomtextheight), "Coffinroom", fill=256, font=font) elif numlr[1] == 2: draw.text((150+img.size[0]/6, lowerroomtextheight), "Common Room", fill=256, font=font) elif numlr[1] == 3: draw.text((190+img.size[0]/6, lowerroomtextheight), "Washroom", fill=256, font=font) #r3 if mb == "lr3": draw.text((4*img.size[0]/6, lowerroomtextheight), "Master Bedroom", fill=256, font=font) elif cr == "lr3": draw.text((4*img.size[0]/6, lowerroomtextheight), "Common Room", fill=256, font=font) else: if numlr[2] == 0: draw.text((50+4*img.size[0]/6, lowerroomtextheight), "Bedroom", fill=256, font=font) elif numlr[2] == 1: draw.text((30+4*img.size[0]/6, lowerroomtextheight), "Coffinroom", fill=256, font=font) elif numlr[2] == 2: draw.text((4*img.size[0]/6, lowerroomtextheight), "Common Room", fill=256, font=font) elif numlr[2] == 3: draw.text((40+4*img.size[0]/6, lowerroomtextheight), "Washroom", fill=256, font=font) #r4 if mb == "lr4": draw.text((100+5*img.size[0]/6, lowerroomtextheight), "Master Bedroom", fill=256, font=font) elif cr == "lr4": draw.text((125+5*img.size[0]/6, lowerroomtextheight), "Common Room", fill=256, font=font) else: if numlr[3] == 0: draw.text((150+5*img.size[0]/6, lowerroomtextheight), "Bedroom", fill=256, font=font) elif numlr[3] == 1: draw.text((130+5*img.size[0]/6, lowerroomtextheight), "Coffinroom", fill=256, font=font) elif numlr[3] == 2: draw.text((100+5*img.size[0]/6, lowerroomtextheight), "Common Room", fill=256, font=font) elif numlr[3] == 3: draw.text((140+5*img.size[0]/6, lowerroomtextheight), "Washroom", fill=256, font=font) #end of lower set of rooms # #beginning of vert hallway draw.line((lefthall, n1o1d3, lefthall, n29o1d48), fill=256, width=13) draw.line((righthall, n1o1d3, righthall, n29o1d48), fill=256, width=13) # #end of vert hallway img.save('layout.png')

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null

0

5

d72f3be93543f65c8d2221e772fc03c724629c77

89

py

Python

Chapter12/testunit/cap.py

carrenolg/python-code

f4bbdb2897c740217dda0a0aa2ebb97abb822e03

[ "Apache-2.0" ]

null

Chapter12/testunit/cap.py

carrenolg/python-code

f4bbdb2897c740217dda0a0aa2ebb97abb822e03

[ "Apache-2.0" ]

null

Chapter12/testunit/cap.py

carrenolg/python-code

f4bbdb2897c740217dda0a0aa2ebb97abb822e03

[ "Apache-2.0" ]

null

# module def just_do_it(text): from string import capwords return capwords(text)

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0.730337

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1

0

1

0

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5

d759d2360be47dabd6dd431b72062f4092950c98

148

py

Python

rio_tiler/io/__init__.py

wouellette/rio-tiler

bea9f7770f06fe8719c176b7379399d617672242

[ "BSD-3-Clause" ]

null

rio_tiler/io/__init__.py

wouellette/rio-tiler

bea9f7770f06fe8719c176b7379399d617672242

[ "BSD-3-Clause" ]

null

rio_tiler/io/__init__.py

wouellette/rio-tiler

bea9f7770f06fe8719c176b7379399d617672242

[ "BSD-3-Clause" ]

null

"""rio-tiler.io""" from .base import BaseReader, MultiBaseReader # noqa from .cogeo import COGReader # noqa from .stac import STACReader # noqa

24.666667

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ad216972036906fb6762f22b6a9fd855b5cfc13a

25

py

Python

tests/bungee_plugins_test.py

harshavardhana/bungee-plugins

d1042b4fd3654890d67393ed65e0064bb30086f8

[ "ECL-2.0", "Apache-2.0" ]

null

tests/bungee_plugins_test.py

harshavardhana/bungee-plugins

d1042b4fd3654890d67393ed65e0064bb30086f8

[ "ECL-2.0", "Apache-2.0" ]

1

2021-03-05T13:16:30.000Z

2021-03-05T15:20:22.000Z

tests/bungee_plugins_test.py

harshavardhana/bungee-plugins

d1042b4fd3654890d67393ed65e0064bb30086f8

[ "ECL-2.0", "Apache-2.0" ]

null

## WRITE TEST CASES HERE

12.5

24

0.72

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4.5

1

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25

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5

ad226dc4c514a0febe71db200c673560ffd1f8bf

156

py

Python

graphgallery/nn/layers/tensorflow/__init__.py

EdisonLeeeee/GraphGallery

4eec9c5136bda14809bd22584b26cc346cdb633b

[ "MIT" ]

300

2020-08-09T04:27:41.000Z

2022-03-30T07:43:41.000Z

graphgallery/nn/layers/tensorflow/__init__.py

EdisonLeeeee/GraphGallery

4eec9c5136bda14809bd22584b26cc346cdb633b

[ "MIT" ]

5

2020-11-05T06:16:50.000Z

2021-12-11T05:05:22.000Z

graphgallery/nn/layers/tensorflow/__init__.py

EdisonLeeeee/GraphGallery

4eec9c5136bda14809bd22584b26cc346cdb633b

[ "MIT" ]

51

2020-09-23T15:37:12.000Z

2022-03-05T01:28:56.000Z

from .conv import * from .top_k import Top_k_features from .dropout import * from .misc import SparseConversion, Scale, Sample, Gather, Laplacian, Mask

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0

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5

ad5a93f6f68fd9a58e65b43713c532cc8dbe4382

914

py

Python

tests/test_news_list.py

brianwachira/watchtower

154deea7f007e4aa55db7a53d44c1db0ac2e32c0

[ "MIT" ]

null

tests/test_news_list.py

brianwachira/watchtower

154deea7f007e4aa55db7a53d44c1db0ac2e32c0

[ "MIT" ]

null

tests/test_news_list.py

brianwachira/watchtower

154deea7f007e4aa55db7a53d44c1db0ac2e32c0

[ "MIT" ]

null

import unittest from app.models import News_List class News_ListTest(unittest.TestCase): ''' Test Class to test behaviour of News_list class ''' def setUp(self): ''' Set up method that runs before every Test ''' self.new_news_list = News_List('branham','A Week Later','loerm ipsum ipsum lorem','http://www.abc.com','http://www.bca.com','kmart','lorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsumlorem ipsum ipsum ipsum ipsum ipsum') def test_instance(self): self.assertTrue(isinstance(self.new_news_list,News_List))

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null

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ad6449918a08b114d365e829574c8b5000866c9a

97

py

Python

src/pyquickhelper/sphinxext/sphinximages/sphinxtrib/__init__.py

Pandinosaurus/pyquickhelper

326276f656cf88989e4d0fcd006ada0d3735bd9e

[ "MIT" ]

18

2015-11-10T08:09:23.000Z

2022-02-16T11:46:45.000Z

src/pyquickhelper/sphinxext/sphinximages/sphinxtrib/__init__.py

Pandinosaurus/pyquickhelper

326276f656cf88989e4d0fcd006ada0d3735bd9e

[ "MIT" ]

321

2015-06-14T21:34:28.000Z

2021-11-28T17:10:03.000Z

src/pyquickhelper/sphinxext/sphinximages/sphinxtrib/__init__.py

Pandinosaurus/pyquickhelper

326276f656cf88989e4d0fcd006ada0d3735bd9e

[ "MIT" ]

10

2015-06-20T01:35:00.000Z

2022-01-19T15:54:32.000Z

""" @file @brief Shortcuts to images. """ from .images import ImageDirective, setup, image_node

13.857143

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true

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ad7144128087936c2a124d18952c1fe69cb6578e

1,679

py

Python

tests/integration/test_warcraft_client_mythic_keystone_dungeon.py

tehmufifnman/BattleMuffin-Python

f0bb5ee7024624191b33441aeecf3fb29570abe7

[ "MIT" ]

7

2020-05-15T18:09:23.000Z

2021-03-08T16:10:37.000Z

tests/integration/test_warcraft_client_mythic_keystone_dungeon.py

tehmufifnman/BattleMuffin-Python

f0bb5ee7024624191b33441aeecf3fb29570abe7

[ "MIT" ]

2

2020-04-20T04:42:37.000Z

2020-10-28T23:27:07.000Z

tests/integration/test_warcraft_client_mythic_keystone_dungeon.py

tehmufifnman/BattleMuffin-Python

f0bb5ee7024624191b33441aeecf3fb29570abe7

[ "MIT" ]

2

2020-05-18T06:58:53.000Z

2021-03-08T16:10:27.000Z

import os from battlemuffin.clients.warcraft_client import WarcraftClient from battlemuffin.config.region_config import Locale, Region def test_get_mythic_keystone_dungeons_index(snapshot): client = WarcraftClient( os.getenv("CLIENT_ID"), os.getenv("CLIENT_SECRET"), Region.us, Locale.en_US ) response = client.get_mythic_keystone_dungeons_index() assert response == snapshot def test_get_mythic_keystone_dungeon(snapshot): client = WarcraftClient( os.getenv("CLIENT_ID"), os.getenv("CLIENT_SECRET"), Region.us, Locale.en_US ) response = client.get_mythic_keystone_dungeon(353) assert response == snapshot def test_get_mythic_keystone_index(snapshot): client = WarcraftClient( os.getenv("CLIENT_ID"), os.getenv("CLIENT_SECRET"), Region.us, Locale.en_US ) response = client.get_mythic_keystone_index() assert response == snapshot def test_get_mythic_keystone_period(snapshot): client = WarcraftClient( os.getenv("CLIENT_ID"), os.getenv("CLIENT_SECRET"), Region.us, Locale.en_US ) response = client.get_mythic_keystone_period(641) assert response == snapshot def test_get_mythic_keystone_seasons_index(snapshot): client = WarcraftClient( os.getenv("CLIENT_ID"), os.getenv("CLIENT_SECRET"), Region.us, Locale.en_US ) response = client.get_mythic_keystone_seasons_index() assert response == snapshot def test_get_mythic_keystone_season(snapshot): client = WarcraftClient( os.getenv("CLIENT_ID"), os.getenv("CLIENT_SECRET"), Region.us, Locale.en_US ) response = client.get_mythic_keystone_season(1) assert response == snapshot

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5

ad8e9c0f9a975bd30471a44d0ceb64110534ac21

204

py

Python

philoseismos/processing/__init__.py

iod-ine/philoseismos

79240d11cf82c3552c4a49d4e19a79b003fa9929

[ "MIT" ]

3

2020-09-28T17:57:29.000Z

2021-08-18T03:54:46.000Z

philoseismos/processing/__init__.py

iod-ine/philoseismos

79240d11cf82c3552c4a49d4e19a79b003fa9929

[ "MIT" ]

1

2021-04-12T15:18:28.000Z

philoseismos/processing/__init__.py

iod-ine/philoseismos

79240d11cf82c3552c4a49d4e19a79b003fa9929

[ "MIT" ]

1

2020-05-12T07:00:59.000Z

""" philoseismos: engineering seismologist's toolbox. author: Ivan Dubrovin e-mail: io.dubrovin@icloud.com """ from philoseismos.processing.spectra import average_spectrum_of_dm, dispersion_image_of_dm

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null

0

1

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1

0

1

0

5

d102ba7748c73bbfe7b7013c0ba7885ffdd70f5c

12,958

py

Python

gym_minigrid/envs/customs.py

utnnproject/gym-minigrid

9e8f9c12964dd36a3c940783d510be525a17e5a8

[ "Apache-2.0" ]

null

gym_minigrid/envs/customs.py

utnnproject/gym-minigrid

9e8f9c12964dd36a3c940783d510be525a17e5a8

[ "Apache-2.0" ]

null

gym_minigrid/envs/customs.py

utnnproject/gym-minigrid

9e8f9c12964dd36a3c940783d510be525a17e5a8

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from gym_minigrid.minigrid import * from gym_minigrid.register import register import random class SimpleCorridor(MiniGridEnv): def __init__(self, size=19, agent_pos=None, goal_pos=None, obstacle_type=None): self.obstacle_type = obstacle_type self._agent_default_pos = agent_pos self.goal_type = 0 super().__init__(grid_size=size, max_steps=100) def _gen_grid(self, width, height): # Create the grid self.grid = Grid(width, height) middle_height = height // 2 middle_widht = width // 2 self._agent_default_pos = (middle_height, middle_widht - 1 + random.randint(0, 1)) # random goal type goalType = (self.goal_type + 1) % 4 self.goal_type = goalType if (goalType == 0): goal_pos = (4 + random.randint(0, 1), 1) elif (goalType == 1): goal_pos = (width - 5 + random.randint(0, 1), 1) elif (goalType == 2): goal_pos = (4 + random.randint(0, 1), height-2) elif (goalType == 3): goal_pos = (width - 5 + random.randint(0, 1), height-2) self._goal_default_pos = goal_pos # Generate the surrounding walls self.grid.horz_wall(3, 0, 4) self.grid.horz_wall(3, height - 1, 4) self.grid.vert_wall(middle_height - 2, 0, middle_height - 2) self.grid.vert_wall(middle_height - 2, middle_height + 3, middle_height - 2) self.grid.horz_wall(middle_height - 2, middle_height - 2, middle_height - 4) self.grid.horz_wall(middle_height - 2, middle_height + 2, middle_height - 4) self.grid.vert_wall(middle_height + 2, 0, middle_height - 2) self.grid.vert_wall(middle_height + 2, middle_height + 3, middle_height - 2) self.grid.horz_wall(middle_height + 3, 0, 3) self.grid.horz_wall(middle_height + 3, height - 1, 3) self.grid.vert_wall(3, 0, height) self.grid.vert_wall(width - 4, 0, height) # Randomize the player start position and orientation if self._agent_default_pos is not None: self.agent_pos = self._agent_default_pos self.grid.set(*self._agent_default_pos, None) self.agent_dir = self._rand_int(0, 4) # assuming random start direction else: self.place_agent() if self._goal_default_pos is not None: goal = Goal() self.put_obj(goal, *self._goal_default_pos) goal.init_pos, goal.cur_pos = self._goal_default_pos else: self.place_obj(Goal()) self.mission = ( "Get to the green goal square" ) def step(self, action): obs, reward, done, info = MiniGridEnv.step(self, action) return obs, reward, done, info class SimpleCorridor16x16(MiniGridEnv): def __init__(self, agent_pos=None, goal_pos=None, obstacle_type=None, numObjs=4): self.obstacle_type = obstacle_type self._agent_default_pos = agent_pos self.numObjs = numObjs super().__init__(grid_size=16, max_steps=100, agent_view_size=7) def _gen_grid(self, width, height): # Create the grid self.grid = Grid(width, height) self._agent_default_pos = (6 + self._rand_int(0, 8), 7 + self._rand_int(0, 2)) # random goal type goalType = self._rand_int(1, 4) self.goal_type = goalType if (goalType == 1): goal_pos = (1,7 + self._rand_int(0, 2)) elif (goalType == 2): goal_pos = (13 + self._rand_int(0, 2), 1 ) elif (goalType == 3): goal_pos = (13 + self._rand_int(0, 2),14) self._goal_default_pos = goal_pos self._goal_default_pos = goal_pos # Generate the surrounding walls self.grid.horz_wall(0, 6, 12) self.grid.horz_wall(0, 9, 12) self.grid.vert_wall(0, 6, 3) self.grid.vert_wall(12, 0, 7) self.grid.vert_wall(12, 9, 6) self.grid.horz_wall(12, 0, 3) self.grid.horz_wall(12, 15, 3) self.grid.vert_wall(15, 0, 16) # Randomize the player start position and orientation if self._agent_default_pos is not None: self.agent_pos = self._agent_default_pos self.grid.set(*self._agent_default_pos, None) self.agent_dir = self._rand_int(0, 4) # assuming random start direction else: self.place_agent() if self._goal_default_pos is not None: goal = Goal() self.put_obj(goal, *self._goal_default_pos) goal.init_pos, goal.cur_pos = self._goal_default_pos else: self.place_obj(Goal()) self.mission = ( "Get to the green goal square" ) def step(self, action): obs, reward, done, info = MiniGridEnv.step(self, action) return obs, reward, done, info class SimpleCorridor18x18(MiniGridEnv): def __init__(self, agent_pos=None, goal_pos=None, obstacle_type=None, numObjs=4): self.obstacle_type = obstacle_type self._agent_default_pos = agent_pos self.numObjs = numObjs self.goal_type = 0 super().__init__(grid_size=18, max_steps=100, agent_view_size=7) def _gen_grid(self, width, height): # Create the grid self.grid = Grid(width, height) agent_pos = (6 + random.randint(0, 7), 8 + random.randint(0, 2)) # random goal type goalType = (self.goal_type + 1) % 3 self.goal_type = goalType if (goalType == 0): goal_pos = (1, 8 + self._rand_int(0, 2)) elif (goalType == 1): goal_pos = (13 + self._rand_int(0, 2), 1 ) elif (goalType == 2): goal_pos = (13 + self._rand_int(0, 2),16) self._agent_default_pos = agent_pos self._goal_default_pos = goal_pos # Generate the surrounding walls self.grid.horz_wall(0, 7, 12) self.grid.horz_wall(0, 10, 12) self.grid.vert_wall(0, 7, 3) self.grid.vert_wall(12, 0, 8) self.grid.vert_wall(12, 10, 8) self.grid.horz_wall(12, 0, 3) self.grid.horz_wall(12, 17, 3) self.grid.vert_wall(15, 0, 18) # Randomize the player start position and orientation if self._agent_default_pos is not None: self.agent_pos = self._agent_default_pos self.grid.set(*self._agent_default_pos, None) self.agent_dir = self._rand_int(0, 4) # assuming random start direction else: self.place_agent() if self._goal_default_pos is not None: goal = Goal() self.put_obj(goal, *self._goal_default_pos) goal.init_pos, goal.cur_pos = self._goal_default_pos else: self.place_obj(Goal()) self.mission = ( "Get to the green goal square" ) def step(self, action): obs, reward, done, info = MiniGridEnv.step(self, action) return obs, reward, done, info class SimpleCorridor23x23(SimpleCorridor): def __init__(self, **kwargs): super().__init__(size=23, **kwargs) class LineCorridor(MiniGridEnv): def __init__(self, size=22, agent_pos=None, goal_pos=None, obstacle_type=None): self.obstacle_type = obstacle_type self._agent_default_pos = agent_pos self.goal_type = 0 super().__init__(grid_size=size, max_steps=100) def _gen_grid(self, width, height): # Create the grid self.grid = Grid(width, height) middle_height = height // 2 middle_widht = width // 2 self._agent_default_pos = (middle_height, middle_widht - 1 + random.randint(0, 1)) # random goal type goalType = (self.goal_type + 1) % 2 self.goal_type = goalType if (goalType == 0): goal_pos = (1, middle_height - 1 + random.randint(0, 2)) elif (goalType == 1): goal_pos = (width - 2, middle_height - 1 + random.randint(0, 2)) self._goal_default_pos = goal_pos # Generate the surrounding walls self.grid.horz_wall(0, middle_height - 2, width) self.grid.horz_wall(0, middle_height + 2, width) self.grid.vert_wall(0, middle_height - 2, 4) self.grid.vert_wall(height - 1, middle_height - 2, 4) # Randomize the player start position and orientation if self._agent_default_pos is not None: self.agent_pos = self._agent_default_pos self.grid.set(*self._agent_default_pos, None) self.agent_dir = self._rand_int(0, 4) # assuming random start direction else: self.place_agent() if self._goal_default_pos is not None: goal = Goal() self.put_obj(goal, *self._goal_default_pos) goal.init_pos, goal.cur_pos = self._goal_default_pos else: self.place_obj(Goal()) self.mission = ( "Get to the green goal square" ) def step(self, action): obs, reward, done, info = MiniGridEnv.step(self, action) return obs, reward, done, info class LineCorridor19x19(LineCorridor): def __init__(self, **kwargs): super().__init__(size=19, **kwargs) class LineCorridor25x25(LineCorridor): def __init__(self, **kwargs): super().__init__(size=25, **kwargs) class LineCorridor28x28(LineCorridor): def __init__(self, **kwargs): super().__init__(size=28, **kwargs) class OneLineCorridor(MiniGridEnv): def __init__(self, size=22, agent_pos=None, goal_pos=None, obstacle_type=None): self.obstacle_type = obstacle_type self._agent_default_pos = agent_pos self.goal_type = 0 super().__init__(grid_size=size, max_steps=100) def _gen_grid(self, width, height): # Create the grid self.grid = Grid(width, height) middle_height = height // 2 middle_widht = width // 2 self._agent_default_pos = (middle_height, middle_widht) # random goal type goalType = (self.goal_type + 1) % 2 self.goal_type = goalType if (goalType == 0): goal_pos = (1, middle_height) elif (goalType == 1): goal_pos = (width - 2, middle_height) self._goal_default_pos = goal_pos # Generate the surrounding walls self.grid.horz_wall(0, middle_height - 1, width) self.grid.horz_wall(0, middle_height + 1, width) self.grid.vert_wall(0, middle_height - 1, 3) self.grid.vert_wall(height - 1, middle_height - 1, 3) # Randomize the player start position and orientation if self._agent_default_pos is not None: self.agent_pos = self._agent_default_pos self.grid.set(*self._agent_default_pos, None) self.agent_dir = self._rand_int(0, 4) # assuming random start direction else: self.place_agent() if self._goal_default_pos is not None: goal = Goal() self.put_obj(goal, *self._goal_default_pos) goal.init_pos, goal.cur_pos = self._goal_default_pos else: self.place_obj(Goal()) self.mission = ( "Get to the green goal square" ) def step(self, action): obs, reward, done, info = MiniGridEnv.step(self, action) return obs, reward, done, info class OneLineCorridor25x25(OneLineCorridor): def __init__(self, **kwargs): super().__init__(size=25, **kwargs) register( id='MiniGrid-Customs-SimpleCorridor-v0', entry_point='gym_minigrid.envs:SimpleCorridor' ) register( id='MiniGrid-Customs-SimpleCorridor16x16-v0', entry_point='gym_minigrid.envs:SimpleCorridor16x16' ) register( id='MiniGrid-Customs-SimpleCorridor18x18-v0', entry_point='gym_minigrid.envs:SimpleCorridor18x18' ) register( id='MiniGrid-Customs-LineCorridor-v0', entry_point='gym_minigrid.envs:LineCorridor' ) register( id='MiniGrid-Customs-LineCorridor19x19-v0', entry_point='gym_minigrid.envs:LineCorridor19x19' ) register( id='MiniGrid-Customs-LineCorridor25x25-v0', entry_point='gym_minigrid.envs:LineCorridor25x25' ) register( id='MiniGrid-Customs-LineCorridor28x28-v0', entry_point='gym_minigrid.envs:LineCorridor28x28' ) register( id='MiniGrid-Customs-OneLineCorridor-v0', entry_point='gym_minigrid.envs:OneLineCorridor' ) register( id='MiniGrid-Customs-OneLineCorridor25x25-v0', entry_point='gym_minigrid.envs:OneLineCorridor25x25' )

29.857143

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12,958

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0

1

0

1

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null

0

5

d1404470db4638c22765cbf9bfb567414200965c

751

py

Python

cases/calculation.py

minakoyang/YY_python2.7_interpreter_in_CPP

e949f4bbd27752e6dbfef0a887d9567345d512f4

[ "MIT" ]

1

2019-04-30T16:27:19.000Z

cases/calculation.py

minakoyang/YY_python2.7_interpreter_in_CPP

e949f4bbd27752e6dbfef0a887d9567345d512f4

[ "MIT" ]

null

cases/calculation.py

minakoyang/YY_python2.7_interpreter_in_CPP

e949f4bbd27752e6dbfef0a887d9567345d512f4

[ "MIT" ]

null

print -1/2 print -1/2-1/2 print -1/2+(-1/2) print 3/8.0*(-3/4) print (4/7*1.2-3.34)-2*(3/4.0) print ++++3.5-------1 print ------------1 print 2.5--------1 print 2.5+.5 print -1-------1 print -1--------1 print 15**2 print 3.6**1.0 print 2.5**3 print 2**6.0 print (-4.5)**3 print 8**(-1.0) print 18//21 print 8.7//4.5 print 8.9//3 print 3//4.5 print -5//2 print 89.8//-2 print -24.5//-4.6 print 2%3 print 9.9%5.68 print 5.6%6 print 2.5%-4 print -7%3.2 print -15%-4 print -3.5%-8.9 x = -0.5 print x**-2 print 7.3//x print 8%x print x//3 + x%9.5 x = 1/2 print x print 4**x print 4.5**x y=8 print x%y print x*y-2*y+x/6 print x**(y/3)-x%2 y=-4.5 print y**3 + x//7 print x%4.8 - y//5.6 print x//y*8 print (x%y)/(y**x) z=+++++++3 print z print ---z

11.920635

30

0.539281

209

751

1.937799

0.100478

0.133333

0.08642

0.039506

0.111111

0.061728

0

0.21519

0.158455

751

62

31

12.112903

0.425633

0

null

0

null

0.903846

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null

0

1

0

1

0

null

0

1

0

1

0

5

d152482fe386f995eeff94ed94f565300aca41a4

113

py

Python

pdover2t/misc.py

geiragustsson/PDover2t_fork_rev002

45719043fe6ce30766e8a40505e4106ab9c86765

[ "MIT" ]

null

pdover2t/misc.py

geiragustsson/PDover2t_fork_rev002

45719043fe6ce30766e8a40505e4106ab9c86765

[ "MIT" ]

null

pdover2t/misc.py

geiragustsson/PDover2t_fork_rev002

45719043fe6ce30766e8a40505e4106ab9c86765

[ "MIT" ]

null

def water_depth_press(wd: "h_l", rho_water, g=9.81) -> "p_e": p_e = rho_water * g * abs(wd) return p_e

18.833333

61

0.60177

24

113

2.5

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0.3

0

0.034483

0.230089

113

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null

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1

0

5

d16e3af186dcd49375815b8b5235fff25248fa81

61

py

Python

dagapp/__init__.py

i2mint/dagapp

d9a8071076bedd6af63d3b2c7a1086d3640c8eed

[ "Apache-2.0" ]

2

2021-11-13T00:36:36.000Z

2021-12-21T05:43:20.000Z

dagapp/__init__.py

i2mint/dagapp

d9a8071076bedd6af63d3b2c7a1086d3640c8eed

[ "Apache-2.0" ]

2

2021-07-29T16:25:31.000Z

2021-08-04T16:52:50.000Z

dagapp/__init__.py

i2mint/dagapp

d9a8071076bedd6af63d3b2c7a1086d3640c8eed

[ "Apache-2.0" ]

null

"""Making apps from DAGs""" from dagapp.base import dag_app

15.25

31

0.737705

10

61

4.4

0.9

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5

66fbf2927629ec694872c8bae0f246d185ffc6d4

151

py

Python

sponge-integration-tests/examples/core/knowledge_base_from_archive_2.py

mnpas/sponge

7190f23ae888bbef49d0fbb85157444d6ea48bcd

[ "Apache-2.0" ]

9

2017-12-16T21:48:57.000Z

2022-01-06T12:22:24.000Z

sponge-integration-tests/examples/core/knowledge_base_from_archive_2.py

mnpas/sponge

7190f23ae888bbef49d0fbb85157444d6ea48bcd

[ "Apache-2.0" ]

3

2020-12-18T11:56:46.000Z

2022-03-31T18:37:10.000Z

sponge-integration-tests/examples/core/knowledge_base_from_archive_2.py

mnpas/sponge

7190f23ae888bbef49d0fbb85157444d6ea48bcd

[ "Apache-2.0" ]

2

2019-12-29T16:08:32.000Z

2020-06-15T14:05:34.000Z

""" Sponge Knowledge Base Test - KB from an archive file """ class Action2FromArchive(Action): def onCall(self, arg): return arg.lower()

15.1

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19

151

5.315789

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0.218543

151

9

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0

null

0

1

0

1

0

5

0f217ba09a4040b1dbf7f531d045e097a17733cf

52

py

Python

fpi/example.py

omkarjc/FPI-Function-Portal-Interface-

b87623797108d2177edae154041908025a1d18d3

[ "MIT" ]

null

fpi/example.py

omkarjc/FPI-Function-Portal-Interface-

b87623797108d2177edae154041908025a1d18d3

[ "MIT" ]

null

fpi/example.py

omkarjc/FPI-Function-Portal-Interface-

b87623797108d2177edae154041908025a1d18d3

[ "MIT" ]

null

from base import spydoc print(spydoc.fpi("add;1;4"))

26

28

0.75

10

52

3.9

0.9

0

0.041667

0.076923

52

2

28

26

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0.132075

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true

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0

null

0

1

0

1

0

1

0

5

0f45a9c2f466781da32622dcffb8cfbfad9e3217

2,281

py

Python

src/report/store_db.py

joelmpiper/bill_taxonomy

9284dfae905ca8efa558b4fd93469d03cf4b8074

[ "MIT" ]

null

src/report/store_db.py

joelmpiper/bill_taxonomy

9284dfae905ca8efa558b4fd93469d03cf4b8074

[ "MIT" ]

null

src/report/store_db.py

joelmpiper/bill_taxonomy

9284dfae905ca8efa558b4fd93469d03cf4b8074

[ "MIT" ]

null

from sqlalchemy.orm import sessionmaker from sqlalchemy import create_engine from src.ingest.setup_database import US_Score from src.ingest.setup_database import NY_Score def store_us_db(dbname, bills, subject, y_prob, y_true, cfg): if (subject.split(' ')[0] == 'Bank'): subject = subject.replace('capital', 'and capital') subject = subject.replace(' ', '_') subject = subject.replace(',', '') host = cfg['dbwrite_host'] dbwrite_user = cfg['dbwrite_user'] engine = create_engine('postgres://%s@%s/%s' % (dbwrite_user, host, dbname)) # Open a session and connect to the database engine Session = sessionmaker(bind=engine) session = Session() for i, bill in enumerate(bills.iterrows()): # one_bill = US_Score(subject=subject, bill_num=bills['bill_num'][i], # actual=bool(y_true[i]), score=y_prob[i]) # session.add(one_bill) score_column = cfg['score_column'] bill_num = bills['bill_num'][i] session.query(US_Score).filter(US_Score.bill_num == bill_num, US_Score.subject == subject).update( {score_column: y_prob[i]}) session.commit() session.close() return 0 def store_ny_db(dbname, bills, subject, y_prob, cfg): if (subject.split(' ')[0] == 'Bank'): subject = subject.replace('capital', 'and capital') subject = subject.replace(' ', '_') subject = subject.replace(',', '') host = cfg['dbwrite_host'] dbwrite_user = cfg['dbwrite_user'] engine = create_engine('postgres://%s@%s/%s' % (dbwrite_user, host, dbname)) # Open a session and connect to the database engine Session = sessionmaker(bind=engine) session = Session() for i, bill in enumerate(bills.iterrows()): score_column = cfg['score_column'] # one_bill = NY_Score(subject=subject, bill_num=bills['bill_num'][i], # score=y_prob[i]) # session.add(one_bill) bill_num = bills['bill_num'][i] session.query(NY_Score).filter(NY_Score.bill_num == bill_num, NY_Score.subject == subject).update( {score_column: y_prob[i]}) session.commit() session.close() return 0

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null

0

1

0

1

0

null

0

5

0f59e1a07b6f93abbe00c43910be323e2e2374b0

146

py

Python

tsengine/orm/__init__.py

ccccxjin/TsEngine

5f8deed436eb9756be40f78a7bf52be9e910b501

[ "MIT" ]

1

2020-07-10T09:11:38.000Z

tsengine/orm/__init__.py

ccccxjin/tsengine

5f8deed436eb9756be40f78a7bf52be9e910b501

[ "MIT" ]

null

tsengine/orm/__init__.py

ccccxjin/tsengine

5f8deed436eb9756be40f78a7bf52be9e910b501

[ "MIT" ]

null

from .insert import Insert from .query import Query from .session import Session from .session_factory import SessionFactory, ScopeSessionFactory

29.2

64

0.849315

18

146

6.833333

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0.178862

0

0.116438

146

4

65

36.5

0.953488

0

1

0

true

0

1

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1

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

0f5dadb76d6b96043b999be804825395ee7ea7c2

83

py

Python

python/doit/05/argv_test.py

gangserver/py_test

869bdfa5c94c3b6a15b87e0c3de6b2cdaca821f4

[ "Apache-2.0" ]

null

python/doit/05/argv_test.py

gangserver/py_test

869bdfa5c94c3b6a15b87e0c3de6b2cdaca821f4

[ "Apache-2.0" ]

null

python/doit/05/argv_test.py

gangserver/py_test

869bdfa5c94c3b6a15b87e0c3de6b2cdaca821f4

[ "Apache-2.0" ]

null

import sys print(sys.argv) for i, name in enumerate(sys.argv): print(i, name)

13.833333

35

0.686747

15

83

3.8

0.6

0.245614

0

0.180723

83

6

36

13.833333

0.838235

0

1

0

true

0

0.25

0

0.25

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

5

7e81ecbff7dd74a6ae1dc190d157d31f8db829bf

77

py

Python

acd/interfaces.py

clld/acd

9782fdcd55ebfe7f01a65c0bcfaedb8cc42c8f67

[ "Apache-2.0" ]

1

2021-08-07T11:39:56.000Z

acd/interfaces.py

clld/acd

9782fdcd55ebfe7f01a65c0bcfaedb8cc42c8f67

[ "Apache-2.0" ]

7

2021-07-13T07:13:37.000Z

2021-08-16T17:21:06.000Z

acd/interfaces.py

clld/acd

9782fdcd55ebfe7f01a65c0bcfaedb8cc42c8f67

[ "Apache-2.0" ]

null

from zope.interface import Interface class IFormset(Interface): """"""

12.833333

36

0.701299

8

77

6.75

0.75

0

0.168831

77

5

37

15.4

0.84375

0

1

0

true

0

0.5

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7e853ae068d76a57b2820dd0c961df8252f15239

102

py

Python

transmute_core/tests/frameworks/test_flask/conftest.py

pwesthagen/transmute-core

4282d082377e522f5e60fe740d0cbe2315f76f50

[ "MIT" ]

42

2016-06-04T00:16:16.000Z

2021-06-11T02:09:31.000Z

transmute_core/tests/frameworks/test_flask/conftest.py

pwesthagen/transmute-core

4282d082377e522f5e60fe740d0cbe2315f76f50

[ "MIT" ]

55

2016-06-11T13:58:46.000Z

2021-12-21T06:29:20.000Z

transmute_core/tests/frameworks/test_flask/conftest.py

pwesthagen/transmute-core

4282d082377e522f5e60fe740d0cbe2315f76f50

[ "MIT" ]

18

2016-05-18T20:50:53.000Z

2021-11-18T09:09:59.000Z

import pytest from .example import app @pytest.fixture def test_app(): return app.test_client()

12.75

28

0.745098

15

102

4.933333

0.666667

0

0.166667

102

7

29

14.571429

0.870588

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

5

0e22ccbc75346642932ee0282360e1263f62d7d0

443

py

Python

src/genie/libs/parser/ios/show_acl.py

nujo/genieparser

083b01efc46afc32abe1a1858729578beab50cd3

[ "Apache-2.0" ]

204

2018-06-27T00:55:27.000Z

2022-03-06T21:12:18.000Z

src/genie/libs/parser/ios/show_acl.py

nujo/genieparser

083b01efc46afc32abe1a1858729578beab50cd3

[ "Apache-2.0" ]

468

2018-06-19T00:33:18.000Z

2022-03-31T23:23:35.000Z

src/genie/libs/parser/ios/show_acl.py

nujo/genieparser

083b01efc46afc32abe1a1858729578beab50cd3

[ "Apache-2.0" ]

309

2019-01-16T20:21:07.000Z

2022-03-30T12:56:41.000Z

"""show_acl.py supported commands: * show access-lists """ # import iosxe parser from genie.libs.parser.iosxe.show_acl import ShowAccessLists as ShowAccessLists_iosxe class ShowAccessLists(ShowAccessLists_iosxe): """Parser for show access-lists show ip access-lists show ip access-lists <acl> show ipv6 access-lists show ipv6 access-lists <acl>""" pass

29.533333

85

0.636569

51

443

5.45098

0.392157

0.23741

0.161871

0.122302

0.161871

0

0.006349

0.288939

443

15

86

29.533333

0.87619

0.460497

0

1

0

true

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0

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0

null

1

0

null

0

1

0

1

0

5

0e443bcb59753efbf64bab29153147533fca39e3

96

py

Python

venv/lib/python3.8/site-packages/numpy/core/_type_aliases.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/core/_type_aliases.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/core/_type_aliases.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/e9/f4/3a/5ab7f7070c3492fe7c9e1e48ab43576c6af8c6cb88cc46daeca07f9157

96

0.895833

9

96

9.555556

1

0

0.375

0

96

1

96

0.520833

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

null

1

0

1

0

5

0e45f8a39076cab848244b2e2f1e5d66b17d922f

2,165

py

Python

mysite/pages/migrations/0036_hero1model_hero2model.py

cjlee112/socraticqs2

2e7dd9d2ec687f68ca8ca341cf5f1b3b8809c820

[ "Apache-2.0" ]

8

2015-06-02T15:34:44.000Z

2019-03-21T12:27:30.000Z

mysite/pages/migrations/0036_hero1model_hero2model.py

cjlee112/socraticqs2

2e7dd9d2ec687f68ca8ca341cf5f1b3b8809c820

[ "Apache-2.0" ]

761

2015-01-07T05:13:08.000Z

2022-02-10T10:23:37.000Z

mysite/pages/migrations/0036_hero1model_hero2model.py

cjlee112/socraticqs2

2e7dd9d2ec687f68ca8ca341cf5f1b3b8809c820

[ "Apache-2.0" ]

12

2015-01-28T20:09:36.000Z

2018-03-20T13:32:11.000Z

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('cms', '0020_old_tree_cleanup'), ('pages', '0035_helpcentermodel_intercomarticlemodel'), ] operations = [ migrations.CreateModel( name='Hero1Model', fields=[ ('cmsplugin_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, related_name='pages_hero1model', serialize=False, to='cms.CMSPlugin')), ('hidden', models.BooleanField(default=False)), ('title', models.CharField(blank=True, max_length=200)), ('description', models.TextField(blank=True)), ('best_prattices_text', models.CharField(max_length=200)), ('best_prattices_link', models.URLField()), ('bp1_text', models.CharField(max_length=200)), ('bp1_link', models.URLField()), ('video_url', models.URLField()), ], options={ 'abstract': False, }, bases=('cms.cmsplugin',), ), migrations.CreateModel( name='Hero2Model', fields=[ ('cmsplugin_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, related_name='pages_hero2model', serialize=False, to='cms.CMSPlugin')), ('hidden', models.BooleanField(default=False)), ('title', models.CharField(blank=True, max_length=200)), ('description', models.TextField(blank=True)), ('best_prattices_text', models.CharField(max_length=200)), ('best_prattices_link', models.URLField()), ('bp2_text', models.CharField(max_length=200)), ('bp2_link', models.URLField()), ('video_url', models.URLField()), ], options={ 'abstract': False, }, bases=('cms.cmsplugin',), ), ]

43.3

226

0.570439

198

2,165

6.040404

0.323232

0.075251

0.060201

0.073579

0.762542

0.710702

0

0.022049

0.28776

2,165

49

227

44.183673

0.753567

0

0.577778

0

0.178291

0.028637

0

1

0

false

0

0.044444

0

0.111111

0

null

0

1

0

null

0

5

0e468a116288f3374cb9926df577dd40b334093d

154

py

Python

gengine/base/settings.py

greck2908/gamification-engine

4a74086bde4505217e4b9ba36349a427a7042b4b

[ "MIT" ]

347

2015-03-03T14:25:59.000Z

2022-03-09T07:46:31.000Z

gengine/base/settings.py

greck2908/gamification-engine

4a74086bde4505217e4b9ba36349a427a7042b4b

[ "MIT" ]

76

2015-03-05T23:37:31.000Z

2022-03-31T13:41:42.000Z

gengine/base/settings.py

greck2908/gamification-engine

4a74086bde4505217e4b9ba36349a427a7042b4b

[ "MIT" ]

115

2015-03-04T23:47:25.000Z

2021-12-24T06:24:06.000Z

_settings = None def set_settings(settings): global _settings _settings = settings def get_settings(): global _settings return _settings

17.111111

27

0.733766

17

154

6.235294

0.411765

0.45283

0.415094

0

0.214286

154

9

28

17.111111

0.876033

0

0.285714

0

1

0.285714

false

0

0.428571

0

1

0

null

1

0

1

0

null

0

1

0

5

0e944a626be349e3bade9acca661afdab2dff6c8

90

py

Python

EnergyIntensityIndicators/Residential/__init__.py

NREL/EnergyIntensityIndicators

6d5a6d528ecd27b930d82088055224473ba2d63e

[ "BSD-3-Clause" ]

7

2020-07-30T15:02:23.000Z

2022-01-23T20:02:55.000Z

EnergyIntensityIndicators/Residential/__init__.py

NREL/EnergyIntensityIndicators

6d5a6d528ecd27b930d82088055224473ba2d63e

[ "BSD-3-Clause" ]

36

2020-06-18T15:47:32.000Z

2021-09-13T21:20:49.000Z

EnergyIntensityIndicators/Residential/__init__.py

NREL/EnergyIntensityIndicators

6d5a6d528ecd27b930d82088055224473ba2d63e

[ "BSD-3-Clause" ]

2

2020-06-18T13:30:43.000Z

2020-11-17T11:34:10.000Z

""" Residential Data Module """ from .residential_floorspace import ResidentialFloorspace

18

57

0.822222

8

90

9.125

0.875

0

0.1

90

5

57

18

0.901235

0.255556

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

7d05f9c37ef5cdad78b0dac34998974678fe5cbe

236

py

Python

synphot/filter_parameterization/__init__.py

spacetelescope/pysynphot_DONOTUSE

2a382d7bdf29cc4a1e6b69e59d5c1d0f82dabffc

[ "BSD-3-Clause" ]

null

synphot/filter_parameterization/__init__.py

spacetelescope/pysynphot_DONOTUSE

2a382d7bdf29cc4a1e6b69e59d5c1d0f82dabffc

[ "BSD-3-Clause" ]

null

synphot/filter_parameterization/__init__.py

spacetelescope/pysynphot_DONOTUSE

2a382d7bdf29cc4a1e6b69e59d5c1d0f82dabffc

[ "BSD-3-Clause" ]

null

"""This subpackage handles filter parameterization. The algorithms in this subpackage were originally developed by Brett Morris as part of the `tynt <https://github.com/bmorris3/tynt>`_ package. """ from .filter_fft import * # noqa

23.6

70

0.766949

32

236

5.59375

0.84375

0.156425

0

0.004951

0.144068

236

9

71

26.222222

0.881188

0.838983

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

5

7d1e6e547cf6973954cdb6881803208e3adb749a

552

py

Python

terrascript/resource/HarryEMartland/appdynamics.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

terrascript/resource/HarryEMartland/appdynamics.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

terrascript/resource/HarryEMartland/appdynamics.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# terrascript/resource/HarryEMartland/appdynamics.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:11:50 UTC) import terrascript class appdynamics_action(terrascript.Resource): pass class appdynamics_health_rule(terrascript.Resource): pass class appdynamics_policy(terrascript.Resource): pass class appdynamics_transaction_detection_rule(terrascript.Resource): pass __all__ = [ "appdynamics_action", "appdynamics_health_rule", "appdynamics_policy", "appdynamics_transaction_detection_rule", ]

19.714286

73

0.78442

59

552

7.033898

0.457627

0.228916

0.221687

0.20241

0.281928

0

0.025105

0.134058

552

27

74

20.444444

0.843096

0.221014

0

0.266667

1

0

0.227166

0.142857

0

1

0

false

0.266667

0.066667

0

0.333333

0

null

1

0

1

0

null

0

1

0

5

adda1ce9d6c53904616dd5424fce8bc674f2942b

77

py

Python

singlecellmultiomics/bamProcessing/__init__.py

zztin/SingleCellMultiOmics

d3035c33eb1375f0703cc49537417b755ad8a693

[ "MIT" ]

17

2019-05-21T09:12:16.000Z

2022-02-14T19:26:58.000Z

singlecellmultiomics/bamProcessing/__init__.py

zztin/SingleCellMultiOmics

d3035c33eb1375f0703cc49537417b755ad8a693

[ "MIT" ]

70

2019-05-20T08:08:45.000Z

2021-06-22T15:58:01.000Z

singlecellmultiomics/bamProcessing/__init__.py

zztin/SingleCellMultiOmics

d3035c33eb1375f0703cc49537417b755ad8a693

[ "MIT" ]

7

2020-04-09T15:11:12.000Z

2022-02-14T15:23:31.000Z

from .bamFunctions import * from .bamFeatures import * from .pileup import *

19.25

27

0.766234

9

77

6.555556

0.555556

0.338983

0

0.155844

77

3

28

25.666667

0.907692

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

adf9e3bdfc9bdfe11efe639b00f3fa85eb5508a5

16

py

Python

ola.py

njascanu/hello-world

9aff9ae8397bdea74e6cf11257677552b154bfd0

[ "MIT" ]

null

ola.py

njascanu/hello-world

9aff9ae8397bdea74e6cf11257677552b154bfd0

[ "MIT" ]

2

2018-11-05T12:34:52.000Z

2018-11-05T14:00:21.000Z

ola.py

njascanu/hello-world

9aff9ae8397bdea74e6cf11257677552b154bfd0

[ "MIT" ]

null

print("ola ola")

16

0.6875

3

16

3.666667

0.666667

0

0.0625

16

1

16

0.733333

0

0.411765

0

1

0

true

0

1

0

null

0

1

0

null

0

1

0

1

0

5

bc238f43f5e684a3b473d8d4b632c0826fcce7cd

48

py

Python

tests/components/hvv_departures/__init__.py

tbarbette/core

8e58c3aa7bc8d2c2b09b6bd329daa1c092d52d3c

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/hvv_departures/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/hvv_departures/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Tests for the HVV Departures integration."""

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py

Python

selfcontact/__init__.py

GuyTevet/selfcontact

bcb062220539601f86aa8329ce24e85ed6e85311

[ "zlib-acknowledgement", "Xnet", "X11" ]

null

selfcontact/__init__.py

GuyTevet/selfcontact

bcb062220539601f86aa8329ce24e85ed6e85311

[ "zlib-acknowledgement", "Xnet", "X11" ]

null

selfcontact/__init__.py

GuyTevet/selfcontact

bcb062220539601f86aa8329ce24e85ed6e85311

[ "zlib-acknowledgement", "Xnet", "X11" ]

null

from .utils.mesh import winding_numbers from .utils.mesh import batch_face_normals, \ batch_pairwise_dist, \ winding_numbers from .body_segmentation import BatchBodySegment from .utils.sparse import sparse_batch_mm from .selfcontact import SelfContact, SelfContactSmall

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Python

tests/test_model_evaluation.py

MarieRoald/component-viz

c1728cdc672dcffc9dd5c396acbb1cbfc57657b3

[ "MIT" ]

null

tests/test_model_evaluation.py

MarieRoald/component-viz

c1728cdc672dcffc9dd5c396acbb1cbfc57657b3

[ "MIT" ]

null

tests/test_model_evaluation.py

MarieRoald/component-viz

c1728cdc672dcffc9dd5c396acbb1cbfc57657b3

[ "MIT" ]

null

import numpy as np import pytest import scipy.linalg as sla import tensorly as tl from component_vis import factor_tools, model_evaluation from tensorly.random import random_cp def _estimate_core_tensor(factors, X): lhs = factors[0] for factor in factors[1:]: lhs = np.kron(lhs, factor) rhs = X.reshape(-1, 1) return sla.lstsq(lhs, rhs)[0].ravel() def test_estimate_core_tensor_against_reference(rng): """Test that the fast core estimation algorithm by Papalexakis and Faloutsos coincide with the reference """ A = rng.standard_normal(size=(4, 3)) B = rng.standard_normal(size=(5, 3)) C = rng.standard_normal(size=(6, 3)) core = rng.standard_normal(size=(3, 3, 3)) tucker_tensor = (core, (A, B, C)) X = factor_tools.construct_tucker_tensor(tucker_tensor) X += rng.standard_normal(X.shape) slow_estimate = _estimate_core_tensor((A, B, C), X) np.testing.assert_allclose( model_evaluation.estimate_core_tensor((A, B, C), X).ravel(), slow_estimate ) D = rng.standard_normal(size=(7, 3)) core = rng.standard_normal(size=(3, 3, 3, 3)) tucker_tensor = (core, (A, B, C, D)) X = factor_tools.construct_tucker_tensor(tucker_tensor) X += rng.standard_normal(X.shape) slow_estimate = _estimate_core_tensor((A, B, C, D), X) np.testing.assert_allclose( model_evaluation.estimate_core_tensor((A, B, C, D), X).ravel(), slow_estimate ) def test_estimate_core_tensor_known_tucker(rng): A = rng.standard_normal(size=(4, 3)) B = rng.standard_normal(size=(5, 3)) C = rng.standard_normal(size=(6, 3)) core = rng.standard_normal(size=(3, 3, 3)) tucker_tensor = (core, (A, B, C)) X = factor_tools.construct_tucker_tensor(tucker_tensor) np.testing.assert_allclose( model_evaluation.estimate_core_tensor((A, B, C), X), core ) D = rng.standard_normal(size=(7, 3)) core = rng.standard_normal(size=(3, 3, 3, 3)) tucker_tensor = (core, (A, B, C, D)) X = factor_tools.construct_tucker_tensor(tucker_tensor) np.testing.assert_allclose( model_evaluation.estimate_core_tensor((A, B, C, D), X), core ) def test_core_consistency_cp_tensor(rng): A = rng.standard_normal(size=(4, 3)) B = rng.standard_normal(size=(5, 3)) C = rng.standard_normal(size=(6, 3)) cp_decomposition = (None, (A, B, C)) X = factor_tools.construct_cp_tensor(cp_decomposition) cc = model_evaluation.core_consistency(cp_decomposition, X) assert cc == pytest.approx(100) D = rng.standard_normal(size=(7, 3)) cp_decomposition = (None, (A, B, C, D)) X = factor_tools.construct_cp_tensor(cp_decomposition) cc = model_evaluation.core_consistency(cp_decomposition, X) assert cc == pytest.approx(100) w = rng.standard_normal(size=(3)) cp_decomposition = (w, (A, B, C, D)) X = factor_tools.construct_cp_tensor(cp_decomposition) cc = model_evaluation.core_consistency(cp_decomposition, X) assert cc == pytest.approx(100) def test_core_consistency_with_known_tucker(rng): A = factor_tools.normalise(rng.standard_normal(size=(4, 3))) B = factor_tools.normalise(rng.standard_normal(size=(5, 3))) C = factor_tools.normalise(rng.standard_normal(size=(6, 3))) core = rng.standard_normal(size=(3, 3, 3)) tucker_tensor = (core, (A, B, C)) cp_tensor = (None, (A, B, C)) X = factor_tools.construct_tucker_tensor(tucker_tensor) superdiagonal_ones = np.zeros((3, 3, 3)) for i in range(3): superdiagonal_ones[i, i, i] = 1 core_error = np.sum((core - superdiagonal_ones) ** 2) core_consistency = 100 - 100 * core_error / 3 assert model_evaluation.core_consistency( cp_tensor, X, normalised=False ) == pytest.approx(core_consistency) core_consistency = 100 - 100 * core_error / np.sum(core ** 2) assert model_evaluation.core_consistency( cp_tensor, X, normalised=True ) == pytest.approx(core_consistency) def test_core_consistency_with_one_component(rng): """ The core consistency of a one component model should be 100. The one component model is fitted with TensorLy. """ X = np.array( [ [ [ 0.31147131, 0.52783545, 0.26642189, 0.64235561, 0.21453002, 0.52733376, ], [ 0.90453298, 0.72577025, 0.42906596, 0.82763775, 0.80431794, 0.60144761, ], [ 0.38229538, 0.62663986, 0.26112048, 0.68714129, 0.29639633, 0.57257494, ], [ 0.58335707, 0.53130365, 0.20064246, 0.48084977, 0.55589144, 0.36965256, ], [0.28321635, 0.62896352, 0.31016972, 0.7812717, 0.15945839, 0.6549802], ], [ [ 0.16519647, 0.35396283, 0.06433226, 0.29446942, 0.15126687, 0.26900462, ], [0.32653605, 0.59199309, 0.0887053, 0.45340927, 0.31944195, 0.41431456], [0.27286571, 0.58159856, 0.07688624, 0.4459044, 0.26453377, 0.4159955], [0.35707497, 0.7036691, 0.06581244, 0.49629388, 0.36419535, 0.4691295], [0.1729032, 0.39037184, 0.07657362, 0.33475942, 0.15348332, 0.30513679], ], [ [ 0.30037468, 0.43326544, 0.15389418, 0.43000351, 0.25913448, 0.35877758, ], [ 0.98137716, 0.80696452, 0.38246394, 0.80405414, 0.91380948, 0.59427858, ], [ 0.41751219, 0.63848692, 0.16936067, 0.56614412, 0.38196243, 0.48741497, ], [ 0.70775975, 0.77087099, 0.21936273, 0.64419824, 0.68493827, 0.52504628, ], [ 0.24952121, 0.46833168, 0.15317907, 0.46932296, 0.20135204, 0.40443773, ], ], [ [ 0.25346554, 0.56232452, 0.22228408, 0.62677006, 0.17027687, 0.53685895, ], [0.49892347, 0.7102087, 0.23699528, 0.6830915, 0.4398167, 0.57215165], [0.34165029, 0.72866965, 0.21686842, 0.7156926, 0.27110747, 0.62780439], [0.40707853, 0.64414682, 0.1028226, 0.48663565, 0.40321353, 0.43691713], [0.27148235, 0.67695523, 0.28095653, 0.78163621, 0.16013074, 0.6707594], ], ] ) A = np.array([[2.88566669], [1.89412896], [2.84850993], [2.6549183]]) B = np.array([[0.34164683], [0.57733088], [0.43787481], [0.45825688], [0.3850214]]) C = np.array( [ [0.38033551], [0.52306521], [0.18119524], [0.5080392], [0.33523002], [0.42242025], ] ) cc = model_evaluation.core_consistency((None, (A, B, C)), X, normalised=False) assert cc == pytest.approx(100) def test_core_consistency_against_matlab(rng): """Test against the MATLAB implementation by Vagelis Papalexakis https://www.cs.ucr.edu/~epapalex/code.html The components are fitted using TensorLy. """ X = np.array( [ [ [ 0.31147131, 0.52783545, 0.26642189, 0.64235561, 0.21453002, 0.52733376, ], [ 0.90453298, 0.72577025, 0.42906596, 0.82763775, 0.80431794, 0.60144761, ], [ 0.38229538, 0.62663986, 0.26112048, 0.68714129, 0.29639633, 0.57257494, ], [ 0.58335707, 0.53130365, 0.20064246, 0.48084977, 0.55589144, 0.36965256, ], [0.28321635, 0.62896352, 0.31016972, 0.7812717, 0.15945839, 0.6549802], ], [ [ 0.16519647, 0.35396283, 0.06433226, 0.29446942, 0.15126687, 0.26900462, ], [0.32653605, 0.59199309, 0.0887053, 0.45340927, 0.31944195, 0.41431456], [0.27286571, 0.58159856, 0.07688624, 0.4459044, 0.26453377, 0.4159955], [0.35707497, 0.7036691, 0.06581244, 0.49629388, 0.36419535, 0.4691295], [0.1729032, 0.39037184, 0.07657362, 0.33475942, 0.15348332, 0.30513679], ], [ [ 0.30037468, 0.43326544, 0.15389418, 0.43000351, 0.25913448, 0.35877758, ], [ 0.98137716, 0.80696452, 0.38246394, 0.80405414, 0.91380948, 0.59427858, ], [ 0.41751219, 0.63848692, 0.16936067, 0.56614412, 0.38196243, 0.48741497, ], [ 0.70775975, 0.77087099, 0.21936273, 0.64419824, 0.68493827, 0.52504628, ], [ 0.24952121, 0.46833168, 0.15317907, 0.46932296, 0.20135204, 0.40443773, ], ], [ [ 0.25346554, 0.56232452, 0.22228408, 0.62677006, 0.17027687, 0.53685895, ], [0.49892347, 0.7102087, 0.23699528, 0.6830915, 0.4398167, 0.57215165], [0.34165029, 0.72866965, 0.21686842, 0.7156926, 0.27110747, 0.62780439], [0.40707853, 0.64414682, 0.1028226, 0.48663565, 0.40321353, 0.43691713], [0.27148235, 0.67695523, 0.28095653, 0.78163621, 0.16013074, 0.6707594], ], ] ) weights = np.array([1.0, 1.0]) A = np.array( [ [2.76226953, -0.53390772], [1.75876538, -0.34060455], [1.79354941, -0.71522305], [3.22462239, -0.33290826], ] ) B = np.array( [ [-0.38495969, 0.26550081], [-0.28370532, 1.14039406], [-0.44012596, 0.4361716], [-0.22939878, 0.88900254], [-0.50484759, 0.16563351], ] ) C = np.array( [ [-0.12695248, -1.08474105], [-0.40821875, -0.84407922], [-0.14334649, -0.29662345], [-0.44798284, -0.68956002], [-0.06783491, -1.07348081], [-0.39525733, -0.50830426], ] ) cc = model_evaluation.core_consistency((weights, (A, B, C)), X, normalised=False) assert cc == pytest.approx(99.830437445788107) def test_sse(rng): cp = random_cp((4, 5, 6), 3, random_state=rng) tensor = cp.to_tensor() noise = rng.random_sample((4, 5, 6)) sse = model_evaluation.sse(cp, tensor + noise) assert sse == pytest.approx(tl.sum(noise ** 2)) def test_relative_sse(rng): cp = random_cp((4, 5, 6), 3, random_state=rng) tensor = cp.to_tensor() noise = rng.random_sample((4, 5, 6)) rel_sse = model_evaluation.relative_sse(cp, tensor + noise) assert rel_sse == pytest.approx(tl.sum(noise ** 2) / tl.sum((tensor + noise) ** 2)) def test_fit(rng): cp = random_cp((4, 5, 6), 3, random_state=rng) tensor = cp.to_tensor() noise = rng.random_sample((4, 5, 6)) fit = model_evaluation.fit(cp, tensor + noise) assert fit == pytest.approx(1 - tl.sum(noise ** 2) / tl.sum((tensor + noise) ** 2))

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python/testData/intentions/PyAnnotateTypesIntentionTest/typeComment_after.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/intentions/PyAnnotateTypesIntentionTest/typeComment_after.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

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2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/intentions/PyAnnotateTypesIntentionTest/typeComment_after.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

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2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

def foo(x, y): # type: (object, object) -> object pass

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Python

3-Patron/python/Mazmorra.py

TanZng/patrones-combinados

93796d9d5296649bd76b7b2ee7c723156b9a120f

[ "MIT" ]

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2021-09-13T15:45:48.000Z

3-Patron/python/Mazmorra.py

TanZng/patrones-combinados

93796d9d5296649bd76b7b2ee7c723156b9a120f

[ "MIT" ]

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3-Patron/python/Mazmorra.py

TanZng/patrones-combinados

93796d9d5296649bd76b7b2ee7c723156b9a120f

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2021-09-24T03:00:47.000Z

class Mazmorra(): def __init__(self): self.__salas = [] @property def salas(self): return self.__salas def addSala(self, sala): self.__salas.append(sala)

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moff/attribute/__init__.py

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moff/attribute/__init__.py

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moff/attribute/__init__.py

Tikubonn/moff

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from .srcset import Srcset, SrcsetAttribute from .sizes import Sizes, SizesAttribute

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Python

xlsxlite/test/test_utils.py

nyaruka/xlsxlite

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[ "MIT" ]

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2018-07-09T01:55:33.000Z

2019-06-20T21:12:26.000Z

xlsxlite/test/test_utils.py

nyaruka/xlsxlite

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2018-06-13T22:29:44.000Z

2021-03-04T22:00:22.000Z

xlsxlite/test/test_utils.py

nyaruka/xlsxlite

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from datetime import datetime import pytest import pytz from xlsxlite.utils import datetime_to_serial def test_datetime_to_serial(): assert datetime_to_serial(datetime(2013, 1, 1, 12, 0, 0)) == 41275.5 assert datetime_to_serial(datetime(2018, 6, 15, 11, 24, 30, 0)) == 43266.47534722222 # try with a non-naive datetime with pytest.raises(ValueError): datetime_to_serial(datetime(2018, 6, 15, 11, 24, 30, 0, pytz.UTC))

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unfollowery/__init__.py

LUKASANUKVARI/unfollowery

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2020-08-16T14:11:59.000Z

2020-09-21T04:00:16.000Z

unfollowery/__init__.py

luccario/InstaUnfollowers

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unfollowery/__init__.py

luccario/InstaUnfollowers

f780640ca5b55669b4b296faafedd4bbab51e1bf

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from unfollowery.crawler import Profile

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py

Python

models/monitors/monitor.py

trae-horton/secret-bridge

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models/monitors/monitor.py

trae-horton/secret-bridge

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2019-09-30T11:04:10.000Z

2022-02-23T18:44:46.000Z

models/monitors/monitor.py

trae-horton/secret-bridge

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2019-09-25T16:57:04.000Z

2021-11-25T05:21:16.000Z

from abc import ABC, abstractmethod class MonitorModel(ABC): @abstractmethod def poll(self): """Polls the appropriate Github Events API for new events associated with this model. """ pass

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Data Scientist Career Path/3. Python Fundamentals/11. Python Files/6. with.py

myarist/Codecademy

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shivaniverma1/Data-Scientist

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Data Scientist Career Path/3. Python Fundamentals/11. Python Files/6. with.py

shivaniverma1/Data-Scientist

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2021-06-08T01:32:04.000Z

2022-03-18T15:38:09.000Z

with open('fun_file.txt') as close_this_file: setup = close_this_file.readline() punchline = close_this_file.readline() print(setup)

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1

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5

cbe16f6f1f0dfc43dfd9224ba4bf86f1dc67f892

123

py

Python

Lib/lib2to3/tests/data/fixers/myfixes/fix_explicit.py

shawwn/cpython

0ff8a3b374286d2218fc18f47556a5ace202dad3

[ "0BSD" ]

52,316

2015-01-01T15:56:25.000Z

2022-03-31T23:19:01.000Z

Lib/lib2to3/tests/data/fixers/myfixes/fix_explicit.py

shawwn/cpython

0ff8a3b374286d2218fc18f47556a5ace202dad3

[ "0BSD" ]

25,286

2015-03-03T23:18:02.000Z

2022-03-31T23:17:27.000Z

Lib/lib2to3/tests/data/fixers/myfixes/fix_explicit.py

shawwn/cpython

0ff8a3b374286d2218fc18f47556a5ace202dad3

[ "0BSD" ]

31,623

2015-01-01T13:29:37.000Z

2022-03-31T19:55:06.000Z

from lib2to3.fixer_base import BaseFix class FixExplicit(BaseFix): explicit = True def match(self): return False

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null

0

1

0

1

0

5

1de2386207bca35ae35a41156defa759c9b3e485

119

py

Python

emol/emol/models/tests/test_user.py

lrt512/emol

e1dd3462632a525c3b9701d4fd9a332d19c93b85

[ "MIT" ]

null

emol/emol/models/tests/test_user.py

lrt512/emol

e1dd3462632a525c3b9701d4fd9a332d19c93b85

[ "MIT" ]

null

emol/emol/models/tests/test_user.py

lrt512/emol

e1dd3462632a525c3b9701d4fd9a332d19c93b85

[ "MIT" ]

null

import pytest from werkzeug.exceptions import Unauthorized from emol.models import User, Role, UserRole, Discipline

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56

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119

6.533333

0.8

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0

1

0

true

0

1

0

1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

5

1dfcc91db7d47b4e4009f87847f7a10aa5756276

651

py

Python

tests/test_plots/__init__.py

SMargreitter/ChemCharts

174acfb09c06049a41a48b14b89ba987ee0683ee

[ "Apache-2.0" ]

16

2022-01-29T05:32:13.000Z

2022-03-02T15:19:17.000Z

tests/test_plots/__init__.py

SMargreitter/ChemCharts

174acfb09c06049a41a48b14b89ba987ee0683ee

[ "Apache-2.0" ]

7

2022-02-01T22:34:57.000Z

2022-03-11T23:02:27.000Z

tests/test_plots/__init__.py

SMargreitter/ChemCharts

174acfb09c06049a41a48b14b89ba987ee0683ee

[ "Apache-2.0" ]

1

2022-01-19T12:41:38.000Z

from tests.test_plots.test_base_plot import TestBasePlot from tests.test_plots.test_contour_plot import TestContourPlot from tests.test_plots.test_hexagonal_plot import TestHexagonalPlot from tests.test_plots.test_histogram_plot import TestHistogramPlot from tests.test_plots.test_scatter_boxplot_plot import TestScatterBoxplotPlot from tests.test_plots.test_scatter_interactive import TestScatterInteractivePlot from tests.test_plots.test_scatter_static_plot import TestScatterStaticPlot from tests.test_plots.test_trisurf_interactive_plot import TestTrisurfInteractivePlot from tests.test_plots.test_trisurf_static_plot import TestTrisurfStaticPlot

65.1

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0.917051

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86

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0

1

0

1

0

5

38020e67e7229da6b90b34047af4d9f9871ed285

51

py

Python

models/__init__.py

artBoffin/GooeyBrain

60b08c713c97a4ae24ad9a47a73d69dab41d27ea

[ "Apache-2.0" ]

2

2017-07-13T04:53:47.000Z

2020-08-20T03:48:46.000Z

models/__init__.py

artBoffin/GooeyBrain

60b08c713c97a4ae24ad9a47a73d69dab41d27ea

[ "Apache-2.0" ]

11

2017-02-19T01:28:43.000Z

2022-03-11T23:15:40.000Z

models/__init__.py

artBoffin/GooeyBrain

60b08c713c97a4ae24ad9a47a73d69dab41d27ea

[ "Apache-2.0" ]

null

import base_model BaseModel = base_model.BaseModel

17

32

0.862745

7

51

6

0.571429

0.428571

0.857143

0

0.098039

51

2

33

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1

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null

1

0

1

0

null

0

1

0

5

382aecb2f5a7cb74ab464f0b5088d1db0b59e5f9

141

py

Python

python_fishc/13.0.py

iisdd/Courses

a47d202e0d7e1ba85a38c6fe3dd9619eceb1045c

[ "MIT" ]

1

2020-11-29T14:42:01.000Z

python_fishc/13.0.py

iisdd/Courses

a47d202e0d7e1ba85a38c6fe3dd9619eceb1045c

[ "MIT" ]

null

python_fishc/13.0.py

iisdd/Courses

a47d202e0d7e1ba85a38c6fe3dd9619eceb1045c

[ "MIT" ]

null

''' 0.写一个元组生成器（类似于列表推导式） ''' # 用tuple1.__next__()一个一个显示 tuple1 = (x**2 for x in range(10)) def jixu(): return tuple1.__next__()

17.625

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0.609929

19

141

4.105263

0.842105

0

0.0625

0.205674

141

7

36

20.142857

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0.340426

0

1

0.333333

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0

0.333333

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

5

697ef623f999454920ee513d0a464183e0204fa9

69

py

Python

animals/__init__.py

przemekkot/object_forge

84d4d364ed0dbbb97878df1c22ff9aec4564c8f4

[ "MIT" ]

null

animals/__init__.py

przemekkot/object_forge

84d4d364ed0dbbb97878df1c22ff9aec4564c8f4

[ "MIT" ]

null

animals/__init__.py

przemekkot/object_forge

84d4d364ed0dbbb97878df1c22ff9aec4564c8f4

[ "MIT" ]

null

# encoding: utf-8 from .animals import Animals from .vet import Vet

13.8

28

0.753623

11

69

4.727273

0.636364

0

0.017544

0.173913

69

4

29

17.25

0.894737

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0

1

0

true

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1

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null

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0

null

0

1

0

1

0

1

0

5

698067ca8815ad55adb3c0b9d9eab63ed1e75eb7

52

py

Python

playlist_manager/util/errors.py

jkleve/auto-playlist

40982d428405b92cd68664d295c2b443bc0f9db0

[ "MIT" ]

null

playlist_manager/util/errors.py

jkleve/auto-playlist

40982d428405b92cd68664d295c2b443bc0f9db0

[ "MIT" ]

null

playlist_manager/util/errors.py

jkleve/auto-playlist

40982d428405b92cd68664d295c2b443bc0f9db0

[ "MIT" ]

null

class InitializationException(Exception): pass

13

41

0.788462

4

52

10.25

1

0

0.153846

52

4

42

13

0.931818

0

1

0

true

0.5

0

0.5

0

1

0

1

null

0

1

0

null

0

1

0

5

6986bfb7d1a443957cabdb505359fbf587be6eed

48

py

Python

example/gan/mxgan/__init__.py

Liuxg16/BrainMatrix

0ec70edd4e12dd3719d20dd14d4e24438c60326f

[ "Apache-2.0" ]

249

2016-06-17T17:59:14.000Z

2021-05-31T09:56:17.000Z

example/gan/mxgan/__init__.py

Liuxg16/BrainMatrix

0ec70edd4e12dd3719d20dd14d4e24438c60326f

[ "Apache-2.0" ]

9

2016-09-29T06:11:41.000Z

2018-11-18T16:09:30.000Z

example/gan/mxgan/__init__.py

Liuxg16/BrainMatrix

0ec70edd4e12dd3719d20dd14d4e24438c60326f

[ "Apache-2.0" ]

64

2016-06-17T22:40:26.000Z

2020-05-16T00:31:58.000Z

"""T-test: random experiment code by Tianqi """

24

47

0.6875

7

48

4.714286

1

0

0.145833

48

1

48

0.804878

0.833333

0

null

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null

0

null

0

null

1

null

true

0

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1

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null

0

1

0

1

0

null

0

1

0

5

69ac6c298889a1d3807dcaee16c12b3b7baf5058

142

py

Python

vedaseg/metrics/__init__.py

YuxinZou/vedaseg

b586ecb195af9682d0429d424ed8ad999e1ae9a7

[ "Apache-2.0" ]

438

2019-12-17T12:31:15.000Z

2022-03-03T08:43:15.000Z

vedaseg/metrics/__init__.py

YuxinZou/vedaseg

b586ecb195af9682d0429d424ed8ad999e1ae9a7

[ "Apache-2.0" ]

25

2019-12-23T08:57:47.000Z

2021-12-05T02:50:10.000Z

vedaseg/metrics/__init__.py

YuxinZou/vedaseg

b586ecb195af9682d0429d424ed8ad999e1ae9a7

[ "Apache-2.0" ]

56

2019-12-17T12:06:08.000Z

2021-12-16T06:23:19.000Z

from .builder import build_metrics from .metrics import (Accuracy, DiceScore, IoU, MIoU, MultiLabelIoU, MultiLabelMIoU)

35.5

68

0.690141

14

142

6.928571

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0

0.246479

142

3

69

47.333333

0.906542

0

1

0

true

0

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0

null

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1

0

1

0

1

0

5

69ce337a1d2dfd2bc001cc8226d7fba133f23bcf

599

py

Python

gym_environments/gym_shadow_hand/__init__.py

szahlner/shadow-teleop

360c7d7c2586e9295c45fca0b4850b43d230bcda

[ "MIT" ]

1

2022-03-02T20:27:20.000Z

gym_environments/gym_shadow_hand/__init__.py

szahlner/shadow-teleop

360c7d7c2586e9295c45fca0b4850b43d230bcda

[ "MIT" ]

null

gym_environments/gym_shadow_hand/__init__.py

szahlner/shadow-teleop

360c7d7c2586e9295c45fca0b4850b43d230bcda

[ "MIT" ]

null

from gym.envs.registration import register register( id="shadow_hand_reach-v0", entry_point="gym_shadow_hand.envs:ShadowHandReachEnvV0" ) register( id="shadow_hand_reach-v1", entry_point="gym_shadow_hand.envs:ShadowHandReachEnv" ) register( id="shadow_hand_reach_goalenv-v1", entry_point="gym_shadow_hand.envs:ShadowHandReachGoalEnv" ) register( id="shadow_hand_block-v1", entry_point="gym_shadow_hand.envs:ShadowHandManipulateBlockEnv" ) register( id="shadow_hand_block_goalenv-v1", entry_point="gym_shadow_hand.envs:ShadowHandManipulateBlockGoalEnv" )

22.185185

71

0.781302

73

599

6.041096

0.287671

0.226757

0.181406

0.226757

0.639456

0.356009

0.294785

0.163265

0

0.011321

0.115192

599

26

72

23.038462

0.820755

0

0.238095

0

0.569282

0.469115

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1

0

true

0

0.047619

0

0.047619

0

null

1

0

1

null

0

1

0

5

38a18af7fedb6450efb07405d82eef3c1816138e

151

py

Python

__init__.py

sys-bio/sbmlLayout

872783ff8f1a485d921d86cab4b1da600af9f23b

[ "MIT" ]

null

__init__.py

sys-bio/sbmlLayout

872783ff8f1a485d921d86cab4b1da600af9f23b

[ "MIT" ]

null

__init__.py

sys-bio/sbmlLayout

872783ff8f1a485d921d86cab4b1da600af9f23b

[ "MIT" ]

null

#from .sbLayout import sbmlLayout from . import editSBML from . import exportSBML from . import sbLayout from sbmlLayout._version import __version__

18.875

43

0.81457

18

151

6.555556

0.388889

0.254237

0

0.145695

151

7

44

21.571429

0.914729

0.211921

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

38ac85280946c4ff3ec2417f02eee9a44b2ce968

324

py

Python

simplified_scrapy/core/requesttm_base.py

yiyedata/simplified-scrapy

ccfdc686c53b2da3dac733892d4f184f6293f002

[ "Apache-2.0" ]

7

2019-08-11T10:31:03.000Z

2021-03-08T10:07:52.000Z

simplified_scrapy/core/requesttm_base.py

yiyedata/simplified-scrapy

ccfdc686c53b2da3dac733892d4f184f6293f002

[ "Apache-2.0" ]

1

2020-12-29T02:30:18.000Z

2021-01-25T02:49:37.000Z

simplified_scrapy/core/requesttm_base.py

yiyedata/simplified-scrapy

ccfdc686c53b2da3dac733892d4f184f6293f002

[ "Apache-2.0" ]

4

2019-10-22T02:14:35.000Z

2021-05-13T07:01:56.000Z

#!/usr/bin/python #coding=utf-8 class RequestTmBase: def addRecode(self, ssp, url, tmSpan, state, concurrency,countPer10s,size): raise NotImplementedError def startRecode(self): raise NotImplementedError def startServer(self): raise NotImplementedError def endRecode(self): raise NotImplementedError

27

77

0.762346

35

324

7.057143

0.657143

0.388664

0.327935

0.251012

0

0.010909

0.151235

324

12

78

27

0.887273

0.08642

0

0.444444

0

1

0.444444

false

0

0.555556

0

null

1

0

null

0

1

0

1

0

5

38b55ef0484fe13cf0cddba73cdb791d9562b120

98

py

Python

app/tests/__init__.py

NobeKanai/netease_backup

00b0f5138f6b788d6db1d75a1431e5880db35631

[ "MIT" ]

1

2020-05-05T03:54:01.000Z

app/tests/__init__.py

NobeKanai/netease_backup

00b0f5138f6b788d6db1d75a1431e5880db35631

[ "MIT" ]

null

app/tests/__init__.py

NobeKanai/netease_backup

00b0f5138f6b788d6db1d75a1431e5880db35631

[ "MIT" ]

null

import sys import os sys.path.insert(0, os.path.join(os.path.dirname(__file__), os.path.pardir))

19.6

75

0.755102

18

98

3.888889

0.555556

0.257143

0

0.011111

0.081633

98

4

76

24.5

0.766667

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

38cfdb61fa6827cffd9ae05c736312e22c5f8930

327

py

Python

django/cheese/apps/core/views.py

whutch/cheese

9fea560a81d2b26c70551cdc30ff0befc9069c75

[ "MIT" ]

null

django/cheese/apps/core/views.py

whutch/cheese

9fea560a81d2b26c70551cdc30ff0befc9069c75

[ "MIT" ]

null

django/cheese/apps/core/views.py

whutch/cheese

9fea560a81d2b26c70551cdc30ff0befc9069c75

[ "MIT" ]

null

# -*- coding: utf-8 -*- """View functions for core app.""" # Part of Cheese (https://github.com/whutch/cheese) # :copyright: (c) 2015 Will Hutcheson # :license: MIT (https://github.com/whutch/cheese/blob/master/LICENSE.txt) from django.shortcuts import render def home(request): return render(request, "core/home.html")

27.25

74

0.703364

46

327

5

0.76087

0.095652

0.121739

0.173913

0.226087

0

0.017483

0.125382

327

11

75

29.727273

0.786713

0.642202

0

0.12963

0

1

0.333333

false

0

0.333333

1

0

null

0

1

0

null

0

1

0

1

0

5

2a0f763ef96ea9744e1f5c90fdfa47ad497e1b28

55

py

Python

src/objects/WebApp.sikuli/WebApp.py

adrianpothuaud/Sikuli-WS

6210a949768fb4eb2b80693818ae3eb31ec9c406

[ "MIT" ]

1

2018-02-20T16:28:45.000Z

src/objects/WebApp.sikuli/WebApp.py

adrianpothuaud/Sikuli-WS

6210a949768fb4eb2b80693818ae3eb31ec9c406

[ "MIT" ]

null

src/objects/WebApp.sikuli/WebApp.py

adrianpothuaud/Sikuli-WS

6210a949768fb4eb2b80693818ae3eb31ec9c406

[ "MIT" ]

null

# -*- coding:utf-8 -*- """ """ from sikuli import *

6.875

22

0.472727

6

55

4.333333

1

0

0.02381

0.236364

55

7

23

7.857143

0.595238

0.363636

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2a91a498c75313be2aa269d2f03f3648b7a5dfb7

161

py

Python

scraping/admin.py

stepacool/work4sharingPy

c6ffdb3ac8a2956181c290bc4617e3cb11e13dba

[ "MIT" ]

null

scraping/admin.py

stepacool/work4sharingPy

c6ffdb3ac8a2956181c290bc4617e3cb11e13dba

[ "MIT" ]

5

2021-03-19T01:48:51.000Z

2021-09-22T18:52:20.000Z

scraping/admin.py

stepacool/work4sharingPy

c6ffdb3ac8a2956181c290bc4617e3cb11e13dba

[ "MIT" ]

2

2020-04-16T20:23:11.000Z

2020-04-18T12:35:46.000Z

from django.contrib import admin # Register your models here. from scraping.models import Employee, Job admin.site.register(Employee) admin.site.register(Job)

20.125

41

0.807453

23

161

5.652174

0.565217

0.138462

0.261538

0

0.111801

161

7

42

23

0.909091

0.161491

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

aa6d835956fca3037285362f00357a730863d426

601

py

Python

precise/skaters/portfoliostatic/acdc.py

ChandanKSingh/precise

3c420fe34d252a557b7a17ed9f7637be2fa86e20

[ "MIT" ]

null

precise/skaters/portfoliostatic/acdc.py

ChandanKSingh/precise

3c420fe34d252a557b7a17ed9f7637be2fa86e20

[ "MIT" ]

null

precise/skaters/portfoliostatic/acdc.py

ChandanKSingh/precise

3c420fe34d252a557b7a17ed9f7637be2fa86e20

[ "MIT" ]

null

from precise.skaters.portfoliostatic.weakportfactory import weak_portfolio_factory from precise.skaters.portfoliostatic.acdcfactory import schur_complement_portfolio_factory # Acca Dacca Portfolio method # # It's gonna rock you ... eventually def acdc_weak_s5_port(cov=None, pre=None): return schur_complement_portfolio_factory(port=weak_portfolio_factory, cov=cov, pre=pre, n_split=5) def acdc_weak_s25_port(cov=None, pre=None): return schur_complement_portfolio_factory(port=weak_portfolio_factory, cov=cov, pre=pre, n_split=25) ACDC_PORT = [] # Not working yet ACDC_LONG_PORT = []

31.631579

104

0.810316

87

601

5.298851

0.436782

0.208243

0.130152

0.201735

0.420824

0

0.011173

0.106489

601

18

105

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0

1

0.25

false

0

0.25

0.75

0

null

1

0

1

0

null

0

1

0

1

0

5

aa7f9f5b777b78278cfacb287c182dc67cace8ff

63

py

Python

handlers/__init__.py

Wilidon/MrushNotice

d03979f8b271c2f2df10482090631664b14fef1d

[ "MIT" ]

1

2022-02-13T08:19:32.000Z

handlers/__init__.py

Wilidon/MrushNotice

d03979f8b271c2f2df10482090631664b14fef1d

[ "MIT" ]

null

handlers/__init__.py

Wilidon/MrushNotice

d03979f8b271c2f2df10482090631664b14fef1d

[ "MIT" ]

null

from .main import dp from .callback import dp __all__ = ['dp']

15.75

24

0.714286

10

63

4.1

0.6

0.390244

0

0.174603

63

4

25

15.75

0.788462

0

0.03125

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

aab16ad116831c58cf0d6b064be71f9d9a7b741d

20

py

Python

checkov/version.py

DarwinJS/checkov

98b6679f01debf82132705e467d5a509dc9c05ca

[ "Apache-2.0" ]

null

checkov/version.py

DarwinJS/checkov

98b6679f01debf82132705e467d5a509dc9c05ca

[ "Apache-2.0" ]

null

checkov/version.py

DarwinJS/checkov

98b6679f01debf82132705e467d5a509dc9c05ca

[ "Apache-2.0" ]

null

version = '1.0.640'

10

19

0.6

4

20

3

1

0

0.294118

0.15

20

1

20

0.411765

0

0.35

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

aacffa5327b6e7c2332c6a57d738c1834f42c037

171

py

Python

tests/test_account.py

IanMadlenya/kaleidoscope

be3e28c1b4b958f81d8d67df03abebbebcf7965a

[ "MIT" ]

1

2020-08-30T15:29:04.000Z

tests/test_account.py

IanMadlenya/kaleidoscope

be3e28c1b4b958f81d8d67df03abebbebcf7965a

[ "MIT" ]

null

tests/test_account.py

IanMadlenya/kaleidoscope

be3e28c1b4b958f81d8d67df03abebbebcf7965a

[ "MIT" ]

2

2019-09-18T07:13:32.000Z

2020-11-20T18:15:18.000Z

from unittest import TestCase class TestAccount(TestCase): def test_process_order(self): self.fail() def test_update_account(self): self.fail()

17.1

34

0.684211

21

171

5.380952

0.666667

0.123894

0.212389

0

0.22807

171

9

35

19

0.856061

0

0.333333

0

1

0.333333

false

0

0.166667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

aad48fcd9b4928429883d4b5cfed414ed1386787

225

py

Python

Python/No Idea/No Idea!.py

sachinprabhu007/HackerRank-Solutions

f42d3c1e989b288e42b4674a926d007aa22940a1

[ "MIT" ]

null

Python/No Idea/No Idea!.py

sachinprabhu007/HackerRank-Solutions

f42d3c1e989b288e42b4674a926d007aa22940a1

[ "MIT" ]

1

2019-01-16T12:13:29.000Z

2019-01-16T14:57:57.000Z

Python/No Idea/No Idea!.py

sachinprabhu007/HackerRank-Solutions

f42d3c1e989b288e42b4674a926d007aa22940a1

[ "MIT" ]

null

# Enter your code here. Read input from STDIN. Print output to STDOUT n, m = input().split() sc_ar = input().split() A = set(input().split()) B = set(input().split()) print sum([(i in A) - (i in B) for i in sc_ar])

25

70

0.604444

41

225

3.268293

0.585366

0.298507

0.19403

0

0.213333

225

8

71

28.125

0.757062

0.297778

0

0.125

0

null

0

null

0.2

0

null

1

0

1

0

null

0

1

0

1

0

5

632d708614d44a85faa8a4ed6adebf07d047fc0b

36

py

Python

avalanche/benchmarks/generators/__init__.py

aishikhar/avalanche

39c361aba1663795ed33f093ab2e15cc5792026e

[ "MIT" ]

1

2021-08-11T19:43:38.000Z

avalanche/benchmarks/generators/__init__.py

aishikhar/avalanche

39c361aba1663795ed33f093ab2e15cc5792026e

[ "MIT" ]

null

avalanche/benchmarks/generators/__init__.py

aishikhar/avalanche

39c361aba1663795ed33f093ab2e15cc5792026e

[ "MIT" ]

1

2021-04-09T08:10:27.000Z

from .scenario_generators import *

18

35

0.805556

4

36

7

1

0

0.138889

36

1

36

0.903226

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

2db80c00f6b0724fd3320958c0b094da276e4a2b

48

py

Python

src/python/ensureconda/__main__.py

ForgottenProgramme/ensureconda

fbb35dff988f5324d2af7f4b53425d32fa4e5e8c

[ "MIT" ]

7

2020-10-27T21:13:27.000Z

2021-11-15T17:37:25.000Z

src/python/ensureconda/__main__.py

ForgottenProgramme/ensureconda

fbb35dff988f5324d2af7f4b53425d32fa4e5e8c

[ "MIT" ]

10

2020-09-10T17:29:52.000Z

2022-03-23T18:00:49.000Z

src/python/ensureconda/__main__.py

ForgottenProgramme/ensureconda

fbb35dff988f5324d2af7f4b53425d32fa4e5e8c

[ "MIT" ]

4

2020-09-11T14:08:02.000Z

2022-03-23T04:30:56.000Z

from .cli import ensureconda_cli as cli cli()

9.6

39

0.75

8

48

4.375

0.625

0

0.1875

48

4

40

12

0.897436

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

9340da790cc100cafc8662fb043af7d2e599ee59

613

py

Python

pineapple/tracking.py

735tesla/python-pineapple

e108f1a1685e3fe045e27ceacc7827fb498da669

[ "MIT" ]

11

2016-05-14T03:47:44.000Z

2018-06-08T12:19:21.000Z

pineapple/tracking.py

735tesla/python-pineapple

e108f1a1685e3fe045e27ceacc7827fb498da669

[ "MIT" ]

2

2017-09-14T23:37:54.000Z

2018-05-15T17:04:42.000Z

pineapple/tracking.py

735tesla/python-pineapple

e108f1a1685e3fe045e27ceacc7827fb498da669

[ "MIT" ]

8

2016-05-16T04:19:58.000Z

2018-04-16T00:51:54.000Z

from module import Module class Tracking(Module): def __init__(self, api): super(Tracking, self).__init__(api, 'Tracking') def getScript(self): return self.request('getScript') def setScript(self, script): return self.request('saveScript', {'trackingScript': script}) def getTrackingList(self): return self.request('getTrackingList') def addMac(self, mac): return self.request('addMac', {'mac': mac}) def removeMac(self, mac): return self.request('removeMac', {'mac': mac}) def clearMacs(self): return self.request('clearMacs')

34.055556

69

0.649266

68

613

5.735294

0.323529

0.153846

0.261538

0.161538

0.123077

0

0.212072

613

17

70

36.058824

0.807453

0

0.140294

0

1

0.4375

false

0

0.0625

0.375

0.9375

0

null

0

1

0

null

0

1

0

1

0

5

93463b29d9ecca86249743d467e7180741cabf69

156

py

Python

tester/test_handlers/test_link_handler.py

seaioheroes/TorCMS-master

266aa6dcf8e2d36024dd278e6f56fd638c88c6ff

[ "MIT" ]

null

tester/test_handlers/test_link_handler.py

seaioheroes/TorCMS-master

266aa6dcf8e2d36024dd278e6f56fd638c88c6ff

[ "MIT" ]

null

tester/test_handlers/test_link_handler.py

seaioheroes/TorCMS-master

266aa6dcf8e2d36024dd278e6f56fd638c88c6ff

[ "MIT" ]

null

# -*- coding:utf-8 -*- ''' Test ''' from torcms.handlers.link_handler import LinkHandler def test_ab(): ''' Test ''' assert LinkHandler

10.4

52

0.589744

17

156

5.294118

0.823529

0

0.008475

0.24359

156

14

53

11.142857

0.754237

0.198718

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

null

0

1

0

1

0

1

0

5

fa832f200b75ab9a74cc5da576fcb692a95ee1c6

171

py

Python

lib/JumpScale/lib/cloudproviders/__init__.py

rudecs/jumpscale_core7

30c03f26f1cdad3edbb9d79d50fbada8acc974f5

[ "Apache-2.0" ]

null

lib/JumpScale/lib/cloudproviders/__init__.py

rudecs/jumpscale_core7

30c03f26f1cdad3edbb9d79d50fbada8acc974f5

[ "Apache-2.0" ]

4

2016-08-25T12:08:39.000Z

2018-04-12T12:36:01.000Z

lib/JumpScale/lib/cloudproviders/__init__.py

rudecs/jumpscale_core7

30c03f26f1cdad3edbb9d79d50fbada8acc974f5

[ "Apache-2.0" ]

3

2016-03-08T07:49:34.000Z

2018-10-19T13:56:43.000Z

from JumpScale import j def cb(): from .factory import Factory return Factory() j.base.loader.makeAvailable(j, 'tools') j.tools._register('cloudproviders', cb)

17.1

39

0.719298

23

171

5.304348

0.608696

0.098361

0

0.157895

171

9

40

19

0.847222

0

0.111765

0

1

0.166667

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

fa9dc00dc678af1f1c9b1cf279196b3244082d8f

146

py

Python

Point.py

matthew99carroll/VoronoiDiagram

354a6ff08bb0858972b542b4b90973ce31996978

[ "MIT" ]

null

Point.py

matthew99carroll/VoronoiDiagram

354a6ff08bb0858972b542b4b90973ce31996978

[ "MIT" ]

null

Point.py

matthew99carroll/VoronoiDiagram

354a6ff08bb0858972b542b4b90973ce31996978

[ "MIT" ]

null

class Point(object): def __init__(self, x, y): self.x = x self.y = y def ToString(self): return '("+x+", "+y+")'

18.25

31

0.465753

20

146

3.2

0.5

0.15625

0

0.342466

146

7

32

20.857143

0.666667

0

0.09589

0

1

0.333333

false

0

0.166667

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

5

fa9e056c09b88a31dc7a7de2278a785f934bd371

23

py

Python

exmol/stoned/__init__.py

varnerjwpu/exmol

6458c00272a4ae0b58c627c3fcb4f529eb8e1fdf

[ "MIT" ]

null

exmol/stoned/__init__.py

varnerjwpu/exmol

6458c00272a4ae0b58c627c3fcb4f529eb8e1fdf

[ "MIT" ]

null

exmol/stoned/__init__.py

varnerjwpu/exmol

6458c00272a4ae0b58c627c3fcb4f529eb8e1fdf

[ "MIT" ]

null

from .stoned import *

11.5

22

0.695652

3

23

5.333333

1

0

0.217391

23

1

23

0.888889

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

fab94f48d30fd91d4874dc05f0563a9007c997ea

443

py

Python

tests/test_main.py

ine-rmotr-projects/itp-w1-highest-number-cubed

0c59febb9391dafdcd3df551e7797c262525535d

[ "MIT" ]

1

2017-03-24T01:37:51.000Z

tests/test_main.py

ine-rmotr-projects/itp-w1-highest-number-cubed

0c59febb9391dafdcd3df551e7797c262525535d

[ "MIT" ]

6

2017-01-17T00:09:01.000Z

2017-06-02T01:27:42.000Z

tests/test_main.py

ine-rmotr-projects/itp-w1-highest-number-cubed

0c59febb9391dafdcd3df551e7797c262525535d

[ "MIT" ]

108

2016-10-20T22:07:37.000Z

2019-01-11T05:04:02.000Z

import unittest from highest_number_cubed import highest_number_cubed class TestHighestNumberCubed(unittest.TestCase): def test_three(self): self.assertEqual(highest_number_cubed(30), 3) def test_two(self): self.assertEqual(highest_number_cubed(12), 2) def test_one(self): self.assertEqual(highest_number_cubed(3), 1) def test_big(self): self.assertEqual(highest_number_cubed(12000), 22)

23.315789

57

0.731377

58

443

5.310345

0.413793

0.253247

0.350649

0.337662

0.480519

0

0.041209

0.17833

443

18

58

24.611111

0.804945

0

0.363636

1

0.363636

false

0

0.181818

0

0.636364

0

null

1

0

null

0

1

0

1

0

5

facb70b29a213bd6e2a942903e5c92638870715e

96

py

Python

venv/lib/python3.8/site-packages/numpy/ma/bench.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/numpy/ma/bench.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/numpy/ma/bench.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/b5/2b/26/9f97f9f6b3b1e9eed4194de4c8079d3e1246daca7ef0b6477993e56ba8

96

0.895833

9

96

9.555556

1

0

0.395833

0

96

1

96

0.5

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

null

1

0

1

0

5

878ddec9cfa1020116bc90e343f5eef51b639723

90

py

Python

1-iniciante/1095.py

marcobarone-dev/uri

82bf0b244d3966673b10a42948dcdeabcde07e76

[ "MIT" ]

1

2018-07-04T02:42:29.000Z

1-iniciante/1095.py

marcobarone-dev/uri-python

82bf0b244d3966673b10a42948dcdeabcde07e76

[ "MIT" ]

null

1-iniciante/1095.py

marcobarone-dev/uri-python

82bf0b244d3966673b10a42948dcdeabcde07e76

[ "MIT" ]

null

i, j = 1, 60 while j >= 0: print('I={} J={}'.format(i, j)) i += 3 j -= 5

15

36

0.344444

17

90

1.823529

0.588235

0.193548

0

0.109091

0.388889

90

5

37

18

0.454545

0

0.105882

0

1

0

true

0

0.2

1

0

1

null

0

1

0

1

0

null

0

1

0

5

87df96c9e49a33b62860b29d713df67ba2cb690a

207

py

Python

mopidy/core/__init__.py

swinton/mopidy

c32c73f5112c29ef7ccccf36a508c571adb39759

[ "Apache-2.0" ]

null

mopidy/core/__init__.py

swinton/mopidy

c32c73f5112c29ef7ccccf36a508c571adb39759

[ "Apache-2.0" ]

null

mopidy/core/__init__.py

swinton/mopidy

c32c73f5112c29ef7ccccf36a508c571adb39759

[ "Apache-2.0" ]

null

from .current_playlist import CurrentPlaylistController from .library import LibraryController from .playback import PlaybackController, PlaybackState from .stored_playlists import StoredPlaylistsController

41.4

55

0.89372

19

207

9.631579

0.684211

0

0.082126

207

4

56

51.75

0.963158

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

87e26dc05248672362821b3acdac537c68bc2361

157

py

Python

sunsynk/__init__.py

dirkackerman/sunsynk

cf1a95e73f1595271aea677e7ce6739cb830e648

[ "MIT" ]

11

2021-11-02T20:16:38.000Z

2022-03-21T19:22:43.000Z

sunsynk/__init__.py

dirkackerman/sunsynk

cf1a95e73f1595271aea677e7ce6739cb830e648

[ "MIT" ]

27

2021-11-11T07:29:22.000Z

2022-03-28T16:26:05.000Z

sunsynk/__init__.py

dirkackerman/sunsynk

cf1a95e73f1595271aea677e7ce6739cb830e648

[ "MIT" ]

10

2021-11-06T09:54:53.000Z

2022-03-28T10:02:15.000Z

"""Sunsynk library.""" # pylint: disable=unused-import # flake8: noqa from .sensor import Sensor, group_sensors, update_sensors from .sunsynk import Sunsynk

26.166667

57

0.77707

20

157

6

0.65

0

0.007194

0.11465

157

5

58

31.4

0.856115

0.382166

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

355b34857734e1a5a8298ab160c476aee7695dd9

142

py

Python

utils/gpu_op.py

maestrojeong/Deep-Hash-Table-ICML18-

0c7efa230f950d5a2cd1928ac9f5d99f4276d2b5

[ "MIT" ]

70

2018-06-03T04:19:13.000Z

2021-11-08T10:40:46.000Z

utils/gpu_op.py

maestrojeong/Deep-Hash-Table-ICML18-

0c7efa230f950d5a2cd1928ac9f5d99f4276d2b5

[ "MIT" ]

1

2019-06-21T11:50:15.000Z

2019-06-24T05:38:27.000Z

utils/gpu_op.py

maestrojeong/Deep-Hash-Table-ICML18-

0c7efa230f950d5a2cd1928ac9f5d99f4276d2b5

[ "MIT" ]

14

2018-06-03T16:34:55.000Z

2020-09-09T17:02:30.000Z

import os def selectGpuById(id): os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(id)

23.666667

56

0.697183

20

142

4.65

0.7

0.086022

0.236559

0.322581

0

0.133803

142

5

57

28.4

0.756098

0

0.34507

0

1

0.25

false

0

0.25

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

5

3586fd2c76124d2b3ca54e76a56ca4cfc531ef90

107

py

Python

app/forms/__init__.py

RoodeAwakening/BoxIt

026153bf97de3c7aa7417b9f87a04b2d5d810b43

[ "MIT", "PostgreSQL", "Unlicense" ]

3

2021-04-07T05:59:26.000Z

2021-04-09T22:53:42.000Z

app/forms/__init__.py

RoodeAwakening/BoxIt

026153bf97de3c7aa7417b9f87a04b2d5d810b43

[ "MIT", "PostgreSQL", "Unlicense" ]

13

2021-03-30T05:44:58.000Z

2021-03-30T05:54:41.000Z

app/forms/__init__.py

RoodeAwakening/BoxIt

026153bf97de3c7aa7417b9f87a04b2d5d810b43

[ "MIT", "PostgreSQL", "Unlicense" ]

1

2021-04-07T16:56:51.000Z

from .login_form import LoginForm from .signup_form import SignUpForm from .comment_form import CommentForm

35.666667

37

0.869159

15

107

6

0.6

0.333333

0

0.102804

107

3

37

35.666667

0.9375

0

1

0

true

0

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null

0

1

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5

35d3a7f273dd287b8e4929190fdfcfc1cc5c4cba

40

py

Python

flexipy/exceptions.py

Vitexus/flexipy

9f0e13369082c32e7431ab5dda257f610acfeab9

[ "BSD-3-Clause" ]

3

2016-03-30T16:11:09.000Z

2017-12-05T12:11:54.000Z

flexipy/exceptions.py

Vitexus/flexipy

9f0e13369082c32e7431ab5dda257f610acfeab9

[ "BSD-3-Clause" ]

2

2017-02-16T15:19:55.000Z

2021-02-18T16:21:31.000Z

flexipy/exceptions.py

Vitexus/flexipy

9f0e13369082c32e7431ab5dda257f610acfeab9

[ "BSD-3-Clause" ]

3

2017-07-10T22:38:53.000Z

2021-01-28T16:29:53.000Z

class FlexipyException(Exception): pass

20

34

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8.5

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0

null

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1

0

null

0

1

0

5

ea0b5c3fb8906b1c3343eff6bc4e0e70edddc628

208

py

Python

src/cotea/wrappers/ansi_breakpoint.py

ispras/cotea

9be1c038be968de726e9245e8796c994b128ef73

[ "Apache-2.0" ]

null

src/cotea/wrappers/ansi_breakpoint.py

ispras/cotea

9be1c038be968de726e9245e8796c994b128ef73

[ "Apache-2.0" ]

null

src/cotea/wrappers/ansi_breakpoint.py

ispras/cotea

9be1c038be968de726e9245e8796c994b128ef73

[ "Apache-2.0" ]

null

class ansi_breakpoint: def __init__(self, sync_obj, label): self.sync_obj = sync_obj self.label = label def stop(self): self.sync_obj.continue_runner_with_stop(self.label)

29.714286

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1

0

1

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null

0

1

0

5

ea381fac5abb63e14b864ade2fc1a398ed63e63c

113

py

Python

helpers/get_listing_image_upload_path.py

viveklaata/E-Commerce-Portal

5594a3302e74288a484db8e19edaf5b3270a4a28

[ "Apache-2.0" ]

1

2020-06-14T18:33:24.000Z

helpers/get_listing_image_upload_path.py

viveklaata/E-Commerce-Portal

5594a3302e74288a484db8e19edaf5b3270a4a28

[ "Apache-2.0" ]

null

helpers/get_listing_image_upload_path.py

viveklaata/E-Commerce-Portal

5594a3302e74288a484db8e19edaf5b3270a4a28

[ "Apache-2.0" ]

null

def get_listing_image_upload_path(instance, filename): return 'listing/{0}/{1}'.format(instance.code, filename)

56.5

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1

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null

0

1

0

1

0

5

ea48e11b7225a8b35bed96ad54788bf3f3e61769

123

py

Python

reactant/__init__.py

neil-vqa/reactant

32e993f1f4b03a121d60c5121bb92cdc53c0fef5

[ "MIT" ]

15

2021-09-11T16:48:25.000Z

2021-12-06T10:01:31.000Z

reactant/__init__.py

neil-vqa/reactant

32e993f1f4b03a121d60c5121bb92cdc53c0fef5

[ "MIT" ]

1

2021-11-24T01:01:31.000Z

2021-11-24T03:30:18.000Z

reactant/__init__.py

neil-vqa/reactant

32e993f1f4b03a121d60c5121bb92cdc53c0fef5

[ "MIT" ]

1

2021-11-23T15:30:13.000Z

__version__ = "0.6.0" from pydantic import Field from reactant.main import DjangoORM, PeeweeORM, SQLAlchemyORM, generate

20.5

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0.796748

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123

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1

0

1

0

null

0

1

0

1

0

5

ea627cdc23a10f57b71c1db184dc5fb859eb832b

145

py

Python

blog_extension/admin.py

getHarsh/getHarsh

35fb22dab3fdda81b5960bdb3df88e631564c07c

[ "MIT" ]

null

blog_extension/admin.py

getHarsh/getHarsh

35fb22dab3fdda81b5960bdb3df88e631564c07c

[ "MIT" ]

null

blog_extension/admin.py

getHarsh/getHarsh

35fb22dab3fdda81b5960bdb3df88e631564c07c

[ "MIT" ]

1

2021-07-26T18:23:26.000Z

from blog_extension.models import Subscriptions from django.contrib import admin # Register your models here. admin.site.register(Subscriptions)

29

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145

6.421053

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true

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1

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null

0

1

0

1

0

1

0

5

578df5ecf49934dbc80fe52d981dbe634ee45b98

116

py

Python

flamingo/core/plugins/__init__.py

rschwebel/flamingo

b861bdc2e1194ec7d58be365000cb70a07721df4

[ "Apache-2.0" ]

null

flamingo/core/plugins/__init__.py

rschwebel/flamingo

b861bdc2e1194ec7d58be365000cb70a07721df4

[ "Apache-2.0" ]

null

flamingo/core/plugins/__init__.py

rschwebel/flamingo

b861bdc2e1194ec7d58be365000cb70a07721df4

[ "Apache-2.0" ]

null

from .meta_data import MetaDataDefaults # NOQA from .static import Static # NOQA from .media import Media # NOQA

29

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116

5.5

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116

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true

0

1

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1

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

17c79e96cc1f7c9dd62fec01a61295b41130554a

25

py

Python

15_echo/brain/nucleus.py

Tjorriemorrie/trading

aafa15a6c564bfa86948ab30e33d554172b38a3e

[ "MIT" ]

2

2017-07-02T09:06:28.000Z

2020-09-11T04:23:14.000Z

15_echo/brain/nucleus.py

Tjorriemorrie/trading

aafa15a6c564bfa86948ab30e33d554172b38a3e

[ "MIT" ]

2

2021-03-31T19:14:07.000Z

2021-06-01T23:34:32.000Z

15_echo/brain/nucleus.py

Tjorriemorrie/trading

aafa15a6c564bfa86948ab30e33d554172b38a3e

[ "MIT" ]

2

2016-03-29T07:51:16.000Z

2016-10-30T04:53:58.000Z

class Nucleus: pass

6.25

14

0.64

3

25

5.333333

1

0

0.32

25

4

15

6.25

0.941176

0

1

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true

0.5

0

0.5

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1

0

null

0

1

0

null

0

1

0

5

aa048142a0dbd6a1bcd197ca083c257ed9c3eb3f

201

py

Python

createdb.py

olaruandreea/flaskbootstrapblog

462f2f700c3b73a794842f1fad4b318c6406c678

[ "MIT" ]

null

createdb.py

olaruandreea/flaskbootstrapblog

462f2f700c3b73a794842f1fad4b318c6406c678

[ "MIT" ]

4

2020-05-03T11:45:01.000Z

2020-06-13T19:43:26.000Z

createdb.py

olaruandreea/flaskbootstrapblog

462f2f700c3b73a794842f1fad4b318c6406c678

[ "MIT" ]

null

from website import * from website.models.user import * from website.models.blog_post import * def create_db(): app = create_app() with app.app_context(): db.create_all() create_db()

18.272727

38

0.696517

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201

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201

11

39

18.272727

0.82716

0

1

0.125

false

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0.5

0

1

0

null

1

0

null

0

1

0

5

aa226848e2bf6066f2ebb5785db790b7af91dc79

172

py

Python

setup.py

ravensorb/traefik-certificate-exporter

479da800ce7b2977c4fbaba8346f01644a8e7272

[ "MIT" ]

null

setup.py

ravensorb/traefik-certificate-exporter

479da800ce7b2977c4fbaba8346f01644a8e7272

[ "MIT" ]

null

setup.py

ravensorb/traefik-certificate-exporter

479da800ce7b2977c4fbaba8346f01644a8e7272

[ "MIT" ]

null

from setuptools import setup #import versioneer if __name__ == "__main__": #setup( version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass()) setup()

24.571429

82

0.738372

19

172

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0.145349

172

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0.795918

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true

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null

1

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1

0

null

0

1

0

1

0

5

aa44821e35992081d9387bb335f0e75f97954bbd

134

py

Python

my_deepsoccer_training/src/utils/wan.py

kimbring2/jetbot_gazebo

385348a6bd451fb9d752bf0417138b2eacf91e48

[ "Apache-1.1" ]

19

2020-04-07T07:07:39.000Z

2022-02-12T22:24:06.000Z

my_deepsoccer_training/src/utils/wan.py

kimbring2/jetbot_gazebo

385348a6bd451fb9d752bf0417138b2eacf91e48

[ "Apache-1.1" ]

5

2020-03-07T07:30:47.000Z

2021-02-04T13:11:03.000Z

my_deepsoccer_training/src/utils/wan.py

kimbring2/jetbot_gazebo

385348a6bd451fb9d752bf0417138b2eacf91e48

[ "Apache-1.1" ]

16

2020-03-17T00:17:54.000Z

2022-03-21T18:27:13.000Z

import wandb import tensorflow as tf wandb.init(config=tf.flags.FLAGS, sync_tensorboard=True, anonymous='allow', project="DeepMine")

26.8

95

0.798507

19

134

5.578947

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0

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134

4

96

33.5

0.861789

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0

1

0

true

0

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null

0

1

0

null

0

1

0

1

0

1

0

5

a4b7c4f1352c5333024cab026c595dfc149d822c

745

py

Python

swagger_client/api/__init__.py

arthrex/arthrexvip-ucb-python-client

afd7ecdd2a4e4bf9e635e60f2006deb8411427d4

[ "MIT" ]

null

swagger_client/api/__init__.py

arthrex/arthrexvip-ucb-python-client

afd7ecdd2a4e4bf9e635e60f2006deb8411427d4

[ "MIT" ]

null

swagger_client/api/__init__.py

arthrex/arthrexvip-ucb-python-client

afd7ecdd2a4e4bf9e635e60f2006deb8411427d4

[ "MIT" ]

null

from __future__ import absolute_import # flake8: noqa # import apis into api package from swagger_client.api.builds_api import BuildsApi from swagger_client.api.buildtargets_api import BuildtargetsApi from swagger_client.api.config_api import ConfigApi from swagger_client.api.credentials_api import CredentialsApi from swagger_client.api.orgs_api import OrgsApi from swagger_client.api.projects_api import ProjectsApi from swagger_client.api.public_api import PublicApi from swagger_client.api.shares_api import SharesApi from swagger_client.api.status_api import StatusApi from swagger_client.api.userdevices_api import UserdevicesApi from swagger_client.api.users_api import UsersApi from swagger_client.api.webhooks_api import WebhooksApi

41.388889

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0.383387

0

0.001466

0.084564

745

17

64

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0.916422

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true

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0

null

1

0

null

0

1

0

1

0

5

a4bbc82f7c573241310fa0ebed8f1876a6eac575

133

py

Python

src/log_formatter/__init__.py

casualkex/mintwatch

91d63acc513dfa5182617f06e0300eabad744086

[ "MIT" ]

5

2022-02-11T20:01:43.000Z

2022-02-18T11:07:38.000Z

src/log_formatter/__init__.py

casualkex/mintwatch

91d63acc513dfa5182617f06e0300eabad744086

[ "MIT" ]

null

src/log_formatter/__init__.py

casualkex/mintwatch

91d63acc513dfa5182617f06e0300eabad744086

[ "MIT" ]

1

2022-02-17T14:02:11.000Z

from log_formatter.formatter import LogFormatter from log_formatter.response import Response __all__ = ["Response", "LogFormatter"]

26.6

48

0.827068

15

133

6.933333

0.466667

0.134615

0.307692

0

0.097744

133

4

49

33.25

0.866667

0

0.150376

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

a4cce65b35e0bbfd033df9dcbc328b9ff3ff7a8b

182,073

py

Python

data/typing/pandas.core.series.py

pydata-apis/python-api-record

684cffbbb6dc6e81f9de4e02619c8b0ebc557b2b

[ "MIT" ]

67

2020-08-17T11:53:26.000Z

2021-11-08T20:16:06.000Z

data/typing/pandas.core.series.py

data-apis/python-record-api

684cffbbb6dc6e81f9de4e02619c8b0ebc557b2b

[ "MIT" ]

36

2020-08-17T11:09:51.000Z

2021-12-15T18:09:47.000Z

data/typing/pandas.core.series.py

pydata-apis/python-api-record

684cffbbb6dc6e81f9de4e02619c8b0ebc557b2b

[ "MIT" ]

7

2020-08-19T05:06:47.000Z

2020-11-04T05:10:38.000Z

from typing import * class Series: # usage.dask: 4 # usage.hvplot: 1 __module__: ClassVar[object] # usage.dask: 7 # usage.sklearn: 1 __name__: ClassVar[object] # usage.koalas: 1 to_dict: ClassVar[object] # usage.koalas: 1 to_latex: ClassVar[object] # usage.koalas: 1 to_markdown: ClassVar[object] # usage.koalas: 1 to_string: ClassVar[object] @overload @classmethod def __getitem__(cls, _0: Type[int], /): """ usage.koalas: 2 """ ... @overload @classmethod def __getitem__(cls, _0: int, /): """ usage.dask: 16 usage.geopandas: 3 usage.koalas: 10 usage.prophet: 20 usage.statsmodels: 32 usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["int"], /): """ usage.koalas: 1 """ ... @overload @classmethod def __getitem__(cls, _0: List[Literal["max", "min"]], /): """ usage.koalas: 1 """ ... @overload @classmethod def __getitem__(cls, _0: numpy.float64, /): """ usage.koalas: 4 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, int, None], /): """ usage.koalas: 11 usage.prophet: 1 usage.statsmodels: 29 """ ... @overload @classmethod def __getitem__(cls, _0: slice[int, None, int], /): """ usage.hvplot: 4 usage.koalas: 2 usage.statsmodels: 23 """ ... @overload @classmethod def __getitem__(cls, _0: slice[int, int, int], /): """ usage.koalas: 3 usage.prophet: 1 usage.statsmodels: 24 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["2013-01-04T00:00:00"], /): """ usage.koalas: 1 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, Literal["2013-01-04T00:00:00"], None], /): """ usage.koalas: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[Literal["2013-01-02T00:00:00"], None, Literal["2013-01-02T00:00:00"]], /, ): """ usage.koalas: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[ Literal["2013-01-02T00:00:00"], Literal["2013-01-04T00:00:00"], Literal["2013-01-02T00:00:00"], ], /, ): """ usage.koalas: 1 """ ... @overload @classmethod def __getitem__(cls, _0: pandas.core.series.Series, /): """ usage.alphalens: 8 usage.dask: 6 usage.geopandas: 2 usage.koalas: 4 usage.prophet: 3 usage.seaborn: 28 usage.statsmodels: 11 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["A"], /): """ usage.dask: 4 usage.koalas: 1 usage.modin: 4 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["B"], /): """ usage.dask: 1 usage.koalas: 1 usage.modin: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["a"], /): """ usage.dask: 3 usage.koalas: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Tuple[Literal["a"], Literal["lama"]], /): """ usage.koalas: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Type[float], /): """ usage.koalas: 1 """ ... @overload @classmethod def __getitem__(cls, _0: pandas.core.indexes.base.Index, /): """ usage.alphalens: 2 usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: cftime._cftime.DatetimeNoLeap, /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["0001"], /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[Literal["0001-01-01"], Literal["0001-12-30"], Literal["0001-01-01"]], /, ): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, Literal["0001-12-30"], None], /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[ cftime._cftime.DatetimeNoLeap, cftime._cftime.DatetimeNoLeap, cftime._cftime.DatetimeNoLeap, ], /, ): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, cftime._cftime.DatetimeNoLeap, None], /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: cftime._cftime.Datetime360Day, /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[ cftime._cftime.Datetime360Day, cftime._cftime.Datetime360Day, cftime._cftime.Datetime360Day, ], /, ): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, cftime._cftime.Datetime360Day, None], /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: cftime._cftime.DatetimeJulian, /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[ cftime._cftime.DatetimeJulian, cftime._cftime.DatetimeJulian, cftime._cftime.DatetimeJulian, ], /, ): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, cftime._cftime.DatetimeJulian, None], /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: cftime._cftime.DatetimeAllLeap, /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[ cftime._cftime.DatetimeAllLeap, cftime._cftime.DatetimeAllLeap, cftime._cftime.DatetimeAllLeap, ], /, ): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, cftime._cftime.DatetimeAllLeap, None], /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: cftime._cftime.DatetimeGregorian, /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[ cftime._cftime.DatetimeGregorian, cftime._cftime.DatetimeGregorian, cftime._cftime.DatetimeGregorian, ], /, ): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, cftime._cftime.DatetimeGregorian, None], /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: cftime._cftime.DatetimeProlepticGregorian, /): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[ cftime._cftime.DatetimeProlepticGregorian, cftime._cftime.DatetimeProlepticGregorian, cftime._cftime.DatetimeProlepticGregorian, ], /, ): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__( cls, _0: slice[None, cftime._cftime.DatetimeProlepticGregorian, None], / ): """ usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, None, None], /): """ usage.seaborn: 1 usage.xarray: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["lower_dl"], /): """ usage.statsmodels: 4 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["upper_dl"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["prob_exceedance"], /): """ usage.statsmodels: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["nuncen_above"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["ncen_equal"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["det_limit_index"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["rank"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["censored"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["cen"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["L1.0->0"], /): """ usage.statsmodels: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["fb(0).cov.chol[1,1]"], /): """ usage.statsmodels: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["llf"], /): """ usage.statsmodels: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["sse"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["mse"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: slice[None, Literal["initial_level"], None], /): """ usage.statsmodels: 2 """ ... @overload @classmethod def __getitem__(cls, _0: List[numpy.int64], /): """ usage.statsmodels: 12 """ ... @overload @classmethod def __getitem__(cls, _0: List[int], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["ds"], /): """ usage.prophet: 3 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["col2"], /): """ usage.modin: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["col1"], /): """ usage.modin: 4 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["C"], /): """ usage.modin: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["idx"], /): """ usage.modin: 1 """ ... @overload @classmethod def __getitem__(cls, _0: numpy.ndarray, /): """ usage.dask: 1 usage.geopandas: 5 usage.seaborn: 8 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["edges"], /): """ usage.seaborn: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["widths"], /): """ usage.seaborn: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["heights"], /): """ usage.seaborn: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["geometry"], /): """ usage.geopandas: 5 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["address"], /): """ usage.geopandas: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["amount"], /): """ usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["name"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["id"], /): """ usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["aa"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["bb"], /): """ usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["Edith"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["-500"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["numbers"], /): """ usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["names"], /): """ usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["more_numbers"], /): """ usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["integers"], /): """ usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["dates"], /): """ usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["Date"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["Close"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["b"], /): """ usage.dask: 2 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["c-d"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["x"], /): """ usage.dask: 3 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["y"], /): """ usage.dask: 4 """ ... @overload @classmethod def __getitem__(cls, _0: Tuple[slice[int, int, int]], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["25%"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["50%"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["75%"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["z"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["d"], /): """ usage.dask: 1 """ ... @overload @classmethod def __getitem__(cls, _0: Literal["e"], /): """ usage.dask: 1 """ ... @classmethod def __getitem__(cls, _0: object, /): """ usage.alphalens: 10 usage.dask: 72 usage.geopandas: 16 usage.hvplot: 4 usage.koalas: 47 usage.modin: 12 usage.prophet: 28 usage.seaborn: 40 usage.statsmodels: 155 usage.xarray: 23 """ ... @overload @classmethod def __ne__(cls, _0: Type[pandas.core.series.Series], /): """ usage.dask: 4 """ ... @overload @classmethod def __ne__(cls, _0: pandas.core.series.Series, /): """ usage.koalas: 4 usage.statsmodels: 2 """ ... @overload @classmethod def __ne__(cls, _0: Literal["ALL"], /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __ne__(cls, _0: int, /): """ usage.statsmodels: 3 """ ... @overload @classmethod def __ne__(cls, _0: float, /): """ usage.statsmodels: 1 """ ... @overload @classmethod def __ne__(cls, _0: numpy.ndarray, /): """ usage.pandas: 6 """ ... @classmethod def __ne__(cls, _0: object, /): """ usage.dask: 4 usage.koalas: 4 usage.pandas: 6 usage.statsmodels: 7 """ ... @classmethod def __rmod__(cls, _0: Union[numpy.timedelta64, numpy.float64, str], /): """ usage.pandas: 2 usage.sklearn: 1 """ ... # usage.statsmodels: 3 T: object # usage.dask: 6 # usage.geopandas: 1 # usage.sklearn: 1 __class__: Type[geopandas.geoseries.GeoSeries] # usage.dask: 2 _constructor: object # usage.dask: 1 _data: object # usage.dask: 3 _values: object # usage.geopandas: 1 array: object # usage.koalas: 2 at: object # usage.koalas: 2 axes: object # usage.dask: 28 # usage.seaborn: 10 cat: object # usage.dask: 1 div: object # usage.dask: 1 divide: object # usage.prophet: 1 ds: object # usage.dask: 2 # usage.hvplot: 1 # usage.koalas: 33 # usage.prophet: 2 # usage.xarray: 2 dt: object # usage.dask: 64 # usage.geopandas: 7 # usage.hvplot: 1 # usage.koalas: 7 # usage.prophet: 1 # usage.pyjanitor: 1 # usage.seaborn: 2 # usage.sklearn: 9 # usage.statsmodels: 2 dtype: object # usage.sklearn: 2 dtypes: object # usage.dask: 1 # usage.geopandas: 1 empty: object # usage.koalas: 6 hasnans: object # usage.prophet: 3 holiday: object # usage.hvplot: 1 hvplot: object # usage.koalas: 1 iat: object # usage.dask: 31 # usage.geopandas: 1 # usage.koalas: 27 # usage.prophet: 9 # usage.seaborn: 1 # usage.sklearn: 1 # usage.statsmodels: 102 # usage.xarray: 3 iloc: object # usage.alphalens: 34 # usage.dask: 83 # usage.geopandas: 5 # usage.koalas: 42 # usage.modin: 7 # usage.seaborn: 12 # usage.sklearn: 3 # usage.statsmodels: 252 # usage.xarray: 23 index: object # usage.koalas: 4 is_unique: object # usage.alphalens: 1 # usage.dask: 19 # usage.koalas: 30 # usage.seaborn: 1 # usage.sklearn: 1 # usage.statsmodels: 43 # usage.xarray: 3 loc: object # usage.statsmodels: 1 lower_ci: object # usage.statsmodels: 1 multiply: object # usage.alphalens: 20 # usage.dask: 48 # usage.geopandas: 3 # usage.koalas: 16 # usage.modin: 1 # usage.seaborn: 9 # usage.sklearn: 6 # usage.statsmodels: 31 # usage.xarray: 5 name: Union[ Tuple[Literal["X", "Y", "x", "y"], Literal["A", "B", "a", "z"]], float, int, str, None, ] # usage.koalas: 1 names: List[Literal["koalas", "hello"]] # usage.dask: 2 nbytes: object # usage.dask: 6 # usage.sklearn: 4 # usage.statsmodels: 19 ndim: object # usage.alphalens: 9 # usage.hvplot: 1 # usage.koalas: 13 plot: object # usage.dask: 1 rdiv: object # usage.dask: 7 # usage.koalas: 2 # usage.prophet: 1 # usage.seaborn: 1 # usage.sklearn: 12 # usage.statsmodels: 53 shape: object # usage.dask: 2 # usage.seaborn: 25 # usage.statsmodels: 9 size: object # usage.dask: 26 # usage.geopandas: 2 # usage.koalas: 103 # usage.pyjanitor: 3 # usage.seaborn: 2 str: object # usage.statsmodels: 1 upper_ci: object # usage.alphalens: 8 # usage.dask: 40 # usage.geopandas: 15 # usage.koalas: 11 # usage.modin: 1 # usage.prophet: 38 # usage.pyjanitor: 2 # usage.seaborn: 22 # usage.statsmodels: 280 # usage.xarray: 16 values: object @overload def __add__(self, _0: int, /): """ usage.alphalens: 6 usage.dask: 35 usage.koalas: 65 usage.modin: 1 usage.pyjanitor: 1 usage.statsmodels: 6 """ ... @overload def __add__(self, _0: numpy.int64, /): """ usage.dask: 2 usage.koalas: 49 usage.statsmodels: 1 """ ... @overload def __add__(self, _0: pandas.core.series.Series, /): """ usage.dask: 14 usage.koalas: 6 usage.prophet: 4 usage.pyjanitor: 2 usage.seaborn: 1 usage.statsmodels: 17 """ ... @overload def __add__(self, _0: Literal["_lit"], /): """ usage.koalas: 1 """ ... @overload def __add__(self, _0: numpy.ndarray, /): """ usage.dask: 2 usage.prophet: 1 usage.seaborn: 1 usage.statsmodels: 17 """ ... @overload def __add__(self, _0: float, /): """ usage.dask: 2 usage.seaborn: 1 usage.statsmodels: 2 """ ... @overload def __add__(self, _0: numpy.float64, /): """ usage.dask: 1 usage.statsmodels: 1 """ ... @overload def __add__(self, _0: Literal[")"], /): """ usage.statsmodels: 1 """ ... @overload def __add__(self, _0: Literal["*"], /): """ usage.statsmodels: 3 """ ... @overload def __add__(self, _0: Literal[""], /): """ usage.statsmodels: 1 """ ... @overload def __add__(self, _0: Literal["Q"], /): """ usage.statsmodels: 1 """ ... @overload def __add__(self, _0: Union[numpy.ndarray, numpy.timedelta64, numpy.datetime64], /): """ usage.pandas: 39 """ ... @overload def __add__(self, _0: Literal["-"], /): """ usage.pyjanitor: 1 """ ... @overload def __add__(self, _0: dask.dataframe.core.Scalar, /): """ usage.dask: 1 """ ... def __add__(self, _0: object, /): """ usage.alphalens: 6 usage.dask: 57 usage.koalas: 121 usage.modin: 1 usage.pandas: 39 usage.prophet: 5 usage.pyjanitor: 4 usage.seaborn: 3 usage.statsmodels: 50 """ ... @overload def __and__(self, _0: pandas.core.series.Series, /): """ usage.dask: 5 usage.geopandas: 4 usage.koalas: 3 usage.prophet: 3 usage.seaborn: 2 usage.statsmodels: 4 """ ... @overload def __and__(self, _0: numpy.ndarray, /): """ usage.pandas: 3 usage.seaborn: 1 """ ... @overload def __and__(self, _0: bool, /): """ usage.geopandas: 2 """ ... def __and__(self, _0: Union[pandas.core.series.Series, numpy.ndarray, bool], /): """ usage.dask: 5 usage.geopandas: 6 usage.koalas: 3 usage.pandas: 3 usage.prophet: 3 usage.seaborn: 3 usage.statsmodels: 4 """ ... def __array_wrap__(self, /, result: numpy.ndarray): """ usage.dask: 3 """ ... @overload def __contains__(self, _0: Literal["missing"], /): """ usage.statsmodels: 1 """ ... @overload def __contains__(self, _0: Literal["nobs"], /): """ usage.statsmodels: 1 """ ... @overload def __contains__(self, _0: Literal["bool"], /): """ usage.pandas: 1 """ ... def __contains__(self, _0: Literal["bool", "nobs", "missing"], /): """ usage.pandas: 1 usage.statsmodels: 2 """ ... @overload def __eq__(self, _0: Literal["c"], /): """ usage.koalas: 2 usage.seaborn: 20 """ ... @overload def __eq__(self, _0: Literal["a"], /): """ usage.dask: 1 usage.koalas: 2 usage.seaborn: 24 """ ... @overload def __eq__(self, _0: Literal["b"], /): """ usage.koalas: 2 usage.seaborn: 22 """ ... @overload def __eq__(self, _0: pandas.core.series.Series, /): """ usage.dask: 24 usage.geopandas: 2 usage.koalas: 4 usage.statsmodels: 2 """ ... @overload def __eq__(self, _0: int, /): """ usage.alphalens: 1 usage.dask: 5 usage.hvplot: 3 usage.koalas: 32 usage.prophet: 1 usage.seaborn: 3 usage.statsmodels: 4 """ ... @overload def __eq__(self, _0: numpy.int64, /): """ usage.dask: 1 usage.koalas: 1 usage.seaborn: 3 """ ... @overload def __eq__(self, _0: float, /): """ usage.alphalens: 1 usage.dask: 1 usage.statsmodels: 10 """ ... @overload def __eq__(self, _0: Literal["Duncan"], /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: Literal["carData"], /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: Literal["Guerry"], /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: Literal["HistData"], /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: Literal["ALL"], /): """ usage.statsmodels: 2 """ ... @overload def __eq__(self, _0: numpy.float64, /): """ usage.dask: 1 usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: Literal["ND"], /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: Literal["1"], /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: Literal["iris"], /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: Literal["datasets"], /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: Literal["quakes"], /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: pandas.core.frame.DataFrame, /): """ usage.statsmodels: 1 """ ... @overload def __eq__(self, _0: object, /): """ usage.pandas: 52 """ ... @overload def __eq__(self, _0: pandas._libs.tslibs.timedeltas.Timedelta, /): """ usage.prophet: 1 """ ... @overload def __eq__(self, _0: Literal["Yes"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["No"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["Lunch"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["Dinner"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["rest"], /): """ usage.seaborn: 3 """ ... @overload def __eq__(self, _0: Literal["walking"], /): """ usage.seaborn: 3 """ ... @overload def __eq__(self, _0: Literal["running"], /): """ usage.seaborn: 3 """ ... @overload def __eq__(self, _0: Literal["no fat"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["low fat"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["A"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["B"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["C"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["D"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["E"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["F"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["G"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["First"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["Second"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["Third"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["male"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["female"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["Male"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["Female"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["II"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["III"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["j"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["d"], /): """ usage.seaborn: 7 """ ... @overload def __eq__(self, _0: Literal["e"], /): """ usage.seaborn: 7 """ ... @overload def __eq__(self, _0: Literal["f"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["g"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["h"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["i"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["k"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["l"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["m"], /): """ usage.seaborn: 17 """ ... @overload def __eq__(self, _0: numpy.bool_, /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["n"], /): """ usage.seaborn: 13 """ ... @overload def __eq__(self, _0: Literal["t"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["u"], /): """ usage.seaborn: 2 """ ... @overload def __eq__(self, _0: Literal["v"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: Literal["x"], /): """ usage.seaborn: 3 """ ... @overload def __eq__(self, _0: Literal["y"], /): """ usage.seaborn: 3 """ ... @overload def __eq__(self, _0: Literal["o"], /): """ usage.seaborn: 3 """ ... @overload def __eq__(self, _0: Literal["numeric"], /): """ usage.seaborn: 1 """ ... @overload def __eq__(self, _0: pandas.core.indexes.datetimes.DatetimeIndex, /): """ usage.hvplot: 2 """ ... @overload def __eq__(self, _0: Literal["geometry"], /): """ usage.geopandas: 2 """ ... @overload def __eq__(self, _0: numpy.ndarray, /): """ usage.geopandas: 5 """ ... @overload def __eq__(self, _0: Literal["Africa"], /): """ usage.geopandas: 1 """ ... @overload def __eq__(self, _0: Literal["Queens"], /): """ usage.geopandas: 3 """ ... @overload def __eq__(self, _0: Literal["Bronx"], /): """ usage.geopandas: 2 """ ... @overload def __eq__(self, _0: Literal["Polygon"], /): """ usage.geopandas: 2 """ ... @overload def __eq__(self, _0: Literal["MultiPolygon"], /): """ usage.geopandas: 2 """ ... @overload def __eq__(self, _0: Literal["LineString"], /): """ usage.geopandas: 2 """ ... @overload def __eq__(self, _0: Literal["MultiLineString"], /): """ usage.geopandas: 2 """ ... @overload def __eq__(self, _0: Literal["LinearRing"], /): """ usage.geopandas: 2 """ ... @overload def __eq__(self, _0: Literal["Point"], /): """ usage.geopandas: 2 """ ... @overload def __eq__(self, _0: Literal["MultiPoint"], /): """ usage.geopandas: 2 """ ... @overload def __eq__(self, _0: bool, /): """ usage.pyjanitor: 1 """ ... @overload def __eq__(self, _0: Literal["category"], /): """ usage.dask: 4 """ ... @overload def __eq__(self, _0: Literal["i8"], /): """ usage.dask: 1 """ ... @overload def __eq__(self, _0: Literal["object"], /): """ usage.dask: 1 """ ... @overload def __eq__(self, _0: Type[numpy.float64], /): """ usage.sklearn: 3 """ ... @overload def __eq__(self, _0: List[Type[numpy.float64]], /): """ usage.sklearn: 2 """ ... @overload def __eq__( self, _0: List[ Union[pandas.core.dtypes.dtypes.CategoricalDtype, Type[numpy.float64]] ], /, ): """ usage.sklearn: 3 """ ... @overload def __eq__( self, _0: List[ Union[Type[numpy.float64], pandas.core.dtypes.dtypes.CategoricalDtype] ], /, ): """ usage.sklearn: 1 """ ... @overload def __eq__( self, _0: List[Union[type, pandas.core.dtypes.dtypes.CategoricalDtype]], / ): """ usage.sklearn: 1 """ ... def __eq__(self, _0: object, /): """ usage.alphalens: 2 usage.dask: 39 usage.geopandas: 29 usage.hvplot: 5 usage.koalas: 43 usage.pandas: 52 usage.prophet: 2 usage.pyjanitor: 1 usage.seaborn: 180 usage.sklearn: 10 usage.statsmodels: 29 """ ... @overload def __floordiv__(self, _0: int, /): """ usage.dask: 1 usage.koalas: 36 """ ... @overload def __floordiv__(self, _0: numpy.timedelta64, /): """ usage.koalas: 3 """ ... @overload def __floordiv__(self, _0: Union[numpy.timedelta64, numpy.ndarray], /): """ usage.pandas: 6 """ ... @overload def __floordiv__(self, _0: float, /): """ usage.dask: 2 """ ... def __floordiv__(self, _0: Union[int, float, numpy.timedelta64, numpy.ndarray], /): """ usage.dask: 3 usage.koalas: 39 usage.pandas: 6 """ ... @overload def __ge__(self, _0: int, /): """ usage.alphalens: 2 usage.dask: 6 """ ... @overload def __ge__(self, _0: numpy.float64, /): """ usage.dask: 1 usage.seaborn: 1 usage.statsmodels: 1 """ ... @overload def __ge__(self, _0: numpy.int64, /): """ usage.dask: 1 usage.statsmodels: 1 """ ... @overload def __ge__(self, _0: numpy.ndarray, /): """ usage.pandas: 7 """ ... @overload def __ge__(self, _0: pandas.core.series.Series, /): """ usage.prophet: 3 """ ... @overload def __ge__(self, _0: pandas._libs.tslibs.timestamps.Timestamp, /): """ usage.dask: 1 usage.pyjanitor: 1 """ ... @overload def __ge__(self, _0: Literal["c"], /): """ usage.dask: 1 """ ... @overload def __ge__(self, _0: Literal["Zelda"], /): """ usage.dask: 1 """ ... @overload def __ge__(self, _0: Literal["d"], /): """ usage.dask: 1 """ ... @overload def __ge__(self, _0: Literal["B"], /): """ usage.dask: 1 """ ... @overload def __ge__(self, _0: Literal["3"], /): """ usage.dask: 1 """ ... @overload def __ge__(self, _0: None, /): """ usage.dask: 1 """ ... @overload def __ge__(self, _0: pandas._libs.tslibs.nattype.NaTType, /): """ usage.dask: 1 """ ... @overload def __ge__(self, _0: Literal["2"], /): """ usage.dask: 1 """ ... def __ge__(self, _0: object, /): """ usage.alphalens: 2 usage.dask: 17 usage.pandas: 7 usage.prophet: 3 usage.pyjanitor: 1 usage.seaborn: 1 usage.statsmodels: 2 """ ... @overload def __gt__(self, _0: int, /): """ usage.alphalens: 1 usage.dask: 55 usage.geopandas: 1 usage.hvplot: 4 usage.koalas: 12 usage.statsmodels: 4 """ ... @overload def __gt__(self, _0: float, /): """ usage.koalas: 1 usage.statsmodels: 2 """ ... @overload def __gt__(self, _0: numpy.ndarray, /): """ usage.statsmodels: 1 """ ... @overload def __gt__(self, _0: Union[numpy.timedelta64, numpy.ndarray], /): """ usage.pandas: 5 """ ... @overload def __gt__(self, _0: pandas._libs.tslibs.timestamps.Timestamp, /): """ usage.prophet: 2 """ ... @overload def __gt__(self, _0: pandas._libs.tslibs.timedeltas.Timedelta, /): """ usage.prophet: 2 """ ... @overload def __gt__(self, _0: pandas.core.series.Series, /): """ usage.dask: 2 usage.prophet: 3 """ ... @overload def __gt__(self, _0: Literal["2014-01-01"], /): """ usage.prophet: 1 """ ... @overload def __gt__(self, _0: pandas.core.frame.DataFrame, /): """ usage.dask: 1 """ ... @overload def __gt__(self, _0: numpy.float64, /): """ usage.dask: 1 """ ... @overload def __gt__(self, _0: numpy.int64, /): """ usage.dask: 2 """ ... def __gt__(self, _0: object, /): """ usage.alphalens: 1 usage.dask: 61 usage.geopandas: 1 usage.hvplot: 4 usage.koalas: 13 usage.pandas: 5 usage.prophet: 8 usage.statsmodels: 7 """ ... @overload def __iadd__(self, _0: pandas.core.series.Series, /): """ usage.koalas: 1 usage.seaborn: 1 usage.statsmodels: 1 """ ... @overload def __iadd__(self, _0: numpy.ndarray, /): """ usage.statsmodels: 1 """ ... @overload def __iadd__(self, _0: List[float], /): """ usage.statsmodels: 1 """ ... @overload def __iadd__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): """ usage.pandas: 2 """ ... @overload def __iadd__(self, _0: int, /): """ usage.prophet: 1 usage.seaborn: 1 """ ... @overload def __iadd__(self, _0: float, /): """ usage.prophet: 5 """ ... def __iadd__(self, _0: object, /): """ usage.koalas: 1 usage.pandas: 2 usage.prophet: 6 usage.seaborn: 2 usage.statsmodels: 3 """ ... @overload def __iand__(self, _0: numpy.ndarray, /): """ usage.statsmodels: 1 """ ... @overload def __iand__(self, _0: pandas.core.series.Series, /): """ usage.seaborn: 1 """ ... def __iand__(self, _0: Union[pandas.core.series.Series, numpy.ndarray], /): """ usage.seaborn: 1 usage.statsmodels: 1 """ ... @overload def __imul__(self, _0: float, /): """ usage.statsmodels: 1 """ ... @overload def __imul__(self, _0: numpy.ndarray, /): """ usage.statsmodels: 2 """ ... @overload def __imul__(self, _0: numpy.float64, /): """ usage.seaborn: 1 """ ... def __imul__(self, _0: Union[numpy.float64, float, numpy.ndarray], /): """ usage.seaborn: 1 usage.statsmodels: 3 """ ... def __invert__(self, /): """ usage.dask: 1 usage.geopandas: 5 usage.koalas: 1 usage.prophet: 1 usage.pyjanitor: 1 usage.seaborn: 1 usage.statsmodels: 22 usage.xarray: 1 """ ... @overload def __ior__(self, _0: pandas.core.series.Series, /): """ usage.statsmodels: 1 """ ... @overload def __ior__(self, _0: numpy.ndarray, /): """ usage.statsmodels: 3 """ ... def __ior__(self, _0: Union[numpy.ndarray, pandas.core.series.Series], /): """ usage.statsmodels: 4 """ ... @overload def __isub__(self, _0: numpy.float64, /): """ usage.statsmodels: 1 """ ... @overload def __isub__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): """ usage.pandas: 2 """ ... @overload def __isub__(self, _0: float, /): """ usage.prophet: 1 """ ... @overload def __isub__(self, _0: int, /): """ usage.seaborn: 1 """ ... def __isub__( self, _0: Union[int, numpy.timedelta64, numpy.ndarray, numpy.float64, float], / ): """ usage.pandas: 2 usage.prophet: 1 usage.seaborn: 1 usage.statsmodels: 1 """ ... def __iter__(self, /): """ usage.dask: 4 usage.koalas: 2 usage.modin: 2 usage.prophet: 1 usage.pyjanitor: 2 usage.seaborn: 7 usage.sklearn: 12 usage.statsmodels: 16 """ ... @overload def __itruediv__(self, _0: numpy.int64, /): """ usage.alphalens: 2 """ ... @overload def __itruediv__(self, _0: pandas.core.series.Series, /): """ usage.dask: 1 usage.statsmodels: 1 """ ... @overload def __itruediv__(self, _0: numpy.float64, /): """ usage.statsmodels: 1 """ ... @overload def __itruediv__(self, _0: int, /): """ usage.seaborn: 1 """ ... def __itruediv__( self, _0: Union[pandas.core.series.Series, numpy.float64, numpy.int64, int], / ): """ usage.alphalens: 2 usage.dask: 1 usage.seaborn: 1 usage.statsmodels: 2 """ ... @overload def __le__(self, _0: numpy.float64, /): """ usage.dask: 1 usage.seaborn: 1 usage.statsmodels: 2 """ ... @overload def __le__(self, _0: float, /): """ usage.dask: 1 usage.statsmodels: 1 """ ... @overload def __le__(self, _0: int, /): """ usage.dask: 3 usage.statsmodels: 1 """ ... @overload def __le__(self, _0: Union[numpy.ndarray, numpy.float64], /): """ usage.pandas: 7 """ ... @overload def __le__(self, _0: pandas.core.series.Series, /): """ usage.prophet: 3 """ ... @overload def __le__(self, _0: pandas._libs.tslibs.timestamps.Timestamp, /): """ usage.prophet: 4 usage.pyjanitor: 1 """ ... @overload def __le__(self, _0: numpy.int64, /): """ usage.dask: 1 """ ... def __le__(self, _0: object, /): """ usage.dask: 6 usage.pandas: 7 usage.prophet: 7 usage.pyjanitor: 1 usage.seaborn: 1 usage.statsmodels: 4 """ ... @overload def __lt__(self, _0: int, /): """ usage.alphalens: 3 usage.dask: 9 usage.koalas: 4 usage.prophet: 1 usage.statsmodels: 1 """ ... @overload def __lt__(self, _0: pandas.core.frame.DataFrame, /): """ usage.alphalens: 1 usage.dask: 1 """ ... @overload def __lt__(self, _0: numpy.float64, /): """ usage.dask: 1 usage.statsmodels: 2 """ ... @overload def __lt__(self, _0: float, /): """ usage.dask: 1 usage.statsmodels: 4 """ ... @overload def __lt__(self, _0: Union[numpy.ndarray, numpy.int64, numpy.float64], /): """ usage.pandas: 4 """ ... @overload def __lt__(self, _0: pandas.core.series.Series, /): """ usage.dask: 2 usage.prophet: 3 """ ... @overload def __lt__(self, _0: Literal["2013-01-01"], /): """ usage.prophet: 1 """ ... @overload def __lt__(self, _0: pandas._libs.tslibs.timedeltas.Timedelta, /): """ usage.dask: 2 """ ... def __lt__(self, _0: object, /): """ usage.alphalens: 4 usage.dask: 16 usage.koalas: 4 usage.pandas: 4 usage.prophet: 5 usage.statsmodels: 7 """ ... @overload def __mod__(self, _0: int, /): """ usage.dask: 5 usage.geopandas: 2 usage.koalas: 16 """ ... @overload def __mod__(self, _0: Union[numpy.timedelta64, numpy.ndarray], /): """ usage.pandas: 57 """ ... def __mod__(self, _0: Union[int, numpy.ndarray, numpy.timedelta64], /): """ usage.dask: 5 usage.geopandas: 2 usage.koalas: 16 usage.pandas: 57 """ ... @overload def __mul__(self, _0: int, /): """ usage.alphalens: 7 usage.dask: 2 usage.hvplot: 1 usage.koalas: 5 usage.pyjanitor: 1 usage.seaborn: 2 usage.statsmodels: 4 """ ... @overload def __mul__(self, _0: pandas.core.series.Series, /): """ usage.dask: 4 usage.koalas: 6 usage.prophet: 1 usage.seaborn: 1 usage.statsmodels: 5 """ ... @overload def __mul__(self, _0: float, /): """ usage.alphalens: 1 usage.dask: 1 usage.pyjanitor: 3 usage.statsmodels: 9 """ ... @overload def __mul__(self, _0: numpy.float64, /): """ usage.prophet: 2 usage.statsmodels: 4 """ ... @overload def __mul__(self, _0: numpy.ndarray, /): """ usage.prophet: 1 usage.statsmodels: 2 """ ... @overload def __mul__(self, _0: numpy.int64, /): """ usage.statsmodels: 2 """ ... @overload def __mul__( self, _0: Union[ numpy.float32, numpy.ndarray, numpy.timedelta64, numpy.int64, numpy.float64 ], /, ): """ usage.pandas: 29 """ ... def __mul__(self, _0: object, /): """ usage.alphalens: 8 usage.dask: 7 usage.hvplot: 1 usage.koalas: 11 usage.pandas: 29 usage.prophet: 4 usage.pyjanitor: 4 usage.seaborn: 3 usage.statsmodels: 26 """ ... def __neg__(self, /): """ usage.dask: 2 usage.koalas: 17 usage.statsmodels: 2 """ ... @overload def __or__(self, _0: pandas.core.series.Series, /): """ usage.dask: 3 usage.geopandas: 8 usage.koalas: 3 usage.prophet: 1 """ ... @overload def __or__(self, _0: numpy.ndarray, /): """ usage.pandas: 3 usage.seaborn: 1 """ ... def __or__(self, _0: Union[pandas.core.series.Series, numpy.ndarray], /): """ usage.dask: 3 usage.geopandas: 8 usage.koalas: 3 usage.pandas: 3 usage.prophet: 1 usage.seaborn: 1 """ ... @overload def __pow__(self, _0: int, /): """ usage.dask: 3 usage.koalas: 1 usage.prophet: 7 usage.statsmodels: 10 """ ... @overload def __pow__(self, _0: float, /): """ usage.dask: 2 usage.statsmodels: 1 """ ... @overload def __pow__(self, _0: numpy.timedelta64, /): """ usage.pandas: 1 """ ... def __pow__(self, _0: Union[float, int, numpy.timedelta64], /): """ usage.dask: 5 usage.koalas: 1 usage.pandas: 1 usage.prophet: 7 usage.statsmodels: 11 """ ... @overload def __radd__(self, _0: pandas.core.frame.DataFrame, /): """ usage.koalas: 48 usage.statsmodels: 2 """ ... @overload def __radd__(self, _0: pandas.core.series.Series, /): """ usage.dask: 14 usage.koalas: 6 usage.prophet: 4 usage.pyjanitor: 2 usage.seaborn: 1 usage.statsmodels: 17 """ ... @overload def __radd__(self, _0: Literal["_lit"], /): """ usage.koalas: 1 """ ... @overload def __radd__(self, _0: numpy.ndarray, /): """ usage.alphalens: 1 usage.dask: 2 usage.prophet: 2 usage.statsmodels: 2 """ ... @overload def __radd__(self, _0: int, /): """ usage.dask: 1 usage.prophet: 1 usage.statsmodels: 1 """ ... @overload def __radd__(self, _0: Literal["("], /): """ usage.statsmodels: 1 """ ... @overload def __radd__(self, _0: numpy.float64, /): """ usage.statsmodels: 1 """ ... @overload def __radd__(self, _0: object, /): """ usage.pandas: 46 """ ... @overload def __radd__(self, _0: dask.dataframe.core.Scalar, /): """ usage.dask: 1 """ ... def __radd__(self, _0: object, /): """ usage.alphalens: 1 usage.dask: 18 usage.koalas: 55 usage.pandas: 46 usage.prophet: 7 usage.pyjanitor: 2 usage.seaborn: 1 usage.statsmodels: 24 """ ... @overload def __rand__(self, _0: pandas.core.series.Series, /): """ usage.dask: 5 usage.geopandas: 4 usage.koalas: 3 usage.prophet: 3 usage.seaborn: 2 usage.statsmodels: 4 """ ... @overload def __rand__(self, _0: numpy.ndarray, /): """ usage.seaborn: 1 """ ... def __rand__(self, _0: Union[pandas.core.series.Series, numpy.ndarray], /): """ usage.dask: 5 usage.geopandas: 4 usage.koalas: 3 usage.prophet: 3 usage.seaborn: 3 usage.statsmodels: 4 """ ... def __rfloordiv__( self, _0: Union[numpy.float64, numpy.int64, numpy.ndarray, numpy.timedelta64], / ): """ usage.pandas: 6 """ ... def __rmatmul__(self, _0: numpy.ndarray, /): """ usage.statsmodels: 3 """ ... @overload def __rmul__(self, _0: float, /): """ usage.koalas: 2 usage.sklearn: 1 usage.statsmodels: 2 """ ... @overload def __rmul__(self, _0: pandas.core.series.Series, /): """ usage.dask: 4 usage.koalas: 6 usage.prophet: 1 usage.seaborn: 1 usage.statsmodels: 5 """ ... @overload def __rmul__(self, _0: int, /): """ usage.alphalens: 1 usage.koalas: 3 usage.seaborn: 1 usage.sklearn: 1 usage.statsmodels: 11 """ ... @overload def __rmul__(self, _0: bool, /): """ usage.alphalens: 1 """ ... @overload def __rmul__(self, _0: numpy.ndarray, /): """ usage.prophet: 1 usage.statsmodels: 6 """ ... @overload def __rmul__(self, _0: numpy.float64, /): """ usage.statsmodels: 9 """ ... @overload def __rmul__(self, _0: pandas.core.frame.DataFrame, /): """ usage.statsmodels: 4 """ ... @overload def __rmul__(self, _0: object, /): """ usage.pandas: 30 """ ... def __rmul__(self, _0: object, /): """ usage.alphalens: 2 usage.dask: 4 usage.koalas: 11 usage.pandas: 30 usage.prophet: 2 usage.seaborn: 2 usage.sklearn: 2 usage.statsmodels: 37 """ ... @overload def __ror__(self, _0: pandas.core.series.Series, /): """ usage.dask: 3 usage.geopandas: 8 usage.koalas: 3 usage.prophet: 1 """ ... @overload def __ror__(self, _0: numpy.ndarray, /): """ usage.pandas: 1 """ ... def __ror__(self, _0: Union[pandas.core.series.Series, numpy.ndarray], /): """ usage.dask: 3 usage.geopandas: 8 usage.koalas: 3 usage.pandas: 1 usage.prophet: 1 """ ... def __rpow__(self, _0: Union[numpy.float64, numpy.int64, numpy.timedelta64], /): """ usage.pandas: 10 """ ... @overload def __rsub__(self, _0: pandas.core.series.Series, /): """ usage.alphalens: 1 usage.dask: 5 usage.geopandas: 1 usage.koalas: 14 usage.prophet: 16 usage.seaborn: 2 usage.statsmodels: 14 """ ... @overload def __rsub__(self, _0: Literal["2013-03-11"], /): """ usage.koalas: 1 """ ... @overload def __rsub__(self, _0: pandas._libs.tslibs.timestamps.Timestamp, /): """ usage.koalas: 1 """ ... @overload def __rsub__(self, _0: pandas.core.frame.DataFrame, /): """ usage.alphalens: 2 usage.dask: 5 usage.seaborn: 2 usage.statsmodels: 8 """ ... @overload def __rsub__(self, _0: numpy.ndarray, /): """ usage.alphalens: 1 usage.statsmodels: 7 """ ... @overload def __rsub__(self, _0: int, /): """ usage.pyjanitor: 1 usage.statsmodels: 6 """ ... @overload def __rsub__(self, _0: object, /): """ usage.pandas: 44 """ ... def __rsub__(self, _0: object, /): """ usage.alphalens: 4 usage.dask: 10 usage.geopandas: 1 usage.koalas: 16 usage.pandas: 44 usage.prophet: 16 usage.pyjanitor: 1 usage.seaborn: 4 usage.statsmodels: 35 """ ... @overload def __rtruediv__(self, _0: pandas.core.series.Series, /): """ usage.alphalens: 2 usage.dask: 8 usage.koalas: 5 usage.prophet: 2 usage.statsmodels: 10 """ ... @overload def __rtruediv__(self, _0: pandas.core.frame.DataFrame, /): """ usage.seaborn: 2 usage.statsmodels: 5 """ ... @overload def __rtruediv__(self, _0: int, /): """ usage.statsmodels: 1 """ ... @overload def __rtruediv__(self, _0: numpy.ndarray, /): """ usage.statsmodels: 6 """ ... @overload def __rtruediv__(self, _0: object, /): """ usage.pandas: 37 """ ... def __rtruediv__(self, _0: object, /): """ usage.alphalens: 2 usage.dask: 8 usage.koalas: 5 usage.pandas: 37 usage.prophet: 2 usage.seaborn: 2 usage.statsmodels: 22 """ ... def __rxor__(self, _0: pandas.core.series.Series, /): """ usage.dask: 1 """ ... @overload def __setitem__(self, _0: pandas.core.series.Series, _1: float, /): """ usage.alphalens: 2 usage.dask: 1 usage.seaborn: 1 usage.statsmodels: 2 """ ... @overload def __setitem__( self, _0: pandas.core.series.Series, _1: pandas.core.series.Series, / ): """ usage.alphalens: 2 usage.seaborn: 2 usage.statsmodels: 1 """ ... @overload def __setitem__(self, _0: int, _1: float, /): """ usage.statsmodels: 8 usage.xarray: 2 """ ... @overload def __setitem__(self, _0: slice[None, None, None], _1: int, /): """ usage.seaborn: 1 usage.statsmodels: 1 """ ... @overload def __setitem__(self, _0: slice[int, int, int], _1: float, /): """ usage.statsmodels: 4 """ ... @overload def __setitem__(self, _0: slice[int, int, int], _1: float, /): """ usage.statsmodels: 3 """ ... @overload def __setitem__(self, _0: int, _1: numpy.dtype, /): """ usage.statsmodels: 1 """ ... @overload def __setitem__(self, _0: Literal["L1.0->0"], _1: numpy.float64, /): """ usage.statsmodels: 2 """ ... @overload def __setitem__(self, _0: Literal["fb(0).cov.chol[1,1]"], _1: numpy.float64, /): """ usage.statsmodels: 2 """ ... @overload def __setitem__(self, _0: numpy.ndarray, _1: bool, /): """ usage.statsmodels: 1 """ ... @overload def __setitem__(self, _0: slice[None, int, None], _1: float, /): """ usage.modin: 1 """ ... @overload def __setitem__(self, _0: slice[int, None, int], _1: float, /): """ usage.modin: 1 """ ... @overload def __setitem__(self, _0: pandas.core.series.Series, _1: int, /): """ usage.dask: 2 usage.seaborn: 1 """ ... def __setitem__( self, _0: Union[ pandas.core.series.Series, numpy.ndarray, int, Literal["fb(0).cov.chol[1,1]", "L1.0->0"], slice[Union[int, None], Union[int, None], Union[int, None]], ], _1: object, /, ): """ usage.alphalens: 4 usage.dask: 3 usage.modin: 2 usage.seaborn: 5 usage.statsmodels: 25 usage.xarray: 2 """ ... @overload def __sub__(self, _0: pandas.core.series.Series, /): """ usage.alphalens: 1 usage.dask: 5 usage.geopandas: 1 usage.koalas: 14 usage.prophet: 16 usage.seaborn: 2 usage.statsmodels: 14 """ ... @overload def __sub__(self, _0: Literal["2012-01-01"], /): """ usage.koalas: 1 """ ... @overload def __sub__(self, _0: int, /): """ usage.dask: 4 usage.koalas: 3 usage.statsmodels: 2 """ ... @overload def __sub__(self, _0: pandas._libs.tslibs.timestamps.Timestamp, /): """ usage.koalas: 1 usage.prophet: 2 """ ... @overload def __sub__(self, _0: numpy.float64, /): """ usage.alphalens: 3 usage.dask: 9 usage.prophet: 1 usage.statsmodels: 10 """ ... @overload def __sub__(self, _0: numpy.ndarray, /): """ usage.prophet: 1 usage.seaborn: 3 usage.statsmodels: 4 """ ... @overload def __sub__(self, _0: Union[numpy.ndarray, numpy.datetime64, numpy.timedelta64], /): """ usage.pandas: 35 """ ... @overload def __sub__(self, _0: float, /): """ usage.prophet: 2 """ ... @overload def __sub__(self, _0: Literal["1970-01-01T00:00:00"], /): """ usage.prophet: 1 """ ... @overload def __sub__(self, _0: datetime.timedelta, /): """ usage.pyjanitor: 1 """ ... @overload def __sub__(self, _0: numpy.int64, /): """ usage.pyjanitor: 1 """ ... def __sub__(self, _0: object, /): """ usage.alphalens: 4 usage.dask: 18 usage.geopandas: 1 usage.koalas: 19 usage.pandas: 35 usage.prophet: 23 usage.pyjanitor: 2 usage.seaborn: 5 usage.statsmodels: 30 """ ... @overload def __truediv__(self, _0: pandas.core.series.Series, /): """ usage.alphalens: 2 usage.dask: 8 usage.koalas: 5 usage.prophet: 2 usage.statsmodels: 10 """ ... @overload def __truediv__(self, _0: int, /): """ usage.dask: 1 usage.koalas: 1 usage.prophet: 1 usage.pyjanitor: 1 usage.statsmodels: 4 """ ... @overload def __truediv__(self, _0: float, /): """ usage.dask: 1 usage.koalas: 1 usage.prophet: 1 usage.statsmodels: 1 """ ... @overload def __truediv__(self, _0: numpy.float64, /): """ usage.alphalens: 2 usage.prophet: 3 usage.statsmodels: 10 """ ... @overload def __truediv__(self, _0: numpy.int64, /): """ usage.alphalens: 1 usage.pyjanitor: 1 """ ... @overload def __truediv__(self, _0: numpy.ndarray, /): """ usage.prophet: 1 usage.statsmodels: 5 """ ... @overload def __truediv__( self, _0: Union[numpy.float64, numpy.timedelta64, numpy.ndarray], / ): """ usage.pandas: 29 """ ... @overload def __truediv__(self, _0: pandas._libs.tslibs.timedeltas.Timedelta, /): """ usage.prophet: 2 """ ... def __truediv__(self, _0: object, /): """ usage.alphalens: 5 usage.dask: 10 usage.koalas: 7 usage.pandas: 29 usage.prophet: 10 usage.pyjanitor: 2 usage.statsmodels: 30 """ ... @overload def __xor__(self, _0: numpy.ndarray, /): """ usage.pandas: 1 """ ... @overload def __xor__(self, _0: pandas.core.series.Series, /): """ usage.dask: 1 """ ... def __xor__(self, _0: Union[pandas.core.series.Series, numpy.ndarray], /): """ usage.dask: 1 usage.pandas: 1 """ ... def abs(self, /): """ usage.alphalens: 1 usage.dask: 3 usage.koalas: 2 usage.prophet: 2 """ ... @overload def add(self, /, other: int): """ usage.alphalens: 1 """ ... @overload def add(self, /, other: pandas.core.series.Series, level: int): """ usage.statsmodels: 1 """ ... @overload def add(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 4 """ ... @overload def add(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... @overload def add(self, /, other: pandas.core.series.Series): """ usage.dask: 1 """ ... @overload def add(self, /, other: pandas.core.series.Series, fill_value: None): """ usage.dask: 1 """ ... def add( self, /, other: Union[int, pandas.core.series.Series], level: int = ..., fill_value: Union[None, int] = ..., ): """ usage.alphalens: 1 usage.dask: 7 usage.statsmodels: 1 """ ... def add_prefix(self, /, prefix: Literal["item_"]): """ usage.koalas: 2 """ ... def add_suffix(self, /, suffix: Literal["_item"]): """ usage.koalas: 2 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["inner"], axis: None, fill_value: None, ): """ usage.dask: 1 """ ... @overload def align(self, /, other: pandas.core.series.Series, join: Literal["inner"]): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["inner"], axis: None, fill_value: int, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["inner"], fill_value: int, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["outer"], axis: None, fill_value: None, ): """ usage.dask: 1 """ ... @overload def align(self, /, other: pandas.core.series.Series, join: Literal["outer"]): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["outer"], axis: None, fill_value: int, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["outer"], fill_value: int, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["left"], axis: None, fill_value: None, ): """ usage.dask: 1 """ ... @overload def align(self, /, other: pandas.core.series.Series, join: Literal["left"]): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["left"], axis: None, fill_value: int, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["left"], fill_value: int, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["right"], axis: None, fill_value: None, ): """ usage.dask: 1 """ ... @overload def align(self, /, other: pandas.core.series.Series, join: Literal["right"]): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["right"], axis: None, fill_value: int, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["right"], fill_value: int, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["inner"], axis: int, fill_value: None, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["outer"], axis: int, fill_value: None, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["left"], axis: int, fill_value: None, ): """ usage.dask: 1 """ ... @overload def align( self, /, other: pandas.core.series.Series, join: Literal["right"], axis: int, fill_value: None, ): """ usage.dask: 1 """ ... def align( self, /, other: pandas.core.series.Series, join: Literal["right", "left", "outer", "inner"], axis: Union[int, None] = ..., fill_value: Union[None, int] = ..., ): """ usage.dask: 20 """ ... @overload def all(self, /): """ usage.alphalens: 3 usage.dask: 20 usage.geopandas: 7 usage.hvplot: 8 usage.koalas: 8 usage.prophet: 5 usage.seaborn: 13 """ ... @overload def all(self, /, axis: int, skipna: bool): """ usage.dask: 2 """ ... def all(self, /, axis: int = ..., skipna: bool = ...): """ usage.alphalens: 3 usage.dask: 22 usage.geopandas: 7 usage.hvplot: 8 usage.koalas: 8 usage.prophet: 5 usage.seaborn: 13 """ ... @overload def any(self, /): """ usage.alphalens: 2 usage.dask: 6 usage.geopandas: 5 usage.koalas: 3 usage.prophet: 4 usage.seaborn: 6 usage.sklearn: 2 usage.statsmodels: 3 usage.xarray: 1 """ ... @overload def any(self, /, axis: int, skipna: bool): """ usage.dask: 2 """ ... def any(self, /, axis: int = ..., skipna: bool = ...): """ usage.alphalens: 2 usage.dask: 8 usage.geopandas: 5 usage.koalas: 3 usage.prophet: 4 usage.seaborn: 6 usage.sklearn: 2 usage.statsmodels: 3 usage.xarray: 1 """ ... @overload def append(self, /, to_append: pandas.core.series.Series): """ usage.dask: 2 usage.koalas: 2 """ ... @overload def append(self, /, to_append: pandas.core.series.Series, ignore_index: bool): """ usage.koalas: 1 """ ... def append(self, /, to_append: pandas.core.series.Series, ignore_index: bool = ...): """ usage.dask: 2 usage.koalas: 3 """ ... @overload def apply(self, /, func: Callable): """ usage.alphalens: 2 usage.dask: 6 usage.koalas: 1 usage.prophet: 1 usage.pyjanitor: 5 usage.statsmodels: 2 """ ... @overload def apply(self, /, func: numpy.ufunc): """ usage.koalas: 1 """ ... @overload def apply(self, /, func: numpy.ufunc): """ usage.koalas: 1 """ ... @overload def apply(self, /, func: numpy.ufunc): """ usage.koalas: 1 """ ... @overload def apply(self, /, func: Type[str]): """ usage.koalas: 11 """ ... @overload def apply(self, /, func: Callable, args: Tuple[pandas.core.frame.DataFrame]): """ usage.statsmodels: 1 """ ... @overload def apply(self, /, func: Callable, args: Tuple[Literal["%tc"]]): """ usage.statsmodels: 1 """ ... @overload def apply(self, /, func: Callable, args: Tuple[Literal["%tC"]]): """ usage.statsmodels: 1 """ ... @overload def apply(self, /, func: Callable, args: Tuple[Literal["%td"]]): """ usage.statsmodels: 1 """ ... @overload def apply(self, /, func: Callable, args: Tuple[Literal["%tw"]]): """ usage.statsmodels: 1 """ ... @overload def apply(self, /, func: Callable, args: Tuple[Literal["%tm"]]): """ usage.statsmodels: 1 """ ... @overload def apply(self, /, func: Callable, args: Tuple[Literal["%tq"]]): """ usage.statsmodels: 1 """ ... @overload def apply(self, /, func: Callable, args: Tuple[Literal["%th"]]): """ usage.statsmodels: 1 """ ... @overload def apply(self, /, func: Callable, args: Tuple[Literal["%ty"]]): """ usage.statsmodels: 1 """ ... @overload def apply(self, /, func: Callable): """ usage.pyjanitor: 1 """ ... @overload def apply(self, /, func: functools.partial): """ usage.pyjanitor: 1 """ ... @overload def apply(self, /, func: numpy.ufunc): """ usage.pyjanitor: 1 """ ... @overload def apply(self, /, func: Callable, convert_dtype: bool, args: Tuple[None, ...]): """ usage.dask: 3 """ ... @overload def apply(self, /, func: Callable): """ usage.sklearn: 1 """ ... def apply( self, /, func: Union[Callable, functools.partial, Callable, numpy.ufunc, Type[str]], convert_dtype: bool = ..., args: Tuple[Union[pandas.core.frame.DataFrame, str, None], ...] = ..., ): """ usage.alphalens: 2 usage.dask: 9 usage.koalas: 15 usage.prophet: 1 usage.pyjanitor: 8 usage.sklearn: 1 usage.statsmodels: 11 """ ... @overload def asof(self, /, where: int): """ usage.koalas: 4 """ ... @overload def asof(self, /, where: List[int]): """ usage.koalas: 2 """ ... def asof(self, /, where: Union[List[int], int]): """ usage.koalas: 6 """ ... @overload def astype(self, /, dtype: Type[numpy.int32]): """ usage.dask: 1 usage.koalas: 1 usage.statsmodels: 1 """ ... @overload def astype(self, /, dtype: Type[bool]): """ usage.geopandas: 2 usage.koalas: 5 """ ... @overload def astype(self, /, dtype: Literal["category"]): """ usage.alphalens: 7 usage.dask: 17 usage.seaborn: 5 usage.sklearn: 2 usage.statsmodels: 1 """ ... @overload def astype(self, /, dtype: Type[int]): """ usage.dask: 3 usage.pyjanitor: 3 usage.seaborn: 1 usage.statsmodels: 4 usage.xarray: 1 """ ... @overload def astype(self, /, dtype: Type[float]): """ usage.dask: 5 usage.prophet: 1 usage.pyjanitor: 1 usage.statsmodels: 1 """ ... @overload def astype(self, /, dtype: Literal["int32"]): """ usage.statsmodels: 1 """ ... @overload def astype(self, /, dtype: Type[numpy.int64]): """ usage.statsmodels: 1 """ ... @overload def astype(self, /, dtype: Literal["bool"]): """ usage.prophet: 1 """ ... @overload def astype(self, /, dtype: Type[object]): """ usage.geopandas: 1 usage.seaborn: 2 """ ... @overload def astype(self, /, dtype: Type[str]): """ usage.dask: 3 usage.pyjanitor: 2 usage.seaborn: 2 """ ... @overload def astype(self, /, dtype: Literal["int"]): """ usage.hvplot: 4 usage.sklearn: 1 """ ... @overload def astype(self, /, dtype: Literal["int64"]): """ usage.dask: 2 usage.geopandas: 1 """ ... @overload def astype(self, /, dtype: Literal["geometry"]): """ usage.geopandas: 1 """ ... @overload def astype(self, /, dtype: Type[float], errors: Literal["ignore"]): """ usage.pyjanitor: 1 """ ... @overload def astype(self, /, dtype: Literal["float"]): """ usage.dask: 2 usage.sklearn: 1 """ ... @overload def astype(self, /, dtype: numpy.dtype): """ usage.dask: 6 """ ... @overload def astype(self, /, dtype: Literal["datetime64[ns]"]): """ usage.dask: 1 """ ... @overload def astype(self, /, dtype: Literal["f8"]): """ usage.dask: 5 """ ... @overload def astype(self, /, dtype: pandas.core.dtypes.dtypes.CategoricalDtype): """ usage.dask: 2 """ ... @overload def astype(self, /, dtype: Literal["i8"]): """ usage.dask: 3 """ ... @overload def astype(self, /, dtype: Literal["float32"]): """ usage.dask: 3 """ ... @overload def astype(self, /, dtype: Type[numpy.float64], copy: bool): """ usage.sklearn: 1 """ ... @overload def astype(self, /, dtype: pandas.core.dtypes.dtypes.CategoricalDtype, copy: bool): """ usage.sklearn: 1 """ ... @overload def astype(self, /, dtype: Type[object], copy: bool): """ usage.sklearn: 1 """ ... @overload def astype(self, /, dtype: Type[numpy.float16]): """ usage.sklearn: 2 """ ... @overload def astype(self, /, dtype: Type[numpy.int16]): """ usage.sklearn: 1 """ ... def astype( self, /, dtype: Union[ pandas.core.dtypes.dtypes.CategoricalDtype, numpy.dtype, str, type ], errors: Literal["ignore"] = ..., copy: bool = ..., ): """ usage.alphalens: 7 usage.dask: 53 usage.geopandas: 5 usage.hvplot: 4 usage.koalas: 6 usage.prophet: 2 usage.pyjanitor: 7 usage.seaborn: 10 usage.sklearn: 10 usage.statsmodels: 9 usage.xarray: 1 """ ... def autocorr(self, /, lag: int): """ usage.dask: 4 """ ... @overload def between(self, /, left: numpy.float64, right: numpy.float64): """ usage.koalas: 1 """ ... @overload def between(self, /, left: int, right: int, inclusive: bool): """ usage.dask: 1 """ ... @overload def between(self, /, left: int, right: int): """ usage.dask: 1 """ ... def between( self, /, left: Union[int, numpy.float64], right: Union[int, numpy.float64], inclusive: bool = ..., ): """ usage.dask: 2 usage.koalas: 1 """ ... @overload def bfill(self, /): """ usage.koalas: 2 """ ... @overload def bfill(self, /, inplace: bool): """ usage.koalas: 1 """ ... def bfill(self, /, inplace: bool = ...): """ usage.koalas: 3 """ ... @overload def clip(self, /, lower: int, upper: int): """ usage.dask: 3 usage.koalas: 2 """ ... @overload def clip(self, /): """ usage.koalas: 1 """ ... @overload def clip(self, /, lower: int): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def clip(self, /, upper: int): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def clip(self, /, lower: int, upper: None): """ usage.dask: 1 """ ... @overload def clip(self, /, lower: None, upper: int): """ usage.dask: 1 """ ... @overload def clip(self, /, lower: float, upper: float): """ usage.dask: 2 """ ... @overload def clip(self, /, lower: float, upper: None): """ usage.dask: 1 """ ... @overload def clip(self, /, lower: float): """ usage.dask: 1 """ ... @overload def clip(self, /, lower: None, upper: float): """ usage.dask: 1 """ ... @overload def clip(self, /, upper: float): """ usage.dask: 1 """ ... def clip( self, /, lower: Union[float, int, None] = ..., upper: Union[None, int, float] = ..., ): """ usage.dask: 13 usage.koalas: 5 """ ... @overload def combine(self, /, other: shapely.geometry.point.Point, func: Callable): """ usage.geopandas: 1 """ ... @overload def combine(self, /, other: shapely.geometry.point.Point, func: Callable): """ usage.geopandas: 1 """ ... @overload def combine(self, /, other: pandas.core.series.Series, func: Callable): """ usage.geopandas: 1 """ ... @overload def combine(self, /, other: pandas.core.series.Series, func: Callable): """ usage.geopandas: 1 """ ... @overload def combine( self, /, other: pandas.core.series.Series, func: Callable, fill_value: None ): """ usage.dask: 2 """ ... @overload def combine( self, /, other: pandas.core.series.Series, func: Callable, fill_value: int ): """ usage.dask: 2 """ ... @overload def combine( self, /, other: pandas.core.series.Series, func: Callable, fill_value: None ): """ usage.dask: 2 """ ... def combine( self, /, other: Union[pandas.core.series.Series, shapely.geometry.point.Point], func: Callable, fill_value: Union[None, int] = ..., ): """ usage.dask: 6 usage.geopandas: 4 """ ... def combine_first(self, /, other: pandas.core.series.Series): """ usage.dask: 5 usage.koalas: 5 usage.pyjanitor: 1 """ ... def copy(self, /): """ usage.alphalens: 2 usage.dask: 3 usage.koalas: 2 usage.modin: 1 usage.seaborn: 1 usage.statsmodels: 94 """ ... def corr(self, /, other: pandas.core.series.Series): """ usage.koalas: 2 """ ... @overload def count(self, /): """ usage.dask: 9 """ ... @overload def count(self, /, *, axis: int): """ usage.dask: 1 """ ... @overload def count(self, /, *, axis: Literal["columns"]): """ usage.dask: 1 """ ... def count(self, /, *, axis: Union[Literal["columns"], int] = ...): """ usage.dask: 11 """ ... @overload def cov(self, /, other: pandas.core.series.Series): """ usage.dask: 1 """ ... @overload def cov(self, /, other: pandas.core.series.Series, min_periods: int): """ usage.dask: 1 """ ... def cov(self, /, other: pandas.core.series.Series, min_periods: int = ...): """ usage.dask: 2 """ ... @overload def cummax(self, /): """ usage.dask: 3 usage.koalas: 3 """ ... @overload def cummax(self, /, skipna: bool): """ usage.koalas: 2 """ ... @overload def cummax(self, /, axis: None, skipna: bool): """ usage.dask: 1 """ ... def cummax(self, /, axis: None = ..., skipna: bool = ...): """ usage.dask: 4 usage.koalas: 5 """ ... @overload def cummin(self, /): """ usage.dask: 3 usage.koalas: 3 """ ... @overload def cummin(self, /, skipna: bool): """ usage.koalas: 2 """ ... @overload def cummin(self, /, axis: None, skipna: bool): """ usage.dask: 1 """ ... def cummin(self, /, axis: None = ..., skipna: bool = ...): """ usage.dask: 4 usage.koalas: 5 """ ... @overload def cumprod(self, /): """ usage.dask: 3 usage.koalas: 3 """ ... @overload def cumprod(self, /, skipna: bool): """ usage.koalas: 2 """ ... @overload def cumprod(self, /, axis: None, skipna: bool): """ usage.dask: 1 """ ... def cumprod(self, /, axis: None = ..., skipna: bool = ...): """ usage.dask: 4 usage.koalas: 5 """ ... @overload def cumsum(self, /): """ usage.dask: 4 usage.koalas: 3 usage.statsmodels: 1 """ ... @overload def cumsum(self, /, skipna: bool): """ usage.koalas: 2 """ ... @overload def cumsum(self, /, axis: None, skipna: bool): """ usage.dask: 1 """ ... def cumsum(self, /, axis: None = ..., skipna: bool = ...): """ usage.dask: 5 usage.koalas: 5 usage.statsmodels: 1 """ ... @overload def describe(self, /): """ usage.dask: 4 """ ... @overload def describe(self, /, include: List[Literal["number"]], exclude: None): """ usage.dask: 1 """ ... @overload def describe(self, /, include: List[Type[numpy.timedelta64]], exclude: None): """ usage.dask: 1 """ ... @overload def describe(self, /, include: List[Literal["object", "number"]], exclude: None): """ usage.dask: 1 """ ... def describe( self, /, include: List[ Union[Type[numpy.timedelta64], Literal["number", "object"]] ] = ..., exclude: None = ..., ): """ usage.dask: 7 """ ... @overload def diff(self, /): """ usage.dask: 3 usage.hvplot: 4 usage.prophet: 3 usage.seaborn: 1 usage.statsmodels: 33 """ ... @overload def diff(self, /, periods: int): """ usage.dask: 4 usage.statsmodels: 1 """ ... def diff(self, /, periods: int = ...): """ usage.dask: 7 usage.hvplot: 4 usage.prophet: 3 usage.seaborn: 1 usage.statsmodels: 34 """ ... def divmod(self, /, other: int): """ usage.koalas: 2 """ ... def dot(self, /, other: pandas.core.series.Series): """ usage.koalas: 3 """ ... def drop(self, /, labels: Literal["initial_level"]): """ usage.statsmodels: 2 """ ... @overload def drop_duplicates(self, /): """ usage.dask: 6 usage.koalas: 1 usage.statsmodels: 4 """ ... @overload def drop_duplicates(self, /, keep: Literal["last"]): """ usage.dask: 3 usage.koalas: 2 """ ... @overload def drop_duplicates(self, /, keep: bool, inplace: bool): """ usage.koalas: 1 """ ... @overload def drop_duplicates(self, /, keep: Literal["first"]): """ usage.dask: 3 usage.koalas: 1 """ ... @overload def drop_duplicates(self, /, keep: bool): """ usage.koalas: 1 """ ... def drop_duplicates( self, /, keep: Union[Literal["last", "first"], bool] = ..., inplace: bool = ... ): """ usage.dask: 12 usage.koalas: 6 usage.statsmodels: 4 """ ... @overload def droplevel(self, /, level: int): """ usage.koalas: 2 """ ... @overload def droplevel(self, /, level: Literal["level_1"]): """ usage.koalas: 1 """ ... @overload def droplevel(self, /, level: List[int]): """ usage.koalas: 2 """ ... @overload def droplevel(self, /, level: List[Literal["level_1"]]): """ usage.koalas: 1 """ ... @overload def droplevel(self, /, level: Tuple[int]): """ usage.koalas: 1 """ ... @overload def droplevel(self, /, level: Tuple[Literal["level_1"]]): """ usage.koalas: 1 """ ... @overload def droplevel(self, /, level: List[Literal["level_3", "level_1"]]): """ usage.koalas: 1 """ ... @overload def droplevel(self, /, level: Tuple[int, int]): """ usage.koalas: 1 """ ... @overload def droplevel(self, /, level: Tuple[Literal["level_2"], Literal["level_3"]]): """ usage.koalas: 1 """ ... @overload def droplevel(self, /, level: List[Tuple[Literal["a", "c"], Literal["1", "3"]]]): """ usage.koalas: 1 """ ... def droplevel( self, /, level: Union[ List[ Union[ Literal["level_1", "level_3"], int, Tuple[Literal["a", "c"], Literal["1", "3"]], ] ], int, Literal["level_1"], Tuple[Union[Literal["level_1", "level_3", "level_2"], int], ...], ], ): """ usage.koalas: 12 """ ... @overload def dropna(self, /): """ usage.alphalens: 3 usage.dask: 7 usage.geopandas: 1 usage.koalas: 3 usage.pyjanitor: 1 usage.seaborn: 1 usage.statsmodels: 17 usage.xarray: 2 """ ... @overload def dropna(self, /, inplace: bool): """ usage.koalas: 1 """ ... def dropna(self, /, inplace: bool = ...): """ usage.alphalens: 3 usage.dask: 7 usage.geopandas: 1 usage.koalas: 4 usage.pyjanitor: 1 usage.seaborn: 1 usage.statsmodels: 17 usage.xarray: 2 """ ... @overload def duplicated(self, /, keep: Literal["last"]): """ usage.xarray: 1 """ ... @overload def duplicated(self, /): """ usage.statsmodels: 1 """ ... def duplicated(self, /, keep: Literal["last"] = ...): """ usage.statsmodels: 1 usage.xarray: 1 """ ... @overload def eq(self, /, other: Literal["MultiPolygon"]): """ usage.geopandas: 1 """ ... @overload def eq(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... def eq( self, /, other: Union[pandas.core.series.Series, Literal["MultiPolygon"]], fill_value: int = ..., ): """ usage.dask: 1 usage.geopandas: 1 """ ... def equals(self, /, other: pandas.core.series.Series): """ usage.dask: 3 usage.statsmodels: 1 usage.xarray: 8 """ ... def expanding(self, /, min_periods: int): """ usage.koalas: 3 """ ... def explode(self, /): """ usage.dask: 5 """ ... @overload def ffill(self, /): """ usage.koalas: 2 """ ... @overload def ffill(self, /, inplace: bool): """ usage.koalas: 1 """ ... def ffill(self, /, inplace: bool = ...): """ usage.koalas: 3 """ ... @overload def fillna(self, /, value: int): """ usage.dask: 1 usage.koalas: 3 usage.pyjanitor: 3 usage.statsmodels: 2 """ ... @overload def fillna(self, /, value: float): """ usage.koalas: 1 usage.statsmodels: 1 """ ... @overload def fillna(self, /, value: int, inplace: bool): """ usage.koalas: 3 """ ... @overload def fillna(self, /, method: Literal["ffill"]): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def fillna(self, /, method: Literal["bfill"]): """ usage.dask: 2 usage.koalas: 1 """ ... @overload def fillna(self, /, value: numpy.float64, inplace: bool): """ usage.statsmodels: 1 """ ... @overload def fillna(self, /, value: numpy.int32, inplace: bool): """ usage.statsmodels: 1 """ ... @overload def fillna(self, /, value: Literal["white"]): """ usage.seaborn: 1 """ ... @overload def fillna(self, /, value: shapely.geometry.point.Point): """ usage.geopandas: 1 """ ... @overload def fillna(self, /, value: numpy.float64): """ usage.dask: 1 usage.pyjanitor: 1 """ ... @overload def fillna(self, /, value: int, method: None, axis: int, limit: None): """ usage.dask: 2 """ ... @overload def fillna(self, /, value: numpy.float64, method: None, axis: int, limit: None): """ usage.dask: 2 """ ... @overload def fillna(self, /, value: None, method: Literal["pad"], axis: int, limit: None): """ usage.dask: 1 """ ... @overload def fillna(self, /, method: Literal["pad"]): """ usage.dask: 1 """ ... @overload def fillna(self, /, method: Literal["ffill"], limit: None): """ usage.dask: 1 """ ... @overload def fillna(self, /, value: None, method: Literal["bfill"], axis: int, limit: None): """ usage.dask: 1 """ ... @overload def fillna(self, /, method: Literal["bfill"], limit: None): """ usage.dask: 1 """ ... @overload def fillna(self, /, value: None, method: Literal["pad"], axis: int, limit: int): """ usage.dask: 1 """ ... @overload def fillna(self, /, method: Literal["pad"], limit: int): """ usage.dask: 1 """ ... @overload def fillna(self, /, method: Literal["ffill"], limit: int): """ usage.dask: 1 """ ... @overload def fillna(self, /, value: None, method: Literal["bfill"], axis: int, limit: int): """ usage.dask: 1 """ ... @overload def fillna(self, /, method: Literal["bfill"], limit: int): """ usage.dask: 2 """ ... @overload def fillna(self, /, value: pandas.core.series.Series): """ usage.dask: 2 """ ... @overload def fillna( self, /, value: pandas.core.series.Series, method: None, axis: int, limit: None ): """ usage.dask: 1 """ ... def fillna( self, /, value: object = ..., method: Union[Literal["bfill", "ffill", "pad"], None] = ..., axis: int = ..., limit: Union[None, int] = ..., ): """ usage.dask: 23 usage.geopandas: 1 usage.koalas: 9 usage.pyjanitor: 4 usage.seaborn: 1 usage.statsmodels: 5 """ ... @overload def filter(self, /, items: List[Literal["three", "one"]]): """ usage.koalas: 1 """ ... @overload def filter(self, /, regex: Literal["e$"]): """ usage.koalas: 1 """ ... @overload def filter(self, /, like: Literal["hre"]): """ usage.koalas: 1 """ ... @overload def filter(self, /, items: List[Tuple[Literal["one", "three"], Literal["x", "z"]]]): """ usage.koalas: 1 """ ... def filter( self, /, regex: Literal["e$"] = ..., items: List[ Union[ Literal["three", "one"], Tuple[Literal["one", "three"], Literal["x", "z"]], ] ] = ..., like: Literal["hre"] = ..., ): """ usage.koalas: 4 """ ... @overload def first(self, /, offset: Literal["0d"]): """ usage.dask: 1 """ ... @overload def first(self, /, offset: Literal["100h"]): """ usage.dask: 1 """ ... @overload def first(self, /, offset: Literal["20d"]): """ usage.dask: 1 """ ... @overload def first(self, /, offset: Literal["20B"]): """ usage.dask: 1 """ ... @overload def first(self, /, offset: Literal["3W"]): """ usage.dask: 1 """ ... @overload def first(self, /, offset: Literal["3M"]): """ usage.dask: 1 """ ... @overload def first(self, /, offset: Literal["400d"]): """ usage.dask: 1 """ ... @overload def first(self, /, offset: Literal["13M"]): """ usage.dask: 1 """ ... def first(self, /, offset: str): """ usage.dask: 8 """ ... def first_valid_index(self, /): """ usage.koalas: 1 """ ... @overload def floordiv(self, /, other: int): """ usage.koalas: 1 """ ... @overload def floordiv(self, /, other: float): """ usage.koalas: 1 """ ... @overload def floordiv(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def floordiv(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... def floordiv( self, /, other: Union[pandas.core.series.Series, int, float], fill_value: int = ..., ): """ usage.dask: 2 usage.koalas: 2 """ ... def ge(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def get(self, /, key: Literal["lower_window"], default: int): """ usage.prophet: 1 """ ... @overload def get(self, /, key: Literal["upper_window"], default: int): """ usage.prophet: 1 """ ... @overload def get(self, /, key: Literal["prior_scale"], default: float): """ usage.prophet: 1 """ ... def get( self, /, key: Literal["prior_scale", "upper_window", "lower_window"], default: Union[float, int], ): """ usage.prophet: 3 """ ... @overload def groupby(self, /, by: pandas.core.series.Series): """ usage.alphalens: 4 usage.dask: 20 usage.koalas: 72 usage.seaborn: 14 """ ... @overload def groupby(self, /, by: pandas.core.series.Series, axis: int): """ usage.koalas: 1 """ ... @overload def groupby(self, /, by: pandas.core.series.Series, axis: Literal["index"]): """ usage.koalas: 1 """ ... @overload def groupby(self, /, level: Literal["date"]): """ usage.alphalens: 2 """ ... @overload def groupby(self, /, level: List[Literal["date"]]): """ usage.alphalens: 1 """ ... @overload def groupby(self, /, by: pandas.core.resample.TimeGrouper): """ usage.alphalens: 1 usage.xarray: 1 """ ... @overload def groupby(self, /, by: numpy.ndarray): """ usage.statsmodels: 1 """ ... @overload def groupby(self, /, by: List[Literal["b", "a"]]): """ usage.seaborn: 2 """ ... @overload def groupby(self, /, by: List[Literal["d", "c", "b", "a"]]): """ usage.seaborn: 1 """ ... @overload def groupby(self, /, by: pandas.core.series.Series, sort: bool): """ usage.seaborn: 1 """ ... @overload def groupby(self, /, by: pandas.core.arrays.categorical.Categorical, sort: bool): """ usage.seaborn: 1 """ ... @overload def groupby(self, /, level: int): """ usage.dask: 1 """ ... @overload def groupby(self, /, by: pandas.core.series.Series, group_keys: bool): """ usage.dask: 4 """ ... @overload def groupby(self, /, by: List[pandas.core.series.Series]): """ usage.dask: 6 """ ... @overload def groupby(self, /, level: int, sort: bool): """ usage.dask: 1 """ ... @overload def groupby(self, /, level: int, dropna: bool): """ usage.dask: 1 """ ... @overload def groupby(self, /, by: Callable): """ usage.dask: 2 """ ... @overload def groupby(self, /, by: Callable, group_keys: bool): """ usage.dask: 2 """ ... @overload def groupby(self, /, by: List[Callable]): """ usage.dask: 1 """ ... @overload def groupby(self, /, by: pandas.core.indexes.base.Index, group_keys: bool): """ usage.dask: 2 """ ... @overload def groupby(self, /, by: List[pandas.core.indexes.base.Index]): """ usage.dask: 1 """ ... @overload def groupby(self, /, by: Literal["a"]): """ usage.dask: 2 """ ... @overload def groupby(self, /, by: Literal["a"], group_keys: bool): """ usage.dask: 3 """ ... @overload def groupby(self, /, by: pandas.core.indexes.numeric.Int64Index, group_keys: bool): """ usage.dask: 2 """ ... @overload def groupby(self, /, by: pandas.core.indexes.numeric.Int64Index): """ usage.dask: 2 """ ... @overload def groupby(self, /, level: List[int], sort: bool): """ usage.dask: 2 """ ... @overload def groupby(self, /, by: list): """ usage.dask: 2 """ ... @overload def groupby(self, /, by: Literal["x"]): """ usage.dask: 2 """ ... @overload def groupby(self, /, by: pandas.core.groupby.ops.BaseGrouper): """ usage.dask: 1 """ ... @overload def groupby(self, /, by: List[pandas.core.series.Series], group_keys: bool): """ usage.dask: 4 """ ... @overload def groupby(self, /, by: Literal["foo"]): """ usage.dask: 1 """ ... @overload def groupby(self, /, by: List[Literal["foo"]]): """ usage.dask: 1 """ ... @overload def groupby(self, /, by: dask.dataframe.core.DataFrame): """ usage.dask: 1 """ ... @overload def groupby(self, /, by: List[dask.dataframe.core.DataFrame]): """ usage.dask: 1 """ ... def groupby( self, /, level: Union[int, List[Union[Literal["date"], int]], Literal["date"]] = ..., dropna: bool = ..., sort: bool = ..., by: object = ..., group_keys: bool = ..., ): """ usage.alphalens: 8 usage.dask: 65 usage.koalas: 74 usage.seaborn: 19 usage.statsmodels: 1 usage.xarray: 1 """ ... def gt(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def head(self, /): """ usage.koalas: 2 """ ... @overload def head(self, /, n: int): """ usage.dask: 7 usage.koalas: 4 """ ... def head(self, /, n: int = ...): """ usage.dask: 7 usage.koalas: 6 """ ... @overload def idxmax(self, /): """ usage.dask: 2 usage.koalas: 3 usage.prophet: 2 usage.seaborn: 1 usage.statsmodels: 1 """ ... @overload def idxmax(self, /, skipna: bool): """ usage.dask: 4 usage.koalas: 3 """ ... @overload def idxmax(self, /, axis: int, skipna: bool): """ usage.dask: 1 """ ... @overload def idxmax(self, /, axis: int): """ usage.dask: 1 """ ... def idxmax(self, /, skipna: bool = ..., axis: int = ...): """ usage.dask: 8 usage.koalas: 6 usage.prophet: 2 usage.seaborn: 1 usage.statsmodels: 1 """ ... @overload def idxmin(self, /): """ usage.koalas: 3 usage.prophet: 2 usage.statsmodels: 1 """ ... @overload def idxmin(self, /, skipna: bool): """ usage.dask: 4 usage.koalas: 3 """ ... def idxmin(self, /, skipna: bool = ...): """ usage.dask: 4 usage.koalas: 6 usage.prophet: 2 usage.statsmodels: 1 """ ... def infer_objects(self, /): """ usage.seaborn: 2 """ ... @overload def isin(self, /, values: List[Literal["lama", "cow"]]): """ usage.koalas: 1 """ ... @overload def isin(self, /, values: set): """ usage.koalas: 1 usage.prophet: 1 """ ... @overload def isin(self, /, values: List[bool]): """ usage.prophet: 1 """ ... @overload def isin(self, /, values: pandas.core.series.Series): """ usage.dask: 3 usage.prophet: 2 """ ... @overload def isin(self, /, values: List[Literal["Mary", "Mark"]]): """ usage.pyjanitor: 1 """ ... @overload def isin(self, /, values: List[Literal["Ruth", "John"]]): """ usage.pyjanitor: 1 """ ... @overload def isin(self, /, values: List[Literal["Esther", "Peter", "Eve", "Mary"]]): """ usage.pyjanitor: 1 """ ... @overload def isin(self, /, values: List[Literal["Ruth", "Esther"]]): """ usage.pyjanitor: 1 """ ... @overload def isin(self, /, values: List[int]): """ usage.dask: 3 usage.pyjanitor: 2 """ ... @overload def isin(self, /, values: range): """ usage.pyjanitor: 1 """ ... def isin( self, /, values: Union[ List[Union[str, int, bool]], pandas.core.series.Series, set, range ], ): """ usage.dask: 6 usage.koalas: 2 usage.prophet: 4 usage.pyjanitor: 7 """ ... def isna(self, /): """ usage.seaborn: 1 """ ... def isnull(self, /): """ usage.alphalens: 2 usage.dask: 5 usage.koalas: 8 usage.prophet: 2 usage.seaborn: 3 usage.statsmodels: 1 usage.xarray: 1 """ ... def item(self, /): """ usage.koalas: 1 """ ... def items(self, /): """ usage.dask: 1 usage.geopandas: 1 usage.koalas: 1 usage.statsmodels: 2 """ ... def iteritems(self, /): """ usage.dask: 4 usage.geopandas: 1 usage.koalas: 1 usage.xarray: 1 """ ... def keys(self, /): """ usage.koalas: 1 usage.statsmodels: 1 """ ... @overload def last(self, /, offset: Literal["0d"]): """ usage.dask: 1 """ ... @overload def last(self, /, offset: Literal["100h"]): """ usage.dask: 1 """ ... @overload def last(self, /, offset: Literal["20d"]): """ usage.dask: 1 """ ... @overload def last(self, /, offset: Literal["20B"]): """ usage.dask: 1 """ ... @overload def last(self, /, offset: Literal["3W"]): """ usage.dask: 1 """ ... @overload def last(self, /, offset: Literal["3M"]): """ usage.dask: 1 """ ... @overload def last(self, /, offset: Literal["400d"]): """ usage.dask: 1 """ ... @overload def last(self, /, offset: Literal["13M"]): """ usage.dask: 1 """ ... def last(self, /, offset: str): """ usage.dask: 8 """ ... def last_valid_index(self, /): """ usage.koalas: 3 """ ... def le(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... def lt(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... def mad(self, /): """ usage.koalas: 4 """ ... @overload def map(self, /, arg: dict): """ usage.koalas: 1 """ ... @overload def map(self, /, arg: collections.defaultdict): """ usage.koalas: 1 """ ... @overload def map(self, /, arg: Callable): """ usage.dask: 2 usage.koalas: 1 usage.statsmodels: 1 """ ... @overload def map( self, /, arg: Dict[Literal["virginica", "versicolor", "setosa"], Literal["g", "b", "r"]], ): """ usage.seaborn: 1 """ ... @overload def map(self, /, arg: Callable, na_action: None): """ usage.dask: 3 """ ... @overload def map(self, /, arg: Dict[numpy.int64, numpy.int64], na_action: None): """ usage.dask: 1 """ ... @overload def map(self, /, arg: Dict[numpy.int64, numpy.int64]): """ usage.dask: 2 """ ... @overload def map(self, /, arg: pandas.core.series.Series, na_action: None): """ usage.dask: 1 """ ... @overload def map(self, /, arg: pandas.core.series.Series): """ usage.dask: 7 """ ... def map( self, /, arg: Union[pandas.core.series.Series, collections.defaultdict, dict, Callable], na_action: None = ..., ): """ usage.dask: 16 usage.koalas: 3 usage.seaborn: 1 usage.statsmodels: 1 """ ... @overload def mask(self, /, cond: pandas.core.series.Series): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def mask(self, /, cond: pandas.core.series.Series, other: float): """ usage.dask: 1 """ ... @overload def mask( self, /, cond: pandas.core.series.Series, other: pandas.core.series.Series ): """ usage.dask: 2 """ ... def mask( self, /, cond: pandas.core.series.Series, other: Union[pandas.core.series.Series, float] = ..., ): """ usage.dask: 4 usage.koalas: 1 """ ... @overload def max(self, /): """ usage.alphalens: 5 usage.dask: 11 usage.geopandas: 2 usage.hvplot: 1 usage.koalas: 3 usage.prophet: 11 usage.pyjanitor: 1 usage.seaborn: 8 usage.statsmodels: 2 """ ... @overload def max(self, /, skipna: bool): """ usage.dask: 4 usage.xarray: 1 """ ... @overload def max(self, /, axis: int, skipna: bool): """ usage.dask: 2 """ ... @overload def max(self, /, axis: int): """ usage.dask: 1 """ ... @overload def max(self, /, axis: Literal["columns"]): """ usage.dask: 1 """ ... def max(self, /, axis: Union[Literal["columns"], int] = ..., skipna: bool = ...): """ usage.alphalens: 5 usage.dask: 19 usage.geopandas: 2 usage.hvplot: 1 usage.koalas: 3 usage.prophet: 11 usage.pyjanitor: 1 usage.seaborn: 8 usage.statsmodels: 2 usage.xarray: 1 """ ... @overload def mean(self, /): """ usage.alphalens: 8 usage.dask: 17 usage.koalas: 8 usage.prophet: 2 usage.seaborn: 2 usage.statsmodels: 12 """ ... @overload def mean(self, /, skipna: bool): """ usage.dask: 2 usage.xarray: 1 """ ... @overload def mean(self, /, axis: int): """ usage.dask: 1 usage.statsmodels: 1 """ ... @overload def mean(self, /, axis: int, skipna: bool): """ usage.dask: 1 """ ... @overload def mean(self, /, axis: Literal["columns"]): """ usage.dask: 1 """ ... def mean(self, /, axis: Union[int, Literal["columns"]] = ..., skipna: bool = ...): """ usage.alphalens: 8 usage.dask: 22 usage.koalas: 8 usage.prophet: 2 usage.seaborn: 2 usage.statsmodels: 13 usage.xarray: 1 """ ... def median(self, /): """ usage.alphalens: 1 usage.koalas: 1 usage.prophet: 1 usage.xarray: 1 """ ... @overload def memory_usage(self, /, index: bool): """ usage.dask: 2 """ ... @overload def memory_usage(self, /, index: bool, deep: bool): """ usage.dask: 4 """ ... def memory_usage(self, /, index: bool, deep: bool = ...): """ usage.dask: 6 """ ... @overload def min(self, /): """ usage.alphalens: 3 usage.dask: 7 usage.geopandas: 2 usage.hvplot: 1 usage.koalas: 52 usage.prophet: 12 usage.pyjanitor: 1 usage.seaborn: 11 usage.statsmodels: 5 """ ... @overload def min(self, /, skipna: bool): """ usage.dask: 3 usage.xarray: 1 """ ... @overload def min(self, /, axis: int, skipna: bool): """ usage.dask: 2 """ ... @overload def min(self, /, axis: int): """ usage.dask: 1 """ ... @overload def min(self, /, axis: Literal["columns"]): """ usage.dask: 1 """ ... def min(self, /, axis: Union[Literal["columns"], int] = ..., skipna: bool = ...): """ usage.alphalens: 3 usage.dask: 14 usage.geopandas: 2 usage.hvplot: 1 usage.koalas: 52 usage.prophet: 12 usage.pyjanitor: 1 usage.seaborn: 11 usage.statsmodels: 5 usage.xarray: 1 """ ... @overload def mod(self, /, other: pandas.core.series.Series): """ usage.koalas: 4 """ ... @overload def mod(self, /, other: int): """ usage.koalas: 3 """ ... @overload def mod(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def mod(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... def mod( self, /, other: Union[pandas.core.series.Series, int], fill_value: int = ... ): """ usage.dask: 2 usage.koalas: 7 """ ... def mode(self, /): """ usage.dask: 1 """ ... @overload def mul(self, /, other: pandas.core.series.Series, level: int): """ usage.statsmodels: 1 """ ... @overload def mul(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 2 """ ... @overload def mul(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... @overload def mul(self, /, other: pandas.core.series.Series): """ usage.dask: 1 """ ... def mul( self, /, other: Union[int, pandas.core.series.Series], level: int = ..., fill_value: int = ..., ): """ usage.dask: 4 usage.statsmodels: 1 """ ... def ne(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def nlargest(self, /, n: int): """ usage.dask: 4 usage.koalas: 1 """ ... @overload def nlargest(self, /): """ usage.koalas: 2 """ ... def nlargest(self, /, n: int = ...): """ usage.dask: 4 usage.koalas: 3 """ ... def notna(self, /): """ usage.dask: 1 usage.seaborn: 5 """ ... def notnull(self, /): """ usage.dask: 3 usage.koalas: 2 usage.prophet: 1 usage.seaborn: 5 usage.statsmodels: 1 """ ... @overload def nsmallest(self, /, n: int): """ usage.dask: 2 usage.koalas: 1 """ ... @overload def nsmallest(self, /): """ usage.koalas: 2 """ ... def nsmallest(self, /, n: int = ...): """ usage.dask: 2 usage.koalas: 3 """ ... @overload def nunique(self, /): """ usage.dask: 5 usage.koalas: 1 """ ... @overload def nunique(self, /, dropna: bool): """ usage.koalas: 1 """ ... def nunique(self, /, dropna: bool = ...): """ usage.dask: 5 usage.koalas: 2 """ ... @overload def pct_change(self, /): """ usage.koalas: 2 """ ... @overload def pct_change(self, /, periods: int): """ usage.koalas: 8 """ ... def pct_change(self, /, periods: int = ...): """ usage.koalas: 10 """ ... def pop(self, /, item: Tuple[Literal["lama"], Literal["speed"]]): """ usage.koalas: 1 """ ... @overload def pow(self, /, other: float): """ usage.alphalens: 1 """ ... @overload def pow(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def pow(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... @overload def pow(self, /, other: int): """ usage.dask: 1 """ ... def pow( self, /, other: Union[pandas.core.series.Series, int, float], fill_value: int = ..., ): """ usage.alphalens: 1 usage.dask: 3 """ ... @overload def prod(self, /): """ usage.dask: 2 usage.koalas: 7 """ ... @overload def prod(self, /, min_count: int): """ usage.koalas: 6 """ ... @overload def prod(self, /, skipna: bool, min_count: int): """ usage.xarray: 1 """ ... @overload def prod(self, /, axis: int, skipna: bool): """ usage.dask: 2 """ ... @overload def prod(self, /, skipna: bool): """ usage.dask: 1 """ ... @overload def prod(self, /, axis: int): """ usage.dask: 1 """ ... @overload def prod(self, /, axis: Literal["columns"]): """ usage.dask: 1 """ ... def prod( self, /, axis: Union[Literal["columns"], int] = ..., skipna: bool = ..., min_count: int = ..., ): """ usage.dask: 7 usage.koalas: 13 usage.xarray: 1 """ ... @overload def quantile(self, /, q: numpy.ndarray): """ usage.dask: 2 """ ... @overload def quantile(self, /, q: List[float]): """ usage.dask: 2 """ ... @overload def quantile(self, /, q: float): """ usage.dask: 1 """ ... @overload def quantile(self, /): """ usage.dask: 1 """ ... @overload def quantile(self, /, q: list): """ usage.dask: 1 """ ... def quantile(self, /, q: Union[List[float], numpy.ndarray, float] = ...): """ usage.dask: 7 """ ... @overload def radd(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def radd(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... def radd(self, /, other: Union[int, pandas.core.series.Series], fill_value: int): """ usage.dask: 2 """ ... @overload def rank(self, /): """ usage.koalas: 2 """ ... @overload def rank(self, /, ascending: bool): """ usage.koalas: 1 """ ... @overload def rank(self, /, method: Literal["min"]): """ usage.koalas: 1 """ ... @overload def rank(self, /, method: Literal["max"]): """ usage.koalas: 1 """ ... @overload def rank(self, /, method: Literal["first"]): """ usage.koalas: 1 """ ... @overload def rank(self, /, method: Literal["dense"]): """ usage.koalas: 1 """ ... @overload def rank(self, /, method: Literal["dense"], ascending: bool): """ usage.xarray: 1 """ ... def rank( self, /, method: Literal["dense", "first", "max", "min"] = ..., ascending: bool = ..., ): """ usage.koalas: 7 usage.xarray: 1 """ ... def ravel(self, /): """ usage.statsmodels: 1 """ ... def rdivmod(self, /, other: int): """ usage.koalas: 2 """ ... @overload def reindex(self, /, index: pandas.core.indexes.range.RangeIndex): """ usage.seaborn: 1 usage.statsmodels: 1 """ ... @overload def reindex(self, /, index: pandas.core.indexes.multi.MultiIndex): """ usage.dask: 3 usage.statsmodels: 1 """ ... @overload def reindex(self, /, index: pandas.core.indexes.base.Index): """ usage.statsmodels: 1 """ ... @overload def reindex(self, /, index: pandas.core.indexes.numeric.Int64Index): """ usage.dask: 3 """ ... @overload def reindex(self, /, index: numpy.ndarray): """ usage.dask: 1 """ ... @overload def reindex(self, /, index: pandas.core.indexes.datetimes.DatetimeIndex): """ usage.dask: 1 """ ... def reindex(self, /, index: object): """ usage.dask: 8 usage.seaborn: 1 usage.statsmodels: 3 """ ... @overload def rename(self, /, index: Literal["A"]): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def rename(self, /, index: Literal["b"]): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def rename(self, /, index: Literal["a"]): """ usage.dask: 1 usage.koalas: 13 usage.seaborn: 1 """ ... @overload def rename(self, /): """ usage.dask: 1 usage.koalas: 54 """ ... @overload def rename(self, /, index: None): """ usage.dask: 1 usage.koalas: 1 usage.seaborn: 1 """ ... @overload def rename(self, /, index: Tuple[Literal["x"], Literal["b"]]): """ usage.koalas: 1 """ ... @overload def rename(self, /, index: Tuple[Literal["x"], Literal["a"]]): """ usage.koalas: 5 """ ... @overload def rename(self, /, index: Literal["d"]): """ usage.koalas: 1 """ ... @overload def rename(self, /, index: Literal["car_id"]): """ usage.koalas: 1 """ ... @overload def rename(self, /, index: Literal["B"]): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def rename(self, /, index: Literal["ABC"]): """ usage.koalas: 2 """ ... @overload def rename(self, /, index: Literal["c"]): """ usage.koalas: 2 """ ... @overload def rename(self, /, index: Literal["left"]): """ usage.koalas: 2 """ ... @overload def rename(self, /, index: Literal["y"]): """ usage.dask: 2 usage.koalas: 3 usage.seaborn: 1 """ ... @overload def rename(self, /, index: Literal["z"], *, inplace: bool): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def rename(self, /, index: Literal["end_date"]): """ usage.koalas: 2 """ ... @overload def rename(self, /, index: Literal["col1"]): """ usage.koalas: 1 """ ... @overload def rename(self, /, index: Literal["col2"]): """ usage.koalas: 1 """ ... @overload def rename(self, /, index: Literal["count"]): """ usage.statsmodels: 1 """ ... @overload def rename(self, /, index: Literal["weight"]): """ usage.statsmodels: 2 """ ... @overload def rename(self, /, index: Literal["estimate (prev)"]): """ usage.statsmodels: 1 """ ... @overload def rename(self, /, index: Literal["impact of revisions"]): """ usage.statsmodels: 1 """ ... @overload def rename(self, /, index: Literal["impact of news"]): """ usage.statsmodels: 1 """ ... @overload def rename(self, /, index: Literal["estimate (new)"]): """ usage.statsmodels: 1 """ ... @overload def rename(self, /, index: Literal["heights"]): """ usage.seaborn: 1 """ ... @overload def rename(self, /, index: Literal["x"]): """ usage.seaborn: 1 """ ... @overload def rename(self, /, index: Literal["2a+b"]): """ usage.pyjanitor: 1 """ ... @overload def rename(self, /, index: Literal["idx"]): """ usage.dask: 1 """ ... @overload def rename(self, /, index: Literal["renamed"]): """ usage.dask: 1 """ ... @overload def rename(self, /, index: Literal["z"]): """ usage.dask: 1 """ ... @overload def rename(self, /, index: Callable): """ usage.dask: 5 """ ... @overload def rename(self, /, index: pandas.core.series.Series): """ usage.dask: 4 """ ... @overload def rename(self, /, index: Callable, *, inplace: bool): """ usage.dask: 1 """ ... @overload def rename(self, /, index: Literal["Name"]): """ usage.dask: 1 """ ... @overload def rename(self, /, index: Literal["Num"]): """ usage.dask: 1 """ ... @overload def rename(self, /, index: Literal["AA"]): """ usage.dask: 1 """ ... def rename( self, /, index: Union[ Callable, None, pandas.core.series.Series, str, Tuple[Literal["x"], Literal["b", "a"]], ] = ..., *, inplace: bool = ..., ): """ usage.dask: 25 usage.koalas: 93 usage.pyjanitor: 1 usage.seaborn: 5 usage.statsmodels: 7 """ ... @overload def repeat(self, /, repeats: pandas.core.series.Series): """ usage.koalas: 2 """ ... @overload def repeat(self, /, repeats: int): """ usage.koalas: 2 """ ... def repeat(self, /, repeats: Union[int, pandas.core.series.Series]): """ usage.koalas: 4 """ ... @overload def replace(self, /, to_replace: Dict[float, None]): """ usage.koalas: 2 """ ... @overload def replace(self, /): """ usage.koalas: 1 """ ... @overload def replace(self, /, to_replace: dict): """ usage.koalas: 1 """ ... @overload def replace(self, /, to_replace: float, value: float): """ usage.alphalens: 2 """ ... @overload def replace(self, /, to_replace: Dict[int, str]): """ usage.statsmodels: 2 """ ... @overload def replace(self, /, to_replace: Literal["c"], value: float): """ usage.dask: 1 """ ... @overload def replace(self, /, to_replace: int, value: int): """ usage.dask: 1 """ ... @overload def replace(self, /, to_replace: int, value: int, regex: bool): """ usage.dask: 1 """ ... @overload def replace(self, /, to_replace: Dict[int, int]): """ usage.dask: 1 """ ... @overload def replace(self, /, to_replace: Dict[int, int], value: None, regex: bool): """ usage.dask: 1 """ ... def replace( self, /, to_replace: Union[Literal["c"], int, float, dict] = ..., value: Union[int, float, None] = ..., regex: bool = ..., ): """ usage.alphalens: 2 usage.dask: 5 usage.koalas: 4 usage.statsmodels: 2 """ ... @overload def resample(self, /, rule: Literal["24H"]): """ usage.xarray: 2 """ ... @overload def resample(self, /, rule: Literal["24H"], loffset: Literal["-12H"]): """ usage.xarray: 2 """ ... @overload def resample(self, /, rule: Literal["3H"]): """ usage.xarray: 8 """ ... @overload def resample(self, /, rule: Literal["1H"]): """ usage.xarray: 2 """ ... @overload def resample(self, /, rule: Literal["M"], convention: Literal["end"]): """ usage.statsmodels: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Minute, closed: Literal["right"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Minute, closed: Literal["right"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["30T"], closed: Literal["right"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["30T"], closed: Literal["right"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Minute, closed: Literal["left"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Minute, closed: Literal["left"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["30T"], closed: Literal["left"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["30T"], closed: Literal["left"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Hour, closed: Literal["right"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Hour, closed: Literal["right"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["h"], closed: Literal["right"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["h"], closed: Literal["right"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Hour, closed: Literal["left"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Hour, closed: Literal["left"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["h"], closed: Literal["left"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["h"], closed: Literal["left"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Day, closed: Literal["right"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Day, closed: Literal["right"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["d"], closed: Literal["right"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["d"], closed: Literal["right"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Day, closed: Literal["left"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Day, closed: Literal["left"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["d"], closed: Literal["left"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["d"], closed: Literal["left"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Week, closed: Literal["right"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Week, closed: Literal["right"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["w"], closed: Literal["right"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["w"], closed: Literal["right"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Week, closed: Literal["left"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Week, closed: Literal["left"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["w"], closed: Literal["left"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["w"], closed: Literal["left"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.MonthEnd, closed: Literal["right"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.MonthEnd, closed: Literal["right"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["M"], closed: Literal["right"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["M"], closed: Literal["right"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.MonthEnd, closed: Literal["left"], label: Literal["left"], ): """ usage.dask: 3 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.MonthEnd, closed: Literal["left"], label: Literal["right"], ): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: Literal["M"], closed: Literal["left"], label: Literal["right"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: Literal["M"], closed: Literal["left"], label: Literal["left"] ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Minute, closed: None, label: Literal["left"], ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Minute, closed: None, label: None ): """ usage.dask: 2 """ ... @overload def resample(self, /, rule: Literal["30min"]): """ usage.dask: 1 """ ... @overload def resample(self, /, rule: Literal["10min"]): """ usage.dask: 2 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Hour, closed: None, label: Literal["left"], ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Hour, closed: None, label: None ): """ usage.dask: 2 """ ... @overload def resample(self, /, rule: Literal["2h"]): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.MonthEnd, closed: None, label: Literal["left"], ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.MonthEnd, closed: None, label: None ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.QuarterEnd, closed: None, label: Literal["left"], ): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Day, closed: None, label: Literal["left"], ): """ usage.dask: 1 """ ... @overload def resample(self, /, rule: Literal["57T"]): """ usage.dask: 1 """ ... @overload def resample(self, /, rule: Literal["1d"]): """ usage.dask: 1 """ ... @overload def resample( self, /, rule: pandas._libs.tslibs.offsets.Day, closed: None, label: None ): """ usage.dask: 2 """ ... def resample( self, /, rule: object, closed: Union[None, Literal["left", "right"]] = ..., label: Union[None, Literal["left", "right"]] = ..., ): """ usage.dask: 88 usage.statsmodels: 1 usage.xarray: 14 """ ... @overload def reset_index(self, /): """ usage.alphalens: 2 usage.dask: 1 usage.koalas: 8 usage.prophet: 1 usage.seaborn: 1 usage.statsmodels: 2 """ ... @overload def reset_index(self, /, name: Literal["values"]): """ usage.koalas: 2 """ ... @overload def reset_index(self, /, drop: bool): """ usage.dask: 6 usage.koalas: 6 """ ... @overload def reset_index(self, /, drop: bool, inplace: bool): """ usage.koalas: 3 """ ... @overload def reset_index(self, /, level: int): """ usage.koalas: 1 """ ... @overload def reset_index(self, /, level: List[int]): """ usage.koalas: 1 usage.statsmodels: 4 """ ... @overload def reset_index(self, /, name: Literal["s"]): """ usage.koalas: 1 """ ... @overload def reset_index(self, /, drop: bool, name: Literal["s"]): """ usage.koalas: 1 """ ... @overload def reset_index(self, /, inplace: bool): """ usage.koalas: 1 """ ... @overload def reset_index(self, /, name: Literal["heights"]): """ usage.seaborn: 1 """ ... def reset_index( self, /, drop: bool = ..., name: Literal["heights", "s", "values"] = ..., inplace: bool = ..., ): """ usage.alphalens: 2 usage.dask: 7 usage.koalas: 24 usage.prophet: 1 usage.seaborn: 2 usage.statsmodels: 6 """ ... @overload def rmod(self, /, other: pandas.core.series.Series): """ usage.koalas: 4 """ ... @overload def rmod(self, /, other: int): """ usage.koalas: 3 """ ... @overload def rmod(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def rmod(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... def rmod( self, /, other: Union[pandas.core.series.Series, int], fill_value: int = ... ): """ usage.dask: 2 usage.koalas: 7 """ ... @overload def rmul(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def rmul(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... def rmul(self, /, other: Union[int, pandas.core.series.Series], fill_value: int): """ usage.dask: 2 """ ... @overload def rolling(self, /, window: int): """ usage.alphalens: 2 usage.dask: 2 usage.koalas: 3 usage.statsmodels: 2 """ ... @overload def rolling(self, /, window: int, min_periods: None, center: bool): """ usage.xarray: 1 """ ... @overload def rolling(self, /, window: int, min_periods: int, center: bool): """ usage.xarray: 2 """ ... @overload def rolling(self, /, window: int, center: bool): """ usage.dask: 1 usage.statsmodels: 1 """ ... @overload def rolling(self, /, window: Literal["2D"]): """ usage.dask: 1 """ ... @overload def rolling( self, /, window: int, min_periods: None, center: bool, win_type: None, axis: int ): """ usage.dask: 2 """ ... @overload def rolling(self, /, window: int, axis: int): """ usage.dask: 1 """ ... @overload def rolling(self, /, window: Literal["1S"]): """ usage.dask: 1 """ ... @overload def rolling( self, /, window: Literal["1S"], min_periods: None, center: bool, win_type: None, axis: int, ): """ usage.dask: 2 """ ... @overload def rolling(self, /, window: Literal["2S"]): """ usage.dask: 1 """ ... @overload def rolling( self, /, window: Literal["2S"], min_periods: None, center: bool, win_type: None, axis: int, ): """ usage.dask: 2 """ ... @overload def rolling(self, /, window: Literal["3S"]): """ usage.dask: 1 """ ... @overload def rolling( self, /, window: Literal["3S"], min_periods: None, center: bool, win_type: None, axis: int, ): """ usage.dask: 2 """ ... @overload def rolling(self, /, window: pandas._libs.tslibs.offsets.Second): """ usage.dask: 1 """ ... @overload def rolling( self, /, window: pandas._libs.tslibs.offsets.Second, min_periods: None, center: bool, win_type: None, axis: int, ): """ usage.dask: 2 """ ... def rolling( self, /, window: Union[ Literal["3S", "2S", "1S", "2D"], int, pandas._libs.tslibs.offsets.Second ], min_periods: Union[None, int] = ..., center: bool = ..., win_type: None = ..., axis: int = ..., ): """ usage.alphalens: 2 usage.dask: 19 usage.koalas: 3 usage.statsmodels: 3 usage.xarray: 3 """ ... @overload def round(self, /, decimals: int): """ usage.alphalens: 4 usage.dask: 1 usage.koalas: 1 """ ... @overload def round(self, /): """ usage.dask: 1 """ ... def round(self, /, decimals: int = ...): """ usage.alphalens: 4 usage.dask: 2 usage.koalas: 1 """ ... @overload def rpow(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def rpow(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... def rpow(self, /, other: Union[int, pandas.core.series.Series], fill_value: int): """ usage.dask: 2 """ ... @overload def rsub(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def rsub(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... def rsub(self, /, other: Union[int, pandas.core.series.Series], fill_value: int): """ usage.dask: 2 """ ... @overload def rtruediv(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 2 """ ... @overload def rtruediv(self, /, other: int, fill_value: int): """ usage.dask: 2 """ ... def rtruediv( self, /, other: Union[int, pandas.core.series.Series], fill_value: int ): """ usage.dask: 4 """ ... def searchsorted(self, /, value: pandas.core.series.Series, side: Literal["right"]): """ usage.dask: 1 """ ... @overload def sem(self, /, axis: int, skipna: None, ddof: int): """ usage.dask: 1 """ ... @overload def sem(self, /): """ usage.dask: 2 """ ... @overload def sem(self, /, ddof: int): """ usage.dask: 1 """ ... @overload def sem(self, /, axis: int, skipna: bool, ddof: int): """ usage.dask: 1 """ ... @overload def sem(self, /, skipna: bool): """ usage.dask: 1 """ ... @overload def sem(self, /, skipna: bool, ddof: int): """ usage.dask: 1 """ ... @overload def sem(self, /, axis: int): """ usage.dask: 1 """ ... @overload def sem(self, /, axis: Literal["columns"]): """ usage.dask: 1 """ ... def sem( self, /, axis: Union[Literal["columns"], int] = ..., skipna: Union[bool, None] = ..., ddof: int = ..., ): """ usage.dask: 9 """ ... @overload def shift(self, /, periods: int, fill_value: int): """ usage.koalas: 1 """ ... @overload def shift(self, /, periods: int): """ usage.alphalens: 1 usage.dask: 6 usage.statsmodels: 5 usage.xarray: 1 """ ... @overload def shift(self, /): """ usage.dask: 1 """ ... @overload def shift(self, /, periods: int, freq: pandas._libs.tslibs.offsets.Second): """ usage.dask: 1 """ ... @overload def shift(self, /, periods: int, freq: Literal["S"]): """ usage.dask: 4 """ ... @overload def shift(self, /, periods: int, freq: Literal["W"]): """ usage.dask: 3 """ ... @overload def shift(self, /, periods: int, freq: pandas._libs.tslibs.timedeltas.Timedelta): """ usage.dask: 5 """ ... @overload def shift(self, /, periods: int, freq: pandas._libs.tslibs.offsets.Day): """ usage.dask: 1 """ ... @overload def shift(self, /, periods: int, freq: pandas._libs.tslibs.offsets.Hour): """ usage.dask: 1 """ ... @overload def shift(self, /, periods: int, freq: Literal["B"]): """ usage.dask: 3 """ ... @overload def shift(self, /, periods: int, freq: Literal["D"]): """ usage.dask: 3 """ ... @overload def shift(self, /, periods: int, freq: Literal["H"]): """ usage.dask: 3 """ ... @overload def shift(self, /, periods: int, freq: pandas._libs.tslibs.offsets.Minute): """ usage.dask: 1 """ ... def shift(self, /, periods: int = ..., freq: object = ...): """ usage.alphalens: 1 usage.dask: 32 usage.koalas: 1 usage.statsmodels: 5 usage.xarray: 1 """ ... @overload def sort_index(self, /): """ usage.alphalens: 2 usage.dask: 3 usage.koalas: 247 usage.seaborn: 1 usage.statsmodels: 4 """ ... @overload def sort_index(self, /, ascending: bool): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def sort_index(self, /, na_position: Literal["first"]): """ usage.koalas: 1 """ ... @overload def sort_index(self, /, inplace: bool): """ usage.koalas: 1 """ ... @overload def sort_index(self, /, level: List[int]): """ usage.koalas: 1 """ ... def sort_index( self, /, ascending: bool = ..., na_position: Literal["first"] = ..., inplace: bool = ..., level: List[int] = ..., ): """ usage.alphalens: 2 usage.dask: 4 usage.koalas: 251 usage.seaborn: 1 usage.statsmodels: 4 """ ... @overload def sort_values(self, /): """ usage.alphalens: 1 usage.dask: 4 usage.koalas: 11 usage.prophet: 1 """ ... @overload def sort_values(self, /, ascending: bool): """ usage.dask: 1 usage.koalas: 1 """ ... @overload def sort_values(self, /, na_position: Literal["first"]): """ usage.koalas: 1 """ ... @overload def sort_values(self, /, inplace: bool): """ usage.koalas: 1 """ ... def sort_values( self, /, ascending: bool = ..., na_position: Literal["first"] = ..., inplace: bool = ..., ): """ usage.alphalens: 1 usage.dask: 5 usage.koalas: 14 usage.prophet: 1 """ ... @overload def squeeze(self, /): """ usage.dask: 1 usage.koalas: 4 usage.statsmodels: 4 """ ... @overload def squeeze(self, /, axis: int): """ usage.modin: 1 """ ... def squeeze(self, /, axis: int = ...): """ usage.dask: 1 usage.koalas: 4 usage.modin: 1 usage.statsmodels: 4 """ ... @overload def std(self, /): """ usage.alphalens: 2 usage.dask: 3 usage.koalas: 3 usage.prophet: 1 usage.statsmodels: 1 """ ... @overload def std(self, /, ddof: int): """ usage.dask: 1 usage.seaborn: 1 """ ... @overload def std(self, /, axis: int, skipna: bool): """ usage.dask: 1 """ ... @overload def std(self, /, skipna: bool): """ usage.dask: 1 """ ... @overload def std(self, /, skipna: bool, ddof: int): """ usage.dask: 1 """ ... @overload def std(self, /, axis: int): """ usage.dask: 1 """ ... @overload def std(self, /, axis: Literal["columns"]): """ usage.dask: 1 """ ... def std( self, /, axis: Union[Literal["columns"], int] = ..., skipna: bool = ..., ddof: int = ..., ): """ usage.alphalens: 2 usage.dask: 9 usage.koalas: 3 usage.prophet: 1 usage.seaborn: 1 usage.statsmodels: 1 """ ... @overload def sub(self, /, other: int): """ usage.alphalens: 1 """ ... @overload def sub(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 1 """ ... @overload def sub(self, /, other: int, fill_value: int): """ usage.dask: 1 """ ... def sub( self, /, other: Union[pandas.core.series.Series, int], fill_value: int = ... ): """ usage.alphalens: 1 usage.dask: 2 """ ... @overload def sum(self, /): """ usage.alphalens: 4 usage.dask: 35 usage.koalas: 11 usage.prophet: 3 usage.seaborn: 3 usage.sklearn: 1 usage.statsmodels: 5 """ ... @overload def sum(self, /, skipna: bool): """ usage.dask: 1 usage.xarray: 1 """ ... @overload def sum(self, /, skipna: bool, min_count: int): """ usage.xarray: 1 """ ... @overload def sum(self, /, axis: int, skipna: bool): """ usage.dask: 2 """ ... @overload def sum(self, /, axis: int): """ usage.dask: 1 """ ... @overload def sum(self, /, axis: Literal["columns"]): """ usage.dask: 1 """ ... @overload def sum(self, /, level: int): """ usage.dask: 1 """ ... def sum(self, /, axis: Union[Literal["columns"], int] = ..., skipna: bool = ...): """ usage.alphalens: 4 usage.dask: 41 usage.koalas: 11 usage.prophet: 3 usage.seaborn: 3 usage.sklearn: 1 usage.statsmodels: 5 usage.xarray: 2 """ ... @overload def tail(self, /): """ usage.koalas: 1 """ ... @overload def tail(self, /, n: int): """ usage.dask: 4 usage.koalas: 5 usage.prophet: 4 """ ... def tail(self, /, n: int = ...): """ usage.dask: 4 usage.koalas: 6 usage.prophet: 4 """ ... @overload def take(self, /, indices: List[int]): """ usage.koalas: 2 """ ... @overload def take(self, /, indices: range): """ usage.koalas: 2 """ ... @overload def take(self, /, indices: numpy.ndarray): """ usage.dask: 1 """ ... def take(self, /, indices: Union[numpy.ndarray, range, List[int]]): """ usage.dask: 1 usage.koalas: 4 """ ... @overload def to_csv( self, /, path_or_buf: None, sep: Literal[","], na_rep: Literal[""], columns: None, header: bool, index: bool, quotechar: Literal['"'], date_format: None, escapechar: None, ): """ usage.koalas: 1 """ ... @overload def to_csv(self, /, header: bool, index: bool): """ usage.koalas: 1 """ ... @overload def to_csv( self, /, path_or_buf: None, sep: Literal[","], na_rep: Literal["null"], columns: None, header: bool, index: bool, quotechar: Literal['"'], date_format: None, escapechar: None, ): """ usage.koalas: 1 """ ... @overload def to_csv(self, /, na_rep: Literal["null"], header: bool, index: bool): """ usage.koalas: 1 """ ... def to_csv( self, /, header: bool, index: bool, path_or_buf: None = ..., sep: Literal[","] = ..., na_rep: Literal["null", ""] = ..., columns: None = ..., quotechar: Literal['"'] = ..., date_format: None = ..., escapechar: None = ..., ): """ usage.koalas: 4 """ ... def to_dict(self, /): """ usage.dask: 5 usage.koalas: 1 usage.statsmodels: 3 """ ... @overload def to_frame(self, /): """ usage.dask: 12 usage.hvplot: 1 usage.koalas: 5 usage.sklearn: 2 usage.statsmodels: 6 usage.xarray: 2 """ ... @overload def to_frame(self, /, name: Literal["a"]): """ usage.dask: 3 usage.koalas: 2 """ ... @overload def to_frame(self, /, name: Literal["s"]): """ usage.dask: 2 """ ... @overload def to_frame(self, /, name: None): """ usage.dask: 2 """ ... @overload def to_frame(self, /, name: Literal["bar"]): """ usage.dask: 3 """ ... @overload def to_frame(self, /, name: Literal["__series__"]): """ usage.dask: 2 """ ... @overload def to_frame(self, /, name: Literal["A"]): """ usage.dask: 3 """ ... def to_frame( self, /, name: Union[None, Literal["a", "A", "__series__", "bar", "s"]] = ... ): """ usage.dask: 27 usage.hvplot: 1 usage.koalas: 7 usage.sklearn: 2 usage.statsmodels: 6 usage.xarray: 2 """ ... def to_markdown(self, /): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: None): """ usage.koalas: 7 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["B"]): """ usage.koalas: 3 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["A"]): """ usage.koalas: 3 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["animal"]): """ usage.koalas: 2 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["Col1"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["Col2"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["x"]): """ usage.koalas: 3 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["koalas"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["dates"]): """ usage.koalas: 2 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["id"]): """ usage.koalas: 2 """ ... @overload def to_string( self, /, dtype: numpy.dtype, name: Tuple[Literal["num"], Literal["b"]] ): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["numeric1"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["one"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["class"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["0.5"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["b"]): """ usage.koalas: 3 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["shield"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["max_speed"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["value"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["a"]): """ usage.koalas: 3 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["1"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["C"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["value1"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["c"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["my_name"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["0"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Tuple[Literal["x"], Literal["a"]]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["species"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["population"]): """ usage.koalas: 1 """ ... @overload def to_string(self, /, dtype: numpy.dtype, name: Literal["Koalas"]): """ usage.koalas: 2 """ ... @overload def to_string(self, /, length: bool): """ usage.koalas: 1 """ ... @overload def to_string(self, /, max_rows: int): """ usage.dask: 1 """ ... def to_string( self, /, dtype: numpy.dtype = ..., name: Union[str, Tuple[Literal["num", "x"], Literal["b", "a"]], None] = ..., ): """ usage.dask: 1 usage.koalas: 51 """ ... def to_timestamp(self, /, freq: Literal["Q"]): """ usage.statsmodels: 1 """ ... def tolist(self, /): """ usage.dask: 4 usage.geopandas: 5 usage.koalas: 2 usage.prophet: 1 usage.pyjanitor: 3 usage.seaborn: 6 usage.statsmodels: 9 """ ... def transform(self, /, func: Callable): """ usage.koalas: 1 """ ... @overload def truediv(self, /, other: int): """ usage.koalas: 2 """ ... @overload def truediv(self, /, other: float): """ usage.koalas: 2 """ ... @overload def truediv(self, /, other: pandas.core.series.Series, fill_value: int): """ usage.dask: 3 """ ... @overload def truediv(self, /, other: int, fill_value: int): """ usage.dask: 3 """ ... def truediv( self, /, other: Union[pandas.core.series.Series, int, float], fill_value: int = ..., ): """ usage.dask: 6 usage.koalas: 4 """ ... @overload def truncate(self, /): """ usage.koalas: 1 """ ... @overload def truncate(self, /, before: int): """ usage.koalas: 1 """ ... @overload def truncate(self, /, after: int): """ usage.koalas: 1 """ ... @overload def truncate(self, /, copy: bool): """ usage.koalas: 1 """ ... @overload def truncate(self, /, before: int, after: int, copy: bool): """ usage.koalas: 2 """ ... @overload def truncate(self, /, before: int, after: int): """ usage.koalas: 1 """ ... def truncate(self, /, before: int = ..., after: int = ..., copy: bool = ...): """ usage.koalas: 7 """ ... def unique(self, /): """ usage.alphalens: 2 usage.dask: 9 usage.geopandas: 2 usage.prophet: 8 usage.seaborn: 63 usage.statsmodels: 1 """ ... @overload def unstack(self, /, level: int): """ usage.koalas: 2 usage.statsmodels: 2 """ ... @overload def unstack(self, /): """ usage.alphalens: 2 usage.seaborn: 1 usage.xarray: 1 """ ... @overload def unstack(self, /, level: Literal["factor_quantile"]): """ usage.alphalens: 1 """ ... @overload def unstack(self, /, level: List[int]): """ usage.statsmodels: 4 """ ... def unstack( self, /, level: Union[List[int], int, Literal["factor_quantile"]] = ... ): """ usage.alphalens: 3 usage.koalas: 2 usage.seaborn: 1 usage.statsmodels: 6 usage.xarray: 1 """ ... def update(self, /, other: pandas.core.series.Series): """ usage.koalas: 1 """ ... @overload def value_counts(self, /): """ usage.dask: 8 usage.koalas: 2 usage.statsmodels: 1 """ ... @overload def value_counts(self, /, normalize: bool): """ usage.koalas: 6 usage.statsmodels: 1 """ ... @overload def value_counts(self, /, ascending: bool): """ usage.dask: 1 usage.koalas: 6 """ ... @overload def value_counts(self, /, normalize: bool, dropna: bool): """ usage.koalas: 6 """ ... @overload def value_counts(self, /, ascending: bool, dropna: bool): """ usage.dask: 1 usage.koalas: 6 """ ... @overload def value_counts(self, /, sort: bool): """ usage.dask: 1 """ ... @overload def value_counts(self, /, dropna: bool): """ usage.dask: 2 """ ... def value_counts( self, /, ascending: bool = ..., normalize: bool = ..., dropna: bool = ... ): """ usage.dask: 13 usage.koalas: 26 usage.statsmodels: 2 """ ... @overload def var(self, /): """ usage.dask: 4 usage.koalas: 3 usage.seaborn: 1 """ ... @overload def var(self, /, skipna: bool, ddof: int): """ usage.dask: 1 usage.xarray: 1 """ ... @overload def var(self, /, axis: int, skipna: bool): """ usage.dask: 1 """ ... @overload def var(self, /, axis: int, skipna: None): """ usage.dask: 1 """ ... @overload def var(self, /, ddof: int): """ usage.dask: 1 """ ... @overload def var(self, /, skipna: bool): """ usage.dask: 1 """ ... @overload def var(self, /, axis: int): """ usage.dask: 1 """ ... @overload def var(self, /, axis: Literal["columns"]): """ usage.dask: 1 """ ... def var( self, /, axis: Union[Literal["columns"], int] = ..., skipna: Union[None, bool] = ..., ddof: int = ..., ): """ usage.dask: 11 usage.koalas: 3 usage.seaborn: 1 usage.xarray: 1 """ ... @overload def where(self, /, cond: pandas.core.series.Series): """ usage.dask: 1 usage.koalas: 1 usage.xarray: 1 """ ... @overload def where( self, /, cond: pandas.core.series.Series, other: pandas.core.series.Series, axis: int, ): """ usage.dask: 2 """ ... @overload def where( self, /, cond: pandas.core.series.Series, other: numpy.float64, axis: int ): """ usage.dask: 2 """ ... @overload def where(self, /, cond: pandas.core.series.Series, other: float): """ usage.dask: 1 """ ... @overload def where( self, /, cond: pandas.core.series.Series, other: pandas.core.series.Series ): """ usage.dask: 2 """ ... def where( self, /, cond: pandas.core.series.Series, axis: int = ..., other: Union[pandas.core.series.Series, numpy.float64, float] = ..., ): """ usage.dask: 8 usage.koalas: 1 usage.xarray: 1 """ ... def xs(self, /, key: Tuple[Literal["a"], Literal["lama"], Literal["speed"]]): """ usage.koalas: 1 """ ...

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null

0

1

0

1

0

null

0

1

0

5

105adf9efb9bf3258660494a5f9d558d4b64d186

57

py

Python

example-project/src-server/app/service/auth/signin.py

syarig/codecrumbs

4a8e7b7dc8db342768052059aa517d049f5639b8

[ "BSD-3-Clause" ]

2,735

2018-05-03T10:50:17.000Z

2022-03-31T14:43:26.000Z

example-project/src-server/app/service/auth/signin.py

christyjacob4/codecrumbs

628862d024c912c04b17a313699b4d3c20c51b62

[ "BSD-3-Clause" ]

75

2019-01-14T20:42:28.000Z

2021-09-25T18:29:32.000Z

example-project/src-server/app/service/auth/signin.py

christyjacob4/codecrumbs

628862d024c912c04b17a313699b4d3c20c51b62

[ "BSD-3-Clause" ]

116

2019-01-14T20:53:54.000Z

2022-02-28T06:10:31.000Z

#cc:signin#2;send request #cc:signin#4;respond to client

19

30

0.77193

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57

4

0.818182

0.363636

0

0.038462

0.087719

57

3

30

19

0.807692

0.894737

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null

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null

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null

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null

1

null

true

0

null

1

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null

1

0

1

0

1

0

null

0

1

0

5

10af7945cc48da010e72010284acf855140a1fc0

432

py

Python

lib/exceptions.py

danechitoaie/entropy

70fd1bdd82281ec04e26b75fe399651ca82af2b3

[ "MIT" ]

null

lib/exceptions.py

danechitoaie/entropy

70fd1bdd82281ec04e26b75fe399651ca82af2b3

[ "MIT" ]

null

lib/exceptions.py

danechitoaie/entropy

70fd1bdd82281ec04e26b75fe399651ca82af2b3

[ "MIT" ]

null

class EntropyException(Exception): pass class EntropyHttpUnauthorizedException(EntropyException): pass class EntropyHttpMkcolException(EntropyException): pass class EntropyHttpPropFindException(EntropyException): pass class EntropyHttpNoContentException(EntropyException): pass class EntropyHttpPutException(EntropyException): pass class EntropyVerifyCodeDirectoryException(EntropyException): pass

20.571429

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0.824074

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12.714286

0.357143

0.151685

0.351124

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0.127315

432

20

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null

0

1

0

null

0

1

0

5

5e2ce8e8a8f06f30081e4e87cc8aa5c32c4be960

27

py

Python

simulite/__init__.py

wolray/simulite

cedc185fbb85c637edef37bb2989fcaa44413d3b

[ "MIT" ]

null

simulite/__init__.py

wolray/simulite

cedc185fbb85c637edef37bb2989fcaa44413d3b

[ "MIT" ]

null

simulite/__init__.py

wolray/simulite

cedc185fbb85c637edef37bb2989fcaa44413d3b

[ "MIT" ]

null

from simulite.env import *

13.5

26

0.777778

4

27

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1

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27

1

27

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1

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null

0

1

0

1

0

5

eacf797847dcfe8215e28772afa05e493bbb0f04

6,544

py

Python

overhang/dnastorage_utils/codec/phys.py

dna-storage/DINOS

65f4142e80d646d7eefa3fc16d747d21ec43fbbe

[ "BSD-2-Clause-FreeBSD" ]

null

overhang/dnastorage_utils/codec/phys.py

dna-storage/DINOS

65f4142e80d646d7eefa3fc16d747d21ec43fbbe

[ "BSD-2-Clause-FreeBSD" ]

null

overhang/dnastorage_utils/codec/phys.py

dna-storage/DINOS

65f4142e80d646d7eefa3fc16d747d21ec43fbbe

[ "BSD-2-Clause-FreeBSD" ]

null

from dnastorage.codec.base import * from dnastorage.exceptions import * class CombineCodewords(BaseCodec): def __init__(self,CodecObj=None,Policy=None): BaseCodec.__init__(self,CodecObj=CodecObj,Policy=Policy) def _encode(self, codeword_list): return "".join(codeword_list) def _decode(self, s): assert ("not used for decoding") class NormalizeStrandLength(BaseCodec): def __init__(self,length,CodecObj=None,Policy=None): BaseCodec.__init__(self,CodecObj=CodecObj,Policy=Policy) self.length = length def _encode(self, phys_s): if len(phys_s) > self.length: e = DNAStrandPayloadWrongSize("NormalizeStrandLength: Strand is too long ({})".format(len(phys_s))) if self._Policy.allow(e): pass else: raise e elif len(phys_s) < self.length: add = self.length - len(phys_s) phys_s = phys_s + "".join([ random.choice('AGCT') for _ in range(add) ]) return phys_s def _decode(self, s): assert ("not used for decoding") class InsertMidSequence(BaseCodec): def __init__(self,seq,CodecObj=None,Policy=None): BaseCodec.__init__(self,CodecObj=CodecObj,Policy=Policy) self._seq = seq def _encode(self,strand): if strand.find(self._seq) != -1: err = DNAStrandPoorlyFormed("Found sequence already present while prepending {}"\ .format(self._seq)) if self._Policy.allow(err): pass else: raise err middle = len(strand)/2 return strand[0:middle]+self._seq+strand[middle:] def _decode(self,strand): index = strand.find(self._seq) if index != -1: return strand[0:index]+strand[index+len(self._seq):] else: err = DNAStrandMissingSequence("{} should have had {} inside it.".format(strand,self._seq)) # there could be errors in the cut preventing us from seeing it # with an exact match, so now we look for an inexact match if self._Policy.allow(err): middle = len(strand)/2 slen = len(self._seq) res = [] for m in range(middle-slen,middle): sli = strand[m:m+slen] res.append( ed.eval(sli,self._seq) ) mn = min(res) if mn < slen/3: place = middle - slen + res.index(mn) return strand[0:place]+strand[place+slen:] else: # just leave the strand along, and hopefully codewords can # still be extracted properly return strand else: raise err class PrependSequence(BaseCodec): def __init__(self,seq,CodecObj=None,Policy=None,isPrimer=False): BaseCodec.__init__(self,CodecObj=CodecObj,Policy=Policy) self._seq = seq[:] self.is_primer = isPrimer def _encode(self,strand): if strand.find(self._seq) != -1: err = DNAStrandPoorlyFormed("Found sequence already present while prepending {}"\ .format(self._seq)) if self._Policy.allow(err): pass else: raise err return self._seq + strand def _decode(self,strand): index = strand.find(self._seq) if index != -1: # expected at beginning return strand[index+len(self._seq):] else: err = DNAStrandMissingSequence("{} should have had {} at beginning.".format(strand,self._seq)) # there could be errors in the cut preventing us from seeing it # with an exact match, so now we look for an inexact match if self._Policy.allow(err): slen = len(self._seq) res = [] # FIXME: how far in should we look? for m in range(0,50): sli = strand[m:m+slen] res.append( ed.eval(sli,self._seq) ) mn = min(res) idx = res.index(mn) #print res,mn if mn < 5: return strand[idx+slen:] else: if self.is_primer: raise DNAMissingPrimer("Missing primer {}".format(self._seq)) # just leave the strand along, and hopefully codewords can # still be extracted properly return strand else: raise err class AppendSequence(BaseCodec): def __init__(self,seq,CodecObj=None,Policy=None,isPrimer=False): BaseCodec.__init__(self,CodecObj=CodecObj,Policy=Policy) self._seq = seq self.is_primer = isPrimer def _encode(self,strand): if strand.find(self._seq) != -1: err = DNAStrandPoorlyFormed("Found sequence already present while appending {}"\ .format(self._seq)) if self._Policy.allow(err): pass else: raise err return strand + self._seq def _decode(self,strand): index = strand.find(self._seq) slen = len(self._seq) if index != -1: # expected at end return strand[:index] else: err = DNAStrandMissingSequence("{} should have had {} at end.".format(strand,self._seq)) # there could be errors in the cut preventing us from seeing it # with an exact match, so now we look for an inexact match if self._Policy.allow(err): slen = len(self._seq) res = [] for m in range(len(strand)-2*slen,len(strand)): sli = strand[m:m+slen] res.append( ed.eval(sli,self._seq) ) mn = min(res) idx = res.index(mn) if mn < 5: return strand[:idx+len(strand)-2*slen] else: if self.is_primer: raise DNAMissingPrimer("Missing primer".format(self._seq)) # just leave the strand along, and hopefully codewords can # still be extracted properly return strand else: raise err

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null

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null

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5

eadd3895ad1b734316b040a891c090af793060a8

55

py

Python

src/mrp_library/models/__init__.py

Yermouth/mrp2019

da77c07bcbcdca5ce96c3fc7e670b55ad680844d

[ "Apache-2.0" ]

1

2019-10-10T02:31:31.000Z

src/mrp_library/models/__init__.py

Yermouth/mrp2019

da77c07bcbcdca5ce96c3fc7e670b55ad680844d

[ "Apache-2.0" ]

null

src/mrp_library/models/__init__.py

Yermouth/mrp2019

da77c07bcbcdca5ce96c3fc7e670b55ad680844d

[ "Apache-2.0" ]

null

from mrp_library.models.generalizer import Generalizer

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5

eae3a90f1f36531ddfdafc4ff473cb94a9f6f138

22

py

Python

python/testData/completion/excludedSubPackage/a.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/completion/excludedSubPackage/a.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/completion/excludedSubPackage/a.py

truthiswill/intellij-community

fff88cfb0dc168eea18ecb745d3e5b93f57b0b95

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

import pkg1.sub<caret>

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1

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0

5

eaf6e5331c3137494dfc93aef98bd5aae8fbf678

6,757

py

Python

hes_off/core/deprecated/tests/numba_tests/process_plots.py

luca-riboldi/HES-OFF

67f4d7750436db63be10d0f4158c72e21b1fc2e8

[ "MIT" ]

1

2021-05-11T14:31:06.000Z

hes_off/core/deprecated/tests/numba_tests/process_plots.py

edvardronglanlundin/HES-OFF

95523c730ac46676cfe8326ef7498d1484cdf3b2

[ "MIT" ]

null

hes_off/core/deprecated/tests/numba_tests/process_plots.py

edvardronglanlundin/HES-OFF

95523c730ac46676cfe8326ef7498d1484cdf3b2

[ "MIT" ]

2

2021-06-22T11:46:29.000Z

2021-09-10T13:04:15.000Z

import numpy as np import matplotlib.pyplot as plt # Define font settings fontsize = 12 plt.rc('text', usetex=False) plt.rcParams['font.family'] = 'serif' # 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace' plt.rcParams['font.serif'] = 'times new roman' # 'cmr10', 'palatino', 'times new roman' plt.rcParams['mathtext.fontset'] = 'stix' # 'cm' (latex style), 'stix' (times new roman style), 'stixsans' # ------------------------------------------------------------------------------------------------------------------ ## # Results plotting functions # ------------------------------------------------------------------------------------------------------------------ ## def plot_hydrogen_level(results): """ Plot hydrogen storage level over time """ n_axes = results["times"].shape[0] fig = plt.figure(figsize=(6.0, 5.5)) fig.suptitle('Hydrogen storage level over the year (kg)', fontsize=fontsize+1, fontweight='normal', color='k') axes = fig.subplots(n_axes) for index, ax in enumerate(axes): x, y = results["times"][index, :] / 24, results["H2_level"][index, :] for t in ax.xaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) for t in ax.yaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) ax.plot([0.0], [0.0], linestyle="", marker="", label="Period " + str(index + 1)) ax.plot(x, y, linewidth=0.75, linestyle='-', color='k', label="", marker="") ax.set_ylabel('H$_2$ level (kg)', fontsize=fontsize, color='k', labelpad=fontsize) if index + 1 == n_axes: ax.set_xlabel('Time (days)', fontsize=fontsize, color='k', labelpad=fontsize) ax.legend(ncol=1, loc='lower right', fontsize=fontsize-1, edgecolor='k', framealpha=1.0, handlelength=0.0) dy = np.max(y) ax.set_ylim([-dy/5, np.max(y)+dy/5]) fig.tight_layout() return fig, axes def plot_hydrogen_balance(results): """ Plot the hydrogen balance over time """ n_axes = results["times"].shape[0] fig = plt.figure(figsize=(6.0, 5.5)) fig.suptitle('Hydrogen production and utilization over the year', fontsize=fontsize+1, fontweight='normal', color='k') axes = fig.subplots(n_axes) for index, ax in enumerate(axes): x1, y1 = results["times"][index, :] / 24, +results["H2_produced"][index, :] x2, y2 = results["times"][index, :] / 24, -results["H2_utilized"][index, :] for t in ax.xaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) for t in ax.yaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) ax.plot([0.0], [0.0], linestyle="", marker="", label="Period " + str(index + 1)) ax.plot(x1, y1, linewidth=0.75, linestyle='-', color='k', label="Produced") ax.plot(x2, y2, linewidth=0.75, linestyle='-', color='r', label="Utilized") ax.set_ylabel('Mass flow (kg/s)', fontsize=fontsize, color='k', labelpad=fontsize) if index + 1 == n_axes: ax.set_xlabel('Time (days)', fontsize=fontsize, color='k', labelpad=fontsize) ax.legend(ncol=1, loc='lower right', fontsize=fontsize-1, edgecolor='k', framealpha=1.0) dy = max(np.max(y1)-np.min(y2), 0.02) ax.set_ylim([np.min(y2)-dy/5, np.max(y1)+dy/5]) fig.tight_layout() return fig, axes def plot_power_deficit(results): """ Plot the energy deficit over time """ n_axes = results["times"].shape[0] fig = plt.figure(figsize=(6.0, 5.5)) fig.suptitle('Power deficit over the year', fontsize=fontsize+1, fontweight='normal', color='k') axes = fig.subplots(n_axes) for index, ax in enumerate(axes): x, y = results["times"][index, :] / 24, results["power_deficit"][index, :] / 1e6 for t in ax.xaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) for t in ax.yaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) ax.plot(x, y, linewidth=0.75, linestyle='-', color='k', label="Period "+str(index+1), marker="") ax.set_ylabel('Deficit (MW)', fontsize=fontsize, color='k', labelpad=fontsize) if index + 1 == n_axes: ax.set_xlabel('Time (days)', fontsize=fontsize, color='k', labelpad=fontsize) ax.legend(ncol=1, loc='lower right', fontsize=fontsize-1, edgecolor='k', framealpha=1.0, handlelength=0.0) dy = max(1, np.max(y)) ax.set_ylim([-dy/5, np.max(y)+dy/5]) fig.tight_layout() return fig, axes def plot_carbon_dioxide_emissions(results): """ Plot the carbon dioxide emissions over time """ n_axes = results["times"].shape[0] fig = plt.figure(figsize=(6.0, 5.5)) fig.suptitle('CO$_2$ emissions accumulated over the year', fontsize=fontsize+1, fontweight='normal', color='k') axes = fig.subplots(n_axes) for index, ax in enumerate(axes): x, y = results["times"][index, :] / 24, np.cumsum(results["CO2_produced"][index, :]) / 1e6 for t in ax.xaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) for t in ax.yaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) ax.plot(x, y, linewidth=0.75, linestyle='-', color='k', label="Period "+str(index+1), marker="") ax.set_ylabel('CO$_2$ emissions (Mt)', fontsize=fontsize, color='k', labelpad=fontsize) if index + 1 == n_axes: ax.set_xlabel('Time (days)', fontsize=fontsize, color='k', labelpad=fontsize) ax.legend(ncol=1, loc='lower right', fontsize=fontsize-1, edgecolor='k', framealpha=1.0, handlelength=0.0) dy = np.max(y) ax.set_ylim([-dy/5, np.max(y)+dy/5]) fig.tight_layout() return fig, axes def plot_flag(results): """ Plot the operation flag over time """ n_axes = results["times"].shape[0] fig = plt.figure(figsize=(6.0, 5.5)) fig.suptitle('Process flag over the year', fontsize=fontsize+1, fontweight='normal', color='k') axes = fig.subplots(n_axes) for index, ax in enumerate(axes): x, y = results["times"][index, :] / 24, results["flag"][index, :] for t in ax.xaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) for t in ax.yaxis.get_major_ticks(): t.label1.set_fontsize(fontsize) ax.plot(x, y, linewidth=0.75, linestyle='', color='k', label="Period "+str(index+1), marker="o", markerfacecolor="w", markeredgecolor="k", markersize=3.0, markeredgewidth=0.75) ax.set_ylabel('Flag', fontsize=fontsize, color='k', labelpad=fontsize) if index + 1 == n_axes: ax.set_xlabel('Time (days)', fontsize=fontsize, color='k', labelpad=fontsize) ax.legend(ncol=1, loc='lower right', fontsize=fontsize-1, edgecolor='k', framealpha=1.0, handlelength=0.0) ax.set_ylim([-0.5, 5.5]) fig.tight_layout() return fig, axes

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d8229ebe6abc1c7f5c6cb426f2743a6da719da8a

105

py

Python

tests/fixtures/fixtures.py

pcorpet/zenaton-python

099c41627482a6fdc619833c2bec59dbc68cbcd4

[ "MIT" ]

28

2017-09-19T11:53:22.000Z

2019-12-17T12:18:43.000Z

tests/fixtures/fixtures.py

pcorpet/zenaton-python

099c41627482a6fdc619833c2bec59dbc68cbcd4

[ "MIT" ]

21

2018-10-25T14:47:56.000Z

2020-07-28T14:56:03.000Z

tests/fixtures/fixtures.py

pcorpet/zenaton-python

099c41627482a6fdc619833c2bec59dbc68cbcd4

[ "MIT" ]

2

2019-06-17T06:38:05.000Z

2019-07-24T09:46:00.000Z

import pytest class DummyClass: pass @pytest.fixture def dummy_object(): return DummyClass()

9.545455

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null

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null

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1

0

1

0

5

dc42f9fe188d4a98e1bd9bd605d241083e966cfc

1,354

py

Python

src/error/customs.py

0417taehyun/studeep-backend

5a13fd6b20b8fda8adceb7c82e44efe87b644da0

[ "Apache-2.0" ]

1

2021-07-05T06:25:43.000Z

src/error/customs.py

0417taehyun/studeep-backend

5a13fd6b20b8fda8adceb7c82e44efe87b644da0

[ "Apache-2.0" ]

26

2021-05-01T05:56:34.000Z

2021-05-21T10:07:32.000Z

app/errors/customs.py

YAPP-18th/ML-Team-Backend

7da5430ab07e180d88ca62d005d760c729f1de9c

[ "Apache-2.0" ]

null

def get_detail(param: str, field: str, message: str, err: str): detail = [ { 'loc': [ f'{param}', # ex. body f'{field}' # ex. title ], "msg": message, # ex. field required, not found "type": f"{err}.missing" # ex. value_error } ] return detail class InternalException(Exception): def __init__(self, message: str): self.message = message def __str__(self): return self.message class NoSuchElementException(Exception): def __init__(self, message: str): self.message = message def __str__(self): return self.message class InvalidArgumentException(Exception): def __init__(self, message: str): self.message = message def __str__(self): return self.message class RequestConflictException(Exception): def __init__(self, message: str): self.message = message def __str__(self): return self.message class ForbiddenException(Exception): def __init__(self, message: str): self.message = message def __str__(self): return self.message class RequestInvalidException(Exception): def __init__(self, message: str): self.message = message def __str__(self): return self.message

20.515152

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0.626649

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null

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1

0

1

0

5

dc9b3041b77e53c6b1a8fc8ce3707e0f58e25eab

166

py

Python

venv/python_resources/visualize.py

cglorsung/becker-lorsung-ddi

bdfc7e17bc91468ce684e5ee0288589b11a4edab

[ "MIT" ]

null

venv/python_resources/visualize.py

cglorsung/becker-lorsung-ddi

bdfc7e17bc91468ce684e5ee0288589b11a4edab

[ "MIT" ]

null

venv/python_resources/visualize.py

cglorsung/becker-lorsung-ddi

bdfc7e17bc91468ce684e5ee0288589b11a4edab

[ "MIT" ]

null

# Authors: Conor Lorsung and Kyle Becker from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt import numpy as np from arffMain import returnVals

20.75

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5

dcb29a04e8de140afbcd19deca813aec3cbe92ba

5,546

py

Python

tests/tests_preprocessing/test_goldenfeatures_transformer.py

stjordanis/mljar-supervised

8c3f9d1ed527dfcfdaef91cf82e2779c5832e294

[ "MIT" ]

1,882

2018-11-05T13:20:54.000Z

2022-03-31T14:31:46.000Z

tests/tests_preprocessing/test_goldenfeatures_transformer.py

stjordanis/mljar-supervised

8c3f9d1ed527dfcfdaef91cf82e2779c5832e294

[ "MIT" ]

499

2019-03-14T09:57:51.000Z

2022-03-30T06:00:43.000Z

tests/tests_preprocessing/test_goldenfeatures_transformer.py

stjordanis/mljar-supervised

8c3f9d1ed527dfcfdaef91cf82e2779c5832e294

[ "MIT" ]

277

2019-02-08T21:32:13.000Z

2022-03-29T03:26:05.000Z

import unittest import tempfile import json import shutil import numpy as np import pandas as pd from numpy.testing import assert_almost_equal from sklearn import datasets from supervised.preprocessing.goldenfeatures_transformer import ( GoldenFeaturesTransformer, ) from supervised.algorithms.registry import ( BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, ) class GoldenFeaturesTransformerTest(unittest.TestCase): automl_dir = "automl_testing" def tearDown(self): shutil.rmtree(self.automl_dir, ignore_errors=True) def test_transformer(self): X, y = datasets.make_classification( n_samples=100, n_features=10, n_informative=6, n_redundant=1, n_classes=2, n_clusters_per_class=3, n_repeated=0, shuffle=False, random_state=0, ) df = pd.DataFrame(X, columns=[f"f{i}" for i in range(X.shape[1])]) with tempfile.TemporaryDirectory() as tmpdir: gft = GoldenFeaturesTransformer(tmpdir, "binary_classification") gft.fit(df, y) df = gft.transform(df) gft3 = GoldenFeaturesTransformer(tmpdir, "binary_classification") gft3.from_json(gft.to_json(), tmpdir) def test_subsample_regression_10k(self): rows = 10000 X = np.random.rand(rows, 3) X = pd.DataFrame(X, columns=[f"f{i}" for i in range(3)]) y = pd.Series(np.random.rand(rows), name="target") gft3 = GoldenFeaturesTransformer(self.automl_dir, REGRESSION) X_train, X_test, y_train, y_test = gft3._subsample(X, y) self.assertTrue(X_train.shape[0], 2500) self.assertTrue(X_test.shape[0], 2500) self.assertTrue(y_train.shape[0], 2500) self.assertTrue(y_test.shape[0], 2500) def test_subsample_regression_4k(self): rows = 4000 X = np.random.rand(rows, 3) X = pd.DataFrame(X, columns=[f"f{i}" for i in range(3)]) y = pd.Series(np.random.rand(rows), name="target") gft3 = GoldenFeaturesTransformer(self.automl_dir, REGRESSION) X_train, X_test, y_train, y_test = gft3._subsample(X, y) self.assertTrue(X_train.shape[0], 2000) self.assertTrue(X_test.shape[0], 2000) self.assertTrue(y_train.shape[0], 2000) self.assertTrue(y_test.shape[0], 2000) def test_subsample_multiclass_10k(self): rows = 10000 X = np.random.rand(rows, 3) X = pd.DataFrame(X, columns=[f"f{i}" for i in range(3)]) y = pd.Series(np.random.randint(0, 4, rows), name="target") gft3 = GoldenFeaturesTransformer(self.automl_dir, MULTICLASS_CLASSIFICATION) X_train, X_test, y_train, y_test = gft3._subsample(X, y) self.assertTrue(X_train.shape[0], 2500) self.assertTrue(X_test.shape[0], 2500) self.assertTrue(y_train.shape[0], 2500) self.assertTrue(y_test.shape[0], 2500) for uni in [np.unique(y_train), np.unique(y_test)]: for i in range(4): self.assertTrue(i in uni) def test_subsample_multiclass_4k(self): rows = 4000 X = np.random.rand(rows, 3) X = pd.DataFrame(X, columns=[f"f{i}" for i in range(3)]) y = pd.Series(np.random.randint(0, 4, rows), name="target") gft3 = GoldenFeaturesTransformer(self.automl_dir, MULTICLASS_CLASSIFICATION) X_train, X_test, y_train, y_test = gft3._subsample(X, y) self.assertTrue(X_train.shape[0], 2000) self.assertTrue(X_test.shape[0], 2000) self.assertTrue(y_train.shape[0], 2000) self.assertTrue(y_test.shape[0], 2000) for uni in [np.unique(y_train), np.unique(y_test)]: for i in range(4): self.assertTrue(i in uni) def test_subsample_binclass_4k(self): rows = 4000 X = np.random.rand(rows, 3) X = pd.DataFrame(X, columns=[f"f{i}" for i in range(3)]) y = pd.Series(np.random.randint(0, 2, rows), name="target") gft3 = GoldenFeaturesTransformer(self.automl_dir, BINARY_CLASSIFICATION) X_train, X_test, y_train, y_test = gft3._subsample(X, y) self.assertTrue(X_train.shape[0], 2000) self.assertTrue(X_test.shape[0], 2000) self.assertTrue(y_train.shape[0], 2000) self.assertTrue(y_test.shape[0], 2000) for uni in [np.unique(y_train), np.unique(y_test)]: for i in range(2): self.assertTrue(i in uni) def test_features_count(self): N_COLS = 10 X, y = datasets.make_classification( n_samples=100, n_features=N_COLS, n_informative=6, n_redundant=1, n_classes=2, n_clusters_per_class=3, n_repeated=0, shuffle=False, random_state=0, ) df = pd.DataFrame(X, columns=[f"f{i}" for i in range(X.shape[1])]) with tempfile.TemporaryDirectory() as tmpdir: FEATURES_COUNT = 42 gft = GoldenFeaturesTransformer( tmpdir, "binary_classification", features_count=FEATURES_COUNT ) gft.fit(df, y) self.assertEqual(len(gft._new_features), FEATURES_COUNT) gft3 = GoldenFeaturesTransformer(tmpdir, "binary_classification") gft3.from_json(gft.to_json(), tmpdir) df = gft3.transform(df) self.assertEqual(df.shape[1], N_COLS + FEATURES_COUNT)

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dcbb44f14b1a709e19afbdbb8c228dd24854d0e9

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Python

nni/retiarii/__init__.py

qfyin/nni

59a1ccf8eba68b94974e84fc3834f38d851faf89

[ "MIT" ]

1

2022-03-03T06:04:34.000Z

nni/retiarii/__init__.py

qfyin/nni

59a1ccf8eba68b94974e84fc3834f38d851faf89

[ "MIT" ]

1

2021-01-17T08:53:56.000Z

nni/retiarii/__init__.py

qfyin/nni

59a1ccf8eba68b94974e84fc3834f38d851faf89

[ "MIT" ]

1

2020-12-21T11:15:54.000Z

from .operation import Operation from .graph import * from .execution import * from .mutator import * from .utils import register_module

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