hexsha string | size int64 | ext string | lang string | max_stars_repo_path string | max_stars_repo_name string | max_stars_repo_head_hexsha string | max_stars_repo_licenses list | max_stars_count int64 | max_stars_repo_stars_event_min_datetime string | max_stars_repo_stars_event_max_datetime string | max_issues_repo_path string | max_issues_repo_name string | max_issues_repo_head_hexsha string | max_issues_repo_licenses list | max_issues_count int64 | max_issues_repo_issues_event_min_datetime string | max_issues_repo_issues_event_max_datetime string | max_forks_repo_path string | max_forks_repo_name string | max_forks_repo_head_hexsha string | max_forks_repo_licenses list | max_forks_count int64 | max_forks_repo_forks_event_min_datetime string | max_forks_repo_forks_event_max_datetime string | content string | avg_line_length float64 | max_line_length int64 | alphanum_fraction float64 | qsc_code_num_words_quality_signal int64 | qsc_code_num_chars_quality_signal float64 | qsc_code_mean_word_length_quality_signal float64 | qsc_code_frac_words_unique_quality_signal float64 | qsc_code_frac_chars_top_2grams_quality_signal float64 | qsc_code_frac_chars_top_3grams_quality_signal float64 | qsc_code_frac_chars_top_4grams_quality_signal float64 | qsc_code_frac_chars_dupe_5grams_quality_signal float64 | qsc_code_frac_chars_dupe_6grams_quality_signal float64 | qsc_code_frac_chars_dupe_7grams_quality_signal float64 | qsc_code_frac_chars_dupe_8grams_quality_signal float64 | qsc_code_frac_chars_dupe_9grams_quality_signal float64 | qsc_code_frac_chars_dupe_10grams_quality_signal float64 | qsc_code_frac_chars_replacement_symbols_quality_signal float64 | qsc_code_frac_chars_digital_quality_signal float64 | qsc_code_frac_chars_whitespace_quality_signal float64 | qsc_code_size_file_byte_quality_signal float64 | qsc_code_num_lines_quality_signal float64 | qsc_code_num_chars_line_max_quality_signal float64 | qsc_code_num_chars_line_mean_quality_signal float64 | qsc_code_frac_chars_alphabet_quality_signal float64 | qsc_code_frac_chars_comments_quality_signal float64 | qsc_code_cate_xml_start_quality_signal float64 | qsc_code_frac_lines_dupe_lines_quality_signal float64 | qsc_code_cate_autogen_quality_signal float64 | qsc_code_frac_lines_long_string_quality_signal float64 | qsc_code_frac_chars_string_length_quality_signal float64 | qsc_code_frac_chars_long_word_length_quality_signal float64 | qsc_code_frac_lines_string_concat_quality_signal float64 | qsc_code_cate_encoded_data_quality_signal float64 | qsc_code_frac_chars_hex_words_quality_signal float64 | qsc_code_frac_lines_prompt_comments_quality_signal float64 | qsc_code_frac_lines_assert_quality_signal float64 | qsc_codepython_cate_ast_quality_signal float64 | qsc_codepython_frac_lines_func_ratio_quality_signal float64 | qsc_codepython_cate_var_zero_quality_signal bool | qsc_codepython_frac_lines_pass_quality_signal float64 | qsc_codepython_frac_lines_import_quality_signal float64 | qsc_codepython_frac_lines_simplefunc_quality_signal float64 | qsc_codepython_score_lines_no_logic_quality_signal float64 | qsc_codepython_frac_lines_print_quality_signal float64 | qsc_code_num_words int64 | qsc_code_num_chars int64 | qsc_code_mean_word_length int64 | qsc_code_frac_words_unique null | qsc_code_frac_chars_top_2grams int64 | qsc_code_frac_chars_top_3grams int64 | qsc_code_frac_chars_top_4grams int64 | qsc_code_frac_chars_dupe_5grams int64 | qsc_code_frac_chars_dupe_6grams int64 | qsc_code_frac_chars_dupe_7grams int64 | qsc_code_frac_chars_dupe_8grams int64 | qsc_code_frac_chars_dupe_9grams int64 | qsc_code_frac_chars_dupe_10grams int64 | qsc_code_frac_chars_replacement_symbols int64 | qsc_code_frac_chars_digital int64 | qsc_code_frac_chars_whitespace int64 | qsc_code_size_file_byte int64 | qsc_code_num_lines int64 | qsc_code_num_chars_line_max int64 | qsc_code_num_chars_line_mean int64 | qsc_code_frac_chars_alphabet int64 | qsc_code_frac_chars_comments int64 | qsc_code_cate_xml_start int64 | qsc_code_frac_lines_dupe_lines int64 | qsc_code_cate_autogen int64 | qsc_code_frac_lines_long_string int64 | qsc_code_frac_chars_string_length int64 | qsc_code_frac_chars_long_word_length int64 | qsc_code_frac_lines_string_concat null | qsc_code_cate_encoded_data int64 | qsc_code_frac_chars_hex_words int64 | qsc_code_frac_lines_prompt_comments int64 | qsc_code_frac_lines_assert int64 | qsc_codepython_cate_ast int64 | qsc_codepython_frac_lines_func_ratio int64 | qsc_codepython_cate_var_zero int64 | qsc_codepython_frac_lines_pass int64 | qsc_codepython_frac_lines_import int64 | qsc_codepython_frac_lines_simplefunc int64 | qsc_codepython_score_lines_no_logic int64 | qsc_codepython_frac_lines_print int64 | effective string | hits int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
e81824b8131ba03318ca6b4849e36efce3c0e115 | 276 | py | Python | s3direct/urls.py | richiefi/django-s3direct | b0bbe67c4387f4a111da12381f0454e64fcaee81 | [
"MIT"
] | 1 | 2020-08-11T11:38:09.000Z | 2020-08-11T11:38:09.000Z | s3direct/urls.py | richiefi/django-s3direct | b0bbe67c4387f4a111da12381f0454e64fcaee81 | [
"MIT"
] | null | null | null | s3direct/urls.py | richiefi/django-s3direct | b0bbe67c4387f4a111da12381f0454e64fcaee81 | [
"MIT"
] | null | null | null | from django.urls import path
from s3direct.views import get_upload_params, generate_aws_v4_signature
urlpatterns = [
path("get_upload_params/", get_upload_params, name="s3direct"),
path("get_aws_v4_signature/", generate_aws_v4_signature, name="s3direct-signing"),
]
| 30.666667 | 86 | 0.789855 | 38 | 276 | 5.342105 | 0.447368 | 0.133005 | 0.221675 | 0.216749 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.024292 | 0.105072 | 276 | 8 | 87 | 34.5 | 0.797571 | 0 | 0 | 0 | 1 | 0 | 0.228261 | 0.076087 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
1c2b29ee506620ebaf32b44c7796a7ef747c0674 | 31 | py | Python | learn.py | ishmandoo/FlapPyBird-Neural | 2cd733db090dd972e698a5d951b90f76f091babe | [
"MIT"
] | 2 | 2019-11-13T22:14:30.000Z | 2019-11-13T22:15:24.000Z | learn.py | ishmandoo/FlapPyBird | 2cd733db090dd972e698a5d951b90f76f091babe | [
"MIT"
] | null | null | null | learn.py | ishmandoo/FlapPyBird | 2cd733db090dd972e698a5d951b90f76f091babe | [
"MIT"
] | null | null | null | from flappy import main
main() | 10.333333 | 23 | 0.774194 | 5 | 31 | 4.8 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16129 | 31 | 3 | 24 | 10.333333 | 0.923077 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
1c404242f0b6bd6bdb19467bcc2784e17f1dafc5 | 228 | py | Python | backend/apps/contact/admin.py | skiv23/portfolio | 3c1a7b0cf0fb67148ce4b0491132e3a01375c9b0 | [
"MIT"
] | null | null | null | backend/apps/contact/admin.py | skiv23/portfolio | 3c1a7b0cf0fb67148ce4b0491132e3a01375c9b0 | [
"MIT"
] | null | null | null | backend/apps/contact/admin.py | skiv23/portfolio | 3c1a7b0cf0fb67148ce4b0491132e3a01375c9b0 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from django.contrib import admin
from . import models
admin.site.register(models.Title)
admin.site.register(models.Contact)
admin.site.register(models.ContactMeEntry)
admin.site.register(models.Photo)
| 20.727273 | 42 | 0.776316 | 31 | 228 | 5.709677 | 0.483871 | 0.20339 | 0.384181 | 0.519774 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004808 | 0.087719 | 228 | 10 | 43 | 22.8 | 0.846154 | 0.092105 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
1c4e3cb8c781b998ec2999b4d075c32bf090784e | 64 | py | Python | optimizer/__init__.py | mpraiser/wsn_routing | a813622d701b195980c22578926d7b16e3e9cbbc | [
"MIT"
] | null | null | null | optimizer/__init__.py | mpraiser/wsn_routing | a813622d701b195980c22578926d7b16e3e9cbbc | [
"MIT"
] | null | null | null | optimizer/__init__.py | mpraiser/wsn_routing | a813622d701b195980c22578926d7b16e3e9cbbc | [
"MIT"
] | null | null | null | from .common import optimize, Target
from .optimizer import jso
| 21.333333 | 36 | 0.8125 | 9 | 64 | 5.777778 | 0.777778 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.140625 | 64 | 2 | 37 | 32 | 0.945455 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
1c6e302995aec47ce59adedddae9a2c20e3ed7d3 | 9,092 | py | Python | mmdet/ops/nms/rnms_wrapper.py | GuoBo98/ShipDet | 2979c39c5a56be3b99ba77833cfe556a8a0fc97e | [
"Apache-2.0"
] | null | null | null | mmdet/ops/nms/rnms_wrapper.py | GuoBo98/ShipDet | 2979c39c5a56be3b99ba77833cfe556a8a0fc97e | [
"Apache-2.0"
] | null | null | null | mmdet/ops/nms/rnms_wrapper.py | GuoBo98/ShipDet | 2979c39c5a56be3b99ba77833cfe556a8a0fc97e | [
"Apache-2.0"
] | null | null | null | import numpy as np
import torch
import math
import pdb
from mmdet.core.poly_box import polyiou
def pesudo_nms_poly(dets, iou_thr):
keep = torch.range(0, len(dets))
return dets, keep
def py_cpu_nms_poly_fast(dets, iou_thr):
# TODO: check the type numpy()
if dets.shape[0] == 0:
keep = dets.new_zeros(0, dtype=torch.long)
keep = keep.cpu().numpy()
device = dets.device
if isinstance(dets, torch.Tensor):
dets = dets.cpu().numpy().astype(np.float64)
if isinstance(iou_thr, torch.Tensor):
iou_thr = iou_thr.cpu().numpy().astype(np.float64)
else:
device = dets.device
if isinstance(dets, torch.Tensor):
dets = dets.cpu().numpy().astype(np.float64)
if isinstance(iou_thr, torch.Tensor):
iou_thr = iou_thr.cpu().numpy().astype(np.float64)
obbs = dets[:, 0:-1]
# pdb.set_trace()
x1 = np.min(obbs[:, 0::2], axis=1)
y1 = np.min(obbs[:, 1::2], axis=1)
x2 = np.max(obbs[:, 0::2], axis=1)
y2 = np.max(obbs[:, 1::2], axis=1)
scores = dets[:, 8]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
polys = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([dets[i][0], dets[i][1],
dets[i][2], dets[i][3],
dets[i][4], dets[i][5],
dets[i][6], dets[i][7]])
polys.append(tm_polygon)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
# if order.size == 0:
# break
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
# w = np.maximum(0.0, xx2 - xx1 + 1)
# h = np.maximum(0.0, yy2 - yy1 + 1)
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
hbb_inter = w * h
hbb_ovr = hbb_inter / (areas[i] + areas[order[1:]] - hbb_inter)
# h_keep_inds = np.where(hbb_ovr == 0)[0]
h_inds = np.where(hbb_ovr > 0)[0]
tmp_order = order[h_inds + 1]
for j in range(tmp_order.size):
iou = polyiou.iou_poly(polys[i], polys[tmp_order[j]])
hbb_ovr[h_inds[j]] = iou
# ovr.append(iou)
# ovr_index.append(tmp_order[j])
# ovr = np.array(ovr)
# ovr_index = np.array(ovr_index)
# print('ovr: ', ovr)
# print('thresh: ', thresh)
try:
if math.isnan(ovr[0]):
pdb.set_trace()
except:
pass
inds = np.where(hbb_ovr <= iou_thr)[0]
# order_obb = ovr_index[inds]
# print('inds: ', inds)
# order_hbb = order[h_keep_inds + 1]
order = order[inds + 1]
# pdb.set_trace()
# order = np.concatenate((order_obb, order_hbb), axis=0).astype(np.int)
return torch.from_numpy(dets[keep, :]).to(device), torch.from_numpy(np.array(keep)).to(device)
def py_cpu_nms_poly_fast_np(dets, thresh):
try:
obbs = dets[:, 0:-1]
except:
print('fail index')
pdb.set_trace()
x1 = np.min(obbs[:, 0::2], axis=1)
y1 = np.min(obbs[:, 1::2], axis=1)
x2 = np.max(obbs[:, 0::2], axis=1)
y2 = np.max(obbs[:, 1::2], axis=1)
scores = dets[:, 8]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
polys = []
for i in range(len(dets)):
tm_polygon = polyiou.VectorDouble([dets[i][0], dets[i][1],
dets[i][2], dets[i][3],
dets[i][4], dets[i][5],
dets[i][6], dets[i][7]])
polys.append(tm_polygon)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
ovr = []
i = order[0]
keep.append(i)
# if order.size == 0:
# break
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
# w = np.maximum(0.0, xx2 - xx1 + 1)
# h = np.maximum(0.0, yy2 - yy1 + 1)
w = np.maximum(0.0, xx2 - xx1)
h = np.maximum(0.0, yy2 - yy1)
hbb_inter = w * h
hbb_ovr = hbb_inter / (areas[i] + areas[order[1:]] - hbb_inter)
# h_keep_inds = np.where(hbb_ovr == 0)[0]
h_inds = np.where(hbb_ovr > 0)[0]
tmp_order = order[h_inds + 1]
for j in range(tmp_order.size):
iou = polyiou.iou_poly(polys[i], polys[tmp_order[j]])
hbb_ovr[h_inds[j]] = iou
# ovr.append(iou)
# ovr_index.append(tmp_order[j])
# ovr = np.array(ovr)
# ovr_index = np.array(ovr_index)
# print('ovr: ', ovr)
# print('thresh: ', thresh)
try:
if math.isnan(ovr[0]):
pdb.set_trace()
except:
pass
inds = np.where(hbb_ovr <= thresh)[0]
# order_obb = ovr_index[inds]
# print('inds: ', inds)
# order_hbb = order[h_keep_inds + 1]
order = order[inds + 1]
# pdb.set_trace()
# order = np.concatenate((order_obb, order_hbb), axis=0).astype(np.int)
return keep
def py_cpu_nms(dets, thresh):
"""Pure Python NMS baseline."""
#print('dets:', dets)
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
## index for dets
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thresh)[0]
order = order[inds + 1]
return keep
def bbox_poly2hbb(boxes):
"""
with label
:param boxes: (x1, y1, ... x4, y4, score) [n, 9]
:return: hbb: (xmin, ymin, xmax, ymax, score) [n, 5]
"""
n = boxes.shape[0]
hbbs = np.zeros((n, 4))
xs = np.reshape(boxes[:, : -1], (n, 4, 2))[:, :, 0]
ys = np.reshape(boxes[:, : -1], (n, 4, 2))[:, :, 1]
# pdb.set_trace()
hbbs[:, 0] = np.min(xs, axis=1)
hbbs[:, 1] = np.min(ys, axis=1)
hbbs[:, 2] = np.max(xs, axis=1)
hbbs[:, 3] = np.max(ys, axis=1)
hbbs = np.hstack((hbbs, boxes[:, -1, np.newaxis]))
return hbbs
def obb_HNMS(dets, iou_thr=0.5):
"""
do nms on obbs by corresponding hbbs
:param dets: shape (n, 9) (x1, y1, ..., score)
:param iou_thr:
:return:
"""
if dets.shape[0] == 0:
# TODO: use warp of function to reimplement it
keep = dets.new_zeros(0, dtype=torch.long)
keep = keep.cpu().numpy()
device = dets.device
if isinstance(dets, torch.Tensor):
dets = dets.cpu().numpy().astype(np.float64)
else:
device = dets.device
if isinstance(dets, torch.Tensor):
dets = dets.cpu().numpy().astype(np.float64)
if isinstance(iou_thr, torch.Tensor):
iou_thr = iou_thr.cpu().numpy().astype(np.float64)
h_dets = bbox_poly2hbb(dets)
keep = py_cpu_nms(h_dets, iou_thr)
return torch.from_numpy(dets[keep, :]).to(device), torch.from_numpy(np.array(keep)).to(device)
def obb_hybrid_NMS(dets, thresh_hbb=0.5, thresh_obb=0.3):
"""
do nms on obbs by 1. corresponding hbbs on relative high thresh 2. then nms by obbs on obbs
:param dets:
:param thresh:
:return:
"""
if dets.shape[0] == 0:
keep = dets.new_zeros(0, dtype=torch.long)
keep = keep.cpu().numpy()
device = dets.device
if isinstance(dets, torch.Tensor):
dets = dets.cpu().numpy().astype(np.float64)
else:
device = dets.device
if isinstance(dets, torch.Tensor):
dets = dets.cpu().numpy().astype(np.float64)
if isinstance(thresh_hbb, torch.Tensor):
thresh_hbb = thresh_hbb.cpu().numpy().astype(np.float64)
if isinstance(thresh_obb, torch.Tensor):
thresh_obb = thresh_obb.cpu().numpy().astype(np.float64)
h_dets = bbox_poly2hbb(dets)
h_keep = py_cpu_nms(h_dets, thresh_hbb)
keeped_o_dets = dets[h_keep, :]
o_keep = py_cpu_nms_poly_fast(keeped_o_dets, thresh_obb)
final_keep = h_keep[o_keep]
return torch.from_numpy(dets[final_keep, :]).to(device), torch.from_numpy(np.array(final_keep)).to(device)
| 34.052434 | 110 | 0.507589 | 1,290 | 9,092 | 3.465116 | 0.113178 | 0.007606 | 0.034452 | 0.039374 | 0.771812 | 0.752796 | 0.722819 | 0.711409 | 0.693512 | 0.685011 | 0 | 0.046063 | 0.326661 | 9,092 | 266 | 111 | 34.180451 | 0.68409 | 0.157281 | 0 | 0.740331 | 0 | 0 | 0.001326 | 0 | 0 | 0 | 0 | 0.007519 | 0 | 1 | 0.038674 | false | 0.01105 | 0.027624 | 0 | 0.104972 | 0.005525 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
1c861ca14ad6b414908f7984a494078a60ae9b82 | 329,634 | py | Python | software/qt_examples/src/pyqt-official/quick/animation/animation_rc.py | idetore/CASPER | 48725e40580b942e20bea760c681d99395ac7557 | [
"MIT"
] | null | null | null | software/qt_examples/src/pyqt-official/quick/animation/animation_rc.py | idetore/CASPER | 48725e40580b942e20bea760c681d99395ac7557 | [
"MIT"
] | null | null | null | software/qt_examples/src/pyqt-official/quick/animation/animation_rc.py | idetore/CASPER | 48725e40580b942e20bea760c681d99395ac7557 | [
"MIT"
] | 1 | 2020-02-14T21:43:29.000Z | 2020-02-14T21:43:29.000Z | # -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.8.0)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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"
qt_resource_name = b"\
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qt_resource_struct_v1 = b"\
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"
qt_resource_struct_v2 = b"\
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"
qt_version = QtCore.qVersion().split('.')
if qt_version < ['5', '8', '0']:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| 64.181075 | 129 | 0.726782 | 79,773 | 329,634 | 3.002695 | 0.003811 | 0.123941 | 0.16115 | 0.182003 | 0.573939 | 0.568028 | 0.563027 | 0.55724 | 0.552631 | 0.548461 | 0 | 0.375636 | 0.015833 | 329,634 | 5,135 | 130 | 64.193574 | 0.362721 | 0.000458 | 0 | 0.347529 | 0 | 0.965814 | 0.000012 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0.000391 | false | 0 | 0.000195 | 0 | 0.000586 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
1c9347a195ac306c6e4952de3dbe96ff7b28345f | 107 | py | Python | shared/templates/yamlfile_value/template.py | y-cann/scap-security-guide | 1fb06f2f45b2830dc269f3967d440a47e5d8987d | [
"BSD-3-Clause"
] | null | null | null | shared/templates/yamlfile_value/template.py | y-cann/scap-security-guide | 1fb06f2f45b2830dc269f3967d440a47e5d8987d | [
"BSD-3-Clause"
] | null | null | null | shared/templates/yamlfile_value/template.py | y-cann/scap-security-guide | 1fb06f2f45b2830dc269f3967d440a47e5d8987d | [
"BSD-3-Clause"
] | null | null | null | def preprocess(data, lang):
data["ocp_data"] = data.get("ocp_data", "false") == "true"
return data
| 26.75 | 62 | 0.626168 | 15 | 107 | 4.333333 | 0.6 | 0.215385 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.17757 | 107 | 3 | 63 | 35.666667 | 0.738636 | 0 | 0 | 0 | 0 | 0 | 0.233645 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
1c93a35f90bc622aef58b88c117283a37fc39be3 | 109 | py | Python | gamepie-ios/system.py | JadedTuna/gamepie | d49ba3597457bca0b43eb8fc5218683c5e6eef59 | [
"Zlib"
] | 4 | 2017-04-23T20:47:53.000Z | 2021-05-09T07:08:58.000Z | gamepie-ios/system.py | JadedTuna/gamepie | d49ba3597457bca0b43eb8fc5218683c5e6eef59 | [
"Zlib"
] | null | null | null | gamepie-ios/system.py | JadedTuna/gamepie | d49ba3597457bca0b43eb8fc5218683c5e6eef59 | [
"Zlib"
] | 1 | 2015-05-19T18:58:45.000Z | 2015-05-19T18:58:45.000Z | import os
def getOS():
return "iOS"
def getSystemPath(path):
return os.path.join(*path.split("/"))
| 13.625 | 41 | 0.642202 | 15 | 109 | 4.666667 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.183486 | 109 | 7 | 42 | 15.571429 | 0.786517 | 0 | 0 | 0 | 0 | 0 | 0.036697 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0 | 0.2 | 0.4 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
98d8da0ccc157b1521b30787d8991f88e18abe8c | 144 | py | Python | server/websockets/admin.py | nking1232/html5-msoy | 6e026f1989b15310ad67c050beb69a168c3bdd5f | [
"MIT"
] | null | null | null | server/websockets/admin.py | nking1232/html5-msoy | 6e026f1989b15310ad67c050beb69a168c3bdd5f | [
"MIT"
] | null | null | null | server/websockets/admin.py | nking1232/html5-msoy | 6e026f1989b15310ad67c050beb69a168c3bdd5f | [
"MIT"
] | 2 | 2020-12-18T19:19:38.000Z | 2020-12-18T19:53:56.000Z | from django.contrib import admin
from .models import ChannelRoom, Participant
admin.site.register(ChannelRoom)
admin.site.register(Participant) | 28.8 | 44 | 0.847222 | 18 | 144 | 6.777778 | 0.555556 | 0.147541 | 0.278689 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.076389 | 144 | 5 | 45 | 28.8 | 0.917293 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
c7022dc31cf4a2d963675d28330bd4076a3ec8ad | 286 | py | Python | test/orm/declarative/test_tm_future_annotations.py | petit87/sqlalchemy | 67d674bd63ca36ac32b23f96e2b19e9dac6b0863 | [
"MIT"
] | null | null | null | test/orm/declarative/test_tm_future_annotations.py | petit87/sqlalchemy | 67d674bd63ca36ac32b23f96e2b19e9dac6b0863 | [
"MIT"
] | null | null | null | test/orm/declarative/test_tm_future_annotations.py | petit87/sqlalchemy | 67d674bd63ca36ac32b23f96e2b19e9dac6b0863 | [
"MIT"
] | null | null | null | from __future__ import annotations
from .test_typed_mapping import MappedColumnTest # noqa
from .test_typed_mapping import RelationshipLHSTest # noqa
"""runs the annotation-sensitive tests from test_typed_mappings while
having ``from __future__ import annotations`` in effect.
"""
| 28.6 | 69 | 0.818182 | 35 | 286 | 6.285714 | 0.571429 | 0.109091 | 0.177273 | 0.245455 | 0.236364 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125874 | 286 | 9 | 70 | 31.777778 | 0.88 | 0.031469 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
c70aeac7db42dcf0c9ec7ad29c07d71d54cab035 | 254 | py | Python | examples/context.py | Mabo-IoT/simpati | 93cd0fd06260e55c70f248f51c9b1a049a176276 | [
"MIT"
] | null | null | null | examples/context.py | Mabo-IoT/simpati | 93cd0fd06260e55c70f248f51c9b1a049a176276 | [
"MIT"
] | 1 | 2018-08-22T02:50:15.000Z | 2018-08-22T02:50:15.000Z | examples/context.py | Mabo-IoT/simpati | 93cd0fd06260e55c70f248f51c9b1a049a176276 | [
"MIT"
] | 1 | 2018-09-03T06:16:24.000Z | 2018-09-03T06:16:24.000Z | import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from simpati.request import Request
from simpati.client import Client
from simpati.response import Response
from simpati.transition import Transition | 31.75 | 82 | 0.807087 | 38 | 254 | 5.289474 | 0.421053 | 0.218905 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004329 | 0.090551 | 254 | 8 | 83 | 31.75 | 0.865801 | 0 | 0 | 0 | 0 | 0 | 0.007843 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.857143 | 0 | 0.857143 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
c72aa7c4915529585b64d52e437b02cdb3a1b505 | 15,271 | py | Python | lib/CbasLib/CBASOperations_Rest.py | pavithra-mahamani/TAF | ff854adcc6ca3e50d9dc64e7756ca690251128d3 | [
"Apache-2.0"
] | null | null | null | lib/CbasLib/CBASOperations_Rest.py | pavithra-mahamani/TAF | ff854adcc6ca3e50d9dc64e7756ca690251128d3 | [
"Apache-2.0"
] | null | null | null | lib/CbasLib/CBASOperations_Rest.py | pavithra-mahamani/TAF | ff854adcc6ca3e50d9dc64e7756ca690251128d3 | [
"Apache-2.0"
] | null | null | null | """
Created on Sep 25, 2017
@author: riteshagarwal
"""
import json
import urllib
from connections.Rest_Connection import RestConnection
from membase.api import httplib2
class CBASHelper(RestConnection):
def __init__(self, master, cbas_node):
super(CBASHelper, self).__init__(cbas_node)
self.cbas_base_url = "http://{0}:{1}".format(self.ip, 8095)
def createConn(self, bucket, username, password):
pass
def closeConn(self):
pass
def execute_statement_on_cbas(self, statement, mode, pretty=True,
timeout=70, client_context_id=None,
username=None, password=None,
analytics_timeout=120, time_out_unit="s"):
if not username:
username = self.username
if not password:
password = self.password
api = self.cbas_base_url + "/analytics/service"
headers = self._create_capi_headers(username, password)
params = {'statement': statement, 'mode': mode, 'pretty': pretty,
'client_context_id': client_context_id,
'timeout': str(analytics_timeout) + time_out_unit}
params = json.dumps(params)
status, content, header = self._http_request(
api, 'POST', headers=headers, params=params, timeout=timeout)
if status:
return content
elif str(header['status']) == '503':
self.log.info("Request Rejected")
raise Exception("Request Rejected")
elif str(header['status']) in ['500','400']:
json_content = json.loads(content)
msg = json_content['errors'][0]['msg']
if "Job requirement" in msg and "exceeds capacity" in msg:
raise Exception("Capacity cannot meet job requirement")
else:
return content
else:
self.log.error("/analytics/service status:{0}, content:{1}"
.format(status, content))
raise Exception("Analytics Service API failed")
def execute_parameter_statement_on_cbas(self, statement, mode, pretty=True,
timeout=70, client_context_id=None,
username=None, password=None,
analytics_timeout=120,
parameters=[]):
if not username:
username = self.username
if not password:
password = self.password
api = self.cbas_base_url + "/analytics/service"
headers = self._create_capi_headers(username, password)
params = {'statement': statement, 'mode': mode, 'pretty': pretty,
'client_context_id': client_context_id,
'timeout': str(analytics_timeout) + "s"}
for i in range(len(parameters)):
params.update(parameters[i])
params = json.dumps(params)
status, content, header = self._http_request(
api, 'POST', headers=headers, params=params, timeout=timeout)
if status:
return content
elif str(header['status']) == '503':
self.log.info("Request Rejected")
raise Exception("Request Rejected")
elif str(header['status']) in ['500', '400']:
json_content = json.loads(content)
msg = json_content['errors'][0]['msg']
if "Job requirement" in msg and "exceeds capacity" in msg:
raise Exception("Capacity cannot meet job requirement")
else:
return content
else:
self.log.error("/analytics/service status:{0}, content:{1}"
.format(status, content))
raise Exception("Analytics Service API failed")
def delete_active_request_on_cbas(self, payload, username=None,
password=None):
if not username:
username = self.username
if not password:
password = self.password
api = self.cbas_base_url + "/analytics/admin/active_requests"
status, content, header = self._http_request(
api, 'DELETE',params=payload, timeout=60)
if status:
return header['status']
elif str(header['status']) == '404':
self.log.info("Request Not Found")
return header['status']
else:
self.log.error("/analytics/admin/active_requests "
"status:{0}, content:{1}"
.format(status, content))
raise Exception("Analytics Admin API failed")
def analytics_tool(self, query, port=8095, timeout=650, query_params={},
is_prepared=False, named_prepare=None,
verbose = True, encoded_plan=None, servers=None):
key = 'prepared' if is_prepared else 'statement'
headers = None
content=""
prepared = json.dumps(query)
if is_prepared:
if named_prepare and encoded_plan:
http = httplib2.Http()
if len(servers)>1:
url = "http://%s:%s/query/service" % (servers[1].ip, port)
else:
url = "http://%s:%s/query/service" % (self.ip, port)
headers = {'Content-type': 'application/json'}
body = {'prepared': named_prepare, 'encoded_plan':encoded_plan}
response, content = http.request(
url, 'POST', headers=headers, body=json.dumps(body))
return eval(content)
elif named_prepare and not encoded_plan:
params = 'prepared=' + urllib.quote(prepared, '~()')
params = 'prepared="%s"'% named_prepare
else:
prepared = json.dumps(query)
prepared = str(prepared.encode('utf-8'))
params = 'prepared=' + urllib.quote(prepared, '~()')
if 'creds' in query_params and query_params['creds']:
headers = self._create_headers_with_auth(
query_params['creds'][0]['user'].encode('utf-8'),
query_params['creds'][0]['pass'].encode('utf-8'))
api = "%s/analytics/service?%s" % (self.cbas_base_url, params)
self.log.info("%s" % api)
else:
params = {key : query}
if 'creds' in query_params and query_params['creds']:
headers = self._create_headers_with_auth(
query_params['creds'][0]['user'].encode('utf-8'),
query_params['creds'][0]['pass'].encode('utf-8'))
del query_params['creds']
params.update(query_params)
params = urllib.urlencode(params)
if verbose:
self.log.info('Query params: {0}'.format(params))
api = "%s/analytics/service?%s" % (self.cbas_base_url, params)
status, content, header = self._http_request(
api, 'POST', timeout=timeout, headers=headers)
try:
return json.loads(content)
except ValueError:
return content
def operation_log_level_on_cbas(self, method, params=None,
logger_name=None, log_level=None,
timeout=120, username=None,
password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
if params is not None:
api = self.cbas_base_url + "/analytics/cluster/logging"
else:
api = self.cbas_base_url + "/analytics/cluster/logging/" + logger_name
# In case of SET action we can set logging level of a specific logger
# and pass log_level as text string in body
if log_level:
params = log_level
status, content, response = self._http_request(
api, method=method, params=params, headers=headers,
timeout=timeout)
return status, content, response
def operation_config_on_cbas(self, method="GET", params=None,
username=None, password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
api = self.cbas_base_url + "/analytics/node/config"
status, content, response = self._http_request(
api, method=method, params=params, headers=headers)
return status, content, response
def restart_cbas(self, username=None, password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
api = self.cbas_base_url + "/analytics/cluster/restart"
status, content, response = self._http_request(
api, method="POST", headers=headers)
return status, content, response
def fetch_cbas_stats(self, username=None, password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
cbas_base_url = "http://{0}:{1}".format(self.ip, 9110)
api = cbas_base_url + "/analytics/node/stats"
status, content, response = self._http_request(
api, method="GET", headers=headers)
return status, content, response
def operation_service_parameters_configuration_cbas(self, method="GET",
params=None,
username=None,
password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
cbas_base_url = "http://{0}:{1}".format(self.ip, 8095)
api = cbas_base_url + "/analytics/config/service"
status, content, response = self._http_request(
api, method=method, params=params, headers=headers)
return status, content, response
def operation_node_parameters_configuration_cbas(self, method="GET",
params=None,
username=None,
password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
cbas_base_url = "http://{0}:{1}".format(self.ip, 8095)
api = cbas_base_url + "/analytics/config/node"
status, content, response = self._http_request(
api, method=method, params=params, headers=headers)
return status, content, response
def restart_analytics_cluster_uri(self, username=None, password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
api = self.cbas_base_url + "/analytics/cluster/restart"
status, content, response = self._http_request(
api, method="POST", headers=headers)
return status, content, response
def restart_analytics_node_uri(self, node_ip, port=8095, username=None,
password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
node_url = "http://{0}:{1}".format(node_ip, port)
api = node_url + "/analytics/node/restart"
status, content, response = self._http_request(
api, method="POST", headers=headers)
return status, content, response
def fetch_bucket_state_on_cbas(self, method="GET", username=None,
password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
api = self.cbas_base_url + "/analytics/buckets"
status, content, response = self._http_request(
api, method=method, headers=headers)
return status, content, response
def fetch_pending_mutation_on_cbas_node(self, node_ip, port=9110,
method="GET", username="None",
password="None"):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
node_url = "http://{0}:{1}".format(node_ip, port)
api = node_url + "/analytics/node/stats"
status, content, response = self._http_request(
api, method=method, headers=headers)
return status, content, response
def fetch_pending_mutation_on_cbas_cluster(self, port=9110, method="GET",
username="None",
password="None"):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
node_url = "http://{0}:{1}".format(self.ip, port)
api = node_url + "/analytics/node/agg/stats/remaining"
status, content, response = self._http_request(
api, method=method, headers=headers)
return status, content, response
def fetch_dcp_state_on_cbas(self, dataset, method="GET",
dataverse="Default", username=None,
password=None):
if not username:
username = self.username
if not password:
password = self.password
headers = self._create_capi_headers(username, password)
api = self.cbas_base_url + "/analytics/dataset/dcp/{0}/{1}" \
.format(dataverse, dataset)
status, content, response = self._http_request(
api, method=method, headers=headers)
return status, content, response
# return analytics diagnostics info
def get_analytics_diagnostics(self, cbas_node, timeout=120):
analytics_base_url = "http://{0}:{1}/".format(cbas_node.ip, 8095)
api = analytics_base_url + 'analytics/node/diagnostics'
status, content, header = self._http_request(api, timeout=timeout)
if status:
json_parsed = json.loads(content)
return json_parsed
else:
raise Exception("Unable to get jre path from analytics")
| 43.756447 | 82 | 0.564862 | 1,594 | 15,271 | 5.24655 | 0.11606 | 0.049743 | 0.060265 | 0.03659 | 0.74997 | 0.731317 | 0.719359 | 0.706565 | 0.689346 | 0.681454 | 0 | 0.012214 | 0.335211 | 15,271 | 348 | 83 | 43.882184 | 0.811564 | 0.012573 | 0 | 0.665584 | 0 | 0 | 0.103391 | 0.029199 | 0 | 0 | 0 | 0 | 0 | 1 | 0.064935 | false | 0.207792 | 0.012987 | 0 | 0.152597 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
c765f83f7eab12d2fece5febe33d287cba14584f | 160 | py | Python | ddcm/__init__.py | SkyZH/ddcm-protocol | 676a4ccff8783badf8d518c359d44e7454e1588c | [
"BSD-3-Clause"
] | 4 | 2019-12-27T03:40:49.000Z | 2021-05-29T17:02:49.000Z | ddcm/__init__.py | SkyZH/ddcm-protocol | 676a4ccff8783badf8d518c359d44e7454e1588c | [
"BSD-3-Clause"
] | 7 | 2016-01-24T14:36:56.000Z | 2018-08-16T13:10:35.000Z | ddcm/__init__.py | skyzh/ddcm-protocol | 676a4ccff8783badf8d518c359d44e7454e1588c | [
"BSD-3-Clause"
] | null | null | null | from .Service import Service
from .Node import Node
from .Route import Route
from .Remote import Remote
from .KBucket import KBucket
from .Logger import Logger
| 22.857143 | 28 | 0.8125 | 24 | 160 | 5.416667 | 0.333333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.15 | 160 | 6 | 29 | 26.666667 | 0.955882 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
c77815f9cac4254f39cc2fd8ec74442e742ddc52 | 122 | py | Python | commander/commander/auth.py | gkrizek/beam | fcb2e35cff8b4d5f9d398f276cf3eff661013abb | [
"Apache-2.0"
] | 5 | 2018-11-03T17:01:09.000Z | 2019-05-18T09:19:41.000Z | commander/commander/auth.py | gkrizek/beam | fcb2e35cff8b4d5f9d398f276cf3eff661013abb | [
"Apache-2.0"
] | null | null | null | commander/commander/auth.py | gkrizek/beam | fcb2e35cff8b4d5f9d398f276cf3eff661013abb | [
"Apache-2.0"
] | 3 | 2018-11-23T02:56:42.000Z | 2021-01-02T15:05:16.000Z |
def CheckAuth(headers, body):
# We will impliment this later when we actually use API Gateway
return 'granted' | 17.428571 | 67 | 0.713115 | 17 | 122 | 5.117647 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.229508 | 122 | 7 | 68 | 17.428571 | 0.925532 | 0.5 | 0 | 0 | 0 | 0 | 0.122807 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
c7ce728d30ba4cb4bba9bd8e6ab4a977ff839c27 | 1,462 | py | Python | silo/benchmarks/results/ben-3-8-13.py | anshsarkar/TailBench | 25845756aee9a892229c25b681051591c94daafd | [
"MIT"
] | 274 | 2015-01-23T16:24:09.000Z | 2022-02-22T03:16:14.000Z | silo/benchmarks/results/ben-3-8-13.py | anshsarkar/TailBench | 25845756aee9a892229c25b681051591c94daafd | [
"MIT"
] | 3 | 2015-03-17T11:52:36.000Z | 2019-07-22T23:04:25.000Z | silo/benchmarks/results/ben-3-8-13.py | anshsarkar/TailBench | 25845756aee9a892229c25b681051591c94daafd | [
"MIT"
] | 94 | 2015-01-07T06:55:36.000Z | 2022-01-22T08:14:15.000Z | RESULTS = [({'scale_factor': 80, 'threads': 80, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (232976.0, 3.76111)), ({'scale_factor': 72, 'threads': 72, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (254892.0, 3.73888)), ({'scale_factor': 64, 'threads': 64, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (242310.0, 3.46666)), ({'scale_factor': 56, 'threads': 56, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (237052.0, 3.57222)), ({'scale_factor': 48, 'threads': 48, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (230557.0, 3.74444)), ({'scale_factor': 40, 'threads': 40, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (234613.0, 3.87222)), ({'scale_factor': 32, 'threads': 32, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (224310.0, 3.7)), ({'scale_factor': 24, 'threads': 24, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (190809.0, 2.90555)), ({'scale_factor': 16, 'threads': 16, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (142130.0, 2.01667)), ({'scale_factor': 8, 'threads': 8, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (76759.6, 1.21667)), ({'scale_factor': 4, 'threads': 4, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (38735.2, 0.533333)), ({'scale_factor': 2, 'threads': 2, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (20094.2, 0.305555)), ({'scale_factor': 1, 'threads': 1, 'txn_flags': 1, 'db': 'ndb-proto2', 'bench': 'tpcc'}, (10286.7, 0.0))]
| 731 | 1,461 | 0.579343 | 222 | 1,462 | 3.698198 | 0.234234 | 0.174178 | 0.142509 | 0.174178 | 0.459196 | 0.459196 | 0.459196 | 0.459196 | 0 | 0 | 0 | 0.175614 | 0.108071 | 1,462 | 1 | 1,462 | 1,462 | 0.453988 | 0 | 0 | 0 | 0 | 0 | 0.435705 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
1bf98990b55b079f59746c728142b3595c3bb94a | 162 | py | Python | Lectures_Codes/examples-06/lecture06/myhello/myhellopackage/greetings/hello.py | MichalKyjovsky/NPRG065_Programing_in_Python | 14436fbf8f0e547ab084083135a84c8ae49e083c | [
"MIT"
] | null | null | null | Lectures_Codes/examples-06/lecture06/myhello/myhellopackage/greetings/hello.py | MichalKyjovsky/NPRG065_Programing_in_Python | 14436fbf8f0e547ab084083135a84c8ae49e083c | [
"MIT"
] | null | null | null | Lectures_Codes/examples-06/lecture06/myhello/myhellopackage/greetings/hello.py | MichalKyjovsky/NPRG065_Programing_in_Python | 14436fbf8f0e547ab084083135a84c8ae49e083c | [
"MIT"
] | null | null | null | # Module initialization code
print(f"Initializing module {__name__} ...")
# The only content of the module
def say_hello():
print(f"Hello from {__name__}")
| 20.25 | 44 | 0.716049 | 22 | 162 | 4.863636 | 0.681818 | 0.11215 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.160494 | 162 | 7 | 45 | 23.142857 | 0.786765 | 0.351852 | 0 | 0 | 0 | 0 | 0.539216 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0 | 0 | 0.333333 | 0.666667 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
40157e2c3b43016933cef9ec008837dfa0c6b779 | 243 | py | Python | prettyqt/widgets/styleoptionrubberband.py | phil65/PrettyQt | 26327670c46caa039c9bd15cb17a35ef5ad72e6c | [
"MIT"
] | 7 | 2019-05-01T01:34:36.000Z | 2022-03-08T02:24:14.000Z | prettyqt/widgets/styleoptionrubberband.py | phil65/PrettyQt | 26327670c46caa039c9bd15cb17a35ef5ad72e6c | [
"MIT"
] | 141 | 2019-04-16T11:22:01.000Z | 2021-04-14T15:12:36.000Z | prettyqt/widgets/styleoptionrubberband.py | phil65/PrettyQt | 26327670c46caa039c9bd15cb17a35ef5ad72e6c | [
"MIT"
] | 5 | 2019-04-17T11:48:19.000Z | 2021-11-21T10:30:19.000Z | from __future__ import annotations
from prettyqt import widgets
from prettyqt.qt import QtWidgets
QtWidgets.QStyleOptionRubberBand.__bases__ = (widgets.StyleOption,)
class StyleOptionRubberBand(QtWidgets.QStyleOptionRubberBand):
pass
| 20.25 | 67 | 0.839506 | 23 | 243 | 8.521739 | 0.608696 | 0.122449 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 243 | 11 | 68 | 22.090909 | 0.907407 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.166667 | 0.5 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 5 |
4015a3a6840865f5e24c58a8bb8e937811e14e8b | 81 | py | Python | pythoncalculator/__init__.py | Danillaneru/python-calculator | d89d20e6a18ad96213d1535bc072ad633fea2901 | [
"MIT"
] | null | null | null | pythoncalculator/__init__.py | Danillaneru/python-calculator | d89d20e6a18ad96213d1535bc072ad633fea2901 | [
"MIT"
] | 2 | 2021-05-24T14:19:49.000Z | 2021-05-24T15:14:29.000Z | pythoncalculator/__init__.py | Danillaneru/python-calculator | d89d20e6a18ad96213d1535bc072ad633fea2901 | [
"MIT"
] | null | null | null | from .add import add
from .subtract import subtract
from .divide import divide
| 20.25 | 31 | 0.790123 | 12 | 81 | 5.333333 | 0.416667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.17284 | 81 | 3 | 32 | 27 | 0.955224 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
402999a8a315340e58486085ad1fd7928949acd4 | 114 | py | Python | setup.py | yanyuliren/tt | e7e5ece9aeec6cee9f9bc1194dcfdfd42d9d63d3 | [
"MIT"
] | 58 | 2018-11-16T18:33:36.000Z | 2022-03-21T10:25:01.000Z | setup.py | yanyuliren/tt | e7e5ece9aeec6cee9f9bc1194dcfdfd42d9d63d3 | [
"MIT"
] | 14 | 2018-11-16T18:29:56.000Z | 2020-01-31T10:15:46.000Z | setup.py | roshniRam/Printed_text_recognition_and_conversion | a6885a4128a4992ef8a905d27790efa67023063f | [
"MIT"
] | 29 | 2018-11-16T18:51:46.000Z | 2022-01-03T22:04:16.000Z | #for the building the executables
from distutils.core import setup
import py2exe
setup(console=['ui.py'])
| 16.285714 | 34 | 0.736842 | 16 | 114 | 5.25 | 0.8125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010638 | 0.175439 | 114 | 6 | 35 | 19 | 0.882979 | 0.280702 | 0 | 0 | 0 | 0 | 0.066667 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
4069f46704e35c85396779f33574bf0c3f850031 | 129 | py | Python | youtube/admin.py | shrutiverma2107/video-library-django | d3018fc3c270f16ffe27572e6506e160de52e2e5 | [
"MIT"
] | null | null | null | youtube/admin.py | shrutiverma2107/video-library-django | d3018fc3c270f16ffe27572e6506e160de52e2e5 | [
"MIT"
] | null | null | null | youtube/admin.py | shrutiverma2107/video-library-django | d3018fc3c270f16ffe27572e6506e160de52e2e5 | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import *
from embed_video.admin import AdminVideoMixin
admin.site.register(Videos) | 25.8 | 45 | 0.837209 | 18 | 129 | 5.944444 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.100775 | 129 | 5 | 46 | 25.8 | 0.922414 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.75 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
40d25f9503add8f5c5c9e877990bc28432a56549 | 221 | py | Python | run_app_common_logger.py | NathanKr/python-logger-playground | 8a10f9199bfd7cf42902e9e66984299013f52e7e | [
"MIT"
] | null | null | null | run_app_common_logger.py | NathanKr/python-logger-playground | 8a10f9199bfd7cf42902e9e66984299013f52e7e | [
"MIT"
] | null | null | null | run_app_common_logger.py | NathanKr/python-logger-playground | 8a10f9199bfd7cf42902e9e66984299013f52e7e | [
"MIT"
] | null | null | null | from log_utils.utils import configure_root_logger
from common_logger.utils1 import add
from common_logger.utils2 import sub
configure_root_logger()
print(f'write log to file root.log')
print(add(1,2.3))
print(sub(4,5)) | 24.555556 | 49 | 0.80543 | 40 | 221 | 4.275 | 0.55 | 0.152047 | 0.222222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.035176 | 0.099548 | 221 | 9 | 50 | 24.555556 | 0.824121 | 0 | 0 | 0 | 0 | 0 | 0.117117 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.428571 | 0 | 0.428571 | 0.428571 | 0 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 5 |
40eb6985941c170b0ffaf529b670200958c1a785 | 89 | py | Python | web/apps/bot/admin.py | vitaliyharchenko/django_template | 41fa00cb0b8be6c5cf67b7a334d4340163255160 | [
"MIT"
] | null | null | null | web/apps/bot/admin.py | vitaliyharchenko/django_template | 41fa00cb0b8be6c5cf67b7a334d4340163255160 | [
"MIT"
] | 1 | 2018-02-02T20:25:41.000Z | 2018-02-02T20:25:41.000Z | web/apps/bot/admin.py | vitaliyharchenko/django_template | 41fa00cb0b8be6c5cf67b7a334d4340163255160 | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import Dialog
admin.site.register(Dialog)
| 17.8 | 32 | 0.820225 | 13 | 89 | 5.615385 | 0.692308 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.11236 | 89 | 4 | 33 | 22.25 | 0.924051 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
dc022f8aed3cb77219029638adaaf82bc3889109 | 5,900 | py | Python | tests/test_api.py | exolever/django-typeform-feedback | 5784523b880e4890172b9f61d848187f5c24237e | [
"MIT"
] | null | null | null | tests/test_api.py | exolever/django-typeform-feedback | 5784523b880e4890172b9f61d848187f5c24237e | [
"MIT"
] | 15 | 2019-03-22T09:04:53.000Z | 2019-12-13T08:15:10.000Z | tests/test_api.py | exolever/django-typeform-feedback | 5784523b880e4890172b9f61d848187f5c24237e | [
"MIT"
] | null | null | null | from django.conf import settings
from django.urls import reverse
from rest_framework import status
from rest_framework.test import APITestCase
from foo.models import Foo
from typeform_feedback.models import UserGenericTypeformFeedback, GenericTypeformFeedback
from typeform_feedback.helpers import random_string
from .test_mixin import TypeformTestMixin
class TestAPI(TypeformTestMixin, APITestCase):
def test_action_validate_pending_user_response(self):
# PREPARE DATA
user, generic_typeform = self.create_base_context()
user_response = UserGenericTypeformFeedback(
feedback=generic_typeform,
user=user,
)
user_response.save()
# DO ACTION
response = self.client.post(
reverse('api:action-validate', kwargs={'uuid': user_response.uuid}),
data={},
content_type='application/json',
)
# ASSERTIONS
user_response.refresh_from_db()
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertEqual(
user_response.status,
settings.TYPEFORM_FEEDBACK_USER_FEEDBACK_STATUS_PENDING,
)
def test_action_validate_user_response(self):
# PREPARE DATA
user, generic_typeform = self.create_base_context()
user_response = UserGenericTypeformFeedback(
feedback=generic_typeform,
user=user,
)
user_response.save()
user_response_payload = self.typeform_with_hidden_fields_response_payload(
user_pk=user.pk,
typeform_id=generic_typeform.typeform_id,
)
user_response.set_typeform_response(user_response_payload)
# DO ACTION
response = self.client.post(
reverse('api:action-validate', kwargs={'uuid': user_response.uuid}),
data={},
content_type='application/json',
)
# ASSERTIONS
user_response.refresh_from_db()
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(
user_response.status,
settings.TYPEFORM_FEEDBACK_USER_FEEDBACK_STATUS_DONE,
)
def test_action_validate_previous_failed_response_for_user(self):
# PREPARE DATA
user, generic_typeform = self.create_base_context()
user_response = UserGenericTypeformFeedback(
feedback=generic_typeform,
user=user,
)
user_response.save()
user_response_payload = self.typeform_with_hidden_fields_response_payload(
user_pk=user.pk,
typeform_id=generic_typeform.typeform_id,
)
user_response.set_typeform_response(user_response_payload)
user_response.mark_as_fail()
# DO ACTION
response = self.client.post(
reverse('api:action-validate', kwargs={'uuid': user_response.uuid}),
data={},
content_type='application/json',
)
# ASSERTIONS
user_response.refresh_from_db()
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(
user_response.status,
settings.TYPEFORM_FEEDBACK_USER_FEEDBACK_STATUS_DONE,
)
def test_action_invalidate_user_response(self):
# PREPARE DATA
user, generic_typeform = self.create_base_context()
user_response = UserGenericTypeformFeedback(
feedback=generic_typeform,
user=user,
)
user_response.save()
user_response_payload = self.typeform_with_hidden_fields_response_payload(
user_pk=user.pk,
typeform_id=generic_typeform.typeform_id,
)
user_response.set_typeform_response(user_response_payload)
# DO ACTION
response = self.client.post(
reverse('api:action-invalidate', kwargs={'uuid': user_response.uuid}),
data={},
content_type='application/json',
)
# ASSERTIONS
user_response.refresh_from_db()
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(
user_response.status,
settings.TYPEFORM_FEEDBACK_USER_FEEDBACK_STATUS_FAIL,
)
def test_get_typeform_url(self):
user, generic_typeform = self.create_base_context()
user.set_password('abc')
user.save()
self.client.login(username=user.username, password='abc')
# DO ACTION
response = self.client.get(
reverse('api:get-url', kwargs={'quiz_slug': generic_typeform.quiz_slug})
)
# ASSERTIONS
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(
response.json(),
{'url': '{}?user_id={}'.format(
settings.TYPEFORM_FEEDBACK_DEFAULT_URL.format(generic_typeform.typeform_id),
user.pk
)}
)
def test_get_typeform_url_for_not_real_typeform(self):
user, _ = self.create_base_context()
user.set_password('abc')
user.save()
foo = Foo(bar='bar')
foo.save()
generic_typeform = GenericTypeformFeedback.create_typeform(
linked_object=foo,
slug=random_string(),
typeform_id='',
url='https://fakeurl.com',
)
self.client.login(username=user.username, password='abc')
# DO ACTION
response = self.client.get(
reverse('api:get-url', kwargs={'quiz_slug': generic_typeform.quiz_slug})
)
# ASSERTIONS
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(
response.json(),
{'url': '{}?user_id={}'.format(
generic_typeform.url,
user.pk
)}
)
| 32.596685 | 92 | 0.63322 | 605 | 5,900 | 5.842975 | 0.153719 | 0.112023 | 0.052051 | 0.035644 | 0.794908 | 0.774823 | 0.774823 | 0.774823 | 0.769448 | 0.769448 | 0 | 0.004238 | 0.280169 | 5,900 | 180 | 93 | 32.777778 | 0.828114 | 0.03 | 0 | 0.635037 | 0 | 0 | 0.046267 | 0.00368 | 0 | 0 | 0 | 0 | 0.087591 | 1 | 0.043796 | false | 0.029197 | 0.058394 | 0 | 0.109489 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
dc14552722fbfc6ca94239f37be8534fc5fcc835 | 138 | py | Python | bib2web/__main__.py | Juvawa/bib2web | 8d6c2244e46eefee1a519f8b3b656a143aa8bd9e | [
"MIT"
] | null | null | null | bib2web/__main__.py | Juvawa/bib2web | 8d6c2244e46eefee1a519f8b3b656a143aa8bd9e | [
"MIT"
] | null | null | null | bib2web/__main__.py | Juvawa/bib2web | 8d6c2244e46eefee1a519f8b3b656a143aa8bd9e | [
"MIT"
] | null | null | null | from main import main
import sys
if __name__ == '__main__':
#print sys.argv[1]
#start = time()
main()
#end = time()
#print end-start | 15.333333 | 26 | 0.65942 | 21 | 138 | 3.952381 | 0.571429 | 0.240964 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008929 | 0.188406 | 138 | 9 | 27 | 15.333333 | 0.732143 | 0.42029 | 0 | 0 | 0 | 0 | 0.103896 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
9074cab84165b83a687245a0b4350086927b7be3 | 42 | py | Python | tests/components/solarlog/__init__.py | domwillcode/home-assistant | f170c80bea70c939c098b5c88320a1c789858958 | [
"Apache-2.0"
] | 30,023 | 2016-04-13T10:17:53.000Z | 2020-03-02T12:56:31.000Z | tests/components/solarlog/__init__.py | jagadeeshvenkatesh/core | 1bd982668449815fee2105478569f8e4b5670add | [
"Apache-2.0"
] | 31,101 | 2020-03-02T13:00:16.000Z | 2022-03-31T23:57:36.000Z | tests/components/solarlog/__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 solarlog integration."""
| 21 | 41 | 0.714286 | 5 | 42 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.119048 | 42 | 1 | 42 | 42 | 0.810811 | 0.833333 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
9097d2ee008a64188c99088eee1465c38263b344 | 316 | py | Python | test/unittests/test_GRLostBarnNSum.py | mudkipmaster/gwlf-e | 9e058445537dd32d1916f76c4b73ca64261771cd | [
"Apache-2.0"
] | null | null | null | test/unittests/test_GRLostBarnNSum.py | mudkipmaster/gwlf-e | 9e058445537dd32d1916f76c4b73ca64261771cd | [
"Apache-2.0"
] | 6 | 2018-07-24T22:46:28.000Z | 2018-07-29T19:13:09.000Z | test/unittests/test_GRLostBarnNSum.py | mudkipmaster/gwlf-e | 9e058445537dd32d1916f76c4b73ca64261771cd | [
"Apache-2.0"
] | 1 | 2018-07-24T18:22:01.000Z | 2018-07-24T18:22:01.000Z | from VariableUnittest import VariableUnitTest
class TestGRLostBarnNSum(VariableUnitTest):
def test_GRLostBarnNSum(self):
pass
# z = self.z
# np.testing.assert_array_almost_equal(
# GRLostBarnNSum.GRLostBarnNSum_2(),
# GRLostBarnNSum.GRLostBarnNSum(), decimal=7)
| 28.727273 | 57 | 0.686709 | 28 | 316 | 7.571429 | 0.714286 | 0.264151 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.008264 | 0.234177 | 316 | 10 | 58 | 31.6 | 0.867769 | 0.427215 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0.25 | 0.25 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 |
90a6f2c69fbe037a2915ca124a25d4a4dda7c842 | 336 | py | Python | docker/home/views.py | tribuit003/pyweb-pro | 605d11364f20942a96183f070eb0e0e829eb31fc | [
"MIT"
] | null | null | null | docker/home/views.py | tribuit003/pyweb-pro | 605d11364f20942a96183f070eb0e0e829eb31fc | [
"MIT"
] | 1 | 2021-07-29T14:29:19.000Z | 2021-07-29T14:30:10.000Z | docker/home/views.py | tribuit003/pyweb-pro | 605d11364f20942a96183f070eb0e0e829eb31fc | [
"MIT"
] | null | null | null | from django.shortcuts import render
from requests import get
# Create your views here.
def index(request):
ip = get('https://api.ipify.org').text
return render(request, 'base.html', {'ip':ip})
def cv_page(request):
return render(request, 'home.html')
def learn_html(request):
return render(request, 'learn_html.html') | 25.846154 | 50 | 0.71131 | 49 | 336 | 4.816327 | 0.55102 | 0.152542 | 0.241525 | 0.220339 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.14881 | 336 | 13 | 51 | 25.846154 | 0.825175 | 0.068452 | 0 | 0 | 0 | 0 | 0.179487 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.222222 | 0.222222 | 0.888889 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
90b307c9acbb5f80a8d1a6b2c0c46130d75172ac | 54 | py | Python | test2_hello2.py | eega15/test2 | 592fbfb55265c0e2b11ac809b6bc6c93b1594ba8 | [
"Unlicense"
] | null | null | null | test2_hello2.py | eega15/test2 | 592fbfb55265c0e2b11ac809b6bc6c93b1594ba8 | [
"Unlicense"
] | null | null | null | test2_hello2.py | eega15/test2 | 592fbfb55265c0e2b11ac809b6bc6c93b1594ba8 | [
"Unlicense"
] | null | null | null | ### test2_hello2.py ###
### 10523
print( 'hello2...' ) | 18 | 23 | 0.555556 | 6 | 54 | 4.833333 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.170213 | 0.12963 | 54 | 3 | 24 | 18 | 0.446809 | 0.407407 | 0 | 0 | 0 | 0 | 0.391304 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
29538954a8346ec3dc0de18d082e7cbc08f1e1f1 | 964 | py | Python | gooddata-sdk/gooddata_sdk/catalog/data_source/action_requests/ldm_request.py | hkad98/gooddata-python-sdk | 64942080ecb44c2d8e914e57f7a591daa6cca205 | [
"MIT"
] | 7 | 2022-01-24T16:27:06.000Z | 2022-02-25T10:18:49.000Z | gooddata-sdk/gooddata_sdk/catalog/data_source/action_requests/ldm_request.py | hkad98/gooddata-python-sdk | 64942080ecb44c2d8e914e57f7a591daa6cca205 | [
"MIT"
] | 29 | 2022-01-20T15:45:38.000Z | 2022-03-31T09:39:25.000Z | gooddata-sdk/gooddata_sdk/catalog/data_source/action_requests/ldm_request.py | hkad98/gooddata-python-sdk | 64942080ecb44c2d8e914e57f7a591daa6cca205 | [
"MIT"
] | 7 | 2022-01-20T07:11:15.000Z | 2022-03-09T14:50:17.000Z | # (C) 2022 GoodData Corporation
from __future__ import annotations
from typing import Optional, Type
import attr
from gooddata_metadata_client.model.generate_ldm_request import GenerateLdmRequest
from gooddata_sdk.catalog.base import Base
@attr.s(auto_attribs=True, kw_only=True)
class CatalogGenerateLdmRequest(Base):
separator: str = "__"
generate_long_ids: Optional[bool] = None
table_prefix: Optional[str] = None
view_prefix: Optional[str] = None
primary_label_prefix: Optional[str] = None
secondary_label_prefix: Optional[str] = None
fact_prefix: Optional[str] = None
date_granularities: Optional[str] = None
grain_prefix: Optional[str] = None
reference_prefix: Optional[str] = None
grain_reference_prefix: Optional[str] = None
denorm_prefix: Optional[str] = None
wdf_prefix: Optional[str] = None
@staticmethod
def client_class() -> Type[GenerateLdmRequest]:
return GenerateLdmRequest
| 31.096774 | 82 | 0.752075 | 117 | 964 | 5.940171 | 0.444444 | 0.174101 | 0.23741 | 0.302158 | 0.161151 | 0 | 0 | 0 | 0 | 0 | 0 | 0.004981 | 0.167012 | 964 | 30 | 83 | 32.133333 | 0.860523 | 0.030083 | 0 | 0 | 1 | 0 | 0.002144 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.043478 | true | 0 | 0.217391 | 0.043478 | 0.913043 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
461dbcb6b78b6bdcb4048ebc80229917c83665ba | 210 | py | Python | app/ac_common/exceptions.py | marwahaha/allecena | b8f8a15ca0dbc80e745febf0e81263ec197e7363 | [
"Apache-2.0"
] | 3 | 2018-04-29T15:40:37.000Z | 2020-04-15T20:37:08.000Z | app/ac_common/exceptions.py | marwahaha/allecena | b8f8a15ca0dbc80e745febf0e81263ec197e7363 | [
"Apache-2.0"
] | 1 | 2019-10-30T20:35:46.000Z | 2019-10-30T20:35:46.000Z | app/ac_common/exceptions.py | marwahaha/allecena | b8f8a15ca0dbc80e745febf0e81263ec197e7363 | [
"Apache-2.0"
] | 2 | 2019-08-04T02:54:22.000Z | 2021-03-03T21:03:11.000Z | # coding: utf-8
class AllecenaException(Exception):
pass
class AllegroException(Exception):
pass
class EbayException(Exception):
pass
class AllecenaComputationException(Exception):
pass
| 11.666667 | 46 | 0.742857 | 19 | 210 | 8.210526 | 0.526316 | 0.333333 | 0.346154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005848 | 0.185714 | 210 | 17 | 47 | 12.352941 | 0.906433 | 0.061905 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
46421effcf85576088ae60b1066aca5532cb9444 | 22 | py | Python | ode-clients/Libraries/odeClient/testClients/__init__.py | OSADP/SEMI-ODE | 37cba20a7a54d891338068c1134c797ae977f279 | [
"Apache-2.0"
] | null | null | null | ode-clients/Libraries/odeClient/testClients/__init__.py | OSADP/SEMI-ODE | 37cba20a7a54d891338068c1134c797ae977f279 | [
"Apache-2.0"
] | null | null | null | ode-clients/Libraries/odeClient/testClients/__init__.py | OSADP/SEMI-ODE | 37cba20a7a54d891338068c1134c797ae977f279 | [
"Apache-2.0"
] | null | null | null | __author__ = '562474'
| 11 | 21 | 0.727273 | 2 | 22 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.315789 | 0.136364 | 22 | 1 | 22 | 22 | 0.315789 | 0 | 0 | 0 | 0 | 0 | 0.272727 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
465fa540d47e6d1cf202bf19c5cfe517759ec70b | 2,254 | py | Python | app/rooms/api/tests.py | DakobedBard/Bookings | 6738fd52d2bcd5ab16228b7bfe9c06fee3ee49aa | [
"MIT"
] | null | null | null | app/rooms/api/tests.py | DakobedBard/Bookings | 6738fd52d2bcd5ab16228b7bfe9c06fee3ee49aa | [
"MIT"
] | null | null | null | app/rooms/api/tests.py | DakobedBard/Bookings | 6738fd52d2bcd5ab16228b7bfe9c06fee3ee49aa | [
"MIT"
] | null | null | null | import json
from django.urls import reverse
from rest_framework.authtoken.models import Token
from rest_framework.test import APITestCase
from rest_framework import status
from rooms.models import Room
from utils.test_utils.date_seeder import DataSeeder
class RoomTestCase(APITestCase):
def setUp(self) -> None:
DataSeeder.seed_host('utils/test_utils/host_directory.csv')
def test_create_room(self):
response = self.client.post(
path="http://127.0.0.1:8000/rooms/create_room/",
data=json.dumps({
"host":1,
'state': 'Arkansas',
'city': 'bonerville'
}),
content_type='application/json'
)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
roomID = 1
room = Room.objects.get(id=roomID)
response = self.client.post(
path="http://127.0.0.1:8000/rooms/%d/delete" % roomID,
data=json.dumps({
"host":1,
'state': 'Arkansas',
'city': 'bonerville'
}),
content_type='application/json'
)
# print("Response data " + response)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def test_remove_room(self):
pass
# response = self.client.post(
# path="http://127.0.0.1:8000/rooms/create_room/",
# data=json.dumps({
# "host":1,
# 'state': 'Arkansas',
# 'city': 'bonerville'
# }),
# content_type='application/json'
# )
# self.assertEqual(response.status_code, status.HTTP_201_CREATED)
# #
# roomID = 1
# room = Room.objects.get(id=roomID)
# response = self.client.post(
# path="http://127.0.0.1:8000/rooms/%d/delete" % roomID,
# data=json.dumps({
# "host":1,
# 'state': 'Arkansas',
# 'city': 'bonerville'
# }),
# content_type='application/json'
# )
#
# print("Response data " + response.data)
# # self.assertEqual(response.status_code, status.HTTP_201_CREATED)
#
| 33.641791 | 75 | 0.543922 | 236 | 2,254 | 5.067797 | 0.262712 | 0.050167 | 0.060201 | 0.073579 | 0.700669 | 0.700669 | 0.700669 | 0.700669 | 0.700669 | 0.61204 | 0 | 0.038233 | 0.326974 | 2,254 | 66 | 76 | 34.151515 | 0.750165 | 0.314108 | 0 | 0.457143 | 0 | 0 | 0.135884 | 0.023087 | 0 | 0 | 0 | 0 | 0.057143 | 1 | 0.085714 | false | 0.028571 | 0.2 | 0 | 0.314286 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
465fcbd488f2bd640473822d0241e651ef201012 | 682 | py | Python | src/sdk/test/test_pong_api.py | Chace-wang/bk-user | 057f270d66a1834312306c9fba1f4e95521f10b1 | [
"MIT"
] | null | null | null | src/sdk/test/test_pong_api.py | Chace-wang/bk-user | 057f270d66a1834312306c9fba1f4e95521f10b1 | [
"MIT"
] | null | null | null | src/sdk/test/test_pong_api.py | Chace-wang/bk-user | 057f270d66a1834312306c9fba1f4e95521f10b1 | [
"MIT"
] | 1 | 2021-12-31T06:48:41.000Z | 2021-12-31T06:48:41.000Z | # coding: utf-8
"""
蓝鲸用户管理 API
User management APIs for BlueKing # noqa: E501
OpenAPI spec version: v2
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import unittest
import bkuser_sdk
from api.pong_api import PongApi # noqa: E501
from bkuser_sdk.rest import ApiException
class TestPongApi(unittest.TestCase):
"""PongApi unit test stubs"""
def setUp(self):
self.api = api.pong_api.PongApi() # noqa: E501
def tearDown(self):
pass
def test_pong(self):
"""Test case for pong
"""
pass
if __name__ == '__main__':
unittest.main()
| 17.05 | 68 | 0.653959 | 87 | 682 | 4.91954 | 0.574713 | 0.056075 | 0.046729 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.021443 | 0.247801 | 682 | 39 | 69 | 17.487179 | 0.812866 | 0.353372 | 0 | 0.142857 | 1 | 0 | 0.01995 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.214286 | false | 0.142857 | 0.357143 | 0 | 0.642857 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 5 |
46656a94629b685c5975bcef2eeefc80c29b68f4 | 1,144 | py | Python | application/auth/models.py | Luukuton/keiji-tsoha-hy2020 | 68128d862f47d2d3db6568b0ef73d9bdb91f86c8 | [
"MIT"
] | 1 | 2020-06-10T22:50:34.000Z | 2020-06-10T22:50:34.000Z | application/auth/models.py | Luukuton/keiji-tsoha-hy2020 | 68128d862f47d2d3db6568b0ef73d9bdb91f86c8 | [
"MIT"
] | 2 | 2020-04-04T13:35:49.000Z | 2020-04-25T20:58:36.000Z | application/auth/models.py | Luukuton/keiji-tsoha-hy2020 | 68128d862f47d2d3db6568b0ef73d9bdb91f86c8 | [
"MIT"
] | null | null | null | from application import db
from application.models import Base
from werkzeug.security import generate_password_hash, check_password_hash
class User(Base):
__tablename__ = "account"
nickname = db.Column(db.String(16), nullable=False)
username = db.Column(db.String(32), nullable=False, unique=True)
password = db.Column(db.String(256), nullable=False)
language = db.Column(db.String(2), nullable=False)
categories = db.relationship(
"Category",
backref='account',
lazy=True
)
def __init__(self, nickname, username, password, language):
self.nickname = nickname
self.username = username
self.encrypt_password(password)
self.language = language
def get_id(self):
return self.id
def is_active(self):
return True
def is_anonymous(self):
return False
def is_authenticated(self):
return True
def encrypt_password(self, plain_password):
self.password = generate_password_hash(plain_password)
def check_password(self, password):
return check_password_hash(self.password, password)
| 26 | 73 | 0.683566 | 136 | 1,144 | 5.566176 | 0.338235 | 0.063408 | 0.05284 | 0.084544 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.009029 | 0.225524 | 1,144 | 43 | 74 | 26.604651 | 0.845372 | 0 | 0 | 0.064516 | 1 | 0 | 0.019231 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.225806 | false | 0.258065 | 0.096774 | 0.16129 | 0.709677 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 5 |
466c248074f12761b009642d1e996a9370af4b39 | 2,532 | py | Python | mesh_restart.py | wangzishuo111/doraemon | dcad07891c6658deef30025e431250527d3e43d1 | [
"MIT"
] | null | null | null | mesh_restart.py | wangzishuo111/doraemon | dcad07891c6658deef30025e431250527d3e43d1 | [
"MIT"
] | null | null | null | mesh_restart.py | wangzishuo111/doraemon | dcad07891c6658deef30025e431250527d3e43d1 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from base.log import *
import os
from multiprocessing import Process
import redis_pool
def gen_id():
r = redis_pool.get('dornaemon')
return r.incr('deploy_id')
def do_start(server_name, deploy_id):
log_path = 'html/deploy_logs/%d.txt' % deploy_id
cmd = "cat /home/op/mesh_deploy_5.80/%s | python /home/op/mesh_deploy_5.80/guizhou_start.py > %s 2>&1" % (server_name, log_path)
logger().info('cmd:%s', cmd)
if 0 != os.system(cmd):
msg = '[Finish] Failed to deploy [%s]' % deploy_id
else:
msg = '[Finish] Success to deploy [%s]' % deploy_id
logger().error(msg)
open(log_path, 'a').write(msg)
def do_stop(server_name, deploy_id):
log_path = 'html/deploy_logs/%d.txt' % deploy_id
cmd = "cat /home/op/mesh_deploy_5.80/%s | python /home/op/mesh_deploy_5.80/guizhou_stop.py > %s 2>&1" % (server_name, log_path)
logger().info('cmd:%s', cmd)
if 0 != os.system(cmd):
msg = '[Finish] Failed to deploy [%s]' % deploy_id
else:
msg = '[Finish] Success to deploy [%s]' % deploy_id
logger().error(msg)
open(log_path, 'a').write(msg)
def do_restart(server_name, deploy_id):
log_path = 'html/deploy_logs/%d.txt' % deploy_id
cmd = "cat /home/op/mesh_deploy_5.80/%s | python /home/op/mesh_deploy_5.80/guizhou_restart.py > %s 2>&1" % (server_name, log_path)
logger().info('cmd:%s', cmd)
if 0 != os.system(cmd):
msg = '[Finish] Failed to deploy [%s]' % deploy_id
else:
msg = '[Finish] Success to deploy [%s]' % deploy_id
logger().error(msg)
open(log_path, 'a').write(msg)
def start(server_name):
deploy_id = gen_id()
p = Process(target=do_start, args=(server_name, deploy_id))
p.start()
logger().info('start deploy [%s]', deploy_id)
return deploy_id
def stop(server_name):
deploy_id = gen_id()
p = Process(target=do_stop, args=(server_name, deploy_id))
p.start()
logger().info('start deploy [%s]', deploy_id)
return deploy_id
def restart(server_name):
deploy_id = gen_id()
p = Process(target=do_restart, args=(server_name, deploy_id))
p.start()
logger().info('start deploy [%s]', deploy_id)
return deploy_id
def enter(server_name, oprate_item):
if oprate_item == 'start':
deploy_id = start(server_name)
return deploy_id
elif oprate_item == 'stop':
deploy_id = stop(server_name)
return deploy_id
elif oprate_item == 'restart':
deploy_id = restart(server_name)
return deploy_id
else:
assert False, 'your enter a wrong item'
def main():
deploy_id = enter('kpms', 'start')
print deploy_id
while True:
time.sleep(1)
if __name__ == '__main__':
main()
| 29.103448 | 131 | 0.691943 | 422 | 2,532 | 3.914692 | 0.184834 | 0.159806 | 0.087167 | 0.098063 | 0.776029 | 0.742131 | 0.742131 | 0.742131 | 0.696126 | 0.696126 | 0 | 0.013364 | 0.14297 | 2,532 | 86 | 132 | 29.44186 | 0.747926 | 0.008294 | 0 | 0.540541 | 0 | 0.040541 | 0.271531 | 0.111643 | 0 | 0 | 0 | 0 | 0.013514 | 0 | null | null | 0 | 0.054054 | null | null | 0.013514 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
467d1b9fe60acec27a0eba62b2a0fe5b19905b42 | 26,608 | py | Python | saxo_openapi/endpoints/portfolio/responses/positions.py | shyrwinsia/saxo_openapi | 8e5c1bf336654d059ea87ba2ff7e7aaef33d1262 | [
"MIT"
] | 52 | 2019-03-13T13:27:36.000Z | 2022-03-18T08:27:22.000Z | saxo_openapi/endpoints/portfolio/responses/positions.py | shyrwinsia/saxo_openapi | 8e5c1bf336654d059ea87ba2ff7e7aaef33d1262 | [
"MIT"
] | 15 | 2019-03-14T19:42:51.000Z | 2021-12-19T16:14:02.000Z | saxo_openapi/endpoints/portfolio/responses/positions.py | shyrwinsia/saxo_openapi | 8e5c1bf336654d059ea87ba2ff7e7aaef33d1262 | [
"MIT"
] | 23 | 2019-03-13T13:45:22.000Z | 2022-02-26T21:20:49.000Z | # -*- coding: utf-8 -*-
"""Responses.
responses serve both testing purpose aswell as dynamic docstring replacement.
"""
responses = {
"_v3_SinglePosition": {
"url": "/openapi/port/v1/positions/{PositionId}",
"params": {'ClientKey': 'Cf4xZWiYL6W1nMKpygBLLA=='},
"response": {
"NetPositionId": "EURUSD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": -100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "46dc6b2a-5b6f-43c8-b747-6b530da9110e",
"ExecutionTimeOpen": "2019-03-04T00:10:23.040641Z",
"IsMarketOpen": True,
"OpenPrice": 1.13715,
"RelatedOpenOrders": [],
"SourceOrderId": "76271915",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 21,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212561926",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.88199,
"ConversionRateOpen": 0.88199,
"CurrentPrice": 1.1339,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Ask",
"Exposure": -100000,
"ExposureCurrency": "EUR",
"ExposureInBaseCurrency": -100000,
"InstrumentPriceDayPercentChange": -0.24,
"ProfitLossOnTrade": 325,
"ProfitLossOnTradeInBaseCurrency": 286.65,
"TradeCostsTotal": -11.36,
"TradeCostsTotalInBaseCurrency": -10.02
}
}
},
"_v3_SinglePositionDetails": {
"url": "/openapi/port/v1/positions/{PositionId}/details",
"params": {'ClientKey': 'Cf4xZWiYL6W1nMKpygBLLA==',
'AccountKey': 'Cf4xZWiYL6W1nMKpygBLLA=='},
"response": {
"DisplayAndFormat": {
"Currency": "USD",
"Decimals": 4,
"Description": "Euro/US Dollar",
"Format": "AllowDecimalPips",
"Symbol": "EURUSD"
},
"Exchange": {
"Description": "Inter Bank",
"ExchangeId": "SBFX",
"IsOpen": True
},
"NetPositionId": "EURUSD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": -100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "46dc6b2a-5b6f-43c8-b747-6b530da9110e",
"ExecutionTimeOpen": "2019-03-04T00:10:23.040641Z",
"IsMarketOpen": True,
"OpenPrice": 1.13715,
"RelatedOpenOrders": [],
"SourceOrderId": "76271915",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 21,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionDetails": {
"CloseCost": {
"Commission": 5.67
},
"CloseCostInBaseCurrency": {
"Commission": 5
},
"CorrelationKey": "46dc6b2a-5b6f-43c8-b747-6b530da9110e",
"LockedByBackOffice": False,
"MarketValue": 351,
"OpenCost": {
"Commission": 5.69
},
"OpenCostInBaseCurrency": {
"Commission": 5.02
},
"SourceOrderId": "76271915"
},
"PositionId": "212561926",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.882195,
"ConversionRateOpen": 0.882195,
"CurrentPrice": 1.13364,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Ask",
"Exposure": -100000,
"ExposureCurrency": "EUR",
"ExposureInBaseCurrency": -100000,
"InsTrumentPriceDayPercentChange": -0.26,
"ProfitLossOnTrade": 351,
"ProfitLossOnTradeInBaseCurrency": 309.65,
"TradeCostsTotal": -11.36,
"TradeCostsTotalInBaseCurrency": -10.02
}
}
},
"_v3_PositionsMe": {
"url": "/openapi/port/v1/positions/me",
"params": {},
"response": {
"__count": 4,
"Data": [
{
"NetPositionId": "EURUSD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": -100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "46dc6b2a-5b6f-43c8-b747-6b530da9110e",
"ExecutionTimeOpen": "2019-03-04T00:10:23.040641Z",
"IsMarketOpen": True,
"OpenPrice": 1.13715,
"RelatedOpenOrders": [],
"SourceOrderId": "76271915",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 21,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212561926",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.882595,
"ConversionRateOpen": 0.882595,
"CurrentPrice": 1.13312,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Ask",
"Exposure": -100000,
"ExposureCurrency": "EUR",
"ExposureInBaseCurrency": -100000,
"InstrumentPriceDayPercentChange": -0.31,
"ProfitLossOnTrade": 403,
"ProfitLossOnTradeInBaseCurrency": 355.69,
"TradeCostsTotal": -11.36,
"TradeCostsTotalInBaseCurrency": -10.03
}
},
{
"NetPositionId": "EURUSD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": 100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "50fae087-b7d4-49ab-afa2-5145cd56a7c5",
"ExecutionTimeOpen": "2019-03-04T00:04:11.340151Z",
"IsMarketOpen": True,
"OpenPrice": 1.1371,
"RelatedOpenOrders": [],
"SourceOrderId": "76271912",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 21,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212561892",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.882595,
"ConversionRateOpen": 0.882595,
"CurrentPrice": 1.13292,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Bid",
"Exposure": 100000,
"ExposureCurrency": "EUR",
"ExposureInBaseCurrency": 100000,
"InstrumentPriceDayPercentChange": -0.31,
"ProfitLossOnTrade": -418,
"ProfitLossOnTradeInBaseCurrency": -368.92,
"TradeCostsTotal": -11.35,
"TradeCostsTotalInBaseCurrency": -10.02
}
},
{
"NetPositionId": "GBPAUD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": 500000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "206cceed-2240-43f8-8c46-840e8b722549",
"ExecutionTimeOpen": "2019-03-03T23:35:08.243690Z",
"IsMarketOpen": True,
"OpenPrice": 1.86391,
"RelatedOpenOrders": [],
"SourceOrderId": "76271862",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 22,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212550212",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.6254,
"ConversionRateOpen": 0.6254,
"CurrentPrice": 1.85999,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Bid",
"Exposure": 500000,
"ExposureCurrency": "GBP",
"ExposureInBaseCurrency": 581757.5,
"InstrumentPriceDayPercentChange": -0.25,
"ProfitLossOnTrade": -1960,
"ProfitLossOnTradeInBaseCurrency": -1225.78,
"TradeCostsTotal": -93.1,
"TradeCostsTotalInBaseCurrency": -58.22
}
},
{
"NetPositionId": "GBPCAD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": 100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "19c44107-6858-4191-805c-764a69d27491",
"ExecutionTimeOpen": "2019-03-03T23:34:51.823660Z",
"IsMarketOpen": True,
"OpenPrice": 1.75824,
"RelatedOpenOrders": [],
"SourceOrderId": "76271861",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 23,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212550210",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.663595,
"ConversionRateOpen": 0.663595,
"CurrentPrice": 1.75294,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Bid",
"Exposure": 100000,
"ExposureCurrency": "GBP",
"ExposureInBaseCurrency": 116351.5,
"InstrumentPriceDayPercentChange": -0.18,
"ProfitLossOnTrade": -530,
"ProfitLossOnTradeInBaseCurrency": -351.71,
"TradeCostsTotal": -17.55,
"TradeCostsTotalInBaseCurrency": -11.65
}
}
]
}
},
"_v3_PositionsQuery": {
"url": "/openapi/port/v1/positions/",
"params": {'ClientKey': 'Cf4xZWiYL6W1nMKpygBLLA=='},
"response": {
"__count": 4,
"Data": [
{
"NetPositionId": "EURUSD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": -100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "46dc6b2a-5b6f-43c8-b747-6b530da9110e",
"ExecutionTimeOpen": "2019-03-04T00:10:23.040641Z",
"IsMarketOpen": True,
"OpenPrice": 1.13715,
"RelatedOpenOrders": [],
"SourceOrderId": "76271915",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 21,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212561926",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.882905,
"ConversionRateOpen": 0.882905,
"CurrentPrice": 1.13273,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Ask",
"Exposure": -100000,
"ExposureCurrency": "EUR",
"ExposureInBaseCurrency": -100000,
"InstrumentPriceDayPercentChange": -0.34,
"ProfitLossOnTrade": 442,
"ProfitLossOnTradeInBaseCurrency": 390.24,
"TradeCostsTotal": -11.35,
"TradeCostsTotalInBaseCurrency": -10.02
}
},
{
"NetPositionId": "EURUSD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": 100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "50fae087-b7d4-49ab-afa2-5145cd56a7c5",
"ExecutionTimeOpen": "2019-03-04T00:04:11.340151Z",
"IsMarketOpen": True,
"OpenPrice": 1.1371,
"RelatedOpenOrders": [],
"SourceOrderId": "76271912",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 21,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212561892",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.882905,
"ConversionRateOpen": 0.882905,
"CurrentPrice": 1.13253,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Bid",
"Exposure": 100000,
"ExposureCurrency": "EUR",
"ExposureInBaseCurrency": 100000,
"InstrumentPriceDayPercentChange": -0.34,
"ProfitLossOnTrade": -457,
"ProfitLossOnTradeInBaseCurrency": -403.49,
"TradeCostsTotal": -11.35,
"TradeCostsTotalInBaseCurrency": -10.02
}
},
{
"NetPositionId": "GBPAUD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": 500000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "206cceed-2240-43f8-8c46-840e8b722549",
"ExecutionTimeOpen": "2019-03-03T23:35:08.243690Z",
"IsMarketOpen": True,
"OpenPrice": 1.86391,
"RelatedOpenOrders": [],
"SourceOrderId": "76271862",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 22,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212550212",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.62534,
"ConversionRateOpen": 0.62534,
"CurrentPrice": 1.86127,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Bid",
"Exposure": 500000,
"ExposureCurrency": "GBP",
"ExposureInBaseCurrency": 582115,
"InstrumentPriceDayPercentChange": -0.19,
"ProfitLossOnTrade": -1320,
"ProfitLossOnTradeInBaseCurrency": -825.45,
"TradeCostsTotal": -93.13,
"TradeCostsTotalInBaseCurrency": -58.24
}
},
{
"NetPositionId": "GBPCAD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": 100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "19c44107-6858-4191-805c-764a69d27491",
"ExecutionTimeOpen": "2019-03-03T23:34:51.823660Z",
"IsMarketOpen": True,
"OpenPrice": 1.75824,
"RelatedOpenOrders": [],
"SourceOrderId": "76271861",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 23,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212550210",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.66389,
"ConversionRateOpen": 0.66389,
"CurrentPrice": 1.75321,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Bid",
"Exposure": 100000,
"ExposureCurrency": "GBP",
"ExposureInBaseCurrency": 116423,
"InstrumentPriceDayPercentChange": -0.17,
"ProfitLossOnTrade": -503,
"ProfitLossOnTradeInBaseCurrency": -333.94,
"TradeCostsTotal": -17.56,
"TradeCostsTotalInBaseCurrency": -11.66
}
}
]
}
},
"_v3_PositionListSubscription": {
"url": "/openapi/port/v1/positions/subscriptions",
"params": {},
"body": {
"Arguments": {
"ClientKey": "Cf4xZWiYL6W1nMKpygBLLA=="
},
"ContextId": "explorer_1551702571343",
"ReferenceId": "C_702"
},
"response": {
"ContextId": "explorer_1551702571343",
"Format": "application/json",
"InactivityTimeout": 30,
"ReferenceId": "C_702",
"RefreshRate": 1000,
"Snapshot": {
"Data": [
{
"NetPositionId": "EURUSD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": -100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "46dc6b2a-5b6f-43c8-b747-6b530da9110e",
"ExecutionTimeOpen": "2019-03-04T00:10:23.040641Z",
"IsMarketOpen": True,
"OpenPrice": 1.13715,
"RelatedOpenOrders": [],
"SourceOrderId": "76271915",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 21,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212561926",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.883135,
"ConversionRateOpen": 0.883135,
"CurrentPrice": 1.13243,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Ask",
"Exposure": -100000,
"ExposureCurrency": "EUR",
"ExposureInBaseCurrency": -100000,
"InstrumentPriceDayPercentChange": -0.37,
"ProfitLossOnTrade": 472,
"ProfitLossOnTradeInBaseCurrency": 416.84,
"TradeCostsTotal": -11.35,
"TradeCostsTotalInBaseCurrency": -10.02
}
},
{
"NetPositionId": "EURUSD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": 100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "50fae087-b7d4-49ab-afa2-5145cd56a7c5",
"ExecutionTimeOpen": "2019-03-04T00:04:11.340151Z",
"IsMarketOpen": True,
"OpenPrice": 1.1371,
"RelatedOpenOrders": [],
"SourceOrderId": "76271912",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 21,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212561892",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.883135,
"ConversionRateOpen": 0.883135,
"CurrentPrice": 1.13223,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Bid",
"Exposure": 100000,
"ExposureCurrency": "EUR",
"ExposureInBaseCurrency": 100000,
"InstrumentPriceDayPercentChange": -0.37,
"ProfitLossOnTrade": -487,
"ProfitLossOnTradeInBaseCurrency": -430.09,
"TradeCostsTotal": -11.35,
"TradeCostsTotalInBaseCurrency": -10.02
}
},
{
"NetPositionId": "GBPAUD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": 500000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "206cceed-2240-43f8-8c46-840e8b722549",
"ExecutionTimeOpen": "2019-03-03T23:35:08.243690Z",
"IsMarketOpen": True,
"OpenPrice": 1.86391,
"RelatedOpenOrders": [],
"SourceOrderId": "76271862",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 22,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212550212",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.625415,
"ConversionRateOpen": 0.625415,
"CurrentPrice": 1.86215,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Bid",
"Exposure": 500000,
"ExposureCurrency": "GBP",
"ExposureInBaseCurrency": 582455,
"InstrumentPriceDayPercentChange": -0.14,
"ProfitLossOnTrade": -880,
"ProfitLossOnTradeInBaseCurrency": -550.37,
"TradeCostsTotal": -93.15,
"TradeCostsTotalInBaseCurrency": -58.26
}
},
{
"NetPositionId": "GBPCAD__FxSpot",
"PositionBase": {
"AccountId": "9226397",
"Amount": 100000,
"AssetType": "FxSpot",
"CanBeClosed": True,
"ClientId": "9226397",
"CloseConversionRateSettled": False,
"CorrelationKey": "19c44107-6858-4191-805c-764a69d27491",
"ExecutionTimeOpen": "2019-03-03T23:34:51.823660Z",
"IsMarketOpen": True,
"OpenPrice": 1.75824,
"RelatedOpenOrders": [],
"SourceOrderId": "76271861",
"SpotDate": "2019-03-06",
"Status": "Open",
"Uic": 23,
"ValueDate": "2019-03-06T00:00:00.000000Z"
},
"PositionId": "212550210",
"PositionView": {
"CalculationReliability": "Ok",
"ConversionRateCurrent": 0.66362,
"ConversionRateOpen": 0.66362,
"CurrentPrice": 1.75496,
"CurrentPriceDelayMinutes": 0,
"CurrentPriceType": "Bid",
"Exposure": 100000,
"ExposureCurrency": "GBP",
"ExposureInBaseCurrency": 116491,
"InstrumentPriceDayPercentChange": -0.07,
"ProfitLossOnTrade": -328,
"ProfitLossOnTradeInBaseCurrency": -217.67,
"TradeCostsTotal": -17.56,
"TradeCostsTotalInBaseCurrency": -11.65
}
}
],
"MaxRows": 100000
},
"State": "Active"
}
},
"_v3_PositionSubscriptionPageSize": {
"url": "/openapi/port/v1/positions/subscriptions/"
"{ContextId}/{ReferenceId}",
"body": {
"NewPageSize": 25630
},
"response": ''
},
"_v3_PositionSubscriptionRemoveMultiple": {
"url": "/openapi/port/v1/positions/subscriptions/{ContextId}",
"params": {
"Tag": "..."
},
"response": ''
},
"_v3_PositionSubscriptionRemove": {
"url": "/openapi/port/v1/positions/subscriptions/"
"{ContextId}/{ReferenceId}",
"response": ''
},
}
| 41.381026 | 77 | 0.441296 | 1,456 | 26,608 | 8.028846 | 0.190247 | 0.021557 | 0.032335 | 0.040719 | 0.775877 | 0.758768 | 0.745595 | 0.734987 | 0.715483 | 0.664671 | 0 | 0.162834 | 0.434531 | 26,608 | 642 | 78 | 41.445483 | 0.614117 | 0.004209 | 0 | 0.662992 | 0 | 0 | 0.40832 | 0.185284 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
467e7ff85a2d765a69e9a9050598e5645cd19a1c | 45 | py | Python | Python/OpenCV/test.py | S-c-r-a-t-c-h-y/coding-projects | cad33aedb72720c3e3a37c7529e55abd3edb291a | [
"MIT"
] | null | null | null | Python/OpenCV/test.py | S-c-r-a-t-c-h-y/coding-projects | cad33aedb72720c3e3a37c7529e55abd3edb291a | [
"MIT"
] | null | null | null | Python/OpenCV/test.py | S-c-r-a-t-c-h-y/coding-projects | cad33aedb72720c3e3a37c7529e55abd3edb291a | [
"MIT"
] | null | null | null | # https://www.youtube.com/watch?v=WQeoO7MI0Bs | 45 | 45 | 0.777778 | 7 | 45 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.045455 | 0.022222 | 45 | 1 | 45 | 45 | 0.75 | 0.955556 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
4687e0e6dbe19a9ca14e8240cb0a3bbf09e9be2f | 46 | py | Python | atmos_space_flight/Python/gravity.py | als0052/AtmosSpaceDynamics | acf20f4ba320f55bf7e33d959539e7938a4b24d2 | [
"CNRI-Python"
] | null | null | null | atmos_space_flight/Python/gravity.py | als0052/AtmosSpaceDynamics | acf20f4ba320f55bf7e33d959539e7938a4b24d2 | [
"CNRI-Python"
] | null | null | null | atmos_space_flight/Python/gravity.py | als0052/AtmosSpaceDynamics | acf20f4ba320f55bf7e33d959539e7938a4b24d2 | [
"CNRI-Python"
] | null | null | null | #!/usr/bin/env python
# Filename: gravity.py
| 11.5 | 22 | 0.695652 | 7 | 46 | 4.571429 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.130435 | 46 | 3 | 23 | 15.333333 | 0.8 | 0.891304 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
4687f3f3ede65252208b5d368d3d46bc0032366e | 28 | py | Python | cgen/hsm/__init__.py | jszeman/chsm | 0283d722553aea09477e039a718b9500d814f0ae | [
"MIT"
] | 4 | 2021-04-16T18:28:00.000Z | 2022-03-01T07:29:21.000Z | cgen/hsm/__init__.py | xsession/chsm | 1c502897c8fce56bb48ad49de6d642f2bb66ff95 | [
"MIT"
] | 6 | 2020-10-25T19:56:59.000Z | 2022-03-24T05:00:10.000Z | cgen/hsm/__init__.py | xsession/chsm | 1c502897c8fce56bb48ad49de6d642f2bb66ff95 | [
"MIT"
] | 1 | 2021-05-27T07:01:28.000Z | 2021-05-27T07:01:28.000Z | from .sm import StateMachine | 28 | 28 | 0.857143 | 4 | 28 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.107143 | 28 | 1 | 28 | 28 | 0.96 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
468d7aa63260a2fe906d224ba46dfb003ef5ae38 | 25 | py | Python | anvil/http/__init__.py | benlawraus/pyDALAnvilWorks | 8edc67b0fbe65bdcc0ef6fd2424f55046cacba7c | [
"MIT"
] | 6 | 2021-11-14T22:49:40.000Z | 2022-03-26T17:40:40.000Z | anvil/http/__init__.py | benlawraus/pyDALAnvilWorks | 8edc67b0fbe65bdcc0ef6fd2424f55046cacba7c | [
"MIT"
] | null | null | null | anvil/http/__init__.py | benlawraus/pyDALAnvilWorks | 8edc67b0fbe65bdcc0ef6fd2424f55046cacba7c | [
"MIT"
] | 1 | 2022-01-31T01:18:32.000Z | 2022-01-31T01:18:32.000Z | from .anvilHttp import *
| 12.5 | 24 | 0.76 | 3 | 25 | 6.333333 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.16 | 25 | 1 | 25 | 25 | 0.904762 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
469d1a3b8d63cd1e4ab582c09a139ed549483bc2 | 58 | py | Python | __init__.py | pqrs6/clee-fast | e08a2e1c88024f640f14def2618dcac18b2dfc99 | [
"MIT"
] | null | null | null | __init__.py | pqrs6/clee-fast | e08a2e1c88024f640f14def2618dcac18b2dfc99 | [
"MIT"
] | 1 | 2016-10-26T12:49:09.000Z | 2016-10-26T12:49:09.000Z | __init__.py | pqrs6/clee_fast | e08a2e1c88024f640f14def2618dcac18b2dfc99 | [
"MIT"
] | null | null | null | #import main_rt
#import main as main_rts
#import main_tau
| 14.5 | 24 | 0.810345 | 11 | 58 | 4 | 0.545455 | 0.681818 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.137931 | 58 | 3 | 25 | 19.333333 | 0.88 | 0.896552 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
d3b5ed547755115bdc006da7c0f701a0ff354cd9 | 583 | py | Python | nn_numpy/loss.py | EmanuelFontelles/nn_numpy | 0919b4ada752b930bf6cf829a9a83ac60599f141 | [
"MIT"
] | null | null | null | nn_numpy/loss.py | EmanuelFontelles/nn_numpy | 0919b4ada752b930bf6cf829a9a83ac60599f141 | [
"MIT"
] | null | null | null | nn_numpy/loss.py | EmanuelFontelles/nn_numpy | 0919b4ada752b930bf6cf829a9a83ac60599f141 | [
"MIT"
] | null | null | null | """
loss function to optimizer functions
"""
import numpy as np
from nn_numpy.tensor import Tensor
class Loss:
def loss(self, predicted: Tensor, actual: Tensor) -> float:
raise NotImplementedError
def grad(self, predicted: Tensor, actual: Tensor) -> Tensor:
raise NotImplementedError
class MSE(Loss):
"""
MSE is mean square error
"""
def loss(self, predicted: Tensor, actual: Tensor) -> float:
return np.sum((predicted-actual)**2)
def grad(self, predicted: Tensor, actual: Tensor) -> Tensor:
return 2*(predicted-actual) | 26.5 | 64 | 0.667238 | 71 | 583 | 5.464789 | 0.408451 | 0.134021 | 0.195876 | 0.257732 | 0.448454 | 0.448454 | 0.448454 | 0.448454 | 0 | 0 | 0 | 0.004405 | 0.221269 | 583 | 22 | 65 | 26.5 | 0.85022 | 0.104631 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.166667 | 0.166667 | 0.833333 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
d3ded18891448215fe2f691ab9c716135ce2b6f8 | 19,789 | py | Python | gchemplots.py | dyvasey/geoscripts | b9b387a4514efc9cb263a3bdf48be219b1372477 | [
"MIT"
] | null | null | null | gchemplots.py | dyvasey/geoscripts | b9b387a4514efc9cb263a3bdf48be219b1372477 | [
"MIT"
] | null | null | null | gchemplots.py | dyvasey/geoscripts | b9b387a4514efc9cb263a3bdf48be219b1372477 | [
"MIT"
] | null | null | null | """
Module for making geochemical plots.
"""
import string
import matplotlib.pyplot as plt
import pandas as pd
import pyrolite.plot
from mpltern.ternary.datasets import get_triangular_grid
def TAS(SiO2,Na2O,K2O,ax=None,first= [],**plt_kwargs):
"""
Plots total alkali-silica (TAS) diagram after Le Bas et al., 1986.
Plot divided into alkaline and subalkaline fields after Irvine and
Barangar, 1971. Values used for plot lines were taken from source code
of GCDKit (Janousek et al., 2006).
Parameters:
SiO2: List of SiO2 values (wt. %)
Na2O: List of Na2O values (wt. %)
K2O: List of K2O values (wt. %)
ax: Axes on which to plot the diagram
first: Empty list by default. If empty, lines/labels will plot
Returns:
ax: Axes with TAS plotted
"""
if ax is None:
ax = plt.gca()
# Calculate total alkalis
alkalis = Na2O + K2O
# Plot data
ax.scatter(SiO2,alkalis, **plt_kwargs)
# Check if first empty to avoid repeat plotting of TAS grid/labels
if first == []:
# Create lines
line1 = [[30,41,41,45,48.4,52.5,30],[0,0,7,9.4,11.5,14,24.15]]
line2 = [[41,45,45,41],[0,0,3,3]]
line3 = [[45,52,52,45],[0,0,5,5]]
line4 = [[52,57,57,52],[0,0,5.9,5]]
line5 = [[57,63,63,57],[0,0,7,5.9]]
line6 = [[63,77,69,63],[0,0,8,7]]
line7 = [[77,100,100,69,69],[0,0,25,25,8]]
line8 = [[45,52,49.4],[5,5,7.3]]
line9 = [[52,57,53,49.4],[5,5.9,9.3,7.3]]
line10 = [[57,63,57.6,53],[5.9,7,11.7,9.3]]
line11 = [[63,69,69,57.6],[7,8,17.73,11.7]]
line12 = [[41,45,45,49.4,45,41],[3,3,5,7.3,9.4,7]]
line13 = [[49.4,53,48.4,45],[7.3,9.3,11.5,9.4]]
line14 = [[53,57.6,52.5,48.4],[9.3,11.7,14,11.5]]
line15 = [[57.6,69,30],[11.7,17.73,24.15]]
lines = [line1,line2,line3,line4,line5,line6,line7,line8,line9,line10,
line11,line12,line13,line14,line15]
# Create labels
labelsx = [43,48.5,54.8,59.9,67,75,63.5,57.8,52.95,49.2,45,49.2,53,57,
43]
labelsy = [1.55,2.8,3,3,3,8,11,8.5,7,5.65,7,9.3,11.5,14,12]
labeltext = ['Picrobasalt','Basalt','Basaltic\nAndesite','Andesite',
'Dacite','Rhyolite','Trachyte/Trachydacite',
'Trachy-andesite','Basaltic-\ntrachy-andesite',
'Trachy-basalt','Tephrite/\nBasanite','Phono-tephrite',
'Tephri-phonolite','Phonolite','Foidite']
# Create subalkaline/alkaline fields
subalkx = [39.2,40,43.2,45,48,50,53.7,55,60,65,77.4]
subalky = [0,0.4,2,2.8,4,4.75,6,6.4,8,8.8,10]
# Plot Subalkaline/Alkaline line
ax.plot(subalkx,subalky,'r--')
ax.text(38,2,'Alkaline',rotation=45,color='r',ha='center',va='center')
ax.text(49,2,'Subalkaline',rotation=45,color='r',ha='center',
va='center')
#Set axes limits
ax.set_xlim(35,80)
ax.set_ylim(0,16)
for z in range(15): # Loop through and plot TAS lines
ax.plot(lines[z][0],lines[z][1],'k')
ax.text(labelsx[z],labelsy[z],labeltext[z],color='k',
ha='center',va='center',fontsize=10)
# Avoid repeat grid plotting
first.append('Not First')
return(ax)
def TASsm(SiO2,Na2O,K2O,ax=None,first= [],**plt_kwargs):
"""
Plots small total alkali-silica (TAS) diagram after Le Bas et al., 1986.
This version plots a small TAS diagram with minimal text/labels in order
to accomodate the diagram on multi-axes plots. Plot divided into alkaline
and subalkaline fields after Irvine and Barangar, 1971. Values used for
plot lines were taken from source code of GCDKit (Janousek et al., 2006).
Parameters:
SiO2: List of SiO2 values (wt. %)
Na2O: List of Na2O values (wt. %)
K2O: List of K2O values (wt. %)
ax: Axes on which to plot the diagram
first: Empty list by default. If empty, lines/labels will plot
Returns:
ax: Axes with TAS plotted
"""
if ax is None:
ax = plt.gca()
# Calculate total alkalis
alkalis = Na2O + K2O
#Plot data
ax.scatter(SiO2,alkalis, **plt_kwargs)
# Check if first empty to avoid repeat plotting of TAS grid/labels
if first == []:
# Create lines
line1 = [[30,41,41,45,48.4,52.5,30],[0,0,7,9.4,11.5,14,24.15]]
line2 = [[41,45,45,41],[0,0,3,3]]
line3 = [[5,52,52,45],[0,0,5,5]]
line4 = [[52,57,57,52],[0,0,5.9,5]]
line5 = [[57,63,63,57],[0,0,7,5.9]]
line6 = [[63,77,69,63],[0,0,8,7]]
line7 = [[77,100,100,69,69],[0,0,25,25,8]]
line8 = [[45,52,49.4],[5,5,7.3]]
line9 = [[52,57,53,49.4],[5,5.9,9.3,7.3]]
line10 = [[57,63,57.6,53],[5.9,7,11.7,9.3]]
line11 = [[63,69,69,57.6],[7,8,17.73,11.7]]
line12 = [[41,45,45,49.4,45,41],[3,3,5,7.3,9.4,7]]
line13 = [[49.4,53,48.4,45],[7.3,9.3,11.5,9.4]]
line14 = [[53,57.6,52.5,48.4],[9.3,11.7,14,11.5]]
line15 = [[57.6,69,30],[11.7,17.73,24.15]]
lines = [line1,line2,line3,line4,line5,line6,line7,line8,line9,line10,
line11,line12,line13,line14,line15]
# Create abbreviated labels
labelsx = [43,48.5,54.8,59.9,67,75,63.5,57.8,52.95,49.2,45,49.2,53,57,
43]
labelsy = [1.55,2.8,3,3,3,8,11,8.5,7,5.65,7,9.3,11.5,14,12]
labeltext = ['PB','B','BA','A','D','R','T/TD','TA','BTA','TB',
'TEP/\nBSN','PHT','TPH','PH','FOI']
# Create subalkaline/alkaline fields
subalkx = [39.2,40,43.2,45,48,50,53.7,55,60,65,77.4]
subalky = [0,0.4,2,2.8,4,4.75,6,6.4,8,8.8,10]
#Plot Subalkaline/Alkaline line without text
ax.plot(subalkx,subalky,'r--')
#Set axes limits
ax.set_xlim(35,80)
ax.set_ylim(0,16)
for z in range(15): #loop through TAS lines
ax.plot(lines[z][0],lines[z][1],'k')
ax.text(labelsx[z],labelsy[z],labeltext[z],color='k',
ha='center',va='center',fontsize=6)
# Set small fonts
ax.set_xlabel('$\mathregular{SiO_2}$ (wt. %)',fontsize=8)
ax.set_ylabel('$\mathregular{Na{_2}O + K{_2}O}$ (wt. %)',fontsize=8)
ax.tick_params(axis='both', which='major', labelsize=6)
# Avoid repeat grid plotting
first.append('Not First')
return(ax)
def cabanis(Tb,Th,Ta,ax=None,grid=False,first=[],**plt_kwargs):
"""
Plot Th-3Tb-2Ta diagram of Cabanis and Thieblemont (1988).
Parameters:
Tb: List of Tb values
Th: List of Th values
Ta: List of Ta values
ax: Axes on which to plot, requires "ternary" projection from mpltern
grid: Boolean for whether to add grid to diagram
first: Empty list by default. If empty and grid is True, plot grid
Returns:
ax: Axes with diagram plotted
"""
if ax is None:
ax = plt.gca()
# Calculate 3Tb and 2Ta
Tb3 = Tb*3
Ta2 = Ta*2
# Set plot labels
ax.set_tlabel('3Tb',fontsize=8)
ax.set_llabel('Th',fontsize=8)
ax.set_rlabel('2Ta',fontsize=8)
# Plot grid
if (grid==True) & (first==[]):
t, l, r = get_triangular_grid()
ax.triplot(t, l, r,color='gray',linestyle='--')
first.append('NotFirst')
# Plot data
ax.scatter(Tb3,Th,Ta2,**plt_kwargs)
# Set plot labels
ax.set_tlabel('3Tb',fontsize=8)
ax.set_llabel('Th',fontsize=8)
ax.set_rlabel('2Ta',fontsize=8)
# Remove plot ticks
ax.taxis.set_ticks([])
ax.laxis.set_ticks([])
ax.raxis.set_ticks([])
return(ax)
def cabanisd(Tb,Th,Ta,ax=None,grid=False,**plt_kwargs):
"""
Plot Th-3Tb-2Ta diagram of Cabanis and Thieblemont (1988) as KDE.
Uses KDE functionality of pyrolite (Williams et al., 2020).
Parameters:
Tb: List of Tb values
Th: List of Th values
Ta: List of Ta values
ax: Axes on which to plot, requires "ternary" projection from mpltern
grid: Boolean for whether to add grid to diagram
Returns:
ax: Axes with diagram plotted
"""
if ax is None:
ax = plt.gca()
# Calculate 3Tb and 2Ta
Tb3 = Tb*3
Ta2 = Ta*2
Th1 = Th*1
# Plot grid
if grid==True:
t, l, r = get_triangular_grid()
ax.triplot(t, l, r,color='gray',linestyle='--')
# Make into Pandas dataframe and plot using pyrolite
df = pd.concat([Tb3,Th1,Ta2],axis=1)
df.pyroplot.density(ax=ax,**plt_kwargs)
# Set plot labels
ax.set_tlabel('3Tb',fontsize=8)
ax.set_llabel('Th',fontsize=8)
ax.set_rlabel('2Ta',fontsize=8)
# Remove plot ticks
ax.taxis.set_ticks([])
ax.laxis.set_ticks([])
ax.raxis.set_ticks([])
return(ax)
def harker(df,fig=None,axs=None,**plt_kwargs):
"""
Plot silica variation ("Harker") diagrams for major oxides.
Plots SiO2 (wt. %) against TiO2, Al2O3, FeOt, P2O5, CaO, MgO, Na2O, and
K2O
Parameters:
df: Pandas dataframe with major oxide information. Requires Fe input
as FeOt
fig: Figure on which to plot diagrams
axs: Set of axs within figure on which to plot
Returns:
fig: Figure with diagrams plotted
axs: Axes within figure with diagrams plotted
"""
# Create figure if not specified
if fig is None:
fig, axs = plt.subplots(4,2, sharex=True, figsize=(6.5,9),dpi=300)
# Set axs limits and oxides
plt.setp(axs,xlim=(40,75))
oxides = ['TiO2','Al2O3','FeOt','P2O5','CaO','MgO','Na2O','K2O']
ylims = [(0,2.5),(12,22),(0,15),(0,1.2),(0,15),(0,10),(0,7),(0,4)]
# Nested for loops to plot each oxide
for x in range(4):
for y in range(2):
df.plot.scatter(x = 'SiO2',y = oxides[2*x+y], ax=axs[x][y],ylim = ylims[2*x+y],
**plt_kwargs)
axs[x][y].tick_params(axis='both', which='major', labelsize=6)
plt.tight_layout()
return(fig,axs)
def spiders(df,**plt_kwargs):
"""
Plots figure of rare earth element plot and immobile element plot.
Plots rare earth element plot and imbbolie element plot after Pearce,
2014. Rare earth element plot does not include Pm or Tm, given low
availability in used data. Somewhat deprecated in favor of REE and
immobile below, which are more flexible for multi-axes figures.
Parameters:
df: Pandas dataframe with necessary trace element data
Retruns:
fig: Figure with both plots
"""
# Convert P and Ti from oxides to ppm, if needed, using pyrolite
PTioxides = df[['P2O5','TiO2']] #isolate oxides only
pti = PTioxides.pyrochem.convert_chemistry(to=["P", "Ti"]) #Convert
pti_ppm = pti.pyrochem.scale('wt%','ppm')
# Set of if statements for how to proceed depending on if P/Ti were
# previously reported
if pd.Series(['P', 'Ti']).isin(df.columns).all():
df.update(pti_ppm)
print(1)
elif 'P' in df.columns:
df.update(pti_ppm)
df = pd.concat([df,pti_ppm['Ti']],axis=1)
print(2)
elif 'Ti' in df.columns:
df.update(pti_ppm)
df = pd.concat([df,pti_ppm['P']],axis=1)
print(3)
else:
df = pd.concat([df,pti_ppm],axis=1)
print(4)
# List of elements for both plots
trace = df[["La", "Ce", "Pr", "Nd", "Sm", "Eu","Gd", "Tb", "Dy", "Ho",
"Er", "Yb","Lu","Th","Nb","Ta","P","Zr","Hf","Ti","Y"]]
# Normalize to primitive mantle
norm = trace.pyrochem.normalize_to(reference="PM_SM89", units="ppm")
# Define rare earth elements
ree = ["La", "Ce", "Pr", "Nd", "Sm", "Eu",
"Gd", "Tb", "Dy", "Ho", "Er", "Yb", "Lu"]
# Define immobile elements
imm = ["Th", "Nb", "Ta", "La", "Ce", "P", "Nd",
"Zr", "Hf", "Sm", "Eu", "Ti", "Gd", "Tb", "Y", "Yb"]
# Set up figure and plot
fig, ax = plt.subplots(1, 2, figsize=(20, 5))
plt.setp(ax, ylim=(0.1,1000))
norm.pyroplot.spider(
ax=ax[0],
**plt_kwargs,
unity_line=True,
components=ree,
)
norm.pyroplot.spider(
ax=ax[1],
**plt_kwargs,
unity_line=True,
components=imm,
)
# Add titles
ax[0].set_title('REE Elements')
ax[1].set_title('Incompatible Elements')
return(fig)
def REE(df,ax=None,**plt_kwargs):
"""
Plot rare earth element digaram, normalized to primitive mantle.
Plot normalized to primitive mantle values of Sun and McDonough, 1989.
Does not contain seldom-used Pm or Tm. Uses pyrolite extensively.
Parameters:
df: Pandas dataframe with geochemical data.
ax: Axes on which to plot diagram
Returns:
ax: Axes with diagram plotted
"""
if ax is None:
ax = plt.gca()
# Convert P and Ti from oxides to ppm, if needed, using pyrolite
PTioxides = df[['P2O5','TiO2']] #isolate oxides only
pti = PTioxides.pyrochem.convert_chemistry(to=["P", "Ti"]) #Convert
pti_ppm = pti.pyrochem.scale('wt%','ppm')
# Set of if statements for how to proceed depending on if P/Ti were
# previously reported
if pd.Series(['P', 'Ti']).isin(df.columns).all():
df.update(pti_ppm)
print(1)
elif 'P' in df.columns:
df.update(pti_ppm)
df = pd.concat([df,pti_ppm['Ti']],axis=1)
print(2)
elif 'Ti' in df.columns:
df.update(pti_ppm)
df = pd.concat([df,pti_ppm['P']],axis=1)
print(3)
else:
df = pd.concat([df,pti_ppm],axis=1)
print(4)
# Set rare earth elements
ree = ["La", "Ce", "Pr", "Nd", "Sm", "Eu",
"Gd", "Tb", "Dy", "Ho", "Er", "Yb", "Lu"]
# Get values from dataframe and normalize
trace = df[ree]
norm = trace.pyrochem.normalize_to(reference="PM_SM89", units="ppm")
norm.pyroplot.spider(
ax=ax,
**plt_kwargs,
unity_line=True,
components=ree,
)
ax.set_ylim(0.1,1000)
ax.tick_params(axis='both', which='major', labelsize=6)
ax.set_ylabel('Sample/Primitive Mantle',fontsize=8)
return(ax)
def immobile(df,ax=None,**plt_kwargs):
"""
Plot immobile element digaram, normalized to primitive mantle.
Plot normalized to primitive mantle values of Sun and McDonough, 1989.
After Pearce, 2014. Uses pyrolite extensively.
Parameters:
df: Pandas dataframe with geochemical data.
ax: Axes on which to plot diagram
Returns:
ax: Axes with diagram plotted
"""
if ax is None:
ax = plt.gca()
# Convert P and Ti from oxides to ppm, if needed, using pyrolite
PTioxides = df[['P2O5','TiO2']] #isolate oxides only
pti = PTioxides.pyrochem.convert_chemistry(to=["P", "Ti"]) #Convert
pti_ppm = pti.pyrochem.scale('wt%','ppm')
# Set of if statements for how to proceed depending on if P/Ti were
# previously reported
if pd.Series(['P', 'Ti']).isin(df.columns).all():
df.update(pti_ppm)
print(1)
elif 'P' in df.columns:
df.update(pti_ppm)
df = pd.concat([df,pti_ppm['Ti']],axis=1)
print(2)
elif 'Ti' in df.columns:
df.update(pti_ppm)
df = pd.concat([df,pti_ppm['P']],axis=1)
print(3)
else:
df = pd.concat([df,pti_ppm],axis=1)
print(4)
# Set immobile elements
imm = ["Th", "Nb", "Ta", "La", "Ce", "P", "Nd",
"Zr", "Hf", "Sm", "Eu", "Ti", "Gd", "Tb", "Y", "Yb"]
# Get values from dataframe and normalize
trace = df[imm]
norm = trace.pyrochem.normalize_to(reference="PM_SM89", units="ppm")
norm.pyroplot.spider(
ax=ax,
**plt_kwargs,
unity_line=True,
components=imm,
)
ax.set_ylim(0.1,1000)
ax.tick_params(axis='both', which='major', labelsize=6)
ax.set_ylabel('Sample/Primitive Mantle',fontsize=8)
return(ax)
def NdSr(eNd,Sr,init=False,ax=None,**plt_kwargs):
"""
Plot diagram of epsilon Nd vs. 87Sr/86Sr.
Parameters:
eNd: Values for epsilon Nd
Sr: Values for 87Sr/86Sr
init: Boolean for if values are initial values
ax: Axes on which to plot diagram
Returns:
ax: Axes with diagram plotted
"""
if ax is None:
ax = plt.gca()
ax.scatter(Sr,eNd,**plt_kwargs)
# Set labels according to whether initial or present day values
if init==False:
ax.set_xlabel('$\mathregular{^{87}Sr/^{86}Sr}$',fontsize=8)
ax.set_ylabel('\u03B5Nd',fontsize=8)
elif init==True:
ax.set_xlabel('$\mathregular{^{87}Sr/^{86}Sr_i}$',fontsize=8)
ax.set_ylabel('$\mathregular{\u03B5Nd_i}$',fontsize=8)
ax.set_xlim(0.700,0.712)
ax.set_ylim(-12,15)
ax.axvline(0.7045,c='gray',zorder=0)
ax.axhline(0,c='gray',zorder=0)
ax.tick_params(axis='both', which='major', labelsize=6)
return(ax)
def NdSrd(df,init=False,ax=None,**plt_kwargs):
"""
Plot diagram of epsilon Nd vs. 87Sr/86Sr as KDE using pyrolite.
Parameters:
df: Pandas dataframe with epsilon Nd and 87Sr/86Sr. Requires columns
labeled '87Sr/86Sr' and '\u03B5Nd'.
init: Boolean for if values are initial values
ax: Axes on which to plot diagram
Returns:
ax: Axes with diagram plotted
"""
if ax is None:
ax = plt.gca()
# Plot using appropriate labels, depending on whether values are initial.
if init==False:
df.loc[:,['87Sr/86Sr','\u03B5Nd']].pyroplot.density(
ax=ax,
extent=[0.700,0.712,-12,15],
vmin=0.05,
bins=100,
**plt_kwargs
)
ax.set_xlabel('$\mathregular{^{87}Sr/^{86}Sr}$',fontsize=8)
ax.set_ylabel('\u03B5Nd',fontsize=8)
elif init==True:
df.loc[:,['87Sr/86Sri','\u03B5Ndi']].pyroplot.density(
ax=ax,
extent=[0.700,0.712,-12,15],
vmin=0.05,
bins=100,
**plt_kwargs
)
ax.set_xlabel('$\mathregular{^{87}Sr/^{86}Sr_i}$',fontsize=8)
ax.set_ylabel('$\mathregular{\u03B5Nd_i}$',fontsize=8)
ax.set_xlim(0.700,0.712)
ax.set_ylim(-12,15)
ax.axvline(0.7045,c='gray',zorder=0)
ax.axhline(0,c='gray',zorder=0)
ax.tick_params(axis='both', which='major', labelsize=6)
return(ax)
def subfig(fig,xloc=0,yloc=1,fontsize=16,**plt_kwargs):
"""
Add subfigure labels to axes in figure using axes coordinates
Parameters:
fig: Figure on which to apply labels
xloc: X location for label, in axes coordinates (0-1)
yloc: Y location for label, in axes coordinates (0-1)
fontsize: Font size for axes labels
Returns:
fig: Figure with labels applied
"""
axes = fig.get_axes() # Get all axes in figure
letters = list(string.ascii_lowercase)
for x in range(len(axes)):
axes[x].text(xloc, yloc,'('+letters[x]+')',transform=axes[x].transAxes,
fontsize=fontsize,va='top',**plt_kwargs)
return(fig) | 32.926789 | 91 | 0.560463 | 2,964 | 19,789 | 3.701754 | 0.154858 | 0.014127 | 0.014036 | 0.016588 | 0.754375 | 0.72521 | 0.718556 | 0.70288 | 0.676996 | 0.666788 | 0 | 0.087874 | 0.284047 | 19,789 | 601 | 92 | 32.926789 | 0.686547 | 0.315984 | 0 | 0.757377 | 0 | 0 | 0.087934 | 0.021007 | 0 | 0 | 0 | 0 | 0 | 1 | 0.036066 | false | 0 | 0.016393 | 0 | 0.052459 | 0.039344 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
d3ed90ba2bcb07ab267dea3af4d18f6a5c61a051 | 68 | py | Python | wsgi.py | afg984/nthucourses | 9f28f8e9480b9d7a9db1f9c023955fb23b1a28aa | [
"BSD-3-Clause"
] | null | null | null | wsgi.py | afg984/nthucourses | 9f28f8e9480b9d7a9db1f9c023955fb23b1a28aa | [
"BSD-3-Clause"
] | null | null | null | wsgi.py | afg984/nthucourses | 9f28f8e9480b9d7a9db1f9c023955fb23b1a28aa | [
"BSD-3-Clause"
] | null | null | null | import nthucourses.wsgi
application = nthucourses.wsgi.application
| 17 | 42 | 0.852941 | 7 | 68 | 8.285714 | 0.571429 | 0.517241 | 0.896552 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088235 | 68 | 3 | 43 | 22.666667 | 0.935484 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
313963ae54d8e147e3b6d9a6562f761e4adce3b2 | 240 | py | Python | tests/test_forced_dumps_implementation.py | vikorbit/PyLaTeX | 9dee695c88857b4894ec110765a4b3c7f84ca853 | [
"MIT"
] | 4 | 2019-10-12T21:36:01.000Z | 2021-12-21T10:03:31.000Z | tests/test_forced_dumps_implementation.py | vikorbit/PyLaTeX | 9dee695c88857b4894ec110765a4b3c7f84ca853 | [
"MIT"
] | null | null | null | tests/test_forced_dumps_implementation.py | vikorbit/PyLaTeX | 9dee695c88857b4894ec110765a4b3c7f84ca853 | [
"MIT"
] | 1 | 2020-11-25T08:47:30.000Z | 2020-11-25T08:47:30.000Z | from pylatex.base_classes import LatexObject
from nose.tools import raises
class BadObject(LatexObject):
pass
@raises(TypeError)
def test_latex_object():
LatexObject()
@raises(TypeError)
def test_bad_object():
BadObject()
| 14.117647 | 44 | 0.758333 | 29 | 240 | 6.103448 | 0.62069 | 0.169492 | 0.20339 | 0.248588 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.154167 | 240 | 16 | 45 | 15 | 0.871921 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | true | 0.1 | 0.2 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
3152f0e829a417fffb0e6eea457ec20f38182572 | 43 | py | Python | rpi-config/scripts/simpleFunction.py | DiamondLightSource/rpi-config | 617f5e176c0621e3ea1b567e9586e96ba0f8b5db | [
"Apache-2.0"
] | 4 | 2016-08-23T12:13:21.000Z | 2018-08-22T12:55:55.000Z | rpi-config/scripts/simpleFunction.py | DiamondLightSource/rpi-config | 617f5e176c0621e3ea1b567e9586e96ba0f8b5db | [
"Apache-2.0"
] | null | null | null | rpi-config/scripts/simpleFunction.py | DiamondLightSource/rpi-config | 617f5e176c0621e3ea1b567e9586e96ba0f8b5db | [
"Apache-2.0"
] | 2 | 2016-09-15T19:17:30.000Z | 2018-03-06T06:34:13.000Z | def simpleFunction():
print "Hello World" | 21.5 | 22 | 0.744186 | 5 | 43 | 6.4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.139535 | 43 | 2 | 23 | 21.5 | 0.864865 | 0 | 0 | 0 | 0 | 0 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0.5 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
31540b459a702a690eba4d2cbc0c627b2f33c5f4 | 77 | py | Python | src/bin/sensemaking-cli.py | deepsensemaking/sensemaking | ba50f75ba591b583f37528f127141588798606e3 | [
"Apache-2.0"
] | null | null | null | src/bin/sensemaking-cli.py | deepsensemaking/sensemaking | ba50f75ba591b583f37528f127141588798606e3 | [
"Apache-2.0"
] | null | null | null | src/bin/sensemaking-cli.py | deepsensemaking/sensemaking | ba50f75ba591b583f37528f127141588798606e3 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*
print("s e n s e m a k i n g")
| 15.4 | 30 | 0.532468 | 19 | 77 | 2.157895 | 0.842105 | 0.097561 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.017241 | 0.246753 | 77 | 4 | 31 | 19.25 | 0.689655 | 0.532468 | 0 | 0 | 0 | 0 | 0.617647 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
3154ea9942ef20be6140f4dc7dc20035b9b83bca | 73 | py | Python | decomp/corpus/__init__.py | esteng/decomp | a6996b379e4a5e1a70a28b2b6f86bf39160ee10b | [
"MIT"
] | 48 | 2019-10-01T13:33:24.000Z | 2022-02-14T13:58:57.000Z | decomp/corpus/__init__.py | esteng/decomp | a6996b379e4a5e1a70a28b2b6f86bf39160ee10b | [
"MIT"
] | 15 | 2019-10-01T15:01:36.000Z | 2021-05-25T17:23:22.000Z | decomp/corpus/__init__.py | esteng/decomp | a6996b379e4a5e1a70a28b2b6f86bf39160ee10b | [
"MIT"
] | 9 | 2020-03-02T17:54:17.000Z | 2021-06-17T19:53:53.000Z | """Module for defining abstract corpus readers"""
from .corpus import *
| 18.25 | 49 | 0.739726 | 9 | 73 | 6 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.150685 | 73 | 3 | 50 | 24.333333 | 0.870968 | 0.589041 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
3162aa5fc5159c53161d2d8387f0ba13382f7418 | 106 | py | Python | aslam_offline_calibration/kalibr/python/kalibr_imu_rs_camera_calibration/__init__.py | huangqinjin/kalibr | 5bc7b73ce8185c734152def716e7d657a2736ec5 | [
"BSD-4-Clause"
] | null | null | null | aslam_offline_calibration/kalibr/python/kalibr_imu_rs_camera_calibration/__init__.py | huangqinjin/kalibr | 5bc7b73ce8185c734152def716e7d657a2736ec5 | [
"BSD-4-Clause"
] | null | null | null | aslam_offline_calibration/kalibr/python/kalibr_imu_rs_camera_calibration/__init__.py | huangqinjin/kalibr | 5bc7b73ce8185c734152def716e7d657a2736ec5 | [
"BSD-4-Clause"
] | null | null | null | from IrscCalibrator import *
import IrscUtil as util
import IrscPlots as plots
import IrscSensors as sens
| 21.2 | 28 | 0.839623 | 15 | 106 | 5.933333 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.150943 | 106 | 4 | 29 | 26.5 | 0.988889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
3171d3e254c88173bc9e7e7276a220cee76cd0fd | 61 | py | Python | deepspeed/runtime/pipe/__init__.py | ConnollyLeon/DeepSpeed | 2d84d1c185ef0345eaf43a7240d61b33eda43497 | [
"MIT"
] | 6,728 | 2020-02-07T23:53:18.000Z | 2022-03-31T20:02:53.000Z | deepspeed/runtime/pipe/__init__.py | ConnollyLeon/DeepSpeed | 2d84d1c185ef0345eaf43a7240d61b33eda43497 | [
"MIT"
] | 1,104 | 2020-02-08T00:26:15.000Z | 2022-03-31T22:33:56.000Z | deepspeed/runtime/pipe/__init__.py | ConnollyLeon/DeepSpeed | 2d84d1c185ef0345eaf43a7240d61b33eda43497 | [
"MIT"
] | 801 | 2020-02-10T15:33:42.000Z | 2022-03-29T16:32:33.000Z | from .module import PipelineModule, LayerSpec, TiedLayerSpec
| 30.5 | 60 | 0.852459 | 6 | 61 | 8.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.098361 | 61 | 1 | 61 | 61 | 0.945455 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
3175516fd9a614bf4bdb4257685a9ab5a84463e6 | 64 | py | Python | research/GenKGC/lit_models/__init__.py | zjunlp/PromptKG | 791bf82390eeadc30876d9f95e8dd26cd05de3dc | [
"MIT"
] | 11 | 2022-02-04T12:32:37.000Z | 2022-03-25T11:49:48.000Z | research/GenKGC/lit_models/__init__.py | zjunlp/PromptKG | 791bf82390eeadc30876d9f95e8dd26cd05de3dc | [
"MIT"
] | null | null | null | research/GenKGC/lit_models/__init__.py | zjunlp/PromptKG | 791bf82390eeadc30876d9f95e8dd26cd05de3dc | [
"MIT"
] | 4 | 2022-02-04T05:08:23.000Z | 2022-03-16T02:07:52.000Z | from .transformer import TransformerLitModel
from .base import * | 32 | 44 | 0.84375 | 7 | 64 | 7.714286 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.109375 | 64 | 2 | 45 | 32 | 0.947368 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
319aa51f86949514ba65a4cb6e08566d0ecc79c7 | 293 | py | Python | pyfo/utils/__init__.py | bradleygramhansen/pyfo | 559678080f27e7d9f3f194a0c28e9e8bfe71a7f3 | [
"MIT"
] | 3 | 2018-06-11T09:16:13.000Z | 2019-03-08T05:22:43.000Z | pyfo/utils/__init__.py | bradleygramhansen/pyfo | 559678080f27e7d9f3f194a0c28e9e8bfe71a7f3 | [
"MIT"
] | null | null | null | pyfo/utils/__init__.py | bradleygramhansen/pyfo | 559678080f27e7d9f3f194a0c28e9e8bfe71a7f3 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
Author: Bradley Gram-Hansen
Time created: 12:28
Date created: 20/11/2017
License: MIT
'''
from ..utils.core import DualAveraging, _generate_log_pdf, _grad_logp, _to_leaf
all = ['DualAveraging','_generate_log_pdf','_grad_logp', '_to_leaf'] | 24.416667 | 79 | 0.720137 | 43 | 293 | 4.581395 | 0.790698 | 0.213198 | 0.243655 | 0.274112 | 0.416244 | 0.416244 | 0.416244 | 0.416244 | 0 | 0 | 0 | 0.054054 | 0.116041 | 293 | 12 | 80 | 24.416667 | 0.706564 | 0.450512 | 0 | 0 | 1 | 0 | 0.313725 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
319f6a8a0bea6b1e467e328d6e39aec7898bad08 | 728 | py | Python | src/skdh/features/lib/__init__.py | PfizerRD/scikit-digital-health | f834a82d750d9e3cdd35f4f5692a0a388210b821 | [
"MIT"
] | 1 | 2022-03-31T20:56:49.000Z | 2022-03-31T20:56:49.000Z | src/skdh/features/lib/__init__.py | PfizerRD/scikit-digital-health | f834a82d750d9e3cdd35f4f5692a0a388210b821 | [
"MIT"
] | null | null | null | src/skdh/features/lib/__init__.py | PfizerRD/scikit-digital-health | f834a82d750d9e3cdd35f4f5692a0a388210b821 | [
"MIT"
] | null | null | null | from skdh.features.lib.entropy import *
from skdh.features.lib import entropy
from skdh.features.lib.smoothness import *
from skdh.features.lib import smoothness
from skdh.features.lib.statistics import *
from skdh.features.lib import statistics
from skdh.features.lib.frequency import *
from skdh.features.lib import frequency
from skdh.features.lib.misc import *
from skdh.features.lib import misc
from skdh.features.lib.moments import *
from skdh.features.lib import moments
from skdh.features.lib.wavelet import *
from skdh.features.lib import wavelet
__all__ = (
entropy.__all__
+ smoothness.__all__
+ statistics.__all__
+ frequency.__all__
+ misc.__all__
+ moments.__all__
+ wavelet.__all__
)
| 29.12 | 42 | 0.78022 | 99 | 728 | 5.414141 | 0.131313 | 0.208955 | 0.41791 | 0.496269 | 0.404851 | 0.404851 | 0 | 0 | 0 | 0 | 0 | 0 | 0.14011 | 728 | 24 | 43 | 30.333333 | 0.85623 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.608696 | 0 | 0.608696 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
31e4b09ba2aae8285fea64d6d2bee49fcd3b839a | 9,055 | py | Python | RLBotPack/VirxEB/util/tools.py | L0laapk3/RLBotPack | f54038475d2a57428f3784560755f96bfcf8015f | [
"MIT"
] | 13 | 2019-05-25T20:25:51.000Z | 2022-03-19T13:36:23.000Z | RLBotPack/VirxEB/util/tools.py | L0laapk3/RLBotPack | f54038475d2a57428f3784560755f96bfcf8015f | [
"MIT"
] | 53 | 2019-06-07T13:31:59.000Z | 2022-03-28T22:53:47.000Z | RLBotPack/VirxEB/util/tools.py | L0laapk3/RLBotPack | f54038475d2a57428f3784560755f96bfcf8015f | [
"MIT"
] | 78 | 2019-06-30T08:42:13.000Z | 2022-03-23T20:11:42.000Z | from util.routines import Aerial, double_jump, ground_shot, jump_shot, virxrlcu
from util.utils import Vector, math, side, cap
def find_ground_shot(agent, target, weight=None, cap_=6):
return find_shot(agent, target, weight, cap_, can_aerial=False, can_double_jump=False, can_jump=False)
def find_any_ground_shot(agent, cap_=6):
return find_any_shot(agent, cap_, can_aerial=False, can_double_jump=False, can_jump=False)
def find_jump_shot(agent, target, weight=None, cap_=6):
return find_shot(agent, target, weight, cap_, can_aerial=False, can_double_jump=False, can_ground=False)
def find_any_jump_shot(agent, cap_=6):
return find_any_shot(agent, cap_, can_aerial=False, can_double_jump=False, can_ground=False)
def find_double_jump(agent, target, weight=None, cap_=6):
return find_shot(agent, target, weight, cap_, can_aerial=False, can_jump=False, can_ground=False)
def find_any_double_jump(agent, cap_=6):
return find_any_shot(agent, cap_, can_aerial=False, can_jump=False, can_ground=False)
def find_aerial(agent, target, weight=None, cap_=6):
return find_shot(agent, target, weight, cap_, can_double_jump=False, can_jump=False, can_ground=False)
def find_any_aerial(agent, cap_=6):
return find_any_shot(agent, cap_, can_double_jump=False, can_jump=False, can_ground=False)
def find_shot(agent, target, weight=None, cap_=6, can_aerial=True, can_double_jump=True, can_jump=True, can_ground=True):
if not can_aerial and not can_double_jump and not can_jump and not can_ground:
agent.print("WARNING: All shots were disabled when find_shot was ran")
return
# Takes a tuple of (left,right) target pairs and finds routines that could hit the ball between those target pairs
# Only meant for routines that require a defined intercept time/place in the future
# Assemble data in a form that can be passed to C
targets = (
tuple(target[0]),
tuple(target[1])
)
me = agent.me.get_raw(agent)
game_info = (
agent.boost_accel,
agent.best_shot_value
)
gravity = tuple(agent.gravity)
max_aerial_height = 1200 if len(agent.friends) == 0 and len(agent.foes) == 1 else math.inf
min_aerial_height = 551 if max_aerial_height > 1200 and agent.me.location.z >= 2044 - agent.me.hitbox.height * 1.1 else (150 if agent.boost_amount == 'unlimited' or agent.me.airborne else 450)
is_on_ground = not agent.me.airborne
can_ground = is_on_ground and can_ground
can_jump = is_on_ground and can_jump
can_double_jump = is_on_ground and can_double_jump
if not can_ground and not can_jump and not can_double_jump and not can_aerial:
return
# Here we get the slices that need to be searched - by defining a cap, we can reduce the number of slices and improve search times
slices = get_slices(agent, cap_, weight=weight)
if slices is None:
return
# Loop through the slices
for ball_slice in slices:
# Gather some data about the slice
intercept_time = ball_slice.game_seconds
time_remaining = intercept_time - agent.time - (1 / 120)
if time_remaining <= 0:
return
ball_location = (ball_slice.physics.location.x, ball_slice.physics.location.y, ball_slice.physics.location.z)
if abs(ball_location[1]) > 5212.75:
return # abandon search if ball is scored at/after this point
ball_info = (ball_location, (ball_slice.physics.velocity.x, ball_slice.physics.velocity.y, ball_slice.physics.velocity.z))
# Check if we can make a shot at this slice
# This operation is very expensive, so we use C to improve run time
shot = virxrlcu.parse_slice_for_shot_with_target(can_ground, can_jump, can_double_jump, can_aerial and (min_aerial_height < ball_location[2] < max_aerial_height), time_remaining, *game_info, gravity, ball_info, me, targets)
if shot['found'] == 1:
if shot['shot_type'] == 3:
return Aerial(intercept_time, (Vector(*shot['targets'][0]), Vector(*shot['targets'][1])), shot['fast'])
shot_switch = [
ground_shot,
jump_shot,
double_jump
]
return shot_switch[shot['shot_type']](intercept_time, (Vector(*shot['targets'][0]), Vector(*shot['targets'][1])))
def find_any_shot(agent, cap_=6, can_aerial=True, can_double_jump=True, can_jump=True, can_ground=True):
if not can_aerial and not can_double_jump and not can_jump and not can_ground:
agent.print("WARNING: All shots were disabled when find_any_shot was ran")
return
# Only meant for routines that require a defined intercept time/place in the future
# Assemble data in a form that can be passed to C
me = agent.me.get_raw(agent)
game_info = (
agent.boost_accel,
agent.best_shot_value
)
gravity = tuple(agent.gravity)
max_aerial_height = 1200 if len(agent.friends) == 0 and len(agent.foes) == 1 else math.inf
min_aerial_height = 551 if max_aerial_height > 1200 and agent.me.location.z >= 2044 - agent.me.hitbox.height * 1.1 else (150 if agent.boost_amount == 'unlimited' or agent.me.airborne else 450)
is_on_ground = not agent.me.airborne
can_ground = is_on_ground and can_ground
can_jump = is_on_ground and can_jump
can_double_jump = is_on_ground and can_double_jump
if not can_ground and not can_jump and not can_double_jump and not can_aerial:
return
# Here we get the slices that need to be searched - by defining a cap, we can reduce the number of slices and improve search times
slices = get_slices(agent, cap_)
if slices is None:
return
# Loop through the slices
for ball_slice in slices:
# Gather some data about the slice
intercept_time = ball_slice.game_seconds
time_remaining = intercept_time - agent.time - (1 / 120)
if time_remaining <= 0:
return
ball_location = (ball_slice.physics.location.x, ball_slice.physics.location.y, ball_slice.physics.location.z)
if abs(ball_location[1]) > 5212.75:
return # abandon search if ball is scored at/after this point
ball_info = (ball_location, (ball_slice.physics.velocity.x, ball_slice.physics.velocity.y, ball_slice.physics.velocity.z))
# Check if we can make a shot at this slice
# This operation is very expensive, so we use C to improve run time
shot = virxrlcu.parse_slice_for_shot(can_ground, can_jump, can_double_jump, can_aerial and (min_aerial_height < ball_location[2] < max_aerial_height), time_remaining, *game_info, gravity, ball_info, me)
if shot['found'] == 1:
if shot['shot_type'] == 3:
return Aerial(intercept_time, fast_aerial=shot['fast'])
shot_switch = [
ground_shot,
jump_shot,
double_jump
]
return shot_switch[shot['shot_type']](intercept_time)
def get_slices(agent, cap_, weight=None, start_slice=12):
# Get the struct
struct = agent.ball_prediction_struct
min_time_to_ball = agent.predictions['self_min_time_to_ball'] - (1 / 15)
# Make sure it isn't empty
if struct is None or min_time_to_ball > cap_:
return
if start_slice / 60 < min_time_to_ball:
start_slice = round(min_time_to_ball * 60) - 1
ball_y = agent.ball.location.y * side(agent.team)
foes = len(tuple(foe for foe in agent.foes if not foe.demolished and foe.location.y * side(agent.team) < ball_y + 75))
if not agent.predictions['goal'] and agent.ball_to_goal > 2560 and agent.ball.location.dist(agent.foe_goal.location) > 900 and foes > 0:
factor = 1.2 - 0.04 * foes
cap_ = min(agent.predictions['enemy_time_to_ball'] * factor, cap_)
end_slices = None
# If we're shooting, crop the struct
if agent.shooting and agent.shot_weight != -1:
# Get the time remaining
time_remaining = agent.stack[0].intercept_time - agent.time
if time_remaining < 0.5 and time_remaining >= 0:
return
# if the shot is done but it's working on it's 'follow through', then ignore this stuff
if time_remaining > 0:
# Convert the time remaining into number of slices, and take off the minimum gain accepted from the time
min_gain = 0.05 if weight is None or weight is agent.shot_weight else -(agent.max_shot_weight - agent.shot_weight + 1)
end_slice = round(min(time_remaining - min_gain, cap_) * 60)
if end_slices is None:
# Cap the slices
end_slice = round(cap_ * 60)
# We can't end a slice index that's lower than the start index
if end_slice <= start_slice:
return
# for every second worth of slices that we have to search, skip 1 more slice (for performance reasons) - min 1 and max 3
skip = cap(end_slice - start_slice / 60, 1, 3)
return struct.slices[start_slice:end_slice:skip]
| 41.347032 | 231 | 0.690558 | 1,409 | 9,055 | 4.208659 | 0.151171 | 0.038786 | 0.03946 | 0.018887 | 0.734739 | 0.71973 | 0.71973 | 0.715514 | 0.715008 | 0.715008 | 0 | 0.020214 | 0.224186 | 9,055 | 218 | 232 | 41.536697 | 0.823915 | 0.170955 | 0 | 0.523438 | 0 | 0 | 0.034358 | 0.002807 | 0 | 0 | 0 | 0 | 0 | 1 | 0.085938 | false | 0 | 0.015625 | 0.0625 | 0.304688 | 0.015625 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
9ee0f3f3ad5f5331413fdeb116cfdc8373c42ea3 | 99 | py | Python | Sprites/Animation.py | ProSerg/Asteroid | 0815cc1d043ecf2b2653179399da4f62afe85fd7 | [
"MIT"
] | null | null | null | Sprites/Animation.py | ProSerg/Asteroid | 0815cc1d043ecf2b2653179399da4f62afe85fd7 | [
"MIT"
] | 3 | 2017-05-13T13:21:34.000Z | 2017-05-13T13:23:02.000Z | Sprites/Animation.py | ProSerg/Asteroid | 0815cc1d043ecf2b2653179399da4f62afe85fd7 | [
"MIT"
] | null | null | null | from Asteroid.common import Sprite
class Animation(Sprite):
def __init__(self):
pass
| 14.142857 | 34 | 0.69697 | 12 | 99 | 5.416667 | 0.916667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.232323 | 99 | 6 | 35 | 16.5 | 0.855263 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0.25 | 0.25 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 5 |
730ed5d3ad1e9581c8a33cb7ecd4308adc1b5a2d | 56 | py | Python | interest/handler/__init__.py | interest-hub/interest | e6e1def4f2999222aac2fb1d290ae94250673b89 | [
"MIT"
] | 14 | 2015-02-15T09:29:26.000Z | 2016-03-24T15:30:54.000Z | interest/handler/__init__.py | roll/interest-py | e6e1def4f2999222aac2fb1d290ae94250673b89 | [
"MIT"
] | 17 | 2015-02-15T22:52:07.000Z | 2016-02-28T23:40:18.000Z | interest/handler/__init__.py | roll/interest-py | e6e1def4f2999222aac2fb1d290ae94250673b89 | [
"MIT"
] | 1 | 2016-11-11T11:14:05.000Z | 2016-11-11T11:14:05.000Z | from .handler import Handler
from .record import Record
| 18.666667 | 28 | 0.821429 | 8 | 56 | 5.75 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.142857 | 56 | 2 | 29 | 28 | 0.958333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
730f162ec851e01347accb486e77acb193a4f51c | 93 | py | Python | restapiserver/medicine/admin.py | LiveCoronaDetector/coronaAPIserver | b42e2e32dfe37f9963aec071a0ee8fe98c9808cc | [
"MIT"
] | 1 | 2020-08-29T02:36:39.000Z | 2020-08-29T02:36:39.000Z | restapiserver/medicine/admin.py | LiveCoronaDetector/coronaAPIserver | b42e2e32dfe37f9963aec071a0ee8fe98c9808cc | [
"MIT"
] | null | null | null | restapiserver/medicine/admin.py | LiveCoronaDetector/coronaAPIserver | b42e2e32dfe37f9963aec071a0ee8fe98c9808cc | [
"MIT"
] | 2 | 2020-03-13T03:48:34.000Z | 2020-08-29T02:36:40.000Z | from django.contrib import admin
from .models import Pharmacy
admin.site.register(Pharmacy)
| 18.6 | 32 | 0.827957 | 13 | 93 | 5.923077 | 0.692308 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.107527 | 93 | 4 | 33 | 23.25 | 0.927711 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
732bc26c304e731e8a44b4d8c90f6ac644557120 | 107 | py | Python | tools/version.py | ra2u18/cpu6502 | 7676faab72276b7de77b6e692b6d825284615944 | [
"Apache-2.0"
] | null | null | null | tools/version.py | ra2u18/cpu6502 | 7676faab72276b7de77b6e692b6d825284615944 | [
"Apache-2.0"
] | null | null | null | tools/version.py | ra2u18/cpu6502 | 7676faab72276b7de77b6e692b6d825284615944 | [
"Apache-2.0"
] | null | null | null | import globals, sys
print("netlemon tools - v{}.{}".format(globals.V_MAJOR, globals.V_MINOR))
sys.exit(0) | 21.4 | 73 | 0.719626 | 17 | 107 | 4.411765 | 0.705882 | 0.213333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.010309 | 0.093458 | 107 | 5 | 74 | 21.4 | 0.762887 | 0 | 0 | 0 | 0 | 0 | 0.212963 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0.333333 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
7df499b95284421b2e635fe9a9027428b8eee815 | 165 | py | Python | Task 1E.py | asew4/Flood-Warning-System-8 | 66f436caf8307232604b830e4dc4ab385de0556e | [
"MIT"
] | null | null | null | Task 1E.py | asew4/Flood-Warning-System-8 | 66f436caf8307232604b830e4dc4ab385de0556e | [
"MIT"
] | null | null | null | Task 1E.py | asew4/Flood-Warning-System-8 | 66f436caf8307232604b830e4dc4ab385de0556e | [
"MIT"
] | 1 | 2022-02-06T02:27:29.000Z | 2022-02-06T02:27:29.000Z | from floodsystem import geo
from floodsystem.stationdata import build_station_list
stations = build_station_list()
print(geo.rivers_by_station_number(stations,9)) | 23.571429 | 54 | 0.854545 | 23 | 165 | 5.826087 | 0.608696 | 0.223881 | 0.238806 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006623 | 0.084848 | 165 | 7 | 55 | 23.571429 | 0.880795 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0.25 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
b405ba23ab4765a5abf3207190141299fa294076 | 96 | py | Python | venv/lib/python3.8/site-packages/jedi/api/replstartup.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/jedi/api/replstartup.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | 19 | 2021-11-20T04:09:18.000Z | 2022-03-23T15:05:55.000Z | venv/lib/python3.8/site-packages/jedi/api/replstartup.py | DesmoSearch/Desmobot | b70b45df3485351f471080deb5c785c4bc5c4beb | [
"MIT"
] | null | null | null | /home/runner/.cache/pip/pool/6d/f0/fb/736b2b7aee2fd1ee9ea084ead3e581b5eb7d99a954f2525648b10ecee0 | 96 | 96 | 0.895833 | 9 | 96 | 9.555556 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.354167 | 0 | 96 | 1 | 96 | 96 | 0.541667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
b41a130ed931f880dac8f5943ad00d450aa747a5 | 30,878 | py | Python | AppServer/google/appengine/tools/devappserver2/admin/datastore_viewer_test.py | loftwah/appscale | 586fc1347ebc743d7a632de698f4dbfb09ae38d6 | [
"Apache-2.0"
] | 790 | 2015-01-03T02:13:39.000Z | 2020-05-10T19:53:57.000Z | AppServer/google/appengine/tools/devappserver2/admin/datastore_viewer_test.py | loftwah/appscale | 586fc1347ebc743d7a632de698f4dbfb09ae38d6 | [
"Apache-2.0"
] | 1,361 | 2015-01-08T23:09:40.000Z | 2020-04-14T00:03:04.000Z | AppServer/google/appengine/tools/devappserver2/admin/datastore_viewer_test.py | loftwah/appscale | 586fc1347ebc743d7a632de698f4dbfb09ae38d6 | [
"Apache-2.0"
] | 155 | 2015-01-08T22:59:31.000Z | 2020-04-08T08:01:53.000Z | #!/usr/bin/env python
#
# Copyright 2007 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""Tests for devappserver2.admin.datastore_viewer."""
import datetime
import os
import unittest
import google
import mox
import webapp2
from google.appengine.api import apiproxy_stub_map
from google.appengine.api import datastore
from google.appengine.api import datastore_types
from google.appengine.datastore import datastore_pb
from google.appengine.datastore import datastore_stub_util
from google.appengine.tools.devappserver2 import api_server
from google.appengine.tools.devappserver2.admin import admin_request_handler
from google.appengine.tools.devappserver2.admin import datastore_viewer
class PropertyNameToValuesTest(unittest.TestCase):
"""Tests for datastore_viewer._property_name_to_value(s)."""
def setUp(self):
self.app_id = 'myapp'
self.entity1 = datastore.Entity('Kind1', id=123, _app=self.app_id)
self.entity1['cat'] = 5
self.entity1['dog'] = 10
self.entity2 = datastore.Entity('Kind1', id=124, _app=self.app_id)
self.entity2['dog'] = 15
self.entity2['mouse'] = 'happy'
def test_property_name_to_values(self):
self.assertEqual({'cat': [5],
'dog': mox.SameElementsAs([10, 15]),
'mouse': ['happy']},
datastore_viewer._property_name_to_values([self.entity1,
self.entity2]))
def test_property_name_to_value(self):
self.assertEqual({'cat': 5,
'dog': mox.Func(lambda v: v in [10, 15]),
'mouse': 'happy'},
datastore_viewer._property_name_to_value([self.entity1,
self.entity2]))
class GetWriteOpsTest(unittest.TestCase):
"""Tests for DatastoreRequestHandler._get_write_ops."""
def setUp(self):
self.app_id = 'myapp'
os.environ['APPLICATION_ID'] = self.app_id
# Use a consistent replication strategy so the puts done in the test code
# are seen immediately by the queries under test.
consistent_policy = datastore_stub_util.MasterSlaveConsistencyPolicy()
api_server.test_setup_stubs(
app_id=self.app_id,
application_root=None, # Needed to allow index updates.
datastore_consistency=consistent_policy)
def test_no_properties(self):
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
self.assertEquals(
2, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
def test_indexed_properties_no_composite_indexes(self):
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
entity['p1'] = None # 2 writes.
entity['p2'] = None # 2 writes.
entity['p3'] = [1, 2, 3] # 6 writes.
self.assertEquals(
12, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
def test_unindexed_properties_no_composite_indexes(self):
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
entity['u1'] = None # 0 writes.
entity['u2'] = None # 0 writes.
entity['u3'] = [1, 2, 3] # 0 writes.
entity.set_unindexed_properties(('u1', 'u2', 'u3'))
# unindexed properties have no impact on cost
self.assertEquals(
2, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
def test_composite_index(self):
ci = datastore_pb.CompositeIndex()
ci.set_app_id(datastore_types.ResolveAppId(None))
ci.set_id(0)
ci.set_state(ci.WRITE_ONLY)
index = ci.mutable_definition()
index.set_ancestor(0)
index.set_entity_type('Yar')
prop = index.add_property()
prop.set_name('this')
prop.set_direction(prop.ASCENDING)
prop = index.add_property()
prop.set_name('that')
prop.set_direction(prop.DESCENDING)
stub = apiproxy_stub_map.apiproxy.GetStub('datastore_v3')
stub.CreateIndex(ci)
self.assertEquals(1, len(datastore.GetIndexes()))
# no properties, no composite indices.
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
# We only have the 2 built-in index writes because the entity doesn't have
# property values for any of the index properties.
self.assertEquals(
2, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
entity['this'] = 4
# Unindexed property so no additional writes
entity.set_unindexed_properties(('this',))
self.assertEquals(
2, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
entity['that'] = 4
# Unindexed property so no additional writes
entity.set_unindexed_properties(('this', 'that'))
self.assertEquals(
2, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# no indexed property value on 'that'
entity.set_unindexed_properties(('that',))
# 2 writes for the entity.
# 2 writes for the single indexed property.
self.assertEquals(
4, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# indexed property value on both 'this' and 'that'
entity.set_unindexed_properties(())
# 2 writes for the entity
# 4 writes for the indexed properties
# 1 writes for the composite index
self.assertEquals(
7, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# now run tests with null property values
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
entity['this'] = None
# 2 for the entity
# 2 for the single indexed property
self.assertEquals(
4, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
entity['that'] = None
# 2 for the entity
# 4 for the indexed properties
# 1 for the composite index
self.assertEquals(
7, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# now run tests with a repeated property
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
entity['this'] = [1, 2, 3]
# 2 for the entity
# 6 for the indexed properties
self.assertEquals(
8, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
entity['that'] = None
# 2 for the entity
# 8 for the indexed properties
# 3 for the Composite index
self.assertEquals(
13, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
entity['that'] = [4, 5]
# 2 for the entity
# 10 for the indexed properties
# 6 for the Composite index
self.assertEquals(
18, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
def test_composite_index_no_properties(self):
ci = datastore_pb.CompositeIndex()
ci.set_app_id(datastore_types.ResolveAppId(None))
ci.set_id(0)
ci.set_state(ci.WRITE_ONLY)
index = ci.mutable_definition()
index.set_ancestor(0)
index.set_entity_type('Yar')
stub = apiproxy_stub_map.apiproxy.GetStub('datastore_v3')
stub.CreateIndex(ci)
self.assertEquals(1, len(datastore.GetIndexes()))
# no properties, and composite index with no properties.
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
# We have the 2 built-in index writes, and one for the entity key in the
# composite index despite the fact that there are no proerties defined in
# the index.
self.assertEquals(
3, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# now with a repeated property
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
entity['this'] = [1, 2, 3]
# 2 for the entity
# 6 for the indexed properties
# 1 for the composite index
self.assertEquals(
9, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
def test_composite_ancestor_index(self):
ci = datastore_pb.CompositeIndex()
ci.set_app_id(datastore_types.ResolveAppId(None))
ci.set_id(0)
ci.set_state(ci.WRITE_ONLY)
index = ci.mutable_definition()
index.set_ancestor(1)
index.set_entity_type('Yar')
prop = index.add_property()
prop.set_name('this')
prop.set_direction(prop.ASCENDING)
prop = index.add_property()
prop.set_name('that')
prop.set_direction(prop.DESCENDING)
stub = apiproxy_stub_map.apiproxy.GetStub('datastore_v3')
stub.CreateIndex(ci)
self.assertEquals(1, len(datastore.GetIndexes()))
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
entity['this'] = 4
entity['that'] = 4
# 2 for the entity
# 4 for the indexed properties
# 1 for the composite index
self.assertEquals(
7, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# Now use the same entity but give it an ancestor
parent_entity = datastore.Entity('parent', id=123, _app=self.app_id)
entity = datastore.Entity(
'Yar',
parent=parent_entity.key(),
id=123,
_app=self.app_id) # 2 writes.
entity['this'] = 4
entity['that'] = 4
# 2 writes for the entity.
# 4 writes for the indexed properties.
# 2 writes for the composite indices.
self.assertEquals(
8, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# Now use the same entity but give it 2 ancestors.
grandparent_entity = datastore.Entity(
'grandparent', id=123, _app=self.app_id)
parent_entity = datastore.Entity(
'parent', parent=grandparent_entity.key(), id=123, _app=self.app_id)
entity = datastore.Entity(
'Yar',
parent=parent_entity.key(),
id=123,
_app=self.app_id) # 2 writes.
entity['this'] = 4
entity['that'] = 4
# 2 writes for the entity.
# 4 writes for the indexed properties.
# 3 writes for the composite indices.
self.assertEquals(
9, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# Now try it with a multi-value prop
entity['this'] = [None, None, None]
# 2 writes for the entity.
# 8 writes for the indexed properties.
# 9 writes for the composite indices.
self.assertEquals(
19, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# Now try it with 2 multi-value props.
entity['that'] = [None, None]
# 2 writes for the entity.
# 10 writes for the indexed properties.
# 18 writes for the composite indices.
self.assertEquals(
30, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
def test_composite_ancestor_index_no_properties(self):
ci = datastore_pb.CompositeIndex()
ci.set_app_id(datastore_types.ResolveAppId(None))
ci.set_id(0)
ci.set_state(ci.WRITE_ONLY)
index = ci.mutable_definition()
index.set_ancestor(1)
index.set_entity_type('Yar')
stub = apiproxy_stub_map.apiproxy.GetStub('datastore_v3')
stub.CreateIndex(ci)
self.assertEquals(1, len(datastore.GetIndexes()))
entity = datastore.Entity('Yar', id=123, _app=self.app_id) # 2 writes.
entity['this'] = [None, None]
# 2 writes for the entity.
# 4 writes for the indexed properties.
# 1 writes for the composite index.
self.assertEquals(
7, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
# Now use the same entity but give it an ancestor
parent_entity = datastore.Entity('parent', id=123, _app=self.app_id)
entity = datastore.Entity(
'Yar',
parent=parent_entity.key(),
id=123,
_app=self.app_id) # 2 writes.
entity['this'] = [None, None]
# 2 writes for the entity.
# 4 writes for the indexed properties.
# 2 writes for the composite indices.
self.assertEquals(
8, datastore_viewer.DatastoreRequestHandler._get_write_ops(entity))
class GetEntitiesTest(unittest.TestCase):
"""Tests for DatastoreRequestHandler._get_entities."""
def setUp(self):
self.app_id = 'myapp'
os.environ['APPLICATION_ID'] = self.app_id
# Use a consistent replication strategy so the puts done in the test code
# are seen immediately by the queries under test.
consistent_policy = datastore_stub_util.MasterSlaveConsistencyPolicy()
api_server.test_setup_stubs(
app_id=self.app_id,
datastore_consistency=consistent_policy)
self.entity1 = datastore.Entity('Kind1', id=123, _app=self.app_id)
self.entity1['intprop'] = 1
self.entity1['listprop'] = [7, 8, 9]
datastore.Put(self.entity1)
self.entity2 = datastore.Entity('Kind1', id=124, _app=self.app_id)
self.entity2['stringprop'] = 'value2'
self.entity2['listprop'] = [4, 5, 6]
datastore.Put(self.entity2)
self.entity3 = datastore.Entity('Kind1', id=125, _app=self.app_id)
self.entity3['intprop'] = 3
self.entity3['stringprop'] = 'value3'
self.entity3['listprop'] = [1, 2, 3]
datastore.Put(self.entity3)
self.entity4 = datastore.Entity('Kind1', id=126, _app=self.app_id)
self.entity4['intprop'] = 4
self.entity4['stringprop'] = 'value4'
self.entity4['listprop'] = [10, 11, 12]
datastore.Put(self.entity4)
def test_ascending_int_order(self):
entities, total = datastore_viewer._get_entities(kind='Kind1',
namespace='',
order='intprop',
start=0,
count=100)
self.assertEqual([self.entity1, self.entity3, self.entity4], entities)
self.assertEqual(3, total)
def test_decending_string_order(self):
entities, total = datastore_viewer._get_entities(kind='Kind1',
namespace='',
order='-stringprop',
start=0,
count=100)
self.assertEqual([self.entity4, self.entity3, self.entity2], entities)
self.assertEqual(3, total)
def test_start_and_count(self):
entities, total = datastore_viewer._get_entities(kind='Kind1',
namespace='',
order='listprop',
start=1,
count=2)
self.assertEqual([self.entity2, self.entity1], entities)
self.assertEqual(4, total)
class GetEntityTemplateDataTest(unittest.TestCase):
def setUp(self):
self.app_id = 'myapp'
os.environ['APPLICATION_ID'] = self.app_id
# Use a consistent replication strategy so the puts done in the test code
# are seen immediately by the queries under test.
consistent_policy = datastore_stub_util.MasterSlaveConsistencyPolicy()
api_server.test_setup_stubs(
app_id=self.app_id,
datastore_consistency=consistent_policy)
self.entity1 = datastore.Entity('Kind1', id=123, _app=self.app_id)
self.entity1['intprop'] = 1
self.entity1['listprop'] = [7, 8, 9]
datastore.Put(self.entity1)
self.entity2 = datastore.Entity('Kind1', id=124, _app=self.app_id)
self.entity2['stringprop'] = 'value2'
self.entity2['listprop'] = [4, 5, 6]
datastore.Put(self.entity2)
self.entity3 = datastore.Entity('Kind1', id=125, _app=self.app_id)
self.entity3['intprop'] = 3
self.entity3['listprop'] = [1, 2, 3]
datastore.Put(self.entity3)
self.entity4 = datastore.Entity('Kind1', id=126, _app=self.app_id)
self.entity4['intprop'] = 4
self.entity4['stringprop'] = 'value4'
self.entity4['listprop'] = [10, 11]
datastore.Put(self.entity4)
def test(self):
headers, entities, total_entities = (
datastore_viewer.DatastoreRequestHandler._get_entity_template_data(
request_uri='http://next/',
kind='Kind1',
namespace='',
order='intprop',
start=1))
self.assertEqual(
[{'name': 'intprop'}, {'name': 'listprop'}, {'name': 'stringprop'}],
headers)
self.assertEqual(
[{'attributes': [{'name': u'intprop',
'short_value': '3',
'value': '3'},
{'name': u'listprop',
'short_value': mox.Regex(r'\[1L?, 2L?, 3L?\]'),
'value': mox.Regex(r'\[1L?, 2L?, 3L?\]')},
{'name': u'stringprop',
'short_value': '',
'value': ''}],
'edit_uri': '/datastore/edit/{0}?next=http%3A//next/'.format(
self.entity3.key()),
'key': datastore_types.Key.from_path(u'Kind1', 125, _app=u'myapp'),
'key_id': 125,
'key_name': None,
'shortened_key': 'agVteWFw...',
'write_ops': 10},
{'attributes': [{'name': u'intprop',
'short_value': '4',
'value': '4'},
{'name': u'listprop',
'short_value': mox.Regex(r'\[10L?, 11L?\]'),
'value': mox.Regex(r'\[10L?, 11L?\]')},
{'name': u'stringprop',
'short_value': u'value4',
'value': u'value4'}],
'edit_uri': '/datastore/edit/{0}?next=http%3A//next/'.format(
self.entity4.key()),
'key': datastore_types.Key.from_path(u'Kind1', 126, _app=u'myapp'),
'key_id': 126,
'key_name': None,
'shortened_key': 'agVteWFw...',
'write_ops': 10}],
entities)
self.assertEqual(3, total_entities)
class DatastoreRequestHandlerGetTest(unittest.TestCase):
"""Tests for DatastoreRequestHandler.get."""
def setUp(self):
self.app_id = 'myapp'
os.environ['APPLICATION_ID'] = self.app_id
api_server.test_setup_stubs(app_id=self.app_id)
self.mox = mox.Mox()
self.mox.StubOutWithMock(admin_request_handler.AdminRequestHandler,
'render')
def tearDown(self):
self.mox.UnsetStubs()
def test_empty_request_and_empty_datastore(self):
request = webapp2.Request.blank('/datastore')
response = webapp2.Response()
handler = datastore_viewer.DatastoreRequestHandler(request, response)
handler.render('datastore_viewer.html',
{'entities': [],
'headers': [],
'kind': None,
'kinds': [],
'message': None,
'namespace': '',
'num_pages': 0,
'order': None,
'paging_base_url': '/datastore?',
'order_base_url': '/datastore?',
'page': 1,
'select_namespace_url': '/datastore?namespace=',
'show_namespace': False,
'start': 0,
'total_entities': 0})
self.mox.ReplayAll()
handler.get()
self.mox.VerifyAll()
def test_empty_request_and_populated_datastore(self):
entity = datastore.Entity('Kind1', id=123, _app=self.app_id)
entity['intprop'] = 1
entity['listprop'] = [7, 8, 9]
datastore.Put(entity)
request = webapp2.Request.blank('/datastore')
response = webapp2.Response()
handler = datastore_viewer.DatastoreRequestHandler(request, response)
self.mox.ReplayAll()
handler.get()
self.mox.VerifyAll()
self.assertEqual(302, response.status_int)
self.assertEqual('http://localhost/datastore?kind=Kind1',
response.location)
def test_kind_request_and_populated_datastore(self):
entity = datastore.Entity('Kind1', id=123, _app=self.app_id)
entity['intprop'] = 1
entity['listprop'] = [7, 8, 9]
datastore.Put(entity)
request = webapp2.Request.blank('/datastore?kind=Kind1')
response = webapp2.Response()
handler = datastore_viewer.DatastoreRequestHandler(request, response)
handler.render(
'datastore_viewer.html',
{'entities': mox.IgnoreArg(), # Tested with _get_entity_template_data.
'headers': mox.IgnoreArg(), # Tested with _get_entity_template_data.
'kind': 'Kind1',
'kinds': ['Kind1'],
'message': None,
'namespace': '',
'num_pages': 1,
'order': None,
'order_base_url': '/datastore?kind=Kind1',
'page': 1,
'paging_base_url': '/datastore?kind=Kind1',
'select_namespace_url': '/datastore?kind=Kind1&namespace=',
'show_namespace': False,
'start': 0,
'total_entities': 1})
self.mox.ReplayAll()
handler.get()
self.mox.VerifyAll()
def test_order_request(self):
entity = datastore.Entity('Kind1', id=123, _app=self.app_id)
entity['intprop'] = 1
entity['listprop'] = [7, 8, 9]
datastore.Put(entity)
request = webapp2.Request.blank(
'/datastore?kind=Kind1&order=intprop')
response = webapp2.Response()
handler = datastore_viewer.DatastoreRequestHandler(request, response)
handler.render(
'datastore_viewer.html',
{'entities': mox.IgnoreArg(), # Tested with _get_entity_template_data.
'headers': mox.IgnoreArg(), # Tested with _get_entity_template_data.
'kind': 'Kind1',
'kinds': ['Kind1'],
'message': None,
'namespace': '',
'num_pages': 1,
'order': 'intprop',
'order_base_url': '/datastore?kind=Kind1',
'page': 1,
'paging_base_url': '/datastore?kind=Kind1&order=intprop',
'select_namespace_url':
'/datastore?kind=Kind1&namespace=&order=intprop',
'show_namespace': False,
'start': 0,
'total_entities': 1})
self.mox.ReplayAll()
handler.get()
self.mox.VerifyAll()
def test_namespace_request(self):
entity = datastore.Entity('Kind1',
id=123,
_app=self.app_id,
_namespace='google')
entity['intprop'] = 1
entity['listprop'] = [7, 8, 9]
datastore.Put(entity)
request = webapp2.Request.blank(
'/datastore?kind=Kind1&namespace=google')
response = webapp2.Response()
handler = datastore_viewer.DatastoreRequestHandler(request, response)
handler.render(
'datastore_viewer.html',
{'entities': mox.IgnoreArg(), # Tested with _get_entity_template_data.
'headers': mox.IgnoreArg(), # Tested with _get_entity_template_data.
'kind': 'Kind1',
'kinds': ['Kind1'],
'message': None,
'namespace': 'google',
'num_pages': 1,
'order': None,
'order_base_url': '/datastore?kind=Kind1&namespace=google',
'page': 1,
'paging_base_url': '/datastore?kind=Kind1&namespace=google',
'select_namespace_url':
'/datastore?kind=Kind1&namespace=google',
'show_namespace': True,
'start': 0,
'total_entities': 1})
self.mox.ReplayAll()
handler.get()
self.mox.VerifyAll()
def test_page_request(self):
for i in range(1000):
entity = datastore.Entity('Kind1', id=i+1, _app=self.app_id)
entity['intprop'] = i
datastore.Put(entity)
request = webapp2.Request.blank(
'/datastore?kind=Kind1&page=3')
response = webapp2.Response()
handler = datastore_viewer.DatastoreRequestHandler(request, response)
handler.render(
'datastore_viewer.html',
{'entities': mox.IgnoreArg(), # Tested with _get_entity_template_data.
'headers': mox.IgnoreArg(), # Tested with _get_entity_template_data.
'kind': 'Kind1',
'kinds': ['Kind1'],
'message': None,
'namespace': '',
'num_pages': 50,
'order': None,
'order_base_url': '/datastore?kind=Kind1&page=3',
'page': 3,
'paging_base_url': '/datastore?kind=Kind1',
'select_namespace_url':
'/datastore?kind=Kind1&namespace=&page=3',
'show_namespace': False,
'start': 40,
'total_entities': 1000})
self.mox.ReplayAll()
handler.get()
self.mox.VerifyAll()
class DatastoreEditRequestHandlerTest(unittest.TestCase):
"""Tests for DatastoreEditRequestHandler."""
def setUp(self):
self.app_id = 'myapp'
os.environ['APPLICATION_ID'] = self.app_id
# Use a consistent replication strategy so that the test can use queries
# to verify that an entity was written.
consistent_policy = datastore_stub_util.MasterSlaveConsistencyPolicy()
api_server.test_setup_stubs(
app_id=self.app_id,
datastore_consistency=consistent_policy)
self.mox = mox.Mox()
self.mox.StubOutWithMock(admin_request_handler.AdminRequestHandler,
'render')
self.entity1 = datastore.Entity('Kind1', id=123, _app=self.app_id)
self.entity1['intprop'] = 1
self.entity1['listprop'] = [7, 8, 9]
self.entity1['dateprop'] = datastore_types._OverflowDateTime(2**60)
datastore.Put(self.entity1)
self.entity2 = datastore.Entity('Kind1', id=124, _app=self.app_id)
self.entity2['stringprop'] = 'value2'
self.entity2['listprop'] = [4, 5, 6]
datastore.Put(self.entity2)
self.entity3 = datastore.Entity('Kind1', id=125, _app=self.app_id)
self.entity3['intprop'] = 3
self.entity3['listprop'] = [1, 2, 3]
datastore.Put(self.entity3)
self.entity4 = datastore.Entity('Kind1', id=126, _app=self.app_id)
self.entity4['intprop'] = 4
self.entity4['stringprop'] = 'value4'
self.entity4['listprop'] = [10, 11]
datastore.Put(self.entity4)
def tearDown(self):
self.mox.UnsetStubs()
def test_get_no_entity_key_string(self):
request = webapp2.Request.blank(
'/datastore/edit?kind=Kind1&next=http://next/')
response = webapp2.Response()
handler = datastore_viewer.DatastoreEditRequestHandler(request, response)
handler.render(
'datastore_edit.html',
{'fields': [('dateprop',
'overflowdatetime',
mox.Regex('^<input class="overflowdatetime".*'
'value="".*$')),
('intprop',
'int',
mox.Regex('^<input class="int".*value="".*$')),
('listprop', 'list', ''),
('stringprop',
'string',
mox.Regex('^<input class="string".*$'))],
'key': None,
'key_id': None,
'key_name': None,
'kind': 'Kind1',
'namespace': '',
'next': 'http://next/',
'parent_key': None,
'parent_key_string': None})
self.mox.ReplayAll()
handler.get()
self.mox.VerifyAll()
def test_get_no_entity_key_string_and_no_entities_in_namespace(self):
request = webapp2.Request.blank(
'/datastore/edit?kind=Kind1&namespace=cat&next=http://next/')
response = webapp2.Response()
handler = datastore_viewer.DatastoreEditRequestHandler(request, response)
self.mox.ReplayAll()
handler.get()
self.mox.VerifyAll()
self.assertEqual(302, response.status_int)
self.assertRegexpMatches(
response.location,
r'/datastore\?kind=Kind1&message=Cannot+.*&namespace=cat')
def test_get_entity_string(self):
request = webapp2.Request.blank(
'/datastore/edit/%s?next=http://next/' % self.entity1.key())
response = webapp2.Response()
handler = datastore_viewer.DatastoreEditRequestHandler(request, response)
handler.render(
'datastore_edit.html',
{'fields': [('dateprop',
'overflowdatetime',
mox.Regex('^<input class="overflowdatetime".*'
'value="1152921504606846976".*$')),
('intprop',
'int',
mox.Regex('^<input class="int".*value="1".*$')),
('listprop', 'list', mox.Regex(r'\[7L?, 8L?, 9L?\]'))],
'key': str(self.entity1.key()),
'key_id': 123,
'key_name': None,
'kind': 'Kind1',
'namespace': '',
'next': 'http://next/',
'parent_key': None,
'parent_key_string': None})
self.mox.ReplayAll()
handler.get(str(self.entity1.key()))
self.mox.VerifyAll()
def test_post_no_entity_key_string(self):
request = webapp2.Request.blank(
'/datastore/edit',
POST={'kind': 'Kind1',
'overflowdatetime|dateprop': '2009-12-24 23:59:59',
'int|intprop': '123',
'string|stringprop': 'Hello',
'next': 'http://redirect/'})
response = webapp2.Response()
handler = datastore_viewer.DatastoreEditRequestHandler(request, response)
self.mox.ReplayAll()
handler.post()
self.mox.VerifyAll()
self.assertEqual(302, response.status_int)
self.assertEqual('http://redirect/', response.location)
# Check that the entity was added.
query = datastore.Query('Kind1')
query.update({'dateprop': datetime.datetime(2009, 12, 24, 23, 59, 59),
'intprop': 123,
'stringprop': 'Hello'})
self.assertEquals(1, query.Count())
def test_post_entity_key_string(self):
request = webapp2.Request.blank(
'/datastore/edit/%s' % self.entity4.key(),
POST={'overflowdatetime|dateprop': str(2**60),
'int|intprop': '123',
'string|stringprop': '',
'next': 'http://redirect/'})
response = webapp2.Response()
handler = datastore_viewer.DatastoreEditRequestHandler(request, response)
self.mox.ReplayAll()
handler.post(str(self.entity4.key()))
self.mox.VerifyAll()
self.assertEqual(302, response.status_int)
self.assertEqual('http://redirect/', response.location)
# Check that the entity was updated.
entity = datastore.Get(self.entity4.key())
self.assertEqual(2**60, entity['dateprop'])
self.assertEqual(123, entity['intprop'])
self.assertEqual([10, 11], entity['listprop'])
self.assertNotIn('stringprop', entity)
if __name__ == '__main__':
unittest.main()
| 36.412736 | 79 | 0.620636 | 3,534 | 30,878 | 5.254952 | 0.098472 | 0.016423 | 0.025201 | 0.023262 | 0.814496 | 0.776856 | 0.74094 | 0.702762 | 0.681116 | 0.6564 | 0 | 0.029691 | 0.251733 | 30,878 | 847 | 80 | 36.455726 | 0.774075 | 0.130611 | 0 | 0.680315 | 0 | 0 | 0.147946 | 0.03517 | 0 | 0 | 0 | 0 | 0.07874 | 1 | 0.050394 | false | 0 | 0.022047 | 0 | 0.08189 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
b46ce4058a5bf748c57e3ba9d528ba42ef803d19 | 134 | py | Python | ENCODN/TOOLS/COMMUNICATION/TELECOM/MULTITAP/MULTITAP_FRAME.py | akshitadixit/ENCODN | 7b4ecaba10314f9f59f53e9b479016b21f8b632b | [
"RSA-MD"
] | 6 | 2020-10-07T13:09:38.000Z | 2021-01-16T17:16:51.000Z | ENCODN/TOOLS/COMMUNICATION/TELECOM/MULTITAP/MULTITAP_FRAME.py | akshitadixit/ENCODN | 7b4ecaba10314f9f59f53e9b479016b21f8b632b | [
"RSA-MD"
] | 27 | 2020-10-09T09:14:23.000Z | 2021-01-22T07:16:43.000Z | ENCODN/TOOLS/COMMUNICATION/TELECOM/MULTITAP/MULTITAP_FRAME.py | DSC-IIIT-Kalyani/ENCODN | 62752ac7e368b294ec9613a1f73cb3f1f7c878f5 | [
"RSA-MD"
] | 14 | 2020-10-07T14:25:59.000Z | 2021-02-21T16:54:37.000Z | from tkinter import *
from tkinter import ttk
def MULTITAP_FRAME(master=None):
s = ttk.Style(master)
s.theme_use('awdark')
| 16.75 | 33 | 0.708955 | 20 | 134 | 4.65 | 0.7 | 0.236559 | 0.365591 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.186567 | 134 | 7 | 34 | 19.142857 | 0.853211 | 0 | 0 | 0 | 0 | 0 | 0.047244 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0.4 | 0 | 0.6 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
b483759769b5498c5b4fed6a83f8f045f612faa4 | 288 | py | Python | mofa/analytics/models.py | BoxInABoxICT/BoxPlugin | ad351978faa37ab867a86d2f4023a2b3e5a2ce19 | [
"Apache-2.0"
] | null | null | null | mofa/analytics/models.py | BoxInABoxICT/BoxPlugin | ad351978faa37ab867a86d2f4023a2b3e5a2ce19 | [
"Apache-2.0"
] | null | null | null | mofa/analytics/models.py | BoxInABoxICT/BoxPlugin | ad351978faa37ab867a86d2f4023a2b3e5a2ce19 | [
"Apache-2.0"
] | null | null | null | # This program has been developed by students from the bachelor Computer Science at Utrecht University within the
# Software and Game project course
# ©Copyright Utrecht University Department of Information and Computing Sciences.
from django.db import models
# Create your models here.
| 41.142857 | 113 | 0.819444 | 41 | 288 | 5.780488 | 0.853659 | 0.14346 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.152778 | 288 | 6 | 114 | 48 | 0.967213 | 0.864583 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
81ee59b63d37ff57bce30e5d0a723a58eb69ebcd | 40,888 | py | Python | distil/utils/supervised_strategy_wrappers.py | SatyadevNtv/distil | c8c3489920a24537a849eb8446efc9c2e19ab193 | [
"MIT"
] | 1 | 2021-08-15T07:50:46.000Z | 2021-08-15T07:50:46.000Z | distil/utils/supervised_strategy_wrappers.py | chipsh/distil | c8c3489920a24537a849eb8446efc9c2e19ab193 | [
"MIT"
] | null | null | null | distil/utils/supervised_strategy_wrappers.py | chipsh/distil | c8c3489920a24537a849eb8446efc9c2e19ab193 | [
"MIT"
] | null | null | null | import apricot
import math
import numpy as np
import torch
from scipy.sparse import csr_matrix
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from torch.utils.data import Subset
from torch.utils.data.sampler import SubsetRandomSampler
from .calculate_class_budgets import calculate_class_budgets
from .gradmatch_solvers import OrthogonalMP_REG_Parallel, Fixed_Weight_Greedy_Parallel
class DataSelectionStrategy(object):
"""
Implementation of Data Selection Strategy class which serves as base class for other
dataselectionstrategies for general learning frameworks.
Parameters
----------
trainloader: class
Loading the training data using pytorch dataloader
valloader: class
Loading the validation data using pytorch dataloader
model: class
Model architecture used for training
num_classes: int
Number of target classes in the dataset
linear_layer: bool
If True, we use the last fc layer weights and biases gradients
If False, we use the last fc layer biases gradients
loss: class
PyTorch Loss function
"""
def __init__(self, trainloader, valloader, model, num_classes, linear_layer, loss, device):
"""
Constructer method
"""
self.trainloader = trainloader # assume its a sequential loader.
self.valloader = valloader
self.model = model
self.N_trn = len(trainloader.sampler)
self.N_val = len(valloader.sampler)
self.grads_per_elem = None
self.val_grads_per_elem = None
self.numSelected = 0
self.linear_layer = linear_layer
self.num_classes = num_classes
self.trn_lbls = None
self.val_lbls = None
self.loss = loss
self.device = device
def select(self, budget, model_params):
pass
def get_labels(self, valid=False):
for batch_idx, (inputs, targets) in enumerate(self.trainloader):
if batch_idx == 0:
self.trn_lbls = targets.view(-1, 1)
else:
self.trn_lbls = torch.cat((self.trn_lbls, targets.view(-1, 1)), dim=0)
self.trn_lbls = self.trn_lbls.view(-1)
if valid:
for batch_idx, (inputs, targets) in enumerate(self.valloader):
if batch_idx == 0:
self.val_lbls = targets.view(-1, 1)
else:
self.val_lbls = torch.cat((self.val_lbls, targets.view(-1, 1)), dim=0)
self.val_lbls = self.val_lbls.view(-1)
def compute_gradients(self, valid=False, batch=False, perClass=False):
"""
Computes the gradient of each element.
Here, the gradients are computed in a closed form using CrossEntropyLoss with reduction set to 'none'.
This is done by calculating the gradients in last layer through addition of softmax layer.
Using different loss functions, the way we calculate the gradients will change.
For LogisticLoss we measure the Mean Absolute Error(MAE) between the pairs of observations.
With reduction set to 'none', the loss is formulated as:
.. math::
\\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad
l_n = \\left| x_n - y_n \\right|,
where :math:`N` is the batch size.
For MSELoss, we measure the Mean Square Error(MSE) between the pairs of observations.
With reduction set to 'none', the loss is formulated as:
.. math::
\\ell(x, y) = L = \\{l_1,\\dots,l_N\\}^\\top, \\quad
l_n = \\left( x_n - y_n \\right)^2,
where :math:`N` is the batch size.
Parameters
----------
valid: bool
if True, the function also computes the validation gradients
batch: bool
if True, the function computes the gradients of each mini-batch
perClass: bool
if True, the function computes the gradients using perclass dataloaders
"""
if perClass:
embDim = self.model.get_embedding_dim()
for batch_idx, (inputs, targets) in enumerate(self.pctrainloader):
inputs, targets = inputs.to(self.device), targets.to(self.device, non_blocking=True)
if batch_idx == 0:
out, l1 = self.model(inputs, last=True, freeze=True)
loss = self.loss(out, targets).sum()
l0_grads = torch.autograd.grad(loss, out)[0]
if self.linear_layer:
l0_expand = torch.repeat_interleave(l0_grads, embDim, dim=1)
l1_grads = l0_expand * l1.repeat(1, self.num_classes)
if batch:
l0_grads = l0_grads.mean(dim=0).view(1, -1)
if self.linear_layer:
l1_grads = l1_grads.mean(dim=0).view(1, -1)
else:
out, l1 = self.model(inputs, last=True, freeze=True)
loss = self.loss(out, targets).sum()
batch_l0_grads = torch.autograd.grad(loss, out)[0]
if self.linear_layer:
batch_l0_expand = torch.repeat_interleave(batch_l0_grads, embDim, dim=1)
batch_l1_grads = batch_l0_expand * l1.repeat(1, self.num_classes)
if batch:
batch_l0_grads = batch_l0_grads.mean(dim=0).view(1, -1)
if self.linear_layer:
batch_l1_grads = batch_l1_grads.mean(dim=0).view(1, -1)
l0_grads = torch.cat((l0_grads, batch_l0_grads), dim=0)
if self.linear_layer:
l1_grads = torch.cat((l1_grads, batch_l1_grads), dim=0)
torch.cuda.empty_cache()
if self.linear_layer:
self.grads_per_elem = torch.cat((l0_grads, l1_grads), dim=1)
else:
self.grads_per_elem = l0_grads
if valid:
for batch_idx, (inputs, targets) in enumerate(self.pcvalloader):
inputs, targets = inputs.to(self.device), targets.to(self.device, non_blocking=True)
if batch_idx == 0:
out, l1 = self.model(inputs, last=True, freeze=True)
loss = self.loss(out, targets).sum()
l0_grads = torch.autograd.grad(loss, out)[0]
if self.linear_layer:
l0_expand = torch.repeat_interleave(l0_grads, embDim, dim=1)
l1_grads = l0_expand * l1.repeat(1, self.num_classes)
if batch:
l0_grads = l0_grads.mean(dim=0).view(1, -1)
if self.linear_layer:
l1_grads = l1_grads.mean(dim=0).view(1, -1)
else:
out, l1 = self.model(inputs, last=True, freeze=True)
loss = self.loss(out, targets).sum()
batch_l0_grads = torch.autograd.grad(loss, out)[0]
if self.linear_layer:
batch_l0_expand = torch.repeat_interleave(batch_l0_grads, embDim, dim=1)
batch_l1_grads = batch_l0_expand * l1.repeat(1, self.num_classes)
if batch:
batch_l0_grads = batch_l0_grads.mean(dim=0).view(1, -1)
if self.linear_layer:
batch_l1_grads = batch_l1_grads.mean(dim=0).view(1, -1)
l0_grads = torch.cat((l0_grads, batch_l0_grads), dim=0)
if self.linear_layer:
l1_grads = torch.cat((l1_grads, batch_l1_grads), dim=0)
torch.cuda.empty_cache()
if self.linear_layer:
self.val_grads_per_elem = torch.cat((l0_grads, l1_grads), dim=1)
else:
self.val_grads_per_elem = l0_grads
else:
embDim = self.model.get_embedding_dim()
for batch_idx, (inputs, targets) in enumerate(self.trainloader):
inputs, targets = inputs.to(self.device), targets.to(self.device, non_blocking=True)
if batch_idx == 0:
out, l1 = self.model(inputs, last=True, freeze=True)
loss = self.loss(out, targets).sum()
l0_grads = torch.autograd.grad(loss, out)[0]
if self.linear_layer:
l0_expand = torch.repeat_interleave(l0_grads, embDim, dim=1)
l1_grads = l0_expand * l1.repeat(1, self.num_classes)
if batch:
l0_grads = l0_grads.mean(dim=0).view(1, -1)
if self.linear_layer:
l1_grads = l1_grads.mean(dim=0).view(1, -1)
else:
out, l1 = self.model(inputs, last=True, freeze=True)
loss = self.loss(out, targets).sum()
batch_l0_grads = torch.autograd.grad(loss, out)[0]
if self.linear_layer:
batch_l0_expand = torch.repeat_interleave(batch_l0_grads, embDim, dim=1)
batch_l1_grads = batch_l0_expand * l1.repeat(1, self.num_classes)
if batch:
batch_l0_grads = batch_l0_grads.mean(dim=0).view(1, -1)
if self.linear_layer:
batch_l1_grads = batch_l1_grads.mean(dim=0).view(1, -1)
l0_grads = torch.cat((l0_grads, batch_l0_grads), dim=0)
if self.linear_layer:
l1_grads = torch.cat((l1_grads, batch_l1_grads), dim=0)
torch.cuda.empty_cache()
if self.linear_layer:
self.grads_per_elem = torch.cat((l0_grads, l1_grads), dim=1)
else:
self.grads_per_elem = l0_grads
if valid:
for batch_idx, (inputs, targets) in enumerate(self.valloader):
inputs, targets = inputs.to(self.device), targets.to(self.device, non_blocking=True)
if batch_idx == 0:
out, l1 = self.model(inputs, last=True, freeze=True)
loss = self.loss(out, targets).sum()
l0_grads = torch.autograd.grad(loss, out)[0]
if self.linear_layer:
l0_expand = torch.repeat_interleave(l0_grads, embDim, dim=1)
l1_grads = l0_expand * l1.repeat(1, self.num_classes)
if batch:
l0_grads = l0_grads.mean(dim=0).view(1, -1)
if self.linear_layer:
l1_grads = l1_grads.mean(dim=0).view(1, -1)
else:
out, l1 = self.model(inputs, last=True, freeze=True)
loss = self.loss(out, targets).sum()
batch_l0_grads = torch.autograd.grad(loss, out)[0]
if self.linear_layer:
batch_l0_expand = torch.repeat_interleave(batch_l0_grads, embDim, dim=1)
batch_l1_grads = batch_l0_expand * l1.repeat(1, self.num_classes)
if batch:
batch_l0_grads = batch_l0_grads.mean(dim=0).view(1, -1)
if self.linear_layer:
batch_l1_grads = batch_l1_grads.mean(dim=0).view(1, -1)
l0_grads = torch.cat((l0_grads, batch_l0_grads), dim=0)
if self.linear_layer:
l1_grads = torch.cat((l1_grads, batch_l1_grads), dim=0)
torch.cuda.empty_cache()
if self.linear_layer:
self.val_grads_per_elem = torch.cat((l0_grads, l1_grads), dim=1)
else:
self.val_grads_per_elem = l0_grads
def update_model(self, model_params):
"""
Update the models parameters
Parameters
----------
model_params: OrderedDict
Python dictionary object containing models parameters
"""
self.model.load_state_dict(model_params)
class OMPGradMatchStrategy(DataSelectionStrategy):
"""
Implementation of OMPGradMatch Strategy from the paper :footcite:`sivasubramanian2020gradmatch` for supervised learning frameworks.
OMPGradMatch strategy tries to solve the optimization problem given below:
.. math::
\\min_{\\mathbf{w}, S: |S| \\leq k} \\Vert \\sum_{i \\in S} w_i \\nabla_{\\theta}L_T^i(\\theta) - \\nabla_{\\theta}L(\\theta)\\Vert
In the above equation, :math:`\\mathbf{w}` denotes the weight vector that contains the weights for each data instance, :math:`\mathcal{U}` training set where :math:`(x^i, y^i)` denotes the :math:`i^{th}` training data point and label respectively,
:math:`L_T` denotes the training loss, :math:`L` denotes either training loss or validation loss depending on the parameter valid,
:math:`S` denotes the data subset selected at each round, and :math:`k` is the budget for the subset.
The above optimization problem is solved using the Orthogonal Matching Pursuit(OMP) algorithm.
Parameters
----------
trainloader: class
Loading the training data using pytorch DataLoader
valloader: class
Loading the validation data using pytorch DataLoader
model: class
Model architecture used for training
loss_type: class
The type of loss criterion
eta: float
Learning rate. Step size for the one step gradient update
device: str
The device being utilized - cpu | cuda
num_classes: int
The number of target classes in the dataset
linear_layer: bool
Apply linear transformation to the data
selection_type: str
Type of selection -
- 'PerClass': PerClass method is where OMP algorithm is applied on each class data points seperately.
- 'PerBatch': PerBatch method is where OMP algorithm is applied on each minibatch data points.
- 'PerClassPerGradient': PerClassPerGradient method is same as PerClass but we use the gradient corresponding to classification layer of that class only.
valid : bool, optional
If valid==True we use validation dataset gradient sum in OMP otherwise we use training dataset (default: False)
lam : float
Regularization constant of OMP solver
eps : float
Epsilon parameter to which the above optimization problem is solved using OMP algorithm
"""
def __init__(self, trainloader, valloader, model, loss_type,
eta, device, num_classes, linear_layer, selection_type, valid=True, lam=0, eps=1e-4, r=1):
"""
Constructor method
"""
super().__init__(trainloader, valloader, model, num_classes, linear_layer, loss_type, device)
self.loss_type = loss_type
self.eta = eta # step size for the one step gradient update
self.device = device
self.init_out = list()
self.init_l1 = list()
self.selection_type = selection_type
self.valid = valid
self.lam = lam
self.eps = eps
def ompwrapper(self, X, Y, bud):
reg = OrthogonalMP_REG_Parallel(X, Y, nnz=bud,
positive=True, lam=self.lam,
tol=self.eps, device=self.device)
ind = torch.nonzero(reg).view(-1)
return ind.tolist(), reg[ind].tolist()
def select(self, budget, model_params):
"""
Apply OMP Algorithm for data selection
Parameters
----------
budget: int
The number of data points to be selected
model_params: OrderedDict
Python dictionary object containing models parameters
Returns
----------
idxs: list
List containing indices of the best datapoints,
gammas: weights tensors
Tensor containing weights of each instance
"""
self.update_model(model_params)
if self.selection_type == 'PerClass':
self.get_labels(valid=self.valid)
idxs = []
gammas = []
# Calculate the class budgets to be used.
class_budgets = calculate_class_budgets(budget, self.num_classes, self.trn_lbls, self.N_trn)
for i in range(self.num_classes):
if class_budgets[i] == 0:
print("SKIPPING CLASS", i, "AS THERE IS NO BUDGET")
continue
trn_subset_idx = torch.where(self.trn_lbls == i)[0].tolist()
trn_data_sub = Subset(self.trainloader.dataset, trn_subset_idx)
self.pctrainloader = DataLoader(trn_data_sub, batch_size=self.trainloader.batch_size,
shuffle=False, pin_memory=True)
if self.valid:
val_subset_idx = torch.where(self.val_lbls == i)[0].tolist()
val_data_sub = Subset(self.valloader.dataset, val_subset_idx)
self.pcvalloader = DataLoader(val_data_sub, batch_size=self.trainloader.batch_size,
shuffle=False, pin_memory=True)
self.compute_gradients(self.valid, batch=False, perClass=True)
trn_gradients = self.grads_per_elem
if self.valid:
sum_val_grad = torch.sum(self.val_grads_per_elem, dim=0)
else:
sum_val_grad = torch.sum(trn_gradients, dim=0)
idxs_temp, gammas_temp = self.ompwrapper(torch.transpose(trn_gradients, 0, 1),
sum_val_grad, class_budgets[i])
idxs.extend(list(np.array(trn_subset_idx)[idxs_temp]))
gammas.extend(gammas_temp)
elif self.selection_type == 'PerBatch':
self.compute_gradients(self.valid, batch=True, perClass=False)
idxs = []
gammas = []
trn_gradients = self.grads_per_elem
if self.valid:
sum_val_grad = torch.sum(self.val_grads_per_elem, dim=0)
else:
sum_val_grad = torch.sum(trn_gradients, dim=0)
idxs_temp, gammas_temp = self.ompwrapper(torch.transpose(trn_gradients, 0, 1),
sum_val_grad, math.ceil(budget/self.trainloader.batch_size))
batch_wise_indices = list(self.trainloader.batch_sampler)
for i in range(len(idxs_temp)):
tmp = batch_wise_indices[idxs_temp[i]]
idxs.extend(tmp)
gammas.extend(list(gammas_temp[i] * np.ones(len(tmp))))
diff = budget - len(idxs)
if diff > 0:
remainList = set(np.arange(self.N_trn)).difference(set(idxs))
new_idxs = np.random.choice(list(remainList), size=diff, replace=False)
idxs.extend(new_idxs)
gammas.extend([1 for _ in range(diff)])
idxs = np.array(idxs)
gammas = np.array(gammas)
if self.selection_type in ["PerClass"]:
rand_indices = np.random.permutation(len(idxs))
idxs = list(np.array(idxs)[rand_indices])
gammas = list(np.array(gammas)[rand_indices])
return idxs, gammas
class FixedWeightGradMatchStrategy(DataSelectionStrategy):
"""
Implementation of OMPGradMatch Strategy from the paper :footcite:`sivasubramanian2020gradmatch` for supervised learning frameworks.
OMPGradMatch strategy tries to solve the optimization problem given below:
.. math::
\\min_{\\mathbf{w}, S: |S| \\leq k} \\Vert \\sum_{i \\in S} w_i \\nabla_{\\theta}L_T^i(\\theta) - \\nabla_{\\theta}L(\\theta)\\Vert
In the above equation, :math:`\\mathbf{w}` denotes the weight vector that contains the weights for each data instance, :math:`\mathcal{U}` training set where :math:`(x^i, y^i)` denotes the :math:`i^{th}` training data point and label respectively,
:math:`L_T` denotes the training loss, :math:`L` denotes either training loss or validation loss depending on the parameter valid,
:math:`S` denotes the data subset selected at each round, and :math:`k` is the budget for the subset.
The above optimization problem is solved using the Orthogonal Matching Pursuit(OMP) algorithm.
Parameters
----------
trainloader: class
Loading the training data using pytorch DataLoader
valloader: class
Loading the validation data using pytorch DataLoader
model: class
Model architecture used for training
loss_type: class
The type of loss criterion
eta: float
Learning rate. Step size for the one step gradient update
device: str
The device being utilized - cpu | cuda
num_classes: int
The number of target classes in the dataset
linear_layer: bool
Apply linear transformation to the data
selection_type: str
Type of selection -
- 'PerClass': PerClass method is where OMP algorithm is applied on each class data points seperately.
- 'PerBatch': PerBatch method is where OMP algorithm is applied on each minibatch data points.
- 'PerClassPerGradient': PerClassPerGradient method is same as PerClass but we use the gradient corresponding to classification layer of that class only.
valid : bool, optional
If valid==True we use validation dataset gradient sum in OMP otherwise we use training dataset (default: False)
lam : float
Regularization constant of OMP solver
eps : float
Epsilon parameter to which the above optimization problem is solved using OMP algorithm
"""
def __init__(self, trainloader, valloader, model, loss_type,
eta, device, num_classes, linear_layer, selection_type, valid=True, r=1):
"""
Constructor method
"""
super().__init__(trainloader, valloader, model, num_classes, linear_layer, loss_type, device)
self.loss_type = loss_type
self.eta = eta # step size for the one step gradient update
self.device = device
self.init_out = list()
self.init_l1 = list()
self.selection_type = selection_type
self.valid = valid
def fixed_weight_wrapper(self, X, Y, val_set_size, bud):
reg = Fixed_Weight_Greedy_Parallel(X, Y, val_set_size, nnz=bud, device=self.device)
ind = torch.nonzero(reg).view(-1)
return ind.tolist()
def select(self, budget, model_params):
"""
Apply OMP Algorithm for data selection
Parameters
----------
budget: int
The number of data points to be selected
model_params: OrderedDict
Python dictionary object containing models parameters
Returns
----------
idxs: list
List containing indices of the best datapoints,
gammas: weights tensors
Tensor containing weights of each instance
"""
self.update_model(model_params)
if self.selection_type == 'PerClass':
self.get_labels(valid=self.valid)
idxs = []
# Calculate the class budgets to be used.
class_budgets = calculate_class_budgets(budget, self.num_classes, self.trn_lbls, self.N_trn)
for i in range(self.num_classes):
if class_budgets[i] == 0:
print("SKIPPING CLASS", i, "AS THERE IS NO BUDGET")
continue
trn_subset_idx = torch.where(self.trn_lbls == i)[0].tolist()
trn_data_sub = Subset(self.trainloader.dataset, trn_subset_idx)
self.pctrainloader = DataLoader(trn_data_sub, batch_size=self.trainloader.batch_size,
shuffle=False, pin_memory=True)
if self.valid:
val_subset_idx = torch.where(self.val_lbls == i)[0].tolist()
val_data_sub = Subset(self.valloader.dataset, val_subset_idx)
self.pcvalloader = DataLoader(val_data_sub, batch_size=self.trainloader.batch_size,
shuffle=False, pin_memory=True)
self.compute_gradients(self.valid, batch=False, perClass=True)
trn_gradients = self.grads_per_elem
if self.valid:
val_set_size = self.val_grads_per_elem.shape[0]
sum_val_grad = torch.sum(self.val_grads_per_elem, dim=0)
else:
val_set_size = self.trn_gradients.shape[0]
sum_val_grad = torch.sum(trn_gradients, dim=0)
idxs_temp = self.fixed_weight_wrapper(torch.transpose(trn_gradients, 0, 1),
sum_val_grad, val_set_size, class_budgets[i])
idxs.extend(list(np.array(trn_subset_idx)[idxs_temp]))
elif self.selection_type == 'PerBatch':
self.compute_gradients(self.valid, batch=True, perClass=False)
idxs = []
trn_gradients = self.grads_per_elem
if self.valid:
val_set_size = self.val_grads_per_elem.shape[0]
sum_val_grad = torch.sum(self.val_grads_per_elem, dim=0)
else:
val_set_size = self.trn_gradients.shape[0]
sum_val_grad = torch.sum(trn_gradients, dim=0)
idxs_temp = self.fixed_weight_wrapper(torch.transpose(trn_gradients, 0, 1),
sum_val_grad, val_set_size, math.ceil(budget/self.trainloader.batch_size))
batch_wise_indices = list(self.trainloader.batch_sampler)
for i in range(len(idxs_temp)):
tmp = batch_wise_indices[idxs_temp[i]]
idxs.extend(tmp)
# Account for the labeled set weights/indices by adding labeled_set_size to budget.
diff = budget - len(idxs)
if diff > 0:
remainList = set(np.arange(self.N_trn)).difference(set(idxs))
new_idxs = np.random.choice(list(remainList), size=diff, replace=False)
idxs.extend(new_idxs)
idxs = np.array(idxs)
if self.selection_type in ["PerClass"]:
rand_indices = np.random.permutation(len(idxs))
idxs = list(np.array(idxs)[rand_indices])
return idxs
class CRAIGStrategy(DataSelectionStrategy):
"""
Implementation of CRAIG Strategy from the paper :footcite:`mirzasoleiman2020coresets` for supervised learning frameworks.
CRAIG strategy tries to solve the optimization problem given below for convex loss functions:
.. math::
\\sum_{i\\in \\mathcal{U}} \\min_{j \\in S, |S| \\leq k} \\| x^i - x^j \\|
In the above equation, :math:`\\mathcal{U}` denotes the training set where :math:`(x^i, y^i)` denotes the :math:`i^{th}` training data point and label respectively,
:math:`L_T` denotes the training loss, :math:`S` denotes the data subset selected at each round, and :math:`k` is the budget for the subset.
Since, the above optimization problem is not dependent on model parameters, we run the subset selection only once right before the start of the training.
CRAIG strategy tries to solve the optimization problem given below for non-convex loss functions:
.. math::
\\sum_{i\\in \\mathcal{U}} \\min_{j \\in S, |S| \\leq k} \\| \\nabla_{\\theta} {L_T}^i(\\theta) - \\nabla_{\\theta} {L_T}^j(\\theta) \\|
In the above equation, :math:`\\mathcal{U}` denotes the training set, :math:`L_T` denotes the training loss, :math:`S` denotes the data subset selected at each round,
and :math:`k` is the budget for the subset. In this case, CRAIG acts an adaptive subset selection strategy that selects a new subset every epoch.
Both the optimization problems given above are an instance of facility location problems which is a submodular function. Hence, it can be optimally solved using greedy selection methods.
Parameters
----------
trainloader: class
Loading the training data using pytorch DataLoader
valloader: class
Loading the validation data using pytorch DataLoader
model: class
Model architecture used for training
loss_type: class
The type of loss criterion
device: str
The device being utilized - cpu | cuda
num_classes: int
The number of target classes in the dataset
linear_layer: bool
Apply linear transformation to the data
if_convex: bool
If convex or not
selection_type: str
Type of selection:
- 'PerClass': PerClass Implementation where the facility location problem is solved for each class seperately for speed ups.
- 'Supervised': Supervised Implementation where the facility location problem is solved using a sparse similarity matrix by assigning the similarity of a point with other points of different class to zero.
"""
def __init__(self, trainloader, valloader, model, loss,
device, num_classes, linear_layer, if_convex, selection_type, optimizer='lazy'):
"""
Constructer method
"""
super().__init__(trainloader, valloader, model, num_classes, linear_layer, loss, device)
self.if_convex = if_convex
self.selection_type = selection_type
self.optimizer = optimizer
def distance(self, x, y, exp=2):
"""
Compute the distance.
Parameters
----------
x: Tensor
First input tensor
y: Tensor
Second input tensor
exp: float, optional
The exponent value (default: 2)
Returns
----------
dist: Tensor
Output tensor
"""
n = x.size(0)
m = y.size(0)
d = x.size(1)
x = x.unsqueeze(1).expand(n, m, d)
y = y.unsqueeze(0).expand(n, m, d)
dist = torch.pow(x - y, exp).sum(2)
# dist = torch.exp(-1 * torch.pow(x - y, 2).sum(2))
return dist
def compute_score(self, model_params, idxs):
"""
Compute the score of the indices.
Parameters
----------
model_params: OrderedDict
Python dictionary object containing models parameters
idxs: list
The indices
"""
trainset = self.trainloader.sampler.data_source
subset_loader = torch.utils.data.DataLoader(trainset, batch_size=self.trainloader.batch_size, shuffle=False,
sampler=SubsetRandomSampler(idxs),
pin_memory=True)
self.model.load_state_dict(model_params)
self.N = 0
g_is = []
if self.if_convex:
for batch_idx, (inputs, targets) in enumerate(subset_loader):
inputs, targets = inputs, targets
if self.selection_type == 'PerBatch':
self.N += 1
g_is.append(inputs.view(inputs.size()[0], -1).mean(dim=0).view(1, -1))
else:
self.N += inputs.size()[0]
g_is.append(inputs.view(inputs.size()[0], -1))
else:
embDim = self.model.get_embedding_dim()
for batch_idx, (inputs, targets) in enumerate(subset_loader):
inputs, targets = inputs.to(self.device), targets.to(self.device, non_blocking=True)
if self.selection_type == 'PerBatch':
self.N += 1
else:
self.N += inputs.size()[0]
out, l1 = self.model(inputs, freeze=True, last=True)
loss = self.loss(out, targets).sum()
l0_grads = torch.autograd.grad(loss, out)[0]
if self.linear_layer:
l0_expand = torch.repeat_interleave(l0_grads, embDim, dim=1)
l1_grads = l0_expand * l1.repeat(1, self.num_classes)
if self.selection_type == 'PerBatch':
g_is.append(torch.cat((l0_grads, l1_grads), dim=1).mean(dim=0).view(1, -1))
else:
g_is.append(torch.cat((l0_grads, l1_grads), dim=1))
else:
if self.selection_type == 'PerBatch':
g_is.append(l0_grads.mean(dim=0).view(1, -1))
else:
g_is.append(l0_grads)
self.dist_mat = torch.zeros([self.N, self.N], dtype=torch.float32)
first_i = True
if self.selection_type == 'PerBatch':
g_is = torch.cat(g_is, dim=0)
self.dist_mat = self.distance(g_is, g_is).cpu()
else:
for i, g_i in enumerate(g_is, 0):
if first_i:
size_b = g_i.size(0)
first_i = False
for j, g_j in enumerate(g_is, 0):
self.dist_mat[i * size_b: i * size_b + g_i.size(0),
j * size_b: j * size_b + g_j.size(0)] = self.distance(g_i, g_j).cpu()
self.const = torch.max(self.dist_mat).item()
self.dist_mat = (self.const - self.dist_mat).numpy()
def compute_gamma(self, idxs):
"""
Compute the gamma values for the indices.
Parameters
----------
idxs: list
The indices
Returns
----------
gamma: list
Gradient values of the input indices
"""
if self.selection_type in ['PerClass', 'PerBatch']:
gamma = [0 for i in range(len(idxs))]
best = self.dist_mat[idxs] # .to(self.device)
rep = np.argmax(best, axis=0)
for i in rep:
gamma[i] += 1
elif self.selection_type == 'Supervised':
gamma = [0 for i in range(len(idxs))]
best = self.dist_mat[idxs] # .to(self.device)
rep = np.argmax(best, axis=0)
for i in range(rep.shape[1]):
gamma[rep[0, i]] += 1
return gamma
def get_similarity_kernel(self):
"""
Obtain the similarity kernel.
Returns
----------
kernel: ndarray
Array of kernel values
"""
for batch_idx, (inputs, targets) in enumerate(self.trainloader):
if batch_idx == 0:
labels = targets
else:
tmp_target_i = targets
labels = torch.cat((labels, tmp_target_i), dim=0)
kernel = np.zeros((labels.shape[0], labels.shape[0]))
for target in np.unique(labels):
x = np.where(labels == target)[0]
# prod = np.transpose([np.tile(x, len(x)), np.repeat(x, len(x))])
for i in x:
kernel[i, x] = 1
return kernel
def select(self, budget, model_params):
"""
Data selection method using different submodular optimization
functions.
Parameters
----------
budget: int
The number of data points to be selected
model_params: OrderedDict
Python dictionary object containing models parameters
optimizer: str
The optimization approach for data selection. Must be one of
'random', 'modular', 'naive', 'lazy', 'approximate-lazy', 'two-stage',
'stochastic', 'sample', 'greedi', 'bidirectional'
Returns
----------
total_greedy_list: list
List containing indices of the best datapoints
gammas: list
List containing gradients of datapoints present in greedySet
"""
for batch_idx, (inputs, targets) in enumerate(self.trainloader):
if batch_idx == 0:
labels = targets
else:
tmp_target_i = targets
labels = torch.cat((labels, tmp_target_i), dim=0)
# per_class_bud = int(budget / self.num_classes)
total_greedy_list = []
gammas = []
if self.selection_type == 'PerClass':
self.get_labels(False)
class_budgets = calculate_class_budgets(budget, self.num_classes, self.trn_lbls, self.N_trn)
for i in range(self.num_classes):
idxs = torch.where(labels == i)[0]
self.compute_score(model_params, idxs)
fl = apricot.functions.facilityLocation.FacilityLocationSelection(random_state=0, metric='precomputed',
n_samples=class_budgets[i],
optimizer=self.optimizer)
sim_sub = fl.fit_transform(self.dist_mat)
greedyList = list(np.argmax(sim_sub, axis=1))
gamma = self.compute_gamma(greedyList)
total_greedy_list.extend(idxs[greedyList])
gammas.extend(gamma)
rand_indices = np.random.permutation(len(total_greedy_list))
total_greedy_list = list(np.array(total_greedy_list)[rand_indices])
gammas = list(np.array(gammas)[rand_indices])
elif self.selection_type == 'Supervised':
for i in range(self.num_classes):
if i == 0:
idxs = torch.where(labels == i)[0]
N = len(idxs)
self.compute_score(model_params, idxs)
row = idxs.repeat_interleave(N)
col = idxs.repeat(N)
data = self.dist_mat.flatten()
else:
idxs = torch.where(labels == i)[0]
N = len(idxs)
self.compute_score(model_params, idxs)
row = torch.cat((row, idxs.repeat_interleave(N)), dim=0)
col = torch.cat((col, idxs.repeat(N)), dim=0)
data = np.concatenate([data, self.dist_mat.flatten()], axis=0)
sparse_simmat = csr_matrix((data, (row.numpy(), col.numpy())), shape=(self.N_trn, self.N_trn))
self.dist_mat = sparse_simmat
fl = apricot.functions.facilityLocation.FacilityLocationSelection(random_state=0, metric='precomputed',
n_samples=budget, optimizer=self.optimizer)
sim_sub = fl.fit_transform(sparse_simmat)
total_greedy_list = list(np.array(np.argmax(sim_sub, axis=1)).reshape(-1))
gammas = self.compute_gamma(total_greedy_list)
elif self.selection_type == 'PerBatch':
idxs = torch.arange(self.N_trn)
N = len(idxs)
self.compute_score(model_params, idxs)
fl = apricot.functions.facilityLocation.FacilityLocationSelection(random_state=0, metric='precomputed',
n_samples=math.ceil(
budget / self.trainloader.batch_size),
optimizer=self.optimizer)
sim_sub = fl.fit_transform(self.dist_mat)
temp_list = list(np.array(np.argmax(sim_sub, axis=1)).reshape(-1))
gammas_temp = self.compute_gamma(temp_list)
batch_wise_indices = list(self.trainloader.batch_sampler)
for i in range(len(temp_list)):
tmp = batch_wise_indices[temp_list[i]]
total_greedy_list.extend(tmp)
gammas.extend(list(gammas_temp[i] * np.ones(len(tmp))))
return total_greedy_list, gammas
# Define a GradMatch Active Select handler
class SupervisedSelectHandler(Dataset):
def __init__(self, wrapped_handler):
self.wrapped_handler = wrapped_handler
def __getitem__(self, index):
if self.wrapped_handler.select == False:
x, y, index = self.wrapped_handler.__getitem__(index)
return x, y
else:
x, index = self.wrapped_handler.__getitem__(index)
return x
def __len__(self):
return len(self.wrapped_handler.X) | 48.047004 | 251 | 0.571953 | 4,920 | 40,888 | 4.585366 | 0.09248 | 0.017996 | 0.017287 | 0.018839 | 0.785284 | 0.759663 | 0.746055 | 0.730009 | 0.700177 | 0.686658 | 0 | 0.013476 | 0.335746 | 40,888 | 851 | 252 | 48.047004 | 0.817158 | 0.273259 | 0 | 0.680556 | 0 | 0 | 0.008813 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.039683 | false | 0.001984 | 0.021825 | 0.001984 | 0.093254 | 0.003968 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
81f2361369290c75c442f5053463a34e2c368be1 | 82 | py | Python | scripts/field/autogen_enter_underbase.py | hsienjan/SideQuest-Server | 3e88debaf45615b759d999255908f99a15283695 | [
"MIT"
] | null | null | null | scripts/field/autogen_enter_underbase.py | hsienjan/SideQuest-Server | 3e88debaf45615b759d999255908f99a15283695 | [
"MIT"
] | null | null | null | scripts/field/autogen_enter_underbase.py | hsienjan/SideQuest-Server | 3e88debaf45615b759d999255908f99a15283695 | [
"MIT"
] | null | null | null | # Character field ID when accessed: 310010000
# ObjectID: 0
# ParentID: 310010000
| 20.5 | 45 | 0.768293 | 10 | 82 | 6.3 | 0.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.275362 | 0.158537 | 82 | 3 | 46 | 27.333333 | 0.637681 | 0.914634 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
c326f5061236613808252d90975b07496261d4e4 | 415 | py | Python | Python/804_Unique_Morse_Code_Words.py | ImedAdel/LeetCode | e994b783de9b6167a6ec6c261dcafc8c0753bd54 | [
"Unlicense"
] | 3 | 2019-03-27T22:14:45.000Z | 2020-01-07T14:43:27.000Z | Python/804_Unique_Morse_Code_Words.py | ImedAdel/LeetCode | e994b783de9b6167a6ec6c261dcafc8c0753bd54 | [
"Unlicense"
] | null | null | null | Python/804_Unique_Morse_Code_Words.py | ImedAdel/LeetCode | e994b783de9b6167a6ec6c261dcafc8c0753bd54 | [
"Unlicense"
] | null | null | null | class Solution:
def uniqueMorseRepresentations(self, words):
"""
:type words: List[str]
:rtype: int
"""
MORSE = [".-","-...","-.-.","-..",".","..-.","--.","....","..",".---","-.-",".-..","--","-.","---",".--.","--.-",".-.","...","-","..-","...-",".--","-..-","-.--","--.."]
return len({"".join(MORSE[ord(c) - ord("a")] for c in word) for word in words})
| 41.5 | 177 | 0.313253 | 29 | 415 | 4.482759 | 0.724138 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.212048 | 415 | 9 | 178 | 46.111111 | 0.397554 | 0.081928 | 0 | 0 | 0 | 0 | 0.237822 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
c33466a1adaa540eaaa6035d0ba27980b696591b | 54 | py | Python | venv/Lib/site-packages/mpl_toolkits/axes_grid/axisline_style.py | arnoyu-hub/COMP0016miemie | 59af664dcf190eab4f93cefb8471908717415fea | [
"MIT"
] | 353 | 2020-12-10T10:47:17.000Z | 2022-03-31T23:08:29.000Z | venv/Lib/site-packages/mpl_toolkits/axes_grid/axisline_style.py | arnoyu-hub/COMP0016miemie | 59af664dcf190eab4f93cefb8471908717415fea | [
"MIT"
] | 80 | 2020-12-10T09:54:22.000Z | 2022-03-30T22:08:45.000Z | venv/Lib/site-packages/mpl_toolkits/axes_grid/axisline_style.py | arnoyu-hub/COMP0016miemie | 59af664dcf190eab4f93cefb8471908717415fea | [
"MIT"
] | 63 | 2020-12-10T17:10:34.000Z | 2022-03-28T16:27:07.000Z | from mpl_toolkits.axisartist.axisline_style import *
| 27 | 53 | 0.851852 | 7 | 54 | 6.285714 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.092593 | 54 | 1 | 54 | 54 | 0.897959 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
c36b2a769a9111e5b6b638a208bd3b546bd118e1 | 20 | py | Python | example_project/some_modules/third_modules/a70.py | Yuriy-Leonov/cython_imports_limit_issue | 2f9e7c02798fb52185dabfe6ce3811c439ca2839 | [
"MIT"
] | null | null | null | example_project/some_modules/third_modules/a70.py | Yuriy-Leonov/cython_imports_limit_issue | 2f9e7c02798fb52185dabfe6ce3811c439ca2839 | [
"MIT"
] | null | null | null | example_project/some_modules/third_modules/a70.py | Yuriy-Leonov/cython_imports_limit_issue | 2f9e7c02798fb52185dabfe6ce3811c439ca2839 | [
"MIT"
] | null | null | null | class A70:
pass
| 6.666667 | 10 | 0.6 | 3 | 20 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 0.35 | 20 | 2 | 11 | 10 | 0.769231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
5ee7906ad27079beff89d024b06423ecd3a5a4a4 | 33 | py | Python | python/testData/codeInsight/controlflow/MatchStatementSingleClauseWildcardPattern.py | 06needhamt/intellij-community | 63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b | [
"Apache-2.0"
] | null | null | null | python/testData/codeInsight/controlflow/MatchStatementSingleClauseWildcardPattern.py | 06needhamt/intellij-community | 63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b | [
"Apache-2.0"
] | null | null | null | python/testData/codeInsight/controlflow/MatchStatementSingleClauseWildcardPattern.py | 06needhamt/intellij-community | 63d7b8030e4fdefeb4760e511e289f7e6b3a5c5b | [
"Apache-2.0"
] | null | null | null | match 42:
case _:
y
z | 8.25 | 11 | 0.424242 | 5 | 33 | 2.6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 0.515152 | 33 | 4 | 12 | 8.25 | 0.6875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
5eefc1919bb78626980cbbc4bb831a82e357fde0 | 149 | py | Python | lab4_sln/text_recognizer/datasets/__init__.py | sergeyktest/fsdl-text-recognizer-project | 0a35a2cb5b55cfcc1cde661b305c5c635272657c | [
"MIT"
] | 44 | 2018-08-01T07:39:37.000Z | 2021-10-31T10:11:54.000Z | lab4_sln/text_recognizer/datasets/__init__.py | sanzgiri/fsdl-text-recognizer-project | 1bf7b8655f36149e3c1fcd7726be120d6b0e7b63 | [
"MIT"
] | 1 | 2018-08-03T19:38:09.000Z | 2018-08-03T19:38:09.000Z | lab4_sln/text_recognizer/datasets/__init__.py | sanzgiri/fsdl-text-recognizer-project | 1bf7b8655f36149e3c1fcd7726be120d6b0e7b63 | [
"MIT"
] | 182 | 2018-08-01T01:50:40.000Z | 2022-03-25T19:45:36.000Z | from .emnist import EmnistDataset
##### Hide lines below until Lab 2
from .emnist_lines import EmnistLinesDataset
##### Hide lines above until Lab 2
| 29.8 | 44 | 0.771812 | 21 | 149 | 5.428571 | 0.571429 | 0.175439 | 0.157895 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.015748 | 0.147651 | 149 | 4 | 45 | 37.25 | 0.88189 | 0.38255 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
6f069586a51de5f50e98fb9a22b5eb26f2ec80ba | 136 | py | Python | pype/hosts/tvpaint/api/plugin.py | simonebarbieri/pype | a6dc83aa1300738749cbe8e5e2e6d2d1794e0289 | [
"MIT"
] | null | null | null | pype/hosts/tvpaint/api/plugin.py | simonebarbieri/pype | a6dc83aa1300738749cbe8e5e2e6d2d1794e0289 | [
"MIT"
] | null | null | null | pype/hosts/tvpaint/api/plugin.py | simonebarbieri/pype | a6dc83aa1300738749cbe8e5e2e6d2d1794e0289 | [
"MIT"
] | null | null | null | from pype.api import PypeCreatorMixin
from avalon.tvpaint import pipeline
class Creator(PypeCreatorMixin, pipeline.Creator):
pass
| 19.428571 | 50 | 0.816176 | 16 | 136 | 6.9375 | 0.6875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.132353 | 136 | 6 | 51 | 22.666667 | 0.940678 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.25 | 0.5 | 0 | 0.75 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 5 |
6f2f0a89c7c024c7b03714a92e95e5f9e0b12dbc | 140 | py | Python | profiles_api/admin.py | Quzeem/profiles-rest-api | 9f01af2ca3896f92c54fa15e9da4aa38bdbeb29c | [
"MIT"
] | null | null | null | profiles_api/admin.py | Quzeem/profiles-rest-api | 9f01af2ca3896f92c54fa15e9da4aa38bdbeb29c | [
"MIT"
] | null | null | null | profiles_api/admin.py | Quzeem/profiles-rest-api | 9f01af2ca3896f92c54fa15e9da4aa38bdbeb29c | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import User, UserProfileFeed
admin.site.register(User)
admin.site.register(UserProfileFeed)
| 20 | 41 | 0.828571 | 18 | 140 | 6.444444 | 0.555556 | 0.155172 | 0.293103 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.092857 | 140 | 6 | 42 | 23.333333 | 0.913386 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
6f358571f29290a2c4ba20ce08a90af836fb50f0 | 1,820 | py | Python | yt/run_test.py | nikicc/anaconda-recipes | 9c611a5854bf41bbc5e7ed9853dc71c0851a62ef | [
"BSD-3-Clause"
] | 130 | 2015-07-28T03:41:21.000Z | 2022-03-16T03:07:41.000Z | yt/run_test.py | nikicc/anaconda-recipes | 9c611a5854bf41bbc5e7ed9853dc71c0851a62ef | [
"BSD-3-Clause"
] | 119 | 2015-08-01T00:54:06.000Z | 2021-01-05T13:00:46.000Z | yt/run_test.py | nikicc/anaconda-recipes | 9c611a5854bf41bbc5e7ed9853dc71c0851a62ef | [
"BSD-3-Clause"
] | 72 | 2015-07-29T02:35:56.000Z | 2022-02-26T14:31:15.000Z | import yt.analysis_modules.halo_finding.fof.EnzoFOF
import yt.geometry.grid_visitors
import yt.utilities.lib.primitives
import yt.analysis_modules.halo_finding.hop.EnzoHop
import yt.utilities.spatial._distance_wrap
import yt.analysis_modules.ppv_cube.ppv_utils
import yt.analysis_modules.photon_simulator.utils
import yt.utilities.lib.origami
import yt.utilities.lib.line_integral_convolution
import yt.utilities.lib.image_utilities
import yt.utilities.lib.bitarray
import yt.utilities.lib.mesh_utilities
import yt.utilities.lib.write_array
import yt.utilities.lib.interpolators
import yt.utilities.lib.partitioned_grid
import yt.utilities.lib.marching_cubes
import yt.utilities.lib.element_mappings
import yt.utilities.lib.points_in_volume
import yt.utilities.lib.ray_integrators
import yt.utilities.lib.fortran_reader
import yt.utilities.lib.depth_first_octree
import yt.utilities.lib.basic_octree
import yt.utilities.lib.particle_mesh_operations
import yt.utilities.lib.quad_tree
import yt.utilities.lib.lenses
import yt.utilities.lib.geometry_utils
import yt.utilities.spatial.ckdtree
import yt.geometry.oct_visitors
import yt.geometry.fake_octree
import yt.utilities.lib.bounding_volume_hierarchy
import yt.utilities.lib.mesh_triangulation
import yt.utilities.lib.alt_ray_tracers
import yt.utilities.lib.grid_traversal
import yt.geometry.particle_oct_container
import yt.utilities.lib.pixelization_routines
import yt.utilities.lib.ragged_arrays
import yt.geometry.particle_deposit
import yt.geometry.grid_container
import yt.geometry.particle_smooth
import yt.utilities.lib.image_samplers
import yt.utilities.lib.contour_finding
import yt.utilities.lib.amr_kdtools
import yt.geometry.oct_container
import yt.utilities.lib.misc_utilities
import yt.frontends.artio._artio_caller
import yt.geometry.selection_routines
| 38.723404 | 51 | 0.877473 | 275 | 1,820 | 5.610909 | 0.290909 | 0.238496 | 0.35256 | 0.388853 | 0.2372 | 0.085548 | 0 | 0 | 0 | 0 | 0 | 0 | 0.050549 | 1,820 | 46 | 52 | 39.565217 | 0.89294 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
6f4a31dfef6492842a20bf2e9c7c1d7418107c59 | 436 | py | Python | gnosis/safe/__init__.py | b1u3h4t/gnosis-py | 880f72c2ece80fc5884a6441402a820095412cd6 | [
"MIT"
] | 1 | 2021-10-21T06:43:52.000Z | 2021-10-21T06:43:52.000Z | gnosis/safe/__init__.py | HonzaDajc/gnosis-py | a2ab705f98479c4e46ca2b225a4bc75a773d69a7 | [
"MIT"
] | null | null | null | gnosis/safe/__init__.py | HonzaDajc/gnosis-py | a2ab705f98479c4e46ca2b225a4bc75a773d69a7 | [
"MIT"
] | 2 | 2021-07-14T10:02:16.000Z | 2021-08-02T22:04:41.000Z | # flake8: noqa F401
from .exceptions import (CannotEstimateGas, CouldNotPayGasWithEther,
InvalidChecksumAddress, InvalidInternalTx,
InvalidMultisigTx, InvalidPaymentToken,
InvalidSignaturesProvided, SafeServiceException,
SignatureNotProvidedByOwner)
from .proxy_factory import ProxyFactory
from .safe import Safe, SafeOperation, SafeTx
| 48.444444 | 73 | 0.672018 | 26 | 436 | 11.230769 | 0.807692 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.012903 | 0.288991 | 436 | 8 | 74 | 54.5 | 0.929032 | 0.038991 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.428571 | 0 | 0.428571 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
6f731779efd9c8f1d076b1d5eb89af11330a412f | 3,466 | py | Python | python/fate_flow/tests/api_tests/data_access_test.py | hubert-he/FATE | 6758e150bd7ca7d6f788f9a7a8c8aea7e6500363 | [
"Apache-2.0"
] | 3,787 | 2019-08-30T04:55:10.000Z | 2022-03-31T23:30:07.000Z | python/fate_flow/tests/api_tests/data_access_test.py | hubert-he/FATE | 6758e150bd7ca7d6f788f9a7a8c8aea7e6500363 | [
"Apache-2.0"
] | 1,439 | 2019-08-29T16:35:52.000Z | 2022-03-31T11:55:31.000Z | python/fate_flow/tests/api_tests/data_access_test.py | hubert-he/FATE | 6758e150bd7ca7d6f788f9a7a8c8aea7e6500363 | [
"Apache-2.0"
] | 1,179 | 2019-08-29T16:18:32.000Z | 2022-03-31T12:55:38.000Z | import os
import time
import unittest
import requests
from fate_arch.common import file_utils, conf_utils
from fate_flow.settings import HTTP_PORT, API_VERSION, WORK_MODE, FATEFLOW_SERVICE_NAME
from fate_flow.entity.types import JobStatus
class TestDataAccess(unittest.TestCase):
def setUp(self):
self.data_dir = os.path.join(file_utils.get_project_base_directory(), "examples", "data")
self.upload_guest_config = {"file": os.path.join(self.data_dir, "breast_hetero_guest.csv"), "head": 1,
"partition": 10, "work_mode": WORK_MODE, "namespace": "experiment",
"table_name": "breast_hetero_guest", "use_local_data": 0, 'drop': 1, 'backend': 0, "id_delimiter": ',',}
self.upload_host_config = {"file": os.path.join(self.data_dir, "breast_hetero_host.csv"), "head": 1,
"partition": 10, "work_mode": WORK_MODE, "namespace": "experiment",
"table_name": "breast_hetero_host", "use_local_data": 0, 'drop': 1, 'backend': 0, "id_delimiter": ',',}
self.download_config = {"output_path": os.path.join(file_utils.get_project_base_directory(),
"fate_flow/fate_flow_unittest_breast_b.csv"),
"work_mode": WORK_MODE, "namespace": "experiment",
"table_name": "breast_hetero_guest"}
ip = conf_utils.get_base_config(FATEFLOW_SERVICE_NAME).get("host")
self.server_url = "http://{}:{}/{}".format(ip, HTTP_PORT, API_VERSION)
def test_upload_guest(self):
response = requests.post("/".join([self.server_url, 'data', 'upload']), json=self.upload_guest_config)
self.assertTrue(response.status_code in [200, 201])
self.assertTrue(int(response.json()['retcode']) == 0)
job_id = response.json()['jobId']
for i in range(60):
response = requests.post("/".join([self.server_url, 'job', 'query']), json={'job_id': job_id})
self.assertTrue(int(response.json()['retcode']) == 0)
if response.json()['data'][0]['f_status'] == JobStatus.SUCCESS:
break
time.sleep(1)
def test_upload_host(self):
response = requests.post("/".join([self.server_url, 'data', 'upload']), json=self.upload_host_config)
self.assertTrue(response.status_code in [200, 201])
self.assertTrue(int(response.json()['retcode']) == 0)
job_id = response.json()['jobId']
for i in range(60):
response = requests.post("/".join([self.server_url, 'job', 'query']), json={'job_id': job_id})
self.assertTrue(int(response.json()['retcode']) == 0)
if response.json()['data'][0]['f_status'] == JobStatus.SUCCESS:
break
time.sleep(1)
def test_upload_history(self):
response = requests.post("/".join([self.server_url, 'data', 'upload/history']), json={'limit': 2})
self.assertTrue(response.status_code in [200, 201])
self.assertTrue(int(response.json()['retcode']) == 0)
def test_download(self):
response = requests.post("/".join([self.server_url, 'data', 'download']), json=self.download_config)
self.assertTrue(response.status_code in [200, 201])
self.assertTrue(int(response.json()['retcode']) == 0)
if __name__ == '__main__':
unittest.main()
| 51.731343 | 140 | 0.60502 | 419 | 3,466 | 4.75895 | 0.231504 | 0.070211 | 0.045637 | 0.072217 | 0.714142 | 0.714142 | 0.714142 | 0.714142 | 0.713139 | 0.648445 | 0 | 0.019245 | 0.23543 | 3,466 | 66 | 141 | 52.515152 | 0.733208 | 0 | 0 | 0.444444 | 0 | 0 | 0.167725 | 0.024827 | 0 | 0 | 0 | 0 | 0.185185 | 1 | 0.092593 | false | 0 | 0.12963 | 0 | 0.240741 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
48b9c757a1e4bc4c9fc0e566d769d6dbce352261 | 202 | py | Python | scrapy/contrib/linkextractors/__init__.py | h4ck3rm1k3/scrapy | 59dcdbe84769c9d204f552a2b545b1e096a2d42c | [
"BSD-3-Clause"
] | 26 | 2015-02-07T17:35:26.000Z | 2020-04-27T21:11:00.000Z | scrapy/contrib/linkextractors/__init__.py | h4ck3rm1k3/scrapy | 59dcdbe84769c9d204f552a2b545b1e096a2d42c | [
"BSD-3-Clause"
] | 15 | 2015-01-12T02:28:23.000Z | 2015-02-03T03:41:07.000Z | scrapy/contrib/linkextractors/__init__.py | h4ck3rm1k3/scrapy | 59dcdbe84769c9d204f552a2b545b1e096a2d42c | [
"BSD-3-Clause"
] | 9 | 2015-09-21T08:17:20.000Z | 2021-02-07T02:31:36.000Z | """
scrapy.contrib.linkextractors
This package contains a collection of Link Extractors.
For more info see docs/topics/link-extractors.rst
"""
from .lxmlhtml import LxmlLinkExtractor as LinkExtractor
| 22.444444 | 56 | 0.811881 | 26 | 202 | 6.307692 | 0.923077 | 0.170732 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.118812 | 202 | 8 | 57 | 25.25 | 0.921348 | 0.673267 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
48bdb2df02928db7877a4ccfddb4bbc0d3330739 | 20,256 | py | Python | tests/test_string.py | nickhodaly/assertpy | f3faf0539b7bd536904494dd050b4853963f2bd2 | [
"BSD-3-Clause"
] | null | null | null | tests/test_string.py | nickhodaly/assertpy | f3faf0539b7bd536904494dd050b4853963f2bd2 | [
"BSD-3-Clause"
] | null | null | null | tests/test_string.py | nickhodaly/assertpy | f3faf0539b7bd536904494dd050b4853963f2bd2 | [
"BSD-3-Clause"
] | null | null | null | # Copyright (c) 2015-2019, Activision Publishing, Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from grxtestassert import assert_that, fail
import sys
if sys.version_info[0] == 3:
unicode = str
else:
unicode = unicode
def test_is_length():
assert_that('foo').is_length(3)
def test_is_length_failure():
try:
assert_that('foo').is_length(4)
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo> to be of length <4>, but was <3>.')
def test_contains():
assert_that('foo').contains('f')
assert_that('foo').contains('o')
assert_that('foo').contains('fo', 'o')
assert_that('fred').contains('d')
assert_that('fred').contains('fr', 'e', 'd')
def test_contains_single_item_failure():
try:
assert_that('foo').contains('x')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo> to contain item <x>, but did not.')
def test_contains_multi_item_failure():
try:
assert_that('foo').contains('f', 'x', 'z')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to("Expected <foo> to contain items <'f', 'x', 'z'>, but did not contain <'x', 'z'>.")
def test_contains_multi_item_single_failure():
try:
assert_that('foo').contains('f', 'o', 'x')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to("Expected <foo> to contain items <'f', 'o', 'x'>, but did not contain <x>.")
def test_contains_ignoring_case():
assert_that('foo').contains_ignoring_case('f')
assert_that('foo').contains_ignoring_case('F')
assert_that('foo').contains_ignoring_case('Oo')
assert_that('foo').contains_ignoring_case('f', 'o', 'F', 'O', 'Fo', 'Oo', 'FoO')
def test_contains_ignoring_case_type_failure():
try:
assert_that(123).contains_ignoring_case('f')
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string or iterable')
def test_contains_ignoring_case_missinge_item_failure():
try:
assert_that('foo').contains_ignoring_case()
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('one or more args must be given')
def test_contains_ignoring_case_single_item_failure():
try:
assert_that('foo').contains_ignoring_case('X')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo> to case-insensitive contain item <X>, but did not.')
def test_contains_ignoring_case_single_item_type_failure():
try:
assert_that('foo').contains_ignoring_case(12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given arg must be a string')
def test_contains_ignoring_case_multi_item_failure():
try:
assert_that('foo').contains_ignoring_case('F', 'X', 'Z')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to("Expected <foo> to case-insensitive contain items <'F', 'X', 'Z'>, but did not contain <'X', 'Z'>.")
def test_contains_ignoring_case_multi_item_type_failure():
try:
assert_that('foo').contains_ignoring_case('F', 12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given args must all be strings')
def test_contains_ignoring_case_list():
assert_that(['foo']).contains_ignoring_case('Foo')
assert_that(['foo', 'bar', 'baz']).contains_ignoring_case('Foo')
assert_that(['foo', 'bar', 'baz']).contains_ignoring_case('Foo', 'bAr')
assert_that(['foo', 'bar', 'baz']).contains_ignoring_case('Foo', 'bAr', 'baZ')
def test_contains_ignoring_case_list_elem_type_failure():
try:
assert_that([123]).contains_ignoring_case('f')
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val items must all be strings')
def test_contains_ignoring_case_list_multi_elem_type_failure():
try:
assert_that(['foo', 123]).contains_ignoring_case('f')
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val items must all be strings')
def test_contains_ignoring_case_list_missinge_item_failure():
try:
assert_that(['foo']).contains_ignoring_case()
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('one or more args must be given')
def test_contains_ignoring_case_list_single_item_failure():
try:
assert_that(['foo']).contains_ignoring_case('X')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to("Expected <['foo']> to case-insensitive contain items <X>, but did not contain <X>.")
def test_contains_ignoring_case_list_single_item_type_failure():
try:
assert_that(['foo']).contains_ignoring_case(12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given args must all be strings')
def test_contains_ignoring_case_list_multi_item_failure():
try:
assert_that(['foo', 'bar']).contains_ignoring_case('Foo', 'X', 'Y')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to("Expected <['foo', 'bar']> to case-insensitive contain items <'Foo', 'X', 'Y'>, but did not contain <'X', 'Y'>.")
def test_contains_ignoring_case_list_multi_item_type_failure():
try:
assert_that(['foo', 'bar']).contains_ignoring_case('F', 12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given args must all be strings')
def test_does_not_contain():
assert_that('foo').does_not_contain('x')
assert_that('foo').does_not_contain('x', 'y')
def test_does_not_contain_single_item_failure():
try:
assert_that('foo').does_not_contain('f')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo> to not contain item <f>, but did.')
def test_does_not_contain_list_item_failure():
try:
assert_that('foo').does_not_contain('x', 'y', 'f')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to("Expected <foo> to not contain items <'x', 'y', 'f'>, but did contain <f>.")
def test_does_not_contain_list_multi_item_failure():
try:
assert_that('foo').does_not_contain('x', 'f', 'o')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to("Expected <foo> to not contain items <'x', 'f', 'o'>, but did contain <'f', 'o'>.")
def test_is_empty():
assert_that('').is_empty()
def test_is_empty_failure():
try:
assert_that('foo').is_empty()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo> to be empty string, but was not.')
def test_is_not_empty():
assert_that('foo').is_not_empty()
def test_is_not_empty_failure():
try:
assert_that('').is_not_empty()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected not empty string, but was empty.')
def test_is_equal_ignoring_case():
assert_that('FOO').is_equal_to_ignoring_case('foo')
assert_that('foo').is_equal_to_ignoring_case('FOO')
assert_that('fOO').is_equal_to_ignoring_case('foo')
def test_is_equal_ignoring_case_failure():
try:
assert_that('foo').is_equal_to_ignoring_case('bar')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo> to be case-insensitive equal to <bar>, but was not.')
def test_is_equal_ignoring_case_bad_value_type_failure():
try:
assert_that(123).is_equal_to_ignoring_case(12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string')
def test_is_equal_ignoring_case_bad_arg_type_failure():
try:
assert_that('fred').is_equal_to_ignoring_case(12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given arg must be a string')
def test_starts_with():
assert_that('fred').starts_with('f')
assert_that('fred').starts_with('fr')
assert_that('fred').starts_with('fred')
def test_starts_with_failure():
try:
assert_that('fred').starts_with('bar')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <fred> to start with <bar>, but did not.')
def test_starts_with_bad_value_type_failure():
try:
assert_that(123).starts_with(12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string or iterable')
def test_starts_with_bad_arg_none_failure():
try:
assert_that('fred').starts_with(None)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given prefix arg must not be none')
def test_starts_with_bad_arg_type_failure():
try:
assert_that('fred').starts_with(123)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given prefix arg must be a string')
def test_starts_with_bad_arg_empty_failure():
try:
assert_that('fred').starts_with('')
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('given prefix arg must not be empty')
def test_ends_with():
assert_that('fred').ends_with('d')
assert_that('fred').ends_with('ed')
assert_that('fred').ends_with('fred')
def test_ends_with_failure():
try:
assert_that('fred').ends_with('bar')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <fred> to end with <bar>, but did not.')
def test_ends_with_bad_value_type_failure():
try:
assert_that(123).ends_with(12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string or iterable')
def test_ends_with_bad_arg_none_failure():
try:
assert_that('fred').ends_with(None)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given suffix arg must not be none')
def test_ends_with_bad_arg_type_failure():
try:
assert_that('fred').ends_with(123)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given suffix arg must be a string')
def test_ends_with_bad_arg_empty_failure():
try:
assert_that('fred').ends_with('')
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('given suffix arg must not be empty')
def test_matches():
assert_that('fred').matches(r'\w')
assert_that('fred').matches(r'\w{2}')
assert_that('fred').matches(r'\w+')
assert_that('fred').matches(r'^\w{4}$')
assert_that('fred').matches(r'^.*?$')
assert_that('123-456-7890').matches(r'\d{3}-\d{3}-\d{4}')
def test_matches_failure():
try:
assert_that('fred').matches(r'\d+')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <fred> to match pattern <\\d+>, but did not.')
def test_matches_bad_value_type_failure():
try:
assert_that(123).matches(12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string')
def test_matches_bad_arg_type_failure():
try:
assert_that('fred').matches(123)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given pattern arg must be a string')
def test_matches_bad_arg_empty_failure():
try:
assert_that('fred').matches('')
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('given pattern arg must not be empty')
def test_does_not_match():
assert_that('fred').does_not_match(r'\d+')
assert_that('fred').does_not_match(r'\w{5}')
assert_that('123-456-7890').does_not_match(r'^\d+$')
def test_does_not_match_failure():
try:
assert_that('fred').does_not_match(r'\w+')
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <fred> to not match pattern <\\w+>, but did.')
def test_does_not_match_bad_value_type_failure():
try:
assert_that(123).does_not_match(12)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string')
def test_does_not_match_bad_arg_type_failure():
try:
assert_that('fred').does_not_match(123)
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('given pattern arg must be a string')
def test_does_not_match_bad_arg_empty_failure():
try:
assert_that('fred').does_not_match('')
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('given pattern arg must not be empty')
def test_is_alpha():
assert_that('foo').is_alpha()
def test_is_alpha_digit_failure():
try:
assert_that('foo123').is_alpha()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo123> to contain only alphabetic chars, but did not.')
def test_is_alpha_space_failure():
try:
assert_that('foo bar').is_alpha()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo bar> to contain only alphabetic chars, but did not.')
def test_is_alpha_punctuation_failure():
try:
assert_that('foo,bar').is_alpha()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo,bar> to contain only alphabetic chars, but did not.')
def test_is_alpha_bad_value_type_failure():
try:
assert_that(123).is_alpha()
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string')
def test_is_alpha_empty_value_failure():
try:
assert_that('').is_alpha()
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('val is empty')
def test_is_digit():
assert_that('123').is_digit()
def test_is_digit_alpha_failure():
try:
assert_that('foo123').is_digit()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo123> to contain only digits, but did not.')
def test_is_digit_space_failure():
try:
assert_that('1 000 000').is_digit()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <1 000 000> to contain only digits, but did not.')
def test_is_digit_punctuation_failure():
try:
assert_that('-123').is_digit()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <-123> to contain only digits, but did not.')
def test_is_digit_bad_value_type_failure():
try:
assert_that(123).is_digit()
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string')
def test_is_digit_empty_value_failure():
try:
assert_that('').is_digit()
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('val is empty')
def test_is_lower():
assert_that('foo').is_lower()
assert_that('foo 123').is_lower()
assert_that('123 456').is_lower()
def test_is_lower_failure():
try:
assert_that('FOO').is_lower()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <FOO> to contain only lowercase chars, but did not.')
def test_is_lower_bad_value_type_failure():
try:
assert_that(123).is_lower()
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string')
def test_is_lower_empty_value_failure():
try:
assert_that('').is_lower()
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('val is empty')
def test_is_upper():
assert_that('FOO').is_upper()
assert_that('FOO 123').is_upper()
assert_that('123 456').is_upper()
def test_is_upper_failure():
try:
assert_that('foo').is_upper()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <foo> to contain only uppercase chars, but did not.')
def test_is_upper_bad_value_type_failure():
try:
assert_that(123).is_upper()
fail('should have raised error')
except TypeError as ex:
assert_that(str(ex)).is_equal_to('val is not a string')
def test_is_upper_empty_value_failure():
try:
assert_that('').is_upper()
fail('should have raised error')
except ValueError as ex:
assert_that(str(ex)).is_equal_to('val is empty')
def test_is_unicode():
assert_that(unicode('unicorn')).is_unicode()
assert_that(unicode('unicorn 123')).is_unicode()
assert_that(unicode('unicorn')).is_unicode()
def test_is_unicode_failure():
try:
assert_that(123).is_unicode()
fail('should have raised error')
except AssertionError as ex:
assert_that(str(ex)).is_equal_to('Expected <123> to be unicode, but was <int>.')
def test_chaining():
assert_that('foo').is_type_of(str).is_length(3).contains('f').does_not_contain('x')
assert_that('fred').starts_with('f').ends_with('d').matches(r'^f.*?d$').does_not_match(r'\d')
| 32.670968 | 154 | 0.683501 | 3,011 | 20,256 | 4.329791 | 0.077051 | 0.130398 | 0.045563 | 0.092046 | 0.852037 | 0.793741 | 0.747488 | 0.702232 | 0.65015 | 0.592468 | 0 | 0.010627 | 0.191647 | 20,256 | 619 | 155 | 32.723748 | 0.785575 | 0.073904 | 0 | 0.482679 | 0 | 0.013857 | 0.244141 | 0 | 0 | 0 | 0 | 0 | 0.454965 | 1 | 0.180139 | false | 0 | 0.004619 | 0 | 0.184758 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
48db4763ae16317f3e900c64e4bf6a6c526a8fb7 | 52 | py | Python | orientacao_objetos_exercicios/exercicio6/excecoes.py | montalvas/python | 483c2097f6f91bfae127dafcb63e3006eeecad1d | [
"MIT"
] | null | null | null | orientacao_objetos_exercicios/exercicio6/excecoes.py | montalvas/python | 483c2097f6f91bfae127dafcb63e3006eeecad1d | [
"MIT"
] | null | null | null | orientacao_objetos_exercicios/exercicio6/excecoes.py | montalvas/python | 483c2097f6f91bfae127dafcb63e3006eeecad1d | [
"MIT"
] | null | null | null | class SaldoInsuficienteError(RuntimeError):
pass | 26 | 43 | 0.826923 | 4 | 52 | 10.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115385 | 52 | 2 | 44 | 26 | 0.934783 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
48fee7dfa558aba13a389127b2e590d720e5faf5 | 17 | py | Python | dp.py | hackerhoursla/tech_track | cd1d2253bf8c8cf353d4a736c9477c845c99b6a0 | [
"MIT"
] | null | null | null | dp.py | hackerhoursla/tech_track | cd1d2253bf8c8cf353d4a736c9477c845c99b6a0 | [
"MIT"
] | 1 | 2021-03-24T03:45:31.000Z | 2021-03-24T03:45:31.000Z | dp.py | hackerhoursla/tech_track | cd1d2253bf8c8cf353d4a736c9477c845c99b6a0 | [
"MIT"
] | 1 | 2021-03-24T01:49:53.000Z | 2021-03-24T01:49:53.000Z | # new code for DP | 17 | 17 | 0.705882 | 4 | 17 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.235294 | 17 | 1 | 17 | 17 | 0.923077 | 0.882353 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
d286adc560b580293705c9932136a12e504323c7 | 194 | py | Python | thread-renderer/src/configloader/__init__.py | FToovvr/adnmb-quests-tools | eb3c594cb94ff803edde4705ab67e8de060c7efb | [
"MIT"
] | null | null | null | thread-renderer/src/configloader/__init__.py | FToovvr/adnmb-quests-tools | eb3c594cb94ff803edde4705ab67e8de060c7efb | [
"MIT"
] | null | null | null | thread-renderer/src/configloader/__init__.py | FToovvr/adnmb-quests-tools | eb3c594cb94ff803edde4705ab67e8de060c7efb | [
"MIT"
] | null | null | null | from .configloader import DivisionsConfiguration, DivisionRule, DivisionType
from .matchrule import MatchRule, MatchUntil, MatchOnly, Collect, Include
from .postrules import PostRules, PostRule
| 48.5 | 76 | 0.850515 | 19 | 194 | 8.684211 | 0.684211 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.097938 | 194 | 3 | 77 | 64.666667 | 0.942857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
d287541177e48fc345d3ce790784c7e244626f44 | 141 | py | Python | src/keep_awake.py | b83s/notion | e30dfbd1e02827b4109b510ee84b60567384ecaa | [
"MIT"
] | null | null | null | src/keep_awake.py | b83s/notion | e30dfbd1e02827b4109b510ee84b60567384ecaa | [
"MIT"
] | 1 | 2021-01-28T23:56:06.000Z | 2021-01-28T23:56:06.000Z | src/keep_awake.py | b83s/notion | e30dfbd1e02827b4109b510ee84b60567384ecaa | [
"MIT"
] | 1 | 2021-01-28T23:55:33.000Z | 2021-01-28T23:55:33.000Z | #!/usr/bin/env -S PATH="${PATH}:/usr/local/bin" python3
import requests
def ping():
requests.get('https://notion-gijs.herokuapp.com/') | 20.142857 | 55 | 0.673759 | 21 | 141 | 4.52381 | 0.809524 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007937 | 0.106383 | 141 | 7 | 56 | 20.142857 | 0.746032 | 0.382979 | 0 | 0 | 0 | 0 | 0.390805 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
9609cd5839855878aab54be0b97234b8c1f4719f | 96 | py | Python | py_tdlib/constructors/message_chat_change_photo.py | Mr-TelegramBot/python-tdlib | 2e2d21a742ebcd439971a32357f2d0abd0ce61eb | [
"MIT"
] | 24 | 2018-10-05T13:04:30.000Z | 2020-05-12T08:45:34.000Z | py_tdlib/constructors/message_chat_change_photo.py | MrMahdi313/python-tdlib | 2e2d21a742ebcd439971a32357f2d0abd0ce61eb | [
"MIT"
] | 3 | 2019-06-26T07:20:20.000Z | 2021-05-24T13:06:56.000Z | py_tdlib/constructors/message_chat_change_photo.py | MrMahdi313/python-tdlib | 2e2d21a742ebcd439971a32357f2d0abd0ce61eb | [
"MIT"
] | 5 | 2018-10-05T14:29:28.000Z | 2020-08-11T15:04:10.000Z | from ..factory import Type
class messageChatChangePhoto(Type):
photo = None # type: "photo"
| 16 | 35 | 0.729167 | 11 | 96 | 6.363636 | 0.727273 | 0.257143 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 96 | 5 | 36 | 19.2 | 0.875 | 0.135417 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.333333 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
824a1a4be14d271ccf912c62273cd57eaa78d742 | 30 | py | Python | src/examples/VRP/__init__.py | calebebrim/GeneticAlgorithm | 93475adfac4bba145054e1bbb3acfad77505fa85 | [
"MIT"
] | null | null | null | src/examples/VRP/__init__.py | calebebrim/GeneticAlgorithm | 93475adfac4bba145054e1bbb3acfad77505fa85 | [
"MIT"
] | null | null | null | src/examples/VRP/__init__.py | calebebrim/GeneticAlgorithm | 93475adfac4bba145054e1bbb3acfad77505fa85 | [
"MIT"
] | null | null | null | from ..utils import binary_ops | 30 | 30 | 0.833333 | 5 | 30 | 4.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 30 | 1 | 30 | 30 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
825ca5f45cb8bb15009ea73ebb9eaf52650cb9b4 | 10,531 | py | Python | classifier/classifier.py | jamesdunham/cso-classifier | dad7eb53ce1e4608222358686bfd2ee45eac50be | [
"Apache-2.0"
] | 1 | 2019-07-30T13:30:49.000Z | 2019-07-30T13:30:49.000Z | classifier/classifier.py | jamesdunham/cso-classifier | dad7eb53ce1e4608222358686bfd2ee45eac50be | [
"Apache-2.0"
] | null | null | null | classifier/classifier.py | jamesdunham/cso-classifier | dad7eb53ce1e4608222358686bfd2ee45eac50be | [
"Apache-2.0"
] | null | null | null | import math
from functools import partial
from multiprocessing.pool import Pool
from classifier import misc
from classifier.semanticmodule import CSOClassifierSemantic as sema
from classifier.syntacticmodule import CSOClassifierSyntactic as synt
def run_cso_classifier(paper, modules="both", enhancement="first"):
"""Run the CSO Classifier.
It takes as input the text from abstract, title, and keywords of a research paper and outputs a list of relevant
concepts from CSO.
This function requires the paper (please note, one single paper, no batch mode) and few flags:
(i) modules, determines whether to run only the syntactic module, or the semantic module, or both;
(ii) enhancement, controls whether the classifier should infer super-topics, i.e., their first direct
super-topics or the whole set of topics up until root.
Args:
paper (dictionary): contains the metadata of the paper, e.g., title, abstract and keywords {"title": "",
"abstract": "","keywords": ""}.
modules (string): either "syntactic", "semantic" or "both" to determine which modules to use when
classifying. "syntactic" enables only the syntactic module. "semantic" enables only the semantic module.
Finally, with "both" the classifier takes advantage of both the syntactic and semantic modules. Default =
"both".
enhancement (string): either "first", "all" or "no". With "first" the CSO classifier returns only the topics
one level above. With "all" it returns all topics above the resulting topics. With "no" the CSO Classifier
does not provide any enhancement.
Returns:
fcso (dictionary): contains the CSO Ontology.
fmodel (dictionary): contains a cache of the model, i.e., each token is linked to the corresponding CSO topic.
"""
if modules not in ["syntactic", "semantic", "both"]:
raise ValueError("Error: Field modules must be 'syntactic', 'semantic' or 'both'")
if enhancement not in ["first", "all", "no"]:
raise ValueError("Error: Field enhances must be 'first', 'all' or 'no'")
# Loading ontology and model
cso, model = misc.load_ontology_and_chached_model()
# Passing parameters to the two classes (synt and sema)
synt_module = synt(cso, paper)
sema_module = sema(model, cso, paper)
# initializing variable that will contain output
class_res = dict()
class_res["syntactic"] = list()
class_res["semantic"] = list()
class_res["union"] = list()
class_res["enhanced"] = list()
if modules == 'syntactic' or modules == 'both':
class_res["syntactic"] = synt_module.classify_syntactic()
if modules == 'semantic' or modules == 'both':
class_res["semantic"] = sema_module.classify_semantic()
union = list(set(class_res["syntactic"] + class_res["semantic"]))
class_res["union"] = union
if enhancement == 'first':
enhanced = misc.climb_ontology(cso, union, "first")
class_res["enhanced"] = [x for x in enhanced if x not in union]
elif enhancement == 'all':
enhanced = misc.climb_ontology(cso, union, "all")
class_res["enhanced"] = [x for x in enhanced if x not in union]
elif enhancement == 'no':
pass
return class_res
def run_cso_classifier_batch_mode(papers, workers=1, modules="both", enhancement="first"):
"""Run the CSO Classifier in *BATCH MODE* and with multiprocessing.
It takes as input a set of papers, which include abstract, title, and keywords and for each one of them returns a
list of relevant concepts from CSO. This function requires a dictionary of papers, with each id corresponding to
the metadata of a paper, and few flags: (i) modules, determines whether to run only the syntactic module,
or the semantic module, or both; (ii) enhancement, controls whether the classifier should infer super-topics,
i.e., their first direct super-topics or the whole set of topics up until root.
Args:
papers (dictionary): contains the metadata of the papers, e.g., for each paper, there is title, abstract and
keywords {"id1":{"title": "","abstract": "","keywords": ""},"id2":{"title": "","abstract": "","keywords": ""}}.
workers (integer): number of workers. If 1 is in single thread, otherwise multithreaded
modules (string): either "syntactic", "semantic" or "both" to determine which modules to use when
classifying. "syntactic" enables only the syntactic module. "semantic" enables only the semantic module.
Finally, with "both" the classifier takes advantage of both the syntactic and semantic modules. Default =
"both".
enhancement (string): either "first", "all" or "no". With "first" the CSO classifier returns only the topics
one level above. With "all" it returns all topics above the resulting topics. With "no" the CSO Classifier
does not provide any enhancement.
Returns:
fcso (dictionary): contains the CSO Ontology.
fmodel (dictionary): contains a cache of the model, i.e., each token is linked to the corresponding CSO topic.
"""
if modules not in ["syntactic", "semantic", "both"]:
raise ValueError("Error: Field modules must be 'syntactic', 'semantic' or 'both'")
if enhancement not in ["first", "all", "no"]:
raise ValueError("Error: Field enhances must be 'first', 'all' or 'no'")
if workers < 1:
raise ValueError("Error: Number of workers must be equal or greater than 1")
if type(workers) != int:
raise ValueError("Error: Number of workers must be integer")
size_of_corpus = len(papers)
chunk_size = math.ceil(size_of_corpus / workers)
papers_list = list(misc.chunks(papers, chunk_size))
annotate = partial(run_cso_classifier_batch_model_single_worker, modules=modules, enhancement=enhancement)
with Pool(workers) as p:
result = p.map(annotate, papers_list)
class_res = {k: v for d in result for k, v in d.items()}
return class_res
def run_cso_classifier_batch_model_single_worker(papers, modules="both", enhancement="first"):
"""Run the CSO Classifier in *BATCH MODE*.
It takes as input a set of papers, which include abstract, title, and keywords and for each one of them returns a
list of relevant concepts from CSO. This function requires a dictionary of papers, with each id corresponding to
the metadata of a paper, and few flags:
(i) modules, determines whether to run only the syntactic module, or the semantic module, or both;
(ii) enhancement, controls whether the classifier should infer super-topics, i.e., their first direct
super-topics or the whole set of topics up until root.
Args:
papers (dictionary): contains the metadata of the papers, e.g., for each paper, there is title, abstract and
keywords {"id1":{"title": "","abstract": "","keywords": ""},"id2":{"title": "","abstract": "","keywords": ""}}.
modules (string): either "syntactic", "semantic" or "both" to determine which modules to use when
classifying. "syntactic" enables only the syntatcic module. "semantic" enables only the semantic module.
Finally, with "both" the classifier takes advantage of both the syntactic and semantic modules. Default =
"both".
enhancement (string): either "first", "all" or "no". With "first" the CSO classifier returns only the topics
one level above. With "all" it returns all topics above the resulting topics. With "no" the CSO Classifier
does not provide any enhancement.
Returns:
fcso (dictionary): contains the CSO Ontology.
fmodel (dictionary): contains a cache of the model, i.e., each token is linked to the corresponding CSO topic.
"""
if modules not in ["syntactic", "semantic", "both"]:
raise ValueError("Error: Field modules must be 'syntactic', 'semantic' or 'both'")
if enhancement not in ["first", "all", "no"]:
raise ValueError("Error: Field enhances must be 'first', 'all' or 'no'")
# Loading ontology and model
cso, model = misc.load_ontology_and_chached_model()
# Passing parameters to the two classes (synt and sema)
synt_module = synt(cso)
sema_module = sema(model, cso)
# initializing variable that will contain output
class_res = dict()
for paper_id, paper_value in papers.items():
print("Processing:", paper_id)
# In this case we avoid computing other fields. We select only title, abstract and keywords
paper = dict()
paper["title"] = paper_value["title"] if "title" in paper_value and not paper_value["title"] is None else ""
paper["abstract"] = paper_value["abstract"] if "abstract" in paper_value and not paper_value[
"abstract"] is None else ""
paper["keywords"] = paper_value["keywords"] if "keywords" in paper_value and not paper_value[
"keywords"] is None else ""
# just in case the value keywords contains an array of keywords
if isinstance(paper["keywords"], list):
paper["keywords"] = ', '.join(paper["keywords"])
class_res[paper_id] = dict()
class_res[paper_id]["syntactic"] = list()
class_res[paper_id]["semantic"] = list()
class_res[paper_id]["union"] = list()
class_res[paper_id]["enhanced"] = list()
if modules == 'syntactic' or modules == 'both':
synt_module.set_paper(paper)
class_res[paper_id]["syntactic"] = synt_module.classify_syntactic()
if modules == 'semantic' or modules == 'both':
sema_module.set_paper(paper)
class_res[paper_id]["semantic"] = sema_module.classify_semantic()
union = list(set(class_res[paper_id]["syntactic"] + class_res[paper_id]["semantic"]))
class_res[paper_id]["union"] = union
if enhancement == 'first':
enhanced = misc.climb_ontology(cso, union, "first")
class_res[paper_id]["enhanced"] = [x for x in enhanced if x not in union]
elif enhancement == 'all':
enhanced = misc.climb_ontology(cso, union, "all")
class_res[paper_id]["enhanced"] = [x for x in enhanced if x not in union]
elif enhancement == 'no':
pass
return class_res
| 49.674528 | 120 | 0.664704 | 1,394 | 10,531 | 4.944763 | 0.142037 | 0.033657 | 0.022632 | 0.026113 | 0.804439 | 0.774264 | 0.769331 | 0.749166 | 0.69926 | 0.683882 | 0 | 0.000991 | 0.233786 | 10,531 | 211 | 121 | 49.909953 | 0.853266 | 0.485139 | 0 | 0.423913 | 0 | 0 | 0.181483 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.032609 | false | 0.021739 | 0.065217 | 0 | 0.130435 | 0.01087 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
827199c693053201c1ea7d5f7a89e0d0350a0079 | 136 | py | Python | lms/lms_app/admin.py | teamdiniz/AC-s-LPII- | 7a2582e729c245ecb8d186c0f5caa4361dbfbbc3 | [
"Apache-2.0"
] | null | null | null | lms/lms_app/admin.py | teamdiniz/AC-s-LPII- | 7a2582e729c245ecb8d186c0f5caa4361dbfbbc3 | [
"Apache-2.0"
] | null | null | null | lms/lms_app/admin.py | teamdiniz/AC-s-LPII- | 7a2582e729c245ecb8d186c0f5caa4361dbfbbc3 | [
"Apache-2.0"
] | null | null | null | from django.contrib import admin
# Register your models here.
from lms_app.models import Professor
admin.site.register(Professor)
| 22.666667 | 37 | 0.794118 | 19 | 136 | 5.631579 | 0.684211 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.147059 | 136 | 5 | 38 | 27.2 | 0.922414 | 0.191176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
827ab788d2bd0b33791019c1b8e9c4c4f9c2e16b | 142 | py | Python | DEPENDENCIES/utf/tests/ut_stagedtestmultiline_missing_out.py | kevinkenzhao/Repy2 | a7afb4c8ba263c8a74775a6281a50d94880a8d34 | [
"MIT"
] | null | null | null | DEPENDENCIES/utf/tests/ut_stagedtestmultiline_missing_out.py | kevinkenzhao/Repy2 | a7afb4c8ba263c8a74775a6281a50d94880a8d34 | [
"MIT"
] | null | null | null | DEPENDENCIES/utf/tests/ut_stagedtestmultiline_missing_out.py | kevinkenzhao/Repy2 | a7afb4c8ba263c8a74775a6281a50d94880a8d34 | [
"MIT"
] | null | null | null | # This test should fail since error message one is not printed out.
#pragma out message one
#pragma out message two
print "Test message two"
| 23.666667 | 67 | 0.774648 | 24 | 142 | 4.583333 | 0.625 | 0.181818 | 0.290909 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.183099 | 142 | 5 | 68 | 28.4 | 0.948276 | 0.767606 | 0 | 0 | 0 | 0 | 0.551724 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0 | null | null | 1 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 5 |
828f923e52f0e703a931809af0018412cd975144 | 52 | py | Python | oas_dev/util/output_overview.py | sarambl/OAS-DEV | 8dec6d29ef23dee8135bc937cd6ee1ef5b64d304 | [
"CC0-1.0"
] | null | null | null | oas_dev/util/output_overview.py | sarambl/OAS-DEV | 8dec6d29ef23dee8135bc937cd6ee1ef5b64d304 | [
"CC0-1.0"
] | null | null | null | oas_dev/util/output_overview.py | sarambl/OAS-DEV | 8dec6d29ef23dee8135bc937cd6ee1ef5b64d304 | [
"CC0-1.0"
] | null | null | null | from oas_dev.project_root import get_project_base
| 13 | 49 | 0.865385 | 9 | 52 | 4.555556 | 0.888889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115385 | 52 | 3 | 50 | 17.333333 | 0.891304 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
82985325daa2a5514647aa4979efadfd0c5b1dda | 101 | py | Python | office365/onedrive/columns/currency_column.py | rikeshtailor/Office365-REST-Python-Client | ca7bfa1b22212137bb4e984c0457632163e89a43 | [
"MIT"
] | 544 | 2016-08-04T17:10:16.000Z | 2022-03-31T07:17:20.000Z | office365/onedrive/columns/currency_column.py | rikeshtailor/Office365-REST-Python-Client | ca7bfa1b22212137bb4e984c0457632163e89a43 | [
"MIT"
] | 438 | 2016-10-11T12:24:22.000Z | 2022-03-31T19:30:35.000Z | office365/onedrive/columns/currency_column.py | rikeshtailor/Office365-REST-Python-Client | ca7bfa1b22212137bb4e984c0457632163e89a43 | [
"MIT"
] | 202 | 2016-08-22T19:29:40.000Z | 2022-03-30T20:26:15.000Z | from office365.runtime.client_value import ClientValue
class CurrencyColumn(ClientValue):
pass
| 16.833333 | 54 | 0.821782 | 11 | 101 | 7.454545 | 0.909091 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.034091 | 0.128713 | 101 | 5 | 55 | 20.2 | 0.897727 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0.333333 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 5 |
829afbc95107fcf0e1ea4e30df54af4c91d21650 | 112 | py | Python | WEEKS/CD_Sata-Structures/general/practice/PYTHON/28 - alphabeticShift.py | webdevhub42/Lambda | b04b84fb5b82fe7c8b12680149e25ae0d27a0960 | [
"MIT"
] | null | null | null | WEEKS/CD_Sata-Structures/general/practice/PYTHON/28 - alphabeticShift.py | webdevhub42/Lambda | b04b84fb5b82fe7c8b12680149e25ae0d27a0960 | [
"MIT"
] | null | null | null | WEEKS/CD_Sata-Structures/general/practice/PYTHON/28 - alphabeticShift.py | webdevhub42/Lambda | b04b84fb5b82fe7c8b12680149e25ae0d27a0960 | [
"MIT"
] | null | null | null | def alphabeticShift(inputString):
return "".join(chr(ord(i) + 1) if i != "z" else "a" for i in inputString)
| 37.333333 | 77 | 0.651786 | 18 | 112 | 4.055556 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.01087 | 0.178571 | 112 | 2 | 78 | 56 | 0.782609 | 0 | 0 | 0 | 0 | 0 | 0.017857 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | false | 0 | 0 | 0.5 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 5 |
82ad67a9c17365c07278ccebd2cbd62d1b1a2655 | 516 | py | Python | nipype/interfaces/semtools/filtering/__init__.py | PAmcconnell/nipype | 39fbd5411a844ce7c023964d3295eb7643b95af5 | [
"Apache-2.0"
] | null | null | null | nipype/interfaces/semtools/filtering/__init__.py | PAmcconnell/nipype | 39fbd5411a844ce7c023964d3295eb7643b95af5 | [
"Apache-2.0"
] | 2 | 2018-04-26T12:09:32.000Z | 2018-04-27T06:36:49.000Z | nipype/interfaces/semtools/filtering/__init__.py | PAmcconnell/nipype | 39fbd5411a844ce7c023964d3295eb7643b95af5 | [
"Apache-2.0"
] | 1 | 2019-11-14T14:16:57.000Z | 2019-11-14T14:16:57.000Z | # -*- coding: utf-8 -*-
from .denoising import UnbiasedNonLocalMeans
from .featuredetection import (
GenerateSummedGradientImage, CannySegmentationLevelSetImageFilter,
DilateImage, TextureFromNoiseImageFilter, FlippedDifference, ErodeImage,
GenerateBrainClippedImage, NeighborhoodMedian, GenerateTestImage,
NeighborhoodMean, HammerAttributeCreator, TextureMeasureFilter, DilateMask,
DumpBinaryTrainingVectors, DistanceMaps, STAPLEAnalysis,
GradientAnisotropicDiffusionImageFilter, CannyEdge)
| 51.6 | 79 | 0.835271 | 28 | 516 | 15.392857 | 0.928571 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.002169 | 0.106589 | 516 | 9 | 80 | 57.333333 | 0.932755 | 0.040698 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.25 | 0 | 0.25 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
82ccb9b5ff785a9e3d11029788ca770b6bd57466 | 101 | py | Python | voynich/__init__.py | jacoblee628/voynich | ac62cabc0337af13733f83df10a121e3e60c5ebe | [
"MIT"
] | 1 | 2021-11-04T23:48:50.000Z | 2021-11-04T23:48:50.000Z | voynich/__init__.py | jacoblee628/voynich | ac62cabc0337af13733f83df10a121e3e60c5ebe | [
"MIT"
] | null | null | null | voynich/__init__.py | jacoblee628/voynich | ac62cabc0337af13733f83df10a121e3e60c5ebe | [
"MIT"
] | null | null | null | __all__ = ['VoynichManuscript', 'Line', 'Page']
from .voynich import VoynichManuscript, Line, Page
| 20.2 | 50 | 0.732673 | 10 | 101 | 7 | 0.7 | 0.6 | 0.714286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.128713 | 101 | 4 | 51 | 25.25 | 0.795455 | 0 | 0 | 0 | 0 | 0 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 5 |
7d9b9acdf98df0594a41d3aacbdfbaac92f5c74d | 333 | py | Python | src/utilities/validation.py | danodea/basic-blog | b93793f4cadbd0863e60305857fbcc6da92c67f4 | [
"MIT"
] | null | null | null | src/utilities/validation.py | danodea/basic-blog | b93793f4cadbd0863e60305857fbcc6da92c67f4 | [
"MIT"
] | null | null | null | src/utilities/validation.py | danodea/basic-blog | b93793f4cadbd0863e60305857fbcc6da92c67f4 | [
"MIT"
] | null | null | null | import re
USER_RE = re.compile(r"^[a-zA-Z0-9_-]{3,20}$")
PASSWORD_RE = re.compile(r"^.{3,20}$")
EMAIL_RE = re.compile(r"^[\S]+@[\S]+.[\S]+$")
def validate_username(input):
return USER_RE.match(input)
def validate_password(input):
return PASSWORD_RE.match(input)
def validate_email(input):
return EMAIL_RE.match(input) | 23.785714 | 46 | 0.678679 | 55 | 333 | 3.927273 | 0.363636 | 0.055556 | 0.152778 | 0.166667 | 0.212963 | 0 | 0 | 0 | 0 | 0 | 0 | 0.027119 | 0.114114 | 333 | 14 | 47 | 23.785714 | 0.705085 | 0 | 0 | 0 | 0 | 0 | 0.146707 | 0.062874 | 0 | 0 | 0 | 0 | 0 | 1 | 0.3 | false | 0.3 | 0.1 | 0.3 | 0.7 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 5 |
7dc65e44c5b6e7ebc8cb55bffefd2f85b1e90d6a | 145 | py | Python | lsspipe/__init__.py | zdu863/lss-pipeline-stages | f55440b02a316fbde24a2af03e017eae006ece6c | [
"BSD-3-Clause"
] | null | null | null | lsspipe/__init__.py | zdu863/lss-pipeline-stages | f55440b02a316fbde24a2af03e017eae006ece6c | [
"BSD-3-Clause"
] | null | null | null | lsspipe/__init__.py | zdu863/lss-pipeline-stages | f55440b02a316fbde24a2af03e017eae006ece6c | [
"BSD-3-Clause"
] | null | null | null | # Make sure any stages you want to use in a pipeline
# are imported here.
from ceci import PipelineStage
from .lsspipeStage1 import LSSPipeStage1 | 36.25 | 52 | 0.813793 | 22 | 145 | 5.363636 | 0.863636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016393 | 0.158621 | 145 | 4 | 53 | 36.25 | 0.95082 | 0.475862 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
81bf38f1cb9389952766909f353f6d339fa992a4 | 76 | py | Python | pmfp/entrypoint/docker_/compose/__init__.py | Python-Tools/pmfp | 832273890eec08e84f9c68d03f3316b2c8139133 | [
"MIT"
] | 4 | 2017-09-15T03:38:56.000Z | 2019-12-16T02:03:14.000Z | pmfp/entrypoint/docker_/compose/__init__.py | Python-Tools/pmfp | 832273890eec08e84f9c68d03f3316b2c8139133 | [
"MIT"
] | 1 | 2021-04-27T10:51:42.000Z | 2021-04-27T10:51:42.000Z | pmfp/entrypoint/docker_/compose/__init__.py | Python-Tools/pmfp | 832273890eec08e84f9c68d03f3316b2c8139133 | [
"MIT"
] | null | null | null | from .new import new_dockercompose
from .deploy import deploy_dockercompose
| 25.333333 | 40 | 0.868421 | 10 | 76 | 6.4 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.105263 | 76 | 2 | 41 | 38 | 0.941176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
81c048ef8bbd5e245146fb56f3d57a6023633b17 | 119,371 | py | Python | typy/std/__init__.py | cyrus-/tydy | 0fea76d82663e18a809735c02d09529950dbb5ba | [
"MIT"
] | 39 | 2016-09-12T14:44:56.000Z | 2017-04-06T16:08:00.000Z | typy/std/__init__.py | cyrus-/tydy | 0fea76d82663e18a809735c02d09529950dbb5ba | [
"MIT"
] | 30 | 2016-10-05T04:14:27.000Z | 2017-02-06T20:16:07.000Z | typy/std/__init__.py | cyrus-/typy | 0fea76d82663e18a809735c02d09529950dbb5ba | [
"MIT"
] | 1 | 2016-09-13T13:41:52.000Z | 2016-09-13T13:41:52.000Z | """typy standard library"""
import ast
from collections import OrderedDict
from .. import util as _util
from ..util import astx
from .._contexts import BlockTransMechanism
from .._fragments import Fragment
from .._ty_exprs import CanonicalTy, TypeKind
from .._errors import TypeValidationError, TyError
from .. import _terms
try:
integer_types = (int, long)
except NameError:
integer_types = (int,)
class unit(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
return _check_trivial_idx_ast(idx_ast)
@classmethod
def ana_Tuple(cls, ctx, e, idx):
if len(e.elts) != 0:
raise TyError(
"Tuple must be empty to be a unit value.", e)
@classmethod
def trans_Tuple(cls, ctx, e, idx):
return astx.copy_node(e)
@classmethod
def ana_pat_Tuple(cls, ctx, pat, idx):
if len(pat.elts) != 0:
raise TyError(
"Tuple pattern must be empty to match unit values.", pat)
return { }
@classmethod
def trans_pat_Tuple(cls, ctx, pat, idx, scrutinee_trans):
return (ast.copy_location(
ast.NameConstant(value=True), pat), { })
@classmethod
def syn_Compare(cls, ctx, e):
ctx.ana(e.left, unit_ty)
for op, comparator in zip(e.ops, e.comparators):
if isinstance(op, (ast.Lt, ast.LtE, ast.Gt, ast.GtE, ast.In, ast.NotIn)):
raise TyError("Invalid comparison operator on unit.", comparator)
ctx.ana(comparator, unit_ty)
return CanonicalTy(boolean, ())
@classmethod
def trans_Compare(cls, ctx, e):
left_tr = ctx.trans(e.left)
comp_trs = [ ]
for comparator in e.comparators:
comp_trs.append(ctx.trans(comparator))
return ast.copy_location(
ast.Compare(
left = left_tr,
ops = e.ops,
comparators = comp_trs), e)
unit_ty = CanonicalTy(unit, ())
class boolean(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
return _check_trivial_idx_ast(idx_ast)
@classmethod
def ana_NameConstant(cls, ctx, e, idx):
value = e.value
if value is True or value is False:
return
else:
raise TyError("Invalid name constant: " + str(value), e)
@classmethod
def trans_NameConstant(cls, ctx, e, idx):
return ast.copy_location(
ast.NameConstant(value=e.value), e)
@classmethod
def ana_pat_NameConstant(cls, ctx, pat, idx):
value = pat.value
if value is True or value is False:
return {}
else:
raise TyError("Invalid name constant: " + str(value), pat)
@classmethod
def trans_pat_NameConstant(cls, ctx, pat, idx, scrutinee_trans):
if pat.value:
condition = scrutinee_trans
else:
condition = ast.fix_missing_locations(ast.copy_location(
ast.UnaryOp(
op = ast.Not(),
operand=scrutinee_trans), pat))
return condition, {}
@classmethod
def syn_BoolOp(cls, ctx, e):
for value in e.values:
ctx.ana(value, boolean_ty)
return boolean_ty
@classmethod
def ana_BoolOp(cls, ctx, e, idx):
for value in e.values:
ctx.ana(value, boolean_ty)
@classmethod
def trans_BoolOp(cls, ctx, e, idx=None):
values_tr = [ ]
for value in e.values:
values_tr.append(ctx.trans(value))
return ast.copy_location(
ast.BoolOp(
op = e.op,
values = values_tr), e)
@classmethod
def syn_Compare(cls, ctx, e):
ctx.ana(e.left, boolean_ty)
for op, comparator in zip(e.ops, e.comparators):
if isinstance(op, (ast.Lt, ast.LtE, ast.Gt, ast.GtE, ast.In, ast.NotIn)):
raise TyError("Invalid comparison operator on unit.", comparator)
ctx.ana(comparator, boolean_ty)
return boolean_ty
@classmethod
def trans_Compare(cls, ctx, e):
left_tr = ctx.trans(e.left)
comp_trs = [ ]
for comparator in e.comparators:
comp_trs.append(ctx.trans(comparator))
return ast.copy_location(
ast.Compare(
left = left_tr,
ops = e.ops,
comparators = comp_trs), e)
@classmethod
def check_If(cls, ctx, stmt, idx):
cls.syn_If(ctx, stmt, idx)
@classmethod
def trans_checked_If(cls, ctx, stmt, mechanism):
return cls.trans_If(cls, ctx, stmt, mechanism)
@classmethod
def syn_If(cls, ctx, e, idx):
body_block = e.body_block = _terms.Block(e.body)
body_ty = ctx.syn_block(body_block)
orelse_block = e.orelse_block = _terms.Block(e.orelse)
orelse_ty = ctx.ana_block(orelse_block, body_ty)
return body_ty
@classmethod
def ana_If(cls, ctx, e, idx, ty):
body_block = e.body_block = _terms.Block(e.body)
orelse_block = e.orelse_block = _terms.Block(e.orelse)
ctx.ana_block(body_block, ty)
ctx.ana_block(orelse_block, ty)
@classmethod
def trans_If(cls, ctx, e, idx, mechanism):
return [ast.copy_location(
ast.If(
test=ctx.trans(e.test),
body=ctx.trans_block(e.body_block, mechanism),
orelse=ctx.trans_block(e.orelse_block, mechanism)), e)]
@classmethod
def syn_IfExp(cls, ctx, e, idx):
body_ty = ctx.syn(e.body)
ctx.ana(e.orelse, body_ty)
return body_ty
@classmethod
def ana_IfExp(cls, ctx, e, idx, ty):
ctx.ana(e.body, ty)
ctx.ana(e.orelse, ty)
@classmethod
def trans_IfExp(cls, ctx, e, idx):
return ast.copy_location(
ast.IfExp(
test=ctx.trans(e.test),
body=ctx.trans(e.body),
orelse=ctx.trans(e.orelse)), e)
boolean_ty = CanonicalTy(boolean, ())
class string(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
return _check_trivial_idx_ast(idx_ast)
@classmethod
def ana_Str(cls, ctx, e, idx):
return
@classmethod
def trans_Str(cls, ctx, e, idx):
return astx.copy_node(e)
@classmethod
def ana_pat_Str(cls, ctx, pat, idx):
return {}
@classmethod
def trans_pat_Str(cls, ctx, pat, idx, scrutinee_trans):
condition = ast.copy_location(
ast.Compare(
left=scrutinee_trans,
ops=[ast.Eq()],
comparators=[ast.copy_location(
ast.Str(s=pat.s),
pat)]),
pat)
return condition, {}
@classmethod
def ana_JoinedStr(cls, ctx, e, idx):
values = e.values
for value in values:
if isinstance(value, ast.Str): continue
else: # FormattedValue
if value.conversion != -1:
raise TyError(
"string types do not support conversions.", value)
if value.format_spec is not None:
raise TyError(
"string types do not support format specifications.",
value)
ctx.ana(value.value, string_ty)
@classmethod
def trans_JoinedStr(cls, ctx, e, idx):
return ast.copy_location(
ast.JoinedStr(
values=[
ast.copy_location(ast.Str(s=value.s), value)
if isinstance(value, ast.Str) else
ast.copy_location(
ast.FormattedValue(
value=ctx.trans(value.value),
conversion=value.conversion,
format_spec=value.format_spec),
value)
for value in e.values]),
e)
@classmethod
def ana_pat_JoinedStr(cls, ctx, pat, idx):
values = pat.values
before_str = None
after_str = None
formatted_pat = None
for value in values:
if isinstance(value, ast.Str):
if formatted_pat is None:
before_str = value.s
else:
after_str = value.s
else: # FormattedValue
if formatted_pat is None:
formatted_pat = value.value
else:
raise TyError(
"Can only have one formatted value in format string "
"pattern.", value)
if value.format_spec is not None:
raise TyError(
"Cannot use format specification in format string "
"pattern.", value)
if value.conversion != -1:
raise TyError(
"Cannot use conversions in format string pattern.",
value)
pat.before_str = before_str
pat.after_str = after_str
pat.formatted_pat = formatted_pat
if formatted_pat is not None:
return ctx.ana_pat(formatted_pat, string_ty)
@classmethod
def trans_pat_JoinedStr(cls, ctx, pat, idx, scrutinee_trans):
before_str, formatted_pat, after_str = \
pat.before_str, pat.formatted_pat, pat.after_str
conditions = []
min_length = 0
if before_str is not None:
min_length += len(before_str)
before_str_condition = ast.fix_missing_locations(ast.copy_location(
astx.method_call(
scrutinee_trans,
"startswith",
[ast.Str(s=before_str)]),
pat))
else: before_str_condition = None
if after_str is not None:
min_length += len(after_str)
after_str_condition = ast.fix_missing_locations(ast.copy_location(
astx.method_call(
scrutinee_trans,
"endswith",
[ast.Str(s=after_str)]),
pat))
else: after_str_condition = None
if before_str is not None and after_str is not None and min_length > 0:
length_condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=astx.builtin_call('len', [scrutinee_trans]),
ops=[ast.GtE()],
comparators=[ast.Num(n=min_length)]),
pat)) # TODO do the other things do length checks properly?
conditions.append(length_condition)
if before_str_condition is not None:
conditions.append(before_str_condition)
f_lower = ast.Num(n=len(before_str))
else: f_lower = None
if after_str_condition is not None:
conditions.append(after_str_condition)
f_upper = ast.Num(n=-len(after_str))
else: f_upper = None
if f_lower is not None or f_upper is not None:
formatted_pat_scrutinee = \
ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Slice(
lower=f_lower,
upper=f_upper,
step=None),
ctx=astx.load_ctx), pat))
else:
formatted_pat_scrutinee = scrutinee_trans
formatted_pat_condition, binding_translations = \
ctx.trans_pat(formatted_pat, formatted_pat_scrutinee)
conditions.append(formatted_pat_condition)
if len(conditions) >= 2:
condition = ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=conditions), pat)
else:
condition = conditions[0]
return condition, binding_translations
@classmethod
def ana_FormattedValue(cls, ctx, e, idx):
e.pretend_e = pretend_e = ast.copy_location(
ast.JoinedStr(
values=[e]), e)
cls.ana_JoinedStr(ctx, pretend_e, idx)
@classmethod
def trans_FormattedValue(cls, ctx, e, idx):
return cls.trans_JoinedStr(ctx, e.pretend_e, idx)
@classmethod
def ana_pat_FormattedValue(cls, ctx, pat, idx):
pat.pretend_pat = pretend_pat = ast.copy_location(
ast.JoinedStr(
values=[pat]), pat)
return cls.ana_pat_JoinedStr(ctx, pretend_pat, idx)
@classmethod
def trans_pat_FormattedValue(cls, ctx, pat, idx, scrutinee_trans):
return cls.trans_pat_JoinedStr(ctx, pat.pretend_pat, idx,
scrutinee_trans)
@classmethod
def ana_pat_BinOp(cls, ctx, pat, idx):
op = pat.op
if isinstance(op, ast.Add):
left, right = pat.left, pat.right
if isinstance(left, ast.Str):
if left.s == "":
raise TyError(
"Literal pattern in + pattern must be non-empty.",
left)
return ctx.ana_pat(right, string_ty)
elif isinstance(right, ast.Str):
if right.s == "":
raise TyError(
"Literal pattern in + pattern must be non-empty.",
right)
return ctx.ana_pat(left, string_ty)
else:
raise TyError("One side of + pattern must be a literal.", pat)
else:
raise TyError("Invalid pattern operator on strings.", pat)
@classmethod
def trans_pat_BinOp(cls, ctx, pat, idx, scrutinee_trans):
left, right = pat.left, pat.right
if isinstance(left, ast.Str):
left_condition = ast.fix_missing_locations(ast.copy_location(
astx.method_call(
scrutinee_trans,
"startswith",
[left]),
pat))
remainder = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Slice(ast.Num(n=len(left.s)), None, None),
ctx=astx.load_ctx),
pat)) # scrutinee_trans[len(left):]
right_condition, binding_translations = ctx.trans_pat(right, remainder)
condition = ast.copy_location(
astx.make_binary_And(left_condition, right_condition),
pat)
else:
right_condition = ast.fix_missing_locations(ast.copy_location(
astx.method_call(
scrutinee_trans,
"endswith",
[right]),
pat))
remainder = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Slice(None, ast.Num(n=-len(right.s)), None),
ctx=astx.load_ctx),
pat))
left_condition, binding_translations = ctx.trans_pat(left, remainder)
condition = ast.copy_location(
astx.make_binary_And(left_condition, right_condition),
pat)
return condition, binding_translations
@classmethod
def syn_BinOp(cls, ctx, e):
left, op, right = e.left, e.op, e.right
string_ty = CanonicalTy(cls, ())
if isinstance(op, ast.Add):
ctx.ana(left, string_ty)
ctx.ana(right, string_ty)
return string_ty
else:
raise TyError("Invalid string operator.", e)
@classmethod
def trans_BinOp(cls, ctx, e):
return ast.copy_location(
ast.BinOp(
left=ctx.trans(e.left),
op=e.op,
right=ctx.trans(e.right)),
e)
@classmethod
def syn_Subscript(cls, ctx, e, idx):
slice = e.slice
if isinstance(slice, ast.Index):
ctx.ana(slice.value, num_ty)
return string_ty
elif isinstance(slice, ast.Slice):
lower, upper, step = slice.lower, slice.upper, slice.step
if lower is not None:
ctx.ana(lower, num_ty)
if upper is not None:
ctx.ana(upper, num_ty)
if step is not None:
ctx.ana(step, num_ty)
return string_ty
else:
raise TyError("Invalid string slice.", e)
@classmethod
def trans_Subscript(cls, ctx, e, idx):
slice = e.slice
if isinstance(slice, ast.Index):
slice_tr = ast.copy_location(
ast.Index(value=ctx.trans(slice.value)),
slice)
else:
lower, upper, step = slice.lower, slice.upper, slice.step
lower_tr = ctx.trans(lower) if lower is not None else None
upper_tr = ctx.trans(upper) if upper is not None else None
step_tr = ctx.trans(step) if step is not None else None
slice_tr = ast.copy_location(
ast.Slice(lower_tr, upper_tr, step_tr),
slice)
return ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=ctx.trans(e.value),
slice=slice_tr,
ctx=e.ctx),
e))
@classmethod
def syn_Compare(cls, ctx, e):
left, ops, comparators = e.left, e.ops, e.comparators
ctx.ana(left, string_ty)
for op, comparator in zip(ops, comparators):
if isinstance(op, (ast.In, ast.NotIn)):
raise TyError("Invalid comparison operator for strings.",
comparator)
ctx.ana(comparator, string_ty)
return boolean_ty
@classmethod
def trans_Compare(cls, ctx, e):
return ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=ctx.trans(e.left),
ops=e.ops,
comparators=[
ctx.trans(comparator)
for comparator in e.comparators]),
e))
string_ty = CanonicalTy(string, ())
class num(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
return _check_trivial_idx_ast(idx_ast)
@classmethod
def ana_Num(cls, ctx, e, idx):
n = e.n
if isinstance(n, integer_types):
return
else:
raise TyError("Invalid literal for num type.", e)
@classmethod
def ana_UnaryOp(cls, ctx, e, idx):
if isinstance(e.op, ast.Not):
raise TyError("Invalid unary operator 'not' for num type.", e)
ctx.ana(e.operand, CanonicalTy(num, ()))
@classmethod
def trans_Num(cls, ctx, e, idx):
return ast.copy_location(
ast.Num(n=e.n), e)
@classmethod
def ana_pat_Num(cls, ctx, pat, idx):
n = pat.n
if isinstance(n, integer_types):
return {}
else:
raise TyError("Invalid pattern literal for num type.", pat)
@classmethod
def trans_pat_Num(cls, ctx, pat, idx, scrutinee_trans):
condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left = scrutinee_trans,
ops=[ast.Eq()],
comparators=[ast.Num(n=pat.n)]), pat))
return condition, {}
@classmethod
def ana_pat_UnaryOp(cls, ctx, pat, idx):
op = pat.op
if isinstance(op, (ast.UAdd, ast.USub)):
return ctx.ana_pat(pat.operand, num_ty)
else:
raise TyError("Invalid pattern for num type.", pat)
@classmethod
def trans_pat_UnaryOp(cls, ctx, pat, idx, scrutinee_trans):
if isinstance(pat.op, ast.USub):
cond_op = ast.Lt()
new_scrutinee_trans = ast.fix_missing_locations(ast.copy_location(
ast.UnaryOp(
op=ast.USub(),
operand=scrutinee_trans), pat))
else:
cond_op = ast.Gt()
new_scrutinee_trans = scrutinee_trans
this_condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=scrutinee_trans,
ops=[cond_op],
comparators=[ast.Num(n=0)]), pat))
operand_condns, bindings = ctx.trans_pat(pat.operand,
new_scrutinee_trans)
condition = ast.fix_missing_locations(ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=[this_condition, operand_condns]), pat))
return condition, bindings
@classmethod
def syn_BinOp(cls, ctx, e):
op = e.op
if isinstance(op, ast.MatMult):
raise TyError("Invalid operator on numbers.", e)
else:
left = e.left
try:
ctx.ana(left, num_ty)
left_ty = num_ty
except TyError:
ctx.ana(left, ieee_ty)
left_ty = ieee_ty
right = e.right
try:
ctx.ana(right, num_ty)
right_ty = num_ty
except TyError:
ctx.ana(right, ieee_ty)
right_ty = num_ty
if isinstance(e.op, ast.Div):
return ieee_ty
else:
if left_ty is ieee_ty or right_ty is ieee_ty:
return ieee_ty
else:
return num_ty
@classmethod
def ana_BinOp(cls, ctx, e):
if isinstance(e.op, ast.MatMult):
raise TyError("Invalid operator on numbers.", e)
elif isinstance(e.op, ast.Div):
raise TyError("Cannot use division at num type.", e)
else:
ctx.ana(e.left, num_ty)
ctx.ana(e.right, num_ty)
@classmethod
def trans_BinOp(cls, ctx, e):
return ast.copy_location(
ast.BinOp(
left=ctx.trans(e.left),
op=e.op,
right=ctx.trans(e.right)), e)
@classmethod
def syn_UnaryOp(cls, ctx, e, idx):
if isinstance(e.op, ast.Not):
raise TyError("Invalid unary operator 'not' for num type.", e)
return CanonicalTy(num, ())
@classmethod
def trans_UnaryOp(cls, ctx, e, idx=None):
return ast.copy_location(
ast.UnaryOp(
op=e.op,
operand=ctx.trans(e.operand)), e)
@classmethod
def syn_Compare(cls, ctx, e):
left = e.left
try:
ctx.ana(left, num_ty)
except TyError:
ctx.ana(left, ieee_ty)
for op, comparator in zip(e.ops, e.comparators):
if isinstance(op, (ast.In, ast.NotIn)):
raise TyError(
"Invalid comparison operator for num.",
comparator)
try:
ctx.ana(comparator, num_ty)
except TyError:
ctx.ana(comparator, ieee_ty)
return boolean_ty
@classmethod
def trans_Compare(cls, ctx, e):
return ast.copy_location(
ast.Compare(
left=ctx.trans(e.left),
ops=e.ops,
comparators=[
ctx.trans(comparator)
for comparator in e.comparators]), e)
num_ty = CanonicalTy(num, ())
class ieee(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
return _check_trivial_idx_ast(idx_ast)
@classmethod
def ana_Num(cls, ctx, e, idx):
return
@classmethod
def trans_Num(cls, ctx, e, idx):
return ast.copy_location(
ast.Num(n=e.n), e)
@classmethod
def ana_Name(cls, ctx, e, idx):
id = e.id
if id == "NaN" or id == "Inf" or id == "Infinity":
return
else:
raise TyError("Invalid name constant for ieee type.", e)
@classmethod
def trans_Name(cls, ctx, e, idx):
return ast.fix_missing_locations(ast.copy_location(
astx.builtin_call(
'float', [ast.Str(s=e.id)]), e))
@classmethod
def ana_UnaryOp(cls, ctx, e, idx):
if isinstance(e.op, (ast.Not, ast.Invert)):
raise TyError("Invalid unary operator for ieee type.", e)
ctx.ana(e.operand, CanonicalTy(ieee, ()))
@classmethod
def ana_pat_Num(cls, ctx, pat, idx):
return {}
@classmethod
def trans_pat_Num(cls, ctx, pat, idx, scrutinee_trans):
condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left = scrutinee_trans,
ops=[ast.Eq()],
comparators=[ast.Num(n=pat.n)]), pat))
return condition, {}
@classmethod
def ana_pat_Name(cls, ctx, pat, idx):
id = pat.id
if id == "NaN" or id == "Inf" or id == "Infinity":
return {}
else:
raise TyError("Invalid name constant for ieee type: " + id, pat)
@classmethod
def trans_pat_Name(cls, ctx, pat, idx, scrutinee_trans):
id = pat.id
if id == "Inf" or id == "Infinity":
condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=scrutinee_trans,
ops=[ast.Eq()],
comparators=[
astx.builtin_call('float', [ast.Str(s=pat.id)])]),
pat))
else: # NaN
math = ctx.add_import("math")
condition = ast.fix_missing_locations(ast.copy_location(
ast.Call(
func=ast.Attribute(
value=ast.Name(id=math, ctx=astx.load_ctx),
attr="isnan",
ctx=astx.load_ctx),
args=[scrutinee_trans],
keywords=[]), pat))
return condition, {}
@classmethod
def ana_pat_UnaryOp(cls, ctx, pat, idx):
if isinstance(pat.op, (ast.UAdd, ast.USub)):
return ctx.ana_pat(pat.operand, ieee_ty)
else:
raise TyError("Invalid pattern for ieee type.", pat)
@classmethod
def trans_pat_UnaryOp(cls, ctx, pat, idx, scrutinee_trans):
if isinstance(pat.op, ast.USub):
cond_op = ast.Lt()
new_scrutinee_trans = ast.fix_missing_locations(ast.copy_location(
ast.UnaryOp(
op=ast.USub(),
operand=scrutinee_trans), pat))
else:
cond_op = ast.Gt()
new_scrutinee_trans = scrutinee_trans
this_condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=scrutinee_trans,
ops=[cond_op],
comparators=[ast.Num(n=0.0)]), pat))
operand_condns, bindings = ctx.trans_pat(pat.operand,
new_scrutinee_trans)
condition = ast.fix_missing_locations(ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=[this_condition, operand_condns]), pat))
return condition, bindings
precedence = set([num])
@classmethod
def syn_BinOp(cls, ctx, e):
op = e.op
if isinstance(op, (ast.MatMult, ast.BitOr, ast.BitXor,
ast.BitAnd, ast.LShift, ast.RShift)):
raise TyError("Invalid operator on ieee.", e)
else:
left = e.left
try:
ctx.ana(left, ieee_ty)
except TyError:
ctx.ana(left, num_ty)
right = e.right
try:
ctx.ana(right, ieee_ty)
except TyError:
ctx.ana(right, num_ty)
return ieee_ty
@classmethod
def ana_BinOp(cls, ctx, e):
if isinstance(op, (ast.MatMult, ast.BitOr, ast.BitXor,
ast.BitAnd, ast.LShift, ast.RShift)):
raise TyError("Invalid operator on ieee.", e)
else:
left = e.left
try:
ctx.ana(left, ieee_ty)
except TyError:
ctx.ana(left, num_ty)
right = e.right
try:
ctx.ana(right, ieee_ty)
except TyError:
ctx.ana(right, num_ty)
@classmethod
def trans_BinOp(cls, ctx, e):
return ast.copy_location(
ast.BinOp(
left=ctx.trans(e.left),
op=e.op,
right=ctx.trans(e.right)), e)
@classmethod
def syn_UnaryOp(cls, ctx, e, idx):
if isinstance(e.op, (ast.Not, ast.Invert)):
raise TyError("Invalid unary operator for ieee type.", e)
return ieee_ty
@classmethod
def trans_UnaryOp(cls, ctx, e, idx=None):
return ast.copy_location(
ast.UnaryOp(
op=e.op,
operand=ctx.trans(e.operand)), e)
@classmethod
def syn_Compare(cls, ctx, e):
left = e.left
try:
ctx.ana(left, ieee_ty)
except TyError:
ctx.ana(left, num_ty)
for op, comparator in zip(e.ops, e.comparators):
if isinstance(op, (ast.In, ast.NotIn)):
raise TyError(
"Invalid comparison operator for num.",
comparator)
try:
ctx.ana(comparator, ieee_ty)
except TyError:
ctx.ana(comparator, num_ty)
return boolean_ty
@classmethod
def trans_Compare(cls, ctx, e):
return ast.copy_location(
ast.Compare(
left=ctx.trans(e.left),
ops=e.ops,
comparators=[
ctx.trans(comparator)
for comparator in e.comparators]), e)
ieee_ty = CanonicalTy(ieee, ())
class cplx(Fragment):
# TODO init_idx
# TODO intro_forms
# TODO other operations
# TODO pattern matching
pass
def _update_name_bindings_disjoint(bindings, new_bindings):
for name_ast, ty in new_bindings.items():
for name_ast_orig, _ in bindings.items():
if name_ast.id == name_ast_orig.id:
raise TyError("Duplicated binding.", name_ast)
bindings[name_ast] = ty
class record(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
if isinstance(idx_ast, ast.Slice):
# Python special cases single slices
# we don't want that
idx_ast = ast.ExtSlice(dims=[idx_ast])
if isinstance(idx_ast, ast.ExtSlice):
idx_value = dict() # returned below
for dim in idx_ast.dims:
if (isinstance(dim, ast.Slice) and
dim.step is None and
dim.upper is not None and
isinstance(dim.lower, ast.Name)):
lbl = dim.lower.id
if lbl in idx_value:
raise TypeValidationError(
"Duplicate label.", dim)
ty = ctx.as_type(dim.upper)
idx_value[lbl] = ty
else:
raise TypeValidationError(
"Invalid field specification.", dim)
return idx_value
else:
raise TypeValidationError(
"Invalid record specification.", idx_ast)
@classmethod
def ana_Dict(cls, ctx, e, idx):
for lbl, value in zip(e.keys, e.values):
if isinstance(lbl, ast.Name):
id = lbl.id
if id in idx: ctx.ana(value, idx[id])
else:
raise TyError("Invalid label: " + id, lbl)
else:
raise TyError("Label is not an identifier.", lbl)
if len(idx) != len(e.keys):
raise TyError("Labels do not match those in type.", e)
@classmethod
def trans_Dict(cls, ctx, e, idx):
ast_dict = dict((k.id, v)
for k, v in zip(e.keys, e.values))
return ast.copy_location(ast.Tuple(
elts=list(
ctx.trans(ast_dict[lbl])
for lbl in sorted(idx.keys())
), ctx=ast.Load()),
e)
@classmethod
def ana_pat_Dict(cls, ctx, pat, idx):
keys, values = pat.keys, pat.values
n_keys = len(pat.keys)
n_fields = len(idx)
if n_keys < n_fields:
raise TyError("Missing fields.", pat)
elif n_keys > n_fields:
raise TyError("Too many fields.", pat)
else:
bindings = { }
for key, value in zip(keys, values):
if isinstance(key, ast.Name):
key_id = key.id
try:
ty = idx[key_id]
except KeyError:
raise TyError("Invalid field name: " + key_id, key)
else:
new_bindings = ctx.ana_pat(value, ty)
_update_name_bindings_disjoint(bindings, new_bindings)
else:
raise TyError("Invalid record field.", key)
return bindings
@classmethod
def trans_pat_Dict(cls, ctx, pat, idx, scrutinee_trans):
keys, values = pat.keys, pat.values
sorted_idx = sorted(idx.keys())
conditions = []
binding_translations = { }
for key, value in zip(keys, values):
key_id = key.id
key_scrutinee = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(
value=ast.Num(n=_util._seq_pos_of(key_id, sorted_idx))),
ctx=astx.load_ctx),
key))
condition, key_binding_translations = ctx.trans_pat(value, key_scrutinee)
conditions.append(condition)
binding_translations.update(key_binding_translations)
condition = ast.fix_missing_locations(ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=conditions),
pat))
return condition, binding_translations
@classmethod
def ana_pat_Set(cls, ctx, pat, idx):
elts = pat.elts
n_elts = len(elts)
n_fields = len(idx)
if n_elts < n_fields:
raise TyError("Missing fields.", pat)
elif n_elts > n_fields:
raise TyError("Too many fields.", pat)
else:
bindings = { }
for elt in elts:
if isinstance(elt, ast.Name):
id = elt.id
try:
ty = idx[id]
except KeyError:
raise TyError("Invalid field name: " + id, elt)
else:
new_bindings = ctx.ana_pat(elt, ty)
_update_name_bindings_disjoint(bindings, new_bindings)
else:
raise TyError("Invalid record field.", elt)
return bindings
@classmethod
def trans_pat_Set(cls, ctx, pat, idx, scrutinee_trans):
elts = pat.elts
sorted_idx = sorted(idx.keys())
binding_translations = { }
for elt in elts:
key_id = elt.id
key_scrutinee = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(
value=ast.Num(n=_util._seq_pos_of(key_id, sorted_idx))),
ctx=astx.load_ctx),
elt))
_, key_binding_translations = ctx.trans_pat(elt, key_scrutinee)
binding_translations.update(key_binding_translations)
condition = ast.copy_location(
ast.NameConstant(True),
pat)
return condition, binding_translations
@classmethod
def syn_Attribute(cls, ctx, e, idx):
try:
return idx[e.attr]
except KeyError:
raise TyError("Invalid field label: " + e.attr, e)
@classmethod
def trans_Attribute(cls, ctx, e, idx):
pos = _util._seq_pos_of(e.attr, sorted(idx.keys()))
return ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=ctx.trans(e.value),
slice=ast.Index(ast.Num(n=pos)),
ctx=e.ctx),
e))
class tpl(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
if isinstance(idx_ast, ast.Slice):
# special case for a single
idx_ast = ast.ExtSlice(dims=[idx_ast])
elif isinstance(idx_ast, ast.Index):
value = idx_ast.value
if isinstance(value, ast.Tuple):
idx_ast = ast.ExtSlice(
dims=[ast.Index(value=elt)
for elt in value.elts])
else:
idx_ast = ast.ExtSlice(dims=[idx_ast])
if isinstance(idx_ast, ast.ExtSlice):
idx_value = OrderedDict()
for n, dim in enumerate(idx_ast.dims):
if isinstance(dim, ast.Index):
lbl = n
ty_ast = dim.value
elif (isinstance(dim, ast.Slice)
and dim.step is None
and dim.upper is not None and
isinstance(dim.lower, ast.Name)):
lbl = dim.lower.id
ty_ast = dim.upper
else:
raise TypeValidationError(
"Invalid tpl specification.", idx_ast)
if lbl in idx_value:
raise TypeValidationError(
"Duplicate label.", dim)
ty = ctx.as_type(ty_ast)
idx_value[lbl] = ty
return idx_value
else:
raise TypeValidationError(
"Invalid tpl specification.", idx_ast)
@classmethod
def ana_Tuple(cls, ctx, e, idx):
elts = e.elts
for elt, ty in zip(elts, idx.values()):
ctx.ana(elt, ty)
if len(elts) != len(idx):
raise TyError("Incorrect nunmber of elements.", e)
@classmethod
def trans_Tuple(cls, ctx, e, idx):
return ast.copy_location(
ast.Tuple(
elts=[ctx.trans(elt) for elt in e.elts],
ctx=e.ctx),
e)
@classmethod
def _get_key(cls, lbl):
if isinstance(lbl, ast.Name):
key = lbl.id
elif isinstance(lbl, ast.Num):
key = lbl.n
else:
raise TyError("Invalid label.", lbl)
return key
@classmethod
def ana_Dict(cls, ctx, e, idx):
for lbl, value in zip(e.keys, e.values):
key = cls._get_key(lbl)
try:
ty = idx[key]
except KeyError:
raise TyError("Label not found in type.", lbl)
else:
ctx.ana(value, ty)
if len(idx) != len(e.keys):
raise TyError("Labels do not match those in type.", e)
@classmethod
def trans_Dict(cls, ctx, e, idx):
# TODO order of evaluation?
ast_dict = dict((cls._get_key(k), v)
for k, v in zip(e.keys, e.values))
return ast.copy_location(
ast.Tuple(
elts=[
ctx.trans(ast_dict[key])
for key in idx.keys()],
ctx=astx.load_ctx),
e)
@classmethod
def ana_pat_Tuple(cls, ctx, pat, idx):
elts = pat.elts
bindings = { }
for elt, ty in zip(elts, idx.values()):
new_bindings = ctx.ana_pat(elt, ty)
_update_name_bindings_disjoint(bindings, new_bindings)
if len(elts) != len(idx):
raise TyError("Incorrect number of elements.", pat)
return bindings
@classmethod
def trans_pat_Tuple(cls, ctx, pat, idx, scrutinee_trans):
conditions = []
binding_translations = { }
for i, elt in enumerate(pat.elts):
elt_scrutinee = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(value=ast.Num(n=i)),
ctx=astx.load_ctx),
elt))
condition, elt_binding_translations = \
ctx.trans_pat(elt, elt_scrutinee)
conditions.append(condition)
binding_translations.update(elt_binding_translations)
condition = ast.fix_missing_locations(ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=conditions),
pat))
return condition, binding_translations
@classmethod
def ana_pat_Dict(cls, ctx, pat, idx):
keys, values = pat.keys, pat.values
n_keys = len(pat.keys)
n_fields = len(idx)
if n_keys < n_fields:
raise TyError("Missing fields.", pat)
elif n_keys > n_fields:
raise TyError("Too many fields.", pat)
else:
bindings = { }
for key, value in zip(keys, values):
k = cls._get_key(key)
try:
ty = idx[k]
except KeyError:
raise TyError("Field not found.", key)
else:
new_bindings = ctx.ana_pat(value, ty)
_update_name_bindings_disjoint(bindings, new_bindings)
return bindings
@classmethod
def trans_pat_Dict(cls, ctx, pat, idx, scrutinee_trans):
keys, values = pat.keys, pat.values
conditions = []
binding_translations = { }
idx_lbls = list(idx.keys())
for key, value in zip(keys, values):
k = cls._get_key(key)
key_scrutinee = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(
value=ast.Num(n=_util._seq_pos_of(k, idx_lbls))),
ctx=astx.load_ctx),
key))
condition, key_binding_translations = \
ctx.trans_pat(value, key_scrutinee)
conditions.append(condition)
binding_translations.update(key_binding_translations)
condition = ast.fix_missing_locations(ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=conditions),
pat))
return condition, binding_translations
@classmethod
def ana_pat_Set(cls, ctx, pat, idx):
elts = pat.elts
n_elts = len(elts)
n_fields = len(idx)
if n_elts < n_fields:
raise TyError("Missing fields.", pat)
elif n_elts > n_fields:
raise TyError("Too many fields.", pat)
else:
bindings = { }
for elt in elts:
if isinstance(elt, ast.Name):
id = elt.id
try:
ty = idx[id]
except KeyError:
raise TyError("Invalid field name: " + id, elt)
else:
new_bindings = ctx.ana_pat(elt, ty)
_update_name_bindings_disjoint(bindings, new_bindings)
else:
raise TyError("Invalid record field.", elt)
return bindings
@classmethod
def trans_pat_Set(cls, ctx, pat, idx, scrutinee_trans):
elts = pat.elts
idx_keys = list(idx.keys())
binding_translations = { }
for elt in elts:
key_id = elt.id
key_scrutinee = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(
value=ast.Num(n=_util._seq_pos_of(key_id, idx_keys))),
ctx=astx.load_ctx),
elt))
_, key_binding_translations = ctx.trans_pat(elt, key_scrutinee)
binding_translations.update(key_binding_translations)
condition = ast.copy_location(
ast.NameConstant(True),
pat)
return condition, binding_translations
@classmethod
def syn_Attribute(cls, ctx, e, idx):
try:
return idx[e.attr]
except KeyError:
raise TyError("Invalid field label: " + e.attr, e)
@classmethod
def trans_Attribute(cls, ctx, e, idx):
pos = _util._seq_pos_of(e.attr, idx.keys())
return ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=ctx.trans(e.value),
slice=ast.Index(ast.Num(n=pos)),
ctx=e.ctx),
e))
@classmethod
def syn_Subscript(cls, ctx, e, idx):
slice = e.slice
if isinstance(slice, ast.Index):
value = slice.value
if isinstance(value, ast.Num):
try:
return idx[value.n]
except KeyError:
raise TyError("Invalid field position.", value)
else:
raise TyError("Invalid field position.", value)
else:
raise TyError("Invalid subscript.", e)
@classmethod
def trans_Subscript(cls, ctx, e, idx):
n = e.slice.value.n
pos = _util._seq_pos_of(n, idx.keys())
return ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=ctx.trans(e.value),
slice=ast.Index(ast.Num(n=pos)),
ctx=e.ctx),
e))
class variant(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
if isinstance(idx_ast, ast.Index):
value = idx_ast.value
if not isinstance(value, ast.Tuple):
value = ast.Tuple(elts=[value], ctx=astx.load_ctx)
idx = { }
for elt in value.elts:
if isinstance(elt, ast.Name):
tag = elt.id
tag_ast = elt
ty_asts = []
elif (isinstance(elt, ast.Call)
and isinstance(elt.func, ast.Name)
and len(elt.keywords) == 0):
tag = elt.func.id
tag_ast = elt.func
ty_asts = elt.args
else:
raise TypeValidationError(
"Invalid case specification.", elt)
if not tag[0].isupper():
raise TypeValidationError(
"Tag must start with an uppercase letter.", tag_ast)
if tag in idx:
raise TypeValidationError(
"Duplicate tag: " + tag, tag_ast)
types = tuple(
ctx.as_type(ty_ast)
for ty_ast in ty_asts)
idx[tag] = types
return idx
else:
raise TypeValidationError(
"Invalid case specification.", elt)
@classmethod
def ana_Name(cls, ctx, e, idx):
tag = e.id
try: types = idx[tag]
except: raise TyError("Invalid tag: " + tag, e)
if len(types) != 0:
raise TyError(
"Missing arguments to constructor.", e)
@classmethod
def trans_Name(cls, ctx, e, idx):
return ast.fix_missing_locations(ast.copy_location(
ast.Tuple(
elts=[ast.Str(s=e.id)],
ctx=astx.load_ctx),
e))
@classmethod
def ana_Call(cls, ctx, e, idx):
func, args, keywords = e.func, e.args, e.keywords
if len(keywords) != 0:
raise TyError("Keyword arguments are not supported.", e)
if isinstance(func, ast.Name):
tag = func.id
try: types = idx[tag]
except: raise TyError("Invalid tag: " + tag, func)
n_types = len(types)
n_args = len(args)
if n_args > n_types:
raise TyError("Too many arguments.", e)
elif n_args < n_types:
raise TyError("Too few arguments.", e)
else:
for arg, ty in zip(args, types):
ctx.ana(arg, ty)
else:
raise TyError("Invalid tag.", func)
@classmethod
def trans_Call(cls, ctx, e, idx=None):
elts = [ast.copy_location(ast.Str(s=e.func.id), e)]
args = e.args
elts.extend([
ctx.trans(arg)
for arg in args])
return ast.copy_location(
ast.Tuple(
elts=elts,
ctx=astx.load_ctx),
e)
@classmethod
def ana_pat_Name(cls, ctx, pat, idx):
tag = pat.id
try: types = idx[tag]
except: raise TyError("Invalid tag.", pat)
if len(types) != 0:
raise TyError("Missing arguments to constructor.", pat)
return { }
@classmethod
def trans_pat_Name(cls, ctx, pat, idx, scrutinee_trans):
condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(value=ast.Num(n=0)),
ctx=astx.load_ctx),
ops=[ast.Eq()],
comparators=[ast.Str(s=pat.id)]),
pat))
return condition, { }
@classmethod
def ana_pat_Call(cls, ctx, pat, idx):
func, args, keywords = pat.func, pat.args, pat.keywords
if len(keywords) != 0:
raise TyError("Keyword arguments are not supported.", pat)
if isinstance(func, ast.Name):
tag = func.id
try: types = idx[tag]
except: raise TyError("Invalid tag: " + tag, pat)
n_types = len(types)
n_args = len(args)
if n_args > n_types:
raise TyError("Too many arguments.", pat)
elif n_args < n_types:
raise TyError("Too few arguments.", pat)
else:
bindings = { }
for arg, ty in zip(args, types):
arg_bindings = ctx.ana_pat(arg, ty)
_update_name_bindings_disjoint(bindings, arg_bindings)
return bindings
else:
raise TyError("Invalid tag.", func)
@classmethod
def trans_pat_Call(cls, ctx, pat, idx, scrutinee_trans):
tag = pat.func.id
tag_condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(value=ast.Num(n=0)),
ctx=astx.load_ctx),
ops=[ast.Eq()],
comparators=[ast.Str(s=tag)]),
pat))
conditions = [tag_condition]
binding_translations = { }
for i, arg in enumerate(pat.args):
arg_scrutinee = ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.fix_missing_locations(ast.copy_location(
ast.Index(value=ast.Num(n=1 + i)),
pat)),
ctx=astx.load_ctx),
pat)
arg_condition, arg_binding_translations = ctx.trans_pat(arg, arg_scrutinee)
conditions.append(arg_condition)
binding_translations.update(arg_binding_translations)
condition = ast.copy_location(
ast.BoolOp(
op=ast.copy_location(ast.And(), pat),
values=conditions),
pat)
return condition, binding_translations
class fn(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
if isinstance(idx_ast, ast.Index):
value = idx_ast.value
if isinstance(value, ast.Compare):
left, ops, comparators = value.left, value.ops, value.comparators
if len(ops) == len(comparators) == 1:
if isinstance(ops[0], ast.Gt):
if isinstance(left, ast.Tuple):
if len(left.elts) == 0:
arg_type_asts = []
else:
raise TypeValidationError(
"Invalid type index format.", idx_ast)
else:
arg_type_asts = [left]
return_ty_ast = comparators[0]
else:
raise TypeValidationError(
"Invalid type index format.", idx_ast)
else:
raise TypeValidationError(
"Invalid type index format.", idx_ast)
elif isinstance(value, ast.Tuple):
elts = value.elts
if len(elts) < 2:
raise TypeValidationError(
"Invalid type index format.", idx_ast)
arg_type_asts = elts[0:-1]
final_elt = elts[-1]
if isinstance(final_elt, ast.Compare):
left, ops, comparators = \
final_elt.left, final_elt.ops, final_elt.comparators
if len(ops) == len(comparators) == 1:
if isinstance(ops[0], ast.Gt):
arg_type_asts.append(left)
return_ty_ast = comparators[0]
else:
raise TypeValidationError(
"Invalid type index format.", idx_ast)
else:
raise TypeValidationError(
"Invalid type index format.", idx_ast)
else:
raise TypeValidationError(
"Invalid type index format.", idx_ast)
else:
raise TypeValidationError(
"Invalid type index format.", idx_ast)
arg_types = tuple(
ctx.as_type(arg_ty)
for arg_ty in arg_type_asts)
return_ty = ctx.as_type(return_ty_ast)
return (arg_types, return_ty)
else:
raise typy.TypeValidationError(
"Invalid type index format.", idx_ast)
@classmethod
def syn_FunctionDef(cls, ctx, stmt):
# process decorators
decorator_list = stmt.decorator_list
if len(decorator_list) > 1:
raise TyError(
"fn does not support additional decorators.",
decorator_list[1])
# process args
arguments = stmt.args
if arguments.vararg is not None:
raise TyError(
"fn does not support varargs", arguments)
if len(arguments.kwonlyargs) > 0:
raise TyError(
"fn does not support kw only args", arguments)
if arguments.kwarg is not None:
raise TyError(
"fn does not support kw arg", arguments)
if len(arguments.defaults) > 0:
raise TyError(
"fn does not support defaults", arguments)
args = arguments.args
def _process_args():
for arg in args:
arg_id = arg.arg
arg_ann = arg.annotation
if arg_ann is None:
raise TyError(
"Missing argument type on " + arg_id,
arg)
arg_ty = ctx.as_type(arg_ann)
# simulate a name for the error reporting system
name = ast.Name(id=arg_id,
lineno=arg.lineno,
col_offset=arg.col_offset)
yield (name, arg_ty)
arg_sig = stmt.arg_sig = OrderedDict(_process_args())
arg_types = tuple(arg_sig.values())
# process return type annotation
returns = stmt.returns
if returns is not None:
rty = ctx.as_type(returns)
else:
rty = None
# push bindings
if rty is not None:
self_name = ast.copy_location(
ast.Name(id=stmt.name),
stmt)
self_ty = CanonicalTy(cls, (arg_types, rty))
ctx.push_var_bindings({self_name : self_ty})
stmt.uniq_arg_sig = ctx.push_var_bindings(dict(arg_sig))
# process docstring
body = stmt.body
if (len(body) > 1
and isinstance(body[0], ast.Expr)
and isinstance(body[0].value, ast.Str)):
proper_body = stmt.proper_body = body[1:]
docstring = stmt.docstring = body[0].value.s
else:
proper_body = stmt.proper_body = body
docstring = stmt.docstring = None
# make sure there is at least one remaining statement
if len(proper_body) == 0:
raise TyError(
"Must be at least one statement, "
"other than the docstring, in the body.",
stmt)
# check statements in proper_body
proper_body_block = stmt.proper_body_block = _terms.Block(proper_body)
if rty is None:
rty = ctx.syn_block(proper_body_block)
else:
ctx.ana_block(proper_body_block, rty)
# return canonical type
return CanonicalTy(fn, (arg_types, rty))
@classmethod
def ana_FunctionDef(cls, ctx, stmt, idx):
# process decorators
decorator_list = stmt.decorator_list
if len(decorator_list) == 1:
asc = decorator_list[0]
try: ty = ctx.as_type(asc)
except:
try: fragment = ctx.static_env.eval_expr_ast(asc)
except:
raise TyError(
"Decorator is neither a type nor a fragment.", stmt)
else:
if fragment != cls:
raise TyError("Decorator is not fn.", stmt)
else:
canonical_ty = ctx.canonicalize(ty)
asc_idx = canonical_ty.idx
if idx != asc_idx:
raise TyError("Decorator is inconsistent with expected type.", stmt)
if len(decorator_list) > 1:
raise TyError(
"fn does not support decorators in analytic position.",
decorator_list[1])
# process args
arguments = stmt.args
if arguments.vararg is not None:
raise TyError(
"fn does not support varargs", arguments)
if len(arguments.kwonlyargs) > 0:
raise TyError(
"fn does not support kw only args", arguments)
if arguments.kwarg is not None:
raise TyError(
"fn does not support kw arg", arguments)
if len(arguments.defaults) > 0:
raise TyError(
"fn does not support defaults", arguments)
args = arguments.args
arg_types = idx[0]
n_args = len(args)
n_arg_types = len(arg_types)
if n_args < n_arg_types:
raise TyError(
"Too few arguments", stmt)
elif n_args > n_arg_types:
raise TyError(
"Too many arguments", stmt)
def _process_args():
for arg, arg_ty in zip(args, arg_types):
arg_id = arg.arg
arg_ann = arg.annotation
if arg_ann is not None:
given_arg_ty = ctx.as_type(arg_ann)
if not ctx.ty_expr_eq(arg_ty, given_arg_ty, TypeKind):
raise TyError(
"Given type annotation is inconsistent "
"with ascription.", arg_ann)
name = ast.Name(id=arg_id,
lineno=arg.lineno,
col_offset=arg.col_offset)
yield (name, arg_ty)
arg_sig = stmt.arg_sig = OrderedDict(_process_args())
returns = stmt.returns
rty = idx[1]
if returns is not None:
ann_rty = ctx.as_type(returns)
if not ctx.ty_expr_eq(rty, ann_rty, TypeKind):
raise TyError(
"Given return type annotation is inconsistent "
"with ascription.", returns)
# push bindings
self_name = ast.copy_location(
ast.Name(id=stmt.name),
stmt)
self_ty = CanonicalTy(cls, (arg_types, rty))
ctx.push_var_bindings({self_name : self_ty})
stmt.uniq_arg_sig = ctx.push_var_bindings(dict(arg_sig))
# process docstring
body = stmt.body
if (len(body) > 1
and isinstance(body[0], ast.Expr)
and isinstance(body[0].value, ast.Str)):
proper_body = stmt.proper_body = body[1:]
docstring = stmt.docstring = body[0].value.s
else:
proper_body = stmt.proper_body = body
docstring = stmt.docstring = None
# make sure there is at least one remaining statement
if len(proper_body) == 0:
raise TyError(
"Must be at least one statement, "
"other than the docstring, in the body.",
stmt)
# check statements in proper_body
proper_body_block = stmt.proper_body_block = _terms.Block(proper_body)
ctx.ana_block(proper_body_block, rty)
# bindings
ctx.pop_var_bindings()
@classmethod
def trans_FunctionDef(cls, ctx, stmt, idx, mechanism):
uniq_id = stmt.uniq_id
# translate arguments
arguments_tr = ast.arguments(
args= [
ast.arg(
arg=stmt.uniq_arg_sig[arg.arg][0],
annotation=None,
lineno=arg.lineno,
col_offset=arg.col_offset)
for arg in stmt.args.args
],
vararg=None,
kwonlyargs=[],
kw_defaults=[],
kwarg=None,
defaults=[])
# translate body
body_tr = ctx.trans_block(stmt.proper_body_block,
BlockTransMechanism.Return)
return [ast.copy_location(
ast.FunctionDef(
name=uniq_id,
args=arguments_tr,
body=body_tr,
decorator_list=[],
returns=None),
stmt)]
@classmethod
def ana_Lambda(cls, ctx, e, idx):
# process args
arguments = e.args
if arguments.vararg is not None:
raise TyError(
"fn does not support varargs", arguments)
if len(arguments.kwonlyargs) > 0:
raise TyError(
"fn does not support kw only args", arguments)
if arguments.kwarg is not None:
raise TyError(
"fn does not support kw arg", arguments)
if len(arguments.defaults) > 0:
raise TyError(
"fn does not support defaults", arguments)
args = arguments.args
arg_types = idx[0]
n_args = len(args)
n_arg_types = len(arg_types)
if n_args < n_arg_types:
raise TyError(
"Too few arguments", e)
elif n_args > n_arg_types:
raise TyError(
"Too many arguments", e)
def _process_args():
for arg, arg_ty in zip(args, arg_types):
arg_id = arg.arg
name = ast.Name(id=arg_id,
lineno=arg.lineno,
col_offset=arg.col_offset)
yield (name, arg_ty)
arg_sig = e.arg_sig = OrderedDict(_process_args())
e.uniq_arg_sig = ctx.push_var_bindings(dict(arg_sig))
rty = idx[1]
ctx.ana(e.body, rty)
ctx.pop_var_bindings()
@classmethod
def trans_Lambda(cls, ctx, e, idx):
args=e.args
aa = args.args
return ast.copy_location(
ast.Lambda(
args=ast.arguments(
args=[
ast.arg(
arg=a.arg,
annotation=None,
lineno=a.lineno,
col_offset=a.col_offset)
for a in aa
],
vararg=args.vararg,
kwonlyargs=args.kwonlyargs,
kw_defaults=args.kw_defaults,
kwarg=args.kwarg,
defaults=args.defaults),
body=ctx.trans(e.body)),
e)
@classmethod
def syn_Call(cls, ctx, e, idx):
if len(e.keywords) != 0:
raise TyError("fn does not support keyword arguments.", e)
# check args
args = e.args
arg_types, rty = idx
n_args = len(args)
n_args_reqd = len(arg_types)
if n_args < n_args_reqd:
raise TyError("Too few arguments provided.", e)
elif n_args > n_args_reqd:
raise TyError("Too many arguments provided.", e)
for arg, arg_ty in zip(args, arg_types):
ctx.ana(arg, arg_ty)
# return type
return rty
@classmethod
def trans_Call(cls, ctx, e, idx):
return ast.copy_location(
ast.Call(
func=ctx.trans(e.func),
args=[
ctx.trans(arg)
for arg in e.args],
keywords=[]),
e)
@classmethod
def check_Assign(cls, ctx, stmt):
targets = stmt.targets
if len(targets) != 1:
# TODO support for multiple targets
raise TyError(
"Too many assignment targets.", targets[1])
target = targets[0]
pat, ann = _terms.get_pat_and_ann(target)
if ann is not None:
ty = ctx.as_type(ann)
ctx.ana(stmt.value, ty)
else:
ty = ctx.syn(stmt.value)
bindings = stmt.bindings = ctx.ana_pat(pat, ty)
stmt.uniq_bindings = ctx.add_bindings(bindings)
@classmethod
def trans_checked_Assign(cls, ctx, stmt):
target_name = tuple(stmt.bindings.keys())[0]
target_tr = ast.copy_location(
ast.Name(id=stmt.uniq_bindings[target_name.id][0],
ctx=astx.store_ctx),
target_name)
value = stmt.value
value_tr = ast.copy_location(
ctx.trans(value),
value)
return [ast.copy_location(
ast.Assign(
targets=[target_tr],
value=value_tr),
stmt)]
@classmethod
def integrate_static_FunctionDef(cls, ctx, stmt):
name_ast = ast.copy_location(
ast.Name(id=stmt.name, ctx=astx.load_ctx),
stmt)
stmt.uniq_bindings = uniq_bindings = ctx.add_bindings({ name_ast: stmt.ty })
stmt.uniq_id = uniq_bindings[stmt.name][0]
@classmethod
def integrate_trans_FunctionDef(cls, ctx, stmt, translation, mechanism):
uniq_id = stmt.uniq_id
if mechanism == BlockTransMechanism.Return:
translation.append(ast.copy_location(
ast.Return(
value=ast.copy_location(
ast.Name(
id=uniq_id,
ctx=astx.load_ctx),
stmt)),
stmt))
class py(Fragment):
@classmethod
def init_idx(cls, ctx, idx_ast):
return _check_trivial_idx_ast(idx_ast)
@classmethod
def check_Assign(cls, ctx, stmt, idx):
target = stmt.targets[0]
if isinstance(target, ast.Subscript):
cls._ana_slice(ctx, target.slice)
ctx.ana(stmt.value, py_type)
@classmethod
def check_AugAssign(cls, ctx, stmt, idx):
target = stmt.target
if isinstance(target, ast.Subscript):
cls._ana_slice(ctx, target.slice)
ctx.ana(stmt.value, py_type)
@classmethod
def syn_Assign(cls, ctx, stmt, idx):
cls.check_Assign(ctx, stmt, idx)
return py_type
@classmethod
def syn_AugAssign(cls, ctx, stmt, idx):
cls.check_AugAssign(ctx, stmt, idx)
return py_type
@classmethod
def trans_Assign(cls, ctx, stmt, idx,
mechanism=BlockTransMechanism.Statement):
return cls._trans_Assign_AugAssign(ctx, stmt, idx, mechanism)
@classmethod
def trans_AugAssign(cls, ctx, stmt, idx,
mechanism=BlockTransMechanism.Statement):
return cls._trans_Assign_AugAssign(ctx, stmt, idx, mechanism)
@classmethod
def _trans_Assign_AugAssign(cls, ctx, stmt, idx, mechanism):
if isinstance(stmt, ast.Assign):
target = stmt.targets[0]
else:
target = stmt.target
if isinstance(target, ast.Attribute):
# target_translation = ast.copy_location(
# ast.Name(id="ABCABDBDASD", ctx=astx.store_ctx), target)
target_translation = ast.copy_location(
ast.Attribute(
value=ctx.trans(target.value),
attr=target.attr,
ctx=target.ctx),
stmt)
else:
target_translation = ast.copy_location(
ast.Subscript(
value=ctx.trans(target.value),
slice=cls._trans_slice(ctx, target.slice),
ctx=target.ctx),
target)
if isinstance(stmt, ast.Assign):
assign_translation = ast.copy_location(
ast.Assign(
targets=[target_translation],
value=ctx.trans(stmt.value)),
stmt)
else:
assign_translation = ast.copy_location(
ast.AugAssign(
target=target_translation,
op=stmt.op,
value=ctx.trans(stmt.value)),
stmt)
if mechanism == BlockTransMechanism.Statement:
statement = [assign_translation]
elif mechanism == BlockTransMechanism.Returns:
return_translation = ast.copy_location(
ast.Return(value=None), stmt)
statement = [assign_translation, return_translation]
else: raise Exception("Unexpected mechanism.")
return statement
@classmethod
def syn_If(cls, ctx, stmt, idx):
body_block = stmt.body_block = _terms.Block(stmt.body)
body_ty = ctx.syn_block(body_block)
orelse = stmt.orelse
if len(orelse) > 0:
orelse_block = stmt.orelse_block = _terms.Block(orelse)
ctx.ana_block(orelse_block, body_ty)
return body_ty
@classmethod
def ana_If(cls, ctx, stmt, idx, ty):
body_block = stmt.body_block = _terms.Block(stmt.body)
ctx.ana_block(body_block, ty)
orelse = stmt.orelse
if len(orelse) > 0:
orelse_block = stmt.orelse_block = _terms.Block(stmt.orelse)
ctx.ana_block(orelse_block, ty)
@classmethod
def trans_If(cls, ctx, stmt, id, mechanism):
orelse = stmt.orelse
if len(orelse) > 0:
orelse_translation = ctx.trans_block(stmt.orelse_block, mechanism)
else:
orelse_translation=[]
return [ast.fix_missing_locations(ast.copy_location(
ast.If(
test=ctx.trans(stmt.test),
body=ctx.trans_block(stmt.body_block, mechanism),
orelse=orelse_translation),
stmt))]
@classmethod
def ana_Num(cls, ctx, e, idx):
return
@classmethod
def trans_Num(cls, ctx, e, idx):
return ast.copy_location(
ast.Num(n=e.n),
e)
@classmethod
def ana_pat_Num(cls, ctx, pat, idx):
return {}
@classmethod
def trans_pat_Num(cls, ctx, pat, idx, scrutinee_tr):
condition = ast.copy_location(
ast.Compare(
left=scrutinee_tr,
ops=[ast.Eq()],
comparators=[ast.copy_location(
ast.Num(n=pat.n),
pat)]),
pat)
return condition, {}
@classmethod
def ana_UnaryOp(cls, ctx, e, idx):
ctx.ana(e.operand, py_type)
@classmethod
def trans_UnaryOp(cls, ctx, e, idx=None):
return ast.copy_location(
ast.UnaryOp(
op=e.op,
operand=ctx.trans(e.operand)),
e)
@classmethod
def ana_pat_UnaryOp(cls, ctx, pat, idx):
op, operand = pat.op, pat.operand
if isinstance(op, (ast.USub, ast.UAdd)):
if isinstance(operand, ast.Num):
return {}
else:
raise TyError("Invalid operand pattern.", operand)
elif isinstance(op, ast.Not):
if isinstance(operand, ast.NameConstant) and operand.value is None:
return {}
else:
raise TyError("Invalid operand pattern.", operand)
else:
raise TyError("Invalid operand pattern.", operand)
@classmethod
def trans_pat_UnaryOp(cls, ctx, pat, idx, scrutinee_tr):
op, operand = pat.op, pat.operand
if isinstance(op, (ast.USub, ast.UAdd)):
condition = ast.copy_location(
ast.Compare(
left=scrutinee_tr,
ops=[ast.Eq()],
comparators=[pat]),
pat)
else: # not None
condition = ast.copy_location(
ast.Compare(
left=scrutinee_tr,
ops=[ast.IsNot()],
comparators=[
ast.copy_location(
ast.NameConstant(None),
pat)]),
pat)
return condition, {}
@classmethod
def ana_Str(cls, ctx, e, idx):
return
@classmethod
def trans_Str(cls, ctx, e, idx):
return ast.copy_location(
ast.Str(s=e.s),
e)
@classmethod
def ana_pat_Str(cls, ctx, pat, idx):
return {}
@classmethod
def trans_pat_Str(cls, ctx, pat, idx, scrutinee_tr):
condition = ast.copy_location(
ast.Compare(
left=scrutinee_tr,
ops=[ast.Eq()],
comparators=[ast.copy_location(
ast.Str(s=pat.s),
pat)]),
pat)
return condition, {}
@classmethod
def ana_JoinedStr(cls, ctx, e, idx):
values = e.values
for value in values:
if isinstance(value, ast.Str): continue
else: # FormattedValue
fvalue = value.value
ctx.ana(fvalue, py_type)
format_spec = value.format_spec
if format_spec is not None:
ctx.ana(format_spec, py_type)
@classmethod
def trans_JoinedStr(cls, ctx, e, idx):
return ast.copy_location(
ast.JoinedStr(
values=[
ast.copy_location(ast.Str(s=value.s), value)
if isinstance(value, ast.Str) else
ast.copy_location(
ast.FormattedValue(
value=ctx.trans(value.value),
conversion=value.conversion,
format_spec=(
None
if value.format_spec is None else
ctx.trans(value.format_spec))),
value)
for value in e.values]),
e)
@classmethod
def ana_FormattedValue(cls, ctx, e, idx):
pretend_e = e.pretend_e = ast.copy_location(
ast.JoinedStr(
values=[e]), e)
return cls.ana_JoinedStr(ctx, pretend_e, idx)
@classmethod
def trans_FormattedValue(cls, ctx, e, idx):
return cls.trans_JoinedStr(ctx, e.pretend_e, idx)
@classmethod
def ana_pat_JoinedStr(cls, ctx, pat, idx):
return string.ana_pat_JoinedStr(ctx, pat, idx)
@classmethod
def trans_pat_JoinedStr(cls, ctx, pat, idx, scrutinee_tr):
str_condition, binding_translations = \
string.trans_pat_JoinedStr(ctx, pat, idx, scrutinee_tr)
cls_condition = ast.fix_missing_locations(ast.copy_location(
astx.builtin_call('isinstance', []), pat))
cls_condition.args.append(scrutinee_tr)
cls_condition.args.append(
ast.fix_missing_locations(ast.copy_location(ast.Attribute(
value=ast.Name(id="__builtins__", ctx=astx.load_ctx),
attr="str",
ctx=astx.load_ctx), pat))
)
condition = ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=[cls_condition, str_condition]),
pat)
return condition, binding_translations
@classmethod
def ana_pat_FormattedValue(cls, ctx, pat, idx):
pat.pretend_pat = pretend_pat = ast.copy_location(
ast.JoinedStr(values=[pat]), pat)
return cls.ana_pat_JoinedStr(ctx, pretend_pat, idx)
@classmethod
def trans_pat_FormattedValue(cls, ctx, pat, idx, scrutinee_trans):
return cls.trans_pat_JoinedStr(ctx, pat.pretend_pat, idx,
scrutinee_trans)
@classmethod
def ana_NameConstant(cls, ctx, e, idx):
return
@classmethod
def trans_NameConstant(cls, ctx, e, idx):
return ast.copy_location(
ast.NameConstant(value=e.value),
e)
@classmethod
def ana_pat_NameConstant(cls, ctx, pat, idx):
return {}
@classmethod
def trans_pat_NameConstant(cls, ctx, pat, idx, scrutinee_tr):
condition = ast.copy_location(
ast.Compare(
left=scrutinee_tr,
ops=[ast.Is()],
comparators=[
ast.copy_location(
ast.NameConstant(pat.value),
pat)]),
pat)
return condition, {}
@classmethod
def ana_Name(cls, ctx, e, idx):
id = e.id
if id == 'NotImplemented' or id == 'Ellipsis':
return
else:
raise TyError("Invalid constant of type py.", e) # TODO defer to lifting
@classmethod
def trans_Name(cls, ctx, e, idx):
return ast.copy_location(
ast.Name(
id=e.id,
ctx=e.ctx),
e)
@classmethod
def ana_pat_Name(cls, ctx, pat, idx):
id = pat.id
if id == 'NotImplemented' or id == 'Ellipsis':
return {}
else:
raise TyError("Invalid constant pattern of type py.", pat)
@classmethod
def trans_pat_Name(cls, ctx, pat, idx, scrutinee_tr):
condition = ast.copy_location(
ast.Compare(
left=scrutinee_tr,
ops=[ast.Eq()],
comparators=[
ast.copy_location(
ast.Name(id=pat.id, ctx=pat.ctx),
pat)]),
pat)
return condition, {}
@classmethod
def ana_Dict(cls, ctx, e, idx):
for key, val in zip(e.keys, e.values):
ctx.ana(key, py_type)
ctx.ana(val, py_type)
@classmethod
def trans_Dict(cls, ctx, e, idx):
return ast.copy_location(
ast.Dict(
keys=[ctx.trans(key)
for key in e.keys],
values=[ctx.trans(val)
for val in e.values]),
e)
@classmethod
def ana_pat_Dict(cls, ctx, pat, idx):
used_keys = set()
bindings = { }
for key, value in zip(pat.keys, pat.values):
if isinstance(key, ast.Str):
key.id = key_id = key.s
elif isinstance(key, ast.Name):
key_id = key.id
else:
raise TyError("Invalid key in dict pattern.", key)
if key_id in used_keys:
raise TyError(
"Duplicate key.", key)
used_keys.add(key_id)
new_bindings = ctx.ana_pat(value, py_type)
_update_name_bindings_disjoint(bindings, new_bindings)
return bindings
@classmethod
def trans_pat_Dict(cls, ctx, pat, idx, scrutinee_trans):
# ('a', 'b', 'c') == tuple(scrutinee_trans.keys())
dict_condition = ast.fix_missing_locations(ast.copy_location(
astx.isinstance_builtin_id(scrutinee_trans, 'dict'),
pat))
keys_condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=ast.Set(
elts=[
ast.Str(s=key.id)
for key in pat.keys
]),
ops=[ast.Eq()],
comparators=[
astx.builtin_call(
'set',
[
ast.Call(
func=ast.Attribute(
value=scrutinee_trans,
attr='keys',
ctx=astx.load_ctx),
args=[],
keywords=[])]),
]),
pat))
conditions = [dict_condition, keys_condition]
binding_translations = { }
for key, value in zip(pat.keys, pat.values):
cur_scrutinee_tr = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(value=ast.Str(s=key.id)),
ctx=astx.load_ctx),
key))
cur_condition, cur_binding_translations = \
ctx.trans_pat(value, cur_scrutinee_tr)
conditions.append(cur_condition)
binding_translations.update(cur_binding_translations)
condition = ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=conditions),
pat)
return condition, binding_translations
@classmethod
def ana_Set(cls, ctx, e, idx):
for elt in e.elts:
ctx.ana(elt, py_type)
@classmethod
def trans_Set(cls, ctx, e, idx):
return ast.copy_location(
ast.Set(
elts=[
ctx.trans(elt)
for elt in e.elts]),
e)
@classmethod
def ana_List(cls, ctx, e, idx):
for elt in e.elts:
ctx.ana(elt, py_type)
@classmethod
def trans_List(cls, ctx, e, idx):
return ast.copy_location(
ast.List(
elts=[
ctx.trans(elt)
for elt in e.elts],
ctx=e.ctx),
e)
@classmethod
def ana_pat_List(cls, ctx, pat, idx):
bindings = { }
for elt in pat.elts:
new_bindings = ctx.ana_pat(elt, py_type)
_update_name_bindings_disjoint(bindings, new_bindings)
return bindings
@classmethod
def trans_pat_List(cls, ctx, pat, idx, scrutinee_trans):
list_condition = ast.fix_missing_locations(ast.copy_location(
astx.isinstance_builtin_id(scrutinee_trans, 'list'),
pat))
length_condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=astx.builtin_call('len', [scrutinee_trans]),
ops=[ast.Eq()],
comparators=[ast.Num(n=len(pat.elts))]),
pat))
conditions = [list_condition, length_condition]
binding_translations = { }
for i, elt in enumerate(pat.elts):
elt_scrutinee_trans = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(value=ast.Num(n=i)),
ctx=astx.load_ctx),
elt))
elt_condition, elt_binding_translations = \
ctx.trans_pat(elt, elt_scrutinee_trans)
conditions.append(elt_condition)
binding_translations.update(elt_binding_translations)
condition = ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=conditions),
pat)
return condition, binding_translations
@classmethod
def ana_Tuple(cls, ctx, e, idx):
for elt in e.elts:
ctx.ana(elt, py_type)
@classmethod
def trans_Tuple(cls, ctx, e, idx):
return ast.copy_location(
ast.Tuple(
elts=[
ctx.trans(elt)
for elt in e.elts],
ctx=e.ctx),
e)
@classmethod
def ana_pat_Tuple(cls, ctx, pat, idx):
bindings = { }
for elt in pat.elts:
new_bindings = ctx.ana_pat(elt, py_type)
_update_name_bindings_disjoint(bindings, new_bindings)
return bindings
@classmethod
def trans_pat_Tuple(cls, ctx, pat, idx, scrutinee_trans):
return cls._trans_pat_Tuple(ctx, pat, pat,
None, None,
None, scrutinee_trans)
@classmethod
def _trans_pat_Tuple(cls, ctx, pat, tpl_pat,
extender, extender_on_right,
binder,
scrutinee_trans):
tuple_condition = ast.fix_missing_locations(ast.copy_location(
astx.isinstance_builtin_id(scrutinee_trans, 'tuple'),
pat))
length_condition = ast.fix_missing_locations(ast.copy_location(
ast.Compare(
left=astx.builtin_call('len', [scrutinee_trans]),
ops=[ast.Eq() if extender is None else ast.GtE()],
comparators=[ast.Num(n=len(tpl_pat.elts))]),
pat))
conditions = [tuple_condition, length_condition]
binding_translations = { }
elts = tpl_pat.elts
n_elts = len(elts)
for i, elt in enumerate(elts):
elt_scrutinee_trans = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=ast.Index(
value=ast.Num(
n=i if extender is None or extender_on_right else -n_elts + i)),
ctx=astx.load_ctx),
elt))
elt_condition, elt_binding_translations = \
ctx.trans_pat(elt, elt_scrutinee_trans)
conditions.append(elt_condition)
binding_translations.update(elt_binding_translations)
if extender is not None:
if extender_on_right:
extender_slice = ast.Slice(
lower=ast.Num(n_elts),
upper=None,
step=None)
else:
extender_slice = ast.Slice(
lower=None,
upper=ast.Num(n=-n_elts),
step=None)
extender_translation = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=extender_slice,
ctx=astx.load_ctx),
pat))
binding_translations[extender.id] = extender_translation
if binder is not None:
if extender is None:
binder_translation = scrutinee_trans
else:
if extender_on_right:
binder_slice = ast.Slice(
lower=None,
upper=ast.Num(n=n_elts),
step=None)
else:
binder_slice = ast.Slice(
lower=ast.Num(n=-n_elts),
upper=None,
step=None)
binder_translation = ast.fix_missing_locations(ast.copy_location(
ast.Subscript(
value=scrutinee_trans,
slice=binder_slice,
ctx=astx.load_ctx),
pat))
binding_translations[binder.id] = binder_translation
condition = ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=conditions),
pat)
return condition, binding_translations
@classmethod
def ana_Ellipsis(cls, ctx, e, idx):
return
@classmethod
def trans_Ellipsis(cls, ctx, e, idx):
return ast.copy_location(
ast.Ellipsis(),
e)
@classmethod
def ana_Bytes(cls, ctx, e, idx):
return
@classmethod
def trans_Bytes(cls, ctx, e, idx):
return ast.copy_location(
ast.Bytes(s=e.s),
e)
@classmethod
def ana_pat_Bytes(cls, ctx, pat, idx):
return {}
@classmethod
def trans_pat_Bytes(cls, ctx, pat, idx, scrutinee_tr):
condition = ast.copy_location(
ast.Compare(
left=scrutinee_tr,
ops=[ast.Eq()],
comparators=[ast.copy_location(
ast.Bytes(s=pat.s),
pat)]),
pat)
return condition, {}
@classmethod
def syn_FunctionDef(cls, ctx, stmt):
cls.ana_FunctionDef(ctx, stmt, ())
return py_type
@classmethod
def ana_FunctionDef(cls, ctx, stmt, idx):
# process decorators
decorator_list = stmt.decorator_list
if hasattr(stmt, "fragment_ascription"):
decorator_list = decorator_list[1:]
if len(decorator_list) > 0:
for decorator in decorator_list:
ctx.ana(decorator, py_type)
# process arguments
def _process_arg(arg):
annotation = arg.annotation
if annotation is not None:
ctx.ana(annotation, py_type)
name = ast.Name(id=arg.arg,
lineno=arg.lineno,
col_offset=arg.col_offset)
return (name, py_type)
def _process_args():
arguments = stmt.args
for arg in arguments.args:
yield _process_arg(arg)
vararg = arguments.vararg
if vararg is not None:
yield _process_arg(vararg)
kwonlyargs = arguments.kwonlyargs
if kwonlyargs is not None:
for kwonlyarg in kwonlyargs:
yield _process_arg(kwonlyarg)
kw_defaults = arguments.kw_defaults
if kw_defaults is not None:
for kw_default in kw_defaults:
ctx.ana(kw_default, py_type)
kwarg = arguments.kwarg
if kwarg is not None:
yield _process_arg(kwarg)
defaults = arguments.defaults
if defaults is not None:
for default in defaults:
ctx.ana(default, py_type)
arg_sig = stmt.arg_sig = OrderedDict(_process_args())
# return annotation
returns = stmt.returns
if returns is not None:
ctx.ana(returns, py_type)
# push bindings
self_name = ast.copy_location(
ast.Name(id=stmt.name),
stmt)
ctx.push_var_bindings({self_name : py_type})
uniq_arg_sig = { }
for name, ty in arg_sig.items():
id = name.id
ctx.add_id_var_binding(id, id, ty)
uniq_arg_sig[id] = (id, ty)
stmt.uniq_arg_sig = uniq_arg_sig
# process docstring
body = stmt.body
if (len(body) > 1
and isinstance(body[0], ast.Expr)
and isinstance(body[0].value, ast.Str)):
proper_body = stmt.proper_body = body[1:]
docstring = stmt.docstring = body[0].value.s
else:
proper_body = stmt.proper_body = body
docstring = stmt.docstring = None
# check statements in proper_body
proper_body_block = stmt.proper_body_block = _terms.Block(proper_body)
ctx.ana_block(proper_body_block, py_type)
# bindings
ctx.pop_var_bindings()
@classmethod
def trans_FunctionDef(cls, ctx, stmt, idx, mechanism):
uniq_id = stmt.uniq_id
# translate arguments
args = stmt.args
arguments_tr = ast.arguments(
args= [
ast.arg(
arg=stmt.uniq_arg_sig[arg.arg][0],
annotation=None if arg.annotation is None else ctx.trans(arg.annotation),
lineno=arg.lineno,
col_offset=arg.col_offset)
for arg in args.args
],
vararg=None if args.vararg is None else ast.copy_location(
ast.arg(
arg=args.vararg.arg,
annotation=None if args.vararg.annotation is None
else ctx.trans(args.vararg.annotation)), args.vararg),
kwonlyargs=[
ast.copy_location(
ast.arg(
arg=arg.arg,
annotation=None if arg.annotation is None
else ctx.trans(arg.annotation)), arg)
for arg in args.kwonlyargs
],
kw_defaults=[
ctx.trans(kw_default)
for kw_default in args.kw_defaults
],
kwarg=None if args.kwarg is None else ast.copy_location(
ast.arg(
arg=args.kwarg.arg,
annotation=None if args.kwarg.annotation is None
else ctx.trans(args.kwarg.annotation)), args.kwarg),
defaults=[
ctx.trans(default)
for default in args.defaults
])
# translate body
body_tr = ctx.trans_block(stmt.proper_body_block,
BlockTransMechanism.Return)
return [ast.copy_location(
ast.FunctionDef(
name=uniq_id,
args=arguments_tr,
body=body_tr,
decorator_list=[],
returns=None),
stmt)]
@classmethod
def integrate_static_FunctionDef(cls, ctx, stmt):
name_ast = ast.copy_location(
ast.Name(id=stmt.name, ctx=astx.load_ctx),
stmt)
stmt.uniq_bindings = uniq_bindings = ctx.add_bindings({ name_ast: stmt.ty })
stmt.uniq_id = uniq_bindings[stmt.name][0]
@classmethod
def integrate_trans_FunctionDef(cls, ctx, stmt, translation, mechanism):
uniq_id = stmt.uniq_id
if mechanism == BlockTransMechanism.Return:
translation.append(ast.copy_location(
ast.Return(
value=ast.copy_location(
ast.Name(
id=uniq_id,
ctx=astx.load_ctx),
stmt)),
stmt))
@classmethod
def ana_Lambda(cls, ctx, e, idx):
# process args
arguments = e.args
for default in arguments.defaults:
ctx.ana(default, py_type)
for default in arguments.kw_defaults:
ctx.ana(default, py_type)
def _process_arg(arg):
name = ast.Name(id=arg.arg,
lineno=arg.lineno,
col_offset=arg.col_offset)
return (name, py_type)
def _process_args():
for arg in arguments.args:
yield _process_arg(arg)
vararg = arguments.vararg
if vararg is not None:
yield _process_arg(vararg)
kwonlyargs = arguments.kwonlyargs
if kwonlyargs is not None:
for kwonlyarg in kwonlyargs:
yield _process_arg(kwonlyarg)
kwarg = arguments.kwarg
if kwarg is not None:
yield _process_arg(kwarg)
arg_sig = e.arg_sig = OrderedDict(_process_args())
# push bindings
ctx.push_var_bindings({})
uniq_arg_sig = { }
for name, ty in arg_sig.items():
id = name.id
ctx.add_id_var_binding(id, id, ty)
uniq_arg_sig[id] = (id, ty)
e.uniq_arg_sig = uniq_arg_sig
# body
ctx.ana(e.body, py_type)
ctx.pop_var_bindings()
@classmethod
def trans_Lambda(cls, ctx, e, idx):
# translate arguments
args = e.args
arguments_tr = ast.arguments(
args=args.args,
vararg=args.vararg,
kwonlyargs=args.kwonlyargs,
kw_defaults=[
ctx.trans(kw_default)
for kw_default in args.kw_defaults
],
kwarg=args.kwarg,
defaults=[
ctx.trans(default)
for default in args.defaults
])
# translate body
body_tr = ctx.trans(e.body)
return ast.copy_location(
ast.Lambda(
args=arguments_tr,
body=body_tr), e)
@classmethod
def ana_DictComp(cls, ctx, e, idx):
generators = e.generators
for generator in generators:
ctx.ana(generator.iter, py_type)
target = generator.target
bindings = ctx.ana_pat(generator.target, py_type)
var_bindings = ctx.push_var_bindings(bindings)
generator.var_bindings = var_bindings
for cond in generator.ifs:
ctx.ana(cond, py_type)
ctx.ana(e.key, py_type)
ctx.ana(e.value, py_type)
for generator in generators:
ctx.pop_var_bindings()
@classmethod
def trans_DictComp(cls, ctx, e, idx):
return ast.copy_location(
ast.DictComp(
key=ctx.trans(e.key),
value=ctx.trans(e.value),
generators=[
ast.comprehension(
target=cls._trans_simple_pat(ctx,
generator.var_bindings,
generator.target),
iter=ctx.trans(generator.iter),
ifs=[ctx.trans(cond)
for cond in generator.ifs],
is_async=generator.is_async)
for generator in e.generators]),
e)
@classmethod
def _trans_simple_pat(cls, ctx, var_bindings, target):
if isinstance(target, ast.Tuple):
return ast.copy_location(
ast.Tuple(
elts=[
cls._trans_simple_pat(ctx, elt)
for elt in target.elts],
ctx=target.ctx),
target)
elif isinstance(target, ast.List):
return ast.copy_location(
ast.List(
elts=[
cls._trans_simple_pat(ctx, elt)
for elt in target.elts],
ctx=target.ctx),
target)
elif isinstance(target, ast.Name):
return ast.copy_location(
ast.Name(
id=var_bindings[target.id][0],
ctx=target.ctx),
target)
else:
raise TyError("Invalid pattern form in generator.", target)
@classmethod
def ana_SetComp(cls, ctx, e, idx):
generators = e.generators
for generator in generators:
ctx.ana(generator.iter, py_type)
target = generator.target
bindings = ctx.ana_pat(generator.target, py_type)
var_bindings = ctx.push_var_bindings(bindings)
generator.var_bindings = var_bindings
for cond in generator.ifs:
ctx.ana(cond, py_type)
ctx.ana(e.elt, py_type)
for generator in generators:
ctx.pop_var_bindings()
@classmethod
def trans_SetComp(cls, ctx, e, idx):
return ast.copy_location(
ast.SetComp(
elt=ctx.trans(e.elt),
generators=[
ast.comprehension(
target=cls._trans_simple_pat(ctx,
generator.var_bindings,
generator.target),
iter=ctx.trans(generator.iter),
ifs=[ctx.trans(cond)
for cond in generator.ifs],
is_async=generator.is_async)
for generator in e.generators]),
e)
@classmethod
def ana_ListComp(cls, ctx, e, idx):
generators = e.generators
for generator in generators:
ctx.ana(generator.iter, py_type)
target = generator.target
bindings = ctx.ana_pat(generator.target, py_type)
var_bindings = ctx.push_var_bindings(bindings)
generator.var_bindings = var_bindings
for cond in generator.ifs:
ctx.ana(cond, py_type)
ctx.ana(e.elt, py_type)
for generator in generators:
ctx.pop_var_bindings()
@classmethod
def trans_ListComp(cls, ctx, e, idx):
return ast.copy_location(
ast.ListComp(
elt=ctx.trans(e.elt),
generators=[
ast.comprehension(
target=cls._trans_simple_pat(ctx,
generator.var_bindings,
generator.target),
iter=ctx.trans(generator.iter),
ifs=[ctx.trans(cond)
for cond in generator.ifs],
is_async=generator.is_async)
for generator in e.generators]),
e)
@classmethod
def ana_GeneratorExp(cls, ctx, e, idx):
generators = e.generators
for generator in generators:
ctx.ana(generator.iter, py_type)
target = generator.target
bindings = ctx.ana_pat(generator.target, py_type)
var_bindings = ctx.push_var_bindings(bindings)
generator.var_bindings = var_bindings
for cond in generator.ifs:
ctx.ana(cond, py_type)
ctx.ana(e.elt, py_type)
for generator in generators:
ctx.pop_var_bindings()
@classmethod
def trans_GeneratorExp(cls, ctx, e, idx):
return ast.copy_location(
ast.GeneratorExp(
elt=ctx.trans(e.elt),
generators=[
ast.comprehension(
target=cls._trans_simple_pat(ctx,
generator.var_bindings,
generator.target),
iter=ctx.trans(generator.iter),
ifs=[ctx.trans(cond)
for cond in generator.ifs],
is_async=generator.is_async)
for generator in e.generators]),
e)
@classmethod
def syn_BoolOp(cls, ctx, e):
for value in e.values:
ctx.ana(value, py_type)
return py_type
@classmethod
def ana_BoolOp(cls, ctx, e, idx):
for value in e.values:
ctx.ana(value, py_type)
@classmethod
def trans_BoolOp(cls, ctx, e, idx=None):
return ast.copy_location(
ast.BoolOp(
op=e.op,
values=[ctx.trans(value)
for value in e.values]),
e)
@classmethod
def syn_BinOp(cls, ctx, e):
ctx.ana(e.left, py_type)
ctx.ana(e.right, py_type)
return py_type
@classmethod
def trans_BinOp(cls, ctx, e):
return ast.copy_location(
ast.BinOp(
left=ctx.trans(e.left),
op=e.op,
right=ctx.trans(e.right)),
e)
@classmethod
def ana_pat_BinOp(cls, ctx, pat, idx):
left, op, right = pat.left, pat.op, pat.right
if isinstance(op, ast.Add):
if isinstance(left, ast.Str) or isinstance(right, ast.Str):
pretend_pat = ast.fix_missing_locations(ast.copy_location(
ast.Call(
func=ast.Name(id="str", ctx=astx.load_ctx),
args=[],
keywords=[]),
pat))
pretend_pat.args.append(astx.deep_copy_node(pat))
pat._pretend_pat = pretend_pat
return ctx.ana_pat(pretend_pat, py_type)
else:
if isinstance(left, ast.Tuple) and isinstance(right, ast.Name):
extender = right
extender_on_right = True
binder_pat = None
tuple_pat = left
elif isinstance(right, ast.Tuple) and isinstance(left, ast.Name):
extender = left
extender_on_right = False
binder_pat = None
tuple_pat = right
elif cls._is_Tuple_binder(left) and isinstance(right, ast.Name):
extender = right
extender_on_right = True
binder_pat = left
tuple_pat = left.func
elif cls._is_Tuple_binder(right) and isinstance(left, ast.Name):
extender = left
extender_on_right = False
binder_pat = right
tuple_pat = right.func
else:
raise TyError("Invalid pattern.", pat)
pat._extender = extender
pat._extender_on_right = extender_on_right
pat._binder_pat = binder_pat
pat._tuple_pat = tuple_pat
if binder_pat is None:
tuple_bindings = ctx.ana_pat(tuple_pat, py_type)
pat._binder = None
else:
tuple_bindings = ctx.ana_pat(binder_pat, py_type)
pat._binder = binder_pat.args[0]
bindings = dict(tuple_bindings)
_update_name_bindings_disjoint(bindings, { extender : py_type })
return bindings
else:
raise TyError("Invalid pattern.", pat)
@classmethod
def trans_pat_BinOp(cls, ctx, pat, idx, scrutinee_trans):
try: pretend_pat = pat._pretend_pat
except AttributeError:
return cls._trans_pat_Tuple(
ctx, pat, pat._tuple_pat,
pat._extender, pat._extender_on_right,
pat._binder,
scrutinee_trans)
else:
return ctx.trans_pat(pretend_pat, scrutinee_trans)
@classmethod
def syn_UnaryOp(cls, ctx, e, idx):
ctx.ana(e.operand, py_type)
return py_type
@classmethod
def syn_Compare(cls, ctx, e):
ctx.ana(e.left, py_type)
for comparator in e.comparators:
ctx.ana(comparator, py_type)
return py_type
@classmethod
def trans_Compare(cls, ctx, e):
return ast.copy_location(
ast.Compare(
left=ctx.trans(e.left),
ops=e.ops,
comparators=[
ctx.trans(comparator)
for comparator in e.comparators]),
e)
@classmethod
def syn_IfExp(cls, ctx, e, idx):
body_ty = ctx.syn(e.body)
ctx.ana(e.orelse, body_ty)
return body_ty
@classmethod
def ana_IfExp(cls, ctx, e, idx, ty):
ctx.ana(e.body, ty)
ctx.ana(e.orelse, ty)
@classmethod
def trans_IfExp(cls, ctx, e, idx):
return ast.copy_location(
ast.IfExp(
test=ctx.trans(e.test),
body=ctx.trans(e.body),
orelse=ctx.trans(e.orelse)),
e)
@classmethod
def syn_Call(cls, ctx, e, idx):
for arg in e.args:
if isinstance(arg, ast.Starred):
ctx.ana(arg.value, py_type)
else:
ctx.ana(arg, py_type)
used_kws = set()
for keyword in e.keywords:
kw_arg, kw_value = keyword.arg, keyword.value
if kw_arg in used_kws:
raise TyError("Duplicate keyword arguments.", kw_value)
used_kws.add(kw_arg)
ctx.ana(kw_value, py_type)
return py_type
@classmethod
def trans_Call(cls, ctx, e, idx):
return ast.copy_location(
ast.Call(
func=ctx.trans(e.func),
args=[
ast.copy_location(
ast.Starred(
value=ctx.trans(arg.value),
ctx=arg.ctx),
arg)
if isinstance(arg, ast.Starred)
else ctx.trans(arg)
for arg in e.args],
keywords=[
ast.keyword(
arg=kw.arg,
value=ctx.trans(kw.value))
for kw in e.keywords]),
e)
@classmethod
def _is_Tuple_binder(cls, pat):
return (isinstance(pat, ast.Call) and
isinstance(pat.func, ast.Tuple) and
len(pat.args) == 1 and
isinstance(pat.args[0], ast.Name))
@classmethod
def ana_pat_Call(cls, ctx, pat, idx):
func, args, keywords = pat.func, pat.args, pat.keywords
pat._is_instance_pattern = False
if isinstance(func, ast.Name):
func_id = func.id
if func_id == "str":
if len(args) == 1 and len(keywords) == 0:
return ctx.ana_pat(args[0], string_ty)
else:
raise TyError("Too many arguments in pattern.", pat)
elif func_id == "int":
if len(args) == 1 and len(keywords) == 0:
return ctx.ana_pat(args[0], num_ty)
else:
raise TyError("Too many arguments in pattern.", pat)
elif func_id == "float":
if len(args) == 1 and len(keywords) == 0:
return ctx.ana_pat(args[0], ieee_ty)
else:
raise TyError("Too many arguments in pattern.", pat)
elif func_id == "bool" and len(keywords) == 0:
if len(args) == 1:
return ctx.ana_pat(args[0], boolean_ty)
else:
raise TyError("Too many arguments in pattern.", pat)
elif isinstance(func, ast.Tuple):
if len(args) == 1 and isinstance(args[0], ast.Name):
func_bindings = ctx.ana_pat(func, py_type)
bindings = dict(func_bindings)
new_binding = { args[0] : CanonicalTy(tpl, OrderedDict(
(i, py_type)
for i in range(len(func.elts))
)) }
_update_name_bindings_disjoint(bindings, new_binding)
return bindings
else:
raise TyError("Invalid pattern format.", pat)
if len(args) == 0:
pat._is_instance_pattern = True
ctx.ana(func, py_type)
used_keywords = set()
bindings = {}
for keyword in keywords:
arg = keyword.arg
if arg in used_keywords:
raise TyError("Duplicate keyword: " + arg, pat)
used_keywords.add(arg)
kw_bindings = ctx.ana_pat(keyword.value, py_type)
_update_name_bindings_disjoint(bindings, kw_bindings)
return bindings
else:
raise TyError("Invalid pattern.")
@classmethod
def trans_pat_Call(cls, ctx, pat, idx, scrutinee_trans):
if pat._is_instance_pattern:
cls_translation = ctx.trans(pat.func)
cls_condition = ast.fix_missing_locations(ast.copy_location(
astx.builtin_call('isinstance', []), pat))
cls_condition.args.append(scrutinee_trans)
cls_condition.args.append(cls_translation)
conditions = [cls_condition]
binding_translations = { }
for keyword in pat.keywords:
arg = keyword.arg
arg_scrutinee = ast.copy_location(
ast.Attribute(
value=scrutinee_trans,
attr=arg,
ctx=astx.load_ctx),
pat)
kw_condition, kw_binding_translations = \
ctx.trans_pat(keyword.value, arg_scrutinee)
conditions.append(kw_condition)
binding_translations.update(kw_binding_translations)
condition = ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=conditions),
pat)
else:
func = pat.func
if isinstance(func, ast.Name):
func_id = func.id
if func_id == "str":
tag_condition = astx.isinstance_builtin_id(scrutinee_trans, 'str')
elif func_id == "int":
tag_condition = astx.isinstance_builtin_id(scrutinee_trans, 'int')
elif func_id == "float":
tag_condition = astx.isinstance_builtin_id(scrutinee_trans, 'float')
elif func_id == "bool":
tag_condition = astx.isinstance_builtin_id(scrutinee_trans, 'bool')
tag_condition = ast.fix_missing_locations(ast.copy_location(
tag_condition, pat))
rec_condition, binding_translations = \
ctx.trans_pat(pat.args[0], scrutinee_trans)
condition = ast.copy_location(
ast.BoolOp(
op=ast.And(),
values=[tag_condition, rec_condition]),
pat)
else: # if isinstance(func, ast.Tuple):
return cls._trans_pat_Tuple(ctx, pat, pat.func,
None, None,
pat.args[0],
scrutinee_trans)
return condition, binding_translations
@classmethod
def syn_Attribute(cls, ctx, e, idx):
return py_type
@classmethod
def trans_Attribute(cls, ctx, e, idx):
return ast.copy_location(
ast.Attribute(
value=ctx.trans(e.value),
attr=e.attr,
ctx=e.ctx),
e)
@classmethod
def syn_Subscript(cls, ctx, e, idx):
cls._ana_slice(ctx, e.slice)
return py_type
@classmethod
def _ana_slice(cls, ctx, slice):
if isinstance(slice, ast.Index):
ctx.ana(slice.value, py_type)
elif isinstance(slice, ast.Slice):
lower, upper, step = slice.lower, slice.upper, slice.step
if lower is not None and not astx.is_underscore(lower):
ctx.ana(lower, py_type)
if upper is not None and not astx.is_underscore(upper):
ctx.ana(upper, py_type)
if step is not None and not astx.is_underscore(step):
ctx.ana(step, py_type)
else: # ExtSlice
for dim in slice.dims:
cls._ana_slice(ctx, dim)
@classmethod
def trans_Subscript(cls, ctx, e, idx):
return ast.copy_location(
ast.Subscript(
value=ctx.trans(e.value),
slice=cls._trans_slice(ctx, e.slice),
ctx=e.ctx),
e)
@classmethod
def _trans_slice(cls, ctx, slice):
if isinstance(slice, ast.Index):
return ast.copy_location(
ast.Index(
value=ctx.trans(slice.value)),
slice)
elif isinstance(slice, ast.Slice):
lower, upper, step = slice.lower, slice.upper, slice.step
lower_trans = (None if lower is None or astx.is_underscore(lower)
else ctx.trans(lower))
upper_trans = (None if upper is None or astx.is_underscore(upper)
else ctx.trans(upper))
step_trans = (None if step is None or astx.is_underscore(step)
else ctx.trans(step))
return ast.copy_location(
ast.Slice(
lower=lower_trans,
upper=upper_trans,
step=step_trans),
slice)
else: # ExtSlice
return ast.copy_location(
ast.ExtSlice(
dims=[
cls._trans_slice(ctx, dim)
for dim in slice.dims]),
slice)
py_type = CanonicalTy(py, ())
def _check_trivial_idx_ast(idx_ast):
if (isinstance(idx_ast, ast.Index) and
isinstance(idx_ast.value, ast.Tuple) and
len(idx_ast.value.elts) == 0):
return ()
else:
raise TypeValidationError(
"unit type can only have trivial index.", idx_ast)
# TODO bytes
# TODO ilist
# TODO dict
# TODO mlist
# TODO complex?
# TODO decimal?
# Maybe not in the standard library?
# TODO proto
# TODO string_in
# TODO numpy stuff
# TODO cl stuff
| 35.358709 | 93 | 0.51598 | 13,150 | 119,371 | 4.510875 | 0.031635 | 0.05334 | 0.043494 | 0.047945 | 0.812856 | 0.76068 | 0.721484 | 0.683014 | 0.645875 | 0.610708 | 0 | 0.001439 | 0.394669 | 119,371 | 3,375 | 94 | 35.369185 | 0.819469 | 0.012398 | 0 | 0.723143 | 0 | 0 | 0.037661 | 0 | 0 | 0 | 0 | 0.000296 | 0 | 1 | 0.077918 | false | 0.000332 | 0.003316 | 0.024536 | 0.153183 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
81d879cb40285dc4c726ee0a9dcf6927ac070008 | 256 | py | Python | app/model.py | opendata-yamanashi/aed-kofu-api | 163cb39e65caaaefaedc9c3da6b8d6e6e535bfb5 | [
"MIT"
] | null | null | null | app/model.py | opendata-yamanashi/aed-kofu-api | 163cb39e65caaaefaedc9c3da6b8d6e6e535bfb5 | [
"MIT"
] | null | null | null | app/model.py | opendata-yamanashi/aed-kofu-api | 163cb39e65caaaefaedc9c3da6b8d6e6e535bfb5 | [
"MIT"
] | null | null | null | from pydantic import BaseModel, Field
class AED_Data(BaseModel):
facility: str = Field(None, alias="設置施設")
facility_detail: str = Field(None, alias="設置箇所(詳細に)")
address: str = Field(None, alias="住所")
control: str = Field(None, alias="所管")
| 32 | 57 | 0.679688 | 35 | 256 | 4.914286 | 0.571429 | 0.186047 | 0.27907 | 0.395349 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.171875 | 256 | 7 | 58 | 36.571429 | 0.811321 | 0 | 0 | 0 | 0 | 0 | 0.066406 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.166667 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 5 |
81d8d32606486624ebddd57513d1eb8eeec74ffe | 41 | py | Python | Python/Tests/TestData/AddExistingFolder/Program.py | techkey/PTVS | 8355e67eedd8e915ca49bd38a2f36172696fd903 | [
"Apache-2.0"
] | 404 | 2019-05-07T02:21:57.000Z | 2022-03-31T17:03:04.000Z | Python/Tests/TestData/AddExistingFolder/Program.py | techkey/PTVS | 8355e67eedd8e915ca49bd38a2f36172696fd903 | [
"Apache-2.0"
] | 1,672 | 2019-05-06T21:09:38.000Z | 2022-03-31T23:16:04.000Z | Python/Tests/TestData/AddExistingFolder/Program.py | techkey/PTVS | 8355e67eedd8e915ca49bd38a2f36172696fd903 | [
"Apache-2.0"
] | 186 | 2019-05-13T03:17:37.000Z | 2022-03-31T16:24:05.000Z | print('hello world')
while True:
pass | 13.666667 | 20 | 0.682927 | 6 | 41 | 4.666667 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.195122 | 41 | 3 | 21 | 13.666667 | 0.848485 | 0 | 0 | 0 | 0 | 0 | 0.261905 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.333333 | 0 | 0 | 0 | 0.333333 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
81db4369dee9666c7329029cb7ebe5a229952054 | 240 | py | Python | unit6/spiders/p4_pipeline_handson/p4_pipeline_handson/settings.py | nulearn3296/scrapy-training | 8981dbc33b68bd7246839eee34ca8266d5a0066f | [
"BSD-3-Clause"
] | 182 | 2017-04-05T23:39:22.000Z | 2022-02-22T19:49:52.000Z | unit6/spiders/p4_pipeline_handson/p4_pipeline_handson/settings.py | nulearn3296/scrapy-training | 8981dbc33b68bd7246839eee34ca8266d5a0066f | [
"BSD-3-Clause"
] | 3 | 2017-04-18T07:16:39.000Z | 2019-05-04T22:54:53.000Z | unit6/spiders/p4_pipeline_handson/p4_pipeline_handson/settings.py | nulearn3296/scrapy-training | 8981dbc33b68bd7246839eee34ca8266d5a0066f | [
"BSD-3-Clause"
] | 53 | 2017-04-07T03:25:54.000Z | 2022-02-21T21:51:01.000Z | BOT_NAME = 'p4_pipeline_handson'
SPIDER_MODULES = ['p4_pipeline_handson.spiders']
NEWSPIDER_MODULE = 'p4_pipeline_handson.spiders'
ROBOTSTXT_OBEY = True
ITEM_PIPELINES = {
'p4_pipeline_handson.pipelines.SaveToFilesPipeline': 300,
}
| 20 | 61 | 0.795833 | 28 | 240 | 6.357143 | 0.607143 | 0.224719 | 0.382022 | 0.269663 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.03271 | 0.108333 | 240 | 11 | 62 | 21.818182 | 0.799065 | 0 | 0 | 0 | 0 | 0 | 0.508333 | 0.429167 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
81e054604932a6070fd4371cb65f3d7622e5da61 | 3,828 | py | Python | leetcode/LeetCode_79_WordSearch.py | pursue-wind/leetcode | dd2b6734d3f95b49664a4f6a7775443996c8e4da | [
"Apache-2.0"
] | null | null | null | leetcode/LeetCode_79_WordSearch.py | pursue-wind/leetcode | dd2b6734d3f95b49664a4f6a7775443996c8e4da | [
"Apache-2.0"
] | null | null | null | leetcode/LeetCode_79_WordSearch.py | pursue-wind/leetcode | dd2b6734d3f95b49664a4f6a7775443996c8e4da | [
"Apache-2.0"
] | null | null | null | from typing import List
class Solution:
opt = [(0, -1), (1, 0), (0, 1), (-1, 0)]
def exist(self, board: List[List[str]], word: str) -> bool:
m = len(board)
if m == 0:
return False
n = len(board[0])
visited = [[False for _ in range(n)] for _ in range(m)]
for i in range(m):
for j in range(n):
if self.word_search(board, visited, word, 0, i, j):
return True
return False
def word_search(self, board, visited, word, index, x, y) -> bool:
def in_area(m, n) -> bool:
return 0 <= m < len(board) and 0 <= n < len(board[0])
# 如果是最后一个元素
if len(word) - 1 == index:
return word[index] == board[x][y]
# 如果当前元素匹配
if word[index] == board[x][y]:
visited[x][y] = True
for op in self.opt:
n_x = x + op[0]
n_y = y + op[1]
if in_area(n_x, n_y) and not visited[n_x][n_y] \
and self.word_search(board, visited, word, index + 1, n_x, n_y):
return True
visited[x][y] = False
return False
class Solution3:
def __init__(self):
self.visited = set()
opt = [(0, -1), (1, 0), (0, 1), (-1, 0)]
def exist(self, board: List[List[str]], word: str) -> bool:
m, n = len(board), len(board[0])
for i in range(m):
for j in range(n):
if self.word_search(board, word, 0, i, j):
return True
return False
def word_search(self, board, word, index, x, y) -> bool:
def in_area(m, n) -> bool:
return 0 <= m < len(board) and 0 <= n < len(board[0])
# 如果是最后一个元素
if len(word) - 1 == index:
return word[index] == board[x][y]
# 如果当前元素匹配
if word[index] == board[x][y]:
self.visited.add((x, y))
for a, b in self.opt:
n_x, n_y = x + a, y + b
if in_area(n_x, n_y) and (n_x, n_y) not in self.visited and self.word_search(board, word, index + 1,
n_x, n_y):
return True
self.visited.remove((x, y))
return False
class Solution4:
opt = [(0, -1), (1, 0), (0, 1), (-1, 0)]
def exist(self, board: List[List[str]], word: str) -> bool:
m, n = len(board), len(board[0])
need = dict()
for x in word:
need[x] = word.count(x)
for i in range(m):
for j in range(n):
if board[i][j] in need: need[board[i][j]] -= 1
if any(map(lambda x: x > 0, need.values())):
return False
def word_search(index, x, y) -> bool:
def in_area(m, n) -> bool:
return 0 <= m < len(board) and 0 <= n < len(board[0])
# 如果是最后一个元素
if len(word) - 1 == index: return word[index] == board[x][y]
# 如果当前元素匹配
if word[index] == board[x][y]:
visited[x][y] = True
for a, b in self.opt:
n_x, n_y = x + a, y + b
if in_area(n_x, n_y) and not visited[n_x][n_y] and word_search(index + 1, n_x, n_y):
return True
visited[x][y] = False
return False
visited = [[False for _ in range(n)] for _ in range(m)]
for i in range(m):
for j in range(n):
if board[i][j] == word[0]:
if word_search(0, i, j): return True
return False
s = Solution4()
b = s.exist(
[
["A", "B", "C", "E"],
["S", "F", "C", "S"],
["A", "D", "E", "E"]
],
"SEE")
print(b)
| 30.624 | 116 | 0.439655 | 550 | 3,828 | 2.974545 | 0.110909 | 0.018337 | 0.020171 | 0.026895 | 0.787897 | 0.753056 | 0.732885 | 0.718215 | 0.718215 | 0.7011 | 0 | 0.024032 | 0.413009 | 3,828 | 124 | 117 | 30.870968 | 0.70405 | 0.014629 | 0 | 0.55914 | 0 | 0 | 0.003984 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.107527 | false | 0 | 0.010753 | 0.032258 | 0.376344 | 0.010753 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
c499de86c6aaf1d0b684233f3a557bec36e619ba | 94 | py | Python | demoapp/models.py | mattykay/django-celery-poc | 80c1aa78bb80686e6b5be2870a0df3d2250e8e8e | [
"MIT"
] | null | null | null | demoapp/models.py | mattykay/django-celery-poc | 80c1aa78bb80686e6b5be2870a0df3d2250e8e8e | [
"MIT"
] | 5 | 2021-03-19T02:24:20.000Z | 2022-02-10T14:07:24.000Z | demoapp/models.py | mattykay/django-celery-poc | 80c1aa78bb80686e6b5be2870a0df3d2250e8e8e | [
"MIT"
] | null | null | null | from __future__ import absolute_import, unicode_literals
from django.db import models # noqa | 31.333333 | 56 | 0.840426 | 13 | 94 | 5.615385 | 0.769231 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.12766 | 94 | 3 | 57 | 31.333333 | 0.890244 | 0.042553 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 5 |
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