text string | size int64 | token_count int64 |
|---|---|---|
"""Represent a class to generate a meme."""
from PIL import Image, ImageDraw, ImageFont
from os import makedirs
from random import randint
from textwrap import fill
class MemeGenerator:
"""
A class to generate a meme.
The following responsibilities are defined under this class:
- Loading of an image from a disk.
- Transforming the image by resizing to a maximum width of 500px while
maintaining the input aspect ratio.
- Adding a caption to the image with a body and author to a random location
on the image.
"""
def __init__(self, output_dir: str):
"""
Create a new `MemeGenerator`.
Parameters
----------
`output_dir`: str
output directory to save the generated meme.
"""
self.output_dir = output_dir
makedirs(self.output_dir, exist_ok=True)
def validate_width(self, width: int) -> ValueError:
"""
Assert whether desired width of the image does not exceed 500px.
Raise `ValueError` if width greater than 500px.
Parameters
----------
`width`: int
width of the image.
"""
if width > 500:
raise ValueError('Width of the image cannot exceed 500px.')
def make_meme(self, img_path: str, text: str,
author: str, width: int = 500) -> str:
"""
Return path of the saved image of the meme after adding caption to it.
Parameters
----------
`img_path`: str
path of the original image.
`text`: str
quote of the meme.
`author`: str
author of the meme.
`width`: int
desired width of the image, default = 500px.
"""
# Opening the image.
img = Image.open(img_path)
# Try-except block to handle instances where width > 500px.
try:
self.validate_width(width)
except ValueError as val_err:
print(val_err)
else:
# Resizing the image proportionate to the given width.
ratio = width / float(img.size[0])
height = int(ratio * float(img.size[1]))
img = img.resize((width, height), Image.NEAREST)
# Adding the caption to the image.
caption = fill(f'{text} - {author}', width=40)
font = ImageFont.truetype('./_data/font/Candara.ttf', size=20)
draw = ImageDraw.Draw(img)
draw.text(xy=(randint(10, 40),
randint(20, 70)),
text=caption,
font=font,
fill='white')
# Saving the image to the output directory.
output_path = f'{self.output_dir}/{randint(0, 1000)}.png'
try:
img.save(output_path)
except Exception as e:
print(e)
return output_path
| 2,889 | 844 |
from collections import Counter
class Workload:
def __init__(self, tasks):
self.per_person = Counter(task.assignee for task in tasks)
def assign(self, task, person):
task.assignee = person
self.per_person[person] += 1
return task
def assigned_to(self, person):
return self.per_person[person]
def get_least_busy_person(self, people):
if not people:
return None
return sorted(people, key=lambda person: self.assigned_to(person))[0] | 519 | 158 |
"""
This script produces the summary analysis required to provide input data for the OSeMOSYS soft-link
"""
SET_TOTAL_ENERGY_PER_CELL = "TotalEnergyPerCell"
SET_ELEC_FINAL_CODE = "FinalElecCode"
SET_ELEC_CURRENT = 'ElecStart'
SET_POP = 'Pop'
SET_NEW_CONNECTIONS = 'NewConnections'
SET_NEW_CAPACITY = 'NewCapacity'
SET_INVESTMENT_COST = 'InvestmentCost'
SET_TRANSMISSION_INV = 'TransmissionInvestmentCost'
SET_GHI = 'GHI'
SET_WINDCF = 'WindCF'
import os
import tkinter as tk
from tkinter import filedialog, messagebox
import pandas as pd
root = tk.Tk()
root.withdraw()
root.attributes("-topmost", True)
messagebox.showinfo('OnSSET', 'Open the csv file with results data')
result_csv_path = filedialog.askopenfilename()
messagebox.showinfo('OnSSET', 'Browse to SUMMARIES folder and name the scenario to save outputs')
summary_folder = filedialog.askdirectory()
df_in = pd.read_csv(result_csv_path)
df_in['PVType2018'] = 1
df_in['NewConnections2018'] = 0
df_in['NewCapacity2018'] = 0
df_in['InvestmentCost2018'] = 0
df_in['TransmissionInvestmentCost2018'] = 0
df_in['TotalEnergyPerCell2018'] = df_in['ElecPopCalib'] * df_in['ResidentialDemandTierCustom2018'] * df_in[SET_ELEC_CURRENT]
elements = ["1.Population", "2.New_Connections", "3.Capacity", "4.Investment", "5. Demand", "6. Transmission summaries", "7. Distribution summaries", "8. Capacity factor"]
techs = ["Grid", "MG_Diesel", "MG_PV", "MG_Wind", "MG_Hydro", "SA_PV_1", "SA_PV_2", "SA_PV_3", "SA_PV_4", "SA_PV_5"]
years = [2018, 2025, 2030, 2040, 2050, 2060, 2070]
tech_codes = [1, 4, 5, 6, 7, 8, 9, 10, 11, 12]
grid_power_plants_capital_cost = {2025: 663,
2030: 373,
2040: 3067,
2050: 982,
2060: 1583,
2070: 1352}
mg_diesel_capital_cost = {2025: 721,
2030: 721,
2040: 721,
2050: 721,
2060: 721,
2070: 721}
mg_pv_capital_cost = {2025: 2829,
2030: 2540,
2040: 2252,
2050: 1977,
2060: 1703,
2070: 1428}
mg_wind_capital_cost = {2025: 4356,
2030: 4285,
2040: 4213,
2050: 4117,
2060: 4021,
2070: 3926}
mg_hydro_capital_cost = {2025: 3000,
2030: 3000,
2040: 3000,
2050: 3000,
2060: 3000,
2070: 3000}
sa_pv_capital_cost_1 = {2025: 9067,
2030: 8143,
2040: 7218,
2050: 6338,
2060: 5458,
2070: 4577}
### Stand-alone PV capital cost (USD/kW) for household systems between 21-50 W
sa_pv_capital_cost_2 = {2025: 8487,
2030: 7621,
2040: 6756,
2050: 5932,
2060: 5108,
2070: 4285}
### Stand-alone PV capital cost (USD/kW) for household systems between 51-100 W
sa_pv_capital_cost_3 = {2025: 6165,
2030: 5537,
2040: 4908,
2050: 4310,
2060: 3711,
2070: 3113}
### Stand-alone PV capital cost (USD/kW) for household systems between 101-1000 W
sa_pv_capital_cost_4 = {2025: 4316,
2030: 3876,
2040: 3436,
2050: 3017,
2060: 2598,
2070: 2179}
### Stand-alone PV capital cost (USD/kW) for household systems over 1 kW
sa_pv_capital_cost_5 = {2025: 6710,
2030: 6026,
2040: 5342,
2050: 4690,
2060: 4039,
2070: 3387}
tech_costs = {1: grid_power_plants_capital_cost, 4: mg_diesel_capital_cost, 5: mg_pv_capital_cost, 6: mg_wind_capital_cost, 7: mg_hydro_capital_cost,
8: sa_pv_capital_cost_1, 9: sa_pv_capital_cost_2, 10: sa_pv_capital_cost_3, 11: sa_pv_capital_cost_4, 12: sa_pv_capital_cost_5}
for year in years:
df_in.loc[(df_in[SET_ELEC_FINAL_CODE + '{}'.format(year)] == 3) & (df_in['PVType' + '{}'.format(year)] == 1), SET_ELEC_FINAL_CODE + '{}'.format(year)] = 8
df_in.loc[(df_in[SET_ELEC_FINAL_CODE + '{}'.format(year)] == 3) & (df_in['PVType' + '{}'.format(year)] == 2), SET_ELEC_FINAL_CODE + '{}'.format(year)] = 9
df_in.loc[(df_in[SET_ELEC_FINAL_CODE + '{}'.format(year)] == 3) & (df_in['PVType' + '{}'.format(year)] == 3), SET_ELEC_FINAL_CODE + '{}'.format(year)] = 10
df_in.loc[(df_in[SET_ELEC_FINAL_CODE + '{}'.format(year)] == 3) & (df_in['PVType' + '{}'.format(year)] == 4), SET_ELEC_FINAL_CODE + '{}'.format(year)] = 11
df_in.loc[(df_in[SET_ELEC_FINAL_CODE + '{}'.format(year)] == 3) & (df_in['PVType' + '{}'.format(year)] == 5), SET_ELEC_FINAL_CODE + '{}'.format(year)] = 12
df_electrified = df_in.loc[df_in[SET_ELEC_CURRENT] == 1]
df_unelectrified = df_in.loc[df_in[SET_ELEC_CURRENT] == 0]
sumtechs = []
for element in elements:
for tech in techs:
sumtechs.append(element + "_" + tech)
total_rows = len(sumtechs)
electrified_sumtechs = pd.DataFrame(columns=years)
unelectrified_sumtechs = pd.DataFrame(columns=years)
combined_sumtechs = pd.DataFrame(columns=years)
for row in range(0, total_rows):
electrified_sumtechs.loc[sumtechs[row]] = 0
unelectrified_sumtechs.loc[sumtechs[row]] = 0
combined_sumtechs.loc[sumtechs[row]] = 0
i = 0
# Adding pop summary (Million ppl
for code in tech_codes:
for year in years:
electrified_sumtechs[year][sumtechs[i]] = sum(df_electrified.loc[df_electrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_POP + "{}".format(year)])/1000000
unelectrified_sumtechs[year][sumtechs[i]] = sum(df_unelectrified.loc[df_unelectrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_POP + "{}".format(year)])/1000000
combined_sumtechs[year][sumtechs[i]] = sum(df_in.loc[df_in[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_POP + "{}".format(year)]) / 1000000
i += 1
# Adding connections summary (Million ppl)
for code in tech_codes:
for year in years:
electrified_sumtechs[year][sumtechs[i]] = sum(df_electrified.loc[df_electrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_NEW_CONNECTIONS + "{}".format(year)])/1000000
unelectrified_sumtechs[year][sumtechs[i]] = sum(df_unelectrified.loc[df_unelectrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_NEW_CONNECTIONS + "{}".format(year)])/1000000
i += 1
# Adding capacity summaries (MW)
for code in tech_codes:
for year in years:
electrified_sumtechs[year][sumtechs[i]] = sum(df_electrified.loc[df_electrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_NEW_CAPACITY + "{}".format(year)])/1000
unelectrified_sumtechs[year][sumtechs[i]] = sum(df_unelectrified.loc[df_unelectrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_NEW_CAPACITY + "{}".format(year)])/1000
i += 1
# Adding investment summaries (Million USD)
for code in tech_codes:
for year in years:
electrified_sumtechs[year][sumtechs[i]] = sum(df_electrified.loc[df_electrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_INVESTMENT_COST + "{}".format(year)])/1000000
unelectrified_sumtechs[year][sumtechs[i]] = sum(df_unelectrified.loc[df_unelectrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_INVESTMENT_COST + "{}".format(year)])/1000000
i += 1
# Adding demand summaries (GWh)
for code in tech_codes:
for year in years:
electrified_sumtechs[year][sumtechs[i]] = sum(df_electrified.loc[df_electrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_TOTAL_ENERGY_PER_CELL + "{}".format(year)])/1000000
unelectrified_sumtechs[year][sumtechs[i]] = sum(df_unelectrified.loc[df_unelectrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_TOTAL_ENERGY_PER_CELL + "{}".format(year)])/1000000
i += 1
# Adding transmission summaries (Million USD)
for code in tech_codes:
for year in years:
if code == 1:
electrified_sumtechs[year][sumtechs[i]] = sum(df_electrified.loc[df_electrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code, SET_TRANSMISSION_INV + "{}".format(year)]) / 1000000
unelectrified_sumtechs[year][sumtechs[i]] = sum(df_unelectrified.loc[df_unelectrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code, SET_TRANSMISSION_INV + "{}".format(year)])/1000000
i += 1
# Adding distribution summaries (USD/kW)
for code in tech_codes:
for year in years:
if electrified_sumtechs[year][sumtechs[i-40]] > 0:
electrified_sumtechs[year][sumtechs[i]] = (electrified_sumtechs[year][sumtechs[i-30]]*1000000 -
electrified_sumtechs[year][sumtechs[i-10]]*1000000 -
electrified_sumtechs[year][sumtechs[i-40]]*1000 * tech_costs[code][year])/(electrified_sumtechs[year][sumtechs[i-40]]*1000)
if unelectrified_sumtechs[year][sumtechs[i-40]] > 0:
unelectrified_sumtechs[year][sumtechs[i]] = (unelectrified_sumtechs[year][sumtechs[i-30]]*1000000 -
unelectrified_sumtechs[year][sumtechs[i-10]]*1000000 -
unelectrified_sumtechs[year][sumtechs[i-40]]*1000 * tech_costs[code][year])/(unelectrified_sumtechs[year][sumtechs[i-40]]*1000)
i += 1
for code in tech_codes:
for year in years:
if code == 5 or code > 7:
if electrified_sumtechs[year][sumtechs[i-40]] > 0:
electrified_sumtechs[year][sumtechs[i]] = df_electrified.loc[df_electrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_GHI].mean()/8760
if unelectrified_sumtechs[year][sumtechs[i - 40]] > 0:
unelectrified_sumtechs[year][sumtechs[i]] = df_unelectrified.loc[df_unelectrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_GHI].mean() / 8760
elif code == 6:
if electrified_sumtechs[year][sumtechs[i - 40]] > 0:
electrified_sumtechs[year][sumtechs[i]] = df_electrified.loc[df_electrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_WINDCF].mean()
if unelectrified_sumtechs[year][sumtechs[i - 40]] > 0:
unelectrified_sumtechs[year][sumtechs[i]] = df_unelectrified.loc[df_unelectrified[SET_ELEC_FINAL_CODE + "{}".format(year)] == code][SET_WINDCF].mean()
i += 1
all_sumtechs = electrified_sumtechs + unelectrified_sumtechs
unelectrified_path = os.path.join(summary_folder, 'Unelectrified_Summary.csv')
electrified_path = os.path.join(summary_folder, 'Electrified_summary.csv')
combined_path = os.path.join(summary_folder, 'Combined_summary.csv')
unelectrified_sumtechs.to_csv(unelectrified_path, index=sumtechs)
electrified_sumtechs.to_csv(electrified_path, index=sumtechs)
all_sumtechs.to_csv(combined_path, index=sumtechs) | 11,386 | 4,845 |
from collections import Counter
from functools import cache, partial, reduce
from math import prod
import pdb
import aoc
import networkx as nx
from toolz import keyfilter, sliding_window
from aoc import Point, adjacent_transforms, generate_bounded_coords
from tadhg_utils import (
lcompact,
lconcat,
lfilter,
lmap,
splitstrip,
splitstriplines,
)
INPUT, TEST = aoc.get_inputs(__file__)
TA1 = 1588
TA2 = 2188189693529
A1 = 2657
A2 = None
def parse_data(data):
start, raw_rules = data.split("\n\n")
rules = lmap(lambda x: splitstrip(x, sep="->"), splitstriplines(raw_rules))
rules = dict(rules)
return start, rules
def process_step(curr, rules):
pairs = lmap("".join, sliding_window(2, curr))
def alter_pair(pair):
if pair in rules:
pair = pair[0] + rules[pair] + pair[1]
return pair
def merge(couplets):
st = ""
for i, couplet in enumerate(couplets):
if not st:
st = couplet
else:
st = st[: 2 * (i)] + couplet
return st
xx = merge(lmap(alter_pair, pairs))
xx2 = merge(xx[0])
return xx2, rules
def make_quads(term):
s, e = 0, 4
refs = []
while e <= len(term):
refs.append((s, e))
s = s + 3
e = e + 3
return [term[s:e] for s, e in refs]
@cache
def make_quads2(quarter, term):
s, e, i = 0, quarter, 0
refs = []
while len(refs) < 4:
refs.append((s, e))
s = s + quarter - i
e = e + quarter - i
i = i + 1
return [term[s:e] for s, e in refs]
def naive_step(curr, rules):
@cache
def naive_step_inner(curr):
@cache
def alter_pair(pair):
return pair[0] + rules[pair] + pair[1]
pairs = tuple(lmap("".join, sliding_window(2, curr)))
return do_merge(tuple(lmap(alter_pair, pairs)))
return naive_step_inner(curr)
def naive_steps(curr, rules, num):
for i in range(num):
curr = naive_step(curr, rules)
print(i)
return curr
def process_one(data):
start, rules = data
curr = start
def better_step(curr):
def merge(couplets):
st = ""
for i, couplet in enumerate(couplets):
if not st:
st = couplet
else:
st = st[: 2 * (i)] + couplet
return st
def alter_pair(pair):
if pair in rules:
pair = pair[0] + rules[pair] + pair[1]
return pair
@cache
def alter_quad(quad):
pairs = lmap("".join, sliding_window(2, quad))
return merge(lmap(alter_pair, pairs))
quads = make_quads(curr)
altered_quads = map(alter_quad, quads)
stuff = ""
for q in altered_quads:
stuff = stuff[:-1] + q
return stuff
for i in range(10):
prev_length = 3 * (2 ** (i - 1)) + 1
p1, p2 = prev_length, prev_length - 1
if prev_length > 7:
curr = better_step(curr[:p1]) + better_step(curr[p2:])[1:]
else:
curr = better_step(curr)
counted = Counter(curr)
return counted.most_common()[0][1] - counted.most_common()[-1][1]
def do_merge(segments):
stuff = ""
for segment in segments:
if not stuff:
stuff = segment
else:
stuff = stuff + segment[1:]
return stuff
def process_two(data):
start, rules = data
num = 5
curr = naive_steps(start, rules, num)
curr2 = naive_steps(start, rules, 2)
naive2 = naive_steps(start, rules, 2)
nn2, nc2, cb2 = [naive_steps(x, rules, 2) for x in ["NN", "NC", "CB"]]
chec2 = curr2 == do_merge((nn2, nc2, cb2))
curr3 = naive_steps(start, rules, 3)
nn3, nc3, cb3 = [naive_steps(x, rules, 3) for x in ["NN", "NC", "CB"]]
chec3 = curr3 == do_merge((nn3, nc3, cb3))
curr4 = naive_steps(start, rules, 4)
nn4, nc4, cb4 = [naive_steps(x, rules, 4) for x in ["NN", "NC", "CB"]]
chec4 = curr4 == do_merge((nn4, nc4, cb4))
twos = {rule: naive_steps(rule, rules, 2) for rule in rules}
ones = {rule: naive_steps(rule, rules, 1) for rule in rules}
innc3 = [x for x in twos.values() if x in nc3]
inc3 = [(x, v) for x, v in twos.items() if v in curr3]
nn = naive_steps("NN", rules, num)
nc = naive_steps("NC", rules, num)
cb = naive_steps("CB", rules, num)
"""
xx = [do_steps("BN", rules, i) for i in range(num - 1)]
stripped = (
curr.replace(nn, nn[-1]).replace(nc, "|" + nc[-1]).replace(cb, cb[-1])
)
matches = lfilter(
lambda x: curr[25:32] in x,
[do_steps("BN", rules, i) for i in range(num - 1)],
)
"""
"""
xcurr = do_steps(start, rules, 10)
xstuff = ""
for k in xcurr:
xstuff = xstuff + k * xcurr[k]
"""
pdb.set_trace()
nn40 = naive_steps("NN", rules, 40)
return counted.most_common()[0][1] - counted.most_common()[-1][1]
def xdo_steps(curr, rules, num):
pairs = lmap("".join, sliding_window(2, curr))
couplets = {p: 1 for p in pairs}
for i in range(num):
print(i, couplets, sum(couplets.values()))
new_couplets = {}
for couplet in couplets:
triplet = couplet[0] + rules[couplet] + couplet[1]
newpairs = lmap("".join, sliding_window(2, triplet))
for np in newpairs:
if np in new_couplets:
new_couplets[np] = new_couplets[np] + 1
else:
new_couplets[np] = 1
for k in couplets:
if k in new_couplets:
new_couplets[k] = new_couplets[k] * couplets[k]
couplets = new_couplets
return couplets
def do_steps(curr, rules, num):
sequences = {}
def merge(couplets):
st = ""
for i, couplet in enumerate(couplets):
if not st:
st = couplet
else:
st = st[: 2 * (i)] + couplet
return st
@cache
def alter_pair(pair):
if pair in rules:
pair = pair[0] + rules[pair] + pair[1]
return pair
for pair in rules:
sequences[pair] = alter_pair(pair)
@cache
def alter_quad(quad):
pairs = lmap("".join, sliding_window(2, quad))
return merge(lmap(alter_pair, pairs))
@cache
def better_step(curr):
print(len(sequences))
if curr in sequences:
return sequences[curr]
elif len(curr) > 7:
quads = make_quads(curr)
altered_quads = map(alter_quad, quads)
stuff = ""
for q in altered_quads:
stuff = stuff[:-1] + q
if curr not in sequences:
sequences[curr] = stuff
return stuff
else:
pairs = lmap("".join, sliding_window(2, curr))
return merge(lmap(alter_pair, pairs))
for i in range(num):
curr = better_step(curr)
print("len:", len(curr))
"""
prev_length = 3 * (2 ** (i - 2)) + 1
print(i, len(curr), prev_length)
p1, p2 = prev_length, prev_length - 1
if False:
# if any(seq in curr for seq in sequences):
for seq in sequences:
if curr.startswith(seq):
curr = curr.replace(seq, sequences[seq])
else:
curr = curr.replace(seq, sequences[seq][1:])
else:
if i > 3:
qs = make_quads2(prev_length, curr)
curr = (
better_step(qs[0])
+ better_step(qs[1])[1:]
+ better_step(qs[2])[1:]
+ better_step(qs[3])[1:]
)
else:
oldcurr = curr
curr = better_step(curr)
if oldcurr not in sequences:
sequences[oldcurr] = curr
"""
"""
for seq in sorted(sequences.keys(), key=lambda x: -len(x))
curr =
if seq in curr:
p1 = curr.index(seq)
p2 = p1 + len(seq)
curr = (
better_step(curr[:p1])
+ sequences[seq][1:]
+ better_step(curr[p2:])
)
in_seq = True
if not in_seq:
if i > 3:
qs = make_quads2(prev_length, curr)
curr = (
better_step(qs[0])
+ better_step(qs[1])[1:]
+ better_step(qs[2])[1:]
+ better_step(qs[3])[1:]
)
else:
curr = better_step(curr)
if i > 3:
qs = make_quads2(prev_length, curr)
curr = (
better_step(qs[0])
+ better_step(qs[1])[1:]
+ better_step(qs[2])[1:]
+ better_step(qs[3])[1:]
)
else:
curr = better_step(curr)
"""
"""
prev_length = 3 * (2 ** (i - 1)) + 1
print(i, len(curr), prev_length)
p1, p2 = prev_length, prev_length - 1
if prev_length > 7:
curr = better_step(curr[:p1]) + better_step(curr[p2:])[1:]
else:
curr = better_step(curr)
"""
print(Counter(curr))
return curr
def cli_main() -> None:
input_funcs = [parse_data]
data = aoc.load_and_process_input(INPUT, input_funcs)
aoc.run_tests(TEST, TA1, TA2, A1, input_funcs, process_one, process_two)
# result_one = process_one(data)
# print(result_one)
# result_two = process_two(data)
# aoc.finish(result_one, A1, result_two, A2)
if __name__ == "__main__":
cli_main()
"""
--- Day 13: Transparent Origami ---
You reach another volcanically active part of the cave. It would be nice if you
could do some kind of thermal imaging so you could tell ahead of time which
caves are too hot to safely enter.
Fortunately, the submarine seems to be equipped with a thermal camera! When you
activate it, you are greeted with:
Congratulations on your purchase! To activate this infrared thermal imaging
camera system, please enter the code found on page 1 of the manual.
Apparently, the Elves have never used this feature. To your surprise, you
manage to find the manual; as you go to open it, page 1 falls out. It's a large
sheet of transparent paper! The transparent paper is marked with random dots
and includes instructions on how to fold it up (your puzzle input). For
example:
6,10
0,14
9,10
0,3
10,4
4,11
6,0
6,12
4,1
0,13
10,12
3,4
3,0
8,4
1,10
2,14
8,10
9,0
fold along y=7
fold along x=5
The first section is a list of dots on the transparent paper. 0,0 represents
the top-left coordinate. The first value, x, increases to the right. The second
value, y, increases downward. So, the coordinate 3,0 is to the right of 0,0,
and the coordinate 0,7 is below 0,0. The coordinates in this example form the
following pattern, where # is a dot on the paper and . is an empty, unmarked
position:
...#..#..#.
....#......
...........
#..........
...#....#.#
...........
...........
...........
...........
...........
.#....#.##.
....#......
......#...#
#..........
#.#........
Then, there is a list of fold instructions. Each instruction indicates a line
on the transparent paper and wants you to fold the paper up (for horizontal
y=... lines) or left (for vertical x=... lines). In this example, the first
fold instruction is fold along y=7, which designates the line formed by all of
the positions where y is 7 (marked here with -):
...#..#..#.
....#......
...........
#..........
...#....#.#
...........
...........
-----------
...........
...........
.#....#.##.
....#......
......#...#
#..........
#.#........
Because this is a horizontal line, fold the bottom half up. Some of the dots
might end up overlapping after the fold is complete, but dots will never appear
exactly on a fold line. The result of doing this fold looks like this:
#.##..#..#.
#...#......
......#...#
#...#......
.#.#..#.###
...........
...........
Now, only 17 dots are visible.
Notice, for example, the two dots in the bottom left corner before the
transparent paper is folded; after the fold is complete, those dots appear in
the top left corner (at 0,0 and 0,1). Because the paper is transparent, the dot
just below them in the result (at 0,3) remains visible, as it can be seen
through the transparent paper.
Also notice that some dots can end up overlapping; in this case, the dots merge
together and become a single dot.
The second fold instruction is fold along x=5, which indicates this line:
#.##.|#..#.
#...#|.....
.....|#...#
#...#|.....
.#.#.|#.###
.....|.....
.....|.....
Because this is a vertical line, fold left:
#####
#...#
#...#
#...#
#####
.....
.....
The instructions made a square!
The transparent paper is pretty big, so for now, focus on just completing the
first fold. After the first fold in the example above, 17 dots are visible -
dots that end up overlapping after the fold is completed count as a single dot.
How many dots are visible after completing just the first fold instruction on
your transparent paper?
Your puzzle answer was 631.
--- Part Two ---
Finish folding the transparent paper according to the instructions. The manual
says the code is always eight capital letters.
What code do you use to activate the infrared thermal imaging camera system?
Your puzzle answer was EFLFJGRF.
"""
| 13,575 | 4,589 |
#!/usr/bin/env python3
import json
import sys
import asyncio
import aiohttp
from aiohttp.resolver import AsyncResolver
import requests
import websockets
MM_URL = 'http://127.0.0.1:8065/api/v4'
MM_WSURL = 'ws://127.0.0.1:8065/api/v4/websocket'
MM_LOGIN = 'bot@localhost.local'
MM_PASSWORD = '12345678'
CALLBACK_URL = 'https://api.myapp.com/messages'
CALLBACK_TIMEOUT = 10
def login():
loginData = {'login_id': MM_LOGIN, 'password': MM_PASSWORD}
loginRequest = requests.post(MM_URL + '/users/login', json=loginData)
if 'Token' in loginRequest.headers:
return loginRequest.headers['Token']
else:
raise Exception("Authentification failed")
async def createConnection():
async with websockets.connect(MM_WSURL) as websocket:
await authenticateWebsocket(websocket)
await startLoop(websocket)
async def startLoop(websocket):
while True:
try:
await asyncio.wait_for(waitForMessage(websocket), timeout=60)
except asyncio.TimeoutError:
await websocket.pong()
continue
async def authenticateWebsocket(websocket):
jsonData = json.dumps({
"seq": 1,
"action": "authentication_challenge",
"data": {
"token": token
}
}).encode('utf8')
await websocket.send(jsonData)
response = await websocket.recv()
status = json.loads(response)
if 'event' in status and status['event'] == 'hello':
print("Auth OK")
return True
else:
return False
async def waitForMessage(websocket):
print("Waiting for messages")
while True:
event = await websocket.recv()
await eventHandler(event)
async def eventHandler(event):
print(event)
data = json.loads(event)
if 'event' in data and data['event'] == 'posted':
loop.create_task(sendCallback(event))
async def sendCallback(event):
conn = aiohttp.TCPConnector(resolver=dnsResolver)
try:
async with aiohttp.ClientSession(connector=conn, loop=loop) as session:
await session.post(CALLBACK_URL, json=event, timeout=CALLBACK_TIMEOUT)
await session.close()
except asyncio.TimeoutError:
pass
if __name__ == '__main__':
loop = asyncio.get_event_loop()
dnsResolver = AsyncResolver()
token = login()
try:
sys.exit(loop.run_until_complete(createConnection()))
except KeyboardInterrupt:
print("Shutting down, press Ctrl+C to force exit.", flush=True)
def shutdown_exception_handler(l, context):
if "exception" not in context \
or not isinstance(context["exception"], asyncio.CancelledError):
l.default_exception_handler(context)
loop.set_exception_handler(shutdown_exception_handler)
tasks = asyncio.gather(*asyncio.Task.all_tasks(loop=loop),
loop=loop,
return_exceptions=True)
tasks.add_done_callback(lambda t: loop.stop())
tasks.cancel()
while not tasks.done() and not loop.is_closed():
loop.run_forever()
finally:
loop.close()
| 3,169 | 982 |
#-------------------------------------------------------------------------------
# Name: module1
# Purpose:
#
# Author: mo-mo-
#
# Created: 23/09/2018
# Copyright: (c) mo-mo- 2018
# Licence: <your licence>
#-------------------------------------------------------------------------------
from math import factorial, sqrt
n, m = map(int, input().split())
ans = 1
if m != 1:
factorization = {}
while True:
for i in range (2, int(sqrt(m))+1):
if m % i == 0:
if i in factorization:
factorization[i] += 1
else:
factorization[i] = 1
m = m // i
break
else:
if m in factorization:
factorization[m] += 1
else:
factorization[m] = 1
break
for i in factorization.keys():
k = factorization[i]
num = 1
for i in range(k):
num *= (n+k-1-i)
num //= factorial(k)
ans *= num % (10**9+7)
ans = ans % (10**9+7)
print(ans)
| 1,165 | 397 |
import sys
sys.stdin = open('input.txt')
while True:
N = int(input())
if not N:
break
strN = str(N)
while len(strN) > 1:
N = sum(int(i) for i in strN)
strN = str(N)
print strN
| 221 | 89 |
import json
from flask import jsonify, request, make_response, render_template
from flask.blueprints import Blueprint
from app.daos import tenant_dao
from app.firebase_utils import *
from app.services import user_service
from app.user_dao import user_dao
users_bp = Blueprint("users_bp", __name__)
@users_bp.route("/users", methods=["GET"])
def get_multiple():
accessible_roles = ["SUPER_ADMIN","TENANT_ADMIN"]
returned_value = have_claims(request.headers.get("Authorization"),accessible_roles)
if returned_value["have_access"]:
params = user_service.url_args_to_query_params_dict(request.args, False)
values_for_return = user_dao.get_users(
returned_value,
params['filters'],
params['sort'],
params['range']
)
return user_service.response(values_for_return['data'],values_for_return['count'], 200)
else:
return user_service.response(status_code=403)
@users_bp.route("/users", methods=["POST"])
def create_tenant_admin():
accessible_roles = ["SUPER_ADMIN","TENANT_ADMIN"]
returned_value = have_claims(request.headers.get("Authorization"),accessible_roles)
if returned_value["have_access"]:
data = request.json
success = user_dao.create_user(**data)
if success:
return user_service.response(success,status_code=200)
else:
return user_service.response(status_code=500)
else:
return user_service.response(status_code=403)
@users_bp.route("/users/<id>", methods=["DELETE"])
def del_tenant(id):
accessible_roles = ["SUPER_ADMIN","TENANT_ADMIN"]
returned_value = have_claims(request.headers.get("Authorization"),accessible_roles)
if returned_value["have_access"]:
'''success = user_dao.delete_user(id)
if success:
return user_service.response() '''
return user_service.response(user_dao.update_user(have_claims, id, {"role":"", "tenantId": ""}))
return user_service.response(status_code=403)
@users_bp.route("/users/<id>", methods=["PUT"])
def update_tenant_admin(id):
accessible_roles = ["SUPER_ADMIN", "TENANT_ADMIN", "USER"]
returned_value = have_claims(request.headers.get("Authorization"),accessible_roles)
if returned_value["have_access"]:
data = request.json
success = user_dao.update_user(returned_value, id, data)
if success:
return user_service.response(success,status_code=200)
else:
return user_service.response(status_code=500)
return user_service.response(status_code=403)
@users_bp.route("/users/<id>", methods=["GET"])
def get_one_user(id):
accessible_roles = ["SUPER_ADMIN","TENANT_ADMIN", "USER"]
returned_value = have_claims(request.headers.get("Authorization"),accessible_roles)
if returned_value["have_access"]:
return user_service.response(user_dao.get_one(returned_value, id))
else:
return user_service.response(status_code=403) | 3,057 | 1,023 |
import unittest
from credentials import Credential
class TestCredential(unittest.TestCase):
"""
Test class that defines test cases for the credential class behaviours,
the arguments help in creating test cases.
"""
def setUp(self):
"""
this method runs before each test case, carries the instructions of what is to be done
"""
self.new_password = Credential("max")
def test_init(self):
"""
used to test if the objects have been initialized properly
"""
self.assertEqual(self.new_password.credential_detail,"max")
if __name__ == '__main__':
unittest.main() | 657 | 177 |
"""Python project."""
from __future__ import annotations
import logging
import shutil
from typing import TYPE_CHECKING, ClassVar, Optional, Set, Tuple
from runway.compat import cached_property
from typing_extensions import Literal
from ..base_classes import Project
from ..constants import BASE_WORK_DIR
from ..models.args import PythonHookArgs
from ._python_docker import PythonDockerDependencyInstaller
from .dependency_managers import (
Pip,
Pipenv,
PipenvNotFoundError,
Poetry,
PoetryNotFoundError,
is_pip_project,
is_pipenv_project,
is_poetry_project,
)
if TYPE_CHECKING:
from pathlib import Path
LOGGER = logging.getLogger(f"runway.{__name__}")
class PythonProject(Project[PythonHookArgs]):
"""Python project."""
DEFAULT_CACHE_DIR_NAME: ClassVar[str] = "pip_cache"
"""Name of the default cache directory."""
@cached_property
def docker(self) -> Optional[PythonDockerDependencyInstaller]:
"""Docker interface that can be used to build the project."""
return PythonDockerDependencyInstaller.from_project(self)
@cached_property
def metadata_files(self) -> Tuple[Path, ...]:
"""Project metadata files.
Files are only included in return value if they exist.
"""
if self.project_type == "poetry":
config_files = [
self.project_root / config_file for config_file in Poetry.CONFIG_FILES
]
elif self.project_type == "pipenv":
config_files = [
self.project_root / config_file for config_file in Pipenv.CONFIG_FILES
]
else:
config_files = [
self.project_root / config_file for config_file in Pip.CONFIG_FILES
]
return tuple(path for path in config_files if path.exists())
@cached_property
def runtime(self) -> str:
"""Runtime of the build system.
Value should be a valid Lambda Function runtime
(https://docs.aws.amazon.com/lambda/latest/dg/lambda-runtimes.html).
"""
if self._runtime_from_docker:
return self._validate_runtime(self._runtime_from_docker)
return self._validate_runtime(
f"python{self.pip.python_version.major}.{self.pip.python_version.minor}"
)
@cached_property
def pip(self) -> Pip:
"""Pip dependency manager."""
return Pip(self.ctx, self.project_root)
@cached_property
def pipenv(self) -> Optional[Pipenv]:
"""Pipenv dependency manager.
Return:
If the project uses pipenv and pipenv is not explicitly disabled,
an object for interfacing with pipenv will be returned.
Raises:
PipenvNotFoundError: pipenv is not installed or not found in PATH.
"""
if self.project_type != "pipenv":
return None
if Pipenv.found_in_path():
return Pipenv(self.ctx, self.project_root)
raise PipenvNotFoundError
@cached_property
def poetry(self) -> Optional[Poetry]:
"""Poetry dependency manager.
Return:
If the project uses poetry and poetry is not explicitly disabled,
an object for interfacing with poetry will be returned.
Raises:
PoetryNotFound: poetry is not installed or not found in PATH.
"""
if self.project_type != "poetry":
return None
if Poetry.found_in_path():
return Poetry(self.ctx, self.project_root)
raise PoetryNotFoundError
@cached_property
def project_type(self) -> Literal["pip", "pipenv", "poetry"]:
"""Type of python project."""
if is_poetry_project(self.project_root):
if self.args.use_poetry:
return "poetry"
LOGGER.warning(
"poetry project detected but use of poetry is explicitly disabled"
)
if is_pipenv_project(self.project_root):
if self.args.use_pipenv:
return "pipenv"
LOGGER.warning(
"pipenv project detected but use of pipenv is explicitly disabled"
)
return "pip"
@cached_property
def requirements_txt(self) -> Optional[Path]:
"""Dependency file for the project."""
if self.poetry: # prioritize poetry
return self.poetry.export(output=self.tmp_requirements_txt)
if self.pipenv:
return self.pipenv.export(output=self.tmp_requirements_txt)
requirements_txt = self.project_root / "requirements.txt"
if is_pip_project(self.project_root, file_name=requirements_txt.name):
return requirements_txt
return None
@cached_property
def supported_metadata_files(self) -> Set[str]:
"""Names of all supported metadata files.
Returns:
Set of file names - not paths.
"""
file_names = {*Pip.CONFIG_FILES}
if self.args.use_poetry:
file_names.update(Poetry.CONFIG_FILES)
if self.args.use_pipenv:
file_names.update(Pipenv.CONFIG_FILES)
return file_names
@cached_property
def tmp_requirements_txt(self) -> Path:
"""Temporary requirements.txt file.
This path is only used when exporting from another format.
"""
return BASE_WORK_DIR / f"{self.source_code.md5_hash}.requirements.txt"
def cleanup(self) -> None:
"""Cleanup temporary files after the build process has run."""
if (self.poetry or self.pipenv) and self.tmp_requirements_txt.exists():
self.tmp_requirements_txt.unlink()
shutil.rmtree(self.dependency_directory, ignore_errors=True)
if not any(self.build_directory.iterdir()):
# remove build_directory if it's empty
shutil.rmtree(self.build_directory, ignore_errors=True)
def install_dependencies(self) -> None:
"""Install project dependencies."""
if self.requirements_txt:
LOGGER.debug("installing dependencies to %s...", self.dependency_directory)
if self.docker:
self.docker.install()
else:
self.pip.install(
cache_dir=self.args.cache_dir,
extend_args=self.args.extend_pip_args,
no_cache_dir=not self.args.use_cache,
no_deps=bool(self.poetry or self.pipenv),
requirements=self.requirements_txt,
target=self.dependency_directory,
)
LOGGER.debug(
"dependencies successfully installed to %s", self.dependency_directory
)
else:
LOGGER.info("skipped installing dependencies; none found")
| 6,810 | 1,887 |
# Lucas J. Koerner
# 05/2018
# koerner.lucas@stthomas.edu
# University of St. Thomas
from instrbuilder.instrument_opening import open_by_name
import time
KEYSIGHT = False
RIGOL = True
if KEYSIGHT:
osc = open_by_name(name='msox_scope') # name within the configuration file (config.yaml)
osc.set('time_range', 1e-3)
osc.set('chan_scale', 0.8, configs={'chan': 1})
osc.set('chan_offset', -0.2, configs={'chan': 1})
osc.set('single_acq')
time.sleep(0.1)
# save a PNG screen-shot to host computer
t = osc.save_display_data('test')
if RIGOL:
# rigol_ds is the name in my YAML file
osc = open_by_name(name='rigol_ds')
osc.set('time_range', 1e-3)
osc.set('chan_scale', 0.8, configs={'chan': 1})
osc.set('chan_offset', -0.2, configs={'chan': 1})
osc.set('single_acq')
time.sleep(0.1)
# save a PNG screen-shot to host computer
t = osc.save_display_data('test')
osc.set('stop_acq')
osc.set('run_acq')
# connect a fg to CHAN1: 400 mVpk-pk, 1kHz, high-z output
time.sleep(2)
osc.set('trigger_mode', 'EDGE')
osc.set('trigger_sweep', 'NORM')
osc.set('trigger_source', 1)
osc.set('trigger_level', 0.2)
time.sleep(2)
# specific measure
vp = osc.get('meas_vpp', configs={'chan':1})
# general measure -- input measure type
vavg = osc.get('meas', configs={'meas_type':'VAVG', 'chan':1})
print('Measured pk-pk voltage of = {}'.format(vp))
print('Measured avg voltage of = {}'.format(vavg))
| 1,435 | 628 |
#/usr/bin/env python
import codecs
import os
import sys
from setuptools import setup, find_packages
if 'publish' in sys.argv:
os.system('python setup.py sdist upload')
sys.exit()
read = lambda filepath: codecs.open(filepath, 'r', 'utf-8').read()
# Dynamically calculate the version based on redator.VERSION.
version = __import__('redator').get_version()
setup(
name='django-redator',
version=version,
description=(
'Django Redator (sic) is a application for the Django Web Framework to '
'help you integrate Redactor <http://imperavi.com/redactor/>, a '
'beautiful and easy-to-use WYSIWYG HTML editor, into your projects.'
),
long_description=read(os.path.join(os.path.dirname(__file__), 'README.rst')),
keywords = 'django app wysiwyg editor redactor',
author='Vladimir Sidorenko, Guilherme Gondim',
author_email='semente+django-redator@taurinus.org',
maintainer='Guilherme Gondim',
maintainer_email='semente+django-redator@taurinus.org',
license='BSD License',
url='https://bitbucket.org/semente/django-redator/',
download_url='https://bitbucket.org/semente/django-redator/downloads/',
packages=find_packages(),
zip_safe=False,
include_package_data=True,
classifiers=[
'Development Status :: 5 - Production/Stable',
'Environment :: Web Environment',
'Framework :: Django',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Operating System :: OS Independent',
'Programming Language :: Python',
'Topic :: Software Development :: Libraries :: Python Modules',
],
)
| 1,668 | 516 |
#! /usr/bin/env python
# $Id: test_parser.py 7463 2012-06-22 19:49:51Z milde $
# Author: Stefan Rank <strank(AT)strank(DOT)info>
# Copyright: This module has been placed in the public domain.
"""
Tests for basic functionality of parser classes.
"""
import sys
import unittest
import DocutilsTestSupport # must be imported before docutils
import docutils
from docutils import parsers, utils, frontend
from docutils._compat import b
class RstParserTests(unittest.TestCase):
def test_inputrestrictions(self):
parser_class = parsers.get_parser_class('rst')
parser = parser_class()
document = utils.new_document('test data', frontend.OptionParser(
components=(parser, )).get_default_values())
if sys.version_info < (3,):
# supplying string input is supported, but only if ascii-decodable
self.assertRaises(UnicodeDecodeError,
parser.parse, b('hol%s' % chr(224)), document)
else:
# input must be unicode at all times
self.assertRaises(TypeError, parser.parse, b('hol'), document)
if __name__ == '__main__':
unittest.main()
| 1,185 | 364 |
################################################################################
#
# Copyright (c) 2010 The MadGraph5_aMC@NLO Development team and Contributors
#
# This file is a part of the MadGraph5_aMC@NLO project, an application which
# automatically generates Feynman diagrams and matrix elements for arbitrary
# high-energy processes in the Standard Model and beyond.
#
# It is subject to the MadGraph5_aMC@NLO license which should accompany this
# distribution.
#
# For more information, visit madgraph.phys.ucl.ac.be and amcatnlo.web.cern.ch
#
################################################################################
## Diagram of Class
##
## Variable <--- aloha_lib.Variable
## |
## +- LorentzObject <--- Gamma
## |
## +- Sigma
## |
## +- P
##
## list <--- AddVariable
## |
## +- MultVariable <--- MultLorentz
##
## list <--- LorentzObjectRepresentation <-- ConstantObject
##
################################################################################
from __future__ import division
import aloha.aloha_lib as aloha_lib
import aloha
import cmath
#===============================================================================
# P (Momenta)
#===============================================================================
class L_P(aloha_lib.LorentzObject):
""" Helas Object for an Impulsion """
contract_first = 1
def __init__(self, name, lorentz1, particle):
self.particle = particle
aloha_lib.LorentzObject.__init__(self, name,[lorentz1], [],['P%s'%particle])
aloha_lib.KERNEL.add_tag((name,))
def create_representation(self):
self.sub0 = aloha_lib.DVariable('P%s_0' % self.particle)
self.sub1 = aloha_lib.DVariable('P%s_1' % self.particle)
self.sub2 = aloha_lib.DVariable('P%s_2' % self.particle)
self.sub3 = aloha_lib.DVariable('P%s_3' % self.particle)
self.representation= aloha_lib.LorentzObjectRepresentation(
{(0,): self.sub0, (1,): self.sub1, \
(2,): self.sub2, (3,): self.sub3},
self.lorentz_ind, [])
class P(aloha_lib.FactoryLorentz):
""" Helas Object for an Impulsion """
object_class = L_P
#def __init__(self, lorentz1, particle):
@classmethod
def get_unique_name(self, lorentz1, particle):
return '_P^%s_%s' % (particle, lorentz1)
#===============================================================================
# Pslash
#===============================================================================
class L_PSlash(aloha_lib.LorentzObject):
""" Gamma Matrices """
#gamma0 = [[0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0]]
#gamma1 = [[0, 0, 0, 1], [0, 0, 1, 0], [0, -1, 0, 0], [-1, 0, 0, 0]]
#gamma2 = [[0, 0, 0, -complex(0,1)],[0, 0, complex(0,1), 0],
# [0, complex(0,1), 0, 0], [-complex(0,1), 0, 0, 0]]
#gamma3 = [[0, 0, 1, 0], [0, 0, 0, -1], [-1, 0, 0, 0], [0, 1, 0, 0]]
#
#gamma = [gamma0, gamma1, gamma2, gamma3]
def __init__(self, name, spin1, spin2, particle):
self.particle = particle
aloha_lib.LorentzObject.__init__(self,name,[], [spin1, spin2])
def create_representation(self):
"""create representation"""
p0 = aloha_lib.DVariable('P%s_0' % self.particle)
p1 = aloha_lib.DVariable('P%s_1' % self.particle)
p2 = aloha_lib.DVariable('P%s_2' % self.particle)
p3 = aloha_lib.DVariable('P%s_3' % self.particle)
gamma = {
(0, 0): 0, (0, 1): 0, (0, 2): p0-p3, (0, 3): -1*p1+1j*p2,
(1, 0): 0, (1, 1): 0, (1, 2): -1*p1-1j*p2, (1, 3): p0+p3,
(2, 0): p0+p3, (2, 1): p1-1j*p2, (2, 2): 0, (2, 3): 0,
(3, 0): p1+1j*p2, (3, 1): p0-p3, (3, 2): 0, (3, 3): 0}
self.representation = aloha_lib.LorentzObjectRepresentation(gamma,
self.lorentz_ind,self.spin_ind)
class PSlash(aloha_lib.FactoryLorentz):
object_class = L_PSlash
@classmethod
def get_unique_name(self, spin1, spin2, particle):
return '_P%s/_%s_%s' % (particle, spin1,spin2)
#===============================================================================
# Mass
#===============================================================================
class L_Mass(aloha_lib.LorentzObject):
""" Helas Object for a Mass"""
def __init__(self, name, particle):
self.particle = particle
aloha_lib.LorentzObject.__init__(self, name,[], [])
def create_representation(self):
mass = aloha_lib.DVariable('M%s' % self.particle)
self.representation = aloha_lib.LorentzObjectRepresentation(
mass, self.lorentz_ind, self.spin_ind)
class Mass(aloha_lib.FactoryLorentz):
object_class = L_Mass
@classmethod
def get_unique_name(self, particle):
return '_M%s' % particle
#===============================================================================
# Mass
#===============================================================================
class L_Coup(aloha_lib.LorentzObject):
""" Helas Object for a Mass"""
def __init__(self, name, nb):
self.nb = nb
aloha_lib.LorentzObject.__init__(self, name,[], [])
def create_representation(self):
coup = aloha_lib.Variable('COUP%s' % self.nb)
self.representation = aloha_lib.LorentzObjectRepresentation(
coup, self.lorentz_ind, self.spin_ind)
class Coup(aloha_lib.FactoryLorentz):
object_class = L_Coup
@classmethod
def get_unique_name(self, nb):
return 'coup%s' % nb
#===============================================================================
# FCT
#===============================================================================
class L_FCT(aloha_lib.LorentzObject):
""" Helas Object for a Mass"""
def __init__(self, name, id):
self.fctid = id
aloha_lib.LorentzObject.__init__(self, name,[], [])
def create_representation(self):
var = aloha_lib.Variable('FCT%s' % self.fctid)
self.representation = aloha_lib.LorentzObjectRepresentation(
var, self.lorentz_ind, self.spin_ind)
class FCT(aloha_lib.FactoryLorentz):
object_class = L_FCT
@classmethod
def get_unique_name(self, name):
return '_FCT%s' % name
#===============================================================================
# OverMass2
#===============================================================================
class L_OverMass2(aloha_lib.LorentzObject):
""" Helas Object for 1/M**2 """
def __init__(self, name, particle):
self.particle = particle
aloha_lib.LorentzObject.__init__(self, name, [], [], tags=['OM%s' % particle])
def create_representation(self):
mass = aloha_lib.DVariable('OM%s' % self.particle)
self.representation = aloha_lib.LorentzObjectRepresentation(
mass, self.lorentz_ind, self.spin_ind)
class OverMass2(aloha_lib.FactoryLorentz):
object_class = L_OverMass2
@classmethod
def get_unique_name(self, particle):
return '_OM2_%s' % particle
#===============================================================================
# Width
#===============================================================================
class L_Width(aloha_lib.LorentzObject):
""" Helas Object for an Impulsion """
def __init__(self, name, particle):
self.particle = particle
aloha_lib.LorentzObject.__init__(self, name, [], [])
def create_representation(self):
width = aloha_lib.DVariable('W%s' % self.particle)
self.representation= aloha_lib.LorentzObjectRepresentation(
width, self.lorentz_ind, self.spin_ind)
class Width(aloha_lib.FactoryLorentz):
object_class = L_Width
@classmethod
def get_unique_name(self, particle):
return '_W%s' % particle
#===============================================================================
# Param
#===============================================================================
class L_Param(aloha_lib.LorentzObject):
""" Object for a Model Parameter """
def __init__(self, Lname, name):
self.varname = name
aloha_lib.LorentzObject.__init__(self, name, [], [])
def create_representation(self):
param = aloha_lib.Variable( self.varname, aloha_lib.ExtVariable)
self.representation= aloha_lib.LorentzObjectRepresentation(
param, [], [])
class Param(aloha_lib.FactoryLorentz):
object_class = L_Param
@classmethod
def get_unique_name(self, name):
if name == 'Pi':
KERNEL.has_pi = True
return 'Param_%s' % name
#===============================================================================
# Scalar
#===============================================================================
class L_Scalar(aloha_lib.LorentzObject):
""" Helas Object for a Spinor"""
def __init__(self, name, particle):
self.particle = particle
aloha_lib.LorentzObject.__init__(self, name, [], [])
def create_representation(self):
rep = aloha_lib.Variable('S%s_1' % self.particle)
self.representation= aloha_lib.LorentzObjectRepresentation(
rep, [], [])
class Scalar(aloha_lib.FactoryLorentz):
object_class = L_Scalar
@classmethod
def get_unique_name(self,particle):
return '_S%s' % particle
#===============================================================================
# Spinor
#===============================================================================
class L_Spinor(aloha_lib.LorentzObject):
""" Helas Object for a Spinor"""
contract_first = 1
def __init__(self, name, spin1, particle, prefactor=1):
self.particle = particle
aloha_lib.LorentzObject.__init__(self, name,[], [spin1])
def create_representation(self):
self.sub0 = aloha_lib.Variable('F%s_1' % self.particle)
self.sub1 = aloha_lib.Variable('F%s_2' % self.particle)
self.sub2 = aloha_lib.Variable('F%s_3' % self.particle)
self.sub3 = aloha_lib.Variable('F%s_4' % self.particle)
self.representation= aloha_lib.LorentzObjectRepresentation(
{(0,): self.sub0, (1,): self.sub1, \
(2,): self.sub2, (3,): self.sub3},
[],self.spin_ind)
class Spinor(aloha_lib.FactoryLorentz):
""" Helas Object for a Spinor"""
object_class = L_Spinor
@classmethod
def get_unique_name(self,spin1, particle):
return '_F%s_%s' % (particle,spin1)
#===============================================================================
# Vector
#===============================================================================
class L_Vector(aloha_lib.LorentzObject):
""" Helas Object for a Vector"""
contract_first = 1
def __init__(self, name, lorentz, particle):
self.particle = particle
aloha_lib.LorentzObject.__init__(self, name, [lorentz], [])
def create_representation(self):
self.sub0 = aloha_lib.Variable('V%s_1' % self.particle)
self.sub1 = aloha_lib.Variable('V%s_2' % self.particle)
self.sub2 = aloha_lib.Variable('V%s_3' % self.particle)
self.sub3 = aloha_lib.Variable('V%s_4' % self.particle)
self.representation= aloha_lib.LorentzObjectRepresentation(
{(0,): self.sub0, (1,): self.sub1, \
(2,): self.sub2, (3,): self.sub3},
self.lorentz_ind, [])
class Vector(aloha_lib.FactoryLorentz):
object_class = L_Vector
@classmethod
def get_unique_name(self, lor, particle):
return '_V%s_%s' % (particle, lor)
#===============================================================================
# Spin3/2
#===============================================================================
class L_Spin3Half(aloha_lib.LorentzObject):
""" Helas Object for a Spin2"""
def __init__(self, name, lorentz, spin, particle):
self.particle = particle
aloha_lib.LorentzObject.__init__(self, name, [lorentz], [spin])
def create_representation(self):
self.sub00 = aloha_lib.Variable('R%s_1' % self.particle)
self.sub01 = aloha_lib.Variable('R%s_2' % self.particle)
self.sub02 = aloha_lib.Variable('R%s_3' % self.particle)
self.sub03 = aloha_lib.Variable('R%s_4' % self.particle)
self.sub10 = aloha_lib.Variable('R%s_5' % self.particle)
self.sub11 = aloha_lib.Variable('R%s_6' % self.particle)
self.sub12 = aloha_lib.Variable('R%s_7' % self.particle)
self.sub13 = aloha_lib.Variable('R%s_8' % self.particle)
self.sub20 = aloha_lib.Variable('R%s_9' % self.particle)
self.sub21 = aloha_lib.Variable('R%s_10' % self.particle)
self.sub22 = aloha_lib.Variable('R%s_11' % self.particle)
self.sub23 = aloha_lib.Variable('R%s_12' % self.particle)
self.sub30 = aloha_lib.Variable('R%s_13' % self.particle)
self.sub31 = aloha_lib.Variable('R%s_14' % self.particle)
self.sub32 = aloha_lib.Variable('R%s_15' % self.particle)
self.sub33 = aloha_lib.Variable('R%s_16' % self.particle)
rep = {(0,0): self.sub00, (0,1): self.sub01, (0,2): self.sub02, (0,3): self.sub03,
(1,0): self.sub10, (1,1): self.sub11, (1,2): self.sub12, (1,3): self.sub13,
(2,0): self.sub20, (2,1): self.sub21, (2,2): self.sub22, (2,3): self.sub23,
(3,0): self.sub30, (3,1): self.sub31, (3,2): self.sub32, (3,3): self.sub33}
self.representation= aloha_lib.LorentzObjectRepresentation( rep, \
self.lorentz_ind, self.spin_ind)
class Spin3Half(aloha_lib.FactoryLorentz):
object_class = L_Spin3Half
@classmethod
def get_unique_name(self, lor, spin, part):
return 'Spin3Half%s^%s_%s' % (part, lor, spin)
#===============================================================================
# Spin2
#===============================================================================
class L_Spin2(aloha_lib.LorentzObject):
""" Helas Object for a Spin2"""
def __init__(self, name, lorentz1, lorentz2, particle):
self.particle = particle
aloha_lib.LorentzObject.__init__(self, name, [lorentz1, lorentz2], [])
def create_representation(self):
self.sub00 = aloha_lib.Variable('T%s_1' % self.particle)
self.sub01 = aloha_lib.Variable('T%s_2' % self.particle)
self.sub02 = aloha_lib.Variable('T%s_3' % self.particle)
self.sub03 = aloha_lib.Variable('T%s_4' % self.particle)
self.sub10 = aloha_lib.Variable('T%s_5' % self.particle)
self.sub11 = aloha_lib.Variable('T%s_6' % self.particle)
self.sub12 = aloha_lib.Variable('T%s_7' % self.particle)
self.sub13 = aloha_lib.Variable('T%s_8' % self.particle)
self.sub20 = aloha_lib.Variable('T%s_9' % self.particle)
self.sub21 = aloha_lib.Variable('T%s_10' % self.particle)
self.sub22 = aloha_lib.Variable('T%s_11' % self.particle)
self.sub23 = aloha_lib.Variable('T%s_12' % self.particle)
self.sub30 = aloha_lib.Variable('T%s_13' % self.particle)
self.sub31 = aloha_lib.Variable('T%s_14' % self.particle)
self.sub32 = aloha_lib.Variable('T%s_15' % self.particle)
self.sub33 = aloha_lib.Variable('T%s_16' % self.particle)
rep = {(0,0): self.sub00, (0,1): self.sub01, (0,2): self.sub02, (0,3): self.sub03,
(1,0): self.sub10, (1,1): self.sub11, (1,2): self.sub12, (1,3): self.sub13,
(2,0): self.sub20, (2,1): self.sub21, (2,2): self.sub22, (2,3): self.sub23,
(3,0): self.sub30, (3,1): self.sub31, (3,2): self.sub32, (3,3): self.sub33}
self.representation= aloha_lib.LorentzObjectRepresentation( rep, \
self.lorentz_ind, [])
class Spin2(aloha_lib.FactoryLorentz):
object_class = L_Spin2
@classmethod
def get_unique_name(self, lor1, lor2, part):
return 'Spin2^%s_%s_%s' % (part, lor1, lor2)
#===============================================================================
# Gamma
#===============================================================================
class L_Gamma(aloha_lib.LorentzObject):
""" Gamma Matrices """
#gamma0 = [[0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0]]
#gamma1 = [[0, 0, 0, 1], [0, 0, 1, 0], [0, -1, 0, 0], [-1, 0, 0, 0]]
#gamma2 = [[0, 0, 0, -complex(0,1)],[0, 0, complex(0,1), 0],
# [0, complex(0,1), 0, 0], [-complex(0,1), 0, 0, 0]]
#gamma3 = [[0, 0, 1, 0], [0, 0, 0, -1], [-1, 0, 0, 0], [0, 1, 0, 0]]
#
#gamma = [gamma0, gamma1, gamma2, gamma3]
gamma = { #Gamma0
(0, 0, 0): 0, (0, 0, 1): 0, (0, 0, 2): 1, (0, 0, 3): 0,
(0, 1, 0): 0, (0, 1, 1): 0, (0, 1, 2): 0, (0, 1, 3): 1,
(0, 2, 0): 1, (0, 2, 1): 0, (0, 2, 2): 0, (0, 2, 3): 0,
(0, 3, 0): 0, (0, 3, 1): 1, (0, 3, 2): 0, (0, 3, 3): 0,
#Gamma1
(1, 0, 0): 0, (1, 0, 1): 0, (1, 0, 2): 0, (1, 0, 3): 1,
(1, 1, 0): 0, (1, 1, 1): 0, (1, 1, 2): 1, (1, 1, 3): 0,
(1, 2, 0): 0, (1, 2, 1): -1, (1, 2, 2): 0, (1, 2, 3): 0,
(1, 3, 0): -1, (1, 3, 1): 0, (1, 3, 2): 0, (1, 3, 3): 0,
#Gamma2
(2, 0, 0): 0, (2, 0, 1): 0, (2, 0, 2): 0, (2, 0, 3): -1j,
(2, 1, 0): 0, (2, 1, 1): 0, (2, 1, 2): 1j, (2, 1, 3): 0,
(2, 2, 0): 0, (2, 2, 1): 1j, (2, 2, 2): 0, (2, 2, 3): 0,
(2, 3, 0): -1j, (2, 3, 1): 0, (2, 3, 2): 0, (2, 3, 3): 0,
#Gamma3
(3, 0, 0): 0, (3, 0, 1): 0, (3, 0, 2): 1, (3, 0, 3): 0,
(3, 1, 0): 0, (3, 1, 1): 0, (3, 1, 2): 0, (3, 1, 3): -1,
(3, 2, 0): -1, (3, 2, 1): 0, (3, 2, 2): 0, (3, 2, 3): 0,
(3, 3, 0): 0, (3, 3, 1): 1, (3, 3, 2): 0, (3, 3, 3): 0
}
def __init__(self, name, lorentz, spin1, spin2):
aloha_lib.LorentzObject.__init__(self,name,[lorentz], [spin1, spin2])
def create_representation(self):
self.representation = aloha_lib.LorentzObjectRepresentation(self.gamma,
self.lorentz_ind,self.spin_ind)
class Gamma(aloha_lib.FactoryLorentz):
object_class = L_Gamma
@classmethod
def get_unique_name(self, lor, spin1, spin2):
return 'Gamma^%s_%s_%s' % (lor, spin1, spin2)
#===============================================================================
# Sigma
#===============================================================================
class L_Sigma(aloha_lib.LorentzObject):
""" Sigma Matrices """
#zero = [[0,0,0,0]]*4
#i = complex(0,1)
#sigma01 = [[ 0, -i, 0, 0], [-i, 0, 0, 0], [0, 0, 0, i], [0, 0, i, 0]]
#sigma02 = [[ 0, -1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, -1, 0]]
#sigma03 = [[-i, 0, 0, 0], [0, i, 0, 0], [0, 0, i, 0], [0, 0, 0, -i]]
#sigma12 = [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 1, 0], [0, 0, 0, -1]]
#sigma13 = [[0, i, 0, 0], [-i, 0, 0, 0], [0, 0, 0, i], [0, 0, -i, 0]]
#sigma23 = [[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]]
#def inv(matrice):
# out=[]
# for i in range(4):
# out2=[]
# out.append(out2)
# for j in range(4):
# out2.append(-1*matrice[i][j])
# return out
#
#sigma =[[zero, sigma01, sigma02, sigma03], \
# [inv(sigma01), zero, sigma12, sigma13],\
# [inv(sigma02), inv(sigma12), zero, sigma23],\
# [inv(sigma03), inv(sigma13), inv(sigma23), zero]]
sigma={(0, 2, 0, 1): -0.5, (3, 1, 2, 0): 0, (3, 2, 3, 1): 0, (1, 3, 1, 3): 0,
(2, 3, 3, 2): 0.5, (2, 1, 3, 1): 0, (0, 2, 2, 1): 0, (3, 1, 0, 0): 0,
(2, 3, 3, 1): 0, (3, 3, 1, 2): 0, (3, 1, 0, 3): 0, (1, 1, 0, 3): 0,
(0, 1, 2, 2): 0, (3, 2, 3, 2): -0.5, (2, 1, 0, 1): 0, (3, 3, 3, 3): 0,
(1, 1, 2, 2): 0, (2, 2, 3, 2): 0, (2, 1, 2, 1): 0, (0, 1, 0, 3): 0,
(2, 1, 2, 2): -0.5, (1, 2, 2, 1): 0, (2, 2, 1, 3): 0, (0, 3, 1, 3): 0,
(3, 0, 3, 2): 0, (1, 2, 0, 1): 0, (3, 0, 3, 1): 0, (0, 0, 2, 2): 0,
(1, 2, 0, 2): 0, (2, 0, 0, 3): 0, (0, 0, 2, 1): 0, (0, 3, 3, 2): 0,
(3, 0, 1, 1): -0.5j, (3, 2, 0, 1): -0.5, (1, 0, 1, 0): 0.5j, (0, 0, 0, 1): 0,
(0, 2, 1, 1): 0, (3, 1, 3, 2): 0.5j, (3, 2, 2, 1): 0, (1, 3, 2, 3): 0.5j,
(1, 0, 3, 0): 0, (3, 2, 2, 2): 0, (0, 2, 3, 1): 0, (1, 0, 3, 3): 0,
(2, 3, 2, 1): 0, (0, 2, 3, 2): -0.5, (3, 1, 1, 3): 0, (1, 1, 1, 3): 0,
(1, 3, 0, 2): 0, (2, 3, 0, 1): 0.5, (1, 1, 1, 0): 0, (2, 3, 0, 2): 0,
(3, 3, 0, 3): 0, (1, 1, 3, 0): 0, (0, 1, 3, 3): 0, (2, 2, 0, 1): 0,
(2, 1, 1, 0): 0, (3, 3, 2, 2): 0, (2, 3, 1, 0): 0.5, (2, 2, 2, 3): 0,
(0, 3, 0, 3): 0, (0, 1, 1, 2): 0, (0, 3, 0, 0): -0.5j, (2, 3, 1, 1): 0,
(1, 2, 3, 0): 0, (2, 0, 1, 3): 0, (0, 0, 3, 1): 0, (0, 3, 2, 0): 0,
(2, 3, 1, 2): 0, (2, 0, 1, 0): -0.5, (1, 2, 1, 0): 0, (3, 0, 0, 2): 0,
(1, 0, 0, 2): 0, (0, 0, 1, 1): 0, (1, 2, 1, 3): 0, (2, 3, 1, 3): 0,
(2, 0, 3, 0): 0, (0, 0, 1, 2): 0, (1, 3, 3, 3): 0, (3, 2, 1, 0): -0.5,
(1, 3, 3, 0): 0, (1, 0, 2, 3): -0.5j, (0, 2, 0, 0): 0, (3, 1, 2, 3): -0.5j,
(3, 2, 3, 0): 0, (1, 3, 1, 0): -0.5j, (3, 2, 3, 3): 0, (0, 2, 2, 0): 0,
(2, 3, 3, 0): 0, (3, 3, 1, 3): 0, (0, 2, 2, 3): 0.5, (3, 1, 0, 2): 0,
(1, 1, 0, 2): 0, (3, 3, 1, 0): 0, (0, 1, 2, 3): 0.5j, (1, 1, 0, 1): 0,
(2, 1, 0, 2): 0, (0, 1, 2, 0): 0, (3, 3, 3, 0): 0, (1, 1, 2, 1): 0,
(2, 2, 3, 3): 0, (0, 1, 0, 0): 0, (2, 2, 3, 0): 0, (2, 1, 2, 3): 0,
(1, 2, 2, 2): 0.5, (2, 2, 1, 0): 0, (0, 3, 1, 2): 0, (0, 3, 1, 1): 0.5j,
(3, 0, 3, 0): 0, (1, 2, 0, 3): 0, (2, 0, 0, 2): 0, (0, 0, 2, 0): 0,
(0, 3, 3, 1): 0, (3, 0, 1, 0): 0, (2, 0, 0, 1): 0.5, (3, 2, 0, 2): 0,
(3, 0, 1, 3): 0, (1, 0, 1, 3): 0, (0, 0, 0, 0): 0, (0, 2, 1, 2): 0,
(3, 1, 3, 3): 0, (0, 0, 0, 3): 0, (1, 3, 2, 2): 0, (3, 1, 3, 0): 0,
(3, 2, 2, 3): -0.5, (1, 3, 2, 1): 0, (1, 0, 3, 2): -0.5j, (2, 3, 2, 2): 0,
(0, 2, 3, 3): 0, (3, 1, 1, 0): 0.5j, (1, 3, 0, 1): 0.5j, (1, 1, 1, 1): 0,
(2, 1, 3, 2): 0, (2, 3, 0, 3): 0, (3, 3, 0, 2): 0, (1, 1, 3, 1): 0,
(3, 3, 0, 1): 0, (2, 1, 3, 3): 0.5, (0, 1, 3, 2): 0.5j, (1, 1, 3, 2): 0,
(2, 1, 1, 3): 0, (3, 0, 2, 1): 0, (0, 1, 3, 1): 0, (3, 3, 2, 1): 0,
(2, 2, 2, 2): 0, (0, 1, 1, 1): 0, (2, 2, 2, 1): 0, (0, 3, 0, 1): 0,
(3, 0, 2, 2): -0.5j, (1, 2, 3, 3): -0.5, (0, 0, 3, 2): 0, (0, 3, 2, 1): 0,
(2, 0, 1, 1): 0, (2, 2, 0, 0): 0, (0, 3, 2, 2): 0.5j, (3, 0, 0, 3): 0,
(1, 0, 0, 3): 0, (1, 2, 1, 2): 0, (2, 0, 3, 1): 0, (1, 0, 0, 0): 0,
(0, 0, 1, 3): 0, (2, 0, 3, 2): 0.5, (3, 2, 1, 3): 0, (1, 3, 3, 1): 0,
(1, 0, 2, 0): 0, (2, 2, 0, 2): 0, (0, 2, 0, 3): 0, (3, 1, 2, 2): 0,
(1, 3, 1, 1): 0, (3, 1, 2, 1): 0, (2, 2, 0, 3): 0, (3, 0, 0, 1): 0,
(1, 3, 1, 2): 0, (2, 3, 3, 3): 0, (0, 2, 2, 2): 0, (3, 1, 0, 1): -0.5j,
(3, 3, 1, 1): 0, (1, 1, 0, 0): 0, (2, 1, 0, 3): 0, (0, 1, 2, 1): 0,
(3, 3, 3, 1): 0, (2, 1, 0, 0): -0.5, (1, 1, 2, 0): 0, (3, 3, 3, 2): 0,
(0, 1, 0, 1): -0.5j, (1, 1, 2, 3): 0, (2, 2, 3, 1): 0, (2, 1, 2, 0): 0,
(0, 1, 0, 2): 0, (1, 2, 2, 3): 0, (2, 0, 2, 1): 0, (2, 2, 1, 1): 0,
(1, 2, 2, 0): 0, (2, 2, 1, 2): 0, (0, 3, 1, 0): 0, (3, 0, 3, 3): 0.5j,
(2, 1, 3, 0): 0, (1, 2, 0, 0): 0.5, (0, 0, 2, 3): 0, (0, 3, 3, 0): 0,
(2, 0, 0, 0): 0, (3, 2, 0, 3): 0, (0, 3, 3, 3): -0.5j, (3, 0, 1, 2): 0,
(1, 0, 1, 2): 0, (3, 2, 0, 0): 0, (0, 2, 1, 3): 0, (1, 0, 1, 1): 0,
(0, 0, 0, 2): 0, (0, 2, 1, 0): 0.5, (3, 1, 3, 1): 0, (3, 2, 2, 0): 0,
(1, 3, 2, 0): 0, (1, 0, 3, 1): 0, (2, 3, 2, 3): 0.5, (0, 2, 3, 0): 0,
(3, 1, 1, 1): 0, (2, 3, 2, 0): 0, (1, 3, 0, 0): 0, (3, 1, 1, 2): 0,
(1, 1, 1, 2): 0, (1, 3, 0, 3): 0, (2, 3, 0, 0): 0, (2, 0, 2, 0): 0,
(3, 3, 0, 0): 0, (1, 1, 3, 3): 0, (2, 1, 1, 2): 0, (0, 1, 3, 0): 0,
(3, 3, 2, 0): 0, (2, 1, 1, 1): 0.5, (2, 0, 2, 2): 0, (3, 3, 2, 3): 0,
(0, 1, 1, 0): -0.5j, (2, 2, 2, 0): 0, (0, 3, 0, 2): 0, (3, 0, 2, 3): 0,
(0, 1, 1, 3): 0, (2, 0, 2, 3): -0.5, (1, 2, 3, 2): 0, (3, 0, 2, 0): 0,
(0, 0, 3, 3): 0, (1, 2, 3, 1): 0, (2, 0, 1, 2): 0, (0, 0, 3, 0): 0,
(0, 3, 2, 3): 0, (3, 0, 0, 0): 0.5j, (1, 2, 1, 1): -0.5, (1, 0, 0, 1): 0.5j,
(0, 0, 1, 0): 0, (2, 0, 3, 3): 0, (3, 2, 1, 2): 0, (1, 3, 3, 2): -0.5j,
(1, 0, 2, 1): 0, (3, 2, 1, 1): 0, (0, 2, 0, 2): 0, (1, 0, 2, 2): 0}
def __init__(self, name, lorentz1, lorentz2, spin1, spin2):
aloha_lib.LorentzObject.__init__(self, name, [lorentz1, lorentz2], \
[spin1, spin2])
def create_representation(self):
self.representation = aloha_lib.LorentzObjectRepresentation(self.sigma,
self.lorentz_ind,self.spin_ind)
class Sigma(aloha_lib.FactoryLorentz):
object_class = L_Sigma
@classmethod
def get_unique_name(self, lorentz1, lorentz2, spin1, spin2):
return 'Sigma_[%s,%s]^[%s,%s]' % (spin1, spin2, lorentz1, lorentz2)
#===============================================================================
# Gamma5
#===============================================================================
class L_Gamma5(aloha_lib.LorentzObject):
#gamma5 = [[-1, 0, 0, 0, 0], [0, -1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]
gamma5 = {(0,0): -1, (0,1): 0, (0,2): 0, (0,3): 0,\
(1,0): 0, (1,1): -1, (1,2): 0, (1,3): 0,\
(2,0): 0, (2,1): 0, (2,2): 1, (2,3): 0,\
(3,0): 0, (3,1): 0, (3,2): 0, (3,3): 1}
def __init__(self, name, spin1, spin2):
aloha_lib.LorentzObject.__init__(self, name, [], [spin1, spin2])
def create_representation(self):
self.representation = aloha_lib.LorentzObjectRepresentation(self.gamma5,
self.lorentz_ind,self.spin_ind)
class Gamma5(aloha_lib.FactoryLorentz):
object_class = L_Gamma5
@classmethod
def get_unique_name(self, spin1, spin2):
return 'Gamma5_%s_%s' % (spin1, spin2)
#===============================================================================
# Conjugate Matrices
#===============================================================================
class L_C(aloha_lib.LorentzObject):
#[0, -1, 0, 0] [1,0,0,0] [0,0,0,1],[0,0,-1,0]
Cmetrix = {(0,0): 0, (0,1): -1, (0,2): 0, (0,3): 0,\
(1,0): 1, (1,1): 0, (1,2): 0, (1,3): 0,\
(2,0): 0, (2,1): 0, (2,2): 0, (2,3): 1,\
(3,0): 0, (3,1): 0, (3,2): -1, (3,3): 0}
def __init__(self, name, spin_list):
# spin_list is automatically ordered. The sign for the symmetrization
# is set in the Factory routine
aloha_lib.LorentzObject.__init__(self, name, [], spin_list)
def create_representation(self):
self.representation = aloha_lib.LorentzObjectRepresentation(self.Cmetrix,
self.lorentz_ind,self.spin_ind)
class C(aloha_lib.FactoryLorentz):
object_class = L_C
def __new__(cls, spin1, spin2):
spin_list = [spin1, spin2]
spin_list.sort()
sign = give_sign_perm(spin_list, [spin1, spin2])
name = cls.get_unique_name(spin_list)
if sign == 1:
return aloha_lib.FactoryVar.__new__(cls, name, cls.object_class, spin_list)
else:
out = aloha_lib.FactoryVar.__new__(cls, name, cls.object_class, spin_list)
out.prefactor = -1
return out
@classmethod
def get_unique_name(cls, spin_list):
return "C_%s_%s" % tuple(spin_list)
#===============================================================================
# EPSILON
#===============================================================================
#Helpfull function
def give_sign_perm(perm0, perm1):
"""Check if 2 permutations are of equal parity.
Assume that both permutation lists are of equal length
and have the same elements. No need to check for these
conditions.
"""
assert len(perm0) == len(perm1)
perm1 = list(perm1) ## copy this into a list so we don't mutate the original
perm1_map = dict((v, i) for i,v in enumerate(perm1))
transCount = 0
for loc, p0 in enumerate(perm0):
p1 = perm1[loc]
if p0 != p1:
sloc = perm1_map[p0] # Find position in perm1
perm1[loc], perm1[sloc] = p0, p1 # Swap in perm1
perm1_map[p0], perm1_map[p1] = loc, sloc # Swap the map
transCount += 1
# Even number of transposition means equal parity
return -2 * (transCount % 2) + 1
# Practical definition of Epsilon
class L_Epsilon(aloha_lib.LorentzObject):
""" The fully anti-symmetric object in Lorentz-Space """
def give_parity(self, perm):
"""return the parity of the permutation"""
assert set(perm) == set([0,1,2,3])
i1 , i2, i3, i4 = perm
#formula found on wikipedia
return -self.sign * ((i2-i1) * (i3-i1) *(i4-i1) * (i3-i2) * (i4-i2) *(i4-i3))/12
# DEFINE THE REPRESENTATION OF EPSILON
def __init__(self, name, lorentz1, lorentz2, lorentz3, lorentz4):
lorentz_list = [lorentz1 , lorentz2, lorentz3, lorentz4]
#order_lor = list(lorentz_list)
#order_lor.sort()
#self.sign = give_sign_perm(order_lor, lorentz_list)
self.sign=1
aloha_lib.LorentzObject.__init__(self, name, lorentz_list, [])
def create_representation(self):
if not hasattr(self, 'epsilon'):
# init all element to zero
epsilon = dict( ((l1, l2, l3, l4), 0)
for l1 in range(4) \
for l2 in range(4) \
for l3 in range(4) \
for l4 in range(4))
# update non trivial one
epsilon.update(dict(
((l1, l2, l3, l4), self.give_parity((l1,l2,l3,l4)))
for l1 in range(4) \
for l2 in range(4) if l2 != l1\
for l3 in range(4) if l3 not in [l1,l2]\
for l4 in range(4) if l4 not in [l1,l2,l3]))
L_Epsilon.epsilon = epsilon
self.representation = aloha_lib.LorentzObjectRepresentation(self.epsilon,
self.lorentz_ind,self.spin_ind)
class Epsilon(aloha_lib.FactoryLorentz):
object_class = L_Epsilon
@classmethod
def get_unique_name(cls,l1,l2,l3,l4):
return '_EPSILON_%s_%s_%s_%s' % (l1,l2,l3,l4)
#===============================================================================
# Metric
#===============================================================================
class L_Metric(aloha_lib.LorentzObject):
metric = {(0,0): 1, (0,1): 0, (0,2): 0, (0,3): 0,\
(1,0): 0, (1,1): -1, (1,2): 0, (1,3): 0,\
(2,0): 0, (2,1): 0, (2,2): -1, (2,3): 0,\
(3,0): 0, (3,1): 0, (3,2): 0, (3,3): -1}
#[[1, 0, 0,0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, -1]]
def __init__(self, name, lorentz1, lorentz2):
aloha_lib.LorentzObject.__init__(self,name,[lorentz1, lorentz2], [])
def create_representation(self):
self.representation = aloha_lib.LorentzObjectRepresentation(self.metric,
self.lorentz_ind,self.spin_ind)
class Metric(aloha_lib.FactoryLorentz):
object_class = L_Metric
@classmethod
def get_unique_name(cls,l1,l2):
if l1<l2:
return '_ETA_%s_%s' % (l1,l2)
else:
return '_ETA_%s_%s' % (l2,l1)
#===============================================================================
# Identity
#===============================================================================
class L_Identity(aloha_lib.LorentzObject):
#identity = [[1, 0, 0,0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]
identity = {(0,0): 1, (0,1): 0, (0,2): 0, (0,3): 0,\
(1,0): 0, (1,1): 1, (1,2): 0, (1,3): 0,\
(2,0): 0, (2,1): 0, (2,2): 1, (2,3): 0,\
(3,0): 0, (3,1): 0, (3,2): 0, (3,3): 1}
def __init__(self, name, spin1, spin2):
aloha_lib.LorentzObject.__init__(self, name, [],[spin1, spin2])
def create_representation(self):
self.representation = aloha_lib.LorentzObjectRepresentation(self.identity,
self.lorentz_ind,self.spin_ind)
class Identity(aloha_lib.FactoryLorentz):
object_class = L_Identity
@classmethod
def get_unique_name(self, spin1, spin2):
return 'Id_%s_%s' % (spin1, spin2)
#===============================================================================
# IdentityL
#===============================================================================
class L_IdentityL(aloha_lib.LorentzObject):
#identity = [[1, 0, 0,0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]
identity = {(0,0): 1, (0,1): 0, (0,2): 0, (0,3): 0,\
(1,0): 0, (1,1): 1, (1,2): 0, (1,3): 0,\
(2,0): 0, (2,1): 0, (2,2): 1, (2,3): 0,\
(3,0): 0, (3,1): 0, (3,2): 0, (3,3): 1}
def __init__(self, name, l1, l2):
aloha_lib.LorentzObject.__init__(self, name, [l1,l2], [])
def create_representation(self):
self.representation = aloha_lib.LorentzObjectRepresentation(self.identity,
self.lorentz_ind,self.spin_ind)
class IdentityL(aloha_lib.FactoryLorentz):
object_class = L_IdentityL
@classmethod
def get_unique_name(self, l1, l2):
return 'IdL_%s_%s' % (l1, l2)
#===============================================================================
# ProjM
#===============================================================================
class L_ProjM(aloha_lib.LorentzObject):
""" A object for (1-gamma5)/2 """
#projm = [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
projm= {(0,0): 1, (0,1): 0, (0,2): 0, (0,3): 0,\
(1,0): 0, (1,1): 1, (1,2): 0, (1,3): 0,\
(2,0): 0, (2,1): 0, (2,2): 0, (2,3): 0,\
(3,0): 0, (3,1): 0, (3,2): 0, (3,3): 0}
def __init__(self,name, spin1, spin2):
"""Initialize the object"""
aloha_lib.LorentzObject.__init__(self, name, [], [spin1, spin2])
def create_representation(self):
self.representation = aloha_lib.LorentzObjectRepresentation(self.projm,
self.lorentz_ind,self.spin_ind)
class ProjM(aloha_lib.FactoryLorentz):
object_class = L_ProjM
@classmethod
def get_unique_name(self, spin1, spin2):
return 'PROJM_%s_%s' % (spin1, spin2)
#===============================================================================
# ProjP
#===============================================================================
class L_ProjP(aloha_lib.LorentzObject):
"""A object for (1+gamma5)/2 """
#projp = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]
projp = {(0,0): 0, (0,1): 0, (0,2): 0, (0,3): 0,\
(1,0): 0, (1,1): 0, (1,2): 0, (1,3): 0,\
(2,0): 0, (2,1): 0, (2,2): 1, (2,3): 0,\
(3,0): 0, (3,1): 0, (3,2): 0, (3,3): 1}
def __init__(self,name, spin1, spin2):
"""Initialize the object"""
aloha_lib.LorentzObject.__init__(self, name, [], [spin1, spin2])
def create_representation(self):
self.representation = aloha_lib.LorentzObjectRepresentation(self.projp,
self.lorentz_ind, self.spin_ind)
class ProjP(aloha_lib.FactoryLorentz):
object_class = L_ProjP
@classmethod
def get_unique_name(self, spin1, spin2):
return 'PROJP_%s_%s' % (spin1, spin2)
#===============================================================================
# Denominator Propagator
#===============================================================================
class DenominatorPropagator(aloha_lib.LorentzObject):
"""The Denominator of the Propagator"""
def __new__(cls, particle):
name = 'DenomP%s' % particle
return aloha_lib.Variable.__new__(cls, name)
def __init__(self, particle):
if self:
return
self.particle = particle
aloha_lib.LorentzObject.__init__(self, [], [])
def get_unique_name(self,*args):
return 'DenomP%s' % self.particle
def simplify(self):
"""Return the Denominator in a abstract way"""
mass = Mass(self.particle)
width = Width(self.particle)
denominator = P('i1', self.particle) * P('i1', self.particle) - \
mass * mass + complex(0,1) * mass* width
return denominator
def create_representation(self):
"""Create the representation for the Vector propagator"""
object = self.simplify()
self.representation = object.expand()
#===============================================================================
# Numerator Propagator
#===============================================================================
SpinorPropagatorout = lambda spin1, spin2, particle: -1 * (Gamma('mu', spin1, spin2) * \
P('mu', particle) - Mass(particle) * Identity(spin1, spin2))
SpinorPropagatorin = lambda spin1, spin2, particle: (Gamma('mu', spin1, spin2) * \
P('mu', particle) + Mass(particle) * Identity(spin1, spin2))
VectorPropagator = lambda l1, l2, part: complex(0,1)*(-1 * Metric(l1, l2) + OverMass2(part) * \
Metric(l1,'I3')* P('I3', part) * P(l2, part))
VectorPropagatorMassless= lambda l1, l2, part: complex(0,-1) * Metric(l1, l2)
Spin3halfPropagatorin = lambda mu, nu, s1, s2, part: (\
-1/3 * (Gamma(mu,s1,-2) + Identity(s1, -2) * P(mu, part) * Mass(part) * OverMass2(part))* \
(PSlash(-2,-3, part) - Identity(-2,-3) * Mass(part)) * \
( Gamma(nu, -3, s2)+ Mass(part) * OverMass2(part) * Identity(-3, s2) * P(nu, part) ) - \
(PSlash(s1,s2, part) + Mass(part) * Identity(s1,s2)) * (Metric(mu, nu) - OverMass2(part) * P(mu, part) * P(nu,part)))
Spin3halfPropagatorout = lambda mu, nu, s1, s2, part: ( \
-1/3 * (Gamma(mu,s1,-2) - Identity(s1, -2) * P(mu, part) * Mass(part) * OverMass2(part))* \
(-1*PSlash(-2,-3, part) - Identity(-2,-3) * Mass(part)) * \
( Gamma(nu, -3, s2)- Mass(part) * OverMass2(part) * Identity(-3, s2) * P(nu, part) ) - \
(-1*PSlash(s1,s2, part)
+ Mass(part) * Identity(s1,s2)) * (Metric(mu, nu) - OverMass2(part) * P(mu, part) * P(nu,part)))
Spin3halfPropagatorMasslessOut = lambda mu, nu, s1, s2, part: Gamma(nu, s1,-1) * PSlash(-1,-2, part) * Gamma(mu,-2, s2)
Spin3halfPropagatorMasslessIn = lambda mu, nu, s1, s2, part: -1 * Gamma(mu, s1,-1) * PSlash(-1,-2, part) * Gamma(nu,-2, s2)
Spin2masslessPropagator = lambda mu, nu, alpha, beta: 1/2 *( Metric(mu, alpha)* Metric(nu, beta) +\
Metric(mu, beta) * Metric(nu, alpha) - Metric(mu, nu) * Metric(alpha, beta))
Spin2Propagator = lambda mu, nu, alpha, beta, part: Spin2masslessPropagator(mu, nu, alpha, beta) + \
- 1/2 * OverMass2(part) * (Metric(mu,alpha)* P(nu, part) * P(beta, part) + \
Metric(nu, beta) * P(mu, part) * P(alpha, part) + \
Metric(mu, beta) * P(nu, part) * P(alpha, part) + \
Metric(nu, alpha) * P(mu, part) * P(beta , part) )+ \
1/6 * (Metric(mu,nu) + 2 * OverMass2(part) * P(mu, part) * P(nu, part)) * \
(Metric(alpha,beta) + 2 * OverMass2(part) * P(alpha, part) * P(beta, part))
| 41,975 | 18,010 |
"""Functions that either provide an instruction flow or a string to display."""
import os
import os.path as op
import numpy as np
import pandas as pd
import sp_experiment
from sp_experiment.utils import get_final_choice_outcomes
from sp_experiment.define_settings import (txt_color,
color_magnitude, color_probability,
font, lang, max_nsamples_opt_stop,
max_ntrls, max_nsamples, block_size,
maxwait, exchange_rate,
KEYLIST_SAMPLES)
def print_human_readable_instrs(kind, track_eyes, opt_stop, fpath=None):
"""Print the instructions in readable format.
Parameters
----------
kind : str
Can be 'active', 'passive', or 'description'
track_eyes : bool
Whether or not to render instructions for eyetracking.
opt_stop : bool
Whether or not to render instructions for optional stopping.
fpath : str | None
Path to the file to be written. If None, will print to console. If
file already exists, it will be deleted.
"""
if opt_stop:
texts = run_instructions(kind, return_text_only=True,
track_eyes=track_eyes, opt_stop=opt_stop,
max_nsamples=max_nsamples_opt_stop)
else:
texts = run_instructions(kind, return_text_only=True,
track_eyes=track_eyes, opt_stop=opt_stop)
if fpath is None:
for text in texts:
print(text)
else:
if op.exists(fpath):
os.remove(fpath)
with open(fpath, 'w', encoding='utf-8') as fout:
for text in texts:
fout.write(text + '\n\n')
def _navigate_instrs(ith_text, texts, txt_stim, win):
"""Navigate in instructions."""
from psychopy import event
do_break = False
key = event.waitKeys()
if key[0] == KEYLIST_SAMPLES[1]:
ith_text += 1
elif key[0] == KEYLIST_SAMPLES[0]:
ith_text -= 1
elif key[0] == 'x':
do_break = True
else:
# print help and leave ith_text unchanged
txt_stim.text = ('Nutzen Sie die linke und rechte Taste zum '
'Navigieren.')
txt_stim.draw()
win.flip()
event.waitKeys()
# Check whether to stop
if ith_text >= len(texts):
do_break = True
# ith_text is never negative
if ith_text < 0:
ith_text = 0
return ith_text, do_break
def run_instructions(kind, monitor='testMonitor', font=font, lang=lang,
max_ntrls=max_ntrls, max_nsamples=max_nsamples,
block_size=block_size, maxwait=maxwait,
exchange_rate=exchange_rate, opt_stop=True,
return_text_only=False, track_eyes=False):
"""Show experiment instructions on the screen.
Parameters
----------
kind : str
Can be 'general', 'active', 'passive', or 'description'
monitor : str
Name of monitor to be used
font : str
Name of font to be used
lang : str
Language to be used, can be 'de' or 'en'
max_ntrls : int
Maximum number of trials for this run.
max_nsamples : int
Maximum number of samples per trial.
block_size : int
Number of trials after which feedback is provided
maxwait : int | float
Maximum time to wait for a response until time out
exchange_rate : float
Conversion rate of points to Euros
opt_stop : bool
True if optional stopping is allowed in this run, False otherwise ...
return_text_only : bool
If True, will only return the texts. No impact if False
track_eyes : bool
Defaults to False: will not change the instruction strings to
accommodate for eyetracking. Will automatically switch to True if an
eyetracker is detected in Runtime
"""
if not return_text_only:
from psychopy import visual, monitors
import tobii_research as tr
# Check if we have an eyetracker ... if yes, switch to showing
# instructions with eyetracker instructions as well
found_eyetrackers = tr.find_all_eyetrackers()
if not track_eyes and len(found_eyetrackers) > 0:
track_eyes = True
# Define monitor specific window object
my_monitor = monitors.Monitor(name=monitor)
win = visual.Window(color=(0, 0, 0), # Background color: RGB [-1,1]
fullscr=True, # Fullscreen for better timing
monitor=my_monitor,
winType='pyglet',
size=my_monitor.getSizePix())
# Hide the cursor
win.mouseVisible = False
# prepare text object
txt_stim = visual.TextStim(win,
units='deg',
color=txt_color)
txt_stim.height = 1
txt_stim.font = font
# prepare image stim
img_stim = visual.ImageStim(win)
img_stim.pos = (0.5, 0)
# general image directorys
init_dir = op.dirname(sp_experiment.__file__)
img_dir = op.join(init_dir, 'image_data')
# START INSTRUCTIONS
if kind == 'general':
texts = _provide_general_instr_str(lang=lang)
if return_text_only:
return texts
ith_text = 0
while True:
text = texts[ith_text]
txt_stim.text = text
txt_stim.draw()
win.flip()
# Check whether to proceed, go gack, or stay
ith_text, do_break = _navigate_instrs(ith_text, texts, txt_stim,
win)
if do_break:
break
elif kind == 'active':
texts = _provide_active_instr_strs(lang, max_ntrls, max_nsamples,
block_size, maxwait, exchange_rate,
track_eyes, opt_stop)
if return_text_only:
return texts
ith_text = 0
while True:
text = texts[ith_text]
txt_stim.text = text
txt_stim.draw()
if 'Kugeln mit Zahlen' in text:
img_stim.image = op.join(img_dir, 'any_ball.png')
img_stim.draw()
if 'Taste drücken.' in text:
img_stim.image = op.join(img_dir, 'bbox_photo.png')
img_stim.draw()
if 'Wie bereits erwähnt' in text:
img_stim.image = op.join(img_dir, 'bbox_photo_stop_button.png')
img_stim.draw()
if 'Farbe der Punkte' in text:
img_stim.image = op.join(img_dir, 'final_ball.png')
img_stim.draw()
if 'Hilfestellung' in text:
img_stim.image = op.join(img_dir, 'start_cropped.png')
img_stim.draw()
if 'zentralen Stimulus weiß' in text:
img_stim.image = op.join(img_dir, 'action_cropped.png')
img_stim.draw()
if 'kurz zu blau' in text:
img_stim.image = op.join(img_dir, 'choice_cropped.png')
img_stim.draw()
if 'Sekunden Zeit' in text:
img_stim.image = op.join(img_dir, 'error_cropped.png')
img_stim.draw()
if 'Zusammenfassend' in text:
img_stim.image = op.join(img_dir, 'fix_stims.png')
img_stim.draw()
win.flip()
# Check whether to proceed, go gack, or stay
ith_text, do_break = _navigate_instrs(ith_text, texts, txt_stim,
win)
if do_break:
break
elif kind == 'passive':
texts = _provide_passive_instr_strs(lang, max_ntrls, max_nsamples,
block_size, maxwait, exchange_rate,
track_eyes, opt_stop)
if return_text_only:
return texts
ith_text = 0
while True:
text = texts[ith_text]
txt_stim.text = text
txt_stim.draw()
if 'Kugeln mit Zahlen' in text:
img_stim.image = op.join(img_dir, 'any_ball.png')
img_stim.draw()
if 'Taste drücken.' in text:
img_stim.image = op.join(img_dir, 'bbox_photo.png')
img_stim.draw()
if 'Farbe der Punkte' in text:
img_stim.image = op.join(img_dir, 'final_ball.png')
img_stim.draw()
if 'Hilfestellung' in text:
img_stim.image = op.join(img_dir, 'start_cropped.png')
img_stim.draw()
if 'zentralen Stimulus weiß' in text:
img_stim.image = op.join(img_dir, 'action_cropped.png')
img_stim.draw()
if 'kurz zu blau' in text:
img_stim.image = op.join(img_dir, 'choice_cropped.png')
img_stim.draw()
if 'Sekunden Zeit' in text:
img_stim.image = op.join(img_dir, 'error_cropped.png')
img_stim.draw()
if 'Zusammenfassend' in text:
img_stim.image = op.join(img_dir, 'fix_stims.png')
img_stim.draw()
win.flip()
# Check whether to proceed, go gack, or stay
ith_text, do_break = _navigate_instrs(ith_text, texts, txt_stim,
win)
if do_break:
break
elif kind == 'description':
texts = _provide_description_instr_str(lang=lang)
if return_text_only:
return texts
ith_text = 0
while True:
text = texts[ith_text]
txt_stim.text = text
txt_stim.draw()
# Draw red and blue lottery
txt_stim1 = visual.TextStim(win, units='deg',
color=color_magnitude,
height=1, font=font, text='1\n5',
pos=(-0.5, -7))
txt_stim2 = visual.TextStim(win, units='deg',
color=color_probability,
height=1, font=font, text='90\n10',
pos=(0.5, -7))
txt_stim1.draw()
txt_stim2.draw()
# Flip and go
win.flip()
# Check whether to proceed, go gack, or stay
ith_text, do_break = _navigate_instrs(ith_text, texts, txt_stim,
win)
if do_break:
break
win.close()
def _provide_active_instr_strs(lang, max_ntrls, max_nsamples, block_size,
maxwait, exchange_rate, track_eyes,
opt_stop=True):
"""Provide active instr texts."""
# Eyetracking special replacement strings
if track_eyes:
eyetrackstr1 = ' Wenn Sie während eines Versuchs wiederholt nicht ausreichend fixieren, muss der Versuch erneut gestartet werden.' # noqa: E501
eyetrackstr2 = ' Die Farbe wechselt auch zu rot, wenn Sie wiederholt nicht ausreichend den zentralen Stimulus fixiert haben.' # noqa: E501
eyetrackstr3 = 'oder haben wiederholt nicht den zentralen Stimulus fixiert ' # noqa: E501
else:
eyetrackstr1 = ''
eyetrackstr2 = ''
eyetrackstr3 = ''
# Optional stopping special replacement strings
if opt_stop:
opt_stop_str1 = 'bis zu einem Maximum von {}'.format(max_nsamples)
opt_stop_str2 = 'von Ihnen gewählte Anzahl an Kugeln (bis zu maximal {})'.format(max_nsamples) # noqa: E501
opt_stop_str3 = 'Nach dem Drücken der blauen "Stopp" Taste (oder maximal {} Zügen)'.format(max_nsamples) # noqa: E501
else:
opt_stop_str1 = max_nsamples
opt_stop_str2 = '{} Kugeln'.format(max_nsamples)
opt_stop_str3 = 'Wenn Sie nach {} Zügen ein weiteres Mal die linke oder rechte Taste drücken,'.format(max_nsamples) # noqa: E501
texts = list()
if lang == 'de':
texts.append('Instruktionen Aufgabe A. Bitte lesen Sie aufmerksam den folgenden Text. Nutzen Sie die linke und rechte Taste zum Navigieren. Achten Sie auf die Details in der Beschreibung! In dieser Aufgabe werden Sie selbst nach Informationen suchen.') # noqa: E501
texts.append('Bitte fixieren Sie während des Experiments mit ihrem Blick immer den zentralen Stimulus in der Bildschirmmitte.{}'.format(eyetrackstr1)) # noqa: E501
texts.append('Links und rechts von dem zentralen Stimulus befinden sich zwei unsichtbare Urnen. In den Urnen befinden sich jeweils 100 Kugeln mit Zahlen darauf. Die Zahlen stehen für Spielpunkte, die zu einem Wechselkurs von {} in Euro umgewandelt werden. Dieses Geld in Euro wird Ihnen am Ende des Experimentes als Bonus ausgezahlt.'.format(exchange_rate)) # noqa: E501
texts.append('Es gibt in dieser Aufgabe viele Durchgänge. In jedem Durchgang gibt es neue Urnen, und ihre Aufgabe wird jedes Mal sein, sich am Ende der Aufgabe für eine der beiden Urnen zu entscheiden. Um etwas über den Inhalt der Urnen zu erfahren, dürfen Sie in jedem Durchgang zunächst insgesamt {} mal blind eine Kugel ziehen. Dies können Sie tun, indem Sie die linke oder die rechte Taste drücken. Sie können also jedes Mal selbst wählen, aus welcher Urne die nächste Kugel gezogen wird. Die Kugel wird jedesmal zufällig aus der jeweiligen Urne gezogen und Ihnen kurz gezeigt. Danach wird die Kugel zurück in die ursprüngliche Urne gelegt. Es sind also IMMER 100 Kugeln in jeder Urne. In anderen Worten, der Inhalt der Urnen wird durch Ihre Ziehung(en) nicht verändert.'.format(opt_stop_str1)) # noqa: E501 E999
if opt_stop:
# If optional stopping, append an extra explanation on how to stop
texts.append('Wie bereits erwähnt können Sie in jedem Durchgang maximal {} mal blind eine Kugel ziehen, um mehr über die Urnen zu lernen. Sie können jedoch auch schon früher aufhören, Kugeln zu ziehen. Das können Sie mit der blauen Taste über der rechten Taste anzeigen. Beim Drücken der dieser blauen "Stopp" Taste wird sofort die nächste Phase des momentanen Durchgangs eingeleitet, wie im Folgenden beschrieben.'.format(max_nsamples)) # noqa: E501
texts.append('Nachdem Sie sich die {} angeschaut haben, müssen Sie sich final für eine der Urnen entscheiden. Ihr Ziel sollte natürlich sein, dabei immer die jeweils bessere Urne zu wählen. Nach dieser finalen Entscheidung wird aus der gewählten Urne nochmals eine Kugel (zufällig) gezogen. Die Punkte auf dieser finalen Kugel werden Ihrem Konto gutgeschrieben. Dies wird durch die grüne Farbe der Punkte gezeigt. Danach beginnt ein neuer Durchgang mit komplett neuen Urnen. Insgesamt gibt es {} Durchgänge und alle {} Durchgänge werden Sie Zeit für eine kurze Pause haben.'.format(opt_stop_str2, max_ntrls, block_size)) # noqa: E501
texts.append('Als Hilfestellung zeigt Ihnen die Farbe des zentralen Stimulus an, was während der Durchgänge als nächstes passiert: Zu Beginn eines Durchgangs ist der Stimulus kurz grün und dann weiß. Das bedeutet, dass neue unsichtbaren Urnen links und rechts aufgestellt wurden.') # noqa: E501
texts.append('Danach bleibt die Farbe des zentralen Stimulus weiß. Das bedeutet, dass Sie jetzt eine Kugel aus einer der Urnen ziehen können, durch Drücken der linken oder der rechten Taste. Während Sie darauf warten, dass die Kugel gezeigt wird, wird die Farbe des zentralen Stimulus auch noch weiß sein.') # noqa: E501
texts.append('{} wechselt die Farbe des zentralen Stimulus kurz zu blau und wird dann wieder weiß. Das bedeutet, dass Sie sich nun final zwischen den Urnen entscheiden müssen. Zur Erinnerung: Nur die Kugel die nach der finalen Entscheidung gezogen wird bestimmt, wie viele Punkte ihrem Konto hinzugefügt werden.'.format(opt_stop_str3)) # noqa: E501
texts.append('Für Ihre Entscheidungen haben Sie jeweils {} Sekunden Zeit. Wenn Sie länger warten, wechselt die Farbe des zentralen Stimulus zu rot und der momentane Durchgang wird abgebrochen.{} Direkt danach wird ein neuer Durchgang gestartet.'.format(maxwait, eyetrackstr2)) # noqa: E501
texts.append('Zusammenfassend bedeuten die Farben das folgende:\n\ngrün: neuer Durchgang mit neuen Urnen\n\nweiß: Urne wählen oder auf zufällig gezogene Kugel warten\n\nblau: nächste Entscheidung ist die finale Entscheidung für diesen Durchgang\n\nrot: Sie haben länger als {} Sekunden gewartet {}und der Durchgang wird abgebrochen'.format(maxwait, eyetrackstr3)) # noqa: E501
texts.append('Die Instruktionen sind abgeschlossen. Drücken Sie die rechte Taste um fortzufahren.') # noqa: E501
elif lang == 'en':
texts.append('LANGUAGE NOT IMPLEMENTED YET')
return texts
def _provide_passive_instr_strs(lang, max_ntrls, max_nsamples, block_size,
maxwait, exchange_rate, track_eyes,
opt_stop=True):
"""Provide passive instr texts."""
# Eyetracking special replacement strings
if track_eyes:
eyetrackstr1 = ' Wenn Sie während eines Versuchs wiederholt nicht ausreichend fixieren, muss der Versuch erneut gestartet werden.' # noqa: E501
eyetrackstr2 = ' Die Farbe wechselt auch zu rot, wenn Sie wiederholt nicht ausreichend den zentralen Stimulus fixiert haben.' # noqa: E501
eyetrackstr3 = 'oder haben wiederholt nicht den zentralen Stimulus fixiert ' # noqa: E501
else:
eyetrackstr1 = ''
eyetrackstr2 = ''
eyetrackstr3 = ''
# Optional stopping special replacement strings
if opt_stop:
opt_stop_str1 = 'bis zu einem Maximum von {}'.format(max_nsamples)
opt_stop_str2 = 'vom Computer gewählte Anzahl an Kugeln (bis zu maximal {})'.format(max_nsamples) # noqa: E501
opt_stop_str3 = 'alle gewollten'
else:
opt_stop_str1 = max_nsamples
opt_stop_str2 = '{} Kugeln'.format(max_nsamples)
opt_stop_str3 = '{}'.format(max_nsamples)
texts = list()
if lang == 'de':
texts.append('Instruktionen Aufgabe B. Bitte lesen Sie aufmerksam den folgenden Text. Nutzen Sie die linke und rechte Taste zum Navigieren. Achten Sie auf die Details in der Beschreibung! In dieser Aufgabe wird der Computer Sie mit Informationen versorgen.') # noqa: E501
texts.append('Bitte fixieren Sie während des Experiments mit ihrem Blick immer den zentralen Stimulus in der Bildschirmmitte.{}'.format(eyetrackstr1)) # noqa: E501
texts.append('Links und rechts von dem zentralen Stimulus befinden sich zwei unsichtbare Urnen. In den Urnen befinden sich jeweils 100 Kugeln mit Zahlen darauf. Die Zahlen stehen für Spielpunkte, die zu einem Wechselkurs von {} in Euro umgewandelt werden. Dieses Geld in Euro wird Ihnen am Ende des Experimentes als Bonus ausgezahlt.'.format(exchange_rate)) # noqa: E501
texts.append('Es gibt in dieser Aufgabe viele Durchgänge. In jedem Durchgang gibt es neue Urnen, und ihre Aufgabe wird jedes Mal sein, sich am Ende der Aufgabe für eine der beiden Urnen zu entscheiden. Der Computer wird zunächst insgesamt {} mal blind eine Kugel ziehen. Hierzu wird der Computer entscheiden, aus welcher Urne die nächste Kugel gezogen wird. Die Kugel wird jedesmal zufällig aus der jeweiligen Urne gezogen und Ihnen kurz gezeigt. Danach wird die Kugel zurück in die ursprüngliche Urne gelegt. Es sind also IMMER 100 Kugeln in jeder Urne. In anderen Worten, der Inhalt der Urnen wird durch Ihre Ziehung(en) nicht verändert.'.format(opt_stop_str1)) # noqa: E501
texts.append('Nachdem Sie sich die {} angeschaut haben, müssen Sie sich final für eine der Urnen entscheiden. Dies können Sie tun, indem Sie die linke oder die rechte Taste drücken. Ihr Ziel sollte natürlich sein, dabei immer die jeweils bessere Urne zu wählen. Nach dieser finalen Entscheidung wird aus der gewählten Urne nochmals eine Kugel (zufällig) gezogen. Die Punkte auf dieser finalen Kugel werden Ihrem Konto gutgeschrieben.'.format(opt_stop_str2)) # noqa: E501
texts.append('Dies wird durch die grüne Farbe der Punkte gezeigt. Danach beginnt ein neuer Durchgang mit komplett neuen Urnen. Insgesamt gibt es {} Durchgänge und alle {} Durchgänge werden Sie Zeit für eine kurze Pause haben.'.format(max_ntrls, block_size)) # noqa: E501
texts.append('Als Hilfestellung zeigt Ihnen die Farbe des zentralen Stimulus an, was während der Durchgänge als nächstes passiert: Zu Beginn eines Durchgangs ist der Stimulus kurz grün und dann weiß. Das bedeutet, dass neue unsichtbaren Urnen links und rechts aufgestellt wurden.') # noqa: E501
texts.append('Danach bleibt die Farbe des zentralen Stimulus weiß. Das bedeutet, dass der Computer jetzt eine Kugel aus einer der Urnen ziehen wird. Während Sie darauf warten, dass die Kugel gezeigt wird, wird die Farbe des zentralen Stimulus auch noch weiß sein.') # noqa: E501
texts.append('Wenn der Computer {} Züge getätigt hat, wechselt die Farbe des zentralen Stimulus kurz zu blau und wird dann wieder weiß. Das bedeutet, dass Sie sich nun final zwischen den Urnen entscheiden müssen. Zur Erinnerung: Nur die Kugel die nach der finalen Entscheidung gezogen wird bestimmt, wie viele Punkte ihrem Konto hinzugefügt werden.'.format(opt_stop_str3)) # noqa: E501
texts.append('Für Ihre Entscheidungen haben Sie jeweils {} Sekunden Zeit. Wenn Sie länger warten, wechselt die Farbe des zentralen Stimulus zu rot und der momentane Durchgang wird abgebrochen.{} Direkt danach wird ein neuer Durchgang gestartet.'.format(maxwait, eyetrackstr2)) # noqa: E501
texts.append('Zusammenfassend bedeuten die Farben das folgende:\n\ngrün: neuer Durchgang mit neuen Urnen\n\nweiß: Der Computer wählt eine Urne oder auf zufällig gezogene Kugel warten\n\nblau: nächste Entscheidung ist die finale Entscheidung für diesen Durchgang\n\nrot: Sie haben länger als {} Sekunden gewartet {}und der Durchgang wird abgebrochen'.format(maxwait, eyetrackstr3)) # noqa: E501
texts.append('Die Instruktionen sind abgeschlossen. Drücken Sie die rechte Taste um fortzufahren.') # noqa: E501
elif lang == 'en':
texts.append('LANGUAGE NOT IMPLEMENTED YET')
return texts
def _provide_general_instr_str(lang):
"""Provide a welcome screen text."""
texts = list()
if lang == 'de':
texts.append('Wilkommen! Sie werden drei Aufgaben nacheinander '
'ausführen. Im Folgenden bekommen Sie Anweisungen zur '
'ersten Aufgabe. Dann dürfen Sie einen Test der '
'ersten Aufgabe durchführen. Danach wird die erste '
'Aufgabe gestartet. Wenn Sie mit dieser Aufgabe '
'fertig sind, wird die zweite Aufgabe in den selben '
'Schritten durchgeführt. Das heißt: Erst Anweisung, '
'dann Test, dann Durchführung der Aufgabe. '
'Zum Schluss gibt es noch eine kurze dritte Aufgabe, '
'gefolgt von 4 Matheproblemen. '
'Drücken Sie eine beliebige Taste.')
elif lang == 'en':
texts.append('LANGUAGE NOT IMPLEMENTED YET')
return texts
def provide_blockfbk_str(data_file, current_nblocks, nblocks, lang):
"""Provide a string to be displayed during block feedback.
Parameters
----------
data_file : str
Path to the data file from which to calculate current amount of points.
current_nblocks : int
nblocks : int
lang : str
Language, can be 'de' or 'en' for German or English.
Returns
-------
block_feedback : str
"""
# Current number of points
df_tmp = pd.read_csv(data_file, sep='\t')
outcomes = get_final_choice_outcomes(df_tmp)
points = int(np.sum(outcomes))
if lang == 'de':
block_feedback = ('Block {}/{} beendet!' # noqa: E999 E501
' Sie haben in dieser Aufgabe bisher {} '
'Punkte gesammelt.'
' Am Ende des Experiments werden Ihre Punkte'
' in Euro umgerechnet und Ihnen als Bonus gezahlt.'
' Machen Sie jetzt eine kurze Pause.'
' Drücken Sie eine beliebige Taste um'
' fortzufahren.'
.format(current_nblocks, nblocks, points))
elif lang == 'en':
block_feedback = ('Block {}/{} done!' # noqa: E999 E501
' You earned {} points in this task '
'so far.'
' Remember that your points will be '
' converted to Euros and paid to you at'
' the end of the experiment as a bonus.'
' Take a short break now.'
' Then press any key to continue.'
.format(current_nblocks, nblocks, points))
return block_feedback
def provide_start_str(is_test, condition, lang):
"""Provide a string for beginning of the task."""
if condition == 'active':
condi = 'A'
elif condition == 'passive':
condi = 'B'
else:
condi = 'C'
mod = ' TEST ' if is_test else ' '
if lang == 'de':
start_str = ('Beginn der{}Aufgabe {}. Drücken Sie eine '
'beliebige Taste um zu beginnen.'
.format(mod, condi))
elif lang == 'en':
start_str = ('Starting the{}for task {}. '
'Press any key to start.'
.format(mod, condi))
return start_str
def provide_stop_str(is_test, lang):
"""Provide a string for end of the task."""
mod = ' TEST ' if is_test else ' '
if lang == 'de':
stop_str = ('Die{}Aufgabe ist beendet. Drücken Sie eine beliebige'
' Taste.'.format(mod))
elif lang == 'en':
stop_str = 'The{}task is over. Press any key to quit.'.format(mod)
return stop_str
def _provide_description_instr_str(lang='de'):
"""Provide instructions."""
texts = list()
if lang == 'de':
instruct_str = ('Im Folgenden werden Sie auf der linken und rechten '
'Seite jeweils eine Lotterie sehen. Benutzen Sie die '
'linke oder rechte Taste, um sich für die Lotterie '
'zu entscheiden, die Sie spielen wollen. '
'Danach wird ihnen ihr Gewinn angezeigt.')
instruct_str += ('Unten sehen Sie ein Beispiel für *eine* Lotterie. '
'Diese Lotterie bedeutet: 1 Punkt mit 90%iger oder '
'5 Punkte mit 10%iger Chance. Die Punktzahl wird '
'in pink angezeigt, die Wahrscheinlichkeit in blau.')
instruct_str += ('\n\n\n\n\nDrücken Sie eine beliebige Taste um zu '
'beginnen.')
texts.append(instruct_str)
elif lang == 'en':
raise RuntimeError('English language not yet implemented.')
return texts
| 27,516 | 8,211 |
"""
Perceptron.
A simple implementation of Perceptron classifier algorithm
described in Bishop(2006).
Author: Shota Horii <sh.sinker@gmail.com>
Test: tests/test_perceptron.py
References:
C.M. Bishop (2006). Pattern Recognition and Machine Learning. Springer. 192-196.
"""
import numpy as np
from ..base import BinaryClassifier
from ..utils.preprocessing import real2sign
class Perceptron(BinaryClassifier):
"""
Perceptron Classifier (Binary classification only)
Parameters
----------
n_epoch: int
number of epoch to iterate optimisation process
shuffle: bool
if true, shuffle data before optimisation
seed: int
random seed
"""
def __init__(self, n_epoch=10, shuffle=True, seed=0):
self.w = None
self.b = None
self.n_epoch = n_epoch
self.shuffle = shuffle
self.seed = seed
def _fit(self, X, y):
"""
Parameters
----------
X: np.ndarray (n,d) of real (-inf, inf)
n: number of samples
d: number of features
y: np.ndarray (n,) of int {-1,1}
n: number of samples
Returns
-------
self: Perceptron
"""
# convert target variable from {0,1} -> {-1, 1}
#y = binary2sign(y)
# initialise the weights and bias
self.w = np.zeros(X.shape[1])
self.b = 0.0
idx = np.arange(X.shape[0])
# shuffle data
if self.shuffle:
np.random.seed(self.seed)
np.random.shuffle(idx)
# SGD
for _ in range(self.n_epoch):
has_misclassification = False
for i in idx:
y_pred = real2sign(self.w @ X[i] + self.b)
if y_pred != y[i]:
has_misclassification = True
self.w += X[i] * y[i]
self.b += y[i]
# terminate if all samples were classified correctly
if not has_misclassification:
break
return self
def _predict(self, X):
"""
Parameters
----------
X: np.ndarray (n,d) of real (-inf, inf)
n: number of samples
d: number of features
Returns
-------
np.ndarray (n,) of int {-1,1}
n: number of samples
"""
return real2sign(self.w @ X.T + self.b)
| 2,435 | 761 |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.15.2)
#
# 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 = [int(v) for v in 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()
| 283,339 | 260,743 |
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta
args = {
'owner': 'garanin',
'start_date': datetime(2018, 11, 1),
'provide_context': True} # настройки дага, которые перадаем каждому дагу через дефолтный аргумент
with DAG("Hello_world", description='Hello_world', schedule_interval='*/1 * * * *', catchup=False,
default_args=args) as dag:
# "hello-world" - название дага
# description - описание дага
# */1 * * * * - каждую минуту Cron
# catchup - если true будет исполняться каждую минуту без пауз с момента datetime(2018, 11, 1)
t1 = BashOperator(
task_id='task_1',
bash_command='echo "Hello World from Task 1"')
t2 = BashOperator(
task_id='task_2',
bash_command='echo "Hello World from Task 2"')
t3 = BashOperator(
task_id='task_3',
bash_command='echo "Hello World from Task 3"')
t4 = BashOperator(
task_id='task_4',
bash_command='echo "Hello World from Task 4"')
t1 >> t2
t1 >> t3
t2 >> t4
t3 >> t4
| 1,116 | 396 |
"""Evaluate outcome (+CATE) of datasets"""
from scipy.stats import spearmanr
import numpy as np
def safe_spearmanr(arr_a, arr_b):
"Compute the spearman-R correlation, but 0 if all equal"
if np.all(arr_a[0] == arr_a) or np.all(arr_b[0] == arr_b):
return 0
return spearmanr(arr_a, arr_b).correlation
def evaluate_outcome(model, dataset, k=5, n=1):
"""Evaluate the outcome of a model with a dataset
Arguments
---------
model: BaseModel
model to be trained and evaluated on the dataset.
dataset: BaseDataset
Dataset on which the model is evaluated.
k: int
Number of folds
n: int
Number of iterations to evaluate over
"""
results = []
for _ in range(n):
for train_data, test_data in dataset.kfolds(k=k):
model.train(model.preprocess(train_data.standard_df))
model_outcome = model.predict_outcome(test_data.standard_df)
if (np.all(model_outcome == model_outcome[0])
or np.all(test_data.outcome == test_data.outcome[0])):
corr = 0
else:
corr = spearmanr(model_outcome, test_data.outcome).correlation
results.append(corr)
return results
def evaluate_performance(model, dataset, k=5, n=1):
"""Evaluate the outcome + CATE of a model with a dataset
Arguments
---------
model: BaseModel
model to be trained and evaluated on the dataset.
dataset: BaseDataset
Dataset on which the model is evaluated.
k: int
Number of folds
n: int
Number of iterations to evaluate over
"""
cate_corr = []
outcome_corr = []
for _ in range(n):
for train_data, test_data in dataset.kfolds(k=k):
model.train(model.preprocess(train_data.standard_df))
test_df = test_data.standard_df
cate = model.predict_cate(test_df)
outcome = model.predict_outcome(test_df)
cate_corr.append(safe_spearmanr(cate, test_data.cate))
outcome_corr.append(safe_spearmanr(outcome, test_data.outcome))
return cate_corr, outcome_corr
| 2,159 | 682 |
#!/usr/bin/env python
"""
Return JSON metadata for GOES Imagery
"""
import cgi
import json
import os
import glob
import datetime
from pyiem.util import ssw
BIRDS = ['EAST', 'WEST']
PRODUCTS = ['WV', 'VIS', 'IR']
def parse_time(text):
"""
Convert ISO something into a datetime
"""
try:
if len(text) == 17:
date = datetime.datetime.strptime(text, '%Y-%m-%dT%H:%MZ')
elif len(text) == 20:
date = datetime.datetime.strptime(text, '%Y-%m-%dT%H:%M:%SZ')
else:
date = datetime.datetime.utcnow()
except Exception as _:
date = datetime.datetime.utcnow()
return date
def find_scans(root, bird, product, start_gts, end_gts):
""" Find GOES SCANs """
if bird not in BIRDS or product not in PRODUCTS:
return
now = start_gts.replace(hour=0, minute=0, second=0)
while now < end_gts:
mydir = now.strftime("/mesonet/ARCHIVE/data/%Y/%m/%d/GIS/sat/awips211")
if os.path.isdir(mydir):
os.chdir(mydir)
filenames = glob.glob("GOES_%s_%s_*.wld" % (bird, product))
filenames.sort()
for filename in filenames:
ts = datetime.datetime.strptime(filename[:-4].split("_")[3],
"%Y%m%d%H%M")
if ts >= start_gts and ts <= end_gts:
root['scans'].append(ts.strftime("%Y-%m-%dT%H:%M:00Z"))
now += datetime.timedelta(hours=24)
def list_files(form):
"""
List available GOES files based on the form request
"""
bird = form.getvalue('bird', 'EAST').upper()
product = form.getvalue('product', 'VIS').upper()
# default to a four hour period
utc0 = datetime.datetime.utcnow()
utc1 = utc0 - datetime.timedelta(hours=4)
start_gts = parse_time(form.getvalue('start',
utc1.strftime("%Y-%m-%dT%H:%MZ")))
end_gts = parse_time(form.getvalue('end',
utc0.strftime("%Y-%m-%dT%H:%MZ")))
root = {'scans': []}
find_scans(root, bird, product, start_gts, end_gts)
return root
def main():
"""
Do something fun and educational
"""
form = cgi.FieldStorage()
operation = form.getvalue('operation', 'list')
callback = form.getvalue('callback', None)
if callback is not None:
ssw("Content-type: application/javascript\n\n")
ssw("%s(" % (callback,))
else:
ssw("Content-type: text/plain\n\n")
if operation == "list":
ssw(json.dumps(list_files(form)))
if callback is not None:
ssw(')')
if __name__ == "__main__":
main()
| 2,671 | 929 |
from fanstatic import Library, Resource
library = Library('scrolltofixed', 'resources')
scrolltofixed = Resource(library, 'jquery-scrolltofixed.js',
minified='jquery-scrolltofixed-min.js')
| 216 | 57 |
#!/usr/bin/env python
import json
import os
import click
import ray
from ray.serve.api import Deployment
from ray.serve.config import DeploymentMode
from ray._private.utils import import_attr
from ray import serve
from ray.serve.constants import (
DEFAULT_CHECKPOINT_PATH,
DEFAULT_HTTP_HOST,
DEFAULT_HTTP_PORT,
)
@click.group(help="[EXPERIMENTAL] CLI for managing Serve instances on a Ray cluster.")
@click.option(
"--address",
"-a",
default=os.environ.get("RAY_ADDRESS", "auto"),
required=False,
type=str,
help="Address of the running Ray cluster to connect to. " 'Defaults to "auto".',
)
@click.option(
"--namespace",
"-n",
default="serve",
required=False,
type=str,
help='Ray namespace to connect to. Defaults to "serve".',
)
@click.option(
"--runtime-env-json",
default=r"{}",
required=False,
type=str,
help=(
"Runtime environment dictionary to pass into ray.init. " "Defaults to empty."
),
)
def cli(address, namespace, runtime_env_json):
ray.init(
address=address,
namespace=namespace,
runtime_env=json.loads(runtime_env_json),
)
@cli.command(help="Start a detached Serve instance on the Ray cluster.")
@click.option(
"--http-host",
default=DEFAULT_HTTP_HOST,
required=False,
type=str,
help="Host for HTTP servers to listen on. " f"Defaults to {DEFAULT_HTTP_HOST}.",
)
@click.option(
"--http-port",
default=DEFAULT_HTTP_PORT,
required=False,
type=int,
help="Port for HTTP servers to listen on. " f"Defaults to {DEFAULT_HTTP_PORT}.",
)
@click.option(
"--http-location",
default=DeploymentMode.HeadOnly,
required=False,
type=click.Choice(list(DeploymentMode)),
help="Location of the HTTP servers. Defaults to HeadOnly.",
)
@click.option(
"--checkpoint-path",
default=DEFAULT_CHECKPOINT_PATH,
required=False,
type=str,
hidden=True,
)
def start(http_host, http_port, http_location, checkpoint_path):
serve.start(
detached=True,
http_options=dict(
host=http_host,
port=http_port,
location=http_location,
),
_checkpoint_path=checkpoint_path,
)
@cli.command(help="Shutdown the running Serve instance on the Ray cluster.")
def shutdown():
serve.api._connect()
serve.shutdown()
@cli.command(
help="""
[Experimental]
Create a deployment in running Serve instance. The required argument is the
import path for the deployment: ``my_module.sub_module.file.MyClass``. The
class may or may not be decorated with ``@serve.deployment``.
""",
hidden=True,
)
@click.argument("deployment")
@click.option(
"--options-json",
default=r"{}",
required=False,
type=str,
help="JSON string for the deployments options",
)
def deploy(deployment: str, options_json: str):
deployment_cls = import_attr(deployment)
if not isinstance(deployment_cls, Deployment):
deployment_cls = serve.deployment(deployment_cls)
options = json.loads(options_json)
deployment_cls.options(**options).deploy()
| 3,114 | 985 |
def naive(a, b):
c = 0
while a > 0:
c = c + b
a = a - 1
return c
def russian(a, b):
"""
The Russian Peasant Algorithm:
Multiply one integer by the other integer.
Input: a, b: integers
Returns: a*b
"""
c = 0
while a > 0:
if a % 2 == 1:
c = c + b
b = b << 1
a = a >> 1
return c
def rec_russian(a, b):
if a == 0:
return 0
elif a % 2 == 0:
return 2 * rec_russian(a / 2, b)
else:
return b + 2 * rec_russian((a - 1) / 2, b)
| 562 | 236 |
# Python Tuples
# Ordered, Immutable collection of items which allows Duplicate Members
# We can put the data, which will not change throughout the program, in a Tuple
# Tuples can be called as "Immutable Python Lists" or "Constant Python Lists"
employeeTuple = ("Sam", "Sam" "Mike", "John", "Harry", "Tom", "Sean", "Justin")
# to check the variable type
print(type(employeeTuple))
# to check whether the type is "Tuple" or not
print(isinstance(employeeTuple, tuple))
# to print all the elements in the Tuple
for employeeName in employeeTuple:
print("Employee: " + employeeName)
print("**********************************************************")
# Other functions
# to display an element using index
print(employeeTuple[0])
# to display the length of the Tuple
print(len(employeeTuple))
# employeeTuple[2] = "David" # This will throw a TypeError since Tuple cannot be modified
print(employeeTuple)
print("**********************************************************")
# we can use the tuple() constructor to create a tuple
employeeName2 = tuple(("Richard", "Henry", "Brian"))
print(employeeName2)
# we can also omit the use of brackets to create a tuple
employeeName3 = "David", "Michael", "Shaun"
print(employeeName3)
print(type(employeeName3))
print("**********************************************************")
# Difference between a Tuple and a String
myStr = ("Sam") # This is a String
print(type(myStr))
# This is a Tuple (for a Tuple, comma is mandatory) with one item
myTuple1 = ("Sam",)
print(type(myTuple1))
print(len(myTuple1))
# This is an empty Tuple
myTuple2 = ()
print(type(myTuple2))
print(len(myTuple2))
print("**********************************************************")
# Value Swapping using Tuple
myNumber1 = 2
myNumber2 = 3
myNumber1, myNumber2 = myNumber2, myNumber1
print(myNumber1)
print(myNumber2)
print("**********************************************************")
# Nested Tuples
employeeName4 = employeeName3, ("Raj", "Vinith")
print(employeeName4)
print("**********************************************************")
# Tuple Sequence Packing
packed_tuple = 1, 2, "Python"
print(packed_tuple)
# Tuple Sequence Unpacking
number1, number2, string1 = packed_tuple
print(number1)
print(number2)
print(string1)
print("**********************************************************")
| 2,314 | 672 |
import dht
class DHT22Sensor:
provides = ["temperature", "humidity"]
def __init__(self, port):
self._sensor = dht.DHT22(port)
def readout(self):
self._sensor.measure()
return {"temperature": self._sensor.temperature(), "humidity": self._sensor.humidity()}
| 296 | 104 |
from datetime import timedelta
from django.contrib.auth import get_user_model
from django.utils import timezone
from oauthlib.oauth2 import (InvalidClientIdError, MissingClientIdError,
InvalidRedirectURIError, MismatchingRedirectURIError)
from rest_framework import status
from oauth_api.compat import reverse
from oauth_api.models import get_application_model, AuthorizationCode, RefreshToken
from oauth_api.settings import oauth_api_settings
from oauth_api.tests.utils import TestCaseUtils
from oauth_api.tests.views import RESPONSE_DATA
Application = get_application_model()
User = get_user_model()
class BaseTest(TestCaseUtils):
def setUp(self):
self.test_user = User.objects.create_user('test_user', 'test_user@example.com', '1234')
self.dev_user = User.objects.create_user('dev_user', 'dev_user@example.com', '1234')
self.application = Application(
name='Test Application',
redirect_uris='http://localhost http://example.com',
user=self.dev_user,
client_type=Application.CLIENT_CONFIDENTIAL,
authorization_grant_type=Application.GRANT_AUTHORIZATION_CODE,
)
self.application.save()
self.application_public = Application(
name='Test Application (Public)',
redirect_uris='http://localhost http://example.com',
user=self.dev_user,
client_type=Application.CLIENT_PUBLIC,
authorization_grant_type=Application.GRANT_AUTHORIZATION_CODE,
)
self.application_public.save()
class TestAuthorizationCode(BaseTest):
def test_invalid_client(self):
"""
Test for invalid client information
"""
self.client.login(username='test_user', password='1234')
query_string = {
'client_id': 'invalid',
'response_type': 'code',
}
response = self.client.get(reverse('oauth_api:authorize'), data=query_string)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn('error', response.context)
error = response.context['error']
self.assertTrue(isinstance(error, InvalidClientIdError))
def test_missing_client(self):
"""
Test for missing client information
"""
self.client.login(username='test_user', password='1234')
query_string = {
'response_type': 'code',
}
response = self.client.get(reverse('oauth_api:authorize'), data=query_string)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn('error', response.context)
error = response.context['error']
self.assertTrue(isinstance(error, MissingClientIdError))
def test_valid_client(self):
"""
Test for valid client information
"""
self.client.login(username='test_user', password='1234')
query_string = {
'client_id': self.application.client_id,
'response_type': 'code',
'state': 'random_state_string',
'scope': 'read write',
'redirect_uri': 'http://localhost',
}
response = self.client.get(reverse('oauth_api:authorize'), data=query_string)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertIn('form', response.context)
form = response.context['form']
self.assertEqual(form['redirect_uri'].value(), 'http://localhost')
self.assertEqual(form['state'].value(), 'random_state_string')
self.assertEqual(form['scopes'].value(), 'read write')
self.assertEqual(form['client_id'].value(), self.application.client_id)
def test_invalid_response_type(self):
"""
Test for invalid response_type
"""
self.client.login(username='test_user', password='1234')
query_string = {
'client_id': self.application.client_id,
'response_type': 'invalid',
}
response = self.client.get(reverse('oauth_api:authorize'), data=query_string)
self.assertEqual(response.status_code, status.HTTP_302_FOUND)
self.assertIn('error=unsupported_response_type', response['Location'])
def test_missing_response_type(self):
"""
Test for missing response_type
"""
self.client.login(username='test_user', password='1234')
query_string = {
'client_id': self.application.client_id,
}
response = self.client.get(reverse('oauth_api:authorize'), data=query_string)
self.assertEqual(response.status_code, status.HTTP_302_FOUND)
self.assertIn('error=invalid_request', response['Location'])
def test_default_redirect_uri(self):
"""
Test for default redirect uri
"""
self.client.login(username='test_user', password='1234')
query_string = {
'client_id': self.application.client_id,
'response_type': 'code',
}
response = self.client.get(reverse('oauth_api:authorize'), data=query_string)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertIn('form', response.context)
form = response.context['form']
self.assertEqual(form['redirect_uri'].value(), 'http://localhost')
def test_forbidden_redirect_uri(self):
"""
Test for forbidden redirect_uri (Not defined in list of allowed URIs)
"""
self.client.login(username='test_user', password='1234')
query_string = {
'client_id': self.application.client_id,
'response_type': 'code',
'redirect_uri': 'http://invalid.local.host',
}
response = self.client.get(reverse('oauth_api:authorize'), data=query_string)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn('error', response.context)
error = response.context['error']
self.assertTrue(isinstance(error, MismatchingRedirectURIError))
def test_invalid_redirect_uri(self):
"""
Test for malformed redirect_uri value
"""
self.client.login(username='test_user', password='1234')
query_string = {
'client_id': self.application.client_id,
'response_type': 'code',
'redirect_uri': 'invalid',
}
response = self.client.get(reverse('oauth_api:authorize'), data=query_string)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
self.assertIn('error', response.context)
error = response.context['error']
self.assertTrue(isinstance(error, InvalidRedirectURIError))
def test_authorization_code_post_allow(self):
"""
Test for resource owner authorized the client
"""
self.client.login(username='test_user', password='1234')
form_data = {
'client_id': self.application.client_id,
'state': 'random_state_string',
'scopes': 'read write',
'redirect_uri': 'http://localhost',
'response_type': 'code',
'allow': True,
}
response = self.client.post(reverse('oauth_api:authorize'), data=form_data)
self.assertEqual(response.status_code, status.HTTP_302_FOUND)
self.assertIn('http://localhost?', response['Location'])
self.assertIn('state=random_state_string', response['Location'])
self.assertIn('code=', response['Location'])
def test_authorization_code_post_denied(self):
"""
Test for resource owner did not authorize the client
"""
self.client.login(username='test_user', password='1234')
form_data = {
'client_id': self.application.client_id,
'state': 'random_state_string',
'scopes': 'read write',
'redirect_uri': 'http://localhost',
'response_type': 'code',
'allow': False,
}
response = self.client.post(reverse('oauth_api:authorize'), data=form_data)
self.assertEqual(response.status_code, status.HTTP_302_FOUND)
self.assertIn('error=access_denied', response['Location'])
def test_authorization_code_post_invalid_response_type(self):
"""
Test for authorization code is given for an allowed request with a invalid response_type
"""
self.client.login(username='test_user', password='1234')
form_data = {
'client_id': self.application.client_id,
'state': 'random_state_string',
'scopes': 'read write',
'redirect_uri': 'http://localhost',
'response_type': 'invalid',
'allow': True,
}
response = self.client.post(reverse('oauth_api:authorize'), data=form_data)
self.assertEqual(response.status_code, status.HTTP_302_FOUND)
self.assertIn('error=unsupported_response_type', response['Location'])
self.assertIn('state=random_state_string', response['Location'])
def test_authorization_code_post_invalid_redirect_uri(self):
"""
Test for authorization code is given for an allowed request with a invalid redirect_uri
"""
self.client.login(username='test_user', password='1234')
form_data = {
'client_id': self.application.client_id,
'state': 'random_state_string',
'scopes': 'read write',
'redirect_uri': 'http://invalid.local.host',
'response_type': 'code',
'allow': True,
}
response = self.client.post(reverse('oauth_api:authorize'), data=form_data)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
class TestAuthorizationCodeTokenView(BaseTest):
def test_basic_auth(self):
"""
Test for requesting access token using Basic Authentication
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['token_type'], 'Bearer')
self.assertEqual(response.data['scope'], 'read write')
self.assertEqual(response.data['expires_in'], oauth_api_settings.ACCESS_TOKEN_EXPIRATION)
def test_basic_auth_invalid_secret(self):
"""
Test for requesting access toke using invalid secret via Basic Authentication
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
'invalid'))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_invalid_auth_code(self):
"""
Test for requesting access token using invalid authorization code
"""
self.client.login(username='test_user', password='1234')
token_request = {
'grant_type': 'authorization_code',
'code': 'invalid',
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_invalid_grant_type(self):
"""
Test for requesting access token using invalid grant_type
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'invalid',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_expired_authorization_code(self):
"""
Test for requesting access code when authorization code has been expired
"""
self.client.login(username='test_user', password='1234')
ac = AuthorizationCode(application=self.application, user=self.test_user, code='BANANA', expires=timezone.now(),
redirect_uri='', scope='')
ac.save()
token_request = {
'grant_type': 'authorization_code',
'code': 'BANANA',
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_refresh_token(self):
"""
Test for requesting access token using refresh token
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('refresh_token' in response.data)
# Make second token request to be sure that previous refresh token
# remains valid.
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.post(reverse('oauth_api:token'), token_request)
# Request new access token using the refresh token from the first
# request
token_request = {
'grant_type': 'refresh_token',
'refresh_token': response.data['refresh_token'],
'scope': response.data['scope'],
}
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('access_token' in response.data)
# Refresh tokens cannot be used twice
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
self.assertTrue('invalid_grant' in response.data.values())
def test_refresh_token_override_authorization(self):
"""
Test overriding Authorization header by providing client ID and secret as param
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('refresh_token' in response.data)
token_request = {
'grant_type': 'refresh_token',
'refresh_token': response.data['refresh_token'],
'scope': response.data['scope'],
'client_id': self.application.client_id,
'client_secret': self.application.client_secret,
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth('invalid_client_id',
'invalid_client_secret'))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('access_token' in response.data)
def test_refresh_token_default_scopes(self):
"""
Test for requesting access token using refresh token while not providing any scopes
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('refresh_token' in response.data)
token_request = {
'grant_type': 'refresh_token',
'refresh_token': response.data['refresh_token'],
}
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('access_token' in response.data)
def test_refresh_token_invalid_scopes(self):
"""
Test for requesting access token using refresh token while providing invalid scopes
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('refresh_token' in response.data)
token_request = {
'grant_type': 'refresh_token',
'refresh_token': response.data['refresh_token'],
'scope': 'read write invalid',
}
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_refresh_token_repeating_request_fail(self):
"""
Test for requesting access token using refresh token and repeating the request
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('refresh_token' in response.data)
token_request = {
'grant_type': 'refresh_token',
'refresh_token': response.data['refresh_token'],
'scope': response.data['scope'],
}
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_refresh_token_expired(self):
"""
Test for requesting access token using expired refresh token
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('refresh_token' in response.data)
token_request = {
'grant_type': 'refresh_token',
'refresh_token': response.data['refresh_token'],
'scope': response.data['scope'],
}
# Set expiration time to expire the token
RefreshToken.objects.update(expires=timezone.now())
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_refresh_token_not_expired(self):
"""
Test for requesting access token using refresh token with expiration date
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
}
self.client.credentials(HTTP_AUTHORIZATION=self.get_basic_auth(self.application.client_id,
self.application.client_secret))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertTrue('refresh_token' in response.data)
token_request = {
'grant_type': 'refresh_token',
'refresh_token': response.data['refresh_token'],
'scope': response.data['scope'],
}
# Set expiration time to future
RefreshToken.objects.update(expires=timezone.now() + timedelta(days=7))
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_public(self):
"""
Test for requesting access token using client_type 'public'
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code(self.application_public.client_id)
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': 'http://localhost',
'client_id': self.application_public.client_id,
}
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['token_type'], 'Bearer')
self.assertEqual(response.data['scope'], 'read write')
self.assertEqual(response.data['expires_in'], oauth_api_settings.ACCESS_TOKEN_EXPIRATION)
def test_public_with_invalid_redirect_uri(self):
"""
Test for requesting access token using client_type 'public' and providing
invalid redirect_uri
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code(self.application_public.client_id)
token_request = {
'grant_type': 'authorization_code',
'code': authorization_code,
'redirect_uri': '/../',
'client_id': self.application_public.client_id,
}
response = self.client.post(reverse('oauth_api:token'), token_request)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
class TestAuthorizationCodeResourceAccess(BaseTest):
def test_access_allowed(self):
"""
Test for accessing resource using valid access token
"""
self.client.login(username='test_user', password='1234')
authorization_code = self.get_authorization_code()
access_token = self.get_access_token(authorization_code)
self.client.credentials(HTTP_AUTHORIZATION='Bearer %s' % access_token)
response = self.client.get(reverse('resource-view'))
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data, RESPONSE_DATA)
def test_access_denied(self):
"""
Test for accessing resource using invalid access token
"""
self.client.force_authenticate(user=self.test_user)
self.client.credentials(HTTP_AUTHORIZATION='Bearer invalid')
response = self.client.get(reverse('resource-view'))
self.client.force_authenticate(user=None)
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
| 26,757 | 7,490 |
# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You may not
# use this file except in compliance with the License. A copy of the License
# is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file 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.
"""
Decoders for sequence-to-sequence models.
"""
import logging
from abc import ABC, abstractmethod
from typing import Callable, List, NamedTuple, Tuple
from typing import Optional
import mxnet as mx
from sockeye.config import Config
from sockeye.utils import check_condition
from . import attention as attentions
from . import constants as C
from . import encoder
from . import layers
from . import lexicon as lexicons
from . import rnn
from . import transformer
from . import utils
logger = logging.getLogger(__name__)
def get_decoder(config: Config,
lexicon: Optional[lexicons.Lexicon] = None,
embed_weight: Optional[mx.sym.Symbol] = None) -> 'Decoder':
if isinstance(config, RecurrentDecoderConfig):
return RecurrentDecoder(config=config, lexicon=lexicon, embed_weight=embed_weight, prefix=C.DECODER_PREFIX)
elif isinstance(config, transformer.TransformerConfig):
return TransformerDecoder(config=config, embed_weight=embed_weight, prefix=C.DECODER_PREFIX)
else:
raise ValueError("Unsupported decoder configuration")
class Decoder(ABC):
"""
Generic decoder interface.
A decoder needs to implement code to decode a target sequence known in advance (decode_sequence),
and code to decode a single word given its decoder state (decode_step).
The latter is typically used for inference graphs in beam search.
For the inference module to be able to keep track of decoder's states
a decoder provides methods to return initial states (init_states), state variables and their shapes.
"""
@abstractmethod
def decode_sequence(self,
source_encoded: mx.sym.Symbol,
source_encoded_lengths: mx.sym.Symbol,
source_encoded_max_length: int,
target: mx.sym.Symbol,
target_lengths: mx.sym.Symbol,
target_max_length: int,
source_lexicon: Optional[mx.sym.Symbol] = None) -> mx.sym.Symbol:
"""
Decodes given a known target sequence and returns logits
with batch size and target length dimensions collapsed.
Used for training.
:param source_encoded: Encoded source: (source_encoded_max_length, batch_size, encoder_depth).
:param source_encoded_lengths: Lengths of encoded source sequences. Shape: (batch_size,).
:param source_encoded_max_length: Size of encoder time dimension.
:param target: Target sequence. Shape: (batch_size, target_max_length).
:param target_lengths: Lengths of target sequences. Shape: (batch_size,).
:param target_max_length: Size of target sequence dimension.
:param source_lexicon: Lexical biases for current sentence.
Shape: (batch_size, target_vocab_size, source_seq_len)
:return: Logits of next-word predictions for target sequence.
Shape: (batch_size * target_max_length, target_vocab_size)
"""
pass
@abstractmethod
def decode_step(self,
prev_word_id: mx.sym.Symbol,
source_encoded_max_length: int,
*states: mx.sym.Symbol) \
-> Tuple[mx.sym.Symbol, mx.sym.Symbol, List[mx.sym.Symbol]]:
"""
Decodes a single time step given the previous word id and previous decoder states.
Returns logits, attention probabilities, and next decoder states.
Implementations can maintain an arbitrary number of states.
:param prev_word_id: Previous word id. Shape: (batch_size,).
:param source_encoded_max_length: Length of encoded source time dimension.
:param states: Arbitrary list of decoder states.
:return: logits, attention probabilities, next decoder states.
"""
pass
@abstractmethod
def reset(self):
"""
Reset decoder method. Used for inference.
"""
pass
@abstractmethod
def init_states(self,
source_encoded: mx.sym.Symbol,
source_encoded_lengths: mx.sym.Symbol,
source_encoded_max_length: int) -> List[mx.sym.Symbol]:
"""
Returns a list of symbolic states that represent the initial states of this decoder.
Used for inference.
:param source_encoded: Encoded source. Shape: (batch_size, source_encoded_max_length, encoder_depth).
:param source_encoded_lengths: Lengths of encoded source sequences. Shape: (batch_size,).
:param source_encoded_max_length: Size of encoder time dimension.
:return: List of symbolic initial states.
"""
return []
@abstractmethod
def state_variables(self) -> List[mx.sym.Symbol]:
"""
Returns the list of symbolic variables for this decoder to be used during inference.
:return: List of symbolic variables.
"""
return []
@abstractmethod
def state_shapes(self,
batch_size: int,
source_encoded_max_length: int,
source_encoded_depth: int) -> List[mx.io.DataDesc]:
"""
Returns a list of shape descriptions given batch size, encoded source max length and encoded source depth.
Used for inference.
:param batch_size: Batch size during inference.
:param source_encoded_max_length: Size of encoder time dimension.
:param source_encoded_depth: Depth of encoded source.
:return: List of shape descriptions.
"""
return []
@abstractmethod
def get_rnn_cells(self) -> List[mx.rnn.BaseRNNCell]:
"""
Returns a list of RNNCells used by this decoder.
:raises: NotImplementedError
"""
return []
class TransformerDecoder(Decoder):
"""
Transformer decoder as in Vaswani et al, 2017: Attention is all you need.
In training, computation scores for each position of the known target sequence are compouted in parallel,
yielding most of the speedup.
At inference time, the decoder block is evaluated again and again over a maximum length input sequence that is
initially filled with zeros and grows during beam search with predicted tokens. Appropriate masking at every
time-step ensures correct self-attention scores and is updated with every step.
:param config: Transformer configuration.
:param embed_weight: Optionally use an existing embedding matrix instead of creating a new target embedding.
"""
def __init__(self,
config: transformer.TransformerConfig,
embed_weight: Optional[mx.sym.Symbol] = None,
prefix: str = C.TRANSFORMER_DECODER_PREFIX) -> None:
self.config = config
self.prefix = prefix
self.layers = [transformer.TransformerDecoderBlock(
config, prefix="%s%d_" % (prefix, i)) for i in range(config.num_layers)]
# Embedding & output parameters
if embed_weight is None:
embed_weight = mx.sym.Variable(C.TARGET_EMBEDDING_PREFIX + "weight")
self.embedding = encoder.Embedding(num_embed=config.model_size,
vocab_size=config.vocab_size,
prefix=C.TARGET_EMBEDDING_PREFIX,
dropout=config.dropout_residual,
embed_weight=embed_weight,
add_positional_encoding=config.positional_encodings)
if self.config.weight_tying:
logger.info("Tying the target embeddings and prediction matrix.")
self.cls_w = embed_weight
else:
self.cls_w = mx.sym.Variable("%scls_weight" % prefix)
self.cls_b = mx.sym.Variable("%scls_bias" % prefix)
def decode_sequence(self,
source_encoded: mx.sym.Symbol,
source_encoded_lengths: mx.sym.Symbol,
source_encoded_max_length: int,
target: mx.sym.Symbol,
target_lengths: mx.sym.Symbol,
target_max_length: int,
source_lexicon: Optional[mx.sym.Symbol] = None) -> mx.sym.Symbol:
"""
Decodes given a known target sequence and returns logits
with batch size and target length dimensions collapsed.
Used for training.
:param source_encoded: Encoded source: (source_encoded_max_length, batch_size, encoder_depth).
:param source_encoded_lengths: Lengths of encoded source sequences. Shape: (batch_size,).
:param source_encoded_max_length: Size of encoder time dimension.
:param target: Target sequence. Shape: (batch_size, target_max_length).
:param target_lengths: Lengths of target sequences. Shape: (batch_size,).
:param target_max_length: Size of target sequence dimension.
:param source_lexicon: Lexical biases for current sentence.
Shape: (batch_size, target_vocab_size, source_seq_len)
:return: Logits of next-word predictions for target sequence.
Shape: (batch_size * target_max_length, target_vocab_size)
"""
# (1, target_max_length, target_max_length)
target_bias = transformer.get_autoregressive_bias(target_max_length, name="%sbias" % self.prefix)
# (batch_size, source_max_length, num_source_embed)
source_encoded = mx.sym.swapaxes(source_encoded, dim1=0, dim2=1)
# target: (batch_size, target_max_length, model_size)
target, target_lengths, target_max_length = self.embedding.encode(target, target_lengths, target_max_length)
for layer in self.layers:
target = layer(target, target_lengths, target_max_length, target_bias,
source_encoded, source_encoded_lengths, source_encoded_max_length)
# target: (batch_size * target_max_length, model_size)
target = mx.sym.reshape(data=target, shape=(-3, -1))
# logits: (batch_size * target_max_length, vocab_size)
logits = mx.sym.FullyConnected(data=target, num_hidden=self.config.vocab_size,
weight=self.cls_w, bias=self.cls_b, name=C.LOGITS_NAME)
return logits
def decode_step(self,
prev_word_id: mx.sym.Symbol,
source_encoded_max_length: int,
*states: mx.sym.Symbol) \
-> Tuple[mx.sym.Symbol, mx.sym.Symbol, List[mx.sym.Symbol]]:
"""
Decodes a single time step given the previous word id and previous decoder states.
Returns logits, attention probabilities, and next decoder states.
Implementations can maintain an arbitrary number of states.
:param prev_word_id: Previous word id. Shape: (batch_size,).
:param source_encoded_max_length: Length of encoded source time dimension.
:param states: Arbitrary list of decoder states.
:return: logits, attention probabilities, next decoder states.
"""
target_max_length = self._get_target_max_length(source_encoded_max_length)
# sequences: (batch_size, target_max_length)
source_encoded, source_encoded_lengths, sequences, lengths = states
# (batch_size, target_max_length)
mask = mx.sym.one_hot(indices=lengths, depth=target_max_length, on_value=1, off_value=0)
# all zeros but length position is set to prev_word_id
# (batch_size, target_max_length)
prev_word_id = mx.sym.broadcast_mul(mx.sym.expand_dims(prev_word_id, axis=1), mask)
# append/insert prev_word_id to sequences
# (batch_size, target_max_length)
sequences = sequences + prev_word_id
lengths += 1
# (1, target_max_length, target_max_length)
target_bias = transformer.get_autoregressive_bias(target_max_length, name="%sbias" % self.prefix)
# (batch_size, target_max_length, model_size)
target, target_lengths, target_max_length = self.embedding.encode(sequences, lengths, target_max_length)
for layer in self.layers:
target = layer(target, target_lengths, target_max_length, target_bias,
source_encoded, source_encoded_lengths, source_encoded_max_length)
# set all target positions to zero except for current time-step
# target: (batch_size, target_max_length, model_size)
target = mx.sym.broadcast_mul(target, mx.sym.expand_dims(mask, axis=2))
# reduce to single prediction
# target: (batch_size, model_size)
target = mx.sym.sum(target, axis=1, keepdims=False)
# logits: (batch_size, vocab_size)
logits = mx.sym.FullyConnected(data=target, num_hidden=self.config.vocab_size,
weight=self.cls_w, bias=self.cls_b, name=C.LOGITS_NAME)
# TODO(fhieber): no attention probs for now
attention_probs = mx.sym.sum(mx.sym.zeros_like(source_encoded), axis=2, keepdims=False)
# next states
new_states = [source_encoded, source_encoded_lengths, sequences, lengths]
return logits, attention_probs, new_states
def reset(self):
pass
def init_states(self,
source_encoded: mx.sym.Symbol,
source_encoded_lengths: mx.sym.Symbol,
source_encoded_max_length: int) -> List[mx.sym.Symbol]:
"""
Returns a list of symbolic states that represent the initial states of this decoder.
Used for inference.
:param source_encoded: Encoded source. Shape: (batch_size, source_encoded_max_length, encoder_depth).
:param source_encoded_lengths: Lengths of encoded source sequences. Shape: (batch_size,).
:param source_encoded_max_length: Size of encoder time dimension.
:return: List of symbolic initial states.
"""
target_max_length = self._get_target_max_length(source_encoded_max_length)
# 0s: (batch_size, target_max_length)
sequences = mx.sym.broadcast_axis(mx.sym.expand_dims(mx.sym.zeros_like(source_encoded_lengths), axis=1),
axis=1,
size=target_max_length)
# 0s: (batch_size, source_encoded_max_length, encoder_depth)
lengths = mx.sym.zeros_like(source_encoded_lengths)
return [source_encoded, source_encoded_lengths, sequences, lengths]
def state_variables(self) -> List[mx.sym.Symbol]:
"""
Returns the list of symbolic variables for this decoder to be used during inference.
:return: List of symbolic variables.
"""
return [mx.sym.Variable(C.SOURCE_ENCODED_NAME),
mx.sym.Variable(C.SOURCE_LENGTH_NAME),
mx.sym.Variable('sequences'),
mx.sym.Variable('lengths')]
def state_shapes(self,
batch_size: int,
source_encoded_max_length: int,
source_encoded_depth: int) -> List[mx.io.DataDesc]:
"""
Returns a list of shape descriptions given batch size, encoded source max length and encoded source depth.
Used for inference.
:param batch_size: Batch size during inference.
:param source_encoded_max_length: Size of encoder time dimension.
:param source_encoded_depth: Depth of encoded source.
:return: List of shape descriptions.
"""
target_max_length = self._get_target_max_length(source_encoded_max_length)
return [mx.io.DataDesc(C.SOURCE_ENCODED_NAME,
(batch_size, source_encoded_max_length, source_encoded_depth),
layout=C.BATCH_MAJOR),
mx.io.DataDesc(C.SOURCE_LENGTH_NAME, (batch_size,), layout="N"),
mx.io.DataDesc('sequences', (batch_size, target_max_length), layout="NC"),
mx.io.DataDesc('lengths', (batch_size,), layout="N")]
def get_rnn_cells(self) -> List[mx.rnn.BaseRNNCell]:
"""
Returns a list of RNNCells used by this decoder.
"""
return super().get_rnn_cells()
@staticmethod
def _get_target_max_length(source_encoded_max_length: int):
# TODO(fhieber): we need to hardcode this for the inference algorithm to work.
# This is currently in line with the beam_search algorithm but not ideal.
return source_encoded_max_length * C.TARGET_MAX_LENGTH_FACTOR
RecurrentDecoderState = NamedTuple('RecurrentDecoderState', [
('hidden', mx.sym.Symbol),
('layer_states', List[mx.sym.Symbol]),
])
"""
RecurrentDecoder state.
:param hidden: Hidden state after attention mechanism. Shape: (batch_size, num_hidden).
:param layer_states: Hidden states for RNN layers of RecurrentDecoder. Shape: List[(batch_size, rnn_num_hidden)]
"""
class RecurrentDecoderConfig(Config):
"""
Recurrent decoder configuration.
:param vocab_size: Target vocabulary size.
:param max_seq_len_source: Maximum source sequence length
:param num_embed: Target word embedding size.
:param rnn_config: RNN configuration.
:param attention_config: Attention configuration.
:param embed_dropout: Dropout probability for target embeddings.
:param hidden_dropout: Dropout probability on next decoder hidden state.
:param weight_tying: Whether to share embedding and prediction parameter matrices.
:param zero_state_init: If true, initialize RNN states with zeros.
:param context_gating: Whether to use context gating.
:param layer_normalization: Apply layer normalization.
"""
def __init__(self,
vocab_size: int,
max_seq_len_source: int,
num_embed: int,
rnn_config: rnn.RNNConfig,
attention_config: attentions.AttentionConfig,
embed_dropout: float = .0,
hidden_dropout: float = .0,
weight_tying: bool = False,
zero_state_init: bool = False,
context_gating: bool = False,
layer_normalization: bool = False) -> None:
super().__init__()
self.vocab_size = vocab_size
self.max_seq_len_source = max_seq_len_source
self.num_embed = num_embed
self.rnn_config = rnn_config
self.attention_config = attention_config
self.embed_dropout = embed_dropout
self.hidden_dropout = hidden_dropout
self.weight_tying = weight_tying
self.zero_state_init = zero_state_init
self.context_gating = context_gating
self.layer_normalization = layer_normalization
class RecurrentDecoder(Decoder):
"""
RNN Decoder with attention.
The architecture is based on Luong et al, 2015: Effective Approaches to Attention-based Neural Machine Translation.
:param config: Configuration for recurrent decoder.
:param lexicon: Optional Lexicon.
:param embed_weight: Optionally use an existing embedding matrix instead of creating a new target embedding.
:param prefix: Decoder symbol prefix.
"""
def __init__(self,
config: RecurrentDecoderConfig,
lexicon: Optional[lexicons.Lexicon] = None,
embed_weight: Optional[mx.sym.Symbol] = None,
prefix: str = C.DECODER_PREFIX) -> None:
# TODO: implement variant without input feeding
self.config = config
self.rnn_config = config.rnn_config
self.attention = attentions.get_attention(config.attention_config, config.max_seq_len_source)
self.lexicon = lexicon
self.prefix = prefix
self.num_hidden = self.rnn_config.num_hidden
if self.config.context_gating:
self.gate_w = mx.sym.Variable("%sgate_weight" % prefix)
self.gate_b = mx.sym.Variable("%sgate_bias" % prefix)
self.mapped_rnn_output_w = mx.sym.Variable("%smapped_rnn_output_weight" % prefix)
self.mapped_rnn_output_b = mx.sym.Variable("%smapped_rnn_output_bias" % prefix)
self.mapped_context_w = mx.sym.Variable("%smapped_context_weight" % prefix)
self.mapped_context_b = mx.sym.Variable("%smapped_context_bias" % prefix)
if self.rnn_config.residual:
utils.check_condition(self.config.rnn_config.first_residual_layer >= 2,
"Residual connections on the first decoder layer are not supported as input and "
"output dimensions do not match.")
self.rnn = rnn.get_stacked_rnn(self.rnn_config, self.prefix)
if not self.config.zero_state_init:
self._create_state_init_parameters()
# Hidden state parameters
self.hidden_w = mx.sym.Variable("%shidden_weight" % prefix)
self.hidden_b = mx.sym.Variable("%shidden_bias" % prefix)
self.hidden_norm = layers.LayerNormalization(self.num_hidden,
prefix="%shidden_norm" % prefix) \
if self.config.layer_normalization else None
# Embedding & output parameters
if embed_weight is None:
embed_weight = mx.sym.Variable(C.TARGET_EMBEDDING_PREFIX + "weight")
self.embedding = encoder.Embedding(self.config.num_embed,
self.config.vocab_size,
prefix=C.TARGET_EMBEDDING_PREFIX,
dropout=config.embed_dropout,
embed_weight=embed_weight)
if self.config.weight_tying:
check_condition(self.num_hidden == self.config.num_embed,
"Weight tying requires target embedding size and rnn_num_hidden to be equal")
logger.info("Tying the target embeddings and prediction matrix.")
self.cls_w = embed_weight
else:
self.cls_w = mx.sym.Variable("%scls_weight" % prefix)
self.cls_b = mx.sym.Variable("%scls_bias" % prefix)
def _create_state_init_parameters(self):
"""
Creates parameters for encoder last state transformation into decoder layer initial states.
"""
self.init_ws, self.init_bs, self.init_norms = [], [], []
for state_idx, (_, init_num_hidden) in enumerate(self.rnn.state_shape):
self.init_ws.append(mx.sym.Variable("%senc2decinit_%d_weight" % (self.prefix, state_idx)))
self.init_bs.append(mx.sym.Variable("%senc2decinit_%d_bias" % (self.prefix, state_idx)))
if self.config.layer_normalization:
self.init_norms.append(layers.LayerNormalization(num_hidden=init_num_hidden,
prefix="%senc2decinit_%d_norm" % (
self.prefix, state_idx)))
def decode_sequence(self,
source_encoded: mx.sym.Symbol,
source_encoded_lengths: mx.sym.Symbol,
source_encoded_max_length: int,
target: mx.sym.Symbol,
target_lengths: mx.sym.Symbol,
target_max_length: int,
source_lexicon: Optional[mx.sym.Symbol] = None) -> mx.sym.Symbol:
"""
Decodes given a known target sequence and returns logits
with batch size and target length dimensions collapsed.
Used for training.
:param source_encoded: Encoded source: (source_encoded_max_length, batch_size, encoder_depth).
:param source_encoded_lengths: Lengths of encoded source sequences. Shape: (batch_size,).
:param source_encoded_max_length: Size of encoder time dimension.
:param target: Target sequence. Shape: (batch_size, target_max_length).
:param target_lengths: Lengths of target sequences. Shape: (batch_size,).
:param target_max_length: Size of target sequence dimension.
:param source_lexicon: Lexical biases for current sentence.
Shape: (batch_size, target_vocab_size, source_seq_len)
:return: Logits of next-word predictions for target sequence.
Shape: (batch_size * target_max_length, target_vocab_size)
"""
# embed and slice target words
# target_embed: (batch_size, target_seq_len, num_target_embed)
target_embed, target_lengths, target_max_length = self.embedding.encode(target, target_lengths,
target_max_length)
# target_embed: target_seq_len * (batch_size, num_target_embed)
target_embed = mx.sym.split(data=target_embed, num_outputs=target_max_length, axis=1, squeeze_axis=True)
# get recurrent attention function conditioned on source
source_encoded_batch_major = mx.sym.swapaxes(source_encoded, dim1=0, dim2=1, name='source_encoded_batch_major')
attention_func = self.attention.on(source_encoded_batch_major, source_encoded_lengths, source_encoded_max_length)
attention_state = self.attention.get_initial_state(source_encoded_lengths, source_encoded_max_length)
# initialize decoder states
# hidden: (batch_size, rnn_num_hidden)
# layer_states: List[(batch_size, state_num_hidden]
state = self.get_initial_state(source_encoded, source_encoded_lengths)
# hidden_all: target_seq_len * (batch_size, 1, rnn_num_hidden)
hidden_all = []
# TODO: possible alternative: feed back the context vector instead of the hidden (see lamtram)
lexical_biases = []
self.reset()
for seq_idx in range(target_max_length):
# hidden: (batch_size, rnn_num_hidden)
state, attention_state = self._step(target_embed[seq_idx],
state,
attention_func,
attention_state,
seq_idx)
# hidden_expanded: (batch_size, 1, rnn_num_hidden)
hidden_all.append(mx.sym.expand_dims(data=state.hidden, axis=1))
if source_lexicon is not None:
assert self.lexicon is not None, "source_lexicon should not be None if no lexicon available"
lexical_biases.append(self.lexicon.calculate_lex_bias(source_lexicon, attention_state.probs))
# concatenate along time axis
# hidden_concat: (batch_size, target_seq_len, rnn_num_hidden)
hidden_concat = mx.sym.concat(*hidden_all, dim=1, name="%shidden_concat" % self.prefix)
# hidden_concat: (batch_size * target_seq_len, rnn_num_hidden)
hidden_concat = mx.sym.reshape(data=hidden_concat, shape=(-1, self.num_hidden))
# logits: (batch_size * target_seq_len, target_vocab_size)
logits = mx.sym.FullyConnected(data=hidden_concat, num_hidden=self.config.vocab_size,
weight=self.cls_w, bias=self.cls_b, name=C.LOGITS_NAME)
if source_lexicon is not None:
# lexical_biases_concat: (batch_size, target_seq_len, target_vocab_size)
lexical_biases_concat = mx.sym.concat(*lexical_biases, dim=1, name='lex_bias_concat')
# lexical_biases_concat: (batch_size * target_seq_len, target_vocab_size)
lexical_biases_concat = mx.sym.reshape(data=lexical_biases_concat, shape=(-1, self.config.vocab_size))
logits = mx.sym.broadcast_add(lhs=logits, rhs=lexical_biases_concat,
name='%s_plus_lex_bias' % C.LOGITS_NAME)
return logits
def decode_step(self,
prev_word_id: mx.sym.Symbol,
source_encoded_max_length: int,
*states: mx.sym.Symbol) \
-> Tuple[mx.sym.Symbol, mx.sym.Symbol, List[mx.sym.Symbol]]:
"""
Decodes a single time step given the previous word id and previous decoder states.
Returns logits, attention probabilities, and next decoder states.
Implementations can maintain an arbitrary number of states.
:param prev_word_id: Previous word id. Shape: (batch_size,).
:param source_encoded_max_length: Length of encoded source time dimension.
:param states: Arbitrary list of decoder states.
:return: logits, attention probabilities, next decoder states.
"""
source_encoded, prev_dynamic_source, source_encoded_length, prev_hidden, *layer_states = states
word_vec_prev, _, _ = self.embedding.encode(prev_word_id, None, 1)
attention_func = self.attention.on(source_encoded, source_encoded_length, source_encoded_max_length)
prev_state = RecurrentDecoderState(prev_hidden, list(layer_states))
prev_attention_state = attentions.AttentionState(context=None, probs=None, dynamic_source=prev_dynamic_source)
# state.hidden: (batch_size, rnn_num_hidden)
# attention_state.dynamic_source: (batch_size, source_seq_len, coverage_num_hidden)
# attention_state.probs: (batch_size, source_seq_len)
state, attention_state = self._step(word_vec_prev,
prev_state,
attention_func,
prev_attention_state)
# logits: (batch_size, target_vocab_size)
logits = mx.sym.FullyConnected(data=state.hidden, num_hidden=self.config.vocab_size,
weight=self.cls_w, bias=self.cls_b, name=C.LOGITS_NAME)
new_states = [source_encoded,
attention_state.dynamic_source,
source_encoded_length,
state.hidden] + state.layer_states
return logits, attention_state.probs, new_states
def reset(self):
"""
Calls reset on the RNN cell.
"""
self.rnn.reset()
# TODO remove this once mxnet.rnn.SequentialRNNCell.reset() invokes recursive calls on layer cells
for cell in self.rnn._cells:
# TODO remove this once mxnet.rnn.ModifierCell.reset() invokes reset() of base_cell
if isinstance(cell, mx.rnn.ModifierCell):
cell.base_cell.reset()
cell.reset()
def init_states(self,
source_encoded: mx.sym.Symbol,
source_encoded_lengths: mx.sym.Symbol,
source_encoded_max_length: int) -> List[mx.sym.Symbol]:
"""
Returns a list of symbolic states that represent the initial states of this decoder.
Used for inference.
:param source_encoded: Encoded source. Shape: (batch_size, source_encoded_max_length, encoder_depth).
:param source_encoded_lengths: Lengths of encoded source sequences. Shape: (batch_size,).
:param source_encoded_max_length: Size of encoder time dimension.
:return: List of symbolic initial states.
"""
source_encoded_time_major = mx.sym.swapaxes(source_encoded, dim1=0, dim2=1)
hidden, layer_states = self.get_initial_state(source_encoded_time_major, source_encoded_lengths)
context, attention_probs, dynamic_source = self.attention.get_initial_state(source_encoded_lengths,
source_encoded_max_length)
states = [source_encoded, dynamic_source, source_encoded_lengths, hidden] + layer_states
return states
def state_variables(self) -> List[mx.sym.Symbol]:
"""
Returns the list of symbolic variables for this decoder to be used during inference.
:return: List of symbolic variables.
"""
return [mx.sym.Variable(C.SOURCE_ENCODED_NAME),
mx.sym.Variable(C.SOURCE_DYNAMIC_PREVIOUS_NAME),
mx.sym.Variable(C.SOURCE_LENGTH_NAME),
mx.sym.Variable(C.HIDDEN_PREVIOUS_NAME)] + \
[mx.sym.Variable("%senc2decinit_%d" % (self.prefix, i)) for i in
range(len(self.rnn.state_info))]
def state_shapes(self,
batch_size: int,
source_encoded_max_length: int,
source_encoded_depth: int) -> List[mx.io.DataDesc]:
"""
Returns a list of shape descriptions given batch size, encoded source max length and encoded source depth.
Used for inference.
:param batch_size: Batch size during inference.
:param source_encoded_max_length: Size of encoder time dimension.
:param source_encoded_depth: Depth of encoded source.
:return: List of shape descriptions.
"""
return [mx.io.DataDesc(C.SOURCE_ENCODED_NAME,
(batch_size, source_encoded_max_length, source_encoded_depth),
layout=C.BATCH_MAJOR),
mx.io.DataDesc(C.SOURCE_DYNAMIC_PREVIOUS_NAME,
(batch_size, source_encoded_max_length, self.attention.dynamic_source_num_hidden),
layout=C.BATCH_MAJOR),
mx.io.DataDesc(C.SOURCE_LENGTH_NAME,
(batch_size,),
layout="N"),
mx.io.DataDesc(C.HIDDEN_PREVIOUS_NAME,
(batch_size, self.num_hidden),
layout="NC")] + \
[mx.io.DataDesc("%senc2decinit_%d" % (self.prefix, i),
(batch_size, num_hidden),
layout=C.BATCH_MAJOR) for i, (_, num_hidden) in enumerate(self.rnn.state_shape)]
def get_rnn_cells(self) -> List[mx.rnn.BaseRNNCell]:
"""
Returns a list of RNNCells used by this decoder.
"""
return [self.rnn]
def get_initial_state(self,
source_encoded: mx.sym.Symbol,
source_encoded_length: mx.sym.Symbol) -> RecurrentDecoderState:
"""
Computes initial states of the decoder, hidden state, and one for each RNN layer.
Optionally, init states for RNN layers are computed using 1 non-linear FC
with the last state of the encoder as input.
:param source_encoded: Concatenated encoder states. Shape: (batch_size, source_seq_len, encoder_num_hidden).
:param source_encoded_length: Lengths of source sequences. Shape: (batch_size,).
:return: Decoder state.
"""
# we derive the shape of hidden and layer_states from some input to enable
# shape inference for the batch dimension during inference.
# (batch_size, 1)
zeros = mx.sym.expand_dims(mx.sym.zeros_like(source_encoded_length), axis=1)
# last encoder state
source_encoded_last = mx.sym.SequenceLast(data=source_encoded,
sequence_length=source_encoded_length,
use_sequence_length=True) if not self.config.zero_state_init else None
# decoder hidden state
hidden = mx.sym.tile(data=zeros, reps=(1, self.num_hidden))
# initial states for each layer
layer_states = []
for state_idx, (_, init_num_hidden) in enumerate(self.rnn.state_shape):
if self.config.zero_state_init:
init = mx.sym.tile(data=zeros, reps=(1, init_num_hidden))
else:
init = mx.sym.FullyConnected(data=source_encoded_last,
num_hidden=init_num_hidden,
weight=self.init_ws[state_idx],
bias=self.init_bs[state_idx],
name="%senc2decinit_%d" % (self.prefix, state_idx))
if self.config.layer_normalization:
init = self.init_norms[state_idx].normalize(init)
init = mx.sym.Activation(data=init, act_type="tanh",
name="%senc2dec_inittanh_%d" % (self.prefix, state_idx))
layer_states.append(init)
return RecurrentDecoderState(hidden, layer_states)
def _step(self,
word_vec_prev: mx.sym.Symbol,
state: RecurrentDecoderState,
attention_func: Callable,
attention_state: attentions.AttentionState,
seq_idx: int = 0) -> Tuple[RecurrentDecoderState, attentions.AttentionState]:
"""
Performs single-time step in the RNN, given previous word vector, previous hidden state, attention function,
and RNN layer states.
:param word_vec_prev: Embedding of previous target word. Shape: (batch_size, num_target_embed).
:param state: Decoder state consisting of hidden and layer states.
:param attention_func: Attention function to produce context vector.
:param attention_state: Previous attention state.
:param seq_idx: Decoder time step.
:return: (new decoder state, updated attention state).
"""
# (1) RNN step
# concat previous word embedding and previous hidden state
rnn_input = mx.sym.concat(word_vec_prev, state.hidden, dim=1,
name="%sconcat_target_context_t%d" % (self.prefix, seq_idx))
# rnn_output: (batch_size, rnn_num_hidden)
# next_layer_states: num_layers * [batch_size, rnn_num_hidden]
rnn_output, layer_states = self.rnn(rnn_input, state.layer_states)
# (2) Attention step
attention_input = self.attention.make_input(seq_idx, word_vec_prev, rnn_output)
attention_state = attention_func(attention_input, attention_state)
# (3) Combine previous word vector, rnn output, and context
hidden_concat = mx.sym.concat(rnn_output, attention_state.context,
dim=1, name='%shidden_concat_t%d' % (self.prefix, seq_idx))
if self.config.hidden_dropout > 0:
hidden_concat = mx.sym.Dropout(data=hidden_concat, p=self.config.hidden_dropout,
name='%shidden_concat_dropout_t%d' % (self.prefix, seq_idx))
if self.config.context_gating:
hidden = self._context_gate(hidden_concat, rnn_output, attention_state, seq_idx)
else:
hidden = self._hidden_mlp(hidden_concat, seq_idx)
return RecurrentDecoderState(hidden, layer_states), attention_state
def _hidden_mlp(self, hidden_concat: mx.sym.Symbol, seq_idx: int) -> mx.sym.Symbol:
hidden = mx.sym.FullyConnected(data=hidden_concat,
num_hidden=self.num_hidden, # to state size of RNN
weight=self.hidden_w,
bias=self.hidden_b,
name='%shidden_fc_t%d' % (self.prefix, seq_idx))
if self.config.layer_normalization:
hidden = self.hidden_norm.normalize(hidden)
# hidden: (batch_size, rnn_num_hidden)
hidden = mx.sym.Activation(data=hidden, act_type="tanh",
name="%snext_hidden_t%d" % (self.prefix, seq_idx))
return hidden
def _context_gate(self,
hidden_concat: mx.sym.Symbol,
rnn_output: mx.sym.Symbol,
attention_state: attentions.AttentionState,
seq_idx: int) -> mx.sym.Symbol:
gate = mx.sym.FullyConnected(data=hidden_concat,
num_hidden=self.num_hidden,
weight=self.gate_w,
bias=self.gate_b,
name = '%shidden_gate_t%d' % (self.prefix, seq_idx))
gate = mx.sym.Activation(data=gate, act_type="sigmoid",
name='%shidden_gate_act_t%d' % (self.prefix, seq_idx))
mapped_rnn_output = mx.sym.FullyConnected(data=rnn_output,
num_hidden=self.num_hidden,
weight=self.mapped_rnn_output_w,
bias=self.mapped_rnn_output_b,
name="%smapped_rnn_output_fc_t%d" % (self.prefix, seq_idx))
mapped_context = mx.sym.FullyConnected(data=attention_state.context,
num_hidden=self.num_hidden,
weight=self.mapped_context_w,
bias=self.mapped_context_b,
name="%smapped_context_fc_t%d" % (self.prefix, seq_idx))
hidden = gate * mapped_rnn_output + (1 - gate) * mapped_context
if self.config.layer_normalization:
hidden = self.hidden_norm.normalize(hidden)
# hidden: (batch_size, rnn_num_hidden)
hidden = mx.sym.Activation(data=hidden, act_type="tanh",
name="%snext_hidden_t%d" % (self.prefix, seq_idx))
return hidden
| 42,174 | 12,020 |
import random
import pygame
# 刷新帧率
FRAME_PER_SEC = 60
# 屏幕属性
SCREEN_RECT = pygame.Rect(0, 0, 480, 700)
# 创建敌军事件
CREATE_ENEMY_EVENT = pygame.USEREVENT
# 创建英雄发射子弹事件
HERO_FIRE_EVENT = pygame.USEREVENT + 1
class GameSprite(pygame.sprite.Sprite):
"""飞机大战游戏精灵"""
def __init__(self, image_name_load, speed=1):
# 1, 调用父类的方法 super().__init__()
super().__init__()
# 2. 图像位置速度
self.image = pygame.image.load(image_name_load)
# 获取图像的位置
self.rect = self.image.get_rect()
# 赋值速度
self.speed = speed
def update(self):
# 在屏幕的垂直方向上移动
self.rect.y += self.speed
# # 判断位置, 超出部分, 重置精灵起始位置
# if self.rect.y >= SCREEN_RECT.height:
# self.rect.y = SCREEN_RECT.y
class Background(GameSprite):
"""游戏背景精灵"""
def update(self):
# 1. 调用父类方法实现
super().update()
# 2. 判断是移除屏幕, 将图像摄者到图像上方
if self.rect.y >= SCREEN_RECT.height:
self.rect.y = -SCREEN_RECT.height
def __init__(self, is_alt=False):
""""""
# 1. 调用父类方法创建精灵, 创建背景图片
super().__init__("./images/background.png")
# 2. 判断is_alt 修改图像位置
if is_alt:
self.rect.y = -SCREEN_RECT.height
class Enemy(GameSprite):
"""敌机精灵"""
def __init__(self):
# 初始化父类方法
super().__init__("./images/enemy1.png")
# 指定敌机的初始随机速度
random_speed = random.randint(1, 3)
self.speed = random_speed
# 敌机初始随机位置, 最大值获取
max_x = SCREEN_RECT.width-self.rect.width
# 设置 x 的位置
self.rect.x = random.randint(0, max_x)
# 设置 Y 方向初始位置
self.rect.bottom = 0
def update(self):
# 调用父类方法保持垂直方向飞行
super().update()
# 判断是否飞出屏幕, 如果飞出, 需要从精灵族删除敌机
if self.rect.y >= SCREEN_RECT.height:
# print("飞出屏幕, 需要删除...")
# 删除精灵
# self.__del__()
# sprite 提供的kill()方法, 会将精灵在精灵族和内存中删除, 同时也调用__del__方法
self.kill()
# def __del__(self):
# # print("销毁敌机: %s" % self.rect)
# pass
class Hero(GameSprite):
"""英雄"""
def __init__(self, key_up_down=0, key_left_right=0):
# 调用父类方法
super().__init__("./images/me1.png", 0)
# 设置英雄位置
self.rect.centerx = SCREEN_RECT.centerx
self.rect.bottom = SCREEN_RECT.bottom - 120
self.k_up_down = key_up_down
self.k_left_right = key_left_right
self.bullet_group = pygame.sprite.Group()
pass
def update(self):
# 英雄在水平方向上运动
self.rect.y += self.k_up_down
self.rect.x += self.k_left_right
# 设置水平方向的运行轨迹界限
if self.rect.right >= SCREEN_RECT.right:
self.rect.right = SCREEN_RECT.right
elif self.rect.x <= 0:
self.rect.x = 0
# 试着垂直方向上的运行轨迹界限
if self.rect.bottom >= SCREEN_RECT.bottom:
self.rect.bottom = SCREEN_RECT.bottom
elif self.rect.y <= 0:
self.rect.y = 0
def fire(self):
# print("发射子弹... ")
# 创建子弹
bu = Bullet()
# 子弹位置
bu.rect.bottom = self.rect.y
bu.rect.centerx = self.rect.centerx
# 添加到精灵族
self.bullet_group.add(bu)
class Bullet(GameSprite):
"""子弹精灵"""
def __init__(self):
# 创建子弹
super().__init__("./images/bullet1.png", -2)
def update(self):
# 调用父类方法, 子弹严垂直方向飞行
super().update()
# 越界, 删除精灵
if self.rect.bottom <= 0:
self.kill()
def __del__(self):
print("bullet kill...")
| 3,595 | 1,737 |
#!/usr/bin/env python
"""
# Authors: Shengquan Chen, Boheng Zhang, Xiaoyang Chen
# Created Time : Sat 28 Nov 2020 08:31:29 PM CST
# File Name: utils.py
# Description: stPlus: reference-based enhancement of spatial transcriptomics
"""
__author__ = "Xiaoyang Chen"
__email__ = "xychen20@mails.tsinghua.edu.cn"
from .model import *
| 332 | 135 |
"""Virtualna mašina za rad s listama; kolokvij 31. siječnja 2011. (Puljić).
9 registara (L1 do L9) koji drže liste cijelih brojeva (počinju od prazne),
2 naredbe (ubacivanje i izbacivanje elementa po indeksu),
3 upita (duljina i praznost liste, dohvaćanje elementa po indeksu)."""
from vepar import *
class T(TipoviTokena):
LISTA, UBACI, IZBACI = 'lista', 'ubaci', 'izbaci'
KOLIKO, PRAZNA, DOHVATI = 'koliko', 'prazna', 'dohvati'
class ID(Token): pass
class BROJ(Token):
def vrijednost(self): return int(self.sadržaj)
class MINUSBROJ(BROJ): """Negativni broj."""
def list_lexer(lex):
for znak in lex:
if znak.isspace(): lex.zanemari()
elif znak == 'L':
nakonL = lex.čitaj()
if nakonL.isdecimal():
n = lex.prirodni_broj(nakonL)
tok = lex.token(T.ID)
if 1 <= n <= 9: yield tok
else: raise tok.krivi_sadržaj('očekivan broj liste između 1 i 9')
else:
lex.zvijezda(str.isalpha)
yield lex.literal(T, case=False)
elif znak.isalpha():
lex.zvijezda(str.isalpha)
yield lex.literal(T, case=False)
elif znak.isdecimal():
lex.prirodni_broj(znak)
yield lex.token(T.BROJ)
elif znak == '-':
lex.prirodni_broj('', nula=False)
yield lex.token(T.MINUSBROJ)
else: raise lex.greška()
### Beskontekstna gramatika (jezik je regularan!)
# start -> naredba start | ''
# naredba -> deklaracija | provjera | ubaci | izbaci | dohvati | duljina
# deklaracija -> LISTA ID
# provjera -> PRAZNA ID
# ubaci -> UBACI ID BROJ BROJ | UBACI ID MINUSBROJ BROJ
# izbaci -> IZBACI ID BROJ
# dohvati -> DOHVATI ID BROJ
# duljina -> KOLIKO ID
### Apstraktna sintaksna stabla
# Program: naredbe:[naredba]
# naredba: Deklaracija: lista:ID
# Provjera: lista:ID
# Ubaci: lista:ID vrijednost:BROJ|MINUSBROJ indeks:BROJ
# Izbaci: lista:ID indeks:BROJ
# Dohvati: lista:ID indeks:BROJ
# Duljina: lista:ID
class P(Parser):
lexer = list_lexer
def start(self):
naredbe = []
while not self > KRAJ: naredbe.append(self.naredba())
return Program(naredbe)
def naredba(self):
if self >= T.UBACI: return Ubaci(self >> T.ID,
self >> {T.BROJ,T.MINUSBROJ}, self >> T.BROJ)
elif self >= T.LISTA: return Deklaracija(self >> T.ID)
elif self >= T.PRAZNA: return Provjera(self >> T.ID)
elif self >= T.IZBACI: return Izbaci(self >> T.ID, self >> T.BROJ)
elif self >= T.DOHVATI: return Dohvati(self >> T.ID, self >> T.BROJ)
elif self >= T.KOLIKO: return Duljina(self >> T.ID)
else: raise self.greška()
class Program(AST('naredbe')):
"""Program u jeziku listâ."""
def izvrši(self):
mem = Memorija(redefinicija=False)
for nar in self.naredbe: print(nar, nar.izvrši(mem), sep=' --> ')
class Deklaracija(AST('lista')):
"""Deklaracija liste."""
def izvrši(self, memorija): memorija[self.lista] = []
class Provjera(AST('lista')):
"""Je li lista prazna?"""
def izvrši(self, memorija): return not memorija[self.lista]
class Duljina(AST('lista')):
"""Broj elemenata u listi."""
def izvrši(self, memorija): return len(memorija[self.lista])
class Dohvati(AST('lista indeks')):
"""Element zadanog indeksa (brojeći od 0). Prevelik indeks javlja grešku."""
def izvrši(self, memorija):
l, i = memorija[self.lista], self.indeks.vrijednost()
if i < len(l): return l[i]
else: raise self.indeks.iznimka('Prevelik indeks')
class Izbaci(AST('lista indeks')):
"""Izbacuje element zadanog indeksa iz liste ili javlja grešku izvođenja."""
def izvrši(self, memorija):
l, i = memorija[self.lista], self.indeks.vrijednost()
if i < len(l): del l[i]
else: raise self.indeks.iznimka('Prevelik indeks')
class Ubaci(AST('lista element indeks')):
"""Ubacuje vrijednost u listu na zadanom indeksu, ili javlja grešku."""
def izvrši(self, memorija):
l, i = memorija[self.lista], self.indeks.vrijednost()
if i <= len(l): l.insert(i, self.element.vrijednost())
else: raise self.indeks.iznimka('Prevelik indeks')
P.tokeniziraj('lista L1 prazna ubaci-2345izbaci L9 dohvati 3 koliko')
source = '''lista L1 lista L3
ubaci L3 45 0 dohvati L3 0
koliko L1 koliko L3
prazna L1 prazna L3
Lista L5 ubaci L5 6 0 ubaci L5 -7 1 ubaci L5 8 1 ubaci L5 9 0
dohvati L5 0 dohvati L5 1 dohvati L5 2 dohvati L5 3 koliko L5
izbaci L5 1 dohvati L5 0 dohvati L5 1 dohvati L5 2 koliko L5'''
P(source).izvrši()
with očekivano(LeksičkaGreška): P.tokeniziraj('L0')
with očekivano(SintaksnaGreška): P('ubaci L5 6 -2')
with očekivano(SemantičkaGreška): P('ubaci L7 5 0').izvrši()
with očekivano(LeksičkaGreška): P.tokeniziraj('ubaci L3 5 -0')
| 4,961 | 2,039 |
# Generated by Django 2.1.5 on 2019-01-30 09:57
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('applications', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='repository',
name='auth_pwd',
field=models.CharField(max_length=128, null=True),
),
migrations.AlterField(
model_name='repository',
name='auth_token',
field=models.CharField(max_length=128, null=True),
),
migrations.AlterField(
model_name='repository',
name='auth_username',
field=models.CharField(max_length=128, null=True),
),
]
| 746 | 231 |
from dataclasses import dataclass
from lahja import BaseEvent
@dataclass
class NewUPnPMapping(BaseEvent):
ip: str
| 121 | 39 |
# -*- coding: utf-8 -*-
"""
Breadcrumb resolving
====================
"""
try:
from django.core.urlresolvers import Resolver404, get_resolver
except ImportError:
from django.urls import Resolver404, get_resolver
from autobreadcrumbs.registry import breadcrumbs_registry
class BreadcrumbRessource(object):
"""
Simple crumb ressource model to contain all datas about a ressource.
"""
def __init__(self, path, name, title, view_args, view_kwargs, link_type_settings={}):
self.path = path
self.name = name
self.title = title
self.view_args = view_args
self.view_kwargs = view_kwargs
self.link_type_settings = link_type_settings
def __repr__(self):
return "<BreadcrumbRessource: {0}>".format(self.name)
def __str__(self):
# NOTE: should be __unicode__() because passed paths can be unicode...
# right ?
return self.path
class PathBreadcrumbResolver(object):
"""
Resolve given path as breadcrumbs
Arguments:
root_urlconf (string): Python path to an url conf file, usually
``settings.ROOT_URLCONF``. It will be used as the url map to
resolve every given path.
"""
def __init__(self, root_urlconf):
self.urlresolver = get_resolver(root_urlconf)
def cut(self, path):
"""
Cut given path into segments
Arguments:
path (string): An url path like ``/foo/bar/``.
Returns:
list: List of path segments, each segment is a part of the url path
starting from ``/`` and ending on the full path.
Such as for ``/foo/bar/`` segments will be: ::
- /
- /foo
- /foo/bar
"""
# Cut the path in segments
segments = ['/']
tmp = '/'
for item in path.split('/'):
if item:
tmp += item + '/'
segments.append(tmp)
return segments
def format_title(self, value):
"""
Manage title format
Arguments:
name (string): Url name.
value (string): Crumb value.
Keyword Arguments:
request (django.http.request.HttpRequest): Optional Django request
object used with custom crumb function. If not given, crumb
functions is ignored (so the crumb ressource still be
available).
Returns:
string: Crumb title.
"""
title = value
if value is None:
return None
# Force unicode on lazy translation else it will trigger an exception
# with templates
if hasattr(value, '_proxy____unicode_cast'):
title = unicode(value)
return title
def get_current(self, elements):
"""
Return current Breadcrumb from elements.
This is pretty simple as the current element is allways the last
element (if element list is not empty).
Arguments:
elements (list): List of breadcrumb elements.
Returns:
BreadcrumbRessource or None: The last element from given
``elements`` if any, else None.
"""
if len(elements) > 0:
return elements[-1]
return None
def resolve(self, path, request=None):
"""
Return resolved breadcrumbs datas from given path.
Cut the path in segments and check each of them to find breadcrumb
details if any.
Crumb value can be a simple string, a Django lazy unicode or a tuple
``(title, custom_function)``.
Crumb ``custom_function`` take url name and request object as arguments
and will return ``False`` to ignore crumb (won't be in breadcrumbs) or
``True`` to keep crumb element.
Arguments:
path (string): An url path like ``/foo/bar/``.
Keyword Arguments:
request (django.http.request.HttpRequest): Optional Django request
object used with custom crumb function. If not given, crumb
functions will be ignored (so the crumb ressources still be
available).
Returns:
Dict: Datas from resolved crumbs:
* ``autobreadcrumbs_elements``: Resolved bread crumbs for each
segment;
* ``autobreadcrumbs_current``: Breadcrumb for current (the last
one) path.
"""
breadcrumbs_elements = []
link_type_settings = {}
path_segments = self.cut(path)
# Resolve each segment
for seg in path_segments:
try:
resolved = self.urlresolver.resolve(seg)
except Resolver404:
pass
else:
view_control = None
namespace = resolved.namespace
title = name = resolved.url_name
if not name:
continue
if namespace:
name = ':'.join([namespace, name])
# Ignore ressource without a crumb title
if not breadcrumbs_registry.has_title(name):
continue
# Get defined title
title = breadcrumbs_registry.get_title(name)
# Custom function usage
if isinstance(title, tuple) or isinstance(title, list):
title, link_type_settings = title
title = self.format_title(title)
# Ignore element if empty
if title is None:
continue
# Finally append the part to the knowed crumbs list
breadcrumbs_elements.append(
BreadcrumbRessource(seg, name, title, resolved.args,
resolved.kwargs, link_type_settings)
)
return {
'autobreadcrumbs_elements': breadcrumbs_elements,
'autobreadcrumbs_current': self.get_current(breadcrumbs_elements),
}
| 6,125 | 1,588 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from BlockDataWrapper import BlockDataWrapper
from BlockInputsWrapper import BlockInputsWrapper
from BlockLinkerWrapper import BlockLinkerWrapper
from BlockRandomWrapper import BlockRandomWrapper
from BlockVoterWrapper import BlockVoterWrapper
from BlockForwardWrapper import BlockForwardWrapper
from BlockDistributeWrapper import BlockDistributeWrapper
from BlockTriggerWrapper import BlockTriggerWrapper
from BlockWriterWrapper import BlockWriterWrapper
class SpellbookWrapper():
def __init__(self, url='http://bitcoinspellbook.appspot.com'):
self.url = url
def blockdata(self):
return BlockDataWrapper(self.url)
def blockinputs(self):
return BlockInputsWrapper(self.url)
def blocklinker(self):
return BlockLinkerWrapper(self.url)
def blockrandom(self):
return BlockRandomWrapper(self.url)
def blockvoter(self):
return BlockVoterWrapper(self.url)
def blockforward(self):
return BlockForwardWrapper(self.url)
def blockdistribute(self):
return BlockDistributeWrapper(self.url)
def blocktrigger(self):
return BlockTriggerWrapper(self.url)
def blockwriter(self):
return BlockWriterWrapper(self.url) | 1,276 | 344 |
from smeagol.encode import *
import os
import pytest
script_dir = os.path.dirname(__file__)
rel_path = "data"
data_path = os.path.join(script_dir, rel_path)
def test_integer_encode():
record = SeqRecord(Seq('ACGTNWSMKRYBDHVZ'), id='id', name='name')
result = integer_encode(record, rc=False)
assert np.all(result == [1,2,3,4,6,7,8,9,10,11,12,13,14,15,16,0])
record = SeqRecord(Seq('ACGE'))
with pytest.raises(KeyError):
integer_encode(record, rc='none')
record = SeqRecord(Seq('AGCAU'))
result = integer_encode(record, rc=True)
assert np.all(result == [1, 5, 3, 2, 5])
def test_SeqEncoding():
records = [SeqRecord(Seq('AGC'), id='id1', name='name1'),
SeqRecord(Seq('ACA'), id='id2', name='name2')]
result = SeqEncoding(records, sense='+')
assert result.len == 3
assert np.all(result.ids == ['id1', 'id2'])
assert np.all(result.names == ['name1', 'name2'])
assert np.all(result.seqs == np.array([[1,3,2], [1,2,1]]))
assert np.all(result.senses == ['+', '+'])
assert type(result.senses) == np.ndarray
assert type(result.ids) == np.ndarray
assert type(result.names) == np.ndarray
assert type(result.senses) == np.ndarray
result = SeqEncoding(records, sense='+', rcomp='only')
assert np.all(result.ids == ['id1', 'id2'])
assert np.all(result.names == ['name1', 'name2'])
assert np.all(result.seqs == np.array([[3,2,4], [4,3,4]]))
assert np.all(result.senses == ['-', '-'])
result = SeqEncoding(records, sense='+', rcomp='both')
assert np.all(result.ids == ['id1', 'id2', 'id1', 'id2'])
assert np.all(result.names == ['name1', 'name2', 'name1', 'name2'])
assert np.all(result.seqs == np.array([[1,3,2], [1,2,1], [3,2,4], [4,3,4]]))
assert np.all(result.senses == ['+', '+', '-', '-'])
def test_SeqGroups():
records = os.path.join(data_path, 'test.fa.gz')
result = SeqGroups(records, sense='+')
expected = [SeqEncoding([SeqRecord(seq=Seq('ATTAAATA'), id='Seg1', name='Seg1')], sense='+', rcomp='none'), SeqEncoding([SeqRecord(seq=Seq('CAAAATCTTTAGGATTAGCAC'), id='Seg2', name='Seg2')], sense='+', rcomp='none')]
for i in range(2):
assert np.all(result.seqs[i].seqs == expected[i].seqs)
assert result.seqs[i].ids == expected[i].ids
assert result.seqs[i].senses == expected[i].senses
assert result.seqs[i].names == expected[i].names
records = [SeqRecord(Seq('AGC'), id='id1', name='name'),
SeqRecord(Seq('ACA'), id='id2', name='name')]
result = SeqGroups(records, sense='-', rcomp='both', group_by='name')
assert len(result.seqs) == 1
expected = SeqEncoding(records, sense='-', rcomp='both')
assert np.all(result.seqs[0].seqs == expected.seqs)
assert np.all(result.seqs[0].ids == expected.ids)
assert np.all(result.seqs[0].names == expected.names)
assert np.all(result.seqs[0].senses == expected.senses)
| 2,958 | 1,119 |
# automatically generated by the FlatBuffers compiler, do not modify
# namespace: tflite
class BuiltinOperator(object):
ADD = 0
AVERAGE_POOL_2D = 1
CONCATENATION = 2
CONV_2D = 3
DEPTHWISE_CONV_2D = 4
EMBEDDING_LOOKUP = 7
FULLY_CONNECTED = 9
HASHTABLE_LOOKUP = 10
L2_NORMALIZATION = 11
L2_POOL_2D = 12
LOCAL_RESPONSE_NORMALIZATION = 13
LOGISTIC = 14
LSH_PROJECTION = 15
LSTM = 16
MAX_POOL_2D = 17
RELU = 19
RELU6 = 21
RESHAPE = 22
RESIZE_BILINEAR = 23
RNN = 24
SOFTMAX = 25
SPACE_TO_DEPTH = 26
SVDF = 27
TANH = 28
CONCAT_EMBEDDINGS = 29
SKIP_GRAM = 30
CALL = 31
CUSTOM = 32
| 688 | 362 |
#!/usr/bin/python
__author__ = 'kilroy'
# (c) 2014, WasHere Consulting, Inc.
# Written for Infinite Skills
# need pycrypto package
from Crypto.Cipher import AES
# need PIL and stepic packages
import Image, stepic
import binascii
# key has to be 16, 24 or 32 bytes long
cryptObj = AES.new("This is my key42", AES.MODE_CBC, "16 character vec")
# notice the spaces -- that's to pad it out to a multiple of 16 bytes
plaintext = "This is some text we need to encrypt because it's very secret "
ciphertext = cryptObj.encrypt(plaintext)
# we need to convert to ASCII to store it nicely
binval = binascii.b2a_base64(ciphertext)
i = Image.open("bullpuppies.jpg")
print("ASCII: ", binval)
stego = stepic.encode(i, binval)
stego.save("stegencrypt.bmp", "BMP")
newim = Image.open("stegencrypt.bmp")
data = stepic.decode(newim).rstrip('\n')
print("What we have out: ", data)
# convert from ASCII back to binary
encrypted = binascii.a2b_base64(data)
newcryptObj = AES.new("This is my key42", AES.MODE_CBC, "16 character vec")
result = newcryptObj.decrypt(encrypted)
print(result) | 1,080 | 410 |
self.toolBar.widgetForAction(self.backAction).setPopupMode(QToolButton.DelayedPopup)
self.toolBar.widgetForAction(self.forwardAction).setPopupMode(QToolButton.DelayedPopup)
self.toolBar.widgetForAction(self.upAction).setPopupMode(QToolButton.DelayedPopup)
| 256 | 88 |
class Table(object):
def __init__(self, name):
self.name = name
self.columns = []
def createColumn(self, column):
self.columns.append(column)
def readColumn(self, name):
for value in self.columns:
if value.name == name:
return value
def updateColumn(self, name, column):
for i in range(0,len(self.columns)):
if self.columns[i].name == name:
self.columns[i] = column
break
def deleteColumn(self, name):
for i in range(0, len(self.columns)):
if self.columns[i].name == name:
del self.columns[i]
break
| 703 | 194 |
class Config:
api_host = "https://api.frame.io"
default_page_size = 50
default_concurrency = 5
| 107 | 40 |
"""
Profile ../profile-datasets-py/div83/073.py
file automaticaly created by prof_gen.py script
"""
self["ID"] = "../profile-datasets-py/div83/073.py"
self["Q"] = numpy.array([ 1.956946, 2.801132, 4.230252, 5.419981, 6.087733,
6.419039, 6.547807, 6.548787, 6.504738, 6.432189,
6.370779, 6.31601 , 6.30854 , 6.30967 , 6.3021 ,
6.284641, 6.249721, 6.198102, 6.145852, 6.099373,
6.058663, 5.986194, 5.884295, 5.747187, 5.574639,
5.373091, 5.135734, 4.845567, 4.50543 , 4.128883,
3.740616, 3.373579, 3.08385 , 2.839622, 2.645253,
2.513544, 2.431584, 2.357084, 2.259045, 2.163875,
2.110346, 2.096796, 2.099166, 2.141685, 2.178575,
2.199215, 2.274125, 2.462204, 2.771892, 3.16592 ,
3.663737, 4.257632, 4.884156, 6.018014, 7.350006,
8.070105, 8.34401 , 8.37078 , 8.312201, 8.323041,
8.553217, 9.130147, 10.2062 , 11.93486 , 14.44259 ,
17.91218 , 22.60939 , 28.81827 , 34.92618 , 38.93378 ,
56.09235 , 71.77225 , 85.79204 , 102.7434 , 118.9449 ,
125.5382 , 132.4874 , 122.411 , 122.1301 , 128.6215 ,
118.193 , 100.195 , 96.30662 , 102.0976 , 123.4148 ,
225.838 , 205.4768 , 199.0024 , 192.8198 , 186.9111 ,
181.2631 , 175.8601 , 170.6889 , 165.7365 , 160.9931 ,
156.4465 , 152.0869 , 147.9041 , 143.8903 , 140.0354 ,
136.3324 ])
self["P"] = numpy.array([ 5.00000000e-03, 1.61000000e-02, 3.84000000e-02,
7.69000000e-02, 1.37000000e-01, 2.24400000e-01,
3.45400000e-01, 5.06400000e-01, 7.14000000e-01,
9.75300000e-01, 1.29720000e+00, 1.68720000e+00,
2.15260000e+00, 2.70090000e+00, 3.33980000e+00,
4.07700000e+00, 4.92040000e+00, 5.87760000e+00,
6.95670000e+00, 8.16550000e+00, 9.51190000e+00,
1.10038000e+01, 1.26492000e+01, 1.44559000e+01,
1.64318000e+01, 1.85847000e+01, 2.09224000e+01,
2.34526000e+01, 2.61829000e+01, 2.91210000e+01,
3.22744000e+01, 3.56505000e+01, 3.92566000e+01,
4.31001000e+01, 4.71882000e+01, 5.15278000e+01,
5.61260000e+01, 6.09895000e+01, 6.61253000e+01,
7.15398000e+01, 7.72396000e+01, 8.32310000e+01,
8.95204000e+01, 9.61138000e+01, 1.03017000e+02,
1.10237000e+02, 1.17778000e+02, 1.25646000e+02,
1.33846000e+02, 1.42385000e+02, 1.51266000e+02,
1.60496000e+02, 1.70078000e+02, 1.80018000e+02,
1.90320000e+02, 2.00989000e+02, 2.12028000e+02,
2.23442000e+02, 2.35234000e+02, 2.47408000e+02,
2.59969000e+02, 2.72919000e+02, 2.86262000e+02,
3.00000000e+02, 3.14137000e+02, 3.28675000e+02,
3.43618000e+02, 3.58966000e+02, 3.74724000e+02,
3.90893000e+02, 4.07474000e+02, 4.24470000e+02,
4.41882000e+02, 4.59712000e+02, 4.77961000e+02,
4.96630000e+02, 5.15720000e+02, 5.35232000e+02,
5.55167000e+02, 5.75525000e+02, 5.96306000e+02,
6.17511000e+02, 6.39140000e+02, 6.61192000e+02,
6.83667000e+02, 7.06565000e+02, 7.29886000e+02,
7.53628000e+02, 7.77790000e+02, 8.02371000e+02,
8.27371000e+02, 8.52788000e+02, 8.78620000e+02,
9.04866000e+02, 9.31524000e+02, 9.58591000e+02,
9.86067000e+02, 1.01395000e+03, 1.04223000e+03,
1.07092000e+03, 1.10000000e+03])
self["CO2"] = numpy.array([ 373.9833, 373.985 , 373.9884, 373.995 , 374.0057, 374.0216,
374.0366, 374.0466, 374.0776, 374.1326, 374.2046, 374.2866,
374.3886, 374.5236, 374.6526, 374.7416, 374.8237, 374.8957,
374.9497, 374.9817, 374.9777, 374.8958, 374.7168, 374.3068,
373.8689, 373.399 , 373.1931, 373.0142, 372.9923, 372.9705,
372.9696, 372.9687, 372.9958, 373.0249, 373.093 , 373.1831,
373.3081, 373.5051, 373.7122, 374.0992, 374.5252, 375.0212,
375.5992, 376.2002, 376.6862, 377.1942, 377.6051, 377.9581,
378.329 , 378.7248, 379.1366, 379.5774, 380.0341, 380.4167,
380.7802, 381.0429, 381.2468, 381.3648, 381.4278, 381.4508,
381.4547, 381.4305, 381.3971, 381.3314, 381.2585, 381.1682,
381.0824, 381.001 , 380.9507, 380.9132, 380.8856, 380.8667,
380.8483, 380.8259, 380.7977, 380.7672, 380.7296, 380.6944,
380.6635, 380.66 , 380.705 , 380.7848, 380.8093, 380.8081,
380.801 , 380.763 , 380.7717, 380.7742, 380.7766, 380.7788,
380.781 , 380.783 , 380.785 , 380.7869, 380.7887, 380.7904,
380.7921, 380.7937, 380.7952, 380.7967, 380.7981])
self["CO"] = numpy.array([ 3.371623 , 3.275051 , 3.089317 , 2.793105 , 2.386455 ,
1.898358 , 1.286632 , 0.6786696 , 0.2446264 , 0.1117883 ,
0.07406993, 0.06797857, 0.06665738, 0.06541499, 0.0627382 ,
0.05708244, 0.05198118, 0.0482764 , 0.04517182, 0.04224734,
0.04012826, 0.03841507, 0.03688128, 0.0355552 , 0.035034 ,
0.03503531, 0.03689901, 0.03936141, 0.0440788 , 0.0492937 ,
0.04925202, 0.04920743, 0.04473116, 0.04025199, 0.0366714 ,
0.03351052, 0.03137962, 0.03123803, 0.03108933, 0.03244993,
0.03412033, 0.03639812, 0.03949292, 0.04300421, 0.0464721 ,
0.05039819, 0.05490918, 0.06008545, 0.06627392, 0.07433206,
0.08375509, 0.0936498 , 0.1051005 , 0.1153623 , 0.1259091 ,
0.1348409 , 0.1428668 , 0.1493407 , 0.1547317 , 0.1586747 ,
0.1618646 , 0.1637585 , 0.1653183 , 0.165877 , 0.1662896 ,
0.166244 , 0.1661372 , 0.1659742 , 0.1657402 , 0.1654906 ,
0.1652257 , 0.1649732 , 0.1647089 , 0.1644201 , 0.1640755 ,
0.1636954 , 0.1632974 , 0.1629001 , 0.1624932 , 0.1620122 ,
0.1613979 , 0.1608719 , 0.1609615 , 0.1616045 , 0.162019 ,
0.1622284 , 0.1622896 , 0.1621097 , 0.1619268 , 0.1617408 ,
0.1615517 , 0.1613596 , 0.1611645 , 0.1609663 , 0.1607661 ,
0.1605619 , 0.1603556 , 0.1601463 , 0.159934 , 0.1597186 ,
0.1595013 ])
self["T"] = numpy.array([ 182.651, 193.018, 211.533, 232.314, 250.519, 264.537,
275.437, 284.487, 292.885, 299.567, 304.326, 304.11 ,
298.775, 290.318, 281.349, 274.469, 269.03 , 262.729,
255.232, 247.095, 239.554, 233.573, 228.85 , 225.016,
221.726, 218.548, 214.604, 211.054, 207.796, 204.77 ,
202.096, 200.364, 198.049, 195.456, 192.937, 190.933,
189.903, 190.14 , 191.564, 193.628, 195.422, 195.904,
194.759, 193.011, 191.99 , 192.292, 193.427, 194.638,
195.439, 196.086, 196.942, 197.885, 198.685, 199.239,
199.659, 200.124, 200.563, 200.928, 201.22 , 201.562,
202.071, 202.831, 203.902, 205.287, 206.924, 208.756,
210.742, 212.832, 214.977, 217.117, 219.157, 221.102,
223.008, 224.906, 226.796, 228.809, 230.866, 232.815,
234.6 , 236.299, 237.853, 239.205, 240.549, 241.851,
242.628, 240.741, 240.155, 240.155, 240.155, 240.155,
240.155, 240.155, 240.155, 240.155, 240.155, 240.155,
240.155, 240.155, 240.155, 240.155, 240.155])
self["N2O"] = numpy.array([ 1.07999800e-03, 8.19997700e-04, 6.29997300e-04,
4.79997400e-04, 3.49997900e-04, 2.49998400e-04,
2.49998400e-04, 4.39997100e-04, 1.14999300e-03,
1.39999100e-03, 3.20998000e-03, 5.38996600e-03,
7.70995100e-03, 9.55994000e-03, 1.17199300e-02,
1.49099100e-02, 1.73398900e-02, 1.84498900e-02,
1.95798800e-02, 2.11198700e-02, 2.25798600e-02,
2.06298800e-02, 1.70899000e-02, 1.36999200e-02,
1.13299400e-02, 9.15995100e-03, 7.07996400e-03,
6.14997000e-03, 5.60997500e-03, 5.08997900e-03,
5.26998000e-03, 8.14997300e-03, 1.09399700e-02,
1.36499600e-02, 1.70299500e-02, 2.36499400e-02,
3.00799300e-02, 3.63299100e-02, 6.36398600e-02,
9.82897900e-02, 1.31219700e-01, 1.68939600e-01,
2.03099600e-01, 2.35689500e-01, 2.62639400e-01,
2.85029400e-01, 3.02059300e-01, 3.12939200e-01,
3.16769100e-01, 3.16769000e-01, 3.16768800e-01,
3.16768700e-01, 3.16768500e-01, 3.16768100e-01,
3.16767700e-01, 3.16767400e-01, 3.16767400e-01,
3.16767300e-01, 3.16767400e-01, 3.16767400e-01,
3.16767300e-01, 3.16767100e-01, 3.16766800e-01,
3.16766200e-01, 3.16765400e-01, 3.16764300e-01,
3.16762800e-01, 3.16760900e-01, 3.16758900e-01,
3.16757700e-01, 3.16752200e-01, 3.16747300e-01,
3.16742800e-01, 3.16737500e-01, 3.16732300e-01,
3.16730200e-01, 3.16728000e-01, 3.16731200e-01,
3.16731300e-01, 3.16729300e-01, 3.16732600e-01,
3.16738300e-01, 3.16739500e-01, 3.16737700e-01,
3.16730900e-01, 3.16698500e-01, 3.16704900e-01,
3.16707000e-01, 3.16708900e-01, 3.16710800e-01,
3.16712600e-01, 3.16714300e-01, 3.16715900e-01,
3.16717500e-01, 3.16719000e-01, 3.16720400e-01,
3.16721800e-01, 3.16723100e-01, 3.16724400e-01,
3.16725600e-01, 3.16726800e-01])
self["O3"] = numpy.array([ 1.092958 , 0.9333394 , 0.7369399 , 0.7964717 , 0.9379673 ,
1.079703 , 1.237282 , 1.401171 , 1.54559 , 1.718999 ,
1.962487 , 2.289906 , 2.740603 , 3.343639 , 4.033965 ,
4.681221 , 5.141878 , 5.436586 , 5.582286 , 5.602026 ,
5.534786 , 5.136629 , 4.572073 , 3.916747 , 3.242732 ,
2.627226 , 2.238409 , 1.97481 , 1.707352 , 1.378664 ,
0.9718754 , 0.4739234 , 0.1691735 , 0.07579148, 0.06013094,
0.06060745, 0.06021215, 0.05955906, 0.06177556, 0.07662733,
0.1108608 , 0.1167128 , 0.07390804, 0.05683548, 0.05512018,
0.06082157, 0.08559211, 0.1410637 , 0.2397043 , 0.2934931 ,
0.3111259 , 0.3108367 , 0.2986955 , 0.2690934 , 0.2461522 ,
0.2268442 , 0.1992463 , 0.1731716 , 0.1505927 , 0.124096 ,
0.09637658, 0.07470752, 0.06088038, 0.05258867, 0.04738112,
0.04395351, 0.04182795, 0.04072743, 0.04045889, 0.0410274 ,
0.0409915 , 0.03975355, 0.03971269, 0.04038045, 0.04089773,
0.04011796, 0.03950926, 0.0416304 , 0.04225274, 0.03989827,
0.03871712, 0.03680791, 0.03578315, 0.03928659, 0.04175655,
0.04199231, 0.0311443 , 0.0311445 , 0.03114469, 0.03114488,
0.03114505, 0.03114522, 0.03114538, 0.03114554, 0.03114568,
0.03114583, 0.03114596, 0.03114609, 0.03114622, 0.03114634,
0.03114645])
self["CH4"] = numpy.array([ 0.0996805, 0.1110867, 0.1195645, 0.1268483, 0.1474111,
0.1701019, 0.1716519, 0.1735449, 0.1782488, 0.1907058,
0.2214456, 0.2588824, 0.2996521, 0.3522618, 0.4052884,
0.4652571, 0.5254507, 0.5900453, 0.652122 , 0.7166726,
0.7781653, 0.834089 , 0.8861858, 0.9360986, 0.9791765,
1.019945 , 1.059185 , 1.097075 , 1.133645 , 1.169365 ,
1.207695 , 1.248736 , 1.292576 , 1.332766 , 1.371486 ,
1.408036 , 1.441656 , 1.471497 , 1.485487 , 1.500237 ,
1.515757 , 1.532077 , 1.549197 , 1.566077 , 1.580627 ,
1.595846 , 1.611256 , 1.627016 , 1.642985 , 1.658145 ,
1.673924 , 1.688113 , 1.702712 , 1.71622 , 1.729587 ,
1.739776 , 1.747785 , 1.752725 , 1.755075 , 1.756095 ,
1.756145 , 1.755794 , 1.755242 , 1.754189 , 1.753005 ,
1.751649 , 1.75037 , 1.74924 , 1.748419 , 1.747772 ,
1.747412 , 1.747125 , 1.74691 , 1.746811 , 1.746822 ,
1.746941 , 1.747158 , 1.747506 , 1.747867 , 1.748135 ,
1.748353 , 1.748495 , 1.748612 , 1.748701 , 1.748684 ,
1.748515 , 1.748561 , 1.748572 , 1.748593 , 1.748603 ,
1.748613 , 1.748622 , 1.748631 , 1.74864 , 1.748648 ,
1.748656 , 1.748664 , 1.748671 , 1.748678 , 1.748685 ,
1.748692 ])
self["CTP"] = 500.0
self["CFRACTION"] = 0.0
self["IDG"] = 0
self["ISH"] = 0
self["ELEVATION"] = 0.0
self["S2M"]["T"] = 240.155
self["S2M"]["Q"] = 136.332410939
self["S2M"]["O"] = 0.03114645315
self["S2M"]["P"] = 717.06042
self["S2M"]["U"] = 0.0
self["S2M"]["V"] = 0.0
self["S2M"]["WFETC"] = 100000.0
self["SKIN"]["SURFTYPE"] = 0
self["SKIN"]["WATERTYPE"] = 1
self["SKIN"]["T"] = 240.155
self["SKIN"]["SALINITY"] = 35.0
self["SKIN"]["FOAM_FRACTION"] = 0.0
self["SKIN"]["FASTEM"] = numpy.array([ 3. , 5. , 15. , 0.1, 0.3])
self["ZENANGLE"] = 0.0
self["AZANGLE"] = 0.0
self["SUNZENANGLE"] = 0.0
self["SUNAZANGLE"] = 0.0
self["LATITUDE"] = -74.316
self["GAS_UNITS"] = 2
self["BE"] = 0.0
self["COSBK"] = 0.0
self["DATE"] = numpy.array([2006, 10, 20])
self["TIME"] = numpy.array([0, 0, 0])
| 13,342 | 10,301 |
'''
>>> c = Conf('climon.conf.test')
>>> list(c.iter_ids('sensor'))
['sine', 'web']
>>> list(c.iter_ids('toggle'))
['fake']
>>> c.get_element('sensor', 'sine') # doctest: +ELLIPSIS
<function sine.<locals>.sine_sensor at ...>
>>> c.get_element('toggle', 'fake') # doctest: +ELLIPSIS
<toggles.FakeToggle object at ...>
>>> list(c.iter_elements('sensor')) # doctest: +ELLIPSIS
[('sine', <function sine... at ...>)]
>>> list(c.iter_elements('toggle')) # doctest: +ELLIPSIS
[('fake', <toggles.FakeToggle object at...>)]
'''
from functools import lru_cache
from toggles import TOGGLES, InvertedToggle
from sensors import SENSORS
ELEMENTS = {
'toggle': TOGGLES,
'sensor': SENSORS,
}
def new_element(element_type, element_conf):
make_element = ELEMENTS[element_type][element_conf['type'].upper()]
element = make_element(element_conf['source'])
if element_type == 'toggle' and element_conf.get('invert', None) == 'true':
element = InvertedToggle(element)
element.conf = element_conf
element.conf['element_type'] = element_type
return element
class Conf(object):
def __init__(self, fname):
import configparser
self.raw = configparser.ConfigParser()
self.raw.read(fname)
def get_section(self, element_type, element_id):
s = self.raw['%s:%s' % (element_type, element_id)]
s['id'] = element_id
s['element_type'] = element_type
return s
@lru_cache(maxsize=None)
def get_element(self, element_type, element_id):
return new_element(element_type, self.get_section(element_type, element_id))
def iter_ids(self, element_type):
for section in self.raw.sections():
if section.startswith('%s:' % element_type):
_, element_id = section.split(':', 1)
yield element_id
def iter_sections(self, element_type):
for element_id in self.iter_ids(element_type):
yield element_id, self.get_section(element_type, element_id)
def iter_elements(self, element_type):
for element_id in self.iter_ids(element_type):
yield element_id, self.get_element(element_type, element_id)
@staticmethod
def section_type_id(section):
try:
s_type, s_id = section.split(':', 1)
return s_type, s_id
except ValueError:
return None, None
def iter_group_elements(self, group):
for section in self.raw.sections():
element_type, element_id = self.section_type_id(section)
if element_type in ELEMENTS and self.raw[section].get('group', '') == group:
yield section, self.get_section(element_type, element_id)
def iter_groups(self):
groups = [group for group_id, group in self.iter_sections('group')]
for group in sorted(groups, key=lambda g: g['order']):
yield group, list(self.iter_group_elements(group['id']))
class ParsedConf(object):
def __init__(self, fname):
self.conf = Conf(fname)
self.groups = list(self.conf.iter_groups())
self.sensors = dict(self.conf.iter_sections('sensor'))
self.toggles = dict(self.conf.iter_sections('toggle'))
| 3,204 | 1,059 |
#© 2017-2020, ETH Zurich, D-INFK, lubu@inf.ethz.ch
import random
import unittest
from kademlia.node import Node
from kademlia.utils import digest
from talosdht.protocolsecurity import generate_secret_key
from talosdht.talosprotocol import TalosSKademliaProtocol
from talosvc.talosclient.restapiclient import TalosVCRestClient
class DummyProt(TalosSKademliaProtocol):
def callPing(self, nodeToAsk):
print "Ping %s" % nodeToAsk
class TestBuckets(unittest.TestCase):
def test_bucket_basic(self):
ecd_key = generate_secret_key()
sourceNode = Node(digest(random.getrandbits(255)), ip="127.0.0.1", port=12345)
dummy_protocol = DummyProt(ecd_key, sourceNode, None, 4, talos_vc=None)
nodes = []
for i in range(1000):
nodes.append(Node(digest(random.getrandbits(255)), ip="127.0.0.1", port=i+10000))
for i in range(1000):
dummy_protocol.router.addContact(nodes[i])
for i in range(1000):
self.assertFalse(dummy_protocol.router.isNewNode(nodes[i]))
| 1,058 | 400 |
import requests
from bs4 import BeautifulSoup
import re
import time
import _thread
class Client():
base = "https://temp-mail.org/en"
def __init__(self, address: str = None):
self.address = address
self.session = requests.Session()
realisticBrowser = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36"
}
if self.address != None:
self.session.cookies.set("mail", self.address.replace("@", "%40"))
self.session.headers.update(realisticBrowser)
self.session.get(self.base)
try:
self.address = self.session.cookies["mail"].replace("%40", "@")
except:
raise ValueError("The email address you supplied is not supported on temp-mail")
self.recent = []
def checkloop(self, callback=lambda m: m, async=True):
if async == True:
_thread.start_new_thread(self.checkloop, (), {"callback": callback, "async": False})
else:
self.check()
time.sleep(2)
while True:
ilen = len(self.recent)
self.check()
if len(self.recent) > ilen:
for i in self.recent[ilen:]:
callback(i)
time.sleep(10)
def check(self):
r = self.session.get(self.base + "/option/refresh/")
soup = BeautifulSoup(r.text, "html.parser")
mails = []
for mail in soup.find('tbody').findChildren("tr"):
info = mail.findChildren("td")[0].findChildren()[0]
view_url = info["href"]
subject = info["title"]
by = re.findall(r".*<(.+)>", info.decode_contents())
creq = self.session.get(view_url)
contentsoup = BeautifulSoup(creq.text, "html.parser")
content = contentsoup.find("div", {"class":"pm-text"}).decode_contents()
themail = Email(author=by, content=content,to=self.address,subject=subject)
mails.append(themail)
self.recent = mails
return mails
class Email():
def __init__(self, author=None, content=None, to=None, subject=None):
self.author = author
self.content = content
self.to = to
self.subject = subject
def __str__(self):
return str({"author":self.author, "content": self.content, "to": self.to, "subject":self.subject})
def links(self):
soup = BeautifulSoup(self.content, "html.parser")
lnks = []
for link in soup.findAll("a"):
lnks.append(link["href"])
return lnks | 2,275 | 917 |
from ._TestFillEmbedTime import *
from ._TestFillSimple import *
from ._TestManyFields import *
from ._TestMsgArray import *
from ._TestPrimitiveArray import *
from ._TestString import *
| 187 | 57 |
# Introductory examples
name = 'Maurizio'
surname = 'Petrelli'
print('-------------------------------------------------')
print('My name is {}'.format(name))
print('-------------------------------------------------')
print('My name is {} and my surname is {}'.format(name, surname))
print('-------------------------------------------------')
# Decimal Number formatting
PI = 3.14159265358979323846
print('----------------------------------------------------')
print("The 2 digit Archimedes' constant is equal to {:.2f}".format(PI))
print("The 3 digit Archimedes' constant is equal to {:.3f}".format(PI))
print("The 4 digit Archimedes' constant is equal to {:.4f}".format(PI))
print("The 5 digit Archimedes' constant is equal to {:.5f}".format(PI))
print('----------------------------------------------------')
'''Results
-------------------------------------------------
My name is Maurizio
-------------------------------------------------
My name is Maurizio and my surname is Petrelli
-------------------------------------------------
----------------------------------------------------
The 2 digit Archimedes' constant is equal to 3.14
The 3 digit Archimedes' constant is equal to 3.142
The 4 digit Archimedes' constant is equal to 3.1416
The 5 digit Archimedes' constant is equal to 3.14159
----------------------------------------------------
'''
| 1,355 | 357 |
"""Finance Database view"""
__docformat__ = "numpy"
import os
import pandas as pd
from tabulate import tabulate
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal.etf import financedatabase_model
from gamestonk_terminal.helper_funcs import export_data
def display_etf_by_name(
name: str,
limit: int,
export: str = "",
):
"""Display a selection of ETFs based on name filtered by total assets. [Source: Finance Database]
Parameters
----------
name: str
Search by name to find ETFs matching the criteria.
limit: int
Limit of ETFs to display
export: str
Type of format to export data
"""
data = financedatabase_model.get_etfs_by_name(name)
if not data:
print("No data was found with that name\n")
return
tabulate_data = pd.DataFrame(data).T[
["long_name", "family", "category", "total_assets"]
]
tabulate_data_sorted = tabulate_data.sort_values(by="total_assets", ascending=False)
tabulate_data_sorted["total_assets"] = tabulate_data_sorted["total_assets"] / 1e6
if gtff.USE_TABULATE_DF:
print(
tabulate(
tabulate_data_sorted.iloc[:limit],
showindex=True,
headers=["Name", "Family", "Category", "Total Assets [M]"],
floatfmt=".2f",
tablefmt="fancy_grid",
),
"\n",
)
else:
print(tabulate_data_sorted.iloc[:limit].to_string(), "\n")
export_data(export, os.path.dirname(os.path.abspath(__file__)), "ln_fd", data)
def display_etf_by_description(
description: str,
limit: int,
export: str = "",
):
"""Display a selection of ETFs based on description filtered by total assets. [Source: Finance Database]
Parameters
----------
description: str
Search by description to find ETFs matching the criteria.
limit: int
Limit of ETFs to display
export: str
Type of format to export data
"""
data = financedatabase_model.get_etfs_by_description(description)
if not data:
print("No data was found with that description\n")
return
tabulate_data = pd.DataFrame(data).T[
["long_name", "family", "category", "total_assets"]
]
tabulate_data_sorted = tabulate_data.sort_values(by="total_assets", ascending=False)
tabulate_data_sorted["total_assets"] = tabulate_data_sorted["total_assets"] / 1e6
if gtff.USE_TABULATE_DF:
print(
tabulate(
tabulate_data_sorted.iloc[:limit],
showindex=True,
headers=["Name", "Family", "Category", "Total Assets [M]"],
floatfmt=".2f",
tablefmt="fancy_grid",
),
"\n",
)
else:
print(tabulate_data_sorted.iloc[:limit].to_string(), "\n")
export_data(export, os.path.dirname(os.path.abspath(__file__)), "ld", data)
def display_etf_by_category(
category: str,
limit: int,
export: str = "",
):
"""Display a selection of ETFs based on a category filtered by total assets. [Source: Finance Database]
Parameters
----------
description: str
Search by description to find ETFs matching the criteria.
limit: int
Limit of ETFs to display
export: str
Type of format to export data
"""
data = financedatabase_model.get_etfs_by_category(category)
if not data:
print("No data was found on that category\n")
return
tabulate_data = pd.DataFrame(data).T[
["long_name", "family", "category", "total_assets"]
]
tabulate_data_sorted = tabulate_data.sort_values(by="total_assets", ascending=False)
tabulate_data_sorted["total_assets"] = tabulate_data_sorted["total_assets"] / 1e6
if gtff.USE_TABULATE_DF:
print(
tabulate(
tabulate_data_sorted.iloc[:limit],
showindex=True,
headers=["Name", "Family", "Category", "Total Assets [M]"],
floatfmt=".2f",
tablefmt="fancy_grid",
),
"\n",
)
else:
print(tabulate_data_sorted.iloc[:limit].to_string(), "\n")
export_data(
export,
os.path.join(os.path.dirname(os.path.abspath(__file__)), "screener"),
"sbc",
data,
)
| 4,405 | 1,352 |
from .clients import MovesAppClient
from .settings import SETUP_URL_NAME
from ..core import views
class MovesAppDialogRedirect(views.OAuthDialogRedirectView):
"""
View that handles the redirects for the Moves app authorization dialog.
"""
client_class = MovesAppClient
class MovesAppCallback(views.OAuthCallbackView):
"""
View that handles the Moves app OAuth callback.
"""
client_class = MovesAppClient
class MovesAppSetup(views.OAuthSetupView):
"""
View that handles the setup of a retrieved Moves app account.
"""
client_class = MovesAppClient
setup_url = SETUP_URL_NAME
class MovesAppOAuthDisconnect(views.OAuthDisconnectView):
"""
View that handles the disconnect of a Moves app account.
"""
client_class = MovesAppClient
| 803 | 232 |
import random
import torch
import numpy as np
import time
import os
from model.net import Net
from model.loss import Loss
from torch.autograd import Variable
import itertools
import pandas as pd
from main.dataset import LunaDataSet
from torch.utils.data import DataLoader
from configs import VAL_PCT, TOTAL_EPOCHS, DEFAULT_LR, OUTPUT_PATH
from glob import glob
def get_lr(epoch):
if epoch <= TOTAL_EPOCHS * 0.5:
lr = DEFAULT_LR
elif epoch <= TOTAL_EPOCHS * 0.8:
lr = 0.1 * DEFAULT_LR
else:
lr = 0.01 * DEFAULT_LR
return lr
def train(data_loader, net, loss, epoch, optimizer, get_lr, save_dir='./models/'):
print("****************training:*******************")
start_time = time.time()
net.train()
lr = get_lr(epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
metrics = []
for i, (data, target, coord) in enumerate(data_loader):
if torch.cuda.is_available():
data = Variable(data.cuda())
target = Variable(target.cuda())
coord = Variable(coord.cuda())
data = data.float()
target = target.float()
coord = coord.float()
output = net(data, coord)
loss_output = loss(output, target)
optimizer.zero_grad()
loss_output[0].backward()
optimizer.step()
loss_output[0] = loss_output[0].item()
metrics.append(loss_output)
break
metrics = np.asarray(metrics, np.float32)
if epoch % 10 == 0:
net_state_dict = net.state_dict()
for key in net_state_dict.keys():
net_state_dict[key] = net_state_dict[key].cpu()
torch.save({
'epoch': epoch,
'save_dir': save_dir,
'model_state_dict': net_state_dict,
'optimizer_state_dict': optimizer.state_dict(),
'loss': np.mean(metrics[:, 0])}, os.path.join(save_dir, f'''{epoch}.ckpt'''))
end_time = time.time()
print(f'''Epoch {epoch} (lr {lr})''')
print(f'''Train: tpr {100.0 * np.sum(metrics[:, 6]) / np.sum(metrics[:, 7])},
tnr {100.0 * np.sum(metrics[:, 8]) / np.sum(metrics[:, 9])},
total pos {np.sum(metrics[:, 7])}, total neg {np.sum(metrics[:, 9])},
time {end_time - start_time}''')
print(f'''loss {np.mean(metrics[:, 0])}, classify loss {np.mean(metrics[:, 1])},
regress loss {np.mean(metrics[:, 2])}, {np.mean(metrics[:, 3])},
{np.mean(metrics[:, 4])}, {np.mean(metrics[:, 5])}''')
def validate(data_loader, net, loss):
print("****************validation:*******************")
start_time = time.time()
net.eval()
metrics = []
for i, (data, target, coord) in enumerate(data_loader):
if torch.cuda.is_available():
data = Variable(data.cuda())
target = Variable(target.cuda())
coord = Variable(coord.cuda())
data = data.float()
target = target.float()
coord = coord.float()
output = net(data, coord)
loss_output = loss(output, target, train=False)
loss_output[0] = loss_output[0].item()
metrics.append(loss_output)
break
end_time = time.time()
metrics = np.asarray(metrics, np.float32)
print(f'''time {end_time - start_time}''')
print(f'''loss {np.mean(metrics[:, 0])}, classify loss {np.mean(metrics[:, 1])},
regress loss {np.mean(metrics[:, 2])}, {np.mean(metrics[:, 3])},
{np.mean(metrics[:, 4])}, {np.mean(metrics[:, 5])}''')
def run(load_last_checkpoint=False):
save_dir = f'{OUTPUT_PATH}/models/'
os.makedirs(save_dir, exist_ok=True)
neural_net = Net()
loss_fn = Loss()
optim = torch.optim.SGD(neural_net.parameters(), DEFAULT_LR, momentum=0.9, weight_decay=1e-4)
starting_epoch = 0
initial_loss = None
if load_last_checkpoint:
model_paths = glob(f'''{save_dir}*.ckpt''')
model_names = [int(i.split('/')[-1][:-5]) for i in model_paths]
latest_model_path = f'''{save_dir}{max(model_names)}.ckpt'''
print('loading latest model from:', latest_model_path)
checkpoint = torch.load(latest_model_path)
neural_net.load_state_dict(checkpoint['model_state_dict'])
optim.load_state_dict(checkpoint['optimizer_state_dict'])
starting_epoch = checkpoint['epoch']
initial_loss = checkpoint['loss']
if torch.cuda.is_available():
neural_net = neural_net.cuda()
loss_fn = loss_fn.cuda()
print(f'''Training from epoch: {starting_epoch} towards: {TOTAL_EPOCHS},
with learning rate starting from: {get_lr(starting_epoch)}, and loss: {initial_loss}''')
meta = pd.read_csv(f'{OUTPUT_PATH}/augmented_meta.csv', index_col=0).sample(frac=1).reset_index(drop=True)
meta_group_by_series = meta.groupby(['seriesuid']).indices
list_of_groups = [{i: list(meta_group_by_series[i])} for i in meta_group_by_series.keys()]
random.Random(0).shuffle(list_of_groups)
val_split = int(VAL_PCT * len(list_of_groups))
val_indices = list(itertools.chain(*[list(i.values())[0] for i in list_of_groups[:val_split]]))
train_indices = list(itertools.chain(*[list(i.values())[0] for i in list_of_groups[val_split:]]))
ltd = LunaDataSet(train_indices, meta)
lvd = LunaDataSet(val_indices, meta)
train_loader = DataLoader(ltd, batch_size=1, shuffle=False)
val_loader = DataLoader(lvd, batch_size=1, shuffle=False)
for ep in range(starting_epoch, TOTAL_EPOCHS):
train(train_loader, neural_net, loss_fn, ep, optim, get_lr, save_dir=save_dir)
validate(train_loader, neural_net, loss_fn)
if __name__ == '__main__':
run(load_last_checkpoint=False)
| 5,638 | 2,024 |
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
import numpy as np
import sys
import os
import argparse
import json
import random
import multiprocessing as mp
def mp_run(data, process_num, func, *args):
""" run func with multi process
"""
level_start = time.time()
partn = max(len(data) / process_num, 1)
start = 0
p_idx = 0
ps = []
while start < len(data):
local_data = data[start:start + partn]
start += partn
p = mp.Process(target=func, args=(local_data, p_idx) + args)
ps.append(p)
p.start()
p_idx += 1
for p in ps:
p.join()
for p in ps:
p.terminate()
return p_idx
def read(train_data_file, test_data_file):
behavior_dict = dict()
train_sample = dict()
test_sample = dict()
user_id = list()
item_id = list()
cat_id = list()
behav_id = list()
timestamp = list()
start = time.time()
itobj = zip([train_data_file, test_data_file], [train_sample, test_sample])
for filename, sample in itobj:
with open(filename, 'rb') as f:
for line in f:
arr = line.strip().split(',')
if len(arr) != 5:
break
user_id.append(int(arr[0]))
item_id.append(int(arr[1]))
cat_id.append(int(arr[2]))
if arr[3] not in behavior_dict:
i = len(behavior_dict)
behavior_dict[arr[3]] = i
behav_id.append(behavior_dict[arr[3]])
timestamp.append(int(arr[4]))
sample["USERID"] = np.array(user_id)
sample["ITEMID"] = np.array(item_id)
sample["CATID"] = np.array(cat_id)
sample["BEHAV"] = np.array(behav_id)
sample["TS"] = np.array(timestamp)
user_id = []
item_id = []
cat_id = []
behav_id = []
timestamp = []
print("Read data done, {} train records, {} test records"
", elapsed: {}".format(
len(train_sample["USERID"]),
len(test_sample["USERID"]), time.time() - start))
return behavior_dict, train_sample, test_sample
def gen_user_his_behave(train_sample):
user_his_behav = dict()
iterobj = zip(train_sample["USERID"], train_sample["ITEMID"],
train_sample["TS"])
for user_id, item_id, ts in iterobj:
if user_id not in user_his_behav:
user_his_behav[user_id] = list()
user_his_behav[user_id].append((item_id, ts))
for _, value in user_his_behav.items():
value.sort(key=lambda x: x[1])
return user_his_behav
def split_train_sample(train_dir, train_sample_seg_cnt):
segment_filenames = []
segment_files = []
for i in range(train_sample_seg_cnt):
filename = "{}/part_{}".format(train_dir, i)
segment_filenames.append(filename)
segment_files.append(open(filename, 'wb'))
with open("train_tmp", 'rb') as fi:
for line in fi:
i = random.randint(0, train_sample_seg_cnt - 1)
segment_files[i].write(line)
for f in segment_files:
f.close()
os.remove("train_tmp")
# Shuffle
for fn in segment_filenames:
lines = []
with open(fn, 'rb') as f:
for line in f:
lines.append(line)
random.shuffle(lines)
with open(fn, 'wb') as f:
for line in lines:
f.write(line)
def partial_gen_train_sample(users, user_his_behav, filename, pipe, seq_len,
min_len):
stat = dict()
with open(filename, 'wb') as f:
for user in users:
value = user_his_behav[user]
count = len(value)
if count < min_len:
continue
arr = [0 for i in range(seq_len - min_len)] + \
[v[0] for v in value]
for i in range(len(arr) - seq_len + 1):
sample = arr[i:i + seq_len]
f.write('{}_{}'.format(user, i)) # sample id
f.write('\t{}'.format(sample[-1])) # label feature
for j in range(seq_len - 1):
if sample[j] != 0:
f.write("\tslot_{}:{}".format(j + 1, sample[j]))
f.write('\n')
if sample[-1] not in stat:
stat[sample[-1]] = 0
stat[sample[-1]] += 1
pipe.send(stat)
def gen_train_sample(train_sample, args):
user_his_behav = gen_user_his_behave(train_sample)
print("user_his_behav len: {}".format(len(user_his_behav)))
users = user_his_behav.keys()
process = []
pipes = []
parall = args.parall
job_size = int(len(user_his_behav) / parall)
if len(user_his_behav) % parall != 0:
parall += 1
for i in range(parall):
a, b = mp.Pipe()
pipes.append(a)
p = mp.Process(
target=partial_gen_train_sample,
args=(users[i * job_size:(i + 1) * job_size], user_his_behav,
'train_tmp.part_{}'.format(i), b, args.seq_len,
args.min_seq_len))
process.append(p)
p.start()
stat = dict()
for pipe in pipes:
st = pipe.recv()
for k, v in st.items():
if k not in stat:
stat[k] = 0
stat[k] += v
for p in process:
p.join()
# Merge partial files
with open("train_tmp", 'wb') as f:
for i in range(parall):
filename = 'train_tmp.part_{}'.format(i)
with open(filename, 'rb') as f1:
f.write(f1.read())
os.remove(filename)
# Split train sample to segments
split_train_sample(args.train_dir, args.train_sample_seg_cnt)
return stat
def gen_test_sample(test_dir, test_sample, seq_len, min_seq_len):
user_his_behav = gen_user_his_behave(test_sample)
with open("{}/part-0".format(test_dir), 'wb') as f:
for user, value in user_his_behav.items():
if len(value) / 2 + 1 < min_seq_len:
continue
mid = int(len(value) / 2)
left = value[:mid][-seq_len + 1:]
right = value[mid:]
left = [0 for i in range(seq_len - len(left) - 1)] + \
[l[0] for l in left]
f.write('{}_{}'.format(user, 'T')) # sample id
labels = ','.join(map(str, [item[0] for item in right]))
f.write('\t{}'.format(labels))
# kvs
for j in range(seq_len - 1):
if left[j] != 0:
f.write("\tslot_{}:{}".format(j + 1, left[j]))
f.write('\n')
def prepare_sample_set(train_dir, sample_dir, process_num=12, feature_num=69):
def parse_data(files, idx, feature_num=69):
history_ids = [0] * feature_num
samples = dict()
process = 0
for filename in files:
process += 1
print("process {} / {}.".format(process, len(files)))
with open(filename) as f:
print("Begin to handle {}.".format(filename))
for line in f:
features = line.strip().split("\t")
item_id = int(features[1])
for item in features[2:]:
slot, feasign = item.split(":")
slot_id = int(slot.split("_")[1])
history_ids[slot_id - 1] = int(feasign)
if item_id not in samples:
samples[item_id] = list()
samples[item_id].append(history_ids)
with open("parse_data_{}.json".format(idx), 'w') as json_file:
json.dump(samples, json_file)
files = ["{}/{}".format(train_dir, f) for f in os.listdir(train_dir)]
real_process_num = mp_run(files, process_num, parse_data, feature_num)
num = 0
all_samples = dict()
for i in range(real_process_num):
filename = "parse_data_{}.json".format(i)
with open(filename, 'r') as json_file:
each_samples = json.load(json_file)
for key in each_samples:
if key not in all_samples:
all_samples[key] = []
all_samples[key] += each_samples[key]
num += len(each_samples[key])
os.remove(filename)
for ck in all_samples:
with open("{}/samples_{}.json".format(sample_dir, ck), 'w') as f:
json.dump(all_samples[ck], f)
if __name__ == '__main__':
_PARSER = argparse.ArgumentParser(description="DataProcess")
_PARSER.add_argument("--train_file", required=True, help="Train filename")
_PARSER.add_argument("--test_file", required=True, help="Test filename")
_PARSER.add_argument(
"--item_cate_filename",
default="./Item_Cate.txt",
help="item cate filename, used to init the first tree.")
_PARSER.add_argument(
"--stat_file", default="./Stat.txt", help="Stat filename")
_PARSER.add_argument(
"--train_dir", default="./train_data", help="Train directory")
_PARSER.add_argument(
"--sample_dir", default="./samples", help="Sample directory")
_PARSER.add_argument(
"--test_dir", default="./test_data", help="Test directory")
_PARSER.add_argument(
'--parall', type=int, help="parall process used", default=16)
_PARSER.add_argument(
"--train_sample_seg_cnt",
type=int,
default=20,
help="count of train sample segments file")
_PARSER.add_argument(
"--seq_len",
type=int,
help="sequence length of the sample record",
default=70)
_PARSER.add_argument(
"--min_seq_len",
type=int,
help="Min length of the sample sequence record",
default=8)
args = _PARSER.parse_args()
os.system("rm -rf ./{} && mkdir -p {}".format(args.train_dir,
args.train_dir))
os.system("rm -rf ./{} && mkdir -p {}".format(args.test_dir,
args.test_dir))
os.system("rm -rf ./{} && mkdir -p {}".format(args.sample_dir,
args.sample_dir))
behavior_dict, train_sample, test_sample = read(args.train_file,
args.test_file)
print(repr(behavior_dict))
stat = gen_train_sample(train_sample, args)
with open(args.stat_file, 'w') as f:
json.dump(stat, f)
gen_test_sample(args.test_dir, test_sample, args.seq_len, args.min_seq_len)
item_cate = dict()
for sample in [train_sample, test_sample]:
iterobj = zip(sample["ITEMID"], sample["CATID"])
for item_id, cat_id in iterobj:
if item_id not in item_cate:
item_cate[item_id] = cat_id
with open(args.item_cate_filename, 'w') as f:
for key in item_cate:
f.write("{}\t{}\n".format(key, item_cate[key]))
prepare_sample_set(
args.train_dir,
args.sample_dir,
args.parall,
feature_num=args.seq_len - 1)
| 11,765 | 3,780 |
import json
from typing import Any, Dict
import requests
def lambda_handler(event: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
print(f"request: {json.dumps(event)}")
uuid_message = get_uuid_message()
return {
"statusCode": 200,
"headers": {"Content-Type": "text/plain"},
"body": uuid_message,
}
def get_uuid_message() -> str:
response = requests.get("https://httpbin.org/uuid")
uuid = response.json()["uuid"]
return f"Hello, CDK! Here is your UUID: {uuid}"
| 530 | 184 |
#!/usr/bin/python3
"""Plot histograms of images. Possible nans and infinities are ignored."""
import argparse
from collections import OrderedDict
import logging
import numpy as np
import pylab as pl
from scipy import interpolate
import dwi.files
import dwi.util
def parse_args():
"""Parse command-line arguments."""
p = argparse.ArgumentParser(description=__doc__)
p.add_argument('--verbose', '-v', action='count',
help='increase verbosity')
p.add_argument('--input', nargs='+',
help='input files')
p.add_argument('--param', type=int, nargs='*',
help='image parameter index to use')
p.add_argument('--fig', required=True,
help='output figure file')
p.add_argument('--smooth', action='store_true',
help='smoothen the histogram by spline interpolation')
return p.parse_args()
def histogram(a, m1=None, m2=None, inclusive=True, bins='doane'):
"""Create histogram from data between (m1, m2), with bin centers."""
a = np.asarray(a)
if m1 is not None:
if inclusive:
a = a[a >= m1]
else:
a = a[a > m1]
if m2 is not None:
if inclusive:
a = a[a <= m2]
else:
a = a[a < m2]
mn, mx = a.min(), a.max()
hist, bin_edges = np.histogram(a, bins=bins, density=False)
bin_centers = [np.mean(t) for t in zip(bin_edges, bin_edges[1:])]
return hist, bin_centers, mn, mx
def smoothen(x, y):
"""Smoothen histogram."""
x_smooth = np.linspace(min(x), max(x), 300)
y_smooth = interpolate.spline(x, y, x_smooth)
y_smooth[y_smooth < 0] = 0 # Don't let it dive negative.
return x_smooth, y_smooth
def plot_histograms(Histograms, outfile, smooth=False):
"""Plot subfigures, each having several histograms bundled together."""
nrows = len({x[0] for x in Histograms})
ncols = len({x[1] for x in Histograms})
# logging.warning('## %s ', [nrows, ncols])
fig = pl.figure(figsize=(ncols * 6, nrows * 6))
# pl.yscale('log')
for i, ((param, rng), histograms) in enumerate(Histograms.items(), 1):
# logging.warning('#### %s ', [i, param, rng, len(histograms)])
if histograms:
fig.add_subplot(nrows, ncols, i)
minmin, maxmax = None, None
for hist, bins, mn, mx in histograms:
x, y = bins, hist
if smooth:
x, y = smoothen(x, y)
pl.plot(x, y)
# pl.bar(x, y, width=x[1] - x[0])
if minmin is None:
minmin = mn
if maxmax is None:
maxmax = mx
minmin = min(minmin, mn)
maxmax = max(maxmax, mx)
pl.title(f'{param}; {len(histograms)}; {rng}; '
f'[{minmin:.5g}, {maxmax:.5g}]')
# pl.tight_layout()
logging.info('Plotting to %s...', outfile)
pl.savefig(outfile, bbox_inches='tight')
pl.close()
def add_histograms(hists, path, img, param, ranges, verbose):
"""Add histograms for a file."""
original_shape, original_size = img.shape, img.size
img = img[dwi.util.bbox(img)]
img = img[np.isfinite(img)]
if np.any(img < 0):
# negatives = img[img < 0]
logging.warning('Image contains negatives: %s', path)
if verbose:
print(f'Read {original_shape}, {img.dtype}, '
f'{img.size / original_size:.1%}, {np.mean(img):.4g}, '
f'{dwi.util.fivenums(img)}, {param}, {path}')
for rng in ranges:
if isinstance(rng, list):
incl = True
if isinstance(rng, tuple):
incl = False
m1, m2 = np.percentile(img, rng)
key = param, str(rng)
hists.setdefault(key, []).append(histogram(img, m1, m2, incl))
# hists[0].append(histogram(img, None, None))
# hists[1].append(histogram(img, 0, 100))
# hists[2].append(histogram(img, 0.1, 99.9))
# hists[3].append(histogram(img, 1, 99))
# hists[4].append(histogram(img, 2, 98))
def main():
"""Main."""
args = parse_args()
logging.basicConfig(level=logging.INFO)
ranges = [[0, 100], (0, 100), [0, 99], (1, 95)]
hists = OrderedDict()
for path in args.input:
img, attrs = dwi.files.read_pmap(path, params=args.param,
dtype=np.float32)
for i, param in enumerate(attrs['parameters']):
add_histograms(hists, path, img[..., i], param, ranges,
args.verbose)
plot_histograms(hists, args.fig, smooth=args.smooth)
if __name__ == '__main__':
main()
| 4,685 | 1,684 |
from django.db import models
from django.contrib.auth.models import AbstractBaseUser, BaseUserManager, \
PermissionsMixin
class UserManager(BaseUserManager):
def create_user(self, email, password=None, **extra_fields):
"""Creates and saves a new user"""
if not email: raise ValueError('No valid email provided')
user = self.model(email=self.normalize_email(email), **extra_fields)
user.set_password(password)
user.save(using=self._db)
return user
def create_superuser(self, email, password):
"""Creates and saves a new superuser"""
user = self.create_user(email, password)
user.is_staff = True
user.is_superuser = True
user.save(using=self._db)
return user
class User(AbstractBaseUser, PermissionsMixin):
"""Custom user model that supports using email instead of username"""
email = models.EmailField(max_length=255, unique=True)
name = models.CharField(max_length=255)
is_active = models.BooleanField(default=True)
is_staff = models.BooleanField(default=False)
objects = UserManager()
USERNAME_FIELD = 'email'
class TestUser(models.Model):
"""Delme asap"""
field = models.EmailField(max_length=255, unique=True)
| 1,191 | 406 |
import requests
import bs4
import pdf2image
import os
import glob
import json
import platform
if platform.system() == 'Windows':
import win32com.client #pywin32
class Circolari:
def __init__(self, archiveDir=None):
self.__pageTemplate__ = 'https://www.itispaleocapa.edu.it/circolari/page/'
if archiveDir is not None:
self.__archiveDir__ = archiveDir
if not os.path.exists(archiveDir):
os.mkdir(archiveDir)
if os.path.exists(archiveDir + '/archive.json'):
with open(archiveDir + '/archive.json') as file:
self.__archive__ = json.load(file)
else:
self.__archive__ = {'dir': archiveDir}
else:
self.__archiveDir__ = None
self.__archive__ = None
def __saveArchive__(self):
with open(self.__archiveDir__ + '/archive.json', 'w') as file:
file.write(json.dumps(self.__archive__, indent=4, sort_keys=True))
def getPages(self, no, _range=True):
circolariList = list()
no = int(no)
if no < 1:
return []
if _range:
pList = range(no)
else:
pList = [no-1]
for i in pList:
x = requests.get(self.__pageTemplate__ + str(i + 1)).text
soup = bs4.BeautifulSoup(x, 'html.parser')
tags = soup.find_all(class_='post-box-archive')
for circolare in tags:
aTag = circolare.find_all('a')[1]
cURL = aTag['href']
cNo = [value for value in [i for o in [value.replace('/', '').split(' ') for value in aTag.text.lower().replace('bis', '').replace('ter', '').replace('-', '').split('.')] for i in o] if value.isnumeric()][0]
while True:
if cNo.startswith('0'):
cNo = cNo[1:]
else:
break
if 'bis' in aTag.text.lower():
cNo = cNo + ' bis'
elif 'ter' in aTag.text.lower():
cNo = cNo + ' ter'
cDate = circolare.find_all(class_='hdate')[0].text
dMAE = circolare.find(class_='members-access-error')
if dMAE:
cRestricted = True
cTitle = dMAE.text
else:
cRestricted = False
cTitle = circolare.find('p').text
circolariList.append(self.Circolare(cNo, cTitle, cDate, cURL, cRestricted, self.__archiveDir__, self.__archive__))
if self.__archive__ is not None:
for value in circolariList:
if value.number not in self.__archive__:
self.__archive__[value.number] = {
'name': value.name,
'date': value.date,
'url': value.url,
'restricted': value.restricted
}
self.__saveArchive__()
return circolariList
def get(self, no, _timeout=5):
no = str(no)
noN = int(no.split(' ')[0])
if self.__archive__ is not None:
if no in self.__archive__:
return self.Circolare(no, self.__archive__[no]['name'], self.__archive__[no]['date'], self.__archive__[no]['url'], self.__archive__[no]['restricted'], self.__archiveDir__, self.__archive__)
if noN < 1:
return 'Inesistente'
cList = self.getPages(1, _range=False)
cNos = [value.number for value in cList]
if no in cNos:
return [value for value in cList if value.number == no][0]
iterations = 0
page = 1
while True:
cNoN = [int(value.split(' ')[0]) for value in cNos]
last = cNoN[0]
first = cNoN[-1]
if first < noN and last > first:
return 'Inesistente'
if noN > last:
return 'Inesistente'
page = page + int((last - noN) / 10)
if str(noN) + ' bis' in cNos and not str(noN) in cNos:
page = page + 1
cList = self.getPages(page, _range=False)
cNos = [value.number for value in cList]
if no in cNos:
return [value for value in cList if value.number == no][0]
iterations = iterations + 1
if iterations == _timeout:
return 'Timeout'
def getFrom(self, startCircN, includeFirst=False):
startCircN = str(startCircN)
i = 1
pageList = []
while True:
page = self.getPages(i, _range=False)
cNos = [value.number for value in page]
maxC = int([value for value in cNos if value.isnumeric()][0])
if i == 1 and maxC < int(startCircN.split(' ')[0]) and includeFirst or i == 1 and maxC <= int(startCircN.split(' ')[0]) and not includeFirst:
return []
if startCircN in cNos:
if includeFirst:
page = page[:cNos.index(startCircN) + 1]
else:
page = page[:cNos.index(startCircN)]
pageList.append(page)
break
pageList.append(page)
i = i + 1
return [i for o in pageList for i in o]
class Circolare:
def __init__(self, number, name, date, url, restricted, archiveDir, archive):
self.number = number
self.name = name
self.date = date
self.url = url
self.restricted = restricted
self.__archiveDir__ = archiveDir
self.__archive__ = archive
self.__downloadInfo__ = None
def __saveArchive__(self):
with open(self.__archiveDir__ + '/archive.json', 'w') as file:
file.write(json.dumps(self.__archive__, indent=4, sort_keys=True))
def __convertToPng__(self, infile, outfile, poppler=None):
tmpFilesList = []
if poppler:
pages = pdf2image.convert_from_path(infile, poppler_path=poppler)
else:
pages = pdf2image.convert_from_path(infile)
for page in range(len(pages)):
pages[page].save(outfile + '-' + str(page+1) + '.png', 'PNG')
tmpFilesList.append(outfile + '-' + str(page+1) + '.png')
return tmpFilesList
def download(self, path=None, pngConvert=False, docConvert=False, keepDoc=False, poppler=None):
fileList = {}
if self.__archive__ is not None:
path = self.__archive__['dir']
dirpath = path + '/' + self.number + '/'
try:
if self.__archive__[self.number]['attachments'] is None:
return {}
except:
pass
try:
for num, filename in enumerate(self.__archive__[self.number]['attachments']):
tmpFilename = self.__archive__[self.number]['attachments'][filename]['filename']
tmpExtension = tmpFilename.split('.')[-1]
if not os.path.exists(self.__archive__[self.number]['attachments'][filename]['filename']) or (tmpExtension == 'pdf' and pngConvert and not glob.glob(dirpath + tmpFilename.split('-')[1].split('.')[0] + '-*.png')) or (tmpExtension in ['doc', 'docx'] and docConvert and not glob.glob(dirpath + self.number + '-*.pdf')):
raise 'error'
except:
pass
else:
if not pngConvert and not docConvert:
return self.__archive__[self.number]['attachments']
try:
if any(not os.path.exists(i) for o in [self.__archive__[self.number]['attachments'][f]['files'] for f in self.__archive__[self.number]['attachments']] for i in o):
raise 'error'
except:
pass
else:
return self.__archive__[self.number]['attachments']
dirpath = path + '/' + self.number + '/'
dPage = requests.get(self.url).text
soup = bs4.BeautifulSoup(dPage, 'html.parser')
word = None
if docConvert:
word = win32com.client.Dispatch('Word.Application')
word.visible = 0
for num, value in enumerate(soup.find_all(class_='post-attachment')):
tmpFilesArray = {}
if not glob.glob(dirpath + self.number + '-' + str(num+1) + '.*') or self.__archive__ is None:
pdfPage = requests.get(value.find('a')['href'])
pdfFile = pdfPage.content
cExtension = pdfPage.url.split('.')[-1]
if not os.path.isdir(path + '/' + self.number):
os.mkdir(path + '/' + self.number)
file = open(dirpath + self.number + '-' + str(num+1) + '.' + cExtension, 'wb')
file.write(pdfFile)
file.close()
tmpFilesArray['name'] = value.find('a').text
tmpFilesArray['filename'] = dirpath + self.number + '-' + str(num+1) + '.' + cExtension
else:
tmpFilesArray['name'] = self.__archive__[self.number]['attachments'][str(num+1)]['name']
tmpFilesArray['filename'] = self.__archive__[self.number]['attachments'][str(num+1)]['filename']
cExtension = tmpFilesArray['filename'].split('.')[-1]
tmpFilesArray['files'] = glob.glob(dirpath + str(num+1) + '-*.png')
if not tmpFilesArray['files']:
del tmpFilesArray['files']
if pngConvert and cExtension == 'pdf' and not glob.glob(dirpath + str(num+1) + '-*.png'):
tmpFilesArray['files'] = self.__convertToPng__(tmpFilesArray['filename'], dirpath + str(num+1), poppler=poppler)
elif docConvert and cExtension in ['doc', 'docx'] and not os.path.exists(dirpath + self.number + '-' + str(num+1) + '.pdf'):
wb = word.Documents.Open(os.path.abspath(tmpFilesArray['filename']))
wb.SaveAs2(os.path.abspath(tmpFilesArray['filename'].replace('.' + cExtension, '') + '.pdf'), FileFormat=17)
wb.Close()
if not keepDoc:
os.remove(tmpFilesArray['filename'])
tmpFilesArray['filename'] = tmpFilesArray['filename'].replace('.' + cExtension, '') + '.pdf'
if pngConvert and not glob.glob(dirpath + str(num+1) + '-*.png'):
tmpFilesArray['files'] = self.__convertToPng__(tmpFilesArray['filename'], dirpath + str(num+1) , poppler=poppler)
fileList[str(num+1)] = tmpFilesArray
if word is not None:
word.Quit()
if not fileList:
fileList = None
if self.__archive__ is not None:
try:
self.__archive__[self.number]
except:
self.__archive__[self.number] = {
'name': self.name,
'date': self.date,
'url': self.url,
'restricted': self.restricted
}
if not 'attachments' in self.__archive__[self.number] or (not 'files' in self.__archive__[self.number]['attachments'] and pngConvert) or docConvert:
self.__archive__[self.number]['attachments'] = fileList
self.__saveArchive__()
self.__downloadInfo__ = fileList
return fileList
def delete(self, archive=True, files=True):
if files:
try:
if self.__downloadInfo__ is None:
self.__downloadInfo__ = self.__archive__[self.number]['attachments']
except:
pass
if self.__downloadInfo__ is not None:
dirpath = os.path.dirname(self.__downloadInfo__['1']['filename'])
if os.path.exists(dirpath):
for file in glob.glob(dirpath + '/*'):
os.remove(file)
os.rmdir(dirpath)
if self.__archive__ is not None:
del self.__archive__[self.number]['attachments']
self.__downloadInfo__ = None
try:
del self.__archive__[self.number]['attachments']
except:
pass
if archive and self.__archive__ is not None:
del self.__archive__[self.number]
if self.__archive__ is not None:
self.__saveArchive__()
| 13,739 | 4,017 |
from data.recipe import Recipe
from data import RecipeGroup
from typing import List
class RecipeIO:
"""
Controls saving and loading of recipes from the filesystem.
"""
@staticmethod
def save(recipe: Recipe) -> bool:
raise NotImplementedError()
@staticmethod
def save_completed(recipe: Recipe) -> bool:
raise NotImplementedError()
@staticmethod
def load(recipe_name: str) -> List[Recipe]:
raise NotImplementedError()
@staticmethod
def place_tombstone(recipe_group: RecipeGroup) -> bool:
raise NotImplementedError()
| 596 | 180 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: Alex
# @Date: 2015-11-16 19:15:59
# @Last Modified by: Alex
# @Last Modified time: 2015-12-28 22:28:23
from django.db import models
from Inventationery.core.models import TimeStampedModel
from Inventationery.apps.Customer.models import CustomerModel
from Inventationery.apps.Inventory.models import ItemModel, LocationModel
from Inventationery.apps.Payments.models import PaymentModel, PaymModeModel
from Inventationery.core.models import Countries
# Function: Get currency codes from Countries model
def Get_CurrencyCodes():
CurrencyCodes = Countries.objects.values('currency_code')
Codes_list = ()
for Currency in CurrencyCodes:
Codes_list.append()
# Function: Get new sequence number for Sales Order
def Get_SalesId():
prefix = 'OV-'
try:
last = SalesOrderModel.objects.latest('created')
except:
last = None
if last:
no = int(filter(unicode.isdigit, last.SalesId))
no = no + 1
return str(prefix + str(no).zfill(5))
else:
return str(prefix + str(1).zfill(5))
# Class: Model for Sales Order
# ---------------------------------------------------------------------------
class SalesOrderModel(TimeStampedModel):
# SALES TYPE OPTIONS
SALES_ORDER = 'SO'
RESTORED_ORDER = 'RO'
SALES_TYPE = (
(SALES_ORDER, 'Orden de venta'),
(RESTORED_ORDER, 'Orden devuelta'),
)
# SALES STATUS OPTIONS
OPEN = 'OPE'
# BACKORDER = 'BAC'
REDUCED = 'RED'
INVOICED = 'INV'
CASHED = 'CAS'
CANCELED = 'CAN'
REDUCED_CASHED = 'RCA'
SALES_STATUS = (
(OPEN, 'Abierta'),
# (BACKORDER, 'Back order'),
(REDUCED, 'Reducido'),
(INVOICED, 'Facturado'),
(CASHED, 'Cobrado'),
(CANCELED, 'Cancelado'),
(REDUCED_CASHED, 'Reducido/Cobrado'),
)
# DOCUMENT STATE OPTIONS
OPEN = 'Abierto'
PROCESS = 'Proceso'
CLOSED = 'Cerrado'
DOC_STATE = (
(OPEN, 'Abierto'),
(PROCESS, 'En proceso'),
(CLOSED, 'Cerrado'),
)
# DELIVERY MODE
HOME = 'HOM'
BRANCH = 'BRA'
DLV_MODE = (
(HOME, 'A domicilio'),
(BRANCH, 'En sucursal'),
)
SalesId = models.CharField(
max_length=45, default=Get_SalesId, unique=True)
SalesName = models.CharField(max_length=100)
SalesType = models.CharField(
max_length=50, choices=SALES_TYPE, default=SALES_ORDER)
SalesStatus = models.CharField(
max_length=100, default=OPEN, choices=SALES_STATUS)
# DocumentStatus //Pendiente
WorkerSalesPlacer = models.CharField(
max_length=100, blank=True, null=True) # DirParty-Name
LanguageCode = models.CharField(
max_length=5, default='es-mx') # DirParty-LanguageCode
DeliveryName = models.CharField(
max_length=200, blank=True, null=True) # CustModel-get_PrimaryAddress
DeliveryDate = models.DateField(blank=True, null=True)
ConfirmedDlv = models.DateField(blank=True, null=True)
DlvMode = models.CharField(max_length=20, default=HOME, choices=DLV_MODE)
CurrencyCode = models.CharField(
default='MXN',
max_length=3) # CustModel-CurrencyCode
# Catalogo de pagos a 30 dias
Payment = models.ForeignKey(PaymentModel, blank=True, null=True)
# Catalogo de tipo de pagos
PaymMode = models.ForeignKey(PaymModeModel, null=True, blank=True)
Remarks = models.TextField(
default=None, blank=True, null=True)
SubTotal = models.DecimalField(
max_digits=20, decimal_places=2, blank=True, null=True)
Total = models.DecimalField(
max_digits=20, decimal_places=2, blank=True, null=True)
Paid = models.DecimalField(
max_digits=20, decimal_places=2, blank=True, null=True)
Balance = models.DecimalField(
max_digits=20, decimal_places=2, blank=True, null=True)
Enabled = models.BooleanField(default=True)
DocumentState = models.CharField(
max_length=20, choices=DOC_STATE, default=CLOSED)
Customer = models.ForeignKey(CustomerModel, default=None)
Location = models.ForeignKey(LocationModel, blank=True, null=True)
def __unicode__(self):
return "{0}".format(self.SalesId)
# Class: Model for Sales Order
# ---------------------------------------------------------------------------
class SalesLineModel(TimeStampedModel):
# SALES STATUS OPTIONS
BACKORDER = 'BAC'
REDUCED = 'RED'
INVOICED = 'INV'
CASHED = 'CAS'
CANCELED = 'CAN'
SALES_STATUS = (
(BACKORDER, 'Back order'),
(REDUCED, 'Reducido'),
(INVOICED, 'Facturado'),
(CASHED, 'Cobrado'),
(CANCELED, 'Cancelado'),
)
ItemId = models.ForeignKey(ItemModel, blank=True, null=True)
ItemName = models.CharField(max_length=50, blank=True, null=True)
SalesQty = models.PositiveIntegerField(blank=True, null=True)
SalesUnit = models.CharField(max_length=10, blank=True, null=True)
SalesPrice = models.DecimalField(
blank=True, null=True, max_digits=10, decimal_places=2)
LineDisc = models.DecimalField(
max_digits=10, decimal_places=2, blank=True, null=True)
LinePercent = models.DecimalField(
max_digits=10, decimal_places=2, blank=True, null=True)
LineAmount = models.DecimalField(
max_digits=20, decimal_places=2, blank=True, null=True)
SalesLineStatus = models.CharField(max_length=100,
default=BACKORDER,
choices=SALES_STATUS,
blank=True,
null=True)
LineNum = models.PositiveSmallIntegerField(blank=True, null=True)
SalesOrder = models.ForeignKey(
SalesOrderModel, null=True, blank=True)
| 5,847 | 2,001 |
# Generated by Django 3.2.5 on 2021-07-13 09:12
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('account', '0002_auto_20210712_1851'),
]
operations = [
migrations.AlterField(
model_name='customuser',
name='identity_number',
field=models.CharField(max_length=11, unique=True, verbose_name='Kimlik Numarası'),
),
]
| 444 | 160 |
# for new opencv
#import os,sys
#os.chdir(os.path.expanduser('~/opencv-2.4.6.1/lib'))
#sys.path.append(os.path.expanduser('~/opencv-2.4.6.1/lib/python2.7/dist-packages'))
# before starting
#export PYTHONPATH=~/opencv-2.4.6.1/lib/python2.7/dist-packages
import os
#import cv
import cv2
import math
import argparse
import numpy as np
global inputParser # just a reminder, it's used as a global variable
global inputArgs # just a reminder, it's used as a global variable
def parseInput() :
global inputParser
global inputArgs
inputParser = argparse.ArgumentParser(description='Match features between two stereo images.')
inputParser.add_argument('-l', '--left', dest='left', action='store', default="", type=str, help='left image')
inputParser.add_argument('-r', '--right', dest='right', action='store', default="", type=str, help='right image')
inputParser.add_argument('-n', '--name', dest='name', action='store', default="fm_out", type=str, help='name of the current set (used to save output values)')
inputParser.add_argument('-f', '--feature', dest='feature', action='store', default="sift", type=str, help='feature to use: sift, surf, orb, brisk')
inputParser.add_argument('-m', '--match', dest='match', action='store', default="bf", type=str, help='match using: bf (bruteforce), flann')
inputParser.add_argument('-p', '--proportion', dest='proportion', action='store', default=0.25, type=float, help='Lowe\'s distance test ratio')
inputParser.add_argument('-s', '--stddev', dest='stddev', action='store', default=0.0, type=float, help='max standard deviation between angles of each point pair (stereo cheat, 0.0 don\'t; use, > 0.0 use this)')
inputParser.add_argument('-d', '--debug', action='store_true', help='debug output')
inputArgs = inputParser.parse_args()
def processInput() :
print ""
if inputArgs.left == "" or inputArgs.right == "":
print "Missing images!"
quit()
# here we go ...
# load image pair
img_l = cv2.imread(inputArgs.left)
img_r = cv2.imread(inputArgs.right)
if img_l == None or img_r == None:
print "Missing images!"
quit()
# we like them gray
gray_l = cv2.cvtColor(img_l, cv2.COLOR_BGR2GRAY)
gray_r = cv2.cvtColor(img_r, cv2.COLOR_BGR2GRAY)
# which decetor are we using
if inputArgs.feature == 'sift':
detector = cv2.SIFT()
norm = cv2.NORM_L2
elif inputArgs.feature == 'surf':
detector = cv2.SURF(800)
norm = cv2.NORM_L2
elif inputArgs.feature == 'orb':
detector = cv2.ORB(400)
norm = cv2.NORM_HAMMING
elif inputArgs.feature == 'brisk':
detector = cv2.BRISK()
norm = cv2.NORM_HAMMING
else:
print "Wrong feature detector!"
quit()
# how are we matching detected features
if inputArgs.match == 'bf':
matcher = cv2.BFMatcher(norm)
elif inputArgs.match == 'flann':
# borrowed from: https://github.com/Itseez
FLANN_INDEX_KDTREE = 1 # bug: flann enums are missing
FLANN_INDEX_LSH = 6
flann_params = []
if norm == cv2.NORM_L2:
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
else:
flann_params = dict(algorithm = FLANN_INDEX_LSH,
table_number = 6, # 12
key_size = 12, # 20
multi_probe_level = 1) #2
matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
print "Using: " + inputArgs.feature + " with " + inputArgs.match
print ""
print "detecting ..."
# find the keypoints and descriptors
kp_l, des_l = detector.detectAndCompute(gray_l, None)
kp_r, des_r = detector.detectAndCompute(gray_r, None)
print "Left image features: " + str(len(kp_l))
print "Right image features: " + str(len(kp_l))
print ""
# visualization
if inputArgs.debug == 1:
# left
img_l_tmp = img_l.copy()
#for kp in kp_l:
# x = int(kp.pt[0])
# y = int(kp.pt[1])
# cv2.circle(img_l_tmp, (x, y), 2, (0, 0, 255))
img_l_tmp = cv2.drawKeypoints(img_l_tmp, kp_l, img_l_tmp, (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DEFAULT)
head, tail = os.path.split(inputArgs.left)
cv2.imwrite(head+"/"+"feat_"+tail, img_l_tmp)
# right
img_r_tmp = img_r.copy()
#for kp in kp_r:
# x = int(kp.pt[0])
# y = int(kp.pt[1])
# cv2.circle(img_r_tmp, (x, y), 2, (0, 0, 255))
img_r_tmp = cv2.drawKeypoints(img_r_tmp, kp_r, img_r_tmp, (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DEFAULT)
head, tail = os.path.split(inputArgs.right)
cv2.imwrite(head+"/"+"feat_"+tail, img_r_tmp)
print "matching ..."
# match
raw_matches = matcher.knnMatch(des_l, trainDescriptors = des_r, k = 2)
print "Raw matches: " + str(len(raw_matches))
# filter matches: per Lowe's ratio test
filtered_matches = []
mkp_l = []
mkp_r = []
for m in raw_matches:
if len(m) == 2 and m[0].distance < m[1].distance * inputArgs.proportion:
filtered_matches.append(m)
mkp_l.append( kp_l[m[0].queryIdx] )
mkp_r.append( kp_r[m[0].trainIdx] )
print "Filtered matches: " + str(len(filtered_matches))
# visualization
if inputArgs.debug == 1:
# draw points
img_l_tmp = cv2.drawKeypoints(img_l_tmp, mkp_l, img_l_tmp, (255, 0, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
head, tail = os.path.split(inputArgs.left)
#cv2.imwrite(head+"/"+"feat_"+tail, img_l_tmp)
img_r_tmp = cv2.drawKeypoints(img_r_tmp, mkp_r, img_r_tmp, (255, 0, 0), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
head, tail = os.path.split(inputArgs.right)
#cv2.imwrite(head+"/"+"feat_"+tail, img_r_tmp)
# merge image side by side
h_l, w_l = img_l_tmp.shape[:2]
h_r, w_r = img_r_tmp.shape[:2]
img_tmp = np.zeros((max(h_l, h_l), w_r+w_r, 3), np.uint8)
img_tmp[:h_l, :w_l] = img_l_tmp
img_tmp[:h_r, w_l:w_l+w_r] = img_r_tmp
# draw lines
for m in filtered_matches:
cv2.line(img_tmp, (int(round(kp_l[m[0].queryIdx].pt[0])), int(round(kp_l[m[0].queryIdx].pt[1]))), (int(w_l + round(kp_r[m[0].trainIdx].pt[0])), int(round(kp_r[m[0].trainIdx].pt[1]))), (255, 0, 0), 1)
cv2.imwrite(inputArgs.name + "_features.jpg", img_tmp)
# filter matches: per direction (since it's a stereo pair, most of the points should have the same angle between them)
if inputArgs.stddev != 0.0:
ang_stddev = 360.0
stddev = 180.0
while abs(stddev) > inputArgs.stddev:
ang_stddev = stddev
raw_matches = [] # silly !!!
for m in filtered_matches: # silly !!!
raw_matches.append(m) # silly !!!
filtered_matches = []
mkp_l = []
mkp_r = []
ang = []
for m in raw_matches:
xDiff = kp_r[m[0].trainIdx].pt[0] - kp_l[m[0].queryIdx].pt[0] #p2.x - p1.x
yDiff = kp_r[m[0].trainIdx].pt[1] - kp_l[m[0].queryIdx].pt[1] #p2.y - p1.y
#print math.degrees(math.atan2(yDiff,xDiff))
ang.append(math.degrees(math.atan2(yDiff,xDiff)))
mean = np.mean(ang)
differences = [(value - mean)**2 for value in ang]
stddev = np.mean(differences) ** 0.5
#print mean
#print stddev
ang = []
for m in raw_matches:
xDiff = kp_r[m[0].trainIdx].pt[0] - kp_l[m[0].queryIdx].pt[0] #p2.x - p1.x
yDiff = kp_r[m[0].trainIdx].pt[1] - kp_l[m[0].queryIdx].pt[1] #p2.y - p1.y
ang_tmp = math.degrees(math.atan2(yDiff,xDiff))
if (mean + stddev) > (mean - stddev):
if (mean + stddev) >= ang_tmp and (mean - stddev) <= ang_tmp:
filtered_matches.append(m)
mkp_l.append( kp_l[m[0].queryIdx] )
mkp_r.append( kp_r[m[0].trainIdx] )
ang.append(math.degrees(math.atan2(yDiff,xDiff)))
else:
if (mean + stddev) <= ang_tmp and (mean - stddev) >= ang_tmp:
filtered_matches.append(m)
mkp_l.append( kp_l[m[0].queryIdx] )
mkp_r.append( kp_r[m[0].trainIdx] )
ang.append(math.degrees(math.atan2(yDiff,xDiff)))
##print np.median(ang)
mean = np.mean(ang)
differences = [(value - mean)**2 for value in ang]
stddev = np.mean(differences) ** 0.5
#print mean
#print stddev
if (abs(ang_stddev) - abs(stddev)) < 0.001:
break
print "Filtered matches cheat: " + str(len(filtered_matches))
mkp_pairs = zip(mkp_l, mkp_r)
file = open(inputArgs.name + "_kp.txt", "w")
for p in mkp_pairs:
# left x , left y ; right x , right y
file.write(str(p[0].pt[0]) + "," + str(p[0].pt[1]) + ";" + str(p[1].pt[0]) + "," + str(p[1].pt[1]) + "\n")
file.close()
# visualization
if inputArgs.debug == 1:
# draw lines
for m in filtered_matches:
cv2.line(img_tmp, (int(round(kp_l[m[0].queryIdx].pt[0])), int(round(kp_l[m[0].queryIdx].pt[1]))), (int(w_l + round(kp_r[m[0].trainIdx].pt[0])), int(round(kp_r[m[0].trainIdx].pt[1]))), (0, 255, 0), 1)
cv2.imwrite(inputArgs.name + "_features.jpg", img_tmp)
if __name__ == "__main__": # this is not a module
parseInput() # what do we have to do
processInput() # doing what we have to do
print "" # for estetic output
| 8,678 | 4,056 |
from lexer import (
TokenKeyword,
TokenOpenBrace,
TokenCloseBrace,
TokenOpenParen,
TokenCloseParen,
TokenSemicolon,
TokenAssignmentOperator,
TokenAdditionOperator,
TokenMultiplicationOperator,
TokenIncrementOperator,
TokenEqualityOperator,
TokenInequalityOperator,
TokenLogicalOperator,
TokenIdentifier,
TokenInteger,
TokenFloat,
TokenString,
)
class Int():
'''An integer literal'''
def __init__(self, value):
self.value = value
def __str__(self):
return "(Int {0})".format(self.value)
class Constant():
'''A constant value'''
def __init__(self, value):
self.value = value
def __str__(self):
return "(Constant {0})".format(self.value)
class Variable():
'''A variable'''
def __init__(self, value):
self.value = value
def __str__(self):
return "(Variable {0})".format(self.value)
class UnaryOperationExpression():
'''A prefix expression'''
def __init__(self, operator, operand):
self.operator = operator
self.operand = operand
def __str__(self):
return "(UnaryOp {0} {1})".format(self.operator.value, self.operand)
class BinaryOperationExpression():
''' An infix expression'''
def __init__(self, operator, lhs, rhs):
self.operator = operator
self.lhs = lhs
self.rhs = rhs
def __str__(self):
return "(BinaryOp {0} {1} {2})".format(self.operator.value, self.lhs, self.rhs)
class IfStatement():
''' An if statement'''
def __init__(self, condition, body, else_body):
self.condition = condition
self.body = body
self.else_body = else_body
def __str__(self):
return "(IfStatement {0} {1} {2})".format(self.condition, self.body, self.else_body)
class AssignmentStatement():
''' An assignment statement'''
def __init__(self, op, lhs, rhs):
self.op = op
self.lhs = lhs
self.rhs = rhs
def __str__(self):
return "(AssignmentStatement {0} {1} {2})".format(self.op.value, self.lhs, self.rhs)
def __repr__(self):
return str(self)
class ReturnStatement():
''' A return statement'''
def __init__(self, expression):
self.expression = expression
def __str__(self):
return "(ReturnStatement {0})".format(self.expression)
def __repr__(self):
return str(self)
class Argument():
''' A function argument'''
def __init__(self, type_name, name):
self.type_name = type_name
self.name = name
def __str__(self):
return "(Argument {0} {1})".format(self.type_name, self.name)
class Function():
''' A function contains a list of statements'''
def __init__(self, return_type, name, arguments=[], statements=[]):
self.return_type = return_type
self.name = name
self.arguments = arguments
self.statements = statements
def __str__(self):
return "(Function {0} {1} ({2}) {3})".format(
self.return_type,
self.name,
self.arguments,
str(self.statements))
class Call():
''' A function call contains a function name and a list of arguments'''
def __init__(self, name, arguments = []):
self.name = name
self.arguments = arguments
def __str__(self):
return "(Call {0} ({1}))".format(self.name, self.arguments)
class Program():
''' A Program contains a list of functions'''
def __init__(self, functions):
self.functions = functions
def __str__(self):
return "(Program {0})".format(str(self.functions))
def parse_constant(tokens):
if (isinstance(peek(tokens), TokenInteger)):
current_token = accept(TokenInteger, tokens)
node = Constant(Int(current_token.value))
elif (isinstance(peek(tokens), TokenFloat)):
current_token = accept(TokenFloat, tokens)
node = Constant(Float(current_token.value))
elif (isinstance(peek(tokens), TokenFloat)):
current_token = accept(TokenString, tokens)
node = Constant(current_token.value)
else:
node = None
return node
def parse_variable(tokens):
if isinstance(peek(tokens), TokenIdentifier):
current_token = tokens.pop(0)
node = Variable(current_token.value)
else:
node = None
return node
def parse_primary(tokens):
expr = parse_constant(tokens) or parse_variable(tokens)
if expr:
return expr
open_paren = accept(TokenOpenParen, tokens)
if open_paren:
expr = parse_expression(tokens)
expect(TokenCloseParen, tokens)
return expr
def parse_unary(tokens):
if any(isinstance(peek(tokens), x) for x in (
TokenLogicalOperator,
TokenAdditionOperator,
)):
operator = (accept(TokenLogicalOperator, tokens)
or accept(TokenAdditionOperator, tokens))
rhs = parse_unary(tokens)
return UnaryOperationExpression(operator, rhs)
else:
return parse_primary(tokens)
def parse_multiplication(tokens):
expr = parse_unary(tokens)
while isinstance(peek(tokens), TokenMultiplicationOperator):
operator = accept(TokenMultiplicationOperator, tokens)
rhs = parse_multiplication(tokens)
expr = BinaryOperationExpression(operator, expr, rhs)
return expr
def parse_addition(tokens):
expr = parse_multiplication(tokens)
while isinstance(peek(tokens), TokenAdditionOperator):
operator = accept(TokenAdditionOperator, tokens)
rhs = parse_addition(tokens)
expr = BinaryOperationExpression(operator, expr, rhs)
return expr
def parse_comparison(tokens):
expr = parse_addition(tokens)
while isinstance(peek(tokens), TokenInequalityOperator):
operator = accept(TokenInequalityOperator, tokens)
rhs = parse_comparison(tokens)
expr = BinaryOperationExpression(operator, expr, rhs)
return expr
def parse_equality(tokens):
expr = parse_comparison(tokens)
while isinstance(peek(tokens), TokenEqualityOperator):
operator = accept(TokenEqualityOperator, tokens)
rhs = parse_equality(tokens)
expr = BinaryOperationExpression(operator, expr, rhs)
next_token = peek(tokens)
return expr
def parse_expression(tokens):
return parse_equality(tokens)
def parse_return(tokens):
if accept_value(TokenKeyword, 'return', tokens):
expr = parse_expression(tokens)
expect(TokenSemicolon, tokens)
return ReturnStatement(expr)
def peek(tokens):
if len(tokens) < 1:
return None
else:
return tokens[0]
def accept(tok_type, tokens):
if isinstance(peek(tokens), tok_type):
return tokens.pop(0)
else:
return None
def accept_value(tok_type, tok_value, tokens):
if isinstance(peek(tokens), tok_type) and peek(tokens).value == tok_value:
return tokens.pop(0)
else:
return None
def expect(tok, tokens):
match = accept(tok, tokens)
if not match:
tok_instance = tok()
if hasattr(tok_instance, 'value'):
raise Exception("Expected token {0}".format(tok_instance.value))
else:
raise Exception("Expected token {0}".format(tok.__name__))
return match
def parse_assignment(tokens):
lhs = parse_variable(tokens)
if lhs:
if isinstance(peek(tokens), TokenAssignmentOperator):
op = accept(TokenAssignmentOperator, tokens)
rhs = parse_expression(tokens)
expect(TokenSemicolon, tokens)
return AssignmentStatement(op, lhs, rhs)
return None
def parse_initializing_assignment(tokens):
# handle initializing assignment statement
if accept(TokenKeyword, tokens):
if accept(TokenIdentifier, tokens):
if accept(TokenAssignmentOperator, tokens):
expr = parse_expression(tokens)
expect(TokenSemicolon, tokens)
return AssignmentStatement(expr)
def parse_function_call(tokens):
if accept(TokenOpenParen, tokens):
# handle function call
args = []
while not accept(TokenCloseParen, tokens):
args.append(parse_function_argument(tokens))
expect(TokenSemicolon, tokens)
return Call()
def parse_statement(tokens):
# Declarations
# int a;
# Assignment statements:
# a = 1;
# int a = 1;
# Return statements:
# return a;
# return a + 1;
# Other keyword statements:
# break;
# continue;
# check if first token is keyword or identifier name
stmt = (parse_return(tokens)
or parse_assignment(tokens)
or parse_init(tokens)
or parse_call(tokens))
return stmt
def parse_function_argument(tokens):
# does not handle * pointer yet
current_token = tokens.pop(0)
if not isinstance(current_token, TokenKeyword):
raise Exception("Expected argument type declaration")
type_name = current_token
if not(isinstance(peek(tokens), TokenIdentifier)):
raise Exception("Expected identifier for argument name")
name = parse_variable(tokens)
return Argument(type_name, name)
def parse_function_declaration(tokens):
current_token = tokens.pop(0)
if isinstance(current_token, TokenKeyword):
return_type = current_token
else:
raise Exception("Expected return type declaration")
current_token = tokens.pop(0)
if isinstance(current_token, TokenIdentifier):
name = current_token
else:
raise Exception("Expected identifier for function name")
current_token = tokens.pop(0)
args = []
if isinstance(current_token, TokenOpenParen):
while not isinstance(peek(tokens), TokenCloseParen):
args.append(parse_function_argument(tokens))
else:
raise Exception("Expected token (")
tokens.pop(0) # swallow )
statements = []
current_token = tokens.pop(0) # should be {
if isinstance(current_token, TokenOpenBrace):
while not isinstance(peek(tokens), TokenCloseBrace):
statements.append(parse_statement(tokens))
else:
raise Exception("Expected token {")
return Function(return_type, name, args, statements)
def parse(tokens):
functions = []
while len(tokens) > 0:
functions.append(parse_function(tokens))
return Program(functions)
| 10,472 | 3,042 |
# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Executes benchmark testing for 3D Unet model."""
# pylint: disable=line-too-long
from __future__ import print_function
import functools
import os
import time
from typing import Optional
from absl import flags
import tensorflow as tf # pylint: disable=g-bad-import-order
from official.benchmark import benchmark_wrappers
from official.benchmark import keras_benchmark
from official.benchmark import owner_utils
from official.vision.segmentation import unet_main as unet_training_lib
from official.vision.segmentation import unet_model as unet_model_lib
UNET3D_MIN_ACCURACY = 0.90
UNET3D_MAX_ACCURACY = 0.98
UNET_TRAINING_FILES = 'gs://mlcompass-data/unet3d/train_data/*'
UNET_EVAL_FILES = 'gs://mlcompass-data/unet3d/eval_data/*'
UNET_MODEL_CONFIG_FILE = 'gs://mlcompass-data/unet3d/config/unet_config.yaml'
FLAGS = flags.FLAGS
class Unet3DAccuracyBenchmark(keras_benchmark.KerasBenchmark):
"""Benchmark accuracy tests for UNet3D model in Keras."""
def __init__(self,
output_dir: Optional[str] = None,
root_data_dir: Optional[str] = None,
**kwargs):
"""A benchmark class.
Args:
output_dir: directory where to output e.g. log files
root_data_dir: directory under which to look for dataset
**kwargs: arbitrary named arguments. This is needed to make the
constructor forward compatible in case PerfZero provides more named
arguments before updating the constructor.
"""
flag_methods = [unet_training_lib.define_unet3d_flags]
# UNet3D model in Keras."""
self.training_file_pattern = UNET_TRAINING_FILES
self.eval_file_pattern = UNET_EVAL_FILES
# TODO(hongjunchoi): Create and use shared config file instead.
self.config_file = UNET_MODEL_CONFIG_FILE
super(Unet3DAccuracyBenchmark, self).__init__(
output_dir=output_dir, flag_methods=flag_methods)
def _set_benchmark_parameters(self, experiment_name):
"""Overrides training parameters for benchmark tests."""
FLAGS.model_dir = self._get_model_dir(experiment_name)
FLAGS.mode = 'train'
FLAGS.training_file_pattern = self.training_file_pattern
FLAGS.eval_file_pattern = self.eval_file_pattern
FLAGS.config_file = self.config_file
FLAGS.lr_init_value = 0.00005
FLAGS.lr_decay_rate = 0.5
FLAGS.epochs = 3
@benchmark_wrappers.enable_runtime_flags
def _run_and_report_benchmark(self,
experiment_name: str,
min_accuracy: float = UNET3D_MIN_ACCURACY,
max_accuracy: float = UNET3D_MAX_ACCURACY,
distribution_strategy: str = 'tpu',
epochs: int = 10,
steps: int = 0,
epochs_between_evals: int = 1,
dtype: str = 'float32',
enable_xla: bool = False,
run_eagerly: bool = False):
"""Runs and reports the benchmark given the provided configuration."""
params = unet_training_lib.extract_params(FLAGS)
strategy = unet_training_lib.create_distribution_strategy(params)
input_dtype = params.dtype
if input_dtype == 'float16' or input_dtype == 'bfloat16':
policy = tf.keras.mixed_precision.experimental.Policy(
'mixed_bfloat16' if input_dtype == 'bfloat16' else 'mixed_float16')
tf.keras.mixed_precision.experimental.set_policy(policy)
stats = {}
start_time_sec = time.time()
with strategy.scope():
unet_model = unet_model_lib.build_unet_model(params)
history = unet_training_lib.train(
params, strategy, unet_model,
functools.partial(unet_training_lib.get_train_dataset, params),
functools.partial(unet_training_lib.get_eval_dataset, params))
stats['accuracy_top_1'] = history.history['val_metric_accuracy'][-1]
stats['training_accuracy_top_1'] = history.history['metric_accuracy'][-1]
wall_time_sec = time.time() - start_time_sec
super(Unet3DAccuracyBenchmark, self)._report_benchmark(
stats,
wall_time_sec,
top_1_min=min_accuracy,
top_1_max=max_accuracy,
total_batch_size=params.train_batch_size)
def _get_model_dir(self, folder_name):
return os.path.join(self.output_dir, folder_name)
@owner_utils.Owner('tf-model-garden')
def benchmark_4x4_tpu_bf16(self):
"""Test Keras model with 4x4 TPU, fp16."""
experiment_name = 'benchmark_4x4_tpu_fp16'
self._setup()
self._set_benchmark_parameters(experiment_name)
self._run_and_report_benchmark(
experiment_name=experiment_name,
dtype='bfloat16',
distribution_strategy='tpu')
@owner_utils.Owner('tf-graph-compiler')
def benchmark_4x4_tpu_bf16_mlir(self):
"""Test Keras model with 4x4 TPU, fp16 and MLIR enabled."""
experiment_name = 'benchmark_4x4_tpu_fp16_mlir'
tf.config.experimental.enable_mlir_bridge()
self._setup()
self._set_benchmark_parameters(experiment_name)
self._run_and_report_benchmark(
experiment_name=experiment_name,
dtype='bfloat16',
distribution_strategy='tpu')
if __name__ == '__main__':
tf.test.main()
| 5,983 | 1,951 |
try:
import subprocess32 as sp
except ModuleNotFoundError:
import subprocess as sp
import shlex
def run_command(cmd):
p = sp.run(shlex.split(cmd), stdout=sp.PIPE, stderr=sp.PIPE)
return p.stdout, p.stderr
| 222 | 80 |
# -*- coding: utf-8 -*-
from typing import Any, Dict
from PIL import Image
from pygerber.parser.pillow.parser import DEFAULT_COLOR_SET_GREEN, ColorSet
from .parser.pillow.api import PillowProjectSpec, _render_file
def render_from_spec(spec: Dict[str, Any]) -> Image.Image:
"""Render 2D image from specfile alike dictionary.
:param spec: specfile parameters dictionary.
:type spec: Dict
:return: rendered and merged image.
:rtype: Image.Image
"""
return PillowProjectSpec(spec).render()
def render_from_yaml(file_path: str) -> Image.Image:
"""Render 2D image from specfile written in yaml.
:param file_path: yaml specfile path.
:type file_path: str
:return: rendered and merged image.
:rtype: Image.Image
"""
return PillowProjectSpec.from_yaml(file_path).render()
def render_from_json(file_path: str) -> Image.Image:
"""Render 2D image from specfile written in json.
:param file_path: json specfile path.
:type file_path: str
:return: rendered and merged image.
:rtype: Image.Image
"""
return PillowProjectSpec.from_json(file_path).render()
def render_from_toml(file_path: str) -> Image.Image:
"""Render 2D image from specfile written in toml.
:param file_path: toml specfile path.
:type file_path: str
:return: rendered and merged image.
:rtype: Image.Image
"""
return PillowProjectSpec.from_toml(file_path).render()
def render_file_and_save(
file_path: str,
save_path: str,
*,
dpi: int = 600,
colors: ColorSet = DEFAULT_COLOR_SET_GREEN,
ignore_deprecated: bool = True,
image_padding: int = 0,
):
"""Loads, parses, renders file from file_path and saves it in save_path.
:param file_path: Path to gerber file.
:type file_path: str
:param save_path: Path to save render.
:type save_path: str
:param dpi: DPI of output image, defaults to 600
:type dpi: int, optional
:param colors: Color set to use, defaults to DEFAULT_COLOR_SET_GREEN
:type colors: ColorSet, optional
:param ignore_deprecated: If true, causes parser to not stop when deprecated syntax is found, defaults to True
:type ignore_deprecated: bool, optional
:param image_padding: Additional pixel padding for image, defaults to 0
:type image_padding: int, optional
"""
image = render_file(
file_path,
dpi=dpi,
colors=colors,
ignore_deprecated=ignore_deprecated,
image_padding=image_padding,
)
image.save(save_path)
def render_file(
file_path: str,
*,
dpi: int = 600,
colors: ColorSet = DEFAULT_COLOR_SET_GREEN,
ignore_deprecated: bool = True,
image_padding: int = 0,
) -> Image.Image:
"""
Loads, parses and renders file from given path and returns its render as PIL.Image.Image.
:param file_path: Path to gerber file to render.
:type file_path: str
:param dpi: Output image DPI, defaults to 600
:type dpi: int, optional
:param colors: Color specification, defaults to DEFAULT_COLOR_SET_GREEN
:type colors: ColorSet, optional
:param ignore_deprecated: If false causes parser to stop when deprecated syntax is met, defaults to True
:type ignore_deprecated: bool, optional
:param image_padding: Additional image padding, defaults to 0
:type image_padding: int, optional
:return: Output image.
:rtype: Image.Image
"""
return _render_file(
file_path,
dpi,
colors,
ignore_deprecated,
image_padding,
)
| 3,556 | 1,159 |
from transformers import model_paths
def test_candy_model():
assert model_paths.CANDY_FAST_NEURAL_TRANSFER_MODEL == "models/candy.t7"
def test_feathers_model():
assert model_paths.FEATHERS_FAST_NEURAL_TRANSFER_MODEL == "models/feathers.t7"
def test_mosaic_model():
assert model_paths.MOSAIC_FAST_NEURAL_TRANSFER_MODEL == "models/mosaic.t7"
def test_the_scream_model():
assert model_paths.THE_SCREAM_FAST_NEURAL_TRANSFER_MODEL == "models/the_scream.t7"
def test_udnie_model():
assert model_paths.UDNIE_FAST_NEURAL_TRANSFER_MODEL == "models/udnie.t7"
def test_celeba_distill_model():
assert model_paths.CELEBA_DISTILL_ANIME_GAN == "models/celeba_distill.pt"
def test_face_paint_model():
assert model_paths.FACE_PAINT_ANIME_GAN == "models/face_paint_512_v2.pt"
def test_paprika_model():
assert model_paths.PAPRIKA_ANIME_GAN == "models/paprika.pt"
| 868 | 377 |
from imutils.perspective import four_point_transform
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import matplotlib as plt
import os
from itertools import groupby
def angleCos(p0, p1, p2):
d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float')
return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )
# the function that an pic, find sqaures and
#return the sqaures and return the x, y, contour, and parenthierachy
# sorted the list top to bottom and left to right
def findSquaresHier(img):
## rerun the whole process by draging the hierachy around
img= cv2.imread(img)
#img = cv2.GaussianBlur(img, (5, 5), 0)
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
squares = []
_retval, bin = cv2.threshold(imgGray, 0, 255, cv2.THRESH_BINARY_INV+ cv2.THRESH_OTSU)
bin, contours, _hierarchy = cv2.findContours(bin, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
hierarchy = _hierarchy[0]
sqaures =[]
contoursHier = []
for comp in zip(contours, hierarchy):
currentContour = comp[0]
currentHierachy = comp[1]
child = currentHierachy[2]
parent = currentHierachy[3]
#print currentContour , parent
#if currentHierachy[3] == -1 and currentHierachy[2] != -1 :
cnt_len = cv2.arcLength(currentContour, True)
currentContour = cv2.approxPolyDP(currentContour, 0.03*cnt_len, True)
if len(currentContour) == 4 and cv2.contourArea(currentContour) < 1024 and cv2.contourArea(currentContour) > 200 and cv2.isContourConvex(currentContour):
currentContour = currentContour.reshape(-1, 2)
max_cos = np.max([angleCos( currentContour[i], currentContour[(i+1) % 4], currentContour[(i+2) % 4] ) for i in xrange(4)])
if max_cos < 0.2:
(x, y, w, h) = cv2.boundingRect(currentContour)
squares.append(currentContour)
ar = w / float(h)
if (w > 22 and w<= 35 and h >22 and h <= 35 and ar >=0.8 and ar <=1.3 and y > 100 and x > 500):
pt =(x+ y+ w+h)
coord = x+y
cntHier = (y,x,coord, currentContour, parent)
contoursHier.append(cntHier)
questions = sorted(contoursHier, key=lambda x: (x[0], x[1]))
return questions
# create that will create bin for the squares and group them
# if they are betwen +/- y ranges
# input: array of the squares
# output: Question number, (y,x,cnt, hier)
def GroupSquaresByAxis(arr):
# Create the bin and get teh all the y values
_bins = []
yValues = []
for i in range(0, len(arr)):
a= arr[i][0]
yValues.append(a)
if len(_bins) ==0:
_bins.append(a+10)
elif a not in range(0, _bins[-1]+4):
_bins.append(a+10)
# digitize the y values using the the _bin array
inds = np.digitize(yValues,np.array(_bins),right=True)
# step six : zip the bin with the with the sorted the list from step three
#squaresWithInds =[]
squaresWithInds = zip(arr, inds)
# group the elements by the bins
squaresGroupedByInds=[]
for key,valuesiter in groupby(squaresWithInds, key=lambda s:s[1]):
somet = (key, list(v[0]for v in valuesiter))
squaresGroupedByInds.append(somet)
return squaresGroupedByInds
# function to select only two squares by questions:
#input: result of the GroupSquaresByAxis function
# output : Array of question, 2 questions+ hier
def selectTwoSquares(squaresGroupedByInds):
# for sqaures with Ind:
# if the number of hier -2 take the two element:
# If not :
twoSquares =[]
squareSize = len(squaresGroupedByInds)
#hier_ext = [0]* squareSize
#hier_int = [0]* squareSize
for i in range(len(squaresGroupedByInds)):
q =squaresGroupedByInds[i][0]
theArr = squaresGroupedByInds[i][1] # list of the array in the questions
arrSize = len(theArr)
theContours =[]
#print q, hier, y, x, pt
#print j
# if the number of array >2:
# if the total number of hier -1 is 2 take them.
# if the number hier(-1) is 1: check if the number of the inside is2 :if it is 2 then take them
# if the number of array is 2:
#check if different x: Add the array
# if the same x # mark the question as unsawerable:
# get the count number hier(-1)
#hier_ext = [0]* squareSize
hier_ext =0
hier_int =0
#hier_int = [0]* squareSize
for j in range(len(theArr)):
hier = theArr[j][4]
if hier ==-1 : hier_ext += 1
elif hier !=-1 : hier_int +=1
# get the count number hier(not -1)
#print hier_int
if arrSize ==3 : # check if the size is more than 2
if hier_ext !=2: # check if the ext size is 2
for j in theArr:
contour =j[3]
y = j[0]
x = j[1]
pt = j[2]
hier =j[4]
asso = (contour,x, hier)
if hier != -1 :theContours.append(asso)
#print theContours
da = (q,theContours)
twoSquares.append(da)
if hier_ext ==2: # check if the ext size is 2
for j in theArr:
contour =j[3]
y = j[0]
x = j[1]
pt = j[2]
hier =j[4]
asso = (contour,x, hier)
if hier == -1 :theContours.append(asso)
#print theContours
da = (q,theContours)
twoSquares.append(da)
elif arrSize == 4 : # check if the size is more than 2
for j in theArr:
contour =j[3]
y = j[0]
x = j[1]
pt = j[2]
hier =j[4]
asso = (contour,x, hier)
if hier == -1 :theContours.append(asso)
#print theContours
da = (q,theContours)
twoSquares.append(da)
elif arrSize == 2:
xvalue1 = theArr[0][1]
xvalue2 = theArr[1][1]
if abs(xvalue1 - xvalue2) < 6:
da = (q, [])
twoSquares.append(da)
if abs(xvalue1 - xvalue2) > 6:
for j in theArr:
contour =j[3]
y = j[0]
x = j[1]
pt = j[2]
hier =j[4]
asso = (contour, x,hier)
theContours.append(asso)
#print theContours
da = (q,theContours)
twoSquares.append(da)
return twoSquares
# create a function that will take image and ouput an array of answer:
def secondPageAnswers(img):
imgcv2 = cv2.imread(img)
imgGray = cv2.cvtColor(imgcv2, cv2.COLOR_BGR2GRAY)
paper = cv2.threshold(imgGray , 0, 255, cv2.THRESH_BINARY_INV+ cv2.THRESH_OTSU)[1]
#mask the image with zeros
mask = np.zeros(paper.shape, dtype="uint8")
mask = cv2.bitwise_and(paper, paper, mask=mask)
answers= []
filename, file_extension = os.path.splitext(img)
contractNumber = str((filename.split("-")[0]).strip())
# get the sorted questions
# to do: if the total numbers of questions is not equal to 32 then move mark the file as unreadable
# Squares Finder:
squareFind = findSquaresHier(img)
# group by Axis:
groupByAxis = GroupSquaresByAxis(squareFind)
#print len(groupByAxis)
# get only two squares per questions:
twoSquares = selectTwoSquares (groupByAxis)
#print twoSquares[1]
answers =[]
que = []
for (j,v) in twoSquares:
vSorted = sorted(v, key=lambda x: x[1])
siz = len(vSorted)
result =[]
bub =None
if siz ==2:
for (k,a) in enumerate(vSorted):
cnt = a[0]
x = a[1]
hier = a[2]
#print j,cnt,x
mask = np.zeros(paper.shape, dtype="uint8")
cv2.drawContours(mask, [cnt], -1, 255, -1)
mask = cv2.bitwise_and(paper, paper, mask=mask)
total = cv2.countNonZero(mask)
_result = (k,total)
#if bub is None or
result.append(_result)
#print result
##get the result
if abs(result[0][1]- result[1][1]) >50:
if result[0][1] > result[1][1]:
res ='Y'
YAnwser =(j,res)
answers.append(YAnwser)
else :
res ='N'
NAnwser =(j,res)
answers.append(NAnwser)
#print j, res
else:
NullAnswer =(j,"Null")
answers.append(NullAnswer)
else:
cant =(j, "cant answer")
answers.append(cant)
return answers
| 9,244 | 3,090 |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.11.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Shapelets and the Shapelet Transform with sktime
#
# Introduced in [1], a shapelet is a time series subsequences that is identified as being representative of class membership. Shapelets are a powerful approach for measuring _phase-independent_ similarity between time series; they can occur at any point within a series and offer _interpretable_ results for how matches occur. The original research extracted shapelets to build a decision tree classifier.
#
# The example below illustrates how leaf shape can be represented as a one-dimensional time series (blue line) to distinguish between two species.[2]
#
# <img src = "img/leaf_types.png">
# <img src = "img/verdena_shapelet.png">
#
# The highlighted red subsection of the time series (i.e., "subsequences") above is the shapelet that distinguishes *Verbena urticifolia* from *Urtica dioica*.
#
# ## The Shapelet Transform
#
# Much research emphasis has been placed on shapelet-based approaches for time series classification (TSC) since the original research was proposed. The current state-of-the-art for shapelets is the **shapelet transform** (ST) [3, 4]. The transform improves upon the original use of shapelets by separating shapelet extraction from the classification algorithm, allowing interpretable phase-independent classification of time series with any standard classification algorithm (such as random/rotation forest, neural networks, nearest neighbour classifications, ensembles of all, etc.). To facilitate this, rather than recursively assessing data for the best shapelet, the transform evaluates candidate shapelets in a single procedure to rank them based on information gain. Then, given a set of _k_ shapelets, a time series can be transformed into _k_ features by calculating the distance from the series to each shapelet. By transforming a dataset in this manner any vector-based classification algorithm can be applied to a shapelet-transformed time series problem while the interpretability of shapelets is maintained through the ranked list of the _best_ shapelets during transformation.
#
# Shapelets can provide interpretable results, as seen in the figure below:
#
# <img src = "img/leaves_shapelets.png">
#
# The shapelet has "discovered" where the two plant species distinctly differ. *Urtica dioica* has a stem that connects to the leaf at almost 90 degrees, whereas the stem of *Verbena urticifolia* connects to the leaf at a wider angle.
#
# Having found shapelet, its distance to the nearest matching subsequence in all objects in the database can be recorded. Finally, a simple decision tree classifier can be built to determine whether an object $Q$ has a subsequence within a certain distance from shapelet $I$.
#
# <img src = "img/shapelet_classifier.png">
#
# #### References
# [1] Ye, Lexiang, and Eamonn Keogh. "Time series shapelets: a novel technique that allows accurate, interpretable and fast classification." Data mining and knowledge discovery 22, no. 1-2 (2011): 149-182.
#
# [2] Ye, Lexiang, and Eamonn Keogh. "Time series shapelets: a new primitive for data mining." In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 947-956. 2009.
#
# [3] Lines, Jason, Luke M. Davis, Jon Hills, and Anthony Bagnall. "A shapelet transform for time series classification." In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 289-297. ACM, 2012.
#
# [4] Hills, Jon, Jason Lines, Edgaras Baranauskas, James Mapp, and Anthony Bagnall. "Classification of time series by shapelet transformation." Data Mining and Knowledge Discovery 28, no. 4 (2014): 851-881.
#
# [5] Bostrom, Aaron, and Anthony Bagnall. "Binary shapelet transform for multiclass time series classification." In Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXII, pp. 24-46. Springer, Berlin, Heidelberg, 2017.
#
# ## Example: The Shapelet Transform in sktime
#
# The following workbook demonstrates a full workflow of using the shapelet transform in `sktime` with a `scikit-learn` classifier with the [OSU Leaf](http://www.timeseriesclassification.com/description.php?Dataset=OSULeaf) dataset, which consists of one dimensional outlines of six leaf classes: *Acer Circinatum*, *Acer Glabrum*, *Acer Macrophyllum*, *Acer Negundo*, *Quercus Garryana* and *Quercus Kelloggii*.
# + tags=[]
from sktime.datasets import load_osuleaf
from sktime.transformations.panel.shapelets import ContractedShapeletTransform
train_x, train_y = load_osuleaf(split="train", return_X_y=True)
test_x, test_y = load_osuleaf(split="test", return_X_y=True)
# + tags=[]
# How long (in minutes) to extract shapelets for.
# This is a simple lower-bound initially;
# once time is up, no further shapelets will be assessed
time_contract_in_mins = 1
# The initial number of shapelet candidates to assess per training series.
# If all series are visited and time remains on the contract then another
# pass of the data will occur
initial_num_shapelets_per_case = 10
# Whether or not to print on-going information about shapelet extraction.
# Useful for demo/debugging
verbose = 2
st = ContractedShapeletTransform(
time_contract_in_mins=time_contract_in_mins,
num_candidates_to_sample_per_case=initial_num_shapelets_per_case,
verbose=verbose,
)
st.fit(train_x, train_y)
# + tags=[]
# %matplotlib inline
import matplotlib.pyplot as plt
# for each extracted shapelet (in descending order of quality/information gain)
for s in st.shapelets[0:5]:
# summary info about the shapelet
print(s)
# plot the series that the shapelet was extracted from
plt.plot(train_x.iloc[s.series_id, 0], "gray")
# overlay the shapelet onto the full series
plt.plot(
list(range(s.start_pos, (s.start_pos + s.length))),
train_x.iloc[s.series_id, 0][s.start_pos : s.start_pos + s.length],
"r",
linewidth=3.0,
)
plt.show()
# + tags=[]
# for each extracted shapelet (in descending order of quality/information gain)
for i in range(0, min(len(st.shapelets), 5)):
s = st.shapelets[i]
# summary info about the shapelet
print("#" + str(i) + ": " + str(s))
# overlay shapelets
plt.plot(
list(range(s.start_pos, (s.start_pos + s.length))),
train_x.iloc[s.series_id, 0][s.start_pos : s.start_pos + s.length],
)
plt.show()
# + tags=[]
import time
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sktime.datasets import load_osuleaf
train_x, train_y = load_osuleaf(split="train", return_X_y=True)
test_x, test_y = load_osuleaf(split="test", return_X_y=True)
# example pipeline with 1 minute time limit
pipeline = Pipeline(
[
(
"st",
ContractedShapeletTransform(
time_contract_in_mins=time_contract_in_mins,
num_candidates_to_sample_per_case=10,
verbose=False,
),
),
("rf", RandomForestClassifier(n_estimators=100)),
]
)
start = time.time()
pipeline.fit(train_x, train_y)
end_build = time.time()
preds = pipeline.predict(test_x)
end_test = time.time()
print("Results:")
print("Correct:")
correct = sum(preds == test_y)
print("\t" + str(correct) + "/" + str(len(test_y)))
print("\t" + str(correct / len(test_y)))
print("\nTiming:")
print("\tTo build: " + str(end_build - start) + " secs")
print("\tTo predict: " + str(end_test - end_build) + " secs")
# -
| 7,747 | 2,398 |
from __future__ import print_function
import unittest
import six
from pychemy.inchi import InChI
class InchiTest(unittest.TestCase):
def test_unicode_inchi(self):
inchi_str = six.u('InChI=1S/C14H18O8/c1-20-9-4-7(5-15)2-3-8(9)21-14-13(19)12(18)11(17)10(6-16)22-14/h2-5,10-14,16-19H,6H2,1H3/t10-,11-,12+,13-,14-/m1/s1')
inchi = InChI(inchi_str)
formula = inchi.formula
self.assertEqual(formula.formula, 'C14H18O8')
| 435 | 245 |
from abc import *
from collections.abc import Iterable
from enum import Enum
class Individual(metaclass=ABCMeta):
""" Collects information about one point in design space. """
class State(Enum):
EMPTY = 0
IN_PROGRESS = 1
EVALUATED = 2
FAILED = 3
@classmethod
def to_string(cls, state):
if state == cls.State.EMPTY:
return 'empty'
if state == cls.State.IN_PROGRESS:
return 'in_progress'
if state == cls.State.EVALUATED:
return 'evaluated'
if state == cls.State.FAILED:
return 'failed'
counter: int = 0
def __init__(self, vector: list = [], features=dict()):
self.id = Individual.counter
Individual.counter += 1
self.vector = vector.copy()
self.costs = []
self.costs_signed = []
self.state = self.State.EMPTY
self.population_id = -1
self.algorithm_id = 0
self.parents = []
self.children = []
self.features = dict()
self.features["start_time"] = 0.0
self.features["finish_time"] = 0.0
self.features["feasible"] = 0.0 # the distance from the feasibility region in min norm, its an index, not a
self.features["precision"] = 7 # the default value of the considered decimals
for key, value in features.items():
if not key in self.features:
self.features[key] = value
self.custom = {}
def calc_signed_costs(self, p_signs):
"""
This function calculates the signed costs for every vector and insert the feasibility after
:return:
"""
self.costs_signed = list(map(lambda x, y: x * round(y, ndigits=self.features["precision"]), p_signs, self.costs))
self.costs_signed.append(not self.features["feasible"])
def __repr__(self):
""" :return: [vector[p1, p2, ... pn]; costs[c1, c2, ... cn]] """
string = "vector: ["
for i, number in enumerate(self.vector):
string += str(number)
if i < len(self.vector) - 1:
string += ", "
string = string[:len(string) - 1]
string += "]"
string += "; costs:["
for i, number in enumerate(self.costs):
string += str(number)
if i < len(self.costs) - 1:
string += ", "
string += "]"
if len(self.custom) > 0:
string += "; custom:["
for key, value in self.custom.items():
string += "'{}': {}".format(key, value)
if i < len(self.custom) - 1:
string += ", "
string += "]"
if len(self.features) > 0:
string += "; features:["
for key, value in self.features.items():
string += "'{}': {}".format(key, value)
if i < len(self.features) - 1:
string += ", "
string += "]"
# string += "; state: '{}', ".format(Individual.State.to_string(self.state))
# string += "; info: {}, ".format(self.info)
return string
def __eq__(self, other):
if self.costs_signed == []:
diff = set(self.vector) - set(other.vector)
return diff == set()
else:
diff = set(self.costs_signed) - set(other.costs_signed)
return diff == set()
def __hash__(self):
#return id(self)
return hash(tuple(self.vector))
def sync(self, individual):
self.vector = individual.vector
self.costs = individual.costs
self.costs_signed = individual.costs_signed
self.state = individual.state
self.population_id = individual.population_id
self.algorithm_id = individual.algorithm_id
self.parents = individual.parents
self.children = individual.children
self.features = individual.features
self.custom = individual.custom
def to_dict(self):
output = {'id': self.id,
'vector': list(self.vector),
'costs': list(self.costs),
'costs_signed': self.costs_signed,
'state': self.to_string(self.state),
'population_id': self.population_id,
'algorithm_id': self.algorithm_id,
'custom': self.custom,
'features': self.features}
parents = []
for parent in self.parents:
parents.append(self._replace_individual_id(parent))
output['parents'] = parents
children = []
for child in self.children:
children.append(self._replace_individual_id(child))
output['children'] = children
features = dict()
for key, value in self.features.items():
features[key] = self._replace_individual_id(value)
output['features'] = features
return output
def _replace_individual_id(self, value):
if isinstance(value, Iterable):
val = []
for item in value:
val.append(self._replace_individual_id(item))
return val
elif isinstance(value, Individual):
return value.id
else:
return value
@staticmethod
def from_dict(dictionary):
individual = Individual()
individual.id = dictionary['id']
individual.vector = dictionary['vector']
individual.costs = dictionary['costs']
individual.state = dictionary['state']
individual.costs_signed = dictionary['costs_signed']
individual.population_id = dictionary['population_id']
individual.algorithm_id = dictionary['algorithm_id']
individual.custom = dictionary['custom']
individual.features = dictionary['features']
# for feature in individual.features.items():
# key = 'feature_' + feature[0]
# if isinstance(feature[1], Iterable):
# value = []
# for item in feature[1]:
# if isinstance(item, Individual):
# value.append(item.id)
# else:
# value.append(item)
# else:
# value = feature[1]
return individual
| 6,332 | 1,785 |
class PartialFailure(Exception):
"""
Error indicating either send_messages or delete_messages API call failed partially
"""
def __init__(self, result, *args):
self.success_count = len(result['Successful'])
self.failure_count = len(result['Failed'])
self.result = result
super().__init__(*args)
| 343 | 92 |
from block import *
from logging import ERROR, WARN, INFO, DEBUG
class duplicates_query(Block):
def on_load(self, config):
self.add_port("query", Port.QUERY, Port.UNNAMED, [])
self.log(INFO, "Duplicate-query block loaded")
def do_task(self):
yield
log = Log()
res = self.query("query", log).log
for i in range(len(res["hash"])):
self.log(INFO, "%s: " % res["hash"][i])
for f in res["files"][i]:
self.log(INFO, "\t%s" % f) | 471 | 171 |
# Copyright (C) 2015 Hewlett-Packard Development Company, L.P.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import os
import fixtures
import voluptuous
import yaml
from nodepool import config as nodepool_config
from nodepool import provider_manager
from nodepool import tests
class TestShadeIntegration(tests.IntegrationTestCase):
def _cleanup_cloud_config(self):
os.remove(self.clouds_path)
def _use_cloud_config(self, config):
config_dir = fixtures.TempDir()
self.useFixture(config_dir)
self.clouds_path = os.path.join(config_dir.path, 'clouds.yaml')
self.useFixture(fixtures.MonkeyPatch(
'os_client_config.config.CONFIG_FILES',
[self.clouds_path]))
with open(self.clouds_path, 'w') as h:
yaml.safe_dump(config, h)
self.addCleanup(self._cleanup_cloud_config)
def test_nodepool_provider_config_bad(self):
# nodepool doesn't support clouds.yaml-less config anymore
# Assert that we get a nodepool error and not an os-client-config
# error.
self.assertRaises(
voluptuous.MultipleInvalid,
self.setup_config, 'integration_noocc.yaml')
def test_nodepool_occ_config(self):
configfile = self.setup_config('integration_occ.yaml')
auth_data = {'username': 'os_real',
'project_name': 'os_real',
'password': 'os_real',
'auth_url': 'os_real'}
occ_config = {'clouds': {'real-cloud': {'auth': auth_data}}}
self._use_cloud_config(occ_config)
config = nodepool_config.loadConfig(configfile)
self.assertIn('real-provider', config.providers)
pm = provider_manager.get_provider(
config.providers['real-provider'], use_taskmanager=False)
pm.start()
self.assertEqual(pm._client.auth, auth_data)
def test_nodepool_occ_config_reload(self):
configfile = self.setup_config('integration_occ.yaml')
auth_data = {'username': 'os_real',
'project_name': 'os_real',
'password': 'os_real',
'auth_url': 'os_real'}
occ_config = {'clouds': {'real-cloud': {'auth': auth_data}}}
self._use_cloud_config(occ_config)
pool = self.useNodepool(configfile, watermark_sleep=1)
pool.updateConfig()
provider_manager = pool.config.provider_managers['real-provider']
self.assertEqual(provider_manager._client.auth, auth_data)
# update the config
auth_data['password'] = 'os_new_real'
os.remove(self.clouds_path)
with open(self.clouds_path, 'w') as h:
yaml.safe_dump(occ_config, h)
pool.updateConfig()
provider_manager = pool.config.provider_managers['real-provider']
self.assertEqual(provider_manager._client.auth, auth_data)
| 3,405 | 1,028 |
from .bboxConstant import BboxConstant
import netaddr as net
import socket
class BboxAPIUrl:
"""
Used to handle API url
"""
API_PREFIX = "api/v1"
def __init__(self, api_class, api_method, ip=BboxConstant.DEFAULT_LOCAL_IP):
"""
:param api_class: string
:param api_method: string
:param ip: string
:return:
"""
self.api_class = api_class
self.api_method = api_method
self.ip = ip
self.authentication_type = None
self.url = None
self.build_url_request()
def get_api_class(self):
return self.api_class
def get_api_method(self):
return self.api_method
def get_ip(self):
return self.ip
def get_url(self):
return self.url
def get_authentication_type(self):
return self.authentication_type
def set_api_name(self, api_class, api_method):
self.api_class = api_class
self.api_method = api_method
self.build_url_request()
def build_url_request(self):
"""
Build the url to use for making a call to the Bbox API
:return: url string
"""
# Check if the ip is LAN or WAN
if net.IPAddress(socket.gethostbyname(self.ip)).is_private():
url = BboxConstant.DEFAULT_BBOX_URL
self.authentication_type = BboxConstant.AUTHENTICATION_TYPE_LOCAL
else:
url = "https://{}:{}".format(self.ip,
BboxConstant.DEFAULT_REMOTE_PORT)
self.authentication_type = BboxConstant.AUTHENTICATION_TYPE_REMOTE
if self.api_class is None:
url = "{}/{}".format(url, self.API_PREFIX)
else:
url = "{}/{}/{}".format(url, self.API_PREFIX, self.api_class)
if self.api_method is None:
self.url = url
else:
self.url = "{}/{}".format(url, self.api_method)
| 1,944 | 610 |
from tletools import TLE
import matplotlib.pyplot as plt
import numpy as np
import math
from math import *
import sys
import datetime as dt
import tleToKeplerianElements as ttke
# uses TLE to return the current distance of the satellite to the focus
def distanceToCentralBody(tle, curr_trueA):
lan, argp, inc, ecc, n, M, a, E, v = ttke.tleToKepler(tle)
eccA = atan2(sqrt(1 - ecc ** 2) * sin(curr_trueA), ecc + cos(curr_trueA))
eccA = eccA % (2 * pi)
r = a * (1 - ecc * cos(eccA))
return r
# calculates the orbital plane cartesian coordinates for one point in time
def getOrbitalCartesianCoords(tle, curr_trueA):
r = distanceToCentralBody(tle, curr_trueA)
x = r * cos(curr_trueA)
y = r * sin(curr_trueA)
return x, y | 753 | 273 |
import re
import json
import random
from duorequest import DuoRequest
class DuoVoice():
def __init__(self, session, langData):
self._lang_data = langData
self._user_session = session
self._tts_voices = None
self._cloudfront_server_url = None
self._homepage_text = None
self.voice_url_dict = None
#TODO: Get a faster way of getting the urls
def get_audio_url(self, word, language_abbr=None, rand=True, voice=None):
# Check word is in vocab
if word is None:
raise Exception('A word must be specified to use this function')
word = word.lower()
# Get default language abbr
if not language_abbr:
language_abbr = list(self._lang_data.keys())[0]
if self.voice_url_dict is None or language_abbr not in self.voice_url_dict:
self._populate_voice_url_dictionary(language_abbr)
# If no audio exists for a word, return None
if word not in self.voice_url_dict[language_abbr]:
return None
# Get word audio links
word_links = list(self.voice_url_dict[language_abbr][word])
# If a voice is specified, get that one or None
if voice:
for word_link in word_links:
if "/{}/".format(voice) in word_link:
return word_link
return None
# If random, shuffle
if rand:
return random.choice(word_links)
return word_links[0]
@property
def _homepage(self):
if self._homepage_text:
return self._homepage_text
homepage_url = "https://www.duolingo.com"
request = DuoRequest.do_request(homepage_url, self._user_session)
self._homepage_text = request.text
return self._homepage
@property
def _cloudfront_server(self):
if self._cloudfront_server_url:
return self._cloudfront_server_url
server_list = re.search('//.+\.cloudfront\.net', self._homepage)
self._cloudfront_server_url = "https:{}".format(server_list.group(0))
return self._cloudfront_server_url
def _populate_voice_url_dictionary(self, lang_abbr):
if self.voice_url_dict is None:
self.voice_url_dict = {}
self.voice_url_dict[lang_abbr] = {}
# Get skill IDs
skill_ids = []
for skill in self._lang_data[lang_abbr]['skills']:
skill_ids.append(skill['id'])
# Scrape all sessions and create voice url dictionary
for skill_id in skill_ids:
req_data = {
"fromLanguage": "en" if lang_abbr != "en" else "de",
"learningLanguage": lang_abbr,
"challengeTypes": ["definition", "translate"],
"skillId": skill_id,
"type": "SKILL_PRACTICE",
"juicy": True,
"smartTipsVersion": 2
}
resp = DuoRequest.do_request("https://www.duolingo.com/2017-06-30/sessions", self._user_session, req_data)
if resp.status_code != 200:
continue
resp_data = resp.json()
for challenge in resp_data['challenges']:
self._add_to_voice_url_dict(
lang_abbr, challenge['prompt'], challenge['tts'])
if challenge.get("metadata") and challenge['metadata'].get("non_character_tts"):
for word, url in challenge['metadata']['non_character_tts']['tokens'].items():
self._add_to_voice_url_dict(lang_abbr, word, url)
for token in challenge['tokens']:
if token.get("tts") and token.get("value"):
self._add_to_voice_url_dict(
lang_abbr, token['value'], token['tts'])
def _add_to_voice_url_dict(self, lang_abbr, word, url):
word = word.lower()
if word not in self.voice_url_dict[lang_abbr]:
self.voice_url_dict[lang_abbr][word] = set()
self.voice_url_dict[lang_abbr][word].add(url)
| 4,091 | 1,205 |
# -*- coding: UTF-8 -*-
"""
Entry point logic for available backend worker tasks
"""
from celery import Celery
from vlab_api_common import get_task_logger
from vlab_onefs_api.lib import const
from vlab_onefs_api.lib.worker import vmware, setup_onefs
app = Celery('onefs', backend='rpc://', broker=const.VLAB_MESSAGE_BROKER)
@app.task(name='onefs.show', bind=True)
def show(self, username, txn_id):
"""Obtain basic information about onefs
:Returns: Dictionary
:param username: The name of the user who wants info about their default gateway
:type username: String
:param txn_id: A unique string supplied by the client to track the call through logs
:type txn_id: String
"""
logger = get_task_logger(txn_id=txn_id, task_id=self.request.id, loglevel=const.VLAB_ONEFS_LOG_LEVEL.upper())
resp = {'content' : {}, 'error': None, 'params': {}}
logger.info('Task starting')
try:
info = vmware.show_onefs(username)
except ValueError as doh:
logger.error('Task failed: {}'.format(doh))
resp['error'] = '{}'.format(doh)
else:
logger.info('Task complete')
resp['content'] = info
return resp
@app.task(name='onefs.create', bind=True)
def create(self, username, machine_name, image, front_end, back_end, ram, cpu_count, txn_id):
"""Deploy a new OneFS node
:Returns: Dictionary
:param username: The name of the user who wants to create a new default gateway
:type username: String
:param machine_name: The name of the new OneFS node
:type machine_name: String
:param image: The image version of OneFS to create
:type image: String
:param front_end: The network to hook up the external network to
:type front_end: String
:param back_end: The network to hook the internal network to
:type back_end: String
:param ram: The number of GB of memory to provision the node with
:type ram: Integer
:param cpu_count: The number of CPU cores to allocate to the vOneFS node
:type cpu_count: Integer
:param txn_id: A unique string supplied by the client to track the call through logs
:type txn_id: String
"""
logger = get_task_logger(txn_id=txn_id, task_id=self.request.id, loglevel=const.VLAB_ONEFS_LOG_LEVEL.upper())
resp = {'content' : {}, 'error': None, 'params': {}}
logger.info('Task starting')
try:
resp['content'] = vmware.create_onefs(username, machine_name, image, front_end, back_end, ram, cpu_count, logger)
except ValueError as doh:
logger.error('Task failed: {}'.format(doh))
resp['error'] = '{}'.format(doh)
logger.info('Task complete')
return resp
@app.task(name='onefs.delete', bind=True)
def delete(self, username, machine_name, txn_id):
"""Destroy a OneFS node
:Returns: Dictionary
:param username: The name of the user who wants to create a new default gateway
:type username: String
:param machine_name: The name of the instance of onefs
:type machine_name: String
:param txn_id: A unique string supplied by the client to track the call through logs
:type txn_id: String
"""
logger = get_task_logger(txn_id=txn_id, task_id=self.request.id, loglevel=const.VLAB_ONEFS_LOG_LEVEL.upper())
resp = {'content' : {}, 'error': None, 'params': {}}
logger.info('Task starting')
try:
vmware.delete_onefs(username, machine_name, logger)
except ValueError as doh:
logger.error('Task failed: {}'.format(doh))
resp['error'] = '{}'.format(doh)
else:
logger.info('Task complete')
return resp
@app.task(name='onefs.image', bind=True)
def image(self, txn_id):
"""Obtain the available OneFS images/versions that can be deployed
:Returns: Dictionary
:param username: The name of the user who wants to create a new default gateway
:type username: String
:param txn_id: A unique string supplied by the client to track the call through logs
:type txn_id: String
"""
logger = get_task_logger(txn_id=txn_id, task_id=self.request.id, loglevel=const.VLAB_ONEFS_LOG_LEVEL.upper())
resp = {'content' : {}, 'error': None, 'params': {}}
logger.info('Task starting')
resp['content'] = {'image': vmware.list_images()}
logger.info('Task complete')
return resp
@app.task(name='onefs.config', bind=True)
def config(self, cluster_name, name, username, version, int_netmask, int_ip_low,
int_ip_high, ext_netmask, ext_ip_low, ext_ip_high, gateway, dns_servers,
encoding, sc_zonename, smartconnect_ip, join_cluster, compliance, txn_id):
"""Turn a blank OneFS node into a usable device
:Returns: Dictionary
:param cluster_name: The name of the OneFS cluster
:type cluster_name: String
:param name: The name of the OneFS node
:type name: String
:param int_netmask: The subnet mask for the internal OneFS network
:type int_netmask: String
:param int_ip_low: The smallest IP to assign to an internal NIC
:type int_ip_low: String (IPv4 address)
:param int_ip_high: The largest IP to assign to an internal NIC
:type int_ip_high: String (IPv4 address)
:param ext_ip_low: The smallest IP to assign to an external/public NIC
:type ext_ip_low: String (IPv4 address)
:param ext_ip_high: The largest IP to assign to an external/public NIC
:type ext_ip_high: String (IPv4 address)
:param gateway: The IP address for the default gateway
:type gateway: String (IPv4 address)
:param dns_servers: A common separated list of IPs of the DNS servers to use
:type dns_servers: String
:param encoding: The filesystem encoding to use.
:type encoding: String
:param sc_zonename: The SmartConnect Zone name to use. Skipped if None.
:type sc_zonename: String
:param smartconnect_ip: The IPv4 address to use as the SIP
:type smartconnect_ip: String (IPv4 address)
:param join_cluster: Add the node to an existing cluster
:type join_cluster: Boolean
:param compliance: Set to True when creating a Compliance mode cluster
:type compliance: Boolean
:param txn_id: A unique string supplied by the client to track the call through logs
:type txn_id: String
"""
logger = get_task_logger(txn_id=txn_id, task_id=self.request.id, loglevel=const.VLAB_ONEFS_LOG_LEVEL.upper())
resp = {'content' : {}, 'error': None, 'params': {}}
logger.info('Task starting')
nodes = vmware.show_onefs(username)
node = nodes.get(name, None)
if not node:
error = "No node named {} found".format(name)
resp['error'] = error
logger.error(error)
return resp
elif node['meta']['configured']:
error = "Cannot configure a node that's already configured"
resp['error'] = error
logger.error(error)
else:
# Lets set it up!
logger.info('Found node')
console_url = node['console']
if join_cluster:
logger.info('Joining node to cluster {}'.format(cluster_name))
setup_onefs.join_existing_cluster(console_url, cluster_name, compliance, logger)
else:
logger.info('Setting up new cluster named {}'.format(cluster_name))
setup_onefs.configure_new_cluster(version=version,
console_url=console_url,
cluster_name=cluster_name,
int_netmask=int_netmask,
int_ip_low=int_ip_low,
int_ip_high=int_ip_high,
ext_netmask=ext_netmask,
ext_ip_low=ext_ip_low,
ext_ip_high=ext_ip_high,
gateway=gateway,
dns_servers=dns_servers,
encoding=encoding,
sc_zonename=sc_zonename,
smartconnect_ip=smartconnect_ip,
compliance=compliance,
logger=logger)
node['meta']['configured'] = True
vmware.update_meta(username, name, node['meta'])
logger.info('Task complete')
return resp
@app.task(name='onefs.modify_network', bind=True)
def modify_network(self, username, machine_name, new_network, txn_id):
"""Change the network an OneFS node is connected to"""
logger = get_task_logger(txn_id=txn_id, task_id=self.request.id, loglevel=const.VLAB_ONEFS_LOG_LEVEL.upper())
resp = {'content' : {}, 'error': None, 'params': {}}
logger.info('Task starting')
try:
vmware.update_network(username, machine_name, new_network)
except ValueError as doh:
logger.error('Task failed: {}'.format(doh))
resp['error'] = '{}'.format(doh)
logger.info('Task complete')
return resp
| 9,120 | 2,736 |
# coding: utf-8
# In[1]:
import json
import os
import time
def events_dict(events, num_files):
for i in range(num_files):
events['event'+str(i+1000000)] = ['event'+str(i+1000000)]
with open('events.json', 'w') as json_file:
json.dump(events, json_file)
return events
def keywords_dict(keywords, events, num_files, fp):
for i in range(num_files):
print(i)
f = open(fp+'event'+str(i+18778+1000000)+'.txt', 'r')
full = f.readlines()
f.close()
keys = full[0].strip().split(' ')
events['event'+str(i+1000000)] += keys
for k in keys:
try:
keywords[k].append('event'+str(i+1000000))
except:
keywords[k] = [k, 'event'+str(i+1000000)]
# if i % 10000 == 0:
# print('Saving dictionary ...')
# with open('events.json', 'w') as json_file:
# json.dump(events, json_file)
# with open('keywords.json', 'w') as json_file:
# json.dump(keywords, json_file)
# print('Saved.')
del full
del keys
with open('events.json', 'w') as json_file:
json.dump(events, json_file)
with open('keywords.json', 'w') as json_file:
json.dump(keywords, json_file)
if __name__ == "__main__":
start_time = time.time()
events = {}
keywords = {}
fp = 'news/'
# fp = 'Data/'
num_files = 9000000
print('Totally ', num_files)
print('Initializing events dictionary ...')
events = events_dict(events, num_files)
print('Initializing keywords dictionary and Adding keywords&events relations ...')
keywords_dict(keywords, events, num_files, fp)
print('Done!!!')
with open('time.txt', 'a') as f:
f.write('Initializing dict totally takes %.3f minutes\n'%((time.time()-start_time)/60))
| 1,878 | 669 |
#---------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
#---------------------------------------------------------------------------------------------
import urllib
from requests.auth import AuthBase
import requests
from msrest.authentication import Authentication
from . import HttpBearerChallenge
from . import HttpBearerChallengeCache as ChallengeCache
class KeyVaultAuthBase(AuthBase):
def __init__(self, authorization_callback):
self._callback = authorization_callback
self._token = None
def __call__(self, request):
# attempt to pre-fetch challenge if cached
if self._callback:
challenge = ChallengeCache.get_challenge_for_url(request.url)
if challenge:
# if challenge cached, retrieve token and update the request
self.set_authorization_header(request, challenge)
else:
# send the request to retrieve the challenge, then request the token and update
# the request
# TODO: wire up commons flag for things like Fiddler, logging, etc.
response = requests.Session().send(request)
if response.status_code == 401:
auth_header = response.headers['WWW-Authenticate']
if HttpBearerChallenge.is_bearer_challenge(auth_header):
challenge = HttpBearerChallenge(response.request.url, auth_header)
ChallengeCache.set_challenge_for_url(response.request.url, challenge)
self.set_authorization_header(request, challenge)
return request
def set_authorization_header(self, request, challenge):
auth = self._callback(
challenge.get_authorization_server(),
challenge.get_resource(),
challenge.get_scope())
request.headers['Authorization'] = '{} {}'.format(auth[0], auth[1])
class KeyVaultAuthentication(Authentication):
def __init__(self, authorization_callback):
super(KeyVaultAuthentication, self).__init__()
self.auth = KeyVaultAuthBase(authorization_callback)
self._callback = authorization_callback
def signed_session(self):
session = super(KeyVaultAuthentication, self).signed_session()
session.auth = self.auth
return session
| 2,550 | 589 |
from math import sqrt
side_a = 4
side_b = 6
side_c = 3
semiperimiter = (side_a + side_b + side_c)/2
diameter = (side_a* side_b* side_c)/ 2*sqrt(semiperimiter * (semiperimiter - side_a) * (semiperimiter - side_b) * (semiperimiter - side_c))
print ('The radius of a circle with sides of length 4, 6 and 3 is', diameter/2)
| 326 | 140 |
import numpy as np
import zern
import matplotlib.pyplot as pl
from tqdm import tqdm
import scipy.special as sp
def _even(x):
return x%2 == 0
def _zernike_parity( j, jp):
return _even(j-jp)
class KL(object):
def __init__(self):
pass
# tmp = np.load('kl/kl_data.npy')
# self.noll_KL = tmp[:,0].astype('int')
# self.noll_Z = tmp[:,1].astype('int')
# self.cz = tmp[:,2]
def precalculate(self, npix_image, first_noll=1):
"""
Precalculate KL modes. We skip the first mode, which is just the
aperture. The second and third mode (first and second one in the return)
are tip-tilt using standard Zernike modes. The rest are KL modes
obtained by the diagonalization of the Kolmogorov Noll's matrix
Parameters
----------
npix_image : int
Number of pixels on the pupil plane
Returns
-------
float array
KL modes
"""
self.npix_image = npix_image
print("Computing KL modes...")
Z_machine = zern.ZernikeNaive(mask=[])
x = np.linspace(-1, 1, npix_image)
xx, yy = np.meshgrid(x, x)
rho = np.sqrt(xx ** 2 + yy ** 2)
theta = np.arctan2(yy, xx)
aperture_mask = rho <= 1.0
self.KL = np.zeros((self.n_modes_max, self.npix_image, self.npix_image))
for mode in (range(self.n_modes_max)):
j = mode + first_noll
# if (i <= 2):
# n, m = zern.zernIndex(i + first_noll)
# Z = Z_machine.Z_nm(n, m, rho, theta, True, 'Jacobi')
# self.KL[i,:,:] = Z * aperture_mask
# else:
indx = np.where(self.noll_KL == j)[0]
tmp = vh[mode,:]
print(mode, self.cz[indx], tmp[np.abs(tmp) > 0])
for i in range(len(indx)):
jz = self.noll_Z[indx[i]]
cz = self.cz[indx[i]]
n, m = zern.zernIndex(jz)
Z = Z_machine.Z_nm(n, m, rho, theta, True, 'Jacobi')
self.KL[mode,:,:] += cz * Z * aperture_mask
return self.KL
def precalculate_covariance(self, npix_image, n_modes_max, first_noll=1, overfill=1.0):
"""
Precalculate KL modes. We skip the first mode, which is just the
aperture. The second and third mode (first and second one in the return)
are tip-tilt using standard Zernike modes. The rest are KL modes
obtained by the diagonalization of the Kolmogorov Noll's matrix
Parameters
----------
npix_image : int
Number of pixels on the pupil plane
n_modes_max : int
Maximum number of nodes to consider
first_noll : int
First Noll index to consider. j=1 is the aperture. j=2/3 are the tip-tilts
Returns
-------
float array
KL modes
"""
self.npix_image = npix_image
self.first_noll = first_noll - 1
self.n_modes_max = n_modes_max + first_noll
print("Computing Kolmogorov covariance...")
covariance = np.zeros((self.n_modes_max, self.n_modes_max))
for j in range(self.n_modes_max):
n, m = zern.zernIndex(j+1)
for jpr in range(self.n_modes_max):
npr, mpr = zern.zernIndex(jpr+1)
deltaz = (m == mpr) and (_zernike_parity(j, jpr) or m == 0)
if (deltaz):
phase = (-1.0)**(0.5*(n+npr-2*m))
t1 = np.sqrt((n+1)*(npr+1))
t2 = sp.gamma(14./3.0) * sp.gamma(11./6.0)**2 * (24.0/5.0*sp.gamma(6.0/5.0))**(5.0/6.0) / (2.0*np.pi**2)
Kzz = t2 * t1 * phase
t1 = sp.gamma(0.5*(n+npr-5.0/3.0))
t2 = sp.gamma(0.5*(n-npr+17.0/3.0)) * sp.gamma(0.5*(npr-n+17.0/3.0)) * sp.gamma(0.5*(n+npr+23.0/3.0))
covariance[j,jpr] = Kzz * t1 / t2
covariance[0,:] = 0.0
covariance[:,0] = 0.0
covariance[0,0] = 1.0
print("Diagonalizing Kolmogorov covariance...")
u, s, vh = np.linalg.svd(covariance)
vh[np.abs(vh) < 1e-10] = 0.0
print("Computing KL modes...")
Z_machine = zern.ZernikeNaive(mask=[])
x = np.linspace(-1, 1, npix_image)
xx, yy = np.meshgrid(x, x)
rho = overfill * np.sqrt(xx ** 2 + yy ** 2)
theta = np.arctan2(yy, xx)
aperture_mask = rho <= 1.0
self.KL = np.zeros((self.n_modes_max, self.npix_image, self.npix_image))
noll_Z = 1 + np.arange(self.n_modes_max)
for mode in tqdm(range(self.n_modes_max)):
cz = vh[mode,:]
ind = np.where(cz != 0)[0]
for i in range(len(ind)):
jz = noll_Z[ind[i]]
coeff = cz[ind[i]]
n, m = zern.zernIndex(jz)
Z = Z_machine.Z_nm(n, m, rho, theta, True, 'Jacobi')
self.KL[mode,:,:] += coeff * Z * aperture_mask
self.KL = self.KL[self.first_noll+1:,:,:]
return self.KL
if (__name__ == '__main__'):
tmp = KL()
tmp.precalculate_covariance(npix_image=128, n_modes_max=25, first_noll=3)
f, ax = pl.subplots(nrows=4, ncols=4)
for i in range(16):
ax.flat[i].imshow(tmp.KL[i, :, :])
pl.show()
mat = np.zeros((20,20))
for i in range(20):
for j in range(20):
mat[i,j] = np.sum(tmp.KL[i,:,:]*tmp.KL[j,:,:])
#pl.imshow(np.log(np.abs(mat)))
#pl.show() | 5,757 | 2,192 |
from tf_models.utils import train, save_model
def train_and_save(name: str, corpus: str, pos_label: str, root: str = ""):
print("Start training {}...".format(name))
mlp_model, _, vec = train(corpus, pos_label, root)
save_model(mlp_model, vec, name, root)
if __name__ == "__main__":
# Train intent model
# fmt: off
train_and_save(
name="intent",
corpus="intent_corpus.csv",
pos_label="weather",
root="intent"
)
# fmt: on
# Train flow control model
train_and_save(
name="flow_control",
corpus="flow_control_corpus.csv",
pos_label="continue",
root="flow_control",
)
| 677 | 229 |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
# sys.path.append('../')
# sys.path.insert(0, os.path.abspath('.'))
sys.path.insert(0, os.path.abspath('../'))
sys.path.insert(0, os.path.abspath('../sospcat'))
# -- Project information -----------------------------------------------------
project = 'SoSpaCATpy'
copyright = '2020, Jonas I. Liechti'
author = 'Jonas I. Liechti'
# ############################################################################
# ############################################################################
# The full version, including alpha/beta/rc tags
def get_version_and_cmdclass(package_path):
"""Load version.py module without importing the whole package.
Template code from miniver
"""
import os
from importlib.util import module_from_spec, spec_from_file_location
spec = spec_from_file_location(
"version", os.path.join(package_path, "_version.py")
)
module = module_from_spec(spec)
spec.loader.exec_module(module)
return module.__version__ # , module.cmdclass
# major/minor
_release = get_version_and_cmdclass('../sospcat')
print(_release)
release = _release.split('+')[0]
version = '.'.join(release.split('.')[:2])
# ############################################################################
# ############################################################################
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.doctest',
'sphinx.ext.coverage',
'sphinx.ext.todo',
'sphinx.ext.intersphinx',
'sphinx.ext.mathjax',
'sphinx.ext.ifconfig',
'sphinx.ext.viewcode',
'sphinx.ext.githubpages',
'sphinx.ext.autosummary',
'sphinx.ext.extlinks',
'sphinx.ext.napoleon',
'm2r2'
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# ############################################################################
# ############################################################################
source_suffix = ['.rst', '.md']
master_doc = 'index'
todo_include_todos = True
# ############################################################################
# ############################################################################
# ############################################################################
# ############################################################################
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if not on_rtd: # only import and set the theme if we're building docs locally
import sphinx_rtd_theme
html_theme = "sphinx_rtd_theme"
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# ############################################################################
# ############################################################################
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
# html_theme = 'alabaster'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# ############################################################################
# ############################################################################
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {
'python': ('https://docs.python.org/3', None),
'numpy': ('http://docs.scipy.org/doc/numpy/', None),
'matplotlib': ('http://matplotlib.org', None),
'sphinx': ('http://www.sphinx-doc.org/en/stable/', None),
'sklearn': (
'http://scikit-learn.org/stable',
(None, './_intersphinx/sklearn-objects.inv')
)
}
# ############################################################################
# ############################################################################
# ############################################################################
# ############################################################################
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
add_module_names = False
# ############################################################################
# ############################################################################
# Use both docstring of class and docstring of __init__ methods
autoclass_content = 'both'
| 5,641 | 1,453 |
"""empty message
Revision ID: f1896d92dddc
Revises: bdd283763bd2
Create Date: 2020-08-21 22:08:42.863607
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = 'f1896d92dddc'
down_revision = 'bdd283763bd2'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('bills', sa.Column('created', sa.Date(), nullable=True))
op.add_column('paychecks', sa.Column('created', sa.Date(), nullable=True))
op.add_column('savings_goals', sa.Column('created', sa.Date(), nullable=True))
op.add_column('total_savings_log', sa.Column('created', sa.Date(), nullable=True))
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('total_savings_log', 'created')
op.drop_column('savings_goals', 'created')
op.drop_column('paychecks', 'created')
op.drop_column('bills', 'created')
# ### end Alembic commands ###
| 1,039 | 397 |
from states import Territories_Abbreviated
from states import Territories_Full_Name
from states import Uninhabited_Territories
from states import Associated_States
from states import Territories
import unittest
class Abbreviated_Test(unittest.TestCase):
def check_lenght(self):
"""
Checks there is only 5 territories.
"""
all_territories = Territories_Abbreviated().all_territories
self.assertEqual(5,len(all_territories),'There is either less or more than 5 territories recognized.')
class Full_Name_Test(unittest.TestCase):
def check_lenght(self):
"""
Checks there is only 5 territories.
"""
all_territories = Territories_Full_Name().all_territories
self.assertEqual(5,len(all_territories),'There is either less or more than 5 territories recognized.')
class Uninhabited_Territories_Test(unittest.TestCase):
def check_lenght(self):
"""
Checks there is only 7 territories.
"""
all_territories = Uninhabited_Territories().all_territories
self.assertEqual(7,len(all_territories),'There is either less or more than 7 territories recognized.')
class Associated_States_Test(unittest.TestCase):
def check_lenght(self):
"""
Checks there is only 3 territories.
"""
all_territories = Associated_States().pacific_abbreviated
self.assertEqual(3,len(all_territories),'There is either less or more than 3 territories recognized.')
class Territories_Test(unittest.TestCase):
def check_search_returns_info(self):
"""
Checks that the search function returns the correct value.
"""
PR = Territories().get_territory_info('Puerto Rico')
full_name = PR['full_name']
self.assertTrue(full_name == 'Puerto Rico', 'Search function is not retrieving the right value. Used Puerto Rico as a Test.')
if __name__ == "__main__":
unittest.main() | 1,969 | 586 |
from BinarySerachTree_def.BinaryTree import BinaryTreeS
binarytree=BinaryTreeS()
#create a binary tree with fibonnaci
binarytree.insert(0)
binarytree.insert(1)
binarytree.insert(1)
binarytree.insert(2)
binarytree.insert(3)
binarytree.insert(5)
binarytree.insert(8)
binarytree.insert(13)
binarytree.getTraverseInOrder()
print('------------------------------')
#Remove the last element
binarytree.remove(13)
binarytree.getTraverseInOrder()
print('------------------------------')
#Remove the first element different of zero
binarytree.remove(1)
binarytree.getTraverseInOrder()
print('------------------------------')
print 'Max Value:'
print binarytree.getMax()
print 'Min Value:'
print binarytree.getMin() | 714 | 219 |
import torch
import torch.nn as nn
from torch.autograd import Variable
from data_loading import *
from rdkit import Chem
'''
the model
'''
class generative_model(nn.Module):
def __init__(self, vocabs_size, hidden_size, output_size, embedding_dimension, n_layers):
super(generative_model, self).__init__()
self.vocabs_size = vocabs_size
self.hidden_size = hidden_size
self.output_size = output_size
self.embedding_dimension = embedding_dimension
self.n_layers = n_layers
self.embedding = nn.Embedding(vocabs_size, embedding_dimension)
self.rnn = nn.LSTM(embedding_dimension, hidden_size, n_layers, dropout = 0.2)
self.linear = nn.Linear(hidden_size, output_size)
def forward(self, input, hidden):
batch_size = input.size(0)
input = self.embedding(input)
output, hidden = self.rnn(input.view(1, batch_size, -1), hidden)
output = self.linear(output.view(batch_size, -1))
return output, hidden
def init_hidden(self, batch_size):
hidden=(Variable(torch.zeros(self.n_layers, batch_size, self.hidden_size)),
Variable(torch.zeros(self.n_layers, batch_size, self.hidden_size)))
return hidden
data,vocabs=load_data()
data = set(list(data))
vocabs = list(vocabs)
vocabs_size = len(vocabs)
output_size = len(vocabs)
batch_size = 128
cuda = True
hidden_size = 1024
embedding_dimension = 248
n_layers=3
end_token = ' '
model = generative_model(vocabs_size,hidden_size,output_size,embedding_dimension,n_layers)
model.load_state_dict(torch.load('mytraining.pt'))
if cuda:
model = model.cuda()
model.eval()
def evaluate(prime_str='!', temperature=0.4):
max_length = 200
inp = Variable(tensor_from_chars_list(prime_str,vocabs,cuda)).cuda()
batch_size = inp.size(0)
hidden = model.init_hidden(batch_size)
if cuda:
hidden = (hidden[0].cuda(), hidden[1].cuda())
predicted = prime_str
while True:
output, hidden = model(inp, hidden)
# Sample from the network as a multinomial distribution
output_dist = output.data.view(-1).div(temperature).exp()
top_i = torch.multinomial(output_dist, 1)[0]
# Add predicted character to string and use as next input
predicted_char = vocabs[top_i]
if predicted_char ==end_token or len(predicted)>max_length:
return predicted
predicted += predicted_char
inp = Variable(tensor_from_chars_list(predicted_char,vocabs,cuda)).cuda()
return predicted
def valid_smile(smile):
return Chem.MolFromSmiles(smile) is not None
def get_canonical_smile(smile):
return Chem.MolToSmiles(Chem.MolFromSmiles(smile))
def valid_smiles_at_temp(temp):
range_test = 100
c=0
for i in range(range_test):
s= evaluate(prime_str='!', temperature=temp)[1:] # remove the first character !.
if valid_smile(s):
print(s)
c+=1
return float(c)/range_test
def smiles_in_db(smile):
smile = '!'+get_canonical_smile(smile)+' '
if smile in data:
return True
return False
def percentage_variety_of_valid_at_temp(temp):
range_test = 100
c_v=0
c_nd=0
for i in range(range_test):
s= evaluate(prime_str='!', temperature=temp)[1:] # remove the first character !.
if valid_smile(s):
c_v+=1
if not smiles_in_db(s):
c_nd+=1
return float(c_nd)/c_v
| 3,469 | 1,199 |
# coding: utf-8
"""
Paragon Insights APIs
API interface for PI application # noqa: E501
OpenAPI spec version: 4.0.0
Contact: healthbot-feedback@juniper.net
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class HbGraphsQuery(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'group_name': 'str',
'group_type': 'str',
'device_name': 'str',
'measurement_name': 'str',
'measurement_type': 'str',
'transformation': 'str',
'field_name': 'str',
'field_type': 'str',
'field_aggregation': 'str',
'where': 'HbGraphsQueryWhere',
'group_by_interval': 'str',
'group_by_fill': 'str',
'group_by_tag_key': 'str',
'retention_policy': 'str'
}
attribute_map = {
'group_name': 'group_name',
'group_type': 'group_type',
'device_name': 'device_name',
'measurement_name': 'measurement_name',
'measurement_type': 'measurement_type',
'transformation': 'transformation',
'field_name': 'field_name',
'field_type': 'field_type',
'field_aggregation': 'field_aggregation',
'where': 'where',
'group_by_interval': 'group_by_interval',
'group_by_fill': 'group_by_fill',
'group_by_tag_key': 'group_by_tag_key',
'retention_policy': 'retention_policy'
}
def __init__(self, group_name=None, group_type=None, device_name=None, measurement_name=None, measurement_type=None, transformation=None, field_name=None, field_type=None, field_aggregation=None, where=None, group_by_interval=None, group_by_fill=None, group_by_tag_key=None, retention_policy=None): # noqa: E501
"""HbGraphsQuery - a model defined in Swagger""" # noqa: E501
self._group_name = None
self._group_type = None
self._device_name = None
self._measurement_name = None
self._measurement_type = None
self._transformation = None
self._field_name = None
self._field_type = None
self._field_aggregation = None
self._where = None
self._group_by_interval = None
self._group_by_fill = None
self._group_by_tag_key = None
self._retention_policy = None
self.discriminator = None
if group_name is not None:
self.group_name = group_name
if group_type is not None:
self.group_type = group_type
if device_name is not None:
self.device_name = device_name
if measurement_name is not None:
self.measurement_name = measurement_name
if measurement_type is not None:
self.measurement_type = measurement_type
if transformation is not None:
self.transformation = transformation
if field_name is not None:
self.field_name = field_name
if field_type is not None:
self.field_type = field_type
if field_aggregation is not None:
self.field_aggregation = field_aggregation
if where is not None:
self.where = where
if group_by_interval is not None:
self.group_by_interval = group_by_interval
if group_by_fill is not None:
self.group_by_fill = group_by_fill
if group_by_tag_key is not None:
self.group_by_tag_key = group_by_tag_key
if retention_policy is not None:
self.retention_policy = retention_policy
@property
def group_name(self):
"""Gets the group_name of this HbGraphsQuery. # noqa: E501
Device/Network group name # noqa: E501
:return: The group_name of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._group_name
@group_name.setter
def group_name(self, group_name):
"""Sets the group_name of this HbGraphsQuery.
Device/Network group name # noqa: E501
:param group_name: The group_name of this HbGraphsQuery. # noqa: E501
:type: str
"""
self._group_name = group_name
@property
def group_type(self):
"""Gets the group_type of this HbGraphsQuery. # noqa: E501
Device/Network group type # noqa: E501
:return: The group_type of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._group_type
@group_type.setter
def group_type(self, group_type):
"""Sets the group_type of this HbGraphsQuery.
Device/Network group type # noqa: E501
:param group_type: The group_type of this HbGraphsQuery. # noqa: E501
:type: str
"""
allowed_values = ["device", "network"] # noqa: E501
if group_type not in allowed_values:
raise ValueError(
"Invalid value for `group_type` ({0}), must be one of {1}" # noqa: E501
.format(group_type, allowed_values)
)
self._group_type = group_type
@property
def device_name(self):
"""Gets the device_name of this HbGraphsQuery. # noqa: E501
label name # noqa: E501
:return: The device_name of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._device_name
@device_name.setter
def device_name(self, device_name):
"""Sets the device_name of this HbGraphsQuery.
label name # noqa: E501
:param device_name: The device_name of this HbGraphsQuery. # noqa: E501
:type: str
"""
self._device_name = device_name
@property
def measurement_name(self):
"""Gets the measurement_name of this HbGraphsQuery. # noqa: E501
Measurement name (topic/rule name) # noqa: E501
:return: The measurement_name of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._measurement_name
@measurement_name.setter
def measurement_name(self, measurement_name):
"""Sets the measurement_name of this HbGraphsQuery.
Measurement name (topic/rule name) # noqa: E501
:param measurement_name: The measurement_name of this HbGraphsQuery. # noqa: E501
:type: str
"""
self._measurement_name = measurement_name
@property
def measurement_type(self):
"""Gets the measurement_type of this HbGraphsQuery. # noqa: E501
Measurement type: Field table/Trigger table/Rollup table # noqa: E501
:return: The measurement_type of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._measurement_type
@measurement_type.setter
def measurement_type(self, measurement_type):
"""Sets the measurement_type of this HbGraphsQuery.
Measurement type: Field table/Trigger table/Rollup table # noqa: E501
:param measurement_type: The measurement_type of this HbGraphsQuery. # noqa: E501
:type: str
"""
allowed_values = ["Field table", "Trigger table", "Rollup table"] # noqa: E501
if measurement_type not in allowed_values:
raise ValueError(
"Invalid value for `measurement_type` ({0}), must be one of {1}" # noqa: E501
.format(measurement_type, allowed_values)
)
self._measurement_type = measurement_type
@property
def transformation(self):
"""Gets the transformation of this HbGraphsQuery. # noqa: E501
Transformation value for query # noqa: E501
:return: The transformation of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._transformation
@transformation.setter
def transformation(self, transformation):
"""Sets the transformation of this HbGraphsQuery.
Transformation value for query # noqa: E501
:param transformation: The transformation of this HbGraphsQuery. # noqa: E501
:type: str
"""
allowed_values = ["derivative", "spread", "non-negative-derivative", "difference", "cumulative-sum", "elapsed"] # noqa: E501
if transformation not in allowed_values:
raise ValueError(
"Invalid value for `transformation` ({0}), must be one of {1}" # noqa: E501
.format(transformation, allowed_values)
)
self._transformation = transformation
@property
def field_name(self):
"""Gets the field_name of this HbGraphsQuery. # noqa: E501
Field name of a measurement # noqa: E501
:return: The field_name of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._field_name
@field_name.setter
def field_name(self, field_name):
"""Sets the field_name of this HbGraphsQuery.
Field name of a measurement # noqa: E501
:param field_name: The field_name of this HbGraphsQuery. # noqa: E501
:type: str
"""
self._field_name = field_name
@property
def field_type(self):
"""Gets the field_type of this HbGraphsQuery. # noqa: E501
Field type of the measurement (int, float, string, uint) # noqa: E501
:return: The field_type of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._field_type
@field_type.setter
def field_type(self, field_type):
"""Sets the field_type of this HbGraphsQuery.
Field type of the measurement (int, float, string, uint) # noqa: E501
:param field_type: The field_type of this HbGraphsQuery. # noqa: E501
:type: str
"""
self._field_type = field_type
@property
def field_aggregation(self):
"""Gets the field_aggregation of this HbGraphsQuery. # noqa: E501
Data aggregation type of the field/key # noqa: E501
:return: The field_aggregation of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._field_aggregation
@field_aggregation.setter
def field_aggregation(self, field_aggregation):
"""Sets the field_aggregation of this HbGraphsQuery.
Data aggregation type of the field/key # noqa: E501
:param field_aggregation: The field_aggregation of this HbGraphsQuery. # noqa: E501
:type: str
"""
allowed_values = ["mean", "mode", "median", "count", "sum", "integral", "distinct"] # noqa: E501
if field_aggregation not in allowed_values:
raise ValueError(
"Invalid value for `field_aggregation` ({0}), must be one of {1}" # noqa: E501
.format(field_aggregation, allowed_values)
)
self._field_aggregation = field_aggregation
@property
def where(self):
"""Gets the where of this HbGraphsQuery. # noqa: E501
:return: The where of this HbGraphsQuery. # noqa: E501
:rtype: HbGraphsQueryWhere
"""
return self._where
@where.setter
def where(self, where):
"""Sets the where of this HbGraphsQuery.
:param where: The where of this HbGraphsQuery. # noqa: E501
:type: HbGraphsQueryWhere
"""
self._where = where
@property
def group_by_interval(self):
"""Gets the group_by_interval of this HbGraphsQuery. # noqa: E501
Group by interval of the query # noqa: E501
:return: The group_by_interval of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._group_by_interval
@group_by_interval.setter
def group_by_interval(self, group_by_interval):
"""Sets the group_by_interval of this HbGraphsQuery.
Group by interval of the query # noqa: E501
:param group_by_interval: The group_by_interval of this HbGraphsQuery. # noqa: E501
:type: str
"""
self._group_by_interval = group_by_interval
@property
def group_by_fill(self):
"""Gets the group_by_fill of this HbGraphsQuery. # noqa: E501
Group by fill value of the query # noqa: E501
:return: The group_by_fill of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._group_by_fill
@group_by_fill.setter
def group_by_fill(self, group_by_fill):
"""Sets the group_by_fill of this HbGraphsQuery.
Group by fill value of the query # noqa: E501
:param group_by_fill: The group_by_fill of this HbGraphsQuery. # noqa: E501
:type: str
"""
allowed_values = ["fill(null)", "none"] # noqa: E501
if group_by_fill not in allowed_values:
raise ValueError(
"Invalid value for `group_by_fill` ({0}), must be one of {1}" # noqa: E501
.format(group_by_fill, allowed_values)
)
self._group_by_fill = group_by_fill
@property
def group_by_tag_key(self):
"""Gets the group_by_tag_key of this HbGraphsQuery. # noqa: E501
Group by tag key value of the query # noqa: E501
:return: The group_by_tag_key of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._group_by_tag_key
@group_by_tag_key.setter
def group_by_tag_key(self, group_by_tag_key):
"""Sets the group_by_tag_key of this HbGraphsQuery.
Group by tag key value of the query # noqa: E501
:param group_by_tag_key: The group_by_tag_key of this HbGraphsQuery. # noqa: E501
:type: str
"""
self._group_by_tag_key = group_by_tag_key
@property
def retention_policy(self):
"""Gets the retention_policy of this HbGraphsQuery. # noqa: E501
Retention policy name # noqa: E501
:return: The retention_policy of this HbGraphsQuery. # noqa: E501
:rtype: str
"""
return self._retention_policy
@retention_policy.setter
def retention_policy(self, retention_policy):
"""Sets the retention_policy of this HbGraphsQuery.
Retention policy name # noqa: E501
:param retention_policy: The retention_policy of this HbGraphsQuery. # noqa: E501
:type: str
"""
self._retention_policy = retention_policy
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(HbGraphsQuery, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, HbGraphsQuery):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| 16,206 | 5,144 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .position import LearnedEmbedding
class FactorizedAttentionBlock(nn.Module):
def __init__(self, inplanes, feat_planes, out_planes=None, kernel_size=1, stride=1, position_embedding=None,
**kwargs):
super(FactorizedAttentionBlock, self).__init__()
self.pe_use = False
if position_embedding is not None:
self.pe_use = True
if position_embedding == "Learned Embedding":
self.pe = LearnedEmbedding(inplanes)
else:
raise NotImplementedError()
self.inplanes = inplanes
self.feat_planes = feat_planes
self.basis = nn.Conv2d(inplanes, feat_planes, kernel_size, padding=kernel_size // 2)
self.coef = nn.Conv2d(inplanes, feat_planes, kernel_size, padding=kernel_size // 2)
self.value = nn.Conv2d(inplanes, inplanes, kernel_size, padding=kernel_size // 2)
self.final_use = False
if out_planes is not None:
self.final_use = True
if stride == 1:
self.final = nn.Conv2d(inplanes, feat_planes, 1)
else:
self.final = nn.Conv2d(inplanes, feat_planes, kernel_size=1, stride=2, padding=1)
def forward(self, x):
if self.pe_use:
x = self.pe(x) + x
B = x.shape[0]
C = self.inplanes
M = self.feat_planes
H = x.shape[2]
W = x.shape[3]
# B*M*H*W
basis = self.basis(x)
basis = F.softmax(basis.view(-1, H * W), dim=1).view(B, M, H, W)
# B*M*H*W
coef = self.coef(x)
coef = F.softmax(coef, dim=1)
# B*C*H*W
value = self.value(x)
# B*HW*M
basis = basis.view(B, M, -1)
basis = torch.transpose(basis, 1, 2)
# B*C*HW
value = value.view(B, C, -1)
# B * C * M
res = torch.bmm(value, basis)
# B * M * HW
coef = coef.view(B, M, -1)
# B * C * HW
output = torch.bmm(res, coef)
# B * C * H * W
output = output.view(B, C, H, W)
# output = self.bn(output)
# output = self.up(output)
output = output + x
if self.final_use:
output = self.final(output)
return output
class FactorizedAttention(nn.Module):
def __init__(self, **kwargs):
super(FactorizedAttention, self).__init__()
self.inplanes = kwargs['inplanes']
self.out_planes = self.inplanes
self.num_level = kwargs['num_level']
for lvl_idx in range(self.num_level):
if isinstance(self.inplanes, int):
planes = self.inplanes
else:
planes = self.inplanes[lvl_idx]
kwargs['inplanes'] = planes
self.add_module(
self.get_lateral_name(lvl_idx),
FactorizedAttentionBlock(**kwargs)
)
def get_lateral_name(self, idx):
return 'c{}_lateral'.format(idx)
def get_lateral(self, idx):
return getattr(self, self.get_lateral_name(idx))
def get_outplanes(self):
return self.out_planes
def forward(self, input):
features = input['features']
assert self.num_level == len(features)
laterals = [self.get_lateral(i)(x) for i, x in enumerate(features)]
features = []
for lvl_idx in range(self.num_level):
out = laterals[lvl_idx]
features.append(out)
return {'features': features}
| 3,564 | 1,253 |
import subprocess
commit_sha_base = 'c855573fd1acadf2dd3b1cdc1e4581cd49c77f05'
cmake_command = 'cmake -S . -B cmake_build -DTFM_PLATFORM=arm/mps2/an521 \
-DTFM_TOOLCHAIN_FILE=toolchain_GNUARM.cmake \
-DCMAKE_BUILD_TYPE=Release \
-DTFM_PROFILE=profile_small'
build_command = 'cmake --build cmake_build -- install -j32'
# p = subprocess.Popen(, shell=True,
# stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
# for line in p.stdout.readlines():
# if line.find('reference is not a tree') > 0:
# print("Python exec shell command error")
# exit(2)
# retval = p.wait()
def sys_run(command):
p = subprocess.Popen(command, shell=True,
stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
for line in p.stdout.readlines():
print(line)
retval = p.wait()
return p.stdout
sys_run('git checkout ' + commit_sha_base)
sys_run('rm -rf cmake_build')
sys_run(cmake_command)
sys_run(build_command) | 1,029 | 390 |
from collections import defaultdict
import copy
class CooSparseMatrix:
def _prepare_coords(self, coords):
i, j = tuple(map(int, coords))
if 0 > i or i >= self.Shape[0] or 0 > j or j >= self.Shape[1]:
raise TypeError
return i, j
def __get_copy(self):
return copy.deepcopy(self)
def __init__(self, ijx_list, shape=None):
self.data = defaultdict(dict)
for *coords, x in ijx_list:
i, j = tuple(map(int, coords))
if (i, j) in self.data:
raise TypeError
self.data[i][j] = x
self.Shape = shape
def __getitem__(self, coords):
if len(coords) == 2:
i, j = self._prepare_coords(coords)
return self.data.get(coords[0], dict()).get(coords[1], 0.)
i, _ = self._prepare_coords((coords[0], 0))
return CooSparseMatrix([(i, j, v) for j, v in self.data[i].items()],
Shape=(1, self.Shape[1]))
def __setitem__(self, coords, value):
i, j = self._prepare_coords(coords)
if value == 0:
if i in self.data and j in self.data[i]:
del self.data[i][j]
else:
self.data[i][j] = value
def __add__(self, other):
if self.Shape != other.Shape:
raise TypeError
res = self.__get_copy()
for i, d in other.data.items():
if i not in res.data:
res.data[i] = copy.copy(other.data[i])
continue
for j, v in d.items():
cur_val = res.data[i].get(j, 0) + v
if cur_val == 0:
res.data[i].pop(j, None)
else:
res.data[i][j] = cur_val
if len(res.data[i]) == 0:
del res.data[i]
return res
def __mul__(self, value):
if value == 0:
return CooSparseMatrix([], Shape=self.Shape)
res = self.__get_copy()
for i in self.data.keys():
for j in self.data[i].keys():
res.data[i][j] *= value
return res
def __rmul__(self, value):
return self * value
def __sub__(self, other):
return self + other * -1
def __deepcopy__(self, memo):
res = CooSparseMatrix([], shape=self.Shape)
res.data = copy.deepcopy(self.data)
return res
def __setattr__(self, attr, value):
if attr == 'shape':
if not isinstance(value, tuple):
raise TypeError
if len(value) != 2 or type(value[0]) is not int or type(value[1]) is not int:
raise TypeError
if value[0] * value[1] != self.Shape[0] * self.Shape[1]:
raise TypeError
res = CooSparseMatrix([], value)
for i, d in self.data.items():
for j, v in d.items():
pos = i * self.Shape[1] + j
res[pos // value[1], pos % value[1]] = v
self.Shape = value
self.data = res.data
elif attr == 'T':
raise AttributeError
else:
self.__dict__[attr] = value
def __getattr__(self, attr):
if attr == 'shape':
return self.Shape
elif attr == 'T':
res = CooSparseMatrix([], self.Shape[::-1])
for i, d in self.data.items():
for j, v in d.items():
res[j, i] = v
return res
else:
return self.__dict__[attr]
| 3,557 | 1,107 |
#!/usr/bin/env python
#----------------------------------------------------------------------
# Copyright (c) 2013-2016 Raytheon BBN Technologies
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and/or hardware specification (the "Work") to
# deal in the Work without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Work, and to permit persons to whom the Work
# is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Work.
#
# THE WORK IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE WORK OR THE USE OR OTHER DEALINGS
# IN THE WORK.
#----------------------------------------------------------------------
'''Stitching client: Call the Stitching Computation Service to expand a single request RSpec.
Then use Omni to allocate / createsliver reservations at all necessary aggregates. Return
the combined manifest RSpec.'''
# Call this just like omni:
# $ python ./src/stitcher.py -o createsliver <valid slice name> <path to RSpec file>
# (assuming a valid omni_config in the usual spots)
# 'createsliver' or 'allocate' commands with an RSpec that requires stitching will be processed
# by the stitcher code.
# All other calls will be passed directly to Omni.
# All calls are APIv2 (hard-coded) currently.
# Input request RSpec does _not_ need a stitching extension, but should
# be a single RSpec for all resources that you want in your slice.
# To create a request that needs stitching, include at least 1 <link> elements with
# more than 1 different <component_manager> elements (and no
# shared_vlan element or link_type of other than VLAN)
# Selected known issues / todos
# - Thread calls to omni
# - Support AM API v3
# - Consolidate constants
# - Fully handle a VLAN_UNAVAILABLE error from an AM
# - Fully handle negotiating among AMs for a VLAN tag to use
# As in when the returned suggestedVLANRange is not what was requested
# - fakeMode is incomplete
# - Tune counters, sleep durations, etc
# - Return a struct with detailed results (not just comments in manifest)
# - Return a struct on errors
# - Get AM URLs from the Clearinghouse
# - Use Authentication with the SCS
# - Support Stitching schema v2
# - Time out omni calls in case an AM hangs
# - opts.warn is used to suppress omni output. Clean that up. A scriptMode option?
# - Implement confirmSafeRequest to ensure no dangerous requests are made
# - Handle known EG error messages
# - Loop checking to see if EG sliverstatus says success or failure
import json
import logging
import logging.handlers
import optparse
import os
import sys
import gcf.oscript as omni
from gcf.omnilib.util import OmniError, AMAPIError
from gcf.omnilib.stitchhandler import StitchingHandler
from gcf.omnilib.stitch.utils import StitchingError, prependFilePrefix
from gcf.omnilib.stitch.objects import Aggregate
import gcf.omnilib.stitch.objects
#from gcf.omnilib.stitch.objects import DCN_AM_RETRY_INTERVAL_SECS as DCN_AM_RETRY_INTERVAL_SECS
# URL of the SCS service
SCS_URL = "https://geni-scs.net.internet2.edu:8443/geni/xmlrpc"
DEFAULT_CAPACITY = 20000 # in Kbps
# Call is the way another script might call this.
# It initializes the logger, options, config (using omni functions),
# and then dispatches to the stitch handler
def call(argv, options=None):
if options is not None and not options.__class__==optparse.Values:
raise OmniError("Invalid options argument to call: must be an optparse.Values object")
if argv is None or not type(argv) == list:
raise OmniError("Invalid argv argument to call: must be a list")
##############################################################################
# Get a parser from omni that understands omni options
##############################################################################
parser = omni.getParser()
# update usage for help message
omni_usage = parser.get_usage()
parser.set_usage("\n" + "GENI Omni Stitching Tool\n" + "Copyright (c) 2013-2016 Raytheon BBN Technologies\n" +
omni_usage+
"\nstitcher.py reserves multi-aggregate fully bound topologies, including stitching, if the call is createsliver or allocate; else it just calls Omni.\n")
##############################################################################
# Add additional optparse.OptionParser style options
# Be sure not to re-use options already in use by omni for
# different meanings, otherwise you'll raise an OptionConflictError
##############################################################################
parser.add_option("--defaultCapacity", default=DEFAULT_CAPACITY,
type="int", help="Default stitched link capacity in Kbps - default is 20000 meaning ~20Mbps")
parser.add_option("--excludehop", metavar="HOP_EXCLUDE", action="append",
help="Hop URN to exclude from any path")
parser.add_option("--includehop", metavar="HOP_INCLUDE", action="append",
help="Hop URN to include on every path - use with caution")
parser.add_option("--includehoponpath", metavar="HOP_INCLUDE PATH", action="append", nargs=2,
help="Hop URN to include and then path (link client_id) to include it on")
parser.add_option("--fixedEndpoint", default=False, action="store_true",
help="RSpec uses a static endpoint - add a fake node with an interface on every link")
parser.add_option("--noExoSM", default=False, action="store_true",
help="Always use local ExoGENI racks, not the ExoSM, where possible (default %default)")
parser.add_option("--useExoSM", default=False, action="store_true",
help="Always use the ExoGENI ExoSM, not the individual EG racks, where possible (default %default)")
parser.add_option("--fileDir", default=None,
help="Directory for all output files generated. By default some files go in /tmp, some in the CWD, some in ~/.gcf.")
parser.add_option("--logFileCount", default=5, type="int",
help="Number of backup log files to keep, Default %default")
parser.add_option("--ionRetryIntervalSecs", type="int",
help="Seconds to sleep before retrying at DCN aggregates (default: %default)",
default=gcf.omnilib.stitch.objects.DCN_AM_RETRY_INTERVAL_SECS)
parser.add_option("--ionStatusIntervalSecs", type="int",
help="Seconds to sleep between sliverstatus calls at DCN aggregates (default %default)",
default=30)
parser.add_option("--noReservation", default=False, action="store_true",
help="Do no reservations: just generate the expanded request RSpec (default %default)")
parser.add_option("--scsURL",
help="URL to the SCS service. Default: Value of 'scs_url' in omni_config or " + SCS_URL,
default=None)
parser.add_option("--timeout", default=0, type="int",
help="Max minutes to allow stitcher to run before killing a reservation attempt (default %default minutes, 0 means no timeout).")
parser.add_option("--noAvailCheck", default=False, action="store_true",
help="Disable checking current VLAN availability where possible.")
parser.add_option("--genRequest", default=False, action="store_true",
help="Generate and save an expanded request RSpec, but do no reservation.")
parser.add_option("--noDeleteAtEnd", default=False, action="store_true",
help="On failure or Ctrl-C do not delete any reservations completed at some aggregates (default %default).")
parser.add_option("--noTransitAMs", default=False, action="store_true",
help="Do not reserve resources at intermediate / transit aggregates; allow experimenter to manually complete the circuit (default %default).")
parser.add_option("--noSCS", default=False, action="store_true",
help="Do not call the SCS to expand or add a stitching extension. Use this only if supplying any needed stitching extension and the SCS would fail your request. (default %default).")
parser.add_option("--fakeModeDir",
help="Developers only: If supplied, use canned server responses from this directory",
default=None)
parser.add_option("--savedSCSResults", default=None,
help="Developers only: Use this saved file of SCS results instead of calling SCS (saved previously using --debug)")
parser.add_option("--useSCSSugg", default=False, action="store_true",
help="Developers only: Always use the VLAN tags the SCS suggests, not 'any'.")
parser.add_option("--noEGStitching", default=False, action="store_true",
help="Developers only: Use GENI stitching, not ExoGENI stitching.")
parser.add_option("--noEGStitchingOnLink", metavar="LINK_ID", action="append",
help="Developers only: Use GENI stitching on this particular link only, not ExoGENI stitching.")
# parser.add_option("--script",
# help="If supplied, a script is calling this",
# action="store_true", default=False)
# Put our logs in a different file by default
parser.set_defaults(logoutput='stitcher.log')
# Configure stitcher with a specific set of configs by default
# First, set the default logging config file
lcfile = os.path.join(sys.path[0], os.path.join("gcf","stitcher_logging.conf"))
# Windows & Mac binaries do not get the .conf file in the proper archive apparently
# And even if they did, it appears the logging stuff can't readily read .conf files
# from that archive.
# Solution 1 that fails (no pkg_resources on windows so far, needs the file in the .zip)
# lcfile = pkg_resources.resource_filename("gcf", "stitcher_logging.conf")
# Solution2 is to use pkgutil to read the file from the archive
# And write it to a temp file that the logging stuff can use.
# Note this requires finding some way to get the file into the archive
# With whatever I do, I want to read the file direct from source per above if possible
if not os.path.exists(lcfile):
# File didn't exist as a regular file among python source
# Try it where py2exe (Windows) puts resources (one directory up, parallel to zip).
lcfile = os.path.join(os.path.normpath(os.path.join(sys.path[0], '..')), os.path.join("gcf","stitcher_logging.conf"))
if not os.path.exists(lcfile):
# File didn't exist in dir parallel to zip of source
# Try one more up, but no gcf sub-directory - where py2app (Mac) puts it.
lcfile = os.path.join(os.path.normpath(os.path.join(os.path.join(sys.path[0], '..'), '..')), "stitcher_logging.conf")
if not os.path.exists(lcfile):
# Now we'll try a couple approaches to read the .conf file out of a source zip
# And put it in a temp directory
tmpdir = os.path.normpath(os.getenv("TMPDIR", os.getenv("TMP", "/tmp")))
if tmpdir and tmpdir != "" and not os.path.exists(tmpdir):
os.makedirs(tmpdir)
lcfile = os.path.join(tmpdir, "stitcher_logging.conf")
try:
# This approach requires the .conf be in the source.zip (e.g. library.zip, python27.zip)
# On Windows (py2exe) this isn't easy apparently. But it happens by default on Mac (py2app)
# Note that could be a manual copy & paste possibly
import pkgutil
lconf = pkgutil.get_data("gcf", "stitcher_logging.conf")
with open(lcfile, 'w') as file:
file.write(lconf)
#print "Read config with pkgutils %s" % lcfile
except Exception, e:
#print "Failed to read .conf file using pkgutil: %s" % e
# If we didn't get the file in the archive, use the .py version
# I find this solution distasteful
from gcf import stitcher_logging_deft
try:
with open(lcfile, 'w') as file:
file.write(stitcher_logging_deft.DEFT_STITCHER_LOGGING_CONFIG)
except Exception, e2:
sys.exit("Error configuring logging: Could not write (from python default) logging config file %s: %s" % (lcfile, e2))
#print "Read from logging config from .py into tmp file %s" % lcfile
parser.set_defaults(logconfig=lcfile)
# Have omni use our parser to parse the args, manipulating options as needed
options, args = omni.parse_args(argv, parser=parser)
# If there is no fileDir, then we try to write to the CWD. In some installations, that will
# fail. So test writing to CWD. If that fails, set fileDir to a temp dir to write files ther.
if not options.fileDir:
testfile = None
handle = None
try:
import tempfile
handle, testfile = tempfile.mkstemp(dir='.')
#print "Can write to CWD: created %s" % testfile
os.close(handle)
except Exception, e:
#print "Cannot write to CWD '%s' for output files: %s" % (os.path.abspath('.'), e)
tmpdir = os.path.normpath(os.getenv("TMPDIR", os.getenv("TMP", "/tmp")))
if tmpdir and tmpdir != "" and not os.path.exists(tmpdir):
os.makedirs(tmpdir)
testfile1 = None
handle1 = None
try:
import tempfile
handle1, testfile1 = tempfile.mkstemp(dir=tmpdir)
os.close(handle1)
options.fileDir = tmpdir
except Exception, e1:
sys.exit("Cannot write to temp directory '%s' for output files. Try setting `--fileDir` to point to a writable directory. Error: %s'" % (tmpdir, e1))
finally:
try:
os.unlink(testfile1)
except Exception, e2:
pass
finally:
try:
os.unlink(testfile)
except Exception, e2:
pass
# Create the dirs for fileDir option as needed
if options.fileDir:
fpDir = os.path.normpath(os.path.expanduser(options.fileDir))
if fpDir and fpDir != "":
if not fpDir.endswith(os.sep):
fpDir += os.sep
fpd2 = os.path.abspath(fpDir)
if not os.path.exists(fpd2):
try:
os.makedirs(fpd2)
except Exception, e:
sys.exit("Failed to create '%s' for saving files per --fileDir option: %s" % (fpd2, e))
if not os.path.isdir(fpd2):
sys.exit("Path specified in '--fileDir' is not a directory: %s" % fpd2)
testfile = None
handle = None
try:
import tempfile
handle, testfile = tempfile.mkstemp(dir=fpd2)
os.close(handle)
except Exception, e:
sys.exit("Cannot write to directory '%s' specified by '--fileDir': %s" % (fpDir, e))
finally:
try:
os.unlink(testfile)
except Exception, e2:
pass
options.fileDir = fpDir
options.logoutput = os.path.normpath(os.path.join(os.path.abspath(options.fileDir), options.logoutput))
# Set up the logger
# First, rotate the logfile if necessary
if options.logoutput:
options.logoutput = os.path.normpath(os.path.expanduser(options.logoutput))
if options.logoutput and os.path.exists(options.logoutput) and options.logFileCount > 0:
backupCount = options.logFileCount
bfn = options.logoutput
# Code from python logging.handlers.RotatingFileHandler.doRollover()
for i in range(backupCount - 1, 0, -1):
sfn = "%s.%d" % (bfn, i)
dfn = "%s.%d" % (bfn, i + 1)
if os.path.exists(sfn):
if os.path.exists(dfn):
os.remove(dfn)
os.rename(sfn, dfn)
dfn = bfn + ".1"
if os.path.exists(dfn):
os.remove(dfn)
if os.path.exists(bfn):
try:
os.rename(bfn, dfn)
except OSError, e:
# Issue #824 partial solution
if "being used by another process" in str(e):
# On Windows, when another stitcher instance running in same directory, so has stitcher.log open
# WindowsError: [Error 32] The process cannot access the file because it is being used by another process
sys.exit("Error: Is another stitcher process running in this directory? Run stitcher from a different directory, or re-run with the option `--fileDir <separate directory for this run's output files>`")
else:
raise
# Then have Omni configure the logger
try:
omni.configure_logging(options)
except Exception, e:
sys.exit("Failed to configure logging: %s" % e)
# Now that we've configured logging, reset this to None
# to avoid later log messages about configuring logging
options.logconfig = None
logger = logging.getLogger("stitcher")
if options.fileDir:
logger.info("All files will be saved in the directory '%s'", os.path.abspath(options.fileDir))
# We use the omni config file
# First load the agg nick cache
# First, suppress all but WARN+ messages on console
if not options.debug:
lvl = logging.INFO
handlers = logger.handlers
if len(handlers) == 0:
handlers = logging.getLogger().handlers
for handler in handlers:
if isinstance(handler, logging.StreamHandler):
lvl = handler.level
handler.setLevel(logging.WARN)
break
config = omni.load_agg_nick_config(options, logger)
# Load custom config _after_ system agg_nick_cache,
# which also sets omni_defaults
config = omni.load_config(options, logger, config)
if config.has_key('omni_defaults') and config['omni_defaults'].has_key('scs_url'):
if options.scsURL is not None:
logger.debug("Ignoring omni_config default SCS URL of '%s' because commandline specified '%s'", config['omni_defaults']['scs_url'], options.scsURL)
else:
options.scsURL = config['omni_defaults']['scs_url']
logger.debug("Using SCS URL from omni_config: %s", options.scsURL)
else:
if options.scsURL is None:
options.scsURL = SCS_URL
logger.debug("Using SCS URL default: %s", SCS_URL)
else:
logger.debug("Using SCS URL from commandline: %s", options.scsURL)
if not options.debug:
handlers = logger.handlers
if len(handlers) == 0:
handlers = logging.getLogger().handlers
for handler in handlers:
if isinstance(handler, logging.StreamHandler):
handler.setLevel(lvl)
break
#logger.info("Using AM API version %d", options.api_version)
# Make any file prefix be part of the output file prefix so files go in the right spot
if options.prefix and options.fileDir:
pIsDir = (options.prefix and options.prefix.endswith(os.sep))
if not os.path.isabs(options.prefix):
options.prefix = os.path.normpath(os.path.join(options.fileDir, options.prefix))
else:
# replace any directory in prefix and use the fileDir
options.prefix = prependFilePrefix(options.fileDir, options.prefix)
if pIsDir:
options.prefix += os.sep
elif options.fileDir:
options.prefix = options.fileDir
# logger.debug("--prefix is now %s", options.prefix)
# Create the dirs needed for options.prefix if specified
if options.prefix:
fpDir = os.path.normpath(os.path.expanduser(os.path.dirname(options.prefix)))
if fpDir and fpDir != "" and not os.path.exists(fpDir):
try:
os.makedirs(fpDir)
except Exception, e:
sys.exit("Failed to create '%s' for saving files per --prefix option: %s" % (fpDir, e))
if options.fakeModeDir:
if not os.path.isdir(options.fakeModeDir):
logger.error("Got Fake Mode Dir %s that is not a directory!", options.fakeModeDir)
raise StitchingError("Fake Mod path not a directory: %s" % options.fakeModeDir)
else:
logger.info("Running with Fake Mode Dir %s", options.fakeModeDir)
Aggregate.PAUSE_FOR_DCN_AM_TO_FREE_RESOURCES_SECS = options.ionRetryIntervalSecs
Aggregate.SLIVERSTATUS_POLL_INTERVAL_SEC = options.ionStatusIntervalSecs
nondefOpts = omni.getOptsUsed(parser, options)
if options.debug:
logger.info(omni.getSystemInfo() + "\nStitcher: " + omni.getOmniVersion())
logger.info("Running stitcher ... %s Args: %s" % (nondefOpts, " ".join(args)))
else:
# Force this to the debug log file only
logger.debug(omni.getSystemInfo() + "\nStitcher: " + omni.getOmniVersion())
logger.debug("Running stitcher ... %s Args: %s" % (nondefOpts, " ".join(args)))
omni.checkForUpdates(config, logger)
if options.defaultCapacity < 1:
logger.warn("Specified a tiny default link capacity of %dKbps!", options.defaultCapacity)
# FIXME: Warn about really big capacities too?
if options.useExoSM and options.noExoSM:
sys.exit("Cannot specify both useExoSM and noExoSM")
if options.useExoSM and options.noEGStitching:
sys.exit("Cannot specify both useExoSM and noEGStitching")
if options.useExoSM and options.noEGStitchingOnLink:
sys.exit("Cannot specify both useExoSM and noEGStitchingOnLink")
if options.noExoSM:
if not options.noEGStitching:
logger.debug("Per options avoiding ExoSM. Therefore, not using EG Stitching")
options.noEGStitching = True
# Note that the converse is not true: You can require noEGStitching and still use
# the ExoSM, assuming we edit the request to the ExoSM carefully.
if options.noTransitAMs:
logger.info("Per options not completing reservations at transit / SCS added aggregates")
if not options.noDeleteAtEnd:
logger.debug(" ... therefore setting noDeleteAtEnd")
options.noDeleteAtEnd = True
handler = StitchingHandler(options, config, logger)
return handler.doStitching(args)
# Goal of main is to call the 'call' method and print the result
def main(argv=None):
if argv is None:
argv = sys.argv[1:]
# FIXME: Print other header stuff?
try:
text, item = call(argv)
# FIXME: If called from a script, then anything here?
# if options.script:
# return json
# return result
# else:
print text
except AMAPIError, ae:
if ae.returnstruct and isinstance(ae.returnstruct, dict) and ae.returnstruct.has_key('code'):
if isinstance(ae.returnstruct['code'], int) or isinstance(ae.returnstruct['code'], str):
sys.exit(int(ae.returnstruct['code']))
if isinstance(ae.returnstruct['code'], dict) and ae.returnstruct['code'].has_key('geni_code'):
sys.exit(int(ae.returnstruct['code']['geni_code']))
sys.exit(ae)
except OmniError, oe:
sys.exit(oe)
if __name__ == "__main__":
sys.exit(main())
| 24,209 | 7,027 |
from fixture.orm import ORMFixture
from model.group import Group
from fixture.contact import Contacthelper
db = ORMFixture(host='localhost', name='addressbook', user='root', password='')
try:
l = db.get_contact_list()
for item in l:
print(item.all_phones_from_page)
print(len(l))
finally:
pass
| 320 | 106 |
"""
URL definitions for version 1 of the CSRF API.
"""
from django.conf.urls import url
from .views import CsrfTokenView
urlpatterns = [
url(r'^token$', CsrfTokenView.as_view(), name='csrf_token'),
]
| 208 | 76 |
# coding=utf-8
#
"""
Copyright (c) 2020, Alexander Magola. All rights reserved.
license: BSD 3-Clause License, see LICENSE for more details.
"""
from zm.pyutils import struct
class AnyStrKey(object):
""" Any amount of string keys"""
__slots__ = ()
def __eq__(self, other):
if not isinstance(other, AnyStrKey):
# don't attempt to compare against unrelated types
return NotImplemented # pragma: no cover
return True
def __hash__(self):
# necessary for instances to behave sanely in dicts and sets.
return hash(self.__class__)
ANYSTR_KEY = AnyStrKey()
_PATHS_SCHEME_DICT_VARS = {
'incl' : { 'type': ('str', 'list-of-strs') },
'excl' : { 'type': ('str', 'list-of-strs') },
'ignorecase' : { 'type': 'bool' },
'startdir' : { 'type': 'str' },
}
PATHS_SCHEME = {
'type' : ('str', 'list', 'dict'),
'dict' : {
'vars' : _PATHS_SCHEME_DICT_VARS,
},
'list' : {
'vars-type' : ('str', 'dict'),
'dict-vars' : _PATHS_SCHEME_DICT_VARS,
},
'traits' : ['complex-path'],
}
ConfNode = struct('ConfNode', 'val, traits')
| 1,158 | 428 |
from __future__ import absolute_import
import logging
log = logging.getLogger(__name__)
from ..message import Message
from . import register
@register
class ok_1(Message):
''' Define the ``OK`` message (revision 1) for acknowledging successful
handling of a previous message.
The ``content`` fragment of for this message is empty.
'''
msgtype = 'OK'
revision = 1
@classmethod
def create(cls, request_id, **metadata):
''' Create an ``OK`` message
Args:
request_id (str) :
The message ID for the message the precipitated the OK.
Any additional keyword arguments will be put into the message
``metadata`` fragment as-is.
'''
header = cls.create_header(request_id=request_id)
return cls(header, metadata, {})
| 832 | 228 |
#Importing libraries that are needed for processing
import pandas as pd
from sklearn import preprocessing
import numpy as np
from sklearn.metrics.pairwise import pairwise_distances
# Reading the csv file of term document frequency generated in R
input_data = pd.read_csv("finamatrix.csv",index_col=0, header =0 )
# Normalizing the data within a scale range
x = preprocessing.MinMaxScaler()
data_standard = x.fit_transform(input_data)
# Calculating jaccardian similarity scores
jac_similarity = pairwise_distances(data_standard, metric='jaccard')
# Reshaping array
jac_similarity = jac_similarity.reshape(jac_similarity.size,1)
print jac_similarity[0:10]
# sorting scores
sorted_jac = np.sort(jac_similarity, axis = None)[::-1]
sorted_jac = np.roll(sorted_jac, -np.count_nonzero(np.isnan(jac_similarity)))
#Top three paired article scores are
top_jac = sorted_jac[0:3]
print sorted_jac[0:20]
print("top three jacardian score paired articles are")
print(top_jac)
| 973 | 332 |
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
# load data
(x_train, _), _ = keras.datasets.mnist.load_data()
x_train = x_train.astype('float32').reshape((-1, 28, 28, 1)) / 255.
# build with tf
encoder_input = keras.Input(shape=(28, 28, 1), name='original_img')
x = layers.Conv2D(16, 3, activation='relu')(encoder_input)
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.MaxPooling2D(3)(x)
x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.Conv2D(16, 3, activation='relu')(x)
encoder_output = layers.GlobalMaxPooling2D()(x)
x = layers.Reshape((4, 4, 1))(encoder_output)
x = layers.Conv2DTranspose(16, 3, activation='relu')(x)
x = layers.Conv2DTranspose(32, 3, activation='relu')(x)
x = layers.UpSampling2D(3)(x)
x = layers.Conv2DTranspose(16, 3, activation='relu')(x)
decoder_output = layers.Conv2DTranspose(1, 3, activation='relu')(x)
autoencoder = keras.Model(encoder_input, decoder_output, name='autoencoder')
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)
autoencoder.fit(x_train,
x_train,
epochs=50,
batch_size=256,
shuffle=True,
callbacks=[callback])
decoded_imgs = autoencoder.predict(x_train)
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_train[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, n + i + 1)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show() | 1,858 | 766 |
#!/usr/bin/env python
# Copyright 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
'''Copies the liblouis braille translation tables to a destination.'''
import liblouis_list_tables
import optparse
import os
import shutil
def LinkOrCopyFiles(sources, dest_dir):
def LinkOrCopyOneFile(src, dst):
if os.path.exists(dst):
os.unlink(dst)
try:
os.link(src, dst)
except:
shutil.copy(src, dst)
if not os.path.exists(dest_dir):
os.makedirs(dest_dir)
for source in sources:
LinkOrCopyOneFile(source, os.path.join(dest_dir, os.path.basename(source)))
def WriteDepfile(depfile, infiles):
stampfile = depfile + '.stamp'
with open(stampfile, 'w'):
os.utime(stampfile, None)
content = '%s: %s' % (stampfile, ' '.join(infiles))
open(depfile, 'w').write(content)
def main():
parser = optparse.OptionParser(description=__doc__)
parser.add_option('-D', '--directory', dest='directories',
action='append', help='Where to search for table files')
parser.add_option('-e', '--extra_file', dest='extra_files', action='append',
default=[], help='Extra liblouis table file to process')
parser.add_option('-d', '--dest_dir', action='store', metavar='DIR',
help=('Destination directory. Used when translating ' +
'input paths to output paths and when copying '
'files.'))
parser.add_option('--depfile', metavar='FILENAME',
help=('Store .d style dependencies in FILENAME and touch '
'FILENAME.stamp after copying the files'))
options, args = parser.parse_args()
if len(args) != 1:
parser.error('Expecting exactly one argument')
if not options.directories:
parser.error('At least one --directory option must be specified')
if not options.dest_dir:
parser.error('At least one --dest_dir option must be specified')
files = liblouis_list_tables.GetTableFiles(args[0], options.directories,
options.extra_files)
LinkOrCopyFiles(files, options.dest_dir)
if options.depfile:
WriteDepfile(options.depfile, files)
if __name__ == '__main__':
main()
| 2,327 | 729 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
data_dir ディレクトリ内のすべてのファイルを、PGM_P ( = const char * ) として埋め込むスクリプト。
Build/Upload の前に、自動的に実行される。
"""
Import("env", "projenv")
import os, glob, re, hashlib
header_file_header = """// auto-generated by script tools/embed_resource.py
// DO NOT EDIT BY HAND
#ifndef ESP8266Clock_resource_data_H_
#define ESP8266Clock_resource_data_H_
#include <Arduino.h>
namespace Resource {
"""
header_file_footer = """
} // namespace Resource
#endif // ESP8266Clock_resource_data_H_
"""
cpp_file_header = """// auto-generated by script tools/embed_resource.py
// DO NOT EDIT BY HAND
#include <resource.h>
#include <resource-data.h>
namespace Resource {
"""
cpp_file_footer = """
} // namespace Resource
"""
data_dir = env.subst("$PROJECT_DATA_DIR")
src_dir = env.subst("$PROJECT_SRC_DIR")
# 変数名に使えない文字を削除する用
pattern = re.compile(r'[^0-9A-Za-z_]')
def del_invalid_char(input):
"""変数名に使えない文字をアンダーバーに置換"""
return pattern.sub('_', input)
def get_var_name(file_path):
"""ファイルパスから変数名を取得する"""
# 数字から始まるファイル名対策として、先頭のパス区切り文字をわざと残す
return del_invalid_char(file.replace(data_dir, ''))
def wrap_ifdef_if(condition, defined, action):
if condition:
header_file.write('#ifdef %s\n' % (defined))
cpp_file.write('#ifdef %s\n' % (defined))
action()
if condition:
header_file.write('#endif\n')
cpp_file.write('#endif\n')
def embed_as_binary(file_path, header_file, cpp_file):
"""ファイル file_path を .h/.cpp ファイルに埋め込む"""
var_name = get_var_name(file_path)
with open(file_path, 'rb') as f:
bs = f.read()
header_file.write('extern const char %s[] PROGMEM;\n' % (var_name))
header_file.write('extern const size_t len%s;\n' % (var_name))
header_file.write('extern const char etag%s[] PROGMEM;\n' % (var_name))
cpp_file.write('const char %s[] PROGMEM = {\n' % (var_name))
for i, b in enumerate(bs):
cpp_file.write('%s,' % (hex(b)))
if i % 32 == 31:
cpp_file.write('\n')
cpp_file.write('};\n')
cpp_file.write('const size_t len%s = %d;\n' % (var_name, len(bs)))
hs = hashlib.sha256(bs).hexdigest()
cpp_file.write('const char etag%s[] PROGMEM = "W/\\"%s\\"";\n' % (var_name, hs[:12]))
with open(os.path.join(src_dir, 'resource-data.h'), 'w') as header_file:
with open(os.path.join(src_dir, 'resource.cpp'), 'w') as cpp_file:
header_file.write(header_file_header)
cpp_file.write(cpp_file_header)
files = glob.glob(os.path.join(data_dir, '**'))
for file in files:
wrap_ifdef_if(os.path.basename(file) == 'public.key',
'ENABLE_BINARY_SIGNING',
lambda: embed_as_binary(file, header_file, cpp_file))
print("Embedding %s" % (file))
cpp_file.write('resource_t searchByPath(const String &path) {\n')
for file in files:
if os.path.basename(file) != 'public.key':
cpp_file.write(' if (path == F("%s"))\n' % (file.replace(data_dir, '').replace('\\', '/')))
var_name = get_var_name(file)
cpp_file.write(' return {%s, len%s, etag%s};\n' % (var_name, var_name, var_name))
cpp_file.write(' return {0, 0, 0};\n')
cpp_file.write('}')
header_file.write(header_file_footer)
cpp_file.write(cpp_file_footer)
| 3,199 | 1,413 |
# Generated by Django 3.0.8 on 2020-08-19 11:57
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('rest', '0031_auto_20200819_2056'),
]
operations = [
migrations.AlterField(
model_name='culture_event',
name='fare',
field=models.CharField(max_length=200),
),
]
| 389 | 145 |
from watchFaceParser.elements.basicElements.imageSet import ImageSet
class Icon:
definitions = {
1: { 'Name': 'Images', 'Type': ImageSet},
2: { 'Name': 'NoWeatherImageIndex', 'Type': 'long'},
}
| 220 | 69 |
from django.db import migrations
import utilities.fields
class Migration(migrations.Migration):
dependencies = [
('dcim', '0132_cable_length'),
]
operations = [
migrations.AddField(
model_name='frontport',
name='color',
field=utilities.fields.ColorField(blank=True, max_length=6),
),
migrations.AddField(
model_name='frontporttemplate',
name='color',
field=utilities.fields.ColorField(blank=True, max_length=6),
),
migrations.AddField(
model_name='rearport',
name='color',
field=utilities.fields.ColorField(blank=True, max_length=6),
),
migrations.AddField(
model_name='rearporttemplate',
name='color',
field=utilities.fields.ColorField(blank=True, max_length=6),
),
]
| 905 | 259 |