hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d439ffdbf06785b49342634199820579ecf19f5f
| 102
|
py
|
Python
|
app/routes/__init__.py
|
abcnever/euchre-game
|
5446e345e0dfdcf83d5fe87c3d2cedc31b3ae669
|
[
"MIT"
] | 1
|
2018-12-31T05:38:56.000Z
|
2018-12-31T05:38:56.000Z
|
app/routes/__init__.py
|
abcnever/euchre-game
|
5446e345e0dfdcf83d5fe87c3d2cedc31b3ae669
|
[
"MIT"
] | 4
|
2018-11-03T15:51:13.000Z
|
2019-01-12T21:09:23.000Z
|
app/routes/__init__.py
|
abcnever/euchre-game
|
5446e345e0dfdcf83d5fe87c3d2cedc31b3ae669
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
routes = Blueprint('routes', __name__)
from . import index
import rooms
| 14.571429
| 38
| 0.77451
| 13
| 102
| 5.769231
| 0.615385
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.156863
| 102
| 6
| 39
| 17
| 0.872093
| 0
| 0
| 0
| 0
| 0
| 0.058824
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.75
| 0
| 0.75
| 0.5
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
|
0
| 6
|
2e03f35b0822759a335c0fe0661869bd17581090
| 19,035
|
py
|
Python
|
domrl/agents/my_agents.py
|
santiagonasar/DomRL
|
00ce010ea2d5d3cb1b56910f2304c114d82d3198
|
[
"MIT"
] | null | null | null |
domrl/agents/my_agents.py
|
santiagonasar/DomRL
|
00ce010ea2d5d3cb1b56910f2304c114d82d3198
|
[
"MIT"
] | null | null | null |
domrl/agents/my_agents.py
|
santiagonasar/DomRL
|
00ce010ea2d5d3cb1b56910f2304c114d82d3198
|
[
"MIT"
] | null | null | null |
import string
import numpy
import copy
from domrl.engine.agent import Agent
"""
class Agent(object):
def choose(self, decision, state):
return decision.moves[0]
class StdinAgent(Agent):
def choose(self, decision, state):
# Autoplay
if len(decision.moves) == 1:
return [0]
player = decision.player
print(f" ==== Decision to be made by {player} ==== ")
print(f"Actions: {player.actions} | Buys: {player.buys} | Coins: {player.coins}")
print("Hand: ", list(map(str, player.hand)))
print(decision.prompt)
for idx, move in enumerate(decision.moves):
print(f"{idx}: {move}")
# Get user input and process it.
while True:
user_input = input()
if user_input == "?":
state.event_log.print(player)
print(state)
else:
try:
ans = list(map(lambda x: int(x.strip()), user_input.split(',')))
except:
print('Clearly invalid input. Please try again.')
continue
break
return ans
class APIAgent(Agent):
def choose(self, decision, state):
# Autoplay
# if len(decision.moves) == 1:
# return [0]
player = decision.player
actions = player.actions
buys = player.buys
coins = player.coins
moves = decision.moves
hand = player.hand
state
while True:
user_input = input()
if user_input == "?":
state.event_log.print(player)
print(state)
else:
ans = list(map(lambda x: int(x.strip()), user_input.split(',')))
break
return ans
"""
class RandomAgent(Agent):
def policy(self, decision, state):
if 'Trash up to 4' in decision.prompt: # for chapel
my_list = []
range_max = numpy.random.randint(0, min(len(decision.moves), 4) + 1, 1, int)
for idx in range(0, range_max[0]):
new_item = -1
while new_item == -1 or new_item in my_list:
new_item = numpy.random.randint(0, len(decision.moves), 1, int)[0]
my_list.append(new_item)
return my_list
if len(decision.moves) == 0:
return []
if 'Discard down to 3 cards' in decision.prompt: # for militia
my_list = []
range_max = max(len(decision.player.hand) - 3, 0)
for idx in range(0, range_max):
new_item = -1
while new_item == -1 or new_item in my_list:
new_item = numpy.random.randint(0, len(decision.moves), 1, int)[0]
my_list.append(new_item)
return my_list
value = list(numpy.random.randint(0, len(decision.moves), 1, int))
return value
class PassOnBuySemiAgent(Agent):
def policy(self, decision, state):
if 'Buy' in decision.prompt:
return [0]
class CleverAgentOld(Agent):
def __init__(self, agent):
self.agent = agent
def policy(self, decision, state):
initialDecision = copy.deepcopy(decision)
# Automove If One Move
if len(decision.moves) == 1:
return [0]
for idx in range(0, len(initialDecision.moves)):
move = initialDecision.moves[idx]
if "Buy: Curse" in move.__str__():
decision.moves.pop(idx)
if hasattr(move, "card") and (
move.card.add_actions > 0 or ("treasure" in decision.prompt.lower() and move.card.coins > 0)):
return self.restrictDecision(decision.moves, initialDecision.moves, idx)
restrictedChoice = self.agent.policy(decision, state)
return self.restrictDecision(decision.moves, initialDecision.moves, restrictedChoice[0])
def restrictDecision(self, moves, initialMoves, chosen):
for idx in range(0, len(initialMoves)):
if str(initialMoves[idx]) == str(moves[chosen]):
return list([idx])
return [chosen]
class RulesSemiAgent(Agent):
def policy(self, decision, state):
# Automove If One Move
if len(decision.moves) == 1:
return [0]
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if "Bandit" in str(move): # currently does not work
decision.moves.pop(idx)
if "Remodel" in str(move): # currently does not work
decision.moves.pop(idx)
class CleverSemiAgent(Agent):
def policy(self, decision, state):
# Automove If One Move
if len(decision.moves) == 1:
return [0]
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if "Buy: Curse" in move.__str__():
decision.moves.pop(idx)
if hasattr(move, "card") and (
move.card.add_actions > 0 or ("treasure" in decision.prompt.lower() and move.card.coins > 0)):
return [idx]
class ApplySemiAgent(Agent):
def __init__(self, semiAgents, agent):
self.semiAgents = semiAgents
self.agent = agent
def policy(self, decision, state):
for semiAgent in self.semiAgents:
value = semiAgent.policy(decision, state)
if value is not None:
return value
return self.agent.policy(decision, state)
class BigMoneySemiAgent(Agent):
def policy(self, decision, state):
for stringDesired in ["Buy: Province", "Buy: Gold", "Buy: Silver"]:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if stringDesired in move.__str__():
return [idx]
class SmithySemiAgent(Agent):
def policy(self, decision, state):
for stringDesired in ["Play: Smithy"]:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if stringDesired in move.__str__():
return [idx]
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if "Buy: Smithy" in move.__str__() and (
sum(1 for c in decision.player.all_cards if 'Smithy' in str(c)) / len(
decision.player.all_cards) < 0.1):
return [idx]
class DontBuyCopperOrEstateSemiAgent(Agent):
def policy(self, decision, state):
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if 'Buy: Copper' in str(move) or 'Buy: Estate' in str(move):
decision.moves.pop(idx)
class MyHeuristicSemiAgent(Agent):
def policy(self, decision, state):
for stringDesired in []:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if stringDesired in move.__str__():
return [idx]
if 'Action' in decision.prompt:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if 'Militia' in str(move) or 'Smithy' in str(move):
return [idx]
if 'Buy' not in decision.prompt and 'Choose a pile to gain card from.' not in decision.prompt:
return
desired_deck = {'Festival': 1, 'Market': 1, 'Militia': 1, 'Smithy': 0.1, 'Village': 0.2}
if numpy.random.randint(0, 2, 1, int) == 1:
desired_deck = {'Market': 1, 'Festival': 1, 'Smithy': 0.1, 'Militia': 1, 'Village': 0.2}
for wish in desired_deck:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if wish in str(move) and (
sum(1 for c in decision.player.all_cards if wish in str(c)) / len(
decision.player.all_cards) < desired_deck[wish]):
return [idx]
class MarketSemiAgent(Agent):
def policy(self, decision, state):
if 'Action' in decision.prompt:
for stringDesired in ['Empty']:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if 'Militia' in str(move):
return [idx]
if 'Smithy' in str(move) and decision.player.actions > 1:
return [idx]
if stringDesired in str(move):
return [idx]
if 'Buy' not in decision.prompt and 'Choose a pile to gain card from.' not in decision.prompt:
return
desired_deck = {'Market': 1, 'Militia': 0.001, 'Smithy': 0.001, 'Village': 0.2}
for wish in desired_deck:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if wish in str(move):
if sum(1 for c in decision.player.all_cards if wish in str(c)) / len(
decision.player.all_cards) < desired_deck[wish]:
return [idx]
class CustomHeuristicsSemiAgent(Agent):
def __init__(self, desired_decks):
self.desired_deck = desired_decks
def policy(self, decision, state):
if 'Action' in decision.prompt:
for stringDesired in ['Empty']:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if 'Militia' in str(move):
return [idx]
if 'Smithy' in str(move) and decision.player.actions > 1:
return [idx]
if stringDesired in str(move):
return [idx]
if 'Buy' not in decision.prompt and 'Choose a pile to gain card from.' not in decision.prompt:
return
for wish in self.desired_deck:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if wish in str(move):
if sum(1 for c in decision.player.all_cards if wish in str(c)) / len(
decision.player.all_cards) < self.desired_deck[wish]:
return [idx]
class MarketNoSmithySemiAgent(Agent):
def policy(self, decision, state):
if 'Action' in decision.prompt:
for stringDesired in ['Empty']:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if 'Militia' in str(move):
return [idx]
if 'Smithy' in str(move) and decision.player.actions > 1:
return [idx]
if stringDesired in str(move):
return [idx]
if 'Buy' not in decision.prompt and 'Choose a pile to gain card from.' not in decision.prompt:
return
desired_deck = {'Market': 1, 'Militia': 0.1, 'Village': 0.2}
for wish in desired_deck:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if wish in str(move):
if sum(1 for c in decision.player.all_cards if wish in str(c)) / len(
decision.player.all_cards) < desired_deck[wish]:
return [idx]
class MarketNoSmithySemiAgent2(Agent):
def policy(self, decision, state):
if 'Action' in decision.prompt:
for stringDesired in ['Empty']:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if 'Militia' in str(move):
return [idx]
if 'Smithy' in str(move) and decision.player.actions > 1:
return [idx]
if stringDesired in str(move):
return [idx]
if 'Buy' not in decision.prompt and 'Choose a pile to gain card from.' not in decision.prompt:
return
desired_deck = {'Market': 1, 'Militia': 0.2, 'Village': 0.2}
for wish in desired_deck:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if wish in str(move):
if sum(1 for c in decision.player.all_cards if wish in str(c)) / len(
decision.player.all_cards) < desired_deck[wish]:
return [idx]
class OnlyBuyCopperIfSemiAgent(Agent):
def policy(self, decision, state):
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if "Buy: Copper" in str(move):
if sum(c.coins for c in decision.player.all_cards) < 5:
return [idx]
else:
decision.moves.pop(idx)
class ChapelSemiAgent(Agent):
def policy(self, decision, state):
if 'Action' in decision.prompt:
for c in decision.player.hand:
if 'Estate' in str(c):
for idx in range(0, len(decision.moves)):
if 'Play: Chapel' in str(decision.moves[idx]):
return [idx]
if 'Trash up to 4' in decision.prompt:
moves = []
for idx in range(0, len(decision.moves)):
if len(moves) >= 4:
break
try:
move = decision.moves[idx]
except:
break
if "Choose: Estate" in move.__str__():
moves.append(idx)
for idx in range(0, len(decision.moves)):
if len(moves) >= 4:
break
try:
move = decision.moves[idx]
except:
break
if "Choose: Copper" in move.__str__() and (
sum(c.coins for c in decision.player.all_cards) -
sum(1 for planned_move in moves if 'Copper' in str(planned_move)) > 5):
moves.append(idx)
return moves
if 'Buy' in decision.prompt:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if 'Buy: Chapel' in str(move) and decision.player.coins < 4 and (
sum(1 for c in decision.player.all_cards if 'Chapel' in str(c)) == 0):
return [idx]
class AggressiveChapelSemiAgent(ChapelSemiAgent):
def policy(self, decision, state):
if 'Action' in decision.prompt:
for c in decision.player.hand:
if 'Estate' in str(c) or ('Copper' in str(c) and sum(c.coins for c in decision.player.all_cards) > 5):
for idx in range(0, len(decision.moves)):
if 'Play: Chapel' in str(decision.moves[idx]):
return [idx]
if 'Trash' in decision.prompt:
moves = []
for idx in range(0, len(decision.moves)):
if len(moves) >= 4:
break
try:
move = decision.moves[idx]
except:
break
if "Choose: Estate" in str(move):
moves.append(idx)
for idx in range(0, len(decision.moves)):
if len(moves) >= 4:
break
try:
move = decision.moves[idx]
except:
break
if "Choose: Copper" in str(move) and (
sum(c.coins for c in decision.player.all_cards) -
sum(1 for planned_move in moves if 'Copper' in str(decision.moves[planned_move])) > 5):
moves.append(idx)
return moves
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if "Buy: Chapel" in str(move) and (sum(1 for c in decision.player.all_cards if 'Chapel' in str(c)) > 0):
decision.moves.pop(idx)
if "Buy: Chapel" in str(move) and decision.player.coins < 4 and (
sum(1 for c in decision.player.all_cards if 'Chapel' in str(c)) == 0):
return [idx]
class ProvinceSemiAgent(Agent):
def policy(self, decision, state):
for stringDesired in ["Buy: Province"]:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if stringDesired in move.__str__():
return [idx]
class ProvinceNeverLoseSemiAgent(Agent):
def policy(self, decision, state):
desired_strings = ["Buy: Province"]
if (state.supply_piles['Province'].qty == 1 and
(6 + decision.player.total_vp() <
max(state.other_players, key=lambda pr: pr.total_vp()).total_vp())):
desired_strings = ["Buy: Duchy"]
for stringDesired in desired_strings:
for idx in range(0, len(decision.moves)):
try:
move = decision.moves[idx]
except:
break
if stringDesired in str(move):
return [idx]
| 32.262712
| 118
| 0.492094
| 2,095
| 19,035
| 4.409547
| 0.083532
| 0.111171
| 0.065815
| 0.045031
| 0.790431
| 0.767049
| 0.75774
| 0.731868
| 0.708811
| 0.682615
| 0
| 0.012843
| 0.41098
| 19,035
| 589
| 119
| 32.317487
| 0.811095
| 0.006987
| 0
| 0.760204
| 0
| 0
| 0.049746
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.058673
| false
| 0.002551
| 0.010204
| 0
| 0.247449
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
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| 1
| 0
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| null | 0
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| 0
| 0
| 0
| 0
|
0
| 6
|
2e0969f7a34ab55314e5540bfd52964a79256105
| 7,452
|
py
|
Python
|
permabots/serializers/state.py
|
eafanasev/permabots
|
24de0376e8c482800f4214c021c133d81b9de69f
|
[
"BSD-3-Clause"
] | 81
|
2016-05-18T02:34:10.000Z
|
2021-08-28T17:25:13.000Z
|
permabots/serializers/state.py
|
eafanasev/permabots
|
24de0376e8c482800f4214c021c133d81b9de69f
|
[
"BSD-3-Clause"
] | 15
|
2016-05-27T08:51:46.000Z
|
2021-03-19T21:42:21.000Z
|
permabots/serializers/state.py
|
eafanasev/permabots
|
24de0376e8c482800f4214c021c133d81b9de69f
|
[
"BSD-3-Clause"
] | 34
|
2016-05-29T14:37:01.000Z
|
2022-03-24T17:16:53.000Z
|
from rest_framework import serializers
from permabots.models import State, TelegramChatState, TelegramChat, TelegramUser, KikChatState, KikChat, KikUser, MessengerChatState
from django.utils.translation import ugettext_lazy as _
class StateSerializer(serializers.HyperlinkedModelSerializer):
id = serializers.ReadOnlyField(help_text=_("State ID"))
class Meta:
model = State
fields = ['id', 'created_at', 'updated_at', 'name']
read_only_fields = ('id', 'created_at', 'updated_at',)
class TelegramChatStateSerializer(serializers.ModelSerializer):
id = serializers.ReadOnlyField(help_text=_("Chat State ID"))
chat = serializers.IntegerField(source="chat.id", help_text=_("Chat identifier. Telegram API format. https://core.telegram.org/bots/api#chat"))
state = StateSerializer(many=False, help_text=_("State associated to the Chat"))
user = serializers.IntegerField(source="user.id", help_text=_("User indentifier. Telegram API format. https://core.telegram.org/bots/api#chat"))
class Meta:
model = TelegramChatState
fields = ['id', 'created_at', 'updated_at', 'chat', 'user', 'state']
read_only_fields = ('id', 'created_at', 'updated_at',)
def create(self, validated_data):
chat = TelegramChat.objects.get(pk=validated_data['chat'])
user = TelegramUser.objects.get(pk=validated_data['user'])
state = State.objects.get(name=validated_data['state']['name'])
chat_state = TelegramChatState.objects.create(chat=chat,
state=state,
user=user)
return chat_state
def update(self, instance, validated_data):
chat = TelegramChat.objects.get(pk=validated_data['chat']['id'])
user = TelegramUser.objects.get(pk=validated_data['user']['id'])
state = State.objects.get(name=validated_data['state']['name'])
instance.chat = chat
instance.user = user
instance.state = state
instance.save()
return instance
class TelegramChatStateUpdateSerializer(TelegramChatStateSerializer):
chat = serializers.IntegerField(source="chat.id", required=False,
help_text=_("Chat identifier. Telegram API format. https://core.telegram.org/bots/api#chat"))
state = StateSerializer(many=False, required=False, help_text=_("State associated to the Chat"))
user = serializers.IntegerField(source="user.id", required=False,
help_text=_("User identifier. Telegram API format. https://core.telegram.org/bots/api#chat"))
def update(self, instance, validated_data):
if 'user' in validated_data:
instance.user = TelegramUser.objects.get(pk=validated_data['user']['id'])
if 'chat' in validated_data:
instance.chat = TelegramChat.objects.get(pk=validated_data['chat']['id'])
if 'state' in validated_data:
instance.state = State.objects.get(name=validated_data['state']['name'])
instance.save()
return instance
class KikChatStateSerializer(serializers.ModelSerializer):
id = serializers.ReadOnlyField(help_text=_("Chat State ID"))
chat = serializers.CharField(source="chat.id", help_text=_("Chat identifier. Kik API format."))
state = StateSerializer(many=False, help_text=_("State associated to the Chat"))
user = serializers.CharField(source="user.username", help_text=_("User indentifier. Kik API format"))
class Meta:
model = KikChatState
fields = ['id', 'created_at', 'updated_at', 'chat', 'user', 'state']
read_only_fields = ('id', 'created_at', 'updated_at',)
def create(self, validated_data):
chat = KikChat.objects.get(pk=validated_data['chat'])
user = KikUser.objects.get(pk=validated_data['user'])
state = State.objects.get(name=validated_data['state']['name'])
chat_state = KikChatState.objects.create(chat=chat,
state=state,
user=user)
return chat_state
def update(self, instance, validated_data):
chat = KikChat.objects.get(pk=validated_data['chat']['id'])
user = KikUser.objects.get(pk=validated_data['user']['username'])
state = State.objects.get(name=validated_data['state']['name'])
instance.chat = chat
instance.user = user
instance.state = state
instance.save()
return instance
class KikChatStateUpdateSerializer(KikChatStateSerializer):
chat = serializers.CharField(source="chat.id", required=False,
help_text=_("Chat identifier. Kik API format."))
state = StateSerializer(many=False, required=False, help_text=_("State associated to the Chat"))
user = serializers.CharField(source="user.username", required=False,
help_text=_("User identifier. Kik API format."))
def update(self, instance, validated_data):
if 'user' in validated_data:
instance.user = KikUser.objects.get(pk=validated_data['user']['username'])
if 'chat' in validated_data:
instance.chat = KikChat.objects.get(pk=validated_data['chat']['id'])
if 'state' in validated_data:
instance.state = State.objects.get(name=validated_data['state']['name'])
instance.save()
return instance
class MessengerChatStateSerializer(serializers.ModelSerializer):
id = serializers.ReadOnlyField(help_text=_("Chat State ID"))
chat = serializers.CharField(help_text=_("Chat identifier. Messenger API format."))
state = StateSerializer(many=False, help_text=_("State associated to the Chat"))
class Meta:
model = MessengerChatState
fields = ['id', 'created_at', 'updated_at', 'chat', 'state']
read_only_fields = ('id', 'created_at', 'updated_at',)
def create(self, validated_data):
chat = validated_data['chat']
state = State.objects.get(name=validated_data['state']['name'])
chat_state = MessengerChatState.objects.create(chat=chat,
state=state)
return chat_state
def update(self, instance, validated_data):
chat = validated_data['chat']
state = State.objects.get(name=validated_data['state']['name'])
instance.chat = chat
instance.state = state
instance.save()
return instance
class MessengerChatStateUpdateSerializer(MessengerChatStateSerializer):
chat = serializers.CharField(required=False,
help_text=_("Chat identifier. Messenger API format."))
state = StateSerializer(many=False, required=False, help_text=_("State associated to the Chat"))
def update(self, instance, validated_data):
if 'chat' in validated_data:
instance.chat = validated_data['chat']
if 'state' in validated_data:
instance.state = State.objects.get(name=validated_data['state']['name'])
instance.save()
return instance
| 47.164557
| 148
| 0.625872
| 780
| 7,452
| 5.833333
| 0.103846
| 0.117143
| 0.056044
| 0.055385
| 0.827912
| 0.82044
| 0.778022
| 0.746593
| 0.716264
| 0.658462
| 0
| 0
| 0.255502
| 7,452
| 158
| 149
| 47.164557
| 0.820115
| 0
| 0
| 0.601626
| 0
| 0
| 0.163022
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.073171
| false
| 0
| 0.02439
| 0
| 0.422764
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
2e1d52600cecea1b471b062e4a225acf8590bb5b
| 201
|
py
|
Python
|
robotathome/__init__.py
|
goyoambrosio/RobotAtHome_API
|
91864b4cf06202656def6b66ac348708337a9d52
|
[
"MIT"
] | 1
|
2021-02-21T09:31:25.000Z
|
2021-02-21T09:31:25.000Z
|
robotathome/__init__.py
|
goyoambrosio/RobotAtHome_API
|
91864b4cf06202656def6b66ac348708337a9d52
|
[
"MIT"
] | null | null | null |
robotathome/__init__.py
|
goyoambrosio/RobotAtHome_API
|
91864b4cf06202656def6b66ac348708337a9d52
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__version__ = "0.4.9"
from robotathome.version import *
from robotathome.helpers import *
from robotathome.toolbox import *
from robotathome.log import *
| 22.333333
| 33
| 0.731343
| 27
| 201
| 5.296296
| 0.62963
| 0.41958
| 0.440559
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022989
| 0.134328
| 201
| 8
| 34
| 25.125
| 0.798851
| 0.208955
| 0
| 0
| 0
| 0
| 0.031847
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.8
| 0
| 0.8
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
5cf06ac7a7b0b3cfe37cd14963dd9859601b68ba
| 716
|
py
|
Python
|
dojo/__init__.py
|
VIVelev/PyDojo
|
d932b3df841636208611192be1f881390c361289
|
[
"MIT"
] | 4
|
2018-10-29T22:01:39.000Z
|
2019-01-15T14:46:40.000Z
|
dojo/__init__.py
|
VIVelev/PyDojo
|
d932b3df841636208611192be1f881390c361289
|
[
"MIT"
] | 3
|
2018-12-16T15:35:52.000Z
|
2020-03-31T01:14:53.000Z
|
dojo/__init__.py
|
VIVelev/PyDojo
|
d932b3df841636208611192be1f881390c361289
|
[
"MIT"
] | null | null | null |
from . import (
anomaly,
base,
bayes,
cluster,
dimred,
ensemble,
evolution,
linear,
metrics,
nlp,
nn,
plot,
preprocessing,
split,
svm,
tree,
tuning,
activations,
exceptions,
losses,
misc,
optimizers,
regularizers,
statistics,
)
__all__ = [
"anomaly",
"base",
"bayes",
"cluster",
"dimred",
"ensemble",
"evolution",
"linear",
"metrics",
"nlp",
"nn",
"plot",
"preprocessing",
"split",
"svm",
"tree",
"tuning",
"activations",
"exceptions",
"losses",
"misc",
"optimizers",
"regularizers",
"statistics",
]
__version__ = "0.4.9"
| 12.785714
| 21
| 0.49581
| 55
| 716
| 6.309091
| 0.563636
| 0.063401
| 0.092219
| 0.132565
| 0.933718
| 0.933718
| 0.933718
| 0.933718
| 0.933718
| 0.933718
| 0
| 0.006494
| 0.354749
| 716
| 55
| 22
| 13.018182
| 0.744589
| 0
| 0
| 0
| 0
| 0
| 0.23324
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.018868
| 0
| 0.018868
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
cf15d443632a0a7ff7b603303f52089b10666d75
| 29
|
py
|
Python
|
faculty_sync/__init__.py
|
Matt-Haugh/faculty-sync
|
56bac90badfe6812f44bad13f56715e4e16f4e57
|
[
"Apache-2.0"
] | 6
|
2019-02-08T10:36:07.000Z
|
2021-11-30T06:04:56.000Z
|
faculty_sync/__init__.py
|
Matt-Haugh/faculty-sync
|
56bac90badfe6812f44bad13f56715e4e16f4e57
|
[
"Apache-2.0"
] | 32
|
2018-04-29T13:54:39.000Z
|
2019-01-18T16:14:54.000Z
|
faculty_sync/__init__.py
|
Matt-Haugh/faculty-sync
|
56bac90badfe6812f44bad13f56715e4e16f4e57
|
[
"Apache-2.0"
] | 3
|
2020-01-09T17:03:31.000Z
|
2021-04-04T10:37:25.000Z
|
from .app import run # noqa
| 14.5
| 28
| 0.689655
| 5
| 29
| 4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.241379
| 29
| 1
| 29
| 29
| 0.909091
| 0.137931
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cf15dd2f22f5f6ead7a60c2bea1f3a755aad40b2
| 180
|
py
|
Python
|
schema/schema.py
|
akarapun/elearning
|
fe116d5815925269819061ea183cbfdb773844cf
|
[
"MIT"
] | 1
|
2020-03-14T11:00:14.000Z
|
2020-03-14T11:00:14.000Z
|
schema/schema.py
|
akarapun/elearning
|
fe116d5815925269819061ea183cbfdb773844cf
|
[
"MIT"
] | null | null | null |
schema/schema.py
|
akarapun/elearning
|
fe116d5815925269819061ea183cbfdb773844cf
|
[
"MIT"
] | null | null | null |
import graphene
from root_query import RootQuery as query
from root_mutation import RootMutation as mutation
def get():
return graphene.Schema(query=query, mutation=mutation)
| 25.714286
| 58
| 0.816667
| 25
| 180
| 5.8
| 0.52
| 0.110345
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.133333
| 180
| 6
| 59
| 30
| 0.929487
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| true
| 0
| 0.6
| 0.2
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
cf3333692ac60d08a7e6536dccc579d614d6a1e3
| 25
|
py
|
Python
|
pimkl/models/__init__.py
|
PhosphorylatedRabbits/pimkl
|
824fe70027d7950ea6775c8db2ac587d8504ff3d
|
[
"MIT"
] | 3
|
2019-10-01T10:05:53.000Z
|
2021-03-08T12:16:17.000Z
|
pimkl/models/__init__.py
|
PhosphorylatedRabbits/pimkl
|
824fe70027d7950ea6775c8db2ac587d8504ff3d
|
[
"MIT"
] | 82
|
2019-10-18T16:01:26.000Z
|
2022-02-03T16:56:04.000Z
|
pimkl/models/__init__.py
|
PhosphorylatedRabbits/pimkl
|
824fe70027d7950ea6775c8db2ac587d8504ff3d
|
[
"MIT"
] | null | null | null |
from .pimkl import PIMKL
| 12.5
| 24
| 0.8
| 4
| 25
| 5
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16
| 25
| 1
| 25
| 25
| 0.952381
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cf495150ff1ac19fcbe331ab5bc19db881f77aa9
| 14,735
|
py
|
Python
|
mooringlicensing/tests/test_proposal_vessel_logic.py
|
GraemeMuller/mooringlicensing
|
2b2594189fb88f4add3fbc979a60a05397aaa491
|
[
"Apache-2.0"
] | null | null | null |
mooringlicensing/tests/test_proposal_vessel_logic.py
|
GraemeMuller/mooringlicensing
|
2b2594189fb88f4add3fbc979a60a05397aaa491
|
[
"Apache-2.0"
] | null | null | null |
mooringlicensing/tests/test_proposal_vessel_logic.py
|
GraemeMuller/mooringlicensing
|
2b2594189fb88f4add3fbc979a60a05397aaa491
|
[
"Apache-2.0"
] | 15
|
2021-03-02T01:40:12.000Z
|
2022-02-15T08:26:09.000Z
|
from mooringlicensing.settings import HTTP_HOST_FOR_TEST
from mooringlicensing.tests.test_setup import APITestSetup
from mooringlicensing.components.proposals.models import MooringBay, Proposal, Vessel
from mooringlicensing.components.proposals.utils import proposal_submit
from datetime import datetime
import pytz
from ledger.settings_base import TIME_ZONE
#from mooringlicensing.tests.test_manage_vessels import ManageVesselTests
class VesselTests(APITestSetup):
#def test_proposal_wla_vessel_logic(self):
#self.wla_vessel_logic()
def test_create_bare_vessel_add_to_proposal(self):
create_vessel_data = {
'vessel': {
'new_vessel': True,
'rego_no': '20210503_1',
'vessel_details': {
'read_only': False,
'vessel_name': '20210503_1',
'berth_mooring': 'home',
'vessel_length': '23',
'vessel_overall_length': '34',
'vessel_weight': '45',
'vessel_draft': '56',
'vessel_type': 'tender'
},
'vessel_ownership': {
'registered_owner': 'current_user',
'individual_owner': True,
'percentage': '35'
}
}
}
self.client.login(email=self.customer1, password='pass')
self.client.enforce_csrf_checks=True
create_response = self.client.post(
'/api/vessel/',
#self.create_proposal_data,
create_vessel_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
#vessel_details_id_1 = create_response.data.get('vessel_details').get('id')
vessel_id_1 = create_response.data.get('id')
vessel = Vessel.objects.get(id=vessel_id_1)
vessel_details_id_1 = vessel.latest_vessel_details.id
#import ipdb; ipdb.set_trace()
#manage_vessel_test_cases = ManageVesselTests()
#vessel_id_1, vessel_details_id_1 = manage_vessel_test_cases.test_manage_vessels()
## vessel is now in 'draft' status
#vessel_details_id_1 = proposal.vessel_details.id
#vessel_ownership_id_1 = proposal.vessel_ownership.id
#vessel_id_1 = proposal.vessel_details.vessel_id
## Proposal 2 - add vessel from Proposal
create_response_2 = self.client.post(
'/api/waitinglistapplication/',
#self.create_proposal_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(create_response_2.status_code, 200)
self.assertTrue(create_response_2.data.get('id') > 0)
proposal_2_id = create_response_2.data.get('id')
# get proposal
url2 = 'http://localhost:8071/api/proposal/{}.json'.format(proposal_2_id)
get_response_2 = self.client.get(url2, HTTP_HOST=HTTP_HOST_FOR_TEST,)
self.assertEqual(get_response_2.status_code, 200)
# save Proposal2
draft_proposal_data = {
"proposal": {},
"vessel": {
"vessel_details": {
"id": vessel_details_id_1
},
"vessel_ownership": {
#"id": vessel_ownership_id_1
},
"id": vessel_id_1,
"read_only": True,
}
}
draft_response = self.client.post(
'/api/proposal/{}/draft/'.format(proposal_2_id),
draft_proposal_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(draft_response.status_code, 302)
## add DoT rego papers
rego_papers_response = self.client.post(
'/api/proposal/{}/process_vessel_registration_document/'.format(proposal_2_id),
self.rego_papers_data,
#format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(rego_papers_response.status_code, 200)
# submit Proposal2
# submit api endpoint
submit_proposal_2_data = {
"proposal": {
"preferred_bay_id": MooringBay.objects.last().id,
"silent_elector": False,
},
"vessel": {
"vessel_details": {
"id": vessel_details_id_1
},
"vessel_ownership": {
#"id": vessel_ownership_id_1
"org_name": "Company1",
"percentage": "65", # increase to 66 to cause serializer validation error
"individual_owner": False
},
"id": vessel_id_1,
"read_only": True,
}
}
submit_2_response = self.client.post(
'/api/proposal/{}/submit/'.format(proposal_2_id),
submit_proposal_2_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(submit_2_response.status_code, 200)
### proposal_submit(instance, request) - need a request obj, so we just make changes manually here
#proposal_2 = Proposal.objects.get(id=proposal_2_id)
#proposal_2.lodgement_date = datetime.now(pytz.timezone(TIME_ZONE))
#proposal_2.processing_status = 'with_assessor'
#proposal_2.customer_status = 'with_assessor'
#proposal_2.save()
## proposal and proposal2 should now share the same vessel_details
#self.assertEqual(proposal.vessel_details, proposal_2.vessel_details)
#self.assertEqual(proposal.vessel_ownership.vessel, proposal_2.vessel_ownership.vessel)
#self.assertNotEqual(proposal.vessel_ownership, proposal_2.vessel_ownership)
def test_proposal_wla_vessel_logic(self):
#def wla_vessel_logic(self):
print("test_proposal_wla_vessel_logic")
self.client.login(email=self.customer, password='pass')
self.client.enforce_csrf_checks=True
create_response = self.client.post(
'/api/waitinglistapplication/',
#self.create_proposal_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(create_response.status_code, 200)
self.assertTrue(create_response.data.get('id') > 0)
proposal_id = create_response.data.get('id')
# get proposal
url = 'http://localhost:8071/api/proposal/{}.json'.format(proposal_id)
get_response = self.client.get(url, HTTP_HOST=HTTP_HOST_FOR_TEST,)
self.assertEqual(get_response.status_code, 200)
#######################
draft_proposal_data = {
"proposal": {},
"vessel": {
"vessel_details": {
"vessel_type": "cabin_cruiser",
"vessel_name": "gfhj",
"vessel_overall_length": "45",
"vessel_length": "34",
"vessel_draft": "67",
"vessel_beam": "0.00",
"vessel_weight": "56",
"berth_mooring": "fghx"
},
"vessel_ownership": {
"org_name": None,
"percentage": "26",
"individual_owner": None
},
"rego_no": "20210407_1",
"vessel_id": None
}
}
draft_response = self.client.post(
'/api/proposal/{}/draft/'.format(proposal_id),
draft_proposal_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(draft_response.status_code, 302)
## add DoT rego papers
rego_papers_response = self.client.post(
'/api/proposal/{}/process_vessel_registration_document/'.format(proposal_id),
self.rego_papers_data,
#format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(rego_papers_response.status_code, 200)
## add Silent Elector papers
electoral_roll_doc_response = self.client.post(
'/api/proposal/{}/process_electoral_roll_document/'.format(proposal_id),
self.electoral_roll_doc_data,
#format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(electoral_roll_doc_response.status_code, 200)
## submit api endpoint
#submit_proposal_data = {
# "proposal": {
# "preferred_bay_id": MooringBay.objects.first().id,
# }
# }
submit_proposal_data = {
"proposal": {
"silent_elector": True,
"preferred_bay_id": MooringBay.objects.first().id,
},
"vessel": {
"vessel_details": {
"vessel_type": "cabin_cruiser",
"vessel_name": "gfhj",
"vessel_overall_length": "45",
"vessel_length": "34",
"vessel_draft": "67",
"vessel_beam": "0.00",
"vessel_weight": "56",
"berth_mooring": "fghx"
},
"vessel_ownership": {
"org_name": None,
"percentage": "26",
"individual_owner": True
},
"rego_no": "20210407_1",
"vessel_id": None
}
}
submit_response = self.client.post(
'/api/proposal/{}/submit/'.format(proposal_id),
submit_proposal_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(submit_response.status_code, 200)
## proposal_submit(instance, request) - need a request obj, so we just make changes manually here
proposal = Proposal.objects.get(id=proposal_id)
proposal.lodgement_date = datetime.now(pytz.timezone(TIME_ZONE))
proposal.processing_status = 'with_assessor'
proposal.customer_status = 'with_assessor'
proposal.save()
## vessel is now in 'draft' status
vessel_details_id_1 = proposal.vessel_details.id
vessel_ownership_id_1 = proposal.vessel_ownership.id
vessel_id_1 = proposal.vessel_details.vessel_id
## Proposal 2 - add vessel from Proposal
create_response_2 = self.client.post(
'/api/waitinglistapplication/',
#self.create_proposal_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(create_response_2.status_code, 200)
self.assertTrue(create_response_2.data.get('id') > 0)
proposal_2_id = create_response_2.data.get('id')
# get proposal
url2 = 'http://localhost:8071/api/proposal/{}.json'.format(proposal_2_id)
get_response_2 = self.client.get(url2, HTTP_HOST=HTTP_HOST_FOR_TEST,)
self.assertEqual(get_response_2.status_code, 200)
# save Proposal2
draft_proposal_data = {
"proposal": {},
"vessel": {
"vessel_details": {
"id": vessel_details_id_1
},
"vessel_ownership": {
#"id": vessel_ownership_id_1
},
"id": vessel_id_1,
"read_only": True,
}
}
draft_response = self.client.post(
'/api/proposal/{}/draft/'.format(proposal_2_id),
draft_proposal_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(draft_response.status_code, 302)
## add DoT rego papers
rego_papers_response = self.client.post(
'/api/proposal/{}/process_vessel_registration_document/'.format(proposal_2_id),
self.rego_papers_data,
#format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(rego_papers_response.status_code, 200)
# submit Proposal2
# submit api endpoint
submit_proposal_2_data = {
"proposal": {
"preferred_bay_id": MooringBay.objects.last().id,
"silent_elector": False,
},
"vessel": {
"vessel_details": {
"id": vessel_details_id_1
},
"vessel_ownership": {
#"id": vessel_ownership_id_1
"org_name": "Company1",
"percentage": "26",
"individual_owner": False
},
"id": vessel_id_1,
"read_only": True,
}
}
submit_2_response = self.client.post(
'/api/proposal/{}/submit/'.format(proposal_2_id),
submit_proposal_2_data,
format='json',
HTTP_HOST=HTTP_HOST_FOR_TEST,
)
self.assertEqual(submit_2_response.status_code, 200)
## proposal_submit(instance, request) - need a request obj, so we just make changes manually here
proposal_2 = Proposal.objects.get(id=proposal_2_id)
proposal_2.lodgement_date = datetime.now(pytz.timezone(TIME_ZONE))
proposal_2.processing_status = 'with_assessor'
proposal_2.customer_status = 'with_assessor'
proposal_2.save()
# proposal and proposal2 should now share the same vessel_details
self.assertEqual(proposal.vessel_details, proposal_2.vessel_details)
self.assertEqual(proposal.vessel_ownership.vessel, proposal_2.vessel_ownership.vessel)
self.assertNotEqual(proposal.vessel_ownership, proposal_2.vessel_ownership)
| 40.259563
| 106
| 0.532813
| 1,413
| 14,735
| 5.20736
| 0.121019
| 0.038054
| 0.026909
| 0.036695
| 0.833107
| 0.792607
| 0.782006
| 0.732536
| 0.726284
| 0.706986
| 0
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| 0.371225
| 14,735
| 365
| 107
| 40.369863
| 0.769563
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| 1
| 0.007491
| false
| 0.007491
| 0.026217
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| 0.003745
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| null | 0
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| null | 0
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| 0
|
0
| 6
|
cf87f5c8e02f38ea8f3193bac50bb2b12e7a1fd6
| 187
|
py
|
Python
|
landlab/plot/network_sediment_transporter/__init__.py
|
amanaster2/landlab
|
ea17f8314eb12e3fc76df66c9b6ff32078caa75c
|
[
"MIT"
] | 257
|
2015-01-13T16:01:21.000Z
|
2022-03-29T22:37:43.000Z
|
landlab/plot/network_sediment_transporter/__init__.py
|
amanaster2/landlab
|
ea17f8314eb12e3fc76df66c9b6ff32078caa75c
|
[
"MIT"
] | 1,222
|
2015-02-05T21:36:53.000Z
|
2022-03-31T17:53:49.000Z
|
landlab/plot/network_sediment_transporter/__init__.py
|
amanaster2/landlab
|
ea17f8314eb12e3fc76df66c9b6ff32078caa75c
|
[
"MIT"
] | 274
|
2015-02-11T19:56:08.000Z
|
2022-03-28T23:31:07.000Z
|
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 18 14:22:27 2019
@author: pfeif
"""
from .plot_network_and_parcels import plot_network_and_parcels
__all__ = ["plot_network_and_parcels"]
| 18.7
| 62
| 0.73262
| 29
| 187
| 4.275862
| 0.724138
| 0.266129
| 0.33871
| 0.508065
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.080247
| 0.13369
| 187
| 9
| 63
| 20.777778
| 0.685185
| 0.395722
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| 0
| 0
| 0.228571
| 0.228571
| 0
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| false
| 0
| 0.5
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| null | 1
| 1
| 1
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| null | 0
| 0
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| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
d867db88e4ca21f6719c78ccee656b292d5cb54d
| 87
|
py
|
Python
|
rin/models/builders/__init__.py
|
an-dyy/Rin
|
70066f04157a20a08cfe65ce9235ce65d35f8be3
|
[
"MIT"
] | 13
|
2022-01-15T17:29:17.000Z
|
2022-02-17T05:43:39.000Z
|
rin/models/builders/__init__.py
|
an-dyy/Rin
|
70066f04157a20a08cfe65ce9235ce65d35f8be3
|
[
"MIT"
] | 3
|
2022-01-16T18:05:58.000Z
|
2022-02-18T03:55:50.000Z
|
rin/models/builders/__init__.py
|
an-dyy/Rin
|
70066f04157a20a08cfe65ce9235ce65d35f8be3
|
[
"MIT"
] | 6
|
2022-01-16T16:45:45.000Z
|
2022-02-12T18:49:20.000Z
|
from .ansi import *
from .embed import *
from .intents import *
from .message import *
| 17.4
| 22
| 0.724138
| 12
| 87
| 5.25
| 0.5
| 0.47619
| 0
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| 0
| 0.183908
| 87
| 4
| 23
| 21.75
| 0.887324
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| true
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| 1
| 0
| 1
| 0
|
0
| 6
|
d886d8769ff3fc634a5efc50044c8c5b5c253a34
| 125
|
py
|
Python
|
psql/students/views.py
|
Dickens-odera/Django-postgreSQL
|
3d169e5146b72ee0c70872f9bcaa68d83bb4d145
|
[
"MIT"
] | 4
|
2019-03-09T21:51:33.000Z
|
2019-03-12T13:59:24.000Z
|
psql/students/views.py
|
Dickens-odera/Django-postgreSQL
|
3d169e5146b72ee0c70872f9bcaa68d83bb4d145
|
[
"MIT"
] | 21
|
2020-01-28T22:37:42.000Z
|
2022-03-11T23:42:12.000Z
|
psql/students/views.py
|
Dickens-odera/Django-postgreSQL
|
3d169e5146b72ee0c70872f9bcaa68d83bb4d145
|
[
"MIT"
] | null | null | null |
from django.shortcuts import render
from django.views import View
class Student(View):
# default constructor
pass
| 17.857143
| 35
| 0.752
| 16
| 125
| 5.875
| 0.75
| 0.212766
| 0
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| 0
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| 0.2
| 125
| 6
| 36
| 20.833333
| 0.94
| 0.152
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| true
| 0.25
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| 1
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| 0
| 0
|
0
| 6
|
d8a1c0d4af343154a7b6e862553ccd0f9afc6287
| 35
|
py
|
Python
|
munimap/__init__.py
|
MrSnyder/bielefeldGEOCLIENT
|
17c78b43fc2055d23a1bc4b5091da164756bf767
|
[
"Apache-2.0"
] | 2
|
2022-02-07T13:20:45.000Z
|
2022-02-14T21:40:06.000Z
|
munimap/__init__.py
|
MrSnyder/bielefeldGEOCLIENT
|
17c78b43fc2055d23a1bc4b5091da164756bf767
|
[
"Apache-2.0"
] | 4
|
2021-06-17T07:53:53.000Z
|
2021-12-17T10:55:48.000Z
|
munimap/__init__.py
|
MrSnyder/bielefeldGEOCLIENT
|
17c78b43fc2055d23a1bc4b5091da164756bf767
|
[
"Apache-2.0"
] | 2
|
2021-06-01T09:41:55.000Z
|
2022-02-14T17:33:33.000Z
|
from .application import create_app
| 35
| 35
| 0.885714
| 5
| 35
| 6
| 1
| 0
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| 0
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| 0.085714
| 35
| 1
| 35
| 35
| 0.9375
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| null | 0
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| null | 0
| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d8cecb88a76ac706a809e4cc08eab584a400780c
| 95
|
py
|
Python
|
testproj/models.py
|
SweetProcess/django-postgres-queue
|
55486edd311605ce9da43b330a097effc9d8d0c4
|
[
"BSD-2-Clause"
] | 12
|
2020-02-28T10:13:16.000Z
|
2022-02-07T13:02:42.000Z
|
testproj/models.py
|
SweetProcess/django-postgres-queue
|
55486edd311605ce9da43b330a097effc9d8d0c4
|
[
"BSD-2-Clause"
] | 7
|
2020-04-29T07:10:06.000Z
|
2022-02-18T04:47:14.000Z
|
testproj/models.py
|
SweetProcess/django-postgres-queue
|
55486edd311605ce9da43b330a097effc9d8d0c4
|
[
"BSD-2-Clause"
] | 2
|
2020-04-29T01:07:58.000Z
|
2020-07-24T07:34:50.000Z
|
from django.db import models
from pgq.models import BaseJob
class AltJob(BaseJob):
pass
| 11.875
| 30
| 0.757895
| 14
| 95
| 5.142857
| 0.714286
| 0
| 0
| 0
| 0
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| 0
| 0.189474
| 95
| 7
| 31
| 13.571429
| 0.935065
| 0
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| 0.25
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| null | 0
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| null | 0
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| 1
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| 0
| 1
| 0
|
0
| 6
|
d8f4296e2254125124f067ff59d13993bf9ff9fb
| 44
|
py
|
Python
|
_ED/_ED_exercicios_lista_01/A_e_B_soma.py
|
CarlosJunn/Aprendendo_Python
|
cddb29b5ee2058c3fb612574eb4af414770b7422
|
[
"MIT"
] | null | null | null |
_ED/_ED_exercicios_lista_01/A_e_B_soma.py
|
CarlosJunn/Aprendendo_Python
|
cddb29b5ee2058c3fb612574eb4af414770b7422
|
[
"MIT"
] | null | null | null |
_ED/_ED_exercicios_lista_01/A_e_B_soma.py
|
CarlosJunn/Aprendendo_Python
|
cddb29b5ee2058c3fb612574eb4af414770b7422
|
[
"MIT"
] | null | null | null |
def soma(var1, var2):
print(var1 + var2)
| 22
| 22
| 0.636364
| 7
| 44
| 4
| 0.714286
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114286
| 0.204545
| 44
| 2
| 22
| 22
| 0.685714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0
| 0.5
| 0.5
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
d8f51b1a28eed2fc2cb6f5a816793efa909e560a
| 44
|
py
|
Python
|
light_cnns/ShuffleNet/__init__.py
|
murufeng/awesome_lightweight_networks
|
dfa19bd7ee491a7b7ade360175244c81b3c0e322
|
[
"MIT"
] | 318
|
2021-08-15T10:33:27.000Z
|
2022-03-31T16:42:50.000Z
|
light_cnns/ShuffleNet/__init__.py
|
x779250919/awesome_lightweight_networks
|
dfa19bd7ee491a7b7ade360175244c81b3c0e322
|
[
"MIT"
] | 6
|
2021-11-16T06:27:34.000Z
|
2022-02-08T07:57:52.000Z
|
light_cnns/ShuffleNet/__init__.py
|
x779250919/awesome_lightweight_networks
|
dfa19bd7ee491a7b7ade360175244c81b3c0e322
|
[
"MIT"
] | 67
|
2021-11-01T13:06:48.000Z
|
2022-03-24T12:59:41.000Z
|
from .blocks import *
from .models import *
| 22
| 22
| 0.727273
| 6
| 44
| 5.333333
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 44
| 2
| 23
| 22
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
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| 1
| 1
| 0
| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2b11c153be4b8832c6040020cb7d31f81bd55212
| 177
|
py
|
Python
|
python/testData/inspections/AddCallSuperCommentAfterColonPreserved_after.py
|
jnthn/intellij-community
|
8fa7c8a3ace62400c838e0d5926a7be106aa8557
|
[
"Apache-2.0"
] | 2
|
2019-04-28T07:48:50.000Z
|
2020-12-11T14:18:08.000Z
|
python/testData/inspections/AddCallSuperCommentAfterColonPreserved_after.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 173
|
2018-07-05T13:59:39.000Z
|
2018-08-09T01:12:03.000Z
|
python/testData/inspections/AddCallSuperCommentAfterColonPreserved_after.py
|
Cyril-lamirand/intellij-community
|
60ab6c61b82fc761dd68363eca7d9d69663cfa39
|
[
"Apache-2.0"
] | 2
|
2020-03-15T08:57:37.000Z
|
2020-04-07T04:48:14.000Z
|
class Example1:
def __init__(self):
self.field1 = 1
class Example2(Example1):
def __init__(self): # Some valuable comment here
Example1.__init__(self)
| 22.125
| 53
| 0.666667
| 21
| 177
| 5.047619
| 0.571429
| 0.226415
| 0.283019
| 0.358491
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.044776
| 0.242938
| 177
| 8
| 54
| 22.125
| 0.746269
| 0.146893
| 0
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
991a18d03a14e31eda4cb23c385430ed43c9548f
| 584
|
py
|
Python
|
erri/python/lesson_51/divisibility.py
|
TGITS/programming-workouts
|
799e805ccf3fd0936ec8ac2417f7193b8e9bcb55
|
[
"MIT"
] | null | null | null |
erri/python/lesson_51/divisibility.py
|
TGITS/programming-workouts
|
799e805ccf3fd0936ec8ac2417f7193b8e9bcb55
|
[
"MIT"
] | 16
|
2020-05-30T12:38:13.000Z
|
2022-02-19T09:23:31.000Z
|
erri/python/lesson_51/divisibility.py
|
TGITS/programming-workouts
|
799e805ccf3fd0936ec8ac2417f7193b8e9bcb55
|
[
"MIT"
] | null | null | null |
def numbers_divisible_by_5_and_7_between_values(start_value, end_value):
result = []
for e in range(start_value, end_value + 1, 5):
if e % 7 == 0:
result.append(e)
return result
def numbers_divisible_by_5_and_13_between_values(start_value, end_value):
result = []
for e in range(start_value, end_value + 1, 5):
if e % 13 == 0:
result.append(e)
return result
if __name__ == "__main__":
print(numbers_divisible_by_5_and_7_between_values(300, 450))
print(numbers_divisible_by_5_and_13_between_values(300, 450))
| 29.2
| 73
| 0.683219
| 91
| 584
| 3.901099
| 0.307692
| 0.180282
| 0.202817
| 0.214085
| 0.929577
| 0.929577
| 0.738028
| 0.738028
| 0.4
| 0.4
| 0
| 0.068282
| 0.222603
| 584
| 19
| 74
| 30.736842
| 0.713656
| 0
| 0
| 0.533333
| 0
| 0
| 0.013699
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.133333
| false
| 0
| 0
| 0
| 0.266667
| 0.133333
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
51524a18696e566854cad187baecc6f9f444c5a9
| 137
|
py
|
Python
|
minato_namikaze/bot_files/lib/functions/__init__.py
|
ooliver1/yondaime-hokage
|
3552887dc022c8ace13de9dae01392b9471e5f58
|
[
"Apache-2.0"
] | 1
|
2021-11-04T13:20:36.000Z
|
2021-11-04T13:20:36.000Z
|
minato_namikaze/bot_files/lib/functions/__init__.py
|
ooliver1/yondaime-hokage
|
3552887dc022c8ace13de9dae01392b9471e5f58
|
[
"Apache-2.0"
] | null | null | null |
minato_namikaze/bot_files/lib/functions/__init__.py
|
ooliver1/yondaime-hokage
|
3552887dc022c8ace13de9dae01392b9471e5f58
|
[
"Apache-2.0"
] | null | null | null |
from .meek_moe import *
from .moderation import *
from .owneronly import *
from .tools import *
from .user import *
from .votes import *
| 19.571429
| 25
| 0.737226
| 19
| 137
| 5.263158
| 0.473684
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175182
| 137
| 6
| 26
| 22.833333
| 0.884956
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
5acfc300def397c83d7759b0f2da4ebf3d019307
| 75
|
py
|
Python
|
boomdiff/__init__.py
|
team-boomeraang/cs107-FinalProject
|
93d854ea2c6dd3a8de68eeacc0bb31f412dbb94a
|
[
"MIT"
] | null | null | null |
boomdiff/__init__.py
|
team-boomeraang/cs107-FinalProject
|
93d854ea2c6dd3a8de68eeacc0bb31f412dbb94a
|
[
"MIT"
] | 17
|
2020-10-05T23:55:45.000Z
|
2020-12-11T00:25:55.000Z
|
boomdiff/__init__.py
|
team-boomeraang/cs107-FinalProject
|
93d854ea2c6dd3a8de68eeacc0bb31f412dbb94a
|
[
"MIT"
] | 2
|
2020-12-08T22:13:40.000Z
|
2021-12-09T04:39:45.000Z
|
from .autodiff import AD
from . import optimize
from . import loss_function
| 25
| 27
| 0.813333
| 11
| 75
| 5.454545
| 0.636364
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.146667
| 75
| 3
| 27
| 25
| 0.9375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
5ad84beee014aae6de8a2c4d64e5d9db308890c2
| 3,267
|
py
|
Python
|
meterpreter/windows_x86/shellcode_bindtcprc4.py
|
cobranail/redteam
|
a21091ac0aef289b61dd05771fff7296fb7c4e7e
|
[
"MIT"
] | null | null | null |
meterpreter/windows_x86/shellcode_bindtcprc4.py
|
cobranail/redteam
|
a21091ac0aef289b61dd05771fff7296fb7c4e7e
|
[
"MIT"
] | null | null | null |
meterpreter/windows_x86/shellcode_bindtcprc4.py
|
cobranail/redteam
|
a21091ac0aef289b61dd05771fff7296fb7c4e7e
|
[
"MIT"
] | null | null | null |
buf = b""
buf += b"\x48\x31\xc9\x48\x81\xe9\xb1\xff\xff\xff\x48\x8d\x05"
buf += b"\xef\xff\xff\xff\x48\xbb\xf8\x3d\x59\x54\x46\x6d\x59"
buf += b"\x99\x48\x31\x58\x27\x48\x2d\xf8\xff\xff\xff\xe2\xf4"
buf += b"\x04\x75\xda\xb0\xb6\x85\x95\x99\xf8\x3d\x18\x05\x07"
buf += b"\x3d\x0b\xc8\xae\x75\x68\x86\x23\x25\xd2\xcb\x98\x75"
buf += b"\xd2\x06\x5e\x25\xd2\xcb\xd8\x75\xd2\x26\x16\x25\x56"
buf += b"\x2e\xb2\x77\x14\x65\x8f\x25\x68\x59\x54\x01\x38\x28"
buf += b"\x44\x41\x79\xd8\x39\xf4\x54\x15\x47\xac\xbb\x74\xaa"
buf += b"\x7c\x08\x1c\xcd\x3f\x79\x12\xba\x01\x11\x55\x96\x0b"
buf += b"\xd8\xe1\xe0\x36\x5b\x5b\xc3\x1f\x59\x99\xf8\xb6\xd9"
buf += b"\xdc\x46\x6d\x59\xd1\x7d\xfd\x2d\x33\x0e\x6c\x89\xc9"
buf += b"\x73\x75\x41\x10\xcd\x2d\x79\xd0\xf9\xed\xba\x02\x0e"
buf += b"\x92\x90\xd8\x73\x09\xd1\x1c\x47\xbb\x14\xa8\x31\x75"
buf += b"\x68\x94\xea\x2c\x98\x50\xf5\x7c\x58\x95\x7e\x8d\x2c"
buf += b"\x68\xb4\x3e\x15\x70\x4e\x28\x60\x48\x8d\xe5\x01\x10"
buf += b"\xcd\x2d\x7d\xd0\xf9\xed\x3f\x15\xcd\x61\x11\xdd\x73"
buf += b"\x7d\x45\x1d\x47\xbd\x18\x12\xfc\xb5\x11\x55\x96\x2c"
buf += b"\x01\xd8\xa0\x63\x00\x0e\x07\x35\x18\xc0\xb9\x67\x11"
buf += b"\xd7\xaa\x4d\x18\xcb\x07\xdd\x01\x15\x1f\x37\x11\x12"
buf += b"\xea\xd4\x12\xab\xb9\x92\x04\xd0\x46\x4a\x2a\x66\x19"
buf += b"\x5e\x6b\x99\xf8\x7c\x0f\x1d\xcf\x8b\x11\x18\x14\x9d"
buf += b"\x58\x54\x46\x24\xd0\x7c\xb0\x0c\x99\x04\x16\x24\xe5"
buf += b"\x9b\xf8\x1d\xa9\x54\x46\x6d\x59\xd8\xac\x74\xd0\xb0"
buf += b"\x0a\xe4\xa8\xd8\x42\x71\x2e\x72\x41\x92\x8c\xd5\x71"
buf += b"\xd7\x31\x55\x47\x6d\x59\xc0\xb9\x87\x70\xd4\x2d\x6d"
buf += b"\xa6\x4c\x92\x3f\x00\x04\x16\x20\x68\x50\xb5\x0c\x99"
buf += b"\x1c\xb9\xad\x11\x10\x3a\x7c\xe3\xbe\x49\xb2\xb9\x66"
buf += b"\x2d\x75\xd0\x93\x2c\x7d\x18\xc1\xb4\xb4\xbb\x1c\xcf"
buf += b"\x94\x18\x23\x3a\xe6\x6e\x33\xb9\xb8\x11\xa8\x2a\x75"
buf += b"\xd0\xad\x07\xd7\xee\x70\xc0\xc2\xa6\x81\x0b\x5c\x99"
buf += b"\xd1\xc9\xef\x11\xdd\xbf\x2c\xe3\xed\x14\x06\xb8\xab"
buf += b"\x93\x25\xd0\x60\xb0\xb4\x9e\x15\xfc\x18\x37\xd4\x99"
buf += b"\xc2\x8c\x1c\xc7\xa9\xe9\x9b\xf8\x3d\x11\xd7\xaa\x7d"
buf += b"\x11\x10\x1a\x70\x68\x9d\x2c\x69\x18\xc1\xb0\xb4\xa0"
buf += b"\x15\xfc\x6f\x80\x51\xa7\xc2\x8c\x1c\xc5\xa9\x79\xc7"
buf += b"\x71\xcb\xd8\xa2\x74\xd6\x41\x2f\xb4\xb0\xc7\x54\x47"
buf += b"\x6d\x59\xf3\xb8\x7c\x00\x3c\x46\x7d\x59\x99\xb9\x65"
buf += b"\x11\xdd\xb4\x25\x68\x50\xb9\x87\x01\xf0\x15\x88\xa6"
buf += b"\x4c\xb0\xb0\xc1\x54\x47\x6d\x59\xd0\x71\xe2\x0a\x02"
buf += b"\x16\x20\x68\x50\xb1\xb4\xa9\x1c\xcf\xb7\x11\x10\x01"
buf += b"\x7c\xe3\x56\x9f\xa5\x06\x66\x2d\x75\xda\x90\x66\x25"
buf += b"\x58\x5a\xb0\x14\x9f\x21\xa6\x24\xd0\x67\xa7\x64\x18"
buf += b"\x0d\x07\x3b\xb1\x89\xf8\x3d\x59\x79\x16\xf6\x59\xa1"
buf += b"\xb0\xe2\xd3\xf9\xc5\xba\x57\x09\x5d\xf3\xde\x0a\x0e"
buf += b"\x5c\x99\xd0\x71\xc5\xf3\xaa\x86\x18\xa2\xd1\xc9\xe6"
buf += b"\x18\x56\x5a\x6d\x11\x10\x3a\xbd\xbb\x5b\x44\x71\x4f"
buf += b"\xd8\x72\x29\x59\x15\xc0\x79\x41\xd8\x70\x29\x59\xaa"
buf += b"\x86\x18\xba\xd1\xc9\xe6\xa7\x94\x07\x6f\x45\x99\xb9"
buf += b"\xb7\x4d\x54\x07\xeb\x4d\x81\xb9\xb5\x4d\x54\x07\x6f"
buf += b"\x4d\x81\xb9\xb7\x4d\x44\x07\x5d\x48\xd0\x07\xfc\x11"
buf += b"\xab\x8f\x18\x82\xc6\xb9\xc2\xbe\x0c\x2c\x6d\x00\xd0"
buf += b"\x3f\xff\xa9\xe1\xe4\x3b\xa6\x4c"
| 60.5
| 62
| 0.681053
| 777
| 3,267
| 2.863578
| 0.266409
| 0.095281
| 0.012135
| 0.010787
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.289025
| 0.048975
| 3,267
| 53
| 63
| 61.641509
| 0.4271
| 0
| 0
| 0
| 0
| 0.962264
| 0.821549
| 0.821549
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
8506de82d6055b04a2943e77fdb439071ce4d984
| 217
|
py
|
Python
|
2016/Day3/day3.py
|
dh256/adventofcode
|
428eec13f4cbf153333a0e359bcff23070ef6d27
|
[
"MIT"
] | null | null | null |
2016/Day3/day3.py
|
dh256/adventofcode
|
428eec13f4cbf153333a0e359bcff23070ef6d27
|
[
"MIT"
] | null | null | null |
2016/Day3/day3.py
|
dh256/adventofcode
|
428eec13f4cbf153333a0e359bcff23070ef6d27
|
[
"MIT"
] | null | null | null |
from Triangles import Triangles
triangles = Triangles("input.txt")
print(f'Part 1 Possible Triangles = {triangles.possible(columns=False)}')
print(f'Part 2 Possible Triangles = {triangles.possible(columns=True)}')
| 27.125
| 73
| 0.769585
| 28
| 217
| 5.964286
| 0.5
| 0.431138
| 0.11976
| 0.407186
| 0.491018
| 0
| 0
| 0
| 0
| 0
| 0
| 0.010256
| 0.101382
| 217
| 7
| 74
| 31
| 0.846154
| 0
| 0
| 0
| 0
| 0
| 0.623256
| 0.32093
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.25
| 0
| 0.25
| 0.5
| 1
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
85130f3e4ad5c5120ad2e6f0fda31687cde600e1
| 48
|
py
|
Python
|
tests/unit/__init__.py
|
Agi-dev/pylaas
|
7cb6a3e1d560843886b27d4fa5aaf9ff74a555f7
|
[
"MIT"
] | null | null | null |
tests/unit/__init__.py
|
Agi-dev/pylaas
|
7cb6a3e1d560843886b27d4fa5aaf9ff74a555f7
|
[
"MIT"
] | null | null | null |
tests/unit/__init__.py
|
Agi-dev/pylaas
|
7cb6a3e1d560843886b27d4fa5aaf9ff74a555f7
|
[
"MIT"
] | null | null | null |
from pylaas.pylaas import Pylaas
Pylaas.init()
| 12
| 32
| 0.791667
| 7
| 48
| 5.428571
| 0.571429
| 0.631579
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 48
| 3
| 33
| 16
| 0.904762
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
51e0a0bde3e606a8d923a4a2f6c574587c3699aa
| 94
|
py
|
Python
|
gdcdatamodel/validators/__init__.py
|
uc-cdis/gdcdatamodel
|
731c6fee160586dde4e5ff3bc76f131201c71543
|
[
"Apache-2.0"
] | 27
|
2016-06-24T20:32:44.000Z
|
2022-01-17T07:53:48.000Z
|
gdcdatamodel/validators/__init__.py
|
NCI-GDC/gdcdatamodel
|
924fc8ab695b1cbb0131636ffcb6d3881db2e200
|
[
"Apache-2.0"
] | 63
|
2016-07-20T21:40:11.000Z
|
2021-08-12T18:39:21.000Z
|
gdcdatamodel/validators/__init__.py
|
uc-cdis/gdcdatamodel
|
731c6fee160586dde4e5ff3bc76f131201c71543
|
[
"Apache-2.0"
] | 5
|
2016-10-20T20:00:09.000Z
|
2020-08-14T08:55:40.000Z
|
from .json_validators import GDCJSONValidator
from .graph_validators import GDCGraphValidator
| 31.333333
| 47
| 0.893617
| 10
| 94
| 8.2
| 0.7
| 0.390244
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.085106
| 94
| 2
| 48
| 47
| 0.953488
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cfdf91ae1cb6e8022118a3f1b41c2937678fb7cc
| 160
|
py
|
Python
|
reports/admin.py
|
mikael19/activity
|
3932de42d9b423bff5739f7e06520035df213fc6
|
[
"Apache-2.0"
] | 60
|
2020-02-13T17:20:43.000Z
|
2022-03-12T19:26:04.000Z
|
reports/admin.py
|
mikael19/activity
|
3932de42d9b423bff5739f7e06520035df213fc6
|
[
"Apache-2.0"
] | 449
|
2020-02-12T22:18:00.000Z
|
2022-03-11T23:36:59.000Z
|
reports/admin.py
|
mikael19/activity
|
3932de42d9b423bff5739f7e06520035df213fc6
|
[
"Apache-2.0"
] | 31
|
2020-03-07T21:00:54.000Z
|
2021-07-14T18:37:34.000Z
|
#!/usr/bin/python3
# -*- coding: utf-8 -*-
from django.contrib import admin
from .models import Report, ReportAdmin
admin.site.register(Report, ReportAdmin)
| 17.777778
| 40
| 0.7375
| 21
| 160
| 5.619048
| 0.761905
| 0.288136
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.014286
| 0.125
| 160
| 8
| 41
| 20
| 0.828571
| 0.24375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cfe98e2e60d9e9fba419167cf7355bbd889f6279
| 181
|
py
|
Python
|
students/k3342/laboratory_works/Evgenov_Sergei/laboratory_work_1/homework_project_evgenov/homework_app/admin.py
|
TonikX/ITMO_ICT_-WebProgramming_2020
|
ba566c1b3ab04585665c69860b713741906935a0
|
[
"MIT"
] | 10
|
2020-03-20T09:06:12.000Z
|
2021-07-27T13:06:02.000Z
|
students/k3342/laboratory_works/Evgenov_Sergei/laboratory_work_1/homework_project_evgenov/homework_app/admin.py
|
TonikX/ITMO_ICT_-WebProgramming_2020
|
ba566c1b3ab04585665c69860b713741906935a0
|
[
"MIT"
] | 134
|
2020-03-23T09:47:48.000Z
|
2022-03-12T01:05:19.000Z
|
students/k3342/laboratory_works/Evgenov_Sergei/laboratory_work_1/homework_project_evgenov/homework_app/admin.py
|
TonikX/ITMO_ICT_-WebProgramming_2020
|
ba566c1b3ab04585665c69860b713741906935a0
|
[
"MIT"
] | 71
|
2020-03-20T12:45:56.000Z
|
2021-10-31T19:22:25.000Z
|
from django.contrib import admin
# Register your models here.
from .models import Homework
from .models import Comment
admin.site.register(Homework)
admin.site.register(Comment)
| 18.1
| 32
| 0.80663
| 25
| 181
| 5.84
| 0.48
| 0.136986
| 0.219178
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121547
| 181
| 9
| 33
| 20.111111
| 0.918239
| 0.143646
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.6
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
3204af199cb003e852ca0760cb9defcf59d47794
| 151
|
py
|
Python
|
active_learning_ts/instance_properties/objectives/constant_instance_objective.py
|
hassberg/active_learning_ts
|
7ebdabd3349d3ac4ea2761a8aa869b8d222a2d83
|
[
"MIT"
] | 1
|
2022-02-14T09:38:22.000Z
|
2022-02-14T09:38:22.000Z
|
active_learning_ts/instance_properties/objectives/constant_instance_objective.py
|
hassberg/active_learning_ts
|
7ebdabd3349d3ac4ea2761a8aa869b8d222a2d83
|
[
"MIT"
] | 1
|
2022-02-11T12:13:31.000Z
|
2022-02-11T12:13:31.000Z
|
active_learning_ts/instance_properties/objectives/constant_instance_objective.py
|
hassberg/active_learning_ts
|
7ebdabd3349d3ac4ea2761a8aa869b8d222a2d83
|
[
"MIT"
] | 2
|
2021-12-15T12:56:30.000Z
|
2022-02-01T15:31:08.000Z
|
from active_learning_ts.instance_properties.instance_objective import InstanceObjective
class ConstantInstanceObjective(InstanceObjective):
pass
| 25.166667
| 87
| 0.880795
| 14
| 151
| 9.214286
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.086093
| 151
| 5
| 88
| 30.2
| 0.934783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
5c89e099dbb6487518321274d452146bff4c881c
| 31
|
py
|
Python
|
d3rlpy/metrics/__init__.py
|
YangRui2015/d3rlpy
|
da778b2a2b0afbafe25395296baecd0d4d0cd0d5
|
[
"MIT"
] | 1
|
2021-05-08T06:21:05.000Z
|
2021-05-08T06:21:05.000Z
|
d3rlpy/metrics/__init__.py
|
YangRui2015/d3rlpy
|
da778b2a2b0afbafe25395296baecd0d4d0cd0d5
|
[
"MIT"
] | null | null | null |
d3rlpy/metrics/__init__.py
|
YangRui2015/d3rlpy
|
da778b2a2b0afbafe25395296baecd0d4d0cd0d5
|
[
"MIT"
] | null | null | null |
from . import comparer, scorer
| 15.5
| 30
| 0.774194
| 4
| 31
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.16129
| 31
| 1
| 31
| 31
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
7a91c7a166a3924e77fb2de527555edf8c09057c
| 27
|
py
|
Python
|
utils/__init__.py
|
xdr940/monodepth2_Xavier
|
6d7d522237df8552644c1c10f97b309be5b53498
|
[
"MIT"
] | 2
|
2020-10-26T08:14:03.000Z
|
2020-11-19T07:49:25.000Z
|
utils/__init__.py
|
maomingyang/monodepth2_Xavier
|
80bb9d34cacdfe7d1852a67405c2f8611f1f90e1
|
[
"MIT"
] | null | null | null |
utils/__init__.py
|
maomingyang/monodepth2_Xavier
|
80bb9d34cacdfe7d1852a67405c2f8611f1f90e1
|
[
"MIT"
] | 1
|
2020-10-26T08:14:06.000Z
|
2020-10-26T08:14:06.000Z
|
from .logger import Writer
| 13.5
| 26
| 0.814815
| 4
| 27
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 27
| 1
| 27
| 27
| 0.956522
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
8f8443c5e076bbde5f69f55086b6374ba65b00ae
| 18,503
|
py
|
Python
|
Tools/lib/python/eupath/BiomFileMicrobiomeDbExporter.py
|
VEuPathDB/EuPathGalaxy
|
39768986ebe9555870d9435b523da768935fa4fe
|
[
"Apache-2.0"
] | null | null | null |
Tools/lib/python/eupath/BiomFileMicrobiomeDbExporter.py
|
VEuPathDB/EuPathGalaxy
|
39768986ebe9555870d9435b523da768935fa4fe
|
[
"Apache-2.0"
] | null | null | null |
Tools/lib/python/eupath/BiomFileMicrobiomeDbExporter.py
|
VEuPathDB/EuPathGalaxy
|
39768986ebe9555870d9435b523da768935fa4fe
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/python
from . import EupathExporter
import biom
from biom.cli.table_validator import _validate_table
from biom.parse import load_table
class BiomExport(EupathExporter.Export):
BIOM_TYPE = "BIOM"
BIOM_VERSION = "1.0, 2.0, or 2.1"
def __init__(self, args):
"""
Initializes the gene list export class with the parameters needed to accomplish the particular
type of export.
:param args: parameters provided from tool form
"""
EupathExporter.Export.__init__(self,
BiomExport.BIOM_TYPE,
BiomExport.BIOM_VERSION,
None,
args)
# generic 7 arguments and then dataset file path
if len(args) < 8:
raise EupathExporter.ValidationException("The tool was passed an insufficient numbers of arguments:", args)
self._dataset_file_path = args[7]
def validate_datasets(self):
# try read a file
# gives stupid errors like "Invalid format 'Biological Observation Matrix 0.9.1-dev', must be '1.0.0'"
# valid, report = _validate_table(self._dataset_file_path)
# if not valid:
# raise EupathExporter.ValidationException(report)
try:
table=load_table(self._dataset_file_path)
except ValueError, e:
raise EupathExporter.ValidationException("Could not load the file as BIOM - does it conform to the specification on https://biom-format.org?", e)
give_table_extra_methods(table)
generated_by="MicrobiomeDb exporter"
with open(self._dataset_file_path+".metadata.json", 'w') as f1:
table.to_json_but_only_metadata(generated_by, direct_io=f1)
with open(self._dataset_file_path+".data.tsv", 'w') as f2:
table.to_json_but_only_data_and_not_json_but_tsv(generated_by, direct_io=f2)
def identify_dependencies(self):
return []
def identify_projects(self):
return ["MicrobiomeDB"]
def identify_supported_projects(self):
return ["MicrobiomeDB"]
def identify_dataset_files(self):
return [
{"name": "uploaded.biom", "path": self._dataset_file_path},
{"name": "metadata.json", "path": self._dataset_file_path+".metadata.json"},
{"name": "data.tsv", "path": self._dataset_file_path + ".data.tsv"}
]
def output_success(self):
header = "<html><body><h1>Good news!</h1><br />"
msg = """
<h2>Results of the VEuPathDB Export Tool<br />BIOM files to MicrobiomeDB</h2>
<h3>Your BIOM file was exported from Galaxy to your account in VEuPathDB.
For file access, go to the My Data Sets section on MicrobiomeDB:
<a href='http://microbiomedb.org/mbio/app/workspace/datasets'>My Data Sets</a><br />
</h3><br />
</body></html>
"""
with open(self._output, 'w') as file:
file.write("%s%s" % (header,msg))
def give_table_extra_methods(table):
# just my looking at the name you know this is gonna be good isn't it
# takes a table and attaches these two methods to it:
# - to_json_but_only_metadata
# - to_json_but_only_data_and_not_json_but_tsv
#
# Done by twice replicating the 200 lines long method from
# https://github.com/biocore/biom-format/blob/fd84172794d14a741a5764234d7a28416b9dba08/biom/table.py#L4451
# and judiciously commenting stuff out
#
# View in python-coloring editor to see what got commented out or diff with the package code to find out how they were changed
# Scroll to the bottom of the file to see how they get added
#
#
# Here are the globals
#
from future.utils import string_types
def get_biom_format_version_string(version=None):
"""Returns the current Biom file format version.
Parameters
----------
version : tuple
a tuple containing the version number of the biom table
"""
if version is None:
return "Biological Observation Matrix 1.0.0"
else:
return "Biological Observation Matrix %s.%s.0" % (version[0],
version[1])
def get_biom_format_url_string():
return "http://biom-format.org"
from datetime import datetime
from json import dumps
def to_json_but_only_metadata(self, generated_by, direct_io=None):
"""Returns a JSON string representing the table in BIOM format.
Parameters
----------
generated_by : str
a string describing the software used to build the table
direct_io : file or file-like object, optional
Defaults to ``None``. Must implementing a ``write`` function. If
`direct_io` is not ``None``, the final output is written directly
to `direct_io` during processing.
Returns
-------
str
A JSON-formatted string representing the biom table
"""
if not isinstance(generated_by, string_types):
raise TableException("Must specify a generated_by string")
# Fill in top-level metadata.
if direct_io:
direct_io.write(u'{')
direct_io.write(u'"id": "%s",' % str(self.table_id))
direct_io.write(
u'"format": "%s",' %
get_biom_format_version_string((1, 0))) # JSON table -> 1.0.0
direct_io.write(
u'"format_url": "%s",' %
get_biom_format_url_string())
direct_io.write(u'"generated_by": "%s",' % generated_by)
direct_io.write(u'"date": "%s",' % datetime.now().isoformat())
else:
id_ = u'"id": "%s",' % str(self.table_id)
format_ = u'"format": "%s",' % get_biom_format_version_string(
(1, 0)) # JSON table -> 1.0.0
format_url = u'"format_url": "%s",' % get_biom_format_url_string()
generated_by = u'"generated_by": "%s",' % generated_by
date = u'"date": "%s",' % datetime.now().isoformat()
# Determine if we have any data in the matrix, and what the shape of
# the matrix is.
try:
num_rows, num_cols = self.shape
except: # noqa
num_rows = num_cols = 0
has_data = True if num_rows > 0 and num_cols > 0 else False
# Default the matrix element type to test to be an integer in case we
# don't have any data in the matrix to test.
test_element = 0
if has_data:
test_element = self[0, 0]
# Determine the type of elements the matrix is storing.
if isinstance(test_element, int):
matrix_element_type = u"int"
elif isinstance(test_element, float):
matrix_element_type = u"float"
elif isinstance(test_element, string_types):
matrix_element_type = u"str"
else:
raise TableException("Unsupported matrix data type.")
# Fill in details about the matrix.
if direct_io:
direct_io.write(
u'"matrix_element_type": "%s",' %
matrix_element_type)
direct_io.write(u'"shape": [%d, %d],' % (num_rows, num_cols))
else:
matrix_element_type = u'"matrix_element_type": "%s",' % \
matrix_element_type
shape = u'"shape": [%d, %d],' % (num_rows, num_cols)
# Fill in the table type
if self.type is None:
type_ = u'"type": null,'
else:
type_ = u'"type": "%s",' % self.type
if direct_io:
direct_io.write(type_)
# Fill in details about the rows in the table and fill in the matrix's
# data. BIOM 2.0+ is now only sparse
if direct_io:
direct_io.write(u'"matrix_type": "sparse",')
"""
direct_io.write(u'"data": [')
"""
else:
matrix_type = u'"matrix_type": "sparse",'
"""
data = [u'"data": [']
"""
data=[]
max_row_idx = len(self.ids(axis='observation')) - 1
max_col_idx = len(self.ids()) - 1
rows = [u'"rows": [']
have_written = False
for obs_index, obs in enumerate(self.iter(axis='observation')):
# i'm crying on the inside
if obs_index != max_row_idx:
rows.append(u'{"id": %s, "metadata": %s},' % (dumps(obs[1]),
dumps(obs[2])))
else:
rows.append(u'{"id": %s, "metadata": %s}],' % (dumps(obs[1]),
dumps(obs[2])))
# turns out its a pain to figure out when to place commas. the
# simple work around, at the expense of a little memory
# (bound by the number of samples) is to build of what will be
# written, and then add in the commas where necessary.
built_row = []
for col_index, val in enumerate(obs[0]):
if float(val) != 0.0:
built_row.append(u"[%d,%d,%r]" % (obs_index, col_index,
val))
"""
if built_row:
# if we have written a row already, its safe to add a comma
if have_written:
if direct_io:
direct_io.write(u',')
else:
data.append(u',')
if direct_io:
direct_io.write(u','.join(built_row))
else:
data.append(u','.join(built_row))
have_written = True
"""
"""
# finalize the data block
if direct_io:
direct_io.write(u"],")
else:
data.append(u"],")
"""
# Fill in details about the columns in the table.
columns = [u'"columns": [']
for samp_index, samp in enumerate(self.iter()):
if samp_index != max_col_idx:
columns.append(u'{"id": %s, "metadata": %s},' % (
dumps(samp[1]), dumps(samp[2])))
else:
columns.append(u'{"id": %s, "metadata": %s}]' % (
dumps(samp[1]), dumps(samp[2])))
if rows[0] == u'"rows": [' and len(rows) == 1:
# empty table case
rows = [u'"rows": [],']
columns = [u'"columns": []']
rows = u''.join(rows)
columns = u''.join(columns)
if direct_io:
direct_io.write(rows)
direct_io.write(columns)
direct_io.write(u'}')
else:
return u"{%s}" % ''.join([id_, format_, format_url, matrix_type,
generated_by, date, type_,
matrix_element_type, shape,
u''.join(data), rows, columns])
# This is also copy pasted from
# https://github.com/biocore/biom-format/blob/fd84172794d14a741a5764234d7a28416b9dba08/biom/table.py#L4451
def to_json_but_only_data_and_not_json_but_tsv(self, generated_by, direct_io=None):
"""Returns a JSON string representing the table in BIOM format.
Parameters
----------
generated_by : str
a string describing the software used to build the table
direct_io : file or file-like object, optional
Defaults to ``None``. Must implementing a ``write`` function. If
`direct_io` is not ``None``, the final output is written directly
to `direct_io` during processing.
Returns
-------
str
A JSON-formatted string representing the biom table
"""
"""
if not isinstance(generated_by, string_types):
raise TableException("Must specify a generated_by string")
# Fill in top-level metadata.
if direct_io:
direct_io.write(u'{')
direct_io.write(u'"id": "%s",' % str(self.table_id))
direct_io.write(
u'"format": "%s",' %
get_biom_format_version_string((1, 0))) # JSON table -> 1.0.0
direct_io.write(
u'"format_url": "%s",' %
get_biom_format_url_string())
direct_io.write(u'"generated_by": "%s",' % generated_by)
direct_io.write(u'"date": "%s",' % datetime.now().isoformat())
else:
id_ = u'"id": "%s",' % str(self.table_id)
format_ = u'"format": "%s",' % get_biom_format_version_string(
(1, 0)) # JSON table -> 1.0.0
format_url = u'"format_url": "%s",' % get_biom_format_url_string()
generated_by = u'"generated_by": "%s",' % generated_by
date = u'"date": "%s",' % datetime.now().isoformat()
# Determine if we have any data in the matrix, and what the shape of
# the matrix is.
try:
num_rows, num_cols = self.shape
except: # noqa
num_rows = num_cols = 0
has_data = True if num_rows > 0 and num_cols > 0 else False
# Default the matrix element type to test to be an integer in case we
# don't have any data in the matrix to test.
test_element = 0
if has_data:
test_element = self[0, 0]
# Determine the type of elements the matrix is storing.
if isinstance(test_element, int):
matrix_element_type = u"int"
elif isinstance(test_element, float):
matrix_element_type = u"float"
elif isinstance(test_element, string_types):
matrix_element_type = u"str"
else:
raise TableException("Unsupported matrix data type.")
# Fill in details about the matrix.
if direct_io:
direct_io.write(
u'"matrix_element_type": "%s",' %
matrix_element_type)
direct_io.write(u'"shape": [%d, %d],' % (num_rows, num_cols))
else:
matrix_element_type = u'"matrix_element_type": "%s",' % \
matrix_element_type
shape = u'"shape": [%d, %d],' % (num_rows, num_cols)
# Fill in the table type
if self.type is None:
type_ = u'"type": null,'
else:
type_ = u'"type": "%s",' % self.type
if direct_io:
direct_io.write(type_)
# Fill in details about the rows in the table and fill in the matrix's
# data. BIOM 2.0+ is now only sparse
if direct_io:
direct_io.write(u'"matrix_type": "sparse",')
direct_io.write(u'"data": [')
else:
matrix_type = u'"matrix_type": "sparse",'
data = [u'"data": [']
max_row_idx = len(self.ids(axis='observation')) - 1
max_col_idx = len(self.ids()) - 1
rows = [u'"rows": [']
have_written = False
"""
for obs_index, obs in enumerate(self.iter(axis='observation')):
"""
# i'm crying on the inside
if obs_index != max_row_idx:
rows.append(u'{"id": %s, "metadata": %s},' % (dumps(obs[1]),
dumps(obs[2])))
else:
rows.append(u'{"id": %s, "metadata": %s}],' % (dumps(obs[1]),
dumps(obs[2])))
# turns out its a pain to figure out when to place commas. the
# simple work around, at the expense of a little memory
# (bound by the number of samples) is to build of what will be
# written, and then add in the commas where necessary.
built_row = []
"""
for col_index, val in enumerate(obs[0]):
if float(val) != 0.0:
if direct_io:
direct_io.write(u"%d\t%d\t%r\n" % (obs_index, col_index,
val))
else:
data.append([obs_index, col_index, val])
"""
built_row.append(u"[%d,%d,%r]" % (obs_index, col_index,
val))
if built_row:
# if we have written a row already, its safe to add a comma
if have_written:
if direct_io:
direct_io.write(u',')
else:
data.append(u',')
if direct_io:
direct_io.write(u','.join(built_row))
else:
data.append(u','.join(built_row))
have_written = True
"""
"""
# finalize the data block
if direct_io:
direct_io.write(u"],")
else:
data.append(u"],")
# Fill in details about the columns in the table.
columns = [u'"columns": [']
for samp_index, samp in enumerate(self.iter()):
if samp_index != max_col_idx:
columns.append(u'{"id": %s, "metadata": %s},' % (
dumps(samp[1]), dumps(samp[2])))
else:
columns.append(u'{"id": %s, "metadata": %s}]' % (
dumps(samp[1]), dumps(samp[2])))
if rows[0] == u'"rows": [' and len(rows) == 1:
# empty table case
rows = [u'"rows": [],']
columns = [u'"columns": []']
rows = u''.join(rows)
columns = u''.join(columns)
if direct_io:
direct_io.write(rows)
direct_io.write(columns)
direct_io.write(u'}')
else:
return u"{%s}" % ''.join([id_, format_, format_url, matrix_type,
generated_by, date, type_,
matrix_element_type, shape,
u''.join(data), rows, columns])
"""
#
#
# Here's the patching
# Taken from https://stackoverflow.com/a/28060251
#
#
table.to_json_but_only_metadata = to_json_but_only_metadata.__get__(table)
table.to_json_but_only_data_and_not_json_but_tsv = to_json_but_only_data_and_not_json_but_tsv.__get__(table)
| 39.620985
| 157
| 0.529806
| 2,253
| 18,503
| 4.166001
| 0.146028
| 0.052845
| 0.048476
| 0.043256
| 0.764223
| 0.737375
| 0.711485
| 0.708928
| 0.708928
| 0.703068
| 0
| 0.014602
| 0.355996
| 18,503
| 466
| 158
| 39.706009
| 0.773078
| 0.103983
| 0
| 0.207101
| 0
| 0.011834
| 0.175399
| 0.005239
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.005917
| 0.04142
| null | null | 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
8fad947383ebed4a8dc5105f198868d325d01faf
| 26
|
py
|
Python
|
docs/test.py
|
sn696/nlp
|
2fecf255e138f770281e41c416d7943c88e984b0
|
[
"Apache-2.0"
] | null | null | null |
docs/test.py
|
sn696/nlp
|
2fecf255e138f770281e41c416d7943c88e984b0
|
[
"Apache-2.0"
] | null | null | null |
docs/test.py
|
sn696/nlp
|
2fecf255e138f770281e41c416d7943c88e984b0
|
[
"Apache-2.0"
] | null | null | null |
def bla():
print("HI")
| 13
| 15
| 0.5
| 4
| 26
| 3.25
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.230769
| 26
| 2
| 15
| 13
| 0.65
| 0
| 0
| 0
| 0
| 0
| 0.074074
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
8fb2489cd1dd2952b86b11284654e9047e428b6f
| 20,760
|
py
|
Python
|
unit_tests/test_case.py
|
LandRegistry/digital-street-case-management-api
|
e1a44deede52c257dd0d2655e276242fbedb406d
|
[
"MIT"
] | null | null | null |
unit_tests/test_case.py
|
LandRegistry/digital-street-case-management-api
|
e1a44deede52c257dd0d2655e276242fbedb406d
|
[
"MIT"
] | null | null | null |
unit_tests/test_case.py
|
LandRegistry/digital-street-case-management-api
|
e1a44deede52c257dd0d2655e276242fbedb406d
|
[
"MIT"
] | 3
|
2019-04-26T06:37:25.000Z
|
2021-04-11T05:22:28.000Z
|
from unittest import TestCase, mock
from case_management_api.exceptions import ConflictError
from case_management_api.main import app
from case_management_api.extensions import db
from case_management_api.models import Case, User, Address, X500Name
import json
import copy
# Test data
address = Address("1", "Digital Street", "Bristol", "Bristol", "United Kingdom", "BS2 8EN")
seller_address = Address("11", "Digital Street", "Bristol", "Bristol", "United Kingdom", "BS2 8EN")
seller = User(1, "Lisa", "Seller", "lisa.seller@example.com", "12345678901", seller_address)
seller_conveyancer_address = Address("12", "Digital Street", "Bristol", "Bristol", "United Kingdom", "BS2 8EN")
seller_conveyancer1 = User(2, "Natasha",
"Conveyancer",
"natasha.conveyancer@example.com", "10293847565",
seller_conveyancer_address)
seller_conveyancer2 = User(3, "Tash",
"Conveyancer",
"natasha2.conveyancer@example.com", "10293847567",
seller_conveyancer_address)
buyer_address = Address("13", "Digital Street", "Bristol", "Bristol", "United Kingdom", "BS2 8EN")
buyer = User(4, "David", "Buyer", "david.buyer@example.com", "10987654321", buyer_address)
buyer_conveyancer_address = Address("14", "Digital Street", "Bristol", "Bristol", "United Kingdom", "BS2 8EN")
buyer_conveyancer = User(5, "Samuel",
"Conveyancer",
"samuel.conveyancer@example.com", "10293847566",
buyer_conveyancer_address)
case1 = Case("sell", "ABCD123",
seller_conveyancer1, seller,
buyer, X500Name("Conveyancer B", "Plymouth", "GB"), buyer_conveyancer,
address)
case2 = Case("sell", "ABCD123",
seller_conveyancer2, seller,
buyer, X500Name("Conveyancer B", "Plymouth", "GB"), buyer_conveyancer,
address)
case2.title_number = "ZQV888860"
case3 = Case("sell", "DCBA321",
seller_conveyancer1, seller,
buyer, X500Name("Conveyancer B", "Plymouth", "GB"), buyer_conveyancer,
address)
case3.title_number = "ZQV888860"
case3.status = "completed"
standard_dict = {
"case_reference": "ABCD123".upper(),
"case_type": "buy",
"assigned_staff_id": 3,
"client_id": 1,
"status": "active",
"address": {
"house_name_number": "1",
"street": "Digital Street",
"town_city": "Bristol",
"county": "Bristol",
"country": "England",
"postcode": "BS2 8EN"
},
"title_number": "ZQV888860",
"counterparty_id": 2,
"counterparty_conveyancer_org": {
"organisation": "Generic Conveyancing Company",
"locality": "Plymouth",
"country": "GB",
"state": "Devon"
},
"counterparty_conveyancer_contact_id": 4
}
# Tests the Case endpoints
class TestCases(TestCase):
def setUp(self):
"""Sets up the tests."""
self.app = app.test_client()
@mock.patch.object(db.Model, 'query')
def test_001_get_cases(self, mock_db_query):
"""Gets a list of all cases."""
mock_db_query.all.return_value = [case1, case2]
response = self.app.get('/v1/cases', headers={'accept': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 200)
self.assertEqual(len(response.json), 2)
@mock.patch.object(db.Model, 'query')
def test_002_get_cases_for_assigned_staff(self, mock_db_query):
"""Gets a list of all cases with the assigned member of staff."""
mock_db_query.filter_by.return_value.all.return_value = [case1, case3]
response = self.app.get('/v1/cases?assigned_staff_id=1', headers={'accept': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 200)
self.assertEqual(len(response.json), 2)
@mock.patch.object(db.Model, 'query')
def test_003_get_cases_for_title_number(self, mock_db_query):
"""Gets a list of all cases with the title number."""
mock_db_query.filter_by.return_value.all.return_value = [case2, case3]
response = self.app.get('/v1/cases?title_number=ZQV888860', headers={'accept': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 200)
self.assertEqual(len(response.json), 2)
@mock.patch.object(db.Model, 'query')
def test_004_get_cases_for_status(self, mock_db_query):
"""Gets a list of all cases with the status."""
mock_db_query.filter_by.return_value.all.return_value = [case1, case2]
response = self.app.get('/v1/cases?status=active', headers={'accept': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 200)
self.assertEqual(len(response.json), 2)
@mock.patch.object(db.Model, 'query')
def test_004_get_cases_for_status_and_title_number(self, mock_db_query):
"""Gets a list of all cases with the status."""
mock_db_query.filter_by.return_value.filter_by.return_value.all.return_value = [case2]
response = self.app.get('/v1/cases?status=active&title_number=ZQV888860',
headers={'accept': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 200)
self.assertEqual(len(response.json), 1)
@mock.patch.object(db.Model, 'query')
def test_005_get_case(self, mock_db_query):
"""Gets a specified case."""
mock_db_query.get.return_value = case1
response = self.app.get('/v1/cases/' + case1.case_reference, headers={'accept': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 200)
self.assertEqual(response.json['case_reference'], 'ABCD123')
@mock.patch.object(db.Model, 'query')
def test_006_get_case_invalid_case_ref(self, mock_db_query):
"""The given case reference does not exist."""
mock_db_query.get.return_value = None
response = self.app.get('/v1/cases/N0-1D', headers={'accept': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 404)
self.assertIn('Case not found', response.json['error_message'])
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
def test_007_create_case(self, mock_db_query, mock_db_add, mock_db_commit):
"""Creates a case."""
mock_db_query.get.side_effect = [
case1.assigned_staff,
case1.client,
case1.counterparty,
case1.counterparty_conveyancer_contact
]
response = self.app.post('/v1/cases', data=json.dumps(standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 201)
self.assertEqual(response.json['status'], 'active')
# Check we call the correct two database methods
self.assertTrue(mock_db_add.called)
self.assertTrue(mock_db_commit.called)
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
@mock.patch.object(Case, 'set_title_number')
def test_010_create_case_title_number_already_exists(self,
mock_case_set_title_number,
mock_db_query,
mock_db_add,
mock_db_commit):
"""The given title number already exists for an active case."""
mock_db_query.filter_by.return_value.first.side_effect = [
case1.assigned_staff,
case1.client,
case1.counterparty,
case1.counterparty_conveyancer_contact
]
mock_case_set_title_number.side_effect = ConflictError('An active case with this title number already exists')
response = self.app.post('/v1/cases', data=json.dumps(standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 409)
# Check we do not call the any database methods
self.assertFalse(mock_db_add.called)
self.assertFalse(mock_db_commit.called)
self.assertIn('An active case with this title number already exists', response.json['error_message'])
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
@mock.patch.object(Case, 'set_status')
def test_010_create_case_invalid_status(self, mock_case_set_status, mock_db_query, mock_db_add, mock_db_commit):
"""The given case reference does not exist."""
mock_db_query.filter_by.return_value.first.side_effect = [
case1.assigned_staff,
case1.client,
case1.counterparty,
case1.counterparty_conveyancer_contact
]
mock_case_set_status.side_effect = ValueError('Status is invalid')
local_standard_dict = copy.deepcopy(standard_dict)
local_standard_dict['status'] = 'invalid status here'
response = self.app.post('/v1/cases', data=json.dumps(local_standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 400)
# Check we do not call the any database methods
self.assertFalse(mock_db_add.called)
self.assertFalse(mock_db_commit.called)
self.assertTrue(
'Status is invalid' in response.json['error_message'] or 'is not one of' in response.json['error_message']
)
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
@mock.patch.object(Case, 'set_status')
def test_010_create_case_title_number_already_exists_status(self,
mock_case_set_status,
mock_db_query,
mock_db_add,
mock_db_commit):
"""The given case reference does not exist."""
mock_db_query.filter_by.return_value.first.side_effect = [
case1.assigned_staff,
case1.client,
case1.counterparty,
case1.counterparty_conveyancer_contact
]
mock_case_set_status.side_effect = ConflictError('An active case with this title number already exists')
response = self.app.post('/v1/cases', data=json.dumps(standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 409)
# Check we do not call the any database methods
self.assertFalse(mock_db_add.called)
self.assertFalse(mock_db_commit.called)
self.assertIn('An active case with this title number already exists', response.json['error_message'])
# @mock.patch.object(db.session, 'commit')
# @mock.patch.object(db.session, 'add')
# @mock.patch.object(db.Model, 'query')
# def test_008_create_case_invalid_json(self, mock_db_query, mock_db_add, mock_db_commit):
# """The json data used to create the case is invalid."""
# local_standard_dict = copy.deepcopy(standard_dict)
# del local_standard_dict['title_number']
# response = self.app.post('/v1/cases', data=json.dumps(local_standard_dict),
# headers={'accept': 'application/json', 'content-type': 'application/json'})
# print(response.get_data().decode())
# self.assertEqual(response.status_code, 400)
# self.assertIn('"error_message":"\'case_reference\' is a required property', response.json['error_message'])
# # check we haven't tried calling the postgres database
# self.assertFalse(mock_db_query.called)
# # Check we do not call the any database methods
# self.assertFalse(mock_db_add.called)
# self.assertFalse(mock_db_commit.called)
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
def test_009_update_case(self, mock_db_query, mock_db_add, mock_db_commit):
"""Updates the details of a case."""
mock_db_query.get.side_effect = [
case1,
case1.address
]
response = self.app.put('/v1/cases/' + standard_dict['case_reference'], data=json.dumps(standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 200)
# Check we call the correct two database methods
self.assertTrue(mock_db_add.called)
self.assertTrue(mock_db_commit.called)
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
def test_010_update_case_invalid_case_ref(self, mock_db_query, mock_db_add, mock_db_commit):
"""The given case reference does not exist."""
mock_db_query.get.side_effect = [
None,
case1.address
]
response = self.app.put('/v1/cases/N0-1D', data=json.dumps(standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 404)
# Check we do not call the any database methods
self.assertFalse(mock_db_add.called)
self.assertFalse(mock_db_commit.called)
self.assertIn('Case not found', response.json['error_message'])
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
def test_011_update_case_mismatch_case_ref(self, mock_db_query, mock_db_add, mock_db_commit):
"""The case reference in the url does not match the case reference in the request body."""
mock_db_query.get.side_effect = [
case1,
case1.address
]
response = self.app.put('/v1/cases/WR0NG-1D', data=json.dumps(standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 400)
# Check we do not call the any database methods
self.assertFalse(mock_db_add.called)
self.assertFalse(mock_db_commit.called)
self.assertIn('Case Reference mismatch', response.json['error_message'])
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
@mock.patch.object(Case, 'set_title_number')
def test_010_update_case_title_number_already_exists(self,
mock_case_set_title_number,
mock_db_query,
mock_db_add,
mock_db_commit):
"""The given title number already exists for an active case."""
mock_db_query.get.side_effect = [
case1,
case1.address
]
mock_case_set_title_number.side_effect = ConflictError('An active case with this title number already exists')
response = self.app.put('/v1/cases/' + standard_dict['case_reference'], data=json.dumps(standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 409)
# Check we do not call the any database methods
self.assertFalse(mock_db_add.called)
self.assertFalse(mock_db_commit.called)
self.assertIn('An active case with this title number already exists', response.json['error_message'])
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
@mock.patch.object(Case, 'set_status')
def test_010_update_case_invalid_status(self, mock_case_set_status, mock_db_query, mock_db_add, mock_db_commit):
"""The given case reference does not exist."""
mock_db_query.get.side_effect = [
case1,
case1.address
]
mock_case_set_status.side_effect = ValueError('Status is invalid')
local_standard_dict = copy.deepcopy(standard_dict)
local_standard_dict['status'] = 'invalid status here'
response = self.app.put('/v1/cases/' + local_standard_dict['case_reference'],
data=json.dumps(local_standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 400)
# Check we do not call the any database methods
self.assertFalse(mock_db_add.called)
self.assertFalse(mock_db_commit.called)
self.assertTrue(
'Status is invalid' in response.json['error_message'] or 'is not one of' in response.json['error_message']
)
@mock.patch.object(db.session, 'commit')
@mock.patch.object(db.session, 'add')
@mock.patch.object(db.Model, 'query')
@mock.patch.object(Case, 'set_status')
def test_010_update_case_title_number_already_exists_status(self,
mock_case_set_status,
mock_db_query,
mock_db_add,
mock_db_commit):
"""The given case reference does not exist."""
mock_db_query.get.side_effect = [
case1,
case1.address
]
mock_case_set_status.side_effect = ConflictError('An active case with this title number already exists')
response = self.app.put('/v1/cases/' + standard_dict['case_reference'], data=json.dumps(standard_dict),
headers={'accept': 'application/json', 'content-type': 'application/json'})
print(response.get_data().decode())
self.assertEqual(response.status_code, 409)
# Check we do not call the any database methods
self.assertFalse(mock_db_add.called)
self.assertFalse(mock_db_commit.called)
self.assertIn('An active case with this title number already exists', response.json['error_message'])
# @mock.patch.object(db.session, 'commit')
# @mock.patch.object(db.session, 'add')
# @mock.patch.object(db.Model, 'query')
# def test_012_update_case_invalid_json(self, mock_db_query, mock_db_add, mock_db_commit):
# """The json data used to update the case is invalid."""
# local_standard_dict = copy.deepcopy(standard_dict)
# del local_standard_dict['title_number']
# response = self.app.put('/v1/cases/' + local_standard_dict['case_reference'],
# data=json.dumps(local_standard_dict),
# headers={'accept': 'application/json', 'content-type': 'application/json'})
# self.assertEqual(response.status_code, 400)
# self.assertIn('"error_message":"\'case_reference\' is a required property', response.json['error_message'])
# # check we haven't tried calling the postgres database
# self.assertFalse(mock_db_query.called)
# # Check we do not call the any database methods
# self.assertFalse(mock_db_add.called)
# self.assertFalse(mock_db_commit.called)
| 44.741379
| 118
| 0.623603
| 2,453
| 20,760
| 5.059111
| 0.091724
| 0.041579
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| 0.847381
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| 0.797019
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| 20,760
| 463
| 119
| 44.838013
| 0.782778
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|
0
| 6
|
8fd2e82aebac5ad690a708e50d0e59e898bbc4d9
| 148
|
py
|
Python
|
6 kyu/How Much.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 6
|
2020-09-03T09:32:25.000Z
|
2020-12-07T04:10:01.000Z
|
6 kyu/How Much.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | 1
|
2021-12-13T15:30:21.000Z
|
2021-12-13T15:30:21.000Z
|
6 kyu/How Much.py
|
mwk0408/codewars_solutions
|
9b4f502b5f159e68024d494e19a96a226acad5e5
|
[
"MIT"
] | null | null | null |
def howmuch(m, n):
return [[f"M: {i}",f"B: {5+i//63*9}" ,f"C: {4+i//63*7}" ] for i in range(min(m,n), max(m, n)+1) if ( i>=37 and (i-37)%63==0)]
| 74
| 129
| 0.47973
| 39
| 148
| 1.820513
| 0.615385
| 0.084507
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| 0.129032
| 0.162162
| 148
| 2
| 129
| 74
| 0.443548
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| 0.228188
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| 1
| 1
| 0
|
0
| 6
|
8fea4bed48fa59ec55fe07f8f6ad1b99d153bbb8
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/cleo/parser.py
|
Retraces/UkraineBot
|
3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/cleo/parser.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/cleo/parser.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/7b/7f/96/63a574971b9bef03664f7734b5fe4d077878ca6eb0d0b03b788da826f9
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
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| 0.4375
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0
| 6
|
890af031eb6be83b77fc37f7c9187ae08b527b00
| 340
|
py
|
Python
|
backend/application/schemas/__init__.py
|
uesleicarvalhoo/Ecommerce
|
1d8d0f0c522dcd27fd90e315989b6fa93caf62b8
|
[
"MIT"
] | null | null | null |
backend/application/schemas/__init__.py
|
uesleicarvalhoo/Ecommerce
|
1d8d0f0c522dcd27fd90e315989b6fa93caf62b8
|
[
"MIT"
] | null | null | null |
backend/application/schemas/__init__.py
|
uesleicarvalhoo/Ecommerce
|
1d8d0f0c522dcd27fd90e315989b6fa93caf62b8
|
[
"MIT"
] | null | null | null |
from backend.domain.schemas import Token, TokenData
from .crud import NewClientSchema, NewOrderItemSchema, NewProductSchema, UpdateClientSchema
from .entities import ClientSchema, OrderItemSchema, OrderSchema, PaymentInfoSchema, PaymentResultSchema, ProductSchema
from .query import QueryClientSchema, QueryOrderSchema, QueryProductSchema
| 56.666667
| 119
| 0.870588
| 29
| 340
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|
0
| 6
|
891141474009e5e3bb5335af572453341185f7e8
| 94
|
py
|
Python
|
code/misc/__init__.py
|
niuwk/infonets
|
274e97c9a86144dd52cbe90caffff578a2f5d178
|
[
"BSD-3-Clause"
] | 8
|
2018-06-20T23:20:43.000Z
|
2020-01-12T01:32:06.000Z
|
code/misc/__init__.py
|
niuwk/infonets
|
274e97c9a86144dd52cbe90caffff578a2f5d178
|
[
"BSD-3-Clause"
] | null | null | null |
code/misc/__init__.py
|
niuwk/infonets
|
274e97c9a86144dd52cbe90caffff578a2f5d178
|
[
"BSD-3-Clause"
] | 4
|
2018-06-26T20:28:13.000Z
|
2021-06-17T13:39:56.000Z
|
from __future__ import absolute_import, division, print_function
from .predataset import *
| 15.666667
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| 0.819149
| 11
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| 1
|
0
| 6
|
8f1621ab1057ac7a0d606f8f58c524319c41c036
| 1,067
|
py
|
Python
|
pyenv/lib/python3.6/weakref.py
|
ronald-rgr/ai-chatbot-smartguide
|
c9c830feb6b66c2e362f8fb5d147ef0c4f4a08cf
|
[
"Apache-2.0"
] | null | null | null |
pyenv/lib/python3.6/weakref.py
|
ronald-rgr/ai-chatbot-smartguide
|
c9c830feb6b66c2e362f8fb5d147ef0c4f4a08cf
|
[
"Apache-2.0"
] | 3
|
2020-03-23T18:01:51.000Z
|
2021-03-19T23:15:15.000Z
|
pyenv/lib/python3.6/weakref.py
|
ronald-rgr/ai-chatbot-smartguide
|
c9c830feb6b66c2e362f8fb5d147ef0c4f4a08cf
|
[
"Apache-2.0"
] | null | null | null |
XSym
0074
be01ab2100c1c1d1fb2b73cffbbd8141
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/weakref.py
| 213.4
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| 0.893158
| 1,067
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| 949
| 213.4
| 0.684211
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0
| 6
|
8f444eb6a59441c478006a9a76544b297c438d0a
| 16,740
|
py
|
Python
|
tests/test_image.py
|
shivamvats/autolab_core
|
cda081d2e07e3fe6cc9f3e8c86eea92330910d20
|
[
"Apache-2.0"
] | 68
|
2017-07-02T22:14:47.000Z
|
2022-03-30T19:09:37.000Z
|
tests/test_image.py
|
shivamvats/autolab_core
|
cda081d2e07e3fe6cc9f3e8c86eea92330910d20
|
[
"Apache-2.0"
] | 14
|
2017-06-29T18:27:12.000Z
|
2022-02-02T20:59:02.000Z
|
tests/test_image.py
|
shivamvats/autolab_core
|
cda081d2e07e3fe6cc9f3e8c86eea92330910d20
|
[
"Apache-2.0"
] | 35
|
2017-07-17T01:44:59.000Z
|
2022-03-30T19:09:28.000Z
|
"""
Tests the image class.
Author: Jeff Mahler
"""
import os
import logging
import numpy as np
import unittest
from .constants import IM_HEIGHT, IM_WIDTH, BINARY_THRESH, COLOR_IM_FILEROOT
from autolab_core import (
ColorImage,
DepthImage,
BinaryImage,
SegmentationImage,
GrayscaleImage,
PointCloudImage,
NormalCloudImage,
)
class TestImage(unittest.TestCase):
def test_color_init(self):
# valid data
random_valid_data = (
255.0 * np.random.rand(IM_HEIGHT, IM_WIDTH, 3)
).astype(np.uint8)
im = ColorImage(random_valid_data)
self.assertEqual(im.height, IM_HEIGHT)
self.assertEqual(im.width, IM_WIDTH)
self.assertEqual(im.channels, 3)
self.assertTrue(np.allclose(im.data, random_valid_data))
# invalid channels
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH).astype(np.uint8)
caught_bad_channels = False
try:
im = ColorImage(random_data)
except ValueError:
caught_bad_channels = True
self.assertTrue(caught_bad_channels)
# invalid type
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 3).astype(np.float32)
caught_bad_dtype = False
try:
im = ColorImage(random_data)
except ValueError:
caught_bad_dtype = True
self.assertTrue(caught_bad_dtype)
def test_depth_init(self):
# valid data
random_valid_data = np.random.rand(IM_HEIGHT, IM_WIDTH).astype(
np.float32
)
im = DepthImage(random_valid_data)
self.assertEqual(im.height, IM_HEIGHT)
self.assertEqual(im.width, IM_WIDTH)
self.assertEqual(im.channels, 1)
self.assertEqual(im.type, np.float32)
self.assertTrue(np.allclose(im.data, random_valid_data))
# invalid channels
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 3).astype(np.float32)
caught_bad_channels = False
try:
im = DepthImage(random_data)
except ValueError:
caught_bad_channels = True
self.assertTrue(caught_bad_channels)
# invalid type
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH).astype(np.uint8)
caught_bad_dtype = False
try:
im = DepthImage(random_data)
except ValueError:
caught_bad_dtype = True
self.assertTrue(caught_bad_dtype)
def test_binary_init(self):
# valid data
random_valid_data = (
255.0 * np.random.rand(IM_HEIGHT, IM_WIDTH)
).astype(np.uint8)
binary_data = 255 * (random_valid_data > BINARY_THRESH)
im = BinaryImage(random_valid_data, threshold=BINARY_THRESH)
self.assertEqual(im.height, IM_HEIGHT)
self.assertEqual(im.width, IM_WIDTH)
self.assertEqual(im.channels, 1)
self.assertTrue(np.allclose(im.data, binary_data))
# invalid channels
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 3).astype(np.uint8)
caught_bad_channels = False
try:
im = BinaryImage(random_data)
except ValueError:
caught_bad_channels = True
self.assertTrue(caught_bad_channels)
# invalid type
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH).astype(np.float32)
caught_bad_dtype = False
try:
im = BinaryImage(random_data)
except ValueError:
caught_bad_dtype = True
self.assertTrue(caught_bad_dtype)
def test_grayscale_init(self):
# valid data
random_valid_data = (
255.0 * np.random.rand(IM_HEIGHT, IM_WIDTH)
).astype(np.uint8)
im = GrayscaleImage(random_valid_data)
self.assertEqual(im.height, IM_HEIGHT)
self.assertEqual(im.width, IM_WIDTH)
self.assertEqual(im.channels, 1)
self.assertTrue(np.allclose(im.data, random_valid_data))
# invalid channels
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 5).astype(np.uint8)
caught_bad_channels = False
try:
im = GrayscaleImage(random_data)
except ValueError:
caught_bad_channels = True
self.assertTrue(caught_bad_channels)
# invalid type
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH).astype(np.float32)
caught_bad_dtype = False
try:
im = GrayscaleImage(random_data)
except ValueError:
caught_bad_dtype = True
self.assertTrue(caught_bad_dtype)
def test_segment_init(self):
# valid data
random_valid_data = (
255.0 * np.random.rand(IM_HEIGHT, IM_WIDTH)
).astype(np.uint8)
im = SegmentationImage(random_valid_data)
self.assertEqual(im.height, IM_HEIGHT)
self.assertEqual(im.width, IM_WIDTH)
self.assertEqual(im.channels, 1)
self.assertTrue(np.allclose(im.data, random_valid_data))
# invalid channels
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 3).astype(np.uint8)
caught_bad_channels = False
try:
im = SegmentationImage(random_data)
except ValueError:
caught_bad_channels = True
self.assertTrue(caught_bad_channels)
# invalid type
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH).astype(np.float32)
caught_bad_dtype = False
try:
im = SegmentationImage(random_data)
except ValueError:
caught_bad_dtype = True
self.assertTrue(caught_bad_dtype)
def test_pc_init(self):
# valid data
random_valid_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 3).astype(
np.float32
)
im = PointCloudImage(random_valid_data)
self.assertEqual(im.height, IM_HEIGHT)
self.assertEqual(im.width, IM_WIDTH)
self.assertEqual(im.channels, 3)
self.assertTrue(np.allclose(im.data, random_valid_data))
# invalid channels
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH).astype(np.float32)
caught_bad_channels = False
try:
im = PointCloudImage(random_data)
except ValueError:
caught_bad_channels = True
self.assertTrue(caught_bad_channels)
# invalid type
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 3).astype(np.uint8)
caught_bad_dtype = False
try:
im = PointCloudImage(random_data)
except ValueError:
caught_bad_dtype = True
self.assertTrue(caught_bad_dtype)
def test_nc_init(self):
# valid data
random_valid_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 3).astype(
np.float32
)
random_valid_data = random_valid_data / np.tile(
np.linalg.norm(random_valid_data, axis=2)[:, :, np.newaxis],
[1, 1, 3],
)
im = NormalCloudImage(random_valid_data)
self.assertEqual(im.height, IM_HEIGHT)
self.assertEqual(im.width, IM_WIDTH)
self.assertEqual(im.channels, 3)
self.assertTrue(np.allclose(im.data, random_valid_data))
# invalid channels
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH).astype(np.float32)
caught_bad_channels = False
try:
im = NormalCloudImage(random_data)
except ValueError:
caught_bad_channels = True
self.assertTrue(caught_bad_channels)
# invalid type
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 3).astype(np.uint8)
caught_bad_dtype = False
try:
im = NormalCloudImage(random_data)
except ValueError:
caught_bad_dtype = True
self.assertTrue(caught_bad_dtype)
# invalid norm
random_data = np.random.rand(IM_HEIGHT, IM_WIDTH, 3).astype(np.float32)
caught_bad_norm = False
try:
im = NormalCloudImage(random_data)
except ValueError:
caught_bad_norm = True
self.assertTrue(caught_bad_norm)
def test_resize(self):
random_valid_data = (
255.0 * np.random.rand(IM_HEIGHT, IM_WIDTH, 3)
).astype(np.uint8)
im = ColorImage(random_valid_data)
big_scale = 2.0
big_im = im.resize(big_scale)
self.assertEqual(big_im.height, big_scale * IM_HEIGHT)
self.assertEqual(big_im.width, big_scale * IM_WIDTH)
small_scale = 0.5
small_im = im.resize(small_scale)
self.assertEqual(small_im.height, small_scale * IM_HEIGHT)
self.assertEqual(small_im.width, small_scale * IM_WIDTH)
def test_transform(self):
random_valid_data = (
255.0 * np.random.rand(IM_HEIGHT, IM_WIDTH, 3)
).astype(np.uint8)
im = ColorImage(random_valid_data)
translation = np.array([2, 2])
im_tf = im.transform(translation, 0.0)
self.assertTrue(np.allclose(im[0, 0], im_tf[2, 2]))
def test_shape_comp(self):
random_valid_data = (
255.0 * np.random.rand(IM_HEIGHT, IM_WIDTH, 3)
).astype(np.uint8)
im1 = ColorImage(random_valid_data)
random_valid_data = (
255.0 * np.random.rand(IM_HEIGHT, IM_WIDTH, 3)
).astype(np.uint8)
im2 = ColorImage(random_valid_data)
self.assertTrue(im1.is_same_shape(im2))
random_valid_data = (
255.0 * np.random.rand(2 * IM_HEIGHT, 2 * IM_WIDTH, 3)
).astype(np.uint8)
im3 = ColorImage(random_valid_data)
self.assertFalse(im1.is_same_shape(im3))
def test_mask_by_ind(self):
random_valid_data = (
255.0 * np.random.rand(IM_HEIGHT, IM_WIDTH, 3)
).astype(np.uint8)
im = ColorImage(random_valid_data)
ind = np.array([[0, 0]])
im2 = im.mask_by_ind(ind)
self.assertEqual(np.sum(im2[1, 1]), 0.0)
def test_indexing(self, height=50, width=100):
color_data = (255 * np.random.rand(height, width, 3)).astype(np.uint8)
im = ColorImage(color_data, "a")
# test valid indexing on color images
i = int(height * np.random.rand())
j = int(width * np.random.rand())
k = int(3 * np.random.rand())
logging.info("Indexing with i=%d, j=%d, k=%d" % (i, j, k))
c_true = color_data[i, j, k]
c_read = im[i, j, k]
self.assertTrue(
np.sum(np.abs(c_true - c_read)) < 1e-5,
msg="Image ijk indexing failed",
)
c_true = color_data[i, j, :]
c_read = im[i, j]
self.assertTrue(
np.sum(np.abs(c_true - c_read)) < 1e-5,
msg="Image ij indexing failed",
)
c_true = color_data[i, :, :]
c_read = im[i]
self.assertTrue(
np.sum(np.abs(c_true - c_read)) < 1e-5,
msg="Image i indexing failed",
)
# test valid slicing on color images
i_start = 0
j_start = 0
k_start = 0
i_stop = int(height * np.random.rand())
j_stop = int(width * np.random.rand())
k_stop = int(3 * np.random.rand())
i_step = 1
j_step = 1
k_step = 1
logging.info(
"Slicing with i_start=%d, i_stop=%d, i_step=%d, \
j_start=%d, j_stop=%d, j_step=%d, \
k_start=%d, k_stop=%d, k_step=%d"
% (
i_start,
i_stop,
i_step,
j_start,
j_stop,
j_step,
k_start,
k_stop,
k_step,
)
)
c_true = color_data[
i_start:i_stop:i_step, j_start:j_stop:j_step, k_start:k_stop:k_step
]
c_read = im[
i_start:i_stop:i_step, j_start:j_stop:j_step, k_start:k_stop:k_step
]
self.assertTrue(
np.sum(np.abs(c_true - c_read)) < 1e-5,
msg="Image ijk slicing failed",
)
# test out of bounds indexing on color image
caught_out_of_bounds = False
try:
c_read = im[-1, j, k]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[i, -1, k]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[i, j, -1]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[height, j, k]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[i, width, k]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[i, j, 3]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
# test out of bounds slicing on color image. (Python slicing does not
# cause out of bound)
caught_out_of_bounds = False
try:
c_read = im[
-1:i_stop:i_step, j_start:j_stop:j_step, k_start:k_stop:k_step
]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[
i_start:i_stop:i_step, -1:j_stop:j_step, k_start:k_stop:k_step
]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[
i_start:i_stop:i_step, j_start:j_stop:j_step, -1:k_stop:k_step
]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[
i_start : height + 1 : i_step,
j_start:j_stop:j_step,
k_start:k_stop:k_step,
]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[
i_start:i_stop:i_step,
j_start : width + 1 : j_step,
k_start:k_stop:k_step,
]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
caught_out_of_bounds = False
try:
c_read = im[
i_start:i_stop:i_step, j_start:j_stop:j_step, k_start:4:k_step
]
except ValueError:
caught_out_of_bounds = True
self.assertTrue(caught_out_of_bounds)
def test_io(self, height=50, width=100):
color_data = (255 * np.random.rand(height, width, 3)).astype(np.uint8)
im = ColorImage(color_data, "a")
file_root = COLOR_IM_FILEROOT
if not os.path.exists(os.path.dirname(file_root)):
os.makedirs(os.path.dirname(file_root))
# save and load png
filename = file_root + ".png"
im.save(filename)
loaded_im = ColorImage.open(filename)
self.assertTrue(
np.sum(np.abs(loaded_im.data - im.data)) < 1e-5,
msg="ColorImage data changed after load png",
)
os.remove(filename)
# save and load jpg
filename = file_root + ".jpg"
im.save(filename)
loaded_im = ColorImage.open(filename)
os.remove(filename)
# save and load npy
filename = file_root + ".npy"
im.save(filename)
loaded_im = ColorImage.open(filename)
self.assertTrue(
np.sum(np.abs(loaded_im.data - im.data)) < 1e-5,
msg="ColorImage data changed after load npy",
)
os.remove(filename)
# save and load npz
filename = file_root + ".npz"
im.save(filename)
loaded_im = ColorImage.open(filename)
self.assertTrue(
np.sum(np.abs(loaded_im.data - im.data)) < 1e-5,
msg="ColorImage data changed after load npz",
)
os.remove(filename)
os.rmdir(os.path.dirname(file_root))
if __name__ == "__main__":
unittest.main()
| 32.695313
| 79
| 0.590562
| 2,132
| 16,740
| 4.363508
| 0.072233
| 0.041277
| 0.044932
| 0.065785
| 0.820703
| 0.789208
| 0.763087
| 0.756853
| 0.748791
| 0.742771
| 0
| 0.016882
| 0.317085
| 16,740
| 511
| 80
| 32.759296
| 0.796886
| 0.036858
| 0
| 0.638756
| 0
| 0
| 0.016537
| 0
| 0
| 0
| 0
| 0
| 0.169856
| 1
| 0.0311
| false
| 0
| 0.014354
| 0
| 0.047847
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
8f55d899ff508eb8a1d177c797a6fd9fbcf92c39
| 91
|
py
|
Python
|
bempy/django/blocks/href/__init__.py
|
svetlyak40wt/bempy
|
ad87982d17c2d14c344d9e3d91a48c37dfb72535
|
[
"BSD-3-Clause"
] | 1
|
2015-04-29T15:19:45.000Z
|
2015-04-29T15:19:45.000Z
|
bempy/django/blocks/href/__init__.py
|
svetlyak40wt/bempy
|
ad87982d17c2d14c344d9e3d91a48c37dfb72535
|
[
"BSD-3-Clause"
] | null | null | null |
bempy/django/blocks/href/__init__.py
|
svetlyak40wt/bempy
|
ad87982d17c2d14c344d9e3d91a48c37dfb72535
|
[
"BSD-3-Clause"
] | 1
|
2019-06-10T16:08:54.000Z
|
2019-06-10T16:08:54.000Z
|
from bempy import block
@block()
def href(text, url):
return dict(text=text, url=url)
| 15.166667
| 35
| 0.692308
| 15
| 91
| 4.2
| 0.666667
| 0.222222
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| 0
| 0.175824
| 91
| 5
| 36
| 18.2
| 0.84
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| 0.25
| false
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| 0.75
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| 0
| 1
| 1
| 0
|
0
| 6
|
56de048f5c2b7053194f6036eac5cfdc158231ae
| 119
|
py
|
Python
|
blaze/expr/scalar/__init__.py
|
chdoig/blaze
|
caa5a497e1ca1ceb1cf585483312ff4cd74d0bda
|
[
"BSD-3-Clause"
] | 1
|
2015-01-18T23:59:57.000Z
|
2015-01-18T23:59:57.000Z
|
blaze/expr/scalar/__init__.py
|
chdoig/blaze
|
caa5a497e1ca1ceb1cf585483312ff4cd74d0bda
|
[
"BSD-3-Clause"
] | null | null | null |
blaze/expr/scalar/__init__.py
|
chdoig/blaze
|
caa5a497e1ca1ceb1cf585483312ff4cd74d0bda
|
[
"BSD-3-Clause"
] | null | null | null |
from .core import *
from .numbers import *
from .boolean import *
from .interface import *
from .parser import exprify
| 19.833333
| 27
| 0.756303
| 16
| 119
| 5.625
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| 119
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| 23.8
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|
0
| 6
|
7103ed3a8dc345aca78dea3c696fddc42f31afa8
| 4,939
|
py
|
Python
|
z2/part2/interactive/jm/random_fuzzy_arrows_1/946790979.py
|
kozakusek/ipp-2020-testy
|
09aa008fa53d159672cc7cbf969a6b237e15a7b8
|
[
"MIT"
] | 1
|
2020-04-16T12:13:47.000Z
|
2020-04-16T12:13:47.000Z
|
z2/part2/interactive/jm/random_fuzzy_arrows_1/946790979.py
|
kozakusek/ipp-2020-testy
|
09aa008fa53d159672cc7cbf969a6b237e15a7b8
|
[
"MIT"
] | 18
|
2020-03-06T17:50:15.000Z
|
2020-05-19T14:58:30.000Z
|
z2/part2/interactive/jm/random_fuzzy_arrows_1/946790979.py
|
kozakusek/ipp-2020-testy
|
09aa008fa53d159672cc7cbf969a6b237e15a7b8
|
[
"MIT"
] | 18
|
2020-03-06T17:45:13.000Z
|
2020-06-09T19:18:31.000Z
|
from part1 import (
gamma_board,
gamma_busy_fields,
gamma_delete,
gamma_free_fields,
gamma_golden_move,
gamma_golden_possible,
gamma_move,
gamma_new,
)
"""
scenario: test_random_actions
uuid: 946790979
"""
"""
random actions, total chaos
"""
board = gamma_new(5, 7, 6, 4)
assert board is not None
assert gamma_move(board, 1, 3, 2) == 1
assert gamma_move(board, 1, 1, 5) == 1
assert gamma_move(board, 2, 0, 2) == 1
assert gamma_move(board, 2, 3, 3) == 1
assert gamma_move(board, 3, 6, 0) == 0
assert gamma_move(board, 4, 2, 2) == 1
assert gamma_move(board, 4, 3, 6) == 1
assert gamma_golden_possible(board, 4) == 1
assert gamma_move(board, 5, 3, 1) == 1
assert gamma_golden_possible(board, 5) == 1
assert gamma_move(board, 6, 0, 6) == 1
assert gamma_move(board, 1, 4, 3) == 1
assert gamma_move(board, 1, 0, 6) == 0
assert gamma_move(board, 2, 1, 4) == 1
assert gamma_move(board, 2, 0, 2) == 0
assert gamma_move(board, 3, 2, 4) == 1
assert gamma_free_fields(board, 3) == 24
assert gamma_move(board, 4, 2, 6) == 1
assert gamma_move(board, 4, 0, 5) == 1
assert gamma_free_fields(board, 4) == 22
assert gamma_move(board, 5, 0, 0) == 1
assert gamma_move(board, 5, 1, 5) == 0
assert gamma_move(board, 6, 4, 4) == 1
assert gamma_move(board, 6, 1, 0) == 1
assert gamma_free_fields(board, 6) == 19
assert gamma_move(board, 1, 6, 4) == 0
assert gamma_move(board, 1, 1, 6) == 1
assert gamma_golden_possible(board, 1) == 1
assert gamma_move(board, 2, 6, 4) == 0
assert gamma_move(board, 2, 3, 2) == 0
assert gamma_move(board, 3, 5, 2) == 0
assert gamma_move(board, 4, 1, 4) == 0
assert gamma_move(board, 4, 3, 0) == 1
assert gamma_busy_fields(board, 4) == 5
assert gamma_move(board, 5, 5, 2) == 0
assert gamma_move(board, 6, 4, 0) == 1
assert gamma_move(board, 6, 2, 4) == 0
assert gamma_move(board, 1, 1, 2) == 1
assert gamma_move(board, 1, 4, 3) == 0
assert gamma_move(board, 2, 5, 2) == 0
assert gamma_free_fields(board, 2) == 15
assert gamma_move(board, 5, 4, 0) == 0
assert gamma_busy_fields(board, 5) == 2
assert gamma_move(board, 6, 4, 0) == 0
assert gamma_move(board, 6, 4, 5) == 1
assert gamma_move(board, 2, 3, 3) == 0
assert gamma_move(board, 3, 4, 6) == 1
assert gamma_golden_possible(board, 3) == 1
assert gamma_move(board, 4, 0, 1) == 0
assert gamma_move(board, 5, 3, 1) == 0
assert gamma_move(board, 5, 3, 0) == 0
assert gamma_move(board, 6, 1, 2) == 0
assert gamma_move(board, 6, 0, 5) == 0
assert gamma_golden_move(board, 6, 6, 2) == 0
assert gamma_move(board, 1, 5, 3) == 0
assert gamma_free_fields(board, 1) == 4
assert gamma_move(board, 2, 2, 3) == 1
assert gamma_move(board, 3, 3, 0) == 0
assert gamma_move(board, 5, 1, 4) == 0
assert gamma_move(board, 6, 4, 0) == 0
assert gamma_move(board, 1, 1, 0) == 0
assert gamma_golden_move(board, 1, 5, 0) == 0
assert gamma_move(board, 2, 0, 2) == 0
assert gamma_move(board, 2, 2, 6) == 0
assert gamma_move(board, 3, 1, 0) == 0
assert gamma_move(board, 3, 4, 5) == 0
assert gamma_free_fields(board, 4) == 5
assert gamma_move(board, 6, 4, 3) == 0
assert gamma_golden_possible(board, 1) == 1
assert gamma_move(board, 2, 0, 4) == 1
assert gamma_move(board, 3, 5, 2) == 0
assert gamma_move(board, 4, 1, 6) == 0
assert gamma_move(board, 4, 2, 5) == 1
assert gamma_move(board, 5, 1, 4) == 0
assert gamma_move(board, 6, 2, 3) == 0
assert gamma_busy_fields(board, 6) == 5
assert gamma_move(board, 1, 1, 1) == 1
assert gamma_move(board, 1, 0, 4) == 0
assert gamma_move(board, 4, 1, 0) == 0
assert gamma_move(board, 4, 2, 1) == 1
assert gamma_move(board, 5, 0, 2) == 0
assert gamma_move(board, 6, 2, 6) == 0
assert gamma_move(board, 1, 1, 4) == 0
assert gamma_move(board, 2, 1, 0) == 0
assert gamma_move(board, 2, 1, 1) == 0
assert gamma_move(board, 3, 5, 3) == 0
assert gamma_move(board, 3, 0, 1) == 1
assert gamma_free_fields(board, 3) == 7
assert gamma_move(board, 4, 2, 1) == 0
assert gamma_move(board, 5, 0, 2) == 0
assert gamma_move(board, 6, 5, 3) == 0
assert gamma_move(board, 1, 2, 0) == 0
assert gamma_free_fields(board, 1) == 2
assert gamma_move(board, 2, 1, 4) == 0
board159991877 = gamma_board(board)
assert board159991877 is not None
assert board159991877 == ("61443\n"
"414.6\n"
"223.6\n"
"..221\n"
"2141.\n"
"3145.\n"
"56.46\n")
del board159991877
board159991877 = None
assert gamma_move(board, 3, 0, 5) == 0
assert gamma_move(board, 3, 4, 6) == 0
assert gamma_move(board, 4, 5, 3) == 0
assert gamma_move(board, 4, 3, 2) == 0
assert gamma_move(board, 5, 1, 3) == 1
assert gamma_busy_fields(board, 5) == 3
assert gamma_move(board, 6, 1, 4) == 0
assert gamma_move(board, 6, 1, 4) == 0
assert gamma_move(board, 1, 1, 4) == 0
assert gamma_move(board, 1, 1, 1) == 0
assert gamma_move(board, 2, 3, 4) == 1
assert gamma_move(board, 3, 3, 2) == 0
assert gamma_move(board, 3, 1, 2) == 0
assert gamma_move(board, 4, 2, 4) == 0
gamma_delete(board)
| 33.598639
| 46
| 0.651751
| 929
| 4,939
| 3.310011
| 0.051668
| 0.386341
| 0.434146
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| 0.84065
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| 0.358374
| 0.218537
| 0.217886
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| 4,939
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0
| 6
|
710b961e1ad5947f0ed951b546cf5901fe703f83
| 29
|
py
|
Python
|
SoundcloudDownloader/__init__.py
|
AmirMohammad2003/soundcloud-downloader
|
298cb4819013bed0d1fe4ac33900b562aa005cdf
|
[
"MIT"
] | 1
|
2022-02-15T15:51:23.000Z
|
2022-02-15T15:51:23.000Z
|
SoundcloudDownloader/__init__.py
|
AmirMohammad2003/soundcloud-downloader
|
298cb4819013bed0d1fe4ac33900b562aa005cdf
|
[
"MIT"
] | null | null | null |
SoundcloudDownloader/__init__.py
|
AmirMohammad2003/soundcloud-downloader
|
298cb4819013bed0d1fe4ac33900b562aa005cdf
|
[
"MIT"
] | null | null | null |
from .downloader import SCDL
| 14.5
| 28
| 0.827586
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| 29
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| 1
| 0
|
0
| 6
|
711bf1c98d07609f3f236bbc8c2ef1df6122afa1
| 14,950
|
py
|
Python
|
apps/drl/chpA01/e01/chp_a01_e01.py
|
yt7589/iching
|
6673da38f4c80e7fd297c86fedc5616aee8ac09b
|
[
"Apache-2.0"
] | 32
|
2020-04-14T08:32:18.000Z
|
2022-02-09T07:05:08.000Z
|
apps/drl/chpA01/e01/chp_a01_e01.py
|
trinh-hoang-hiep/iching
|
e1feae5741c3cbde535d7a275b01d4f0cf9e21ed
|
[
"Apache-2.0"
] | 1
|
2020-04-08T10:42:15.000Z
|
2020-04-15T01:38:03.000Z
|
apps/drl/chpA01/e01/chp_a01_e01.py
|
trinh-hoang-hiep/iching
|
e1feae5741c3cbde535d7a275b01d4f0cf9e21ed
|
[
"Apache-2.0"
] | 4
|
2020-08-25T03:56:46.000Z
|
2021-05-11T05:55:51.000Z
|
#
import numpy as np
import torch
from torch.utils.data import DataLoader
import sklearn.model_selection as skm
from apps.drl.chpA01.e01.chp_a01_e01_ds import ChpA01E01Ds
from apps.drl.chpA01.e01.chp_a01_e01_model import ChpA01E01Model
class ChpA01E01(object):
def __init__(self):
self.name = ''
self.model_file = './work/lnrn.pt'
def startup(self, args={}):
print('线性回归 adam')
#self.lnrn_plain()
#self.lnrn_sgd()
#self.lnrn_adam()
#self.lnrn_adam_mse()
#self.lnrn_with_ds()
#self.lnrn_with_model()
#self.lnrn_gpu()
#self.lnrn_eval()
#self.lnrn_save_load()
self.lnrn_ds_split() # train and valid split
def lnrn_ds_split(self):
print('分配训练、验证、测试数据集 v0.0.1')
# load dataset
ds = ChpA01E01Ds(num=1000)
total_count= len(ds)
train_count = int(0.7 * total_count)
valid_count = int(0.2 * total_count)
test_count = total_count - train_count - valid_count
train_ds, valid_ds, test_ds = torch.utils.data.random_split(ds, (train_count, valid_count, test_count))
train_batch_size = 10
valid_batch_size = 23
test_batch_size = 88
train_dl = DataLoader(train_ds, batch_size=train_batch_size, shuffle=True)
valid_dl = DataLoader(valid_ds, batch_size=valid_batch_size, shuffle=False)
test_dl = DataLoader(test_ds, batch_size=test_batch_size, shuffle=False)
# define the model
device = self.get_exec_device()
model = ChpA01E01Model().to(device)
# define the loss function
criterion = torch.nn.MSELoss()
# define optimization method
#learning_params = model.parameters() # 需要epochs=100才能收敛
learning_params = []
for k, v in model.named_parameters():
if k == 'w001':
learning_params.append({'params': v, 'lr': 0.01})
elif k == 'b001':
learning_params.append({'params': v, 'lr': 0.1})
optimizer = torch.optim.Adam(learning_params, lr=0.001)
epochs = 10
best_loss = 10000.0
unimproved_loop = 0
improved_threshold = 0.000000001
max_unimproved_loop = 5
train_done = False
for epoch in range(epochs):
model.train()
for X, y_hat in train_dl:
optimizer.zero_grad()
X, y_hat = X.to(device), y_hat.to(device)
y = model(X)
loss = criterion(y, y_hat)
lossv = 0.0
for Xv, yv_hat in valid_dl:
with torch.no_grad():
Xv, yv_hat = Xv.to(device), yv_hat.to(device)
yv = model(Xv)
lossv += criterion(yv, yv_hat)
lossv /= valid_count
if lossv < best_loss:
# save the model
torch.save(model.state_dict(), self.model_file)
if lossv < best_loss - improved_threshold:
unimproved_loop = 0
else:
unimproved_loop += 1
best_loss = lossv
if unimproved_loop >= max_unimproved_loop:
train_done = True
break
# early stopping处理
loss.backward()
optimizer.step()
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, model.w001, model.b001, loss))
if train_done:
break
# 模型验证
test_loss = 0
for X, y_hat in test_dl:
X, y_hat = X.to(device), y_hat.to(device)
with torch.no_grad():
y = model(X)
test_loss += criterion(y, y_hat)
test_loss /= len(test_ds)
print('测试集上代价函数值：{0};'.format(test_loss))
# 载入模型
ckpt = torch.load(self.model_file)
m1 = ChpA01E01Model()
print('初始值：w={0}; b={1};'.format(m1.w001, m1.b001))
m1.load_state_dict(ckpt)
print('载入值：w={0}; b={1};'.format(m1.w001, m1.b001))
def lnrn_eval(self):
# load dataset
ds = ChpA01E01Ds(num=1000)
batch_size = 10
dl = DataLoader(ds, batch_size=batch_size, shuffle=True)
# define the model
device = self.get_exec_device()
model = ChpA01E01Model().to(device)
# define the loss function
criterion = torch.nn.MSELoss()
# define optimization method
#learning_params = model.parameters() # 需要epochs=100才能收敛
learning_params = []
for k, v in model.named_parameters():
if k == 'w001':
learning_params.append({'params': v, 'lr': 0.01})
elif k == 'b001':
learning_params.append({'params': v, 'lr': 0.1})
optimizer = torch.optim.Adam(learning_params, lr=0.001)
epochs = 10
for epoch in range(epochs):
model.train()
for X, y_hat in dl:
optimizer.zero_grad()
X, y_hat = X.to(device), y_hat.to(device)
y = model(X)
loss = criterion(y, y_hat)
loss.backward()
optimizer.step()
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, model.w001, model.b001, loss))
# 模型验证
test_num = 100
test_ds = ChpA01E01Ds(num=test_num)
model.eval()
preds = []
batch_size = 30
test_dl = DataLoader(ds, batch_size=batch_size, shuffle=False)
test_loss = 0
for X, y_hat in test_dl:
X, y_hat = X.to(device), y_hat.to(device)
with torch.no_grad():
y = model(X)
test_loss += criterion(y, y_hat)
test_loss /= test_num
print('测试集上代价函数值：{0};'.format(test_loss))
def lnrn_save_load(self):
# load dataset
ds = ChpA01E01Ds(num=1000)
batch_size = 10
dl = DataLoader(ds, batch_size=batch_size, shuffle=True)
# define the model
device = self.get_exec_device()
model = ChpA01E01Model().to(device)
# define the loss function
criterion = torch.nn.MSELoss()
# define optimization method
#learning_params = model.parameters() # 需要epochs=100才能收敛
learning_params = []
for k, v in model.named_parameters():
if k == 'w001':
learning_params.append({'params': v, 'lr': 0.01})
elif k == 'b001':
learning_params.append({'params': v, 'lr': 0.1})
optimizer = torch.optim.Adam(learning_params, lr=0.001)
epochs = 10
for epoch in range(epochs):
model.train()
for X, y_hat in dl:
optimizer.zero_grad()
X, y_hat = X.to(device), y_hat.to(device)
y = model(X)
loss = criterion(y, y_hat)
loss.backward()
optimizer.step()
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, model.w001, model.b001, loss))
# 模型验证
test_num = 100
test_ds = ChpA01E01Ds(num=test_num)
model.eval()
preds = []
batch_size = 30
test_dl = DataLoader(ds, batch_size=batch_size, shuffle=False)
test_loss = 0
for X, y_hat in test_dl:
X, y_hat = X.to(device), y_hat.to(device)
with torch.no_grad():
y = model(X)
test_loss += criterion(y, y_hat)
test_loss /= test_num
print('测试集上代价函数值：{0};'.format(test_loss))
print('模型保存和加载测试')
# 保存模型
torch.save(model.state_dict(), self.model_file)
# 载入模型
ckpt = torch.load(self.model_file)
m1 = ChpA01E01Model()
print('初始值：w={0}; b={1};'.format(m1.w001, m1.b001))
m1.load_state_dict(ckpt)
print('载入值：w={0}; b={1};'.format(m1.w001, m1.b001))
def ds_exp(self):
ds = ChpA01E01Ds(num=1000)
batch_size = 10
dl = DataLoader(ds, batch_size=batch_size, shuffle=True)
for X, y in dl:
print('X: {0}; y: {1};'.format(X, y))
break
def lnrn_with_ds(self):
# load dataset
ds = ChpA01E01Ds(num=1000)
batch_size = 10
dl = DataLoader(ds, batch_size=batch_size, shuffle=True)
# define the model
w = torch.tensor(1.0, requires_grad=True)
b = torch.tensor(0.0, requires_grad=True)
# define the loss function
criterion = torch.nn.MSELoss()
# define optimization method
optimizer = torch.optim.Adam([
{'params': w, 'lr': 0.01},
{'params': b, 'lr': 0.1}
], lr=0.001)
epochs = 10
for epoch in range(epochs):
for X, y_hat in dl:
optimizer.zero_grad()
y = w * X + b
loss = criterion(y, y_hat)
loss.backward()
optimizer.step()
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, w, b, loss))
def lnrn_with_model(self):
# load dataset
ds = ChpA01E01Ds(num=1000)
batch_size = 10
dl = DataLoader(ds, batch_size=batch_size, shuffle=True)
# define the model
model = ChpA01E01Model()
# define the loss function
criterion = torch.nn.MSELoss()
# define optimization method
#learning_params = model.parameters() # 需要epochs=100才能收敛
learning_params = []
for k, v in model.named_parameters():
if k == 'w001':
learning_params.append({'params': v, 'lr': 0.01})
elif k == 'b001':
learning_params.append({'params': v, 'lr': 0.1})
optimizer = torch.optim.Adam(learning_params, lr=0.001)
epochs = 10
for epoch in range(epochs):
model.train()
for X, y_hat in dl:
optimizer.zero_grad()
y = model(X)
loss = criterion(y, y_hat)
loss.backward()
optimizer.step()
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, model.w001, model.b001, loss))
def get_exec_device(self):
gpu_num = torch.cuda.device_count()
for gi in range(gpu_num):
print(torch.cuda.get_device_name(gi))
pref_gi = 0
if torch.cuda.is_available():
if pref_gi is not None:
device = 'cuda:{0}'.format(pref_gi)
else:
device = 'cuda'
else:
device = 'cpu'
#device1 = 'cuda' if torch.cuda.is_available() else 'cpu'
return device
def lnrn_gpu(self):
# load dataset
ds = ChpA01E01Ds(num=1000)
batch_size = 10
dl = DataLoader(ds, batch_size=batch_size, shuffle=True)
# define the model
device = self.get_exec_device()
model = ChpA01E01Model().to(device)
# define the loss function
criterion = torch.nn.MSELoss()
# define optimization method
#learning_params = model.parameters() # 需要epochs=100才能收敛
learning_params = []
for k, v in model.named_parameters():
if k == 'w001':
learning_params.append({'params': v, 'lr': 0.01})
elif k == 'b001':
learning_params.append({'params': v, 'lr': 0.1})
optimizer = torch.optim.Adam(learning_params, lr=0.001)
epochs = 10
for epoch in range(epochs):
model.train()
for X, y_hat in dl:
optimizer.zero_grad()
X, y_hat = X.to(device), y_hat.to(device)
y = model(X)
loss = criterion(y, y_hat)
loss.backward()
optimizer.step()
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, model.w001, model.b001, loss))
def lnrn_plain(self):
X, y_hat = self.load_ds()
w = torch.tensor(1.0, requires_grad=True)
w_lr = 0.01
b = torch.tensor(0.0, requires_grad=True)
b_lr = 0.1
epochs = 6000
X = torch.tensor(X)
y_hat = torch.tensor(y_hat)
for epoch in range(epochs):
y = w * X + b
tl = 0.5 * (y - y_hat)**2
loss = tl.sum() / 1000.0
loss.backward()
with torch.no_grad():
w -= w_lr * w.grad
w.grad = torch.zeros_like(w.grad)
b -= b_lr * b.grad
b.grad = torch.zeros_like(b.grad)
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, w, b, loss))
def lnrn_sgd(self):
X, y_hat = self.load_ds()
w = torch.tensor(1.0, requires_grad=True)
b = torch.tensor(0.0, requires_grad=True)
epochs = 6000
optimizer = torch.optim.SGD([
{'params': w, 'lr': 0.01},
{'params': b, 'lr': 0.1}
], 0.001)
X = torch.tensor(X)
y_hat = torch.tensor(y_hat)
for epoch in range(epochs):
optimizer.zero_grad()
y = w * X + b
tl = 0.5 * (y - y_hat)**2
loss = tl.sum() / 1000.0
loss.backward()
optimizer.step()
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, w, b, loss))
def lnrn_adam(self):
X, y_hat = self.load_ds()
w = torch.tensor(1.0, requires_grad=True)
b = torch.tensor(0.0, requires_grad=True)
epochs = 6000
optimizer = torch.optim.Adam([
{'params': w, 'lr': 0.01},
{'params': b, 'lr': 0.1}
], lr=0.001)
X = torch.tensor(X)
y_hat = torch.tensor(y_hat)
for epoch in range(epochs):
optimizer.zero_grad()
y = w * X + b
tl = 0.5 * (y - y_hat)**2
loss = tl.sum() / 1000.0
loss.backward()
optimizer.step()
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, w, b, loss))
def lnrn_adam_mse(self):
X, y_hat = self.load_ds()
w = torch.tensor(1.0, requires_grad=True)
w_lr = 0.01
b = torch.tensor(0.0, requires_grad=True)
b_lr = 0.1
epochs = 1000
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam([
{'params': w, 'lr': 0.01},
{'params': b, 'lr': 0.1}
], lr=0.001)
X = torch.tensor(X)
y_hat = torch.tensor(y_hat)
for epoch in range(epochs):
optimizer.zero_grad()
y = w * X + b
loss = criterion(y, y_hat)
loss.backward()
optimizer.step()
print('{0}: w={1}; b={2}; loss={3};'.format(epoch, w, b, loss))
def load_ds(self):
b = 1.6
w = 0.3
X = np.linspace(0, 1.0, num=1000)
y = w*X + b
return X, y
| 36.286408
| 111
| 0.515853
| 1,905
| 14,950
| 3.891339
| 0.091864
| 0.0259
| 0.016188
| 0.035074
| 0.769189
| 0.759881
| 0.751787
| 0.751787
| 0.734251
| 0.734251
| 0
| 0.054282
| 0.357993
| 14,950
| 412
| 112
| 36.286408
| 0.718066
| 0.071104
| 0
| 0.760933
| 0
| 0
| 0.047419
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.043732
| false
| 0
| 0.017493
| 0
| 0.069971
| 0.06414
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
713306af80f9bda8ed19e15977036787dfe99363
| 65
|
py
|
Python
|
common/schemas/base.py
|
GymWorkoutApp/gwa_common
|
0ff307ed6786e2991a26fb4c0afc33767ea9697d
|
[
"Unlicense"
] | null | null | null |
common/schemas/base.py
|
GymWorkoutApp/gwa_common
|
0ff307ed6786e2991a26fb4c0afc33767ea9697d
|
[
"Unlicense"
] | 1
|
2019-01-14T11:05:45.000Z
|
2019-01-14T11:05:45.000Z
|
common/schemas/base.py
|
GymWorkoutApp/gwa_common
|
0ff307ed6786e2991a26fb4c0afc33767ea9697d
|
[
"Unlicense"
] | null | null | null |
from schematics import Model
class BaseSchema(Model):
pass
| 10.833333
| 28
| 0.753846
| 8
| 65
| 6.125
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 65
| 5
| 29
| 13
| 0.942308
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
85542a696f124dad5f9fbd2a37905013b91e33cb
| 47
|
py
|
Python
|
__init__.py
|
ari-s/XpyY
|
384500b8112a4475f2df3e736f324ab8724f66c4
|
[
"Artistic-2.0"
] | null | null | null |
__init__.py
|
ari-s/XpyY
|
384500b8112a4475f2df3e736f324ab8724f66c4
|
[
"Artistic-2.0"
] | null | null | null |
__init__.py
|
ari-s/XpyY
|
384500b8112a4475f2df3e736f324ab8724f66c4
|
[
"Artistic-2.0"
] | null | null | null |
from . import inputfilter, helpers, operations
| 23.5
| 46
| 0.808511
| 5
| 47
| 7.6
| 1
| 0
| 0
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| 0
| 0
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| 0
| 0.12766
| 47
| 1
| 47
| 47
| 0.926829
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| 1
| 0
| 1
| 0
|
0
| 6
|
8554be15e09c1822d22634b758aa964fa9ed48d7
| 54
|
py
|
Python
|
pbj/mesh/__init__.py
|
bem4solvation/pbj
|
4fa9c111596359192539787ae241a79d4316b15b
|
[
"MIT"
] | null | null | null |
pbj/mesh/__init__.py
|
bem4solvation/pbj
|
4fa9c111596359192539787ae241a79d4316b15b
|
[
"MIT"
] | 1
|
2022-02-18T17:34:37.000Z
|
2022-02-18T17:34:37.000Z
|
pbj/mesh/__init__.py
|
bem4solvation/pbj
|
4fa9c111596359192539787ae241a79d4316b15b
|
[
"MIT"
] | null | null | null |
from .mesh_tools import *
from .charge_tools import *
| 18
| 27
| 0.777778
| 8
| 54
| 5
| 0.625
| 0.55
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| 0.148148
| 54
| 2
| 28
| 27
| 0.869565
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| 1
| 0
|
0
| 6
|
8598df774d9eeecdd024d7959965af4b9f6bb33b
| 33
|
py
|
Python
|
bitbucketcli/__init__.py
|
jscheiber22/bitbucket-cli
|
e59cfec6b623d3b0764c57b87ffc30b6dc93ab49
|
[
"MIT"
] | null | null | null |
bitbucketcli/__init__.py
|
jscheiber22/bitbucket-cli
|
e59cfec6b623d3b0764c57b87ffc30b6dc93ab49
|
[
"MIT"
] | null | null | null |
bitbucketcli/__init__.py
|
jscheiber22/bitbucket-cli
|
e59cfec6b623d3b0764c57b87ffc30b6dc93ab49
|
[
"MIT"
] | null | null | null |
from .bitbucket import Bitbucket
| 16.5
| 32
| 0.848485
| 4
| 33
| 7
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 33
| 1
| 33
| 33
| 0.965517
| 0
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| true
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| null | 0
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| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
85a0765a2793633cf4c9cac1fd0f21aaddc91051
| 21
|
py
|
Python
|
test/refactor/import_tree/pkgx/__init__.py
|
kirat-singh/jedi
|
65bc1c117b3175cb4d492484775c3fd7f207bc92
|
[
"MIT"
] | 4,213
|
2015-01-02T15:43:22.000Z
|
2022-03-31T16:15:01.000Z
|
test/refactor/import_tree/pkgx/__init__.py
|
kirat-singh/jedi
|
65bc1c117b3175cb4d492484775c3fd7f207bc92
|
[
"MIT"
] | 1,392
|
2015-01-02T18:43:39.000Z
|
2022-03-27T18:43:59.000Z
|
test/refactor/import_tree/pkgx/__init__.py
|
PeterJCLaw/jedi
|
070f191f550990c23220d7f209df076178307cf6
|
[
"MIT"
] | 525
|
2015-01-02T19:07:31.000Z
|
2022-03-13T02:03:20.000Z
|
def pkgx():
pass
| 7
| 11
| 0.52381
| 3
| 21
| 3.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 21
| 2
| 12
| 10.5
| 0.785714
| 0
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| 1
| 0.5
| true
| 0.5
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| null | 0
| 0
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| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
85c6e6e27c4642f174380f5896d05b5c193d6322
| 98
|
py
|
Python
|
template_filters.py
|
codeskyblue/bootstrap-tornado
|
bcfa0959b0125abdc2f018e15db39a07abc5d8b0
|
[
"MIT"
] | 5
|
2017-10-18T19:30:16.000Z
|
2018-11-29T01:50:29.000Z
|
template_filters.py
|
codeskyblue/bootstrap-tornado
|
bcfa0959b0125abdc2f018e15db39a07abc5d8b0
|
[
"MIT"
] | null | null | null |
template_filters.py
|
codeskyblue/bootstrap-tornado
|
bcfa0959b0125abdc2f018e15db39a07abc5d8b0
|
[
"MIT"
] | null | null | null |
# coding: utf-8
#
import hashlib
def hashmd5(handler, s):
return hashlib.md5(s).hexdigest()
| 12.25
| 37
| 0.683673
| 14
| 98
| 4.785714
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.037037
| 0.173469
| 98
| 8
| 37
| 12.25
| 0.790123
| 0.132653
| 0
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| 0
| 0
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| 0
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| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
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| null | 0
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| 0
| 0
| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
a470cb91df1bd038e05291850dfc417d25cf4c6e
| 36
|
py
|
Python
|
src/models/__init__.py
|
jbed94/Faster-R-CNN
|
ebfc3ff0a84deca9672155085e57d09199023a85
|
[
"MIT"
] | 5
|
2019-07-09T09:28:14.000Z
|
2020-09-04T13:56:02.000Z
|
src/models/__init__.py
|
jbed94/Faster-R-CNN
|
ebfc3ff0a84deca9672155085e57d09199023a85
|
[
"MIT"
] | null | null | null |
src/models/__init__.py
|
jbed94/Faster-R-CNN
|
ebfc3ff0a84deca9672155085e57d09199023a85
|
[
"MIT"
] | 1
|
2020-01-04T14:41:28.000Z
|
2020-01-04T14:41:28.000Z
|
from .faster_rcnn import FasterRCNN
| 18
| 35
| 0.861111
| 5
| 36
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0.111111
| 36
| 1
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| 36
| 0.9375
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| 1
| 0
| 1
| 0
|
0
| 6
|
a48f3157ee8581ce9c031bbdf34b08f860994e0e
| 5,010
|
py
|
Python
|
MSDuplicateCheck.py
|
SLINGhub/MSOrganiser
|
918acda503093963a87a272f73bf6b07e8363e19
|
[
"MIT"
] | null | null | null |
MSDuplicateCheck.py
|
SLINGhub/MSOrganiser
|
918acda503093963a87a272f73bf6b07e8363e19
|
[
"MIT"
] | null | null | null |
MSDuplicateCheck.py
|
SLINGhub/MSOrganiser
|
918acda503093963a87a272f73bf6b07e8363e19
|
[
"MIT"
] | null | null | null |
import sys
from collections import Counter
def check_duplicated_columns_in_wide_data(input_wide_data, output_option,
logger = None, ingui = True,
allow_multiple_istd = False):
"""Function to check for duplicate column names (usually Transition Name) in a given wide data.
Args:
input_wide_data (pandas DataFrame): A data frame of sample as rows and transition names as columns
output_option (str): The name of the contents that the data frame contains. Example: Area, RT etc...
logger (object): logger object created by start_logger in MSOrganiser
ingui (bool): if True, print analysis status to screen
allow_multiple_istd (bool): if True, allow input_wide_data to have mulitple internal standards
"""
# Convert the dataframe column name to a list
column_name_list = input_wide_data.columns.values.tolist()
# Get a list of duplicated column names
duplicated_column_name_list = [key for key in Counter(column_name_list).keys() if Counter(column_name_list)[key] > 1]
# When there are duplicated
if len(duplicated_column_name_list) > 0:
# Convert the list into a string
duplicated_column_name_string = ""
if allow_multiple_istd:
duplicated_column_name_string = ", ".join(map(str, duplicated_column_name_list))
else:
duplicated_column_name_string = ", ".join(duplicated_column_name_list)
# Inform the user and stop the program
if logger:
logger.warning('In the %s data frame, ' +
'there are column names (Transition_Name) in the output files that are duplicated. ' +
'The data in these duplicated column names may be different. ' +
'Please check the input files especially if you are concatenating by columns. ' +
'Duplicated columns are %s',
output_option, duplicated_column_name_string)
if ingui:
print('In the ' + output_option + ' data frame, ' +
'there are column names (Transition_Name) in the output files that are duplicated. ' +
'The data in these duplicated column names may be different. ' +
'Please check the input files especially if you are concatenating by columns. ' +
'Duplicated columns are ' + duplicated_column_name_string, flush=True)
sys.exit(-1)
def check_duplicated_sample_names_in_wide_data(input_wide_data, output_option,
logger = None, ingui = True,
allow_multiple_istd = False):
"""Function to check for duplicate sample names in a given wide data.
Args:
input_wide_data (pandas DataFrame): A data frame of sample as rows and transition names as columns
output_option (str): The name of the contents that the data frame contains. Example: Area, RT etc...
logger (object): logger object created by start_logger in MSOrganiser
ingui (bool): if True, print analysis status to screen
allow_multiple_istd (bool): if True, allow input_wide_data to have mulitple internal standards
"""
# Convert the sample name column to a list
unique_Sample_Name_list = []
if allow_multiple_istd:
unique_Sample_Name_list = input_wide_data[("Sample_Name","")].tolist()
else:
unique_Sample_Name_list = input_wide_data["Sample_Name"].tolist()
# Get a list of duplicated column names
duplicated_Sample_Name_list = [key for key in Counter(unique_Sample_Name_list).keys() if Counter(unique_Sample_Name_list)[key] > 1]
# When there are duplicated
if len(duplicated_Sample_Name_list) > 0:
# Convert the list into a string
duplicated_Sample_Name_string = ", ".join(duplicated_Sample_Name_list)
# Inform the user and stop the program
if logger:
logger.warning('In the %s data frame, ' +
'there are sample names in the output files that are duplicated. ' +
'The data in these duplicated row names may be different. ' +
'Please check the input files especially if you are concatenating by rows. ' +
'Duplicated sample names are %s',
output_option, duplicated_Sample_Name_string)
if ingui:
print('In the ' + output_option + ' data frame, ' +
'there are sample names in the output files that are duplicated. ' +
'The data in these duplicated row names may be different. ' +
'Please check the input files especially if you are concatenating by rows. ' ,
'Duplicated sample names are ' + duplicated_Sample_Name_string, flush = True)
sys.exit(-1)
| 51.649485
| 135
| 0.632735
| 626
| 5,010
| 4.870607
| 0.166134
| 0.039357
| 0.038373
| 0.042637
| 0.861594
| 0.790423
| 0.790423
| 0.755658
| 0.755658
| 0.726796
| 0
| 0.001725
| 0.305589
| 5,010
| 96
| 136
| 52.1875
| 0.874677
| 0.28024
| 0
| 0.538462
| 0
| 0
| 0.296906
| 0
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| 0
| 0
| 0
| 0
| 1
| 0.038462
| false
| 0
| 0.038462
| 0
| 0.076923
| 0.038462
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
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| null | 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
a495c2d0574d766741a95b95cca307a0fd5ed011
| 156
|
py
|
Python
|
tests/tensortrade/unit/actions/test_action_scheme.py
|
bwcknr/tensortrade
|
376f5e4cc4ad7df271774088884fbe88f8feb7d8
|
[
"Apache-2.0"
] | 34
|
2020-06-05T22:39:53.000Z
|
2022-01-09T03:09:12.000Z
|
tests/tensortrade/unit/actions/test_action_scheme.py
|
bwcknr/tensortrade
|
376f5e4cc4ad7df271774088884fbe88f8feb7d8
|
[
"Apache-2.0"
] | 1
|
2022-01-17T06:38:27.000Z
|
2022-01-17T06:38:27.000Z
|
tests/tensortrade/unit/actions/test_action_scheme.py
|
bwcknr/tensortrade
|
376f5e4cc4ad7df271774088884fbe88f8feb7d8
|
[
"Apache-2.0"
] | 8
|
2020-06-01T12:09:53.000Z
|
2022-01-18T14:45:29.000Z
|
from gym.spaces import Discrete
from tensortrade import TradingContext
from tensortrade.actions import ActionScheme
from tensortrade.orders import Trade
| 19.5
| 44
| 0.858974
| 19
| 156
| 7.052632
| 0.578947
| 0.335821
| 0
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| 0
| 0
| 0.121795
| 156
| 7
| 45
| 22.285714
| 0.978102
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| 1
| 0
|
0
| 6
|
f1147796939ac2e1d57e13e3f8ea0bfc0440a336
| 21
|
py
|
Python
|
example_project/some_modules/third_modules/a154.py
|
Yuriy-Leonov/cython_imports_limit_issue
|
2f9e7c02798fb52185dabfe6ce3811c439ca2839
|
[
"MIT"
] | null | null | null |
example_project/some_modules/third_modules/a154.py
|
Yuriy-Leonov/cython_imports_limit_issue
|
2f9e7c02798fb52185dabfe6ce3811c439ca2839
|
[
"MIT"
] | null | null | null |
example_project/some_modules/third_modules/a154.py
|
Yuriy-Leonov/cython_imports_limit_issue
|
2f9e7c02798fb52185dabfe6ce3811c439ca2839
|
[
"MIT"
] | null | null | null |
class A154:
pass
| 7
| 11
| 0.619048
| 3
| 21
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.214286
| 0.333333
| 21
| 2
| 12
| 10.5
| 0.714286
| 0
| 0
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| 0
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| 0
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| 1
| 0
| true
| 0.5
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| null | 0
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| 0
| 0
| 1
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| 1
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| null | 0
| 0
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| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
f119b5d4beb70730381e86765733b13ef1a9853c
| 13
|
py
|
Python
|
PTTLibrary/Version.py
|
csongs/PTTLibrary
|
8bfe17fb90bdbc15395ef6bf7d77eb6a2df4ddad
|
[
"MIT"
] | null | null | null |
PTTLibrary/Version.py
|
csongs/PTTLibrary
|
8bfe17fb90bdbc15395ef6bf7d77eb6a2df4ddad
|
[
"MIT"
] | null | null | null |
PTTLibrary/Version.py
|
csongs/PTTLibrary
|
8bfe17fb90bdbc15395ef6bf7d77eb6a2df4ddad
|
[
"MIT"
] | 1
|
2019-11-21T15:17:01.000Z
|
2019-11-21T15:17:01.000Z
|
Ver = '0.7.3'
| 13
| 13
| 0.461538
| 4
| 13
| 1.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0.272727
| 0.153846
| 13
| 1
| 13
| 13
| 0.272727
| 0
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| 0.357143
| 0
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| 0
| false
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| 1
| 1
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| 1
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| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
f13762d4600499acd31000ec315081d6b392f111
| 96
|
py
|
Python
|
web-app/backend/emotion/main/__init__.py
|
syzroy/4099-Emotion-Analyser
|
de5b0fc28f97fbe7c55cf50413587fd55ae572ea
|
[
"MIT"
] | 1
|
2019-09-02T09:18:19.000Z
|
2019-09-02T09:18:19.000Z
|
web-app/backend/emotion/main/__init__.py
|
syzroy/4099-Emotion-Analyser
|
de5b0fc28f97fbe7c55cf50413587fd55ae572ea
|
[
"MIT"
] | null | null | null |
web-app/backend/emotion/main/__init__.py
|
syzroy/4099-Emotion-Analyser
|
de5b0fc28f97fbe7c55cf50413587fd55ae572ea
|
[
"MIT"
] | 1
|
2020-03-31T22:48:55.000Z
|
2020-03-31T22:48:55.000Z
|
from flask import Blueprint
blueprint = Blueprint('main', __name__)
from . import controllers
| 16
| 39
| 0.78125
| 11
| 96
| 6.454545
| 0.636364
| 0.507042
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.145833
| 96
| 5
| 40
| 19.2
| 0.865854
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| 0
| 0
| 1
| 0
| 1
| 1
|
0
| 6
|
f14527fea38073a7de574084fec0d41b8bb443a3
| 59
|
py
|
Python
|
handlers/__init__.py
|
arbaishev/urlshortener-tg-bot
|
e758a1235f3bb6a5b057cd9f6f9b6ecdc586bc27
|
[
"MIT"
] | null | null | null |
handlers/__init__.py
|
arbaishev/urlshortener-tg-bot
|
e758a1235f3bb6a5b057cd9f6f9b6ecdc586bc27
|
[
"MIT"
] | null | null | null |
handlers/__init__.py
|
arbaishev/urlshortener-tg-bot
|
e758a1235f3bb6a5b057cd9f6f9b6ecdc586bc27
|
[
"MIT"
] | null | null | null |
from . import basic_commands
from . import custom_commands
| 19.666667
| 29
| 0.830508
| 8
| 59
| 5.875
| 0.625
| 0.425532
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135593
| 59
| 2
| 30
| 29.5
| 0.921569
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| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
f17e98f8e95a8abbe3aa433b765288baef0cfbc8
| 6,404
|
py
|
Python
|
tasks/tests/test_todolist_endpoint.py
|
Shizuku-Asami/todolist
|
2bbd2f8cc20176e3871945f604135280da606875
|
[
"MIT"
] | null | null | null |
tasks/tests/test_todolist_endpoint.py
|
Shizuku-Asami/todolist
|
2bbd2f8cc20176e3871945f604135280da606875
|
[
"MIT"
] | null | null | null |
tasks/tests/test_todolist_endpoint.py
|
Shizuku-Asami/todolist
|
2bbd2f8cc20176e3871945f604135280da606875
|
[
"MIT"
] | null | null | null |
import pytest
from django.test import Client
from rest_framework import status
import logging
from users.models import User
from tasks.models import TodoList, TodoItem
LOGGER = logging.getLogger(__name__)
@pytest.fixture
def login_data():
data = {
"email": "testuser@example.com",
"password": "12345678",
}
return data
@pytest.fixture
def data():
"""
Minimal data to create a todolist object in the database.
"""
data = {
"todoitem_todolist": [],
"name": "test todolist",
"description": "A description for the new todolist",
}
return data
@pytest.fixture
def data_extra():
"""
Todolist data with todoitems.
"""
data = {
"name": "test todolist",
"description": "A description for the new todolist",
"todoitem_todolist": [
{
"name": "item 1",
"description": "easy",
"is_done": False,
},
],
}
return data
@pytest.fixture
def data_extra1():
"""
Todolist data with todoitems.
"""
data = {
"name": "test todolist",
"description": "A description for the new todolist",
"todoitem_todolist": [
{
"name": "item 1",
"description": "easy",
"is_done": False,
},
{
"name": "item 2",
"description": "medium",
"is_done": False,
},
],
}
return data
@pytest.fixture
def user():
return User.objects.create_user(email="testuser@example.com", password="12345678")
@pytest.fixture
def client():
client = Client(
enforce_csrf_checks=True,
HTTP_USER_AGENT="Mozilla/5.0",
)
return client
@pytest.fixture
@pytest.mark.django_db
def auth_client(client, login_data, user):
response = client.post("/users/login", login_data)
access_token = response.data["tokens"]["access"]
headers = {"HTTP_AUTHORIZATION": "Bearer " + access_token}
client.defaults = headers
return client
@pytest.mark.django_db
def test_create_todolist_with_minimal_data_returns_http_201_created(
auth_client, data, user
):
data["user"] = user.id
response = auth_client.post("/todolists/", data)
assert response.status_code == status.HTTP_201_CREATED
@pytest.mark.django_db
def test_create_todolist_with_at_least_one_todoitem_returns_http_201_created(
auth_client, data_extra, user
):
data_extra["user"] = user.id
response = auth_client.post("/todolists/", data_extra)
assert response.status_code == status.HTTP_201_CREATED
@pytest.mark.django_db
def test_create_todolist_with_many_todoitems_returns_http_201_created(
auth_client, data_extra1, user
):
data_extra1["user"] = user.id
response = auth_client.post("/todolists/", data_extra1)
assert response.status_code == status.HTTP_201_CREATED
@pytest.mark.django_db
def test_create_todolist_without_todoitems_returns_valid_response(
auth_client, data, user
):
data["user"] = user.id
response = auth_client.post("/todolists/", data)
LOGGER.info("Response data: %s", response.data)
for key in data.keys():
assert key in response.data.keys()
assert data[key] == response.data[key]
@pytest.mark.django_db
def test_create_todolist_with_one_todoitem_returns_valid_response(
auth_client, data_extra, user
):
data_extra["user"] = user.id
response = auth_client.post("/todolists/", data_extra)
LOGGER.info("Response data: %s", response.data)
for key in data_extra.keys():
if key == "todoitem_todolist":
for i in data_extra[key]:
assert i in response.data[key].keys()
assert data_extra[key][i] == response.data[key][i]
else:
assert key in response.data.keys()
assert data_extra[key] == response.data[key]
@pytest.mark.django_db
def test_create_todolist_with_todoitems_returns_valid_response(
auth_client, data_extra1, user
):
data_extra1["user"] = user.id
response = auth_client.post("/todolists/", data_extra1)
LOGGER.info("Response data: %s", response.data)
for key in data_extra1.keys():
if key == "todoitem_todolist":
for i in data_extra1[key]:
assert i in response.data[key].keys()
assert data_extra1[key][i] == response.data[key][i]
else:
assert key in response.data.keys()
assert data_extra1[key] == response.data[key]
@pytest.mark.django_db
def test_get_todolist_returns_http_200_ok(auth_client, user):
pass
@pytest.mark.django_db
def test_get_all_todolist_for_current_user(auth_client, user):
response = auth_client.get("/todolists/")
LOGGER.info("Response data: %s", response.data)
@pytest.mark.django_db
def test_update_todolist_name_returns_http_200_ok(auth_client, user):
pass
@pytest.mark.django_db
def test_update_todolist_name_returns_updated_todolist(auth_client, user):
pass
@pytest.mark.django_db
def test_update_todolist_description_returns_http_200_ok(auth_client, user):
pass
@pytest.mark.django_db
def test_update_todolist_description_returns_updated_todolist(auth_client, user):
pass
@pytest.mark.django_db
def test_update_todolist_todoitems_returns_http_200_ok(auth_client, user):
pass
@pytest.mark.django_db
def test_update_todolist_todoitems_returns_updated_todolist(auth_client, user):
pass
@pytest.mark.django_db
def test_update_todolist_multiple_fields_returns_http_200_ok(auth_client, user):
pass
@pytest.mark.django_db
def test_update_todolist_multiple_fields_returns_updated_todolist(auth_client, user):
pass
@pytest.mark.django_db
def test_delete_todolist_returns_http_200_ok(auth_client, user):
pass
@pytest.mark.django_db
def test_delete_todolist_removes_todolist_data_from_database(auth_client, client):
pass
@pytest.mark.django_db
def test_user_cannot_create_todolist_for_another_user(auth_client, user):
pass
@pytest.mark.django_db
def test_user_cannot_get_todolist_of_another_user(auth_client, user):
pass
@pytest.mark.django_db
def test_user_cannot_update_todolist_of_another_user(auth_client, user):
pass
@pytest.mark.django_db
def test_user_cannot_delete_todolist_of_another_user(auth_client, user):
pass
| 25.212598
| 86
| 0.687851
| 829
| 6,404
| 4.992762
| 0.13269
| 0.072481
| 0.08891
| 0.100024
| 0.796328
| 0.785939
| 0.746799
| 0.707659
| 0.674076
| 0.637352
| 0
| 0.013418
| 0.20862
| 6,404
| 253
| 87
| 25.312253
| 0.803275
| 0.01827
| 0
| 0.612022
| 0
| 0
| 0.106874
| 0
| 0
| 0
| 0
| 0.011858
| 0.071038
| 1
| 0.15847
| false
| 0.092896
| 0.032787
| 0.005464
| 0.229508
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
f18d49e62425073162c0a2b27cec8d6f89224cc4
| 129
|
py
|
Python
|
tests/test_datapublic.py
|
ConsultingMD/covid-data-public
|
2b7091f7cc3877df45a7887709e999b0ebdf30ec
|
[
"MIT"
] | 17
|
2020-03-26T19:40:09.000Z
|
2021-08-31T04:07:30.000Z
|
tests/test_datapublic.py
|
ConsultingMD/covid-data-public
|
2b7091f7cc3877df45a7887709e999b0ebdf30ec
|
[
"MIT"
] | 78
|
2020-03-27T23:10:51.000Z
|
2021-09-20T21:41:27.000Z
|
tests/test_datapublic.py
|
ConsultingMD/covid-data-public
|
2b7091f7cc3877df45a7887709e999b0ebdf30ec
|
[
"MIT"
] | 11
|
2020-03-29T00:23:44.000Z
|
2021-02-12T23:36:07.000Z
|
from covidactnow.datapublic.common_fields import CommonFields
def test_import_worked():
assert CommonFields.DATE == "date"
| 21.5
| 61
| 0.79845
| 15
| 129
| 6.666667
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.124031
| 129
| 5
| 62
| 25.8
| 0.884956
| 0
| 0
| 0
| 0
| 0
| 0.031008
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.666667
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 1
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
74b8ab1aaeacf133316533b1f7ae93886ac7521c
| 71,951
|
py
|
Python
|
do_experiment.py
|
figlerg/network_tracing
|
97ee6bbcad1a4ca30736d23ee0c00b0f2a6ae5f2
|
[
"BSD-3-Clause"
] | null | null | null |
do_experiment.py
|
figlerg/network_tracing
|
97ee6bbcad1a4ca30736d23ee0c00b0f2a6ae5f2
|
[
"BSD-3-Clause"
] | null | null | null |
do_experiment.py
|
figlerg/network_tracing
|
97ee6bbcad1a4ca30736d23ee0c00b0f2a6ae5f2
|
[
"BSD-3-Clause"
] | null | null | null |
import hashlib
import os
import pickle
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import FormatStrFormatter
import matplotlib as mpl
from globals import *
from net import Net
from tqdm import tqdm
import cycler
import random
random.seed(12345)
# Direct input
plt.rcParams['text.latex.preamble'] = r"\usepackage{bm} \usepackage{amsmath}"
# plt.rcParams['text.latex.preamble']=[r"\usepackage{lmodern}"]
# Options
params = {'text.usetex': True,
'font.size': 10,
'font.family': 'serif'
# 'font.family' : 'lmodern',
}
plt.rcParams.update(params)
columwidth = 251.8 / 72.27 # 251.80688[pt] / 72.27[pt/inch]
def estimateQuotientCI(ax, xvalues, mean1, sd1, mean2, sd2, color, mccount, p=95):
iters = 2000
lowers = list()
uppers = list()
percs = [(100 - p) / 2, 100 - (100 - p) / 2]
"""
Monte Carlo mean 1/N*sum(X_i) implies:
V(1/N*sum(X_i))=1/(N^2)*sum(V(X_i))=1/(N^2)*N*V(X)=V(X)/N
=> Variance of monte carlo mean is 1/N times variance of single model result
=> SD is 1/sqrt(N) times SD of model result
"""
sd11 = sd1 / (mccount ** 0.5)
sd21 = sd2 / (mccount ** 0.5)
for m1, s1, m2, s2 in zip(mean1, sd11, mean2, sd21):
quotients = list()
for i in range(iters):
"""
since (sum(X_i)-mu)/(sqrt(N)*sigma) converges towards Normal(0,1) we may
assume 1/N*sum(X_i) approx ~ Normal(mu,sigma/sqrt(N))
"""
denom = random.normalvariate(m2, s2)
if denom <= 0: # truncate normal dist - negative values dont make sense
continue
nom = random.normalvariate(m1, s1)
if nom < 0: # truncate normal dist - negative values dont make sense
continue
quotients.append(nom / denom)
ps = np.percentile(quotients, percs)
lowers.append(ps[0])
uppers.append(ps[1])
ax.fill_between(xvalues, lowers, uppers, color=color, alpha=0.2, zorder=-1)
# pickling disabled for now, uncomment plot lines for that
def simple_experiment_old(n, p, p_i, mc_iterations, max_t, seed=0, mode=None, force_recompute=False, path=None,
clustering: float = None, dispersion=None):
# this creates the net, runs monte carlo on it and saves the resulting timeseries plot, as well as pickles for net and counts
assert not (dispersion and clustering), "Cannot set a dispersion target and " \
"a clustering target at the same time"
if dispersion:
chosen_epsilon = epsilon_disp
else:
chosen_epsilon = epsilon_clustering
if path:
dirname = path
else:
dirname_parent = os.path.dirname(__file__)
dirname = os.path.join(dirname_parent, 'Experiments')
# the cache is now tagged with a hash from all important parameters instead of the above.
# Any change to the model parameters will certainly trigger a recompute now
id_params = (
n, p, p_i, mc_iterations, max_t, seed, mode, clustering, dispersion, t_i, t_c, t_r, t_d, t_t, p_q, p_t,
quarantine_time, resolution, chosen_epsilon)
# normal hashes are salted between runs -> use something that is persistent
tag = str(hashlib.md5(str(id_params).encode('utf8')).hexdigest())
# disables loading pickled results
if force_recompute:
# if false, it looks at saved experiments and reuses those
net = Net(n=n, p=p, p_i=p_i, max_t=max_t, seed=seed, clustering_target=clustering, dispersion_target=dispersion)
counts, sd, achieved_clustering, achieved_disp = net.monte_carlo(mc_iterations, mode=mode)
with open(os.path.join(dirname, tag + '_net.p'), 'wb') as f:
pickle.dump((net, achieved_clustering, achieved_disp), f)
with open(os.path.join(dirname, tag + '_counts.p'), 'wb') as f:
pickle.dump((counts, sd), f)
else:
try:
with open(os.path.join(dirname, tag + "_counts.p"), 'rb') as f:
counts, sd = pickle.load(f)
with open(os.path.join(dirname, tag + "_net.p"), 'rb') as f:
net, achieved_clustering, achieved_disp = pickle.load(f)
print('Experiment results have been loaded from history.')
except FileNotFoundError:
net = Net(n=n, p=p, p_i=p_i, max_t=max_t, seed=seed, clustering_target=clustering,
dispersion_target=dispersion)
counts, sd, achieved_clustering, achieved_disp = net.monte_carlo(mc_iterations, mode=mode)
with open(os.path.join(dirname, tag + '_net.p'), 'wb') as f:
pickle.dump((net, achieved_clustering, achieved_disp), f)
with open(os.path.join(dirname, tag + '_counts.p'), 'wb') as f:
pickle.dump((counts, sd), f)
exposed = counts[EXP_STATE, :]
infected = counts[INF_STATE, :]
ep_curve = exposed + infected
# compute when the peak happens and what the ratio of infected is then
t_peak = np.argmax(ep_curve, axis=0)
peak_height = ep_curve[t_peak] / n
# compute the ratio of all exposed people at end of sim to the number of indiv.
# (also check heuristically whether an equilibrium has been reached
recovered = counts[REC_STATE, :]
virus_contacts = ep_curve + recovered
sensitivity = max(1, n / 100) # increasing divisor makes this more sensitive
equilib_flag = abs(
virus_contacts[-1] - virus_contacts[-2]) < sensitivity # just a heuristic, see whether roc is low
period_prevalence = virus_contacts[-1] / n
return net, counts, sd, t_peak, peak_height, equilib_flag, period_prevalence, achieved_clustering, achieved_disp
from do_experiment_parallel import \
simple_experiment # this is the new, parallel version of the above function. By Martin!
def vary_p(res, n, p_i, mc_iterations, max_t, interval=(0, 1), seed=0, mode=None, force_recompute=False, path=None):
# here I want to systematically check what varying the edge probability does. Should return something like a 1d heatmap?
# return value should use one of the values t_peak, peak_height, equilib_flag, period_prevalence
peak_times = np.ndarray(res)
mean_peak_heights = np.ndarray(res)
mean_period_prevalences = np.ndarray(res)
sd_peak_heights = np.ndarray(res)
sd_period_prevalences = np.ndarray(res)
ps = np.linspace(interval[0], interval[1], endpoint=True, num=res)
for i, p in enumerate(ps):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, clustering, dispersion = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i, mode=mode,
force_recompute=force_recompute, path=path)
peak_times[i] = t_peak
mean_peak_heights[i] = mean_peak
sd_peak_heights[i] = sd_peak
mean_period_prevalences[i] = mean_prevalence
sd_period_prevalences[i] = sd_prevalence
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(16 * 1.5, 9 * 1.5))
ax1, ax2, ax3 = axes
if mode:
ax1.set_title(mode)
else:
ax1.set_title('vanilla')
ax1.plot(ps, peak_times)
# ax1.set_xlabel('p')
ax1.set_ylabel('Peak time')
ax2.plot(ps, mean_peak_heights)
# ax2.set_xlabel('p')
ax2.set_ylabel('Peak prevalence')
ax3.plot(ps, mean_period_prevalences)
ax3.set_ylabel('Fraction of affected')
ax3.set_xlabel('p')
# labels = [interval[0],] + list(['' for i in range(len(ps)-2)]) + [interval[1],]
ax3.set_xticks(ps[1:-2], minor=True)
ax3.set_xticks([interval[0], interval[1]])
plt.tick_params(
axis='x', # changes apply to the x-axis
which='minor', # both major and minor ticks are affected
# bottom=False, # ticks along the bottom edge are off
# top=False, # ticks along the top edge are off
labelbottom=False) # labels along the bottom edge are off
# plt.xticks([interval[0],interval[1]])
if mode:
fig.savefig(os.path.join(path, 'pvaried_n{}_p{}_{}'.format(
n, str(interval[0]) + 'to' + str(interval[1]), mode) + '.png'))
else:
fig.savefig(os.path.join(path, 'pvaried_n{}_p{}'.format(
n, str(interval[0]) + 'to' + str(interval[1])) + '.png'))
def vary_p_plot_cache(res, n, p_i, mc_iterations, max_t, interval=(0, 1), seed=0, force_recompute=False, path=None):
# utility function that loads all the pickles (or runs them first) and plots the three scenarios
# is a modified copy of vary_p !
peak_times = np.ndarray(res)
peak_heights = np.ndarray(res)
period_prevalences = np.ndarray(res)
peak_times_q = np.ndarray(res)
peak_heights_q = np.ndarray(res)
period_prevalences_q = np.ndarray(res)
peak_times_t = np.ndarray(res)
peak_heights_t = np.ndarray(res)
period_prevalences_t = np.ndarray(res)
peak_heights_sd = np.ndarray(res)
peak_heights_q_sd = np.ndarray(res)
peak_heights_t_sd = np.ndarray(res)
period_prevalences_sd = np.ndarray(res)
period_prevalences_q_sd = np.ndarray(res)
period_prevalences_t_sd = np.ndarray(res)
ps = np.linspace(interval[0], interval[1], endpoint=True, num=res)
# all 3 modes
for i, p in tqdm(enumerate(ps), total=res, desc='Vanilla'):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i + res, mode=None,
force_recompute=force_recompute, path=path)
peak_times[i] = t_peak
peak_heights[i] = mean_peak
peak_heights_sd[i] = sd_peak
period_prevalences[i] = mean_prevalence
period_prevalences_sd[i] = sd_prevalence
for i, p in tqdm(enumerate(ps), total=res, desc='Quarantine'):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i + 2 * res, mode='quarantine',
force_recompute=force_recompute,
path=path)
peak_times_q[i] = t_peak
peak_heights_q[i] = mean_peak
peak_heights_q_sd[i] = sd_peak
period_prevalences_q[i] = mean_prevalence
period_prevalences_q_sd[i] = sd_prevalence
for i, p in tqdm(enumerate(ps), total=res, desc='Tracing'):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i + 3 * res, mode='tracing',
force_recompute=force_recompute,
path=path)
peak_times_t[i] = t_peak
peak_heights_t[i] = mean_peak
peak_heights_t_sd[i] = sd_peak
period_prevalences_t[i] = mean_prevalence
period_prevalences_t_sd[i] = sd_prevalence
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(14, 14 / 16 * 9))
ax1, ax2, ax3 = axes
ax1.plot(ps, peak_times, ps, peak_times_q, ps, peak_times_t)
ax1.set_ylabel('Peak time')
ax2.plot(ps, peak_heights, ps, peak_heights_q, ps, peak_heights_t)
ax2.set_ylabel('Peak prevalence')
ax3.plot(ps, period_prevalences, ps, period_prevalences_q, ps, period_prevalences_t)
ax3.set_ylabel('Fraction of affected')
ax3.set_xlabel('p')
ax3.set_xticks(ps[1:-2], minor=True)
ax3.set_xticks([interval[0], interval[1]])
plt.legend(['Vanilla', 'Quarantine', 'Tracing'])
plt.tick_params(
axis='x',
which='minor',
# bottom=False, # ticks along the bottom edge are off
# top=False, # ticks along the top edge are off
labelbottom=False) # labels along the bottom edge are off
# plt.xticks([interval[0],interval[1]])
parent = os.path.dirname(path)
fig.savefig(os.path.join(parent, 'Pics', 'pvaried_n{}_mc{}_{}'.format(n, mc_iterations, 'comp') + '.png'),
bbox_inches='tight')
# this feels pretty uninteresting:
def vary_p_i(res, n, p, mc_iterations, max_t, seed=0, mode=None, force_recompute=False, path=None):
# here I want to systematically check what varying the edge probability does. Should return something like a 1d heatmap?
# return value should use one of the values t_peak, peak_height, equilib_flag, period_prevalence
peak_times = np.ndarray(res)
peak_heights = np.ndarray(res)
peak_heights_sd = np.ndarray(res)
# flags = np.ndarray(res)
period_prevalences = np.ndarray(res)
period_prevalences_sd = np.ndarray(res)
p_is = np.linspace(0, 1, endpoint=True, num=res)
for i, p_inf in enumerate(p_is):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_inf, mc_iterations, max_t, seed=seed + i, mode=mode,
force_recompute=force_recompute, path=path)
# TODO seed inside simple_experiment is constant, think about whether that's ok!
peak_times[i] = t_peak
peak_heights[i] = mean_peak
peak_heights_sd[i] = sd_peak
period_prevalences[i] = mean_prevalence
period_prevalences_sd[i] = sd_prevalence
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(16 * 1.5, 9 * 1.5))
# fig.subplots_adjust(wspace = 0.5)
ax1, ax2, ax3 = axes
ax1.plot(p_is, peak_times)
# ax1.set_xlabel('p')
ax1.set_ylabel('peak-times')
ax2.plot(p_is, peak_heights)
# ax2.set_xlabel('p')
ax2.set_ylabel('peak-height')
ax3.plot(p_is, period_prevalences)
# ax3.set_xlabel('p')
ax3.set_ylabel('percentage of affected')
ax3.set_xlabel('infection probability')
ax3.set_xticks(p_is)
# plt.show()
if mode:
fig.savefig(os.path.join(path, 'pivaried_n{}_p{}_{}'.format(n, p, mode) + '.png'))
else:
fig.savefig(os.path.join(path, 'pivaried_n{}_p{}'.format(n, p) + '.png'))
def vary_C(res, n, p, p_i, mc_iterations, max_t, interval=None, seed=0, mode=None, force_recompute=False, path=None):
# measure effect of clustering coeff on tracing effectiveness
if not interval:
# THEORY: the average clustering coeff of erdos renyi networks is p!
# so I test around that to see what changed
interval = (0.5 * p, 10 * p)
peak_times = np.ndarray(res)
peak_heights = np.ndarray(res)
peak_heights_sd = np.ndarray(res)
period_prevalences = np.ndarray(res)
period_prevalences_sd = np.ndarray(res)
Cs = np.linspace(interval[0], interval[1], endpoint=True, num=res)
unsuccessful_flag = []
for i, C in tqdm(enumerate(Cs), total=res):
try:
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i, mode=mode,
force_recompute=force_recompute,
path=path, clustering=C)
peak_times[i] = t_peak
peak_heights[i] = mean_peak
peak_heights_sd[i] = sd_peak
period_prevalences[i] = mean_prevalence
period_prevalences_sd[i] = sd_prevalence
# Cs[i] = net.final_cluster_coeff # in the end I want to plot the actual coeff, not the target
# should specify this in the paper
except AssertionError:
print('Clustering target not reached')
unsuccessful_flag.append(i)
peak_times[i] = np.nan
peak_heights[i] = np.nan
peak_heights_sd[i] = np.nan
period_prevalences[i] = np.nan
period_prevalences_sd[i] = np.nan
dirname_parent = os.path.dirname(__file__)
dirname = os.path.join(dirname_parent, 'Experiments', 'Paper', 'Cache')
id_params = (
n, p, p_i, mc_iterations, max_t, seed, mode, interval, t_i, t_c, t_r, t_d, t_t, p_q, p_t, quarantine_time,
resolution,
epsilon_disp, 'disp')
# normal hashes are salted between runs -> use something that is persistent
tag = str(hashlib.md5(str(id_params).encode('utf8')).hexdigest())
with open(os.path.join(dirname, tag + '_metrics.p'), 'wb') as f:
out = [Cs, unsuccessful_flag, peak_times, peak_heights, period_prevalences]
pickle.dump(out, f)
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(14, 14 / 16 * 9))
# fig.subplots_adjust(wspace = 0.5)
ax1, ax2, ax3 = axes
colordict = {'vanilla': 'C0', 'quarantine': 'C1', 'tracing': 'C2'}
if mode:
ax1.set_title(mode)
else:
ax1.set_title('Vanilla')
ax1.plot(Cs, peak_times, colordict[mode])
ax1.set_ylabel('Peak time')
ax2.plot(Cs, peak_heights, colordict[mode])
ax2.set_ylabel('Peak prevalence')
ax3.plot(Cs, period_prevalences, colordict[mode])
ax3.set_ylabel('Fraction of affected')
ax3.set_xlabel('C(g)')
# labels = [interval[0],] + list(['' for i in range(len(ps)-2)]) + [interval[1],]
ax3.set_xticks(Cs[1:-1], minor=True)
ax3.set_xticks([interval[0], interval[1]])
# plt.tick_params(
# axis='x', # changes apply to the x-axis
# which='minor', # both major and minor ticks are affected
# # bottom=False, # ticks along the bottom edge are off
# # top=False, # ticks along the top edge are off
# labelbottom=False) # labels along the bottom edge are off
# plt.xticks([interval[0],interval[1]])
if mode:
parent = os.path.dirname(path)
fig.savefig(os.path.join(parent, 'Pics', 'Cvaried_n{}_p{}_{}'.format(
n, str(interval[0]) + 'to' + str(interval[1]), mode) + '.png'), bbox_inches='tight')
else:
parent = os.path.dirname(path)
fig.savefig(os.path.join(parent, 'Pics', 'Cvaried_n{}_C{}'.format(
n, str(interval[0]) + 'to' + str(interval[1])) + '.png'), bbox_inches='tight')
return out # Cs, unsuccessful_flags, times, peaks, period_prev
def vary_disp(res, n, p, p_i, mc_iterations, max_t, interval=None, seed=0, mode=None, force_recompute=False, path=None):
# measure effect of clustering coeff on tracing effectiveness
if not interval:
# THEORY: the average clustering coeff of erdos renyi networks is p!
# so I test around that to see what changed
interval = (0.5 * p, 10 * p)
peak_times = np.ndarray(res)
peak_heights = np.ndarray(res)
peak_heights_sd = np.ndarray(res)
period_prevalences = np.ndarray(res)
period_prevalences_sd = np.ndarray(res)
Ds = np.linspace(interval[0], interval[1], endpoint=True, num=res)
unsuccessful_flag = []
for i, D in tqdm(enumerate(Ds), total=res, desc='Varying dispersion values'):
try:
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i, mode=mode,
force_recompute=force_recompute,
path=path, dispersion=D)
peak_times[i] = t_peak
peak_heights[i] = mean_peak
peak_heights_sd[i] = sd_peak
period_prevalences[i] = mean_prevalence
period_prevalences_sd[i] = sd_prevalence
print('last_disp{}, target_disp{}'.format(net.final_dispersion, D))
# Cs[i] = net.final_cluster_coeff # in the end I want to plot the actual coeff, not the target
# should specify this in the paper
except AssertionError:
print('Dispersion target not reached')
unsuccessful_flag.append(i)
peak_times[i] = np.nan
peak_heights[i] = np.nan
peak_heights_sd[i] = np.nan
period_prevalences[i] = np.nan
period_prevalences_sd[i] = np.nan
dirname_parent = os.path.dirname(__file__)
dirname = os.path.join(dirname_parent, 'Experiments', 'Paper', 'Cache')
id_params = (
n, p, p_i, mc_iterations, max_t, mode, seed, interval, t_i, t_c, t_r, t_d, t_t, p_q, p_t, quarantine_time,
resolution,
epsilon_disp)
# normal hashes are salted between runs -> use something that is persistent
tag = str(hashlib.md5(str(id_params).encode('utf8')).hexdigest())
with open(os.path.join(dirname, tag + '_metrics.p'), 'wb') as f:
out = [Ds, unsuccessful_flag, peak_times, peak_heights, period_prevalences]
pickle.dump(out, f)
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(14, 14 / 16 * 9))
# fig.subplots_adjust(wspace = 0.5)
ax1, ax2, ax3 = axes
colordict = {'vanilla': 'C0', 'quarantine': 'C1', 'tracing': 'C2'}
if mode:
ax1.set_title(mode)
else:
ax1.set_title('Vanilla')
ax1.plot(Ds, peak_times, colordict[mode])
ax1.set_ylabel('Peak time')
ax2.plot(Ds, peak_heights, colordict[mode])
ax2.set_ylabel('Peak prevalence')
ax3.plot(Ds, period_prevalences, colordict[mode])
ax3.set_ylabel('Fraction of affected')
ax3.set_xlabel('C(g)')
# labels = [interval[0],] + list(['' for i in range(len(ps)-2)]) + [interval[1],]
ax3.set_xticks(Ds[1:-1], minor=True)
ax3.set_xticks([interval[0], interval[1]])
# plt.tick_params(
# axis='x', # changes apply to the x-axis
# which='minor', # both major and minor ticks are affected
# # bottom=False, # ticks along the bottom edge are off
# # top=False, # ticks along the top edge are off
# labelbottom=False) # labels along the bottom edge are off
# plt.xticks([interval[0],interval[1]])
if mode:
parent = os.path.dirname(path)
fig.savefig(os.path.join(parent, 'Pics', 'dispvaried_n{}_p{}_{}'.format(
n, str(interval[0]) + 'to' + str(interval[1]), mode) + '.png'), bbox_inches='tight')
else:
parent = os.path.dirname(path)
fig.savefig(os.path.join(parent, 'Pics', 'dispvaried_n{}_C{}'.format(
n, str(interval[0]) + 'to' + str(interval[1])) + '.png'), bbox_inches='tight')
return out # Cs, unsuccessful_flags, times, peaks, period_prev
def vary_C_comp(res, n, p, p_i, mc_iterations, max_t, interval=None, seed=0, force_recompute=False, path=None):
# measure effect of clustering coeff on tracing effectiveness
if not interval:
# THEORY: the average clustering coeff of erdos renyi networks is p!
# so I test around that to see what changed
interval = (0.5 * p, 10 * p)
Cs = np.linspace(interval[0], interval[1], endpoint=True, num=res)
peak_times_1 = np.ndarray(res)
peak_heights_1 = np.ndarray(res)
peak_heights_sd_1 = np.ndarray(res)
period_prevalences_1 = np.ndarray(res)
period_prevalences_sd_1 = np.ndarray(res)
unsuccessful_flags_1 = []
for i, C in tqdm(enumerate(Cs), total=res, desc='Vanilla'):
try:
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i, mode='vanilla',
force_recompute=force_recompute,
path=path, clustering=C)
peak_times_1[i] = t_peak
peak_heights_1[i] = mean_peak
peak_heights_sd_1[i] = sd_peak
period_prevalences_1[i] = mean_prevalence
period_prevalences_sd_1[i] = sd_prevalence
# Cs[i] = net.final_cluster_coeff # in the end I want to plot the actual coeff, not the target
# should specify this in the paper
except AssertionError:
print('Clustering target not reached')
unsuccessful_flags_1.append(i)
peak_times_1[i] = np.nan
peak_heights_1[i] = np.nan
peak_heights_sd_1[i] = np.nan
period_prevalences_1[i] = np.nan
period_prevalences_sd_1[i] = np.nan
peak_times_2 = np.ndarray(res)
peak_heights_2 = np.ndarray(res)
peak_heights_sd_2 = np.ndarray(res)
period_prevalences_2 = np.ndarray(res)
period_prevalences_sd_2 = np.ndarray(res)
unsuccessful_flags_2 = []
for i, C in tqdm(enumerate(Cs), total=res, desc='Quarantine'):
try:
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i + res, mode='quarantine',
force_recompute=force_recompute,
path=path, clustering=C)
peak_times_2[i] = t_peak
peak_heights_2[i] = mean_peak
peak_heights_sd_2[i] = sd_peak
period_prevalences_2[i] = mean_prevalence
period_prevalences_sd_2[i] = sd_prevalence
# Cs[i] = net.final_cluster_coeff # in the end I want to plot the actual coeff, not the target
# should specify this in the paper
except AssertionError:
print('Clustering target not reached')
unsuccessful_flags_2.append(i)
peak_times_2[i] = np.nan
peak_heights_2[i] = np.nan
peak_heights_sd_2[i] = np.nan
period_prevalences_2[i] = np.nan
period_prevalences_sd_2[i] = np.nan
peak_times_3 = np.ndarray(res)
peak_heights_3 = np.ndarray(res)
peak_heights_sd_3 = np.ndarray(res)
period_prevalences_3 = np.ndarray(res)
period_prevalences_sd_3 = np.ndarray(res)
unsuccessful_flags_3 = []
for i, C in tqdm(enumerate(Cs), total=res, desc='Tracing'):
try:
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i + 2 * res, mode='tracing',
force_recompute=force_recompute,
path=path, clustering=C)
peak_times_3[i] = t_peak
peak_heights_3[i] = mean_peak
peak_heights_sd_3[i] = sd_peak
period_prevalences_3[i] = mean_prevalence
period_prevalences_sd_3[i] = sd_prevalence
# Cs[i] = net.final_cluster_coeff # in the end I want to plot the actual coeff, not the target
# should specify this in the paper
except AssertionError:
print('Clustering target not reached')
unsuccessful_flags_3.append(i)
peak_times_3[i] = np.nan
peak_heights_3[i] = np.nan
peak_heights_sd_3[i] = np.nan
period_prevalences_3[i] = np.nan
period_prevalences_sd_3[i] = np.nan
dirname_parent = os.path.dirname(__file__)
dirname = os.path.join(dirname_parent, 'Experiments', 'Paper', 'Cache')
id_params = (
n, p, p_i, mc_iterations, max_t, interval, seed, t_i, t_c, t_r, t_d, t_t, p_q, p_t, quarantine_time, resolution,
epsilon_clustering)
# normal hashes are salted between runs -> use something that is persistent
tag = str(hashlib.md5(str(id_params).encode('utf8')).hexdigest())
with open(os.path.join(dirname, tag + '_metrics.p'), 'wb') as f:
out = [Cs, unsuccessful_flags_1, peak_times_1, peak_heights_1, period_prevalences_1,
Cs, unsuccessful_flags_2, peak_times_2, peak_heights_2, period_prevalences_2,
Cs, unsuccessful_flags_3, peak_times_3, peak_heights_3, period_prevalences_3]
pickle.dump(out, f)
fig, axes = plt.subplots(3, 1, sharex=True, figsize=(14, 14 / 16 * 9))
# fig.subplots_adjust(wspace = 0.5)
ax1, ax2, ax3 = axes
ax1.plot(Cs, peak_times_1, Cs, peak_times_2, Cs, peak_times_3)
ax1.set_ylabel('Peak time')
ax2.plot(Cs, peak_heights_1, Cs, peak_heights_2, Cs, peak_heights_3)
ax2.set_ylabel('Peak prevalence')
ax3.plot(Cs, period_prevalences_1, Cs, period_prevalences_2, Cs, period_prevalences_3)
ax3.set_ylabel('Fraction of affected')
ax3.set_xlabel('C(g)')
# labels = [interval[0],] + list(['' for i in range(len(ps)-2)]) + [interval[1],]
ax3.set_xticks(Cs[1:-1], minor=True)
ax3.set_xticks([interval[0], interval[1]])
# plt.tick_params(
# axis='x', # changes apply to the x-axis
# which='minor', # both major and minor ticks are affected
# # bottom=False, # ticks along the bottom edge are off
# # top=False, # ticks along the top edge are off
# labelbottom=False) # labels along the bottom edge are off
# plt.xticks([interval[0],interval[1]])
plt.legend(['Vanilla', 'Quarantine', 'Tracing'])
parent = os.path.dirname(path)
fig.savefig(os.path.join(parent, 'Pics', 'Cvaried_n{}_C{}_comp'.format(
n, str(interval[0]) + 'to' + str(interval[1])) + '.png'), bbox_inches='tight')
return out # Cs, unsuccessful_flags, times, peaks, period_prev
# OLD, now this is in vary_C_pi_comp_corrected
# def vary_C_comp_corrected(res, n, p, p_i, mc_iterations, max_t, interval=None, seed=0, force_recompute=False,
# path=None):
# # BROKEN! Since martin's commit?
#
# # measure effect of clustering coeff on tracing effectiveness. Here we scale according to the vanilla outcome
#
# if not interval:
# # THEORY: the average clustering coeff of erdos renyi networks is p!
# # so I test around that to see what changed
# interval = (0.5 * p, 10 * p)
#
# Cs = np.linspace(interval[0], interval[1], endpoint=True, num=res)
#
# # the following two variables save the actual values that were achieved by the heuristic.
# # In theory, these should be approximately the same in each net
# achieved_clusterings = np.zeros((3, res))
# achieved_disps = np.zeros((3, res))
#
# # vanilla
# peak_times_1 = np.ndarray(res)
# peak_heights_1 = np.ndarray(res)
# peak_heights_sd_1 = np.ndarray(res)
# period_prevalences_1 = np.ndarray(res)
# period_prevalences_sd_1 = np.ndarray(res)
# unsuccessful_flags_1 = []
# for i, C in tqdm(enumerate(Cs), total=res,desc='Vanilla'):
# net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag , achieved_clustering, achieved_disp = \
# simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i, mode='vanilla',
# force_recompute=force_recompute,
# path=path, clustering=C)
#
# assert equilib_flag, 'Sim not complete?'
#
# peak_times_1[i] = t_peak
# peak_heights_1[i] = mean_peak
# peak_heights_sd_1[i] = sd_peak
# period_prevalences_1[i] = mean_prevalence
# period_prevalences_sd_1[i] = sd_prevalence
#
# achieved_clusterings[0, i] = achieved_clustering
# achieved_disps[0, i] = achieved_disp
#
#
# # exposed = counts[EXP_STATE, :]
# # infected = counts[INF_STATE, :]
# # ep_curve = exposed + infected
# #
# # exposed_sd = sd[EXP_STATE, :]
# # infected_sd = sd[INF_STATE, :]
# # ep_curve_sd = exposed_sd + infected_sd
# #
# # # these are the point prevalence +- sd
# # upper_alpha = (ep_curve[t_peak] + ep_curve_sd[t_peak])/n
# # lower_alpha = (ep_curve[t_peak] - ep_curve_sd[t_peak])/n
# #
# # recovered = counts[REC_STATE, :]
# # recovered_sd = sd[REC_STATE, :]
# #
# #
# # upper_beta = recovered[-1]-recovered_sd/n
#
#
# # quarantine
# peak_times_2 = np.ndarray(res)
# peak_heights_2 = np.ndarray(res)
# peak_heights_sd_2 = np.ndarray(res)
# period_prevalences_2 = np.ndarray(res)
# period_prevalences_sd_2 = np.ndarray(res)
# unsuccessful_flags_2 = []
# for i, C in tqdm(enumerate(Cs), total=res,desc='Quarantine'):
# net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag , achieved_clustering, achieved_disp = \
# simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i + res, mode='quarantine',
# force_recompute=force_recompute,
# path=path, clustering=C)
#
# assert equilib_flag, 'Sim not complete?'
#
# peak_times_2[i] = t_peak
# peak_heights_2[i] = mean_peak / peak_heights_1[i]
# peak_heights_sd_2[i] = sd_peak
# period_prevalences_2[i] = mean_prevalence / period_prevalences_1[i]
# period_prevalences_sd_2[i] = sd_prevalence
#
# achieved_clusterings[1, i] = achieved_clustering
# achieved_disps[1, i] = achieved_disp
#
#
#
#
# # tracing
# peak_times_3 = np.ndarray(res)
# peak_heights_3 = np.ndarray(res)
# peak_heights_sd_3 = np.ndarray(res)
# period_prevalences_3 = np.ndarray(res)
# period_prevalences_sd_3 = np.ndarray(res)
# unsuccessful_flags_3 = []
# for i, C in tqdm(enumerate(Cs), total=res,desc='Tracing'):
# net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag , achieved_clustering, achieved_disp = \
# simple_experiment(n, p, p_i, 2*mc_iterations, max_t, seed=seed + i + 2 * res, mode='tracing',
# force_recompute=force_recompute,
# path=path, clustering=C)
#
# assert equilib_flag, 'Sim not complete?'
#
# peak_times_3[i] = t_peak
# peak_heights_3[i] = mean_peak / peak_heights_1[i]
# peak_heights_sd_3[i] = sd_peak
# period_prevalences_3[i] = mean_prevalence / period_prevalences_1[i]
# period_prevalences_3_sd_2[i] = sd_prevalencea
#
# achieved_clusterings[2, i] = achieved_clustering
# achieved_disps[2, i] = achieved_disp
#
# dirname_parent = os.path.dirname(__file__)
# dirname = os.path.join(dirname_parent, 'Experiments', 'Paper', 'Cache')
#
# id_params = (
# n, p, p_i, mc_iterations, max_t, interval, seed, t_i, t_c, t_r, t_d, t_t, p_q, p_t, quarantine_time, resolution,
# epsilon_clustering)
# # normal hashes are salted between runs -> use something that is persistent
# tag = str(hashlib.md5(str(id_params).encode('utf8')).hexdigest())
#
# with open(os.path.join(dirname, tag + '_metrics_corrected.p'), 'wb') as f:
# out = [Cs, unsuccessful_flags_1, peak_times_1, peak_heights_1, period_prevalences_1,
# Cs, unsuccessful_flags_2, peak_times_2, peak_heights_2, period_prevalences_2,
# Cs, unsuccessful_flags_3, peak_times_3, peak_heights_3, period_prevalences_3,
# achieved_clusterings, achieved_disps]
#
# pickle.dump(out, f)
#
# # two modes for visualization
# show_both = False
# if show_both:
# fig, axes = plt.subplots(2, 1, sharex=True, figsize=(14, 14 / 16 * 9))
#
# # fig.subplots_adjust(wspace = 0.5)
# ax2, ax3 = axes
#
# # ax1.plot(Cs, peak_times_1,Cs, peak_times_2,Cs, peak_times_3)
# # ax1.set_ylabel('Peak time')
#
# ax2.plot(Cs, peak_heights_2, 'C1')
# ax2.plot(Cs, peak_heights_3, 'C2')
# ax2.set_ylabel('Scaled peak height')
#
# ax3.plot(Cs, period_prevalences_2, 'C1')
# ax3.plot(Cs, period_prevalences_3, 'C2')
# ax3.set_ylabel('Scaled period prevalence')
# ax3.set_xlabel('C(g)')
# # labels = [interval[0],] + list(['' for i in range(len(ps)-2)]) + [interval[1],]
# ax3.set_xticks(Cs, minor=False)
# ax3.xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
#
# # ax3.set_xticks([interval[0], interval[1]])
#
# # plt.tick_params(
# # axis='x', # changes apply to the x-axis
# # which='minor', # both major and minor ticks are affected
# # # bottom=False, # ticks along the bottom edge are off
# # # top=False, # ticks along the top edge are off
# # labelbottom=False) # labels along the bottom edge are off
#
# ax_upper_axis = ax2.twiny()
#
# ax_upper_axis.set_xlim(ax3.get_xlim())
# ax_upper_axis.set_xticks(Cs)
# ax_upper_axis.set_xticklabels(["{:.3f}".format(a) for a in achieved_disps.mean(axis=0)])
# # ax_upper_axis.xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
# ax_upper_axis.set_xlabel('D(g)')
#
# # plt.xticks([interval[0],interval[1]])
# ax3.legend(['Quarantine', 'Tracing'])
#
# parent = os.path.dirname(path)
# fig.savefig(os.path.join(parent, 'Pics', 'Cvaried_n{}_C{}_comp_corrected'.format(
# n, str(interval[0]) + 'to' + str(interval[1])) + '.png'), bbox_inches='tight')
# else:
# fig, axes = plt.subplots(2, 1, sharex=True, figsize=(14, 14 / 16 * 9))
#
# # fig.subplots_adjust(wspace = 0.5)
# ax2, ax3 = axes
#
# # ax1.plot(Cs, peak_times_1,Cs, peak_times_2,Cs, peak_times_3)
# # ax1.set_ylabel('Peak time')
#
# ax2.plot(Cs, peak_heights_3, 'C2')
# ax2.set_ylabel('Scaled peak height')
#
# ax3.plot(Cs, period_prevalences_3, 'C2')
# ax3.set_ylabel('Scaled period prevalence')
# ax3.set_xlabel('C(g)')
# ax3.set_xticks(Cs, minor=False)
# ax3.xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
#
# # ax3.set_xticks(Cs[1:-1], minor=True)
# # ax3.set_xticks([interval[0], interval[1]])
# # ax3.set_xticks(Cs, minor=True)
#
# ax_upper_axis = ax2.twiny()
#
# ax_upper_axis.set_xlim(ax3.get_xlim())
# ax_upper_axis.set_xticks(Cs)
# ax_upper_axis.set_xticklabels(["{:.3f}".format(a) for a in achieved_disps.mean(axis=0)])
# # ax_upper_axis.xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
# ax_upper_axis.set_xlabel('D(g)')
#
# # plt.legend(['Quarantine', 'Tracing'])
# ax3.legend(['Tracing', ])
#
# parent = os.path.dirname(path)
# fig.savefig(os.path.join(parent, 'Pics', 'Cvaried_n{}_C{}_comp_corrected_tracing'.format(
# n, str(interval[0]) + 'to' + str(interval[1])) + '.png'), bbox_inches='tight')
#
# return out
def vary_C_pi_comp_corrected(res, n, p, p_is: tuple, mc_iterations, max_t, interval=None, seed=0, force_recompute=False,
path=None):
# measure effect of clustering coeff on tracing effectiveness. Here we scale according to the vanilla outcome
# Several values for infectvity p_i are used
Cs = np.linspace(interval[0], interval[1], endpoint=True, num=res)
n_p_i = len(p_is)
assert n_p_i <= 5, 'Less values for p_i should be selected for visibility'
# the following two variables save the actual values that were achieved by the heuristic.
# In theory, these should be approximately the same in each net
achieved_clusterings = np.zeros((3 * n_p_i, res))
achieved_disps = np.zeros((3 * n_p_i, res))
# vanilla
peak_times_1 = np.ndarray((res, n_p_i))
peak_heights_1 = np.ndarray((res, n_p_i))
peak_heights_sd_1 = np.ndarray((res, n_p_i))
period_prevalences_1 = np.ndarray((res, n_p_i))
period_prevalences_sd_1 = np.ndarray((res, n_p_i))
for i, C in tqdm(enumerate(Cs), total=res, desc='Vanilla'):
for j, p_inf in enumerate(p_is):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_inf, mc_iterations, max_t, seed=j * 156484 + seed + i, mode='vanilla',
force_recompute=force_recompute,
path=path, clustering=C)
assert equilib_flag, 'Sim not complete?'
peak_times_1[i, j] = t_peak
peak_heights_1[i, j] = mean_peak
peak_heights_sd_1[i, j] = sd_peak
period_prevalences_1[i, j] = mean_prevalence
period_prevalences_sd_1[i, j] = sd_prevalence
achieved_clusterings[j, i] = achieved_clustering
achieved_disps[j, i] = achieved_disp
# quarantine
peak_times_2 = np.ndarray((res, n_p_i))
peak_heights_2 = np.ndarray((res, n_p_i))
peak_heights_sd_2 = np.ndarray((res, n_p_i))
period_prevalences_2 = np.ndarray((res, n_p_i))
period_prevalences_sd_2 = np.ndarray((res, n_p_i))
for i, C in tqdm(enumerate(Cs), total=res, desc='Quarantine'):
for j, p_inf in enumerate(p_is):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_inf, mc_iterations, max_t, seed=j * 84265 + seed + i + res, mode='quarantine',
force_recompute=force_recompute,
path=path, clustering=C)
assert equilib_flag, 'Sim not complete?'
peak_times_2[i, j] = t_peak
peak_heights_2[i, j] = mean_peak
peak_heights_sd_2[i, j] = sd_peak
period_prevalences_2[i, j] = mean_prevalence
period_prevalences_sd_2[i, j] = sd_prevalence
achieved_clusterings[n_p_i + j, i] = achieved_clustering
achieved_disps[n_p_i + j, i] = achieved_disp
# tracing
peak_times_3 = np.ndarray((res, n_p_i))
peak_heights_3 = np.ndarray((res, n_p_i))
peak_heights_sd_3 = np.ndarray((res, n_p_i))
period_prevalences_3 = np.ndarray((res, n_p_i))
period_prevalences_sd_3 = np.ndarray((res, n_p_i))
for i, C in tqdm(enumerate(Cs), total=res, desc='Tracing'):
for j, p_inf in enumerate(p_is):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_inf, mc_iterations, max_t, seed=j * 543513 + seed + i + 2 * res,
mode='tracing',
force_recompute=force_recompute,
path=path, clustering=C)
assert equilib_flag, 'Sim not complete?'
peak_times_3[i, j] = t_peak
peak_heights_3[i, j] = mean_peak
peak_heights_sd_3[i, j] = sd_peak
period_prevalences_3[i, j] = mean_prevalence
period_prevalences_sd_3[i, j] = sd_prevalence
achieved_clusterings[2 * n_p_i + j, i] = achieved_clustering
achieved_disps[2 * n_p_i + j, i] = achieved_disp
dirname_parent = os.path.dirname(__file__)
dirname = os.path.join(dirname_parent, 'Experiments', 'Paper', 'Cache')
id_params = (
n, p, p_is, mc_iterations, max_t, interval, seed, t_i, t_c, t_r, t_d, t_t, p_q, p_t, quarantine_time,
resolution,
epsilon_clustering)
# normal hashes are salted between runs -> use something that is persistent
tag = str(hashlib.md5(str(id_params).encode('utf8')).hexdigest())
with open(os.path.join(dirname, tag + '_metrics_corrected.p'), 'wb') as f:
out = [Cs, peak_times_1, peak_heights_1, period_prevalences_1,
peak_times_2, peak_heights_2, period_prevalences_2,
peak_times_3, peak_heights_3, period_prevalences_3,
achieved_clusterings, achieved_disps]
pickle.dump(out, f)
# two modes for visualization
scale = 1
fig, axes = plt.subplots(4, 1, figsize=(columwidth, 2 * columwidth), dpi=1000)
# fig.subplots_adjust(wspace = 0.5)
# (axul, axur), (axll, axlr) = axes # upper left, upper right, lower left, lower right
ax1, ax2, ax3, ax4 = axes # reordered to be 4x1.
# ax1.set_ylabel('$\\alpha_q$')
# ax3.set_ylabel('$\\alpha_t$')
# ax2.set_ylabel('$\\beta_q$')
# ax4.set_ylabel('$\\beta_t$')
ax1.set_ylabel('peak ratio ($\\alpha$)')
ax3.set_ylabel('peak ratio ($\\alpha$)')
ax2.set_ylabel('overall ratio ($\\beta$)')
ax4.set_ylabel('overall ratio ($\\beta$)')
ax1.set_ylim([0, 1])
ax2.set_ylim([0, 1])
ax3.set_ylim([0, 1])
ax4.set_ylim([0, 1])
# ax1.set_ylabel('Scaled peak height')
# ax3.set_ylabel('Scaled peak height')
# ax2.set_ylabel('Scaled period prevalence')
# ax4.set_ylabel('Scaled period prevalence')
# ax1.set_ylabel('Scaled peak height')
# ax3.set_ylabel('Scaled peak height')
# ax2.set_ylabel('Scaled period prevalence')
# ax4.set_ylabel('Scaled period prevalence')
# ax2.set_xlabel('C(g)')
ax4.set_xlabel('C(g)')
# axll.set_xticks(Cs, minor=False)
# axll.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# axlr.set_xticks(Cs, minor=False)
# axlr.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# ax1.set_title('Quarantine')
# ax3.set_title('Tracing')
# ax1.set_prop_cycle(color=['orange','orange','orange',],linestyle=['-','--',':'])
# ax2.set_prop_cycle(color=['orange','orange','orange',],linestyle=['-','--',':'])
# ax3.set_prop_cycle(color=['green','green','green',],linestyle=['-','--',':'])
# ax4.set_prop_cycle(color=['green','green','green',],linestyle=['-','--',':'])
oranges = plt.get_cmap('Blues')
greens = plt.get_cmap('Greens')
n_colors = n_p_i
col_vals = np.linspace(0.7, 1, n_colors)
colors = [oranges(col_vals[0]), oranges(col_vals[1]), oranges(col_vals[2]), oranges(col_vals[3]),
oranges(col_vals[4]),
greens(col_vals[0]), greens(col_vals[1]), greens(col_vals[2]), greens(col_vals[3]), greens(col_vals[4]), ]
linestyles = ['-', '--', ':', '-.', (0, (5, 10))]
line_artists = [None, ] * 2 * n_p_i
for i in range(n_p_i):
linestyle = linestyles[i]
l1 = ax1.plot(Cs, peak_heights_2[:, i] / peak_heights_1[:, i], color=colors[i], linestyle=linestyle, zorder=1)
estimateQuotientCI(ax1, Cs, peak_heights_2[:, i], peak_heights_sd_2[:, i], peak_heights_1[:, i],
peak_heights_sd_1[:, i], color=colors[i], mccount=mc_iterations, p=95)
l3 = ax3.plot(Cs, peak_heights_3[:, i] / peak_heights_1[:, i], color=colors[n_p_i + i], linestyle=linestyle,
zorder=1)
estimateQuotientCI(ax3, Cs, peak_heights_3[:, i], peak_heights_sd_3[:, i], peak_heights_1[:, i],
peak_heights_sd_1[:, i], color=colors[n_p_i + i], mccount=mc_iterations, p=95)
l2 = ax2.plot(Cs, period_prevalences_2[:, i] / period_prevalences_1[:, i], color=colors[i], linestyle=linestyle,
zorder=1)
estimateQuotientCI(ax2, Cs, period_prevalences_2[:, i], period_prevalences_sd_2[:, i],
period_prevalences_1[:, i],
period_prevalences_sd_1[:, i], color=colors[i], mccount=mc_iterations, p=95)
l4 = ax4.plot(Cs, period_prevalences_3[:, i] / period_prevalences_1[:, i], color=colors[n_p_i + i],
linestyle=linestyle, zorder=1)
estimateQuotientCI(ax4, Cs, period_prevalences_3[:, i], period_prevalences_sd_3[:, i],
period_prevalences_1[:, i],
period_prevalences_sd_1[:, i], color=colors[n_p_i + i], mccount=mc_iterations, p=95)
line_artists[i] = l1[0]
line_artists[n_p_i + i] = l3[0]
labels1 = list(['quar.: $p_i$=' + str(val) for val in p_is])
labels2 = list(['trac.: $p_i$=' + str(val) for val in p_is])
# line_labels = [None,]*2*n_p_i
# line_labels[::2] = labels1
# line_labels[1::2] = labels2
line_labels = labels1 + labels2
fig.legend(handles=line_artists, # The line objects
labels=line_labels, # The labels for each line
loc="center", # Position of legend
bbox_to_anchor=(0.5, -0.1),
borderaxespad=0.1, # Small spacing around legend box
ncol=2
)
plt.subplots_adjust(bottom=0.01)
# ax1.legend(['$p_i$=' + str(val) for val in p_is],loc='upper center', bbox_to_anchor=(0.5, -0.25), ncol=3) # looks bad
# ax2.legend(['$p_i$=' + str(val) for val in p_is],bbox_to_anchor=(0., 1.02, 1., .102), loc='lower left',ncol=3, mode="expand") # looks worse
# ax3.legend(['$p_i$=' + str(val) for val in p_is], loc='upper center', bbox_to_anchor=(0.5, -0.25),fancybox=True, shadow=True, ncol=3)
# # left upper axis for dispersion values
# ax_upper_axis = axul.twiny()
# ax_upper_axis.set_xlim(axul.get_xlim())
# ax_upper_axis.set_xticks(Cs)
# ax_upper_axis.set_xticklabels(["{:.2f}".format(a) for a in achieved_disps.mean(axis=0)])
# ax_upper_axis.set_xlabel('D(g)')
#
# # right upper axis for dispersion values
# ax_upper_axis = axur.twiny()
# ax_upper_axis.set_xlim(axul.get_xlim())
# ax_upper_axis.set_xticks(Cs)
# ax_upper_axis.set_xticklabels(["{:.2f}".format(a) for a in achieved_disps.mean(axis=0)])
# ax_upper_axis.set_xlabel('D(g)')
# ax2.plot(Cs, peak_heights_2, 'C1')
# ax2.plot(Cs, peak_heights_3, 'C2')
# ax2.set_ylabel('Scaled peak height')
#
# ax2.plot(Cs, peak_heights_2, 'C1')
# ax2.plot(Cs, peak_heights_3, 'C2')
# ax2.set_ylabel('Scaled peak height')
#
# ax3.plot(Cs, period_prevalences_2, 'C1')
# ax3.plot(Cs, period_prevalences_3, 'C2')
# ax3.set_ylabel('Scaled period prevalence')
# ax3.set_xlabel('C(g)')
# # labels = [interval[0],] + list(['' for i in range(len(ps)-2)]) + [interval[1],]
# ax3.set_xticks(Cs, minor=False)
# ax3.xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
#
#
# ax_upper_axis = ax2.twiny()
#
# ax_upper_axis.set_xlim(ax3.get_xlim())
# ax_upper_axis.set_xticks(Cs)
# ax_upper_axis.set_xticklabels(["{:.3f}".format(a) for a in achieved_disps.mean(axis=0)])
# # ax_upper_axis.xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
# ax_upper_axis.set_xlabel('D(g)')
#
# # plt.xticks([interval[0],interval[1]])
# ax3.legend(['Quarantine', 'Tracing'])
plt.tight_layout()
parent = os.path.dirname(path)
fig.savefig(os.path.join(parent, 'Pics', 'Cvaried_n{}_C{}_comp_corrected'.format(
n, str(interval[0]) + 'to' + str(interval[1])) + '.pdf'), bbox_inches='tight', pad_inches=0)
return out
def vary_C_pi_comp_corrected_flipped(res, n, p, p_is: tuple, mc_iterations, max_t, interval=None, seed=0,
force_recompute=False,
path=None):
# measure effect of clustering coeff on tracing effectiveness. Here we scale according to the vanilla outcome
# Several values for infectvity p_i are used
Cs = np.linspace(interval[0], interval[1], endpoint=True, num=res)
n_p_i = len(p_is)
# assert n_p_i <= 5, 'Less values for p_i should be selected for visibility'
# the following two variables save the actual values that were achieved by the heuristic.
# In theory, these should be approximately the same in each net
achieved_clusterings = np.zeros((3 * n_p_i, res))
achieved_disps = np.zeros((3 * n_p_i, res))
# vanilla
peak_times_1 = np.ndarray((res, n_p_i))
peak_heights_1 = np.ndarray((res, n_p_i))
peak_heights_sd_1 = np.ndarray((res, n_p_i))
period_prevalences_1 = np.ndarray((res, n_p_i))
period_prevalences_sd_1 = np.ndarray((res, n_p_i))
for i, C in tqdm(enumerate(Cs), total=res, desc='Vanilla'):
for j, p_inf in enumerate(p_is):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_inf, mc_iterations, max_t, seed=j * 156484 + seed + i, mode='vanilla',
force_recompute=force_recompute,
path=path, clustering=C)
assert equilib_flag, 'Sim not complete?'
peak_times_1[i, j] = t_peak
peak_heights_1[i, j] = mean_peak
peak_heights_sd_1[i, j] = sd_peak
period_prevalences_1[i, j] = mean_prevalence
period_prevalences_sd_1[i, j] = sd_prevalence
achieved_clusterings[j, i] = achieved_clustering
achieved_disps[j, i] = achieved_disp
# quarantine
peak_times_2 = np.ndarray((res, n_p_i))
peak_heights_2 = np.ndarray((res, n_p_i))
peak_heights_sd_2 = np.ndarray((res, n_p_i))
period_prevalences_2 = np.ndarray((res, n_p_i))
period_prevalences_sd_2 = np.ndarray((res, n_p_i))
for i, C in tqdm(enumerate(Cs), total=res, desc='Quarantine'):
for j, p_inf in enumerate(p_is):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_inf, mc_iterations, max_t, seed=j * 84265 + seed + i + res, mode='quarantine',
force_recompute=force_recompute,
path=path, clustering=C)
assert equilib_flag, 'Sim not complete?'
peak_times_2[i, j] = t_peak
peak_heights_2[i, j] = mean_peak
peak_heights_sd_2[i, j] = sd_peak
period_prevalences_2[i, j] = mean_prevalence
period_prevalences_sd_2[i, j] = sd_prevalence
achieved_clusterings[n_p_i + j, i] = achieved_clustering
achieved_disps[n_p_i + j, i] = achieved_disp
# tracing
peak_times_3 = np.ndarray((res, n_p_i))
peak_heights_3 = np.ndarray((res, n_p_i))
peak_heights_sd_3 = np.ndarray((res, n_p_i))
period_prevalences_3 = np.ndarray((res, n_p_i))
period_prevalences_sd_3 = np.ndarray((res, n_p_i))
for i, C in tqdm(enumerate(Cs), total=res, desc='Tracing'):
for j, p_inf in enumerate(p_is):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_inf, mc_iterations, max_t, seed=j * 543513 + seed + i + 2 * res,
mode='tracing',
force_recompute=force_recompute,
path=path, clustering=C)
assert equilib_flag, 'Sim not complete?'
peak_times_3[i, j] = t_peak
peak_heights_3[i, j] = mean_peak
peak_heights_sd_3[i, j] = sd_peak
period_prevalences_3[i, j] = mean_prevalence
period_prevalences_sd_3[i, j] = sd_prevalence
achieved_clusterings[2 * n_p_i + j, i] = achieved_clustering
achieved_disps[2 * n_p_i + j, i] = achieved_disp
dirname_parent = os.path.dirname(__file__)
dirname = os.path.join(dirname_parent, 'Experiments', 'Paper', 'Cache')
id_params = (
n, p, p_is, mc_iterations, max_t, interval, seed, t_i, t_c, t_r, t_d, t_t, p_q, p_t, quarantine_time,
resolution,
epsilon_clustering)
# normal hashes are salted between runs -> use something that is persistent
tag = str(hashlib.md5(str(id_params).encode('utf8')).hexdigest())
with open(os.path.join(dirname, tag + '_metrics_corrected.p'), 'wb') as f:
out = [Cs, peak_times_1, peak_heights_1, period_prevalences_1,
peak_times_2, peak_heights_2, period_prevalences_2,
peak_times_3, peak_heights_3, period_prevalences_3,
achieved_clusterings, achieved_disps]
pickle.dump(out, f)
# two modes for visualization
scale = 1
fig, axes = plt.subplots(4, 1, figsize=(columwidth, 2 * columwidth), dpi=1000)
# fig.subplots_adjust(wspace = 0.5)
# (axul, axur), (axll, axlr) = axes # upper left, upper right, lower left, lower right
ax1, ax2, ax3, ax4 = axes # reordered to be 4x1.
# ax1.set_ylabel('$\\alpha_q$')
# ax3.set_ylabel('$\\alpha_t$')
# ax2.set_ylabel('$\\beta_q$')
# ax4.set_ylabel('$\\beta_t$')
ax1.set_ylabel('peak ratio ($\\alpha$)')
ax3.set_ylabel('peak ratio ($\\alpha$)')
ax2.set_ylabel('overall ratio ($\\beta$)')
ax4.set_ylabel('overall ratio ($\\beta$)')
ax1.set_ylim([0, 1])
ax2.set_ylim([0, 1])
ax3.set_ylim([0, 1])
ax4.set_ylim([0, 1])
# ax1.set_ylabel('Scaled peak height')
# ax3.set_ylabel('Scaled peak height')
# ax2.set_ylabel('Scaled period prevalence')
# ax4.set_ylabel('Scaled period prevalence')
# ax1.set_ylabel('Scaled peak height')
# ax3.set_ylabel('Scaled peak height')
# ax2.set_ylabel('Scaled period prevalence')
# ax4.set_ylabel('Scaled period prevalence')
# ax2.set_xlabel('C(g)')
# ax1.set_ylabel('$p_i$')
# ax2.set_ylabel('$p_i$')
# ax3.set_ylabel('$p_i$')
ax4.set_xlabel('$p_i$')
# axll.set_xticks(Cs, minor=False)
# axll.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# axlr.set_xticks(Cs, minor=False)
# axlr.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# ax1.set_title('Quarantine')
# ax3.set_title('Tracing')
# ax1.set_prop_cycle(color=['orange','orange','orange',],linestyle=['-','--',':'])
# ax2.set_prop_cycle(color=['orange','orange','orange',],linestyle=['-','--',':'])
# ax3.set_prop_cycle(color=['green','green','green',],linestyle=['-','--',':'])
# ax4.set_prop_cycle(color=['green','green','green',],linestyle=['-','--',':'])
step = 6
# quick and dirty, only get values for a number of Cs
Cs = Cs[0::step]
peak_heights_1 = peak_heights_1[0::step,:]
peak_heights_2 = peak_heights_2[0::step,:]
peak_heights_3 = peak_heights_3[0::step,:]
peak_heights_sd_1 = peak_heights_sd_1[0::step,:]
peak_heights_sd_2 = peak_heights_sd_2[0::step,:]
peak_heights_sd_3 = peak_heights_sd_3[0::step,:]
period_prevalences_1 = period_prevalences_1[0::step,:]
period_prevalences_2 = period_prevalences_2[0::step,:]
period_prevalences_3 = period_prevalences_3[0::step,:]
period_prevalences_sd_1 = period_prevalences_sd_1[0::step,:]
period_prevalences_sd_2 = period_prevalences_sd_2[0::step,:]
period_prevalences_sd_3 = period_prevalences_sd_3[0::step,:]
n_Cs = len(Cs)
oranges = plt.get_cmap('Blues')
greens = plt.get_cmap('Greens')
n_colors = n_p_i
col_vals = np.linspace(0.7, 1, n_Cs)
colors = list([oranges(col_val) for col_val in col_vals]) + list([greens(col_val) for col_val in col_vals])
# colors = [oranges(col_vals[0]), oranges(col_vals[1]), oranges(col_vals[2]),
# greens(col_vals[0]), greens(col_vals[1]), greens(col_vals[2]) ]
linestyles = ['-', '--', ':', '-.', (0, (5, 10))]
line_artists = [None, ] * 2 * n_Cs
for i in range(n_Cs):
linestyle = linestyles[i]
l1 = ax1.plot(p_is, peak_heights_2[i,:] / peak_heights_1[i,:], color=colors[i], linestyle=linestyle, zorder=1)
estimateQuotientCI(ax1, p_is, peak_heights_2[i,:], peak_heights_sd_2[i,:], peak_heights_1[i,:],
peak_heights_sd_1[i,:], color=colors[i], mccount=mc_iterations, p=95)
l3 = ax3.plot(p_is, peak_heights_3[i,:] / peak_heights_1[i,:], color=colors[n_Cs + i], linestyle=linestyle,
zorder=1)
estimateQuotientCI(ax3, p_is, peak_heights_3[i,:], peak_heights_sd_3[i,:], peak_heights_1[i,:],
peak_heights_sd_1[i,:], color=colors[n_Cs + i], mccount=mc_iterations, p=95)
l2 = ax2.plot(p_is, period_prevalences_2[i,:] / period_prevalences_1[i,:], color=colors[i], linestyle=linestyle,
zorder=1)
estimateQuotientCI(ax2, p_is, period_prevalences_2[i,:], period_prevalences_sd_2[i,:],
period_prevalences_1[i,:],
period_prevalences_sd_1[i,:], color=colors[i], mccount=mc_iterations, p=95)
l4 = ax4.plot(p_is, period_prevalences_3[i,:] / period_prevalences_1[i,:], color=colors[n_Cs + i],
linestyle=linestyle, zorder=1)
estimateQuotientCI(ax4, p_is, period_prevalences_3[i,:], period_prevalences_sd_3[i,:],
period_prevalences_1[i,:],
period_prevalences_sd_1[i,:], color=colors[n_Cs + i], mccount=mc_iterations, p=95)
line_artists[i] = l1[0]
line_artists[n_Cs + i] = l3[0]
labels1 = list(['quar.: $C(g)$={:.2f}'.format(val) for val in Cs])
labels2 = list(['trac.: $C(g)$={:.2f}'.format(val) for val in Cs])
# line_labels = [None,]*2*n_p_i
# line_labels[::2] = labels1
# line_labels[1::2] = labels2
line_labels = labels1 + labels2
fig.legend(handles=line_artists, # The line objects
labels=line_labels, # The labels for each line
loc="center", # Position of legend
bbox_to_anchor=(0.5, -0.1),
borderaxespad=0.1, # Small spacing around legend box
ncol=2
)
plt.subplots_adjust(bottom=0.01)
# ax1.legend(['$p_i$=' + str(val) for val in p_is],loc='upper center', bbox_to_anchor=(0.5, -0.25), ncol=3) # looks bad
# ax2.legend(['$p_i$=' + str(val) for val in p_is],bbox_to_anchor=(0., 1.02, 1., .102), loc='lower left',ncol=3, mode="expand") # looks worse
# ax3.legend(['$p_i$=' + str(val) for val in p_is], loc='upper center', bbox_to_anchor=(0.5, -0.25),fancybox=True, shadow=True, ncol=3)
# # left upper axis for dispersion values
# ax_upper_axis = axul.twiny()
# ax_upper_axis.set_xlim(axul.get_xlim())
# ax_upper_axis.set_xticks(Cs)
# ax_upper_axis.set_xticklabels(["{:.2f}".format(a) for a in achieved_disps.mean(axis=0)])
# ax_upper_axis.set_xlabel('D(g)')
#
# # right upper axis for dispersion values
# ax_upper_axis = axur.twiny()
# ax_upper_axis.set_xlim(axul.get_xlim())
# ax_upper_axis.set_xticks(Cs)
# ax_upper_axis.set_xticklabels(["{:.2f}".format(a) for a in achieved_disps.mean(axis=0)])
# ax_upper_axis.set_xlabel('D(g)')
# ax2.plot(Cs, peak_heights_2, 'C1')
# ax2.plot(Cs, peak_heights_3, 'C2')
# ax2.set_ylabel('Scaled peak height')
#
# ax2.plot(Cs, peak_heights_2, 'C1')
# ax2.plot(Cs, peak_heights_3, 'C2')
# ax2.set_ylabel('Scaled peak height')
#
# ax3.plot(Cs, period_prevalences_2, 'C1')
# ax3.plot(Cs, period_prevalences_3, 'C2')
# ax3.set_ylabel('Scaled period prevalence')
# ax3.set_xlabel('C(g)')
# # labels = [interval[0],] + list(['' for i in range(len(ps)-2)]) + [interval[1],]
# ax3.set_xticks(Cs, minor=False)
# ax3.xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
#
#
# ax_upper_axis = ax2.twiny()
#
# ax_upper_axis.set_xlim(ax3.get_xlim())
# ax_upper_axis.set_xticks(Cs)
# ax_upper_axis.set_xticklabels(["{:.3f}".format(a) for a in achieved_disps.mean(axis=0)])
# # ax_upper_axis.xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
# ax_upper_axis.set_xlabel('D(g)')
#
# # plt.xticks([interval[0],interval[1]])
# ax3.legend(['Quarantine', 'Tracing'])
plt.tight_layout()
parent = os.path.dirname(path)
fig.savefig(os.path.join(parent, 'Pics', 'Cvaried_n{}_C{}_comp_corrected_flipped'.format(
n, str(interval[0]) + 'to' + str(interval[1])) + '.pdf'), bbox_inches='tight', pad_inches=0)
return out
def vary_C_comp_epcurves(res, n, p, p_i, mc_iterations, max_t, interval, seed=0, force_recompute=False,
path=None):
# measure effect of clustering coeff on tracing effectiveness. Here we scale according to the vanilla outcome
# res parameter defines how many points on [0,1] are used
Cs = np.linspace(interval[0], interval[1], endpoint=True, num=res)
# the following two variables save the actual values that were achieved by the heuristic.
# In theory, these should be approximately the same in each net
achieved_clusterings = np.zeros((3, res))
achieved_disps = np.zeros((3, res))
# set up the plots
# fig, axes = plt.subplots(1, 4, figsize=(8*scale, 4*scale),gridspec_kw={'width_ratios': [5,5,5,0.3]}, dpi=1000)
fig = plt.figure(figsize=(columwidth, columwidth))
rows = 3
columns = 2
grid = fig.add_gridspec(rows, columns, wspace=.25, hspace=.35, width_ratios=[10, 0.4])
# plt.subplot(grid[0, :])
# plt.annotate('sub1', xy = (0.5, -0.5), va = 'center', ha = 'center', weight='bold', fontsize = 15)
# plt.plot(x, y)
#
# plt.subplot(grid[1, 0])
# plt.annotate('sub2', xy = (0.5, -0.5), va = 'center', ha = 'center', weight='bold', fontsize = 15)
# plt.plot(x, y)
#
# plt.subplot(grid[1, 1])
# plt.annotate('sub3', xy = (0.5, -0.5), va = 'center', ha = 'center', weight='bold', fontsize = 15)
# plt.plot(x, y)
#
# plt.subplot(grid[1, 2])
# plt.annotate('sub4', xy = (0.5, -0.5), va = 'center', ha = 'center', weight='bold', fontsize = 15)
# plt.plot(x, y)
# plt.show()
ax1 = fig.add_subplot(grid[0, 0])
ax2 = fig.add_subplot(grid[1, 0])
ax3 = fig.add_subplot(grid[2, 0])
cbar_ax = fig.add_subplot(grid[:, 1])
# ax1, ax2, ax3, cbar_ax = axes
ax1.set_ylabel('Infected')
ax2.set_ylabel('Infected')
ax3.set_ylabel('Infected')
ax1.set_xlabel('t')
ax2.set_xlabel('t')
ax3.set_xlabel('t')
# cbar_ax.axis('off')
norm = matplotlib.colors.Normalize(vmin=Cs[0], vmax=Cs[-1], clip=False)
cmap = plt.cm.jet
cb1 = mpl.colorbar.ColorbarBase(cbar_ax, cmap=cmap, norm=norm, orientation='vertical')
cbar_ax.set_ylabel('C(g)')
# set up colorcycles
# color = plt.cm.viridis(Cs)
# norm = mpl.colors.Normalize(vmin=Cs[0], vmax=Cs[-1])
# fig.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=color), ax=ax1)
# fig.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=color), ax=ax2)
# fig.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=color), ax=ax3)
# color.cycle_cmap(res)
# mpl.rcParams['axes.prop_cycle'] = cycler.cycler('color', color)
# ax3.legend(list(['C = '+ str(C) for C in Cs]))
# vanilla
peak_times_1 = np.ndarray(res)
peak_heights_1 = np.ndarray(res)
period_prevalences_1 = np.ndarray(res)
unsuccessful_flags_1 = []
for i, C in tqdm(enumerate(Cs), desc='Vanilla', total=res):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i, mode='vanilla',
force_recompute=force_recompute,
path=path, clustering=C)
peak_times_1[i] = t_peak
peak_heights_1[i] = mean_peak
period_prevalences_1[i] = mean_prevalence
achieved_clusterings[0, i] = achieved_clustering
achieved_disps[0, i] = achieved_disp
# epidemiological curve
ax1.plot(mean_counts[2, :], color=cmap(norm(C)), linewidth=0.75)
# quarantine
peak_times_2 = np.ndarray(res)
peak_heights_2 = np.ndarray(res)
period_prevalences_2 = np.ndarray(res)
unsuccessful_flags_2 = []
for i, C in tqdm(enumerate(Cs), desc='Quarantine', total=res):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, mc_iterations, max_t, seed=seed + i + res, mode='quarantine',
force_recompute=force_recompute,
path=path, clustering=C)
peak_times_2[i] = t_peak
peak_heights_2[i] = mean_peak / peak_heights_1[i]
period_prevalences_2[i] = mean_prevalence / period_prevalences_1[i]
achieved_clusterings[1, i] = achieved_clustering
achieved_disps[1, i] = achieved_disp
# epidemiological curve
ax2.plot(mean_counts[2, :], color=cmap(norm(C)), linewidth=0.75)
# tracing
peak_times_3 = np.ndarray(res)
peak_heights_3 = np.ndarray(res)
period_prevalences_3 = np.ndarray(res)
unsuccessful_flags_3 = []
for i, C in tqdm(enumerate(Cs), desc='Tracing', total=res):
net, mean_counts, sd_counts, t_peak, mean_peak, sd_peak, mean_prevalence, sd_prevalence, equilib_flag, achieved_clustering, achieved_disp = \
simple_experiment(n, p, p_i, 2 * mc_iterations, max_t, seed=seed + i + 2 * res, mode='tracing',
force_recompute=force_recompute,
path=path, clustering=C)
peak_times_3[i] = t_peak
peak_heights_3[i] = mean_peak / peak_heights_1[i]
period_prevalences_3[i] = mean_prevalence / period_prevalences_1[i]
achieved_clusterings[2, i] = achieved_clustering
achieved_disps[2, i] = achieved_disp
# epidemiological curve
ax3.plot(mean_counts[2, :], color=cmap(norm(C)), linewidth=0.75)
parent = os.path.dirname(path)
dirname_parent = os.path.dirname(__file__)
dirname = os.path.join(dirname_parent, 'Experiments', 'Paper', 'Cache')
id_params = (
n, p, p_i, mc_iterations, max_t, interval, seed, t_i, t_c, t_r, t_d, t_t, p_q, p_t, quarantine_time, resolution,
epsilon_clustering)
# normal hashes are salted between runs -> use something that is persistent
tag = str(hashlib.md5(str(id_params).encode('utf8')).hexdigest())
with open(os.path.join(dirname, tag + '_metrics_corrected.p'), 'wb') as f:
out = [Cs, unsuccessful_flags_1, peak_times_1, peak_heights_1, period_prevalences_1,
Cs, unsuccessful_flags_2, peak_times_2, peak_heights_2, period_prevalences_2,
Cs, unsuccessful_flags_3, peak_times_3, peak_heights_3, period_prevalences_3,
achieved_clusterings, achieved_disps]
pickle.dump(out, f)
# plt.tight_layout()
fig.align_ylabels()
fig.savefig(os.path.join(dirname_parent, 'Experiments', 'Paper', 'Pics', 'Cvaried_n{}_C{}_comp_epcurves'.format(
n, str(interval[0]) + 'to' + str(interval[1])) + '.pdf'), bbox_inches='tight')
return out
if __name__ == '__main__':
res = 20
n = 500
p = 0.1
p_i = 0.5
mc_iterations = 50
max_t = 200
path = r'C:\Users\giglerf\Google Drive\Seminar_Networks\Experiments\vary_params'
vary_p(res=res, n=n, p_i=p_i, mc_iterations=mc_iterations, max_t=max_t, force_recompute=False, path=path)
vary_p(res=res, n=n, p_i=p_i, mc_iterations=mc_iterations, max_t=max_t, mode='quarantine', force_recompute=False,
path=path)
vary_p(res=res, n=n, p_i=p_i, mc_iterations=mc_iterations, max_t=max_t, mode='tracing', force_recompute=False,
path=path)
vary_p_i(res=res, n=n, p=p, mc_iterations=mc_iterations, max_t=max_t, force_recompute=False, path=path)
vary_p_i(res=res, n=n, p=p, mc_iterations=mc_iterations, max_t=max_t, mode='quarantine', force_recompute=False,
path=path)
vary_p_i(res=res, n=n, p=p, mc_iterations=mc_iterations, max_t=max_t, mode='tracing', force_recompute=False,
path=path)
# vary_p(res=3,n=100,p_i=0.5, mc_iterations=1, max_t=20 path = r'C:\Users\giglerf\Google Drive\Seminar_Networks\Experiments\vary_params')
| 43.42245
| 153
| 0.630026
| 10,297
| 71,951
| 4.154414
| 0.059241
| 0.043714
| 0.029454
| 0.017579
| 0.88424
| 0.862032
| 0.845481
| 0.839964
| 0.830544
| 0.819183
| 0
| 0.024974
| 0.239246
| 71,951
| 1,656
| 154
| 43.448672
| 0.756545
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| 0
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| 0.003947
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| 0.015222
| 1
| 0.012881
| false
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| 0.016393
| 0
| 0.037471
| 0.008197
| 0
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| 0
| null | 0
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0
| 6
|
74d177778420438ff2eca413f2fefcfb8e7d85fb
| 43
|
py
|
Python
|
modelexp/__init__.py
|
DomiDre/modelexp
|
1ec25f71e739dac27716f9a8637fa6ab067499b9
|
[
"MIT"
] | null | null | null |
modelexp/__init__.py
|
DomiDre/modelexp
|
1ec25f71e739dac27716f9a8637fa6ab067499b9
|
[
"MIT"
] | null | null | null |
modelexp/__init__.py
|
DomiDre/modelexp
|
1ec25f71e739dac27716f9a8637fa6ab067499b9
|
[
"MIT"
] | null | null | null |
from ._app import App
from ._cli import Cli
| 21.5
| 21
| 0.790698
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
74ec24e9c8e87181d24709864b41d8c1ddcf4e40
| 37
|
py
|
Python
|
schicluster/impute/__init__.py
|
zhoujt1994/HiCluster
|
ee7431c33d8b565cd8b92b633e6f79b2267c1535
|
[
"MIT"
] | null | null | null |
schicluster/impute/__init__.py
|
zhoujt1994/HiCluster
|
ee7431c33d8b565cd8b92b633e6f79b2267c1535
|
[
"MIT"
] | null | null | null |
schicluster/impute/__init__.py
|
zhoujt1994/HiCluster
|
ee7431c33d8b565cd8b92b633e6f79b2267c1535
|
[
"MIT"
] | null | null | null |
from .snakemake import prepare_impute
| 37
| 37
| 0.891892
| 5
| 37
| 6.4
| 1
| 0
| 0
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| 0
| 0
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| 0.081081
| 37
| 1
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| 37
| 0.941176
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| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2d198a2f244324ff0128838b9a5ae9d9ce102ecd
| 143
|
py
|
Python
|
src/dicomweb_client/__init__.py
|
michaelloewenstein/dicomweb-client
|
0ea37db68a2b9c8373c964e610acea945b7b07b7
|
[
"MIT"
] | null | null | null |
src/dicomweb_client/__init__.py
|
michaelloewenstein/dicomweb-client
|
0ea37db68a2b9c8373c964e610acea945b7b07b7
|
[
"MIT"
] | null | null | null |
src/dicomweb_client/__init__.py
|
michaelloewenstein/dicomweb-client
|
0ea37db68a2b9c8373c964e610acea945b7b07b7
|
[
"MIT"
] | null | null | null |
__version__ = '0.52.0'
from dicomweb_client.api import DICOMwebClient # noqa
from dicomweb_client.uri import URI, URISuffix, URIType # noqa
| 28.6
| 63
| 0.783217
| 20
| 143
| 5.3
| 0.65
| 0.226415
| 0.339623
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0.03252
| 0.13986
| 143
| 4
| 64
| 35.75
| 0.829268
| 0.062937
| 0
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| 0
| 0.045802
| 0
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| 0
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| false
| 0
| 0.666667
| 0
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| 0
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| null | 1
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| 0
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| 0
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| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
740fe8cbb416d0ab36b1379f3546e419288814df
| 97
|
py
|
Python
|
__init__.py
|
joeliu1985/learnseldom
|
759c30bdd60bad8877f6f156536d7158d861cb95
|
[
"Apache-2.0"
] | 359
|
2020-08-15T12:36:42.000Z
|
2022-03-31T03:07:06.000Z
|
__init__.py
|
liumengjia/UIAutoDemo
|
a7d7fecf37045357bdc2a98371edc5f8b15bc50a
|
[
"Apache-2.0"
] | 6
|
2020-10-15T12:06:23.000Z
|
2021-07-01T03:28:55.000Z
|
__init__.py
|
liumengjia/UIAutoDemo
|
a7d7fecf37045357bdc2a98371edc5f8b15bc50a
|
[
"Apache-2.0"
] | 63
|
2020-08-16T00:52:21.000Z
|
2022-03-15T13:35:22.000Z
|
"""
更新时间：2020-08-27
版本说明：
1、seldom：1.6.0
2、poium：0.6.3
3、Python：3.7.4
"""
| 13.857143
| 22
| 0.463918
| 20
| 97
| 2.25
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.298507
| 0.309278
| 97
| 7
| 23
| 13.857143
| 0.373134
| 0.917526
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
7411749e416475591e8cc5ecaa9729e38a18a59a
| 360
|
py
|
Python
|
apps/chat/consumers.py
|
SeniorDev34/Django_React_Chat
|
5234fd14e65902e06a8bb14eae5e411798ddece9
|
[
"BSD-3-Clause"
] | 58
|
2016-09-26T14:30:14.000Z
|
2022-02-15T21:38:32.000Z
|
apps/chat/consumers.py
|
Webdev889/Django_React_Chat
|
5234fd14e65902e06a8bb14eae5e411798ddece9
|
[
"BSD-3-Clause"
] | 1
|
2020-06-05T20:31:09.000Z
|
2020-06-05T20:31:09.000Z
|
apps/chat/consumers.py
|
Webdev889/Django_React_Chat
|
5234fd14e65902e06a8bb14eae5e411798ddece9
|
[
"BSD-3-Clause"
] | 15
|
2016-09-26T14:38:24.000Z
|
2020-03-30T12:00:06.000Z
|
from channels.sessions import channel_session
from .engine import ChatEngine
@channel_session
def ws_connect(message):
# TODO Move many LOGIN_USER actions from ws_message into ws_add
pass
@channel_session
def ws_message(message):
ChatEngine.dispatch(message)
@channel_session
def ws_disconnect(message):
ChatEngine(message).disconnect()
| 18
| 67
| 0.788889
| 47
| 360
| 5.829787
| 0.489362
| 0.20438
| 0.186131
| 0.208029
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147222
| 360
| 19
| 68
| 18.947368
| 0.892508
| 0.169444
| 0
| 0.272727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.052632
| 0
| 1
| 0.272727
| false
| 0.090909
| 0.181818
| 0
| 0.454545
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
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| 0
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| 0
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| 0
| null | 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
7421110cf1ee2b53032b9a766c6c2971fc7e112a
| 4,657
|
py
|
Python
|
tests/test_auditor/test_auditor_notebook.py
|
elyase/polyaxon
|
1c19f059a010a6889e2b7ea340715b2bcfa382a0
|
[
"MIT"
] | null | null | null |
tests/test_auditor/test_auditor_notebook.py
|
elyase/polyaxon
|
1c19f059a010a6889e2b7ea340715b2bcfa382a0
|
[
"MIT"
] | null | null | null |
tests/test_auditor/test_auditor_notebook.py
|
elyase/polyaxon
|
1c19f059a010a6889e2b7ea340715b2bcfa382a0
|
[
"MIT"
] | null | null | null |
# pylint:disable=ungrouped-imports
from unittest.mock import patch
import pytest
import activitylogs
import auditor
import tracker
from event_manager.events import notebook as notebook_events
from factories.factory_plugins import NotebookJobFactory
from factories.factory_projects import ProjectFactory
from tests.utils import BaseTest
@pytest.mark.auditor_mark
class AuditorNotebookTest(BaseTest):
"""Testing subscribed events"""
DISABLE_RUNNER = True
def setUp(self):
self.notebook = NotebookJobFactory(project=ProjectFactory())
auditor.validate()
auditor.setup()
tracker.validate()
tracker.setup()
activitylogs.validate()
activitylogs.setup()
super().setUp()
@patch('tracker.service.TrackerService.record_event')
@patch('activitylogs.service.ActivityLogService.record_event')
def test_notebook_started(self, activitylogs_record, tracker_record):
auditor.record(event_type=notebook_events.NOTEBOOK_STARTED,
instance=self.notebook,
target='project')
assert tracker_record.call_count == 1
assert activitylogs_record.call_count == 0
@patch('tracker.service.TrackerService.record_event')
@patch('activitylogs.service.ActivityLogService.record_event')
def test_notebook_started_triggered(self, activitylogs_record, tracker_record):
auditor.record(event_type=notebook_events.NOTEBOOK_STARTED_TRIGGERED,
instance=self.notebook,
target='project',
actor_id=1,
actor_name='foo')
assert tracker_record.call_count == 1
assert activitylogs_record.call_count == 1
@patch('tracker.service.TrackerService.record_event')
@patch('activitylogs.service.ActivityLogService.record_event')
def test_notebook_stopped(self, activitylogs_record, tracker_record):
auditor.record(event_type=notebook_events.NOTEBOOK_STOPPED,
instance=self.notebook,
target='project')
assert tracker_record.call_count == 1
assert activitylogs_record.call_count == 0
@patch('tracker.service.TrackerService.record_event')
@patch('activitylogs.service.ActivityLogService.record_event')
def test_notebook_stopped_triggered(self, activitylogs_record, tracker_record):
auditor.record(event_type=notebook_events.NOTEBOOK_STOPPED_TRIGGERED,
instance=self.notebook,
target='project',
actor_id=1,
actor_name='foo')
assert tracker_record.call_count == 1
assert activitylogs_record.call_count == 1
@patch('tracker.service.TrackerService.record_event')
@patch('activitylogs.service.ActivityLogService.record_event')
def test_notebook_viewed(self, activitylogs_record, tracker_record):
auditor.record(event_type=notebook_events.NOTEBOOK_VIEWED,
instance=self.notebook,
target='project',
actor_id=1,
actor_name='foo')
assert tracker_record.call_count == 1
assert activitylogs_record.call_count == 1
@patch('tracker.service.TrackerService.record_event')
@patch('activitylogs.service.ActivityLogService.record_event')
def test_experiment_new_status(self, activitylogs_record, tracker_record):
auditor.record(event_type=notebook_events.NOTEBOOK_NEW_STATUS,
instance=self.notebook,
target='project')
assert tracker_record.call_count == 1
assert activitylogs_record.call_count == 0
@patch('tracker.service.TrackerService.record_event')
@patch('activitylogs.service.ActivityLogService.record_event')
def test_experiment_failed(self, activitylogs_record, tracker_record):
auditor.record(event_type=notebook_events.NOTEBOOK_FAILED,
instance=self.notebook,
target='project')
assert tracker_record.call_count == 1
assert activitylogs_record.call_count == 0
@patch('tracker.service.TrackerService.record_event')
@patch('activitylogs.service.ActivityLogService.record_event')
def test_experiment_succeeded(self, activitylogs_record, tracker_record):
auditor.record(event_type=notebook_events.NOTEBOOK_SUCCEEDED,
instance=self.notebook,
target='project')
assert tracker_record.call_count == 1
assert activitylogs_record.call_count == 0
| 39.803419
| 83
| 0.683702
| 470
| 4,657
| 6.514894
| 0.140426
| 0.086218
| 0.07838
| 0.057479
| 0.801437
| 0.801437
| 0.801437
| 0.801437
| 0.801437
| 0.801437
| 0
| 0.005324
| 0.233627
| 4,657
| 116
| 84
| 40.146552
| 0.85262
| 0.012669
| 0
| 0.593407
| 0
| 0
| 0.17966
| 0.165505
| 0
| 0
| 0
| 0
| 0.175824
| 1
| 0.098901
| false
| 0
| 0.098901
| 0
| 0.21978
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
742264cd4616cf3ba21074ffcf7f198ab86e0ed1
| 6,231
|
py
|
Python
|
macapype/pipelines/extract_brain.py
|
Macatools/macapype
|
50820e2ab948c91c5362771d51688edd09b72499
|
[
"BSD-3-Clause"
] | 7
|
2020-07-04T04:04:03.000Z
|
2022-03-24T14:35:45.000Z
|
macapype/pipelines/extract_brain.py
|
Macatools/macapype
|
50820e2ab948c91c5362771d51688edd09b72499
|
[
"BSD-3-Clause"
] | 95
|
2020-01-02T16:41:20.000Z
|
2021-12-07T15:50:41.000Z
|
macapype/pipelines/extract_brain.py
|
Macatools/macapype
|
50820e2ab948c91c5362771d51688edd09b72499
|
[
"BSD-3-Clause"
] | 9
|
2019-11-14T12:46:14.000Z
|
2022-01-26T09:44:21.000Z
|
"""
Pipelines for brain extraction
"""
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
from nipype.interfaces import ants
import nipype.interfaces.fsl as fsl
import nipype.interfaces.afni as afni
from ..nodes.extract_brain import AtlasBREX
from ..utils.utils_nodes import NodeParams, parse_key
def create_extract_pipe(params_template, params={}, name="extract_pipe"):
"""
Description: Extract T1 brain using AtlasBrex
Params:
- norm_intensity (see `N4BiasFieldCorrection <https://nipype.readthedocs\
.io/en/0.12.1/interfaces/generated/nipype.interfaces.ants.segmentation.\
html#n4biasfieldcorrection>`_ for arguments)
- atlas_brex (see :class:`AtlasBREX \
<macapype.nodes.extract_brain.AtlasBREX>` for arguments) - also \
available as :ref:`indiv_params <indiv_params>`
Inputs:
inputnode:
restore_T1:
preprocessed (debiased/denoised) T1 file name
restore_T2:
preprocessed (debiased/denoised)T2 file name
arguments:
params_template:
dictionary of info about template
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "extract_pipe")
Outputs:
smooth_mask.out_file:
Computed mask (after some smoothing)
"""
# creating pipeline
extract_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(fields=['restore_T1', 'restore_T2',
"indiv_params"]),
name='inputnode')
# atlas_brex
atlas_brex = NodeParams(AtlasBREX(),
params=parse_key(params, "atlas_brex"),
name='atlas_brex')
extract_pipe.connect(inputnode, "restore_T1",
atlas_brex, 't1_restored_file')
atlas_brex.inputs.NMT_file = params_template["template_head"]
atlas_brex.inputs.NMT_SS_file = params_template["template_brain"]
extract_pipe.connect(
inputnode, ("indiv_params", parse_key, "atlas_brex"),
atlas_brex, 'indiv_params')
# mask_brex
mask_brex = pe.Node(fsl.UnaryMaths(), name='mask_brex')
mask_brex.inputs.operation = 'bin'
extract_pipe.connect(atlas_brex, 'brain_file', mask_brex, 'in_file')
# smooth_mask
smooth_mask = pe.Node(fsl.UnaryMaths(), name='smooth_mask')
smooth_mask.inputs.operation = "bin"
smooth_mask.inputs.args = "-s 1 -thr 0.5 -bin"
extract_pipe.connect(mask_brex, 'out_file', smooth_mask, 'in_file')
# mult_T1
mult_T1 = pe.Node(afni.Calc(), name='mult_T1')
mult_T1.inputs.expr = "a*b"
mult_T1.inputs.outputtype = 'NIFTI_GZ'
extract_pipe.connect(inputnode, "restore_T1", mult_T1, 'in_file_a')
extract_pipe.connect(smooth_mask, 'out_file', mult_T1, 'in_file_b')
# mult_T2
mult_T2 = pe.Node(afni.Calc(), name='mult_T2')
mult_T2.inputs.expr = "a*b"
mult_T2.inputs.outputtype = 'NIFTI_GZ'
extract_pipe.connect(inputnode, 'restore_T2', mult_T2, 'in_file_a')
extract_pipe.connect(smooth_mask, 'out_file', mult_T2, 'in_file_b')
return extract_pipe
def create_extract_T1_pipe(params_template, params={},
name="extract_T1_pipe"):
"""
Description: Extract T1 brain using AtlasBrex
Params:
- norm_intensity (see `N4BiasFieldCorrection <https://nipype.readthedocs.\
io/en/0.12.1/interfaces/generated/nipype.interfaces.ants.segmentation.html\
#n4biasfieldcorrection>`_ for arguments)
- atlas_brex (see :class:`AtlasBREX \
<macapype.nodes.extract_brain.AtlasBREX>` for arguments) - also available \
as :ref:`indiv_params <indiv_params>`
Inputs:
inputnode:
restore_T1:
preprocessed (debiased/denoised) T1 file name
arguments:
params_template:
dictionary of info about template
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "extract_pipe")
Outputs:
smooth_mask.out_file:
Computed mask (after some smoothing)
"""
# creating pipeline
extract_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(fields=['restore_T1',
"indiv_params"]),
name='inputnode')
# N4 intensity normalization with parameters from json
norm_intensity = NodeParams(ants.N4BiasFieldCorrection(),
params=parse_key(params, "norm_intensity"),
name='norm_intensity')
extract_pipe.connect(inputnode, 'restore_T1',
norm_intensity, "input_image")
# atlas_brex
atlas_brex = NodeParams(AtlasBREX(),
params=parse_key(params, "atlas_brex"),
name='atlas_brex')
extract_pipe.connect(norm_intensity, "output_image",
atlas_brex, 't1_restored_file')
atlas_brex.inputs.NMT_file = params_template["template_head"]
atlas_brex.inputs.NMT_SS_file = params_template["template_brain"]
extract_pipe.connect(
inputnode, ("indiv_params", parse_key, "atlas_brex"),
atlas_brex, 'indiv_params')
# mask_brex
mask_brex = pe.Node(fsl.UnaryMaths(), name='mask_brex')
mask_brex.inputs.operation = 'bin'
extract_pipe.connect(atlas_brex, 'brain_file', mask_brex, 'in_file')
# smooth_mask
smooth_mask = pe.Node(fsl.UnaryMaths(), name='smooth_mask')
smooth_mask.inputs.operation = "bin"
smooth_mask.inputs.args = "-s 1 -thr 0.5 -bin"
extract_pipe.connect(mask_brex, 'out_file', smooth_mask, 'in_file')
# mult_T1
mult_T1 = pe.Node(afni.Calc(), name='mult_T1')
mult_T1.inputs.expr = "a*b"
mult_T1.inputs.outputtype = 'NIFTI_GZ'
extract_pipe.connect(inputnode, 'restore_T1', mult_T1, 'in_file_a')
extract_pipe.connect(smooth_mask, 'out_file', mult_T1, 'in_file_b')
return extract_pipe
| 30.247573
| 79
| 0.640989
| 731
| 6,231
| 5.199726
| 0.154583
| 0.066561
| 0.071034
| 0.049724
| 0.837674
| 0.833465
| 0.784267
| 0.784267
| 0.784267
| 0.769271
| 0
| 0.013118
| 0.253731
| 6,231
| 205
| 80
| 30.395122
| 0.804301
| 0.319371
| 0
| 0.657895
| 0
| 0
| 0.168531
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.026316
| false
| 0
| 0.092105
| 0
| 0.144737
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
745a92b2c8ad9dcb7b7a23f1c35b004d8b21958a
| 9,287
|
py
|
Python
|
vumi/middleware/tests/test_provider_setter.py
|
seidu626/vumi
|
62eae205a07029bc7ab382086715694548001876
|
[
"BSD-3-Clause"
] | 199
|
2015-01-05T09:04:24.000Z
|
2018-08-15T17:02:49.000Z
|
vumi/middleware/tests/test_provider_setter.py
|
seidu626/vumi
|
62eae205a07029bc7ab382086715694548001876
|
[
"BSD-3-Clause"
] | 187
|
2015-01-06T15:22:38.000Z
|
2018-07-14T13:15:29.000Z
|
vumi/middleware/tests/test_provider_setter.py
|
seidu626/vumi
|
62eae205a07029bc7ab382086715694548001876
|
[
"BSD-3-Clause"
] | 86
|
2015-01-31T02:47:08.000Z
|
2018-12-01T11:59:47.000Z
|
"""Tests for vumi.middleware.provider_setter."""
from vumi.middleware.provider_setter import (
StaticProviderSettingMiddleware, AddressPrefixProviderSettingMiddleware,
ProviderSettingMiddlewareError)
from vumi.tests.helpers import VumiTestCase, MessageHelper
class TestStaticProviderSettingMiddleware(VumiTestCase):
def setUp(self):
self.msg_helper = self.add_helper(MessageHelper())
def mk_middleware(self, config):
dummy_worker = object()
mw = StaticProviderSettingMiddleware(
"static_provider_setter", config, dummy_worker)
mw.setup_middleware()
return mw
def test_set_provider_on_inbound_if_unset(self):
"""
The statically configured provider value is set on inbound messages
that have no provider.
"""
mw = self.mk_middleware({"provider": "MY-MNO"})
msg = self.msg_helper.make_inbound(None)
self.assertEqual(msg.get("provider"), None)
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "MY-MNO")
def test_replace_provider_on_inbound_if_set(self):
"""
The statically configured provider value replaces any existing provider
a message may already have set.
"""
mw = self.mk_middleware({"provider": "MY-MNO"})
msg = self.msg_helper.make_inbound(None, provider="YOUR-MNO")
self.assertEqual(msg.get("provider"), "YOUR-MNO")
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "MY-MNO")
def test_set_provider_on_outbound_if_unset(self):
"""
Outbound messages are left as they are.
"""
mw = self.mk_middleware({"provider": "MY-MNO"})
msg = self.msg_helper.make_outbound(None)
self.assertEqual(msg.get("provider"), None)
processed_msg = mw.handle_outbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "MY-MNO")
class TestAddressPrefixProviderSettingMiddleware(VumiTestCase):
def setUp(self):
self.msg_helper = self.add_helper(MessageHelper())
def mk_middleware(self, config):
dummy_worker = object()
mw = AddressPrefixProviderSettingMiddleware(
"address_prefix_provider_setter", config, dummy_worker)
mw.setup_middleware()
return mw
def assert_middleware_error(self, msg):
[err] = self.flushLoggedErrors(ProviderSettingMiddlewareError)
self.assertEqual(str(err.value), msg)
def test_set_provider_unique_matching_prefix(self):
"""
If exactly one prefix matches the address, its corresponding provider
value is set on the inbound message.
"""
mw = self.mk_middleware({"provider_prefixes": {
"+123": "MY-MNO",
"+124": "YOUR-MNO",
}})
msg = self.msg_helper.make_inbound(None, from_addr="+12345")
self.assertEqual(msg.get("provider"), None)
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "MY-MNO")
def test_set_provider_longest_matching_prefix(self):
"""
If more than one prefix matches the address, the provider value for
the longest matching prefix is set on the inbound message.
"""
mw = self.mk_middleware({"provider_prefixes": {
"+12": "YOUR-MNO",
"+123": "YOUR-MNO",
"+1234": "YOUR-MNO",
"+12345": "MY-MNO",
"+123456": "YOUR-MNO",
}})
msg = self.msg_helper.make_inbound(None, from_addr="+12345")
self.assertEqual(msg.get("provider"), None)
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "MY-MNO")
def test_no_provider_for_no_matching_prefix(self):
"""
If no prefix matches the address, the provider value will be set to
``None`` on the inbound message.
"""
mw = self.mk_middleware({"provider_prefixes": {
"+124": "YOUR-MNO",
"+125": "YOUR-MNO",
}})
msg = self.msg_helper.make_inbound(None, from_addr="+12345")
self.assertEqual(msg.get("provider"), None)
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), None)
def test_set_provider_no_normalize_msisdn(self):
"""
If exactly one prefix matches the address, its corresponding provider
value is set on the inbound message.
"""
mw = self.mk_middleware({
"provider_prefixes": {
"083": "MY-MNO",
"+2783": "YOUR-MNO",
},
})
msg = self.msg_helper.make_inbound(None, from_addr="0831234567")
self.assertEqual(msg.get("provider"), None)
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "MY-MNO")
def test_set_provider_normalize_msisdn(self):
"""
If exactly one prefix matches the address, its corresponding provider
value is set on the inbound message.
"""
mw = self.mk_middleware({
"normalize_msisdn": {"country_code": "27"},
"provider_prefixes": {
"083": "YOUR-MNO",
"+2783": "MY-MNO",
},
})
msg = self.msg_helper.make_inbound(None, from_addr="0831234567")
self.assertEqual(msg.get("provider"), None)
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "MY-MNO")
def test_set_provider_normalize_msisdn_strip_plus(self):
"""
If exactly one prefix matches the address, its corresponding provider
value is set on the inbound message.
"""
mw = self.mk_middleware({
"normalize_msisdn": {"country_code": "27", "strip_plus": True},
"provider_prefixes": {
"083": "YOUR-MNO",
"+2783": "YOUR-MNO",
"2783": "MY-MNO",
},
})
msg = self.msg_helper.make_inbound(None, from_addr="0831234567")
self.assertEqual(msg.get("provider"), None)
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "MY-MNO")
def test_set_provider_on_outbound(self):
"""
Outbound messages are left as they are.
"""
mw = self.mk_middleware({"provider_prefixes": {"+123": "MY-MNO"}})
msg = self.msg_helper.make_outbound(
None, to_addr="+1234567", from_addr="+12345")
self.assertEqual(msg.get("provider"), None)
processed_msg = mw.handle_outbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "MY-MNO")
def test_provider_not_overwritten_for_inbound(self):
"""
If a provider already exists for an inbound message, it isn't
overwritten.
"""
mw = self.mk_middleware({"provider_prefixes": {"+123": "MY-MNO"}})
msg = self.msg_helper.make_inbound(
None, to_addr="+345", from_addr="+12345", provider="OTHER-MNO")
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "OTHER-MNO")
def test_provider_not_overwritten_for_outbound(self):
"""
If a provider already exists for an outbound message, it isn't
overwritten.
"""
mw = self.mk_middleware({"provider_prefixes": {"+123": "MY-MNO"}})
msg = self.msg_helper.make_outbound(
None, to_addr="+1234567", from_addr="+345", provider="OTHER-MNO")
processed_msg = mw.handle_outbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), "OTHER-MNO")
def test_provider_logs_no_address_error_for_inbound(self):
"""
If the from_addr of an inbound message is None, an error should be
logged and the message returned.
"""
mw = self.mk_middleware({"provider_prefixes": {"+123": "MY-MNO"}})
msg = self.msg_helper.make_inbound(
None, to_addr="+1234567", from_addr=None)
processed_msg = mw.handle_inbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), None)
self.assert_middleware_error(
"Address for determining message provider cannot be None,"
" skipping message")
def test_provider_logs_no_address_error_for_outbound(self):
"""
If the to_addr of an outbound message is None, an error should be
logged and the message returned.
"""
mw = self.mk_middleware({"provider_prefixes": {"+123": "MY-MNO"}})
msg = self.msg_helper.make_outbound(
None, to_addr=None, from_addr="+345")
processed_msg = mw.handle_outbound(msg, "dummy_connector")
self.assertEqual(processed_msg.get("provider"), None)
self.assert_middleware_error(
"Address for determining message provider cannot be None,"
" skipping message")
| 41.459821
| 79
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0
| 6
|
74786f79345d33d879c08d04d1f7b830de6cd146
| 11
|
py
|
Python
|
tall.py
|
philiptrae/teknakurs
|
50f37fe14daaeca952b97cb3e49cf8c609a7475f
|
[
"MIT"
] | null | null | null |
tall.py
|
philiptrae/teknakurs
|
50f37fe14daaeca952b97cb3e49cf8c609a7475f
|
[
"MIT"
] | null | null | null |
tall.py
|
philiptrae/teknakurs
|
50f37fe14daaeca952b97cb3e49cf8c609a7475f
|
[
"MIT"
] | 1
|
2020-03-02T18:09:41.000Z
|
2020-03-02T18:09:41.000Z
|
a=[1,2,3];
| 5.5
| 10
| 0.363636
| 4
| 11
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0
| 6
|
74997f909a3788cec02da019c265634f6d10dad6
| 91
|
py
|
Python
|
src/__init__.py
|
sarthaxxxxx/AAI-ALS
|
48703525eb3490e2ba5cb555dc69fd9d10bf859a
|
[
"MIT"
] | 1
|
2021-06-03T04:16:10.000Z
|
2021-06-03T04:16:10.000Z
|
src/__init__.py
|
sarthaxxxxx/AAI-ALS
|
48703525eb3490e2ba5cb555dc69fd9d10bf859a
|
[
"MIT"
] | null | null | null |
src/__init__.py
|
sarthaxxxxx/AAI-ALS
|
48703525eb3490e2ba5cb555dc69fd9d10bf859a
|
[
"MIT"
] | null | null | null |
from .loss import *
from .models import *
from .tools import *
from .runner import trainer
| 18.2
| 27
| 0.747253
| 13
| 91
| 5.230769
| 0.538462
| 0.441176
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| 91
| 4
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| 22.75
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| 1
| 0
|
0
| 6
|
74a2689f039096440175d756bf6f41e9552c7d9e
| 181
|
py
|
Python
|
django/contrib/gis/gdal/base.py
|
jpmallarino/django
|
659d2421c7adbbcd205604002d521d82d6b0b465
|
[
"BSD-3-Clause",
"0BSD"
] | 61,676
|
2015-01-01T00:05:13.000Z
|
2022-03-31T20:37:54.000Z
|
checkerista/.env/Lib/site-packages/django/contrib/gis/gdal/base.py
|
LybaFatimaNasir/CS311S20PID02
|
bc29a8c4c9ee508c74d231c015a57b1ca4dfcb39
|
[
"MIT"
] | 8,884
|
2015-01-01T00:12:05.000Z
|
2022-03-31T19:53:11.000Z
|
checkerista/.env/Lib/site-packages/django/contrib/gis/gdal/base.py
|
LybaFatimaNasir/CS311S20PID02
|
bc29a8c4c9ee508c74d231c015a57b1ca4dfcb39
|
[
"MIT"
] | 33,143
|
2015-01-01T02:04:52.000Z
|
2022-03-31T19:42:46.000Z
|
from django.contrib.gis.gdal.error import GDALException
from django.contrib.gis.ptr import CPointerBase
class GDALBase(CPointerBase):
null_ptr_exception_class = GDALException
| 25.857143
| 55
| 0.834254
| 23
| 181
| 6.434783
| 0.608696
| 0.135135
| 0.22973
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0
| 6
|
77706b655274f443964d068b9edb8b67d0fad362
| 65
|
py
|
Python
|
aula18.py
|
lhardt/PythonCourse
|
c0654bfc589f5faf1c26f419917683a0a2d6a0de
|
[
"MIT"
] | null | null | null |
aula18.py
|
lhardt/PythonCourse
|
c0654bfc589f5faf1c26f419917683a0a2d6a0de
|
[
"MIT"
] | null | null | null |
aula18.py
|
lhardt/PythonCourse
|
c0654bfc589f5faf1c26f419917683a0a2d6a0de
|
[
"MIT"
] | null | null | null |
# Shallow Copy
meta.append(arr)
# Deep Copy
meta.append(arr[:])
| 10.833333
| 19
| 0.692308
| 10
| 65
| 4.5
| 0.6
| 0.355556
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| 0.138462
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| 5
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0
| 6
|
77745557be22d6ecd552560854aa1ad92ae5e98d
| 142
|
py
|
Python
|
setup.py
|
Shakil-Mahmud-Programmer/Currency-Exchange-System-GUI-
|
7bcd342591edf9468fc1da80242966ff76a41772
|
[
"MIT"
] | null | null | null |
setup.py
|
Shakil-Mahmud-Programmer/Currency-Exchange-System-GUI-
|
7bcd342591edf9468fc1da80242966ff76a41772
|
[
"MIT"
] | null | null | null |
setup.py
|
Shakil-Mahmud-Programmer/Currency-Exchange-System-GUI-
|
7bcd342591edf9468fc1da80242966ff76a41772
|
[
"MIT"
] | null | null | null |
import subprocess
subprocess.run('pip install tk')
subprocess.run('pip install requests')
subprocess.run('pip install pycopy-webbrowser')
| 28.4
| 48
| 0.78169
| 18
| 142
| 6.166667
| 0.5
| 0.351351
| 0.432432
| 0.621622
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| 0.105634
| 142
| 4
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| 35.5
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0
| 6
|
77b2369349daaf703172e5a5b0f7043175b7576e
| 203
|
py
|
Python
|
jova/splits/__init__.py
|
bbrighttaer/jova_baselines
|
336ec88e6069e16ab959cbd38aa58730e15e2e0a
|
[
"MIT"
] | 3
|
2020-08-17T22:03:34.000Z
|
2021-09-08T11:52:24.000Z
|
jova/splits/__init__.py
|
bbrighttaer/jova_baselines
|
336ec88e6069e16ab959cbd38aa58730e15e2e0a
|
[
"MIT"
] | null | null | null |
jova/splits/__init__.py
|
bbrighttaer/jova_baselines
|
336ec88e6069e16ab959cbd38aa58730e15e2e0a
|
[
"MIT"
] | 1
|
2020-12-21T12:10:04.000Z
|
2020-12-21T12:10:04.000Z
|
"""
Gathers all splitters in one place for convenient imports
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from .splitters import *
| 20.3
| 57
| 0.817734
| 26
| 203
| 5.846154
| 0.653846
| 0.197368
| 0.315789
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 203
| 9
| 58
| 22.555556
| 0.873563
| 0.280788
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0.25
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
77e4715af0db51c9efb74a294431a3d9065c3e56
| 86
|
py
|
Python
|
pipeline/loggers/__init__.py
|
HSE-LAMBDA/RheologyReconstruction
|
fe89dea28ab0873d075e69c51e9ae2aeb07fe8e2
|
[
"Apache-2.0"
] | 1
|
2021-01-12T11:43:31.000Z
|
2021-01-12T11:43:31.000Z
|
pipeline/loggers/__init__.py
|
HSE-LAMBDA/RheologyReconstruction
|
fe89dea28ab0873d075e69c51e9ae2aeb07fe8e2
|
[
"Apache-2.0"
] | null | null | null |
pipeline/loggers/__init__.py
|
HSE-LAMBDA/RheologyReconstruction
|
fe89dea28ab0873d075e69c51e9ae2aeb07fe8e2
|
[
"Apache-2.0"
] | null | null | null |
from .tensorboard_logger import TensorboardLogger
# from .logger import GenericLogger
| 28.666667
| 49
| 0.860465
| 9
| 86
| 8.111111
| 0.666667
| 0.328767
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.104651
| 86
| 2
| 50
| 43
| 0.948052
| 0.383721
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
7acf02d76b945702db1d5fb40d686d68a034532f
| 48
|
py
|
Python
|
neispy/__init__.py
|
kijk2869/neispy
|
c6ff7982d35660d2d2bf69e8cef67b37e6137374
|
[
"MIT"
] | null | null | null |
neispy/__init__.py
|
kijk2869/neispy
|
c6ff7982d35660d2d2bf69e8cef67b37e6137374
|
[
"MIT"
] | null | null | null |
neispy/__init__.py
|
kijk2869/neispy
|
c6ff7982d35660d2d2bf69e8cef67b37e6137374
|
[
"MIT"
] | null | null | null |
from .client import Client
from .error import *
| 16
| 26
| 0.770833
| 7
| 48
| 5.285714
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 48
| 2
| 27
| 24
| 0.925
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
7ad381d354b6e8acccb6ace42115a7c9431ad04a
| 68
|
py
|
Python
|
tests/bad_servers/no_init.py
|
MSLNZ/msl-loadlib
|
60f100221774e7c8bac067b50f427fd1d99d2552
|
[
"MIT"
] | 51
|
2017-02-20T18:13:18.000Z
|
2022-03-02T21:46:36.000Z
|
tests/bad_servers/no_init.py
|
MSLNZ/msl-loadlib
|
60f100221774e7c8bac067b50f427fd1d99d2552
|
[
"MIT"
] | 31
|
2017-02-20T18:09:43.000Z
|
2022-03-02T15:21:37.000Z
|
tests/bad_servers/no_init.py
|
MSLNZ/msl-loadlib
|
60f100221774e7c8bac067b50f427fd1d99d2552
|
[
"MIT"
] | 15
|
2017-02-20T18:13:25.000Z
|
2020-04-06T12:27:43.000Z
|
from msl.loadlib import Server32
class NoInit(Server32):
pass
| 11.333333
| 32
| 0.75
| 9
| 68
| 5.666667
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.072727
| 0.191176
| 68
| 5
| 33
| 13.6
| 0.854545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
bb0bbb9fa3e7e39bf316166058f7301e3221a253
| 3,053
|
py
|
Python
|
utils/migrations/0001_initial.py
|
remigermain/chernobyl-disaster.org-backend
|
28f45a1946c7052246421444522208880369e263
|
[
"MIT"
] | 1
|
2022-02-16T06:19:06.000Z
|
2022-02-16T06:19:06.000Z
|
utils/migrations/0001_initial.py
|
remigermain/chernobyl-disaster.org-backend
|
28f45a1946c7052246421444522208880369e263
|
[
"MIT"
] | 1
|
2022-03-12T01:01:54.000Z
|
2022-03-12T01:01:54.000Z
|
utils/migrations/0001_initial.py
|
remigermain/chernobyl-disaster.org-backend
|
28f45a1946c7052246421444522208880369e263
|
[
"MIT"
] | null | null | null |
# Generated by Django 3.1.1 on 2020-10-01 10:21
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('contenttypes', '0002_remove_content_type_name'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Issue',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('date', models.DateTimeField(auto_now_add=True)),
('created', models.BooleanField(default=False)),
('object_id', models.PositiveIntegerField()),
('uuid', models.CharField(max_length=200)),
('message', models.TextField()),
('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.contenttype')),
('creator', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='issue_creator', to=settings.AUTH_USER_MODEL)),
],
options={
'ordering': ['-id'],
'abstract': False,
},
),
migrations.CreateModel(
name='Contact',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('date', models.DateTimeField(auto_now_add=True)),
('created', models.BooleanField(default=False)),
('email', models.EmailField(max_length=254)),
('message', models.TextField()),
('creator', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='contact_creator', to=settings.AUTH_USER_MODEL)),
],
options={
'ordering': ['-id'],
'abstract': False,
},
),
migrations.CreateModel(
name='Commit',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('date', models.DateTimeField(auto_now_add=True)),
('created', models.BooleanField(default=False)),
('object_id', models.PositiveIntegerField()),
('uuid', models.CharField(max_length=200)),
('updated_fields', models.TextField(blank=True, null=True)),
('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.contenttype')),
('creator', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='commit_creator', to=settings.AUTH_USER_MODEL)),
],
options={
'ordering': ['-id'],
'abstract': False,
},
),
]
| 44.897059
| 177
| 0.580085
| 297
| 3,053
| 5.794613
| 0.272727
| 0.032539
| 0.048809
| 0.0767
| 0.732714
| 0.732714
| 0.732714
| 0.732714
| 0.732714
| 0.732714
| 0
| 0.012733
| 0.279725
| 3,053
| 67
| 178
| 45.567164
| 0.769895
| 0.01474
| 0
| 0.633333
| 1
| 0
| 0.118097
| 0.025615
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.05
| 0
| 0.116667
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
bb20dda4881babcbbe510e339b203dcec69326a1
| 8,452
|
py
|
Python
|
removDup.py
|
zsyc/LeetAlgo
|
70793a26900824e308f69ec2b2299e04eb9c7646
|
[
"MIT"
] | null | null | null |
removDup.py
|
zsyc/LeetAlgo
|
70793a26900824e308f69ec2b2299e04eb9c7646
|
[
"MIT"
] | null | null | null |
removDup.py
|
zsyc/LeetAlgo
|
70793a26900824e308f69ec2b2299e04eb9c7646
|
[
"MIT"
] | null | null | null |
def removeDuplicates(s: str) -> str:
i = 0
while i < len(s)-1:
if s[i] == s[i+1]:
s = s[i+2:] if s==0 else s[:i]+s[i+2:]
i = 0 if i==0 else i-1
else:
i+=1
return s
print(removeDuplicates("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"))
| 704.333333
| 8,219
| 0.982134
| 47
| 8,452
| 176.617021
| 0.319149
| 0.001205
| 0.000723
| 0.000964
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.001198
| 0.012186
| 8,452
| 11
| 8,220
| 768.363636
| 0.993053
| 0
| 0
| 0
| 0
| 0
| 0.969238
| 0.969238
| 0
| 1
| 0
| 0
| 0
| 1
| 0.1
| false
| 0
| 0
| 0
| 0.2
| 0.1
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
bb28f7ec80ef51c0ffa3ad8fac04c259d92c2d12
| 260
|
py
|
Python
|
models/__init__.py
|
pgmikhael/MLExperiments
|
d3613a70e537ea5aaa0453ddaa76938c32637c49
|
[
"MIT"
] | null | null | null |
models/__init__.py
|
pgmikhael/MLExperiments
|
d3613a70e537ea5aaa0453ddaa76938c32637c49
|
[
"MIT"
] | null | null | null |
models/__init__.py
|
pgmikhael/MLExperiments
|
d3613a70e537ea5aaa0453ddaa76938c32637c49
|
[
"MIT"
] | null | null | null |
from models.trained_models import Resnet18
from models.trained_models import AlexNet
from models.trained_models import VGG16
from models.trained_models import DenseNet161
from models.trained_models import Inception_v3
from models.alexnet import Vanilla_AlexNet
| 43.333333
| 46
| 0.888462
| 37
| 260
| 6.054054
| 0.297297
| 0.267857
| 0.379464
| 0.513393
| 0.647321
| 0
| 0
| 0
| 0
| 0
| 0
| 0.033755
| 0.088462
| 260
| 6
| 47
| 43.333333
| 0.911392
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
24738f3028f23e2c6f3e2d3909704cf38e1fd6c6
| 40
|
py
|
Python
|
src/nodes/corenodes/adjust/brightness_contrast_node/__init__.py
|
Correct-Syntax/GimelStudio
|
db6e2db35730e11bcb25f5ba82823e68b86003f1
|
[
"Apache-2.0"
] | 134
|
2021-02-27T08:28:09.000Z
|
2022-03-30T17:46:27.000Z
|
src/nodes/corenodes/adjust/brightness_contrast_node/__init__.py
|
Correct-Syntax/GimelStudio
|
db6e2db35730e11bcb25f5ba82823e68b86003f1
|
[
"Apache-2.0"
] | 127
|
2021-04-13T13:34:20.000Z
|
2022-02-14T21:16:12.000Z
|
src/nodes/corenodes/adjust/brightness_contrast_node/__init__.py
|
Correct-Syntax/GimelStudio
|
db6e2db35730e11bcb25f5ba82823e68b86003f1
|
[
"Apache-2.0"
] | 20
|
2021-03-23T20:06:05.000Z
|
2022-01-20T18:24:53.000Z
|
from .brightness_contrast_node import *
| 20
| 39
| 0.85
| 5
| 40
| 6.4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 40
| 1
| 40
| 40
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
700f4850abe57d50058b8c652917b8d7bb495949
| 95
|
py
|
Python
|
graphmodels/inference/__init__.py
|
DLunin/pygraphmodels
|
4ea8ebed74f3a7d5d56af4d5f189a514aab420f9
|
[
"MIT"
] | null | null | null |
graphmodels/inference/__init__.py
|
DLunin/pygraphmodels
|
4ea8ebed74f3a7d5d56af4d5f189a514aab420f9
|
[
"MIT"
] | null | null | null |
graphmodels/inference/__init__.py
|
DLunin/pygraphmodels
|
4ea8ebed74f3a7d5d56af4d5f189a514aab420f9
|
[
"MIT"
] | null | null | null |
from .inference import InferenceStrategy, SumProductInference, NaiveInference, random_ordering
| 47.5
| 94
| 0.884211
| 8
| 95
| 10.375
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.073684
| 95
| 1
| 95
| 95
| 0.943182
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
7065952138eb4401a0c0aa9c75d0634225525f9b
| 165
|
py
|
Python
|
pytest_eventlet/plugin.py
|
nameko/pytest-eventlet
|
5860d3e919cd499fd120ad32b8fe34a535fd95e2
|
[
"Apache-2.0"
] | null | null | null |
pytest_eventlet/plugin.py
|
nameko/pytest-eventlet
|
5860d3e919cd499fd120ad32b8fe34a535fd95e2
|
[
"Apache-2.0"
] | null | null | null |
pytest_eventlet/plugin.py
|
nameko/pytest-eventlet
|
5860d3e919cd499fd120ad32b8fe34a535fd95e2
|
[
"Apache-2.0"
] | null | null | null |
# -*- coding: utf-8 -*-
def pytest_load_initial_conftests():
# make sure we monkey_patch before local conftests
import eventlet
eventlet.monkey_patch()
| 23.571429
| 54
| 0.715152
| 21
| 165
| 5.380952
| 0.809524
| 0.19469
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007463
| 0.187879
| 165
| 6
| 55
| 27.5
| 0.835821
| 0.424242
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
7072c12cae7043cdd5c66711119c5fd1d8675caf
| 159
|
py
|
Python
|
fastcord/utils/color.py
|
dskprt/botnolib
|
dd17aff956df0a54838980257249a7dfb725ab23
|
[
"MIT"
] | 3
|
2020-03-17T13:08:42.000Z
|
2021-07-07T10:58:04.000Z
|
fastcord/utils/color.py
|
dskprt/botnolib
|
dd17aff956df0a54838980257249a7dfb725ab23
|
[
"MIT"
] | 1
|
2020-04-07T12:46:09.000Z
|
2020-04-07T12:46:09.000Z
|
fastcord/utils/color.py
|
dskprt/botnolib
|
dd17aff956df0a54838980257249a7dfb725ab23
|
[
"MIT"
] | 1
|
2020-04-12T17:37:32.000Z
|
2020-04-12T17:37:32.000Z
|
def int_from_rgb(r, g, b):
return (r << 16) + (g << 8) + b
def rgb_from_int(color):
return ((color >> 16) & 255), ((color >> 8) & 255), (color & 255)
| 26.5
| 69
| 0.522013
| 27
| 159
| 2.925926
| 0.444444
| 0.202532
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 0.245283
| 159
| 5
| 70
| 31.8
| 0.533333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0
| 0
| 0.5
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
7079f165c0da30c2f8bc2b267d56315a885599c8
| 258
|
py
|
Python
|
import-from-dir/otherfiles/Misc.py
|
brenordv/python-snippets
|
aa69d4d64f7b9cea958ad852248210f4e869fe50
|
[
"MIT"
] | 2
|
2020-04-10T21:20:22.000Z
|
2021-01-17T19:28:32.000Z
|
import-from-dir/otherfiles/Misc.py
|
brenordv/python-snippets
|
aa69d4d64f7b9cea958ad852248210f4e869fe50
|
[
"MIT"
] | null | null | null |
import-from-dir/otherfiles/Misc.py
|
brenordv/python-snippets
|
aa69d4d64f7b9cea958ad852248210f4e869fe50
|
[
"MIT"
] | 2
|
2020-07-20T20:24:01.000Z
|
2022-02-27T15:40:40.000Z
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Misc.py: Just a sample file with a function.
This material is part of this post:
http://raccoon.ninja/pt/dev-pt/python-importando-todos-os-arquivos-de-um-diretorio/
"""
def hey_ho():
return "Let's go!"
| 21.5
| 83
| 0.678295
| 44
| 258
| 3.954545
| 0.909091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004505
| 0.139535
| 258
| 12
| 84
| 21.5
| 0.779279
| 0.802326
| 0
| 0
| 0
| 0
| 0.214286
| 0
| 0
| 0
| 0
| 0.083333
| 0
| 1
| 0.5
| true
| 0
| 0
| 0.5
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
709245c54b2fc2d5807e7e673098113b160a8c0b
| 30
|
py
|
Python
|
RST/ODSAextensions/odsa/extrtoolembed/__init__.py
|
dwgillies/OpenDSA
|
e012925896070a86bd7c3a4cbb75fa5682d9b9e2
|
[
"MIT"
] | 200
|
2015-02-08T05:27:52.000Z
|
2022-03-23T02:44:38.000Z
|
RST/ODSAextensions/odsa/extrtoolembed/__init__.py
|
dwgillies/OpenDSA
|
e012925896070a86bd7c3a4cbb75fa5682d9b9e2
|
[
"MIT"
] | 119
|
2015-03-22T22:38:21.000Z
|
2022-03-15T04:38:52.000Z
|
RST/ODSAextensions/odsa/extrtoolembed/__init__.py
|
dwgillies/OpenDSA
|
e012925896070a86bd7c3a4cbb75fa5682d9b9e2
|
[
"MIT"
] | 105
|
2015-01-03T08:55:00.000Z
|
2022-03-19T00:51:45.000Z
|
from .extrtoolembed import *
| 10
| 28
| 0.766667
| 3
| 30
| 7.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 30
| 2
| 29
| 15
| 0.92
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
5689760c0527699177f621ddf2b82ac355cf773f
| 39,782
|
py
|
Python
|
build/lib/nn_common_modules/modules.py
|
jyotirmay123/nn-common-modules
|
014de12c68330c434e90989099f3af77067484ad
|
[
"MIT"
] | null | null | null |
build/lib/nn_common_modules/modules.py
|
jyotirmay123/nn-common-modules
|
014de12c68330c434e90989099f3af77067484ad
|
[
"MIT"
] | null | null | null |
build/lib/nn_common_modules/modules.py
|
jyotirmay123/nn-common-modules
|
014de12c68330c434e90989099f3af77067484ad
|
[
"MIT"
] | null | null | null |
"""
Description
++++++++++++++++++++++
Building blocks of segmentation neural network
Usage
++++++++++++++++++++++
Import the package and Instantiate any module/block class you want to you::
from nn_common_modules import modules as additional_modules
dense_block = additional_modules.DenseBlock(params, se_block_type = 'SSE')
Members
++++++++++++++++++++++
"""
import torch
import torch.nn as nn
from squeeze_and_excitation import squeeze_and_excitation as se
import torch.nn.functional as F
import torch.distributions as tdist
class DenseBlock(nn.Module):
"""Block with dense connections
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: forward passed tensor
:rtype: torch.tonsor [FloatTensor]
"""
def __init__(self, params, se_block_type=None):
super(DenseBlock, self).__init__()
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSELayer(params['num_filters'])
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
else:
self.SELayer = None
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
conv1_out_size = int(params['num_channels'] + params['num_filters'])
conv2_out_size = int(
params['num_channels'] + params['num_filters'] + params['num_filters'])
self.conv1 = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv2 = nn.Conv2d(in_channels=conv1_out_size, out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv3 = nn.Conv2d(in_channels=conv2_out_size, out_channels=params['num_filters'],
kernel_size=(1, 1),
padding=(0, 0),
stride=params['stride_conv'])
self.batchnorm1 = nn.BatchNorm2d(num_features=params['num_channels'])
self.batchnorm2 = nn.BatchNorm2d(num_features=conv1_out_size)
self.batchnorm3 = nn.BatchNorm2d(num_features=conv2_out_size)
self.prelu = nn.PReLU()
if params['drop_out'] > 0:
self.drop_out_needed = True
self.drop_out = nn.Dropout2d(params['drop_out'])
else:
self.drop_out_needed = False
def forward(self, input):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:return: Forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
o1 = self.batchnorm1(input)
o2 = self.prelu(o1)
o3 = self.conv1(o2)
o4 = torch.cat((input, o3), dim=1)
o5 = self.batchnorm2(o4)
o6 = self.prelu(o5)
o7 = self.conv2(o6)
o8 = torch.cat((input, o3, o7), dim=1)
o9 = self.batchnorm3(o8)
o10 = self.prelu(o9)
out = self.conv3(o10)
return out
class EncoderBlock(DenseBlock):
"""Dense encoder block with maxpool and an optional SE block
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: output tensor with maxpool, output tensor without maxpool, indices for unpooling
:rtype: torch.tensor [FloatTensor], torch.tensor [FloatTensor], torch.tensor [LongTensor]
"""
def __init__(self, params, se_block_type=None):
super(EncoderBlock, self).__init__(params, se_block_type=se_block_type)
self.maxpool = nn.MaxPool2d(
kernel_size=params['pool'], stride=params['stride_pool'], return_indices=True)
def forward(self, input, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param weights: Weights used for squeeze and excitation, shape depends on the type of SE block, defaults to None
:type weights: torch.tensor, optional
:return: output tensor with maxpool, output tensor without maxpool, indices for unpooling
:rtype: torch.tensor [FloatTensor], torch.tensor [FloatTensor], torch.tensor [LongTensor]
"""
out_block = super(EncoderBlock, self).forward(input)
if self.SELayer:
out_block = self.SELayer(out_block, weights)
if self.drop_out_needed:
out_block = self.drop_out(out_block)
out_encoder, indices = self.maxpool(out_block)
return out_encoder, out_block, indices
class DecoderBlock(DenseBlock):
"""Dense decoder block with maxunpool and an optional skip connections and SE block
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
def __init__(self, params, se_block_type=None):
super(DecoderBlock, self).__init__(params, se_block_type=se_block_type)
self.unpool = nn.MaxUnpool2d(
kernel_size=params['pool'], stride=params['stride_pool'])
def forward(self, input, out_block=None, indices=None, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param out_block: Tensor for skip connection, shape = (N x C x H x W), defaults to None
:type out_block: torch.tensor [FloatTensor], optional
:param indices: Indices used for unpooling operation, defaults to None
:type indices: torch.tensor, optional
:param weights: Weights used for squeeze and excitation, shape depends on the type of SE block, defaults to None
:type weights: torch.tensor, optional
:return: Forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
if indices is not None:
unpool = self.unpool(input, indices)
else:
# TODO: Implement Conv Transpose
print("You have to use Conv Transpose")
if out_block is not None:
concat = torch.cat((out_block, unpool), dim=1)
else:
concat = unpool
out_block = super(DecoderBlock, self).forward(concat)
if self.SELayer:
out_block = self.SELayer(out_block, weights)
if self.drop_out_needed:
out_block = self.drop_out(out_block)
return out_block
class ClassifierBlock(nn.Module):
"""
Last layer
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_c':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:return: forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
def __init__(self, params):
super(ClassifierBlock, self).__init__()
self.conv = nn.Conv2d(
params['num_channels'], params['num_class'], params['kernel_c'], params['stride_conv'])
def forward(self, input, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param weights: Weights for classifier regression, defaults to None
:type weights: torch.tensor (N), optional
:return: logits
:rtype: torch.tensor
"""
batch_size, channel, a, b = input.size()
if weights is not None:
weights, _ = torch.max(weights, dim=0)
weights = weights.view(1, channel, 1, 1)
out_conv = F.conv2d(input, weights)
else:
out_conv = self.conv(input)
return out_conv
class GenericBlock(nn.Module):
"""
Generic parent class for a conv encoder/decoder block.
:param params: {'kernel_h': 5
'kernel_w': 5
'num_channels':64
'num_filters':64
'stride_conv':1
}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
def __init__(self, params, se_block_type=None):
super(GenericBlock, self).__init__()
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
else:
self.SELayer = None
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
self.out_channel = params['num_filters']
self.conv = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.prelu = nn.PReLU()
self.batchnorm = nn.BatchNorm2d(num_features=params['num_filters'])
if params['drop_out'] > 0:
self.drop_out_needed = True
self.drop_out = nn.Dropout2d(params['drop_out'])
else:
self.drop_out_needed = False
def forward(self, input, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param weights: Custom weights for convolution, defaults to None
:type weights: torch.tensor [FloatTensor], optional
:return: [description]
:rtype: [type]
"""
_, c, h, w = input.shape
if weights is None:
x1 = self.conv(input)
else:
weights, _ = torch.max(weights, dim=0)
weights = weights.view(self.out_channel, c, 1, 1)
x1 = F.conv2d(input, weights)
x2 = self.prelu(x1)
x3 = self.batchnorm(x2)
return x3
class SDnetEncoderBlock(GenericBlock):
"""
A standard conv -> prelu -> batchnorm-> maxpool block without dense connections
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: output tensor with maxpool, output tensor without maxpool, indices for unpooling
:rtype: torch.tensor [FloatTensor], torch.tensor [FloatTensor], torch.tensor [LongTensor]
"""
def __init__(self, params, se_block_type=None):
super(SDnetEncoderBlock, self).__init__(params, se_block_type)
self.maxpool = nn.MaxPool2d(
kernel_size=params['pool'], stride=params['stride_pool'], return_indices=True)
def forward(self, input, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param weights: Weights used for squeeze and excitation, shape depends on the type of SE block, defaults to None
:type weights: torch.tensor, optional
:return: output tensor with maxpool, output tensor without maxpool, indices for unpooling
:rtype: torch.tensor [FloatTensor], torch.tensor [FloatTensor], torch.tensor [LongTensor]
"""
out_block = super(SDnetEncoderBlock, self).forward(input, weights)
if self.SELayer:
out_block = self.SELayer(out_block, weights)
if self.drop_out_needed:
out_block = self.drop_out(out_block)
out_encoder, indices = self.maxpool(out_block)
return out_encoder, out_block, indices
class SDnetDecoderBlock(GenericBlock):
"""Standard decoder block with maxunpool -> skipconnections -> conv -> prelu -> batchnorm, without dense connections and an optional SE blocks
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
def __init__(self, params, se_block_type=None):
super(SDnetDecoderBlock, self).__init__(params, se_block_type)
self.unpool = nn.MaxUnpool2d(
kernel_size=params['pool'], stride=params['stride_pool'])
def forward(self, input, out_block=None, indices=None, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param out_block: Tensor for skip connection, shape = (N x C x H x W), defaults to None
:type out_block: torch.tensor [FloatTensor], optional
:param indices: Indices used for unpooling operation, defaults to None
:type indices: torch.tensor, optional
:param weights: Weights used for squeeze and excitation, shape depends on the type of SE block, defaults to None
:type weights: torch.tensor, optional
:return: Forward pass
:rtype: torch.tensor
"""
unpool = self.unpool(input, indices)
if out_block is not None:
concat = torch.cat((out_block, unpool), dim=1)
else:
concat = unpool
out_block = super(SDnetDecoderBlock, self).forward(concat, weights)
if self.SELayer:
out_block = self.SELayer(out_block, weights)
if self.drop_out_needed:
out_block = self.drop_out(out_block)
return out_block
class SDNetNoBNEncoderBlock(nn.Module):
"""
Encoder Block for Bayesian Network
"""
def __init__(self, params):
super(SDNetNoBNEncoderBlock, self).__init__()
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
self.out_channel = params['num_filters']
self.conv = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(
kernel_size=params['pool'], stride=params['stride_pool'], return_indices=True)
def forward(self, input):
x1 = self.conv(input)
x2 = self.relu(x1)
out_encoder, indices = self.maxpool(x2)
return out_encoder, x2, indices
class SDNetNoBNDecoderBlock(nn.Module):
"""
Decoder Block for Bayesian Network
"""
def __init__(self, params):
super(SDNetNoBNDecoderBlock, self).__init__()
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
self.out_channel = params['num_filters']
self.conv = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.relu = nn.ReLU()
self.unpool = nn.MaxUnpool2d(
kernel_size=params['pool'], stride=params['stride_pool'])
def forward(self, input, out_block=None, indices=None):
unpool = self.unpool(input, indices)
if out_block is not None:
concat = torch.cat((out_block, unpool), dim=1)
else:
concat = unpool
x1 = self.conv(concat)
x2 = self.relu(x1)
return x2
class ConcatBlock(nn.Module):
def __init__(self, params):
super(ConcatBlock, self).__init__()
self.broadcasting_needed = params['broadcasting_needed']
def forward(self, input, another_input):
if self.broadcasting_needed:
n, c, h, w = input.shape
modified_inp = another_input.expand(h, w)
else:
modified_inp = another_input
if len(modified_inp.shape) == 3:
modified_inp = modified_inp.unsqueeze(0)
concat = torch.cat((input, modified_inp), dim=1)
return concat
class DenseBlockNoBN(nn.Module):
"""Block with dense connections
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: forward passed tensor
:rtype: torch.tonsor [FloatTensor]
"""
def __init__(self, params, se_block_type=None):
super(DenseBlockNoBN, self).__init__()
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSELayer(params['num_filters'])
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
else:
self.SELayer = None
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
conv1_out_size = int(params['num_channels'] + params['num_filters'])
conv2_out_size = int(
params['num_channels'] + params['num_filters'] + params['num_filters'])
self.conv1 = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv2 = nn.Conv2d(in_channels=conv1_out_size, out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv3 = nn.Conv2d(in_channels=conv2_out_size, out_channels=params['num_filters'],
kernel_size=(1, 1),
padding=(0, 0),
stride=params['stride_conv'])
# self.batchnorm1 = nn.BatchNorm2d(num_features=params['num_channels'])
# self.batchnorm2 = nn.BatchNorm2d(num_features=conv1_out_size)
# self.batchnorm3 = nn.BatchNorm2d(num_features=conv2_out_size)
self.prelu = nn.PReLU()
if params['drop_out'] > 0:
self.drop_out_needed = True
self.drop_out = nn.Dropout2d(params['drop_out'])
else:
self.drop_out_needed = False
def forward(self, input):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:return: Forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
# o1 = self.batchnorm1(input)
o2 = self.prelu(input)
o3 = self.conv1(o2)
o4 = torch.cat((input, o3), dim=1)
# o5 = self.batchnorm2(o4)
o6 = self.prelu(o4)
o7 = self.conv2(o6)
o8 = torch.cat((input, o3, o7), dim=1)
# o9 = self.batchnorm3(o8)
o10 = self.prelu(o8)
out = self.conv3(o10)
return out
class EncoderBlockNoBN(DenseBlockNoBN):
"""Dense encoder block with maxpool and an optional SE block
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: output tensor with maxpool, output tensor without maxpool, indices for unpooling
:rtype: torch.tensor [FloatTensor], torch.tensor [FloatTensor], torch.tensor [LongTensor]
"""
def __init__(self, params, se_block_type=None):
super(EncoderBlockNoBN, self).__init__(params, se_block_type=se_block_type)
self.maxpool = nn.MaxPool2d(
kernel_size=params['pool'], stride=params['stride_pool'], return_indices=True)
def forward(self, input, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param weights: Weights used for squeeze and excitation, shape depends on the type of SE block, defaults to None
:type weights: torch.tensor, optional
:return: output tensor with maxpool, output tensor without maxpool, indices for unpooling
:rtype: torch.tensor [FloatTensor], torch.tensor [FloatTensor], torch.tensor [LongTensor]
"""
out_block = super(EncoderBlockNoBN, self).forward(input)
if self.SELayer:
out_block = self.SELayer(out_block, weights)
if self.drop_out_needed:
out_block = self.drop_out(out_block)
out_encoder, indices = self.maxpool(out_block)
return out_encoder, out_block, indices
class DecoderBlockNoBN(DenseBlockNoBN):
"""Dense decoder block with maxunpool and an optional skip connections and SE block
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
def __init__(self, params, se_block_type=None):
super(DecoderBlockNoBN, self).__init__(params, se_block_type=se_block_type)
self.unpool = nn.MaxUnpool2d(
kernel_size=params['pool'], stride=params['stride_pool'])
def forward(self, input, out_block=None, indices=None, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param out_block: Tensor for skip connection, shape = (N x C x H x W), defaults to None
:type out_block: torch.tensor [FloatTensor], optional
:param indices: Indices used for unpooling operation, defaults to None
:type indices: torch.tensor, optional
:param weights: Weights used for squeeze and excitation, shape depends on the type of SE block, defaults to None
:type weights: torch.tensor, optional
:return: Forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
if indices is not None:
unpool = self.unpool(input, indices)
else:
# TODO: Implement Conv Transpose
print("You have to use Conv Transpose")
if out_block is not None:
concat = torch.cat((out_block, unpool), dim=1)
else:
concat = unpool
out_block = super(DecoderBlockNoBN, self).forward(concat)
if self.SELayer:
out_block = self.SELayer(out_block, weights)
if self.drop_out_needed:
out_block = self.drop_out(out_block)
return out_block
class FullyPreActivatedResBlock(nn.Module):
def __init__(self, params, concat_extra):
super(FullyPreActivatedResBlock, self).__init__()
# padding_h = int((params['kernel_h'] - 1) / 2)
# padding_w = int((params['kernel_w'] - 1) / 2)
# self.conv = nn.Conv2d(in_channels=params['num_channels']+concat_extra, out_channels=params['num_filters'],
# kernel_size=(
# params['kernel_h'], params['kernel_w']),
# padding=(padding_h, padding_w),
# stride=params['stride_conv'])
input_size = params['num_channels']+concat_extra
self.conv1 = nn.Conv2d(in_channels=input_size, out_channels=params['num_filters'],
kernel_size= (3,3),
padding=(1,1),
stride=params['stride_conv'])
self.conv2 = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=( 3,3),
padding=(1,1),
stride=params['stride_conv'])
self.conv3 = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=( 3,3),
padding=(1,1),
stride=params['stride_conv'])
self.conv4 = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=( 3,3),
padding=(1,1),
stride=params['stride_conv'])
self.batchnorm1 = nn.BatchNorm2d(num_features=input_size)
self.batchnorm2 = nn.BatchNorm2d(num_features=params['num_channels'])
self.batchnorm3 = nn.BatchNorm2d(num_features=params['num_channels'])
self.batchnorm4 = nn.BatchNorm2d(num_features=params['num_channels'])
self.prelu = nn.PReLU()
def forward(self, input, depth):
#return input
# input = self.conv(input)
if depth >= 1:
o1 = self.batchnorm1(input)
o2 = self.prelu(o1)
o4 = self.conv1(o2)
# # out = o3
# o5 = self.batchnorm2(o3)
# o6 = self.prelu(o5)
# o7 = self.conv2(o6)
#
# o8 = o3 + o7
# # o8 = torch.stack([o3,o7], dim=0).sum(dim=1)
#
# #
# o9 = self.batchnorm2(o8)
# o10 = self.prelu(o9)
# o11 = self.conv2(o10)
# #
# # # o12 = o7 + o11
# # #
# # # o13 = self.batchnorm4(o12)
# # # o14 = self.prelu(o13)
# # # o15 = self.conv4(o14)
out = o4
if depth >= 2:
o5 = self.batchnorm2(o4)
o6 = self.prelu(o5)
o7 = self.conv2(o6)
o8 = o4 + o7
out = o8
if depth >= 3:
o9 = self.batchnorm3(o8)
o10 = self.prelu(o9)
o11 = self.conv3(o10)
o12 = o11 + o8
out = o12
if depth >= 4:
o13 = self.batchnorm4(o12)
o14 = self.prelu(o13)
o15 = self.conv4(o14)
o16 = o15 + o12
out = o16
if depth > 4:
raise Exception('Depth more than 4 does not supported!!!')
return out
class FullBayesianDenseBlock(nn.Module):
"""Block with dense connections
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: forward passed tensor
:rtype: torch.tonsor [FloatTensor]
"""
def __init__(self, params, se_block_type=None):
super(FullBayesianDenseBlock, self).__init__()
if se_block_type == se.SELayer.CSE.value:
self.SELayer = se.ChannelSELayer(params['num_filters'])
elif se_block_type == se.SELayer.SSE.value:
self.SELayer = se.SpatialSELayer(params['num_filters'])
elif se_block_type == se.SELayer.CSSE.value:
self.SELayer = se.ChannelSpatialSELayer(params['num_filters'])
else:
self.SELayer = None
padding_h = int((params['kernel_h'] - 1) / 2)
padding_w = int((params['kernel_w'] - 1) / 2)
conv1_out_size = int(params['num_channels'] + params['num_filters'])
conv2_out_size = int(params['num_filters'] + params['num_filters'])
self.conv1_mu = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv2_mu = nn.Conv2d(in_channels=conv1_out_size, out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv3_mu = nn.Conv2d(in_channels=conv2_out_size, out_channels=params['num_filters'],
kernel_size=(1, 1),
padding=(0, 0),
stride=params['stride_conv'])
self.conv1_sigma = nn.Conv2d(in_channels=params['num_channels'], out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv2_sigma = nn.Conv2d(in_channels=conv1_out_size, out_channels=params['num_filters'],
kernel_size=(
params['kernel_h'], params['kernel_w']),
padding=(padding_h, padding_w),
stride=params['stride_conv'])
self.conv3_sigma = nn.Conv2d(in_channels=conv2_out_size, out_channels=params['num_filters'],
kernel_size=(1, 1),
padding=(0, 0),
stride=params['stride_conv'])
self.tanh = nn.Tanh()
self.normal = tdist.Normal(torch.tensor([0.0]), torch.tensor([1.0]))
if params['drop_out'] > 0:
self.drop_out_needed = True
self.drop_out = nn.Dropout2d(params['drop_out'])
else:
self.drop_out_needed = False
def reparameterization(self, x_mean, x_sigma):
# using logvar as log(sigma**2) or 2*log(sigma)
sz = x_sigma.size()
x_sigma_noise = torch.mul((x_sigma/2).exp(), self.normal.sample(sz).squeeze().cuda())
out = x_mean + x_sigma_noise
return out
def get_kl_loss(self, mu, logvar):
# using logvar as log(sigma**2) or 2*log(sigma)
kl_loss = -0.5 * torch.mean(1 + logvar - mu.pow(2) - logvar.exp())
return kl_loss
def forward(self, input):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:return: Forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
o1_mu = self.conv1_mu(input)
o1_sigma = self.conv1_sigma(input)
o2_mu = self.tanh(o1_mu)
o2_sigma = self.tanh(o1_sigma)
o3 = self.reparameterization(o2_mu, o2_sigma)
kl_1 = self.get_kl_loss(o2_mu, o2_sigma)
o4 = torch.cat((input, o3), dim=1)
o5_mu = self.conv2_mu(o4)
o5_sigma = self.conv2_sigma(o4)
o6_mu = self.tanh(o5_mu)
o6_sigma = self.tanh(o5_sigma)
o7 = self.reparameterization(o6_mu, o6_sigma)
kl_2 = self.get_kl_loss(o6_mu, o6_sigma)
o8 = torch.cat((o3, o7), dim=1)
o9_mu = self.conv3_mu(o8)
o9_sigma = self.conv3_sigma(o8)
o10_mu = self.tanh(o9_mu)
o10_sigma = self.tanh(o9_sigma)
out = self.reparameterization(o10_mu, o10_sigma)
kl_3 = self.get_kl_loss(o10_mu, o10_sigma)
kl_loss = 0.33 * (kl_1 + kl_2 + kl_3)
return out, kl_loss
class FullBayesianEncoderBlock(FullBayesianDenseBlock):
"""Dense encoder block with maxpool and an optional SE block
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: output tensor with maxpool, output tensor without maxpool, indices for unpooling
:rtype: torch.tensor [FloatTensor], torch.tensor [FloatTensor], torch.tensor [LongTensor]
"""
def __init__(self, params, se_block_type=None):
super(FullBayesianEncoderBlock, self).__init__(params, se_block_type=se_block_type)
self.maxpool = nn.MaxPool2d(
kernel_size=params['pool'], stride=params['stride_pool'], return_indices=True)
def forward(self, input, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param weights: Weights used for squeeze and excitation, shape depends on the type of SE block, defaults to None
:type weights: torch.tensor, optional
:return: output tensor with maxpool, output tensor without maxpool, indices for unpooling
:rtype: torch.tensor [FloatTensor], torch.tensor [FloatTensor], torch.tensor [LongTensor]
"""
out_block, kl_loss = super(FullBayesianEncoderBlock, self).forward(input)
if self.SELayer:
out_block = self.SELayer(out_block, weights)
if self.drop_out_needed:
out_block = self.drop_out(out_block)
out_encoder, indices = self.maxpool(out_block)
return out_encoder, out_block, indices, kl_loss
class FullBayesianDecoderBlock(FullBayesianDenseBlock):
"""Dense decoder block with maxunpool and an optional skip connections and SE block
:param params: {
'num_channels':1,
'num_filters':64,
'kernel_h':5,
'kernel_w':5,
'stride_conv':1,
'pool':2,
'stride_pool':2,
'num_classes':28,
'se_block': se.SELayer.None,
'drop_out':0,2}
:type params: dict
:param se_block_type: Squeeze and Excite block type to be included, defaults to None
:type se_block_type: str, valid options are {'NONE', 'CSE', 'SSE', 'CSSE'}, optional
:return: forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
def __init__(self, params, se_block_type=None):
super(FullBayesianDecoderBlock, self).__init__(params, se_block_type=se_block_type)
self.unpool = nn.MaxUnpool2d(
kernel_size=params['pool'], stride=params['stride_pool'])
def forward(self, input, out_block=None, indices=None, weights=None):
"""Forward pass
:param input: Input tensor, shape = (N x C x H x W)
:type input: torch.tensor [FloatTensor]
:param out_block: Tensor for skip connection, shape = (N x C x H x W), defaults to None
:type out_block: torch.tensor [FloatTensor], optional
:param indices: Indices used for unpooling operation, defaults to None
:type indices: torch.tensor, optional
:param weights: Weights used for squeeze and excitation, shape depends on the type of SE block, defaults to None
:type weights: torch.tensor, optional
:return: Forward passed tensor
:rtype: torch.tensor [FloatTensor]
"""
if indices is not None:
unpool = self.unpool(input, indices)
else:
# TODO: Implement Conv Transpose
print("You have to use Conv Transpose")
if out_block is not None:
concat = torch.cat((out_block, unpool), dim=1)
else:
concat = unpool
out_block, kl_loss = super(FullBayesianDecoderBlock, self).forward(concat)
if self.SELayer:
out_block = self.SELayer(out_block, weights)
if self.drop_out_needed:
out_block = self.drop_out(out_block)
return out_block, kl_loss
| 38.698444
| 146
| 0.58813
| 4,825
| 39,782
| 4.658238
| 0.050984
| 0.027718
| 0.030833
| 0.024026
| 0.844056
| 0.834624
| 0.82795
| 0.819585
| 0.806905
| 0.79418
| 0
| 0.021062
| 0.298225
| 39,782
| 1,027
| 147
| 38.736125
| 0.78401
| 0.352471
| 0
| 0.677966
| 0
| 0
| 0.067436
| 0
| 0
| 0
| 0
| 0.002921
| 0
| 1
| 0.076271
| false
| 0
| 0.010593
| 0
| 0.163136
| 0.006356
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
3b2030e0fb65fbf77df1b38a50ff4028317a6e41
| 116
|
py
|
Python
|
flask/ytmicro/ytegg/ytegg/debug/__init__.py
|
hyteer/starter
|
8e8c62ef29d195c657d029648b4c7a657debea0d
|
[
"Apache-2.0"
] | null | null | null |
flask/ytmicro/ytegg/ytegg/debug/__init__.py
|
hyteer/starter
|
8e8c62ef29d195c657d029648b4c7a657debea0d
|
[
"Apache-2.0"
] | 6
|
2019-12-26T16:38:51.000Z
|
2020-01-06T18:55:03.000Z
|
flask/ytmicro/ytegg/ytegg/debug/__init__.py
|
hyteer/starter
|
8e8c62ef29d195c657d029648b4c7a657debea0d
|
[
"Apache-2.0"
] | null | null | null |
from flask import Blueprint
debug = Blueprint('debug', __name__, template_folder='templates')
from . import views
| 19.333333
| 65
| 0.775862
| 14
| 116
| 6.071429
| 0.714286
| 0.329412
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12931
| 116
| 5
| 66
| 23.2
| 0.841584
| 0
| 0
| 0
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| 0
| 0.12069
| 0
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| 0
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| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0.666667
| 1
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| null | 1
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| 0
| 1
| 1
|
0
| 6
|
3b4e1cac62e15c368308150fd45077b3f6c6c900
| 2,041
|
py
|
Python
|
tests/test_optimizers/test_opt_algos_simple.py
|
Wollala/Gradient-Free-Optimizers
|
8fb1608c264431b87f66fd2d233b76a0fa75316c
|
[
"MIT"
] | 1
|
2022-02-25T03:14:48.000Z
|
2022-02-25T03:14:48.000Z
|
tests/test_optimizers/test_opt_algos_simple.py
|
Wollala/Gradient-Free-Optimizers
|
8fb1608c264431b87f66fd2d233b76a0fa75316c
|
[
"MIT"
] | null | null | null |
tests/test_optimizers/test_opt_algos_simple.py
|
Wollala/Gradient-Free-Optimizers
|
8fb1608c264431b87f66fd2d233b76a0fa75316c
|
[
"MIT"
] | null | null | null |
from gradient_free_optimizers.optimizers import search_tracker
import pytest
from gradient_free_optimizers import (
HillClimbingOptimizer,
StochasticHillClimbingOptimizer,
RepulsingHillClimbingOptimizer,
SimulatedAnnealingOptimizer,
DownhillSimplexOptimizer,
RandomSearchOptimizer,
GridSearchOptimizer,
RandomRestartHillClimbingOptimizer,
PowellsMethod,
PatternSearch,
RandomAnnealingOptimizer,
ParallelTemperingOptimizer,
ParticleSwarmOptimizer,
EvolutionStrategyOptimizer,
BayesianOptimizer,
TreeStructuredParzenEstimators,
ForestOptimizer,
OneDimensionalBayesianOptimization,
ParallelAnnealingOptimizer,
EnsembleOptimizer,
LocalBayesianOptimizer,
VariableResolutionBayesianOptimizer,
EvoSubSpaceBayesianOptimizer,
)
from surfaces.test_functions import SphereFunction
optimizers = (
"Optimizer",
[
(HillClimbingOptimizer),
(StochasticHillClimbingOptimizer),
(RepulsingHillClimbingOptimizer),
(SimulatedAnnealingOptimizer),
(DownhillSimplexOptimizer),
(RandomSearchOptimizer),
(GridSearchOptimizer),
(RandomRestartHillClimbingOptimizer),
(PowellsMethod),
(PatternSearch),
(RandomAnnealingOptimizer),
(ParallelTemperingOptimizer),
(ParticleSwarmOptimizer),
(EvolutionStrategyOptimizer),
(BayesianOptimizer),
(TreeStructuredParzenEstimators),
(ForestOptimizer),
(OneDimensionalBayesianOptimization),
(ParallelAnnealingOptimizer),
(EnsembleOptimizer),
(LocalBayesianOptimizer),
(VariableResolutionBayesianOptimizer),
(EvoSubSpaceBayesianOptimizer),
],
)
sphere_function = SphereFunction(n_dim=2, metric="score")
@pytest.mark.parametrize(*optimizers)
def test_opt_algos_0(Optimizer):
opt = Optimizer(sphere_function.search_space())
opt.search(sphere_function, n_iter=15)
_ = opt.best_para
_ = opt.best_score
_ = opt.search_data
| 27.958904
| 62
| 0.733464
| 109
| 2,041
| 13.541284
| 0.504587
| 0.028455
| 0.02168
| 0.03523
| 0.752033
| 0.752033
| 0.752033
| 0.752033
| 0.752033
| 0.752033
| 0
| 0.002445
| 0.198432
| 2,041
| 72
| 63
| 28.347222
| 0.899756
| 0
| 0
| 0
| 0
| 0
| 0.006859
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.015625
| false
| 0
| 0.0625
| 0
| 0.078125
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
8e5f88947c3835217065dd11e5835134dc3d3860
| 104
|
py
|
Python
|
managers/__init__.py
|
CharlesDLandau/api_consumer
|
da598727fef10b4f318446ca884b86cd9b7a1deb
|
[
"MIT"
] | null | null | null |
managers/__init__.py
|
CharlesDLandau/api_consumer
|
da598727fef10b4f318446ca884b86cd9b7a1deb
|
[
"MIT"
] | null | null | null |
managers/__init__.py
|
CharlesDLandau/api_consumer
|
da598727fef10b4f318446ca884b86cd9b7a1deb
|
[
"MIT"
] | 1
|
2018-07-24T02:37:44.000Z
|
2018-07-24T02:37:44.000Z
|
from .celery_manager import make_celery
from .api_consumer_config import *
from .celery_config import *
| 26
| 39
| 0.836538
| 15
| 104
| 5.466667
| 0.533333
| 0.243902
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 104
| 3
| 40
| 34.666667
| 0.891304
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
8e74f86d008428d75e3955506380a6241287a8cb
| 106
|
py
|
Python
|
machop/commands.py
|
phlip9/machop
|
e2d0b1b655a2b49df0b4042814f4d3dd18bdcd2c
|
[
"BSD-2-Clause"
] | 1
|
2016-01-05T17:26:08.000Z
|
2016-01-05T17:26:08.000Z
|
machop/commands.py
|
phlip9/machop
|
e2d0b1b655a2b49df0b4042814f4d3dd18bdcd2c
|
[
"BSD-2-Clause"
] | null | null | null |
machop/commands.py
|
phlip9/machop
|
e2d0b1b655a2b49df0b4042814f4d3dd18bdcd2c
|
[
"BSD-2-Clause"
] | null | null | null |
class MachopCommand(object):
def shutdown(self):
pass
def cleanup(self):
pass
| 10.6
| 28
| 0.575472
| 11
| 106
| 5.545455
| 0.727273
| 0.262295
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.339623
| 106
| 9
| 29
| 11.777778
| 0.871429
| 0
| 0
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0.4
| 0
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 6
|
8e862dde8b0576bb91f6e2909e00353ca06c999e
| 222
|
py
|
Python
|
cleverly/agglomerative/__init__.py
|
rayandrews/clustering-algorithm
|
14336545b11e25d7259b9f3e9314ea4a36674cc2
|
[
"MIT"
] | 1
|
2021-11-03T21:14:08.000Z
|
2021-11-03T21:14:08.000Z
|
cleverly/agglomerative/__init__.py
|
rayandrews/cleverly
|
14336545b11e25d7259b9f3e9314ea4a36674cc2
|
[
"MIT"
] | null | null | null |
cleverly/agglomerative/__init__.py
|
rayandrews/cleverly
|
14336545b11e25d7259b9f3e9314ea4a36674cc2
|
[
"MIT"
] | null | null | null |
# from .Agglomerative import Agglomerative
# from .linkage import average, average_group, complete, single
# __all__ = [
# 'Agglomerative',
# 'average',
# 'average_group',
# 'complete',
# 'single'
# ]
| 20.181818
| 63
| 0.635135
| 19
| 222
| 7.105263
| 0.473684
| 0.207407
| 0.281481
| 0.4
| 0.488889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.225225
| 222
| 10
| 64
| 22.2
| 0.784884
| 0.90991
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
8e93cf78056143ebad2ee79e366db2acb62faee8
| 9,975
|
py
|
Python
|
build/sandbox/overlay_test.py
|
TinkerBoard2-Android/tools-treble
|
4a70b7b8a10119fe886bb009c634646a533f7db0
|
[
"Apache-2.0"
] | 1
|
2022-02-10T21:17:20.000Z
|
2022-02-10T21:17:20.000Z
|
build/sandbox/overlay_test.py
|
TinkerBoard2-Android/tools-treble
|
4a70b7b8a10119fe886bb009c634646a533f7db0
|
[
"Apache-2.0"
] | null | null | null |
build/sandbox/overlay_test.py
|
TinkerBoard2-Android/tools-treble
|
4a70b7b8a10119fe886bb009c634646a533f7db0
|
[
"Apache-2.0"
] | null | null | null |
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Test overlay."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import shutil
import subprocess
import tempfile
import unittest
from . import overlay
import re
class BindOverlayTest(unittest.TestCase):
def setUp(self):
self.source_dir = tempfile.mkdtemp()
self.destination_dir = tempfile.mkdtemp()
os.mkdir(os.path.join(self.source_dir, 'base_dir'))
os.mkdir(os.path.join(self.source_dir, 'base_dir', 'base_project'))
os.mkdir(os.path.join(self.source_dir, 'base_dir', 'base_project', '.git'))
os.mkdir(os.path.join(self.source_dir, 'overlays'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest1'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest1', 'from_dir'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest1', 'from_dir', '.git'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest1', 'upper_subdir'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest1', 'upper_subdir',
'lower_subdir'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest1', 'upper_subdir',
'lower_subdir', 'from_unittest1'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest1', 'upper_subdir',
'lower_subdir', 'from_unittest1', '.git'))
os.symlink(
os.path.join(self.source_dir, 'overlays', 'unittest1',
'upper_subdir', 'lower_subdir'),
os.path.join(self.source_dir, 'overlays', 'unittest1',
'upper_subdir', 'subdir_symlink')
)
open(os.path.join(self.source_dir,
'overlays', 'unittest1', 'from_file'), 'a').close()
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest2'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest2', 'upper_subdir'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest2', 'upper_subdir',
'lower_subdir'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest2', 'upper_subdir',
'lower_subdir', 'from_unittest2'))
os.mkdir(os.path.join(self.source_dir,
'overlays', 'unittest2', 'upper_subdir',
'lower_subdir', 'from_unittest2', '.git'))
def tearDown(self):
shutil.rmtree(self.source_dir)
def testValidTargetOverlayBinds(self):
with tempfile.NamedTemporaryFile('w+t') as test_config:
test_config.write(
'<?xml version="1.0" encoding="UTF-8" ?>'
'<config>'
' <target name="unittest">'
' <overlay name="unittest1"/>'
' </target>'
'</config>'
)
test_config.flush()
o = overlay.BindOverlay(
config_file=test_config.name,
target='unittest',
source_dir=self.source_dir)
self.assertIsNotNone(o)
bind_mounts = o.GetBindMounts()
bind_source = os.path.join(self.source_dir, 'overlays/unittest1/from_dir')
bind_destination = os.path.join(self.source_dir, 'from_dir')
self.assertEqual(bind_mounts[bind_destination], overlay.BindMount(bind_source, False))
def testMultipleOverlays(self):
with tempfile.NamedTemporaryFile('w+t') as test_config:
test_config.write(
'<?xml version="1.0" encoding="UTF-8" ?>'
'<config>'
' <target name="unittest">'
' <overlay name="unittest1"/>'
' <overlay name="unittest2"/>'
' </target>'
'</config>'
)
test_config.flush()
o = overlay.BindOverlay(
config_file=test_config.name,
target='unittest',
source_dir=self.source_dir)
self.assertIsNotNone(o)
bind_mounts = o.GetBindMounts()
bind_source = os.path.join(self.source_dir,
'overlays/unittest1/upper_subdir/lower_subdir/from_unittest1')
bind_destination = os.path.join(self.source_dir, 'upper_subdir/lower_subdir/from_unittest1')
self.assertEqual(bind_mounts[bind_destination], overlay.BindMount(bind_source, False))
bind_source = os.path.join(self.source_dir,
'overlays/unittest2/upper_subdir/lower_subdir/from_unittest2')
bind_destination = os.path.join(self.source_dir,
'upper_subdir/lower_subdir/from_unittest2')
self.assertEqual(bind_mounts[bind_destination], overlay.BindMount(bind_source, False))
def testMultipleOverlaysWithWhitelist(self):
with tempfile.NamedTemporaryFile('w+t') as test_config:
test_config.write(
'<?xml version="1.0" encoding="UTF-8" ?>'
'<config>'
' <target name="unittest">'
' <overlay name="unittest1"/>'
' <overlay name="unittest2"/>'
' </target>'
'</config>'
)
test_config.flush()
rw_whitelist = set('overlays/unittest1/uppser_subdir/lower_subdir/from_unittest1')
o = overlay.BindOverlay(
config_file=test_config.name,
target='unittest',
source_dir=self.source_dir)
self.assertIsNotNone(o)
bind_mounts = o.GetBindMounts()
bind_source = os.path.join(self.source_dir,
'overlays/unittest1/upper_subdir/lower_subdir/from_unittest1')
bind_destination = os.path.join(self.source_dir, 'upper_subdir/lower_subdir/from_unittest1')
self.assertEqual(
bind_mounts[bind_destination],
overlay.BindMount(source_dir=bind_source, readonly=False))
bind_source = os.path.join(self.source_dir,
'overlays/unittest2/upper_subdir/lower_subdir/from_unittest2')
bind_destination = os.path.join(self.source_dir,
'upper_subdir/lower_subdir/from_unittest2')
self.assertEqual(bind_mounts[bind_destination], overlay.BindMount(bind_source, False))
def testValidOverlaidDir(self):
with tempfile.NamedTemporaryFile('w+t') as test_config:
test_config.write(
'<?xml version="1.0" encoding="UTF-8" ?>'
'<config>'
' <target name="unittest">'
' <overlay name="unittest1"/>'
' </target>'
'</config>'
)
test_config.flush()
o = overlay.BindOverlay(
config_file=test_config.name,
target='unittest',
source_dir=self.source_dir,
destination_dir=self.destination_dir)
self.assertIsNotNone(o)
bind_mounts = o.GetBindMounts()
bind_source = os.path.join(self.source_dir, 'overlays/unittest1/from_dir')
bind_destination = os.path.join(self.destination_dir, 'from_dir')
self.assertEqual(bind_mounts[bind_destination], overlay.BindMount(bind_source, False))
def testValidFilesystemViewDirectoryBind(self):
with tempfile.NamedTemporaryFile('w+t') as test_config:
test_config.write(
'<?xml version="1.0" encoding="UTF-8" ?>'
'<config>'
' <target name="unittest">'
' <view name="unittestview"/>'
' </target>'
' <view name="unittestview">'
' <path source="overlays/unittest1/from_dir" '
' destination="to_dir"/>'
' </view>'
'</config>'
)
test_config.flush()
o = overlay.BindOverlay(
config_file=test_config.name,
target='unittest',
source_dir=self.source_dir)
self.assertIsNotNone(o)
bind_mounts = o.GetBindMounts()
bind_source = os.path.join(self.source_dir, 'overlays/unittest1/from_dir')
bind_destination = os.path.join(self.source_dir, 'to_dir')
self.assertEqual(bind_mounts[bind_destination], overlay.BindMount(bind_source, False))
def testValidFilesystemViewFileBind(self):
with tempfile.NamedTemporaryFile('w+t') as test_config:
test_config.write(
'<?xml version="1.0" encoding="UTF-8" ?>'
'<config>'
' <target name="unittest">'
' <view name="unittestview"/>'
' </target>'
' <view name="unittestview">'
' <path source="overlays/unittest1/from_file" '
' destination="to_file"/>'
' </view>'
'</config>'
)
test_config.flush()
o = overlay.BindOverlay(
config_file=test_config.name,
target='unittest',
source_dir=self.source_dir)
self.assertIsNotNone(o)
bind_mounts = o.GetBindMounts()
bind_source = os.path.join(self.source_dir, 'overlays/unittest1/from_file')
bind_destination = os.path.join(self.source_dir, 'to_file')
self.assertEqual(bind_mounts[bind_destination], overlay.BindMount(bind_source, False))
def testInvalidTarget(self):
with tempfile.NamedTemporaryFile('w+t') as test_config:
test_config.write(
'<?xml version="1.0" encoding="UTF-8" ?>'
'<config>'
' <target name="unittest">'
' <overlay name="unittest1"/>'
' </target>'
'</config>'
)
test_config.flush()
with self.assertRaises(KeyError):
overlay.BindOverlay(
config_file=test_config.name,
target='unknown',
source_dir=self.source_dir)
if __name__ == '__main__':
unittest.main()
| 39.426877
| 96
| 0.626767
| 1,125
| 9,975
| 5.354667
| 0.129778
| 0.076195
| 0.092795
| 0.081341
| 0.800631
| 0.791999
| 0.791999
| 0.791999
| 0.784031
| 0.777058
| 0
| 0.009905
| 0.240902
| 9,975
| 252
| 97
| 39.583333
| 0.785658
| 0.056441
| 0
| 0.684685
| 0
| 0
| 0.261763
| 0.077177
| 0
| 0
| 0
| 0
| 0.067568
| 1
| 0.040541
| false
| 0
| 0.045045
| 0
| 0.09009
| 0.004505
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
8e9b411cad2e535c9b32761377b543e387000be4
| 15,416
|
py
|
Python
|
action-server/tests/actions/test_question_answering_form.py
|
dialoguemd/covidflow
|
b159b76dc68462f272614db4cbf716844872ebca
|
[
"MIT"
] | 7
|
2020-05-23T07:07:26.000Z
|
2021-11-29T05:58:51.000Z
|
action-server/tests/actions/test_question_answering_form.py
|
dialoguemd/covidflow
|
b159b76dc68462f272614db4cbf716844872ebca
|
[
"MIT"
] | 210
|
2020-04-13T17:21:55.000Z
|
2021-04-20T15:46:26.000Z
|
action-server/tests/actions/test_question_answering_form.py
|
dialoguemd/covidflow
|
b159b76dc68462f272614db4cbf716844872ebca
|
[
"MIT"
] | 3
|
2020-04-09T14:38:09.000Z
|
2020-07-29T15:06:11.000Z
|
from unittest.mock import MagicMock, patch
from rasa_sdk.events import ActionExecuted, Form, SlotSet, UserUttered
from rasa_sdk.forms import REQUESTED_SLOT
from covidflow.actions.answers import QuestionAnsweringResponse, QuestionAnsweringStatus
from covidflow.actions.question_answering_form import (
ANSWERS_KEY,
ANSWERS_SLOT,
ASKED_QUESTION_SLOT,
FEEDBACK_KEY,
FEEDBACK_NOT_GIVEN,
FEEDBACK_SLOT,
FORM_NAME,
QUESTION_KEY,
QUESTION_SLOT,
SKIP_QA_INTRO_SLOT,
STATUS_KEY,
STATUS_SLOT,
QuestionAnsweringForm,
)
from .form_test_helper import FormTestCase
def QuestionAnsweringResponseMock(*args, **kwargs):
mock = MagicMock(*args, **kwargs)
async def mock_coroutine(*args, **kwargs):
return mock(*args, **kwargs)
mock_coroutine.mock = mock
return mock_coroutine
DOMAIN = {"responses": {"utter_ask_feedback_error": [{"text": ""}],}}
QUESTION = "What is covid?"
ANSWERS = [
"It's a virus!",
"It's the greatest plea since the plague!",
"No, it's SU-PER BAD!",
]
SUCCESS_RESULT = QuestionAnsweringResponse(
answers=ANSWERS, status=QuestionAnsweringStatus.SUCCESS
)
FAILURE_RESULT = QuestionAnsweringResponse(status=QuestionAnsweringStatus.FAILURE)
OUT_OF_DISTRIBUTION_RESULT = QuestionAnsweringResponse(
status=QuestionAnsweringStatus.OUT_OF_DISTRIBUTION
)
NEED_ASSESSMENT_RESULT = QuestionAnsweringResponse(
status=QuestionAnsweringStatus.NEED_ASSESSMENT
)
FULL_RESULT_SUCCESS = {
QUESTION_KEY: QUESTION,
ANSWERS_KEY: ANSWERS,
STATUS_KEY: QuestionAnsweringStatus.SUCCESS,
FEEDBACK_KEY: True,
}
class TestQuestionAnsweringForm(FormTestCase):
def setUp(self):
super().setUp()
self.form = QuestionAnsweringForm()
def test_form_activation_first_time_without_qa_samples(self):
tracker = self.create_tracker(active_form=False, intent="ask_question")
self.run_form(tracker, DOMAIN)
self.assert_events(
[
Form(FORM_NAME),
SlotSet(SKIP_QA_INTRO_SLOT, True),
SlotSet(REQUESTED_SLOT, QUESTION_SLOT),
]
)
self.assert_templates(
[
"utter_can_help_with_questions",
"utter_qa_disclaimer",
"utter_ask_active_question",
]
)
def test_form_activation_first_time_with_qa_samples(self):
tracker = self.create_tracker(active_form=False, intent="ask_question")
self.run_form(
tracker, domain={"responses": {"utter_qa_sample_foo": [{"text": "bar"}]}}
)
self.assert_events(
[
Form(FORM_NAME),
SlotSet(SKIP_QA_INTRO_SLOT, True),
SlotSet(REQUESTED_SLOT, QUESTION_SLOT),
]
)
self.assert_templates(
[
"utter_can_help_with_questions",
"utter_qa_disclaimer",
"utter_qa_sample",
"utter_ask_active_question",
]
)
def test_form_activation_not_first_time(self):
tracker = self.create_tracker(
slots={ASKED_QUESTION_SLOT: FULL_RESULT_SUCCESS, SKIP_QA_INTRO_SLOT: True},
active_form=False,
intent="ask_question",
)
self.run_form(tracker, DOMAIN)
self.assert_events([Form(FORM_NAME), SlotSet(REQUESTED_SLOT, QUESTION_SLOT)])
self.assert_templates(["utter_ask_active_question"])
def test_form_activation_affirm(self):
tracker = self.create_tracker(
slots={ASKED_QUESTION_SLOT: FULL_RESULT_SUCCESS},
active_form=False,
intent="affirm",
text="What is covid?",
)
self.run_form(tracker, DOMAIN)
self.assert_events([Form(FORM_NAME), SlotSet(REQUESTED_SLOT, QUESTION_SLOT)])
self.assert_templates(["utter_ask_active_question"])
def test_form_activation_fallback(self):
tracker = self.create_tracker(
slots={ASKED_QUESTION_SLOT: FULL_RESULT_SUCCESS, SKIP_QA_INTRO_SLOT: True},
active_form=False,
intent="affirm",
)
self.run_form(tracker, DOMAIN)
self.assert_events([Form(FORM_NAME), SlotSet(REQUESTED_SLOT, QUESTION_SLOT)])
self.assert_templates(["utter_ask_active_question"])
@patch("covidflow.actions.question_answering_form.QuestionAnsweringProtocol")
def test_provide_question_success(self, mock_protocol):
mock_protocol.return_value.get_response = QuestionAnsweringResponseMock(
return_value=SUCCESS_RESULT
)
tracker = self.create_tracker(
slots={REQUESTED_SLOT: QUESTION_SLOT}, text=QUESTION
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
SlotSet(QUESTION_SLOT, QUESTION),
SlotSet(STATUS_SLOT, QuestionAnsweringStatus.SUCCESS),
SlotSet(ANSWERS_SLOT, ANSWERS),
SlotSet(REQUESTED_SLOT, FEEDBACK_SLOT),
]
)
self.assert_templates([None, "utter_ask_feedback"])
self.assert_texts([ANSWERS[0], None])
@patch("covidflow.actions.question_answering_form.QuestionAnsweringProtocol")
def test_provide_question_failure(self, mock_protocol):
mock_protocol.return_value.get_response = QuestionAnsweringResponseMock(
return_value=FAILURE_RESULT
)
tracker = self.create_tracker(
slots={REQUESTED_SLOT: QUESTION_SLOT}, text=QUESTION
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
SlotSet(QUESTION_SLOT, QUESTION),
SlotSet(STATUS_SLOT, QuestionAnsweringStatus.FAILURE),
SlotSet(ANSWERS_SLOT, None),
SlotSet(QUESTION_SLOT, None),
SlotSet(FEEDBACK_SLOT, None),
SlotSet(
ASKED_QUESTION_SLOT,
{
QUESTION_KEY: QUESTION,
STATUS_KEY: QuestionAnsweringStatus.FAILURE,
ANSWERS_KEY: None,
FEEDBACK_KEY: None,
},
),
Form(None),
SlotSet(REQUESTED_SLOT, None),
]
)
self.assert_templates([])
@patch("covidflow.actions.question_answering_form.QuestionAnsweringProtocol")
def test_provide_question_out_of_distribution(self, mock_protocol):
mock_protocol.return_value.get_response = QuestionAnsweringResponseMock(
return_value=OUT_OF_DISTRIBUTION_RESULT
)
tracker = self.create_tracker(
slots={REQUESTED_SLOT: QUESTION_SLOT}, text=QUESTION
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
SlotSet(QUESTION_SLOT, QUESTION),
SlotSet(STATUS_SLOT, QuestionAnsweringStatus.OUT_OF_DISTRIBUTION),
SlotSet(ANSWERS_SLOT, None),
SlotSet(QUESTION_SLOT, None),
SlotSet(FEEDBACK_SLOT, None),
SlotSet(
ASKED_QUESTION_SLOT,
{
QUESTION_KEY: QUESTION,
STATUS_KEY: QuestionAnsweringStatus.OUT_OF_DISTRIBUTION,
ANSWERS_KEY: None,
FEEDBACK_KEY: None,
},
),
Form(None),
SlotSet(REQUESTED_SLOT, None),
]
)
self.assert_templates([])
def test_provide_feedback_affirm(self):
tracker = self.create_tracker(
slots={
REQUESTED_SLOT: FEEDBACK_SLOT,
QUESTION_SLOT: QUESTION,
ANSWERS_SLOT: ANSWERS,
STATUS_SLOT: QuestionAnsweringStatus.SUCCESS,
},
intent="affirm",
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
SlotSet(FEEDBACK_SLOT, True),
SlotSet(QUESTION_SLOT, None),
SlotSet(FEEDBACK_SLOT, None),
SlotSet(ASKED_QUESTION_SLOT, FULL_RESULT_SUCCESS),
Form(None),
SlotSet(REQUESTED_SLOT, None),
]
)
self.assert_templates([])
def test_provide_feedback_deny(self):
tracker = self.create_tracker(
slots={
REQUESTED_SLOT: FEEDBACK_SLOT,
QUESTION_SLOT: QUESTION,
ANSWERS_SLOT: ANSWERS,
STATUS_SLOT: QuestionAnsweringStatus.SUCCESS,
},
intent="deny",
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
SlotSet(FEEDBACK_SLOT, False),
SlotSet(QUESTION_SLOT, None),
SlotSet(FEEDBACK_SLOT, None),
SlotSet(
ASKED_QUESTION_SLOT, {**FULL_RESULT_SUCCESS, FEEDBACK_KEY: False}
),
Form(None),
SlotSet(REQUESTED_SLOT, None),
]
)
self.assert_templates(["utter_post_feedback"])
def test_provide_feedback_not_given(self):
tracker = self.create_tracker(
slots={
REQUESTED_SLOT: FEEDBACK_SLOT,
QUESTION_SLOT: QUESTION,
ANSWERS_SLOT: ANSWERS,
STATUS_SLOT: QuestionAnsweringStatus.SUCCESS,
},
text="some text with",
intent="another_intent",
entities=[{"and": "entities"}],
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
SlotSet(FEEDBACK_SLOT, FEEDBACK_NOT_GIVEN),
SlotSet(QUESTION_SLOT, None),
SlotSet(FEEDBACK_SLOT, None),
SlotSet(
ASKED_QUESTION_SLOT,
{**FULL_RESULT_SUCCESS, FEEDBACK_KEY: FEEDBACK_NOT_GIVEN},
),
Form(None),
SlotSet(REQUESTED_SLOT, None),
ActionExecuted("utter_ask_another_question"),
ActionExecuted("action_listen"),
UserUttered(
"some text with",
parse_data={
"text": "some text with",
"intent": {"name": "another_intent"},
"intent_ranking": [],
"entities": [{"and": "entities"}],
},
),
Form(None),
SlotSet(REQUESTED_SLOT, None),
]
)
self.assert_templates([])
@patch("covidflow.actions.question_answering_form.QuestionAnsweringProtocol")
def test_fallback_question_success(self, mock_protocol):
mock_protocol.return_value.get_response = QuestionAnsweringResponseMock(
return_value=SUCCESS_RESULT
)
tracker = self.create_tracker(
active_form=False, intent="fallback", text=QUESTION
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
Form(FORM_NAME),
SlotSet(QUESTION_SLOT, QUESTION),
SlotSet(STATUS_SLOT, QuestionAnsweringStatus.SUCCESS),
SlotSet(ANSWERS_SLOT, ANSWERS),
SlotSet(REQUESTED_SLOT, FEEDBACK_SLOT),
]
)
self.assert_templates([None, "utter_ask_feedback"])
self.assert_texts([ANSWERS[0], None])
@patch("covidflow.actions.question_answering_form.QuestionAnsweringProtocol")
def test_fallback_question_failure(self, mock_protocol):
mock_protocol.return_value.get_response = QuestionAnsweringResponseMock(
return_value=FAILURE_RESULT
)
tracker = self.create_tracker(
active_form=False, intent="fallback", text=QUESTION
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
Form(FORM_NAME),
SlotSet(QUESTION_SLOT, QUESTION),
SlotSet(STATUS_SLOT, QuestionAnsweringStatus.FAILURE),
SlotSet(ANSWERS_SLOT, None),
SlotSet(QUESTION_SLOT, None),
SlotSet(FEEDBACK_SLOT, None),
SlotSet(
ASKED_QUESTION_SLOT,
{
QUESTION_KEY: QUESTION,
STATUS_KEY: QuestionAnsweringStatus.FAILURE,
ANSWERS_KEY: None,
FEEDBACK_KEY: None,
},
),
Form(None),
SlotSet(REQUESTED_SLOT, None),
]
)
self.assert_templates([])
@patch("covidflow.actions.question_answering_form.QuestionAnsweringProtocol")
def test_fallback_question_out_of_distribution(self, mock_protocol):
mock_protocol.return_value.get_response = QuestionAnsweringResponseMock(
return_value=OUT_OF_DISTRIBUTION_RESULT
)
tracker = self.create_tracker(
active_form=False, intent="fallback", text=QUESTION
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
Form(FORM_NAME),
SlotSet(QUESTION_SLOT, QUESTION),
SlotSet(STATUS_SLOT, QuestionAnsweringStatus.OUT_OF_DISTRIBUTION),
SlotSet(ANSWERS_SLOT, None),
SlotSet(QUESTION_SLOT, None),
SlotSet(FEEDBACK_SLOT, None),
SlotSet(
ASKED_QUESTION_SLOT,
{
QUESTION_KEY: QUESTION,
STATUS_KEY: QuestionAnsweringStatus.OUT_OF_DISTRIBUTION,
ANSWERS_KEY: None,
FEEDBACK_KEY: None,
},
),
Form(None),
SlotSet(REQUESTED_SLOT, None),
]
)
self.assert_templates([])
@patch("covidflow.actions.question_answering_form.QuestionAnsweringProtocol")
def test_fallback_question_need_assessment(self, mock_protocol):
mock_protocol.return_value.get_response = QuestionAnsweringResponseMock(
return_value=NEED_ASSESSMENT_RESULT
)
tracker = self.create_tracker(
active_form=False, intent="fallback", text=QUESTION
)
self.run_form(tracker, DOMAIN)
self.assert_events(
[
Form(FORM_NAME),
SlotSet(QUESTION_SLOT, QUESTION),
SlotSet(STATUS_SLOT, QuestionAnsweringStatus.NEED_ASSESSMENT),
SlotSet(ANSWERS_SLOT, None),
SlotSet(QUESTION_SLOT, None),
SlotSet(FEEDBACK_SLOT, None),
SlotSet(
ASKED_QUESTION_SLOT,
{
QUESTION_KEY: QUESTION,
STATUS_KEY: QuestionAnsweringStatus.NEED_ASSESSMENT,
ANSWERS_KEY: None,
FEEDBACK_KEY: None,
},
),
Form(None),
SlotSet(REQUESTED_SLOT, None),
]
)
self.assert_templates([])
| 32.116667
| 88
| 0.572782
| 1,349
| 15,416
| 6.203113
| 0.092661
| 0.055927
| 0.037643
| 0.043021
| 0.806883
| 0.788838
| 0.780354
| 0.77892
| 0.76924
| 0.763145
| 0
| 0.000198
| 0.34568
| 15,416
| 479
| 89
| 32.183716
| 0.829384
| 0
| 0
| 0.565657
| 0
| 0
| 0.077193
| 0.045537
| 0
| 0
| 0
| 0
| 0.080808
| 1
| 0.042929
| false
| 0
| 0.015152
| 0
| 0.065657
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
8ea831799aea2aeef5fc8f77fd4d7ab78df0e408
| 46
|
py
|
Python
|
mflops/__init__.py
|
shuncyu/mflops
|
81fddf9407bcbdca02b9c57f6b03640b3fb94101
|
[
"MIT"
] | 1
|
2020-12-17T03:09:20.000Z
|
2020-12-17T03:09:20.000Z
|
mflops/__init__.py
|
shuncyu/mflops
|
81fddf9407bcbdca02b9c57f6b03640b3fb94101
|
[
"MIT"
] | null | null | null |
mflops/__init__.py
|
shuncyu/mflops
|
81fddf9407bcbdca02b9c57f6b03640b3fb94101
|
[
"MIT"
] | null | null | null |
from .model_info import get_model_compute_info
| 46
| 46
| 0.913043
| 8
| 46
| 4.75
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.065217
| 46
| 1
| 46
| 46
| 0.883721
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
8ed785f9ecac41cc4671b1c00a6044aaca380a37
| 31
|
py
|
Python
|
aiotuyalan/lib/__init__.py
|
zachcheatham/aiotuyalan
|
cc07703509ae4b618995668d59e0624904c9a75f
|
[
"MIT"
] | null | null | null |
aiotuyalan/lib/__init__.py
|
zachcheatham/aiotuyalan
|
cc07703509ae4b618995668d59e0624904c9a75f
|
[
"MIT"
] | null | null | null |
aiotuyalan/lib/__init__.py
|
zachcheatham/aiotuyalan
|
cc07703509ae4b618995668d59e0624904c9a75f
|
[
"MIT"
] | null | null | null |
from .client import TuyaClient
| 15.5
| 30
| 0.83871
| 4
| 31
| 6.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129032
| 31
| 1
| 31
| 31
| 0.962963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
8ef2f5a9f4f1a846fe1bc7ce7c66cfad8a5a8285
| 23
|
py
|
Python
|
vogue/commands/load/__init__.py
|
mayabrandi/vogue
|
463e6417a9168eadb0d11dea2d0f97919494bcd3
|
[
"MIT"
] | 111
|
2015-01-15T11:53:20.000Z
|
2022-03-26T19:55:24.000Z
|
vogue/commands/load/__init__.py
|
mayabrandi/vogue
|
463e6417a9168eadb0d11dea2d0f97919494bcd3
|
[
"MIT"
] | 2,995
|
2015-01-15T16:14:20.000Z
|
2022-03-31T13:36:32.000Z
|
vogue/commands/load/__init__.py
|
mayabrandi/vogue
|
463e6417a9168eadb0d11dea2d0f97919494bcd3
|
[
"MIT"
] | 55
|
2015-05-31T19:09:49.000Z
|
2021-11-01T10:50:31.000Z
|
from .base import load
| 11.5
| 22
| 0.782609
| 4
| 23
| 4.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 23
| 1
| 23
| 23
| 0.947368
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d932b4c968ef33aec273d7a41dbca435dc81e22a
| 14,460
|
py
|
Python
|
tests/test_package_checker.py
|
jsoref/centos-package-cron
|
0c7e3e24b91619916a515c8ef492dcfa863dae66
|
[
"BSD-2-Clause"
] | 83
|
2015-03-19T09:07:57.000Z
|
2021-10-14T02:19:58.000Z
|
tests/test_package_checker.py
|
jsoref/centos-package-cron
|
0c7e3e24b91619916a515c8ef492dcfa863dae66
|
[
"BSD-2-Clause"
] | 26
|
2015-01-08T17:29:10.000Z
|
2020-03-04T19:56:19.000Z
|
tests/test_package_checker.py
|
jsoref/centos-package-cron
|
0c7e3e24b91619916a515c8ef492dcfa863dae66
|
[
"BSD-2-Clause"
] | 21
|
2016-05-17T19:22:56.000Z
|
2021-02-15T14:27:08.000Z
|
#!/usr/bin/python
import unittest
import sys
from centos_package_cron import package_checker
from centos_package_cron import package_fetcher
from centos_package_cron import errata_fetcher
from centos_package_cron.errata_fetcher import ErrataType
from centos_package_cron.errata_fetcher import ErrataSeverity
from mock import Mock
from centos_package_cron.package import Package
class PackageCheckerTest(unittest.TestCase):
def testAdvisoryPackageMeantForCurrentOsCentOs5(self):
# arrange
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='5')
errata = Mock()
pkg = Mock()
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
advisory_packages = [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el4.2', 'arch':'x86_64'},
{'name': 'xen-libs','version':'3.0.3', 'release':'135.el5.2', 'arch':'x86_64'}]
# act
result = map(lambda a: checker._advisoryPackageMeantForCurrentOs(a), advisory_packages)
# assert
assert result == [False, True]
def testAdvisoryPackageMeantForCurrentOsCentOs_AllVersions(self):
# arrange
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='6')
errata = Mock()
pkg = Mock()
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
advisory_packages = [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el4.2', 'arch':'x86_64'},
{'name': 'kernel', 'version':'2.6.32', 'release':'642.13.1.el6', 'arch':'i686'},
{'name': 'xen-libs','version':'3.0.3', 'release':'135.el6.2', 'arch':'x86_64'}]
# act
result = map(lambda a: checker._advisoryPackageMeantForCurrentOs(a), advisory_packages)
# assert
assert result == [False, True, True]
def testAdvisoryPackageMeantForCurrentOsCentOs7(self):
# arrange
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
errata = Mock()
pkg = Mock()
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
advisory_packages = [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el6.2', 'arch':'x86_64'},
{'name': 'xen-libs','version':'3.0.3', 'release':'135.el7.2', 'arch':'x86_64'},
{'name': 'xen-libs','version':'3.0.3', 'release':'135.el7_0.2', 'arch':'x86_64'}]
# act
result = map(lambda a: checker._advisoryPackageMeantForCurrentOs(a), advisory_packages)
# assert
assert result == [False, True, True]
def testSameVersionOfAnotherPackageInstalled(self):
# arrange
errata = Mock()
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['i686','x86_64'], ['7'], [{'name': 'libcacard-tools','version':'1.5.3', 'release':'60.el7_0.5', 'arch':'x86_64'}],[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('libgcrypt', '1.5.3', '4.el7', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
os_fetcher.get_mid_level_version = Mock(return_value='7.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
assert result == []
def testFindAdvisoriesOnInstalledPackagesNotInstalled(self):
# arrange
errata = Mock()
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['7'], [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el5_8.2', 'arch':'x86_64'}],[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('bash','1.0', '4.el7', 'x86_64', 'updates'),
Package('openssl','2.0', '4.el7', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
os_fetcher.get_mid_level_version = Mock(return_value='7.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
self.assertEquals(result, [])
def testFindAdvisoriesOnInstalledPackagesInstalledButCurrentAlready(self):
# arrange
errata = Mock()
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['7'], [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el7.2', 'arch':'x86_64'}],[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('xen-libs','3.0.3', '135.el7.2', 'x86_64', 'updates'),
Package('openssl','2.0', '4.el7', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
os_fetcher.get_mid_level_version = Mock(return_value='7.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
self.assertEquals(result, [])
def testFindAdvisoriesOnInstalledPackagesInstalledButNewerVersion(self):
# arrange
errata = Mock()
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['7'], [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el7.2', 'arch':'x86_64'}],[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('xen-libs','3.0.4', '135.el5_8.2', 'x86_64', 'updates'),
Package('openssl','2.0', '4.el7', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
os_fetcher.get_mid_level_version = Mock(return_value='7.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
self.assertEquals(result, [])
def testFindAdvisoriesOnInstalledPackagesVersionComparisonWith2Digits(self):
# arrange
errata = Mock()
errata_packages = [
{'arch': 'x86_64',
'name': 'glibc',
'release': '55.el7.1',
'version': '2.17'},
{'arch': 'x86_64',
'name': 'glibc',
'release': '118.el7.3',
'version': '2.5'}]
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['7'], errata_packages,[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('glibc','2.17', '55.el7.1', 'x86_64', 'updates'),
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
os_fetcher.get_mid_level_version = Mock(return_value='7.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
assert result == []
def testFindAdvisoriesOnInstalledPackagesBothOldAndNewInstalled(self):
# arrange
errata = Mock()
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['7'], [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el7.2', 'arch':'x86_64'}],[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('xen-libs','3.0.3', '132.el7.2', 'x86_64', 'updates'),
Package('xen-libs','3.0.4', '135.el7.2', 'x86_64', 'updates'),
Package('openssl','2.0', '4.el7', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
os_fetcher.get_mid_level_version = Mock(return_value='7.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
assert result == []
def testFindAdvisoriesOnInstalledPackagesInstalledButLowerVersion(self):
# arrange
errata = Mock()
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['7'], [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el7.2', 'arch':'x86_64'}],[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('xen-libs','3.0.2', '135.el7.2', 'x86_64', 'updates'),
Package('openssl','2.0', '4.el7', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
os_fetcher.get_mid_level_version = Mock(return_value='7.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
self.assertNotEquals(result, [])
def testFindAdvisoriesOnInstalledPackagesInstalledButNewerRelease(self):
# arrange
errata = Mock()
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['7'], [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el7.2', 'arch':'x86_64'}],[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('xen-libs','3.0.3', '135.el7.3', 'x86_64', 'updates'),
Package('openssl','2.0', '4.el7', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
os_fetcher.get_mid_level_version = Mock(return_value='7.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
self.assertEquals(result, [])
def testFindAdvisoriesOnInstalledPackagesInstalledAndNeedsUpdatingButWrongCentOsVersion(self):
# arrange
errata = Mock()
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['7'], [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el7.2', 'arch':'x86_64'}],[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('xen-libs','3.0.3', '135.el7.1', 'x86_64', 'updates'),
Package('openssl','2.0', '4.el6', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='6')
os_fetcher.get_mid_level_version = Mock(return_value='6.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
self.assertEquals(result, [])
def testFindAdvisoriesOnInstalledPackagesInstalledAndNeedsUpdatingButWrongCentOsVersionOnPackage(self):
# https://github.com/wied03/centos-package-cron/issues/5
# arrange
errata = Mock()
adv_packages = [{'name': 'bash','version':'3.2', 'release':'33.el5.1', 'arch':'i386'},
{'name': 'bash','version':'3.2', 'release':'33.el5.1', 'arch':'src'},
{'name': 'bash','version':'3.2', 'release':'33.el5.1', 'arch':'x86_64'},
{'name': 'bash','version':'4.1.2', 'release':'15.el6_5.1', 'arch':'i686'},
{'name': 'bash','version':'4.1.2', 'release':'15.el6_5.1', 'arch':'src'},
{'name': 'bash','version':'4.1.2', 'release':'15.el6_5.1', 'arch':'x86_64'},
{'name': 'bash','version':'4.2.45', 'release':'5.el7_0.2', 'arch':'src'},
{'name': 'bash','version':'4.2.45', 'release':'5.el7_0.2', 'arch':'x86_64'}]
errata.get_errata = Mock(return_value=[
errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['5', '6', '7'], adv_packages,[])
])
pkg = Mock()
pkg.fetch_installed_packages = Mock(return_value=[
Package('bash','4.1.2', '29.el6', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='6')
os_fetcher.get_mid_level_version = Mock(return_value='6.6')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
self.assertEquals(result, [])
def testFindAdvisoriesOnInstalledPackagesInstalledAndNeedsUpdating(self):
# arrange
errata = Mock()
advisory = errata_fetcher.ErrataItem('adv id', ErrataType.SecurityAdvisory,ErrataSeverity.Important, ['x86_64'], ['7'], [{'name': 'xen-libs','version':'3.0.3', 'release':'135.el7.2', 'arch':'x86_64'}],[])
errata.get_errata = Mock(return_value=[advisory])
pkg = Mock()
xen_package = Package('xen-libs','3.0.3', '135.el7.1', 'x86_64', 'updates')
pkg.fetch_installed_packages = Mock(return_value=[
xen_package,
Package('openssl','2.0', '4.el7', 'x86_64', 'updates')
])
os_fetcher = Mock()
os_fetcher.get_top_level_version = Mock(return_value='7')
os_fetcher.get_mid_level_version = Mock(return_value='7.0')
checker = package_checker.PackageChecker(errata,pkg,os_fetcher)
# act
result = checker.findAdvisoriesOnInstalledPackages()
# assert
assert len(result) == 1
first = result[0]
assert first['advisory'] == advisory
assert first['installed_packages'] == [xen_package]
if __name__ == "__main__":
unittest.main()
| 44.085366
| 216
| 0.615214
| 1,618
| 14,460
| 5.289864
| 0.074784
| 0.055731
| 0.082369
| 0.06426
| 0.827433
| 0.819021
| 0.801612
| 0.785138
| 0.781984
| 0.781984
| 0
| 0.054264
| 0.223859
| 14,460
| 327
| 217
| 44.220183
| 0.708367
| 0.023306
| 0
| 0.689076
| 0
| 0
| 0.152994
| 0
| 0
| 0
| 0
| 0
| 0.067227
| 1
| 0.058824
| false
| 0
| 0.084034
| 0
| 0.147059
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
d96f5c5299748f4e8e43da2e826f19d01f03be8b
| 34
|
py
|
Python
|
pettingzoo/classic/rps_v1.py
|
vstark21/PettingZoo
|
0ebd8fb540e195f9dd91d996f190e9a89dedcf26
|
[
"Apache-2.0"
] | 4
|
2021-12-17T08:00:28.000Z
|
2022-02-12T12:25:24.000Z
|
pettingzoo/classic/rps_v1.py
|
vstark21/PettingZoo
|
0ebd8fb540e195f9dd91d996f190e9a89dedcf26
|
[
"Apache-2.0"
] | null | null | null |
pettingzoo/classic/rps_v1.py
|
vstark21/PettingZoo
|
0ebd8fb540e195f9dd91d996f190e9a89dedcf26
|
[
"Apache-2.0"
] | 1
|
2021-01-25T22:57:41.000Z
|
2021-01-25T22:57:41.000Z
|
from .rps.rps import env, raw_env
| 17
| 33
| 0.764706
| 7
| 34
| 3.571429
| 0.714286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147059
| 34
| 1
| 34
| 34
| 0.862069
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
795af4d864995ffc8fae39868fb4eb2dfaeab3d0
| 332
|
py
|
Python
|
colcon_export_command/xml/models/__init__.py
|
maciejmatuszak/colcon-export-command
|
a7747996cbff25d8611306a9c1987cea3966271e
|
[
"Apache-2.0"
] | null | null | null |
colcon_export_command/xml/models/__init__.py
|
maciejmatuszak/colcon-export-command
|
a7747996cbff25d8611306a9c1987cea3966271e
|
[
"Apache-2.0"
] | null | null | null |
colcon_export_command/xml/models/__init__.py
|
maciejmatuszak/colcon-export-command
|
a7747996cbff25d8611306a9c1987cea3966271e
|
[
"Apache-2.0"
] | null | null | null |
from colcon_export_command.xml.models.project import (
AdditionalGenerationEnvironment,
Component,
Configuration,
Configurations,
Env,
Envs,
Project,
)
__all__ = [
"AdditionalGenerationEnvironment",
"Component",
"Configuration",
"Configurations",
"Env",
"Envs",
"Project",
]
| 16.6
| 54
| 0.650602
| 23
| 332
| 9.130435
| 0.652174
| 0.380952
| 0.504762
| 0.638095
| 0.771429
| 0.771429
| 0.771429
| 0
| 0
| 0
| 0
| 0
| 0.240964
| 332
| 19
| 55
| 17.473684
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0.243976
| 0.093373
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.055556
| 0
| 0.055556
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
797ba3ce5ce939dc298e57e18fc7ff032d596f0b
| 70
|
py
|
Python
|
double3/double3sdk/depth/depth.py
|
CLOMING/winter2021_double
|
9b920baaeb3736a785a6505310b972c49b5b21e9
|
[
"Apache-2.0"
] | null | null | null |
double3/double3sdk/depth/depth.py
|
CLOMING/winter2021_double
|
9b920baaeb3736a785a6505310b972c49b5b21e9
|
[
"Apache-2.0"
] | null | null | null |
double3/double3sdk/depth/depth.py
|
CLOMING/winter2021_double
|
9b920baaeb3736a785a6505310b972c49b5b21e9
|
[
"Apache-2.0"
] | null | null | null |
from double3sdk.double_api import _DoubleAPI
class _Depth:
pass
| 11.666667
| 44
| 0.785714
| 9
| 70
| 5.777778
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017544
| 0.185714
| 70
| 5
| 45
| 14
| 0.894737
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
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